biodegradation of winery biomass wastes by developing a ...€¦ · i, avinash v karpe, hereby...

Biodegradation of winery biomass wastes by

developing a symbiotic multi-fungal consortium

Submitted in total fulfilment requirements for the degree of

Doctor of Philosophy By

Avinash Vasant Karpe

Department of Chemistry and Biotechnology Faculty of Science, Engineering and Technology

Swinburne University of Technology

August 2015

iii

Abstract

Australia is sixth largest global wine grape producer with an annual production

of 1.75 million metric tonnes in 2012-13. Owing to its poor digestibility and minor

phenolic toxicity, winery waste (about a half of total grape biomass) has limited use as

either animal feed or compost fertilizer. During fermentation, Saccharomyces cerevisiae

generates about 12-15% ethanol from the raw material, with the residual biomass

(approx. 85%) consisting of spent wash, seeds, marcs and pomace generating landfill

waste.

Fungi from the divisions Ascomycota and Basidiomycota are considered as

efficient biomass degraders. These fungi not only hydrolyse the complex molecules like

cellulose to simple sugars, but also generate metabolites of industrial and medicinal

importance. However, the inherent nature of the lignocellulosic complex makes it

recalcitrant towards numerous physical, chemical or biological treatments. Besides their

lower inherent activities, cellulases suffer from product inhibition from cellulose and

lignin degradation products, thus making the biomass degradation more difficult. Also,

no naturally evolved organism possesses the entire spectrum of the necessary biomass

degrading biochemical machinery.

The experiments described herein explore the possibilities of developing a

mixed fungal consortium to achieve enhanced grape biomass degradation. The

processes utilized cultures of Ascomycota fungi (Trichoderma harzianum, Aspergillus

niger, Penicillium chrysogenum and Penicillium citrinum) alone or together with a

Basidiomycota fungus (Phanerochaete chrysosporium). The conventional and popular

submerged fermentation (SmF) and the emerging Solid State Fermentation (SSF)

methods were tested. Statistical analysis was performed to generate an optimised mixed

fungal “cocktail” and achieve a bioreactor-based degradation process with increased

biomass degradation. Additionally, co-culturing the optimized ascomycete fungal

mixture with Ph. chrysosporium resulted in enhanced degradation. In addition to

classifying and characterizing the important metabolites generated and consumed during

the biodegradation process, metabolomics was used to identify the critical points of

fungal metabolism which can be altered to improve the overall bioconversion process.

iv

Initial experiments involved single cultures of the ascomycetes added to thermal

pre-treated (autoclaved) winery waste. Pre-treatment resulted in 18% mass loss of

biomass with enzyme activities, especially that of β-glucosidase, improving in most

cultures (compared to non-autoclaved waste), indicating considerable hydrolyzation of

cellulose and hemicelluloses. P. chrysogenum was observed to degrade about 9% lignin

in the presence of free sugars. For SSF, the ratio between substrate and fungal growth

medium was kept at 1:1. Cellulase and xylanase activities were moderately or

considerably higher in SSF conditions with respect to SmF. However, β-glucosidase

activities were higher under SmF than SSF. Lignin degradation in SSF conditions was

considerably lower at 2.2 % as compared to 9% in SmF.

To balance the overall enzyme activities and achieve higher lignin degradation

while minimizing competitive and product inhibitions, full factorial statistical modelling

was used to determine an optimal fungal mixture. Following autoclaving pre-treatment,

a mixture of A. niger: P. chrysogenum: T. harzianum: P. citrinum in a percent ratio of

60:14:4:2 was applied for biomass degradation. The substrate to medium ratio was held

at 0.39. It was observed that the enzyme activities of cellulases, glucosidase and

xylanases increased considerably, especially xylanase activities which increased from

1430 U/mL in monoculture to 3550 U/mL in optimized conditions. Total lignin

degradation of 17.9 % in 5 days was noted. Metabolic profiling indicated considerable

production of sugar alcohols, fatty acids and secondary metabolites, such as gallic acid.

Further improvement in degradation of thermal pre-treated waste was achieved

by a cocktail of Ph. chrysosporium and the statistically optimized mix of ascomycetes.

Over 16 days, the nature of fungal mix prevented product inhibition of cellulases, β-

glucosidase and xylanase activities, thus resulting in enhanced activities. Xylanase

activity increased about 2-fold (> 6000 U/mL) with respect to ascomycete mixture.

Considerable laccase and lignin peroxidase activities were observed, which was

surprising since Ph. chrysosporium is generally unable to express laccase activities

when grown on various substrates. Metabolic profiling displayed lignin degradation

products such as suberic acid, 1, 3 benzenedicarboxylate and cadaverine, which were

further degraded during the course of fermentation. Similarly, considerable amounts of

commercially important metabolites such as sugar alcohols, sugar acids and fatty acids

were generated.

v

Metabolic flux profiling of A. niger and P. chrysogenum indicated considerable

accumulation of free sugars. GC-MS based 2H2O-flux profiling of A. niger and P.

chrysogenum showed 17 and 36 active metabolic pathways, respectively, merging in

and out of glycolysis and TCA cycle pathways. Fungal enzymes encountered product

inhibition after 5 days of fermentation, indicating the need for continuous batch

conditions of lower intervals of 4-5 days to generate better degradation. Based on the

assessment of A. niger metabolic pathways, it is proposed that adding keto acid

carboxylases will reroute amino acid biosynthesis pathways to butanol isomers, which

are widely used as fuel molecules. Moderate generation of xylitol, an intermediate of

ethanol production, was observed during P. chrysogenum metabolism. This pentose

sugar oxidation pathway was absent in the other ascomycetes under investigation.

Moderate lignin and tannin degradation was also observed during the early phase of

biomass degradation. It is proposed that P. chrysogenum forms an ideal candidate for

inclusion in a fungal consortium to minimize pentose based enzyme inhibition and

convert this carbohydrate into sugar alcohols, eventually leading to ethanol production.

Overall, the results of this study show that a mixed fungal approach in

combination with simple pre-treatment process such as autoclaving is able to generate

considerable amount of biomass degradation. This degradation generates numerous

metabolites such as sugars, alcohols and fatty acids which can be applied to generate

biofuel molecules and metabolites of medicinal interests.

vi

Dedicated in the loving memory of

Devendra R Jagtap

17th Jun. 1987 – 2nd Oct. 2013

A brother and the best of friends

viii

Acknowledgements

This thesis has been a result of an arduous, yet a momentous journey of some

considerable years of my life. So, as it happens with every journey, there are a number

of people who have been instrumental in shaping the story of this journey. My gurus,

Professors Enzo Palombo and Ian Harding and Dr. David Beale of all the people have

been the biggest constructors of my PhD’s journey. Enzo, you not only guided me as a

master, but also acted like my godfather during all these times. Ian, the PhD would not

have been as cheery without your numerous stories and corrections. Lastly, David; you

have been like an elder and wiser friend, guiding me on the basis of your own research

experiences. You are the people who have still kept my hunger for research and

knowledge alive. I cannot thank you enough gentlemen!

I would also like to thank my colleagues at Faculty and Science, Engineering

and Technology, Swinburne University and CSIRO. You have made this journey more

of a fun and entertaining rather than a boring one. I am grateful to Chris Key, Dr.

Huimei Wu, Savithri Galappathie, Angela McKellar, Ngan Nguyen, Dr. Rebecca

Alfred, Dr. Adrian Dinsdale, Dr. François Malherbe, Dr. Daniel Eldridge and Professor

Mrinal Bhave. This acknowledgement will remain incomplete without mentioning Dr.

Jacqui McRae from The Australian Wine Research Institute, who provided the research

with grape substrate to work on.

The story of this journey will especially remain incomplete with mentioning vast

number of friends who always made the life ‘Greater than 9000’. The gang of

‘Breakers’ in India, Rohan Shah, Ravi Nirmal, Snehal Jadhav, Amol Ghodke, Shaan

Agrawal, Balasaheb Sonawane, Richi Shah, Maitri Patel, Dr Atul Kamboj, Matt Quinn,

Simon Grossemy, Dr. Vi Khanh Truong, Dr Mohammad Al Kobaisi, Dr. Shakuntla

Gondalia and Nainesh Godhani, my PhD journey would have been not possible without

you guys. Thanks!

Finally, big thanks to my parents, brother and other family members for

unconditional love, unshakable belief and very long patience. Although, no thanks is big

enough, that’s the only thing I can give you. I know that I have made you proud and

hope that all your sacrifices were worth it!

ix

Declaration

I, Avinash V Karpe, hereby declare, to best of my knowledge, the thesis entitled

“Biodegradation of winery biomass wastes by developing a symbiotic multi-fungal

consortium” is no more than 100,000 words in length, exclusive of tables, figures,

appendices, references and footnotes. I also declare that this thesis, in complete or in

part has been submitted or previously published for any other degree, diploma or

independent publishing by me or any other person. Where the work is a result of

collaborations and joint research, the thesis and its resultant research articles indicate

contributions of all contributors and respective workers. I also declare that this thesis

has been appropriately edited; however, the extent of the editing addressed only the

style and grammar of the thesis, and not its substantive content.

-------------------------------

Avinash V Karpe

x

Publications arising from this thesis work

Peer-reviewed journal articles

Karpe AV, Harding IH & Palombo EA (2014) Comparative degradation of

hydrothermal pretreated winery grape wastes by various fungi. Industrial Crops &

Products 59: 228-233

Karpe AV, Beale DJ, Harding IH & Palombo EA (In press) Optimization of degradation

of winery-derived biomass wastes by Ascomycetes. Journal of Chemical Technology &

Biotechnology. DOI:10.1002/jctb.4486

Karpe AV, Beale DJ, Morrison PD, Harding IH & Palombo EA (2015) Untargeted

Metabolic Profiling of Vitis vinifera during Fungal Degradation. FEMS Microbiology

Letters. 362 (10), fnv060

Karpe AV, Beale DJ, Godhani, N, Morrison PD, Harding IH & Palombo EA (2015)

Metabolic profiling of winery-derived biomass waste degradation by Aspergillus niger.

Journal of Chemical Technology & Biotechnology, DOI: 10.1002/jctb.4749

Conference presentations

Karpe AV, Beale DJ, Harding IH & Palombo EA. Application of metabolomics in

fungal mediated winery biomass degradation. XIVth International Congress of

Mycology and Eukaryotic Microbiology, Montréal, Canada; 07/2014 (Oral

presentation)

Karpe AV, Beale DJ, Harding IH & Palombo EA. Optimization of degradation of

winery-derived biomass waste by Ascomycetes. Australian Society for Microbiology

Annual General Meeting & Exposition, Melbourne, Australia; 07/2014 (Oral

presentation)

Karpe AV, Harding IH & Palombo EA. Comparative degradation of grape pomace

from winery wastes by various fungal cultures. 4th International Conference for Young

Chemists, Penang, Malaysia; 01/2013 (Oral presentation)

xi

Related miscellaneous peer-reviewed journal articles

Beale DJ, Karpe AV, Jadhav, S, Muster, T & Palombo EA (2015) 'Omic' approaches

and their use in the assessment of microbial influenced corrosion of metals. Corrosion

Reviews

Jadhav, S, Gulati, V, Fox, EM, Karpe, A, Beale, DJ, Sevior, D, Bhave, M & Palombo,

EA. (2015). Rapid identification and source-tracking of Listeria monocytogenes using

MALDI-TOF mass spectrometry. International Journal of Food Microbiology 202, 1-9

xii

Table of contents

Abstract ..................................................................................................................... iii

Dedication ................................................................................................................. vi

Acknowledgements ................................................................................................... vii

Declaration ................................................................................................................ viii

Publications arising from the thesis .......................................................................... ix

Table of contents ....................................................................................................... xi

List of tables .............................................................................................................. xvii

List of figures ............................................................................................................ xviii

Annotation ................................................................................................................. xxv

1. Chapter 1 ............................................................................................................. 1

1.1. Overview .................................................................................................. 2

1.2. Problems associated with biomass degradation ....................................... 3

1.3. Various biomass degradation approaches ................................................ 4

1.4. Aims and strategies of the project ............................................................. 5

2. Chapter 2 ............................................................................................................. 7

2.1. Biomass composition ....................................................................................... 8

2.1.1. Cellulose ..................................................................................................... 8

2.1.2. Hemicelluloses ............................................................................................ 10

2.1.2.1. Xylans ..................................................................................................... 10

2.1.2.2. Xyloglucans ............................................................................................ 11

2.1.2.3. Mannans ................................................................................................. 11

2.1.2.4. β-glucans ................................................................................................ 12

2.1.2.5. Pectins .................................................................................................... 14

2.1.3. . Lignins ........................................................................................................ 15

2.2. Biomass biodegradation ................................................................................... 18

2.2.1. Biodegradation: Issues and problems ......................................................... 19

2.2.2. Structure of lignocellulose complex ........................................................... 19

2.2.3. Microbial and chemical limitations ............................................................ 21

2.2.4. Enzymatic limitations ................................................................................. 21

xiii

2.2.5. Product inhibition ........................................................................................ 21

2.3. Biomass pre-treatment ..................................................................................... 22

2.3.1. Physical pre-treatment methods .................................................................. 22

2.3.1.1. Milling .................................................................................................... 23

2.3.1.2. Irradiation and sonication ....................................................................... 23

2.3.2. Physico-chemical treatment ........................................................................ 24

2.3.3. Chemical treatments .................................................................................... 26

2.3.3.1. Acid pre-treatment .................................................................................. 26

2.3.3.2. Alkali pre-treatment ............................................................................... 27

2.3.3.3. Organosolv treatments ............................................................................ 27

2.3.4. Biological pre-treatment ............................................................................. 28

2.4. Fungal degradation of biomass ........................................................................ 28

2.4.1. Cellulases .................................................................................................... 29

2.4.2. Hemicellulases ............................................................................................ 32

2.4.3. Lignases ...................................................................................................... 33

2.4.3.1. Laccases ................................................................................................. 35

2.4.3.2. Heme peroxidases .................................................................................. 38

2.4.3.2.1. Lignin peroxidases .......................................................................... 38

2.4.3.2.2. Manganese peroxidases ................................................................... 39

2.4.3.2.3. Versatile peroxidases ...................................................................... 42

2.5. Biodegradation methods .................................................................................. 45

2.5.1. First generation: Submerged fermentation .................................................. 45

2.5.2. Second Generation: Solid State Fermentation ............................................ 47

2.5.3. Third generation: Consolidated Bioprocessing ........................................... 50

2.5.4. Symbiotic fermentation: SSF and thermophilic degradation ...................... 53

2.6. Application of metabolomics in fungal biomass degradation ......................... 56

2.7. Overview .......................................................................................................... 59

3. Chapter 3 ............................................................................................................. 60

3.1. Chemicals ........................................................................................................ 61

3.1.1. Chemicals and media ................................................................................... 61

3.1.2. Growth media .............................................................................................. 61

3.1.3. Enzymes ...................................................................................................... 63

xiv

3.1.4. Buffers and Reagents .................................................................................. 63

3.1.5. Maintenance of storage and other conditions .............................................. 65

3.2. Organisms ........................................................................................................ 66

3.3. Genomic identification of fungi and bacteria .................................................. 67

3.4. Grape samples .................................................................................................. 67

3.5. Fungal and bacterial growth conditions ........................................................... 67

3.6. Calculating bacterial and fungal concentrations .............................................. 68

3.7. Biochemical tests ............................................................................................. 69

3.7.1. Determination of total soluble sugars ......................................................... 69

3.7.2. Determination of reducing sugars ................................................................ 69

3.7.3. Determination of pentoses .......................................................................... 70

3.7.4. Total protein content ................................................................................... 70

3.7.5. Lignin content measurement ....................................................................... 70

3.7.6. Total nitrogen and carbon content ............................................................... 71

3.8. Enzyme assays ................................................................................................. 72

3.8.1. Cellulase activity assay ............................................................................... 72

3.8.2. β-glucosidase activity assay ......................................................................... 73

3.8.3. Xylanase activity assay ................................................................................ 73

3.8.4. Laccase activity assay ................................................................................. 74

3.8.5. Lignin peroxidase activity assay ................................................................. 75

3.9. Silyl derivatization and gas chromatography-mass spectrometry(GC-MS) ..... 76

3.9.1. Silyl derivatization ...................................................................................... 76

3.9.2. GC-MS analysis .......................................................................................... 77

3.10. Chemometric and statistical analysis ............................................................ 78

3.10.1. Biochemical analysis and enzyme assays ................................................. 78

3.10.2. Statistical modeling for degradation optimization ................................... 78

3.10.3. Metabolic profiling and flux analysis ....................................................... 79

4. Chapter 4 ............................................................................................................. 81

4.1. Introduction ...................................................................................................... 82

4.2. Overview .......................................................................................................... 83

4.3. Results and discussions ................................................................................... 84

4.3.1. Effects of hydrothermal pre-treatment ........................................................ 84

xv

4.3.2. Utilization of Total Soluble Sugars ............................................................ 85

4.3.3. Utilization of reducing sugars and pentoses ............................................... 87

4.3.4. Change in the lignin content ....................................................................... 90

4.3.5. Cellulase activity ......................................................................................... 92

4.3.6. β-glucosidase activity ................................................................................. 94

4.3.7. Xylanase activity ......................................................................................... 96

4.4. Conclusions ..................................................................................................... 97

4.5. Experimental summary .................................................................................... 98

5. Chapter 5 ............................................................................................................. 100

5.1. Introduction ...................................................................................................... 101

5.2. Overview .......................................................................................................... 102


5.3.1. Total Protein Content .................................................................................. 102

5.3.2. Reducing sugars .......................................................................................... 104

5.3.3. Lignin mineralization .................................................................................. 107

5.3.4. Cellulase activities during SSF ................................................................... 110

5.3.5. β-glucosidase activities .............................................................................. 112

5.3.6. Xylanase activities ...................................................................................... 115

5.4. Conclusions ..................................................................................................... 118

5.5. Summary .......................................................................................................... 119

6. Chapter 6 ............................................................................................................. 121

6.1. Introduction ...................................................................................................... 122

6.2. Overview .......................................................................................................... 123


6.3.1. Compositional analysis of grape waste ....................................................... 124

6.3.2. Statistics: Design of experiment and analysis ............................................. 125

6.3.3. Grape biomass degradation ......................................................................... 126

6.3.4. Enzyme activities ........................................................................................ 128

6.3.5. Grape biomass degradation in bioreactor ................................................... 137

6.3.6. Cellulolytic enzyme production in bioreactor culture ................................ 137

6.3.7. Gas Chromatography-Mass Spectrometry (GC-MS) .................................. 138

xvi

6.3.8. Metabolic output of mixed fungal degradation ........................................... 139

6.4. Conclusions ..................................................................................................... 145

6.5. Summary .......................................................................................................... 146

7. Chapter 7 ............................................................................................................. 148

7.1. Introduction ...................................................................................................... 149

7.2. Overview .......................................................................................................... 149


7.3.1. Lignin degradation ...................................................................................... 150

7.3.2. Cellulase activity ......................................................................................... 152

7.3.3. β-glucosidase activity ................................................................................. 154

7.3.4. Xylanase activity ......................................................................................... 156

7.3.5. Ligninase activities ..................................................................................... 158

7.3.5.1. Laccase activity ...................................................................................... 158

7.3.5.2. Lignin peroxidase activity ...................................................................... 160

7.3.6. Metabolic output of sequential fermentation .............................................. 162

7.3.6.1. Mass spectral analysis and PLS-DA ...................................................... 163

7.4. Conclusions ..................................................................................................... 174

7.5. Summary .......................................................................................................... 175

8. Chapter 8 ............................................................................................................. 176

8.1. Introduction ...................................................................................................... 177

8.2. Overview .......................................................................................................... 178

8.3. Results and discussions .................................................................................... 179

8.3.1. A. niger metabolic profile ........................................................................... 179

8.3.2. Mass spectral analysis and PLS-DA ........................................................... 180

8.3.3. Metabolic flux of A. niger during biomass degradation .............................. 186

8.3.3.1. Glucose junction ..................................................................................... 186

8.3.3.2. Glyceraldehyde-3-phosphate junction .................................................... 187

8.3.3.3. Acetyl-CoA junction .............................................................................. 192

8.3.3.4. Succinate junction .................................................................................. 197

8.3.4. Glycolysis and TCA cycle .......................................................................... 197

8.3.5. P. chrysogenum metabolic profile .............................................................. 198

xvii

8.3.6. Mass spectral analysis and PLS-DA ........................................................... 200

8.3.7. Metabolic flux of P. chrysogenum during biomass degradation ................ 206

8.3.7.1. Glucose/ glucose-1-phosphate junction ................................................. 206

8.3.7.2. Glycerate-3-phosphate junction ............................................................. 211

8.3.7.3. Acetyl CoA junction ............................................................................... 215

8.4. Conclusions ..................................................................................................... 221

8.5. Summary .......................................................................................................... 222

9. Chapter 9 ............................................................................................................. 224

9.1. General discussions ......................................................................................... 225

9.1.1. Hydrothermal pre-treatment and submerged fermentation .................. 226

9.1.2. Solid state fermentation ....................................................................... 227

9.1.3. Statistical optimization of fungal biomass degradation ....................... 227

9.1.4. Biomass degradation by co-culture of basidiomycetes and ascomycetes

.............................................................................................................. 228

9.1.5. Fungal metabolic flux analysis to improve biomass degradation ........ 229

9.2. Close and future aspects .................................................................................. 230

Bibliography ........................................................................................................ 232

Appendices .......................................................................................................... 264

xviii

List of tables

Table 2.1 Major classification of different mannans ................................................ 12

Table 3.1 General composition of various minerals/ organic sources in the growth media

for isolating and characterizing cellulolytic organisms ............................ 62

Table 3.2 Composition of buffers and reagents used during the experiments .......... 62

Table 3.3 Micro-organisms isolated from various sources and characterized by Sanger

sequencing ................................................................................................. 67

Table 3.4 Working protocol for laccase assay .......................................................... 74

Table 3.5 Working protocol for lignin peroxidase assay .......................................... 75

Table 6.1 General composition of Shiraz grape waste .............................................. 125

Table 6.2 ANOVA for Cellulases, Xylanases and β-glucosidase using Adjusted SS for

tests ........................................................................................................... 133

Table 6.3 Total cellulase, xylanase and β-glucosidase activities (U/mL) of different

fungi under experimental conditions of Design of Experiment ................ 135

Table 6.4 Most significant features generated during the optimized biomass degradation

as identified by the volcano plot with their fold change (FC) and P values

.................................................................................................................... 143

Table 7.1 The table represents most significantly generated metabolites by D8 (end of

Ph chrysosporium incubation) and D16 (end of overall degradation) of winery

biomass waste degradation process ........................................................... 168

Table A1 List of most significant metabolites differentiated on the basis of D2O

mediated metabolic flux of A niger during winery biomass degradation . 271

Table A2 List of most significant metabolites differentiated on the basis of D2O

mediated metabolic flux in P chrysogenum .............................................. 276

xix

List of figures

Figure 2.1 Fragment of cellulose with the reducing, non-reducing and cellobiose component of

cellulose ........................................................................................................... 9

Figure 2.2 Hydrogen bonds in cellulose molecule, represented by dashes (---) ................... 10

Figure 2.3 (A) Structure of xylopyranose, which is the primary component of Xylans. (B)

Structure of β-galactopyranose molecule. (C) Structure of 4-O-methyl glucopyranoxyl

uronic acid (MeGLcA), which is a primary component of glucurunoxylans. (D)

Structure of arabinose (arabinofuranose) ............................................................. 11

Figure 2.4 General structure of a portion of β-glucan polymer. The arrows mark the presence of

β (1, 3) glycosidic bonds ...................................................................................... 13

Figure 2.5 General structure of pectin chain, which is supposed to have in it the components like

RG I, XGA, HG and RG II, attached either in a linear manner or as the side chains to

pectic chain .......................................................................................................... 15

Figure 2.6 Structures of (A) Hydroxycinnamyl alcohols-p-coumaryl alcohol, coniferyl alcohol

and sinapyl alcohol and (B) derived structural units H, G and S ......................... 16

Figure 2.7 General structure of lignocellulose complex ....................................................... 20

Figure 2.8 Structure of copper centres Type 1, type 2 and type 3 of laccase enzyme catalytic

cluster .................................................................................................................. 36

Figure 2.9 A general representation of laccase catalysis mechanism of 2, 6-dimethoxy phenol

using Cu2+ site .................................................................................................... 37

Figure 2.10 A conceptual design of MBM reactor to achieve a CBP to generate ethanol from pre-

treated wheat straw .............................................................................................. 52

Figure 4.1 Total soluble sugar content (Kg/m3) in fungal cultures of non-autoclaved, autoclaved

and untreated Shiraz grape waste. Values for non-autoclaved and autoclaved samples

are significantly different to untreated samples (p < 0.05) .................................. 86

Figure 4.2 Reducing sugar content (kg/m3) in fungal cultures of non-autoclaved, autoclaved and

untreated Shiraz grape waste. Values for non-autoclaved and autoclaved samples are

significantly different to untreated samples (p < 0.05) ........................................ 88

xx

Figure 4.3 Pentose sugar content (kg/m3) in fungal cultures of non-autoclaved, autoclaved and

untreated Shiraz grape waste. Values for non-autoclaved and autoclaved samples are


Figure 4.4 Lignin content (%) in fungal cultures of non-autoclaved, autoclaved and untreated

Shiraz grape waste. Values for non-autoclaved and autoclaved samples are


Figure 4.5 Cellulase activity (U/mL) of the fungal cultures in non-autoclaved and autoclaved


significantly different between the samples (p ≤ 0.05), except for the asterisk (*)

marked values (p > 0.05) ..................................................................................... 93

Figure 4.6 β-glucosidase activity (U/mL) of the fungal cultures in non-autoclaved and autoclaved


significantly different between the samples (p ≤ 0.05) ........................................ 95

Figure 4.7 Xylanase activity (U/mL) of the fungal cultures in non-autoclaved and autoclaved


significantly different between the samples (p ≤ 0.05) ........................................ 97

Figure 5.1 Total protein content of fungal degraded Shiraz grape waste over 2 weeks of SSF.

Data from autoclaved fermented samples are used for comparison purposes. All the

values are the mean of triplicate data (P ≤ 0.05) .................................................. 103

Figure 5.2 Total reducing sugar content of fungal degraded Shiraz grape waste over 2 weeks of

SSF. Data from autoclaved fermented samples are used for comparison purposes. The

values are the mean of triplicate data and P ≤ 0.05, except for the bar marked with the

asterisk ................................................................................................................. 104

Figure 5.3 Lignin content of fungal degraded Shiraz grape waste over 2 weeks of SSF. The

untreated sample refers to the lignin content of grape samples before any treatment or

addition of growth medium. The values are means of triplicate data (P < 0.05) . 107

Figure 5.4 Comparison of cellulase activity (U/mL) in the fungal degraded Shiraz grape waste

over 2 weeks of SSF compared to autoclaved pre-treated samples. The values are

means of triplicate data and P < 0.05, except for the bars marked with an asterisk (*)

.............................................................................................................................. 111

Figure 5.5 Comparison of β-glucosidase activity (U/mL) in the fungal degraded Shiraz grape

waste over 2 weeks of SSF compared to autoclaved pre-treated samples. The values

are means of triplicate data and P < 0.05 ............................................................. 114

xxi

Figure 5.6 Comparison of xylanase activity (U/mL) in the fungal degraded Shiraz grape waste

over 2 weeks of SSF compared to autoclaved pre-treated samples. The values are

means of triplicate data and P < 0.05 ................................................................... 116

Figure 6.1 Total protein content of 1 week SSF degraded grape biomass and hydrothermal pre-

treated submerged grape biomass. The values from untreated sample are for

comparison purposes. All values are average of triplicate data with p < 0.05 between

the treatment methods .......................................................................................... 127

Figure 6.2 Main effects plot showing the relationship between various fungal cultures across

different growth media in terms of cellulase activity. The values represented in

parentheses denote standard errors ...................................................................... 129


different growth media, in terms of β-glucosidase activity. The values represented in



different growth media, in terms of xylanase activity. The values represented in


Figure 6.5 Matrix plot shows the fungal enzyme activities under different conditions. The top X-

axis labels of 1, 2, 3 and 4 refer to T. harzianum, A. niger, P. chrysogenum and P.

citrinum, respectively .......................................................................................... 132

Figure 6.6 Enzyme activities observed in individual cultures under different conditions compared

to that of the optimized mixed culture (n=3) ....................................................... 137

Figure 6.7 (A) PCA score scatter plot displaying the metabolite output pattern of mixed fungal

degradation of grape biomass. (B) DModX line plot of PCA metabolites. .......... 140

Figure 6.8. (A) PLS-DA score scatter plot and (B) Loading scatter plot of degraded Shiraz

substrate according to fungal species analysed using GC-MS ............................ 142

Figure 6.9 Important features selected by volcano plot with fold change threshold (x-axis) 2 and

t-tests threshold (y-axis) 0.05 .............................................................................. 145

Figure 7.1 Total lignin content and lignin degradation over 16 days by the co-culture of wood-rot

fungus, Ph. chrysosporium and statistically optimized Ascomycota mix ........... 151

Figure 7.2 Cellulase activity observed over 16 days by the co-culture of wood-rot fungus, Ph.

chrysosporium and statistically optimized Ascomycota mix ............................... 153

xxii

Figure 7.3 β-glucosidase activity observed over 16 days by the co-culture of wood-rot fungus,

Ph. chrysosporium and statistically optimized Ascomycota mix ........................ 155

Figure 7.4 Xylanase activity observed over 16 days by the co-culture of wood-rot fungus, Ph.


Figure 7.5. Laccase activity observed over 16 days by the co-culture of wood-rot fungus, Ph.


Figure 7.6 Lignin peroxidase activity observed over 16 days by the co-culture of wood-rot

fungus, Ph. chrysosporium and statistically optimized Ascomycota mix ........... 161

Figure 7.7 PCA model of winery biomass degradation by the co-culture of Ph. chrysosporium

and statistically optimized ascomycota mix ........................................................ 162

Figure 7.8 ‘DModX’ or ‘Distance of observation’ plot of winery biomass degradation by the co-

culture of Ph. chrysosporium and statistically optimized ascomycota mix. The

samples refer to Control (1-5), Day 4 (6-10), Day 8 (11-15), Day 12 (16-20) and Day

16 (21-25) ............................................................................................................ 163

Figure 7.9. PLS-DA model of winery biomass degradation by the co-culture of Ph.

chrysosporium and statistically optimized ascomycota mix ................................ 164

Figure 7.10A. OPLS-DA model of winery biomass degradation by the co-culture of Ph.

chrysosporium and statistically optimized ascomycota mix. ............................... 165

Figure 7.10B. Loading scatter plot of winery biomass degradation by the co-culture of Ph.

chrysosporium and statistically optimized ascomycota mix ................................ 166

Figure 7.11 Volcano plots displaying significantly generated metabolites (D8 and D16) during

winery biomass degradation by the co-culture of Ph. chrysosporium and statistically

optimized ascomycota mix .................................................................................. 167

Figure 8.1 Principal Component Analysis of Aspergillus niger flux over 8 days during winery

biomass degradation ............................................................................................ 179

Figure 8.2 ‘DModX’ or ‘Distance of observation’ plot of Aspergillus niger flux over 8 days of

winery biomass degradation ................................................................................ 180

Figure 8.3 Partial Least Square- Discriminant Analysis derived score scatter plot of Aspergillus

niger flux over 8 days during winery biomass degradation ................................. 181

Figure 8.4 Partial Least Square- Discriminant Analysis derived loading scatter plot of

Aspergillus niger flux over 8 days during winery biomass degradation .............. 182

xxiii

Figure 8.5 Volcano plot displaying the differential expressing metabolites in deuterated and non-

deuterated media .................................................................................................. 183

Figure 8.6 Metabolic pathways for A. niger mediated SSF degradation of winery biomass waste

over 8 days ........................................................................................................... 184

Figure 8.7 Changes in the composition of metabolites leading to or from the ‘glucose/glucose-6-

phosphate’ junction in the glycolysis pathway during A. niger mediated grape waste


Figure 8.8. Changes in the features of the D-ribulose metabolism path of the pentose phosphate

pathway ................................................................................................................ 188

Figure 8.9. Changes in the features of the D-ribulose metabolism path of pentose phosphate

pathway ................................................................................................................ 189

Figure 8.10. Changes in the features of the D-ribose metabolism path of pentose phosphate

pathway ................................................................................................................ 189

Figure 8.11. Changes in the features of gluconate metabolism path of pentose phosphate pathway

.............................................................................................................................. 190

Figure 8.12. Changes in the features of significant shikimate pathway metabolites ............... 191

Figure 8.13. Changes in the significant metabolite features of significant fucose degradation

pathway ................................................................................................................ 191

Figure 8.14. Changes in the features of acetyl-CoA and most significantly observed pathway

intermediates of valine, leucine and isoleucine biosynthesis in A. niger ............. 194

Figure 8.15. Changes in the features of acetyl-CoA and R-3-hydroxy butanoate, metabolites

leading to BHP biosynthesis in A. niger .............................................................. 194

Figure 8.16. Medium chain fatty acid conversion by A. niger during degradation of Shiraz grape

waste over 8 days ................................................................................................. 196

Figure 8.17. Intermediate metabolites of urea cycle and Yang cycle/methionine salvage pathway

.............................................................................................................................. 196

Figure 8.18. Principal Component Analysis of P. chrysogenum flux over 8 days during winery


Figure 8.19. DModX’ or ‘Distance of observation’ plot of Aspergillus niger flux over 8 days of


xxiv

Figure 8.20. PLS-DA derived score scatter plot of P. chrysogenum flux over 8 days during winery


Figure 8.21. PLS-DA derived loading scatter plot of P. chrysogenum flux over 8 days during


Figure 8.22. Volcano plot displays the differentially expressing metabolites in deuterated and non-

deuterated media of P. chrysogenum ................................................................... 203

Figure 8.23. Metabolic pathway of P. chrysogenum during SSF based degradation of winery

biomass waste over 8 days ................................................................................... 204

Figure 8.24. Metabolic variation of glucose and L-sorbose metabolism by P. chrysogenum during

winery biomass waste degradation of Shiraz grapes ........................................... 207

Figure 8.25. Metabolism of melibiose with respect to glucose by P. chrysogenum during winery

biomass waste degradation of Shiraz grapes over 8 days .................................... 208

Figure 8.26. Metabolism of chitin biosynthesis intermediates with respect to glucose-1-phosphate

by P. chrysogenum during winery biomass waste degradation of Shiraz grapes over 8

days ...................................................................................................................... 209

Figure 8.27. Biosynthesis and metabolism of melanin pigments by P. chrysogenum during SSF

based winery biomass waste degradation ............................................................ 210

Figure 8.28. Metabolism of glycerate-3-phosphate and sugars from pentose phosphate pathway by

P. chrysogenum during winery biomass waste degradation of Shiraz grapes over 8

days ...................................................................................................................... 211

Figure 8.29. Metabolism of pentose acids by P. chrysogenum during winery biomass waste

degradation of Shiraz grapes over 8 days ............................................................ 212

Figure 8.30. Xylitol production by P. chrysogenum during xylose metabolism by winery biomass

waste degradation of Shiraz grapes over 8 days .................................................. 213

Figure 8.31. Propanol production by P. chrysogenum during metabolism by winery biomass waste


Figure 8.32. Pectin degradation by P. chrysogenum during metabolism by winery biomass waste


Figure 8.33. Isoprenoid biosynthesis by P. chrysogenum during metabolism by winery biomass

waste degradation of Shiraz grapes over 8 days. α-tocopherol has been scaled on the

secondary axis ...................................................................................................... 217

xxv

Figure 8.34. Lignin and tannin degradation via benzoate degradation pathway by P. chrysogenum

during metabolism by winery biomass waste degradation of Shiraz grapes over 8 days

.............................................................................................................................. 219

Figure 8.35. Penicillin production via lysine degradation pathway by P. chrysogenum during

metabolism by winery biomass waste degradation of Shiraz grapes over 8 days 220

Figure 8.36. Fatty acid biosynthesis by P. chrysogenum during metabolism by winery biomass waste


xxvi

Annotations

All the acronyms including chemical names, general terminologies, SI units used in the

thesis.

AATCC American Association of Textile Chemists and Colourists

ABS Australian Bureau of Statistics

ABTS 2,2'-azino-bis(3-ethylbenzothiazoline-6-sulphonic acid)

Acetyl-CoA Acetyl-coenzyme A

AFEX Ammonia fibre explosion

AIL Acid Insoluble Lignin

AIR Acid Insoluble Residue

ANOVA Analysis of variance

AP Apiogalacturonan

AR Analytical grade

ASL Acid Soluble Lignin

AWRI Australian Wine Research Institute

AX Arabinoxylans

BOD Biochemical Oxygen Demand

BSA Bovine Serum Albumin

BSTFA N,O-Bis(trimethylsilyl)trifluoroacetamide

CAZy Carbohydrate active enzyme classification

CBH Cellobiohydrolase

CBP Consolidated Bio-processing

xxvii

CDH Cellobiose dehydrogenase

CHD Coronary Heart Disease

CMCase Carboxymethyl cellulase

COD Chemical Oxygen Demand

COVAIN Covariance-Inverse

CSIRO Commonwealth Scientific and Industrial Research Organization

DAHP 7-phospho-2-deoxy-3-D-arabino heptanoate

Dcrit Critical value of DModX

DMAPP Dimethylallyl pyrophosphate

DNSA 3, 5-dinitrosalicylic acid

EC Enzyme Commission number

EG Endoglucanases

EI Electron ionization

EPA Environment Protection Agency

eV Electron Volt

FAO Food and Agriculture Organization

FC Fold change

FEBF Palm empty fruit bunch fibre

FF Furfurals

FPA Filter Paper Activity

GC-MS Gas Chromatography-Mass Spectrometry

GDHB γ-glutaminyl-3,4-dihydroxybenzene

xxviii

GH Glycoside hydrolase

HCW Hot-compressed water

HETCOR Heteronuclear correlation spectra

HMF- Hydroxymethyl furfurals

HPLC High Pressure Liquid Chromatography

HSD Honestly significant difference

HT Hydrothermal treatment

IBM International business Machines

IPP Isopentyl pyrophosphate

IU International unit

IUPAC International Union of Pure and Applied Chemistry

kDa Kilo-Daltons

KDC 2-keto-acid decarboxylases

KEGG Kyoto Encyclopaedia of Genes and Genomes

kGy Kilo Gray

kHz Kilo-Hertz

kJ Kilo-Joules

kPa kilo Pascals

Kw Molal ionisation product of water

kWh Kilowatt- hours

LC-MS Liquid chromatography-mass spectrometry

LDL Low density lipoproteins

xxix

LiP Lignin peroxidase

LSD Least significant difference

MBM Multi-species biofilm membrane reactor

MCFAs Medium chain fatty acids

MEP 4-phospho-2-C-methyl erythritol

MnP Manganese peroxidase

MSI Metabolomics Standard Initiative

NA Nutrient Agar

NAG N-acetyl glucosamine

NB Nutrient Broth

NIH National Institutes of Health

NIST National Institute of Standards and Technology

NMR Nuclear magnetic resonance

OIV L'Organisation Internationale de la Vigne et du Vin

OPLS-DA Orthogonal PLS-DA

PCA Principal Component Analysis

PDA Potato Dextrose Agar

PKs Polyketide synthases

PLS-DA Partial Least Square-Discriminant Analysis

pNPG para-Nitrophenyl β-D-glucopyranoside

PRESS Model's predictive ability

RG I Rhamnogalacturonan I

xxx

RG II Rhamnogalacturonan II

RSD Residual standard deviation

RuBisCO Ribulose biphosphate carboxylase

SE Steam explosion

SmF Submerged fermentation

SPSS Statistical Package for the Social Sciences

SS Adjusted sums of squares

SSF Solid state fermentation

SW Supercritical water

TCA Tricarboxylic acid

TG-FTIR Thermo-gravimetric analyser coupled with Fourier Transform Infrared

Spectrometry

TIC Total Ion Chromatogram

TMCS Trimethylchlorosilane

VP Versatile peroxidases

1

CHAPTER 1

Introduction

Chapter 1 Introduction

2

1. Introduction

1.1. Overview

Fermentation is chiefly used for liquor production from various sources which

include barley, grapes, and molasses from sugarcane. Brewery wastes consist of dry

matter (25%), proteins (20-30%), ethers (3%), crude fibres (20%) and amino acids

(Agblevor et al., 2003, Alonso et al., 2004, Bowling et al., 2011, Rjiba et al., 2007). In

any fermentation process, the organisms like Saccharomyces cerevisiae are able to

ferment to give ethanol of up to 12-15% from the raw material, which is either used as

beer, ale or wine after clarification and as whisky, brandy or rum etc. after distillation

(depending on the source of raw material). Rest of 80-85% of raw material with unused

live or dead fermenting organisms is discarded as spent wash, which in addition to (used

as solvent) adds up to 6-15 times the actual volume of alcohol produced (Ozdural, 2004,

Strong and Burgess, 2008). The Biochemical Oxygen Demand and Chemical Oxygen

Demand (COD) values in these spent washes range about 20000-60000 ppm and 50000-

200000 ppm, respectively (Mohana et al., 2009, Wakelyn, 2006).

In wine industry, besides the spent wash and other post-fermentation wastes, raw

wastes like grape seeds, skin, pomace, marcs, stalks and skin pulp, which were

traditionally dumped after the fermentation process. However, it has been reported that

these parts are composed of valuable components like ethanol, organic acids, oil,

phenolics, colloids and dietary fibres (Arvanitoyannis et al., 2006). Presence of various

secondary metabolites has been reported antioxidants in winery wastes derived from

grapes. Grape seeds have been reported to contain polymers of flavanols like cathechins

and proanthocyanidins have been reported to be antiulcer, anti-carcinogenic and

antiviral in nature (González-Paramás et al., 2003). The polyphenols obtained from the

grape peels, marcs and stalks have been reported to lower Coronary Heart Disease

(CHD) and low density lipoproteins (LDLs) (Alonso et al., 2002, Guendez et al., 2005).

Various workers have reported fungi as the primary decomposers of cellulose,

hemicelluloses and pectins, followed by bacteria, acting as the secondary degraders in

most instances (Kumar et al., 2008, Szostak-Kotowa, 2004, Wood and Garcia-

Campayo, 1994). A large number of microbes are employed for degradation of

wastewaters from breweries. These not only reduce BOD and COD levels by about 80-

90%, but also are responsible for conversion of complex biological molecules to simple

ones, many of which serve as the sources of biofuels. These include fungi like


3

Aspergillus niger, Aspergillus fumigatus, Aspergillus niveus and Penicillium lignorum

(Rao et al., 2007, Sánchez, 2009). Thermophiles such as Clostridium thermocellum are

efficient bioethanol producers with an increased tolerance towards ethanol (Georgieva

et al., 2007, Rani and Seenayya, 1999). These organisms can be utilised in a symbiotic

manner to degrade the biomass and produce biofuel molecules at commercially viable

volumes.

However, fungi belonging to the division Ascomycota, such as Trichoderma

spp., Aspergillus spp. and Penicillium spp., are known for their biomass degrading

ability. These fungi have been well studied for their high cellulase and hemicellulase

enzyme production (Brijwani et al., 2010, Klyosov, 1987b). Such enzymes have the

potential to be used in generating important molecules such as alcohols, flavonoids,

organic acids and phenolics (Arvanitoyannis et al., 2006, Sánchez, 2009, Strong and

Burgess, 2008). A number of fungi from division Basidiomycota, or more commonly

known as white and brown-rot fungi are also known for considerable lignin degradation,

a major impediment in biomass degradation. These fungi include species such as

Phanerochaete spp. and Trametes spp. which have been known for their ability to

completely mineralize the lignin component of biomass to CO2 and H2O (Sánchez,

2009, Singh Arora and Kumar Sharma, 2010).

1.2. Problems associated with biomass degradation

Although, biomass degradation seems like a straightforward process, due to the

involvement of large amounts of factors, it is a very complex process. One of the main

factors related to the biomass degradation is the structure of the biomass itself. The

lignocellulose complex, which forms the basic structure of biomass, is made up of

complex links between cellulose, hemicelluloses and lignins (Eriksson, 1990,

Grisebach, 1985, Leistner, 1985, Brune, 2014). This structure limits the access of

enzymes in the lignocellulose complex to degrade the constituent molecules such as

cellulose, hemicellulose and lignins. In addition to this, there is no single organism that

can degrade all the components. Lignocellulosic sources like woods contain various

molecules like terpenes, phenolics and aliphatics and do not contain any transport water,

which ceases or slows down any microbial reaction significantly (Eriksson, 1990,

Sjöström, 1993). In addition to these, the enzymes which catalyse the lignocellulose

complex themselves have numerous limitations. Lignins not only act as the physical


4

barriers to the microbial degradation, but also as the non-productive binders to the

cellulases (Alvira et al., 2010, Andrić et al., 2010a, Esteghlalian Ali et al., 2000). In

addition to the above conditions, cellulases themselves have very low catalytic activity

(Sainz, 2009, Zhang and Lynd, 2002). Product inhibition is a long known phenomenon,

which adds up the difficulties for lignocellulose degradation. It has been reported by

numerous workers, especially in cellulose degradation, that cellobiose, the chief product

of cellulose degradation and glucose, the minor product, act as the inhibitors for the

cellulases (Baldrian and Valášková, 2008, Carere et al., 2008, Klyosov, 1987b, Sainz,

2009).

1.3. Various biomass degradation approaches

There are various approaches which can be applied on individual level or in

combination with different methods to improve the biomass degradation and its

successive conversion to products of interest. One of the preliminary methods of

biomass degradation consists of pre-treatment procedures such as acid or alkali

treatment or steam blowing techniques. The biomass applied to such treatments then can

be degraded by normal microbial processes. For example, a successive hydrothermal

pretreatment and fungal treatment produces greater lignocellulose degradation as

compared to regular fermentation (Papadimitriou, 2010).

Solid state fermentation (SSF) is one of the second generation bioconversion

processes for the production of industrially important molecules like bio-hydrogen and

bio-ethanol. The method employs a mixture of variable organisms and/or derived

enzymes from them in a single step to generate a considerable higher bioconversion of

biomass as compared to submerged fermentation (Brijwani et al., 2010, Kausar et al.,

2010, Lee, 1997, Sarkar et al., 2012). As the water content in SSF is equal to or slightly

higher than the substrate, the method allows greater aeration and surface attachment to

the filamentous fungi. This approach allows for a production of vast array of

lignocellulolytic enzymes. It also increases the amount of substrate to be degraded in a

single step from 1-2% in submerged fermentation to more than 10% in SSF (Lee, 1997,

Ng et al., 2010).


5

Consolidated Bio-processing (CBP) is one of the upcoming techniques which

employ a number of different strategies to improve the biomass degradation process.

Briefly, it involves a single step process to degrade the substrate and convert it in to the

product of interest. This not only saves considerable amounts of time, but also saves a

number of other resources such as raw materials and more importantly, finance, thereby

making it an economic technique. CBP involves numerous strategies such as genomic

strain improvements in the microbial populations involved in biomass degradation to

yield the products of interest, metabolic engineering and utilization of multiple

organisms, including thermophilic microbes to enhance the one-step degradation and

successive bioconversion process (Demain et al., 2005, Lynd et al., 2005).

One of the simpler methods of biomass degradations however is the

establishment of microbial symbiotic system in order to improve the degradation. To

overcome numerous limitations associated with first and second generation biomass

degradation processes, mixed fungal culture degradation has been suggested (Brijwani

et al., 2010, Chu et al., 2011). It is known that, apart from satisfactory cellulase

production, Aspergillus sp. generate highly efficient xylanases and β-glucosidases in

exceptional quantities (Betini et al., 2009, Singhania, 2012). Additionally, Penicillium

spp. can be used for lignin mineralization to enhance the overall degradation process

(Rodríguez et al., 1994, Rodriguez et al., 1996).

1.4. Aims and strategies of the project

The aims associated with this project, regarding improvement in degradation of

winery derived biomass waste are listed below. The list also briefly overviews the

strategies applied for each aim.

• Applying hydrothermal pre-treatment method followed by fungal biomass

degradation to improve the biomass degradation under the submerged fermentation

conditions.

• Application SSF by the fungi belonging to division Ascomycota and

Basidiomycota. One of the primary reasons for using this strategy was to conserve

the amount of water used in the process and increasing the amount of substrate

being degraded during the process.


6

• Application of statistical based approach to obtain a balance between submerged

fermentation and SSF based approaches. Statistical models were created from the

results based on submerged fermentation and SSF. The models were then used to

predict the optimized mixture of fungi to be used and the ratio of medium and

substrate to increase the biomass degradation.

• Metabolome analysis of fungal mediated biomass degradation in order to further

improve the biomass degradation. Unique metabolites and metabolic pathways for

individual fungi were tracked by metabolic flux analysis. The critical points in

these pathways were observed for altering the entire pathways to generate the

products of interest during biomass degradation.

• Generation of sequential biomass degradation process involving basidiomycetes,

followed by ascomycetes, in combination with application of metabolic pathway

analysis to achieve further improvement in biomass degradation.

7

CHAPTER 2

Literature review

Chapter 2 Literature review

8

2.1. Biomass composition

Grapes are one of the major global horticultural crops with an estimated production

of 69.1 million metric tonnes during 2012. Of this, approximately 80% are wine grapes

(OIV, 2013). Australia is one of the major grape producing regions and during 2012-13,

1.75 million metric tonnes of grapes were crushed for wine production (ABS, 2013).

Wineries produce large amounts of biomass waste, amounting to about 50-60 % of total

grape crushed during the process (Devesa-Rey et al., 2011, Gerling, 2011). In addition

to this, wineries have high wastewater generation of as much as 8 litres per bottle of

wine (Christ and Burritt, 2013). Winery wastes have been classified as pollutants by the

European Union and subsequent treatment and post-product processing are required to

make these wastes less hazardous (Devesa-Rey et al., 2011).

Grape wastes consist of grape berries and plant-derived fibres, grape seeds, skin,

marcs, stalk and skin pulp. Unlike other agricultural by-products, grape biomass waste

has limited use as an animal feed stock due to its poor nutrient value and low

digestibility (high concentration of tannins and polyphenols). The polyphenols also slow

down microbial utilization of this biomass. In addition, the winery grapes have high

amounts of potassium (1.2-7.3 %) and acidity and thus have limited application as either

animal feed or agricultural compost fertilizer. As a result, a majority of these winery

biomass wastes end up as toxic landfill waste (Devesa-Rey et al., 2011).

Fermentation is mainly used for liquor production from various sources which

include barley, grapes, and molasses from sugarcane. Brewery wastes consist of dry

matter (25%), proteins (20-30%), ethers (3%), crude fibres (20%) and amino acids. The

major components of dry grape biomass waste are cellulose, pectins and lignins

(including tannins).

2.1.1. Cellulose

Cellulose is the most abundant and most studied polysaccharide and constitutes

the basic structure of cell walls of all plants and algae. It comprises about 20-50% of

plant biomass and is the largest contributor to the lignocellulose complex, which

comprises cellulose, lignin, hemicellulose and trace amounts of protein and fatty acids

(Eriksson, 1990, Hori, 1985, Sánchez, 2009). Cellulose chiefly occurs in the primary


9

layer of the plant cell wall and its presence decreases in the secondary and tertiary cell

wall layers. The molecule occurs as an aggregated, lateral microfibril network, which

form mesh-like structures of a highly complex nature, which has only been partially

understood until now even by employing various complicated methods like NMR, X-

ray crystallography and electron microscopy (O'Sullivan, 1997, Fernandes et al., 2011).

Figure 2.1. Fragment of cellulose with the reducing, non-reducing and cellobiose component of

cellulose

Cellulose is an almost linear molecule made up of β-D-glucopyranose, linked by

β-1, 4-polyanhydroglucose with cellobiose as the smallest repetitive unit (Figure 2.1),

thus, is a β-glucan (Kumar et al., 2008, O'Neill et al., 2004, Stone, 1958). Until the

1970s, glucose and cellobiose were considered the monomer units of cellulose.

However, by applying model-building strategies, it has been more recently confirmed

that glucose is not the main basic structural component of cellulose, but rather

cellobiose is. Interestingly, cellobiose is a disaccharide, made up of two glucose units

linked by β-1, 4 bonds (O'Sullivan, 1997).

The microfibrils forming the cellulose molecule are arranged in a parallel

manner, thus giving the whole molecule a defined crystalline nature. The individual

glucopyranose structures have been reported not only linked to each other by covalent

bonds, but also with the neighbouring pyranose molecules, lying in a parallel fashion by

Hydrogen bonds and Van der Waals forces of attraction (Figure 2.2). This makes the

whole molecule highly rigid and tolerant to natural degradation compared to other

biopolymers, which are more heterogeneous; cellulose is made of only single β-4-D-

pyranose structure (Dashtban et al., 2009, Eriksson, 1990, McNeil et al., 1984, Wood

and Garcia-Campayo, 1994).


10

Figure 2.2. Hydrogen bonds in cellulose molecule, represented by dashes (---).

Cellulose molecule has been long known as the molecule with a very high

degree of polymerization, which varies from 6000 in certain algae to 14000 in cotton

(Nakamura et al., 2002, Zieher, 2010), with a fold occurring every 30 nm (Fengel, 1971,

O'Sullivan, 1997). Crystallographic methods showed that the native cellulose (cellulose

isolated from the plants) is about 70% of crystalline and 30% amorphous. The minor

amorphous nature of cellulose makes it slightly susceptible to degradation by various

enzymes and other chemicals (McNeil et al., 1984, O'Sullivan, 1997, Wood and Garcia-

Campayo, 1994).

2.1.2 Hemicelluloses

Various hemicelluloses have been reported to be directly or indirectly associated

with cellulose. They include sugars such as xylans, xyloglucans, mannans, pectins,

homogalacturonans, rhamnogalacturonans, arabinans and galactans (Kumar et al., 2008,

Pérez et al., 2002).

2.1.2.1. Xylans

Xylans are the most numerous of all hemicelluloses, composing about 25% of

all saccharides, second only to cellulose to which they are tightly bound by covalent and

other bonds in a complex manner (Fengel, 1971, Goksu et al., 2007, Hansen and

Plackett, 2008) (Figure 2.3).

They are made of β (1, 4) D-xylopyranoses with non-crystalline hexose uronic

acids such as galacturonic acid (Figure 2.3) attached to them.


11

Figure 2.3. (A) Structure of xylopyranose, which is the primary component of Xylans. (B) Structure

of β-galactopyranose molecule. (C) Structure of 4-O-methyl glucopyranoxyl uronic acid (MeGLcA),

which is a primary component of glucurunoxylans. (D) Structure of arabinose (arabinofuranose).

The xylans are further classified as homoxylans, glucurunoxylans, arabino-

glucurunoxylans, arabinoxylans (AX), glucuruno arabinoxylans and heteroxylans,

depending on the types of substitutions made in xylose chain.

2.1.2.2. Xyloglucans

Chiefly related with the seed cotton and primary cell walls of numerous

dicotyledons, xyloglucans are multidimensional hemicelluloses composed of various

monosaccharides such as D-glucose, D-xylose and D-galactose as common components

in varied ratios joined by β-1, 4 linkages. The core generally consists of a β-(1, 4)

glucan skeleton to which xylose is attached by α-(1, 6) linkages along with fucose,

galactose or arabinose (in minor concentrations) (Hori, 1985, Hayashi, 1989).

2.1.2.3. Mannans

Mannans have been classified into three classes consisting of galactomannan;

linear mannans, glucomannans and galactoglucomannans (Table 2.1). These polymers

have been reported in numerous plants like date, coffee (Meier, 1958, Wolfrom et al.,

1961), ivory nut (Aspinall et al., 1953, Nieduszynski and Marchessault, 1972),

Arabidopsis (Chanzy et al., 1984, Handford et al., 2003) and numerous algae. They also

occur in wood in conjugation with β (1, 4) D-glup (Alonso-Sande et al., 2009, Franz,

1973).


12

Table 2.1 Major classification of different mannans

Mannans Source Properties References Glucomannans Angiosperms,

Gymnosperms

Linear β (1, 4) D-manp and D-glup chains. DP of 15-80. Amorphous, heavy acetylation for cellular defence

(Brink and Vries, 2011, Chanzy et al., 1984, Handford et al., 2003, Meier, 1958, Moreira and Filho, 2008, Nieduszynski and Marchessault, 1972, Northcote, 1972, Painter, 1983, Petkowicz et al., 2007)

Galactomannan Angiosperms [β (1, 4) D-manp and D-glup chains] attached to α (1, 6) D-galp. High molecular weight and high DP. Variations: β (1,3)D-manp attached to β (1,4)D-galp

(Dea and Morrison, 1975, Ishurd et al., 2004)

Galactoglucomannans Angiosperms Most numerous of all mannans. Randomized β (1, 4)-D-glup and D-manp linkages.

(Moreira and Filho, 2008, Northcote, 1972)

2.1.2.4. β-glucans

β-glucans are the generic names for the non-cellulosic polysaccharides grouped

as β (1, 3), β (1, 4) and β (1, 3; 1, 4) linked D-glucans (Figure 2.4). They have been

reported to be restricted to members of the Poaceae (formerly Graminae) family

(McNeil et al., 1984); however, they also have been found in lichen and fungi (Fontaine

et al., 2000, Lazaridou et al., 2003, Lazaridou et al., 2004). Additionally, β (1, 3; 1, 4)

glucans have been reported in cotton (Li and Brown, 1993). The polymers have been

reported in seed endosperms of wheat (Philippe et al., 2006), barley (Coles, 1979,

Seefeldt et al., 2009), oat and rice (Brown et al., 1997). They are reportedly formed in

golgi membranes by β-glucan synthetase (Autio, 2006, Tsuchiya et al., 2005).


13

Figure 2.4. General structure of a portion of β-glucan polymer. The arrows mark the presence of β

(1, 3) glycosidic bonds.

The polymers are linear in nature and are made up of D-glup molecules linked

by both β (1, 3) and β (1, 4) glycosidic linkage either separately [as in (1, 3) and β (1, 4)

glucans] and simultaneously [β (1, 3; 1, 4) glucan]. Additionally, trace amounts of

arabinose and xylose also have been found in association with these polymers

(Lazaridou et al., 2003, Lazaridou et al., 2004, McNeil et al., 1984).

Amongst the two types of glycosidic bonds, β (1, 4) bonds are the majority

(about 70%) with β (1, 3) constituting the remaining portion. β (1, 4) bonds are always

located on the reducing ends of the polymer and form continuous adjacent linkages. β

(1, 3) bonds, on the other hand, do not form continuous linkages (Buliga et al., 1986,

Woodward et al., 1983).

Three types of molecules, namely cellobiosyl sugars, cellotriosyl sugars and

cellodextrins, were reportedly released upon the actions of bacterial endoglucanase

enzymes; β-D-glucanohyrolase from Bacillus subtilis (Carpita, 1996) and endo-(1, 3),

(1, 4) -β-glucanase from B. amyloliquifaciens (Tsuchiya et al., 2005). These molecules

comprised 2-3 β (1, 4) linked molecules, which constitutes about 90% of the polymer.

The rest of the polymer is composed of about 4-15% β (1, 4) linked D-glup residues.

Most researchers have reported the presence of cellotriosyl and cellotetraosyl residues

as the smallest components obtained by glucanase action. These molecules are linked to

each other by β (1, 3) glycosidic linkages (Burton and Fincher, 2009, Burton et al.,

2010, Tsuchiya et al., 2005) at the O-3 or O-4 positions (Burton and Fincher, 2009).

However, the ratio of β (1, 4) and β (1, 3) linkages vary from plant to plant and is highly


14

random (thus, determining functional properties of the polymers in different species)

with variations from 1.15:1 in sorghum to 2.6:1 in other cereals like barley (Burton and

Fincher, 2009, Burton et al., 2004).

Among the constituent oligosaccharides, cellotriosyl makes up the major part (~

90%) and cellotetraosyl comprises the remainder (Burton and Fincher, 2009). Due to

these variations, the structure and the molecular weight of β-glucans differ significantly

across different species. The molecular weight of oat β-glucans has been reported as 20

kDa by size exclusion chromatography (Liu and White, 2011, Vaikousi et al., 2004).

2.1.2.5. Pectins

Pectins are complex polysaccharides composed of 1, 4-D-galacturonic acid

residues. These polysaccharides are localized chiefly on the primary cell wall region of

plant tissue with components covalently and ionically bound to each other to give the

pectic compounds an overall complex structure (Burton et al., 2010, Caffall and

Mohnen, 2009, O'Neill et al., 2004, Pérez et al., 2003). Due to this heterogeneous

nature, pectins have a range of molecular weights (from 50-100 kDa) and differential

chemical structures (Pérez et al., 2003). Apart from α-1, 4-galacturonic acid (as the

main chain), pectins also contain substituted residues of rhamnogalacturonan I. The Gal

A is in 4C1 chair conformation with a carboxyl group in the centre, surrounded by

various linkages and ionic covalent bonds (Burton et al., 2010, O'Neill et al., 2004).

Figure 2.5. General structure of pectin chain, which is supposed to have in it the components like

RG I, XGA, HG and RG II, attached either in a linear manner or as the side chains to pectic chain

(Adapted from Mohnen, 2008).


15

Pectins are currently distinguished in five classes which include polysaccharides

such as rhamnogalacturonan I (RG I), rhamnogalacturonan II (RG II),

homogalacturonan (Martens et al.), xylogalacturonan (von Gal Milanezi et al.) and

apiogalacturonan (AP) (Figure 2.5). They form one of the larger parts of primary cell

wall along with hemicelluloses and make up about 35% of the total primary cell wall

composition. They function in various important aspects such as growth, cell and

organogenesis, cell-cell adhesion, defence, abscission, ionic binding and cell maturity

(Naran et al., 2008, O'Neill et al., 2004, Ridley et al., 2001, Vincken et al., 2003).

Although they are found in both Type I and Type II primary cell walls, their chief

occurrence is in the former where they play an important role in cell-cell adhesion by

forming a gelatinous region, which in turn affects the porosity of the cell wall. This

disrupts H-bond formation in cellulose and xyloglucans, thus allowing the cell wall to

expand and insert new molecules in its structure without breakage (Bouton et al., 2002).

2.1.3. Lignins

Lignins are the second most abundant biopolymers after cellulose. In most

plants, especially, in higher plants, they function as the prominent components of

xylem. Due to their hydrophobic nature, they aid in water transport throughout the plant

system. This nature also makes them tolerant towards the biodegradation (Boerjan et al.,

2003).

Lignin biosynthesis and deposition occurs in the secondary cell wall after the

cell has completed its growth. During primary growth, cellulose and hemicellulose are

deposited on the cell wall, followed by sequential deposition of lignin in the secondary

walls. However, most lignin deposition takes place in the next stage, which is followed

by cell death (Baucher et al., 1998).

Lignins are made up of hydroxycinnamyl alcohols i.e. p-coumaryl alcohol,

coniferyl alcohol and sinapyl alcohol (Figure 2.6) and their methoxy derivatives

(Boerjan et al., 2003, Higuchi, 2006, Vanholme et al., 2010). The chief methoxy

products of these three hydroxy cinnamyl alcohols, which form the structural units of

lignin polymers, are known as general p-hydroxyphenyl units (H), general guaiacyl

units (G) and general syringyl unit (S) (Baucher et al., 1998, Boerjan et al., 2003,

Higuchi, 2006, Ralph et al., 2004, Vanholme et al., 2010). However, due to the very

complex nature of the lignin polymer, they have not been successfully isolated and


16

characterized (Vanholme et al., 2010) from natural sources. Higher plants have greater

amounts of G and S units, while grasses and other monocots are rich in H units

(Baucher et al., 1998, Boerjan et al., 2003, Yang et al., 2010a).

Figure 2.6. Structures of (A) Hydroxycinnamyl alcohols-p-coumaryl alcohol, coniferyl alcohol and

sinapyl alcohol and (B) derived structural units H, G and S.

During lignin formation, the p-hydroxycinnamyl alcohols interact with either of

the units (H, G or S) to form monolignols, which are secondary precursors. Besides

monolignols, there are at least three major groups in lignins which form a major part of

the lignin polymer. These include acetylated lignin units, ferulates and dihydroxy

coniferyl alcohol and guaiacylpropane-1, 3-diol derived units. Acetylated lignin units

are derived from two or more lignin monomers like p-coumaryl alcohol, sinapyl

alcohol, 5-hydroxyconiferyl alcohol, hydroxycinnamyl aldehydes, hydroxybenzyl

aldehydes, hydroxycinnamate esters, dihydrocinnamyl alcohols, arylpropane -1, 3-diol,

aryl glycerols, hydroxycinnamyl acetates, hydroxycinnamyl p-hydroxybenzoates,

hydroxycinnamyl p-coumarates and tyramine hydroxycinnamates. These molecules are

primarily attached at γ-positions to form acetylated units. They may be regarded as the

foremost of all lignin components, constituting more than half of the lignin polymer (Lu

and Ralph, 2002).

Ferulates are formed chiefly of hydroxycinnamate esters and guaiacyl units of

monolignols. They are one of the important groups of lignin polymers, although, they

are not as abundant as the acetylated groups. However, these molecules and their

hydroxy derivatives provide important polysaccharide-polysaccharide crosslinking,

which is vital for lignocellulose binding, thus creating strong bonds in the


17

lignocellulosic complex. The ferulates and their derivatives are chiefly found in the

Poaceae family where they form the centre for aggregation to form lignin polymer

(Ishii, 1997, Ralph et al., 1995).

The third and minor group of lignin forming units are derivatives of dihydroxy

coniferyl alcohol and guaiacyl propane-1, 3-diol, which are respectively formed by their

association with guaiacyl unit. As their name suggests, they are chiefly found in

conifers (Gymnosperms) and are minor elements in angiosperms (both monocots and

dicots). The chief linkages by which monolignols and their derivatives join each other

to form lignin polymers are β-O-4 and β-5, 5-5 dibenzodioxocin and 4-O-5 biphenyl

ether linkages. Additionally, β-β-resinol and β-1-oligomer couplings have also been

reported (Ralph et al., 2004).

Among these, β-O-4 linkages are most common and most readily favoured

between the monolignols. Additionally, 5-5-dibenzodioxocin related linkages show

some unexpected 8-membered ring formation by the process of radical coupling of β-O-

4 bonds in rings, followed by an internal trapping of an external ring structure. These

structures form branching points of lignins. In addition, due to their higher complexity

compared to β-O-4 linkages, they are difficult to break open during degradation

processes (Ralph et al., 2004).

During lignification, β-O-4 coupling leads to the formation of two isomers of β-

ether units called erythro- and threo- units, formed in approximately equal amounts.

These two isomers differ significantly in their physical and chemical properties, with

erythro- isomers having higher degradability under alkaline conditions (Kirk, 1984).

The β-β coupling occurring in monolignols, which also occurs in lignins, forms

resinols which are the initiation points for lignin polymer formation. However, the type

of β-β coupling differs in softwoods and in grasses, wherein β-β coupling appears as 4-

O-5 links in the former (Önnerud and Gellerstedt, 2003). In grasses, these bonds are

probably formed due to the prevention of –OH group addition to quinone methide

groups under acidic conditions, which contrasts to softwood β-β coupling, wherein,

addition of –OH groups assist the bond formation (Ralph et al., 2004, Zhang et al.,

2006).

β-1 couplings are the least common of all the linkages found in lignins due to

their high complexity with respect to the other linkages (Lundquist et al., 1967, Weng

and Chapple, 2010). The linkage is found as the secondary type of bond in already

present β-O-4 linkage. These types of linkages occur between G-G and G-S units and


18

are prevalent in conifers and softwoods such as birch (Li et al., 1997), poplar

(Ämmälahti et al., 1998) and spruce (Zhang et al., 2006). However, syringyl based (S)

β-1 linkages have been reported in grass fibres (Ralph et al., 2004, Zhang et al., 2006).

Apart from the previously discussed major molecules, a minor amount of

phenolics are also associated with lignins. Those form a minor portion in softwoods, but

are found in larger amount in grasses in their acetylated form (Boerjan et al., 2003,

Ralph et al., 2004). Some of the Poaceae family members contain as high as 17%

phenolic content in lignin present as acetylated γ-p-coumarate derivatives. The

phenolics are highly complex in their occurrence in the lignin structure and the

mechanism of their structure and involvement in lignification is still unknown (Liu et

al., 2011, Ralph et al., 2004).

Other molecules that have been reported to be associated with lignins have been

determined by the method of pyrolysis of lignins by Thermo-gravimetric analyser

coupled with Fourier Transform Infrared Spectrometer (TG-FTIR). These include

phenol, methoxy phenol, guaiacol and cresol in addition to simpler molecules like

methanol, formaldehyde, acetaldehyde, acetic acid and some simple hydrocarbons with

CO, CO2 and H2O (Liu et al., 2011).

2.2. Biomass degradation

Numerous agricultural activities, especially harvesting and post-harvest

processes generate significant amounts of biomass wastes globally. It has been

estimated that the major crops; corn, barley, oat, rice, sorghum and sugarcane generate

about 73.9 × 1010 metric tonnes of biomass waste per year (Kim and Dale, 2004). The

global agriculture sector plays a major role in generating waste biomass. As of 2012,

reported global agricultural production was about 2.16 × 1010 metric tonnes of which

only 3.43 × 109 metric tonnes of processed products were produced. In addition, the

Food and Agriculture Organization (FAO) of the United Nations indicated the

generation of about 1.06 × 1010 metric tonnes of agricultural wastes during 2011 of

which 5.62 × 108 metric tonnes was of lignocellulosic nature (FAO, 2015). Most of this

biomass waste remains underutilized. The following sections discuss some of the issues

which limit biomass degradation, particularly in terms of obtaining by-products of

commercial interest. Possible solutions to achieve biomass degradation by micro-

organisms, especially fungi, will also be discussed.


19

2.2.1. Biodegradation: issues and problems

Due to their abundance, lignocelluloses have been long considered as a good

substrate for microbial degradation processes. From the late 1970s, several workers

have reported the processing of lignocellulose complexes to derive single cell proteins

(Poulsen and Petersen, 1988); biofuels like ethanol, H2 and CH4 (Demain, 2014, Pauly

and Keegstra, 2008) and metabolites such as polyphenols and flavonoids (Arnous and

Meyer, 2009, Pinelo et al., 2006, Vicens et al., 2009) have been obtained, albeit in very

low quantities. Despite this research, biodegradation of the lignocellulose structure faces

numerous problems which have not yet been fully resolved. Some of the chief issues

related to lignocellulose degradation are given below.

2.2.2. Structure of lignocellulose complex

As mentioned earlier, the lignocellulose complex is made up of three major

components, cellulose, hemicelluloses and lignins, and other components

(glycoproteins, lipids, free amino acids and other metabolites) which interact with the

major components (Eriksson, 1990, Grisebach, 1985, Leistner, 1985). Among the major

components, lignin forms the outermost structure of lignocelluloses and is the most

complex molecule. Unlike cellulose, lignin is made up of wide range of aromatic and

aliphatic molecules (Eriksson, 1990, Grisebach, 1985, Leistner, 1985). Over the years

workers have proposed a generally accepted model for lignocelluloses (Fengel, 1971,

O'Sullivan, 1997, Chundawat et al., 2011) as given in Figure. 2.7.


20

Figure. 2.7. General structure of lignocellulose complex.

The lignocellulose system is chiefly associated with the secondary cell wall of

the plants as the primary cell wall has been reportedly composed of about 90-95%

cellulose with rest of the components being either of the hemicellulosic components,

xylan or glycoprotein (Carpita, 1996, Singh et al., 2009).

Most microbial or chemical degradation methods have not been successful

against lignin due to its variable and intractable nature (Baucher et al., 1998, Boerjan et

al., 2003, Ralph et al., 2004). Hemicelluloses include various types of molecules packed

together by H-bonds. Other molecules are equally complex, such as pectins which are

composed of as many as five different polysaccharides (10-50 saccharide molecules

long) (Mohnen, 2008, Naran et al., 2008, Ridley et al., 2001). Pectins are highly varied

and consist of galactose (or galacturonic acid) rich residues called homogalacturonans,

rhamnogalacturonans, arabinogalacturonans and xylogalacturonans (Burton and

Fincher, 2009, Caffall and Mohnen, 2009, Naran et al., 2008).As yet, no single

microorganism has been reported that can degrade such a wide range of biomolecules.

The amount of hemicelluloses also determines the ease of biodegradation of

lignocellulosic complexes. As mentioned earlier, cellulose microfibrils are surrounded

by a layer of hemicelluloses and lignins (Fengel, 1971, O'Sullivan, 1997).

Due to their very high complexity, hemicelluloses increase the recalcitrant

nature of the overall lignocellulose complex. The recalcitrant nature of hemicelluloses

increases due to their high levels of cross-interactions with cellulose microfibrils

(Brijwani et al., 2010, Carpita and Gibeaut, 1993, Zieher, 2010).


21

2.2.3. Microbial and chemical limitations

As discussed above, due to the varied nature of lignins and hemicelluloses, there

is no single organism that can degrade all the components of ligniocellulose.

Lignocellulosic sources like woods contain various molecules such as terpenes,

phenolics and aliphatics and do not contain transport water, which ceases or

significantly slows down any microbial reaction (Eriksson, 1990, Sjöström, 1993).

Quinones and some flavonoids, which inhibit microbes and microbial enzymes

function, have been reported from many plants, especially woody ones from the

Leguminosae family (Dalbergia plants) and Acacia, (Leistner, 1985).

2.2.4. Enzymatic limitations

One of the other major factors impeding biomass degradation is the physical

interaction of major enzymes, i.e. cellulases and hemicellulases, with their respective

substrates. Lignins not only act as physical barriers to microbial degradation, but also as

non-productive binders of cellulases (Alvira et al., 2010, Duarte et al., 2012b). In

addition to the above conditions, the cellulase enzyme complex overall has very low

catalytic activity. In Trichoderma spp., one of the highest producers of cellulases, it has

been observed that the specific activity of celluloses on their substrates is very low as

compared to the other enzymes (Sainz, 2009, Zhang and Lynd, 2002). Cellulose activity

on filter paper has been shown only at 0.6-0.7 IU/mg of enzyme, which corresponds to

a catalytic constant of 0.5-0.6/sec for the enzymes, which is well below glucoamylase

activity of 69 IU/mg corresponding to a catalytic constant of 58/sec (Klyosov, 1987b).

Additionally, within the cellulases, activity varies leading to decreased reaction rates or

complete inhibition. Within the same experimental set up, it was reported

that carboxymethyl cellulase and xylanases have activities of about 350 IU/ml and 500

IU/ ml, respectively, as against cellobiohydrolases (8 IU/ ml) and β-glucosidase (8

IU/ml) (Andrić et al., 2010a, Hideno et al., 2011, Juhász et al., 2005).

2.2.5. Product inhibition

Product inhibition is a well-known phenomenon which adds to the difficulties of

lignocellulose degradation. It has been reported by numerous workers, especially with

respect to cellulose degradation, that cellobiose (the chief product of cellulose

degradation) and glucose (the minor product) act as cellulase inhibitors (Baldrian and


22

Valášková, 2008, Duarte et al., 2012b). The main reason for this is the exceptionally

low catalytic activity of β-glucosidase as compared to the other enzymes present in the

cellulase enzyme complex (Baldrian and Valášková, 2008, Hideno et al., 2011,

Klyosov, 1987b). Additionally, the glucose produced in the process is unable to be

oxidised or reduced and so cannot be easily remediated from the system (Kim et al.,

2012a, Liu et al., 2012, Liu and White, 2011, Sainz, 2009), i.e. there is an inability of

many cellulolytic microbes to utilize it during simultaneous cellulolytic processes

(Baldrian and Valášková, 2008). It has been reported that concentrations of glucose and

cellobiose as low as 6% inhibit the enzymatic catalysis of cellulose (Sainz, 2009). In

the case of lignin and hemicellulose degrading enzymes, much has been reported about

the enzyme kinetics, especially inhibition and competition kinetic mechanisms.

However, xylanases are probably inhibited due to cellobiose as they are conjugated with

cellulose (Qing et al., 2010).

2.3. Biomass pre-treatment

Pre-treatment forms one of the important steps in biomass conversion towards

commercially/industrially useful products. The process breaks several linkages and

bonds between cellulose, hemicelluloses and lignins, exposing considerable amounts of

hemicelluloses and celluloses to subsequent enzyme/microbial based degradation

(Sarkar et al., 2012). There are several physical, chemical and biological methods of

biomass pre-treatment, which are used according to the biomass type, source and

required type of product recovery. No single pre-treatment is adequate for complete

biomass conversion. However, a mix of two or more types (physical-chemical,

physical-biological and chemical-biological) has been observed to generate higher

biodegradation (Khuong et al., 2014, Kovacs et al., 2009, Lu et al., 2013, Maeda et al.,

2011, Pribowo et al., 2012).

2.3.1. Physical pre-treatment methods

These methods are generally applied to increase the overall surface area of

biomass, thus exposing higher amounts of cellulose and hemicellulosic residues. This

leads to a decrease in recalcitrance, enabling subsequent chemical, enzymatic or

microbial treatment to be more effective (due to the availability of a higher proportion

of substrate). There are various types of physical pre-treatments; however they can be


23

broadly categorised as milling, irradiation, extrusion, hydrothermal treatment and

expansion.

2.3.1.1. Milling

Milling is a very common method applied in biomass pre-treatments. There are

several types of milling methods used including ball milling, hammer milling, grinding,

and chipping. Milling and grinding break several linkages between biomass

components, thus decreases the particle size and increasing the overall surface area.

These techniques are not necessarily very efficient in themselves, but they enhance the

effectiveness of any following pre-treatments such as steam explosion or hydrothermal

treatment (Karimi et al., 2013).

The economic viability of the entire milling process varies according to the

actual and required particle size of biomass substrates, total moisture content and nature

(high lignin or low lignin) of the waste. Particle size is one of the major factors we hope

to control by physical pre-treatment processes. A very high particle size decreases the

efficiency of any following chemical or biological conversion, whereas particles of very

fine size can lead to aggregation, which results in substrate channelling in liquid-based

treatments. This not only decreases efficiency of the following treatment, but also the

economic viability of the entire process. The amount of energy required for wheat straw

milling to 0.8 mm particle size was observed as 51.6 kWh per metric tonne of biomass

(Sarkar et al., 2012). Similarly, the process of milling followed by 2 mm sieving and

screw pressing was observed to utilize 27.7 kWh of energy and 0.6 m3 of water per

metric tonne, while multi-sieving followed by pulping and dispersion utilized about

83.5 kWh of energy and 1.1 m3 of water per metric tonne (Bernstad et al., 2013). The

examples show that the economic viability of the entire process decreases considerably

as the number of physical treatment processes increase.

2.3.1.2. Irradiation and sonication

Irradiation methods use the property of polar bond vibrations to expand the

biomass, subsequently causing an internal explosion, resulting in the breakage of

several bonds and other linkages. Milling can be utilized to decrease the particle size,

although it is not an essential pre-process for microwave irradiation. The process


24

requires an aqueous medium due to its effects on polar bonds in the biomass. It has been

observed that microwave-assisted heating in the presence of water causes an

endothermic deviation peaking at 134°C, followed by a decrease in heat flow at 170°C

due to depolymerisation. The crystallanity of cellulose decreases considerably at 180°C

due to an increase in the rotating ability of polar bonds, which also leads to a further

decomposition of amorphous cellulose. Microwave products have a calorific value of

33.1 kJ/g against conventional pyrolysis (30 kJ/g). Similarly, microwave treatment has

been shown to be economically viable as the heat of combustion is lower (about 18

kJ/g) in microwave-treated products compared to conventional pyrolysis (25 kJ/g) at

280°C (Budarin et al., 2010). Another method of irradiation is the electron beam, which

uses variation in magnetic fields to rotate the polar bonds in biomass. This process

results in room temperature disruption in cellulose and hemicellulose structures and

some mineralization of lignins. Recently, it was observed that the degree of

polymerization of cellulose in hardwood Kraft pulp and bamboo chips decreased almost

2.5-3-fold following a radiation dose of 15 kGy. However, this dose of electron beam

radiation was not very effective on hemicelluloses (Ma et al., 2014).

A more common pre-treatment method is sonication which has been

increasingly used in recent research. The process utilizes ultrasonic frequencies ranging

from 16-100 kHz to disrupt linkages and bonds in the biomass. In an alternating

sequence, cavitation bubbles are formed and imploded in the biomass containing

aqueous phase, thus increasing local temperature and pressure at minute scales, causing

a physical breakdown on lignocellulose components. It has been reported that

sonication of 20-25 minutes at 20-25 kHz in alkaline conditions removed about 80%

lignin and hemicellulose and more than 95% cellulose from sugarcane bagasse. Variable

but promising results from numerous biomass substrates have indicated this method to

be a potentially simpler technique to improve biomass conversion in a direct/indirect

process (Karimi et al., 2013, Rehman et al., 2013).

2.3.2. Physico-chemical treatment

These methods utilize both physical and chemical (mild and strong) techniques

to decompose/degrade the lignocellulose complex. As mentioned earlier, standalone

physical methods such as milling and pyrolysis require higher amounts of input energy

to break down lignocellulose complex. They are not highly economical treatments, with


25

an efficiency of about 45-55% bioconversion. Microwave, although more energy and

time viable, is also only able to convert about half of the biomass (Sarkar et al., 2012).

It has been widely observed that the application of more than one type of

treatment increases biomass conversion efficiency. Physico-chemical techniques utilize

the physical attributes of temperature and pressure along with the chemical attributes of

water, acids and alkaline chemicals to either separate biomass components or alter the

recalcitrant structure to make it more degradable. Hydrothermal treatment (HT), steam

explosion (SE), liquid hot water treatment (LWH) and ammonia fibre explosion

(AFEX) are some of the most commonly used physico-chemical methods used at

laboratory and industrial scales.

Hydrothermal treatment utilizes the property of the molal ionisation product

(Kw) of water for biomass hydrolysis. The Kw of water is dependent on the temperature

of the system. Kw increases from 0.64 × 10-14 at 18°C to 54.6 × 10-14 at 100°C and 268

× 10-14 at about 150°C. Kw reaches its highest value at about 250°C, when it is 634 ×

10-14. This increase in ionic product is important as it is directly proportional to the

water solubility. The solubility of substrates (such as cellulose and hemicelluloses)

increases with the rise in Kw value. This relationship is always taken into account when

applied to several hydrothermal processes such as hot-compressed water (HCW), Steam

explosion (SE) and supercritical water (SW) treatments. Therefore, temperatures around

250°C or above are applied in these processes (Ando et al., 2000, Goto et al., 2004). It

was observed that autoclaving 130°C for 1 hour resulted in about 11.7 % increase in

dissolved organic carbon, indicating a mass loss of co-mingled household waste.

However, due to the increasing Kw, this conversion increased to about 16% at 200°C.

Although, considerable amounts of cellulose (13%) and some hemicellulose (6%) were

recovered by autoclaving at 160°C, lignin loss was absent or negligible in samples

treated at 200°C (Papadimitriou, 2010).

SE utilizes chemical catalysts such as H2SO4 or SO2 to decompose the biomass,

albeit at lower temperature with respect to HT. Experiments on Douglas fir softwood

chips showed that with the mild H2SO4 treated samples, an SO2 based SE at 120°C

resulted in 80-90% hemicellulose release within 1 hour of treatment (Shevchenko et al.,

2000).These methods are simple in nature, however have major drawbacks in the

requirements of high temperature systems (>160°C) with or without corrosive


26

chemicals and the generation of considerable hydroxymethyl furfurals (HMF) and

furfurals (FF) which, if present in a microbial degradation system, cause severe growth

inhibition (Sanchez and Bautista, 1988, Duarte et al., 2012b).

Ammonia fibre explosion (AFEX) is a type of SE which utilizes NH3 instead of

acids or SO2-based catalysis. The conditions are similar to those in SE; however the

alkaline conditions produced due to NH3 mean that inhibitors such as HMF and

furfurals are not generated in this process. Similar to SE, this method is efficient for

biomasses with considerable hemicellulose and moderate cellulose content but cannot

reduce or mineralize the lignin component of the lignocellulose complex. Also, the

higher costs associated with NH3 limits the use of AFEX in industrial applications

(Sarkar et al., 2012).

2.3.3. Chemical treatments

Chemical treatments use strong alkaline or acidic reagents in diluted forms

under room temperature or elevated temperatures. Although oxidising agents such as

H2SO4 and NaOH are frequently used, SO2, CO2, formic acid and acetic acid have also

been used.

2.3.3.1. Acid pre-treatment

Acid pre-treatment is one of the most common methods used in research and in

limited industrial processes. Dilute acids are used at elevated temperatures of around

100-120°C. They cause substantial breakage of hydrogen bonds in cellulose and

especially in hemicelluloses. This process decreases the crystallanity of cellulose to a

considerable extent, thus increasing its degradability in follow-up microbial degradation

processes. Acidic treatment causes monomerization of hemicelluloses to produce free

pentose sugars in the aqueous phase. In an SE experiment where 3% H2SO4 was

applied at 120°C, the release of about 14-36% pentoses and 18-27 % hexoses was

observed (Shevchenko et al., 2000). However, up to 90% hemicellulose removal has

been reported (Karimi et al., 2013). Although this method in itself is cost effective as

compared to milling, pyrolysis and AFEX, it has its own disadvantages. The

requirement of high temperature conditions in acid pre-treatment enhances reactor

vessel corrosion. Thus, acid-tolerant vessels are required, which increases overall


27

expenses. Besides, this process, like HT, generates moderate quantities of inhibitory

molecules such as HMF and furfural in addition to the loss of sugars due to their linkage

with acid-soluble lignins. However, the major costs involved are due to downstream

neutralization of acid-treated biomass which involves expensive alkaline treatments and

generates considerable amounts of wastewaters (Karimi et al., 2013, Sarkar et al.,

2012).

2.3.3.2. Alkali pre-treatment

Alkaline treatment is more focussed towards lignin removal as opposed to

cellulose or hemicellulose degradation, and is widely used in paper and pulp industries.

The process specifically degrades lignin in substantial proportions allowing the

exposure of cellulose and hemicellulose to downstream catalysis. As opposed to acid

pre-treatment, alkali treatments do not require elevated temperatures. An application of

2% NaOH in combination with ultrasonic treatment for 20 minutes at 50°C has been

reported to remove about 81% hemicellulose and 91% lignin from sugarcane bagasse.

Follow-up bacterial treatment with Cellulomonas flavigena (MTCC 7450) resulted in

about 91% glucose yield of the theoretical maximum (Velmurugan and Muthukumar,

2012). In recent experiments, an application of 5% NaOH at 121°C for 1 hour resulted

in a decrease of lignin in sugarcane bagasse from 23.5% to 5.2%. It also resulted in the

loss of about 15.5% xylan. However, glucan content increased by about 28%.

Subsequent biodegradation by the fungus Phlebia spp. MG-60 resulted in about an

ethanol yield of 66% of the theoretical maximum (Khuong et al., 2014). One of the

issues related with alkaline treatment of biomass is the transformation of the cellulose

component. Utilization of alkaline compounds such as NaOH in long-term treatments

results in mercerization, a process that converts natural cellulose (Cellulose I) to

mercerized cellulose (Cellulose II), which is more recalcitrant towards numerous

biodegradation techniques (Karimi et al., 2013, O'Sullivan, 1997).

2.3.3.3. Organosolv treatments

Organosolv treatments utilize organic solvents in either standalone (e.g. 100%

methanol) mode or in combination (e.g. methanol: acetonitrile: acetone in a percentile

ratio of 40:20:40) to degrade biomass. The method uses polar or non-polar solvents

such as methanol, ethanol, and acetone in the presence of catalysts such as formic acid,


28

acetic acid, sulphuric acid or hydrochloric acid. Due to the use of highly volatile

molecules, this process is used for lignin removal due to their high hydrophobicity and

their high solubility in non-polar solvents.

The process has been reported to generate as much as 97% glucose from rice

straw cellulose (Sarkar et al., 2012). Similarly, a Pinus radiata substrate treated with an

acetone-water mixture at 195°C for 5 minutes resulted in an ethanol yield of

approximately 99.5%. The disadvantages of this treatment method are its high costs and

volatility of non-polar molecules which are flammable and explosive at elevated

treatment temperatures (Haghighi Mood et al., 2013).

2.3.4. Biological pre-treatment

Biological pre-treatments rely on microbial cells, especially wood rot fungi, to

degrade biomass. The chief aim of this method is delignification, although numerous

fungi also hydrolyse cellulose and hemicellulose components. Fungi such as white-rot

and brown-rot fungi are utilized for this pre-treatment. White-rot fungi such as Ph.

Chrysosporium, T. versicolor and Phlebia spp. are able to produce universal biomass

degradation with lignin reduction followed by cellulose and hemicellulose hydrolysis.

On the other hand, brown-rot fungi such as Postia spp. and Laetiporus spp. reduce only

the lignin component of biomass, leaving cellulose and hemicellulose needing further

treatment. The application of these fungi in biomass treatment is highly cost-effective

compared to chemical and physical pre-treatment, however the overall effectiveness of

standalone biological pre-treatment is still low without pre-treatment. In combination,

the pre-treatment process of milling and Acremonium spp. enzymes treatment has been

found to be highly effective and has reported to yield as much as 90% hexose and 77%

pentose degradation (Sarkar et al., 2012).

2.4. Fungal degradation of biomass

Cellulose is the chief source of carbon in lignocellulose complex, as it is the

richest source of glucose, the chief source of carbon for the microorganisms. Cellulose,

however, exists in the core of the complex, surrounded by layers of hemicellulose and

lignins (Figure 2.7) and is often inaccessible. To improve access to the cellulose, fungi

have to penetrate through these outer layers. Numerous workers have shown that fungi


29

are better degraders than the bacteria in this aspect (Alvira et al., 2010, Arantes and

Saddler, 2010, Zuroff and Curtis, 2012). Fungi ranging from Trichoderma reesei to

some of the less studied brown/white-rot fungi apply a battery of enzymes to achieve

this. The enzymes include cellulases (complexes of endo- and exo-glucanases and

glucosidases), hemicellulases (such as xylanases, mannanases and arabinases) and

lignases (Dashtban et al., 2009, Pérez et al., 2003, Sánchez, 2009).

Lignocellulosic fungi secrete large amounts of different enzymes which degrade

a wide variety of substrates to make the utilizable carbon source available. The systems

differ in composition to hemicellulases, whose secretions, even under the similar

conditions (Baldrian and Valášková, 2008, Dashtban et al., 2009, Sánchez, 2009, Sipos

et al., 2010) depends on substrate variability. Fungi reportedly produce cellulases with

varied individual enzyme composition. For example, it was reported that T. koningii

produced 31.3 U/ml of endoglucanse which is significantly greater than T. reesei (5

U/ml) and A. niger (7.5 U/ml) (Liu et al., 2012).

2.4.1. Cellulases

Cellulases are complex enzyme systems which generally consist of the three

enzyme classes; endoglucanases, exoglucanases and β-glucosidases.

Endoglucanase (EC 3.2.1.4) form the first enzymes to be secreted from the

lignocellulolytic enzyme complex when degrading a biomass substrate. These are also

called endo-1, 4-glucanases or carboxymethyl cellulases (CMCases). These are the

enzymes that convert crystalline cellulose to the amorphous form making it more

accessible to cellobiohydrolases (Dashtban et al., 2009, Sweeney and Xu, 2012, Zhang

and Lynd, 2002). At present, 10 glycoside hydrolases (GH) of endo-1, 4-glucanases are

known. Among those, 5-6 are known to occur in most of the cellulolytic fungi.

Endoglucanases generally bind through H-bonds in amino acid. Residues within

the enzyme cleft to the cellulose molecule, then either stretch the glycosidic bonds, thus

breaking them, or distort the normal chair 4C1 orientation to the skew/boat orientation,

thus changing the glucosyl structure from -1 to +1 (Davies et al., 2003, Davies et al.,

1995). Almost all EGs have highly specific substrate activity. Some families of EGs,

such as Cel45 EGs, are especially cellulolytic, thus are unable to act on any other

substrates (like hemicelluloses), while others, like Cel5 EGs (arabinoxylan,

mannan/galactomannan and xyloglucan), are variable in their activity (Cantarel et al.,


30

2009, Davies et al., 1995, Gloster et al., 2007, Lawoko et al., 2000, Sweeney and Xu,

2012, Vincken et al., 1997, Vlasenko et al., 2010).

Among the EGs, Cel5 shows higher activities on amorphous cellulose than

crystalline cellulose. However, it is also reported to show less activity on amorphous

cellulose, which occurs in conjugation with lignins. Other EGs such as Cel7 and Cel12

have wider activity, as stated earlier. Cel45 activity is dependent chiefly on H-bonding

to cellulose molecules and not on π stacking as in most of the known EGs. Due to this,

the enzyme does not does not distort the β-glycoside linkage, but elongates it until it

breaks (Gloster et al., 2007, Hilge et al., 1998, Hirvonen and Papageorgiou, 2003,

Karlsson et al., 2002, Schagerlöf et al., 2007, Sweeney and Xu, 2012, Vlasenko et al.,

2010).

EGs are temperature sensitive enzymes with their optimal activity at 60°C and

pH range of 4-5. The stability for different EGs varies with many being highly stable at

40-50°C. Cel5 EGs, however, have been reported to be fairly stable and have been

shown to have good activity at 80°C, probably due to their thermophilic origin

(Baldrian and Valášková, 2008, Cantarel et al., 2009, Dashtban et al., 2009,

Maheshwari et al., 2000, Vlasenko et al., 2010). Unlike CBHs, EGs are monomeric in

nature and vary from 22-45 kDa. However, in some fungi such as Scletrotium rolfsii,

they have been reported to be about 80 kDa and to have catalytic groves (clefts) of up to

4 kDa (Uzcategui et al., 1991).

Cellobiohydrolases (CBH) or exoglucanases (EC 3.3.1.91) are known to be the

main cellulase components. They act by degrading the extended or distorted cellulose

molecules to convert it into cellobiose. In addition to any amorphous cellulose, CBH

also degrades crystalline cellulose. Like endoglucanases, CBHs are classified into

families (glycoside hydrolases; GH). Three families, GH6, GH7 and GH48, are known

to be the precursors of CBHs. Among these, CBH I is related to GH7 and is commonly

found in cellulolytic fungi. CBH II is known to occur in numerous fungi and reportedly

belongs to the GH6 family (Sweeney and Xu, 2012). The enzymes target β-1, 4-

glycosidic linkages as their cleavage sites on cellulose, while CBH II enzymes are

known to catalyse the non-reducing ends of the polymer. In some fungi, such as

Phanerochaete chrysosporium, CBH I has been reported to belong to the group of

enzymes called CBH58 and CBH62 (Baldrian and Valášková, 2008). Recently, CBH I

has been classified in the GH7 family and CBH II in GH6 (Cantarel et al., 2009).


31

CBH I is the chief enzyme in cellulose degradation, comprising 70% of the total

cellulase system (Colussi et al., 2012, Divne et al., 1994, Sánchez, 2009, Sweeney and

Xu, 2012). All the CBH I enzymes are non-glycosylated monomers of about 45-66 kDa.

The single enzyme is composed of a core and carbohydrate binding domains (CBDs).

The enzyme is primarily made up of β-sheets with the remaining part made of α-helix

content (about 25-32%) and β-strand loops joined by nine disulphide bridges

comprising the remainder. The CBD tunnels in CBH I are twice as long as those of

CBH II and consist of loops extending from the –COOH terminus. The tunnels are

complex network of glycosidic interacting amino acid sites (Divne et al., 1994,

Francieli Colussi et al., 2011, Sweeney and Xu, 2012).

The operating conditions of CBH are similar to those of the endoglucanases.

They show an optimum activity from 40°C-60°C between pH 4-5. However, it has been

reported that 50°C and pH 5 are the optimal conditions, while temperatures below 25°C

and above 55°C slowed enzyme activity (Baldrian and Valášková, 2008, Colussi et al.,

2012, Dashtban et al., 2009, Francieli Colussi et al., 2011, Sweeney and Xu, 2012).

CHBs are inhibited by their major (cellobiose) and minor (glucose) products

with concentrations of 3-6 % of either or both sugars inhibiting the cellulolytic action of

the enzyme by competitive inhibition (Baldrian and Valášková, 2008, Igarashi et al.,

1998, Klyosov, 1987b).

β-glucosidases (EC 3.2.1.21) are exo-glycoside hydrolases which catalyse the β-

1, 4 glycosidic bonds in cellobiose or other β-oligodextroses to produce glucose. Like

endoglucanases and exoglucanases, β-glucosidases are also categorised among GH

families 1 and 3 (Dashtban et al., 2009, Henrissat and Davies, 1997).

These enzymes have been reported from a large group of organisms from

bacteria to mammals (Dan et al., 2000, Dashtban et al., 2009, Eyzaguira et al., 2005,

Sweeney and Xu, 2012). β-glucosidases are generally produced by numerous

cellulolytic organisms (especially fungi) due to the competitive inhibition of endo- and

exoglucanases by their end products (cellobiose). However, in the case of Ph.

chrysosporium, β-glucosidases do not effectively degrade cellobiose and their primary

substrates have been reported as β-glucans. Nonetheless, they also degrade cellobiose at

significant levels (Igarashi et al., 1998, Nijikken et al., 2007).

The molecular weights of β-glucosidases, as estimated by gel filtration

chromatography, have been reported as approximately 46 kDa (Nijikken et al., 2007).

However, the molecular weights determined by amino acid sequencing and SDS-PAGE


32

were 52.6 and 53 kDa, respectively (Tsukada et al., 2006a), illustrating a degree of

polydispersity – different techniques will emphasise larger sizes leading to a higher

average molecular weight, and vice versa. In plants, the enzyme has been reported to

occur in multimeric forms, namely dimers (Burmeister et al., 2000, Czjzek et al., 2001,

Nijikken et al., 2007, Verdoucq et al., 2004), tetramers (Aguilar et al., 1997, Chi et al.,

1999) and hexamers (Sue et al., 2006).

As with the other cellulose enzyme constituents, β-glucosidase also suffers from

product inhibition, primarily from glucose and furfurals. In addition, the catalytic

activity of the enzyme has been found to be very low as compared to cellobiohydrolases

and endoglucanases (Baldrian and Valášková, 2008, Juhász et al., 2005, Kim et al.,

2012a).

2.4.2. Hemicellulases

Like cellulases, hemicellulases are a complex mixture of numerous enzyme

catalysing specific substrates. Due to the large number of sugars that constitute

hemicelluloses, the number of individual enzymes in hemicellulases is far greater than

the number of enzymes in cellulases. Hemicellulases are composed mainly of xylanases,

xyloglucanases, β-xylosidases, β-mannases, arabinofuranosidases and α-L-arabinases

(Dashtban et al., 2009, Shallom and Shoham, 2003). In addition, accessory

hemicellulases include esterases like xylan esterase, ferruloyl esterase and glucuronoyl

esterase (Dashtban et al., 2009, Sweeney and Xu, 2012).

Xylanases are a group of various hemicellulases responsible for the degradation

of various xylans by hydrolysing their β-(1, 4) linked D-xylopyranoside units. They

comprise up to 1% of total fungal lignocellulolytic enzymes and may work

symbiotically with other hemicellulases and cellulases, thereby facilitating a consortial

mix for degradation of biomass substrates. Like cellulases and numerous

hemicellulases, xylanases are also classified as glycoside hydrolases and most belong to

the GH 8, 10, 11, 30 and 43 families (Dashtban et al., 2009, Sweeney and Xu, 2012,

Cantarel et al., 2009).

The majority of fungal xylanases are reported to have optimal activities and

stability at a pH of 4 to 6. While a number of fungal species are known to have

xylanases which are active in extreme conditions, this property is seen chiefly in

extremophilic bacteria such as Thermonospora fusca (Bachmann and McCarthy, 1991,


33

Dhiman, 2008). Also, in some species, the optimal enzyme activity has been seen at

more acidic levels as in Aspergillus kawachii (2-6) and A. niger (3.0) (Ito et al.,

1992).The xylanases obtained from Aspergillus spp. have been repeatedly shown to be

active at pH 5 to 6. Unlike cellulases, xylanases have been reported to have high

activities of 650-930 U/ mg protein. It is also reported that in some species, certain

additives such as nitrogen increase this activity up to 30-36 U/mL as against 1.2 U/mL

under nitrogen depleted conditions. The optimal temperature for A. niger and A.

ochraceus xylanases in solid fermentation is about 65°C, while that of A. niveus is in the

range of 55- 65°C (Betini et al., 2009). Xylanases are more stable towards temperature

change, compared to cellulases, as exampled by Aspergillus cultures, where more than

70% of activity was retained at 75°C. However, even under optimal conditions, those

enzymes have a very low half-life of about 1 hour, which reduces significantly as the

temperature rises (Betini et al., 2009).

Although xylanase activity in a fermentation filtrate is not directly inhibited by

free sugars, they probably limit the range of activity over the degradation time. SDS-

PAGE and zymogram analyses show the enzyme size to be about 30 kDa. However, the

enzymes have ability to change conformation and pass through very small spaces (10

kDa cut-off membranes). They can thus catalyse a high amount of substrate in plant

cells (von Gal Milanezi et al., 2012). The enzyme follows Michaelis-Menten kinetics

with Km and Vmax of 47.08 mg/mL and 3.02 IU/mL, respectively. (Edivaldo Filho et al.,

1993, von Gal Milanezi et al., 2012).

Most fungal xylanases are active and stable at 40-55°C, with the optimum

condition dependent on the type of substrate. Where most Aspergillus spp. displayed

activity in 45-60°C, A. niger displayed optimal activity at 40°C on xylan (Garcia

Medeiros and Hanada, 2003), 48°C on wheat bran (Zhao et al., 2006)) and 45-50°C

(von Gal Milanezi et al., 2012) on sugarcane bagasse.

2.4.3. Lignases

Lignases are the group of enzymes that hydrolyse the lignin component in the

lignocellulose complex. Fungi, especially, those belonging to division Basidiomycota,

are however known for their lignin degrading abilities (Baldrian and Valášková, 2008).

Basidiomycetes are categorized as either ‘white-rot’ fungi or ‘brown-rot’ fungi,

depending on the nature of their lignin degradation. White-rot fungi comprise species


34

such as Phanerochaete spp., Trametes spp. and Ganoderma spp., while brown-rot fungi

include species of Fomitopsis spp. and Postia spp.

Basidiomycetes form the major portion of wood rotting fungi and can degrade

cellulose and hemicellulose in addition to the lignin component of biomass owing to the

large array of enzymes they produce. Numerous white-rot basidiomycota are known to

degrade a wide spectrum of biomass components. For example, Trametes spp. M23 has

been reported to effectively degrade humic acid and other humic substances in addition

to utilizing the free sugars and cellulosic components in the growing medium,

particularly in low nitrogen media (Grinhut et al., 2011). Humic substances are the

organic materials chiefly consisting of lignin, cutin, cutan, tannin and humic substances.

Some other basidiomycetes, such as Phlebia spp., Physisporinus rivulous and

Dichomitus squalens, degrade lignin selectively without affecting the other components.

The brown-rot basidiomycetes, such as Postia spp. and Laetiporus spp., can

degrade cellulosic and hemicellulosic substrates, but are unable to degrade lignins; a

property similar to some biomass degrading ascomycetes, as previously discussed

(Dashtban et al., 2010).

Most of these fungi use Fenton-like reactions in order to degrade the lignins in

biomass. This process involves a variety of enzymes which utilize the –OH- radicals

generated from the reaction of hydrogen peroxide and ferrous ions (Fe2+) or manganese

ions (Mn2+). The –OH- thus generated oxidises numerous lignin components to produce

small molecular weight polyphenols and quinones (Grinhut et al., 2011). Some of the

basidiomycetes, such as Phanerochaete velutina, have been observed to use this

property periodically or sequentially to degrade hemicellulose followed by cellulose.

The same property was noticed during the degradation of lignin, where selective lignin

degradation was observed for approximately the first 45 days of degradation

experiment. During this time, vanillin and dihydroxybenzoic acid (phenolic acids),

pimaric acid and palustric acid (resin acids) and sterols such as stigmasterols and

pinosylvin were observed to be degraded or oxidised (Valentín et al., 2010).

Most of the fungal lignases are extracellular in nature, i.e. they are secreted by

the lignin degrading fungi and act externally to the organism, in this case oxidising the

lignin components. This mechanism is activated to access the more useful and energy


35

beneficial components, namely cellulose and hemicelluloses. Fungal lignases can be

classified in two broad categories of phenol oxidases and heme peroxidases. The first

category comprises enzymes such as laccases, while the second category is comprised

of enzymes such as lignin peroxidases, manganese peroxidases and versatile

peroxidases.

2.4.3.1. Laccases

Laccases (EC 1.10.3.2) belong to the phenol oxidase group of lignases and the

family of multimeric copper oxidases that catalyse single electron oxidation processes

in many phenolic compounds. These enzymes have been reported from a number of

fungi associated with ascomycetes and deuteromycetes. However, most of the

commercial enzymes have been sourced from basidiomycetes. Whereas, the white-rot

fungi are commercially the major producers of laccases, brown-rot fungi such as Postia

placenta and Fomitopsis pinicola have been shown to produce comparatively lesser

amounts of laccases. Laccase has also been reported to be produced by Trichoderma

spp., however, only in the spore stage. These enzymes are glycoproteins with a

carbohydrate content of up to 20%. They are large molecules with a molecular weight

of about 60-80 KDa. Molecular oxygen is utilized by these enzymes as the electron

acceptor and is reduced to water in the following equation:

O2 + 4e- + 4H+= 2H2O

Laccases generally contain four copper atoms at their active site centres,

although fewer have been occasionally reported. These copper centres are bound by one

cysteine, ten histidine and, in some cases, one phenylalanine residues. The laccase

catalytic clusters made up of four copper atoms are classified into type 1, type 2 and

type 3 coppers. The catalytic region with the copper centre in the type 1 structure is

generally co-ordinated between two histidine nitrogens and one cysteine sulphur region

(Figure 2.8). This particular arrangement gives a typical blue coloration to the enzymes.

This co-ordination geometry is generally referred to as a distorted trigonal bipyramidal

and is unusual due to its intermediary between Cu+ and Cu2+ states. Type 2 and 3

structures have their active region copper ions arranged in a trinuclear centre (Figure

2.8). These copper centres are co-ordinated to eight histidine residues, arranged in four

His-X-His motifs (Manole et al., 2008, Singh Arora and Kumar Sharma, 2010).


36

Figure 2.8. structure of copper centres (a) Type 1, (b) Type 2 and (c) Type 3 of laccase enzyme

catalytic cluster.

Due to the presence of numerous types of catalytic centres, laccases are able to

oxidise a large number of substrates such as catechol, hydroquinone and guaiacol. Other

substrates, however, are too large to accommodate in the active site of catalytic region.

Chemical mediators are then required, acting as intermediate substrates which become

oxidised and are then able to interact with the actual target substrates with high redox

potential (Singh Arora and Kumar Sharma, 2010).

Laccases have been reported to possess high catalytic activity in acidic

conditions, although each laccase has its own pH optimum. The pH optimum also

changes depending on the nature of substrate being targeted. For example, it was found

that the highest laccase activity for tannic acid and syringaldazine oxidation was

observed at pH 5, whereas, the optimum pH for vanillic acid and hydroquinone

oxidation was around pH 4 (Manole et al., 2008). The nature of the substrate also

determines the other catalytic properties and, eventually, the catalytic preferences of the

laccases. It has been shown that ortho- compounds such as guaiacol, caffeic acid,

catechol, gallic acid and pyrogallol are better substrates than para-compounds such as

orcinol, resorcinol and phloroglucinol (Blaich and Esser, 1975). The temperature range

of laccase activities lies in mesophilic conditions i.e. from 20°C to 37°C, although more

stable thermophilic fungal laccases have been reported (Manole et al., 2008).

It has been shown that laccases are one of the most necessary components in

lignin degradation. In the case of Pycnoporus cinnabarinus, it has been demonstrated

that laccase mutants were unable to degrade Kraft lignin. Laccases cause radical

coupling by substituting electrons from phenolic hydroxyl groups to form phenoxy

radicals by the mechanism of one-electron subtraction. These radicals undergo partial

(a) (b) (c)


37

polymerization via an alkyl-aryl cleavage in their propyl side chains. There are three

types of reactions catalysed by laccases – C1-C2 cleavage (Cα-Cβ), C1-aryl cleavage

(alkyl-aryl) and C1 oxidation. In the presence of a free radical, an electron is donated to

the central cupric (Cu2+) site to convert it to the cuprous state (Cu+). Molecular oxygen

then converts this cuprous state back to the cupric state (Manole et al., 2008, Singh

Arora and Kumar Sharma, 2010) (Figure 2.9).

Figure 2.9. A general representation of the laccase catalysis mechanism of 2, 6-dimethoxy phenol

using the Cu2+ site.

As mentioned above, laccase activity is influenced by a number of factors.

Similarly, a number of conditions influence the production of laccases. Substrates rich

in nitrogen, such as malt or yeast extracts, have been reported to increase laccase

production and activity. An interesting observations made was (Srinivasan et al., 1995)

the production of laccases by Phanerochaete chrysosporium when cultured on cellulose

medium supplemented with a nitrogenous source such as ammonium tartarate.

Numerous other compounds, especially phenolics such as gallic acid, humic acid

ferulates and benzoates, also induce laccase production. Similar to other enzymes such

as cellulases and glucosidases, laccases are generally inhibited or repressed by sugars.

However, this is not as widespread as in cellulases and laccases in some fungi behave

variably to the concentrations of different sugars (Singh Arora and Kumar Sharma,

2010). Other reagents such as fatty acids, thiourea, glutathione, L-cysteine and ion

groups such as halides, cyanides, hydroxide Mg2+, Ca2+, Mn2+ and Zn2+ are known to

inhibit laccase activity (Dashtban et al., 2010, Singh Arora and Kumar Sharma, 2010)


38

2.4.3.2.Heme peroxidases

Heme peroxidases are ligninases which use heme groups as a cofactor during

their catalysis process, and may have one of several different catalytic cores. For

example, the catalytic core of lignin peroxidases is made up of ferrous ions, while that

of manganese peroxidases is made chiefly of manganese ions (Mn2+) or less commonly

Ca2+ or Cd2+ ions.

2.4.3.2.1. Lignin peroxidases

Lignin peroxidases (LiPs) are a group of isozymes which depolymerize non-

phenolic lignins and β-O-4 non-phenolic lignin compounds by H2O2-based oxidation

(EC 1.11.1.14). In addition to these polymers, the LiPs degrade numerous phenolic

molecules including guaiacol, vanillin, catechol and syringic acid. These enzymes were

first isolated from Ph. chrysosporium in 1983 and have been reported since to occur

widely in a number of white-rot fungi such as T. versicolor and Panus spp.

Interestingly, the enzymes also have been reported in lignin active bacteria such as

Acinetobacter spp. and Streptomyces spp. (Doyle et al., 1998, Baldrian and Valášková,

2008, Dashtban et al., 2010).

LiPs are isozymes and have molecular weights of about 36-50 KDa. The number

of isozymes varies in each fungal species, e.g., T. versicolor LiP is made up of 16

different isozymes (Janusz et al., 2013), while 15 isozymes form LiP for Ph.

chrysosporium (Ollikka et al., 1993). LiPs convert the target substrates to intermediate

radicals by single- or multi-step electron transfer mechanisms. These intermediate

radicals, due to their reactivity, become depolymerized. Unlike laccases, these isozymes

do not require mediator molecules or ions to catalyse substrates with high redox

potential. Also, unlike laccases, the heme sites with the enzyme active site are located

deep within the peroxidase enzyme unit. The substrate has to either pass through a

protein channel or a molecule such as veratryl alcohol attaches to the active site through

these channels and mediates the substrate oxidation. The enzymes catalyse the

substrates in a two-step reaction where, in the presence of an oxidizer such as H2O2, the

catalytic Fe3+ centre is oxidised by a one-step electron transfer to form an oxoferryl

compound. This radicalized enzyme then reacts with the substrate in a second reaction.

The equation of this process is shown below.


39

(Fe3+)POx + H2O2 (Fe(IV)=O)2+POx + H2O

(Fe(IV)=O)2+POx + Substrate (Fe(IV)=O)2+POx +Product

(Fe(IV)=O)2+POx + Substrate (Fe3+)Pox + Product

Protoporphyrin acts as the heme-possessing prosthetic group in these enzymes.

The Fe3+ reaction centre is sandwiched between the four pyrrole nitrogens of the heme

group and a nitrogen atom in the imidazole group of histidine. There is thus a strong

pentacoordinated linkage, generally referred to as a Fe-imidazolic nitrogen (Fe-Nε2)

linkage. The ability to weaken this linkage is directly proportional to the ability to

catalyse the high redox potential substrate. In LiPs, Ser177 and Asp201 residues are

responsible for the weakening of this linkage (Conesa et al., 2002).

LiPs oxidise their substrates under acidic conditions, similar to cellulases,

hemicellulases and laccases. However, their pH optima are generally in the pH range of

3-3.5 and this range can further widen due to the variability of substrates. For example,

the pH optimum of LiP during the catalysis of veratryl alcohol and 4-[(3,5-difluoro-4-

hydroxyphenyl) azo] benzenesulfonate (DFAD) has been reported within the range of

2.5-3, whereas for 2, 2’-azinobis- (3-ethylbenzthiazoline)-6-sulfonic acid (ABTS), the

range is shifted to 3-3.5 (Doyle et al., 1998). The temperature zones for LiPs range from

25°C to 50°C. However, considerable activities of some isozymes of Ph. chrysosporium

LiPs have been reported at 60°C. One of the peculiar observations made for LiP is their

variable temperature optimum with respect to the pH. For example, Tuisel et al (Tuisel

et al., 1990) reported that the temperature optimum changes from 25°C at pH 2.5 to

45°C at pH 3.5 and 45-60°C at pH 4.5. Similar to other biomass-degrading enzymes,

LiPs have a low shelf-life and are generally stable at rest for about 48 hours when

incubated up to 50°C. However, about 20% enzyme loss has been recorded for a

working enzyme at 40°C in about 21 hours. Similarly, outside the range of pH 4-7.5, the

enzymes lose all activity within 5 hours. LiPs are generally not inhibited by some

halides, an uncommon feature among the biomass-degrading enzymes. They are

inhibited by EDTA, Mn2+, cyanides, azides and excess H2O2 in the absence of any

reducing agent (Tuisel et al., 1990).

2.4.3.2.2. Manganese Peroxidases

Manganese peroxidases (MnPs) (EC 1.11.1.13) are also glycoproteins, similar to

LiPs, which play a crucial role in lignin degradation (EC 1.11.1.13). These enzymes


40

were first reported in 1985 in Ph. chrysosporium (Dashtban et al., 2010). Since then,

enzyme isoforms have been regularly reported from numerous basidiomycetes including

Trametes versicolor, Agaricus bisporus and Panus tigrinus. The number of MnP

isoforms varies in different fungal species. Ceriporiopsis subvermispora has been

reported to possess 13 different putative isoforms (Fernández-Fueyo et al., 2012),

whereas, Physisporinus rivulosus grown on spruce wood was reported to be composed

of 4 different isomers (Hakala et al., 2005). The most widely known MnP producers,

Ph. chrysosporium and T. versicolor, possess 5 (Fernandez-Fueyo et al., 2012) and 3

MnP isozymes (Carabajal et al., 2013), respectively.

Similar to LiPs, these enzymes have iron protoporphyrin from heme sources as

the catalytic core. As the name suggests, the catalysis process employed by these

enzymes is based on peroxidase-dependent oxidation mediated by manganese. The

Mn2+ cation, in the presence of a peroxide group such as H2O2, is oxidised to Mn3+ on

the enzyme surface. This more chemically active oxidised ion then forms a complex

with chelating agents such as oxalate or malonate and forms a redox mediator for the

oxidative degradation of phenolic substrates. The chelating agents act as the

physiological regulators of MnP, where they aid in dissociating Mn3+ ion from the

enzyme’s surface, thereby enhancing enzyme activity. The attachment of Mn3+ also

causes oxidation of the chelating agent, such as oxalic acid, to produce a formate radical

(HCO-2) which forms a superoxide (O-

2) in a chemical reaction with O2. The chelated

form of Mn3+ then acts as a strong redox mediator and oxidises phenolic lignin and

related structures (Saroj et al., 2013), as illustrated in the reaction.

(Fe3+)MnPH2O2→H2O�⎯⎯⎯⎯⎯⎯� [(Fe4+) = O]MnP

Mn2+→Mn3+�⎯⎯⎯⎯⎯⎯⎯⎯� [(Fe4+) = O]MnP

[(Fe4+) = O]MnPMn2+→Mn3+�⎯⎯⎯⎯⎯⎯⎯⎯� (Fe3+)MnP

The enzymes are also able to degrade non-phenolic polymers if, for example,

organic acids are present which form a complex with Mn3+ (Dashtban et al., 2010). In

the absence of H2O2, which is the primary exogenous inducer, MnP oxidizes the Cα-Cβ

linkage of lignin to generate reduced aryl products and H2O2. This process not only

causes a primary degradation of lignin, but also supplements the reaction machinery


41

with the generated H2O2 which is used by the peroxidase to further oxidise lignin

molecules (Saroj et al., 2013).

The iron in the centre of MnPs is in the Fe3+ state and is linked via a

pentacoordinated linkage to a proximal histidine residue. A hexa-coordinated linkage

has also been observed with the Mn2+ ion in Ph. chrysosporium, which is the chief

catalytic moiety of MnP. This ion is linked to Glu35, Glu39 and Asp179 amino acid

residues in their carboxylic positions. The remaining three linkages are with two water-

based oxygen atoms and a heme propionate oxygen (Conesa et al., 2002).

Similar to cellulases and laccases, MnPs are generally produced as isozymes

encoded by closely-related gene clusters. Thus, differences in expression levels of those

genes determine the variable MnP activities in different fungi. Five genes related to

MnP expression (mnp 1, mnp 2, mnp 3, mnp 4 and mnp 5) have been reported in Ph.

chrysosporium (Kersten and Cullen, 2007), while 13 are reported in another wood-

degrading basidiomycete, Ceriporiopsis subvermispora (Fernandez-Fueyo et al., 2012).

The common occurrence of the MnP gene cluster has been recently shown in Ph.

crassa, where a single gene cluster of WD1694 has been observed to possess at least 4

MnP genes (mnpA2, mnpA3, mnpB2 and mnpB3) (Takano et al., 2013). The MnP genes

from C. subvermispora were found to be highly up-regulated in the presence of ball-

milled aspen as the sole carbon source (as compared to glucose) (Fernandez-Fueyo et

al., 2012).

Another element determining MnP activity is nitrogen. In the presence of

supplemented N2, Plebia radiata has been reported to generate high MnP activities over

an 18-day period incubation under submerged conditions in yeast extract glucose

medium supplemented with succinate. In semi-solid cultures, this activity was

considerably enhanced with the addition of crushed charcoal (Mäkelä et al., 2013). It

has been also reported that numerous white-rot fungi, especially Ph. chrysosporium,

accumulate extracellular oxalic acid in higher quantities during lignin degradation as it

stimulates MnP activities by increasing the stability of Mn3+ ions present in the enzyme

active sites (Zeng et al., 2010). Addition of Mn2+ also increases MnP activity. The MnP

encoding genes mnp1 and mnp 2 of Ph. chrysosporium are positively affected by

increased Mn2+ in a low N2 medium, whereas mnp 3 is not (Kersten and Cullen, 2007).

Similarly, absence of Mn2+ is known to cause a large decrease in MnP activity (Grinhut

et al., 2011). On the other hand, high concentrations of FeSO4 in the fermentation

medium, especially above 0.1 g/L, were observed to not only suppress overall fungal


42

growth, but also MnP activity (Bak et al., 2009). This was a surprising observation

since this mineral is suggested to be one of the important cofactors in Fenton reactions

causing lignin degradation. It has been shown that exogenous addition of a heme group

up to a critical concentration considerably increases MnP production and activity.

Addition of 1 g/L heme increased the activity of MnP in an expression host, Pichia

pastoris SMD1168H, and resulted in an increase of MnP production from 6 U/L to 185

U/L. However, the increase was more prominent at 2490 U/L after addition of 0.5 g/L

heme under similar conditions. The authors proposed that the higher heme

concentration in the fermentation medium increased the bio-bleaching difficulties of

recombinant strains (Jiang et al., 2008). Supplementation of Tween-80 to nitrogen-rich

medium has also been shown to improve MnP activity. (Fujian et al., 2001) showed that

an addition of 0.1 % Tween-80 to medium containing 0.1 % (NH4)2SO4 increased MnP

activity to 1375 U/L within 4 days of solid state fermentation.

MnPs are generally monomeric and, in addition to Mn2+, have been observed to

be induced by several other ions such as Ca2+, Cd2+and Sm3+. A number of mediators in

addition to veratryl alcohol are known to induce and enhance the activities of these

enzymes. These mediators include organic acids, unsaturated fatty acids and thiols.

MnPs are mostly mesophilic in nature and are produced at room temperature or at

around 37°C. However, on a wider scale, a temperature range of 30-60°C within a pH

range of 2.5 and 6.5 results in good MnP activity in various organisms (Dashtban et al.,

2010).

2.4.3.2.3. Versatile peroxidases

Versatile peroxidases (EC 1.11.1.16) (VPs) are recently discovered lignases.

They are oxidoreductases belonging to Class II fungal peroxidases (with LiPs and

MnPs) and have been further classified as reactive-black-5: hydrogen-peroxide

oxidoreductases (EC 1.11.1.16). They are glycoproteins and possess the catalysing

abilities of both LiPs and MnPs, i.e., they have the ability to oxidize general LiP and

MnP substrates such as phenolics and Mn2+ ions. In contrast to other peroxidases, they

have not been found in Ph. chrysosporium, but are found in various other wood rotting

fungi such as Pleurotus spp. and Bjerkandera spp. during the late 1990s (Ruiz-Duenas

et al., 2001). It is known that LiPs are unable to catalyse their substrates in the absence

of mediators such as veratryl alcohol and MnPs are not very efficient in the absence of


43

Mn2+ ions. VPs, on the other hand, are not restricted by limited availability or absence

of these mediators (Dashtban et al., 2010, Ruiz-Duenas et al., 2001).

Although VPs and LiPs share the ability to use veratryl alcohol, the major VP

mediators/substrates are Mn2+, hydroquinones, ferulic acid, dyes and α-napthol. Indeed,

VPs possess greater affinity towards these mediators than veratryl alcohol. Additionally,

like MnPs, VPs show their maximal activities in the presence of Mn2+ (Ruiz-Duenas et

al., 2001). Pleurotus ostreatus, a common edible mushroom, has been reported to

possess nine mnp genes, of which mnp 2, 4 and 5 are classified as VP-encoding genes

for VP2, VP4 and VP5 enzymes, respectively. The genes mnp 3, 6, 7, 8 and 9 are highly

expressed in Mn2+-supplemented medium, while mnp 4 shows predominant expression

in Mn2+-deficient medium. The expression of this gene is dependent on the fungal age

and it shows exceptionally high expression during tropophase, early idiophase and

idiophase. Depending on the age of fungal culture, the substrates were also found to

differ. Whereas this enzyme could degrade other substrates to considerable levels during

every fungal growth stage, some azo dyes such as Orange II were only degraded during

the idiophase in Mn2+-deficient medium, where the activities of MnPs and LiPs were

negligible (Knop et al., 2014).

Similar to other peroxidases, VPs have a heme centre which is sandwiched

between four pyrrole nitrogens of the heme group and a nitrogen atom in the imidazole

group of histidine by pentacoordinated linkage. This linkage is generally referred to as

an Fe-imidazolic nitrogen (Fe-Nε2) linkage. However, in contrast to other peroxidases

where there are generally two histidine residues flanking the heme group (proximal and

distal), one of the histidines in VPs is substituted by either one aspartate/glutamate or

one cysteine and one glutamate. There are multiple catalytic sites present on the VP

surface and these can be broadly classified into two parts. The Mn2+ oxidation site

possesses multiple acidic Mn2+ cation binding residues while the lignin oxidation site

oxidises lignin-related substrates by a tryptophan-mediated electron transfer

mechanism. Additionally, VPs, especially VP4, has been observed to possess about 20

lysine residues in their structure compared to other peroxidases such as MnPs which

contain about half this number (Fernández-Fueyo et al., 2014). The following chemical

equation shows a schematic representation of phenolic substrate degradation by VPs by

utilizing both H2O2 and Mn2+ as mediators, thus showing combined activities of both

LiPs and MnPs.


44

VPH2O2→H2O�⎯⎯⎯⎯⎯⎯� VPRed I

Mn2+→Mn3+�⎯⎯⎯⎯⎯⎯⎯⎯� VPRed I

Mn3+→Mn2+�⎯⎯⎯⎯⎯⎯⎯⎯� VPRed II

VPRed IIMn2+→Mn3+�⎯⎯⎯⎯⎯⎯⎯⎯� VPRed II

Mn3+→Mn2+�⎯⎯⎯⎯⎯⎯⎯⎯� VP

Detailed studies conducted on the evolutionary distribution of fungal biomass

degrading enzymes, it was shown that VPs are closely related to LiPs than MnPs

(Floudas et al., 2012). However, VPs were shown to be evolved from short MnPs (those

possessing a peptide of 21 amino acids starting with the N-terminus and ending at a

KEX2 cleavage site with dibasic Arg-Arg or Lys-Arg residues before the main protein

sequence), which in turn evolved from long MnPs. Due to their intermediary nature,

VPs did not lose the parental active sites for both Mn2+ binding and Trp-homolog sites

responsible for lignin oxidation (Floudas et al., 2012), the former of which (Mn2+)

appeared to be lost from LiPs during evolution. Follow-up analysis also indicated the

presence of the copies of genes encoding VPs in white-rot fungi such as T. versicolor

and 1 copy of putative VP genes in Ph. Chrysosporium (Choi et al., 2014). However,

the presence active forms of these genes remains to be characterized.

A recent secretome analysis of T. versicolor during the fermentation of tomato

juice medium supplemented with Cu2+ and Mn2+ revealed the presence of a VP enzyme.

The purified VP, denoted ‘VP ID 26239’, had a molecular weight of 44.6 kDa and 45.5

kDa, as determined by HPLC-SEC and SDS-PAGE, respectively. The enzyme

displayed greater glycosylation of about 16% as compared to other peroxidases. This

property (glycosylation) resulted in a change of molecular weight from previous

observations which were based on its sequence length of 38.1 kDa. The level of post-

translation glycosylation also had an effect on altered pI of less than 4, which differs

from the theoretical pI value range of 4-7.5. Of the five secreted proteins related to

Class II heme-peroxidases, four belonged to MnPs while the last protein chain was

shown to be related to VP. This protein displayed a VP peculiar Trp-residue required for

both lignin oxidation and long range electron transfer (LRET) pathway types of LRET

I, II and III. Among all the five secretome Class II heme-peroxidases, only VP

displayed the presence of active sites responsible for the LRET II pathway. The overall

activity of purified VP (540 U) was considerably less than that of MnP I, II and III,

which displayed the activities of more than 3800 U (Carabajal et al., 2013).


45

These enzymes are also inactivated in an indirect manner due to product

inhibition of the LiP active site by the addition of excess H2O2. This is due to the co-

operative nature of VP with other peroxidases such as LiPs and MnPs, as confirmed by

the presence of biphasic kinetics-based oxidation of humic acid and fulvic acid.

Isothermal titration calorimetry combined with LC-ESI-MS indicated that VPs have the

ability to degrade humic substances such as humic acid, as observed from its m/z shift

from a range of 500-1000 Da to lower values of up to 393 Da (Siddiqui et al., 2014).

VPs have become attractive enzymes for lignin degradation purposes, especially

in the paper and pulp industries. However, owing to their low copy number in most of

basidiomycetes, they are not as highly secreted as MnPs or LiPs. However, successful

expression of mnp2, 4 and 5 genes, encoding VP 2, 4 and 5, respectively¸ in other fungi

such as Aspergillus nidulans has been reported. Additionally, LiPs and MnPs can also

be re-engineered to convert them in to VPs by adding Mn2+ and Trp-homolog based

LRET pathways, as has been recently shown in Ph. chrysosporium (Dashtban et al.,

2010).

2.5. Biodegradation methods

2.5.1. First generation: Submerged Fermentation

Submerged fermentation is the classical fermentation method used for ethanol

production and the manufacture of fermented food. In the case of biomass degradation,

this method was, thus, the earliest experimented procedure. While it has been very

successful for the production of various alcohols and other fermented products from

sugar sources, it is not necessarily very efficient in degradation of complex and

recalcitrant biomass. . Nonetheless, this method is reported to generate good activities

for selective enzymes such as β-glucosidases.

Submerged fermentation/shake flask fermentation or ‘SmF’ is an aqueous phase

fermentation process where the medium-to-substrate ratio is generally high (in excess of

10:1). Although the technique is well-established for wine and related alcohol

production, its industrial applications flourished in Western countries during late 1940s

for enhanced penicillin production from P. chrysogenum. The basic methods have been

greatly optimized due to the requirement for scaling up of SmF (> 3000-5000 litres).

Such optimizations include supplementation of oxygen, heat exchange, agitation,

prevention of foaming and culture loads. SmF remains the most important standardized


46

fermentation process largely because it has been thoroughly investigated and optimised

(Humphrey, 1998).

During the past 3-4 decades, the importance of glycoside hydrolase (GH)

enzymes has increased significantly in a large number of bioprocess industries. These

enzymes, especially cellulases, are utilized primarily in cotton industries, juice

extraction and, most importantly, in paper manufacturing and recycling. They are also

increasingly used for the production of biofuels such as ethanol. Cellulases are one of

the highest produced enzymes worldwide and a majority of these enzymes, either from

bacterial or fungal sources, are commercially produced by SmF-based technologies

which are easy to manage with simple handling and monitoring systems (Singhania et

al., 2010).

However, even with optimization, SmF methods are inadequate for either

enhanced production of lignocellulolytic enzyme or effective degradation of the

lignocellulose complex. Numerous fungal-based experiments have shown that shake

flask methods tend to produce cellulases and hemicellulases upon addition of pure,

crystalline sources of cellulose, such as Solka-Floc or Avicel (Sipos et al., 2010). It was

also shown that, under shake flask conditions, the specific activities of cellulases, β-

glucosidases and xylanases were 0.6, 1.9 and 16.9 U/mg, respectively, when Solka-Floc

was used as the sole carbon source (Hideno et al., 2011). However, these activities

dropped to 0.4, 0.5 and 10.2 U/mg, respectively, when milled rice straw was used as the

carbon source. These results agree with previous observations (Juhász et al., 2005).

(Viniegra-González et al., 2003) argue that the observed productivity (Γobs) curve

corresponds directly to the ratio of broth volume (P) and time (t) of fermentation. This

relationship is represented in the equation:

𝛤𝑜𝑏𝑠 = �𝑃𝑡�𝑚𝑎𝑥

In the case of SmF of A. niger during invertase production, this curve showed

hyperbolic saturation with a maximal equilibrium level of biomass (Xm) density of 14.6

g/L, whereas, the curve displayed a linear relationship in a solid state fermentation

(SSF) system with an Xm value of over 35 g/L. This was irrespective of the higher level

of inhibition in SSF conditions as compared to SmF conditions.

It has been long debated that SmF conditions create a stress environment for

fungal growth after their spore stage. It has been observed in recent studies that SmF


47

conditions, especially due to agitation under excess water, may result in mycelial

rupture. Secretome studies of Neurospora sitophila grown under SmF conditions

displayed the presence of five intracellular proteins (formate dehydrogenase, ubiquitin-

60S ribosomal protein L40, superoxide dismutase, enolase and fructose biphosphatase)

in the extracellular filtrate (Li et al., 2013). These proteins are known to be involved in

the metabolism of glyoxylate and dicarboxylate, glycolysis, gluconeogenesis and

protection against oxidative stress. The presence of these enzymes in the secretome

indicated the possibilities of fungal cell lysis.

Another issue related to SmF is the necessity of sterilization. Due to the aqueous

conditions, SmF is more favourable for bacterial growth as compared to fungi, thus,

SmF is highly prone to bacterial contamination. To prevent this, the medium is

generally sterilized, primarily by hydrothermal methods such as autoclaving or

steaming. However, the application of these treatments generally results in the

generation of furfurals and low molecular weight organic and fatty acids which inhibit

enzyme activity and overall fungal growth (Merino and Cherry, 2007, Sanchez and

Bautista, 1988).

2.5.2. Second Generation: Solid State Fermentation

Solid State Fermentation (SSF) is a microbial bioprocessing method, conducted

at very low levels of free water. This technique has been for a long time in bread

making and for at least 3000 years for food processing in Asian countries. For example,

the Koji process for food fermentation utilizes Aspergillus oryzae. Production of sake is

another example of SSF and utilizes Trichoderma spp. Both of these applications are

well-known in Japanese and Chinese culture. In Europe, SSF is also used to make

traditional French blue cheese (Couto and Sanromán, 2006, Hölker and Lenz, 2005).

One of the main reasons for its lesser use in Western cultures was the optimization of

penicillin production by SmF and its increased economic viability with quicker

productions during the late 1940s. This was followed by further optimization of SmF

linked to the production of various antibiotics, thus improving yields and decreasing

microbial inhibitory factors (Hölker and Lenz, 2005, Humphrey, 1998, Singhania et al.,

2010).

SSF provides the natural growth conditions to fungi, especially the filamentous

fungi. Fungal spores and, ultimately, the enzymes generated in SSF exhibit higher


48

tolerance to the environmental conditions. Thus, higher quantities of enzymes are

produced under SSF conditions than SmF conditions. This pattern becomes more

evident in mixed fungal cultures where a number of different enzymes are generated

and utilized for various purposes. A number of experiments have shown that the fungal

and enzymatic behaviours in SSF conditions are generally synergistic or symbiotic and

competitive inhibition is not a widespread phenomenon (Gruntjes, 2013, Singhania et

al., 2010).

In the last 2-3 decades, SSF has been widely used for a number of industrial

bioprocesses such as biomass conversion. Degradation and bioconversion of the

lignocellulose complex from various biomass sources has increased the production of

industrial metabolites, biofuels and secondary metabolites which can be used for

medicinal purposes. The use of biomass addresses the issues related to managing the

considerable amounts of waste material generated during numerous agricultural and

allied processes. SSF employs a mixture of different organisms and/or enzymes derived

from them in a single step to generate considerably higher bioconversion of biomass as

compared to SmF (Brijwani et al., 2010, Kausar et al., 2010, Lee, 1997, Sarkar et al.,

2012). As the water content in SSF is equal to or slightly higher than the substrate, the

method allows greater aeration and surface attachment for the filamentous fungi,

allowing production of a vast array of lignocellulolytic enzymes. SSF increases the

amount of substrate to be degraded in a single step from 1-2% in SmF to more than 10%

(Lee, 1997, Ng et al., 2010) and reduces the need for separate enzyme production,

isolation and application in biomass degradation. Thus, SSF fulfils all the requirements

for bioconversion in a single step which makes it more cost-effective than SmF (Betini

et al., 2009, Brijwani et al., 2010, Dashtban et al., 2010, Sarkar et al., 2012).

During SSF degradation of steam-exploded wheat straw, increased protein

production of more than 100-fold was displayed by Neurospora sitophila compared to

SmF. This reflected an increase in enzyme activities. For example, CMCase-specific

activities increased from 0.2 U/mg in SmF to about 0.6 U/mg under SSF conditions.

Similarly, β-glucosidase activities increased from 0.12 U/mg to 0.42 U/mg. Secretome

analysis showed that more cellulolytic enzymes were released during wheat straw

degradation in SSF conditions as compared to SmF (Li et al., 2013). In basidiomycete-

mediated degradation of eucalyptus, it was shown that some strains of Lentinus edodes

and Pleurotus spp. had considerably better activities of cellulases, laccases and MnPs.


49

In particular, MnP activity increased many-fold in SSF as compared to SmF conditions

(Elisashvili et al., 2008).

Numerous biodegradation procedures adopt a pre-treatment process during

which the biomass substrate is first degraded using a fungal culture, followed by either

enzyme-based degradation or mixed fungal fermentation. The uniformity of the biomass

not only results in a production of high amounts of lignocellulolytic enzymes, but also

produces a considerably higher degradation of biomass. Recently, (Cheng and Liu,

2012) showed the effects of pre-treatment in hydrogen production from milled

cornstalk. SSF meditated by Trichoderma reesei Rut-30 was applied to this substrate,

followed by sludge seeding at 35°C and 55°C. It was observed that within 4 days of

seeding, the 6-day pre-treated substrate generated about 200 mL H2, when incubated at

55°C. Additionally, considerable amounts of low molecular weight fatty acids and

ethanol were produced. In a similar pattern, rice straw pre-treated with Ph.

chrysosporium displayed considerable LiP and MnP activities. It was observed that after

30 days of pre-treatment, these enzymes had degraded about 33% of the lignin. Follow-

up enzymatic degradation resulted in the degradation of about 17% of the glucan and

3% of the xylan (Bak et al., 2009).

Other types of pre-treatments include physical processes such as autoclaving,

acid/alkali hydrolysis, ammonia and steam explosion (Sarkar et al., 2012). Pre-

treatment of palm empty fruit bunch fibre (EFBF) with a combination of steam and 5%

acetic acid at 110°C/30 minutes resulted in the release of about 100 mg xylose and 280

mg glucose per gram of substrate after enzymatic degradation (Hassan et al., 2013).

Experiments involving steam pre-treatment followed by Trichoderma atroviride TUB

F-1663-mediated degradation were also conducted on wheat straw, sugarcane bagasse

and milled spruce biomass (Kovacs et al., 2009). These experiments yielded increased

cellulose and xylan degradation and the cellulase, glucosidase and xylanase activities

increased to 56, 3 and 750 IU/g, respectively, indicating that the steam pre-treatment

had increased the degradation levels considerably.

Another type of SSF application is the production of biofuel molecules

especially ethanol. SSF of pre-steamed and SO2 -treated corn stalk by Ph.

chrysosporium was shown to cause loss of 34% biomass with 41% Klason lignin

reduction. Additionally, a follow-up anaerobic submerged fermentation resulted in the

generation of 1.7% ethanol. However, this production further increased to 3% with

yeast co-culture (Shrestha et al., 2008).


50

Similar to SmF-based biomass degradation, fungal degradation under SSF is

also dependent on the nutrient content of the biomass. It is well known that the

composition of the substrate strongly influences its degradation efficiency. Various

chemical components such as free proteins, sugars and structural components such as

cellulose and lignin affect the overall efficiency of degradation, usually providing a

barrier to such degradation (Duarte et al., 2012b). The carbon-nitrogen (C/N) ratio of

the substrate also plays an important role in SSF biodegradation with higher nitrogen

content resulting in greater fungal degradation ability (Brijwani et al., 2010). The higher

the nitrogen content, especially from organic sources (proteins, for example), the greater

the overall enzyme activities (Kim et al., 2012a). During degradation by A. niger, A.

niveus and A. ochraceus, for example, it was noticed that high nitrogen content

additives such as yeast extract and peptone improved xylanase production. The authors

of this research also noted that a mixture of corncob: wheat bran and rice straw: wheat

bran in the ratio of 1:1 increased the xylanase activity by 10-fold, indicating the

importance of nutritional supplementation (Betini et al., 2009). One of the other factors

affecting SSF is the availability of oxygen and its level of permeability through the

biomass structure. This particular aspect is one of the most critical factors for biomass

degradation, but has often been neglected during SSF process optimization.

Additionally, the substrate particle size also played an important role in O2 availability.

Although O2 distribution was observed to be more homogeneous on larger sized

particles, it did not result in higher fungal growth due to its decreased adsorption ability.

However, the fungi showed higher O2 consumption on smaller sized (0.4 cm) particles,

causing greater O2 utilization and, thus, showing greater decrease of air permeability

(Wang et al., 2014).

SSF is reported as an intermediate step in the development of Consolidated Bio-

processing (see below), which has been estimated to reduce the production costs of

biofuels by at least 4-5 fold (Lynd et al., 2005, Yamada et al., 2013).

2.5.3. Third generation: Consolidated Bioprocessing

Consolidated bioprocessing (CBP) is a relatively new methodology for

improving biomass conversion to products of commercial interest (Brethauer and

Studer, 2014, Lynd et al., 2005). CBP combines the individual processes of biomass

hydrolysis and subsequent fermentation to generate products such as ethanol in a single


51

step. It eliminates the time-consuming separate biological pre-treatment process. It also

eliminates the need for separate fermentations of different sugars, such as tetroses or

pentoses, which cannot be fermented by general industrial fermenters such as

Saccharomyces cerevisiae (Lynd et al., 2005).

The combination of multiple processes in a single step makes CBP a very

efficient and economical method. However, there is no single microorganism which can

ferment the entire spectrum of sugars generated in biomass degradation. In addition, the

majority of microorganisms have lower tolerance towards metabolic inhibitory products

such as saccharides, alcohols, fatty acids and organic acid salts (Duarte et al., 2012b,

Lynd et al., 2005).

CBP relies on the development of cellulolytic organisms via strategies such as

metabolic engineering or genetic engineering of native species. The first strategy has

been reported to generate about 0.47 g ethanol/g hexose using the thermophilic

bacterium, Geobacillus thermoglucosidasius. Clostridium thermocellum and C.

cellulolyticum, two thermophilic biomass-degrading bacteria, have been reported to

generate about 50 g/L ethanol on pre-treated cellulose. The second strategy involves the

(recombinant DNA technology) heterologous expression of cellulolytic enzymes in

naturally-occurring biomass-degrading microorganisms. These strategies have been

successful at the laboratory scale, however, success under industrial conditions has not

yet been reported (Olson et al., 2012).

Compared to bacterial CBP, developments in fungal CBP have been limited and

not many fungi have been tested for CBP until very recently (Olson et al., 2012). In

terms of biofuel, and especially ethanol production, utilization of fungi such as

Aspergillus, Rhizopus, Fusarium and Trichoderma has been reported (Hasunuma et al.,

2013a). Among these, Trichoderma spp., especially Trichoderma reesei, has been

observed to generate moderate amounts of ethanol while simultaneously degrading

cellulosic biomass. One of the major limitations of this fungus, however, is its strict

aerobic growth, which prevents the production of commercially-viable levels of

alcohols such as ethanol. The utilization of facultative ethnologenic organisms, such as

Pichia stipites, Candida spp. and S. cerevisiae, can improve this situation. Bacteria such

as Zymomonas mobilis also have considerable potential to improve this process. This

bacterium removes certain deficiencies of yeast-based fermentation, such as

heterologous enzyme secretion and mass accumulation, resulting in recent

developments in CBP (Linger and Darzins, 2013).


52

A recent report on CBP for ethanol production involved construction of a

laboratory-scale multi-species biofilm membrane reactor (MBM reactor), as shown in

Figure 2.10.

Figure 2.10. The figure shows a conceptual design of the MBM reactor for CBP to generate ethanol

from pre-treated wheat straw (Diagram taken from Brethauer and Studer, 2014).

Experimental analyses of Avicel-based CBP using the MBM reactor showed that

a co-culture of S. cerevisiae and T. reesei RUT C30 generated 7.2 g/L ethanol in the gas

phase upon exogenous addition of β-glucosidase. When acid-treated wheat straw

replaced Avicel as the carbon source, addition of Scheffersomyces stipites (formerly

Pichia stipites) generated about 9 g/L ethanol with complete utilization of xylose. The

authors proposed that either the addition of a high β-glucosidase-producing organism or

an ethnologenic strain, such as Dekkera bruxxelensis, would further improve ethanol

production (Brethauer and Studer, 2014).

In a fungal (mushroom)-only based CBP experiment, (Kamei et al., 2014)

described the use of spent sawdust waste from Lentinula edodes cultivation as a

substrate for white-rot fungus, Phlebia spp. MG-60. They observed about 45% ethanol

production from the mushroom sawdust waste in about 400 hours due to increased

saccharification. A slightly different approach utilizing a two-step process of alkali pre-

treatment followed by Phlebia spp. MG-60 biodegradation was utilized for ethanol

production from sugarcane bagasse. After a biodegradation period of 10 days, 4.5 g/L

ethanol was generated, yielding about 66% of the theoretical maximum (Khuong et al.,

2014).

Besides the above-mentioned symbiotic or synergistic approaches, recombinant

DNA technologies have been widely used in developing CBP for biofuel production.

However, their application to filamentous fungi remains limited due to the complex


53

functional and expression systems. Hence, CBP has been more successful with simpler

microorganisms, namely bacteria and yeasts; Indeed, S. cerevisiae has been the subject

of numerous recombinant applications for CBP. Endoglucanases from several

organisms including Trichoderma spp., Aspergillus spp., Clostridium thermocellum and

Bacillus subtilis have been successfully expressed in this yeast. However, due to the

exogenous nature of these genes and the high level of metabolic pressure created on

host cells, extracellular endoglucanase production is lower than in the organisms of

origin (Linger and Darzins, 2013). The other successful gene expression system is that

of E. coli. A number of genes related to exoglucanase and cellulose-binding domains

from Cellulomonas fimi have been expressed in E. coli in addition to the β-glucosidase

gene from Thermobifida fusca. Similarly, pyruvate decarboxylase and alcohol

dehydrogenase genes were expressed in E. coli KO11. This strain utilized sugar beet

pulp as a carbon source and was able to produce ethanol. Other bacteria (B. subtilis, B.

coagulans, Corynebacteria glutamicum and lactic acid bacteria) have been successfully

developed as gene expression systems for CBP (Hasunuma et al., 2013b).

Overall, there is no single microorganism which has the ability to fulfil every

requirement of biomass conversion. Although various technologies have evolved to the

point of developing a commercial-scale CBP process, most of the fundamental research

is still in progress. Considerable developments have been made into bacterial-based

CBP processes by introducing recombinant expression systems. However, significant

research remains to be done in relation to integrating major biomass degraders, i.e.,

filamentous fungi, into CBP.

2.5.4. Symbiotic fermentation: SSF and thermophilic degradation

Symbiotic fermentation may be carried out as part of CBP. The strategy is to

develop a symbiotic consortium of different micro-organisms or their lignocellulolytic

enzymes in either a single batch or continuous batch fermentation. It has been shown

previously that fungi such as Trichoderma spp. and Aspergillus spp. have the ability to

co-culture during cellulose degradation (Brijwani et al., 2010, Gupte and Madamwar,

1997). A similar approach has been investigated in lignocellulose degradation by

termites (Brune, 2014) and lignin degradation by co-culture of wood-rot fungi (Qi-he et

al., 2011). In addition, it has been also shown that lignocellulose degradation is

enhanced in a co-culture of different fungi (Singhania et al., 2010).


54

While testing the effects of a number of filamentous fungi on a rice straw

composting process, it was observed that cultures of T. viride and A. niger were able to

grow in a synergistic manner to degrade cellulose and hemicellulose. In the same

experiment, the commensal behaviour of a number of other strains was assessed, but

these were found to be present at very low levels or absent (Kausar et al., 2010). A

slightly different approach was adopted by Chu et al. (2011). Instead of a pure, known

fungal culture, mixed microbiota was taken from biogas digested sludge and inoculated

onto wheat stalk. The system produced 37 mL/ g of H2 after 200 hours of incubation. In

addition, the concentration of volatile fatty acids, such as acetate, propionate and

butyrate, increased considerably. This was in addition to about 75% cellulose being

solubilized during this process (Chu et al., 2011). A fungal mix was also observed to

degrade soybean hulls supplemented with wheat bran (a 4:1 mix) during an SSF

process. This mix was then degraded by a composite culture of T. reesei and A. oryzae

added in an equivalent ratio. It was observed that after the SSF process of 4 days, the

cellulase and β-glucosidase activities in mixed culture reached 10.8 U/g as compared to

6.5-6.7 U/g in the individual cultures (Brijwani et al., 2010). A similar increase in

enzymatic activities was observed in a mixed fungal culture of T. reesei RUT C30 and

A. niger on corn stover pre-treated by nano-shearing (Lu et al., 2013).It was observed

that the overall activities of cellulases and glucosidases increase considerably under the

symbiotic consortium conditions.

Another form of symbiotic fermentation consists of a sequential fermentation

where the first biomass degrader is used for pre-treatment followed by the second

biomass degrader during the main degradation step. In this process, the enzymes

released by the first biodegrading microorganism act in symbiosis with the second

biodegrading organism to not only enhance the degradation, but also to achieve

bioconversion to the product of interest (Cheng and Liu, 2012). The authors reported

that corn stalk substrate pre-treated with T. reesei RUT C30 by SSF and then seeded

with winery sludge was able to generate H2 gas up to 200 mL within 6 days.

In addition to ascomycetes, wood-rot fungi are known to generate important

molecules such as ethanol in addition to degrading difficult biomass complexes. Corn

stalks pretreated with hot water and steeping SO2 was applied to Ph. Chrysosporium-

mediated degradation. This was followed by depleting total oxygen from this medium to

generate anaerobic conditions. The medium was further fermented at 35°C for up to 11


55

days. It was observed that the total mass and lignin mass loss were about 34% and 41%,

respectively, and the production of ethanol was about 1.7 % (Shrestha et al., 2008).

A recent study demonstrated a symbiotic relationship between bacterial

cellulosomes and fungal extracellular cellulases. It was shown that, individually, C.

thermocellum cellulosomes were able to convert 100% Avicel to monosaccharides and

oligosaccharides, as opposed to about 40% achieved by fungal cellulases. Similarly,

about 40% Whatman no. 1 filter paper conversion was achieved by cellulosome

application, whereas, about 10% conversion was achieved with fungal cellulases. These

studies show the enhanced degrading ability of celulosomes on crystalline cellulose.

Contrastingly, fungal cellulases work better on pre-treated biomass substances such as

switchgrass and poplar, where they showed more than 80% conversion as opposed to

less than 50% conversion by celulosomes. Interestingly, when both of these were mixed

in equivalent amounts in an Avicel-containing medium, their synergistic behaviour

resulted in 100% bioconversion within 24 hours. This study suggested the possibilities

of using enhanced biomass degradation using thermophilic bacteria and general

biomass-degrading fungi in symbiosis for various purposes, including biofuel

production (Resch et al., 2013). A recent experiment displaying the synergistic

properties of cellulolytic enzymes was recently reported. Crude, filtered and dialysed

fungal enzyme samples obtained from SSF were mixed to an already operating SmF

biodegradation sample. The resulting symbiosis of Trichoderma and Aspergillus

enzymes caused the degradation of cellulose and hemicelluloses, as evidenced from a

conversion of up to 64% biomass to reducing sugars. Additionally, under the anaerobic

conditions, these enzyme mix in combination with S. cerevisiae was able to generate up

to 43 g/L ethanol (about 84% of the theoretical maximum) within 7 hours (Pirota et al.,

2014).

Thermophilic fungi can also be included in symbiotic consortia, similar to the

approach of using thermophilic bacteria described above. It is known that most

cellulolytic enzymes have their maximal activities in the range of 50-60°C

(temperatures that are typically achieved during biodegradation processes, such as

composting). However, most biomass-degrading fungi cannot survive at these

temperatures. The application of thermophilic fungi can overcome this problem and has

the potential to increase the biomass degradation potential of fungal systems. A recent

secretome analysis of the thermophilic fungus Thermomyces lanuginosus SSBP

growing on corn cobs showed the presence of 74 different proteins, a number of which


56

were found to be glycoside hydrolase enzymes. Although, other enzyme activities were

comparable to mesophilic fungi, this fungus was observed to possess an exceptionally

high xylanase activity of 3500 U/mL at 70°C with a wide pH range of activity (3-12)

(Winger et al., 2014). Another thermophilic cellulolytic fungus, Myceliophthora

thermophila, was recently shown to display cellulase activities (when grown on pre-

treated wheat straw at 70 °C) of 200 nmol min-1mg protein-1, equivalent to Trichoderma

reesei but higher xylanase activity of about 4 nmol min-1mg protein-1 at its optimal

temperature. One of the peculiar features of M. thermophila was the rate of reaction or

biomass degradation, which was much faster than either Trichoderma spp. or

Aspergillus spp. (van den Brink et al., 2013).

The natural symbiotic mechanism of biomass degradation and conversion to

important products requires careful selection. The process also avoids the much debated

technique of genetic engineering under in-vivo conditions due to better yields without

resorting to genetic engineering techniques. The output of symbiotic consortia can be

further optimized in their natural growth conditions by the application of new

technologies such as metabolomics and metabolic engineering.

2.6. Application of metabolomics in fungal biomass degradation

The study of metabolomics has the potential to provide biochemical information

in order to understand and characterise the various mechanisms related to fungal

biomass degradation. Metabolomics have been applied to investigate bacterial processes

related to preventative health (Bi et al., 2013, Marcinowska et al., 2011), environmental

pollution (Beale et al., 2013a), food (Beale et al., 2014) and fungal metabolism on

various substrates including benzoic acid and Chardonnay grape berries (Hong et al.,

2012, Matsuzaki et al., 2008). However, within the context of fungal-mediated biomass

degradation, its application has been limited. Analysis of the metabolic flux, however,

has been shown to have the capacity to enable a good understanding of the nature, time-

dependence and substrate-based limitations in regard to the metabolism of fungal cells.

Metabolomics also assist in extricating the correlation between cell phenotypes and

their metabolic patterns and stoichiometry (Meijer et al., 2009). Metabolic flux studies

have previously been applied in toxicology and medicine (Maier et al., 2009, Niklas et

al., 2010), plant proteomes (Nelson et al., 2014) and microbial respiratory systems

(Driouch et al., 2012, Pedersen et al., 1999).


57

Numerous methods are available for metabolic flux analysis. The majority of

these involve the use of heavy isotopes due to their considerably superior tracking

abilities. In commonly utilized analyses, such as nuclear magnetic resonance (NMR)

and gas chromatography-mass spectrometry (GC-MS), metabolite tracking is performed

using isotopes such as 1H, 2H, 13C, 14N, 15N and 18O. While every isotope has its

advantages and shortcomings, 13C-molecules have been used by most researchers

(Nelson et al., 2014), probably because of its wide-spread use in general chemistry.2H is

a much more abundant element in nature, comprising about half of the peptide atomic

population (Yang et al., 2010b).It is much more economical as compared to other

isotopes, has a comparatively higher invasive rate and a slower decay rate (Kim et al.,

2012b). Complete 2H labelling is not feasible, however, due to the limited tolerance of

multicellular organisms, thereby resulting in partial labelling, generally from 8% (Kim

et al., 2012b) to 30% (Yang et al., 2010b).

Limited metabolomics-based research has been performed on biomass

conversion compared to clinical applications. However, a number of studies in this area

have recently been described. For example, the supramolecular effects of pre-treatment

and successive microbial degradation of rice straw have recently been described (Ogura

et al., 2013). This biomass was pre-treated by ZrO2 ball milling for 6 hours followed by

metabolic analysis by 2D 13C-1H heteronuclear correlation spectra (HETCOR) in solid

state NMR and thermo-gravimetric analyses. The pre-treated biomass was

supplemented with paddy soil and the products were analysed by 1H-NMR

spectroscopy. The presence of syringyl, guaiacyl, hydroxyphenyl and coumarate

derivatives suggest considerable lignin linkage breakdown. Similarly, sugars and sugar

acids resulted from degradation of the cellulose and hemicellulose components.

Deformation of cellulose was noticed from the appearance of the C4 peak (NMR

spectroscopy) of crystalline cellulose and increasing intensity of amorphous cellulose,

indicating its conversion to the amorphous form. With respect to other pre-treatments,

the activation energy of ball-milling reduced considerably, from 166 KJ/mol to 96

KJ/mol, suggesting an increase in amorphous cellulose. Successive microbial

degradation of this biomass resulted in further generation of pentoses, hexoses and

disaccharides (from 3-4 ppm) and small chain fatty acids, especially butyrate (up to 2.2

ppm).Principal Component Analysis suggested the termination of degradation at 21

days (Ogura et al., 2013). In an extension to this experiment, bacterial cellulose


58

generated by Gluconacetobacter xylinus was degraded by anaerobic sludge and the

products were analysed by solid state-, solution state- and gas state- NMR. While solid

state-NMR showed decreasing crystallanity of cellulose over 84 hours, solution state-

NMR indicated that the major metabolite output consisted of small-chained fatty acids

and ethanol. These were generated during the early phase of fermentation and most

were re-utilized after 60 hours of fermentation. Similarly, CO2 and CH4 were generated

by oxidation and reduction of acetate, as detected by gas state-NMR (Yamazawa et al.,

2013).

The metabolomic approach can also be applied to prevent or minimise process

inhibition, either competitive or product, as shown in recent research using GC-MS-

based analysis (Zha et al., 2014) . It was observed that biomass pre-treatment products

such as hydroxymethyl furfural (HMF) and furfural prolonged the log-phase of yeast

growth, thereby allowing considerable growth inhibition. Similarly, other metabolites

such as aldehydes, phenolics, vanillin and sorbic acid, negatively affected the growth

rate. It was observed that S. cerevisiae counters the effects of HMF and furfural by

converting these molecules to their respective acids. It was also noted that amino acids,

when initially present in considerable amounts, compensated for the inhibitory and

toxic effects of various molecules during fermentation (Zha et al., 2014). Metabolic

profiling can be applied to obtain a metabolic flux, following which metabolic

engineering tools such as induction of expression systems can be utilized. These

techniques not only decrease the overall inhibition, but also aid in developing systems

which generate desired products. This has been reported recently where a Zymomonas

mobilis ethanolic pathway system was expressed in Sphingomonas spp. A1, to convert

alginate to ethanol (Takeda et al., 2011).

Due to the complex nature of biomass composition and the allosteric nature of

most lignocellulolytic enzymes, standard biochemical tests have proven less effective

compared to metabolomic approaches for deciphering biomass conversion. Moreover,

metabolic profiling methods indicate signature metabolites and critical pathway points

which can be exploited to not only improve the biomass degradation process, but also to

convert this biomass to the products of industrial and medicinal interests.


59

2.7. Overview

This chapter presented an overview of biomass composition and fungal

degradation of biomass components, and included a discussion of the various methods

and techniques to improve biodegradation. In the earlier sections, the chapter dealt with

biomass composition and specific characteristics of various biomass components. These

components include cellulose, hemicellulose molecules such as xylans, xylosides,

glucans, mannans and pectins and lignin, tannin and phenolic components.

Later sections described various issues and problems related to biomass

degradation, highlighting the parameters which are still prevalent in industrial

bioprocessing and prevent the utilization of biomass as the major source of energy

production and medicinal metabolite generation. To counter these limitations, various

fungal-mediated biomass processing have been developed. Major enzymes secreted by

biomass-degrading fungi and their effects with possible commercial outputs have been

reviewed. These enzymes include cellulases, hemicellulases and ligninases from a wide

variety of fungi such as ascomycetes and basidiomycetes. Various methods of applying

these fungi and their improvement in symbiotic and SSF conditions have been indicated

by numerous researchers to be the effective measures of not only enhancing biomass

degradation, but also to convert these primary products of biodegradation to secondary

products with maximal economic viability.

Lastly, this chapter reviewed the novel approach of metabolomic analysis and its

application to further develop symbiotic fungal consortia to obtain superior

bioconversion with minimal inhibition effects. Overall, this review presents numerous

approaches for biomass waste utilization to generate potentially useful products which

are important in numerous industrial and medicinal applications. Various approaches

such as SSF, fungal consortia and some consolidated bioprocessing will be later utilized

during the course of this thesis to improve the biomass degradation. The degradation

approaches will be analysed by numerous biochemical and enzyme activity analyses.

Metabolomic approaches using GC-MS techniques will be used to find the metabolic

profile of grape biomass degradation. The technique will also aid in finding the critical

points in fungal biomass degradation pathways to further improve the overall

bioconversion.

60

CHAPTER 3

Materials and methods

61

3.1. Chemicals

3.1.1. Chemicals and media

The chemicals used for all the experiments were purchased from Sigma-Aldrich

Co. LLC (Sydney, Australia) and were of analytical grade (AR) levels, unless specified.

These included sugars such as glucose, fructose, galactose, arabinose, cellobiose,

mannose, xylose, cellulose and para-Nitrophenyl β-D-glucopyranoside (pNPG); and

other chemicals such as cysteine hydrochloride, syringaldazine, ABTS, tyrosine, 3, 5-

dinitrosalicylic acid (DNSA), Bovine Serum Albumin (BSA), pyridine and deuterium

oxide (2H2O).

3.1.2. Growth media

Bacterial and fungal growth media of Nutrient Agar (NA), Potato Dextrose Agar

(PDA), Nutrient Broth (NB) and Sabouraud broth were purchased from Oxoid

(Basingstoke, UK).

AATCC (American Association of Textile Chemists and Colourists) mineral

salts iron medium, Clostridium thermocellum medium and Clostridium stercorarium

medium were made in-house as per the pre-requisites (Atlas, 2010). The composition of

each of these media is given in Table 3.1. The cellulose/ biomass content changed from

30 g/L in the submerged fermentation (SmF) to 500 g/L in solid state fermentation

(SSF). The pH level of AATCC medium was kept at 5 ± 0.2 during all the fungal

growth conditions in submerged and solid state conditions, unless specified otherwise.

For the bacterial growth purposes, the pH was set at 7.4 ± 0.2. Similarly, the pH level

was maintained at 7.4 ± 0.2 in Clostridium thermocellum and Clostridium stercorarium

media during every experiment, unless specified otherwise.

62

Table 3.1. General composition of various minerals and organic sources in the growth media for

isolating and characterizing cellulolytic organisms.

Component AATC

C (g/L)

Cl. thermocellum

(g/L)

Cl. stercorarium

(g/L)

NH4NO3 3.0 - -

KH2PO4 2.5 1.0 1.65

K2HPO4 2.0 1.0 -

MgSO4 0.2 - -

FeSO4.7H2O 0.1 0.00125 -

NH4SO4 - - 1.6

Yeast extract - 6.0 1.0

NaCl - - 0.96

Cysteine

HCl.H2O

- 0.3 0.5

CaCl2 - 0.05 0.096

Resazurin - 0.001 0.001

Urea - 2.0 -

MgCl2 - 0.5 -

Cellobiose - 5.0 -

MOPS - 10.0 -

Trisodium citrate - 30 -

Cellulose/biomass 30-500 - 30.0

63

3.1.3. Enzymes

The enzymes used in the study included cellulase (from Trichoderma viride),

xylanase (from Aspergillus niger), β-glucosidase (from almonds), laccase (from

Trametes versicolor), peroxidase (from Horseradish) and tyrosinase (from mushroom).

All the enzymes were purchased from Sigma-Aldrich Co LLC (Sydney, Australia).

3.1.4. Buffers and reagents

All the buffers used for the study were made in the laboratory using AR grade

constituents, (unless specified) are given in Table 3.2

Table 3.2. Composition of buffers and reagents used during the experiments

Reagent/Buffer Composition

DNSA reagent 1 g 3, 5-DNSA, 300 g sodium potassium tartrate were

dissolved in deionised water. 200 mL of 2M NaOH was

added and volume made to 1 L with deionised water.

Mollisch’s reagent 5% α-napthol solution was made up to 1 L with 95% H2SO4

Biuret reagent 1.5 g CuSO4.5H2O and 6 g NaK-tartrate were dissolved in

500 mL deionised water. 300 mL 10% NaOH was then added

and total volume was made to 1 L with deionised water.

Bial’s reagent 160 mg ethanolic orcinol was dissolved in 80 mL

concentrated HCl and 0.2 mL 10% FeCl3. Total volume was

made to 1 L with deionised water.

0.05 M citrate

buffer

10.5 g citric acid monohydrate was dissolved in 800 mL

deionized water and pH was adjusted to 4.5 with NaOH

solution. Total volume was made to 1 L with deionised water

so that the final pH was 4.8.

0.1 M acetate buffer 13.608 g sodium acetate was dissolved in 900 mL deionized

water and pH was adjusted to 5.0 by 1 M HCl. Total volume

was made to 1 L with deionised water.

0.2 M Na2CO3

solution

21.191 g Na2CO3 was dissolved in deionized water and total

volume was made to 1 L with deionized water.

64

Table 3.2. Composition of buffers and reagents used during the experiments (…continued).


0.01 M phosphate

buffer, pH 7

0.12 g NaH2PO4 (monobasic) was dissolved in 900 mL

deionized water followed by adding 2 g BSA. The pH was

adjusted to 7 and total volume was made to 1 L with

deionised water.

0.1 M phosphate

buffer, pH 6.5

13.609 g KH2PO4 (monobasic) was dissolved in deionized

water and pH was adjusted to 6.5 with 1 M KOH. Total

volume was made to 1 L with deionised water.

0.1 M phosphate

buffer, pH 5


water. The pH was adjusted to 5 with 1 M KOH. Total


0.04 M phosphate

buffer, pH 6.8


water followed by 2.5 g BSA and 5 g triton X-100. The pH

was adjusted to 6.8 with 1 M KOH. Total volume was made

to 1 L with deionised water.

Tris-HCl buffer 0.1 M Tris (12.114 g/L) was dissolved in deionized water

followed by addition of 0.1 N HCl (9.8 mL/L). The pH was

adjusted to 7.8 and total volume was made to 1 L with

deionised water.

50X Tris acetate

EDTA (TAE) buffer

242 g Tris was dissolved in deionized water with 57.1 mL

glacial acetic acid and 100 mL 0.5 M EDTA. Final pH was

adjusted to 8 and total volume was made to 1 L with

deionised water.

Syringaldazine

solution

0.0778 g syringaldazine was dissolved in absolute methanol

to produce 0.216 mM solution.

ABTS solution 4.993 g ABTS was dissolved in 0.1 M KH2PO4 (monobasic).

The pH was adjusted to 5 with 1 M KOH and total volume

was made to 1 L with deionised water.

0.3 % H2O2 solution 10 mL of 30 % H2O2 was added to deionised water and total


65

Table 3.2. Composition of buffers and reagents used during the experiments (…continued).


0.125 M Sodium

tartrate buffer, pH 3

28.76 g sodium tartrate was dissolved in deionized water and

pH was adjusted to 3 with 1M HCl. Total volume was made

to 1 L with deionised water.

Veratryl alcohol

solution

1.682 g (w/v) was dissolved in deionized water and total


0.002 M H2O2

solution

0.1706 mL of 35% H2O2 was dissolved in deionised water to

make the final volume of 1 L.

Cellulase solution Cellulase (from Trichoderma viride, 9.8 U/mg) was

dissolved in citrate buffer, pH 4.8 at a concentration of 1

mg/mL and later used at appropriate dilutions. It was stored

at -20°C until used.

β-glucosidase

solution

β-glucosidase (from almond, 6.9 U/mg) was dissolved in ice

cold tris HCl buffer, pH 7.8 at a concentration of 1 mg/mL

and later used at appropriate dilutions. It was stored at -20°C

until used.

Xylanase solution Xylanase (from Trichoderma viride, 105 U/mg) was

dissolved in citrate buffer, pH 4.8 at a concentration of 1

mg/mL and later used at appropriate dilutions. It was stored

at -20°C until used.

Laccase solution Laccase (from Agaricus bisporus, 0.8 U/mg) was dissolved

in 0.1 M KH2PO4 (monobasic) buffer at a concentration of 1

mg/mL and was later used at appropriate dilutions. It was

stored at -20°C until used.

Lignin peroxidase

solution

Lignin peroxidase (0.1 U/mg) was dissolved in 0.1 M

KH2PO4 (monobasic) buffer at a concentration of 1 mg/mL

and was later used at appropriate dilutions. It was stored at -

20°C until used.

3.1.5. Maintenance of storage and other conditions

Fungal degraded samples were snap frozen by using liquid nitrogen and later

stored at the temperature of -80°C until further analysis. Fungal degraded samples

66

utilized for metabolomic purposes were freeze dried at -52°C in a Cryodos-50 freeze

drier (Telstar Industrial, S.L., Terrassa, Spain) and again stored at -80°C until further

analysis.

All glassware and bioreactors utilized in the process were thoroughly washed

and applied to autoclaving at 121°C, 101 kPa for 20 minutes before utilization.

Similarly, the non-utilized biomass waste from fungal degradation, unused fungal and

bacterial cultures and any organic waste products were autoclaved at 121°C, 101 kPa

for 20 minutes prior to discarding.

3.2. Organisms

Ascomycota fungi Aspergillus niger (ATCC 10577) and Saccharomyces

cerevisiae (ATCC 287) were collected from the culture collection at Swinburne

University of Technology (Hawthorn, Victoria, Australia). Trichoderma harzianum

(AG 46) and Penicillium chrysogenum (AG 47) were obtained from Agpath Pty. Ltd.

(Vervale, Victoria, Australia). Basidiomycota fungi Phanerochaete chrysosporium

(16543), Trametes versicolor (4483) and Ganoderma applanatum (2403) were kindly

provided by Dr. Geoff Dumsday, CSIRO Manufacturing Flagship (Clayton, Victoria,

Australia). All fungi were first grown in Sabouraud agar for 5 days before any

subculturing. Ascomycota fungi were incubated at 30°C, whereas basidiomycota fungi

were incubated at 35°C. Fungal cultures were stored in petri plates at 4°C on a short-

term basis and under medicinal paraffin oil (specific gravity 0.865-0.89) immersion

slants at 4°C for long-term basis.

3.3. Genomic identification of fungi and bacteria

Thermophilic anaerobic bacterium was isolated from the domestic compost

samples held in Department of Chemistry and Biotechnology, Swinburne University of

Technology. The compost samples were cultured on Clostridium thermocellum

medium. This bacterium was used in the accessory experiments and establishing

primary bacterial cellulose degradation at thermophilic conditions (60°C). However, it

was not applied during advanced biomass degradation studies. Besides this, fungi were

isolated from Tamborine National Park, Gold Coast, Queensland, Australia. These fungi

were identified by ITS region sequencing as given below. However, due to their

67

similarities to standard fungi already available in the laboratory, they were not used for

further experiments. The details of these fungi are provided in Table 3.3 below.

Table 3.3. Micro-organisms isolated from various sources and characterized by Sanger sequencing.

Type Primers Identification GenBank ID

Bacteria 27F forward

1513R reverse

Brevundimonas spp. clone viniA711 KC161226

Fungus ITS1 forward

ITS4 reverse

Trichoderma harzianum isolate

Viniti KF316951

Fungus ITS1 forward

ITS4 reverse

Candida homilentoma isolate swin KF316952

Fungus ITS1 forward

ITS4 reverse

Fusarium spp. VinV4 ITS 1, partial

sequence KF316953

Fungus ITS1 forward

ITS4 reverse

Nectria haematococca isolate PVK KF316954

3.4. Grape samples

Post-fermentation wastes of Vitis vinifera vars. Shiraz, Grenache and Cabernet

were obtained from the Australian Wine Research Institute (AWRI), Glen Osmond,

South Australia; Australia. These samples were stored at 4°C in airtight containers

unless used. The grape biomass waste was oven-dried at 70°C for 96 hours and ground

using HR2094 domestic blender (Philips Electronics Australia, North Ryde, NSW;

Australia). The dried, ground substrate was further oven dried for 96 hours at 70°C

before its application to experimental conditions.

3.5. Fungal and bacterial growth conditions

All the fungi mediated biodegradation processes, whether submerged

fermentation or solid state fermentation, were carried out in 250 mL conical flasks. The

68

flasks were filled up to 150 mL levels during submerged fermentation with AATCC

mineral medium. This was followed by addition of dried grape biomass at the

concentration levels of 30 g/L. During solid state fermentation, 10 g of dried grape

biomass was put in to the flasks with 10 mL AATCC mineral medium. The flasks were

tightly sealed with non-absorbent cotton before fungal spore transfer. The flasks

categorised for submerged fermentation were autoclaved at carbohydrate cycle of

115°C for 10 minutes. The flasks categorised for solid state fermentation were not

autoclaved. After the addition of fungal spores, these flasks were incubated at 30°C/ 200

rpm for 1 week duration.

The conditions similar to fungal submerged fermentation were applied to

bacterial shake flask conditions. The only exception was replacement of grape biomass

by pure cellulose at a concentration of 30 g/L.

3.6. Calculating bacterial and fungal concentrations

Bacterial and fungal concentrations were monitored and regulated for all

experimental purposes. For bacteria, this concentration was kept at 1×109 cells/mL,

while the fungal spore concentration was kept at 1×107 cells/mL, which was in line with

numerous previous experiments performed with different fungi (Betini et al., 2009, Gal

Milanezi et al., 2012, Gao et al., 2008, Gupte and Madamwar, 1997, Teixeira et al.,

2010). Initially, the fungi grown on Sabouraud agar plates and bacteria grown on

nutrient agar plates were harvested and added to aseptic 0.1% tween-20 solution. This

method proved to be simple, yet efficient to remove debris material from solid bacterial

cultures.

Fungi obtained from standard collections were inoculated on Sabouraud agar

petri plate. All fungi inoculated plates were incubated at 30°C for 5 days. The grown

fungal biomass consisting of mycelia and spores was then sampled from these plates

and added to 10 mL sterile tween 20 solutions (0.1% in distilled sterile water). The

mixture was then vortexed for about 30 seconds and filtered through sterile Whatman

no. 1 filter paper (pore size: 11 μm). Spores present in the filtrate were counted by

microscopic observation on a haemocytometer and calculated to the required spore

count by using the equation given below.

69

𝑆𝑝𝑜𝑟𝑒 𝑛𝑜. = 𝑁 × 1𝑉

× 𝑆 Eq. 3.1

Where,

N = number of spores observed in an area

V = volume of haemocytometer under consideration (0.004 mm3)

S = Total volume of sample considered for final calculation (1 mL=103 mm3)

These calculated amount of spores/bacterial cells were then inoculated in the respective

growth media, under submerged or solid state fermentation conditions.

3.7. Biochemical tests

3.7.1. Determination of total soluble sugars

Total soluble sugars in non-degraded and degraded grape pomace were

quantitatively determined by Mollisch’s assay. Filtrate was used as the sample, 0.02 ml

of which was mixed with 0.2 ml ethanolic α-napthol. This was followed by a slow and

careful addition of 1 ml 95% H2SO4. The mixture was gently vortexed after 10 minutes

of incubation at room temperature followed by a further incubation at the same

temperature for 30 minutes. The sugar content was then determined by taking the

absorbance at 490 nm and interpolating the values with the glucose standard (Ahmed,

2005).

3.7.2. Determination of reducing sugars

The quantitative determination of reducing sugars in the filtrate of degraded

grape waste was performed by the dinitrosalicylic acid (DNSA) assay. Grape waste

filtrate (100 μL sample) was mixed with 1ml DNSA and incubated in a boiling water

bath for 5 minutes followed by cooling on ice to stop further reaction and bring the

sample to room temperature. The absorbance was taken at 540 nm to determine the

concentration of reducing sugars. A glucose gradient was used to derive the standard

reducing sugar (Plummer, 1987).

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3.7.3. Determination of pentoses

The pentose sugar concentration was quantified by Bial’s assay (Pramod and

Venkatesh, 2006). Grape waste filtrate (200 μL) was mixed with 1 mL Bial’s reagent

(80 mg ethanolic orcinol in 40 mL concentrated HCl and 0.1 mL 10% FeCl3) and kept

in a boiling water bath for 5 minutes. The samples were then cooled to room

temperature on ice to terminate the reaction and the absorbance was taken at 660 nm.

The concentration was determined on the basis of the standard curve of arabinose.

3.7.4. Total protein content

Protein determination was performed using Biuret assay. Appropriately diluted

filtrate in the volume of 0.2 ml from the degraded samples was mixed with 0.8 ml

Biuret reagent. The mixture was vortexed and incubated at room temperature for 30

minutes. Total protein content was determined by absorbance at 546 nm. Bovine serum

albumin was used as the standard for the test.

3.7.5. Lignin content measurement

Lignins were determined as Acid Soluble Lignin (ASL) and Acid Insoluble

Lignin (AIL) by the NREL procedure (Sluiter et al., 2011). 0.1 g dried grape was

incubated in 1 mL 72% H2SO4 at 30°C for 1 hour. This was followed by dilution of the

acid hydrolysed sample to 4% H2SO4 by addition of deionized H2O. The mixture was

then autoclaved at 121°C for 1 hour followed by cooling to room temperature. The

supernatant was collected as the ASL fraction after a brief centrifugation. The pellet was

rinsed with distilled water and was dried at 105°C for 4 hours to ensure complete

drying. The dried sample was weighed as Acid Insoluble Residue and was vaporized in

a muffle furnace at 575°C for 1 hour followed by cooling to room temperature. The

weight of this sample was considered as ash. ASL in each sample was determined by

the absorbance of the centrifuged filtrate at 320 nm using the equation 3.2:

%𝐴𝑆𝐿 = 𝐴𝐵𝑆 × 𝑣𝑜𝑙𝑢𝑚𝑒 × 𝐷𝑓𝜀×𝑊𝑆1 × 𝑝𝑎𝑡ℎ𝑙𝑒𝑛𝑔𝑡ℎ

× 100 Eq. 3.2

Where,

71

ABS = Absorbance at 320 nm

Volume = Volume of total filtrate (30.35 mL)

ε = Absorptivity of biomass at 320 nm (30 L/g.cm)

WS1 = Oven dried weight of sample (milligrams)

Pathlength = Pathlength of the cell (1 cm)

AIL was determined by the ratio of difference between dry Acid Insoluble

Residue (AIR) and ash to the original dry weight of grape waste as given in the equation

below (Equation 3.3).

% 𝐴𝐼𝐿 = 𝑊𝑆2− 𝑊𝑆3𝑊𝑆1

× 100 Eq. 3.3

Where,

WS1 = Oven dried weight of sample (milligrams)

WS2 = Weight of AIR (milligrams)

WS3 = Weight of ash (milligrams)

The total lignin content was calculated as the cumulative ASL and AIL.

3.7.6. Total nitrogen and carbon content

Total nitrogen and carbon content were measured using a Carbon and Nitrogen

analyser (CN 2000, Leco Corporation, St. Joseph, Michigan, USA). Following

calibration and drift correction, 100-130 mg oven dried control and fermented samples

were fed into the analyser. Total nitrogen and carbon content was calculated by

automated sampling.

All the samples were dried at 105°C for 4 hours before analysis to remove any

traces of moisture. The analyser utilizes a convection furnace to oxidise the test samples

at elevated temperatures. Under the current analysis, 1050°C temperature was applied,

which was specified by the supplier. Ultra high purity grade oxygen (Grade 4.5) was

used to oxidize the samples by both direct supplementations over the sample and

background purging in a controlled pulsed mechanism. The analyser utilizes the

72

properties of thermal conductivity to determine total nitrogen percentage, while it uses

an infra-red analyser to detect the total carbon released from a given sample.

All blanks were determined by placing empty ceramic crucibles and adjusting

the ‘zero’ or ‘Blank’ readings. Supplied standards of EDTA (41.05% C, 5.55% H and

9.58% N), rye flour (44.04% C, 6.51% H, 1.83% N and 0.143 % S), complete coal

(71.93 % C and 1.24 % N) and soil (2.7 % C, 0.028 % N and 0.028 % S) were used to

calibrate the nitrogen and carbon contents. All the samples and calibration standards

were used in at least triplicates for establishing validity and increasing the accuracy of

analysis.

3.8. Enzyme assays

The activities of various lignocellulose degrading enzymes was studied which

involved cellulase, β-glucosidase, xylanase. Ligninases activity assays were performed

with laccase and Lignin peroxidase. The assay protocols are mentioned below. For

assay purposes, the crude filtrates from fungal degraded winery biomass were used and

were mixed with citrate buffer in appropriate ratio (for cellulase and xylanase assays) or

relevant phosphate buffers (for β-glucosidase, laccase and peroxidase assays). These

filtrates were centrifuged at 2500 g for 15 minutes at 0°C to separate any fungal and

grape biomass. The supernatant from this centrifugation process was transferred to fresh

tubes. The tubes were applied to snap freezing by liquid nitrogen and stored at -80°C

until further use.

3.8.1. Cellulase activity assay

The crude filtrate obtained (as mentioned above) was thawed on ice bath before

any use. This filtrate was mixed in equal proportion (v/v) of citrate buffer (0.05 M, pH

4.8). The mixture solution was briefly vortexed and stored at -20°C until used further,

while during the use, it was always stored in ice. Cellulase activity was measured in

terms of Filter Paper Activity (FPA) as per IUPAC protocols (Ghose, 1987). Whatman

no. 1 filter paper (50 mg) was used as the substrate and was added to 0.5 ml of

appropriately diluted filtrate in 0.05 M sodium citrate buffer (pH 4.8). The mixture was

vortexed and incubated at 50°C for 1 hour. DNSA reagent (3 mL) was added to this

73

reaction mixture before boiling for 5 minutes. The reaction was quenched on ice prior to

adding 20 ml of de-ionized water. The sample mixture was then vortexed vigorously

and allowed to settle for 20 minutes at room temperature. Absorbance was taken at 540

nm to determine the cellulose activity in terms of FPA. One International Unit (IU) of

cellulase is defined as the amount of enzyme required to liberate 1 µmol glucose per

minute under assay conditions.

3.8.2. β-glucosidase activity assay

The filtrate sample was mixed in equal proportion (v/v) with ice cold tris HCl

buffer (pH 7.8) and then further diluted in phosphate buffer supplemented with 0.2%

BSA (0.04 M, pH 6.8). This mixture was kept at -20°C until used further, while during

the use, it was always stored in ice. β-glucosidase activity was determined by p-

nitrophenyl-β-D-glucoside (pNPG) assay according to the method of Kovacs et al.

(Kovacs et al., 2009) with slight modifications. Briefly, 1 ml of sodium acetate buffer

(0.1 M, pH 5) and 0.5 ml of 0.02 M p-nitrophenyl-β-D-glucosidase (pNPG) were added

to appropriately diluted enzyme sample in sodium acetate buffer. The mixture was

incubated at 50°C for 5 minutes. The reaction was terminated by addition of 2 ml

Na2CO3 solution (0.2 M). β-glucosidase activity was determined by measuring the

optical density against water at 400 nm. One IU of β-glucosidase is defined as the

amount of enzyme required to liberate 1 µmol p-nitrophenol per minute under assay

conditions.

3.8.3. Xylanase activity assay


any use. This filtrate was mixed in equal proportion (v/v) of citrate buffer (0.05 M, pH

4.8). The mixture solution was briefly vortexed and stored at -20°C until used further,

while during the use, it was always stored in ice. Xylanase activity was measured by

Highely’s method (Highley, 1997). 1.8 ml of Birchwood xylan (1%) in 0.05 M Na-

citrate buffer was mixed with 200 µl of appropriately diluted enzyme sample and

incubated at 50°C for exactly 5 minutes. 3 ml DNSA reagent was added to this mixture

before boiling for 5 minutes. The reaction was terminated on ice. Absorbance was taken

74

at 540 nm to determine enzyme activity. One IU of xylanase is defined as the amount of

enzyme required to liberate 1 µmol xylose per minute under assay conditions.

3.8.4. Laccase activity assay


any use. This filtrate was mixed in equal proportion (v/v) of potassium phosphate buffer

(0.1 M, pH 6.5). The mixture solution was briefly vortexed and stored at -20°C until

used further, while during the use, it was always stored in ice. Laccase activity was

measured by using slight variations mentioned previously (Manole et al., 2008). Crude

filtrate was appropriately diluted in potassium phosphate buffer. Fresh potassium

phosphate buffer was added with 0.5 mL of filtrate sample. The mixture was

equilibrated at 37°C for 5 minutes before the addition of methanolic syringaldazine

(0.216 mM). Table 3.4 elaborates the test method of laccase activity.

Table 3.4. Working protocol for laccase assay

Components Sample Blank

KH2PO4 (0.1 M) 2.2 2.2

Filtrate sample 0.5 0

H2O 0 0.5

Equilibration at 37°C for 5 minutes

Syringaldazine 0.3 0.3

Total volume 3.0 3.0

Upon addition of syringaldazine, the testing cuvette with sample was quickly

inverted for a quick vortex mixing of all components. Laccase activity was measured by

spectrophotometer at 525 nm wavelength. The reaction time in cuvette was 10 minutes

and difference in final and initial absorbance was taken in to account for calculation.

The activity was measured by equation 3.4.

𝐿𝑎𝑐𝑐𝑎𝑠𝑒 � 𝑈𝑚𝐿� = �(∆𝐴𝑠𝑎𝑚𝑝𝑙𝑒−∆𝐴𝑏𝑙𝑎𝑛𝑘)×𝑉×𝐷𝑓�

𝜀×𝑉1 Eq. 3.4

Where,

ΔA = difference of absorbance (final absorbance – initial absorbance)

75

V = total volume of mixture (3 mL)

Df = dilution factor

ε = extinction coefficient of syringaldazine (65000 M-1 cm-1)

V1 = volume of enzyme used (0.5 mL)

The enzyme activity was expressed in U/mL, where one unit (U) of Laccase

activity was defined as the amount of enzyme catalysing the oxidation of 1 μmole

syringaldazine to form quinone per minute at 30°C, pH 6.5 in a 3 mL reaction mixture.

3.8.5. Lignin peroxidase (LiP) activity assay


any use. This filtrate was mixed in equal proportion (v/v) of potassium phosphate buffer

(0.1 M, pH 6.5). The mixture solution was briefly vortexed and stored at -20°C until

used further, while during the use, it was always stored in ice. LiP activity was

measured by using the method mentioned previously (Solarska, 2009). Crude filtrate

was appropriately diluted in Na2HPO4-citric acid buffer (0.2 M). Fresh Na2HPO4-citric

acid buffer was added with 0.5 mL of filtrate sample followed by veratryl alcohol (0.02

M). The mixture was equilibrated at 37°C for 5 minutes before the addition of H2O2

(0.004 M) to initiate the reaction. Table 3.5 elaborates the test method of lignin

peroxidase activity.

Table 3.5. Working protocol for lignin peroxidase assay

Components Sample Blank

Na2HPO4-citric acid 0.84 0.84

Filtrate sample 1.56 0

Veratryl alcohol 0.3 0.3

Equilibration at 37°C for 5 minutes

H2O2 0.3 0.3

Total volume 3.0 3.0

Upon addition of H2O2, the testing cuvette with sample was quickly inverted for

a quick vortex mixing of all components. LiP activity was measured by

spectrophotometer at 310 nm wavelength. The reaction time in cuvette was 5 minutes

76

and difference in final and initial absorbance was taken in to account for calculation.

The activity was measured by the equation 3.5 given below.

𝐿𝑖𝑔𝑛𝑖𝑛 𝑝𝑒𝑟𝑜𝑥𝑖𝑑𝑎𝑠𝑒 � 𝑈𝑚𝐿� = �(∆𝐴𝑠𝑎𝑚𝑝𝑙𝑒−∆𝐴𝑏𝑙𝑎𝑛𝑘)×𝑉×𝐷𝑓�

𝜀×𝑉1 Eq. 3.5

Where,

ΔA = difference of absorbance (final absorbance – initial absorbance)

V = total volume of mixture (3 mL)

Df = dilution factor

ε = extinction coefficient of veratryl alcohol (9300 M-1 cm-1)

V1 = volume of enzyme used (1.56 mL)

The enzyme activity was expressed in U/mL, where one unit (U) of lignin

peroxidase activity was defined as the amount of enzyme catalysing the oxidation of 1

μmole veratryl alcohol to form veratraldehyde per minute at 30°C, pH 4.5 in a 3 mL

reaction mixture.

3.9. Silyl derivatization and gas chromatography-mass spectrometry(GC-MS)

3.9.1. Silyl derivatization

Silyl derivatization was utilized for GC-MS based analysis of fungal metabolism

during the biomass degradation process. The necessity of derivatization was felt as the

aqueous samples from the fermentation process not only overload the chromatography

column and mass spectra detector, but also damages the column by bleeding it. Besides,

the polar molecules such as sugars, some amino acids and alcohols cannot be detected

readily by GCMS unlike many volatile compounds. Therefore, a derivatization agent

such as N,O-Bis(trimethylsilyl)trifluoroacetamide (BSTFA) is utilized. BSTFA is

tagged to the metabolite of interest and converts it in to a non-polar volatile molecule,

which can then be easily detected by GC-MS based approaches.

The silyl derivatization was started as a conventional thermal heating process.

Samples derived from the optimized bioreactor degradation process were further

analysed by gas chromatography-mass spectrometry (GC-MS). A 1 ml aliquot of

methanol (LC grade, ScharLab, Sentemanat, Spain) was added to 40 (±2) mg post

degraded freeze dried sample, then vortexed briefly before centrifugation at 572.5 g/4°C

77

for 15 minutes. A 50 µL aliquot of the supernatant was then transferred to a fresh tube

and dried in an RVC 2-18 centrifugal evaporator at 40°C/210 g (MARTIN CHRIST

Gefriertrocknungsanlagen GmbH; Osterode, Germany). All samples were stored at -

80°C until further use (Ng et al., 2012). In order to derivatize the samples for GC-MS

analysis, 40 µL methoxamine HCl (2% in pyridine) was added to each sample and

incubated for 45 minutes at 37°C. To complete the derivatization, silylation was

performed by adding 70µL BSTFA in 1% TMCS. Samples were then incubated for an

additional hour at 70°C. Samples were diluted with 190 µL pyridine, vortexed and

centrifuged at 15682 g for 5 minutes before transferring to GC-MS vials.

However, due to the requirement of more time for this derivatization process, a

new microwave assisted derivatization process was developed after optimization. This

process not only reduced the time of derivation to about 3 minutes as compared to about

2.5 hours by thermal heating process, but also decreased the formation of number of

unnecessary metabolites which were classified as artefacts during the GC-MS process.

In order to derivatize the samples for GC-MS analysis, 40.0 µL methoxyamine HCl (20

mg/mL in pyridine) followed by 70.0 µL N,O-Bis(trimethylsilyl)trifluoroacetamide

(BSTFA) in 1% trimethylchlorosilane (TMCS) were added to the dried samples.

Samples were then briefly vortexed before being transferred to clean GC-MS vials.

These were then derivatized in a Multiwave 3000 microwave (PerkinElmer Inc.,

Melbourne, Australia) for 3 minutes at 120°C/ 600 W before transferring to the GC-MS

for analysis. Pre-derivatized 13C-Sorbitol (Kovats Retention Index = 1918.76, m/z =

620.00 [10µg/mL, HPLC grade, Sigma-Aldrich, Castle Hill, NSW, Australia]) was

added as the second internal standard at this point in order to verify instrument stability

over the run time. This increased the reliability and prevented any miscalculations

arising from sample loss during GC-MS.

3.9.2. GC-MS analysis

The derivatized samples were analysed using an Agilent 6890B Gas

Chromatograph (GC) oven coupled with a 5973A Mass spectrometer (MS) detector

(Agilent Technologies, Mulgrave, Victoria, Australia), as described previously (Beale et

al., 2013b, Beale et al., 2014). The GC-MS system was fitted with a 30 m DB-5MS

column, 0.25mm ID and 0.25 µm film thickness. All injections were performed in

78

splitless mode with 1.0 µL volume; the oven was held at an initial temperature of 70°C

for 2.0 min before increasing to 325°C at 7.5°C min-1; the final temperature was held for

4.5 min. The transfer line was held at 280°C and the detector voltages at 1054 V. Total

Ion Chromatogram (TIC) mass spectra were acquired from 45 to 550 m/z, at an

acquisition frequency of 1.08 spectra s-1. The ionisation source used was electron

ionization (EI) and the energy was 70 eV. The solvent delay time of 7.5 minutes

ensured that the source filament was not saturated and damaged with derivatization

reagent. Data acquisition and spectral analysis were performed using MassHunter.

Quantitative identification of the compounds was performed according to the

Metabolomics Standard Initiative (MSI) Chemical Analysis Workgroup (Sumner et al.,

2007) using standard GC-MS reference metabolite libraries of Wiley, NIST 11 and

NIST EPA/NIH using Kovats retention Indices based on the referenced n-alkane

retention times (C8-C40 Alkanes Calibration Standard, Sigma-Aldrich, Castle Hill,

NSW, Australia). For peak integration, a 5 point detection filtering (default settings)

was set with a start threshold of 0.2 and stop threshold of 0.0 for 10 scans per sample.

3.10. Chemometric and statistical analysis

3.10.1. Biochemical analysis and enzyme assays

Statistical analysis for the analysis of biochemical tests and enzyme assay was

performed by one way analysis of variance (one-way ANOVA). All data were presented

as the mean values of triplicate data samples with their standard error. The consistency

and deviations between the data were analyzed by one way ANOVA using IBM®

SPSS® 20.0 statistics software.

3.10.2. Statistical modeling for degradation optimization

For developing the statistically optimized method for improved fungal

degradation, statistical modelling possessing predictive abilities was used. Experimental

design and statistical analysis was performed using a full factorial design method

developed using Minitab® 16 (Minitab Pty Ltd., Sydney, Australia). One of the simplest

methods for complex experimental design, it can analyse multiple factors with each

factor containing multiple levels of experimental conditions. In our experiments, we

used two factors of fungi (4 types) and methods (2 types) optimising cellulases,

79

xylanases and β-glucosidases (3 factors), thus yielding a 24 experimental condition

model (Montgomery, 2008). For the experimental design, maximum enzyme activities

were the primary responses sought after. However, the responses for reducing sugar and

lignin concentrations were also taken into consideration for the experimental design.

3.10.3. Metabolic profiling and flux analysis

Data generated by GC-MS process for metabolite profiling and metabolic flux

analysis were applied to various statistical approaches. GC-MS quantitative analysis

was performed by Quantitative analysis system of MassHunter workstation software,

version B.06.00/ Build 6.0.388.0 (Agilent Technologies, Mulgrave, Victoria, Australia).

The data was generated in a tabular form and was readily exported to Excel® 2010

spread sheet (Microsoft Corporation, Redmond, WA, USA). The earliest data filtration

was done manually where the peak features generated from non-derivatized samples or

‘artefact peak features’ were removed. Chemometric and statistical and analysis was

undertaken for this filtered data using SIMCA 13, a chemometric software package

(Umetrics AG, Umeå, Sweden), and MetaboAnalyst 2.0, an online statistical package

(TMIC, Edmonton, Canada). Peak areas were taken in to consideration for statistical

analyses. Chromatography peaks were considered significant where the signal to noise

ratio > 50, the Fold Change (FC) was > 2.0, and p-values were ≤ 0.05. Similarly, the

metabolites with FC values < 0.5 were considered as the fungal degraded/ utilized

metabolites. The data generated by mass spectral analyses were thus normalized with

respect to internal standards (RSD = 7.39%), where a magnitude of 1 fold change (FC)

referred to the concentration of 10 mg/L. Also, the FC values of less than 0.5 were

indicated as fungal utilized metabolites. This was followed by Principal Component

Analysis (PCA) based on score scatter

To accommodate the outliers and differentiating between the groups based on

metabolic pattern, which PCA could not fully accomplish; Partial Least Square-

Discriminant Analysis (PLS-DA) was employed. PLS-DA is used to analyse large

datasets and has the ability to assess linear/polynomial correlation between variable

matrices by lowering the dimensions of the predictive model, enabling easy

discrimination between samples and the metabolite features that cause the

discrimination (Wold, et al., 2001).

80

Where the PLS-DA was unable to provide satisfactory discrimination,

Orthogonal PLS-DA (OPLS-DA) was used. Orthogonal PLS-DA (OPLS-DA) was, thus

applied to yield better metabolite discrimination, leading to a better grouping and better

model predictability. Whereas, PLS-DA builds the model from a two-component

system of systematic and residual variables, OPLS-DA utilizes a three component

system. In addition to residual variables, the systematic variability component in OPLS-

DA is derived from adding the correlated variability (predictive) and non-correlated

variability (orthogonal) between X-Y axis components. Generally, due to the presence

of single Y-axis component, OPLS provides only one predictive component. However,

multiple T-axes components are represented as the orthogonal variations to generate a

multiple predictive OPLS-DA model (Trygg and Wold, 2002). For single response, X-

axis variation is represented by equations 3.6 and 3.7 below.

𝑋 = 𝑙𝑥′� + 𝑡𝑝′ + 𝑇0𝑃′0 + 𝑒 Eq. 3.6

and, Y-axis based prediction is represented by:

𝑌 = 𝑦′� + 𝑡𝑞′ + 𝑓 Eq. 3.7

Where, TP’ and TQ’ = matrix products

T = score vector

P’ and Q’ = loading vectors

e and f = residual variables

The FC values obtained in MetaboAnalyst 2.0 were replotted on Excel® 2010

spread sheet to distinguish significant metabolites. These metabolites were then

validated by calculating their Kovats retention indices.

To analyse metabolic flux, in addition to abovementioned statistical approaches,

MATLAB statistical software was also used. The identified metabolites were crossed-

checked against the KEGG database of metabolic pathways

(http://www.kegg.jp/kegg/pathway.html). The data obtained was then applied to

MATLAB 2014a statistical software using Covariance-Inverse (COVAIN) script

(Doerfler et al., 2014). The resultant data were applied as a correlation between the

different metabolites in a time series analysis.

http://www.kegg.jp/kegg/pathway.html

81

CHAPTER 4

Fungal degradation of hydrothermal pretreated

winery grape wastes

Chapter 4 Fungal degradation of hydrothermal pretreated winery grape wastes

82

4.1. Introduction

Fermentation is the preferred method for alcohol production from various

sources such as grape, corn and barley. In the fermentation process, microorganisms

such as Saccharomyces cerevisiae are able to generate up to 12-15% alcohol from the

raw material, which is used as either beverage alcohol or as an industrial reagent. The

rest, and the bulk, of the material are discarded as spent wash, and may include unused

live or dead fermenting organisms. Wine waste will be the focus of study here. Its waste

consists of dry matter including crude fibres, grape seeds, skin, waste, marcs, stalk and

skin pulp, proteins, ethers and amino acids. Apart from the moisture content, the major

components of grape waste are dietary fibre (30-40%), lipids (0.5-1%), soluble sugars

(2-3%), proteins (2.5-3.5%) and ash (2-3%). Dietary fibre consists mainly of cellulose

(27-37%), pectins (37-40%) and lignins (33-35%) (Bravo and Saura-Calixto, 1998, Ping

et al., 2011, Vicens et al., 2009).

Various fungi such as Trichoderma spp., Aspergillus spp. and Penicillium spp.

have been reported as extensive biomass degraders owing to their ability to generate an

array of enzymes including endo- and exo-glucanases, β-glucosidase, xylanases,

arabinofuranosidases and pectinases (Brink and Vries, 2011, González-Centeno et al.,

2010). This degradation generates useful industrial and medicinal biomolecules such as

ethanol, flavonoids, phenolic compounds, anthocyanins and hydroxybenzoic acid

(Arvanitoyannis et al., 2006, Sánchez, 2009, Strong and Burgess, 2008). Additionally,

fungi such as Penicillium spp. can be used for lignin mineralization during the

degradation process (Rodríguez et al., 1994, Singh Arora and Kumar Sharma, 2010).

These fungi, and ultimately the enzymes derived from them, convert the

lignocellulose complex to various soluble sugars, which are then converted into other

secondary products. However, due to the relatively recalcitrant nature of the

lignocellulose complex, pretreatment is usually required to facilitate access of fungal

enzymes to the cellulose and hemicelluloses which are the primary sources of carbon

for these organisms. Hydrothermal treatment has been shown to be a comparatively

efficient pretreatment technique for the breakdown of these biomolecules

(Papadimitriou, 2010).


83

4.2. Overview

Hydrothermal treatment has been shown to be a comparatively efficient

pretreatment technique for the breakdown of recalcitrant biomolecules such as lignin.

The experiments described here were performed to study the effects of fungal

degradation on the composition of winery grape waste to achieve biodegradation of its

lignocellulose components, namely cellulose, hemicelluloses and lignins. The

degradation patterns of various ascomycete fungi such as P. chrysogenum, P. citrinum,

A. niger and T. harzianum were compared, and optimsed, so as to formulate the ideal

fungal/enzymatic combination to yield maximum bioconversion.

4.3. Results and Discussion

During the pilot protocol, fungi were grown in Yeast Mannitol broth to develop

a starter culture. This was followed by their subculturing on Sabouraud agar for spore

propagation at 30°C for 48 hours. The spores were then filtered from fungal mycelia

and were transferred to growth media consisting of pure cellulose as the only carbon

source. These media included American Association of Textile Chemists and Colourists

(AATCC) iron minimal nutrient medium, Clostridium stercorarium and Clostridium

thermocellum media (Atlas, 1993); LC-3 and LC-4 media (Van Gool et al., 2011) . The

biochemical tests and enzyme assays were performed for the pure cellulose degradation

during various pilot protocols with and without the provision of hydrothermal pre-

treatment through autoclaving. It was observed that due to the minimal nature of

AATCC, this medium induced higher cellulose degradation as compared to other media.

Also, FeSO4.7H2O, which was only present in this medium, might have played a role in

enhancing the fungal cellulose degradation. It is known that Fe2+ ions increase the

biomass degradation abilities, especially during the stage of pyrolysis (autoclaving

being one of the examples). It has been reported that addition of a Fe2+ ion source, such

as FeSO4, causes cellulose degradation during pyrolysis with minimal release of either

CO2 or CO. This is in contrast with the other moieties such as ZnCl2 or NiCl2, which

increase the release of these gases during pyrolysis. Additionally, Fe2+ also releases

significant amounts of H2, which is known as an important ingredient of a large number

of enzyme reactions (Williams and Horne, 1994). Also, due to its minimal nutrient

nature, AATCC was found to favour fungal growth during cellulose degradation with


84

respect to that of bacterial growth, which ensured minimal treatment associated with

this medium as compared to others.

After the medium composition optimization, cellulose was replaced by post-

fermented crushed grape biomass waste. The compositional analysis of Shiraz grapes

indicated the presence of soluble proteins (2.2%), total soluble sugars (1.6%), reducing

sugars (1.8%), acid insoluble lignins (35.2%), acid soluble lignins (0.8%), ash +

insoluble proteins (2.8%) and cellulose + hemicellulose (57.4%).

4.3.1. Effects of hydrothermal pre-treatment

Grape pomace samples were added to AATCC medium at a dose of 30 g/L. The

pH of this medium was adjusted to 5.6 before autoclaving it at 121°C for 15 minutes.

The pre-treated grapes were collected. The filtrate was separated from the solids by

centrifuging at 15300 g for 15 minutes at 4°C. The resultant filtrate was further filtered

using Whatman no. 1 filter paper to remove any particulates and was then used for the

determination of free sugars and soluble total protein content. The solid part was taken

and oven-dried at 105°C for 48 hours before any compositional analysis. This part was

then analyzed for lignin content and total carbon and nitrogen content.

Autoclaving altered the composition of winery grape waste, with an overall

decrease in dry mass. The overall dry mass of the grape pomace was observed to be

reduced by about 18%, from 3.00 g to 2.56 g, in a non-autoclaved grape pomace. The

autoclaving process also increased the Total Soluble Sugar (Bengtsson and Åman)

content by about 4 times from 16.9 kg/m3 to 66 kg/m3 (Figure 4.1). Pentose content in

the filtrate increased from 1 kg/m3 to 10 kg/m3. The reducing sugars also increased

significantly from 1.76 kg/m3 to 2.21 kg/m3.

It has been reported by numerous workers that biomass tends to undergo

hydrolysis during hydrothermal processing. Although most of the reports have used

temperatures around 180°C, hydrolysis of biomass has also been documented to occur

at autoclaving temperatures (Papadimitriou, 2010). The molal ionic product of water

(Kw) is dependent on the temperature of the system. Kw increases from 0.64 × 10-14 at

18°C to 54.6 × 10-14 at 100°C and 268 × 10-14 at about 150°C. Kw reaches its highest

value at about 250°C, when it is 634 × 10-14. This increase in ionic product is important


85

as it is directly proportional to the solubility of water. The solubility of substrates (such

as cellulose and hemicelluloses) increases with the rise in Kw value. This relationship is

always taken into account when applied to several hydrothermal processes such as hot-

compressed water (HCW), steam explosion (SE) and supercritical water (SW)

treatments. Therefore, temperatures around 250°C or above are applied in these

processes (Ando et al., 2000, Goto et al., 2004). As compared to HCW or SE processes,

the Kw value during autoclaving is minimal, but is nevertheless sufficient to ensure a

substantially high substrate solubilisation without yielding strong microbial inhibitors

such as 2-furfural and 5-hydroxymethyl furfural (5-HMF). These are generated during

hydrothermal treatments, especially during SE treatment (Klinke et al., 2004, Sanchez

and Bautista, 1988). This allows efficient subsequent microbial degradation without

requiring multiple rinsing of substrate with deionised water to remove these inhibitors,

thereby saving important time and resources.

4.3.2. Utilization of Total Soluble Sugars

During the incubation period, TSS was observed to be significantly utilized by

all the cultures. However, the most significant utilizers were T. harzianum and A. niger,

which utilized about 27.3 kg/m3 and 26 kg/m3 of TSS present, respectively. On the other

hand, P. notatum and P. citrinum were not able to utilize TSS as effectively, displaying

only 17.4 kg/m3 and 7.9 kg/m3 TSS utilization, respectively (Figure. 4.1).


86

Figure 4.1. Total soluble sugar content (kg/m3) in fungal cultures of non-autoclaved, autoclaved and

untreated Shiraz grape waste. Values for non-autoclaved and autoclaved samples are significantly

different to untreated samples (p < 0.05).

However, none of the fungi were able to utilize the complete TSS in the

respective media. In T. harzianum, it was probably due to a very low β-glucosidase

activity as compared with the endoglucanases and exoglucanases in the similar

cellulolytic system. While endoglucanases have a high catalytic activity of about 35-70

IU/ mg, the overall cellulolytic activity becomes very low to 0.6-0.7 IU/ mg due to low

β-glucosidase activity. Product inhibition by glucose and cellobiose, the final products

of cellulose degradation effectively reduces the β-glucosidase activity (Klyosov, 1987a,

Liu et al., 2012, Lynd et al., 2005, Sweeney and Xu, 2012, Andrić et al., 2010b). A.

niger has been reported to have lower endoglucanse and exoglucanase activities [1.11

IU/ mg and 8 IU/mg, respectively] as compared to T. harzianum. However, it has a very

high β-glucosidase activity at 12.5 IU.mg, which might explain its comparative TSS

utilization against T. harzianum (Liu et al., 2012). P. citrinum secretes significant

amounts of endoglucanases [111.5 IU/ mg] and exoglucanases (Dutta et al., 2008, Ng et

al., 2010, Dutta et al., 2007). However, its production ability of β-glucosidase has not

been reported for the broth cultures, although, it has been reported during the solid state

fermentation under the optimal source of carbon and nitrogen sources (Camassola and

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Dillon, 2007, Gao et al., 2008, Ng et al., 2010). This may explain the comparative

inability of the Penicillium cultures to utilize a substantial amount of TSS in the media.

4.3.3. Utilization of reducing sugars and pentoses

Reducing sugars such as glucose are the primary carbon sources for microbial

growth. Cellulose is the most abundant polysaccharide and makes up the basic structure

of plant cell walls. It is an almost linear molecule made up of the reducing sugar β-D-

glucopyranose, linked by β-1, 4-polyanhydroglucose with cellobiose (also a reducing

sugar) as the smallest repetitive unit, thus is a β-glucan (Fengel, 1971).

It was observed that the process of autoclaving released considerable amounts

free sugars in the filtrate. The final sugar concentration suggests complete utilization of

these sugars by growing fungi. The subsequent degrading process of cellulose and

hemicelluloses generated further free sugars (2.2 kg/m3). Noticeable amounts of these

sugars were then consumed by fungi during their growth. It was observed that

substantial amounts of reducing sugars accumulated in the autoclaved samples. The

highest amount of sugar utilization was observed for T. harzianum and P. citrinum

cultures, where the concentration of reducing sugars decreased from 2.2 kg/m3 to 1

kg/m3 and 1.5 kg/m3, respectively (a decrease of 1.2 kg/m3 and 0.7 kg/m3, respectively).

A. niger also showed noticeable degradation amounting to about 0.4 kg/m3 utilization as

the reducing sugar concentration decreased from 2.2 kg/m3 to 1.8 kg/m3 (Figure 4.2),

whereas P. chrysogenum did not show significant utilization . The fungal degraded non-

autoclaved grapes accumulated lower amounts of reducing sugars, ranging from 0.9

kg/m3 (A. niger) to 1.4 kg/m3 (P. citrinum).


88

Figure 4.2. Reducing sugar content (kg/m3) in fungal cultures of non-autoclaved, autoclaved and



Noticeable amount of sugars were utilized by T. harzianum as the sugar

concentration decreased by 0.8 kg/m3 (from 1.8 kg/m3). A similar result was seen in A.

niger culture, where the reducing sugars decreased to 0.9 kg/m3 from 1.9 kg/m3

(untreated control). By contrast, P. chrysogenum and P. citrinum cultures both

accumulated 1.4 kg/m3 sugars, decreasing from 1.8 kg/m3 in the untreated control. Thus,

they were able to utilize only 0.4 kg/m3 of sugars. Overall, proportionally higher

amounts of reducing sugars were observed to be utilized in autoclaved substrates as

compared to non-autoclaved substrates.

It was observed (Figure 4.3) that high amounts of pentose sugars accumulate in

autoclaved samples. The pentose sugar content of non-autoclaved substrate cultured

with T. harzianum, A. niger, P. chrysogenum and P. citrinum was 5.6 kg/m3, 7.6 kg/m3,

5.1 kg/m3 and 5.6 kg/m3, respectively. These values increased significantly in the

autoclaved samples where the pentose content increased to 9.1 kg/m3, 11.1 kg/m3, 8.5

kg/m3 and 9.1 kg/m3 in the same order (Figure 4.3). This may indicate higher

hemicellulosic degradation in autoclaved samples, as also reflected in the higher

0.0

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89

xylanase activities in those substrates as compared to the non-autoclaved substrates (see

below).

Figure 4.3. Pentose sugar content (kg/m3) in fungal cultures of non-autoclaved, autoclaved and



Hydrothermal heating weakens the lignin crosslinks within cellulose and

hemicellulose, thus exposing them to physical, chemical and/or biological degradation

(Hassan et al., 2013, Papadimitriou, 2010). Further, the hydrothermal process, as it

occurs in an autoclave, enhances the breakdown of hemicellulosic polymers to form its

constituent pentose and hexose sugars, as well as other derived products such as

galacturonic and/or glucuronic acids. The highly heterogeneous composition of

hemicelluloses makes them more susceptible to physical and chemical reactions

compared to cellulose. The noticeable content of hexoses and minor amount of heptoses

might explain the extraordinary utilization of reducing sugars in T. harzianum and P.

citrinum cultures (Hassan et al., 2013, Papadimitriou, 2010).

The initial low pH (5.6) probably intensified the degradation process of

hemicelluloses and cellulose in both cultures (Hassan et al., 2013, Takashima and

Tanaka, 2008). The further release of sugars and their ultimate utilization explains the

degradation ability of the fungal cells. Additionally, T. harzianum, A. niger and P.

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citrinum are known to produce a significant amount of hemicellulose-hydrolyzing

enzymes such as xylanases, arabinases and pectinases among many others (Brink and

Vries, 2011, Kumar et al., 2008).

4.3.4. Change in the lignin content

Lignins are abundant biopolymers, second only to cellulose. In most plants,

especially in higher plants, they function as the prominent components of xylem. They

are composed of hydroxycinnamyl alcohols specifically p-coumaryl alcohol, coniferyl

alcohol and sinapyl alcohol, and their methoxy derivatives. Due to their hydrophobic

nature, they aid in efficient water transport throughout the plant system. However, this

very nature also makes them tolerant towards biodegradation (Boerjan et al., 2003,

Vanholme et al., 2010). Lignins not only act as the physical barriers to microbial

degradation, but also as non-productive binders of cellulases, thus acting as cellulase

inhibitors (Duarte et al., 2012b)

The total lignin (Acid Soluble Lignin and Acid Insoluble Lignin) was observed

to increase in every sample when compared to the untreated control (Figure 4.4). This

was expected since T. harzianum and A. niger do not have the ability to degrade lignin

and Penicillium spp. only have limited ability to mineralize lignin (Rodríguez et al.,

1994). As other material is degraded, the remaining material, lignin, will increase as a

percent content.

Autoclaving was previously observed to degrade considerable amounts of

sugars, thereby substantially increasing the lignin content of the substrate from an

average of about 36% in untreated controls to about 63% in autoclaved substrates.

Subsequent fungal growth consumed the sugars, but their inability to mineralize lignin

resulted in further lignin accumulation in the growth media. This increase was

especially noticeable in T. harzianum and P. citrinum cultures, where the lignin content

spiked from 65.7 % to 68 % and 63.8 % to 74 %, respectively, thus, showing inability

of these fungi to degrade lignin. Interestingly, however, P. chrysogenum was able to

mineralize some lignin in autoclaved substrate as total lignin in the culture showed a

decrease of about 9 %, from 64% to 53.1 % (Figure 4.4). On the other hand, the non-

autoclaved substrate from the P. chrysogenum culture displayed an increase of about


91

14% lignin from 36.1% to 50.3%, thus, suggesting the inability of this fungus to

degrade lignin in non-pretreated substrates.

Figure 4.4. Lignin content (%) in fungal cultures of non-autoclaved, autoclaved and untreated Shiraz

grape waste. Values for non-autoclaved and autoclaved samples are significantly different to

untreated samples (p < 0.05).

Similar changes were observed with autoclaved substrate cultured with A. niger.

Similar to P. chrysogenum, a decrease in lignin content by 4.2 % (from 63.7 % to 59.6

%) was observed in this culture. This was unexpected since A. niger has not been

reported to have any lignin degrading properties. The observation encourages further

study on the degradation pattern of A. niger on winery wastes. One of the other

interesting observations was the limited increase in lignin content in non-autoclaved

substrate cultured with A. niger. In this culture, the lignin content rose slightly from

35.8 % in the untreated control to 39.5% in the non-autoclaved sample. One of the

reasons for this might be a lower cellulase activity of A. niger, which resulted in

minimal substrate degradation, thereby, almost conserving the original lignin

composition.

Although all fungi used in this study generated substantial amounts of cellulases

and hemicellulases, all but P. chrysogenum do not have the ability to synthesize

lignases. P. chrysogenum, however, generates lignases which degrade the low molecular

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92

weight lignin components such as cinnamic acid, ferulic acid and vanilic acid

(Rodríguez et al., 1994). In the case of A. niger, the fungus might possess minor lignin

mineralization ability for its major substrates, cellulose and hemicellulose. Indeed, there

are few data available regarding the quantification of lignin degradation/transformation

by these fungi on wooden substrates (Hassan et al., 2013, Singh Arora and Kumar

Sharma, 2010). To authors’ knowledge, the current study is the first to investigate the

degradation of the lignin component of winery biomass waste.

4.3.5. Cellulase activity

Cellulose is the main source of carbon in lignocellulose complexes as it is the

richest source of glucose, the primary source of carbon for most microorganisms.

However, cellulose exists in the core of lignocellulosic complexes, surrounded by

hemicellulose and lignins. To achieve access to cellulose, fungi have to penetrate these

layers (Fengel, 1971). Cellulases are enzyme complexes with slight variations in their

constituent enzyme ratios. However, three enzymes types; endoglucanase, exoglucanase

and β-glucosidase, are most common and are found in almost every lignocellulose

degrading fungus. Aerobic ascomycetes such as T. harzianum and Aspergillus spp. have

cellulases composed of endoglucanase and exoglucanase, reportedly of varied

individual enzyme composition depending on substrate availability and specificity

(Kovacs et al., 2009, Liu et al., 2012, Sipos et al., 2010). Endoglucanases (EC 3.2.1.4)

are the enzymes that convert the crystalline cellulose to the amorphous form making it

more accessible to enzymes like cellobiohydrolases. Cellobiohydrolases or exo

glucanases (EC 3.3.1.91) have been shown to be one of the main cellulase components

(Sweeney and Xu, 2012). They act by degrading the extended or distorted cellulose

molecules to convert them into cellobiose as the product. In addition to any amorphous

cellulose, cellobiohydrolases also degrade crystalline cellulose.

Cellulase activity is thus effectively measured by combined enzyme activities of

both endo and exoglucanases (Ghose, 1987).The highest cellulase activity of about 45

U/mL was observed in the A. niger culture, significantly higher than the enzyme

activities of 19.6 U/mL, 24.1 U/mL and 15.1 U/mL observed in T. harzianum, P.

chrysogenum and P. citrinum cultures, respectively (Figure 4.5). This is in contrast to

reportedly higher activities in Trichoderma with respect to Aspergillus (Kovacs et al.,


93

2009, Liu et al., 2012). One of the reasons for this might be the limited requirement for

enzyme production due to the availability of large amounts of free sugars which result

from the process of autoclaving the substrate. Another reason might be significant

product inhibition of the fungal cellulases by free sugars produced, most of which are

known to be product inhibitors of cellulolytic enzymes (Brink and Vries, 2011, Duarte

et al., 2012a, Duarte et al., 2012b).

Figure 4.5. Cellulase activity (U/mL) of the fungal cultures in non-autoclaved and autoclaved

Shiraz grape waste. Values for non-autoclaved and autoclaved samples are significantly different

between the samples (p ≤ 0.05), except for the asterisk (*) marked values (p > 0.05).

The cellulase activities of the non-autoclaved samples were found to be 48.1

U/mL, 16.6 U/mL, 9.2 U/mL and 64.6 U/mL in T. harzianum, A. niger, P. chrysogenum

and P. citrinum cultures, respectively. The most contrasting difference of activities was

seen in T. harzianum and P. citrinum cultures where the cellulase activities were about

3-4 times higher in non-autoclaved samples as compared to autoclaved samples (Figure

4.5). This was expected in T. harzianum cultures as the species is known (and was

observed) to have a higher cellulase activity, but very low β-glucosidase activity. This

results in product inhibition of Trichoderma cellulase by glucose and cellobiose thus

lowering the activity, as seen in the autoclaved samples, which had considerable

amounts of those free sugars. However, the considerable difference of cellulase activity

*

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in the P. citrinum culture is more difficult to interpret, since it displayed higher β-

glucosidase and xylanase activity in autoclaved and non-autoclaved substrates (Sections

4.3.6 and 4.3.7). Higher cellulase activity was observed in autoclaved substrate further

degraded by A. niger. This was expected since the species, although having

comparatively lower cellulase activity possesses high β-glucosidase activity which

prevented product inhibition of its cellulase enzyme.

Product inhibition is a well-known phenomenon which contributes to the

difficulties observed in lignocellulose degradation. It has been reported by numerous

authors, especially in relation to cellulose degradation, that cellobiose (the chief product

of cellulose degradation) and glucose (the minor product) act as inhibitors of cellulases.

The main reason for this is the exceptionally low catalytic activity of β-glucosidase as

compared to the other enzymes present in the cellulase enzyme complex (Duarte et al.,

2012b, Kovacs et al., 2009, Liu et al., 2012, Sipos et al., 2010).

4.3.6. β-glucosidase activity

β-glucosidases are exo-glycoside hydrolases which catalyse the β-1, 4 glycosidic

bonds in cellobiose or other β-oligodextroses to give glucose as the by-product. Like

endoglucanases and exoglucanases, β-glucosidases are also categorised as Glycoside

Hydrolase families 1 and 3 (Dashtban et al., 2009, Henrissat and Davies, 1997). β-

glucosidases are generally produced by numerous cellulolytic organisms (especially

fungi) due to the competitive inhibition of endo- and exoglucanases by their

degradation end products of cellobiose.

The activity and secreted amount of β-glucosidase is one of the important

contributing factors towards cellulase activities. β-glucosidase hydrolyses the β-(1-6)

glycosidic bonds in cellobiose (the smallest functional unit of the cellulose molecule) to

glucose units which can be readily utilized by chemical or microbial actions (Brink and

Vries, 2011, Kim et al., 2012a). However, numerous previously reported works and the

current findings suggested very low activity (26 U/mL) of these enzymes in

Trichoderma spp. (Baldrian and Valášková, 2008, Juhász et al., 2005, Kovacs et al.,

2009) as compared to other fungi (Figure 4.6), especially, A. niger (Singhania, 2012),

which displayed enzyme activity of 155 U/mL in our work. As found with the other

cellulose enzyme constituents, β-glucosidase also suffers from product inhibition,


95

especially from cellobiose and glucose. This might explain the overall lower cellulase

activity in the T. harzianum culture. As seen earlier in Figures 4.1 and 4.2, high

amounts of sugars observed after hydrothermal pre-treatment may have increased the

product inhibition of β-glucosidase in the T. harzianum culture, thereby decreasing the

activity over the course of fermentation. In addition to this, the catalytic activity of the

enzyme has been found to be very low as compared to other enzymes like

cellobiohydrolases and endoglucanases (Baldrian and Valášková, 2008, Juhász et al.,

2005, Kim et al., 2012a). Penicillium cultures also displayed significant β-glucosidase

activities, which probably resulted in their overall high cellulolytic activities (Figure

4.6).

Figure 4.6. β-glucosidase activity (U/mL) of the fungal cultures in non-autoclaved and autoclaved


between the samples (p ≤ 0.05).

The β-glucosidase activity of T. harzianum from the non-autoclaved substrate

(16.8 U/mL) did not vary much from the autoclaved substrate, although, it showed a

decrease of about 9 U/mL over the fermentation period. Similar results were seen in the

P. citrinum culture where β-glucosidase activity in non-autoclaved substrate was 160.9

U/mL as compared to 180.1 U/mL in autoclaved substrate. However, considerably

lower activities were found in the non-autoclaved substrates cultured with A. niger and

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P. chrysogenum. The β-glucosidase activity of the autoclaved substrate cultured with A.

niger was 154.9 U/mL, which was more than 3 times the value of the non-autoclaved

substrate (49.6 U/mL). Similarly, the activity in P. chrysogenum culture reduced from

84.9 U/mL in autoclaved substrate to 40.6 U/mL in non-autoclaved substrate, a greater

than two-fold decrease in activity. Overall, the higher β-glucosidase activity in

autoclaved substrate as compared to non-autoclaved substrate indicates the hydrolysis

of a major part of cellulose and hemicelluloses to disaccharides, such as cellobiose,

which is the substrate for enzymes such as β-glucosidase (Figure 4.6).

4.3.7. Xylanase activity

Xylans are perhaps most numerous of all hemicelluloses, comprising about 25%

of all saccharides, second only to cellulose to which they are tightly bound by covalent

and other bonds in a complex manner (Fengel, 1971, Goksu et al., 2007, Hansen and

Plackett, 2008). They are comprised of β (1, 4) D-xylopyranoses with non-crystalline

hexoses such as galactose attached to them.

Xylanases are a group of differential hemicellulases responsible for the

degradation of various xylans by hydrolysing their β-(1, 4) linked D-xylopyranoside

units. Comprising up to 1% of total fungal lignocellulolytic enzymes, xylanases may

work symbiotically with other hemicellulases and cellulases, thereby facilitating a

consortial mix for degradation of biomass substrate. Like cellulases and numerous

hemicellulases, xylanases are also classified as glycoside hydrolases (GH) and most of

them belong to families of GH 8, 10, 11, 30 and 43 (Dashtban et al., 2009, Sweeney and

Xu, 2012, Cantarel et al., 2009).

Xylanase activities across all the fungi were lower than expected. The highest

activity of 335 U was found in A. niger followed by P. citrinum and P. chrysogenum at

234 U and 191.1 U, respectively (Figure 4.7). T. harzianum displayed the lowest

enzyme activity of 38.7 U. The low activity of xylanase reflects the presence of very

low amounts of hemicellulose substrates in the medium following thermal hydrolysis.


97

Figure 4.7. Xylanase activity (U/mL) of the fungal cultures in non-autoclaved and autoclaved


between the samples (p ≤ 0.05).

The xylanase activities in non-autoclaved substrates were lower than that of the

autoclaved substrate. Enzyme activities of 13.8 U, 277.3 U and 201.1 U were observed

in T. harzianum, A. niger and P. citrinum non-autoclaved cultures, respectively. The

most significant decrease was seen in P. chrysogenum, where the xylanase activity

decreased dramatically from 191.1 U in autoclaved substrate to 7.2 U in non-autoclaved

substrate. The activities reported here are significantly lower than those reported

previously (Betini et al., 2009, Hideno et al., 2011). The higher activity of xylanase in

autoclaved substrate indicates possible hydrolysis of hemicelluloses to oligomers rather

than mono-saccharides, which acted as the substrates to xylanases. It was seen that

although xylanase activity in fermentation filtrates was not inhibited by free sugars, it

probably limited the range of activity over the degradation time.

4.4. Conclusions

In this study, degradation of winery discarded grape waste was assessed after

hydrothermal treatment, followed by degradation with T. harzianum, A. niger, P.

chrysogenum or P. citrinum. Variation in the percent composition of cellulose,

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hemicelluloses and lignin in the grape waste was determined by various assays after an

incubation period of 5 days.

Significant amounts of reducing sugars and pentoses were secreted in the

medium after the hydrothermal treatment, presumably resulting from the partial and/or

total degradation of cellulose and hemicelluloses, respectively. Subsequent fungal

treatments not only utilized the free sugars in the medium, but also enhanced further

degradation of the grape waste. The process of autoclaving increased the composition of

total soluble sugars to 66 kg/m3, reducing sugars to about 22.6 kg/ m3 and pentose

concentration from about 0.94 kg/m3 in non-autoclaved sample to 9.9 kg/m3 to 10% in

the autoclaved sample. Acid insoluble lignin, which forms the majority of lignin

content, increased to about 63.7%, showing an increase of 28.5% (from 35.2%) in the

non-treated sample.

Total lignin was noticeably mineralized by P. chrysogenum and to a minor

extent by A. niger over the incubation period. The other fungi did not show any lignin

degrading abilities. A. niger displayed substantial xylanase, β-glucosidase and xylanase

activities, marginally higher than P. citrinum except for β-glucosidase.

Although T. harzianum showed comparable cellulase activities, it displayed

lower xylanase and β-glucosidase activities with respect to the other fungi. Autoclaving

hydrolyzed hemicelluloses and crystalline cellulose, converting the latter to a more

degradable amorphous form. Overall, a significant increase in the efficiency of fungal

enzyme activities and subsequent degradation of winery waste was observed after

hydrothermal treatment compared to that seen in a normal fermentation process.

4.5. Experimental summary

The experiments described in this chapter form one of the intermediate protocols

to improve the fungal biodegradation of winery derived biomass waste. The objective of

this protocol was to optimize fungal mediated winery biomass degradation. For the

experimental purposes, fungi belonging to division Ascomycota, such as T. harzianum,

A. niger, P. chrysogenum and P. citrinum, were used.

The fungi inoculated after autoclaving utilized considerable amounts of sugars

as discussed in section 4.3. Penicillium spp. are known to degrade lignin to some extent,


99

especially the smaller lignin structures. Not surprisingly, P. chrysogenum was observed

to degrade minor amounts of lignin during the fermentation process which could have

resulted from loosening or breaking of the linkages between lignins, cellulose and

hemicelluloses during autoclaving process. This process might have provided more

access for biomass degrading enzymes to degrade lignins and cellulose.

Overall, it was observed that a simple autoclaving process could improve the

biomass degradation abilities of fungi during a successive fermentation process. Also,

the pre-treatment autoclaving process decreased the time required to degrade grape

biomass considerably. Current methodologies are focussed on individual fungal cultures

in combination with economically viable physical processes in order to obtain

maximum lignocellulose degradation for gradual production of ethanol, H2 and other

commercially important molecules. The initial protocol was directed at producing a

standard positive control with respect to the cellulose degradation by a different

combination of organisms.

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CHAPTER 5

Solid state fermentation of winery

biomass waste by Ascomycota fungi

Chapter 5 SSF of winery biomass wastes by Ascomycota fungi

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5.1. Introduction

Solid state fermentation (SSF) is one of the second generation bioconversion

processes for the production of industrially important molecules such as bio-hydrogen

and bio-ethanol (Lee, 1997, Brijwani et al., 2010, Sarkar et al., 2012, Kausar et al.,

2010). The method employs either single microbial cultures or a mixture of various

organisms and/or their derived enzymes, in a single step and usually generates a

considerably higher bioconversion of biomass as compared to submerged fermentation.

This approach eliminates the need for a separate pre-treatment method such as

hydrolysis. Strong hydrolysing techniques such as steam explosion, water-compression

and ammonia freeze explosion methods do strongly hydrolyse lignocellulose

components; however they may decrease successive microbial bioconversion (and

degradation) efficiency. This results from the generation of several low molecular

weight molecules such as furfural, 5-hydroxymethylfurfural, small chain organic acids,

phenolics and inorganics, any of which can cause enzyme inhibition of fermentation

micro-organisms (Dashtban et al., 2009, Klinke et al., 2004). In many cases, this

microbial inhibition can be overcome by washing. However, this results in the

generation of considerable wastewater, which is of environmental concern and counter-

productive to the ultimate aim of a cleaner society. These pre-treatment processes also

increase the biomass treatment and successive bioconversion costs. SSF-based

approaches, eliminate the production of microbial inhibitory molecules, ameliorating

the need for washing, and are worth testing for their competiveness against pre-

treatment followed by fungal degradation.

As the water content in SSF is equal to or slightly higher than the substrate, the

method allows greater aeration and surface attachment of filamentous fungi. This

approach allows for the production of a vast array of lignocellulolytic enzymes. It also

increases the amount of substrate degraded in a single step from 1-2% in submerged

fermentation to more than 10% in SSF (Lee, 1997, Ng et al., 2010). The process also

reduces the need for separate enzyme production, isolation and application in biomass

degradation as these all occur in a single step, thus, reducing critical error points and

reducing economic inputs (Betini et al., 2009, Brijwani et al., 2010, Dashtban et al.,

2009, Sarkar et al., 2012). The SSF process is also reported as an intermediate step in

the development of Consolidated Bio-processing (CBP), which has been estimated to


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reduce the production costs of biofuels by a factor of 4-5 or more (Lynd et al., 2005,

Yamada et al., 2013).

5.2. Overview

The experiments described in this chapter were performed to study the

comparative degradation of winery grape wastes by various fungi, and across two

biodegradation processes (SSF and submerged media). Further, they were aimed to

specifically to achieve biodegradation of the winery grape waste lignocellulose

components. One of the primary reasons for using the SSF strategy was to conserve the

amount of water used in the process and increase the amount of substrate being

degraded during the process. The ratio between fungal substrate and growth media was

kept at 1:1 across the various fungi and for both SSF and submerged media, which

meant that the substrate concentration increased from 3% in submerged fermentation to

50% in SSF. The degradation patterns of various white-rot fungi (P. chrysogenum and

P. citrinum), brown-rot fungi (A. niger) and T. harzianum were compared over 2 weeks,

so as to formulate the ideal fungal/enzymatic combination and the optimum time-frame

to yield maximum degradation. These experiments form a process in the development

of a CBP method for the production of biofuel molecules, such as ethanol or hydrogen.

5.3. Results and discussion

5.3.1. Total Protein Content

It was observed that the total protein content in the filtrate dropped significantly

from 11.5 Kg/m3 after 1 week to 1.9 kg/m3 after 2 weeks in T. harzianum cultures. The

content in A. niger also dropped significantly, from 4 kg/m3 to 2.3 mg/ml. The total

content drop in Penicillium spp. was however, not as striking. P. chrysogenum

displayed a drop of 2.2 kg/m3 from 10.6 kg/m3. Moreover, no drop was observed for P.

citrinum cultures, with total protein content staying at 6.2 kg/m3 (Figure 5.1).

Total protein content in the filtrate reflects not only the amount of enzymes

(especially, lignocellulolytic enzymes) secreted, but also reflects the growth stage of

individual fungal cells. The fungi release high amounts of proteins in the filtrate during

their log phase of growth. However, as the stationary phase approaches, the amount of

proteins in the filtrate decreases (Kausar et al., 2010). Thus, the protein content in the


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filtrate varies across the organisms, type of substrate (Sipos et al., 2010) and stage of

incubation.

Figure 5.1. Total protein content of fungal degraded Shiraz grape waste over 2 weeks of SSF. Data

from autoclaved fermented samples are used for comparison purposes. All the values are the mean of

triplicate data.

It has been reported that the presence of specific hemicellulose sugars in the

growth medium increases proteinase activity. However, this activity is inversely

proportional to the cellulase or avicelase activities (Poulsen and Petersen, 1988). This

relation probably reflects the increased activities of cellulase and β-glucosidase and

decreased activities of xylanase in most of the fungal cultures during the current studies.

The protein content in the autoclaved grape substrates (fungal degradation time

of 5 days) was much higher than the SSF cultures. The observed protein concentrations

in T. harzianum and A. niger were 31.9 and 21.9 kg/m3, respectively, by far the highest

values obtained. The total protein in Penicillium cultures differed to a lesser degree.

Against concentrations of 10.6 kg/m3 and 6.22 kg/m3 in SSF cultures after 1 week, the

protein concentrations were observed to be at 11.9 kg/m3 and 13 kg/m3 in P.

chrysogenum and P. citrinum, respectively. The increase in total protein content for

autoclaved samples was not reflected in an increase of any enzyme activity observed in

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those samples, suggesting that the proteins probably originated from the grape substrate

due to the autoclaving process and accumulated in the filtrate. This observation is in

agreement with previous work (Izydorczyk et al., 2000).

5.3.2. Reducing sugars

The reducing sugars in two of the cultures increased significantly from 1 to 2

weeks, whilst the other two did not significantly alter. Specifically, the reducing sugar

levels in T. harzianum cultures decreased from 8.6 kg/m3 to 6.7 kg/m3, whilst of P.

citrinum cultures showed a minimal drop from 7.8 kg/m3 to 7.3 kg/m3. In both cases, the

change was within experimental error, however, the sugar levels in the A. niger and P.

chrysogenum cultures increased significantly by almost 12 kg/m3 (5.7 mg/ml to 17

kg/m3) and 22 kg/m3 (16 kg/m3 to 38.3 kg/m3), respectively (Figure 5.2).

Figure 5.2. Total reducing sugar content of fungal degraded Shiraz grape waste over 2 weeks of

SSF. Data from autoclaved fermented samples are used for comparison purposes. The values are the

mean of triplicate data.

The reducing sugar content in the untreated grape biomass was found to be 1.7

Kg/m3, which increased in each fungal culture under the experimental conditions. It was

also observed that the sugar content increased considerably during the second week, T.

harzianum and P. citrinum being the exceptions. One of the peculiar observations about

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the T. harzianum cultures was the high accumulation of reducing sugars during week 1

of SSF (8.7 kg/m3) which was followed by a drop to 6.7 kg/m3 after the second week.

One of the reasons behind this could be product inhibition of Trichoderma cellulases. It

is known that Trichoderma spp. produce considerable amounts of cellulases (Baldrian

and Valášková, 2008, Klyosov, 1987b). However, the cellulases produced by these

fungi suffer from a lower catalytic activity as compared to other enzymes such as

amylases and cellulases derived from other biomass degraders such as Aspergillus spp.

and Phanerochaete spp. The fungus also generates lower amounts of glucosidases

which are responsible for catalyzing products of cellulose degradation such as

cellobiose. This deficiency contributes to the problem of enhanced product inhibition

(Andrić et al., 2010a, Duarte et al., 2012b, Klyosov, 1987b, Kumar et al., 2008).

By contrast, P. chrysogenum was observed to generate high levels of reducing

sugars at week 1, which increased to even higher values after 2 weeks. This was

observed to be directly proportional to the considerable increase in cellulase activity of

this fungus over 2 weeks (as compared to week 1) and a considerably higher β-

glucosidase activity during the same time. As a result, this fungus probably suffered

from very low product inhibition, which was observed prominently in both the T.

harzianum and P. citrinum cultures. It has been reported that enzyme activities change

according to the nature of substrate (Juhász et al., 2005). Lignin composition also plays

a role in the efficiency of biomass degradation, with the lignin concentration directly

proportional to non-specific inhibition as is discussed in section 5.3.3. Under the current

experimental conditions, it was observed that the grape biomass wastes, due to their

structural complexity, induced higher activities in Penicillium spp. and Aspergillus

niger rather than T. harzianum.

In all cases, the content of reducing sugars in the autoclaved substrate cultures

was considerably lower than that of the SSF cultures. Previous analysis has shown that

the amount of reducing sugars increased considerably in the autoclaved media (Chapter

4, section 4.3.3), which forms a major source of carbon for the fungi. This initial growth

was observed to utilize the substantial amounts of sugars in the medium. Reducing

sugar contents were found to be 1.5 kg/m3, 1.8 kg/m3, 2 kg/m3 and 1.5 kg/m3 in

autoclaved substrates cultured with T. harzianum, A. niger, P. chrysogenum and P.

citrinum, respectively. The most significant difference in reducing sugar content was

observed in P. chrysogenum cultures, where the level was lowered by about a factor of


106

8 than that of 1 week SSF. However, as with the protein content, this difference of sugar

utilization was not reflected in increased enzyme activities, except for A. niger which

displayed a marginally increased cellulase activity (Figure 5.4) and significantly

enhanced β-glucosidase activity (Figure 5.5). The process also seemed to increase β-

glucosidase activity in P. citrinum cultures as compared to the SSF process.

Fungi, especially those belonging to divisions Ascomycota and Basidiomycota,

produce better degradation under solid state conditions than submerged fermentations

due to the provision of higher surface area. Additionally, during the current SSF

experimental set up, autoclaving was not used as the pre-treatment method. As

discussed earlier, free sugars are probably released in the growth medium during

autoclaving as a direct result of hydrolyzation of cellulose and hemicellulosic

components of the biomass. This would not, therefore, happen under the current SSF

experimental protocol. Fungi may, therefore, grow slowly during the initial phase but

induce quicker cellulase and hemicellulase production which in turn causes rapid

cellulose and hemicellulose degradation generating high amounts of reducing sugars

(Figure 5.2). The non-hydrolyzation of cellulose and hemicelluloses also makes the

overall process less prone to the product inhibition of cellulases and hemicellulases

caused by free monosaccharides (e.g. glucose) or disaccharides (e.g. cellobiose) (Kumar

et al., 2008, Duarte et al., 2012b).

Reducing sugars, such as cellobiose, glucose, xylose and arabinose, are the most

common units in cellulose and hemicelluloses and are produced during chemical or

microbial hydrolysis of those molecules. However, it is known that the cellulolytic

enzymes from numerous fungi such as Trichoderma spp. are highly susceptible to

product inhibition, which can decrease further enzyme activity of those fungi. This is

compounded by a very low natural β-glucosidase activity (Baldrian and Valášková,

2008, Klyosov, 1987b). The experimental observations are consistent with these

previous reports as they show a decreasing reducing sugar trend in T. harzianum

cultures, which was directly proportional to significantly decreased cellulase and

xylanase activities in the second week. This resulted in a lower amount of biomass

degradation in T. harzianum cultures. A. niger has also been reported to possess very

high β-glucosidase activity in addition to its moderate cellulase production (Maeda et

al., 2011, Singhania, 2012).


107

5.3.3. Lignin mineralization

Lignins are a group of polymers seemingly arranged in a random and complex

manner. Lignins form the most abundant biomass after cellulose and can comprise up to

40% of total biomass. Lignins are bounded to both cellulose and hemicelluloses in a

highly complex structural pattern (Dashtban et al., 2009, Fengel, 1971). In most plants,

especially, in higher plants, they function as the prominent components of xylem. Due

to their hydrophobic nature, they aid in efficient water transport throughout the plant.

However, this very nature also makes them tolerant towards biodegradation (Boerjan et

al., 2003).

Figure 5.3. Lignin content of fungal degraded Shiraz grape waste over 2 weeks of SSF. The

untreated sample refers to the lignin content of grape samples before any treatment or addition of

growth medium.

Acetylated lignin units are derived from two or more lignin monomers such as

p-coumaryl alcohol, sinapyl alcohol, 5-hydroxyconiferyl alcohol, hydroxycinnamyl

aldehyde, hydroxybenzyl aldehyde, hydroxycinnamate ester, dihydrocinnamyl alcohol,

arylpropane -1, 3-diol, aryl glycerol, hydroxycinnamyl acetate, hydroxycinnamyl p-

hydroxybenzoate, hydroxycinnamyl p-coumarate and tyramine hydroxycinnamate. They

may be regarded as the foremost of all lignin components, constituting more than half of

the lignin polymer (Lu and Ralph, 2002). Lignin degradation or removal is one of the

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chief aspects of biomass degradation as they act as non-productive inhibitors of

cellulolytic enzymes, especially cellulases, which have a strong binding tendency to

lignins (Alvira et al., 2010, Duarte et al., 2012b). The importance of this property

further increases in grape waste due to its high content of lignin-derived products such

as phenolics and tannins (Bravo and Saura-Calixto, 1998, González-Centeno et al.,

2010).

According to literature, most of the fungi used in the current study are

considered as non-lignolytic. Indeed, there was an absence of lignin-degrading enzymes

such as laccases and peroxidases in those fungi (data not shown). However, Penicillium

spp. has been previously reported to possess minor lignin mineralization abilities. These

fungi, especially, P. chrysogenum, are known to produce up to 8% degradation of low

molecular weight lignin molecules under appropriate conditions (Rodríguez et al., 1994,

Rodriguez et al., 1996).

Results obtained during the given experiments indicated a similar trend, but to a

lesser degree. As determined from previous experiments (see Section 4.3.4), the lignin

composition of Shiraz grape waste, and prior to treatment, was found to be about 36%,

which is in agreement with previous reports (Bravo and Saura-Calixto, 1998, González-

Centeno et al., 2010). The lignin content following various treatments is given in Fig

5.3. The terms “Untreated’ and ‘Control’ refer to the substrates before and after the

addition of AATCC mineral medium, respectively The total lignin content increased in

all the cultures, in variable amounts, following SSF treatment (Figure 5.3). This is

presumed due to cellulose and hemicellulose degradation (reducing the total amount of

material), but little lignin degradation (increasing the content of the lignin in the

remaining sample). The untreated grape samples consisted of about 36% lignin.

Probably due to the addition of AATCC-based minerals, this level increased to about

46.9% in control samples (treated with S. cerevisiae). As the substrate was not subjected

to autoclaving before SSF, it did not display a sharp increase in lignin content caused by

hydrolyzation of cellulose and hemicellulose. Therefore, the lignin contents observed

during autoclave pre-treated substrate followed by submerged fermentation (Chapter 4)

were not taken in to consideration during the current experiments.

Used as a control due to its inability to degrade biomass, the lignin content from

S. cerevisiae was expected (data not presented in graph). The lignin content of this yeast


109

increased to about 47%, which was 0.1% above that of the control sample (46.9%). In

P. chrysogenum and P. citrinum cultures, the lignin contents were found at 44.8 % and

49.1%, respectively. These cultures displayed a drop of 2.2% and 1.6% lignin content,

which, although not amounting to considerable lignin degradation, showed a similar

trend to that observed in the literature mentioned above. During the current experiments,

negligible laccase activity was observed in Penicillium cultures, but lignin peroxidases

and tyrosinase activities were absent. The laccase enzyme group present in wood rot

fungi such as Trametes spp. is responsible for a majority of lignin oxidation. The azo

group (R-N=N-R’), which is present in both azo dyes such as Acid Red B, is also found

in a majority of lignin molecule structures. Probably due to this, the laccases from

Penicillium spp. have been effective in exhibiting minor lignin degradation. However, it

has been suggested that, unlike Trametes spp., laccases in Penicillium spp. show bio-

adsorption properties rather than the actual biodegradation of lignin residues (Gou et al.,

2009).

A. niger is known as a lignin non-degrader. This inability was clearly observed

during the experiments here. The lignin content in this culture increased to about 65%,

which was an increase of about 18%. However, this sharp increase could not be

accounted for any other content due to the process involved in lignin content

determination (Sluiter et al., 2011). However, the probability of this occurring due to a

lower cellulose and hemicellulose degradation of grape waste cannot be ruled out. The

lignin content observed in A. niger treated SSF grape contrasts to previous observations

(Kausar et al., 2010) where lignin mineralization was mediated by a consortium of A.

niger and T. viride, even though both fungi are individually known as non-lignolytic in

nature.

One of the surprising results obtained was from T. harzianum cultures. The

lignin content observed in this fungus amounted to 43.6%, which was a drop of about

3.4%. This decrease in lignin was unexpected since no laccase, lignin peroxidase or

tyrosinase activities were found in this fungus. Kirk and Farrell (1987) reviewed 18

strains of Trichoderma spp., including T. harzianum and reported that none of these

fungi possess lignolytic activities. Therefore, the apparent degradation may have been

the result of cellular adsorption to the substrate rather than an actual degradation (Kirk

and Farrell, 1987). In an experiment performed with 14 isolates of Trichoderma spp.,

only one type of phenol oxidase (of only one subtype - classified as a laccase system


110

enzyme), was isolated in minor quantities from some selective fungi. However, no

correlation was observed between this isolated enzyme and lignin degradation as it had

been observed to be in an inactive state (Assavanig et al., 1992). These observations, in

addition to the current experimental observations on enzyme activities (see sections

below), confirmed that lignin degradation of about 3.4% observed was probably due to

the adsorption mechanism or deposition of some polymers on the substrate by this

fungus which were not hydrolysed by sulphuric acid during the process of lignin

determination.

5.3.4. Cellulase activities during SSF

Cellulase activity is measured as the combined activities of endoglucanases and

exoglucanases. The cellulose activity of autoclaved (control) cultures is compared with

SSF at week 1 and at week 2 for the four chosen fungi in Fig 5.4. T. harzianum cultures

displayed a large decrease in cellulase activity from 39 U/mL in week 1 to 12.9 U/mL in

week 2, probably caused by gradually increasing product inhibition during this period.

It has been reported that Trichoderma spp. produce considerable amounts of cellulases

which are prone to lower catalytic activities on their substrates as compared to other

enzymes (Sainz, 2009, Zhang and Lynd, 2002). Cellulose activity on filter paper is only

0.6-0.7 IU/mg of enzyme. This contrasts to glucoamylase activity of 69 IU/mg

(Klyosov, 1987b). The fungus also generates low amounts of glucosidases which are

responsible for catalyzing the products of cellulose degradation, such as cellobiose. This

deficiency contributes to enhance product inhibition (Andrić et al., 2010a, Duarte et al.,

2012b, Klyosov, 1987b, Kumar et al., 2008).

Cellulase activity of P. citrinum did not show drastic change, although it

dropped from 27.7 U/mL in week 1 to 21.9 U/mL in week 2 (Figure 5.4).Contrastingly,

A. niger and P. chrysogenum displayed an increment in cellulase activity. The activity

of A. niger cellulase increased from 28.9 U/mL in week 1 to 43.5 U/mL in week 2,

while that of P. chrysogenum went from 30.7 U/mL to 97.6 U/mL during the same

period.


111

Figure 5.4. Comparison of cellulase activity (U/mL) in the fungal degraded Shiraz grape waste over

2 weeks of SSF compared to autoclaved pre-treated samples. The values are means of triplicate data.

The overall cellulase activities in all SSF cultures employed in the current

investigation were either comparable or substantially higher than the submerged

fermentation from previous experiments (Chapter 4) and other literature reports

(Brijwani et al., 2010, Kovacs et al., 2009, Liu et al., 2012, Sipos et al., 2010).

Due to low β-glucosidase production ability, the resultant cellulase activity in T.

harzianum was severely depleted over the second week. Although, the cellulase activity

was expected to gradually gradual increase over 2 weeks, it was observed to decrease

over the second week. Additionally, the second week cellulase activity of T. harzianum

was unexpectedly lower than the cellulase activities of P. chrysogenum over the same

period. This suggests that grape waste is a better substrate for P. chrysogenum T.

harzianum. However, this claim needs further investigation.

It has been reported that xylan in the biomass acts as one of the competitive

inhibitors of cellulose enzymes (Duarte et al., 2012a, Zhang et al., 2012). Thus, a

significantly lower activity is likely to occur in organisms with lower xylanase activity,

consistent with the results to be shown in Section 5.3.6.

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The cellulase activity of the autoclaved substrate varied according to the

organism and was found to be generally lower than the SSF grapes (week 1 or week 2).

The cellulase activity of T. harzianum cultured on the autoclaved substrate was about

19.7 U/mL (see fig. 4.5), slightly higher than week 2 SSF substrate (12.9 U/mL), but

considerably lower than week 1 SSF (39 U/mL). A similar trend was seen in P.

chrysogenum cultures where this activity was 24.1 U/mL as compared to week 1 (30.7

U/mL) and week 2 (97.6 U/mL) SSF. P. citrinum displayed a slightly lower cellulase

activity for the autoclaved substrate (15.2 U/mL) than either week 1 or week 2 SSF. The

results for week 2 SSF for A. niger cultures were in stark contrast, with cellulase

activity (see fig. 4.5) much higher than for the autoclaved grape waste (45.1 U/mL) and

the week 2 SSF (see fig. 4.5). This could be partly due to a higher β-glucosidase activity

as compared to the grapes applied to SSF. A. niger is generally known to possess high

β-glucosidase activity (Shin et al., 2011). The process of autoclaving probably increased

the amount of oligosaccharides and cellobiose in the filtrate which induced the higher

production of β-glucosidase enzyme. This probably hydrolysed cellobiose, one of the

product inhibitor of cellulases, during the initial incubation period.

Cellulase activities are also affected by the lignin composition of the biomass.

Generally, the lignin concentration is inversely proportional to cellulase activity.

Lignins not only act as physical barriers to microbial degradation, but also as non-

productive binders of cellulases (Alvira et al., 2010, Andrić et al., 2010a, Esteghlalian

Ali et al., 2000, Duarte et al., 2012b). Lignins form the outermost structure of

lignocelluloses and are the most complex molecules of all three biomass components,

i.e. cellulose, hemicellulose and lignins. Unlike cellulose, lignins are made up of a wide

range of aromatic and aliphatic molecules. They are linked to hemicelluloses and

cellulose by highly complex linkages and bonds, which increase the overall recalcitrant

nature of lignocellulose complex towards cellulase degradation (Eriksson, 1990,

Grisebach, 1985, Leistner, 1985). In the case of T. harzianum and P. citrinum, this was

the likely reason for low cellulase activities.

5.3.5. β-glucosidase activities

Contrary to other enzyme groups, β-glucosidase activity in all cultures

marginally or significantly increased over two weeks (Figure 5.5). T. harzianum


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displayed a minor increase in enzyme activity from 27.9 U/mL to 30.2 U/mL and

displayed an activity which was lower than the other fungal cultures. This observation

was not surprising since it is well known that Trichoderma spp. have limited β-

glucosidase activity compared to other biomass-degrading fungi.

A similar marginal increase was observed with P. chrysogenum cultures where

enzyme activities increased marginally from 106 U/mL after 1 week to 118.9 U/mL

after 2 weeks. Although, this increase was minimal, the overall activity was high, and

considered enough to prevent or minimise the product inhibition of cellulase. As seen

previously (section 5.3.4), the highest cellulase activity was observed in P.

chrysogenum cultures. It can be argued from these results that once activities of β-

glucosidase reach a threshold level, the overall cellulase activity is improved or, at least,

enzyme inhibition is prevented. One of the surprising observations was seen in A. niger

cultures, where the enzyme activities were observed to be lower than expected. The

fungus displayed an increase in enzyme activity of only about 20 U/mL, from 28.7

U/mL after the first week to 50.2 U/mL after two weeks of SSF. These values are

considerably lower than previously reported. For example, an activity of 128 U/g was

observed with corn stover after 4 days on incubation (Gao et al., 2008, Singhania,

2012). Similarly, β-glucosidase activities of 68.3 U/g have been reported with Ca(OH)2

pre-treated sugarcane bagasse degradation after 8 days of incubation (Gupte and

Madamwar, 1997). However, the activities of β-glucosidase have been reported to

increase either after a pre-treatment as observed by Gupte and Dutta (1997) or at higher

temperatures of 35-45°C as shown by Gao et al (2008). A similar increase was observed

after hydrothermal pre-treatment (autoclaving) of grape biomass as discussed below and

elsewhere (Chapter 4, section 4.3.6).

The most significant increase, however, was observed in P. citrinum cultures,

which displayed a major increase of about 181 U/mL; from 30.3 U/mL after the first

week to 211.5 U/mL after the second week (Figure 5.5). P. citrinum is known to

generate β-glucosidase with high enzyme activities under various conditions. However,

the activities observed during the current experiments were found to be even higher than

those reported previously, e.g. P. citrinum degradation of rice bran (Ng et al., 2010).

The high activity in the current study after 2 weeks SSF contrasts with a moderate

activity after just 1 week SSF. Previously studies with Penicillium spp., for example,

which showed activities of 59 U/g after 4 days of SSF (Camassola and Dillon, 2007, Ng


114

et al., 2010). but were marginally greater than the reported 26.6 U/mL after 3 days of

fermentation (Maeda et al., 2011) and much greater than the 1.835 U/mL observed on

sugarcane bagasse after 15 days (Castro et al., 2010). It is likely that the nature and

composition of substrate, i.e. grape biomass, has a positive effect on β-glucosidase

activity with respect to the time of fermentation.

Figure 5.5. Comparison of β-glucosidase activity (U/mL) in the fungal degraded Shiraz grape waste

over 2 weeks of SSF compared to autoclaved pre-treated samples. The values are means of triplicate

data.

Although, the overall composition of lignocellulolytic enzymes has been

observed to be well balanced in Penicillium spp. and the production level of β-

glucosidase is significant (Castro et al., 2010), the current β-glucosidase activities and

production by both the Penicillium spp. were much higher than earlier reported by

others (Camassola and Dillon, 2007, Castro et al., 2010, Maeda et al., 2011), but

marginally higher than 156.5 U/g (Ng et al., 2010) and the previously worked out

submerged fermentation experiment (Chapter 4).

This increase in β-glucosidase activities of all the fungi might be indicative of

higher enzyme production and activity during SSF as compared to the submerged

0

50

100

150

200

250


β-gl

ucos

idas

e ac

tivity

(U/m

L)



115

fermentation. Additionally, the substrate used in the current study (non-autoclaved

grape waste) may have contributed to the increased enzyme activity compared to other

substrates previously studied (Kovacs et al., 2009, Liu et al., 2012, Ng et al., 2010,

Sipos et al., 2010).

The β-glucosidase activities in T. harzianum cultures grown on autoclaved

grapes were lower than those of the SSF grapes. The enzyme activity in T. harzianum

was observed at 25.8 U/mL. In P. chrysogenum cultures, this activity was observed to

be 84.9 U/mL. β-glucosidase activity in P. citrinum cultures grown on autoclaved

grapes was observed at 180.1 U/mL, lower than that observed after 2 weeks of SSF, but

significantly higher than week 1 SSF culture. However, one of the contrasting outcomes

seen in these cultures was the comparatively lower cellulase activity even with

considerable β-glucosidase activity, which was unexpected. One of the reasons for this

might be an inherently lower cellulase activity of this fungal species. The lower

cellulase activity is in line with previously reported experiments which showed the

activities at 1.7 U/mL and 1.1 U/mL on wheat bran and rice straw, respectively, after 5

days (Dutta et al., 2008) and 4.8 U/g and 0.6 U/g on rice straw and rice bran,

respectively, after 4 days (Ng et al., 2010).

β-glucosidase activity in A. niger cultures grown on autoclaved grapes (154.9

U/mL) was much higher than the substrates undergoing SSF. This activity was also

higher than the activities reported by other Aspergillus spp. grown on SSF corn stover

(Gao et al., 2008) or on untreated or pre-treated sugarcane bagasse, wheat bran, wheat

straw, wheat bran and groundnut shells (Gupte and Madamwar, 1997). This β-

glucosidase activity probably translated into the comparatively higher cellulase activity

of A. niger observed on autoclaved substrate (see figures 4.5 and 5.4). Similar positive

correlations between β-glucosidase activities and cellulase activities have been observed

in the literature findings mentioned above.

5.3.6. Xylanase activities

It is known that β-glucosidases are similar to β-xylanases. Some belong to the

same family of GH5 Glycoside hydrolases (Pollet et al., 2010)) and their activities are

usually proportional to each other. In all the cultures, a high xylanase activity was

observed by the end of week 1. However, this activity declined rapidly during the


116

second week, although, in almost all the cases, the activity was higher than that on the

autoclaved substrate. A. niger showed highest xylanase activity after the first week at

1430 U/mL, which rapidly dropped to 214.3 U/mL (Figure 5.6).

Figure 5.6. Comparison of xylanase activity (U/mL) in the fungal degraded Shiraz grape waste over

2 weeks of SSF compared to autoclaved pre-treated samples. The values are means of triplicate data.

A. niger is a well-known xylanase producing fungus and has been used for

commercial production of this enzyme. As mentioned before with respect to other

enzymes, xylanase activities are also dependent on the nature of substrate on which the

fungus is growing. Betini et al (2009) reported utilization of several substrates for

xylanase production such as corn cob, eucalyptus sawdust, oatmeal, industrial sludge,

rice straw and sugarcane bagasse and found lower xylanase activities over 4 days at

30°C. However, on a mixture of corncob, wheat bran and rice straw, this activity

increased 3-10 times as compared to individual substrates. When grown on wheat bran,

the fungus also displayed enhanced xylanase activity of about 928 U under the same

conditions (Betini et al., 2009). Much higher xylanase activities of 3182 U/g were

obtained in a mixture of wheat bran and sugarcane bagasse within a period of 3 days

(Lu et al., 2008). Also noted from these experiments and previous observations was that

xylanase activities increase earlier than either cellulases or glucosidases. The enzyme

0

200

400

600

800

1000

1200

1400

1600


Xyl

anas

e ac

tivity

(U/m

L)



117

activity is very high during the initial phases of biomass degradation and gradually

depletes, either due to a complete xylan sugar utilization or product inhibition.

T. harzianum displayed considerable depletion of about 550 U/mL between

week 1 and week 2 of SSF (Figure 5.6). The activity decreased from 772.7 U/mL at the

end of first week to 114.9 U/mL after 2 weeks. Although, Trichoderma spp. generally

produces large amounts of cellulases, they appear to produce poor quantities of

xylanases. Also, it was observed that, similar to cellulase and β-glucosidase activities,

xylanase activity of T. harzianum was lower on grape biomass with respect to other

fungi used in these experiments. (Chen et al., 1997) observed low xylanase activity (20

U/mL, 35°C, 5 days) and a gradual depletion of this activity after 4 days of

Trichoderma spp. fermentation. However, this may be due to the submerged nature of

this fermentation rather than SSF, where this activity was much higher at 320 U/mL

within 3 days (Maeda et al., 2011).

P. chrysogenum also displayed substantial depletion (620 U/mL) from 936.8

U/mL after the first week to 320 U/mL after week 2 of SSF. Grape biomass appeared to

a better substrate for Penicillium spp. as these fungi have been reported to generate

lower xylanase activities. For example, xylanases generated by P. chrysogenum

displayed the activities of 6.5 U/mL on wheat bran after 4 days, while an even lower

activity of 1.4 U/mL was observed on sugarcane pulp after 6 days (Okafor et al., 2007).

Similarly, the xylanase activities of Penicillium spp. were up to only 168 U/mL in

various supplemented basal media at 30°C after the 4th day of fermentation (Adsul et

al., 2007). One of the peculiar properties of xylanase activity was observed in P.

citrinum cultures with a decrease of about 270 U/mL (from 1100.8 U/mL to 822.5

U/mL) over 2 weeks of SSF. This decrease of xylanase activity in P. citrinum was not

as significant as in the other cultures (Figure 5.6). These results indicated that the

xylanase enzyme produced by P. citrinum culture was observed to possess a higher

stability as compared to other fungi used in the current experiments.

The xylanase activities of the majority of the fungal cultures grown on

autoclaved grape were found to be substantially lower than those in SSF cultures. T.

harzianum, P. chrysogenum and P. citrinum displayed xylanase activities at 38.7 U/mL,

191.1 U/mL and 234.2 U/mL, respectively. However, the xylanase activity of A. niger

was found to be 335.3 U/mL. The activity, although, much lower than SSF after week 1,


118

was substantially higher than SSF after week 2. The trend of xylanase activity was

found to be somewhat similar to β-glucosidase activity. However, this seemingly was

due to β-glucosidase itself as its higher activity in A. niger culture probably prevented

accumulation of cellobiose. The only exception to this relationship between β-

glucosidase and xylanase activities was noticed in P. citrinum, which displayed a lower

xylanase activity with respect to its β-glucosidase activity. These results showed that the

fungal xylanase activities were considerably higher in SSF rather than submerged

fermentations either with or without pre-treatment processes, and were in agreement

with results obtained previously (Betini et al., 2009, Brijwani et al., 2010, Lu et al.,

2008, Maeda et al., 2011).

5.4. Conclusions

The experiments presented in this chapter were performed to assess the

comparative degradation of grape biomass waste by Trichoderma harzianum,

Aspergillus niger, P. chrysogenum and P. citrinum in an SSF system over 2 weeks.

Total proteins were observed to decrease in all cultures but the extent was considerably

greater in T. harzianum and A. niger than P. chrysogenum and P. citrinum cultures. In

particular, the levels remained more or less constant at about 6.2 kg/m3 in P. citrinum

culture over two weeks. This decrease possibly reflected the lowered activities of

xylanases of all the fungi. However, other enzyme activities were mostly unaffected.

Reducing sugars in T. harzianum and P. citrinum cultures dropped marginally

while they displayed a sharp rise in A. niger and P. chrysogenum cultures. It was

observed that the concentration of reducing sugars correlated directly to the cellulase

activities in all cultures. As expected, the cellulase activity of T. harzianum and P.

citrinum decreased over two weeks of SSF, while that of A. niger and P. chrysogenum

increased during the same period. The cellulase activities over 2 weeks in all fungal

cultures correlated positively with reducing sugar accumulation, indicating possible

product inhibition of the respective enzymes due to increasing concentrations of

cellobiose/glucose in the media.

T. harzianum cultures displayed very minor increases in β-glucosidase activities

between the first and second week in which was not surprising as this fungus cannot

produce high amounts of β-glucosidases. A. niger, P. chrysogenum and P. citrinum


119

cultures, on the other hand, displayed considerable rises in β-glucosidase activities over

the same period. Xylanase activities dropped in every culture as the fermentation

progressed. The highest activity of about 1430 U/mL was observed in A. niger after one

week. This rapid rise followed by a gradual decrease in xylanase activities was

consistent with observations from the literature.

None of the fungi were able achieve considerable lignin degradation in the SSF

process over 2 weeks. Lower amounts of lignins were degraded in the substrate applied

to SSF with respect to the same substrate subjected to hydrothermal pre-treatment

process before fungal degradation (Chapter 4). Nonetheless, higher lignocellulolytic

enzyme activities were observed in SSF as compared to submerged fermentation.,

Further improvement in lignin degradation and the need to minimise product inhibition

are required to increase the overall bioconversion process for the production of

important molecules like industrial alcohols, acids and numerous metabolites.

5.5. Summary

Fungal enzyme activities during SSF of grape biomass were found to be significantly

higher, in most of the cases observed, than submerged fermentation (Chapter 4) or in

previously reported works (Juhász et al., 2005, Kovacs et al., 2009, Sipos et al., 2010).

The cellulase and β-glucosidase activities were either comparable or substantially

higher in the SSF process than in the autoclaved substrates, except for β-glucosidase

activity in A. niger cultures. Similarly, xylanase activities were significantly higher than

the steam pre-treated grape waste degraded by the fungi over 1 week. However, the

cellulase and xylanase activities over SSF grape wastes decreased during the second

week of incubation. Lignin mineralization was surprisingly lower in SSF as compared

to submerged fermentation. It is known that SSF provides better growth conditions for

fungi by increasing the surface area for fungal attachment. The low substrate particle

size (typically 1-4 mm) (Gupte and Madamwar, 1997, Shi et al., 2008) increase the

aeration and thus increase the degradation of more recalcitrant molecules such as lignin

(Lee, 1997). However, longer incubation was observed to decrease the activity of

numerous enzymes. Overall, a better cellulose and hemicellulose degradation was

observed in SSF as compared to autoclaved substrate, as evident from the increased


120

enzyme activities. However, the experiment highlighted a need to improve lignin

degradation further.

121

CHAPTER 6

Optimizing degradation of winery-derived biomass waste by

Ascomycetes

Chapter 6 Optimizing degradation of winery-derived biomass waste by Ascomycetes

122

6.1. Introduction

Grapes are one of the major global horticultural crops with an estimated

production of 69.1 million tonnes during 2012 of which approximately 80% were wine

grapes (OIV, 2013). Australia is an important grape producing region and 1.75 million

tonnes of grapes were crushed for wine production during 2012-13 (ABS, 2013). Like

the rest of the world, Australian wineries produce large amounts of biomass waste,

amounting to about 50-60% of the total grape crushed during the process (Devesa-Rey

et al., 2011, Gerling, 2011). In addition, wineries are a high wastewater generator,

producing as much as 8 litres per bottle of wine (Christ and Burritt, 2013). The problem

is ubiquitous and, in recent years, winery wastes have been classified as pollutants by

the European Union (Wang et al.). Subsequent treatment, specifically post-product

processing, is thus required to make winery wastes less hazardous, both in nature and

volume (Devesa-Rey et al., 2011). Grape wastes consist of grape berries, plant-derived

fibres, grape seeds, skin, marcs, stalk and skin pulp. Unlike other agricultural by-

products, grape biomass waste has limited use as an animal feed stock due to its poor

nutrient value and low digestibility (high concentration of tannins and polyphenols).

The polyphenols also slow down microbial utilization of this biomass. A majority of

these winery biomass wastes thus end up as toxic landfill (Devesa-Rey et al., 2011).

The major components of grape biomass waste are cellulose, pectins and lignins

(including tannins). Fungi belonging to the division Ascomycota, such as Trichoderma

spp., Aspergillus spp. and Penicillium spp., are known for their biomass degrading

ability and should prove useful in grape biomass degradation. The fungi have been well

studied for their ability to produce high levels of degradative enzymes, such as

cellulases and hemicellulases (Brink and Vries, 2011, Klyosov, 1987b). Such enzymes

also have potential in generating useful metabolic by-products such as alcohols,

flavonoids, organic acids and phenolics (Sánchez, 2009).

Thus, there is great potential for biomass degradation as well as utilization of the

degradation products. However, the fungal enzymes, depending on their parent fungi,

have several limitations. A limitation of cellulases, for example, is their low

comparative activity, on a weight-by-weight basis, compared to many other enzymes

such as amylases (Klyosov, 1987b). Enzymes capable of degrading grape biomass


123

suffer greatly from product inhibition, especially by cellobiose (direct inhibition) and

glucose (indirect inhibition), even at low concentrations (Andrić et al., 2010a).

Several strategies have been employed to overcome these limitations. For

example, the use of mixed fungal culture degradation has been suggested and reported

(Brijwani et al., 2010, Chu et al., 2011). This enables the different strengths of different

fungi to be utilised at the same time. For example, it is known that, apart from

satisfactory cellulase production, Aspergillus spp. generates highly efficient xylanases

and β-glucosidases in exceptional quantities (Betini et al., 2009). Penicillium spp. can

be used for lignin mineralization to enhance the overall degradation process (Rodríguez

et al., 1994). The use of multiple fungi simultaneously would, hopefully, result in

enhanced degradation as a direct result of their complementary degradation pathways.

Other strategies could involve pre-treatment designed to increase access of enzymes to

celluloses and hemicelluloses and the overall surface area of the substrate for more

efficient breakdown (Papadimitriou, 2010).

6.2. Overview

The experiments described herein explore a statistical-based optimization of

mixed fungal cultures to achieve enhanced grape biomass degradation. The optimization

experiments utilized cultures of Trichoderma harzianum, Aspergillus niger, Penicillium

chrysogenum and Penicillium citrinum for degradation purposes.

The conventional and popular submerged fermentation and the emerging Solid

State Fermentation (SSF) methods were tested. As seen in the previous chapter when a

single fungal organism was used, the submerged fermentation method for grape

biomass degradation had some advantages over the SSF method. For example, β-

glucosidase activities were often observed to be considerably higher during pre-treated

biomass degradation by submerged fermentation than SSF. Also, due to pre-treatment

involved in the submerged fermentation method, lignin degradation was higher than in

SSF. However, the enzyme activities responsible for a major part of degradation were

considerably higher during SSF. Part of the purpose of the work in this chapter is to

determine whether or not these trends remain when using a mixed fungal culture.


124

When using a single culture, each of the processes (submerged fermentation and

SSF) suffered some limitations, which would be amplified on a commercial scale. For

example, strong enzyme inhibition was observed in submerged fermentation of pre-

treated grapes. The low substrate concentration in this process (30 g/L) was observed to

generate large amounts of waste water. Conversely, a major limitation observed in SSF

was lower lignin degradation over 2 weeks. In both cases, many of these limitations

might be expected to be reduced if a mixed culture were to be used.

Statistical analysis was performed to generate an optimised mixed fungal

“cocktail” and achieve a bioreactor-based degradation process with increased biomass

degradation. The activities of all enzymes and other parameters were used as the input

values to generate an optimal working design using statistical modelling.

Finally, metabolomic techniques were used to analyse the degradation products

and to identify several metabolites of industrial and medicinal interest. The

biodegradation process was scaled up from flasks to a small industrial scale bioreactor

to increase the reproducibility for future commercial scale biomass degradation.

6.3. Results and Discussions

6.3.1. Compositional analysis of grape waste

It is well known that the composition of the substrate strongly influences its

degradation efficiency. Various non-structural components such as free proteins,

sugars and structural components such as cellulose and lignin affect the overall

efficiency of degradation, usually providing a barrier to such degradation (Duarte et

al., 2012b).The carbon-nitrogen (C/N) ratio plays an important role in biodegradation.

It has been found that a higher the nitrogen content in the substrate results in greater

degradation (Brijwani et al., 2010). This may also reflect the observation that the

higher the nitrogen content, especially from organic sources (e.g. proteins), the greater

the overall enzyme activities (Kim et al., 2012a).

The total compositional analysis of untreated grape wastes used in this study is

given in Table 6.1.


125

Table 6.1. General composition of Shiraz grape waste (n=3).

Component Content (g/100 g

mass)

Composition (previous works)

Proteins 2.22 ± 0.01 3.3 ± 0.1 (González-Centeno et al., 2010)

Total sugars 1.92 ± 0.18 2.1 ± 0.01 1 (González-Centeno et al.,

2010)

Lignins 35.96 ± 0.13 36.2 ± 0.1 (Bravo and Saura-Calixto,

1998)

Cellulose +

Hemicellulose

57.84 ± 0.13 47- 65 (Deng et al., 2011, Spigno et al.,

2008)

Ash 2.81 ± 0.01 3 ± 0.1 (González-Centeno et al., 2010)

6.3.2. Statistics: Design of experiment and analysis

Experimental design and statistical analysis was performed using a full factorial

design method developed using Minitab® 16 (Minitab Pty Ltd., Sydney, Australia). One

of the simplest methods for complex experimental design, it can analyse multiple factors

with each factor containing multiple levels of experimental conditions. In the current

experiments, we used fungi type (4 types) and degradation methods (2 types) to provide

8 factors, each of which were optimised for the component cellulases, xylanases and b-

glucosidases (3 factors), thus yielding a 24 experimental condition model (Montgomery,

2008).

The model used a regression analysis given by the equation:

𝑦 = 𝑏0 + 𝑏1𝑥1 + 𝑏2𝑥2 + 𝑏3𝑥3 + 𝑏4𝑥1𝑥2 + 𝑏5𝑥2𝑥3 + 𝑏6𝑥1𝑥3 + 𝑏7𝑥1𝑥2𝑥3 + 𝑒𝑖 Eq. 6.1

where,

b0 = co-efficient of intercept

b (1,…, 7) = factor mean difference

x (1, 2, 3) = Factor variables

ei = residual for ith unit

Predictability of the model response can be assessed by the equation:


126

𝑅𝑝𝑟𝑒𝑑2 = 1− 𝑃𝑅𝐸𝑆𝑆𝑆𝑆

= 1 −∑ � 𝑒𝑖

1−ℎ𝑖�2

𝑛1

∑(𝑦𝑖−ӯ)2 Eq. 6.2

where,

n= number of observations

ei= ith residual

hi= ith diagonal element of predictor matrix [𝑥(𝑥′𝑥)−1𝑥−1]

yi= ith observed response

ӯ= mean response

PRESS = model's predictive ability

SS = adjusted sums of squares

An increasing response value indicates a higher predictive ability of the given

model (Montgomery, 2008).

For the current experimental design, maximum enzyme activities were the

primary responses. However, the responses for reducing sugar levels and lignin levels

were also used for experiment development.

6.3.3. Grape biomass degradation

In this study, it was observed that the process of hydrothermal treatment

increased the protein content in the medium as compared to that in the SSF process

(Figure 6.1). To maintain similar conditions, the values of hydrothermal pre-treated

substrate were compared with SSF samples which had been treated for one week only.

The observed protein concentration in T. harzianum cultured under the submerged

condition was 31.9 kg/m3, however, this value decreased under SSF conditions, where it

was 11.5 kg/m3. A significant decrease in total protein content was also observed for A.

niger cultures where the protein content decreased from 21.9 kg/m3 to 4.5 kg/m3 which

was also reflected in a decrease in β-glucosidase activity (see Figure 6.3).


127

Figure 6.1. Total protein content of 1 week SSF degraded grape biomass and hydrothermal pre-

treated submerged grape biomass (labelled “autoclaved”). The values from untreated sample are for

comparison purposes. All values are averages of triplicate data with p < 0.05 in all cases.

In addition to protein content, lignin content is an important factor to measure, as

it can determine ultimate biomass degradation. Lignins are aromatic polymers and are

very complex in their chemical structure (compared to celluloses and hemicelluloses).

They form a considerable challenge for degradation, both chemically and biologically

(Higuchi, 2006). They are bound to both cellulose and hemicellulose polymers by

various cross linkages (Dashtban et al., 2009, Fengel, 1971), thus inhibiting the

degradation of those polymers as well. Lignin degradation, or at least its removal and

separation from cellulose and hemicellulose, is therefore one of the chief aims of

biomass degradation. Lignin can act as a non-productive inhibitor of cellulolytic

enzymes, especially cellulases which tend to have strong binding to lignins (Duarte et

al., 2012b). The importance of this intractability is particularly relevant to grape waste

because of the high content of lignin-derived products such as phenolics and tannins

(Bravo and Saura-Calixto, 1998, González-Centeno et al., 2010). In this study, it was

observed that P. chrysogenum was the only fungus able to degrade lignin to an

appreciable extent. Degradation of 4.7 % and 2.2% lignin content were seen under the

submerged and SSF conditions, respectively. None of the other fungi used were found

0

5

10

15

20

25

30

35


Prot

ein

cont

ent (

kg/m

3 )

1 week SSF Autoclaved Untreated


128

to be lignolytic organisms. However, Penicillium spp. has been previously reported to

possess minor lignin mineralization abilities, including P. chrysogenum, and are known

to produce lignin degradation (about 8%) under appropriate conditions (Rodríguez et

al., 1994). It is quite possible that the lignin degrading ability of P. chrysogenum results

in an overall increased biomass degradation efficiency, thus requiring considerably less

protein as compared to other fungi under similar conditions.

Finally, autoclaving caused a considerable reduction in winery grape biomass

waste. Overall, dry mass decreased by about 18% in autoclaved grape waste. It has been

reported by numerous authors that biomass undergoes hydrolysis during hydrothermal

processing. Although most of the reports have utilized temperatures around 180°C, the

hydrolysis of biomass has been documented to occur even at autoclaving temperature of

121°C (Papadimitriou, 2010). This occurs due to the increase in ion products of water

with a simultaneous decrease in the dielectric constant as the temperature rises. These

factors have been reported to significantly increase the solvent capacity of water (Goto

et al., 2004). This, in turn, increases biomass degrading ability of the system, as

observed during course of the mentioned experiment.

6.3.4. Enzyme activities

Cellulase activity was measured as the combined activities of endoglucanases

and exoglucanases as measured by the filter paper assay. The production of cellulases,

β-glucosidases and xylanase is important from a biodegradation perspective. High

activities of β-glucosidases play a crucial role as they prevent the product inhibition of

cellulases (Duarte et al., 2012b, Klyosov, 1987b). To assess the overall activity of these

enzymes in all fungal cultures and to generate a better grape biomass degradation

method, process optimization was carried out. Generally, 1:1 cellulase:β-glucosidase

activities have been reported to yield highest outputs under SSF conditions (Brijwani et

al., 2010, Chahal, 1985), although the exact ratio varies across the species and type of

substrates.

The highest activity of cellulase under the full factorial design was noted as 43.7 U/mL,

as described by a main effects plot (Figure 6.2).


129

Figure 6.2. Main effects plot showing the relationship between various fungal cultures across

different growth media in terms of cellulase activity. The values represented in parentheses denote

standard errors.

The overall equation measured in terms of cellulase activity was (Eq. 6.3), where

𝑦 = 29.03 + 0.32𝑥1 + 6.54𝑥2 − 1.55𝑥3 − 7.06𝑥1𝑥2 + 9.20𝑥2𝑥3 − 0.69𝑥1𝑥2 −

2.62𝑥1𝑥2𝑥3 Eq. 6.3

The highest activities of β-glucosidase were observed at 181.4 U/mL as described by

the main effects plot (Figure 6.3). The equation for β-glucosidase was (Eq. 6.4), where

𝑦 = 79.81− 53.45𝑥1 + 12.17𝑥2 + 15.83𝑥3 − 32.21𝑥1𝑥2 + 31.31𝑥2𝑥3 −

42.34𝑥1𝑥2 + 31.64𝑥1𝑥2𝑥3 Eq. 6.4


130


different growth media, in terms of β-glucosidase activity. The values represented in parentheses

denote standard errors.

The highest activities of xylanase were observed at 1414 U/mL, as observed in main

effects plot (Figure 6.4). The equation for xylanase was (Eq. 6.5), where

𝑦 = 611.17 − 240.25𝑥1 + 263.43𝑥2 − 52.18𝑥3 + 79.13𝑥1𝑥2 − 128.04𝑥2𝑥3 +

43.51𝑥1𝑥2 − 411.37𝑥1𝑥2𝑥3 Eq. 6.5

P. citrinum

P.chrysogen

um

A. niger

T. harzi

anum

110

100

90

80

70

60

50

40

30

20

SSF

Submerged

Fungi

β-gl

ucos

idas

e ac

tivity

(U/m

L)

Method

(1.1)

(0.69)(0.54)

(0.48)

(0.94)

(0.47)


131


different growth media, in terms of xylanase activity. The values represented in parentheses denote

standard errors.

The full factorial modelling method also generated a complete interaction matrix plot

which displayed the overall activities of different enzymes during both SSF and

submerged fermentation (Figure 6.5). Relationships between different conditions can

be inferred from the interaction plot, based on which the statistical model predicted the

optimal conditions, i.e. appropriate ratio between different fungi and ratio between

substrate and medium to achieve maximum amount of biomass degradation within the

least possible time.

P. citr

inum

P. chryso

genum

A. niger

T. harzi

anum

1100

1000

900

800

700

600

500

400

300

200

SSF

Submerged

Fungi

Xyla

nase

act

ivity

(U/m

L)

Method

(19.58)

(11.62)

(15.81)

(17.74)

(11.34)

(21.03)


132

Figure 6.5. Matrix plot shows the fungal enzyme activities under different conditions. The top X-

axis labels of 1, 2, 3 and 4 refer to T. harzianum, A. niger, P. chrysogenum and P. citrinum,

respectively.

The fitted ANOVA model was generated using the data provided in Figures 6.2,

6.3 and 6.4, and is given below in Table 6.2. High predictive responses (cumulative Q2

= 0.929) were observed across the species and experimental conditions, suggesting good

predictive capability of the model used for the enzyme activities of different fungi under

varied conditions of growth (Equations 6.1, 6.2, 6.3, 6.4, 6.5).

4321

40

30

201600

800

0200

100

0

Cel

lula

ses

Xyl

anas

es

Fungi

β-gl

ucos

idas

eSubmerged

SSF

Method


133

Table 6.2. ANOVA for Cellulases, Xylanases and β-glucosidase using Adjusted SS for Tests

Enzyme Source DF Seq SS Adj MS F P

Cellulase Fungi 3 440.6 146.87 42.05 0.001

Method 1 164.75 164.75 47.17 0.003

Fungi*Method 2 822.93 274.31 78.53 0

Error 16 55.89 3.49

Total 23 1484.17

S = 1.86896 R2 = 96.23%

Xylanase Fungi 3 784082 261361 984.86 0.001

Method 1 4061345 4061345 15304.04 0

Fungi*Method 2 147477 49159 185.24 0.002

Error 16 4246 265

Total 23 4997150

S = 1.54204 R2 = 99.88%

β-glucosidase Fungi 3 784082 261361 984.86 0.001

Method 1 4061345 4061345 15304.04 0

Fungi*Method 2 147477 49159 185.24 0.002

Error 16 4246 265

Total 23 4997150

S = 16.2904 R2 = 99.92%

The highest activity of cellulase under the full factorial design was observed at

43.7 U/mL, while those of xylanase and β-glucosidase were observed at 1414 U/mL and

181.4 U/mL, respectively. The cellulase activity with the autoclaved substrate varied as

per organism, but was found to be generally lower than for SSF. For example, the

cellulase activity of T. harzianum for the autoclaved substrate was 19.7 U/mL, lower

than SSF (39 U/mL). A similar trend was seen in the P. chrysogenum culture where the

activity was 24.1 U/mL, lower than for SSF (30.7 U/mL) and for the P. citrinum culture

where the activity was 15.2 U/mL, lower than for SSF conditions (27.7U/mL). The

exception was the A. niger culture, where cellulase activity in the autoclaved grape

(45.1 U/mL) was higher than for SSF conditions (28.9 U/mL) (Table 6.3). This could

be partly due to a higher β-glucosidase activity as compared to the grapes applied to

SSF. A. niger is generally known to possess high β-glucosidase activity (Shin et al.,

2011). The process of autoclaving is likely to have increased the amount of


134

oligosaccharides and cellobiose in the filtrate which in turn increased the production of

β-glucosidase. This probably led to hydrolysis of cellobiose, one of the product

inhibitors of cellulases, during the initial incubation period.

The β-glucosidase activity of T. harzianum was observed at 25.8 U/mL under

submerged conditions, comparable to 27.9 U/mL from SSF. A similar trend was seen

with P. chrysogenum, where the activity was observed at about 106.3 U/mL under SSF

conditions as opposite to 84.9 U/mL under submerged conditions. β-glucosidase activity

in the P. citrinum culture with autoclaved grapes was 180.1 U/mL, which was

significantly higher than the 1 week SSF culture (30.4 U/mL). Thus, one of the

contrasting outcomes seen in this culture is the comparatively low cellulase activity

even with considerably high β-glucosidase activity, a result which was unexpected. One

plausible explanation for this might be an inherently lower cellulase activity of P.

citrinum. Conversely, the β-glucosidase activity in the A. niger culture with autoclaved

grapes was much higher than the SSF substrates. The activity in the autoclaved

substrate was found to be 154.9 U/mL compared to 29.1 U/mL in SSF. The high β-

glucosidase activity was probably related to the comparatively high cellulase activity of

A. niger on autoclaved substrate.


135

Table 6.3. Total cellulase, xylanase and β-glucosidase activities (U/mL) of different fungi under

experimental conditions of Design of Experiment.

It is known that β-glucosidases are similar to xylan degrading enzymes,

especially xylosidases, with some of them belonging to the same family of GH5

glycoside hydrolases. Their activities are generally interdependent and are directly

proportional to each other (Pollet et al., 2010). Confounding this is that cellobiose, the

Method Fungi Cellulases

(U/mL)

Xylanases

(U/mL)

β-glucosidase

(U/mL)

SSF P. chrysogenum 31.2 926.9 105.7

SSF P. citrinum 28.1 1046.2 30.1

Submerged T. harzianum 16.7 32.1 26.4

Submerged P. citrinum 21.2 221.0 178.6

SSF A. niger 29.4 1414.0 28.8

Submerged A. niger 39.2 315.1 156.6

SSF T. harzianum 39.8 703.2 29.6

Submerged P. chrysogenum 25.7 156.3 86.0

SSF A. niger 28.5 1414.0 29.2




SSF P. citrinum 27.6 1046.2 30.5




SSF P. citrinum 27.6 1046.2 30.5



SSF A. niger 29.0 1414.0 29.2






136

chief substrate for β-glucosidases, is inhibitory towards xylanase activity (Brijwani et

al., 2010). In this case, T. harzianum, A. niger, P. chrysogenum and P. citrinum

displayed xylanase activities for autoclaved grape waste at 38.7 U/mL, 335.3 U/mL,

191.1 U/mL and 234.2 U/mL, respectively. These activities were much lower than for

the SSF substrate, which showed substantially higher xylanase activities of 772.7 U/mL,

1430.6 U/mL, 936.8 U/mL and 1100.8 U/mL, respectively. This trend of xylanase

activity was found to be similar to that of β-glucosidase activity (Table 6.3), however

the correlation is probably not a causal relationship and simply reflects the

interdependence noted above.

The higher activity of β-glucosidase in the SSF cultures, particularly in the A.

niger culture, presumably prevents accumulation of cellobiose, in turn resulting in

higher xylanase activity. The only exception to the relationship between β-glucosidase

and xylanase was noticed in P. citrinum, which displayed much lower comparative

xylanase activity with respect to its β-glucosidase activity for the SSF culture. It has

been reported that xylans in biomass act as one of the competitive inhibitors of cellulase

enzymes (Duarte et al., 2012b). Thus, a significantly lower activity is likely to occur in

the organisms with lower xylanase activity, which was mostly consistent with the

current experimental outcomes.

The main effects plots and matrix plot generated were able to provide optimum

relationships necessary to increase the degradation abilities (Figures 6.2, 6.3 6.4 and

6.5). The Full Factorial Design was able to provide the optimum conditions and mix of

various fungi for maximizing the degradation of grape biomass. As predicted by the

model, the substrate: media ratio was adjusted to 0.3:1 for optimal results while the

percent fungal ratio for A. niger: P. chrysogenum: T. harzianum: P. citrinum of

60:14:4:2 was selected so as to yield maximum possible output without considerable

enzyme inhibition.


137

6.3.5. Grape biomass degradation in bioreactor

The pre-dried and ground grape waste was weighed, adjusted to a 0.3:1 ratio

with AATCC medium and sterilized by autoclaving at 115°C for 10 minutes in a 3.6 L

bioreactor with controlled pH, oxygen, temperature, nutrient feed and agitation speed

(Infors AG, Bottmingen, Switzerland). The fungal cultures were added at the design

levels determined from the Minitab modelling (Tables 2 and 3). The treated grape

waste was incubated with this fungal culture for 1 week at 30°C with constant agitation

at 200 rpm. This incubation procedure was identical to that of the individual species and

the results were compared with individual species results (Figure 6.6).

Figure 6.6. Enzyme activities observed in individual cultures under different conditions compared to

that of the optimized mixed culture (n=3).

6.3.6. Cellulolytic enzyme production in bioreactor culture

The activities of all the enzymes were observed to increase under the statistically

optimized conditions (Figure 6.6). After 5 days of culturing, cellulase activity in the

bioreactor was observed at 78.4 U/mL, more than twice that of A. niger, (the fungus

which displayed the highest individual cellulase activity) by itself (under the submerged

fermentation conditions). A less dramatic, but still considerable, increase was seen in

0

500

1000

1500

2000

2500

3000

3500

4000

0

50

100

150

200

250

300

Subm

erge

d

SSF

Subm

erge

d

SSF

Subm

erge

d

SSF

Subm

erge

d

SSF

T. harzianum A. niger P. chrysogenum P. citrinum MixedX

ylan

ase

activ

ity (U

/mL

)

Cel

lula

se/β

-glu

cosi

dase

act

ivity

(U/m

L)

Cellulase β-glucosidase Xylanase


138

the activity of β-glucosidase. Under the statistically optimized bioreactor conditions, the

β-glucosidase activity was observed at 250.9 U/mL, considerably higher than that of P.

citrinum, the fungus which displayed the highest β-glucosidase activity when used

individually. The most significant increase was seen in xylanase activity, which was

observed at 3544.7 U/mL, almost twice that of the activity seen during the process

optimization stage for individual species. Cellulase activities observed during the

bioreactor-mediated mixed fermentation were similar to those of the SSF process at day

15, while the activities of β-glucosidase and xylanase were considerable higher.

A significant spike in xylanase activity is probably one of the main reasons for

the increase in activities of other enzymes. It is known that the hemicellulases form

complex cross linking with cellulose and lignins in a biomass (Sánchez, 2009).

Xylanases, especially those derived from P. citrinum, have been reported to be active

across a wide pH range and temperature conditions spanning 30-50°C (Dutta et al.,

2007). Also, due to the close association between cellulases and β-glucosidase, the

activity of xylanase is dependent on the activities of these enzymes and vice-versa.

A considerable decrease in lignin content was observed in the degraded

substrate, where 17.9% lignin was found to be degraded or mineralized during the

process. This rate was considerably higher than the 4.7 % and 2.2% found for the

control in submerged and SSF treatments, respectively.

6.3.7. Gas Chromatography-Mass Spectrometry (GC-MS)

Samples derived from the optimized bioreactor degradation process were further

analysed by gas chromatography-mass spectrometry (GC-MS). A 1 ml aliquot of

methanol (LC grade, ScharLab, Sentemanat, Spain) was added to 40 mg post degraded

freeze dried sample, then vortexed briefly before centrifugation at 572 g /4°C for 15

minutes. A 50 µL aliquot of the supernatant was then transferred to a fresh tube and

dried in an RVC 2-18 centrifugal evaporator at 40°C/210 g (Martin Christ

Gefriertrocknungsanlagen GmbH; Osterode, Germany). All samples were stored at -

80°C until further use (Ng et al., 2012). In order to derivatize the samples for GC-MS

analysis, 40 µL methoxamine HCl (2% in pyridine) was added to each sample and

incubated for 45 minutes at 37°C. To complete the derivatization, silylation was

performed by adding 70µL BSTFA in 1% TMCS. Samples were then incubated for an


139

additional hour at 70°C. Samples were diluted with 190 µL pyridine, vortexed and

centrifuged at 15682 g for 5 minutes before transferring to GC-MS vials.

GC-MS was performed as previously explained in ‘Materials and methods

section’ (3.10.3).

6.3.8. Metabolic output of mixed fungal degradation

The mixed fungal degradation products were analysed by GC-MS to yield a

metabolic profile of ca. 220 peak features, which is generally expected for a microbial

metabolism processes. The data generated by mass spectral analyses were displayed as

the magnitude of 1 fold change (FC) with respect to the peak areas between mixed

fungal culture samples and control samples. This was followed by Principal Component

Analysis (PCA) based on score scatter (Figure 6.7 A) and DModX plots (Figure 6.7

B).


140

Figure 6.7. (A) PCA score scatter plot displaying the metabolite output pattern of mixed fungal

degradation of grape biomass. (B) DModX line plot of PCA metabolites. Note: Each point on the

scatter plot refers to single sample, with R2X (cumulative) = 91.6 % and Q2 (cumulative) = 80.3

%.All the values are within the Dcrit value range of 0.05, as marked by dotted line.

(A)

(B)


141

PCA is a method for correlating variable transformation to non-correlated

variables. During PCA, these variables, or components, are arranged in a decreasing

order of importance to minimise the dimensionality of data under consideration.

Generally, 2-3 principal components are used to obtain a PCA dataset, however, the

number of components can increase based on the complexity of data. PCA modelling is

performed based on the equation (Equation 6.6) given below.

𝑋 = 1 × 𝑋� + 𝑇𝑃′ + 𝐸 Eq. 6.6

Where,

X = Correlation of data matrix

𝑋� = Average of x variables in the dataset

P = matrix showing influence of variables

E = residual matrix or deviation between original values and projected values

The DModX (Distance of Observation) plot of PCA is the normalized

observational distance between variable set and X modal plane and is proportional to a

variable’s residual standard deviation (RSD). Dcrit (critical value of DModX) is derived

from the F-distribution and calculates the size of observational area under analysis.

Values which are twice that of Dcrit’ values are generally considered as moderate

outliers (Jackson, 2005).

In order to interrogate the data further, the samples were processed using Partial

Least Square-Discriminant Analysis (PLS-DA). PLS-DA is used to analyse large

datasets and has the ability to assess linear/polynomial correlation between variable

matrices by lowering the dimensions of the predictive model, enabling easy

dissemination between the samples and the metabolite features that cause the

dissemination (Wold et al., 2001).

Of the 220 metabolites, 78 were identified as statistically significant by PLS-DA

(Figure 6.8). Moreover, the change in metabolite concentration during degradation was

analysed by one way ANOVA using Fisher’s least significant difference method

(Fisher’s LSD) and Tukey’s Honestly Significant Difference (Tukey’s HSD).


142

Figure 6.8. (A) PLS-DA score scatter plot and (B) loading scatter plot of degraded Shiraz grape

waste according to fungal species analysed using GC-MS. Note: the PLS-DA plot eclipse represents

the 95% confidence interval. The black star labels on the loading scatter plot refer to each fungal

group; with each yellow circles referring to single metabolite feature.

(A)

(B)


143

Numerous metabolites, including organic acids, alcohols, sugars, sugar acids and

amino acids, were observed to be either consumed or generated during the biomass

degradation process. Table 6.4 represents the most significant metabolites analysed by

one way ANOVA.

Table 6.4. Most significant features generated during the optimized biomass degradation as

identified by the volcano plot with their fold change (FC) and P values

Peaks FC value P-value

Galactonic acid-1,4-lactone 12.031 2.06 e-7

Hexacosanoic acid 711.99 9.89 e-7

FructoseBP 177.47 3.34 e-6

DL-Fucose 3.373 4.07 e-6

Ethyl phosphoric acid 16174 6.89 e-6

Arabinofuranose 27.731 6.95 e-6

alpha-DL-Lyxofuranoside 54.94 6.98 e-6

2-Monopalmitin 9.5698 8.89 e-6

myo- Inositol 49.643 1.06 e-5

Phosphoric acid 143890 1.06 e-5

Oleanolic acid 4170.7 1.15 e-5

Gulose 5.3276 1.17 e-5

D-Fructose O-methyloxime Results 229.8 1.20 e-5

α-D-Mannopyranoside 269.66 1.20 e-5

D-Fructose 207.12 1.34 e-5

Glyceric acid 4215.4 1.35 e-5

Heptadecanoic acid 310.16 1.37 e-5

Arabitol 4.0971 1.63 e-5

Tetradecanoic acid 1760 1.80 e-5


144

Table 6.4. Most significant features generated during the optimized biomass degradation as

identified by the volcano plot with their fold change (FC) and P values (…continued)

Peaks FC value P-value

Saccharic acid 4.1996 2.09 e-5

Gallic acid 38.383 2.14 e-5

D- Mannitol 119.86 2.17 e-5

Hexadecanoic acid 17611 2.30 e-5

Docosanoic acid 322 2.32 e-5

β-Sitosterol 18.036 2.57 e-5

Maleic acid, 2-methyl- (2TMS) Results 0.068538 0.000125

Xylitol, 1,2,3,4,5-pentakis-O-(trimethylsilyl) 0.18821 0.000701

Phosphoric acid, bis(trimethylsilyl) 2,3-

bis[(trimethylsilyl)oxy] propyl ester

0.19696 0.002067

Glycerol-3-phosphate (4TMS) Results 0.19714 0.00207

Stigmasterol trimethylsilyl ether 0.20688 4.94 e-5

1,2,3-Propanetricarboxylic acid, 2-

[(trimethylsilyl)oxy]-,tris(trimethylsilyl) ester

0.29432 0.009638

Citric acid (4TMS) Results 0.29432 0.009638

D-Xylonic acid, 2,3,5-tris-O-(trimethylsilyl)-,γ-lactone 0.30923 0.003607

A volcano plot was further applied to assess the metabolite input and output. It

was observed that a majority of metabolites present in the substrate were consumed by

the fungi during the degradation process whilst other metabolites were generated

(Figure 6.9).


145

0

1

2

3

4

5

6

7

8

-5 0 5 10 15 20

- Log

10 (P

-val

ue)

Log2 (Fold Change)

Control Optimized mix

Figure 6.9. Important features selected by volcano plot with fold change threshold (x-axis) 2 and t-

tests threshold (y-axis) 0.05. Note: both fold changes and p-values are log transformed. The further

its position away from the (0, 0), the more significant the feature is. The yellow circles represent the

significant metabolites, while the black triangles represent non-significant metabolites.

The primary significantly accumulated metabolites were stigmasterol, maleic

acid, xylitol and glycerol. Although statistically less significant, other metabolites of

interest generated were γ-lactone-d-xylonic acid, d-glucopyranoside, citric acid,

ethylene and arabinitol.

6.4. Conclusions

Recently, winery wastes have been classified as pollutants by the European

Union and post-product processing is required to lower their hazardous nature.

Individual fungal enzymes have limited capacity so mixed fungal degradation combined

with pre-treatment can decrease biomass recalcitrance for more efficient breakdown.


146

The aim of this part of the thesis was the application of a statistical based

approach to obtain optimization for both submerged fermentation and SSF. Statistical

models were created from the results based on submerged fermentation and SSF

experiments. The models were then used to predict the optimized mixture of fungi to be

used as well as the ratio of medium to substrate. The resultant optimised conditions

were then tested and compared across submerged fermentation and SSF against the

most successful single organism result for each case.

Winery biomass degradation by a mixture of Trichoderma harzianum,

Aspergillus niger, Penicillium chrysogenum and P. citrinum in submerged fermentation

and SSF was evaluated. Higher cellulase and β-glucosidase activities were observed in

SSF and submerged fermentation, respectively. Statistical modelling predicted the

fungal percentage ratio of 60:14:4:2 for A. niger: P. chrysogenum: T. harzianum: P

citrinum with a substrate: medium ratio of 0.39:1.

Under the optimized conditions, cellulase, xylanase and β-glucosidase activities

increased to 78.5, 3544.7 and 250.9 U/mL, respectively. Cellulase and xylanase activity

both increased more than two-fold. Lignin degradation increased to 17.9% with respect

to submerged fermentation (P. chrysogenum) under optimized conditions within 5 days.

GC-MS analysis identified 78 significant metabolites, of which stigmasterol, glycerol,

maleic acid, xylitol and citric acid were the most significant generated by fungal

degradation

Enhanced degradation of winery-derived biomass was achieved using mixed

fungal cultures and GC-MS analysis indicated the production of commercially

important metabolites during the process.

6.5. Summary

A series of experiments were performed to obtain an optimized protocol for

degrading winery biomass waste. Both submerged fermentation and SSF processes were

assessed using the fungi T. harzianum, A. niger, P. chrysogenum and P. citrinum.

Following degradation, the parameters of cellulase, xylanase and β-glucosidase

activities, carbon/nitrogen content and lignin content were determined. A full factorial


147

design to predict the optimal ratio of fungi and substrate to medium ratios was

employed to demonstrate the effectiveness of a mixed fungi culture.

Enzyme activities and lignin degradation were observed to be considerably

enhanced under these optimized conditions, illustrating the significance of a multi-

factorial design to mixed culture degradation. In addition to noticeable increases in

cellulase and β-glucosidase activities, a greater than two fold increase was seen in

xylanase activity during the mixed fungal fermentation within 5 days as against 7 days

in submerged fermentation and SSF. In addition, several metabolites of industrial and

medicinal interest such as alcohols, acids and monosaccharides were observed to be

produced during the mixed fermentation process.


148

CHAPTER 7

Degradation of hydrothermally pre-

treated winery biomass by a co-culture of

basidiomycetes and ascomycetes

Chapter 7 Winery biomass degradation by a co-culture of basidiomycetes and ascomycetes

149

7.1. Introduction

Grapes are one of Australia’s major agricultural products although the waste

material (lignocellulosic biomass and water) generated by wineries is an environmental

concern. It has been reported that a symbiotic consortium of lignocellulose degrading

micro-organisms effectively degrades such biomass (Chu et al., 2011), and is

considerably more efficient than monoculture fungal degradation and lignin related

metabolite degradation (Gou et al., 2009). However, there have been few reports of

mixed or sequential fermentation of biomass using different phyla of fungi. Numerous

fungi, especially those belonging to division Basidiomycota, are known for their lignin

degrading abilities (Baldrian and Valášková, 2008). Basidiomycetes are categorized as

either ‘white-rot’ fungi or ‘brown-rot’ fungi, depending on the nature of their lignin

degradation (white-rot fungi being more effective than brown-rot fungi). White-rot

fungi include Phanerochaete spp., Trametes spp. and Ganoderma spp., while brown-rot

fungi include Fomitopsis spp. and Postia spp.

Fungi belonging to the division Ascomycota, such as Trichoderma spp.,

Aspergillus spp. and Penicillium spp., are known for their biomass degrading ability and

should prove useful in grape biomass degradation. The fungi have been well studied for

their ability to produce high levels of cellulase and hemicellulase degrading enzymes

(Brink and Vries, 2011, Klyosov, 1987b). Basidiomycetes form the major wood rotting

fungi and can degrade the cellulose, hemicellulose and lignin component of the biomass

due to the large array of enzymes they produce (Grinhut et al., 2011).

7.2. Overview

As mentioned above, utilizing a mixed fungal culture to degrade biomass

produces a higher degradation compared to fungal monocultures. Indeed, the work

described in this thesis has shown that a mixed ascomycete fungal culture was more

effective than the respective monocultures. This chapter involves the study of biomass

degradation achieved by a mixture of a white-rot fungus, Phanerochaete chrysosporium

and the optimised mix of ascomycetes described in the previous chapter. It is

hypothesized that the combination of a wood-rot basidiomycete and the mixture of

ascomycetes would produce better lignocellulose degradation than the ascomycetes

alone.


150

7.3. Results and Discussions

Grape samples were added to AATCC minimal iron medium in an Infors HT 3.6

L bioreactor (Infors AG, Bottmingen, Switzerland) and autoclaved using a ‘gentle’

cycle (115°C). The substrate and medium ratio was maintained at 0.39:1, as determined

by the statistical modelling in the preceding chapter. After cooling to room temperature,

this medium was oxygenated overnight. A spore suspension from a 120-hour culture of

Ph. chrysosporium (about 1× 107 spores/mL) was added to the bioreactor for grape

biomass degradation. This degradation was allowed to proceed for 8 days after which a

mixture of A. niger: P. chrysogenum: T. harzianum: P. citrinum (percentage ratio of

60:14:4:2) was added and the degradation allowed to proceed for a further 8 days.

Degradation was performed at 30°C and 200 rpm for the entire experiment. Samples

were taken every 4 days and analysed for lignin content and enzyme activity, namely

cellulase, β-glucosidase, xylanase, laccase, lignin peroxidase and tyrosinase. Similarly,

the samples were analysed by GC-MS in order to quantify the degradation products

generated at the end of each stage of degradation.

7.3.1. Lignin degradation

As discussed earlier, lignins form a considerable part of grape biomass. The

winery waste grapes obtained for experimentation here consisted of a just over 36%

lignin (35.4 % AIL + 0.7% ASL). It was also observed that a major part of this lignin

was not degraded during autoclaving. However, considerable amounts of sugars were

released into the medium due to the degradation of cellulosic and hemicellulosic

components, forming monosaccharides and oligosaccharides. Autoclaving was observed

to dramatically increase the total lignin content to about 63.7%, indicative that other

ingredients are degraded leaving a high percentage content of lignin behind. This point,

prior to addition of degradation agents, is referred to as the control.

Lignin degradation was enhanced during the co-culture of Ph. chrysosporium

and ascomycetes (Figure 7.1) and, interestingly, that lignin degradation tapered off

slowly and gradually by the end of experimental period. At the end of day four (D4), the

total lignin content in the sample dropped to 59.4%. This content then dropped further

to 54.7%, 48.2% and 41.2% after D8, D12 and D16, respectively (Figure 7.1).


151

Figure 7.1. Total lignin content and lignin degradation over 16 days by the co-culture of wood-rot

fungus, Ph. chrysosporium, and the optimized Ascomycota mix.

Although the duration of this lignin degradation experiment was twice that of

the mixed ascomycete degradation described in Chapter 6, overall degradation was

considerably higher than the former process. One of the issues related to the mixed

ascomycota degradation, as will be discussed in the next chapter, was the autolytic

property of P. chrysogenum (and possibly of P. citrinum) towards the nutritional

deficiency period. Autophagy is quite common in filamentous fungi, especially during

the carbon depletion period of their growth. It is utilized by these fungi to recycle the

nutrient resources for their survival. Chitinases are a group of enzymes responsible for

the onset of autophagy in P. chrysogenum. Although, the enzymes are produced by P.

chrysogenum along with chitin synthase enzymes, their activity increases considerably

towards the carbon starvation period, i.e. from the 4th day of incubation (Sámi et al.,

2001). These enzyme activities have been shown to decrease the hypal concentration in

the tested samples (Sámi et al., 2001), indicating P. chrysogenum autolysis, primarily

after D5 of incubation. The application of Ph. chrysosporium may have enabled the

entire system to counter this deficiency by initiating lignin degradation, thus causing a

release of considerable amounts of free sugar into the medium to be later utilized by

ascomycetes during their early growth phase.

0

10

20

30

40

50

60

70

Control Day 4 Day 8 Day 12 Day 16

Lig

nin

cont

ent (

%)

Lignin Lignin degraded (%)


152

7.3.2. Cellulase activity

Cellulases are enzyme complexes with moderately variable enzyme constituents.

Three enzyme classes, endoglucanases, exoglucanases and β-glucosidases, are most

common and are found in almost every lignocellulose degrading fungus. Aerobic

ascomycetes, such as T. harzianum and Aspergillus sp., have cellulases composed of

endoglucanases and exoglucanases, reportedly of varied individual enzyme composition

depending on substrate availability and specificity (Kovacs et al., 2009, Liu et al., 2012,

Sipos et al., 2010). Numerous experiments, including those reported in the previous

chapters of this thesis, have investigated cellulase activities of these fungi under

different conditions with variable carbon sources. There have been fewer reports,

however, for Ph. chrysosporium cellulase activity although, it is known to have

lignocellulolytic activity (Dashtban et al., 2010). Previous reports have indicated lower

cellulase activities of up to 1 U/mL on cotton stalks (Shi et al., 2008), CMC and avicel

(Uzcategui et al., 1991). Contrasting results were observed under the current

experimental conditions. Although, the overall cellulase activities were not as high as

the mixed ascomycete experiment, they were consistently higher than most of the

monoculture fungal cultures either in submerged or SSF conditions. As expected,

cellulase activities dropped during the latter phase of the Ph. chrysosporium degradation

period (i.e. D8) and early phase of ascomycete mix inoculation, i.e. by D12. The

cellulase activities displayed a considerable increase towards the end of degradation

period, possibly due to an increased production of these enzymes by ascomycetes.

Additionally, these fungi also possibly decreased the overall cellulase inhibition by

utilizing the sugars. Cellulase activity was about 82.5 U/mL on D4, while it dropped to

64.9 U/mL by D12. However, by D16, it again increased to 75.7 U/mL (Figure 7.2).


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Figure 7.2. Cellulase activity observed over 16 days by the co-culture of wood-rot fungus, Ph.

chrysosporium, and the optimized Ascomycota mix.

It has been noted that the carbon and nitrogen ratio is an important factor which

decides the efficiency of biomass degradation by microbes, especially fungi (Mäkelä et

al., 2013). It has been found that the higher the nitrogen content in the substrate, the

greater the degradation ability of fungi (Brijwani et al., 2010). In addition, the higher

the nitrogen content, especially from organic sources (proteins, for example), the greater

the overall enzyme activities (Kim et al., 2012a). As observed during the previous

chapter (Chapter 6, Section 6.3.1), soluble proteins comprised 2.2% of the Shiraz grape

biomass, which may have induced an exceptionally high cellulase activity of Ph.

chrysosporium during the early phase of biodegradation. The results obtained in these

experiments contrasted to the observations reported in a previous study by (Shi et al.,

2008) where a prolonged pre-treatment for more than 15 days resulted in lower

enzymatic activities during subsequent biomass degradation. One of the other reasons

for the absence (or at least reduction) of enzyme inhibition could be the presence of the

enzyme cellobiose dehydrogenase (CDH). This enzyme is an oxidoreductase and is

generally categorised as a hemofavoenzyme (due to presence of both heme and flavin

domains). CDH specifically binds to cellulose and oxidises cellobiose as its substrate to

convert it to either glucose or other monosaccharide lactones (Bao et al., 1993). The

presence of CDH may explain the increased cellulase activity at the end of the

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fermentation at D16, where the total cellulase activity increased from 64.8 U/ml to 75.7

U/mL.

7.3.3. β-glucosidase activity

β-glucosidase activity plays an important role in cellulase efficiency and overall

biomass degradation. β-glucosidases are generally produced by numerous cellulolytic

organisms (especially fungi) due to the competitive inhibition of endo- and exo-

glucanases by their end product of cellobiose. β-glucosidase hydrolyses the β-(1-4)

glycosidic bonds in cellobiose (the smallest functional unit of the cellulose molecule) to

glucose units which can be readily utilized in microbial biochemical reactions (Brink

and Vries, 2011, Kim et al., 2012a).

Autoclaving altered the composition of winery grape waste, with an overall

decrease in dry mass. This pre-treatment process has been reported previously to affect

the biomass composition due to changes in the molal activities of water (Papadimitriou

and Barton, 2007). Hydrothermal heating weakens the lignin crosslinks within cellulose

and hemicellulose, thus exposing them to physical, chemical and/or biological

degradation (Papadimitriou, 2010). Further, the hydrothermal process, as it occurs in an

autoclave, enhances the breakdown of hemicellulosic polymers to form their constituent

pentose and hexose sugars, in addition to other derived products such as galacturonic

and/or glucuronic acids. The highly heterogeneous composition of hemicelluloses

makes them more susceptible to physical and chemical reactions compared to cellulose.

The presence of considerable amounts of oligosaccharide residues resulting from

autoclaving possibly led to exceptionally high β-glucosidase activity during the early

phase of grape biomass degradation.

Secretion of CDH by Ph. chrysosporium results in a two way degradation of

cellulose (and ultimately the biomass) by separate pathways. Due to the complex nature

of CDH, it has an independent pathway for cellulose degradation. The other pathway

consists of cellulase activities followed by β-glucosidase based degradation, similar to

numerous other biomass degrading fungi (Tsukada et al., 2006b). Although, Ph.

chrysosporium is not one of the major β-glucosidase generating fungi, about 11 different

types of β-glucosidase enzyme types have been reported in this organism; belonging to

either glycoside hydrolase (GH) classes GH3 or GH5 (Martinez et al., 2004).


155

During the current experiment, Ph. chrysosporium displayed higher β-

glucosidase activity than the individual fungi or the combined ascomycetes. However,

the activity of 316.5 U/mL at the first measured point, D4, dropped to 253 U/mL by D8

and 193.8 U/mL by D12 before increasing again at D16 to 445.5 U/mL. This decrease

during degradation is probably due to the overall inhibition of fungal activity caused by

increasing nutrient starvation (Figure 7.3).

Figure 7.3. β-glucosidase activity observed over 16 days by the co-culture of wood-rot fungus, Ph.


Ph. chrysosporium generates both intra- and extra-cellular β-glucosidases (Smith

and Gold, 1979) and the overall activity varies according to nature of the substrate.

However, the β-glucosidase activity observed during first four days of the current

experiment was found to be similar to previous observations (Lymar et al., 1995). β-

glucosidase activity increased considerably to 445.5 U/mL at the end of biodegradation

period (D16), indicating the combined activities of β-glucosidases from Ph.

chrysosporium and ascomycetes. Ascomycetes such as A. niger and P. citrinum are

known to generate β-glucosidase enzyme with high enzyme activities under various

conditions. However, the activities observed during the current experiments were found

to be even higher than ones reported previously (e.g. on rice bran (Ng et al., 2010)). It is

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possible that the nature and composition of the substrate, i.e. grape biomass, might have

a positive effect on β-glucosidase activity with respect to the time of fermentation.

7.3.4. Xylanase activity

Xylanase activity is an important parameter to measure hemicellulose

degradation as a major part of hemicellulose is comprised of xylans. Xylanases are a

group of differential hemicellulases responsible for the degradation of various xylans by

hydrolysing their β-(1, 4) linked D-xylopyranoside units. Comprising up to 1% of total

fungal lignocellulolytic enzymes, xylanases may work symbiotically with other

hemicellulases and cellulases, thereby facilitating a consortium for degradation of

biomass substrates (Dashtban et al., 2009, Cantarel et al., 2009).

During the current experiments, noteworthy xylanase activity was observed

during Ph. chrysosporium based grape biomass degradation. During the early phase of

degradation, i.e., D4, a xylanase activity of 1626.4 U/mL was observed, considerably

higher than A. niger (a commercial producer of xylanase) (Sections 4.3.7 and 5.3.6).

Although, the activity dropped by D8 to 1493.9 U/mL, it was still higher than the

ascomycete xylanase activities mentioned above and in ‘cellulose + xylan’ based

substrate degradation (Hori et al., 2011). This was surprising considering the low levels

of xylanase secreted by Ph. chrysosporium in cellulose and wood supplemented growth

media (Sato et al., 2007). This activity increased significantly after the addition of the

ascomycetes mix at the end of D8. By D12, xylanase activity was observed at 6131.5

U/mL. Although, this activity dropped considerably by D16 to 4661 U/mL, it was

significantly higher than any xylanase activity measured in previous experiments

(Figure 7.4).


157

Figure 7.4. Xylanase activity observed over 16 days by the co-culture of wood-rot fungus, Ph.


Due to its wider spectrum of lignocellulose degradation, Ph. chrysosporium has

the ability to degrade the hemicellulosic component of its substrate. With respect to

other enzymes such as cellulases and β-glucosidases, xylanase and xylosidase activities

are reported to be considerably higher in Ph. chrysosporium. However, as with the other

enzymes, these activities also depend on the nature of substrate. For example, corn stalk

has been shown to enhance xylanase activities with respect to avicel or oat spelts

(Dobozi et al., 1992).

The observations noted in this experiment indicated enhancement of xylanase

activities, probably due to nature of substrate and symbiotic behaviour between fungal

hemicellulases (of Ph. chrysosporium and ascomycetes) and cellulases. Xylanases not

only increased the overall activities of hemicellulases and cellulases, but also prevented

an overall product inhibition of these enzymes. Carbohydrate Active Enzyme (CAZy)

classification lists xylanases in subclass GH5 (http://www.cazy.org/GH5.html), which is

one of the largest groups of glycoside hydrolase (GH) enzymes. This subclass consists

of glucanases, mannanases, glucosidases, xylanase, xyloglucanase and galactanase. Due

to close relatedness of these enzymes, both in genomic and proteomic nature, they have

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generally been reported to work in very close associations (Huy et al., Das et al., 2013,

Cantarel et al., 2009).

7.3.5. Ligninase activities

Lignases are the group of enzymes that hydrolyse the lignin component in

lignocellulose complex to smaller molecules. As discussed earlier, lignin is the second

most abundant carbon sink after cellulose. However, unlike cellulose, lignin is formed

of more than one type of molecule and, due to its complex structure, the normally

employed ascomycetes such as Aspergillus spp. and Trichoderma spp. do not have the

capacity to degrade lignin (Dashtban et al., 2010). However, numerous other fungi do -

especially those belonging to division Basidiomycota (Baldrian and Valášková, 2008).

Fungal lignases can be classified into the two broad categories of phenol

oxidases and heme peroxidases. The first category comprises enzymes such as laccases,

while the second category is comprised of enzymes such as lignin peroxidases.

7.3.5.1. Laccase activity

Laccases belong to the phenol oxidase group of lignases. They further belong to

the family of multimeric copper oxidases and catalyse single electron oxidation

processes in a large number of phenolic compounds. Laccases are complex, having

multiple catalytic centres, and this enables them to oxidise a large number of substrates

including catechol, hydroquinone and guaiacol (Dashtban et al., 2010)

During the current experiment, considerable laccase activity was observed

during the early phase of degradation, i.e. during the period of Ph. chrysosporium based

degradation. The initial laccase activities (D4) were low at about 24 U/mL. However, by

D8, the activities increased considerably to about 74 U/mL. After this time, the activities

dropped again to 34 U/mL by D12 and 18 U/mL by D16 (Figure 7.5).This result was

expected since the ascomycetes inoculated after D8 did not possess any laccase

activities, as has been observed during the previous studies (data not shown).


159

Figure 7.5. Laccase activity observed over 16 days by the co-culture of wood-rot fungus, Ph.


It has been shown that the process of adsorption not only decreases the

competitive and product inhibition but also increases the half-life of an enzyme complex

as compared to the free enzyme. A recent experiment (Wu et al., 2014) showed that

Trametes versicolor laccases retained their activities for longer when adsorbed on

humus and humic acid related minerals as compared to free laccase, which displayed a

sharp decrease in activity after just 4 days. However, during the first 8 days, laccase

activities were observed to increase in the current experiments which might indicate a

positive effect of grape biomass on this enzyme’s activity. One of the reasons for this

phenomenon might be the autoclaving pre-treatment process of the grape biomass.

Although the previous experiments indicated that autoclaving was not very efficient in

hydrolysing the lignin component, this process breaks or weakens numerous types of

lignin-lignin and lignin-cellulose bonds, thereby increasing lignin exposure

(Papadimitriou, 2010) and, consequently, fungal lignin degradation efficiency.

Generally, Ph. chrysosporium does not generate or exhibit insignificant amounts

of laccases during biomass degradation, although it possesses laccase encoding genes.

Substrates rich in nitrogen, such as malt or yeast extracts, have been reported to increase

the laccase production and activities (Srinivasan et al., 1995). Additionally, brewery and

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apple pomace waste has also been reported to induce laccase activities to exceptional

levels (>700 U/g) in the presence of inducers such as copper sulphate during Ph.

chrysosporium mediated degradation (Gassara et al., 2010). Similarly, under the current

experimental conditions, the high content of nitrogen in the grape biomass and the

considerable nitrogenous content of the basal medium (3g/L NH4NO3) induced and

later enhanced the laccase activity during early phase of degradation (up to D8).

7.3.5.2.Lignin peroxidase activity

Lignin peroxidases (LiP) are a group of isozymes which depolymerize the non-

phenolic lignins and β-O-4 non phenolic lignin compounds by H2O2 based oxidation. In

addition to these polymers, the LiPs are known to degrade numerous phenolic molecules

such as guaiacol, vanillin, catechol and syringic acid. These enzymes were first isolated

from Ph. chrysosporium in 1983 and have since been reported to occur widely in a

number of white-rot fungi.

During the current experiment, highest LiP activities were observed during first

8 days of biomass degradation. By D4, LiP activity was observed at 126.5 U/mL, which

increased to 147 U/mL after D8 (Figure 7.6). Higher enzyme activity was expected

during the early phase of grape biomass degradation. However, lignin peroxidase

generally has a pH optimum at the low pH of around 3.0 to 3.5 and this value can be

even lower depending on the substrates (Doyle et al., 1998). Similar to laccase, LiP

activities are also influenced by inducing entities. Oxygen and nitrogen act as positive

inducers of LiP activities causing marginal to considerable increase in LiP activities

(Belinky et al., 2003, Martı́nez, 2002). Similarly, veratryl alcohol is known to enhance

LiP activities in brewery and pomace to considerable levels after a prolonged incubation

of about 9 days; although, this activity was either absent or insignificant during the

earlier phase (Gassara et al., 2010). Due to the presence of oxygen and nitrogenous

compounds, the activities during the early phase in the current experiments were

comparable to previous observations around D8 (Gassara et al., 2010), but considerably

better in earlier and later periods.

As expected, due to the inability of ascomycetes to produce LiP, overall activity

decreased by D12 to 102.5 U/mL and further by D16 to 61.5 U/mL (Figure 7.6).


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Figure 7.6. Lignin peroxidase activity observed over 16 days by the co-culture of wood-rot fungus,

Ph. chrysosporium, and the optimized Ascomycota mix.

The drop of LiP activity from D12 to D16, although considerable, was surprising

since none of the previous studies had detected enzyme activity during long

fermentation periods (Gassara et al., 2010, Fujian et al., 2001).Possible explanations for

this may be the consistently high cellulase activities combined with considerably higher

levels of β-glucosidase activities during the later phase of grape biomass, which either

increased or maintained the levels of glucose and other sugars. Dosoretz et al. (1990)

has shown that an increasing glucose in Ph. chrysosporium medium minimises the loss

of LiP activities. Increasing concentrations of glucose in the medium resulted in

inhibition of proteases, which act as LiP repressors (Dosoretz et al., 1990). One of the

other possibilities may be the presence of soybean peroxidase in the degradation

medium. This enzyme, secreted by Ph. chrysosporium, is a catalytically similar enzyme

to LiP. However, the enzyme has high thermostability and longer half-life- as compared

to LiP, as it has been observed to maintain its stability long-term below 70°C and for up

to 2.5 hours at 85°C. (McEldoon and Dordick, 1996). Whether or not this enzyme is

produced, and what activity that would lead to during winery biomass waste

degradation by Ph. chrysosporium remains to be tested.

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7.3.6. Metabolic output of sequential fermentation

A complete metabolomic profile was obtained by GC-MS at regular intervals

(D4, D8, D12 and D16) to analyse the metabolic behaviour of the fungi utilized during

the biomass degradation process. The aim was to increase the understanding of temporal

fungal metabolic behaviour by assessing the levels of accumulation or utilization of

metabolites during the biomass degradation process. The mixed fungal degradation

product was analysed by GC-MS (5 replicates per sample) to yield a metabolic profile

of ca. 339 peak features, which is generally expected for a microbial metabolism

processes. The data generated by mass-spectral analysis were then exported to

Microsoft® Excel to manually normalize peak areas with respect to the internal standard.

This data was then applied to SIMCA 13 for preliminary multivariate Principal

Component Analysis (PCA). The score scatter and DModX plots are shown in Figure

7.7 and Figure 7.8, respectively.

Figure 7.7. PCA model of winery biomass degradation by the co-culture of Ph. chrysosporium and

the optimized ascomycota mix. Note: Each point on the scatter plot refers to single sample, with R2X

(cumulative) = 87.9% and Q2 (cumulative) = 67.6%. Each point on the plot refers to single treated

sample. Note: the OPLS-DA plot eclipse represents the 95% confidence interval


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Figure 7.8. ‘DModX’ or ‘Distance of observation’ plot of winery biomass degradation by the co-

culture of Ph. chrysosporium and the optimized ascomycota mix. The samples refer to Control (1-5),

Day 4 (6-10), Day 8 (11-15), Day 12 (16-20) and Day 16 (21-25).

7.3.6.1. Mass spectral analysis and PLS-DA

In order to interrogate the data further, the samples were processed using Partial

Least Square-Discriminant Analysis (PLS-DA). Although, PLS-DA improved the data

predictability to about 78.9% with respect to PCA, the metabolite segregation by PLS-

DA was unable to group the metabolites based solely on their discriminant levels

(Figure 7.9).


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Figure 7.9. PLS-DA model of winery biomass degradation by the co-culture of Ph. chrysosporium

and the optimized ascomycota mix. Note: Each point on the scatter plot refers to single sample, with

R2X (cumulative) = 95.1%, R2Y (cumulative) = 94.1% and Q2 (cumulative) = 78.9%. Each point on

the plot refers to single treated sample. Note: the OPLS-DA plot eclipse represents the 95%

confidence interval

Orthogonal PLS-DA (OPLS-DA) was, thus, applied to yield better metabolite

discrimination, leading to better grouping and model predictability. Whereas PLS-DA

builds the model from a two-component system of systematic and residual variables,

OPLS-DA utilizes a three component system. In addition to residual variables, the

systematic variability component in OPLS-DA is derived from adding the correlated

variability (predictive) and non-correlated variability (orthogonal) between X-Y axis

components. Generally, due to the presence of single Y-axis component, OPLS

provides only one predictive component. However, multiple T-axes components are

represented as the orthogonal variations to generate a multiple predictive OPLS-DA

model (Trygg and Wold, 2002). For single response, X-axis variation is represented by

equations 7.1 and 7.2 below.

𝑋 = 𝑙𝑥′� + 𝑡𝑝′ + 𝑇0𝑃′0 + 𝑒 Eq. 7.1

and, Y-axis based prediction is represented by:


165

𝑌 = 𝑦′� + 𝑡𝑞′ + 𝑓 Eq. 7.2

Where, TP’ and TQ’ = matrix products

T = score vector

P’ and Q’ = loading vectors

e and f = residual variables

Of the 339 metabolites, 153 were identified as statistically significant by OPLS-

DA (Figure 7.10). Moreover, the change in metabolite concentration during

degradation was analysed by one way ANOVA using Fisher’s least significant

difference method (Fisher’s LSD) and Tukey’s Honestly Significant Difference

(Tukey’s HSD).

Figure 7.10A. OPLS-DA model of winery biomass degradation by the co-culture of Ph.

chrysosporium and the optimized ascomycota mix. Note: Each point on the scatter plot refers to

single sample, with R2X (cumulative) = 94.1%, R2Y (cumulative) = 91.8% and Q2 (cumulative) =

79.7%. Each point on the plot refers to single treated sample. Note: the OPLS-DA plot eclipse

represents the 95% confidence interval.


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Figure 7.10B. Loading scatter plot of winery biomass degradation by the co-culture of Ph.

chrysosporium and the optimized ascomycota mix. Note: The black star labels on the loading scatter

plot refer to each fungal group; with each yellow circles referring to single metabolite feature.

The filtered sample data was applied to univariate differential analyses methods

of T-test, analysis of variance (Vranova et al., 2013). Although, the metabolic outputs of

all samples, i.e. D4, D8, D12 and D16, were taken into account, major metabolite

turnovers were observed after day 8 and day 16 of degradation. Based on the volcano

plot generated from these tests, the total number of generated metabolites by fungal

biomass degradation was discriminated from the degraded or utilized metabolites

(Figure 7.11). The filtered data are displayed as the magnitude of fold change (FC) with

respect to the peak areas between mixed fungal culture samples and control samples.

The FC cut-off values are set at 2-fold change in a metabolite noting that a single-fold

change of metabolite refers to 10 mg/L of 13C-sorbitol, which was used as an internal

standard. Due to considerable degradation of cellulose and hemicellulose, hexose and

pentose production were expected.


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Figure 7.11. Volcano plots displaying significantly generated metabolites during winery biomass

degradation (D8 and D16) by the co-culture of Ph. chrysosporium and the optimized ascomycota

mix.

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Table 7.1. The table represents most significantly generated metabolites by D8 (end of Ph. chrysosporium incubation) and D16 (end of overall degradation) of winery

biomass waste degradation process.

DAY 8 Metabolites KEGG

ID m/z RT FC p-value InChI code

Ribitol, D- (5TMS) C01068 513.052 22.09/19.16 7.0702 5.83E-07 SUZLPERYXSOGNY-ACDBMABISA-N

Tyrosine, DL- (3TMS) C00082 397.732 22.098 7.0702 5.83E-07 WMWBCQXPKSQMOK-UHFFFAOYSA-N

Compound_58 73.2 21.85 6.9319 1.03E-06 Mannitol, D- (6TMS) C00392 615.259 21.935 6.9319 1.03E-06 USBJDBWAPKNPCK-

MOUTVQLLSA-N Compound_60 21.657 6.8961 1.01E-06 alpha-DL-Lyxofuranoside, methyl 2,3,5-tris-O-(trimethylsilyl)-

C00476 380.699 18.2 6.6012 4.40E-07 WZJRJUAWWFXXSN-UHFFFAOYSA-N

Xylose, D- (1MEOX) (4TMS) C00181 467.895 18.09 6.6005 4.40E-07 ZBEJHGUYYNELJI-RCCFBDPRSA-N

D-Leucrose R000405 342.297 18.19 6.6001 4.40E-07 alpha-D-Ribofuranoside, methyl 2,3,5-tris-O-(trimethylsilyl)-


Galactonic acid (6TMS) C00880 629.242 22.772 6.2367 1.71E-06 IVIMVRAMMWQMHJ-WZYRSQIMSA-N

Octanedioic acid, bis(trimethylsilyl) ester C08278 318.556 18.791 4.9772 6.22E-06 LWDVKORSFQXPHL-UHFFFAOYSA-N

Glucose, D- (1MEOX) (5TMS) C00031 570.102 21.35 4.5298 0.011337 LLVFXXKDPAPSCJ-DXBBTUNJSA-N

9,12-Octadecadienoic acid (Z,Z)-, trimethylsilyl ester

C01595 352.626 25.526 4.298 1.28E-05 MXGBYOVWUYSJSN-UTJQPWESSA-N

Propanedioic acid, bis(trimethylsilyl) ester C00383 248.424 9.802 3.6293 0.00683 ATCKJLDGNXGLAO-UHFFFAOYSA-N

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biomass waste degradation process (…continued)

DAY 8 Metabolites KEGG ID m/z RT FC p-value InChI code (-)-Riboflavin C00255 376.365 25.679 3.4399 3.04E-06 AUNGANRZJHBGPY-

SCRDCRAPSA-N 1,3-Benzenedicarboxylic acid, bis(trimethylsilyl) ester

C14097 310.493 20.121 3.2662 0.006857 XBYWPSUAELKTET-UHFFFAOYSA-N

Inulotriose (impurity: 1-Kestose) R000842 504.438 25.746 3.1825 2.68E-05 Glucopyranose, D- (5TMS) C00031 541.062 18.89/22.55 2.9742 0.000683 PPFHNIVPOLWPCF-

AWGDKMGJSA-N alpha-D-Xylopyranose, 1,2,3,4-tetrakis-O-(trimethylsilyl)-

C00181 438.854 18.959 2.9733 0.000687 KEOUSSOURMHEKN-UHFFFAOYSA-N

Lysine, L- (4TMS) C00047 434.912 21.871 2.6924 0.015148 NAMWUGJQNPIVND-KRWDZBQOSA-N

Cadaverine tetratms C01672 390.902 21.78 2.5958 0.006888 VLLJOGZFMDUFGI-UHFFFAOYSA-N

Per(trimethylsilyl)-D-ribose C00121 380.699 17.487 2.5228 0.061273 WZJRJUAWWFXXSN-UHFFFAOYSA-N

L-Asparagine C00152 132.118 10.374 2.4911 0.037694 DCXYFEDJOCDNAF-REOHCLBHSA-N

Propanoic acid, 3-[(trimethylsilyl)oxy]-, trimethylsilyl ester

C00163 234.440 9.525 2.0314 0.091801 IHWRJZSKTAMEMR-UHFFFAOYSA-N

DAY 16 Galactonic acid (6TMS) C00880 629.242 22.772 23.647 1.15E-08 IVIMVRAMMWQMHJ-

WZYRSQIMSA-N Per-O-(trimethylsilyl)-alpha-D-galactofuranuronic acid

C00333 555.045 19.832 22.936 2.57E-10 SNQPUYIVUCUQEU-UHFFFAOYSA-N

2,3,4-Trihydroxybutyric acid tetraTMS C01620 424.828 19.838 22.936 2.57E-10 IEXXQBIANMZJJP-LSDHHAIUSA-N

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DAY 16 Metabolites KEGG ID m/z RT FC p-value InChI code (-)-Perillyl alcohol C02452 152.234 19.896 22.936 2.57E-10 NDTYTMIUWGWIMO-

SNVBAGLBSA-N 1,3-Benzenedicarboxylic acid, bis(trimethylsilyl) ester

C14097 310.493 20.121 21.039 3.13E-10 XBYWPSUAELKTET-UHFFFAOYSA-N

Propanedioic acid, bis(trimethylsilyl) ester C00383 248.424 9.802 16.289 0.001619 ATCKJLDGNXGLAO-UHFFFAOYSA-N

Butanoic acid, 3-[(trimethylsilyl)oxy]-, trimethylsilyl ester

C01089 248.466 9.804 16.289 0.001619 YWRIHOYCAHATJN-UHFFFAOYSA-N

alpha-D-Ribofuranoside, methyl 2,3,5-tris-O-(trimethylsilyl)-


alpha-DL-Lyxofuranoside, methyl 2,3,5-tris-O-(trimethylsilyl)-


Xylose, D- (1MEOX) (4TMS) C00181 467.895 18.09 15.987 1.75E-09 ZBEJHGUYYNELJI-RCCFBDPRSA-N

D-Leucrose R000405 342.297 18.19 15.986 1.75E-09 Ribitol, D- (5TMS) C01068 513.052 22.09/19.16 13.296 5.42E-08 SUZLPERYXSOGNY-

ACDBMABISA-N Tyrosine, DL- (3TMS) C00082 397.732 22.098 13.296 5.42E-08 WMWBCQXPKSQMOK-

UHFFFAOYSA-N Octanedioic acid, bis(trimethylsilyl) ester C08278 318.556 18.791 12.962 5.60E-09 LWDVKORSFQXPHL-

UHFFFAOYSA-N Pentanedioic acid, 2-[(trimethylsilyl)oxy]-, bis(trimethylsilyl) ester

C02630 364.658 16.895 12.034 0.1097 GFDIGKRHNMDEJF-GFCCVEGCSA-N

1,4-Benzenedicarboxylic acid, bis(trimethylsilyl) ester

C06337 310.493 20.11 12.007 0.03366 XNDCJULFVFHXBR-UHFFFAOYSA-N

Compound_60 21.657 10.759 5.17E-08

171



DAY 16 Metabolites KEGG ID m/z RT FC p-value InChI code alpha-D-Glucose 1,6-bisphosphate potassium salt hydrate

C01231 379.214 10.61/20.96 9.8338 4.48E-07 OVRXVCAVDXBPQT-QMKHLHGBSA-J

Glucose, D- (1MEOX) (5TMS) C00031 570.102 21.35 8.8565 0.005113 LLVFXXKDPAPSCJ-DXBBTUNJSA-N

Propanoic acid, 3-[(trimethylsilyl)oxy]-, trimethylsilyl ester

C00163 234.440 9.525 7.8948 0.002846 IHWRJZSKTAMEMR-UHFFFAOYSA-N

3-Methyl-1,3-bis(trimethylsilyloxy)butane 248.509 9.509 7.8089 0.29133 FHZUQOQCMZAXQH-UHFFFAOYSA-N

D-Arabinose 4TMS C00216 467.896 18.522 7.8025 2.97E-07 ZBEJHGUYYNELJI-VWQWLJPISA-N

Glucose, 1,6-anhydro, beta-D- (3TMS) C00221 378.684 18.826 7.7984 2.96E-07 OEPKLYQQZLBNKR-HHHGZCDHSA-N

Per(trimethylsilyl)-D-ribose C00121 380.699 17.487 7.7868 0.006846 WZJRJUAWWFXXSN-UHFFFAOYSA-N

Glucopyranose, D- (5TMS) C00031 541.062 18.89/22.55 7.4222 6.72E-06 PPFHNIVPOLWPCF-AWGDKMGJSA-N

alpha-D-Galactose 1-phosphate dipotassium salt pentahydrate

C00446 317.250 18.415 7.3892 2.34E-07 ZYBRMLNCQMITLC-JFYWUTKTSA-L

Compound_57 443.6 21.695 7.1796 1.14E-06 Phenylalanine, DL- (2TMS) C00079 309.552 17.713 7.1789 1.61E-06 DWNFNBPPSFIIBG-

AWEZNQCLSA-N Vitamin E acetate C13202 472.744 17.270 6.1295 0.14582 ZAKOWWREFLAJOT-

UHFFFAOYSA-N alpha-D-Xylopyranose, 1,2,3,4-tetrakis-O-(trimethylsilyl)-

C00181 438.854 18.959 4.9366 2.09E-05 KEOUSSOURMHEKN-UHFFFAOYSA-N

Compound_84 216.6 24.468 4.7245 2.48E-05

172



DAY 16 Metabolites KEGG ID m/z RT FC p-value InChI code 1,3-Diphenyl-1-trimethylsilyloxy-1-pentene 310.505 23.197 4.5244 3.84E-05 SDLWASLJDGYSTN-

SILNSSARSA-N alpha-D-Glucose-1-phosphate, dipotassium salt dihydrate

C00103 276.179 18.197 3.3807 0.032003 JXLMWHHJPUWTEV-QMKHLHGBSA-L

Compound_74 147.2 24.462 3.218 0.000682 1H-Indole, 7-methyl-1-(trimethylsilyl)- C08313 203.355 10.384 2.9527 0.030054 FSBIHSWERZRMQS-

UHFFFAOYSA-N Creatinine (3TMS) C00791 329.662 10.371 2.9061 0.030415 XLSJWTRDAZBATG-

UHFFFAOYSA-N L-Asparagine C00152 132.118 10.374 2.9061 0.030415 DCXYFEDJOCDNAF-

REOHCLBHSA-N d-(+)-Xylose, tetrakis(trimethylsilyl) ether C00181 467.895 19.68 2.6816 0.0039 ZBEJHGUYYNELJI-

UHFFFAOYSA-N Compound_81 340.8 25.898 2.1797 0.086601


173

Figure 7.10 and Table 7.1 represent the metabolites generated after Day 8 and

Day 16 of winery biomass degradation. During the first 8 days of biodegradation, Ph.

chrysosporium degraded the overall lignocellulose complex to generate a wide variety

of products, as expected. These mainly consisted of sugars such as glucose, xylose,

lyxose and galactose, sugar alcohols, short chain fatty acids and sugar acids.

During this early phase, considerable numbers of lignin degradation products

were also observed, including octanedioic acid, 1, 3-benzenedicarboxylic acid and

cadaverine. These were released into the medium by Ph. chrysosporium within the first

4 days of initiating the biodegradation process. By Day 4, 160 mg/L of octanedioic acid

(generally referred to as suberic acid) was accumulated in the medium. However, by

Day 8 this accumulation dropped considerably to 32 mg/L indicating its further

degradation by the fungus. This fatty acid is known to be produced in plants for defence

against primary or secondary infections (Best et al., 2012) and has been linked with

humic and fulvic acids, which are the products of lignin components of plant biomass

(Xiaoli et al., 2008). The concentration, however, dropped considerably (12.3 mg/L) by

Day 16, due to fungal degradation first by Ph. chrysosporium and later by ascomycetes.

Similar to suberic acid, 1, 3-benzenedicarboxylic acid has been found in

association with humic and fulvic substances and has been observed in significant

quantities in humic substances and plant biomass pre-treatment processes (Yip et al.,

2009, Olivella et al., 2002). In the current experiments, this metabolite was observed to

be present in the grape biomass in very low quantities (23.2 mg/L), indicating that a

majority of it may be present in the bound form in the lignin complex and therefore not

detected. The subsequent autoclaving and Ph. chrysosporium based oxidation resulted

in very high concentrations of this metabolite during the early phase of biomass

degradation. Such high levels of 1, 3-benzenedicarboxylic acid have also been observed

during the pyrolysis of bamboo biomass (Yip et al., 2009). During the early phase of

Phanerochaete spp. based degradation, the concentration increased to about 2290 mg/L,

indicating considerable lignin degradation. Also, similar to suberic acid, the

concentration of 1, 3-benzenedicarboxylic acid dropped considerably to 108.8 mg/L by

Day 16, indicating its metabolism by Ph. chrysosporium and ascomycetes.


174

7.4. Conclusions

The winery biomass degradation process described in this chapter utilized a

basidiomycete fungus, Ph. chrysosporium, and a statistically optimized mix (percent

ratio of 60:14:4:2) of A. niger, P. chrysogenum, P. citrinum and T. harzianum as

determined during a previous experiment (Chapter 6). Pre-treatment of the biomass by

autoclaving was followed by Ph. chrysosporium degradation for 8 days and subsequent

co-culturing for a further 8 days with the ascomycete mix.

During the total biodegradation period of 16 days, a considerable amount of

lignin was degraded by enzyme mediated oxidation. At the end of the biodegradation,

about 22.5% lignin degradation was observed not only due to the breaking of numerous

lignocellulose complex linkages, but also from the ligninase activities of Ph.

chrysosporium followed by the lignin mineralization ability of the ascomycete fungi

mix.

Cellulase activities remained stable throughout the degradation period, while β-

glucosidase and xylanase activities increased significantly towards the later period of

degradation. The increment of these activities was due to the enhanced CDH enzyme,

which probably prevented product inhibition of cellulases and β-glucosidases. Very

high xylanase activities were recorded during the Ph. chrysosporium: Ascomycota mix.

These values were recorded as the highest for any experiment conducted in this thesis,

possibly due to the symbiotic nature of these enzymes with not only other

hemicellulases, but also with cellulases and glucosidases. Considerably higher laccase

activities were noticed during the earlier phase (up to Day 8) of winery biomass

degradation. This was peculiar since Ph. chrysosporium generally does not have

capability of laccase secretion. However, the high nitrogen content of the grape waste

and supplementation of nitrogenous sources in the media are likely to have induced

laccase activity in this fungus. Similarly, considerable lignin peroxidase (LiP) activities

were observed during first 8 days of biomass degradation. Surprisingly, LiP activity did

not decrease significantly after the commencement of co-culturing with ascomycetes.

This was unexpected since LiP has a very short half-life. One of the possibilities for this

stable LiP activity could be the presence of another similarly catalytic enzyme (such as

soybean peroxidase) with a longer half-life and possessing greater temperature

tolerance. Another reason for this might be repression of proteases caused by the


175

presence of considerable levels of glucose. This protease repression also might have

caused LiP stabilization for an extended period of biomass degradation.

Many metabolites arising from the initial degradation of lignin accumulated

during the early phase of biomass degradation due to a combination of hydrothermal

pre-treatment, and Ph. chrysosporium, degradation. However, this accumulation

decreased considerably during the later phase of degradation due to metabolism of

numerous fungal mediated biodegradation.

7.5. Summary

In summary, the rapid degradation of winery biomass waste was improved

considerably by application of hydrothermal pre-treatment and subsequent application

of a multi-fungal consortium. Due to the nature of this system, product and competitive

inhibition of lignocellulose degrading enzymes, especially cellulases and β-glucosidase,

was decreased considerably, leading to more efficient biomass degradation.

Composition of the biomass also played an important role as higher levels of nitrogen

and oxygen not only induced laccase activity, but also had roles in suppressing

inhibition of ligninase enzyme over an extended duration. Numerous metabolites were

generated during the biomass degradation process including sugars, sugar acids, sugar

alcohols and lignin degradation products. However, the biomass degradation could be

further improved by generating a more complex microbial system, particularly the

inclusion of a thermophilic bacterium (or fungus) to assist the current mixed fungal

system. Additionally, a wider spectrum of metabolites remains to be identified which

can be achieved by using a combination of GC-MS and LC-MS based analytical

approaches.

176

CHAPTER 8

Metabolic profiling of ascomycetes during SSF based winery biomass

degradation

Chapter 8 Metabolic profiling of ascomycetes during SSF-based biomass degradation

177

8.1. Introduction

Fungi belonging to the division Ascomycota, such as Aspergillus spp. and

Penicillium spp., are known for their biomass degrading abilities. These fungi have been

well studied for their high cellulase and hemicellulase enzyme production (Brink and

Vries, 2011, Klyosov, 1987b). These enzymes have the potential to be used in

generating important molecules such as alcohols, flavonoids, organic acids and

phenolics (Arvanitoyannis et al., 2006, Sánchez, 2009, Strong and Burgess, 2008). It is

known that, apart from cellulase production, Aspergillus spp. generate xylanases and β-

glucosidases in exceptional quantities (Betini et al., 2009, Singhania, 2012).

Additionally, as observed in the previous chapters, P. chrysogenum possess a

considerable cellulolytic ability in addition to a limited lignolytic activity, which is

absent in all other ascomycetes used under this project.

The field of metabolomics has the potential to provide biochemical information

in order to understand and characterise the various mechanisms related to fungal

biomass degradation. Among numerous areas, metabolomics has been applied to

investigate bacterial processes related to preventative health (Bi et al., 2013,

Marcinowska et al., 2011), environmental pollution (Beale et al., 2013a), food (Beale et

al., 2014) and fungal metabolism on various substrates such as benzoic acid and

Chardonnay grape berries, respectively (Hong et al., 2012, Matsuzaki et al., 2008).

However, within the context of fungal-mediated biomass degradation, its application

has been rather limited. Additionally, the analysis of the metabolic flux has the capacity

to enable a greater understanding of the nature, time dependence and substrate-based

limitations in regard to the metabolism of fungal cells. The process also assists in

understanding of correlation between cell phenotypes and their metabolic patterns and

stoichiometry (Meijer et al., 2009). Metabolic flux studies have previously been applied

in toxicology and medicine (Maier et al., 2009, Niklas et al., 2010), plant proteome

(Nelson et al., 2014), bacterial and fungal respiratory systems (Driouch et al., 2012,

Pedersen et al., 1999).

Numerous methods are available for metabolic flux analysis. The majority of

these involve the use of heavy isotopes due to their considerably superior tracking

abilities. In commonly utilized analyses, such as nuclear magnetic resonance (NMR)

and gas chromatography-mass spectrometry (GC-MS), metabolite tracking is performed


178

using isotopes such as 1H, 2H, 13C, 14N, 15N and 18O. While every isotope has its

advantages and shortcomings, 13C-molecules have been used by most researchers

(Nelson et al., 2014). However, 2H is a much more abundant element in nature,

comprising about half of the peptide atomic population (Yang et al., 2010b). Besides, it

is much more economical as compared to other isotopes, has a comparatively higher

invasive rate and a slower decay rate (Kim et al., 2012b). However, complete 2H

labelling is not feasible due to the limited tolerance of multicellular organisms, thereby

resulting in partial labelling, generally from 8% (Kim et al., 2012b) to 30% (Yang et al.,

2010b).

8.2. Overview

The current study described herein studied the metabolic flux of the periodic

metabolic change during the degradation of winery-derived biomass waste by A. niger

and P. chrysogenum. The study utilized 2H-flux due to its rapid incorporation in fungal

metabolism as compared to 13C. The flux experiment was designed to differentiate the

metabolites derived from the substrate and those generated by the fungi. This process

also aided in differentiating and quantifying significant metabolites generated by fungi

at different intervals, thereby, optimizing a particular process in order to maximize the

generation of specific products or generate numerous products in significant quantities

in a single bioconversion process setup.

8.3. Results and discussions

8.3.1. A. niger metabolic profile

A number of studies related to the degradation of various substrates by

Aspergillus spp. have been reported. However, the study of the degradation mechanism

of these fungi on winery biomass wastes has not been reported. The current section,

thus, contributes towards the understanding of metabolic behaviour over time and the

levels of accumulation or utilization of metabolites during the biomass degradation

process. The data generated by mass-spectral analysis was then exported to Microsoft®

Excel to manually normalize obtained peak areas with respect to the internal standard.

This data was then applied to ‘SIMCA 13’ based preliminary multivariate analysis of


179

Principal Component Analysis (PCA), represented in the score scatter (Figure 8.1) and

DModX (Figure 8.2) plots.

Figure 8.1. Principal Component Analysis of Aspergillus niger flux over 8 days during winery

biomass degradation. Note: Each point on the scatter plot refers to single sample, with R2X

(cumulative) = 90.3% and Q2 (cumulative) = 73.5%. Samples 1-27 (green circles) refer to D0 to D8

of deuterated samples, while samples 31-57 (yellow triangles) to D0 to D8 of control samples (all

triplicates). Note: the PCA plot eclipse represents the 95% confidence interval


180

Figure 8.2. ‘DModX’ or ‘Distance of observation’ plot of Aspergillus niger flux over 8 days of

winery biomass degradation. Note: Samples 1-27 refer to D0 to D8 of deuterated samples, while

samples 31-57 refer to D0 to D8 of control samples (all triplicates)

8.3.2. Mass spectral analysis and PLS-DA

The mass spectrometry analysis yielded ca. 640 peaks in growth media

supplemented with H2O and 30% D2O. The analysis was performed by PLS-DA

(Figures 8.3 and 8.4), which enabled easy discrimination between samples and the

metabolite features, as discussed previously (Section 6.3.8) and elsewhere(Wold et al.,

2001).


181

Figure 8.3. Partial Least Square- Discriminant Analysis derived score scatter plot of Aspergillus

niger flux over 8 days during winery biomass degradation. Note: Each point on the scatter plot

refers to single sample, with R2X (cumulative) = 69.3%, R2Y (cumulative) = 93.1% and Q2

(cumulative) = 86.8%. Samples 1-27 (green circles) refer to D0 to D8 of deuterated samples, while

samples 31-57 (yellow triangles) refer to D0 to D8 of control samples. Note: the PLS-DA plot eclipse

represents the 95% confidence interval.

The data obtained from SIMCA 13 required analyses for further discrimination

of the unique metabolites from a large group to more accurate levels. MetaboAnalyst

2.0 was, thus utilized to further differentiate and classify the most significant

metabolites in D2O and non-D2O based media. Metaboanalyst 2.0 employs a pre-

processing data filtering and normalization before its application to differential

expression analyses and two-group or multi-group analysis (Xia et al., 2012).


182

Figure 8.4. Partial Least Square- Discriminant Analysis derived loading scatter plot of Aspergillus

niger flux over 8 days during winery biomass degradation. Note: Five pointed star labels denote the

days of analysis (D2O day 0 to D2O day 8 and AATCC day 0 to AATCC day 8). Green circles

represent all the metabolites under consideration and their orientation. The metabolites in the vicinity

of five pointed star labels represent most significant metabolites.

The filtered sample data was applied to univariate differential analyses methods

of T-test, analysis of variance (Vranova et al.). Based on volcano plot model generated

from these tests, the total number of differentially expressed metabolites in D2O was

reduced to 37 (Figure 8.5; Appendix 3, Table 1).


183

Figure 8.5. Volcano plot displaying the differential expressing metabolites in deuterated and non-deuterated media. Note: The significant metabolites expressed in deuterated medium (black triangles) were taken into consideration for further metabolic flux analyses.

0

2

4

6

8

10

12

14

16

18

20

-5 -3 -1 1 3 5

-Log

10 (P

-val

ue)

Log2 (Fold Change)

Non-deuterated medium Deuterated medium


184

Figure 8.6. Metabolic pathways for A. niger mediated SSF degradation of winery biomass waste over 8 days. Note: The following table illustrates all the metabolite numbers in this pathway.


185


186

8.3.3. Metabolic flux analysis of A. niger during winery biomass degradation

The identified metabolites were then crossed-checked against the KEGG

database of metabolic pathways (http://www.kegg.jp/kegg/pathway.html). The data

obtained was then applied to MATLAB 2014a statistical software using Covariance-

Inverse (COVAIN) script (Doerfler et al., 2014). The resultant data were applied as a

correlation between the different metabolites in a time series analysis. Glycolysis

leading to the tricarboxylic acid (TCA) cycle was considered as the basic skeletal

pathway with respect to any other upstream or downstream metabolic pathways. The

major junctions were marked at glucose, glyceraldehyde-3-phosphate, acetyl-coenzyme

A (acetyl-CoA) and succinate, where an increase or decrease in other metabolites was

considered with respect to these metabolites. Overall, including glycolysis-TCA, 18

different pathways were projected on the basis of biosynthesis or degradation of

metabolites during the 8 day fermentation (Figure 8.6)

8.3.3.1. Glucose junction

Three pathways led in or out of glucose or glucose-6-phosphate. These included

galactose metabolism, N-acetyl glucosamine synthesis towards the biosynthesis of

chitins and sucrose / psicose degradation leading towards the glycolysis pathway. Major

degradation of L-sorbose leading to glucose was observed over 8 days of biomass

degradation. A considerable amount of this degradation was observed in samples D2 to

D5, when the sorbose concentration dropped from 564 mg/L to 5.9 mg/mL. During the

same period, the glucose concentration expectedly dropped from 66 mg/L to 1.8 mg/L.

However, the glucose concentration increased to 56 mg/L in D7, probably reflecting

sorboseglucose conversion (Figure 8.7).

The occurrence of N-acetyl glucosamine (NAG) is insignificant in grape

biomass. However, it forms a considerable proportion of fungal cell wall (Bernard and

Latgé, 2001). As expected, a constant increase of NAG from 5.4 mg/L in D0 to 83.5

mg/L in D7 was observed, indicating a constant increase in fungal biomass during the

biodegradation process (Figure 8.7). All the soluble sugars, such as glucose, galactose

and melibiose, showed uniform increments in accumulation from D5 onwards, probably

marking a late cellulase/β-glucosidase inhibition.



187

Figure 8.7. Changes in the composition of metabolites leading to or from the ‘glucose/glucose-6-

phosphate’ junction in the glycolysis pathway during A. niger mediated grape waste biomass

degradation.

8.3.3.2. Glyceraldehyde-3-phosphate junction

It was observed that a total of five pathways of biosynthesis or degradation

merged into the glycolysis pathway at the glyceraldehyde-3-phosphate junction. A

majority of these consisted of pentose phosphate pathways, related to the metabolism of

D-ribulose, L-arabinose, ribose and pectins. One pathway belonged to L-tyrosine

metabolism.

In the D-ribulose metabolism pathway, it was observed that the concentration of

glyceraldehyde-3-phosphate was inversely proportional to the sugar concentration over

the entire degradation period. Glyceraldehyde-3-phosphate accumulation reduced from

112 mg/L to 4.3 mg/L, barring D5, when it unexpectedly spiked to 95.5 mg/L. D-

ribulose displayed a constant increase from 5.4 mg/L to 83.5 mg/L during this time.

Sedoheptulose-7-phosphate remained more or less constant except in D5, when it

dropped to 6.3 mg/L (Figure 8.8).

0

100

200

300

400

500

600

0 1 5 6 7

Con

cent

ratio

n (m

g/L

)

Glucose metabolism (days) D-Galactose N-Acetyl-Glucosamine D-MelibioseL-Sorbose D-Glucose D-Psicose


188

Figure 8.8. Changes in the features of the D-ribulose metabolism path of the pentose phosphate

pathway

In the second part of pentose phosphate pathway, L-arabinose metabolism, all

the metabolites except glyceraldehyde-3-phosphate displayed accumulation. Especially

interesting were the sudden spikes in L-arabinose content from 12.2 mg/L in D4 to 84

mg/L in D5 and decrease in xylitol concentration from 37.4 mg/L in D4 to 15.2 mg/L in

D5 (Figure 8.9).

Ribose metabolism, in addition to gulonate and gluconate metabolism, also

displayed similarities to arabinose metabolism. However, from D4 onwards, the

concentration of both ribose and 5-phospho-alpha-D-ribose-1-diphosphate (PRPP)

displayed a direct proportionality to glyceraldehyde-3-phosphate (Figures 8.10 and

8.11). Molecules such as arabinose, arabitol, gulonates, gluconates and other pentose

acids and alcohols are primary degradation products of hemicellulose components of

biomass, such as xylans and pectins (Gírio et al., 2010). From the observed metabolic

behaviour of these degradation products over the fermentation time, it could be easily

interpreted that treatment with A. niger for 4-5 days would not only make a majority of

these components available to a further independent bioconversion, but also will

prevent the product inhibition of xylan degrading enzymes, thereby, further improving

the biomass degradation rate (Duarte et al., 2012b).

0

20

40

60

80

100

120

140

0 4 5 7

Con

cent

ratio

n (m

g/L

)

Ribulose pathway (days)

Glyceraldehyde-3-Phosphate D- RibuloseD-Erythrose-4-Phosphate Sedoheptulose-7-Phosphate


189

Figure 8.9. Changes in the features of the D-ribulose metabolism path of pentose phosphate pathway.

Figure 8.10. Changes in the features of the D-ribose metabolism path of pentose phosphate pathway

0

20

40

60

80

100

120

140

0 4 5 7

Con

cent

ratio

n (m

g/L

)

Pentose phosphate pathway (days) Glyceraldehyde-3-Phosphate L-Arabinose Xylitol Xylonate Gulconate

0

20

40

60

80

100

120

140

0 4 5 7

Con

cent

ratio

n (m

g/L

)

Ribose metabolism (days) Glyceraldehyde-3-Phosphate P- Ribosyl-PP D-Ribose


190

Figure 8.11. Changes in the features of gluconate metabolism path of pentose phosphate pathway

One of the other metabolic paths leading from glyceraldehyde-3-phosphate via

phosphoenol pyruvate was the shikimate pathway. Shikimate (or shikimic acid) and 3,

5-Diiodo-L-tyrosine were the only metabolites of this pathway which were observed

during our experiments. The shikimate pathway is one of the most important pathways

in plants as shikimate is the only source of benzene ring structures to synthesize further

complex biomolecules. Therefore, shikimate is the source of aromatic amino acids,

alkaloids, terpenoids, coumarins, stilbenes, tannins and lignins. Many of these

molecules serve in structural, defence or growth induction mechanisms in plants

(Bochkov et al., 2012). In addition, shikimate is used for the commercial production of

Oseltamivir phosphate (Tamiflu®), which is used to treat influenza H1N1 and H5N1

infections (Cortés-Tolalpa et al., 2014). In the context of this experiment, the shikimate

pathway was considered necessary to evaluate the lignin degradation potential of A.

niger. It was observed that the shikimate concentration decreased rapidly from 548

mg/L to 8.8 mg/L over the first four days of biomass degradation. After this, it

continued to decrease further over the next four days and reached 4.6 mg/L in D7. This

seemed more or less proportional to glyceraldehyde-3-phosphate during the first four

days. A minor part of the shikimate decrease may be explained in the increment of 3, 5-

diiodo-L-tyrosine from 0.5 mg/L to 3 mg/L over 8 days (Figure 8.12).

0

20

40

60

80

100

120

140

0 4 5 7

Con

cent

ratio

n (m

g/L

)

Glucouronate metabolism (days)

Glyceraldehyde-3-Phosphate D-Glucuronate 2-Keto-D-gluconate


191

Figure 8.12. Changes in the features of significant shikimate pathway metabolites.

Figure 8.13. Changes in the significant metabolite features of significant fucose degradation

pathway.

This molecule has been reported as an intermediate metabolite in the formation

of fungal melanins, such as DOPA melanin, γ-glutaminyl-3,4-dihydroxybenzene

0

100

200

300

400

500

600

0 4 5 7

Con

cent

ratio

n (m

g/L

)

Shikimate pathway (days)

Glyceraldehyde-3-Phosphate Shikimate 3,5-Diiodo-L-Tyrosine

0

100

200

300

400

500

600

0 4 5 7

Con

cent

ratio

n (m

g/L

)

Fucose degradation (days)

Glyceraldehyde-3-Phosphate Fucose


192

(GDHB) melanin and catechol melanin (Bell and Wheeler, 1986). Due to the increase in

fungal mass, as evident from an increase in NAG (Figure 8.7), the accumulation of 3,

5-diiodo-L-tyrosine was expected. However, due to the status of shikimate as a

precursor to virtually all molecules with a benzene ring, it could be possible that during

the degradation process, A. niger converted shikimate into a large number of secondary

metabolites, which appeared as statistically insignificant during the metabolic pathway

analysis.

Fucose was observed to decrease during the overall fermentation period. The

initial concentration of 548.6 mg/L decreased rapidly to 7.9 mg/L within four days of

fermentation, remaining more or less linear thereafter (Figure 8.13). The outcome was

expected as this sugar is not only one of the chief precursors of nucleotide sugars, but

also is an active metabolite in fructose and mannose metabolism leading to glycolysis.

Due to this role in metabolic pathways, it forms a considerable part of fruit cell walls,

enabling them to be resistant towards numerous microbial degrading enzymes (Vincken

et al., 1996).

8.3.3.3. Acetyl-CoA junction

Acetyl-CoA forms one of the most important junction points towards the

beginning of the TCA cycle. It acts as an intermediate between pyruvate, the end

product of glycolysis and citrate, the starting point of the TCA cycle. It is estimated that

38 pathways directly or indirectly merge in or lead from TCA/glycolysis at the acetyl-

CoA junction (KEGG, 2014). In the current experimental analyses, seven metabolic

pathways either merged at the acetyl-CoA junction via pyruvate or led from the TCA

cycle with acetyl-CoA as the precursor metabolite. These included isoleucine

biosynthesis, butanoate metabolism, xylene degradation, fatty acid biosynthesis, valine

biosynthesis and secondary metabolite biosynthesis.

In recent years, it has been observed in bacteria such as Geobacter

sulfurreducens and Methanocaldococcus jannaschii that a major proportion of

isoleucine biosynthesis flux is attributed to the citramalate pathway. The enzyme,

citramalate synthase, condenses pyruvate and acetyl-CoA to form R-citramalate (or 2-

methyl malate), which under the subsequent enzyme is converted to L-isoleucine (Risso

et al., 2008, Drevland et al., 2007). During our flux analysis based on 2H2O labelled

metabolites, it was observed that the acetyl-CoA concentration dropped rapidly from


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143.3 mg/L in D0 to 31.6 mg/L in D3. Barring an increment in D4, this concentration

further decreased to 9.8 mg/L in D7. Under the same conditions, citramalate

concentrations steadily increased from 8 mg/L to 11.9 mg/L in 7 days (Figure 8.14). To

our knowledge, this study shows the first reported occurrence of isoleucine biosynthesis

from pyruvate and acetyl-CoA condensation. However, further experiments are

important to confirm the level of importance of R-citramalate during isoleucine

biosynthesis in fungi, such as A. niger. The other metabolites observed which belonged

to the valine, leucine and isoleucine biosynthesis pathway were 2-ketovaline/ 2-oxo

isovalerate and 4-methyl-2-oxopentanoate/ 2-oxoisocaproate. They serve as the

precursors for L-valine and L-leucine synthesis, respectively. It was observed that,

barring D5, the concentration of this intermediate increased from 78.6 mg/L to 99.3

mg/L. However, the concentration of 4-methyl-2-oxopentanoate remained more or less

constant at 0.3 mg/L during the entire fermentation process (Figure 8.14). It has been

suggested that the elimination of acetolactate group enzymes such as acetohydroxy acid

synthase and application of 2-keto-acid decarboxylases (KDCs) followed by alcohol

dehydrogenases (Xia et al.) during the generation of 2-ketovaline in E. coli eliminates

downstream amino acid biosynthesis and alters the pathway towards 1-butanol, 2-

butanol and 3-methyl-butanol production, some of the major commercial fuel molecules

(Atsumi et al., 2008, Si et al., 2014).

One of the metabolites related to butanoate metabolism, R-3-hydroxy butanoate,

is among the most abundant R-3-hydroxy alkanoate polymers (PHBs). It is an important

ingredient for microbial spore formation. However, the industrial importance of these

metabolites lies in their use as biodegradable plastics. The metabolite is generated from

a double condensation of acetyl-CoA to first form acetoacetyl-CoA, followed by

acetoacetate and R-3-hydroxy butanoate, which is catalysed by the enzyme PHB

synthase (Thakor et al., 2006). In the current studies, it was observed that the

concentration of acetyl-CoA was inversely proportional to R-3-hydroxy butanoate

during the initial phase of biomass degradation. The concentration of acetyl-CoA

dropped by about 42 mg/L, which was also reflected in an increase of 40 mg/L of R-3-

hydroxy butanoate over the initial four days of fermentation. However, after this period,

probably due to inhibition of cellulolytic enzymes leading to decreasing levels of sugars

in the culture, A. niger may have utilized the polymer for nutritional or spore forming


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purposes. This reflected the reduction of R-3-hydroxy butanoate levels by about 39

mg/L from D5 to D8 (Figure 8.15).

Figure 8. 14. Changes in the features of acetyl-CoA and most significantly observed pathway

intermediates of valine, leucine and isoleucine biosynthesis in A. niger.

Figure 8.15. Changes in the features of acetyl-CoA and R-3-hydroxy butanoate, metabolites leading

to BHP biosynthesis in A. niger.

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In the fatty acid metabolism pathways, hexadecanoate was observed to degrade

marginally over the eight days of fermentation from 9.6 mg/L to 6.4 mg/L (Figure 4C).

It is reported that grapes, especially the grape seeds, store considerable amounts of

unsaturated fatty acids. Hexadecanoate, or palmitic acid, forms about 6-13% of total oil

content of grape biomass (Sabir et al., 2012). Medium chain fatty acids (MCFAs) are

produced by fungal cells during the fermentation process. However, after a period of 3-

4 days post-fermentation, the MCFAs drop the internal cell pH of fungi, thereby

inhibiting further growth and resultant fermentation (Edwards et al., 1990, Legras et al.,

2010). During our experiment, dodecanoate was observed to increase from 78 mg/L in

D0 to 99.3 mg/L in D7. However, the major increase was observed during the initial

four days, when the concentration increased from 78.6 mg/L to 96.8 mg/L, indicating

considerable fungal growth. The increase plateaued after four days, indicating possible

fungal inhibition (Figure 8.16). The fatty acid increment curve confirmed the outputs

from pentose sugar metabolism indicating that a continuous batch fermentation of four

days will enhance the overall biomass degradation as compared to single batch

fermentation.

Minor quantities of urea and S-methyl-5-thio-D-ribose-1-phosphate were also

observed during the flux analysis. The former is generated in the urea cycle with L-

aspartate as the precursor. L-aspartate also serves as the precursor to ethylene

biosynthesis via the Yang cycle/methionine salvage pathway to which S-methyl-5-thio-

D-ribose-1-phosphate serves as an intermediate metabolite and a catalysed product of S-

adenosine methionine (Larsson et al., 2011). However, according to our experiment, the

concentration of S-methyl-5-thio-D-ribose-1-phosphate remained more or less stable

(2.9 mg/L in D0 and 3.9 mg/L in D7), implying that this metabolite originates from the

grape biomass rather than being a fungal product. The urea content initially increased

from 1.6 mg/L on Day 1 to 9.6 mg/L, perhaps owing to active fungal metabolism

(Figure 8.17). However, as observed with sugar and fatty acid metabolism, possible

product inhibition after day 4 caused a decrease in total urea content to 1 mg/L,

probably due to its catalysis in an aqueous environment to ammonia and CO2.


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Figure 8.16. Medium chain fatty acid conversion by A. niger during degradation of Shiraz grape

waste over 8 days

Figure 8.17. Intermediate metabolites of urea cycle and Yang cycle/methionine salvage pathway.

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8.3.3.4. Succinate junction

Succinate is one of the very important components of metabolic pathways as it forms

one of the active parts of the TCA cycle. However, due to its role as an intermediate

metabolite of citrate or malate in the TCA cycle, it generally does not remain in high

concentrations in the fungal cell (Brown et al., 2013). In the current study, the only

pathway merging into the TCA cycle at the succinate junction was pentanoate

degradation. The concentration of pentanoate increased from 1.6 mg/L to 20.8 mg/L

during the initial two days, during which succinate decreased from 5.5 mg/L to 2.1

mg/L. However, the fatty acid gradually degraded over the fermentation period to 1.9

mg/L during the same time, while succinate levels increased to 63.4 mg/L, thus showing

an inverse relation with respect to pentanoate. Pentanoate and iso-pentanoate are the

major C5 fatty acids occurring in plant essential oils and are catalysed by hydroxyacyl-

CoA dehydrogenase followed by methylmalonyl epimerase to succinate (Layden et al.,

2013).

8.3.4. Glycolysis and TCA cycle

It was seen across the different pathways that the concentration of a number of

metabolites was inversely proportional to that of their pathways’ junction point

metabolites in glycolysis and TCA cycle pathways. It was also observed that A. niger

degraded the biomass better during the first four days of fermentation, after which the

accumulation of hexoses such as glucose and medium chain fatty acids started to inhibit

the fungal biodegradation efficiency. In the case of glycolysis and the TCA cycle, it was

observed that almost all the metabolites displayed a decreased concentration over the

initial four days of fermentation, suggesting active biomass degradation by fungi. This

was reflected in the increase of metabolites leading to biosynthesis of medium chained

fatty acids, branched-chain hydrophobic amino acids and chitin. A considerable increase

of 2-ketovaline indicated its possible use to produce biofuel molecules such as iso-

butanol by the post-fermentation application of 2-keto-acid decarboxylases after

eliminating acetolactate group enzymes (Atsumi et al., 2008, Si et al., 2014).

Additionally, it has been observed that a pre-treatment prior to SSF and a mixed fungal

degradation considerably increases the biomass degradation. A continuous batch

fermentation of the degraded biomass in a simultaneous and/or sequential manner by


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Saccharomyces cerevisiae, Pichia stipitisi or Escherichia coli has been shown to utilize

the mixed pentoses and hexoses (Gupta et al., 2009, Kim et al., 2010) generated by A.

niger enzymes. The in-process utilization of these sugars not only converts those sugars

to products such as ethanol, but also prevents the product inhibition of A. niger

cellulolytic enzymes.

8.3.5. P. chrysogenum metabolic profile

Penicillium chrysogenum or Penicillium notatum, as it was previously known is

one of the most recognised fungi. This fungus, belonging to division ascomycota, has

been used for a very long time for penicillin manufacturing, one of the most used

antibiotics in the world. However, this fungus has been known to be a highly versatile

eukaryote as, apart from penicillin production, it has been known to degrade several

types of lignocellulosic wastes. Similar to other ascomycetes such as Trichoderma spp.

and Aspergillus spp., it has been known to produce enzymes such as cellulases and

hemicellulases among many. Also, unlike some of the other species such as those

mentioned above, Penicillium spp., and especially, P. chrysogenum although minor, has

an inherent property of lignin degradation. It has been observed to degrade the lignin

component of varied sources such as wheat straw, pine lignocellulose (Rodríguez et al.,

1994, Rodriguez et al., 1996) and grape biomass wastes from wineries. However, due to

lower enzyme productions as compared to Trichoderma and Aspergillus fungi,

Penicillium spp. are not widely utilized in bioprocess industry. However, as discussed in

previous chapters, Penicillium spp., especially, P. chrysogenum has been observed to

produce comparable winery biomass degradation with respect to A. niger, while its

activities were considerably higher than Trichoderma harzianum. Besides this, it also

degraded a minor amount of lignins, a property found to be absent in the other species

used during various experiments.

Similar to A. niger, P. chrysogenum has been heavily reported in several

industrial processes, especially, penicillin production. The metabolic profiling of this

fungus has been reported from numerous groups for the purpose of developing

metabolic engineering to enhance penicillin production from this fungus. However, the

metabolic behaviour of this fungus on biomass degradation has been neglected. While

few researches mention biomass degradation properties of this fungus, none of the


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currently reported researches have presented any metabolic behaviour of P.

chrysogenum on any type of biomass conversion. The current thesis therefore forms one

of the foundation works for metabolic profiling of this P. chrysogenum during winery

biomass waste degradation over periodic intervals. The approach used for metabolic

flux analysis was similar to that discussed earlier during A. niger mediated biomass

degradation. This data was then applied to ‘SIMCA 13’ based preliminary multivariate

analysis of Principal Component Analysis (PCA). The score scatter (Figure 8.18) and

DModX (Figure 8.19) plots.

Figure 8.18. Principal Component Analysis of P. chrysogenum flux over 8 days during winery


(cumulative) = 84.4% and Q2 (cumulative) = 68.9%. Samples D2O-28 to D2O-54 (brown triangles)

refer to D0 to D8 of deuterated samples, while samples AA-82 to AA-108 (yellow squares) refer to

D0 to D8 of control samples (all triplicates). Note: the PCA plot eclipse represents the 95%

confidence interval


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Figure 8.19. ‘DModX’ or ‘Distance of observation’ plot of P. chrysogenum flux over 8 days of

winery biomass degradation. Note: Samples D2O-28 to D2O-54 refer to D0 to D8 of deuterated

samples, while samples AA-82 to AA-108 refer to D0 to D8 of control samples (all triplicates)

8.3.6. Mass spectral analysis and PLS-DA

The mass spectrometry analysis yielded ca. 640 peaks in growth media

supplemented with H2O and 30% D2O, whose analysis was performed by PLS-DA

(Figures 8.20 and 8.21).


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Figure 8.20. PLS-DA derived score scatter plot of P. chrysogenum flux over 8 days during winery


(cumulative) = 79.7%, R2Y (cumulative) = 99.6% and Q2 (cumulative) = 93.1%. Samples D2O-28 to

D2O-54 (brown triangles) refer to D0 to D8 of deuterated samples, while samples AA-82 to AA-108

(yellow squares) refer to D0 to D8 of control samples (all triplicates). Note: the OPLS-DA plot

eclipse represents the 95% confidence interval.


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Figure 8.21. PLS-DA derived loading scatter plot of P. chrysogenum flux over 8 days during winery biomass degradation. Note: Five pointed star labels denote the days of analysis (2H2O day 0 to D2O day 8 and AATCC day 0 to AATCC day 8). Green circles represent all the metabolites under consideration and their orientation.

The data obtained from SIMCA 13 required analyses for further discrimination

of the unique metabolites from a large group to more accurate levels. MetaboAnalyst

2.0 was, thus utilized to further differentiate and classify the most significant

metabolites in D2O and non-D2O based media. Metaboanalyst 2.0 employs a pre-

processing data filtering and normalization before its application to differential

expression analyses and two-group or multi-group analysis (Xia et al., 2012). The

filtered sample data was applied to univariate differential analyses methods of T-test,

analysis of variance (Vranova et al.). Based on volcano plot model generated from these

tests, the total number of differentially expressed metabolites in D2O was reduced to 94

(Figure 8.22; Appendix 3, Table 2).


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Figure 8.22. Volcano plot displays the differentially expressing metabolites in deuterated and non-

deuterated media of P. chrysogenum. Note: The significant metabolites expressed in deuterated

medium (yellow circles) were taken into consideration for further metabolic flux analyses.

0123456789

1011121314

-4 -2 0 2 4

-Log

10 (

P-va

lue)

Log2 (Fold Change)

Non-deuterated medium Deuterated medium

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Figure 8.23. Metabolic pathway of P. chrysogenum during SSF based degradation of winery biomass waste over 8 days. Note: Numbers in the table represent

identities of GCMS detected pathway metabolites.


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8.3.7. Metabolic flux analysis of P. chrysogenum during winery biomass

degradation

The identified metabolites were then crossed-checked against the KEGG

database of metabolic pathways (http://www.kegg.jp/kegg/pathway.html). The data

obtained was then applied to MATLAB 2014a statistical software using Covariance-

Inverse (COVAIN) script (Doerfler et al., 2014). The resultant data were applied as a

correlation between the different metabolites in a time series analysis. Glycolysis

leading to the tricarboxylic acid (TCA) cycle was considered as the basic skeletal

pathway with respect to any other upstream or downstream metabolic pathways. The

major junctions were marked at glucose, glycerate-3-phosphate, acetyl-coenzyme A

(acetyl-CoA) and fumarate, where an increase or decrease in other metabolites was

considered with respect to these metabolites. The pathways merging at glycolysis

junction point of glyceraldehyde-3-phosphate was mapped with respect to glycerate-3-

phosphate, the immediate next metabolite. Similarly, the pathways merging at pyruvate

junction were mapped with acetyl-CoA. Overall, including glycolysis-TCA, 23 distinct

(and 36 individual) pathways with respect to glycolysis and TCA were projected on the

basis of biosynthesis or degradation of metabolites during the 8 day fermentation

(Figure 8.23).

8.3.7.1. Glucose/ glucose-1-phosphate junction

Glucose is one of the most important molecules in any metabolic pathway. It

marks the initial metabolite of glycolysis and TCA cycle pathways; the pathways which

are required for energy production from storage molecules in all organisms. During the

experiments, initial glucose concentration on D0 was about 4.7 mg/L in the substrate.

This was expected since the winery biomass waste consists of very low amounts of this

sugar. Due to the fungal degradation, however, this concentration increased to about

85.1 mg/L on D5. This concentration gradually decreased to 1.3 mg/L on D7, indicating

the utilization of this sugar by the fungus. However, an increase to about 28.7 mg/L on

D8 suggested accumulation of glucose due to a probable onset of cellulase enzyme

inhibition from D7 onwards (Figure 8.24).

It was observed that metabolic pathways of 7 different sugars and sugar alcohols

linked directly to glucose. These consisted primarily of galactose, melibiose, L-sorbose,



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maltose and psicose. Contrary to the previous studies (Barbe et al., 2001), L-sorbose

was observed to be in higher concentrations in Shiraz grape biomass. Considerable

amount of this sugar was utilized by P. chrysogenum. The initial concentration of 632.6

mg/L detected on D0 rapidly dropped to 16.1 mg/L by D4. This was inversely

proportional to glucose concentration, indicating that a considerable amount of sorbose

was converted to glucose via a rapid sorbitol reduction process, as observed from a

constantly low sorbitol levels throughout the experimental period (Figure 8.24). The

probability of a rapid sorbitol conversion can be inferred from the previous observations

which indicated the presence of very low concentrations or complete absence of sorbitol

in grapes (Pilando and Wrolstad, 1992) in contrast to other fruit sources such as pear or

pineapple.

Figure 8.24. Metabolic variation of glucose and L-sorbose metabolism by P. chrysogenum during

winery biomass waste degradation of Shiraz grapes.

Melibiose, which was earlier reported as a trace sugar in grape berries (Kliewer,

1965), is a disaccharide made up of α (1-6) linkage between glucose and galactose. As

contrary to previous reports about the low concentration of melibiose(Bhat et al., 2010,

Kliewer, 1965), the current experimental observations showed the presence of about 80

mg/L of melibiose, which increased to about 101.3 mg/L from D0 to D5 and later

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dropped to 6.7 mg/L by D7, thus indicating a considerable metabolism, directly

proportional to glucose metabolism (Figure 8.25).

Figure 8.25. Metabolism of melibiose with respect to glucose by P. chrysogenum during winery

biomass waste degradation of Shiraz grapes over 8 days.

N-acetyl glucosamine (NAG) is one of the other major metabolite synthesized

during fungal growth on a substrate; it being an essential component of fungal cell wall.

It was observed that NAG content of P. chrysogenum degraded biomass initially

increased from 4.51 mg/L on D0 to 111.4 mg/L on D5. However, it then depreciated

rapidly to 39.9 mg/L by D8 (Figure 8.26). Although unexpected, this depletion in NAG

levels was not surprising due to a possible autophagy by P. chrysogenum. Autophagy is

not very uncommon in filamentous fungi, especially during the carbon depletion period

of their growth. It is utilized by these fungi to recycle the nutrient resources for their

survival. In case of P. chrysogenum, autolysis has been linked with proteolysis activity

of fungus after 3 days of substrate degradation.

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D-Glucose D-Galactose Melibose


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Figure 8.26. Metabolism of chitin biosynthesis intermediates with respect to glucose-1-phosphate by

P. chrysogenum during winery biomass waste degradation of Shiraz grapes over 8 days.

Proteases showed enhanced activities during earlier phase of Penicillium

metabolism. However, these activities dropped sharply after 5th day of incubation,

coinciding with ammonia release, thus marking the onset of autophagy (McIntyre et al.,

2000). Chitinases are the other group of enzymes responsible for the onset of autophagy

in P. chrysogenum. Although, the enzymes are produced by P. chrysogenum along

chitin synthase enzymes, their activity increases considerably towards the carbon

starvation period, i.e. from 4th day of incubation (Sámi et al., 2001). These enzyme

activities have shown to decrease the hypal concentration in the tested samples,

indicating P. chrysogenum autolysis, primarily after D5 of incubation. Our results were

found to be in agreement with these previously performed works and showed a

considerable depletion of NAG after D5. Similarly, N-acetyl mannosamine, one of the

intermediates of chitin biosynthesis, also displayed an overall depletion from 3.4 mg/L

to 0.25 mg/L during P. chrysogenum mediated biomass degradation.

Similar to A. niger, P. chrysogenum was also observed to generate melanin

products via tryptophan metabolism. One end product, melatonin and an intermediate,

3, 5-diiodo-L-tyrosine were detected in culture samples tested. Melanins are some of the

numerous polyketides produced by filamentous fungi. Their production is induced by a

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group of enzymes called as Polyketide synthases (PKS enzymes), which in turn, are

regulated by PKS genes. Although, the number of these genes vary according to

species, they occur in high concentrations (23 copies) in Penicillium spp. (Woo et al.,

2010). Although, melanins have been observed from disease causing fungi such as

Penicillium marneffei, as reported by Woo et al (2010), the pigments have also been

observed during penicillin production by P. chrysogenum due to de-repression of

melanin synthesis genes (Sigl et al., 2011). Current experiments showed that the

concentration of melatonin and 3, 5-diiodo-L-tyrosine increased during the lag phase of

fungal growth. Melatonin deposition increased from 6.1 mg/L on D0 to 9.8 mg/L on D3,

whereas, 3, 5-diiodo-L-tyrosine concentration increased from 2.1 mg/L to 5.7 mg/L

during the same duration. However, during the later phases, this concentration dropped

rapidly to 3.7 mg/L and 0.6 mg/L on D8 for melatonin and 3, 5-diiodo-L-tyrosine,

respectively (Figure 8.27). This drop in the melanin levels can be attributed to the late

autolysis observed in P. chrysogenum culture, as indicated by the decrease in chitin

biosynthesis intermediates (Figure 8.26).

Figure 8.27. Biosynthesis and metabolism of melanin pigments by P. chrysogenum during SSF

based winery biomass waste degradation.

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3,5-Diido-L-tyrosine 7-Phospho-2-dehydro-3-deoxy-D-arabinoheptanoate Melatonin


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8.3.7.2.Glycerate-3-phosphate junction

Glycerate-3-phosphate or 3-phosphogleceric acid is the downstream metabolite

of glyceraldehyde-3-phosphate in glycolysis pathway. During the experiments, it was

observed that at least 6 pathways converged at Glycerate-3-phosphate junction either

directly or via glyceraldehyde-3-phosphate (not detected as a statistically significant

metabolite during P. chrysogenum flux). These consisted of pentose phosphate

pathways, lecithin metabolism via serine biosynthesis, terpenoids metabolism, deoxy

sugar metabolism, glycerol biosynthesis and pectin metabolism. The molecule is

generated during glycolysis pathway by type I and type III D-Glycerate kinases in the

cell cytoplasm, as has been reported from Saccharomyces spp. (yeast) and Nostoc spp.

(bacteria) (Bartsch et al., 2008). In our experiments, owing to hemicellulose degradation

by P. chrysogenum, numerous monosaccharides such as D-ribulose, erythrose, xylose

accumulated during the earlier phase of degradation, especially, during first 3 days of

SSF. These accumulated underwent fungal mediated metabolism to generate glycerate-

3-P. Due to this, glycerate-3-P increased from an initial concentration of 160.9 mg/L to

252.5 mg/L during first 3 days (Figure 8.28). However, owing to biosynthesis of

downstream molecules such as serine and glycerol, this concentration depleted

considerably to 12.9 mg/L by D8.

Figure 8.28. Metabolism of glycerate-3-phosphate and sugars from pentose phosphate pathway by

P. chrysogenum during winery biomass waste degradation of Shiraz grapes over 8 days.

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Glycerate-3-P D-Arabitol D-Ribulose Sedoheptulose

D-Erythronate-4-P D-Erythrose Erythritol


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The only sugar showing a late accumulation during the fermentation was D-

ribulose. Initial concentration of this pentose sugar was 4.5 mg/L on D0, which

increased to 6 mg/L. However, this concentration increased to 40 mg/L by D8, possibly

showing the onset of either xylanase inhibition. However, it has been long known that

ribulose and ribulose-5-phosphate are two of the most important metabolites of pentose

phosphate pathway. It is known that CO2 is assimilated in the metabolic pathways,

especially in TCA or Calvin cycle via ribulose and ribulose-5-phosphate. This CO2

fixation or acquisition is catalysed by ribulose biphosphate carboxylase (RuBisCO).

This pathway is very common in plants, algae and photosynthetic bacteria.

Additionally, RuBisCO-like enzymes catalyse a reverse TCA pathway, causing the

generation of ribulose and ribulose-1-phosphate in sulphur bacteria such as Chlorobium

limicola and Thiocapsa roseopersicina (van der Meer et al., 1998). However, in non-

photosynthetic organisms such as fungi, ribulose is one of the intermediates of reduction

of pentose sugar alcohols such as arabitol (Guo et al., 2006) or from pentose acid

reduction of molecules such as gluconates or idonates, as observed in our studies

(Figure 8.29). In an inversely proportional relation to ribulose, concentration of these

acids depleted from about 6.2 mg/L on D6 to about 3.4 mg/L on D8.

Figure 8.29. Metabolism of pentose acids by P. chrysogenum during winery biomass waste

degradation of Shiraz grapes over 8 days.

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Glycerate-3-P 2-Keto-D-gluconate 2-Dihydro-L-idonate


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One of the interesting features observed during the metabolic profiling of P.

chrysogenum during the SSF of winery biomass waste was the accumulation of pentose

alcohols such as arabitol and xylitol. As mentioned above, arabitol was more or less

reduced to ribulose, thereafter merging in to glycolysis pathway at glycerate-3-

phosphate junction. Due to this, its accumulation in culture sample remained in a range

of 18-19 mg/L during the degradation period. Xylose is one of the major hemicellulosic

residues. However, a large number of wild type fungi, including yeasts are unable to

utilize this sugar. During this experiment, it was observed that xylose accumulated in

the early phase of P. chrysogenum mediated degradation due to xylanase activities for

hemicellulose degradation. During this period, xylose concentration increased more

than two-folds from 15.1 mg/L on D0 to 37.7 mg/L on D3. However, this concentration

depleted to 1.4 mg/L by D8, displaying xylose metabolism either towards glycolysis

cycle via xylulose-5-phosphate or xylitol biosynthesis. During the experiments, it was

also noticed that during xylose depletion period, considerable amounts of xylitol

accumulated in the media. Xylitol was observed to accumulate during later phase of

fermentation and its concentration (2.6 mg/L, D0) increased to 24 mg/L by D8 (Figure

8.30).

Figure 8.30. Xylitol production by P. chrysogenum during xylose metabolism by winery biomass

waste degradation of Shiraz grapes over 8 days.

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Glycerate-3-P D-Xylono lactone D-Xylonate Xylitol D-Xylose


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P. chrysogenum therefore, was observed to possess the property of xylose

conversion. This property can be applied to co-generate biofuel molecules such as

ethanol while biodegradation of lignocellulose components. Studies utilizing S.

cerevisiae (Lee et al., 2003) and Candida maltosa (Guo et al., 2006) based D-

xylulokinase catalysing activities to generate ethanol from xylose have been reported.

One of the other peculiar results observed was the propanol biosynthesis via glycerol

metabolism pathway. Although, not generated in high concentration as xylitol, the SSF

process utilizing P. chrysogenum showed the potential to generate greater amounts of

propanol, one of the other important molecules which can be used as a biofuel molecule.

By D6, about 6.2 mg/L propanol was generated. However, probably due to the

stationary growth phase of this fungus, this metabolite was reutilized as an energy

source by Penicillium, thereby; ultimately depleting the propanol levels to 0.6 mg/L.

similarly, Sn-glycerol-3-phosphate was produced during the earlier growth phase and

reached its peak value of 18 mg/L at D6, followed by a depletion t 1.1 mg/L by D8

(Figure 8.31).

Figure 8.31. Propanol production by P. chrysogenum during metabolism by winery biomass waste


From the obtained results, it can be deduced that P. chrysogenum has the ability

to generate propanol during the lag-phase of its growth. However, the experiments also

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highlighted the necessity for extraction of propanol from the medium before the onset of

stationary phase. The study also indicates a necessity to further improve the Penicillium

mediated biomass degradation to generate higher quantities of industrially useful

alcohols such as propanol and glycerol.

P. chrysogenum was observed to possess pectin degradation as well, a feature

observed less prominently during A. niger metabolic flux. The concentration of

glucuronate (Figure 8.11) was more or less stationary during A. niger mediated biomass

degradation. Additionally, threonate and galacturonate degradation was almost absent.

However, P. chrysogenum was found to degrade pectin substrate of biomass via inositol

metabolism pathway. Galacturonate concentration dropped from 79.6 mg/L on D3 to 3.1

mg/L on D8. Additionally, glucuronate concentration depleted from 4.3 mg/L on D0 to

0.2 mg/L on D8 (Figure 8.32).

Figure 8.32. Pectin degradation by P. chrysogenum during metabolism by winery biomass waste


8.3.7.3.Acetyl CoA junction

Acetyl-CoA is arguably the most distinct molecule in metabolic pathway as it

forms a connecting linkage between glycolysis and TCA cycle. Due to its presence at

this critical position, large number of pathways either mere in or merge out at Acetyl-

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CoA junction. In the current experiments, about 7 pathways merged at this junction

directly and 3 pathways merged (indirectly) via pyruvate. These pathways consisted of

metabolisms of isoprenoids, fatty acids such as Hexadecanoate, dodecanoate,

octadecanoate, linoleate and docosanoate; secondary metabolites and amino acids such

as leucine, isoleucine, lysine, cysteine, methionine and lysine.

Unlike glycerate-3-phosphate, overall concentration of acetyl-CoA remained

unexpectedly low during the entire SSF period. This was in contrast to the levels

observed during A. niger metabolism, where, these levels were higher than 100 mg/L

during early phase of metabolism (Section 8.3.3.3). However, one of the reasons for

this might be the biosynthesis of amino acids, fatty acids and isoprenoids, as explained

in this section.

One of the major biosynthesis seen from acetyl-CoA junction was isoprenoid

biosynthesis, resulting in the formation of α-tocopherol via mevalonate pathway.

Tocopherols are the large groups of isoprenoids generally produced by the plants as

their defence molecules in response to fungal infection. González-Candelas et al (2010)

during their experiments reported the presence of multiple ESTs responsible for

secondary metabolite production in citrus plants via 7-phospho-2-deoxy-3-D-arabino

heptanoate (DAHP). This molecule was also observed during the current experiments as

an intermediate metabolite during melanin pigment synthesis (Figure 8.27). Another

molecule acting as α-tocopherol biosynthesis intermediate observed during the

metabolic profiling was 4-(Cystediene-5diphospho)-2-C-methyl-D-erythritol. It has

been reported in numerous researches and had been reviewed that this molecule belongs

to 4-phospho-2-C-methyl erythritol (MEP) pathway and is one of the most widely used

precursor for α-tocopherol biosynthesis in plant plastids and prokaryotes. However, in

fungi and other eukaryotes, this isoprenoid is biosynthesized from acetyl-CoA as its

starter metabolite. Isopentyl pyrophosphate (IPP) acts its active isoprene precursor in

mevalonate pathway, which combines with dimethylallyl pyrophosphate (DMAPP) to

form geranyl pyrophosphate (GPP). GPP serves as the immediate precursor to

carotenoids, tocopherols and numerous other fat soluble vitamins (Demain, 2014).

During the experiments, it was observed that although, α-tocopherol was found

at levels less than 1 mg/L, its precursors i.e., mevalonate and MEP were present at

higher concentrations.


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Figure 8.33. Isoprenoid biosynthesis by P. chrysogenum during metabolism by winery biomass

waste degradation of Shiraz grapes over 8 days. α-tocopherol has been scaled on the secondary axis.

At D0, the culture sample consisted of 73 mg/L and 2.2 mg/L of these

metabolites, respectively. This indicated a significant presence of some isoprenoids in

the grape biomass before the onset of its degradation. However, the fungus probably

synthesized α-tocopherol chiefly via mevalonate pathway, and surprisingly, to a minor

extent by MEP pathway, which is not a common way of isoprenoid synthesis in fungi.

Mevalonate levels increased to 109.8 mg/L by D3, which was directly proportional to a

considerable increase in α-tocopherol levels from 0.003 mg/L to 0.03 mg/L during the

same period. After D3, the drop in mevalonate levels to 67.4 mg/L by D7 resulted in

depletion of α-tocopherol to 0.01 mg/L. However, this Tocopherol concentration

increased to 0.03 mg/ L on D8, probably as a result of increasing MEP levels from 2.2

mg/L (D0) to 5.7 mg/L (D7) (Figure 8.33). Other isoprenoids were not detected in

significant levels by GCMS analysis. An application of LCMS based approach is

expected to detect these metabolites to resolve these deficiencies.

Tannins and lignins are some of the major constituents of grape biomass and can

comprise up to 35-40% of dry weight of this biomass (Bravo and Saura-Calixto, 1998).

It has been demonstrated that P. chrysogenum has the ability to degrade minor amounts

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of low molecular weight lignin components such as vanilates and ferrulates (Rodríguez

et al., 1994, Rodriguez et al., 1996). Additionally, Penicillium spp. have been also

known to possess tannin degrading enzymes such as tannin acyl hydrolase, which

catalyses the hydrolyzation of tannic acid (Sharma and Saxena, 2012). However, this

property has not been widely reported in P. chrysogenum.

During SSF based degradation of Shiraz grape biomass, P. chrysogenum was

observed to free up and then degrade syrinagate or syringic acid component of grape

tannins via benzoate degradation pathway. Initial syringate content of grape biomass

was recorded at 1.03 ± 0.01 mg/L (D0). Considering that the majority of syringate ends

up in grape juice used for wine production, the obtained results we in line with previous

observations that had reported Shiraz grape to contain up to 3 mg/L of syringate

(Fanzone et al., 2012). This metabolite was observed to accumulate to about 3.2 mg/L

during early phase (D3) of P. chrysogenum mediated degradation. However, this

concentration depleted to 0.6 mg/L by D8 (Figure 8.34).

Lignin degradation has been previously observed in P. chrysogenum cultures, as

mentioned above. During this experiment, small molecular weight lignins, especially

polypropanoids such as vanillates, caffetaes and ferrulates were possibly degraded, as

indicated by the presence of 4-hydroxy benzoate. At D0, this metabolite was

accumulated at the level of 6.1 mg/L. An increase in its concentration (15.4 mg/L) by

D3 indicated that the lignin degradation started during the early phases of P.

chrysogenum growth. This was further confirmed by a heavy accumulation of 4-amino

benzoate, immediate degradation product of 4-hydroxy benzoate. The level of 4-amino

benzoate increased rapidly from 4.4 mg/L on D0 to 34 mg/L on D3. During the later

phases, concentration of benzoates dropped (14 mg/L and 2.5 mg/L of 4-amino

benzoate and 4-hydroxy benzoate, respectively, by D8) due to their degradation via

benzoate pathway (Figure 8.34).


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Figure 8.34. Lignin and tannin degradation via benzoate degradation pathway by P. chrysogenum

during metabolism by winery biomass waste degradation of Shiraz grapes over 8 days

During the SSF based degradation, P. chrysogenum also displayed the

production of penicillin, especially during the log phase of its growth. It was observed

that lysine degradation pathway was utilized for penicillin production as evidenced from

the presence of considerable amounts of glutarate and L-pipecolate. L-pipecolate or 2-

piperidine carboxylic acid is an intermediate of lysine synthesis. However, in P.

chrysogenum, it also acts as the precursor to 2-aminoadipic acid, an important starter

metabolite for penicillin biosynthesis. L-pipecolate dehydrogenase catalyses L-

pipecolate first, to L-2aminoadipate-6-semialdehyde, followed by L-2-aminoadipate. L-

2-aminoadipate then enters penicillin biosynthesis pathway by N-(5-amino-5-

carboxypentanoyl)-L-cysteinyl-D-valine synthase to form isopenicillin N, which is the

precursor to all forms of penicillins (Vranova et al., 2013).

The lag phase of P. chrysogenum growth displayed slow lysine degradation, as

evident from lowered accumulation of glutarate. During first 3 days, glutarate levels

slightly dropped from 4.8 mg/L to 3.8 mg/L. This possibly was due to diversion of

lysine towards penicillin production, as L-pipecolate, the intermediate metabolite in

penicillin production started accumulating during this period. The original concentration

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(D0) of L-pipecolate increased from 4.1 mg/L to 7.7 mg/L by D3. During the later

period, the concentrations of L-pipecolate dropped, especially after D7, most likely due

to autolysis of P. chrysogenum caused by nutrient starvation (Figure 8.35).

Figure 8.35. Penicillin production via lysine degradation pathway by P. chrysogenum during metabolism by winery biomass waste degradation of Shiraz grapes over 8 days.

One of the most distinct pathways leaving from acetyl-CoA was fatty acid

biosynthesis. Unlike A. niger, fatty acids were not produced in higher quantities by P.

chrysogenum over the entire biomass degradation period. However, 5 fatty acids

(hexadecanoate, octadecanoate, 9Z-octadecanoate, docosanoate and linoleate) were

significantly produced against just 2 (hexadecanoate and dodecanoate) observed during

A. niger metabolism. Similar to A. niger metabolic pattern, fatty acids accumulated over

the entire period of SSF. Especially, hexadecanoate concentration increased from 1.8

mg/L to 14.5 mg/L over 8 days (Figure 8.36). It is normally observed during a

microbial degradation that after a period of 3-4 days post-fermentation, the MCFAs

drop the internal cell pH of fungi, thereby inhibiting further growth and resultant

fermentation (Edwards et al., 1990, Legras et al., 2010). However, due to the low

accumulation of all fatty acids, the enzyme and growth inhibition was delayed to about

6 days in P. chrysogenum as compared to 4 days in A. niger.

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Figure 8.36. Fatty acid biosynthesis by P. chrysogenum during metabolism by winery biomass waste degradation of Shiraz grapes over 8 days.

8.4. Conclusions

A targeted metabolomic approach was used to understand the metabolic flux

during winery waste biomass degradation by A. niger and P. chrysogenum. Deuterium

oxide was used as metabolite tagging molecule in an 8 day experiment. Successive

applications of multivariate statistics and covariance inverse statistics to GC-MS data

were used to characterise the metabolic output of winery waste biomass degradation. It

was observed that 37 unique metabolites related to 18 different pathways were chiefly

involved during the A. niger metabolic process, while 94 unique metabolites related to

23 distinct pathways were involved in P. chrysogenum metabolism.

It was found that product inhibition started at the fifth day of fermentation under

solid state fermentation conditions in A. niger. On the other hand, inhibition started after

D6 in P. chrysogenum mediated biomass degradation. However, one of the peculiar

observations noticed with P. chrysogenum was the onset of autophagy or autolysis

during the later period of incubation (D7-D8).

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The addition of external enzyme complexes after the fourth day may result in the

generation of bio-fuel molecules such as 1-butanol and 2-butanol by altering the amino

acid biosynthesis pathways. Additionally, an initial mixed fungal treatment and

continuous fermentation with selective yeasts after day 5 will not only prevent the

product inhibition, but will also considerably increase the rate of biomass degradation.

P. chrysogenum, on other hand displayed the production of numerous commercially

important metabolites such as propanol and xylitol. Additionally, it also displayed the

degradation of lignin and tannin degradation, which makes it one of the important

contributors for developing the process for an efficient conversion of biomass to

biofuels and other commercially important metabolites. However, due to feedback

inhibition and individual enzymatic limitations, both of these fungi have not proven to

be highly effective. However, it has been observed that in a symbiotic consortium

followed by simple pre-treatments, they prove to be highly effective in degrading

biomass, causing up to about 18% lignin degradation (see Section 6.3.6), which

otherwise is less than 6% in case of single culture of P. chrysogenum .

8.5. Summary

Abovementioned chapter describes the assessment of metabolic pathways in

biomass degrading fungi, A. niger and P. chrysogenum. These fungi were selected due

to their overall better cellulolytic enzyme activities. Additionally, P. chrysogenum was

selected based on its lignin degrading ability observed during the previous experiments

under submerged and SSF conditions.

The pathways were built upon the 2H2O based metabolic flux of those fungi

over a period of 8 days during SSF based degradation of winery biomass waste. GC-MS

approach was used for data generation. These data were then applied to quality control

and were filtered by multivariate statistical approaches such as PCA, PLS-DA, t-tests

and one way ANOVA. Unique metabolites, based on 2H2O labelling were then obtained

for further analysis. 37 metabolites were obtained in A. niger degraded samples,

whereas, 94 unique metabolites were obtained in P. chrysogenum degraded samples.

These metabolites were then applied to Covariance-Inverse (COVAIN) statistical

toolbox was applied in MATLAB© toolbox to generate metabolic behavioural pattern

over the entire period of biomass degradation.


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It was observed that both the fungi degraded more efficiently during their early

growth phase. After this, both product and competitive inhibitions began, causing

decrease in the efficiency of biomass degradation. For A. niger, growth stationary phase

and inhibitions started from 5th day of fermentation onset. P. chrysogenum culture, on

the other hand displayed the decrease in efficiency after 6th day of SSF. After this time,

degradation ability of P. chrysogenum not only decreased, but it also displayed

autolysis, due to nutrient starvation.

During the SSF, A. niger displayed a greater degradation of cellulosic and

hemicellulosic residues, as evident from high accumulation of free hexoses and

pentoses. However, this fungus was also prone to an early inhibition, most likely due to

high amounts of fatty acid deposition. Although, P. chrysogenum was not effective as A.

niger, especially during hemicellulose degradation, it was able to degrade tannins and

lignins to some extent, a feature absent in Aspergillus spp. Also, its culture accumulated

considerably low amounts of fatty acids, thereby showing a late inhibition as compared

to A. niger.

From the metabolic flux analysis, it could be suggested that these fungi, if kept

under a continuous batch fermentation of about 5 days, will produce greater biomass

degradation than single batch fermentation. Additionally, eliminating the leucine,

isoleucine and valine biosynthesis pathways by adding external enzyme complexes such

as 2-keta acid decarboxylases is likely to reroute the metabolic pathway towards butanol

isomers biosynthesis, which have the utility as biofuel molecules. Similarly, minor

amounts of propanol and xylitol were observed in P. chrysogenum. Improving the P.

chrysogenum SSF conditions by preventing autolysis is expected to generate ethanol,

which is also considered as an important biofuel molecule.

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CHAPTER 9

General Discussions and Conclusions

Chapter 9 General Discussions and Conclusions

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9.1. General Discussions

Grapes are one of the major global horticultural crops, with a production of

about 70 million tonnes in 2012-13. Majority of these are classified as wine grapes,

Australia is 6th largest global wine grape producer. However, such a massive production

also generates considerable pre- and post-process wastes. It is estimated that about half

of grape biomass ends up as waste. Also, wineries generate massive amounts of

wastewater during wine production. Owing to its poor digestibility and minor phenolic

toxicity, the winery waste has a very limited use as either animal feed or compost

fertilizer. End result, it ends up as a landfill waste. Therefore, novel techniques for its

bioconversion are necessary.

Lignocellulose complex in biomass is made up of three major components of

cellulose, hemicelluloses and lignins. Apart from there, there are glycoproteins, lipids,

free amino acids and other metabolites, which occur in the interaction with the major

components. Among those, lignins form the outermost structure of lignocelluloses and

are the most complex molecules of all the three. Due to the varied nature of lignins and

hemicelluloses, there is no single organism that can degrade all the components. Other

major reason impeding the biomass degradation is interaction of majority of enzymes

like cellulases and hemicellulases with their respective substrates. A number of products

generated by biomass degradation themselves cause product inhibitions of

lignocellulolytic enzymes.

The experiments described herein explore the possibilities of developing mixed

fungal consortium to achieve enhanced grape biomass degradation. The degradation

processes utilized cultures of ascomycota fungi such as Trichoderma harzianum,

Aspergillus niger, Penicillium chrysogenum and Penicillium citrinum, and

basidiomycota fungus Ph. chrysosporium for degradation purposes. The conventional

and popular submerged fermentation and the emerging Solid State Fermentation (SSF)

methods were tested. Statistical analysis was performed to generate an optimised mixed

fungal “cocktail” and achieve a bioreactor-based degradation process with increased

biomass degradation. Additionally, co-culturing was experimented with Ph.

chrysosporium and statistically optimized ascomycete fungal mixture. Metabolomics

was applied in classifying and characterizing important metabolites during the

biodegradation process. It was used to pin point the critical points of fungal


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biodegradation process, which can be altered to improve the required bioconversion

process.

9.1.1. Hydrothermal pre-treatment and Submerged fermentation

Routinely used autoclaving method was used as a pre-treatment technique on

grape biomass waste. According to numerous literature sources, hydrothermal pre-

treatment processes such as hot-compressed water (HCW), steam explosion (SE),

ammonia fibre explosion (AFEX) and supercritical water (SW) treatments degrade the

biomass contents, especially celluloses and hemicelluloses to greater degrees. These

methods also have been observed to degrade lignin to considerable levels (Ando et al.,

2000, Goto et al., 2004, Papadimitriou, 2010). However, these techniques have been

known to solubilize considerable levels of lignin composition to free phenolics. These

resultant metabolites not only inhibit the overall lignocellulose degrading enzyme

activities, but also have adverse effects on fungal growth. Additionally, other

metabolites such as furfurals are generated in considerable amounts due to hydrothermal

treatments (Duarte et al., 2012b). However, the GC-MS analysis of grape biomass

before and after the process of autoclaving indicated an absence of considerable levels

of phenolics. Additionally, it was observed that although autoclaving was able to

hydrolyse hemicelluloses and cellulose to considerable extents, this method did not

generate any furfural molecules, possibly due a lower molal ionic product of water as

compared to HCW or SE processes.

Autoclaving resulted in 18% mass loss of biomass, thus indicating its

considerable effects. The biochemical analysis showed a considerable release of

pentoses and other free sugars due to this process. During the initial phase of growth,

presence of free sugars possibly enhanced an overall fungal growth, thus enabling fungi

to increase overall biomass degradation rate in less amount of time with respect to a

non-treated biomass substrate. Enzyme activities especially that of β-glucosidase

improved in many cultures, indicating a considerable hydrolyzation of cellobiose and

other disaccharides forming functional units of cellulose and hemicelluloses. The

hydrothermal treatment was also observed to solubilize some linkages in lignins.

Resultantly, in the presence of free sugars, fungi such as P. chrysogenum were able to

degrade about 9% lignin, a majority of which were possibly smaller chained lignins.


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9.1.2. Solid state fermentation

The SSF was performed to study the comparative fungal degradation of winery

grape wastes to achieve biodegradation of its lignocellulose components. One of the

primary reasons for using this strategy was to conserve the amount of water used in the

process and increase the amount of substrate being degraded during the process. The

ratio between substrate fungal growth media was kept at 1:1, which meant that the

substrate concentration increased from 3% in submerged fermentation to 50% in

SSF.The degradation patterns of various fungi such as T. harzianum, A. niger, P.

chrysogenum and P. citrinum were compared over 2 weeks.

Cellulase activities were moderately or considerably higher in SSF conditions

with respect to pre-treated submerged fermented biomass. Similarly, xylanase activity

was significantly higher in SSF conditions than the SmF. However, β-glucosidase

activities were either comparable or were higher under SmF conditions than SSF. The

cellulase activities increased considerably in some fungal cultures, while it remained

stationary or decreased in others. This indicated that long term fermentation did not

necessarily improve the biodegradation. Xylanase activities decreased significantly after

week 2 with respect to week 1. The only improvement was in β-glucosidase activity,

where it was considerable in some and marginal in other cultures after 2 weeks of SSF.

The lignin degradation in SSF conditions was considerably lower at 2.2 % as compared

to 9% in pre-treated SmF conditions. For primary enzymes which degrade macro

molecules such as cellulose and xylans, SSF proved to be a better candidate; it still

resulted in the inhibition of those enzymes due to lower activities of secondary enzymes

such as β-glucosidases, which further hydrolyze the products of cellulose hydrolysis

which created product inhibition of those primary enzymes. The longer duration of

week 2 improvements was considered as a less economically viable trait. Due to

abovementioned shortcomings of SmF and SSF processes, a necessity of a new working

model was felt to improve overall degradation efficiency.

9.1.3. Statistical optimization of fungal biomass degradation

The conventional and popular submerged fermentation and the emerging Solid

State Fermentation (SSF) methods were observed to have their advantages at some


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aspects. However, each of the processes suffered from some limitations, which on

commercial scale would accumulate to larger scales.

To balance the overall enzyme activities combined with higher lignin

degradation, with minimizing competitive and product inhibitions, a statistical approach

was applied. The enzyme activities from SmF and SSF processes were applied to a

combination coefficient correlation and ANOVA to generate a predicted method with

higher biomass degradation efficiency.

The statistical approach yielded a combination of all ascomycota fungi in

variable percent ratio. The fungal mixture of A. niger: P. chrysogenum: T. harzianum:

P. citrinum in a percent ratio of 60:14:4:2 was made and applied for biomass

degradation. Additionally, the pre-treatment method of autoclaving was provided to

biomass before fungal inoculation. Substrate to medium ratio was suggested to be held

at 0.39 to improve biodegradation. It was observed that the enzyme activities of

cellulases, glucosidase and xylanases increased considerably. Especially, the xylanase

activities increased more than 2-folds. Also the total lignin degradation during the

biodegradation process was 17.9 % in a shorter duration of 5 days, as compared to 7

days in SSF. This method indicated the positive synergistic approach of numerous fungi

under the same conditions. This fungal behaviour was observed to not only compensate

each other’s limitations, but also to produce a better biomass conversion than the

individual processes. The metabolic profile indicated a generation of several hexoses,

pentoses and fatty acids along important medicinal metabolites such as gallic acid.

9.1.4. Biomass degradation by co-culture of basidiomycete and ascomycete

The next experiments involved the study of biomass degradation achieved by the

simultaneous mixture of a white-rot fungus, Phanerochaete chrysosporium and

statistically optimized mix of ascomycetes. It was hypothesized that addition of wood-

rotting basidiomycetes would produce better lignin degradation as compared to a mixed

ascomycete culture. This treatment, followed after the pre-treatment method application

of autoclaving or ‘hydrothermal pre-treatment’ was predicted to considerably improve

an overall biomass degradation and conversion.


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The grape samples were pre-treated by autoclaving in a bioreactor before

applying to fungal degradation. The ratio between substrate and medium was

maintained at 0.39:1, as determined by the statistical modelling. Ph. chrysosporium

degradation was allowed for 8 days and was followed by a mixture of A. niger: P.

chrysogenum: T. harzianum: P. citrinum in a percentage ratio of 60:14:4:2 for further 8

days. It was observed that over 16 days, due to the nature of fungal mix, cellulase

activities did not suffer product inhibition and displayed considerable activities. Similar

observations were noted regarding β-glucosidase and xylanase activity, especially the

latter’s activity increased about 2-folds with respect to that observed in ascomycota

fungal mixture. In a peculiar observation, considerable laccase activities were observed

during early phase of degradation, which was surprising since Ph. chrysosporium is

generally unable to express laccase activities on numerous substrates. However, it is

predicted that due to moderately high nitrogen content of grape biomass and added

nitrogen supplementation resulted in elevated laccase expression during degradation

process. Good activities of lignin peroxidase were also observed. Metabolic profiling

displayed considerable production of lignin degradation products such as suberic acid,

1, 3 benzenedicarboxylate and cadaverine, which were further degraded by fungal cells

in the course of fermentation. Similarly, considerable amounts of commercially

important metabolites such as sugar alcohols, sugar acids and fatty acids were

generated.

9.1.5. Fungal metabolic flux analysis to improve biomass degradation

Among numerous areas, metabolomics has been applied to investigate bacterial

processes related to preventative health, environmental pollution and food research.

However, within the context of fungal-mediated biomass degradation, its application

has been rather limited. The process assists in understanding of correlation between cell

phenotypes, metabolic patterns and stoichiometry. It also enables better understanding

of the nature, time dependence and substrate-based limitations of fungal metabolism.

Metabolic flux during the degradation of winery-derived biomass waste by A.

niger and P. chrysogenum was studied. The study utilized 2H-flux due to its rapid

incorporation in fungal metabolism as compared to 13C. The flux experiment was

designed to differentiate the metabolites derived from the substrate and those generated


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by the fungi. This process also aided in differentiating and quantifying significant

metabolites generated by fungi at different intervals, thereby, optimizing a particular

process in order to maximize the generation of specific products or generate numerous

products in significant quantities in a single bioconversion process setup.

Over an 8 day period, metabolic flux profile of both fungi indicated considerable

biomass degrading ability, as evidenced from considerable accumulation of free sugars.

GC-MS based 2H2O-flux profile of A. niger showed about 17 active pathways merging

in and out of glycolysis and TCA cycle pathways. It was observed that the fungal

enzymes encountered product inhibition from 5 days of initiation, thus, indicating a

necessity of continuous batch degradations of lower intervals to generate better

degradation. Considerable amounts of amino acid biosynthesis were observed. It is

proposed that a pathway alteration by the addition of keto acid carboxylases will reroute

these pathways from amino acid synthesis to butanol isomers, which are widely used as

fuel molecules.

P. chrysogenum metabolic flux displayed about 36 active pathways merging in

and out of glycolysis and TCA cycle pathways. Similar to A. niger, this fungus also

encountered enzyme inhibition from day 5 onwards during biomass degradation.

Additionally, this fungus was observed to be prone to autolysis in a nutrient drought

conditions. This indicated a necessity of lower duration continuous fermentation,

similar to A. niger. Also observed was moderate generation of xylitol, an intermediate

of ethanol production with a property of pentose sugar oxidation, a property absent in

other ascomycetes under study. Moderate lignin and tannin degradation was also

observed during early phase of biomass degradation, as evidenced from the presence of

their degradation intermediates. It is proposed that P. chrysogenum forms a good

candidate for development of fungal consortium; especially to minimize pentose based

enzyme inhibitions and its ability to convert them in to sugar alcohols leading to ethanol

production.

9.2. Close and Future Aspects

The process elaborated in this document was focussed towards developing a

system of multiple fungi to improve the winery biomass waste conversion. The project

dealt with applying the simple yet effective and economically viable methods in the


231

process of developing a waste management system for winery grape biomass, which

remains a considerable environmental hazard due to its massive production in Australia.

The project successfully demonstrated that cost effective techniques such as

autoclaving, followed by a multiple fungal consortium was not only able to improve the

degradation of winery biomass waste, but also minimised the inhibition of degrading

enzymes. Additionally, the process was able to degrade considerable lignin composition

with respect to monoculture micro-organisms and numerous standalone physio-

chemical techniques. Considerable sugar alcohols, which form the intermediates of

biofuels such as ethanol and butanol, were generated during multi-fungal fermentation.

However, a considerable development is still required to develop more complex

consortia of biomass degrading fungi and thermophilic bacteria in order to improve

bioconversion efficiency.

The project also showed the need of increasing the application of metabolomics

in biomass degradation process. Metabolic flux profiling in A. niger and P.

chrysogenum was able to provide the critical points in their biomass degradation

pathways. A further work on those critical points or critical pathways in collaboration

with other ‘omic’ approaches such as proteomics, transcriptomics and genomics is

expected not only to improve the biodegradation efficiency, but also the production of

metabolites of commercial/ industrial interests. The preliminary work performed on

these ascomycetes can be easily applied on other fungi of commercial interests to

enhance their output efficiencies.

232

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264

Appendices

265

Appendix 1

Calibration curves of biochemical tests and enzyme assays

Figure A1. Standard curve for reducing sugars by DNSA assay @ 540 nm.

Figure A2. Standard curve for total sugars by Mollisch’s assay @ 400 nm.

y = 0.0586x + 0.0026

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5

Abs

orba

nce

(Au)

@ 5

40 n

m

Glucose Concentration (mg/mL)

y = 0.3098x + 0.0017

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

0 1 2 3 4 5 6

Abs

orba

nce

(Au)

@ 4

90 n

m

Glucose concentration (mg/mL)

266

Figure A3. Standard curve for pentose sugars by Bial’s assay @ 490 nm.

Figure A4. Standard curve for total proteins by Biuret assay @ 546 nm.

y = 1.7671x - 0.0022

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

0 0.02 0.04 0.06 0.08 0.1 0.12

Abs

orba

nce

(490

nm

)

Arabinose concentration (mg/mL)

y = 0.0523x - 0.0022

0

0.05

0.1

0.15

0.2

0.25

0.3

0 1 2 3 4 5 6

Abs

orba

nce

@ 5

46 n

m

BSA concentration (mg/mL)

267

Figure A4. Standard curve to determine β-glucosidase activity by release of p-

Nitrophenol from pNPG @ 400 nm.

y = 0.0301x + 0.0064

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0 1 2 3 4 5 6

Abs

orba

nce

@ 4

00 n

m

p-Nitrophenol concentration (mg/mL)

268

Appendix 2

Sequence information of isolated and characterized bacteria and fungi during the

project

Locus: KC161226 Length: 690 bp DNA: linear

Definition: Uncultured Brevundimonas spp. clone viniA711 16S ribosomal RNA gene,

partial sequence.

Origin:

gttcggaata gctcctggaa actggtggta atgccgaatg tgcccttcgg

gggaaagatt tatcgccttt agagcggacc gcgtctgatt agctatttgg

tgaggtaatg gctcaccaag gcgacgatca gtagctggtc tgacaggatg

accatccaca ttgggactga gacacggacc aaactcctac gggaggcaac

actggggaat cttgcgcaat gggcgaaagc ctgacgcttc catgccgggt

gaatgatgaa ggtcttagga ttgtaaaatt ctttcaccgg ggacgataat

gacggtaccc ggagaagaag ccccggctaa cttcgtgcca ccagccgcgg

taatacgaag ggggctagcg ttgctcggaa ttactgggcg taaagggcgc

gtaggcggac atttaagtca ggggtgaaat cccagagctc aactctggaa

ctgcctttga tactgggtgt cttgagtgtg atacaggtat gtggaactcc

gactgtatag gtgaaattcg tacatattca gaagaaaacc agtggcaaat

gcgacatact ggctcattac tgacgctgag gcgcgaaatc ctgaggagca

aacaggatta cataccctgg tagtccactc cgtaaacaat gattgctagt

tgtcgggctg catgcacctc ggtgacgcac ctaacacatt

Locus: KF316951 Length: 254 bp DNA: linear

Definition: Trichoderma harzianum isolate Viniti 18S ribosomal RNA gene, partial

sequence; internal transcribed spacer 1, 5.8S ribosomal RNA gene, and internal

transcribed spacer 2, complete sequence; and 28S ribosomal RNA gene, partial

sequence.

Origin:

269

atgcctgtcc gaacgtcctt tcaaccctcg aaaccctccg gggggtcggc

gttggggaac ggccctgcct ctggcggtgg ccgtctccca aatacagtgg

cggtctcgcc ccaacctctc ctgcgcaata atttgcacac tcccatcggg

agcgcggcgc gtccacagcc gttaaacacc caacttctga aatgttgacc

tcggatcagg taggaatacc cgctgaactt aagcatatca ataagcggaa gaaa


Definition: Candida homilentoma isolate swin 18S ribosomal RNA gene, partial



sequence.

Origin:

tgagtattct ataacttaac acacaattaa ttagtcaaca caaacataac

ctcaaaactt tcaacaacgg atctcttggt tctcgcatcg atgaagaacg

cagcgaaatg cgataagtaa tatgaattgc agattgtgaa tcatcgaatc

tttgaacgca cattgcaccg tgtggcattc cacacggtat gcctgtttga

gcgtggtttc tctctcagcc cgcgttcctt tttttaagga gcttgggctg

gcggtgagcg gcacacgagt gtttgcttga aagctagtac gactgatagt

acactttaca caaactcccc ctcaaatcag gtaggactac ccgctgaact

taagcatatc aataagcgga gga


Definition: Fusarium spp. VinV4 internal transcribed spacer 1, partial sequence; 5.8S

ribosomal RNA gene and internal transcribed spacer 2, complete sequence; and 28S

ribosomal RNA gene, partial sequence.

Origin:

ctttcaacaa cggatctctt ggctctggca tctatgaaaa acgcaccaaa

atgtgataaa taatgtgaat tgcttaattc agtgaatcat caaatctttg

aacgcacatt gcgcccgcca ttattctggc gggcatgcct gttcaagcgt

270

cattacaacc ctcacgcccc cgggcctggc gttggggacc ggtcgaaccg

ccttttgacc cccccaaccc ccccgggggg tcggcgttg


Definition: Nectria haematococca isolate PVK 18S ribosomal RNA gene, partial



sequence.

Origin

tgggtaaggg taataactca tcaccctgtg acatacctaa acgttgcttc

ggcgggaata gacggccccg taaaacgggc cgccgccgcc agaggaccct

taactctgtt tctataatgt ttcttctgag taaaacaagc aaataaatta

aaactttcta caacggatct cttggcactg gcatcaatga ataacgctgc

gaaatgcgaa aagtaatgtg aattgcataa ttcattgaat ctgcaactct

tagaacgcac attgaccccc ctcctattca ggttggcagc cagtaagcca

ttgactacac tccgtaggac catgagcatg ccggagacgg aatatttagc

actataatac tgtcaggggc atggtaccaa cggttcactt tcagtgga

271

Appendix 3 Table A1. List of most significant metabolites differentiated on the basis of D2O mediated metabolic flux of A. niger during winery biomass degradation

Metabolite Kovats

Index

m/z KEGG

ID

FC P-value InChI Key

Galactose oxime hexakis(trimethylsilyl) 2129.565 628.2 C00124 7.4475 2.40e-09 VHAWQMKIXQYXH

C-UHFFFAOYSA-N

Glucose oxime hexakis(trimethylsilyl) 2129.482 628.3 C00031 7.3366 2.56e-09 VHAWQMKIXQYXH

C-UHFFFAOYSA-N

Xylitol 5TMS 1699.223 513.1 C00379 2.7273 0.017916 SUZLPERYXSOGNY-

UHFFFAOYSA-N

1-O-Methyl-alpha-D-Galactoside 1798.013 194.2 C01019 2.4776 0.12344 UIDVFSCIFIMDHO-

SPOLIRPYSA-N

Shikimic acid (4TMS) 1807.463 462.9 C00493 2.4542 0.13224 LMFAUEGOCMAFIW

-KZNAEPCWSA-N

2,3-Dimethylsuccinic acid 1701.943 146.1 C00042 2.3732 0.023395 KLZYRCVPDWTZLH-

UHFFFAOYSA-N

D-Ribulose 1472.389 150.1 C00309 2.3732 0.023395 ZAQJHHRNXZUBTE-

NQXXGFSBSA-N

N-acetyl-D-glucosamine 1702.012 221.2 C00140 2.3732 0.023395 MBLBDJOUHNCFQT-

UHFFFAOYSA-N

272

Table A1. List of most significant metabolites differentiated on the basis of D2O mediated metabolic flux of A. niger during winery biomass degradation (…continued)

Metabolite Kovats

Index

m/z KEGG

ID


3,5-Diiodo-L-Tyrosine 2424.713 432.9 C02645 1.8409 0.028522 NYPYHUZRZVSYKL-

UHFFFAOYSA-N

2-keto-d-gluconic acid 5TMS 2142.731 555.1 C06473 1.6884 0.00055 VBUYCZFBVCCYFD-

JJYYJPOSSA-N

D-Psicose, pentakis(trimethylsilyl) ether (mixed anomers) 2142.814 488.6 C06468 1.6884 0.00055 BJHIKXHVCXFQLS-

UHFFFAOYSA-N

2-Keto-L-gulonic acid hydrate 2142.897 194.14 C14899 1.6884 0.00055 WTAHRPBPWHCMH

W-LWKDLAHASA-N

D-(+)-Melibiose monohydrate 2748.823 360.3 C05402 1.6608 0.035676 RGFAEJSLSYUKCO-

RWCKWOMLSA-N

Sedoheptulose-1-phosphate 2118.387 290.2 C05382 1.6517 0.020511 JPTRNFAYXMBCLJ-

VVXDDPDLNA-N

D-Altro-2-Heptulose, anhydrotetrakis-O-(trimethylsilyl)- 2118.387 824.5 C02076 1.6512 0.020569 JDDUOQRGYRTILL-

QCWLDUFUSA-N

Hexanoic acid, 5-oxo-, trimethylsilyl ester 1307.325 218.3 C06762 1.6157 0.22696 PXSCQEMSXNKQJK-

UHFFFAOYSA-N

(+/-)-1,2,4-Butanetriol 1358.391 106.1 C01089 1.5923 0.055938 ARXKVVRQIIOZGF-

UHFFFAOYSA-N

273


Metabolite Kovats

Index

m/z KEGG

ID


D-alpha-Hydroxyglutaric acid disodium 1349.982 192.1 C00026 1.5179 0.079283 GFDIGKRHNMDEJF-

GFCCVEGCSA-N

L-(+)-Citramalic acid 1461.449 148.1 C02612 1.447 0.35398 XFTRTWQBIOMVPK-

UHFFFAOYSA-N

D-(+)-Malic acid 1426.514 134.1 C00149 1.4346 0.11695 BJEPYKJPYRNKOW-

REOHCLBHSA-N

3-Trimethylsiloxycapric acid, trimethylsilyl ester 1441.805 276.5 C00249 1.425 0.40954 HWIXAIPBPDYHHJ-

UHFFFAOYSA-N

Arabinose, 2,3,4,5-tetrakis-O-(trimethylsilyl)- 1731.758 438.9 C00259 1.4202 0.31946 DWTGGLMCGFIELV-

UHFFFAOYSA-N

d-Ribose, 2,3,4,5-tetrakis-O-(trimethylsilyl)-, O-

methyloxime BP

1756.810 422.5 C00119 1.4201 0.31952 ZBEJHGUYYNELJI-

RGEXLXHISA-N

D-Glucuronic acid, 2,3,4,5-tetrakis-O-(trimethylsilyl)-,

trimethylsilyl ester

2412.068 555.0 C00191 1.324 0.10818 HDGFBGPNRURMOV-

UHFFFAOYSA-N

Butanal, 2,3,4-tris[(trimethylsilyl)oxy]-3-

[[(trimethylsilyl)oxy] methyl]-, O-methyloxime, (S)-

1649.556 467.9 C00279 1.2591 0.62864 OKWPSPPSKQXZNS-

XMHGGMMESA-N

274


Metabolite Kovats

Index

m/z KEGG

ID


Ribose, D- (1MEOX) (4TMS) 1656.452 467.9 C00121 1.2591 0.62864 ZBEJHGUYYNELJI-

XMHGGMMESA-N

Urea (2TMS) 1461.332 205.0 C00086 1.2557 0.55849

MASDFXZJIDNRTR-

UHFFFAOYSA-N

Pentanoic acid, 3-methyl-3,5-bis[(trimethylsilyl)oxy]-,

methyl ester

1461.449 306.5 C00803 1.2556 0.5585 KKMAJHUYGPAZQG-

UHFFFAOYSA-N

Acetic acid, [(trimethylsilyl)oxy]-, trimethylsilyl ester 1448.569 220.4 C00024 1.2477 0.53841 MAEQOWMWOCEXK

P-UHFFFAOYSA-N

Glyceraldehyde (1MEOX) (2TMS) 1216.385 263.5 C00118 1.2136 0.46383 MNQZXJOMYWMBO

U-VKHMYHEASA-N

Xylonic acid, 2,3,5-tris-O-(trimethylsilyl)-, gamma-lactone,

D-

1644.570 364.7 C00502 1.1732 0.75409 RJMKTJPGOIYTQZ-

UHFFFAOYSA-N

Dodecanoic acid, n- ( 1TMS) 1649.688 272.5 C02679 1.154 0.75502 RHDZBWSXEZTMPK-

UHFFFAOYSA-N

Sorbopyranose, 1,2,3,4,5-pentakis-O-(trimethylsilyl)-, L- 2274.988 488.6 C00247 1.0591 0.80946 PJXWXHJDJODISP-

UHFFFAOYSA-N

275


Metabolite Kovats

Index

m/z KEGG

ID


Butanoic acid, 2-(methoxyimino)-3-methyl-, trimethylsilyl

ester

1052.143 217.3 C00141 1.0513 0.028223 CALQXVJPGUTRRJ-

CSKARUKUSA-N

D-5-Deoxyribofuranose, 5-S-methyl-5-thio-1,2,3-tris-O-

(trimethylsilyl)-

1667.271 396.8 C04188 1.0148 0.95195 VSJSAJSCVZDVTO-

UHFFFAOYSA-N

2-Ketoisocaproic acid oxime, bis(trimethylsilyl)- derivative 1165.945 289.5 C00233 1.0033 0.79154 RBTBRFPPEAKNKU-

UHFFFAOYSA-N

276

Table A 2. List of most significant metabolites differentiated on the basis of D2O mediated metabolic flux in P. chrysogenum.

Metabolite Kovats

Index

m/z KEGG

ID


2-Ketoisocaproic acid oxime 1165.973 289.5187 C00233 1.1426 8.88e-14 RBTBRFPPEAKNKU-UHFFFAOYSA-N

Butanoic acid, 3-methyl-3-[(trimethylsilyl)oxy]-, trimethylsilyl ester 1205.645 262.4933 C04405 1.8101

0.000922

CEMBCVWMQGTNRL-UHFFFAOYSA-N

Benzoic acid 1244.733 194.3025 C00180 2.164

9.67e-05

VFFKJOXNCSJSAQ-UHFFFAOYSA-N

Serine, L- (2TMS) 1253.029 249.455 C00065 1.6295

0.01423

MWERNBWITQOYEZ-QMMMGPOBSA-N

Ethanolamine (3TMS) 1262.909 277.627 C00189 1.6522

0.009685

MZTOEZRSUGVLOG-UHFFFAOYSA-N

Phosphoric acid, bis(trimethylsilyl) 2,3-bis[(trimethylsilyl)oxy]propyl ester 1755.238 460.7982 C00009 0.5694

0.035744

OSNKBCBCOYHYNF-UHFFFAOYSA-N

2-Deoxy ribose O,O',O''-tris(trimethylsilyl)- 1298.497 350.6739 C01801 1.0695

0.60459

WJEJOIVWMARKGR-UHFFFAOYSA-N

2-Ketoisocaproic acid, trimethylsilyl ester 1349.757 202.3229 C02504 1.0696

0.60412

MCPFMKJHOFUFFW-UHFFFAOYSA-N

Succinic acid (2TMS) 1312.052 262.451 C00042 1.0626

0.6436

QUUDZINXVRFXLB-UHFFFAOYSA-N

Propanoic acid, 2,3-bis[(trimethylsilyl)oxy]-, trimethylsilyl ester 1325.581 322.6207 C00163 1.0696

0.60413

VTLZEKUPFUIGJQ-UHFFFAOYSA-N

Glyceric acid (3TMS) 1326.021 322.621 C00258 1.0696

0.60412

VTLZEKUPFUIGJQ-NSHDSACASA-N

Fumaric acid (2TMS) 1347.824 260.435 C00122 1.0798

0.64332

OITVFMRNHJZOHF-BQYQJAHWSA-N

277

Table A 2. List of most significant metabolites differentiated on the basis of D2O mediated metabolic flux in P. chrysogenum (…continued).

Metabolite Kovats

Index

m/z KEGG

ID


Mevalonic acid-1,5-lactone (1TMS) 1362.839 202.323 C00418 1.0838

0.67966

CNPIMSSHNMVFPM-UHFFFAOYSA-N

Threonic acid-1,4-lactone (2TMS) 1367.765 262.451 C01620 1.4582

0.000956

ASNCBNVBJKSVIT-BDAKNGLRSA-N

2(3H)-Furanone, dihydro-3,4-bis[(trimethylsilyl)oxy]-, cis- 1367.867 262.451 C03393 1.4581

0.000957

ASNCBNVBJKSVIT-BDAKNGLRSA-N

Glutaric acid (2TMS) 1401.385 276.477 C00489 1.5381

0.009207

UWZGPXOJVOOLSG-UHFFFAOYSA-N

Uridine-5'-diphosphogalactose disodium salt 1409.930 610.266 C00052 2.8227

1.52e-05

PKJQEQVCYGYYMM-OUJOOSCPSA-L

β-Alanine (3TMS) 1423.217 305.637 C00099 1.9927

0.000205

MKLCEJSOJVLNMP-UHFFFAOYSA-N

Acetic acid, [(trimethylsilyl)oxy]-, trimethylsilyl ester 1448.576 220.4136 C00033 0.1546

6.50e-05

MAEQOWMWOCEXKP-UHFFFAOYSA-N

Butanoic acid, 2-methyl-3-[(trimethylsilyl)oxy]-, trimethylsilyl ester 1451.390 262.4933 C00141 1.0629

0.78476

REMFBAMFOOTXSI-UHFFFAOYSA-N

Butyric acid, 4-amino- (3TMS) 1525.218 319.663 C00334 1.4476

0.041965

HMZQEGNJWXKANK-UHFFFAOYSA-N

3,3,3-Tris(trimethylsilyl)propan-1-ol 1527.767 276.638 C05979 1.9332

0.000881

BDERNNFJNOPAEC-UHFFFAOYSA-N

L-(+)-Tartaric acid 1543.186 150.0868 C00898 1.0372

0.84074

FEWJPZIEWOKRBE-LWMBPPNESA-N

L-Arabinonic acid, γ-lactone 1543.259 C01114 148.11

4 1.037

0.84149

CUOKHACJLGPRHD-YVZJFKFKSA-N

278


Metabolite Kovats

Index

m/z KEGG

ID


α-D-Galactofuranosiduronic acid, methyl 2,3,5-tris-O-(trimethylsilyl)-, methyl ester 1543.395 438.7359 C00426 1.0289

0.87843

PUYBWLWFNZAWFM-UHFFFAOYSA-N

Glutaric acid, 2-hydroxy- (3TMS) 1553.943 364.658 C00026 2.3018

0.05978

GFDIGKRHNMDEJF-GFCCVEGCSA-N

D-Ribonic acid, 2,3,5-tris-O-(trimethylsilyl)-, γ-lactone 1586.376 364.6574 C01685 1.3237

0.057253

RJMKTJPGOIYTQZ-UHFFFAOYSA-N

D-Arabino-Hexitol, 2-deoxy-1,3,4,5,6-pentakis-O-(trimethylsilyl)- 1591.007 527.078 C04691 1.3529

0.17245

ULCPMKYKEVLMSQ-UHFFFAOYSA-N

D-Erythro-Pentitol, 2-deoxy-1,3,4,5-tetrakis-O-(trimethylsilyl)- 1591.040 424.8709 C11435 1.3529

0.17245

HXWBUBLOJVCOEO-UHFFFAOYSA-N

2-Piperidine carboxylic acid, 1-(trimethylsilyl)-5-[(trimethylsilyl)oxy]-, trimethylsilyl ester 1593.623 273.5193 C00408 1.5593

0.005006

CPDPZJUOATWIOD-UHFFFAOYSA-N

Pentanedioic acid, 3-methyl-3-[(trimethylsilyl)oxy]-, bis(trimethylsilyl) ester 1596.584 378.684 C06007 1.6915

0.000694

SWDDFRVEZJUJJD-UHFFFAOYSA-N

Fucose, DL- (1MEOX) (4TMS) 1602.643 481.923 C01019 1.0973

0.56492

SXUVIFIZLGMRBJ-ORJDBQSNSA-N

D-Ribofuranose, 1,2,3,5-tetrakis-O-(trimethylsilyl)- 1609.245 438.8544 C00121 1.1589

0.4671

LDFPXMNJVPETIY-UHFFFAOYSA-N

α-DL-Arabinopyranose, 1,2,3,4-tetrakis-O-(trimethylsilyl)- 1609.103 438.8544 438.8544

1.1586

0.46794

KEOUSSOURMHEKN-YYIAUSFCSA-N

D-Xylopyranose, 1,2,3,4-tetrakis-O-(trimethylsilyl)- 1621.056 438.85438

438.85438

2.5262

1.11e-06

KEOUSSOURMHEKN-UHFFFAOYSA-N

Xylonic acid, 2,3,4-tris-O-(trimethylsilyl)-, δ-lactone, D- 1621.404 364.6574 364.6574

1.804

8.01e-05

RWGTVHCZLUFFAU-UHFFFAOYSA-N

279


Metabolite Kovats

Index

m/z KEGG

ID


Lyxose, D- (1MEOX) (4TMS) 1656.440 467.8956 C00476 1.505

0.04653

ZBEJHGUYYNELJI-KZNAEPCWSA-N

Xylose, D- (1MEOX) (4TMS) 1656.455 467.8956 C00181 1.505

0.04653

ZBEJHGUYYNELJI-RCCFBDPRSA-N

D-5-Deoxyribofuranose, 5-S-methyl-5-thio-1,2,3-tris-O-(trimethylsilyl)- 1667.270 396.7654

4 C04188 1.4259

0.25347

VSJSAJSCVZDVTO-UHFFFAOYSA-N

Erythritol (4TMS) 1680.215 410.845 C00503 2.0797

0.00275

ZBFVOPVQUXWTHM-IYBDPMFKSA-N

2,3,5-Tri-o-trimethylsilyl-xylono-1,5-lactone 1680.290 148.114 C02266 2.0797

0.00275

XXBSUZSONOQQGK-FLRLBIABSA-N

Mannose, 6-deoxy-2,3,4,5-tetrakis-O-(trimethylsilyl)-, L- 1686.424 481.923 C00507 2.8706

6.32e-07

SXUVIFIZLGMRBJ-HMMYKYKNSA-N

Xylitol 5TMS 1699.245 513.052 C00379 1.1663

0.72239

SUZLPERYXSOGNY-UHFFFAOYSA-N

2,3-Dimethylsuccinic acid 1701.972 146.1412 C00091 6.255

0.000768

KLZYRCVPDWTZLH-UHFFFAOYSA-N

D-Ribulose 1472.212/ 1184.853

150.13 C00309 6.255

0.000768

ZAQJHHRNXZUBTE-NQXXGFSBSA-N

N-acetyl-D-glucosamine 1702.041 221.2078 C00140 6.255

0.000768

MBLBDJOUHNCFQT-UHFFFAOYSA-N

Ribitol, D- (5TMS) 1713.261 513.052 C00474 2.5064

0.00227

SUZLPERYXSOGNY-ACDBMABISA-N

Arabitol, D- (5TMS) 1718.419 513.052 C01904 1.4384

0.006646

SUZLPERYXSOGNY-RTBURBONSA-N

280


Metabolite Kovats

Index

m/z KEGG

ID


Putrescine (4TMS) 1731.797 376.876 C00134 1.189

0.40852

HALMUIBMTWWREW-UHFFFAOYSA-N

Glycerol-3-phosphate, DL- (4TMS) 1754.952 460.798 C00093 2.9436

0.000658

OSNKBCBCOYHYNF-HNNXBMFYSA-N

Sorbopyranose, 1,2,3,4,5-pentakis-O-(trimethylsilyl)-, L- 1808.785/ 2274.986

541.0615 C00247 2.2887

0.008467

PJXWXHJDJODISP-UHFFFAOYSA-N

Citric acid (4TMS) 1812.142 480.848 C00158 1.5279

0.098174

VFGAVMGYDWDESE-UHFFFAOYSA-N

Fructose, D- (1MEOX) (5TMS) 1864.373/ 2729.258

541.062 C00095 1.5059

0.046356

PLNWQGWZBNJIQM-UHFFFAOYSA-N

Trimethylsilyl 3,5-dimethoxy-4-(trimethylsilyloxy)benzoate 1890.594 342.5349 C10833 1.8027

0.000322

KWOFGVXBWXVAJU-UHFFFAOYSA-N

Galacturonic acid, D- (1MEOX) (5TMS) 1934.981 584.087 C00333 1.2679

0.20619

FKLGHFYFJKVJFQ-WOCUKSDZSA-N

Benzoic acid, 3,4,5-tris(trimethylsiloxy)-, trimethylsilyl ester 1950.357 458.844 C00156 1.23

0.15086

KCIFUQKNKZTHGI-UHFFFAOYSA-N

Inositol, 1,2,3,4,5,6-hexakis-O-(trimethylsilyl)-, scyllo- 2019.775 613.2426 C06153 1.1663

0.31948

FRTKXRNTVMCAKI-QJJHVYBSSA-N

1H-Indole-3-acetic acid, 1-(trimethylsilyl)-, ethyl ester 2034.280 275.41824

C00954 1.15

0.37418

ZYFCMPGFRITKAM-UHFFFAOYSA-N

Hexadecanoic acid, n- (1TMS) 2042.652 328.606 C00249 1.8481

0.001537

HKNNSWCACQFVIK-UHFFFAOYSA-N

Inositol, myo- (6TMS) 2082.670 613.243 C00137 1.8481

0.001537

FRTKXRNTVMCAKI-HPEVMFQJSA-N

281


Metabolite Kovats

Index

m/z KEGG

ID


D-Mannose, 2-(acetylamino)-2-deoxy-, tetrakis(trimethylsilyl) deriv. 2084.912 509.932 C00645 1.2383

0.27276

DPZWOYBZRJCTQP-UHFFFAOYSA-N

Sedoheptulose TMS 2118.444 643.2686 C02076 1.6839

0.000789

UZWIKIWXLGTVJV-UHFFFAOYSA-N

Glucose oxime hexakis(trimethylsilyl) 2129.521 628.2572 C00031 1.7574

0.086144

VHAWQMKIXQYXHC-UHFFFAOYSA-N

Galactose oxime hexakis(trimethylsilyl) 2129.641 628.2572 C00124 1.7594

0.085678

VHAWQMKIXQYXHC-UHFFFAOYSA-N

2-keto-d-gluconic acid 5TMS 2142.758 584.087 C06473 1.3093

0.27124

VBUYCZFBVCCYFD-JJYYJPOSSA-N

D-Psicose (mixed anomers) 2142.837 180.156 C06468 1.3093

0.27124

BJHIKXHVCXFQLS-UHFFFAOYSA-N

2-Keto-L-gulonic acid hydrate 2142.936 194.1394

C15673 1.3093

0.27124

WTAHRPBPWHCMHW-LWKDLAHASA-N

Linoleic acid ethyl ester 2158.334 308.4986 C01595 2.0934

0.063675

FMMOOAYVCKXGMF-MURFETPASA-N

Ethyl Oleate 2164.847 310.5145 D04090 2.7904

0.011851

LVGKNOAMLMIIKO-QXMHVHEDSA-N

Oleic acid, trimethylsilyl ester 2225.456 354.6425 C00712 3.9425

0.006763

GAODWSYPSJBKGB-SEYXRHQNSA-N

Galactitol, D- (6TMS) 2243.612/ 2515.697

615.259 C01697 1.0966

0.726

USBJDBWAPKNPCK-NVPYSNMXSA-N

Octadecanoic acid, trimethylsilyl ester 2279.143 356.6584 C01530 1.1007

0.40744

DDLPZVTUKLKVQB-UHFFFAOYSA-N

282


Metabolite Kovats

Index

m/z KEGG

ID


Sucrose, D- (8TMS) 2289.902 919.746 C00089 1.2191

0.18686

JDAONBLATRVCRV-YYEYMFTQSA-N

Inulobiose (impurity: Sucrose) 2595.917/ 2951.808

342.297 C01711 1.7779

0.007807

WOHYVFWWTVNXTP-SQOWBOMSSA-N

Galactopyranoside, 1-O-methyl-, α-D- (4TMS) 2290.969 482.907 C02985 1.3395

0.052446

UIDVFSCIFIMDHO-SPOLIRPYSA-N

D-(-)-Citramalic acid 2291.697 148.11402

C02612 1.2201

0.18532

XFTRTWQBIOMVPK-RXMQYKEDSA-N

D-Altrose, 2,3,4,5,6-pentakis-O-(trimethylsilyl)- 2333.160 527.0349 C06464 1.1783

0.39585

DMYXNOJXHNOHDN-WAPOTWQKSA-N

Melatonin 2343.197 232.2783 C01598 1.0512

0.72168

DRLFMBDRBRZALE-UHFFFAOYSA-N

D-Glucuronic acid, 2,3,4,5-tetrakis-O-(trimethylsilyl)-, trimethylsilyl ester 2412.129 555.045 C00191 2.4483

0.000416

HDGFBGPNRURMOV-UHFFFAOYSA-N

3,5-Diiodo-L-Tyrosine 2424.825 432.9816 C01060 2.9471

7.63e-05

NYPYHUZRZVSYKL-UHFFFAOYSA-N

Mannonic acid, 2,3,4,6-tetrakis-O-(trimethylsilyl)-, lactone 2520.426 466.8645 C03107 1.1105

0.60704

VNGTZLYNGGLPIZ-VQHPVUNQSA-N

α-D-Glucose-1-phosphate, dipotassium salt dihydrate 2523.959 276.179 C00103 0.7856

0.24065

JXLMWHHJPUWTEV-QMKHLHGBSA-L

Gulose, 2,3,4,5,6-pentakis-O-(trimethylsilyl)- 2562.538/ 2875.852

541.0615 C15923 1.0192

0.92422

PPTMWEDTYQRQBC-UHFFFAOYSA-N

D-(+)-Melibiose monohydrate 2748.831 360.3118 C05402 2.5419

8.65e-05

RGFAEJSLSYUKCO-RWCKWOMLSA-N

283


Metabolite Kovats

Index

m/z KEGG

ID


Trehalose, α, α'-, D - (8TMS) 2759.013 919.746 C01083 1.1582

0.31803

YQFZNYCPHIXROS-UHFFFAOYSA-N

Maltose, octakis(trimethylsilyl)- 2893.459 919.7454 C00208 1.3086

0.21046

QJZFVYJIGWEKIR-UHFFFAOYSA-N

Docosanoic acid, trimethylsilyl ester 3020.779 412.7647 C08281 1.0835

0.67229

BJBBFOVJCKMBAL-UHFFFAOYSA-N

Tocopherol, α- (1TMS) 3101.754 502.888 C02477 1.2082

0.28607

MJWXSUWKOCZGBM-NCLPYWGKSA-N

Appendices

284

Appendix 4

P. chrysogenum metabolic flux: Secondary data

Figure A5. Periodic concentration of glycolysis pathway metabolites detected in P. chrysogenum

during winery biomass waste degradation of Shiraz grapes over 8 days.

Figure A6. Periodic concentration of TCA cycle metabolites detected in P. chrysogenum during

winery biomass waste degradation of Shiraz grapes over 8 days.

020406080

100120140160180

0 4 5 7 8

Con

cent

ratio

n (m

g/L

)

Gycolysis duration (days) Glucose-1-P D-Glucose UDP-Galactose Galactitol

D-Galactose Glycerate-3-P Acetyl-CoA

0

100

200

300

400

500

600

0 4 5 7 8

Con

cent

ratio

n (m

g/L

)

TCA duration (days)

Citrate 2-oxo-glutarate Succinyl-CoA Succinate Fumarate

Appendices

285

Figure A7. Gluconolactone metabolism pathway metabolites detected in P. chrysogenum during


Figure A8. Maltose metabolism pathway metabolites detected in P. chrysogenum during winery


0102030405060708090

100

0 4 5 7 8

Con

cent

ratio

n (m

g/L

)

Glucono-1,5-lactone metabolism duration (days)

D-Glucose D-Glucono-1,5-lactone

0

50

100

150

200

250

300

350

0 4 5 7 8

Con

cent

ratio

n (m

g/L

)

Maltose metabolism duration (days)

D-Glucose α, α'-Trehalose Maltose

Appendices

286

Figure A9. Sucrose metabolism pathway metabolites detected in P. chrysogenum during winery


Figure A10. Fructose metabolism pathway metabolites detected in P. chrysogenum during winery


0102030405060708090

100

0 4 5 7 8

Con

cent

ratio

n (m

g/L

)

Sucrose metabolism duration (days)

D-Glucose Sucrose Psicose

0

50

100

150

200

250

300

350

400

0 4 5 7 8

Con

cent

ratio

n (m

g/L

)

Fructose metabolism duration (days)

Glucose-1-P Inulobiose D-Fructose

Appendices

287

Figure A11. Lecithin metabolism pathway metabolites detected in P. chrysogenum during winery


Figure A12. Terpenoid metabolism pathway metabolites detected in P. chrysogenum during winery


0

50

100

150

200

250

300

0 3 6 8

Con

cent

ratio

n (m

g/L

)

Lecithin metabolism duration (days) Glycerate-3-P L-Serine Propanol L-Tartarate Ethanolamine

0

0.01

0.02

0.03

0.04

0.05

0.06

0

50

100

150

200

250

300

0 3 6 8

Con

cent

ratio

n (m

g/L

)

Con

cent

ratio

n (m

g/L

)

Terpenoid metabolism duration (days) Glycerate-3-P

4-(Cystediene-5-diphospho)-2-C-methyl-D-erythritol

α-Tocopherol

Appendices

288

Figure A13. Deoxy sugar metabolism pathway metabolites detected in P. chrysogenum during


Figure A14. Fucose metabolism pathway metabolites detected in P. chrysogenum during winery


0

50

100

150

200

250

300

0 3 6 8

Con

cent

ratio

n (m

g/L

)

Deoxy sugar metabolism duration (days) Glycerate-3-P D-Ribonate D-Ribose 5-P-α-D-Ribose-1-P Deoxyribose

0

50

100

150

200

250

300

0 3 6 8

Con

cent

ratio

n (m

g/L

)

Fucose metabolism duration (days)

Glycerate-3-P L-Rhamnose L-Fucose L-Fucose-1-P

Appendices

289

Figure A15. Pectin metabolism pathway metabolites detected in P. chrysogenum during winery


Figure A16. Isoleucine metabolism pathway metabolites detected in P. chrysogenum during winery


0

50

100

150

200

250

300

0 3 6 8

Con

cent

ratio

n (m

g/L

)

Pectin metabolism duration (days) Glycerate-3-P 2-Keto-D-gluconate 2-Dihydro-L-idonate

02468

1012141618

0 3 7 8

Con

cent

ratio

n (m

g/L

)

Isoleucine biosynthesis duration (days)

Acetyl-CoA (R)-2,3-Dihydroxy-3-methyl valerate

Appendices

290

Figure A17. Yan cycle metabolism pathway metabolites detected in P. chrysogenum during winery


Figure A18. Arginine metabolism pathway metabolites detected in P. chrysogenum during winery


0

10

20

30

40

50

60

0 3 7 8

Con

cent

ratio

n (m

g/L

)

Yang cycle duration (days)

Acetate L-Serine S-Methyl-5-thio-D-ribose-1-P

02468

1012141618

0 3 7 8

Con

cent

ratio

n (m

g/L

)

Arginine metabolism duration (days)

Acetyl-CoA Fumarate Putrescine

Appendices

291

Figure A19. Ascorbate metabolism pathway metabolites detected in P. chrysogenum during winery

biomass waste degradation of Shiraz

0

20

40

60

80

100

120

0 3 7 8

Con

cent

ratio

n (m

g/L

)

Ascorbate metabolism duration (days) Acetyl-CoA L-Gulose D-Glucuronate L-Threonate

biodegradation of winery biomass wastes by developing a ...€¦ · i, avinash v karpe, hereby...

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