DETERMINING THE DOSE RESPONSE OF CELLULASE ENZYME COCKTAILS

UNDER VARIABLE TEMPERATURE, pH AND IONIC LIQUID CONDITIONS

A Report of a Senior Study

By

Davis Raymond Hu

Major: Biochemistry

Maryville College

Spring, 2014

Date approved____________________, by ________________________________

Faculty Supervisor

Date approved____________________, by ________________________________

Division Chair

iii

ABSTRACT

In today’s society, fuel technologies are becoming more expensive while

affecting the environment negatively and contributing to temperature and weather changing

patterns. The next generation of fuel technologies derives from the shift of finite fossil

sources to a development of biofuels – liquid fuels generated from solar energy stored in

plant biomass. The five-year mission of the Joint BioEnergy Institute (JBEI) in Emeryville,

California is to discover methods of harnessing solar energy in biomass that could meet the

nation’s annual transportation energy needs in ways that have less impact on global climate

change. The Deconstruction division of JBEI is working to develop a targeted cellulase

enzyme cocktail under optimized temperature, pH, and ionic liquid conditions in order to

generate fermentable glucose sugars from biofuels. Complex sugars generated from cellulase

sources are ultimately fed to engineered microbes which produce biofuels as well as other

valuable chemical products. In the experiment, the Dinitrosalicylic (DNS) assay, which

quantifies reducing sugars, was used to determine the optimal temperature and pH at which

several enzymes catalyze the production of glucose from cellulose. The catalytic efficacy of

combinations of these enzymes at different total enzyme dose volumes was also measured.

Initial experiments with enzymes Cel_9A and Cel_5A revealed optimal temperatures of

saccharification at 65°C and 85°C respectively. With commercial enzymes from Megazyme

International, Endo-Cellulase shows highest enzyme activity and is most favored at pH 5.

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TABLE OF CONTENTS

Page

Chapter I

Introduction 1

Chapter II

Materials and Methods 10

Chapter III

Results 20

Chapter IV

Discussion and Conclusions 27

Works Cited 30

v

LIST OF FIGURES

Figure Page

1 Figure 1. Cellulose Enzyme Breakage Types 7

2 Figure 2. Dinitrosalicylic (DNS) Colorimetric Assay 13

3 Figure 3. DNS Enzyme Reaction Schematic Diagram 14

4 Figure 4. Reaction Diagram Table 15

5 Figure 5. Whatman 350 Unifilter Plate 16

6 Figure 6. 96-well Schematic Temperature Diagram 17

7 Figure 7. DNS Enzyme 96-well Reaction 18

8 Figure 8. Temperature Variance Variable Graph for Cel_9A and Cel_5A 21

9 Figure 9. Temperature and pH 5 Variance Variables for Enzymes 22

10 Figure 10. Temperature and pH 6 Variance Variables for Enzymes 23

11 Figure 11. Temperature and pH 7 Variance Variables for Enzymes 24

12 Figure 12. Absorbance vs. Enzyme concentration of ILSG and Avicel 26

13 Figure 13. Glucose vs. Enzyme Concentration of ILSG and Avicel 26

14 Figure 14. Temperature and pH 5, 6, 7 Variance Variables for Enzymes 29

vi

Acknowledgements

This work was supported and made possible by the Center of Science and

Engineering Education at Lawrence Berkeley National Laboratory, U.S. Department of

Energy, Office of Science, and The Joint BioEnergy Institute. I would like to thank my

wonderful supporting mentors, University of California Berkeley Ph.D. graduate Vimalier

Reyes-Oritz and enzyme optimization scientist Kenneth Sale. I would also like to thank my

safety work lead supervisor Steve Singer. Lastly, I would also like to thank Vice President

Blake Simmons who made my internship possible by his wise selection of me for my

participation in this program. This work conducted by the Joint BioEnergy Institute was

supported by the Office of Science, Office of Biological and Environmental Research, of the

U. S. Department of Energy under Contract No. DE-AC02-05CH11231. I would also like to

thank my wonderful advisor/mentor/professor Dr. Angelia Gibson for her support by making

my thesis possible at Maryville College.

1

CHAPTER I

INTRODUCTION

One of the major global issues and concerns of the 21st century is the question on the

development of new sources of energy fuels to meet the increasing demands for generations

and beyond. Foreign oil dependence is not a solution for the future of energy sustainability

because it is a limited finite resource. In order for America to build an energy-independent

infrastructure, new strategies need to be developed and implemented to find new solutions to

solve this crisis. The desire for energy independence combined with rapid depletion of fossil

fuel reserves is a major catalyst for developing alternative fuels.

For many years, the transportation sector has relied mainly on liquid fuels, such as

gasoline and diesel because they are energy dense and fungible [1]. Fossil fuels provided 85-

95% of all energy production from 1950 to 2005 [2]. Fossil fuel energy production grew

521% over the course of fifty years from 63.9 to 396.8 quadrillion Btu (quads) [2].

Consumption of these fossil fuel resources in the United States was 28% more than it

produced; and 63% of its oil consumption was dependent on imports [3]. Of the total energy

consumed, approx. 85.1% was derived from fossil fuels, approx. 52.3% was consumed by

commerce and industry, approx. 25.8% by transportation, and approx. 21.1% in residential

1

use [3]. In terms of the rate of current consumption of these fossil fuel resources, world

petroleum is projected to last another 92 years, natural gas 160 years, and coal 224 years [4].

The development of alternative renewable transportation fuels is critical to energy,

environmental, and economic security that leads to a reduced reliance on fossil fuels.

A major proportion consisting about 85% of fossil fuels and energy consumed

worldwide is derived from petroleum oil, coal and natural gas [4]. These sources of fuels are

not sustainable because they are limited in quantity and are not environmentally friendly due

to the pollution they give off, which is a threat to global climate change. The dirtiest fuel

source is coal along with petroleum ranking second, which emits sulfur dioxide, nitrogen

oxides and mercury and is used in 500 older generators that produce more than half of the

power in the United States [5]. Natural gas is a cleaner form of energy which accounts 10%

of all electricity, but it cannot be ensured with a steady fuel supply and is more expensive

than coal or petroleum [5]. This effect has stimulated interest in alternative energy from

wind, solar and biomass sources.

While there have been advances in alternative energy sources using bioethanol

produced from corn starch through hydrolysis and fermentation [1], there is a major concern

due to the nature of this fuel being derived from food products like starch, sucrose, and

oilseed feedstock. This could cause part of the food sector prices to rise at an unexpected

rate. The rising price of food fuel sources such as corn crops account for 40% of U.S.

production in 2011, up from 31% in 2008-2009 [6]. Bioethanol is also not fully “drop-in”

compatible or fungible with existing transportation energy infrastructure. This means it does

not enable maximum leverage of high capital investment for fuel production and distribution

infrastructure because of the costly expenses to run a corn bioethanol factory and it does not

2

generate a maximal output in terms of fuel efficiency and cost effectiveness [5]. This implies

that ethanol fuels from corn, starch, and oilseed sources may not be the ideal carbon source

for long-term fuels.

The next big idea comes from the most basic and abundant source of fermentable

sugars in life, biomass. How can we utilize biomass and convert it into biofuels? Recently,

scientists and researchers may have found the potential fuel source derived from cellulosic

biomass, which provides a more sustainable and non-food source of fermentable sugar such

as ionic liquid pretreated switchgrass [1]. It is estimated that there are a billion tons of

biomass available annually in the United States [7]. More than half of that biomass is

composed of cellulose, which after the conversion to glucose from hydrolysis, can be

fermented into cellulosic biofuels [8, 9]. This means there is a large, untapped resource of

lignocellulosic biomass that could provide a renewable domestic source of nearly carbon-

neutral, advanced (drop-in and/or fungible) liquid fuels [10-12].

At the Joint BioEnergy Institute (JBEI) in Emeryville, California, there are four

divisions that are working synergistically to perform the basic science behind the conversion

of lignocellulosic biomass to advanced biofuels. The Feedstocks Division is where new

biomass is harvested and grown from previously obtained biomass. Then the Deconstruction

Division is responsible for breaking down the cellulolytic matter and converting it into usable

sugars. One of the difficult barriers for the conversion of cellulolytic matter to glucose sugars

is biomass recalcitrance of plant cell wall polysaccharides to enzymatic hydrolysis [13]. The

current pretreatment method being developed at JBEI is to “dissolve” lignocellulose biomass

and hydrolyze the resulting liquor into sugars using ionic liquids. These liquids are composed

of salts that are in liquid form rather than crystals at room temperature. This ionic liquid is

3

known as 1-ethyl-3-methylimidazolium acetate (abbreviated as [C2mim][OAc]), which is

used to dissolve switchgrass biomass into three components – cellulose, hemicellulose, and

lignin [1]. This process has been shown to dramatically reduce biomass recalcitrance and

enhances the enzymatic hydrolysis of fermentable sugars by saccharification at high

temperatures and in presence of high ionic liquid concentrations in which this cocktail can be

converted into biodiesel (fatty acid ethyl-esters or FAEEs) by a metabolically engineered

strain of Escherichia coli. [13].

Ionic liquid pretreatment results in successful deconstruction of biomass matter. The

IL-pretreated switchgrass as recommended in a review article by Sluiter et al. describes an

experiment performed by Keasling research group about ionic liquid pretreatment of

switchgrass. The switchgrass was pretreated with [C2mim][OAc] ionic liquid at a 10:1

weight/weight ratio at 120°C for 3 hours [18], then washed multiple times with water and

ethanol. Next, the biomass was dried by using a lyophilization technique. The IL-pretreated

switchgrass end result was composed of 41% glucan and 13% xylan [18]. The composition

resembles that β-1,4-glucan cellulose have been broken into smaller fragments. The

remaining amount of ionic liquid in switchgrass was estimated using measured IL

concentrations in the hydrolyzate product after saccharification. The IL concentration in

hydrolysate was 0.05% at a 10% weight/volume loading of IL-pretreated biomass [18]. The

total saccharification product equals approximately 0.5% IL left in biomass after

pretreatment [18]. The experiment has demonstrated that IL is an effective salt in liquid

solution that effectively degrades cellulsae into fragmented sugar products.

Cellulose is then extracted from the ionic liquid pretreated biomass and sent to a

process called saccharification to generate glucose-rich sugars. But, ionic liquid pretreatment

4

presents challenges to using commercial enzymes to liberate the glucose from pretreated

cellulose, because these enzymes are not active in the presence of ionic liquids.

Saccharification requires at least three enzymes to work synergistically in order for this

method to work as expected. The three enzymes used for this experiment consists of the

following: endoglucanases, cellobiohydrolases, and β –glucosidases. Cellulose is formed

from glucose polymers in which glucose molecules are linked via β-(1,4) glycosidic bonds.

Endoglucanases hydrolyze bonds at random spots on cellulose by creating, reducing and non-

reducing ends. Cellobiohydrolases catalyze hydrolysis of β-(1,4) bonds at the reducing and

non-reducing ends created by endoglucanases, creating glucose dimers called cellobiose. β –

glucosidases catalyze hydrolysis of the β-(1,4) bond of cellobiose to produce two glucose

molecules.

The Microbial Communities, Enzyme Optimization and Fungal Biotechnology

groups at JBEI are actively pursuing a mission to optimize a cellulose cocktail for the

maximum release of glucose from ionic liquid pretreated switchgrass that functions at high

temperatures (>70°C) and in 20% [C2mim][Oac] ionic liquid. The utilization of three

thermophilic enzymes from Megazyme, Ireland in a cocktail solution working synergistically

achieves the maximal glucose output: Endo-Cellulase (Endo) – Cel_9A & Cel_5A,

Cellobiohydralase (CBH) – Csac, and β-Glucoside (BG) – βG. These enzymes work

synergistically to catalyze the cleavage of the (1-4) bonds between glucose subunits to

produce the monosaccharide glucose as illustrated in Figure 1 on the next page.

5

Figure 1. Enzyme cellulose fibril breakages for corresponding enzymes Endo, CBH and

βG Cellulose is a crystalline form of glucose polymers joined by β linkages (Image from

Hu, 2013).

Finally, the sugars are then fed to engineered microbes to produce biofuels in the

Fuels Synthesis Division. When new fuels have been generated successfully, the

Technologies Division then develops techniques for high yields of the biofuels. While the

production of these biofuels sounds very promising, there are roadblocks that hamper the

development of these cost and energy-efficient processes to convert lignocellulose into

advanced biofuels which include: lack of scalable and sustainable energy crops, difficulty in

6

separating and breaking down biomass, expense of enzymes used to produce fermentable

sugars, and the need for microbial routes to produce advanced biofuels.

The Keasling research group at JBEI has conducted research utilizing a cellulase

cocktail solution being fed to engineered microbial strains for the production of cellulosic

biofuels. Ethanol is being used increasingly, but it has limitations for long term fuel

sustainability such as incompatibilities with existing petroleum based fuel resources. The

next generation of advanced biofuels will be derived from microorganisms such as

Escherichia coli and Saccharomyces cerevisiae [9]. The production of advanced biofuels

from these bacteria and yeast strains are developed from metabolic pathway engineering.

Once the strains have been genetically engineered, the deconstructed fermentable sugars are

fed to the bacteria which in result produce long chain isoprenoid and fatty acid alcohol based

biofuels.

The Steen experimental group examined saccharification of IL-pretreated switchgrass

hydrolysates that were generated to determine whether it would support growth of an

engineered Escherichia coli strain to produce fatty acid ethyl-ester (FAEE) biodiesel [19].

Through their method, they demonstrated the engineering of bacterial strain E. coli to

produce structurally tailored fatty esters (biodiesel), fatty alcohols, and waxes directly from

simple sugars. The end result of biofuel production includes determining the optimal

proportion of each enzyme and understanding the production of glucose as a function of the

enzyme dose and how the required dose responds to changes in the temperature and

concentration of ionic liquid in the reaction mixture. By determining the favorable

temperature, pH, and dose response conditions for the enzymatic cocktail, maximum yield of

biofuels could be produced from the genetically engineered bacteria strains.

7

The enzyme optimization team’s experiment is unique in the way that the ratio

mixture of enzymes that releases a maximized output of glucose from the determined optimal

proportion, temperature and pH is specified through the dose response of the enzyme. Next,

the team measures the amount of sugar produced by absorbance spectroscopy through mixing

each tested sample in a dinitrosalicylic acid (DNS) colorimetric indicator solution. This

method tests for the presence of free carbonyl groups (C=O), also known as reducing sugars.

This involves the oxidation of the aldehyde functional group present in glucose and the

ketone functional group fructose. The aldehyde group is oxidized to carboxyl group while the

dinitrosalicylic is reduced to 3-amino, 5-nitrosalicylic acid. Side reactions such as the

decomposition of sugar competes for the availability of 3,5-dinitrosalicylic acid.

The Zhang research group uses DNS assays to test for each enzyme that delignifies

biofuels. Dinitrosalicylic acid is an effective indicator solution to measure the amount of

sugars that are produced after exposed to reaction conditions such as temperature and pH for

enzymes endoglucanases, exoglucanases, β-Glucoside [20]. The chemistry behind the

yellow/orange color shift is due to the amount of sugar content after exposed to temperature

and pH conditions. Understanding the dose response concept is important from an economic

perspective because enzymes still represent approximately $1.00 to $1.50 of the minimum

ethanol selling price per gallon. Thus, we want to be able to minimize the total amount of

enzyme used, which requires the understanding of the concept of dose response [12].

1

CHAPTER II

MATERIALS AND METHODS

Biofuels are produced by fermenting the sugars derived from pretreatment and

saccharification of plant biomass to produce relatively pure glucose. At JBEI, biomass is

pretreated with ionic liquids at high temperatures and, upon dilution with water the process

produces a crystalline form of glucose polymers. Then enzymes are used to break down the

cellulose in order to obtain glucose through a process called saccharification. From there, the

glucose is fed to yeast and bacteria whose metabolisms have been engineered to utilize

glucose to produce various biofuels [1].

A key step in this process is the saccharification of cellulose to glucose, which

requires a mixture of enzymes that has been optimized to function under the pretreatment

conditions. In the case of JBEI this means optimized to perform at high temperatures and in

the presence of ionic liquids. The goal of this work is to determine the maximized release of

glucose at an optimized temperature and pH. This experiment is divided into several parts:

preparing enzymes in test tubes, reacting enzymes in ionic liquid/water source and oil,

1

preparation of ionic liquid switch grass and avicel substrate, and preparation/running a DNS

assay under various temperature and pH conditions.

Materials

Thermophilic Enzymes from Megazyme Ireland

The three thermophilic enzymes used from Megazyme, Ireland are Endo-Cellulase

(Endo) – Cel_9A & Cel_5A, Cellobiohydralase (CBH) – Csac, and β-Glucoside (BG) – βG.

Reaction Conditions

The thermophilic enzymes were exposed to a reaction temperature condition range

from 50°C-80°C in order to prevent contamination and to increase the rate of the reaction.

The ionic liquid content was varied from 0%-20% concentration levels. The enzyme load

consisted 20 mg of enzyme/g of glucon.

Chemicals

The required chemicals involved in the experiment included: ionic liquid (1-n-ethyl-

3-methylimidazolium acetate), deionized water, 1M pH 5 citrate buffer, avicel and 1%

carboxymethylcellulose.

Characterization Equipment

High Performance Liquid Chromatography (HPLC) was used to separate different

components in solution/sample. The solvent that is in a reservoir gets sent to a pump solvent

manager/delivery system. Then the solvent gets separated by molecular weight of the glucose

and water. Then the sample is injected to the autosampler sample manager, which is then sent

to a high pressure column. The detector then senses and sends the signal reading to a

chromatogram to plot the data.

2

2950 Biochemistry Analyzer (YSI) is used to analyze glucose content more

efficiently than HPLC by measuring liquid solution. The biochemistry analyzer analyzes

biochemical substances such as glucose and sucrose sugars, lactose and lactate, galactose,

glutamate and glutamine, choline, ethanol and methanol alcohols and peroxides [21]. An

immobilized enzyme biosensor takes a very small sample and then analyzes the amount of

chemical composition. The methodology behind YSI technology is using two membrane

layers, polycarbonate and cellulose acetate. This allows the substrate to be oxidized as it

enters an enzyme layer, which generates hydrogen peroxide. This substance is then passed

through the cellulose acetate layer to a platinum electrode where the peroxide is oxidized.

The final current is proportional to the concentration of the substrate.

SpectraMax M2 Assay Spectrophotometer is used to analyze the wavelength

absorbance of many samples in a 96-well plate. The instrument contains a rapid scanning

light beam that measures each sample within a second and generates an absorbance value

within the range of the visible light spectrum.

The Dinitrosalicylic Acid Colorimetric Assay (DNS) in conjunction with the

SpectraMax M2 Instrument measures the color intensity of a sample with insoluble biomass,

soluble sugars, and cellodextrins for the presence of reducing sugars from free carbonyl

groups.

DNS is a chemical substance used to react with reducing sugars with yellow color λ-

max = 580 nm and orange color around 620 nm. A spectrophotometer is used to measure the

amount of light absorbance in a particular sample. Refer to Figure 2 below for a colormetric

progression of using a DNS reagent on a sample.

3

Figure 2. DNS colorimetric progression from yellow to red due to raised temperature

(Image from Hu, 2013).

Refer to Figure 7 below regarding how the DNS assay affects the coloration of the samples.

Figure 3. Schematic diagram the addition of enzyme and DNS reagent (Image from Hu,

2013).

4

Sample Preparation and Data Collection

Preparing Enzymes in Test Tubes

The materials needed for this part are micropipettes (L10: 0.5 µL to 10 µL, L20: 2 µL

to 20 µL, L200: 20 µL to 200 µL), 17 test tube vials with caps, test tube holders, red and

green pipette tips. The enzymes tested are Endo-Cellulase 5A, Endo-Cellulase 9A,

Cellobiohydralase, and β-Glucosidase. The methods: micropipettes are used to fill each tube

with specific amounts of enzyme following the cocktail preparation and reaction preparation

table. This is done for 17 vials each for Cel_5A and Cel_9A with corresponding ratios of

other enzymes.

Reacting Enzymes in Ionic Liquid, Water Source and Oil

The reaction diagram for specified amounts of reagents for each sample well is

followed in Figure 3 below.

Figure 4. Reaction Diagram Table for indicated amounts of each specified reagent

(Image from Hu, 2013).

5

Individual wells in the 96-well plate with pellets were filled with the specified

amount of enzyme, ionic liquid, sterile water and oil. 1.0 M citrate buffer with a pH of 4.8

was added without ionic liquids. This was then repeated for four sets of vials total (Cel_5A

@ 50°C, Cel_5A @ 70°C, Cel_9A @ 50°C, and Cel_9A @ 70°C).

After all of the solutions were added, VWR Type 19 oil was used to cover the layers

of the liquid in each well. This prevented liquid evaporation. Plates were placed in an

INFORS Multitron incubator/shaker at their designated temperatures for 24 hours.

After 24 hours, the plates were removed from incubator/shaker and only the sugar

solution was transferred to a Whatman 350 unifilter plate. Plates were centrifuged for 10

minutes until all sugar solution was filtered to the plate. Refer to Figure 4 below for a picture

of the filtered glucose solution. Glucose content was then measured using the YSI 2950

Biochemistry Analyzer.

Figure 5. Filtered glucose solution by using Whatman 350 unifilter plate and centrifuge

(Image from Hu, 2013).

6

Preparation and Running a DNS assay for Temperature Variance with enzymes Cel_9A

and Cel_5A

The goal was to find enzyme optimal temperature. Carboxymethyl Cellulose (CMC)

is a cellulose derivative that makes up the cellulose backbone and has been used extensively

to characterize enzyme activity from endoglucanases. It is a highly specific substrate for

endo-acting cellulases, as the structure has been engineered to decrystallize cellulose and

create amorphous sites that are ideal for endoglucanase action. CMC is desirable because of

the catalysis product (glucose) as its easily measured using a reducing sugar assay such as

DNS assay. Using CMC in enzyme assays is important in regard to screening for cellulose

enzymes that are needed for more efficient cellulosic ethanol conversion. In this case it is

used as the substrate and when the enzymes are added, the sugar fiber chains break apart.

When DNS reagent was added, the solution will turn red under the presence of heat. This

involves the use of a substrate (1% CMC) and enzyme (Cel_9A and Cel_5A). We achieved a

cleaved CMC product after mixing the solutions together.

The methods: a 96-well plate were obtained filled with 165 µL of 1% CMC, 25 µL

Enzyme Cel_9A and Cel_5A into four wells column wise alternatively between the enzymes

according to schematic diagram and 10 µL buffer. Refer to Figure 5 below for temperature

and enzyme setup. A 200 µL pipette were then used to mix each well thoroughly.

7

Figure 6. Schematic diagram of enzymes Cel_9A and Cel_5A placement in 96-well plate

along with specified temperatures (Image from Hu, 2013).

The wells were then covered with a metal sticker plate and placed in a Veriti 96 well

Thermal Cycler heater for 30 minutes using settings of 45°C to 70°C with 5°C increments for

each bi-column of Cel_9A and Cel_5A enzyme.

Once the initial heating was completed, the enzymes Cel_9A and Cel_5A were

transferred 60 µL to a new 96-well plate along with the DNS reagent. Color changes should

appear golden yellow. Refer to Figure 6 below for a picture of the glucose solution that

turned golden yellow upon adding DNS reagent.

Figure 7. Enzyme solution turning yellow after DNS reagent has been added (Image

from Hu, 2013).

8

The plate were placed in the Veriti Thermal Cycler again for 5 minutes using DNS

reagent settings. After second heating, the orange solutions were transferred to a 96-well

transparent colorimetric reader plate and were analyzed for absorbance at 540 nm using the

SpectraMax M2 instrument. The absorbance for four samples of each enzyme at each

temperature was taken. Then the average between the four were taken and absorbance vs.

temperature was graphed along with standard error bars in Microsoft Excel.

Preparation and Running a DNS assay for Temperature and pH Variance with enzymes

Endo-cellulase, Cellobiohydrase and β-Glucosidase

The experimental procedure for creating a DNS assay is repeated similarly to that of

enzymes Cel_9A and Cel_5A. This time, three samples of each enzyme were tested at each

temperature increment 30°C to 85°C at pH 5, 6 and 7. Once plate with enzyme and DNS

reagent has been made, the 96-well transparent plate were placed in the SpectraMax M2 for

data acquisition.

Preparation and Running a DNS assay for an Enzyme Cocktail Solution

The experiment was continued by now combining the three enzymes together to form

a multi-component enzyme mixture or cocktail. The substrates, ionic liquid pretreated switch

grass (ILSG) and avicel, were used for this experiment. The experimental setup consists of

three 25 mg of ILSG for nanomolar concentration of 25 nM, 50 nM, 100 nM, 200 nM and

400 nM which means 5 different concentrations times three sets of 25 mg totals 15 samples.

The same setup goes for avicel totaling another 15 samples. Three control samples were also

included. Each sample tube were filled with specified calculated amounts of enzyme, 50 µL

of 1M citrate buffer and water. Once all samples were filled, they were placed in an

9

Eppendorf Thermomixer at 60°C at 1400 rpm for 24 hours. By the next day, a DNS assay

were performed then measured with SpectraMax M2 for data acquisition.

1

CHAPTER III

RESULTS

Collection of Data for Temperature Variance Variable with enzymes Cel_9A and Cel_5A

Once again, we are measuring the efficacy of saccharification of Cel_9A and Cel_5A

enzymes out of varying temperature ranges. By measuring glucose production using the DNS

assay, we were able to determine the optimal temperature for the enzymes. Four absorbance

measurements were obtained for each temperature for enzymes Cel_5A and Cel_9A. The

data from the SpectraMax M2 instrument were imported to Microsoft Excel, and a

temperature row were created from 45°C to 90°C with 5°C increments. The mean and

standard deviation of the four measurements were taken and plotted as a scatter plot of

absorbance vs. temperature. According to Figure 8, it is apparent that Cel_9A had an optimal

temperature at 65°C with an absorbance of 0.340 and Cel_5A had an optimal temperature at

85°C with an absorbance of 0.345. By the physical appearance of the graph, Cel_9A and

Cel_5A have a general inverse proportional trend crossing point at about 75°C, with Cel_9A

decreasing activity at higher temperatures and Cel_5A increasing at higher temperatures.

After crossing the 85°C threshold, Cel_9A activity significantly decreases due to extreme

temperatures.

1

Figure 8. Temperature Variance Variable Graph for Cel_9A and Cel_5A. We are

measuring for determining the optimal temperature of the enzymes Cel_9A and Cel_5A

which are at 65°C and 85°C respectively.

Collection of Data for Temperature and pH Variance Variables with enzymes Endo-

cellulase, Cellobiohydrase and β-Glucosidase

Once again, we are measuring the efficacy of saccharification of enzymes endo-

cellulase, cellobiohydrase and β-Glucosidase out of varying temperature and pH ranges from

5-7. By measuring glucose production using the DNS assay, we were able to determine the

optimal temperature and pH condition for each of the enzymes.

0.340 0.345

0.000

0.050

0.100

0.150

0.200

0.250

0.300

0.350

0.400

35 45 55 65 75 85 95

Ab

sorb

ance

λ=

620

nm

Temperature (°C)

Cel_9A

Cel_5A

2

pH 5 Data

The results from the data were imported to Microsoft Excel from the SpectraMax M2

instrument. The same procedure was followed except this time with pH variance of 5. The

temperature range this time was from 30°C to 85°C. The data chart in Figure 9 shows that

endo-cellulase enzyme had a maximum temperature peak point at 40°C at pH 5 with an

absorbance of 0.455, cellobiohydralase at 35°C with an absorbance of 0.150, and β-

Glucosidase at 45°C with an absorbance of 0.148. The graph shows a consistent measure of

absorbance around 0.150 range over all temperatures for enzymes cellobiohydralase and β-

Glucosidase. By physical appearance of the graph, endo-cellulase had the most activity with

high and low peak points while cellobiohydralase and βG had similar minimal changes.

Figure 9. Temperature and pH 5Variance Variables for Endo, CBH and βG enzymes.

We are measuring for determining the optimal temperature of the enzymes endo-

cellulase, cellobiohydralase, and β-glucosidase which are 40°C, 35°C, and 45°C

respectively.

0.455

0.150 0.148

0.000

0.100

0.200

0.300

0.400

0.500

0.600

25 35 45 55 65 75 85

Ab

sorb

ance

λ=

620

nm

Temperature (°C)

ENDO

CBH

βG

3

pH 6 Data

The temperature range was the same from 30°C to 85°C. The data chart in Figure 10

shows that again, the enzyme Endo-Cellulase had the most activity where the maximum

temperature peak point is at 65°C with an absorbance of 0.401. Cellobiohydralase and β-

Glucosidase had minimal non-significant change between all temperatures. The maximum of

CBH was at 65°C with an absorbance of 0.147 and βG was at 35°C with an absorbance of

0.155. The graph shows a consistent measure of absorbance around 0.150 range over all

temperatures for enzymes cellobiohydralase and β-Glucosidase.

Figure 10. Temperature and pH 6 Variance Variables for Endo, CBH and βG enzymes.

We are measuring for determining the optimal temperature of the enzymes endo-

cellulase, cellobiohydralase, and β-glucosidase which are 65°C, 70°C, and 35°C

respectively.

0.401

0.1470.155

0.000

0.100

0.200

0.300

0.400

0.500

0.600

25 35 45 55 65 75 85

Ab

sorb

ance

λ=

620

nm

Temperature (°C)

ENDO

CBH

βG

4

pH 7 Data

The temperature range was the same from 30°C to 85°C. The data chart in Figure 11

shows that again, the enzyme Endo-Cellulase had the most activity where the maximum

temperature peak point is at 50°C with an absorbance of 0.313. The difference between pH 7

data graph and the others is that the enzymes Cellobiohydralase and β-Glucosidase had slight

more activity where CBH had a maximum peak point at 45°C with an absorbance of 0.148

and βG had a maximum peak point at 75°C with an absorbance of 0.155. It is also to note

that the enzyme absorbance begins to overlap one another starting from 75°C as opposed to

the other pH data graphs where Endo-Cellulase remained more active at higher temperatures.

Figure 11. Temperature and pH 7 Variance Variables for Endo, CBH and βG enzymes.

We are measuring for determining the optimal temperature of the enzymes endo-

cellulase, cellobiohydralase, and β-glucosidase which are 50°C, 45°C, and 75°C

respectively.

0.313

0.148 0.155

0.000

0.050

0.100

0.150

0.200

0.250

0.300

0.350

25 35 45 55 65 75 85

Ab

sorb

ance

λ=

620

nm

Temperature (°C)

ENDO

CBH

βG

5

Collection of Data for Enzyme Cocktail Solution

The data obtained for the enzyme cocktail solution for two substrates, ionic liquid

switchgrass (ILSG) and avicel, shows that glucose production appears to be a logarithmic

function of enzyme dose. With ILSG, there is a steady increase in absorbance from 0 to 400

nM because of successful saccharification of ILSG and is expected to continue grow beyond

that. To the contrary, avicel reached a plateau at 200 nM and no further growth was expected

even at a higher concentration of 400 nM was present. Refer to data charts in Figure 12 and

13. These data show that, at the same enzyme dose, the cellulose produced from ionic liquid

pretreatment (ILSG) is easier to convert to glucose; there is a 2 to 3X higher glucose yield

from the ILSG compared to that produced from avicel according to Figure 13. This is likely

due to the cellulose produced from IL pretreatment have a much lower crystallinity than the

highly crystalline structure of avicel. The glucose concentrations were calculated by taking

the averages of ILSG and avicel at each concentration then substituted into a given linear

equation by my mentor Absorbance = 0.1376x + 0.0628. The graph in Figure 13 displays the

glucose concentrations with respect to enzyme concentrations.

Figure 12. Absorbance vs. Enzyme concentration of ILSG and Avicel

-0.1

0.1

0.3

0.5

0.7

0.9

1.1

0 25 50 100 200 400

Ab

sorb

ance

Enzyme Cocktail (EG, CBH, βG) and Concentration [nM]

ILSG

6

Figure 13. Glucose vs. Enzyme Concentration of ILSG and Avicel

0

1

2

3

4

5

6

7

8

0 100 200 300 400

Glu

cose

Co

nce

ntr

atio

n (

nM

)

Enzyme Concentration [nM]

GC ILSG

GC Avicel

1

CHAPTER IV

DISCUSSION AND CONCLUSION

In this project, we used the DNS assay to determine the optimal temperature and pH

at which several enzymes required to catalyze the production of glucose from cellulose

function optimally, and we then measured the performance of combinations of these enzymes

at different total enzyme doses. Through the first run of preliminary experiments with

Cel_9A and Cel_5A, we conclude that Cel_9A had optimal absorbance temperature 65°C

and Cel_5A had optimal absorbance at temperature 85°C when running the DNS assay

indicator solution for maximal glucose output. When experimenting with commercial

enzymes Endo-Cellulase, Cellobiohydralse and β-Glucosidase, it is conclusive that at pH 5,

Endo-Cellulase was the most favored which shows highest enzyme activity. The optimal

temperature conditions for each enzyme tested at three pH scales shows that Endo-Cellulase

had the highest peak at 40°C for pH 5 with 0.455 absorbance, Cellobiohydralse had the

highest peak at 45°C for pH 5 with 0.148 absorbance and β-Glucosidase had the highest peak

at 35°C for pH 6 and 75°C for pH 7 with 0.155 absorbance. The higher the absorbance value

when testing using DNS assay, the optimal ideal temperature condition is favored for the

maximal glucose output at the three pH levels.

1

In summary, detection of higher absorbance from depolymerized cellulose was

present at lower pH or more acidic conditions in which pH of 5 in this case represented.

More activity was present at a lower pH range. This applies particularly for enzyme Endo-

Cellulase, but for the enzyme β-Glucosidase, slightly higher pH had a slight more absorbance

activity. Refer to Figure 14 for a complete overview graph of all the enzymes with pH

variance of 5, 6, and 7 altogether. In the next part of the experiment when a cocktail of

enzymes were mixed, the substrate ILSG had a steady increase in activity when the enzyme

concentration increased but avicel did not. Refer to Figure 12. Next the glucose concentration

was calculated for each enzyme concentration (0 nM, 25 nM, 50 nM, 100 nM, 200 nM, 400

nM) with the given formula by my mentor: Absorbance (y) = 0.1376x + 0.0628 which

therefore means x = (y-0.0628)/0.1376 where x is the glucose concentration. Then a graph of

glucose vs. enzyme concentration was plotted where shows a logarithmic trend. Refer to

Figure 13. It is apparent that ILSG had excellent activity which means at a higher enzyme

cocktail concentration, the substrate is being saccharified to glucose sugars.

2

Figure 14. Complete Graph of Temperature and pH 5, 6, 7 Variance Variables for Endo,

CBH and βG enzymes.

The sources of error could have been from a mistake from tedious pipetting. Although

no significant observations are made, the results show a promising initiative for further

research on global biofuel production. With these experiments performed, we now know the

optimal temperatures for each enzyme at various pH concentrations. The limitation of

findings is that we still do not know how to produce biofuels on a massive scale at this time.

This study could be extended to more research on producing an enzyme cocktail at a specific

temperature and pH condition that would maximize the breakdown of cellulolytic biomass to

producing the highest possible yield of glucose to be fed to microbes engineered to utilize

glucose for production of biofuels, which could possibly help to solve the energy crisis in the

near future.

0.000

0.050

0.100

0.150

0.200

0.250

0.300

0.350

0.400

0.450

0.500

25 35 45 55 65 75 85

Ab

sorb

ance

Temperature (°C)

ENDO pH 5

ENDO pH 6

ENDO pH 7

CBH pH 5

CBH pH 6

CBH pH 7

βG pH 5

βG pH 6

βG pH 7

1

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