an integrated investigation of ruminal microbial communities...
TRANSCRIPT
AN INTEGRATED INVESTIGATION OF RUMINAL MICROBIAL COMMUNITIES
USING 16S rRNA GENE-BASED TECHNIQUES
DISSERTATION
Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy
in the Graduate School of The Ohio State University
By
Min Seok Kim
Graduate Program in Animal Sciences
The Ohio State University
2011
Dissertation Committee:
Dr. Mark Morrison, Advisor
Dr. Zhongtang Yu, Co-Advisor
Dr. Jeffrey L. Firkins
Dr. Michael A. Cotta
ii
ABSTRACT
Ruminant animals obtain most of their nutrients from fermentation products
produced by ruminal microbiome consisting of bacteria, archaea, protozoa and fungi. In
the ruminal microbiome, bacteria are the most abundant domain and greatly contribute to
production of the fermentation products. Some studies showed that ruminal microbial
populations between the liquid and adherent fraction are considerably different. Many
cultivation-based studies have been conducted to investigate the ruminal microbiome, but
culturable species only accounted for a small portion of the ruminal microbiome. Since
the 16S rRNA gene (rrs) was used as a phylogenetic marker in studies of the ruminal
microbiome, the ruminal microbiome that is not culturable has been identified. Most of
previous studies were dependent on sequences recovered using DGGE and construction
of rrs clone libraries, but these two techniques could recover only small number of rrs
sequences. Recently microarray or pyrosequencing analysis have been used to examine
microbial communities in various environmental samples and greatly contributed to
identifying numerous rrs sequences at the same time. However, few studies have used the
microarray or pyrosequencing analysis to investigate the ruminal microbiome. The
overall objective of my study was to examine ruminal microbial diversity as affected by
dietary modification and to compare microbial diversity between the liquid and adherent
fractions using the microarray and pyrosequencing analysis.
In the first study (Chapter 3), a meta-analysis of all the rrs sequences of rumen
origin deposited in the RDP database was performed. Collectively, 5,271 and 943 OTUs
of bacteria and archaea, respectively, were identified at 0.03 phylogenetic distance. The
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predominant bacterial phyla were Firmicutes and Bacteroidetes, while the largest
archaeal phylum was Euryarchaeota. More than 50% of all the bacterial sequences could
not be classified into any known genus. The bacterial OTUs identified in this study were
used to develop a phylogenetic microarray as demonstrated in Chapter 6. In the second
study (Chapter 4), select cultured bacteria and uncultured bacteria were quantified using
specific real-time PCR assays in order to compare the abundance between the cultured
bacteria and uncultured bacteria. The populations of some uncultured bacteria were as
abundant as those of major cellulolytic cultured bacteria such as Fibrobacter
succinogenes, Ruminococcus albus and Ruminococcus flavefaciens. In the third study
(Chapter 5), the diversity of ruminal microbiome in cattle was examined using rrs clone
libraries. Six known rrs clones were used to validate the phylogenetic microarray
(Chapter 6). The phylogenetic data of the cloned sequences supported the predominance
of Firmicutes and Bacteroidetes and the abundance of unclassified groups as described in
the meta-analysis (Chapter 3). In the fourth study (Chapter 6), a phylogenetic microarray
that detects 1,600 OTUs of ruminal bacteria was developed in a 6×5K format based on
the OTUs identified in Chapter 3. The utility of the phylogenetic microarray (referred to
as RumenArray) was tested in comparative analysis of fractionated bacterial microbiomes
obtained from sheep fed two different diets.
Species-level OTUs are commonly defined at 0.03 phylogenetic distance based on
full-length rrs sequences. However, the current 454 pyrosequencing method is not able to
produce full-length rrs sequences. Because sequence divergence is not distributed evenly
along the rrs, pyrosequencing analysis of different rrs regions can lead to overestimated
or underestimated species richness. To identify a region or phylogenetic distance that can
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support species richness estimate as reliably as full-length rrs sequences, in the fifth
study I compared datasets of partial rrs sequences corresponding to different variable
regions with a dataset of nearly full-length rrs sequences (Chapter 7). The results
indicated that the V1-V3 and the V1-V4 regions at 0.04 distance provide more accurate
estimates than other partial regions. Based on the results obtained in Chapter 7,
pyrosequencing analysis was performed to investigate bacterial diversity in the rumen of
cattle as affected by supplementation of monensin, or 4% fat from distillers grains,
roasted soybeans and an animal vegetable blend in my sixth study (Chapter 8).
Supplementary fat resulted in significant shift of bacterial populations when compared to
the control diet, but supplementary monensin did not. As shown in the previous Chapters,
the pyrosequencing analysis showed that numerous rrs sequences that cannot be assigned
to any characterized genus were predominant in the rumen.
The overall results of the above studies provided further insights into the ruminal
microbiome as affected by different diets and different fractions. Integration of
RumenArray and pyrosequencing techniques will improve our understanding of the
ruminal microbial microbiome and its integration with nutritional studies.
v
ACKNOWLEDGMENTS
I first would like to thank my advisors, Drs. Mark Morrison and Zhongtang Yu,
for their support and guidance during my graduate study. I would like to express special
thanks to Dr. Zhongtang Yu for his support and thoughtful discussions during individual
meetings. I would like to thank Drs. Jeff Firkins and Mike Cotta for their service on my
dissertation committee. I wish to thank Dr. Kichoon Lee for his service on my candidacy
committee.
I wish to acknowledge all former and present colleagues for their friendship in
the Morrison/Yu lab: Seungha Kang, Jill Stiverson, Mike Nelson, Mike Cressman,
Lingling Wang, Shan Wei, Wen Lv, Katie Shaw, Yueh-Fen Li, Amanda Gutek, Amlan
Patra, Phongthorn Kongmun, Gunilla Bech-Nielsen, Sally Adams, Yan Zhang, Zhenming,
Mohd Saufi Bastami, Jing Chen, Premaraj, Bethany and anyone else I missed. I am
especially grateful to Jill Stiverson for her help and technical support when I first joined
the lab. I especially thank Mike Nelson for his help in bioinformatics analysis when I first
started pyrosequencing analysis. I would like to thank all my fellow graduate students
working in the Department of Animal Sciences for their friendship. I would like to thank
Sangsu Shin for his friendship since 1995. I would like to thank Sunghee Park and
Changsoo Lee working in the Department of Food Science & Technology for their
friendship.
I wish to acknowledge all my family members for their support, love and
encouragement.
vi
VITA
April 1977 ......................................................Born-Kwangju, Republic of Korea
2002 ...............................................................B.A., Department of Animal Sciences, Seoul
National University, Republic of Korea
2002 to February 2004 ..................................M.S., Department of Animal Sciences,
Seoul National University, Republic of
Korea
March 2004 to August 2006 .........................Researcher, Department of Agricultural
Sciences, Korea National Open University
September 2006 to present ............................Graduate Research Associate, Department
of Animal Sciences, The Ohio State
University
Publications
Kim, M., Morrison, M., Yu, Z., 2011. Phylogenetic diversity of bacterial communities in
bovine rumen as affected by diets and microenvironments. Folia Microbiologica,
DOI 10.1007/s12223-011-0066-5
Kim, M., Morrison, M., Yu, Z., 2011. Status of the phylogenetic diversity census of
ruminal microbiomes. FEMS Microbiology Ecology, 76, 49-63
Kim, M., Morrison, M., Yu, Z., 2011. Evaluation of different partial 16S rRNA gene
sequence regions for phylogenetic analysis of microbiomes. Journal of
Microbiological Methods, 84, 81-87
vii
Nam, E.S., Kim, M.S., Lee, H.B., Ahn, J.K., 2010. β-Glycosidase of Thermus
thermophilus KNOUC202: Gene and biochemical properties of the enzyme
expressed in Escherichia coli. Applied Biochemistry Microbiology, 46, 515-524
Kim, M.S., Sung, H.G., Kim, H.J., Lee, S.S., Chang, J.S., Ha, J.K., 2005. Study on
rumen cellulolytic bacterial attachment and fermentation dependent on initial pH
by cPCR. Journal of Animal Science and Technology (in Korean). 47, 615-624
Fields of Study
Major Field: Animal Science
Focus: Rumen Microbial Ecology
viii
LIST OF TABLES
Table 3.1 The number of OTUs for total bacteria, total archaea and major groups of
bacteria, and their percentage coverage at three phylogenetic distances .......................... 49
Table 4.1 Primers and a TaqMan probe used in the real-time PCR assays for total
bacteria, total archaea or cultured bacteria ....................................................................... 72
Table 4.2 Primers used in the real-time PCR assays for uncultured bacteria .................. 73
Table 7.1 Estimates of species-level OTUs calculated from partial and full-length
archaeal 16S rRNA gene sequences .............................................................................. 132
Table 7.2 Estimates of genus- and family-level OTUs calculated from partial and full-
length archaeal 16S rRNA gene sequences ................................................................... 133
Table 7.3 Estimates of species-level OTUs calculated from partial and full-length
bacterial 16S rRNA gene sequences .............................................................................. 134
Table 7.4 Estimates of genus- and family-level OTUs calculated from partial sequence
regions and full length of bacterial 16S rRNA gene sequences ..................................... 135
Table 7.5 Estimates of OTUs calculated from partial and full-length archaeal 16S rRNA
gene sequences at 0.01 distance ..................................................................................... 136
Table 7.6 Estimates of OTUs calculated from partial and full-length bacterial 16S rRNA
gene sequences at 0.01 distance ..................................................................................... 137
ix
Table 7.7 Estimates of bacterial species-level OTUs calculated from full-length and
short partial bacterial 16S rRNA gene sequences .......................................................... 138
Table 8.1 Ingredient composition of dietary treatments .............................................. 155
Table 8.2 Sequence data and alpha diversity indices for the six fractions .................. 156
Table A List of bacterial primers for pyrosequencing analysis ................................... 199
x
LIST OF FIGURES
Figure 3.1 Bacterial phyla represented by the 16S rRNA gene sequences of rumen origin
........................................................................................................................................... 50
Figure 3.2 A taxonomic tree showing the genera of ruminal archaea identified by the
RDP database sequences ................................................................................................... 51
Figure 3.3 A taxonomic tree showing the bacteria (grouped into genera) isolated from
the rumen ......................................................................................................................... 52
Figure 3.4 A taxonomic tree showing the archaea (grouped into genera) isolated from
the rumen ......................................................................................................................... 55
Figure 3.5 A taxonomic tree showing all the genera of ruminal bacteria identified by the
13478 16S rRNA gene sequences of rumen origin .......................................................... 56
Figure 4.1 Populations of total archaea and total bacteria in the rumen of cattle .......... 74
Figure 4.2 Populations of three major cellulolytic bacteria and Butyrivibrio spp. in the
rumen ............................................................................................................................... 75
Figure 4.3 Populations of major non-cellulolytic cultured bacteria in the rumen ......... 76
Figure 4.4 Populations of uncultured bacteria originally identified from the rumen of
sheep ................................................................................................................................ 77
xi
Figure 5.1 Clustering analysis of DGGE banding profiles based on the V3 region of 16S
rRNA genes ...................................................................................................................... 87
Figure 5.2 A taxonomic tree showing the bacterial genera represented by the 144
sequences ......................................................................................................................... 88
Figure 5.3 A Venn diagram showing the numbers of species-level OTUs shared among
the four composite samples .............................................................................................. 89
Figure 5.4 A PCA analysis plot comparing the bacterial communities in the four
composite samples ........................................................................................................... 90
Figure 6.1 Linear range of detection of the RumenArray as determined using cRNA
pools of the 6 positive clones ......................................................................................... 108
Figure 6.2 A venn diagram showing the number of detected OTUs ........................... 109
Figure 6.3 A hierarchical tree showing signal intensities and similarity among the
fractionated samples........................................................................................................ 110
Figure 6.4 PCA for comparison among all the fractionated samples ........................... 111
Figure 8.1 Sequence distribution at the phylum level for each fraction ...................... 157
Figure 8.2 The distribution of sequences and OTUs at genus or the lowest classifiable
rank in the phylum Firmicutes ........................................................................................ 158
Figure 8.3 The distribution of sequences and OTUs at genus or the lowest classifiable
rank in the phylum Bacteroidetes ................................................................................... 160
Figure 8.4 The distribution of sequences and OTUs at the lowest classifiable rank in
minor phyla ..................................................................................................................... 161
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Figure 8.5 Principal coordinates analysis for the six fractions .................................... 162
Figure 8.6 Principal coordinates analysis for comparison among the three datasets ... 163
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TABLE OF CONTENTS
ABSTRACT ....................................................................................................................... ii
ACKNOWLEDGMENTS ................................................................................................. v
VITA ................................................................................................................................. vi
LIST OF TABLES .......................................................................................................... viii
LIST OF FIGURES ........................................................................................................... x
CHAPTER 1: INTRODUCTION ...................................................................................... 1
CHAPTER 2: REVIEW OF LITERATURE ..................................................................... 6
2.1 Microbial communities in the rumen ....................................................................... 6
2.1.1 Bacterial communities ....................................................................................... 6
2.1.1.1 Major cellulolytic bacteria ......................................................................... 6
2.1.1.2 Minor cellulolytic bacteria .......................................................................... 8
2.1.1.3 Uncultured cellulolytic bacteria ................................................................. 9
2.1.2 Archaeal communities ..................................................................................... 10
2.2 Methods to investigate microbial diversity ............................................................ 12
2.2.1 Cultivation-dependent methods ....................................................................... 12
2.2.2 Cultivation-independent methods .................................................................... 13
2.3 Bioinformatics analysis to investigate microbial diversity ..................................... 19
2.4 The use of monensin and its role in rumen fermentation........................................ 20
xiv
2.5 Biohydrogenation in the rumen .............................................................................. 22
2.6 Summary ................................................................................................................. 24
CHAPTER 3: STATUS OF THE PHYLOGENETIC DIVERSITY CENSUS OF
RUMINAL MICROBIOMES .......................................................................................... 25
3.1 Abstract ................................................................................................................... 25
3.2 Introduction ............................................................................................................ 25
3.3 Materials and Methods ........................................................................................... 28
3.3.1 Sequence data collection and phylogenetic analyses ...................................... 28
3.3.2 Diversity estimate ............................................................................................ 29
3.4 Results ..................................................................................................................... 30
3.4.1 Data summary .................................................................................................. 31
3.4.2 Firmicutes ........................................................................................................ 31
3.4.3 Bacteroidetes.................................................................................................... 35
3.4.4 Proteobacteria ................................................................................................. 37
3.4.5 Minor phyla ...................................................................................................... 38
3.4.6 Archaea ............................................................................................................ 40
3.4.7 Estimates of OTU richness ............................................................................. 42
3.5 Discussion .............................................................................................................. 43
3.5.1 Phylogenetic diversity ...................................................................................... 43
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3.5.2 Diversity estimates ........................................................................................... 47
CHAPTER 4: QUANTITATIVE COMPARISONS OF CULTURED AND
UNCULTURED MICROBIAL POPULATIONS IN THE RUMEN OF CATTLE FED
DIFFERENT DIETS ......................................................................................................... 63
4.1 Abstract ................................................................................................................... 63
4.2 Introduction ............................................................................................................ 64
4.3 Materials and Methods ........................................................................................... 65
4.3.1 Sample collection, fractionation and DNA extraction ..................................... 65
4.3.2 Real-time PCR assays ...................................................................................... 66
4.4 Results and Discussion ........................................................................................... 67
4.4.1 Quantification of populations of total bacteria and total archaea .................... 67
4.4.2 Quantification of cultured bacteria .................................................................. 67
4.4.3 Quantification of uncultured bacteria .............................................................. 69
4.5 Conclusions ............................................................................................................. 71
CHAPTER 5: PHYLOGENETIC DIVERSITY OF BACTERIAL COMMUNITIES IN
BOVINE RUMEN AS AFFECTED BY DIETS AND MICROENVIRONMENTS ...... 78
5.1 Abstract ................................................................................................................... 78
5.2 Introduction ............................................................................................................ 78
5.3 Materials and Methods ........................................................................................... 79
5.3.1 Sample collection, fractionation and DNA extraction ..................................... 79
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5.3.2 DGGE analysis................................................................................................. 80
5.3.3 Construction of rrs clone libraries ................................................................... 80
5.3.4 Restriction fragment length polymorphism (RFLP) analysis, DNA sequencing
and phylogenetic analysis ......................................................................................... 81
5.3.5 Comparison of ruminal bacterial communities among the four composite
samples ...................................................................................................................... 82
5.3.6 Nucleotide sequence accession numbers ......................................................... 82
5.4 Results and Discussion ........................................................................................... 82
CHAPTER 6: DEVELOPMENT OF A PHYLOGENETIC MICROARRAY FOR
COMPREHENSIVE ANALYSIS OF RUMINAL MICROBIOME ............................... 91
6.1 Abstract ................................................................................................................... 91
6.2 Introduction ............................................................................................................ 92
6.3 Materials and Methods ........................................................................................... 94
6.3.1 Oligonucleotide probe design and microarray fabrication............................... 94
6.3.2 Sample collection, fractionation and DNA extraction ..................................... 95
6.3.3 Sample preparation and labeling ...................................................................... 95
6.3.4 Microarray hybridization ................................................................................. 96
6.3.5 Signal detection and data analysis ................................................................... 96
6.3.6 Determination of the specificity and detection limit........................................ 97
xvii
6.3.7 Comparison between microarray and real-time PCR data ............................... 98
6.4 Results and Discussion ........................................................................................... 98
6.4.1 Validation of the specificity, sensitivity and detection limit ........................... 98
6.4.2 Data summary .................................................................................................. 99
6.4.3 Diversity of ruminal bacteria assigned to known genus ................................ 100
6.4.4 Diversity of ruminal bacteria that are not assigned to any known genus ...... 103
6.4.5 PCA for comparison between fractions ......................................................... 106
6.4.6 Comparison of RumenArray and real-time PCR data ................................... 106
6.5 Conclusions ........................................................................................................... 107
CHAPTER 7: EVALUATION OF DIFFERENT PARTIAL 16S rRNA GENE
SEQUENCE REGIONS FOR PHYLOGENETIC ANALYSIS OF MICROBIOMES . 112
7.1 Abstract ................................................................................................................. 112
7.2 Introduction .......................................................................................................... 112
7.3 Materials and Methods ......................................................................................... 116
7.3.1 Sequence collection, alignment, and clipping................................................ 116
7.3.2 Diversity estimates ......................................................................................... 117
7.3.3 UniFrac analysis............................................................................................. 118
7.3.4 Analysis of sequence datasets recovered from uncultured bacteria ............... 118
7.3.5 Analysis of short partial sequence regions..................................................... 118
xviii
7.4 Results ................................................................................................................... 119
7.4.1 Analysis of partial archaeal sequences .......................................................... 120
7.4.2 Analysis of partial bacterial sequences .......................................................... 122
7.4.3 UniFrac analysis............................................................................................. 125
7.4.4 Analysis of uncultured bacterial sequences ................................................... 125
7.4.5 Analysis of short partial sequence regions..................................................... 126
7.5 Discussion ............................................................................................................. 127
CHAPTER 8: INVESTIGATION OF RUMINAL BACTERIAL DIVERSITY IN
CATTLE FED SUPPLEMENTARY MONENSIN OR FAT USING
PYROSEQUENCING ANALYSIS................................................................................ 139
8.1 Abstract ................................................................................................................. 139
8.2 Introduction .......................................................................................................... 140
8.3 Materials and Methods ......................................................................................... 141
8.3.1 Sample collection ........................................................................................... 141
8.3.2 Metagenomic DNA extraction ....................................................................... 142
8.3.3 Pyrosequencing .............................................................................................. 142
8.3.4 Sequence processing and bioinformatics analysis ......................................... 143
8.3.5 Comparison among three datasets ................................................................. 144
8.4 Results and Discussion ......................................................................................... 145
8.4.1 Data summary ................................................................................................ 145
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8.4.2 Firmicutes ...................................................................................................... 145
8.4.3 Bacteroidetes.................................................................................................. 150
8.4.4 Minor phyla .................................................................................................... 152
8.4.5 Comparison among the diets .......................................................................... 152
8.4.6 Comparison among three datasets ................................................................. 153
8.5 Conclusions ........................................................................................................... 154
CHAPTER 9: GENERAL DISCUSSION ...................................................................... 164
WORKS CITED ............................................................................................................. 170
APPENDIX A: ADDITIONAL PYROSEQUENCING METHODS ............................ 197
1
CHAPTER 1
INTRODUCTION
Ruminal fermentation is mediated by a complex microbiome consisting of
Bacteria, Archaea, and Eukaryota. Although many studies have been reported that
characterize the ruminal microbiome using cultivation-based methods, the isolated
species accounted for only a small proportion of the ruminal microbiome (Kim et al.,
2011b; Stevenson and Weimer, 2007). Since 16S rRNA gene (rrs) sequences were
applied to investigation of the diversity of ruminal bacteria and archaea (Stahl et al.,
1988), the complex microbial diversity in the rumen has begun to be revealed and
appreciated.
The microbial diversity of ruminal microbiome has been a research focus of many
studies since the late 1980’s. Cloning and sequencing of 16S rRNA genes were used to
identify ruminal microorganisms in primarily domesticated ruminant animals, but also
wild ruminant animals (Sundset et al., 2007; Nelson et al., 2003). Because of the limited
numbers of clones sequenced in individual studies, only predominant members of the
ruminal microbiome were identified. In addition, individual studies were biased due to
techniques used and limited scopes of sampling (e.g., small numbers of animals, diets,
and geographic areas sampled). In an effort to assess the current status of species richness
that has been revealed in the rumen, we performed a meta-analysis on all the rrs
sequences (more than 10,000) of rumen origin found in public databases. This meta-
2
analysis provided a global phylogenetic framework which can be used in guiding future
studies and tool development.
From the above meta-analysis of 16S rRNA gene sequences, many thousands of
OTUs were identified and they represent about 70% of the predicted diversity in the
rumen. All these sequences were determined using the Sanger DNA sequencing
technology. Although the Sanger technology is not as cost-effective as pyrosequencing
on a per-sequence basis, it produces sequence data that are more accurate than those
obtained using pyrosequencing technologies. As researchers started to choose
pyrosequencing over Sanger sequencing in characterizing microbiome, we performed a
study using Sanger sequencing of clone libraries with an objective to determine if
conventional clone libraries can still contribute to discovery of novel diversity in the
rumen. This study also created the clones that were used in validation of the phylogenetic
microarray we developed.
Monensin is an ionophores and it has been fed to feedlot cattle to improve
production efficiency (Russell, 2002). It has been proposed that Gram-positive bacteria
are more sensitive to monensin than Gram-negative bacteria in in-vitro cultures due to
lack of outer membrane (Nagaraja et al., 1997; Callaway et al., 2003). Thus, monensin
can inhibit Gram-positive bacteria, including H2-producing bacteria. As a result,
monensin can reduce methane production in the rumen and shift fermentation towards
more reduced VFA (e.g. propionate), decreasing acetate:propionate ratio (Callaway et al.,
2003). However, some bacteria do not display the above “norm” (Russell and Houlihan,
2003). Monensin resistance is thought to be mediated by extracellular polysaccharides
and results from a physiological selection (Russell and Houlihan, 2003). Some in vivo
3
studies using rrs-based techniques (Stahl et al., 1988; Weimer et al., 2008) showed that
supplementary monensin did not shift ruminal bacterial populations due to monensin
resistance. In addition, some in vivo studies showed that supplementary monensin did not
decrease the acetate:propionate ratio (e.g. Firkins et al., 2008; Oelker et al., 2009). Firkins
et al. (2008) indicated that long-term change in ruminal bacterial communities could be
attributed to other dietary factors than sensitivity to monensin. As the first comprehensive
study to examine the bacterial effect of monensin supplementation in cattle, we analyzed
the ruminal bacteriome in Holstein dairy cattle fed monensin to examine to what extent
monensin alters bacterial populations.
Lipids, especially unsaturated oils, function to modulate the ruminal microbiome
and improve some aspects of rumen function, such as decreasing methanogenesis and
ammonia production (Martin et al., 2010). Unsaturated fat can readily be incorporated
into rations for high producing dairy cows to improve energy intake. However, findings
and conclusions vary among studies with respect to the magnitude of efficacy or,
conversely, their negative effect of feeding fat (e.g., decreased fiber digestibility, milk fat
depression, and milk protein decrease). Conceivably, depending on the composition of
the lipids, different groups of ruminal microbes could be affected by lipids to different
extents. Fibrolytic bacteria can be inhibited by coating feed particles with lipids that
interferes in their adhesion or activity (Calsamiglia et al., 2007). We contend that
delineating and understanding the impact of lipids on the different members of the
ruminal microbiome will help understand inconsistent outcomes of lipid
supplementations. To this end, we examined the effects of lipids on ruminal microbiome
using pyrosequencing analysis.
4
To support the above pyrosequencing analysis, we also evaluated different
regions (500 - 700 bp) of this phylogenetic marker to identify the best partial sequence
region(s) and suitable phylogenetic distance values that can provide as reliable species
richness estimates as full-length sequences. We were able to identify that the V1-V3
region at 0.04 distance is the best region to define species-level OTUs. In another study,
the populations of select cultured and uncultured bacteria present in different ruminal
samples were determined to assess their prevalence in the rumen. The results showed that
some of the uncultured bacteria can be as important, at least numerically, as some of the
previously cultured bacteria that have been perceived to be important to rumen function.
Pyrosequencing has been increasingly used in analysis of microbiomes, including
ruminal microbiomes (Callaway et al., 2010). Although it can support detailed diversity
analysis of ruminal samples, it produces considerable amounts of artifactual sequences
(Quince et al., 2009). Because these artifactual sequences are produced randomly, the
true population sizes and community structure are difficult to assess. Although some of
the artifactual sequences can be filtered out, diversity is still overestimated considerably
(11% to 35%) (Gomez-Alvarez et al., 2009; Kunin et al., 2010). Phylogenetic
microarrays do not have these limitations and have been proven to be useful in analysis
of complex microbiomes in human gut (Palmer et al., 2006; Rajilic-Stojanovic et al.,
2009). Using the phylogenetic framework established from the meta-analysis, we
developed a phylogenetic microarray dedicated to comprehensive analysis of ruminal
bacteria. The microarray, referred to as RumenArray, was validated in terms of probe
specificity, detection limits, and dynamic range of detection. We also tested the utility of
the RumenArray by analyzing some fractionated ruminal samples collected from sheep
5
fed two different diets. The RumenArray is the first phylogenetic microarray dedicated to
comprehensive analysis of ruminal bacteriome, and it may be used in support of semi-
quantitative analysis of rumen bacteria in nutritional studies.
Collectively, the series of studies described here have advanced our understanding
of the ruminal microbiome, providing detailed information on the effects on monensin
and dietary fat on ruminal bacteria, and developed useful tools that can help significantly
improve future studies of ruminal microbiome.
6
CHAPTER 2
REVIEW OF LITERATURE
2.1 Microbial communities in the rumen
The rumen harbors a complex microbiome consisting of diverse bacteria, archaea,
fungi, protozoa, and viruses. This microbiome co-evolves with the host and forms a
stable yet dynamic climax community. The very survival and well-being of ruminant
animals depends on digestion and subsequent fermentation of ingested feed by a
functional ruminal microbiome.
2.1.1 Bacterial communities
Bacteria dominate in the rumen. Bacteria play the most important role in
converting ingested feed to nutrients (e.g., acetic, propionic, and butyric acids) that can
be assimilated by the ruminant hosts. Bacteria also make the greatest contribution to the
nitrogen supply to the host animals. Although amylolytic and lipolytic bacteria also
contribute to feed digestion, cellulolytic bacteria are the focus of many studies on the
ruminal microbiome because degradation of cellulose, which is recalcitrant, is often the
limiting step in feed digestion.
2.1.1.1 Major cellulolytic bacteria
7
Fibrobacter succinogenes, Ruminococcus albus, and R. flavefaciens are three
major cellulolytic bacteria commonly isolated from the rumen. F. succinogenes is a
Gram-negative and rod-shaped anaerobe first isolated from the rumen of cattle (Hungate,
1950). Strains tested of F. succinogenes degrade fiber and even crystalline cellulose more
actively than those of R. albus or R. flavefaciens (Kobayashi et al., 2008). Because F.
succinogenes is phylogenetically diverse, Kobayashi et al. (2008) divided F.
succinogenes into four phylogenetic groups and indicated that one of the four groups
(group 1) is more important in fiber degradation than the other three groups. The genus
Fibrobacter was represented by 26 operational taxonomic units (OTUs), and 11 of the 26
OTUs were associated with F. succinogenes, supporting the diversity of F. succinogenes
(Kim et al., 2011b). Six of the 11 OTUs were represented by less than 5 rrs sequences,
whereas the remaining 5 OTUs were represented by more than 5 rrs sequences. The 6
OTU groups may play more important role in cellulose degradation than the 5 less
prevalent OTU groups.
Phylogenetic analysis of the genus Ruminococcus on the basis of 16S rRNA gene
(rrs) sequence comparisons showed that all species previously isolated fall within the
Class Clostridia of phylum Firmicutes. However, a few species were classified to class
Bacilli. The newly defined genus Ruminococcus is a monophylogenic group and contains
both the Ruminococcus species isolated from the rumen: R. albus and R. flavefaciens. R.
albus is a Gram-positive, non-pigmented (white) and coccus-shaped anaerobe first
isolated from the rumen of dairy cattle (Hungate, 1957) and subsequently from other
herbivorous animals (Hobson and Stewart, 1997). The growth of R. albus in the medium
containing cellulose as a sole energy source was shown to be dependent on the addition
8
of rumen fluid in the growth medium (Wood et al., 1982). About the same time, a
distinguishing feature of R. albus strain 8 was determined as its dependence on the
provision of micromolar concentrations of phenylacetic acid (PPA) and phenylpropionic
acids (PPA) for optimal growth and cellulose degradation (Hungate and Stack, 1982;
Stack and Cotta, 1986; Stack and Hungate, 1984). Subsequent work with other strains of
R. albus as well as other cellulolytic ruminal bacteria established the effects of PAA/PPA
were species-specific, and it is now widely accepted that cellulose but not xylan
degradation by R. albus strains is conditional on the availability of PAA/PPA (Morrison
et al. 1990; Reveneau et al. 2003; Stack and Cotta, 1986).
Ruminococcus flavefaciens, which is a Gram-variable and coccus-shaped
anaerobe produces a yellow pigment. R. flavefaciens is generally less abundant than R.
albus in the rumen because R. flavefaciens is inhibited by a bacteriocin produced by some
strains of R. albus (Russell, 2002). However, some studies including a study in Chapter 4
showed that R. flavefaciens is more abundant than R. albus (Kongmun et al., 2011;
Mosoni et al., 2011). R. flavefaciens mainly digested epidermis, schlerenchyma and
phloem cells via attachment to their cut edges (Hobson and Stewart, 1997). Unlike R.
albus, PAA/PPA is not required for optimum growth of R. flavefaciens.
2.1.1.2 Minor cellulolytic bacteria
Butyrivibrio fibrisolvens is a Gram-negative and rod-shaped anaerobe first
isolated from the bovine rumen (Hungate, 1950). Some strains of B. fibrisolvens were
involved in cellulose degradation, although they have a limited ability to degrade
cellulose compared to F. succinogenes, R. albus and R. flavefaciens (Dehority, 2003).
9
Therefore, B. fibrisolvens strains are thought to play a minor role in cellulose degradation
in the rumen. Eubacterium cellulosolvens is a Gram-positive and rod-shaped anaerobe
first isolated from the bovine rumen, and it degrades cellulose (Bryant et al., 1958).
However, E. cellulosolvens is thought to play an unimportant role in cellulose
degradation due to its low abundance (Bryant et al., 1958). Four cellulolytic Clostridium
species, which are C. cellobioparus, C. locheadii, C. longisporum and C.
polysaccharolyticum, were isolated from the rumen (Dehority, 2003). Their contribution
to cellulose degradation in the rumen remains to be determined. A cellulolytic
Micromonospora strain was isolated from the ovine rumen but it is thought not to be
important in cellulose degradation (Maluszynska and Janota-Bassalik, 1974). Other
cellulolytic bacteria isolated from the rumen include Fusobacterium polysaccharolyticum
(Van Gylswyk, 1980), a Cellulomonas strain (Kim et al., 2011b), and Cellulosilyticum
ruminicola (Cai and Dong, 2010). Future studies are needed to evaluate their significance
in rumen feed digestion.
2.1.1.3 Uncultured cellulolytic bacteria
Fibrobacter, Ruminococcus, and Butyrivibrio had many species-level OTUs
identified from the rrs sequences of uncultured ruminal bacteria (Kim et al., 2011b).
These uncultured Fibrobacter, Ruminococcus and Butyrivibrio OTUs are presumed to
play an important role in cellulose degradation (Kim et al., 2011b). Cellulolytic
Acetivibrio cellulolyticus and Aectivibrio cellulosolvens had been isolated from sewage
sludge (Patel et al., 1980; Khan et al., 1984). Ruminal Acetivibrio had 106 species-level
OTUs defined from ruminal rrs sequences (Kim et al., 2011b). The real-time PCR assay
10
showed that one uncultured Acetivibrio OTU named Ad-H1-14-1 was more abundant in
sheep fed hay than in sheep fed corn:hay and as numerous as R. flavefaciens (Stiverson et
al., 2011). Therefore, some strains of Acetivibrio may contribute significantly to cellulose
degradation. Larue et al. (2005) reported that many rrs clones were closely related to
Clostridium, and some of these uncultured Clostridium spp. may be important to
cellulose degradation. This premise corroborates the finding of a diverse Clostridium spp.,
such as C. cellobioparus, C. locheadii, C. longisporum and C. polysaccharolyticum, in
the rumen (Dehority, 2003).
Numerous rrs sequences recovered from the adherent fraction of rumen contents
were assigned to unclassified Ruminococcaceae, unclassified Clostridiales, or
unclassified Lachnospiraceae (Kim et al., 2011b). High abundance of uncultured bacteria
classified into these groups was confirmed using the real-time PCR assays (Stiverson et
al., 2011). Many of the uncultured bacteria assigned to these three groups could be
important to cellulose degradation in the rumen.
2.1.2 Archaeal communities
Methane is produced as an end-product of rumen fermentation, and approximately
17 liters per an hour can be produced by a cow (Russell, 2002). Because of the short
retention time in the rumen, almost all the methanogens found in the rumen are
hydrogenotrophic methanogens, which grow much faster than acetotrophic methanogens.
Smith and Hungate (1958) showed that methanogens are present in the 10-7
dilution of
rumen liquid. Janssen and Kirs (2008) reported that, as of 2008, methanogens had been
classified to 28 genera and 113 species of which only a few were recovered from the
11
rumen. Methanobrevibacter is the predominant genus of methanogen. It is not only a
genus frequently reported from isolations but also the genus represented by most rrs
sequences (Kim et al., 2010b). Methanobrevibacter ruminantium was the first
methanogen isolated from the rumen (Smith and Hungate 1958). Two new species of
Methanobrevibacter, M. millerae and M. olleyae, were isolated in recent years from cattle
and sheep, respectively (Rea et al., 2007). Methanomicrobium mobile is another
predominant cultured ruminal methanogen species, reaching a population as high as >
108/ ml (Hobson and Stewart, 1997). Methanobacterium formicicum (Gilbert et al. 2010)
and Methanobacterium bryantii (Janssen and Kirs, 2008) are two species isolated from
bovine rumen. Methanobacterium beijingense was represented by only one rrs sequence
recovered from goat rumen (Kim et al., 2011b). Methanoculleus spp. were only recently
isolated from the rumen. Janssen and Kirs (2008) isolated a Methanoculleus olentangyi
strain from the rumen, but no ruminal rrs sequence corresponding to this isolate was
found in the RDP database (Kim et al., 2011b). Although not published, three
Methanoculleus marisnigri strains of rumen origin were also recorded in the RDP
database (Kim et al., 2011b). Methanosarcina was reported (Hobson and Stewart, 1977)
in the rumen, but not as a numerous methanogen (Janssen and Kirs, 2008). Only two
ruminal rrs sequences recovered from unpublished studies were classified to
Methanosarcina barkeri (Kim et al., 2011b). Ruminal Methanosaet spp., the obligate
acetotrophic methanogens, has not been isolated or represented in rrs sequence databases.
Collectively, cultured methanogens only account for a small part of diversity of ruminal
methanogens (Janssen and Kirs, 2008), and based on a meta-analysis, rrs sequences
12
recovered from methanogen isolates account for less than 2% of all the methanogen
sequences of rumen origin (Kim et al. 2011b).
2.2 Methods to investigate microbial diversity
2.2.1 Cultivation-dependent methods
Investigation of ruminal bacteria has been done with cultivation-based methods
for many decades. Cultivation-based studies have helped to elucidate some of the
important metabolic functions in the rumen from in vitro studies of model organisms
(Hobson and Stewart, 1997).
Anaerobic roll tubes from which individual colonies could be selected had been
used to isolate ruminal bacteria (Dehority, 2003). After anaerobic glove boxes were
developed, Petri plates could be easily used to isolate ruminal bacteria in the anaerobic
glove boxes (Leedle and Hespell, 1980). Hungate (1950) primarily developed an
anaerobic technique using tubes sealed with rubber stoppers under O2 free CO2.
Cellulolytic bacteria such as F. succinogenes, R. albus and R. flavefaciens were first
isolated from the bovine rumen using the anaerobic technique by Hungate (1950).
Butyrivibrio fibrisolvens and Prevotella ruminicola, predominant hemicellulose
degrading bacteria, were also isolated from the rumen using the anaerobic technique
(Dehority, 2003). The anaerobic technique also helped isolate the following amylolytic
bacteria: Streptococcus bovis, Ruminobacter amylophilus, Succinimonas amylolytica, and
Selenomonas ruminantium (Dehority, 2003). In addition, lactate-utilizing bacteria and
methanogens could be isolated from the rumen using the Hungate anaerobic technique
13
(Dehority, 2003). These culturable species have been used as models of rumen microbial
ecology.
However, rrs-based studies showed that culturable ruminal bacteria could explain
only a small proportion of the ruminal bacteria (Deng et al., 2008; Kim et al., 2011b).
Ruminal bacteria are very sensitive to oxygen because they are strict anaerobes. Also,
liquid media for isolation of pure cultures may not represent the true ruminal
environment. These limitations led to the use of the rrs-based techniques. However,
isolation of pure cultures is still very important in our understanding of ruminal microbial
ecology. New isolation methods need to be developed for future studies.
2.2.2 Cultivation-independent methods
Woese et al. (1983) first suggested rrs as a phylogenetic marker because it is
phylogenetically conserved and not laterally transferred (Gentry et al., 2006). All the
microbes have rrs sequences consisting of hypervariable and universal regions (Yu et al.,
2004a; Gentry et al., 2006). Therefore, the hypervariable regions can be used to identify
individual species whereas the universal regions can be targeted to analyze broad groups
of microbes. Since rrs-targeted analysis was first applied to examining ruminal microbial
diversity (Stahl et al., 1998), numerous studies have attempted to investigate ruminal
bacterial diversity by rrs-targeted analysis using several molecular approaches (Kim et
al., 2011b).
For the past three decades, construction of rrs clone libraries has been used to
identify predominant bacteria in the rumen. Most studies using rrs clone libraries have
focused on ruminal bacteria present in rumen fluid (e.g. Tajima et al., 2000, 2007;
14
Ozutsumi et al., 2005; Zhou et al., 2009), whereas other studies examined ruminal
bacteria attached to plant particles (Larue et al., 2005; Yu et al., 2006; Brulc et al., 2009).
A few studies focused on ruminal bacteria present on the rumen wall (Cho et al., 2006;
Lukas et al., 2010). These studies showed that ruminal bacterial diversity differed among
these microenvironments. All the ruminal sequences that passed quality controls are
archived in the RDP database, while GenBank serves as the main depository of all the
sequences (including sequences of poor quality and chimeric sequences) recovered from
any source, including the rumen. Analysis of rrs clone libraries has contributed to
identifying predominant and novel ruminal bacteria that are both culturable and
unculturable. However, it is laborious to use this method and the results are not
quantitative. Also, it is difficult to identify less numerous members of the population
using this method due to the limited number of clones that researchers can afford to
sequence. Nonetheless, this method is still important because a small number of rrs
clones can still help find novel species-level OTUs as demonstrated recently (Kim et al.,
2011c).
Fluorescence in situ hybridization (FISH) has been used to visually detect target
organisms using probes labeled fluorescently. The probes can hybridize to rRNA within
the undamaged microbial cell (Deng et al., 2008) and detect microbes in situ within its
natural environment. Yanagita et al. (2000) examined the diversity of ruminal
methanogens using the FISH method and showed that Methanomicrobium mobile
accounts for 54% of all the methanogens. Mackie et al. (2003) identified the presence of
ruminal Oscillospira species using FISH. The FISH method has helped to visualize both
culturable and unculturable bacteria in situ within a ruminal environment. However it is a
15
laborious and non-quantitative method (McSweeney et al., 2007), and its application is
limited due to the constraint of probe design for FISH (Deng et al., 2008). Numerous
uncultured bacteria were identified in the ruminal adherent fraction, and they may be
associated with fiber degradation (Kim et al., 2011b). Their attachment to plant biomass
can be confirmed using the FISH method.
Real-time PCR assays have been used to quantitatively estimate microbial
populations in complex environmental samples (McSweeney et al., 2007). Tajima et al.
(2001a) designed primer sets for 12 ruminal species and quantified these using a real-
time PCR assay. Ruminal archaea, fungi and protozoa have also been quantified using
real-time PCR (Sylvester et al., 2004; Denman and McSweeney, 2006; Jeyanathan et al.,
2011). Stiverson et al. (2011) reported the first study that quantified uncultured bacteria
represented by rrs sequences in the rumen using real-time PCR. This method is not
suitable for discovery of novel diversity because primers and probes have to be designed
from known sequences. However, it is the most suitable method to evaluate dietary effect
on ruminal microbes because dietary manipulation often results in quantitative changes in
population sizes, which cannot be precisely determined by other methods, such as clone
libraries, DGGE, or pyrosequencing.
DGGE has been used to separate rrs PCR fragments amplified from complex
environmental DNA. Yu et al. (2004a) used DGGE to examine diversity of total ruminal
bacteria and identified the most useful variable region based on rrs sequences. Genera
Prevotella and Treponema in the rumen were analyzed using the DGGE technique with
genus-specific primers (Bekele et al., 2010, 2011). Yu et al. (2008) also reported that the
V3 region is the best target in DGGE profiling of ruminal archaea. The rrs sequences of
16
novel bacteria can be detected using this method, but it is difficult to confirm non-
predominant bacteria. As a profiling technique, DGGE can help identify represent
samples that can be analyzed in detail using other molecular techniques, such as clone
libraries and DNA sequencing.
Terminal restriction fragment length polymorphisms (T-RFLP) were first used to
rapidly screen microbial diversity in sludge, aquifer sand, and termite guts (Liu et al.,
1997). Metagenomic DNA extracted from the environment sample is subjected to PCR
using universal primers of which one is labeled at the 5’-end, and then PCR products are
digested with a restriction enzyme. A DNA sequencer is commonly used to visualize the
terminal fragment that is fluorescently labeled (Kitts, 2001). Fernando et al. (2010)
analyzed microbial diversity in feedlot cattle during shift from a high-forage diet to a
high-grain diet using the T-RFLP technique. Frey et al. (2010) also examined microbial
diversity in rumen, duodenum, ileum and feces of cattle using the T-RFLP technique. The
use of the DNA sequencer helps provide greatly reproducible data for repeated samples
(Liu et al., 1997). However, rrs sequence information cannot be obtained directly from
the T-RFLP profile, and two different rrs sequences representing different species can
have the same peak if a terminal restriction site is shared. Kim et al. (2011b) retrieved
more than 10,000 bacterial rrs sequences of rumen origin, and they can be used as a
reference set in inferring the bacteria represented by individual TRFs recovered from
ruminal bacterial communities. Such a ruminal dataset can reduce ambiguity that results
from large generic databases.
A phylogenetic microarray is a small gene chip that includes numerous
oligonucleotide probes and allows comprehensive simultaneous detection of numerous
17
rrs targets. Phylogenetic microarrays have been used to examine microbial communities
in various environments such as soil (Small et al., 2001; Liles et al., 2010), human gut
(Palmer et al., 2006; Kang et al., 2010), human feces (Rajilic-Stojanovic et al., 2009),
activated sludge (Adamczyk et al., 2003), and lake (Castiglioni et al., 2004). Although a
phylogenetic microarray can rapidly and simultaneously detect numerous microbial
populations, it is only semi-quantitative and has lower sensitivity and specificity than
real-time PCR (McSweeney et al., 2007). However, it is still very useful in evaluating
dietary effect because microarray allows simultaneous semi-quantification of multiple
species of bacteria. No ruminal phylogenetic microarray has been reported until this
study.
Since the 454 Genome Sequencer (GS) (Roche, Branford, Connecticut) was
developed for massively parallel sequencing applications (Margulies et al., 2005), it has
been applied to analyze microbiomes in various samples (Turnbaugh et al., 2006; Roesch
et al., 2007; Qu et al., 2008; Pope et al., 2010), including ruminal samples (Callaway et
al., 2010). The original 454 GS FLX system could read approximately 250 bp spanning
one or two variable rrs regions. As the sequencing technology continues to improve, the
current 454 GS FLX Titanium system is able to produce read lengths of about 500 bp.
Recently 454 Life Sciences has announced a new GS FLX system that will produce 1,000
bp reads. It is expected that sequencing of full-length rrs (1.5 kb) will be achieved in the
near future. For analysis of ruminal microbiomes, Brulc et al. (2009) examined
microbiomes in liquid and adherent fractions of rumen digesta using the GS20 system
and showed that ruminal microbial diversity of three cattle fed the same diet were
noticeably different. Pitta et al. (2010) compared ruminal microbiomes between cattle fed
18
bermudagrass hay diet and cattle grazing winter wheat. Callaway et al. (2010) compared
ruminal microbiomes among cattle fed diets containing 0, 25, and 50% dried distillers
grain plus solubles (DDGS), and they reported that dietary supplementation with DDGS
altered the ruminal microbiomes. Another study assessed in vitro fermentation dynamics
of corn products and examined differences in ruminal microbiomes between two different
fermentation periods of time (Williams et al., 2010). However, the number of rare OTUs
could be overestimated due to pyrosequencing errors. Kunin et al. (2010) evaluated the
pyrosequencing errors using only Escherichia coli MG1655 as a reference rrs sequence,
and they found that the number of OTUs was greatly overestimated. Kunin et al. (2010)
concluded that the use of stringent quality-based trimming and the calculation of OTUs at
≤ 97% identity could reduce, but not eliminate, the pyrosequencing errors. It remains to
be a challenge to distinguish real sequences from artifactual sequences produced during
pyrosequencing. Recently, Huse has developed a pseudo-single linkage algorithm that
can remove rrs sequences that seem to result from the pyrosequencing errors
(unpublished study), and it was added in the Mothur program (Schloss et al., 2009).
These approaches can contribute to improved diversity analysis. However, until
sequencing accuracy is improved to a level comparable to that of the Sanger sequencing,
pyrosequencing data need to be interpreted with caution. Additionally, prevalence of
individual sequence reads has been used to calculate the relative abundance of the
bacteria or archaea represented. However, it should be kept in mind that pyrosequencing
subjects to PCR bias because two rounds of PCR are involved in pyrosequencing:
generation of PCR amplicons and emulsion PCR. Therefore, relative abundance and
community structure calculated from sequence prevalence can be questionable.
19
2.3 Bioinformatics analysis to investigate microbial diversity
Rarefaction has been used to analyze and compare species richness based on
OTUs among samples of different sizes (Hughes et al., 2001). Rarefaction curves can be
constructed by computing species-level OTUs for the number of rrs sequences sampled
as described previously (Schloss et al., 2004). Rarefaction curves increase quickly at first
and then approach an asymptote where few new OTUs are found with increased
sampling. However, asymptotic richness cannot be read off rarefaction curves directly
because rarefaction curves rarely reach plateau. The asymptotic richness can be estimated
using the non-linear model procedure (PROC NLIN) of SAS (V9.1, SAS Inst. Inc., Cary,
NC) as described previously (Larue et al., 2005). Nonparametric estimators, such as
Chao1 and ACE, can also be used to examine microbial species richness. These two
nonparametric estimators use mark-release-recapture (MRR) statistics that uses the ratio
of OTUs that have been observed and singleton OTUs (Hughes et al., 2001). Chao1 and
ACE tend to underestimate richness when sample sizes are small (Hughes et al., 2001).
Because the morphology of most microbes cannot be distinguished under a
microscope, cultivation-independent analysis using rrs sequences has been used to
estimate microbial phylogenetic diversity. To evaluate the microbial phylogenetic
diversity from numerous rrs sequences, systematic analysis has been developed using
various bioinformatics programs (Schloss and Handelsman, 2004). The rrs sequences
need to be aligned prior to microbial phylogenetic analysis. The alignment format can be
directly downloaded from the RDP database (Cole et al., 2009) or created using
bioinformatic programs such as ClustalW (Larkin et al., 2007), Greengenes (Desantis et
20
al., 2006) and Silva (Pruesse et al., 2007), and then distance matrices can be created using
the DNADIST program in the PHYLIP package (Schloss and Handelsman, 2005). OTUs
can be generated from the distance matrices using the DOTUR program, and then
rarefaction curves can be constructed (Schloss and Handelsman, 2005). OTUs at 0.03,
0.05, and 0.10 phylogenetic distances are conventionally used to represent species, genus,
and family, respectively, based on the full-length rrs sequences (Kim et al., 2011a). The
maximum number of OTUs can be estimated from the rarefaction curves using the non-
linear model, and the percent coverage can be computed from observed and maximum
numbers of OTUs as described previously (Larue et al., 2005; Kim et al., 2011b). Since
the Mothur program containing various bioinformatic tools was recently developed,
alignment, distance matrices, and OTUs can be generated directly (Schloss et al., 2009).
Caporaso et al. (2010) also developed a bioinformatics program, ‘quantitative insights
into microbial ecology’ (QIIME), to analyze microbial diversity. The QIIME program
also includes a variety of bioinformatics tools to analyze and visualize microbial diversity,
and it rapidly calculates OTUs using Uclust that is a clustering, alignment and search
algorithm for the analysis of large sequence datasets (Caporaso et al., 2010).
2.4 The use of monensin and its role in rumen fermentation
Monensin, an ionophore, has been used to improve feed efficiency in ruminant
animals (Russell and Strobel, 1989). Monensin has been reported to decrease methane
production, reducing energy loss, and increase propionate at the expense of acetate
(Russell and Strobel, 1989). Monensin also can decrease urinary ammonia excretion,
ruminal acidosis and liver abscesses (Callaway et al., 2003). As a result, efficient feed
21
utilization results in improvement of production in ruminant animals (Callaway et al.,
2003). However, some studies did not show that monensin supplementation affects the
acetate:propionate ratio (e.g. Firkins et al., 2008; Oelker et al., 2009; Mathew et al., 2011).
Monensin can replace H+ with Na
+ or K
+ as a metal/proton antiporter (Callaway et al.,
2003). Intracellular K+ is replaced with extracellular H
+, whereas extracellular Na
+ is
substituted for intracellular H+ (Callaway et al., 2003). Because the gradient of K
+ is
higher than that of Na+, accumulated H
+ decreases pH (Callaway et al., 2003). The
increased H+ activates a reversible ATPase and then pumps the intracellular H
+ out of the
cell (Callaway et al., 2003). Other ATP-dependent pumps were also activated to restore
Na+/K+ gradients, resulting in cell death by reduction in intracellular ATP pools (Russell
and Strobel, 1989).
Lipophilic monensin inhibits Gram-positive bacteria more than Gram-negative,
because Gram-positive bacteria lack an outer membrane present in Gram-negative
bacteria (Nagaraja et al., 1997; Callaway et al., 2003). In pure cultures, the growth of
Gram-positive bacteria was inhibited by monensin, while the growth of Gram-negative
Escherichia coli O157:H7 or K12 was not reduced by monensin (Buchko et al., 2000).
However, the outer membrane may not be the only factor for monensin sensitivity
(Russell and Strobel, 1989). This is exemplified by the finding that some Gram-negative
bacteria were susceptible to monensin, and monensin tolerance was developed in both
Gram-positive and Gram-negative bacteria (Callaway and Russell, 2000). Therefore,
supplementary monensin should result in shift towards monensin-resistant microbial
populations (Callaway et al., 2003). However, a conflicting finding was reported from an
early rrs-based study where no significant change in microbial population was detected
22
after dietary monensin supplement (Stahl et al., 1988). Other dietary and intraruminal
environmental factors other than supplementary monensin may be associated with long
term change of ruminal microbial communities (Firkins et al., 2008). The use of
pyrosequencing analyses will help provide deep insight into the shift of microbial
populations as affected by supplementary monensin.
2.5 Biohydrogenation in the rumen
Lipids in the rumen are metabolized through lipolysis and subsequent
biohydrogenation (BH) of unsaturated fatty acids. Dietary lipids are hydrolyzed by
ruminal microbial and plant lipase, releasing polyunsaturated fatty acids (PUFA) that are
commonly contained in dietary grass and feedstuff for ruminant animals. These PUFA,
such as linoleic acid (cis-9, cis-12 18:2) and α-linolenic acid (cis-9, cis-12, cis-15 18:3),
are converted to saturated fatty acids through a BH process carried out by ruminal
bacteria. As intermediates of the BH, conjugated linoleic acid (CLA) is formed from
dietary linoleic acid in the rumen. The final end-product of this BH is stearic acid
(Hobson and Stewart, 1997).
In BH of PUFA, the initial isomerization step results in conversion of linoleic
acid (cis-9, cis-12 18:2) to cis-9, trans-11 CLA and trans-10, cis-12 CLA. These CLA
isomers are hydrogenated to trans-11 18:1 and trans-10 18:1 and then finally reduced to
stearic acid (18:0). The cis-9, trans-11 CLA also can be formed from endogenous
conversion of vaccenic acid (trans-11 18:1) in the host mammary glands through
oxidation by △9-desaturase (Jenkins et al., 2008; Or-Rashid et al., 2009). The BH of 20:5
and 22:6 in fish oil has not been clearly identified. Isomerization, hydrogenation, or chain
23
shortening might be involved in the BH of these two PUFA (Jenkins et al., 2008). In
addition, the Moate model described that 16:1 is hydrogenated to 16:0 (Jenkins et al.,
2008).
Two groups of ruminal microorganisms, termed group A and group B, are
involved in the BH process (Hobson and Stewart, 1997). Group A microorganisms have
the ability to convert linoleic acid to trans 18:1 isomers but cannot hydrogenate trans
18:1 isomers further. Butyrivibrio fibrisolvens is involved in this BH process (Jenkins et
al., 2008). Because trans 18:1 isomers are formed via cis-9, trans-11 CLA, Group A
microorganisms are thought to be cis-9, trans-11 CLA producing microorganisms. Group
B microorganisms can convert not only 18:1 isomers but also 18:2 isomers to stearic
acid. Consequently, both group A and B microorganisms are required for complete
conversion to stearic acid (Hobson and Stewart, 1997). Kemp et al. (1975) identified that
Fusocillus spp. are Group B microorganisms. A recent study (Boeckaert et al., 2009)
showed that ruminal BH in the solid fraction of rumen contents is primarily responsible
for complete conversion of linoleic acid (cis-9, cis-12 18:2) to stearic acid (18:0), but in
the liquid fraction, BH is associated with the conversion of linoleic acid (cis-9, cis-12
18:2) to trans-10 18:1/ trans-11 18:1. Few studies have been conducted on ruminal BH of
α-linolenic acid (cis-9, cis-12, cis-15 18:3). Or-Rashid et al. (2009) presumed that α-
linolenic acid is converted to cis-9, trans-11, cis-15 18:3, trans-9, cis-11, cis-15 18:3,
trans-10, cis-12, cis-15 18:3, trans-9, trans-11, cis-15 18:3, and trans-10, trans-12, cis-15
18:3 isomers by certain rumen microorganisms.
Fellner et al. (1997) described that 18:1 isomers are increased by the inhibition of
the BH process of linoleic acid by supplementary monensin. It seems that monensin
24
hinders the terminal step of the BH process at which 18:1 isomers are converted to stearic
acid (Jenkins et al., 2003). The concentrations of trans-12 18:1, trans-15 18:1 and trans-
16/cis-14 18:1 were increased by supplementary monensin, whereas those of trans-10
and trans-11 18:1 were not affected (Mathew et al., 2011). However, Oelker et al. (2009)
reported that trans-11 18:1 tended to be increased by supplementary monensin. The
concentration of trans-11, cis-15 18:2 also tended to increase when monensin was fed
(Lourenço et al., 2008; Mathew et al., 2011). The concentrations of cis-9, trans-11 CLA
or total CLA were not affected by supplementary monensin (Oelker et al., 2011), but this
result is contradictory with a previous study (Odongo et al., 2007).
2.6 Summary
The ruminal microbiome is complex and diverse. The phylogenetic microarray
allows for detection and semi-quantification of abundant microbes in a comparative
manner. Less abundant microbes could be detected and quantified by real-time PCR with
species- or genus-specific primers. Also, the construction of rrs clone libraries can still
contribute to finding novel ruminal OTUs, which can be used to design new microarray
probes. Further, pyrosequencing can help detect numerous rrs sequences, including those
representing minor species. Integrated investigation of ruminal microbial communities
using these rrs-based techniques will help elucidate the ruminal microbial communities
more precisely than ever before and contribute to understanding rumen function.
25
CHAPTER 3
STATUS OF THE PHYLOGENETIC DIVERSITY CENSUS OF RUMINAL
MICROBIOMES
3.1 Abstract
In this study, the collective microbial diversity in rumen was examined by
performing a meta-analysis of all the curated 16S rRNA gene (rrs) sequences deposited
in the RDP database. As of November 2010, 13,478 bacterial and 3,516 archaeal rrs
sequences were found. The bacterial sequences were assigned to 5,271 operational
taxonomic units (OTUs) at species level (0.03 phylogenetic distance) representing 19
existing phyla, of which the Firmicutes (2,958 OTUs), Bacteroidetes (1,610 OTUs), and
Proteobacteria (226 OTUs) were the most predominant. These bacterial sequences were
grouped into more than 3500 OTUs at genus level (0.05 distance), but only 180 existing
genera were represented. Nearly all of the archaeal sequences were assigned to 943
species-level OTUs in the phylum Euryarchaeota. Although clustered into 670 genus-
level OTUs, only 12 existing archaeal genera were represented. Based on rarefaction
analysis, the current percent coverage at species level reached 71% for bacteria and 65%
for archaea. At least 78,218 bacterial and 24,480 archaeal sequences would be needed to
reach 99.9% coverage. The results of this study may serve as a framework to assess the
significance of individual populations to rumen functions and to guide future studies to
identify the alpha and global diversity of ruminal microbiomes.
3.2 Introduction
26
The rumen has evolved to digest various plant materials by a complex
microbiome consisting of bacteria, archaea, protozoa, and fungi. Within this microbiome,
bacteria are the most abundant domain and make the greatest contribution to digestion
and conversion of feeds to short chain fatty acids (SCFA) and microbial proteins (Hobson
and Stewart, 1997). Ruminal archaea are mostly methanogens that belong to the phylum
Euryarchaeota. Utilizing the CO2 and H2 produced from bacterial fermentation, these
methanogens produce methane, a potent greenhouse gas that is implicated in global
warming (Janssen and Kirs, 2008). Both bacterial and archaeal populations can be
affected by many factors, such as species and age of hosts, diets, seasons, and geographic
regions (Tajima et al., 2001a; Zhou et al., 2009). Numerous efforts have been attempted
to optimize rumen functions by enhancing feed digestion, improving conversion of
dietary nitrogen to microbial proteins, and reducing methane emission and nitrogen
excretion by manipulating the ruminal microbiome through dietary means. Although
limited success has been achieved, few of these dietary manipulations achieved persistent
effect without negatively affecting the overall rumen function (van Nevel and Demeyer,
1996; Calsamiglia et al., 2007; Patra and Saxena, 2009). The lack of sufficient
understanding of the ruminal microbiome is considered one of the major knowledge gaps
that hinder effective enhancement of rumen function (Firkins and Yu, 2006).
The ruminal microbiome, as with other microbiomes, has been investigated
primarily using cultivation-based methods for many decades until the 1980’s when 16S
rRNA gene-targeted analysis was applied (Stahl et al., 1988). Cultured bacteria and
archaea helped in defining some of the important metabolic activities underpinning
rumen functions (Hobson and Stewart, 1997); however, it soon became evident that most
27
of the rumen microbes escaped laboratory cultivation (Whitford et al., 1998). Thereafter,
most studies attempted to characterize the ruminal microbiome by phylogenetic analysis
of 16S rRNA gene (rrs) sequences recovered in clone libraries by direct PCR
amplification (reviewed by Edwards et al., 2004; Deng et al., 2008). Some DNA-based
studies focused on the microbes present in rumen fluid (e.g., Tajima et al., 2000, 2007;
Ozutsumi et al., 2005; Zhou et al., 2009), while other studies analyzed the microbes
partitioned in rumen fluid or embedded in the biofilm adhering to feed particles
separately (e.g., Larue et al., 2005; Yu et al., 2006; Brulc et al., 2009). Because of
practical considerations, most of the studies reported hitherto focused on the ruminal
microbiome of domesticated ruminant animals, but a few studies examined the ruminal
microbiome of wild ruminant species, including reindeer (Sundset et al., 2007) and
several species of wild African ruminants (Nelson et al., 2003). These studies showed
that the ruminal microbiome of wild ruminant animals contains ruminal microbiome
distinct from that of domesticated ruminant animals.
Rarefaction analysis (Hughes et al., 2001) is typically used to estimate the depth
of coverage of diversity in most studies on microbiomes, including the ruminal
microbiome. Because a limited number of rrs sequences were sequenced in individual
studies reported hitherto, the prokaryotic diversity resident in the rumen has only been
partially uncovered. Even the two studies that sequenced thousands of rrs sequences (Yu
et al., 2006; Brulc et al., 2009) failed to achieved complete coverage. Additionally, some
of the rrs sequences recovered from the rumen were deposited in public databases but
have not been reported in the literature, contributing little to characterization and
understanding of ruminal microbial diversity.
28
Besides the limited depth of coverage of diversity, the scope of these studies was
also narrow with respect to species and number of sampled animals, diets, and
geographic regions. Typically, rumen digesta of only a few animals of the same species
(primarily cattle and sheep) fed one or few diets were sampled and analyzed in a small
number of countries. Edwards et al. (2004) noted that individual 16S clone libraries
might have been biased towards certain microbial phyla due to PCR biases and that the
prokaryotic diversity in the rumen might be much greater than that indicated by
individual studies. The limited scope of sampling in individual studies might be a major
factor contributing to such bias. Thus, we hypothesize that the general prokaryotic
diversity of ruminal microbiome can be better defined by a meta-analysis of rrs
sequences (both published and unpublished) recovered from all the rumens that have
been analyzed. Edwards et al. (2004) performed a collective analysis, but only the data
from 3 studies (Whitford et al., 1998; Tajima et al., 1999, 2000) were analyzed.
Sequences shorter than 1 kb were also excluded. In this study, we performed a global
phylogenetic analysis of all publicly available rrs sequences of rumen origin in an effort
to provide a collective and progressive census of the ruminal prokaryotes and to gain
further insight into the ruminal microbiome. Finally, we estimated the current coverage of
the prokaryotic diversity already identified and the number of sequences that would be
needed to fully describe the prokaryotic diversity in the rumen in general.
3.3 Materials and Methods
3.3.1 Sequence data collection and phylogenetic analyses
29
As of November 2010, the RDP database (Release 10, Update 22) was searched
for rrs sequences of rumen origin using the search terms ‘rumen’ and ‘ruminal’ to collect
sequences of both ruminal bacteria and archaea. Sequences of ‘suspect quality’ were
excluded using the option of ‘Quality’ in the RDP database. To ensure the rrs sequences
of cultured ruminal bacteria and archaea were included in our analysis, databases of
ATCC, CCUG, DSMZ, and JCM culture collections were searched using the same search
terms ‘rumen’ and ‘ruminal’. The rrs sequences of those isolates were added to the above
sequence selections if they were not found in the initial search in RDP. The sequences for
total bacteria, bacterial isolates, total archaea, and archaeal isolates were downloaded
separately from the RDP database in aligned format with common gaps removed. The
navigation tree for each of these sequence datasets was also downloaded. Each of the
sequence datasets and the associated navigation tree were imported into ARB, a software
environment that can store, manage and analyze rrs sequences (Ludwig et al., 2004).
Taxonomic trees with the Bergey’s taxonomy applied were generated from the imported
navigation trees using the ARB program.
All the rrs sequences of both ruminal bacteria (≥ 274bp) and archaea (≥ 200bp)
were aligned against the rrs Greengenes database (DeSantis et al., 2006). The resultant
aligned sequences were inserted into the Greengenes database ARB tree to generate a
detailed phylogenetic tree using the positional variance by parsimony method (Ludwig et
al., 2004).
3.3.2 Diversity estimate
30
Numbers of OTUs were calculated for total bacteria, total archaea, and major
groups of bacteria using the Mothur program (Schloss et al., 2009). Briefly, the aligned
sequences for each group of bacteria or archaea were separated from the rest of the
sequences in the Greengenes alignment. Distance matrix was constructed at 0.03
(equivalent to species), 0.05 (genus) and 0.10 (family) phylogenetic distances. The
number of OTUs observed within each group was calculated based on rarefaction
analysis implemented in the Mothur program at each of the distances. From each of the
rarefaction curves, the asymptote that indicates the maximum number of OTUs
represented by each group of sequences was estimated at species, genus and family levels
using the non-linear model procedure (PROC NLIN) of SAS (V9.1, SAS Inst. Inc., Cary,
NC) as described previously (Larue et al., 2005). The percent coverage was calculated by
dividing the observed number of OTUs by the maximum number of OTUs estimated. The
number of sequences that would be required to provide 99.9% coverage at these 3 levels
was predicted using the same non-linear model (Larue et al., 2005). The Chao1 and ACE
estimates were also calculated using the Mothur program.
3.4 Results
A “naïve meta-analysis” of ruminal microbiome was conducted using all publicly
available rrs sequences that have been recovered worldwide from both domesticated and
wild ruminant animals using the Sanger DNA sequencing technology. By “naïve meta-
analysis” we mean we collectively analyzed all the sequences irrespective of the
taxonomic associations reported in the respective studies. Thus, this analysis enabled a
fresh, broad view on the global diversity of ruminal microbiome. The result is an updated
31
consolidated perspective of the ruminal microbiome based on most current phylogenetic
diversity information.
3.4.1 Data summary
In total, 13,478 bacterial and 3,516 archaeal sequences of rumen origin were
analyzed (Figs. 1 and 2). The bacterial and archaeal sequences accounted for
approximately 79% and 21%, respectively, of the sequence dataset analyzed. Nineteen
bacterial phyla were represented, with Firmicutes, Bacteroidetes, and Proteobacteria
being the most numerous phyla, accounting for 57.8, 26.7, and 6.9% of the total bacterial
sequences, respectively (Figure 3.1). The remaining 16 phyla, collectively referred to as
‘minor phyla’, each was represented by <3% of the total bacterial sequences. About
99.6% of the archaeal sequences were assigned to phylum Euryarchaeota, and only 11
sequences (0.3%) were assigned to phylum Crenarchaeota (Figure 3.2). The sequences
recovered from cultured bacteria and archaea accounted for only 6.5% and 1.7% of all the
respective sequences, respectively, and represented 88 existing bacterial (Figure 3.3) and
6 known archaeal (Figure 3.4) genera. The generic views depicted by all the bacterial and
the archaeal sequences are provided as Figure 3.5 and Figure 3.2, respectively. The
cultured species within each bacterial genus were also listed in Figure 3.3.
3.4.2 Firmicutes
Approximately 90.6% of the Firmicutes sequences were assigned to class
Clostridia, with the rest assigned to Bacilli, Erysipelotrichi and Unclassified_Firmicutes
(Figure 3.5). The Firmicutes sequences were assigned to a total of 78 known genera, and
32
54 of these genera are within class Clostridia (Figure 3.5). Within class Bacilli, lactic
acid-producing genera Streptococcus and Carnobacterium were most predominant. Some
genera (e.g., Streptococcus and Lactobacillus) were represented mostly by rumen
isolates, while others (e.g., Carnobacterium and Planococcus) comprised entirely
uncultured bacteria. Except for Bacillus, Planococcus, Planomicrobium, Enterococcus
and Lactobacillus, other genera were each represented by <10 sequences. It seems likely
that these minor genera are probably not residents in rumen. Within class Clostridia,
Lachnospiraceae and Ruminococcaceae were the largest families, accounting for 23.8%
and 25.8% of the Clostridia sequences, respectively, followed by Veillonellaceae (7.7%)
(Figure 3.5). The predominant genera included Butyrivibrio (4.8% of the Clostridia
sequences), Acetivibrio (4.5%), Ruminococcus (4.1%), Succiniclasticum (3.7%),
Pseudobutyrivbrio (2.3%) and Mogibacterium (2.3%).
The Butyrivibrio sequences were assigned to 134 species-level OTUs. The
largest OTU contained 17 sequences recovered from cattle, buffaloes, or sheep in at least
10 studies conducted in 7 countries, reflecting its wide distribution. Cultured isolates
contributed 42 sequences within 27 OTUs, and 25 of these sequences were derived from
bacteria named B. fibrisolvens, while 4 sequences from bacteria named B. hungatei
(Figure 3.3). The second largest genus, Acetivibrio, had 106 species-level OTUs, but only
2 sequences came from isolates including a Clostridium cellobioparum strain. The most
abundant Acetivbrio OTU contained 47 sequences recovered from 15 studies on cattle,
sheep, buffaloes, camels, yaks, or reindeer in several continents (Tajima et al., 1999,
2007; Koike et al., 2003a; Ozutsumi et al., 2005; Sundset et al., 2007; Brulc et al., 2009;
Yang et al., 2010a, b). Ruminococcus was the third largest genus represented by 108
33
species-level OTUs, with the two most abundant OTUs containing 16 sequences each.
Fifty-three sequences were recovered from isolates named R. albus, R. flavefaciens, R.
bromii, or Ruminococcus sp. (Figure 3.5). These cultured Ruminococcus sequences were
assigned to 24 OTUs and 7 of them were each represented by one isolate. The fourth
largest genus, Succiniclasticum, was represented by 58 species-level OTUs. The largest
OTU comprised 37 sequences recovered in 11 studies. Except one sequence recovered
from a Succiniclasticum ruminis strain initially isolated from cattle (van Gylswyk, 1995),
all the Succiniclasticum sequences were recovered from uncultured bacteria from cattle,
sheep, camels, gayals, or yaks (Tajima et al., 2007; Brulc et al., 2009; Yang et al., 2010a).
Pseudobutyrivibrio, the fifth largest genus closely related to Butyrivibrio, was
represented by 27 species-level OTUs. This genus was well represented by cultured
isolates (66 in total), primarily isolates previously named Butyrivibrio fibrisolvens
(Figure 3.3). Mogibacterium was the sixth largest genus represented by 35 species-level
OTUs. However, this genus was not represented by any cultured isolate. The most
abundant OTU comprised 40 sequences recovered from rumen epithelium in an
unpublished study (GenBank record).
Other genera represented by >1% but <2% of the bacterial sequences included
Anaerovorax, Coprococcus, Oscillibacter, and Selenomonas. Selenomonas and
Oscillibacter are common genera well represented by rumen isolates, but Coprococcus
and Anaerovorax were primarily represented by uncultured bacteria (Figure 3.5). The
Coprococcus sequences were assigned to 42 species-level OTUs. The most abundant
OTU had 21 sequences, including one recovered from Lachnospira multipara ATCC
19207, an isolate from bovine rumen. Anaerovorax comprised 67 species-level OTUs,
34
with the most abundant OTU containing 16 sequences, 14 of which were recovered from
rumen epithelium in the same unpublished study.
Besides the predominant or familiar genera mentioned above, quite a few genera
that have been rarely reported in the rumen were also represented by the sequence
dataset, including Lachnobacterium, Moryella, Oribacterium, Roseburia,
Syntrophococcus, Papillibacter, and Dialster. Most of these uncommon genera had no
cultured isolates (Figure 3.5).
Within the smallest class Erysipelotrichi, Sharpea, a new genus established in
2008 (Morita et al., 2008), was the largest genus (Figure 3.5). Although bacteria of this
new genus were initially isolated from horse feces, 22 of all the 50 Sharpea sequences
found in RDP were recovered from the rumen, suggesting the rumen as a common habitat
for bacteria of this genus.
A large number of sequences (approx. 60% of the Firmicutes sequences) have
not been classified to any existing family, order, or genus within class Clostridia (Figure
3.5). The largest group (1,858 sequences) of related sequences remains to be classified to
a family in the order Clostridiales. These Unclassified_Clostridiales sequences
represented 606 species-level OTUs, with the largest OTU containing 64 sequences
recovered from cattle in the USA (Brulc et al., 2009) and Japan (Tajima et al., 2007), and
buffalos in China (Yang et al., 2010a). Twelve isolates were found within this group, and
they represented 9 OTUs, with 6 of them being represented by one isolate each: 3
Clostridium sp., one C. sticklandii, one C. clostridioforme, and one unnamed isolate
(Figure 3.3). The second largest unclassified sequence group (1,177 sequences) was
found within family Ruminococcaceae. This group of sequences represented 524 species-
35
level OTUs, with two major OTUs containing 19 sequences each. Sequences in one of
these two OTUs were recovered from cattle or sheep in 6 studies conducted in Canada,
Japan, and the USA (Tajima et al., 1999; Brulc et al., 2009). The other major OTU
contained sequences that were recovered from cattle in the USA (Brulc et al., 2009). This
unclassified group was represented by 4 isolates, including one named R. albus, which
represented 4 OTUs (Figure 3.3). The third largest group, Unclassified_Lachnospiraceae,
contained 1090 sequences that represent 588 species-level OTUs. The largest OTU had
15 sequences recovered from sheep in Belgium (unpublished data). Twenty-five of the
588 OTUs had cultured representatives: Cellulosilyticum ruminicola, Clostridium sp., C.
aminovalericum, C. polysaccharolyticum, C. aminophilum, C. aerotolerans, C.
symbiosum, R. gnavus, E. uniforme, E. rangiferina, L. multipara, P. xylanivorans, and 14
unnamed isolates.
3.4.3 Bacteroidetes
Bacteroidetes was the second most numerous phylum (3,605 sequences). Most
(88.5%) of these sequences were assigned to class Bacteroidia (Figure 3.5), and the rest
of the sequences were assigned to class Sphingobacteria (38 sequences) and
Unclassified_Bacteroidetes (378 sequences). The Bacteroidetes sequences were assigned
to 15 genera within Bacteroidia and 2 genera within Sphingobacteria (Figure 3.5). Of
these genera, 6 were represented by rumen isolates. Prevotella was the most numerous
genus, accounting for 41.5% of the Bacteroidetes sequences or 11.1% of all the bacterial
sequences. The large number of Prevotella sequences reflects the predominance of this
genus in the rumen in general (Stevenson and Weimer 2007; Bekele et al., 2010). The
36
1,496 Prevotella sequences were assigned to 637 OTUs, of which 20 OTUs contained
cultured sequences (58 in total). Four OTUs were represented by only one cultured
bacterium. Most of the Prevotella isolates were classified as P. ruminicola, followed by
P. brevis, P. bryantii, and P. albensis (Figure 3.3). Thirteen isolates remain to be
classified to a Prevotella species. The remaining 617 OTUs were represented only by
uncultured bacteria. The most abundant OTU had 34 sequences recovered in two
unpublished studies.
Nine of the 15 represented genera within Bacteroidia had no cultured
representative, including the second most abundant genus Paraprevotella (represented by
44 OTUs) and the third most abundant genus Barnesiella (24 OTUs) (Figure 3.5). Except
for Barnesiella, Rikenella, and Paraprevotella, other genera were only represented by
<1% of the Bacteroidetes sequences. Genera Hallella and Rikenella might be adapted to
the rumen-like environment because 21.4 and 17.6% of all the respective sequences
found in RDP were of rumen origin.
More than half (53%) of the Bacteroidetes sequences has not been classified to
any existing genus, family, or class (Figure 3.5). These unclassified sequences were
mostly placed into 4 groups in the RDP database: Unclassified_Bacteroidales (27.5%,
395 species-level OTUs), Unclassified_Bacteroidetes (10.5%, 212 species-level OTUs),
Unclassified_Prevotellaceae (8.0%, 236 species-level OTUs), and Unclassified
Porphyromonadaceae (5.7%) (Figure 3.5). All these unclassified sequences were
recovered from uncultured ruminal bacteria (Lodge-Ivey et al., 2005; Brulc et al., 2009;
Yang et al., 2010a), except one isolate unclassified within Porphyromonadaceae, three
isolates unclassified within Bacteroidales, and one isolate unclassified within
37
Bacteroidetes. All the 38 sequences of Sphingobacteria were recovered from uncultured
bacteria. The Unclassified_Bacteroidales sequences accounted for about 22% of all the
respective rumen sequences found in RDP.
3.4.4 Proteobacteria
All the 5 classes of Proteobacteria were represented in the dataset (Figure 3.5).
Class -Proteobacteria was entirely represented by a small number of sequences
recovered from uncultured bacteria. Phenylobacterium was the largest genus (32.8% of
the -Proteobacteria sequences) but all its sequences were recovered from semi-
continuous RUSITEC. Within class β-Proteobacteria, 17 genera were recognized, most
of which were represented by only several sequences. Aquabacterium was the most
predominant genus (32.4% of the β-Proteobacteria sequences); however, all its
sequences were recovered from uncultured bacteria from semi-continuous RUSITEC or
ruminal protozoal cultures. Four sequences were recovered from cultured isolates: one
Comamonas terrigena strain, two Lampropedia hyalina strains, and one Oxalobacter
formigenes strain. γ-Proteobacteria was the most predominant class, which was
represented by about 73% of all the proteobacterial sequences. Twenty-three existing
genera within this class were represented, with Ruminobacter, Succinivibrio,
Escherichia/Shigella, and Pseudomonas each accounting for ≥2% of the γ-proteobacterial
sequences (Figure 3.5). Most of the genera within γ-Proteobacteria were represented by
at least one rumen isolate (Figure 3.3), including well characterized species, such as
Ruminobacter amylophilus, Succinimonas amylolytica, Succinivibrio dextrinosolvens,
Actinobacillus succinogenes, Mannheimia ruminalis, Mannheimia succiniciproducens,
38
Acinetobacter lwoffii, Pseudomonas putida, and Pseudomonas oryzihabitans. Again,
most of these genera were represented by sequences recovered in a few studies,
suggesting paucity of the bacteria represented by these sequences. It should be noted that
genus Psychrobacter comprised >58% of the γ-Proteobacteria sequences, but all its
sequences were recovered from 3 steers in a single study (Brulc et al., 2009). Given that
all the characterized bacteria within this genus are strictly aerobic psychrophiles (Bozal et
al., 2003) and that rumen is a mesophilic environment, the occurrence of Psychrobacter-
like bacteria needs to be replicated. Class δ-Proteobacteria was represented by 47
sequences, with Desulfovibrio being the most predominant genus. Except 5 sequences
from isolates of Desulfovibrio, most of the δ-proteobacterial sequences were recovered
from uncultured bacteria unclassified within Desulfovibrionaceae. ε-Proteobacteria was
the smallest class represented by only 20 sequences, but 5 of them were recovered from
cultured strains: one from Campylobacter sp. and 4 from Wolinella succinogenes (Figure
3.3). Twenty-one of the 53 identified genera of Proteobacteria were represented by at
least one isolate from the rumen.
3.4.5 Minor phyla
Approximately 7% (949 sequences) of the bacterial sequences were assigned to
16 minor phyla (Figure 3.1). These phyla varied in number of sequences, with
Synergistetes being represented by 382 sequences, while Acidobacteria by only one
sequence. Most (95.5%) sequences within phylum Synergistetes were recovered from
rumen epithelium (an unpublished study). Except genera Pyramidobacter and
Thermovirga, each of which accounted for 14% of the sequences, most sequences remain
39
unclassified within family Synergistaceae (Figure 3.5). These results suggest that
Synergistetes might be predominant inhabitants of rumen epithelial surface. Two ruminal
isolates were found within this phylum, and one of them belonged to Synergistes jonesii,
a species isolated from the rumen and capable of degrading toxic pyridinediols (Allison
et al., 1992). Spirochaetes was the second largest minor phylum, and most of its
sequences (79.2%) were assigned to genus Treponema (Figure 3.5). However,
Spirochaetes is a very small phylum, accounting for only 1.1% of all the bacterial
sequences. All the Fibrobacteres sequences belonged to the cellulolytic genus
Fibrobacter, and >50% of its sequences were assigned to F. succinogenes (Figure 3.5).
This species contained 58 of the 59 Fibrobacter isolates (Figure 3.3), reflecting the
predominance of this species within Fibrobacter and the justified effort made to isolate
and characterize it (Bera-Maillet et al., 2004; Shinkai et al., 2010). Actinobacteria was
represented by 107 sequences, and most of its sequences were assigned to Olsenella and
Bifidobacterium (Figure 3.5). The former genus has one species (i.e., Olsenella oviles)
initially isolated from the rumen (Dewhirst et al., 2001), while the latter genus contained
sequences derived from cultured isolates that belonged to several species. The 16
Tenericutes sequences were assigned to genus Asteroleplasma, Acholeplasma or
Anaeroplasma, the last of which was represented by the type species and strains isolated
from the rumen (Figures 3.3 and 3.5). Fusobacteria comprised 10 sequences recovered
from cultured isolates named Fusobacterium sp. (Figures 3.3 and 3.5). The remaining
minor phyla, including Acidobacteria, Chloroflexi, Deferribacteres, Lentisphaerae,
Planctomycetes, Verrucomicrobia, OP10, Cyanobacteria, SR1, and TM7 were
represented by a very small number of sequences recovered from uncultured bacteria
40
(Figure 3.5). It remains to be determined if they are resident bacteria and have any
significant role in the rumen.
3.4.6 Archaea
Both phyla Crenarchaeota and Euryarchaeota were represented by the sequence
dataset, but the former comprised only 11 sequences from uncultured or unclassified
Thermoprotei (Figure 3.2). About 94% of all the archaeal sequences were assigned to 4
classes within phylum Euryarchaeota (Figure 3.2): Methanobacteria (70.3% of all the
archaeal sequences), Methanomicrobia (16.4%), Thermoplasmata (7.4%), and
Methanopyri (0.03%). Within these classes, 12 genera were represented (Figure 3.2).
Within Methanobacteria, 3 genera of family Methanobacteriaceae were represented:
Methanobrevibacter, Methanosphaera and Methanobacterium. There were 239
sequences unclassified to any existing genus. The Methanobrevibacter sequences
represented 201 species-level OTUs, with the largest OTU containing 456 sequences.
This OTU contained sequences recovered from cattle and sheep (Tajima et al., 2001b;
Whitford et al., 2001a; Wright et al., 2004, 2007, 2008; Skillman et al., 2006; Rea et al.,
2007; Zhou et al., 2009) and reindeer (Sundset et al., 2009) in 13 studies. This OTU also
contained 4 isolates named Methanobrevibacter sp.. Genus Methanobrevibacter was
represented by 31 isolate sequences that were assigned to 16 OTUs and classified as M.
ruminantium, M. olleyae and M. millerae, or designated as Methanobrevibacter sp..
These results further confirmed the predominance and ubiquity of hydrogenotrophic
Methanobrevibacter spp. in the rumen (Janssen and Kirs, 2008).
41
The Methanosphaera sequences were assigned to 225 species-level OTUs with
no cultured representative, suggesting its predominance in the rumen but poor
culturability in the laboratory. The most abundant OTU had 25 sequences recovered from
cattle (Whitford et al., 2001a), goat (Cheng et al., 2009), reindeer (Sundset et al., 2009),
and muskoxen (an unpublished study). On the contrary, Methanobacterium was a minor
genus, but well represented by isolates, including M. beijingenes, M. bryantii, M.
formicicum, and 7 other Methanobacterium sp. isolates.
Within class Methanomicrobia, Methanomicrobium was the largest genus
(92.3% of the Methanomicrobia sequences), but except for 6 sequences recovered from 2
species and one unnamed isolate (Figure 3.2), most of the sequences were recovered from
uncultured methanomicrobial archaea. In total, 263 species-level OTUs were recognized,
with the most abundant OTU containing 86 sequences recovered from cattle in Korea (an
unpublished study). Six OTUs were represented by one cultured isolate each, named as
Methanomicrobium mobile, Methanoculleus marisnigri, or Methanobacterium sp..
Methanimicrococcus was the second most abundant genus within Methanomicrobia, and
it was represented only by uncultured methanogens. On the contrary, Methanoculleus,
Methanofollis, and Methanosarcina were represented by a few sequences that were
recovered from isolates (Figure 3.2). These sequences may represent archaeal organisms
that are readily culturable but scarce in the rumen. Methanosaeta, the obligate
acetoclastic methanogen genus, was only represented by one uncultured sequence, while
the facultative acetoclastic Methanosarcina was represented by 4 rumen isolates. The
class Thermoplasmata was only represented by uncultured archaea in the genus
Thermogymnomonas or Unclassified Thermoplasmatales. The Thermogymnomonas
42
sequences were assigned to 31 species-level OTUs, with the most abundant OTU
containing 18 sequences recovered from cattle (Wright et al., 2007, and unpublished
studies), reindeer (Sundset et al., 2009), sheep and buffaloes (unpublished studies). All
the isolates in this genus were recovered from rice field or deep-sea hydrothermal vents
(based on GenBank records). Further studies are needed to examine their occurrence in
the rumen.
Three groups of archaeal sequences remain to be classified (Figure 3.2). The
Unclassified_Euryarchaeota group was assigned to 86 species-level OTUs, with the most
abundant one containing 18 sequences recovered from cattle (Wright et al., 2007), sheep
(Wright et al., 2004; Nicholson et al., 2007), reindeer (Sundset et al., 2009), and buffaloes
(an unpublished study). The Unclassified_Methanobacteriaceae sequences represented
92 species-level OTUs, and the most abundant OTU contained 29 sequences recovered
from cattle and muskoxen (unpublished studies). Finally, the
Unclassified_Thermoplasmatales sequences were assigned to 57 species-level OTUs,
with the largest OTU containing 27 sequences recovered from sheep (Wright et al., 2006;
Ohene-Adjei et al., 2007), goats (Cheng et al., 2009), and semi-continuous RUSITEC (an
unpublished study).
3.4.7 Estimates of OTU richness
The numbers of OTUs of Bacteria, Archaea, the major bacterial phyla, 4
predominant genera, and 3 unclassified groups were estimated (Table 3.1). Nearly 5,300
bacterial and 950 archaeal species-level OTUs were defined by the current dataset.
Firmicutes had approximately twice as many OTUs as Bacteroidetes. A large number of
43
OTUs were defined in the unclassified groups that contained numerous sequences
recovered from the biofilm adhering to feed particles in the rumen of sheep (Larue et al.,
2005) and cattle (Brulc et al., 2009) fed hay. The percentage coverage reached 65% and
71% at species levels for archaea and bacteria, respectively. For the 4 major known
bacterial genera (Fibrobacter, Ruminococcus, Butyrivibrio, and Prevotella), coverage at
species level ranged from 73 to 81% (Table 3.1). The coverage for the unclassified
groups was comparable to that of classified groups. As expected, higher coverage was
noted at genus and family levels (Table 3.1).
The maximum numbers of OTUs predicted with rarefaction were lower than the
Chao1 or ACE estimates (Table 3.1). Based on rarefaction estimate, the rumen might
contain >7400 and 1400 species-level OTUs of bacteria and archaea, respectively.
Firmicutes and Bacteroidetes were estimated to have >3900 and >2300 species-level
OTUs, respectively. The 3 unclassified groups might have >700 species-level OTUs
each. Ruminococcus, Butyrivibrio, and Prevotella were predicted to have a large number
of species-level OTUs. At least 32 species-level OTUs could be found in genus
Fibrobacter in the rumen. To reach nearly complete (99.9%) coverage at species level, at
least 78,000 and 24,000 new sequences would be required for bacteria and archaea,
respectively. For bacteria, most of these additional sequences are expected to be from
species within Firmicutes and Bacteroidetes.
3.5. Discussion
3.5.1 Phylogenetic diversity
44
A few phyla were typically reported by individual studies (Whitford et al., 1998;
Tajima et al., 2000; Larue et al., 2005; Brulc et al., 2009), but collectively 19 phyla of
bacteria were represented by the consolidated rrs sequence dataset analyzed in this study.
At low taxonomic ranks (genus and species), the collected global diversity also exceeded
the -diversity, which refers to the biodiversity within a particular habitat, reported in
individual rumens (Table 3.1). This discrepancy might be attributed to the limited number
of rrs sequenced, diets and animals used, the geographic regions and seasons sampled,
and bias associated with the methods used in individual studies. Firmicutes and
Bacteroidetes were the predominant phyla with respect to numbers of both sequences and
species-level OTUs. Although numbers of sequences or OTUs within rrs sequence
datasets may not necessarily reflect distribution or abundance, these two phyla are
recognized to be omnipresent and dominant in the rumen (Edwards et al., 2004; Larue et
al., 2005; Brulc et al., 2009). Based on the dataset used in this study, the phylum
Firmicutes is much more predominant and diverse than Bacteroidetes. However, this
might not be the case in individual studies, and the relative abundance of these two
important phyla varied among studies (e.g., Whitford et al., 1998; Larue et al., 2005;
Ozutsumi et al., 2005; Tajima et al., 2007; Brulc et al., 2009). The variations might result
from differences in PCR primers used (Edwards et al., 2004), fractions (liquid vs. solid)
of rumen samples analyzed, DNA extraction methods used, and coverage achieved.
Indeed, Leser et al. (2002) noted that relative abundance of phylogenetic groups could
differ greatly between separate libraries generated from the same sample due to small
numbers of clones analyzed, or factors that affect PCR amplification. Therefore, the
45
meta-analysis reported in this study might have helped to account for among study effects
and might offer new insight into ruminal microbial diversity.
Ten of the 19 bacterial phyla represented by the sequence dataset did not contain
any isolates from the rumen. This might be due to their dearth in the rumen and/or their
recalcitrance to laboratory cultivation. As mentioned above, several phyla (e.g.,
Acidobacteria, Chloroflexi, Deferribacteres, Lentisphaerae, OP10, and Cyanobacteria)
might not be resident bacteria in the rumen. However, some minor phyla are likely
resident ruminal bacteria. For example, phylum Synergistetes was only represented by 17
sequences until January of 2010, but >360 sequences classified into this phylum were
recently recovered from rumen epithelium (NCBI, unpublished data) that was rarely
analyzed. Thus, caution needs to be exercised when ruling out certain bacteria or archaea
as resident members of the rumen.
Three large groups of unclassified bacteria: the Unclassified_Clostridiales,
Unclassified_Lachnospiraceae, and Unclassified_Ruminococcaceae, are probably
predominant ruminal bacteria. A quantitative analysis of 6 uncultured bacteria within
these groups using respective specific real-time PCR assays showed that these uncultured
bacteria were as abundant as R. albus, R. flavefaciens, and F. succinogenes in the rumen
of both sheep and dairy cattle (unpublished data). These 3 unclassified bacterial groups
are likely competitive in the rumen and some of their species might have an important
role in ruminal feed digestion. Yet, except a few isolates orphaned from other genera or
species, all 3 groups are primarily represented by uncultured bacteria, especially bacteria
from adherent fraction (Larue et al., 2005; Brulc et al., 2009). Isolation and
46
characterization of representative strains belonging to these groups will greatly assist in
better understand their ecology, physiology, and contributions to rumen function.
A large number of species-level OTUs was identified by the consolidated
sequence dataset (Table 3.1), but only 10 archaeal species have been described within
five genera (Figure 3.4). The poor representations by cultured archaea reflect the general
difficulties to isolate rumen methanogens and the limited numbers of cultivation-based
studies conducted hitherto. New species, genera, and families are needed to accommodate
the archaeal diversity represented by the unclassified sequences. We note the many
archaeal sequences that were assigned to Thermogymnomonas, which is a new genus
described in 2007 (Itoh et al., 2007). Based on the type species, Thermogymnomonas
acidicola, this genus is acidophilic, strictly aerobic, and moderately thermophilic. Many
sequences recovered from the rumen, an anaerobic and mesophilic environment suggest
adaptation to the rumen.
Methanobrevibacter, Methanomicrobium and Methanosphaera accounted for 50,
15, and 13% of all the archaeal sequences. The overall predominance of
Methanobrevibacter was in general agreement with its abundance noted in individual
studies (Janssen and Kirs, 2008). The predominance of these hydrogenotrophic
methanogens is likely attributed to their ability to grow relatively rapidly (so avoiding
washout) and to competitively utilize H2 and CO2, major fermentation products in the
rumen. Future mitigation of rumen methane emission might be directed towards
inhibition of these three hydrogenotrophic genera. Of course, alternative H2 sink, such as
homoacetogenesis, is needed to reduce possible negative effect on fiber digestion.
47
3.5.2 Diversity estimates
Although large numbers of bacterial and archaeal species-level OTUs have been
identified by the rrs sequences, more species-level OTUs remain to be identified. The
diversity estimates for all the bacterial groups (Table 3.1) are much greater than
previously suggested (Edwards et al., 2004; Yu et al., 2006). As noted earlier, the dataset
used in this study included sequences recovered from different animals fed different diets
in different countries, and thus might help reach this expanded phylogenetic view of
ruminal microbiome. For the well-studied genera Fibrobacter, Ruminococcus,
Butyrivibrio, and Prevotella, the numbers of OTUs estimated and predicted for each
genus were also much greater than seeming possible. This surprise might be attributable
to, among other factors, a lack of a reliable taxonomy that can classify sequences to
species. For example, nearly all the Fibrobacter sequences were assigned to F.
succinogenes, yet F. succinogenes contains a diverse group of bacteria (Amann et al.,
1992). Based on a 0.03 phylogenetic distance, the Fibrobacter sequences can be assigned
to 26 species-level OTUs. The genus Ruminococcus was also shown to contain two
distinct and unrelated clusters (Rainey and Janssen, 1995). Defined reclassification of
species within these genera will provide a taxonomic framework to support future studies
of these important groups of ruminal bacteria.
The current coverage at species level is still incomplete, even with the well-
studied genera (Table 3.1). Therefore, novel bacteria and archaea remain to be identified.
According to the estimates from the collected sequence datasets, to achieve 99.9%
coverage of ruminal global diversity at species level, >70,000 bacterial and >20,000
archaeal rrs sequences would need to be recovered from multiple animals fed different
48
diets across broad geographic regions. It should be noted, however, that the current
coverage might be an underestimate because with increasing number of sequences, the
predicted maximum richness tended to increase (Yu et al., 2006; Roesch et al., 2007).
Therefore, more sequences than that estimated here might be required. Although
identifying the full diversity in ruminal microbiome has loomed as technically
challenging, the recent advancement of next- or third-generation DNA sequencing
technologies coupled with coordinated international efforts can help achieve this goal.
Indeed, three large datasets of rrs sequences have been generated recently using the
Solexa 1G Genome Analyzer (an unpublished study, GenBank accession numbers:
SRX007415, SRX007414, approx. 70 bp) and the 454 FLX system (GenBank accession
number: SRA009223.8, <300 bp) (Pitta et al., 2010). These sequence datasets are very
large, but they only contain very short sequences, especially those generated by the
Solexa 1G Genome Analyzer. These short sequences were not included or analyzed in
this study because they could not be reliably analyzed together with the longer sequences
of our sequence dataset. With the most recent 454 FLX Titanium system, rrs sequences
up to 800 bp can be massively sequenced. Partial rrs sequences of this length can be
added to the consolidate sequence dataset used in this study. Eventually, the full diversity
of ruminal microbiome can be defined, which may serve as a guideline in design of future
studies on rumen nutrition and provide a framework to assess the significance of
individual population in the rumen.
49
Gro
up
Ob
serv
ed #
of
OT
Us
(% c
over
age)
M
axim
um
# o
f O
TU
s C
lon
es n
eed
ed f
or
99.9
% c
over
age
Rar
efac
tion
ric
hn
ess
Chao
1
AC
E
0.0
3
0.0
5
0.1
0
0.0
3
0.0
5
0.1
0
0.0
3
0.0
5
0.1
0
0.0
3
0.0
5
0.1
0
0.0
3
0.0
5
0.1
0
Tota
l A
rch
aea
949
(6
5)
670
(7
5)
278
(9
1)
1469
895
307
2309
1375
442
4017
2145
550
2448
0
1874
6
11
829
Tota
l B
acte
ria
5271
(71
) 3
774
(82
) 1
785
(96
) 7
426
4599
1859
11
257
6466
2356
1661
4
8507
2370
7821
8
5816
8
3488
2
Fir
mic
ute
s 2
958
(74
) 2
050
(84
) 9
36
(9
8)
3993
2442
956
5826
3407
11
66
8381
4411
11
81
4195
1
3175
0
18111
Ba
cter
oid
etes
1
610
(69
) 11
62
(83
) 5
18
(9
7)
2344
1399
536
3573
1884
681
5232
2384
673
2229
1
1490
3
9042
Pro
teob
act
eria
2
26
(7
1)
172
(8
3)
97 (
94
) 3
20
207
103
508
230
145
927
419
131
5578
3955
2587
Un
clas
sifi
ed
Clo
stri
dia
les
606
(7
6)
400
(8
0)
189
(9
1)
798
502
207
1300
751
276
1947
1044
277
9567
8652
5928
Un
clas
sifi
ed
La
chno
spir
ace
ae
588
(6
6)
452
(8
0)
8
97
567
7
252
4919
Un
clas
sifi
ed
Ru
min
oco
ccace
ae
524
(7
0)
381
(7
8)
7
50
486
11
32
714
1
833
1055
7
077
5674
Fib
roba
cter
2
6 (
81
)
3
2
39
66
488
Ru
min
oco
ccu
s 1
08
(8
1)
134
170
182
1302
Bu
tyri
vib
rio
134
(7
7)
173
251
334
1686
Pre
vote
lla
637
(7
3)
869
1282
1750
8149
Ta
ble
3.1
. T
he
nu
mb
er o
f O
TU
s fo
r to
tal
bac
teri
a, t
ota
l ar
chae
a an
d m
ajo
r g
rou
ps
of
bac
teri
a, a
nd
th
eir
per
cen
tag
e co
ver
age
at t
hre
e
ph
ylo
gen
etic
dis
tan
ces.
50
Figure 3.1. Bacterial phyla represented
by the 16S rRNA gene sequences of
rumen origin. The taxonomic tree was
created using the ARB program. In
total 19 existing bacterial phyla were
represented by the 13,478 bacterial
sequences. All the sequences in
rectangle bars were classified down to
the same taxonomic rank, whereas
sequences in sloped bars were
classified down to different taxonomic
ranks.
51
Figure 3.2. A taxonomic tree showing the genera of ruminal archaea identified by the
RDP database sequences. The lineage at class level is labeled: Mb, class Methanobacteria;
Mm, class Methanomicrobia; Tp, class Thermoplasmata. In total, 12 known genera of
archaea were represented by the 3,516 archaeal sequences. The number in parentheses
indicate the number of sequences recovered from isolates.
52
Figure 3.3. A taxonomic tree showing the bacteria (grouped into genera) isolated from the rumen.
In total, 882 bacterial isolates were classified into 88 known bacterial genera, accounting for 6.5%
of all the bacterial sequences. The most predominant species or isolates were indicated in
brackets. The numbers in parentheses indicate the total numbers of isolates for each species.
Figure 3.3 Continued
53
Figure 3.3 Continued
Figure 3.3 Continued
54
Figure 3.3 Continued
55
Figure 3.4. A taxonomic tree showing the archaea (grouped into genera) isolated from the
rumen. In total, 68 archaeal isolates were classified into 6 known archaeal genera,
accounting for only 1.9% of all the archaeal sequences. The most predominant species or
isolates were indicated in brackets. The numbers in parentheses indicate the total numbers
of isolates for each species.
56
Figure 3.5. A taxonomic tree showing all the genera of ruminal bacteria identified by the 13,478
16S rRNA gene sequences of rumen origin. The taxonomic tree was constructed as described in
Figure 3.1. Of all the 13,478 sequences, 5,845 sequences were assigned to 179 existing genera
within 19 known phyla. The remaining 7,633 sequences could not be assigned to any known
genus. The numbers in parentheses indicate the number of sequences recovered from cultured
isolates.
Figure 3.5 Continued
57
Figure 3.5 Continued
Figure 3.5 Continued
58
Figure 3.5 Continued
Figure 3.5 Continued
59
Figure 3.5 Continued
Figure 3.5 Continued
60
Figure 3.5 Continued
Figure 3.5 Continued
61
Figure 3.5 Continued
Figure 3.5 Continued
62
Figure 3.5 Continued
63
CHAPTER 4
QUANTITATIVE COMPARISONS OF CULTURED AND UNCULTURED
MICROBIAL POPULATIONS IN THE RUMEN OF CATTLE FED DIFFERENT
DIETS
4.1 Abstract
Sequencing analysis of 16S rRNA genes (rrs) amplified by PCR is the primary
method used in examining diversity and populations of bacteria in various samples,
including ruminal samples. However, PCR amplification has bias, and it is often difficult
to infer population functions from rrs sequence data. Thus, sequence frequencies and
sequence comparison often do not provide adequate information on the abundance and
function of the bacteria represented. In this study we used real-time PCR to quantify the
populations of select uncultured bacteria to assess their distribution as affected by diets
and microenvironments within the rumen. The liquid, adherent and solid fractions were
obtained from the rumen of cattle fed two different diets (hay alone vs. hay plus grain).
Specific real-time PCR assays were used to quantify the populations of six uncultured
bacteria present in each fraction. The abundance of major cultured bacteria was also
quantified for comparison. The population of total bacteria was more than 108 rrs copies/
µg DNA and similar across all the fractions, while the population of total archaea was
less than 105 rrs copies/ µg DNA and approximately 10 times higher in cattle fed hay
than in cattle fed hay plus grain. The population of Prevotella spp. was more than 107 rrs
copies/ µg DNA, being the most abundant among all the cultured and the uncultured
bacteria quantified. The populations of Fibrobacter succinogenes, Ruminococcus
flavefaciens and Butyrivibrio spp. were more than 106 rrs copies/ µg DNA, while the
population of Ruminococcus albus was less than 106 rrs copies/ µg DNA. The
64
populations of four of the six uncultured bacteria were approximately 106 rrs copies/ µg
DNA across all the fractions. These four uncultured bacteria were similar in abundance to
F. succinogenes, R. flavefaciens and Butyrivibrio spp. In addition, the populations of the
six uncultured bacteria were slightly higher in the adherent and solid fractions than in the
liquid fraction. These uncultured bacteria may be associated with fiber degradation.
4.2 Introduction
A complex ruminal microbiome mediates hydrolysis of polymeric feedstuffs and
subsequent fermentation of hydrolytic products to volatile fatty acids (VFA) that are used
as the energy source for ruminant animals. Microbial biomass also constitutes the sources
of major protein and B vitamins for the host animals. Being the major contributors to
rumen functions, bacteria have been the focus of microbiological studies of the rumen
microbiome. Cultivation-based methods were used to investigate ruminal bacteria until
the 1980s. As a result, various cultured bacteria were identified, and their functions were
determined through physiological studies of model species or strains. Since rrs sequences
were used to investigate diversity of ruminal bacteria, it became evident that cultured
ruminal bacteria represent only a small portion of the ruminal bacteriome (Stevenson and
Weimer, 2007). Kim et al. (2011b) reported that rrs sequences obtained from cultured
bacteria represent only 7% of all the bacterial sequences of rumen origin. More than 55%
of all the bacterial sequences were assigned to unclassified groups that could not be
classified into any known genus (Kim et al., 2011b). Therefore, uncultured members of
the ruminal bacteriome probably play a greater role in rumen functions than the cultured
peers.
65
Frequencies of rrs sequences are often used to infer the abundance of the
uncultured bacteria represented. However, PCR is well documented to have amplification
bias with universal primers. As such, sequence frequency does not necessarily reflect the
relative abundance of the bacterium represented, or the importance or weight to rumen
function. In a previous study (Stiverson et al., 2011), specific real-time PCR assays were
shown to accurately determine the population sizes and distribution of both cultured and
uncultured bacteria in the rumen of sheep. Some uncultured bacteria had abundance
comparable to that of several cultured bacteria that are perceived as major bacteria in the
rumen. We hypothesize that this holds true for the rumen of cattle. To test this
hypothesis, real-time PCR assay quantified the populations of select cultured and
uncultured bacteria in the rumen of cattle fed different diets.
4.3 Materials and Methods
4.3.1 Sample collection, fractionation and DNA extraction
Rumen contents were collected from four cannulated cattle: two Jersey cattle
fed with only forage composed predominantly of Timothy grass (designated as hay, H)
and two Holstein cattle fed a typical dairy diet consisting of 14% alfalfa forage, 42% corn
silage, 6% cottonseed, and 38% grains (designated as including concentrate, C). The two
groups of cattle were fed twice daily (early morning and late afternoon) and adapted to
their respective diets for more than 3 weeks before rumen sampling, which took
place approximately 6 hours after the morning feeding. The liquid and adherent
fractions were obtained as described previously (Larue et al., 2005). Bacteria present in
the liquid fraction (Lq) were recovered by centrifugation, whereas bacteria adherent to
66
the solid digesta were recovered using a detaching buffer (Dehority and Grubb, 1980) and
designated as the adherent fraction (Ad). To recover bacteria that might fail to detach, the
remaining solid digesta was subjected to DNA extraction and designated as the solid
fraction (Sld). Twelve fraction samples (2 cattle × 2 diets × 3 fractions) were stored at -80
oC prior to DNA extraction. Metagenomic DNA was extracted from each of the
fractionated samples as described previously (Yu and Morrison, 2004b).
4.3.2 Real-time PCR assays
Standards for Fibrobacter succinogenes, Ruminococcus albus and Prevotella
ruminicola were amplified from genomic DNA of respective strains using 27F and
1525R primers. A composite sample of the 12 metagenomic DNAs at equal amount was
used to prepare sample-derived standards for total bacteria, total archaea, Butyrivibrio,
Prevotella, Ruminobacter amylophilus, Ruminococcus flavefaciens, Selenomonas
ruminantium and six uncultured bacteria by a regular PCR reaction using specific primers
as described previously (Stiverson et al., 2011). The sample-derived standards are
thought to reduce bias that may result from sequence variation within total bacteria, total
archaea, Butyrivibrio, or Prevotella. On the other hand, the sample-derived standards for
R. amylophilus, R. flavefaciens and S. ruminantium were used because their purified
genomic DNAs were not available. The six uncultured bacteria named Ad-C1-74-3, Lq-
C2-16-3, Lq-C2-58-2, Ad-H1-14-1, Ad-H1-75-1 and Ad-H2-90-2 were recovered from
sheep fed two different diets (Larue et al., 2005; Stiverson et al., 2011). Each standard
was serially diluted and the concentration from 101 to 10
7 rrs copies were used in the
real-time PCR assays. Each real-time PCR assay was conducted in three technical
67
replicates (three PCR reactions from the same template) from which the mean was
calculated. The mean was also calculated from the two biological replicates (two cattle
fed the same diet) of each fraction recovered from each diet. The primers (Table 4.1 and
4.2) and PCR condition used to quantify each target were the same as those used by
Stiverson et al. (2011).
4.4 Results and Discussion
4.4.1 Quantification of populations of total bacteria and total archaea
Total bacterial populations ranged from 1.71×108 to 5.47×10
8 rrs copies/ µg DNA
across all the fractions and were slightly higher in cattle fed hay plus grain than in cattle
fed hay only (Figure 4.1). The increased digestibility concomitant with grain
supplementation is a major factor that increases the total bacterial population. Total
archaeal populations were much higher in the three fractions recovered from cattle fed
hay (2.62×104 ~ 3.67×10
4) than the respective fractions recovered from cattle fed hay
plus grain (7.22×102 ~ 5.34×10
3) as shown in Figure 4.1. It seems that the abundance of
total archaea is affected by the amount of forage in the diet. This result corroborates the
previous finding that more methane is produced by animals fed diets high in forage than
by animals fed diet high in grain (Janssen, 2010).
4.4.2 Quantification of cultured bacteria
Populations of three major cellulolytic bacteria and Butyrivibrio spp. were shown in
Figure 4.2. Among the three cellulolytic bacteria, the populations of F. succinogenes
(1.61×106 ~ 9.96×10
6) and R. flavefaciens (2.56×10
6 ~ 3.03×10
7) were more abundant
68
than the population of R. albus (7.59×104 ~ 8.64×10
5) in any of the fractionated samples.
This result supports the previous findings that the population of F. succinogenes is higher
than that of R. albus (Koike et al., 2003b; Stevenson and Weimer, 2007). However, some
studies showed contradictory results (Martin et al., 2001; Stiverson et al., 2011). The two
studies (Koike et al., 2003b; Stiverson et al., 2011) that used sheep showed that R. albus
is more predominant among the three cellulolytic species. More studies are needed to
verify the predominance of R. albus in the rumen of sheep while F. succinogenes is the
predominant cellulolytic in the rumen of cattle. Although real-time PCR assays showed
the abundance of Fibrobacter succinogenes, few Fibrobacter-like rrs sequences were
identified from rrs clone libraries, the microarray analysis and the pyrosequencing
analysis as described in Chapter 5, 6 and 8. The lack of Fibrobacter-like rrs sequences
seems to be due to the poor efficiency of PCR amplication with universal primers as
demonstrated previously (Larue et al., 2005).
The populations of the three cellulolytic bacteria have a tendency to be higher in
the Sld fractions than in the Ad fractions (Figure 4.2). This result indicates that
cellulolytic bacteria are tightly attached to plant particles, and bacterial detachment by the
detaching buffer (Larue et al., 2005) is incomplete. Therefore, Sld fractions should be
included in future studies to account for the total population of ruminal bacteria attached
to plant particles. The population of Butyrivibrio spp. was greater than 106 rrs copies/ µg
DNA and did not differ among all the fractions (Figure 4.2). Primary niches of
Butyrivibrio are utilization of hemicellulose, starch, pectin, xylan, pentose and hexose
(Russell, 2002), and some strains can even degrade cellulose (Hungate, 1950). Therefore,
69
Butyrivirio spp. present in the adherent fraction may contribute slightly to cellulose
digestion.
The population of genus Prevotella ranged from 4.40×107 to 1.88×10
8 rrs copies/
µg DNA across all the fractions and was slightly higher in cattle fed hay plus grain than
in cattle fed hay (Figure 4.3). The population of Prevotella spp. was the most abundant
among known bacteria. This result supports that Prevotella is the most predominant
genus in the rumen (Kim et al., 2011b; Stevenson and Weimer, 2007). The population of
Prevotella ruminicola was also higher in cattle fed hay plus grain than in cattle fed hay
(Figure 4.3). The population difference between cattle fed the two different diets was
greater for P. ruminicola than for Prevotella spp.. This result indicates that the
populations of some unknown Prevotella spp. are high in the Ad or the Sld fractions than
in the Lq fraction. The abundance of Prevotella spp. in the Ad or the Sld fraction might
suggest their involvement in fiber degradation as described previously (Koike et al.,
2003a; Dodd et al., 2010) and the presence of numerous uncultured Prevotella spp.
(Bekele et al., 2010). Isolation and characterization of uncultured Prevotella spp. would
need to be attempted in future studies. As expected, both lactate-utilizing Selenomonas
ruminantium and starch-utilizing Ruminobacter amylophilus were more abundant in
cattle fed hay plus grain than in cattle fed hay (Figure 4.3).
4.4.3 Quantification of uncultured bacteria
The populations of six different uncultured bacteria were quantified using specific
real-time PCR assays. Ad-C1-74-3, Lq-C2-16-3 and Lq-C2-58-2 were originally
recovered from sheep fed corn:hay, whereas Ad-H1-14-1, Ad-H1-75-1 and Ad-H2-90-2
70
were recovered from sheep fed hay (Larue et al., 2005; Stiverson et al., 2011). The
population of Ad-C1-74-3, which was assigned to Anaerovorax (Stiverson et al. 2011),
was slightly higher in the Ad and the Sld fractions than in the Lq fractions, but it was
similar between the Lq fractions, or between the Ad fractions. Because Matthies et al.
(2000) reported that Anaerovorax of non-rumen origin frequently metabolizes amino
acids, Ad-C1-74-3 may be associated with the degradation of amino acids. Lq-C2-16-3
and Lq-C2-58-2 were assigned to ‘Unclassified Ruminococcaceae’ and ‘Unclassified
Erysipelotrichaceae’, respectively (Stiverson et al. 2011). These two uncultured bacteria
were slightly more abundant in cattle fed hay plus grain than in cattle fed hay and more
abundant in the Ad fractions than in the Lq fractions (Figure 4.4). The population of Lq-
C2-58-2 was greater than 106 rrs copies/ µg DNA across all the fractions. The
populations of Ad-H1-14-1 and Ad-H2-90-2 that were assigned to Acetivibrio and
‘Unclassified Clostridia’, respectively, were about 106 rrs copies/ µg DNA. The
populations of these two bacteria were slightly higher in the Ad fractions than in the Lq
fractions (Figure 4.4). Because Acetivibrio includes cellulolytic species such as A.
cellulolyticus and A. cellulosolvens as described previously (Stiverson et al., 2011), Ad-
H1-14-1 might represent an Acetivibrio bacterium that participates in fiber degradation in
the rumen. Future studies targeting Acetivibrio can help further assess the importance of
this genus to cellulose degradation in the rumen. The population of Ad-H1-75-1, which
was assigned to ‘Unclassified Clostridiales, was much higher in the Ad and the Sld
fractions than the Lq fractions (Figure 4.4). Ad-H1-75-1 is also presumed to be involved
in fiber degradation.
Detailed physiology can only be gained through studies of pure cultures. A reverse
71
metagenomic approach, as demonstrated previously (Nichols, 2007; Pope et al., 2011),
may be used to help isolate these uncultured bacteria. The metagenomic data recovered in
previous studies of ruminal samples can also be used to design selective media for
uncultured bacteria through its metabolic reconstruction, resulting in the isolation and
characterization of the uncultured bacterium.
4.5 Conclusions
In this study, the populations of uncultured bacteria were as great as those of major
cultured bacteria except for Prevotella. They are also ubiquitous in the rumen.
Uncultured bacteria may play as an important role as the cultured bacteria, if not more.
Comparative dynamic studies of uncultured bacteria in response to dietary treatments
might help further reveal their ecological niche and roles in the rumen. Isolation and
characterization of uncultured bacteria in the rumen would need to be attempted to define
the function of uncultured bacteria.
72
P
rim
ers
Seq
uen
ces
(5’
3’)
T
arget
A
nnea
lin
g
tem
per
ature
(oC
)
Am
pli
con
leng
th (
bp
) R
efer
ence
s
2
7f
15
25r
AG
A G
TT
TG
A T
CM
TG
G C
TC
AG
AA
G G
AG
GT
G W
TC
CA
R C
C
To
tal
Bac
teri
a 5
4
1,5
35
Lar
ue
et a
l.,
20
05
3
40
f
80
6r
TC
C T
AC
GG
G A
GG
CA
G C
AG
T
GG
A C
TA
CC
A G
GG
TA
T C
TA
AT
C C
TG
TT
To
tal
Bac
teri
a
60
46
7
Nad
kar
ni
et a
l.,
20
02
T
aqM
an
pro
be
6-F
AM
-5’-
CG
T A
TT
AC
C G
CG
GC
T G
CT
GG
CA
C-3
’-T
AM
RA
70
Bac
30
3f
Bac
70
8r
GA
A G
GT
CC
C C
CA
CA
T T
G
CA
A T
CG
GA
G T
TC
TT
C G
TG
Ba
cter
oid
es a
nd
Pre
vote
lla
5
6
41
8
Bar
tosc
h e
t al
.,
20
04
,
Ber
nhar
d a
nd
Fie
ld,
20
00
A
RC
78
7F
AR
C1
05
9R
AT
T A
GA
TA
C C
CS
BG
T A
GT
CC
GC
C A
TG
CA
C C
WC
CT
C T
T
ota
l A
rchae
a 6
0
27
3
Yu e
t al
., 2
00
5
R
a12
81
f
Ra1
43
9r
CC
C T
AA
AA
G C
AG
TC
T T
AG
TT
C G
CC
T C
CT
TG
C G
GT
TA
G A
AC
A
R.
alb
us
55
17
5
Ko
ike
and
Ko
bay
ash
i, 2
00
1
F
s-f
Fs-
r
GG
T A
TG
GG
A T
GA
GC
T T
GC
GC
C T
GC
CC
C T
GA
AC
T A
TC
F
. su
ccin
og
enes
6
3
44
6
Taj
ima
et a
l.,
20
01
a
5
30
f
Buty
-90
0r
GT
G C
CA
GC
M G
CC
GC
G G
TG
C G
GC
AC
Y G
AC
TC
C C
TA
TG
B
uty
rivi
bri
o
65
37
1
Sti
ver
son e
t al
.,
20
11
S
el-M
it-f
Sel
-Mit
-r
TG
C T
AA
TA
C C
GA
AT
G T
TG
TC
C T
GC
AC
T C
AA
GA
A A
GA
S.
rum
ina
nti
um
and
M.
mu
lta
cid
a
53
51
3
Taj
ima
et a
l.,
20
01
a
R
am
-f
Ram
-r
CA
A C
CA
GT
C G
CA
TT
C A
GA
CA
C T
AC
TC
A T
GG
CA
A C
AT
R
. a
myl
op
hil
us
57
64
2
Taj
ima
et a
l.,
20
01
a
R
f15
4f-
K
Rf4
25
r-K
TC
T G
GA
AA
C G
GA
TG
G T
A
CC
T T
TA
AG
A C
AG
GA
G T
TT
AC
A A
R
. fl
ave
faci
ens
55
29
5
Ko
ike
and
Ko
bay
ash
i, 2
00
1
Tab
le 4
.1. P
rim
ers
and a
Taq
Man
pro
be
use
d i
n t
he
real
-tim
e P
CR
ass
ays
for
tota
l bac
teri
a, t
ota
l ar
chae
a or
cult
ure
d b
acte
ria
(Rep
roduce
d f
rom
Sti
ver
son e
t al
., 2
011)
73
Partial sequences
(GenBank accession no.)
Sequences (5’ 3’) Annealing position
(E. coli numbering)
Amplicon
length (bp)
Ad-C1-74 (AY816616) GAA GGG ACC GGT TAA GGT C 1013-1031 1,024
Lq-C2-16 (AY816578) GAC TTT GCT TCC CTT TGT TTT
G
1245-1265 1,258
Lq-C2-58 (AY816550) AGC CTC CGA TAC ATC TCT GC 1010-1029 1,022
Ad-H1-14 (AY816508) GAT TTG CTT ACC CTC GCG
GGT TT
1260-1282 1,275
Ad-H1-75 (AY816420) CAC ACC TTG TAT CTC TAC
AAG C
1006-1027 1,020
Ad-H2-90 (AY816432) CTT CGA CAG CTG CCT CCT TA 1451-1470 1,463
Table 4.2. Primers used in the real-time PCR assays for uncultured bacteria (Reproduced
from Stiverson et al., 2011)
74
Figure 4.1. Populations of total archaea and total bacteria in the rumen of cattle. Liquid
(Lq), adherent (Ad) and solid (Sld) fractions from the cattle were combined based on the
diet. C, 42% corn silage, 14% alfalfa hay, 6% cotton seed and 38% grain; H, mixed grass
hay including mostly timothy hay. The error bars indicate the standard error of the means
(n=2).
Total Archaea
Lq-H Lq-C Ad-H Ad-C Sld-H Sld-C100
101
102
103
104
105
106
107
108
109
Fraction
rrs c
op
ies/
ug
DN
A
Total Bacteria
Lq-H Lq-C Ad-H Ad-C Sld-H Sld-C100
101
102
103
104
105
106
107
108
109
Fraction
rrs c
op
ies/
ug
DN
A
75
Figure 4.2. Populations of three major cellulolytic bacteria and Butyrivibrio spp. in the
rumen. The sample labeling is the same as those in Figure 4.1. The error bars indicate the
standard error of the means (n=2).
F. succinogenes
Lq-H Lq-C Ad-H Ad-C Sld-H Sld-C100
101
102
103
104
105
106
107
108
109
Fraction
rrs c
op
ies/
ug
DN
A
R. albus
Lq-H Lq-C Ad-H Ad-C Sld-H Sld-C100
101
102
103
104
105
106
107
108
109
Fraction
rrs c
op
ies/
ug
DN
A
R. flavefaciens
Lq-H Lq-C Ad-H Ad-C Sld-H Sld-C100
101
102
103
104
105
106
107
108
109
Fraction
rrs c
op
ies/
ug
DN
A
Butyrivibio
Lq-H Lq-C Ad-H Ad-C Sld-H Sld-C100
101
102
103
104
105
106
107
108
109
Fraction
rrs c
op
ies/
ug
DN
A
76
Figure 4.3. Populations of major non-cellulolytic cultured bacteria in the rumen. The
sample labeling is the same as those in Figure 4.1. The error bars indicate the standard
error of the means (n=2).
P. ruminicola
Lq-H Lq-C Ad-H Ad-C Sld-H Sld-C100
101
102
103
104
105
106
107
108
109
Fraction
rrs c
op
ies/
ug
DN
A
Prevotella
Lq-H Lq-C Ad-H Ad-C Sld-H Sld-C100
101
102
103
104
105
106
107
108
109
Fraction
rrs c
op
ies/
ug
DN
A
S. ruminantium
Lq-H Lq-C Ad-H Ad-C Sld-H Sld-C100
101
102
103
104
105
106
107
108
109
Fraction
rrs c
op
ies/
ug
DN
A
R. amylophilus
Lq-H Lq-C Ad-H Ad-C Sld-H Sld-C100
101
102
103
104
105
106
107
108
109
Fraction
rrs c
op
ies/
ug
DN
A
77
Figure 4.4. Populations of uncultured bacteria originally identified from the rumen of
sheep (Larue et al., 2005; Stiverson et al., 2011). The sample labeling is the same as those
in Figure 4.1. The error bars indicate the standard error of the means (n=2).
Ad-C1-74-3
Lq-H Lq-C Ad-H Ad-C Sld-H Sld-C100
101
102
103
104
105
106
107
108
109
Fraction
rrs c
op
ies/
ug
DN
A
Lq-C2-16-3
Lq-H Lq-C Ad-H Ad-C Sld-H Sld-C100
101
102
103
104
105
106
107
108
109
Fraction
rrs c
op
ies/
ug
DN
A
Lq-C2-58-2
Lq-H Lq-C Ad-H Ad-C Sld-H Sld-C100
101
102
103
104
105
106
107
108
109
Fraction
rrs c
op
ies/
ug
DN
A
Ad-H1-14-1
Lq-H Lq-C Ad-H Ad-C Sld-H Sld-C100
101
102
103
104
105
106
107
108
109
Fraction
rrs c
op
ies/
ug
DN
A
Ad-H1-75-1
Lq-H Lq-C Ad-H Ad-C Sld-H Sld-C100
101
102
103
104
105
106
107
108
109
Fraction
rrs c
op
ies/
ug
DN
A
Ad-H2-90-2
Lq-H Lq-C Ad-H Ad-C Sld-H Sld-C100
101
102
103
104
105
106
107
108
109
Fraction
rrs c
op
ies/
ug
DN
A
78
CHAPTER 5
PHYLOGENETIC DIVERSITY OF BACTERIAL COMMUNITIES IN BOVINE
RUMEN AS AFFECTED BY DIETS AND MICROENVIRONMENTS
5.1 Abstract
Phylogenetic analysis was conducted to examine ruminal bacteria in two ruminal
fractions (adherent fraction vs. liquid fraction) recovered from cattle fed with two
different diets: forage alone vs. forage plus concentrate. One hundred forty-four 16S
rRNA gene (rrs) sequences were obtained from clone libraries constructed from the four
samples. These rrs sequences were assigned to 116 different operational taxonomic units
(OTUs) defined at 0.03 phylogenetic distance. Most of these OTUs could not be assigned
to any known genus. The phylum Firmicutes was represented by approximately 70% of
all the sequences. By comparing to the OTUs already documented in the rumen, 52 new
OTUs were identified. UniFrac, SONS, and denaturing gradient gel electrophoresis
(DGGE) analyses revealed difference in diversity between the two fractions and between
the two diets. This study showed that rrs sequences recovered from small clone libraries
can still help identify novel species-level OTUs.
5.2 Introduction
A complex microbiome consisting of bacteria, archaea, fungi and protozoa has
evolved to efficiently degrade various types of forages in the rumen (Flint, 1997), and a
biofilm formed on the surface of the ruminal forages is especially important to feed
digestion (Cheng et al., 1977). Defining the phylogenetic diversity of ruminal
microbiome, particularly the bacterial community, is intriguingly interesting to many
79
microbiologists because it is essential to functional analysis of this microbiome. Stahl et
al. (1998) primarily used rrs sequence-based analysis in identifying the phylogenetic
diversity of ruminal bacterial community. Most studies focused on the microbes collected
from rumen fluid (e.g. Tajima et al., 2000, 2007; Ozutsumi et al., 2005), though some
studies examined the microbes present in separated liquid and solid fractions (Larue et
al., 2005; Yu et al., 2006; Brulc et al., 2009) and on the rumen wall (Cho et al., 2006;
Lukas et al., 2010). Based on a recent meta-analysis (Kim et al., 2011b), the rrs
sequences of rumen origin that have been archived in the RDP database represent more
than 3500 species-level OTUs. These OTUs represent approximately 70% of the global
bacterial diversity estimated to be present in the rumen. Therefore, more studies are
needed to further discover the phylogenetic diversity of ruminal bacterial communities.
However, it is uncertain if typical clone library-based analysis can still identify new
OTUs. In this study, we analyzed four rrs clone libraries constructed from liquid and
adherent fractions recovered from two cows fed with different diets (forage alone vs.
forage plus concentrate), defined species-level OTUs and compared the defined OTUs
with existing ruminal OTUs reported recently (Kim et al., 2011b). This study enabled us
to identify many novel ruminal OTUs. The difference in phylogenetic diversity between
the two fractions and the two diets were also assessed using UniFrac, SONS and DGGE
analyses.
5.3 Materials and Methods
5.3.1 Sample collection, fractionation and DNA extraction
Whole rumen content was collected from four cannulated cows: two Jersey cattle
80
fed with only forage composed predominantly of Timothy grass and two Holstein cattle
fed with a typical dairy diet consisting of 14% alfalfa forage, 42% corn silage, 6%
cottonseed, and 38% grains. The two groups of cows were fed twice daily (early morning
and late afternoon) and adapted to their respective diets for more than 3 weeks before
rumen sampling, which took place approximately 6 hours after the morning feeding. Both
the liquid fraction and the adherent fraction of each rumen digesta were separated as
reported previously (Larue et al., 2005). Bacteria present in the liquid fraction (Lq) were
recovered by centrifugation, while bacteria adherent to the solid digesta were recovered
by using a detaching buffer containing 0.15% (v/v) Tween-80 (Dehority and Grubb,
1980) and centrifugation. Metagenomic DNA was extracted from each of the fractionated
samples as described previously (Yu and Morrison, 2004b). To recover bacteria that
might fail to detach, the remaining solid digesta was also subjected to DNA extraction.
The resultant metagenomic DNA extracts from the adhering fraction and the solid
particles were combined and designated as the adherent fraction (Ad).
5.3.2 DGGE analysis
DGGE analysis was conducted to profile and compare the bacterial communities
among the four samples using the GC-357f and 519r primer set targeting the V3 region,
and band patterns were analyzed using the BioNumerics program as described previously
(Yu and Morrison, 2004a).
5.3.3 Construction of rrs clone libraries
The DNA extracts were pooled based on diets and fractions, resulting in four
81
composite samples representing the liquid fraction and the adhering fraction recovered
from the two cattle fed with forage alone (Lq-H, and Ad-H, respectively) and from the
two cattle fed with forage plus concentrate (Lq-C, and Ad-C, respectively). The nearly
full-length rrs gene was amplified by PCR from each composite DNA sample using
universal bacterial primers 27F (5’-AGAGTTTGATCMTGGCTCAG-3’) and 1525R (5’-
AAGGAGGTGWTCCARCC-3’) and cloned using a TOPO-TA cloning kit (Invitrogen,
Carlsbad, CA). Three hundred and eighty four random clones from the four libraries (96
clones per library) were subjected to screening for the presence of the insert using PCR
(Yu and Mohn, 2001).
5.3.4 Restriction fragment length polymorphism (RFLP) analysis, DNA sequencing and
phylogenetic analysis
To reduce sequencing redundancy of similar clones, the PCR products of the
aforementioned screening were digested using both HaeIII and AluI as described
previously (Larue et al., 2005). The RFLP patterns were compared using BioNumerics
(BioSystematica, Tavistock, Devon, United Kingdom), and the clones with a unique
RFLP pattern were sequenced using the 27F primer at High-Throughput Genomics Unit
(University of Washington, Seattle, WA).
Low-quality sequence regions at both ends of each sequence read were trimmed off
using the FinchTV program V1.4 (Geospiza, Inc., Seattle, WA). All the sequences were
then subjected to chimera check using the Pintail program (Ashelford et al., 2005), and
suspected chimeric sequences were excluded from further analysis. Classification,
alignment, and construction of a taxonomic tree at genus level were performed using the
82
bioinformatic programs as described previously (Kim et al., 2011b). Using the Mothur
program (Schloss et al., 2009), the species-level OTUs (defined at 0.03 phylogenetic
distance) identified in this study were compared to all the OTUs recognized in the recent
meta-analysis of global diversity of ruminal bacteria (Kim et al., 2011b).
5.3.5 Comparison of ruminal bacterial communities among the four composite samples
Principal coordinate analysis (PCA) was conducted to examine relationship among
the four composite samples using the UniFrac program (Lozupone and Knight, 2005).
The SONS program (Schloss and Handelsman, 2006) was used to determine the numbers
of shared OTUs across the four samples.
5.3.6 Nucleotide sequence accession numbers
The rrs sequences obtained in this study have been deposited in the GenBank
database (JF319298 - JF319441).
5.4 Results and Discussion
The four composite samples were first compared using DGGE to visualize the
impact on the ruminal bacterial communities from the diets and the fractions. As shown
in Figure 5.1, the four samples shared a number of DGGE bands, but bands distinct to
each sample were also evident. Clustering analysis showed that both the diets and the
microenvironments (liquid vs. solid fractions) had affected the ruminal bacterial
communities, with the diets having a greater impact than the fractions. These results are
consistent with a previous study where both corn supplementation and fractions were
83
found to affect the ruminal bacterial communities in sheep (Larue et al., 2005).
The bacterial communities in the composite samples were further examined and
compared using the rrs clone libraries. From the 384 clones screened for the presence of
insert, 288 clones were found to produce a unique RFLP pattern, which were then
sequenced. After removing the sequences of low quality and suspected chimeric
sequences, 144 high-quality rrs sequences were obtained and analyzed phylogenetically.
The phyla Firmicutes, Bacteroidetes, Proteobacteria, Spirochaetes, and Verrucomicrobia
were represented by 140 sequences, and the remaining four sequences could not be
classified into any existing phylum (Figure 5.2). Firmicutes was the most predominant
phylum and accounted for 69.4% of all the 144 sequences. Approximately 60.2% of the
Firmicutes sequences could not be classified into any known family or genus, with
‘Unclassified_Lachnospiraceae’ being the most abundant (27 sequences) group. Genera
Butyrivibrio, Ruminococcus, and Succiniclasticum each were represented by more than
nine sequences, while the remaining genera identified were represented by no more than
five sequences each. The 144 sequences were assigned to 116 species-level OTUs, of
which 52 appeared to be novel when compared to the existing ruminal OTUs reported
previously (Kim et al., 2011b), indicating new ruminal species. This is the first study that
compared OTUs identified in individual studies to those already documented. The results
of this study also demonstrated that small numbers of rrs sequences could still contribute
towards identifying the full phylogenetic diversity of ruminal bacteria. Future individual
studies using different PCR primers, sampling methods, and DNA extraction techniques
would help improve discovery of novel OTUs and eventually lead to full coverage of
phylogenetic diversity (Edwards et al., 2004; Hong et al., 2009).
84
A similar number of species-level OTUs was identified from all the four samples,
with Ad-H, Ad-C, Lq-H, and Lq-C fractions having 31, 29, 34, and 31 OTUs,
respectively. However, based on comparison using the SONS program, three or fewer
OTUs were shared between any two of the four composite samples, and no OTU was
shared by all the four samples (Figure 5.3). Ten of the 31 Ad-H OTUs were classified
into known genera, with Ruminococcus (4 OTUs) being the most abundant. The Ad-C
fraction also included 10 OTUs classified into existing genera, with each genus being
represented by one OTU except Ruminococcus (2 OTUs) and Succiniclasticum (2 OTUs).
As expected, Ruminococcus was predominant in the adherent fraction recovered from the
rumen of cattle fed with only forage. However, most of the OTUs from both the Ad-H
and the Ad-C fractions could not be assigned to any known genus (21 and 19 OTUs,
respectively). Eight OTUs could only be classified to the order Clostridiales in the Ad-H
fraction, while another seven OTUs were only assigned to the family Lachnospiraceae in
the Ad-C fraction. These OTUs might represent new families or genera. The distinct
distribution of these two groups of bacteria is probably attributed to dietary effect. Future
studies using quantitative analysis are needed to confirm and help explain this finding.
The Lq-H and the Lq-C fractions contained 8 and 10 OTUs (of the 34 and 31
OTUs) assigned to known genera, respectively. The most numerous single genus in the
Lq-H sample was Butyrivibrio (4 OTUs), while each genus in the Lq-C sample was
represented by only one of the 10 OTUs except Butyrivibrio (2 OTUs) and Treponema (2
OTUs). It appeared that versatile Butyrivibrio species that can degrade several types of
polysaccharides (hemicellulose, starch, and pectin) are abundant in the liquid fraction
irrespective of the diets. The two OTUs assigned to Treponema are thought to be
85
associated with diets rich in concentrate as described previously (Bekele et al., 2011).
‘Unclassified_Bacteroidetes’ (7 OTUs) and ‘Unclassified_Clostridiales’ (6 OTUs) were
the first and second most abundant unclassified groups in the Lq-H sample, whereas
‘Unclassified_Lachnospiraceae’ (8 OTUs) was the most abundant unclassified group in
the Lq-C sample. Again, ‘Unclassified_Clostridiales’ was predominant in the adhering
fraction of the forage-fed cattle, whereas ‘Unclassified_Lachnospiraceae’ was
predominant in both fractions of cattle fed with both forage and concentrate. As noted
previously (Kim et al., 2011b), ‘Unclassified_Clostridiales’ rather than
‘Unclassified_Lachnospiraceae’ seems to be associated with fiber digestion.
Based on the PCA analysis, the P1 separated the bacterial communities based on
the diets, whereas the P2 separated the ruminal bacterial communities based on the
fractions (Figure 5.4). This result agrees with the DGGE data and the sequence-based
comparison by SONS. However, the DGGE data showed a greater similarity among the
samples than the Venn diagram. This discrepancy is probably due to the limited
resolution of DGGE. These results suggest significant impact on the ruminal bacterial
community from diets and microenvironments (liquid vs. solid surface), although
difference between the two breeds may also have some effect on the ruminal bacterial
community. The effects of diets on the ruminal bacterial community have been
investigated in several studies (e.g. Tajima et al., 2000; Larue et al., 2005). The partition
of the bacterial populations between the solid and the liquid fractions have also been
examined (Michalet-Doreau et al., 2001; Larue et al., 2005). In all the studies reported
(including this present study), the same OTUs were not commonly found between diets
or fractions. More studies are needed to define the core and variable bacterial
86
communities in the rumen. As demonstrated recently (Pitta et al., 2010), studies using
comprehensive analysis may help achieve such a goal.
87
Figure 5.1. Clustering analysis of DGGE banding profiles based on the V3 region of 16S
rRNA genes. Lq-C and Ad-C represent the liquid fraction and the adhering fraction
recovered from cattle fed with forage plus concentrate, while Lq-H and Ad-H represent
the liquid fraction and the adhering fraction recovered from cattle fed with forage alone.
The dendrogram was constructed using the BioNumerics program.
88
Figure 5.2. A taxonomic tree showing the bacterial genera represented by the 144
sequences. The lineage at phylum level is labeled: V, phylum Verrucomicrobia; B,
phylum Bacteroidetes; S, phylum Spirochaetes; P, phylum Proteobacteria; F, phylum
Firmicutes. In total, 14 known genera were represented by 49 sequences, while the
remaining 95 sequences could not be assigned to any existing genus. Numbers in
parenthesis indicate the number of sequences from respective fraction.
89
Figure 5.3. A Venn diagram showing the numbers of species-level OTUs shared among
the four composite samples. The four samples had 116 species-level OTUs in
combination.
90
Figure 5.4. A PCA analysis plot comparing the bacterial communities in the four
composite samples.
91
CHAPTER 6
DEVELOPMENT OF A PHYLOGENETIC MICROARRAY FOR COMPREHENSIVE
ANALYSIS OF RUMINAL MICROBIOME
6.1 Abstract:
Phylogenetic microarray is a powerful tool that enables simultaneous detection
and semi-quantitation of thousands of different members of a microbiome. The objective
of this study was to develop a microarray to support comprehensive analysis of a
complex ruminal bacteriome. All the 16S rRNA gene (rrs) sequences of rumen origin
collected worldwide were retrieved from the RDP database and subjected to phylogenetic
analysis. The rrs sequences assigned to each genus based on the new Bergey’s Taxonomy
were aligned and assigned to species-equivalent operational taxonomic units (OTUs) at a
0.03 phylogenetic distance. One representative sequence was selected from each OTU,
and one specific GoArray probe was designed for each OTU. The specificity of the
probes was verified in silico using the Probe Match function in the RDP database. The
specificity, sensitivity, and linear range of detection are determined using pools of rrs
clones of known sequences. Of approximately 2,500 OTUs identified, 1,664 OTUs that
include 10 OTUs obtained from the known clones were targeted by a specific GoArray
probe on the ruminal microarray (referred to as RumenArray). In addition, 2 GoArray
probes specific to the bovine mitochondrial rrs sequence were added to the RumenArray
as internal controls. The RumenArray has been custom fabricated in a 6x5k format with
each probe being represented in triplicates. The RumenArray can detect approximately
1.0 × 107 copies of targets and had a linear dynamic range of >3 orders of magnitude.
Fractionated rumen samples (liquid fraction vs. adherent fraction) obtained from sheep
92
fed two different diets (hay alone vs. hay plus corn) were used to test the utility of the
RumanArray. More than 300 different OTUs were detected in combination across the
four fractions. Prevotella was the most numerous among known genera. Unclassified
groups represented more than 50% of all the OTUs detected. This is the first phylochip
dedicated to analysis of ruminal bacteria. It enables comprehensive semi-quantitative
analysis of ruminal bacteria in support of nutritional studies of ruminant animals.
6.2 Introduction:
A complex microbiome consisting of bacteria, archaea, protozoa and fungi in the
rumen degrades various feedstuffs ingested by ruminant animals. Within the ruminal
microbiome, Bacteria are the most abundant domain and have a predominant role in
degrading ingested feedstuffs. Dietary manipulations have attempted to optimize ruminal
bacterial fermentation (Calsamiglia et al., 2007; Weimer et al., 2008). Cultivation-based
methods dominated attempts to investigate ruminal bacteria and elucidated important
metabolic functions in the rumen until the 1980’s. However, most ruminal bacteria could
not be isolated due to limitations of cultivation-based methods (Whitford et al., 1998).
Contemporary molecular biology techniques overcome the limitations of
cultivation-based methods and now use rrs sequence-based methods in examining
diversity of ruminal bacteria. Construction of rrs clone libraries has contributed greatly to
understanding the diversity of ruminal bacteria (Tajima et al., 2000; Ozutsumi et al.,
2005; Zhou et al., 2009). Some studies used fractionated rumen contents (liquid fraction
vs. adherent fraction) to investigate diversity of ruminal bacteria as affected by
microenvironments (Larue et al., 2005; Yu et al., 2006; Brulc et al., 2009; Kim et al.,
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2011c), and this fractionation method contributed to understanding the niches of bacterial
species within the rumen (Larue et al., 2005; Kim et al., 2011c). The PCR-DGGE
technique was also used to examine bacterial diversity in the rumen and contributed to
assessment of dietary effects in nutritional studies. This technique, however, provides
limited information on the microbes that are affected by the dietary manipulations. Real-
time PCR assays have been used in quantitatively determine the effects of diets on
selected species or genera of bacteria or methanogens, including uncultured strains
represented by novel rrs sequences (Stiverson et al., 2011). However, only a small
number of species or genera of microbes can be practically analyzed in this manner. This
limitation hinders comprehensive assessment of any dietary manipulation on the ruminal
microbiome. As shown in a recent study by Kim et al. (2011b), the ruminal microbiome
likely contains hundreds of species, and the cultured microbes only account for less than
7% of the entire ruminal microbiome. Therefore, new techniques are needed that support
comprehensive and quantitative analysis of ruminal microbiome, which is required for
simultaneous analysis of large numbers of ruminal samples collected in nutritional
studies.
Phylogenetic microarrays have been used as a powerful tool that enables
simultaneous detection and semi-quantitation of thousands of different rrs. Although the
phylogenetic microarray technique has been applied to investigation of bacteria present in
various environments such as soil, human gut, human feces, sludge and lake (Adamczyk
et al., 2003; Castiglioni et al., 2004; Kang et al., 2010; Palmer et al., 2006; Small et al.,
2001), no phylogenetic microarray has been developed to examine ruminal bacteria. The
objective of this study was to develop a phylochip dedicated to analysis of ruminal
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bacteria. The utility of the microarray we developed (referred to RumenArray) was tested
in examining the ruminal bacteria in the adherent and the liquid fractions recovered from
sheep fed two different diets: hay alone vs. corn plus hay.
6.3 Materials and Methods:
6.3.1 Oligonucleotide probe design and microarray fabrication:
Approximately 10,000 rrs sequences of rumen origin collected worldwide were
retrieved from the RDP database (Release 10, Update 5) and subjected to phylogenetic
analysis as described previously (Kim et al., 2011b). The sequences were classified into
respective genera based on the Bergey’s Taxonomy implemented in the RDP database.
Sequences within each genus were aligned using the Geneious program (Auckland, New
Zealand) and assigned to species-level OTUs at 0.03 phylogenetic distance.
Approximately 2,500 OTUs were identified. One specific probe was designed from the
representative sequence selected from each OTU using the GoArray program (Rimour et
al., 2005). Another ten GoArray probes were designed from sequenced clones (Kim et al.
2011c) and included into the RumenArray to assist in determination of specificity,
sensitivity and detection limit. In addition, two probes designed from the bovine
mitochondrial rrs sequences were added into the RumenArray to serve as internal
controls. Each GoArray probe consisted of two short separated sub-probes (17 mers) and
a short linker (6 mers) inserted between the two sub-probes. The resultant GoArray probe
length was 40 nt. The specificity of the probes was verified in silico using the Probe
Match function in the RDP database. The final 1,666 GoArray probes including the 10
clone probes and the 2 internal control probes were synthesized onto a 6 × 5K custom
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oligonucleotide microarray by MYcroarray (Ann Arbor, MI), with each probe
represented in triplicates.
6.3.2 Sample collection, fractionation and DNA extraction:
Metagenomic DNA samples recovered from sheep fed with two different diets
(100% orchard grass hay vs. a combination of 70% orchard grass hay and 30% corn)
were provided by Stiverson et al. (2011). Briefly, four sheep were assigned to two groups
of two sheep, and a repeated switchover design was used. The liquid (Lq) and the
adherent (Ad) fractions of each sample were recovered as described previously (Larue et
al., 2005). Metagenomic DNA was extracted from each of the sixteen samples (2 sheep x
2 diet x 2 fraction in a repeated switchover design = 16) using the RBB+C method (Yu
and Morrison, 2004b). Fifteen of the 16 metagenomic DNAs were used for RumenArray
analysis due to lack of one metagenomic DNA recovered from one sheep fed with hay.
6.3.3 Sample preparation and labeling:
The nearly full-length 16S rRNA genes were amplified from each metagenomic
DNA sample using the universal primer set 27F (5’-AGA GTT TGA TCM TGG CTC
AG-3’) and T7/1492R (5’-TCT AAT ACG ACT CAC TAT AGG GGG YTA CCT TGT
TAC GAC TT-3’) as described previously (Palmer et al., 2006; Kang et al., 2010). PCR
was performed with 30 cycles (denaturation, 95oC for 30 s; annealing, 55
oC for 30 s; and
extension, 72oC for 90 s) using a PTC-100 thermocycler (MJ Research, Waltham, Mass).
All amplicons were purified using a PCR purification Kit (Qiagen, Valencia, CA, USA),
and then the purified amplicons were used as templates to synthesize single-stranded
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complementary RNA (cRNA) using a MEGAScript T7 in vitro transcription kit (Ambion,
Austin, TX, USA). After cRNA was purified, they were then labeled with Cy5 at 37oC
for 1 hour using the IT uArray Cy5 reagent (Mirus, Madison, WI, USA). The labeled
cRNA was purified to remove the free Cy5 dye using the MEGAclear kit. The labeled
cRNA was quantified using the NanoDropTM
1000 and then stored at -80oC until
microarray hybridization.
6.3.4 Microarray hybridization:
Microarray hybridization was performed using Agilent Technologies’
Hybridization gasket slides that are compatible with the MYcroarray slides. The
hybridization solution containing 6X SSPE, 0.05% Tween-20, 0.01 mg/ml acetylated
BSA, 10% formamide and 1.2 µg labeled cRNA was incubated at 65oC for 5 min and
then placed on ice for at least 5 min. The Agilent Hybridization Cassette, the Agilent
gasket slide and the MYcroarray slide were preheated at 65oC while the hybridization
solution was prepared. The hybridization solution was added to the center of the Agilent
gasket slide, and then the MYcroarray slide was placed over the gasket slide. The cassette
was placed in HB-1000 hybridization oven (UVP, LLC), and then hybridization was
performed for 18 hours at preselected temperatures with rotation set at 10 rpm.
6.3.5 Signal detection and data analysis:
An Axon Genepix 4000B scanner (Axon Instruments, Union City, CA) was used to
scan the microarray slides at 100% laser power, 500-600 PMT photomultiplier
sensitivity, and 5 µm resolution. The GenePix Pro 6.0 program (Axon Instruments, Union
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City, CA) was used to analyze the images and create GenePix Results Format (GPR)
files. Median signal intensity that was transformed to log2 was used for microarray
analysis, and normalization was performed based on the signal of internal control probes
targeting the bovine mitochondrial rrs region. Spots that have less than the signal-to-
noise ratio (SNR) value of 3.0 were removed for accurate quantification as described
previously (He et al., 2007). Significant differences between samples were examined by
one-way ANOVA using the MeV program within the TM4 microarray software suite
(Saeed et al., 2006). Principal component analysis (PCA) using the MeV program (Saeed
et al., 2006) was conducted to compare the fractionated samples.
6.3.6 Determination of the specificity and detection limit:
Ten rrs clones corresponding to the 10 clone probes (referred to as “positive
clones”) and another sixteen rrs clones corresponding to none of the probe (referred to as
“negative clones”) were also used to evaluate the specificity. PCR products were
amplified from each of the clones and then used to synthesize cRNA as described above.
A pool of the 10 positive cRNA and a pool of the 16 negative cRNAs were labeled with
Cy5 in separate labeling reactions and then subjected to microarray hybridization using
separate microarrays. Positive signals for respective cRNA pools were analyzed and
compared at hybridization temperatures of 42oC, 45
oC and 47
oC. In addition, 6 of the 10
positive clones were selected to determine the detection limit and dynamic range of
detection. The pools of the 6 positive cRNAs were serially diluted (from 1010
rrs copies
to 106 rrs copies) and then subjected to microarray hybridization using individual
microarrays.
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6.3.7 Comparison between microarray and real-time PCR data:
Real-time PCR data obtained by using the same sheep samples (Stiverson et al.,
2011) were compared to RumenArray data. Stiverson et al. (2011) used one-way analysis
of variance (ANOVA) to analyze the fraction and diet-based data. Our study used the
analysis, as implemented in the MeV program (Saeed et al., 2006), in analyzing the
microarray data.
6.4 Results and Discussion:
6.4.1 Validation of the specificity, sensitivity and detection limit:
Cy5-labeled cRNA obtained from the 10 positive rrs clones was hybridized to the
RumenArray slide at 42oC, 45
oC or 47
oC. Twenty-one false positives were detected at
42oC, whereas 12 and 7 false positives were detected at 45
oC or
47
oC, respectively.
Although the number of false positives was reduced at 47oC compared to 45
oC, one false
negative was found at 47oC. Another cRNA sample prepared from the 16 negative clones
was also used to examine the specificity at the three different temperatures. Twenty false
positives were detected at 42oC, whereas 9 and 8 false positives were detected at 45
oC
and 47oC, respectively. Because one false negative in the specificity test using the 10
positive rrs clones was found at 47oC, we selected 45
oC as the optimal hybridization
temperature. However, the RumenArray is not completely specific based on the high
number of false positive signals in the two specificity tests. Because only approximately
500 bp of the 1500 bp full-length sequence was sequenced for the clones used in the
specificity test, the region that was not sequenced is probably hybridized to the
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RumenArray.
The Cy5-labeled cRNA synthesized from pools of six positive rrs clones were used
to determine the detection limit at 45oC. The 1 × 10
10 rrs copies of Cy5-labeled cRNA
were serially diluted, and then each diluted Cy5-labeled cRNA was hybridized to the
RumenArray. The signal intensity was normalized using the signal intensity of the cRNA
of bovine mitochondrial rrs. The lowest rrs copies that showed positive signals were
approximately 107 (Figure 6.1). When 2.09 × 10
6 rrs copies were hybridized to the
RumenArray slide, some of the 6 positive rrs clones did not show the positive signal.
Thus, the RumenArray has a dynamics range of at least three orders of magnitude (107 -
1010
rrs copies). The detection limit indicates that the RumenArray is not as sensitive as
real-time PCR in investigating ruminal bacteria with low abundance.
6.4.2 Data summary
In total, 319 OTUs were detected in combination across the four sampled fractions
(Figure 6.2). The Lq-C, Lq-H, Ad-C, and Ad-H fractions had 186, 243, 159, and 176
detected OTUs, respectively. The number of total shared OTUs was 91, and each fraction
had unique OTUs which ranged from 11 to 58 (Figure 6.2). These unique OTUs indicate
that different diets or fractions affect ruminal bacterial populations as described
previously (Larue et al., 2005; Kim et al., 2011c). Even though four sheep were fed the
same diet, signal intensities were different among the four sheep (Figure 6.3). It seems
that the genetic difference between individual sheep affects the populations of
predominant bacteria. However, the similarity was higher between the sheep fed the same
diet than between the sheep fed different diets or fractions (Figure 6.3). The number of
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detected OTUs was greater in the Lq fractions than in the Ad fractions probably because
the majority of sequence dataset used for microarray analysis were recovered from the
liquid fraction. Novel sequences will need to be recovered from the Ad fraction for future
studies.
6.4.3 Diversity of ruminal bacteria assigned to known genus:
The RumenArray analysis showed that Prevotella was the most abundant genus
among known genera, and it was represented by 47 OTUs (of all the 319 OTUs detected)
in combination across all the fractions. However, all the 47 OTUs were represented by
only uncultured bacterial sequences. This result agrees with the previous findings with
respect to the predominance and diversity of Prevotella (Bekele et al., 2010; Kim et al.,
2011b; Stevenson and Weimer, 2007). Thirty-one of the 47 OTUs did not show any
significant difference among the four fractions but slightly more abundant in sheep fed
corn:hay than in sheep fed hay alone. Another twelve OTUs were detected only in sheep
fed hay, whereas another four OTUs were detected only in sheep fed corn:hay.
Ruminococcus is one of the frequently cultured cellulolytic ruminal bacteria, and it
can rapidly attach to the surface of plant materials to digest cellulose in the rumen (Koike
et al., 2003b). Ruminococcus was the second most numerous genus among the detected
genera, and 12 OTUs within this genus were detected. Three of the 12 OTUs showed
significant difference among the fractions (Figure 6.3). Ruminococcus OTU 1
(S000991018, RDP ID) recovered originally from Holstein cattle (unpublished sequence
data, GenBank records) was more abundant (P < 0.05) in the Ad-H and the Lq-H
fractions than in the Ad-C and the Lq-C fractions. Another 3 OTUs were detected only in
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sheep fed hay, and they may be associated with cellulose degradation. Ruminococcus
OTU 2 (S000607272, RDP ID) recovered originally from sheep (unpublished sequence
data, GenBank records) was more abundant (P <0.05) in the Lq-C fraction than in the
other fractions. Ruminococcus OTU 3 (S000991061, RDP ID) recovered originally from
Holstein cattle (unpublished sequence data, GenBank records) was more abundant (P
<0.05) in the Lq-C fraction than in the Ad-C and the Lq-H fractions. The Ruminococcus
OTU 2 and 3 may be associated with amylolytic Ruminococcus spp. such as R. bromii, R.
callidus, R. hydrogenotrophicus, R. obeum and R. schinkii (Larue et al., 2005). Our study
also showed that R. bromii (S000728607, RDP ID) recovered originally from cattle
(Klieve et al., 2007) was detected only in sheep fed corn:hay. Another 3 OTUs were also
detected only in sheep fed corn:hay, and they are presumed to be amylolytic.
The other genera were each represented by less than 8 OTUs. Seven Butyrivibrio
OTUs were detected, and one of them (S000361669, RDP ID) recovered originally from
cattle (unpublished sequence data, GenBank records) was less abundant (P < 0.05) in the
Lq-H fraction than in the other fractions (Figure 6.3). Another one Butyrivibrio OTU
(S000650332, RDP ID) recovered first from reindeer grazing on natural summer pasture
(Sundset et al., 2007) was detected only in sheep fed hay. On the other hand, another two
Butyrivibrio OTUs were detected only in sheep fed corn:hay, and one of the two OTUs
(S000561168, RDP ID) was recovered from acidosis Holstein cattle (Tajima et al., 2000).
Three Acetivibrio OTUs were detected and two of them were detected only in the Ad-H
fraction. Acetivibrio is largely a genus consisting of cellulolytic bacterial species that
were formerly classified as Clostridium spp. These include Clostridium thermocellus, C.
hungatei, C. cellulolyticum, C. termitidis, C. stercorarium, A. cellulolyticus, and A.
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cellulosolvens. The greater abundance of Acetivibrio in the Ad-H fraction suggests a
potentially major role which this genus may play in fiber degradation in the rumen.
Oxalobacter formigenes (S000843932, RDP ID) that can degrade oxalate, which is
contained in plants (Allison et al., 1985), was detected only in the Ad-H fraction. Three
Sporobacter OTUs were detected and two of them were detected only in sheep fed hay.
Because Sporobacter isolated from wood-feeding termites is involved in degradation of
aromatic compounds of lignocellulose (Grech-Mora et al., 1996), ruminal Sporobacter
may be beneficial to fiber degradation. Henderson (1971) reported that Anaerovibrio
lipolytica isolated from rumen produces lipase. Two ruminal Anaerovibrio OTUs
detected in this study are thought to be a major source of lipase. Olsenella umbonata A2,
also a lactic acid bacterium (Kraatz et al., 2011), was detected only in sheep fed corn:hay.
Selenomonas ruminantium K2 (S000012651, unpublished sequence data) and
Selenomonas bovis (S000769745, RDP ID) (Zhang and Dong, 2009) were detected only
in sheep fed corn:hay. Lachnobacterium bovis (S000390937, RDP ID) that ferments
glucose and produce primarily lactic acid (Whitford et al., 2001b) was detected only in
sheep fed corn:hay. Amylolytic Ruminobacter (S001160055, RDP ID) was detected in
sheep fed corn:hay but not in sheep fed hay. Megasphaera elsdenii (S000390583, RDP
ID) is a lactate-utilizing bacterium and mainly found in ruminant animals fed high grain
diets (Ouwerkerk et al., 2002), and it was more abundant (P <0.05) in sheep fed corn:hay
than in sheep fed hay.
The RumenArray analysis did not detect Fibrobacter OTUs. This finding agrees
with the results obtained by using rrs clone libraries and pyrosequencing analysis as
described in Chapter 5 and 8. As shown in Chapter 4, real-time PCR assay showed the
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abundance of Fibrobacter. This discrepancy indicates that commonly used universal
primers cannot efficiently amplify Fibrobacter rrs sequences (Larue et al., 2005). Except
for Fibrobacter, it is not known if other genera also fail to be detected due to the same
reason. Direct labeling and hybridization of rRNA will eliminate this limitation.
6.4.4 Diversity of ruminal bacteria that are not assigned to any known genus:
Unclassified Clostridiales (U_Clostridiales), unclassified Ruminococcaceae
(U_Ruminococcaceae) and unclassified Lachnospiraceae (U_Lachnospiraceae) within
the phylum Firmicutes were detected in high abundance in the RumenArray analysis.
This result supports the prevalence of sequences assigned to these unclassified groups as
discussed previously (Kim et al., 2011b). In total, 28 OTUs classified to U_Clostridiales
were detected, and two of them showed significant difference among the fractions
(Figure 6.3). U_Clostridiales OTU 1 (S000991126, RDP ID) recovered first from cattle
(unpublished sequence data, GenBank records) was more abundant (P < 0.05) in sheep
fed hay than in sheep fed corn:hay. U_Clostridiales OTU 2 (S001382058, RDP ID)
recovered first from semi-continuous RUSITEC (unpublished sequence data, GenBank
records) was also more abundant (P < 0.05) in sheep fed hay than in sheep fed corn:hay.
Another 13 OTUs were detected only in sheep fed hay, and they were recovered from
cattle fed with hay (Brulc et al., 2009), swamp buffaloes fed with rice straw (Yang et al.,
2010a), yaks fed with pelleted lucerne (Yang et al., 2010b), or cattle fed with 66% forage
(Ozutsumi et al., 2005), suggesting that these 13 OTUs are ubiquitous and play important
role in fiber degradation. On the other hand, another 4 OTUs were detected only in sheep
fed corn:hay.
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Thirty-four OTUs assigned to U_Ruminococcaceae were detected in the
RumenArray analysis, and five of them showed significant difference among the
fractions (Figure 6.3). U_Ruminococcaceae OTU 1 (S000650389, RDP ID), recovered
first from reindeer grazing on natural summer pasture (Sundset et al., 2007), was higher
(P <0.05) in abundance in sheep fed hay than in sheep fed corn:hay. U_Ruminococcaceae
OTU 2 (S000361544, unpublished sequence data) and U_Ruminococcaceae OTU 3
(S000566650, RDP ID), both recovered from cattle (Ozutsumi et al., 2005), were more
abundant (P <0.05) in sheep fed hay than in sheep fed corn:hay. U_Ruminococcaceae
OTU 4 (S000888009, RDP ID), recovered from Yunnan yellow cattle in China
(unpublished sequence data, GenBank records), was more abundant (P <0.05) in the Lq-
H and the Lq-C fractions than in the Ad-C fraction. U_Ruminococcaceae OTU 5
(S000616063, RDP ID), recovered first from a goat (unpublished sequence data,
GenBank records), had a higher abundance (P <0.05) in sheep fed corn:hay than in sheep
fed hay. Another 11 OTUs were detected only in the sheep fed hay, of which 9 OTUs
were also recovered from cattle fed hay (Brulc et al., 2009). These organisms may be
associated with fiber degradation in both sheep and cattle.
Forty-three OTUs representing U_Lachnospiraceae were detected in the
RumenArray analysis, and one of them showed significant difference among the fractions
(Figure 6.3). U_Lachnospiraceae OTU 1 (S001143463, RDP ID), first recovered from
cattle fed hay (Brulc et al., 2009), had a higher abundance (P <0.05) in the sheep fed hay
than in the sheep fed corn:hay. U_Lachnospiraceae OTU 2 (S000806419, RDP ID)
named Cellulosilyticum ruminicola, which is a cellulolytic bacterium isolated from a yak
(Cai and Dong, 2010), was detected only in the sheep fed hay. U_Lachnospiraceae OTU
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3 (S000927675, RDP ID) named Eubacterium rangiferina, which is an usnic acid
resistant bacterium isolated from the reindeer rumen (Sundset et al., 2008), was detected
only in the sheep fed corn:hay. Another 11 OTUs were detected only in the sheep fed hay
and recovered from cattle fed hay (Brulc et al., 2009), reindeers grazing on natural
summer pasture (Sundset et al., 2007), cattle fed 66% chopped Sudangrass hay
(Ozutsumi et al., 2005), or cattle fed TMR including 65% forage (Whitford et al., 1998).
These OTUs may represent ubiquitous cellulolytic bacteria. Another 9 OTUs were
detected only in the sheep fed corn:hay and first recovered from cattle fed hay (Brulc et
al., 2009), cattle fed 66% hay (Tajima et al., 2001a), swamp buffaloes fed rice straws
(Yang et al., 2010a), or identified from the rumen of cattle or sheep (unpublished
sequence data, GenBank records).
The RumenArray analysis also detected two unclassified groups: unclassified
Bacteroidales (U_Bacteroidales) and unclassified Prevotellaceae (U_Prevotellaceae)
within the phylum Bacteroidetes. We detected 27 OTUs classified to U_Bacteroidales of
which most were detected in a slightly higher abundance in the sheep fed corn:hay than
in the sheep fed hay, although they showed no significant difference among the fractions.
These OTUs might have amylolytic activity, or at least be stimulated by corn in the diet.
U_Prevotellaceae had 18 OTUs detected, and one of them showed significant difference
among the fractions. U_ Prevotellaceae OTU 1 (S000508062, RDP ID) recovered from
sheep fed hay (Larue et al., 2005) was more abundant (P <0.05) in the sheep fed hay than
in the sheep fed corn:hay, and another 6 U_Prevotellaceae OTUs were detected only in
sheep fed hay. These 7 OTUs may be associated with fiber degradation. Another one
U_Prevotellaceae OTU was detected only in sheep fed corn:hay.
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The abundance of unclassified Clostridiales, unclassified Ruminococcaceae, and
unclassified Lachnospiraceae agrees with the previous meta-analysis (Kim et al., 2011b).
Numerous uncultured bacteria assigned to these three groups are thought to play an
important role in the fermentation of the rumen. These uncultured bacteria will need to be
isolated and characterized. A reverse metagenomic approach, as described previously
(Nichols, 2007; Pope et al., 2011), may help successfully isolate these uncultured bacteria.
6.4.5 PCA for comparison between fractions
PCA, as implemented in the MeV program, showed that PC2 separates the ruminal
bacteria of sheep fed hay from sheep fed hay:corn as shown in Figure 6.4. However,
bacterial communities did not seem to be affected greatly by the diets because PC2
explained only 9.8% variance. On the other hand, PC1 explaining 51.7% variance is
thought to separate the 15 fractions based on the number of detected OTUs observed in
each fractionated samples. No clear separation was seen based on fractions (Figure 6.4).
6.4.6 Comparison of RumenArray and real-time PCR data:
The rumen samples analyzed in the RumenArray analysis previously had been
analyzed by real-time PCR (Stiverson et al., 2011). In that study (Stiverson et al., 2011),
the abundance of select cultured and uncultured bacteria were quantified. However, only
one OTU was analyzed by both methods. This OTU, termed ‘Unclassified Clostridia 1’
in the RumenArray, is the uncultured ruminal bacterium Ad-H2-90-2 (S001790257, RDP
ID) (Stiverson et al., 2011). It was recovered from the adherent fraction of sheep fed hay
and assigned to class Clostridia. The abundance of this OTU was higher (P <0.05) in the
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Lq-H fraction than in the other fractions (Figure 6.3), consistent with the finding by
Stiverson et al. (2011). The relative abundance of this OTU was similar between the real-
time PCR and the RumenArray data except for the Ad-C fractions. The relative
abundance of this OTU in the Ad-C fraction was 2.5-times greater in the RumenArray
data than in real-time PCR data.
6.5 Conclusions:
The RumenArray developed in this study supports simultaneous and rapid
analysis of many predominant ruminal bacteria, both cultured and uncultured. The
preliminary analysis showed that some OTUs are primarily associated with hay-fed
animals, while others are associated with animals that received corn. Some OTUs also
partition between the liquid and the solid fractions. The RumenArray analysis showed
that unclassified Lachnospiraceae, unclassified Ruminococcaceae, and unclassified
Clostridiales were more abundant than the others, supporting the meta-analysis data (Kim
et al., 2011b). The RumenArray can be an alternative method for detection and semi-
quantification of abundant ruminal bacteria in a comparative manner.
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Figure 6.1: Linear range of detection of the RumenArray as determined using cRNA
pools of the 6 positive clones.
109
Figure 6.2: A venn diagram showing the number of detected OTUs. The number of the
total detected OTUs in combination across all the four fractions was 319, and 91 of them
were shared irrespective of diets or fractions.
Lq-C 25
Lq-H 58
11
15
2
19
91
2
17
2
36 Ad-H
13
Ad-C
11
6 11
110
Figure 6.3: A hierarchical tree showing signal intensities and similarity among the
fractionated samples. The hierarchical tree was constructed using the MeV program
(Saeed et al., 2006). Fifteen of all the 319 OTUs showed significant difference among the
four fractions. Signal intensities were also different among four sheep fed the same diet.
111
Figure 6.4: PCA for comparison among all the fractionated samples. PC2 separated
sheep fed hay from sheep fed corn:hay.
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CHAPTER 7
EVALUATION OF DIFFERENT PARTIAL 16S rRNA GENE SEQUENCE REGIONS
FOR PHYLOGENETIC ANALYSIS OF MICROBIOMES
7.1 Abstract:
Operational taxonomic units (OTUs) are conventionally defined at a phylogenetic
distance (0.03-species, 0.05-genus, 0.10-family) based on full-length 16S rRNA gene
sequences. However, partial sequences (700bp or shorter) have been used in most studies.
This discord may affect analysis of diversity and species richness because sequence
divergence is not distributed evenly along the 16S rRNA gene. In this study, we
compared a set each of bacterial and archaeal 16S rRNA gene sequences of nearly full
length with multiple sets of different partial 16S rRNA gene sequences derived therefrom
(approx. 440 - 700 bp), at conventional and alternative distance levels. Our objective was
to identify partial sequence region(s) and distance level(s) that allow more accurate
phylogenetic analysis of partial 16S rRNA genes. Our results showed that no partial
sequence region could estimate OTU richness or define OTUs as reliably as nearly full-
length genes. However, the V1-V4 regions can provide more accurate estimates than
others. For analysis of archaea, we recommend the V1-V3 and the V4-V7 regions and
clustering of species-level OTUs at 0.03 and 0.02 distance, respectively. For analysis of
bacteria, the V1-V3 and the V1-V4 regions should be targeted, with species-level OTUs
being clustered at 0.04 distance in both cases.
7.2 Introduction:
The difficulty in culturing most microbes present in natural or managed
113
environments forces microbiologists to use non-culture based methods to study
populations. The use of 16S rRNA gene sequencing has proven to be an effective
phylogenetic marker in examining microbial diversity and classifying microbes. Even
though the scarcity of well-characterized microbes and the lack of a reliable prokaryotic
taxonomy system often make it difficult to classify microbes to species or sub-species
level with certainty solely based on 16S rRNA gene sequences, 16S rRNA gene
sequences can provide more objective and reliable classification of microbes than
phenotyping (Schloss and Handelsman, 2005). Since Lane et al. (1985) first described the
use of 16S rRNA gene for identifying and classifying uncultured microbes in the
environment, PCR amplification, cloning and sequencing have been the primary
technologies used in determining 16S rRNA gene sequences from various environments.
During the past two decades, more than 1.3 million bacterial and 54,000 archaeal 16S
rRNA gene sequences have been archived in RDP (as of March 20, 2010, Release 10,
Update 18) (Cole et al., 2009). These sequences are curated and include 16S rRNA genes
recovered from both cultured and uncultured prokaryotes, with the latter accounting for
most of the sequences. The 16S rRNA gene sequences in RDP have been classified into
genera among 35 bacterial phyla and 5 archaeal phyla, but many of these phyla are
composed largely or entirely of uncultured prokaryotes (Schloss and Handelsman, 2004).
The 16S rRNA gene sequences generated from microbiomes are typically clustered
into operation taxonomic units (OTUs) at a few distance levels to determine species
richness, diversity, composition, and community structure. Species, genus, family, and
phylum are conventionally defined with distance values of 0.03, 0.05, 0.10 and 0.20,
respectively, based on nearly full-length (approx. 1,540 bp) 16S rRNA gene sequences
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(Schloss and Handelsman, 2004). However, 16S rRNA gene sequences produced in most
studies are partial sequences of 700 bp or shorter due to cost (with the Sanger DNA
sequencing technology) or technology limitations (with the next generation DNA
sequencing technologies). Indeed, in the RDP database less than 44% of the bacterial and
15.3% of the archaeal sequences are longer than 1,200 bp. Only a very small percentage
of the sequences in RDP reached nearly full length. Therefore, most researchers have
used partial 16S rRNA gene sequences to make taxonomic assignments. Such a discord
may create uncertainty in taxonomic placement of OTUs for the following reasons: First,
divergence among different 16S rRNA gene sequences is not distributed evenly along the
16S rRNA gene but concentrated primarily in the nine hypervariable (V) regions
(Stackebrandt and Goebel, 1994). Second, some of the V regions are more variable than
others (Youssef et al., 2009; Yu and Morrison, 2004a). Third, some regions of the 16S
rRNA genes produce more reliable taxonomic assignments than others (Liu et al, 2007,
2008; Wang et al., 2007). We hypothesize that different V regions may produce different
results with respect to estimates on species richness, diversity, and microbiome
composition and structure, and some partial sequence regions may be better suited for
microbiome analysis than others. A different taxonomic cutoff value, or distance level,
may be required for a particular partial sequence region to give rise to similar results as
nearly full-length sequences.
Recently, a number of studies used 454 pyrosequencing in comprehensive analysis
of species richness and diversity present in complex microbiomes (Claesson et al., 2009;
Sogin et al., 2006; Krober et al., 2009; Youssef et al., 2009). These studies generated
large numbers of partial 16S rRNA gene sequences. By necessity, these partial sequences
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were clustered into OTUs using the same conventional distance values that were used for
nearly full-length sequences. In some of these studies, two single V regions are compared
between them and also against a set of nearly full-length sequences (Claesson et al.,
2009; Dethlefsen et al., 2008; Huse et al., 2008) in estimating OTU richness. It is
recognized that the choice of V regions significantly affects estimates on OTU richness
and diversity. One study also showed that the V1-V2 (approx. 350 bp) and the V8 regions
produced different OTU evenness when a termite sample was analyzed (Engelbrektson et
al., 2010). Another study compared eight V regions, either singular or dual, but the length
of the partial sequence regions only ranged from 99 to 361 bp (Youssef et al., 2009).
More importantly, in these studies conclusions were drawn from comparing short partial
sequences recovered from one or a few habitats. As such, the conclusions derived from
these studies may not be applied broadly to other environments. As the read length of
pyrosequencing continues to increase, longer partial sequences (up to 800 currently) of
16S rRNA genes can be sequenced. Thus, there is a need to identify suitable partial
sequence regions and phylogenetic distance cutoff values that can provide reliable
analysis of microbiomes. In this study, we systematically compared all the partial
sequence regions (approx. 450 to 700 bp) delineated by commonly used domain-specific
(bacterial or archaeal) PCR primers against nearly full-length 16S rRNA gene sequences
archived in RDP that represent a broad taxonomy of both cultured bacteria and archaea.
The comparisons were focused on observed OTU richness, parametric and nonparametric
estimates of maximum OTU richness, accuracy of OTU clustering, and community
structure. The objective was to identify a partial region(s) of 16S rRNA gene and a
distance cutoff value(s) that enable analysis of 454 pyrosequencing reads and produce
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comparable results as nearly full-length sequences.
7.3 Materials and Methods:
7.3.1 Sequence collection, alignment, and clipping
All the sequences longer than 1,200 bp were retrieved from the RDP database
(Release 10, Update 18) in March 2010. All these sequences are of good quality as
determined by RDP. For domain Bacteria, only the sequences recovered from type strains
were selected, while for domain Archaea, sequences derived from both type and non-type
strains were chosen because only a small number of archaeal type strains are archived in
RDP. The bacterial and archaeal sequences were downloaded separately. The sequences
that do not have nearly full-length (<1,424 bp), as determined by the absence of the
annealing sites of the domain-specific PCR primer pairs that anneal near the termini of
16S rRNA genes (A2Fa and U1510r for archaea, 27f and 1492r for bacteria), were
removed manually from the datasets. The nearly full-length sequences were aligned using
the NAST aligner according to a core set of alignment templates in the Greengenes
database (DeSantis et al., 2006). Partial sequence regions that were delineated by the
binding sites of domain-specific primer pairs (Tables 7.1 and 7.3) targeting different
hypervariable regions (Baker et al., 2003; Yu and Morrison, 2004a; Yu et al., 2008) were
clipped out from the alignment of the nearly full-length sequences using the Geneious
program (Biomatters Ltd, Auckland, New Zealand), with the original alignment being
retained. The alignment of each partial sequence region and the full-length sequences was
analyzed as a separate ‘clone library’.
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7.3.2 Diversity estimates
From the alignment of the nearly full-length sequences and each partial sequence
region, a distance matrix at distance of 0.03, 0.05, and 0.10 was computed using the
DNADIST program of the PHYLIP package (version 3.69,
http://evolution.genetics.washington.edu/phylip.html) with the Jukes-Cantor correction
applied. A distance matrix was also computed at 0.01 distance, which was suggested to
be a new taxonomic cutoff value for species (Stackebrandt and Ebers, 2006). The
DOTUR program (Schloss and Handelsman, 2005) was used to cluster the sequences into
OTUs (referred to as ‘observed’ OTUs) and determine the maximum number of OTUs
represented by each ‘clone library’ using nonparametric Chao1 and ACE richness
estimates. From the rarefaction output calculated by the DOTUR program, a parametric
estimate of maximum number of OTUs in each ‘clone library’ was also performed using
the non-linear models procedure (PROC NLIN) of SAS (V9.1, SAS Inst. Inc., Cary, NC)
as described previously (Larue et al., 2005). The number of OTUs defined by each partial
sequence ‘clone library’ (referred to as observed OTU richness) and the maximum
number of OTUs predicted from each partial sequence ‘clone library’ (referred to as
maximum OTU richness) were compared to those defined by the corresponding nearly
full-length sequence ‘clone library’.
To identify a distance cutoff value that produces better estimate on species-level
OTUs than the commonly used 0.03 distance, all the partial sequence regions were also
analyzed at 0.02 and 0.04 distances. Distance matrices at 0.02 and 0.04 were computed as
described above. Each of the distance matrices was then used in clustering OTUs and
estimating observed and maximum OTU richness as described above. The sequence
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composition was compared between respective OTUs defined from the nearly full-length
‘clone library’ and from each of the partial sequence ‘clone library’. The ‘accuracy’ of
OTU clustering was introduced as a percentage of partial sequence-based OTUs that have
identical sequence composition as the OTUs defined by the nearly full-length sequences.
7.3.3 UniFrac analysis
The UniFrac program (Lozupone and Knight, 2005) was used to assess differences
in ‘clone libraries’ represented by individual partial sequence datasets and the nearly full-
length sequence dataset. The sequences from all the ‘libraries’ were aligned against the
Greengenes database and then inserted into the ARB tree to build phylogenetic trees. The
constructed trees were subjected to UniFrac significance test and P test.
7.3.4 Analysis of sequence datasets recovered from uncultured bacteria
To verify their applicability, one sequence dataset each recovered from rumen
(Brulc et al., 2009) and deep-sea surface sediment (Schauer et al., 2010) were also
analyzed as described for the composite RDP sequence datasets of bacterial strains. Each
of the two sequence datasets was retrieved from the RDP database, aligned, and analyzed
as mentioned above for the RDP sequence datasets. Because no individual studies
reported large numbers of archaea full-length archaeal sequences, this verification was
not done for archaea.
7.3.5 Analysis of short partial sequence regions
Short partial sequences spanning 1 or 2 consecutive V regions (94 - 362) were also
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evaluated using the composite RDP sequence dataset for bacteria. Due to lack of primers
that anneal to individual V regions of archaeal sequences, this evaluation was not done
for the composite RDP sequences of archaea. The Illumina GAIIx system, which
produces short reads of about 100bp, has been used in microbiome analysis in a recent
study (Caporaso et al., 2011). To evaluate the reliability of such short sequence reads in
estimating richness and diversity, both the 100bp regions downstream of primer F515 and
upstream of primer R806 (primers F515 and R806 were used in the study by Caporaso et
al., 2011) were also analyzed and compared to the nearly full-length sequences in the
bacterial RDP dataset as described above.
7.4 Results:
From the RDP database (Release 10 Update 18), 7,450 bacterial sequences longer
than 1,200 bp were found that were derived from type strains. Of these sequences, 887
have a length of ≥1,458 bp and contain the annealing sites of universal or domain-
specific primers near both ends of 16S rRNA gene. These nearly full-length sequences
represent a broad taxonomic spectrum, including 18 phyla, 25 class, 64 order, 165 family,
361 genera, and 4 unclassified groups above genus level. In total, 8,375 archaeal
sequences were found longer than 1,200 bp. Among them, only 284 were derived from
type strains and have nearly full length. To increase the taxonomic representation, the
nearly full-length sequences derived from non-type strains of archaea were also included.
The archaeal sequence dataset used in this study contained 1,071 nearly full-length
sequences (≥1,435 bp), which represent all the 4 archaeal phyla, 9 class, 14 order, 25
family, 73 genera, and 14 unclassified groups above the phylum, class, order, family or
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genus level. We intentionally selected and used sequences from a broad taxonomic
spectrum so that the results derived can be less biased toward taxa found in particular
habitats and the conclusions drawn can be better applied broadly to various
environments. The lengths of the partial sequence regions ranged from 463 to 702 bp for
archaea and from 446 to 652 bp for bacteria. Each of the partial sequence regions was
compared to the corresponding nearly full-length sequence dataset with respect to
observed OTU richness, maximum OTU richness, community structure, and accuracy of
OTU clustering.
7.4.1 Analysis of partial archaeal sequences
The different partial sequence ‘clone libraries’ (partial sequence regions) of the
archaeal sequence dataset gave rise to varying observed OTU richness at 0.03 distance,
and all the estimates differed from that computed from the full-length archaeal sequences
(Table 7.1). The V1-V3 region resulted in the best estimates of both observed OTU
richness (5.5% overestimate) and the rarefaction-predicted (6.9% overestimate)
maximum OTU richness, followed by the V1-V4 region. For both the Chao1 and ACE
estimates, the V3-V5 region afforded the best predictions, which were lower than (3.9
and 12%, respectively) that estimated from the nearly full-length sequences. The partial
sequence region that generated the second best estimate at species level was the V7-V9
region for Chao1 estimate and the V1-V4 region for ACE estimate. Overall, the V1-V4
region yielded overestimates, while the downstream regions underestimated all the
richness estimates.
None of the partial sequence regions faithfully recaptured all the OTUs that were
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defined by the nearly full-length archaeal sequences at any of the tested distances. At
0.04 distance, all the partial sequence regions produced worse estimates on both the
observed and the maximum OTU richness measurements than at 0.03 distance. The V1-
V3 and the V1-V4 regions rendered worse estimates on OTU richness at 0.02 distance
than at 0.03 distance, but the downstream partial sequence regions gave rise to better
estimates on both observed and maximum richness (Table 7.1). The only exception was
the Chao1 estimate from the V3-V5 region. The V4-V7 region resulted in the best
estimates of observed OTU richness and rarefaction-predicted maximum OTU richness at
0.02 distance, whereas the V3-V5 and the V5-V7 regions resulted in better Chao1 and
ACE estimates at 0.03 and 0.02 distance, respectively. At 0.02 distance, the V4-V7
region produced the highest accuracy (72.2%) of OTU clustering among all the partial
sequence regions, but OTU clustering based on this partial sequence region was 3.4%
more accurate at 0.03 distance. At 0.02 distance, the partial sequence regions downstream
of V1-V4 produced more accurate estimates on OTU richness. These results reflect the
greater sequence divergence of the V1-V4 region than the downstream regions (Yu et al.,
2008).
At 0.01 distance, a new distance cutoff value recommended by Stackebrandt and
Ebers (2006) to define prokaryotic species, the V1-V4 and the V1-V3 were the first and
second best partial sequence regions with respect to estimates of observed OTU richness
and maximum OTU richness irrespective of prediction methods used (Table 7.5).
At 0.05 distance (equivalent to genus), the V1-V4 region produced the best estimates on
both observed and maximum OTU richness (Table 7.2). The V1-V3 region yielded the
second best estimate of maximum OTU richness, though the observed OTU richness was
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overestimated. At 0.10 distance (equivalent to family), the V3-V5 region produced the
best estimates on both the observed OTU richness and the rarefaction-predicted
maximum OTU richness. For Chao1 estimate, the V4-V7 region gave rise to a more
accurate estimate than other partial sequence regions, followed by the V3-V5 region. The
V1-V4 region supported the best ACE estimate, followed by the V4-V7 region. We note
that the V1-V4 region (1-639 bp) tended to overestimate both observed and maximum
OTU richness at 0.05 and 0.10 distances, whereas the downstream regions considerably
underestimated all the estimates of OTU richness (Table 7.2).
7.4.2 Analysis of partial bacterial sequences
The estimates of OTU richness of the bacterial sequence dataset differed also
among the different partial sequence regions analyzed (Table 7.3). The observed species-
level OTU richness estimated from the V6-V9 region delineated by primers 968f-1492r
was the closest (5.9% underestimate) to that calculated from the full-length sequences,
followed by the V1-V3 region delineated by primers 27f-519r. For the rarefaction
estimate of maximum species-level OTU richness, the V6-V9 region delineated by
primers 968f-1492r is the best region (13.8% underestimate), followed by the V1-V3
region delineated by primers 27f-519r. The V6-V9 region delineated by primers 968f-
1492r also generated the best Chao1 (3.0% underestimate) and ACE (14.3%
underestimate) estimates, which was followed by the V6-V9 region delineated by primers
926f-1492r. As in the case of partial archaeal sequences, the upstream regions (V1-V4)
consistently overestimated OTU richness, while the downstream regions underestimated
OTU richness.
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When different partial sequence regions were evaluated in clustering species-level
OTUs at 0.02 and 0.04 distance levels, no partial sequence region completely recaptured
the OTU estimates defined by the nearly full-length sequences either. However, the
upstream regions (i.e. V1-V3 and V1-V4) produced more accurate estimates of observed
and maximum OTU richness at 0.04 than at 0.03 distances, whereas the downstream
partial sequence regions improved estimates at 0.02 distance, with a few exceptions
(Table 7.3). The three estimates on maximum OTU richness based on the V6-V9 region
delineated by primers 968f-1492r and the Chao1 estimate from the V6-V9 region
delineated by primers 926f-1492r were more accurate at 0.03 than at 0.02 distance. The
V1-V3 region delineated by primers 27f-519r afforded nearly the same observed OTU
richness at 0.04 distance as the nearly full-length sequences did at 0.03 distance. The V1-
V4 region delineated by primers 63f-685r also generated a very close estimate of
observed OTU richness at 0.04 distance. At this same distance, the V1-V3 region
delineated by primers 63f-519r, the V1-V4 region delineated by primers 27f-685r, and
the V1-V4 region delineated by primers 63f-685r also supported better estimate on
maximum OTU richness by rarefaction, Chao1 and ACE, respectively, than other partial
sequence regions. The V1-V4 region delineated by primers 27f-685r, however, produced
the best accuracy (86.4%) of OTU clustering at 0.04 distance, while also producing rather
accurate estimate (2.2% underestimate) on observed OTU richness.
At 0.01 distance, the V1-V4 region delineated by primers 27f-685r produced the
best estimate on observed OTU richness, while the V1-V4 region delineated by primers
63f-685r generated the second best estimate (Table 7.6). For rarefaction estimate of
maximum OTU richness, the V6-V9 region delineated by primers 968f-1492r was the
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best region, while the V1-V4 region delineated by primers 27f-685r was the second best
choice. For both Chao1 and ACE estimates, the V1-V4 region delineated by primers 63f-
685r was the best region, while the V1-V4 region delineated by primers 27f-685r
generated the second best estimate. When the rumen sequence dataset (Brulc et al., 2009)
was analyzed at 0.01 distance, the V1-V4 region delineated by primers 27f-685r and 63f-
685r also produced the first and second best estimates on observed and maximum OTU
richness, respectively (data not shown). Therefore, the V1-V4 region is probably the best
region for species richness and diversity estimates at 0.01 distance.
At genus level, the V6-V9 region delineated by primers 968f-1492r allowed the
best estimates on both observed OTU richness and maximum OTU richness (Table 7.4).
However, all these predictions were underestimated. The V1-V4 region delineated by
primers 27f-685r yielded the second closest estimates of observed OTU richness and the
maximum OTU richness that was predicted by rarefaction and Chao1, while the V3-V5
region delineated by primers 357f-907r resulted in the second best ACE estimate of
maximum OTU richness. At family level, the V6-V9 region delineated by primers 968f-
1492r again generated the closest estimates, all of which were underestimated (Table
7.4). The V3-V5 region delineated by primers 357f-907r produced the second closest
estimates on both observed OTU richness and maximum OTU richness. Again, the
upstream regions (V1-V4) overestimated OTU richness, while the downstream regions
underestimated OTU richness (Table 7.4). These results corroborate the greater bacterial
sequence divergence of the V1-V4 region than the downstream regions (Yu and
Morrison, 2004a) and are in general agreement with finding of Youssef et al. (Youssef et
al., 2009).
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7.4.3 UniFrac analysis
Both the UniFrac significance test and the P test showed that none of the
‘microbiomes’ represented by individual partial sequence regions was significantly
different (P ≥ 0.25) than that represented by the full-length sequences. These results
suggest that the locations of the partial sequence regions of the analyzed lengths might
not significantly affect comparison of microbiomes using UniFrac. This conclusion is
consistent with the finding of several previous studies from which several shorter partial
sequence regions were analyzed (Huse et al., 2008; Liu et al., 2007; Wang et al., 2007).
Therefore, any of the partial sequence regions analyzed in this study can depict a
comparable microbiome structure as the full-length sequences.
7.4.4 Analysis of uncultured bacterial sequences
The rumen sequence dataset contained 1,388 sequences (≥1,438 bp) of rumen
origin (Brulc et al., 2009) that represented 9 bacterial phyla, but Firmicutes,
Bacteroidetes, and Proteobacteria predominated. Various partial sequence regions were
compared to their corresponding nearly full-length sequences with respect to estimates on
OTU richness (data not shown). For observed OTU richness, the V1-V4 region
delineated by primers 63f-685r produced better estimate (0.3% overestimate) at 0.04
distance than at other distances, whereas the V1-V4 region delineated by primers 27f-
685r gave rise to the second best estimate (2.4% underestimate) also at 0.04 distance. The
V1-V4 region delineated by primers 27f-685r and 0.04 distance also provided the best
Chao1 (1.8% overestimate) and ACE (0.3% overestimate) estimate and the second best
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rarefaction estimate (1.7% underestimate vs. 1.5% underestimate from the V4-V6
region). All other distances and partial regions produced worse estimates on either
observed OTU richness or maximum OTU richness. When the bacterial sequence dataset
(634 sequences of ≥1,421 bp) recovered from deep-sea surface sediments (Schauer et al.,
2010) was analyzed (data not shown), the V1-V4 region delineated by primers 27f-685r
and 0.04 distance also produced the best estimate on OTU richness (0.3% overestimate)
and the second best rarefaction estimate of maximum OTU richness (2.3% overestimate
vs. 1.4% overestimate from the V6-V9 region delineated by primers 968f-1492r).
Although not the best combination, this V1-V4 region and 0.04 distance only led to 7.6%
and 6.5% overestimate for Chao1 or ACE estimates, respectively. Evidently, the V1-V4
region and 0.04 distance can provide accurate estimate on OTU richness from these two
sets of uncultured bacterial sequences.
7.4.5 Analysis of short partial sequence regions
Short partial sequence regions delineated by primers used in a previous study
(Youssef et al., 2009) were analyzed to evaluate their utility in estimate of species
richness. These short sequences (94-362 bp) span 1 or 2 consecutive V regions (Table
7.7). For observed OTU richness, the V6 region and 0.03 distance produced the best
estimate, while the V1-V2 region gave rise to best rarefaction and ACE estimate and V7-
V8 the best Chao1 estimate. Nevertheless, none of these short partial sequence region
produced accurate estimate at any of the distances (0.01 to 0.05) examined. This also
holds true for the rumen sequence dataset (data not shown). Because different V regions
have been used in many different studies, β-diversity analysis using previously published
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datasets can be difficult.
The 100bp region downstream of the forward primer F515 and the 100bp region
upstream of reverse primer R806, which were generated by the Illumina GAIIx system
and used in analysis of several samples including human feces and soil, fresh water and
freshwater sediments (Caporaso et al., 2011), were compared to the nearly full-length
sequences of RDP to assess the accuracy of such a short region in estimating species
richness (data not shown). Except the 100bp region downstream of F515 and 0.01
distance that produced an accurate estimate on observed OTU richness (3.7%
underestimate), both short regions underestimated both observed OTU richness and
maximum OTU richness by 13.5 to 66% at 0.01, 0.02, or 0.03 distances. Similar results
were observed when the rumen sequence dataset (Brulc et al., 2009) was subjected to this
analysis (data not shown). Thus, although the Illumina GAIIx can produce sequence
reads more cost-effectively, the short sequences probably do not support accurate
analysis of microbiomes.
7.5 Discussion:
Defining the full diversity of microbiomes is essential for microbial ecologists to
assess the functional significance of any bacterial or archaeal species or to determine if
the major members have been accounted for in analysis of specific microbiomes.
Pyrosequencing recently emerged as the enabling technology to comprehensively
characterize complex microbiomes in natural environments (Gilbert et al., 2008),
managed ecosystems (Krause et al., 2008; Liu et al., 2008; Zhang et al., 2009), or human
and animal gut (Claesson et al., 2009; Dowd et al., 2008; Huse et al., 2008). It is difficult
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to assemble individual pyrosequencing reads into full-length 16S rRNA genes because of
the conserved nature of this gene. Therefore, partial 16S rRNA gene sequences are
directly used in microbiome analysis. Because different regions of the 16S rRNA gene
have different divergence, the choice of partial sequence regions can significantly affect
the analysis results (Engelbrektson et al., 2010; Liu et al., 2007; Youssef et al., 2009).
Thus, it is important and useful to determine how a partial 16S rRNA gene sequence
region can support characterization of microbiomes as ‘reliably’ as nearly full-length 16S
rRNA genes.
Unlike other reported studies that compared single or dual V regions (94 - 360
bp), in this study we compared all partial sequence regions that span at least three
consecutive V regions (463-702 bp for archaea, 446-652 bp for bacteria). All these partial
sequence regions can be generated using domain-specific primer pairs so that they can be
amplified and used in pyrosequencing analysis. In addition, instead of using uncultured
bacterial sequences recovered from a particular habitat, we chose the sequences only
recovered from cultured organisms (type strains for bacteria, both type and non-type
strains for archaea). These sequences are better taxonomically characterized and are free
of chimeric artifacts. Furthermore, because the sequences were not recovered from a
particular habitat, the sequences used in this study represent a much broader taxonomy
and diversity. As such, the results of this study might be applied to analysis of different
microbiomes. To test this premise, two large datasets of nearly full-length sequences
were analyzed in parallel to verify if the best partial sequence region(s) and distance(s)
can be applied to individual sequence datasets.
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The 454 GS FLX systems is the primary technology used in most diversity studies
of microbiomes (Droege and Hill, 2008). The previous 454 GS FLX system produces
sequence reads about 250 bp, a length typically spanning single V regions of 16S rRNA
gene. Such a length only allows for classification of 16S rRNA gene sequences to genus
in RDP (Liu et al., 2008). Most studies reported so far pyrosequenced single V regions,
and when compared, two different V regions typically produce different results (Claesson
et al., 2009; Dethlefsen et al., 2008; Huse et al., 2008; Sogin et al., 2006; Youssef et al.,
2009). Several of these studies also compared partial sequences to nearly full-length
sequences in estimating OTU richness (Claesson et al., 2009; Huse et al., 2008; Youssef
et al., 2009). However, few studies have assessed if partial sequences can be clustered
into species-level OTUs as ‘reliably’ as nearly full-length sequences. Thus, this study is
probably among the early studies in a continuum of research that will lead to improved
analysis of 16S rRNA gene sequences.
Different partial sequence regions produced different estimates of OTU richness,
both observed and predicted maximum, at all the three conventional distance levels for
either archaea or bacteria. However, none of the analyzed partial sequence regions of
bacteria or archaea faithfully recaptured the richness estimates (observed or predicted)
that were determined by the nearly full-length sequences at conventional 0.03, 0.05, or
0.10 distances. These results corroborate the finding of a recent study (Schloss, 2010).
Therefore, estimates of OTU richness calculated from partial sequences should be
interpreted with caution. As shown in this study, the V1-V4 region can provide improved
estimates on species richness and accuracy of OTU clustering when clustered at 0.04
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distance, while the downstream partial sequence regions need to be clustered at 0.02
distance (Tables 7.1 and 7.3).
As the 454 pyrosequencing technology is increasingly used in analysis of
microbiomes and the sequence read length continues to increase, longer partial 16S
rRNA gene sequences will be sequenced that will improve accuracy of microbiome
analysis. If a common partial sequence region is targeted by different researchers,
analysis results can be compared among laboratories. A common target region will also
facilitate global analysis of microbial diversity in a particular type of environment of
interest as well as β-diversity across multiple environments. As a beginning of this effort,
for analysis of archaea we recommend the V1-V3 region to be targeted with species-level
OTUs being clustered at 0.03 distance if the current FLX Titanium system is used, or the
V4-V7 region to be targeted with species-level OTUs being clustered at 0.02 distance if
the newest 454 FLX system that generates up to 800bp sequence reads is used. For
analysis of bacteria, the V1-V3 region should be targeted using the FLX Titanium
system, while the V1-V4 should be targeted with the newest 454 FLX system, with
species-level OTUs being clustered at 0.04 distance in both cases. Additionally, these
partial sequence regions also provide better analysis of richness and diversity than other
partial regions if 0.01 distance is used to define OTUs. It should be pointed out that if
distance is set at thousandth or below partial sequence regions may produce similar
richness estimates as nearly full-length sequence. However, it will be time consuming to
compare millions of richness estimates (5x107 for archaea and 5x10
8 for bacteria
sequences). In addition, most OTU clustering algorithms do not support thousandth
distance.
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The V1-V3 or the V1-V4 regions of bacterial 16S rRNA genes provide two
additional advantages: First, the V1-V3 or the V1-V4 regions are more divergent and thus
can provide more phylogenetic resolution than other regions. Greater resolution is
especially important in analysis of microbiomes from specialized habitats, such as
intestinal tract of animals and humans, rumens, anaerobic digesters, biological
wastewater treatment reactors, where great diversity exists at low taxa. Second, RDP and
other databases stored more partial sequences that correspond to the V1-V4 region than
the downstream regions. As such, partial sequences corresponding to this region will
have more database sequences to compare to, greatly facilitating phylogenetic analysis.
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Max
imu
m #
of
OT
Us
±
Rar
efac
tio
n
Ch
ao1
AC
E
A2
Fa
– U
15
10r
V1
-V9
1,4
35
0.0
3
36
3 (
0.0
) 3
63
(1
00
) 4
62
(0
.0)
59
0 (
0.0
) 6
60
(0
.0)
A2
Fa
– 5
19r
V1
-V3
46
7
0.0
3
38
3 (
5.5
) 2
52
(6
5.8
) 4
94
(6
.9)
68
4 (
15
.9)
75
8 (
14
.8)
A2
Fa
– A
69
3r
V1
-V4
63
9
0.0
3
38
8 (
6.9
) 2
69
(6
9.3
) 4
99
(8
.0)
68
0 (
15
.3)
75
7 (
14
.7)
AR
C3
44
f–A
RC
91
5r
V3
-V5
55
3
0.0
3
33
1 (
-8.8
) 2
31
(6
9.8
) 4
01
(-1
3.2
) 5
67
(-3
.9)
58
1 (
-12
.0)
0
.02
38
4 (
5.8
) 2
56
(6
6.7
) 4
95
(7
.1)
69
6 (
18
.0)
72
6 (
10
.0)
U5
19
f –
UA
12
04r
V4
-V7
70
2
0.0
3
31
1 (
-14
.3)
23
5 (
75
.6)
37
7 (
-18
.4)
51
3 (
-13
.1)
55
4 (
-16
.1)
0
.02
36
7 (
1.1
) 2
65
(7
2.2
) 4
73
(2
.4)
68
2 (
15
.6)
73
3 (
11
.1)
A6
79
r§ –
UA
12
04
r V
5-V
7
52
7
0.0
3
27
9 (
-23
.1)
18
9 (
67
.7)
33
1 (
-28
.4)
45
0 (
-23
.7)
48
0 (
-27
.3)
0
.02
34
2 (
-5.8
) 2
31
(6
7.5
) 4
32
(-6
.5)
62
7 (
6.3
) 6
63
(0
.5)
AR
CH
91
5–
U1
51
0r
V6
-V9
58
5
0.0
3
30
7 (
-15
.4)
22
7 (
73
.9)
36
5 (
-21
.0)
46
5 (
-21
.2)
49
8 (
-24
.5)
0
.02
37
8 (
4.1
) 2
51
(6
6.4
) 4
77
(3
.2)
64
6 (
9.5
) 6
71
(1
.7)
A1
04
0f
– U
151
0r
V7
-V9
46
3
0.0
3
32
8 (
-9.6
) 2
31
(7
0.4
) 3
94
(-1
4.7
) 5
20
(-1
1.9
) 5
55
(-1
5.9
)
0
.02
37
7 (
3.9
) 2
43
(6
4.5
) 4
78
(3
.5)
66
0 (
11
.9)
69
6 (
5.5
) †
The
esti
mat
es f
or
nea
rly f
ull
-len
gth
seq
uen
ces
and p
arti
al s
equen
ce r
egio
ns
at 0
.03 (
shad
ed)
are
list
ed. F
or
som
e p
arti
al s
equ
ence
regio
ns,
the
esti
mat
es a
t 0.0
2 o
r 0.0
4 a
re a
lso l
iste
d w
hen
bet
ter
esti
mat
es w
ere
obta
ined
.
* C
alcu
late
d f
rom
conse
nsu
s se
quen
ces
(sam
e as
in o
ther
tab
les)
.
± V
alues
in p
aren
thes
is s
how
the
esti
mat
es r
elat
ive
to t
hat
of
full
-len
gth
seq
uen
ces.
Posi
tive
val
ues
des
ignat
e ov
eres
tim
ates
, an
d
neg
ativ
e val
ues
und
eres
tim
ates
. T
he
val
ues
that
are
both
under
lined
and b
old
ed a
re t
he
bes
t es
tim
ates
, w
hil
e th
e val
ues
that
are
only
under
lined
are
the
seco
nd b
est
esti
mat
es (
sam
e as
in o
ther
tab
les)
.
‡ N
um
ber
of
OT
Us
that
conta
in t
he
sam
e se
quen
ces
as t
he
corr
espondin
g O
TU
s cl
ust
ered
fro
m t
he
nea
rly f
ull
-len
gth
seq
uen
ces.
The
val
ues
in p
aren
thes
is r
epre
sent
accu
racy (
%)
of
OT
U c
lust
erin
g (
sam
e as
in o
ther
tab
les)
.
§ T
he
rever
se c
om
ple
men
tary
of
pri
mer
A693r
rep
ort
ed b
y Y
u e
t al
. (2
008
). S
ame
as i
n o
ther
Tab
le 7
.2.
Tab
le 7
.1.
Est
imat
es o
f sp
ecie
s-le
vel
OT
Us
calc
ula
ted f
rom
par
tial
and f
ull
-len
gth
arc
hae
al 1
6S
rR
NA
gen
e se
qu
ence
s†.
133
Pri
mer
set
V
reg
ions
Seq
uen
ce
length
(bp
)*
Dis
tance
level
# o
f O
TU
s ±
Max
imum
# o
f O
TU
s ±
Rar
efac
tion
C
hao
1
AC
E
A2F
a – U
1510r
V1-V
9
1,4
35
0.0
5
273 (
0.0
)
322 (
0.0
) 469 (
0.0
) 462 (
0.0
) A
2F
a – 5
19r
V1-V
3
467
303 (
11.0
) 355 (
10.2
) 455 (
-3.0
) 495 (
7.1
)
A2F
a – A
693r
V1-V
4
639
300 (
9.9
) 350 (
8.7
) 457 (
-2.6
) 477 (
3.2
)
AR
C344f–
AR
C915r
V3-V
5
553
246 (
-9.9
) 278 (
-13.7
) 374 (
-20.3
) 392 (
-15.2
)
U519f
– U
A1204r
V4-V
7
702
234 (
-14.3
) 268 (
-16.8
) 403 (
-14.1
) 399 (
-13.6
) A
679r§
– U
A1204r
V5-V
7
527
206 (
-24.5
) 227 (
-29.5
) 321 (
-31.6
) 325 (
-29.7
)
AR
CH
915
–U
1510r
V6-V
9
585
231 (
-15.4
) 255 (
-20.8
) 378 (
-19.4
) 368 (
-20.3
)
A1040f
– U
1510r
V7-V
9
463
244 (
-10.6
) 271 (
-15.8
) 372 (
-20.7
) 366 (
-20.8
)
A2F
a – U
1510r
V1-V
9
1435
0.1
0
137 (
0.0
) 142 (
0.0
) 210 (
0.0
) 204 (
0.0
) A
2F
a – 5
19r
V1-V
3
467
168 (
22.6
) 174 (
22.5
) 265 (
26.2
) 238 (
16.7
)
A2F
a – A
693r
V1-V
4
639
165 (
20.4
) 172 (
21.1
) 235 (
11.9
) 235 (
15.2
)
AR
C344f–
AR
C915r
V3-V
5
553
129 (
-5.8
) 130 (
-8.5
) 193 (
-8.1
) 171 (
-16.2
)
U519f
– U
A1204r
V4-V
7
702
122 (
-10.9
) 124 (
-12.7
) 194 (
-7.6
) 172 (
-15.7
)
A679r§
– U
A1204r
V5-V
7
527
105 (
-23.4
) 107 (
-24.6
) 145 (
-31.0
) 143 (
-29.9
)
AR
CH
915
–U
1510r
V6-V
9
585
115 (
-16.1
) 115 (
-19.0
) 150 (
-28.6
) 148 (
-27.5
)
A1040f
– U
1510r
V7-V
9
463
123 (
-10.2
) 125 (
-12.0
) 169 (
-19.5
) 167 (
-18.1
)
Tab
le 7
.2.
Est
imat
es o
f gen
us-
and f
amil
y-l
evel
OT
Us
calc
ula
ted f
rom
par
tial
and f
ull
-len
gth
arc
hae
al 1
6S
rR
NA
gen
e se
qu
ence
s.
134
Pri
mer
set
V
reg
ion
s S
equ
ence
len
gth
(b
p)*
D
ista
nce
level
#
of
OT
Us
±
# o
f id
enti
cal
OT
Us
(%)
‡
Max
imu
m #
of
OT
Us ±
R
aref
acti
on
Ch
ao1
AC
E
27
f –
149
2r
V1
-V9
1,4
58
0.0
3
55
5 (
0.0
) 5
55
(1
00
) 1
10
5 (
0.0
) 1
60
0 (
0.0
) 1
80
9 (
0.0
) 2
7f
– 5
19
r
V1
-V3
48
4
0.0
3
60
2 (
8.5
) 4
59
(7
6.2
) 1
32
9 (
20
.0)
21
26
(33
.0)
22
55
(24
.7)
0
.04
55
6 (
0.2
) 4
59
(8
2.6
) 1
08
0 (
-2.3
) 1
80
7 (
12
.9)
18
59
(2
.8)
27
f –
685
r V
1-V
4
65
2
0.0
3
60
3 (
8.6
) 4
78
(7
9.3
) 1
36
8 (
23
.8)
20
85
(30
.3)
23
85
(31
.8)
0
.04
54
3 (
-2.2
) 4
69
(8
6.4
) 1
01
5 (
-8.1
) 1
54
1 (
-3.7
) 1
68
1 (
-7.1
)
63
f –
519
r V
1-V
3
44
6
0.0
3
61
2 (
10
.3)
45
9 (
75
.0)
13
72
(24
.2)
20
19
(26
.2)
22
35
(23
.5)
0
.04
56
3 (
1.4
) 4
56
(8
1.0
) 1
11
2 (
0.6
) 1
87
5 (
17
.2)
19
50
(7
.8)
63
f –
685
r V
1-V
4
61
4
0.0
3
60
6 (
9.2
) 4
82
(7
9.5
) 1
37
5 (
24
.4)
21
33
(33
.3)
24
20
(33
.8)
0
.04
55
7 (
0.4
) 4
72
(8
4.7
) 1
08
8 (
-1.5
) 1
66
2 (
3.9
) 1
78
4 (
-1.4
)
35
7f
– 9
07
r
V3
-V5
56
3
0.0
3
48
8 (
-12
.1)
40
2 (
82
.4)
82
3 (
-25
.5)
12
44
(-2
2.0
) 1
31
3 (
-27
.4)
0
.02
55
2 (
-0.5
) 4
59
(8
3.2
) 1
09
3 (
-1.1
) 1
67
0 (
4.4
) 1
77
5 (
-1.9
)
53
3f
– 1
10
0r
V4
-V6
59
7
0.0
3
49
9 (
-10
.1)
40
8 (
81
.8)
86
6 (
-21
.6)
13
35
(-1
7.0
) 1
39
6 (
-22
.8)
0
.02
56
7 (
2.2
) 4
68
(8
2.5
) 1
16
9 (
5.8
) 1
81
3 (
13
.3)
20
40
(12
.8)
92
6f
– 1
49
2r
V
6-V
9
60
5
0.0
3
50
5 (
-9.0
) 4
16
(8
2.4
) 8
83
(-2
0.1
) 1
49
3 (
-7.0
) 1
44
1 (
-20
.3)
0
.02
57
3 (
3.2
) 4
61
(8
0.5
) 1
21
9 (
10
.3)
18
55
(15
.9)
20
72
(14
.5)
96
8f
– 1
49
2r
V
6-V
9
54
4
0.0
3
52
2 (
-5.9
) 4
32
(8
2.8
) 9
52
(-1
3.8
) 1
55
4 (
-3.0
) 1
55
1 (
-14
.3)
0
.02
58
7 (
5.8
) 4
62
(7
8.7
) 1
28
6 (
16
.4)
19
46
(21
.6)
22
50
(24
.4)
No
te:
esti
mat
es a
t 0
.03
dis
tan
ce a
re s
had
ed.
Ta
ble
7.3
. E
stim
ates
of
spec
ies-
lev
el O
TU
s ca
lcu
late
d f
rom
par
tial
an
d f
ull
-len
gth
bac
teri
al 1
6S
rR
NA
gen
e se
qu
ence
s†.
135
Pri
mer
set
H
yp
ervar
iable
regio
ns
Seq
uen
ce
length
(bp
)*
Dis
tance
level
# o
f O
TU
s ±
Max
imum
# o
f O
TU
s ±
Rar
efac
tion
C
hao
1
AC
E
27
f –
149
2r
V1-V
9
14
58
0
.05
44
4 (
0.0
) 6
88
(0
.0)
10
87
(0
.0)
10
61
(0
.0)
27
f –
519
r
V1-V
3
48
4
51
2 (
15
.3)
90
9 (
32
.1)
14
52
(34
.0)
15
18
(43
.1)
27
f –
685
r V
1-V
4
65
2
49
6 (
11
.7)
84
6 (
23
.0)
12
82
(17
.9)
13
67
(28
.8)
63
f –
519
r V
1-V
3
44
6
52
2 (
17
.6)
92
8 (
34
.9)
14
34
(31
.9)
15
88
(49
.7)
63
f –
685
r V
1-V
4
61
4
50
7 (
14
.2)
88
8 (
29
.1)
13
99
(28
.7)
14
68
(38
.4)
35
7f
– 9
07
r
V3-V
5
56
3
37
1 (
-16
.4)
52
1 (
-24
.3)
86
3 (
-21
.0)
83
1 (
-21
.7)
53
3f
– 1
10
0r
V4-V
6
59
7
38
4 (
-13
.5)
52
7 (
-23
.4)
74
9 (
-31
.0)
79
0 (
-25
.5)
92
6f
– 1
49
2r
V
6-V
9
60
5
38
1 (
-14
.2)
52
5 (
-23
.7)
81
2 (
-25
.0)
81
8 (
-22
.9)
96
8f
– 1
49
2r
V
6-V
9
54
4
41
5 (
-6.5
) 6
08
(-1
1.6
) 1
00
7 (
-7.0
) 9
64
(-9
.1)
27
f –
149
2r
V1-V
9
14
58
0
.10
24
4 (
0.0
) 2
91
(0
.0)
49
0 (
0.0
) 4
60
(0
.0)
27
f –
519
r
V1-V
3
48
4
32
1 (
31
.6)
42
4 (
45
.7)
67
9 (
39
.0)
67
9 (
47
.6)
27
f –
685
r V
1-V
4
65
2
31
1 (
27
.5)
39
8 (
36
.8)
69
0 (
40
.8)
64
5 (
40
.2)
63
f –
519
r V
1-V
3
44
6
34
5 (
41
.4)
47
1 (
61
.9)
73
2 (
49
.4)
74
9 (
62
.8)
63
f –
685
r V
1-V
4
61
4
32
4 (
32
.8)
42
3 (
45
.4)
76
6 (
56
.3)
68
9 (
49
.8)
35
7f
– 9
07
r
V3-V
5
56
3
20
8 (
-14
.8)
23
9 (
-17
.9)
34
6 (
-29
.0)
35
9 (
-22
.0)
53
3f
– 1
10
0r
V4-V
6
59
7
19
4 (
-20
.5)
21
1 (
-27
.5)
29
5 (
-40
.0)
28
9 (
-37
.2)
92
6f
– 1
49
2r
V
6-V
9
60
5
20
3 (
-16
.8)
22
0 (
-24
.4)
32
0 (
-35
.0)
30
7 (
-33
.3)
96
8f
– 1
49
2r
V
6-V
9
54
4
22
8 (
-6.6
) 2
55
(-1
2.4
) 3
84
(-2
.2)
36
0 (
-21
.7)
Tab
le 7
.4.
Est
imat
es o
f gen
us-
and f
amil
y-l
evel
OT
Us
calc
ula
ted f
rom
par
tial
seq
uen
ce r
egio
ns
and f
ull
len
gth
of
bac
teri
al 1
6S
rR
NA
gen
e se
quen
ces.
136
Pri
mer
set
V
reg
ions
Seq
uen
ce
length
(bp
)*
Dis
tanc
e le
vel
# o
f O
TU
s ±
Max
imum
# o
f O
TU
s ±
Rar
efac
tion
C
hao
1
AC
E
A2F
a – U
1510
r V
1-V
9
1,4
35
552 (
0.0
) 886 (
0.0
) 1337 (
0.0
) 1358 (
0.0
)
A2F
a – 5
19
r V
1-V
3
467
543 (
-1.6
) 831 (
-6.2
) 1184 (
-11.4
) 1265 (
-6.8
)
A2F
a – A
693
r V
1-V
4
639
550 (
-0.4
) 859 (
-3.0
) 1301 (
-2.7
) 1375 (
1.3
)
AR
C344f–
AR
C915r
V3
-V5
553
0.0
1
481 (
-12.9
) 699 (
-21.1
) 995 (
-25.6
) 1144 (
-15.8
)
U519f
– U
A1204r
V4
-V7
702
459 (
-16.8
) 649 (
-26.7
) 990 (
-26.0
) 1061 (
-21.9
)
A679r§
– U
A1204r
V5
-V7
527
437 (
-20.8
) 616 (
-30.5
) 902 (
-32.5
) 958 (
-29.5
)
AR
CH
915
–U
1510r
V6
-V9
585
468 (
-15.2
) 656 (
-26.0
) 959 (
-28.3
) 1021 (
-24.8
)
A1040f
– U
1510r
V7
-V9
463
475 (
-13.9
) 668 (
-24.6
) 917 (
-31.4
) 958 (
-29.5
)
* C
alcu
late
d f
rom
conse
nsu
s se
quen
ces
(sam
e as
in T
able
7.6
).
± V
alues
in p
aren
thes
is s
how
the
esti
mat
es r
elat
ive
to t
hat
of
full
-len
gth
seq
uen
ces.
Posi
tive
val
ues
des
ignat
e over
esti
mat
es,
and
neg
ativ
e val
ues
und
eres
tim
ates
. T
he
val
ues
that
are
both
under
lined
and b
old
ed a
re t
he
bes
t es
tim
ates
, w
hil
e th
e val
ues
that
are
only
under
lined
are
the
seco
nd b
est
esti
mat
es (
sam
e as
in T
able
7.6
).
Tab
le 7
.5. E
stim
ates
of
OT
Us
calc
ula
ted f
rom
par
tial
and f
ull
-len
gth
arc
haea
l 16S
rR
NA
gen
e se
qu
ence
s at
0.0
1 d
ista
nce
.
137
Pri
mer
set
V
reg
ion
s S
equ
ence
len
gth
(b
p)*
Dis
tan
ce
lev
el
# o
f O
TU
s ±
Max
imu
m #
of
OT
Us ±
Rar
efac
tio
n
Ch
ao1
A
CE
27
f –
14
92
r V
1-V
9
1,4
58
69
4 (
0.0
) 2
35
6 (
0.0
) 3
85
7 (
0.0
) 4
77
1 (
0.0
)
27
f –
51
9r
V
1-V
3
48
4
7
29
(5
.0)
32
07
(3
6.1
) 4
82
2 (
25
.0)
64
32
(3
4.8
)
27
f –
68
5r
V1-V
4
65
2
7
23
(4
.2)
30
01
(2
7.4
) 4
61
7 (
19
.7)
58
37
(2
2.3
)
63
f –
51
9r
V1-V
3
44
6
7
27
(4
.8)
31
07
(3
1.9
) 4
62
1 (
19
.8)
61
97
(2
9.9
)
63
f –
68
5r
V1-V
4
61
4
0.0
1
72
5 (
4.5
) 3
00
2 (
27
.4)
44
22
(1
4.6
) 5
80
2 (
21
.6)
35
7f
– 9
07
r
V3-V
5
56
3
6
26
(-9
.8)
16
60
(-2
9.5
) 2
47
5 (
-35
.8)
30
68
(-3
5.7
)
53
3f
– 1
10
0r
V4-V
6
59
7
6
37
(-8
.2)
16
78
(-2
8.8
) 2
90
0 (
-24
.8)
32
55
(-3
1.8
)
92
6f
– 1
49
2r
V
6-V
9
60
5
6
43
(-7
.3)
17
03
(-2
7.7
) 2
79
9 (
-27
.4)
33
01
(-3
0.8
)
96
8f
– 1
49
2r
V
6-V
9
54
4
6
52
(-6
.1)
18
19
(-2
2.8
) 2
87
3 (
-25
.5)
35
23
(-2
6.2
)
Ta
ble
7.6
. E
stim
ates
of
OT
Us
calc
ula
ted
fro
m p
arti
al a
nd
full
-len
gth
bac
teri
al 1
6S
rR
NA
gen
e se
qu
ence
s at
0.0
1 d
ista
nce
.
138
Pri
mer
set
V
reg
ions
Seq
uen
ce
length
(bp)*
D
ista
nce
level
# o
f O
TU
s ±
Max
imum
# o
f O
TU
s ±
Rar
efac
tion
Chao
1
AC
E
27 -
1492
V1-V
9
1,4
58
0.0
3
555 (
0.0
) 1105 (
0.0
) 1600 (
0.0
) 1809 (
0.0
) 27 -
355
V1-V
2
325
0.0
3
626 (
12.8
) 1494 (
35.2
) 2376 (
48.5
) 2527 (
39.7
)
0.0
5
550 (
-0.9
) 1066 (
-3.5
) 1748 (
9.3
) 1855 (
2.5
)
338 -
548
V
3
192
0.0
3
445 (
-19.8
) 729 (
-34.0
) 1149 (
-28.2
) 1259 (
-30.4
)
0.0
1
564 (
1.6
) 1208 (
9.3
) 1763 (
10.2
) 2225 (
23.0
)
530 -
826
V
4
274
0.0
3
449 (
-19.1
) 695 (
-37.1
) 1142 (
-28.6
) 1093 (
-39.6
)
0.0
1
569 (
2.5
) 1204 (
9.0
) 1845 (
15.3
) 2069 (
14.4
)
805–1065
V
5-V
6
275
0.0
3
517 (
-6.8
) 936 (
-15.3
) 1420 (
-11.3
) 1587 (
-12.3
)
0.0
2
583 (
5.0
) 1303 (
17.9
) 2233 (
39.6
) 2443 (
35.0
)
967–1065
V
6
94
0.0
3
557 (
0.4
) 1163 (
5.2
) 2198 (
37.4
) 2236 (
23.6
)
0.0
4
523 (
-5.8
) 1014 (
-8.2
) 1842 (
15.1
) 1937 (
7.1
)
967–1238
V
6-V
7
268
0.0
3
511 (
-7.9
) 888 (
-19.6
) 1401 (
12.4
) 1452 (
-19.7
)
0.0
2
578 (
4.1
) 1212 (
9.7
) 1877 (
17.3
) 2102 (
16.2
)
1046–1238
V
7
191
0.0
3
322 (
-42.0
) 410 (
-62.9
) 632 (
-60.5
) 648 (
-64.2
)
0.0
1
478 (
-13.9
) 827 (
-25.2
) 1439 (
-10.1
) 1467 (
-18.9
)
1046–1406
V
7-V
8
362
0.0
3
406 (
-26.8
) 580 (
-47.5
) 1039 (
-35.1
) 933 (
-48.4
)
0.0
1
552 (
-0.5
) 1051 (
-4.9
) 1557 (
-2.7
) 1708 (
-5.6
)
Note
: es
tim
ates
at
0.0
3 d
ista
nce
are
shad
ed.
Tab
le 7
.7. E
stim
ates
of
bac
teri
al s
pec
ies-
lev
el O
TU
s ca
lcula
ted f
rom
full
-len
gth
and s
hort
par
tial
bac
teri
al 1
6S
rR
NA
gen
e se
quen
ces
139
CHAPTER 8
INVESTIGATION OF RUMINAL BACTERIAL DIVERSITY IN CATTLE FED
SUPPLEMENTARY MONENSIN OR FAT USING PYROSEQUENCING ANALYSIS
8.1 Abstract:
Monensin and dietary fats have been used to improve the efficiency of feed
utilization and reduce methane production. However, the effect of these dietary
manipulations on the ruminal bacteriome has not been examined in detail. The objective
of this study was to examine and compare the effects of monensin or monensin in
combination with fat on ruminal bacterial communities in cattle fed with the following
three diets: (1) a control diet without monensin or fat (C), (2) the control diet
supplemented with monensin as Rumensin (CR), and (3) the control diet supplemented
with both monensin and fat (CRF). The bacteriome in the liquid and adherent fractions
was analyzed using 454 pyrosequencing analysis. In total, 24,792 16S rRNA gene (rrs)
sequences were obtained. Most sequences were assigned to phyla Firmicutes and
Bacteroidetes irrespective of fractions. Firmicutes was more abundant in the adherent
fraction than in the liquid fraction, while Bacteroidetes was less abundant in the adherent
fraction than in the liquid fraction. Gram-positive Firmicutes was not affected by
monensin but stimulated by monensin in combination with fats. Prevotella was dominant
among known genera. However, most sequences were assigned to unclassified groups,
with unclassified Lachnospiraceae and unclassified Clostridiales being predominant. In
total, 9,867 OTUs at 0.04 distance were identified at species-equivalent level from the
24,792 sequences across all the fractions. Although the sequence distribution was similar
between bacterial communities in the C and CR groups, OTU distribution differed
140
between these two groups. It seems that numerous monensin-resistant strains are
stimulated to adapt to a new ruminal environment. Based on both sequence and OTU
distribution, bacterial communities in the CRF group were different from those in C or
the CR groups. Numerous strains involved in lipolysis or biohydrogenation (BH)
appeared to increase in the rumen of cattle fed CRF.
8.2 Introduction:
Monensin has been fed to ruminant animals in order to improve the efficiency of
feed utilization (Russell and Strobel, 1989). Monensin is thought to modulate ruminal
microbiome through its selective effect on Gram-positive bacteria than Gram-negative
bacteria as documented in in vitro cultures (Callaway et al., 2003). As a result, monensin
can decrease methane production as well as acetate:propionate ratio in the rumen
(Callaway et al., 2003). However, in vivo studies using rrs-based techniques showed that
Gram-positive bacterial populations were not significantly affected by monensin due to
monensin resistance (Stahl et al., 1988; Weimer et al., 2008). In addition, some in vivo
studies showed that the acetate:propionate ratio was not altered by monensin (Firkins et
al., 2008; Oelker et al., 2009). Ruminal bacterial populations could be affected by other
dietary factors other than monensin (Firkins et al., 2008). Supplementary monensin often
results in milk fat depression (MFD) that might be associated with specific BH reactions
(Mathew et al., 2011). However, other studies could not support this finding (Duffield et
al., 2003). The inconsistent effects of monensin supplementation might be associated
with interaction between monensin and other dietary factors such as unsaturated fat,
effective fiber, or starch availability (Mathew et al., 2011).
141
Dietary fats are hydrolyzed by lipase produced by rumen microbes and plants. As a
result, polyunsaturated fatty acids (PUFA) are released. These PUFA are converted into
saturated fatty acids through BH by rumen microbes. Fats such as long-chain fatty acids
are also used to reduce methane production and the ratio of acetate and propionate
through manipulation of ruminal fermentation (Nagaraja et al., 1997). However, some
microbes including cellulolytic bacteria can be inhibited by PUFA (Maia et al., 2007).
Supplementation of both monensin and fat might worsen MFD compared to
supplementation of monensin alone (AlZahal et al., 2008). However, this finding is not
supported by a recent study (Mathew et al., 2011).
The objective of this study was to examine the effects of monensin alone or
monensin in combination with fat on the bacterial communities present in the liquid and
the adherent fractions recovered from Holstein dairy cattle using pyrosequencing
analysis.
8.3 Materials and Methods:
8.3.1 Sample collection
All rumen samples were collected in a previous study and kindly provided by Dr.
Eastridge, The Ohio State University. Briefly, whole rumen contents were obtained from
six cannulated lactating Holstein cattle that were used in a 6 x 6 balanced Latin square
design experiment with a 3-week period adaptation on each diet (Mathew et al., 2011).
Three of the six dietary treatments (6 cattle × 3 diets) were selected to examine ruminal
bacterial diversity: (1) C = control diet containing ground corn and long alfalfa hay, (2)
CR = C plus monensin as Rumensin (12 g/ 909 kg DM), and (3) CRF = CR plus 4% fat.
142
Detailed dietary treatments were shown in Table 8.1. The rumen sample obtained from
each treatment was divided into two fractions (the liquid fraction vs. the adherent fraction)
as described previously (Kim et al., 2011c). Bacteria present in the liquid fraction (Lq)
was obtained by centrifugation, while the remaining solid digesta was used to recover
bacteria adherent to solid digesta (Ad) using a detaching buffer (Larue et al., 2005; Kim
et al., 2011c).
8.3.2 Metagenomic DNA extraction
Metagenomic DNA was extracted from each fractionated sample (6 cattle × 3 diets
× 2 fractions = 36 samples) as described previously (Yu and Morrison, 2004b). The DNA
extracts were pooled based on diets and fractions, resulting in the following six
composite samples: the liquid fraction and the adherent fraction recovered from six cattle
fed with C (Lq-C and Ad-C), CR (Lq-CR and Ad-CR), and CRF (Lq-CRF and Ad-CRF).
8.3.3 Pyrosequencing
Each composite DNA sample was amplified with universal primers 27F (5’-
Adaptor primer-AKRGTTYGATYNTGGCTCAG-3’) and 519R (5’-Adaptor primer-
barcode-GTNTBACCGCDGCTGCTG-3’). All 454 sequencing primers designed are
listed in Table A in the appendix. PCR reaction mixtures (50-ul) consisted of 100 ng
composite DNA, 1X PCR buffer (20 mM Tris-HCl [pH 8.4] and 50 mM KCl), 200 uM
deoxynucleoside triphosphates, 100 nM (each) primer, 1.75 mM MgCl2, 670 ng of bovine
serum albumin/ul, and 1.25 U of Platinum Taq DNA polymerase (Invitrogen
Corporation, Carlsbad, CA). PCR amplification of the rrs V1-V3 region was achieved
143
with 30 cycles of PCR (denaturation at 95 oC for 30 s; annealing at 60
oC for 30 s; and
extension at 72 oC for 60 s) using a PTC-100 thermocycler (MJ Research, Waltham,
Mass.). All amplicons were purified using a QIAquick Gel Extraction Kit (Qiagen,
Valencia, CA). The gel-purified amplicons were quantified using a NanoDrop ND-1000
UV-Vis Spectrophotometer (NanoDrop products, Wilmington, DE). Amplicons from
each composite sample were diluted with the Elution buffer of the QIAquick Gel
Extraction Kit to a concentration of 20ng/ µl, and an amplicon pool was prepared by
combining an equal amount of each diluted amplicons. This pool was sequenced at the
Plant-Microbe Genomics Facility, The Ohio State University using the 454 GS FLX
Titanium system (designated as “Dataset A”). The same six composite DNA was also
sequenced using a 454 GS FLX Titanium system at Research and Testing Laboratories
(Lubbock, TX), resulting in “Dataset B”. The composite DNA was sequenced again by
Research and Testing Laboratories (Lubbock, TX) on a different PTP plate, resulting in
“Dataset C”. The three datasets were analyzed individually to assess the repeatability of
the pyrosequencing analysis and then were combined together and analyzed to examine
the effect of monensin alone or monensin plus fat on the ruminal bacteriome.
8.3.4 Sequence processing and bioinformatics analysis
The QIIME program (Caporaso et al, 2010) was used to trim off the barcodes and
primers and sort the six libraries from returned sequences. Sequences with read length
shorter than 200nt or longer than 650nt, mean quality score below Q25, and
homopolymer stretches longer than 8nt were excluded. The sequences that passed the
above quality control were aligned against the Greengenes core dataset using the NAST
144
algorithm as implemented in the Mothur program (Schloss et al., 2009). Sequences
suspected to have resulted from pyrosequencing errors or to be chimeric sequences were
removed from the aligned file using the pre.cluster and the chimera.slayer commands as
implemented in the Mothur program (Schloss et al., 2009). The same number of
sequences for each fraction treatment was selected to normalize sequence data using the
sub.sample command as implemented in the Mothur program (Schloss et al., 2009).
Hierarchical taxa assignment was conducted using the RDP naïve Bayesian rRNA
Classifier as implemented in the QIIME program (Caporaso et al., 2010), and species-
level OTUs were calculated at 0.04 distance as recommended previously (Kim et al.,
2011a) using the furthest neighbor algorithm as implemented in the Mothur program
(Schloss et al., 2009). A rarefaction curve and alpha diversity indices were calculated for
each library using the rarefaction.single and the summary.single commands as
implemented in the Mothur program (Schloss et al., 2009). A full description for
sequence processing is available in Appendix A. Principal coordinate analysis (PCoA)
was conducted using the unweighted UniFrac method (Lozupone and Knight, 2005) as
described previously (Kim et al., 2011b).
8.3.5 Comparison among three datasets
Comparisons among the three datasets were performed using PCoA as
implemented in the UniFrac program (Lozupone and Knight, 2005). A phylogenetic tree
was constructed using the ARB program (Ludwing et al., 2004) and then used as an input
file to run the UniFrac program as described previously (Kim et al., 2011b).
145
8.4 Results and Discussion:
8.4.1 Data summary
After removing sequences resulting from pyrosequencing errors or chimeric
sequences, 24,792 sequences (4,132 sequences × 6 fractions) were obtained from the six
sample fractions (Table 8.2). Approximately 53% of the 24,792 sequences were assigned
to the phylum Firmicutes, while 26% of them were assigned to the phylum Bacteroidetes
(Figure 8.1). The predominance of these two phyla corroborates the previous study (Kim
et al., 2011b). About 17% of the total sequences could not be classified into any known
phylum (Figure 8.1). The other phyla accounted for less than 2% of the total sequences.
In total, 9,867 OTUs were identified at 0.04 distance from all the sequences. The
number of OTUs for each fraction ranged from 2,395 to 2,726 (Table 8.2). More than 70%
of all the OTUs were represented by singletons (Table 8.2). Firmicutes and Bacteroidetes
were represented by 4,952 and 2,657 OTUs, respectively, and the OTUs of these two
phyla accounted for 77% of the total OTUs. The maximum number of OTUs estimated
from a rarefaction curve for each fraction ranged from 4,392 to 5,755 (Table 8.2).
8.4.2 Firmicutes
Firmicutes was the most abundant phylum across all the six fractions, accounting
for 40-75% of all the sequences. The proportion of Firmicutes was higher in the Ad
fractions than in the Lq fractions and the highest in the Ad-CRF fraction (Figure 8.1).
Because both cultured and uncultured fibrolytic bacteria are assigned mostly to
Firmicutes (Kim et al., 2011b), the higher proportion of Firmicutes in the Ad fractions
than in the Lq fractions is not surprising. The proportion of Firmicutes was higher in
146
cattle fed CRF than in cattle fed C or CR, and the predominance of Firmicutes was not
lower in the CR or the CRF samples than in the C samples (Figure 8.1). These findings
are not expected because monensin was proposed to selectively inhibit Gram-positive
bacteria and most members of phylum Firmicutes are Gram-positive bacteria (Wolf et al.,
2004). The mode of action of monensin in the rumen may differ from in vitro cultures
and needs to revise. The high predominance of Firmicutes in the CRF samples than in the
C and the CR samples is also intriguing. It is logic to conclude that Firmicutes was
stimulated, while other bacterial phyla, most of which are Gram-negative bacteria, were
not affected. Alternatively, Firmicutes was not affected, while other bacterial phyla were
inhibited. Phylum-specific quantitative analysis is needed to test the above notions. It
should be noted that fats are degraded through lipolysis and resulting unsaturated fatty
acids inevitably is subjected to biohydrogenation (BH) in the rumen. As well known
examples, Anaerovibrio lipolytica and Butyrivibrio fibrisolvens, both are Gram-positive
bacteria of Firmicutes, are involved in lipolysis and BH, respectively. Supplementation of
fats could result in increases in these two species and related species within these two
genera. Numerous uncultured bacteria including members of Anaerovibrio and
Butyrivibrio can also be involved in lipolysis and BH, resulting in high abundance of
Firmicutes in the Ad-CRF and Lq-CRF fractions. However, the lower relative abundance
of Butyrivibrio (Figure 8.2) does not support this premise.
The majority of Firmicutes sequences were assigned to unclassified Clostridiales,
unclassified Lachnospiraceae, or unclassified Ruminococcaceae (Figure 8.2), with
Unclassified Clostridiales, unclassified Lachnospiraceae and unclassified
Ruminococcaceae being represented by 1,856, 1,537 and 581 OTUs, respectively, in
147
combination across all the 6 fractions. This result supports the predominance of these
three unclassified groups as reported previously (Kim et al., 2011b). The proportions of
all the three groups were higher in the Ad fraction than in the Lq fraction (Figure 8.2),
suggesting their potentially important role in fiber degradation as maintained previously
(Kim et al., 2011b). The proportion of unclassified Clostridiales or unclassified
Ruminococcaceae was higher in cattle fed CRF than in cattle fed C or CR. Ruminal
bacteria involved in lipolysis or BH within these two groups might have increased due to
the fat supplementation. The three unclassified groups each included many unique OTUs
detected only in cattle fed C, CR or CRF. The unique OTUs detected only in cattle fed C
might represent bacteria that are sensitive to monensin, while the unique OTUs detected
only in cattle fed CR and in cattle fed CRF likely represent monensin-resistant bacteria.
The unique OTUs detected only in the cattle fed CRF may also be associated with
lipolysis and/or BH. The use of a reverse metagenomic approach (Nichols, 2007; Pope et
al., 2011) may help isolate these unique OTUs.
The proportion of Gram-positive Butyrivibrio was lower in the Ad-CR fraction than
in the Ad-C fraction (Figure 8.2). Some Butyrivibrio strains involved in BH appeared to
be inhibited by monensin as described previously (Weimer et al., 2008). The proportion
of Butyrivibrio decreased in cattle fed CRF (Figure 8.2). The supplementation of
additional fats might have inhibited Butyrivibrio strains. The proportion of another
unknown bacteria rather than Butyrivibrio might increase for BH. Genus Butyrivibro was
represented by 107 OTUs in combination across all the 6 fractions. Although the
sequence distribution of Butyrivibrio was similar between the Lq-C and the Lq-CR
fractions, the number of OTUs was greater in the Lq-CR fraction than in the Lq-C
148
fraction due to 15 unique OTUs detected only in the Lq-CR fraction (Figure 8.2). These
15 OTUs might be strains highly resistant to monensin. Although the number of
sequences and OTUs was less in cattle fed CRF than in cattle fed C or CR, 21 unique
OTUs were detected only in cattle fed CRF and they may be monensin-resistant as well
as associated with BH.
Sequences classified to any known genera within Firmicutes accounted for no more
than 1% of total sequences, except for Gram-negative genus Succiniclasticum that
ferments succinate to propionate (van Gylswyk, 1995). The proportion of
Succiniclasticum was lower in the Lq-C fraction than in the Lq-CR and the Lq-CRF
fractions and the highest in the Lq-CRF fraction among all the fractions (Figure 8.2). The
high abundance of Succiniclasticum in the Lq-CRF fraction might have resulted from
increase of succinate, the substrate of Succiniclasticum. Succiniclasticum was represented
by 101 OTUs in combination across all the 6 fractions, and 7 OTUs of them were
represented by more sequences in cattle fed CR or in cattle fed CRF than in cattle fed C.
These 7 OTUs might be monensin-resistant and take up the niche left by monensin-
sensitive bacteria.
Genus Anaerovibrio was represented by 10 OTUs in combination across all the
fractions, and 6 of them were detected only in the Lq-CRF fraction. Gram-negative
Anaerovibrio lipolytica is a major lipolytic species and it produces succinate and
propionate main fermentation products (Russell, 2002). As expected, the proportion of
Anaerovibrio sequences was the highest in the Lq-CRF fraction among all the fractions.
Anaerovibrio spp. may positively interact with Succiniclasticum in the rumen when fed
monensin and fats. However, future studies are needed to verify such a positive
149
relationship.
The proportion of Mogibacterium was higher in cattle fed CR or CRF than in cattle
fed C. This result indicates that Gram-positive Mogibacterium is not inhibited by
monensin. Mogibacterium was found more abundant in the Ad fractions than in the Lq
fractions. Mogibacterium is not fibrolytic. Its preferential occurrence in the Ad fraction
cannot be explained. Because it produces phenyl propionic acid (PPA) and phenyl acetic
acid (PAA) (Larue et al., 2005), both of which are needed by R. albus for to adhere and
degrade fiber, there might be a mutualism between these two groups of bacteria in the
rumen. Mogibacterium was represented by 33 OTUs in combination across all the 6
fractions. Five of the 33 OTUs were detected only in the Ad-CR fraction, while another
12 were detected only in the Ad-CRF fraction. All these OTUs should be monensin
resistant. The latter 12 OTUs could play a role in BH.
The proportion of Syntrophococcus was the highest in the Ad-C fraction and
decreased in the Ad-CR and the Ad-CRF fractions. Syntrophococcus seemed to be
inhibited by monensin in terms of sequences and OTUs (Figure 8.2). Because both Gram-
negative S. sucromutans and Gram-positive Eubacterium cellulosolvens were
representative species of genus Syntrophococcus in the RDP database (Kim et al., 2011b),
Syntrophococcus sequences identified in this study might be phylogenetically related to S.
sucromutans or E. cellulosolvens. Gram-positive Anaerovorax was not inhibited by
monensin and its population increased in the Ad-CRF fraction (Figure 8.2). This result
indicates that Anaerovorax may be associated with fiber degradation as well as BH.
Anaerovorax was represented by 20 OTUs, and 7 of them were detected only in the Ad-
CRF fraction. Ruminococcus was represented by less than 10 sequences in each fraction
150
and its population decreased in the rumen of cattle fed CRF, suggesting that
Ruminococcus is sensitive to monensin, which is consistent with the previous finding
(Weimer et al., 2008). Supplementation of the fats further decreased the frequency of
Ruminococcus sequences, corroborating the inhibitory effect of fats towards
Ruminococcus (Maia et al., 2007). Ruminococcus was represented by 15 OTUs in
combination among all the fractions, and 5 OTUs of them were detected only in the
rumen of cattle fed CR. These OTUs could be monensin-resistant members of
Ruminococcus.
Although previous studies based on pure cultures have reported that monensin
inhibits Gram-positive bacteria more than Gram-negative bacteria (Nagaraja et al., 1997;
Callaway et al., 2003), some in vivo studies (Stahl et al., 1988; Weimer et al., 2008) do
not support the above generalization. Our study supports provided further evidence that
some Gram-positive ruminal bacteria are monensin resistant.
8.4.3 Bacteroidetes
Bacteroidetes was the second most abundant phylum across all the six fractions,
accounting for 10-43% of all the sequences. The proportion of Bacteroidetes was higher
in the Lq fractions than in the Ad fractions and the highest in the Lq-CR fraction (Figure
8.1), consistent with the finding of Kim et al. (2011b). The proportions of Bacteroidetes
were similar between cattle fed C and cattle fed CR, whereas the proportion of
Bacteroidetes in cattle fed CRF highly decreased (Figure 8.1). All these results suggest
that Bacteroidetes as a whole is not sensitive to monensin but can be inhibited by dietary
fats (Figure 8.1).
151
Most sequences classified to Bacteroidetes were assigned to unclassified
Bacteroidetes, unclassified Bacteroidales, or unclassified Prevotellaceae (Figure 8.3).
The populations of these three groups were more abundant in the Lq fractions than in the
Ad fractions (Figure 8.3). The proportions of these three groups decreased in cattle fed
CRF compared to cattle fed C or CR. The supplementation of the fats seemed to inhibit
ruminal bacteria assigned to these three groups. Most of the Bacteroidetes OTUs were
also assigned to these three groups: unclassified Bacteroidetes (801 OTUs), unclassified
Bacteroidales (722 OTUs), and unclassified Prevotellaceae (575 OTUs). These three
groups were represented by many unique OTUs detected only in cattle fed C, CR or CRF,
indicating that some bacterial strains were affected by monensin or monensin in
combination with fats.
Prevotella was the most abundant genus among known genera as described
previously (Stevenson et al., 2007; Kim et al., 2011b). The proportion of Prevotella was
higher in the Lq fractions than in the Ad fractions (Figure 8.3). The proportion of
Prevotella was higher in cattle fed CR than in cattle fed C (Figure 8.3), and this result is
consistent with the previous study (Weimer et al., 2008). However, the proportion of
Prevotella decreased in cattle fed CRF compared to cattle fed CR (Figure 8.3). Prevotella
was represented by 375 OTUs in combination across all the 6 fractions, and the number
of unique OTUs detected only in cattle fed C, CR and CRF was 67, 124 and 75,
respectively. The high number of unique OTUs in cattle fed CR indicates that many
Prevotella strains resistant to monensin are stimulated and they may play an important
role in ruminal fermentation when monensin is fed to cattle.
152
8.4.4 Minor phyla
The phylum TM7 has been proposed from rrs sequence data and recognized as
Gram-positive bacteria (Hugenholtz et al., 2001). Because the proportion of TM7, which
was represented by 121 OTUs, was the highest in the Ad-C fraction, it may be associated
with fiber degradation. However, TM7 was inhibited by monensin or monensin in
combination with fat (Figure 8.4). The proportion of the phylum SR1 was the highest in
the Lq-C fraction, and the population of SR1 may be amylolytic. SR1 was also inhibited
by monensin or monensin in combination with fat (Figure 8.4). SR1 was represented by
17 OTUs, and 2 of the OTUs were predominant. Unclassified Enterobacteriaceae within
the phylum Proteobacteria was highly detected in the Lq-C fraction and greatly inhibited
by monensin or monensin in combination with fats, but it was not detected in the Ad
fractions (Figure 8.4). Members of the Enterobacteriaceae are Gram-negative and
contains many pathogens such as Salmonella and Escherichia coli. Although E. coli
O157:H7 (ATCC 43895) and Salmonella (ATCC 6960) were not inhibited by monensin
(Edrington et al., 2003), monensin may inhibit other pathongenic strains within
unclassified Enterobacteriaceae. Unclassified Enterobacteriaceae was represented by 4
OTUs, and 2 of the OTUs were predominant in the Lq-C fraction based on the number of
sequences. However, the 2 predominant OTUs were highly inhibited by monensin. Small
number of sequences was assigned to Fibrobacter, and this result could result from PCR
bias against members of this phylum as described previously (Larue et al., 2005).
8.4.5 Comparison among the diets
To compare the bacterial diversity among the six fractions, fraction correlations
153
were determined using PCoA by the unweighted UniFrac method. P1 explained 23.56%
variation and seemed to separate the liquid fractions from the adherent fractions, while P2
explained 20.81% variation and separated the six fractions based on different diets
(Figure 8.5). This result further indicates that the ruminal bacterial communities were
affected by monensin or monensin in combination with fats. Although the sequence
distribution was similar between cattle fed C and cattle fed CR (Figure 8.1), their
separation along P2 might be attributed to different OTU distributions between these two
diets. Callaway et al. (2003) indicated that highly monensin-resistant bacterial strains are
stimulated to adapt to the monensin-rich environment. Therefore, the different OTU
distributions between cattle fed C and cattle fed CR could result from the selection of
ruminal bacterial strains that are highly resistant to monensin.
8.4.6 Comparison among three datasets
Comparison by PCoA among the three datasets was shown in Figure 8.6. P1
separated the Dataset A from the Dataset B and or C, but P1 only explained 10.81% of
the variation. Axis P2 separated the liquid fractions from the adherent fractions across all
the three datasets and explained 8.95% variation. These results suggest that the
differences among the three datasets are relatively small (<10%) and pyrosequencing
analysis can be repeatable. Slight differences between Dataset A and the other two
datasets might result from difference in barcode sequences, PCR condition (including
primers), or pyrosequencing procedures. The separation of the Ad fractions from the Lq
fractions could be attributed to bias caused by differences in bacterial composition
between these two fractions.
154
8.5 Conclusions:
Ruminal bacterial communities were affected by different diets and fractions. Many
uncultured bacteria present in the adherent fraction may be associated with fiber
degradation. The sequence distribution of cattle fed CR was similar to that of cattle fed C.
Most members of both Gram-positive Firmicutes and Gram-negative Bacteroidetes did
not seem to be affected by monensin. Although previous in vitro studies have showed
that Gram-positive bacteria are sensitive to monensin, our study did not support that
conclusion. Difference in OTU distribution between cattle fed C and cattle fed CR
indicated that highly monensin-resistant strains were stimulated in the presence of
monensin. The proportion of Firmicutes was higher in cattle fed CRF than in cattle fed C
or CR, whereas that of Bacteroidetes was lower in cattle fed CRF than in cattle fed C or
CR. Therefore, many uncultured bacteria within Firmicutes might be involved in
lipolysis or biohydrogenation, whereas many uncultured bacteria within Bacteroidetes
could be inhibited by dietary fats. The OTUs affected by monensin or fats will need to be
isolated and characterized to further understand the mode(s) of action of monensin.
155
Diet, % of DM1
Ingredient C CR CRF
Alfalfa hay 15.0 15.0 15.0
Corn silage 35.0 35.0 35.0
Corn, ground 13.6 11.6 11.6
Soybean mean, 48% CP 8.10 8.10 -
Dried distillers grains 10.0 10.0 -
Dried distillers grains with solubles - - 16.0
Roasted soybeans, cracked - - 12.8
Animal-vegetable blend - - 0.70
Soybean hulls 16.3 16.3 5.16
Dicalcium phosphate 0.68 0.68 0.14
Limestone 0.36 0.36 0.84
Magnesium oxide 0.20 0.20 0.14
Potassium sulfate 0.16 0.16 -
Trace mineralized salt1 0.50 0.50 0.50
Vitamin A2 0.02 0.02 0.02
Vitamin D3 0.04 0.04 0.04
Vitamin E4 0.06 0.06 0.06
Rumensin premix5 - 2.00 2.00
1Included 0.10% Mg; 38.0% Na; 58.0% Cl; 0.04% S; 5,000 mg of Fe/kg; 7,500 mg of Zn/kg; 2,500 mg of
Cu/kg; 6,000 mg of Mn/kg; 100 mg of I/kg; 60 mg of Se/kg; and 50 mg of Co/kg.
2Included 30,000 IU of vitamin A/g.
3Included 3,000 IU of vitamin D/g.
4Included 44 IU of vitamin E/g.
5Elanco Animal Health, Greenfield, IN (initially formulated to provide 12 g of monensin/909 kg of total
dietary DM; based on analysis of the premix, provided 14 g/909 kg).
Table 8.1: Ingredient composition of dietary treatments (Reproduced from Mathew et al.,
2011).
156
Sample
Individual
Sequences
# of
OTUs
Singletons
Doubletons
Chao 1
Rarefaction
Estimatea
Phylogenetic
distance
Ad-C 4132 2726 2090 327 9382 5755 71.24
Ad-CR 4132 2551 1870 352 7501 4895 71.00
Ad-CRF 4132 2395 1730 333 6873 4392 55.19
Lq-C 4132 2573 1913 378 7398 5182 70.40
Lq-CR 4132 2682 2047 368 8357 5764 77.92
Lq-CRF 4132 2544 1891 368 7386 5126 67.63
a: Maximum number of OTUs estimated from rarefaction curves (Kim et al., 2011b)
Table 8.2: Sequence data and alpha diversity indices for the six fractions.
157
Figure 8.1: Sequence distribution at the phylum level for each fraction. Phyla accounting
for less than 1% of all the sequences were not included.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Ad-C Ad-CR Ad-CRF Lq-C Lq-CR Lq-CRF Total
Actinobacteria
TM7
Bacteroidetes
U_Bacteria
Firmicutes
Fraction
158
0
200
400
600
800
Ad-C Ad-CR Ad-CRF Lq-C Lq-CR Lq-CRF
No. of sequences No. of OTUs
0
200
400
600
800
1000
1200
1400
Ad-C Ad-CR Ad-CRF Lq-C Lq-CR Lq-CRF
No. of sequences No. of OTUs
0
100
200
300
400
500
Ad-C Ad-CR Ad-CRF Lq-C Lq-CR Lq-CRF
No. of sequences No. of OTUs
0
10
20
30
40
50
60
Ad-C Ad-CR Ad-CRF Lq-C Lq-CR Lq-CRF
No. of sequences No. of OTUs
0
50
100
150
200
250
300
Ad-C Ad-CR Ad-CRF Lq-C Lq-CR Lq-CRF
No. of sequences No. of OTUs
0
5
10
15
20
25
30
35
Ad-C Ad-CR Ad-CRF Lq-C Lq-CR Lq-CRF
No. of sequences No. of OTUs
Figure 8.2: The distribution of sequences and OTUs at genus or the lowest classifiable
rank in the phylum Firmicutes. Minor taxa accounting for small numbers of sequences
were not included.
Figure 8.2 Continued
U_Clostridiales U_Lachnospiraceae
U_Ruminococcacea
e
Butyrivibrio
Succiniclasticum Mogibacterium
159
0
5
10
15
20
25
30
35
Ad-C Ad-CR Ad-CRF Lq-C Lq-CR Lq-CRF
No. of sequences No. of OTUs
0
2
4
6
8
10
Ad-C Ad-CR Ad-CRF Lq-C Lq-CR Lq-CRF
No. of sequences No. of OTUs
0
2
4
6
8
Ad-C Ad-CR Ad-CRF Lq-C Lq-CR Lq-CRF
No. of sequences No. of OTUs
0
2
4
6
8
10
12
14
Ad-C Ad-CR Ad-CRF Lq-C Lq-CR Lq-CRF
No. of sequences No. of OTUs
Figure 8.2 Continued
Syntrophococcus Ruminococcus
Anaerovibrio Anaerovorax
160
0
100
200
300
400
500
600
Ad-C Ad-CR Ad-CRF Lq-C Lq-CR Lq-CRF
No. of sequences No. of OTUs
0
100
200
300
400
500
Ad-C Ad-CR Ad-CRF Lq-C Lq-CR Lq-CRF
No. of sequences No. of OTUs
0
100
200
300
400
500
Ad-C Ad-CR Ad-CRF Lq-C Lq-CR Lq-CRF
No. of sequences No. of OTUs
0
100
200
300
400
Ad-C Ad-CR Ad-CRF Lq-C Lq-CR Lq-CRF
No. of sequences No. of OTUs
Figure 8.3: The distribution of sequences and OTUs at genus or the lowest classifiable
rank in the phylum Bacteroidetes. Minor taxa accounting for small numbers of sequences
were not included.
U_Bacteroidetes U_Bacteroidales
U_Prevotellaceae Prevotella
161
Figure 8.4: The distribution of sequences and OTUs at the lowest classifiable rank in
minor phyla. Minor taxa accounting for small numbers of sequences were not included.
0
50
100
150
200
Ad-C Ad-CR Ad-CRF Lq-C Lq-CR Lq-CRF
No. of sequences No. of OTUs
0
10
20
30
40
50
Ad-C Ad-CR Ad-CRF Lq-C Lq-CR Lq-CRF
No. of sequences No. of OTUs
0
20
40
60
80
Ad-C Ad-CR Ad-CRF Lq-C Lq-CR Lq-CRF
No. of sequences No. of OTUs
TM7
SR1
U_Enterobacteriaceae
162
Figure 8.5: Principal coordinates analysis for the six fractions.
163
Figure 8.6: Principal coordinates analysis for comparison among the three datasets
Lq-CR (A)
Lq-C (A)
Lq-CRF (A)
Ad-C (A)
Ad-CR (A)
Ad-CRF (A)
Ad-CR (C)
Ad-CRF (B)
Ad-CRF (C)
Ad-C (C)
Ad-CR (B)
Ad-C (B)
Lq-CRF (B)
Lq-CRF (C)
Lq-CR (B)
Lq-C (B)
Lq-CR (C)
Lq-C (C)
164
CHAPTER 9
GENERAL DISCUSSION
Since 16S rRNA gene (rrs) sequences were applied to investigation of the diversity
of ruminal microbiome, numerous studies have been conducted to better understand the
complex ruminal microbiome. Using an integrated approach consisting of conventional
molecular biology techniques and emerging metagenomic and microarray technologies,
we conducted a series of studies on the ruminal microbiome. The goal of these studies
was to provide a new insight into the diversity of the ruminal bacteriome and the effect of
diets and feed additives. Such knowledge might help improve rumen function in the
future.
Most of the previous studies have examined the diversity of the ruminal
microbiome using DGGE and rrs clone libraries. However, these techniques produced
only small numbers of sequences, and as such only limited microbial diversity was
explained. For that reason, we performed a meta-analysis of all the rrs sequences that
were publicly available to better define ruminal microbial diversity. More than 55% of all
the rrs sequences could not be classified into any known genus as described in Chapter 3.
A large number of operational taxonomic units (OTUs) consisting of uncultured bacterial
sequences were predominant based on the number of sequences. Future studies are
needed to verify the predominance of these OTUs. A representative sequence of each
predominant OTU can be used to design PCR primers and probes for quantification of
abundance using a real-time PCR assay. In addition, OTUs that may be associated with
cellulose degradation could be selected using the meta-analysis. The numerous OTUs
assigned to unclassified Clostridiales, unclassified Lachnospiraceae, or unclassified
165
Ruminococcaceae, especially those predominant OTUs found in the adhering fractions,
are good candidates for isolation. The population dynamics of the OTUs putatively
involved in cellulose degradation can be determined in a comparative manner in different
nutritional studies so that their niche can be inferred. Further, the dataset consists of
entirely sequences generated using the Sanger technology and has been stored as an ARB
database. This dataset can be updated regularly as new high-quality sequences from
rumen are available in the RDP database. This database can serve as a central depository
of rrs sequences of rumen origin. A meta-analysis using multiple datasets of large
numbers of sequences in the future may help define a “core microbiome”, which is not
achievable in this study. This core microbiome might play an important role in rumen
function.
Using real-time PCR assays, we showed that some uncultured bacteria can be as
predominant as major cultured bacteria, and several of the analyzed uncultured bacteria
are more predominant in the adhering fraction than in the liquid fraction (Chapter 4).
Further studies are needed to verify if they actually attach to plant biomass in the rumen.
Probes for fluorescence in situ hybridization (FISH) can be designed from the rrs
sequences of select uncultured bacteria. Their attachment to plant biomass in the rumen
can be assessed using FISH. Uncultured bacteria of interest can also be isolated using a
probe-guided approach or a reverse metagenomic approach as described previously
(Nichols, 2007; Pope et al., 2011) for detailed studies of their metabolism and physiology.
More than 3,500 OTUs were identified from the bacterial rrs sequences retrieved
from the RDP database, and yet these OTUs represented only approximately 70% of
ruminal bacterial diversity (Chapter 3). Therefore, novel ruminal OTUs remains to be
166
identified. The study in Chapter 5 showed that conventional rrs clone libraries can still
help identify novel OTUs even though small numbers of rrs clones were used. Therefore,
clone library-based studies are still very useful in finding novel OTUs from the ruminal
microbiome, especially from animals fed different diets in different geographic regions.
These novel OTUs can be used to design new primers or probes for rrs-based analysis
such as real-time PCR assays, FISH, and phylogenetic microarray. The predominance of
the novel OTUs, and thus their ecological importance, can be evaluated using specific
real-time PCR assays as described in Chapter 4. The novel OTUs also can be used to
update new probes for the RumenArray (Chapter 6). It should be noted that although
pyrosequencing can generate sequences more cost effectively and large number of
sequences can be produced from single samples, the high percentage of artifactual
sequences (Kunin et al., 2010) will compound diversity analysis and prevent design of
robust and accurate primers or probes. Therefore, clone library-based studies of ruminal
microbiome should continue until accurate massively parallel sequencing technologies
become available.
A phylogenetic microarray (RumenArray) was developed based on the OTUs
identified from the meta-analysis described in Chapter 3 (Chapter 6). Numerous OTUs
that could not be classified into a known genus were detected by the RumenArray, and
most of the detected OTUs were assigned to unclassified Clostridiales, unclassified
Lachnospiraceae, or unclassified Ruminococcaceae within the phylum Firmicutes. The
data from the RumenArray analysis corroborates the predominance of the OTUs assigned
to these three groups. The RumenArray showed differential distribution of predominant
OTUs between different ruminal fractions and sheep fed different diets. These
167
preliminary results showed that the RumenArray will be a new tool in comprehensive
analysis of predominant ruminal bacteria in a semi-quantitative manner. Feed efficiency
is important to sustainability and economic viability of dairy and beef industries. The
RumenArray can help identify important core species or microbiome structure features
that are associated with feed efficiency because several hundreds of different bacteria can
be detected in a sample. As ruminal rrs sequences constantly increase in the RDP
database, the specificity of the designed microarray probes will need to be regularly
checked (Dugat-Bony et al., 2011). Additionally, as the RDP database is being updated
on a regular basis, new microarray probes will need to be regularly designed and added to
the current version of RumenArray (version 1).
OTUs are typically calculated from full-length rrs sequences at a phylogenetic
distance (0.03-species, 0.05-genus, 0.10-family), but current pyrosequencing system
produces partial sequences (700 bp or shorter). Many sequence reads produced from the
Sanger technology are also no longer than 500 bp. Given that sequence divergence is not
distributed evenly along the rrs gene, we evaluated and identified partial rrs sequence
region(s) and corresponding distance level(s) that produce comparable results as nearly-
full length sequences (Chapter 7). For bacterial analysis, the V1-V3 region at 0.04
distance produced comparable results as the nearly-full length region. The use of the 0.04
distance will help reduce overestimate of OTUs richness defined at 0.03 distance in
future studies. For archaeal analysis, the V1-V3 at 0.03 distance and the V4-V7 at 0.02
distance were recommended. If these recommended partial regions and distances are used
in future studies, comparisons among different research groups will be more accurate and
future meta-analyses will be more greatly facilitated.
168
Cattle have been typically fed diets containing monensin to improve production
efficiency. Although many in vitro studies have showed that Gram-positive bacteria are
more sensitive to monensin than Gram-negative bacteria, this finding was not supported
by two in vivo studies (Stahl et al., 1998; Weimer et al., 2008). Therefore, we
investigated the effect of monensin, either alone or in combination with dietary fats,
using pyrosequencing analysis (Chapter 8). The result showed that some Gram-positive
bacteria were not affected by monensin. However, the OTU distribution within a genus or
an unclassified group was affected by monensin, indicating the stimulation of highly
monensin-resistant bacteria. Further analysis of monensin-resistant bacteria is needed to
verify their abundance using real-time PCR assays. Pure cultures of monensin-resistant
bacteria will help further understand the mode(s) of action of monensin in the rumen. In
addition, the examination of the effect of monensin plus fats on the diversity of ruminal
bacteriome provided an opportunity for us to investigate the interaction between
monensin and fats. Effects of the dietary fats were inferred from comparison between the
monensin group and the monensin-fat group. Future studies will benefit from inclusion of
a fats only group. Understanding the metabolism of fatty acids is important because they
affect fatty acid composition of meats and milk. The greater population of Firmicutes that
increased corresponding to feeding monensin and fats suggests selection of some bacteria
of Firmicutes in fat hydrolysis and subsequent BH. Isolation and studies of pure culture
involved in fat metabolism will contribute to developing accurate modeling of lipolysis
and biohydrogenation in future nutritional studies.
Manipulation of ruminal fermentation is necessary and highly sought after to
improve rumen functions and enhance animal productivity. A better understanding of the
169
ruminal microbiome will help develop efficient fermentation modifiers. The integrated
investigation reported in this dissertation advanced our knowledge and understanding of
ruminal microbiome and developed new tools (real-time PCR approach and
RumenArray), which may be very useful in future studies.
170
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APPENDIX A:
ADDITIONAL PYROSEQUENCING METHODS
Supplementary Materials and Methods for Chapter 8:
Processing of the sequence dataset:
The Dataset A was obtained from the OSU sequencing center, while the Dataset B
and the Dataset C were generated by Research and Testing Laboratory (Lubbock, TX).
Each Dataset was processed using the QIIME bioinformatics software package, v1.3.0.
(Caporaso et al., 2010) or the Mothur program (Schloss et al., 2009). The
split_libraries.py command as implemented in the QIIME program was used to separate
sample libraries with the following parameters: -q qual.file -l 200 -L 650 -H 8 –M 2 –b 8.
These parameters could remove sequences with homopolymer stretches greater than 8nt
(-H 8), primer mismatches greater than 2nt (-M 2), sequence length less than 200 bp (-l
200), sequence length greater than 650 bp (-L 650), overall quality scores less than 25 (-q
qual.file). Forward primers and reverse primers were also trimmed off, and the barcode
sequences were detected (-b 8). The resultant sequences were aligned using the align.seqs
command as implemented in the Mothur program against the Greengenes core set
database. Sequences that are expected to result from pyrosequencing errors were removed
from the aligned file using the pre.cluster command, and then chimeric sequences were
checked using the chimera.slayer command as implemented in the Mothur program. The
high-quality sequences obtained from each Dataset were combined into one composite
198
Dataset. The number of sequences among samples was normalized using the sub.sample
command as implemented in the Mothur program.
The dist.seqs, the cluster, the make.shared, and the summary.single commands as
implemented in the Mothur program were used to generate OTUs and calculate diversity
indices. An OTU list file compatible with the QIIME program was created using the
get.otulist command as implemented in the Mothur program. Representative sequences
for each OTU were selected using the pick_rep_set.py command as implemented in the
QIIME program. The aligned representative sequences were classified to bacterial taxa
using the assign_taxonomy.py command as implemented in the QIIME program. The
OTU table was created from the OTU mapping file and the taxonomy file generated from
the pick_otus.py and assign_taxonomy.py commands as implemented in the QIIME
program. A phylogenetic tree was constructed using the filter_alignment.py and
make_phylogeny.py commands and then used to calculate beta diversity measures using
the beta_diversity.py command as implemented in the QIIME program. Alpha diversity
measures were calculated using the alpha_rarefaction.py command.
199
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