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A Comparative Analysis Of The Moose RumenMicrobiota And The Pursuit Of ImprovingFibrolytic Systems.Suzanne Ishaq PellegriniUniversity of Vermont

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A COMPARATIVE ANALYSIS OF THE MOOSE RUMEN MICROBIOTA AND THE

PURSUIT OF IMPROVING FIBROLYTIC SYSTEMS.

A Dissertation Presented

by

Suzanne Ishaq Pellegrini

to

The Faculty of the Graduate College

of

The University of Vermont

In Partial Fulfillment of the Requirements

For the Degree of Doctor of Philosophy

Specializing in Animal, Nutrition and Food Science

May, 2015

Defense Date: March 19, 2015

Dissertation Examination Committee:

André-Denis G. Wright, Ph.D., Advisor

Indra N. Sarkar, Ph.D., MLIS, Chairperson

John W. Barlow, Ph.D., D.V.M.

Douglas I. Johnson, Ph.D.

Stephanie D. McKay, Ph.D.

Cynthia J. Forehand, Ph.D., Dean of the Graduate College

ABSTRACT

The goal of the work presented herein was to further our understanding of the

rumen microbiota and microbiome of wild moose, and to use that understanding to

improve other processes. The moose has adapted to eating a diet of woody browse,

which is very high in fiber, but low in digestibility due to the complexity of the plant

polysaccharides, and the presence of tannins, lignin, and other plant-secondary

compounds. Therefore, it was hypothesized that the moose would host novel

microorganisms that would be capable of a wide variety of enzymatic functions, such as

improved fiber breakdown, metabolism of digestibility-reducing or toxic plant

compounds, or production of functional metabolites, such as volatile fatty acids, biogenic

amines, etc.

The first aim, naturally, was to identify the microorganisms present in the rumen

of moose, in this case, the bacteria, archaea, and protozoa. This was done using a variety

of high-throughput techniques focusing on the SSU rRNA gene (see CHAPTERS 2-5).

The second aim was to culture bacteria from the rumen of the moose in order to study

their biochemical capabilities (see CHAPTERS 6-7). The final aim was to apply those

cultured bacterial isolates to improve other systems. Specifically, bacteria from the

rumen of the moose was introduced to young lambs in order to colonize the digestive

tract, speed the pace of rumen development, and improve dietary efficiency (see

CHAPTER 8).

ii

CITATIONS

Material from this dissertation has been published in the following form:

Ishaq, S.L. and Wright, A-D.G.. (2012). Insight into the bacterial gut microbiome of the

North American moose (Alces alces). BMC Microbiology, 12:212.

Ishaq, S.L. and Wright, A-D.G.. (2013). Terrestrial Vertebrate Animal Metagenomics,

Wild Ruminants. In: Nelson K. (Ed.) Encyclopedia of Metagenomics: Springer

Reference. Springer-Verlag Berlin Heidelberg.

Ishaq, S.L. and Wright, A-D.G.. (2013). Wild Ruminants. In: Rumen Microbiology –

Evolution to Revolution. AK Puniya, R Singh, DN Kamra (Eds). Springer Publishing.

Ishaq, S.L. and Wright, A-D.G.. (2014). High-throughput DNA sequencing of the

ruminal bacteria from moose (Alces alces) in Vermont, Alaska, and Norway. Microbial

Ecology, 68(2):185-195.

Ishaq, S.L. and Wright, A-D.G.. (2014). Design and validation of four new primers for

next-generation sequencing to target the 18S rRNA gene of gastrointestinal ciliate

protozoa. Applied and Environmental Microbiology, 80(17): ahead of print.

Material from this dissertation has been submitted for publication to the International

Journal of Systemic and Evolutionary Microbiology on December 22, 2014 in the

following form:

Ishaq, S.L., Reis, D., Lachance, H., and Wright, A-D.G.. (2015). Descriptions of

Streptococcus alcis sp. nov. and Streptococcus vermontensis, sp. nov., and proposal of

three new subspecies of Streptococcus gallolyticus isolated from the rumen of moose

(Alces alces) in Vermont.

Material from this dissertation has been submitted for publication to BMC Microbiology

on February 23, 2015 in the following form:

Ishaq, S.L., Reis, D., and Wright, A-D.G.. (2015). Fibrolytic bacteria isolated from the

rumen of North American moose (Alces alces).

iii

Material from this dissertation has been submitted for publication to Applied and

Environmental Microbiology on February 11, 2015 in the following form:

Ishaq, S.L., Sundset, M.A., Crouse, J., and Wright, A-D.G. 2015. High-throughput DNA

sequencing of the moose rumen from different geographical location reveals a core

ruminal methanogenic archaeal diversity and a differential ciliate protozoal diversity.

iv

ACKNOWLEDGEMENTS

First and foremost, I want to thank my advisor and mentor, Dr. André-Denis Wright,

without whom this doctoral project would not have happened. Thanks for supporting

me professionally and personally, and especially for endlessly correcting my poor

spelling, putting up with my dry sense of humor, and helping me to mature into a

professional. Thanks to my other committee members, Dr. John Barlow, Dr. Doug

Johnson, Dr. Stephanie McKay, and Dr. Neil Sarkar for their interest in my work, their

constructive criticism, helpful suggestions, and for reading every page of this tome.

The entire Animal Science Department has my gratitude for too many reasons to list

here, but I would like to thank Jane O’Neil and Helen Maciejewski for all of the

paperwork they’ve done and phone calls they’ve made on my behalf. I’d like to thank

the Wright lab members: the undergraduates who were eager to learn laboratory skills

and help with my project, Dr. Benoit St-Pierre and Rachel P. Smith, M.S. for their

assistance in technical problems and discussions on analysis, Laura Cersosimo for

being my sounding-block and partner in science, and Christina Kim for helping me

collect rumen samples from sheep we had to chase down in a field in the hot July sun

because literally no one else was available. I’d like to thank my collaborators and those

individuals who assisted me in sample collection, as moose parts are not as easy to

come by as one would think.

v

On a more personal note, I’d like to thank my family for their heroic attempts to

understand my work and their unfailing support for my nine grueling years of higher

education. I especially need to thank my parents, Jill and Tony Pellegrini, for instilling

in me a love of reading, organization, and learning, as those have motivated me and

proved to be essential while writing this. I’d like to thank my friends, especially Laura

Cersosimo, Rachel Smith, Amanda Ochoa, Aimee Benjamin, Mital Pandya, and Mike

Haselton, for giving me something to do outside of work, and for always being willing

to inevitably talk about work. I want to thank JP Ishaq for his support for so many

years, even when I didn’t make it easy, and for putting up with me smelling like a wide

variety of animals after work. I’d like to thank everyone who lent a proverbial shoulder

to cry on during this last year, and especially for using those shoulders to move all my

stuff multiple times up and down stairs. I want to thank Lee Warren for his support in

the last and most stressful year of my Ph.D., especially when the only thing to do was

to hand me chocolate and hope for the best, and for being a willing participant in my

adventures. Finally, I would like to thank the Windows 7 version of Word for its help

formatting this beast, and for not being the Windows 8 version of Word.

vi

TABLE OF CONTENTS

CITATIONS…………………………………………………………………………..…..ii

ACKNOWLEDGEMENTS…………………………………………...………………….iv

LIST OF FIGURES…………………………………………………………………….xiii

LIST OF TABLES…………………………………………………………..…………..xv

CHAPTER 1 COMPREHENSIVE LITERATURE REVIEW ................................ 1

1.1. Moose .................................................................................................................. 1

1.1.1 Ecology and anatomy ...................................................................................... 1

1.1.2 Diet and Nutrition ........................................................................................... 3

1.2. Microbial phylogeny and the small-subunit rRNA genes................................... 5

1.3. An overview of the rumen microbiome and its role in digestion ....................... 8

1.3.1 Bacteria ........................................................................................................... 8

1.3.2 Archaea and methanogenesis ........................................................................ 11

1.3.3 Protozoa ........................................................................................................ 14

1.3.4 Fungi ............................................................................................................. 16

1.3.5 Dietary components and volatile fatty acids ................................................. 17

1.4. Probiotics and manipulation of the rumen environment ................................... 22

1.5. Summary ........................................................................................................... 23

1.6. References ......................................................................................................... 24

1.7. Figures............................................................................................................... 42

CHAPTER 2 INSIGHT INTO THE BACTERIAL GUT MICROBIOME OF

THE NORTH AMERICAN MOOSE (ALCES ALCES). .................................................. 43

2.1. Abstract ............................................................................................................. 44

2.2. Background ....................................................................................................... 45

vii

2.3. Results ............................................................................................................... 47

2.3.1 Quantitative Real-Time PCR ........................................................................ 47

2.3.2 PhyloChip Array ........................................................................................... 47

2.3.3 UniFrac analysis............................................................................................ 50

2.4. Discussion ......................................................................................................... 51

2.5. Materials and Methods ...................................................................................... 58

2.5.1 Sample Collection ......................................................................................... 58

2.5.2 DNA extraction ............................................................................................. 59

2.5.3 Quantitative Real-Time PCR ........................................................................ 59

2.5.4 PhyloChip ..................................................................................................... 60

2.5.5 Analysis......................................................................................................... 60

2.6. Competing Interests .......................................................................................... 62

2.7. Authors’ Contributions ..................................................................................... 62

2.8. Acknowledgements ........................................................................................... 62

2.9. References ......................................................................................................... 63

2.10. Figures........................................................................................................... 67

2.11. Tables ............................................................................................................ 73

2.12. Supplemental Material .................................................................................. 75

CHAPTER 3 HIGH-THROUGHPUT DNA SEQUENCING OF THE

RUMINAL BACTERIA FROM MOOSE (ALCES ALCES) IN VERMONT, ALASKA,

AND NORWAY. 81

3.1. Abstract ............................................................................................................. 82

3.2. Background ....................................................................................................... 83

3.3. Methods............................................................................................................. 85

viii

3.3.1 Sample Collection ......................................................................................... 85

3.3.2 DNA extraction and quantification ............................................................... 87

3.3.3 Amplicon library preparation ........................................................................ 87

3.3.4 Sequencing analysis ...................................................................................... 89

3.4. Results ............................................................................................................... 91

3.4.1 Classification of taxa by sample location ..................................................... 91

3.4.2 Statistical analysis of OTUs and clustering .................................................. 93

3.5. Discussion ......................................................................................................... 96

3.5.1 Bacteroidetes:Firmicutes............................................................................... 96

3.5.2 Temperature, region, or life stage? ............................................................... 99

3.6. Conclusion ...................................................................................................... 100

3.7. Availability of Supporting Data ...................................................................... 100

3.8. Competing Interests ........................................................................................ 100

3.9. Authors’ Contributions ................................................................................... 101

3.10. Acknowledgments....................................................................................... 101

3.11. References ................................................................................................... 101

3.12. Figures......................................................................................................... 106

3.13. Tables .......................................................................................................... 109

CHAPTER 4 DESIGN AND VALIDATION OF FOUR NEW PRIMERS FOR

NEXT-GENERATION SEQUENCING TO TARGET THE 18S RRNA GENE OF

GASTROINTESTINAL CILIATE PROTOZOA. .......................................................... 111

4.1. Abstract ........................................................................................................... 112

4.2. Introduction ..................................................................................................... 113

4.3. Materials and Methods .................................................................................... 115

ix

4.3.1 Sample collection and DNA extraction ...................................................... 115

4.3.2 Primer design .............................................................................................. 115

4.3.3 PCR amplification ....................................................................................... 116

4.3.4 Sequence analysis ....................................................................................... 117

4.4. Results ............................................................................................................. 119

4.4.1 Primer set 1: P-SSU-316F + GIC758R ....................................................... 119

4.4.2 Primer set 2: GIC1080F + GIC1578R ........................................................ 121

4.4.3 Primer set 3: GIC1184F + GIC1578R ........................................................ 122

4.4.4 Comparison of the three primer sets ........................................................... 122

4.5. Discussion ....................................................................................................... 123

4.6. Acknowledgements ......................................................................................... 127

4.7. References ....................................................................................................... 127

4.8. Figures............................................................................................................. 132

4.9. Tables .............................................................................................................. 135

4.10. Supplemental Material ................................................................................ 137

CHAPTER 5 HIGH-THROUGHPUT DNA SEQUENCING OF THE MOOSE

RUMEN FROM DIFFERENT GEOGRAPHICAL LOCATION REVEALS A CORE

RUMINAL METHANOGENIC ARCHAEAL DIVERSITY AND A DIFFERENTIAL

CILIATE PROTOZOAL DIVERSITY. .......................................................................... 138

5.1. Abstract ........................................................................................................... 139

5.2. Introduction ..................................................................................................... 140

5.3. Methods........................................................................................................... 141

5.3.1 Sequence analysis ....................................................................................... 142

5.3.2 Real-time PCR ............................................................................................ 143

5.4. Results ............................................................................................................. 144

x

5.4.1 Methanogens ............................................................................................... 144

5.4.2 Protozoa ...................................................................................................... 145

5.5. Discussion ....................................................................................................... 147


5.7. References ....................................................................................................... 151

5.8. Figures............................................................................................................. 157

5.9. Tables .............................................................................................................. 160

CHAPTER 6 DESCRIPTION OF STREPTOCOCCUS ALCIS, SP. NOV., AND

STREPTOCOCCUS VERMONTENSIS, SP. NOV., AND PROPOSAL OF THREE

NEW SUBSPECIES OF STREPTOCOCCUS GALLOLYTICUS ISOLATED FROM

THE RUMEN OF MOOSE (ALCES ALCES) IN VERMONT....................................... 163

6.1. Summary ......................................................................................................... 164

6.2. Description of Streptococcus alcis sp. nov. .................................................... 175

6.3. Description of Streptococcus vermontensis sp. nov. ...................................... 176

6.4. Description of Streptococcus gallolyticus subsp. mannosilyticus subsp. nov. 177

6.5. Description of Streptococcus gallolyticus subsp. melibiosilyticus subsp. nov.

178

6.6. Description of Streptococcus gallolyticus subsp. ruminantium subsp. nov.... 179

6.7. Conflict of interest .......................................................................................... 179


6.9. References ....................................................................................................... 180

6.10. Figures............................................................................................................. 185

6.11. Tables .......................................................................................................... 190


xi

CHAPTER 7 FIBROLYTIC BACTERIA ISOLATED FROM THE RUMEN OF

NORTH AMERICAN MOOSE (ALCES ALCES). ......................................................... 197

7.1. Abstract ........................................................................................................... 198

7.2. Introduction ..................................................................................................... 199

7.3. Methods........................................................................................................... 200

7.4. Results ............................................................................................................. 203

7.5. Discussion ....................................................................................................... 204

7.6. References ....................................................................................................... 206

7.7. Figures............................................................................................................. 210

7.8. Tables .............................................................................................................. 212

CHAPTER 8 THE EFFECTS OF ADMINISTERING A FIBROLYTIC

PROBIOTIC MADE FROM MOOSE RUMEN BACTERIA TO NEONATAL LAMBS

…………...…………………………………………………………………………….213

8.1. Abstract ........................................................................................................... 214

8.2. Introduction ..................................................................................................... 215

8.3. Methods........................................................................................................... 217

8.3.1 Probiotic ...................................................................................................... 218

8.3.2 Production ................................................................................................... 220

8.3.3 Rumen sampling ......................................................................................... 220

8.3.4 Sequencing and DNA data analysis ............................................................ 221

8.3.5 Real-time PCR ............................................................................................ 222

8.4. Results ............................................................................................................. 223

8.4.1 Probiotic ...................................................................................................... 223

8.4.2 Production ................................................................................................... 223

8.4.3 Sequencing .................................................................................................. 225

xii

8.4.4 Real-time PCR ............................................................................................ 228

8.5. Discussion ....................................................................................................... 228


8.7. References ....................................................................................................... 231

8.8. Figures............................................................................................................. 236

8.9. Tables .............................................................................................................. 244


CHAPTER 9 CONCLUSION .............................................................................. 248

9.1. Summary ......................................................................................................... 248

9.2. The moose rumen: summarized findings and unanswered questions ............. 248

9.3. Fibrolytic probiotics in lambs: beneficial or bust? ......................................... 251

9.4. Molecular methodology .................................................................................. 255

9.4.1 Sequencing Platform ................................................................................... 255

9.4.2 Sequencing Target ...................................................................................... 258

9.4.3 Bioinformatics Programs and Analysis ...................................................... 260

9.5. References ....................................................................................................... 261

9.6. Figures............................................................................................................. 267

9.7. Tables .............................................................................................................. 268

CHAPTER 10 COMPREHENSIVE BIBLIOGRAPHY ....................................... 269

xiii

LIST OF FIGURES

Figure 1-1 Diagram comparing the immature ruminant to the mature ruminant. ............ 42

Figure 1-2 The frequency of variability in the bacterial 16S rRNA gene [203]. .............. 42

Figure 1-3 Variable regions of the ciliate protozoal 18S rRNA. ...................................... 42

Figure 2-1 The OTUs found common in all samples (rumen and colon). ........................ 67

Figure 2-2 Breakdown of unclassified families by phylum. ............................................. 68

Figure 2-3 A comparison of the OTUs exclusive to the rumen or the colon. ................... 69

Figure 2-4 Jackknife environment clustering in UniFrac, by sample. .............................. 70

Figure 2-5 Principal component analysis (PCA) scatterplot of the environments

using the weighted UniFrac algorithm. ............................................................................. 71

Figure 2-6 Distribution of PhyloChip OTU’s for all 14 samples. .................................... 72

Figure 3-1 A comparison of sequences per sample, using the Silva bacterial

reference taxonomy for classification. ............................................................................ 106

Figure 3-2 FastUnifrac clustering of all 17 samples. A) weighted, non-

normalized, and B) unweighted. ..................................................................................... 107

Figure 3-3 PCoA graphs colored by variable. ................................................................ 108

Figure 4-1 A map of the full-length protozoal 18S rRNA gene, including variable

(V1-V9) and rumen ciliate signature regions (SR1-SR4), and showing the

respective amplicons of the three primer sets used in the present study. ....................... 132

Figure 4-2 Taxonomy and proportion of unique pyrosequences using NCBI

(BLAST), by forward primers P-SSU-316F (Sylvester et al., 2004), GIC1080F

(present study), and GIC1184F (present study). ............................................................. 133

Figure 4-3 Distribution of sequence identity percentages to known sequences

based on BLAST by sequencing primer. ........................................................................ 134

Figure 5-1 PCoA of moose (A, C, and E) and protozoa (B, D, and F). .......................... 157

Figure 5-2 Moose rumen methanogen taxonomy of total sequences, per sample

from Alaska, Norway and Vermont ................................................................................ 158

xiv

Figure 5-3 Moose rumen protozoal taxonomy of total sequences, per sample from

Norway and Vermont. ..................................................................................................... 159

Figure 6-1 Neighbor-joining tree comparing isolates to type isolates to lactic acid

bacteria. ........................................................................................................................... 185

Figure 6-2 UPGMA trees comparing novel isolates using the housekeeping genes

gmk (A), dpr (B), parC (C), pta (D), pyrC (E), recN(F), and rpoD (G). ........................ 186

Figure 7-1 Phylogenetic comparison of 31 isolates which known sequences

(NCBI). ........................................................................................................................... 210

Figure 7-2 Growth at various temperatures (A) and pHs (B), as measured by

optical density at 600 nm. ............................................................................................... 211

Figure 8-1 Group weight (kg) means over time.............................................................. 236

Figure 8-2 Group pH means over time. .......................................................................... 237

Figure 8-3 Volatile fatty acid (VFA) and ethanol profile of groups

(E=experimental, C=control) for two time points on pasture. ........................................ 238

Figure 8-4 Acetate to propionate ratio, with SEM. ......................................................... 239

Figure 8-5 Principal component analysis of samples by sampling time: May=teal,

June=green, July=red, and October=dark blue. .............................................................. 239

Figure 8-6 Diversity at the phylum level for all sequencing time points........................ 240

Figure 8-7 Diversity at the family level for all sequencing time points. ........................ 241

Figure 8-8 Real-time PCR data for methanogens and protozoa. .................................... 242

Figure 8-9 Comparison of methanogen versus protozoal densities for all samples ....... 243

Figure 9-1 Mean rumen pH over time, with standard deviation bars. ............................ 267

xv

LIST OF TABLES

Table 2-1 Estimated densities (16S rRNA copy numbers per gram wet weight) of

bacteria in the rumen (R) of the moose in October, 2010, Vermont. ............................... 73

Table 2-2 Total number of taxa found in each sample, before screening for

analysis but after background noise was removed and including only OTUs with

> 0.92 positive fraction. .................................................................................................... 74

Table 2-3 Statistics for samples taken from moose shot in October 2010 in

Vermont during the moose hunting season. ...................................................................... 74

Table 3-1 Metadata of the 17 samples from Alaska (AK), Norway (NO), and

Vermont (VT). ................................................................................................................ 109

Table 3-2 Statistical measures using a subset of sequences per sample. ........................ 110

Table 3-3 The number of shared OTUs across different samples at a 3% genetic

distance generated using a shared OTU table in MOTHUR. .......................................... 110

Table 4-1 Gastrointestinal tract ciliate protozoal primer specifications. ........................ 135

Table 4-2 18S rRNA protozoal reference sequences used to determine genetic

distance cutoff. ................................................................................................................ 136

Table 4-3 Comparison of statistical parameters and results for three primer sets. ......... 137

Table 5-1 Statistical measures per sample for methanogens (met) and protozoa

(prot) in Alaska (AK), Norway (NO), and Vermont (VT). ............................................ 160

Table 5-2 The number of shared OTUs and unique sequences across different

samples in Alaska (AK), Norway (NO), and Vermont (VT). ......................................... 161

Table 5-3 Real-time PCR results for methanogenic archaea and ciliate protozoa in

Alaska (AK), Norway (NO), and Vermont (VT). ........................................................... 162

Table 6-1Ability of isolates to produce biogenic amines from selected amino

acids, produce lipase, metabolize citrate, produce a ropy or non-ropy

exopolysaccharide, and extracellular protein production. .............................................. 190

Table 6-2 Acid production and ability of isolates to produce an acid byproduct

from a variety of carbohydrates. ..................................................................................... 192

xvi

Table 6-3 G+C content of isolates and DNA-DNA hybridization to reference

strains Streptococcus gallolyticus gallolyticus (ATCC 700065) and S. gallolyticus

macedonicus (ATCC BAA-249)..................................................................................... 194

Table 7-1 Isolate GenBank ID, closest GenBank match with percent identity,

growth on minimal media or on high salinity, and ability to reduce nitrite.................... 212

Table 8-1 Diversity statistics per sample for each of the four sampling time points. .... 244

Table 9-1 A comparison of the Silva bacterial database vs the Ribosomal

Database Project (RDP) bacterial reference files in ability to classify 16S rRNA

gene bacterial sequences. ................................................................................................ 268

1

CHAPTER 1 COMPREHENSIVE LITERATURE REVIEW

1.1 Moose

1.1.1 Ecology and anatomy

Moose, Alces alces, also known as Eurasian Elk in Europe, are the largest browsing

ruminant of the Cervidae (deer) family. They are unique among ruminants, as they do

not form herds, but will live individually, with the exceptions being mating season and

the first 9 to 10 months of a calf’s life. At maturity they reach upwards of 1.5 to 2 meters

at the shoulder, live up to 25 years in the wild, and weigh an average of 360 kg (females)

to 450 kg (males). Several subspecies of moose are recognized, for which geographic

isolation and adaptation has caused differential characteristics, such as antler shape. For

example, moose in Alaska (A. alces gigas) and eastern Siberia (A. alces buturlini) tend to

be larger, with males reaching up to 600 kg, and moose in Scandinavia (A. alces alces)

have white legs instead of the typical brown.

However, investigations of mitochondrial DNA have revealed conflicting results as to the

genetic validity of some subspecies [1–3]. Moose originally migrated to the United

States from Asia across the Bering Strait approximately 14,000 to 11,000 years ago.

From there, they dispersed across North America and genetic subspecies were eventually

established: A. alces gigas in Alaska and the Canadian Yukon, A. a. andersoni in western

Canada and the great lakes region of the US, A. a. shirasi from the Rocky Mountains and

Colorado to Alberta, Canada, and A. a. americana from the great lakes region to the east

coast [2]. In a comparison of the four proposed subspecies in North America,

2

mitochondrial diversity was slightly higher for populations in the center and lower in the

peripheral populations along the eastern and western cost (excluding Alaska) [2, 3]. The

implication was of a large central population which only relatively recently (as recent as

the 1900s) dispersed to peripheral territories, thus the genetic diversity between

subspecies was not entirely due to geographic isolation [2, 3].

Moose were traditionally found in most boreal and subarctic areas of the northern

hemisphere, but deforestation and over-hunting has reduced their range and, in some

areas, their population [4]. They do not thrive in warmer climates, and adults will often

lose weight during an unusually hot summer, although only calf weight is adversely

affected by overly cold winters [5]. Moose prefer young hardwood forest, deciduous

mixed forest, and salt-rich wetland habitats in the summer. Like all ruminants, moose

have a specialized digestive system with a four chambered stomach: rumen, reticulum,

omasum, and abomasum (Figure 1). The rumen/reticulum fosters a complex consortia of

microorganisms (bacteria, archaea, protozoa, fungi, viruses), and the collection of these is

known as microbiota. It is these microbiota which ferment plant matter that the animal

cannot breakdown on its own [6]. The omasum resorbs water from digesta, and the

abomasum secretes pepsin and rennet, and thus functionally resembles the glandular or

“true stomach”.

Moose are characterized by having wide mouths and long, flexible tongues that have

relatively few taste buds, resulting in browse selection based largely on olfactory cues

[7]. Molars and pre-molars are sharper than in many other ruminants, indicating a

3

specialization towards crushing tougher materials rather than grinding thin grasses [7].

The salivary glands are relatively large for ruminants, increasing in size for the summer,

allowing for excess saliva (specifically serous or enzyme-containing saliva) to pass into

the digestive tract. The saliva of deer also contains tannin-binding proteins [8]. Tannins

are plant-based polyphenols which can bind to proteins in the diet and make them

inaccessible for digestion, thus tannin-binding proteins aid in increasing the digestibility

of the diet.

The rumen is relatively small compared to other browsing ruminants of comparable size,

with a large reticulum capable of filtering larger particles back into circulation for

continued fermentation, a small yet elongated omasum, and an abomasum with unusually

thick mucosa [7]. Openings between stomach chambers are unusually wide and can be

further widened [7]; with the additional saliva this allows faster passage of forage

through the system during summer when food is plentiful. Faster passage of forage

through the digestive system has been shown to reduce methane emissions in domestic

cattle [9].

1.1.2 Diet and Nutrition

Diet selection and a preference for certain plant species can be seen in moose in different

locations, but trends towards certain genera can be seen across all moose. Deciduous or

coniferous leaves, twigs and stems are most often consumed, although moose have been

known to strip bark from ash and maple species. New growth is especially sought out, as

foliage is higher in protein and minerals than grass, and the concentrations of toxic plant

4

secondary compounds is lower. In western North America, the moose diet is

overwhelmingly (75-91%) comprised of willow species (Salix spp.), but will also

incorporate alder, aspen, and birch [10, 11]. In eastern North America, maple, ash,

hemlock, pine, fir, and birch comprised the primary diet of moose [12, 13]. In

Scandinavian countries (i.e. Norway, Sweden, Finland), birch and pine tend to dominate

the diet, as well as blueberry species [13–17].

Dietary efficiency decreases from summer through autumn, especially with respect to

cellulose digestion [18, 19]. Caloric intake decreases from summer into winter as well,

not only from reduced forage quality and quantity, but also from decreased production of

volatile fatty acids in the rumen, especially propionate and butyrate [18, 19].

Interestingly, moose will voluntarily reduce feed intake in the winter regardless of quality

and quantity of feed supplied [20]. Moose commonly lose up to 20% of body weight

over winter [21], a cycle which is common to other arctic cervids, such as reindeer.

Moose, especially pregnant cows, show an increased preference for aquatic wetland plant

and algae species when they are available during the summer, and specifically for those

species with a high salt content, such as green algae, Spirogyra sp., and bladderworts,

Utricularis sp. [22]. An estimated 94-96% of sodium intake for a moose comes from

aquatic species eaten during the summer; not only can moose detect salt concentrations as

low as 1 mmol (or 100 milli-equivalent/liter), but they appear to have an effective method

of sodium retention which reduces excess excretion in urine and feces [22]. In addition

5

to salt, feeding on aquatic plants provides extra water in the diet which is required for

ruminant digestion and peristaltic movement.

1.1.2.1. Modified Diets

There has been some research done on formulated rations for captive moose with variable

success [23, 24]. Moose fed on large quantities of grass forages are prone to declining

health caused by chronic diarrhea and wasting, which can eventually lead to death [24].

Moose, like all ruminants, are also prone to lactic acidosis or “grain overload” [25]. This

is common when an animal switches from a natural cellulose-based diet to a

manufactured or otherwise highly-digestible starch-based diet. The sudden change in

food type causes a sudden shift in rumen bacterial communities, generally from gram-

negative to gram-positive bacteria, and promotes the faster replicating lactic-acid

bacterial species which prefer a starch substrate. Excess lactic acid is produced, lowering

ruminal pH below pH 5.5, which can kill naturally-occurring populations of

microorganisms, such as the rumen protozoa [25]. Not only does this decrease appetite

and feed intake, but larger amounts of ruminal acid are transported across the rumen wall

and into the bloodstream, leading to a more serious metabolic acidosis.

1.2 Microbial phylogeny and the small-subunit rRNA genes

Metagenomic studies do not usually employ culturing techniques, and many rumen

microorganisms are too recalcitrant to culture. Thus, putative identification is made

using pairwise gene sequence comparisons to known species. The small subunit of the

ribosome (SSU rRNA) provides a good platform for both current molecular methods, but

6

comparative phylogeny as well as, although the gene itself does not provide any

information about the phenotypic functionality of the organism. Overall, the rRNA genes

evolve very slowly and, since they are ubiquitous, they can be used for comparison across

wide variety of taxa.

Prokaryotes, such as bacteria and archaea, have a 16S rRNA gene which is approximately

1,600 base pairs in length. Eukaryotes, such as protozoa, fungi, plants, animals, etc.,

have an 18S rRNA gene which is up to 2,300 base pairs in length, depending on the

kingdom. In both cases, the S stands for Svedberg Units, or sedimentation rates of the

RNA molecule, and is a relative measure of weight and size. Thus, the 18S is larger than

the 16S. In both genes, there exist regions which are conserved (identical or near-

identical) across taxa, and nine variable regions (V1-V9) [26]. The variable regions are

not under functional constraint and are prone to higher evolutionary rates (Figures 2, 3),

providing a means for identification and classification through analysis [27–31]. The

conserved areas are targets for primers, as a single primer can bind universally (to all or

nearly-all) to its target taxa.

In addition to a small subunit, ribosomes also possess a large subunit (LSU rRNA), the

23S rRNA in prokaryotes, and the 28S rRNA in eukaryotes. Eukaryotes have an

additional 5.8S subunit which is non-coding, and all small and large units of RNA have

associated proteins which aid in structure and function. Taken together, this gives a

combined 70S ribosome in prokaryotes, and a combined 80S ribosome rRNA in

eukaryotes.

7

The two main challenges facing high-throughput sequencing are in choosing a target for

amplification, and being able to integrate the generated data into an increased

understanding of the microbiome of the environment being studied, both of which are

discussed further on pages 255-260. High-throughput sequencing can currently sequence

thousands to millions of reads which are up to 600 bases in length for amplicons and

1,000 bases in length for genomic DNA (e.g. Roche 454). This has forced studies to

choose which variable regions of the rRNA gene to amplify and sequence, and has

opened up an arena for debate on which variable region to choose [27].

Additionally, the ability to sequence microorganisms without culturing first has led to the

exponential growth of online sequence databases, which have variable amounts of

detailed information about the sequence entry. Indeed, many bacterial sequences in

GenBank are listed as “unclassified” or “uncultured”, making taxonomic analysis for

high-throughput methods difficult. Operational Taxonomic Units (OTUs) are a

bioinformatics tool for grouping sequences based on percent identity to known

sequences, and in this way sequences can have an assigned level of taxonomy even when

the taxonomic resolution is low due to short reads, or when sequences cannot be

putatively identified using public databases. Different metagenomic studies also assign

OTUs at different taxonomic level (97%, 98%, or 99% for bacteria), which can make

analysis across studies difficult.

8

In recent years, the advances in de novo shotgun sequencing has allowed for the large-

scale investigation of a variety of microbiomes [32, 33]. While microbiome refers to the

collective genetic material or genomes of all the microorganisms in a specific

environment, the term is often casually used interchangeably with “microbiota”, or is

used to describe only the genetic material of a specific type of microorganism (i.e.

“microbiome” instead of “bacterial microbiome”). While the same challenge of

sequencing without culturing still applies; in that you can identify which pieces of the

puzzle are present but not always how they fit together, shotgun sequencing allows for

the entire genome to be sequenced. DNA is enzymatically or physically cut into small

pieces which are sequenced, these pieces are assembled into contigs or short sections,

which themselves are then assembled to more or less recreate the entire genome. This

allows for the identification of putative genes for different enzymes, and detailed

comparisons of species across multiple loci instead of just one gene. Naturally, this

process comes with its own drawbacks of technical difficulties, extremely high data

output, and the logistical challenges of reassembling an entire genome. However, it is an

interesting way of identifying form and function in a single method, and is furthering the

field of molecular genetics.

1.3 An overview of the rumen microbiome and its role in digestion

1.3.1 Bacteria

The most important tool a scientist can possess is curiosity; however, the inventor of the

microscope and the “Father of Microbiology” was not a scientist by traditional terms.

Antonie van Leeuwenhoek was a draper, a local politician, and a lens-maker in the 1600s

9

to early 1700s, and it was using these home-made lenses that he began to observe things

on a cellular level. He was the first person to view and describe single-celled organisms,

such as bacteria, protozoa, and spermatozoa, which he referred to as “animalcules” or

“wee beasties”. To date, there are 30 valid bacterial phyla [34, 35], with several more

candidate phyla in use (i.e. OP1, TM7, etc.) [36]; however, only a selection of these are

found to colonize the rumen. Some phyla, such as those which contain aquatic or soil

bacteria (i.e. Chlorobi or Verrucomicrobia, respectively), are often found in the digestive

tract as a result of incidental ingestion, and are thought to be transient members of the

rumen. The two main phyla which tend to dominant the gastrointestinal tract (GIT) are

Bacteroidetes and Firmicutes.

Currently, the phylum Bacteroidetes contains over 7,000 species, genetically adapted to a

wide range of environments, such as soil, water, and the GIT [37]. Bacteria belong to the

phylum Bacteroidetes are gram-negative anaerobes or aerobes, and it is generally

anaerobic members that belong to the class Bacteroidia (formerly Bacteroides) that are

found in all parts of the GIT. As members of the phylum Bacteroidetes possess a wide

range of enzymes, especially those which digest carbohydrates or proteins, the species

profile for the host GIT is determined by the diet of the host. In the GIT, some of them

produce butyrate, which has been implicated in upregulating the GI immune system [38],

and can alter toxic or mutagenic compounds [39].

In healthy humans, Bacteroidetes is the dominant phyla [40–42]; however, in obese

humans Bacteroidetes bacteria are decreased and Firmicutes is the dominant phylum [43–

10

45]. This is in contrast to ruminants, which are more likely to have Firmicutes as the

dominant phylum [46–53]. However, several studies have identified Bacteroidetes as the

dominant phylum in adult ruminants [47, 54], growing ruminants [55], and ruminants

transitioning to a high-starch diet [56]. Firmicutes bacteria are gram-positive, often form

endospores, and are divided into two major classes: Clostridia and Bacilli. Bacteria

belonging to the class Clostridia are strict anaerobes, are also found in soil, and many are

characterized as cellulolytic, such as Butyrivibrio spp. [52], Clostridium spp. [57], or

Ruminococcus spp. [58]. Within the class Bacilli, the major rumen taxa of interest are

cellulolytic Bacillus species [59, 60] or lactic acid bacteria (order Lactobacilliales), such

as Lactobacillus spp., Lactococcus spp., Enterococcus spp., and Streptococcus spp. [61].

Other major phyla include Fibrobacteres, which contains cellulolytic bacteria, is common

to the GIT [62, 63], and is not well understood as a group as they are difficult to culture.

Bacteria belonging to the phylum Proteobacteria are more prevalent in the intestines or

colon [46, 64], and many are pathogenic, such as Helicobacter or Campylobacter.

However, some species of Proteobacteria have been found to be capable of breaking

down plant compounds, such as lignin [65]. Bacteria from the phylum Actinobacteria

can also be found in the GIT. Many Actinobacteria species are acetogenic, produce

antibiotics or other pharmacologically important compounds [66], or are used to create

dairy products, such as Bifidobacterium spp.

11

1.3.2 Archaea and methanogenesis

In 1990, a four page article revolutionized the way we classify microbes [67]. From

humble beginnings comes the story of archaea, which could not be correctly classified

using the Linnaean system of taxonomy, and not even the prokaryote-eukaryote division

could settle the issue. Though studies had already been done on these microorganisms

[68], they did not yet have a place on the tree of life. Thus, the Domain level of

classification was introduced [67], and the potential for microbial research on archaea

was suddenly limitless. The Archaeal domain is divided into two main phyla,

Euryarchaeota, representing methanogens and related species, and Crenarchaeota,

representing the thermophilic extremophiles, and three presumptive phyla: Korachaeota,

Nanoarchaeota, and Thaumarchaeota [69].

Despite providing a multitude of research opportunities, methanogens were quickly

singled out as a potential target for reducing the production of methane from various

agricultural and industrial sources. Methane has 25 times more potential for global

warming than carbon dioxide, 21 times more if you measure it as the combustion of

methane into carbon dioxide [70, 71]. As of 2012 in the US, enteric fermentation from

domestic livestock was the second largest source of human-related agricultural methane,

accounting for 141 Tg CO2 Eq/year (equivalent of a million metric tons of carbon

dioxide) [70]. Nitrous oxide has a potential of 298 times greater than carbon dioxide, and

agricultural soil management creates 204.6 Tg CO2 Eq emissions, while manure

management systems create 17.9 Tg CO2 Eq/year in the US [70]. Why then have we

focused on methane from enteric fermentations?

12

For domestic livestock, methanogenesis represents a loss of dietary efficiency as

compounds such as acetate or hydrogen are sequestered by methanogens instead of being

used by the host for production (i.e. live weight gain, milk production, wool production,

etc.). Much research has been done on methanogenesis and rumen microbial populations

between domestic and wild ruminants, as wild ruminants (bison, elk and deer) are

estimated to produce up to 0.37 Tg CO2 Eq/year [72, 73]. This is a drastically different

figure than that for domestic livestock, yet population differences are not the only factor.

There are, for example, an estimated 300,000 moose and 25 million white-tailed deer [74]

in the US, versus 90 million cattle registered with the USDA [75]. Thus, wild ruminants

are presumed to produce less methane based on a presumed higher dietary efficiency and

lower production demands.

Ammonia, hydrogen, and carbon dioxide gas are also produced by enteric fermentation,

and their partial pressure drives many reactions in the rumen [76]. Methane gas is

created when hydrogen, available as free protons, H2 gas or NADH and NADPH

cofactors, is used to reduce carbon dioxide. It is thermodynamically favorable, and it

prevents the accumulation of hydrogen gas in the digestive tract which can be harmful to

both microorganisms and host [76]. In addition to living freely in rumen fluid, attached

to particulate matter, or attached GIT epithelia [77], many methanogens can be found

attached to the extracellular surface or intracellularly within protozoa as symbiotic way of

capturing the hydrogen that the protozoa releases [78–80]. Methanogenic archaea

13

harness this process to generate energy, although some gamma- and alpha-proteobacteria

are methanotrophs, and are capable of using methane as their carbon source.

A few different one- or two-carbon molecules may also be used to provide the carbon

dioxide and hydrogen necessary for methanogenesis. Acetate, a volatile fatty acid (VFA)

released as a byproduct of enteric fermentation, can be used to form methane along

several different enzyme pathways. Interestingly, acetogenesis can also act as a hydrogen

sink and will inhibit methanogenesis, though it is in not thermodynamically favorable

under normal rumen conditions. The hydrogen threshold of a methanogen is up to 100

times lower than for than for the reductive acetogenesis pathway, thus a large

methanogen population will reduce the hydrogen concentration below the threshold of

acetogenesis and render it inactive [81, 82].

Formic acid can also be broken down to carbon dioxide and hydrogen, both of which can

be used to form methane, by the enzyme hydrogenlyase, known also as hydrogenase or

formate hydrogenlyase [68]. Hydrogenlyase can be found in other archaeal species like

Thermococcus, and has also been studied in the bacterium Escherichia coli, which uses

the enzyme formate hydrogenlyase (formate dehydrogenase coupled hydrogenase, FDH-

MHY) complex to generate electron acceptors under anaerobic conditions, if formate is

available [83]. Formate in the rumen is provided through consumption of fruit or honey,

dietary supplementation, or bacterial production. Various rumen bacteria, such as

Butyrivibrio, Clostridium, Fibrobacter, Lachnospira, Oxalobacter, Prevotella,

Ruminobacter, Ruminococcus, Streptococcus, or Succinovibrio [84] produce formate.

14

Methanol, a breakdown product of pectin, can also be used for methanogenesis.

Regardless of the carbon source and enzyme pathway used, the final step of

methanogenesis is catalyzed by the methyl-coenzyme M reductase (mcr) gene and is

present in all methanogens [85, 86].

The overwhelming majority of rumen methanogen diversity belongs to the genus

Methanobrevibacter (Mbr.) [87–92]. The two main species identified include Mbr.

smithii [91, 93, 94], which been shown to improve polysaccharide fermentation by

bacteria [95, 96], and Mbr. ruminantium [97], which is associated with a lower

calorie/high-forage diet [98]. Methanobrevibacter thaueri is typically low in ruminants,

but was found in reasonably high numbers in impala [99] and moose [100].

Methanosphaera stadtmanae strictly uses methanol for methanogenesis, and thus is more

prevalent in omnivorous or fruitivorous monogastrics [91, 101, 102], and ruminants with

higher pectin diets [99, 100].

1.3.3 Protozoa

After the initial discovery by Antonie van Leeuwenhoek in the 1600s, protozoa were

discovered nearly 200 years ago [103–105]. Rumen protozoa represent the largest

microbial biomass, although they are not the most abundant, and are important

contributors to plant biomass degradation [106–110]. Identification of protozoa in the

digestive tract has traditionally been accomplished via microscopic identification [79,

15

107, 111–117], or other molecular methods [118–121], although a few high-throughput

studies have recently been published [100, 122–126].

The rumen protozoa, all of which possess cilia as a means of transport, are divided into

two main orders: the Entodiniomorphida, some of which are cellulolytic and have one or

two zones or bands of cilia on the anterior end (i.e. Entodinium, Epidinium, etc.), and the

Vestibuliderida, which are completely covered in cilia, and thus, more motile (i.e.

Dasytricha, Isotricha). Common rumen protozoa include Entodinium spp., Epidinium

spp., Eudiplodinium spp., Isotricha spp., Ophryoscolex spp., and Polyplastron

multivesiculatum, [127].

Fibrolytic protozoal species include Polyplastron multivesiculatum [110], Eudiplodinium

spp. [128, 129], Enoploplastron spp. [114], and Diplodinium sp. [114], to name a few.

Entodinium spp. are a major source of starch and bacterial digestion in the rumen [130].

While total protozoal counts are higher in concentrate selectors [131], Entodinium

populations are decreased on a high-concentrate diet versus a roughage diet [131],

possible due to a shift in the bacteria populations which the Entodinium prey upon.

Protozoa are sensitive to changes in pH [132], and factors such as diet [87, 131, 133] and

weaning strategy [134] will have an effect on the diversity and quantity of rumen

protozoa. Likewise, changes in the protozoal population can alter the density of rumen

methanogens which use protozoa as a source for hydrogen in methanogenesis.

Polyplastron, Eudiplodinium, and Entodinium species have been shown to closely

16

associate with some methanogens, such as Methanosphaera stadtmanae and Mbr.

ruminantium [135–137].

1.3.4 Fungi

Despite contributing to the breakdown of plant material in the rumen [138–141],

relatively little work has been done on identifying rumen fungi or understanding their

interactions with other rumen microorganisms. Rumen fungi exist in a plant-associated

vegetative state or a free-living zoospore state, which was previously thought to be

protozoa [138]. Prior to work by C.G. Orpin in the mid-1970s [142, 143], fungi had not

been confirmed to colonize in anaerobic environments.

Currently, all identified anaerobic fungi are classified within the order Neocallimastigales

(phylum Neocallimastigomycota) [144]. Genera commonly found in the rumen include

Anaeromyces, Caecomyces, Cyllamyces, Neocallimastix, Orpinomyces, Piromyces, and

Trichoderma, all of which are fibrolytic [139, 145]. To date, fungi have been identified

in a variety of African ruminants [146], domestic ruminants [140, 146, 147], and

herbivorous reptiles [146, 148]. Fungi have also been investigated for use as a probiotic

for ruminants [149].

In vitro, co-culturing rumen fungi and the archaeal Methanobrevibacter smithii increased

xylanase activity and promoted acetate formation over lactate [96]. Likewise, some

strains of lactate-utilizing bacteria stimulated the cellulolytic capabilities of fungi in co-

culture [150], while cellulolytic bacteria inhibited it [151].

17

Typically, fungi are identified using the internal transcribed spacer (ITS) region of non-

functional DNA between regions coding for ribosomal subunits. Other potential targets

for identification, such as the rRNA genes, nuclear housekeeping genes, or mitochondrial

genes, are complicated by poor-quality PCR amplification or sequencing, products which

are too large for most current next-generation sequencing techniques, or by low gene

variability leading to insufficient resolution for identification [152]. In the case of the

phylum Neocallimastigomycota, which encompasses many genera of rumen fungi,

mitochondria are entirely lacking.

1.3.5 Dietary components and volatile fatty acids

Dietary fiber is the naturally occurring component of plants which is indigestible to

animals that lack the proper digestive enzymes. Fiber is a general term which

encompasses pectin, gums, mucilage, cellulose, hemicellulose, and lignin. Both pectin

and gums are water-soluble, but those fibers that are found in plant cells walls, such as

cellulose, hemicellulose, and lignin, are water-insoluble. Dietary fiber not only provides

bulk to the diet and aids in water-retention in the intestines, but in the case of ruminants,

plant fibers act as substrates to the fibrolytic microorganisms in the GIT, and a certain

percentage of fiber is required in the diet to maintain a healthy, functioning rumen.

Fibrolytic microorganisms use various polysaccharide-digesting enzymes, like cellulases,

hemicellulase, glucosidehydrolases, xylanase, etc., to break down plant biomass.

Cellulose is a linear polysaccharide of 15-10,000 glucose molecules connected by β-1,4

18

glycosidic linkages, and provides the structural support in cell walls, which allows for

plant growth. Thus, is in the most abundant organic polymer on Earth. As the plant

matures and grows in size, the structural components and cellulose content of the plant

are increased, leading to a decrease in the digestibility of the plant and lowering its

nutritional content. Cellulose can exist as a crystalline structure, as in cotton fibers, or as

a manufactured derivative, such as carboxymethylcellulose, which is more water-soluble.

Acidic treatment or very high temperatures can make cellulose amorphous and soluble in

water.

The crystalline structure can vary depending on the organism which created it. For

example, plants create cellulose 1β, which is often mixed with hemicelluloses and lignin

to decrease hydrophobia, while bacteria and algae create cellulose 1α, which is found in

longer strands and has a higher tensile strength. The cellulase enzyme, often also known

as β-1,4 endoglucanase, is actually a group of enzymes. Due to the differing structure of

cellulose, all endoglucanase enzymes breakdown cellulose using slightly different

actions. In the rumen, bacteria, protozoa, and fungi all produces cellulases [58–60, 106,

110, 139, 145, 153].

Hemicelluloses, also called arabinoxylans, are polysaccharides that make up the plant

wall, but are less complex (500-3,000 glucose units) than cellulose, and are found in a

branched, amorphous structure. Hemicelluloses include xylan, arabinoxylan,

glucuronoxylan, glucomannan, and xyloglucan, and as a group are much more abundant

http://en.wikipedia.org/wiki/Arabinoxylan

http://en.wikipedia.org/wiki/Glucuronoxylan

http://en.wikipedia.org/wiki/Glucomannan

19

than cellulose. Hemicellulase enzymes are, likewise, as varied as their substrates and are

also produced by rumen bacteria, protozoa and fungi [141, 145, 154–156].

Starch is another plant polysaccharide comprised of glucose units; however, it is more

easily digested as both microorganisms and animals possess the required enzymes.

Glucose units bound together with α-1,4 glycosidic bonds form the helical sugar amylose,

which is more resistant to digestion due to its shape. Amylopectin, which is also a

polymer of glucose units with α-1,4 glycosidic bonds, has branching which occurs at the

evenly spaced α-1,4 glycosidic bonds. Amylose and amylopectin are the two

components of starches, and while amylopectin will make up at least 70% of the starch,

there will be different proportions of each depending on the type of starch. Amylose, an

enzyme commonly found in saliva and intestines, is able to hydrolyze amylopectin. New

growth plants tend to have higher concentrations of starch, which declines as the season

progresses and the energy from stored starch is used to facilitate plant growth.

Lignin is an important component of plant cell walls, as it is covalently bonded to

hemicellulose, and fills the space in cell walls to provide flexibility and mechanical

strength to the plant. Lignin is hydrophobic, allows for water transport through the plants

vascular system without allowing it to move osmotically across every cell wall.

Although it provides a great deal of carbon, it is of no nutritional value to the ruminant.

Rumen bacteria and fungi, some of which are able to breakdown lignin, are able to

increase the dietary efficiency of the host and the digestibility of the forage [65, 141, 149,

157].

20

The products of these plant polysaccharide-targeting enzymes are smaller molecules of

cellobiose (glucose dimers) or glucose monomers, which are then fermented by the gut

microorganisms. As any free glucose in the rumen is immediately fermented by

microorganisms, the host relies on other products for energy. These microbial by-

products are primarily short-chain or volatile fatty acids (VFAs) like propionic, acetic,

and butyric acids, and to a lesser extent, isobutyrate, valerate, isovalerate, 2-

methylbutyric, hexanoic, and heptanoic acids, which are absorbed across the rumen wall

and can be used as energy by the host [158–160]. For the most part, these VFAs can be

interconverted, and will work towards equilibrium even as new VFAs are introduced into

the rumen.

Fiber digestion in the rumen increases the production of the VFA acetate, which is the

major VFA found in the rumen and accounts for the increasing proportion of acetate

production in the rumen on a higher-forage winter diet [6, 18, 19, 161]. Acetic acid is

used for adenosine triphosphate (ATP) synthesis, body fat synthesis, milk synthesis,

producing acetyl-CoA for lipogenesis, increasing blood flow to the colon, and other

associated health benefits [162].

Sugar, starch, and pectin digestion in the rumen favors the pathway to create the VFA

propionate, which accounts for the higher concentrations of propionate production in

summer as compared to autumn/winter [6, 18, 19]. Propionate is a biologically important

metabolite that is used for gluconeogenesis in the liver, for energy in milk synthesis, it

21

can induce satiety, and has a variety of other health benefits [163, 164]. The production

of propionate reduces the amount of free hydrogen in the rumen, and combined with a

drop in the methanogen-commensal protozoal populations associated with high starch

diets, this leads to a decrease in methanogenesis and total methanogen populations [133,

165]. Thus, a lower acetate to propionate ratio is indicative of increased dietary

efficiency.

Butyrate is an important VFA as it provides energy for the rumen wall itself, as well as

intestinal epithelia. It is also used in fat or milk synthesis, and has been shown to reduce

inflammation and carcinogenesis in the colon [38, 162, 166, 167]. Butyrate production is

increased with a higher fiber diet, and decreased with diets higher in gums [167].

Lactic acid, or lactate, can be used in lieu of glucose as a carbon source by lactose-

fermenting bacteria and yeast [168, 169], and is an intermediate step in the production of

other VFAs, such as propionate [168]. However, when a rapidly-fermentable

carbohydrate, such as some types of starch, is ingested by the ruminant in large

quantities, it can lead to a rapid accumulation of lactic acid, which reduces the pH to a

point where microorganisms are killed and normal rumen fermentation processes are

inhibited [170–173]. Fibrolytic bacteria and protozoa require an environmental pH of

6.3 to 7.0, amylolytic bacteria prefer pH 5.5 to 6.5, and fungi require 6.0 to 7.0.

22

1.4 Probiotics and manipulation of the rumen environment

Probiotics are live microbial cultures which are administered to promote healthy

digestive function and normal microbiota. Probiotics are typically monocultures or

comprised of a few species of bacteria [174–179], although yeast [180], and even fungi

[149] have been used as probiotics in humans and animals. Transfaunation is the process

of transferring rumen contents from one host to another, in contrast to fecal transfer,

which uses material from the intestines or feces as an inoculant and is more common in

monogastrics. In either case, transferring whole contents allows for the transfer of

bacteria, methanogens, protozoa, fungi, and viruses. Prebiotics are chemical or feed

additives which promote a healthy digestive function by improving and supporting the

growth of normal GIT microbiota [163, 181].

Probiotics and transfaunation have long been used to treat GIT dysfunction, especially

after surgery [182–185], but they are also popular as preventative methods to improve

overall health [175, 186–189]. For production animals, probiotics have been

administered to improve meat, milk, or wool output. This has been accomplished by

increasing fibrolysis in the rumen and, thus, dietary efficiency [149, 176–178, 187, 190–

194], or by reducing energy wasted in methanogenesis [82] and GIT dysfunction [183].

Rumen development in young ruminants is linked to colonization of the rumen.

Colonization begins during birth and progresses through to adulthood [195–198], after

which the rumen population may change with diet and health status, but is generally

considered to be stable [199, 200]. Host GIT epithelial cells will also adapt to normal

23

GIT microbiota. Host cells use pattern-recognition receptors to recognize conserved

microbial markers, and in response will activate pro- and anti-inflammatory responses as

well as upregulate epithelial cell signaling [201, 202]. Thus, alteration of the GIT

microbiota is difficult to achieve long-term, especially when the environment is colonized

by well-established and well-adapted microorganisms.

1.5 Summary

It was hypothesized that the moose would host novel microorganisms; that these

microorganisms would be capable of a wide variety of enzymatic functions; and that

these microorganisms could be used to improve other systems. The objectives of this

work were to identify the bacteria, archaea, and protozoa present in the rumen of moose;

to culture bacteria from the rumen of the moose in order to study their biochemical

capabilities; and to apply those cultured bacterial isolates to improve fiber digestion in

neonate lambs.

24

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1.7 Figures

Figure 1-1 Diagram comparing the immature ruminant to the mature ruminant.

Photo courtesy of http://veanavite.com.au/RearingGuide/Calves.aspx

Figure 1-2 The frequency of variability in the bacterial 16S rRNA gene [203].

Figure 1-3 Variable regions of the ciliate protozoal 18S rRNA.

Adapted from Ishaq and Wright, 2014 [122].

43

CHAPTER 2 INSIGHT INTO THE BACTERIAL GUT MICROBIOME OF

THE NORTH AMERICAN MOOSE (ALCES ALCES).

Suzanne L. Ishaq1,*

and André-Denis G. Wright1,2

¹ Department of Animal Science, College of Agriculture and Life Sciences, University of

Vermont, 570 Main St., Burlington, Vermont 05401

² Department of Medicine, University of Vermont, 111 Colchester Ave., Burlington,

Vermont, 05401

* Suzanne Ishaq, Department of Animal Science, University of Vermont, 203 Terrill

Building, 570 Main Street, Burlington VT 05405. [email protected]. Fax) 1-802-656-

8196.

Email addresses:

SLI: [email protected]

ADGW: [email protected]

Keywords: colon/gut microbiome/rumen/Vermont/16S rRNA

Ishaq, S.L. and Wright, A-D.G. (2012). Insight into the bacterial gut microbiome of the

North American moose (Alces alces). BMC Microbiology, 12:212.



44

2.1 Abstract

Background: The work presented here provides the first intensive insight into the

bacterial populations in the digestive tract of the North American moose (Alces alces).

Eight free-range moose on natural pasture were sampled, producing eight rumen samples

and six colon samples. Second generation (G2) PhyloChips were used to determine the

presence of hundreds of operational taxonomic units (OTUs), representing multiple

closely related species/strains (>97% identity), found in the rumen and colon of the

moose.

Results: A total of 789 unique OTUs were used for analysis, which passed the

fluorescence and the positive fraction thresholds. There were 73 OTUs, representing 21

bacterial families, which were found exclusively in the rumen samples: Lachnospiraceae,

Prevotellaceae and several unclassified families, whereas there were 71 OTUs,

representing 22 bacterial families, which were found exclusively in the colon samples:

Clostridiaceae, Enterobacteriaceae and several unclassified families. Overall, there were

164 OTUs that were found in 100% of the samples. The Firmicutes were the most

dominant bacteria phylum in both the rumen and the colon.

Conclusions: Using PhyloTrac and UniFrac computer software, samples

clustered into two distinct groups: rumen and colon, confirming that the rumen and colon

are distinct environments. There was an apparent correlation of age to cluster, which will

be validated by a larger sample size in future studies, but there were no detectable trends

based upon gender.

45

2.2 Background

North American moose, (Alces alces), are the largest browsing ruminant of the

deer family Cervidae, and preferably inhabit young hardwood forests, deciduous mixed

forests, and salt rich wetland habitats that have an abundance of woody browse and salty

aquatic vegetation [1–4]. In northern latitudes, such as Vermont, moose have

traditionally done well, although unregulated hunting and deforested habitats caused a

severe decline in the Vermont population during the 20th

century [5]. It was not until

1993 that moose hunting became regulated again in Vermont and remains strictly

controlled by the state. Vermont provides a wide variety of habitats, with one of the most

suitable regions being in the northeastern corner of the state. Known as the Northeast

Kingdom, the area is rich in bogs and swamps, and is comprised of over 75% deciduous

or mixed forests with growth of various maturities [6]. This area also supports the

highest concentration of moose in the state [6] and traditionally has the highest hunter

success rates: ranging from 38-70% from 2006 to 2009 [7, 8], making it an excellent site

for sample collection.

Like all ruminants, moose have a specialized digestive system with a four

chambered stomach that allows a complex consortium of symbiotic microorganisms to

ferment plant matter that the animal cannot breakdown on its own, especially cellulose

[9, 10]. During the process of fermentation, hydrogen, ammonia, carbon dioxide, and

methane gas are produced [11], as well as volatile fatty acids (VFAs) such as acetate,

butyrate, and propionate. These VFAs are released into the rumen where they can be

absorbed and used by the ruminant as a source of energy [11–13].

46

Limited work has previously been done using classical microbiology to identify

organisms found in the rumen of moose [14]. One male moose from Alaska was shot in

August of 1985, and bacteria which were isolated and characterized consisted of

Streptococcus bovis (21 strains), Butyrivibrio fibrisolvens (9 strains), Lachnospira

multiparus (7 strains), and Selenomonas ruminantium (2 strains) [14].

For the present study, the second generation (G2) PhyloChip (PhyloTech Inc.,

California) was used to survey rumen and colon samples for the presence and

presumptive identification of bacteria. The G2 PhyloChip uses 16S rRNA gene

sequences to rapidly type bacteria and methanogens in a mixed microbial sample without

the use of cloning or sequencing [15, 16]. The PhyloChip contains approximately

500,000 probes on its surface, representing over 8,400 species of bacteria and roughly

300 species of archaea [17]. There are 11, 25mer, probes that are designed to hybridize

to each specific taxon, allowing for specificity in determining taxa present [17].

Depending on what the probes are designed to target, the PhyloChip can be used to

differentiate between different serotypes of Escherichia coli, or determine the presence of

a species regardless of strain. It is already a popular bacterial screening method for air

[15], water [18], and soil [19, 20], and has recently gained favor for digestive tract

samples [21, 22]. Due to their specificity and sensitivity, DNA microarrays have also

been used to categorize diseased and healthy states [22, 23].

The major objectives of the present study were to type the bacteria present in

rumen and colonic samples, and to compare these findings with other studies of

ruminants and herbivores. Given that moose are large browsing herbivores [3], it was

hypothesized that the bacterial populations in the browse-fed wild moose would be more

47

closely related to bacterial populations found in other browse/forage fed animals. This

study reports on the bacteria found in the rumen and colon of the North American moose,

as well as how these environments relate to other studies of the gut microbiome in

various species.

2.3 Results

2.3.1 Quantitative Real-Time PCR

Mean bacteria cell densities were calculated for each rumen sample using

standard curves generated by Bio-Rad’s CFX96 software. Based on a regression line

created using the bacterial standards (R2= 0.997), estimated cell density ranged from

8.46 x 1011

to 2.77 x 1012

copies of 16S rRNA/g in the rumen (Table 1).

2.3.2 PhyloChip Array

1.1.2.2.Combined rumen and colon

A total of 789 unique OTUs were used for analysis which passed the fluorescence

and the positive fraction thresholds. Total numbers for each taxonomic group found are

listed for each sample (Table 2), which represent raw data before initial screening. There

were 789 total distinct OTUs that were found in all the samples combined; 267

Firmicutes, 225 Proteobacteria, and 72 Bacteroidetes being the major phyla. Not all

OTUs were found in every sample, but out the total 789 OTUs there were 164 OTUs,

comprising 25 bacterial families, which were found across all 14 samples (Figure 1). The

most abundant of these families were unclassified, 25%; Lachnospiraceae, 20%;

Clostridiaceae, 16% and Peptostreptococcaceae, 7%. The remaining 21 families

48

represented less than 4% each of the OTUs found in all 14 samples (Figure 1). The

OTUs with unclassified families were then classified by phyla; of the 25% of OTUs with

unclassified families, the phyla Firmicutes represented 22%, Proteobacteria and

Chloroflexi were 17% each, Bacteroidetes was 15%, and all others represented 5% or less

(Figure 2a).

Many of the unclassified sequences were presumptively identified in PhyloTrac,

as well as in GenBank, based upon the environment where they were found as most of

them are uncultured, thereby providing an interesting, if subjective, means of

comparison. Unclassified sequences in the moose were related to a range of

environmental sequences including 102 “termite gut clone” OTUs, 20 “rumen clone”

OTUs, 20 “forest soil/wetland clone” OTUs, 16 “swine intestine/fecal clone” OTUs, six

“human colonic clone” OTUs, six “sludge clone” OTUs, four “penguin dropping clone”

OTUs, four “chicken gut clone” OTUs, two “human mouth clone” OTUs and a large

number of “soil clone” and “water clone” OTUs from various environments. While

many of the forest soil/wetland, soil and water clones may represent transient populations

that are picked up from the environment, these data correlate with summer diets of moose

in Vermont, namely woody browse in forested areas and aquatic plants found in bogs and

marshes.

1.1.2.3. Rumen Samples

The rumen samples contained 575 total OTUs; 192 Firmicutes, 142

Proteobacteria, and 66 Bacteroidetes being the dominant phyla. In the rumen samples,

there was a range of 308 to 465 OTUs/sample, and an average of 350 OTUs/sample

49

(Table 2). There were 237 OTUs found across all eight rumen samples and, of these, 73

OTUs were exclusive to the rumen, representing 21 families (Figure 3). The OTUs with

unclassified families were assigned by phyla (Figure 2b), with the dominant phyla being

Bacteroidetes, 27%; Proteobacteria, 19%; and Chloroflexi and NC10 with 11% each.

NC10 is a candidate phylum consisting of uncultivated and uncharacterized bacteria that

is currently named after the location where the bacteria were sampled, Nullarbor Caves,

Australia. All other phyla represented 10% or less of OTUs with unclassified families

(Figure 2b). Of the unclassified sequences found exclusively in the rumen, there were 51

termite gut clones, 36 marine, wetland, or waterway sediment clones, 13 fecal or colon

clones, 11 rumen clones, nine soil clones, and seven sludge clones.

A previous study on rumen microorganisms in the moose [14] identified

Streptococcus bovis (21 strains), Butyrivibrio fibrisolvens (9 strains), Lachnospira

multiparus (7 strains), and Selenomonas ruminantium (2 strains). The present study

found Streptococcus bovis strains ATCC 43143 and B315 in every sample except for 1C

and 2R. Butyrivibrio fibrisolvens and B. fibrisolvens strain LP1265 were found in all

samples except for 3R, 6R, 2C and 3C, whereas Butyrivibrio fibrisolvens strain WV1 was

found in 8C only. Lachnospira multiparus was not present on the chip. However, all 14

samples did contain Lachnospira pectinoschiza, as well as Selenomonas ruminantium

strains S20 and JCM6582.

1.1.2.4. Colon samples

The colon samples contained a total of 658 OTUs; 248 Firmicutes, 194

Proteobacteria and 46 Bacteroidetes. The colon samples ranged from 307 to 597

50

OTUs/sample, with an average of 413 OTUs/sample (Table 2). There were 235 OTUs

that were found across all six colon samples, and of these, 71 OTUs were exclusive to the

colon, representing 22 families (Figure 3). Again, the OTUs with unclassified families

were assigned by phyla (Figure 2c), with the dominant phyla being Firmicutes,

Proteobacteria and Unclassified, 16% each; Gemmatimonadetes and Chloroflexi, 11%

each, and Bacteroidetes, 10%. All other phyla represented 10% or less of OTUs with

unclassified families (Figure 2c). Again, many unidentified sequences were listed as

uncultured clones by location found. The unidentified sequences found exclusively in the

colon were related to52 “termite gut clone” OTUs, 20 “marine, wetland, or waterway

sediment clone” OTUs, 10 “soil clone” OTUs, eight “fecal/colon clone” OTUs, eight

“sludge clone” OTUs and five “rumen clone” OTUs.

2.3.3 UniFrac analysis

P-test significance was run using all 14 samples together and 100 permutations,

resulting in a corrected p-value of < 0.01, designating that each sample was significantly

different from each other. Environment clusters and jackknife values are provided

(Figure 4), showing a statistical measurement of the correctness of the tree created. The

weighted algorithm accounted for the relative abundance of sequences in a sample, which

is typical for environmental samples. UniFrac and PhyloTrac both clustered the rumen

and colon samples into two distinct groups: the first node was present 100% of the time

in the unweighted and weighted UniFrac clusters. The branching pattern for the rumen

group is different between UniFrac algorithm (Figure 4) and between programs (Figure

5). However, the branching pattern for the colon group is identical between PhyloTrac,

51

and the unweighted and weighted UniFrac outputs. A principal component analysis

(PCA) scatterplot (Figure 5) was also created using the weighted algorithm, which

grouped the rumen and colon samples separately.

The rumen samples also tentatively clustered by age/weight in the unweighted

UniFrac output (Figure 4a), with the youngest/lightest two grouped together (185 kg., 1-

yr old; 186.36 kg, 2-yrs old), the two 3-yr old females, grouped together (244.55 and

259.55 kg), and the three oldest/heaviest males (301.36 kg, 4-yrs old; 319.09 kg, 4-yrs

old; and 405.45 kg, 8-yrs old) grouped together with a male of unspecified age/weight.

The age/weight clusters within the rumen in the weighted UniFrac output (Figure 4b)

were not the same as with the unweighted output, nevertheless, some clusters remained

(c.f. Figures 5a and 5b).

2.4 Discussion

The major objective of this study was to identify bacteria present in the rumen and

colon content samples of the North American moose. This is the first time that the rumen

and colon bacterial populations of the moose have been evaluated on a large scale (i.e.

PhyloChip), with the last work published in 1986 [14]. While Dehority’s [14] results

give the present study an indication of the bacterial population within the rumen of

moose, the findings were limited by a sample size of one animal and the constraints of

classical microbiology. Anaerobic gut microorganisms are difficult to culture, which

continues to present a major obstacle in gut microbial identification. However, genetic

analysis, such as microarray and high-throughput sequencing, allow microbes to be

studied before they are grown in a pure culture.

52

One drawback of using the PhyloChip, and indeed with all methods that forego

culturing, is the inability to distinguish between live and dead microbes. It also cannot

distinguish between colonizing versus transient species, such as the green sulfur bacteria

in the phylum Chlorobi or green non-sulfur bacteria of Chloroflexi, both of which are

photosynthetic and picked up by the moose during feeding. Careful analysis of the data

is required to properly interpret the results. However, even dead and transient bacterial

populations can have a profound impact on the resident bacteria as well as the host,

whether by releasing harmful components when lysed, such as Lipid A, or providing

DNA which may be taken up by live cells in the rumen, as in plasmids that contain genes

that confer antibiotic resistance. Is important to take a holistic view to prevent

marginalizing potentially important species. Like all methods that rely on PCR

amplification, PhyloChip is also subject to PCR bias. This is mediated during sample

preparation by running multiple reactions per sample and minimizing the number of

cycles.

Rumen samples were consistently clustered separately from the colon samples by

PhyloTrac and UniFrac and there were 174 OTUs that were exclusive to either the rumen

or the colon; confirming that the rumen and the colon are two distinct environments.

Similar findings were reported in a study using fecal samples from sheep [24], as a non-

invasive means of modeling the rumen bacteria from captive exotic animals where it is

impractical to obtain rumen contents. It was concluded that bacterial concentrations and

species in the colon were not reliably predictive of the bacterial concentrations or species

in the rumen [24].

53

The rumen contained an average of 1.66 x 1012

copies of 16S rRNA/g (± 7.27 x

1011

SEM). This is comparable to other ruminants: 5.17 x 1011

cells/g (± 3.49 x 1011

) for

Norwegian reindeer [25], 1.86 x 1011

cells/g (± 9.68 x 1010

) and 5.38 x 1011

cells/g (±

2.62 x 1011

) for Svalbard reindeer [26] in April and October, respectively, and 1.60 x 1011

cells/g (± 1.35 x 1011

) for Canadian dairy cattle [27].

The dominant phylum in the moose rumen was Firmicutes with 192 OTUs,

followed by Proteobacteria with 142 OTUs and Bacteroidetes with 66 OTUs. Firmicutes

is often the dominant phylum in gut microbiomes, and many of those found in the moose

were of the class Clostridia, containing sulfate-reducing bacteria (SRB), which can be

pathogenic, endospore forming, and found in soil. Sundset et al. [28] reported that in

rumen samples taken from reindeer in Svalbard, the bacteria cultivated were mainly from

the class Clostridia. It was noted that Fibrobacter succinogenes, Ruminococcus albus,

and R. flavefaciens were not found in the rumen of the reindeer [28], although this may

simply be a bias of the cultivation approach. Fibrobacter and Ruminococcus are both

cellulolytic and have previously been found in the rumen of reindeer [25, 29]. However,

in the present study, F. succinogenes and R. albus were not found, despite both species

being present on the chip with multiple strains. Ruminococcus flavefaciens was detected

in several samples, but only a few of its 11 probes matched, making the result

insignificant. Ruminococcus obeum was detected in the present study.

In a recent paper studying rumen bacteria in dairy cattle, Firmicutes was the

dominant phylum in four cattle rumen samples when using full length 16S rRNA clone

libraries, but was only dominant in three samples with Proteobacteria being dominant in

one sample when using partial 16S rRNA clone libraries or environmental gene tags [30].

54

Gamma- and alpha-Proteobacteria have been shown to be type I and type II

methanotrophs, respectively, meaning they utilize methane as their source of carbon. In

the present study, the species Enterobacter cloacae, of the class gamma-Proteobacteria,

was found in the moose, and in a non-lactating Holstein cow based on PCR of the 16S

rRNA gene to target methanotrophs [31].

In a comparison between the moose rumen data and a study using the PhyloChip

and samples from the crop of the wild folivorous bird, the hoatzin [21], similarities arise.

Godoy-Vitorino et al. [17] showed that bacteria from the crop of the hoatzin clustered

into distinct groups by age: chicks (n=3), juveniles (n=3) and adults (n=3). This

correlates with the present study, as the rumen samples clustered by age/weight in the

unweighted, and to some extent, in the weighted UniFrac jackknife clustering. As in the

moose, some of the differential families found in the crop of the adult hoatzin included

Lachnospiraceae, Acidobacteriaceae, Peptostreptococcaceae, Helicobacteraceae and

Unclassified (phyla: Proteobacteria, Cyanobacteria, NC10, Chloroflexi, etc.) [17]. The

total number of taxonomic groups discovered for hoatzin chicks, juveniles and adults

ranged from 37-40 phyla, 47-49 classes, 88-90 orders, 147-152 families, 305-313

subfamilies, and 1351 to 1521 OTUs, an increase over moose, which possibly arises from

grouping three samples onto one chip, as was done with the hoatzin samples [21].

In the study by Godoy-Vitorino et al. [21], as well as the current study, OTU

cutoff level was predetermined by the PhyloTrac program (i.e. <97%). However, Godoy-

Vitorino et al. [17] used a pf=0.90 to determine if an OTU was present, meaning that

90% of the probes for that OTU were positive. When a pf value of 0.90 was applied to

the current study, effectively lowering the number of probes that needed to be positive to

55

be a match for that OTU, the average number of OTUs present rose from 350 to 488 for

the rumen and from 413 to 524 for the colon. This suggests that moose either have only a

relatively few bacterial species in large quantities, or that there is a wide variety of

bacteria found in the moose which are unique and unable to hybridize to the probes found

on the G2 PhyloChip. The PhyloChip has recently been shown to overestimate species

diversity [32]. The major drawback to using DNA microarray chips is that only known

sequences can be used as probes, thus rendering the chips ineffective for discovering and

typing new species [33]. The G2 PhyloChip was created in 2006, thus any new taxa that

have been identified since then will not be present on the chip, and any re-classification

of sequences that are currently on the chip can only be noted by using the most current

version of PhyloTrac. These data will be validated and expanded upon using high-

throughput DNA sequencing and cultures.

Despite the many similarities between bacteria found in the rumen of the moose

to the hoatzin, reindeer and the previous moose study, there are many bacterial families

found in the present study which were not mentioned in any of the previous studies.

However, many of these bacterial families have been noted in the foregut of the

dromedary camel, a pseudo-ruminant with a three chambered stomach. In a recent study

by Samsudin et al. [34], the following bacterial families were found in the foregut

dromedary camels (n=12) as well as the rumen of the moose in the present study (though

not in every rumen sample): Eubacteriaceae, Clostridiaceae, Prevotellaceae,

Lachnospiraceae, Rikenellaceae, Flexibacteraceae, Bacteroidaceae, Erysipelotrichaceae,

Bacillaceae, Peptococcoceae, and Peptostreptococcaceae. Wild dromedary camels in

Australia survive on a high fiber forage diet [34], which is closer to the diet of wild North

56

American moose. This may explain why the bacterial populations in wild camels appear

to be closer to moose than that of wild reindeer, which eat a diet rich in lichens, despite

the reindeer and the moose being members of the Cervidae family.

In the rumen, there were 51 sequences found that were listed as being related to

termite gut clones, yet many more similarities can be found between the moose and the

termite gut, which have compartmentalized guts containing microbes. Treponema

primitia strain ZAS-1, as well as five other Treponema species, were found in the moose

rumen in the present study, and 109 Treponema phylotypes and species were previously

found in the termite gut [35]. Treponema primitia, belonging to the phylum Spirochetes,

is an acetogenic microorganism capable of degrading mono- and disaccharides such as

cellulose or xylan [35]. Bacteroidetes, Chlorobi, Cyanobacteria, Firmicutes and

Proteobacteria clones were also discovered in the termite [35], as well 49 phylotypes

which represented three new candidate orders in the phylum Fibrobacteres.

To our knowledge, no studies exist using PhyloChip analysis on the fecal samples of

herbivores. However, many other colon studies exist, focusing on medically significant

pathogens in humans. In a recent study on irritable bowel syndrome, the bacterial

families in healthy rats were Rhizobiaceae, Peptococcaceae/Acidaminocoocus,

Clostridiaceae, Lachnospiraceae, Intrasporangiaceae, Succinivibrionaceae,

Alteromonadaceae, Paenibacillaceae and Flavobacteriaceae [36]. Of these, only

Peptococcaceae/Acidaminocoocus, Clostridiaceae and Lachnospiraceae were found in the

moose. In a separate study, fecal samples from cervid species in Norway were tested for

colon bacteria that were known pathogens to humans using selective culturing techniques

[37]. In that study, E. coli O103 was found in 41% of the samples, E. coli O26 and O145

57

were found in small amounts, and E. coli O111 and O157 were not found at all [37]. In

addition, no cervid fecal samples were positive for Salmonella, although one roe deer

(Capreolus capreolus) sample was positive for Campylobacter jejuni jejuni [37]. In the

present study, several samples contained Salmonella, E. coli, or Campylobacter species,

although no strains of verocytotoxic (e.g. O157:H7) or uropathogenic (e.g. CFT073) E.

coli, Shigella or Campylobacter jejuni were found. However, all of the moose colon

samples contained Citrobacter freundii, a nitrate reducing bacteria commonly found in

the environment, which is known to be an opportunistic pathogen in humans.

The moose colon contained 658 OTUs, of which 248 were Firmicutes and 46

Bacteroidetes. In a 2006 study of the mouse gut microbiome in lean and ob/ob obese

mice, it was discovered that transfaunation with microorganisms from the obese mouse

intestine into the lean mice caused increased weight gain and fat deposition [38]. It is

important to note that the bacteria in the obese mice had significantly higher proportions

of Firmicutes than Bacteroidetes [38].

The work presented here provides the first insight into the bacterial populations in

the digestive tract of the North American moose. While the G2 PhyloChip is an excellent

tool for identifying known bacteria, it contains only 300 archaeal sequences, which were

not utilized because bacterial-specific primers were used. Furthermore, there is currently

no microarray that is designed to identify protozoa or fungi. Next generation (high-

throughput) sequencing is needed to validate the bacterial population findings of the

present study, as well as identify the protozoal, archaeal and fungal populations present in

the moose rumen. The PhyloChip, like all methods that do not rely on culturing, cannot

be used to differentiate between transient and colonizing species. It can be assumed that

58

some species found in the moose are simply passing through the digestive tract, having

been picked up from the environment, and are not colonizing the tract. Despite this, these

transient bacteria may still have an impact on the dynamics within the rumen, and it is

important to take a holistic approach when looking at mixed environmental samples. It is

also possible that some of these unclassified bacteria which are presumed transient, such

as the soil or water clones, are actually colonizing the moose digestive tract and are

simply unique to moose.

2.5 Materials and Methods

2.5.1 Sample Collection

All samples were obtained with permission of licensed hunters through the

Vermont Department of Fish and Wildlife. Whole rumen (R) and colon (C) contents

were collected from moose shot during the October 2010 moose hunting season in

Vermont. Samples were collected by hunters within 2 h, if not sooner, of death and put

on ice immediately. Hunters were given a written set of instructions about sample

collection, and had been instructed verbally as well, to fill the collection containers with

material taken from well inside the rumen and colon, and to seal the container quickly to

minimize overexposure to oxygen. Samples were then transferred to the laboratory

within 24 h, and stored at -20ºC until DNA extraction. A total of eight rumen and six

colon samples (Table 3) were collected from eight moose. Twelve of the samples were

paired rumen and colon contents from the same animal, and two rumen samples did not

have corresponding colon samples. Moose were weighed and aged, by examining the

59

wear and replacement of the premolars and molars of the lower jar, by Vermont Fish and

Wildlife biologists at the mandatory reporting stations.

2.5.2 DNA extraction

Samples were fully thawed, and 0.25 gram aliquots of either rumen content or

colonic material, were used for extraction. DNA was extracted from all 14 samples using

the repeated bead-beating plus column (RBB+C) method [39], and the QIAamp DNA

Stool Mini Kit (QIAGEN, Germantown, Maryland). DNA was quantified using a

NanoDrop 2000C Spectrophotometer (ThermoScientific, California), and the purity of

the DNA extract was verified using gel electrophoresis to molecular weight. DNA

extract was also PCR amplified to test quality and verified using gel electrophoresis to

determine correct PCR amplicon length prior to quantitative real-time PCR, or

hybridization to the PhyloChip.

2.5.3 Quantitative Real-Time PCR

Real-time PCR was used to calculate bacterial concentrations in each sample, and

was performed using a CFX96 thermocycler (Bio-Rad, Hercules, CA), using universal

bacterial primers 1114-F (5’-CGGCAACGAGCGCAACCC-3’) and 1275-R (5’-

CCATTGTAGCACGTGTGTAGCC-3’) [40]. Each reaction contained 12.5μL of the iQ

SYBR Green Supermix kit (Bio-Rad, Hercules, CA): 2.5µl of each primer (40mM),

6.5µL of ddH2O, and 1µL of the initial DNA extract which was diluted to approximately

10 ng/μL. The external standard for bacteria, as previously described [40], was a mix of

Ruminococcus flavefaciens and Fibrobacter succinogenes that were serially diluted over

60

four logs. The protocol consisted of an initial denaturing at 95°C for 15 min, then 40

cycles of 95°C for 30s, 60°C for 30s, 72° for 1 min. This was followed by a melt curve,

with a temperature increase 0.5°C every 10s from 65ºC up to 95°C to check for

contamination. Data were analyzed using the CFX Manager Software v1.6 (Bio-Rad,

Hercules, CA).

2.5.4 PhyloChip

DNA (25-50 ng/μl) was sent to the University of Vermont’s Microarray Core

Facility for genotyping using the G2 PhyloChip (PhyloTech Inc., San Francisco, CA).

There, the 16S rRNA gene of bacteria was PCR amplified using the universal bacterial

primers 27F (5’-AGAGTTTGATCCTGGCTCAG-3’) and 1492R (5’-

CTACGGCTACCTTGTTACGA-3’) [41], quantified, fragmented, labeled with biotin,

and hybridized according to manufacturer’s proprietary instructions. Each amplified

sample was hybridized to its own chip, creating 14 total data sets. The analysis platform

used was an Affymetrix 7G scanner, and Gene Chip Operating System (GCOS). Data

generated is available online at ARRAYExpress.

2.5.5 Analysis

PhyloChip data were analyzed using the software program PhyloTrac v2.0

(available from www.phylotrac.org). PhyloTrac automatically removed background

noise as the average of the two least intense fluorescence signals in each chip quadrant,

and used internal standards to create a linear scale to normalize fluorescence intensity

with concentration of that sequence in the original sample [17]. The 16S rRNA

61

sequences on the chip were grouped into Operational Taxonomic Units (OTUs) based on

a 97% or greater sequence identity, which was predetermined by the program. For each

OTU, there are 11 perfect-match probes, and 11 mismatch probes, which are always

analyzed in pairs. For an OTU to be considered a positive match to a probe, the signal

intensity must be 1.3X the intensity of the mismatch probe [13]. The positive fraction is

a measure of how many perfect-match probes matched out of the total number of probe

pairs for that OTU. For this study, a positive fraction of 0.92 was used to determine the

presence of an OTU in a sample; for each OTU, 92% of the perfect-match probes were

positive. A mean intensity threshold of 100 was used, so that only OTUs with signal

intensity greater than that were included in the analysis. All 14 sample files were used in

the comparison.

Data were evaluated down to the taxonomic level of family for most analyses

since each OTU represented more than one species [32]. A heatmap (Figure 6) showing

the presence or absence, and relative intensity of each OTU was created using all 14

samples. Samples were arranged in rows and were clustered on the vertical axis. OTUs

were arranged vertically and were clustered on the horizontal axis. Clustering was done

using PhyloTrac’s heatmap option with Pearson correlation, a measure of the correlation

between two variables, and complete linkage algorithms (farthest neighbor), which

clusters based on the maximum distance between two variables.

UniFrac (available from http:// bmf2.colorado.edu/unifrac/), an online statistical

program, was used to analyze PhyloChip data [42, 43] and to confirm the clustering

functions of PhyloTrac. Data were exported from PhyloTrac for analysis using the

UniFrac statistical software. P-test significance was run using all 14 environments

62

together and 100 permutations, to determine whether each sample was significantly

different from each other. A p-value of < 0.05 states that the environments were

significantly clustered together. Two Jackknife environment clusters were performed

using 100 permutations, the weighted and unweighted UniFrac algorithms, and 307

minimum sequences to keep (UniFrac default for the specified conditions). Jackknife

counts were provided for each node, representing the number of times out of 100 that a

node was present on the tree when the tree was repeatedly rebuilt. A Jackknife

percentage of >50% is considered significant. A principal component analysis (PCA)

scatterplot was also created using the weighted algorithm, a chart which arranged two

potentially related variables into unrelated variables on a graph, revealing underlying

variance within the data.

2.6 Competing Interests

The authors declare no competing interests.

2.7 Authors’ Contributions

SI carried out all DNA extraction, PCR, PhyloTrac and Unifrac analysis, and drafted the

manuscript. AW conceived of the study and participated in its design, and edited the

manuscript. Both authors approved the final manuscript.

2.8 Acknowledgements

The author would like to acknowledge Rachel P. Smith and Dr. Benoit St-Pierre,

Department of Animal Science, University of Vermont, for technical advice; the Vermont

63

Fish and Wildlife Department for their help in sample collection logistics; and Terry

Clifford, Archie Foster, Lenny Gerardi, Ralph Loomis, Beth and John Mayer, and Rob

Whitcomb for collection of samples.

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2.10 Figures

Figure 2-1 The OTUs found common in all samples (rumen and colon).

164 OTUs found common to all samples (n=14). The Unclassified sections are broken down by phyla in

Figure 2a.

68

Figure 2-2 Breakdown of unclassified families by phylum.

(a) OTUs present in all 14 samples. There were 41 OTUs found exclusively in the rumen that were not

classified down to the family level. (b) OTUs found exclusively in the rumen. There were 22 OTUs found

exclusively in the rumen that were not classified down to the family level. (c) OTUs found exclusively in

the colon. There were 19 OTUs found exclusively in the colon that were not classified down to the family

level. Several are candidate phyla and are named by where they were discovered: AD3, soil in Virginia

and Delaware, USA; OP3 and OP10, now Armatimonadetes, Obsidian Pool hot spring in Yellowstone

National Park, USA; NC10, Null Arbor Caves, Australia; TM7, a peat bog in Gifhorn, Germany; WS3, a

contaminated aquifer on Wurtsmith Air Force Base in Michigan, USA.

http://en.wikipedia.org/wiki/Gifhorn

69

Figure 2-3 A comparison of the OTUs exclusive to the rumen or the colon.

A comparison of the 73 OTUs exclusive in the rumen (n=8) or 71 OTUs exclusive in the colon (n=6), by

family. Families with three or more associated OTUs are labeled in the chart; all other families with two or

fewer OTUs are labeled via the legend. The Unclassified sections are broken down by phyla in Figure 2b,

and 2c, respectively.

70

Figure 2-4 Jackknife environment clustering in UniFrac, by sample.

(a) An unweighted UniFrac algorithm and (b) a weighted UniFrac algorithm were used, and were not

normalized as different evolutionary rates of gene did not need to be accounted for. Jackknife counts for

each are provided for each node. The weighted UniFrac algorithm takes into account abundance of

sequences, and is better suited to analysis of mixed bacterial samples. Samples are labeled by individual

moose (1-8) and sample type (rumen, R or colon, C), and gender, weight and age information is provided in

the legend.

71

Figure 2-5 Principal component analysis (PCA) scatterplot of the environments using the weighted

UniFrac algorithm.

Samples are labeled by number (1-8), and groups are shown.

72

Figure 2-6 Distribution of PhyloChip OTU’s for all 14 samples.

Samples (rumen and colon) are arranged in rows and are clustered on the vertical axis (y-axis). OTU’s are

arranged vertically and are on the horizontal axis (x-axis). Clustering was done for each using PhyloTrac’s

heatmap option with Pearson correlations and complete linkage algorithms.

73

2.11 Tables

Table 2-1 Estimated densities (16S rRNA copy numbers per gram wet weight) of bacteria in the

rumen (R) of the moose in October, 2010, Vermont.

All figures based on calculations using standard curves generated by the Bio-Rad CFX manager program:

bacteria (R² = 0.997).

Sample Bacterial copies of 16S rRNA/g (SEM)

1R 8.46 x 1011

2R 1.61 x 1012

3R 2.57 x 1012

4R 2.02 x 1012

5R 9.36 x 1011

6R 1.21 x 1012

7R 2.77 x 1012

8R 1.34 x 1012

Mean (SEM) 1.66 x 1012

(7.27 x 1011

)

74

Table 2-2 Total number of taxa found in each sample, before screening for analysis but after

background noise was removed and including only OTUs with > 0.92 positive fraction.

Not all OTUs were found in every sample.

Sample Phylum Class Order Family Sub-family OTU

1R 20 42 59 83 94 367

2R 21 43 63 90 103 395

3R 19 38 51 75 83 308

4R 23 44 58 80 94 374

5R 23 46 67 97 109 465

6R 23 43 56 84 97 382

7R 22 43 57 86 100 379

8R 23 45 69 98 116 432

Mean rumen 22 43 60 87 100 350

1C 16 33 45 63 72 331

2C 18 36 54 78 90 378

3C 15 30 40 54 65 307

6C 17 34 50 72 84 374

7C 26 49 82 124 146 597

8C 21 42 66 98 115 488

Mean colon 19 37 51 82 95 413

Table 2-3 Statistics for samples taken from moose shot in October 2010 in Vermont during the moose

hunting season.

Moose Sample

location

Sample

name

Gender Weight,

dressed carcass (kg)

Approx.

age (yr)

1 Rumen 1R

F 185 1 Colon 1C

2 Rumen 2R

F 244.55 3 Colon 2C

3 Rumen 3R

M 186.36 2 Colon 3C

4 Rumen 4R M N/A N/A

5 Rumen 5R M 319.09 4

6 Rumen 6R

F 259.55 3 Colon 6C

7 Rumen 7R

M 301.36 4 Colon 7C

8 Rumen 8R

M 405.45 8 Colon 8C

75

2.12 Supplemental Material

Supplementary Table 1 Genus/Identifier and GenBank # of sequences in selected

families, found in all rumen samples (n=8), sequences are non-exclusive to the rumen.

Rumen

Genus or Identifier GenBank ASCN #

Clostridiaceae

Clostridium X71852.1, AB093546.1

cow rumen clone AY244908.1

Great Artesian Basin clone AF407695.1

Forested wetland clone AF523923

Lachnospiraceae AF550610.1

Oral clone AF481208.1

sludge clone AF482434.1

Swine intestine clone AF371790.1, AF371796.1

Termite gut clone

AB100478.1, AB100483.1, AB100493.1, AB100479.1,

AB100476.1, AB100486.1, AB089030.1, AB089043.1,

AB089045.1, AB088977.1, AB089028.1, AB088965.1,

AB088951.1, AB089035.1, AB088984.1, AB089032.1

Lachnospiraceae

Butyrivibrio U41168.1, X89978.1, AF105403.1, AY178635.1

chicken clone AF376201.1, AF376218.1

Clostridium AF067965.1, AY169415.1

Eubacterium AB008552.1

Fusobacterium X85022.1

Granular sludge clone AF332711.1, AF332720.1, AF332721.1

human colonic clone AJ408957.1, AJ408972.1, AJ408993.1, AJ408989.1

Lachnospira AY169414.1

Pseudobutyrivibrio AF202260.1

Swine intestine clone AF371584.1, AF371541.1, AF371648.1

Rumen clone AB034003.1, AB034059.1

Termite gut clone

AB100463.1, AB089002.1, AB088983.2, AB088950.1,

B088993.1, AB088990.1, AB088989.1, AB088998.1,

AB088994.1, B089044.1, AB088968.1, AB088980.1,

AB088952.1, AB089040.1, B088991.1, AB089034.1

Peptostreptococcaceae

Anaerococcus AF542229.1

Finegoldia AB109771.1

municipal wastewater

treatment plant clone CR933145.1

76

oral clone AF538856.1, Y134903.1, AY134904.1

Peptostreptococcus AF481225.1

TCE-dechlorinating clone AY217429.1

termite gut clone

AB100461.1, AB062845.1, AB088986.1, AB088954.2,

AB088971.1, AB088960.2, AB088970.1

Prevotellaceae

Cow rumen clone AY244919.1, AY244931.1, AY244923.1, AY244900.1

Deep marine sediment clone AY093462.1

Prevotella AY699286.1, AY134905.1, AF481227.1

Rumen clone AB185583.1

Swine intestine clone AF371893.1

Unclassified

Arthrobacter X80744.1

Bacillus AF454298.1

Beta proteobacterium,

uncultured AY082479

chicken cecum clone,

uncultured AF376430

Chlorobi, uncultured AY118151.1

coal effluent wetland clone AF523883.1, AF523884.1

cow rumen clone AY244902.1

Cryptoendolith AY250886.1

DCP-dechlorinating clone AJ306749.1, AJ306746.2, AJ306774.1, AJ306741.2,

AJ306793.1

deep marine sediment clone AY093480.1

Delaware River estuary

clone AY274839.1

Desulfotomaculum AY084078.1, Y11573.1, Y11574.1

Elbe river clone AJ401113.1

Feedlot manure, uncultured AF317384.1

Flexistipes AF481216.1

forest soil clone AY913277.1

forested wetland clone AF523973.1, AF524015.1

G+C Gram-positive clone AF465653.1

Holophaga AJ519640

human mouth clone AY207065.1, AY134895.1

hydrothermal vent sediment

clone AF420340.1

marine sediment above

hydrate ridge clone AJ535231.1

Mono Lake clone AF507879.2, AF507860.1, AF507869.2, AF507859.1,

AF507862.1, AF507892.1

77

ocean soil, uncultured AY181050.1

Paralvinella AJ441239.1, AJ441217.1

penguin droppings sediments

clone AY218551.1

Pleurotus AY838556.1

Rocky Mountain alpine soil

clone AY192275.1 AY192273.1

rumen clone AB185532.1

sludge clone AB106352.1, AF234699.1, AF368184.1

soil clone AJ390463.1

sponge clone AJ347025.1, AJ347055.1

temperate estuarine mud

clone AY216458.1

termite gut homogenate

clone

AB088930.1, AB089097.1, AB089109.1, AB089122.1,

AB089008.1

Toolik Lake clone AF534435.1

trichloroethene-

contaminated site clone

AF529128.1

uranium mill tailings waste

clone

AJ519669.1, AJ519650.1, AJ296549.1, AJ518784.1,

AJ519396.1, AJ536867.1

vadose (subterranean water)

clone AY177763.1

vent worm enrichment clone AJ431235.1

78

Supplementary Table 2 Genus/Identifier and GenBank # of sequences in selected

families, found in all colon samples (n=6), sequences are non-exclusive to the colon.

Colon

Genus or Identifier GenBank ASCN #

Clostridiaceae

Chicken intestine clone,

uncultured AF429381.1

Chlorobenzene-degrading

clone AJ488075.1

Clostridium AY007244.1, X76750.1, X71852.1, AB093546.1

Cow rumen clone AY244908.1

Clostridiales oral clone AF481208

Equine intestine clone AJ408137.1

Forested wetland clone,

uncultured AF523923

Granular sludge clone AY261814.1, AF482434.1

Great Artesian Basin clone AF407695.1

Lachnospiraceae AF550610.1


Termite gut clone

AB100493.1, AB100478.1, AB088977.1, AB100475.1,

AB100479.1, AB089043.1, AB089028.1, AB100486.1,

AB088951.1, AB088965.1, AB089045.1, AB089032.1,

AB100469.1, AB100483.1, B089035.1,AB089033.1,

AB088984.1, AB100476.1, AB089030.1

Enterobacteriaceae

Alterococcus AF075271.2

Citrobacter AF025365.1

Coal effluent wetland clone AF523903.1

Enterobacter AJ550468.1

Erwinia AF141891.1, AF373202.1

Escherichia NC_000913.2

Klebsiella X93216.1

Kluyvera AJ627202.1

Opitutus AY695840.1

Pantoea AF130912.1, AF373198.1

Raoultella AF181574.1

Salmonella U92194.1

Serratia AF124036.1

Soda lake clone, uncultured AF507000.1

White-tail deer rumen clone AF084835.1

Lachnospiraceae

79

Butyrivibrio U41168.1

Clostridium * AY169415.1

Chicken clone, uncultured AF376205.1, AF376218.1, AF376201.1

Fusobacterium X85022.1

granular sludge clone AF332721.1, AF332720.1, AF332711.1

human colonic clone AJ408972.1, AJ408989.1

Lachnospira AY169414.1

Roseburia AY804149.1

Rumen clone AB034059.1, AB034003.1

Ruminantium AB008552.1


Termite gut homogenate

clone

AB088950.1, AB089040.1, AB088950.1, AB088990.1,

AB088998.1, AB088993.1, AB100463.1, AB088994.1,

AB089002.1, AB089000.1, AB089044.1, AB088952.1,

AB089036.1, AB089034.1, AB088983.2, AB088980.1,

AB088968.1, AB088991.1

Peptostreptococcaceae

Anaerococcus AF542229.1

Finegoldia AB109771.1

municipal wastewater

treatment plant clone CR933145.1

Oral clone AY134904.1

Peptostreptococcus AF481225.1

Swine manure clone AY167963.1

TCE-dechlorinating clone AY217429.1

Termite gut clone AB088971.1, AB062845.1, AB088954.2, AB088986.1,

AB088970.1

Unclassified

Anaerobic bioreactor clone AJ278169.1

Antarctic sediment clone AY250886.1, AY133397.1, AY177804.1

Arthrobacter X80744.1

Bacillus AF454298.1

Bacteroidetes, uncultured AF449785.1

Beta proteobacterium,

uncultured AY082479

Brackish mud clone,


Chicken cecum, uncultured AF376430

Chlorobi clone, uncultured AY118151.1

cow rumen clone AY244902.1, AB185532.1

DCP-dechlorinating clone AJ306793.1, AJ306749.1

Delaware River estuary

clone AY274839.1

80

Desulfotomaculum Y11574.1, Y11573.1, AY084078.1

Feedlot manure clone,


Ferribacter AF282254.1, AF282252.1

Forest soil clone, uncultured AY913277.1, AF507760.1

forested wetland clone AF523973.1, AF524015.1

Holophaga AJ519640

hot spring clone AF027097.1

Human mouth clone,

uncultured AF515500.1, AY207065.1

hydrothermal sediment clone AF420340.1, U15103.1

Mono Lake clone AF507859.1, AF507869.2, AF507892.1, AF507879.2

Ocean sediment clone,

uncultured AY181050.1, AY114325.1, AY093473.1

Oral clone AY134895.1

Paralvinella AJ441239.1

Penguin droppings sediment

clone AY218551.1

Plectonema AF091110.1

Rocky Mountain alpine soil

clone AY192273.1, AY192275.1

Salmonella AF029226.1

sludge clone AF234699.1

soil clone AJ390463.1

sponge clone AJ347055.1

temperate estuarine mud

clone AY216458.1

termite gut clone AB089097.1, AB089109.1, AB089074.1, AB089008.1

uranium mining waste pile

clone

AJ519397.1, AJ518784.1, AJ519663.1, AJ519396.1,

AJ532728.1

vent worm enrichment clone AJ431234.1, AJ431235.1

* This entry is included under family Lachnospiraceae in PhyloTrac, and as family

Clostridiaceae in GenBank.

81

CHAPTER 3 HIGH-THROUGHPUT DNA SEQUENCING OF THE

RUMINAL BACTERIA FROM MOOSE (ALCES ALCES) IN VERMONT,

ALASKA, AND NORWAY.

Suzanne L. Ishaq1,*

and André-Denis Wright1,2

*1 Department of Animal Science, College of Agriculture and Life Sciences, University

of Vermont, Burlington, Vermont 05401

2 Department of Medicine, University of Vermont, 111 Colchester Ave., Burlington,

Vermont, 05401

*Contact: Suzanne Ishaq, Department of Animal Science, University of Vermont, 203

Terrill Building, 570 Main Street, Burlington VT 05405. [email protected]. Fax) 1-

802-656-8196.

Keywords: 16S rRNA/454 Roche Titanium/Alaska/Norway/rumen/Vermont

Ishaq, S.L. and Wright, A-D.G. (2014). High-throughput DNA sequencing of the

ruminal bacteria from moose (Alces alces) in Vermont, Alaska, and Norway.

Microbial Ecology, 68(2):185-195.

82

3.1 Abstract

In the present study, the rumen bacteria of moose (Alces alces) from three distinct

geographic locations were investigated. Moose are large, browsing ruminants in the deer

family, which subsist on fibrous, woody browse, and aquatic plants. Subspecies exist

which are distinguished by differing body and antler size, and these are somewhat

geographically isolated. Seventeen rumen samples were collected from moose in

Vermont, Alaska and Norway, and bacterial 16S rRNA genes were sequenced using

Roche 454 pyrosequencing with Titanium chemistry. Overall, 109,643 sequences were

generated from the 17 individual samples, revealing 33,622 unique sequences. Members

of the phylum Bacteroidetes were dominant in samples from Alaska and Norway, but

representatives of the phylum Firmicutes were dominant in samples from Vermont.

Within the phylum Bacteroidetes, Prevotellaceae was the dominant family in all three

sample locations, most of which belonged to the genus Prevotella. Within the phylum

Firmicutes, the family Lachnospiraceae was the most prevalent in all three sample

locations. The data set supporting the results of this article is available in the Sequence

Read Archive (SRA), available through NCBI [study accession number SRP022590].

Samples clustered by geographic location and by weight, and were heterogeneous based

on gender, location and weight class (p<0.05). Location was a stronger factor in

determining the core microbiome than either age or weight, but gender did not appear to

be a strong factor. There were no shared operational taxonomic units across all 17

samples, which indicates that these moose may have been isolated long enough to

preclude a core microbiome among moose. Other potential factors discussed include

differences in climate, food quality and availability, gender, and life cycle.

83

3.2 Background

The moose (Alces alces), also known as the Eurasian elk in Europe, is a large

browsing ruminant in the Cervidae (deer) family native to northern latitudes, especially

Canada, the northern United States, Scandinavia and Russia. Their diet typically includes

woody browse from a variety of hardwoods and deciduous species (i.e. willow, aspen,

ash, maple) [1, 2], but during warmer months will also include salt-rich aquatic

vegetation from wetlands and swamps [3]. As a ruminant, they possess a four-chambered

stomach (rumen, reticulum, omasum, and abomasum), of which, the rumen and reticulum

contain a diverse assemblage of microorganisms that break down their fibrous diet and

provide usable nutrients for the host [1, 4].

As many populations of moose are geographically isolated, there are several

recognized subspecies based upon color, structure of the antlers, and facial

features/structure. The present study investigated samples from three different

subspecies of moose in Vermont (VT) (Alces alces americana), Alaska (AK) (Alces alces

gigas), and northern Norway (NO) (Alces alces alces). Alces alces gigas is recognized as

the largest moose subspecies (>600kg for males, >450kg for females), A. alces

americana is mid-range (>450kg for males, >250kg for females), and A. alces alces tends

to be smaller (>250kg for males, >200kg for females). Alces alces alces also have more

distinctive white legs, and have shorter and broader antlers than the other two subspecies.

Generally, moose wean at 6 months, reach sexual maturity at just over 2 years old, and

are known to live up to 15-20 years in the wild [4].

84

Only two published studies currently exist which identify the bacteria present in

the rumen of the moose. The first used traditional culturing techniques to identify the

bacteria in a male moose from Alaska, and identified Streptococcus bovis, Butyrivibrio

fibrisolvens, Lachnospira multiparus, and Selenomonas ruminantium [5]. The second

study used the second generation (G2) PhyloChip to identify hundreds of bacteria from

the rumen and colon of eight moose from Vermont, and reported that the phylum

Firmicutes was dominant, followed by the phylum Proteobacteria [6].

The present study compares three geographic locations of moose using Roche 454

high-throughput sequencing of the 16S rRNA gene using a 500bp amplicon generated

from the universal bacterial primers 27F [7] and 519R [8]. The objectives of this

research were to classify the bacteria present in the rumen of moose from Alaska,

Vermont and Norway; to compare the samples across geographic location, gender, and

weight class to determine possible trends; and to compare samples to published studies

on wild and domesticated ruminants. To date, there has been no study using high-

throughput/next generation sequencing to determine the complexity of the bacterial

community present in the rumen of the moose, nor has there been a comparison of moose

from different geographic locations to determine the core phylotypes which exist in

moose. It was hypothesized that bacterial populations would be distinct based on

geographic location, and that previous studies using classical approaches [5] had

underestimated diversity in the rumen of moose. In previous studies, age [9, 10] and

geographic location [11] play a role in differing core microbiomes.

85

3.3 Methods

3.3.1 Sample Collection

A total of 17 samples (Table 1) were collected from three different geographic

locations over two years. In October 2010, eight whole rumen samples were collected

from moose in Vermont (VT) shot during the 2010 hunting season (October 16-21,

2010). Samples were collected by licensed hunters, during field dressing, within 2h of

death and fixed frozen. Within 24 h, samples were transported to the laboratory and

stored at -20ºC until DNA extraction at the University of Vermont (UVM), Burlington,

VT. Hunters received written and verbal instructions on sample collection to sample

from well inside the rumen and to fill the container to minimize oxygen exposure.

Institutional Animal Care and Use Committee (IACUC) approval was not required to

collect rumen samples from licensed hunters.

Likewise, in the fall of 2011, six samples were collected from licensed hunters in

Norway (NO). The Norwegian moose hunting season is much longer (September 1-

October 31) than that in the United States, thus hunters may be in the field for weeks at a

time. To accommodate this, whole rumen samples were collected during field dressing

and immediately fixed in 70% ethanol until they were brought to the Department of

Arctic Biology, University of Tromsø, Tromsø, Norway. Samples were stored at 4°C

until DNA extraction at the University of Tromsø by the corresponding author. The

DNA extract, containing 0.1 volume of 2M sodium acetate, then two volumes of 100%

ethanol, was shipped to UVM. Once there, samples were centrifuged at 14,000g for 30

min, supernatant was then poured off, and the pellet washed with 100μl of 100% ethanol.

The pellet was air dried and then suspended in 50μl of TE (Tris-EDTA, pH 8.0) buffer.

86

In August, 2012, three whole rumen samples were collected via esophageal tubing

of sedated captive, free-range wild moose at the Moose Research Center, Soldotna,

Alaska (AK) (IACUC protocol #11-021, UVM; ACUC protocol #2011-026, Department

Fish and Game, Alaska). Wild moose there live in an approximately 2 square mile

enclosure where they can forage naturally. Rumen samples were immediately fixed with

70% ethanol and shipped to UVM for DNA extraction. Care was taken to prevent

contamination of rumen digesta sample with saliva. In all samples, approximately 50 g

of whole rumen digesta sample was collected. Previously, it was shown that different

methods of rumen sample collection do not affect the rumen community structure [12].

Vermont samples were collected from October 16-23, 2010 when temperatures

are historically 2-12°C (low/high), however, in 2010 most areas were unusually warm

with daytime highs around 21°C [13]. Norwegian samples were collected between

September 26-October 6, 2011, with temperatures ranging from (low high) 6-13°C in

September, (historically 3-9°C), and 0.8-5°C in October (historically 0-5°C) [13].

Alaskan samples were collected on August 31, 2012, with temperatures ranging from 9-

12°C (historically 5-15°C) [13]. It is important to note that Vermont moose were

sampled during summer-like temperatures, hotter than either Alaskan or Norwegian

moose, despite being sampled at the latest time point in the year. In 2010, Vermont also

received nearly twice the annual average rainfall [14], in 2011, Troms County, Norway

received its highest annual rainfall since 1983 [13]; and in 2012, the region around

Soldotna, Alaska had its highest annual rainfall in six years [13].

87

3.3.2 DNA extraction and quantification

Samples were fully thawed and 0.25 gram aliquots of whole contents (liquid and

particle associated) were used for extraction. DNA was extracted from all 17 samples

individually using he repeated bead-beating plus column (RBB+C) method [14],

combined the QIAamp DNA Stool Mini Kit (QIAGEN, Maryland) and/or the Powersoil

DNA Isolation Kit (MO BIO Laboratories, California). Samples were homogenized

using zirconia beads for 3 min, then incubated with lysis buffer [15] at 70°C for 15 min,

followed by centrifugation at 4°C for 5 min at 16,000G. This was performed twice, and

the supernatant from each was combined and treated with an inhibitex tablet from the

QIAGEN kit. The remainder of the DNA extraction followed the manufacturer’s

instructions. Final elutions were made into 200 μl of TE (Tris-EDTA, pH 8.0) buffer,

and DNA was quantified using a NanoDrop 2000C Spectrophotometer

(ThermoScientific, California). PCR was performed on a C1000 ThermoCycler (Bio-

Rad, California) using the iTaq kit (Bio-Rad, California) to measure the quality of DNA

from the mixed sample of bulk nucleic acids. All PCR results were run on a 1% agarose

gel at 100 volts for 60 min, and imaged on a ChemiDoc XRS+ gel imager (Bio-Rad,

California).

3.3.3 Amplicon library preparation

The variable regions V1-V3 of the bacterial 16S rRNA were recently determined

to be the overall best region to estimate species-level richness using genetic distances of

0.03 and 0.04 for cultured and uncultured bacterial sequences, while providing an optimal

amplicon size of approximately 500b [16]. DNA was PCR amplified with universal

88

bacterial primers (IDT, California): 27F [7], (5’-AGAGTTTGATCCTGGCTCAG -3’)

and 519R [8], (5’-GWATTACCGCGGCKGCTG-3’). These primers were selected to

amplify the first three variable regions (V1-V3) of the 16S rRNA gene in bacteria,

creating an amplicon of ~500bp. The iProof High Fidelity DNA polymerase kit (Bio-Rad,

California) was used: 10µl of 5X High Fidelity buffer which includes MgCl2, 1.0µl of

10mM dNTP mix, 2.5µl each of forward and reverse primer 0.5µl of iProof DNA

polymerase and 31.5µl of ddH2O. For each sample, 2µl of DNA template were added

once the master mix had been aliquoted, to a total reaction volume of 50µl. A PCR

procedure was developed to optimize amplification, as follows: initial denaturing at

98°C for 4 min, then 34 cycles of 98°C for 10 s, 50°C for 30 s, 72° for 2 min, followed

by a final extension step of 72°C for 10 min. All PCR results were run on a 1% agarose

gel, and each sample was run in duplicate or triplicate.

The bands from each moose sample were excised from the agarose gel, combined

per sample, and purified using the QIAGEN QIAQuick Gel Extraction Kit (QIAGEN,

Maryland) according to manufacturer’s instructions. The gel-extracted DNA was re-

eluted into 30µl Buffer EB, and was quantified using a NanoDrop 2000C

Spectrophotometer (ThermoScientific, California) to a minimum required final

concentration of 20ng/µl per 20µl sample. The DNA amplicons were frozen and shipped

overnight to Molecular Research, LP (MR DNA) for Roche 454 pyrosequencing with

Titanium chemistry.

89

3.3.4 Sequencing analysis

Sequences were deposited online in the Sequence Read Archive (SRA) though

NCBI, under study accession number SRP022590. To analyze the DNA sequencing data,

the open-source computer software program MOTHUR ver.1.29 [17, 18] was used.

Sequences from all three locations were processed together using the original standard

flowgram format (sff) output file from the sequencer. Flow grams were denoised using

“shhh.flows”, (i.e. the MOTHUR-integrated version of the PyroNoise algorithm [19]).

The barcode and forward primer were removed, and sequences which contained any of

the following conditions were discarded: < 300 bases, >550 bases, contained

homopolymer runs >8 bases, or contained any mismatches in the barcode. After

trimming, identical sequences were grouped into “unique” sequences using MOTHUR,

which allowed for a comparison of total sequences, as well as those sequences which

were differentiated by at least one base.

Sequences were aligned using the Needleman-Wunsch global alignment

algorithm [20], 8b kmer searching, match reward +1, mismatch penalty -1, and gap

open/extend penalty -2. A reference alignment for bacteria, which had been created and

optimized for this type of data set in the laboratory, was used to align the candidate

sequences. The alignment was then filtered to remove gap-only columns, and sequences

were trimmed to a common length of 392 characters. The alignment was checked for

chimeras using the MOTHUR-integrated version of the program UCHIME [21]; using

abundance as a reference. These putative-chimeric sequences were classified against a

Silva bacterial taxonomy [22, 23], and only those putative-chimeric sequences which had

less than 80% similarity to known sequences were removed from the alignment.

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The remaining aligned sequences were all classified using the Silva reference

taxonomy and an 80% cutoff. Genetic distance was calculated, sequences clustered into

operational taxonomic units (OTUs) at 0.03% and 0.05% genetic distance using the

nearest neighbor method, and a representative sequence chosen for each cluster/OTU at

each distance. Sequences were subsampled from each moose rumen sample, and

diversity was compared using CHAO [24], ACE [25], Good’s Coverage [26] and the

Shannon-Weiner Index [27]. Good’s Coverage, C = 1 - N1/n, where C is the coverage of

a random sample of size n, and N1 is the numbers of “classes” observed once. A large

number of “classes” observed only once will create a value of C approaching 0. Shared

OTUs were generated using the make.shared command in MOTHUR and manual

interpretation of the table. Shared OTUs were also compared using the

get.coremicrobiome command.

A relaxed neighbor-joining tree was calculating using the Clearcut tree making

program within MOTHUR, and then used to run a weighted and unweighted Unifrac

within MOTHUR was run using random sampling. The distance file created from the

weighted Unifrac was used to create a Principal Component Analysis (PCoA) and then

analyzed for population differentiation using an analysis of molecular variance

(AMOVA) test. Unifrac tests were also run online using FastUnifrac [28] to verify

sample structure via clustering, as well as using PCoA plots (viewed in 3D with

Kineimage online), P test significance, and Unifrac significance of all samples.

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3.4 Results

3.4.1 Classification of taxa by sample location

The breakdown of phyla per sample is presented in Figure 1, with the statistical

analysis of the sample populations as follows. The phyla Bacteroidetes, Firmicutes, and

Proteobacteria, and the group “Unclassified” were found in each sample location.

Overall, the phylum Bacteroidetes was dominant in the AK (69.1% of total sequences)

and NO samples (40.4% of total sequences), but not from VT samples (25.7% of total

sequences) (Figure 1). Within the phylum Bacteroidetes, Prevotellaceae was the

dominant family in all three sample locations, with 10,522 unique sequences (33,099

total sequences) across all 17 samples. Within the Prevotellaceae family, 8,526

sequences were identified as belonging to the genus Prevotella: 5,511 sequences from

AK moose (50.4% of unique sequences), 2,625 sequences from NO moose (17.3% of

unique sequences), and 390 sequences (5.2% of unique) from VT. The second largest

group in the phylum Bacteroidetes was the group “RC9” (family Rikenellaceae), with

495 unique sequences (1,827 total sequences) across all samples.

Bacteria belonging to the phylum Firmicutes were dominant in the VT samples

(56.4% of total sequences), were the second most prevalent in NO samples (32.8% of

total sequences), and third most prevalent in AK samples (9.3% of total sequences)

(Figure 1). Lachnospiraceae was the most prevalent family within the phylum Firmicutes

with 6,677 unique sequences (21,252 total) across all samples, followed by

Ruminococcaceae with 1,303 unique sequences (3,018 total). The largest groups at the

genus-level were as follows: Butyrivibrio: 457 unique sequences (1706 total),

Syntrophococcus- 186 unique sequences (514 total), Butyrivibrio-Pseudobutyrivibrio-

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110 unique sequences (402 total), Ruminococcus- 60 unique sequences (155 total),

Mitsuokella- 50 unique sequences (84 total), Moryella- 37 unique sequences (123 total),

Mogibacterium- 30 unique sequences (47 total), and Lachnospira- 24 unique sequences

(64 total).

Uncharacterized and unclassified bacteria at the phylum level represented a large

proportion of sequences: 12.8% unique sequences (18.7% of total) in AK, making it the

second largest group in those samples, 19.5% unique sequences (19.1% of total) in NO

and 14.8% unique sequences (14.7% of total) in VT (Figure 1). Representatives of the

phylum Proteobacteria were the fourth most prevalent bacteria across all 17 samples in

each of the three sample locations. Within this phylum, the most prevalent genera were

Acinetobacter: 173 unique sequences (542 total), and Pseudomonas: 137 unique

sequences (1,247 total). The phyla Cyanobacteria and Actinobacteria were also found in

each sample location, but they were much more prevalent in NO moose (2% and 1% of

sequences, respectively) than in AK or VT moose (<1% and <0.4% of sequences each).

The phyla Lentisphaerae, Spirochaetes, and Synergistetes, as well as the candidate

division “TM7” were found in all three sample locations, with each representing <0.4%

of sequences in any sample location. Fusobacteria was only found in NO samples,

Acidobacteria and Chloroflexi were only found in VT samples, and candidate division

“SR1” was found in NO and VT samples, with <0.4% of sequences in any sample

location.

The following 68 genera were also identified, but with low frequency (i.e. <100

unique sequences): Acetitomaculum, Acetobacter, Acidovorax, Adlercreutzia, Afipia,

Anaerobiospirillum, Anaerococcus, Anaerostipes, Ancalomicrobium, Aquabacterium,

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Atopobium, Barnesiella, Blastomonas, Blautia, Bradyrhizobium, Campylobacter,

Catonella, Caulobacter, Citrobacter, Clostridium, Comamonas, Coprococcus,

Corynebacterium, Desulfovibrio, Duganella, Empedobacter, Eubacterium,

Flavobacterium, Fusobacterium, Howardella, Hydrogenoanaerobacterium, Kurthia,

Lactobacillus, Lactococcus, Leuconostoc, Macrococcus, Massilia, Methylobacterium,

Microbacterium, Mycobacterium, Oerskovia, Olsenella, Oribacterium, Oscillibacter,

Oscillospira, Paenibacillus, Parvimonas, Phascolarctobacterium, Ralstonia, Rhizobium,

Rhodobium, Robinsoniella, Schwartzia, Sediminibacterium, Selenomonas,

Solobacterium, Sphingomonas, Spiroplasma, Stenotrophomonas, Streptococcus,

Succiniclasticum, Succinivibrio, Sutterella, Treponema, Variovorax, Veillonella,

Victivallis, and Xylanibacter.

3.4.2 Statistical analysis of OTUs and clustering

Overall, a total of 109,643 sequences were generated from the 17 samples. Of

these, a total of 37,831 sequences from the three Alaskan samples (AK1R-AK3R)

revealed 10,936 unique sequences, 51,459 total sequences from the six Norwegian

samples (NO1R-NO6R) revealed 15,211 unique sequences, and, 20,353 total sequences

from the eight Vermont samples (VT1R-VT8R) revealed 7,475 unique sequences. The

number of total sequences per sample (Table 1), which passed the quality control and

processing steps, and which were subsequently used in the analysis, ranged from 483 to

19,583. Of the 33,622 unique sequences, 28,111 were classified to phylum (not including

“Unclassified”), 21,122 down to family, and 10,642 down to genus. The unique and total

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number of sequences for each sample location at the family level of classification is

provided (see Supplemental Table 1).

Using a genetic distance of 3%, the 109,662 total sequences were assigned to

17,774 OTUs, of which 15,271 contained a single sequence. Owing to the wide range of

sequences per sample (483 to 19,583), a random subsample of the sequences was

selected, using the smallest sample size as the subsample size, and diversity indices

measured. The CHAO [24] and ACE [25] richness estimators, Good’s [26] coverage,

and Shannon-Weiner [27] diversity index were calculated based a 3% genetic distances

for each sample (Table 2). For the entire dataset, at a 3% genetic distance, and using a

subsample consisting of 483 sequences (i.e. 391 OTUs), the estimated number of OTUs

which should have been observed were CHAO=3,279 and ACE=8,420, Good’s [26]

coverage was 0.261, likely low due to the high proportion of OTU singletons, and

Shannon-Weiner [27] index was 5.726.

No OTUs were shared across all 17 samples, using either get.coremicrobiome or a

shared OTU table (Table 3) at a 3% genetic distance. Overall, only two OTUs were

found at a relative abundance of greater than 1% and were found in at least 10 samples.

No OTUs were present, in any abundance, in 11 or more samples. The Alaskan samples

shared the greatest number of OTUs (n=92), most of which belonged to the genus

Prevotella (Table 3). Among the NO samples (n=6), there were 8 shared OTUs, and

among the VT samples (n=8) there was only 1 shared OTU (Table 3).

When comparing gender, all female samples (n=11) shared 1 OTU, and all male

samples (n=6) shared 6 OTUs (Table 3). When compared by weight, only 12 samples

were taken into account. Five samples were discounted because the three AK samples

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only had estimated live weights, whereas the other 12 samples reported dressed carcass

weight, and samples NO4R and VT4R did not have any recorded weights. The largest

grouping of shared OTUs was seen in the smallest weight class, 0-100kg (NO1R, NO6R),

which shared 67 OTUs, followed by the largest weight class, 301-400kg (VT5R, VT7R,

VT8R) (Table 3). Weight class 101-200kg (NO2R, VT1R, VT3R) shared 26 OTUs

containing 740 unique sequences, and weight class 201-300kg (NO3R, NO5R, VT2R,

VT6R) shared 5 OTUs (Table 3).

Sample clustering was evaluated using weighted and unweighted FastUnifrac

(Figure 2A and B, respectively). For the weighted Unifrac, there was a Unifrac

significance of p=1.0e-03 (corrected), meaning that there is no probability that the branch

lengths would be seen by chance, and a P-Test Significance of p=0.0 (corrected),

meaning that samples were significantly clustered. In both the weighted and unweighted

Unifrac, the three AK samples (AK1R-AK3R) clustered together, and six VT samples

(VT3R-VT8R) clustered together (Figure 2). The remaining two VT samples and the six

NO samples clustered differently between the weighted and unweighted Unifrac. In the

weighted Unifrac, NO1R and NO4R clustered with the large VT clade, while the other

four NO samples clustered separately with VT1R (Figure 2A). Samples NO2R, NO3R,

NO5R, NO6R, and VT1R were all between calf age and approximately 3 years of age. In

the unweighted Unifrac, all six NO samples clustered together, along with VT1R and

VT2R (Figure 2B). The NO moose were all <4 years of age, and VT1R and 2R were 1

year and 3 years old, respectively. However, this is not exclusive, as VT3R and VT6R

were aged 2 years and 3 years, respectively, yet clustered with older and heavier moose.

96

In both weighted and unweighted Unifrac, five out of six males clustered together

excluding NO5R, the only male NO moose sample. Additionally, VT6R (female)

clustered with the males both times, and VT2R (female) clustered with males in the

weighted analysis. The five males and two females mentioned above were also the seven

heaviest animals, with the exception of the one male from Norway which clustered with

the five female NO moose samples.

According to PCoA, samples clustered most significantly by geographic location

(Figure 3A), and males clustered significantly, though females did not (Figure 3B).

There was not a strong clustering based on weight class (Figure 3C) or age (data not

shown). According to AMOVA, when comparing diversity across different factors, it

was shown that groups were statistically heterogeneous based on gender (F-

statistic=2.10077, P=0.042), geographic location (Fs=4.63938, P=<0.001), and weight

class (Fs=2.01592, P=0.003).

3.5 Discussion

3.5.1 Bacteroidetes:Firmicutes

Bacteroidetes was the dominant phylum in rumen samples from AK and NO, and

Firmicutes was the dominant phylum in samples from VT, the latter has been previously

demonstrated [6]. Firmicutes was the dominant phylum in Thompson’s gazelle, Grant’s

gazelle, and eland [29]; in Norwegian reindeer [11], and in dromedary camels in

Australia [30]. There are two studies on rumen bacteria from Svalbard reindeer; one

study reporting Bacteroidetes to be dominant in reindeer on a winter diet [31] and the

other reporting Firmicutes to be dominant in reindeer on a late summer diet [11]. This is

97

contrary to a another study using traditional culturing of bacteria from arctic Svalbard

reindeer, which found that Streptococcus bovis (phylum Bacteroidetes) were more

abundant in summer, along with increased starch utilization, proteolysis and lactate

utilization by rumen microbes, whereas fiber digestion, cellulolysis and xylanolysis were

increased during the winter, along with the cellulolytic bacteria Butyrivibrio fibrisolvens

(phylum Firmicutes) [32]. This shift in bacterial phyla dominance reflects the change in

quality and consistency of seasonal diets, as high starch diets and high fiber diets favor

different rumen bacteria.

In the present study, the Lachnospiraceae represented the largest family in the

phylum Firmicutes for all three sample locations, and it was the most abundant family in

the rumen of VT moose. Rumen Lachnospiraceae are a family of bacteria which produce

butyrate, a short-chain fatty acid, which is readily absorbed and metabolized by rumen

epithelial cells [33]. Butyrate reduces the side effects of gastrointestinal inflammation,

and stimulates rumen development by increasing rumen epithelial papillae growth [34].

The prevalence of Lachnospiraceae has been observed previously in studies of the

Tammar wallaby, an Australian herbivorous marsupial [35], dairy cows [36], North

American moose [6], and various arctic ruminants [27]. Furthermore, Lachnospiraceae

was the second-most abundant bacterial family in dromedary camels in Australia [30]. In

the folivorous bird, the Hoatzin, chicks had the highest proportion of Lachnospiraceae,

which decreased as age increased [9]. In the present study, the observed number of

Lachnospiraceae per sample were plotted against age using the statistical package JMP

ver.9.0, and while there appeared to be an inverse relationship between increase in age

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and decrease in the family Lachnospiraceae, the correlation was not significant (R2=0.20)

(data not shown).

Bacteroidetes has previously been shown to be dominant in growing ruminants

[10], or ruminants transitioning from a high-fiber diet to a high-starch/high-digestibility

diet [37], hibernating ground squirrels [38], in the oral cavity of pregnant women [39],

and has been associated with increased fat deposition [40, 41]. Studies involving fecal

transfers from pregnant women to gnotobiotic mice increased the proportion of the phyla

Proteobacteria and Actinobacteria and decreased the proportion of the phyla Firmicutes

and Bacteroidetes, which mirrored the change in women over the course of gestation

[41]. In the present study, only one female moose, NO4R (age and weight unknown),

was confirmed to have a calf, and all other females had an unknown reproductive status.

Proteobacteria was not elevated in this particular sample, but it had the second highest

prevalence of Actinobacteria, after sample NO2R (female, 1.5 year, 120kg dressed

carcass weight).

Previously, it was shown using the G2 PhyloChip microarray that the rumen of

VT moose was largely made up of bacteria in the phylum Firmicutes, followed closely by

bacteria in the phylum Proteobacteria [6]. In the present study, Proteobacteria

represented 4% of total sequences for NO moose, 2.5% of total sequences for AK moose,

and only 1.2% of total sequences for VT moose. This discrepancy is most likely due to

the bias inherent in the G2 PhyloChip, which was created using 16S rRNA

oligonucleotide probes, most of which are designed to target multiple taxa [40], and

which likely produced a large number of false positives [6].

99

3.5.2 Temperature, region, or life stage?

The samples in the present study were collected during unusually rainy seasons in

all three sample locations, and unusually warm seasons in Vermont and Norway [13, 14].

Previously, it has been shown that hot and rainy summers, and not cold summers or

winters, decreased average moose weight in Norway [43]. We speculate that this would

cause plants to mature faster and transition more quickly during the growing season from

a high proportion of starch to a high proportion of structural carbohydrates. Plant starch

is more nutritious, and is likely to be associated with a higher proportion of ruminal

Bacteroidetes, while structural carbohydrates (i.e. cellulose, hemicellulose, lignin), are

very fibrous and are likely be associated with a higher proportion of ruminal Firmicutes

[37, 44]. The unusually mild fall temperatures and heavy rainfall in Vermont may have

increased the cellulose content of forage and, therefore, resulted in a high proportion of

the primarily cellulolytic phylum Firmicutes in the VT moose rumen. This may explain

why the VT moose had a different microbial population than the AK or NO moose.

Moreover, the cooler temperatures in Alaska may have led to a higher starch content in

forage, resulting in moose which had a high proportion of the phylum Bacteroidetes.

AK samples consistently clustered together and shared the greatest number of

OTUs and unique sequences. This is unsurprising, as these moose cohabited the same 2

square mile enclosure for years, were likely consuming a similar diet, and were roughly

the same age. However, the VT and NO samples each clustered separately with a large

amount of consistency, which was not observed among age or weight groups across all

sample locations. This suggests that geographic location and, thus, available forage,

plays a large role in defining the core microbiome in the three isolated populations in the

100

present study [11]. Age/life stage may play a larger role in defining the core microbiome

within a sample location and account for individual variation [6, 9, 41]. While males did

cluster on PCoA graphs and Unifrac, and shared six OTUs, this was not seen in females,

potentially due to their different reproductive stages. Although there was significant

clustering of females based on weight/age using Unifrac, it was not corroborated using

PCoA, and they shared only one OTU. This discrepancy could be due to the low number

of samples per age/weight group.

3.6 Conclusion

The present study found that samples taken from Vermont (USA), Alaska (USA),

and Norway differ from each other in terms of phylogenetic diversity with respect to

geographical location, gender, and weight of the host animal. The observed clustering

trends, between moose populations, are likely due to different quality of diet between

populations, which may be due to differing location, climate, and sampling season

between the three groups.

3.7 Availability of Supporting Data

The data set supporting the results of this article is available in the Sequence Read

Archive (SRA), available through NCBI [study accession number SRP022590].

3.8 Competing Interests

The authors declare no competing interests.

101

3.9 Authors’ Contributions

SLI performed all laboratory procedures, excluding Roche 454 pyrosequencing, analyzed

the dataset, and wrote the manuscript. ADW conceived of the project, facilitated

Norwegian sample collection, funded all work and edited the manuscript.

3.10 Acknowledgments

The authors would like to acknowledge: the Vermont Fish and Wildlife Dept. for

sample collection logistics; Terry Clifford, Archie Foster, Lenny Gerardi, Ralph Loomis,

Beth and John Mayer, and Rob Whitcomb for collection of Vermont moose samples; Dr.

Even Jørgensen, University of Tromsø, and Dr. Helge K. Johnsen, University of Tromsø,

for collection of Norwegian moose samples; Dr. Monica A. Sunset, University of

Tromsø, for facilitating sample collection and storage, as well as for providing DNA

extraction materials for the Norwegian samples; Dr. John Crouse and Dr. Kimberlee

Beckmen, both of the Alaska Dept. of Fish & Game for collection of Alaskan moose

samples; and Dr. Benoit St-Pierre, University of Vermont for assistance with MOTHUR

and Perl programming.

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3.12 Figures

Figure 3-1 A comparison of sequences per sample, using the Silva bacterial reference taxonomy for

classification.

Alaska (AK): 37,831 total; Norway (NO): 51,459 total; Vermont (VT): 20,353 total; overall: 109,643 total.

107

Figure 3-2 FastUnifrac clustering of all 17 samples. A) weighted, non-normalized, and B)

unweighted.

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Figure 3-3 PCoA graphs colored by variable.

A) geographic location: red=AK, blue=NO, yellow=VT, B) gender: red=female, blue=male, C) weight

class: pink=not available, red=0-100 kg, light blue=101-200kg, yellow=201-300 kg, green=301-400 kg,

and purple=400+ kg for live weights.

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3.13 Tables

Table 3-1 Metadata of the 17 samples from Alaska (AK), Norway (NO), and Vermont (VT).

Sample Sample Site Collection

m-d-y Sex

Weight

(kg)1

Approx

. age2

# Seqs used

for analysis3

AK-1R Soldotna, AK

60°29′N 151°4′W 08-31-12 F

485 (live

wt.)

10y

3mo 5,695

AK-2R “ ” 08-31-12 F 487 (live

wt.)

10y

3mo 12,550

AK-3R “ ” 08-31-12 F 506 (live

wt.)

11y

3mo 19,586

NO-1R Troms County, NO

69°N 20°E 09-26-11 F

61

(dressed) Calf 6,028

NO-2R “ ” 09-26-11 F 120

(dressed) 1.5y 18,543

NO-3R “ ” 09-26-11 F 211

(dressed) >4.5y 3,122

NO-4R “ ” 09-30-11 F N/A N/A4 3,037

NO-5R “ ” 09-27-11 M 290

(dressed) 2-3y 17,624

NO-6R Andørja, Ibestad, NO

68°N 17°E 10-08-11 F

77

(dressed)

Calf,

6-8 mo. 3,105

VT-1R Averill, VT

44°59'N, 71°42'W 10-16-10 F

185

(dressed) 1y 3,484

VT-2R East Haven, VT

44°39’N, 71°53’W 10-16-10 F

244.6

(dressed) 3y 1,744

VT-3R North Danville, VT

44°27’N, 72°5’W 10-17-10 M

186.4

(dressed) 2y 3,405

VT-4R Canaan, VT

44°59’N, 71°32’W 10-16-10 M N/A N/A 3,679

VT-5R Ferdinand, VT

44°42.7’N, 71°45.19’W 10-20-10 M

319.1

(dressed) 4y 3,261

VT-6R Brighton, VT

44°48.3’ N, 71°51.3’W 10-23-10 F

259.6

(dressed) 3y 483

VT-7R Walden, VT

44°26.3’N, 72°13.4’W 10-23-10 M

301.3

(dressed) 4y 2,305

VT-8R Wheelock, VT

44°35.28’N, 72°5.33’W 10-23-10 M

405.5

(dressed) 8y 1,992

1AK moose were weighed live, VT and NO moose carcasses were field dressed and

weighed at reporting stations. 2 AK moose had birthdates, VT and NO moose age was

estimated using wear on teeth. 3 Unique sequences = 33,622

4Moose had a bull calf.

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Table 3-2 Statistical measures using a subset of sequences per sample.

Sample # OTUs in

sample CHAO ACE

Good’s

Coverage

Shannon-Weiner

Index

AK_1R 962 6,012 11,389 0.576 5.700

AK_2R 1,707 9,846 22,158 0.554 6.523

AK_3R 2,374 15,861 45,718 0.642 5.787

NO_1R 1,329 7,737 20,686 0.381 6.813

NO_2R 2,926 18,244 42,501 0.538 7.047

NO_3R 563 3,253 3,792 0.380 5.779

NO_4R 730 4,459 10,654 0.327 6.364

NO_5R 3,554 25,415 62,870 0.409 7.654

NO_6R 774 6,318 18,613 0.273 6.433

VT_1R 987 6,630 15,544 0.279 6.690

VT_2R 539 3,179 3,393 0.268 6.149

VT_3R 992 6,230 15,364 0.282 6.765

VT_4R 1,106 7,430 12,943 0.294 6.77

VT_5R 958 5,776 14,374 0.307 6.649

VT_6R 148 2,122 3,450 0.113 4.957

VT_7R 669 4,407 8,443 0.303 6.335

VT_8R 686 5,607 5,544 0.185 6.471

Table 3-3 The number of shared OTUs across different samples at a 3% genetic distance generated

using a shared OTU table in MOTHUR.

Samples Compared OTUs Shared,

3% distance

Shared Unique

Sequences

AK samples (n=3) 92 4,510

NO samples (n=6) 8 380

VT samples (n=8) 1 136

All 17 samples 0 0

All Females (n=11) 1 114

All Males (n=6) 6 314

Weight: 0-100 kg (NO1R, NO6R) 67 433

Weight: 101-200 kg (NO2R, VT1R, VT3R) 26 740

Weight: 201-300 kg (NO3R, NO5R, VT2R, VT6R) 5 417

Weight: 301-400 kg (VT5R, VT7R, VT8R) 28 276

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CHAPTER 4 DESIGN AND VALIDATION OF FOUR NEW PRIMERS FOR

NEXT-GENERATION SEQUENCING TO TARGET THE 18S RRNA GENE

OF GASTROINTESTINAL CILIATE PROTOZOA.

Suzanne L. Ishaq and André-Denis G. Wright

Department of Animal Science, College of Agriculture and Life Sciences, University of


Contact: Suzanne Ishaq, Department of Animal Science, University of Vermont, 203

Terrill Building, 570 Main Street, Burlington VT 05405. [email protected]. Fax) 1-802-

656-8196.

Keywords: Alaska/Entodiniomorphida/ciliophora/moose/protist/rumen/Vestibuliferida

Ishaq, S.L. and Wright, A-D.G.. (2014). Design and validation of four new primers for


protozoa. Applied and Environmental Microbiology, 80(17): ahead of print.

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4.1 Abstract

Four new primers and one published primer were used to PCR amplify hyper-

variable regions within the protozoal 18S rRNA gene to determine which primer pair

provided the best identification and statistical analysis. PCR amplicons of 394 to 498

bases were generated from three primer sets, sequenced using Roche 454 pyrosequencing

with Titanium, and analyzed using the BLAST (NCBI) database and MOTHUR ver.

1.29. The protozoal diversity of rumen contents from moose in Alaska was assessed. In

the present study, primer set 1, P-SSU-316F + GIC758R (amplicon = 482 bases) gave the

best representation of diversity using BLAST classification, and amplified Entodinium

simplex and Ostracodinium spp., which were not amplified by the other two primer sets.

Primer set 2, GIC1080F + GIC1578R (amplicon = 498 bases), had similar BLAST results

and a slightly higher percentage of sequences that identified with a higher sequence

identity. Primer sets 1 and 2 are recommended for use in ruminants. However, primer set

1 may be inadequate to determine protozoal diversity in non-ruminants. Amplicons

created by primer set 1 were indistinguishable for certain species within the genera

Bandia, Blepharocorys, Polycosta, Tetratoxum, or between Hemiprorodon

gymnoprosthium and Prorodonopsis coli, none of which are normally found in the

rumen.

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4.2 Introduction

Rumen ciliate protozoa represent important functional members of the rumen

environment, as most have some cellulolytic or amylolytic abilities (1–3). Most studies

of rumen ciliate protozoa are performed using microscopy and traditional culturing

techniques (2, 4–10), quantitative PCR (11, 12), denaturing gradient gel electrophoresis

(13), and full-length 18S rRNA clone libraries (13, 14). A few studies of rumen ciliate

protozoa use high-throughput sequencing, although primer selection remains a problem,

as some studies use universal eukaryotic primers, primers which target only one ciliate

protozoa signature region, or primers which produce unsuitable long amplicons for

current high-throughput technology (15–20).

The 18S rRNA gene ranges from 1.5 kb to over 4.5 kb (21), and in rumen ciliate

protozoa it is generally 1.5 kb to 1.8 kb in length. Like the 16S rRNA gene of

prokaryotes, the 18S rRNA gene of eukaryotes has nine hyper-variable regions (V1–V9)

which can be used for genus/species identification. Four gut ciliate signature regions

exist within the rumen protozoal 18S rRNA gene, which represent areas of high

variability that can improve identification down to species level (22,23). Signature

region 1 occurs between 440–460bp (within V3), signature region 2 occurs between 590-

620bp (between V3 and V4), signature region 3 occurs between 1220–1260bp (within

V6), and signature region 4 occurs between 1560–1580bp (after V8) (Figure 1).

Additionally, rumen ciliate protozoa have a slightly different 18S rRNA secondary

structure from non-rumen ciliates, in that rumen protozoa are missing helix E23–5 from

the V4 region and other helices in the region are shorter (21-23). Previously, the V9

region (15) or the V5–V7 regions (13) have been amplified for phylogenetic analysis

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using high-throughput techniques. Ciliated protozoa found in the gastrointestinal tract of

animals belong to the phylum Ciliophora, class Litostomatea, subclass Trichostomatia,

and the orders Entodiniomorphia and Vestibuliferida. The vast majority of rumen

ciliated protozoa belong to the Ophryoscolecidae, the largest family (both in numbers of

species and genera) within the Entodiniomorphia.

In the present study, four new primers were designed to specifically target

conserved 18S rRNA gene regions for ciliate protozoa, which are normally found in the

gastrointestinal tract of herbivores. Using our protocol, these primers did not amplify

other eukaryotic, bacterial, or archaeal species, or non-ciliated protozoa which are not

normally found in a healthy gastrointestinal tract environment. The strategy for the

pairing of the forward and reverse primers was to create amplicons which included at

least one of the four signature regions of the rumen ciliate protozoal 18S rRNA gene.

Current limitations of the Roche 454 and MiSeq ver. 3.0 (with 2 x 300 base paired ends)

platforms, along with few reliable conserved regions, exclusive to ciliate protozoa,

prevent the inclusion of all four signature regions, which was possible when the full 18S

rRNA gene was sequenced using Sanger sequencing technology.

The first objective of the present study were to test these primer pairs on rumen

samples from the North American moose (Alces alces) to determine their suitability and

validity for rumen ciliate identification. The second objective was to compare the primer

sets using CHAO, ACE, Shannon-Weaver, Inverse Simpson, Good’s Coverage, and

Unifrac in order to make a recommendation of the most suitable primer set for rumen

protozoal 18S rRNA gene amplification.

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4.3 Materials and Methods

4.3.1 Sample collection and DNA extraction

On August 31, 2012, whole rumen samples were collected via esophageal tubing

of three captive, free-range wild moose at the Moose Research Center, Soldotna, Alaska

(AK) (IACUC protocol #11-021, University of Vermont; ACUC protocol #2011-026,

Department Fish and Game, Alaska). All three moose were females between 10-11 years

of age. Rumen samples were mixed with 70% ethanol and shipped to the University of

Vermont (Burlington, Vermont, USA) where they were stored at 4°C. To confirm the

sequencing results, all three moose samples were visual inspected using light microscopy

to identify genera of rumen ciliates.

To extract DNA, a 5 ml aliquot of whole rumen contents in ethanol was

centrifuged for 5 min at 16,000 x G, and ethanol was removed by pouring off the liquid

fraction. From the remaining whole contents of all the samples, 0.25 g aliquots of whole

contents (liquid and particle associated) were used for extraction. DNA was extracted

from the three rumen samples using the repeated bead-beating plus column (RBB+C)

method (24), combined with the QIAamp DNA Stool Mini Kit (QIAGEN, Maryland).

The final elutions were made using 200 μl of TE buffer (1M Tris-HCL, 0.5M EDTA, pH

8.0), and eluted DNA was quantified using a NanoDrop 2000C Spectrophotometer

(ThermoScientific, California).

4.3.2 Primer design

The new forward and reverse primers were designed to target signature regions

unique to gastrointestinal tract ciliate protozoa within the 18S rRNA gene. Protozoal 18S

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rRNA gene reference alignments were created and used to select areas, which were

highly conserved among the rumen ciliate protozoa. Four conserved regions were

selected, and a potential primer sequence identified from each of those regions. The four

new primers were given the prefix “GIC” for gastrointestinal ciliates, and are as shown in

Table 1 with specifications. Along with a previously described rumen protozoal primer,

P-SSU-316F (5’-GCTTTCGWTGGTAGTGTATT-3’) (12), primer sequences were

compared to known sequences of gastrointestinal ciliates in GenBank (NCBI) (Table 2)

to determine specificity to amplify only gastrointestinal tract ciliate protozoa.

Primer set 1, P-SSU-316F (12) and GIC758R, created an amplicon of 482 bases

(primers not included) which encompassed variable regions V3-V4, and rumen ciliate

signature regions 1-2 (Figure 1). Primer set 2, GIC1080F and GIC1578R, created an

amplicon of 498b, which encompassed V6-V8 and signature regions 3-4 (Figure 1).

Primer set 3, GIC1184F and GIC1578R, created an amplicon of 394b, which also

encompassed V6-V8, and rumen ciliate signature regions 3-4 (Figure 1).

4.3.3 PCR amplification

The Phusion High-Fidelity DNA polymerase kit (ThermoScientific,

Massachusetts) was used for PCR: 10 µl of 5X High Fidelity buffer (including MgCl2),

1.0 µl of 10 mM dNTP mix, 2.5 µl each of forward and reverse primer at 10 mM

concentration, 0.5 µl of Phusion DNA polymerase (2U/μl), and 31.5 µl of ddH2O. DNA

templates (2 µL of 10-50ng/µL concentration) were added once the master mix had been

aliquoted, to a total reaction volume of 50 µl. The PCR protocol was adapted from

previous literature (12); 94°C for 4 min hot start, followed by 30 cycles of 94°C for 30 s,

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55°C for 30 s, and 72°C for 1 min, with a final extension of 72°C for 6 min on the last

cycle. All PCR results were run on a 1% agarose gel at 100 volts for 60 min, and imaged

on a ChemiDoc XRS+ gel imager (Bio-Rad, California).

DNA bands of the correct amplicon size were excised out of the agarose gel for

DNA purification, using the QIAQuick Gel Extraction Kit (QIAGEN, Maryland)

according to the manufacturer’s instructions. For each primer set, all gel bands from each

of the three moose samples were filtered through the same column. The gel–extracted

DNA from each primer set was quantified using a NanoDrop 2000C Spectrophotometer

(ThermoScientific, California), at a minimum final concentration of 20 ng/µl, at a volume

of 20 µl. The DNA amplicons from the three test primer sets were frozen and shipped

overnight to Molecular Research DNA (MR DNA), Shallowater, Texas, USA, for Roche

454 pyrosequencing with Titanium chemistry.

4.3.4 Sequence analysis

Sequences were deposited online in the Sequence Read Archive (SRA) though

NCBI, under study accession number SRP034591. To analyze the DNA sequencing data,

the open-source computer software program MOTHUR ver.1.29 (25) was used.

Sequences from all three primer sets were processed independently using the original

standard flowgram format (sff) output file from the sequencer. Flow grams were

denoised using the MOTHUR-integrated version of the PyroNoise algorithm (26), which

also creates phylotypes, which is a unique sequence representing multiple identical

sequences. Unique sequences are not equivalent to singletons, which are operational

taxonomic units (OTUs) containing a single sequence. The barcode and primer

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sequences were removed, and sequences which contained any of the following conditions

were discarded: <400 bases for primer set 1 and 2 or <375 bases for primer set 3, >500

bases, homopolymer runs >8 bases, or any mismatches in the barcode.

To determine the genetic distance cutoffs for species-level comparisons, 51 full-

length 18S rRNA valid protozoal reference sequences were obtained from NCBI (Table

2, Supplemental Figure 4). These reference sequences were then trimmed using the three

test primer sets as trim points. BLAST sequences were manually aligned and pairwise

distances were calculated using PHYLIP (ver. 3.69) using a Kimura 2-parameter model

(Table 3). A total of 51 pairwise species (within genus) and 2,926 pairwise distances

between genera were compared using validly recognized species to determine genetic

distances.

Sequences were aligned using the Needleman-Wunsch global alignment

algorithm (27), 8 base kmer searching, match reward +1, mismatch penalty -1, and gap

open/extend penalty -2. An 18S rRNA gene reference alignment, featuring rumen and

non-rumen ciliate protozoal sequences downloaded from NCBI, was created in the

laboratory to provide a better alignment of candidate sequences. The reference alignment

contained 219 full-length 18S rRNA sequences for all available gastrointestinal tract

(rumen, forestomach, cecum, and colon) ciliates sequences, as well as non-ruminant

ciliates and non-ciliate protozoal sequences from BLAST. The reference alignment

contained all 51 sequences previously used to determine genetic distance cutoffs. The

candidate alignment was then filtered to remove gap-only columns, and any sequences

which would not align (<10 sequences per data set). The candidate alignment was

checked for chimeras using the MOTHUR-integrated version of the program UCHIME

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(28); using the ciliate 18S rRNA gene sequence reference alignment which was created in

our laboratory. To determine the specificity of the primers, unique sequences were

classified using BLAST.

Sequences from the three primer sets were trimmed to a uniform length per

library (Table 3), and clustered using the Nearest Neighbor method to determine

observed number of OTUs per library. Libraries were then subsampled equal to the

smallest library (n=424 sequences per library), and Shannon-Weaver Diversity index

(29), Good’s Coverage (30), Inverse Simpson (31), CHAO (32), and ACE (33) were

calculated for each library based on the recommended genetic distance. Like Simpson’s

Diversity, Inverse Simpson measures number and abundance of species. However, it

weights rare species lower than Simpson to prevent a dramatic increase in diversity with

the addition of rare species. Additionally, using MOTHUR, relaxed neighbor-joining

trees were created from trimmed sequences (375 bases) using CLEARCUT, and trees

were clustered using weighted and unweighted Unifrac as a measure of similarity of

abundance and structure between libraries.

4.4 Results

4.4.1 Primer set 1: P-SSU-316F + GIC758R

A total of 12,326 sequences passed quality assurance measures and were used for

sequence analysis. Of these, 769 sequences were unique (Table 3). Using aligned

sequences of valid protozoa to generate pairwise genetic distances (see Supplemental

Figure 1), the species and genus-level cutoffs were determined to be 0.036 and 0.087,

respectively. So, a 4% species-level cutoff and a 9% genus-level cutoff were comparable

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to cutoffs for near full-length gene sequences. Sequences were trimmed to 450 bases, and

various diversity indices calculated for the data set. At a 4% species-level cutoff, 48

species-level OTUs were observed, and at 9% genus-level cutoff, 15 genus-level OTUs

were observed (Table 3). ACE, CHAO, Shannon-Weaver and Inverse Simpson values

can be found in Table 3.

Using BLAST, the most prevalent taxon was Polyplastron multivesiculatum,

which represented nearly 60% of the unique sequences, followed by the genus

Entodinium, which represented just over 20% of unique sequences (Figure 2). The most

prevalent species of Entodinium were Ent. furca dilobum (7% of unique sequences) and

Ent. nanellum (5% of unique sequences). Epidinium caudatum represented 5% of unique

sequences (Figure 2). Primer set 1 amplified Ent. simplex, Ostracodinium gracile, and

other Ostracodinium spp., which were not amplified by other primer sets. The percent

identity to known sequences ranged from 95-100% for primer set 1, with 66% of

sequences having a 99% identity to a known sequence in BLAST (Figure 3). However,

there were six sequences that had a 99-100% identity on only 93-95% of the query

sequence. The average percent identity to Ostracodinium gracile was 98.2% (range 97-

99%). There were 21 unique (63 total) sequences, which had <96% identity to a known

sequence.

During the calculation of genetic distances using pairwise comparisons, it was

noted that the amplicon created by primer set 1 could not differentiate between

Blepharocorys microcorys (AB794975) and Blepharocorys uncinata (AB530162),

between Hemiprorodon gymnoprosthium (AB795028) and Prorodonopsis coli

(AB795029), between Tetratoxum excavatum (AB794971) and Tetratoxum parvum

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(AB794969), between Bandia deveneyi (AY380823) and Bandia smalesae (AF298822),

and between Polycosta roundi (AF298819) and Polycosta turniae (AF298818).

4.4.2 Primer set 2: GIC1080F + GIC1578R

A total of 6,070 sequences for this primer set passed quality assurance measures

and were used for sequence analysis. Of these, 697 sequences were unique (Table 3).

Using aligned sequences of valid protozoa to generate pairwise genetic distances (see

Supplemental Figure 2), the species and genus-level cutoffs were determined to be 0.039

and 0.079, respectively. For statistical analysis, sequences were trimmed to a uniform

length of 450 bases. At a 4% species-level genetic distance cutoff, 25 species-level

OTUs were observed, and at 8% genus-level cutoff, 14 genus-level OTUs were observed

(Table 3). ACE, CHAO, Shannon-Weaver and Inverse Simpson values can be found in

Table 3.

Sequences from this primer set represented over 60% Polyplastron

multivesiculatum (Figure 2). The next predominant genus was Entodinium with 30% of

unique sequences and, of that, Ent. nanellum represented approximately two-thirds of the

genus (20% of unique sequences) (Figure 2). Diploplastron affine and Epidinium

caudatum were the third most prevalent taxa, with 5% of unique sequences each. The

percent identity to known sequences ranged from 94-100% for primer set 2, with 67% of

sequences having a 99% identity to a known sequence in BLAST (Figure 3). Only 1

unique (9 total) sequence had <96% identity to known sequences.

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4.4.3 Primer set 3: GIC1184F + GIC1578R

A total of 8,265 total sequences passed quality assurance measures and were used

for sequence analysis. Of these, 424 sequences were unique and used for BLAST (Table

3). Using aligned sequences of valid protozoal to generate pairwise genetic distances

(see Supplemental Figure 3), the species and genus-level cutoffs were determined to be

0.042 and 0.096, respectively. For statistical analysis, sequences were trimmed to a

uniform length of 375 bases. At a 4% species-level genetic distance cutoff, 20 species-

level OTUs were observed, and at 10% genus-level cutoff, 12 genus-level OTUs were

observed (Table 3). ACE, CHAO, Shannon-Weaver and Inverse Simpson values can be

found in Table 3.

Over 75% of unique sequences in the primer set 3 dataset were classified as

Polyplastron multivesiculatum using BLAST (Figure 2). The next predominant genus

was Entodinium, representing approximately 15% of the unique sequences (Figure 2).

Epidinium was the third most prevalent genus, with <5% of the unique sequences. The

percent identity to known sequences ranged from 95-99% for primer set 3, with 76% of

sequences having a 98% identity to a known sequence in GenBank (Figure 3). There

were 2 unique sequences, which had <96% identity to a publically available sequence.

4.4.4 Comparison of the three primer sets

Primer set 1 (P-SSU-316F + GIC758R) had the highest number of observed

OTUs, as well as the highest ACE, CHAO, Shannon-Weaver and Inverse Simpson values

of all three primer sets, indicating the highest amount of diversity of the three amplicon

libraries (Table 3). Primer set 1 had the lowest Good’s coverage (0.95). Primer set 2

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(GIC1080F + GIC1578R) had the second largest number of observed OTUs, and second

largest CHAO. However, primer set 2 had the lowest ACE, Shannon-Weaver, and

Inverse Simpson values, indicating a low amount of diversity (Table 3). Primer set 3 had

the lowest number of observed OTUs, as well as the lowest CHAO estimate (Table 3).

However, primer set 3 had the second highest ACE, Shannon-Weaver, and Inverse

Simpson values (Table 3). Weighted and unweighted Unifrac analyses were also run

using MOTHUR. Primer sets were not significantly different based on weighted (0.96 –

1.0), or unweighted (0.98 – 1.0, p<0.001) Unifrac. There was no correlation between

sequence length and % identity to known sequences in BLAST for any of the three data

sets.

Genera of rumen ciliates were confirmed using light microscopy. Various species

of Entodinium, as well as Polyplastron multivesiculatum and Epidinium cattanei were

found in abundance in all three samples. Isotricha were found in two samples, and

Ostracodinium was found in one sample.

4.5 Discussion

This study validated three primer sets for the amplification of gastrointestinal tract

ciliate protozoa for use in high-throughput sequencing, as well as determined the

diversity of moose rumen protozoa from Alaska, USA. All three test primer sets were

able to amplify 18S rRNA protozoal sequences. The 18S rRNA gene is highly conserved

among eukaryotes, and finding potential primer sites, which are specific to certain taxa

can be challenging. Using the new primers reported in the present study, under the same

amplification parameters (i.e. PCR annealing temperature, removal of short amplicons),

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only 18S rRNA genes from gastrointestinal tract (rumen, forestomach, cecum and colon)

ciliate protozoa should be targeted for amplification.

Previously used primers for high-throughput sequencing were often universal

eukaryotic primers (15-19), or primers which targeted only one signature region for the

ciliate protozoa (18, 20). In several studies, this resulted in sequences which were not

ciliate or protozoan in nature (17, 19), or which were too short (<200 bases), thereby

increasing the risk for misidentification (18) given the overall high degree of

conservation of the 18S gene across eukaryotic taxa. In the present study, no non-

protozoal eukaryotic sequences (i.e. plant, fungal or host DNA) were amplified.

Previously, using classical microbiology, Dehority (6) identified just five species

of protozoa from the rumen of Alaskan moose, Sládeček (4) identified four species from

moose, and Westerling (34) identified just two species from moose in Finland. In the

present study, 12 species representing 7 genera were found across the three Alaskan

moose. Previously, between 16 to 24 rumen ciliate species, represented by 1 to 9 genera,

were found in in studies of wild reindeer (9, 35–39), wild musk ox (Ovibos moschatus)

(6), wildebeest (Connochaetes spp.) (40), Kafue lechwe antelope (Kobus leche kafuensis)

(41), Sassaby antelope (Damaliscus lunatus) (10), and tsessebe (Damaliscus lunatus

lunatus) (42).

Only one study exists, which investigated the rumen protozoa from three bull-

moose in Alaska, USA, using culturing and microscopy techniques (6). Previously,

Entodinium alces and Entodinium exiguum were reported to be the two dominant species

in moose rumen contents, whereas Entodinium dubardi, Entodinium simplex, and

Entodinium longinucleatum were present in one moose (6). Additionally, Entodinium

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dubardi and Epidinium caudatum were identified in moose rumen samples from Finnish

Lapland (34), and Entodinium dubardi, Entodinium simplex, Ostracodinium obtusum,

and Epidinium ecaudatum were isolated from moose in Slovakia (4). Eudiplodinium

neglectum was also first identified in a moose from Canada (5).

Based on the previous studies which identified the rumen ciliate protozoa in the

moose, it was surprising that Polyplastron are the dominant species in the present study.

Polyplastron produce xylanases, carboxymethylcellulases, and various other

endoglucanases, which digest fiber in the rumen. While the presence of Polyplastron

was validated using light microscopy in the present study, a large number of sequences

related to Polyplastron may be explained by rRNA copy numbers in ciliates, which could

be highly variable from one species to another. Also, there is a lack of publically

available sequences for the rumen ciliates, especially closely related genera to

Polyplastron, such as Elytroplastron and Eudiplodinium. This is also true of Entodinium

alces, Entodinium exiguum, Ostracodinium obtusum, Epidinium ecaudatum, and

Eudiplodinium neglectum, all of which were previously found in moose (4–6), but no

representative sequences exist for these species. This makes identification at a species

level very difficult until additional sequences from all described species are elucidated.

There were 74 total sequences (24 unique sequences), which had <96% identity to

publically available sequences. Given that the genetic distance between Epidinium

caudatum and Epidinium ecaudatum is 1.3%, some sequences in the present analysis with

genetic distances between 1.0-1.5% to Epi. caudatum may represent other species of

Epidinium, such as Epidinium cattanei, which was observed under light microscopy.

Similarly, given that Polyplastron multivesiculatum has 98% sequence identity to

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Ostracodinium gracile and Ostracodinium clipoleum, the large number of sequences

which have <98% identity to P. multivesiculatum, may in fact represent other closely

related species or genera. As we stated previously, more representative sequences from

other rumen ciliates, such as Epi. cattanei, are needed to confirm these interpretations of

the data.

Based on the analysis in the present study, the primer set which gave the best

representation of diversity using BLAST was primer set 1, P-SSU-316F + GIC758R.

Primer set 1 amplified Entodinium simplex and Ostracodinium spp., which were not

amplified by either of the other two primer sets. However, primer set 2 had a slightly

higher percentage of sequences classified at a higher % identity in BLAST than primer

set 1. Both primer sets produced an amplicon of greater than 400 bases. Primer set 1 (P-

SSU-316F + GIC758R) had the highest number of observed OTUs, as well as the highest

ACE, CHAO, Shannon-Weaver and Inverse Simpson values of all three primer sets,

indicating the highest amount of diversity of the three amplicon libraries. Primer set 3

had the second highest ACE, Shannon-Weaver, and Inverse Simpson values. While

primer sets 2 and 3 target the same variable and signature regions, primer set 2 spans a

larger area of conserved region, which may account for the lower estimated diversity than

primer set 3.

It is important to note that primer set 1 (P-SSU-316F + GIC758R) produced

amplicons which could not differentiate between the pairs Blepharocorys microcorys and

Blepharocorys uncinata, Hemiprorodon gymnoprosthium and Prorodonopsis coli,

Tetratoxum excavatum and Tetratoxum parvum, Bandia deveneyi and Bandia smalesae,

and Polycosta roundi and Polycosta turniae. The aforementioned Blepharocorys spp.

127

(43), Hemiprorodon gymnoprosthium (44), Prorodonopsis coli (45), and Tetratoxum

spp. (46) have previously been found in the hind gut of the horse, and Bandia deveneyi,

B. smalesae, Polycosta roundi, and P. turniae in Australian marsupials (47). Since these

ciliate species mainly occur in non-ruminants, either P-SSU-316F + GIC758R or

GIC1080F + GIC1578R could be used in ruminants, but only GIC1080F + GIC1578R

should be used in non-ruminants. However, in order to bring about standardization of the

amplification of rumen ciliates and to better enable comparison across studies, GIC1080F

+ GIC1578R seems to be the better choice for a general gut ciliate primer set.


The authors would like to acknowledge Dr. John Crouse and Dr. Kimberlee Beckmen of

the Alaskan Department of Fish and Game for their assistance in rumen sample

collection.

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4.8 Figures

Figure 4-1 A map of the full-length protozoal 18S rRNA gene, including variable (V1-V9) and rumen

ciliate signature regions (SR1-SR4), and showing the respective amplicons of the three primer sets

used in the present study.

133

Figure 4-2 Taxonomy and proportion of unique pyrosequences using NCBI (BLAST), by forward

primers P-SSU-316F (Sylvester et al., 2004), GIC1080F (present study), and GIC1184F (present

study).

All sequences used passed all quality assurance steps outlined in Methods.

134

Figure 4-3 Distribution of sequence identity percentages to known sequences based on BLAST by

sequencing primer.

135

4.9 Tables

Table 4-1 Gastrointestinal tract ciliate protozoal primer specifications.

Primer

Name Sequence (5’3’)

Tm*

(50mM

NaCl)

Dimer

Formation

Hairpin

Formation

End

Stability

GIC758R CAACTGTCTCTATKAAYCG 47.4°C 2 bp 2 bp Medium

GIC1080F GGGRAACTTACCAGGTCC 53.6°C 3 bp 3 bp Medium

GIC1184F TGTCTGGTTAATTCCGA 47.2°C 4 bp 3 bp High

GIC1578R GTGATRWGRTTTACTTRT 42.1°C 2 bp 2 bp High

*Melting temperature. K=G/T Y=C/T R=A/G W=A/T

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Table 4-2 18S rRNA protozoal reference sequences used to determine genetic distance cutoff.

Species Accession

#

Species Accession

#

Alloiozona trizona AB795026 Epidinium ecaudatum AM158465

Amylovorax dehorityi AF298817 Eremoplastron dilobum AM158472

Amylovorax dogieli AF298825 Eremoplastron neglectum AM158473

Balantidium

ctenopharyngodoni

GU480804 Eremoplastron rostratum AM158469

Balantidium entozoon EU581716 Eudiplodinium maggii U57766

Bandia cribbi AF298824 Gassovskiella galea AB793783

Bandia deveneyi AY380823 Helicozoster indicus AB794981

Bandia smalesae AF298822 Hemiprorodon

gymnoprosthium

AB795028

Bandia tammar AF298823 Isotricha intestinalis U57770

Bitricha tasmaniensis AF298821 Isotricha prostoma AF029762

Blepharoconus hemiciliatus AB795027 Latteuria media AB794983

Blepharocorys angusta AB794976 Latteuria polyfaria AB794982

Blepharocorys curvigula AB534184 Macropodinium ennuensis AF298820

Blepharocorys jubata AB794977 Macropodinium yalabense AF042486

Blepharocorys microcorys AB794975 Neobalantidium coli AF029763

Blepharocorys uncinata AB530162 Ochoterenaia appendiculata AB794973

Bozasella sp AB793744 Ophryoscolex caudatus AM158467

Bundleia benbrooki AB555711 Ophryoscolex purkynjei U57768

Bundleia nana AB555712 Ostracodinium clipeolum AB536717

Bundleia postciliata AB555709 Ostracodinium gracile AB535662

Buxtonella sulcata AB794979 Paraisotricha colpoidea EF632075

Circodinium minimum AB794974 Paraisotricha minuta AB794984

Cochliatoxum periachtum EF632078 Parentodinium sp. AB530164

Cycloposthium bipalmatum AB530165 Polycosta roundi AF298819

Cycloposthium edentatum EF632077 Polycosta turniae AF298818

Cycloposthium ishikawai EF632076 Polydiniella mysorea AB555710

Dasytricha ruminantium U57769 Polyplastron multivesiculatum U57767

Didesmis ovalis AB795025 Prorodonopsis coli AB795029

Diplodinium dentatum U57764 Pseudoentodinium elephantis AB794972

Diploplastron affine AM158457 Raabena bella AB534183

Ditoxum funinucleum AB794091 Spirodinium equi AB794092

Entodinium caudatum U57765 Sulcoarcus pellucidulus AB795024

Entodinium dubardi AM158443 Tetratoxum parvum AB794969

Entodinium longinucleatum AB481099 Triadinium caudatum AB530163

Entodinium nanellum JX876561 Tripalmaria dogieli EF632074

Entodinium simplex AM158466 Triplumaria selenica AB533538

Epidinium caudatum U57763 Troglodytella abrassarti AB437346

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Table 4-3 Comparison of statistical parameters and results for three primer sets.

P-SSU-

316F

GIC758R

GIC1080F

GIC1578R

GIC1184F

GIC1578R

Total sequences before quality assurance steps 19,886 10,143 8,611

Total sequences after quality assurance steps 12,326 6,070 8,265

Unique sequences used for BLAST 769 697 424

Trimmed sequence length (bases) 450 b 450 b 375 b

Subsampled unique sequences used for

statistical analysis 424 424 424

Species-level distance within genera1 0.036 0.039 0.042

Recommended % cutoff for species-level

OTUs 4% 4% 4%

Observed species-level OTUs 48 25 20

CHAO 112 58 42

ACE 226 92 145

Shannon-Weaver 2.36 1.02 1.37

Good’s Coverage 0.94 0. 96 0.97

Inverse Simpson 4.69 1.71 2.59

Genus-level distance across genera2 0.087 0.079 0.096

Recommended % cutoff for genera-level OTUs 9% 8% 10%

Observed genus-level OTUs 15 14 12 1 Using near full-length 18S rRNA gene sequences from valid species, the species-level cutoff was

calculated to be 0.031. 2 Using near full-length 18S rRNA gene sequences, the genus-level cutoff was calculated to be 0.071.


Supplemental Material from this publication includes four lower triangle charts of genetic

distance comparisons, which are too large to be included in this text. Supplemental

Material can be accessed from the journal Applied and Environmental Microbiology:

http://aem.asm.org/content/early/2014/06/23/AEM.01644-14/suppl/DCSupplemental

138

CHAPTER 5 HIGH-THROUGHPUT DNA SEQUENCING OF THE MOOSE

RUMEN FROM DIFFERENT GEOGRAPHICAL LOCATION REVEALS A

CORE RUMINAL METHANOGENIC ARCHAEAL DIVERSITY AND A

DIFFERENTIAL CILIATE PROTOZOAL DIVERSITY.

Suzanne L. Ishaq1,*

, Monica A. Sundset2, John Crouse

3, and André-Denis G. Wright

1,4

*1 Department of Animal Science, University of Vermont, Burlington, Vermont, 05401

2 Department of Arctic and Marine Biology, University of Tromsø, Tromsø, Norway,

9019

3 Alaska Department of Fish and Game, Soldotna, Alaska, 99669

4 Present address: School of Animal and Comparative Biomedical Sciences, University

of Arizona, Tucson, Arizona 85721

*Contact: Suzanne Ishaq, Department of Animal Science, University of Vermont, 203

Terrill Building, 570 Main Street, Burlington VT 05405. [email protected]. Fax) 1-

802-656-8196.

Keywords: 16S rRNA/ 18S rRNA/ 454 Roche Titanium/ Alaska/ MiSeq/ Norway/

rumen/ Vermont

139

5.1 Abstract

Moose rumen samples from Vermont, Alaska, and Norway were investigated for

methanogenic archaeal and protozoal density using real-time PCR, and diversity using

high-throughput sequencing of the 16S rRNA and 18S rRNA gene, respectively.

Vermont moose showed the highest protozoal and methanogen densities. Alaskan

samples had the highest percentages of Methanobrevibacter smithii, followed by the

Norwegian samples. One Norwegian sample contained 43% Methanobrevibacter thaueri,

while all other samples contained <10%. Vermont samples had large percentages of

Methanobrevibacter ruminantium, as did two Norwegian samples. Methanosphaera

stadtmanae represented one third of sequences in three samples. Samples were

heterogeneous based on gender, geographic location, and weight class using AMOVA,

but did not cluster significantly using PCoA or Unifrac. Two Alaskan moose contained

>70% Polyplastron multivesiculatum, and one contained >75% Entodinium sp. Protozoa

from Norwegian moose belonged predominantly (>50%) to the genus Entodinium,

especially Ent. caudatum. Norwegian moose also contained a large proportion of

sequences (25-97%) which could not be classified beyond Ophryoscolecidae. Protozoa

from Vermont samples were predominantly Eudiplodinium rostratum (>75%), with up to

7% Diploplastron affine. Four of the eight Vermont samples also contained 5-12%

Entodinium spp. Samples were heterogeneous based on AMOVA, PCoA, and Unifrac.

Previously, Alaskan moose were speculated to consume a higher starch diet than

Vermont moose, which were presumably consuming a higher forage diet.

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5.2 Introduction

Previous investigations into the microorganisms in the rumen of the moose have focused

on bacteria using cultivation [1] and high-throughput sequencing techniques [2, 3], or on

protozoa using light microscopy [4–7] and high-throughput sequencing [8].

Methanogenic archaea in the rumen of moose have not been previously identified, nor

have methanogens or protozoa from moose been compared across samples from different

geographic locations. Methanogens and protozoa in the rumen are often found in

intracellular or extracellular symbiotic associations involving hydrogen transfer from

protozoa to methanogens. Previously, protozoa from the genera Entodinium,

Polyplastron, Epidinium, and Ophryoscolex have been shown to interact with

methanogens from the orders Methanobacteriales and Methanomicrobiales [9].

The objectives of this research were to identify the methanogens and protozoa present in

the rumen of moose from Alaska, Vermont, and Norway; to measure the density of

methanogens and protozoa in these samples; to compare samples across geographic

location, gender, and weight class to determine possible trends; and to compare samples

to published studies on wild and domesticated ruminants. It was hypothesized that moose

may have fewer total methanogens than domestic ruminants due to a fast rate of passage

through the gastrointestinal tract [10]. In previous studies, age [3, 11, 12] and geographic

location [3, 13] have played a role in differentiating core bacterial microbiomes, and it

was also hypothesized that this would hold true for methanogens in the moose rumen.

141

However, reindeer in various geographic locations have been shown to have similar

protozoal diversity, indicating that the host species may have been isolated long enough

to develop a common profile regardless of geographic location of the host [14].

5.3 Methods

A total of 17 samples were collected from the rumen of moose in Vermont, USA (n=8)

(Oct 2010), Troms County, Norway (n=6) (Sept-Oct 2011), and Soldotna, Alaska (n=3)

(Aug 2012). Sample collection and DNA extraction were previously described [3].

Metadata for each sample collected, including gender, weight, approximate age, and

coordinates of sample collection have also been published elsewhere [3]. Pooled samples

from Alaska (n=3) were previously sequenced and described [8]. Samples were

identified by location (Alaska=AK, Norway=NO, and Vermont=VT), host (m=moose),

individual moose (1-8), and by sample material (r= rumen), consistent with previous

publications [2, 3, 8].

PCR was performed on a C1000 ThermalCycler (Bio-Rad, Hercules, CA) using the

Phusion kit (ThermoScientific, CA) to amplify rDNA. For methanogenic archaea, the V1

to V3 region of the archaeal 16S rRNA gene was amplified using primers 86F (5’-

GCTCAGTAACACGTGG-3’) [15] and 471R (5’-GWRTTACCGCGGCKGCTG-3’)

[16]. The protocol was as follows: initial denaturing at 98°C for 10 min, then 35 cycles

of 98°C for 30s, 58°C for 30s, 72°C for 30s, then a final elongation step of 72°C for 6

142

min. For ciliate protozoa, the V3-V4 and signature regions 1-2 of the 18S rRNA gene

were amplified using primers P-SSU-316F (5’-GCTTTCGWTGGTAGTGTATT-3’) [17]

and GIC758R (5’-CAACTGTCTCTATKAAYCG-3’) [8] following previously described

conditions [8]. PCR amplicons were verified on an agarose gel (100V, 60 min), and

DNA bands were excised and purified as previously described [3]. Amplicons were sent

to MR DNA Laboratories (Shallowater, TX) for MiSeq ver. 3 (methanogens) or Roche

454 pyrosequencing with Titanium (protozoa).

5.3.1 Sequence analysis

All sequences were analyzed using MOTHUR ver. 1.31 [18]. For methanogens,

sequence analysis was as previously described [3], with the following modifications.

Sequences were trimmed to a uniform length of 436 alignment characters (minimum 350

bases), and candidate sequences were aligned against the Ribosomal Database Project

(RDP) reference alignment integrated into MOTHUR with the bacterial sequences

removed. Sequences were classified using the k-nearest neighbor method against the full

RDP alignment, which had been modified to include species-level taxonomy. A 2%

genetic distance cutoff was used to designate species. For protozoa, sequence analysis

was as previously described for primer set 1, using a 4% genetic distance cutoff to

designate species [8]. For each sample, sequences were subsampled, and CHAO [19],

ACE [20], Good’s Coverage [21] and the Shannon-Weiner Index [22] were calculated.

143

An analysis of molecular variance (AMOVA) and Unifrac [23] were used to compare the

heterogeneity of samples.

5.3.2 Real-time PCR

Real-time PCR was used to calculate archaeal and protozoal densities in whole samples.

DNA was amplified using a CFX96 Real-Time System (Bio-Rad, CA) and a C1000

ThermalCycler (Bio-Rad, CA). Data were analyzed using CFX Manager Software ver.

1.6 (Bio-Rad, CA). The iQ SYBR Green Supermix kit (Bio-Rad, CA) was used: 12.5µL

of mix, 2.5µl of each primer (40mM), 6.5µL of ddH2O, and 1µL of the initial DNA

extract diluted to approximately 10 ng/μL. For methanogens, the primers targeted the

methyl coenzyme-M reductase A gene (mcrA), following the protocol by Denman et al.

[24]. The internal standards for methanogens were a mix of Methanobrevibacter smithii,

M. gottschalkii, M. ruminantium and M. millerae (R2=0.998).

For protozoa, the primers, PSSU316F and PSSU539R [17], targeted the 18S rRNA gene,

following the protocol by Sylvester et al. [17], and the internal standards for protozoa

were created in the laboratory using fresh rumen contents which were filtered through

one layer of cheesecloth to remove large particles, and then the protozoa were allowed to

separate for two hours at 39°C. Once a protozoal pellet was visible, 50 ml were drawn

from the bottom of the funnel, and 1 volume of ethanol was added to fix the cells and

DNA. The mix was centrifuged for 5 min at 2,000 x G, the pellet was washed with TE

buffer (1MTris-HCl, 0.5 M EDTA, pH 8.0) and then centrifuged again. Cells were

144

counted microscopically using a Thoma Slide following the protocol by Dehority [5],

(R2=0.998). Both protocols were followed by a melt curve, with a temperature increase

0.5°C every 10s from 65ºC up to 95°C to check for contamination.

5.4 Results

5.4.1 Methanogens

A total of 141,368 sequences, of which, 47,370 were unique sequences, passed quality

assurance steps. For each sample, between 22 and 330 OTUs were assigned using a 2%

genetic distance cutoff, giving a total of 1,942 non-redundant OTUs. CHAO, ACE,

Good’s Coverage and Shannon-Weaver Diversity for each sample are provided in Table

1. The Vermont samples showed the highest Shannon index, CHAO, and ACE, while the

Norwegian samples showed the highest Good’s Coverage. The Alaskan samples showed

the highest observed OTUs. Although there were few shared OTUs among samples,

these shared OTUs represented a large number of shared sequences (Table 2).

Comparing all 17 samples across different factors using AMOVA, groups were

heterogeneous based on gender (p<0.001), geographic location (p=<0.001), and weight

class (p<0.001). In contrast, samples did not cluster significantly based on gender or

weight class using PCoA (Figure 1 A,C,E), although Vermont clustered separately from

Norway and Alaska. When comparing samples using Unifrac, all samples again did not

cluster significantly using either weighted (0.17, p<0.001) or unweighted (0.91, p=0.20)

145

parameters. However, 16 out of 136 pairwise sample comparisons were significantly

different (p<0.001).

Vermont samples contained the highest mean density of methanogens at 1.3E+10,

followed by Alaskan samples and Norwegian samples (5.19E+09 and 3.58E+09,

respectively) (Table 3). Alaskan samples had the highest percentages of

Methanobrevibacter (Mbr.) smithii (16 to 36%), followed by the Norwegian samples (10

to 24%) (Figure 2). The Norwegian sample NO1R, contained the highest percentage of

Mbr. thaueri (43% of total sequences), while all other samples contained <10%.

Vermont samples had large percentages of Mbr. ruminantium (27-51% of total

sequences), as did the Norwegian samples NO3R and NO4R (40 and 41%, respectively)

(Figure 2). Methanosphaera stadtmanae was highest in NO5R (36%), VT8R (35%), and

NO6R (34%) (Figure 2). Less than 36 sequences total were found of each of the

following: Methanocella, Methanospirillum, Methanolobus, Methanosarcina,

Picrophilus, Methanobacterium, Mbr. curvatus, Mbr. cuticularis, or Unclassified at the

genus level (“Other”, Figure 2).

5.4.2 Protozoa

A total of 499,152 sequences, of which, 72,091 were unique sequences, passed quality

assurance steps. For each sample, between 1 and 31 OTUs were estimated using a 4%

genetic distance cutoff, giving a total of 110 non-redundant OTUs. CHAO, ACE, Good’s

146

Coverage and Shannon-Weaver Diversity index for each sample are provided in Table 1.

Both Norwegian and Vermont samples had extremely high coverage (>0.97%), yet low

Shannon diversity, CHAO and ACE values. Although there were few shared OTUs

among samples, these shared OTUs represented a large number of shared sequences

(Table 2). When comparing samples using Unifrac, samples clustered significantly using

weighted (0.71, p<0.001) and unweighted (0.93, p<0.001) parameters. When comparing

the Norway and Vermont samples across different factors using AMOVA, groups were

heterogeneous based on gender (p<0.001), geographic location (p=<0.001), and weight

class (p<0.001). This was also confirmed using PCoA for gender, location, and weight

class (Figure 1B, D, F).

Vermont samples contained the highest mean density of protozoa at 4.70E+06, followed

by Alaskan samples and Norwegian samples (3.83E+06 and 5.17E+04, respectively)

(Table 3). Using a previously described reference alignment and taxonomy of valid

protozoal sequences [8], protozoa were identified (Figure 3). Two Alaskan moose

contained >70% Polyplastron multivesiculatum, and one contained >75% Entodinium sp.

Protozoa from Norwegian moose belonged predominantly (>50% of total sequences) to

the genus Entodinium, especially Ent. caudatum (Figure 3). Norwegian moose also

contained a large proportion of sequences (25-97% of total sequences) which could not

be classified beyond the Ophryoscolecidae family (Figure 3). Protozoa from Vermont

samples were predominantly composed of Eudiplodinium rostratum (>75% of total

147

sequences). Vermont samples also contained up to 7% Diploplastron affine (Figure 3).

Many other species were identified in moose, with <1% each of the following identified:

Anoplodinium denticulatum, Dasytricha spp., Diplodinium dentatum, Enoploplastron

triloricatum, Entodinium bursa, Ent. dubardi, Ent. furca dilobum, Ent. furca monolobum,

Ent. longinucleatum, Ent. simplex, Epidinium caudatum, Epi. ecaudatum caudatum,

Epidinium spp., Eremoplastron dilobum, Eremoplastron rostratum, Eudiplodinium

maggii, Isotricha intestinalis, Isotricha prostoma, Metadinium medium, Metadinium

minorum, Ophryoscolex purkynjei, Ophryoscolex spp., Ostracodinium clipeolum,

Ostracodinium dentatum, Ostracodinium gracile, and Ostracodinium spp.

5.5 Discussion

The present study represents the first insight into the methanogenic archaeal diversity in

the rumen of the moose. Two of three Alaskan moose, as well as two of six Norwegian

moose had a larger proportion of methanogens belonging to the SGMT clade (Mbr.

smithii, Mbr. gottschalkii, Mbr. millerae, and Mbr. thaueri). All eight Vermont moose,

one Alaskan moose and four Norwegian moose had greater proportions of members of

the RO clade (Mbr. ruminantium and Mbr. olleyae). Previously, the SGMT clade was

show to be prevalent in alpaca [25], sheep [26], and reindeer [27]. As with bacteria in a

previous study [3], Alaskan moose shared a large number of methanogenic sequences,

followed by the Norwegian samples, and females shared more archaeal and protozoal

148

sequences than males. Unlike previously, the 202-300 kg weight class shared the greatest

number of archaeal and protozoal sequences of all the weight classes.

Methanobrevibacter smithii, unlike many other methanogens, has been shown to grow at

less than neutral pH [28], is often associated with high-calorie diets, has been shown to

influence weight gain in rats [29], and has been shown to improve polysaccharide

fermentation by bacteria [30, 31]. Conversely, Mbr. ruminantium has been associated

with a low-energy (high forage) diet [32]. Previously, Alaskan moose were speculated to

be on a higher starch/energy diet than those of Vermont moose, presumably on a higher

forage/lower energy diet [2], which may account for the relatively high proportions of

Mbr. smithii in Alaskan moose and high proportions of Mbr. ruminantium in Vermont

moose in the present study. Methanosphaera stadtmanae has previously been associated

with diets involving fruit, as they require methanol which is a byproduct of pectin

fermentation, as has been previously seen in omnivores [33, 34] and ruminants [16, 35,

36]. Though distinct in terms of proportion of taxa present in each of the three moose

populations, the samples were not statistically different between populations. This

suggests that moose have a core methanogen microbiome, as has been suggested for

protozoa in other host species [14].

In the present study, protozoa from Alaska, Norway, and Vermont were distinctly

different. Norwegian samples were dominated by Entodinium spp., while Vermont

149

samples were dominated by Polyplastron multivesiculatum and Eudiplodinium maggii.

The previously published data are limited by the fact that protozoal sequences were

generated using mixed amplicons from three Alaskan moose, not sequenced individually

[8].

Previously, using light microscopy, moose were shown to have primarily Entodinium

spp., including Ent. dubardi and Ent. longinucleatum in Alaska [5], Ent. dubardi and

other Entodinium spp. from Slovakia, and Ent. dubardi and Epi. caudatum in Finish

Lapland [7]. More recently using high-throughput sequencing, moose in Alaska were

shown to have a high percentage of Polyplastron multivesiculatum, and well as a variety

of Entodinium and other species [8], which was also shown in the present study. The

Norwegian samples had a high percentage of Ent. caudatum, Ent. furca dilobum, and

other Entodinium species, giving them a similar profile to moose samples from Alaska

[5], Finland [7], and Slovakia [6] using light microscopy. The Norwegian samples also

contained a large proportion of sequences which could not be identified beyond the

family level, indicating that these moose host novel ciliate species, or that no 18S rRNA

sequences exist for previously identified species.

Given the markedly different protozoal populations found in Alaska, Vermont, and

Norway, as well as the AMOVA analysis confirming statistically different groups, it may

be concluded that moose do not have a typical protozoal diversity as do reindeer [14].

150

Factors such as diet [27, 37, 38], and weaning strategy [39] have an effect on numbers

and type of protozoa. Previously, total protozoal counts were shown to be elevated in

concentrate selectors [38], while Entodinium populations were decreased in animals fed a

higher concentrate diet over those fed a roughage diet [38]. Entodinium spp. are a major

source of starch digestion in the rumen, as well as bacterial digestion [40]. Polyplastron

multivesiculatum produces xylanase and other carbohydrate-degrading enzymes [41],

which allows it to break down hemicellulose in plant cell walls and contribute to fiber

digestion. Eudiplodinium spp. also preferentially ingest structural carbohydrates [42, 43].

It has been shown that protozoal density affects methanogen density [37, 44], as the two

microbial communities are often symbiotically associated with one another. In particular,

Polyplastron, Eudiplodinium maggii, and Entodinium caudatum have been shown to have

>40% association with methanogens [45]. More specifically, Polyplastron was recently

shown to associate with Methanosphaera stadtmanae and Mbr. ruminantium [46].

Methanogen and protozoal densities in reindeer from Norway [47] averaged very closely

to densities found in Norwegian moose, but lower than in Alaskan and Vermont moose.

Domestic steers fed a roughage diet had an average density of 1.34E+09 for

methanogens, which were predominantly Methanobrevibacter spp. [24], and which was

lower than the present study. Holstein dairy cattle on a high-forage diet had an average

density of 6.04E+05 for protozoa [17], which were 2 logs less than densities in moose. It

151

was also shown that densities decreased on a low-forage diet, and the dominant genus

was Entodinium spp. [17]. Roughage diets in livestock have been shown to increase

methane emissions [48], even when the roughage diets are not associated with altered

methanogen densities [32, 49].


Acknowledgments The authors would like to acknowledge the Vermont Fish and

Wildlife Department for sample collection logistics; Terry Clifford, Archie Foster, Lenny

Gerardi, Ralph Loomis, Beth and John Mayer, and Rob Whitcomb for collection of

Vermont moose samples; Dr. Even Jørgensen, University of Tromsø, and Dr. Helge K.

Johnsen, University of Tromsø, for collection of Norwegian moose samples; and Dr.

Kimberlee Beckmen of the Alaska Department of Fish & Game for collection of Alaskan

moose samples.

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157

5.8 Figures

Figure 5-1 PCoA of moose (A, C, and E) and protozoa (B, D, and F).

A, B are colored for gender (red=female and blue=male) for methanogens and protozoa, respectively. C, D

are colored for location (red=Alaska, green= Norway, and blue=Vermont) for methanogens and protozoa,

respectively. E, F are colored for weight class (na= red, 1-100 kg= dark blue, 101-200= right facing green,

201-300=down facing green, 301-400= yellow, 400+= light blue) for methanogens and protozoa,

respectively.

158

Figure 5-2 Moose rumen methanogen taxonomy of total sequences, per sample from Alaska, Norway

and Vermont

“Other” represents the following taxa which has <1% Methanocella, Methanospirillum, Methanolobus,

Methanosarcina, Picrophilus, Methanobacterium, Mbr. curvatus, Mbr. cuticularis, or Unclassified at the

genus level.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

AK

M1R

AK

M2R

AK

M3R

NO

M1R

NO

M2R

NO

M3R

NO

M4R

NO

M5R

NO

M6R

VT

M1R

VT

M2R

VT

M3R

VT

M4R

VT

M5R

VT

M6R

VT

M7R

VT

M8R

Other

Mbr. wolinii

Mbr. woesei

Mbr. sp.ATM

Mbr.

arboriphilus

Msph.

stadtmanae

Mbr.

gottschalkii

Mbr. millerae

Mbr. thaueri

Mbr. smithii

Mbr. olleyae

Mbr.

ruminantium

159

Figure 5-3 Moose rumen protozoal taxonomy of total sequences, per sample from Norway and

Vermont.

Other species constitute <1% each of the following: Anoplodinium denticulatum, Dasytricha spp.,

Diplodinium dentatum, Enoploplastron triloricatum, Entodinium bursa, Ent. dubardi, Ent. furca dilobum,

Ent. furca monolobum, Ent. longinucleatum, Ent. simplex, Epidinium caudatum, Epi. ecaudatum caudatum,

Epidinium spp., Eremoplastron dilobum, Eremoplastron rostratum, Eudiplodinium maggii, Isotricha

intestinalis, Isotricha prostoma, Metadinium medium, Metadinium minorum, Ophryoscolex purkynjei,

Ophryoscolex spp., Ostracodinium clipeolum, Ostracodinium dentatum, Ostracodinium gracile, and

Ostracodinium spp.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

AK

M1R

AK

M2R

AK

M3R

NO

M1R

NO

M2R

NO

M3R

NO

M4R

NO

M5R

NO

M6R

VT

M1R

VT

M2R

VT

M3R

VT

M4R

VT

M5R

VT

M6R

VT

M7R

VT

M8R

Other

Diploplastron

affine

Entodinium

nanellum

Entodinium

caudatum

Entodinium

unclassified

Ophryoscolecidae

unclassified

Polyplastron

multivesiculatum

Eudiplodinium

rostratum

160

5.9 Tables

Table 5-1 Statistical measures per sample for methanogens (met) and protozoa (prot) in Alaska (AK),

Norway (NO), and Vermont (VT). Samples were subsampled using the smallest group for methanogens and protozoa. Species-level cutoff

was 2% for methanogens and 4% for protozoa.

Subsampled Sequences

Sample Total

seqs

Total

OTUs OTUs CHAO ACE

Good’s

Coverage

Shannon-

Weiner

Methanogens

AKM1R 4366 152 18 161 14 0.93 0.47

AKM2R 537 23 20 200 0 0.92 0.52

AKM3R 53648 292 6 17 10 0.98 0.15

Mean 19517 156 15 126 8 0.94 0.38

NOM1R 506 22 22 232 0 0.91 0.56

NOM2R 1830 79 19 163 26 0.93 0.48

NOM3R 19990 70 6 16 5 0.98 0.14

NOM4R 1355 33 15 115 0 0.94 0.39

NOM5R 17130 293 12 56 39 0.96 0.30

NOM6R 7106 70 9 32 41 0.97 0.24

Mean 7986 95 14 102 19 0.95 0.35

VTM1R 2351 102 26 262 328 0.90 0.70

VTM2R 1803 63 23 213 105 0.91 0.59

VTM3R 12855 99 11 46 111 0.96 0.31

VTM4R 4477 149 22 219 47 0.91 0.56

VTM5R 1180 82 38 470 675 0.85 1.02

VTM6R 3142 155 29 325 275 0.89 0.77

VTM7R 790 43 28 389 0 0.89 0.73

VTM8R 8302 330 31 359 538 0.88 0.81

Mean 4363 128 26 285 260 0.90 0.69

Protozoa

AKM1R 81387 31 1 1 0 1.00 0.01

AKM2R 57698 31 1 1 0 1.00 0.01

AKM3R 16200 12 1 1 0 1.00 0.01

Mean 51762 25 1 1 0 1.00 0.01

NOM1R 24605 1 1 1 0 1.00 0.00

NOM2R 15468 4 2 2 0 0.99 0.05

NOM3R 20351 9 3 4 0 0.97 0.14

NOM4R 5354 1 1 1 0 1.00 0.00

NOM5R 35189 7 2 2 0 0.99 0.06

NOM6R 30145 7 2 2 0 0.99 0.08

161

Mean 21852 5 2 2 0 0.99 0.06

VTM1R 24635 2 2 2 0 0.99 0.08

VTM2R 27901 6 2 2 0 0.99 0.06

VTM3R 41131 8 4 7 2 0.96 0.25

VTM4R 27612 3 1 1 0 0.99 0.03

VTM5R 30065 5 3 4 0 0.97 0.15

VTM6R 8334 3 3 3 0 0.98 0.12

VTM7R 27742 2 2 2 0 0.99 0.04

VTM8R 25335 1 1 1 0 1.00 0.00

Mean 26594 4 2 3 0 0.98 0.09

Table 5-2 The number of shared OTUs and unique sequences across different samples in Alaska

(AK), Norway (NO), and Vermont (VT).

Cutoff values of 2% for methanogens and 4% for protozoa were used to generate OTUs.

Methanogens Protozoa

Samples Compared Shared

OTUs

Unique

Seq

Shared

OTUs

Unique

Seq

All samples (n=17) 1 44967 1 48850

AK samples (n=3) 2 19888 2 45572

NO samples (n=6) 2 16227 2 1771

VT samples (n=8) 2 11255 2 1635

All Females (n=11) 2 31300 2 47400

All Males (n=6) 2 16070 2 1578

0-100 kg (NO1R, NO6R) 2 2684 2 519

101-200 kg (NO2R, VT1R, VT3R) 4 4804 2 528

201-300 kg (NO3R, NO5R, VT2R,

VT6R) 2 14007 2 1384

301-400 kg (VT5R, VT7R, VT8R) 2 3729 2 541

162

Table 5-3 Real-time PCR results for methanogenic archaea and ciliate protozoa in Alaska (AK),

Norway (NO), and Vermont (VT).

Sample Corrected cells/ml rumen digesta archaea Corrected cells/ml rumen digesta

protozoa

AKM1R 3.33E+09 3.60E+06

AKM2R 1.91E+09 4.72E+05

AKM3R 1.03E+10 7.43E+06

Mean

(SE) 5.19E+09 (4.51E+09) 3.83E+06 (3.48E+06)

NO1R 8.66E+07 5.92E+03

NO2R 1.54E+08 1.10E+04

NO3R 1.95E+08 5.46E+04

NO4R 1.38E+08 7.26E+03

NO5R 2.17E+10 1.67E+05

NO6R 8.25E+08 6.45E+04

Mean

(SE) 3.58E+09 (8.76E+09) 5.17E+04 (6.20E+04)

VT1R 3.87E+09 2.14E+06

VT2R 1.88E+10 3.00E+06

VT3R 4.26E+10 9.02E+06

VT4R 1.98E+10 5.70E+06

VT5R 7.68E+09 6.53E+06

VT6R 8.93E+08 5.08E+05

VT7R 4.54E+09 5.50E+06

VT8R 6.02E+09 5.18E+06

Mean

(SE) 1.3E+10 (1.38E+10) 4.70E+06 (2.70E+06)

Mean

all (SE) 7.36E+09 (4.95E+09) 2.86E+06 (2.47E+06)

163

CHAPTER 6 DESCRIPTION OF STREPTOCOCCUS ALCIS, SP. NOV., AND

STREPTOCOCCUS VERMONTENSIS, SP. NOV., AND PROPOSAL OF

THREE NEW SUBSPECIES OF STREPTOCOCCUS GALLOLYTICUS

ISOLATED FROM THE RUMEN OF MOOSE (ALCES ALCES) IN

VERMONT.

Suzanne L. Ishaq1*, Doug G. Reis2, Hannah M. Lachance1, and André-Denis G.

Wright1,3

1 Department of Animal Science, College of Agriculture and Life Sciences, University of


2 Department of Microbiology and Molecular Genetics, College of Agriculture and Life

Sciences, University of Vermont, 95 Carrigan Drive., Burlington, Vermont 05405

3 Present Address: School of Animal and Comparative Biomedical Sciences, College of

Agriculture and Life Sciences, University of Arizona, 1117 E. Lowell Street, Tucson,

Arizona. 85721



8196.

Email addresses: SLI: [email protected], DR: [email protected], HL:

[email protected], ADGW: [email protected]

Contents Category: New Taxa (Firmicutes)

Keywords/Cross-reference: 16S rRNA gene, anaerobic culturing, bacteria

GenBank ASCN: KP009806-KP009843

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6.1 Summary

Standard anaerobic and aerobic culturing techniques were used to isolate,

characterize, and investigate the functional abilities of 37 isolates of Streptococcus

bacteria from the rumen of the North American moose. Isolates were able to grow at

higher temperatures and salinities than previously described cultivars, and at a wider

range of pH. All 37 isolates produced acid from fructose, galactose, glucose, glycerol,

lactose, maltose, mannitol, and sucrose. Variable numbers of isolates produced acid from

N-acetylglucosamine, arabinose, cellobiose, cellulose, inulin, mannose, melibiose,

raffinose, and salicin. Twenty-nine isolates produced a ropy exopolysaccharide, three

reduced tellurite, and one isolate produced indole from tryptophan. Two isolates did not

produce biogenic amines from amino acids. Twenty-four isolates are new strains of S.

gallolyticus subsp. gallolyticus, and can produce acid from glycogen, inulin, mannitol,

melibiose, and raffinose. Three isolates are new strains of S. gallolyticus subsp.

macedonicus, which are unable to produce acid from glycogen, inulin, mannitol,

melibiose, and raffinose. Nine isolates were not able to produce acid from glycogen or

raffinose. However, some were able to produce acid from inulin, mannitol or melibiose.

Based on the differential biochemical capabilities, DNA-DNA hybridizations, seven

house-keeping genes, and genetic distances, based upon 16S rRNA gene sequence, of 10

isolates, two new species: S. alcis sp. nov., and S. vermontensis sp. nov.; and three new

subspecies of S. gallolyticus are proposed: S. gallolyticus subsp. mannosilyticus subsp.

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nov., S. gallolyticus subsp. melibiosilyticus subsp. nov., and S. gallolyticus subsp.

ruminantium subsp. nov.

This study used classical culturing techniques to isolate and characterize isolates of

Streptococcus gallolyticus from the rumen of the North American moose (Alces alces). It

was hypothesized that isolates from the moose rumen would be functionally distinct from

previously identified bacteria, and that they might be appropriate for food production.

Only a few studies have been published on the bacteria in the ruminal environment of

moose (Ishaq & Wright, 2014, 2012; Dehority, 1986).

Several species of the lactic acid bacteria (LAB) Streptococcus are non-

pathogenic and have been extremely important economically in the production of dairy

food products due to the secretion of an exopolysaccharide (EPS) that gives dairy

byproducts desired textures (Bolotin et al., 2004; Georgalaki et al., 2000; McSweeney,

2004; Papadimitriou et al., 2012; Tsakalidou et al., 1998; Vincent et al., 2001).

Streptococcus gallolyticus can tolerate high-salinity environments, like those found in

cheese making (Beresford et al., 2001). Many species of Streptococcus possess

decarboxylase enzymes, which make them capable of producing biogenic amines (i.e.

biologically active molecules) from different precursor amino acids. Biogenic amines

can cause a variety of adverse gastrointestinal and cardiovascular symptoms depending

on the amine and the dose. Streptococcus gallolyticus isolates which produced one or

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more of these amines would be considered unfit candidates for food production

applications (Ladero et al., 2010; McSweeney, 2004).

Isolates were cultured from whole rumen digesta samples, which were collected

fresh, during the October 2010 hunting season in Vermont, with permission of licensed

hunters through the Vermont Department of Fish and Wildlife. For information on the

moose age and weight, see Ishaq and Wright (2012). All isolations were performed on

M8 agar (Bryant & Robinson, 1961; Dehority & Grubb, 1976) plates inside an anaerobic

chamber (90% nitrogen, 5% hydrogen, 5% carbon dioxide) (COY Laboratories,

Michigan, US). Samples were serially diluted, plated with 5 replicates, and monitored

for up to 1 week. Colonies were picked and re-isolated on fresh media until pure using

gram staining and colony morphology measurements, and monocultures were identified

using near full-length 16S rRNA gene sequencing performed at the University of

Vermont Cancer Center DNA Analysis Facility (Burlington, Vermont, US). The

bacterial 16S rRNA gene was amplified using the universal bacterial primers 27F and

1494R (Lane, 1991).

PCR was performed using the iTaq DNA Polymerase kit (Bio-Rad, California,

US) per kit instructions. PCR conditions were: initial denaturation of 94°C for 5 min,

then 33 cycles of 94°C for 30 s, 50°C for 30 s, and 72°C for 1 min, followed by a final

extension of 72°C for 6 min. PCR was performed on a C1000 thermal cycler (Bio-Rad,

California, US). Recalcitrant isolates were first extracted using the DNA extraction

protocol in the QIAamp DNA Stool Mini Kit (QIAGEN, Maryland, US).

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Amplification was verified by 1% agarose gel electrophoresis (100V for 60

min), and the remaining PCR product was enzymatically cleaned with ExoSAP-IT

(Affymetrix, California, US). Cycle sequencing primers used included 27F, 1492R,

1494R, 907R (Lane, 1991), and 907F (Blackall et al., 1995). Sequences were proofread

using ChromasPro ver. 1.7.5 (Technelysium Pty. Ltd., Australia), and aligned using the

CLUSTALW algorithm in MEGA ver. 5.05 (Tamura et al., 2011). The alignment was

refined by eye, and then used to calculate pairwise genetic distance using the Kimura 2-

parameter model (Kimura, 1980). Sequences were identified using GenBank’s Basic

Local Alignment Search Tool (BLAST) (Altschul et al., 1990), and compared to

published sequences of lactic acid bacteria from NCBI using a neighbor-joining tree

generated with MEGA (Figure 1).

Near full-length sequences were generated for each isolate and deposited in

NCBI under accession numbers KP009806-KP009843. Sequences had 99% identity to

known sequences of S. gallolyticus subsp. macedonicus using BLAST. When clustered

using a neighbor-joining tree, 37 isolates clustered along with S. gallolyticus subsp.

macedonicus(T)

(Figure 1). Mean genetic identity among the isolates was 99.7% (range

97.7–100%). The mean sequence identity between individual isolates and S. gallolyticus

subsp. macedonicus(T)

was 99.5% (range 98.4–99.7%), while the mean sequence identity

between individual isolates and S. gallolyticus subsp. gallolyticus(T)

was 99% (range

97.6–99.27%). Thirty-six of 37 isolates had a higher percent identity to S. gallolyticus

subsp. macedonicus(T)

than to S. gallolyticus subsp. gallolyticus(T)

, the exception being

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VTM3R19T. The novel isolates had the following sequence identity to S. gallolyticus

subsp. gallolyticus(T)

(VTM3R19T, 99%; VTM4R28

T, 99.2%; VT1R31

T, 95.5%;

VTM3R37T, 94.8%; VTM1R54

T, 95.4%) and S. gallolyticus subsp. macedonicus

(T)

(VTM3R19T, 99.3%; VTM4R28

T, 99.4%; VT1R31

T , 99.2; VTM3R37

T, 98.5%;

VTM1R54T, 98.8%).

Five isolates which were proposed new taxa were also classified based on seven

housekeeping genes as previously described [204]: guanylate kinase, gmk (AB829357-

AB829373); peroxide resistance, dpr (AB829337-AB829356); DNA topoisomerase IV

subunit A, parC (AB829398-AB829412); phosphate acetyltransferase, pta (AB829413-

AB829429); dihydrotase, pyrC (AB829430-AB82944); DNA repair protein, recN

(AB829451-AB829472); and RNA polymerase sigma factor, rpoD (AB829374-

AB829397). Isolates clustered separately from reference sequences for gmk, parC, pta,

recN (Figures 2A, 2C, 2D, 2F), and three of five clustered separately for rpoD (Figure

2G).

Monocultures were maintained on M8 media plates with sodium azide to

prevent potential contamination growth of gram negative species, then transferred to

MRS media (Downes & Ito, 2001; De Mann et al., 1960) for the duration of testing.

Before each test, isolates were subcultured in MRS broth (1% v/v inoculation) for 24 h,

and tests were run in triplicate. Stock aliquots of isolates were mixed with 80% glycerol

and stored at -80°C. Isolates were given unique identifiers (i.e. VTM3R11) containing

the following abbreviations: Vermont (VT), moose (M), individual number (1–4), and

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rumen (R), as well as isolate number. All isolate colonies showed similar morphology:

small, white, irregular to round colonies with an opaque, glistening and butyrous

appearance. Cell morphologies were consistently non-motile, gram-positive cocci in

small chains or groups. All isolates were catalase negative.

Optimal growth parameters were determined by incubating isolates for 24 h at

various temperatures (25–49°C), pH (5.0–10.0) (adjusted prior to autoclaving), or

salinities (0–9% NaCl). Optical density (absorbance, 600 nm) was used to determine

relative growth using a Spectronic 200 (ThermoScientific, CA) (Georgalaki et al., 2002).

Optimal growth ranges were set as isolates measuring >0.5% absorbance. Optimal

temperature ranged from 31°C to 39°C, optimal salinity ranged from 0 to 3% NaCl, and

optimal pH ranged from pH 5.7 to 7.7 (Supplemental Figure 1). Six isolates were able to

grow above 1% absorbance at 25°C (VTM3R15, VTM1R33, VTM3R37T, VTM4R46,

VTM4R49, VTM4R51), and seven isolates were able to grow above 0.5% at 49°C

(VTM3R11, VTM3R26, VTM1R44, VTM4R46, VTM4R49, VTM1R50, VTM4R54).

Six isolates were able to grow above 0.5% at pH 5.0 and above 1% absorbance at pH

10.0 (VTM3R15, VTM2R16, VTM3R37T, VTM3R40, VTM4R46). Only three isolates

were able to grow above 0.5% at 9% NaCl (VTM1R54T, VTM3R42, VTM4R13). In the

present study, isolates tolerated high salinity environments, very high and low pH ranges,

and high and low temperatures: higher than previously described S. macedonicus (sic)

isolates (Tsakalidou et al., 1998).

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Heat tolerance was tested by incubating 48 h old cultures in a 60°C water bath

for 30 min, then inoculating onto MRS agar plates and incubating at 37°C for up to 72 h

to observe for growth. Six isolates showed no change in growth after heat shock

(VTM1R14, VTM3R15, VTM4R46, VTM4R49, VTM4R51, VTM4R54), and three

isolates grew minimally (VTM1R44, VTM1R48, VTM3R32).

Ruthenium red (RR) plates (Stingele & Mollet, 1995) were used to determine if

isolates produced a ropy exopolysaccharide, which prevents RR staining the cell wall of

isolates. Because RR was ineffective at differentiating isolates if added to the media

before autoclaving as specified previously (Stingele & Mollet, 1995), an additional 0.2

g/L RR was added after autoclaving (i.e. 0.28 g/L). Twenty-nine isolates exhibited a

ropy exopolysaccharide, which is preferable in dairy byproducts (Table 1).

Isolates which produced a ropy exopolysaccharide then had extracellular protein

quantified using skimmed milk medium (SMM) (Dabour & LaPointe, 2005; De Vuyst et

al., 1998). The combined protocol is as follows: cultures were heat treated at 90°C for 15

min to inactivate enzymes, then centrifuged at 12,000 x g for 20 min at 4°C to pellet

bacterial cells, and 1 volume of 20% trichloroacetic acid was added to the supernatant to

precipitate protein. The supernatant was centrifuged at 16,000 x g for 20 min at 4°C to

pellet proteins. The supernatant was removed, the pellet air-dried, and then suspended in

ddH2O for the protein assay. The protein assay was performed using the Bio-Rad Protein

Assay Kit, (Bio-Rad, California, US), following the manufacturer’s instructions. Casein

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was used to make protein standards, and assays were read using a Spectronic 200

(ThermoScientific, California, US).

Thirteen isolates produced between 0.5–1 mg/ml of protein each (Table 1). Data

are visualized in Supplemental Figure 2. The quality and quantity of EPS produced by

cultures are important for a successful consumer food product, as it contributes to product

structure, mouth feel, and texture (Folkenberg et al., 2005), especially in low-fat dairy

products, as it maintains the texture and structure that would normally be provided by fat,

and can be used in applications where stabilizers are unavailable for use (Patel &

Prajapati, 2013). In previous studies, some isolates produced no EPS (Georgalaki et al.,

2000).

To measure acid production, isolates were subcultured in skim milk (10% w/v),

and acid production was recorded as the pH value at 1 h, 6 h and 24 h (Georgalaki et al.,

2000), (Table 2), and is visualized in Supplemental Figure 3. In the present study, isolates

produced a similar amount of acid after 6 h of incubation as previously described isolates

(Georgalaki et al., 2000). Isolates were subcultured onto MRS agar plates containing the

pH indicator chlorophenol red, to test for acidic byproducts from different carbohydrates

(Georgalaki et al., 2000). All isolates produced acidic byproducts from D-fructose,

galactose, D+-glucose, glycerol, lactose, maltose and sucrose, and a variable number

produced acids from D-mannose, D-arabinose, salicin, N-acetylglucosamine, cellobiose,

melibiose, inulin, D-raffinose, and cellulose (Table 2). Streptococcus gallolyticus has

previously been shown to utilize cellobiose (Chamkha et al., 2002), and a presumptive

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gene resembling that of a Ruminococcus albus endoglucanase has previously been

identified in S. gallolyticus (Rusniok et al., 2010). Streptococcus gallolyticus subsp.

gallolyticus can produce acid from glycogen, inulin, mannitol, melibiose, pullulan and

raffinose, all of which previous strains of S. gallolyticus subsp. macedonicus were unable

to do (Osawa et al., 1995; Schlegel, 2003; Tsakalidou et al., 1998).

Isolates were tested on mannitol media for their ability to metabolize mannitol

and tolerate potassium tellurite. All isolates fermented mannitol, and two reduced tellurite

and produced black precipitate (VTM3R11, VTM3R17). One isolate did not tolerate

potassium tellurite and was unable to grow (VTM1R33). Isolates were grown for 14 d to

test for the production of indole from tryptophan using Kovac’s reagent (Kovacs, 1928)

added to the culture broth. No isolates produced indole from tryptophan.

To determine whether isolates could hydrolyze esculin (and produce a black

precipitate), strains were plated on esculin media with and without the presence of bile

salts. Three isolates could not tolerate bile salts and exhibited no growth (VTM1R27,

VTM1R33, VTM1R50), while 35 isolates tolerated bile salts and hydrolyzed esculin.

Without bile salts, all 37 isolates were capable of hydrolyzing esculin. Streptococcus

gallolyticus subsp. gallolyticus can hydrolyze esculin (Osawa et al., 1995; Schlegel,

2003; Tsakalidou et al., 1998).

Isolates were subcultured into Simmon’s Citrate slants (Simmons, 1926) for 7d,

and observed for bacterial growth and color change, to indicate the ability use citrate as a

carbon source and ammonia as a nitrogen source (Georgalaki et al., 2000). Fifteen

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isolates used citrate as their sole source of carbon and ammonium ions as their source of

nitrogen (Table 1). To test ability to reduce nitrate to nitrite, isolates were subcultured

into nitrate broth for 3 and 7 d and then tested for color change using potassium iodine

strips moistened with 1N HCl. Three isolates showed variable ability to produce nitrite

from nitrate after 3 d, and six more (n=12) isolates were positive after 7 d. Twenty-five

isolates showed lipase activity (Table 1), as determined by halo formation on MRS agar

plates (pH 6.8) with tributyrin (1% v/v) and arabic gum (1% w/v) (Georgalaki et al.,

2000).

To test biogenic amine production, isolates were inoculated on media containing

a precursor amino acid (2% w/v, ornithine, histidine, lysine, tyrosine) (Joosten &

Northolt, 1989), and observed for blue/green color formation around colonies

(Georgalaki et al., 2000). Most isolates produced biogenic amines from some precursor

amino acids, 11 isolates produced all four amines, and two produced none (Table 1).

Biogenic amines may be used to create functional foods, as some are involved in immune

response, cell growth, and homeostasis regulation (Ladero et al., 2010). However,

certain biogenic amines can be toxic in high concentrations and their presence in certain

foods is not preferred (Ladero et al., 2010). In the present study, isolates created

biogenic amines from histidine, lysine, ornithine, and tyrosine. In a previous study,

strains of Streptococcus produced little to no biogenic amine (Georgalaki et al., 2000).

Using biochemical profiles, as well as 16S sequencing, the present study

classified 24 isolates as S. gallolyticus subsp. gallolyticus and 3 isolates as S. gallolyticus

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subsp. macedonicus. Based on the differential biochemical capabilities, DNA-DNA

hybridizations, seven house-keeping genes, and 16S rRNA genetic distances of 10

isolates, two new species, S. alcis sp. nov. and S. vermontensis sp. nov., and three new

subspecies of S. gallolyticus are proposed: S. gallolyticus subsp. mannosilyticus subsp.

nov., S. gallolyticus subsp. melibiosilyticus subsp. nov., and S. gallolyticus subsp.

ruminantium subsp. nov.

For the novel isolates, the type strain was tested for hemolysis pattern using 5%

sheep’s blood on tryptic soy agar (TSA) plates (ThermoScientific, California, US). Of

the two proposed novel species and three proposed novel subspecies, four were alpha-

hemolytic, and S. gallolyticus subsp. melibiosilyticus subsp. nov. (VTM3R37T) was non-

hemolytic. Each type strain was also genotypically characterized by DNA-DNA

hybridization. G+C content and DNA-DNA hybridization were calculated using a C1000

thermal cycler with CFX96 real-time system (Bio-Rad, California, US) and previously

published protocols (Bowman et al., 1998; Moreira et al., 2011). The reference strains

Streptococcus gallolyticus subsp. gallolyticus(T)

(ATCC 700065) (Osawa et al., 1995) and

S. gallolyticus subsp. macedonicus(T)

(ATCC BAA-249) (Tsakalidou et al., 1998) were

used. DNA-DNA hybridization was calculated using the change in melting temperature

(ΔTm) between the reference and hybrid strains (Moreira et al., 2011) using regression

equations created with previously published DNA-DNA hybridization data (Schlegel at

al., 2003). DDH, GC content, and ΔTm are presented in Table 3. G+C content ranged

from 37.7 to 38.1%, ΔTm was between 0.3 and 2.6°C, and % DNA-DNA hybridization

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to reference strains was between 73 and 91%. The species-level cutoff for DNA-DNA

hybridization is 70%. However, previously published data on DNA-DNA hybridization

ranged from 50–100% for strains of S. gallolyticus subsp. gallolyticus, and 54–100% for

strains of S. gallolyticus subsp. macedonicus (Schlegel et al. 2003), indicating a high

degree of genetic variability even within a subspecies.

6.2 Description of Streptococcus alcis sp. nov.

Streptococcus alcis (al’cis. N.L. gen. n. alcis named after the moose, Alces alces, the

source of the type strain). Cells are Gram-positive cocci, occurring in pairs or short

chains, non-motile, non-sporulating, and catalase-negative. Colonies are circular, 1 mm

in diameter after 24 h at 37 °C, and white to unpigmented. Growth is enhanced in a 5%

CO2 atmosphere, and occurs in MRS broth without gas production. No growth in

6±0.5% (w/v) NaCl broth. Alpha hemolytic on 5% blood agar. The type strain of S.

alcis VTM1R28T

(= ATCC-00408-01T

= DSM XXXXT

=NCBI KP009833) was isolated

from the rumen of a 1-year old female moose in Vermont, USA. Produces acid from N-

Acetylglucosamine, arabinose, cellobiose, cellulose, fructose, galactose, glucose,

glycerol, glycogen, inulin, lactose, maltose, mannose, melibiose, raffinose, salicin and

sucrose. Exopolysaccharide is ropy in consistency. Biogenic amines produced from

histidine and lysine. Lipase is produced. It is tolerant of bile salts and can hydrolyze

esculin. It can use citrate as a sole carbon source and ammonium ions as a sole nitrogen

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source. Characteristics useful in their differentiation from related organisms and also in

the delineation between the two subspecies are listed in Tables 1–3.

6.3 Description of Streptococcus vermontensis sp. nov.

Streptococcus vermontensis (ver.mont.en’sis. N.L. masc. adj. vermontensis named after

the U.S. state where the moose were captured, the source of the type strain). Cells are

Gram-positive cocci, occurring in pairs or short chains, non-motile, non-sporulating, and

catalase-negative. Colonies are circular, 1 mm in diameter after 24 h at 37 °C, and white

to unpigmented. Growth is enhanced in a 5% CO2 atmosphere, and occurs in MRS broth

without gas production. No growth in 6±0.5% (w/v) NaCl broth. Alpha hemolytic on

5% blood agar. The type strain of S. vermontensis VTM3R19T

(= ATCC-00408-06T

=

DSM XXXXXT =NCBI KP009835) was isolated from the rumen of a 2-year old male

moose in Vermont, USA. Produces acid from N-acetylglucosamine, arabinose,

cellobiose, fructose, galactose, glucose, glycerol, lactose, maltose, mannose, melibiose,

salicin and sucrose. Exopolysaccharide is non-ropy in consistency. Biogenic amines

produced from histidine, lysine, and ornithine. It is tolerant of bile salts and can

hydrolyze esculin. Characteristics useful in their differentiation from related organisms

and also in the delineation between the two subspecies are listed in Tables 1–3.

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6.4 Description of Streptococcus gallolyticus subsp. mannosilyticus subsp. nov.

Streptococcus gallolyticus subsp. mannosilyticus (man.no.si.ly'ti.cus. N.L. neut. n.

mannosum mannose; N.L. masc. adj. lyticus (from Gr. neut. adj. lutikos), able to loosen,

able to dissolve; N.L. masc. adj. mannosilyticus breaking down mannose). Cells are

Gram-positive cocci, occurring in pairs or short chains, non-motile, non-sporulating, and

catalase-negative. Colonies are circular, 1 mm in diameter after 24 h at 37 °C, and white

to unpigmented. Growth is enhanced in a 5% CO2 atmosphere, and occurs in MRS broth

without gas production. Some growth in 6±0.5% (w/v) NaCl broth. Alpha hemolytic on

5% blood agar. The type strain of S. gallolyticus subsp. mannosilyticus VTM1R31T

(=ATCC-00408-03T

= DSM XXXXXT =NCBI KP009837) was isolated from the rumen

of a 1-year old female moose in Vermont, USA. Produces acids from fructose, galactose,

glucose, glycerol, lactose, maltose, mannose, and sucrose. Acid production from N-

Acetylglucosamine, arabinose, cellobiose, cellulose, and salicin is variable.

Exopolysaccharide is ropy or non-ropy in consistency. Biogenic amines produced from

lysine, and variably from histidine, ornithine, and tyrosine. Lipase is variably produced.

It is tolerant of bile salts and can hydrolyze esculin. Ability to use citrate as a sole carbon

source and ammonium ions as a sole nitrogen source is variable. Three strains of this

subspecies were also isolated from the moose rumen: VTM3R15, VTM1R44, and

VTM4R49. Characteristics useful in their differentiation from related organisms and also

in the delineation between the two subspecies are listed in Tables 1–3.

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6.5 Description of Streptococcus gallolyticus subsp. melibiosilyticus subsp. nov.

Streptococcus gallolyticus subsp. melibiosilyticus (me.li.bi.o.si.ly'ti.cus. N.L. neut. n.

melibiosum melibiose; N.L. masc. adj. lyticus (from Gr. neut. adj. lutikos), able to

loosen, able to dissolve; N.L. masc. adj. melibiosilyticus breaking down melibiose). Cells

are Gram-positive cocci, occurring in pairs or short chains, non-motile, non-sporulating,

and catalase-negative. Colonies are circular, 1 mm in diameter after 24 h at 37 °C, and

white to unpigmented. Growth is enhanced in a 5% CO2 atmosphere, and occurs in MRS

broth without gas production. Some growth in 6±0.5% (w/v) NaCl broth. Non (gamma)

hemolytic on 5% blood agar. The type strain of S. gallolyticus subsp. melibiosilyticus

VTM3R37T (=ATCC-00408-04

T = DSM XXXXX

T =NCBI KP009841) was isolated

from the rumen of a 1-year old female moose in Vermont, USA. Produces acid from

arabinose, cellobiose, cellulose, fructose, galactose, glucose, glycerol, lactose, maltose,

mannose, melibiose, salicin and sucrose. Acid production from N-Acetylglucosamine is

variable. Exopolysaccharide is ropy in consistency. Biogenic amines produced from

lysine, and variably from histidine, ornithine, and tyrosine. Lipase is variably produced.

It is tolerant of bile salts and can hydrolyze esculin. Ability to use citrate as a sole carbon

source and ammonium ions as a sole nitrogen source is variable. One strain of this

subspecies was also isolated from the moose rumen, VTM3R23. Characteristics useful in

their differentiation from related organisms and also in the delineation between the two

subspecies are listed in Tables 1–3.

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6.6 Description of Streptococcus gallolyticus subsp. ruminantium subsp. nov.

Streptococcus gallolyticus subsp. ruminantium (ru.mi.nan’ti.um. L. part adj. ruminans -

antis, ruminating; N.L. pl. gen. n. ruminantium, of ruminants) named after the ruminant

rumen/forestomach where the bacteria resided. Cells are Gram-positive cocci, occurring

in pairs or short chains, non-motile, non-sporulating, and catalase-negative. Colonies are

circular, 1 mm in diameter after 24 h at 37 °C, and white to unpigmented. Growth is

enhanced in a 5% CO2 atmosphere, and occurs in MRS broth without gas production.

Some growth in 6±0.5% (w/v) NaCl broth. Alpha hemolytic on 5% blood agar. The type

strain of S. gallolyticus subsp. ruminantium VTM1R54T (=ATCC-00408-05

T = DSM

XXXXXT =NCBI KP009842) was isolated from the rumen of a 1-year old female moose

in Vermont, USA. Produces acid from arabinose, fructose, galactose, glucose, glycerol,

lactose, maltose, and sucrose. Acid production from cellulose and melibiose is variable.

Exopolysaccharide is ropy or non-ropy in consistency. Biogenic amines produced from

lysine, ornithine, and tyrosine. Lipase is produced. It is tolerant of bile salts and can

hydrolyze esculin. One strain (VTM4R54) of this subspecies was also isolated from the

rumen of moose in Vermont, US. Characteristics useful in their differentiation from

related organisms and also in the delineation between the two subspecies are listed in

Tables 1–3.

6.7 Conflict of interest

The authors declare no conflict of interest.

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The authors would like to thank Sam Rosenbaum, Ken Wesley, and Emma Hurley for

their help in preparing culture media, collecting optical density data, and for general lab

maintenance; the Vermont Fish and Wildlife Department for their help in rumen sample

collection logistics; and Terry Clifford, Archie Foster, Lenny Gerardi, Ralph Loomis,

Beth and John Mayer, and Rob Whitcomb for collection of rumen samples.

6.9 References

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local alignment search tool. J Molec Bio 215, 403–10.

Austin, P. J., Suchar, L. A., Robbins, C. T. & Hagerman, A. E. (1989). Tannin-

binding proteins in saliva of deer and their absence in saliva of sheep and cattle. J

Chem Ecol 5, 1335–1347.

Belovsky, G. E. (1981). Food plant selection by a generalist herbivore: the moose.

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185

6.10. Figures

Figure 6-1 Neighbor-joining tree comparing isolates to type isolates to lactic acid bacteria.

Tree was generated using MEGA ver. 5.05 (Tamura et al., 2011) and the Kimura 2-parameter model

(Kimura, 1980). (T) = type strain, and numbers at the nodes represent bootstrap values. Lactobacillus

acidophilus (AB680529) and Enterococcus faecium (AJ301830) were used as out-groups.

186

Figure 6-2 UPGMA trees comparing novel isolates using the housekeeping genes gmk (A), dpr (B),

parC (C), pta (D), pyrC (E), recN(F), and rpoD (G).

187



188



189



190

6.11 Tables

Table 6-1Ability of isolates to produce biogenic amines from selected amino acids, produce lipase,

metabolize citrate, produce a ropy or non-ropy exopolysaccharide, and extracellular protein

production.

For biogenic amine production from ornithine, histidine, tyrosine and lysine, and lipase production results

are presented as positive (+), slight positive (~), and negative (-). Simmon’s Citrate results are presented as

positive (+) or negative (-) for both butt (anaerobic) and slant (aerobic) portions, as well as color change

(B). Exopolysaccharide results are presented by type: ropy or non-ropy (NR).

Isolates O

rnit

hin

e

His

tid

ine

Tyro

sin

e

Lysi

ne

Lip

ase

Sim

mon

s

(b/s

)

EP

S t

yp

e

Pro

tein

(mg/m

L)

Streptococcus gallolyticus subsp. gallolyticus

VTM3R11 - - + + + -/- Ropy 0.24

VTM3R12 + - - + + -/- Ropy 0

VTM4R13 + - + + - +/- Ropy n/a

VTM1R14 + - + + + -/- Ropy 0.32

VTM3R17 ~ - + + + +/- NR n/a

VTM4R20 + - + + - -/- Ropy 0.69

VTM3R21 + - + + + -/- Ropy 0.48

VTM3R22 + + + + + -/- Ropy 0.98

VTM3R24 + + + + - -/- Ropy 0.07

VTM1R25 - - - - + +B/- Ropy 0.59

VTM3R26 + - + + + -/- Ropy 0.91

VTM1R27 + + + + + + Ropy n/a

VTM1R29 + + + + + -/- Ropy 0.74

VTM3R32 + ~ + + ~ -/- Ropy 0.63

VTM1R35 + + - + - + Ropy 0.45

VTM1R38 - - - - ~ +/- Ropy 0.69

VTM1R41 ~ - + + - +/- Ropy 0.72

VTM3R42 + + + + + + Ropy 0.76

VTM2R45 + - ~ + - + Ropy n/a

VTM4R46 + - + + + +B/- Ropy 0.43

VTM2R47 + + + + + + NR n/a

VTM1R48 + + + + - -/- Ropy 0

VTM1R50 + + + + - + Ropy 0.3

VTM2R55 + - + + + -/- Ropy 0.69

Streptococcus gallolyticus subsp. macedonicus

VTM1R33 + + - - + -/- NR n/a

191

VTM2R39 + + - + + -/- Ropy 0.63

VTM4R51 + - + + - +B/+B NR n/a

Streptococcus alcis sp. nov.

VTM1R28T - + - + + +B/+ Ropy 0.41

Streptococcus vermontensis sp. nov.

VTM3R19T + - + + - -/- NR n/a

Streptococcus gallolyticus subsp. mannosilyticus subsp. nov.

VTM3R15 - - - + ~ -/- NR n/a

VTM1R31T + ~ + + - -/- Ropy 0.38

VTM1R44 + - + + + + Ropy 0.27

VTM4R49 + - + + + +B/+B NR n/a

Streptococcus gallolyticus subsp. melibiosilyticus subsp. nov.

VTM3R23 + + + + - -/- Ropy 0.72

VTM3R37T - - - + ~ +B/+B Ropy 0.41

Streptococcus gallolyticus subsp. ruminantium subsp. nov.

VTM1R53 + - + + + -/- NR n/a

VTM4R54T + - + + + -/- Ropy 0.63

Total (+)

isolates 31 15 28 34

25

15

29

192

Table 6-2 Acid production and ability of isolates to produce an acid byproduct from a variety of

carbohydrates. Acid production in skim milk was measured as pH over time. Positive acid production results (+) were

indicated by a color change. All isolates produced acid from D-fructose, galactose, D+-glucose, glycerol,

lactose, maltose and sucrose (data not shown).

Isolate Acid production

(pH) Acid production from carbohydrates

6hr 24hr

N-A

cet

ylg

luco

sam

ine

D-a

rab

inose

Cel

lob

iose

Cel

lulo

se

Gly

cogen

Inu

lin

D-M

an

nose

Mel

ibio

se

D-r

aff

inose

Sali

cin

Streptococcus gallolyticus subsp. gallolyticus

VTM3R11 5.83 4.72 + + + - + + + + + +

VTM3R12 6.14 5.11 + + + + + + + + + +

VTM4R13 6.35 5.34 + + + + + + + + + +

VTM1R14 6.13 5.11 + + + + + + + + + +

VTM3R17 6.11 5.38 + + + - + + + + + +

VTM4R20 5.95 4.70 + + + + + + + + + +

VTM3R21 6.15 5.07 + + + + + + + + + +

VTM3R22 5.88 4.69 + + + + + + + + + +

VTM3R24 5.91 4.71 + + + - + + + + + +

VTM1R25 6.04 4.84 + + + + + + + + + +

VTM3R26 6.12 5.12 + + + + + + + + + +

VTM1R27 5.57 4.25 + + + - + + + + + +

VTM1R29 5.95 4.61 + + + + + + + + + +

VTM3R32 6.01 4.96 + + + + + + + + + +

VTM1R35 6.13 4.92 + + + - + + + + + +

VTM1R38 6.25 4.80 + + + + + + + + + +

VTM1R41 5.84 4.47 + + + + + + + + + +

VTM3R42 5.79 4.84 + + + + + + + + + +

VTM2R45 6.07 4.74 + + + + + + + + + +

VTM4R46 6.05 5.57 + + + - + + + + + +

VTM2R47 5.97 4.44 + + + - + + + + + +

VTM1R48 5.91 4.61 + + + - + + + + + +

193

VTM1R50 6.08 5.52 - + + + + + + + + +

VTM2R55 5.88 4.82 + + + - + + + + + +

Streptococcus gallolyticus subsp. macedonicus

VTM1R33 6.07 4.98 - - - - - - - - - -

VTM2R39 5.88 4.57 - + - - - - - - - -

VTM4R51 6.08 5.52 + + - - - - - - - -

Streptococcus alcis sp. nov.

VTM1R28T 5.88 4.64 + + + + + + + + + +

Streptococcus vermontensis sp. nov.

VTM3R19T 6.11 5.09 + + + - - - + + - +

Streptococcus gallolyticus subsp. mannosilyticus subsp. nov.

VTM3R15 6.18 5.85 + - - - - - + - - -

VTM1R31T 5.96 4.61 - + - - - - + - - +

VTM1R44 6.08 5.30 - - - - - - + - - -

VTM4R49 6.10 4.56 + - + - - - + - - +

Streptococcus gallolyticus subsp. melibiosilyticus subsp. nov.

VTM3R23 5.98 5.08 - + + + - - + + - +

VTM3R37T 6.06 5.85 + + + + - - + + - +

Streptococcus gallolyticus subsp. ruminantium subsp. nov.

VTM1R53 6.22 6.21 - + - - - - - - - -

VTM4R54T 6.14 5.67 - + - + - - - + - -

Total (+) isolates 29 33 29 19 25 25 32 29 25 30

194

Table 6-3 G+C content of isolates and DNA-DNA hybridization to reference strains Streptococcus

gallolyticus gallolyticus (ATCC 700065) and S. gallolyticus macedonicus (ATCC BAA-249).

DNA-DNA hybridization (DDH) was calculated based on ΔTm between reference and hybrid strains, and

regression equations generated from previously published DHH data for S. gallolyticus gallolyticus

(R2=0.734) and S. gallolyticus macedonicus (R2=0.481) (Schlegel at el., 2003).

% G+C

Content

S. gallolyticus

gallolyticus

S. gallolyticus

macedonicus

Isolate ΔTm

°C

%

DDH

ΔTm

°C

%

DDH

VTM1R28T S. alcis sp. nov. 38.0 1.0 84.2 1.2 83.0

VTM3R19T S. vermontensis sp. nov. 37.9 1.2 82.2 0.4 90.6

VTM1R31T

S. gallolyticus

mannosilyticus subsp.

nov.

37.9 1.9 77.4 2.6 70.0

VTM3R37T

S. gallolyticus

melibiosilyticus subsp.

nov.

38.1 2.0 76.7 2.0 75.4

VTM1R54T

S. gallolyticus

ruminantium subsp. nov. 38.1 2.4 73.7 1.0 84.9

195


Supplemental Figure 1 Growth of isolates at varying temperatures (A), salinities (B) and pH (C),

measured as absorbance at 600 nm.

196

Supplemental Figure 2 Protein production of ropy-strain isolates, measured as protein precipitate at

750nm. Casein standards were used to generate a standard curve (R2=0.995).

Supplemental Figure 3 Acid production of isolates in skim milk media incubated at 37°C. The pH of

cultures was measured at 1, 6 and 24 hours to determine acid production over time. Initial pH of the

media was calculated using a sterile blank as control.

0.0

0.5

1.0

1.5

2.0

2.5

0 0.5 1 1.5

mg/m

l p

rote

in

Absorbance at 750nm

Casein

Isolates

0

1

2

3

4

5

6

7

1 h 6 h 24 h

pH

Control

197

CHAPTER 7 FIBROLYTIC BACTERIA ISOLATED FROM THE RUMEN

OF NORTH AMERICAN MOOSE (ALCES ALCES).

Suzanne L. Ishaq*,1

, Doug Reis2, and André-Denis G. Wright

1,3

1 Department of Animal Science, College of Agriculture and Life Sciences, University of

Vermont, Burlington, Vermont 05405

2 Department of Microbiology and Molecular Genetics, College of Agriculture and Life

Sciences, University of Vermont, Burlington, Vermont 05405

3 Present address: School of Animal and Comparative Biomedical Sciences, College of

Agriculture and Life Sciences, University of Arizona, Tucson, Arizona 85721

*Suzanne Ishaq, Department of Animal Science, University of Vermont, 203 Terrill


8196.

Keywords: Bacillus/bacteria/fibrolytic/moose/probiotic

198

7.1 Abstract

Fibrolytic bacteria were isolated from the rumen of North American moose

(Alces alces), which eat a high-fiber diet of woody browse. Thirty-one isolates were

cultured from moose rumen digesta samples collected in Vermont. Using Sanger

sequencing of the 16S rRNA gene, culturing techniques, and optical densities, isolates

were identified and screened for biochemical properties important to plant carbohydrate

degradation. The 31 isolates had the following percent identities to known sequences in

the NCBI database: Bacillus licheniformis, 98–100% (n=22); B. foraminis, 98% (n=1); B.

firmus, 98% (n=1); B. flexus, 100% (n=1); B. niabensis, 98% (n=1); Paenibacillus

woosongensis, 98% (n=1); and Staphylococcus saprophyticus, 99–100% (n=4). Isolates

were able to digest cellulose (n=31), cellobiose (n=28), xylan (n=26), starch (n=21),

carboxymethylcellulose (n=21), and lignin (n=18) under minimal nutritional conditions.

Fifteen isolates were able to digest all six carbohydrates or plant components tested.

Isolates were able to tolerate up to 10% (n=16) salinity, between pH 4.0 (n=27) and pH

10.0 (n=27), and between 20°C (n=28) and 55°C (n=30). Isolates were tolerant to

sodium azide (n=30), could reduce potassium tellurite (n=3), metabolize mannitol (n=29),

produce indole from tryptophan (n=4), and all isolates could use citrate or propionate as a

sole carbon source, as well as ammonium ions for nitrogen.

199

7.2 Introduction

Fibrolytic bacteria in the digestive tract of ruminants are instrumental in the

digestion of plant matter for the host. The North American moose (Alces alces) is a large

cervid, which consumes a high-fiber diet of woody browse: mainly willow, pine, maple,

and fir (Belovsky and Jordan, 1981; Shipley, 2010). They also consume seasonally

available aquatic vegetation, which is higher in sodium that arboreal vegetation

(Belovsky and Jordan, 1981). This diet provides several nutritional challenges for which

the moose has adapted. Moose produce tannin-binding salivary proteins to reduce the

digestibility-reducing effects of tannins (Austin et al., 1989), and have a large liver: body

size which may help them detoxify secondary metabolites found in willow and conifers

(Shipley, 2010). Species of rumen bacteria that are resistant to secondary metabolites

have been identified in some ruminants (Odenyo and Osuji, 1998; Dailey et al., 2008;

Sundset et al., 2008), but have not yet been described in moose.

Few studies have identified the rumen bacteria of moose (Ishaq and Wright,

2012, 2014), or used culturing techniques to isolate bacteria from the rumen of moose

(Dehority, 1986). Previously, it was shown that moose from Vermont contained a higher

proportion of bacteria belonging to the phylum Firmicutes, which are mostly fibrolytic

(Ishaq and Wright, 2014).

200

7.3 Methods

Fresh rumen samples were collected during the October 2010 hunting season in

Vermont, with permission of licensed hunters through the Vermont Department of Fish

and Wildlife. Hunters were given written and verbal instructions to collect samples from

well inside the rumen, and to seal the container quickly to reduce oxygen exposure.

Whole rumen samples (i.e. fluid and particulate matter) collected during field dressing

were frozen within 2 h of death, and were transferred to the laboratory within 24 h, where

they were mixed with an equal volume of 80% glycerol and stored at -80°C until

culturing. Additional information regarding the hosts can be found in Ishaq & Wright

(2012). Isolates were given unique identifiers (i.e. VTM3R11) containing the following

abbreviations: Vermont (VT), moose (M), individual number (1–4), and rumen (R), as

well as isolate number.

Bacteria were isolated on M8 agar plates (Bryant and Robinson, 1961; Dehority

and Grubb, 1976), with an added 2 g/L of cellulose and cellobiose, inside an anaerobic

chamber (COY Laboratories, Michigan, US). Whole rumen contents were serial diluted,

and all dilutions (10-1

to 10-9

) were plated with five replicates. Plates were monitored for

up to 7 d, and colonies were picked and re-isolated on fresh media until colonies were

shown to be pure using gram staining and colony morphology measurements. A total of

31 isolates were cultured from four individual moose rumen samples, and stock aliquots

of each isolate were stored at -80°C. Isolates were tested for their catalase reaction

(Gordon et al., 1973).

201

Monocultures were identified using automated cycle sequencing at the

University of Vermont DNA Analysis Facility. The bacterial 16S rRNA gene was

amplified using the universal bacterial primers 27F and 1494R (Lane, 1991). PCR was

performed using the iTaq DNA Polymerase kit (Bio-Rad, California, US) following the

manufacturer’s instructions. PCR conditions were: initial denaturation of 94°C for 5 min,

then 33 cycles of 94°C for 30 s, 50°C for 30 s, and 72°C for 1 min, followed by a final

extension of 72°C for 6 min. PCR was performed on a C1000 thermal cycler (Bio-Rad,

California, US). Recalcitrant isolates were first extracted using the DNA extraction

protocol in the QIAamp DNA Stool Mini Kit (QIAGEN, Maryland, USA). Sequences

were proofread using ChromasPro ver. 1.7.5, and aligned using the CLUSTALW

algorithm in MEGA ver. 6.0. The alignment was visually inspected, and then used to

calculate pairwise genetic distance using the Kimura 2-parameter model (Tamura et al.,

2011). Sequences were classified using BLAST (NCBI) and compared to published

sequences of fibrolytic bacteria from NCBI, and a neighbor joining tree was generated

using MEGA.

As cellulose in the broth media prevented accurate optical density

measurements, isolates were subcultured into 10 ml of tryptic soy broth (TSB) (1%

vol/vol inoculation), and then incubated for 24 h at various temperatures or pH (adjusted

prior to autoclaving). Optical density was used to determine relative growth using a

Spectronic 200 (ThermoScientific, California, US), with absorbance measured 600 nm

(Georgalaki et al., 2002). All samples were run in triplicate, and optimal ranges were set

202

as all isolates measuring >0.5% absorbance. Optimal salinity was measured as growth on

tryptic soy agar (TSA) media with 4-15% NaCl. Heat tolerance was tested by immersing

48 h old cultures in a 60°C water bath for 30 min, then inoculating TSA plates (1%

vol/vol inoculation) and incubating at 37°C for up to 72 h to observe for growth. Isolates

which were able to survive >55°C were tested for their ability to tolerate sodium azide.

Isolates were grown on azide dextrose media (tryptone, 15g/L; beef extract, 4.5g/L;

glucose, 7.5g/L; sodium chloride, 7.5g/L; and sodium azide, 0.2g/L; pH 7.2) and

incubated at 45°C for 5 d and observed for growth.

Isolates were tested for their ability to digest complex carbohydrates (cellulose,

cellobiose, carboxymethylcellulose, xylan, and starch) or plant components (lignin) on

minimal media (Bandounas et al., 2011). Minimal media plates were incubated at 37°C

for up to two weeks to observe for growth. Isolates were tested on mannitol media for

their ability to metabolize mannitol and tolerate potassium tellurite. To test for the

production of the aromatic compound indole from the amino acid tryptophan, isolates

were grown in 1% w/v tryptone broth for 14 d, after which Kovac’s reagent was added to

the culture broth to test for a color reaction (Kovacs, 1928). Isolates were subcultured

into Simmon’s Citrate slants (Simmons, 1926) and Propionate Slants (Gordon et al.,

1973) for 7 d, and observed for bacterial growth and color change to indicate the ability

to use citrate or propionate, respectively, as a carbon source and ammonia as a nitrogen

source (Georgalaki et al., 2000). To test the ability to reduce nitrate to nitrite, isolates

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were subcultured into nitrate broth for 2 and 7 d and then tested for color change using

potassium iodine strips moistened with 1N HCl.

7.4 Results

All 31 isolates were gram positive and catalase positive. Isolates had the

following percent identity to known sequences in NCBI: Bacillus licheniformis, 98–

100% (n=22); B. foraminis, 98% (n=1); B. firmus, 98% (n=1); B. flexus, 100% (n=1); B.

niabensis, 98% (n=1); Paenibacillus woosongensis, 98% (n=1); and Staphylococcus

saprophyticus, 99–100% (n=4) (Table 1, Figure 1). All 16S rRNA sequences are

available from NCBI (KP245773- KP245803).

All 31 isolates tolerated 4% NaCl (data not shown). Isolates (n=16) were able to

tolerate up to 10% salinity (Table 1), including B. firmus, B. flexus, some B.

licheniformis, B. niabensis, P. woosongensis, and some S. saprophyticus. Isolates from

all species grew to >0.5 absorbance between pH 4.0 (n=27) and pH 10.0 (n=27), and

between 20°C (n=28) and 55°C (n=30) (Figure 2). Twenty-nine isolates exhibited

excellent growth after heat shock, but two isolates (B. niabensis VTM4R58, and S.

saprophyticus VTM2R99) exhibited no growth. All but one B. licheniformis isolate

(VTM3R64) tolerated sodium azide and exhibited growth after 5 d.

Under minimal conditions, isolates were able to digest cellobiose (n=28), xylan

(n=26), starch (n=21), carboxymethylcellulose (n=21), and lignin (n=18) (Table 1). All

31 isolates were able to grow on cellulose, glucose, and lactose (data not shown), and 15

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isolates were able to digest all six carbohydrates tested (Table 1). Twenty-seven isolates

were able to metabolize mannitol and produce a color change, but four B. licheniformis

could not (VTM2R66, VTM1R71, VTM1R80, VTM1R88). Only two B. licheniformis

isolates (VTM2R66, VTM2R82) and one B. foraminis isolate (VTM4R85) could reduce

tellurite. Two B. licheniformis isolates (VTM1R74, VTM1R75), one B. firmus isolate

(VTM2R84), and one B. foraminis isolate (VTM4R85) were able to produce indole from

tryptophan. All isolates were able to use citrate and propionate as their carbon source,

and use ammonia for nitrogen. Twelve isolates were able to reduce nitrite to nitrate after

48 h, and an additional two isolates (S. saprophyticus VTM2R99 and B. firmus

VTM2R84) were able to reduce nitrite to nitrate after 7 d of growth (Table 1).

7.5 Discussion

Thirty-one fibrolytic bacterial isolates were examined for their biochemical

capabilities and potential as a probiotic for ruminants. Based on their ability to survive a

wide range of growth parameters and digest complex carbohydrates even on minimal

media, many of the thirty-one fibrolytic isolates in the present study have the potential for

use in agricultural or industrial applications. However, the ability to survive in the

developing digestive tract using milk or milk replacer as a substrate, as well as the ability

to consistently grow well in culture, are also important considerations for a viable

probiotic product.

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Bacillus licheniformis is an important member of the rumen community as it

produces a variety of extracellular enzymes which can digest lignocelluloses (Archana

and Satyanarayana, 1997), starches (Saito, 1973; Pen et al., 1992), keratin (Lin et al.,

1992), and acetate (Veith et al., 2004). It is able to digest glucose anaerobically (Veith et

al., 2004), and certain strains show antibiotic resistance (Pollock, 1965; Moews et al.,

1990). The industrial applications of B. licheniformis are extensive due to the breadth of

its enzymatic products, but also because many are thermophilic or halophilic (Gordon et

al., 1973; Veith et al., 2004; Wei et al., 2010). The present study identified 22 isolates

which had greater than 98% sequence identity to B. licheniformis, as well as four isolates,

which had greater than 98% sequence identity to B. foraminis, B. firmus, B. flexus, and B.

niabensis.

Staphylococcus saprophyticus is a cellulolytic bacterium originally isolated from

the termite gut (Paul et al., 1986). The present study identified four isolates which had

greater than 99% sequence identity to S. saprophyticus. Paenibacillus woosongensis was

originally isolated from forest soil, and was shown to digest a variety of carbohydrates,

including cellulose and xylan (Lee and Yoon, 2008). One isolate in the present study had

a 98% sequence identity to P. woosongensis.

The present study found that 15 out of 31 isolates were able to digest all six

different carbohydrates and plant components investigated on minimal media. Lignin is

an aromatic alcohol polymer found in the cell walls of plants and some algae. In the cell

wall it is often bonded to cellulose or hemicelluloses, which increases the durability of

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plants cell walls, but decreases their digestibility. After cellulose, lignin is the second

most abundant polymer on Earth. Xylans are hemicelluloses which are found in the cell

wall of plants, especially hardwoods, and some algae, and if it is not fermented by gut

microorganisms it can decrease the absorption of minerals in the intestines (Jiang K,

1986). Mannitol is a sugar found in species of deciduous flowering ash.

Carboxymethylcellulose is a purified form of cellulose that is more soluble in water, thus

it in used in food production, pharmaceuticals, or industrial applications.

The ability to survive under restrictive nutritional conditions is especially

important trait for bacteria used in industrial applications, but can also provide an

advantage over competitive species or strains of bacteria which require vitamins or other

substrates in the rumen. Additionally, bacteria which can positively impact the host,

would be beneficial to overall animal health in addition to increasing dietary efficiency.

Indole production often takes place in the intestines, and is used as a quorum-sensing

signal molecule between gut bacteria. However, its presence in the intestines also

stimulates cellular junction-associated molecules in gut epithelial cells, and promotes

resistance to dextran sodium sulfate (DSS)-induced colitis (Shimada et al., 2013).

7.6 References

1. Shipley LA: Fifty years of food and foraging in moose: lessons in ecology from a

model herbivore. Alces 2010, 46:1–13.

2. Belovsky GE, Jordan PA: Sodium dynamics and adaptations of a moose

population. J Mammal 1981, 62:613–621.

207

3. Austin PJ, Suchar LA, Robbins CT, Hagerman AE: Tannin-binding proteins in

saliva of deer and their absence in saliva of sheep and cattle. J Chem Ecol 1989,

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4. Odenyo AA, Osuji PO: Tannin-tolerant ruminal bacteria from East African

ruminants. Can J Microbiol 1998, 44:905–909.

5. Dailey RN, Montgomery DL, Ingram JT, Siemion R, Vasquez M, Raisbeck MF:

Toxicity of the lichen secondary metabolite (+)-usnic acid in domestic sheep. Vet

Pathol 2008, 45:19–25.

6. Sundset MA, Kohn A, Mathiesen SD, Praesteng KE: Eubacterium rangiferina, a

novel usnic acid-resistant bacterium from the reindeer rumen. Naturwissenschaften

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10. Dehority BA, Grubb JA: Basal medium for the selective enumeration of rumen

bacteria utilizing specific energy sources. Appl Env Microbiol 1976, 32:703–710.

11. Bryant MP, Robinson IM: An improved nonselective culture medium for ruminal

bacteria and its use in determining diurnal variation in numbers of bacteria in the

rumen. J Dairy Sci 1961, 44:1446–1456.

12. Gordon RE, Pang CH-N, Haynes WC: The Genus Bacillus. Washington: Agricultural

Research Service; 1973.

13. Lane DJ: 16S/23S rRNA sequencing. In Nucleic acid Tech Bact Syst. Edited by

Stackebrandt E, Goodfellow M. New York City: John Wiley and Sons; 1991:115–175.

14. Tamura K, Peterson D, Peterson N, Stecher G, Nei M, Kumar S: MEGA5:

Molecular Evolutionary Genetics Analysis using maximum likelihood, evolutionary

distance, and maximum parsimony methods. Mol Bio Evol 2011, 28:2731–2739.

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15. Georgalaki MD, Van Den Berghe E, Kritikos D, Devreese B, Van Beeumen J,

Kalantzopoulos G, De Vuyst L, Tsakalidou E: Macedocin, a food-grade lantibiotic

produced by Streptococcus macedonicus ACA-DC 198. Appl Env Microbiol 2002,

68:5891–5903.

16. Bandounas L, Wierckx NJ, de Winde JH, Ruijssenaars HJ: Isolation and

characterization of novel bacterial strains exhibiting ligninolytic potential. BMC

Biotechnol 2011, 11:94.

17. Kovacs N: Eine vereinfachte Methode zum Nachwei s der Indolbildung durch

Bakterien. Z Immunitats Forsch Exp Ther 1928, 55:311.

18. Simmons JS: A culture medium for differentiating organisms of typhoid-colon

aerogenes groups and for isolation of certain fungi. J Infec Dis 1926, 39:209.

19. Georgalaki MD, Sarantinopoulos P, Ferreira ES, De Vuyst L, Kalantzopoulos G,

Tsakalidou E: Biochemical properties of Streptococcus macedonicus strains isolated

from Greek Kasseri cheese. J Appl Microbiol 2000, 88:817–825.

20. Archana A, Satyanarayana T: Xylanase production by thermophilic Bacillus

licheniformis A99 in solid-state fermentation. Enzym Microb Tech 1997, 21:12–17.

21. Pen J, Molendijk L, Quax WJ, Sijmons PC, van Ooyen AJJ, van den Elzen PJM,

Rietveld K, Hoekema A: Production of active Bacillus licheniformis alpha-amylase in

tobacco and its application in starch liquefaction. Bio/Technology 1992, 10:292–296.

22. Saito N: A thermophilic extracellular α-amylase from Bacillus licheniformis. Arch

Biochem Biophys 1973, 155:290–298.

23. Lin X, Lee C-G, Casale ES, Shih JCH: Purification and characterization of a

keratinase from a feather-degrading Bacillus licheniformis strain. Appl Env

Microbiol 1992, 58:3271–3275.

24. Veith B, Herzberg C, Steckel S, Feesche J, Maurer KH, Ehrenreich P, Bäumer S,

Henne A, Liesegang H, Merkl R, Ehrenreich A, Gottschalk G: The complete genome

sequence of Bacillus licheniformis DSM13, an organism with great industrial

potential. J Mol Microbiol Biotech 2004, 7:204–211.

25. Moews PC, Knox JR, Dideberg O, Charlier P, Frère JM: Beta-lactamase of Bacillus

licheniformis 749/C at 2 A resolution. Proteins 1990, 7:156–71.

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26. Pollock MR: Purification and properties of penicillinases from two strains of

Bacillus licheniformis: a chemical, physiochemical and physiological comparison. Biochem J 1965, 94:666–75.

27. Wei X, Ji Z, Chen S: Isolation of halotolerant Bacillus licheniformis WX-02 and

regulatory effects of sodium chloride on yield and molecular sizes of poly-gamma-

glutamic acid. Appl Biochem Biotechnol 2010, 160:1332–40.

28. Paul J, Sarkar A, Varma AK: In vitro studies of cellulose digesting properties of

Staphylococcus saprophyticus isolated from termite gut. Curr Sci 1986, 55:710–714.

29. Lee J-C, Yoon K-H: Paenibacillus woosongensis sp. nov., a xylanolytic bacterium

isolated from forest soil. IJSEM 2008, 58:612–616.

30. Jiang K S: Effects of dietary cellulose and xylan on absorption and tissue

contents of zinc and copper in rats. J Nutr 1986, 116:999–1006.

31. Shimada Y, Kinoshita M, Harada K, Mizutani M, Masahata K, Kayama H, Takeda K:

Commensal bacteria-dependent indole production enhances epithelial barrier

function in the colon. PLoS One 2013, 8:e80604.

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7.7 Figures

Figure 7-1 Phylogenetic comparison of 31 isolates which known sequences (NCBI).

A neighbor-joining tree was created using MEGA ver. 6 and the Kimura 2-paramter

model.

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Figure 7-2 Growth at various temperatures (A) and pHs (B), as measured by optical density at 600

nm.

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7.8 Tables

Table 7-1 Isolate GenBank ID, closest GenBank match with percent identity, growth on minimal

media or on high salinity, and ability to reduce nitrite.

Isolate GenBank

ID

Closest GenBank

Identification

CM

C

Cel

lob

iose

Lig

nin

Sta

rch

Xy

lan

8%

Na

Cl

10

% N

aC

l

Nit

rate

red

uct

ion

VTM4R85 KP245773 98% B. foraminis + + + + + - - -

VTM2R84 KP245774 98% B. firmus + + + + + + + +

VTM1R86 KP245775 100% B. flexus + + - + + + + +

VTM4R61 KP245776 99% B. licheniformis + + + + + + + +

VTM1R62 KP245777 99% B. licheniformis + + + - + + + -

VTM4R63 KP245778 99% B. licheniformis - + - + + + + -


VTM1R65 KP245780 98% B. licheniformis + + + + + + + -

VTM2R66 KP245781 99% B. licheniformis + + + + + + - -

VTM2R67 KP245782 100% B. licheniformis - + - - + - - -

VTM4R68 KP245783 98% B. licheniformis - + + + + + + +


VTM4R70 KP245785 99% B. licheniformis - + - - + - - -

VTM1R71 KP245786 99% B. licheniformis + + - + + + - +

VTM1R72 KP245787 99% B. licheniformis - + - - - + + -

VTM2R73 KP245788 99% B. licheniformis + + - + + - - +


VTM1R75 KP245790 99% B. licheniformis - - - - - - - -

VTM3R76 KP245791 99% B. licheniformis + + + + + + + -

VTM3R77 KP245792 99% B. licheniformis - + + - - - - -

VTM3R78 KP245793 99% B. licheniformis - - + - + - - -


VTM2R81 KP245795 99% B. licheniformis - - - - - - - -

VTM2R82 KP245796 99% B. licheniformis + + + + + + - +


VTM4R58 KP245798 98% B. niabensis + + + - + + + -

VTM1R92 KP245799 98% P. woosongensis + + + + + + + +

VTM1R96 KP245800 100% S. saprophyticus bovis + + + + + + + -

VTM4R98 KP245802 100% S. saprophyticus bovis + + + + + + + -

VTM4R97 KP245801 99% S. saprophyticus s, - + - - - - - -

VTM2R99 KP245803 99% S. saprophyticus s. + + - + + - - +

Total positive (n=31) 21 28 18 21 26 19 16 14

B = Bacillus; P = Paenibacillus; S = Staphylococcus

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CHAPTER 8 THE EFFECTS OF ADMINISTERING A FIBROLYTIC

PROBIOTIC MADE FROM MOOSE RUMEN BACTERIA TO NEONATAL

LAMBS.

Suzanne L. Ishaq1*, Christina J. Kim

1, and André-Denis G. Wright

1,2

1

Department of Animal Science, College of Agriculture and Life Sciences, University of


2 Present address: School of Animal and Comparative Biomedical Sciences, College of

Agriculture and Life Sciences, University of Arizona, Tucson, Arizona 85721



8196.

Keywords/Cross-reference: 16S rRNA gene, lambs, moose, probiotic

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8.1 Abstract

The present study investigated the effect of a fibrolytic probiotic, using bacteria isolated

from the rumen of the North American moose (Alces alces), and which was administered

daily to neonate lambs until 1 week after weaning. It was hypothesized that regular

administration of a fibrolytic probiotic to neonate animals through weaning would

increase the developing rumen bacterial diversity, increase animal production, and allow

for long-term colonization of the probiotic species. Neither weight gain nor wool quality

was improved in lambs given a probiotic, but dietary efficiency was increased as

evidenced by the reduced feed intake (and rearing costs) without a loss to weight gain.

Additionally, the probiotic lambs had a lower acetate to propionate ratio than control

lambs, which has previously been shown to indicate increased dietary efficiency.

Sampling coverage was high in the first two time points, after which it decreased.

Conversely, Shannon, Inverse Simpson, CHAO, and ACE were low and increased over

time, all of which is a function of the increasing diversity of the rumen microbiota as the

rumen develops. The experimental group had a higher diversity at the beginning of the

experiment. Fibrolytic bacteria made up the majority of sequences. In all time points

and both groups, Prevotella was the most prevalent genus, while Butyrivibrio and

Ruminococcus were also prevalent. While protozoal densities increased over time and

were stable, methanogen densities varied greatly in the first six months of life for lambs.

This is likely due to the changing diet and bacterial populations in the rumen.

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8.2 Introduction

Over the first few months of life, the rumen microbiome of the neonate ruminant

undergoes rapid shifts as the animal weans and changes diets, and the rumen develops.

As the rumen develops, it increases in size until it is the largest stomach chamber, its

rumen papillae become longer and more differentiated, and a stable microbiota is

established. Initially, lactic acid bacteria (LAB), such as Streptococcus thermophilus,

Lactobacillus acidophilus, or Bifidobacterium bifidus, tend to dominate, as well as

Escherichia coli (Fonty et al., 1987; Minato et al., 1992). While cellulolytic bacteria do

appear in the rumen within the first few days of life (Fonty et al., 1987; Minato et al.,

1992; Morvan et al., 1994), it is not until weaning and a transition to a plant-based diet

that they become the dominant type of rumen bacteria (Sinha and Ranganathan, 1983;

Ishaq and Wright, 2014). As the microbial diversity adapts to the rumen environment

and the diet provided, so, too, do the gastrointestinal tract epithelia adapt to the

microbiota. Host cells can eventually recognize conserved cell-surface microbial

markers, and the presence of these will active pro- or anti-inflammatory responses, as

well as host epithelial cell signaling (Chang, 2008; Abreu, 2010). Thus, introducing new

microbiota after these host-microbiota interactions have been made may not be

successful.

Different weaning practices can influence the developing microbiota. Including a creep

feed or hay along with a milk diet can encourage larger populations of fibrolytic bacteria

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(Yáñez-Ruiz et al., 2010), improve starch and fiber digestion (Poe et al., 1971), increase

volatile fatty acid production (especially acetate and butyrate) (Laarman et al., 2012), and

improve rumen development (Norouzian et al., 2011). Likewise, development of the

rumen microbiota can be encouraged through early colonization by administering a

probiotic. Probiotics for livestock are generally comprised of LAB or fibrolytic bacteria.

LAB probiotics are more common in pre-weaned ruminants (Abe et al., 1995; Vlková et

al., 2010; Ripamonti et al., 2011) or monogastrics (Abe et al., 1995; Pajarillo et al.,

2015), but are also used in adult ruminants (Aikman et al., 2011; Boyd et al., 2011).

Fibrolytic probiotics have more often been used to improve digestive function in adult

ruminants (Kumar and Sirohi, 2013; Præsteng et al., 2013), as well as for pre-weaned

ruminants (Sun et al., 2010). Many studies report short-term beneficial effects only,

either due to the production animals reaching market weight, or because the probiotic

failed to colonize the digestive tract long-term.

It was hypothesized that regular administration of a fibrolytic probiotic to neonate

animals through weaning would increase the developing rumen bacterial diversity,

increase animal production, and allow for long-term colonization of the probiotic species.

The present study investigated the effect of a fibrolytic probiotic, which had been created

using bacteria isolated from the rumen of the North American moose (Alces alces), and

which was administered daily to neonate lambs until 1 week after weaning at 9 weeks of

age. Neonatal ruminants undergo rumen development over a period of 8-12 weeks,

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during which the rumen and reticulum increase in size and functionality (Hobson and

Fonty, 1997), making the weaning period an ideal time period for rumen manipulation.

Moose were chosen as the source for probiotic strains as they are highly likely to host

efficient species or strains of bacteria, which can digest cellulose, hemicellulose, and

lignin. Moose subsist on a diet of woody browse which is very high in fiber (Belovsky,

1981; Molvar et al., 1993; Routledge and Roese, 2004). Additionally, their body

temperature and dry matter intake is more similar to lambs than to calves or goat kids,

thus improving the likelihood of long-term rumen colonization by the species of interest

(Gasaway and Coady, 1974; Franzmann et al., 1984; Piccione et al., 2003; Dwyer and

Morgan, 2006; Committee on the Nutrient Requirements of Small Ruminants, 2007;

Kochan, 2007; Piccione et al., 2007).

8.3 Methods

All procedures were approved by the UVM Institutional Animal Care and Use

Committee (protocol (14-008). Results are presented by date and experimental week

(week).

Twenty Dorset-cross lambs, 4-7 days of age, were purchased from Bonnieview Farm,

Craftsbury, VT. Lambs were group housed at the Miller Research Farm at the University

of Vermont (UVM), Burlington VT, beginning on April 22, 2014. Eighteen male and

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two female lambs were randomly assigned to either the Control (n=10) or the

Experimental (n=10) group with nine males and one female per group. Males were

castrated within the first two weeks of the study, and groups had similar weights (mean

5.9±0.2 kg) prior to the beginning of the study. Water was provided ad libitum. For four

weeks, lambs were fed DuMOR lamb milk replacer (Tractor Supply Co, Shelburne VT)

using bucket feeding systems (Premier 1 Supplies, Washington, IO), and group intake

was recorded. Beginning in week five, lambs were also given DuMOR sheep starter

pelleted grain feed (Tractor Supply Co, Shelburne VT), and group intakes were recoded.

At week six, lambs were weaned off of the milk replacer and were fed grain pellets and

timothy hay, and again group intake was recorded. At eight weeks (June 26, 2014) lambs

were transferred to Sterling College in Craftsbury, VT, where they were maintained as a

single mob grazing on pasture until mid-October, 2014.

8.3.1 Probiotic

Five bacterial isolates were chosen for use as a probiotic based on a previous study

(Ishaq, Reis, et al., 2015). Isolates are as follows, with GenBank accessions numbers in

parentheses: Bacillus foraminis VTM4R85 (KP245773), B. firmus VTM2R84

(KP245774), B. licheniformis VTM2R66 (KP245781), B. licheniformis VTM1R74

(KP245789), and Staphylococcus saprophyticus bovis VTM1R96 (KP245800). Isolates

were selected based on their ability to digest carboxymethylcellulose, cellobiose,

219

cellulose, lignin, starch, and xylan on minimal media. Isolates were also able to survive

at a wide range of temperatures, salinities, and pH.

Isolates were cultured separately in M8+cellulose broth for approximately six months to

determine whether the isolate could be maintained for an extended period at sufficient

concentrations to be used as a probiotic. Purity was determined via weekly gram

staining, and occasional Sanger sequencing as previously described (Ishaq, Reis, et al.,

2015). Concentration was measured by number of colony forming units (CFUs) on a

plate count, performed in duplicate. As per Food and Drug Administration (FDA)

regulations, probiotics must maintain 107 CFUs for the duration of its shelf life. The five

isolates were then tested for their ability to survive in commercial milk replacer for up to

72 hr at 37°C, and maintain a minimum density of 107. Isolates were cultured for 24, 48

and 72 hr in DuMOR Blue Ribbon lamb milk replacer (DuMOR 06-9551-0234),

reconstituted according to manufacturer instructions, and then replated on M8+cellulose

for plate counts at 24 hr.

Isolates were grown individually in M8 broth supplemented with cellulose as previously

described (Ishaq, Reis, et al., 2015). Cultures were checked regularly for purity using

gram staining, and concentrations were measured using standard plate counts. Twenty-

four hour old cultures were combined at equal concentration within 1 hour of

administration and kept cool during transport. One ml of inoculant or blank media was

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administered orally to experimental and control lambs, respectively, daily between noon

and 1 pm. After two weeks, when lambs were approximately 20 days old, the dose was

increased to 2 ml/day. Probiotic or blank media was given daily for 9 weeks until

weaning at 9.5 to 10 weeks of age.

8.3.2 Production

Lambs were weighed every 2-3 days for the first three weeks, then weekly through

weaning, then monthly for the duration of the study. At study week 8, when lambs were

weaned and put on pasture, a 2 x 2 in patch was shaved on the side, within 3-5 in of the

spine (i.e. mid-side sample). Wool was allowed to grow out for 14 weeks, after which a

1 x 1 in patch was shaved, dried, weighed, and sent for fiber testing to Yocom-McColl

Testing Labs in Denver, CO. Significance for this and other measurements was

calculated using Student’s T-test, and deviation is presented as standard deviation (SD) or

standard error mean (SEM).

8.3.3 Rumen sampling

Rumen samples were collected weekly for eight weeks, then monthly once lambs were on

pasture. Samples were collected in the morning, prior to weaning this was within one to

two hours of feeding. Esophageal tubing was used to obtain samples directly from the

rumen, from which up to 15 ml of fluid and particulate matter (ruminal contents) were

collected and put on ice immediately. Some rumen fluid was separated out and used to

221

measure pH and volatile fatty acids. Rumen pH was tested using a MW101 pH meter

(Milwaukee, NC). Volatile fatty acids were measured using gas chromatography at the

William H. Miner Agricultural Research Institute (Chazy, NY). Thawed rumen samples

were centrifuged for 20 min at 4° at 10,000 x G. Supernatant was filtered through a

single layer of Whatman filter paper, and 0.8 ml of filtrate was mixed with an equal

volume of internal standards (oxalic acid and trimethylamine).

8.3.4 Sequencing and DNA data analysis

DNA was extracted from individual samples using the QIAamp DNA stool fast kit

(QIAGEN, MD), and the V1-V3 region of the 16S rRNA gene was amplified using

previously described protocols (Ishaq and Wright, 2014). Amplicons were sent to

Molecular Research, LP (MR DNA) in Shallowater, TX for MiSeq ver. 4.

Sequences were analyzed using MOTHUR ver. 1.31 (Schloss et al., 2009; Kozich et al.,

2013). Sequences were trimmed to remove barcodes and primers, as well as any

sequence that contained a mismatch in the barcode, more than two mismatches in the

primer, sequences with homopolymers >8, sequences < 475 bases or >570 bases, and

sequences with an average quality score <32 over 5 bases. Sequences were aligned to the

Silva 16S rRNA bacteria MOTHUR reference file, which had been modified to include

fibrolytic isolates cultured in the laboratory, including the five which were used in the

probiotic. The reference alignment was also trimmed to begin at 27F and end after 800

222

bases. Chimeras were identified using UCHIME (Edgar et al., 2011) and removed.

Sequences were identified using the k nearest neighbor method. Data were subsampled

to 10,000 sequences per sample, clustered using the nearest neighbor method, and

diversity parameters were measured. ACE (Chao and Shen, 2003), CHAO (Chao and

Shen, 2010), Good’s Coverage (Good, 1953), Shannon-Weiner diversity (Shannon and

Weaver, 1949), AMOVA and Unifrac values are presented as group mean.

In order to compare control and experimental groups from all four time points, sequences

which passed QA were pooled, and were subsampled to 2,000 sequences per sample,

giving 20,000 per group per time point. This subsample was used to create a neighbor-

joining tree using the mother-integrated algorithms for Clearcut (Evans et al., 2006),

linear discriminant analysis (LDA) using the mother-integrated Lefse (Segata et al.,

2011), and principal component analysis (PCoA).

8.3.5 Real-time PCR

Real-time PCR was used to calculate archaeal and protozoal densities in whole samples.

DNA was amplified using a CFX96 Real-Time System (Bio-Rad, CA) and a C1000

ThermalCycler (Bio-Rad, CA). Data were analyzed using CFX Manager Software ver.

1.6 (Bio-Rad, CA). The iQ SYBR Green Supermix kit (Bio-Rad, CA) was used: 12.5µL

of mix, 2.5µl of each primer (40mM), 6.5µL of ddH2O, and 1µL of the initial DNA

extract diluted to approximately 10 ng/μL. For methanogens, the primers targeted the

223

methyl coenzyme-M reductase A gene (mcrA), following the protocol by Denman et al.

(2007). The internal standards for methanogens were a mix of Methanobrevibacter

smithii, M. gottschalkii, M. ruminantium and M. millerae (R2=0.998). For protozoa, the

primers, PSSU316F and PSSU539R (Sylvester et al., 2004), targeted the 18S rRNA gene,

following the protocol by Sylvester et al. (2004), and The internal standards for protozoa

were created in the laboratory using fresh rumen contents as previously described (Ishaq

et al., unpublished). Both protocols were followed by a melt curve, with a temperature

increase 0.5°C every 10s from 65ºC up to 95°C to check for contamination.

8.4 Results

8.4.1 Probiotic

Five isolates (VTM2R66, VTM1R74, VTM2R84, VTM4R85, VTM1R96), which were

selected for further testing, maintained concentrations ranging from 107 to 10

10 CFUs

over six months. The same five isolates were able to maintain densities greater than 107

in liquid lamb replacer over 72 hours.

8.4.2 Production

Total group weight was higher in the control group for nearly the duration of the study,

with the exception of the week 8 weighing; however, this time point was the only one

which came close to statistical significance (p=0.06). The total cost at the end of

weaning (week 8) of milk replacer, starter grain, and timothy hay for the experimental

224

groups was $1564.63, at a weaning weight of 248.95 kg for the group. This gives a yield

of 6.28 $/kg. The total cost of the control group was $1592.17, and at a weaning weight

of 263.25 kg this yields 6.05 $/kg. When taking into account the total group weights at

market weight (aged six months, week 23), the cost/group drops to 6.08 $/kg for the

experimental group and increases to 6.19 $/kg for the control group.

Mid-side sample wool weight was higher (p=0.02) in the experimental group (mean=0.83

g, SEM=0.5) than in the control group (mean=0.67 g, SEM=0.07). Mean fiber diameter

(MFD) was not significantly different (p=0.14) between experimental (MFD=34µ,

SEM=0.6, SD=7.4) and control (MFD=33.1µ, SEM=0.6, SD=7.7) groups. The

experimental group did have a significantly (p=0.04) lower coefficient of variation

(CoV=21.8) than the control group (CoV=23.4). Although the experimental group had a

higher percentage of fibers that were >30µm (66.8%, SEM=3.4 experimental, 62.1%,

SEM=2.7 control), this was not statistically significant (p=0.11).

The average pH over the course of the experiment was 7.2 for the experimental group and

7.0 for the control group (Figure 2). The experimental group had a higher average pH for

the first seven weeks of the experiment and lower variability within the group, while the

control group were more likely to have a higher average for the remainder of the study.

Seven out of 12 sampling time points were significantly different (Figure 2).

225

Total volatile fatty acids were not significantly different between Experimental and

Control lambs (Figure 3); however, groups were significantly different at weeks 5, 11 and

23 when comparing total VFAs including ethanol. Total VFAs were highest at weeks 8

and 15, and lowest at week 9 after being on a hay only diet for one week (Figure 3).

Acetate, proprionate, and butyrate were significantly (p<0.05) higher in experimental

lambs at weeks 15 and 23, while lambs were on pasture. The acetic acid to propionic

acid ratio was statistically lower in the experimental group at weeks 9, 11 and 15 (Figure

4).

8.4.3 Sequencing

Between 11,000 and 95,000 unique sequences passed quality assurance steps per sample,

giving a total of 500,000 to 1.1 million sequences per data set of 20 samples. At the first

sampling time point, week 2, after administering the probiotic for a week, there was little

statistical difference in rumen diversity between groups, except for AMOVA, and

unweighted and weighted Unifrac (Table 1). At week 6, CHAO, ACE, Shannon, and

Coverage were different between groups, with higher diversity in the experimental

groups. Groups were not statistically different on any diversity measure except for

unweighted Unifrac at week 11 in July, after being on pasture for two weeks (Table 1).

However, by week 23 in October, the control group showed higher diversity according to

Shannon and Inverse Simpson, and groups clustered separately by weighted and

unweighted Unifrac.

226

When comparing all time points together in the smaller subsampled data set, there were

1,787 OTUs identified from the 160,000 sequences, with 88 (4.9%) discriminatory OTUs

with LDA >2 (p<0.05). The number of discriminatory OTUs were as follows: week 2,

five experimental, four control; week 6, five experimental, six control; week 11, 37

experimental, 17 control; and week 23, nine experimental, five control. PCoA graphs

showed a strong clustering of groups by sampling time (Figure 5), but not by treatment

(data not shown), indicating that the change of rumen bacteria over the course of rumen

development is a stronger indicator of variance.

Bacteroidetes was the most prevalent phylum (38-73% of total sequences) in both groups

for the duration of the study, with the exception of the first sampling of the control group

(Figure 6). Firmicutes was the second most prevalent (23-59%). In the control group,

Bacteroidetes increased while Firmicutes decreased for the first three sampling (weeks 2,

6 and 11), while the experimental had a general trend of decreasing Bacteroidetes and

increasing Firmicutes over time. Other prominent phyla tended to peak at one or two

time points, including Actinobacteria and Proteobacteria (week 6), Fibrobacteres (week

11), and Synergistetes (weeks 11, 23).

Major families identified are shown in Figure 6, with families belonging to Bacteroidetes

as shades of blue and members of Firmicutes in shades of green. Prevotellaceae (mostly

227

species Prevotella) was a prominent family in all time points and in both groups, but was

significantly higher in the experimental group at week 2. Lachnospiraceae was also

prominent in all samples, although it was significantly higher in the control group at

week 2. The experimental group had more Ruminococcaceae than the control group at

weeks 2 and 6. There were also families which were prominent in only one time point,

such as Bacteroidaceae, Streptococcaceae, and the candidate family p-2534-18B5 in

week 2; Coriobacteriaceae (mostly species Olsenella) in week 6, Fibrobacteraceae in

week 11, and the candidate Family XI of the class Bacilli (phylum Firmicutes) in week

23.

While the genera Bacillus and Staphylococcus were found in both groups at all time

points, there was not enough resolution in the sequencing amplicons to accurately

identify probiotic sequences down to species or strain. The total genera identified were

as follows: week 2, 301 experimental, 273 control; week 6, 183 experimental, 184

control; week 11, 292 experimental, 331 control; and week 23, 482 experimental, 483

control (Supplemental Material). Overall, 694 genera were identified across both groups

and all time points. The most prevalent genus in all groups and time points was

Prevotella. Other prominent genera included Bacteroides, Butyrivibrio, Catabacter,

Clostridium, Dialister, Lactobacillus, Olsenella, Oribacterium, Parvimonas, RC9,

Ruminococcus, Selemonas, and Streptococcus (Supplemental Material).

228

8.4.4 Real-time PCR

Protozoal density in both control and experimental groups increased over time until they

leveled off at approximately 2 x 103

(Figure 8). Control group had statistically higher

(p<0.05) densities at weeks 8 and 23. Methanogen densities increased for the first month,

then rapidly decreased at week 6 (Figure 8). Levels peaked at week 8, at which point

densities decreased to week 11, then peaked again at week 15. While average density

was higher in control lambs for most time points, due to the variability of densities within

groups this was not statistically significant. In lambs with elevated protozoal densities,

methanogen density was also elevated (Figure 9). When protozoal densities were low,

methanogen densities were also low. However, this trend was not statistically significant

(R2=0.376)

8.5 Discussion

Neither weight gain nor wool quality was improved in lambs given a probiotic, however,

dietary efficiency was increased as evidenced by the reduced feed intake (and rearing

costs) without a significant loss to weight gain. This reduction in rearing costs would be

further amplified using more traditional husbandry practices, such as rearing lambs

outside and giving them access to grass during weaning, thus precluding the need to

supplement with hay. Additionally, the probiotic lambs had a lower acetate to propionate

ratio than control lambs, which has previously been shown to indicate increased dietary

229

efficiency (Van Soest, 1982; Morgavi et al., 2012). An increased production of

propionate reduces free hydrogen in the rumen, making it less available to methanogens.

Sampling coverage was high in the first two time points, after which it decreased.

Conversely, Shannon, Inverse Simpson, CHAO, and ACE were low and increased over

time, all of which is a function of the increasing diversity of the rumen microbiota as the

rumen develops. While there were differences between groups in terms of statistical

diversity, there was no difference in OTUs/ sample between groups, and this is likely due

to evenly subsampling the data set. The experimental group had a higher diversity at the

beginning of the experiment, but this was not persistent.

Fibrolytic bacteria made up the majority of sequences identified in samples, and while

some fibrolytic genera were elevated in the experimental group, this was not consistent

across all time points. In all time points and both groups, Prevotella was the most

prevalent genus, while Butyrivibrio and Ruminococcus were also prevalent. All three

genera have previously been shown to be fibrolytic (Smith et al., 1973; Maglione et al.,

1992; Daniel et al., 1995; Cotta and Forster, 2006; Suen et al., 2011). However,

sequences from the probiotic strains could not be confidently reported in the sequenced

samples.

230

While protozoal densities increased over time and were stable, methanogen densities

varied greatly in the first six months of life for lambs. This is likely due to the changing

diet and bacterial populations in the rumen. For example, when methanogen density

decreased at week 6, the proportion of acetogenic bacteria increased (i.e. phylum

Actinobacteria, and species such as Acetivibrio and Acetitomaculum in the phylum

Firmicutes, class Clostridia), fostering competitive pathways to methanogenesis (Lopez

et al., 1999). Reducing methanogenesis would not only make for more eco-friendly

livestock, but would also reduce the amount of energy lost to the host which might have

otherwise been used for production.

Despite the small increase in dietary efficiency, a more dramatic increase in production

might result from altering the probiotic administered in the present study. Increasing the

dosage, using a different mix of fibrolytic bacteria, or using a probiotic with fibrolytic

and lactic-acid bacterial strains, are all potential methods of improving upon the results

presented here.


The authors would like to thank Matt Bodette, Laura Cersosimo, Sam Frawley, Emma

Hurley, Anjana Mangalat, Katy Nelligan, Scott Shumway, Sam Rosenbaum, Lee Warren,

and Sarah Zegler for their assistance with animal husbandry and sample collection,

Louise Calderwood for facilitating the transfer of lambs from UVM to Sterling College,

231

Mike Richards for care of sheep at Sterling College, and Dr. Sabrina Greenwood for

assistance with UVM Miller Farm facilities used for this project.

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236

8.8 Figures

Figure 8-1 Group weight (kg) means over time.

Significance is denoted with *, and error bars show standard error mean.

237

Figure 8-2 Group pH means over time.

Significance is denoted with *, and error bars show standard error mean.

238

Figure 8-3 Volatile fatty acid (VFA) and ethanol profile of groups (E=experimental, C=control) for

two time points on pasture.

0

50

100

150

200µ

Mol/

ml

(SE

M)

Ethanol

Valerate

Lactate

Isovalerate

Isobutyrate

Butyrate

Propionate

Acetate

*

*

*

239

Figure 8-4 Acetate to propionate ratio, with SEM.

Figure 8-5 Principal component analysis of samples by sampling time: May=teal, June=green,

July=red, and October=dark blue.

0

2

4

6

8

10

wk5 wk6 wk7 wk8 wk9 wk11 wk15 wk23

Exp

Con

* * *

240

Figure 8-6 Diversity at the phylum level for all sequencing time points.

Error bars show standard error mean.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

other

Synergistetes

Proteobacteria

Fibrobacteres

Actinobacteria

Firmicutes

Bacteroidetes

241

Figure 8-7 Diversity at the family level for all sequencing time points. Error bars show standard error mean.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100% other

Succinivibrionaceae

Fibrobacteraceae

Coriobacteriaceae

Lactobacillaceae

Synergistaceae

Family_XI_Incertae_Se

dis

Streptococcaceae

Erysipelotrichaceae

Veillonellaceae

Ruminococcaceae

Lachnospiraceae

Bacteroidaceae

BS11

S24-7

p-2534-18B5

Rikenellaceae

Prevotellaceae

242

Figure 8-8 Real-time PCR data for methanogens and protozoa. Significance is denoted with *, and error bars show standard error mean.

243

Figure 8-9 Comparison of methanogen versus protozoal densities for all samples

.

244

8.9 Tables

Table 8-1 Diversity statistics per sample for each of the four sampling time points.

Results are listed by group, Experimental (n=10) and Control (n=10), or All (n=20).

Group 5-1-14 6-4-14 7-10-14 10-1-14

Total sequences

which passed QA

steps

All 953,581 1,002,520 501,909 1,007,093

Subsampled to 10,000/sample (200,000/time point)

Good’s Coverage

Exp 0.85 0.83 * 0.63 0.32 *

Con 0.87 0.87 0.62 0.28

Shannon-Weiner

Diversity

Exp 3.23 3.69 * 5.82 7.86 *

Con 3.25 3.09 5.72 8.10

Inverse Simpson

Exp 6.34 8.02 21 220 *

Con 6.35 6.15 19 447

CHAO Exp 10,907 11,489 * 50,674 95,992

Con 8,513 7,712 57,141 111,950

ACE Exp 31,864 31,738 * 149,728 278,155

Con 22,578 21,378 171,878 352,438

Total species-level

OTUs All 20,706 21,035 70,062 106,823

Mean species-level

OTUs/sample

Exp 1,271 1,425 3,918 5,700

Con 1,156 999 3,977 6,190

Shared OTUS

(sequences) All 9 (65,731) 7 (68,258) 20 (64,589) 19 (19,215)

Group shared OTUs

(sequences)

Exp 13

(72,661)

12

(65,582) 38 (96,060) 31 (78,967)

Con 12

(77,050)

12

(63,501) 37 (95,910) 33 (81,424)

AMOVA (p-value)* All 0.01* 0.73 0.30 0.14

Weighted Unifrac* All 0.80 * 0.47 0.44 0.57 *

Unweighted Unifrac* All 0.87 * 0.67 * 0.77 * 0.87 *

*Denotes statistically significant value (p<0.05) between groups at that time point.

245


This table has been modified from the original, which is too large to include here. It was

modified to include the top genera by abundance.

taxon

Exp

erim

enta

l w

eek

2 %

Con

trol

wee

k 2

%

Exp

erim

enta

l w

eek

6 %

Con

trol

wee

k 6

%

Exp

erim

enta

l w

eek

11 %

Con

trol

wee

k 1

1 %

Exp

erim

enta

l w

eek

23 %

Con

trol

wee

k 2

3 %

Acholeplasma 0.0 0.0 0.6 0.3 0.1 0.3 0.5 0.2

Acinetobacter 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Actinobacillus 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0

Aggregatibacter 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Alysiella 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Anaeroplasma 0.0 0.0 0.1 0.2 1.2 0.3 0.0 0.0

Anaerostipes 0.0 0.0 0.0 0.1 0.1 0.1 0.1 0.1

Anaerovorax 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0

Bacillus 0.0 0.0 0.1 0.0 0.0 0.0 0.0 0.0

Bacteroides 3.7 13.6 0.1 0.1 1.2 1.0 1.3 1.6

Bergeriella 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Blautia 0.1 2.3 1.1 1.9 0.3 0.3 0.3 0.4

Butyrivibrio 0.2 0.1 3.1 0.5 2.8 1.5 1.2 2.6

Butyrivibrio-

Pseudobutyrivibrio 0.0 0.0 0.2 0.1 0.5 0.4 0.5 1.0

Catabacter 0.1 2.9 0.2 0.0 0.1 0.1 0.1 0.1

Clostridium 0.0 0.0 0.0 0.0 0.0 0.0 1.2 0.6

Coprococcus 0.1 0.0 0.1 0.1 0.3 0.3 0.6 1.0

Desulfovibrio 0.0 0.0 0.0 0.0 0.0 0.0 0.2 0.2

Dialister 0.0 0.0 0.0 0.0 0.0 0.0 2.7 2.6

Dorea 0.1 0.2 0.0 0.0 0.0 0.0 0.0 0.0

Erysipelothrix 0.0 0.3 0.0 0.0 0.0 0.0 0.0 0.0

Eubacterium 0.5 0.2 0.0 0.0 0.1 0.1 0.1 0.1

Faecalibacterium 0.0 0.0 0.1 0.0 0.2 1.0 0.1 0.2

Fastidiosipila 0.2 0.3 0.3 0.0 0.1 0.2 0.2 0.2

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Filifactor 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Fusobacterium 0.1 0.2 0.0 0.0 0.0 0.0 0.0 0.0

Geosporobacter 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Guggenheimella 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Helcococcus 0.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0

Hydrogenoanaerobacterium 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1

Incertae_Sedis 2.2 4.3 6.4 3.6 1.6 1.7 3.2 4.1

Johnsonella 0.0 0.0 0.0 0.0 0.1 0.1 0.4 0.4

Lactobacillus 1.8 4.0 0.0 0.0 0.0 0.0 0.0 0.0

Mannheimia 0.2 0.2 0.0 0.0 0.0 0.0 0.0 0.0

Megasphaera 0.0 0.0 0.1 0.0 0.0 0.0 0.1 0.1

Mogibacterium 0.1 0.4 0.1 0.0 0.1 0.1 0.0 0.0

Moraxella 0.1 0.1 0.0 0.0 0.0 0.0 0.0 0.0

Moryella 0.0 0.0 0.2 0.2 0.2 0.2 0.2 0.3

Neisseria 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Olsenella 0.0 0.0 11.2 10.4 0.1 0.1 0.1 0.1

Oribacterium 0.0 0.0 9.7 10.5 0.2 0.4 0.1 0.1

Oscillibacter 0.1 0.0 0.0 0.0 0.1 0.1 0.0 0.1

Oscillospira 0.0 0.0 0.0 0.0 0.6 0.3 0.0 0.0

Parabacteroides 0.1 0.0 0.0 0.0 0.2 0.2 0.1 0.1

Parvimonas 0.0 0.0 0.0 0.0 0.0 0.0 6.4 2.4

Pedobacter 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.1

Peptostreptococcus 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Porphyromonas 0.8 0.4 0.0 0.0 0.0 0.0 0.0 0.0

Prevotella 33.3 13.7 35.4 38.2 42.4 42.9 31.9 32.9

RC9 0.7 0.5 1.8 0.6 5.5 4.4 6.9 6.6

Roseburia 0.0 0.1 0.2 0.1 0.1 0.2 0.3 0.5

Ruminococcus 0.1 0.0 2.4 2.6 1.8 1.1 0.7 0.8

Selenomonas 0.0 0.0 0.2 0.0 0.6 0.4 8.8 5.7

Soehngenia 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Solobacterium 0.2 0.9 0.2 0.1 0.3 2.8 0.1 0.2

Spirochaeta 1.2 0.4 0.1 0.2 0.0 0.0 0.0 0.0

Streptococcus 0.2 9.9 0.0 0.0 0.0 0.0 0.0 0.0

Subdoligranulum 0.3 0.1 0.0 0.0 0.0 0.0 0.1 0.1

Succiniclasticum 0.0 0.0 0.1 0.2 0.0 0.0 0.4 0.3

Tannerella 0.1 0.1 0.0 0.0 0.1 0.1 0.1 0.1

Treponema 0.0 0.0 0.5 0.6 0.1 0.1 0.0 0.1

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vadinBC27 5.9 2.6 0.1 0.0 0.2 0.2 0.0 0.2

Victivallis 0.0 0.0 0.1 0.0 0.1 0.2 0.0 0.1

Xylanibacter 0.0 0.0 0.1 0.1 0.2 0.1 0.3 0.5

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CHAPTER 9 CONCLUSION

9.1 Summary

It was hypothesized that the moose would host novel microorganisms; that these

microorganisms would be capable of a wide variety of enzymatic functions; and that

these microorganisms could be used to improve other systems. The objectives of this

work were to identify the bacteria (CHAPTERS 2, 3), archaea (CHAPTER 5), and

protozoa (CHAPTERS 4, 5) present in the rumen of moose; to culture bacteria from the

rumen of the moose in order to study their biochemical capabilities (CHAPTERS 6, 7);

and to apply those cultured bacterial isolates to improve fiber digestion in neonate lambs

(CHAPTER 8).

9.2 The moose rumen: summarized findings and unanswered questions

Despite the extensive work presented here on the moose rumen microbiota, it cannot be

said that the moose rumen is yet fully understood. A large proportion of bacteria and

protozoa taxa studied here could not be identified, indicating that there are yet more

novel taxa which need to be isolated, cultured, classified, and deposited into reference

databases. This is particularly necessary for the protozoa, since relatively few species

have been cultured and identified. As much of that work was done prior to the

emergence of DNA sequencing technology, and given the difficulty in maintaining

protozoa in culture, even fewer of the known GIT species have 18S rRNA sequences

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available in public databases (currently <250 sequences). In addition, fungi have barely

been investigated in the moose GIT [1], and the bacteriophages not at all.

Aside from the literature and work presented here on SSU rRNA, no work has been done

on the moose rumen microbiome: the enzymes at work, the myriad beneficial or toxic

products being produced, and the wealth of undocumented microbial genetic material

present. The bacteria in the rumen of the moose are predominantly fibrolytic, as they

should be given the diet of moose. However, is this fibrolytic dominance seasonally tied

to diet quality, forage content, and cellulose intake, as speculated and shown previously

[2–4], or is the fibrolysis in fact accomplished by different bacterial species in different

locations regardless of season. The genus Prevotella has been shown to be amylolytic

and fibrolytic, but without increased sensitivity of identification it remains to be seen

which species or subspecies is present, and which digestive function those found in the

Alaskan moose were performing. Targeted sequencing of known genes which code for

fibrolytic enzymes, or shotgun sequencing of the entire rumen contents to target full-

genomes, would reveal the enzymatic potential of the rumen as a whole. Known and

putative enzymes could be identified from DNA sequencing or microarrays, and the

functional microbiome can be studied using RNA sequencing or microarrays.

Previously, in vitro [5] or in vivo [6, 7] work was used to study digestibility of plant

matter by the moose rumen microorganisms.

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In the work presented here, methanogens in the moose rumen were identified for the first

time; although actual methane production in moose was not studied, either in vitro or in

vivo. It was speculated here that moose might have a lower production of methane than

other ruminants due to the high proportion of forage in their diet [8] and faster rate of

food passage through their GIT [9]. Previously, an in vitro study of biogas production

from GIT methanogens showed that moose rumen microorganisms produced less biogas

than those from beaver [10]. An ability to efficiently digest cellulose leads to a reduction

in methanogenesis [11]; however, the digestion of starch, also can reduce methanogenesis

via two means. Firstly, the production of propionate is increased which sequesters free H

and prevents its use in methanogenesis [12]. Second, the increase in amylolytic bacteria

reduces the pH, which in turn reduces the protozoal population, again providing less H

for methanogenesis [12]. Thus, it is likely that moose produce less methane than other

ruminants, although it is possible that this may be location/diet specific.

Methane production from moose can be most easily studied using anaerobic culturing,

either of pure methanogen cultures or whole rumen contents. One method would be to

use a tabletop methane digester, but any containment system which allowed for the

introduction of plant biomass and the collection of biogas could be used. An in vivo

study could be performed using methane collection systems such as a respiration

chamber, combined feeder/gas measurement systems, or by estimating methane output by

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measuring the output of a tracer gas [13]. Given the size of moose and the relative few

numbers of captive moose available, this would seem impractical.

9.3 Fibrolytic probiotics in lambs: beneficial or bust?

The administered probiotic did not produce the expected results; however, it cannot be

considered a failed hypothesis. Weight gain was not improved in experimental lambs

over control lambs, with the exception of a period after starter grain was introduced in

which experimental lambs gained 30% body weight over the previous week, which was

the highest percent gain at any point in either group. This may be an indication that the

rumen and rumen microbiota of experimental lambs were more developed, and were able

to take advantage of a solid food diet more quickly than those of control lambs. Rumen

papillae and intestinal villi length measurements were not able to taken; however, they

would have provided insight into whether the GIT of probiotics lambs were, in fact, more

developed. This might have been performed using GIT epithelial samples for traditional

histological slides or scanning with micro-computed tomography (micro-CT) [14] to

measure length and differentiation. When put in the context of feeding costs per kg of

body weight; however, the lower cost of raising the probiotic lambs without sacrificing

weight gain is indicative of an increased dietary efficiency.

Although total wool growth was significantly higher in experimental lambs, wool quality

was not improved over that of control lambs. Wool quality is determined by staple

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(fiber) length, strength, diameter, and diameter uniformity. Thinner diameter wool (<22

microns) can be woven into a thinner, finer yarn, and thus is more valuable. The sheep

used in this study were varieties of Dorset-crosses. Dorset is a dual-purpose breed,

producing good quality meat and medium quality (avg. 27-33 microns) white wool. On

the farm from which the lambs were obtained, sheep were mainly bred for meat and dairy

(cheese) production, although wool was also collected and sold. It may be that any

positive effects of the probiotic were not enough to overcome the genotypic

determination of wool quality.

The probiotic lambs had a higher and more stable pH than control lambs through weaning

(Figure 1). A stable pH is important when transitioning between diets, especially when

transitioning to a highly digestible starch, such as grain, to prevent a low rumen pH

which can kill rumen microorganisms and disrupt digestive function. Sheep are not

prone to rumen acidosis, unless they gain access to a high-starch feedstuff and gorge

themselves. However, dairy cattle are more sensitive to rumen acidosis, which can

decrease production and may require medical intervention (SECTION 1.3.5). As the

lambs in the probiotic study were acting as a model for cattle, a stable rumen pH would

be an important benefit of the probiotic.

It is difficult to determine whether the probiotic strains properly colonized the rumen or

not, as the amplicons used for analysis did not have the resolution to be identified past

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genus or, in some cases, family. Difficulty identifying Illumina sequences down to genus

level has previously been seen [15]. In the present work, a variety of reference databases

(i.e. GenBank, RDP, Silva) were used, as well as multiple classification methods (i.e. k

nearest neighbor, wang [16]) and confidence cutoff levels, in an effort to classify down to

species and subspecies, but to no avail. The genera Bacillus and Staphylococcus were

identified, but in low numbers in both groups. The probiotic strains may have colonized

the rumen of experimental lambs, validating the hypothesis that the lamb rumen diversity

could be altered long-term, but without providing the anticipated beneficial effects.

It is possible that the dosage of bacteria was not high enough to allow for larger-scale

colonization of the rumen. It may also be the case that the isolates tested were not ideal

for colonization in some way which was not investigated beforehand (0). For example,

isolates may be sensitive to antimicrobial products released by other rumen

microorganisms, or may have been subject to predation by protozoa. To improve the

identification of probiotic strains, sequencing using longer amplicons or with another

platform could be attempted to improve the resolution. Another method would be to

create strain-specific real-time PCR primers and look at 16S copy number in whole

samples. PCR product would have to be amplified using additional cycles in order to

generate a detectable signal if there are very low copy numbers. Potentially, whole

rumen samples could be used to reisolate the probiotic strains in culture; however, this

method would be the least efficient.

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Probiotic strains were shown to survive in milk replacer for a period of time, which

allows for the potential of administering during bulk-feeding. The probiotic was not

tested as a top-dressing; either as a live culture or dried cells, but this represents another

possible means of administering the probiotic to many animals simultaneously.

However, despite the benefits provided by the probiotic as outlined previously, the

probiotic is not a viable product in its current form. To be useful to producers, a

significant economic difference in animal product quality or production cost must be

shown. Though a reduction in production (rearing) cost was shown, it is not enough to

compensate for the estimated cost of adding a probiotic product. A more dramatic

increase in production might result from altering the probiotic administered in the present

study. Increasing the dosage, using a different mix of fibrolytic bacteria, or using a

probiotic with fibrolytic and lactic-acid bacterial strains are all potential methods of

improving upon the results presented in CHAPTER 8. Additionally, probiotic benefits

may be more pronounced when administered to dairy cattle, or when also measuring milk

production. Immunological measurements, such as inflammation in the gut, GIT

cytokines, blood cortisol, blood cholesterol, or meat quality measurements, such as

concentrations of fatty acids, could also be used to detect beneficial changes caused by

the probiotic other than a direct increase in production.

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9.4 Molecular methodology

9.4.1 Sequencing Platform

High-throughput sequencing was chosen as the platform for the main work presented in

this dissertation, as the previous investigations into the moose rumen had been using

anaerobic culturing or light microscopy [17–21]. Initially, preliminary investigations into

the rumen and colon bacteria of the moose were made using DNA microarray chips [22].

It quickly became clear that this did not provide the sensitivity to detect specific genera

of interest, nor was it capable of identifying unknown taxa. The DNA chip was able to

quickly determine broad differences in diversity between samples, and in the years since

the DNA microarray experiment was performed in 2011 [22], the chip has been

redesigned to detect 50,000 bacterial taxa using over a million different probes.

Multiplexed sequencing-by-synthesis was the next obvious choice, using barcoded

primers to sequence multiple samples simultaneously; however, there were multiple

platforms to choose from. In 2011, when the first data sets were being considered for

sequencing, Roche 454 was the more appropriate platform. It was capable of producing

de novo sequences up to 400-500 bases in length, with tens of thousands of sequences

generated per sample. To perform pyrosequencing, the Roche platform uses a cyclic

flow of nucleotides over a plate containing wells, with one strand of ssDNA per well.

When a nucleotide triphosphate matched the candidate sequence it would hybridize via

256

DNA polymerase. This would release pyrophosphate, which is converted by ATP

sulfurylase to ATP and used by luciferase to convert luciferin to oxyluciferin.

This reaction produces a measurable amount of light, which is measured by a camera and

converted to a “flow value”, and is outputted as a flowgram. Flow values do not directly

translate to a specific number of nucleotides, thus is was necessary to use bioinformatics

algorithms such as PyroNoise [23] to determine the number of bases in the flow using

maximum likelihood. This provided an extra step of quality assurance, as candidate

flowgrams can be compared to reference flowgrams generated from mock data sets and

adjusted to remove noise [23, 24].

Aside from Roche, the most promising platform being developed was Illumina, which at

the time boasted 25-100 bases, with millions of sequences generated. The Illumina

platforms were faster, as four different fluorescent dyes were used to tag nucleotide

bases, thus negating the need to pause between read steps. Once the nucleotide base was

incorporated, the dye was cleaved and diffused out of the read zone. However, the

sequence length being generated was simply not long enough for taxonomic resolution.

For large contig-assembly and full-genome sequencing, the shotgun sequencing approach

of Illumina works well and continues to expand its potential [25].

257

The Illumina platforms rapidly evolved, and just a few short years later were able to

produce amplicons of 400-500 bases using paired-end reads, although by this time Roche

was still outpacing them at 800-1,000 base non-paired reads [26]. It also became more

economical than Roche sequencing, as fewer reagents were required and sequencing

preparation time was greatly reduced [27]. In late 2013, Roche announced that it was

closing the 454 Life Sciences subsidiary and discontinuing the 454 reagents by 2016,

further driving an industry-wide switch from Roche 454 to Illumina platforms.

The Illumina platforms came with their own drawbacks. Raw error rates were higher [27,

28], especially after the first 60 nucleotide bases [15], although consensus finished-read

accuracy was 99% [29]. Data output came in the form of fasta files, as opposed to Roche

which provided flowgrams that could undergo noise reduction. Additionally, Illumina

sequencers showed lower coverage in high GC regions [30]. The sheer number of

sequence reads produced created problems during analysis as random access memory

(RAM) and hard drive space became limiting.

With respect to the data generated in this project, Roche 454 was the optimal platform for

investigating bacterial 16S rRNA sequences and protozoal 18S rRNA sequences, and

Illumina’s MiSeq platform was optimal for investigating archaeal 16S rRNA sequences.

For example, when sequencing protozoa from three pooled Alaskan moose rumen

samples using Roche 454, a total of 12,326 sequences passed QA steps, which were

258

represented by 769 unique (non-redundant) sequences [31]. When sequencing protozoa

from these same three samples individually using MiSeq ver. 3, a total of 200,513

sequences passed the same QA steps, represented by 99,895 unique sequences [32]. This

was a gross overestimation of protozoan diversity, and required more stringent analysis

parameters to correct the data, such as using a higher quality score cutoff, or condensing

sequences which had one or two polymorphic differences between them.

Likewise, when analyzing bacterial sequences generated from the lamb probiotic study,

MiSeq ver. 3 generated over 1 million raw sequences per data set of 20 samples

CHAPTER 8). After stringent QA steps, between 500,000 and just over 1,000,000 total

sequences were retained per data set, over 75% of which were considered unique. Again,

this was an overestimation of diversity, but without the ability to reduce sequencing noise

in the original sequences there was no way to detect raw sequencing errors. Additionally,

computer RAM became limiting, and to accommodate such a massive amount of data

needed to calculate genetic distance and cluster sequences into OTUs, it was necessary to

subsample the data sets.

9.4.2 Sequencing Target

Properly selecting a sequencing target is extremely important as it impacts all aspects of

the project: from PCR amplification, to sequencing reactions, to DNA sequence analysis.

As previously mentioned (SECTION 1.2), the SSU rRNA gene was chosen as the target

259

for phylogenetic studies. As current high-throughput sequencing techniques were limited

in the length of high-quality, low-error sequences which could be generated, a section of

the rRNA gene was needed as a proxy for the full-length sequence. In addition to being

the optimal size for high-throughput sequencing (~500 b), the amplicon needed to be easy

to generate in the lab, have a trustworthy and expansive (when possible) reference

database of sequences available for comparison [33–35], and not be liable to

amplification bias by primers [15, 36, 37]. Most importantly, the amplicon needed to

provide a similar statistical estimate of diversity and resolution for identification as when

using the full-length gene [15, 38–40]. Thus, it was determined that for the presented

work, the V1-V3 region of the 16S for bacteria and archaea, and the V3-V4 region of the

18S for protozoa, provided the overall best solution to the current constraints and the

selected sequencing platform.

Most studies which investigate the microbiota of a certain environment tend to focus on

one domain at a time, or employ different methods to investigate different taxa (i.e.

bacteria, archaea, protozoa, and fungi). However, some phylogenetic studies attempt to

use the same primers and amplification parameters across multiple kingdoms. While this

may be a more efficient approach in terms of time, money, and effort in the laboratory, it

is not an adequate means of investigation. For example, studies which use “universal

prokaryotic primers” to co-amplify bacteria and archaea result in very few archaeal

sequences as compared to the number of bacterial sequences generated [41–43]. In the

260

GI tract, archaeal density is only 1-2 logs lower than bacterial density, thus “universal”

PCR parameters for the 16S gene overwhelmingly underrepresents archaea. Likewise,

“universal eukaryotic primers” often marginalize protozoal sequences [42, 44]. In some

instances, primers which were prokaryote-specific have been used to amplify eukaryotic

18S sequences [45, 46].

9.4.3 Bioinformatics Programs and Analysis

MOTHUR [24] was selected as the bioinformatics platform of choice as it integrated a

comprehensive list of analytical functions within the same platform, was more user-

friendly than other programs, and was compatible with Windows, Mac, and Linux

operating systems. Notably, it integrated a version of PyroNoise [23] which reduced

noise in pyrosequencing flowgrams generated from Roche 454 platforms, as well as a

wide variety of chimera checking algorithms.

Two of the more popular chimera checking programs, UCHIME [47] and ChimeraSlayer

[48], were compared using bacterial 16S rRNA sequences from the moose rumen

generated using the Roche 454 platform (CHAPTER 3). UCHIME identified 18,146

chimeras out of a total 40,514 sequences, or 45% of sequences as chimeric, when using

abundance to estimate chimeras. Using the same data set, again using abundance to

estimate chimeras, ChimeraSlayer identified 17,225 chimeric sequences, or 43%.

However, 14,933 out of the 18,146 UCHIME putative chimeric sequences (82%) were

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able to classify with >80% confidence. For ChimeraSlayer, 14,268 out of the 17,225

putative chimeric sequences (83%) could be classified with >80% confidence. Using the

Silva bacterial 16S reference file to estimate chimeric sequences reduced the percentage

of sequences being identified as chimeric, as well as reduced the percentage of those

which could actually be classified with a reasonable amount of confidence. Thus,

UCHIME with a reference database was used for all further analyses.

Reference alignments for bacteria, archaea, and eukaryota from the Silva [49],

Greengenes [50], and RDP [34] databases were provided for use with MOTHUR. The

Silva database for bacteria was found to generate better alignments and classification than

the RDP reference database (Table 1). Owing to the unique nature of the archaeal and

protozoan sequences being investigated, and the low number of available rumen ciliate

protozoan sequences in any database, it was necessary to generate in-house reference

alignments for both using publically available sequences [31, 32].

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9.6 Figures

Figure 9-1 Mean rumen pH over time, with standard deviation bars.

268

9.7 Tables

Table 9-1 A comparison of the Silva bacterial database vs the Ribosomal Database Project (RDP)

bacterial reference files in ability to classify 16S rRNA gene bacterial sequences.

Silva Silva % RDP RDP %

Total Unique sequences 33,622 33,622

Total classified as Bacteria 33,622 100% 33,610 99.96%

Total classified as Archaea 0 0% 1 ~0%

Unclassified at the Domain level 0 0% 11 0.03%

Total classified at the Phylum level

(excl. Unclassified) 28,111 83.61% 26,769 79.6%

Total classified at the Family level


Total classified at the Genus level


Total classified at the Species level 0 0% N/A

Total Unclassified bacteria 5,511 16.39% 6,841 20.34%

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