Improving the Efficiency of Rumen Function: When to Intervene Windu Negara M.Sc. Animal Science

A thesis submitted for the degree of Doctor of Philosophy at The University of Queensland in 2019 School of Agriculture and Food Sciences

Abstract Rumen microbial development and adaptation are essential in ruminal feed digestion. Many studies have been undertaken to improve ruminal fermentation by modulating the profile of the rumen microbial community i.e. using biological and dietary treatments. However, the result of trying to modulate the rumen microbial community has often been inconsistent. Previous studies have indicated that host specificity influences the rumen microbiome resulting in variation between animals. However, the core rumen microbial community across animals, even with different diets and from differing geographical locations, tends to be similar demonstrating a resilience of the core microbiome against interruptive events. Thus, information related to these characteristics is important for the design and formulation of treatments for modulating the rumen microbiome to enable interventions to persist in the long term. In this study, the changes to the rumen microbial community were investigated during rumen perturbation at three different time frames, i.e. pre-ruminant, post-weaning, and adult. The treatments consisted of probiotic administered to young calves (Chapter 3), feed supplementation of post-weaned cattle (Chapter 4), and movement and change of feed in adult cows (Chapter 5). The plasticity of the core rumen microbiome was investigated when cattle were moved onto a higher quality floodplain pasture-based diet (Chapter 5). Results tend to suggest that the rumen microbial community was less significantly impacted in pre-weaning animals compared to post-weaning and adult animals. Bacillus amylolyquefaciens strain H57, used as a probiotic, was inoculated into four-day old dairy calves (22 in total) through milk replacer for eight weeks. Although populations of Prevotella and Shuttleworthia increased in the core rumen microbiome, the overall rumen microbial diversity was not affected by H57 inoculation. Addition of H57 also increased the predicted functional genes for peptide metabolism in the core rumen microbial community. However, the results also suggested that H57 may have failed to establish in the rumen of dairy calves, as indicated by a low number of H57 in the rumen of treated calves. The low number of H57 in the rumen might be caused by the oesophageal groove mechanism bypassing milk replacer containing H57 into the lower GI tract rather than to the rumen. Recently weaned beef cattle heifers were fed a low-quality hay (38 g/kg DM of CP) and supplemented with copra meal and corn or no supplement as control (Chapter 4). The results showed that the rumen microbial diversity and predicted functional genes of the core rumen microbiome were significantly affected by feed supplementation. Feed supplementation reduced the diversity and species richness of the rumen microbial community. In both non-core and core microbiome data sets, feed supplementation significantly increased the relative abundance of Christensenellaceae R-7 group, Ruminococcus, Ruminococcaceae UCG-014, and Lachnospiraceae XPB1014. The predicted

ii functional profile of the core rumen microbiome showed feed supplementation significantly altered the functional genes related with energy harvesting such as glycolysis, pyruvate and propionate metabolism. The findings in taxonomic and functional gene profiles indicated the adaptation of the rumen microbial community to the feed supplement. Modification of the rumen microbiome in adult cattle was observed in cattle being moved from a variety of extensive pasture-based diets to co-grazing a better-quality floodplain-based diet in northern Australia (Chapter 5). The species of grasses differed between the extensive pastures and floodplain pastures with the latter being of higher nutritive value. Two groups of animals originating from the research station (BHRF; 9 cows) and a variety of commercial properties (COM; 32 cows) remote to the Research Facility, were grazed together on floodplain pasture for 137 days (Study 1). In the following year, 17 COM cows were backgrounded for approximately one month with black spear grass pasture prior to being moved onto the floodplain for 80 days (Study 2). Both studies showed that the rumen microbial community changed, largely in association with improved nutrient quality of the pasture due to rain events. The rumen microbial diversity and species richness decreased along with improved quality pasture. Beta diversity analysis indicated that the changes in rumen microbial diversity were mainly driven by diet quality. In addition, the studies showed the ability of the core rumen microbiome to adjust to feeding on the floodplain pastures. Upon arrival at the trial site, the rumen microbial community differed in accordance to property of origin (birthplace), i.e., Hayfield and Kalala for the feed supplementation study (Chapter 4) and BHRF and COM (including birthplace categories) for the floodplain study (Chapter 5). This specific property-based profile of the rumen microbial community diminished soon after animals were fed the same diet and grazed together as a herd. The analysis of unique core OTUs may demonstrate the incidence of horizontal transmission of microbes between animals in both studies. In the feed supplementation trial, the initial property-specific rumen microbial community, merged into a new microbial community which lasted until the end of the pen trial. The number of unique OTUs that transmitted between animals from Hayfield and Kalala stations was in balance for both groups (169- 178 OTUs). The results of the floodplain study showed the profile of the rumen microbial community in COM animals during the transitional period was adjusted to a similar profile as the rumen microbial profile in BHRF animals (locally reared). Accordingly, horizontal transfer of core OTUs was mainly from BHRF animals to the COM animals. However, the main changes in rumen microbial community caused by a feed changes (supplementation treatment; extensive to floodplain pastures) which favoured organisms that were previously minor players, even below levels of detection. In conclusion, this study explored the possibility of modulating the rumen microbial profile at three different physiological stages of animals (pre ruminating, post weaning, and adult). However,

iii the success rate of intervention was influenced by the type of treatment and diet was shown to have the biggest impact to the structure of rumen microbial community. This study also showed the plasticity of core microbiome and the response the minor rumen microbial community to diet changes, providing a mechanism for adaptation to a new diet. Adjusting the diet may have encouraged the growth of some bacteria species, which were previously below detection limits. This study demonstrated for the first time that the rumen microbiome of co-grazing, adult cows, can co-evolve during adaptation to diet changes experienced in northern Australia.

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Declaration by author This thesis is composed of my original work, and contains no material previously published or written by another person except where due reference has been made in the text. I have clearly stated the contribution by others to jointly-authored works that I have included in my thesis.

I have clearly stated the contribution of others to my thesis as a whole, including statistical assistance, survey design, data analysis, significant technical procedures, professional editorial advice, financial support and any other original research work used or reported in my thesis. The content of my thesis is the result of work I have carried out since the commencement of my higher degree by research candidature and does not include a substantial part of work that has been submitted to qualify for the award of any other degree or diploma in any university or other tertiary institution. I have clearly stated which parts of my thesis, if any, have been submitted to qualify for another award.

I acknowledge that an electronic copy of my thesis must be lodged with the University Library and, subject to the policy and procedures of The University of Queensland, the thesis be made available for research and study in accordance with the Copyright Act 1968 unless a period of embargo has been approved by the Dean of the Graduate School.

I acknowledge that copyright of all material contained in my thesis resides with the copyright holder(s) of that material. Where appropriate I have obtained copyright permission from the copyright holder to reproduce material in this thesis and have sought permission from co-authors for any jointly authored works included in the thesis.

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Publications included in this thesis No Publications included

Submitted manuscripts included in this thesis No manuscripts submitted for publication

Other publications during candidature Negara, W., Gilbert, R., Ouwerkerk, D., Klieve, A., Silva T.A.C.C., McCosker, K., Schatz, T., Poppi, D. (2018) ‘The Effect of Post Weaning Supplementation on the Rumen Microbial Populations of Brahman- Cross Heifers from Northern Australia’. Rowett-INRA 2018 ‘Gut Microbiology: No longer the forgotten organ’, 11th joint symposium, Aberdeen, Scotland, UK. 11-14 June 2018.

Contributions by others to the thesis This thesis would not been done without any advice from my advisors, Athol Klieve, Diane Ouwerkerk, Ros Gilbert, and Prof Dennis Poppi. The probiotic used in Chapter 3 was provided by Peter Dart with the assistance of Benjamin Schofield and Yadav Bagajav. Milk replacer was specifically provided for the trial by Ridley AgriProduct Pty Ltd (Melbourne, Australia) under the supervision of Mr Matthew Callaghan. Dairy calves were provided by UQ Gatton dairy complex. Animal trial design and arrangement was made by Dr David McNeill assisted by Dagong Zhang organizing day to day animal care. I acknowledge Oanh Thi Le for sampling and data analysis of dairy calves performance and health status parameters. The cattle clinicians in Veterinary Science School and the team of Queensland Animal Science Precinct of the University of Queensland were involved in technical support of dairy calves trial. I acknowledge Kieren McCosker, Tim Schatz and personnel in Department of Primary Industries and Fisheries – Northern Territory (DPIF NT); Victoria River Research Station, Katherine Research Station and Beatrice for organizing and conducting the animal trial in post weaning supplementation and floodplain grazing. Rumen fluid samples in feed supplementation trial were provided by Tiago Da Silva and his team from UQ and DPIF NT. I acknowledge Benjamin Schofield, Catherine Minchin, Anita Maguire, and Jenny Gravel for assistance in laboratory works; Allan Lisle and Kerri Chandra for their assistance in statistical analysis; and Ros Gilbert for her help in bioinformatics analysis.

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Statement of parts of the thesis submitted to qualify for the award of another degree No works submitted towards another degree have been included in this thesis.

Research Involving Human or Animal Subjects The experiment in Chapter 3. was approved by the Animal Welfare Unit, The University of Queensland, approval number SVS/064/15/ARC. Whilst the experiment and all the procedures involved in Chapter 4 and 5 were in accordance with the Australian Code of Practice for the Care and Use of Animals for Scientific Purposes and was approved by the Animal Ethics Committee of the Charles Darwin University with the approval number A14002 and A15017, respectively.

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Acknowledgements “A journey of a thousand miles begins with a single step”. This study is a first step for me to explore the rumen microbial world. All the experiences and the achievements during this journey would not be meaningful without any support from my little family. I would like to thank my beloved wife Intan for her patience and the encouragement she has done. I would like to also thank my cheeky children Nisa, Aisha, and Shafiyah for making our home cheerful all day. I believe your future could be much better than me. Special thanks to my parents in Indonesia for all support that they gave to us during our stay in Australia. I would like to acknowledge my extraordinary supervisors Athol Klieve, Diane Ouwerkerk, Ros Gilbert, and Dennis Poppi for all knowledge, patience, and caring that you provided during our interaction. I am assuming supervising me was the hardest supervision job that you ever had in your career. The topic of rumen microbial ecology along with the molecular techniques was the big step in my career as an animal nutrition researcher. I would not pass this big step smoothly without any support and help from you. This thesis would not be possible without the collaboration with the Department of Primary Industries and Fisheries, Northern Territory. Particularly, I would like to thank Kieren McCoskeer, Tim Schatz for offering me a great experience in investigating the rumen microbiome of postweaning supplemented heifers and floodplain grazing cows. I also would like to recognise David McNeill, Peter Dart, and Dagong Zhang for their attention and friendship during the calves trial. In addition, I would like to thank the staff at Katherine Research Station- Northern Territory, Beatrice Hills Research Farm- Northern Territory, and Queensland Animal Science Precinct for the support and help during the animal trial and rumen fluid sample collection. To the superb and cheerful staff of the Rumen Ecology Unit: Jenny Gravel, Anita Maguire, Catherine and Minchin, thanks for the attention and support that provided during my lab work. You inspired me on how to keep enjoyed in the laboratory although I had tens of rumen fluid samples needed to be extracted. Many thanks also for Benjamin Schofield, Lee Oanh, and Tiago Silva for your cooperation in animal trial and bioinformatic analysis. Special thanks to Indonesian student association in Gatton (INDOGA), you are my true family during my stay in Gatton. I would also like to thank postgraduate students in the room 139 Animal Study Building: Yaqoub Al Hosni, Salman Sarwar, M. Ilyas, Risa Antari, Md. Ali Akber, Adhitya Kiloes, Yanti Muflikh, and Mayo for the friendship and lunch time discussion.

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Financial support This study was supported by a Research and Innovation in Science and Technology Project scholarship (RISET-PRO) from Indonesian Government, Ridley AgriProducts Pty Ltd (Melbourne, Australia), ARC linkage program, Department Primary Industry Northern Territory, and Meat and Livestock Australia.

Keywords Rumen microbial profiling, core microbiome, floodplain grazing, feed supplementation, probiotic, Bacillus amyloliquefaciens H57, ruminants

Australian and New Zealand Standard Research Classifications (ANZSRC) ANZSRC code: 060504, Microbial Ecology, 70% ANZSRC code: 070202, Animal Growth and Development, 30%

Fields of Research (FoR) Classification FoR code: 0605, Microbiology, 70% FoR code: 0702, Animal Production, 30%

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Table of Contents Chapter 1. Introduction and literature review ...... 1

1.1. General introduction ...... 1

1.2. Dynamic profiles of the rumen microbial community ...... 2

1.2.1. The Ruminant digestive system ...... 2

1.2.1.1. Reticulorumen ...... 3

1.2.2. Rumen physiology and function ...... 4

1.2.2.1. VFA production ...... 5

1.2.2.2. Protein microbial production ...... 7

1.2.3. Rumen development ...... 11

1.2.4. Rumen microbes ...... 12

1.2.4.1. Bacteria ...... 13

1.2.4.2. Protozoa ...... 19

1.2.4.3. Anaerobic fungi ...... 21

1.2.4.4. Archaea ...... 23

1.2.4.5. Bacteriophages ...... 24

1.2.5. Modulating the rumen microbial community ...... 25

1.2.5.1 Diet treatments ...... 25

1.2.5.2. Biological treatments ...... 28

1.2.6. Host specificity, persistence and plasticity of the rumen microbiota ...... 32

1.2.6.1. Host specificity and rumen microbial stability ...... 32

1.2.6.2. The plasticity of rumen microbiota ...... 35

1.2.7. Next generation sequencing of 16S rRNA gene PCR amplicons for assessing rumen microbial ecology ...... 36

1.3. Conclusions and thesis direction ...... 40

Chapter 2. General Materials and Methods ...... 43

2.1. Introduction ...... 43

2.2. Rumen fluid (RF) sample collection ...... 43 x

2.3. DNA Extraction ...... 43

2.4. Bioinformatic Analysis ...... 46

2.4.1. Quality Filtering and Upstream Analysis...... 46

2.4.2. Downstream Analysis ...... 48

2.4.3. Predicting metagenome functional ...... 49

2.5. Statistical Analysis ...... 49

Chapter 3. Changes in the Rumen Microbiome of Dairy Calves Supplemented with Bacillus amyloliquefaciens H57 ...... 51

3.1. Introduction ...... 51

3.1.1. Background ...... 51

3.2. Methods ...... 52

3.2.1. Animal Ethics and Experimental Location ...... 52

3.2.2. Preparation of probiotics ...... 52

3.2.3. Experimental Design and Treatments ...... 53

3.2.3. Rumen Fluid Sampling and DNA extraction ...... 54

3.2.4. Quantitative PCR ...... 54

3.2.5. Bioinformatic analysis ...... 55

3.3. Results ...... 56

3.3.1. Quantification of H57 probiotic in the rumen ...... 56

3.3.2. Rumen microbiome profile ...... 57

3.3.3. Rumen core microbiome profile ...... 60

3.3.4. Predicted functional metagenome ...... 62

3.4. Discussion ...... 63

3.5. Conclusion ...... 68

Chapter 4. The Effect of Post Weaning Supplementation on the Rumen Microbial Populations of Brahman-Cross Heifers in Northern Australia ...... 69

4.1. Introduction ...... 69

4.2. Methods ...... 70

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4.2.1. Animal Ethics ...... 70

4.2.2. Experimental Location ...... 70

4.2.3. Experimental Design and Treatments ...... 70

4.2.5. Rumen Fluid Sampling ...... 72

4.2.6. DNA Extraction and 16S rRNA Gene Sequencing ...... 73

4.2.7. Bioinformatic and Statistical Analysis ...... 73

4.3. Results ...... 75

4.3.1 The initial (baseline) rumen microbial community of heifers with different origins and weaning weights...... 75

4.3.1.1. Non-core rumen microbial community ...... 75

4.3.1.1.1. Rumen microbial diversity ...... 75 4.3.1.1.2. Taxonomic profile ...... 78 4.3.1.2. Core rumen microbial community ...... 82

4.3.1.2.1. Taxonomic profile ...... 82 4.3.2. The changes to the initial rumen microbiome following six months of feeding with control diet ...... 86

4.3.2.1. Non-core rumen microbiome ...... 86

4.3.2.1.1. Rumen microbial diversity ...... 86 4.3.2.1.2. Taxonomic profile ...... 87 4.3.2.2. Core rumen microbiome ...... 88

4.3.2.3. The unique core microbiome after six months of the feeding trial ...... 89

4.3.3. The effect of feed supplementation on the rumen microbiome ...... 90

4.3.3.1. Non-core rumen microbiome ...... 90

4.3.3.1.1. Rumen microbial diversity ...... 90 4.3.3.2. Core microbiome ...... 95

4.3.4. Predicted functional profiles of the rumen bacterial community ...... 98

4.4. Discussion ...... 99

4.4.1. The initial (baseline) rumen microbial community of heifers with different origins and weaning weights...... 99

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4.4.2 The changes to the initial rumen microbiome following six months of feeding with control diet ...... 101

4.4.3. The effect of feed supplementation on rumen microbial diversity ...... 102

4.4.3. Predictive functional analysis ...... 106

4.5. Conclusion ...... 106

Chapter 5. Factors affecting the rumen microbiome profile of culled cows during transition period on Northern Australia floodplain ...... 107

5.1. Introduction ...... 107

5.2. Material and Methods ...... 108

5.2.1. Animal ethics ...... 108

5.2.2. Time and experimental location ...... 109

5.2.3. Experimental Design and Treatments ...... 110

5.2.4. Rumen Fluid Sampling ...... 112

5.2.5. DNA Extraction and 16S rRNA Gene Sequencing ...... 112

5.2.6. Bioinformatic and Statistic Analysis ...... 112

5.3. Results ...... 115

5.3.1. Study 1...... 115

5.3.1.1. Rumen microbial diversity ...... 115

5.3.1.2. Changes in rumen microbial populations during floodplain grazing ...... 123

5.3.1.2.1. Rumen microbial profile between birthplace groups at day 1 and day 34 ...... 124

5.3.1.2.2. Rumen microbial profile between day 34 and 137 floodplain grazing ...... 126

5.3.1.3. Movement of unique core rumen microbial OTUs during floodplain grazing ...... 127

5.3.1.4. Predicted functional genes of rumen microbial community ...... 130

5.3.1.4.1. Profile of functional genes between birthplace groups at day 1 and day 34..... 130

5.3.1.4.2. Profile of functional genes between day 34 and day 137 floodplain grazing .... 131

5.3.2. Study 2 ...... 133

5.3.2.1. Rumen microbial diversity across floodplains grazing cows ...... 133

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5.3.2.2. The changes in profile of core rumen microbial community during floodplain grazing ...... 136

5.3.2.2.1. Profile of core rumen microbial community based on grazing period ...... 136 5.3.2.2.2. Profile of core rumen microbial community of floodplain grazing cows in accordance to body condition score...... 137 5.3.2.2. Predicted functional gene of rumen microbiome at different grazing period ...... 138

5.4. Discussion ...... 140

5.4.1. Study 1 ...... 140

5.4.2. Study 2 ...... 143

5.5. Conclusion ...... 145

Chapter 6. General discussion and Conclusion ...... 146

6.1. General Discussion ...... 146

6.2. Implication of the study ...... 151

6.3. Conclusion ...... 153

Reference list ...... 155

Appendix 1: Supplementary material for Chapter 4 ...... 192

Appendix 2: Supplementary material for Chapter 5 ...... 194

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List of figures Figure 1.1. The digestive system of ruminant (reproduced from Russell and Rychlik (2001))...... 3 Figure 1.2. Pathways of VFA production from feed fermentation by rumen microbes anaerobically. (Reproduced from Klein (2012))...... 6 Figure 1.4. The synthesis of rumen MP with three different protein and carbohydrate supply scenarios: well matched supply (A), excess carbohydrate relative to protein (B), and excess protein relative to carbohydrate (C). (Reproduced from Klein (2012))...... 9 Figure 1.5. Groups of rumen bacterial species in accordance to the type of substrates utilisation (Reproduced from Yokoyama and Johnson (1993))...... 14 Figure 1.6. The types of lignin-polysaccharide bonds in cell wall plant. (a) Hydroxycinnamate ester, (b) Hydroxycinnamate ether, (c) Ferulate ester-ether bridge, (d) Dehydrodiferulate ester bridge, and (e) Dehydrodiferulate ester-ether bridge. (Reproduced from Carpita and McCann (2015))...... 16 Figure 1.7. Steps involved in cellulose degradation by glycoside hydrolase enzymes from rumen cellulolytic bacteria. (Reproduced from Krause et al. (2003))...... 17 Figure 1.8. The life cycle of anaerobic fungi. (Reproduced from Gruninger et al. (2014))...... 22 Figure 1.9. 2D and 3D structures of bacteriophages from the family Myoviridae (Reddy 2013) ...... 24 Figure 1.10. Ruminal acidosis cycle and its impact to rumen fermentation. (Reproduced from https://ruminantdigestivesystem.com)...... 27 Figure 1.11. The effects of probiotics in modulating host health through modifying the epithelial barrier function. The epithelial barrier function is improved by; A) inducing mucus production; B) increasing the secretion of IgA; and C) competing with other bacteria. Ohland and MacNaughton (2010)...... 30 Figure 1.12. Diagram of the nine regions of 16S rRNA (from E. coli) which are indicated by different colours and designated v1-v9. The legend shows the length of each region in bases. (Reproduced from Yarza et al. (2014))...... 37 Figure 1.13. The PCR amplification process. (Reproduced from Pelt-Verkuil et al. (2008))...... 38 Figure 2.1. The overview of bioinformatic analysis that was performed in this study...... 47 Figure 3.1. Alpha rarefaction graphics for each alpha diversity matrix of the rumen microbiome in calves fed H57 ( ) and Control calves ( ) ...... 57 Figure 3.2. PCoA plot of rumen microbial community in calves between treatment groups in accordance to Unweighted UniFrac index...... 58

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Figure 3.3. Heatmap of rumen microbial OTUs with significant difference in their abundance in accordance to the treatment groups (P<0.05). Each row represents the OTUs name within genus (g_) level with the lighter color showing the more abundance OTUs...... 59 Figure 3.4. The relative abundance of dominant genera rumen microbial in dairy calves in accordance to probiotic treatments. The letter before rumen microbial name is showing taxonomy level (o=order; f=family)...... 60 Figure 3.5. Heatmap of rumen microbial OTUs with significant difference in their abundance in accordance to the treatment groups (P<0.05). Each row represents the OTUs name within genus (g_) or family (f_) level with lighter color showing the higher abundance OTUs. ... 61 Figure 3.6. The relative abundance of dominant core genera rumen microbial in dairy calves in accordance to probiotic treatments. The letter before rumen microbial name is showing taxonomy level (o=order; f=family). **= very significantly different (P<0.01); *= significantly different (P<0.05) ...... 62 Figure 3.7. Composition of KEGG pathways tier 2 predicted functional metagenome of non core rumen microbial community within all treatment groups. Only pathways with relative abundance more than 1% are shown. *Represent significant difference in relative abundance of functional genes between control and H57 groups (FDR P<0.05)...... 63 Figure 4.1. Differences in initial rumen microbial community of heifers based on station origins shown using PCoA plots of Unweighted UniFrac (left) and Weighted UniFrac (right). Hayfield station= , Kalala station= ...... 77 Figure 4.2. Differences in initial rumen microbial community of heifers according to weaning weight x animal origin shown using PCoA plots of Weighted Unifrac. Light weaning weight (LWW; left), heavy weaning weight (HWW; right). Hayfield station= , Kalala station= ...... 77 Figure 4.3. Differences in initial rumen microbial community of heifers originated from Hayfield station according to weaning weight shown using PCoA plot of Weighted Unifrac. Heavy weaning weight (HWW)= , light weaning weight (LWW)= ...... 78 Figure 4.4. Differences in relative abundance of dominant genus within initial rumen microbial community according to animal origins group. The differences between groups determined by ANOVA analysis with asterix superscripts after the taxon name indicating the P-value (*= P<0.05, **= P<0.01, ***= P<0.001)...... 79 Figure 4.5. Differences in relative abundance of 50 most abundant OTUs of initial rumen microbial community shown using heatmap. Only OTUs with relative abundance that significantly differed between animal’s origin as determined by ANOVA (FDR P<0.05) are shown in heatmap...... 80

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Figure 4.6. Differences in relative abundance of dominant genus within initial rumen microbial community according to weaning weight group (heavy: HWW; and light: LWW). The significant differences between groups determined by ANOVA analysis with asterix superscripts after taxon name indicating the P-value (*= P<0.05, **= P<0.01)...... 81 Figure 4.7. Differences in relative abundance of dominant genera within initial core rumen microbial community according to animal’s origin group. The significant differences between groups determined by ANOVA analysis with asterix superscripts after taxon name indicating the P- value (*= P<0.05, **= P<0.01, and ***= P<0.001)...... 83 Figure 4.8. Differences in relative abundance of 50 most abundant OTUs of the core rumen microbial community shown using heatmap. Only OTUs with relative abundance that significantly differed between animal’s origin groups as determined by ANOVA (FDR P<0.05) are shown in the heatmap...... 84 Figure 4.9. Differences in relative abundance of dominant genera within core rumen microbial community according to weaning weight group. The significant differences between groups determined by ANOVA analysis with asterix superscripts after taxon name indicating the P-value (**= P<0.01)...... 85 Figure 4.11. Venn diagram of shared unique core OTUs that were obtained at baseline sampling (initial group) in heifers and after six months of being fed the control diet (Control group). The observations were within group (A) and between groups (B) based on the animal’s origin (Hayfield and Kalala Stations)...... 90 Figure 4.12. Rarefaction curves of species richness measures of the rumen microbial community in heifers according to feed supplementation treatments (supplemented= ; control= ). . 91 Figure 4.13. Differences in rumen microbial community between Control and Supplemented heifers shown using Unweighted (A) and Weighted (B) UniFrac. Supplemented= ; Control= .... 92 Figure 4.14. Differences in relative abundance of 50 most abundant OTUs in Control and Supplement Groups. Only the significantly affected OTUs between diet groups as determined by ANOVA (FDR P<0.05) are shown in heatmap...... 93 Figure 4.15. Differences in relative abundance of dominant rumen microbial genera in Control and Supplemented heifers. The significant differences between groups determined by ANOVA analysis with asterix superscripts after taxon name indicating the P-value (* =P<0.05; **= P<0.01; ***=P<0.001)...... 94 Figure 4.16. Difference in relative abundances of 50 most abundant rumen microbial OTUs in the Control and Supplemented heifers. Only the OTUs significantly affected by diet treatments as determined by ANOVA are shown in heatmap (FDR P<0.05)...... 96

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Figure 4.17. Differences in relative abundance of dominant core rumen microbial genera in Control and Supplemented heifers. The significant differences between groups were determined by ANOVA analysis with asterix superscripts after taxon name indicating the P-value (*=P<0.05; **=P<0.01; ***=P<0.001)...... 97 Figure 4.18. Differences in relative abundance of level 3 KEGG pathways of rumen microbiome data set grouped by carbohydrate metabolism pathway (a) and amino acids metabolism pathway (b). Only the significantly affected KEGG pathways by feed treatment are shown (FDR P<0.05)...... 99 Figure 5.1. Rainfall recorded at Beatrice Hill Research Farm. Study 1 and Study 2 were conducted in 2016 and 2017, respectively...... 109 Figure 5.2. Ground cover fractional data of Beatrice Hill Research Farm during the research period...... 110 Figure 5.3. Changes in alpha diversity of the rumen microbiome in Beatrice Hill Research Farm (BHRF) and commercial (COM) cows from day 1 until day 137 grazing in floodplain. The differences between grazing period were statistically tested using repeated measures (REML) with P<0.05 significance level...... 117 Figure 5.4. Boxplot distribution of alpha diversity measures for each grazing period. The significant differences between grazing periods were statistically tested using repeated measures with REML method. Boxplots with the same letter were not significantly different at the P<0.05 significance level...... 118 Figure 5.5. Differences in the rumen microbial community between days grazing on floodplain. Principal Co-ordinates Analysis (PCoA) plots were generated according to Weighted (left) and Unweighted UniFrac (right)...... 120 Figure 5.6. Changes in rumen microbial community of BHRF and Commercial cows during grazing on floodplains as a herd. Principal Co-ordinates Analysis (PCoA) plots were generated according to Weighted (left) and Unweighted UniFrac (right)...... 121 Figure 5.7. Changes in rumen microbial community of cows during grazing on floodplains as a herd according to their birth places (Commercial: OOH, BCO, KP1; Beatrice Hills Research Farm: BHRF). Principal Co-ordinates Analysis (PCoA) plots were generated according to Weighted (left) and Unweighted UniFrac (right)...... 122 Figure 5.8. The number of core OTUs in floodplain grazing cows in accordance birthplace (OOH, BCO, and KPI for COM animals; BHRF). Whilst, the horizontal lines are showing the number of core OTUs in accordance to property of origin (COM and BHRF)...... 124

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Figure 5.9. Differences in relative abundance of dominant core rumen microbial genera between animals according to birthplace groups (COM: OOH, BCO, KPI; and BHRF) at day 1. The significant differences between animal groups were generated using ANOVA analysis with asterix superscripts showing the level of significance (*= P<0.05, **= P<0.01, ***= P<0.001)...... 125 Figure 5.10. Differences in relative abundance of dominant core rumen microbial genera between animals according to birthplace groups (COM: OOH, BCO, KPI; and BHRF) at day 34. The significant differences between animal groups were generated using ANOVA analysis with asterix superscripts showing the level of significance (**= P<0.01)...... 125 Figure 5.11. Changes in the number of unique core OTUs in cows according to birthplaces (OOH, BCO, KPI and BHRF) during 137 days of floodplain grazing...... 127 Figure 5.12. The number of unique OTU from day 1 that may be passed between animals according to birthplace group at day 34...... 128 Figure 5.13. Taxonomic information - day 1 unique OTUs of BHRF animals that may be passed to OOH, BCO, and KPI cows at day 34...... 128 Figure 5.14. The number of unique OTU from day 1 that passed between animals according to birthplace group at day 137...... 129 Figure 5.15. Taxonomic information of day 1 unique OTUs of BHRF animals that passed into COM cows (OOH, BCO, and KPI) at day 137...... 129 Figure 5.16. Differences in rumen microbial community of cows between grazing period on floodplain. Principal Co-ordinates Analysis (PCoA) plots were generated according to Weighted (left) and Unweighted UniFrac (right)...... 134 Figure 5.17. Differences in rumen microbial community of grazing floodplain cows in accordance to body condition score (BCS) groups. Principal Co-ordinates Analysis (PCoA) plots were generated according to Weighted (left) and Unweighted UniFrac (right)...... 136

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List of tables Table 1.1. The types of plant cell walls in accordance to the composition of wall components...... 15 Table 3.1. Chemical composition of the experimental diet† ...... 54 Table 3.2. Specific PCR primers and probe for Bacillus amyloliquefaciens...... 55 Table 3.3. Differences in alpha diversity indexes between calves fed H57 and Control calves (mean ± SEM) ...... 58 Table 4.1. Nutritional information of feed1 ...... 72 Table 4.2. Differences of alpha diversity measures values (mean) of initial rumen microbial community. The significant differences between groups were determined by ANOVA analysis...... 76 Table 4.3. Statistical analysis of beta diversity measures (Weighted and Unweighted Unifrac) of initial rumen microbial community. P-value was generated by ANOSIM analysis...... 76 Table 4.4. Differences in relative abundance of dominant genera within initial rumen microbial community according to animal origin x weaning weight groups. The significant differences between groups were determined by ANOVA analysis...... 82 Table 4.5. Differences in relative abundance of dominant genera within core rumen microbial community according to animal origin x weaning weight groups. Significant differences between groups were determined by ANOVA analysis...... 85 Table 4.6. Differences of alpha diversity measures values (mean) of control rumen microbial community according to animal’s group. The significant differences between groups were determined by ANOVA analysis...... 86 Table 4.7. Statistical analysis of beta diversity measures (Weighted and Unweighted Unifrac) of control rumen microbial community...... 87 Table 4.8. Differences in relative abundance of dominant genera rumen microbial community of heifers after six months fed control diet. P-value was calculated by ANOVA analysis...... 88 Table 4.9. Differences in relative abundance of dominant genera core rumen microbial community of heifers after six months fed control diet. P-value was calculated by ANOVA analysis ...... 89 Table 4.10. Effect of feed supplementation on alpha diversity measures of the rumen microbiome (mean±s.e.). P-value was generated by ANOVA analysis...... 91 Table 4.13. Differences in relative abundance of core rumen microbiome phyla in Control and Supplemented heifers. P-values were generated by ANOVA analysis ...... 95 Table 5.1. The number of rumen fluid samples and animals for each metadata category...... 111

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Table 5.2. Statistical analysis of four alpha diversity measures of rumen microbial diversity in accordance to animal groups within the three sampling periods. P-values were generated by repeated measures with the REML method...... 116 Table 5.3. Differences in alpha diversity of the rumen microbiome in cows within start weight groups for days 1, 34, and 137 grazing on floodplains (mean ± stdev). The significant differences between groups were statistically tested using repeated measures with REML method...... 118 Table 5.4. Statistical analysis of beta diversity measures in accordance to metadata categories. P- values were generated by PERMANOVA analysis. R values were generated by ANOSIM analysis...... 119 Table 5.5. Differences in relative abundance of non-core OTUs in accordance with metadata category...... 123 Table 5.6. The changes in core rumen microbial populations between day 34 and day 137 (means ± stdev; %). P-values were generated by repeated measures with REML method...... 126 Table 5.7. Differences in relative abundance predicted functional genes of core rumen microbial community at day 1 in accordance to birthplace groups. FDR P-values were generated by ANOVA analysis...... 130 Table 5.8. Differences in relative abundance of predicted functional genes from the core rumen microbial community at day 34 in accordance to birthplace groups. FDR P-values were generated by ANOVA analysis...... 131 Table 5.9. Differences in relative abundance of predicted metagenomic functional genes of rumen microbial community at day 34 and day 137. Only metabolism gene pathways that significantly differed between grazing periods are shown (FDR P<0.05)...... 132 Table 5.10. Differences in alpha diversity measures of rumen microbial diversity in accordance to animal groups. The significant differences between fixed effects were analysed by repeated measures (REML) and ANOVA for grazing period only...... 133 Table 5.11. Differences in beta diversity measures of rumen microbial community in floodplain grazing cows in accordance to metadata categories. P values and R values were generated by PERMANOVA and ANOSIM analyses, respectively...... 135 Table 5.12. Differences in relative abundance (Mean and SEM) of dominant core rumen microbial genera according to grazing period. P-values were generated by ANOVA analysis...... 137 Table 5.13. Differences in relative abundance of dominant core rumen microbial genera at day 80 relative to body condition score (BCS). P-values were generated by ANOVA analysis .... 138

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Table 5.14. Differences in relative abundance of predicted metagenomic functional genes of the rumen microbial community at day 1 and day 80. Only metabolism gene pathways that had significantly different relative abundances between grazing periods are shown (FDR P<0.05)...... 139 Appendix 1.2. Differences in relative abundance of rumen bacterial phyla between Control and Supplemented heifers. P-values were generated by ANOVA analysis...... 192

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List of abbreviations

ADF Acid detergent fiber ADG Average dairy gain AGP Animal growth promotant ANOSIM Analysis of similarities ANOVA Analysis of variance AO Animal origin ATP Adenosine triphosphate BCS Body condition score BHRF Beatrice Hills Research Farm BLAST Basic Local Alignment Search Tool bp Base pairs BWG Body weight gain CFU Colony forming unit

CH4 Methane CMCase Carboxymethylcellulase

CO2 Carbon dioxide DAF Department of Agriculture and Fisheries DM Dry matter DMI Dry matter intake DNA Deoxyribonucleic acid EDTA Ethylenediaminetetraacetic acid FDR False discovery rate Gb Giga base pairs GE Gel electrophoresis GH Glycoside hydrolase GI Gastrointestinal

H2 Hydrogen H57 Bacillus amylolyquefaciens H57 kb Kilo base pairs KEGG Kyoto Encyclopedia of Genes and Genomes KO KEGG Orthology KRS Katherine Research Station

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LSD Least Significant Differences MOI Multiplicity of infection MP Microbial protein

N2O Nitrous oxide NAD+ and NADH Nicotinamide adenine dinucleotide NDF Neutral detergent fibre NGS Next generation sequencing

NH3 Ammonia NSC Non-structural carbohydrate NT Northern Territory OM Organic matter OTU Operational Taxonomic Unit PCoA Principal Coordinate Analysis PCR Polymerase chain reaction PERMANOVA Permutational analysis of variance pH Scale of acidity PICRUSt Phylogenetic Investigation of Communities by Reconstruction of Unobserved States PSM Plant secondary metabolites QASP Queensland Animal Science Precinct QIIME Quantitative Insight Into Microbial Ecology qPCR Quantitative PCR REML Residual Maximum Likelihood RF Rumen fluid rRNA ribosome-Ribonucleic acid SARA Sub-acute ruminal acidosis SEM Standard error of mean VFAs Volatile fatty acids WW Weaning weight

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Chapter 1. Introduction and literature review

1.1. General introduction Ruminants (beef and dairy cattle, sheep and goats) are essential animals in human agriculture, contributing to food production (meat and milk) and nonfood purposes (wool, leather, and draught). The ability to utilise the partially digestible, complex carbohydrates from plant material (e.g. cellulose) makes cattle one of the most populous domesticated mammals. Cattle make an important contribution to the Australian economy and are considered one of the major agricultural commodities. In 2017, the population of cattle in Australia was approximately 26.2 million head, with an economic value of $11.4 billion for the beef industry (MLA 2018). Since ruminants are largely dependant on rumen fermentation products for their nutrition, developing strategies to program the rumen microbial community and improved fermentation efficiency is essential for increasing cattle productivity. Feed efficiency is one of the important phenotypes in cattle productivity and is a driving factor in both the economic feasibility and environmental sustainability of cattle production (Nkrumah et al. 2006; Herd et al. 2014). As well as genetic and physiological factors, the structure of the rumen microbial community plays an important role determining the feed efficiency and nutrition of an animal (Hernandez-Sanabria et al. 2010; McCann et al. 2014b). Numerous studies have been published on the effect of diet on rumen microbial populations (Pitta et al. 2014a, b) . In general, changes in the composition of the rumen microbial community occur in response to the diet, being influenced by the type and structure of the carbohydrates, protein content as well as diet-specific factors such as secondary plant compounds (Bodas et al. 2012; Henderson et al. 2015). Diet can also have a wider impact on the rumen populations of protozoa, fungi, cellulolytic bacteria and proteolytic bacteria (Foiklang et al. 2011; Bainbridge et al. 2018; Belanche et al. 2019). Another production strategy is using a microbial agent or probiotic, to modify the rumen microbial community. The use of micro-organisms as a feed supplement for cattle has been established since 1924 (Eckles et al. 1924). In general, probiotics aim to control and maintain the health and diversity of gut microbiota especially, in the first week of an animal’s life when the microbial community in its digestive tract is still unstable (Kocher 2006). The stable rumen conditions will support the growth of beneficial rumen microorganisms, increasing growth performance and lowering the incidence of metabolic disease i.e. rumen acidosis (Timmerman et al. 2005; Agazzi et al. 2014). However, the result of manipulation of the rumen microbiota structure is often unstable and not long lasting (Yáñez-Ruiz et al. 2015). Studies have suggested that the prevention of long-term 1 establishment may be due to resistance from the established rumen microbiome and host specificity (Weimer et al. 2010; Signorini et al. 2012). To date the literature reveals there is lack information about the best intervention time and the effect of the host specificity on effecting stable, long term changes to the rumen microbiota, particularly the bacterial community. Bacteria are the most abundant microbes in the rumen. Therefore, bacteria can represent the profile of the microbial community in rumen. Bacteria also contribute to the overall rumen fermentation process. It is thought that any attempt to modulate the bacterial community would significantly affect the overall rumen microbial community and rumen fermentation. Bacteria play a major role in degrading the fibrous feed material and provide fermentation products for the host in the form of energy (volatile fatty acids), protein, and vitamins (Van Soest 1994; Girard et al. 2009). In fact, the rumen microbial community provides up to 70% and 80% of the energy and protein requirements, respectively, that are needed by cattle (Flint & Bayer 2008). Thus It has been shown that there is variation in the rumen bacterial profile in accordance with the breed and geographical location of the animal (Henderson et al. 2015) and there is a currently a lack of information for the cattle breeds and environmental conditions as experienced in Northern Australia. In the current study, the effect of using a probiotic or diet treatment to modify the rumen bacterial community was investigated in different production systems (i.e. intensive and extensive systems). The study also investigated the impact of on-farm management on rumen microbial communities. The study’s data therefore provide important new information on the dynamic rumen bacterial populations found in Australian cattle.

1.2. Dynamic profiles of the rumen microbial community

1.2.1. The Ruminant digestive system There is significant modification of the stomach region of ruminant species compared with monogastric species. Ruminants have four compartments of the stomach; the reticulum, rumen, omasum, and abomasum (Figure 1.1). Compared with other sections of gastrointestinal (GI) tract, the stomach of ruminant animals is relatively large. According to Kellems and Church (2010), the stomach of cattle is about 28% and 45% of the total body and GI tract weights, respectively. The rumen and reticulum, often considered together due to their similar function and mixing between both sections, are the largest stomach compartment (62%) compared with the omasum and abomasum, at 24% and 14%, respectively. However, at birth, the rumen is undeveloped and only occupies 35% of the total stomach. At this stage of development, the abomasum is the largest stomach compartment accounting for 51% of the whole stomach (Lyford Jr 1993). The rumen and reticulum are designed to 2 function as a microbial fermentation pouch. It is also developed to efficiently utilise a diet which is mostly plant fibre or complex carbohydrates (Dyce et al. 2002). Unlike monogastric animals, ruminants have a pre-gastric microbial fermentation. As much as 60% to 75% of ingested feed is fermented by the microbes in the rumen, before being exposed to the lower digestive tract (Kellems & Church 2010).

Figure 1.1. The digestive system of ruminant (reproduced from Russell and Rychlik (2001)).

1.2.1.1. Reticulorumen The reticulum and rumen are also known as the reticulorumen due to their proximity to one another and similarity in function. The reticulum and rumen are separated by a thick layer of muscle called the ruminoreticular fold. In calves, there is a reticular groove, which transforms into a tube that bypasses the rumen and connects directly to the omasum when suckling. The function of this groove is to prevent milk from entering the rumen and instead delivers liquid to the omasum and onto the abomasum (Frandson et al. 2009). The reticulum is located in the anterior of the forestomach and sits adjacent to the diaphragm. The primary function of the reticulum is to screen the size of digesta particles with the smaller particles being passed through to the omasum, and the larger particles remaining in the rumen for further digestion (Parish 2011). The rumen is posterior to the reticulum and extends from the fold to the pelvis, particularly on the left side of the abdominal cavity (Frandson et al. 2009). The rumen’s mucosa is layered with papillae which are considered essential for nutrient absorption (Hofmann 1993). Moreover, according to Lyford Jr (1993), feeding habits, feed type, and feed digestibility influence the distribution, size, and the number of ruminal papillae. Rumen capacity depends on age and body weight; adult cattle have a rumen capacity of approximately 100 litres (Frandson et al. 2009).

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1.2.1.2. Omasum and Abomasum The omasum is a spherical shaped organ and connected to the reticulum by the ostium reticulo-omasicum (Hofmann 1993; Parish 2011). The omasum is also known as the “book stomach”, because it is filled with muscular laminae which look like the pages of a book (Dyce et al. 2002). The main function of omasum is for absorbing nutrients and water, particularly volatile fatty acids (VFA) and bicarbonate from ingesta (Parish 2011; Klein 2012). In cattle, the omasum can absorb as much as 30-60% of the water and 40-69% of the VFA content of digesta (Van Soest 1994). Since VFAs and bicarbonate can impair the pH condition in abomasum, it is important for the omasum to absorb as much of the VFAs and bicarbonate present in digesta before it flows through to the abomasum. Thus, the VFA concentration in the omasum will impact the rate of feed passage and therefore feed intake (Hofmann 1993). Another study showed high VFA concentration in the omasum decreased dry matter intake (DMI) and feed passage rate from the rumen (Afzalzadeh & Hovell 2002). The abomasum is the true gastric stomach in ruminants. The abomasum is acidic with a pH range from 3.5 to 4.0, because it produces hydrochloric acid, and also digestive enzymes, to break down nutrients which assists with nutrient absorption (Parish 2011). The secretion of gastric juices in ruminants is stimulated by the presence of VFAs and lactic acid in digesta (Van Soest 1994). The abomasal secretions are also affected by feeding habits and are significantly decreased during fasting (Merchen 1993). In young animals fed milk, the gastric juice in abomasum contains the proteolytic enzyme rennin to coagulate casein from the milk (Merchen 1993). In addition, rennin requires higher pH condition than pepsin to working optimally in the abomasum. Therefore, abomasal pH in young animals fed milk is higher than in adult animals (Merchen 1993).

1.2.2. Rumen physiology and function In the rumen, feed is continually mixed with saliva by rhythmic contractions of the rumen wall (McDonald 2011). Ingested feed, if too large or coarse to pass out of the rumen, will eventually be reduced to fine particles through the rumination process. In the rumination process, ingesta from the reticulum is drawn back into the oesophagus and into the mouth (regurgitation) for re-mastication and re-swallowing (Van Soest 1994). When re-masticating, the fluid from the bolus is immediately swallowed again and the coarse material is re-chewed before being re-ingested (McDonald 2011). Rumination is generated by tactile stimulation of the anterior rumen epithelium (McDonald 2011). Different nutrient types within feed, especially neutral detergent fibre (NDF) with a particle size greater than 1.18 mm, can stimulate rumination (Mertens 1997). NDF from forage and concentrate can have a positive correlation with rumination time (Yang et al. 2001; Yang & Beauchemin 2007; Byskov et al. 2015). Moreover, the interval time between rumination events or rumination time can be useful in predicting dry matter intake (Clement et al. 2014). The rumination process is cyclic and 4 closely correlates with reticulorumen motility which is divided into primary and secondary movements (Van Soest 1994). Reticulorumen motility is beneficial in mixing ingested feed, eructation of fermentation gases and in moving the feed into the omasum (Ruckebusch 1993). The motility is affected by the physical form of feed (McDonald 2011). The finest materials will cause a slower rate of contraction and shortest motion intervals (Ruckebusch 1993). Kendall & McLeay (1996), in an in vitro study, showed that VFAs can stimulate the contraction of rumen wall tissue. The purpose of fermentative digestion in the rumen is to convert the ingested dietary material into microbial biomass and end products (particularly VFA) that can be utilised by the animal. The rumen provides a favorable environment for microbial growth, for example, by adding saliva as a pH buffer and removing acids as fermentation products out of the rumen (Owens. & Goetsch 1993). This and other mechanisms maintain rumen pH, temperature, and moisture at optimum conditions for microbial growth. In return, ruminants are able to utilise fibrous plant material to fulfill their nutrition requirements, and also have the ability to harvest vitamin B and essential amino acids produced by the rumen microbes (Van Soest 1994). The fermentation of forage diets in the rumen to produce VFAs and other by products, provides 50-85% of the metabolizable energy used by the animal (Owens. & Goetsch 1993).

1.2.2.1. VFA production Volatile fatty acids represent waste products from carbohydrate fermentation, produced by rumen microbes. During fermentative digestion, rumen microbes break down feed molecules, mainly by enzymatic hydrolysis (Klein 2012). Bacterial adherence to feed particles is an initial stage in feed digestion with about 40 to 75% of ruminal bacteria adhering to feed (Owens. & Goetsch 1993). Furthermore, particle-attached microorganisms have a primary role in most feed digestion in the rumen (Mcallister et al. 1994). The fermentation process itself takes place under anaerobic conditions and the main pathways are shown in Figure 1.2. (Klein 2012). To maintain anaerobic conditions, the presence of oxygen in the rumen from the ingested feed or diffusing from rumen wall is removed by facultative anaerobic bacteria (Hobson & Stewart 1997). The main VFAs from rumen microbial fermentation are acetic, propionic, butyric, lactic, succinic, and formic acids (McDonald 2011). These VFAs are also often named according to their ionic form: that is, acetate, propionate, butyrate, lactate, succinate, and formate.

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Figure 1.2. Pathways of VFA production from feed fermentation by rumen microbes anaerobically. (Reproduced from Klein (2012)).

Different species of rumen bacteria can have specific substrates that they can use for growth. This means that the structure of rumen bacterial populations is affected by the type of feed consumed by ruminants (Henderson et al. 2015) and thus, the VFA profile in the rumen can also differ with different types of feed. Generally, acetate and propionate dominate the VFA content, comprising up to 70% of total VFA in animals eating high-forage and high-grain diets (Klein 2012). Wanapat et al. (2014) showed that the population of amylolytic bacteria increased linearly while cellulolytic bacteria decreased linearly with lower forage to concentrate (starch) ratio. As a result, this change decreased the acetate: propionate ratio in the rumen along with dropping the rumen pH to below 6. However, the population of fungi, protozoa and proteolytic bacteria were not affected by the roughage: concentrate ratio showing these groups are able to utilise various substrates compared with specialist fermenter i.e. cellulolytic bacteria (Mao et al. 2016). However, an accumulation of VFAs in the rumen will drop the rumen pH and may impair the fermentation process. Shortly after feeding, particularly feed with highly fermentable nutrients, rapid fermentation can reduce rumen pH drastically (Owens. & Goetsch 1993). Low rumen pH can reduce cellulolytic activity, and lead to ruminal acidosis (Owens et al. 1998; Mouriño et al. 2001). Farenzena 6 et al. (2014) reported that there was a positive and linear correlation between bacterial adherence to plant material in roughage diets and increase in ruminal pH. The number of adhered microbial was observed lowest at pH 5.5 for 0.41 mg of microbial protein/g of residual DM of ryegrass. The amount of adhered microbial protein was then increased linearly with an increase in pH from 6.0, 6.5, and 7.0 to 0.47, 0.57, and 0.67, respectively (Farenzena et al. 2014). Mouriño et al. (2001) observed that lowered pH in the rumen lysed and detached cellulolytic bacteria from feed particles. Therefore, lowering the rumen pH will affect the groups of cellulolytic bacteria reducing the digestion rate of fibrous plant material in the rumen. To maintain a favourable rumen pH between 5.5 and 6.5, short chain VFAs, mainly acetate and propionate, will be absorbed largely through the rumen epithelium and lactate will be further fermented by lactic-utilizing bacteria (Russell & Rychlik 2001). To reduce the acidity in the rumen, phosphate and bicarbonate content in saliva also acts as a buffer solution to maintain rumen pH (McDonald 2011). Buffers also can be added to the diet as a feed additive to regulate pH within the favourable range for rumen microbial fermentation (McDonald 2011; Kang & Wanapat 2018). Moreover, the reticulorumen motility controls the normal flow of ingesta and the rumen dilution rate which can also maintain pH at a preferred level (Klein 2012).

During the fermentation of feed, H2 and CO2 are generated as by-products and these can be used as sources of energy by methanogens and acetogenic microbes to synthesise methane gas and acetate respectively (Rieu-Lesme et al. 1996; Morgavi et al. 2010) (Figure 1.2.). In contrast to propionate, the production of acetate and butyrate from pyruvate molecules does not regenerate nicotinamide adenine dinucleotide (NAD+) from NADH (Klein 2012). The excess NADH from acetate and butyrate pathways is then converted into NAD+ which releases free hydrogen which can be used in the methanogenesis cycle (Martin et al. 2010). High concentrations of hydrogen in the rumen may impair electron transfer in bacteria, especially NADH dehydrogenase and lead to NADH accumulation which can halt rumen fermentation (Morgavi et al. 2010).

1.2.2.2. Protein microbial production Apart from the production of VFAs as a by-product of the fermentation of feed, rumen microbes also benefit ruminants as a source of amino acids, providing microbial protein. During the fermentation of carbohydrates, rumen microbes produce energy in the form of adenosine triphosphate (ATP), for their own use, which is used for microbial growth (Hackmann & Firkins 2015). Rumen microbial cells that pass into the abomasum are digested and the resulting amino acids and peptides are used as nutrients by the host. In fact, microbial protein supplies 60-85% of the amino acids that reach the lower digestive tract (Storm et al. 1983), therefore, microbial protein is crucial for cattle productivity. This is particularly the case where cattle have an insufficient protein supply from

7 their diet, such as in tropical regions where forages are often low in nitrogen and non-structural carbohydrates (NSC) (Ian 2011). In the rumen, proteins as well as carbohydrates are degraded by rumen microbes. Ingested protein is degraded by rumen microbial enzymes on the surface of microbes, mainly trypsin endopeptidase, into short-chain peptides (Klein 2012). Some amino acids as well as non-protein nitrogen molecules from diet are further digested to ammonia by certain groups of rumen bacteria (McDonald 2011). Protein metabolism in the rumen occurs in four different ways (Figure 1.3). First, rumen microbial proteins are synthesised from extracellular protein that has been degraded by peptidase into amino acids (Figure 1.3; A). Secondly, microbial protein is generated from external ammonia (NH3) and VFAs (Figure 1.3; C). These extracellular substrates are absorbed by microbes to synthesise amino acids that can be used either for growth (microbial protein/biomass) (Figure 1.3; B) or energy production through VFA pathways (Figure 1.3; D), while ruminal undegradable protein or unfermented protein from feed flows to the small intestine for further digestion (McDonald 2011). There are many factors that affect the synthesis of rumen microbial protein (MP) (Pathak 2008; Hackmann & Firkins 2015). Generally, the yield of MP is driven by diet composition and rumen physiological conditions (Pathak 2008). Rumen MP is synthesised from dietary nitrogen in the form of either non-protein nitrogen, peptides, or amino acids, generally using glucose as an energy and carbohydrate source (Maeng & Baldwin 1976). However, protein and carbohydrates are converted by microbes not only into MP but also as VFAs, NH3, methane (CH4) and CO2 (Klein 2012). Therefore, the process of generating MP can be considered inefficient, as only a third of these nutrients are converted into MP (Stouthamer 1973) with the remainder being used for maintenance and energy storage (Hackmann & Firkins 2015). Maintaining a balanced supply between protein and carbohydrate in the rumen may improve the efficiency of MP synthesis. The synthesis of MP from protein (peptides/amino acids) is dependent upon the supply of ATP obtained from carbohydrate fermentation into VFAs (Van Soest 1994; Perry 2016). Therefore, the matching the supply of nitrogen and carbohydrate in the diet can increase the yield of MP (Figure 1.4; A). While, a less than optimal supply of nitrogen or carbohydrate will lead to more energy being used for maintenance purposes (Figure 1.4; B) or peptides and amino acids will be further degraded to produce the required energy (Figure 1.4; C).

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Figure 1.3. Protein metabolism pathways of rumen microbes. There are four pathways of nitrogen metabolism in microbial cells A. Microbial protein syhthesised directly from extracellular protein; B. Amino acids are synthesised intracellularly from VFA and NH3; C. Some microbes only are able to synthesise amino acids from extracellular NH3 and VFA rather than using peptides; D. Unused amino acid for microbial protein synthesis will be degraded back into VFA and NH3. (Reproduced from Klein (2012)).

Figure 1.4. The synthesis of rumen MP with three different protein and carbohydrate supply scenarios: well matched supply (A), excess carbohydrate relative to protein (B), and excess protein relative to carbohydrate (C). (Reproduced from Klein (2012)).

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The pH and flow rate of digesta out of the rumen are physiological conditions that are well associated with the yield of rumen MP (Pathak 2008). Lowered pH reduces the microbial fermentation and results in lower feed digestion rates (Owens. & Goetsch 1993). Generally, rumen bacteria mainly dominated by plant-fibre degrading bacteria which counts for 40-75% of total rumen bacteria population (Owens. & Goetsch 1993). Thus, the acidic rumen condition significantly reduced the population of plant-fibre degrading which then led to lower feed digestion. An in vitro study by Mouriño et al. (2001) found that the rate of cellulolytic digestion of plant material was reduced as pH was lowered and below pH 5.3 substantially stopped cellulose degradation. During acidic conditions, rumen microbes diverted the energy that could be utilised for growth into maintaining intracellular pH (Booth 1985). The rumen dilution rate determines the synthesis rate of ruminal MP (Klein 2012). The combination between dilution rate and the type of feed (i.e. high fibre or high starch) affects the continuity supply of nutrient for rumen microbial growth. Martínez et al. (2009) reported a high dilution rate (5.42%/h of fermentor volume) in a fermentor significantly increased the activity of rumen micro-organisms that resulted in augmented total VFAs production, increased fibre degradation and proteolytic activities, whilst Meng et al. (1999) showed the positive association between dilution rate and the yield and growth efficiency of ruminal MP. The high yield and growth efficiency of ruminal MP in the study showed that increased dilution rate along with feeding a high- protein diet improved the nitrogen utilisation by rumen micro-organisms. However, high dilution rates in low fermentable, fibre-based feeds resulted in lower ruminal microbial growth efficiency due to a lack of supply of energy and nutrients required for microbial growth (Meng et al. 1999). The dilution rate also serves to increase rumen microbial growth by avoiding saturated microbial populations in the rumen. An in vitro study by Isaacson et al. (1975) showed the level of glucose doses had less effect on the yield of microbial cells. However, increasing the dilution rate significantly increased the yield of microbial cells from glucose fermentation from 42 to 84 grams with a 0.1% (per hour of total volume fermenter) increment in dilution rate (Isaacson et al. 1975). Increasing microbial growth rates will reduce the energy utilisation by cells for non-growth functions (Hackmann & Firkins 2015), therefore increasing the MP production per gram of digestible substrate (Dijkstra et al. 1998). The mechanisms of dilution rate in increasing ruminal MP production 1) favour the growth of fast-growing species, 2) condition rumen microbes to an exponential growth phase, reducing autolysis, and decreased energy utilisation of bacteria for non-growth purposes, and 3) decrease the incidence of bacteria predation by suppressing the protozoal population (Meng et al. 1999).

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1.2.3. Rumen development The rumen develops from a primitive gut in the embryo to, at the 6th-week the primordia of the four compartments being present, and at the 9th week, the ruminal sacs and pillars become apparent (Lyford Jr 1993). At birth, the abomasum is the largest stomach compartment accounting for 51% of the total stomach compared with 35% for the rumen (Lyford Jr 1993). The more developed abomasum, compared to rumen, is associated with the feed type (mainly milk) available to newborn animals (Parish 2011). The rumen develops further during the preweaning period and becomes mature after weaning, associated with increasing forage in the diet (Hobson & Stewart 1997). Rumen development is affected by diet, inoculation of the rumen with microbes (Van Soest 1994; De Barbieri et al. 2015a) and the subsequent fermentative production of VFAs (Tamate et al. 1962). The type of diet stimulates the rate of rumen development. Physically, solid feed can increase the weight of the reticulorumen and increase tissue growth faster than in calves fed only milk, as measured at 3 and 12 weeks of age (Stobo et al. 1966). Several studies reported similar findings, that calves fed with dry feed had increased rumen volume and tissue growth as well as more developed rumen papillary, compared to milk-only fed calves (Warner et al. 1956; Suarez et al. 2006). However, a more recent study by Prevedello et al. (2012) showed that the effect of solid feed was more driven by carbohydrate content rather than physical condition. Therefore, the addition of non-solid feeds, such as molasses in starter diets, can also increase rumen development (Lesmeister & Heinrichs 2005; Sato et al. 2010; Dias et al. 2018). Microbial colonisation and inoculation have been linked to rumen development (Frizzo et al. 2010). Rumen colonisation and inoculation initially occurs soon after birth (Hobson & Stewart 1997). Strictly anaerobic bacteria are found in the rumen of lambs just two days after birth (Fonty et al. 1987). In agreement with previous studies, a study exploring the rumen bacterial community from birth to adult detected some rumen bacteria in the rumen of 1 day old calves (Jami et al. 2013). The report from Jami et al. (2013) also indicated that the detected bacteria were similar to bacteria found in the mature rumen. The bacteria in this early rumen inoculation may originate from the mother during delivery and grooming (Hobson & Stewart 1997). The rumen bacteria is then enriched by microbial inoculation from the immediate environment (i.e. feed, and other animals) (Becker & Hsiung 1929; Stewart et al. 1988; Beharka et al. 1998; Rey et al. 2014). The rumen bacterial community during early life is still developing and not stable (Rey et al. 2014). Studies show that ruminal bacterialdiversity in early life undergoes dramatic shifts according to the diet and age (Rey et al. 2014; Wang et al. 2016). This immature bacterial community in early life could allow for opportunities to modulate the rumen microbiota in ways that might be long lasting, even extending to the adult animal (Yáñez-Ruiz et al. 2015). Supporting this theory, several studies

11 indicated the possibility of early life intervention of ruminal bacterialdiversity (Abecia et al. 2014a; Guzman et al. 2015). However, the persistence of changes still needs further clarification. A study on the effect of inoculating the rumen of pre-weaned lambs with rumen fluid from mature sheep, indicated that significant differences in the rumen bacteria diversity occurred following inoculation, however this diminished two months after weaning (De Barbieri et al. 2015b). The rumen inoculation in early life of animals not only aims to increase the success rate of microbial interventions (to improve feed-utilisation efficiency) but also to improve rumen development itself. It has been shown that buffalo calves inoculated with adult rumen content, containing ciliate protozoa, increased the rate of rumen development as evidenced by higher rumen VFA concentration, ammonia concentrations and faster growth rate (Borhami et al. 1967). Another study reported inoculation with rumen fluid from the ewes in the first five weeks after birth was positively associated with improved rumen function in lambs (De Barbieri et al. 2015c). The role of microbes in improving rumen development may be related to the production of VFAs from feed fermentation. There was a positive relationship found between VFA concentration in rumen liquor and rumen development (Suarez et al. 2006). VFAs which appeared from microbial fermentation or were added to the diet could improve the rate of rumen development in calves (Tamate et al. 1962; Van Soest 1994). In particular propionate and butyrate appear to stimulate the growth of rumen papillae in calves (Tamate et al. 1962; Sakata & Tamate 1978; Gorka et al. 2011b). Gut physiology (e.g. development of the gastrointestinal epithelium), may also be an important determining factor in healthy rumen development (Steele et al. 2016). Rumen papillae are responsible for the absorption of VFAs and other metabolites from the rumen (Hofmann 1993). Therefore, improving ruminal development rate (i.e., rumen papillae), increases the absorption rate of VFAs (McGovern et al. 2018) which are used by the host as an energy source for production (McDonald 2011). In turn, animal performance and health will be improved (Gorka et al. 2011a).

1.2.4. Rumen microbes The relationship that occurs between ruminant animals and the microbes of their gut is sometime described as a host-associated microbiota (Deusch et al. 2017; Huws et al. 2018). As the host, the ruminant provides a favorable rumen environment for optimal microbial growth and in exchange, ruminants can utilise feed fibrous material to fulfill their nutritional requirements for energy (VFAs) and protein (microbial cells) . According to Wright and Klieve (2011), the rumen ecosystem consists of thousands of species of microorganisms. It consists of bacteria (1010–1011 cells/ml), archaea (107–109 cells/ml), protozoa (104–106 cells/ml), fungi (103–106 cells/ml), and viruses (109–1010 cells/ml). These microorganisms 12 interact with each other with these interactions influenced by factors such as diet and physical rumen conditions such as temperature, pH, osmotic pressure, and redox potential (Dehority 2003). In general, rumen bacteria ferment the feed producing VFAs, H2 and CO2 as by-products. VFAs are the main energy source for the ruminant, while H2 and CO2 are not utilised by the host, however, all of these products must be discharged from the rumen to maintain the favorable condition for microbes.

1.2.4.1. Bacteria In general, the number of bacteria in the rumen is about 1010 to 1011 cells/ml of rumen contents with obligate anaerobes as the predominant bacteria and the number of facultative anaerobic bacteria up to 108 cells/g rumen contents (Yokoyama & Johnson 1993). According to the original taxonomic studies of the rumen, for example, Stewart et al. (1997), there are 26 major taxa of culturable rumen bacteria including Prevotella spp., Ruminobacter amylolyphilus, Fibrobacter succinogenes, Selenomonas ruminantium, Ruminococcus spp., Streptococcus equinus, and Butyrivibrio fibrisolvens. In addition, based on the substrate preferences, there are at least 10 groups of major rumen bacteria (Yokoyama & Johnson 1993) (Figure 1.5). More recent studies have revealed the true extent of rumen microbial taxonomic diversity however these major, culturable groups are almost always represented (Creevey et al. 2014; Henderson et al. 2015; Seshadri et al. 2018a). Rumen bacteria mainly ferment monomers and oligomers of substrate that are obtained from the hydrolysis of plant polymers such as cellulose, hemicellulose, pectin, starch, and protein (Stewart et al. 1997). Some of these genera are considered as generalists, i.e. B. fibrisolvens and Prevotella spp., due to their ability to utilise a broad range of substrates (Avgustin et al. 1997; Stewart et al. 1997; Wallace et al. 1997a). In addition, In addition, there are also specific bacteria that obtain energy from the breakdown of plant secondary metabolites (PSM) (Stewart et al. 1997). Studies have investigated the ability of rumen bacteria to degrade certain PSM present in the animal’s diet, i.e. Oxalabacter formigenes and Synergistes jonesii have the capacity to breakdown oxalic acid and 3,4-and 2,3-dihydroxypyridine (DHP) from ruminal mimosine degradation, respectively (Allison et al. 1985; Allison et al. 1992). While other rumen bacteria such as nitrate-utilisers (i.e. Denitribacterium detoxificans and Wolinella succinogenes), sulfate-reducing bacteria (i.e. Desulfovibrio and Desulfotomaculum), and acetogens

(i.e. Clostridium glycolicum and Blautia hydrogenotrophica) are able to oxidise H2 and CO2 using alternative electron acceptor pathways (Weimer 1998; Anderson et al. 2000; Simon 2002; Ouwerkerk et al. 2009; Gagen et al. 2014; Jeyanathan et al. 2014).

13

Figure 1.5. Groups of rumen bacterial species in accordance to the type of substrates utilisation (Reproduced from Yokoyama and Johnson (1993)).

Carbohydrate content in feed particles is mainly in the form of polysaccharides arising from either plant storage (starch) or structural cell wall (fibrous components such as cellulose and hemicellulose) components (Chesson & Forsberg 1997). Starch can also be found in plants as seeds (e.g. corn, wheat, barley) or in tubers like potatoes and cassava. Starch is an easily fermented substrate, with approximately 90% of ingested starch being degraded in the rumen (Orskov 1986). In the rumen, starch particles are hydrolysed mainly by amylolytic bacteria (Chesson & Forsberg 1997) using endo-amylase and exo-amylase enzymes, into oligosaccharides (Tester et al. 2004). Some of the predominant culturable rumen amylolytic bacteria include Ruminobacter amylophilus, Prevotella ruminicola, Streptococcus equinus, Succinimonas amylolytica, Butyrivibrio fibrisolvens, Eubacterium ruminantium, and some Clostridium spp. (Chesson & Forsberg 1997).

14

Oligosaccharides obtained from starch are further fermented to VFAs, and these can be used as source of energy for the host animal or for the synthesis of microbial protein (Van Soest 1994). Metabolomic studies have shown that diets high in starch result in an increase in the ruminal content of VFAs, pyruvic acid, lactic acid and biogenic amines (Xue et al. 2018; Zhang et al. 2018). Although, these products are important for the host as a source of energy, excess concentrations of lactic acid and biogenic amines have detrimental effects on the growth of rumen microbes (Petri et al. 2017; Humer et al. 2018). In high starch diets, amylolytic bacteria can proliferate at the expense of cellulolytic bacteria (Pan et al. 2016). The ratio of VFAs produced then shifts from acetate to propionate and lactate (Valente et al. 2017; Zhang et al. 2018). Thus, excess concentrations of propionate, lactate, and biogenic amines can dramatically decrease ruminal pH and impair the growth and metabolism of cellulolytic bacteria (Petri et al. 2017; Humer et al. 2018). Structural carbohydrates present in plant cell walls consist of cellulose in a matrix with hemicellulose and pectin (Akin & Barton 1983). According to Chesson and Forsberg (1997), there are two types of plant cell walls based on their composition (Table 1.1), while the composition of cell walls itself can vary according to the plant species, stage of plant maturity, and the season (Wilson & Hatfield 1997; Bortolussi et al. 2005). Furthermore the structure and composition, particularly the bonds between polysaccharides and lignin, determines the degradation (fermentation) rate of the plant cell walls (Li et al. 2016b). There are at least five types of polysaccharide-lignin bonds found in plant cell walls (Krause et al. 2003)(Figure 1.6).

Table 1.1. The types of plant cell walls in accordance to the composition of wall components. Cell wall composition (%) Wall component Type I Type II Rhamnogalacturonan 4 16 Arabinan - 10 Xyloglucan 11 21 Arabinoxylan 21 - Mixed-linked glucan 3 - Cellulose 46 23 Protein 7 10 Sourced from Chesson and Forsberg (1997).

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Figure 1.6. The types of lignin-polysaccharide bonds in cell wall plant. (a) Hydroxycinnamate ester, (b) Hydroxycinnamate ether, (c) Ferulate ester-ether bridge, (d) Dehydrodiferulate ester bridge, and (e) Dehydrodiferulate ester-ether bridge. (Reproduced from Carpita and McCann (2015)).

The degradation of cell walls involves the interactions between bacteria, protozoa, and fungi (Lee et al. 2000). Bacteria and fungi have been well acknowledged to possess a broad range of enzymes for plant fibre degradation (Suen et al. 2011b; Wang et al. 2011; Fliegerova et al. 2015), while the role of protozoa in fibre degradation is less significant compared with fibrolytic bacteria (Dijkstra & Tamminga 1995). Fungi have specialised to breakdown lignin by penetrating the plant cell wall and delivering hydrolase enzymes (Fliegerova et al. 2015). However, the role of fungi in ruminal fibrolytic degradation of plant cell walls is outnumbered by bacteria due to small proportion of fungi, approximately 8% of total ruminal microbial mass, within the rumen (Orpin & Joblin 1997). Some major rumen bacteria considered to be fibrolytic include F. succinogenes, R. albus, R. flavefaciens, Prevotella ruminicola, Eubacterium cellulosolvens, and Eubacterium ruminantium (Stewart et al. 1997). Fibrolytic bacteria have the ability to synthesise glycoside hydrolase (GH) enzymes such as cellulase (endo and exo), β-glucosidase, endoxylanase, β-xylodase, α-amylase, licheninase and mannanase (Krause et al. 2003). The process of plant cell wall degradation is started with the hydrolysis of polysaccharide bonds by fibrolytic bacteria (Mcallister et al. 1994). Briefly, fibrolytic bacteria attach to feed particles during the initiation of fibre degradation (Krause et al. 2003). Cellulose is then hydrolysed by GH enzymes through three major steps to glucose as the final product (Figure 1.7). Cellulose is randomly cut by endoglucanase releasing cello-oligosaccharides and glucose. Exoglucanase hydrolyses the terminal ends of the cello-oligosaccharides into cellobiose. Finally, cellobiose is hydrolysed to glucose 16 by β-glucosidase and cellobiase enzymes (Bhat & Hazlewood 2001). Once cell walls have been hydrolysed, non-cellulolytic bacteria are then able to utilise the substrates i.e. oligosaccharides and monosaccharides that arising from hydrolysing process (Flint 1997).

Figure 1.7. Steps involved in cellulose degradation by glycoside hydrolase enzymes from rumen cellulolytic bacteria. (Reproduced from Krause et al. (2003)).

Rumen bacterial populations can also be divided into three fractions according to where they colonise within the rumen: associated with rumen fluid (liquid-associated), attached to solid feed particles (particle-associated) or associated with the rumen epithelium (epimural) (Dehority 2003). A study of 16S rRNA gene sequences from these fractions within the rumen found a different diversity of bacteria present in each fraction and suggested that the bacterial populations from each fraction played a specific role in rumen metabolism (Cho et al. 2006). Studies by Mcallister et al. (1994), McCann et al. (2014a) and Abecia et al. (2014b) showed the roles of particle-associated, liquid- associated, and epimural rumen bacterial populations were linked with fibre degradation, ruminal MP production, and VFAs absorption, respectively. The particle-associated bacteria are the predominant group in the rumen (Olubobokun et al. 1988) and accounted for 80% of the total rumen endoglucanase activity (Cho et al. 2006). This group of rumen bacteria was also found to be more diverse than the liquid-associated bacteria (Michelland et al. 2011). The predominant rumen cellulolytic bacteria such as F. succinogenes and Ruminococcus

17 spp. were found to be more abundant in the solid fraction of rumen contents in cattle fed a high fibre diet (Petri et al. 2012). Bacterial attachment is required for fibre degradation, especially with the cellulolytic bacteria (Bhat et al. 1990; Hess et al. 2011). Bowman and Firkins (1993) reported an increased availability of the feed surface area for bacterial adhesion and colonization improved the rate of NDF and acid detergent fibre (ADF) disappearance. The attachment mechanism for cellulolytic bacteria was mainly by the formation of a glycocalyx layer on the feed surface (Mcallister et al. 1994). Moreover, during feed degradation, the first attachment on feed particles was performed by primary fermenters which are mainly cellulolytic bacteria using their glycocalyces and is recognised as the primary fermentation (Mcallister et al. 1994). The primary fermentation releases soluble substrates or fermentation products that attracts the secondary fermenters such as Selemonomonas ruminantium which convert pyruvate and succinate from the primary fermentation into propionate, lactate, and malate (Petri et al. 2012). The liquid-associated bacteria are usually found moving freely in rumen fluid and are responsible for digesting the soluble feed components within the rumen (Mcallister et al. 1994). Cho et al. (2006) found that rumen fluid contains specific bacteria such as S. maltophilia, S. ruminantium and P. ruminicola which are not detected on the solid particles or ruminal epithelium. Schären et al. (2017) reported that Prevotellaceae was the most abundant family of liquid-associated bacteria in dairy cows. This group of bacteria are easily washed out from the rumen to the abomasum through the rumen fluid discharge and researchers commonly use the presence of liquid-associated bacteria for measuring the ruminal microbial N synthesis (Belenguer et al. 2002) and microbial N flow from the rumen to the intestine (Perez et al. 1998; Abecia et al. 2014b). Therefore, research efforts aimed at increasing the population of liquid-associated bacteria through feeding strategies (i.e. diets containing alternative plant compounds or easily degraded sugars) may benefit the host by also augmenting the supply of microbial protein to the intestine. Wanapat et al. (2014) reported that the supply of rumen microbial protein synthesis increased when the diet consisted of 60% concentrate. Rumen microbial protein increased along with the increased availability of rumen ammonia and energy source for rumen microbial growth (Polyorach & Wanapat 2015). The role of bacteria in ruminal protein metabolism has been discussed previously (Section 1.2.2.2. Protein microbial production). Studies reported the addition of saponin increased the N content of liquid-associated bacteria i.e. Prevotella spp, Ruminococcae UCG002, and Christensenellaceae R-7 which also increased the microbial protein synthesis as well (Castro-Montoya et al. 2011; Wang et al. 2019). Abecia et al. (2014b) reported that diet containing olive cake and different protein sources influenced the community profile of liquid-associated bacteria in the rumen. The results of the study showed that the profile of the liquid-associated bacteria community was

18 clustered according to the diet treatments (Abecia et al. 2014b). This was expected as the liquid- associated bacteria are dominated by Prevotellaceae which is well known as generalist bacteria which utilise various substrates including proteins and starches (Wallace et al. 1997a; Kovár et al. 1999; Walker et al. 2005). The last group of bacteria are defined as epimural bacteria. This group plays a role in oxygen removal, urea digestion and tissue recycling (Cheng et al. 1979). The populations of these bacteria are positively correlated with the molar proportion of VFAs, particularly acetate, isobutyrate, and isovalerate (Chen et al. 2011). Moreover, this group of bacteria may also affect rumen development and the immune system in preweaned ruminants (Malmuthuge et al. 2014). The composition of the epimurial bacterial community is quite different compared with the other groups (Mueller et al. 1984; Schären et al. 2017; Wang et al. 2017). This group of bacteria was less diverse compared with solid and liquid-associated bacteria (Cho et al. 2006). The core populations of epimural bacteria consisted of bacteria from the families Lachnospiraceae, Ruminococcaceae, Rikenellaceae, Prevotellaceae, Desulfobulbaceae, Erysipelotrichaceae, and uncultured Family XIII Incertae Sedis (Clostridiales) (Petri et al. 2013; Schären et al. 2017). Although the population structure of epimural bacteria appear to be independent of other groups of bacteria (Wallace et al. 1979), the diversity profiles of these bacteria is also diet dependant (Petri et al. 2013; Schären et al. 2017).

1.2.4.2. Protozoa Protozoa are much larger than bacteria, ranging from 15 to 250 µm and 10 to 200 µm in length and width, respectively (Dehority 2003). There are two main groups of rumen protozoa, ciliate and flagellate species (Williams & Coleman 1997). Although the ciliate protozoa tend to predominate in the rumen (Dehority 2003), the population of flagellates develops at an early age in calves (Bryant & Small 1960), however, the populations of flagellates then decreases as the ciliate protozoa become established (Eadie 1962). In addition, the populations of flagellate protozoa are also influenced by ruminal pH shown in studies which observed more abundant populations of flagellate protozoa present when the rumen pH is higher than 6.5. (Lengemann & Allen 1959; Bryant & Small 1960). Becker and Everett (1930) found that the flagellate protozoa populations in isolated ciliate-free calves were high and populations significantly decreased as the rumen pH dropped. There are two main groups of rumen ciliates, the holotrichs and oligotrichs (Dehority 2003). Holotrichs consists of protozoans in the families Isotrichidae, Paraisotrichidae, Blepharocorythidae, and Buetschliidae. Oligotrichs are within the family Ophyroscolicidae and are the majority of rumen ciliate protozoa. Rumen ciliates establish in the rumen through inoculation from adult ruminants (often the mother) and when the conditions in the rumen itself are suitable for ciliate growth (Hungate 1966; Borhami et al. 1967; Dehority 1986; Dehority & Orpin 1997). Therefore, studies by Borhami et

19 al. (1967) and Bryant and Small (1960) reported that uninoculated calves could be maintained free of ciliates in their rumen, for at least six months of age unless they were exposed to animals with a ciliate population. According to Dehority (2003), diet and rumen pH are the major factors that affect the number of rumen protozoa. Diet, including frequency of feeding and feeding level, affect the establishment of protozoal populations (Dehority 2003; Monteils et al. 2012). Lengemann and Allen (1959) reported that dairy calves fed milk only until nine weeks of age had less protozoa compared to calves fed a solid diet, which had the same density of protozoa as adult cattle. Martinez et al. (2010) showed that the total number of protozoa increased by about 29% in the sheep with 70% concentrate diet compared to sheep fed 30% concentrate diet. Protozoa are able to metabolise soluble substrates in the rumen i.e. protein, amino acids, starch, and fibre for their maintenance and growth (Dijkstra & Tamminga 1995; Williams & Coleman 1997). The abundant populations of protozoa on high concentrate diets can also be associated with the higher bacterial population. The total number of bacteria is higher in animals fed high concentrate diets (Yáñez-Ruiz et al. 2008; Pan et al. 2016). Bacteria are prey for protozoa, and used as their main source of nitrogen (Coleman 1986). The rate that they engulf bacteria increases linearly with the density of bacteria (Williams & Coleman 1997). Feeding excess concentrate decreases the rumen pH resulting in a reduction in the protozoal population (Franzolin & Dehority 2010). Eadie (1962) found that either in calves or lambs the population of protozoa decreased when the rumen pH was lower than 6.0. Observations of some genera of protozoa in vitro indicated that these genera are able to maintain their population density down to around pH 5.8 but then decreased as pH values dropped below 5.6 (Dehority 2005). However, Hook et al. (2011a) showed that the number of total protozoa increased in cows fed high concentrate diet. Furthermore, the study showed that the total number of protozoa increased even during the lowest rumen pH which measured less than 5.8 (Hook et al. 2011a). The difference in effects of rumen pH on protozoal populations occurs because each genus of protozoa varies in acid resistance and the ability to adapt to acidic conditions (Franzolin & Dehority 1996). A study in calves reported a variance in protozoa populations in accordance to ruminal pH (Sato et al. 2010). The study showed the population of Isotricha and Dasytricha increased by four and twelve folds, respectively, in rumens with an average ruminal pH of 5.89, while the population of Epidinium started increase at a ruminal pH of approximately 6.20 (Sato et al. 2010). The pH sensitivity of some protozoa species may be related also with their role in rumen fermentation. Williams and Coleman (1997) briefly stated that there are beneficial and detrimental effects of rumen protozoa to the host. However, there is no consensus about the value of protozoa to the ruminant. A recent metadata-analysis by Newbold et al. (2015) showed that defaunation (removal of

20 ciliate protozoa) increased rumen microbial protein production by up to 30% and reduced enteric methane gas emission by 10%. However, defaunation also significantly decreased rumen organic matter (OM) digestibility, especially ADF and NDF digestibility by 7%, 20%, and 16%, respectively (Newbold et al. 2015). Isotricha and Dasytricha were Holotrich protozoa which utilise soluble carbohydrate including monosaccharides such as glucose, and fructose (Orpin & Letcher 1978). They are also capable of uptaking complex carbohydrates e.g. starch, maltose, and cellobiose (Williams & Coleman 1997). In addition, Holotrich protozoa have an insignificant role in ruminal nitrogen cycling (Ivan et al. 2000) which was indicated by less predatory activity on rumen bacteria (Ivan 2009). Holotrich species could be beneficial for the animals fed a high concentrate diet by utilising starch to prevent excess bacterial fermentation into lactic acid which can result in subacute ruminal acidosis (SARA) (Mackie et al. 1978). On the contrary, Epidinium among Entodiomorphs are considered as cellulolytic protozoa with greater activity of endoglucanase and xylanase enzymes (Williams & Coleman 1992). Bauchop (1979) reported the direct involvement of Epidinium in plant tissue degradation by engulfing phloem elements and chlorophylls. This group of protozoa also have high rates of engulfing rumen bacteria (Belanche et al. 2012a) and negatively impacted the ruminal MP production and overall protein utilisation by the animals (Ivan 2009; Belanche et al. 2012a). Therefore, the presence of Epidinium as cellulolytic protozoa would benefit animals fed fibre-based diet with sufficient nitrogen content to avoid nitrogen shortage (Belanche et al. 2012b).

1.2.4.3. Anaerobic fungi In the early 1960’s, most researchers regarded zoospores (at least with Neocallimastix frontalis) as rumen flagellate protozoa (Warner 1966). However, Orpin (1977) observed the compounds chitin and cellulose in the cell walls of N. frontalis, Spaeromonas communis, and Piromyces communis, confirming that they were true fungi. This was the very first record of anaerobic fungi. Based on morphological appearance, anaerobic fungi were classified as monocentric Neocallimastix, Caecomyces, Piromyces and the polycentric Orpinomyces, Anaeromyces, and Cyllamyces (Dehority 2003; Fliegerova et al. 2015). Monocentric and polycentric fungi are differed by the number of sporangium which are one and multiple sporangia for monocentric and polycentric, respectively (Borneman & Akin 1994). Briefly, the anaerobic fungi of the rumen have two forms as part of their life cycle, a motile zoospore and rhizoidal, non-motile vegetative form (Dehority 2003). The full life cycle of anaerobic fungi can be seen in Figure 1.8. Rumen fungi have long been recognised as being able to penetrate and degrade plant cell walls (Akin & Borneman 1990). Furthermore, according to Akin and Borneman (1990), fungi produce cellulase, hemicellulase, and xylanase to degrade the plant cell walls. Bernalier et al. (1992) undertook a trial on degradation and fermentation of cellulose using three rumen anaerobic fungi in axenic

21 cultures and/or in association with cellulolytic bacteria. They found that cellulose degradation was higher with anaerobic fungi than for rumen bacteria (Bernalier et al. 1992). Another trial by Bootten et al. (2011), showed that three species of rumen fungi N. frontalis and P. communis, and Caecomyces communis were able to degrade lignified secondary walls of lucerne hay containing pectin polysaccharides, xylan, and cellulose. The study showed N. frontalis had highest percentage of dry matter degradation of lucerne xylem cylinder (27.8%) following by P. communis (16.7%), and C. communis (6.1%). Although N. frontalis was superior in degrading most of neutral monosaccharides component of lucerne, P. communis was observed more active in lignin and arabinose degradation (Bootten et al. 2011), while C. communis had the lowest fibrolytic activity (Bootten et al. 2011).

Figure 1.8. The life cycle of anaerobic fungi. (Reproduced from Gruninger et al. (2014)).

Co-culture with rumen bacteria can improve the cellulolytic degradation ability of rumen fungi. Anaerobic fungi in co-culture with Sphaeromonas communis and Selenomonas ruminantium proved more efficient in cellulose degradation than S. communis by itself (Bernalier et al. 1991). However, the positive or adverse effects of co-culture depends on the species of fungi and bacteria. Bernalier et al. (1992), showed an antagonistic interaction between two rumen anaerobic fungi (N. frontalis MCH3 and P. communis FL,) and the rumen bacterium R. flavefaciens. The antagonism between them reduced overall cellulolytic activity in co-cultures. The importance of fungi in rumen

22 nutrition has been found to be associated with extensive fibre hydrolysis using complex multi hydrolases called cellulosomes (Fliegerova et al. 2015).

1.2.4.4. Archaea Archaea were regarded as a grouping within the domain Bacteria (Archaeobacteria) until separated as a third domain of life by Carl Woese and George Fox in 1977, at the University of Illinois, USA (Woese et al. 1990; Eme & Doolittle 2015). Initially, archaea were found in extreme environments, and were mainly thermophiles, hyperthermophiles, acidophiles, halophiles etc. (Stetter et al. 1981). The definition of archaea did not change until DeLong (1992) observed Deoxyribonucleic acid (DNA) of archaea in coastal environments of North America and quantified that 2% of total extracted 16S ribosome-Ribonucleic Acid (rRNA) genes belonged to Archaea. Further studies demonstrated that archaea are found in a wide variety of environments (DeLong & Pace 2001; Robertson et al. 2005; Colman 2017). Lin et al. (1997), using rRNA-targeted oligonucleotide probes, found that archaea comprised 3-30% of the total rRNA in the GI tract of various domestic animals (bovine, ovine, caprine, and porcine). The diversity and structure of archaeal populations in the rumen have been reported by numerous researchers (Janssen & Kirs 2008; Paul et al. 2017). Archaea in the rumen are mainly methanogenic and responsible for rumen methanogenesis (Stahl et al. 1998; Chagan et al. 1999). A phylogenetic analysis using 16S rRNA genes indicated that populations of rumen methanogens mainly comprised Methanomicrobium mobile, Methanobrevibacter ruminantium, and Methanobrevibacter smithii (Yanagita et al. 2000). However, a recent meta-analysis exploring the structure of rumen archaea reported that 99.9% belonged to the phylum Euyarchaeota and the rest were classified as Crenarchaeota, Thaumarchaeota and an unclassified phylum (Paul et al. 2017). Further classification to genus level showed that rumen archaea clustered into 11 genera that had been already acknowledged and 21 were from unclassified genera (Paul et al. 2017). Methanogens have a major role in maintaining a low partial pressure of hydrogen in the rumen (Wolin 1997). Methanogens represent a hydrogen sink in the rumen and use hydrogen to synthesise methane (CH4) (Janssen & Kirs 2008). This step is necessary to keep the rumen in a favourable condition for feed fermentation (McAllister & Newbold 2008). However, in ruminants, CH4 production is also associated with an inefficient use of energy and accounts for 2-12% of lost dietary energy (Johnson & Johnson 1995). Methane is also a potent greenhouse gas, therefore, reduction of rumen methanogenesis could have a dual benefit, reducing greenhouse gas emission and improving animal performance (Martin et al. 2010). Literature has shown several strategies to minimise methanogenesis by suppressing the population of methanogens in the rumen with dietary lipids, adjusting optimum feed intake and feed quality, and using acetogenic species to dispose ruminal

23 hydrogen via acetogenesis, thus exploring the potential of redirecting hydrogen uptake pathways to supress methane production(Wright & Klieve 2011; Martinez-Fernandez et al. 2016; Tapio et al. 2017; Greening et al. 2018; Popova et al. 2019).

1.2.4.5. Bacteriophages Bacteriophages or bacterial viruses, also known as “phages”, were discovered at about the same time by Frederick William Twort in England (1915) and Fe´lix d’Herelle in France (1917) (Ackermann 2012). Knowledge of bacteriophages developed significantly after the discovery of transmission electron microscopy (TEM) and negative staining (Brenner & Horne 1959). Generally, the structures of most bacteriophages consist of genetic material (DNA) encapsulated by a protein shell (capsid) in the morphologic form as head, collar, and tail (Figure 1.9). Classically, bacteriophages replicate by lytic reproduction, infecting bacteria and injecting the viral genome into bacterial cells to synthesise new bacteriophages (Guttman et al. 2005), although many rumen bacteria are known to form a stable genetic association with phages, therefore harbouring lysogenic (temperate) phages within their genomes (Klieve et al. 1989). As more rumen microbes have their genomes sequenced, more integrated phage genomes (prophages) are being identified (Attwood et al. 2008; Gilbert & Klieve 2015; Kelly et al. 2016).

Figure 1.9. 2D and 3D structures of bacteriophages from the family Myoviridae (Reddy 2013)

Bacteriophage (phage) are the most abundant and prolific viruses in the rumen (Gilbert & Klieve 2015). Early exploration of rumen phages, using TEM (Klieve & Bauchop 1988) observed a large number of bacteriophages within five morphologically distinct groups, with most being tailed phages of the viral order Caudovirales. The first molecular approach to investigate rumen phages reported that the number of phages were approximately 1.6 x 1010 particles per mL of ruminal fluid (Klieve & Swain 1993). Several metagenomic studies (Berg Miller et al. 2012; Ross et al. 2013; Anderson et al. 24

2017; Solden et al. 2018) have now confirmed that the rumen contains dense and highly diverse populations of phages as well as viruses infecting other rumen microbes (archaea and possibly protozoa). Gilbert and Klieve (2015) briefly compiled three roles for bacteriophage in the rumen which are (a) nutrient recycling in the rumen through bacterial lysis, (b) maintaining bacterial population diversity and (c) mediation of bacterial gene transfer. To date, the roles and extent to which phage communities in the rumen contribute to rumen function remain largely unproven however technical advances in sequencing technologies and the inclusion of new rumen phage genomes in sequence databases (Gilbert et al. 2017) are expected to rapidly advance new research into this aspect of the rumen microbial.

1.2.5. Modulating the rumen microbial community The main objective of modulating the rumen microbial community is to increase feed efficiency while at the same way also reducing the drawbacks from rumen fermentation. Globally, food production from ruminants is set to increase by up to 76% and 63% for meat and milk products respectively by 2050 (Alexandratos & Bruinsma 2012). However, the negative impacts of some of the by-products of rumen fermentation on the environment must be considered and without changes in farming practices, these will increase along with the increases in meat and milk production. Methane gas is produced as a by-product of feed fermentation in the rumen and represents up to 12% dietary energy loss (Johnson & Johnson 1995; Perry et al. 2017). Changed into “Methane gas is considered a potent greenhouse gas (GHG) with an estimated 3.1 gigatonnes CO2 -eq or 44% of total anthropogenic methane emissions coming from livestock production (Gerber 2013). Nitrous oxide (N2O) is another GHG emitted from livestock, which contributes approximately 53% of total anthropogenic N2O emissions (Gerber 2013). Ruminants are responsible for emissions of N2O through nitrogen from cattle manure and urine being converted into N2O by soil bacteria (Kingston-Smith et al. 2010). The rumen microbial community is a dynamic system which is subject to modification. The dynamic condition of the rumen microbial community begins with the early processes of rumen colonisation and associated rumen development (Section 2.3. Rumen development). Therefore, delivering treatments for modulating the rumen microbial community during this period could increase success in modification of the community (Yáñez-Ruiz et al. 2015). While it is generally accepted that the phylogenetic composition of the rumen microbial community becomes more stable in adult animals (Li et al. 2012a), the rumen microbial community of adult animals could still be modified through dietary change and biological treatments (Gilbert et al. 2015).

1.2.5.1 Diet treatments Diet is the main driving factor of changes in the rumen microbial community across different animal species and geographical locations (Henderson et al. 2015). This is well understood since the 25 rumen microbial community is dependent on the nutrient content of ingested feed material (Section 1.2.2. Rumen physiology and function). For example, feeding animals with a high starch diet significantly increases the population of rumen amylolytic bacteria (Pan et al. 2016). Phylogenetic studies of the rumen microbial community in animals fed different feed types have been reported in numerous publications and have shown the significant impact of feed on the microbial composition (Fernando et al. 2010; Klevenhusen et al. 2017; Zhang et al. 2018). The profile of rumen VFAs is one of the targets for manipulating the rumen microbiota. A lowered acetate to propionate ratio is considered more desirable as propionate is linked to better animal performance and lower methane emissions (Nagaraja et al. 1997). Propionate has the highest energetic efficiency compared with acetate and butyrate with 36, 20, and 27 Adenosine Triphosphates (ATPs) yielded per molecule of glucose fermented for propionate, acetate, and butyrate respectively (Ryle & Ørskov 1990). Although all VFAs are used as energy sources by ruminant animals, propionate is the only glucogenic VFA and contributes 54% of the glucose from gluconeogenesis (Fahey & Berger 1993). The profile of VFAs reflects the profile of the microbial community within the rumen (de Menezes et al. 2011). As described previously in Section 1.2.2.1. VFA production, a high starch diet leads to increased synthesis of propionate, while a diet high in cellulose leads to increased acetate production (Klein 2012). Increasing the grain ratio in the diet up to 60% significantly increased the populations of amylolytic bacteria such as Mitsuokella sp., Selenomonas ruminantium, and Prevotella bryantii (Fernando et al. 2010). High grain diets also increase the rumen concentration of lactic acid, synthesised by lactic acid-producers such as Streptococcus equinus (Dawson et al. 1997; Valente et al. 2017). High concentrations of lactic acid favour populations of lactic acid utilising bacteria such as Megasphaera elsdenii and Selenomonas ruminantium (Fernando et al. 2010) which convert the lactic acid into VFAs (Russell & Baldwin 1978; Counotte et al. 1981). However, a higher grain ratio can result in reduced rumen pH and if rumen pH remains below 6 for 12 h or more it can be defined as a rumen in a state of SARA (AlZahal et al. 2007). Consequently, the majority of fibrolytic bacteria (Fibrobacter succinogenes and Ruminococcus albus) and lactate-utilizing bacteria (S. ruminantium, and M. elsdenii) are inhibited by the acidic conditions (Russell & Wilson 1996; Fernando et al. 2010; Ding. et al. 2014). On the other hand, the populations of lactate-producing bacteria will increase and lead to an even greater decrease in rumen pH and altered rumen microbial ecology which may result in the animal’s death (Russell & Rychlik 2001; Ding. et al. 2014) (Figure 1.10).

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Figure 1.10. Ruminal acidosis cycle and its impact to rumen fermentation. (Reproduced from https://ruminantdigestivesystem.com).

In beef production, ionophores have been used to favour propionate synthesis and reduce methanogenesis by inhibiting the growth of gram positive bacteria and methanogens (Bell et al. 2015). In addition, ionophores also benefit animals by decreasing protein degradation and the synthesis of lactic acid (Callaway et al. 2003). The main mechanism of ionophores in modifying the rumen bacterial community is by disrupting the bacterial cell membrane, particularly of gram-positive bacteria which have a porous layer of peptidoglycan (Callaway et al. 2003). Gram-positive bacteria mainly produce acetate, butyrate, hydrogen, and ammonia as fermentation products (Bell et al. 2015). The reduction in hydrogen decreases rumen methanogenesis and increases propionate as an alternative electron sink (Russell & Houlihan 2003). Therefore, the populations of propionate producing bacteria such as P. ruminicola and S. ruminantium increase with the addition of ionophores (Callaway et al. 2003; Weimer et al. 2008; Melchior et al. 2018). However, the use of ionophores is no longer being seen as a favourable strategy due to it’s variable effectiveness and the ability of some bacteria to develop ionophore resistance (Russell & Houlihan 2003). Plant secondary metabolites, have been used as natural feed additives to improve rumen fermentation (Weimer 1998). The antimicrobial activity of bioactive compounds has been reported against bacteria, protozoa, archaea, and fungi (Calabrò 2015). Generally, the antimicrobial mechanism of bioactive compounds is by disrupting cell membranes of bacteria or protozoa leading to ionic leakage and disintegration of the cell membrane (Lin & Wang 2010; Bodas et al. 2012). Tannin and saponin are the most intensively studied among other SPC including condensed cyanogenic glycosides, phenolics, glucosinolates, and alkaloids (Kingston-Smith et al. 2010; Calabrò 2015). Tannins are

27 polyphenolic compounds from plants and categorised into hydrolysable and condensed tannins (Makkar et al. 2007b). Studies showed that feeding tannin not only benefits the animals by reducing protein degradation (Weimer 1998) but also modifies the diversity and populations of rumen microbes (De Nardi et al. 2016). A study by Díaz Carrasco et al. (2017) demonstrated the effect of tannin in elevating the ratio of Firmicutes to Bacteroidetes and increasing populations of fibrolytic and amylolytic bacterial communities in the rumen resulting in better fibre digestion. In addition, the antimicrobial effect of tannins was selective, enabling the populations of some species of bacteria to increase due to less competition (Díaz Carrasco et al. 2017). Saponins are bioactive compounds containing steroids or triterpenoid aglycone with glycosidic linkages (Makkar et al. 2007a). Similarly to tannins, saponins also possess antimicrobial activity (Wallace et al. 1994; Hassan et al. 2010). The addition of saponin to the diet of ruminants significantly decreased rumen protozoal populations (Hess et al. 2004) which increased the growth of rumen bacteria due to less protozoal predation (Williams & Coleman 1992). Another study showed an inhibitory effect of saponins to the cellulolytic bacteria F. succinogenes, R. flavefaciens, and R. albus as well as to ruminal fungi N. frontalis and Piromyces rhizinflata (Wang et al. 2000). Looking at the rumen microbiota as a whole, the addition of saponin significantly clustered OTUs of the rumen bacteria and increased the community associated with amino acid and nucleotide metabolism (Wang et al. 2019). However, it is often case that PSM can have both benefits and disadvantages when used as feed additives. Although benefiting as feed additive, PSMs originally function as a plant defensive mechanism against herbivores and may potentially be toxic (Iason 2005). Therefore, the use of PSM in animal diets should be followed by precautions with its toxicity levels. For examples, the inclusion of excessive levels of tannin in animal diets can lead to toxicity. The inclusion of tannin more than 5% of dry matter reduced nutrient digestibility which was followed by lower feed efficiency and animal production (Mueller-Harvey 2006). Nutritionists have considered tannins as antinutritional compounds due to their capacity to bind to the feed constituents (Naumann et al. 2017). Although the rumen microbial community has the ability to neutralise and metabolise much of the PSM content in ingested diet, the excessive levels of PSMs might overwhelm the degradative capacity of the rumen microbial community and cause toxicity to the animal.

1.2.5.2. Biological treatments The use of microbial inoculants, also often referred to as probiotics or direct fed microbials, as feed supplements for cattle has been practiced since 1924 (Eckles et al. 1924). Eckles and coworkers (1924) used dried bakers’ yeast as a source of vitamin B for cattle. However, the term of probiotic was first used by Lilly and Stillwell (1965) when they found substances from protozoa able to support the growth of another species (which today would be better described as a prebiotic). The definition of

28 probiotic, according to FAO/WHO in Hill et al. (2014) is “the live microorganisms that, when administered in adequate amounts, confer a health benefit on the host.” In agriculture, this definition is often widened to include a growth benefit to the host. According to Liong (2011) in his book, probiotics consist of prokaryote and eukaryote organisms. Currently there are prokaryotic probiotics in use, often from the genera Lactobacillus and Bifidobacterium, while with eukaryotic probiotics, Saccharomyces is the dominant genus currently used in animal agriculture. In general, the application of probiotics in the livestock industry is commonly used to improve the animal’s health as well as increase productivity (i.e. improving feed digestion) (Abe et al. 1995; Chiquette 2009; Uyeno et al. 2015). The addition of probiotics as single- or multi-species formulations has been shown to improve the health of cattle (Timmerman et al. 2005). Probiotics improve animal health through lowered mortality rates and a decrease in the incidence of severe diarrhoea (Foditsch et al. 2015). Most of the studies undertaken in investigating the mechanism of a probiotic in altering gut health have been conducted in a non-ruminant animal model (Bajaj-Elliott et al. 2002; Ohland & MacNaughton 2010; Van Tassell & Miller 2011). The mechanism of probiotic action to improve gut health can be through stimulating intestinal epithelial barrier function (Figure 1.11.). Briefly, Ohland and MacNaughton (2010) described three ways by which probiotics induce intestinal barrier function; (A) probiotics promote mucus secretion. Mucus is secreted by goblet cells, enriched by beta-defensin from epithelial cells (Bajaj-Elliott et al. 2002), and considered as the front line barrier against pathogen infection (Van Tassell & Miller 2011); (B) increase the immunity level of the host. Administration of Bifidobacterium longum in mice fed antigens for two weeks resulted in a high level of immunoglobulin A (IgA) and anti- beta-lactoglobulin (β-LG) in intestinal epithelial cells (Takahashi et al. 1998). In addition, Faecalibacterium prausnitzii induced anti-inflammatory cytokine (IL-10) while also reducing the production of pro-inflammatory cytokines (IFN-γ and IL-12) (Sokol et al. 2008; Qiu et al. 2013). Anti- inflammatory cytokines are the most important immune regulator molecule in vertebrates (Opal & DePalo 2000); and (C) out-compete other bacteria, particularly pathogenic bacteria, by producing antimicrobial substances or competing for epithelial adherence. Probiotic bacteria often produce broad types of antimicrobial substances such as organic acids (lactic, acetic, and formic), hydrogen peroxide, and bacteriocins (Nader-Macias et al. 2008; Tejero-Sariñena et al. 2012; Arqués et al. 2015).

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Figure 1.11. The effects of probiotics in modulating host health through modifying the epithelial barrier function. The epithelial barrier function is improved by; A) inducing mucus production; B) increasing the secretion of IgA; and C) competing with other bacteria. Ohland and MacNaughton (2010).

The addition of probiotics has been reported to minimise the impact of acute and subacute rumen acidosis (Lettat et al. 2012). Acidosis is often caused by feeding a high proportion of easily fermented feed in the diet, such as grains (Section 2.7.1. Diet treatments). Lactic acid has the lowest dissociation constant (pKa) compared to propionic, acetic, and butyric acids (Valente et al. 2017), and is often implicated in acidosis. A high concentration of lactic acid dramatically drops the rumen pH to a harmful level for the host (Nocek 1997). A study by Klieve et al. (2003) showed the potency of M. elsdenii as a probiotic for minimising the negative impacts of acidosis. M. elsdenii is mainly found in the rumen of cattle fed high grain diets (Ouwerkerk et al. 2002) and utilises lactic acid as carbon source (Aikman et al. 2011). M. elsdenii regulates ruminal pH by converting lactic acid into propionate and butyrate (Counotte et al. 1981). Chaucheyras-Durand et al. (2008) described the benefit of probiotics in supporting the development of the rumen microbiome and fibre digestion. The addition of probiotics significantly increased ADF digestion in newborn calves (Chaudhary et al. 2008). Probiotic inoculation improved fibre digestion by stimulating the growth of rumen fungi and fibrolytic bacteria (Tripathi & Karim 2011; Ding. et al. 2014). Chaucheyras-Durand et al. (2008) suggested Saccharomyces cerevisae stimulated the growth of bacteria and fungi by providing thiamine, a vitamin that is needed for their growth. In addition, S. cerevisae has the ability to scavenge oxygen in the rumen which favours the growth of other rumen anaerobe microbes (Chaucheyras-Durand et al. 2008). Although known as an anaerobic

30 ecosystem, Scott et al. (1983) showed the presence of oxygen in the rumen up to 1,630 nmol/l. The presence of oxygen introduced via the feeding cycle and blood may disturb redox conditions in the rumen by increasing redox potential which is unfavourable for the metabolism and growth of strictly anaerobic bacteria, such as fibrolytic bacteria (Kalachniuk et al. 1994; Huang et al. 2018). Lee et al. (2008) investigated the cellulase synthesis from Bacillus amyloliquefaciens in degrading rice hull. They found that B. amyloliquefaciens is able to hydrolyse rice hulls as a carbon source for growth. B. amyloliquefaciens isolated from rhinoceros dung had reported carboxymethylcellulase (CMCase) activity at 0.079 U/ml (Singh et al. 2013). Also, several researchers showed that B. amyloliquefaciens has the potential to produce fibre-hydrolysing enzymes such as beta-mannanase, amylase, cellulase, and xylanase (Breccia et al. 1998; Apun et al. 2000; Mabrouk & Ahwany 2008). In fact, analysis of the genome sequence of B. amylolyquefaciens strain H57 showed an abundance of glycolase hydrolase genes including numbers 1, 43, and 13 which are associated with hemicellulose degradation (Schofield et al. 2016). The application of B. amyloliquefaciens strain H57 as a probiotic for cattle and sheep has been reported in several studies (Le et al. 2017a; Le et al. 2017b; Schofield et al. 2018). These studies reported that the benefits of inoculating B. amyloliquefaciens strain H57 were increased dry matter intake, body weight, nitrogen retention in ewes, and improved rumen development in pre-weaned dairy calves. In addition, from a rumen ecology perspective, B. amylolyquefaciens strain H57 significantly modulated the profile of the rumen microbial community in ewes (Schofield et al. 2018). The way B. amyloliquefaciens strain H57 modified rumen microbial population may be associated with the possession of GH and antimicrobial products (i.e. surfactin, iturin, bacillomycin D, and fengycin) (Schofield et al. 2016). However, this mechanism is not completely understood due to the low detection of B.amylolyquefaciens strain H57 in the rumen of all trial animals (Schofield et al. 2018). Probiotics can also be applied to degrade the toxic content in the diet. The complex rumen microbial community can act as a detoxification chamber for the host. Dominguez-Bello (1996) reported a broad range of toxic compounds that can be detoxified in the rumen through microbial activity. An example is detoxification of the fodder plant Leucaena leucocephala by Synergistes jonesii, this bacterium degrades 3-hydroxy-4(H)-pyridone (3,4 DHP) (Allison et al. 1992). 3,4 DHP is the toxic compound produced from mimosine (found in L. leucocephala) degradation in the rumen (Hegarty et al. 1964). Undegraded 3,4 DHP causes toxicity to cattle with reported clinical symptoms such as hair loss, low fertility, weight loss and can lead to mortality (Jones & Hegarty 1984). S. jonesii has been successfully cultured and produced in vitro (Klieve et al. 2002) and used commercially as a probiotic for animals fed Leucaena (Wallace 2008).

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In addition to probiotics, bacteriophages have been recognised as potential biocontrol agents for controlling pathogenic bacteria in livestock production systems (O'Flaherty et al. 2009; Chan et al. 2013; Gutiérrez et al. 2019). The basic information and mechanisms of bacteriophages for controlling bacterial populations have been discussed in Section 2.4.5. Bacteriophage. Bacteriophages have been applied to cattle to control foodborne pathogenic bacteria such as Escherichia coli 0157:H7 (Lili et al. 2017). E. coli 0157:H7 is a food pathogen of high pathogenicity which mainly originates from the ruminant GI tract (Naylor et al. 2003; Teunis et al. 2004). Bacteriophage inoculation significantly reduced overall population density of E. coli 0157:H7 in beef cattle (Niu et al. 2008). The success of phage therapy could be improved by optimising the multiplicity of infection (MOI) to increase the exposure rate of pathogen to phage (Rivas et al. 2010; Abedon 2016). An in vitro study showed that increasing MOI from 10 to 102 plaque forming unit/colony forming unit totally stopped the growth of E. coli 0157:H7 (Sheng et al. 2006). The literature has shown the significance benefits of adding probiotic into diets, however, the establishment of probiotic inoculants is not always successful (Agarwal et al. 2002). Failure to establish in the rumen reduces the benefits of a probiotic that can impact the rumen microbial community and animal’s performance (Frizzo et al. 2011a; Qadis et al. 2014). The effectiveness of the addition of probiotic to animals can be influenced by the type of feed, probiotic strain, animal age, sanitation conditions, and animal health status (Frizzo et al. 2011b; Uyeno et al. 2015). In addition, the establishment of a probiotic is affected by the stability of the current established rumen microbiota and the ability of the probiotic inoculant to occupy a specific rumen niche.

1.2.6. Host specificity, persistence and plasticity of the rumen microbiota

1.2.6.1. Host specificity and rumen microbial stability The effect of the host on the rumen microbiome relates to the morphological, physiological, and behavioural adaptations of ruminants to their environmental conditions (Hofmann 1989; Haworth et al. 2018). The evolution of the rumen microbiome relative to the host and environment has been reported by Zhang et al. (2016). The study showed that the rumen microbiome of animals from high- altitude areas had a convergence of microbial genes associated with VFA production and methanogenesis pathways and showed higher feed-energy conservation compared with their low- altitude relatives fed the same diet. Another study showed the adaptation of the rumen microbiota in accordance to different feeding habits between captive and wild musk deer (Li et al. 2017b). An early study on host specificity, prior to the use of molecular biological techniques, was undertaken by Becker and Hsiung (1929). They cross-inoculated protozoa originating from horses or ruminants and investigated their establishment. They showed that species originating from calves were able to colonise other ruminant species, yet species from horses failed to establish in the rumen 32 of goats. It was concluded that host specificity was less specific between ruminant species (goat, cow, and lamb) but more specific between horses and ruminants. A similar study on host specificity with establishment of rumen microbial populations in lambs and calves was reported by Eadie (1962). It was concluded that the establishment of rumen bacteria in lambs and calves was influenced by variation between individual animals. Later studies on host specificity indicated that individual cows maintain a unique profile of cellulolytic bacteria in their rumen (Weimer et al. 1999). In another study, Weimer et al. (2010) clearly reported the role of the host in controlling ruminal chemical conditions (i.e. pH and VFA) and the rumen microbiota profile after a nearly total rumen content exchange. A global study has shown the variation of rumen microbial communities across ruminant species and geographic areas, yet the core rumen microbiota such as bacteria was similar (Henderson et al. 2015). The term “core rumen bacteria” describes the community of rumen bacteria which appears in all animals or majority of the animals. In a 16S rRNA gene amplicon study, the core rumen bacteria was identical in 16 animals on the same diet and included members of the genus Prevotella within the phylum Bacteriodetes and families Lachnospiraceae and Ruminococcaceae within the phylum Firmicutes (Jami & Mizrahi 2012a). These dominant rumen bacteria are essential in maintaining the overall rumen function as a fermentative organ (Weimer 2015) and can often share a similar biological function (Jami & Mizrahi 2012a; Li & Guan 2017). Weimer (2015) defined some members of this group of bacteria as generalists, for example, Prevotella sp. are able to utilise various types of substrates such as proteins and non-cellulosic polysaccharides and possess a repertoire of proteinase, peptidase, and glycoside hydrolases (Purushe et al. 2010). Another study showed the relative abundance of P. bryantii and P. ruminicola was similar in rumen of sheep fed either hay or concentrate-based diet (Bekele et al. 2010). The rumen bacteria B. fibrisolvens is also considered as a generalist with the ability to utilise starch, cellulose, xylan, and pectin (Stewart et al. 1997; Klieve et al. 2003), whilst Cotta and Hespell (1986) and Sales et al. (2000) considered this bacteria as proteolytic due to its ability to degrade protein and utilise amino acids and ammonia for growth. In addition, this genus is well known as one of the major ruminal butyrate-producing bacteria, although acetate and propionate are produced as well (Stewart et al. 1997). Rumen bacteria variation between individual animals seems to be greater for “specialist” bacteria that occupy specific functional niches. Jami and Mizrahi (2012b) compared the similarity of the rumen microbiota in lactating cows and observed a higher degree of variation for bacteria with specific functions such as M. elsdenii which can stabilise the ruminal pH by disposing of lactic acid during high grain diet feeding (Klieve et al. 2003). A recent metatranscriptomic study between dairy cows with different feed efficiencies showed M. elsdenii was highly correlated with efficient animals. The study also showed the role of M. elsdenii in converting lactate into propionate based on acrylate

33 pathway that is encoded within the genome of M. elsdenii (Shabat et al. 2016). Cantalapiedra-Hijar et al. (2018) reviewed the literature and concluded that the variation of feed efficiency between individual animals was associated with the specialist ruminal microbiota rather than generalist phylotypes. Cantalapiedra-Hijar et al. (2018) also speculated that the individual differences in anatomy and physiology of rumen e.g. rumen size, ruminal absorption capacity, and ruminal tissue development might be indirectly or directly affected and shape the individual’s rumen microbial population. It has been shown that the successful inoculation of probiotics was greater for specialist species such as S. jonesii (Allison et al. 1992) and M. elsdenii (Klieve et al. 2012) which may establish by occupying empty, specific rumen niches. Up to date, Lactobacillus spp. and S. cerevisiae are among the extensively studied probiotic species (Chaucheyras-Durand et al. 2008; Chaucheyras-Durand & Durand 2009). However, the results of the addition of these species as probiotics are still inconsistent (Agarwal et al. 2002; Qadis et al. 2014). This inconsistency may be associated with the persistence of the established rumen microbiota and competition from microbes already occupying rumen niches. Although yeast has the capability to influence fibre degradation (Plata et al. 1994), it appears not to have a direct role in rumen fibre digestion (Angeles C et al. 1998). Studies have shown that the benefits of yeast are more crucial in cattle fed diets with less accessible digestible carbohydrates (i.e. diets with a high lignin content) (Susmel & Stefanon 1993; Guedes et al. 2008). Yeast also increases fibre digestion by maintaining anaerobic conditions that favour the growth of cellulolytic bacteria (Newbold et al. 1996; Mosoni et al. 2007). Whilst, Lactobacillus spp. are epimural bacteria which attach to the rumen epithelial (Petri et al. 2013). Members of lactobacilli such as L. casei, L. acidophilus, L. delbrueckii, and L. johnsonii are able to produce lactic acid, hydrogen peroxide and bacteriocins which are antagonistic against many pathogenic microbial species including Salmonella typhimurium and Escherichia coli (Servin 2004; Pridmore et al. 2008). In addition, L. casei and L. acidophilus can also act as an immunomodulatory with the benefit of improving the host immune system (McAllister et al. 2011). Therefore, the success and benefits of a potential probiotic would be much higher if it is a specialist species rather than generalist species. The chance of success is also increased by targeting an empty niche, for example, in young animals rather than compete for the occupied niche condition in adult animals. Yáñez-Ruiz et al. (2015) proposed the idea for manipulating rumen microbiota in early life stages while the rumen microbiota is still establishing and probably less rumen niches are occupied. Moreover, using probiotics with immunomodulation characteristics may be more crucial in younger animals which can be more susceptible to colonisation of the intestinal tract by pathogens (McAllister et al. 2011).

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1.2.6.2. The plasticity of rumen microbiota The term of “plasticity” refers to the adaptability of the rumen microbiota to adjust to changing environmental conditions, such as changes to diet. A recent study in primates showed that the gastrointestinal microbiota of Chorocebus monkeys was clearly different in accordance to the available food resources in their habitat (Trosvik et al. 2018). In ruminants, although the microbial community is heavily influenced by the diet, the species of the core microbiota can be quite similar (Henderson et al. (2015). Belanche et al. (2019) reported the plasticity of the rumen microbiota during dietary change from concentrate to a pasture-based diet. Interestingly, the appearance of the core community of rumen bacteria in this study was similar for both diet treatment groups and consisted of 35 genera, with the dominant genera including Prevotella, Ruminoccoccus, Ruminobacter, Succinivibrio, Fibrobacter, and Selenomonas. A similar result was reported by Jami and Mizrahi (2012a), who observed 32 of the 35 genera in Belanche et al. (2019), were found across 16 cows fed various diets. The study by Jami and Mizrahi (2012a) suggested that diet had only a small effect on the presence of core species of rumen bacteria. In fact, the core diet-specific communities comprised less than 2% of total core rumen bacteria (Belanche et al. 2019). In addition, the results indicated that the adaption process of the core microbiota was associated with enriching the concentration and diversity of the core community, instead of replacing members of the core. The drawbacks of this plasticity are associated with the difficulties in delivering rumen microbial manipulations that will persist over a long period of time. Studies have shown the re- establishment of the rumen microbiota to the original condition once interruptions came to an end (Yanez-Ruiz et al. 2010; De Barbieri et al. 2015b). This occurred even when most of the rumen content was exchanged with rumen fluid from other donor animals (Weimer et al. 2010). A recent study in rumen content exchange was done between high-efficiency milk production (HE) and low-efficiency milk production (LE) dairy cows (Weimer et al. 2017). Prior to exchange, HE and LE animals had different ruminal pH, VFAs and bacterial profiles. Approximately 95% of rumen content was exchanged between animals within the HE and LE groups. The rumen content exchange resulted in changes of ruminal pH, VFA profile, the ruminal bacterial community, and productivity of recipient animals in accordance to donor animals. However, the ruminal pH and VFAs reverted towards the pre- exchange condition just one day post exchange whilst the re-establishment of the original ruminal microbial community was achieved within 10 days post exchange. The results indicated the role of the host in determining the structure of the microbial community by controlling the physiological conditions of the rumen through VFA absorption, salivary buffering, and passage rate of rumen content. Thus, the control of rumen chemistry enables the re-establishment of the pre-existing rumen microbiota community.

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As described previously, the plasticity characteristic is important for rumen microbial adaptation to the changes in rumen physiology due to any interventions. This plasticity of the rumen microbiota is also a drawback for modulating the rumen microbial community with a long-lasting effect. However, there are possibilities for altering the rumen microbial community through targeting specific or unoccupied niches in the rumen microbiome using specialist inoculants or delivering treatments when the rumen microbiota has not yet established.

1.2.7. Next generation sequencing of 16S rRNA gene PCR amplicons for assessing rumen microbial ecology Historically, researchers used a range of traditional, culture-based, anaerobic techniques for isolating, culturing, and enumeration of rumen microbes (Hungate & Macy 1973). Although the culture-based techniques are still valuable for isolating and culturing rumen microbial species, the majority of rumen microbes are still not able to be cultured and therefore undetected by these methods (Chaucheyras-Durand & Ossa 2014). Creevey et al. (2014) undertook a meta-analysis combining information from culture collections, scientific reports, public genomic databases and 16S rRNA gene-based surveys and revealed that there are many uncultured taxa present within the rumen bacterial community. A global network, the Rumen Microbial Genomic Network (established 2011) has provided further genomic information on the culturable rumen microbiota (Seshadri et al. 2018b), through the Hungate1000 project. This global collaborative project has documented 410 genomes of rumen microbes (Seshadri et al. 2018b). However, this number is still considered low when compared with the number of species observed using DNA sequencing technologies (Huws et al. 2018; Stewart et al. 2018). The use of rRNA gene sequencing has greatly benefited research in rumen ecology as a complement to the traditional culture-based techniques used to describe rumen microbial populations (Krause et al. 2003; Cho et al. 2006; Deng et al. 2007). The 16S and 18S rRNA gene is the small subunit ribosomal RNA (SSU-rRNA) gene in prokaryotes and eukaryotes respectively (Wright et al. 2005). The gene sequences of the SSU-rRNAs are routinely used as a molecular evolutionary clock in microbiology as they have both highly conserved and variable regions, are functionally stable and can be analysed rapidly (Woese 1987; Stackebrandt & Goebel 1994). The 16S rRNA gene has been widely used by researchers to investigate the phylogeny of bacteria and archaea (Weisburg et al. 1991). There are nine variable regions in the 16S rRNA gene (Figure 1.12) with different degrees of DNA sequence conservation for each region (Yarza et al. 2014). The V1-V4 regions of 16S rRNA are preferred for phylogenetic analysis of bacteria, whilst for analysis of archaea the V1-V3 regions are more recommended (Kim et al. 2011). Whilst Yang et al. (2016) considered the V4-V6 as the most reliable regions for phylogenetic analysis of bacteria 36

Figure 1.12. Diagram of the nine regions of 16S rRNA (from E. coli) which are indicated by different colours and designated v1-v9. The legend shows the length of each region in bases. (Reproduced from Yarza et al. (2014)).

The 16S rRNA gene analysis protocols for bacteria have been described thoroughly in reports by Weisburg et al. (1991) and Wright et al. (2005). Briefly, the process is started by extracting DNA from samples of rumen content. Polymerase chain reaction (PCR) amplification is performed using universal primers (fragments of DNA) that target all organisms within a specific domain or clade e.g. bacteria, archaea, fungi or protozoa (Wright et al. 2005). The PCR process exponentially and specifically synthesises the determined DNA region of the gene using a forward and reverse primers (Pelt-Verkuil et al. 2008). A primer is basically a short sequence of nucleic acid which acts as starting

37 point for DNA synthesis by the DNA polymerase enzyme (www.nature.com/scitable). The characteristics of a primer, such as nucleotide composition and length (15 to 22 bp), are essential to ensure a specific section of DNA is amplified resulting in a single PCR amplification product (Isenbarger et al. 2008; Pelt-Verkuil et al. 2008). In addition, the strength and frequency of 3’ terminal hybridisation, mismatches location to sequences cross -hybridisation, interaction between primers, hairpin formation energy, and specificity are all basic requirements to consider in designing PCR primers (Mitsuhashi 1996). Although PCR primers can be designed manually, there are a number of available software programs developed specifically for designing PCR primers such as MEDUSA, Primer3, and PrimerQuest (Pelt-Verkuil et al. 2008). PCR amplification consists of a number of cycles made up of three steps and the first step involves denaturing the double helix DNA strand into single strands. The denaturing process involves heating the DNA at 95oC until separated into two complementary DNA (cDNA) strands (Rakshit 2009). The second step involves the primers then binding (annealing) to their matching complementary sequence on a DNA strand (Pelt-Verkuil et al. 2008). The temperature at which a primer anneals to the DNA strand is determined based on the primer length, percentage of GC content and is usually at temperatures between 40oC and 65oC (Pelt-Verkuil et al. 2008). The third step is DNA amplification or synthesis of a new strand of DNA by a thermostable DNA polymerase (Pelt-Verkuil et al. 2008). During DNA synthesis, thermostable DNA polymerase attaches to the 5’ end of the annealed primer and starts incorporating the deoxyribonucleotide triphosphate (dATP, dGTP, dTTP, dCTP) into a DNA strand in the 5’ to 3’ direction (Figure 1.13) (Pelt-Verkuil et al. 2008). The whole cycle of three steps is then repeated up to 35 times and pieces of double stranded DNA which are known as amplicons are produced for each cycle (Pelt-Verkuil et al. 2008).

Figure 1.13. The PCR amplification process. (Reproduced from Pelt-Verkuil et al. (2008)).

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The development of next generation sequencing (NGS) technologies, which are the second- generation platforms for sequencing DNA, have revolutionised the field of microbial ecology. Bench- top NGS produces large quantities of sequencing data in one run, allowing for entire microbial populations to be sequenced. One NGS platform, Illumina in one run using the Illumina HiSeq X system, can sequence of over 1,800 Gb although each read length is small at only about 300 bp long (van Dijk et al. 2014). To further optimise the cost of sequencing oligonucleotide barcodes can be incorporated into the sequencing adaptors. With barcoded adaptors added to the 16S rRNA gene PCR amplicons, multiple samples can be pooled and run on the same lane of the Illumina flow cell making for a much more cost-effective approach for microbial diversity profiling. For genome sequencing the NGS PacBio sequencing has the advantage over Illumina in that much larger read lengths can be generated. Using the new PacBio RS II system with the P6-C4 chemistry, average read lengths of greater than 20 kb can be achieved which is much greater than the 300 bp reads lengths generated by Illumina sequencing (van Dijk et al. 2014). In rumen ecology, the use of NGS has revealed a far greater extent of prokaryotic and fungal diversity, and even bacteriophages, in the ruminal environment, than had been previously envisaged (Fouts et al. 2012; Ross et al. 2013). The application of NGS occupies a wide area of rumen ecology, from genomic studies of single species genomes to large scale sequencing of whole rumen metagenomes or metatranscriptomes according to species of host animal, breed, age, feed, geography, physiological condition, productivity, production system, and chemical and biological treatments applied (Henderson et al. 2015; Schofield et al. 2016; Wang et al. 2016; Indugu et al. 2017; Popova et al. 2017; Schären et al. 2017; Denman et al. 2018; Schofield et al. 2018; Zhang et al. 2018; Zhu et al. 2018; Belanche et al. 2019; Delgado et al. 2019). In addition, 16S rRNA gene sequences can be used to predict the functional potential of the microbial community in certain ecosystems through predictive functional inference software such as PICRUSt (Langille et al. 2013). However, the application of PICRUSt in the rumen microbial community might be over- or under-estimating genomic functionality since it is largely based on the functional database of the microbial community in the human GI tract (Meale et al. 2017). However, recently the PICRUSt platform combined with the global genomic database of rumen microbes (that is, the data from the Global Rumen Census and Hungate1000 projects) can be used to predict functionality of the rumen microbial community more precisely (Wilkinson et al. 2018). Microbial community profiling (metataxonomic studies) has been used extensively in profiling ruminal microbiota (Li et al. 2016a)using 16S rRNA gene PCR amplicons combined with NGS as it gives a balance between low cost and reasonable accuracy and sequence depth (Hilton et al. 2016). However, the PCR-based protocol used during sequencing raises potential output bias as is a common

39 limitation with PCR analysis (Rajendhran & Gunasekaran 2011; Yu & Firkins 2015) . The main source of bias during amplification is the failure of universal primers to complement all 16S gene sequences equally (Schloss & Handelsman 2004). To overcome the primer bias issue, Isenbarger et al. (2008) proposed engineered polymerase and 10-nucleotide mini primers to better capture diverse 16S rRNA genes in the environment rather than using standard primers. Another limitation comes with the formation of PCR chimeras which could lead to inaccurate classifications and abundances (Schloss et al. 2011). The big challenge of NGS data is in its handling, storage and analysis (Wadapurkar & Vyas 2018). Currently there are several bioinformatic pipelines available for data analysis in metataxonomic studies, e.g. Quantitative Insights Into Microbial Ecology (QIIME) (Caporaso et al. 2010b), Mothur (Schloss et al. 2009) and ARB (Yadhukumar et al. 2004). QIIME is among the most popular open access software pipelines for the analysis of16S rRNA gene PCR amplicon sequencing data (Caporaso et al. 2010b). Caporaso et al. (2010b) showed the capability of QIIME to handle, analysis, and visualisation of massive sequencing microbial community data from NGS technology. QIIME also accommodates various databases i.e., Greengenes (16S rRNA database), SILVA (16S and 18S rRNA databases) and UNITE for internal transcribed spacer (ITS) database (http://qiime.org/home_static/dataFiles.html). A recent study by Wang et al. (2019) showed the application of QIIME to investigate the effect of feed treatment to the profile of rumen microbiota. QIIME was applied in comprehensive steps for quality filtering (i.e. noisy sequences and chimera), OTUs clustering and build OTU table, generating phylogenetic tree as well as diversity, taxonomic profiling and statistical analysis (Navas-Molina et al. 2013; Zhang et al. 2018; Wang et al. 2019). Improvements in the databases used for taxonomic assignment of rumen-specific microbes, has also helped to increase the accuracy of these methods for profiling rumen microbial communities (Seedorf et al. 2014; Henderson et al. 2019).

1.3. Conclusions and thesis direction Ruminants depend on rumen microbial, particularly bacterial fermentation to break down feed and derive their nutrition, through compounds such as VFAs, amino acids, and vitamins. This symbiotic interaction enables ruminants to utilise low-quality abundant cellulosic plant products, amongst other things, as feed to produce valuable products like meat and milk. In return, ruminants provide favorable conditions for microbes to grow in the rumen. The rumen of new born animals is essentially an aseptic environment; then it becomes inhabited with microbes obtained from a variety of sources such as adult animals, feed, water and soil inadvertently consumed. Since microbes have the principal role in feed digestion within the rumen, their presence cannot be separated from animal nutrition and physiology (Hoover & Miller 1991).

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To date, most studies have focused on efforts to change the structure of the rumen microbiota to increase feed efficiency through dietary manipulation and probiotics (Castillo-Lopez et al. 2018; Schofield et al. 2018). However, often the results of interventions are not long lasting and success rates tend to be variable between treatments. The short-term effect and the variability of the intervention may result from a resistance effect of the established rumen microbiota to the given perturbations. The established rumen microbial community is well-adapted to specific or historical conditions within the rumen. A study by Weimer et al. (2010) indicated the effect of host specificity and the plasticity of rumen microbial community to re-establish after major perturbation. Also, unsuccessful rumen interventions using probiotic inoculations might be linked to already occupied rumen niches being targeted by the inoculant (Weimer 2015). The established rumen microbiota can be shaped for certain periods by external factors such as the geography, feeding regime, and production system or host factors such as age, breed, health condition and performance (Stewart et al. 1997; Henderson et al. 2015; Li & Guan 2017; Belanche et al. 2019; Delgado et al. 2019). Intra-ruminal establishment starts as soon as the early rumen colonisation occurs, since some of the microbes commonly found in the mature rumen have been observed in the rumen of three day-old calves (Rey et al. 2014). Therefore, early colonisation, along with the environmental conditions, plays an important role in shaping the specific or unique profile of the rumen microbial community. Thus, an animal background (place of origin and background diet) needs to be considered as a course or source of variation in the rumen microbiota. There is limited literature covering this topic, and this is particularly the case with differing breeds of cattle and under subtropical, tropical and remote environments as experienced in much of northern Australia. This thesis will investigate the dynamic changes in rumen bacterial profiles according to probiotic use, diet change and farm practices, while considering the animal’s background. It is hypothethised that rumen bacterial composition (particularly the core community) will be changed by the addition of probiotic at an early age and by dietary change at older age. Rumen bacterial changes were investigated at three different life stages, i.e. early life, post-weaning, and in the adult ruminant. The first study (Chapter 3. Changes in the rumen microbiota of dairy calves supplemented with Bacillus amyloliquefaciens H57) investigates the effect of probiotic inoculation in early life. The H57 probiotic was administered in milk replacer to modify the rumen microbial community in pre-weaning dairy calves when the rumen microbial community was at an early stage of development. During the post weaning period, the effect of dietary supplementation on rumen microbial composition was investigated (Chapter 4. Effect of post weaning supplementation on the rumen microbial populations of Brahman-cross heifers in Northern Australia). In addition, variation of the rumen microbiota between animals of differing properties of origin was investigated. With these adult cattle,

41 the focus was on changes in the rumen microbiota of cattle being transitioned to feeding on transitory floodplains in the Northern Territory (Chapter 5. Factors affecting the rumen microbiota profile of culled cows during transition period on Northern Australia floodplain). In this study, changes in rumen microbial diversity of cows from differing backgrounds (property of origin/birthplace), performance status (initial body weight and body condition score) and physiological condition (age, teeth condition and body size) were observed during the transition period to feeding on floodplain pasture. Community profiling and functional metagenomic prediction from 16S rRNA gene sequence data was used to provide evidence of changes in populations and functional profiles of the ruminant microbial community. The results from this thesis provide information regarding impacts of probiotics, diet and farm practices in modifying the rumen microbiota and presents implications for future manipulation of the microbial community, based on the resilience and plasticity inherent in the microbiome of the rumen.

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Chapter 2. General Materials and Methods

2.1. Introduction This chapter describes the experimental and analytical procedures used in more than one experiment. Procedures that were specific to one chapter and variations that occurred with animals, their feed and experimental designs between experiments are described in each experimental chapter.

2.2. Rumen fluid (RF) sample collection Rumen fluid samples were collected using the stomach tube method (Klieve et al. 1998; Schofield 2017). A custom-made stomach tube consisted of 1.5 m Powaflex TPR hose (Advanced Industrial Products, Darra, QLD, Australia) with an internal diameter of 8 mm for calves and 20 mm for adult cattle. A custom-made brass filter with 1 mm pore diameter holes was connected to the end of the hose for use with calves only. With adult cattle, 1 mm diameter holes were made directly in the last 10 cm at one end of the hose. A plastic pipe with 50 cm in length and 50 mm diameter was used as a gag to protect the hose. The rumen fluid contents were obtained by applying suction to the tube via a 60 mL catheter tip syringe (Terumo Medical Corporation, Somerset, NJ, USA) or double action hand pump with a 2200 cm3 tube capacity (Wanderer, Darwin, NT, Australia) into a plastic side arm flask, for calves and adult cattle respectively. Rumen fluid samples from calves were then placed into 50 mL sterile plastic tubes (Techno Plas Pty. Ltd., SA, Australia), while for adult animals, the collected rumen fluid was screened using one layer of nylon stocking to remove the solid particles. To minimise contamination between animals, separate pieces of tubing and syringes were used for calves in Treatment and Control groups. The nylon stocking was only used once and the plastic flask for rumen fluid collection was washed and thoroughly cleaned between sample collections before being used for other animals. Four aliquots of 1 mL rumen fluid were transferred into the 1.5 mL sterile microcentrifuge tubes (Eppendorf, Hamburg, Germany). The Eppendorf tubes were centrifuged at 13,793 xg for 10 min and 20 min for calves and adult animals, respectively (Heraeus Biofuge 13; Radiometer, Copenhagen, Denmark). The supernatant then was discarded, and the pellet flash frozen in the liquid nitrogen for transportation to the laboratory. The samples were stored at -80oC until the DNA extraction process.

2.3. DNA Extraction Genomic DNA (gDNA) was extracted from rumen fluid samples using the Repeated Bead- beating and Column method (RBB+C) described by Yu and Forster (2005). Possible bias through extracting DNA from samples at different times (different days) was reduced by randomising samples

43 prior to extraction using the RAND function (Microsoft® Excel® 365 64-bit; (Microsoft Corporation, Washington, USA). First, the RF pellets were thawed and resuspended in 1 mL of Lysis buffer (2.9% NaCl, 0.6% Tris, 0.05 M EDTA pH 8.0, and 4% SDS). In addition, precipitated Lysis buffer needed to be heated at 37oC prior to use. The cell suspension was then transferred into a 2 mL cryotube (Thermo Fisher Scientific Australia Pty Ltd, Victoria, Australia) containing 0.5 g of 0.1 mm zirconia beads and homogenised for 3 min in a bead beater (BioSpec Product Inc., Bartlesville, USA). The samples were then incubated at 70oC for 15 min, then centrifuged in a refrigerated centrifuge (Eppendorf Centrifuge 5415R, Eppendorf, Hamburg, Germany) at 16,100 xg at 4oC for 5 min and the supernatant transferred to a 1.5 mL sterile microcentrifuge tube kept on ice. The pellet was resuspended in 300 µl of Lysis buffer and the process repeated. The two supernatants were pooled into a final volume of approximately 1.3 mL. A 260 µL aliquot of 10 M ammonium acetate was added to the supernatant, mixed well and incubated on ice for 5 min to precipitate contaminating proteins and polysaccharides. The samples were centrifuged at 16,100 xg at 4oC for 10 min and the supernatant transferred to a new tube. The supernatant was weighed, and equal volumes transferred into two microcentrifuge tubes. To each tube an equal volume of isopropanol was added, mixed well and incubated at room temperature for 30 min. Tubes were centrifuged using a refrigerated microcentrifuge at 16,100 xg for 15 min. Then the supernatant was discarded, and 1 mL of 70% ethanol was added to the tubes. The tubes were inverted several times and centrifuged for 5 min. The supernatant was removed, and the pellet dried in a vacuum desiccator (Eppendorf Concentrator 5301, Eppendorf, Hamburg, Germany) at 60oC for 3 min. The dried nucleic acid pellet was then dissolved in 100 µL of TE buffer. The two aliquots of dissolved nucleic acid were pooled for each sample (approx. 200 µL). The nucleic acid solution was then purified using QiAmp® DNA Mini Kit (QIAGEN, Doncaster, Victoria) according to the manufacturer’s instructions with the gDNA eluted into a final volume of approximately 200 µL. The concentration and purity of DNA samples were determined prior to sequencing. The DNA concentration was measured using Qubit® with dsDNA HS Assay Kit (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s instructions using the Qubit® 2.0 Fluorometer (Invitrogen, Carlsbad, CA, USA). This assay used Qubit® 0.5 mL assay tubes (Cat. no. Q32856), Qubit® dsDNA HS Reagent, Qubit® dsDNA HS Buffer, and two Qubit® dsDNA HS Standards in TE buffer with concentrations of 0 ng/µL and 10 ng/µL. The working solution was prepared by diluting the HS Reagent 1:200 in HS Buffer. Then 190 µL of working solution was added into the standard tubes and 198 µL for the assay tubes. The standard solution was made by pipetting 10 µL of dsDNA HS Standards in TE buffer into the 190 µL working solution in the designated standard tube. The standard and working solutions were mixed by vortexing for 2-3 seconds without creating bubbles. For preparing samples, 2 µL of sample was

44 added into 198 µL of working solution in the assay’s tubes. The sample tubes were mixed well by vortexing and incubated at room temperature for two minutes. The Qubit® 2.0. Fluorometer was calibrated using the standards prior to samples assays and then the samples analysed. In addition, agarose gel electrophoresis was performed to confirm the results obtained from the Qubit® dsDNA HS Assay. The Gel Electrophoresis (GE) was done using 1 % SeaKem® LE Agarose (Lonza, Rockland, ME, USA) in 1x TAE buffer (AppliChem, Darmstadt, Germany) containing 1 % GelRed® (Biotium, Fremont, CA, USA). To prepare the gel solution 3 g of agarose powder placed into 500 mL Schott bottle (Duran, Wertheim, Germany) and a 300 mL volume of 1x TAE Buffer added. The solution was microwaved until all of the agarose powder was completely dissolved. The agarose was allowed to cool for several minutes but was still in liquid form when the 30 µL of GelRed was added and gently mixed. After placing the appropriate sample comb into the gel tray, the Agarose solution was poured into gel tray and allowed to set at room temperature for 30 min. The sample comb was removed, and the gel placed in the GE apparatus was filled with 1x TAE Buffer. A 1 µL aliquot of 5x loading dye (Thermo Fisher Scientific, Massachusetts, USA) was mixed with 5 µL of sample and pipetted into a well in agarose gel and 5 µL of 1 Kb DNA Ladder (GeneRuler®, Thermo Fisher Scientific, Massachusetts, USA) was pipetted into the first and the last wells. Finally, the GE apparatus was connected into power pack FisherbrandTM FB300 (Fisher Scientific, Thermo Fisher Scientific, Massachusetts, USA) powered with 95V and 300-400 mA for 45 minutes. The DNA was visualised under UV and an image taken using the Gel Doc Documentation System (Bio-Rad, USA). Approximately 20 µL of extracted gDNA (final extraction product) was pipetted into a labelled 1.5 mL microcentrifuge tube for sequencing sample submission. The gDNA concentration per sample was in the range 10 – 50 ng/µL. Samples of gDNA were transported frozen to either the Australian Centre for Ecogenomics (ACE; University of Queensland, Brisbane, QLD, Australia) or the Australian Genome Research Facility (AGRF, St. Lucia, Queensland, Australia) for microbial diversity sequencing. In total, 240 gDNA samples from four animal trials were submitted for microbial diversity sequencing. The gDNA samples from dairy cattle (Chapter 3.) were sequenced for 16S rRNA gene amplicon sequencing (926F - 1392R; V6-V8 region) at the ACE, whilst gDNA samples from adult cattle (Chapter 4 and Chapter 5) were sent to the AGRF for 16S rRNA gene amplicon sequencing (341F - 806R; V3-V4 region) of the 16S rRNA genes. The primers used were different between Chapter 3 and Chapter 4&5. The different primers were used for different experiments which were conducted at different times and with different sequencing providers. While this was not the preferred option, it was unavoidable and as a consequence, there was no direct comparison between the sequencing results presented in different chapters.

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2.4. Bioinformatic Analysis Most of the bioinformatic analysis was done using the QIIME (Quantitative Insight Into Microbial Ecology) pipeline version 1.9.1 (Caporaso et al. 2010b). Briefly, the QIIME workflow consisted of Pre-analysis for quality filtering, Upstream analysis, and Downstream analysis (Figure 2.1.). The pre-analysis mainly deals with quality filtering and combining the reads. Quality filtering was mainly done using Trimmomatic version 0.36 (Bolger et al. 2014). The upstream analysis processes the output from the pre-analysis into an Operational Taxonomic Unit (OTU) table and phylogeny tree. Meanwhile the downstream steps focus on the statistical analysis and interpretation of results.

2.4.1. Quality Filtering and Upstream Analysis Quality filtering and size trimming of raw sequencing reads was undertaken using Trimmomatic version 0.36 (Bolger et al. 2014). During the filtering process, the adapter and the poor- quality sequences (lower than Phred 33) were removed. The sequencing reads then were trimmed to retain sequences of a set minimum length (either 200 or250 bp). The minimum read length varied on each trial according to the quality of the sequences. The quality of trimmed reads was checked using FastQC version. 0.11.5 (Andrews 2010). The trimmed forward and reverse reads then were joined using fastq-join software in QIIME (Aronesty 2013) or Seqprep software (https://github.com/jstjohn/SeqPrep) and multiplexed into a seqs.fna file using multiple_split_libraries_fastq.py script in QIIME (Caporaso et al. 2010b). The upstream analysis processes the multiplexed reads into an OTU table and creates a phylogenetic tree. Highly related sequences were clustered into OTUs (OTUs Picking) using the open reference method and a 97% threshold of similarity against the Greengenes database version 13_8 (DeSantis et al. (2006);http://greengenes.lbl.gov).Taxonomy was then assigned using Uclust software (Edgar 2010) with taxonomic reference from Greengenes version 13_8 and 97% similarity (DeSantis et al. 2006). while taxonomy assignment using 16S SILVA database version SSU 128 was done using Basic Local Alignment Search Tool (BLAST) software (Altschul et al. 1990) in QIIME with 7 level majority taxonomy (Quast et al. 2013). On occasion, when further taxonomy assignment was required for specific OTUs, a search using the National Center for Biotechnology Information (NCBI; https://www.ncbi.nlm.nih.gov/) 16s rRNA database with the BLASTn website-based program (Altschul et al. (1990); https://blast.ncbi.nlm.nih.gov/Blast.cgi) was undertaken.

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Figure 2.1. The overview of bioinformatic analysis that was performed in this study.

The sequences were also aligned to a Greengenes template alignment (85_otus.fasta; http://greengenes.lbl.gov) using PyNAST software (Caporaso et al. 2010a) with using a 75% minimum threshold of identity. Chimeric sequence artefacts generated during the sequencing process were identified using ChimeraSlayer method in QIIME (Caporaso et al. 2010b; Haas et al. 2011). The chimeric sequences were then removed from the library based on the chimera list from chimeric identification process (Caporaso et al. 2010b). Any gaps in the sequences were filtered out using a 16S alignment lane mask step (Lane 1991) prior to generating the OTU table and phylogeny tree (Caporaso et al. 2010b). An OTU table was then built in QIIME using output files of OTU’s picking, chimera identification, and taxonomy assignment (Caporaso et al. 2010b). A threshold of 0.005% relative 47 abundance was applied to the OTU table to minimize the number of infrequent, potential error reads (Bokulich et al. 2012). The phylogenetic tree was generated in QIIME from clean representative set fasta file using the Fastree method (Price et al. 2009; Caporaso et al. 2010b).

2.4.2. Downstream Analysis Downstream sequence analysis consisted of a rumen microbial diversity analysis, taxonomy profiling, and generating core data sets of the rumen microbial community. Downstream analysis was performed using QIIME in Biolinux version 8.0.7 (Field et al. 2006) through VirtualBox version 5.2.22 (Oracle Corporation, California, USA). The diversity of the total rumen microbial community was investigated using alpha and beta diversity analysis. The diversity of rumen microbial within group of animals was measured using alpha diversity analysis. Alpha diversity was measured using four diversity measures: Observed OTUs, Chao1, Phylogenetic diversity, and the Shannon-Wiener index (Lemos et al. (2011); http://scikit- bio.org/docs/latest/generated/skbio.diversity.alpha.html). Moreover, an alpha rarefaction script was also performed to take into account differing levels of sequencing depth (number of sequence reads obtained per sample) (Caporaso et al. 2010b). Alpha rarefaction was defined within the same sampling depth to provide a fair alpha diversity comparison between each sample group (Gotelli & Colwell 2001). While the beta diversity was observed using Unweighted and Weighted UniFrac measures through QIIME (Caporaso et al. 2010b). Unweighted and Weighted UniFrac measures were used to analyse the difference in the presence and abundance of taxa between the treatment groups, respectively and generated a distance matrix (Lozupone et al. 2011). The sampling depth for beta diversity was based on the lowest number of sequencing reads, therefore. the sampling depth was different for each experimental dataset and is reported separately. Taxonomic profiling of the rumen microbial community was undertaken for both the core and non-core rumen microbial community at phylum and genus level. While the core dataset was defined as the OTUs present in all samples (100% shared) within a given group of samples, identified using the QIIME pipeline (Caporaso et al. 2010b). Once the core OTUs were identified, new OTU tables containing only these core OTUs were generated and the taxonomy was assigned according to 16S Greengenes or SILVA databases (Caporaso et al. 2010b). The generated core OTU tables were then used for taxonomic profile comparison between metadata categories. The changes in the appearance of unique core OTUs was also investigated in this study (Chapter 4 and Chapter 5). The unique core OTUs were defined as the OTUs that were only found in animals within a specific metadata category, for example sample collection time. Unique core OTUs were identified at initial sampling time by discarding the shared core OTUs between animal groups using Microsoft® Excel® 365 64-bit (Microsoft Corporation, Washington, USA). The appearance of 48 unique core OTUs then were tracked at the next sampling time to determine whether they were passed within or between animal groups. Principal Coordinate Analysis (PCoA) plots, box plots, heatmaps, and diagrams (bar, venn, and line) were generated to visualize the results of the downstream analysis. PCoA plots (2D) were generated to visualise the spatial distribution of rumen microbial communities, using distance matrices obtained during beta-diversity analyses (Qiime version 1.9.1, Caporaso et al. (2010b)). Box plots and diagrams were generated to visualize the findings of the alpha diversity and taxonomic profiling of the rumen microbial community. Box plots and diagrams were made using Microsoft® Excel® 365 64-bit (Microsoft Corporation, Washington, USA). Venn diagrams were also generated using interactive web tool (http://bioinformatics.psb.ugent.be/webtools/Venn/), while heatmaps were generated in QIIME to visualise the relative abundant of OTUs between metadata categories (Caporaso et al. 2010b).

2.4.3. Predicting metagenome functional PICRUSt (Langille et al. 2013) and CowPI (Wilkinson et al. 2018) were used to predict the metagenome functional profile of the bacterial populations identified using the 16S rRNA gene data. This analysis was done through the Aberystwyth University’s Galaxy website: https://share- galaxy.ibers.aber.ac.uk following the CowPI protocol (Wilkinson et al. 2018). According to CowPI protocol, the representative set fasta file was then assigned using Usearch software (Edgar 2010) against a 16S sequences database from Global Rumen Census (GRC) (http://www.rmgnetwork.org/global-rumen-census.html) and from fully sequenced genomes of rumen microbes. The OTUs that matched with rRNA sequence from the GRC or genomes were then merged with the OTU table and the number of total OTUs for each rRNA sequence was counted. The OTU abundance was then normalized by a pre-calculated file to take into account the 16S copy number per genome. Metagenome functional profile was predicted from normalized OTU data using the Kyoto Encyclopaedia of Genes and Genomes Orthology (KO) from 497 rumen microbial genomes (Kanehisa & Goto 2000). The predicted KOs then were categorised into tier 1-3 KEGG pathways with a tab-delimited table of predicted KEGG pathway as the final output.

2.5. Statistical Analysis The significant differences in alpha diversity indices and the relative abundance of rumen microbial communities in accordance to metadata categories were analysed by Analysis of Variance (ANOVA) or repeated measures using SAS software v9.4 (SAS Institute, USA). Repeated measures were performed with Residual Maximum Likelihood (REML) method and Ante-dependence order 1 was applied as covariance structure for modelling the correlation between time points based on the 49 repeated measures within animals. Duncan post hoc test and Least Significant Differences (LSD) were done to test the differences between means in ANOVA and repeated measure, respectively. Means with the same letter were not significantly different at the 5% significance level (P<0.05). In addition, ANOVA was also used to compare OTU frequencies across sample groups with additional Benjamini- Hochberg false discovery rate (FDR) correction (Qiime version 1.9.1; Caporaso et al. (2010b)), to reduce the number of false positive results from the analysis (Storey & Tibshirani 2003). The differences in beta diversity measures and the predicted functional metagenomic profile were analysed using permutational analysis of variance (PERMANOVA), analysis of similarities (ANOSIM), and two-sided Welch’s t-test. The differences in Unweighted and Weighted UniFrac indices were statistically tested by PERMANOVA (Anderson 2017) and Analysis of Similarity (ANOSIM) (Anderson & Walsh 2013) using 5% significance level (P<0.05) (Qiime version 1.9.1; Caporaso et al. (2010b)). The degree of segregation between animal’s groups or metadata categories was analysed using ANOSIM in QIIME by generating a R value (Caporaso et al. 2010b). An R value close to 1 was defined as being completely segregated and small values (close to zero) indicating no segregation (Clarke & Warwick 2001). The differences in predicted metagenomic profiles between animal groups were analyzed using a two-sided Welch’s t-test with Benjamini-Hochberg FDR correction using STAMP software v2.1.3. (Parks et al. 2014).

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Chapter 3. Changes in the Rumen Microbiome of Dairy Calves Supplemented with Bacillus amyloliquefaciens H57

3.1. Introduction

3.1.1. Background The rumen of newborn calves is generally recognised as an aseptic environment. Later, microbes from outside the animal colonise the rumen, and the rumen develops as a fermentative organ. However, However, the first rumen colonisation reported occurs in utero and during delivery from the mother (Hobson & Stewart 1997; Alipour et al. 2018) and continues developing until established as in adult animal (Rey et al. 2014). However, During the early period of colonisation, the ruminal microbial community is in an unstable condition (Lucas et al. 2007). The unstable microbial community provides an opportunity for pathogens to enter and proliferate in the rumen thus leading to a reduction in the health and performance of the calves (Frizzo et al. 2011b). Moreover, good health in early life is important to reduce the mortality rate and the costs associated with the use of antibiotics and additional feed. Since 1950, antibiotics were used as growth promoters in domestic animals (Animal growth promotants - AGPs) for increasing feed utilisation efficiency. Antibiotics were also used to reduce enteric infections, and to prevent metabolic and fermentative disorders (Page 2006). However, there is public concern regarding the use of AGPs in animal production. Particularly, relating to increasing antimicrobial resistance and the transference of the resistance genes from animal to human microbiota (Wegener 2006). In fact, more than 60% of 1,415 human pathogenic microorganisms are transmissible from animal to human (Wegener 2006). Probiotics are known as alternatives to antimicrobials. In general, probiotics are used for controlling and maintaining the health and diversity of gut microbiota, especially, in the first week of an animal’s life, when the microbial community in the digestive tract is not well established (Kocher 2006). Probiotics prevent the proliferation of pathogenic microorganisms by stimulation of early rumen microbial establishment and creating a more favorable ecological condition for the rumen microbial community (Chaucheyras-Durand & Durand 2009; Uyeno et al. 2015). Moreover, stable ruminal conditions support the growth of the dominant fermentative rumen microorganisms, increasing growth performance and lowering the incidence of disease (Timmerman et al. 2005; Agazzi et al. 2014). Bacillus is a genus of gram-positive bacteria that are commonly used as probiotics. The important properties from this genus are the ability to produce antimicrobial metabolites and form spores. Bacillus spp. produce antimicrobial compounds that can inhibit multidrug-resistant bacteria 51 such as Staphylococcus aureus, S.epidermidis, Streptococcus pyogenes, and Enterococcus faecalis (Chalasani et al. 2015), while the spore-forming ability protects Bacillus from harsh environment conditions (Riesenman & Nicholson 2000). Bacillus amyloliquefaciens strain H57 (H57) is one of the species in the genus Bacillus that has been shown to improve the performance of Dorper ewes (Le et al. 2017b) . Another study showed the benefit of probiotic H57 by incorporating H57 probiotic in starter pellets by increasing the live weight gain and reducing the risk of diarrhea in dairy calves during the early-life transition period from milk to weaning diet (Le et al. 2017a). Moreover, the H57 probiotic changed the structure and function of the rumen microbial community in dairy calves during the weaning period (Schofield et al. 2018). Based on the success of the previous studies with H57 probiotic in altering rumen microbial communities in sheep (Schofield et al. 2018) and increasing performance and maintaining the health of dairy calves during the weaning period (Le et al. 2017), the current study investigated incorporating the H57 probiotic into the milk replacer of pre-weaned dairy calves. Thus, it was expected that providing H57 probiotic in the early life of dairy calves would have a greater impact on the rumen microbial community and be of greater benefit to animal performance and health status. Rumen development and establishment of microbial populations is crucial in shaping calf performance. Frizzo et al. (2010) showed the positive link between rumen microbial inoculation and rumen development. Therefore, improving rumen development is essential in increasing animal growth rate (Borhami et al. 1967) and lowering the incidence of disease (Timmerman et al. 2005; Agazzi et al. 2014). It was hypothesized that the addition of H57 probiotic during the time of early-life gut colonization would alter the rumen microbiome, providing benefits to animal health and performance.

3.2. Methods

3.2.1. Animal Ethics and Experimental Location The experiment was approved by the Animal Welfare Unit, The University of Queensland, approval number SVS/064/15/ARC. The trial was undertaken over eight weeks starting from 26 June 2015. The trial was conducted at Queensland Animal Science Precinct (QASP) and the Dairy Unit at the University of Queensland, Gatton. DNA extraction was undertaken at the Department of Agriculture and Fisheries (DAF) laboratories at the EcoSciences Precinct, Brisbane.

3.2.2. Preparation of probiotics The H57 probiotic for this study was produced in a 100L fermenter at the University of Queensland (Gatton Campus, Australia) following the protocol of Schofield (2017). In brief, following fermentation, the bacterial cells (mainly spores) were separated from the fermentation product using

52 a Sharples Centrifuge AS26 at 20 000g (Sharples Separator Works, West Chester, Pennsylvania, USA). The pellets of spores and bacterial cells were then resuspended and mixed with a carrier (bentonite) with the particle size approximately 150 µm. The mixed inoculum was then frozen at -20oC, freeze- dried, and powdered in a mortar and pestle (Le 2017). Milk replacer (Ridley Agriproducts Pty Ltd, Melbourne, Australia), with addition H57 probiotic, was subsampled fortnightly and tested for viability (colony forming units (CFU)) to confirm that the H57 probiotic concentration in the milk replacer was approximately 108 CFU/L throughout the trial period (Le 2017).

3.2.3. Experimental Design and Treatments Animals used in this study were selected from the dairy herd at the University of Queensland (UQ) dairy farm, Gatton Campus. There were 22 purebred Holstein-Friesian calves (8 heifers and 14 bulls) with an average liveweight of 39.9kg (± 4.9kg). The calves were separated from their dam within 24 hours of birth, housed in individual pens (1.6m width x 2.2m length) and fed with colostrum two times per day using an artificial teat (Peach Teats, Daviesway Ltd, Victoria, Australia) for the first two days at the UQ dairy farm. At day three the calves were transported to Queensland Animal Science Precinct (QASP), UQ Gatton Campus, and kept in the individual pens (1.5m width x 2.1m length) equipped with plastic barriers on the shared walls to minimise any cross-contamination between animals. The calves were paired on the basis of same-sex then randomly and equally distributed to two treatments (T1: Control and T2: probiotic supplementation) and three cohorts. The H57 probiotic was mixed into milk replacer at a dosage of 108 CFU/L milk replacer. The treatments were given for eight weeks (56 days). The calves were individually fed using an artificial teat with 6 L/d of milk replacer +/- H57 in the morning (07.00 h) and afternoon (16.00 h) each day. Solid feed consisted of pellet (Ridley Agriproducts Pty Ltd, Melbourne, Australia) and wheat chaff offered ad libitum. The afternoon milk ration started to be withdrawn on day 53 until weaning on day 56. On day 56, the calves were moved to a grazing paddock at the UQ dairy farm. The chemical compositon of the experimental diet was analysed by Feed Central Pty Ltd (Charlton, Queensland, Australia) using standard wet chemistry procedures. The chemical composition of the experimental diet is presented in Table 3.1. Milk replacer was made specifically for this trial by Ridley Pty. Ltd. The formulation was essentially based on their commercial product Dairyvite® premium milk replacer,without the addition of mannan oligosacharide and yeast. The product contained full cream milk powder, skim milk powder, whey powder, whey protein concentrate, butter milk powder and other unspecified “dairy powder.

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3.2.3. Rumen Fluid Sampling and DNA extraction Rumen fluid sampling and DNA extraction were described in Chapter 2, General Methods. Briefly, rumen fluid was taken from each calf at the end of the trial (day 56), 4h after morning feeding using a stomach tube with the protocol as described in (Klieve et al. 1998) with modification in sample filtration using a layer of nylon stocking. DNA extraction was done in accordance with the protocol from Yu and Forster (2005). Extracted DNA was analysed for purity and concentration by gel electrophoresis and the Qubit® dsDNA HS Assay Kit and measured with the Qubit® 2.0 Fluorometer (Invitrogen, Carlsbad, CA, USA). The Gel Electrophoresis was done using 1% SeaKem® LE Agarose (Lonza, Rockland, ME, USA) in 1x TAE buffer (AppliChem, Darmstadt, Germany) contained 1% GelRed® (Biotium, Fremont, CA, USA). Then 20 µL of extracted DNA from each animal was transported to the Australian Centre for Ecogenomics (ACE; University of Queensland, Brisbane, QLD, Australia) for the 16S rRNA gene amplicon sequencing (V6-V8 region) using the MiSeq sequencing platform (Illumina, San Diego, CA, USA).

Table 3.1. Chemical composition of the experimental diet† Component (%) Pellets Chaff Control milk H57 milk Metabolisable energy (MJ/kg DM) 14.2 9.10 19.3 19.3 Dry matter 89.3 89.7 - - Crude protein* 20.8 5.70 25.5 25.5 Crude fat* 5.80 2.00 18.8 18.8 Neutral detergent fiber 14.1 59.5 - - Acid detergent fiber 8.20 36.1 - - Total digestible nutrients 84.9 60.4 - - Starch 36.6 1.30 - - Lignin 0.90 3.60 - - Ash 6.30 8.30 - - Calcium 1.10 0.20 - - Magnesium 0.22 0.15 - - Phosphorus 0.49 0.16 - - Sulfur 0.28 0.11 - - Sodium 0.29 0.53 - - H57 probiotic, cfu spore/L - - - 2.01 x 108 † Source: Le (2017), * Laboratory analysis.

3.2.4. Quantitative PCR The quantification of H57 strain was performed using a quantitative real-time Polymerase Chain Reaction (qPCR) assay (Yong et al. 2013). The assay was designed to quantify B. amyloliquefaciens using primers and probe targeting a specific region of the pgsB gene (Table 3.2). A reaction solution with a total volume of 25 µL was made with the 1x RealMasterMix probe (5 PRIME, Gaithersburg, MD, USA). primer and probe were used with concentrations of 0.9 µM and 0.05 µM, respectively. Quantitative real-time PCR amplification of H57 was performed on a Rotor-Gene RG-

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6000 (Qiagen, Valencia, CA, USA) in triplicate with 5 µL of template DNA. Amplification was performed according to protocol by Yong et al. (2013) with an initial denaturation at 95°C for 30 s, continued by 40 cycles of denaturation at 95°C for 5 s and annealing and extension at 60°C for 34 s. The standard curves for H57 probiotic (1010 to 104 cells mL-1) were prepared according to the protocol by Schofield (2017).

3.2.5. Bioinformatic analysis The V6-V8 region of the 16S rRNA genes were amplified using using universal primers for bacteria and archaea (itag926F and itag1392wR; Table 3.2) and the resulting amplicons sequenced by ACE using the Illumina MiSeq platform (Illumina Inc., San Diego, CA, USA). The quality of the sequences obtained were checked using FastQC ver. 0.11.5 (Andrews 2014) and trimmed to a minimum length of 250 bp using Trimmomatic version 0.36 (Bolger et al. 2014). The reverse sequences failed the quality checks and so only the forward reads were used for further analysis. Although only the forward reads were analysed in this study, the quality of the analysis was not affected, with the forward sequence reads of greater that 200bp in length, providing sufficient information for assignment of microbial taxonomy. The QIIME pipeline was able to filter, align, and merge forward reads independently without using paired-end reads, as successfully reported elsewhere (Caporaso et al. 2010; Bokulich et al. 2012; Bolger et al. 2014).

Table 3.2. Specific PCR primers and probe for Bacillus amyloliquefaciens. Primer Sequence (5’-3’) Target Annealing References gene temperature itag926F AAACTYAAAKGAATTGRCGG 16S rRNA 50.0 (Engelbrektson et al. 2010) itag1392R ACGGGCGGTGWGTRC 16S rRNA 57.1 (Engelbrektson et al. 2010) pgsB726-f TGGCGCCATGAGAATCCT pgsB 58.7 (Yong et al. 2013) pgsB791-r GCAAAGCCGTTTACGAAATGA pgsB 58.9 (Yong et al. 2013) pgsB- aFAM- pgsB 69.3 (Yong et al. 2013) probe CGCTGCTCAGCACGAAGGAGC- TAMRAb a FAM (6-carboxy-fluorescein). b TAMRA (6-carboxy-tetramethylrhodamine).

The sequences were classified based on 97% similarity and grouped into Operational Taxonomic Units (OTUs) using the QIIME pipeline version 1.9.1 (Caporaso et al. 2010b). Singletons were removed and OTU abundance was filtered with a threshold of 0.005% to remove very low abundance, erroroneous reads from the sequencing process (Bokulich et al. 2012). The sampling depth for alpha rarefaction and beta diversity analysis was defined according to the minimum number of sequencing reads obtained for all samples, which was 24,386 reads. Alpha diversity analysis was performed with four alpha diversity indices (Chao1, Observed OTUs, PD whole tree, and Shannon- 55

Wiener) to reveal the diversity within the sample (De Barbieri et al. 2015b). Alpha rarefaction was also performed for all four alpha diversity indices using the rarefied OTUs table in accordance to the defined sampling depth. The diversity between samples (beta-diversity) was analysed using the weighted and unweighted Unifrac indices (Lozupone et al. 2011). An OTU table of the core microbiome was generated for each treatment group (i.e. H57 and Control groups). The core microbiome was defined as the species of rumen microbial that were present in all samples (100%). The core OTUs table for each treatment category then was merged into an OTUs table for futher core diversity analysis. The alpha diversity and beta diversity were performed for core microbiome with the similar protocol and metrics as described previously for the non-core dataset. The metagenome functional profile of the microbiome data set was predicted using PICRUSt (Langille et al. 2013) with modified assignment using a “rumen-specific” database in CowPI workflow through the Galaxy website: https://share-galaxy.ibers.aber.ac.uk (Wilkinson et al. 2018). The genes of predicted metagenome functional were defined by the Kyoto Encyclopedia of Genes Genomes (KEGG) Orthology (KO) then collapsed into KEGG pathways (Kanehisa & Goto 2000). The protocol for predicting metagenome function was CowPI (Wilkinson et al. 2018), an adapted PICRUSt workflow (Langille et al. 2013) in Galaxy software https://share-galaxy.ibers.aber.ac.uk. The representative sequences obtained from the OTUs picking and the non-core OTUs table were used as input files for predicting the metagenome function (further described in Chapter 3, General Methods). Analysis of variance (ANOVA) with a Duncan post hoc test, was performed to show the significant differences in rumen bacteria populations and alpha diversity indices using SAS version 9.4 (SAS Institute, USA). Analysis of performance was also used to investigate the effect of feed supplementation on the abundance of OTUs, by comparing the OTUs frequencies across sample groups with Benjamini-Hochberg false discovery rate (FDR) correction. The statistical differences in rumen bacterial profiles between treatments (Weighted and Unweighted UniFrac) were tested using Analysis of Similarity (ANOSIM) through the QIIME pipeline (Caporaso et al. 2010b). Principal Coordinate Analysis (PCoA) was used to visualize the value of the Weighted and Unweighted UniFrac distance matrix. The differences in metagenomic profiles between treatment groups were analysed using a two-sided Welch’s t-test through STAMP software v2.1.3. (Parks et al. 2014).

3.3. Results

3.3.1. Quantification of H57 probiotic in the rumen Quantitative real-time PCR showed that the concentration of H57 probiotic in rumen fluid from both the Control and supplemented animals were less than 104 cells mL-1. Moreover, the precise 56 number of colonies of H57 probiotic in rumen fluid samples cannot be distinguished because it was below the lowest concentration of H57 standards that were used. The standard curves for H57 probiotic that were used in the present study were 104 to 1010 cells mL-1.

3.3.2. Rumen microbiome profile The total number of sequences remaining after filtration to a 0.005% minimum abundance were 1,832,289 sequences with the mean and the minimum number of sequences being 83,285 and 24,386 respectively. The average number of OTUs for the H57 and Control treatment groups were 842 and 833, respectively. Moreover, sequences were classified taxonomically using the Greengenes 16S rRNA gene database version 13_8 (DeSantis et al. 2006) into 11 phyla, 18 classes, 20 orders, 27 families, and 47 genera. The diversity within the sample for each alpha diversity index was visualized with alpha rarefaction curves (Figure 3.1). The rarefaction curves indicated that species richness and biodiversity tended to be slightly higher in calves fed H57 compared to the Control calves. However, analysis of variance of the alpha diversity indexes showed that there was no significant difference in biodiversity (Shannon-Wiener and Phylogenetic distance) and species richness (Chao1 and biodiversity) of the rumen microbial community of the Control calves and calves fed H57 (Table 3.3).

Figure 3.1. Alpha rarefaction graphics for each alpha diversity matrix of the rumen microbiome in calves fed H57 ( ) and Control calves ( ) 57

Table 3.3. Differences in alpha diversity indexes between calves fed H57 and Control calves (mean ± SEM) Treatment Alpha diversity matrixes Control H57 Chao1 779 ± 140 823 ± 143 Phylogenetic distance 44 ± 6.97 45 ± 6.65 Observed OTUs 664 ± 130 701 ± 118 Shannon-Wiener index 6.42 ± 0.55 6.45 ± 0.43

The differences in the rumen microbial communities between the treatment groups were observed using beta diversity analysis. The ANOSIM analysis showed there was a significant difference between the treatment groups in microbial communities according to the Unweighted UniFrac analysis (P=0.019; R=0.156). This analysis indicated that significant differences occured in the rumen microbial community of dairy calves between the two different treatment groups, with these differences due to the presence/absence of certain taxa. However, the PCoA plot of Unweighted UniFrac index showed the rumen bacterial community was not well clustered between heifers in the Control and H57 groups (Figure 3.2). While the ANOSIM analysis for weighted UniFrac matrix showed a non-significant result between treatment groups, indicating that the more dominant microbial communities were similar between the treatment groups.

Figure 3.2. PCoA plot of rumen microbial community in calves between treatment groups in accordance to Unweighted UniFrac index.

At phylum level, the mean relative abundance of rumen microbial community was dominated by Bacteroidetes (43.5%), Firmicutes (23.8%), and Proteobacteria (22.4%). In addition to these three 58 phyla, in total there were seven phyla that categorised as dominant phylum with more than 1% relative abundance such as Actinobacteria (3.44%), unidentified phylum (2.81%), Euryarchaeota (1.07%), and Spirochaetes (1.04%). However, there was no significant difference in the relative abundance of rumen microbial communities between treatment groups when the OTUs were classified at the higher, phylum level. Further analysis (ANOVA) at lower levels of taxonomic classification showed that 57 OTUs were present at significantly different abundances (P<0.05) in between groups (Figure 3.3). While in accordance to FDR P value there was no difference in the abundance of OTUs between treatment groups.

Figure 3.3. Heatmap of rumen microbial OTUs with significant difference in their abundance in accordance to the treatment groups (P<0.05). Each row represents the OTUs name within genus (g_) level with the lighter color showing the more abundance OTUs.

When classified below phylum level, the rumen microbial community was dominated by populations belonging to Prevotella (relative abundance 36.59%), Succinivibrio (relative abundance 59

18.21%), the family Lachnospiraceae (relative abundance 4.49%), an undefined genus of family Veillonellaceae (relative abundance 4.19%), Ruminobacter (relative abundance 3.78%), the order Bacteroidales (relative abundance 3.67%), and the order Clostridiales (relative abundance 3.11%). The list of dominant rumen microbes, with average relative abundance > 1% can be seen in Figure 3.4. The analysis of variance showed there was no difference in relative abundance of dominant rumen microbial genera in dairy calves between treatment groups.

Figure 3.4. The relative abundance of dominant genera rumen microbial in dairy calves in accordance to probiotic treatments. The letter before rumen microbial name is showing taxonomy level (o=order; f=family).

3.3.3. Rumen core microbiome profile The core rumen microbiome for each treatment group was defined as the rumen microbial OTUs that were present in all rumen fluid samples within the group, and of this core there were 206 OTUs. Analysis of variance revealed that there were only six OTUs (Greengenes Taxonomy IDs 594186, 582136, New.Reference OTU334, 579174, 694713, and New.Reference OTU885) which were significantly different in their abundance between the H57 and Control groups (Figure 3.5) (P<0.05). However, when the ANOVA included the FDR correction, there was no significantly different OTUs. Therefore, the abundance of core OTUs between treatment group was similar.

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Figure 3.5. Heatmap of rumen microbial OTUs with significant difference in their abundance in accordance to the treatment groups (P<0.05). Each row represents the OTUs name within genus (g_) or family (f_) level with lighter color showing the higher abundance OTUs.

At phylum level, the core rumen microbial community was dominated by Bacteroidetes (mean relative abundance 41.30%), Proteobacteria (mean relative abundance 32.08%), and Firmicutes (mean relative abundance 21.29%). Apart from these dominant phyla, the core microbial community was comprised of Actinobacteria (mean relative abundance 4.06%), Euryarchaeota (mean relative abundance 1.12%) and unidentified phyla (relative abundance 0.15%). The result of ANOVA indicated there was no difference in the population of core rumen microbial phyla in dairy calves with different probiotic treatments. At genus level, rumen microbial community was dominated by Prevotella (mean relative abundance 36.35%) and Succinivibrio (mean relative abundance 28.85%). Within dominant genera with more than 1% abundance, the relative abundance of Prevotella, family Paraprevotellaceae, and Shuttleworthia was significantly different between the treatment groups

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(Figure 3.6). Dairy calves receiving the H57 probiotic had a greater number of Prevotella compared to the dairy calves in the Control group, relative abundance of 46.43% and 26.27%, respectively (P<0.01). Calves that received H57 probiotic also had significantly higher abundance of the genus Shuttleworthia in their rumen (P<0.05). While family Paraprevotellaceae was observed to be more abundant in calves within the Control group compared to probiotic calves, relative abundance of 4.67% and 0.14%, respectively (P<0.05).

Figure 3.6. The relative abundance of dominant core genera rumen microbial in dairy calves in accordance to probiotic treatments. The letter before rumen microbial name is showing taxonomy level (o=order; f=family). **= very significantly different (P<0.01); *= significantly different (P<0.05)

3.3.4. Predicted functional metagenome The programs CowPI and PICRUSt were used to predict the functional metagenome within the rumen microbial community of the dairy calves. Based on tier 2 KEGG pathways, there were 39 pathways annotated from the non-core OTUs. According to their mean relative abundance, the pathways were dominated by amino acid metabolism (mean 10.22%), carbohydrate metabolism (mean 9.98%), replication and repair (mean 9.50%), membrane transport (mean 8.95%), translation (mean 6.70%), and energy metabolism (mean 5.34%) (Figure 3.7). Further statistical analysis showed there were three metabolism-related pathways that were significantly affected by probiotic treatments (FDR P<0.05). The dairy calves fed H57 probiotic had a rumen microbiome with a

62 significantly higher number of predicted functional genes involved in the metabolism of amino acids and nucleotide metabolism, compared with dairy calves in the Control group, with mean abundances of 12.12% vs 11.75%; and 4.79% vs 4.64%, respectively. In the core dataset, the metabolism-associated tier 2 KEGG pathways that were significantly affected by the probiotic treatments were enzyme- related gene and lipid metabolism (FDR P<0.05). Calves fed H57 probiotic had a significantly higher abundance of enzyme-related genes compared with the Control calves, 2.80% vs 2.55%, respectively, while the genes for lipid metabolism were observed to be higher in Control calves (3.00%) than in calves fed H57 probiotic (2.80%). Further exploration and classification to the tier 3 KEGG pathways of enzyme-related genes, showed that calves fed H57 probiotic had significantly more peptidase enzymes when compared to the Control calves, with 2.20% and 2.1% for H57 and Control groups, respectively.

Figure 3.7. Composition of KEGG pathways tier 2 predicted functional metagenome of non core rumen microbial community within all treatment groups. Only pathways with relative abundance more than 1% are shown. *Represent significant difference in relative abundance of functional genes between control and H57 groups (FDR P<0.05).

3.4. Discussion In the previous study, pellets inoculated with probiotic H57 were shown to significantly improve the performance and health condition of pre- and post-weaned dairy calves (Le et al. 2017). 63

Therefore, the attempt to deliver probiotic H57 through milk replacer, in the early life of dairy calves (three days old) was expected to give a more significant improvement (Le 2017). However, the current study showed no significant improvement in the performance and health status of pre-weaned dairy calves following the addition of probiotic H57. Within the performance parameters measured, addition of probiotic H57 did not improve dry matter intake (1283 vs 1282 g DM/d), daily weight gain (876 vs 874 g/d), case of diarrhoea (0.90 vs 1.20), and duration of diarrhoea (1.05 vs 1.15) when compared to the control calves (Le 2017). In addition, the haematology, plasma hormones, and plasma haptoglobin parameters were also not affected by the addition of probiotic H57 to the milk replacer (Le 2017). Le (2017) concluded, the efficacy of delivering probiotic in liquid diet for neo natal calves was affected by oesophageal groove mechanism and initial health status. In this study, the effect of incorporating the probiotic H57 into the milk replacer of young calves in order to alter the structure of the rumen microbiome, was investigated. Although the presence of H57 probiotic in the rumen was hard to determine, the results showed changes in the rumen microbiome of calves following eight weeks of daily feeding of H57 probiotic. There are three main issues that may have impacted on the use of H57 as a probiotic and its effect on the early rumen microbiome, when administered in the milk starter. Firstly, the oesophageal groove mechanism presents in the pre-ruminant, secondly, the interaction between the early rumen microbial colonies, and thirdly, the effect of bioactive compound that may be produced in fermenter during culture of inoculum. In addition to the 16S rRNA genes analysis, a qPCR test was done to count the number of H57 cells inside the rumen of experimental calves. The analysis was also conducted to verify the H57 population data that was obtained from 16S rRNA analysis. The result of qPCR indicated the population of H57 was below the minimum detection threshold of 1 x 104 cell mL-1, and therefore could not be differentiated from non-specific PCR products. Moreover, analysis of 16S rRNA genes from the ruminal population was not able to detect any member of the family Bacillaceae. A previous study using H57 probiotic determined that to be detected in the rumen, the population of H57 would need to be higher than 6.7 x 104 cells mL-1 (Schofield 2017). Sampling of larger volume and concentration of rumen fluid samples prior to qPCR may increase the numbers of H57 cells detected. We were also anticipating an increase of H57 numbers. If this had occurred and H57 and multiplied in the rumen, then number would have increased considerably (e.g. up to 107 cell more per mL), in which case the relatively low level of detection used in the study, would have been adequate for H57 enumeration. In this trial, H57 probiotic was delivered during the pre-rumination (0-3 weeks) and transitional phases (3-8 weeks). In those periods, the calves are still in pre-ruminant condition and

64 their rumen has the oesophageal groove to accommodate liquid form feed (Wardrop & Coombe 1960; Hofmann 1993). Therefore, liquid feeds, i.e. milk, mostly bypass the rumen through the oesophageal groove into the abomasum. On the other hand, ingested solid feeds mainly pass into the reticulorumen for digestion (Warner et al. 1956). Thus, the incorporation of H57 probiotic into milk replacer may mean that most of the probiotic bypassed to the abomasum instead of into the rumen and H57 was unable to establish within the developing rumen. A similar result was reported by a recent study by Zhang et al. (2017), who tried delivering alternative probiotics to calves using milk replacer. In that study, 16S rRNA gene sequencing showed that both probiotics (L. plantarum and B. subtillis) were undetectable within the rumen (Zhang et al. 2017). The similarity in the alpha and beta diversity results may be reflecting the low population density of H57 probiotic in the rumen or the unsuccessful result of H57 probiotic in colonising the rumen of calves. All alpha diversity indices showed the addition of H57 probiotic did not affect the species richness and biodiversity of the calf rumen microbiome (Table 3.3). While, the result of beta diversity analysis study indicated that feeding H57 probiotic significantly affected the presence or absence of certain taxa in rumen microbial community between treatment groups, however, the statistics supporting this finding were not strong (for example, the PCoA plot of Unweighted Unifrac index showed only a weak effect of H57 probiotic; Figure 3.2). Some researchers have proposed incorporating probiotics in the solid feed to increase the success rate of delivering and establishing populations of probiotics within the rumen (Ushakova et al. 2013; Zhang et al. 2017), especially for application to young ruminants, such as a calves. Incorporating the probiotic into dry feed should avoid the esophageal groove mechanism and there would be more probiotic delivered into the rumen. The result from the previous trial showed that incorporation of H57 probiotic into pelleted feed increased populations of H57 in the rumen of calves beyond the qPCR threshold detection with an average density of 3.59 x 104 ± 2.06 x 103 cells mL-1 (Schofield et al. 2018). However, the study by (Schofield et al. 2018) also showed that the number of H57 detected in the rumen was quite low compared to the number of H57 in the pellets, which was as high as 2.85 x 109 CFU kg-1. Therefore, there may be another issue that hampered the establishment of H57 in the rumen. Interaction between the members of the early rumen microbial community may be affected by the growth of H57 in the rumen. Evidence has shown that pioneer bacterial species shape the rumen microbiome in the early life of calves (Rey et al. 2014). By two days of age, the rumen has been shown to be colonized by a pioneer bacterial community transferred from the immediate environment (Guzman et al. 2015). Rey et al. (2012) reported that in the early colonization of the rumen, the established pioneering bacterial community altered the redox potential inside the rumen. As a

65 consequence, this change allowed for the colonisation by other bacteria, as reported in the human gut (Favier et al. 2003). Oxygen may be available in the rumen of calves during pre-ruminant condition. As an aerobic organism, B. amyloliquefaciens consumes oxygen in the rumen rapidly for growth (Lei et al. 2015). Moreover, the consumption of oxygen creates the anaerobic conditions within the rumen that permits anaerobic species such as Lactobacillus spp. to grow well (Yu et al. 2009; Lei et al. 2015). Consequently, anaerobic conditions inside the rumen suppress the growth of B. amyloliquefaciens and other aerobic species (Priest et al. 1987; Rey et al. 2014). This mechanism may explain the divergence in the core rumen microbiome profile of calves by 8 weeks of age (Figure 3.6). However, the actual mechanism still needs more exploration, particularly assessing the population densities of H57 probiotic in the gut in the early period of H57 probiotic supplementation. In the current study, Bacteroidetes, Firmicutes, and Proteobacteria were identified as the dominant phyla in the rumen of dairy calves across treatment groups. This result was consistent with a report on the establishment of the ruminal bacterial community in dairy calves (Rey et al. 2014). At week eight in the current study, Bacteroidetes, Firmicutes, and Proteobacteria occupied 42.9%, 23.4%, and 23.4% of the total community, respectively. The proportion of these dominant phyla at eight weeks of age was similar to the proportions reported by Jami et al. (2013). Rey et al. (2014) reported that the population of Proteobacteria started to decline within three days of birth and reached the lowest level 15 days after birth. Conversely, the population of Bacteroidetes increased during this period and became the most dominant phylum (Rey et al. 2014). A similar finding was reported in goats with more gradual changes in the proportion of Proteobacteria and Bacteroidetes (Wang et al. 2016). Between seven and 90 days after birth, Proteobacteria dominated in the rumen of newborn goats then gradually decreased, while the abundance of Bacteroidetes continually increased with age. Reflecting on the previous study of H57 by Schofield et al. (2018), the findings of the core rumen microbial population in the current trial may also have indicated the impact of the addition of H57 probiotic to rumen microbial population although it remained below detectable limits. The populations of the genera Prevotella, Shuttleworthia, and unclassified genera of family Paraprevotellaceae in the core rumen microbial community were significantly different between the H57 and Control groups. Prevotella and Shuttleworthia were significantly higher in calves fed H57 probiotic compared with Control calves. On the other hand, calves within the Control group had more abundance of family Paraprevotellaceae. The correlation between the treatment of calves with the H57 probiotic and the relative abundance of Prevotella, Shuttleworthia and Paraprevotellaceae in this study, was not clear since there was no improvement in calves performance with H57 probiotic addition (Le 2017). Genus

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Prevotella (previously classified as Bacteroides) is one of the predominant genera in the digestive tract of ruminants, particularly the rumen of high-performance animals (Indugu et al. 2017; Dias et al. 2018). Taxonomy assignment of the most abundant OTU of Prevotella (OTU 572743) against National Center of Biotechnology Information (NCBI; Altschul et al. (1990)) showed this genus was 94% identical with Prevotella albensis (Avgustin et al. 1997). Prevotella albensis was classified as Prevotella ruminicola and known as one of the major proteolytic bacteria species in the rumen (Sales-Duval et al. 2001). Similar with P. ruminicola, Prevotella albensis also possesses peptidase activity to break down protein and peptides in rumen (Wallace et al. 1997a; Walker et al. 2005). Shuttleworthia was also observed to be more abundant in rumen of calves given H57 probiotic. This genus has previously been found to be more abundant in ruminal liquids and solids of highly efficient cows (Jewell et al. 2015). Recent studies observed this genus as digesta-adherent rumen bacteria in dairy and beef cattle (Mi et al. 2017; Noel et al. 2017). The addition of H57 probiotic also significantly decreased the population of family Paraprevotellaceae. The relative abundance of Paraprevotellaceae in the rumen has previously been shown to be influenced by diet (Pitta et al. 2014b) and a genus of family Paraprevotellaceae has been found more abundant in solid fraction of low residual feed intake dairy cattle (Jewell et al. 2015) but may not necessarily be related to increased feed efficiency (Myer et al. 2015). As the H57 probiotic was not able to reach detectable levels or establish within the rumen of calves in the current experiment, the mechanisms of H57 probiotic in influencing some rumen microbial populations might be indirect, through the absorption of extracellular compounds onto the spores. Pan et al. (2014) reported the absorption of carboxymethylcellulase during late sporulation stage will be absorbed by the spore matrix. The absorption process occurs due to the hydrophobic and electrostatic of the spore surface (Wiencek et al. 1990). This spore surface display system also proved able to maintain the stability of protein molecule i.e. β-galactosidase against extreme environment condition (Sirec et al. 2012). A recent genomic study of Bacillus amyloliquefaciens H57 showed a number of genes related to the production of antimicrobial compounds (ballomycin D, surfactin, Fengcyin, and iturin), and glycoside hydrolase enzyme families 1, 43, and 13 for complex carbohydrate degradation (Schofield et al. 2016). Chowdhury et al. (2015) and Shi et al. (2018) reported synthesis of antimicrobial compounds such as surfaction, fengcyin, iturin, and bacillomycin D from Bacillus amyloliquefaciens sp. CowPI and PICRUSt analysis indicated higher peptidase genes in core rumen microbial community and higher amino acid metabolic pathways in the non-core rumen microbial community of H57 calves. Prevotella was the most dominant genus in core and non-core rumen microbial community of calves fed H57 probiotic. Further taxonomy assignment of the highest abundance OTU

67 belonged to Prevotella (OTU572743) and using the NCBI database indicated that this genus was 94% identical with P. albensis. The genus Prevotella has been commonly found in animals with a high grain diet and has a role in breakdown of protein and amino acid (Sales et al. 2000; Fernando et al. 2010). The report about the nitrogen utilization of Prevotella goes back to Pittman and Bryant (1964), who found that Bacteroides ruminicola utilized peptide nitrogen or ammonia nitrogen for growth. Prevotella spp. are known as the predominant proteolytic bacteria in the rumen with a great diversity of extracellular proteolytic activities among them (Wallace et al. 1997b; Griswold et al. 1999). Walker et al. (2005) identified a pepD peptidase enzyme from Prevotella albensis. In addition, Prevotella spp. produces the diaminopeptidyl peptidases (DAPs), an enzyme which accelerates amino acids fermentation and ammonia production by ‘hyper-ammonia producing’ rumen bacteria (HAP) (Bath et al. 2013). This finding may be reflected with the higher peptidases’ gene in the core rumen microbial community of calves fed H57 probiotic that was predicted by CowPI and PICRUSt analysis. Thus, the results may explain the finding of higher predicted functional genes of amino acids metabolism in non- core rumen microbial community of calves given H57 probiotic. In agreement with the findings reported in the current study, Schofield et al. (2018) reported no significant effect of dietary H57 probiotic in modulating the rumen microbial community of pre- weaned dairy calves. However, dietary H57 supplementation significantly increased live weight gain, and was 20% greater than in control calves (Le et al. 2017). Considering rumen fermentation parameters, calves fed H57 probiotic had a greater concentration of valerate and plasma β-hydroxy butyrate at week 12 and 19, respectively (Le et al. 2017). Therefore, a similar rumen microbial community may have different effects, due to differences in gene expression, on rumen fermentation outcomes in the different treatment groups.

3.5. Conclusion There was an impact of addition of H57 probiotic to the core rumen microbial population and predicted functional genes rumen microbiome of preweaning dairy calves. Calves fed H57 probiotic had more abundance of Prevotella and Shuttleworthia in their core rumen microbial community. The predicted metagenomic profile from 16S rRNA genes also showed calves given H57 probiotic significantly had more genes related with amino acid metabolism and enzyme. However, the mechanism behind this impact was still unclear due to the low number of H57 probiotic in the rumen of calves given milk replacer which failed to reach the detectable levels in the rumen. The low number of H57 probiotic in the rumen indicated that the probiotic was not well established in the rumen of pre-weaning calves given the probiotic in milk replacer.

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Chapter 4. The Effect of Post Weaning Supplementation on the Rumen Microbial Populations of Brahman-Cross Heifers in Northern Australia

4.1. Introduction The rangeland vegetation in tropical regions, such as in the Northern Australia, is mainly dominated by low quality C4 native grasses (Ash & Mcivor 1995) such as Astrebla spp. (Mitchell grass),

Heteropogon contortus (Black spear grass), and Themeada triandra (Kangaroo grass) (Parker & Cobiac 2002). The nutritional characteristics of native grasses such as Astrebla spp. is that they are low in crude protein and of low digestibility, particularly during the dry season (Ian 2011). Therefore, beef producers in this region tend to early wean stock due to the low feed availability. The main objective of early weaning is to maintain the body condition score of the cows during the dry season. Although benefiting the productivity of herd, early weaning is a source of distress to the physiology and behaviour of the cow and calf (Ungerfeld et al. 2011). Weaning is an important event in rumen microbial development. During weaning, calves face a change in their diet from primarily liquid to solid feed, which also impacts on the rumen microbial community (Rey et al. 2014). The rumen microbial community plays an essential role in fermenting plant fibre into volatile fatty acids and producing microbial protein (Van Soest 1994), developing the immune system (Bauer et al. 2006) and accelerating rumen development as the digestion organ through volatile fatty acids production (Suarez et al. 2006; Jiao et al. 2015). Therefore, rumen microbial development in early life is not only important in improving calf health but also for determining downstream performance (Weimer 2015). The abrupt change in feed and the stress during early weaning will interrupt the rumen microbial community development (Enríquez et al. 2011; Conlon & Bird 2015). In the long term, this interruption may negatively impact the productivity of the animals (Oyedipe et al. 1982). In addition to the impact of weaning, the profile of the rumen microbial community can vary based on the animal’s original condition (i.e. farm practices, feeding regime, breeding management, and climate condition). The diversity present in the adult rumen microbial community may be related to rumen microbial development occurring during the pre-weaning period (Yáñez-Ruiz et al. 2015). The rumen microbial development begins after birth with microbial colonization from the dam and the surrounding environment (Curtis & Sloan 2004). In addition, a study by Rey et al. (2014) showed that some members of the mature rumen microbial community have been observed in the undeveloped rumen. Therefore, early rumen colonization has an important role in shaping of a mature rumen microbial community.

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Feed supplementation is widely practiced in northern Australia to reduce the negative impact of early weaning on animal performance (Moriel et al. 2014). Post weaning supplementation strategies supplying rumen degradable N, fermentable carbohydrate, and rumen degradable protein can minimise the negative impacts of early weaning (Silva 2017). Moreover, supplementation with urea-based supplements have resulted in the maintenance of liveweight (LW), whilst a mixture of copra meal plus corn grain supplement markedly increased LW gain (Silva 2017). Although expected, there is still no information to confirm that the improvement in performance also contributes to, or is influenced by, changes in the rumen microbiome post weaning. In the present study, the diversity of the rumen microbiome in animals from differing backgrounds was investigated. The resilience of the unique microbial profile in each background group (animal’s origin and weaning weight) were investigated when the animals were reared in communal pens for six months with the same diet. In addition, the effect of feed supplementation on the profile of the rumen microbiome was studied by adding copra meal and corn to the diet. It was hypothesised that the feed supplementation would be the major driver in shaping the rumen microbiome.

4.2. Methods

4.2.1. Animal Ethics This experiment and all the procedures involved were in accordance with the Australian Code of Practice for the Care and Use of Animals for Scientific Purposes and was approved by the Animal Ethics Committee of the Charles Darwin University with the approval number A14002.

4.2.2. Experimental Location The experiment was conducted over a 22 weeks period at the Katherine Research Station (KRS) in Katherine, Northern Territory, Australia (latitude14°28′0″S, longitude 132°16′0″E, elevation 108m). The climate in the Katherine region is classified as tropical, with a wet season from November until April and a dry season from May until October. The temperature (minimum and maximum) and average rainfall in the Katherine region during this study (May-December 2014) were 12.2-37.4oC and 55.8 mm, respectively. The laboratory-based analyses were done at the Department of Agriculture and Fisheries (DAF) laboratories, Ecosciences Precinct, Dutton Park, Queensland.

4.2.3. Experimental Design and Treatments This study, including the feeding trial and rumen fluid sampling, was part of the PhD study of Silva (2017) but the rumen fluid samples were analysed for their microbiome as part of this PhD. Forty Brahman crossbred heifers from two commercial cattle stations (Kalala and Hayfield) in the Katherine 70 region were selected. Prior to relocation, the animals were ear tagged, dehorned, dewormed (Dectomax® injectable), and vaccinated (Ultravac® botulium and Websters® 5 in 1). All drug administration and handling were standard management practices of KRS. Upon arrival at KRS, sixteen heifers (eight from each station) were randomly selected for baseline rumen fluid sampling. Whilst the remaining 24 heifers were grouped according to supplementation treatment and were sampled for rumen microbial profile at the end of the trial. The heifers were curfewed for 15 h with water available and then grouped into the weaning weight (WW) groups: light weaning (LWW; 118 ± 6 kg liveweight) and heavy weaning (HWW; 183 ± 6 kg liveweight). Each WW group consisted of a balanced number of animals from Kalala and Hayfield stations. The age of these cattle was unknown and hard to predict due to the nature of the management practices employed at the Kalala and Hayfield stations. The differences in weight could be caused either the age of the calf or differences in the milk production of their dam. Generally, in the Northern Territory, differences in weaning weight are due to different age as reflected from the accepted weaning protocol in the cattle industry. There are three groups of weaning age that are widely accepted by the industry which are radical weaning (under 3 months old), early weaning (3-4 months of age), and traditional weaning (4-8 months of age) (Tyler et al. 2012) and it is expected that the light heifers are approximately 4-5 months of age and the heavier heifers 6-7 months of age. Rumen fluid samples were obtained then as baseline samples. After that, the remains 24 heifers were adapted to feed and handling for three weeks. During the first week of adaptation, animals were kept in the holding paddock and fed with Sabi grass hay (Urochloa mosambicensis) ad libitum and 100 g /hd.day copra meal. The copra meal was given to increase the body condition score of the animals. The animals were then moved to a Sabi grass pasture paddock with ad libitum water and fed 100 g /hd.day of copra meal for the next two weeks of adaptation. The experiment was a randomized block design with three factors as treatments. The treatments consisted of two stations (Kalala (K) and Hayfield (H)), two weaning weights (LWW and HWW), and two supplementation levels (T1= without supplementation and T2 = T1 + 10 g /kg LW.day of feed supplement), with three animal replicates. Animals were randomly arranged in 12 pens based on the combination of WW and supplementation treatments. Each pen consisted of the same number of animals from Kalala and Hayfield Stations. The Pen arrangement was from a PhD study (Silva 2017) with a total of 30 pens with a combination two WW and five levels of supplementation and three replicates (pens) (2x5x3). Only the control (T1) and the supplement level (10g/kg W.day; T2) were sampled for rumen microbiota analysis). The feeding trial was conducted over 22 weeks during the dry season. Control feed consisted of Sabi grass hay and a mineral block (Rumenvite® 30% urea + P block; Ridley Agriproducts, Toowoomba, QLD, Australia), both offered ad libitum. Sabi grass hay was

71 chopped prior to feeding, to approximately 2 cm in length using a NDE1402 chopper (New Direct Equipment, Sioux Falls, SD, USA). All animals consumed diets readily but as they were group pens of 8 or 10 heifers/pen there was no opportunity to measure individual intake. The supplement provided was copra meal until week eight and then cracked corn was added into the supplement at 50% (as fed) from week nine until the end of the trial. The corn was added to the supplement because the daily intake of copra meal did not reach the expected voluntary intake. The supplement was offered daily at 07.30 am then checked at 17.00 pm and refilled as necessary. The amount of supplement given per day was adjusted weekly based on the average liveweight for each pen at the start of each week. The nutritional composition of feeds is presented in Table 4.1.

Table 4.1. Nutritional information of feed1 Nutrient contents Sabi grass hay Mineral Block Cracked Copra meal (g/Kg DM2) Lick corn Rumevite Organic matter 897 - 935 935 Crude protein 38 890 78 180 Neutral detergent fibre 612 - 152 506 Phosphorous 1.6 20 2.3 4.7 Calcium 2.8 70 0.1 0.7 1 Source: Silva (2017); 2 DM - Dry Matter

4.2.5. Rumen Fluid Sampling Rumen fluid sampling procedures are described in more detail in Chapter 2. General Methods. Rumen fluid samples were taken twice during the experiment using stomach tube and processed following the procedure described by Schofield et al. (2018). Rumen sampling was undertaken upon arrival of the animals at KRS, with eight animals from each station of origin selected randomly for rumen fluid sampling (total 16 samples). This first sampling was conducted to determine the initial rumen microbiome of animals based on their station of origin and weaning weight. The second rumen fluid sampling was conducted at the end of the pen phase (22 weeks). Rumen fluid was taken from 12 animals per supplementation group (total of 24 samples). Rumen fluid was collected using a stomach tube and hand pump according to Klieve et al. (1998), with minor modification in sample filtration using a layer of nylon stocking. The tubes and plastic flask were washed with clean water after each animal collection. The collected rumen fluid was then screened using a layer of stocking into 15ml plastic bottle and chilled to 4°C. Then four aliquots of 1 mL rumen fluid were transferred into the 1.5 mL Eppendorf tubes and centrifuged at 13,000 g for 20 min. The supernatant then was removed and the pellets were stored at -20 °C for transporting to the laboratory for DNA extraction.

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4.2.6. DNA Extraction and 16S rRNA Gene Sequencing DNA extraction was done using the repeated bead beating plus column method (Yu & Forster 2005) as detailed in Chapter 2, General Methods. Following extraction, purification and quantitation, DNA was sent to the Australian Genome Research Facility (AGRF, St. Lucia, Queensland, Australia) for 16S rRNA gene amplicon sequencing. The 341F – 806R (V3-V4) region of the 16S rRNA genes were PCR amplified using the forward primer 341F (CCTAYGGGRBGCASCAG) and reverse primer 806R (GGACTACNNGGGTATCTAAT). The resulting amplicons for each sample then had unique barcodes attached, were normalised and the samples pooled before sequencing using the Illumina MiSeq platform across two flow cells. The sequences were quality control checked using FastQC ver. 0.11.5 (Andrews 2014) and paired and trimmed to Phred quality 33 and minimum length of 200 bp using Trimmomatic version 0.36 (Bolger et al. 2014). The trimmed forward and reverse reads then were joined and demultiplexed into a seqs.fna file in QIIME (Caporaso et al. 2010b).

4.2.7. Bioinformatic and Statistical Analysis The 16S rRNA gene amplicon sequences from rumen fluid samples were analysed and profiled using the bioinformatics pipeline Quantitative Insights into Microbial Ecology (QIIME) v1.9.1 (Caporaso et al. 2010b) (Section 2.4; Chapter 2. General Methods). The process was started with picking operational taxonomic units (OTUs) with Greengenes version 13_8 as a reference database (DeSantis et al. 2006). Picking OTUs was done using open reference method and a 97% threshold for sequence similarity. Taxonomy assignment was done using Basic Local Alignment Search Tool (BLAST) software (Altschul et al. 1990) against the 16S SILVA database version SSU 128 that has been formatted for QIIME user (Quast et al. 2013) with seven levels of taxonomy. The OTU table was divided by sampling time and diet treatments into the initial rumen sampling, control, and supplementation groups. The OTU table of the initial (baseline) group was to observe the initial rumen microbial profile of heifers upon arrival at the research station in accordance with their origin and weaning weight. The changes from the initial rumen microbial profile for each group of animals after six months were investigated by comparing the rumen microbial profiles to the control group. The effect of feed supplementation was observed by comparing the rumen microbial profiles between the control and supplemented groups. The core microbial community in this study was defined as the OTUs that appeared in 100 % of samples within an animal group (Section 2.4.2; Chapter 2. General Methods). OTU tables of the core microbiome for each group (baseline, control, and supplemented) were generated for each metadata category, such as Hayfield and Kalala station for animal’s origin; light weaning (LWW) and heavy weaning (HWW) for weaning weight; and control and supplemented for treatment. The OTU tables for all metadata categories were then merged into baseline, control, and supplemented groups. The 73

OTU tables for control and supplemented groups also merged to investigate the effect of feed supplementation on the core rumen microbial community, while, core OTU tables for baseline and control groups were merged to observe the changes of initial core rumen microbial community. In addition, the transmission of core rumen microbes between animals was investigated by determining the unique core OTUs after six months reared together and receiving the same diet (control diet). Unique core OTUs were generated for the baseline OTU table by discarding the common core OTUs between animal’s origin groups (Section 2.4.2; Chapter 2. General method). The diversity in rumen non-core microbial communities were investigated using alpha rarefaction, alpha diversity, beta diversity, and the taxonomy profile. While the diversity of the core rumen microbial community was investigated using the taxonomy profile. Alpha diversity analysed the rumen microbial diversity within samples using the Chao1, Phylogenetic Distance, Observed OTU’s, and Shannon-Wiener measures (Lemos et al. 2011), while, the diversity between samples was analyzed through beta diversity analysis using the weighted and unweighted UniFrac measures (Lozupone & Knight 2005). Alpha rarefaction, alpha diversity, and beta diversity were performed using the same sampling depth according to the minimum number of reads in each sample. The analysis of rumen microbial diversity within and between animal groups was based on metadata categories such as the animal’s origin, weaning weight, feed supplementation, and combinations thereof, while, the taxonomy profile was explored by comparing the OTUs’ relative abundance between animal groups and summarised into phylum and genus level. The metagenomic functional profile from the 16S rRNA gene data set was predicted using PICRUSt (Langille et al. 2013) with modified assignment using the “rumen-specific” database in CowPI software through the Galaxy website: https://share-galaxy.ibers.aber.ac.uk (Wilkinson et al. 2018). The genes of predicted metagenome functions were defined and categorised in accordance to Kyoto Encyclopedia of Genes Genomes (KEGG) metabolic pathways (Kanehisa & Goto 2000). The protocol for predicting function from the metagenome was inferred from CowPI. Representative sequences from OTUs were used as input files for predicting function from metagenomics data. More detailed protocols have been described in Section 2.4.3. (Chapter 2. General Method). Analysis of variance (ANOVA) was performed for each data set (i.e. baseline, control, baseline vs control, and control vs supplemented groups). The results of alpha diversity and taxonomy profile of baseline and control data set were analyses using ANOVA in two factorial randomised-block design with SAS software version 9.4. (SAS Institute, USA). The animal’s origin, weaning weight, and animal’s origin x weaning weight were used as the fixed effects. ANOVA for baseline and control groups was also performed to investigate the effect of feed supplementation on the abundance of OTUs by comparing OTU frequencies across sample groups using with Benjamini-Hochberg false discovery rate

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(FDR) correction to reduce the number of false positive results (Storey & Tibshirani 2003). ANOVA in a factorial randomised-block design was used for investigating the effect of feed supplementation to rumen microbial diversity by comparing the alpha diversity and taxonomy profile’s results of control and supplemented groups. ANOVA analysis for alpha diversity measures and taxonomy profiles was performed using SAS software v9.4. (SAS Institute, USA). The result of Unweighted and Weighted UniFrac measures were statistically tested by Analysis of Similarity (ANOSIM) within QIIME (Caporaso et al. 2010b). Principal Coordinate Analysis (PCoA) plots were generated to visualise the spacial differences and clustering of rumen microbial communities between animals. Differences in metagenomic profiles between treatment groups were analyzed using a two-sided Welch’s t-test with Benjamini-Hochberg FDR correction through STAMP software v2.1.3. (Parks et al. 2014). Heatmap graphics were generated to visualise the OTUs with significant differences in their abundances between groups of animals using QIIME v1.9.1 (Caporaso et al. 2010b).

4.3. Results The total number of 16S rRNA gene amplicon sequences obtained from all animals in the trial were 5,250,133. The number of sequences remaining after trimming, aligning, chimera removal, singleton filtering, clustering at 97% similarity (OTU definition) and removing OTUs below 0.005% abundance, were 1,647,885 sequences. Moreover, the number of sequences for baseline, control, and supplemented animal groups were 719,613, 506,647, and 370,876 respectively. While the minimum reads for baseline, control, and supplemented groups were 26,595, 28,168, and 24,258 sequences respectively.

4.3.1 The initial (baseline) rumen microbial community of heifers with different origins and weaning weights.

4.3.1.1. Non-core rumen microbial community 4.3.1.1.1. Rumen microbial diversity Rumen microbial diversity within and between animal’s groups was investigated using alpha and beta diversity analysis, respectively. Alpha diversity analysis was calculated using four alpha diversity measures (Chao1, Observed OTUs, Phylogenetic Diversity, and Shannon-Wiener). Alpha diversity analysis showed that the rumen microbial diversity was only affected by the original location or station from which the animals were first obtained (animal origin) (Table 4.2). There was no difference in species richness (Chao1 and Observed OTUs) and phylogenetic diversity of rumen microbial communities of heifers sourced from different stations. The Shannon-Wiener index was the only alpha diversity index that was significantly affected by animal origin. The Shannon-Wiener index 75 was higher for heifers sourced from Hayfield (P<0.05). The result of Shannon-Wiener index indicated that heifers sourced from Hayfield station had more diverse rumen microbial community compare with heifers originated from Kalala station. Furthermore, there were no significant interaction effects between animal origin and weaning weight.

Table 4.2. Differences of alpha diversity measures values (mean) of initial rumen microbial community. The significant differences between groups were determined by ANOVA analysis. Alpha diversity measures2 Animal’s group1 Chao1 Observed otus Phylogenetic diversity Shannon- Wiener Animal origin Hayfield 1,676.33 1,511.25 142 8.84a Kalala 1,608.50 1,443.13 137 8.40b Weaning weight HWW 1,654.68 1,495.38 141 8.70 LWW 1,630.14 1,459.00 138 8.54 Interaction effect Hayfield x HWW 1,721.09 1,558.75 145 8.98 Hayfield x LWW 1,631.57 1,463.75 138 8.71 Kalala x HWW 1,588.27 1,432.00 136 8.42 Kalala x LWW 1,628.72 1,454.25 138 8.37 1 HWW: Heavy weaning weight, LWW: Light weaning weight; 2 Different superscript letters within the same column indicates significant differences (P<0.05).

Table 4.3. Statistical analysis of beta diversity measures (Weighted and Unweighted Unifrac) of initial rumen microbial community. P-value was generated by ANOSIM analysis. P-value Animal’s group1 Unweighted Unifrac Weighted Unifrac Animal origin 0.001 0.002 Weaning weight 0.860 0.58 Animal origin x HWW 0.1 0.029 Animal origin x LWW 0.28 0.04 Weaning weight x Kalala 0.29 0.28 Weaning weight x Hayfield 0.083 0.015 1 HWW: Heavy weaning weight, LWW: Light weaning weight

The differences in the beta diversity between groups of animals was analysed using Unweighted and Weighted UniFrac. Beta diversity analysis showed that the Unweighted and Weighted Unifrac measures were significantly different between heifers sourced from Kalala and Hayfield stations (Table 4.3) and weaning weight had no effect. Within the interaction effects groupings (e.g. Animal’s origin x weaning weight), only the Weighted UniFrac index indicated that the weaning weight groups were significantly affected by animal origin (P<0.05). Within the animal origin groups, the effect of weaning weight on rumen microbial abundance (Weighted UniFrac) only occurred in heifers sourced from Hayfield station (P<0.05). Visualisation of beta diversity index

76 analyses showed rumen microbial profiles separated significantly between treatment groups (Figures 4.1, 4.2 and 4.3)

Figure 4.1. Differences in initial rumen microbial community of heifers based on station origins shown using PCoA plots of Unweighted UniFrac (left) and Weighted UniFrac (right). Hayfield station= , Kalala station=

Figure 4.2. Differences in initial rumen microbial community of heifers according to weaning weight x animal origin shown using PCoA plots of Weighted Unifrac. Light weaning weight (LWW; left), heavy weaning weight (HWW; right). Hayfield station= , Kalala station=

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Figure 4.3. Differences in initial rumen microbial community of heifers originated from Hayfield station according to weaning weight shown using PCoA plot of Weighted Unifrac. Heavy weaning weight (HWW)= , light weaning weight (LWW)=

4.3.1.1.2. Taxonomic profile The total number of OTUs from initial rumen fluid samples were 1,998 representing a total of 719,613 sequences. Taxonomy assignment of OTUs against 16S SILVA database resulted in 17 phyla, 29 class, 43 order, 62 family, and 159 genera of rumen microbial classification. Statistical analysis of OTUs abundance between metadata categories indicated the abundance of 70 OTUs significantly different between animals that originated from Kalala and Hayfield stations (FDR P<0.05). in addition, there were no differences in the relative abundance of OTUs between weaning weight groups. At phylum level, the profile of rumen microbial community was dominated by Bacteroidetes and Firmicutes (Appendix 1.1.). Bacteroidetes and Firmicutes made up 49.9% and 42.1% of the total abundance, respectively. Within phyla with more than 1.0% abundance, Fibrobacteres and Cyanobacteria were the only phyla that significantly differed in accordance to animal origin and weaning weight. Heifers sourced from Hayfield having significantly greater relative abundance of the phyla Fibrobacteres and Cyanobacteria (P<0.05). Heifers of HWW had a greater number of Fibrobacteres compared to heifers of light weaning weight (P<0.05). in addition, heifers from Hayfield had significantly more Fibrobacteres and Cyanobacteria in the heavy weaned than the lighter weaned Heifers. At genus level, the abundance of 11 rumen microbial genera was significantly different between animals originating from different stations (animal origin). The population of Rikenellaceae RC9 gut group was significantly more than double in heifers originating from Kalala station compared to Hayfield (28.98% VS 11.78%). The heifers from Kalala stations also had significantly higher populations of Saccharofermentas, Butyrivibrio, Lachnospiraceae AC2044 group, and an unclassified genus of family Lachnospiraceae (Figure 4.4). While the abundance of Prevotella was greater in heifers from Hayfield station (25.3%) compared to those from Kalala (14%). Other dominant genera that were 78 significantly more abundant in Hayfield station cattle were Succiniclasticum (3.21% VS 2.00%), Fibrobacter (2.02% VS 0.69%), an unclassified genus of order Gastranaerophilales (1.98% VS 0.53%), Ruminococcaceae UCG-010 (1.51% VS 0.91%), and Ruminococcaceae UCG-014 (1.39% VS 0.71%). Moreover, within 70 OTUs with significantly differed abundance between animal’s origin group (FDR P<0.05), the 50 most abundant OTUs were classified as genera Rikenellaceae RC9 gut group (16%), Prevotella (12%), family Lachnospiraceae (12%), and Ruminococcaceae NK4A214 group (10%) (Figure 4.5). The heatmap indicated that the 50 most abundant OTUs were more abundant in heifers from Kalala station.

Figure 4.4. Differences in relative abundance of dominant genus within initial rumen microbial community according to animal origins group. The differences between groups determined by ANOVA analysis with asterix superscripts after the taxon name indicating the P-value (*= P<0.05, **= P<0.01, ***= P<0.001).

Weaning weight appeared to have less of an effect on the taxonomic groups identified, than animal origin. There were only three genera significantly different between weaning weight groups; Fibrobacter, an unclassified genus of order Gastranaerophilales, and Ruminococcaceae UCG-010.

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These genera were more abundant in heifers of heavy weaning weight. The relative abundance of Fibrobacter and unclassified genus of order Gastranaerophilales were more than double in heifers with heavy weaning weight compared to heifers with a light weaning weigh, with as many as 2.08% VS 0.78% for Fibrobacter and 1.78% VS 0.80% observed for the order Gastranaerophilales, respectively (Figure 4.6). Moreover, Ruminococcaceae UCG-010 was 1.52% and 0.94% in heavy and light weaned heifers, respectively.

Figure 4.5. Differences in relative abundance of 50 most abundant OTUs of initial rumen microbial community shown using heatmap. Only OTUs with relative abundance that significantly differed between animal’s origin as determined by ANOVA (FDR P<0.05) are shown in heatmap.

There was an interaction effect between the animal’s origin and weaning weight (P<0.05) for the genera Christensenellaceae R7 group, [Eubacterium] coprostanoligenes group, Ruminococcus, Fibrobacter, and order Gastranaerophilales. In general, the population of these microbes was more

80 abundant in heifers within the Hayfield and or HWW groups (Table 4.4). The statistical analysis indicated that Christensenellaceae R7 group and [Eubacterium] coprostanoligenes group were observed lower in LWW heifers that originated from Kalala station compared with heifers within other groups. The relative abundance of Ruminococcus was higher in heifers that originated from Hayfield station. However, the population was similar for HWW and LWW groups. While Fibrobacter and order Gastranaerophilales were more abundant in heavy wean heifers sourced from Hayfield station.

Figure 4.6. Differences in relative abundance of dominant genus within initial rumen microbial community according to weaning weight group (heavy: HWW; and light: LWW). The significant differences between groups determined by ANOVA analysis with asterix superscripts after taxon name indicating the P-value (*= P<0.05, **= P<0.01).

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Table 4.4. Differences in relative abundance of dominant genera within initial rumen microbial community according to animal origin x weaning weight groups. The significant differences between groups were determined by ANOVA analysis. Relative abundance (%) Dominant Genus HfHWW HfLWW KaHWW KaLWW Christensenellaceae R-7 group 12.01ab 16.36a 14.35a 7.22b Ruminococcaceae NK4A214 group 1.89 3.38 3.17 2.36 [Eubacterium] coprostanoligenes group 1.32ab 1.93a 1.87a 1.18b Ruminococcus 1.22ab 1.06a 0.94a 1.86b Fibrobacter 3.40a 0.65b 0.76b 0.90b Different superscript letters within same row and group were showing significantly difference (P<0.05). Ka= Kalala; Hf= Hayfield; HWW= Heavy weaning weight; LWW= Light weaning weight. Only genera that were significantly affected by interaction effect are shown.

4.3.1.2. Core rumen microbial community 4.3.1.2.1. Taxonomic profile Analysis of variance of core OTUs showed that the heifers from Kalala and Hayfield stations had 234 OTUs that were significantly different in abundance (P<0.05). Within weaning weight groups, the abundance of 14 OTUs was significantly different between heifers of HWW and LWW groups (FDR P<0.05). In the present study, as many as 1,666 OTUs were defined as core OTUs and classified into 154 genera. Moreover, 21 genera were counted as dominant with more than 1% relative abundance. The core rumen microbiome was dominated by Rikenellaceae RC9 gut group (20.3%), Prevotella (18.2%), and Christensenellaceae R-7 group (13.7%). These genera accounted for 52.2% of the total core population. Ten genera varied significantly between animals from differing properties of origin (Figure 4.7). Heifers from Hayfield station had more abundance of Prevotella, Fibrobacter, Ruminococcaceae UCG-010, unclassified genus of order Gastranaerophilales, and Ruminococcaceae UCG-014. The populations of Prevotella, Fibrobacter, order Gastranaerophilales, and Ruminococcaceae UCG-014 were more than double in heifers from Hayfield compared to heifers from Kalala. The relative abundance of Succiniclasticum and Ruminococcacea UCG-010 was also higher in heifers from Hayfield compared to Kalala. While heifers sourced from Kalala station had an increased abundance of the Rikenellaceae RC9 gut group, Butyrivibrio, Saccharofermentas, Lachnospiraceae AC2044 group, an unclassified genus of family Lachnospiraceae, and Pseudobutyrivibrio. The Rikenellaceae RC9 gut group was found to be almost three times more abundant in heifers sourced from Kalala compared with heifers from Hayfield station (28.7% VS 12.0%). In addition, Lachnospiraceae and Pseudobutyrivibrio were more than twice as abundant in Kalala heifers compared with Hayfield heifers, and Saccharofermentans were nearly five times more abundant in the heifers from Kalala station. Furthermore, within 234 OTUs associated with the animal’s origin the 50 most abundant OTUs

82 were dominated by OTUs belonging to Prevotella (15.0%), Rikenellaceae RC9 gut group (12.4%), Christensenellaceae R-7 group (6.84%), and family Lachnospiraceae (4.27%) (Figure 4.8). Weaning weight only affected the abundance of two genera in the core rumen microbiome which were Fibrobacter and an unclassified genus of order Gastranaerophilales (Figure 4.9). The population of these rumen genera was more abundant in HWW heifers. The number of Fibrobacter was nearly three times more abundant in heifers of HWW compared to those of LWW; 2.22% VS 0.82%, respectively and the population of order Gastranaerophilales was nearly triple the abundance in heifers of HWW compared with LWW heifers; 1.78% VS 0.60%, respectively (P<0.01).

Figure 4.7. Differences in relative abundance of dominant genera within initial core rumen microbial community according to animal’s origin group. The significant differences between groups determined by ANOVA analysis with asterix superscripts after taxon name indicating the P-value (*= P<0.05, **= P<0.01, and ***= P<0.001).

Furthermore, the interaction between animal’s origin and weaning weight was also noticed in the core rumen microbiome community (Table 4.5). The interaction significantly affected the relative abundance of Christensenellaceae R-7 group, family BS11, Ruminococcaceae NK4A214 group, [Eubacterium] coprostanoligenes group, Fibrobacter, Ruminococcus, Ruminococcaceae UCG-010, and order Gastranaerophilales. The relative abundances of Fibrobacter, Ruminococcaceae UCG-010, and 83 order Gastranaerophilales were significantly higher in the HWW heifers from Hayfield Station (P<0.05). The difference in the relative abundance of the Christensenellaceae R-7 group between weaning weight groups was only observed in heifers originating from Kalala station being 15.95% and 7.94% in HWW and LWW groups, respectively (P<0.05). Moreover, there was no difference in relative population of the Christensenellaceae R-7 group between Kalala and Hayfield heifers. A similar finding was also observed for Ruminococcus populations occurring in heifers originating from Kalala station (P<0.05). On the other hand, the differences in populations of the family BS11, were only found in heifers from Hayfield, being 4.57% and 2.53% for HWW and LWW heifers, respectively (P<0.05), while the abundance of Eubacterium coprostanoligenes was significantly different between weaning groups, with a higher abundance in LWW heifers from Hayfield station and HWW heifers from Kalala station (P<0.05).

Figure 4.8. Differences in relative abundance of 50 most abundant OTUs of the core rumen microbial community shown using heatmap. Only OTUs with relative abundance that significantly differed between animal’s origin groups as determined by ANOVA (FDR P<0.05) are shown in the heatmap. 84

Table 4.5. Differences in relative abundance of dominant genera within core rumen microbial community according to animal origin x weaning weight groups. Significant differences between groups were determined by ANOVA analysis. Relative abundance (%) Dominant Genus HfHWW HfLWW KaHWW KaLWW Family BS11 4.57a 2.53b 2.76b 3.19ab Ruminococcaceae NK4A214 group 2.01 3.78 3.55 2.64 Ruminococcus 1.23ab 1.08b 0.93b 1.99a Ruminococcaceae UCG-010 2.24a 1.01b 0.90b 0.97b Order Gastranaerophilales 3.01a 0.74b 0.55b 0.45b Christensenellaceae R-7 group 13.02ab 18.02a 15.95a 7.94b [Eubacterium] coprostanoligenes group 1.29bc 2.03a 1.79ab 1.18c Fibrobacter 3.74a 0.66b 0.69b 0.98b Different superscript letters within same row and group were showing significantly difference (P<0.05). Ka= Kalala; Hf= Hayfield; HWW= Heavy weaning weight; LWW= Light weaning weight.

Figure 4.9. Differences in relative abundance of dominant genera within core rumen microbial community according to weaning weight group. The significant differences between groups determined by ANOVA analysis with asterix superscripts after taxon name indicating the P-value (**= P<0.01).

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4.3.2. The changes to the initial rumen microbiome following six months of feeding with control diet In this section, the changes in the rumen microbial community after six months of the feeding trial was investigated by comparing the profile of the rumen microbiome between animal groups at the second sampling time (control’s data set only).

4.3.2.1. Non-core rumen microbiome 4.3.2.1.1. Rumen microbial diversity Rumen microbial diversity was investigated using alpha and beta diversity analysis. Alpha diversity analysis of rumen microbial community was calculated using four alpha diversity measures (Chao1, Observed OTUs, Phylogenetic Diversity, and Shannon-Wiener). While beta diversity was calculated using Unweighted and Weighted UniFrac measures. The result of alpha diversity analysis showed no significant differences in all alpha diversity measures value (Table 4.6). The result of alpha diversity measures indicated there was no difference in species richness and microbial diversity within the rumen microbiome for all the treatment groups. A similar result was obtained for beta diversity analysis where there were no significant differences observed (Table 4.7). The animal’s origin and weaning weight did not affect the presence or proportion of ruminal species present in the trial animals at the end of six months.

Table 4.6. Differences of alpha diversity measures values (mean) of control rumen microbial community according to animal’s group. The significant differences between groups were determined by ANOVA analysis. Alpha diversity measures (mean) Animal’s group1 Chao1 Observed OTUs Phylogenetic diversity Shannon Animal origin Hayfield 1,693 1,552 141 8.69 Kalala 1,655 1,511 140 8.77 Weaning weight HWW 1,694.79 1,558.50 142 8.83 LWW 1,645 1,495 139 8.62 Animal origin x Weaning weight Hayfield x HWW 1,681 1,536 140 8.64 Hayfield x LWW 1,711 1,576 144 8.76 Kalala x HWW 1,708 1,581 145 9.01 Kalala x LWW 1,601 1,441 136 8.52 1 HWW: Heavy weaning weight; 2 LWW: Light weaning weight

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Table 4.7. Statistical analysis of beta diversity measures (Weighted and Unweighted Unifrac) of control rumen microbial community. P-value Group1 Weighted Unifrac Unweighted Unifrac Animal origin 0.23 0.33 Weaning weight 0.56 0.23 Animal origin x HWW1 0.68 0.83 Animal origin x LWW2 0.39 0.38 Weaning weight x Kalala station 0.21 0.59 Weaning weight x Hayfield station 0.89 0.80 1 HWW: Heavy weaning weight; 2 LWW: Light weaning weight 4.3.2.1.2. Taxonomic profile In total 1998 OTUs was observed for rumen microbial community within control group’s library. The OTUs were classified based on Silva database version 128 into 17 phyla, 29 class, 43 order, 62 family, and 159 genera. Analysis of variance to compare the OTU frequencies across sample groups showed no differences in relative abundance of OTUs between animal origin groups (FDR P>0.05). The same findings were also observed within weaning weight group; there were no differences in relative abundance of OTUs between HWW and LWW groups (FDR P>0.05). ANOVA showed no significant difference in the relative abundance of the rumen microbiome when classified at phylum level. When classified at genus level, the rumen microbiome was dominated by Prevotella (24.2%), Rikenellaceae RC9 gut group (14.3%), and Christensenellaceae R-7 group (13.1%). Within the dominant genera, significant differences occurred in the relative abundance of the Christensenellaceae R-7 group and Ruminococcus (Table 4.8). The relative abundance of the Christensenellaceae R-7 group significantly differed with animal’s origin, weaning weight, and the interaction between the animal’s origin and weaning weight. Furthermore, the relative abundance of Ruminococcus significantly differed between the weaning weight group. The relative abundance of the Christensenellaceae R-7 group was significantly higher in heifers sourced from Hayfield station compared with Kalala’s heifers (14.6% VS 11.9%). Within weaning weight groups, the relative abundance of Christensenellaceae R-7 group was higher in LWW heifers (15.5% VS 11.2%) (P<0.001), whereas Ruminococcus was more abundant in HWW heifers (1.27% VS 2.16%) (P<0.05), while within interaction group (animal’s origin X weaning weight), the relative abundance of Christensenellaceae R-7 group was highest in the LWW heifers originating from Hayfield station.

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Table 4.8. Differences in relative abundance of dominant genera rumen microbial community of heifers after six months fed control diet. P-value was calculated by ANOVA analysis. Mean of relative abundance (%) P-value1 Dominant Genus Hayfield Kalala HWW2 LWW3 AO4 WW5 AO x WW6 Prevotella 22.28 25.73 24.22 24.08 ns ns ns Rikenellaceae RC9 gut group 13.58 14.82 13.96 14.61 ns ns ns Christensenellaceae R-7 group 14.62 11.91 11.21 15.47 0.006 0.001 0.015 f_BacteroidalesBS11 5.35 4.30 5.07 4.42 ns ns ns Succiniclasticum 4.01 4.36 3.82 4.66 ns ns ns o_Gastranaerophilales 2.17 2.28 2.76 1.59 ns ns ns Ruminococcaceae NK4A214 2.02 2.16 1.89 2.35 ns ns ns [Eubacterium] coprostanoligenes 1.78 2.23 1.88 2.19 ns ns ns Saccharofermentans 1.95 2.04 2.08 1.89 ns ns ns Ruminococcus 2.00 1.56 2.16 1.27 ns 0.041 ns Prevotellaceae UCG-003 1.56 1.76 1.68 1.65 ns ns ns Fibrobacter 1.63 1.55 1.92 1.20 ns ns ns Candidatus Saccharimonas 1.08 1.58 1.35 1.36 ns ns ns p_SR1 1.17 1.39 1.14 1.48 ns ns ns Prevotellaceae UCG-001 1.70 0.90 1.25 1.27 ns ns ns Succinivibrionaceae UCG-002 2.22 0.05 1.81 0.12 ns ns ns Ruminococcaceae UCG-014 1.06 0.94 1.02 0.97 ns ns ns Ruminococcaceae UCG-011 1.04 0.92 0.83 1.16 ns ns ns 1 ns= non-significant; 2 HWW= Heavy weaning weight; 3 LWW= Light weaning weight; 4 AO= Animal’s origins; 5 WW= Weaning weight; 6 Interaction between animal’s origins and weaning weight.

4.3.2.2. Core rumen microbiome In total there were 1,759 OTUs defined as core OTUs microbiome in control group’s heifers. Furthermore, the core OTUs were classified against SILVA database into 18 phyla, 28 phyla, 41 order, 59 family, and 159 genera. ANOVA analysis of OTUs frequencies showed the abundance of 7 OTUs (FDR P<0.05) was significantly different between animal’s origin groups. In weaning weight group, the ANOVA indicated that only three OTUs had significant different relative abundance between HWW and LWW groups. When classified at phylum level, the core rumen microbiome was dominated by Bacteroidetes and Firmicutes accounting for 46.9% and 42.7%, respectively. Bacteroidetes, Firmicutes, Cyanobacteria, Fibrobacteres, SR1 (Absconditabacteria), and Saccharibacteria were the dominant phylums, with more than 1% relative abundances. ANOVA analysis indicated there was no difference in the relative abundance of dominant rumen microbes at phylum level. At genus level, the rumen microbiome was dominated by Prevotella (23.3%), Rikenellaceae RC9 gut group (14.3%), and Christensenellaceae R-7 group (14.2%) (Table 4.9). There were no significant differences in the population of dominant rumen microbiome genera in accordance to

88 animal’s origin and the interaction between animal’s origin and weaning weight (P≥0.05). Meanwhile, within weaning weight groupings, the number of Christensenellaceae R-7 group and Ruminococcus was significantly higher in HWW heifers compared to the LWW heifers.

Table 4.9. Differences in relative abundance of dominant genera core rumen microbial community of heifers after six months fed control diet. P-value was calculated by ANOVA analysis Mean of relative abundance (%) P-value1 Dominant Genus Hayfield Kalala HWW2 LWW3 AO4 WW5 AO x WW6 Prevotella 22.82 23.62 24.57 21.68 ns ns ns Rikenellaceae RC9 gut group 13.87 14.69 2.00 1.80 ns ns ns Christensenellaceae R-7 group 15.53 13.08 24.57 21.68 ns 0.004 ns Succiniclasticum 4.34 4.82 14.12 14.54 ns ns ns f_Bacteroidales BS11 4.65 3.91 11.98 16.85 ns ns ns Ruminococcaceae NK4A214 2.21 2.36 4.18 5.10 ns ns ns o_Gastranaerophilales 2.43 2.41 4.41 4.04 ns ns ns Saccharofermentans 2.12 2.24 2.08 2.55 ns ns ns [Eubacterium] coprostanoligenes 1.90 2.43 3.05 1.66 ns ns ns Ruminococcus 2.13 1.63 2.26 2.09 ns * ns Prevotellaceae UCG-003 1.56 1.65 2.03 2.38 ns ns ns Fibrobacter 1.65 1.54 2.30 1.33 ns ns ns p_SR1 1.26 1.51 1.65 1.56 ns ns ns Candidatus Saccharimonas 1.14 1.69 1.91 1.20 ns ns ns Prevotellaceae UCG-001 1.72 0.69 1.25 1.58 ns ns ns Ruminococcaceae UCG-011 1.13 1.04 1.43 1.46 ns ns ns 1 ns= non-significant; 2 HWW= Heavy weaning weight; 3 LWW= Light weaning weight; 4 AO= Animal’s origins; 5 WW= Weaning weight; 6 Interaction between animal’s origins and weaning weight

4.3.2.3. The unique core microbiome after six months of the feeding trial The core OTUs in the initial data set were filtered to retain the unique OTUs that only appeared in the rumen of heifers that originated from Hayfield or Kalala stations. There were 278 and 347 unique core OTUs identified in heifers from Kalala station and Hayfield station, respectively. The Kalala station heifer unique core microbiome was dominated by the Rikenellaceae RC9 gut group (20.5%), Prevotella sp. (10.1%), and an undefined genus of the family BS11 (5.65%). The Hayfield station, unique core OTUs were dominated by Prevotella (28.2%), Christensenellaceae R-7 group, and Rikenellaceae RC9 gut group (5.48%). After the six-month trial period, there were only 169 unique core OTUs and 178 unique core OTU observed in the rumen of control heifers from Kalala and Hayfield stations, respectively (Figure 4.11.A). Moreover, the findings also indicated that some unique OTUs were horizontally crossing between animals (Figure 4.11.B). As many as 176 of the initial unique core OTUs in heifers from Kalala were found in heifers from Hayfield station after 6 months of trial period. The initial unique OTUs in the rumen of heifers from Kalala station that were crossing to heifers from Hayfield were dominated 89 by Rikenellaceae RC9 gut group (15.9%), Prevotella (13.6%), an unclassified genus of family Bacteriodales UCG-001 (5.68%), and Saccharofermentas (5.11%). While 172 unique core OTUs in heifers from Hayfield were observed in heifers from Kalala station after the six-month feeding trial. The OTUs that transferred from Hayfield’s heifers to Kalala heifers were dominated by Prevotella (18.0%), Christensenellacea R-7 group (15.1%), and Ruminococcaceae UCG-014 (5.23%).

Figure 4.11. Venn diagram of shared unique core OTUs that were obtained at baseline sampling (initial group) in heifers and after six months of being fed the control diet (Control group). The observations were within group (A) and between groups (B) based on the animal’s origin (Hayfield and Kalala Stations)..

4.3.3. The effect of feed supplementation on the rumen microbiome

4.3.3.1. Non-core rumen microbiome 4.3.3.1.1. Rumen microbial diversity In this study, rarefaction curves indicated that species richness was greater in the control group heifers compared with the heifers that received feed supplementation (Figure 4.12). Furthermore, alpha diversity analysis using Chao1, Observed OTUs, Shannon-Wiener and Phylogenetic diversity measures indicated that heifers receiving feed supplementation had less diversity than heifers in the control group (Table 4.10).

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Figure 4.12. Rarefaction curves of species richness measures of the rumen microbial community in heifers according to feed supplementation treatments (supplemented= ; control= ).

Table 4.10. Effect of feed supplementation on alpha diversity measures of the rumen microbiome (mean±s.e.). P-value was generated by ANOVA analysis. Treatment Alpha diversity indexes P-value Control Supplemented Chao1 1,713 ± 25.36 1,382 ± 54.98 < 0.0001 Observed OTUs 1,613 ± 30.17 1,240 ± 51.50 < 0.0001 Phylogenetic Diversity 145 ± 1.86 118 ± 4.11 < 0.0001 Shannon-Wiener 8.75 ± 0.09 7.46 ± 0.32 0.0007

Beta diversity analysis showed that feed supplementation affected the presence (Unweighted UniFrac) and the relative abundance (Weighted UniFrac) of rumen bacteria. The visualisation using a distance matrix using PCoA plots indicated that rumen fluid samples clustered according to feed supplementation treatment group (Figure 4.13). Unweighted and Weighted UniFrac analysis showing the presence or absence of OTUs showed two clearly separated clusters according to feed supplementation, explaining a high percentage of the population with 39.2% and 41.7% of the variation observed, respectively. Results of the ANOSIM on the beta diversity measures (i.e. Unweighted and Weighted UniFrac) also indicated a significant effect of dietary supplementation on the microbial population and supported the separation of PCoA clusters (P<0.001).

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Figure 4.13. Differences in rumen microbial community between Control and Supplemented heifers shown using Unweighted (A) and Weighted (B) UniFrac. Supplemented= ; Control=

Taxonomic profile There were 877,523 trimmed, joined, and demultiplexed reads clustered into 1,998 OTUs in total for the Control and Supplemented groups. Furthermore, taxonomy assignment using SILVA database version 128 showed the OTUs pertained to 17 phyla, 29 class, 43 order, 62 family, and 161 genera. Further taxonomic profiling was done by comparing the relative abundance of rumen microbiome at phylum and genus level. The Analysis of variance to compare the OTU frequencies across sample groups showed that the relative abundance of 302 OTUs was significantly different between treatment groups (FDR P<0.05). At the phylum level, Firmicutes and Bacteroidetes dominated the rumen microbiome community and accounted for 47.5% and 40.5%, respectively. Of the dominant phyla (≥1% relative abundance), feed supplementation significantly increased Firmicutes and decreased Bacteroidetes, Cyanobacteria, and Fibrobacteres (Appendix 1.2.). The effect of feed supplementation also affected the relative abundance of minor phyla (<1% abundance). Feed supplementation reduced the overall microbial diversity and therefore the number of phyla observed, however the phylum Actinobacteria was found to increase following feed supplementation. In contrast, the relative abundance of Chloroflexi was not affected by feed supplementation (Appendix 1.2.). At the genus level, the rumen microbiome was dominated by Christensenellaceae R-7 group (18.9%), Prevotella (17.17%), and Rikenellaceae RC9 gut group (14.9%). The 50 most abundant OTUs were dominated by Prevotella (18.87%), order Gastranaerophilales (7.62%), Rikenellaceae RC9 gut group (6.95%), family BS11 (6.29%), Prevotella UCG-003 (5.96%), and [Eubacterium] coprostanoligenes

92 group accounted for 5.30% (Figure 4.14). Furthermore, applying feed treatments into post weaning heifers significantly affected the population of 11 genera of rumen microbes.

Figure 4.14. Differences in relative abundance of 50 most abundant OTUs in Control and Supplement Groups. Only the significantly affected OTUs between diet groups as determined by ANOVA (FDR P<0.05) are shown in heatmap.

Feed supplementation significantly increased populations of the Christensenellaceae R-7 group (P<0.001), Ruminococcus (P<0.05), Ruminococcaceae UCG-014 (P<0.01), and Lachnospiraceae XPB1014 group (P<0.05) (Figure 4.15). The relative proportion of the genus Christensenellaceae R-7 group nearly doubled with feed supplementation from 13.14% to 25.14 %. Feed supplementation also increased the number of Ruminococcus nearly three times compared to numbers present in Control heifers (5.13% VS 1.76%). Feed supplementation also increased the relative abundance of Lachnospiraceae XPB1014 by more than four times compared to the Control heifers (2.36% VS 0.47%).

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While the abundance of Ruminococcaceae UCG-014 was increased with additional feed supplement from 1.00% in control heifers up to 1.70% in supplemented heifers.

Figure 4.15. Differences in relative abundance of dominant rumen microbial genera in Control and Supplemented heifers. The significant differences between groups determined by ANOVA analysis with asterix superscripts after taxon name indicating the P-value (* =P<0.05; **= P<0.01; ***=P<0.001).

However, the relative abundance of the genera Prevotella (P<0.001), Succiniclasticum (P<0.05), Ruminococcaceae NK4A214 group (P<0.05), [Eubacterium] coprostanoligenes group (P<0.001), order Gastranaerophilales (P<0.001), Prevotellaceae UCG-003 (P<0.001), and Fibrobacter (P<0.05) were reduced by feed supplementation (Figure 5.15). The relative abundance of Prevotella and [Eubacterium] coprostanoligenes group were reduced by more than half from 24.2% and 2.02% in the Control group to 10.5% and 0.94% in the Supplemented group, respectively. Meanwhile, feed supplementation significantly dropped the population of order Gastranaerophilales, Prevotella UCG- 003, and Fibrobacter more than three times from 2.23%, 1.67%, and 1.59% into 0.45%, 0.50%, and 0.33% for Control and Supplemented groups, respectively. The other affected dominant genera such

94 as Succiniclasticum and Ruminococcaceae NK4A214 group were decreased in their count by feed supplementation (4.20% and 2.10%) compared to in non-supplemented animals (2.76% and 1.39%).

4.3.3.2. Core microbiome In general, feed supplementation had a strong effect on the core rumen microbial community. Analysis of variance with FDR correction to compare the core OTU frequencies across sample groups showed that feed supplementation affected 279 OTUs (FDR P<0.05). Heatmap visualisation of the 50 most abundant OTUs showed the differences between the diet treatments (Figure 4.16). Taxonomy assignment for core OTUs showed 17 phyla, 26 class, 35 order, 48 family, and 122 genera were present in the core microbiome of both the Control and Supplemented groups. Generally, at the phylum level the core rumen microbial community of both treatment groups was dominated by Firmicutes, Bacteroidetes, and Proteobacteria accounting for 54.1%, 32.7%, and 6.0%, respectively. Regarding relative abundance, there were seven dominant phyla with ≥1% abundance and 10 minor phyla with <1% abundance. Within dominant phylum, feed treatments affected the relative abundance of Bacteroidetes and Proteobacteria. Feed supplementation significantly increased populations of Proteobacteria (2.07% VS 10.22%) but decreased Bacteroidetes (41.4% VS 23.0%) compared with Control group (Table 4.13). Within minor phylum, the relative abundance of Verrucomicrobia, Spirochaetae, Elusimicrobia, Tenericutes and Planctomycetes were significantly reduced by feed supplementation (Appendix 1.3.)

Table 4.13. Differences in relative abundance of core rumen microbiome phyla in Control and Supplemented heifers. P-values were generated by ANOVA analysis Relative abundance (%) (mean±Sem) Phylum P-value1 Control Supplemented Firmicutes 46.78 ± 2.00 62.10 ± 3.07 ns Bacteroidetes 41.44 ± 2.35 23.06 ± 2.37 <0.05 Proteobacteria 2.07 ± 1.18 10.22 ± 4.06 <0.05 Saccharibacteria 1.69 ± 0.22 1.89 ± 0.43 ns Cyanobacteria 2.16 ± 0.23 0.42 ± 0.09 ns Fibrobacteres 1.65 ± 0.40 0.42 ± 0.23 ns SR1 (Absconditabacteria) 1.62 ± 0.35 0.25 ± 0.11 ns 1ns= non-significant (P>0.05)

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Figure 4.16. Difference in relative abundances of 50 most abundant rumen microbial OTUs in the Control and Supplemented heifers. Only the OTUs significantly affected by diet treatments as determined by ANOVA are shown in heatmap (FDR P<0.05).

At the genus level, the core rumen microbiome of heifers in both groups was dominated by Christensenellaceae R-7 group, Prevotella, and Rikenellaceae RC9 gut group with average abundance 22.3%, 14.6%, and 13.2%, respectively. There were 11 dominant genera that were significantly affected by feed supplementation (Figure 4.17). In the current study, feed supplementation increased the proportion of Christensenellaceae R-7 group, Ruminococcus, Lachnospiraceae XPB1014 group, and Ruminococcaceae UCG-014 compared with the control group. Christensenellaceae R-7 group, Ruminococcus, Lachnospiraceae XPB1014 group, and Ruminococcaceae UCG-014 accounted for 29.3% VS 16.0%, 5.27% VS 2.01%, 2.71% VS 0.60%, and 1.79% VS 0.86% in Supplemented and Control groups, respectively. On the other hand, feed supplementation decreased the proportion of Prevotella, 96

Succiniclasticum, Ruminococcaceae NK4A214 group, [Eubacterium] coprostanoligenes group, order Gastranaerophilales, Fibrobacter, and unclassified genus of phylum SR1. These genera accounted for 8.99% VS 19.75%, 3.26% VS 5.23%, 1.65% VS 2.55%, 1.15% VS 2.35%, and 0.42% VS 2.16%, 0.42% VS 1.65%, and 0.25% VS 1.62% in Control and Supplemented groups, respectively. Furthermore, the 50 most abundant OTUs that were significantly affected by the feed treatments belonged to Prevotella (28%), Succinisclasticum (10.00%), Rikenellaceae RC9 gut group (10.00%), Christensenellaceae R-7 group (6.00%), and the order Gastranaerophilales (6.00%).

Figure 4.17. Differences in relative abundance of dominant core rumen microbial genera in Control and Supplemented heifers. The significant differences between groups were determined by ANOVA analysis with asterix superscripts after taxon name indicating the P-value (*=P<0.05; **=P<0.01; ***=P<0.001).

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4.3.4. Predicted functional profiles of the rumen bacterial community The predicted functional genes from CowPi and Picrust were categorised by KEGG pathways then grouped into carbohydrate and amino acid metabolism pathways. The results showed that functional genes for amino sugar and nucleotide sugar metabolism, pyruvate metabolism, Glycolysis/Gluconeogenesis, pentose phosphate pathway, and propionate metabolism were the most abundant of the potential carbohydrate metabolism genes, within the microbiome (Figure 4.18.a). While for the amino acid metabolism genes, the microbiome was dominated by alanine, aspartate and glutamate metabolism, cysteine and methionine metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, and Lysine biosynthesis genes (Figure 4.18.b). Within KEGG pathways of carbohydrate metabolism, feed supplement significantly increased the abundance of pyruvate metabolism, glycolysis/gluconeogenesis, propionate metabolism and inositol phosphate metabolism (FDR P<0.05). However, feed supplementation decreased the relative abundance of pathways associated with amino acid metabolism (FDR P<0.05).

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Figure 4.18. Differences in relative abundance of level 3 KEGG pathways of rumen microbiome data set grouped by carbohydrate metabolism pathway (a) and amino acids metabolism pathway (b). Only the significantly affected KEGG pathways by feed treatment are shown (FDR P<0.05).

4.4. Discussion The results of this study indicated that the rumen microbial community varied in accordance with both the host and diet, in agreement with Henderson et al. (2015). Previous studies have shown that feed is not the only factor that impacts the rumen microbiome. Yáñez-Ruiz et al. (2015) reported that feed interventions to manipulate the rumen microbiome, to increase productivity, were often limited by the resilience of the established rumen microbial community. The composition and population density of the rumen microbial community is well adapted to physiological and environmental changes, e.g. age , antibiotic use and health of the host animal, and varies according to geographical location, season and feeding regime (Stewart et al. 1997). The current study examined the changes to the rumen microbial community in Brahman cross heifers from different backgrounds (place of origin and weaning weight group), and feed supplementation with copra meal and corn grain. However, it is important to note that the lack of metadata information for the weaning weight and age, makes it hard to discuss a clear effect of these factors.

4.4.1. The initial (baseline) rumen microbial community of heifers with different origins and weaning weights. The results of alpha diversity, and beta diversity analysis indicated that the initial rumen microbial community was diverse but differed in accordance to animal’s origin and weaning weight groups. Moreover, the diversity was more closely related to the animal’s origin rather than the

99 weaning weight. The Shannon-Wiener index indicated that heifers from Hayfield station had a greater ruminal diversity compared to those from Kalala station. In addition, the taxonomic profile of the rumen microbiome differed between heifers sourced from different places of origin. Heifers sourced from Hayfield station had more populations of Prevotella sp, Succiniclasticum, Fibrobacter, order Gastranaerophilales, Ruminococcaceae UCG-010, and Ruminococcaceae UCG-014 in non-core microbiome and without Succiniclasticum in core microbiome. Meanwhile, heifers originating from Kalala station had a significantly greater numbers of the Rikenellaceae RC9 gut group, Saccharofermentas, Butyrivibrio, Lachnospiraceae AC2044 group, and an unclassified genus of family Lachnospiraceae; with an additional genus, Pseudobutyrivibrio, in the core microbiome. The differences in the diversity of the rumen microbial communities occurring between animals from different stations of origin may be related to differences occurring in the environmental condition and available feedstuffs (pasture type and quality) at each location. A previous study of the rumen microbiomes from samples collected from across the world showed that the rumen microbial community composition showed variation in overall microbial diversity in accordance with diet, host (i.e. breed, and species of animal), and geographical region (Henderson et al. 2015). At birth, the rumen of calves is relative sterile and is rapidly colonised by microorganisms from the immediate environment, including from the dam, in the days soon after birth (Stewart et al. 1988). Recent studies have shown that some rumen species essential for mature rumen function such as those from the phyla Bacteroidetes, Firmicutes, and Proteobacteria were found in the rumen of calves just three days after birth (Jami et al. 2013; Guzman et al. 2015). More importantly, the species of microbes that are involved in early rumen colonisation may also determine the composition of the mature rumen microbiome (Rey et al. 2014). Feeding management, including weaning management, is also an important factor in shaping the composition of the rumen microbial community. A comprehensive study indicated that new-born goats receiving different feeding management had different rumen microbiomes and different rumen fermentation patterns (Abecia et al. 2014a). In the current study, heifers from two different properties were grouped according to the weaning weight (heavy and light). Since the heifers in this trial were transported to the trial location upon weaning, the differences in weaning weight in this study may also be related to the different age (4-5 months difference; the actual data was not available) that heifers had before entering the trial. Producers in Northern Territory normally wean the calves at eight months old but also do the early weaning to their calves at 3-4 months of age (Tyler et al. 2012). Therefore, heifers in the heavy weaning weight group may have been weaned at an older age compared with the light weaning weight heifers. Although there was no record of weaning age to confirm this effect, there was some anecdotal evidence to confirm this effect.

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The dominant genera Ruminococcaceae UCG010, Fibrobacter, and an unclassified genus of order Gastranaerophilales were more abundant in the heavy weaning weight animals. Moreover, most of the minor genera that differed between the weaning weight groupings were more abundant in heavy weaning weight. These findings were consistent with the study by Jami et al. (2013) that reported the population of cellulolytic species (e.g. Ruminococcus and Fibrobacter), and soluble sugar and lactate utilisers increased with an animal’s age. Although, the heavy weaning weight animals most likely older than the light weaning animals but booth animal groups already had fully functional rumen, and the microbiome data related to these functioning rumens.

4.4.2 The changes to the initial rumen microbiome following six months of feeding with control diet The initial differences in the rumen microbial diversity occurring between the groups of heifers entering the trial (Section 4.4.1.), diminished after six months on the Control diet and being reared together in communal pens and with a short period of 3 weeks of grazing together when they first arrived at KRS. By the end of the trial, the animal’s origin or weaning weight no longer had any significant effects on the animal’s rumen microbial diversity according to alpha and beta diversity measures. The taxonomic profiles also converged in comparison to the initial microbiome, with the original effect of animal origin decreasing such that the number of differentiating rumen bacteria, decreased from 11 genera to only one genus. This may indicate that the rumen microbiomes merged to some extent into a more homogenous rumen microbial community when the heifers were either randomly mixed in communal pens, or when they first came into the trial and were all maintained on the control diet. Animals sourced from different properties of origin, reared in communal pens are exposed to horizontal transmission of rumen microbiota between animals. Particularly, with the provision of a mineral lick-block at the pen which increased the chance of horizontal transmission of microbes between animals, through saliva. This transmission has been previously established, with a pioneering study conducted to emphasise the effect of the environment on rumen microbial transmission showed that cellulolytic bacteria were better established in lambs reared in flocks than lambs reared in isolation (Fonty et al. 1987). Horizontal transmission could explain the finding that some unique rumen microbial OTUs were passed between animals of different property of origin after six months of mixed feeding with control diet. The transmission of rumen microbiota has been noted starting in the early life of the animal (Stewart et al. 1988) and is recognised as an important event in rumen microbial colonisation (Li et al. 2012b). The transmission of rumen microbiota from the surrounding environment continue throughout the animals life as for gut microbiota in mammals (Curtis & Sloan 2004). Therefore, the variation of rumen microbiome profile within source of transmission can cause

101 quite distinct variation of rumen microbial community per animal (Rey et al. 2014), as seen in the heifers at the first sampling (Section 4.4.1.) and after sixth months reared together in a herd. Although some researchers have questioned the effectiveness of bacterial inoculation when provided to an established rumen microbiome, due to the complexity of the existing community, resilience, and host-specificity (Weimer et al. 2010; Weimer 2015; Yáñez-Ruiz et al. 2015), the findings in the current study showed the possibility of individual bacterial species (OTU’s) colonising an already established ruminal microbiome. Some unique rumen bacterial OTUs that were observed in accordance to the property of origin at initial sampling were later passed between animals of different origins. This horizontal inoculation, which occurred at the start of the trial between animals, altered the two different rumen microbiomes into a new microbial community, with this effect lasting until the end of the pen feeding trial. Apart of the horizontal inoculation hypothesis, the merged rumen microbial community at the end of the trial may also be attributed to the effect of diet in changing the profile of an established rumen microbial community. The homogenous diet (basal/control diet) that was fed to the heifers may have encouraged the multiplication of formerly low prevalence organisms, resulting in an apparent “convergence” of microbial communities within the trial animals. According to their function, there are at least two groups of rumen bacteria (1) the polymer-hydrolising bacteria that utilise sugars and other compounds released during the hydrolysis process (i.e. from cellulose, hemmicellulose, starch, lipids, and protein); and (2) highly specialised rumen microorganisms that utilises non hydrolysis products such as plant secondary metabolites (oxalate, mimosine, cyanide), and by products of feed fermentation in the rumen (CO2, H2, and VFAs) (Stewart et al. 1997). Therefore, the nutritive content of the diet impacts on the abundance of both rumen microorganism groups. A recent study by Zhang et al. (2018) showed that the rumen microbiome of goats fed either a high grain diet or a high forage diet, seperated into statistically distinct clusters with a great number of Prevotella, Ruminococcus, Butyrivibrio, Mogibacterium, and Moryella in the high grain diet group. Another study reported changes in the rumen microbial community during the transition from forage to high a grain diet in Angus heifers (Petri et al. 2013).

4.4.3. The effect of feed supplementation on rumen microbial diversity Researchers have previously investigated the effect of the addition of feed supplements such as copra meal and corn to increase the animal’s productivity (Bodine & Purvis 2003; Aregheore 2006), particularly in tropical regions such as Northern Territory (NT), Australia, with low quality feed resources. The native grasses in NT are dominated with C4 type grasses with low nutritional quality (Parker & Cobiac 2002). During the dry season, the nutritional content of these native grasses decline, as indicated by low protein content and lowered digestibility (Ian 2011). Therefore, feed 102 supplementation is beneficial to fulfil the nutrient requirement in cattle as reported by Silva (2017) using corn and copra meal supplementation to increase growth during the dry season in NT. Since ruminants are dependent on microorganisms to ferment feeds in the rumen, therefore feed also has an influence in determining the profile of the ruminal microbial community (Abecia et al. 2014a; Zhang et al. 2018). Moreover, a recent study showed that animal productivity was related to the rumen microbial community profile of animals (McGovern et al. 2018). Feed alters the rumen microbial community by altering the substrates for bacterial growth or by acting as an inhibitor to bacteria (Zened et al. 2013). In the current study, the addition of copra and corn as a feed supplement significantly altered the profile of the rumen microbial community in the Brahman cross heifers. Heifers supplemented with copra and corn had lower rumen microbial richness and diversity when compared to the non- supplemented heifers as indicated by the result of alpha diversity measures. In this study, copra meal and corn (a high starch grain) were supplemented into the diet as a source of protein and energy, respectively. Throughout the trial N was also provided in the form of urea (Mineral block). The findings were similar to a recent study by Zhang et al. (2018) who showed that animals receiving a high grain diet had less richness and diversity in their rumen microbiome compared with animals fed a high forage diet. The reduction in species richness and microbial diversity was correlated to the presence of easily fermentable substrates in the high grain diet, such as starch (Fernando et al. 2010; Zened et al. 2013). In addition to the alterations in species richness and diversity, the taxonomic composition of the rumen microbial community in the supplemented and non-supplemented heifers was also different. The beta diversity distance matrix indicated distinct rumen microbiomes occurring between each dietary group. This observation was consistent with the findings of Mao et al. (2016), who reported three different clusters of rumen microbial communities in accordance with three different grain based diets. In this current study, feed supplementation altered the population of rumen bacteria such as Firmicutes, Bacteroidetes, Cyanobacteria, and Fibrobacteres. These phyla are commonly reported in the rumen (Jami & Mizrahi 2012a; Jami et al. 2013). In the present study, the relative abundance of Firmicutes was higher in supplemented heifers. Firmicutes have been known to be responsible for energy harvesting from rumen fermentation and milk fat deposition in dairy cattle (Jami et al. 2014). Since ruminants are dependent on rumen fermentation to supply most of their energy requirements (Owens. & Goetsch 1993) the population of Firmicutes can reflect the animals’ performance. A study by Myer et al. (2015) showed that Firmicutes were highly abundant in cattle with high average daily gain and suggesting Firmicute populations may also be responsible in modulating feed efficiency. Similar findings were noted in the previous study, where the average body

103 weight gain was higher for supplemented heifers than heifers maintained on the control diet of Sabi grass hay and mineral block (Silva 2017). At the genus level, in both non-core and core microbiome data sets, feed supplementation increased the relative abundance of Christensenellaceae R-7 group, Ruminococcus, Ruminococcaceae UCG-014, and Lachnospiraceae XPB1014, and decreased the relative abundance of Prevotella, Succiniclasticum, Ruminococcaceae NK4A214 group, [Eubacterium] coprostanoligenes group, order Gastranaerophilales, Prevotellaceae UCG-003, and Fibrobacter. Genus Christensenellaceae R-7 is a member of family Christensenellaceae. Family Christensenellaceae have been found attached to plant particles following incubation in the rumen (Liu et al. 2016), and may play a role in plant-fibre degradation (Shen et al. 2017). Similarly, De Mulder et al. (2017) showed that Christensenellaceae were more abundant in the solid fraction of rumen digesta. The highest sequence similarity for the most abundant OTU (OTU 579866) classified within this genus, was only 88% identical to Christensenella massilinensis which in turn is 97.5% similar to Christensenella minuta (Ndongo et al. 2016).. Genera Ruminococcaceae UCG-014 is a member of the family Ruminococaceae, some of which are well known as rumen cellulolytic bacteria (De Mulder et al. 2017). However, the studies by Hook et al. (2011b) and Mao et al. (2013) showed the relative abundance of Ruminococcaceae increased when cattle were fed grain diet and suggested that some of the genera of this family can ferment starch and may be classified as amylolytic bacteria. Ruminococcaceae UCG-014 was also reported abundant in lambs within the higher sub-acute rumen acidosis (SARA) risk group indicating this genus was favoured by grain based diet and resistant to low pH (Li et al. 2017a). Furthermore, feed supplementation also increased the number of Lachnospiraceae XPB1014 and Ruminococcus. According to the Silva database (Quast et al. 2013), Lachnospiraceae XPB1014 is a member of the family Lachnospiraceae. Many members of this family have the ability to produce butyric acid (Meehan & Beiko 2014) from starch (Flint et al. 2012) through the butanoate metabolism pathways (Kanehisa & Goto 2000). Further identification (OTU 582627) indicated that this genus had 93% similarity with Agathobacter ruminis isolated from the rumen of sheep and cows (Rosero et al. 2016). While Ruminococcus (OTU 803010) in this study was 96% identical with Ruminococcus albus (Suen et al. 2011a), Ruminococcus albus was found as one of predominant cellulolytic bacteria in rumen of Chinese Mongolian sheep, along with Ruminococcus flavefaciens (Zeng et al. 2015). Complete genome analysis of R. albus revealed a variety of enzymes for digesting cellulose and hemicellulose such as the families of glycosyl hydrolase (GH) (Suen et al. 2011a), explaining the importance and cellulolytic role of these bacteria in the rumen Feed supplementation decreased the relative abundance of several rumen microbial genera. The relative abundance of Prevotella was lower in heifers fed feed supplement compared with control

104 heifers. This result was inconsistent with other studies that reported this genus increased in abundance when cattle were maintained on grain-based diets (Fernando et al. 2010; Zhang et al. 2018). This inconsistency may be due to the genetic diversity within the genus Prevotella, and how this relates to diet (Matsui et al. 2000; Bekele et al. 2010). Koike et al. (2003) reported a large cluster of Prevotella-related sequences were associated with the fibre associated rumen bacterial community and suggested the possible involvement of Prevotella in fibre breakdown. In the current work, the most abundant OTU classified as Prevotella (OTU 246608), had 92% sequence similarity to Prevotella ruminicola (Purushe et al. 2010). Prevotella ruminicola is a fibrolytic rumen bacterium that degrades complex carbohydrates (Kovár et al. 1999) with propionate as a primary end product of fermentation (Strobel 1992). Succinisclasticum (OTU 204600) had 95% sequence similarity to Succinisclasticum ruminis (Van Gylswyk 1995). This genus produces energy by fermenting succinate into propionate (Van Gylswyk 1995). A recent study reported S. ruminis has been associated with animals with lower efficiency growth (Myer et al. 2015). A more recent study investigating the in-situ fibre-adherent bacterial community suggested that S. ruminis may be a representative of an unstable fibre-associated community which was observed abundant only in an intermittent SARA challenge (Petri et al. 2017). Eubacterium coprostanoligenes ferments arabinose, cellobiose, fructose, glucose, mannose, and melibiose into acetic, formic and succinic acids and hydrogen (Freier et al. 1994) and found lower in the cattle fed lipid and nitrate (Popova et al. 2017). E. coprostanoligenes was also found more abundant in the rumen of musk deer with high fibre diet rather than in deer with mixed diet (Li et al. 2017b) which is similar with the findings in this study. According to NCBI database, genus of Fibrobacter in this study (OTU 262032) was 99% identic with Fibrobacter succinogenes. F. succinogenes is an important cellulolytic member of the rumen bacterial community (Suen et al. 2011b). A decrease in the number of F. succinogenes in supplemented heifers could have been expected. In addition, feed supplementation reduced the population of an unclassified genus of phyla SR1 (Absconditabacteria) in core rumen microbiome. This rumen bacteria was found positively related with diets high in polyunsaturated fatty acids (Castillo-Lopez et al. 2018). In another study, rumen populations of SR1 were decreased by the addition of myristic acid (Bayat et al. 2018), while a study examining risks of SARA in lambs, showed SR1 was more abundant in lambs at lower risk of SARA (Li et al. 2017a). However, there were some inconsistencies in the current study that need further investigation, and these relate to the lower abundance of Ruminococcaceae NK4A21, and Candidatus sacharimonas with feed supplementation. Ruminococcaceae NK4A21 has similar properties to Ruminococcaceae UCG-014 and Ruminococcaceae UCG-011 and relates to a high grain diet (Li et al. 2017a). Populations of Ruminococcaceae NK4A21A have been found to be more abundant in dairy cows fed higher grain diets compared to cows fed a control diet (Pan et al. 2017). In addition, this

105 genus is also found abundant in high SARA risk lambs, therefore indicating that some genera of the family Ruminococcaceae may have different pH sensitivities (Li et al. 2017a). Therefore, providing more detailed information about the animal’s background data (i.e. feeding management and weaning management) and rumen fermentation parameters (i.e. volatile fatty acids and ammonia concentration) may also be important in generating more comprehensive findings in this study.

4.4.3. Predictive functional analysis Functional analysis predicted from the taxonomic profile showed that feed supplementation adjusted the abundance of saccharolytic and non-fibrolytic rumen bacteria (e.g. Christensenellaceae and Lachnospiraceae), in accordance with the higher non-structural carbohydrate (NSC) content of the corn and copra meal feed supplement (i.e. glucose, and starch). The predicted functional profile also showed that glycolysis, and therefore fatty acid synthesis, was higher in the bacteria in supplemented heifers than in control heifers. Furthermore, the number of bacteria with propanoate (propionic acid) metabolism related pathways, was also more abundant in supplemented heifers, due to higher NSC arising from supplementation with corn. These findings were consistent with a metabolomic study that indicated that feeding a high starch diet increased pyruvate and propionate levels in the rumen (Xue et al. 2018). In addition, propionate along with acetate and butyrate are major sources of energy and increasing the proportion of propionate is a major goal in improving rumen fermentation (Wanapat 2000). Therefore, the increase in concentration of propionate in the rumen positively correlates with the better animal performance noted in animals fed the corn and copra meal supplement in a previous study (Silva 2017).

4.5. Conclusion The initial rumen microbial communities of cattle entering the trial were found to differ in accordance with the animal’s origin and weaning weight although the effect of weaning weight was not as strong as the effect of the animal’s origin. It was also hard to draw conclusions for the effect of weaning weight and age on the rumen microbial community, due to the lack of metadata information. However, the initial differences diminished after six months of receiving the Control diet and being reared in communal pens. This indicated that rumen bacteria may have either been exchanged between the heifers or adjusted according to the feed provided during this six-month period, resulting in the establishment of new, stable microbial populations. Feed supplementation then reduced the diversity and species richness of the rumen microbiome, and increased the genes related with energy harvesting such as glycolysis, pyruvate metabolism, and propanoate metabolism. These findings showed the possibility of manipulating the established rumen microbiome at post weaning stage and the change in microbiome is associated with a change in diet. 106

Chapter 5. Factors affecting the rumen microbiome profile of culled cows during transition period on Northern Australia floodplain

5.1. Introduction The sale of surplus to requirement cows, “culled cows”, is an important contributor to the overall profit of northern Australia beef breeding businesses, with the sale of cull cows contributing up to 50% of the total income (Niethe & Holmes 2008). In northern Australian beef enterprises, cows are typically culled from the herd for two reasons: unproductive and culled for age. Reproductively inferior cows usually present as non-pregnant and non-lactating cows at muster and forward store in condition. These cows are readily sold. However, cull cows often present at muster in poor body condition (i.e. low body weight, body condition score, and carcass quality) and are difficult to market for slaughter (Roeber et al. 2001; Niethe & Holmes 2008). The value of cull cows is improved by re- alimenting cows through feed supplementation and intensive feeding to meet higher market values (Holmer et al. 2009; Moreno et al. 2012). The floodplain areas in the Top End region of the Northern Territory (NT) are potential grazing areas that can be utilised for value-adding cull cows over the dry season. NT floodplain regions are zones of land surrounding river systems that are vegetated by palatable improved pastures. (Walczak et al. 2017). Some areas of floodplain have already been used to graze cattle during the dry season (Baars & Ottens 2001; Kettinger et al. 2010), particularly, sub-coastal floodplains which are characterised as a flat area with peaty soil or alluvial soil that tends to flood during the wet season (Cameron & Lemcke 1999). The vast area of sub-coastal floodplains in the Top End region spreads in area to 6,860 km2 with recorded stocking rate as high as 0.4 beast/Ha (Cameron & Lemcke 1999). The availability of grass as cattle feed in floodplains, is abundant during the dry season, and some of the grass in deep flooded areas is able to regrow during the mid-late dry season (Cameron & Lemcke 2003). Therefore, one potential estimate is that as many as 274,400 culled cows could be grazed on sub-coastal floodplains over a dry season. The species of grasses on sub-coastal floodplains are different to grasses in the non-floodplain areas. The grasses in sub-coastal floodplains are dominated by Hymenachne acutigluma (native hymenachne), and Leersia hexandra (swamp rice-grass) in deep flooded areas; and Pseudoraphis spinescens (spiny mud grass), Echinochloa colona (awnless barynyard grass), Oryza australiensis (wild rice), and Paspalum scrobiculatum (scrobic) in shallow flooded areas (Cameron & Lemcke 2003). While in non-flooded rangeland areas, the species of grass are dominated by low quality C4 native grasses such as Astrebla spp. (Mitchell grass), Heteropogon contortus (black spear grass) and Themeda triandra (Kangaroo grass) (Parker & Cobiac 2002).

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To date, literature has shown modifications in the rumen microbial community occurring during transition periods of diet change (Belanche et al. 2019). Rumen microbes are sensitive to diet changes since they obtain the nutrition for growth directly from the fermentation of feed consumed by the host. Diet-dependant changes in the rumen microbiome were found to occur from the pre- weaning rumen development period (Dias et al. 2018). During this period, calves were facing changes in their diet from liquid to solid feed. Feed transition periods also occur in adult cattle due to feed changes. In particular, the transition period onto high grain-based diets in feedlot cattle is well known (Anderson et al. 2016). While changes in rumen microbial communities were reported when cattle transitioned from feedlot diets to pasture grazing systems (Schären et al. 2017). The effect of transition between differing pasture types and quality is less understood. The ability of rumen microbes to adjust to a new diet during the transition period affects cattle performance as rumen microbes are critical to the supply of volatile fatty acids and microbial crude protein for the nutrition of the host (McCann et al. 2014a). The time for microbial populations to adapt to new feed conditions varies according to the extent of changes. Hackman (2015) reported that, in some cases, rumen microbes adjust to a diet shift from high-forage to high-grain feed in a day. While another study showed that the rumen microbiome of dairy steer calves took three to four weeks to adapt from a hay-based diet to grazing on pasture (Nakano et al. 2013). Therefore, a delay in rumen microbial adaptation during dietary transition will affect the performance of the cattle and impact on their economic value. A comprehensive description of the transitional changes occurring in the rumen microbial community of cattle introduced to the floodplain pastures is currently under-reported in the literature. In this study, the change in the rumen microbial communities of cull cows sourced from properties where extensive grazing was undertaken and the effect of dietary transition to floodplain pasture grazing, was investigated. In addition, the effect on the rumen microbial community, of mixing cattle from different backgrounds i.e. property of origins and birthplaces, to the dietary adjustment process and adaptation to floodplain pastures, was also investigated.

5.2. Material and Methods

5.2.1. Animal ethics All animal treatments in this study were undertaken in accordance with animal ethic approval A15017 from Animal Ethics Committee (AEC) Charles Darwin University.

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5.2.2. Time and experimental location This chapter reports on two field trials. The first study was conducted for 137 days from 22nd July 2016 until 7th December 2016. The second study was done in the following year with 80 grazing days from 29 September 2017 until 19 December 2017. Both studies were undertaken on floodplain pastures at Beatrice Hill Research Farm (BHRF). Beatrice Hill Research Farm is located at Middle Point, Northern Territory, with a total area 2,600 hectares. The floodplain area of BHRF is located near the Adelaide River on the Arnhem highway. All laboratory-based analyses were done at the Department of Agriculture and Fisheries (DAF) laboratories, Ecosciences Precinct, Dutton Park, Queensland. The environmental conditions of BHRF are defined as monsoonal with the wet season typically occurring from November until April and characterised by high humidity. A large proportion of the annual rainfall is receiving during this season. The dry season spans from May until October with warm, dry sunny days and lower humidity. Rainfall data during the trial period was obtained from Beatrice Hill and Middle Point weather stations (www.bom.gov.au) (Figure 5.1). The fractional ground cover at BHRF during the study periods was obtained using VegMachine web based software (https://vegmachine.net/) (Karfs et al. 2004) (Figure 5.2).

Figure 5.1. Rainfall recorded at Beatrice Hill Research Farm. Study 1 and Study 2 were conducted in 2016 and 2017, respectively.

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Figure 5.2. Ground cover fractional data of Beatrice Hill Research Farm during the research period.

5.2.3. Experimental Design and Treatments In the first study, 41 Brahman-cross cows were used, consisting of nine cows originating from BHRF and 32 commercial cows (COM) purchased from the Australian Agriculture Company. The COM cows were sourced from several commercial properties. There was no detailed information available regarding the previous management practices including diets provided in each commercial property. All the cows were non-pregnant and non-lactating and co-grazed the BHRF floodplain for 137 days with six muster times. At induction to the study, animals were individually identified using visual management tags and biophysical information recorded against each animal (property of origin, teeth condition, hip height, and year brand). At following musters, body condition score (BCS) was visually assessed and rumen fluid samples were collected. Liveweight was recorded at each muster after a 12 to 15 hour off-feed on-water curfew (Silva 2017). Rumen microbial diversity was analysed based on animal groupings such as background (birthplace and property of origin), anatomy and physiological condition (induction weight, teeth condition, hip height and age), animal performance (body weight gain and BCS), and grazing period. The number of samples and animals for each metadata category is presented in Table 5.1. Property of origin was classified as COM and BHRF. The property brand was used to identify property of birth. Start weight was the initial body weight of the trial animals. Initial body weights were grouped based on start weight into <275 kg, 275-<313 kg, 313-<372 kg and 372-<421 kg. Teeth condition was classified as full mouth, broken mouth, and worn full mouth. Hip height was measured once at day 15 and grouped as short (≤140 cm) and high (>140 cm). Age of animals was classified as <5 years old and >5 years old. Within animal performance metadata, Body Weight Gain (BWG) (%) was calculated as (curfew weight at final muster-initial curfew weight)/initial curfew weight x 100). Curfew body weight was obtained by fasting the cows for overnight (± 12 hrs) in the holding yard prior to weighing. Within the BWG metadata category, animals were grouped into Low (1.22% mean BWG), Middle (8.42%

110 mean BWG) and High (14.8% mean BWG). BCS was quantified based on a numeric value between 1 (very low BCS) and 5 (very high BCS). The numeric value of BCS was then grouped into <2.5 (low), 2.5– 3.5 (average) and >3.5 (high). Grazing periods were classified relative to rumen fluid sampling times into day 1 (upon arrival at BHRF), day 34 (34 days floodplain grazing), and day 137 (137 days floodplain grazing). For Study 2, all cattle were sourced from a commercial property. The animals were backgrounded for approximately one month in a paddock with black spear grass pasture (Heteropogon contortus) at Katherine Research Station prior to transportation to BHRF. They were all non-pregnant and non-lactating. Seventeen Brahman-cross culled cows were used in the trial. These animals grazed BHRF floodplains for 80 days. In this study, animals were grouped for rumen microbial diversity profiling based on grazing period, start weight, BCS, and ADG. ADG was calculated as final BWG/80 days floodplain grazing. Cows were grouped on ADG after the trial finished (Post-hoc); four animals with the highest ADG and four animals with the lowest ADG were grouped into high and low groups, respectively. Whilst the remains of nine animals were grouped as middle ADG group. Grazing period was classified into day 1 (arrival at BHRF) and day 80 (after 80 days of floodplain grazing). Start weight was classified based on start body weight as in Study 1 but animals were grouped into Grid 1 (<323 kg), Grid 2 (323-<340 kg), and combination Grid 3, 4 &5 (340-<437 kg) based on slaughter grid weight categories used by the local abattoir. Slaughter grid refers to the price of cattle based on an individual animal basis rather than average pricing (Graff & Schroeder 2000). BCS was recorded and animals grouped based on their values.

Table 5.1. The number of rumen fluid samples and animals for each metadata category. Number of Number of Number of animals Metadata category categories rumen fluid samples (heads) Study 1 Birthplace 4 81 27 Property of Origin 2 123 41 Start Weight 4 123 41 Teeth Condition 3 123 41 Hip Height 2 108 36 Age 2 78 26 Body Weight Gain 3 123 41 BCS 3 117 39 Grazing period 3 123 41 Study 2 Start Weight 3 34 17 BCS 3 34 17 ADG 2 16 8 Grazing period 2 34 17

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5.2.4. Rumen Fluid Sampling In total, there were 123 rumen fluid samples for further analysis. These samples represented rumen fluid samples from 41 cull cows collected at three different sampling times. While for Study 2, there were 34 samples from 17 individual cull cows at two different sampling times. Rumen fluid was collected using a stomach tube and hand pump according to Klieve et al. (1998), with minor modifications in sample filtration using a layer of nylon stocking (Section 2.2; Chapter 2). The tubes and plastic flask were washed with clean water after each animal collection. The collected rumen fluid was then screened using a layer of stocking into a 15 mL plastic bottle and chilled to 4°C. Four aliquots of 1 mL rumen fluid were transferred into 1.5 mL Eppendorf tubes (Eppendorf, Hamburg, Germany) and centrifuged at 13,793 g for 20 min (Heraeus Biofuge 13; Radiometer, Copenhagen, Denmark). The supernatant was discarded and the pellets were stored at -20 °C for transport to the laboratory and further DNA analysis.

5.2.5. DNA Extraction and 16S rRNA Gene Sequencing The extraction, purification and quantification of rumen microbial DNA were detailed in Section 2.3 (Chapter 2. General Methods). Briefly, DNA from samples of ruminal digesta was extracted following the repeated bead beating plus column method (Yu & Forster 2005). The concentration of extracted DNA was measured using a Qubit® 2.0 Fluorometer with dsDNA HS Assay Kit (Invitrogen, Carlsbad, CA, USA). The quantified result was then confirmed by gel electrophoresis using a 1kb DNA ladder (GeneRuler®, Thermo Fisher Scientific, Massachusetts, USA) and 1% GelRed® (Biotium, Fremont, CA, USA). DNA samples were sent to the Australian Genome Research Facility (AGRF, St. Lucia, Queensland, Australia) for 16S rRNA gene amplicon sequencing. The 341F – 806R (V3-V4) region of the 16S rRNA genes were PCR amplified using the forward primer 341F (CCTAYGGGRBGCASCAG) and reverse primer 806R (GGACTACNNGGGTATCTAAT) (Wang et al. 2019). The DNA sequencing was done using Illumina MiSeq platform across two flow cells (www.agrf.com.au) .

5.2.6. Bioinformatic and Statistic Analysis The bioinformatic analysis of the sequencing data in these studies was done using the Awoonga high performance computer cluster (Research Computing Centre-UQ, Brisbane, QLD) and Biolinux v8.0.7 (Field et al. 2006). Microbial diversity profiling of 16S rRNA gene amplicon sequences from rumen fluid samples were mainly performed in the bioinformatics pipeline Quantitative Insights into Microbial Ecology (QIIME) v1.9.1 (Caporaso et al. 2010b). Briefly, the QIIME workflow consisted of pre-analysis for quality filtering, upstream analysis and downstream analyses as described by Navas-Molina et al. (2013).

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The sequencing data obtained from AGRF was then trimmed into 200 bp and phred 33, joined (reverse and forward sequences), and multiplexed (Section 2.4.1; Chapter 2. General Methods). Operational taxonomic units (OTUs) were picked using the open reference method, with 97% sequence similarity against Greengenes database version 13_8 (DeSantis et al. (2006); http://greengenes.lbl.gov). Taxonomic information for OTUs was assigned against the 16S SILVA database version SSU 128 that has been formatted for QIIME (Quast et al. 2013) with seven levels of taxonomy. OTU tables were built in QIIME (Section 2.4.1; Chapter 2. General Methods) for further downstream analysis (Section 2.4.2; Chapter 2. General Methods). In addition, additional taxonomy assignment for individual OTUs were carried out using Basic Local Alignment Search Tool (BLAST) through the 16S rRNA sequences database at the National Center for Biotechnology Information (NCBI; https://blast.ncbi.nlm.nih.gov/). Non-core rumen microbial diversity was investigated by alpha diversity and beta diversity analysis. Alpha and beta diversity analysis were performed according to metadata categories in each study (Table 5.1). Alpha diversity analysed the diversity and species richness of rumen microbial community within sample and was observed using three measures: Chao1, Phylogenetic Diversity, Observed OTUs, and Shannon index (Lemos et al. 2011). While the diversity between animals (beta diversity) was analyzed using weighted and unweighted UniFrac indices (Lozupone & Knight 2005). The sampling depth in beta diversity analysis was defined according to the lowest number of sequences observed in each metadata category. Statistical analysis was performed to investigate the significant differences in alpha and beta diversity between animal groups. Statistical analysis of alpha diversity measures for both studies was done according to Proc MIXED repeated measures with Residual Maximum Likelihood (REML) method using SAS software version 9.4. (SAS Institute, USA). Anti-dependence order 1 was applied as covariance structure for modelling the correlation between time points based on the repeated measures within animals. Metadata categories (Table 5.1) and combination metadata categories by grazing period were fitted as fixed effects. Pairwise comparisons were tested using least significant differences where means were regarded as not significantly different at P<0.05 significance level. The matrix distance of beta diversity measures for each metadata category were statistically tested using permutational multivariate analysis of variance (PERMANOVA) in the QIIME pipeline. PERMANOVA measures dissimilarity distance in multivariate analysis based on ANOVA design with free distribution permutations to generate p-values (Anderson 2017). While the segregation degree between groups within each metadata category was measured from Weighted and Unweighted UniFrac distance matrices using ANOSIM analysis. The segregation degree was defined by its R value where R values close to 1 define as completely segregated and R values close to zero are less segregated (Clarke &

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Warwick 2001). In addition, principal coordinate analysis (PCoA) plots were generated to visualise the cluster of rumen microbial communities between cows groups. The core rumen microbiome was defined as OTUs that were conserved in 100% of samples within an animal group (Section 2.4.2; Chapter 2. General Methods). OTU tables of the core rumen microbiome were generated for each animal group within a metadata category (Table 5.1), for example OOH, KPI, BCO, and BHRF groups for birthplace category. Core OTU tables of animal groups were then merged into a core OTU table according to the selected metadata category in each study. The differences in profile of the core rumen microbial community was investigated by summarising the taxonomy into phylum and genus levels and comparing its relative abundance between animal groups. Taxonomic profiling at phylum and genus levels was done for the core rumen microbial community in accordance to metadata category. However, taxonomic profiling was only performed for metadata categories that had significant differences between animal groups. In Study 1, taxonomic profiling of the core rumen microbial community was done for birthplace and grazing period categories. The variation in the core rumen microbial community within birthplace was investigated by comparing the taxonomic profile between birthplace groups upon arrival at BHRF (day 1). The change in taxonomic profile between birthplace groups was then investigated after 34 days of floodplain grazing as a single herd. While the changes in taxonomic profile according to grazing period were explored by comparing the profiles between day 34 and day 137. In Study 2, taxonomic profiling was carried out for grazing period and BCS categories. The changes in profile of the rumen microbial community during floodplain grazing was examined between day 1 and day 80. Finally, the association of rumen microbial profiles and BCS was investigated according to BCS group. Statistical analysis in taxonomic profiling of the core microbiome was done using repeated measures with REML and ANOVA in SAS software version 9.4. (SAS Institute, USA). The differences in rumen microbial abundance between animal groups were tested using repeated measures with REML method and ante-dependence order 1 covariance structure. Pairwise comparisons were tested using least significant differences where means were not regarded as significantly different above the 5% significance level (P>0.05). The targeted metadata categories such as birthplace at Study 1 and BCS at Study 2 were fitted as fixed effects. While ANOVA in completely randomised-block design (Silva 2017) and Duncan post hoc test (Hu et al. 2019) was done to analyse the differences in rumen microbial profiles between grazing periods in both studies. Rumen microbial profiling at the OTU level was done for the non-core rumen microbial community. In Study 1, profiles of the non-core rumen microbial community were investigated by comparing the OTU frequency between animal groups within a metadata category (Table 5.1). While

114 in Study 2, non-core OTU frequency was only done for the grazing period category. The significant differences between animal groups were analysed by ANOVA with Benjamini-Hochberg FDR correction through QIIME (Caporaso et al. 2010b). Heatmap graphics were generated in QIIME (Caporaso et al. 2010b) to visualise the OTUs with significant differences in their abundances between animal groups. The horizontal transmission of core rumen microbes between animals during floodplain grazing in Study 1 was investigated by determining the unique core OTUs for each birthplace group. Unique OTUs were generated and defined as the OTUs that were only observed within a particular birthplace group upon arrival on the floodplain. The unique OTUs obtained at day 1 (upon arrival) were then tracked after 34 and 137 days grazing on floodplain as a combined herd. Venn diagrams were generated using Microsoft® Excel® 365 64-bit (Microsoft Corporation, Washington, USA) and interactive web tool (http://bioinformatics.psb.ugent.be/webtools/Venn/). The metagenomic functional profile from the 16S rRNA gene data set was predicted using PICRUSt (Langille et al. 2013) and CowPI (Wilkinson et al. 2018) softwares (Section 2.4.3; Chapter 2. General Methods). The genes of predicted metagenomic functions were defined by Kyoto Encyclopedia of Genes Genomes (KEGG) metabolic pathways and categorised at level three (Kanehisa & Goto 2000). Predicted functional genes were compared and grouped according to animal groups within metadata categories as selected from taxonomic profiling. The differences in metagenomic function profiles between treatment groups were analyzed using a two-sided Welch’s t-test with Benjamini-Hochberg FDR correction through STAMP software v2.1.3. (Parks et al. 2014).

5.3. Results

5.3.1. Study 1. The sequence data obtained from AGRF had an average of 107,772 sequences per sample with minimum and maximum sequences per sample of 39,342 and 235,702, respectively. In total, the number of sequences after trimming, joining, and filtering was 3,228,320 with the average, minimum, and maximum values per sample of 26,246, 9,552, and 51,509 sequences, respectively. These sequences were clustered into 2,129 OTUs and classified into 17 phyla, 31 classes, 39 orders, 50 families, and 164 genera.

5.3.1.1. Rumen microbial diversity Alpha diversity analysis was performed using four measures. All alpha diversity measures were significantly different between animal groups within grazing period and property of origin by grazing period. In addition, Chao1, PD, and Observed OTUs were significantly different between groups

115 according to Start Weight (Table 5.2). More detailed statistical analysis for property of origins by grazing period indicated that upon arrival (day 1) the rumen microbial diversity was significantly different between animals originating from commercial properties and BHRF (Figure 5.3). However, the differences in rumen microbial diversity did not last long, and by day 34 there was less diversity between microbiomes, and this persisted until the end of the trial (day 137).

Table 5.2. Statistical analysis of four alpha diversity measures of rumen microbial diversity in accordance to animal groups within the three sampling periods. P-values were generated by repeated measures with the REML method. Alpha diversity measures (P-values) * Fixed effect Chao1 Phylogenetic Diversity Observed OTUs Shannon Grazing period1 0.0056 0.0004 0.0179 <.0001 Age2 0.4340 0.6632 0.8279 0.9887 BCS3 0.3509 0.3435 0.4376 0.2289 Birthplace4 0.6143 0.9188 0.9097 0.3188 Hip heights5 0.2123 0.1367 0.1617 0.1439 BWG6 0.7975 0.9761 0.9687 0.8181 Property origin7 0.1636 0.2500 0.2559 0.0605 Start weight8 0.0081 0.0340 0.0309 0.4572 Teeth9 0.2566 0.4375 0.3892 0.5357 Age by grazing period 0.2308 0.3437 0.3290 0.8269 BCS by grazing period 0.8954 0.8930 0.9585 0.2933 Birthplace by grazing period 0.1630 0.1066 0.1286 0.1138 Hip heights by grazing period 0.3203 0.2018 0.2531 0.4717 BWG by grazing period 0.7589 0.5834 0.7736 0.9882 Property origin by grazing period 0.0079 0.0022 0.0030 0.0049 Start weight by grazing period 0.2703 0.1082 0.1879 0.2391 Teeth by grazing period 0.6580 0.6063 0.5325 0.7934 1 Days grazing on floodplain: 1, 34, and 137 days; 2Age (years old): <5, > 5; 3Body Condition Score: <2.5 (Low), 2.5–3.5 (Middle), and >3.5 (High); 4 Birthplace of the cows: 00H, BC0, KP1, and TBH; 5 Hip heights: Short (≤140 cms), High (>140 cms); 6 Percentage of curfew Body Weight Gain (mean): Low (1.22%), Middle (8.42%), and High (14.8%); 7 Property of origins: Beatrice Hills Research Farm (BHRF), Commercial properties (COM); 8 Start weight according to slaughter grids: <275, <313, <372, and <421 kg; 9 Teeth conditions: full mouth, broken mouth, and warn full mouth. * Significantly different values with P<0.05 threshold of significance is highlighted.

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Figure 5.3. Changes in alpha diversity of the rumen microbiome in Beatrice Hill Research Farm (BHRF) and commercial (COM) cows from day 1 until day 137 grazing in floodplain. The differences between grazing period were statistically tested using repeated measures (REML) with P<0.05 significance level.

The alpha diversity of the rumen microbiome between grazing periods showed the diversity of the rumen microbial community was affected by grazing period (Figure 5.4). The diversity of the rumen microbial community was observed to be significantly different between day 34 and day 137. Moreover, the alpha diversity of rumen microbiome between day 1 and day 34 was similar, except for the Shannon Index, which has shown a significant difference. In addition, the significant differences between animal groups within start weight categories were also examined (Table 5.3). The values of three alpha diversity measures (all measures excluding the Shannon index) were significantly different between cows with start weights less than 275 kg and 372 kg.

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Figure 5.4. Boxplot distribution of alpha diversity measures for each grazing period. The significant differences between grazing periods were statistically tested using repeated measures with REML method. Boxplots with the same letter were not significantly different at the P<0.05 significance level.

Table 5.3. Differences in alpha diversity of the rumen microbiome in cows within start weight groups for days 1, 34, and 137 grazing on floodplains (mean ± stdev). The significant differences between groups were statistically tested using repeated measures with REML method.

Alpha diversity measures (Mean ± Stdev) * Start weight group (Kg) Chao1 Phylogenetic Diversity Observed OTUs Shannon 275 1828 ± 24.8a 175 ± 2.54 a 1665 ± 32.9 a 8.94 ± 0.07 313 1861 ± 22.9ab 179 ± 2.35 ab 1707 ± 30.5 ab 9.03 ± 0.06 372 1925 ± 15.2b 183 ± 1.56 b 1774 ± 20.1 b 9.07 ± 0.04 421 1904 ± 17.5ab 182 ± 1.80 ab 1758 ± 23.3 ab 9.06 ± 0.05 * Different superscript letter in the same columns showing significant differences (P<0.05).

Beta diversity analyses included the Unweighted UniFrac and Weighted UniFrac measures for each metadata category (Table 5.4). Generally, grazing period had the most influence on the rumen microbiome of floodplain grazing cows. The result of Unweighted and Weighted UniFrac measures were significantly differed between animal groups within grazing period and property of origin categories. Upon arrival (day 1), both beta diversity measures showed significant differences between animal groups according to birthplace and property of origin categories. The differences in beta 118 diversity measures within these categories lasted until day 34. At day 137, the differences in beta diversity within birthplace and property origin categories were only significantly different according to Unweighted UniFrac. In addition, the result of both UniFrac measures was significantly different between animals within age category at day 34. While animals within percentage of body weight gain category had significant differences according to Unweighted UniFrac at day 137.

Table 5.4. Statistical analysis of beta diversity measures in accordance to metadata categories. P- values were generated by PERMANOVA analysis. R values were generated by ANOSIM analysis. No. of P value* R value# Metadata Categories Unweighted Weighted Unweighted Weighted categories UniFrac UniFrac UniFrac UniFrac All data set Grazing period1 3 0.001 0.001 0.55 0.61 Age2 2 0.277 0.229 0.00 0.01 BCS3 2 0.706 0.603 -0.02 -0.03 Birthplace4 4 0.077 0.204 0.01 0.01 Hip heights5 2 0.219 0.307 0.11 0.08 BWG6 3 0.362 0.296 0.03 0.03 PropertyOrigin7 2 0.004 0.013 -0.08 -0.01 SlghtrGrid8 4 0.387 0.834 0.05 0.01 Teeth9 3 0.743 0.997 -0.05 -0.05 Day 1 Age 2 0.403 0.339 0.02 0.00 BCS10 2 0.192 0.295 0.05 0.01 Birthplace 4 0.001 0.001 0.21 0.32 Hip heights 2 0.045 0.555 0.33 0.15 PropertyOrigin 2 0.001 0.001 -0.08 0.11 SlghtrGrid 4 0.324 0.742 0.05 0.01 Teeth 3 0.895 0.998 -0.09 -0.17 Day 34 Age 2 0.022 0.043 0.05 0.06 BCS10 3 0.134 0.095 0.02 0.06 Birthplace 4 0.001 0.016 0.15 0.09 Hip heights 2 0.202 0.284 0.15 0.06 BWG 3 0.007 0.395 0.15 0.06 PropertyOrigin 2 0.001 0.001 0.04 0.00 SlghtrGrid 4 0.375 0.521 0.00 -0.02 Teeth 3 0.895 0.862 -0.18 -0.02 Day 137 Age 2 0.880 0.654 -0.02 -0.03 BCS3 2 0.963 0.847 -0.04 -0.02 Birthplace 4 0.043 0.753 -0.06 0.02 Hip heights 2 0.112 0.059 0.05 0.13 BWG 3 0.028 0.056 0.07 0.11 PropertyOrigin 2 0.005 0.228 -0.04 0.00 SlghtrGrid 4 0.18 0.327 0.11 0.08 Teeth 3 0.444 0.695 -0.04 0.01 1 Days floodplain grazing: 1, 34, and 137 days; 2Age (years old): <5, > 5; 3Body Condition Score: <2.5 (Low), 2.5–3.5 (Middle), and >3.5 (High); 4 Birthplace of the cows: 00H, BC0, KP1, and BHRF; 5 Hip 119 heights: Short (≤140 cms), High (>140 cms); 6 Percentage of curfew Body Weight Gain (mean): Low (1.22%), Middle (8.42%), and High (14.8%); 7 Property of origin: Beatrice Hills Research Farm (BHRF), Commercial properties (COM); 8 Slaughter grid weight: <275, <313, <372, and <421 kg; 9 Teeth conditions: full mouth, broken mouth, and warn full mouth; * Significantly different values with P<0.05 threshold of significance is highlighted. # R value: R values close to 1 define as completely segregated and R values close to zero are showing less segregated (Clarke & Warwick 2001).

PCoA plots were generated to visualise the results from beta diversity analysis, with Unifrac measures showing clearly segregated rumen microbial communities between grazing periods (Figure 5.5). This finding was also supported by a high R value from ANOSIM, which was 0.55 and 0.61 for the Unweighted and Weighted UniFrac analyses, respectively. The PCoA plots showed the rumen microbial community of floodplain grazing animals were similar between day 1 and day 34. However, the rumen microbial community of floodplain grazing animals at day 137 was significantly different and clearly separated from the other grazing period groups.

Figure 5.5. Differences in the rumen microbial community between days grazing on floodplain. Principal Co-ordinates Analysis (PCoA) plots were generated according to Weighted (left) and Unweighted UniFrac (right).

In contrast to grazing period, the PCoA plots indicated an association between rumen microbial community and property of origin but was dependant on grazing time at pasture. COM and BHRF animals had independent microbial communities at day 1 but converged day 34 and were totally mixed by day 137 (Figure 5.6). Moreover, a similar finding was observed for PCoA plots within the birthplace category (Figure 5.7). At day 1 the rumen microbial communities were separated in accordance to birthplace. However, rumen microbial communities merged after the cows were brought together as a herd during floodplain grazing.

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Figure 5.6. Changes in rumen microbial community of BHRF and Commercial cows during grazing on floodplains as a herd. Principal Co-ordinates Analysis (PCoA) plots were generated according to Weighted (left) and Unweighted UniFrac (right).

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Figure 5.7. Changes in rumen microbial community of cows during grazing on floodplains as a herd according to their birth places (Commercial: OOH, BCO, KP1; Beatrice Hills Research Farm: BHRF). Principal Co-ordinates Analysis (PCoA) plots were generated according to Weighted (left) and Unweighted UniFrac (right).

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5.3.1.2. Changes in rumen microbial populations during floodplain grazing ANOVA analysis of OTUs of the non-core rumen microbiome in all data sets (day 1, 34, and 137) showed that grazing period and property of origin significantly affected the OTU frequencies within the rumen microbiomes (Table 5.5). Variation in OTU frequencies were greatest for birthplace and property of origin at day 1 and reduced over time. At 34 days, the differences in OTU frequencies between animal groups within birthplace and property origin categories started to reduce and totally disappeared after 137 days of floodplain grazing. In addition, the relative abundance of the 50 most abundant core OTUs that were significantly different between grazing periods was visualised by heatmap (Appendix 1).

Table 5.5. Differences in relative abundance of non-core OTUs in accordance with metadata category. Metadata Number of OTUs with Dominant taxa of significantly different OTU category significant differences* (%) ǂ Day 1, 34, and 137 Prevotella (9.76), Chhristensenellaceae R-7 (8.63), Grazing period 534 Rikenellaceae RC9 (7.88), and Bacteroidales BS11 (3.94) Day 1 Birthplace1 248 Rikenellaceae RC9 (15.73 %), Bacteriodales BS11 (7.26%) Property origin2 355 Rikenellaceae RC9 (15.50 %), Prevotella (8.17%) Day 34 Birthplace 2 Ruminococcaceae V9D2013 group, Oribacterium PropertyOrigin 9 Rikenellaceae RC9 Day 137 Birthplace 0 - PropertyOrigin 0 - *Significantly affected according to FDR P<0.05 threshold of significance. ǂ Percentage of dominant taxa was calculated from number of OTUs with significant differences except for Day 34 data set the percentage was not shown. 1 Birthplace of the cows: 00H, BC0, KP1, and BHRF. 2 Property of origin: Beatrice Hills Research Farm (BHRF), Commercial properties (COM).

Core OTUs of the rumen microbiome were calculated as being present in 100% of animals within a group. Computation of core rumen microbial community according to property of origin resulted in a lower number of core OTUs compared to calculation based on birthplace (Figure 5.8). The lower number of core OTUs in COM animals showed a high variation of OTUs between birthplaces within the COM animals. Therefore, further analysis of rumen microbial populations was done in accordance to birthplace rather than property of origin category (that is, the COM category).

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Figure 5.8. The number of core OTUs in floodplain grazing cows in accordance birthplace (OOH, BCO, and KPI for COM animals; BHRF). Whilst, the horizontal lines are showing the number of core OTUs in accordance to property of origin (COM and BHRF).

5.3.1.2.1. Rumen microbial profile between birthplace groups at day 1 and day 34 Taxonomic profiling at day 1 showed the core rumen microbial community was dominated by Rikenellaceae RC9 gut group (mean 15.9%), Christensenellaceae R-7 group (mean 14.5%), Prevotella (mean 11.6%), and Fibrobacter (mean 5.98%). Statistical analysis within birthplace category indicated that BHRF animals were significantly more abundant in Fibrobacter, Ruminococcaceae NK4A214, and Prevotellaceae UCG-003 in their core rumen microbial community. While COM animals (OOH, BCO, and KPI) had a greater number of Christensenellaceae R-7 group, and Lachnospiraceae AC2044 (Figure 5.9). Moreover, the population of Prevotella, Papillibacter and Succinislaticum were significantly more prevalent in OOH, KPI, and BCO animals, respectively. In addition, the population of Saccharofermentas was significantly lower in OOH animals compared with other COM animals (BCO and KPI). Taxonomic profiling at day 34 showed more uniform core rumen microbial profiles between birthplace groups rather than at day 1 (Figure 5.10). Prevotella was the only genus observed that had significant difference in its relative abundance in accordance to birthplace group (P<0.01). The relative abundance of Prevotella was greater in OOH and BRFH animals rather than in KPI and BCO animals.

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Figure 5.9. Differences in relative abundance of dominant core rumen microbial genera between animals according to birthplace groups (COM: OOH, BCO, KPI; and BHRF) at day 1. The significant differences between animal groups were generated using ANOVA analysis with asterix superscripts showing the level of significance (*= P<0.05, **= P<0.01, ***= P<0.001).

Figure 5.10. Differences in relative abundance of dominant core rumen microbial genera between animals according to birthplace groups (COM: OOH, BCO, KPI; and BHRF) at day 34. The significant differences between animal groups were generated using ANOVA analysis with asterix superscripts showing the level of significance (**= P<0.01).

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5.3.1.2.2. Rumen microbial profile between day 34 and 137 floodplain grazing Core rumen microbial community of floodplain grazing cows at day 34 and day 137 was dominated by Christensenellaceae R-7 group (19.26%), Prevotella (11.11%), and Rikenellaceae RC9 gut group (9.58%). Taxonomic profiles of the rumen microbial community were significantly changed between day 34 and day 137. The population of some dominant fibrolytic bacteria such as Christensenellaceae R-7 group, Rikenellaceae RC9 gut group, and Fibrobacter decreased at day 137 compared with day 34 (Table 5.6). However, the populations associated with higher quality diets such as Prevotella, Butyrivibrio, and Pseudobutyvibrio significantly increased at day 137. In addition, there were three genera such as Ruminococcus, Ruminococcaceae UCG-010, and Lachnospiraceae ND3007 unchanged in their relative abundance between day 34 and day 137.

Table 5.6. The changes in core rumen microbial populations between day 34 and day 137 (means ± stdev; %). P-values were generated by repeated measures with REML method. Mean ± stdev (%) Rumen microbes1 P-value Change2 Day 34 Day 137 Christensenellaceae R-7 group 20.2 ± 0.82 18.3 ± 1.11 0.0214 Rikenellaceae RC9 gut group 12.2 ± 0.62 6.95 ± 0.23 <.0001

Fibrobacter 5.18 ± 0.91 2.31 ± 0.46 0.0072 f__Bacteroidales BS11 gut group 4.46 ± 0.36 2.69 ± 0.20 <.0001

Saccharofermentans 4.33 ± 0.18 3.27 ± 0.19 0.0004

Ruminococcaceae UCG-011 4.07 ± 0.28 1.53 ± 0.30 <.0001

Succiniclasticum 3.09 ± 0.23 2.14 ± 0.18 0.0007

[Eubacterium] coprostanoligenes group 2.71 ± 0.12 1.73 ± 0.13 <.0001

Ruminococcaceae NK4A214 2.54 ± 0.14 2.10 ± 0.11 0.0043

Lachnospiraceae AC2044 1.73 ±0.13 1.45 ± 0.14 0.0405

Papillibacter 1.72 ± 0.12 1.12 ± 0.09 0.0002 Prevotella 8.81 ± 0.45 13.4 ± 0.80 <.0001 p__SR1 (Absconditabacteria) 1.73 ± 0.20 2.38 ± 0.23 0.0249

f__Lachnospiraceae 1.37 ± 0.09 2.19 ± 0.11 <.0001

Butyrivibrio 1.07 ± 0.06 6.97 ± 0.33 <.0001

Lachnospiraceae XPB1014 1.05 ± 0.08 2.60 ± 0.21 <.0001

Methanobrevibacter 0.90 ± 0.08 1.17 ± 0.13 0.0302

Pseudobutyrivibrio 0.61 ± 0.06 4.87 ± 0.31 <.0001

Lachnospiraceae NK3A20 0.55 ± 0.04 1.42 ± 0.08 <.0001

Ruminococcus 3.35 ± 0.23 3.23 ± 0.33 0.7417

Ruminococcaceae UCG-010 1.19 ± 0.04 1.11 ± 0.06 0.3526

Lachnospiraceae ND3007 0.92 ± 0.07 1.13 ± 0.13 0.1313 1f__: identified to the family level; p__: identified to the phylum level; o__: identified to the order level. Only genera with relative abundance of more than 1% (dominant genera) are shown. 2Change in population between day 34 and day 137 which was decrease ( ), increase ( ), and stable ( )

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5.3.1.3. Movement of unique core rumen microbial OTUs during floodplain grazing The movement of core OTUs between animals was investigated by assessing the movement of unique core OTUs between birthplace groups. Unique core OTUs were defined as the core OTUs that were only observed in one animal group. Observations within birthplace groups showed that unique core OTUs at day 1 were much higher in BHRF animals (312 OTUs) rather than OOH (44 OTUs), BCO (63 OTUs), and KPI (49 OTUs). However, the number of unique OTUs in BHRF animals decreased significantly from 312 OTUs to 130 OTUs after 34 days grazing together with other birthplace groups as a herd (Figure 5.11). Moreover, at day 137 the number of unique core OTUs for all birthplace groups became quite similar with an average of 82 OTUs.

Figure 5.11. Changes in the number of unique core OTUs in cows according to birthplaces (OOH, BCO, KPI and BHRF) during 137 days of floodplain grazing.

The number of unique core OTUs at day 1 which transferred between animals at day 34 was much higher from BHRF animals to KPI, BCO, and OOH (COM) rather than from KPI, BCO, and OOH to BHRF animals (Figure 5.12). The number of OTUs that were unique at day 1 to KPI, BCO, and OOH that transferred into BHRF animals at day 34 were 7, 18, and 15 OTUs, respectively. While the number of OTUs that were unique at day 1 to BHRF that passed to KPI, BCO, and OOH animals at day 34 was 84, 105, and 83 OTUs, respectively. Taxonomic information analysis showed the day 1 unique OTUs of BHRF animals that transferred by day 34 was dominated by family Bacteriodales BS11 gut group (11- 20 OTUs), Prevotella (6-11 OTUs), Rikenellaceae RC9 gut group (6-8 OTUs), and Christensenellaceae R- 7 group (5-7 OTUs) (Figure 5.13).

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Figure 5.12. The number of unique OTU from day 1 that may be passed between animals according to birthplace group at day 34.

Figure 5.13. Taxonomic information - day 1 unique OTUs of BHRF animals that may be passed to OOH, BCO, and KPI cows at day 34.

The movement of day 1 unique OTUs was still observed in core OTUs at day 137 for all birthplace groups (Figure 5.14). The number of unique OTUs at day 1 that were in other groups by day 137 was quite similar to day 34 but more OTUs moved from BHRF animals into other groups. However, the unique core OTUs that transferred from BHRF animals was dominated by OTUs belonged to

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Prevotella (6-10 OTUs) except for KPI animals which were still dominated by family Bacteroidales BS11 (Figure 5.15).

Figure 5.14. The number of unique OTU from day 1 that passed between animals according to birthplace group at day 137.

Figure 5.15. Taxonomic information of day 1 unique OTUs of BHRF animals that passed into COM cows (OOH, BCO, and KPI) at day 137.

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5.3.1.4. Predicted functional genes of rumen microbial community

5.3.1.4.1. Profile of functional genes between birthplace groups at day 1 and day 34 In total there were 54 and 51 genes related with metabolism obtained from 255 predicted functional genes at day 1 and 34 (Appendix 5.1 and Appendix 5.2). At day 1, COM animals had a higher abundance of functional genes such as carbon fixation pathways in prokaryotes, cyanoamino acid metabolism, nitrogen metabolism, pantothenate and CoA biosynthesis, and propionate metabolism (Table 5.7). On the other side, BHRF animals had a greater functional gene pool associated with sulphur metabolism, and linoleic acid metabolism. Whilst within birthplace groups, OOH animals had the highest number of genes related to fructose and mannose metabolism, and starch and sucrose metabolism.

Table 5.7. Differences in relative abundance predicted functional genes of core rumen microbial community at day 1 in accordance to birthplace groups. FDR P-values were generated by ANOVA analysis. Mean (%)1 FDR P- Functional gene 00H BC0 KP1 TBH value Arginine and proline metabolism 1.20b 1.21a 1.21a 1.19c <.0001 Carbon fixation pathways in prokaryotes 0.95a 0.96a 0.95a 0.93b <.0001 Cyanoamino acid metabolism 0.40a 0.38ab 0.39ab 0.37b 0.0376 Cysteine and methionine metabolism 1.03b 1.05ab 1.05ab 1.06a 0.0472 Folate biosynthesis 0.27a 0.27b 0.28b 0.30ab 0.002 Fructose and mannose metabolism 0.65a 0.61b 0.61b 0.64b 0.0135 Galactose metabolism 0.58b 0.57b 0.56a 0.57b 0.0228 Glutathione metabolism 0.18 0.18 0.19 0.18 0.0043 Histidine metabolism 0.65a 0.65b 0.66ab 0.64a 0.0044 Lipoic acid metabolism 0.03 0.03 0.04 0.04 0.0131 Linoleic acid metabolism 0.03b 0.03b 0.03b 0.02a <.0001 Nitrogen metabolism 0.69a 0.69a 0.70a 0.68b 0.0015 Pantothenate and CoA biosynthesis 0.70a 0.69ab 0.69b 0.69b 0.0321 Pentose and glucuronate interconversions 0.68a 0.65b 0.63b 0.65b 0.0188 Propionate metabolism 0.53a 0.53a 0.53a 0.51b 0.0259 Starch and sucrose metabolism 1.41a 1.37b 1.37b 1.37b 0.012 Sulfur metabolism 0.41b 0.41b 0.41b 0.43a 0.0155 Valine, leucine and isoleucine degradation 0.21b 0.22a 0.22ab 0.21b 0.0383 Taurine and hypotaurine metabolism 0.15a 0.15ab 0.15a 0.14b 0.0268 1 The significant differences between birthplace groups were shown by different superscript letter within same row.

At day 34, the number of predicted genes that were significantly different between COM and BHRF animals reduced from 19 at day 1 to 7 genes after 34 days of floodplain grazing (Table 5.8). The findings showed COM animals had a greater abundance of genes for phosphonate and phosphinate

130 metabolism, nitrogen metabolism, and carbon fixation pathways in prokaryotes, while BHRF animals had significantly more genes for biotin metabolism and sulphur metabolism. Within birthplace categories, BHRF and BCO animals had higher numbers of gene for fatty acid biosynthesis and D- Alanine metabolism.

Table 5.8. Differences in relative abundance of predicted functional genes from the core rumen microbial community at day 34 in accordance to birthplace groups. FDR P-values were generated by ANOVA analysis. Mean (%)1 Functional Gene FDR P-value 00H BC0 KP1 TBH Phosphonate and phosphinate metabolism 0.03a 0.03a 0.03a 0.03b 0.004 Nitrogen metabolism 0.70a 0.70a 0.70a 0.69b 0.009 Biotin metabolism 0.11b 0.11b 0.11b 0.12a 0.010 Carbon fixation pathways in prokaryotes 0.96a 0.95a 0.96a 0.94b 0.010 Fatty acid biosynthesis 0.65b 0.66ab 0.65b 0.67a 0.026 Sulfur metabolism 0.40b 0.41b 0.41b 0.43a 0.030 D-Alanine metabolism 0.12b 0.12ab 0.12b 0.13a 0.032 1 The significant differences between birthplace groups were shown by different superscript letters within the same row.

5.3.1.4.2. Profile of functional genes between day 34 and day 137 floodplain grazing COWPi and PICRUSt analysis predicted 68 metabolism genes from 255 level 3 KEGG pathways. Statistical analysis showed the relative abundance of 37 predicted functional genes were significantly different in day 34 and day 137 floodplain grazing. Compared with day 34, rumen microbial community at day 137 had a greater number of functional genes for starch and sucrose metabolism, arginine and proline metabolism, nitrogen metabolism, pantothenate and CoA biosynthesis, butyrate metabolism, pentose and glucuronate interconversion, propionate metabolism, and cyanoamino acid metabolism (Table 5.9).

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Table 5.9. Differences in relative abundance of predicted metagenomic functional genes of rumen microbial community at day 34 and day 137. Only metabolism gene pathways that significantly differed between grazing periods are shown (FDR P<0.05). Mean (%)1 Functional gene FDR P-value Day 34 Day 137 Arginine and proline metabolism 1.22 ± 0.007 1.23 ± 0.009 7.83E-05 Ascorbate and aldarate metabolism 0.12 ± 0.005 0.12 ± 0.006 1.68E-05 beta-Alanine metabolism 0.16 ± 0.007 0.17 ± 0.004 3.71E-05 Biotin metabolism 0.11 ± 0.004 0.11 ± 0.004 5.00E-02 Butyrate metabolism 0.64 ± 0.049 0.67 ± 0.016 8.01E-04 Cyanoamino acid metabolism 0.36 ± 0.017 0.38 ± 0.015 2.68E-06 D-Alanine metabolism 0.12 ± 0.004 0.13 ± 0.004 2.15E-08 Linoleic acid metabolism 0.02 ± 0.003 0.03 ± 0.006 1.25E-11 Nitrogen metabolism 0.69 ± 0.010 0.72 ± 0.011 2.53E-17 Pantothenate and CoA biosynthesis 0.68 ± 0.008 0.69 ± 0.010 3.17E-06 Pentose and glucuronate interconversions 0.59 ± 0.019 0.63 ± 0.038 1.01E-06 Phosphonate and phosphinate metabolism 0.03 ± 0.004 0.04 ± 0.002 2.80E-10 Propionate metabolism 0.56 ± 0.033 0.57 ± 0.018 4.65E-02 Starch and sucrose metabolism 1.34 ± 0.036 1.38 ± 0.027 3.25E-05 Taurine and hypotaurine metabolism 0.14 ± 0.004 0.15 ± 0.005 1.64E-08 Thiamine metabolism 0.43 ± 0.013 0.44 ± 0.012 5.05E-03 Arachidonic acid metabolism 0.04 ± 0.003 0.04 ± 0.004 1.14E-04 Biosynthesis of unsaturated fatty acids 0.21 ± 0.011 0.19 ± 0.006 3.72E-10 Citrate cycle (TCA cycle) 0.50 ± 0.030 0.46 ± 0.019 5.02E-10 Cysteine and methionine metabolism 1.04 ± 0.021 1.01 ± 0.015 7.64E-12 D-Glutamine and D-glutamate metabolism 0.18 ± 0.004 0.17 ± 0.003 0.00E+00 Fatty acid biosynthesis 0.67 ± 0.016 0.61 ± 0.019 0.00E+00 Folate biosynthesis 0.26 ± 0.016 0.23 ± 0.013 5.80E-10 Fructose and mannose metabolism 0.58 ± 0.022 0.56 ± 0.028 1.38E-02 Glycerolipid metabolism 0.33 ± 0.013 0.32 ± 0.015 4.22E-03 Glycine, serine and threonine metabolism 0.96 ± 0.025 0.89 ± 0.026 0.00E+00 Glycolysis / Gluconeogenesis 0.79 ± 0.019 0.73 ± 0.016 0.00E+00 Inositol phosphate metabolism 0.13 ± 0.006 0.11 ± 0.007 0.00E+00 Lipoic acid metabolism 0.03 ± 0.004 0.03 ± 0.003 1.86E-06 Lysine biosynthesis 0.97 ± 0.013 0.94 ± 0.011 0.00E+00 Methane metabolism 1.23 ± 0.031 1.16 ± 0.041 3.96E-12 Phenylalanine metabolism 0.25 ± 0.007 0.24 ± 0.006 3.93E-12 Phenylalanine, tyrosine and tryptophan biosynthesis 1.02 ± 0.019 1.00 ± 0.020 1.09E-04 Sulfur metabolism 0.40 ± 0.020 0.38 ± 0.014 2.60E-06 Tyrosine metabolism 0.19 ± 0.006 0.18 ± 0.004 0.00E+00 Valine, leucine and isoleucine biosynthesis 0.70 ± 0.012 0.69 ± 0.011 1.83E-08 Vitamin B6 metabolism 0.26 ± 0.010 0.24 ± 0.011 0.00E+00 1 Highlighted value is showing higher mean compared with another grazing period.

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5.3.2. Study 2 The sequences received from AGRF for Study 2 had an average of 46,220 sequences per sample and a minimum and maximum of 22,118 and 112,807 sequences per sample, respectively. The total number of sequences (day 1 and 80) reduced to 423 373 sequences after trimmed, joined, and filtered sequences for all data sets. The sequences obtained were clustered into 2,259 OTUs with 97% similarity and classified into 19 phyla, 34 class, 48 order, 61 family, and 178 genera.

5.3.2.1. Rumen microbial diversity across floodplains grazing cows Alpha diversity measures had been done to investigate rumen microbial diversity of floodplain grazing animals according to metadata categories such as grazing period, BCS, start weight, ADG, and interaction between metadata categories and grazing period (Table 5.10). However, statistical analysis of alpha diversity measures indicated there were no differences in rumen microbial diversity within metadata categories.

Table 5.10. Differences in alpha diversity measures of rumen microbial diversity in accordance to animal groups. The significant differences between fixed effects were analysed by repeated measures (REML) and ANOVA for grazing period only. Alpha diversity measures (mean ± stdev)5 Fixed effect Chao1 Phylogenetic Diversity Observed OTUs Shannon Grazing period1 1 1743 ± 34.7 126 ± 2.37 1492 ± 44.1 9.17 ± 0.04 80 1701 ± 38.5 123 ± 2.70 1484 ± 48.2 9.07 ± 0.05 Start Weight 2 Grid 1 1668 ± 65.2 120 ± 4.38 1423 ± 80.1 9.01 ± 0.08 Grid 2 1725 ± 37.6 124 ± 2.53 1485 ± 46.3 9.13 ± 0.05 Grid 3, 4, &5 1750 ± 50.5 127 ± 3.39 1534 ± 62.1 9.18 ± 0.06 Start Weight by grazing period Grid 1 x day 1 1692 ± 86.2 120 ± 5.55 1401 ± 105 9.17 ± 0.10 Grid 1 x day 80 1644 ± 96.6 121 ± 6.86 1445 ± 122 8.84 ± 0.11 Grid 2 x day 1 1739 ± 49.7 125 ± 3.20 1475 ± 60.7 9.17 ± 0.06 Grid 2 x day 80 1712 ± 55.8 123 ± 3.96 1495 ± 70.5 9.08 ± 0.06 Grid 3, 4, &5 x day 1 1783 ± 66.7 131 ± 4.30 1577 ± 81.5 9.19 ± 0.08 Grid 3, 4, &5 x day 80 1717 ± 74.8 123 ± 5.31 1490 ± 94.5 9.17 ± 0.09 BCS3 3 1671 ± 81.2 121 ± 5.53 1432 ± 101 8.98 ± 0.10 3.5 1732 ± 31.8 125 ± 2.17 1497 ± 39.6 9.15 ± 0.04 4 1715 ± 81.2 123 ± 5.53 1489 ± 101 9.08 ± 0.10 BCS by grazing period BCS 3 x day 1 1647 ± 100 122 ± 6.98 1399 ± 127 9.05 ± 0.11 BCS 3 x day 80 1694 ± 114 120 ± 7.69 1465 ± 142 8.92 ± 0.16 BCS 3.5 x day 1 1740 ± 39.3 125 ± 2.74 1480 ± 49.7 9.21 ± 0.04 BCS 3.5 x day 80 1723 ± 44.6 125 ± 3.01 1514 ± 55.6 9.09 ± 0.06 BCS 4 x day 1 1861 ± 100 134 ± 6.98 1664 ± 127 9.11 ± 0.11 BCS 4 x day 80 1569 ± 114 111 ± 7.69 1315 ± 142 9.05 ± 0.16

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ADG4 High 1803 ±77.8 131 ± 4.84 1621 ± 88.8 9.08 ± 0.08 Low 1669 ± 77.8 119 ± 4.84 1387 ± 88.8 9.08 ± 0.08 ADG by grazing period High x day 1 1758 ± 101 127 ± 6.14 1545 ± 114 9.12 ± 0.10 High x day 1 1847 ± 69.5 134 ± 4.57 1697 ± 81 9.04 ± 0.12 Low x day 80 1688 ± 101 121 ± 6.14 1406 ± 114 9.07 ± 0.10 Low x day 80 1650 ± 69.47 116 ± 4.57 1368 ± 81 9.08 ± 0.12 1 Grazing period (days): upon arrived in floodplain (day 1), and after 80 days floodplain grazing (day 80); 2 Start weight according to slaughter grid weight (kg): Grid 1 weight <323, Grid 2 weight <340, and Grid 3, 4, &5 weights < 437; 3Body Condition Score based on a numeric value between 1 (very low BCS) and 5 (very high BCS). 4Average daily gain (ADG): High (0.76 kg) and Low (0.21 kg). 5Different superscript letters within the same column indicates significant differences at P<0.05 significance level (P<0.05).

Beta diversity analysis of rumen microbial diversity between animal groups within metadata categories was determined using the Unweighted and Weighted UniFrac measures. Within all data sets (day 1 and day 80), the presence (Unweighted UniFrac) and the abundance (Weighted UniFrac) of rumen microbial species was significantly different between animal groups within grazing period and BCS categories. The degree of segregation of rumen microbial communities between day 1 and day 80 was very clear as seen in PCoA plots and high R value of Unweighted UniFrac and Weighted UniFrac (Figure 5.16 and Table 5.11).

Figure 5.16. Differences in rumen microbial community of cows between grazing period on floodplain. Principal Co-ordinates Analysis (PCoA) plots were generated according to Weighted (left) and Unweighted UniFrac (right).

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Table 5.11. Differences in beta diversity measures of rumen microbial community in floodplain grazing cows in accordance to metadata categories. P values and R values were generated by PERMANOVA and ANOSIM analyses, respectively. No. of P value* R value# Metadata Categories Unweighted Weighted Unweighted Weighted categories UniFrac UniFrac UniFrac UniFrac Day 1 and day 80 Grazing Period1 2 0.001 0.001 0.963 0.820 Start weight 2 3 0.755 0.655 0.01 -0.001 BCS3 5 0.001 0.001 0.545 0.395 ADG4 2 0.602 0.891 -0.035 -0.041 Day 1 Start weight 3 0.777 0.957 0.004 -0.060 BCS 3 0.119 0.632 0.089 -0.057 ADG 2 0.239 0.12 0.125 0.177 Day 80 Start weight 3 0.063 0.126 0.163 0.064 BCS3 3 0.039 0.195 0.284 -0.062 ADG 2 0.217 0.276 0.094 -0.063 1 Grazing period (days): upon arrived in floodplain (day 1) and after 80 days floodplain grazing (day 80); 2 Start Weight according to slaughter grid weight (kg): Grid 1 weight <323, Grid 2 weight <340, and Grid 3, 4, &5 weights < 437; 3Body Condition Score based on a numeric value between 1 (very low BCS) and 5 (very high BCS). 4Average daily gain (ADG): High (0.76 kg) and Low (0.21 kg). * Significantly different values at P<0.05 threshold of significance is highlighted. # R value: R values close to 1 define as completely segregated and R values close to zero are showing less segregated (Clarke & Warwick 2001).

Findings in beta diversity of BCS category showed rumen microbial community was segregated between low BCS (2, 2.5, and 3) and high BCS (3.5 and 4). However, the segregation patterns in BCS were similar with pattern in grazing time since the initial BCS of animals was low (day 1) and then improved to higher BCS at day 80 (Figure 5.17). Therefore, the findings of beta diversity analysis within BCS groups may simply reflect the effect of grazing period. In addition, Unweighted UniFrac measures also showed significant differences within BCS groups at day 80 (P=0.039). However, the differences were not clearly separated due to low R value (0.284).

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Figure 5.17. Differences in rumen microbial community of grazing floodplain cows in accordance to body condition score (BCS) groups. Principal Co-ordinates Analysis (PCoA) plots were generated according to Weighted (left) and Unweighted UniFrac (right).

5.3.2.2. The changes in profile of core rumen microbial community during floodplain grazing 5.3.2.2.1. Profile of core rumen microbial community based on grazing period Rumen microbial profiling of 16S rRNA gene amplicon according to grazing period showed the core microbiome was dominated by Firmicutes (55.15%) and Bacteroidetes (34.11%) (Appendix 5.4). At genus level, the core rumen microbial community in animals within grazing period group was dominated by Christensenellaceae R-7 group (21.59%), Rikenellaceae RC9 gut group (15.21%), and Prevotella (14.58%). Within dominant genera with more than 1% relative abundance, the population of Christensenellaceae R-7 group, Ruminococcaceae UCG-011, Butyrivibrio, Papillibacter, Lachnospiraceae XPB1014, and Methanobrevibacter was significantly greater in rumen of cows at day 80 (Table 5.12). While at day 1, the core rumen microbiome of cows was significantly more abundant in populations of Rikenellaceae RC9 gut group, Fibrobacter, unclassified genus of phylum Absconditabacteria, and an unclassified genus of order Gastranaerophilales (Table 5.12).

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Table 5.12. Differences in relative abundance (Mean and SEM) of dominant core rumen microbial genera according to grazing period. P-values were generated by ANOVA analysis. Mean ± stdev (%) Rumen microbes1 P-value Day 1 Day 80 Christensenellaceae R-7 group 18.3 ± 0.77 24.8 ± 1.08 0.0002 Rikenellaceae RC9 gut group 18.10 ± 0.70 12.3 ± 0.68 <.0001 Prevotella 13.9 ± 0.97 15.2 ± 1.62 0.4399 Succiniclasticum 3.84 ± 0.16 4.16 ± 0.21 0.1652 Ruminococcaceae UCG-011 2.69 ± 0.14 4.40 ± 0.48 0.0071 Saccharofermentans 2.89 ± 0.17 3.13 ± 0.26 0.4496 Ruminococcaceae NK4A214 2.76 ± 0.22 2.67 ± 0.16 0.7702 [Eubacterium] coprostanoligenes group 2.62 ± 0.23 2.29 ± 0.18 0.3091 f__Bacteroidales BS11 2.01 ± 0.11 2.65 ± 0.32 0.0853 Fibrobacter 3.18 ± 0.29 1.27 ± 0.24 0.0004 p__Absconditabacteria 3.13 ± 0.41 1.25 ± 0.15 <.0001 Butyrivibrio 0.73 ± 0.04 2.42 ± 0.20 <.0001 Pseudobutyrivibrio 1.65 ± 0.24 1.27 ± 0.13 0.1481 o__Gastranaerophilales 2.36 ± 0.30 0.29 ± 0.04 <.0001 Papillibacter 1.11 ± 0.10 1.39 ± 0.13 0.0458 Lachnospiraceae XPB1014 0.96 ± 0.10 1.46 ± 0.21 0.0339 Ruminococcaceae UCG-010 1.11 ± 0.05 1.07 ± 0.07 0.6672 Lachnospiraceae AC2044 1.15 ± 0.08 0.95 ± 0.95 0.1438 Methanobrevibacter 0.50 ± 0.04 1.58 ± 0.24 0.0006 1 f__: identified to family level; p__: identified to phylum level; o__: identified to order level. Only genera with relative abundance of more than 1% (dominant genera) are shown.

5.3.2.2.2. Profile of core rumen microbial community of floodplain grazing cows in accordance to body condition score. Taxonomic profiling according to BCS groups showed 21 dominant genera as members of the core rumen microbial community at day 1 (Appendix 5.5). However, statistical analysis indicated no significant differences in the population of core rumen microbes between BCS groups at day 1. At day 80, there were 19 dominant genera within the core rumen microbial community of BCS groups. The result from ANOVA analysis showed only Ruminococcaceae UCG-010 positively linked with increase body condition score (Table 5.13).

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Table 5.13. Differences in relative abundance of dominant core rumen microbial genera at day 80 relative to body condition score (BCS). P-values were generated by ANOVA analysis Mean ± stdev (%)2 Rumen microbes1 P-value 3 3.5 4 Christensenellaceae R-7 group 23.1 ± 2.21 21.8 ± 3.87 21.2 ± 0.70 0.8657 Prevotella 11.3 ± 2.19 15.0 ± 6.18 10.4 ± 1.86 0.4681 Rikenellaceae RC9 gut group 12.5 ± 0.21 10.4 ± 2.23 12.5 ± 1.99 0.2764 f__Bacteroidales BS11 gut group 5.92 ± 0.34 5.55 ± 1.88 5.09 ± 0.22 0.8912 Succiniclasticum 3.82 ± 0.48 3.75 ± 0.75 4.09 ± 0.71 0.8285 Ruminococcaceae UCG-011 2.91 ± 0.52 3.31 ± 1.80 4.01 ± 0.08 0.8009 [Eubacterium] coprostanoligenes group 2.86 ± 1.12 2.46 ± 0.69 2.89 ± 0.09 0.6142 Ruminococcaceae NK4A214 group 1.78 ± 0.44 2.43 ± 0.53 2.90 ± 0.40 0.1303 Saccharofermentans 1.57 ± 0.86 2.35 ± 0.66 3.34 ± 0.95 0.0723 Butyrivibrio 2.13 ± 0.12 2.10 ± 0.81 2.59 ± 0.30 0.6996 Lachnospiraceae XPB1014 group 1.00 ± 0.48 1.43 ± 0.69 1.06 ± 0.44 0.5877 Ruminococcaceae UCG-010 0.91 ± 0.09 b 1.31 ± 0.31ab 1.76 ± 0.23a 0.0387 Methanobrevibacter 1.21 ± 1.00 1.32 ± 0.74 0.54 ± 0.01 0.4008 Papillibacter 0.63 ± 0.04 1.21 ± 0.40 1.61 ± 0.49 0.0694 Fibrobacter 0.14 ± 0.08 1.24 ± 0.79 1.80 ± 1.82 0.1822 f__Bacteroidales S24-7 group 1.55 ± 0.29 0.99 ± 0.41 1.69 ± 0.77 0.0739 f__Lachnospiraceae 1.04 ± 0.37 0.96 ± 0.28 0.92 ± 0.02 0.9089 Pseudobutyrivibrio 0.78 ± 0.03 0.90 ± 0.43 1.21 ± 0.23 0.5368 p__SR1 (Absconditabacteria) 1.26 ± 0.42 0.88 ± 0.42 0.81 ± 0.70 0.5077 1f__: identified to family level; p__: identified to phylum level; o__: identified to order level. Only genera with a relative abundance of more than 1% (dominant genera) are shown. 2Different superscript letters within the same column indicates significant differences at P<0.05 significance level.

5.3.2.2. Predicted functional gene of rumen microbiome at different grazing period CowPi and PICRUSt analysis indicated 255 predicted level 3 KEGG functional genes from the core rumen microbial community in accordance to grazing period. Within metabolism related genes, upon arrived (day 1), the rumen microbial community had a greater abundance of predicted functional genes for amino sugar and nucleotide sugar metabolism; alanine, aspartate, and glutamate metabolism; phenylalanine, tyrosine and tryptophan biosynthesis; lysine biosynthesis; glycine, serine and threonine metabolism; pentose phosphate pathway; valine, leucine and isoleucine biosynthesis; fatty acid biosynthesis; and fructose and mannose metabolism (Table 5.14). Moreover, after 80 days grazing on the floodplain, the rumen microbial community had a significantly greater number of predicted functional genes for methane metabolism; arginine and proline metabolism; pyruvate metabolism; butyrate metabolism; and propionate metabolism.

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Table 5.14. Differences in relative abundance of predicted metagenomic functional genes of the rumen microbial community at day 1 and day 80. Only metabolism gene pathways that had significantly different relative abundances between grazing periods are shown (FDR P<0.05). Mean ± stdev (%) Functional gene FDR P-value Day 1 Day 80 Arachidonic acid metabolism 0.04 ± 0.003 0.04 ± 0.003 5.88E-03 Arginine and proline metabolism 1.19 ± 0.007 1.22 ±0.009 7.79E-08 Butyrate metabolism 0.61 ± 0.011 0.63 ± 0.011 7.36E-06 Glutathione metabolism 0.18 ± 0.004 0.18 ± 0.005 3.59E-02 Lysine degradation 0.04 ± 0.003 0.05 ± 0.004 4.15E-04 Methane metabolism 1.21 ± 0.023 1.26 ± 0.046 2.17E-03 Phosphonate and phosphinate metabolism 0.02 ± 0.002 0.03 ± 0.003 3.92E-04 Propionate metabolism 0.52 ± 0.014 0.56 ± 0.011 3.17E-07 Pyruvate metabolism 0.81 ± 0.014 0.84 ± 0.013 4.39E-06 Thiamine metabolism 0.41 ± 0.014 0.42 ± 0.019 4.87E-02 Tryptophan metabolism 0.10 ± 0.002 0.10 ± 0.004 8.04E-05 Valine, leucine and isoleucine degradation 0.21 ± 0.004 0.23 ± 0.007 2.27E-05 Alanine, aspartate and glutamate metabolism 1.24 ± 0.018 1.22 ± 0.020 3.03E-03 alpha-Linolenic acid metabolism 0.00 ± 0.000 0.00 ± 0.001 8.17E-05 Amino sugar and nucleotide sugar metabolism 1.44 ± 0.008 1.42 ± 0.014 4.73E-05 Ascorbate and aldarate metabolism 0.13 ± 0.004 0.12 ± 0.006 4.30E-06 Biosynthesis of unsaturated fatty acids 0.19 ± 0.004 0.19 ± 0.006 3.53E-02 C5-Branched dibasic acid metabolism 0.21 ± 0.007 0.20 ± 0.007 1.58E-02 Cyanoamino acid metabolism 0.40 ± 0.015 0.39 ± 0.023 3.91E-02 D-Glutamine and D-glutamate metabolism 0.18 ± 0.003 0.17 ± 0.003 3.20E-05 Fatty acid biosynthesis 0.64 ± 0.014 0.62 ± 0.021 3.03E-02 Folate biosynthesis 0.27 ± 0.012 0.24 ± 0.011 1.82E-07 Fructose and mannose metabolism 0.62 ± 0.022 0.59 ± 0.028 1.32E-03 Galactose metabolism 0.56 ± 0.014 0.54 ± 0.018 2.83E-03 Glycine, serine and threonine metabolism 0.98 ± 0.025 0.95 ± 0.025 3.11E-03 Glycolysis / Gluconeogenesis 0.79 ± 0.012 0.78 ± 0.014 4.89E-02 Lipoic acid metabolism 0.04 ± 0.003 0.03 ± 0.003 9.82E-04 Lysine biosynthesis 0.98 ± 0.010 0.97 ± 0.009 2.15E-02 Pentose phosphate pathway 0.77 ± 0.010 0.74 ± 0.010 4.76E-07 Phenylalanine metabolism 0.26 ± 0.005 0.26 ± 0.005 9.99E-03 Phenylalanine, tyrosine and tryptophan biosynthesis 1.03 ± 0.017 1.01 ± 0.013 1.45E-03 Riboflavin metabolism 0.21 ± 0.009 0.19 ± 0.008 1.35E-05 Sulfur metabolism 0.39 ± 0.010 0.36 ± 0.009 8.41E-08 Tyrosine metabolism 0.19 ± 0.005 0.18 ± 0.005 3.08E-04 Valine, leucine and isoleucine biosynthesis 0.71 ± 0.008 0.70 ± 0.008 2.18E-02 Vitamin B6 metabolism 0.28 ± 0.009 0.26 ± 0.011 5.15E-06

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5.4. Discussion Investigating the profile of rumen microbial community of cattle managed under commercial conditions is novel to the field of rumen microbiology and has led to new insights. This research conducted two studies that investigated the rumen microbial community changes of culled Brahman Cross cows while transitioning to floodplain pastures. In Study 1, the profile of rumen microbiome of commercial and BHRF animals during transition to floodplains was investigated. While in Study 2, the changes in the rumen microbiome during the 80 days on floodplain pasture was investigated with commercial cull cows (Figure 5.2).

5.4.1. Study 1 The results of study 1 showed that much of the variation in the rumen microbiome between animals was significantly associated with different places of origin or birthplace. Alpha and beta diversity at day 1 showed significant differences between animal groups within property of origin and birthplace categories (Section 5.3.1.1). The PCoA plots also supported the findings with segregated plots between animal groups, particularly between BHRF and COM animals (Figure 5.6 and Figure 5.7). The animal-origin-specific rumen microbial community was found in this study also reported by (Henderson et al. 2015). The factors affecting this specific rumen microbial profile are the source of inoculant in early rumen colonisation and the background feeding regime (Rey et al. 2014; Henderson et al. 2015). The process of shaping an animal’s origin-specific rumen microbial community begins in early life with inoculation from the cow (Hobson & Stewart 1997) and continues picking up microbes from surrounds (i.e. other animals and feed) until well established in mature animals (Curtis & Sloan 2004). In fact, Jami et al. (2013) reported that some of bacteria species that are essential for rumen function in mature animals was found in the rumen of one day old animals. The development of rumen microbial community is then also shaped by diet condition (Rey et al. 2014). Diet is well known as the main driver factor that affects the population of the rumen microbial community (Henderson et al. 2015). In the current study, according to pasture species, the floodplain is dominated by Pseudoraphis spinescens (spiny mud grass), Echinochloa colona (awnless barynyard grass), Oryza australiensis (wild rice), and Paspalum scrobiculatum (scrobic) (Cameron & Lemcke 2003). While the species grasses in non-floodplain area where the commercial animals came from is dominated by C4 native grass such as Astrebla spp. (Mitchell grass), Heteropogon contortus (black spear grass) (Parker & Cobiac 2002). The differences of pasture species between extensive commercial properties and floodplain was associated with the differences in the rumen microbial community. Therefore, animals from a particular property may develop a unique rumen microbiome specific to that property and to the diet (e.g. pasture type) usually provided at that property.

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The findings based on the rumen microbial populations and predicted functional genes based on the phylogeny of the 16S rRNA gene sequences also indicated the possibility of different diet conditions between commercial and BHRF properties. Upon arrival, commercial animals, particularly those which originated from OOH, had higher populations of Prevotella in their rumen. BLAST search of the most abundant OTU classified as Prevotella within OOH animals (OTU 578085 and OTU 582088) resulted in 95% similarity with Prevotella ruminicola (Purushe et al. 2010). Previous whole genome- sequencing of this species of bacteria has shown a high abundance of the glycoside hydrolase enzyme for degrading glycosidic linkages in complex carbohydrate such as starch (Purushe et al. 2010). The COWPi and PICRUSt analysis showed COM animals had more abundance of predicted genes related with higher grain (source of energy) diets such as carbon fixation pathways in prokaryotes, cyanoamino acid metabolism, nitrogen metabolism, pantothenate and CoA biosynthesis, and propionate metabolism. Carbon fixation pathways in prokaryotes are associated with energy harnessing in bacteria and archaea (https://www.genome.jp), and biosynthesis of volatile fatty acids (Russell & Rychlik 2001; Fuchs 2011). This gene enriched the abundance of six enzymes that are associated with synthesis of acetate, propionate and butyrate (Zhang et al. 2016). Cyanoamino acid metabolism was observed in cows with a high grain diet and linked to pantothenate and CoA biosynthesis (McCann et al. 2016). While propionate is mainly produced from fermentation of starch by rumen microbes (Xue et al. 2018). On the other side, BHRF animals had a greater population of Fibrobacter. Fibrobacter is a predominant cellulolytic in the rumen and (Suen et al. 2011b) found more abundant in animals with high fiber diet (Zhang et al. 2018). Predicted functional genes analysis showed that BHRF animals had higher number of genes associated with linoleic metabolism in the rumen. Linoleic acid is a major unsaturated fatty acid that is found in the forages (Corl et al. 2000). Therefore, the COM animals in the current study might have had grain as a part of their diet before being transported and introduced to grazing on the floodplains. Unfortunately, there was no detailed information available for this study, regarding the previous diets provided to the COM cows. The significant variation in rumen microbial diversity between animal groups that was observed upon arrival diminished after 34 days of floodplain grazing and became almost homogenous by day 137 (Figure 5.3 and Figure 5.6). Changes in the diversity of the core rumen microbiome indicated that the rumen microbial community of COM animals had adjusted to the floodplain pasture diet by day 34 through changed diet and/or horizontal inoculation from BHRF cows. The ability of rumen microbes to adapt with the new feed has been shown from the early life of the animal when the diet shifted from liquid feed into solid feed (Dias et al. 2018). The ability of the rumen microbiome to adapt to changed diet is important to maintain a sustainable nutrient supply for the animals in the form of VFAs and microbial protein (Storm et al. 1983; Owens. & Goetsch 1993; McCann et al. 2014a).

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In addition, the adjustment period of rumen microbiome to be fully adapted with the new feed can vary from a day (Hackman 2015) to four weeks (Nakano et al. 2013). The movement of core OTUs showed a flow or horizontal transfer of microbes, which occurred mainly from the BHRF to the COM animals (OOH, BCO, and KPI) (Figure 5.11). Horizontal transfer enables the rumen microbial community to adapt to changed diet by enrichment from better adapted (locally sourced) cattle. A similar outcome was reported during early rumen colonization from maternal transfer and their surrounding environment (Curtis & Sloan 2004; Li et al. 2012). Another study by Fonty et al. (1987) showed that lambs that were reared in a herd had more established rumen microbiota compared with lambs raised in isolation pens. In the current study, the marked decrease in unique OTUs of BHRF animals on day 34 showed how the rumen microbial community of COM animals adapted to the feedbase of the floodplain environment, with a more similar microbial profile occurring between the BHRF and COM animals. The OTUs that appeared to be passed from the BHRF to the COM animals after34 days of co-grazing, were predominantly classified as Bacteriodales BS11 (Figure 5.13). Bacteroidales BS11 has been reported as the fifth most predominant microbe in the rumen (Henderson et al. 2015), is enriched in ruminants fed a high-fibre diet (Solden et al. 2017) and associated with the feed-adherent fraction of the microbiome (Koike et al. 2003). Proteomic and rumen metabolites analysis showed BS11 had a role in carbon transformation and sustaining host energy during diet changes condition (Solden et al. 2017). Interestingly, Solden et al. (2017) also reported that BS11 are host associated bacteria which are only found in samples from moose. Therefore, the inoculation of BS11 most likely originated from other cows within the herd, rather than from environment (i.e. grasses). While at day 137, the dominant OTUs from BHRF were dominated by Prevotella (in OOH and BCO) and Bacteriodales BS11 (in KPI). The appearance of unique OTUs belonging to Prevotella at day 137 might be related with the better nutrition content of floodplain pasture at day 137 compared with day 34. In the current study, rumen microbial diversity was significantly associated with grazing period. Particularly between day 34 and day 137. The changes in rumen microbial diversity in this period may be associated with the better nutrition content and nutrient digestibility of the floodplain grasses. A report by Ramírez et al. (2001) showed the dynamic nutrient content and nutrient digestibility of forage in accordance to seasonal conditions. The nutrient content (i.e. crude protein) of tropical forage is higher in the wet season rather than in the dry season (Ian 2011). The data from weather station near the research site showed a major rain event between day 34 and day 137 at BHRF (Figure 5.1; www.bom.gov.au). Increased moisture increases the nutrient content of the pasture, as indicated by the higher green cover fraction at the research site at day 137 compared with day 34 (Figure 5.2; Karfs et al. (2004)).

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The changes in taxonomic profile and predicted functional genes of core rumen microbiome between day 34 and 137 also supported the hypothesis of better pasture condition during the rain event. The profile of the core rumen microbial community of all cows after 137 days grazing on floodplain had a greater number of rumen microbes that could be associated with a better-quality diet such as Prevotella, unclassified genus of family Lachnospiraceae, Lachnospiraceae XPB1014, Lachnospiraceae NK3A20, Butyrivibrio, Pseudobutyvibrio, and Methanobrevibacter. The most abundant OTUs belonged to Prevotella (OTU 326292, OTU 327110, and OTU 150043), with similarity between 91.1% and 97.61% to P. ruminicola (Purushe et al. 2010). The abundance of P. ruminicola is associated with the predicted high gene frequency for propionate metabolism at day 137. P. ruminicola produces propionate along with other VFAs from glucose and xylose fermentation (Kovár et al. 1999). Strobel (1993) reported the utilisation of pentose in plant cell walls by P. ruminicola, which could be reflected in high numbers of pentose and glucuronate interconversion genes, observed in this study. The pathway map of pentose and glucuronate interconversions shows the association of genus Prevotella in the interconversion process (www.genome.jp/kegg/pathway.html; Kanehisa and Goto (2000)). Members of the family Lachnospiraceae, such as Lachnospiraceae XPB1014 and Lachnospiraceae NK3A20 generally have the ability to synthesise butyrate from complex sugars like starch (Kanehisa & Goto 2000; Flint et al. 2012; Meehan & Beiko 2014). Butyrivibrio and Pesudobutyrivibrio are also the main butyrate producers in the rumen (Kopečný et al. 2003) and are more abundant in animals with easily fermentable diet such as starches or soluble sugars (Zhang et al. 2018). The higher populations of these butyrate producing bacteria at day 137 correlates with the high abundance of predicted genes related to butyrate metabolism and starch metabolism at the end of the trial (Table 5.9). COWPi and PICRUSt analysis also showed that floodplain grazing animals at day 137 had more abundant genes related to energy metabolism such as cyanoamino acid metabolism, and pantothenate and CoA biosynthesis, associated with a readily fermentable diet (McCann et al. 2016). In addition, the higher frequency of predicted genes related with nitrogen metabolism, appears indicative of the increased nitrogen content in the rumen at day 137, as reflected in the high nitrogen content in the diet.

5.4.2. Study 2 At study 2, the changes in rumen microbial diversity during the adjustment period to floodplain grazing were under better nutritional pasture conditions than in Study 1. Data from the Vegmachine website (Karfs et al. 2004) showed the trend towards a higher proportion of green feed during study 2 compared with study 1 (Figure 5.2). The increased proportion of green cover is likely related to the high rainfall at the end of previous year (Figure 5.1). The changes in rumen microbial

143 diversity within Study 2 was mainly driven by grazing period since the animals originated from a single source. In the current study, the findings in alpha diversity measures showed no significant differences between animal groups for all metadata categories. While beta diversity measures indicated significant differences of in rumen microbial diversity between grazing periods and BCS categories. The differences between BCS were not as significant as observed for grazing period. The PCoA plots of beta diversity measures showed that animals grouped in the same pattern as for grazing period. Therefore, differences in beta diversity within BCS categories reflect the effect of grazing period instead of the effect BCS on the rumen microbial community profiles. The findings showed the changes of the rumen microbiome to the improved nutrition of floodplain pastures. The OTUs frequency analysis of non-core rumen microbial community also indicated the differences in the feeding condition. At day 1, non-core rumen microbial community had greater OTUs belonging to Rikenellaceae RC9 gut group, Ruminococcaceae UCG-014, undefined genus of ordo Gastranaerophilales, and Prevotella (9.09%). While after 80 days floodplain grazing, the non- core rumen microbial community had greater OTUs dominated by Christensenellaceae R-7 group, Rikenellaceae RC9 gut group, undefined genus of family Bacteroidales BS11 gut group, and Butyrivibrio. Within core rumen microbiome, the changes in microbial diversity during floodplain grazing were influenced by the dynamic nutritive quality of floodplain pasture due to transitional from dry to wet season between day 1 and 80. In fact, the between year variability for pasture quality greatly affects animal performance and is an important issue for beef enterprises in northern Australia (Bortolussi et al. 2005). Rain fell from October 2017, just a month after animals arrived at the floodplain (day 1). In addition, according to the background diet, the animals came from a month of backgrounding on a poor-quality black spear grass pasture. Therefore, the nutritive quality of floodplain pasture was better from day 1 onwards. The core rumen microbial population and predicted functional genes indicated the better-quality pasture condition at day 80. Rumen of floodplain grazing animals at day 80 significantly had more abundance of Christensenellaceae R-7 group, Ruminococcaceae UCG-011, Butyrivibrio, Papillibacter, Lachnospiraceae XPB1014, and Methanobrevibacter. In the current study, Christensenellaceae R-7 group was the most dominant genus in core rumen microbiome of floodplain grazing animals. The abundance of this bacteria during floodplain grazing was in agreement with the association of this bacteria with roughage-based diet condition (Creevey et al. 2014). Some study have reported that the member of family Christensenellaceae have been previously observed to associate with feed particles and therefore have a significant role in plant-fibre degradation (Liu et al. 2016; De Mulder et al. 2017; Shen et al.

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2017). The properties of Ruminococcaceae UCG-011 has not been defined yet. The taxonomic assignment of the most abundance OTU of this genus through BLAST page resulted in a low similarity (89.01%) to Gracilibacter thermotolerants. Hook et al. (2011b) and Mao et al. (2013) reported that some genera of family Ruminococcaceae can ferment starch and are classified as amylolytic bacteria. Butyrivibrio and Lachnospiraceae XPB1014 are known to synthesise butyrate from easily fermented substrate such as starch (Meehan & Beiko 2014; Zhang et al. 2018). Papilibacter is a strictly anaerobe gram positive bacteria that degrades aromatic compounds and organic acids into acetate and butyrate (Defnoun et al. 2000). The abundance of bacteria that produce butyrate at day 80 agreed with the higher number of predicted genes associated with butyrate metabolism. A recent study reported similar findings, of higher butyrate concentration in rumen of cattle grazing the wet season pastures of northern Australia (Perry et al. 2017). There was an increase in the population of Methanobrevibacter in the rumen of cows on the floodplain. The abundance of Methanobrevibacter at day 80 correlates with the high number of genes for methane metabolism. According to BLAST assignment of OTU 842598, this methanogen had 99.29% similarity with Methanobrevibacter millerae (Rea et al. 2007). Methanobrevibacter are predominant methanogens in sheep and cattle (Wright et al. 2004). High methane metabolism in the rumen of cows grazing the floodplain is in agreement with Perry et al. (2017), who reported a 40-50% increase in daily methane production when steers were fed wet season pasture compared with dry season hay. However, the improvement in nutritive quality of the feed actually reduces the ratio of

CH4 production per kilogram dry matter intake due to the increased flow rate of feed through the rumen (Benchaar et al. 2001).

5.5. Conclusion This work may demonstrate microbial transfer between animals as a mechanism for modulating the rumen microbial community of a herd of animals during the transition period to floodplain pastures. However, the changes in rumen microbial community caused by a changed feed base (extensive to floodplain pastures) which then favoured organisms that were previously minor players, even below levels of detection. This work also showed the impact of on-farm practices on the diversity of the rumen microbial community and the dynamic nature of rumen microbial communities of animals grazing floodplain pastures. Both studies reconfirmed that diet quality is the main factor affecting rumen microbial diversity. The property of origin was highly associated with the profile of rumen microbiome on arrival to the floodplain but had a reducing effect over time.

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Chapter 6. General discussion and Conclusion

6.1. General Discussion In this thesis I have investigated the dynamic changes in ruminal microbial diversity in Australian cattle under a range of nutritional and environmental conditions. The objective from this study was to modulate rumen microbial populations through probiotic use, diet change and farm practices in animals of different ages, while considering the animal’s background. We hypothesised that the addition of a probiotic would change the profile of rumen microbial community (particularly the core microbiome) when provided at early age and diet treatments would still impact cows, when applied at an older age. Treatments aimed at changing the rumen microbial community were delivered at three different age classes of cattle, i.e. pre-ruminating calves, post weaning steers and adult cows. Results suggest that the biota of weaned and mature cattle is affected by diet. However, this finding could not be generalised due to the different treatments investigated; probiotic for pre-ruminating calves; feed supplementation for post weaning heifers; and pasture quality change for adult cows. Therefore, the mechanism or factors of relevance to these treatments on the rumen microbial community are likely to be different. To modulate rumen microbial diversity in early life of animals. Young dairy calves (less than a week old) inoculated with Bacillus amyloliquefaciens strain H57 probiotic (H57), as the mechanism to alter the rumen microbiome (Schofield et al. 2018). However, the result indicated that H57 failed to establish in the rumen of dairy calves when administered in the milk replacer. A very low population of H57 appears to have been maintained in the rumen of dairy calves, and it is thought that this was primarily caused by the oesophageal groove mechanism bypassing milk replacer, containing H57, into the lower GI tract rather than to the rumen. A similar situation has been reported by Zhang et al. (2017) in delivering Lactobacillus plantarum and Bacillus subtilis in milk replacer to calves. Therefore, the impact of H57 on the diversity of the rumen microbial community was not as marked as expected. This non-significant result was also reflected in the growth performance of these calves in a parallel study by Le (2017). However, the core rumen microbial community was affected to an extent by the addition of H57. The inoculated calves had a greater abundance of Prevotella and Shuttleworthia in the core community compared with control calves. This effect was also supported by the findings in the predicted functional genome of the core microbiome which showed a higher frequency of peptidase genes. Whether this change would have persisted to adulthood was not investigated. Since the population of ruminal H57 in this study was below the detectable level, the mechanisms by which H57 might modulate the rumen microbial community and has done in other

146 studies (Schofield et al. 2018) is not immediately evident. However, it is possible to speculate on three possible modes of actions. First, the role of H57 in scavenging oxygen in the rumen. The concentration of oxygen is relative higher in the rumen of pre ruminating animals, compares to the mature rumen, as indicated by changes in populations of aerobic and facultative anaerobic bacteria to strictly anerobic bacteria (Jami et al. 2013). Lei et al. (2015) reported that B. amyloliquefaciens consumes oxygen in the rumen for growth. The disposal of ruminal oxygen creates anaerobic conditions that favour the growth of anaerobic species (Yu et al. 2009). H57 also has the ability to grow in anaerobic conditions through nitrate reduction, possessing respiratory nitrate reductase genes (narGHJI) and the transcriptional regulator fnr (Schofield et al. 2016). This property should enable the established of H57 in the rumen as reported by Nakano and Zuber (1998) for the growth of B. subtilis. The second mode of action is associated with antimicrobial character of this species. A recent genomic study of B. amyloliquefaciens H57 showed a number of genes related with antimicrobial compound (ballomycin D, surfactin, Fengcyin, and iturin) (Schofield et al. 2016). Other researchers have reported the synthesis of these active compounds (surfaction, fengcyin, iturin, and bacillomycin) from Bacillus amyloliquefaciens sp (Chowdhury et al. 2015; Shi et al. 2018). In addition, whole genome sequencing also showed H57 possess glycoside hydrolase enzymes for complex carbohydrate degradation (Schofield et al. 2016). The possession of these properties would give a significant support for H57 to alter the fermentation pattern and rumen microbial profile of dairy calves. The question as to whether the timing of rumen intervention can also occur later in the life of the ruminant, at post weaning and in adult cattle was addressed (Chapter 4). The rumen microbial community varies according to diet and geographic location (Henderson et al. 2015). In the current study, the rumen microbiome of post-weaning beef cattle was influenced by feed supplementation. Weaning is an important event in rumen physiology and for the microbial community residing in the rumen due to a major dietary shift from liquid (milk) to solid feed (a plant based diet) (Ungerfeld et al. 2011; Rey et al. 2014). The abrupt change in diet type, i.e. during early weaning negatively impacts the rumen microbial community (Enríquez et al. 2011) and animal productivity (Oyedipe et al. 1982). Feed supplementation has been applied to reduce the negative impact of early weaning on animal productivity (Moriel et al. 2014). In the current study, feed supplementation significantly altered the rumen microbial diversity (alpha and beta diversities) in post weaned cattle. Post weaning supplementation increased populations of plant-fibre (i.e. Christensenellaceae R-7 group and Ruminococcus) and starch utilising bacteria (i.e. Ruminococcaceae UCG-014, and Lachnospiraceae XPB1014) (Flint et al. 2012; Zeng et al. 2015; Li et al. 2017a; Shen et al. 2017). Thus, the feed supplementation enhanced ruminal fermentation as indicated by higher functional genes identified

147 as being associated with the rumen microbiome, including genes involved in glycolysis and propionate metabolism. A similar effect of diet in modulating the rumen microbiome was also observed in adult cattle. The effect of diet change and geographical shift from grazing extensive pasture to sub-coastal floodplain pasture, on the rumen microbiome was investigated. The findings (Chapter 5; Study 1 and 2) showed that the diversity within the rumen microbiome and the predicted functional genes of the core microbiome adapted and adjusted in accordance to the floodplain pasture diet. Although there was no data for the nutrient content of floodplain pasture in each grazing period, satellite images of ground cover at the trial site showed the positive correlation between better green cover and changes in rainfall. The nutrient content and digestibility of forage was higher in the wet season compared to the dry season (Ramírez et al. 2001; Ian 2011). The diversity of the rumen microbial community clearly clustered according to grazing periods associated with the rain. The findings in population of core rumen microbial community showed a greater abundance of rumen microbes which linked to a more nutritious diet i.e. Prevotella, Lachnospiraceae, Butyrivibrio, Pseudobutyrivibrio, Lachnospiraceae XPB1014, and Methanobrevibacter. Whilst the predicted functional genes of the core microbiome indicated a higher abundance of genes for propionate metabolism, butyrate metabolism, starch metabolism, pyruvate metabolism, nitrogen metabolism, and methane metabolism. In these two studies, the species of grasses between respective pastures was significantly different i.e. Pseudoraphis spinescens (spiny mud grass), Echinochloa colona (awnless barynyard grass), Oryza australiensis (wild rice) for floodplains and Astrebla spp. (Mitchell grass), Heteropogon contortus (black spear grass), and Themeada triandra (Kangaroo grass) for extensive pastures (Parker & Cobiac 2002; Cameron & Lemcke 2003). Results indicated that the rumen microbiome changed in order to adjust to the changed pasture conditions. There appears to be a plasticity in the rumen microbiome to allow for adjustment to changed feeding conditions, similar to that reported by Belanche et al. (2019) in the rumen of sheep. This plasticity is associated with the core rumen microbiome, which is essential to overall rumen function (Weimer 2015). Dividing the large number of OTUs from NGS sequencing (Hu et al. 2019; Wang et al. 2019) and the relatively small number of substrate types for ruminal degradation indicated many different species may have overlapping in substrate degradation (Jami & Mizrahi 2012a; Li & Guan 2017). A recent study on the effect of changing feed from non-grazing to grazing diets on the core rumen microbial profile (Belanche et al. (2019) showed the adjustment process within the core microbiome in accordance to a shift of diets was by increasing the concentration and diversity rather than replacing the core members (Belanche et al. 2019). The similar result was observed in this study, the core member of rumen microbiome was similar prior and post floodplain grazing.

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Upon arrival at the trial sites, the rumen microbiome varied in accordance to property of origin (Chapter 4 and 5). The findings of the feed supplementation trial (Chapter 4) also showed differences occurring between the rumen microbial community of heifers originating from individual properties of origin (Hayfield and Kalala) upon arrival at the research site. The taxonomic comparation showed Hayfield heifers had greater populations of Prevotella, Succiniclasticum, Fibrobacter, order Gastranaerophilales, Ruminococcaceae UCG-010, and Ruminococcaceae UCG-014 in their non-core microbiome and lacked Succiniclasticum in core microbiome. Whilst, Kalala heifers had a significantly greater numbers of the Rikenellaceae RC9 gut group, Saccharofermentas, Butyrivibrio, Lachnospiraceae AC2044 group, and an unclassified genus of family Lachnospiraceae; with an additional genus, Pseudobutyrivibrio, in the core microbiome. In Chapter 5, we observed significant differences occurring in the rumen microbial profile of cows originating from commercial properties (COM) and the Beatrice Hills Research Station (BHRF), upon arrival on floodplain site. The COM cows, particularly those which originated from the OOH station, had higher populations of Prevotella in their rumen. In addition, the predicted functional genes from core rumen microbiome showed the COM animals had higher abundance of genes associated with grain feed such as carbon fixation pathways in prokaryotes, cyanoamino acid metabolism, nitrogen metabolism, pantothenate and CoA biosynthesis, and propionate metabolism compared with BHRF animals which fed floodplain pasture- based diet. This specific property or origins-based rumen microbial community might be in accordance with feeding conditions provided at each location. A global study showed the rumen microbial community varies according to the diet (Henderson et al. 2015). Diet during weaning period also shapes the rumen microbial community (Rey et al. 2014). A comprehensive study examining new-born goats, indicated that feeding management had significant impact to the rumen microbiome development and rumen fermentation patterns (Abecia et al. 2014a). Henderson et al. (2015) also reported the specific rumen microbial community in accordance to geographic location. As discussed previously, the specific property-based rumen microbial profile is shaped through source of early inoculation (i.e. mother, herd, and environment). The mother is the main source of early colonisation in newborn animals (Stewart et al. 1997; Li et al. 2012a). Which also indicated by Jami et al. (2013) that reported some microbes that common in adult animals were also observed in a day-old calves. As reported for the inoculation of gut microbiota in mammals, the inoculation of rumen microbiota from the surrounding environment continue throughout the animals life (Curtis & Sloan 2004). Thus, the source of inoculant can cause quite distinct variation of rumen microbial community (Rey et al. 2014). In addition, Zhang et al. (2016) reported the different rumen microbial profile between animals that being reared in high and low altitude area which linked with environmental adaptation

149 mechanism of microbiomes and animal as the host. Therefore, the feeding management and farm location have a significant contribution in variation of rumen microbial profile in adult animals (Martín- García et al. 2013; Henderson et al. 2015; Wang et al. 2016) and in association with the adaptation process of animal (Zhang et al. 2016). However, in our study, the variation of rumen microbial population in accordance to animal origin was diminished after mixing of cattle and being treated as a single herd on the same diet and. The findings indicated, a part of direct feed effect (Chapter 4), the incident of horizontal transfer of microbes between animals was observed as a mechanism to adjust with the new feed condition (Chapter 5). Horizontal inoculation is an essential event in developing ruminal microbial community. Studies in young ruminant showed how rumen being inoculated by horizontal transfer from maternal and their surround environment (Curtis & Sloan 2004; Li et al. 2012a). The horizontal inoculation improved the establishment of rumen microbes better than animals that isolated from other animals (Fonty et al. 1987). Generally, calves that nursed by their dam are weaned progressively, in contrast with calves that immediately separated from their dam after birth and fed with milk replacer (Guilloteau et al. 2009). Currently there is lack of published information regarding with the horizontal inoculation of rumen microbes between adult animals. In our study, we observed the indication of horizontal inoculation of rumen microbes between adult animals, with the exchange of microbes occurring between animals when they were introduced into a herd situation. In Chapter 4, the horizontal inoculation also may have occurred vice-versa, with a similar number of transferred OTUs between animals from Kalala and Hayfield stations. The horizontal inoculation may have contributed to the merging of two distinct core microbiomes (Kalala and Hayfield) into a more homogenous microbial community. Interestingly, the findings in the Chapter 5 (Study 1) showed the direction of horizontal inoculation of core microbiome was mainly flowing from local animals (BHRF) into newcomer animals from commercial properties. BHRF animals had a better adapted condition with floodplain pasture diet compared with COM animal that transported into floodplain. Moreover, the study observed that the unique core OTUs that passed from BHRF animals to COM animals at the first 34 days on floodplains were dominated by Bacteriodales BS11. Solden et al. (2017) analysed the microbial composition from animal derived samples (moose) and non-animal samples (e.g. grass, and soil) and concluded that BS11 were host associated bacteria only and did no arise from the environment Therefore, the species transferred between cows during the first 34 days on floodplain pastures, were more likely sourced from other cows within the herd, rather than non-animal sources. In addition, proteomic and rumen metabolites profiles showed the appearance of BS11 in the rumen had an important role during diet change, by sustaining host energy and carbon transformation (Solden et al. 2017).

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However, there was no supportive analysis to track the passing of rumen microbial between animals from different station of origins have been done in this study. Results from dairy calves study showed the possibility of probiotic inoculant inhabited rumen with low number below the PCR detection limit. Schofield (2017) suggested minimum number 6.7 x 104 cells mL-1 for a species bacteria to be detected in rumen fluid samples by PCR analysis (Schofield 2017). Therefore, the incidence what we call it horizontal transfer can be also explained as the effect of new feed base to the existing low percentage rumen microbial species. Bacteroidales BS11 was found being cultured up in rumen of COM animals during graze in floodplain pasture. At the first day on floodplain, BHRF animals had higher number of unique OTUs Bacteroidales BS11 compared with COM animals which showed this species already adapted with floodplain feeding condition. After grazing 34 days the number of unique OTUs of Bacteroidales BS11 was found similar between COM and BHRF animals. The existing Bacteroidales BS11 may have cultured up in the COM animals along with the intake of feed base in floodplain pasture. Bacteroidales BS11 is normally found in the rumen (Henderson et al. 2015) and enriched in ruminants fed a high- fibre diet (Solden et al. 2017). The similar finding also reported at day 137 when the number of some Prevotella species increased at day 137 due to the better nutrition content of floodplain pasture at day 137 compared with day 34.

6.2. Implication of the study The findings of the current study may have implications for the development of commercial practices to modulate the rumen microbial community at different stages of animal development. The study began with preweaning dairy calves being supplemented with a probiotic. The study then progressed to feed supplementation of weaning animals and finally examined the effects of changing pasture feed-base in adult cattle. The time from birth to weaning is one of the key stress periods affecting lifetime productivity and therefore the economic benefit associated with ruminant livestock. The addition of probiotics during the early life of animals is common practice in the dairy industry, where it is considered to improve calf health status and growth performance (Uyeno et al. 2015). However, it is important to note that the efficacy of probiotic administration is still inconsistent. The dairy calf study (Chapter 3.) showed the efficacy of probiotic supplementation in targeting the rumen microbial community, although the exact mechanisms by which this probiotic affects other rumen microbes could not be determined. The application of probiotics for modifying the gut of the pre-ruminant calf, may be hampered by the oesophageal groove mechanism, particularly when applied in liquid feed such as milk replacer. Therefore, probiotics applied to very young calves may be of for more benefit to the development of the microbiome of the lower intestine rather than in rumen and may be more 151 beneficial to un-healthy rather than healthy animals. Uyeno et al. (2015) noted that the efficacy of probiotics was higher in cattle which were stressed, rather than in healthy animals. A similar result was also obtained in a parallel study to this one, which also used the H57 probiotic. This study showed the benefit of applying the H57 probiotic was not significant in dairy calves, 8 weeks of age which were experiencing low levels of stress (Le 2017). Moreover, in a previous study using dairy calves with lower health status than in the current study, the addition of H57 probiotic significantly reduced the incident of diarrhoea (Le et al. 2017). The mechanisms of probiotic action, to improve gut health, are thought to include (1) stimulation of the intestinal epithelial barrier function (Ohland & MacNaughton 2010) through promoting mucus secretion; (2) increasing the levels of immunoglobulin A (IgA) and anti-beta- lactoglobulin (β-LG) in intestinal epithelial cells; and (3) out-competing pathogenic bacteria by producing antimicrobial substances or competing for epithelial adherence (Takahashi et al. 1998; Bajaj-Elliott et al. 2002; Nader-Macias et al. 2008; Tejero-Sariñena et al. 2012; Arqués et al. 2015). On-farm practices for modulating the rumen microbial communities of adult animals and how established rumen microbial communities respond to diet treatments were investigated in Chapters 5 and 6. At the start of these experiments, the rumen microbial community present in the mature animals, differed markedly between individual animals and between animals sourced from different geographical locations. This rumen microbial communities of mature animals may have been shaped for years and transferred from their herd or adjusted in accordance to diet, environmental conditions and farm management practices such as the use of antibiotics either for growth promotion or for health reasons. These observations were similar to those reported in the global rumen census undertaken by Henderson et al. (2015). In our study, the rumen microbial communities were found to merge following feed supplementation for 22 weeks (Chapter 4) or after just 34 days after grazing together in floodplain pasture (Chapter 5). These studies showed the plasticity of the rumen microbial community and the ability of the rumen to be modulated within a relatively short time-frame, by feed treatments and co-grazing. Modulating an established microbial community such as that found in mature cows, may involve either the import and establishment of new microbes into the pre-existing microbial community, or shifts in the relative abundance of microbial populations which were previously only present at relatively minor or even undetectable concentrations. Both of these mechanisms for rumen population change may have been at play in the feed supplementation study and floodplain study. The floodplain study also used a relatively uncontrolled experimental approach to explore the possibility of horizontal microbial inoculation between co-grazing animals as they are introduced to a new diet and environment. However, additional study i.e. using genetically, and isotopically labelled microorganisms is needed to confirm this “horizontal inoculation” hypothesis.

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Throughout all of these studies, feed was the main factor that affecting the rumen microbial community of cattle. Particularly, in the feed supplementation and floodplain studies when the established rumen microbiota changed in accordance to variation in diet. The findings also raised a big question as to whether it is possible to modulate rumen microbial community and maintain these changes for a long period. As detailed in the literature review Chapter, there are two groups of rumen microbiota based on their function within the rumen fermentation and referred to as generalist and specialist microbes. It will be hard to modulate the generalist species such as Bacteroidales BS11, Prevotella sp, and other dominant species permanently and independently from feed effects. The generalist microbes change in relative abundance as an adaptation mechanism to new feed conditions. The generalist microbes are able to utilise a wide variety of substrates in an attempt to run the essential rumen fermentation pathways of the rumen (Weimer 2015; Trosvik et al. 2018). Therefore, modulation of the generalist microbial community will be confounded by feed conditions. Alternatively, the specialist microbial species may have the opportunity to be modulated with more prolonged effect. Specialist species inhabit specific niches within the rumen and have specific functions in the rumen fermentation. The specialists face much less competition for utilising smaller or less abundant of substrates for example, antinutritional plant compounds (Allison et al. 1992; Klieve et al. 2012; Weimer 2015). Therefore, the specialist species are less influenced by a change in major feed substrates such as protein and fibre, and changes in farm practices. The investigations reported in this thesis used modern DNA sequencing methods for studying rumen microbial communities. The CowPi software was used for predicting rumen metagenomic function analysis, however, this approach should be used with caution since there is possibility for over- or underestimation of enzymatic function. For future studies, the application of additional techniques, such as functional metagenomics and proteomics may be used to provide more specific and accurate information about the mechanisms and enzymatic pathways employed by rumen microbial communities during the adaptation to new diets and on-farm co-grazing. Future studies could also investigate the extent of horizontal microbial transfer between animals, which could not accurately determine in the current trial arrangements. Between-animal microbial transfer could be further investigated using sterile containment experiments (such as those originally developed by Fonty et al. (1987)) and through the use of genetically and isotopically labelled microorganisms to track the microbial transfer between animals.

6.3. Conclusion To conclude, this study explored the possibility of modulating the rumen microbial profile at three different physiological stages of animals (pre ruminating, post weaning, and adult). However, the success rate of intervention was influenced by the type of treatment and diet was shown to have 153 the biggest impact to the structure of rumen microbial community. This study also showed the plasticity of core microbiome and the response the minor rumen microbial community to diet changes, providing a mechanism for adaptation to a new diet. Adjusting the diet may have encouraged the growth of some bacteria species, which were previously below detection limits. This study demonstrated for the first time that the rumen microbiome of co-grazing, adult cows, can co-evolve during adaptation to diet changes experienced in northern Australia.

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Appendix 1: Supplementary material for Chapter 4 Appendix 1.1. Differences in relative abundance of phylum within initial rumen microbial community according to animals’ groups and the interaction between groups. The significant differences between groups were determined by ANOVA analysis.

Animal origin Weaning weight Animal origin x Weaning weight Phylum Hayfield Kalala Heavy Light HfHWW HfLWW KaHWW KaLWW Bacteroidetes 47.99 51.84 47.25 52.59 46.82 49.16 4.01 1.39 Firmicutes 42.01 42.15 43.01 41.15 39.25 44.77 3.53 1.49 Fibrobacteres 2.02a 0.83b 2.08a 0.78b 3.40a 0.65b 0.32b 0.17b Proteobacteria 1.29 1.29 1.46 1.11 1.84 0.73 0.28 0.59 Cyanobacteria 1.98a 0.57b 1.78a 0.78b 2.94a 1.01b 0.15b 0.12b SR1 1.20 0.82 0.95 1.07 1.26 1.15 0.27 0.29 Different superscript letters within same row and group were showing significantly difference (P<0.05). Ka= Kalala; Hf= Hayfield; HWW= Heavy weaning weight; LWW= Light weaning weight.

Appendix 1.2. Differences in relative abundance of rumen bacterial phyla between Control and Supplemented heifers. P-values were generated by ANOVA analysis. Relative abundance (%) (mean±s.e.) Phylum P-value1 Control Supplemented Firmicutes 40.47 ± 1.76 55.20 ± 3.45 <0.01 Bacteroidetes 48.64 ± 2.01 31.53 ± 3.96 <0.01 Proteobacteria 1.91 ± 0.96 9.23 ± 3.71 ns Saccharibacteria 1.35 ± 0.18 1.69 ± 0.48 ns Cyanobacteria 2.23 ± 0.30 0.45 ± 0.09 <0.001 Fibrobacteres 1.59 ±0.38 0.33 ± 0.18 <0.05 SR1 1.29±0.28 0.17±0.08 <0.01 Euryarchaeota 0.52±0.08 0.25±0.06 <0.05 Actinobacteria 0.23±0.02 0.54±0.13 <0.05 Verrucomicrobia 0.51±0.06 0.21±0.04 <0.01 Synergistetes 0.43±0.06 0.09±0.03 <0.001 Spirochaetae 0.27±0.04 0.15±0.02 <0.05 Tenericutes 0.18±0.04 0.05±0.01 <0.05 Elusimicrobia 0.18±0.03 0.01±0.00 <0.001 Chloroflexi 0.10±0.01 0.06±0.01 ns Lentisphaerae 0.06±0.01 0.01±0.00 <0.01 Planctomycetes 0.04±0.01 0.00±0.00 <0.01 1 ns= non-significant (P>0.05)

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Appendix 1.3. Differences in relative abundance of core rumen microbiome phyla in Control and Supplemented heifers. P-values were generated by ANOVA analysis Relative abundance (%) (mean±Sem) Phylum P-value1 Control Supplemented Firmicutes 46.78 ± 2.00 62.10 ± 3.07 ns Bacteroidetes 41.44 ± 2.35 23.06 ± 2.37 <0.05 Proteobacteria 2.07 ± 1.18 10.22 ± 4.06 <0.05 Saccharibacteria 1.69 ± 0.22 1.89 ± 0.43 ns Cyanobacteria 2.16 ± 0.23 0.42 ± 0.09 ns Fibrobacteres 1.65 ± 0.40 0.42 ± 0.23 ns SR1 (Absconditabacteria) 1.62 ± 0.35 0.25 ± 0.11 ns Actinobacteria 0.29 ± 0.03 0.67 ± 0.16 ns Euryarchaeota 0.60 ± 0.10 0.31 ± 0.07 ns Verrucomicrobia 0.49 ± 0.06 0.23 ± 0.04 <0.01 Synergistetes 0.52 ± 0.07 0.11 ± 0.03 ns Spirochaetae 0.27 ± 0.03 0.18 ± 0.03 <0.05 Chloroflexi 0.11 ± 0.01 0.07 ± 0.02 ns Elusimicrobia 0.14 ± 0.02 0.01 ± 0.01 <0.001 Tenericutes 0.09 ± 0.02 0.03 ± 0.01 <0.01 Lentisphaerae 0.05 ± 0.01 0.01 ± 0.00 ns Planctomycetes 0.04 ± 0.01 0.00 ± 0.00 <0.05 1ns= non-significant (P>0.05)

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Appendix 2: Supplementary material for Chapter 5 Appendix 2.1. Heatmap relative abundance of 50 most abundant core rumen microbial’s OTUs in cows grazing on floodplain at three different grazing periods. Only OTUs with significantly differed relative abundance between grazing periods were shown (FDR P<0.05)

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Appendix 2.2. Differences in relative abundant predicted functional genes of core rumen microbial community at day 1 in accordance to birthplace groups. FDR P-values were generated by ANOVA analysis shown by highlighted value. The significant differences between birthplace groups were shown by different superscript letter within same row.

Mean (%) FDR P- Functional gene 00H BC0 KP1 TBH value Alanine, aspartate and glutamate metabolism 1.26 1.25 1.26 1.24 0.0831 Amino sugar and nucleotide sugar metabolism 1.47 1.45 1.46 1.46 0.0774 Arachidonic acid metabolism 0.04 0.04 0.04 0.04 0.116 Arginine and proline metabolism 1.20b 1.21a 1.21a 1.19c <.0001 Ascorbate and aldarate metabolism 0.13 0.12 0.12 0.13 0.7196 beta-Alanine metabolism 0.17 0.16 0.16 0.17 0.0513 Biotin metabolism 0.11 0.11 0.11 0.11 0.2778 Butanoate metabolism 0.61 0.62 0.61 0.60 0.1014 C5-Branched dibasic acid metabolism 0.21 0.20 0.20 0.21 0.4131 Carbon fixation pathways in prokaryotes 0.95a 0.96a 0.95a 0.93b <.0001 Citrate cycle (TCA cycle) 0.49 0.50 0.50 0.50 0.6316 Cyanoamino acid metabolism 0.40a 0.38ab 0.39ab 0.37b 0.0376 Cysteine and methionine metabolism 1.03b 1.05ab 1.05ab 1.06a 0.0472 D-Alanine metabolism 0.12 0.12 0.12 0.12 0.8233 Fatty acid biosynthesis 0.64 0.65 0.66 0.66 0.0625 Folate biosynthesis 0.27a 0.27b 0.28b 0.30ab 0.002 Fructose and mannose metabolism 0.65a 0.61b 0.61b 0.64b 0.0135 Galactose metabolism 0.58b 0.57b 0.56a 0.57b 0.0228 Glutathione metabolism 0.18 0.18 0.19 0.18 0.0043 Glycine, serine and threonine metabolism 0.98 0.97 1.00 0.99 0.2723 Glycolysis / Gluconeogenesis 0.78 0.78 0.78 0.79 0.0748 Glyoxylate and dicarboxylate metabolism 0.52 0.53 0.54 0.53 0.1786 Histidine metabolism 0.65a 0.65b 0.66ab 0.64a 0.0044 Inositol phosphate metabolism 0.14 0.13 0.14 0.14 0.1036 Linoleic acid metabolism 0.03b 0.03b 0.03b 0.02a <.0001 Lipoic acid metabolism 0.03 0.03 0.04 0.04 0.0131 Lysine biosynthesis 0.98 0.98 0.99 0.98 0.5159 Lysine degradation 0.04 0.05 0.05 0.04 0.059 Methane metabolism 1.18 1.20 1.21 1.21 0.2472 Nicotinate and nicotinamide metabolism 0.61 0.60 0.60 0.60 0.2165 Nitrogen metabolism 0.69a 0.69a 0.70a 0.68b 0.0015 Oxidative phosphorylation 0.84 0.84 0.83 0.84 0.7292 Pantothenate and CoA biosynthesis 0.70a 0.69ab 0.69b 0.69b 0.0321 Pentose and glucuronate interconversions 0.68a 0.65b 0.63b 0.65b 0.0188 Pentose phosphate pathway 0.74 0.74 0.75 0.75 0.6882 Phenylalanine metabolism 0.25 0.26 0.26 0.25 0.312 Phenylalanine, tyrosine and tryptophan biosynthesis 1.04 1.05 1.05 1.04 0.5997 195

Phosphonate and phosphinate metabolism 0.03 0.03 0.03 0.03 0.3346 Propionate metabolism 0.53a 0.53a 0.53a 0.51b 0.0259 Pyruvate metabolism 0.83 0.83 0.82 0.81 0.1873 Riboflavin metabolism 0.20 0.20 0.20 0.21 0.4378 Selenocompound metabolism 0.52 0.52 0.51 0.53 0.1455 Starch and sucrose metabolism 1.41a 1.37b 1.37b 1.37b 0.012 Sulfur metabolism 0.41b 0.41b 0.41b 0.43a 0.0155 Taurine and hypotaurine metabolism 0.15a 0.15ab 0.15a 0.14b 0.0268 Thiamine metabolism 0.43 0.42 0.41 0.42 0.0611 Tryptophan metabolism 0.09 0.10 0.10 0.10 0.8095 Tyrosine metabolism 0.18 0.19 0.19 0.19 0.0575 Valine, leucine and isoleucine biosynthesis 0.71 0.71 0.72 0.72 0.673 Valine, leucine and isoleucine degradation 0.21b 0.22a 0.22ab 0.21b 0.0383 Vitamin B6 metabolism 0.29 0.28 0.29 0.29 0.4841

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Appendix 2.3. Differences in relative abundant predicted functional genes of core rumen microbial community at day 34 in accordance to birthplace groups. FDR P-values were generated by ANOVA analysis. The significant differences between birthplace groups were shown by highlighted P value and different superscript letter within same row. Mean (%) Functional Gene FDR P-value 00H BC0 KP1 TBH Alanine, aspartate and glutamate metabolism 1.25 1.25 1.25 1.24 0.788 Amino sugar and nucleotide sugar metabolism 1.46 1.45 1.45 1.45 0.867 Arachidonic acid metabolism 0.04 0.04 0.04 0.04 0.522 Arginine and proline metabolism 1.21 1.22 1.22 1.22 0.301 Ascorbate and aldarate metabolism 0.13 0.13 0.12 0.13 0.687 beta-Alanine metabolism 0.17 0.16 0.16 0.17 0.170 Biotin metabolism 0.11b 0.11b 0.11b 0.12a 0.010 Butanoate metabolism 0.63 0.62 0.62 0.61 0.164 C5-Branched dibasic acid metabolism 0.20 0.20 0.20 0.20 0.244 Carbon fixation pathways in prokaryotes 0.96a 0.95a 0.96a 0.94b 0.010 Citrate cycle (TCA cycle) 0.51 0.51 0.51 0.51 0.851 Cyanoamino acid metabolism 0.38 0.38 0.38 0.36 0.061 Cysteine and methionine metabolism 1.05 1.05 1.05 1.07 0.068 D-Alanine metabolism 0.12b 0.12ab 0.12b 0.13a 0.032 Fatty acid biosynthesis 0.65b 0.66ab 0.65b 0.67a 0.026 Folate biosynthesis 0.27 0.28 0.27 0.29 0.216 Fructose and mannose metabolism 0.61 0.61 0.61 0.62 0.879 Galactose metabolism 0.56 0.56 0.56 0.55 0.073 Glutathione metabolism 0.19 0.19 0.19 0.19 0.522 Glycine, serine and threonine metabolism 1.00 0.99 0.99 0.99 0.907 Glycolysis / Gluconeogenesis 0.80 0.79 0.79 0.79 0.570 Glyoxylate and dicarboxylate metabolism 0.54 0.54 0.54 0.53 0.499 Histidine metabolism 0.65 0.66 0.65 0.66 0.930 Inositol phosphate metabolism 0.14 0.13 0.14 0.13 0.123 Linoleic acid metabolism 0.03 0.03 0.03 0.02 0.123 Lipoic acid metabolism 0.04 0.04 0.03 0.04 0.332 Lysine biosynthesis 0.98 0.98 0.98 0.99 0.857 Lysine degradation 0.05 0.05 0.05 0.05 0.789 Methane metabolism 1.24 1.23 1.23 1.23 0.738 Nicotinate and nicotinamide metabolism 0.60 0.60 0.60 0.60 0.905 Nitrogen metabolism 0.70a 0.70a 0.70a 0.69b 0.009 Oxidative phosphorylation 0.81 0.82 0.81 0.84 0.079 Pantothenate and CoA biosynthesis 0.68 0.68 0.68 0.68 0.945 Pentose and glucuronate interconversions 0.63 0.62 0.62 0.62 0.447 Pentose phosphate pathway 0.75 0.74 0.75 0.73 0.112 Phenylalanine metabolism 0.26 0.25 0.26 0.25 0.609 Phenylalanine, tyrosine and tryptophan biosynthesis 1.03 1.04 1.03 1.04 0.909 Phosphonate and phosphinate metabolism 0.03a 0.03a 0.03a 0.03b 0.004

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Pyruvate metabolism 0.85 0.84 0.84 0.83 0.148 Riboflavin metabolism 0.20 0.20 0.20 0.21 0.784 Selenocompound metabolism 0.51 0.51 0.51 0.53 0.075 Starch and sucrose metabolism 1.35 1.34 1.35 1.33 0.167 Sulfur metabolism 0.40 0.41 0.41 0.43 0.030 Taurine and hypotaurine metabolism 0.14 0.14 0.14 0.14 0.786 Thiamine metabolism 0.41 0.41 0.41 0.42 0.605 Tryptophan metabolism 0.10 0.10 0.10 0.10 0.267 Tyrosine metabolism 0.19 0.19 0.19 0.19 0.811 Valine, leucine and isoleucine biosynthesis 0.71 0.71 0.71 0.71 0.645 Valine, leucine and isoleucine degradation 0.22 0.22 0.22 0.22 0.150 Vitamin B6 metabolism 0.28 0.28 0.28 0.28 0.680

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Appendix 2.4. Differences in relative abundance of core rumen microbial community of floodplain grazing cows in accordance to grazing period. Significant differences between phyla were analysed by ANOVA with asterisk superscript after phylum name is showing P<0.05 significance level.

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Appendix 2.5. Differences in relative abundance of dominant core rumen microbial genera in floodplains grazing cows at day 1 in accordance to body condition score (BCS). P values were generated by ANOVA analysis. Mean Stdev Rumen microbes P value 2.5 3 2.5 3 Rikenellaceae RC9 gut group 17.42 19.00 2.92 4.45 0.406 Christensenellaceae R-7 group 13.24 14.09 3.12 1.86 0.5368 Prevotella 12.41 13.42 3.84 2.72 0.5659 o__Gastranaerophilales 4.43 3.69 2.26 1.66 0.4793 Fibrobacter 3.71 3.02 1.63 1.02 0.346 f__Bacteroidales BS11 gut group 3.29 3.21 1.13 0.68 0.8784 [Eubacterium] coprostanoligenes group 2.69 2.92 0.32 0.87 0.4601 Succiniclasticum 2.81 2.83 0.59 0.44 0.9509 p__SR1 (Absconditabacteria) 2.53 2.35 1.03 1.43 0.7787 Saccharofermentans 2.51 1.96 0.62 0.51 0.0792 Ruminococcaceae NK4A214 group 2.34 2.04 0.89 0.38 0.4293 Ruminococcaceae UCG-014 1.89 1.82 0.76 0.58 0.8341 Ruminococcaceae UCG-011 1.88 1.78 0.52 0.30 0.6508 Ruminococcus 1.56 1.61 0.34 0.49 0.8144 Prevotellaceae UCG-003 1.73 1.42 0.42 0.38 0.1513 Ruminococcaceae UCG-010 1.41 1.40 0.28 0.25 0.9122 c__WCHB1-41 1.12 1.27 0.40 0.69 0.5952 Pseudobutyrivibrio 1.26 1.06 0.91 0.34 0.5941 f__Bacteroidales UCG-001 1.28 0.97 0.55 0.47 0.2512 Lachnospiraceae AC2044 group 1.02 1.09 0.25 0.33 0.6491 Candidatus Saccharimonas 1.08 1.00 0.40 0.31 0.6737

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