Identification and Genomic Characterization of Candidate Starch and Lactate Utilizing Bacteria from the Rumen of Beef Cattle

South Dakota State University Open PRAIRIE: Open Public Research Access Institutional Repository and Information Exchange

Electronic Theses and Dissertations

2020

Venkata Bandarupalli South Dakota State University

Follow this and additional works at: https://openprairie.sdstate.edu/etd

Part of the Animal Sciences Commons

Recommended Citation Bandarupalli, Venkata, "Identification and Genomic Characterization of Candidate Starch and Lactate Utilizing Bacteria from the Rumen of Beef Cattle" (2020). Electronic Theses and Dissertations. 3683. https://openprairie.sdstate.edu/etd/3683

This Dissertation - Open Access is brought to you for free and open access by Open PRAIRIE: Open Public Research Access Institutional Repository and Information Exchange. It has been accepted for inclusion in Electronic Theses and Dissertations by an authorized administrator of Open PRAIRIE: Open Public Research Access Institutional Repository and Information Exchange. For more information, please contact [email protected]. IDENTIFICATION AND GENOMIC CHARACTERIZATION OF CANDIDATE

STARCH AND LACTATE UTILIZING BACTERIA FROM THE RUMEN OF BEEF

CATTLE

VENKATA VINAY KUMAR BANDARUPALLI

A dissertation submitted in partial fulfillment of the requirements for the

Doctor of Philosophy

Major in Animal Science

South Dakota State University

2020 ii

DISSERTATION ACCEPTANCE PAGE

VENKATA VINAY KUMAR BADNDRUPALLI

This dissertation is approved as a creditable and independent investigation by a candidate for the Doctor of Philosophy degree and is acceptable for meeting the dissertation requirements for this degree. Acceptance of this does not imply that the conclusions reached by the candidate are necessarily the conclusions of the major department.

Benoit St Pierre Advisor Date

Joseph P Cassady Department Head Date

Dean, Graduate School Date iii

ACKNOWLEDGEMENTS

I am extremely grateful to my advisor Dr. Benoit St-Pierre for his continuous support during my Ph.D. His expertise in the field of microbiology and his invaluable contribution and guidance helped me to become an independent researcher. Without his support and encouragement, the research project would hardly have been completed.

I would like to extend my sincere thanks to my committee members: Dr. Radhey

Kaushik, Dr. Joy Scaria, and Dr. George Hamer. They gave their time to provide me with insightful suggestions that helped me to improve my research work.

Many thanks to my fellow labmates for the wonderful times that we spent and for their assistance in my research project. To SDSU Agricultural Experimental Station, thank you for your financial support.

Finally, I deeply thank my parents for their encouragement and guidance that helped me to explore the opportunity of pursuing my Ph.D.

TABLE OF CONTENTS

LIST OF FIGURES ...... vii LIST OF TABLES ...... x ABSTRACT ...... xii CHAPTER 1 ...... 1 LITERATURE REVIEW ...... 1 1.1 Importance of Livestock in Ensuring Human Food Security...... 2 1.2 Targeting Feed Efficiency for Improved Livestock Production and Sustainability ...... 3 1.3 Importance of Ruminants for the Sustainable Production of Animal Protein ...... 3 1.4 The function of the rumen ...... 4 1.5 The Development of the Rumen ...... 5 2. Methodologies Used in the Analysis of Rumen Microbial Communities ...... 7 2.1 Culture-Dependent Approaches ...... 7 2.2 Culture-Independent Approaches ...... 8 2.2.1 16S rRNA Gene ...... 9 2.2.2 Taxonomy Dependent and Independent Methods for 16S rRNA Analysis ...... 11 2.3 Omics-Based Approaches ...... 12 2.3.1 Metagenomics ...... 13 2.4 Characterization of the Rumen Microbiome Using the 16S rRNA Gene ...... 14 2.5 Exploring Metabolic Functions of the Rumen Microbiome Using Metagenomics ...... 15 3.1 Effect of Concentrate or Grain-Based Diets on the Rumen Environment ...... 17 3.2 Starch Metabolizing Bacteria in the Rumen Environment ...... 18 3.3 Acidosis, a Digestive Disturbance Resulting from Feeding Concentrate Diets ...... 20 3.4 Lactate Metabolizing Bacteria in the Rumen ...... 22 4.1 Rationale and Approach ...... 24 4.2 Research Objectives ...... 26 References ...... 30 CHAPTER 2 ...... 57 IDENTIFICATION AND GENOMIC CHARACTERIZATION OF CANDIDATE STARCH UTILIZING BACTERIA FROM THE RUMEN OF BEEF CATTLE ...... 57 ABSTRACT ...... 58

1. Introduction ...... 59 2. Materials and Methods ...... 61 2.1 Sample collection and in vitro rumen culture experiments ...... 61 2.2 Microbial genomic DNA purification and PCR amplification of the 16S rRNA gene ...... 62 2.3 Bioinformatic analysis for 16S rRNA gene-based composition analysis ...... 63 2.4 Metagenomic analysis ...... 65 3. Results ...... 66 3.1 Comparative analysis of bacterial communities from donor rumen fluid ...... 66 3.2 Identification of candidate bacterial starch utilizers from rumen fluid ...... 67 3.3 Exploring the metabolic potential of OTUs identified as candidate starch utilizers ...... 68 4. Discussion ...... 71 5. Conclusion ...... 75 References ...... 90 CHAPTER 3 ...... 101 IDENTIFICATION AND GENOMIC CHARACTERIZATION OF CANDIDATE LACTATE UTILIZING BACTERIA FROM THE RUMEN OF BEEF CATTLE ...... 101 ABSTRACT ...... 102 1. Introduction ...... 103 2. Materials and Methods ...... 105 2.1 Sample collection and in vitro rumen culture experiments ...... 105 2.2 Microbial genomic DNA purification and PCR amplification of the 16S rRNA gene ...... 106 2.3 Bioinformatic analysis for 16S rRNA gene-based composition analysis ...... 107 2.4 Metagenomic analysis ...... 108 3. Results ...... 109 3.1 Comparative analysis of bacterial communities from donor rumen fluid ...... 109 3.2 Identification of candidate bacterial lactate utilizers from rumen fluid ...... 109 3.3 Exploring the metabolic potential of OTUs identified as candidate lactate utilizers ...... 110 4. Discussion ...... 113 5. Conclusion ...... 118 References ...... 134 CHAPTER 4 ...... 145 FUTURE EXPERIMENTS ...... 145 4.1 Introduction ...... 145

4.2 Research Findings and Future Experiments...... 146 4.3 Research impact ...... 148 References ...... 150

vii

LIST OF FIGURES

Figure 1.1. Amylolytic, maltose-fermenting, glucose-fermenting and lactic acid fermenting bacteria involved in the fermentation of high-concentrate diets……….27

Figure 2.1. Taxonomic composition of rumen samples used as inocula, prior to setting up batch culture enrichment experiments with starch………………………77

Figure 2.2. RAST gene annotation analysis of contig sets generated from enrichment

Trials 2 (blue), 3 (orange) and 4 (grey)……………………………………………..78

Figure 3.1. Taxonomic composition of rumen samples used as inocula, prior to setting up batch culture enrichment experiments with lactate……………………120

viii

Figure 3.2. RAST gene annotation analysis of contig sets generated from enrichment

Trials 2 (blue) and 3 (orange)……………………………………………………...121

Figure 3.3. KEGG (Kyoto Encyclopedia of Genes and Genomes) annotation of predicted metabolic pathways from the metagenomics analysis of contigs generated from Trial#2 (OTU LacT2_D14_1-15251, affiliated to Butyrivibrio fibrosolvens). A) lactate and pyruvate metabolism…………………………………………………..122

Figure 3.4. KEGG (Kyoto Encyclopedia of Genes and Genomes) annotation of predicted metabolic pathways from the metagenomic analysis of contigs generated from Trial#2 (OUT LacT2_D14_1-15251, affiliated to Butyrivibrio fibrosolvens). B) butanoate metabolism……………………………………………………………...123

Figure 3.5. KEGG (Kyoto Encyclopedia of Genes and Genomes) annotation of predicted metabolic pathways from the metagenomic analysis of contigs generated from Trial#2 (OTU LacT2_D14_1-15251, affiliated to Butyrivibrio fibrosolvens). C) citrate cycle………………………………………………………………………...124

Figure 3.6. KEGG (Kyoto Encyclopedia of Genes and Genomes) annotation of predicted metabolic pathways from the metagenomic analysis of contigs generated from Trial#3 (OTU LT 3.7-00154, affiliated to Anaerococcus prevotii). A) lactate and pyruvate metabolism…………………………………………………………..125

Figure 3.7. KEGG (Kyoto Encyclopedia of Genes and Genomes) annotation of predicted metabolic pathways from the metagenomic analysis of contigs generated from Trial#3 (OTU LT3.7-00154, affiliated to Anaerococcus prevotii). B) glycolysis pathway……………………………………………………………………………126

Figure 3.8. KEGG (Kyoto Encyclopedia of Genes and Genomes) annotation of precited metabolic pathways from the metagenomics analysis of contigs generated from Trial#3 (OTU LT3.7-00154, affiliated to Anaerococcus prevotii). C) glycogen synthesis and mobilization………………………………………………………127

Figure 3.9. KEGG (Kyoto Encyclopedia of Genes and Genomes) annotation of precited metabolic pathways from the metagenomics analysis of contigs generated from Trial#3 (OTU LT3.7-00154, affiliated to Anaerococcus prevotii). D) pentose phosphate pathway………………………………………………………………..128

LIST OF TABLES

Table 1.1. PCR and Non-PCR Based Molecular Approaches………………………28

Table 1.2. Overview of amount of starch fermented in the rumen when the livestock are supplemented with different starch sources…………………………………….29

Table 2.1. Number of high quality-chimera free sequence reads used for OTU analysis in each trial………………………………………………………………...82

Table 2.2: Shared ruminal bacterial OTUs amongst rumen fluid donors used in this study………………………………………………………………………………...83

Table 2.3. Most abundant# ruminal bacterial OTUs that were shared amongst all rumen fluid donors used in this study……………………………………………….84

Table 2.4. Enrichment of OTUs in response to starch……………………………...85

Table 2.5. Carbohydrate-utilizing enzymes identified from the gene annotation analysis of contigs generated from Trial#2 (OTU SD_Bt-00966, affiliated to

Prevotella albensis)…………………………………………………………………86

Table 2.6. Carbohydrate-utilizing enzymes identified from the gene annotation analysis of contigs generated from Trial#3 (OTU SD_Bt-00010, affiliated to

Prevotella albensis)…………………………………………………………………87

Table 2.7. Carbohydrate-utilizing enzymes identified from the gene annotation analysis of contigs generated from Trial#4 (OTU SD_Bt-00010, affiliated to

Prevotella albensis)…………………………………………………………………88

Table 2.8. Number of coding sequences (CDS) assigned to predicted amino acid metabolism-related functions in the contig sets from Trials 2, 3 and 4………….89

Table 3.1. Enrichment of OTUs in response to lactate. Abundance is presented as relative abundance (%) of total non-chimeric reads……………………………..129

Table 3.2. Carbohydrate-utilizing enzymes identified from the gene annotation analysis of contigs generated from Trial#2 (OTU LacT2_D14_1-15251, affiliated to

Butyrivibrio fibrisolvens)…………………………………………………………130

Table 3.3. Carbohydrate-utilizing enzymes identified from the gene annotation analysis of contigs generated from Trial#3 (OTU LT3.7-00154, affiliated to

Anaerococcus prevotii)……………………………………………………………131

Table 3.4. Amino acid metabolizing enzymes identified from the gene annotation analysis of contigs generated from Trial#2 (OTU LacT2_D14_1-15251, affiliated to

Butyrivibrio fibrisolvens)………………………………………………………….132

Table 3.5. Amino acid metabolizing enzymes identified from the gene annotation analysis of contigs generated from Trial#3 (OTU LT3.7-00154, affiliated to

Anaerococcus prevotii)……………………………………………………………133

Table 3.6. Transporters identified from BLASTp database from two experimental trials………………………………………………………………………………..134

xii

ABSTRACT

IDENTIFICATION AND GENOMIC CHARACTERIZATION OF CANDIDATE

STARCH AND LACTATE UTILIZING BACTERIA FROM THE RUMEN OF BEEF

CATTLE

VENKATA VINAY KUMAR BANDARUPALLI 2020 The rumen functions as an anaerobic fermentation chamber where microbial communities ferment feedstuffs into SCFAs. These SCFAs are critical to the ruminant host since they fulfill 70% of its energy requirements. In intensive management systems, ruminants are fed with grain-rich diets, which contain high levels of starch that help meet the energy demands of high producing animals. When grain-based diets are fed to ruminants, ruminal amylolytic and lactate metabolizing bacteria predominate. Grain overload can cause rapid production and accumulation of lactate, resulting in proliferation of amylolytic bacteria and death of lactate metabolizing bacteria.

Consequently, high lactate levels result in a decrease in ruminal pH, a condition referred to as acidosis, which can severely impact animal health. A majority of amylolytic and lactate metabolizing bacteria remain uncharacterized, owing to the complexity of the rumen microbiome. In this context, the main objective of the research described in this dissertation was to identify previously uncharacterized bacteria based on their ability to grow in the presence of starch or lactate using an in-vitro batch culture approach. Rumen fluid collected from cannulated beef cows were maintained in laboratory scale bioreactors at a constant physiological temperature (380C), with three of the replicate cultures supplemented with either starch or lactate. Candidate starch or lactate utilizers

xiii were identified using a 16S rRNA-based analysis by comparing the composition of substrate supplemented and non-supplemented cultures. A total of seven experimental trials were conducted related to starch and lactate, resulting in the identification of ten

Operational Taxonomic Units (OTUs) as candidate utilizers. Metagenomics analysis on the samples showing high OTU enrichment in both starch and lactate trials was performed to gain further insights into the functional potential of these OTUs. Gene annotation using RAST revealed enzymes predicted to be associated with metabolic activities of starch and lactate metabolizing bacteria. Therefore, a better understanding of these highly enriched bacteria in starch and lactate trials will provide greater insights for developing probiotics. Successful probiotic development would be beneficial for animal health and performance.

CHAPTER 1

LITERATURE REVIEW

1.1 Importance of Livestock in Ensuring Human Food Security

The global human population is growing at a fast rate, and it is expected to increase from 7 billion, currently, to 9.73 billion by 2050, potentially reaching up to 11.2 billion by 2100 (Godfray et al., 2010) (FAO, 2017). As not only the world population but also urbanization are increasing, concerns arise whether today’s agricultural system will be able to meet the food demands of a growing population (FAO, 2017). According to the

Food and Agriculture Organization (FAO), consumption of meat and dairy products is projected to increase by 73% and 58%, respectively, by 2050 (FAO, 2018). Valin et al.

(2014) reported that global food production is expected to increase overall by 74% by

2050, with predictions ranging between 54% and 98% depending on the prediction model used (Valin et al., 2014). Deforestation, land degradation, and water scarcity are major factors having a huge negative impact on agricultural productivity, further intensifying the decrease in agricultural productivity globally (FAO, 2017) (FAO, 2011). Production of livestock-derived products, such as milk, meat, and eggs, needs to be increasing rapidly to meet the booming demand of the ever-growing human population (Didanna,

2015) (Bodirsky et al., 2015). Based on models designed to predict global food demand, simply increasing livestock numbers and expanding cropland doesn’t offer a viable solution to meet long-term food demands (Bodirsky et al., 2015). Therefore, one plausible solution for a sustainable global food system is to increase the production efficiency of the livestock sector in order to meet the global demand for animal protein

(Council, 2015) (Gerbens-Leenes, Nonhebel, & Krol, 2010).

1.2 Targeting Feed Efficiency for Improved Livestock Production and Sustainability

While global livestock production has been steadily increasing (Byerly, 1967), higher feed cost have had a major impact on the beef, pork, dairy, poultry, and aquaculture industries, because feed cost represent up to three-quarters of total direct costs (Kenny, Fitzsimons, Waters, & McGee, 2018). Accordingly, research on dairy and beef raised in feedlots has prioritized improvement of feed efficiency. This change in strategy is expected to increase profitability for producers and the sustainability of ruminant production systems (Kenny et al., 2018) (Connor, 2015). Over the past two decades, various studies have been conducted to investigate the nutritional requirements of animals to increase feed efficiency (Tona, 2018), leading to the development of modern biotechnological strategies, such as the use of feed additives, crystalline amino acids, probiotics, and prebiotics (Asmare, 2014) (Ravindran, 2013). However, factors such as diet quality and quantity, environmental conditions, host genetics, microbiome composition as well as age can also impact feed efficiency in animals (Soest, 1994)

(Bruns, Pritchard, & Boggs, 2005) (Barendse et al., 2007) (Kerr, Kellner, & Shurson,

2015). While research has been conducted for many years on this subject, further progress and improvements are still needed to improve feed efficiency in the livestock industry (Shike, 2013).

1.3 Importance of Ruminants for the Sustainable Production of Animal Protein

Ruminants have the ability to thrive on plant biomass material, which is inedible to humans. This is possible through the synergistic action of complex symbiotic microbial communities that ferment plant fibers into short chain fatty acids (SCFAs) and

4 produce microbial proteins, which can both be utilized by their host (Kamra, 2005) (Qi et al., 2011). While the host lacks the necessary enzymes for digestion of plant biomass, ruminal microorganisms as a group express a wide range of enzymes that can hydrolyze plant polysaccharides into monomers, which they then convert into ATP to fulfill their energy requirements, and into SCFAs, which are absorbed by the host for use as energy

(Yue, Li, & Yu, 2013). Assignment of metabolic functions to the ruminal microbial species that participate in fermentation is thus essential for optimizing rumen function and improving the efficiency of milk and meat production (C. S. Stewart, Fonty, &

Gouet, 1988) (Firkins, Karnati, & Yu, 2008) (Eugène, Archimede, & Sauvant, 2004).

However, there are still significant gaps in our knowledge of the key species involved in fermentation activity, and how their metabolism can affect feed efficiency (Kenny et al.,

2018).

1.4 The function of the rumen

The rumen is the largest compartment of the foregut of ruminants. It functions as an anerobic fermentation chamber, where microbial symbiotic communities, consisting of bacteria, archaea, protozoa, and fungi, collectively participate in the digestion of feed into

SCFAs. These represent the main energy source for the host, providing 60%-70% of its energy requirements. (Russell & Mantovani, 2002; Van Soest, 1994). The role of SCFAs for the host animal has been known for quite some time (Elsden, 1945, Masson and

Phillipson, 1951), and it remains a subject of ongoing research of great importance to this day (Cantalapiedra-Hijar, Yanez-Ruiz, Martin-Garcia, & Molina-Alcaide, 2009; Dijkstra,

Forbes, & France, 2005; Elsden, 1945; Flint, 1997; J. Liu et al., 2012; Masson &

Phillipson, 1951; B. Wang et al., 2017). SCFAs are first absorbed through the rumen wall, then they proceed via the bloodstream to the liver and peripheral tissues where they are utilized (den Besten et al., 2013). Acetate can be metabolized for the production of

ATP or used as a precursor for the synthesis of fatty acids, while propionate is used as a precursor for gluconeogenesis in the liver. Gluconeogenesis is the main source of glucose for ruminants, which is utilized for specific functions, such as the synthesis of milk lactose, and contributes to overall animal performance such as live weight gain (Balch &

Rowland, 1957; Linn, 1988; Urrutia & Harvatine, 2017)

Structural and non-structural carbohydrates represent the primary substrates for

SCFA production, with a much greater contribution compared to proteins or lipids. The respective ruminal concentrations for acetate, propionate, and butyrate depend on the type of diet fed to animals. Ruminal acetate concentrations tend to be higher with high fiber diets, whereas propionate is in higher abundance when concentrate-based diets are fed. Other factors that can affect SCFA production and their respective abundance include feed intake, frequency of feeding, as well as composition of the ruminal microbiome (Dijkstra et al., 2005).

1.5 The Development of the Rumen

Ruminants are born with an underdeveloped rumen, and their digestion system functions like that of monogastric animals until they transition to a solid-based diet.

Suckling triggers a reflex mechanism that causes the formation of the esophageal groove, an anatomical structure that allows ingested milk to bypass the rumen and enter the abomasum directly, which is the true stomach of ruminants, where milk is digested by

6 host enzymes (Church, 1988; Davis, 1988; Van Soest, 1994). The transition from pre- ruminant to ruminant is one of the most important stages in the life of a young ruminant

(Jiao et al., 2015). Indeed, the change from suckling to a solid diet requires the development of microbial communities that can metabolize plant biomass as well as anatomical structures for absorption of microbially produced SCFAs. These two processes typically proceed together, and their outcome can greatly impact the future performance of the animal, particularly in intense production systems.

The development of ruminal microbial communities from a pre-ruminant state to fully functional involves multiple cycles of microbial succession, where the composition of communities changes in response to changes in diet and in coordination with host development. Microbial colonization of the rumen starts immediately after birth, from exposure to a wide array of microorganisms present in the immediate environment, such as the dam’s udder, milk, saliva, and manure, as well as from other animals (Church,

1988). While aerobic and facultative anaerobic taxa predominate after birth, they are gradually replaced over a period of 9-13 weeks by taxa that are exclusively anaerobic

(Beharka, Nagaraja, Morrill, Kennedy, & Klemm, 1998; Bryant, Small, Bouma, &

Robinson, 1958; Fonty, Gouet, Jouany, & Senaud, 1987; Minato, Otsuka, Shirasaka,

Itabashi, & Mitsumori, 1992). Functional groups appear to be established early, with amylolytic bacteria appearing first in 1-day-old calves, while cellulolytic bacteria appear later, after 3-5-days (Bryant, Small, Bouma, & Robinson, 1958; Fonty et al., 1987;

Minato et al., 1992). Using the 16S rRNA gene as a marker for bacterial composition,

Jami et al. (Jami, Israel, Kotser, & Mizrahi, 2013) investigated the composition of ruminal bacterial communities in four different age groups: 1–3 day-old calves, 2 month-

7 old calves, 6 month-old heifers, and 2 year-old lactating dairy cows. While Firmicutes,

Bacteroidetes, and Proteobacteria were the main phyla across all age groups, Firmicutes were found to be the most abundant in the youngest animals (1–3 day), in contrast to the older group where Bacteroidetes were the most dominant. Li et al. (2012) also identified

Firmicutes, Bacteroidetes, and Proteobacteria as the most abundant groups in growing bull calves, with Firmicutes being the most abundant at the 14-day stage, while

Bacteroidetes were predominant in older animals later (42 days and 12-months). This study also indicated that metabolic potential developed as early as the pre-ruminant stage, since enzymes predicted to be involved in metabolizing carbohydrates were identified.

Further investigations of ruminal microbial community transitions will be required to better define the impact of solid diets and their ingredient composition on this process as animals develop and mature (Jami et al., 2013; R. W. Li, Connor, Li, Baldwin Vi, &

Sparks, 2012; Rey et al., 2014).

2. Methodologies Used in the Analysis of Rumen Microbial Communities

Due to their nature, ruminal microbial communities are challenging to investigate.

In the following sections, various strategies and approaches that have been developed over the years for this purpose will be described.

2.1 Culture-Dependent Approaches

The main contribution of cultivation-based methods is to provide a deeper understanding of the metabolic potential of individual isolates and their specific

8 contribution to rumen function. For many years, these were the main approaches used for investigating ruminal microbial species, with a primary focus on the ones responsible for metabolizing plant biomass substrates that are resistant to host digestive enzymes (Ze, Le

Mougen, Duncan, Louis, & Flint, 2013). A number of bacterial species were characterized as keystone species, based on their role in initiating the digestion of feed substrates. For instance, Ruminococcus flavefaciens, Ruminococcus albus, and

Fibrobacter succinogenes, which are involved in metabolizing cellulose, were identified and characterized using traditional culturing methods (Shi, Odt, & Weimer, 1997).

Ruminal hemicellulolytic, amylolytic, and lactate utilizing species were also described using similar approaches (Dehority & Grubb, 1977). According to a recent survey, at least 146 ruminal bacterial species have so far been cultured, and these are distributed across 88 different genera and seven phyla (Creevey, Kelly, Henderson, & Leahy, 2014).

Firmicutes is the phylum with the highest number of cultured ruminal species up to this point, which include forty-five species that are affiliated to Lachnospiraceae (Creevey et al., 2014).

2.2 Culture-Independent Approaches

While growth of isolates as individual cultures remains a gold standard when characterizing the metabolic activities of microbial symbionts, one limitation of culture- based methods is their inability to provide community-level assessments, such as composition, diversity, richness, and evenness. Molecular methods, primarily based on analysis of DNA sequences, have provided an effective means of determining the composition of microbial communities without requiring culturing of isolates (Agrawal,

Agrawal, & Shrivastava, 2015; Medlin & Orozco, 2017). The most popular DNA-based strategy has been the use of the small ribosomal RNA subunit (16S rRNA), which has, since its implementation (Pace, Stahl, Lane, & Olsen, 1986), facilitated the investigation of a very wide range of microbial ecosystems and has revealed a level of bacterial species diversity and richness far greater than what had been previously estimated (Hamady &

Knight, 2009).

2.2.1 16S rRNA Gene

The small ribosomal RNA subunit (16S rRNA) is the most commonly used phylogenetic marker for prokaryotic species. Its ubiquity, genetic stability, and length are some of the characteristics that have made this gene a “gold standard” for characterization of unknown microbial species (Ibal, Pham, Park, & Shin, 2019)

(Větrovský & Baldrian, 2013). It is an optimal marker when working with ecological habitats that harbor poorly characterized bacterial communities whose members are difficult to culture, such as communities living in symbiosis with a host (Arumugam et al., 2011; Costello et al., 2009; Turnbaugh et al., 2009) or in a physical environment such as in soils or oceans (Gilbert et al., 2009; Hackl, Zechmeister-Boltenstern, Bodrossy, &

Sessitsch, 2004; Woo, Lau, Teng, Tse, & Yuen, 2008). In clinical microbiology, 16S rRNA gene sequencing has proven to be an effective and comprehensive way of providing information about poorly characterized pathogenic bacteria, thus contributing to a better understanding of diseases and selection of appropriate treatment (Clarridge,

2004).

The 16S rRNA gene is approximately 1,550 bp long, and its basic structure consists of alternating conserved and hypervariable regions. The conserved regions can be used as targets for PCR primer design, while variable regions can be used for classification at various taxonomic levels, which can range from phylum to strain level identification (J. B. Patel, 2001). Nine distinct hypervariable regions (V1-V9), each differing in nucleotide length, sequence, and phylogenetic resolution, can be used for taxonomic classification (Chakravorty, Helb, Burday, Connell, & Alland, 2007; Youssef et al., 2009) (Yu & Morrison, 2004) (Z. Liu, Lozupone, Hamady, Bushman, & Knight,

2007) (Zongzhi Liu, DeSantis, Andersen, & Knight, 2008; Q. Wang, Garrity, Tiedje, &

Cole, 2007).

During the period when Sanger sequencing was the only available option, 16S rRNA-based approaches were limited by clone library construction and costs. The development of Next Generation Sequencing platforms revolutionized this approach by providing much higher sequence yields and greater species coverage for complex microbial communities (Quail et al., 2012). However, most Next Generation Sequencing platforms are limited in the length of individual sequence reads that they can generate, and are thus unable to provide the full sequence of individual 16S rRNA genes. To counter this limitation, a preferred strategy to target a sub-region of the 16S rRNA for sequencing instead of the full-length gene was developed. In an effort to identify sub- regions of the 16S rRNA genes that would be optimal for this purpose, Kim and Morrison

(2011a) used an in-silico approach to compare the phylogenetic resolution of different hypervariable regions to their corresponding full length 16S rRNA gene sequences. They determined that V1-V3 and V1-V4 provided the best phylogenetic resolution.

While the use of the 16S rRNA gene for composition analysis of complex microbial communities has become very popular because it provides many advantages

(Chaucheyras-Durand & Ossa, 2014), there are a number of drawbacks to its use. The variation in gene copy numbers amongst different bacterial species, which can range from 1 to 15 copies, can affect estimates of relative abundance, thus impacting bacterial composition analyses (Klappenbach, Saxman, Cole, & Schmidt, 2001) (Case et al.,

2007). As 16S rRNA-based analyses typically use amplification by PCR to generate

DNA templates for sequencing, this process can introduce biases caused by the primers used, amplification efficiency and / or the production of artifacts (Chandler, 1998; Costea et al., 2017; de Lipthay, Enzinger, Johnsen, Aamand, & Sørensen, 2004; Forney, Zhou, &

Brown, 2004; Morgan, Darling, & Eisen, 2010; Polz & Cavanaugh, 1998; Sipos et al.,

2007; Webster, Newberry, Fry, & Weightman, 2003).

2.2.2 Taxonomy Dependent and Independent Methods for 16S rRNA Analysis

Taxonomy dependent methods, also known as supervised methods, rely on information from reference databases to assign sequences from experimental datasets to taxonomic groups. The major limitation for this approach is its dependence on accurate and robust databases, which can result in its inability to assign bacterial species from poorly characterized phylogenetic lineages to available taxonomic groups (Sedlar,

Kupkova, & Provaznik, 2017). The k-neighbor based tool RDP classifier is one of the most widely used implementations of this approach. Its associated database, the

Ribosomal Database Project (RDP), has a reported ~3 million reference 16S rRNA sequences, which include 44% of its bacterial reference sequences and 15.3% of its

12 archaeal references with a length of at least 1,200 bp (Cole et al., 2014). As research efforts continue to be invested in microbial habitats and further advancements in sequencing technologies, 16S rRNA gene sequence databases are expected to continue to expand and become increasingly more comprehensive.

In contrast, taxonomy independent methods, also known as unsupervised methods, bypass the need for reference database comparisons by using a clustering-based approach. In this case, sequences in an experimental dataset are compared to each other, then divided into distinct groups according to a pre-determined threshold in nucleotide differences, commonly referred to as genetic distance. This approach is an effective strategy for higher resolution when exploring complex microbial systems that consist mostly of uncharacterized bacterial species, such as would be the case for the rumen microbiome for instance (Kim, Morrison, & Yu, 2011a).

2.3 Omics-Based Approaches

While DNA-based approaches have originally relied heavily on PCR-based strategies, they have gradually been overshadowed by the development of high throughput shotgun sequencing technologies, including metagenomics and metatranscriptomics. These have increased our ability to understand microbial community composition and functional potential at greater depths (Franzosa et al., 2015)

(Segal et al., 2019). (Table 1.1).

2.3.1 Metagenomics

One of the first reports on the implementation of metagenomics described a collective analysis of microbial genomes from soil microorganisms (Handelsman et al.

1998), which bypassed the need for traditional culture-dependent methods. As a DNA- based method, metagenomics require extraction of microbial genomic DNA from habitats of interest, just like 16S rRNA based methods, but it does not require PCR, since genomic DNA is sequenced directly in a random fashion, a process commonly designated as ‘shotgun sequencing’. When investigated microbial communities consist of poorly characterized bacterial species for which reference genomes are not available, sequence datasets, which consist of high numbers of short DNA sequences, are analyzed primarily through identification of potential coding sequences and gene annotation. For this purpose, sequence reads can be assembled into contigs, which correspond to segments of microbial genomic DNA from bacterial species of the sampled microbial habitat. A number of online and standalone software packages have been developed for this purpose, including BBAP (Lin et al., 2017), Genovo (Laserson, Jojic, & Koller, 2011;

Sato & Sakakibara, 2013), IDBA-UD (Peng, Leung, Yiu, & Chin, 2012), IVA (Hunt et al., 2015), MAP (Lai, Ding, Li, Duan, & Zhu, 2012), MegaGTA (Medlin & Orozco,

2017), MEGAHIT (D. Li, Liu, Luo, Sadakane, & Lam, 2015; D. Li et al., 2016),

MetaVelvet (Namiki, Hachiya, Tanaka, & Sakakibara, 2012), Omega (Haider et al.,

2014), PRICE (Ruby, Bellare, & DeRisi, 2013), and Ray Meta (Boisvert, Raymond,

Godzaridis, Laviolette, & Corbeil, 2012). Once contigs have been constructed, gene annotation tools such as RAST (Aziz et al., 2008), MEGAN (Huson & Weber, 2013),

IMG/M (Markowitz et al., 2012), Metarep (Goll et al., 2010), or MicroScope (Vallenet et

14 al., 2013), can be used for identification of potential coding sequences in order to match them to proteins of known function. Alternatively, gene annotation can be performed on individual reads without contig assembly, using tools such as MG-RAST (Meyer et al.,

2008). These tools are key in elucidating metabolic pathways of microbial communities from any habitat or environment, by providing critical information on genes present in these communities (Deusch, Tilocca, Camarinha-Silva, & Seifert, 2015).

2.4 Characterization of the Rumen Microbiome Using the 16S rRNA Gene

Kim et al. (2011b) examined bacterial diversity in the rumen by conducting a meta-analysis of curated 16S rRNA gene sequences of rumen origin. They have used

13,478 bacterial and 3516 archaeal sequences to examine the bacterial diversity from the

RDP database. From this analysis, at least 19 bacterial phyla have so far been identified in the rumen, with Firmicutes, Bacteroidetes, and Proteobacteria being the most dominant groups, accounting for 57.8%, 26.7%, and 6.9% of the total bacterial sequences

(Kim, Morrison, & Yu, 2011b). In Firmicutes, 90.6% of sequences are assigned to class

Clostridia, while in Bacteroidetes, 88.5% of the sequences are assigned to class

Bacteroidia. The predominant genera in phylum Firmicutes include Butyrivibrio,

Acetivibrio, Ruminococcus, Succiniclasticum, Pseudobutyrivibrio, and Mogibacterium, whereas Prevotella is the most dominant genus in Bacteroidetes accounting for 41.5% of the Bacteriodetes sequences (Kim et al., 2011b).

2.5 Exploring Metabolic Functions of the Rumen Microbiome Using Metagenomics

Diet is a key factor modulating ruminal microbial community structure, likely because the various enzyme activities that are required for metabolizing feed stuffs are expressed by specialized ruminal microorganisms (Carberry, Kenny, Han, McCabe, &

Waters, 2012). Linking microbial species to their preferred substrates represents critical information to further understand how rumen function impacts animal health and performance (Deusch et al., 2015; Morgavi, Kelly, Janssen, & Attwood, 2013). While

16S rRNA gene-based analyses provide a comprehensive way of identifying unknown bacterial and archaeal species, its major limitation is its inability to accurately provide information on their respective functions and metabolic capabilities (Case et al., 2007)

(von Mering et al., 2007). It is for this reason that intensive metagenomics research on the rumen microbiome has been ongoing.

Metagenomic studies of the rumen microbiome tend to focus on identifying candidate enzymes involved in structural polysaccharide hydrolysis (Brulc et al., 2009;

D. D. Patel et al., 2014; Lingling Wang, Hatem, Catalyurek, Morrison, & Yu, 2013). As one of the earlier metagenomic reports, Ferrer et al. (2005) identified 12 different esterases, nine different forms of endo‐β‐1,4‐glucanases, and one cyclodextrin enzyme from a dairy cow fed a forage-based diet of ryegrass and clover (Ferrer et al., 2005). Hess et al. (2011) later identified 57 different types of cellulose hydrolyzing enzymes out of 90 expressed proteins from the bovine rumen using metagenomic DNA libraries (Hess et al.,

2011). More recently, metagenomics analyses on rumen digesta samples from Holstein–

Friesian crossbred steers that had been fed finger millet straw revealed a diverse

16 carbohydrate-metabolizing enzyme profile, with glycoside hydrolases being the most abundant class of enzymes (Jose, Appoothy, More, & Arun, 2017). Wang et al. (2019) reported on a study that investigated the carbohydrate-metabolizing enzymes diversity of

Holstein cows that had been fed diets with either high forage (70%) or low forage (30%) diets. Glycoside hydrolases, which are predicted to be involved in the digestion of fiber, were found to be the most abundant class of enzymes identified in both diet groups (Lijun

Wang, Zhang, Xu, Xin, & Zhang, 2019). To date, the most comprehensive study on the rumen microbiome using metagenomics reported the assembly of 913 draft genomes from datasets generated from 43 Scottish cattle (R. D. Stewart et al., 2018). Using metagenomic binning and Hi-C-based proximity-guided assembly, the contigs as a group encoded for 1,979,391 genes, of which 69,678 coded for enzymes predicted to be involved in carbohydrate utilization (R. D. Stewart et al., 2018).

In contrast, only a limited number of studies have reported the presence of starch hydrolyzing or lactate utilizing enzymes in the rumen environment using metagenomics.

Pitta et al. (2016) found high oligosaccharide enzyme activity, as well as α-amylases, in primiparous and multiparous cows that had been fed high concentration diets at different stages of lactation, an observation consistent with the abundance of starch in the experimental diets (Pitta et al., 2016). Metagenomics analyses can also reveal genes or coding sequences involved in other bioactivities, such as novel lipases (Bayer, Kunert,

Ballschmiter, & Greiner-Stoeffele, 2010; K. Liu et al., 2009), neutralization of plant toxins (Morgavi et al., 2013), bacterial antibiotic resistance genes (Thomas et al., 2017), and synthesis of specific compounds such as conjugated linoleic acids during milk-fat depression (Morgavi et al., 2013).

While metagenomics has become an indispensable tool in the field of rumen microbiology towards elucidating and understanding the functions of uncultured bacteria and their impact on host nutrition, this approach is still in its infancy (Singh, Gautam,

Verma, Kumar, & Singh, 2008). Since functional annotation is performed primarily for unknown or poorly characterized microorganisms, one of the main challenges has remained the limited number of available reference genomes for ruminal microorganisms

(Denman & McSweeney, 2015).

3.1 Effect of Concentrate or Grain-Based Diets on the Rumen Environment

Grains are rich energy sources because they contain high levels of starch, a glucose polymer that is more readily hydrolyzed than structural polysaccharides such as cellulose and hemicellulose. Grains are used in intensive animal production systems to maximize growth potential, as they provide elevated levels of starch that can satisfy periods of high-energy demands by animals. Amongst grains typically available for livestock production, wheat contains the highest amount of starch (77%), followed by corn and sorghum (72%), then barley and oats (57-58%) (Ferraretto, 2017; Seoane,

1992). However, the amount of starch is not the only factor to consider, as the availability or digestibility of starch in the rumen is not necessarily proportional to its abundance in a particular source (Table 1.2). In addition to the type of grain, other factors can affect starch digestion rates in the rumen, including the processing method used, amount fed, other ingredients present in the diet, intake or amount of feed ingested by the animals, feeding management, and the ability of ruminal microbial communities to adapt their

18 metabolic activities to elevated amounts of starch (Hoffman et al., 2011; Mills, France, &

Dijkstra, 1999).

3.2 Starch Metabolizing Bacteria in the Rumen Environment

The transition from forages to a highly fermentable carbohydrate (concentrate) diet alters the rumen microbiome, with starch hydrolyzing bacteria becoming more predominant compared to fibrolytic bacteria. Robert E. Hungate, pioneer in rumen microbiology, found alterations in the rumen microbiome when animal diets were supplemented with high levels of grain, with significant reductions in cellulolytic bacterial and protozoal counts, while gram-positive bacteria, particularly Streptococcus bovis, increased in abundance (Hungate, Dougherty, Bryant, & Cello, 1952). Similarly, starch metabolizing bacteria were found to range in relative abundance between 90% and

95% in animals fed grain-based diets (Leedle, Bryant, & Hespell, 1982). Bryant et al.

(1958a) reported Succinimonas amylolytica as a starch utilizing ruminal species, and showed that it produced succinate as a main SCFA end product (Bryant, Small, Bouma,

& Chu, 1958). Mackie and Gilchrist (1979) identified additional species belonging to the genera Butyrivibrio, Eubacterium, and Lactobacillus as major players in starch fermentation in the rumen of sheep. In their study, Cotta et al. (1988) tested ruminal starch-utilizing bacterial species in vitro by culturing on starch-based medium, and reported that Streptococcus bovis JB1, Bacteroides ruminicola 23 and B14, and

Butyrivibrio fibrisolvens A38 and 49 were found to have the highest starch hydrolyzing activity. In a later study, McAllister et al. (1990) determined the starch fermentation activity of a few selected species on different diets that included barley, maize, or wheat,

19 and identified Butyrivibrio fibrisolvens A38 as a species capable of high amylolytic activity in their assay (McAllister, Cheng, Rode, & Forsberg, 1990). Ruminobacter amylophilus, an obligate anaerobe expressing amylase, amylopectinase, and pullulanase enzymes, has also been described as an excellent model to investigate starch fermentation in the rumen (Anderson, 1995). More recently, other amylolytic bacteria have been isolated from the rumen (Figure 1.1) (Nagaraja & Titgemeyer, 2007).

Among the various starch-utilizing species characterized from the rumen,

Streptococcus bovis has been studied more extensively because it is considered to be an important starch-fermenter (P. N. Hobson & Macpherson, 1952; Walker, 1965; Walker &

Hope, 1964). The cell density of this species when animals are on forage-based diets is only 104-107 cells/grams, while its cellular density can increase up to 1011 when grain- based diets are used (Nagaraja & Titgemeyer, 2007). High growth rates for S. bovis can be observed during sudden transitions from forage-based diets to concentrate-diets

(Wells, Krause, Callaway, & Russell, 1997). Using a qPCR approach, Prevotella bryantii, Selenomonas ruminantium, and Mitsuokella multiacidus were found to increase in Holstein cows that were switched from a basal diet to a high-grain diet (K. Tajima et al., 2001). Using the same approach, Streptococcus bovis, Selenomonas ruminantium, and

Prevotella bryantii were similarly confirmed to increase in abundance during adaptation to high-concentrate diets (Fernando et al., 2010).

While a number of starch utilizers, such as members of the genera Butyrivibrio,

Eubacterium, Lactobacillus, Prevotella, Ruminobacter, Selenomonas, and Streptococcus, have been isolated using traditional-culturing approaches (Peter N Hobson & Stewart,

2012; Kiyoshi Tajima et al., 2000), culture-independent studies have revealed a much

20 greater diversity and species richness in animals fed with concentrate-based diets than what had been estimated. Using Terminal Restriction Fragment Length Polymorphism

(T-RFLP), Fernando et al. (2010) reported that Bacteroidetes, primarily members of the genus Prevotella, were more dominant in animals fed a high-concentrate diet compared to animals fed on prairie hay. Similarly, but using PCR‐DGGE, greater ruminal bacterial diversity was reported in animals fed a high concentrate diet without forage than in animals fed a standard high concentrate diet (R. M. Petri, Forster, Yang, McKinnon, &

McAllister, 2012). In their study, Zhang et al. (2017) found that the relative abundance of

Fibrobacter decreased significantly in the rumen of Holstein heifers when they were fed increasing levels of concentrate, whereas members of the genera Christensenella and

Turicibacter increased in concentrate-based diets (Zhang et al., 2017).

3.3 Acidosis, a Digestive Disturbance Resulting from Feeding Concentrate Diets

A sudden change in diet from high forage to inclusion of high levels of starch will cause rapid proliferation of starch utilizing bacteria and a faster rate of SCFAs production, resulting in a decrease in ruminal pH. When ruminal pH remains low for extensive periods, a condition referred to as acidosis, an animal may experience health problems. One of the causes of this condition is the accumulation of lactate, a ruminal

SCFAs that is more likely produced from amylolytic bacteria than from cellulolytic bacteria, and which can further exacerbate the impact of accumulating main SCFAs on rumen pH. Typically, bacterial species that predominant under acidotic conditions have the ability to continue to metabolize starch despite the accumulation of lactate, their primary end product. These include Selenomonas ruminantium (Caldwell & Bryant,

1966; Latham, Sharpe, & Sutton, 1971; Ricke, Martin, & Nisbet, 1996) Mitsuokella multiacidus (Peter N Hobson & Stewart, 2012; Paster, Dewhirst, Olsen, Fraser, &

Socransky, 1995), as well as Lactobacillus ruminis and Lactobacillus vitulinus (Sharpe,

Latham, Garvie, Zirngibl, & Kandler, 1973). Many others are likely involved, as Krogh et al. (1963) reported the isolation of 117 Lactobacilli strains from cattle and sheep during acidotic condition (Krogh, 1963).

A number of studies on acidosis have shown that diversity, richness, and evenness of bacterial communities are reduced significantly, which likely impact the production efficiency of affected animal (Fernando et al., 2010; Khafipour, Li, Plaizier, & Krause,

2009; S. Li, Khafipour, Yoon, Plaizier, & Scott, 2016; S. Y. Mao, Zhang, Wang, & Zhu,

2013b; R. M. Petri et al., 2012). At the phylum level, most studies have reported an increase in the relative abundance of Firmicutes with a corresponding decrease in

Bacteroidetes under these conditions (R. W. Li et al., 2012; S. Mao, 2017; S. Y. Mao,

Zhang, Wang, & Zhu, 2013a; Renee M. Petri et al., 2013; J. C. Plaizier et al., 2017), with only a limited number of reports indicating an increase in Bacteroidetes (Fernando et al.,

2010). More variation amongst studies is seen when the composition of bacterial communities is analyzed at higher taxonomic resolution. For instance, an increase in

Ruminococcus, Atopobium, Bifidobacterium, and unclassified Clostridiales was observed in SARA induced animals, while members of the genera Prevotella, Treponema,

Anaeroplasma, Papillibacter, and Acinetobacter were found in lower abundance (S. Y.

Mao et al., 2013a). In their study, Petri et al. (2013b) reported higher abundances of

Prevotella, Acetitomaculum, Pseudobutyrivibrio, and Selenomonas in clinically acidotic heifers (Renee M. Petri et al., 2013), while McCann et al. (2016) found increased relative

22 abundances of Prevotella, Ruminococcus, Streptococcus, and Lactobacillus in SARA challenged animals (McCann et al., 2016). Plaizier et al. (2017b) reported higher abundance of Sharpea, Ruminococcus, Shuttleworthia, and Megasphaera in SARA challenged animals, while levels of Anaerostipes and of the candidate taxon CF231 decreased (Plaizier, Li, Tun, & Khafipour, 2017). Higher abundance of Ruminococcus was observed in SARA challenged animals by Nagata et al. (2018), while Prevotella,

Eubacterium, and Oscillibacter were found in lower abundance (Nagata et al., 2018).

Finally, a metatranscriptomic analysis showed that the relative abundance of

Cutibacterium avidum, Cutibacterium granulosum, Bacteroides vulgatus, and

Dermacoccus nishinomiyaensis increased in response to SARA, while Prevotella denticola and Prevotella scopos showed a decrease in their relative abundance (Ogunade,

Pech-Cervantes, & Schweickart, 2019).

3.4 Lactate Metabolizing Bacteria in the Rumen

Lactate is a ruminal SCFA that accumulates to higher concentrations when animals are fed with highly fermentable carbohydrate rich diets. Since high levels of lactate can be detrimental to the health of ruminants, lactate-utilizing bacterial species have generated a great deal of interest as key players to control lactate accumulation in the rumen. Megasphaera elsdenii and Selenomonas ruminantium subsp. lactilytica have been the best described lactate utilizers in animals adapted to grain diets (Huber, Cooley,

Goetsch, & Das, 1976; Mackie, Gilchrist, Robberts, Hannah, & Schwartz, 1978), with the former considered to be the key player in lactate fermentation because of its tolerance to acidity (Therion, Kistner, & Kornelius, 1982). Indeed, the abundance of Megasphaera

23 elsdenii has been reported to increase during the adaptation to high concentrate diets and to remain elevated when these diets are maintained (Fernando et al., 2010) (R. M. Petri et al., 2012). In their study, Counotte et al. (1981) found that Megasphaera elsdenii was responsible for metabolizing up to 74% of ruminal lactate. Notably, Megasphaera elsdenii can produce butyrate or propionate when metabolizing lactate, through expression of enzymes such as NAD-independent D-lactate dehydrogenase and lactate racemase (Hino & Kuroda, 1993). Intriguingly, both of these enzymes that are needed for lactate utilization are suppressed when Megasphaera elsdenii is grown on glucose (Hino

& Kuroda, 1993). By comparison, Selenomonas ruminantium has a very slow growth rate on lactate medium, and can only utilize D-lactate as this strain has a NAD‐independent

D‐lactate dehydrogenase (iD‐LDH) enzyme (Asanuma & Hino, 2005; Russell &

Baldwin, 1978).

Amongst the limited number of other known ruminal lactate utilizers, five strains of Propionibacterium acnes were originally identified by growth on lactate-supplemented

(Gutierrez, 1953), and Fusobacterium necrophorum, a pathogenic bacterium responsible for causing liver abscesses in cattle, is a lactate utilizer whose abundance can increase up to 106 to 107 cells/g when animals are fed grain diets (Coe et al., 1999; Tan, Nagaraja, &

Chengappa, 1994).

4.1 Rationale and Approach

In ruminal microbial communities, bacteria represent the most abundant and diverse group of microorganisms. They play an essential role in converting components of ingested feed stuffs into SCFAs, which animal hosts can use as energy (Schwartz,

1969). Amongst the different strategies used to increase ruminant production, feeding ingredients rich in starch has become a standard practice. However, feeding high amounts of starch leads to increased production and accumulation of not only the major ruminal

SCFAs, but also of lactate. Elevated ruminal SCFAs and lactate can result in acidosis, a condition that can harm animals, and negatively impact their productivity. In order to design more effective strategies to mitigate the incidence of acidosis, there is a critical need to improve our understand of amylolytic and lactate-utilizing bacteria in the rumen.

Indeed, amylolytic bacteria are critical for digestion of starch, while lactate-utilizing bacteria are essential to prevent the accumulation of lactate, which is a common end product of amylolytic bacteria.

Because of the high microbial diversity that exists in the rumen, the ability to assign specifc metabolic functions to particular bacterial species has remained a challenge. Indeed, not only does the vast majority of ruminal microbial species remain unknown, but ruminant diets can include a wide variety of substrates, which promotes increased functional and microbial diversity.

The traditional approach to this problem has been the use of cultivation-based methods, i.e. using controlled selective culture conditions to identify microbial isolates with metabolic activities of interest. However, the majority of ruminal bacterial species have so far proven resistant to isolation by growth on culture medium, which may be due

25 to factors such as the absence of essential nutrients in current medium formulations or to the necessity of co-culturing with other microorganisms to achieve growth.

One alternative to the selection of isolates is to use an enrichment approach. In this case, samples from the environment of interest are used as the culture medium, which will not only provide a pool of microorganisms with the metabolic activity of interest, but also provide essential nutrients as well as microbial partner species that may be necessary for their growth. Enrichment for bacterial species that express a metabolic activity of interest can be achieved by supplementing a set of replicate cultures with a relevant substrate. Since the majority of ruminal microbial species remain uncharacterized, a

DNA-based method, such as 16S rRNA profiling, can be used to identify enriched species by comparing the bacterial composition of supplemented and unsupplemented cultures. Similarly, metagenomics can be used to gain further insight on the metabolic potential of enriched species through gene annotation of partially reconstructed genomes.

4.2 Research Objectives

In this context, the main objectives of the research presented in this dissertation

were to

 identify bacteria that can grow in batch cultures consisting of rumen fluid

supplemented with starch

 determine the metabolic potential of starch-enriched bacterial species using

metagenomics

 identify bacteria that can grow in batch cultures consisting of rumen fluid

supplemented with lactate

 determine the metabolic potential of lactate-enriched bacterial species using

metagenomics

Figure 1.1. Amylolytic, maltose-fermenting, glucose-fermenting and lactic acid fermenting bacteria involved in the fermentation of high-concentrate diets (Nagaraja & Titgemeyer, 2007).

Table 1.1. PCR and Non-PCR Based Molecular Approaches.

PCR-based molecular techniques References Denaturing/temperature gradient gel (Muyzer, 1999) electrophoresis Single-strand-conformation (Lee, Zo, & Kim, 1996) polymorphism (SSCP) Restriction fragment length (LAGUERRE, RIGOTTIER-GOIS, & polymorphisms (RFLP) LEMANCEAU, 1994) Terminal restriction fragment length (Dunbar, Dragon, Lee, & Baserga, polymorphisms (T-RFLP) 2000) Quantitative PCR (qPCR) (Takai & Horikoshi, 2000)

Non-PCR-based molecular References techniques Fluorescence in situ hybridization (Bodrossy & Sessitsch, 2004; Bottari, (FISH) and microarray Ercolini, Gatti, & Neviani, 2006) Raman microspectroscopy (Huang, Li, Jarvis, Goodacre, & Banwart, 2010) Nano-scale secondary ion mass (Herrmann et al., 2007) spectrometry (NanoSIMS) Next-generation sequencing (NGS) (Nowrousian et al., 2010) methods pyrosequencing Metagenomics (XU, 2006) Metatranscriptomics (Poretsky et al., 2009) Metaproteomics (Wilmes & Bond, 2004)

Table 1.2. Overview of amount of starch fermented in the rumen when the livestock are supplemented with different starch sources.

Grain Starch (%) Ruemn digestability (%) Corn (Herrera- 76.0 72-89.9 Saldana, Huber, & Poore, 1990; Huntington, Harmon, & Richards, 2006) Sorghum (Herrera- 71.3 60-78.4 Saldana et al., 1990; Huntington et al., 2006) Wheat (Herrera- 70.3 88.3-88.1 Saldana et al., 1990; Huntington et al., 2006) Barley (Herrera- 64.3 80.7-84.6 Saldana et al., 1990; Huntington et al., 2006) Oats (Herrera-Saldana 58.1 92.7-94.0 et al., 1990; Huntington et al., 2006) Yucca (Gómez, 80.0 91.0 Posada, & Olivera, 2016)

References

Agrawal, P. K., Agrawal, S., & Shrivastava, R. (2015). Modern molecular approaches for analyzing microbial diversity from mushroom compost ecosystem. 3 Biotech, 5(6), 853-

866. doi:10.1007/s13205-015-0289-2

Anderson, K. L. (1995). Biochemical analysis of starch degradation by Ruminobacter amylophilus 70. Appl Environ Microbiol, 61(4), 1488-1491.

Arumugam, M., Raes, J., Pelletier, E., Le Paslier, D., Yamada, T., Mende, D. R., . . .

Bork, P. (2011). Enterotypes of the human gut microbiome. Nature, 473(7346), 174-180. doi:10.1038/nature09944

ASANUMA, N., & HINO, T. (2005). Ability to utilize lactate and related enzymes of a ruminal bacterium, Selenomonas ruminantium. Animal Science Journal, 76(4), 345-352. doi:10.1111/j.1740-0929.2005.00274.x

Asmare, B. (2014). Biotechnological Advances for Animal Nutrition and Feed

Improvement. World Journal of Agricultural Research, 2(3), 115-118. doi:10.12691/wjar-2-3-5

Aziz, R. K., Bartels, D., Best, A. A., DeJongh, M., Disz, T., Edwards, R. A., . . .

Zagnitko, O. (2008). The RAST Server: rapid annotations using subsystems technology.

BMC Genomics, 9, 75. doi:10.1186/1471-2164-9-75

Balch, D. A., & Rowland, S. J. (1957). Volatile fatty acids and lactic acid in the rumen of dairy cows receiving a variety of diets. Br J Nutr, 11(3), 288-298. doi:10.1079/bjn19570046

Barendse, W., Reverter, A., Bunch, R. J., Harrison, B. E., Barris, W., & Thomas, M. B.

(2007). A Validated Whole-Genome Association Study of Efficient Food Conversion in

Cattle. Genetics, 176(3), 1893. doi:10.1534/genetics.107.072637

Bayer, S., Kunert, A., Ballschmiter, M., & Greiner-Stoeffele, T. (2010). Indication for a

New Lipolytic Enzyme Family: Isolation and Characterization of Two Esterases from a

Metagenomic Library. Journal of Molecular Microbiology and Biotechnology, 18(3),

181-187. doi:10.1159/000315459

Beharka, A. A., Nagaraja, T. G., Morrill, J. L., Kennedy, G. A., & Klemm, R. D. (1998).

Effects of form of the diet on anatomical, microbial, and fermentative development of the rumen of neonatal calves. J Dairy Sci, 81(7), 1946-1955. doi:10.3168/jds.S0022-

0302(98)75768-6

Bodirsky, B. L., Rolinski, S., Biewald, A., Weindl, I., Popp, A., & Lotze-Campen, H.

(2015). Global Food Demand Scenarios for the 21st Century. PLOS ONE, 10(11), e0139201. doi:10.1371/journal.pone.0139201

Bodrossy, L., & Sessitsch, A. (2004). Oligonucleotide microarrays in microbial diagnostics. Current Opinion in Microbiology, 7(3), 245-254. doi:https://doi.org/10.1016/j.mib.2004.04.005

Boisvert, S., Raymond, F., Godzaridis, É., Laviolette, F., & Corbeil, J. (2012). Ray Meta: scalable de novo metagenome assembly and profiling. Genome biology, 13(12), R122.

Bottari, B., Ercolini, D., Gatti, M., & Neviani, E. (2006). Application of FISH technology for microbiological analysis: current state and prospects. Applied Microbiology and

Biotechnology, 73(3), 485-494. doi:10.1007/s00253-006-0615-z

Brulc, J. M., Antonopoulos, D. A., Miller, M. E., Wilson, M. K., Yannarell, A. C.,

Dinsdale, E. A., . . . White, B. A. (2009). Gene-centric metagenomics of the fiber- adherent bovine rumen microbiome reveals forage specific glycoside hydrolases. Proc

Natl Acad Sci U S A, 106(6), 1948-1953. doi:10.1073/pnas.0806191105

Bruns, K. W., Pritchard, R. H., & Boggs, D. L. (2005). The effect of stage of growth and implant exposure on performance and carcass composition in steers. J Anim Sci, 83(1),

108-116. doi:10.2527/2005.831108x

Bryant, M. P., Small, N., Bouma, C., & Chu, H. (1958). Bacteroides ruminicola n. sp. and

Succinimonas amylolytica; the new genus and species; species of succinic acid-producing anaerobic bacteria of the bovine rumen. J Bacteriol, 76(1), 15-23.

Bryant, M. P., Small, N., Bouma, C., & Robinson, I. (1958). Studies on the Composition of the Ruminal Flora and Fauna of Young Calves. Journal of Dairy Science, 41(12),

1747-1767. doi:https://doi.org/10.3168/jds.S0022-0302(58)91160-3

Byerly, T. C. (1967). Efficiency of feed conversion. Science, 157(3791), 890-895. doi:10.1126/science.157.3791.890

Caldwell, D. R., & Bryant, M. P. (1966). Medium without rumen fluid for nonselective enumeration and isolation of rumen bacteria. Applied microbiology, 14(5), 794-801.

Cantalapiedra-Hijar, G., Yanez-Ruiz, D. R., Martin-Garcia, A. I., & Molina-Alcaide, E.

(2009). Effects of forage:concentrate ratio and forage type on apparent digestibility, ruminal fermentation, and microbial growth in goats. J Anim Sci, 87(2), 622-631. doi:10.2527/jas.2008-1142

Carberry, C. A., Kenny, D. A., Han, S., McCabe, M. S., & Waters, S. M. (2012). Effect of phenotypic residual feed intake and dietary forage content on the rumen microbial

33 community of beef cattle. Appl Environ Microbiol, 78(14), 4949-4958. doi:10.1128/aem.07759-11

Case, R. J., Boucher, Y., Dahllof, I., Holmstrom, C., Doolittle, W. F., & Kjelleberg, S.

(2007). Use of 16S rRNA and rpoB genes as molecular markers for microbial ecology studies. Appl Environ Microbiol, 73(1), 278-288. doi:10.1128/aem.01177-06

Chakravorty, S., Helb, D., Burday, M., Connell, N., & Alland, D. (2007). A detailed analysis of 16S ribosomal RNA gene segments for the diagnosis of pathogenic bacteria. J

Microbiol Methods, 69(2), 330-339. doi:10.1016/j.mimet.2007.02.005

Chandler, D. P. (1998). Redefining relativity: quantitative PCR at low template concentrations for industrial and environmental microbiology. Journal of Industrial

Microbiology and Biotechnology, 21(3), 128-140. doi:10.1038/sj.jim.2900546

Chaucheyras-Durand, F., & Ossa, F. (2014). REVIEW: The rumen microbiome:

Composition, abundance, diversity, and new investigative tools. The Professional Animal

Scientist, 30(1), 1-12. doi:10.15232/S1080-7446(15)30076-0

Church, D. C. (1988). The ruminant animal : digestive physiology and nutrition:

Englewood Cliffs (N.J.) : Prentice-Hall.

Clarridge, J. E., 3rd. (2004). Impact of 16S rRNA gene sequence analysis for identification of bacteria on clinical microbiology and infectious diseases. Clinical microbiology reviews, 17(4), 840-862. doi:10.1128/CMR.17.4.840-862.2004

Coe, M. L., Nagaraja, T. G., Sun, Y. D., Wallace, N., Towne, E. G., Kemp, K. E., &

Hutcheson, J. P. (1999). Effect of virginiamycin on ruminal fermentation in cattle during adaptation to a high concentrate diet and during an induced acidosis. J Anim Sci, 77(8),

2259-2268. doi:10.2527/1999.7782259x

Cole, J. R., Wang, Q., Fish, J. A., Chai, B., McGarrell, D. M., Sun, Y., . . . Tiedje, J. M.

(2014). Ribosomal Database Project: data and tools for high throughput rRNA analysis.

Nucleic Acids Res, 42(Database issue), D633-642. doi:10.1093/nar/gkt1244

Connor, E. E. (2015). Invited review: improving feed efficiency in dairy production: challenges and possibilities. Animal, 9(3), 395-408. doi:10.1017/s1751731114002997

Costea, P. I., Zeller, G., Sunagawa, S., Pelletier, E., Alberti, A., Levenez, F., . . . Bork, P.

(2017). Towards standards for human fecal sample processing in metagenomic studies.

Nat Biotechnol, 35(11), 1069-1076. doi:10.1038/nbt.3960

Costello, E. K., Lauber, C. L., Hamady, M., Fierer, N., Gordon, J. I., & Knight, R.

(2009). Bacterial community variation in human body habitats across space and time.

Science (New York, N.Y.), 326(5960), 1694-1697. doi:10.1126/science.1177486

Council, N. R. (2015). Critical Role of Animal Science Research in Food Security and

Sustainability. Washington, DC: The National Academies Press.

Creevey, C. J., Kelly, W. J., Henderson, G., & Leahy, S. C. (2014). Determining the culturability of the rumen bacterial microbiome. Microbial biotechnology, 7(5), 467-479. doi:10.1111/1751-7915.12141

Davis, C. L. D., James K. (1988). The development, nutrition, and management of the young calf. de Lipthay, J. R., Enzinger, C., Johnsen, K., Aamand, J., & Sørensen, S. J. (2004). Impact of DNA extraction method on bacterial community composition measured by denaturing gradient gel electrophoresis. Soil Biology and Biochemistry, 36(10), 1607-1614. doi:https://doi.org/10.1016/j.soilbio.2004.03.011

Dehority, B. A., & Grubb, J. A. (1977). Characterization of the predominant bacteria occurring in the rumen of goats (Capra hircus). Applied and environmental microbiology,

33(5), 1030-1036. den Besten, G., van Eunen, K., Groen, A. K., Venema, K., Reijngoud, D.-J., & Bakker,

B. M. (2013). The role of short-chain fatty acids in the interplay between diet, gut microbiota, and host energy metabolism. Journal of lipid research, 54(9), 2325-2340. doi:10.1194/jlr.R036012

Denman, S. E., & McSweeney, C. S. (2015). The Early Impact of Genomics and

Metagenomics on Ruminal Microbiology. Annual Review of Animal Biosciences, 3(1),

447-465. doi:10.1146/annurev-animal-022114-110705

Deusch, S., Tilocca, B., Camarinha-Silva, A., & Seifert, J. (2015). News in livestock research — use of Omics-technologies to study the microbiota in the gastrointestinal tract of farm animals. Computational and structural biotechnology journal, 13, 55-63. doi:https://doi.org/10.1016/j.csbj.2014.12.005

Didanna, H. L. (2015). The Contribution of Livestock in Meeting Food Production andNutrition in Ethiopia Food Science and Quality Management, 40.

Dijkstra, J., Forbes, J. M., & France, J. (2005). Quantitative Aspects of Ruminant

Digestion and Metabolism.

Dunbar, D. A., Dragon, F., Lee, S. J., & Baserga, S. J. (2000). A nucleolar protein related to ribosomal protein L7 is required for an early step in large ribosomal subunit biogenesis. Proc Natl Acad Sci U S A, 97(24), 13027-13032. doi:10.1073/pnas.97.24.13027

Elsden, S. R. (1945). The Fermentation of Carbohydrates in the Rumen of the Sheep.

Journal of Experimental Biology, 22(1-2), 51.

Eugène, M., Archimede, H., & Sauvant, D. (2004). Quantitative meta-analysis on the effects of defaunation of the rumen on growth, intake and digestion in ruminants.

Livestock Production Science, 85, 81-97. doi:10.1016/S0301-6226(03)00117-9

FAO. (2011). The State of the World's Land and Water Resources for food and agriculture. Paper presented at the Food and Agriculture Organization of the United

Nations, ROME.

FAO. (2017). The future of food and agriculture: Trends and challenges. Paper presented at the Food and Agriculture Organization of the United Nations, Rome.

FAO. (2018). FAO’S WORKON CLIMATECHANGE. Paper presented at the Food and

Agriculture Organization of the United Nations, Tanzania.

Fernando, S. C., Purvis, H. T., 2nd, Najar, F. Z., Sukharnikov, L. O., Krehbiel, C. R.,

Nagaraja, T. G., . . . Desilva, U. (2010). Rumen microbial population dynamics during adaptation to a high-grain diet. Appl Environ Microbiol, 76(22), 7482-7490. doi:10.1128/aem.00388-10

Ferraretto, L. F. (2017). Impact of Starch Content and Digestibility in Dairy Cattle Diets.

Semantic Scholar.

Ferrer, M., Golyshina, O. V., Chernikova, T. N., Khachane, A. N., Reyes-Duarte, D.,

Santos, V. A., . . . Golyshin, P. N. (2005). Novel hydrolase diversity retrieved from a metagenome library of bovine rumen microflora. Environ Microbiol, 7(12), 1996-2010. doi:10.1111/j.1462-2920.2005.00920.x

Firkins, J., Karnati, S., & Yu, Z. (2008). Linking rumen function to animal response by application of metagenomics techniques. Australian Journal of Experimental Agriculture

- AUST J EXP AGR, 48. doi:10.1071/EA08028

Flint, H. J. (1997). The rumen microbial ecosystem—some recent developments. Trends in Microbiology, 5(12), 483-488. doi:https://doi.org/10.1016/S0966-842X(97)01159-1

Fonty, G., Gouet, P., Jouany, J.-P., & Senaud, J. (1987). Establishment of the Microflora and Anaerobic Fungi in the Rumen of Lambs. Microbiology, 133(7), 1835-1843. doi:https://doi.org/10.1099/00221287-133-7-1835

Forney, L. J., Zhou, X., & Brown, C. J. (2004). Molecular microbial ecology: land of the one-eyed king. Curr Opin Microbiol, 7(3), 210-220. doi:10.1016/j.mib.2004.04.015

Franzosa, E. A., Hsu, T., Sirota-Madi, A., Shafquat, A., Abu-Ali, G., Morgan, X. C., &

Huttenhower, C. (2015). Sequencing and beyond: integrating molecular 'omics' for microbial community profiling. Nature reviews. Microbiology, 13(6), 360-372. doi:10.1038/nrmicro3451

Gerbens-Leenes, P. W., Nonhebel, S., & Krol, M. S. (2010). Food consumption patterns and economic growth. Increasing affluence and the use of natural resources. Appetite,

55(3), 597-608. doi:10.1016/j.appet.2010.09.013

Gilbert, J. A., Field, D., Swift, P., Newbold, L., Oliver, A., Smyth, T., . . . Joint, I. (2009).

The seasonal structure of microbial communities in the Western English Channel.

Environ Microbiol, 11(12), 3132-3139. doi:10.1111/j.1462-2920.2009.02017.x

Godfray, H. C., Beddington, J. R., Crute, I. R., Haddad, L., Lawrence, D., Muir, J. F., . . .

Toulmin, C. (2010). Food security: the challenge of feeding 9 billion people. Science,

327(5967), 812-818. doi:10.1126/science.1185383

Goll, J., Rusch, D. B., Tanenbaum, D. M., Thiagarajan, M., Li, K., Methe, B. A., &

Yooseph, S. (2010). METAREP: JCVI metagenomics reports--an open source tool for high-performance comparative metagenomics. Bioinformatics, 26(20), 2631-2632. doi:10.1093/bioinformatics/btq455

Gómez, L. M., Posada, S. L., & Olivera, M. (2016). Starch in ruminant diets: a review.

Revista Colombiana de Ciencias Pecuarias, 29, 77-90.

Gutierrez, J. (1953). Numbers and characteristics of lactate-utilizing organisms in the rumen of cattle. Journal of bacteriology, 66(2), 123-128.

Hackl, E., Zechmeister-Boltenstern, S., Bodrossy, L., & Sessitsch, A. (2004).

Comparison of diversities and compositions of bacterial populations inhabiting natural forest soils. Applied and environmental microbiology, 70(9), 5057-5065. doi:10.1128/AEM.70.9.5057-5065.2004

Haider, B., Ahn, T.-H., Bushnell, B., Chai, J., Copeland, A., & Pan, C. (2014). Omega: an overlap-graph de novo assembler for metagenomics. Bioinformatics, 30(19), 2717-

2722.

Hamady, M., & Knight, R. (2009). Microbial community profiling for human microbiome projects: Tools, techniques, and challenges. Genome research, 19(7), 1141-

1152. doi:10.1101/gr.085464.108

Herrera-Saldana, R. E., Huber, J. T., & Poore, M. H. (1990). Dry Matter, Crude Protein, and Starch Degradability of Five Cereal Grains1. Journal of Dairy Science, 73(9), 2386-

2393. doi:https://doi.org/10.3168/jds.S0022-0302(90)78922-9

Herrmann, A. M., Ritz, K., Nunan, N., Clode, P. L., Pett-Ridge, J., Kilburn, M. R., . . .

Stockdale, E. A. (2007). Nano-scale secondary ion mass spectrometry — A new

39 analytical tool in biogeochemistry and soil ecology: A review article. Soil Biology and

Biochemistry, 39(8), 1835-1850. doi:https://doi.org/10.1016/j.soilbio.2007.03.011

Hess, M., Sczyrba, A., Egan, R., Kim, T. W., Chokhawala, H., Schroth, G., . . . Rubin, E.

M. (2011). Metagenomic discovery of biomass-degrading genes and genomes from cow rumen. Science, 331(6016), 463-467. doi:10.1126/science.1200387

Hino, T., & Kuroda, S. (1993). Presence of lactate dehydrogenase and lactate racemase in

Megasphaera elsdenii grown on glucose or lactate. Applied and environmental microbiology, 59(1), 255-259.

Hobson, P. N., & Macpherson, M. (1952). Amylases of Clostridium butyricum and a

Streptococcus isolated from the rumen of the sheep. The Biochemical journal, 52(4), 671-

679. doi:10.1042/bj0520671

Hobson, P. N., & Stewart, C. S. (2012). The rumen microbial ecosystem: Springer

Science & Business Media.

Hoffman, P. C., Esser, N. M., Shaver, R. D., Coblentz, W. K., Scott, M. P., Bodnar, A.

L., . . . Charley, R. C. (2011). Influence of ensiling time and inoculation on alteration of the starch-protein matrix in high-moisture corn. J Dairy Sci, 94(5), 2465-2474. doi:10.3168/jds.2010-3562

Huang, W. E., Li, M., Jarvis, R. M., Goodacre, R., & Banwart, S. A. (2010). Chapter 5 -

Shining Light on the Microbial World: The Application of Raman Microspectroscopy. In

Advances in Applied Microbiology (Vol. 70, pp. 153-186): Academic Press.

Huber, T. L., Cooley, J. H., Goetsch, D. D., & Das, N. K. (1976). Lactic acid-utilizing bacteria in ruminal fluid of a steer adapted from hay feeding to a high-grain ration. Am J

Vet Res, 37(5), 611-613.

Hungate, R. E., Dougherty, R. W., Bryant, M. P., & Cello, R. M. (1952). Microbiological and physiological changes associated with acute indigestion in sheep. Cornell Vet, 42(4),

423-449.

Hunt, M., Gall, A., Ong, S. H., Brener, J., Ferns, B., Goulder, P., . . . Otto, T. D. (2015).

IVA: accurate de novo assembly of RNA virus genomes. Bioinformatics, 31(14), 2374-

2376.

Huntington, G. B., Harmon, D. L., & Richards, C. J. (2006). Sites, rates, and limits of starch digestion and glucose metabolism in growing cattle. J Anim Sci, 84 Suppl, E14-24. doi:10.2527/2006.8413_supple14x

Huson, D. H., & Weber, N. (2013). Microbial community analysis using MEGAN.

Methods Enzymol, 531, 465-485. doi:10.1016/b978-0-12-407863-5.00021-6

Ibal, J. C., Pham, H. Q., Park, C. E., & Shin, J. H. (2019). Information about variations in multiple copies of bacterial 16S rRNA genes may aid in species identification. PLOS

ONE, 14(2), e0212090. doi:10.1371/journal.pone.0212090

Jami, E., Israel, A., Kotser, A., & Mizrahi, I. (2013). Exploring the bovine rumen bacterial community from birth to adulthood. Isme j, 7(6), 1069-1079. doi:10.1038/ismej.2013.2

Jiao, J., Li, X., Beauchemin, K., Tan, Z., Tang, S., & Zhou, C. (2015). Rumen development process in goats as affected by supplemental feeding v. grazing: Age-related anatomic development, functional achievement and microbial colonisation. The British journal of nutrition, 113, 1-13. doi:10.1017/S0007114514004413

Jose, V. L., Appoothy, T., More, R. P., & Arun, A. S. (2017). Metagenomic insights into the rumen microbial fibrolytic enzymes in Indian crossbred cattle fed finger millet straw.

AMB Express, 7(1), 13-13. doi:10.1186/s13568-016-0310-0

Kamra, D. N. (2005). Rumen microbial ecosystem. Current Science, 89, 124-135.

Kenny, D. A., Fitzsimons, C., Waters, S. M., & McGee, M. (2018). Invited review:

Improving feed efficiency of beef cattle – the current state of the art and future challenges. Animal, 12(9), 1815-1826. doi:10.1017/S1751731118000976

Kerr, B. J., Kellner, T. A., & Shurson, G. C. (2015). Characteristics of lipids and their feeding value in swine diets. Journal of Animal Science and Biotechnology, 6(1), 30. doi:10.1186/s40104-015-0028-x

Khafipour, E., Li, S., Plaizier, J. C., & Krause, D. O. (2009). Rumen microbiome composition determined using two nutritional models of subacute ruminal acidosis.

Applied and environmental microbiology, 75(22), 7115-7124. doi:10.1128/AEM.00739-

Kim, M., Morrison, M., & Yu, Z. (2011a). Evaluation of different partial 16S rRNA gene sequence regions for phylogenetic analysis of microbiomes. J Microbiol Methods, 84(1),

81-87. doi:10.1016/j.mimet.2010.10.020

Kim, M., Morrison, M., & Yu, Z. (2011b). Status of the phylogenetic diversity census of ruminal microbiomes. FEMS Microbiol Ecol, 76(1), 49-63. doi:10.1111/j.1574-

6941.2010.01029.x

Klappenbach, J. A., Saxman, P. R., Cole, J. R., & Schmidt, T. M. (2001). rrndb: the

Ribosomal RNA Operon Copy Number Database. Nucleic acids research, 29(1), 181-

184. doi:10.1093/nar/29.1.181

Krogh, N. (1963). Identification of the gram-positive rumen flora of cattle and sheep in clinical cases of acute indigestion. Acta Veterinaria Scandinavica, 4, 41-51.

LAGUERRE, G., RIGOTTIER-GOIS, L., & LEMANCEAU, P. (1994). Fluorescent

Pseudomonas species categorized by using polymerase chain reaction (PCR)/restriction fragment analysis of 16S rDNA. Molecular Ecology, 3(5), 479-487. doi:10.1111/j.1365-

294X.1994.tb00126.x

Lai, B., Ding, R., Li, Y., Duan, L., & Zhu, H. (2012). A de novo metagenomic assembly program for shotgun DNA reads. Bioinformatics, 28(11), 1455-1462.

Laserson, J., Jojic, V., & Koller, D. (2011). Genovo: de novo assembly for metagenomes.

Journal of Computational Biology, 18(3), 429-443.

Latham, M. J., Sharpe, M. E., & Sutton, J. D. (1971). The Microbial Flora of the Rumen of Cows Fed Hay and High Cereal Rations and its Relationship to the Rumen

Fermentation. Journal of Applied Bacteriology, 34(2), 425-434. doi:10.1111/j.1365-

2672.1971.tb02302.x

Lee, D. H., Zo, Y. G., & Kim, S. J. (1996). Nonradioactive method to study genetic profiles of natural bacterial communities by PCR-single-strand-conformation polymorphism. Appl Environ Microbiol, 62(9), 3112-3120.

Leedle, J. A., Bryant, M. P., & Hespell, R. B. (1982). Diurnal variations in bacterial numbers and fluid parameters in ruminal contents of animals fed low- or high-forage diets. Appl Environ Microbiol, 44(2), 402-412.

Li, D., Liu, C.-M., Luo, R., Sadakane, K., & Lam, T.-W. (2015). MEGAHIT: an ultra- fast single-node solution for large and complex metagenomics assembly via succinct de

Bruijn graph. Bioinformatics, 31(10), 1674-1676.

Li, D., Luo, R., Liu, C.-M., Leung, C.-M., Ting, H.-F., Sadakane, K., . . . Lam, T.-W.

(2016). MEGAHIT v1. 0: a fast and scalable metagenome assembler driven by advanced methodologies and community practices. Methods, 102, 3-11.

Li, R. W., Connor, E. E., Li, C., Baldwin Vi, R. L., & Sparks, M. E. (2012).

Characterization of the rumen microbiota of pre-ruminant calves using metagenomic tools. Environ Microbiol, 14(1), 129-139. doi:10.1111/j.1462-2920.2011.02543.x

Li, S., Khafipour, E., Yoon, I., Plaizier, J. C., & Scott, M. (2016). Impact of

Saccharomyces cerevisiae fermentation product and subacute ruminal acidosis on production, inflammation, and fermentation in the rumen and hindgut of dairy cows.

Animal feed science and technology., 211, 50-60. doi:10.1016/j.anifeedsci.2015.10.010

Lin, Y.-Y., Hsieh, C.-H., Chen, J.-H., Lu, X., Kao, J.-H., Chen, P.-J., . . . Wang, H.-Y.

(2017). De novo assembly of highly polymorphic metagenomic data using in situ generated reference sequences and a novel BLAST-based assembly pipeline. BMC

Bioinformatics, 18(1), 223.

Linn, J. G. (1988). Factors Affecting the Composition of Milk from Dairy Cows.

Washington (DC).

Liu, J., Diao, Q., Zhao, Y., Jiang, C., Li, Y., & Tu, Y. (2012). Effects of dietary

NFC/NDF ratios on rumen pH, NH3-N and VFA of meat sheep. Chinese Journal of

Animal Nutrition, 24(6), 1069-1077.

Liu, K., Wang, J., Bu, D., Zhao, S., McSweeney, C., Yu, P., & Li, D. (2009). Isolation and biochemical characterization of two lipases from a metagenomic library of China

Holstein cow rumen. Biochemical and biophysical research communications, 385(4),

605-611. doi:10.1016/j.bbrc.2009.05.110

Liu, Z., DeSantis, T. Z., Andersen, G. L., & Knight, R. (2008). Accurate taxonomy assignments from 16S rRNA sequences produced by highly parallel pyrosequencers.

Nucleic acids research, 36(18), e120-e120. doi:10.1093/nar/gkn491

Liu, Z., Lozupone, C., Hamady, M., Bushman, F. D., & Knight, R. (2007). Short pyrosequencing reads suffice for accurate microbial community analysis. Nucleic Acids

Res, 35(18), e120. doi:10.1093/nar/gkm541

Mackie, R. I., Gilchrist, F. M. C., Robberts, A. M., Hannah, P. E., & Schwartz, H. M.

(1978). Microbiological and chemical changes in the rumen during the stepwise adaptation of sheep to high concentrate diets. The Journal of Agricultural Science, 90(2),

241-254. doi:10.1017/S0021859600055313

Mao, S. (2017). Impact of Subacute Ruminal Acidosis (SARA) on Ruminal Microbiome,

Lipopoly saccharide and Bioamine and Rumen Epithelial Health of Dairy Cows. Journal of Dairy & Veterinary Sciences, 1. doi:10.19080/JDVS.2017.01.555556

Mao, S. Y., Zhang, R. Y., Wang, D. S., & Zhu, W. Y. (2013a). Impact of subacute ruminal acidosis (SARA) adaptation on rumen microbiota in dairy cattle using pyrosequencing. Anaerobe, 24, 12-19. doi:10.1016/j.anaerobe.2013.08.003

Mao, S. Y., Zhang, R. Y., Wang, D. S., & Zhu, W. Y. (2013b). Impact of subacute ruminal acidosis (SARA) adaptation on rumen microbiota in dairy cattle using pyrosequencing. Anaerobe, 24, 12-19. doi:10.1016/j.anaerobe.2013.08.003

Markowitz, V. M., Chen, I. M., Chu, K., Szeto, E., Palaniappan, K., Grechkin, Y., . . .

Kyrpides, N. C. (2012). IMG/M: the integrated metagenome data management and comparative analysis system. Nucleic Acids Res, 40(Database issue), D123-129. doi:10.1093/nar/gkr975

Masson, M. J., & Phillipson, A. T. (1951). The absorption of acetate, propionate and butyrate from the rumen of sheep. The Journal of physiology, 113(2-3), 189-206. doi:10.1113/jphysiol.1951.sp004565

McAllister, T. A., Cheng, K. J., Rode, L. M., & Forsberg, C. W. (1990). Digestion of barley, maize, and wheat by selected species of ruminal bacteria. Applied and environmental microbiology, 56(10), 3146-3153.

McCann, J. C., Luan, S., Cardoso, F. C., Derakhshani, H., Khafipour, E., & Loor, J. J.

(2016). Induction of Subacute Ruminal Acidosis Affects the Ruminal Microbiome and

Epithelium. Front Microbiol, 7, 701. doi:10.3389/fmicb.2016.00701

Medlin, L. K., & Orozco, J. (2017). Molecular Techniques for the Detection of

Organisms in Aquatic Environments, with Emphasis on Harmful Algal Bloom Species.

Sensors (Basel, Switzerland), 17(5), 1184. doi:10.3390/s17051184

Mills, J. A. N., France, J., & Dijkstra, J. (1999). A review of starch digestion in the lactating dairy cow and proposals for a mechanistic model: 1. Dietary starch characterisation and ruminal starch digestion. Journal of Animal and Feed Sciences, 8(3),

291-340. doi:10.22358/jafs/68938/1999

Minato, H., Otsuka, M., Shirasaka, S., Itabashi, H., & Mitsumori, M. (1992).

Colonization of microorganisms in the rumen of young calves. Journal of General and

Applied Microbiology - J GEN APPL MICROBIOL TOKYO, 38, 447-456. doi:10.2323/jgam.38.447

Morgan, J. L., Darling, A. E., & Eisen, J. A. (2010). Metagenomic Sequencing of an In

Vitro-Simulated Microbial Community. PLOS ONE, 5(4), e10209. doi:10.1371/journal.pone.0010209

Morgavi, D. P., Kelly, W. J., Janssen, P. H., & Attwood, G. T. (2013). Rumen microbial

(meta)genomics and its application to ruminant production. Animal, 7 Suppl 1, 184-201. doi:10.1017/s1751731112000419

Muyzer, G. (1999). DGGE/TGGE a method for identifying genes from natural ecosystems. Current Opinion in Microbiology, 2(3), 317-322. doi:https://doi.org/10.1016/S1369-5274(99)80055-1

Nagaraja, T. G., & Titgemeyer, E. C. (2007). Ruminal acidosis in beef cattle: the current microbiological and nutritional outlook. J Dairy Sci, 90 Suppl 1, E17-38. doi:10.3168/jds.2006-478

Nagata, R., Kim, Y. H., Ohkubo, A., Kushibiki, S., Ichijo, T., & Sato, S. (2018). Effects of repeated subacute ruminal acidosis challenges on the adaptation of the rumen bacterial community in Holstein bulls. J Dairy Sci, 101(5), 4424-4436. doi:10.3168/jds.2017-

13859

Namiki, T., Hachiya, T., Tanaka, H., & Sakakibara, Y. (2012). MetaVelvet: an extension of Velvet assembler to de novo metagenome assembly from short sequence reads.

Nucleic acids research, 40(20), e155-e155.

Nowrousian, M., Stajich, J. E., Chu, M., Engh, I., Espagne, E., Halliday, K., . . . Freitag,

M. (2010). De novo Assembly of a 40 Mb Eukaryotic Genome from Short Sequence

Reads: Sordaria macrospora, a Model Organism for Fungal Morphogenesis. PLOS

Genetics, 6(4), e1000891. doi:10.1371/journal.pgen.1000891

Ogunade, I., Pech-Cervantes, A., & Schweickart, H. (2019). Metatranscriptomic Analysis of Sub-Acute Ruminal Acidosis in Beef Cattle. Animals (Basel), 9(5). doi:10.3390/ani9050232

Pace, N. R., Stahl, D. A., Lane, D. J., & Olsen, G. J. (1986). The Analysis of Natural

Microbial Populations by Ribosomal RNA Sequences. In K. C. Marshall (Ed.), Advances in Microbial Ecology (pp. 1-55). Boston, MA: Springer US.

Paster, B., Dewhirst, F., Olsen, I., Fraser, G., & Socransky, S. (1995). Gram-negative anaerobes: 16S rRNA sequences, phylogeny and DNA probes. Medical and Dental

Aspects of Anaerobes. Northwood, Middlesex: Science Reviews, 373-386.

Patel, D. D., Patel, A. K., Parmar, N. R., Shah, T. M., Patel, J. B., Pandya, P. R., & Joshi,

C. G. (2014). Microbial and Carbohydrate Active Enzyme profile of buffalo rumen metagenome and their alteration in response to variation in the diet. Gene, 545(1), 88-94. doi:10.1016/j.gene.2014.05.003

Patel, J. B. (2001). 16S rRNA gene sequencing for bacterial pathogen identification in the clinical laboratory. Mol Diagn, 6(4), 313-321. doi:10.1054/modi.2001.29158

Peng, Y., Leung, H. C., Yiu, S.-M., & Chin, F. Y. (2012). IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth.

Bioinformatics, 28(11), 1420-1428.

Petri, R. M., Forster, R. J., Yang, W., McKinnon, J. J., & McAllister, T. A. (2012).

Characterization of rumen bacterial diversity and fermentation parameters in concentrate fed cattle with and without forage. J Appl Microbiol, 112(6), 1152-1162. doi:10.1111/j.1365-2672.2012.05295.x

Petri, R. M., Schwaiger, T., Penner, G. B., Beauchemin, K. A., Forster, R. J., McKinnon,

J. J., & McAllister, T. A. (2013). Characterization of the core rumen microbiome in cattle during transition from forage to concentrate as well as during and after an acidotic challenge. PLOS ONE, 8(12), e83424-e83424. doi:10.1371/journal.pone.0083424

Pitta, D. W., Indugu, N., Kumar, S., Vecchiarelli, B., Sinha, R., Baker, L. D., . . .

Ferguson, J. D. (2016). Metagenomic assessment of the functional potential of the rumen microbiome in Holstein dairy cows. Anaerobe, 38, 50-60. doi:10.1016/j.anaerobe.2015.12.003

Plaizier, J. C., Li, S., Danscher, A. M., Derakshani, H., Andersen, P. H., & Khafipour, E.

(2017). Changes in Microbiota in Rumen Digesta and Feces Due to a Grain-Based

Subacute Ruminal Acidosis (SARA) Challenge. Microb Ecol, 74(2), 485-495. doi:10.1007/s00248-017-0940-z

Plaizier, J. C., Li, S., Tun, H. M., & Khafipour, E. (2017). Nutritional Models of

Experimentally-Induced Subacute Ruminal Acidosis (SARA) Differ in Their Impact on

Rumen and Hindgut Bacterial Communities in Dairy Cows. Frontiers in Microbiology, 7,

2128-2128. doi:10.3389/fmicb.2016.02128

Polz, M. F., & Cavanaugh, C. M. (1998). Bias in template-to-product ratios in multitemplate PCR. Applied and environmental microbiology, 64(10), 3724-3730.

Poretsky, R. S., Hewson, I., Sun, S., Allen, A. E., Zehr, J. P., & Moran, M. A. (2009).

Comparative day/night metatranscriptomic analysis of microbial communities in the

North Pacific subtropical gyre. Environ Microbiol, 11(6), 1358-1375. doi:10.1111/j.1462-

2920.2008.01863.x

Qi, M., Wang, P., O'Toole, N., Barboza, P. S., Ungerfeld, E., Leigh, M. B., . . . Forster,

R. J. (2011). Snapshot of the eukaryotic gene expression in muskoxen rumen--a metatranscriptomic approach. PloS one, 6(5), e20521-e20521. doi:10.1371/journal.pone.0020521

Quail, M. A., Smith, M., Coupland, P., Otto, T. D., Harris, S. R., Connor, T. R., . . . Gu,

Y. (2012). A tale of three next generation sequencing platforms: comparison of Ion

Torrent, Pacific Biosciences and Illumina MiSeq sequencers. BMC Genomics, 13(1), 341. doi:10.1186/1471-2164-13-341

Ravindran, V. (2013). Poultry feed availability and nutrition in developing countries.

Poultry Development Review, 60-63.

Rey, M., Enjalbert, F., Combes, S., Cauquil, L., Bouchez, O., & Monteils, V. (2014).

Establishment of ruminal bacterial community in dairy calves from birth to weaning is sequential. J Appl Microbiol, 116(2), 245-257. doi:10.1111/jam.12405

Ricke, S., Martin, S., & Nisbet, D. (1996). Ecology, metabolism, and genetics of ruminal selenomonads. Critical reviews in microbiology, 22(1), 27-65.

Ruby, J. G., Bellare, P., & DeRisi, J. L. (2013). PRICE: software for the targeted assembly of components of (Meta) genomic sequence data. G3: Genes, Genomes,

Genetics, 3(5), 865-880.

Russell, J. B., & Baldwin, R. L. (1978). Substrate preferences in rumen bacteria: evidence of catabolite regulatory mechanisms. Appl Environ Microbiol, 36(2), 319-329.

Russell, J. B., & Mantovani, H. C. (2002). The bacteriocins of ruminal bacteria and their potential as an alternative to antibiotics. J Mol Microbiol Biotechnol, 4(4), 347-355.

Sato, K., & Sakakibara, Y. (2013). An extended genovo metagenomic assembler by incorporating paired-end information. PeerJ, 1, e196.

Schwartz, W. (1969). Robert E. Hungate, The Rumen and its Microbes. X u. 539 S., 62

Abb., 72 Tab. New York-London 1966: Academic Press $ 19.75. Zeitschrift für allgemeine Mikrobiologie, 9(6), 490-490. doi:10.1002/jobm.19690090617

Sedlar, K., Kupkova, K., & Provaznik, I. (2017). Bioinformatics strategies for taxonomy independent binning and visualization of sequences in shotgun metagenomics.

Computational and structural biotechnology journal, 15, 48-55. doi:10.1016/j.csbj.2016.11.005

Segal, J. P., Mullish, B. H., Quraishi, M. N., Acharjee, A., Williams, H. R. T., Iqbal, T., .

. . Marchesi, J. R. (2019). The application of omics techniques to understand the role of the gut microbiota in inflammatory bowel disease. Therapeutic advances in gastroenterology, 12, 1756284818822250-1756284818822250. doi:10.1177/1756284818822250

Seoane, S. D. a. J. R. (1992). Nutritive value of corn, barley, wheat and oats fed with medium quality hay to fattening steers. Semantic Scholar.

Sharpe, M. E., Latham, M. J., Garvie, E. I., Zirngibl, J., & Kandler, O. (1973). Two New

Species of Lactobacillus Isolated from the Bovine Rumen, Lactobacillus ruminis sp.nov. and Lactobacillus vitulinus sp.nov. Microbiology, 77(1), 37-49. doi:https://doi.org/10.1099/00221287-77-1-37

Shi, Y., Odt, C. L., & Weimer, P. J. (1997). Competition for cellulose among three predominant ruminal cellulolytic bacteria under substrate-excess and substrate-limited conditions. Appl Environ Microbiol, 63(2), 734-742.

Shike, D. W. (2013). Beef Cattle Feed Efficiency. Paper presented at the Driftless Region

Beef Conference

Singh, B., Gautam, S. K., Verma, V., Kumar, M., & Singh, B. (2008). Metagenomics in animal gastrointestinal ecosystem: Potential biotechnological prospects. Anaerobe, 14(3),

138-144. doi:10.1016/j.anaerobe.2008.03.002

Sipos, R., Szekely, A. J., Palatinszky, M., Revesz, S., Marialigeti, K., & Nikolausz, M.

(2007). Effect of primer mismatch, annealing temperature and PCR cycle number on 16S rRNA gene-targetting bacterial community analysis. FEMS Microbiol Ecol, 60(2), 341-

350. doi:10.1111/j.1574-6941.2007.00283.x

Soest, P. J. V. (1994). Nutrional ecology of the ruminant.

Stewart, C. S., Fonty, G., & Gouet, P. (1988). The establishment of rumen microbial communities. Animal Feed Science and Technology, 21(2), 69-97. doi:https://doi.org/10.1016/0377-8401(88)90093-4

Stewart, R. D., Auffret, M. D., Warr, A., Wiser, A. H., Press, M. O., Langford, K. W., . . .

Watson, M. (2018). Assembly of 913 microbial genomes from metagenomic sequencing of the cow rumen. Nature Communications, 9(1), 870. doi:10.1038/s41467-018-03317-6

Tajima, K., Aminov, R. I., Nagamine, T., Matsui, H., Nakamura, M., & Benno, Y.

(2001). Diet-dependent shifts in the bacterial population of the rumen revealed with real- time PCR. Appl Environ Microbiol, 67(6), 2766-2774. doi:10.1128/aem.67.6.2766-

2774.2001

Tajima, K., Arai, S., Ogata, K., Nagamine, T., Matsui, H., Nakamura, M., . . . Benno, Y.

(2000). Rumen Bacterial Community Transition During Adaptation to High-grain Diet.

Anaerobe, 6(5), 273-284. doi:https://doi.org/10.1006/anae.2000.0353

Takai, K., & Horikoshi, K. (2000). Rapid detection and quantification of members of the archaeal community by quantitative PCR using fluorogenic probes. Applied and environmental microbiology, 66(11), 5066-5072. doi:10.1128/aem.66.11.5066-5072.2000

Tan, Z. L., Nagaraja, T. G., & Chengappa, M. M. (1994). Selective enumeration of

Fusobacterium necrophorum from the bovine rumen. Applied and environmental microbiology, 60(4), 1387-1389.

Therion, J. J., Kistner, A., & Kornelius, J. H. (1982). Effect of pH on growth rates of rumen amylolytic and lactilytic bacteria. Applied and environmental microbiology, 44(2),

428-434.

Thomas, M., Webb, M., Ghimire, S., Blair, A., Olson, K., Fenske, G. J., . . . Scaria, J.

(2017). Metagenomic characterization of the effect of feed additives on the gut microbiome and antibiotic resistome of feedlot cattle. Scientific reports, 7(1), 12257. doi:10.1038/s41598-017-12481-6

Tona, G. (2018). Current and Future Improvements in Livestock Nutrition and Feed

Resources. In.

Turnbaugh, P. J., Hamady, M., Yatsunenko, T., Cantarel, B. L., Duncan, A., Ley, R. E., .

. . Gordon, J. I. (2009). A core gut microbiome in obese and lean twins. Nature,

457(7228), 480-484. doi:10.1038/nature07540

Urrutia, N. L., & Harvatine, K. J. (2017). Acetate Dose-Dependently Stimulates Milk Fat

Synthesis in Lactating Dairy Cows. J Nutr, 147(5), 763-769. doi:10.3945/jn.116.245001

Valin, H., Sands, R. D., van der Mensbrugghe, D., Nelson, G. C., Ahammad, H., Blanc,

E., . . . Willenbockel, D. (2014). The future of food demand: understanding differences in global economic models. Agricultural Economics, 45(1), 51-67. doi:10.1111/agec.12089

Vallenet, D., Belda, E., Calteau, A., Cruveiller, S., Engelen, S., Lajus, A., . . . Medigue,

C. (2013). MicroScope--an integrated microbial resource for the curation and

53 comparative analysis of genomic and metabolic data. Nucleic Acids Res, 41(Database issue), D636-647. doi:10.1093/nar/gks1194

Van Soest, P. J. (1994). Nutritional Ecology of the Ruminant (2 ed.): Cornell University

Press.

Větrovský, T., & Baldrian, P. (2013). The Variability of the 16S rRNA Gene in Bacterial

Genomes and Its Consequences for Bacterial Community Analyses. PLOS ONE, 8(2), e57923. doi:10.1371/journal.pone.0057923 von Mering, C., Hugenholtz, P., Raes, J., Tringe, S. G., Doerks, T., Jensen, L. J., . . .

Bork, P. (2007). Quantitative phylogenetic assessment of microbial communities in diverse environments. Science, 315(5815), 1126-1130. doi:10.1126/science.1133420

Walker, G. J. (1965). THE CELL-BOUND ALPHA-AMYLASES OF

STREPTOCOCCUS BOVIS. The Biochemical journal, 94(2), 289-298. doi:10.1042/bj0940289

Walker, G. J., & Hope, P. M. (1964). Degradation of starch granules by some amylolytic bacteria from the rumen of sheep. The Biochemical journal, 90(2), 398-408. doi:10.1042/bj0900398

Wang, B., Wang, D., Wu, X., Cai, J., Liu, M., Huang, X., . . . Guan, L. (2017). Effects of dietary physical or nutritional factors on morphology of rumen papillae and transcriptome changes in lactating dairy cows based on three different forage-based diets. BMC

Genomics, 18(1), 353-353. doi:10.1186/s12864-017-3726-2

Wang, L., Hatem, A., Catalyurek, U. V., Morrison, M., & Yu, Z. (2013). Metagenomic

Insights into the Carbohydrate-Active Enzymes Carried by the Microorganisms Adhering

54 to Solid Digesta in the Rumen of Cows. PLOS ONE, 8(11), e78507. doi:10.1371/journal.pone.0078507

Wang, L., Zhang, G., Xu, H., Xin, H., & Zhang, Y. (2019). Metagenomic Analyses of

Microbial and Carbohydrate-Active Enzymes in the Rumen of Holstein Cows Fed

Different Forage-to-Concentrate Ratios. Frontiers in Microbiology, 10(649). doi:10.3389/fmicb.2019.00649

Wang, Q., Garrity, G. M., Tiedje, J. M., & Cole, J. R. (2007). Naïve Bayesian Classifier for Rapid Assignment of rRNA Sequences into the New Bacterial Taxonomy. Applied and environmental microbiology, 73(16), 5261. doi:10.1128/AEM.00062-07

Webster, G., Newberry, C. J., Fry, J. C., & Weightman, A. J. (2003). Assessment of bacterial community structure in the deep sub-seafloor biosphere by 16S rDNA-based techniques: a cautionary tale. J Microbiol Methods, 55(1), 155-164.

Wells, J. E., Krause, D. O., Callaway, T. R., & Russell, J. B. (1997). A bacteriocin- mediated antagonism by ruminal lactobacilli against Streptococcus bovis. FEMS

Microbiology Ecology, 22(3), 237-243. doi:https://doi.org/10.1016/S0168-

6496(96)00095-5

Wilmes, P., & Bond, P. L. (2004). The application of two-dimensional polyacrylamide gel electrophoresis and downstream analyses to a mixed community of prokaryotic microorganisms. Environmental Microbiology, 6(9), 911-920. doi:10.1111/j.1462-

2920.2004.00687.x

Woo, P. C., Lau, S. K., Teng, J. L., Tse, H., & Yuen, K. Y. (2008). Then and now: use of

16S rDNA gene sequencing for bacterial identification and discovery of novel bacteria in

55 clinical microbiology laboratories. Clin Microbiol Infect, 14(10), 908-934. doi:10.1111/j.1469-0691.2008.02070.x

XU, J. (2006). INVITED REVIEW: Microbial ecology in the age of genomics and metagenomics: concepts, tools, and recent advances. Molecular Ecology, 15(7), 1713-

1731. doi:10.1111/j.1365-294X.2006.02882.x

Youssef, N., Sheik, C. S., Krumholz, L. R., Najar, F. Z., Roe, B. A., & Elshahed, M. S.

(2009). Comparison of species richness estimates obtained using nearly complete fragments and simulated pyrosequencing-generated fragments in 16S rRNA gene-based environmental surveys. Appl Environ Microbiol, 75(16), 5227-5236. doi:10.1128/aem.00592-09

Yu, Z., & Morrison, M. (2004). Comparisons of Different Hypervariable Regions of

<em>rrs</em> Genes for Use in Fingerprinting of Microbial Communities by

PCR-Denaturing Gradient Gel Electrophoresis. Applied and environmental microbiology,

70(8), 4800. doi:10.1128/AEM.70.8.4800-4806.2004

Yue, Z. B., Li, W. W., & Yu, H. Q. (2013). Application of rumen microorganisms for anaerobic bioconversion of lignocellulosic biomass. Bioresour Technol, 128, 738-744. doi:10.1016/j.biortech.2012.11.073

Ze, X., Le Mougen, F., Duncan, S. H., Louis, P., & Flint, H. J. (2013). Some are more equal than others: the role of "keystone" species in the degradation of recalcitrant substrates. Gut Microbes, 4(3), 236-240. doi:10.4161/gmic.23998

Zhang, J., Shi, H., Wang, Y., Li, S., Cao, Z., Ji, S., . . . Zhang, H. (2017). Effect of

Dietary Forage to Concentrate Ratios on Dynamic Profile Changes and Interactions of

Ruminal Microbiota and Metabolites in Holstein Heifers. Front Microbiol, 8, 2206. doi:10.3389/fmicb.2017.02206

CHAPTER 2

IDENTIFICATION AND GENOMIC CHARACTERIZATION OF CANDIDATE

STARCH UTILIZING BACTERIA FROM THE RUMEN OF BEEF CATTLE

ABSTRACT

Starch is the major energy component in grain based-diets. Feedlot beef and dairy cattle are fed with grain diets to maximize their performance. Rumen amylolytic or starch metabolizing bacteria predominate when the animals are fed with grain-based diets. Since the specific role of amylolytic bacteria have yet to be clearly elucidated, the primary objective of this research was to identify and determine the metabolic functions of previously uncharacterized amylolytic bacteria. Our general approach consisted of culturing rumen fluid using anaerobic bioreactors for a period of 14 days. Four experimental trials, each performed with a different rumen fluid donor, designated as

Trial 1, Trial 2, Trial 3, and Trial 4, were conducted during this study. Four replicate cultures, each consisting of approximately 2.2 L of rumen fluid per laboratory-scale bioreactors were used. Three of the replicate cultures were supplemented with starch

(11.4g/L, ADM Corn processing, Clinton, Iowa), while the remaining culture was not supplemented with any substrate (Control). Cultures were maintained for 14 days followed by sample collection on day-7 and day-14. Four species-level OTUs, corresponding to genus Ruminobacter and Prevotella were enriched on days 7 and 14.

Prevotella-affiliated OTUs (SD_Bt-00010 and SD_Bt-00966) were then selected for

Shotgun metagenomics analysis. This analysis resulted in the identification of enzymes that were predicted to be involved in starch metabolism. Further characterizing these

Prevotella-affiliated OTUs may contribute towards a better understanding of starch metabolism.

1. Introduction

As a result of their ability to produce high quality protein products such as milk and meat from plant fibers, which cannot be efficiently digested by humans, ruminants have so far played a vital role throughout mankind history, and they are expected to continue to do so in the foreseeable future (Flachowsky, Meyer, & Sudekum, 2017)

(Flint, Scott, Duncan, Louis, & Forano, 2012). In these herbivores, digestion of feed takes place in the rumen, the largest compartment of a four-chambered stomach, through the combined metabolic activities of resident microbial symbionts which precede digestion by host enzymes (Dehority, 2003) (Hungate, 1966). Ruminal microorganisms, consisting of a diverse array of bacteria, methanogens, protozoa, and fungi, are organized into complex communities that work synergistically to ferment ingested feedstuffs, producing short chain fatty acids (SCFAs) and microbial proteins as end products that provide energy and amino acids for their host (S. Wang et al., 2017) (Lengowski et al., 2016).

While ruminants have evolved to utilize structural polysaccharides as their primary source of energy, the inclusion of starch-rich feedstuffs, such as maize or cereal grains, is a common practice in intensive ruminant livestock production systems

(Huntington, Harmon, & Richards, 2006) (Huntington, 1997). Indeed, since starch is a more readily available source of energy compared to plant fiber polysaccharides, it provides a more efficient means of meeting the energy requirements of high producing ruminants (Gómez, Posada, & Olivera, 2016). However, faster rates of fermentation for starch compared to plant fiber components can result in rapid accumulation of short chain fatty acids (SCFAs), which can breach the buffering capacity limits of the rumen, and cause ruminal acidosis (Nagaraja & Titgemeyer, 2007) (Goad, Goad, & Nagaraja, 1998).

Indeed, while fluctuations in ruminal pH are normally observed in healthy individuals, acidotic ruminal pH conditions that are maintained for extended periods of time can have a detrimental effect on the health of affected individuals (Snyder & Credille, 2017). As acidosis not only impairs normal functions of the digestive tract, but can also lead to further complications such as bloat, diarrhea, liver abscesses, and laminitis, it can have a negative impact on animal performance and health (Vyas et al., 2014), resulting in economic losses for ruminant livestock procedures (Grohn & Bruss, 1990).

In intensive production systems, acidosis is most commonly managed by gradually increasing the proportion of starch in the diet over the course of an adaptation period (Owens, Secrist, Hill, & Gill, 1998). While implementation of this practice and other management strategies has greatly helped in reducing the impact of acidosis on the industry, it still remains a concern for animal health and profitability (NejashAbdela,

2016). Since one of the main causes of acidosis is the rapid accumulation of ruminal

SCFAs from microbial digestion of high starch diets, a great deal of effort has been devoted to the identification of ruminal microorganisms that participate in this process

(T. J. DeVries, T. Schwaiger, Beauchemin, & PennerB, 2014). One reported strategy has been to investigate the response or dynamics of ruminal bacterial communities during subacute ruminal acidosis (SARA), a reversible state of pH depression that can further develop into acidosis (Plaizier, Li, Tun, & Khafipour, 2016). In animal nutritional models, induction of SARA using starch-rich feedstuffs was found to be associated with reduced bacterial richness and diversity (Plaizier et al., 2017) (S. Li et al., 2012) (Hook et al., 2011),(Khafipour, Li, Plaizier, & Krause, 2009), with observed reductions in

Bacteroidetes and increases in Firmicutes (Khafipour et al., 2009), as well as changes in a number of well characterized bacterial species that are known to metabolize starch.

Considering that typically only a fraction of microorganisms identified in rumen samples correspond to valid species, it is generally acknowledged that the vast majority of ruminal symbionts remain to be characterized (Creevey, Kelly, Henderson, & Leahy,

2014). Based on this assessment, it was hypothesized that currently unknown ruminal microorganisms include amylolytic bacteria that have yet to be identified. Using a batch- culture system, candidate starch utilizers were enriched from beef cow rumen fluid, then characterized as Operational Taxonomic Units (OTUs) using a 16S rRNA-based approach. Metagenomics analyses were conducted to assess the metabolic potential of candidate ruminal bacterial species, which revealed coding sequences for enzymes that participate in pathways predicted to metabolize starch into SCFAs. Together, the results presented in this report suggest that the bacterial species corresponding to these OTUs function as starch utilizers in the rumen of beef cows.

2. Materials and Methods

2.1 Sample collection and in vitro rumen culture experiments

All animal procedures were approved by the Institutional Animal Care and Use

62 collected (March 2016 to November 2016). Four experimental trials, each performed with a different rumen fluid donor, designated as Trial 1, Trial 2, Trial 3, and Trial 4, were conducted during this study, with the following procedure followed for each trial.

Four replicate cultures, each consisting of approximately 2.2 L of rumen fluid per laboratory-scale bioreactors (Chemglass), were set up within one hour of collection.

Three of the replicate cultures were supplemented with starch (11.4g/L, ADM Corn processing, Clinton, Iowa, USA), while the remaining culture was not supplemented with any substrate (Control). Treatment and control cultures were maintained under anaerobic conditions at a constant physiological temperature (380C), with continuous agitation at

150 rpm using a Rushton-style impeller fixed to a stirring shaft built into the bioreactor.

Each bioreactor allowed for approximately 0.8 L of headspace, and was equipped with a flexible plastic tube for exhaust of excess biogas to prevent pressure build-up. Culture samples (approximately 15 mL/sample) were collected on days 7 (D7) and 14 (D14) to assess their respective bacterial composition. Samples from the rumen inoculum (D0) and cultures were stored frozen at -200C until analyzed.

2.2 Microbial genomic DNA purification and PCR amplification of the 16S rRNA gene

Genomic DNA was extracted from each sample by a repeated bead beating plus column method as previously described (Yu & Morrison, 2004). Briefly, 250µL of rumen sample was lysed in extraction buffer (0.5 M NaCl, 50 mM Tris.HCl, 50Mm EDTA, 4%

SDS) by bead beating followed by sequential extraction with 10M ammonium acetate then isopropanol precipitation. Recovered nucleic acids were then purified using the

QIAamp DNA Stool Kit (QIAGEN, Hilden, Germany) following the manufacturer’s recommended protocol. PCR was performed using the Phusion Taq DNA polymerase

(ThermoFischer Scientific, Waltham, MA, USA) on a 2720 Thermo Cycler

(ThermoFischer Scientific), with the 27F (Edwards, Rogall, Blocker, Emde, & Bottger,

1989) and 519R (Lane et al., 1985) primers to target the V1-V3 regions of the 16S rRNA gene. PCR reactions consisted of a ‘hot start’ (98oC, 3min), followed by 35 consecutive cycles of denaturation (98oC, 30 sec), annealing (50oC, 30 sec), and elongation (72oC, 30 sec), then by a final elongation period (720C, 10 min). Quality of PCR amplicons

(expected approximate length of 500 bp) was determined by agarose gel electrophoresis, with recovery of PCR-generated DNA using the QiaexII Gel extraction kit (Qiagen,

Hilden, Germany). Gel purified amplicons from each sample were then submitted as template for High Throughput Sequencing using an Illumina Miseq(2×300) platform

(Molecular Research DNA, Shallowater, TX).

2.3 Bioinformatic analysis for 16S rRNA gene-based composition analysis

Unless specified, datasets were analyzed using custom written Perl scripts

(available upon request). Raw bacterial 16S rRNA gene V1-V3 amplicon sequences were provided by Molecular Research DNA as contigs assembled from overlapping MiSeq

2x300 paired-end reads from the same flow cell clusters. Reads were then selected to meet the following criteria: presence of both intact 27F (forward) and 519R (reverse) primer nucleotide sequences, length between 400 and 580 nt, and a minimal quality threshold of no more than 1% of nucleotides with a Phred quality score lower than 15.

Following quality screens, sequence reads were aligned, then clustered into

64 not be suitable for the analysis of specific sub-regions since nucleotide sequence variability is not constant across the entire length of the 16S rRNA gene. In this context, if 3% is a commonly accepted clustering cutoff for V4 or V4-V5 regions, which are the least variable of the hypervariable regions, then a higher cutoff should be used for the

V1-V3 region, since V1 is the most variable region of the 16S rRNA gene. OTUs were screened for DNA sequence artifacts using the following methods. Chimeric sequences were first identified with the chimera.uchime and chimera.slayer commands from the

MOTHUR open source software package (Schloss et al., 2009). Secondly, the integrity of the 5’ and 3’ ends of OTUs was evaluated using a database alignment search-based approach; when compared to their closest match of equal or longer sequence length from the NCBI nt database, as determined by BLAST (Basic Local Alignment Search Tool)

(Altschul et al., 1997), OTUs with more than five nucleotides missing from the 5’ or 3’ end of their respective alignments were discarded as artifacts. Single read OTUs were subjected to an additional screen, where only sequences that had a perfect or near perfect match to a sequence in the NCBI nt (National Center for Biotechnology Information) database were kept for analysis, i.e. that the alignment had to span the entire sequence of the OTU, and a maximum of 1% of dissimilar nucleotides was tolerated. The number of non-chimeric and artifact-free sequence reads used for analysis in each trial is shown in

Table 2.1.

After removal of sequence chimeras and artifacts, RDP Classifier (Ribosomal

Database Project) (Q. Wang, Garrity, Tiedje, & Cole, 2007) and BLAST (Altschul et al.,

1997) were used for taxonomic assignment of valid OTUs. The List of Prokaryotic

Names with Standing in Nomenclature (LPSN) was also consulted for information on valid species belonging to taxa of interest (Parte, 2014).

2.4 Metagenomic analysis

A select number of samples exhibiting high levels of enrichment for candidate starch utilizing OTUs were further analyzed using a metagenomics approach. Purified microbial genomic DNA (extracted as described above) was used directly as template for high throughput sequencing using an Illumina Miseq(2×250) platform (Molecular

Research DNA, Shallowater, TX). Raw sequence reads of 200 bp in length or longer were selected using custom written Perl scripts for building of genomic contigs using the de novo assembly program ABySS (Simpson et al., 2009). ABySS was run on a Linux- based high-performance computing cluster maintained by the University Networking and

Research Computing Group at South Dakota State University, with k-mer length set at 64 and all other parameters used at their respective default values.

ABySS-assembled contigs of 4000 bp or greater were selected for further analysis by gene annotation. Coding sequences (CDS) were identified and annotated using RAST

(Rapid Annotations using Subsystems Technology) (Aziz et al., 2008), with assignment of CDS to metabolic pathways performed using KEGG pathways as a model reference

(Kanehisa & Goto, 2000). Further analysis of select CDS was performed against the

NCBI nr database using BLASTp (Altschul et al., 1997) to identify their respective closest homologs.

3. Results

3.1 Comparative analysis of bacterial communities from donor rumen fluid

Analysis of the rumen fluid inocula before culturing with starch revealed that bacteria affiliated to the phyla Bacteroidetes and Firmicutes were overall the most abundant, representing 24.8 - 68.1% and 24.5 - 68.2% of sequence reads, respectively, with their combined totals ranging between 73.5 and 93.0% (Figure 2.1). Prevotellaceae was the predominant Bacteroidetes family (14.4 - 53.1%), while Lachnospiraceae and

Ruminococcaceae were the most highly represented Firmicutes families (1.8 - 16.8% and

1.6 - 22.2%, respectively).

A comparative analysis further revealed that 54 Operational Taxonomic Units

(OTUs) were shared amongst all donors, representing 6.9 - 24.0% of sequence reads in individual donor rumen fluid samples, while the abundance of sequences that were specific or unique to individual donors ranged between 20.9% and 46.1% per sample

(Table 2.2). Amongst the 54 shared OTUs, 10 OTUs were identified as the most abundant, with their respective individual abundance ranging between 0.06% and 5.29% across all samples (Table 2.3). Only one OTU showed species-level sequence identity to its closest valid taxon (Bt-00074; 98.3%), while the other abundant OTUs were found to be more distant (84.6 – 92.4%), and likely corresponded to novel bacterial species. Seven of the most abundant OTUS were affiliated to Bacteroidetes, of which six were predicted to be members of the genus Prevotella, while the remaining three OTUs belonged to

Firmicutes.

3.2 Identification of candidate bacterial starch utilizers from rumen fluid

When compared to their respective culture controls without substrate, three distinct OTUs were found to be the most enriched bacteria in starch-supplemented rumen fluid cultures (Table 2.4). From the Trial 1 donor, OTU SD_Bt-01020 was in highest abundance in starch supplemented cultures at day 7, at an average value that was 1206X greater than the non-supplemented control. The closest valid relative to SD_Bt-01020 was Ruminobacter amylophilus, a well characterized starch digester (Anderson, 1995), with a sequence identity typically considered to be within species-level range for 16S rRNA gene-based analyses. OTU SD_Bt-00966 was identified as a candidate starch utilizer from Trial 2. Compared to other enriched OTUs in this study, it exhibited the most consistent level of enrichment across replicates and time points. Intriguingly, the abundance of this OTU was already elevated in the rumen inoculum of Trial 2, prior to culturing with starch. Prevotella copri, originally isolated from human feces (Hayashi,

Shibata, Sakamoto, Tomita, & Benno, 2007), was identified as its closest bacterial species, but at a degree of 16S rRNA gene sequence identity more consistent with genus- level rather than species-level cutoffs. Lastly, the remaining candidate starch utilizing

OTU identified was SD_Bt-00010. It was enriched from two separate donors (Trial 3 and

Trial 4), and showed the highest abundance averages amongst sets of starch supplemented replicate cultures in this study. SD_Bt-00010 was found to be within species-level sequence identity to Prevotella albensis, for which α-amylase activity has previously been reported (Avgustin, Wallace, & Flint, 1997).

3.3 Exploring the metabolic potential of OTUs identified as candidate starch utilizers

We postulated that sequence datasets generated from samples with high abundance of an OTU would have higher representation of genomic sequences from this

OTU. Consequently, higher sequence representation or coverage would then be favorable for the assembly of contigs that would be of sufficient length to allow for prediction of full-length coding sequences and improved accuracy of gene annotation. From the three

OTUs that were enriched in the presence of starch, we focused our attention on the

Prevotella-affiliated OTUs (SD_Bt-00010 and SD_Bt-00966), since Prevotella-affiliated symbionts are typically the most abundant and diverse group of uncharacterized bacteria in the rumen of cattle.

The most highly enriched samples for SD_Bt-00010 (Trial 3: 67.9% and Trial 4:

74.0%) and for SD_Bt-00966 (Trial 2: 24.9%), were then selected for shotgun sequencing, which generated a total of 9.5 X 106, 9.16 X 106 and 8.4 X 106 paired end reads, respectively. After de novo assembly, contigs were filtered for a minimal length of

4,000 nt, which resulted in the selection of 436 (Trial 3), 397 (Trial 4), and 983 (Trial 2) contigs for further analysis. The maximum contig length from each sample was found to be 68,555 nt (Trial 3), 181,113 nt (Trial 4), and 28,496 nt (Trial 2). The RAST online tool was then used for predicting amino acid coding regions (CDS) from the selected contigs, as well as for performing gene annotation. For the Trial 2 contig set, 974 of 7,422 CDS were annotated successfully, compared to 916 of 5,407 CDS and 1,008 of 4,848 CDS for

Trials 3 and 4, respectively.

Predicted metabolic functional categories from gene annotation included carbohydrates, proteins, amino acids and derivatives, DNA and RNA, phosphorus,

69 potassium and sulfur, regulation and cell signaling, as well as stress response (Figure

2.2). Based on the conditions of the culturing assay, our primary interest was to identify genes involved in carbohydrate metabolism; 345 CDS were assigned to this function from the Trial 2 contig set, compared to 377 CDS for the Trials 3 and 4 datasets.

To gain further insight, data mining for specific functions in carbohydrate metabolism was performed, resulting in the identification of enzymes predicted to be associated with metabolic activities of amylolytic bacteria (Figures 2.3, 2.4, and 2.5).

Coding sequences for α-amylase (EC 3.2.1.1), which catalyzes the hydrolysis of α-1,4- glucosidic bonds in starch to produce maltose, as well as for α-glucosidase (EC 3.2.1.20), which catalyzes the conversion of maltose into glucose, were identified for SD_Bt-00010 and for SD_Bt-00966. All enzymes of the glycolysis pathway were also found for both

OTUs, indicating that glucose released from the hydrolysis of starch would be predicted to be metabolized to pyruvate and yield ATP in these candidate bacterial species (Figures

2.3, 2.4, and 2.5).

For both OTUs, enzymes involved in further metabolizing pyruvate into SCFAs were also identified (Figures 2.3, 2.4, and 2.5). Two outcomes were predicted for pyruvate, with either a conversion to lactate through lactate dehydrogenases (EC 1.1.1.27 and EC 1.1.1.28) or the production of formate and acetyl-CoA through the activity of pyruvate formate-lyase (EC 2.3.1.54). Formate could then be oxidized into CO2 by formate dehydrogenase, while acetyl-CoA could be metabolized sequentially into acetyl phosphate (phosphate acetyltransferase, EC 2.3.1.8), acetate (acetate kinase, EC 2.7.2.1), aldehyde (aldehyde dehydrogenase, EC 1.2.1.3), then ethanol (alcohol dehydrogenases,

EC 1.1.1.1)

In addition to glucose fermentation pathways, enzymes involved in glycogen synthesis and utilization were also identified in SD_Bt-00010 and SD_Bt-00966 affiliated contigs, indicating that glucose released from starch hydrolysis could also be stored as glycogen. Enzymes predicted to be involved in this function included glycogen synthase

(EC 2.4.1.21), which converts ADP-glucose to amylose, as well as 1,4-alpha-glucan branching enzyme (EC 2.4.1.18), which generates glycogen from amylose. Glucose stored as glycogens would then be predicted to be mobilized through the sequential actions of glycogen phosphorylase (EC 2.4.1.1) and phosphoglucomutase (EC 5.4.2.2), which would generate glucose-1-phosphate and glucose-6-phosphate, respectively. The latter could then enter glycolysis to be metabolized into pyruvate and SCFAs.

For amino-related metabolism, contig sets from SD_Bt-00010 and SD_Bt-00966- enriched samples included a high number of coding sequences for proteases and peptidases, as well as transporters for amino acids and oligopeptides (Table 2.8), ranging from 16 to 36 and 30 to 49, respectively. Based on their positioning and close proximity on contigs, coding sequences for transporters suggested they could potentially have represented subunits for multi-protein complexes that function in amino acid transport.

Together, these results suggested that candidate bacterial species SD_Bt-00010 and

SD_Bt-00966 could acquire amino acids through hydrolysis of proteins present in their immediate environment, followed by transport of amino acids for use in protein synthesis or as substrates for fermentation.

4. Discussion

Carbohydrates in ruminant diets are fermented by the collective actions of a wide array of ruminal microorganisms. In animals raised in intensive management systems, diets typically include high levels of grain to favor increased performance yields

(Huntington et al., 2006) (Owens, Zinn, & Kim, 1986). Typically, different groups of bacteria, namely cellulolytic and amylolytic, each specialize in the digestion of either cellulose or starch, respectively (Nafikov & Beitz, 2007). Since starch is one of the most abundant carbohydrates in grain, high grain diets then tend to favor amylolytic bacteria

(Huntington, 1997) (Mills, France, & Dijkstra, 1999). However, as primary starch utilizers, amylolytic bacteria can be instigators of SARA, since the faster rate of starch breakdown results in greater levels of SCFA production, with a more severe impact on ruminal pH. Thus, further investigation of the metabolic activities of indigenous ruminal amylolytic bacteria is critical to the development and improvement of effective strategies to prevent or minimize the onset and effects of SARA / acidosis (Cotta, 1988) (Morgante,

Stelletta, Berzaghi, Gianesella, & Andrighetto, 2007) (T. J. DeVries et al., 2014).

However, one critical gap in our knowledge is that most amylolytic ruminal bacteria likely remain to be identified and characterized, as an estimated 95% of ruminal bacterial species have yet to be assigned a function (Creevey et al., 2014).

In this context, the primary objective of the research presented in this report was to identify uncharacterized rumen bacteria that can metabolize starch, then assess their metabolic potential using a metagenomic approach. The bacterial composition of the four rumen samples prior to culturing in the presence of starch (D0), in which Bacteroidetes and Firmicutes were the most abundant phyla and Prevotellaceae were overall the most

72 predominant family, was consistent with previously reported studies (Pitta et al., 2014)

(Lima et al., 2015) (Opdahl, Gonda, & St-Pierre, 2018) (Jami & Mizrahi, 2012) (Jami,

White, & Mizrahi, 2014) (Jewell, McCormick, Odt, Weimer, & Suen, 2015). From these complex communities, three main OTUs were found to be in higher relative abundance in ruminal fluid cultures when starch was provided as the only supplemented substrate.

Enrichment for OTU SD_Bt-01020, predicted to be a strain of Ruminobacter amylophilus based on 16S rRNA gene sequence comparisons, was maintained to similar levels in both day-7 and day-14 starch-supplemented cultures. Ruminobacter amylophilus has been reported as a key amylolytic species involved in starch hydrolysis based on its amylase, amylopectinase, and pullulanase activities (Anderson, 1995), and it has been found to be an excellent model for investigating starch hydrolysis (Petri, Forster, Yang,

McKinnon, & McAllister, 2012) (Singh et al., 2014) (Anderson, 1995).

The other two enriched OTUs from this study, SD_Bt-00966 and SD_Bt-00010, were assigned to the lineage Prevotella, which has been found to be prevalent in a number of gastro-intestinal environments, including bovine rumen and human gut (Qian et al., 2017) (Jung et al., 2018) (Kovatcheva-Datchary et al., 2015) (Bhute et al., 2016)

(Girard, Tromas, Amyot, & Shapiro, 2017). While SD_Bt-00966 and SD_Bt-00010 were both identified as starch utilizers, they may belong to different groups within this functional category, as they were each related to different species, Prevotella copri and

Prevotella albensis, respectively. Previously, Prevotella copri was reported to be more prominent in humans that ate barley-kernel bread compared to humans eating white wheat flour bread (Kovatcheva-Datchary et al., 2015), while Prevotella albensis was found to utilize starch when strains were supplemented with oat spelt xylan,

73 carboxymethyl cellulose and starch (Avgustin et al., 1997). While 16S rRNA phylogenetic studies can provide a comprehensive picture of bacterial community composition, they are limited in the functional insights that they can provide (Creevey et al., 2014) (Deusch, Tilocca, Camarinha-Silva, & Seifert, 2015). For this reason, a metagenomics analysis was performed on representative high enrichment samples to get an assessment of the metabolic potential of the Prevotella-affiliated OTUs SD_Bt-00966 and SD_Bt-00010.

Metagenomics and metatranscriptomics studies have shown that starch metabolism is one of the prominent core pathways in animals fed grain-based diets, with

α-amylases, the enzymes that catalyze hydrolysis of α-1,4-glucosidic bonds that link chains of glucose monomers in starch (Janecek, Svensson, & MacGregor, 2014)

(Janecek, 1994) (Perry et al., 2007), being encoded by highly expressed genes (Mann,

Wetzels, Wagner, Zebeli, & Schmitz-Esser, 2018) (F. Li & Guan, 2017) (L. Wang,

Hatem, Catalyurek, Morrison, & Yu, 2013). Accordingly, genes encoding α-amylases were identified in contigs from both SD_Bt-00966 and SD_Bt-00010. Besides members of the Prevotella genus, other gut bacterial species can express α-amylase as well as other glycoside hydrolases (GH) that are involved in starch metabolism, including Bacteroides thetaiotaomicron, a gram-negative obligate anaerobe that predominates in the human colon (D'Elia & Salyers, 1996) (Arnal, Cockburn, Brumer, & Koropatkin, 2018) (Cho,

Cho, Wang, & Salyers, 2001) (Baroroh et al., 2017), as well as several strains of

Bifidobacterium (phylum Actinobacteria) (Duranti et al., 2014) (Ryan, Fitzgerald, & van

Sinderen, 2006).

Once glucose has been made available from starch hydrolysis, it can be transported to the intracellular environment of ruminal microorganisms where it can be metabolized or stored as glycogen. Accordingly, CDS for all enzymes of the glycolysis pathway were identified in all contig sets analyzed, indicating that both OTUs had the potential to metabolize glucose into pyruvate (Rodríguez, Sosa, & Rodríguez, 2007). The types of SCFAs that are produced from pyruvate tend to vary depending on the microbial species involved. CDS from the SD_Bt-00010 contig set indicated that this bacterial

OTU would be capable of producing D- and L-lactate, as well as ethanol. This is consistent with work from a number of different groups that had identified starch utilizers as lactate producers. High levels of lactate in the rumen, particularly D-lactate because it is not metabolized by the host, can lead to SARA and acidosis, a syndrome that can cause severe health problems in animals, ultimately resulting in economic losses to producers

(Counotte & Prins, 1981). These results then suggest that SD_Bt-00010 metabolic activity could contribute to the development of acidosis. Currently, one of the main strategies developed to counter SCFA-mediated low pH has been to increase the activity of certain ruminal bacterial species that can metabolize lactate, such as Selenomas ruminatum and Megasphaera elsdenii, thus countering its accumulation and preventing the development of acidotic conditions in the rumen (Fecskeova, Piknova, Javorsky, &

Pristas, 2010). Based on gene annotation analysis of SD_Bt-00010 contigs, an alternative strategy could be to promote production of ethanol instead of lactate. While not as well studied as the latter, ethanol has been reported as a readily detectable ruminal SCFA in concentrate fed animals (Raun & Kristensen, 2011) (Kristensen, Sehested, Jensen, &

Vestergaard, 2007) (K.Pradhan & R.W.Hemken, 1970).

In addition to carbohydrate metabolism, the acquisition of amino acids is a function that is essential for the proliferation of ruminal microorganisms. Based on the predicted enzymatic capabilities from the contig sets analyzed in this study, both enriched

OTUs appeared to rely on proteins from their immediate environment as an amino acid source, since proteases, peptidases, and oligopeptide transporters were well represented in annotated coding sequences, while only a limited set of enzymes for de novo synthesis of amino acids were predicted. Thus, in contrast to most ruminal bacterial models,

SD_Bt-00010 and SD_Bt-00966 may not rely heavily on the use of ammonia for synthesis of amino acids (Mann et al., 2018) (Hart, Creevey, Hitch, & Kingston-Smith,

2018) (Rodríguez et al., 2007).

5. Conclusion

The batch-culture approach used in this study allowed for the identification of previously uncharacterized starch utilizers from the rumen of beef cows. Based on 16S rRNA gene sequence analysis, three ruminal bacterial OTUs enriched with starch as the only provided substrate were found to be affiliated to the genera Ruminococcus and

Prevotella. Further investigations of the Prevotella related OTUs using metagenomics revealed genome-derived coding sequences that could be assembled into predicted pathways that would allow the fermentation of starch into SCFAs. Furthermore, these different OTUs shared other metabolic functions, such as the ability to store glucose as glycogen, as well as to utilize extracellular proteins as a source of amino acids. Together, these characteristics could help define members of a particular subgroup of ruminal

76 starch utilizers, ultimately contributing to a better understanding of metabolic functions and events that impact the onset of SARA and the incidence of acidosis.

Figure 2.1. Taxonomic composition of rumen samples used as inocula, prior to setting up batch culture enrichment experiments with starch. Each experimental trial (T1, T2, T3, T4) is represented on the horizontal axis, with the relative abundance (%) of each major taxonomic group shown on the vertical axis. Families belonging to the same phylum are represented by different shades of the same color: Firmicutes (green), Bacteroidetes (blue), Proteobacteria (red), Planctomycetes (orange), and other families (violet).

Figure 2.2. RAST gene annotation analysis of contig sets generated from enrichment Trials 2 (blue), 3 (orange) and 4 (grey). The relative abundance (%) of coding sequences for each subsystem level category is shown.

Figure 2.3. KEGG (Kyoto Encyclopedia of Genes and Genomes) annotation of predicted metabolic pathways from the metagenomics analysis of contigs generated from Trial#2 (OTU SD_Bt-00966, affiliated to Prevotella copri). A) starch metabolism and glycolysis pathways; B) glycogen synthesis and mobilization. Each enzymes is represented by its Enzyme Commission (EC) number, which is based on the chemical reaction it catalyzes. Please consult Table 2.5 for corresponding enzyme names.

Figure 2.4. KEGG (Kyoto Encyclopedia of Genes and Genomes) annotation of predicted metabolic pathways from the metagenomics analysis of contigs generated from Trial#3 (OTU SD_Bt-00010, affiliated to Prevotella albensis). A) starch metabolism and glycolysis pathways; B) glycogen synthesis and mobilization. Each enzymes is represented by its Enzyme Commission (EC) number, which is based on the chemical reaction it catalyzes. Please consult Table 2.6 for corresponding enzyme names.

Figure 2.5. KEGG (Kyoto Encyclopedia of Genes and Genomes) annotation of predicted metabolic pathways from the metagenomics analysis of contigs generated from Trial#4 (OTU SD_Bt-00010, affiliated to Prevotella albensis). A) starch metabolism and glycolysis pathways; B) glycogen synthesis and mobilization. Each enzymes is represented by its Enzyme Commission (EC) number, which is based on the chemical reaction it catalyzes. Please consult Table 2.7 for corresponding enzyme names.

Table 2.1. Number of high quality-chimera free sequence reads used for OTU analysis in each trial

Trial Number of sequence reads 1 237,750 2 243,235 3 243,316 4 201,202

Table 2.2: Shared ruminal bacterial OTUs amongst rumen fluid donors used in this study. Abundance is presented as relative abundance (%) of total non-chimeric reads per sample.

Trials Shared OTUs Trial 1 Trial 2 Trial 3 Trial 4 T1,T2,T3,T4 54 21.8 6.9 21.9 24.0 T1,T2,T3 247 26.2 8.2 23.1 0 T1,T2,T4 5 1.0 0.2 0 1.2 T1,T3,T4 49 2.9 0 4.1 15.4 T2,T3,T4 12 0 2.6 1.1 4.5 T1,T2 74 3.1 1.6 0 0 T1,T3 555 18.8 0 21.3 0 T1,T4 21 0.7 0 0 4.3 T2,T3 202 0 34.5 5.5 0 T2,T4 5 0 0.08 0 2.1 T3,T4 40 0 0 2.1 9.6 unique - 25.5 46.1 20.9 38.2 Observed OTUs - 2569 1651 3547 375

Table 2.3. Most abundant# ruminal bacterial OTUs that were shared amongst all rumen fluid donors used in this study. Abundance is presented as relative abundance (%) of total non-chimeric reads per sample.

OTU Trial 1 Trial 2 Trial 3 Trial 4 Closest relative Bt-00026F 1.99 0.12 3.13 3.51 Christensenella massiliensis (84.6%) Bt-00028F 0.10 0.21 1.71 1.36 Sporobacter termitidis (87.2%) Bt-00030B 1.66 0.92 1.34 0.97 Prevotella ruminicola (92.4%) Bt-00035F 1.01 0.09 1.28 0.78 Neglecta timonensis (85.3%) Bt-00047B 2.55 1.21 0.30 0.19 Prevotella ruminicola (91.1%) Bt-00051B 0.84 0.10 2.15 0.97 Odoribacter splanchnicus (82.5%) Bt-00071B 0.80 1.34 0.25 0.39 Prevotella ruminicola (98.3%) Bt-00074B 5.29 0.51 0.51 0.97 Prevotella ruminicola (91.4%) Bt-00118B 0.46 0.37 1.25 0.19 Prevotella ruminicola (90.8%) Bt-00119B 0.12 0.06 2.40 0.98 Prevotella brevis (90.8) Total 14.82 4.93 14.32 10.33

# OTUs found at a relative abundance of at least 1.0% in at least one of the rumen fluid donors. F. Firmicutes-affiliated OTU. B: Bacteroidetes-affiliated OTU.

Table 2.4. Enrichment of OTUs in response to starch. Abundance is presented as relative abundance (%) of total non-chimeric reads per sample. D0 (prior to starch enrichment), C-D7 and C-D14 (Control culture day-7 and day-14), S-D7 and S-D14 (Starch culture day-7 and day-14).

OTU C- Donor D0 S-D7* C-D14 S-D14* Closest relative D7 Bt- 21.71 ± Trial 1 0.05 0.03 36.19 ± 5.81 0.15 R. amylophilus (97%) 01020 11.3 Bt- 19.9 27.69 ± Trial 2 0.25 24.85 ± 3.64 0.35 P. copri (91%) 00966 9 4.11 Bt- 42.99 ± Trial 3 0.22 0.03 1.13 5.42 ± 2.98 P. albensis (98%) 00010 21.44 Bt- 33.26 ± 70.71 ± Trial 4 0.11 0.25 0.24 P. albensis (98%) 00010 16.83 2.44

Table 2.5. Carbohydrate-utilizing enzymes identified from the gene annotation analysis of contigs generated from Trial#2 (OTU SD_Bt-00966, affiliated to Prevotella albensis).

Enzyme aa# α-amylase (EC 3.2.1.1) 631 α-glucosidase (EC 3.2.1.20) 714 Glucokinase (EC 2.7.1.2) 156 Glucose-6-phosphate isomerase (EC 5.3.1.9) 453 Phosphofructo kinase (EC 2.7.1.11) 94 Fructose-bisphosphate aldolase class II (EC 4.1.2.13) 468 Glyceraldehyde-3-phosphate dehydrogenase (EC 1.2.1.12) 369 Phosphoglycerate kinase (EC 2.7.2.3) 33 Phosphoglycerate mutase (EC 5.4.2.12) 199 Phosphogluco mutase (EC 5.4.2.2) 314 Enolase (EC 4.2.1.11) 440 Pyruvate kinase (EC 2.7.1.40) 480 Pyruvate formate-lyase (EC 2.3.1.54) 258 Phosphate acetyltransferase (EC 2.3.1.8) 507 Acetate kinase (EC 2.7.2.1) 411 Aldehyde dehydrogenase (EC 1.2.1.3) 1058 Alcohol dehydrogenase (EC 1.1.1.1) 348 Glycogen phosphorylase (EC 2.4.1.1) 851 Glycogen synthase (EC 2.4.1.21) 70 L-lactate dehydrogenase (EC 1.1.1.27) 314 Formate dehydrogenase (EC 1.17.1.9) 136 1,4-α-glucan branching enzyme (EC 2.4.1.18) 750

# aa: length in amino acids

Table 2.6. Carbohydrate-utilizing enzymes identified from the gene annotation analysis of contigs generated from Trial#3 (OTU SD_Bt-00010, affiliated to Prevotella albensis).

Enzyme aa# α-amylase (EC 3.2.1.1) 559 α-glucosidase (EC 3.2.1.20) 538 Glucokinase ( EC 2.7.1.2) 244 Glucose-6-phosphate isomerase (EC 5.3.1.9) 440 Phosphofructo kinase (EC 2.7.1.11) 553 Fructose-bisphosphate aldolase class II (EC 4.1.2.13) 186 Glycerladehyde-3-phosphate dehydrogenase (EC 1.2.1.12) 339 Phosphoglycerate kinase (EC 2.7.2.3) 393 Phosphoglycerate mutase (EC 5.4.2.12) 552 Enolase (EC 4.2.1.11) 471 Pyruvate kinase (EC 2.7.1.40) 485 Pyruvate formate-lyase (EC 2.3.1.54) 682 Phosphate acetyltransferase (EC 2.3.1.8) 330 Acetate kinase (EC 2.7.2.1) 443 Aldehyde dehydrogenase (EC 1.2.1.3) 494 Alcohol dehydrogenase (EC 1.1.1.1) 405 Glycogen phosphorylase (EC 2.4.1.1) 752 Glycogen synthase (EC 2.4.1.21) 482 L-lactate dehydrogenase (EC 1.1.1.27) 319 D-lactate dehydrogenase (EC 1.1.1.28) 324 Formate dehydrogenase (EC 1.17.1.9) 931 1,4-α-glucan branching enzyme (EC 2.4.1.18) 1983 Phosphogluco mutase (EC 5.4.2.2) 564

# aa: length in amino acids

Table 2.7. Carbohydrate-utilizing enzymes identified from the gene annotation analysis of contigs generated from Trial#4 (OTU SD_Bt-00010, affiliated to Prevotella albensis).

Enzyme aa# α-amylase (EC 3.2.1.1) 429 α-glucosidase (EC 3.2.1.20) 538 Glucokinase (EC 2.7.1.2) 304 Glucose-6-phosphate isomerase (EC 5.3.1.9) 440 Phosphofructo kinase (EC 2.7.1.11) 553 Fructose-bisphosphate aldolase class II (EC 4.1.2.13) 300 Glycerladehyde-3-phosphate dehydrogenase (EC 1.2.1.12) 339 Phosphoglycerate kinase (EC 2.7.2.3) 393 Phosphoglycerate mutase (EC 5.4.2.12) 552 Enolase (EC 4.2.1.11) 471 Pyruvate kinase (EC 2.7.1.40) 500 Pyruvate formate-lyase (EC 2.3.1.54) 117 Phosphate acetyltransferase (EC 2.3.1.8) 225 Acetate kinase (EC 2.7.2.1) 392 Aldehyde dehydrogenase (EC 1.2.1.3) 494 Alcohol dehydrogenase (EC 1.1.1.1) 287 Glycogen phosphorylase (EC 2.4.1.1) 819 Glycogen synthase (EC 2.4.1.21) 490 L-lactate dehydrogenase (EC 1.1.1.27) 316 D-lactate dehydrogenase (EC 1.1.1.28) 324 Formate dehydrogenase (EC 1.17.1.9) 945 198 1,4-α-glucan branching enzyme (EC 2.4.1.18) 3 Phosphogluco mutase (EC 5.4.2.2) 560

# aa: length in amino acids

Table 2.8. Number of coding sequences (CDS) assigned to predicted amino acid metabolism-related functions in the contig sets from Trials 2, 3 and 4.

Trial 3 Trial 4 Enzymes and Transporters Trial 2 (CDS) (CDS) (CDS) Proteases and peptidases 28 36 16 # # # Branched chain amino acid transporters 24 (4) 5 (1) 14 (3) # # # Oligopeptide transporters 6 (2) 42 (8) 35 (7) Glutamate dehydrogenase (EC 1.4.1.2) 1 2 1 Glutamate synthase (EC 1.4.1.13) 4 2 2

#Number in parenthesis indicates the number of potential multi-subunit transporter complexes. CDS found sequentially and in close proximity on the same contig were considered to potentially be subunits of a common multi-protein complex.

References

Altschul, S. F., Madden, T. L., Schäffer, A. A., Zhang, J., Zhang, Z., Miller, W., &

Lipman, D. J. J. N. a. r. (1997). Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. 25(17), 3389-3402.

Anderson, K. L. (1995). Biochemical analysis of starch degradation by Ruminobacter amylophilus 70. Appl Environ Microbiol, 61(4), 1488-1491.

Arnal, G., Cockburn, D. W., Brumer, H., & Koropatkin, N. M. (2018). Structural basis for the flexible recognition of alpha-glucan substrates by Bacteroides thetaiotaomicron

SusG. Protein Sci, 27(6), 1093-1101. doi:10.1002/pro.3410

Avgustin, G., Wallace, R. J., & Flint, H. J. (1997). Phenotypic diversity among ruminal isolates of Prevotella ruminicola: proposal of Prevotella brevis sp. nov., Prevotella bryantii sp. nov., and Prevotella albensis sp. nov. and redefinition of Prevotella ruminicola. Int J Syst Bacteriol, 47(2), 284-288. doi:10.1099/00207713-47-2-284

Aziz, R. K., Bartels, D., Best, A. A., DeJongh, M., Disz, T., Edwards, R. A., Zagnitko, O.

(2008). The RAST Server: rapid annotations using subsystems technology. BMC

Genomics, 9, 75. doi:10.1186/1471-2164-9-75

Baroroh, U., Yusuf, M., Rachman, S. D., Ishmayana, S., Syamsunarno, M., Levita, J., &

Subroto, T. (2017). The Importance of Surface-Binding Site towards Starch-Adsorptivity

Level in alpha-Amylase: A Review on Structural Point of View. Enzyme Res, 2017,

4086845. doi:10.1155/2017/4086845

Bhute, S., Pande, P., Shetty, S. A., Shelar, R., Mane, S., Kumbhare, S. V., . . . Shouche,

Y. S. (2016). Molecular Characterization and Meta-Analysis of Gut Microbial

Communities Illustrate Enrichment of Prevotella and Megasphaera in Indian Subjects.

Front Microbiol, 7, 660. doi:10.3389/fmicb.2016.00660

Cho, K. H., Cho, D., Wang, G. R., & Salyers, A. A. (2001). New regulatory gene that contributes to control of Bacteroides thetaiotaomicron starch utilization genes. J

Bacteriol, 183(24), 7198-7205. doi:10.1128/jb.183.24.7198-7205.2001

Cotta, M. A. (1988). Amylolytic activity of selected species of ruminal bacteria. Appl

Environ Microbiol, 54(3), 772-776.

Counotte, G. H., & Prins, R. A. (1981). Regulation of lactate metabolism in the rumen.

Vet Res Commun, 5(2), 101-115. doi:10.1007/bf02214975

Creevey, C. J., Kelly, W. J., Henderson, G., & Leahy, S. C. (2014). Determining the culturability of the rumen bacterial microbiome. Microb Biotechnol, 7(5), 467-479. doi:10.1111/1751-7915.12141

D'Elia, J. N., & Salyers, A. A. (1996). Effect of regulatory protein levels on utilization of starch by Bacteroides thetaiotaomicron. J Bacteriol, 178(24), 7180-7186.

Dehority, B. A. (2003). Rumen microbiology (Vol. 372): Nottingham University Press

Nottingham.

Deusch, S., Tilocca, B., Camarinha-Silva, A., & Seifert, J. (2015). News in livestock research - use of Omics-technologies to study the microbiota in the gastrointestinal tract of farm animals. Comput Struct Biotechnol J, 13, 55-63. doi:10.1016/j.csbj.2014.12.005

Duranti, S., Turroni, F., Lugli, G. A., Milani, C., Viappiani, A., Mangifesta, M., . . .

Ventura, M. (2014). Genomic characterization and transcriptional studies of the starch- utilizing strain Bifidobacterium adolescentis 22L. Appl Environ Microbiol, 80(19), 6080-

6090. doi:10.1128/aem.01993-14

Edwards, U., Rogall, T., Blocker, H., Emde, M., & Bottger, E. C. (1989). Isolation and direct complete nucleotide determination of entire genes. Characterization of a gene coding for 16S ribosomal RNA. Nucleic Acids Res, 17(19), 7843-7853.

Fecskeova, L., Piknova, M., Javorsky, P., & Pristas, P. (2010). Lactate dehydrogenase gene variability among predominant lactate utilizing ruminal bacteria. Folia Microbiol

(Praha), 55(4), 315-318. doi:10.1007/s12223-010-0048-z

Flachowsky, G., Meyer, U., & Sudekum, K. H. (2017). Land Use for Edible Protein of

Animal Origin-A Review. Animals (Basel), 7(3). doi:10.3390/ani7030025

Flint, H. J., Scott, K. P., Duncan, S. H., Louis, P., & Forano, E. (2012). Microbial degradation of complex carbohydrates in the gut. Gut Microbes, 3(4), 289-306. doi:10.4161/gmic.19897

Girard, C., Tromas, N., Amyot, M., & Shapiro, B. J. (2017). Gut Microbiome of the

Canadian Arctic Inuit. mSphere, 2(1). doi:10.1128/mSphere.00297-16

Goad, D. W., Goad, C. L., & Nagaraja, T. G. (1998). Ruminal microbial and fermentative changes associated with experimentally induced subacute acidosis in steers. J Anim Sci,

76(1), 234-241. doi:10.2527/1998.761234x

Gómez, L. M., Posada, S. L., & Olivera, M. (2016). Starch in ruminant diets: a review %J

Revista Colombiana de Ciencias Pecuarias. 29, 77-90.

Grohn, Y. T., & Bruss, M. L. (1990). Effect of diseases, production, and season on traumatic reticuloperitonitis and ruminal acidosis in dairy cattle. J Dairy Sci, 73(9), 2355-

2363. doi:10.3168/jds.S0022-0302(90)78918-7

Hart, E. H., Creevey, C. J., Hitch, T., & Kingston-Smith, A. H. (2018). Meta-proteomics of rumen microbiota indicates niche compartmentalisation and functional dominance in a

93 limited number of metabolic pathways between abundant bacteria. Sci Rep, 8(1), 10504. doi:10.1038/s41598-018-28827-7

Hayashi, H., Shibata, K., Sakamoto, M., Tomita, S., & Benno, Y. (2007). Prevotella copri sp. nov. and Prevotella stercorea sp. nov., isolated from human faeces. Int J Syst Evol

Microbiol, 57(Pt 5), 941-946. doi:10.1099/ijs.0.64778-0

Hook, S. E., Steele, M. A., Northwood, K. S., Dijkstra, J., France, J., Wright, A. D., &

McBride, B. W. (2011). Impact of subacute ruminal acidosis (SARA) adaptation and recovery on the density and diversity of bacteria in the rumen of dairy cows. FEMS

Microbiol Ecol, 78(2), 275-284. doi:10.1111/j.1574-6941.2011.01154.x

Hungate, R. (1966). The Rumen and its Microbes Academic Press NY.

Huntington, G. B. (1997). Starch utilization by ruminants: from basics to the bunk. J

Anim Sci, 75(3), 852-867. doi:10.2527/1997.753852x

Jami, E., & Mizrahi, I. (2012). Composition and similarity of bovine rumen microbiota across individual animals. PLoS One, 7(3), e33306. doi:10.1371/journal.pone.0033306

Jami, E., White, B. A., & Mizrahi, I. (2014). Potential role of the bovine rumen microbiome in modulating milk composition and feed efficiency. PLoS One, 9(1), e85423. doi:10.1371/journal.pone.0085423

Janecek, S. (1994). Sequence similarities and evolutionary relationships of microbial, plant and animal alpha-amylases. Eur J Biochem, 224(2), 519-524.

Janecek, S., Svensson, B., & MacGregor, E. A. (2014). alpha-Amylase: an enzyme specificity found in various families of glycoside hydrolases. Cell Mol Life Sci, 71(7),

1149-1170. doi:10.1007/s00018-013-1388-z

Jewell, K. A., McCormick, C. A., Odt, C. L., Weimer, P. J., & Suen, G. (2015). Ruminal

Bacterial Community Composition in Dairy Cows Is Dynamic over the Course of Two

Lactations and Correlates with Feed Efficiency. Appl Environ Microbiol, 81(14), 4697-

4710. doi:10.1128/aem.00720-15

Jung, D. H., Seo, D. H., Kim, G. Y., Nam, Y. D., Song, E. J., Yoon, S., & Park, C. S.

(2018). The effect of resistant starch (RS) on the bovine rumen microflora and isolation of RS-degrading bacteria. Appl Microbiol Biotechnol, 102(11), 4927-4936. doi:10.1007/s00253-018-8971-z

K.Pradhan, & R.W.Hemken. (1970). Utilization of Ethanol and Its Effect on Fatty Acid

Patterns in Ruminants. J Dairy Sci, 53(12), 1739-1746.

Kanehisa, M., & Goto, S. J. N. a. r. (2000). KEGG: kyoto encyclopedia of genes and genomes. 28(1), 27-30.

Khafipour, E., Li, S., Plaizier, J. C., & Krause, D. O. (2009). Rumen Microbiome

Composition Determined Using Two Nutritional Models of Subacute Ruminal Acidosis.

75(22), 7115-7124. doi:10.1128/AEM.00739-09 %J Applied and Environmental

Microbiology

Kovatcheva-Datchary, P., Nilsson, A., Akrami, R., Lee, Y. S., De Vadder, F., Arora, T., .

. . Backhed, F. (2015). Dietary Fiber-Induced Improvement in Glucose Metabolism Is

Associated with Increased Abundance of Prevotella. Cell Metab, 22(6), 971-982. doi:10.1016/j.cmet.2015.10.001

Kristensen, N. B., Sehested, J., Jensen, S. K., & Vestergaard, M. (2007). Effect of milk allowance on concentrate intake, ruminal environment, and ruminal development in milk- fed Holstein calves. J Dairy Sci, 90(9), 4346-4355. doi:10.3168/jds.2006-885

Lane, D. J., Pace, B., Olsen, G. J., Stahl, D. A., Sogin, M. L., & Pace, N. R. (1985).

Rapid determination of 16S ribosomal RNA sequences for phylogenetic analyses. Proc

Natl Acad Sci U S A, 82(20), 6955-6959.

Lengowski, M. B., Zuber, K. H., Witzig, M., Mohring, J., Boguhn, J., & Rodehutscord,

M. (2016). Changes in Rumen Microbial Community Composition during Adaption to an

In Vitro System and the Impact of Different Forages. PLoS One, 11(2), e0150115. doi:10.1371/journal.pone.0150115

Li, F., & Guan, L. L. (2017). Metatranscriptomic Profiling Reveals Linkages between the

Active Rumen Microbiome and Feed Efficiency in Beef Cattle. 83(9). doi:10.1128/AEM.00061-17 %J Applied and Environmental Microbiology

Li, S., Khafipour, E., Krause, D. O., Kroeker, A., Rodriguez-Lecompte, J. C., Gozho, G.

N., & Plaizier, J. C. (2012). Effects of subacute ruminal acidosis challenges on fermentation and endotoxins in the rumen and hindgut of dairy cows. J Dairy Sci, 95(1),

294-303. doi:10.3168/jds.2011-4447

Lima, F. S., Oikonomou, G., Lima, S. F., Bicalho, M. L. S., Ganda, E. K., de Oliveira

Filho, J. C., . . . Bicalho, R. C. (2015). Prepartum and Postpartum Rumen Fluid

Microbiomes: Characterization and Correlation with Production Traits in Dairy Cows.

81(4), 1327-1337. doi:10.1128/AEM.03138-14 %J Applied and Environmental

Microbiology

Mann, E., Wetzels, S. U., Wagner, M., Zebeli, Q., & Schmitz-Esser, S. (2018).

Metatranscriptome Sequencing Reveals Insights into the Gene Expression and Functional

Potential of Rumen Wall Bacteria. Front Microbiol, 9, 43. doi:10.3389/fmicb.2018.00043

Morgante, M., Stelletta, C., Berzaghi, P., Gianesella, M., & Andrighetto, I. (2007).

Subacute rumen acidosis in lactating cows: an investigation in intensive Italian dairy herds. J Anim Physiol Anim Nutr (Berl), 91(5-6), 226-234. doi:10.1111/j.1439-

0396.2007.00696.x

Nafikov, R. A., & Beitz, D. C. (2007). Carbohydrate and lipid metabolism in farm animals. J Nutr, 137(3), 702-705. doi:10.1093/jn/137.3.702

Nagaraja, T. G., & Titgemeyer, E. C. (2007). Ruminal acidosis in beef cattle: the current microbiological and nutritional outlook. J Dairy Sci, 90 Suppl 1, E17-38. doi:10.3168/jds.2006-478

NejashAbdela. ( 2016). Sub-acute Ruminal Acidosis (SARA) and its Consequence in

Dairy Cattle: A Review of Past and Recent Research at Global Prospective. Achievements in the Life Sciences, 10(2), 187-196.

Opdahl, L. J., Gonda, M. G., & St-Pierre, B. (2018). Identification of Uncultured

Bacterial Species from Firmicutes, Bacteroidetes and CANDIDATUS Saccharibacteria as

Candidate Cellulose Utilizers from the Rumen of Beef Cows. Microorganisms, 6(1). doi:10.3390/microorganisms6010017

Owens, F. N., Secrist, D. S., Hill, W. J., & Gill, D. R. (1998). Acidosis in cattle: a review.

J Anim Sci, 76(1), 275-286.

Owens, F. N., Zinn, R. A., & Kim, Y. K. (1986). Limits to starch digestion in the ruminant small intestine. J Anim Sci, 63(5), 1634-1648.

Parte, A. C. (2014). LPSN--list of prokaryotic names with standing in nomenclature.

Nucleic Acids Res, 42(Database issue), D613-616. doi:10.1093/nar/gkt1111

Perry, G. H., Dominy, N. J., Claw, K. G., Lee, A. S., Fiegler, H., Redon, R., . . . Stone, A.

C. (2007). Diet and the evolution of human amylase gene copy number variation. Nat

Genet, 39(10), 1256-1260. doi:10.1038/ng2123

Petri, R. M., Forster, R. J., Yang, W., McKinnon, J. J., & McAllister, T. A. (2012).

Characterization of rumen bacterial diversity and fermentation parameters in concentrate fed cattle with and without forage. J Appl Microbiol, 112(6), 1152-1162. doi:10.1111/j.1365-2672.2012.05295.x

Pitta, D. W., Kumar, S., Vecchiarelli, B., Shirley, D. J., Bittinger, K., Baker, L. D., . . .

Thomsen, N. (2014). Temporal dynamics in the ruminal microbiome of dairy cows during the transition period. J Anim Sci, 92(9), 4014-4022. doi:10.2527/jas.2014-7621

Plaizier, J. C., Li, S., Danscher, A. M., Derakshani, H., Andersen, P. H., & Khafipour, E.

(2017). Changes in Microbiota in Rumen Digesta and Feces Due to a Grain-Based

Subacute Ruminal Acidosis (SARA) Challenge. Microb Ecol, 74(2), 485-495. doi:10.1007/s00248-017-0940-z

Plaizier, J. C., Li, S., Tun, H. M., & Khafipour, E. (2016). Nutritional Models of

Experimentally-Induced Subacute Ruminal Acidosis (SARA) Differ in Their Impact on

Rumen and Hindgut Bacterial Communities in Dairy Cows. Front Microbiol, 7, 2128. doi:10.3389/fmicb.2016.02128

Qian, W., Li, Z., Ao, W., Zhao, G., Li, G., & Wu, J. (2017). Bacterial community composition and fermentation in the rumen of Xinjiang brown cattle (Bos taurus), Tarim red deer (Cervus elaphus yarkandensis), and Karakul sheep (Ovis aries). Can J

Microbiol, 63(5), 375-383. doi:10.1139/cjm-2016-0596

Raun, B. M., & Kristensen, N. B. (2011). Metabolic effects of feeding ethanol or propanol to postpartum transition Holstein cows. J Dairy Sci, 94(5), 2566-2580. doi:10.3168/jds.2010-3999

Rodríguez, R., Sosa, A., & Rodríguez, Y. (2007). Microbial protein synthesis in rumen and its importance to ruminants. Cuban Journal of Agricultural Science, 41(4), 287-294.

Ryan, S. M., Fitzgerald, G. F., & van Sinderen, D. (2006). Screening for and identification of starch-, amylopectin-, and pullulan-degrading activities in bifidobacterial strains. Appl Environ Microbiol, 72(8), 5289-5296. doi:10.1128/aem.00257-06

Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. B., . . .

Robinson, C. J. J. A. E. M. (2009). Introducing mothur: open-source, platform- independent, community-supported software for describing and comparing microbial communities. 75(23), 7537-7541.

Simpson, J. T., Wong, K., Jackman, S. D., Schein, J. E., Jones, S. J., & Birol, I. (2009).

ABySS: a parallel assembler for short read sequence data. Genome Res, 19(6), 1117-

1123. doi:10.1101/gr.089532.108

Singh, K. M., Pandya, P. R., Tripathi, A. K., Patel, G. R., Parnerkar, S., Kothari, R. K., &

Joshi, C. G. (2014). Study of rumen metagenome community using qPCR under different diets. Meta Gene, 2, 191-199. doi:10.1016/j.mgene.2014.01.001

Snyder, E., & Credille, B. (2017). Diagnosis and Treatment of Clinical Rumen Acidosis.

Vet Clin North Am Food Anim Pract, 33(3), 451-461. doi:10.1016/j.cvfa.2017.06.003

St-Pierre, B., & Wright, A. J. B. M. (2015). Investigation of bacterial and methanogen community composition and diversity in full-scale anaerobic manure digesters. 1, 1-11.

T. J. DeVries, T. Schwaiger, Beauchemin, K. A., & PennerB, G. B. (2014). Impact of severity of ruminal acidosis on feed-sortingbehaviour of beef cattle

Animal Production Science, 54, 1238–1242.

Vyas, D., Uwizeye, A., Mohammed, R., Yang, W. Z., Walker, N. D., & Beauchemin, K.

A. (2014). The effects of active dried and killed dried yeast on subacute ruminal acidosis, ruminal fermentation, and nutrient digestibility in beef heifers. J Anim Sci, 92(2), 724-

732. doi:10.2527/jas.2013-7072

Wang, L., Hatem, A., Catalyurek, U. V., Morrison, M., & Yu, Z. (2013). Metagenomic insights into the carbohydrate-active enzymes carried by the microorganisms adhering to solid digesta in the rumen of cows. PLoS One, 8(11), e78507. doi:10.1371/journal.pone.0078507

Wang, Q., Garrity, G. M., Tiedje, J. M., & Cole, J. R. J. A. E. M. (2007). Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy.

73(16), 5261-5267.

Wang, S., Giller, K., Kreuzer, M., Ulbrich, S. E., Braun, U., & Schwarm, A. (2017).

Contribution of Ruminal Fungi, Archaea, Protozoa, and Bacteria to the Methane

100

Suppression Caused by Oilseed Supplemented Diets. Front Microbiol, 8, 1864. doi:10.3389/fmicb.2017.01864

Yu, Z., & Morrison, M. (2004). Improved extraction of PCR-quality community DNA from digesta and fecal samples. Biotechniques, 36(5), 808-812. doi:10.2144/04365st04

101

CHAPTER 3

IDENTIFICATION AND GENOMIC CHARACTERIZATION OF CANDIDATE

LACTATE UTILIZING BACTERIA FROM THE RUMEN OF BEEF CATTLE

102

ABSTRACT

Grain overload in ruminants results in increased production and accumulation of lactate in the rumen. While lactate utilizing bacteria are involved in lactate metabolism, high lactate levels can result in death of these lactate metabolizing bacteria. Therefore, the main objective of this study was to identify previously uncharacterized bacteria involved in metabolizing lactate using a batch culture approach, then predict their functional potential using metagenomics. Three experimental trials, each performed with a different rumen fluid donor, designated as Trial 1, Trial 2, and Trial 3, were conducted during this study. Four replicate cultures, each consisting of approximately 2.2 L of rumen fluid per laboratory-scale bioreactors were used for the study. Three of the replicate cultures were supplemented with lactate (20 ml/L, Sodium DL-lactate solution,

Sigma), while the remaining culture was not supplemented with any substrate (Control).

Samples collected on days 7 and 14 were analyzed to identify potential lactate utilizers as highly enriched OTUs. Seven species-level OTUs were enriched, which were affiliated to the genera Coprococcus, Butyrivibrio, Clostridium, Anaerococcus, and Anaerosalibacter.

One OTU affiliated to Butyrivibrio fibrisolvens (LacT2_D14_1_15251) and one OTU affiliated to Anaerococcus prevotii (LT3.7-00154) were then selected for shotgun metagenomics analysis. Annotation using RAST revealed genome-derived coding sequences that could be assembled into predicted pathways. Notably, enzymes that were predicted to be involved in lactate metabolism were identified. Further characterizing of these lactate utilizers (OTUs) may provide additional insights towards a better understanding of lactate metabolism and also the events that impact the onset of acidosis.

103

1. Introduction

The rumen is a complex and diverse microbial eco-system that is responsible for fermenting feedstuffs into SCFAs and producing microbial proteins (Bryant, 1959;

Robert E. Hungate, 1966). Since approximately 70% of the energy of the host is provided by SCFAs, ruminal microbial communities are essential contributors to ruminant productivity (Bergman, 1990).

With the aim of increasing their production, feedlot beef and dairy cattle are commonly fed with grain-rich diets. However, grain overload is associated with excessive production and accumulation of ruminal SCFAs, resulting in a pH drop in the rumen (J. Hernández, J. L. Benedito, A. Abuelo, & C. Castillo, 2014). This condition, known as acidosis, can be categorized as either acute or subacute. A rumen pH below 5.0, characterized by increased lactic acid production, is considered acute acidosis, whereas a rumen pH between 5.0 and 5.6 is characterized by increased accumulation of SCFAs and is regarded as subacute acidosis (Krause & Oetzel, 2006; Nagaraja & Titgemeyer, 2007;

Owens, Secrist, Hill, & Gill, 1998). Increased lactic acid production and decreased lactic acid utilization during acute ruminal acidosis is due to the establishment of acid tolerant bacteria as well as to a decrease in the abundance of lactic acid fermenting bacteria

(Therion, Kistner, & Kornelius, 1982) (Russell & Hino, 1985) (Mackie & Gilchrist,

1979a).

Various studies have reported that Streptococcus bovis and Lactobacillus spp are dominant lactic acid-producing bacteria, while Megasphaera elsdenii and Selenomonas ruminantium subsp. lactilytica are potent lactic acid utilizing bacteria (Mackie &

Gilchrist, 1979b) (Ogunade, Pech-Cervantes, & Schweickart, 2019) (R. E. Hungate,

104

Dougherty, Bryant, & Cello, 1952) (Mackenzie, 1967) (Chen et al., 2019). In animal nutritional models, experimental induction of SARA have been associated with reduced bacterial diversity and richness (Plaizier et al., 2017) (Plaizier, Li, Tun, & Khafipour,

2016) (Plaizier, 2004) (E. Khafipour, Krause, & Plaizier, 2009) (Mao, 2017). A study by

McCann et al. (2016) reported that cows with induced SARA had a higher relative abundance of Bacteroidetes in the liquid fraction, whereas an increase in the relative abundance of Firmicutes was observed in the solid fraction of the rumen samples

(McCann et al., 2016). A study by Khafipour et al. (2009) identified increased relative abundance of Bacteroidetes when cows were induced with SARA by feeding alfalfa pellets when compared with the grain diet induced SARA (Ehsan Khafipour, Li, Plaizier,

& Krause, 2009). On their part, a study by Ogunade et al. (2019) investigated the metabolic potential of the rumen microbiome during SARA. It was reported that 68 genes could be mapped to carbohydrate, amino acid, energy, vitamin and co-factor metabolism pathways, and that bacterial biofilm formation pathways were highly expressed in the

SARA-induced beef cows (Ogunade et al., 2019).

2014) (Huws et al., 2018). Based on this assessment, it was hypothesized that currently unknown ruminal microorganisms include lactic acid fermenting bacteria that have yet to be identified. Using a batch-culture system, candidate lactate utilizers were enriched from beef cow rumen fluid, then characterized as Operational Taxonomic Units (OTUs) using a 16S rRNA-based approach. Metagenomics analyses were conducted to assess the

105 metabolic potential of candidate ruminal bacterial species, which revealed coding sequences for enzymes that participate in pathways predicted to metabolize lactate into

SCFAs. Together, the results presented in this chapter suggest that the bacterial species corresponding to these OTUs function as lactate utilizers in the rumen of beef cows.

2. Materials and Methods

2.1 Sample collection and in vitro rumen culture experiments

All animal procedures were approved by the Institutional Animal Care and Use

Committee (IACUC) at South Dakota State University. Fresh rumen fluid was obtained by manually squeezing ruminal digesta collected from fistulated beef cows maintained at the South Dakota State University Cow-Calf Research Facilities. The diet of these animals consisted of pasture hay or haylage during the period when rumen fluid was collected (March 2017 to November 2017). Three experimental trials, each performed with a different rumen fluid donor, designated as Trial 1, Trial 2, and Trial 3, were conducted during this study, with the following procedure followed for each trial. Four replicate cultures, each consisting of approximately 2.2 L of rumen fluid per laboratory- scale bioreactors (Chemglass), were set up within one hour of collection. Three of the replicate cultures were supplemented with lactate (20 ml/L, Sodium DL-lactate solution, sigma), while the remaining culture was not supplemented with any substrate (Control).

Treatment and control cultures were maintained under anaerobic conditions at a constant physiological temperature (380C), with continuous agitation at 150 rpm using a Rushton- style impeller fixed to a stirring shaft built into the bioreactor. Each bioreactor allowed

106 for approximately 0.8 L of headspace, and was equipped with a flexible plastic tube for exhaust of excess biogas to prevent pressure build-up. Culture samples (approximately 15 mL/sample) were collected on days 7 (D7) and 14 (D14) to assess their respective bacterial composition. Samples from the rumen inoculum (D0) and cultures were stored frozen at -200C until analyzed.

2.2 Microbial genomic DNA purification and PCR amplification of the 16S rRNA gene

SDS) by bead beating followed by sequential extraction with 10M ammonium acetate then isopropanol precipitation. Recovered nucleic acids were then purified using the

QIAamp DNA Stool Kit (QIAGEN, Hilden, Germany) following the manufacturer’s recommended protocol. PCR was performed using the Phusion Taq DNA polymerase

(ThermoFischer Scientific, Waltham, MA, USA) on a 2720 Thermo Cycler

(ThermoFischer Scientific), with the 27F (Edwards, Rogall, Blocker, Emde, & Bottger,

(expected approximate length of 500 bp) was determined by agarose gel electrophoresis, with recovery of PCR-generated DNA using the QiaexII Gel extraction kit (Qiagen,

Hilden, Germany). Gel purified amplicons from each sample were then submitted as

107 template for high throughput sequencing using an Illumina Miseq(2×300) platform

(Molecular Research DNA, Shallowater, TX).

2.3 Bioinformatic analysis for 16S rRNA gene-based composition analysis

Unless specified, datasets were analyzed using custom written Perl scripts

(available upon request). Raw bacterial 16S rRNA gene V1-V3 amplicon sequences were provided by Molecular Research DNA as contigs assembled from overlapping MiSeq

Following quality screens, sequence reads were aligned, then clustered into

Operational Taxonomic Units (OTUs) at a genetic distance cutoff of 5% sequence dissimilarity (St-Pierre & Wright, 2015). While 3% is the most commonly used clustering cutoff for 16S rRNA, it was originally recommended for full length sequences, and may not be suitable for the analysis of specific sub-regions since nucleotide sequence variability is not constant across the entire length of the 16S rRNA gene. In this context, if 3% is a commonly accepted clustering cutoff for V4 or V4-V5 regions, which are the least variable of the hypervariable regions, then a higher cutoff should be used for the

MOTHUR open source software package (Schloss et al., 2009). Secondly, the integrity of the 5’ and 3’ ends of OTUs was evaluated using a database alignment search-based

108 approach; when compared to their closest match of equal or longer sequence length from the NCBI nt database, as determined by BLAST (Altschul et al., 1997), OTUs with more than five nucleotides missing from the 5’ or 3’ end of their respective alignments were discarded as artifacts. Single read OTUs were subjected to an additional screen, where only sequences that had a perfect or near perfect match to a sequence in the NCBI nt database were kept for analysis, i.e. that the alignment had to span the entire sequence of the OTU, and a maximum of 1% of dissimilar nucleotides was tolerated.

After removal of sequence chimeras and artifacts, RDP Classifier (Qiong Wang,

Garrity, Tiedje, & Cole, 2007) and BLAST (Altschul et al., 1997) were used for taxonomic assignment of valid OTUs. The List of Prokaryotic Names with Standing in

Nomenclature (LPSN) was also consulted for information on valid species belonging to taxa of interest (Parte, 2014).

2.4 Metagenomic analysis

A select number of samples exhibiting high levels of enrichment for candidate lactate utilizing OTUs were further analyzed using a metagenomics approach. Purified microbial genomic DNA (extracted as described above) was used directly as template for high throughput sequencing using an Illumina Miseq(2×250) platform (Molecular

Research Computing Group at South Dakota State University, with k-mer length set at 64 and all other parameters used at their respective default values.

109

ABySS-assembled contigs of 4000 bp or greater were selected for further analysis by gene annotation. Coding sequences (CDS) were identified and annotated using RAST

(Rapid Annotations using Subsystems Technology) (Aziz et al., 2008), with assignment of CDS to metabolic pathways performed using KEGG pathways as a model reference

(Kanehisa & Goto, 2000). Further analysis of select CDS was performed against the

NCBI nr database using BLASTp (Altschul et al., 1997) to identify their respective closest homologs.

3. Results

3.1 Comparative analysis of bacterial communities from donor rumen fluid

Analysis of the rumen fluid inocula before culturing with lactate revealed that bacteria affiliated to the phyla Bacteroidetes and Firmicutes were overall the most abundant, representing 45.4-58.4% and 30-47% of sequences reads, respectively (Figure

3.1). Prevotellaceae was the predominant Bacteroidetes family (17.1-31.3%), while unclassified Clostridiales was the most highly represented group of Firmicutes at the family level (28.2-34.8%, respectively).

3.2 Identification of candidate bacterial lactate utilizers from rumen fluid

When compared to their respective culture controls without substrate, seven distinct OTUs were found to be the most enriched bacteria in lactate-supplemented rumen fluid cultures (Table 3.1).

110

From the Trial 1 donor, OTU LacT1_D7_3-00877 was in highest abundance in lactate supplemented cultures at day 7 with a relative abundance of 0.16%-22%. The closest valid relative to LacT1_D7_3-00877 was Coprococcus catus, a well characterized lactate utilizer (Holdeman & Moore, 1974a), with a sequence identity of 93% based on

16S rRNA gene-based analyses. OTUs LacT2_D14_1-15251 and LacT2_D14_2-12405 were identified as candidate lactate utilizers from Trial 2. These OTUs were in highest abundance in lactate supplemented cultures at day 14 with a relative abundance of 0.6%-

27% and 0.05%-13%, respectively. The closest valid relative to LacT2_D14_1-15251 and LacT2_D14_2-12405 were respectively Butyrivibrio fibrisolvens (Islam et al., 2019) and Clostridium acetireducens (Örlygsson, Krooneman, Collins, Pascual, & Gottschal,

1996), with a sequence identity 89% and 99%, based on 16S rRNA gene-based analyses.

OTUs LT3.7-00154, LT3.7-01259, LT3.7-08849, and LT3.8-08697 were identified as candidate lactate utilizers from Trial 3. Three of the OTUs (LT3.7-00154, LT3.7-01259, and LT3.7-08849) were in highest abundance in lactate supplemented cultures at day 14, with a relative abundance of 0.01%-39%, 0.05%-16%, and 0.01%-13%, whereas OTU

LT3.8-08697 was in highest abundance in lactate supplemented cultures at day 7, with a relative abundance of 0.03%-11%. The closest valid relative to OTUs LT3.7-00154,

LT3.7-01259, LT3.7-08849, and LT3.8-08697 were respectively Anaerococcus prevotii

(Labutti et al., 2009), Anaerosalibacter bizertensis (Rezgui et al., 2012), Anaerococcus tetradius (Cibis, Gneipel, & Konig, 2016), and Clostridium cochlearium (Schwab, Rago,

Koch, & Harnisch, 2019) with a sequence identity of 96%, 99%, 93%, and 99%.

3.3 Exploring the metabolic potential of OTUs identified as candidate lactate utilizers

111

We postulated that sequence datasets generated from samples with high abundance of an OTU would have higher representation of genomic sequences from this

OTUs that were enriched in the presence of lactate, we focused our attention on two highly enriched OTUs affiliated to Butyrivibrio fibrisolvens (LacT2_D14_1_15251) and

Anaerococcus prevotii (LT3.7-00154).

The most highly enriched samples for LacT2_D14_1_15251 (Trial 2: 27%) and

LT3.7-00154 (Trial 3: 39%), were then selected for shotgun sequencing, which generated a total of 9.4 X 106 and 9.1 X 106 paired end reads, respectively. After de novo assembly, contigs were filtered for a minimal length of 4,000 nt, which resulted in the selection of

324 (Trial 2) and 682 (Trial 3) contigs for further analysis. The maximum contig length from each sample was found to be 38,502 nt (Trial 2) and 24,827 nt (Trial 3). The RAST online tool was then used for predicting amino acid coding regions (CDS) from the selected contigs, as well as for performing gene annotation. For the Trial 2 contig set, 860 of 2,759 CDS were annotated successfully, compared to 741 of 5,629 CDS for Trial 3.

Predicted metabolic functional categories from gene annotation included carbohydrates, proteins, amino acids and derivatives, DNA and RNA, phosphorus, potassium and sulfur, regulation and cell signaling, as well as stress response (Figure

3.2). Based on the conditions of the culturing assay, our primary interest was to identify genes involved in carbohydrate metabolism; 80 CDS were assigned to this function from the Trial 2 contig set, compared to 104 CDS for the Trial 3 dataset.

112

To gain further insight, data mining for specific functions within carbohydrate metabolism was performed, resulting in the identification of enzymes predicted to be associated with metabolic activities of lactate utilizing bacteria (Figures 3.3, 3.4, 3.5, 3.6,

3.7, 3.8, and 3.9).

Coding sequences for lactate dehydrogenase (EC 1.1.2.4, EC 1.1.1.27, EC

1.1.1.28), which can catalyze the conversion of lactate to pyruvate, were identified for

LacT2_D14_1_15251 and for LT3.7-00154 (Figure 3.3 and 3.6).

For both OTUs, enzymes involved in metabolizing pyruvate into SCFAs were also identified (Figure 3.3 and 3.6). In trial 2 and 3, we identified enzymes such as pyruvate dehydrogenase (EC 1.2.4.1), the dihydrolipoamide acetyltransferase component of the pyruvate dehydrogenase complex (EC 2.3.1.12), as well as pyruvate-formate lyase

(EC 2.3.1.54) which converts pyruvate into acetyl-CoA. From the trial 2 dataset, acetyl-

CoA could then be metabolized into acetate by succinyl-CoA:acetate-CoA-transferase

(EC 2.8.3.18), while from the trial 3 dataset, acetyl-CoA could be metabolized sequentially into acetyl phosphate (phosphate acetyltransferase, EC 2.3.1.8) then acetate

(acetate kinase, EC 2.7.2.1). Acetaldehyde dehydrogenase (EC 1.2.1.10) and alcohol dehydrogenases (EC 1.1.1.1) found in both trial 2 and 3 could catalyze the conversion of acetyl-CoA to acetaldehyde and ethanol. Enzymes involved in butanoate metabolism and the citrate cycle were also identified in LacT2_D14_1_15251 affiliated contigs (Figure

3.4 and 3.5).

All enzymes of the glycolysis pathway (Figure 3.7) and enzymes involved in glycogen synthesis and utilization (Figure 3.8) were also identified in LT3.7-00154 affiliated contigs. Enzymes predicted to be involved in this function included glycogen

113 synthase (EC 2.4.1.21), which converts ADP-glucose to amylose, as well as 1,4-alpha- glucan branching enzyme (EC 2.4.1.18), which generates glycogen from amylose.

Glucose stored as glycogen would then be predicted to be metabolized through the sequential actions of glycogen phosphorylase (EC 2.4.1.1) and phosphoglucomutase (EC

5.4.2.2) which would generate glucose-1-phosphate and glucose-6-phosphate, respectively. The latter could then enter glycolysis to be metabolized into pyruvate and

SCFAs.

Contig sets from LacT2_D14_1_15251 and LT3.7-00154 enriched samples included enzymes (EC 1.4.1.13, EC 6.3.5.4, EC 6.3.1.2, EC 1.4.1.4, EC 1.4.1.2) that were involved in amino acid metabolism (Table 3.4 and 3.5). Monocarboxylate and ammonium transporters were identified in contig sets from LacT2_D14_1_15251 and

LT3.7-00154 enriched samples and were involved in lactate and amino acid transportation (Table 3.6).

4. Discussion

Acidosis is one of the most commonly seen clinical disorders in intensive management systems when ruminants are fed with high amounts of grain. This condition has a significant impact on rumen microbial populations and their cell densities, further leading to negative effects on animal health and productivity (Joaquín Hernández et al.,

2014). Lactate is one of the main contributors to acidosis, as increases in its production and its accumulation can have a severe impact on ruminal pH (Mackie & Gilchrist,

1979b). By fermenting lactate, lactate utilizing bacteria can counter this effect, and therefore contribute to regulating lactate levels in the rumen.

114

However, one critical gap in our knowledge is that most lactate metabolizing bacteria likely remain to be identified and characterized, as an estimated 95% of ruminal bacterial species have yet to be assigned a function (Creevey et al., 2014). In this context, the primary objective of the research presented in this chapter was to identify uncharacterized ruminal bacteria that can metabolize lactate, then assess their metabolic potential using a metagenomic approach. The bacterial composition of the three rumen samples prior to culturing in the presence of lactate (D0), in which Bacteroidetes and

Firmicutes were the most abundant phyla and Prevotellaceae were overall the most predominant family, was consistent with previously reported studies (Pitta et al., 2014)

(Lima et al., 2015) (Opdahl, Gonda, & St-Pierre, 2018) (Jami & Mizrahi, 2012) (Jami,

White, & Mizrahi, 2014) (Jewell, McCormick, Odt, Weimer, & Suen, 2015). From these complex communities, seven main OTUs were found to be in higher relative abundance in ruminal fluid cultures when lactate was provided as the only supplemented substrate.

Enrichment for OTU LacT1_D7_3-00877, predicted to be a strain of

Coprococcus catus based on 16S rRNA gene sequence comparisons, was enriched on day-7 with a relative abundance of 22% in one of the lactate supplemented culture.

Coprococcus catus has been reported as a key lactate fermenter from various studies

(Holdeman & Moore, 1974b) (Reichardt et al., 2014; Shabat et al., 2016).

From trial 2, LacT2_D14_1-15251 was found to be enriched in a single culture with an abundance of 27%, and it was most closely related to Butyrivibrio fibrisolvens. A

Study by Ghali et al. (2011) on the foregut of feral camels described three isolates closely related to Butyrivibrio ﬁbrisolvens NCDO 2398 strain, which have been reported to be involved in lactate metabolism, based on culturing with lactic acid medium (LAM) and

115

De Man, Rogosa, Sharpe medium (MRS) medium (Ghali, Scott, Alhadrami, & Al Jassim,

2011). Another study reported that certain strains of Butyrivibrio fibrisolvens have proteolytic activity in the rumen (Cotta & Hespell, 1986). Our observations are then consistent with these previously published results, as they indicate that OTU

LacT2_D14_1-15251 could function as a lactate utilizer. The other OTU enriched in trial

2, LacT2_D14_2-12405, with its highest abundance in a single culture at 13%, was assigned to Clostridium acetireducens. A study by Orlygsson et al. (1996) describes the isolation of Clostridium acetireducens from an anerobic bio-reactor fed on waste from potato starch. While it was not reported as a lactate-utilizer, this anerobic bacteria was found to be capable of deaminating amino acids by using acetate as an electron acceptor while producing butyrate as the reduced end product (Örlygsson et al., 1996).

Four OTUs from trial 3, LT3.7-00154, LT3.7-01259, LT3.7-08849, and LT3.8-

08697, were assigned to three different lineages. OTUs LT3.7-00154 and LT3.7-08849 were found to be affiliated to the genus Anaerococcus, with members having been isolated from human skin, oral cavity, gastro-intestinal environment, vaginal discharges and ovarian abscess (Ezaki, Yamamoto, Ninomiya, Suzuki, & Yabuuchi, 1983) (Cibis et al., 2016). While they have been reported to ferment different sugars (Ezaki et al., 1983;

Murdoch, 1998), no studies have reported the potential role of these species as lactate utilizers. For its part, OTU LT3.7-01259 was most closely affiliated to Anaerosalibacter bizertensis, a bacterial species isolated from sludge, which has the ability to ferment different carbon sources such as glucose, succinate, and pyruvate (Rezgui et al., 2012).

However, no studies have documented the role of Anaerosalibacter bizertensis as a lactate utilizer. Finally, OTU LT3.8-08697 was assigned to Clostridium cochlearium, a

116 bacterial species originally isolated from the mouse gut microbiome (Schwab et al., 2019) and from a slaughter house (Bakhtiary et al., 2018). Known metabolic activities include fermentation of glucose or glutamate to acetate, butyrate, and propionate (Buckel &

Barker, 1974; Cibis et al., 2016), but no reported ability to metabolize lactate.

In light of the limitations of 16S rRNA-based composition analyses to provide functional insights (Creevey et al., 2014) (Deusch, Tilocca, Camarinha-Silva, & Seifert,

2015), a metagenomics analysis was performed on representative high enrichment samples to get an assessment of the metabolic potential of OTUs LacT2_D14_1-15251 and LT3.7-00154. Consistent with lactate utilization, both contig sets included coding sequences for monocarboxylate transporters, which could be involved in lactate transport across biological membranes (Thauer, 1988) (Pösö, 2002), as well as for lactate dehydrogenases (EC 1.1.2.4, EC 1.1.1.28, and EC 1.1.1.27), which would catalyze the conversion of lactate to pyruvate (Garvie, 1980) (J. Hernández, J. Benedito, A. Abuelo, &

C. Castillo, 2014) (Allison, O'Donnell, Hoey, & Fewson, 1985; Goffin, Lorquet,

Kleerebezem, & Hols, 2004). A coding sequence for D-lactate ferricytochrome c oxidoreductase (EC 1.1.2.4), known to be present in Saccharomyces cerevisiae,

Pseudomonas stutzeri, and Acinetobacter (Lodi & Ferrero, 1993) (Pajot & Claisse, 1974)

(C. Ma et al., 2007) (Hao et al., 2007) (C. Q. Ma et al., 2004), was predicted from the contigs assembled from the sample enriched for LacT2_D14_1-15251. Coding sequences for NAD-dependent lactate dehydrogenase enzymes (EC 1.1.1.27 and EC 1.1.1.28), which were identified in the contigs associated with LT3.7-00154, are involved in catalyzing the conversion of lactate to pyruvate. Besides Anaerococcus prevotii,

117

Lactobacillus plantarum can also express this enzyme under aerobic conditions (Goffin et al., 2004).

A number of different enzymes responsible for metabolizing pyruvate into SCFAs were also identified. CDS for enzymes such as pyruvate dehydrogenase complex (EC

1.2.4.1, EC 2.3.1.12), pyruvate formate-lyase (EC 2.3.1.54), acetaldehyde dehydrogenase

(EC 1.2.1.10), and alcohol dehydrogenase (EC 1.1.1.1) were identified in both contig sets analyzed, indicating that both LacT2_D14_1-15251 and LT3.7-00154 had the potential to metabolize pyruvate to SCFAs. Pyruvate dehydrogenase, pyruvate formate-lyase, aldehyde dehydrogenase, and alcohol dehydrogenase enzymes can all be encoded in the same bacterial genome, as shown for Escherichia coli (Clark, 1989) (Qingzhao Wang,

Ou, Kim, Ingram, & Shanmugam, 2010) (Neidhardt & Curtiss, 1996). In addition, CDS for all enzymes of the citrate cycle were identified from the contig set analyzed from trial

2. Previous studies have reported the presence of citrate cycle enzymes in anerobic bacteria (Alteri, Himpsl, Engstrom, & Mobley, 2012; Galushko & Schink, 2000; Thauer,

1988). CDS for all enzymes of the glycolysis pathway were identified in the contig set for

LT3.7-00154, indicating that this OTU had the potential to metabolize glucose into pyruvate (Rodríguez, Sosa, & Rodríguez, 2007). Enzymes that could synthesize glycogen from glucose were also identified from the trial 3 contig set.

Amino acid metabolism is also essential for the proliferation of ruminal microorganism. Based on the predicted enzymatic capabilities from the contig sets analyzed in this study, both enriched OTUs appeared to have enzymes such as glutamate synthase (EC 1.4.1.13), glutamate dehydrogenase (EC 1.4.1.2), glutamine synthetase type

I (EC 6.3.1.2), glutamate dehydrogenase (EC 1.4.1.4), asparagine synthetase (EC 6.3.5.4)

118 and ammonium transporters for the synthesis of amino acids. The presence of ammonium transporters indicates that OTUs LacT2_D14_1-15251 and LT3.7-00154 could use ammonia from the extracellular environment for the synthesis of amino acids (Pengpeng

& Tan, 2013) (Hackette, Skye, Burton, & Segel, 1970) (Marini, Vissers, Urrestarazu, &

André, 1994) (Phibbs & Bernlohr, 1971) (Erfle et al., 1977).

5. Conclusion

The batch-culture approach used in this study allowed for the identification of previously uncharacterized bacteria as candidate lactate utilizers from the rumen of beef cows. Based on 16S rRNA gene sequence analysis, seven ruminal bacterial OTUs enriched with lactate as the only provided substrate were found to be affiliated to the genera Coprococcus, Butyrivibrio, Clostridium, Anaerococcus, and Anaerosalibacter.

Further investigations of the Butyrivibrio and Anaerococcus related OTUs using metagenomics revealed genome-derived coding sequences that could be assembled into predicted pathways that would allow for fermentation of lactate into SCFAs.

Furthermore, these different OTUs shared other metabolic functions, such as the ability to store glucose as glycogen, as well as to utilize extracellular ammonia for the synthesis of amino acids. Together, these characteristics could help define members of a particular subgroup of ruminal lactate utilizers, ultimately contributing to a better understanding of metabolic functions and events that impact the onset of SARA and the incidence of acidosis.

119

Figure 3.1. Taxonomic composition of rumen samples used as inocula, prior to setting up batch culture enrichment experiments with lactate. Each experimental trial (T1, T2, T3) is represented on the horizontal axis, with the relative abundance (%) of each major taxonomic group shown on the vertical axis. Families belonging to the same phylum are represented by different shades of the same color: Firmicutes (green), Bacteroidetes (blue), and other families (red).

120

Figure 3.2. RAST gene annotation analysis of contig sets generated from enrichment Trials 2 (blue) and 3 (orange). The relative abundance (%) of coding sequences for each subsystem level category is shown.

121

Figure 3.3. KEGG (Kyoto Encyclopedia of Genes and Genomes) annotation of predicted metabolic pathways from the metagenomics analysis of contigs generated from Trial#2 (OTU LacT2_D14_1-15251, affiliated to Butyrivibrio fibrosolvens). A) lactate and pyruvate metabolism. Each enzyme is represented by its Enzyme Commission (EC) number, which is based on the chemical reaction it catalyzes. Please consult Table 3.2 for corresponding enzyme names

122

Figure 3.4. KEGG (Kyoto Encyclopedia of Genes and Genomes) annotation of predicted metabolic pathways from the metagenomic analysis of contigs generated from Trial#2 (OUT LacT2_D14_1-15251, affiliated to Butyrivibrio fibrosolvens). B) butanoate metabolism. Each enzyme is represented by its Enzyme Commission (EC) number, which is based on the chemical reaction it catalyzes. Please consult Table 3.2 for corresponding enzyme names.

123

Figure 3.5. KEGG (Kyoto Encyclopedia of Genes and Genomes) annotation of predicted metabolic pathways from the metagenomic analysis of contigs generated from Trial#2 (OTU LacT2_D14_1-15251, affiliated to Butyrivibrio fibrosolvens). C) citrate cycle. Each enzyme is represented by its Enzyme Commission (EC) number, which is based on the chemical reaction it catalyzes. Please consult Table 3.2 for corresponding enzyme names.

124

Figure 3.6. KEGG (Kyoto Encyclopedia of Genes and Genomes) annotation of predicted metabolic pathways from the metagenomic analysis of contigs generated from Trial#3 (OTU LT 3.7-00154, affiliated to Anaerococcus prevotii). A) lactate and pyruvate metabolism. Each enzyme is represented by its Enzyme Commission (EC) number, which is based on the chemical reaction it catalyzes. Please consult Table 3.3 for corresponding enzyme names.

125

Figure 3.7. KEGG (Kyoto Encyclopedia of Genes and Genomes) annotation of predicted metabolic pathways from the metagenomic analysis of contigs generated from Trial#3 (OTU LT3.7-00154, affiliated to Anaerococcus prevotii). B) glycolysis pathway. Each enzyme is represented by its Enzyme Commission (EC) number, which is based on the chemical reaction it catalyzes. Please consult Table 3.3 for corresponding enzyme names.

126

Figure 3.8. KEGG (Kyoto Encyclopedia of Genes and Genomes) annotation of precited metabolic pathways from the metagenomics analysis of contigs generated from Trial#3 (OTU LT3.7-00154, affiliated to Anaerococcus prevotii). C) glycogen synthesis and mobilization. Each enzyme is represented by its Enzyme Commission (EC) number, which is based on the chemical reaction it catalyzes. Please consult Table 3.3 for corresponding enzyme names.

127

Figure 3.9. KEGG (Kyoto Encyclopedia of Genes and Genomes) annotation of precited metabolic pathways from the metagenomics analysis of contigs generated from Trial#3 (OTU LT3.7-00154, affiliated to Anaerococcus prevotii). D) pentose phosphate pathway. Each enzyme is represented by its Enzyme Commission (EC) number, which is based on the chemical reaction it catalyzes. Please consult Table 3.3 for corresponding enzyme names

128

Table 3.1. Enrichment of OTUs in response to lactate. Abundance is presented as relative abundance (%) of total non-chimeric reads. D0 (prior to lactate enrichment), C-D7 and C-D14 (Control culture day-7 and day-14), L-D7 and L-D14 (Lactate culture day-7 and day-14).

Closest Trial OTU D0 C-D7 L-D7* C-D14 L-D14* relative LacT1_D7_ Trial 1 6.7x10--05 4.7x10-05 11 0 4 3-00877 C. catus (93%)

LacT2_ Trial 2 0 0 0.8 0 11 B. fibrosolvens D14_1- (89%) 15251

LacT2_D14 0 0 0.03 0 4 acetireducens _2-12405 (99%)

LT3.7- A. prevotii Trial 3 0.06 0.04 0.04 0.03 13 00154 (96%)

LT3.7- A. bizertensis 0.02 0.02 0.03 6.8x10 6 01259 -05 (99%)

LT3.7- A. tetradius 3.5x10-05 0.02 0.06 0.02 4 08849 (93%)

LT3.8- C. cochlearium 0.0001 0.0002 0.03 6.8x10 0.02 08697 -05 (99%)

129

Table 3.2. Carbohydrate-utilizing enzymes identified from the gene annotation analysis of contigs generated from Trial#2 (OTU LacT2_D14_1-15251, affiliated to Butyrivibrio fibrisolvens).

Enzyme aa# E-value D-Lactate dehydrogenase, cytochrome c-dependent (EC 1.1.2.4) 482 0 Pyruvate dehydrogenase (EC 1.2.4.1) 258 3x10-05 Dihydrolipoamide acetyltransferase component of pyruvate 564 0 dehydrogenase complex (EC 2.3.1.12) Pyruvate kinase (EC 2.7.1.40) 810 0 Acetaldehyde dehydrogenase (EC 1.2.1.10) 319 0 Alcohol dehydrogenase (EC 1.1.1.1) 273 5x10-05 Malate synthase (EC 2.3.3.9) 725 0 Succinyl-CoA: acetate CoA-transferase (EC 2.8.3.18) 503 0 3-Hydroxybutyryl-CoA epimerase (EC 5.1.2.3) 317 1x10-5 3-Hydroxybutyryl-CoA dehydrogenase (EC 1.1.1.157) 317 2x1014 Acetyl-CoA acetyltransferase (EC 2.3.1.9) 405 0 Enoyl-CoA hydratase (EC 4.2.1.17) 272 1x10-12 Butyryl-CoA dehydrogenase (EC 1.3.8.1) 385 0 Fumarate hydratase class I (EC 4.2.1.2) 509 0 Malate dehydrogenase (EC 1.1.1.37) 346 0 Citrate synthase (si) (EC 2.3.3.1) 436 0 Aconitate hydratase 2 (EC 4.2.1.3) 863 0 Isocitrate dehydrogenase [NADP] (EC 1.1.1.42) 418 0 2-Oxoglutarate dehydrogenase E1 component (EC 1.2.4.2) 957 0 Succinyl-CoA ligase [ADP-forming] alpha chain (EC 6.2.1.5) 293 0 Succinate dehydrogenase flavoprotein subunit (EC 1.3.5.1) 568 0

# aa: length in amino acids

130

Table 3.3. Carbohydrate-utilizing enzymes identified from the gene annotation analysis of contigs generated from Trial#3 (OTU LT3.7-00154, affiliated to Anaerococcus prevotii).

Enzyme aa# E-value

L-Lactate dehydrogenase (EC 1.1.1.27) 315 0 Nickle-dependent lactate racemase (EC 5.1.2.1) 430 0 D-lactate dehydrogenase (EC 1.1.1.28) 405 0 Pyruvate formate lyase (EC 2.3.1.54) 692 0 Phosphate acetyltransferase (EC 2.3.1.8) 329 0 Acetate kinase (EC 2.7.2.1) 397 0 Acetaldehyde dehydrogenase (EC 1.2.1.10) 384 0 Alcohol dehydrogenase (EC 1.1.1.1) 317 0.13 Pyruvate phosphate dikinase (EC 2.7.9.1) 874 0 Pyruvate kinase (EC 2.7.1.40) 592 0 Phosphogluco mutase (EC 5.4.2.2) 525 0 Glucose-6-phosphate isomerase (EC 5.3.1.9) 504 0 6-phosphofructokinase (EC 2.7.1.11) 669 0 Fructose-bisphosphate aldolase (EC 4.1.2.13) 319 0 Glyceraldehyde-3-phosphate dehydrogenase (EC 1.2.1.12) 287 7x10-17 Phosphoglycerate kinase (EC 2.7.2.3) 334 0 Phosphoglycerate mutase (EC 5.4.2.12) 564 0 Enolase (EC 4.2.1.11) 397 0 Glucose-1-phosphate adenyltransferase (EC 2.7.7.27) 412 0 Glycogen synthase (EC 2.4.1.21) 510 0 1,4-α-glucan (glycogen) branching enzyme (EC 2.4.1.18) 663 0 Glycogen phosphorylase (EC 2.4.1.1) 751 0 Transketolase (EC 2.2.1.1) 111 4x10-4 Ribulose-phosphate-3-epimerase (EC 5.1.3.1) 136 1x10-8 Ribose-5-phosphate isomerase (EC 5.3.1.6) 154 7x10-7 Ribose-phosphate pyrophosphokinase (EC 2.7.6.1) 327 0

# aa: length in amino acids

131

Table 3.4. Amino acid metabolizing enzymes identified from the gene annotation analysis of contigs generated from Trial#2 (OTU LacT2_D14_1-15251, affiliated to Butyrivibrio fibrisolvens).

Enzyme aa# E-value

Glutamate synthase (NADPH) large chain (EC 1.4.1.13) 1050 0 Aspargine synthetase (EC 6.3.5.4) 631 0 Glutamine synthetase type I (EC 6.3.1.2) 481 0 Glutamate dehydrogenase (NADP+) (EC 1.4.1.4) 431 0 Glutamate dehydrogenase (EC 1.4.1.2) 450 0

# aa: length in amino acids

132

Table 3.5. Amino acid metabolizing enzymes identified from the gene annotation analysis of contigs generated from Trial#3 (OTU LT3.7-00154, affiliated to Anaerococcus prevotii).

Enzyme aa E-value

Glutamate synthase (NADPH) large chain (EC 1.4.1.13) 609 0 Aspargine synthetase (EC 6.3.5.4) 335 1x10-13 Glutamine synthetase type I (EC 6.3.1.2) 129 1x10-13 Glutamate dehydrogenase (NADP+) (EC 1.4.1.4) 75 1x10-13 Glutamate dehydrogenase (EC 1.4.1.2) 111 7.6

# aa: length in amino acids

133

Table 3.6. Transporters identified from BLASTp database from two experimental trials.

Trial Transporters aa# E-value

Trial-2 Monocarboxylate transporter 116 1.9

Ammonium transporter 408 0

Trial-3 Monocarboxylate transporter 118 1.9

Ammonium transporter 517 0

# aa: length in amino acids

134

References

Allison, N., O'Donnell, M. J., Hoey, M. E., & Fewson, C. A. (1985). Membrane-bound lactate dehydrogenases and mandelate dehydrogenases of Acinetobacter calcoaceticus.

Location and regulation of expression. Biochem J, 227(3), 753-757. doi:10.1042/bj2270753

Alteri, C. J., Himpsl, S. D., Engstrom, M. D., & Mobley, H. L. (2012). Anaerobic respiration using a complete oxidative TCA cycle drives multicellular swarming in

Proteus mirabilis. mBio, 3(6). doi:10.1128/mBio.00365-12

Altschul, S. F., Madden, T. L., Schäffer, A. A., Zhang, J., Zhang, Z., Miller, W., &

Lipman, D. J. J. N. a. r. (1997). Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. 25(17), 3389-3402.

Aziz, R. K., Bartels, D., Best, A. A., DeJongh, M., Disz, T., Edwards, R. A., . . .

Zagnitko, O. (2008). The RAST Server: rapid annotations using subsystems technology.

BMC Genomics, 9, 75. doi:10.1186/1471-2164-9-75

Bakhtiary, F., Sayevand, H. R., Remely, M., Hippe, B., Indra, A., Hosseini, H., &

Haslberger, A. G. (2018). Identification of Clostridium spp. derived from a sheep and cattle slaughterhouse by matrix-assisted laser desorption and ionization-time of flight mass spectrometry (MALDI-TOF MS) and 16S rDNA sequencing. Journal of Food

Science and Technology, 55(8), 3232-3240. doi:10.1007/s13197-018-3255-2

Bergman, E. N. (1990). Energy contributions of volatile fatty acids from the gastrointestinal tract in various species. Physiol Rev, 70(2), 567-590. doi:10.1152/physrev.1990.70.2.567

135

Bryant, M. P. (1959). Bacterial species of the rumen. Bacteriological reviews, 23(3), 125-

153.

Buckel, W., & Barker, H. A. (1974). Two pathways of glutamate fermentation by anaerobic bacteria. J Bacteriol, 117(3), 1248-1260.

Chen, L., Shen, Y., Wang, C., Ding, L., Zhao, F., Wang, M., . . . Wang, H. (2019).

Megasphaera elsdenii Lactate Degradation Pattern Shifts in Rumen Acidosis Models.

Frontiers in Microbiology, 10(162). doi:10.3389/fmicb.2019.00162

Cibis, K. G., Gneipel, A., & Konig, H. (2016). Isolation of acetic, propionic and butyric acid-forming bacteria from biogas plants. J Biotechnol, 220, 51-63. doi:10.1016/j.jbiotec.2016.01.008

Clark, D. P. (1989). The fermentation pathways of Escherichia coli. FEMS Microbiol

Rev, 5(3), 223-234. doi:10.1016/0168-6445(89)90033-8

Cotta, M. A., & Hespell, R. B. (1986). Proteolytic activity of the ruminal bacterium

Butyrivibrio fibrisolvens. Applied and environmental microbiology, 52(1), 51-58.

Creevey, C. J., Kelly, W. J., Henderson, G., & Leahy, S. C. (2014). Determining the culturability of the rumen bacterial microbiome. Microb Biotechnol, 7(5), 467-479. doi:10.1111/1751-7915.12141

136

Ezaki T., Yamamoto, N., Ninomiya, K., Suzuki, S., & Yabuuchi, E. (1983). Transfer of

Peptococcus indolicus, Peptococcus asaccharolyticus, Peptococcus prevotii, and

Peptococcus magnus to the Genus Peptostreptococcus and Proposal of

Peptostreptococcus tetradius sp. nov. International Journal of Systematic and

Evolutionary Microbiology, 33(4), 683-698. doi:https://doi.org/10.1099/00207713-33-4-

683

Galushko, A. S., & Schink, B. (2000). Oxidation of acetate through reactions of the citric acid cycle by Geobacter sulfurreducens in pure culture and in syntrophic coculture. Arch

Microbiol, 174(5), 314-321. doi:10.1007/s002030000208

Garvie, E. I. (1980). Bacterial lactate dehydrogenases. Microbiol Rev, 44(1), 106-139.

Ghali, M. B., Scott, P. T., Alhadrami, G. A., & Al Jassim, R. A. M. (2011). Identification and characterisation of the predominant lactic acid-producing and lactic acid-utilising bacteria in the foregut of the feral camel (Camelus dromedarius) in Australia.

Animal Production Science, 51(7), 597-604. doi:https://doi.org/10.1071/AN10197

Goffin, P., Lorquet, F., Kleerebezem, M., & Hols, P. (2004). Major Role of NAD-

Dependent Lactate Dehydrogenases in Aerobic Lactate Utilization in Lactobacillus plantarum during Early Stationary Phase. Journal of Bacteriology, 186(19), 6661-

6666. doi:10.1128/jb.186.19.6661-6666.2004

Hackette, S. L., Skye, G. E., Burton, C., & Segel, I. H. (1970). Characterization of an ammonium transport system in filamentous fungi with methylammonium-14C as the substrate. J Biol Chem, 245(17), 4241-4250.

137

Hao, J., Ma, C., Gao, C., Qiu, J., Wang, M., Zhang, Y., . . . Xu, P. (2007). Pseudomonas stutzeri as a novel biocatalyst for pyruvate production from DL-lactate. Biotechnol Lett,

29(1), 105-110. doi:10.1007/s10529-006-9204-6

Hernández, J., Benedito, J., Abuelo, A., & Castillo, C. (2014). Ruminal Acidosis in

Feedlot: From Aetiology to Prevention. The Scientific World Journal, 2014. doi:10.1155/2014/702572

Hernández, J., Benedito, J. L., Abuelo, A., & Castillo, C. (2014). Ruminal acidosis in feedlot: from aetiology to prevention. TheScientificWorldJournal, 2014, 702572-702572. doi:10.1155/2014/702572

Holdeman, L. V., & Moore, W. E. C. (1974a). New Genus, Coprococcus, Twelve New

Species, and Emended Descriptions of Four Previously Described Species of Bacteria from Human Feces. 24(2), 260-277. doi:https://doi.org/10.1099/00207713-24-2-260

Holdeman, L. V., & Moore, W. E. C. (1974b). New Genus, Coprococcus, Twelve New

Species, and Emended Descriptions of Four Previously Described Species of Bacteria from Human Feces. International Journal of Systematic and Evolutionary Microbiology,

24(2), 260-277. doi:https://doi.org/10.1099/00207713-24-2-260

Hungate, R. E. (1966). CHAPTER II - The Rumen Bacteria. In R. E. Hungate (Ed.), The

Rumen and its Microbes (pp. 8-90): Academic Press.

Hungate, R. E., Dougherty, R. W., Bryant, M. P., & Cello, R. M. (1952). Microbiological and physiological changes associated with acute indigestion in sheep. Cornell Vet, 42(4),

423-449.

Huws, S. A., Creevey, C. J., Oyama, L. B., Mizrahi, I., Denman, S. E., Popova, M., . . .

Morgavi, D. P. (2018). Addressing Global Ruminant Agricultural Challenges Through

138

Understanding the Rumen Microbiome: Past, Present, and Future. Frontiers in

Microbiology, 9(2161). doi:10.3389/fmicb.2018.02161

Islam, M. A., Biswas, P., Sabuj, A. A. M., Haque, Z. F., Saha, C. K., Alam, M. M., . . .

Saha, S. (2019). Microbial load in bio-slurry from different biogas plants in Bangladesh.

J Adv Vet Anim Res, 6(3), 376-383. doi:10.5455/javar.2019.f357

Jami, E., & Mizrahi, I. (2012). Composition and similarity of bovine rumen microbiota across individual animals. PLoS One, 7(3), e33306. doi:10.1371/journal.pone.0033306

Jewell, K. A., McCormick, C. A., Odt, C. L., Weimer, P. J., & Suen, G. (2015). Ruminal

Bacterial Community Composition in Dairy Cows Is Dynamic over the Course of Two

Lactations and Correlates with Feed Efficiency. Appl Environ Microbiol, 81(14), 4697-

4710. doi:10.1128/aem.00720-15

Kanehisa, M., & Goto, S. J. N. a. r. (2000). KEGG: kyoto encyclopedia of genes and genomes. 28(1), 27-30.

Khafipour, E., Krause, D. O., & Plaizier, J. C. (2009). A grain-based subacute ruminal acidosis challenge causes translocation of lipopolysaccharide and triggers inflammation.

Journal of Dairy Science, 92(3), 1060-1070. doi:https://doi.org/10.3168/jds.2008-1389

Khafipour, E., Li, S., Plaizier, J. C., & Krause, D. O. (2009). Rumen microbiome composition determined using two nutritional models of subacute ruminal acidosis.

Applied and environmental microbiology, 75(22), 7115-7124. doi:10.1128/AEM.00739-

139

Krause, K. M., & Oetzel, G. R. (2006). Understanding and preventing subacute ruminal acidosis in dairy herds: A review. Animal Feed Science and Technology, 126(3), 215-

236. doi:https://doi.org/10.1016/j.anifeedsci.2005.08.004

Labutti, K., Pukall, R., Steenblock, K., Glavina Del Rio, T., Tice, H., Copeland, A., . . .

Lapidus, A. (2009). Complete genome sequence of Anaerococcus prevotii type strain

(PC1). Stand Genomic Sci, 1(2), 159-165. doi:10.4056/sigs.24194

Lane, D. J., Pace, B., Olsen, G. J., Stahl, D. A., Sogin, M. L., & Pace, N. R. (1985).

Rapid determination of 16S ribosomal RNA sequences for phylogenetic analyses. Proc

Natl Acad Sci U S A, 82(20), 6955-6959.

Lima, F. S., Oikonomou, G., Lima, S. F., Bicalho, M. L. S., Ganda, E. K., de Oliveira

Filho, J. C., . . . Bicalho, R. C. (2015). Prepartum and Postpartum Rumen Fluid

Microbiomes: Characterization and Correlation with Production Traits in Dairy Cows.

81(4), 1327-1337. doi:10.1128/AEM.03138-14 %J Applied and Environmental

Microbiology

Lodi, T., & Ferrero, I. (1993). Isolation of the DLD gene of Saccharomyces cerevisiae encoding the mitochondrial enzyme D-lactate ferricytochrome c oxidoreductase. Mol Gen

Genet, 238(3), 315-324. doi:10.1007/bf00291989

Ma, C., Gao, C., Qiu, J., Hao, J., Liu, W., Wang, A., . . . Xu, P. (2007). Membrane-bound

L- and D-lactate dehydrogenase activities of a newly isolated Pseudomonas stutzeri strain. Appl Microbiol Biotechnol, 77(1), 91-98. doi:10.1007/s00253-007-1132-4

Ma, C. Q., Xu, P., Qiu, J. H., Zhang, Z. J., Wang, K. W., Wang, M., & Zhang, Y. N.

(2004). An enzymatic route to produce pyruvate from lactate. Appl Microbiol Biotechnol,

66(1), 34-39. doi:10.1007/s00253-004-1646-y

140

Mackenzie, D. D. S. (1967). Production and Utilization of Lactic Acid by the Ruminant.

A Review. Journal of Dairy Science, 50(11), 1772-1786. doi:https://doi.org/10.3168/jds.S0022-0302(67)87715-4

Mackie, R. I., & Gilchrist, F. M. (1979a). Changes in Lactate-Producing and Lactate-

Utilizing Bacteria in Relation to pH in the Rumen of Sheep During Stepwise Adaptation to a High-Concentrate Diet. Appl Environ Microbiol, 38(3), 422-430.

Mackie, R. I., & Gilchrist, F. M. (1979b). Changes in Lactate-Producing and Lactate-

Utilizing Bacteria in Relation to pH in the Rumen of Sheep During Stepwise Adaptation to a High-Concentrate Diet. Applied and environmental microbiology, 38(3), 422-430.

Mao, S. (2017). Impact of Subacute Ruminal Acidosis (SARA) on Ruminal Microbiome,

Lipopoly saccharide and Bioamine and Rumen Epithelial Health of Dairy Cows. Journal of Dairy & Veterinary Sciences, 1. doi:10.19080/JDVS.2017.01.555556

Marini, A. M., Vissers, S., Urrestarazu, A., & André, B. (1994). Cloning and expression of the MEP1 gene encoding an ammonium transporter in Saccharomyces cerevisiae. The

EMBO journal, 13(15), 3456-3463.

McCann, J. C., Luan, S., Cardoso, F. C., Derakhshani, H., Khafipour, E., & Loor, J. J.

(2016). Induction of Subacute Ruminal Acidosis Affects the Ruminal Microbiome and

Epithelium. Front Microbiol, 7, 701. doi:10.3389/fmicb.2016.00701

Murdoch, D. A. (1998). Gram-positive anaerobic cocci. Clin Microbiol Rev, 11(1), 81-

120.

Nagaraja, T. G., & Titgemeyer, E. C. (2007). Ruminal acidosis in beef cattle: the current microbiological and nutritional outlook. J Dairy Sci, 90 Suppl 1, E17-38. doi:10.3168/jds.2006-478

141

Neidhardt, F. C., & Curtiss, R. (1996). Escherichia Coli and Salmonella: Cellular and

Molecular Biology: ASM Press.

Ogunade, I., Pech-Cervantes, A., & Schweickart, H. (2019). Metatranscriptomic Analysis of Sub-Acute Ruminal Acidosis in Beef Cattle. Animals (Basel), 9(5). doi:10.3390/ani9050232

Opdahl, L. J., Gonda, M. G., & St-Pierre, B. (2018). Identification of Uncultured

Bacterial Species from Firmicutes, Bacteroidetes and CANDIDATUS Saccharibacteria as

Candidate Cellulose Utilizers from the Rumen of Beef Cows. Microorganisms, 6(1). doi:10.3390/microorganisms6010017

ÖRLYGSSON, J., KROONEMAN, J., COLLINS, M. D., PASCUAL, C., &

GOTTSCHAL, J. C. (1996). Clostridium acetireducens sp. nov., a Novel Amino Acid-

Oxidizing, Acetate-Reducing Anaerobic Bacterium. International Journal of Systematic and Evolutionary Microbiology, 46(2), 454-459. doi:https://doi.org/10.1099/00207713-

46-2-454

Owens, F. N., Secrist, D. S., Hill, W. J., & Gill, D. R. (1998). Acidosis in cattle: a review.

J Anim Sci, 76(1), 275-286. doi:10.2527/1998.761275x

Pajot, P., & Claisse, M. L. (1974). Utilization by yeast of D-lactate and L-lactate as sources of energy in the presence of antimycin A. Eur J Biochem, 49(1), 275-285. doi:10.1111/j.1432-1033.1974.tb03832.x

Parte, A. C. (2014). LPSN--list of prokaryotic names with standing in nomenclature.

Nucleic Acids Res, 42(Database issue), D613-616. doi:10.1093/nar/gkt1111

Pengpeng, W., & Tan, Z. (2013). Ammonia assimilation in rumen bacteria: a review.

Anim Biotechnol, 24(2), 107-128. doi:10.1080/10495398.2012.756402

142

Phibbs, P. V., Jr., & Bernlohr, R. W. (1971). Purification, properties, and regulation of glutamic dehydrogenase of Bacillus licheniformis. J Bacteriol, 106(2), 375-385.

Pitta, D. W., Kumar, S., Vecchiarelli, B., Shirley, D. J., Bittinger, K., Baker, L. D., . . .

Thomsen, N. (2014). Temporal dynamics in the ruminal microbiome of dairy cows during the transition period. J Anim Sci, 92(9), 4014-4022. doi:10.2527/jas.2014-7621

Plaizier, J. C. (2004). Replacing chopped alfalfa hay with alfalfa silage in barley grain and alfalfa-based total mixed rations for lactating dairy cows. J Dairy Sci, 87(8), 2495-

2505. doi:10.3168/jds.S0022-0302(04)73374-3

Plaizier, J. C., Li, S., Danscher, A. M., Derakshani, H., Andersen, P. H., & Khafipour, E.

(2017). Changes in Microbiota in Rumen Digesta and Feces Due to a Grain-Based

Subacute Ruminal Acidosis (SARA) Challenge. Microb Ecol, 74(2), 485-495. doi:10.1007/s00248-017-0940-z

Plaizier, J. C., Li, S., Tun, H. M., & Khafipour, E. (2016). Nutritional Models of

Experimentally-Induced Subacute Ruminal Acidosis (SARA) Differ in Their Impact on

Rumen and Hindgut Bacterial Communities in Dairy Cows. Front Microbiol, 7, 2128. doi:10.3389/fmicb.2016.02128

Pösö, A. R. (2002). Monocarboxylate transporters and lactate metabolism in equine athletes: a review. Acta veterinaria Scandinavica, 43(2), 63-74. doi:10.1186/1751-0147-

43-63

Reichardt, N., Duncan, S. H., Young, P., Belenguer, A., McWilliam Leitch, C., Scott, K.

P., . . . Louis, P. (2014). Phylogenetic distribution of three pathways for propionate production within the human gut microbiota. Isme j, 8(6), 1323-1335. doi:10.1038/ismej.2014.14

143

Rezgui, R., Maaroufi, A., Fardeau, M. L., Ben Ali Gam, Z., Cayol, J. L., Ben Hamed, S.,

& Labat, M. (2012). Anaerosalibacter bizertensis gen. nov., sp. nov., a halotolerant bacterium isolated from sludge. Int J Syst Evol Microbiol, 62(Pt 10), 2469-2474. doi:10.1099/ijs.0.036566-0

Rodríguez, R., Sosa, A., & Rodríguez, Y. (2007). Microbial protein synthesis in rumen and its importance to ruminants. Cuban Journal of Agricultural Science, 41(4), 287-294.

Russell, J. R., & Hino, T. (1985). Regulation of lactate production in Streptococcus bovis: A spiraling effect that contributes to rumen acidosis. J Dairy Sci, 68(7), 1712-

1721.

Schloss, P. D., Westcott, S. L., Ryabin, T., Hall, J. R., Hartmann, M., Hollister, E. B., . . .

Robinson, C. J. J. A. E. M. (2009). Introducing mothur: open-source, platform- independent, community-supported software for describing and comparing microbial communities. 75(23), 7537-7541.

Schwab, L., Rago, L., Koch, C., & Harnisch, F. (2019). Identification of Clostridium cochlearium as an electroactive microorganism from the mouse gut microbiome.

Bioelectrochemistry, 130, 107334. doi:https://doi.org/10.1016/j.bioelechem.2019.107334

Shabat, S. K., Sasson, G., Doron-Faigenboim, A., Durman, T., Yaacoby, S., Berg Miller,

M. E., . . . Mizrahi, I. (2016). Specific microbiome-dependent mechanisms underlie the energy harvest efficiency of ruminants. Isme j, 10(12), 2958-2972. doi:10.1038/ismej.2016.62

Simpson, J. T., Wong, K., Jackman, S. D., Schein, J. E., Jones, S. J., & Birol, I. (2009).

ABySS: a parallel assembler for short read sequence data. Genome Res, 19(6), 1117-

1123. doi:10.1101/gr.089532.108

144

St-Pierre, B., & Wright, A. J. B. M. (2015). Investigation of bacterial and methanogen community composition and diversity in full-scale anaerobic manure digesters. 1, 1-11.

Thauer, R. K. (1988). Citric-acid cycle, 50 years on. Modifications and an alternative pathway in anaerobic bacteria. Eur J Biochem, 176(3), 497-508. doi:10.1111/j.1432-

1033.1988.tb14307.x

Therion, J. J., Kistner, A., & Kornelius, J. H. (1982). Effect of pH on growth rates of rumen amylolytic and lactilytic bacteria. Applied and environmental microbiology, 44(2),

428-434.

Wang, Q., Garrity, G. M., Tiedje, J. M., & Cole, J. R. J. A. E. M. (2007). Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy.

73(16), 5261-5267.

Wang, Q., Ou, M. S., Kim, Y., Ingram, L. O., & Shanmugam, K. T. (2010). Metabolic flux control at the pyruvate node in an anaerobic Escherichia coli strain with an active pyruvate dehydrogenase. Applied and environmental microbiology, 76(7), 2107-2114. doi:10.1128/AEM.02545-09

Yu, Z., & Morrison, M. (2004). Improved extraction of PCR-quality community DNA from digesta and fecal samples. Biotechniques, 36(5), 808-812. doi:10.2144/04365st04

145

CHAPTER 4

FUTURE EXPERIMENTS

4.1 Introduction

Feedlot beef and dairy cattle are fed diets with high levels of grain to increase their production. Due to the fermentative action of specialized microbial groups that digest starch, there is a rapid accumulation of SCFAs from feeding high grain diets, causing a decrease in ruminal pH. Extended periods of low ruminal pH is a condition referred to as acidosis, and this condition can impact animal health and profitability

(Kennelly, Robinson, & Khorasani, 1999) (T. Nagaraja, Galyean, & Cole, 1998).

Therefore, a great deal of effort has been devoted to the identification of ruminal microorganisms that participate in this process (T. J. DeVries, T. Schwaiger,

Beauchemin, & PennerB, 2014).

For many years, culture-dependent approaches were the main tools available to study microorganisms involved in acidosis. While they are very effective at determining the metabolic activities of isolates, one of their main limitations is their inability to provide community-level assessments. In addition, it has been estimated that the vast majority of ruminal microorganisms still remain to be cultured as isolates. The development of sequence-based DNA analysis has provided an effective means of determining community-level assessments of microbial habitats, such as taxonomic composition, diversity, richness, and evenness. Omics-based approaches (metagenomics and metatranscriptomics) can in addition assess the functional potential and activity of microbial communities during acidosis. While these techniques have provided great insights, many microbial mechanisms involved in acidosis remain unknown.

146

4.2 Research Findings and Future Experiments

Our research resulted in the enrichment of three species-level OTUs from bovine rumen fluid cultures supplemented with starch, and seven species-level OTUs were enriched from bovine rumen fluid cultures supplemented with lactate. Interestingly, one

OTU enriched with starch (SD_Bt-01020) and one OTU enriched with lactate

(LacT1_D7_3-00877) corresponded to known starch and lactate utilizers, Ruminobacter amylophilus and Coprococcus catus, respectively. This suggests that the other OTUs enriched in their respective trials were also involved in starch or lactate metabolism.

Since 16S rRNA gene-based analysis was too limited in scope to provide accurate insights regarding the function of candidate starch or lactate-utilizing OTUs, we explored their metabolic potential using metagenomics. Our analysis on specific samples showing enrichment of OTUs in the presence of starch or lactate resulted in the identification of genes predicted to be involved in utilizing the substrate that was supplemented to treatment cultures. Additional functions, such as glycogen synthesis and mobilization, were also uncovered. While metagenomics does not reveal whether predicted genes are active, future studies using metatranscriptomics, for instance, could be conducted to determine whether mRNAs for enzymes of interest are produced, which would indicate that these genes are active. While a major limitation of metagenomics and metatranscriptomics is their dependence on comprehensive databases, i.e. they can only accurately match coding sequences to a known function if enzymes or proteins have been previously reported for this function, our search for candidate enzymes and other proteins involved in carbohydrate metabolism did not appear to be affected. (Aguiar-Pulido et al.,

2016).

147

In my opinion, the most interesting aspect about the in-vitro batch culture experimental design is the potential to expand on the project. One way would be to supplement rumen fluid cultures with starch or lactate as continuous cultures, anticipating to see higher relative abundance of candidate starch and lactate utilizers (OTUs), as monitored using 16S rRNA analysis, if a substrate is provided continuously instead of provided as a single dose. For example, I would use seven bio-reactors, with three bioreactors supplemented with starch, three bio-reactors supplemented with lactate, and one bio-reactor as a non-supplemented control. Starch or lactate substrates would be added at regular intervals to the rumen fluid cultures for the entire trial duration (14-days). As described in previous chapters of this dissertation, I would follow the same methodology to analyze collected samples. Based on the 16S rRNA gene profile, I could determine whether or not the selected candidate starch and lactate utilizers (OTUs) showed higher enrichments. If a difference in the enrichment was observed, my next approach would be to select samples with the highest enrichment for metagenomics analysis. This would identify genes that are important for the selected starch and lactate utilizers (OTUs) to thrive with either starch or lactate as substrates. Since metagenomics analysis is not able to determine whether genes are active or not, I would employ a metatranscriptomics analysis on samples with high enrichment of an OTU to determine which genes are highly expressed. Other strategies to improve levels of enrichment would be to either maintain rumen fluid cultures for longer periods in bioreactors or to perform serial passages of substrate-enriched cultures. For the latter approach, each passage would involve starting a fresh culture using 10% of an enriched culture that would be transferred to sterilized rumen fluid supplemented with the appropriate substrate for enrichment.

148

Increasing enrichment levels would result in the assembly of longer contigs from metagenomics sequence data, thus providing more accurate predictions of metabolic potential by increasing the number of available annotated genes.

Another possible line of experiments to further pursue this research could be to perform species cultivation. Since cultivation of ruminal bacteria tends to be a challenge, two strategies would be used. First, culture medium would consist of fresh autoclaved rumen fluid mixed with substrate, either starch or lactate, essentially replicating the starting conditions to the bioreactor experiments described in this dissertation. Secondly, the starting pool or source of starch or lactate utilizing bacteria would be from enriched rumen fluid cultures as described above.

4.3 Research impact

Feedlot beef and dairy cattle are fed with grain diets in order to attain greater weight gain and for increased milk production, respectively. Grain overload results in acidosis, which can have a severe impact on the profitability of the beef and dairy industries (Hernández, Benedito, Abuelo, & Castillo, 2014). Starch is the major energy source in grain diets, and its rapid fermentation results in excessive production and accumulation of lactate in the rumen. Various studies have focused on the changes in bacterial diversity, richness, and evenness rather than identifying the key bacterial species, such as amylolytic and lactate fermenting bacteria during lactic acidosis (T. G.

Nagaraja & Titgemeyer, 2007) (McCann et al., 2016) (Plaizier et al., 2017).

The research presented in this dissertation was mainly focused on identifying candidate starch and lactate metabolizing bacteria that could grow in batch cultures

149

consisting of rumen fluid supplemented with starch and lactate. Metagenomics analyses were conducted to determine the functional potential of candidate starch and lactate- enriched bacterial species. Identifying and determining the functional potential of uncharacterized bacteria may give insights into their respective function in the rumen and on how to culture isolates for these species.

One potential long-term impact of this research project may be to provide candidates for the design of probiotics. As described in this dissertation, this research resulted in the identification of only a limited number of candidate starch and lactate utilizers amongst a great deal more that still remain to be identified. However, continuation of this research could lead to the identification of more uncharacterized starch and lactate utilizers (OTUs). In combination with their reconstructed genomes, the uncharacterized species (OTUs) could lead to novel strategies for developing probiotics that are more effective in treating acidosis.

150

References

Aguiar-Pulido, V., Huang, W., Suarez-Ulloa, V., Cickovski, T., Mathee, K., &

Narasimhan, G. (2016). Metagenomics, Metatranscriptomics, and Metabolomics

Approaches for Microbiome Analysis. Evol Bioinform Online, 12(Suppl 1), 5-16. doi:10.4137/ebo.S36436

Hernández, J., Benedito, J., Abuelo, A., & Castillo, C. (2014). Ruminal Acidosis in

Feedlot: From Aetiology to Prevention. The Scientific World Journal, 2014. doi:10.1155/2014/702572

Kennelly, J. J., Robinson, B., & Khorasani, G. (1999). Influence of carbohydrate source and buffer on rumen fermentation characteristics, milk yield, and milk composition in early-lactation Holstein cows. Journal of dairy science, 82(11), 2486-2496.

McCann, J. C., Luan, S., Cardoso, F. C., Derakhshani, H., Khafipour, E., & Loor, J. J.

(2016). Induction of Subacute Ruminal Acidosis Affects the Ruminal Microbiome and

Epithelium. Frontiers in microbiology, 7, 701-701. doi:10.3389/fmicb.2016.00701

Nagaraja, T., Galyean, M. L., & Cole, N. A. (1998). Nutrition and disease. Veterinary

Clinics of North America: Food Animal Practice, 14(2), 257-277.

Nagaraja, T. G., & Titgemeyer, E. C. (2007). Ruminal acidosis in beef cattle: the current microbiological and nutritional outlook. J Dairy Sci, 90 Suppl 1, E17-38. doi:10.3168/jds.2006-478

Plaizier, J. C., Li, S., Danscher, A. M., Derakshani, H., Andersen, P. H., & Khafipour, E.

(2017). Changes in Microbiota in Rumen Digesta and Feces Due to a Grain-Based

Subacute Ruminal Acidosis (SARA) Challenge. Microb Ecol, 74(2), 485-495. doi:10.1007/s00248-017-0940-z

151

T. J. DeVries, T. Schwaiger, Beauchemin, K. A., & PennerB, G. B. (2014). Impact of severity of ruminal acidosis on feed-sortingbehaviour of beef cattle

Animal Production Science, 54, 1238–1242.