Identification of intestinal and fecal microbial biomarkers using a porcine social stress model.

Understanding the relationships between social stress and the gastrointestinal microbiota, and how they influence host health and performance is expected to have many scientific and commercial implementations in different species, including identification and improvement of challenges to animal welfare and health. In particular, the study of the stress impact on the gastrointestinal microbiota of pigs may be of interest as a model for human health. A porcine stress model based on repeated regrouping and reduced space allowance during the last 4 weeks of the finishing period was developed to identify stress-induced changes in the gut microbiome composition. The application of the porcine stress model resulted in a significant increase in salivary cortisol concentration over the course of the trial and decreased growth performance and appetite. The applied social stress resulted in 32 bacteria being either enriched (13) or depleted (19) in the intestine and feces. Fecal samples showed a greater number of microbial genera influenced by stress than caecum or colon samples. Our trial revealed that the opportunistic pathogens Treponema and Clostridium were enriched in colonic and fecal samples from stressed pigs. Additionally, genera such as Streptococcus, Parabacteroides, Desulfovibrio, Terrisporobacter, Marvinbryantia, and Romboutsia were found to be enriched in response to social stress. In contrast, the genera Prevotella, Faecalibacterium, Butyricicoccus, Dialister, Alloprevotella, Megasphaera, and Mitsuokella were depleted. These depleted bacteria are of great interest because they synthesize metabolites [e.g., short-chain fatty acids (SCFA), in particular, butyrate] showing beneficial health benefits due to inhibitory effects on pathogenic bacteria in different animal species. Of particular interest are Dialister and Faecalibacterium, as their depletion was identified in a human study to be associated with inferior quality of life and depression. We also revealed that some pigs were more susceptible to pathogens as indicated by large enrichments of opportunistic pathogens of Clostridium, Treponema, Streptococcus and Campylobacter. Generally, our results provide further evidence for the microbiota-gut-brain axis as indicated by an increase in cortisol concentration due to social stress regulated by the hypothalamic-pituitary-adrenal axis, and a change in microbiota composition, particularly of bacteria known to be associated with pathogenicity and mental health diseases.

Enteric Methane Emissions from Dairy-Beef Steers Supplemented with the Essential Oil Blend Agolin Ruminant.

Agriculture is the largest source of methane globally, and enteric methane accounts for 32% of methane emissions globally. Dairy-beef is an increasingly important contributor to the beef industry. The objective of this study was to investigate if supplementation with a blend of essential oils (Agolin Ruminant) reduced enteric methane emissions from dairy-bred steers. Methane was measured from thirty-six Holstein Friesian steers (18 control and 18 treatment) in open-circuit respiration chambers, at three time-points relative to the introduction of Agolin Ruminant: (i) -3 (pre-additive introduction co-variate), (ii) 46 days after introduction, and (iii) 116 days after introduction. A significantly lower methane yield was observed in treated animals compared to control animals at both 46 days (p < 0.05) and 116 days (p < 0.01) after the introduction of Agolin Ruminant, although there was no difference in methane production (g/day). Control animals appeared to be more affected by isolation in respiration chambers than animals receiving Agolin Ruminant, as indicated by a significant reduction in dry matter intake by control animals in respiration chambers.

Microbiome-driven breeding strategy potentially improves beef fatty acid profile benefiting human health and reduces methane emissions.

BACKGROUND: Healthier ruminant products can be achieved by adequate manipulation of the rumen microbiota to increase the flux of beneficial fatty acids reaching host tissues. Genomic selection to modify the microbiome function provides a permanent and accumulative solution, which may have also favourable consequences in other traits of interest (e.g. methane emissions). Possibly due to a lack of data, this strategy has never been explored. RESULTS: This study provides a comprehensive identification of ruminal microbial mechanisms under host genomic influence that directly or indirectly affect the content of unsaturated fatty acids in beef associated with human dietary health benefits C18:3n-3, C20:5n-3, C22:5n-3, C22:6n-3 or cis-9, trans-11 C18:2 and trans-11 C18:1 in relation to hypercholesterolemic saturated fatty acids C12:0, C14:0 and C16:0, referred to as N3 and CLA indices. We first identified that ~27.6% (1002/3633) of the functional core additive log-ratio transformed microbial gene abundances (alr-MG) in the rumen were at least moderately host-genomically influenced (HGFC). Of these, 372 alr-MG were host-genomically correlated with the N3 index (n=290), CLA index (n=66) or with both (n=16), indicating that the HGFC influence on beef fatty acid composition is much more complex than the direct regulation of microbial lipolysis and biohydrogenation of dietary lipids and that N3 index variation is more strongly subjected to variations in the HGFC than CLA. Of these 372 alr-MG, 110 were correlated with the N3 and/or CLA index in the same direction, suggesting the opportunity for enhancement of both indices simultaneously through a microbiome-driven breeding strategy. These microbial genes were involved in microbial protein synthesis (aroF and serA), carbohydrate metabolism and transport (galT, msmX), lipopolysaccharide biosynthesis (kdsA, lpxD, lpxB), or flagellar synthesis (flgB, fliN) in certain genera within the Proteobacteria phyla (e.g. Serratia, Aeromonas). A microbiome-driven breeding strategy based on these microbial mechanisms as sole information criteria resulted in a positive selection response for both indices (1.36±0.24 and 0.79±0.21 sd of N3 and CLA indices, at 2.06 selection intensity). When evaluating the impact of our microbiome-driven breeding strategy to increase N3 and CLA indices on the environmental trait methane emissions (g/kg of dry matter intake), we obtained a correlated mitigation response of -0.41±0.12 sd. CONCLUSION: This research provides insight on the possibility of using the ruminal functional microbiome as information for host genomic selection, which could simultaneously improve several microbiome-driven traits of interest, in this study exemplified with meat quality traits and methane emissions. Video Abstract.

Measurement Duration but Not Distance, Angle, and Neighbour-Proximity Affects Precision in Enteric Methane Emissions when Using the Laser Methane Detector Technique in Lactating Dairy Cows.

The laser methane detector (LMD), is a proprietary hand-held open path laser measuring device. Its measurements are based on infrared absorption spectroscopy using a semiconductor laser as a collimated excitation source. In the current study, LMD measurements were carried out in two experiments using 20 and 71 lactating dairy cows in Spain and Scotland, respectively. The study aimed at testing four assumptions that may impact on the reliability and repeatability of the LMD measurements of ruminants. The study has verified that there is no difference in enteric methane measurements taken from a distance of 3 m than from those taken at a distance of 2 m; there was no effect to the measurements when the measurement angle was adjusted from 90° to 45°; that the presence of an adjacent animal had no effect on the methane measurements; and that measurements lasting up to 240 s are more precise than those taken for a shorter duration. The results indicate that angle, proximity to other animals, and distance had no effects and that measurements need to last a minimum of 240 s to maintain precision.

Bovine host genome acts on rumen microbiome function linked to methane emissions.

Our study provides substantial evidence that the host genome affects the comprehensive function of the microbiome in the rumen of bovines. Of 1,107/225/1,141 rumen microbial genera/metagenome assembled uncultured genomes (RUGs)/genes identified from whole metagenomics sequencing, 194/14/337 had significant host genomic effects (heritabilities ranging from 0.13 to 0.61), revealing that substantial variation of the microbiome is under host genomic control. We found 29/22/115 microbial genera/RUGs/genes host-genomically correlated (|0.59| to |0.93|) with emissions of the potent greenhouse gas methane (CH4), highlighting the strength of a common host genomic control of specific microbial processes and CH4. Only one of these microbial genes was directly involved in methanogenesis (cofG), whereas others were involved in providing substrates for archaea (e.g. bcd and pccB), important microbial interspecies communication mechanisms (ABC.PE.P), host-microbiome interaction (TSTA3) and genetic information processes (RP-L35). In our population, selection based on abundances of the 30 most informative microbial genes provided a mitigation potential of 17% of mean CH4 emissions per generation, which is higher than for selection based on measured CH4 using respiration chambers (13%), indicating the high potential of microbiome-driven breeding to cumulatively reduce CH4 emissions and mitigate climate change.

Valorization of dairy waste and by-products through microbial bioprocesses.

Waste is an inherent and unavoidable part of any process which can be attributed to various factors such as process inefficiencies, usability of resources and discarding of not so useful parts of the feedstock. Dairy is a burgeoning industry following the global population growth, resulting in generation of waste such as wastewater (from cleaning, processing, and maintenance), whey and sludge. These components are rich in nutrients, organic and inorganic materials. Additionally, the presence of alkaline and acidic detergents along with sterilizing agents in dairy waste makes it an environmental hazard. Thus, sustainable valorization of dairy waste requires utilization of biological methods such as microbial treatment. This review brings forward the current developments in utilization and valorization of dairy waste through microbes. Aerobic and anaerobic treatment of dairy waste using microbes can be a sustainable and green method to generate biofertilizers, biofuels, power, and other biobased products.

Effect of ammonia concentration on rumen microbial protein production in vitro.

We review key findings of one the most cited papers in the 75-year history of BJN. We then identify important consequent developments, as well as opportunities to use analytical and molecular biology advances to maximise conversion of non-protein nitrogen into microbial protein.

Comparison of HPLC and NMR for quantification of the main volatile fatty acids in rumen digesta.

Accurate quantification of volatile fatty acid (VFA) concentrations in rumen fluid are essential for research on rumen metabolism. The study comprehensively investigated the pros and cons of High-performance liquid chromatography (HPLC) and 1H Nuclear magnetic resonance (1H-NMR) analysis methods for rumen VFAs quantification. We also investigated the performance of several commonly used data pre-treatments for the two sets of data using correlation analysis, principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA). The molar proportion and reliability analysis demonstrated that the two approaches produce highly consistent VFA concentrations. In the pre-processing of NMR spectra, line broadening and shim correction may reduce estimated concentrations of metabolites. We observed differences in results using multiplet of different protons from one compound and identified "handle signals" that provided the most consistent concentrations. Different data pre-treatment strategies tested with both HPLC and NMR significantly affected the results of downstream data analysis. "Normalized by sum" pre-treatment can eliminate a large number of positive correlations between NMR-based VFA. A "Combine" strategy should be the first choice when calculating the correlation between metabolites or between samples. The PCA and PLS-DA suggest that except for "Normalize by sum", pre-treatments should be used with caution.

Breeding strategies for improving smallholder dairy cattle productivity in Sub-Saharan Africa.

Breeding strategies for smallholder dairy farming systems in Sub-Saharan Africa (SSA) were simulated and evaluated considering cow traits identified as priorities by farmers in different agro-ecological zones. These traits were related to cow milk yield, fertility, temperament, feed intake and disease resistance. The first breeding strategy was based on continuous importation of genetically superior exotic dairy sire semen to SSA and crossing with local females leading to a gradual upgrade of the indigenous population. The second strategy assumed that semen from elite exotic bulls would be imported to SSA and used on indigenous cows to produce F1 animals. Thereafter, elite animals would be selected from within the F1 and each subsequent generation to establish a new synthetic breed. The third strategy was to improve the indigenous population by genetically selecting the best sires available domestically. Results showed positive genetic progress for all breeding goal traits. After 15 generations of selection, the genetic response of the importation strategy exceeded the corresponding genetic response of the synthetic breed strategy by 20%-60%. The former also exceeded the genetic response of the indigenous breed improvement strategy by 43%-75%. Potentially there is an opportunity for breeders to choose an appropriate breeding strategy that fits a specific need of smallholder dairy farmers.

A heat diffusion multilayer network approach for the identification of functional biomarkers in rumen methane emissions.

A better understanding of rumen microbial interactions is crucial for the study of rumen metabolism and methane emissions. Metagenomics-based methods can explore the relationship between microbial genes and metabolites to clarify the effect of microbial function on the host phenotype. This study investigated the rumen microbial mechanisms of methane metabolism in cattle by combining metagenomic data and network-based methods. Based on the relative abundance of 1461 rumen microbial genes and the main volatile fatty acids (VFAs), a multilayer heterogeneous network was constructed, and the functional modules associated with metabolite-microbial genes were obtained by heat diffusion algorithm. The PLS model by integrating data from VFAs and microbial genes explained 72.98% variation of methane emissions. Compared with single-layer networks, more previously reported biomarkers of methane prediction can be captured by the multilayer network. More biomarkers with the rank of top 20 topological centralities were from the PLS models of diffusion subsets. The heat diffusion algorithm is different from the strategy used by the microbial metabolic system to understand methane phenotype. It inferred 24 novel biomarkers that were preferentially affected by changes in specific VFAs. Results showed that the heat diffusion multilayer network approach improved the understanding of the microbial patterns of VFAs affecting methane emissions which represented by the functional microbial genes.

Identification of Microbial Genetic Capacities and Potential Mechanisms Within the Rumen Microbiome Explaining Differences in Beef Cattle Feed Efficiency.

In this study, Bos Taurus cattle offered one high concentrate diet (92% concentrate-8% straw) during two independent trials allowed us to classify 72 animals comprising of two cattle breeds as "Low" or "High" feed efficiency groups. Digesta samples were taken from individual beef cattle at the abattoir. After metagenomic sequencing, the rumen microbiome composition and genes were determined. Applying a targeted approach based on current biological evidence, 27 genes associated with host-microbiome interaction activities were selected. Partial least square analysis enabled the identification of the most significant genes and genera of feed efficiency (VIP > 0.8) across years of the trial and breeds when comparing all potential genes or genera together. As a result, limited number of genes explained about 40% of the variability in both feed efficiency indicators. Combining information from rumen metagenome-assembled genomes and partial least square analysis results, microbial genera carrying these genes were determined and indicated that a limited number of important genera impacting on feed efficiency. In addition, potential mechanisms explaining significant difference between Low and High feed efficiency animals were analyzed considering, based on the literature, their gastrointestinal location of action. High feed efficiency animals were associated with microbial species including several Eubacterium having the genetic capacity to form biofilm or releasing metabolites like butyrate or propionate known to provide a greater contribution to cattle energy requirements compared to acetate. Populations associated with fucose sensing or hemolysin production, both mechanisms specifically described in the lower gut by activating the immune system to compete with pathogenic colonizers, were also identified to affect feed efficiency using rumen microbiome information. Microbial mechanisms associated with low feed efficiency animals involved potential pathogens within Proteobacteria and Spirochaetales, releasing less energetic substrates (e.g., acetate) or producing sialic acid to avoid the host immune system. Therefore, this study focusing on genes known to be involved in host-microbiome interaction improved the identification of rumen microbial genetic capacities and potential mechanisms significantly impacting on feed efficiency in beef cattle fed high concentrate diet.

Identification of Complex Rumen Microbiome Interaction Within Diverse Functional Niches as Mechanisms Affecting the Variation of Methane Emissions in Bovine.

A network analysis including relative abundances of all ruminal microbial genera (archaea, bacteria, fungi, and protists) and their genes was performed to improve our understanding of how the interactions within the ruminal microbiome affects methane emissions (CH4). Metagenomics and CH4 data were available from 63 bovines of a two-breed rotational cross, offered two basal diets. Co-abundance network analysis revealed 10 clusters of functional niches. The most abundant hydrogenotrophic Methanobacteriales with key microbial genes involved in methanogenesis occupied a different functional niche (i.e., "methanogenesis" cluster) than methylotrophic Methanomassiliicoccales (Candidatus Methanomethylophylus) and acetogens (Blautia). Fungi and protists clustered together and other plant fiber degraders like Fibrobacter occupied a seperate cluster. A Partial Least Squares analysis approach to predict CH4 variation in each cluster showed the methanogenesis cluster had the best prediction ability (57.3%). However, the most important explanatory variables in this cluster were genes involved in complex carbohydrate degradation, metabolism of sugars and amino acids and Candidatus Azobacteroides carrying nitrogen fixation genes, but not methanogenic archaea and their genes. The cluster containing Fibrobacter, isolated from other microorganisms, was positively associated with CH4 and explained 49.8% of its variability, showing fermentative advantages compared to other bacteria and fungi in providing substrates (e.g., formate) for methanogenesis. In other clusters, genes with enhancing effect on CH4 were related to lactate and butyrate (Butyrivibrio and Pseudobutyrivibrio) production and simple amino acids metabolism. In comparison, ruminal genes negatively related to CH4 were involved in carbohydrate degradation via lactate and succinate and synthesis of more complex amino acids by γ-Proteobacteria. When analyzing low- and high-methane emitters data in separate networks, competition between methanogens in the methanogenesis cluster was uncovered by a broader diversity of methanogens involved in the three methanogenesis pathways and larger interactions within and between communities in low compared to high emitters. Generally, our results suggest that differences in CH4 are mainly explained by other microbial communities and their activities rather than being only methanogens-driven. Our study provides insight into the interactions of the rumen microbial communities and their genes by uncovering functional niches affecting CH4, which will benefit the development of efficient CH4 mitigation strategies.

Links between the rumen microbiota, methane emissions and feed efficiency of finishing steers offered dietary lipid and nitrate supplementation.

Ruminant methane production is a significant energy loss to the animal and major contributor to global greenhouse gas emissions. However, it also seems necessary for effective rumen function, so studies of anti-methanogenic treatments must also consider implications for feed efficiency. Between-animal variation in feed efficiency represents an alternative approach to reducing overall methane emissions intensity. Here we assess the effects of dietary additives designed to reduce methane emissions on the rumen microbiota, and explore relationships with feed efficiency within dietary treatment groups. Seventy-nine finishing steers were offered one of four diets (a forage/concentrate mixture supplemented with nitrate (NIT), lipid (MDDG) or a combination (COMB) compared to the control (CTL)). Rumen fluid samples were collected at the end of a 56 d feed efficiency measurement period. DNA was extracted, multiplexed 16s rRNA libraries sequenced (Illumina MiSeq) and taxonomic profiles were generated. The effect of dietary treatments and feed efficiency (within treatment groups) was conducted both overall (using non-metric multidimensional scaling (NMDS) and diversity indexes) and for individual taxa. Diet affected overall microbial populations but no overall difference in beta-diversity was observed. The relative abundance of Methanobacteriales (Methanobrevibacter and Methanosphaera) increased in MDDG relative to CTL, whilst VadinCA11 (Methanomassiliicoccales) was decreased. Trimethylamine precursors from rapeseed meal (only present in CTL) probably explain the differences in relative abundance of Methanomassiliicoccales. There were no differences in Shannon indexes between nominal low or high feed efficiency groups (expressed as feed conversion ratio or residual feed intake) within treatment groups. Relationships between the relative abundance of individual taxa and feed efficiency measures were observed, but were not consistent across dietary treatments.

Unravelling the Role of Rumen Microbial Communities, Genes, and Activities on Milk Fatty Acid Profile Using a Combination of Omics Approaches.

Milk products are an important component of human diets, with beneficial effects for human health, but also one of the major sources of nutritionally undesirable saturated fatty acids (SFA). Recent discoveries showing the importance of the rumen microbiome on dairy cattle health, metabolism and performance highlight that milk composition, and potentially milk SFA content, may also be associated with microorganisms, their genes and their activities. Understanding these mechanisms can be used for the development of cost-effective strategies for the production of milk with less SFA. This work aimed to compare the rumen microbiome between cows producing milk with contrasting FA profile and identify potentially responsible metabolic-related microbial mechanisms. Forty eight Holstein dairy cows were fed the same total mixed ration under the same housing conditions. Milk and rumen fluid samples were collected from all cows for the analysis of fatty acid profiles (by gas chromatography), the abundances of rumen microbiome communities and genes (by whole-genome-shotgun metagenomics), and rumen metabolome (using 500 MHz nuclear magnetic resonance). The following groups: (i) 24 High-SFA (66.9-74.4% total FA) vs. 24 Low-SFA (60.2-66.6%% total FA) cows, and (ii) 8 extreme High-SFA (69.9-74.4% total FA) vs. 8 extreme Low-SFA (60.2-64.0% total FA) were compared. Rumen of cows producing milk with more SFA were characterized by higher abundances of the lactic acid bacteria Lactobacillus, Leuconostoc, and Weissella, the acetogenic Proteobacteria Acetobacter and Kozakia, Mycobacterium, two fungi (Cutaneotrichosporon and Cyphellophora), and at a lesser extent Methanobrevibacter and the protist Nannochloropsis. Cows carrying genes correlated with milk FA also had higher concentrations of butyrate, propionate and tyrosine and lower concentrations of xanthine and hypoxanthine in the rumen. Abundances of rumen microbial genes were able to explain between 76 and 94% on the variation of the most abundant milk FA. Metagenomics and metabolomics analyses highlighted that cows producing milk with contrasting FA profile under the same diet, also differ in their rumen metabolic activities in relation to adaptation to reduced rumen pH, carbohydrate fermentation, and protein synthesis and metabolism.

Improving the Inference of Co-Occurrence Networks in the Bovine Rumen Microbiome.

The importance of the composition and signature of rumen microbial communities has gained increasing attention. One of the key techniques was to infer co-abundance networks through correlation analysis based on relative abundances. While substantial insights and progress have been made, it has been found that due to the compositional nature of data, correlation analysis derived from relative abundance could produce misleading results and spurious associations. In this study, we proposed the use of a framework including a compendium of two correlation measures and three dissimilarity metrics in an attempt to mitigate the compositional effect in the inference of significant associations in the bovine rumen microbiome. We tested the framework on rumen microbiome data including both 16S rRNA and KEGG genes associated with methane production in cattle. Based on the identification of significant positive and negative associations supported by multiple metrics, two co-occurrence networks, e.g., co-presence and mutual-exclusion networks, were constructed. Significant modules associated with methane emissions were identified. In comparison to previous studies, our analysis demonstrates that deriving microbial associations based on the correlations between relative abundances may not only lead to missing information but also produce spurious associations. To bridge together different co-presence and mutual-exclusion relations, a multiplex network model has been proposed for integrative analysis of co-occurrence networks which has great potential to support the prediction of animal phytotypes and to provide additional insights into biological mechanisms of the microbiome associated with the traits.

Correction to: The rumen microbiome as a reservoir of antimicrobial resistance and pathogenicity genes is directly affected by diet in beef cattle.

Following publication of the original article [1], the authors reported an error in the Additional file 1.

Identification of Rumen Microbial Genes Involved in Pathways Linked to Appetite, Growth, and Feed Conversion Efficiency in Cattle.

The rumen microbiome is essential for the biological processes involved in the conversion of feed into nutrients that can be utilized by the host animal. In the present research, the influence of the rumen microbiome on feed conversion efficiency, growth rate, and appetite of beef cattle was investigated using metagenomic data. Our aim was to explore the associations between microbial genes and functional pathways, to shed light on the influence of bacterial enzyme expression on host phenotypes. Two groups of cattle were selected on the basis of their high and low feed conversion ratio. Microbial DNA was extracted from rumen samples, and the relative abundances of microbial genes were determined via shotgun metagenomic sequencing. Using partial least squares analyses, we identified sets of 20, 14, 17, and 18 microbial genes whose relative abundances explained 63, 65, 66, and 73% of the variation of feed conversion efficiency, average daily weight gain, residual feed intake, and daily feed intake, respectively. The microbial genes associated with each of these traits were mostly different, but highly correlated traits such as feed conversion ratio and growth rate showed some overlapping genes. Consistent with this result, distinct clusters of a coabundance network were enriched with microbial genes identified to be related with feed conversion ratio and growth rate or daily feed intake and residual feed intake. Microbial genes encoding for proteins related to cell wall biosynthesis, hemicellulose, and cellulose degradation and host-microbiome crosstalk (e.g., aguA, ptb, K01188, and murD) were associated with feed conversion ratio and/or average daily gain. Genes related to vitamin B12 biosynthesis, environmental information processing, and bacterial mobility (e.g., cobD, tolC, and fliN) were associated with residual feed intake and/or daily feed intake. This research highlights the association of the microbiome with feed conversion processes, influencing growth rate and appetite, and it emphasizes the opportunity to use relative abundances of microbial genes in the prediction of these performance traits, with potential implementation in animal breeding programs and dietary interventions.

Temporal stability of the rumen microbiota in beef cattle, and response to diet and supplements.

BACKGROUND: Dietary intake is known to be a driver of microbial community dynamics in ruminants. Beef cattle go through a finishing phase that typically includes very high concentrate ratios in their feed, with consequent effects on rumen metabolism including methane production. This longitudinal study was designed to measure dynamics of the rumen microbial community in response to the introduction of high concentrate diets fed to beef cattle during the finishing period. A cohort of 50 beef steers were fed either of two basal diet formulations consisting of approximately 10:90 or 50:50 forage:concentrate ratios respectively. Nitrate and oil rich supplements were also added either individually or in combination. Digesta samples were taken at time points over ~ 200 days during the finishing period of the cattle to measure the adaptation to the basal diet and long-term stability of the rumen microbiota. RESULTS: 16S rRNA gene amplicon libraries were prepared from 313 rumen digesta samples and analysed at a depth of 20,000 sequences per library. Bray Curtis dissimilarity with analysis of molecular variance (AMOVA) revealed highly significant (p < 0.001) differences in microbiota composition between cattle fed different basal diets, largely driven by reduction of fibre degrading microbial groups and increased relative abundance of an unclassified Gammaproteobacteria OTU in the high concentrate fed animals. Conversely, the forage-based diet was significantly associated with methanogenic archaea. Within basal diet groups, addition of the nitrate and combined supplements had lesser, although still significant, impacts on microbiota dissimilarity compared to pre-treatment time points and controls. Measurements of the response and stability of the microbial community over the time course of the experiment showed continuing adaptation up to 25 days in the high concentrate groups. After this time point, however, no significant variability was detected. CONCLUSIONS: High concentrate diets that are typically fed to finishing beef cattle can have a significant effect on the microbial community in the rumen. Inferred metabolic activity of the different microbial communities associated with each of the respective basal diets explained differences in methane and short chain fatty acid production between cattle. Longitudinal sampling revealed that once adapted to a change in diet, the rumen microbial community remains in a relatively stable alternate state.

Addressing Global Ruminant Agricultural Challenges Through Understanding the Rumen Microbiome: Past, Present, and Future.

The rumen is a complex ecosystem composed of anaerobic bacteria, protozoa, fungi, methanogenic archaea and phages. These microbes interact closely to breakdown plant material that cannot be digested by humans, whilst providing metabolic energy to the host and, in the case of archaea, producing methane. Consequently, ruminants produce meat and milk, which are rich in high-quality protein, vitamins and minerals, and therefore contribute to food security. As the world population is predicted to reach approximately 9.7 billion by 2050, an increase in ruminant production to satisfy global protein demand is necessary, despite limited land availability, and whilst ensuring environmental impact is minimized. Although challenging, these goals can be met, but depend on our understanding of the rumen microbiome. Attempts to manipulate the rumen microbiome to benefit global agricultural challenges have been ongoing for decades with limited success, mostly due to the lack of a detailed understanding of this microbiome and our limited ability to culture most of these microbes outside the rumen. The potential to manipulate the rumen microbiome and meet global livestock challenges through animal breeding and introduction of dietary interventions during early life have recently emerged as promising new technologies. Our inability to phenotype ruminants in a high-throughput manner has also hampered progress, although the recent increase in "omic" data may allow further development of mathematical models and rumen microbial gene biomarkers as proxies. Advances in computational tools, high-throughput sequencing technologies and cultivation-independent "omics" approaches continue to revolutionize our understanding of the rumen microbiome. This will ultimately provide the knowledge framework needed to solve current and future ruminant livestock challenges.