Including microbiome information in a multi-trait genomic evaluation: a case study on longitudinal growth performance in beef cattle.

BACKGROUND: Growth rate is an important component of feed conversion efficiency in cattle and varies across the different stages of the finishing period. The metabolic effect of the rumen microbiome is essential for cattle growth, and investigating the genomic and microbial factors that underlie this temporal variation can help maximize feed conversion efficiency at each growth stage. RESULTS: By analysing longitudinal body weights during the finishing period and genomic and metagenomic data from 359 beef cattle, our study demonstrates that the influence of the host genome on the functional rumen microbiome contributes to the temporal variation in average daily gain (ADG) in different months (ADG1, ADG2, ADG3, ADG4). Five hundred and thirty-three additive log-ratio transformed microbial genes (alr-MG) had non-zero genomic correlations (rg) with at least one ADG-trait (ranging from |0.21| to |0.42|). Only a few alr-MG correlated with more than one ADG-trait, which suggests that a differential host-microbiome determinism underlies ADG at different stages. These alr-MG were involved in ribosomal biosynthesis, energy processes, sulphur and aminoacid metabolism and transport, or lipopolysaccharide signalling, among others. We selected two alternative subsets of 32 alr-MG that had a non-uniform or a uniform rg sign with all the ADG-traits, regardless of the rg magnitude, and used them to develop a microbiome-driven breeding strategy based on alr-MG only, or combined with ADG-traits, which was aimed at shaping the rumen microbiome towards increased ADG at all finishing stages. Combining alr-MG information with ADG records increased prediction accuracy of genomic estimated breeding values (GEBV) by 11 to 22% relative to the direct breeding strategy (using ADG-traits only), whereas using microbiome information, only, achieved lower accuracies (from 7 to 41%). Predicted selection responses varied consistently with accuracies. Restricting alr-MG based on their rg sign (uniform subset) did not yield a gain in the predicted response compared to the non-uniform subset, which is explained by the absence of alr-MG showing non-zero rg at least with more than one of the ADG-traits. CONCLUSIONS: Our work sheds light on the role of the microbial metabolism in the growth trajectory of beef cattle at the genomic level and provides insights into the potential benefits of using microbiome information in future genomic breeding programs to accurately estimate GEBV and increase ADG at each finishing stage in beef cattle.

Estimating Microbial Protein Synthesis in the Rumen-Can 'Omics' Methods Provide New Insights into a Long-Standing Question?

Rumen microbial protein synthesis (MPS) provides at least half of the amino acids for the synthesis of milk and meat protein in ruminants. As such, it is fundamental to global food protein security. Estimating microbial protein is central to diet formulation, maximising nitrogen (N)-use efficiency and reducing N losses to the environment. Whilst factors influencing MPS are well established in vitro, techniques for in vivo estimates, including older techniques with cannulated animals and the more recent technique based on urinary purine derivative (UPD) excretion, are subject to large experimental errors. Consequently, models of MPS used in protein rationing are imprecise, resulting in wasted feed protein and unnecessary N losses to the environment. Newer 'omics' techniques are used to characterise microbial communities, their genes and resultant proteins and metabolites. An analysis of microbial communities and genes has recently been used successfully to model complex rumen-related traits, including feed conversion efficiency and methane emissions. Since microbial proteins are more directly related to microbial genes, we expect a strong relationship between rumen metataxonomics/metagenomics and MPS. The main aims of this review are to gauge the understanding of factors affecting MPS, including the use of the UPD technique, and explore whether omics-focused studies could improve the predictability of MPS, with a focus on beef cattle.

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.

KOunt: a reproducible KEGG orthologue abundance workflow.

SUMMARY: Accurate gene prediction is essential for successful metagenome analysis. We present KOunt, a Snakemake pipeline, that precisely quantifies KEGG orthologue abundance. AVAILABILITY AND IMPLEMENTATION: KOunt is available on GitHub: https://github.com/WatsonLab/KOunt. The KOunt reference database is available on figshare: https://doi.org/10.6084/m9.figshare.21269715. Test data are available at https://doi.org/10.6084/m9.figshare.22250152 and version 1.2.0 of KOunt at https://doi.org/10.6084/m9.figshare.23607834.

Different microbial genera drive methane emissions in beef cattle fed with two extreme diets.

The ratio of forage to concentrate in cattle feeding has a major influence on the composition of the microbiota in the rumen and on the mass of methane produced. Using methane measurements and microbiota data from 26 cattle we aimed to investigate the relationships between microbial relative abundances and methane emissions, and identify potential biomarkers, in animals fed two extreme diets - a poor quality fresh cut grass diet (GRASS) or a high concentrate total mixed ration (TMR). Direct comparisons of the effects of such extreme diets on the composition of rumen microbiota have rarely been studied. Data were analyzed considering their multivariate and compositional nature. Diet had a relevant effect on methane yield of +10.6 g of methane/kg of dry matter intake for GRASS with respect to TMR, and on the centered log-ratio transformed abundance of 22 microbial genera. When predicting methane yield based on the abundance of 28 and 25 selected microbial genera in GRASS and TMR, respectively, we achieved cross-validation prediction accuracies of 66.5 ± 9% and 85 ± 8%. Only the abundance of Fibrobacter had a consistent negative association with methane yield in both diets, whereas most microbial genera were associated with methane yield in only one of the two diets. This study highlights the stark contrast in the microbiota controlling methane yield between animals fed a high concentrate diet, such as that found on intensive finishing units, and a low-quality grass forage that is often found in extensive grazing systems. This contrast must be taken into consideration when developing strategies to reduce methane emissions by manipulation of the rumen microbial composition.

Genetic associations between human-directed behavior and intraspecific social aggression in growing pigs.

This study estimated the genetic parameters for human-directed behavior and intraspecific social aggression traits in growing pigs, and explored the phenotypic correlations among them. Data on 2,413 growing pigs were available. Pigs were mixed into new social groups of 18 animals, at 69 ± 5.2 d of age and skin lesions (SL) were counted 24 h (SL24h) post-mixing. Individual behavioral responses to isolation in a weighing crate (CRATE) or when alone in an arena while a human directly approached them (IHAT) were assessed within 48 h post-mixing. Additionally, pigs were tested for behavioral responses to the presence of a single human observer walking in their home pen in a circular motion (WTP) within one (T1) and 4 wk post-mixing (T2) noting pigs that followed, nosed or bit the observer. Animal models were used to estimate genetic and phenotypic parameters for all studied traits. Heritabilities (h2) for SL, CRATE and IHAT responses were low to moderate (0.07 to 0.29), with the highest h2 estimated for speed of moving away from the approaching observer. Low but significant h2 were estimated for nosing (0.09) and biting (0.11) the observer at T2. Positive high genetic correlations (rg) were observed between CRATE and IHAT responses (0.52 to 0.93), and within SL traits (0.79 to 0.91) while positive low to high correlations between the estimated breeding values (rEBV) were estimated within the WTP test (0.24 to 0.59) traits. Positive moderate rg were observed between CRATE and central and posterior SL24h. The rEBV of CRATE and IHAT test responses and WTP test traits were low, mostly negative (-0.21 to 0.05) and not significant. Low positive rEBV (0.06 to 0.24) were observed between SL and the WTP test traits. Phenotypic correlations between CRATE and IHAT responses and SL or WTP test traits were mostly low and not significant. Under the conditions of this study, h2 estimates for all studied traits suggest they could be suitable as a method of phenotyping aggression and fear/boldness for genetic selection purposes. Additionally, genetic correlations between aggression and fear indicators were observed. These findings suggest selection to reduce the accumulation of lesions is likely to make pigs more relaxed in a crate environment, but to alter the engagement with humans in other contexts that depends on the location of the lesions under selection.

We estimated genetic and phenotypic correlations and heritabilities for temperament indicators in growing pigs such as fearfulness (i.e., vocal and physical withdrawal response to an approaching human while isolated in an arena; attempts to escape from a weigh crate); boldness (i.e., biting, following or nosing a human walking inside their home pen) and aggression (i.e., skin lesions). Our results indicate that the studied traits were heritable, and some of these traits could potentially be useful for genetic selection. Additionally, genetic correlations were observed between aggression and fear indicators; pigs with a higher count of skin lesions on their flanks, backs, hind quarters and rear legs 24 h post-mixing (i.e., likely subordinate pigs) tended to display more distress while in isolation in a weigh crate, and were less likely to willingly approach a human. The three boldness indicators were associated, indicating that pigs biting the observer were also those that followed and nosed the observer, suggesting a general increase in exploratory drive and/or a reduction in fearfulness in these animals. These findings suggest that selection to reduce lesions to the rear of the body could have a desirable impact on other important behavioral indicators.

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.

Genetic Associations of Novel Behaviour Traits Derived from Social Network Analysis with Growth, Feed Efficiency, and Carcass Characteristics in Pigs.

Reducing harmful aggressive behaviour remains a major challenge in pig production. Social network analysis (SNA) showed the potential in providing novel behavioural traits that describe the direct and indirect role of individual pigs in pen-level aggression. Our objectives were to (1) estimate the genetic parameters of these SNA traits, and (2) quantify the genetic associations between the SNA traits and commonly used performance measures: growth, feed intake, feed efficiency, and carcass traits. The animals were video recorded for 24 h post-mixing. The observed fighting behaviour of each animal was used as input for the SNA. A Bayesian approach was performed to estimate the genetic parameters of SNA traits and their association with the performance traits. The heritability estimates for all SNA traits ranged from 0.01 to 0.35. The genetic correlations between SNA and performance traits were non-significant, except for weighted degree with hot carcass weight, and for both betweenness and closeness centrality with test daily gain, final body weight, and hot carcass weight. Our results suggest that SNA traits are amenable for selective breeding. Integrating these traits with other behaviour and performance traits may potentially help in building up future strategies for simultaneously improving welfare and performance in commercial pig farms.

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.

Genetic Analysis of Novel Behaviour Traits in Pigs Derived from Social Network Analysis.

Social network analysis (SNA) has provided novel traits that describe the role of individual pigs in aggression. The objectives were to (1) estimate the genetic parameters for these SNA traits, (2) quantify the genetic association between SNA and skin lesion traits, and (3) investigate the possible response to selection for SNA traits on skin lesion traits. Pigs were video recorded for 24 h post-mixing. The observed fight and bullying behaviour of each animal was used as input for the SNA. Skin lesions were counted on different body parts at 24 h (SL24h) and 3 weeks (SL3wk) post-mixing. A Bayesian approach estimated the genetic parameters of SNA traits and their association with skin lesions. SNA traits were heritable (h2 = 0.09 to 0.26) and strongly genetically correlated (rg > 0.88). Positive genetic correlations were observed between all SNA traits and anterior SL24h, except for clustering coefficient. Our results suggest that selection for an index that combines the eigenvector centrality and clustering coefficient could potentially decrease SL24h and SL3wk compared to selection for each trait separately. This study provides a first step towards potential integration of SNA traits into a multi-trait selection index for improving pigs' welfare.

Concepts and Consequences of a Core Gut Microbiota for Animal Growth and Development.

Animal microbiomes are occasionally considered as an extension of host anatomy, physiology, and even their genomic architecture. Their compositions encompass variable and constant portions when examined across multiple hosts. The latter, termed the core microbiome, is viewed as more accommodated to its host environment and suggested to benefit host fitness. Nevertheless, discrepancies in its definitions, characteristics, and importance to its hosts exist across studies. We survey studies that characterize the core microbiome, detail its current definitions and available methods to identify it, and emphasize the crucial need to upgrade and standardize the methodologies among studies. We highlight ruminants as a case study and discussthe link between the core microbiome and host physiology and genetics, as well as potential factors that shape it. We conclude with main directives of action to better understand the host-core microbiome axis and acquire the necessary insights into its controlled modulation.

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.

Evaluation of direct and maternal responses in reproduction traits based on different selection strategies for postnatal piglet survival in a selection experiment.

BACKGROUND: Postnatal piglet survival is important both in economic and animal welfare terms. It is influenced by the piglet's own direct genetic effects and by maternal genetic effects of the dam, associated with milk production and mothering abilities. These genetic effects might be correlated, affected by other non-genetic factors and unfavourably associated with other reproduction traits such as litter size, which makes the development of optimal breeding strategies a challenge. To identify the optimum selection strategy for piglet survival, a selection experiment was carried out to compare responses in survival and reproduction traits to selection on only direct, only maternal, or both genetic effects of postnatal survival. The data of the experiment were recorded from outdoor reared pigs, with first- and second-generation sires selected based on their estimated breeding values for maternal and direct effects of postnatal survival of indoor reared offspring, respectively, with the opportunity to identify potential genotype-by-environment interaction. RESULTS: A Bayesian multivariate threshold-linear model that was fitted to data on 22,483 piglets resulted in significant (Pr(h2 > 0) = 1.00) estimates of maternal and direct heritabilities between 0.12 and 0.18 for survival traits and between 0.29 and 0.36 for birth weight, respectively. Selection for direct genetic effects resulted in direct and maternal responses in postnatal survival of 1.11% ± 0.17 and - 0.49% ± 0.10, respectively, while selection for maternal genetic effects led to greater direct and maternal responses, of 5.20% ± 0.34 and 1.29% ± 0.20, respectively, in part due to unintentional within-litter selection. Selection for both direct and maternal effects revealed a significant lower direct response (- 1.04% ± 0.12) in comparison to its expected response from single-effect selection, caused by interactions between direct and maternal effects. CONCLUSIONS: Selection successfully improved post- and perinatal survival and birth weight, which indicates that they are genetically determined and that genotype-by-environment interactions between outdoor (experimental data) and indoor (selection data) housed pigs were not important for these traits. A substantially increased overall (direct plus maternal) response was obtained using selection for maternal versus direct or both direct and maternal effects, suggesting that the maternal genetic effects are the main limiting factor for improving piglet survival on which selection pressure should be emphasized.

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.

A Knowledge-Driven Network-Based Analytical Framework for the Identification of Rumen Metabolites.

Metabolites are the final production of biochemical reactions in the rumen micro-ecological system and are very sensitive to changes in rumen microbes. Nuclear magnetic resonance (NMR) spectroscopy could both identify and quantify the metabolic composition of the ruminal fluid, which reflects the interaction between rumen microbes and diet. The main challenge of untargeted metabolomics is the compound annotation. Based on non-linear and linear associations between microbial gene abundances and integrals derived from NMR spectra, combined with knowledge of enzymatic reaction from the KEGG database, this study developed a knowledge-driven network-based analytical framework for the inference of metabolites. There were 89 potential metabolites inferred from the integral co-occurrence network. The results are supported by dissimilarity network analysis. The coexistence of non-linear and linear associations between microbial gene abundances and spectral integrals was detected. The study successfully found the corresponding integrals for acetate, butyrate and propionate, which are the major volatile fatty acids (VFA) in the rumen. This novel framework could very efficiently infer metabolites to corresponding integrals from NMR spectra.

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.