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.

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.bcdandpccB), 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 CH₄emissions per generation, which is higher than for selection based on measured CH₄using respiration chambers (13%), indicating the high potential of microbiome-driven breeding to cumulatively reduce CH₄emissions and mitigate climate change.

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 orcis-9,trans-11C18:2 andtrans-11C18:1 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, 372alr-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 372alr-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 (aroFandserA), 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.

Our study provides an exhaustive comparison of the microbiome core functionalities (captured by 3,936 microbial gene abundances) between hosts with divergent genotypes for intramuscular lipid deposition. After 10 generations of divergent selection for intramuscular fat in rabbits and 4.14 phenotypic standard deviations (SD) of selection response, we applied a combination of compositional and multivariate statistical techniques to identify 122 cecum microbial genes with differential abundances between the lines (ranging from −0.75 to +0.73 SD). This work elucidates that microbial biosynthesis lipopolysaccharides, peptidoglycans, lipoproteins, mucin components, and NADH reductases, amongst others, are influenced by the host genetic determination for lipid accretion in muscle. We also differentiated between host-genetically influenced microbial mechanisms regulating lipid deposition in body or intramuscular reservoirs, with only 28 out of 122 MGs commonly contributing to both. Importantly, the results of this study are of relevant interest for the efficient development of strategies fighting obesity.

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 (ADG ₁ , ADG ₂ , ADG ₃ , ADG ₄ ). Five hundred and thirty-three additive log-ratio transformed microbial genes ( alr -MG) had non-zero genomic correlations (r _g ) 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 r _g sign with all the ADG-traits, regardless of the r _g 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 r _g 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 r _g 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.

The aim of this study was to evaluate the effect of six generations of selection for intramuscular fat (IMF) in muscle Longissimus thoracis et lumborum (LTL) at 9 wk. in IMF and fatty acid (FA) composition of muscles with diverse metabolic profile in rabbits. Direct response to selection was 0.33 g of IMF/ 100 g of LTL, around 0.4 SD per generation. A positive correlated response was observed in IMF of Biceps femoris, Supraspinatus and Semimembranosus proprius muscles at 9 wk., representing around 0.2 SD of the trait per generation. Selection affected similarly the FA composition of all muscles at 9 wk., high-IMF line showing greater monounsaturated but lower polyunsaturated FA percentages than low-IMF line, whereas no differences were observed for saturated FA. Traits were also measured at 13 wk. and correlated responses were in the same direction. Our results suggest a common genetic background for IMF and FA composition in muscles with different metabolic profile in rabbits.