Ruminant livestock are important sources of human food and global greenhouse gas emissions. Feed degradation and methane formation by ruminants rely on metabolic interactions between rumen microbes and affect ruminant productivity. Rumen and camelid foregut microbial community composition was determined in 742 samples from 32 animal species and 35 countries, to estimate if this was influenced by diet, host species, or geography. Similar bacteria and archaea dominated in nearly all samples, while protozoal communities were more variable. The dominant bacteria are poorly characterised, but the methanogenic archaea are better known and highly conserved across the world. This universality and limited diversity could make it possible to mitigate methane emissions by developing strategies that target the few dominant methanogens. Differences in microbial community compositions were predominantly attributable to diet, with the host being less influential. There were few strong co-occurrence patterns between microbes, suggesting that major metabolic interactions are non-selective rather than specific.

The mammalian gut microbiota is essential in shaping many of its host's functional attributes. One such microbiota resides in the bovine digestive tract in a compartment termed as the rumen. The rumen microbiota is necessary for the proper physiological development of the rumen and for the animal's ability to digest and convert plant mass into food products, making it highly significant to humans. The establishment of this microbial population and the changes occurring with the host's age are important for understanding this key microbial community. Despite its importance, little information about colonization of the microbial populations in newborn animals, and the gradual changes occurring thereafter, exists. Here, we characterized the overall bovine ruminal bacterial populations of five age groups, from 1-day-old calves to 2-year-old cows. We describe the changes occurring in the rumen ecosystem after birth, reflected by a decline in aerobic and facultative anaerobic taxa and an increase in anaerobic ones. Some rumen bacteria that are essential for mature rumen function could be detected as early as 1 day after birth, long before the rumen is active or even before ingestion of plant material occurs. The diversity and within-group similarity increased with age, suggesting a more diverse but homogeneous and specific mature community, compared with the more heterogeneous and less diverse primary community. In addition, a convergence toward a mature bacterial arrangement with age was observed. These findings have also been reported for human gut microbiota, suggesting that similar forces drive the establishment of gut microbiotas in these two distinct mammalian digestive systems.

Ruminants have the remarkable ability to convert human-indigestible plant biomass into human-digestible food products, due to a complex microbiome residing in the rumen compartment of their upper digestive tract. Here we report the discovery that rumen microbiome components are tightly linked to cows' ability to extract energy from their feed, termed feed efficiency. Feed efficiency was measured in 146 milking cows and analyses of the taxonomic composition, gene content, microbial activity and metabolomic composition was performed on the rumen microbiomes from the 78 most extreme animals. Lower richness of microbiome gene content and taxa was tightly linked to higher feed efficiency. Microbiome genes and species accurately predicted the animals' feed efficiency phenotype. Specific enrichment of microbes and metabolic pathways in each of these microbiome groups resulted in better energy and carbon channeling to the animal, while lowering methane emissions to the atmosphere. This ecological and mechanistic understanding of the rumen microbiome could lead to an increase in available food resources and environmentally friendly livestock agriculture.

Microbial communities are essential to the function of virtually all ecosystems and eukaryotes, including humans. However, it is still a major challenge to identify microbial cells active under natural conditions in complex systems. In this study, we developed a new method to identify and sort active microbes on the single-cell level in complex samples using stable isotope probing with heavy water (D 2 O) combined with Raman microspectroscopy. Incorporation of D 2 O-derived D into the biomass of autotrophic and heterotrophic bacteria and archaea could be unambiguously detected via C-D signature peaks in single-cell Raman spectra, and the obtained labeling pattern was confirmed by nanoscaleresolution secondary ion MS. In fast-growing Escherichia coli cells, label detection was already possible after 20 min. For functional analyses of microbial communities, the detection of D incorporation from D 2 O in individual microbial cells via Raman microspectroscopy can be directly combined with FISH for the identification of active microbes. Applying this approach to mouse cecal microbiota revealed that the host-compound foragers Akkermansia muciniphila and Bacteroides acidifaciens exhibited distinctive response patterns to amendments of mucin and sugars. By Ramanbased cell sorting of active (deuterated) cells with optical tweezers and subsequent multiple displacement amplification and DNA sequencing, novel cecal microbes stimulated by mucin and/ or glucosamine were identified, demonstrating the potential of the nondestructive D 2 O-Raman approach for targeted sorting of microbial cells with defined functional properties for singlecell genomics.ecophysiology | single-cell microbiology | carbohydrate utilization | nitrifier | Raman microspectroscopy M icroorganisms play a vital role in many environments. They mediate global biogeochemical cycles, catalyze biotechnological processes, and contribute to health and disease in the human body. The in situ study of microbial activity in natural and engineered ecosystems is therefore of great interest. For this purpose, several elegant methods have been established that use either transcriptional or translational activity of community members (i.e., metatranscriptomics, metaproteomics) (1-3) or the incorporation of isotopically labeled substrates into biomolecules (4-10) to infer the ecophysiology of microbes in such systems. However, these bulk techniques do not offer sufficient spatial resolution to study microbial activities at the micrometer scale. Therefore, important information can be overlooked because microbial communities are frequently spatially structured (e.g., biofilms) (11) and contain populations with life cycles (12,13). Furthermore, even apparently identical cells in clonal populations can have strongly divergent activities (14).Consequently, microbial ecophysiology is ideally studied also at the level of the single cell, but only a restricted number of approaches exist for determining physiological properties of individual cells in a microbial community. For exa...

The bovine rumen houses a complex microbiota which is responsible for cattle's remarkable ability to convert indigestible plant mass into food products. Despite this ecosystem's enormous significance for humans, the composition and similarity of bacterial communities across different animals and the possible presence of some bacterial taxa in all animals' rumens have yet to be determined. We characterized the rumen bacterial populations of 16 individual lactating cows using tag amplicon pyrosequencing. Our data showed 51% similarity in bacterial taxa across samples when abundance and occurrence were analyzed using the Bray-Curtis metric. By adding taxon phylogeny to the analysis using a weighted UniFrac metric, the similarity increased to 82%. We also counted 32 genera that are shared by all samples, exhibiting high variability in abundance across samples. Taken together, our results suggest a core microbiome in the bovine rumen. Furthermore, although the bacterial taxa may vary considerably between cow rumens, they appear to be phylogenetically related. This suggests that the functional requirement imposed by the rumen ecological niche selects taxa that potentially share similar genetic features.

Ruminants are completely dependent on their microbiota for feed digestion and consequently, their viability. It is therefore tempting to hypothesize a connection between the composition and abundance of resident rumen bacterial taxa and the physiological parameters of the host. Using a pyrosequencing approach, we characterized the rumen bacterial community composition in 15 dairy cows and their physiological parameters. We analyzed the degree of divergence between the different animals and found that some physiological parameters, such as milk yield and composition, are highly correlated with the abundance of various bacterial members of the rumen microbiome. One apparent finding was a strong correlation between the ratio of the phyla Firmicutes to Bacteroidetes and milk-fat yield. These findings paralleled human studies showing similar trends of increased adiposity with an increase in Bacteroidetes. This correlation remained evident at the genus level, where several genera showed correlations with the animals' physiological parameters. This suggests that the bacterial community has a role in shaping host physiological parameters. A deeper understanding of this process may allow us to modulate the rumen microbiome for better agricultural yield through bacterial community design.

No abstract

A 1000-cow study across four European countries was undertaken to understand to what extent ruminant microbiomes can be controlled by the host animal and to identify characteristics of the host rumen microbiome axis that determine productivity and methane emissions. A core rumen microbiome, phylogenetically linked and with a preserved hierarchical structure, was identified. A 39-member subset of the core formed hubs in co-occurrence networks linking microbiome structure to host genetics and phenotype (methane emissions, rumen and blood metabolites, and milk production efficiency). These phenotypes can be predicted from the core microbiome using machine learning algorithms. The heritable core microbes, therefore, present primary targets for rumen manipulation toward sustainable and environmentally friendly agriculture.