BackgroundGrazing mammals rely on their ruminal microbial symbionts to convert plant structural biomass into metabolites they can assimilate. To explore how this complex metabolic system adapts to the host animal’s diet, we inferred a microbiome-level metabolic network from shotgun metagenomic data.ResultsUsing comparative genomics, we then linked this microbial network to that of the host animal using a set of interface metabolites likely to be transferred to the host. When the host sheep were fed a grain-based diet, the induced microbial metabolic network showed several critical differences from those seen on the evolved forage-based diet. Grain-based (e.g., concentrate) diets tend to be dominated by a smaller set of reactions that employ metabolites that are nearer in network space to the host’s metabolism. In addition, these reactions are more central in the network and employ substrates with shorter carbon backbones. Despite this apparent lower complexity, the concentrate-associated metabolic networks are actually more dissimilar from each other than are those of forage-fed animals. Because both groups of animals were initially fed on a forage diet, we propose that the diet switch drove the appearance of a number of different microbial networks, including a degenerate network characterized by an inefficient use of dietary nutrients. We used network simulations to show that such disparate networks are not an unexpected result of a diet shift.ConclusionWe argue that network approaches, particularly those that link the microbial network with that of the host, illuminate aspects of the structure of the microbiome not seen from a strictly taxonomic perspective. In particular, different diets induce predictable and significant differences in the enzymes used by the microbiome. Nonetheless, there are clearly a number of microbiomes of differing structure that show similar functional properties. Changes such as a diet shift uncover more of this type of diversity.Electronic supplementary materialThe online version of this article (doi:10.1186/s40168-017-0274-6) contains supplementary material, which is available to authorized users.

Ruminant animals have a symbiotic relationship with the microorganisms in their rumens. In this relationship, rumen microbes efficiently degrade complex plant-derived compounds into smaller digestible compounds, a process that is very likely associated with host animal feed efficiency. The resulting simpler metabolites can then be absorbed by the host and converted into other compounds by host enzymes. We used a microbial community metabolic network inferred from shotgun metagenomics data to assess how this metabolic system differs between animals that are able to turn ingested feedstuffs into body mass with high efficiency and those that are not. We conducted shotgun sequencing of microbial DNA from the rumen contents of 16 sheep that differed in their residual feed intake (RFI), a measure of feed efficiency. Metagenomic reads from each sheep were mapped onto a database-derived microbial metabolic network, which was linked to the sheep metabolic network by interface metabolites (metabolites transferred from microbes to host). No single enzyme was identified as being significantly different in abundance between the low and high RFI animals (P > 0.05, Wilcoxon test). However, when we analyzed the metabolic network as a whole, we found several differences between efficient and inefficient animals. Microbes from low RFI (efficient) animals use a suite of enzymes closer in network space to the host's reactions than those of the high RFI (inefficient) animals. Similarly, low RFI animals have microbial metabolic networks that, on average, contain reactions using shorter carbon chains than do those of high RFI animals, potentially allowing the host animals to extract metabolites more efficiently. Finally, the efficient animals possess community networks with greater Shannon diversity among their enzymes than do inefficient ones. Thus, our system approach to the ruminal microbiome identified differences attributable to feed efficiency in the structure of the microbes' community metabolic network that were undetected at the level of individual microbial taxa or reactions.

Toxin-antitoxin (TA) systems are widely distributed in bacteria and play an important role in maintaining plasmid stability. The leading foodborne pathogen, Campylobacter jejuni, can carry multiple plasmids associated with antibiotic resistance or virulence. Previously a virulence plasmid named pVir was identified in C. jejuni 81-176 and IA3902, but determining the role of pVir in pathogenesis has been hampered because the plasmid cannot be cured. In this study, we report the identification of two TA systems that are located on the pVir plasmid in 81-176 and IA3902, respectively. The virA (proteic antitoxin)/virT (proteic toxin) pair in IA3902 belongs to a Type II TA system, while the cjrA (RNA antitoxin)/cjpT (proteic toxin) pair in 81-176 belongs to a Type I TA system. Notably, cjrA (antitoxin) represents the first noncoding small RNA demonstrated to play a functional role in Campylobacter physiology to date. By inactivating the TA systems, pVir was readily cured from Campylobacter, indicating their functionality in Campylobacter. Using pVir-cured IA3902, we demonstrated that pVir is not required for abortion induction in the guinea pig model. These findings establish the key role of the TA systems in maintaining plasmid stability and provide a means to evaluate the function of pVir in Campylobacter pathobiology.

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Ruminant mammals maintain a symbiotic relationship with their gut microbiome, the organisms of which break complex plant matter into metabolites that are absorbed by the host animal for its nutrition. Building on prior work describing the taxonomic composition of this ecosystem, we sought to explore its biochemical operation with a systems biology approach. We constructed a merged host/microbial metabolic network by using shotgun metagenomic data matched to a reference enzyme database. We used this approach to explore the complexities of the rumen ecosystem and to assess the effects of feed additives in diet, feed efficiency, and breed-differences on this ecosystem. We linked the metabolic network of the microbial community to that of the host animal by defining a set of interface metabolites shared between them. The value of our approach is illustrated with our comparison of the rumen microbiomes of animals of low and high feed efficiencies. Strikingly, there was no individual enzyme that was statistically different in its abundance between these two groups. However, by analyzing the network as a whole, we observed that the high efficiency animals had a microbial network that was closer in network space to the host's reactions. In our antibiotics study, we observed that there were changes in the taxonomic and functional structure when sheep were fed a diet supplemented with antibiotics. Sheep fed antibiotics had a set of microbial enzymes that were closer in in the network space to the host metabolism than were the enzymes of the microbes of sheep not fed antibiotics. There were 546 individual microbial reactions that were significantly different between the two groups. Finally in the third study described, we found that breed differences in these community metabolic networks between Angus and Charolais cattle were only detected by analyzing the global network structure: there were no individual microbial taxa or enzymes that differed significantly between the breeds. These studies have shown the importance of using a metabolic network approach to better characterize the interactions between gut microbiota and host organisms as compared to solely studying the individual taxonomic and metabolic reactions.