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.

We surveyed the ruminal metagenomes of 16 sheep under two different diets using Illumina pair-end DNA sequencing of raw microbial DNA extracted from rumen samples. The resulting sequence data were bioinformatically mapped to known prokaryotic 16S rDNA sequences to identify the taxa present in the samples and then analysed for the presence of potentially new taxa. Strikingly, the majority of the microbial individuals found did not map to known taxa from 16S sequence databases. We used a novel statistical modelling approach to compare the taxonomic distributions between animals fed a forage-based diet and those fed concentrated grains. With this model, we found significant differences between the two groups both in the dominant taxa present in the rumen and in the overall shape of the taxa abundance curves. In general, forage-fed animals have a more diverse microbial ecosystem, whereas the concentrate-fed animals have ruminal systems more heavily dominated by a few taxa. As expected, organisms from methanogenic groups are more prevalent in forage-fed animals. Finally, all of these differences appear to be grounded in an underlying common input of new microbial individuals into the rumen environment, with common organisms from one feed group being present in the other, but at much lower abundance.

Context.— The ability to determine ROS1 status has become mandatory for patients with lung adenocarcinoma, as many global authorities have approved crizotinib for patients with ROS1-positive lung adenocarcinoma. Objective.— To present analytical correlation of the VENTANA ROS1 (SP384) Rabbit Monoclonal Primary Antibody (ROS1 [SP384] antibody) with ROS1 fluorescence in situ hybridization (FISH). Design.— The immunohistochemistry (IHC) and FISH analytical comparison was assessed by using 122 non–small cell lung cancer samples that had both FISH (46 positive and 76 negative cases) and IHC staining results available. In addition, reverse transcription–polymerase chain reaction (RT-PCR) as well as DNA and RNA next-generation sequencing (NGS) were used to further examine the ROS1 status in cases that were discrepant between FISH and IHC, based on staining in the cytoplasm of 2+ or above in more than 30% of total tumor cells considered as IHC positive. Here, we define the consensus status as the most frequent result across the 5 different methods (IHC, FISH, RT-PCR, RNA NGS, and DNA NGS) we used to determine ROS1 status in these cases. Results.— Of the IHC scoring methods examined, staining in the cytoplasm of 2+ or above in more than 30% of total tumor cells considered as IHC positive had the highest correlation with a FISH-positive status, reaching a positive percentage agreement of 97.8% and negative percentage agreement of 89.5%. A positive percentage agreement (100%) and negative percentage agreement (92.0%) was reached by comparing ROS1 (SP384) using a cutoff for staining in the cytoplasm of 2+ or above in more than 30% of total tumor cells to the consensus status. Conclusions.— Herein, we present a standardized staining protocol for ROS1 (SP384) and data that support the high correlation between ROS1 status and ROS1 (SP384) antibody.