Natural variation in infection specificity of Caenorhabditis briggsae isolates by two RNA viruses.

Antagonistic relationships such as host-virus interactions potentially lead to rapid evolution and specificity in interactions. The Orsay virus is so far the only horizontal virus naturally infecting the nematode C. elegans. In contrast, several related RNA viruses infect its congener C. briggsae, including Santeuil (SANTV) and Le Blanc (LEBV) viruses. Here we focus on the host's intraspecific variation in sensitivity to these two intestinal viruses. Many temperate-origin C. briggsae strains, including JU1264 and JU1498, are sensitive to both, while many tropical strains, such as AF16, are resistant to both. Interestingly, some C. briggsae strains exhibit a specific resistance, such as the HK104 strain, specifically resistant to LEBV. The viral sensitivity pattern matches the strains' geographic and genomic relationships. The heavily infected strains mount a seemingly normal small RNA response that is insufficient to suppress viral infection, while the resistant strains show no small RNA response, suggesting an early block in viral entry or replication. We use a genetic approach from the host side to map genomic regions participating in viral resistance polymorphisms. Using Advanced Intercrossed Recombinant Inbred Lines (RILs) between virus-resistant AF16 and SANTV-sensitive HK104, we detect Quantitative Trait Loci (QTLs) on chromosomes IV and III. Building RILs between virus-sensitive JU1498 and LEBV-resistant HK104 followed by bulk segregant analysis, we identify a chromosome II QTL. In both cases, further introgressions of the regions confirmed the QTLs. This diversity provides an avenue for studying virus entry, replication, and exit mechanisms, as well as host-virus specificity and the host response to a specific virus infection.

Interpreting tree ensemble machine learning models with endoR.

Tree ensemble machine learning models are increasingly used in microbiome science as they are compatible with the compositional, high-dimensional, and sparse structure of sequence-based microbiome data. While such models are often good at predicting phenotypes based on microbiome data, they only yield limited insights into how microbial taxa may be associated. We developed endoR, a method to interpret tree ensemble models. First, endoR simplifies the fitted model into a decision ensemble. Then, it extracts information on the importance of individual features and their pairwise interactions, displaying them as an interpretable network. Both the endoR network and importance scores provide insights into how features, and interactions between them, contribute to the predictive performance of the fitted model. Adjustable regularization and bootstrapping help reduce the complexity and ensure that only essential parts of the model are retained. We assessed endoR on both simulated and real metagenomic data. We found endoR to have comparable accuracy to other common approaches while easing and enhancing model interpretation. Using endoR, we also confirmed published results on gut microbiome differences between cirrhotic and healthy individuals. Finally, we utilized endoR to explore associations between human gut methanogens and microbiome components. Indeed, these hydrogen consumers are expected to interact with fermenting bacteria in a complex syntrophic network. Specifically, we analyzed a global metagenome dataset of 2203 individuals and confirmed the previously reported association between Methanobacteriaceae and Christensenellales. Additionally, we observed that Methanobacteriaceae are associated with a network of hydrogen-producing bacteria. Our method accurately captures how tree ensembles use features and interactions between them to predict a response. As demonstrated by our applications, the resultant visualizations and summary outputs facilitate model interpretation and enable the generation of novel hypotheses about complex systems.

Free-Living, Psychrotrophic Bacteria of the Genus Psychrobacter Are Descendants of Pathobionts.

Host-adapted microorganisms are generally assumed to have evolved from free-living, environmental microorganisms, as examples of the reverse process are rare. In the phylum Gammaproteobacteria, family Moraxellaceae, the genus Psychrobacter includes strains from a broad ecological distribution including animal bodies as well as sea ice and other nonhost environments. To elucidate the relationship between these ecological niches and Psychrobacter's evolutionary history, we performed tandem genomic analyses with phenotyping of 85 Psychrobacter accessions. Phylogenomic analysis of the family Moraxellaceae reveals that basal members of the Psychrobacter clade are Moraxella spp., a group of often-pathogenic organisms. Psychrobacter exhibited two broad growth patterns in our phenotypic screen: one group that we called the "flexible ecotype" (FE) had the ability to grow between 4 and 37°C, and the other, which we called the "restricted ecotype" (RE), could grow between 4 and 25°C. The FE group includes phylogenetically basal strains, and FE strains exhibit increased transposon copy numbers, smaller genomes, and a higher likelihood to be bile salt resistant. The RE group contains only phylogenetically derived strains and has increased proportions of lipid metabolism and biofilm formation genes, functions that are adaptive to cold stress. In a 16S rRNA gene survey of polar bear fecal samples, we detect both FE and RE strains, but in in vivo colonizations of gnotobiotic mice, only FE strains persist. Our results indicate the ability to grow at 37°C, seemingly necessary for mammalian gut colonization, is an ancestral trait for Psychrobacter, which likely evolved from a pathobiont.IMPORTANCE Host-associated microbes are generally assumed to have evolved from free-living ones. The evolutionary transition of microbes in the opposite direction, from host associated toward free living, has been predicted based on phylogenetic data but not studied in depth. Here, we provide evidence that the genus Psychrobacter, particularly well known for inhabiting low-temperature, high-salt environments such as sea ice, permafrost soils, and frozen foodstuffs, has evolved from a mammalian-associated ancestor. We show that some Psychrobacter strains retain seemingly ancestral genomic and phenotypic traits that correspond with host association while others have diverged to psychrotrophic or psychrophilic lifestyles.

Syntrophy via Interspecies H2 Transfer between Christensenella and Methanobrevibacter Underlies Their Global Cooccurrence in the Human Gut.

Across human populations, 16S rRNA gene-based surveys of gut microbiomes have revealed that the bacterial family Christensenellaceae and the archaeal family Methanobacteriaceae cooccur and are enriched in individuals with a lean, compared to an obese, body mass index (BMI). Whether these association patterns reflect interactions between metabolic partners, as well as whether these associations play a role in the lean host phenotype with which they associate, remains to be ascertained. Here, we validated previously reported cooccurrence patterns of the two families and their association with a lean BMI with a meta-analysis of 1,821 metagenomes derived from 10 independent studies. Furthermore, we report positive associations at the genus and species levels between Christensenella spp. and Methanobrevibacter smithii, the most abundant methanogen of the human gut. By coculturing three Christensenella spp. with M. smithii, we show that Christensenella spp. efficiently support the metabolism of M. smithii via H2 production far better than Bacteroides thetaiotaomicron does. Christensenella minuta forms flocs colonized by M. smithii even when H2 is in excess. In culture with C. minuta, H2 consumption by M. smithii shifts the metabolic output of C. minuta's fermentation toward acetate rather than butyrate. Together, these results indicate that the widespread cooccurrence of these microorganisms is underpinned by both physical and metabolic interactions. Their combined metabolic activity may provide insights into their association with a lean host BMI.IMPORTANCE The human gut microbiome is made of trillions of microbial cells, most of which are Bacteria, with a subset of Archaea The bacterial family Christensenellaceae and the archaeal family Methanobacteriaceae are widespread in human guts. They correlate with each other and with a lean body type. Whether species of these two families interact and how they affect the body type are unanswered questions. Here, we show that species within these families correlate with each other across people. We also demonstrate that particular species of these two families grow together in dense flocs, wherein the bacteria provide hydrogen gas to the archaea, which then make methane. When the archaea are present, the ratio of bacterial products (which are nutrients for humans) is changed. These observations indicate that when these species grow together, their products have the potential to affect the physiology of their human host.

Human Salivary Amylase Gene Copy Number Impacts Oral and Gut Microbiomes.

Host genetic variation influences microbiome composition. While studies have focused on associations between the gut microbiome and specific alleles, gene copy number (CN) also varies. We relate microbiome diversity to CN variation of the AMY1 locus, which encodes salivary amylase, facilitating starch digestion. After imputing AMY1-CN for ∼1,000 subjects, we identified taxa differentiating fecal microbiomes of high and low AMY1-CN hosts. In a month-long diet intervention study, we show that diet standardization drove gut microbiome convergence, and AMY1-CN correlated with oral and gut microbiome composition and function. The microbiomes of low-AMY1-CN subjects had enhanced capacity to break down complex carbohydrates. High-AMY1-CN subjects had higher levels of salivary Porphyromonas; their gut microbiota had increased abundance of resistant starch-degrading microbes, produced higher levels of short-chain fatty acids, and drove higher adiposity when transferred to germ-free mice. This study establishes AMY1-CN as a genetic factor associated with microbiome composition and function.