Biology subjects

Ruaud, A.

Publications and source records attributed to Ruaud, A..

5 recordsLinked to original sources

bioRxiv2024

Natural variation in infection specificityof 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 hosts 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.

genetics↗

bioRxiv2022

Interpreting tree ensemble machine learning models with endoR

BackgroundTree 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 or genomic content may be associated. Results: We developed endoR, a method to interpret a fitted tree ensemble model. First, endoR simplifies the fitted model into a decision ensemble from which it then extracts information on the importance of individual features and their pairwise interactions and also visualizes these data as an interpretable network. Both the network and importance scores derived from endoR 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 the performance of endoR on both simulated and real metagenomic data. We found endoR to infer true associations with more or comparable accuracy than other commonly used 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 gain insights into components of the microbiome that predict the presence of human gut methanogens, as 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. Conclusion: 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. An implementation of endoR is available as an open-source R-package on GitHub (https://github.com/leylabmpi/endoR).

bioinformatics↗

bioRxiv2020

Free-living psychrophilic bacteria of the genus Psychrobacter are descendents of pathobionts

Host-adapted microbiota are generally thought to have evolved from free-living ancestors. This process is in principle reversible, but examples are few. The genus Psychrobacter (family Moraxellaceae, phylum Gamma-Proteobacteria) includes species inhabiting diverse and mostly polar environments, such as sea ice and marine animals. To probe Psychrobacters evolutionary history, we analyzed 85 Psychrobacter strains by comparative genomics and phenotyping under 24 different growth conditions. Genome-based phylogeny shows Psychrobacter are derived from Moraxella, which are warm-adapted pathobionts. Psychrobacter strains form two ecotypes based on growth temperature: flexible (FE, growth at 4 - 37{degrees}C), and restricted (RE, 4 - 25{degrees}C). FE strains, which can be either phylogenetically basal or derived, have smaller genomes and higher transposon copy numbers. RE strains have larger genomes, and show genomic adaptations towards a psychrophilic lifestyle and are phylogenetically derived only. We then assessed Psychrobacter abundance in 86 mostly wild polar bear stools and tested persistence of select strains in germfree mice. Psychrobacter (both FE and RE) was enriched in stool of polar bears feeding on mammals, but only FE strains persisted in germfree mice. Together these results indicate growth at 37{degrees}C is ancestral in Psychrobacter, lost in many derived species, and likely necessary to colonize the mammalian gut.

microbiology↗

bioRxiv2019

Syntrophy via interspecies H2 transfer between Christensenella and Methanobrevibacter underlies their global co-occurrence 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 co-occur and are enriched in individuals with a lean, compared to an obese, BMI. Whether these association patterns reflect interactions between metabolic partners remains to be ascertained, as well as whether these associations play a role in the lean host phenotype with which they associate. Here, we validated previously reported co-occurrence 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 level between Christensenella spp. and Methanobrevibacter smithii, the most abundant methanogen of the human gut. By co-culturing three Christensenella spp. With M. smithii, we show that Christensenella spp. efficiently support the of M. smithii via H2 production, far better than Bacteroides thetaiotaomicron. C. 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. minutas fermentation towards acetate rather than butyrate. Together, these results indicate that the widespread co-occurrence of these microbiota is underpinned by both physical and metabolic interactions. Their combined metabolic activity may provide insights into their association with a lean host BMI. ImportanceThe 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 showed that species within these families correlate with each other across people. We also demonstrated 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 when these species grow together, their products have the potential to affect the physiology of their human host.

microbiology↗

bioRxiv2018

Human salivary amylase gene copy number impacts oral and gut microbiomes

Host genetic variation influences the composition of the human microbiome. While studies have focused on associations between the microbiome and single nucleotide polymorphisms in genes, their copy number (CN) can also vary. Here, in a study of human subjects including a 2-week standard diet, we relate oral and gut microbiome to CN at the AMY1 locus, which encodes the gene for salivary amylase, active in starch degradation. We show that although diet standardization drove gut microbiome convergence, AMY1-CN influenced oral and gut microbiome composition and function. The gut microbiomes of low-AMY1-CN subjects had an enhanced capacity for breakdown of complex carbohydrates. Those of high-AMY1 subjects were enriched in microbiota linked to resistant starch fermentation, had higher fecal SCFAs, and drove higher adiposity when transferred to germfree mice. Gut microbiota results were validated in a larger separate population. This study establishes AMY1-CN as a genetic factor patterning microbiome composition and function.

genetics↗