Individualized microbiotas dictate the impact of dietary fiber on colitis sensitivity.

BACKGROUND: The observation that the intestinal microbiota is  central in the development of IBD suggests that dietary fiber, the microbiota's primary source of nourishment, could play a central role in these diseases. Accordingly, enriching diets with specific soluble fibers remodels microbiota and modulates colitis sensitivity. In humans, a recent study suggests that the microbiota of select IBD patients might influence the impacts they would experience upon fiber exposure. We sought here to define the extent to which individual microbiotas varied in their responsiveness to purified soluble fiber inulin and psyllium. Moreover, the extent to which such variance might impact proneness to colitis. RESULTS: We observed a high level of inter-individual variation in microbiota responsiveness to fiber inulin and psyllium: while microbiotas from select donors exhibited stark fiber-induced modulation in composition, pro-inflammatory potential, and metabolomic profile, others were only minimally impacted. Mice transplanted with fiber-sensitive microbiomes exhibited colitis highly modulated by soluble fiber consumption, while mice receiving fiber-resistant microbiotas displayed colitis severity irrespective of fiber exposure. CONCLUSION: The extent to which select soluble fibers alter proneness to colitis is highly influenced by an individual's microbiota composition and further investigation of individual microbiota responsiveness toward specific dietary fiber could pave the way to personalized fiber-based intervention, both in IBD patients and healthy individuals. Video Abstract.

Predicting the effect of mutations to investigate recent events of selection across 60,472 Escherichia coli strains.

Microbial genomics studies focusing on the dynamics of selection have often used a small number of distant genomes. As a result, they could only analyze mutations that had become fixed during the divergence between species. However, thousands of genomes of some species are now available in public databases, thanks to high-throughput sequencing. These data provide a more complete picture of the polymorphisms segregating within a species, offering a unique insight into the processes that shape the recent evolution of a species. In this study, we present GLASS (Gene-Level Amino-acid Score Shift), a selection test that is based on the predicted effects of amino acid changes. By comparing the distribution of effects of mutations observed in a gene to the expectation in the absence of selection, GLASS can quantify the intensity of selection. We applied GLASS to a dataset of 60,472 Escherichia coli strains and used this to reexamine the longstanding debate about the role of essentiality versus expression level in the rate of protein evolution. We found that selection has contrasting short-term and long-term dynamics, with essential genes being subject to strong purifying selection in the short term, while expression level determines the rate of gene evolution in the long term. GLASS also found an overrepresentation of inactivating mutations in specific transcription factors, such as efflux pump repressors, which is consistent with selection for antibiotic resistance. These gene-inactivating polymorphisms do not reach fixation, suggesting another contrast between short-term fitness gains and long-term counterselection.

Deciphering polymorphism in 61,157 Escherichia coli genomes via epistatic sequence landscapes.

Characterizing the effect of mutations is key to understand the evolution of protein sequences and to separate neutral amino-acid changes from deleterious ones. Epistatic interactions between residues can lead to a context dependence of mutation effects. Context dependence constrains the amino-acid changes that can contribute to polymorphism in the short term, and the ones that can accumulate between species in the long term. We use computational approaches to accurately predict the polymorphisms segregating in a panel of 61,157 Escherichia coli genomes from the analysis of distant homologues. By comparing a context-aware Direct-Coupling Analysis modelling to a non-epistatic approach, we show that the genetic context strongly constrains the tolerable amino acids in 30% to 50% of amino-acid sites. The study of more distant species suggests the gradual build-up of genetic context over long evolutionary timescales by the accumulation of small epistatic contributions.

A Microbiota-Dependent Response to Anticancer Treatment in an In Vitro Human Microbiota Model: A Pilot Study With Hydroxycarbamide and Daunorubicin.

Background: Anticancer drug efficacy is linked to the gut microbiota's composition, and there is a dire need to better understand these interactions for personalized medicine. In vitro microbiota models are promising tools for studies requiring controlled and repeatable conditions. We evaluated the impact of two anticancer drugs on human feces in the MiniBioReactor Array (MBRA) in vitro microbiota system. Methods: The MBRA is a single-stage continuous-flow culture model, hosted in an anaerobic chamber. We evaluated the effect of a 5-day treatment with hydroxycarbamide or daunorubicine on the fecal bacterial communities of two healthy donors. 16S microbiome profiling allowed analysis of microbial richness, diversity, and taxonomic changes. Results: In this host-free setting, anticancer drugs diversely affect gut microbiota composition. Daunorubicin was associated with significant changes in alpha- and beta-diversity as well as in the ratio of Firmicutes/Bacteroidetes in a donor-dependent manner. The impact of hydroxycarbamide on microbiota composition was not significant. Conclusion: We demonstrated, for the first time, the impact of anticancer drugs on human microbiota composition, in a donor- and molecule-dependent manner in an in vitro human microbiota model. We confirm the importance of personalized studies to better predict drug-associated-dysbiosis in vivo, linked to the host's response to treatment.

The comparative population genetics of Neisseria meningitidis and Neisseria gonorrhoeae.

Neisseria meningitidis and N. gonorrhoeae are closely related pathogenic bacteria. To compare their population genetics, we compiled a dataset of 1,145 genes found across 20 N. meningitidis and 15 N. gonorrhoeae genomes. We find that N. meningitidis is seven-times more diverse than N. gonorrhoeae in their combined core genome. Both species have acquired the majority of their diversity by recombination with divergent strains, however, we find that N. meningitidis has acquired more of its diversity by recombination than N. gonorrhoeae. We find that linkage disequilibrium (LD) declines rapidly across the genomes of both species. Several observations suggest that N. meningitidis has a higher effective population size than N. gonorrhoeae; it is more diverse, the ratio of non-synonymous to synonymous polymorphism is lower, and LD declines more rapidly to a lower asymptote in N. meningitidis. The two species share a modest amount of variation, half of which seems to have been acquired by lateral gene transfer and half from their common ancestor. We investigate whether diversity varies across the genome of each species and find that it does. Much of this variation is due to different levels of lateral gene transfer. However, we also find some evidence that the effective population size varies across the genome. We test for adaptive evolution in the core genome using a McDonald-Kreitman test and by considering the diversity around non-synonymous sites that are fixed for different alleles in the two species. We find some evidence for adaptive evolution using both approaches.