Biology subjects

Vervier, K.

Publications and source records attributed to Vervier, K..

6 recordsLinked to original sources

bioRxiv2021

Two microbiota subtypes identified in Irritable Bowel Syndrome with distinct responses to the low-FODMAP diet

ObjectiveReducing FODMAPs can be clinically beneficial in IBS but the mechanism is poorly understood. We aimed to detect microbial signatures that might predict response to the low FODMAP diet and assess whether microbiota compositional and functional shifts could provide insights into its mode of action. DesignWe used metagenomics to determine high-resolution taxonomic and functional profiles of the stool microbiota from IBS cases and household controls (n=56 pairs) on their usual diet. Clinical response and microbiota changes were studied in 41 pairs after 4 weeks on a low FODMAP diet. ResultsUnsupervised analysis of baseline IBS cases pre-diet identified two distinct microbiota profiles, which we refer to as IBSP (pathogenic-like) and IBSH (health-like) subtypes. IBSP microbiomes were enriched in Firmicutes and genes for amino acid and carbohydrate metabolism, but depleted in Bacteroidetes species. IBSH microbiomes were similar to controls. On the low FODMAP diet IBSH and control microbiota were unaffected, but the IBSP signature shifted towards a health-associated microbiome with an increase in Bacteroidetes (p=0.009), a decrease in Firmicutes species (p=0.004) and normalization of primary metabolic genes. The clinical response to the low FODMAP diet was greater in IBSP subjects compared to IBSH (p = 0.02). Conclusion50% of IBS cases manifested a pathogenic gut microbial signature. This shifted towards the healthy profile on the low FODMAP diet; and IBSP cases showed an enhanced clinical responsiveness to the dietary therapy. The effectiveness of FODMAP exclusion in IBSP may result from the alterations in gut microbiota and metabolites produced. Microbiota signatures could be useful as biomarkers to guide IBS treatment; and investigating IBSP species and metabolic pathways might yield insights regarding IBS pathogenic mechanisms. O_TEXTBOXSignificance of this studyO_ST_ABSWhat is already known on this subject?C_ST_ABSO_LIIBS subjects often respond to a low FODMAP diet. C_LIO_LIThe gut microbiota has been implicated in IBS. C_LIO_LIThe microbiota in IBS subjects may change with diet. C_LI What are the new findings?O_LIWe were able to stratify patients with IBS according to their gut microbiota species and metabolic gene signatures. C_LIO_LIWe identified a distinct gut microbiota subtype with an enhanced clinical response to a low FODMAP diet compared to other IBS subjects. C_LI How might it impact on clinical practice in the foreseeable future?O_LIThe potential development of a microbiota signature as a biomarker to manage IBS cases with a low FODMAP diet recommendation. C_LIO_LIIf the bacteria represented in the IBSP subtype are shown to play a pathogenic role in IBS, perhaps through the metabolic activity this provides a target for new therapies and an intermediate phenotype by which to assess them. C_LI C_TEXTBOX

microbiology↗

bioRxiv2021

Functional and taxonomic comparison of mouse and human gut microbiotas using extensive culturing and metagenomics

Mouse models are essential for biomedical science and drug discovery, yet it is not known how the bacteria in the mouse microbiota - important determinants of phenotypes of health and disease -affect their relevance to human disease. To interrogate the taxonomic and functional differences between the human and mouse gut microbiotas, we developed the Mouse Microbial Genome Collection (MMGC), a compilation of 276 genomes from cultured isolates and 45,218 metagenome-assembled genomes (MAGs) from 1,960 publicly available mouse metagenomes. The MMGC reveals that while only 2.65% of bacterial species are shared between mouse and human, over 80% of annotatable functions are present in both microbiomes. Using drug metabolism and butyrate synthesis as examples, we illustrate that although the species harbouring these key functions can differ between hosts, the MMGC enables identification of functionally equivalent taxa in the mouse and human microbiotas. The MMGC thereby facilitates the informed use of mice in biomedical research by providing access to the conservation and taxonomic locations of bacterial functions of interest.

microbiology↗

bioRxiv2020

Host adaptation in gut Firmicutes is associated with sporulation loss and altered colonisation patterns

Human-to-human transmission of symbiotic, anaerobic bacteria is a fundamental evolutionary adaptation essential for membership of the human gut microbiota. However, despite its importance, the genomic and biological adaptations underpinning symbiont transmission remain poorly understood. Here, we show that sporulation ability, which promotes transmission of anaerobic bacteria, has been independently lost in many distinct evolutionary lineages of gut bacteria belonging to the Firmicutes phyla. Analysis of 1358 genome-sequenced Firmicutes reveals loss of sporulation is associated with features of host-adaptation such as genome reduction and specialized metabolic capabilities. Consistent with these data, analysis of 28,000 gut metagenomes from people around the world demonstrates that bacteria now incapable of sporulation are more abundant but less prevalent in the human population compared to spore-forming bacteria. Our results suggest host adaptation in gut Firmicutes is an evolutionary trade-off between transmission range and colonisation abundance, leading to distinct transmission cycles. We reveal host transmission as an underappreciated process that shapes the evolution, assembly and functions of gut Firmicutes.

microbiology↗

bioRxiv2019

CarboLogR: a Shiny/R application for statistical analysis of bacterial utilisation of carbon sources

SummaryThe Biolog Phenotype Microarray (PM) and Anaerobic MicroPlates (AN) 96-well plates utilise colorimetric redox reactions to rapidly screen bacteria for the ability to utilise different carbon sources and other metabolites. Measurement of substrate utilisation as bacterial growth curves typically involves extended data normalization, outlier detection, and statistical analysis. The CarboLogR package streamlines this process with a Shiny application, guiding users from raw data generated from Biolog assays to growth profile comparison. We applied chemoinformatics approaches to define clusters of carbon sources, based on molecular similarities, increasing statistical power. Altogether, CarboLogR is a novel integrated tool providing automatic and high-level resolution for bacterial growth patterns and carbon source usage.\n\nAvailability and ImplementationCarboLogR application can be downloaded and installed from Github repository https://github.com/kevinVervier/CarboLogR. Tutorial, data, and examples can be downloaded at https://github.com/kevinVervier/CarboLogR/vignettes.\n\nContactkv4@sanger.ac.uk\n\nSupplementary InformationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2018

Impact of genetic variations on the human brain: when and where?

Genetic studies have found candidates in most of neurological and psychiatric conditions. However, it remains challenging to assess, in which brain region, and when in the brain development, a mutation is more likely to have an impact, especially for intergenic loci. Here we present, BRAVA, a publicly available resource for estimating genetic mutations spatio-temporal context on the human brain. Different contexts were observed for schizophrenia associated loci, suggesting both neurodevelopmental and late onset components.

bioinformatics↗

bioRxiv2017

TiSAn: Tissue Specific Variant Annotation

Measures of general deleteriousness, like CADD or PolyPhen, have become indispensable tools in the interpretation of genetic variants. However, these measures say little about where in the organism these deleterious effects will be most apparent. An additional, complementary measure is needed to link deleterious variants (as determined by e.g., CADD) to tissues in which their effect will be most meaningful. Here, we introduce TiSAn (Tissue Specific Annotation), a tool that predicts how related a genomic position is to a given tissue (http://github.com/kevinVervier/TiSAn). TiSAn uses machine learning on genome-scale, tissue-specific data to discriminate variants relevant to a tissue from those having no bearing on the development or function of that tissue. Predictions are then made genome-wide, and these scores can then be used to contextualize and filter variants of interest in whole genome sequencing or genome wide association studies (GWAS). We demonstrate the accuracy and versatility of TiSAn by introducing predictive models for human heart and human brain, and detecting tissue-relevant variations in large cohorts for autism spectrum disorder (TiSAn-brain) and coronary artery disease (TiSAn-heart). We find that TiSAn is better able to prioritize genetic variants according to their tissue-specific action than the current state of the art method, GenoSkyLine.

bioinformatics↗