Biology subjects

Ilott, N. E.

Publications and source records attributed to Ilott, N. E..

3 recordsLinked to original sources

bioRxiv2021

Pharmacologically induced weight loss is associated with distinct gut microbiome changes in obese rats

BackgroundObesity, metabolic disease and some psychiatric conditions are associated with changes to relative abundance of bacterial species and specific genes in the fecal microbiome. Little is known about the impact of pharmacologically induced weight loss on distinct gut microbiome species and their respective gene programs in obese individuals. ResultsUsing shotgun metagenomics, the composition of the microbiome was obtained for two cohorts of obese female Wistar rats (n=10-12, total of 82) maintained on a high fat diet before and after a 42-day treatment with a panel of four investigatory or approved anti-obesity drugs (tacrolimus/FK506, bupropion, naltrexone and sibutramine), alone or in combination. We found that sibutramine treatment induced consistent weight loss through reducing food intake. Decreased weight loss in sibutramine-treated rats was associated with changes to the gut microbiome that included increased beta-diversity, increased Bacteroides/Firmicutes ratio and increased relative abundance of multiple Bacteroides species. In addition, the relative abundance of multiple genes was found to be differentially abundant, including significant reductions in components of flagellum and genes involved in flagellum assembly. ConclusionsThis study provides a large resource comprising complete shotgun metagenomics datasets of the fecal microbiome coupled with weight change and food intake at day 3, day 15 and day 42 from 82 obese rats treated with a range of compounds used for weight loss, which are available to the community for detailed analysis. Furthermore, by conducting a detailed analysis of the microbiome associated with sibutramine-induced weight loss, we have identified multiple weight-loss associated microbial taxa and pathways. These include a reduction in components of flagellum and the flagellum assembly pathway that points to a potential role of sibutramine-induced weight-loss on regulating bacterially driven anti-inflammatory responses.

genomics↗

bioRxiv2020

Accurate identification and quantification of commensal microbiota bound by host immunoglobulins

BackgroundIdentifying which taxa are targeted by immunoglobulins can uncover important host-microbe interactions. Immunoglobulin binding of commensal taxa can be assayed by sorting bound bacteria from samples and using amplicon sequencing to determine their taxonomy, a technique most widely applied to study Immunoglobulin A (IgA-Seq). Previous experiments have scored taxon binding in IgA-Seq datasets by comparing abundances in the IgA bound and unbound sorted fractions. However, as these are relative abundances, such scores are influenced by the levels of the other taxa present and represent an abstract combination of these effects. Diversity in the practical approaches of prior studies also warrants benchmarking of the individual stages involved. Here, we provide a detailed description of the design strategy for an optimised IgA-Seq protocol. Combined with a novel scoring method for IgA-Seq datasets that accounts for the aforementioned effects, this platform enables accurate identification and quantification of commensal gut microbiota targeted by host immunoglobulins. ResultsUsing germ-free and Rag1-/- mice as negative controls, and a strain-specific IgA antibody as a positive control, we determine optimal reagents and fluorescence activated cell sorting (FACS) parameters for IgA-Seq. Using simulated IgA-Seq data, we show that existing IgA-Seq scoring methods are influenced by pre-sort relative abundances. This has consequences for the interpretation of case-control studies where there are inherent differences in microbiota composition between groups. We show that these effects can be addressed using a novel scoring approach based on posterior probabilities. Finally, we demonstrate the utility of both the IgA-Seq protocol and probability-based scores by examining both novel and published data from in vivo disease models. ConclusionsWe provide a detailed IgA-Seq protocol to accurately isolate IgA-bound taxa from intestinal samples. Using simulated and experimental data, we demonstrate novel probability-based scores that adjust for the compositional nature of relative abundance data to accurately quantify taxon-level IgA binding. All scoring approaches are made available in the IgAScores R package. These methods should improve the generation and interpretation of IgA-Seq datasets and could be applied to study other immunoglobulins and sample types.

bioinformatics↗

bioRxiv2019

CGAT-core: a python framework for building scalable, reproducible computational biology workflows

In the genomics era computational biologists regularly need to process, analyse and integrate large and complex biomedical datasets. Analysis inevitably involves multiple dependent steps, resulting in complex pipelines or workflows, often with several branches. Large data volumes mean that processing needs to be quick and efficient and scientific rigour requires that analysis be consistent and fully reproducible. We have developed CGAT-core, a python package for the rapid construction of complex computational workflows. CGAT-core seamlessly handles parallelisation across high performance computing clusters, integration of Conda environments, full parameterisation, database integration and logging. To illustrate our workflow framework, we present a pipeline for the analysis of RNAseq data using pseudoalignment.\n\nAvailabilityCGAT-core is freely available under an MIT licence for installation and use, including source code at https://github.com/cgat-developers/cgat-core\n\nContactandreas.heger@imm.ox.ac.uk (AH), david.sims@imm.ox.ac.uk (DS), adam.cribbs@imm.ox.ac.uk (AC)\n\nSupplementary information:

genomics↗