Human gut flagellome profiling using FlaPro reveals TLR5-related phenotype-specific alterations in IBD
Bogdanova, A. A.; Borbon-Garcia, A.; Ley, R. E.; Tyakht, A. V.
Show abstract
BackgroundFlagellin is the protein monomer of the bacterial flagellum, which confers motility, allowing bacteria to reach their favored niches. Flagellin is highly conserved across bacterial species and thus the target of the innate immune receptor Toll-like receptor 5 (TLR5). In the gut, bacterial flagellin agonizes human TLR5, triggering a pro-inflammatory response. However, the ability to bind and activate TLR5 varies considerably between different flagellins, suggesting that the composition of an individuals flagellin repertoire - the flagellome - may mediate the inflammatory response to the microbiome, with relevance to inflammatory bowel diseases. However, to date, methods to assess the inflammatory potential of a flagellome are lacking. MethodsWe constructed a curated database of human gut microbiome-derived flagellins. To predict the inflammatory potential of the flagellome by sorting flagellins into either "stimulatory" (strong TLR5 agonists) or "silent" (weak TLR5 agonists), we trained a machine learning model on experimentally characterized flagellins with known binding and stimulatory activities. The FlaPro pipeline was implemented using the Snakemake workflow engine for high-throughput analysis and is available at https://github.com/leylabmpi/FlaPro. A publicly available multi-omics dataset from an inflammatory bowel disease (IBD) cohort was used to explore associations between flagellome features and clinical status. FindingsFlaPro enables robust profiling of the human gut flagellome from metagenomic and metatranscriptomic data. Analysis of the IBD datasets revealed a depletion of flagellome diversity and a reduced silent-to-stimulatory flagellin abundance ratio in Crohns disease and ulcerative colitis, observed at both the genomic and transcriptional levels. Multiple condition-specific alterations were identified at the level of individual flagellin clusters. InterpretationThese findings indicate that IBD is associated with distinct alterations in the gut flagellome, particularly in relation to TLR5 recognition. Flagellome features represent a functionally interpretable class of microbiome-derived markers with potential utility in microbiome-wide association studies in the context of human health and disease. FundingThis work was supported by the Max Planck Society and the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme Grant agreement ID: 101142834 (ERC Advanced Grant SilentFlame).
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Interplay Between the Gut Microbiome and Typhoid Fever: Insights from Endemic Countries and a Controlled Human Infection Model 95%
- Accurate identification and quantification of commensal microbiota bound by host immunoglobulins 95%
- Deep learning reveals functional archetypes in the adult human gut microbiome that underlie interindividual variability and confound disease signals 94%
Similar papers in this journal
- Integration of constraint-based modeling with fecal metabolomics reveals large deleterious effects of Fusobacteria species on community butyrate production 96%
- A history of repeated antibiotic usage leads to microbiota-dependent mucus defects 95%
- Gut microbiota transplantation drives the adoptive transfer of colonic genotype-phenotype characteristics between mice lacking catestatin and their wild type counterparts 95%
Similar papers in this journal
- Metagenome-assembled genomes of Estonian Microbiome cohort reveal novel species and their links with prevalent diseases 95%
- A Distinct Contractile Injection System Found in a Majority of Adult Human Microbiomes 94%
- Metagenomics uncovers dietary adaptations for chitin digestion in the gut microbiota of convergent myrmecophagous mammals 94%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.