Human gut flagellome profiling using FlaPro reveals TLR5-related phenotype-specific alterations in IBD
Bibliographic record
Abstract
Abstract Background Flagellin 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 individual’s 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. Methods We 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. Findings FlaPro 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 Crohn’s 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. Interpretation These 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. Funding This work was supported by the Max Planck Society and the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme Grant agreement ID: 101142834 (ERC Advanced Grant SilentFlame).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".