Prevalence, persistence, and genetics of antibody responses to protein toxins and virulence factors
Bibliographic record
Abstract
Abstract Microbial exposures are crucial environmental factors that impact healthspan by sculpting the immune system and microbiota. Antibody profiling via programmable Phage ImmunoPrecipitation Sequencing (PhIP-Seq) provides a high-throughput, costeffective approach for multiplexed detection of exposure and response to thousands of microbial protein products. Here we designed and constructed a library of 95,601 56 amino acid peptide tiles spanning a subset of environmental proteins more likely to be associated with immune responses: those with “toxin” or “virulence factor” keyword annotations. PhIP-Seq was used to profile the circulating antibodies of ~1,000 individuals against this “ToxScan” library of 14,430 toxins and virulence factors from 1,312 genera of organisms. In addition to a detailed analysis of six commonly encountered human commensals and pathogens, we study the age-dependent stability of the ToxScan profile and use a genome-wide association study (GWAS) to find that the MHC-II locus modulates the selection of bacterial epitopes. We detect previously described anti-flagellin antibody responses in a Crohn’s disease cohort and identify a novel association between anti-flagellin antibodies and juvenile dermatomyositis (JDM). PhIP-Seq with the ToxScan library provides a new window into exposure and immune responses to environmental protein toxins and virulence factors, which can be used to study human health and disease at cohort scale.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".