Massively multiplexed serology of inflammatory bowel disease demonstrates association with pentose phosphate shunt autoantibodies 3394
Bibliographic record
Abstract
Abstract Description Introduction Recent advances in high-throughput synthetic biology tools enable massively multiplexed serologic antibody profiling. We apply these tools to investigate inflammatory bowel diseases (IBD). Methods To determine antibody repertoires in IBD, we analyzed serum samples from pediatric controls (N = 30), Crohn’s (N = 30), ulcerative colitis (N = 40), severe ulcerative colitis (N = 28), and adult ulcerative colitis (N = 20). All 148 samples were analyzed using human proteome microarrays (HuProt, >21,000 proteins), human proteome PhIP-Seq (HuScan, >700,000 49-mer peptides), and pan-viral PhIP-Seq (VirScan, >480,000 62-mer peptides). Results HuProt microarray analysis revealed IBD patients frequently exhibited autoantibodies to pentose phosphate shunt pathway proteins (GO:0006098, GO:0009052). This was most pronounced in pediatric ulcerative colitis versus controls (BH-corrected p = 5.08E-05), with nearly all patients showing IgG autoantibodies against at least one of RPIA, TALDO1, PGD, or TKT. Similar reactions were observed in other IBD groups. The top IBD-associated viral antibody was against a peptide from Epstein-Barr virus. Conclusion Autoantibodies can permanently alter health through metabolic disruption; colitis treatments like Cyclosporine A are already thought to work via pentose phosphate pathway modulation of neutrophil behavior. These reported autoantibodies may be directly related to the mechanism of some inflammatory bowel conditions. Funding Sources None Topic Categories Basic Autoimmunity (BA)
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".