Fecal-adherent mucus is a non-invasive source of primary human MUC2 for structural and functional characterization in health and disease
Bibliographic record
Abstract
The O-glycoprotein Mucin-2 (MUC2) forms the protective colon mucus layer. While animal models have demonstrated the importance of Muc2, few studies have explored human MUC2 in similar depth. Recent studies have revealed that secreted MUC2 is bound to human feces. We hypothesized human fecal MUC2 (HF-MUC2) was accessible for purification and downstream structural and functional characterization. We tested this via histologic and quantitative imaging on human fecal sections; extraction from feces for proteomic and O-glycomic characterization; and functional studies via growth and metabolic assays in vitro. Quantitative imaging of solid fecal sections showed a continuous mucus layer of varying thickness along human fecal sections with barrier functions intact. Lectin profiling showed HF-MUC2 bound several lectins but was weak to absent for Ulex europaeus 1 (α1,2 fucose-binding) and Sambucus nigra agglutinin (α2,6 sialic acid-binding), and did not have obvious b1/b2 barrier layers. HF-MUC2 separated by electrophoresis showed high molecular weight glycoprotein bands (∼1-2 MDa). Proteomics and Western analysis confirmed the enrichment of MUC2 and potential MUC2-associated proteins in HF-MUC2 extracts. MUC2 O-glycomics revealed diverse fucosylation, moderate sialylation, and little sulfation versus porcine colonic MUC2 and murine fecal Muc2. O-glycans were functional and supported the growth of Bacteroides thetaiotaomicron (B. theta) and short-chain fatty acid (SCFA) production in vitro. MUC2 could be similarly analyzed from inflammatory bowel disease stools, which displayed an altered glycomic profile and differential growth and SCFA production by B. theta versus healthy samples. These studies describe a new non-invasive platform for human MUC2 characterization in health and disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".