IgGFc-binding protein and MUC2 mucin produced by colonic goblet-like cells spatially interact non-covalently and regulate wound healing
Bibliographic record
Abstract
The colonic mucus bilayer is the first line of innate host defense that at the same time houses and nourishes the commensal microbiota. The major components of mucus secreted by goblet cells are MUC2 mucin and the mucus-associated protein, FCGBP (IgGFc-binding protein). In this study, we determine if FCGBP and MUC2 mucin were biosynthesized and interacted together to spatially enhance the structural integrity of secreted mucus and its role in epithelial barrier function. MUC2 and FCGBP were coordinately regulated temporally in goblet-like cells and in response to a mucus secretagogue but not in CRISPR-Cas9 gene-edited MUC2 KO cells. Whereas ~85% of MUC2 was colocalized with FCGBP in mucin granules, ~50% of FCGBP was diffusely distributed in the cytoplasm of goblet-like cells. STRING-db v11 analysis of the mucin granule proteome revealed no protein-protein interaction between MUC2 and FCGBP. However, FCGBP interacted with other mucus-associated proteins. FCGBP and MUC2 interacted via N-linked glycans and were non-covalently bound in secreted mucus with cleaved low molecular weight FCGBP fragments. In MUC2 KO, cytoplasmic FCGBP was significantly increased and diffusely distributed in wounded cells that healed by enhanced proliferation and migration within 2 days, whereas, in WT cells, MUC2 and FCGBP were highly polarized at the wound margin which impeded wound closure by 6 days. In DSS colitis, restitution and healed lesions in Muc2+/+ but not Muc2-/- littermates, were accompanied by a rapid increase in Fcgbp mRNA and delayed protein expression at 12- and 15-days post DSS, implicating a potential novel endogenous protective role for FCGBP in wound healing to maintain epithelial barrier function.
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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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".