FOXL1+ Telocytes in mouse colon orchestrate extracellular matrix biodynamics and wound repair resolution
Bibliographic record
Abstract
Recent studies have identified FoxL1+-telocytes (TCFoxL1+) as key players in gut epithelial-mesenchymal interactions which can determine the colonic microenvironment. Bone morphogenetic protein signaling disruption in TCFoxL1+ alters the physical and cellular microenvironment and leads to colon pathophysiology. This suggests a role for TCFoxL1+ in stromagenesis, but it is hard to identify the specific contribution of TCFoxL1+ when analyzing whole tissue profiling studies. We performed ex vivo deconstruction of control and BmpR1a△FoxL1+ colon samples, isolated the mesenchyme-enriched fractions, and determined the protein composition of the in vivo extracellular matrix (ECM) to analyze microenvironment variation. Matrisomic analysis of mesenchyme fractions revealed modulations in ECM proteins with functions associated with innate immunity, epithelial wound healing, and the collagen network. These results show that TCFoxL1+ is critical in orchestrating the biodynamics of the colon ECM. TCFoxL1+ disfunction reprograms the gut's microenvironment and drives the intestinal epithelium toward colonic pathologies. In this study, the method that was elected to isolate ECM proteins might not encompass the full extent of ECM proteins in a tissue, due to the protocol chosen, as this protocol by Naba et al., targets more the insoluble part of the matrisome and eliminates the more soluble components in the first steps. However, this ECM-enrichment strategy represents an improvement and interesting avenue to study ECM proteins in the colon compared to total tissue analysis with a background of abundant cellular protein. Thus, the matrisomic approach presented in this study, and its target validation delivered a broader evaluation of the matrix remodeling occurring in the colonic sub-epithelial mesenchyme of the BmpR1a△FoxL1+ mouse model.
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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 | 0.000 |
| 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) | 0.002 | 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".