Functional molecules in mesothelial‐to‐mesenchymal transition revealed by transcriptome analyses
Bibliographic record
Abstract
Peritoneal fibrosis is a common complication of abdominal and pelvic surgery, and can also be triggered by peritoneal dialysis, resulting in treatment failure. In these settings, fibrosis is driven by activated myofibroblasts that are considered to be partly derived by mesothelial-to-mesenchymal transition (MMT). We hypothesized that, if the molecular signature of MMT could be better defined, these insights could be exploited to block this pathological cellular transition. Rat peritoneal mesothelial cells were purified by the use of an antibody against HBME1, a protein present on mesothelial cell microvilli, and streptavidin nanobead technology. After exposure of sorted cells to a well-known mediator of MMT, transforming growth factor (TGF)-β1, RNA sequencing was undertaken to define the transcriptomes of mesothelial cells before and during early-phase MMT. MMT was associated with dysregulation of transcripts encoding molecules involved in insulin-like growth factor (IGF) and bone morphogenetic protein (BMP) signalling. The application of either recombinant BMP4 or IGF-binding protein 4 (IGFBP4) ameliorated TGF-β1-induced MMT in culture, as judged from the retention of epithelial morphological and molecular phenotypes, and reduced migration. Furthermore, peritoneal tissue from peritoneal dialysis patients showed less prominent immunostaining than control tissue for IGFBP4 and BMP4 on the peritoneal surface. In a mouse model of TGF-β1-induced peritoneal thickening, BMP4 immunostaining on the peritoneal surface was attenuated as compared with healthy controls. Finally, genetic lineage tracing of mesothelial cells was used in mice with peritoneal injury. In this model, administration of BMP4 ameliorated the injury-induced shape change and migration of mesothelial cells. Our findings demonstrate a distinctive MMT signature, and highlight the therapeutic potential for BMP4, and possibly IGFBP4, to reduce MMT. © 2018 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".