TDAG51 mediates epithelial-to-mesenchymal transition in human proximal tubular epithelium
Bibliographic record
Abstract
Epithelial-to-mesenchymal transition (EMT) contributes to renal fibrosis in chronic kidney disease. Endoplasmic reticulum (ER) stress, a feature of many forms of kidney disease, results from the accumulation of misfolded proteins in the ER and leads to the unfolded protein response (UPR). We hypothesized that ER stress mediates EMT in human renal proximal tubules. ER stress is induced by a variety of stressors differing in their mechanism of action, including tunicamycin, thapsigargin, and the calcineurin inhibitor cyclosporine A. These ER stressors increased the UPR markers GRP78, GRP94, and phospho-eIF2α in human proximal tubular cells. Thapsigargin and cyclosporine A also increased cytosolic Ca(2+) concentration and T cell death-associated gene 51 (TDAG51) expression, whereas tunicamycin did not. Thapsigargin was also shown to increase levels of active transforming growth factor (TGF)-β1 in the media of cultured human proximal tubular cells. Thapsigargin induced cytoskeletal rearrangement, β-catenin nuclear translocation, and α-smooth muscle actin and vinculin expression in proximal tubular cells, indicating an EMT response. Subconfluent primary human proximal tubular cells were induced to undergo EMT by TGF-β1 treatment. In contrast, tunicamycin treatment did not produce an EMT response. Plasmid-mediated overexpression of TDAG51 resulted in cell shape change and β-catenin nuclear translocation. These results allowed us to develop a two-hit model of ER stress-induced EMT, where Ca(2+) dysregulation-mediated TDAG51 upregulation primes the cell for mesenchymal transformation via Wnt signaling and then TGF-β1 activation leads to a complete EMT response. Thus the release of Ca(2+) from ER stores mediates EMT in human proximal tubular epithelium via the induction of TDAG51.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".