Inhibition of cell surface GRP78 and activated α2M interaction attenuates kidney fibrosis
Bibliographic record
Abstract
We recently showed that cell surface translocation of the endoplasmic reticulum-resident protein GRP78, when bound by activated α 2-macroglobulin (α2M*), induces pro-fibrotic responses in glomerular mesangial cells in response to high glucose and regulates activation of the pro-fibrotic cytokine transforming growth factor-β1 (TGF-β1), implicating a pathogenic role in glomerulosclerosis. Interstitial fibrosis, largely mediated by proximal tubular epithelial cells (PTEC) and renal fibroblasts, develops later in kidney disease and correlates with functional decline. Here we investigated whether interstitial fibrosis was mediated by cell surface GRP78 (csGRP78)/α2M*. High glucose and TGF-β1 increased csGRP78 and α2M* in PTEC and renal fibroblasts, and their inhibition prevented fibrotic protein production. Interestingly, for TGF-β1, this depended on inhibition of noncanonical signaling through YAP/TAZ, with Smad3 activation unaffected. In vivo, type 1 diabetic Akita mice overexpressing TGF-β1 were treated with either a neutralizing antibody for csGRP78 (C38) or α2M* (Fα2M) or an inhibitory peptide blocking csGRP78/α2M* interaction, and mice with unilateral ureteral obstruction were treated with Fα2M or inhibitory peptide. Consistently, inhibition by antibody or peptide attenuated fibrosis and pro-fibrotic signaling. These findings show an important role for csGRP78/α2M* in mediating tubulointerstitial fibrosis in both diabetic and nondiabetic kidney disease and support their inhibition as a potential antifibrotic therapeutic intervention.
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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) | 0.001 | 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) | 0.002 | 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".