Activated Alpha 2-Macroglobulin Is a Novel Mediator of Mesangial Cell Profibrotic Signaling in Diabetic Kidney Disease
Bibliographic record
Abstract
Diabetic kidney disease (DKD) is caused by the overproduction of extracellular matrix proteins (ECM) by glomerular mesangial cells (MCs). We previously showed that high glucose (HG) induces cell surface translocation of GRP78 (csGRP78), mediating PI3K/Akt activation and downstream ECM production. Activated alpha 2-macroglobulin (α2M*) is a ligand known to initiate this signaling cascade. Importantly, increased α2M was observed in diabetic patients’ serum, saliva, and glomeruli. Primary MCs were used to assess HG responses. The role of α2M* was assessed using siRNA, a neutralizing antibody and inhibitory peptide. Kidneys from type 1 diabetic Akita and CD1 mice and human DKD patients were stained for α2M/α2M*. α2M transcript and protein were significantly increased with HG in vitro and in vivo in diabetic kidneys. A similar increase in α2M* was seen in media and kidneys, where it localized to the mesangium. No appreciable α2M* was seen in normal kidneys. Knockdown or neutralization of α2M/α2M* inhibited HG-induced profibrotic signaling (Akt activation) and matrix/cytokine upregulation (collagen IV, fibronectin, CTGF, and TGFβ1). In patients with established DKD, urinary α2M* and TGFβ1 levels were correlated. These data reveal an important role for α2M* in the pathogenesis of DKD and support further investigation as a potential novel therapeutic target.
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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 | 0.000 |
| 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".