TGFBI ( IG-H3) is a diabetes-risk gene based on mouse and human genetic studies
Bibliographic record
Abstract
Transforming growth factor beta-induced (TGFBI/βIG-H3), also known as βig-H3, is a protein inducible by TGFβ1 and secreted by many cell types. It binds to collagen, forms part of the extracellular matrix and interacts with integrins on the cell surface. Recombinant TGFBI and transgenic TGFBI overexpression can promote both islet survival and function. In this study, we generated TGFBI KO mice and further assessed TGFBI function and signaling pathways in islets. Islets from KO mice were of normal size and quantity, and these animals were normoglycemic. However, KO islet survival and function was compromised in vitro. In vivo, KO donor islets became inferior to wild-type donor islets in achieving normoglycemia when transplanted into KO diabetic recipients. TGFBI KO mice were more prone to straptozotocin-induced diabetes than the wild-type counterpart. Phosphoprotein array analysis established that AKT1S1, a molecule linking the AKT and mTORC1 signaling pathways, was modulated by TGFBI in islets. Phosphorylation of four molecules in the AKT and mTORC1 signaling pathway, i.e. AKT, AKT1S1, RPS6 and EIF4EBP1, was upregulated in islets upon TGFBI stimulation. Suppression of AKT activity by a chemical inhibitor, or knockdown of AKT1S1, RPS6 and EIF4EBP1 expression by small interfering RNA, modulated islet survival, proving the relevance of these molecules in TGFBI-triggered signaling. Human genetic studies revealed that in the TGFBI gene and its vicinity, three single-nucleotide polymorphisms were significantly associated with type 1 diabetes risks, and one with type 2 diabetes risks. Our study suggests that TGFBI is a potential risk gene for human diabetes.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 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".