Akt activation and augmented fibronectin production in hyperhexosemia
Bibliographic record
Abstract
Dysmetabolic state in diabetes may lead to augmented synthesis of extracellular matrix (ECM) proteins. In the endothelial cells, we have previously demonstrated that glucose-induced fibronectin (FN) production and that of its splice variant, EDB(+)FN, is regulated by protein kinase B (PKB, also known as Akt). In this study, we investigated the role of Akt1 in ECM protein production in the organs affected by chronic diabetic complications. We studied Akt1/PKBalpha knockout mice and wild-type control littermates. To avoid confounding effects of systemic insulin, we used 30% galactose feeding to induce hyperhexosemia for 8 wk starting at 6 wk of age. We investigated FN mRNA, EDB(+)FN mRNA, and transforming growth factor (TGF)-beta mRNA expression, Akt phosphorylation, Akt kinase activity, and NF-kappaB and AP-1 activation in the retina, heart, and kidney. Renal and cardiac tissues were histologically examined. Galactose feeding caused significant upregulation of FN, EDB(+)FN, and TGF-beta in all tissues. FN protein levels paralleled mRNA. Such upregulation were prevented in Akt1-deficient galactose-fed mice. Galactose feeding caused ECM protein deposition in the glomeruli and in the myocardium, which was prevented in the Akt knockout mice. NF-kappaB and AP-1 activation was pronounced in galactose-fed wild-type mice and prevented in the galactose-fed Akt1/PKBalpha-deficient group. In the retina and kidney, Ser473 was the predominant site for Akt phosphorylation, whereas in the heart it was Thr308. Parallel experiment in streptozotocin-induced diabetic animals showed similar results. The data from this study indicate that hyperhexosemia-induced Akt/PKB activation may be an important mechanism leading to NF-kappaB and AP-1 activation and increased ECM protein synthesis in the organs affected by chronic diabetic complications.
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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".