Heparanase Overexpression Induces Glucagon Resistance and Protects Animals From Chemically Induced Diabetes
Bibliographic record
Abstract
Heparanase, a protein with enzymatic and nonenzymatic properties, contributes toward disease progression and prevention. In the current study, a fortuitous observation in transgenic mice globally overexpressing heparanase (hep-tg) was the discovery of improved glucose homeostasis. We examined the mechanisms that contribute toward this improved glucose metabolism. Heparanase overexpression was associated with enhanced glucose-stimulated insulin secretion and hyperglucagonemia, in addition to changes in islet composition and structure. Strikingly, the pancreatic islet transcriptome was greatly altered in hep-tg mice, with >2,000 genes differentially expressed versus control. The upregulated genes were enriched for diverse functions including cell death regulation, extracellular matrix component synthesis, and pancreatic hormone production. The downregulated genes were tightly linked to regulation of the cell cycle. In response to multiple low-dose streptozotocin (STZ), hep-tg animals developed less severe hyperglycemia compared with wild-type, an effect likely related to their β-cells being more functionally efficient. In animals given a single high dose of STZ causing severe and rapid development of hyperglycemia related to the catastrophic loss of insulin, hep-tg mice continued to have significantly lower blood glucose. In these mice, protective pathways were uncovered for managing hyperglycemia and include augmentation of fibroblast growth factor 21 and glucagon-like peptide 1. This study uncovers the opportunity to use properties of heparanase in management of diabetes.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".