Regenerating 1 and 3b Gene Expression in the Pancreas of Type 2 Diabetic Goto-Kakizaki (GK) Rats
Bibliographic record
Abstract
Regenerating (REG) proteins are associated with islet development, β-cell damage, diabetes and pancreatitis. Particularly, REG-1 and REG-3-beta are involved in cell growth/survival and/or inflammation and the Reg1 promoter contains interleukin-6 (IL-6)-responsive elements. We showed by transcriptome analysis that islets of Goto-Kakizaki (GK) rats, a model of spontaneous type 2 diabetes, overexpress Reg1, 3α, 3β and 3γ, vs Wistar islets. Goto-Kakizaki rat islets also exhibit increased cytokine/chemokine expression/release, particularly IL-6. Here we analyzed Reg1 and Reg3β expression and REG-1 immuno-localization in the GK rat pancreas in relationship with inflammation. Isolated pancreatic islets and acinar tissue from male adult Wistar and diabetic GK rats were used for quantitative RT-PCR analysis. REG-1 immunohistochemistry was performed on paraffin sections with a monoclonal anti-rat REG-1 antibody. Islet cytokine/chemokine release was measured after 48 h-culture. Islet macrophage-positive area was quantified on cryostat sections using anti-CD68 and major histocompatibility complex (MHC) class II antibodies. Pancreatic exocrine-to-endocrine Reg1 and Reg3β mRNA ratios were markedly increased in Wistar vs GK rats. Conversely, both genes were upregulated in isolated GK rat islets. These findings were unexpected, because Reg genes are expressed in the pancreatic acinar tissue. However, we observed REG-1 protein labeling in acinar peri-ductal tissue close to islets and around large, often disorganized, GK rat islets, which may retain acinar cells due to their irregular shape. These large islets also showed peri-islet macrophage infiltration and increased release of various cytokines/chemokines, particularly IL-6. Thus, IL-6 might potentially trigger acinar REG-1 expression and secretion in the vicinity of large diabetic GK rat islets. This increased acinar REG-1 expression might reflect an adaptive though unsuccessful response to deleterious microenvironment.
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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 | 0.001 | 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) | 0.002 | 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".