Differential Expression of Genes Associated with the Progression of Renal Disease in the Kidneys of Liver-Specific Glucokinase Gene Knockout Mice
Bibliographic record
Abstract
Liver glucokinase (GCK) deficient mice possess mild renal complications associated with diabetes. To investigate the progression of kidney disease and identify candidate genes involved in the pathogenesis of renal damage, we examined changes in tissue structure and gene expression in the kidneys of liver-specific GCK knockout (gckw/-) mice and age-matched normal wild-type control (gckw/w) mice as they aged. Suppression subtractive hybridization (SSH) was used to identify candidate genes that showed a pattern of differential expression between kidneys of gckw/- and gckw/w mice at 60 weeks of age. Differential expression of the candidate genes was examined by real-time qPCR in liver-specific gckw/- and gckw/w mice at 16, 26, 40, 60, and 85 weeks of age. Among the candidate genes, only glutathione peroxidase-3 (GPX3) was confirmed to show differential expression by qPCR in the 60-week old mice, however two others genes, MALAT1 and KEG, showed significant changes at other ages. This study shows that liver-specific glucokinase deficient mice display changes in kidney morphology by 40 weeks of age, and that renal complication may be correlated with a reduction in GPX3 levels. Since decreased GPX3 mRNA expression was observed at 26 weeks, which is younger than the age when pathological changes can be seen in kidney biopsies, GPX3 may serve as an early marker for kidney damage.
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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) | 0.001 | 0.001 |
| 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".