The Identification of Potential Factors Associated with the Development of Type 2 Diabetes
Bibliographic record
Abstract
Type 2 diabetes (T2D) arises when pancreatic beta-cells fail to compensate for systemic insulin resistance with appropriate insulin secretion. However, the link between insulin resistance and beta-cell failure in T2D is not fully understood. To explore this association, we studied transgenic MKR mice that initially develop insulin resistance in skeletal muscle but by 8 weeks of age have T2D. In the present study, global islet protein and gene expression changes were characterized in diabetic MKR versus non-diabetic control mice at 10 weeks of age. Using a quantitative proteomics approach (isobaric tags for relative and absolute quantification (iTRAQ)), 159 proteins were differentially expressed in MKR compared with control islets. Marked up-regulation of protein biosynthesis and endoplasmic reticulum stress pathways and parallel down-regulation in insulin processing/secretion, energy utilization, and metabolism were observed. A fraction of the differentially expressed proteins identified (including GLUT2, DNAJC3, VAMP2, RAB3A, and PC1/3) were linked previously to insulin-secretory defects and T2D. However, many proteins for the first time were associated with islet dysfunction, including the unfolded protein response proteins (ERP72, ERP44, ERP29, PPIB, FKBP2, FKBP11, and DNAJB11), endoplasmic reticulum-associated degradation proteins (VCP and UFM1), and multiple proteins associated with mitochondrial energy metabolism (NDUFA9, UQCRH, COX2, COX4I1, COX5A, ATP6V1B2, ATP6V1H, ANT1, ANT2, ETFA, and ETFB). The mRNA expression level corresponding to these proteins was examined by microarray, and then a small subset was validated using quantitative real time PCR and Western blot analyses. Importantly approximately 54% of differentially expressed proteins in MKR islets (including proteins involved in proinsulin processing, protein biosynthesis, and mitochondrial oxidation) showed changes in the proteome but not transcriptome, suggesting post-transcriptional regulation. These results underscore the importance of integrated mRNA and protein expression measurements and validate the use of the iTRAQ method combined with microarray to assess global protein and gene changes involved in the development of T2D.
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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 | 0.001 |
| 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) | 0.001 | 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".