Cystic fibrosis-related diabetes is associated with reduced islet protein expression of GLP-1 receptor and perturbation of cell-specific transcriptional programs
Bibliographic record
Abstract
Insulin secretion is impaired in individuals with cystic fibrosis (CF), contributing to high rates of CF-related diabetes (CFRD) and substantially increasing disease burden. To develop improved therapies for CFRD, better knowledge of pancreatic pathology in CF is needed. Glucagon like peptide-1 (GLP-1) from islet α cells potentiates insulin secretion by binding GLP-1 receptors (GLP-1Rs) on β cells. We determined whether expression of GLP-1 and/or its signaling components are reduced in CFRD, thereby contributing to impaired insulin secretion. Immunohistochemistry of pancreas from humans with CFRD versus no-CF/no-diabetes revealed no difference in GLP-1 immunoreactivity per islet area, whereas GLP-1R immunoreactivity per islet area or per insulin-positive islet area was reduced in CFRD. Using spatial transcriptomics, we observed several differentially expressed α- and/or β-cell genes between CFRD and control pancreas. In CFRD, we found upregulation of α-cell PCSK1 which encodes the enzyme (PC1/3) that generates GLP-1, and downregulation of α-cell PCSK1N which inhibits PC1/3. Gene set enrichment analysis also revealed α and β cell-specific pathway dysregulation in CFRD. Together, our data suggest intra-islet GLP-1 is not limiting in CFRD, but its action may be restricted due to reduced GLP-1R protein levels. Thus, restoring β-cell GLP-1R protein expression may improve β-cell function in CFRD.
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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 | 0.001 |
| 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".