Islet prohormone processing in health and disease
Bibliographic record
Abstract
Biosynthesis of peptide hormones by pancreatic islet endocrine cells is a tightly orchestrated process that is critical for metabolic homeostasis. Like neuroendocrine peptides, insulin and other islet hormones are first synthesized as larger precursor molecules that are processed to their mature secreted products through a series of proteolytic cleavages, mediated by the prohormone convertases Pc1/3 and Pc2, and carboxypeptidase E. Additional posttranslational modifications including C‐terminal amidation of the β‐cell peptide islet amyloid polypeptide (IAPP) by peptidyl‐glycine α‐amidating monooxygenase (Pam) may also occur. Genome‐wide association studies (GWAS) have showed genetic linkage of these processing enzymes to obesity, β‐cell dysfunction, and type 2 diabetes (T2D), pointing to their important roles in metabolism and blood glucose regulation. In both type 1 diabetes (T1D) and T2D, and in the face of metabolic or inflammatory stresses, islet prohormone processing may become impaired; indeed elevated proinsulin:insulin (PI:I) ratios are a hallmark of the β‐cell dysfunction in T2D. Recent studies suggest that genetic or acquired defects in proIAPP processing may lead to the production and secretion of incompletely processed forms of proIAPP that could contribute to T2D pathogenesis, and additionally that impaired processing of both PI and proIAPP may be characteristic of β‐cell dysfunction in T1D. In islet α‐cells, the prohormone proglucagon is normally processed to bioactive glucagon by Pc2 but may express Pc1/3 under certain conditions leading to production of GLP‐1(7‐36 NH2 ). A better understanding of how β‐cell processing of PI and proIAPP, as well as α‐cell processing of proglucagon, are impacted by genetic susceptibility and in the face of diabetogenic stresses, may lead to new therapeutic approaches for improving islet function in diabetes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".