Discovery of Novel Glucose-Regulated Proteins in Isolated Human Pancreatic Islets Using LC–MS/MS-Based Proteomics
Bibliographic record
Abstract
The prevalence of diabetes mellitus is increasing dramatically throughout the world, and the disease has become a major public health issue. The most common form of the disease, type 2 diabetes, is characterized by insulin resistance and insufficient insulin production from the pancreatic beta-cell. Since glucose is the most potent regulator of beta-cell function under physiological conditions, identification of the insulin secretory defect underlying type 2 diabetes requires a better understanding of glucose regulation of human beta-cell function. To this aim, a bottom-up LC-MS/MS-based proteomics approach was used to profile pooled islets from multiple donors under basal (5 mM) or high (15 mM) glucose conditions. Our analysis discovered 256 differentially abundant proteins (∼p < 0.05) after 24 h of high glucose exposure from more than 4500 identified in total. Several novel glucose-regulated proteins were elevated under high glucose conditions, including regulators of mRNA splicing (pleiotropic regulator 1), processing (retinoblastoma binding protein 6), and function (nuclear RNA export factor 1), in addition to neuron navigator 1 and plasminogen activator inhibitor 1. Proteins whose abundances markedly decreased during incubation at 15 mM glucose included Bax inhibitor 1 and synaptotagmin-17. Up-regulation of dicer 1 and SLC27A2 and down-regulation of phospholipase Cβ4 were confirmed by Western blots. Many proteins found to be differentially abundant after high glucose stimulation are annotated as uncharacterized or hypothetical. These findings expand our knowledge of glucose regulation of the human islet proteome and suggest many hitherto unknown responses to glucose that require additional studies to explore novel functional roles.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".