High-content screening identifies a role for Na <sup>+</sup> channels in insulin production
Bibliographic record
Abstract
Insulin production is the central feature of functionally mature and differentiated pancreatic β-cells. Reduced insulin transcription and dedifferentiation have been implicated in type 2 diabetes, making drugs that could reverse these processes potentially useful. We have previously established ratiometric live-cell imaging tools to identify factors that increase insulin promoter activity and promote β-cell differentiation. Here, we present a single vector imaging tool with eGFP and mRFP, driven by the Pdx1 and Ins1 promoters, respectively, targeted to the nucleus to enhance identification of individual cells in a high-throughput manner. Using this new approach, we screened 1120 off-patent drugs for factors that regulate Ins1 and Pdx1 promoter activity in MIN6 β-cells. We identified a number of compounds that positively modulate Ins1 promoter activity, including several drugs known to modulate ion channels. Carbamazepine was selected for extended follow-up, as our previous screen also identified this use-dependent sodium channel inhibitor as a positive modulator of β-cell survival. Indeed, carbamazepine increased Ins1 and Ins2 mRNA in primary mouse islets at lower doses than were required to protect β-cells. We validated the role of sodium channels in insulin production by examining Nav1.7 (Scn9a) knockout mice and remarkably islets from these animals had dramatically elevated insulin content relative to wild-type controls. Collectively, our experiments provide a starting point for additional studies aimed to identify drugs and molecular pathways that control insulin production and β-cell differentiation status. In particular, our unbiased screen identified a novel role for a β-cell sodium channel gene in insulin production.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 0.001 |
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".