High-content screening identifies a role for Na <sup>+</sup> channels in insulin production
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it