Bibliographic record
Abstract
In 2011, Diabetes and prediabetes affected 9 million Canadians and 366 million people worldwide (Canadian Diabetes Website). The underlying pathophysiology of diabetes is beta cell dysfunction leading to loss of appropriate insulin secretion and resulting in hyperglycemia. I have focused on identifying critical molecular regulators of beta cell function and insulin secretion. The CRTC2-CREB pathway is required for maintaining beta cell mass and insulin secretion. I propose that identifying kinases that regulate CRTC-CREB activity will identify other important regulators of pancreatic beta cell survival and function. First, I have identified several AMP kinases as inhibitors of CRTC2-CREB that are activated by an upstream kinase, LKB1. I then went on to generate mice with a beta cell-specific deletion of LKB1 during adulthood. Loss of LKB1 increased insulin secretion and glucose clearance through enhanced beta cell mass and proliferation. The increased insulin secretion was largely the result of loss of AMPK activity and consequent constitutive mTor activity. AMPK is activated under starvation conditions and as such is thought to be a critical regulator of beta cell function. However, the decrease of AMPK activity in high glucose has been a strong argument against it being a critical effector of insulin secretion. I provide genetic evidence supporting the idea that AMPK activity attenuates insulin secretion. During periods of starvation where AMPK activity is high there is a chronic dampening effect on events that prepare beta cells for the next round of insulin secretion. Surprisingly, another downstream kinase of LKB1, SIK2, has opposing functions in the beta cell. I present evidence that the LKB1-AMPK axis attenuates beta cell functions and that targeting this pathway in beta cells may be of therapeutic benefit for T2D.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".