Offspring of Mice Exposed to a Low-Protein Diet in Utero Demonstrate Changes in mTOR Signaling in Pancreatic Islets of Langerhans, Associated with Altered Glucagon and Insulin Expression and a Lower β-Cell Mass
Bibliographic record
Abstract
Low birth weight is a risk factor for gestational and type 2 diabetes (T2D). Since mammalian target of rapamycin (mTOR) controls pancreatic β-cell mass and hormone release, we hypothesized that nutritional insult in utero might permanently alter mTOR signaling. Mice were fed a low-protein (LP, 8%) or control (C, 20%) diet throughout pregnancy, and offspring examined until 130 days age. Mice receiving LP were born 12% smaller and β-cell mass was significantly reduced throughout life. Islet mTOR levels were lower in LP-exposed mice and localized predominantly to α-rather than β-cells. Incubation of isolated mouse islets with rapamycin significantly reduced cell proliferation while increasing apoptosis. mRNA levels for mTORC complex genes mTOR, Rictor and Raptor were elevated at 7 days in LP mice, as were the mTOR and Raptor proteins. Proglucagon gene expression was similarly increased, but not insulin or the immune/metabolic defense protein STING. In human and mouse pancreas STING was strongly associated with islet β-cells. Results support long-term changes in islet mTOR signaling in response to nutritional insult in utero, with altered expression of glucagon and insulin and a reduced β-cell mass. This may contribute to an increased risk of gestational or type 2 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".