Bibliographic record
Abstract
The objective of this study was to assess the role of the mechanistic target of rapamycin complex 1 (mTORC1) in sensing glucose levels and controlling its metabolism in bovine mammary epithelial cells (BMEC). Primary BMEC were isolated from lactating mammary tissue of 3 independent cows. Lactogenic differentiation of BMEC was induced by incubation in DMEM/F12 supplemented with 5 µg/mL insulin, 5 µg/mL prolactin and 5 µg/mL hydrocortisone for 4 days. Protein abundance and site-specific phosphorylation were measured by immunoblotting. Relative mRNA transcript abundance was measured by real-time qPCR. Data were analyzed using a randomized complete block design using PROC MIXED in SAS. To determine the effect of glucose availability on mTORC1 activity, BMEC were incubated in the presence or absence of 4 mmol/L of glucose for up to 4 h. Deprivation of glucose decreased mTORC1 phosphorylation at Ser2448 by 73% (p <0.05) at 4 h compared to cells incubated with glucose. To assess the role of mTORC1 on energy metabolism in BMEC, cells were treated with 100 nM rapamycin, a specific inhibitor of mTORC1, or with a vehicle (control) for 16 h. Phosphorylation of mTORC1 targets 4EBP1 Thr70 and S6K1 Thr389 was reduced by 60% and 55% (p <0.05), respectively, in BMEC treated with rapamycin compared to control cells, confirming inhibition of mTORC1. We then measured the mRNA abundance of key proteins involved in glucose uptake and metabolism. The expression of SLC2A1, which encodes for GLUT1, decreased by 53% (p <0.05) in rapamycin-treated BMEC. Rapamycin also reduced expression VEGF-A, a transcriptional target of HIF-1α, and the glycolytic gene PFKFB1 (p <0.05), but not of G6PD and PGK1. These results demonstrate that mTORC1 senses physiological glucose levels and is involved in control of genes implicated in glycolysis, possibly downstream of HIF-1α, in BMEC.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.010 |
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".