High-resolution glucose tracing and in situ imaging reveals regulated lactate production in human pancreatic β cells
Bibliographic record
Abstract
ABSTRACT Using 13 C 6 glucose labeling coupled to GC-MS and 2D 1 H- 13 C HSQC NMR spectroscopy, we have obtained a comparative high-resolution map of glucose fate underpinning β cell function. In both mouse and human islets, the contribution of glucose to the TCA cycle is similar. Pyruvate-fueling of the TCA cycle is primarily mediated by the activity of pyruvate dehydrogenase, with lower flux through pyruvate carboxylase. While conversion of pyruvate to lactate by lactate dehydrogenase (LDH) can be detected in islets of both species, lactate accumulation is six-fold higher in human islets. Human islets express LDH, with low-moderate LDHA expression and β cell-specific LDHB expression. LDHB inhibition increases glucose-dependent lactate generation in mouse and human β cells, and decreases Ca 2+ -spiking frequency without affecting ATP/ADP levels. Thus, we show that LDHB limits glucose-stimulated lactate generation in β cells. Further studies are warranted to understand how lactate impacts β cell metabolism and/or function. HIGHLIGHTS Human and rodent islets generate lactate following glucose stimulation. β cells specifically express LDHB, which acts to limit lactate generation. LDHB inhibition influences Ca 2+ spiking frequency without affecting ATP/ADP ratio. eTOC Cuozzo et al show that glucose-stimulated rodent and human islets generate lactate. Transcriptomic and imaging analyses reveal that LDHB is specifically expressed in β cells and unexpectedly restrains lactate production. LDHB expression and thus regulated lactate generation might reflect a key mechanism underlying β cell metabolism, function and survival.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".