Lactate preconditioning promotes a HIF-1α-mediated metabolic shift from OXPHOS to glycolysis in normal human diploid fibroblasts
Bibliographic record
Abstract
Recent evidence has emerged that cancer cells can use various metabolites as fuel sources. Restricting cultured cancer cells to sole metabolite fuel sources can promote metabolic changes leading to enhanced glycolysis or mitochondrial OXPHOS. However, the effect of metabolite-restriction on non-transformed cells remains largely unexplored. Here we examined the effect of restricting media fuel sources, including glucose, pyruvate or lactate, on the metabolic state of cultured human dermal fibroblasts. Fibroblasts cultured in lactate-only medium exhibited reduced PDH phosphorylation, indicative of OXPHOS, and a concurrent elevation of ROS. Lactate exposure primed fibroblasts to switch to glycolysis by increasing transcript abundance of genes encoding glycolytic enzymes and, upon exposure to glucose, increasing glycolytic enzyme levels. Furthermore, lactate treatment stabilized HIF-1α, a master regulator of glycolysis, in a manner attenuated by antioxidant exposure. Our findings indicate that lactate preconditioning primes fibroblasts to switch from OXPHOS to glycolysis metabolism, in part, through ROS-mediated HIF-1α stabilization. Interestingly, we found that lactate preconditioning results in increased transcript abundance of MYC and SNAI1, key facilitators of early somatic cell reprogramming. Defined metabolite treatment may represent a novel approach to increasing somatic cell reprogramming efficiency by amplifying a critical metabolic switch that occurs during iPSC generation.
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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".