Dual action of L-Lactate on the activity of NR2B-containing NMDA receptors: from potentiation to neuroprotection
Bibliographic record
Abstract
Abstract L-Lactate is a positive modulator of NMDAR-mediated signaling resulting in plasticity gene induction and memory consolidation. However, L-Lactate is also able to protect neurons against excito-toxic NMDAR activity, an indication of a mitigating action of L-Lactate on NMDA signaling. In this study, we provide experimental evidence that resolves this apparent paradox. Transient co-application of glutamate/glycine (1 μM/100 μM; 2 min) in primary cultures of mouse cortical neurons triggers a NMDA-dependent Ca2+signal positively modulated by L-Lactate (10 mM) or DTT (1 mM) but decreased by Pyruvate (10 mM). This L-Lactate and DTT-induced potentiation is blocked by Ifenprodil (2 μM), a specific blocker of NMDARs containing NR2B sub-units. In contrast, co-application of glutamate/glycine (1 mM/100 μM; 2 min) elicits a NMDAR-dependent excitotoxic death in 49% of neurons. L-Lactate and Pyruvate significantly reduce this rate of cell death processes (respectively to 23% and 9%) while DTT has no effect (54% of neuronal death). This L-Lactate-induced neuroprotection is blocked by carbenoxolone and glibenclamide, respectively blockers of pannexins and KATP. In conclusion, our results show that L-Lactate is involved in two distinct and independent pathways defined as NMDAR-mediated potentiation pathway (or NADH pathway) and a neuroprotective pathway (or Pyruvate/ATP pathway), the prevalence of each one depending on the strength of the glutamatergic stimulus.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".