PDK4 Inhibition Ameliorates Melatonin Therapy by Modulating Cerebral Metabolism and Remyelination in an EAE Demyelinating Mouse Model of Multiple Sclerosis
Bibliographic record
Abstract
We recently showed that melatonin ameliorates the severity of experimental autoimmune encephalomyelitis (EAE), an animal model of MS. However, efficiency of melatonin therapy was associated with side effects, manifested by slowing down of remyelination, through increasing the inhibitory effects of brain pyruvate dehydrogenase kinase-4 (PDK-4) on pyruvate dehydrogenase complex (PDC), a key enzyme in fatty acid (FA) synthesis during remyelination. In this study, we investigated the metabolic profile of FA synthesis using combination therapy of melatonin and diisopropylamine dichloroacetate (DADA), a PDK4 inhibitor, in EAE mice. Disease progression was monitored by recording the disability scores. Immunological, oligodendrogenesis and metabolic factors were also evaluated. Results showed that combination therapy of melatonin and DADA significantly reduced EAE disability scores, compared to melatonin, whereas DADA alone did not have any effect. In addition, co-therapy inhibited pro-inflammatory while increasing anti-inflammatory cytokines, significantly better than melatonin alone. Moreover, administration of combination drugs recovered the declined expression of oligodendrocytic markers in EAE, more potently than melatonin. Furthermore, co-therapy affected cerebral energy metabolism by significantly reducing lactate levels while increasing N-acetylaspartate (NAA) and 3-hydroxy-3-methyl-glutaryl-coenzyme-A reductase (HMGCR) levels. Finally, while melatonin increased lactate and PDK4 expression levels and greatly reduced PDC activity, co-therapy significantly restored PDC function while reducing the lactate levels. In summary, administration of melatonin with DADA increased the efficiency of melatonin treatment by eliminating the inhibitory effects of PDK4 on PDC's function, a critical step for proper FA synthesis during remyelination.
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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 | 0.000 |
| 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) | 0.001 | 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".