Autophagy promotes cell survival by maintaining NAD(H) levels
Bibliographic record
Abstract
Abstract Autophagy is an essential catabolic process that promotes the clearance of surplus or damaged intracellular components1. As a recycling process, autophagy is also important for the maintenance of cellular metabolites to aid metabolic homeostasis2. Loss of autophagy in animal models or malfunction of this process in a number of age-related human pathologies, including neurodegenerative and lysosomal storage diseases, contributes to tissue degeneration3-9. However, it remains unclear which of the many cellular functions of autophagy primarily underlies its role in cell survival. Here we have identified an evolutionarily conserved role of autophagy from yeast to humans in the preservation of nicotinamide adenine dinucleotide (NAD+/NADH) levels, which are critical for cellular survival. In respiring cells, loss of autophagy caused hyperactivation of PARP and Sirtuin families of NADases. Uncontrolled depletion of NAD(H) pool by these enzymes resulted in mitochondrial membrane depolarisation and cell death. Supplementation with NAD(H) precursors improved cell viability in autophagy-deficient models including human pluripotent stem cell-derived neurons with autophagy deficiency or patient-derived neurons with autophagy dysfunction. Our study provides a mechanistic link between autophagy and NAD(H) metabolism, and suggests that boosting NAD(H) levels may have therapeutic benefits in human diseases associated with autophagy dysfunction.
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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) | 0.007 | 0.003 |
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".