Mammalian autophagy is essential for hepatic and renal ketogenesis during starvation
Bibliographic record
Abstract
Autophagy is an intracellular degradation system activated, across species, by starvation. Although accumulating evidence has shown that mammalian autophagy is involved in pathogenesis of several modern diseases, its physiological role to combat starvation has not been fully clarified. In this study, we analysed starvation-induced gluconeogenesis and ketogenesis in mouse strains lacking autophagy in liver, skeletal muscle or kidney. Autophagy-deficiency in any tissue had no effect on gluconeogenesis during starvation. Though skeletal muscle- and kidney-specific autophagy-deficiency did not alter starvation-induced increases in blood ketone levels, liver-specific autophagy-deficiency significantly attenuated this effect. Interestingly, renal as well as hepatic expression of HMG-CoA synthase 2 increased with prolonged starvation. Furthermore, during starvation, mice lacking autophagy both in liver and kidney showed even lower blood ketone levels and physical activity than mice lacking autophagy only in liver. Starvation induced massive lipid droplet formation in extra-adipose tissues including liver and kidney, which was essential for ketogenesis. Moreover, this process was impaired in the autophagy-deficient liver and kidney. These findings demonstrate that hepatic and renal autophagy are essential for starvation-induced lipid droplet formation and subsequent ketogenesis and, ultimately, for maintaining systemic energy homeostasis. Our findings provide novel biological insights into adaptive mechanisms to combat starvation in mammals.
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) | 0.000 | 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".