The stress response factor daf-16/FOXO is required for multiple compound families to prolong the function of neurons with Huntington’s disease
Bibliographic record
Abstract
Helping neurons to compensate for proteotoxic stress and maintain function over time (neuronal compensation) has therapeutic potential in aging and neurodegenerative disease. The stress response factor FOXO3 is neuroprotective in models of Huntington's disease (HD), Parkinson's disease and motor-neuron diseases. Neuroprotective compounds acting in a FOXO-dependent manner could thus constitute bona fide drugs for promoting neuronal compensation. However, whether FOXO-dependent neuroprotection is a common feature of several compound families remains unknown. Using drug screening in C. elegans nematodes with neuronal expression of human exon-1 huntingtin (128Q), we found that 3ß-Methoxy-Pregnenolone (MAP4343), 17ß-oestradiol (17ßE2) and 12 flavonoids including isoquercitrin promote neuronal function in 128Q nematodes. MAP4343, 17ßE2 and isoquercitrin also promote stress resistance in mutant Htt striatal cells derived from knock-in HD mice. Interestingly, daf-16/FOXO is required for MAP4343, 17ßE2 and isoquercitrin to sustain neuronal function in 128Q nematodes. This similarly applies to the GSK3 inhibitor lithium chloride (LiCl) and, as previously described, to resveratrol and the AMPK activator metformin. Daf-16/FOXO and the targets engaged by these compounds define a sub-network enriched for stress-response and neuronally-active pathways. Collectively, these data highlights the dependence on a daf-16/FOXO-interaction network as a common feature of several compound families for prolonging neuronal function in HD.
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) | 0.002 | 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".