Discovery of pan autophagy inhibitors through a high-throughput screen highlights macroautophagy as an evolutionarily conserved process across 3 eukaryotic kingdoms
Bibliographic record
Abstract
Due to the involvement of macroautophagy/autophagy in different pathophysiological conditions such as infections, neurodegeneration and cancer, identification of novel small molecules that modulate the process is of current research and clinical interest. In this work, we developed a luciferase-based sensitive and robust kinetic high-throughput screen (HTS) of small molecules that modulate autophagic degradation of peroxisomes in the budding yeast Saccharomyces cerevisiae. Being a pathway-specific rather than a target-driven assay, we identified small molecule modulators that acted at key steps of autophagic flux. Two of the inhibitors, Bay11 and ZPCK, obtained from the screen were further characterized using secondary assays in yeast. Bay11 inhibited autophagy at a step before fusion with the vacuole whereas ZPCK inhibited the cargo degradation inside the vacuole. Furthermore, we demonstrated that these molecules altered the process of autophagy in mammalian cells as well. Strikingly, these molecules also modulated autophagic flux in a novel model plant, Aponogeton madagascariensis. Thus, using small molecule modulators identified by using a newly developed HTS autophagy assay, our results support that macroautophagy is a conserved process across fungal, animal and plant kingdoms.
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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 | 0.001 | 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".