Identification of organelle-specific autophagy regulators from tandem CRISPR screens
Bibliographic record
Abstract
Autophagy is a conserved degradative process that promotes cellular homeostasis under stress conditions. Under nutrient starvation, autophagy is nonselective, promoting indiscriminate breakdown of cytosolic components. Conversely, selective autophagy is responsible for the specific turnover of damaged organelles. We hypothesized that selective autophagy may be regulated by signaling pathways distinct from those controlling starvation-induced autophagy, thereby promoting organelle turnover. To address this question, we conducted kinome-wide CRISPR screens to identify distinct signaling pathways responsible for the regulation of basal autophagy, starvation-induced autophagy, and two types of selective autophagy, ER-phagy and pexophagy. These parallel screens identified both known and novel autophagy regulators, some common to all conditions and others specific to selective autophagy. More specifically, CDK11A and NME3 were further characterized to be selective ER-phagy regulators. Meanwhile, PAN3 and CDC42BPG were identified as an activator and inhibitor of pexophagy, respectively. Collectively, these datasets provide the first comparative description of the kinase signaling that defines the regulation of selective autophagy and bulk autophagy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".