Identification of stress specific autophagy regulators from tandem CRISPR screens
Bibliographic record
Abstract
Abstract Autophagy is a conserved degradative process that promotes cellular homeostasis under stress conditions. Under nutrient starvation autophagy is largely non-selective, promoting the indiscriminate breakdown of cytosolic components. Conversely, selective autophagy is responsible for the specific turnover of damaged organelles including endoplasmic reticula, lysosomes, mitochondria, and peroxisomes. The mechanisms of selective autophagy are best understood through the activity of cargo-specific receptors called autophagy receptors, which facilitate the engulfment of the targeted cargo within autophagosomes, leading to subsequent degradation. We hypothesized that selective autophagy may be regulated by distinct upstream signaling from starvation induced autophagy, providing an additional layer of regulatory control to targeted autophagic degradation. To comprehensively 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 established and novel autophagy shared regulators under these conditions, as well as kinases specifically required for ER-phagy or pexophagy. More specifically, CDK11A and NME3 were further characterized to be selective ER-phagy regulators. Meanwhile, PAN3 and CDC42BPG were identified as activator or inhibitor of pexophagy, respectively. Collectively, these datasets provide the first comparative description of the kinase signaling specificity, separating regulation of selective autophagy and bulk autophagy. Highlights Parallel pooled kinome genetic knockout screens reveal known and novel regulators of autophagy under basal conditions, nutrient starvation, ER stress, and peroxisomal stress Selective ER and peroxisomal autophagy both have unique activators and inhibitors that distinguish them from bulk autophagy CDK11A and NME3 specifically induce and inhibit ER-phagy, respectively PAN3 and CDC42BPG specifically induce and inhibit pexophagy, respectively
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".