Faculty Opinions recommendation of Genome-Wide Screens Reveal that Resveratrol Induces Replicative Stress in Human Cells.
Bibliographic record
Abstract
Resveratrol is a natural product associated with wide-ranging effects in animal and cellular models, including lifespan extension. To identify the genetic target of resveratrol in human cells, we conducted genome-wide CRISPR-Cas9 screens to pinpoint genes that confer sensitivity or resistance to resveratrol. An extensive network of DNA damage response and replicative stress genes exhibited genetic interactions with resveratrol and its analog pterostilbene. These genetic profiles showed similarity to the response to hydroxyurea, an inhibitor of ribonucleotide reductase that causes replicative stress. Resveratrol, pterostilbene, and hydroxyurea caused similar depletion of nucleotide pools, inhibition of replication fork progression, and induction of replicative stress. The ability of resveratrol to inhibit cell proliferation and S phase transit was independent of the histone deacetylase sirtuin 1, which has been implicated in lifespan extension by resveratrol. These results establish that a primary impact of resveratrol on human cell proliferation is the induction of low-level replicative stress.Copyright © 2020 Elsevier Inc. All rights reserved. PMID: 32755594 Funding information This work was supported by: CIHR, Canada Grant ID: FDN-167277 CIHR, Canada Grant ID: 163051 CIHR, Canada Grant ID: 148936
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.025 |
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".