A platform for the systematic interrogation of genetic interactions in human cells: 26 gene by genome-wide double knockout screens as a proof of concept
Bibliographic record
Abstract
Abstract Genetic interactions are typically studied by looking at the phenotype that results from disruption of pairs of genes, as well as from higher order combinations of perturbations. Systematically interrogating all pairwise combinations provides insights into how genes are organized into pathways and complexes to sustain cellular homeostasis and how interacting genes respond to stressors and external signals. Genetic interactions have been studied extensively in yeast, due, in part, to the availability of a systematic collection of gene knockouts, and the development of Synthetic Genetic Array (SGA) technology. In contrast, such approaches are more challenging in human cells and therefore comparable data for human cells is scarce. This study introduces an innovative approach to functionally characterize genetic interactions in human cells through CRISPR/Cas9 screens using a pooled genome-wide knockout library in NALM6 cells. By combining a single guide RNA (sgRNA) targeting the gene of interest (aka the query) in cells already infected with an inducible genome-wide sgRNA pool, it is possible to achieve near saturation of genome-wide double knockouts. We conducted 26 of these screens, which we term “gene by genome-wide” knockout screens. This approach can be rapidly performed, in part, because it bypasses the need to generate genotyped isogenic knockout clones. Data from these screens identified both expected and novel synthetic lethal and synthetic rescue interactions, demonstrating that this strategy is effective for large-scale genetic research in human cells. Additionally, we show that these GBGW screens can be combined with chemical perturbation to reveal new synthetic interactions that are not apparent without drug treatment. Finally, we show that cDNA overexpression can be incorporated with genome-wide knockouts to systematically explore gain-of-function scenarios. The complete dataset is accessible on the ChemoGenix website (URL: https://chemogenix.iric.ca ).
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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 | 0.002 | 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".