Identification of Novel Regulators of Radiosensitivity Using High-Throughput Genetic Screening
Bibliographic record
Abstract
The biological impact of ionizing radiation (IR) on humans depends not only on the physical properties and absorbed dose of radiation but also on the unique susceptibility of the exposed individual. A critical target of IR is DNA, and the DNA damage response is a safeguard mechanism for maintaining genomic integrity in response to the induced cellular stress. Unrepaired DNA lesions lead to various mutations, contributing to adverse health effects. Cellular sensitivity to IR is highly correlated with the ability of cells to repair DNA lesions, in particular coding sequences of genes that affect that process and of others that contribute to preserving genomic integrity. However, accurate profiling of the molecular events underlying individual sensitivity requires techniques with sensitive readouts. Here we summarize recent studies that have used whole-genome analysis and identified genes that impact individual radiosensitivity. Whereas microarray and RNA-seq provide a snapshot of the transcriptome, RNA interference (RNAi) and CRISPR-Cas9 techniques are powerful tools that enable modulation of gene expression and characterizing the function of specific genes involved in radiosensitivity or radioresistance. Notably, CRISPR-Cas9 has altered the landscape of genome-editing technology with its increased readiness, precision, and sensitivity. Identifying critical regulators of cellular radiosensitivity would help tailor regimens that enhance the efficacy of therapeutic treatments and fast-track prediction of clinical outcomes. It would also contribute to occupational protection based on average individual sensitivity, as well as the formulation of countermeasures to the harmful effects of radiation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".