Identification of Radiation-Responsive Genes<i>In Vitro</i>Using a Gene Trap Strategy Predicts for Modulation of Expression by Radiation<i>In Vivo</i>
Bibliographic record
Abstract
A large number of genes are known to be responsive to ionizing radiation, and there is strong evidence for the existence of inducible radiation resistance in mammalian cells. We have developed a gene trap insertional mutagenesis strategy to identify novel genes involved in responses to radiation. Using this approach, we have isolated four gene-trap integrations in embryonic stem cells. In three cases (9A, 3E and 9H) the trapped genes are radiation-inducible, and in one (7D) the gene is down-regulated. Sequence analysis of fusion transcripts from three of the integrations indicate one novel gene (3E), the mouse homologue (9A) of a known but uncharacterized human gene that encodes a protein with significant homology to several GTPase-activating proteins and a murine locus, Mym (9H). The embryonic stem cell clone with the 9A insertion was introduced into the mouse germline, and the in vivo expression pattern of 9A was studied in detail. A unique, spatially restricted pattern of expression in embryos and adult animals was observed. There is tissue-specific in vivo induction of the 9A gene in adult mice by radiation. This study demonstrates the potential of the gene trap approach for the identification and functional analysis of novel radiation-regulated genes. Similar strategies may facilitate the discovery and characterization of genes involved in other cellular stress responses.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".