Sex-specific microRNAome deregulation in the shielded bystander spleen of cranially exposed mice
Bibliographic record
Abstract
The bystander effect is a phenomenon that occurs when exposed cells signal distress to their naïve, unexposed neighbors. It is now accepted as a ubiquitous consequence of radiation exposure. It is well documented to occur in cultured cells, 3D tissue models, and in organs and organisms. Notwithstanding, the exact mechanisms of the bystander effect remain unclear. Recent studies hinted that bystander effects may, in part, be distinct in males and females, and possibly mediated via short non-coding RNAs, specifically, microRNAs. MicroRNAs are small, abundant and capable of regulating the expression of a wide variety of targets. Yet, their roles in bystander effects have not been analyzed in detail. The mechanisms behind sex differences observed in in vivo bystander effects also remain to be uncovered. We hypothesized that the radiation-induced expression of microRNAs in exposed and bystander tissue may be distinct in males and females. Using a well-establish bystander mouse model when the animal's head is exposed, while the body is completely protected by a medical-grade shield, we have for the first time shown that radiation exposure triggers a significant and sex-specific deregulation of the microRNAome in the non-exposed bystander spleen. The altered miRNA levels were paralleled by sex-specific changes in the levels of the miRNA processing enzyme Dicer and components of the RNA-induced silencing complex (RISC). Sterilization of animals resulted in drastic microRNAome alterations and significantly affected radiation and bystander miRNA responses. Our data may provide a roadmap for further analysis of the role of microRNAome in genotoxic stress responses and may help us explain sex specificity of radiation-induced carcinogenesis.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".