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Record W2280623184 · doi:10.2172/1160287

Low Dose Radiation-Induced Epigenetic Changes in an Animal Model (Final Report)

2014· report· en· W2280623184 on OpenAlexaff
Olga Kovalchuk

Bibliographic record

Venuenot available
Typereport
Languageen
FieldMedicine
TopicEffects of Radiation Exposure
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsEpigeneticsDNA methylationBiologyHistoneAdaptive responseGenome instabilitymicroRNADNA damageGeneticsComputational biologyGeneGene expressionDNA

Abstract

fetched live from OpenAlex

This project sought mechanistic understanding of the epigenetic response of tissues as well as the consequences of those responses, when induced by low dose irradiation in a well-established model system (mouse). Based on solid and extensive preliminary data we investigated the molecular epigenetic mechanisms of in vivo radiation responses, particularly – effects of low, occupationally relevant radiation exposures on the genome stability and adaptive response in mammalian tissues and organisms. We accumulated evidence that low dose irradiation altered epigenetic profiles and impacted radiation target organs of the exposed animals. The main long-term goal was to dissect the epigenetic basis of induction of the low dose radiation-induced genome instability and adaptive response and the specific fundamental roles of epigenetic changes (i.e. DNA methylation, histone modifications and miRNAs) in their generation. We hypothesized that changes in global and regional DNA methylation, global histone modifications and regulatory microRNAs played pivotal roles in the generation and maintenance low-dose radiation-induced genome instability and adaptive response. We predicted that epigenetic changes influenced the levels of genetic rearrangements (transposone reactivation). We hypothesized that epigenetic responses from low dose irradiation were dependent on exposure regimes, and would be greatest when organisms are exposed in a protracted/fractionated manner: fractionated exposures > acute exposures. We anticipated that the epigenetic responses were correlated with the gene expression levels. Our immediate objectives were: • To investigate the exact nature of the global and locus-specific DNA methylation changes in the LDR exposed cells and tissues and dissect their roles in adaptive response • To investigate the roles of histone modifications in the low dose radiation effects and adaptive response • To dissect the roles of regulatory microRNAs and their targets in low dose radiation effects and adaptive response • To correlate the levels of epigenetic changes with genetic rearrangement levels and gene expression patterns. In sum, we determined the precise global and locus-specific DNA methylation patterns in the LDR-exposed cells and tissues of mice, and to correlated DNA methylation changes with the gene expression patterns and manifestations of genome instability. We also determined the alterations of global histone modification pattern in the LDR exposed tissues. Additionally, we established the nature of microRNAome changes in the LDR exposed tissue. In this study we for the first time found that LDR exposure caused profound tissue-specific epigenetic changes in the exposed tissues. We established that LDR exposure affect methylation of repetitive elements in the murine genome, causes changes in histone methylation, acetylation and phosphorylation. Importantly, we found that LDR causes profound and persistent effects on small RNA profiles and gene expression, and that miRNAs are excellent biomarkers of LDR exposure. Furthermore, we extended our analysis and studied LDR effects in rat tissues and human tissues and cell lines. There we also analyzed LDR-induced gene expression, DNA methylation and miRNA changes. Our datasets laid foundation for several new research projects aimed to understand molecular underpinnings of low dose radiation responses, and biological repercussions of low dose radiation effects and radiation carcinogenesis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.389
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.060
GPT teacher head0.353
Teacher spread0.294 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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