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Record W4415954563 · doi:10.3390/cimb47110923

Molecular Mechanisms of DNA Damage Response and Epigenetic Regulation in Cold-Adapted Species: Implications for Genome Stability and Molecular Network Perspective

2025· review· en· W4415954563 on OpenAlexaff
Olawale O. Taiwo, Waliu Alaka, Kenneth B. Storey

Bibliographic record

VenueCurrent Issues in Molecular Biology · 2025
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicEpigenetics and DNA Methylation
Canadian institutionsWestern UniversityCarleton University
Fundersnot available
KeywordsEpigeneticsDNA damageDNA repairChromatinHistoneReprogrammingDNA methylationEpigenomicsDNA mismatch repair

Abstract

fetched live from OpenAlex

Cold-adapted species display remarkable genomic resilience under prolonged freezing and thawing cycles that would be lethal to most organisms. This review synthesizes current knowledge on the molecular mechanisms of DNA damage response (DDR) and epigenetic regulation that collectively safeguard genome integrity in these organisms. We highlight key DNA repair pathways, including base excision repair (BER), nucleotide excision repair (NER), homologous recombination (HR), and non-homologous end joining (NHEJ), that are activated during freeze-thaw stress to repair oxidative and strand break damage. Epigenetic regulators such as DNA methyltransferases (DNMTs), histone methyltransferases, and histone deacetylases (HDACs) dynamically remodel chromatin and modulate DDR signaling, facilitating efficient repair and transcriptional reprogramming during recovery. Comparative evidence from freeze-tolerant vertebrates, hibernating mammals, and polar fish underscores the conservation of these adaptive pathways across taxa. Integrating these insights provides a molecular network perspective (MNP) linking DDR and epigenetic mechanisms to environmental resilience, with potential applications in crop improvement and biotechnological adaptation strategies for extreme environments.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.587
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.032
GPT teacher head0.369
Teacher spread0.337 · 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 designBench or experimental
Domainnot available
GenreReview

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

Citations1
Published2025
Admission routes1
Has abstractyes

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