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Replication-stress-associated DSBs induced by ionizing radiation risk genomic destabilization and associated clonal evolution.

ABSTRACT: Exposure to ionizing radiation is associated with cancer risk. Although multiple types of DNA damage are caused by radiation, it remains unknown how this damage is associated with cancer risk. Here, we show that after repair of double-strand breaks (DSBs) directly caused by radiation (dir-DSBs), irradiated cells enter a state at higher risk of genomic destabilization due to accumulation of replication-stress-associated DSBs (rs-DSBs), ultimately resulting in clonal evolution of cells with abrogated defense systems. These effects were observed over broad ranges of radiation doses (0.25-2 Gy) and dose rates (1.39-909 mGy/min), but not upon high-dose irradiation, which caused permanent cell-cycle arrest. The resultant genomic destabilization also increased the risk of induction of single-nucleotide variants (SNVs), including radiation-associated SNVs, as well as structural alterations in chromosomes. Thus, the radiation-associated risk can be attributed to rs-DSB accumulation and resultant genomic destabilization.

SUBMITTER: Matsuno Y

PROVIDER: S-EPMC8042347 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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Replication-stress-associated DSBs induced by ionizing radiation risk genomic destabilization and associated clonal evolution.

Matsuno Yusuke Y Hyodo Mai M Suzuki Mafuka M Tanaka Yosuke Y Horikoshi Yasunori Y Murakami Yasufumi Y Torigoe Hidetaka H Mano Hiroyuki H Tashiro Satoshi S Yoshioka Ken-Ichi KI

iScience 20210315 4

Exposure to ionizing radiation is associated with cancer risk. Although multiple types of DNA damage are caused by radiation, it remains unknown how this damage is associated with cancer risk. Here, we show that after repair of double-strand breaks (DSBs) directly caused by radiation (dir-DSBs), irradiated cells enter a state at higher risk of genomic destabilization due to accumulation of replication-stress-associated DSBs (rs-DSBs), ultimately resulting in clonal evolution of cells with abroga ...[more]

PMID: 33870130

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Project description:Background: Gene signatures derived from transcriptomic data using machine learning methods have shown promise for biodosimetry testing. These signatures may not be sufficiently robust for large scale testing, as their performance has not been adequately validated on external, independent datasets. The present study develops human and murine signatures with biochemically-inspired machine learning that are strictly validated using k-fold and traditional approaches. Methods: Gene Expression Omnibus (GEO) datasets of exposed human and murine lymphocytes were preprocessed via nearest neighbor imputation and expression of genes implicated in the literature to be responsive to radiation exposure (n=998) were then ranked by Minimum Redundancy Maximum Relevance (mRMR). Optimal signatures were derived by backward, complete, and forward sequential feature selection using Support Vector Machines (SVM), and validated using k-fold or traditional validation on independent datasets. Results: The best human signatures we derived exhibit k-fold validation accuracies of up to 98% ( DDB2, PRKDC, TPP2, PTPRE, and GADD45A) when validated over 209 samples and traditional validation accuracies of up to 92% ( DDB2, CD8A, TALDO1, PCNA, EIF4G2, LCN2, CDKN1A, PRKCH, ENO1, and PPM1D) when validated over 85 samples. Some human signatures are specific enough to differentiate between chemotherapy and radiotherapy. Certain multi-class murine signatures have sufficient granularity in dose estimation to inform eligibility for cytokine therapy (assuming these signatures could be translated to humans). We compiled a list of the most frequently appearing genes in the top 20 human and mouse signatures. More frequently appearing genes among an ensemble of signatures may indicate greater impact of these genes on the performance of individual signatures. Several genes in the signatures we derived are present in previously proposed signatures. Conclusions: Gene signatures for ionizing radiation exposure derived by machine learning have low error rates in externally validated, independent datasets, and exhibit high specificity and granularity for dose estimation.

Project description:BackgroundAdaptation to a stressor can lead to costs on other traits. These costs play an unavoidable role on fitness and influence the evolutionary trajectory of a population. Host defense seems highly subject to these costs, possibly because its maintenance is energetically costly but essential to the survival. When assessing the ecological risk related to pollution, it is therefore relevant to consider these costs to evaluate the evolutionary consequences of stressors on populations. However, to the best of our knowledge, the effects of evolution in irradiate environment on host defense have never been studied. Using an experimental evolution approach, we analyzed fitness across 20 transfers (about 20 generations) in Caenorhabditis elegans populations exposed to 0, 1.4, and 50.0 mGy.h- 1 of 137Cs gamma radiation. Then, populations from transfer 17 were placed in the same environmental conditions without irradiation (i.e., common garden) for about 10 generations before being exposed to the bacterial parasite Serratia marcescens and their survival was estimated to study host defense. Finally, we studied the presence of an evolutionary trade-off between fitness of irradiated populations and host defense.ResultsWe found a lower fitness in both irradiated treatments compared to the control ones, but fitness increased over time in the 50.0 mGy.h- 1, suggesting a local adaptation of the populations. Then, the survival rate of C. elegans to S. marcescens was lower for common garden populations that had previously evolved under both irradiation treatments, indicating that evolution in gamma-irradiated environment had a cost on host defense of C. elegans. Furthermore, we showed a trade-off between standardized fitness at the end of the multigenerational experiment and survival of C. elegans to S. marcescens in the control treatment, but a positive correlation between the two traits for the two irradiated treatments. These results indicate that among irradiated populations, those most sensitive to ionizing radiation are also the most susceptible to the pathogen. On the other hand, other irradiated populations appear to have evolved cross-resistance to both stress factors.ConclusionsOur study shows that adaptation to an environmental stressor can be associated with an evolutionary cost when a new stressor appears, even several generations after the end of the first stressor. Among irradiated populations, we observed an evolution of resistance to ionizing radiation, which also appeared to provide an advantage against the pathogen. On the other hand, some of the irradiated populations seemed to accumulate sensitivities to stressors. This work provides a new argument to show the importance of considering evolutionary changes in ecotoxicology and for ecological risk assessment.

Dataset Information

Replication-stress-associated DSBs induced by ionizing radiation risk genomic destabilization and associated clonal evolution.

Publications

Replication-stress-associated DSBs induced by ionizing radiation risk genomic destabilization and associated clonal evolution.

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