Functional Proteomics Analysis to Study ATM Dependent Signaling in Response to Ionizing Radiation

Cheema, Amrita K.; Varghese, Rency S.; Timofeeva, Olga; Zhang, Lihua; Kirilyuk, Alexander; Zandkarimi, Fereshteh; Kaur, P.; Ressom, Habtom W.; Jung, Mira; Dritschilo, Anatoly

doi:10.1667/rr3198.1

Cited by 12 publications

(12 citation statements)

References 37 publications

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“…More directly, Ephrin A, ERBB4, and Neuregulins have been identified for their potential roles as radiation-associated carcinogenesis biomarkers. Cheema et al 46 showed that the Ephrin signaling pathway responds to irradiation in an ATM-dependent manner. ERBB4 is part of the epidermal growth factor receptor pathway that is activated by radiation, resulting in downstream effects on MAPK and cell proliferation 47 .…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Methods to Identify Genetic Correlates of Radiation-Associated Contralateral Breast Cancer in the WECARE Study

Lee

Liang

Woods

et al. 2019

Preprint

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The purpose of this study is to identify germline single nucleotide polymorphisms (SNPs) that optimally predict radiation-associated contralateral breast cancer (RCBC) and to provide new biological insights into the carcinogenic process.Fifty-two women with contralateral breast cancer and 153 women with unilateral breast cancer were identified within the Women's Environmental Cancer and Radiation Epidemiology (WECARE) Study who were at increased risk of RCBC because they were ≤ 40 years of age at first diagnosis of breast cancer and received a scatter radiation dose > 1 Gy to the contralateral breast. A previously reported algorithm, preconditioned random forest regression, was applied to predict the risk of developing RCBC. The resulting model produced an area under the curve of 0.62 (p=0.04) on hold-out validation data. The biological analysis identified the cyclic AMP-mediated signaling and Ephrin-A as significant biological correlates, which were previously shown to influence cell survival after radiation in an ATM-dependent manner. The key connected genes and proteins that are identified in this analysis were previously identified as relevant to breast cancer, radiation response, or both. In summary, machine learning/bioinformatics methods applied to genome-wide genotyping data have great potential to reveal plausible biological correlates associated with the risk of RCBC.

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Section: Discussionmentioning

confidence: 99%

Machine Learning Methods to Identify Genetic Correlates of Radiation-Associated Contralateral Breast Cancer in the WECARE Study

Lee

Liang

Woods

et al. 2019

Preprint

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“…Besides examining tumor microenvironment, other efforts are made to discover biomarkers useful for predicting outcomes [46][47][48][49][50][51][52][53]. However, the results of these studies are yet of insufficient reliability to be employed in the clinical setting.…”

Section: Proteome Analysis As a Radiation Therapy Predictormentioning

confidence: 99%

Radiation proteomics: A brief overview

Leszczyński

2014

Proteomics

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Acute biological effects caused by the exposure to high doses of radiation, either ionizing or nonionizing, are relatively well-known but the delayed effects, occurring decades after exposure, are difficult to predict. The knowledge of the acute and delayed effects of the low doses of ionizing radiation (e.g. bystander effect) or nonionizing radiation (e.g. radiation emitted by wireless communication devices) is not yet reliably established. Often the acute effects of low doses are small and difficult to discover and replicate in scientific studies. Chronic effects of prolonged exposures to low-dose radiation for decades are virtually unknown and often not possible to predict on the basis of the knowledge gained from acute exposures to high doses of radiation. Physiological significance of the biological effects induced by low doses of radiation is not known. The same lack of predictability of outcomes applies to the delayed effects of high-dose radiation exposures. Proteomics, supplemented with other "omics" techniques, might be the best way forward to find out the target molecules of radiation, the biomarkers of radiation exposure and the physiological and health significance of the acute and delayed biological effects caused by the exposures to high- and low-dose radiation. However, the currently available database of radiation effects on proteomes is far too small to be useful in formulation of new hypotheses concerning health consequences of radiation exposures.

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“…These signaling pathways are governed by translational and post-translational events, leading to the need for analysis of differential protein levels. Since the primary focus has been on the radiation stress response, global changes in the proteome were determined by comparing 2-D gel patterns with and without radiation exposure [11]. …”

Section: 1 Introductionmentioning

confidence: 99%

“…Pathways showing significant perturbations included purine, pyrimidine and aminosugars metabolism (Fig. 15.4) [11]. …”

Section: 1 Introductionmentioning

confidence: 99%

“…As an example, Table 15.4 illustrates pathway integration across all three levels of expression for purine metabolism. The selected ions representing small molecule metabolites were validated by mass spectrometry and empirical formulae by comparing MS/MS fragmentation patterns and retention times of the target ion and the standard metabolite [11]. …”

Section: 1 Introductionmentioning

confidence: 99%

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Human Fibroblasts for Large-Scale “Omics” Investigations of ATM Gene Function

Jung

Timofeeva

Cheema

et al. 2011

Advances in Experimental Medicine and Biology

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ATM (gene mutated in ataxia-telangiectasia) is a critical central component of the pleiotropic responses of cells to ionizing radiation-induced stress. To gain insight into molecular mechanisms and to enhance our understanding of ATM functions, we have advanced a human model cell system, derived from genetically defined immortal fibroblasts, and we have applied high-throughput genomic, proteomic and metabolomic technologies for a systems level analysis. The cellular characterizations reported here provide the background for application of a systems analysis to integrate transcription, post-translational modifications and metabolic activity induced by exposure of cells to ionizing radiation. We present here a summary of the derivation and characterization of cells comprising this model cell system and review applications of this model to systems analysis of ATM functions.

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Functional Proteomics Analysis to Study ATM Dependent Signaling in Response to Ionizing Radiation

Cited by 12 publications

References 37 publications

Machine Learning Methods to Identify Genetic Correlates of Radiation-Associated Contralateral Breast Cancer in the WECARE Study

Machine Learning Methods to Identify Genetic Correlates of Radiation-Associated Contralateral Breast Cancer in the WECARE Study

Radiation proteomics: A brief overview

Human Fibroblasts for Large-Scale “Omics” Investigations of ATM Gene Function

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