Urinary Metabolomics for the Prediction of Radiation-Induced Cardiac Dysfunction

Survivors of acute radiation exposure are likely to experience delayed effects that manifest as injury in late-responding organs such as the heart. Non-invasive indicators of radiation-induced cardiac dysfunction are important in the prediction and diagnosis of this disease. In this study, we aimed...
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Autor*in:	Yaoxiang Li [verfasserIn] Shivani Bansal [verfasserIn] Vijayalakshmi Sridharan [verfasserIn] Sunil Bansal [verfasserIn] Meth M. Jayatilake [verfasserIn] Jose A. Fernández [verfasserIn] John H. Griffin [verfasserIn] Marjan Boerma [verfasserIn] Amrita K. Cheema [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	metabolomics radiation-induced cardiac dysfunction non-invasive indicators mouse model urinary metabolites activated protein C (APCHi)

Übergeordnetes Werk:	In: Metabolites - MDPI AG, 2012, 13(2023), 4, p 525
Übergeordnetes Werk:	volume:13 ; year:2023 ; number:4, p 525

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/metabo13040525

Katalog-ID:	DOAJ089812107

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spelling	10.3390/metabo13040525 doi (DE-627)DOAJ089812107 (DE-599)DOAJ7a8a37e081d041f4a59e039b659f40d1 DE-627 ger DE-627 rakwb eng QR1-502 Yaoxiang Li verfasserin aut Urinary Metabolomics for the Prediction of Radiation-Induced Cardiac Dysfunction 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Survivors of acute radiation exposure are likely to experience delayed effects that manifest as injury in late-responding organs such as the heart. Non-invasive indicators of radiation-induced cardiac dysfunction are important in the prediction and diagnosis of this disease. In this study, we aimed to identify urinary metabolites indicative of radiation-induced cardiac damage by analyzing previously collected urine samples from a published study. The samples were collected from male and female wild-type (C57BL/6N) and transgenic mice constitutively expressing activated protein C (APCHi), a circulating protein with potential cardiac protective properties, who were exposed to 9.5 Gy of γ-rays. We utilized LC-MS-based metabolomics and lipidomics for the analysis of urine samples collected at 24 h, 1 week, 1 month, 3 months, and 6 months post-irradiation. Radiation caused perturbations in the TCA cycle, glycosphingolipid metabolism, fatty acid oxidation, purine catabolism, and amino acid metabolites, which were more prominent in the wild-type (WT) mice compared to the APCHi mice, suggesting a differential response between the two genotypes. After combining the genotypes and sexes, we identified a multi-analyte urinary panel at early post-irradiation time points that predicted heart dysfunction using a logistic regression model with a discovery validation study design. These studies demonstrate the utility of a molecular phenotyping approach to develop a urinary biomarker panel predictive of the delayed effects of ionizing radia-tion. It is important to note that no live mice were used or assessed in this study; instead, we focused solely on analyzing previously collected urine samples. metabolomics radiation-induced cardiac dysfunction non-invasive indicators mouse model urinary metabolites activated protein C (APCHi) Microbiology Shivani Bansal verfasserin aut Vijayalakshmi Sridharan verfasserin aut Sunil Bansal verfasserin aut Meth M. Jayatilake verfasserin aut Jose A. Fernández verfasserin aut John H. Griffin verfasserin aut Marjan Boerma verfasserin aut Amrita K. Cheema verfasserin aut In Metabolites MDPI AG, 2012 13(2023), 4, p 525 (DE-627)718627164 (DE-600)2662251-8 22181989 nnns volume:13 year:2023 number:4, p 525 https://doi.org/10.3390/metabo13040525 kostenfrei https://doaj.org/article/7a8a37e081d041f4a59e039b659f40d1 kostenfrei https://www.mdpi.com/2218-1989/13/4/525 kostenfrei https://doaj.org/toc/2218-1989 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 4, p 525
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allfieldsSound	10.3390/metabo13040525 doi (DE-627)DOAJ089812107 (DE-599)DOAJ7a8a37e081d041f4a59e039b659f40d1 DE-627 ger DE-627 rakwb eng QR1-502 Yaoxiang Li verfasserin aut Urinary Metabolomics for the Prediction of Radiation-Induced Cardiac Dysfunction 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Survivors of acute radiation exposure are likely to experience delayed effects that manifest as injury in late-responding organs such as the heart. Non-invasive indicators of radiation-induced cardiac dysfunction are important in the prediction and diagnosis of this disease. In this study, we aimed to identify urinary metabolites indicative of radiation-induced cardiac damage by analyzing previously collected urine samples from a published study. The samples were collected from male and female wild-type (C57BL/6N) and transgenic mice constitutively expressing activated protein C (APCHi), a circulating protein with potential cardiac protective properties, who were exposed to 9.5 Gy of γ-rays. We utilized LC-MS-based metabolomics and lipidomics for the analysis of urine samples collected at 24 h, 1 week, 1 month, 3 months, and 6 months post-irradiation. Radiation caused perturbations in the TCA cycle, glycosphingolipid metabolism, fatty acid oxidation, purine catabolism, and amino acid metabolites, which were more prominent in the wild-type (WT) mice compared to the APCHi mice, suggesting a differential response between the two genotypes. After combining the genotypes and sexes, we identified a multi-analyte urinary panel at early post-irradiation time points that predicted heart dysfunction using a logistic regression model with a discovery validation study design. These studies demonstrate the utility of a molecular phenotyping approach to develop a urinary biomarker panel predictive of the delayed effects of ionizing radia-tion. It is important to note that no live mice were used or assessed in this study; instead, we focused solely on analyzing previously collected urine samples. metabolomics radiation-induced cardiac dysfunction non-invasive indicators mouse model urinary metabolites activated protein C (APCHi) Microbiology Shivani Bansal verfasserin aut Vijayalakshmi Sridharan verfasserin aut Sunil Bansal verfasserin aut Meth M. Jayatilake verfasserin aut Jose A. Fernández verfasserin aut John H. Griffin verfasserin aut Marjan Boerma verfasserin aut Amrita K. Cheema verfasserin aut In Metabolites MDPI AG, 2012 13(2023), 4, p 525 (DE-627)718627164 (DE-600)2662251-8 22181989 nnns volume:13 year:2023 number:4, p 525 https://doi.org/10.3390/metabo13040525 kostenfrei https://doaj.org/article/7a8a37e081d041f4a59e039b659f40d1 kostenfrei https://www.mdpi.com/2218-1989/13/4/525 kostenfrei https://doaj.org/toc/2218-1989 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 13 2023 4, p 525
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author	Yaoxiang Li
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topic_title	QR1-502 Urinary Metabolomics for the Prediction of Radiation-Induced Cardiac Dysfunction metabolomics radiation-induced cardiac dysfunction non-invasive indicators mouse model urinary metabolites activated protein C (APCHi)
topic	misc QR1-502 misc metabolomics misc radiation-induced cardiac dysfunction misc non-invasive indicators misc mouse model misc urinary metabolites misc activated protein C (APCHi) misc Microbiology
topic_unstemmed	misc QR1-502 misc metabolomics misc radiation-induced cardiac dysfunction misc non-invasive indicators misc mouse model misc urinary metabolites misc activated protein C (APCHi) misc Microbiology
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title	Urinary Metabolomics for the Prediction of Radiation-Induced Cardiac Dysfunction
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title_sort	urinary metabolomics for the prediction of radiation-induced cardiac dysfunction
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title_auth	Urinary Metabolomics for the Prediction of Radiation-Induced Cardiac Dysfunction
abstract	Survivors of acute radiation exposure are likely to experience delayed effects that manifest as injury in late-responding organs such as the heart. Non-invasive indicators of radiation-induced cardiac dysfunction are important in the prediction and diagnosis of this disease. In this study, we aimed to identify urinary metabolites indicative of radiation-induced cardiac damage by analyzing previously collected urine samples from a published study. The samples were collected from male and female wild-type (C57BL/6N) and transgenic mice constitutively expressing activated protein C (APCHi), a circulating protein with potential cardiac protective properties, who were exposed to 9.5 Gy of γ-rays. We utilized LC-MS-based metabolomics and lipidomics for the analysis of urine samples collected at 24 h, 1 week, 1 month, 3 months, and 6 months post-irradiation. Radiation caused perturbations in the TCA cycle, glycosphingolipid metabolism, fatty acid oxidation, purine catabolism, and amino acid metabolites, which were more prominent in the wild-type (WT) mice compared to the APCHi mice, suggesting a differential response between the two genotypes. After combining the genotypes and sexes, we identified a multi-analyte urinary panel at early post-irradiation time points that predicted heart dysfunction using a logistic regression model with a discovery validation study design. These studies demonstrate the utility of a molecular phenotyping approach to develop a urinary biomarker panel predictive of the delayed effects of ionizing radia-tion. It is important to note that no live mice were used or assessed in this study; instead, we focused solely on analyzing previously collected urine samples.
abstractGer	Survivors of acute radiation exposure are likely to experience delayed effects that manifest as injury in late-responding organs such as the heart. Non-invasive indicators of radiation-induced cardiac dysfunction are important in the prediction and diagnosis of this disease. In this study, we aimed to identify urinary metabolites indicative of radiation-induced cardiac damage by analyzing previously collected urine samples from a published study. The samples were collected from male and female wild-type (C57BL/6N) and transgenic mice constitutively expressing activated protein C (APCHi), a circulating protein with potential cardiac protective properties, who were exposed to 9.5 Gy of γ-rays. We utilized LC-MS-based metabolomics and lipidomics for the analysis of urine samples collected at 24 h, 1 week, 1 month, 3 months, and 6 months post-irradiation. Radiation caused perturbations in the TCA cycle, glycosphingolipid metabolism, fatty acid oxidation, purine catabolism, and amino acid metabolites, which were more prominent in the wild-type (WT) mice compared to the APCHi mice, suggesting a differential response between the two genotypes. After combining the genotypes and sexes, we identified a multi-analyte urinary panel at early post-irradiation time points that predicted heart dysfunction using a logistic regression model with a discovery validation study design. These studies demonstrate the utility of a molecular phenotyping approach to develop a urinary biomarker panel predictive of the delayed effects of ionizing radia-tion. It is important to note that no live mice were used or assessed in this study; instead, we focused solely on analyzing previously collected urine samples.
abstract_unstemmed	Survivors of acute radiation exposure are likely to experience delayed effects that manifest as injury in late-responding organs such as the heart. Non-invasive indicators of radiation-induced cardiac dysfunction are important in the prediction and diagnosis of this disease. In this study, we aimed to identify urinary metabolites indicative of radiation-induced cardiac damage by analyzing previously collected urine samples from a published study. The samples were collected from male and female wild-type (C57BL/6N) and transgenic mice constitutively expressing activated protein C (APCHi), a circulating protein with potential cardiac protective properties, who were exposed to 9.5 Gy of γ-rays. We utilized LC-MS-based metabolomics and lipidomics for the analysis of urine samples collected at 24 h, 1 week, 1 month, 3 months, and 6 months post-irradiation. Radiation caused perturbations in the TCA cycle, glycosphingolipid metabolism, fatty acid oxidation, purine catabolism, and amino acid metabolites, which were more prominent in the wild-type (WT) mice compared to the APCHi mice, suggesting a differential response between the two genotypes. After combining the genotypes and sexes, we identified a multi-analyte urinary panel at early post-irradiation time points that predicted heart dysfunction using a logistic regression model with a discovery validation study design. These studies demonstrate the utility of a molecular phenotyping approach to develop a urinary biomarker panel predictive of the delayed effects of ionizing radia-tion. It is important to note that no live mice were used or assessed in this study; instead, we focused solely on analyzing previously collected urine samples.
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title_short	Urinary Metabolomics for the Prediction of Radiation-Induced Cardiac Dysfunction
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Radiation caused perturbations in the TCA cycle, glycosphingolipid metabolism, fatty acid oxidation, purine catabolism, and amino acid metabolites, which were more prominent in the wild-type (WT) mice compared to the APCHi mice, suggesting a differential response between the two genotypes. After combining the genotypes and sexes, we identified a multi-analyte urinary panel at early post-irradiation time points that predicted heart dysfunction using a logistic regression model with a discovery validation study design. These studies demonstrate the utility of a molecular phenotyping approach to develop a urinary biomarker panel predictive of the delayed effects of ionizing radia-tion. 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Urinary Metabolomics for the Prediction of Radiation-Induced Cardiac Dysfunction

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