Abstract
AbstractRecent advancement in integrated multi-omics has significantly contributed to many areas of the biomedical field. Radiation research has also grasped uprising omics technologies in biomarker identification to aid in triage management. Herein, we have used a combinatorial multi-omics approach based on transcriptomics together with metabolomics and lipidomics of blood from murine exposed to 1 Gy (LD) and 7.5 Gy (HD) of total-body irradiation (TBI) for a comprehensive understanding of biological processes through integrated pathways and networking. Both omics displayed demarcation of HD group from controls using multivariate analysis. Dysregulated amino acids, various PC, PE and carnitine were observed along with many dysregulated genes (Nos2, Hmgcs2, Oxct2a, etc.). Joint-Pathway Analysis and STITCH interaction showed radiation exposure resulted in changes in amino acid, carbohydrate, lipid, nucleotide, and fatty acid metabolism. Elicited immune response was also observed by Gene Ontology. BioPAN has predicted Elovl5, Elovl6 and Fads2 for fatty acid pathways, only in HD group. Collectively, the combined omics approach facilitated a better understanding of processes uncovering metabolic pathways. Presumably, this is the first in radiation metabolomics that utilized an integrated omics approach following TBI in mice. Our work showed that omics integration could be a valuable tool for better comprehending the mechanism as well as molecular interactions.
Funder
DRDO | Institute of Nuclear Medicine and Allied Sciences, Defence Research and Development Organisation
University Grants Commission
Council of Scientific and Industrial Research
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Computer Science Applications,Drug Discovery,General Biochemistry, Genetics and Molecular Biology,Modeling and Simulation
Reference49 articles.
1. Kumar, P., Wang, P., Farese, A. M., MacVittie, T. J. & Kane, M. A. Metabolomics of multiorgan radiation injury in non-human primate model reveals system-wide metabolic perturbations. Health Phys. 121, 395–405 (2021).
2. Tyagi, R., Maan, K., Khushu, S. & Rana, P. Urine metabolomics based prediction model approach for radiation exposure. Sci. Rep. 10, 16063 (2020).
3. Maan, K. et al. An integrative chemometric approach and correlative metabolite networking of LC-MS and 1H NMR based urine metabolomics for radiation signatures. Mol. Omics 18, 214–225 (2022).
4. Singh, V. K., Seed, T. M. & Cheema, A. K. Metabolomics-based predictive biomarkers of radiation injury and countermeasure efficacy: current status and future perspectives. Expert Rev. Mol. Diagn. 21, 641–654 (2021).
5. Wang, Z., Gerstein, M. & Snyder, M. RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet 10, 57–63 (2009).
Cited by
16 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献