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Introducing Serine as Cardiovascular Disease Biomarker Candidate via Pathway Analysis

Mostafa Rezaei Tavirani, Mona Zamanian Azodi, Mohammad Rostami-Nejad, Hamideh Morravej, Zahra Razzaghi, Farshad Okhovatian, Majid Rezaei-Tavirani

Background: The rate of death due to cardiovascular disease (CVD) is growing. Investigations about CVD that leading to introduce varieties of metabolites is available. The monitoring of these metabolites to find effective ones in the future of clinic applications is the main aim of this study. Materials and Methods: Numbers of 34 metabolites for the CVD are extracted from literature and designated for interaction determinations by MetScape V 3.1.3. The compound-reaction-enzyme-gene network was constructed and the pathways were analyzed. Based on the presence of metabolites in the pathways the critical compounds were determined. Results: Pathway analysis revealed 18 disturbed pathways related to the CVD.  glycerophospholipid metabolism pathway including 27 compounds is related to the 9 queried metabolites. L-Serine which was communed between 5 pathways and also was presented in the largest pathway was identified as the critical compound. Conclusion: It can be concluded that L-Serine is a proper biomarker candidate for CVD diagnosis and also patients follow up approaches. [GMJ.2020;9:e1696]

Metabolome; Metabolic Networks; Cardiovascular Diseases

Kelly BB, Fuster V. Promoting cardiovascular health in the developing world: a critical challenge to achieve global health. National Academies Press; 2010.

Turk-Adawi K, Sarrafzadegan N, Fadhil I, Taubert K, Sadeghi M, Wenger NK et al. Cardiovascular disease in the Eastern Mediterranean region: epidemiology and risk factor burden. Nat Rev Cardiol. 2018;15(2):106-119.

https://doi.org/10.1038/nrcardio.2017.138

PMid:28933782

Tocchetti CG, Cadeddu C, Di Lisi D, Femminò S, Madonna R, Mele D et al. From molecular mechanisms to clinical management of antineoplastic drug-induced cardiovascular toxicity: a translational overview. Antioxid Redox Signal 2019;30(18):2110-53.

https://doi.org/10.1089/ars.2016.6930

PMid:28398124 PMCid:PMC6529857

Sigurdsson MI, Waldron NH, Bortsov AV, Smith SB, Maixner W. Genomics of cardiovascular measures of autonomic tone. J Cardiovasc Pharmacol. 2018;71(3):180-91.

https://doi.org/10.1097/FJC.0000000000000559

PMid:29300220 PMCid:PMC5839974

Nowak C, Carlsson AC, Östgren CJ, Nyström FH, Alam M, Feldreich T et al. Multiplex proteomics for prediction of major cardiovascular events in type 2 diabetes. Diabetologia. 2018;61(8):1748-57.

https://doi.org/10.1007/s00125-018-4641-z

PMid:29796748 PMCid:PMC6061158

McGarrah RW, Crown SB, Zhang G-F, Shah SH, Newgard CB. Cardiovascular metabolomics. Circulation research. 2018;122(9):1238-58.

https://doi.org/10.1161/CIRCRESAHA.117.311002

PMid:29700070 PMCid:PMC6029726

Enquobahrie DA, Denis M, Tadesse MG, Gelaye B, Ressom HW, Williams MA. Maternal early pregnancy serum metabolites and risk of gestational diabetes mellitus. J Clin Endocrinol Metab. 2015;100(11):4348-56.

https://doi.org/10.1210/jc.2015-2862

PMid:26406294 PMCid:PMC4702451

Nobakht BF, Arefi Oskouie A, Rezaei-Tavirani M, Aliannejad R, Taheri S, Fathi F et al. NMR spectroscopy-based metabolomic study of serum in sulfur mustard exposed patients with lung disease. Biomarkers. 2017;22(5):413-9.

https://doi.org/10.1080/1354750X.2016.1203995

PMid:27319271

Nobakht BF, Aliannejad R, Rezaei-Tavirani M, Arefi Oskouie A, Naseri MT, Parastar H et al. NMR-and GC/MS-based metabolomics of sulfur mustard exposed individuals: a pilot study. Biomarkers. 2016;21(6):479-89.

https://doi.org/10.3109/1354750X.2016.1153725

PMid:26984270

Ghoochani BFNM, Aliannejad R, Oskouie AA, Rezaei-Tavirani M, Kalantari S, Naseri MT et al. Metabolomics diagnostic approach to mustard airway diseases: a preliminary study. Iran. J. Basic Med. Sci. 2018;21(1):59-69.

Ruiz-Canela M, Hruby A, Clish CB, Liang L, Martínez-González MA, Hu FB. Comprehensive metabolomic profiling and incident cardiovascular disease: a systematic review. Am Heart J . 2017;6(10):1-22.

https://doi.org/10.1161/JAHA.117.005705

PMid:28963102 PMCid:PMC5721826

Zojaji H, Tavirani MR, Mansouri V, Salehi AS, Robati RM, Lak E. Metabolic analysis of acute appendicitis by using system biology approach. Gastroenterol Hepatol Bed Bench. 2018;11(Suppl 1):92-97.

https://doi.org/10.5812/ijcm.65701

Zamanian-Azodi M, Tavirani MR, Rostami-Nejad M, Tajik-Rostami F. New molecular aspects of cardiac arrest; promoting cardiopulmonary resuscitation approaches. Emergency. 2018;6(1).1-6.

Santolini M, Barabási A-L. Predicting perturbation patterns from the topology of biological networks Proc Natl Acad Sci.. 2018;115(27):6375-83.

https://doi.org/10.1073/pnas.1720589115

PMid:29925605 PMCid:PMC6142275

Chong J, Soufan O, Li C, Caraus I, Li S, Bourque G et al. MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis. Nucleic Acids Res. 2018;46(1):486-94.

https://doi.org/10.1093/nar/gky310

PMid:29762782 PMCid:PMC6030889

Fiehn O, Kind T, Barupal DK. Data processing, metabolomic databases and pathway analysis. Annual Plant Reviews online. 2018:367-406.

https://doi.org/10.1002/9781119312994.apr0472

Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13(11):2498-504.

https://doi.org/10.1101/gr.1239303

PMid:14597658 PMCid:PMC403769

Gao J, Tarcea VG, Karnovsky A, Mirel BR, Weymouth TE, Beecher CW et al. Metscape: a Cytoscape plug-in for visualizing and interpreting metabolomic data in the context of human metabolic networks. Bioinformatics. 2010;26(7):971-3.

https://doi.org/10.1093/bioinformatics/btq048

PMid:20139469 PMCid:PMC2844990

Shen Q, Xiang W, Ye S, Lei X, Wang L, Jia S et al. Plasma metabolite biomarkers related to secondary hyperparathyroidism and parathyroid hormone. J Cell Biochem. 2019;120(9): 15766-15775.

https://doi.org/10.1002/jcb.28846

PMid:31069832

Herder C, Karakas M, Koenig W. Biomarkers for the prediction of type 2 diabetes and cardiovascular disease. Clin Pharmacol Ther. 2011;90(1):52-66.

https://doi.org/10.1038/clpt.2011.93

PMid:21654741

Gormley M, Dampier W, Ertel A, Karacali B, Tozeren A. Prediction potential of candidate biomarker sets identified and validated on gene expression data from multiple datasets. BMC bioinformatics. 2007;8(1):1-13.

https://doi.org/10.1186/1471-2105-8-415

PMid:17963508 PMCid:PMC2211325

Øvrehus MA, Bruheim P, Ju W, Zelnick LR, Langlo KA, Sharma K et al. Gene expression studies and targeted metabolomics reveal disturbed serine, methionine, and tyrosine metabolism in early hypertensive nephrosclerosis. Kidney Int. 2019;4(2):321-33.

https://doi.org/10.1016/j.ekir.2018.10.007

PMid:30775629 PMCid:PMC6365407

Liu T, Liu M, Shang P, Jin X, Liu W, Zhang Y et al. Investigation into the underlying molecular mechanisms of hypertensive nephrosclerosis using bioinformatics analyses. Mol Med Rep. 2018;17(3):4440-8.

https://doi.org/10.3892/mmr.2018.8405

PMid:29328390 PMCid:PMC5802219

Mishra RC, Tripathy S, Desai KM, Quest D, Lu Y, Akhtar J et al. Nitric oxide synthase inhibition promotes endothelium-dependent vasodilatation and the antihypertensive effect of L-serine. Hypertension. 2008;51(3):791-6.

https://doi.org/10.1161/HYPERTENSIONAHA.107.099598

PMid:18212272

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