1hnmr based metabolomics study of altered metabolic pathways in patients with covid-19

Abstract: coronavirus disease (covid-19) is an infectious respiratory that was first reported in december 2019 in wuhun, china. it is quickly spread around the world by severe acute respiratory syndrome coronavirus 2(sars-cov-2). there is limited information about pathogenesis and targeted treatment strategies. the severity of the disease in patients with covid-19 is divided into three states: mild, moderate and severe, due to differences in the immune response of individuals against the coronavirus [1].metabolomics is a newly developed technique in the field of systematic biology following genomics and proteomics, and the study of metabolomics refers to the wide and quantitative analysis of all metabolites in biological samples [2]. metabolomics has become one of the most important methods in biomedical research using advanced methods such as nuclear magnetic resonance (nmr)and high resolution mass spectrometry (ms). various metabolic studies have been performed around the world to study covid-19, which show metabolic disorders during disease progression [3-4]. nmr metabolomics can identify the differences in low molecular weight molecules that exist in states of health or disease.in this study, serum samples of 61 covid-19 patients and 20 healthy people as the control were examined and the potential spectral markers were identified by multivariate and statistical analysis. analysis show that the following metabolic pathways including glysine,serine and threonine, pirimidine metabolism, histidine metabolism, aminoacyl-trna biosynthesis, pantothenate and coa biosynthesis, beta-alanine metabolism, amino sugar and nucleotide sugar metabolism, ascorbate and aldarate metabolism, amino sugar and nucleotide sugar metabolism, valine, leucine and isoleucine biosynthesis, synthesis and degradation of ketone bodies are changed during illness. our results provide valuable information related to covid-19 serum markers, on the other hand selecting a proper data by pretreatment method is an essential step in the analysis of metabolomics data. in this study, autoscaling and range scaling performed better than the other pretreatment methods.