Mass Spectrometric Analysis of Metabolites Associated with Blood Disorders of Human

Abstract

Metabolomics is an emerging field and serve as the final piece of puzzle in omics sciences, and also being used in wide scientific area that include toxicology, newborn screening, food safety, drug discovery and for identification/discovery of new biomarkers. This dissertation describes the lipidomics and pharmaco metabolomics study of two blood disorders of human including acute leukemia and β-thalassemia, respectively, using advanced spectroscopy techniques. A quantitative method of total fifteen free fatty acids (FFAs) in two sub-types of acute leukemia (AL) i.e. acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) and control groups i.e. pre-leukemic conditions (aplastic anemia (APA) and myelodysplastic syndrome (MDS), and healthy groups, was developed using gas chromatography-multiple reaction monitoring-mass spectrometry (GC MRM-MS). Fifteen FFAs were analyzed in 179 serum samples of MDS, APA, ALL, AML and healthy control. A multivariate statistical method of the random forest (RF) was employed for chemometric analysis. Over all it was found that the concentration range for all fatty acids methyl ester (FAMEs) fall within coefficient (r2 ) ≥ 0.95 and the value of accuracy lies within in the range of 78.0%-116.0%. The generated RF model gave an overall accuracy of 96.6%, 96.0% sensitivity and 96.5% selectivity. Moreover, it was seen that identified FFAs profile not only altered in leukaemia but also in pre-leukaemic stages. The results of the study proposed that metabolism of stearic acid (18:0) was found to be differentiated among healthy, pre-leukemic and AL states, that can be used as a disease development biomarker. Furthermore, the other result reflects that myristic acid (14:0) could be used as a discriminative marker between AML and ALL. Additionally, myristic acid (14:0) may serve as a differentiative marker of pre leukemic (APA and MDS) into AL. Combination of myristic acid (C 14:0) and palmitic acid (16:0) can also be used to monitor progression of APA into MDS or AML. Second part of this dissertation is based on pharmacometabolomics study of β thalassemia patient’s serum samples. This study aimed to improve the understanding of hydroxyurea (HU) therapy in β-thalassemia by metabonomics approach using 1H NMR spectroscopy. This study includes cases of β thalassemia before and after HU therapy along with healthy controls. Carr-Purcell Meiboom-Gill (CPMG) sequence was used to identify forty-one putative metabolites. Generation of models like PLS-DA and OPLS-DA based on different metabolites including lipids, amino acids, glucose, fucose, isobutyrate, and glycerol revealed satisfactory outcomes with 85.2% and 91.1% classification rates, respectively. The concentration, of these metabolites was found to be different in β-thalassemia samples. However, after HU treatment metabolic profile of the same patients showed closeness towards healthy. Deviant metabolic pathways counting lipoprotein changes, glycolysis, TCA cycle, and fatty acid and choline metabolisms were identified as having significant differences among study groups. Conclusively, metabolomics either in the field of disease diagnosis, progression or drug discovery serve as a promising area of research. Additionally, combining data from other analytical techniques would give a better understanding of biological systems.