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In metabolomics, the analytical challenge is to capture the chemical diversity of the metabolome. With the current technologies only a portion of the metabolome can be analysed. As a result there is a drive to direct significant analytical efforts towards capturing the metabolome or changes in the metabolome reliably and reproducibly in biological systems. Apart from analytical challenges, the challenges also include development of appropriate methodologies to quench and extract metabolites which is a crucial parameter in sample preparation and is required to achieve an accurate representation of phenotype. This thesis focuses on addressing both the challenges in mammalian and microalgal metabolomics. Metabolomics in cancer research is gaining momentum as a tool to understand the molecular mechanism of disease progression and for the identification of specific biomarkers which may assist distinguishing between normal, benign and metastatic cancer states. In our first investigation we developed GC-MS based modified direct cell scraping, bead harvesting and LN2 methods for harvesting three adherently grown mammalian cell lines (two breast cancer cell lines MDA-MB 436, MCF7 and an endothelial cell line HMEC1) which provided rapid and reliable route with three fold improved metabolome coverage and reduced the artifacts due to metabolome leakage compared to conventional methods. Later optimized treatments were employed and the influence of various washing and quenching solvents (buffered/unbuffered) on metabolite leakage was investigated for metastatic cancer cell line MDA-MB-231. This identified one washing step with PBS followed by quenching with 60% methanol (buffered with HEPES) as the best washing and quenching solvents. Further validation and comparison of proposed workflows for metabolomic study of two metastatic TNBC cell lines (MDA-MB-231 and MDA-MB-436) resulted in recovery of 154 unique metabolites and demonstrated the robustness and reliability of these methods in pathway based analysis in cancer. In case of GC-MS based microalgal metabolomics, with comprehensive evaluation of selected quenching and extraction methods in model microalga C. reinhardtii, we have successfully demonstrated that the choice of quenching and extraction solvents have significant impact on recovery of different classes of metabolites. Our results clearly indicate that 60% methanol (buffered with HEPES) and 25 % aqueous methanol are the best suited quenching and extraction solvent respectively for untargeted metabolomic analysis of C. reinhardtii, as the highest number of metabolites belonging to various chemical classes were recovered with good intensities and reproducibilities with this miniaturized proposed method compared to other evaluated methods. Later impact of various stages involved in biodiesel production workflow from microalga on recovery of biodiesel was assessed in three microalgal species namely C. reinhardtii, D. salina and N. salina. Within which we have developed an optimized GC-FID method and miniaturized direct TE method for quantification of fatty acids, which can be applied to a small amount of biomass and saves tremendous amounts of time, solvents and reagents required, is less expensive and uses environment friendly solvents making it more suitable for sustainable large scale production. In our final investigation, we directed our efforts towards preliminary optimization and comparative analysis of HILIC and IP-RP-HPLC based separation for the retention and separation of specific metabolites classes. This identified HILIC as the best available column till date for untargeted metabolomic studies. The descriptive understanding gained from each of these investigations provides greater insight into biology of mammalian and algal systems by improving the metabolome coverage for various metabolite classes. These insights illustrating the underlying molecular pathways involved in respective biology's, will help scientific communities in identifying as-of-yet-missing reactions in the metabolic network. In addition these insights will surely help in generating many hypothesis based investigations in microalgal and cancer community. |
