Siklodekstrin Nanopartiküllerin Sitotoksik Etki Mekanizmasının Aydınlatılmasında Biyoenformatik Yaklaşımlar

Abstract

This thesis aims to determine the cytotoxic effect of previously manufactured cyclodextrin nanoparticles on cancer cells by metabolomic studies with different bioinformatic approaches. MDA-MB-231 breast cancer cell lines are treated with cyclodextrin nanoparticles which have been proposed as drug delivery system and the research continued over the maximum amount observed nanoparticle which has the inhibitory effect (antiproliferative effect) on cell proliferation. Cell lines in which proliferation inhibited by cyclodextrin nanoparticles is used as treated cell lines and be referred as T set. The group without treatment in which the proliferation is not prevented by nanoparticles in any way is used as control group and is designated as the group C. The cytosolic fractions is obtained from the lysates solution purified from group T and group C and liquid chromatography mass spectrometry (LC-MS) based metabolomics will be performed in order to metabolite profiling of these fractions. Based on metabolite profiling results, changes in metabolite levels is compared between the group T and the group C. Metabolite profiling (identification) from the raw data obtained by the LC-MS method and their comparison is only possible with the support of appropriate software and data bank. For this purpose, different bioinformatics approaches have been tried: The metabolites have been tried to be identified by CentWave and MatchedFilter algorithms of XCMS, a freely accessible platform which runs under R programming language in metabolite profiling. Besides, the number of identified metabolites is compared with each other using Human Metabolome Database (HMDB) and Metlin Database. During the processing of the raw data, the intersection set of the XCMS Centwave algorithm and MatchedFilter algorithm is associated with MetaboAnalyst. As a result of metabolite profiling, the highest amount of metabolite is identified from the groups T and C. The resulting data is used to elucidate the mechanism of cytotoxic action of cyclodextrin nanoparticles on cancer cells by performing metabolic pathway analysis.