Akademik Veri Yönetim Sistemi
Tezin Türü: Doktora
Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, Fen Edebiyat Fakültesi, Biyolojik Bilimler Bölümü, Türkiye
Tezin Onay Tarihi: 2016
Öğrenci: RAFIG GURBANOV
Danışman: AYŞE GÜL GÖZEN
Özet:Approximately 50 elements with the density larger than five are considered as heavy metals. Some of them are essential for growth, although they also form strong detrimental complexes in living organisms. Moreover, microorganisms can develop resistance toward metal burden. This exceptional microbial feature may have remarkable applications in industry, such as bioremediation of metal-contaminated waters and recovery of important metals from industrial wastes. Considering the above-mentioned potentials, the purposes of this work are: 1) To screen, identify and classify metal resistant bacterial strains 2) To characterize the heavy metal resistance related molecular alterations happening in these bacterial isolates 3) To develop and optimize robust detection techniques for the elucidation of metal resistance in bacteria. In the first part of the study, it was aimed to develop a reliable method for identification of key molecular profile changes and thus for discrimination of Cd and Pb-resistant bacteria using ATR-FTIR spectroscopy coupled with unsupervised multivariate analysis techniques such as Hierarchical Cluster Analysis (HCA) and Principal Component Analysis (PCA). In the second part, it was intended to shed light on the possible mechanisms of Ag resistance and on how these changes are reflected in the cell’s structure and molecular vi composition, especially in E. coli and S. aureus. Furthermore, in the current study, unsupervised chemometric methods, namely PCA and HCA were used, since they are broadly applied methods for the differentiation of microbial communities with impressive accuracy. The third part of the study, originating from the above-mentioned investigations, demonstrates the prompt and successful discrimination and most importantly classification of Ag, Cd and Pb-resistant laboratory and environmental (freshwater) bacteria from their non-resistant counterparts. In this context, a supervised classification pattern-recognition tool, Soft Independent Modeling of Class Analogy (SIMCA), was applied in light of FTIR spectra of these bacterial isolates. Information obtained from the ATR-FTIR spectroscopy coupled with chemometrics could serve as a guide for further studies on heavy metal resistance mechanisms, the detailed elucidation of which could in turn lead to the selection of the bacterial species best suited for bioremediation. The identification and classification of heavy metal resistant life forms in a fast and productive manner would also contribute to the establishment of sustainable, green and worthwhile biogeotechnological operations to rehabilitate the soil and water.