A Multi-omics Study of Human Mitochondrial Proteins During Neurogenesis

dc.contributor.advisorBabu, Mohan
dc.contributor.authorZhang, Qingzhou
dc.contributor.committeememberSuh, Dae-yon
dc.contributor.committeememberFitzpatrick, Dennis
dc.contributor.committeememberWeger, Harold
dc.contributor.externalexaminerHu, Pingzhao
dc.date.accessioned2021-12-13T17:00:52Z
dc.date.available2021-12-13T17:00:52Z
dc.date.issued2021-04
dc.descriptionA Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Biochemistry, University of Regina. xix, 194 p.en_US
dc.description.abstractMitochondria are double membrane organelles in eukaryotic cells, and play vital roles in neurogenesis. Disruptions of mitochondrial functions may lead to neurodegenerative disorders. Yet, the underlying mechanisms remain largely unknown. To elucidate the dynamic changes of transcriptomes during neurogenesis, the single cell RNA sequencing experiments were conducted on both cultured human embryonic carcinoma stem cells and and retinoic acid-induced differentiated neuron-like cells. The systematic analysis framework I have established revealed significant expression alternations between cell states, persistence of heterogeneity in differentiated neuron-like cells, and dynamic modeling during the differentiation, and also identified mitochondrial proteins as novel neuronal markers. To elucidate mitochondrial interactome in neurogenesis, biochemical fractionation coupled with in-depth mass spectrometry profiling experiments were performed in both cell states. The bioinformatics pipeline I have developed resulted in 6,442 high-quality protein-protein interactions among 600 mitochondrial proteins. Computational modeling further predicted that RAB5IF could play a role in the assembly of the respirasome (composed of complexes I, III and IV). Lastly, due to the complexity of BF-MS data and limitation of current software tool kits, I have developed an optimized approach, termed as Statistical Modeling Elution Data (SMED), to infer protein interactions from BF-MS. SMED took advantage of novel statistical modeling and machine learning strategies to achieve better prediction performances compared to previously published studies. Taken together, the multi-omics methods and computational strategies I have established in this study provided not only new insights of mitochondrial biology, but also a systems biology research focus to leverage the deluge of omics data. The findings from this study could accelerate our understanding of how mitochondrial proteins and their interactions play a role in neurobiology.en_US
dc.description.authorstatusStudenten
dc.description.peerreviewyesen
dc.identifier.tcnumberTC-SRU-14459
dc.identifier.thesisurlhttps://ourspace.uregina.ca/bitstream/handle/10294/14459/Zhang_Qingzhou_PhD_BIOC_Fall2021.pdf
dc.identifier.urihttps://hdl.handle.net/10294/14459
dc.language.isoenen_US
dc.publisherFaculty of Graduate Studies and Research, University of Reginaen_US
dc.titleA Multi-omics Study of Human Mitochondrial Proteins During Neurogenesisen_US
dc.typemaster thesisen_US
thesis.degree.departmentDepartment of Chemistry and Biochemistryen_US
thesis.degree.disciplineBiochemistryen_US
thesis.degree.grantorFaculty of Graduate Studies and Research, University of Reginaen
thesis.degree.levelDoctoral -- firsten
thesis.degree.nameDoctor of Philosophy (PhD)en_US

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