Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.716643
Title: Systems analysis of the human cell cycle transcription network
Author: Chen, Sz-Hau
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2016
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Abstract:
Cell division is one of the most fundamental processes of life whereby one cell replicates itself to produce two. The molecular machinery that drives and regulates this fundamental process has been much studied but much remains unknown. This work describes the use of transcriptomics analyses to identify putative new proteins involved with this process and subsequent attempts to prove their association with this pathway. Using the latest array technology, in Chapter 2 I describe studies that examine the expression of genes regulated during different stages of the human cell cycle. Synchronous populations of neonatal human dermal fibroblasts (NHDFs) were generated by serum starvation and analysed in two separate microarray experiments. For the first set array experiments, samples were taken every 6 hours for 48 hours after serum refeeding, and every 2 hours for 24 hours for the second experiment. Using BioLayout Express3D, network structure analyses identified four major clusters of gene expression patterns associated with different stages of the cell cycle: G0-, early G1-, late G1-, and S/G2/M-phase. By comparison with datasets of other human cells and tissues, the list of genes in the S/G2/M cluster was refined; genes were only kept in the list if they were found to be co-expressed in cells and tissues with high levels of cell proliferation. 706 genes that were co-expressed during S/G2/M-phase were selected for further analyses. Manual curation showed that 484 are known cell cycle-associated genes, 78 are genes with putative association to the cell cycle, and 75 have known roles in other biological processes, whilst 69 were entirely uncharacterised genes. In order to investigate the 69 genes with unknown function, in Chapter 3 I describe how RNAi was used to screen 42 of these genes to see if their knockdown resulted in an effect on cell proliferation. After extensive assay optimisation, endoribonuclease-prepared siRNA (esiRNA) was delivered to NHDF cells and the effect of knockdown determined using a real time cell analysis (RTCA) system. This system monitors the change in electrical resistance induced by growing cell populations defined as the cell impedance index (CI). Using a Z-scoring cut-off to determine the hits of the RNAi screening, according to the average value of cell impedance growth rate (CIGR i.e. a value from transformed CI), 19 of 42 genes were found to significantly affect the dynamics of cell proliferation, supporting a potential role in cell division. In order to verify that the unknown proteins localise to structures compatible with a role in the cell cycle, in Chapter 4 I describe protein localisation studies on 11 of 19 genes of ‘hits’ from Chapter 3 (we were unable to obtain clones for the other 8 genes) and other genes of interest. Transfection studies of HEK293T cells with expression clones containing more than 11 ORFs with GFP fused to either the N- or C-terminal were performed. FAM111B and KIAA1549L appeared to be localised to the centrosome. In order to better understand the context in which the novel centrosomal proteins that FAM111B might operate, in Chapter 5 I describe the construction of a large-scale pathway model of centrosome life cycle based on an extensive literature review. The model is composed of 117 of the most important centrosome-associated proteins and has been constructed using the modified Edinburgh Notation (mEPN) scheme. This model was used to better annotate the genes in the original S/G2/M list and understand which of the genes in the model are regulated during cell division. This regulatory network model of the centrosome life cycle represents an important summary of current knowledge and provides a useful resource for further analyses of the novel centrosomal proteins. In summary, a list cell cycle gene was derived from microarray experiments by using network structure analyses. Subsequent analyses filtered the genes that co-expressed during S/G2/M-phase narrowing down into 706 genes. Of this list, 69 genes had not previously been associated with the cell cycle. 42 of these unknown genes were analysed by using real time RNAi screening, 19 of these genes were indeed associated with the cell proliferation, and 2 of these genes with unknown function appear to localise to the centrosome. To predict their involvement in the centrosome life cycle, a pathway map composed of 117 centrosome-associated proteins were formed. Although further research is needed to determine their position in the centrosome life cycle, the pathway can be used for computational modelling testing their putative function in the system.
Supervisor: Freeman, Thomas ; Barnett, Mark Sponsor: Biotechnology and Biological Sciences Research Council (BBSRC)
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.716643  DOI: Not available
Keywords: cell cycle ; transcriptomics ; network analysis ; pathways
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