Naidoo, Shiven2024-10-312024-10-312024Naidoo, Shiven. (2024). Using ChIP-seq and Gene Expression Microarray data to explore transcriptional dysregulation of PXDN and PXDNL in cardiovascular diseases [Master’s dissertation, University of the Witwatersrand, Johannesburg]. WireDSpace.https://hdl.handle.net/10539/42148A Dissertation Submitted in fulfillment of the requirements for the degree Master of Science in Molecular and Cell Biology in the Faculty of Science, University of the Witwatersrand, Johannesburg, South Africa 2024Background: Cardiovascular diseases (CVDs) remain one of the leading causes of death globally. The genes PXDN and PXDNL are both expressed in the cardiovascular system, and their dysregulation has been linked to various disorders, including CVDs, but little is known of their transcriptional regulation in the cardiovascular system or their roles in CVD pathogenesis. Methods: This study developed two custom bioinformatics pipelines in R to mine and analyse ChIP-seq data from ChIP-Atlas and gene expression microarray data from the Gene Expression Omnibus (GEO). The first pipeline used ChIPseeker to identify regulatory transcription factors (TFs) of PXDN and PXDNL in cardiovascular cells and tissues. ChIP-seq data from 400 experiments across 63 TFs was filtered to isolate TFs with high confidence binding peaks in the promoter and first intron of PXDN and PXDNL. The second pipeline used R Bioconductor packages to explore the expression profiles of PXDN, PXDNL, and their TFs in seven microarray datasets across three CVD-related contexts: cardiomyopathies, heart failure and TNF-α stimulation. Results and discussion: This study identified 27 TFs binding to PXDN and 18 TFs binding to PXDNL in cardiovascular cells. Sixteen of these TFs were shared by both PXDN and PXDNL, suggesting potential coregulatory mechanisms in cardiovascular cells where they are both expressed. Unique TFs were also identified for PXDN (11) and PXDNL (2). Differential gene expression analysis revealed no significant change in expression (log2FC > 0.5; p.adj < 0.05) for PXDN, PXDNL and many of their identified TFs in the CVD-related conditions investigated, suggesting that changes at the transcript level may not contribute to the progression of these conditions. Conclusions: This study advances our understanding of the transcriptional regulation of PXDN and PXDNL in healthy cardiovascular cells as well as their expression levels in the investigated CVD-related contexts. This study also contributes a bioinformatics pipeline which can be further developed and applied to analysing data from ChIP-Atlas and GEO. Future research can elucidate the roles of each TF in regulating PXDN and PXDNL in healthy and diseased cell linesen© 2024 University of the Witwatersrand, Johannesburg. All rights reserved. The copyright in this work vests in the University of the Witwatersrand, Johannesburg. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of University of the Witwatersrand, Johannesburg.Transcriptional RegulationDifferential Gene Expression AnalysisBioinformatics PipelinesUCTDSDG-4: Quality educationDissertationUniversity of the Witwatersrand, Johannesburg