Integrative Analysis of Multiomic Data to Dissect Transcriptional Regulatory Mechanisms


Student thesis: Doctoral Thesis

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Awarding Institution
  • Xin WANG (Supervisor)
  • Kristy L. RICHARDS (External person) (External Co-Supervisor)
  • Praveen SETHUPATHY (External person) (External Co-Supervisor)
Award date7 Sep 2021


Transcriptional regulation is one of the critical biological processes occurring in all kinds of organisms, involving multiple molecules like transcription factors (TFs), cofactors, and cis-regulatory elements, and therefore do not function exclusively at the singleomic levels, such as (epi-)genome, transcriptome, proteome, and metabolome. The coordination of these molecules regulates the gene expression that establishes and sustains specific cell states and essential biological functions in organisms. TFs cooperating with cofactors and other chromatin regulators bind to cis-regulatory elements, orchestrating the expression of genes and constructing complex regulatory networks. Dysregulation of the regulatory system may result from abnormal regulation in any layer, leading to a wide range of diseases.

Traditional singleomic analysis focuses on only one kind of molecule, allowing us to capture limited changes in a subset of the regulation system. Although singleomic studies may identify epigenomic modifications or differentially expressed genes in response to a specific stimulation, it fails to provide a systematic explanation of regulatory processes in bacteria or humans. Nowadays, integrative analysis of multiomic data is revolutionizing biomedical research now and providing a more detailed landscape of regulatory mechanisms, thanks to the advancement of high-throughput sequencing technologies and big data.

The work in this thesis focuses on applying integrative network biology analysis in different organisms to better dissect transcriptional regulatory mechanisms based on multiomic data. The studies in each chapter are described below:

Chapter 1: An introduction of essential trans-acting factors and cis-regulatory elements as well as the multiomic data integration in dissecting transcriptional regulatory mechanisms. We discuss the trans-acting TFs and coregulators, cis-regulatory elements enhancers, super-enhancers, and silencers in regulating gene expression, with their critical functions and interactions in maintaining the homeostasis of biology.

Chapter 2: This chapter introduces a genome-wide, network-based approach to dissect the crosstalk between key virulence-related TFs in a superbug, Pseudomonas aeruginosa. By integrating ChIP-seq and RNA-seq data, we describe the regulatory relationships of the TFs with their functional targets in a network that we call ‘Pseudomonas aeruginosa genomic regulatory network’ (PAGnet). Analysis of the network leads to the identification of novel functions for two TFs (ExsA and GacA) in quorum sensing and nitrogen metabolism. We also provide an online platform and R package based on PAGnet to facilitate updating and user-customised analyses.

Chapter 3: In this chapter, we apply integrative analysis of epigenomic and transcriptomic profiling to uncover super-enhancer heterogeneity in triple-negative breast cancer (TNBC) compared to other subtypes and provide clinically relevant biological insights towards TNBC. Using CRISPR/Cas9-mediated gene editing, we identify genes that are specifically regulated by TNBC-specific super-enhancers, including FOXC1, thereby unveiling a mechanism for specific overexpression of the key oncogenes in TNBC, contributing to the dysregulated oncogene expression in breast tumorigenesis.

Chapter 4: This chapter illustrates the epigenomic landscape reprogramming in hepatocellular carcinoma (HCC) by integrative analysis of multiomic data and provides potential clinically prognostic and predictive biomarkers. Thirteen target genes are identified to be significantly upregulated by HCC acquired super-enhancers that are associated with the prognosis of HCC patients. By performing integrative analysis of epigenomic and transcriptomic profiling across normal, paired cirrhotic and HCC patients, we discover the activation patterns of HCC acquired super-enhancers and their putative target genes in the cirrhotic stage, which provides potential early diagnostic biomarkers to this malignancy.

Chapter 5: In this chapter, we provide a summary of the thesis as well as further perspective.