Genome-wide analysis of the transcriptional network controlled by PGC-1α and ERRα in skeletal muscle from microarray and next generation sequencing data

Gene regulation in higher eukaryotes is a complex and dynamic process involving the coordinated action of several transcription factors and coregulators. The assembly of multi-protein complexes at promoter or enhancer elements modulates the activity of whole biological pathways and contributes to the physiological plasticity of cells and tissues. However, given their complexity and the high number of players involved, these regulatory processes are still poorly understood. In this context, next generation sequencing methods like the chromatin immunoprecipitation sequencing technique (ChIP-Seq) provide a novel approach for mapping the interactions between protein complexes and DNA elements on a genome-wide scale.
In this project, we perform a comprehensive coregulator-tailored ChIP-Seq data analysis to study the global recruitment of the peroxisome proliferator-activated receptor gamma coactivator 1 alpha (PGC-1alpha) and of the nuclear estrogen-related receptor alpha (ERRalpha) in skeletal muscle. Moreover, by integrating this information with microarray expression data, we infer the direct and indirect effects of our proteins of interest on their downstream target genes and, thus, on the regulated biological pathways. In addition, by combining several computational techniques, including binding site prediction and principal component analysis, we identify the activator protein 1 (AP-1) and the specificity protein 1 (SP1) as novel transcriptional partners in the PGC-1alpha and ERRalpha-mediated regulation of energy metabolism in skeletal muscle. Our study provides a new approach for the genome-wide analysis of coregulators and sheds light on the transcriptional network controlling skeletal muscle plasticity.
The content of this thesis is organized as follows. In chapter 1 we depict the state-of-the-art regarding nuclear receptors (and in particular ERRalpha), coregulators (with a focus on PGC-1alpha) and next generation sequencing technologies; moreover, we present the aims that motivated the projects described in this thesis. Chapter 2 illustrates our ChIP-Seq data analysis procedure, starting from the raw reads till the peak calling step. As every maturing technique, ChIP-Seq is accompanied by a number of issues that still need to be addressed; for this reason, we focus particularly on the artifacts we found and on our computational approaches to solve them. In chapter 3 we describe the downstream analysis steps of ChIP-Seq studies, including peak annotation, motif search and principal component analysis of peak binding site predictions. The results of the computational techniques described in the previous chapters to dissect PGC-1alpha regulatory network in skeletal muscle are illustrated in chapter 4, whereas in chapter 5 we focus on the interplay between PGC-1alpha and ERRalpha and in particular we examine to which extent ERRalpha is required for PGC-1alpha-mediated effects on skeletal muscle cells. Finally, the discussion will review the main findings of the present thesis and provide an outlook on the possible future developments of this study.