Background Hypertrophic cardiomyopathy (HCM) is the most common genetic disease of the cardiac muscle, frequently caused by mutations in MYBPC3. However, little is known about the upstream pathways... Show moreBackground Hypertrophic cardiomyopathy (HCM) is the most common genetic disease of the cardiac muscle, frequently caused by mutations in MYBPC3. However, little is known about the upstream pathways and key regulators causing the disease. Therefore, we employed a multi-omics approach to study the pathomechanisms underlying HCM comparing patient hearts harboring MYBPC3 mutations to control hearts. Results Using H3K27ac ChIP-seq and RNA-seq we obtained 9310 differentially acetylated regions and 2033 differentially expressed genes, respectively, between 13 HCM and 10 control hearts. We obtained 441 differentially expressed proteins between 11 HCM and 8 control hearts using proteomics. By integrating multi-omics datasets, we identified a set of DNA regions and genes that differentiate HCM from control hearts and 53 protein-coding genes as the major contributors. This comprehensive analysis consistently points toward altered extracellular matrix formation, muscle contraction, and metabolism. Therefore, we studied enriched transcription factor (TF) binding motifs and identified 9 motif-encoded TFs, including KLF15, ETV4, AR, CLOCK, ETS2, GATA5, MEIS1, RXRA, and ZFX. Selected candidates were examined in stem cell-derived cardiomyocytes with and without mutated MYBPC3. Furthermore, we observed an abundance of acetylation signals and transcripts derived from cardiomyocytes compared to non-myocyte populations. Conclusions By integrating histone acetylome, transcriptome, and proteome profiles, we identified major effector genes and protein networks that drive the pathological changes in HCM with mutated MYBPC3. Our work identifies 38 highly affected protein-coding genes as potential plasma HCM biomarkers and 9 TFs as potential upstream regulators of these pathomechanisms that may serve as possible therapeutic targets. Show less
This thesis is concerned with statistical methodology for jointly analyzing multiple types of omics data. These datasets provide information on several biological levels, and an integrated analysis... Show moreThis thesis is concerned with statistical methodology for jointly analyzing multiple types of omics data. These datasets provide information on several biological levels, and an integrated analysis can lead to a better understanding of whole biological system. Due to the strong correlations within and between datasets, high dimensionality, and systematic differences between datasets, novel methods are needed. We consider latent variable modeling where strong correlations are incorporated, dimension reduction is performed, and heterogeneity between omics data is modeled. The first part of the thesis studies current data integration methods applied to population cohorts and their software implementations. In the second part, we propose a novel probabilistic data integration framework to model the relation between omics data: PO2PLS. This framework allows for statistical inference and helps reduce overfitting. The PO2PLS framework can be used to integrate multiple omics data with various study designs. Show less
Bouhaddani, S. el; Uh, H.W.; Jongbloed, G.; Hayward, C.; Klaric, L.; Kielbasa, S.M.; Houwing-Duistermaat, J. 2018
