Novel insights into potential therapeutic targets and biomarkers using integrated multi- omics approaches for dilated and ischemic cardiomyopathies

At present heart failure treatment targets symptoms based on the left ventricle dysfunction severity; however, lack of systemic studies and available biological data to uncover heterogeneous underlying mechanisms on the scale of genomic, transcriptional and expressed protein level signifies the need to shift the analytical paradigm toward network centric and data mining approaches. This study, for the first time, aimed to investigate how bulk and single cell RNA-sequencing as well as the proteomics analysis of the human heart tissue can be integrated to uncover heart failure specific networks and potential therapeutic targets or biomarkers. Furthermore, it was demonstrated that transcriptomics data in combination with minded data from public databases can be used to elucidate specific gene expression profiles. This was achieved using machine learning algorithms to predict the likelihood of the therapeutic target or biomarker tractability based on a novel scoring system also introduced in this study. The described methodology could be very useful for the target selection and evaluation during the pre-clinical therapeutics development stage. Finally, the present study shed new light into the complex etiology of the heart failure differentiating between subtle changes in dilated and ischemic cardiomyopathy on the single cell, proteome and whole transcriptome level. HIGHLIGHTS First report of an integrated multi-omics analysis for dilated and ischemic cardiomyopathies. Identification of metabolic and regulatory network differences for the two types of cardiomyopathies. Introduction of a new scoring system to evaluate genes based on the size of their network and disease association. Two-step machine learning pipeline to uncover potential therapeutic target clusters.