Development of multi-omics heart-kidney expression profiling databases and integrative systems-level disease analysis

Increasing numbers of patients are identified as suffering from chronic kidney disease (CKD): currently estimated at 11% of the general population in the US and 7% in the EU. A sizable number progress to end-stage renal disease (ESRD) which requires renal replacement therapy (RRT) and/or die from cardiovascular disease (CVD). Specifically, it has been suggested that CKD patients are at the highest risk of CVD. So far, the main prognostic indicators in established CKD have been the severity of hypertension and of proteinuria. However, these parameters whilst helpful do not always accurately predict progression as it can occur in established disease although with patients being normotensive and nonproteinuric. Renal histology has long been considered a most valuable indicator of the severity of established CKD and the likelihood of progression. However, repeat renal biopsies to follow progress and response to treatment is an invasive procedure, and an unrealistic option to consider on a routine basis. It is therefore imperative to identify less invasive markers in established CKD that are likely to predict outcomes and there is an urgent need to further elucidate the molecular mechanisms underlying advanced renal damage. The increase in research on the development of disease biomarkers, therapeutic targets and new drugs has provided a wealth of information on individual molecular changes associated with disease. Nevertheless, these findings have not been translated into expected clinical outcomes. Due to the multi-factorial molecular phenotype of disease, it is evident that development of novel therapeutic and disease detection approaches should be based upon the study of the entire “System” simultaneously. System biology-based diagnostic and prognostic models consisting of relevant panels of molecules - key branches of the cellular network, appear to more accurately reflect pathophysiology, consequently, may have a much higher chance of success in the clinical setting. To get a holistic view of a system’s biology, multiple and different types of observations must be combined, such as clinical (pathological, demographical, epidemiological), as well as molecular (including large-scale genotypic, gene expression, proteomics, metabolomics, lipidomics) data. Over the last decade the emergence of high-throughput screening platforms, opened the possibility to mechanistically understand diseases and diseases stages at the molecular level. Simultaneously it has become apparent that systems to integrate and correlate this data are either inadequate or non-existent. With the ever-reducing cost of omics technologies there is great potential in integrative multi-omics research. An unbiased, hypothesis-free approach in disease diagnostics and data analytics has an enormous advantage over a traditional hypothesis-based investigation since no assumptions are made regarding molecules involved in malignant conditions, involved pathways or outcomes. In this purely data-driven context, it is much more likely to identify potential druggable targets, thereby streamlining both the discovery and testing phases. Presently, one of the greatest challenges is to further study and develop methods for downstream data analysis. The methodology used in this work is based on an unbiased, hypothesis-free approach, combining all available –omics data into a global picture of CKD and CVD conditions. Therefore, multi-omics disease profiling databases were created handling differentially expressed (DE) molecules of large-screening studies in CKD—CKDdb database (www.padb.org/ckddb) and CVD—C/VD database (www.padb.org/cvd) collected from the literature and general scope databases followed by an extensive manual curation and annotation. They cover many molecular entities, for instance microRNAs, gene, protein and metabolites in a multitude of tissues and body-accessible fluids of several disease groups and phenotypes related with the renal-cardiovascular axis. Moreover, the developed databases enable integration, harmonization and management of large-scale –omics data, thereby allowing to cross-compare and investigate numerous renal-cardiovascular conditions in parallel, contextualize data and develop new data analytics methods. The next step involved integration of those resources through a systems-biology approach by developing network- and pathway-based disease models. This approach was successful in identifying significant hampered gene clusters modulating the thyroid-stimulating hormone (TSH) signalling and the brain-derived neurotrophic factor (BDNF) pathway in established CKD, as well as aberrant sphingolipid metabolism in CKD progression and association with an inherited rare condition—Fabry disease (FD). Furthermore, in a system-level integrative data analysis of a leading underlying cause of CKD—IgA nephropathy (IgAN), the analysis was indicative of activation of the pathway for pathogen phagocytosis (ITGAM, TYROBP, including an NADPH oxidase co-activator: NCF2) in a way that mimics host- pathogen interactions at the gut epithelium in genetically predisposed IgAN subjects with further development of subclinical inflammation. On the other hand, the coronary artery disease (CAD) showcase, uncovered a global disturbed lipid metabolism profile, including biosynthesis of fatty acids (FA), with blunted ability for cholesterol binding and transport, as well as the involvement of the proliferator-activated receptor (PPAR) signalling pathway. Additionally, indication of disrupted biological processes with distinct increase in expression of molecular elements involved in activation of inflammation, extracellular matrix (ECM)- receptor interaction, and cardiac hypertrophy could be shown. Several avenues should be pursued to further enhance the strength of this approach, presently several steps in the data analytics pipeline must be performed manually or using separate bespoke software solutions. For instance, streamline of data analytics by developing automated computational ways to alleviate this bottleneck. The time and technologies are now ripe to move to the next stage of both transferring these –omics results and technologies to the clinic and combining different biological levels to define the molecular determinants in many renal and cardiovascular conditions