Perspectives on systems biology applications in diabetic kidney disease

Abstract

Diabetic kidney disease (DKD) is a microvascular complication of type 1 and 2 diabetes with a devastating impact on individuals with the disease, their families, and society as a whole. DKD is the single most frequent cause of incident chronic kidney disease cases and accounts for over 40% of the population with end-stage renal disease. Contributing factors for the high prevalence are the increase in obesity and subsequent diabetes combined with an improved long-term survival with diabetes. Environment and genetic variations contribute to DKD susceptibility and progressive loss of kidney function. How the molecular mechanisms of genetic and environmental exposures interact during DKD initiation and progression is the focus of ongoing research efforts. The development of standardized, unbiased high-throughput profiling technologies of human DKD samples opens new avenues in capturing the multiple layers of DKD pathobiology. These techniques routinely interrogate analytes on a genome-wide scale generating comprehensive DKD-associated fingerprints. Linking the molecular fingerprints to deep clinical phenotypes may ultimately elucidate the intricate molecular interplay in a disease stage and subtype-specific manner. This insight will form the basis for accurate prognosis and facilitate targeted therapeutic interventions. In this review, we present ongoing efforts from large-scale data integration translating "-omics" research efforts into improved and individualized health care in DKD.

Figure 1. From Genome–to–Phenome – An Integrative Systems Biology Perspective on Diabetic Kidney Disease

Each biological system (e.g. diabetic kidney) can be regarded as being composed of discrete components. Each component can be independently inquired through unbiased high throughput “–omics” profiling or imaging technologies (ultrasound, nuclear magnetic resonance imaging, positron emission tomography), but individual level data is embedded in an interaction network that connects and integrates the various components in a functional context. Potential interactions between two or more components are indicated through arrows e.g. gene–by–environment interaction (1), gene–by–gene interaction (2), protein–DNA interaction (3), metabolite–protein interaction (4) e.g. allosteric regulation of enzymes by small metabolites and reciprocal Phenome–“omes” interaction (5). Technologies applied to generate –omics level data are: Genome/Epigenome (SNPs, CNV, promoters): e.g. genome sequencing, high–density SNP chips, methylation assays (MeDIP), histone modifications; Transcriptome (mRNA, miRNA, lncRNA, piRNA, tRNA, rRNA): microarray(s) and RNA sequencing; Proteome (Phosphoproteome, Glycoproteome, Acetylome): yeast two–hybrid, coimmunoprecipitation, 2D differential gel electrophoresis; Metabolome (sugars, lipids, amino acids, small molecules): nuclear magnetic resonance spectroscopy and mass spectrometry; Morphome (tissue damage/integrity): histology, immunohistochemistry; Phenome (ACR, GFR, retinal microaneurysm): clinical chemistry, clearance measurement, imaging studies; Environment and Ancestry (Drugs, Nutrition, Comorbidities): nutritional assessment, environmental exposure and socio–economic assessment.