Systems biology of kidney diseases

Abstract

Kidney diseases manifest in progressive loss of renal function, which ultimately leads to complete kidney failure. The mechanisms underlying the origins and progression of kidney diseases are not fully understood. Multiple factors involved in the pathogenesis of kidney diseases have made the traditional candidate gene approach of limited value toward full understanding of the molecular mechanisms of these diseases. A systems biology approach that integrates computational modeling with large-scale data gathering of the molecular changes could be useful in identifying the multiple interacting genes and their products that drive kidney diseases. Advances in biotechnology now make it possible to gather large data sets to characterize the role of the genome, epigenome, transcriptome, proteome, and metabolome in kidney diseases. When combined with computational analyses, these experimental approaches will provide a comprehensive understanding of the underlying biological processes. Multiscale analysis that connects the molecular interactions and cell biology of different kidney cells to renal physiology and pathology can be utilized to identify modules of biological and clinical importance that are perturbed in disease processes. This integration of experimental approaches and computational modeling is expected to generate new knowledge that can help to identify marker sets to guide the diagnosis, monitor disease progression, and identify new therapeutic targets.

Figure 1. ‘Omic’ approaches to profile cellular processes

Both external stimuli and internal signals can initiate the activation of downstream signaling pathways through post-transcriptional modification of signaling molecules, such as protein kinases, by phosphorylation. Activation of the signaling cascade leads to either transcriptional activation by recruitment and assembly of the transcriptional complex on the DNA, transcription factor/DNA (TF/DNA) interaction, or inhibition of transcriptional activation by exclusion of key TFs from the complex. Covalent modifications of the DNA by methylation and of histones by acetylation have a major role in the regulation of gene expression. Variations in the coding region of the genome result in mutations of the gene transcripts, and changes in non-coding region could affect the regulation of gene expression. Gene transcripts as messenger RNAs in the cytoplasm are translated into proteins. The protein products synthesized in response to the initial signal serve to maintain cellular integrity and react to perturbations in the systems by initiating additional signals or catalyze reactions that generate specific metabolites as a by-product. Characterization of these processes at the systems level is known as the ‘omics’ approaches: kinome for phosphorylation of proteins, TF/DNA regulome for regulation of transcription by the interaction of TF with DNA, epigenome for modification of histones and DNA, genome for the sequence of DNA, transcriptome for the mRNA transcripts of the expressed genes, proteome for the protein composition, and metabolome for the metabolites that are generated in a specific tissue or cell (Red P = phosphorylation, Me=methylation, Ac = acetylation, line with poly A tail=mRNA). miRNA, microRNA.

Figure 2. Construction of molecular networks

Networks that integrate binary interactions from heterogeneous sources, such as microRNAs (miRNAs) targeting mRNAs (from databases such as TargetScan or through experiments such as Argonaute HITS-CLIP (black diamond heads), protein/DNA interactions based on ChIP followed by high-throughput sequencing (ChIP-seq) analyses (cyan arrows), protein–protein interactions (orange links), virus/host protein interactions from databases such as VirusMINT (gray lines), kinase–substrate interactions (green arrows), and drug–target interactions (red diamond heads) from databases such as the DrugBank, can be used to build networks made of different types of nodes and links. An example of such network for key components that have a role in human immunodeficiency virus-associated nephropathy (HIVAN) is given on the right. Dasatinib is an Src inhibitor used for the treatment of oncologic disorder, but could be tested for its therapeutic potential in the treatment of HIV-associated nephropathy. Arrowheads indicate activations and diamond heads denote inhibition. NPHS1, nephrosis 1 homolog.

Figure 3. Multiscale analysis of kidney function

Maintenance of renal function requires the coordinated regulation from other organ systems (neuroendocrine and cardiovascular) and various tissue compartments and cells within the kidney. To recapitulate normal renal physiology in biological models, this multiscale organization from organ systems down to cell/gene level will need to be determined. The interactions at several levels have been described: Systems Approach for Physiological Integration of Renal, Cardiac, and Respiratory (SAPHIR) models, as well as models of autoregulation of glomerular blood flow, tubuloglomerular feedback, and tubulovascular exchange. Clinical parameters that we can use to assess and infer the function of organ systems and organs include blood pressure and cardiac function for cardiovascular and neuroendocrine input into the kidney, glomerular filtration rate (GFR), and proteinuria as determinants of the filtration function of the glomeruli, balance of electrolytes as an indicator of tubular function, podocyte number, foot process effacement, mesangial deposition, glomerulosclerosis, and tubulointerstitial fibrosis on renal histology as indicators of disease severity, and genetic variations (mutations, single-nucleotide polymorphisms (SNPs)) and mRNA and protein expression levels as indices of cellular response to internal and external stimuli. RTEC, renal tubular epithelial cell.

Figure 4. Systems biomedicine of kidney diseases

The capability to integrate clinical parameters, understand disease pathophysiology, and discover molecular mechanisms of diseases on the systems level will enable physicians to make more accurate molecular diagnosis, to predict the prognosis of disease, and to formulate targeted therapeutic agents. Together these approaches will facilitate the individualization of patient care.