The KUPNetViz: a biological network viewer for multiple -omics datasets in kidney diseases

Abstract

Background: Constant technological advances have allowed scientists in biology to migrate from conventional single-omics to multi-omics experimental approaches, challenging bioinformatics to bridge this multi-tiered information. Ongoing research in renal biology is no exception. The results of large-scale and/or high throughput experiments, presenting a wealth of information on kidney disease are scattered across the web. To tackle this problem, we recently presented the KUPKB, a multi-omics data repository for renal diseases.

Results: In this article, we describe KUPNetViz, a biological graph exploration tool allowing the exploration of KUPKB data through the visualization of biomolecule interactions. KUPNetViz enables the integration of multi-layered experimental data over different species, renal locations and renal diseases to protein-protein interaction networks and allows association with biological functions, biochemical pathways and other functional elements such as miRNAs. KUPNetViz focuses on the simplicity of its usage and the clarity of resulting networks by reducing and/or automating advanced functionalities present in other biological network visualization packages. In addition, it allows the extrapolation of biomolecule interactions across different species, leading to the formulations of new plausible hypotheses, adequate experiment design and to the suggestion of novel biological mechanisms. We demonstrate the value of KUPNetViz by two usage examples: the integration of calreticulin as a key player in a larger interaction network in renal graft rejection and the novel observation of the strong association of interleukin-6 with polycystic kidney disease.

Conclusions: The KUPNetViz is an interactive and flexible biological network visualization and exploration tool. It provides renal biologists with biological network snapshots of the complex integrated data of the KUPKB allowing the formulation of new hypotheses in a user friendly manner.

Figure 1

A snaphsot of KUPNetViz in action. Under the network view, the KUPKB data mapping tab is displayed, where the researcher can map onto the network several gene/protein/miRNA expression datasets and apply different criteria and combinations of kidney location(s), disease(s) and data from published studies. This snapshot focuses on the down-regulation of TGFβ1 in kidney in two renal disease states, namely in acute renal allograft rejection and interstitial fibrosis and tubular atrophy, and the up-regulation of the miRNA that targets TGFβ1 in interstitial fibrosis and tubular atrophy, as derived from two published studies.

Figure 2

Calreticulin network. Analysis of the calreticulin network shows that not only calreticulin expression is modified in renal graft rejection but also many of the genes/proteins (34%) with known interactions with calreticulin. In addition specific processes/pathways are enriched including extracellular matrix interaction and basement membrane proteins. These are processes and pathways known to be involved in renal graft rejection. Overall projection of the KUPKB data on the calreticulin interaction network clearly exemplifies a role of calreticulin in renal graft rejection.

Figure 3

The IL6/IL6R axis network for human and rat. The IL6/IL6R axis expression network created by querying the KUPNetViz for IL6 and IL6R and using the first level interaction neighbors of IL6. (A) The network associated with IL6/IL6R is mostly up-regulated in patients with PKD as observed after mapping the results of three relevant studies in human (namely “Lai, Proteomics Clin Appl, 2008”, “Mason, Proteomics Clin Appl, 2009” and “Song, Hum Mol Genet, 2009”), (B-D) Progressive upregulation of the IL6/IL6R network in PKD in rat model as derived from the study of Koupepidou et al.[36]. The three figures depict three distinct stages of the disease, specifically (A) day 0, (B) day 6 and (D) day 24, after mapping the three experimental conditions in the dataset “Koupepidou, BMC Nephrol, 2010”.

Figure 4

The modularization of KUPNetViz. A simple description of the architecture behind the KUPNetViz which consists of three simple layers, present in many similar applications: i) the background database, ii) the server PHP code layer handling database querying and parsing iii) the client side of the application where the interaction with the user is taking place. Client–server data exchange is performed using the widely used JavaScript Object Notation (JSON) format. The vertical dashed lines in the data stored in the backend database layer and the PHP layer mark the distinction of the different modules handling the background knowledge and network reconstruction and the KUPKB datasets and their mapping to the network. This distinction allow the evolution of the KUPNetViz to a more general biological network web framework which combines bundled background knowledge on biomolecular interactions with a very simple interface designed for bench researchers wishing simple biological state snapshots and not to go deeper in other network properties.