Understanding kidney disease: toward the integration of regulatory networks across species

Abstract

Animal models have long been useful in investigating both normal and abnormal human physiology. Systems biology provides a relatively new set of approaches to identify similarities and differences between animal models and human beings that may lead to a more comprehensive understanding of human kidney pathophysiology. In this review, we briefly describe how genome-wide analyses of mouse models have helped elucidate features of human kidney diseases, discuss strategies to achieve effective network integration, and summarize currently available web-based tools that may facilitate integration of data across species. The rapid progress in systems biology and orthology, as well as the advent of web-based tools to facilitate these processes, now make it possible to take advantage of knowledge from distant animal species in targeted identification of regulatory networks that may have clinical relevance for human kidney diseases.

Figure 1

Depiction of 56 murine urine metabolite features that were up or down-regulated by >2 fold after normalization to urine creatinine values in urine of diabetic DBA/2 mice with substantial early diabetic nephropathy after 12 weeks of diabetes. These features needed to be present in 5 out of 7 animals tested in each group to be listed. Levels of 35 features in electrospray ionization-positive mode which showed such alterations are depicted.

Figure 2

A: Overview of the matching algorithm of TALE, adapted from Tian et al. The algorithm first matches the most important nodes conserved between the two networks. Of note, not all of these have to be exact orthologs of those in the other species, but fulfill similar functions (defined as connected to the comparable set of genes, as indicated by the matching of different shades of green, yellow and blue/purple). The algorithm then progressively extends these matches by including genes that are connected to those important genes in either species. B: Species-specific transcriptional networks can then be built based on the identified genes and connections using available primary literature based network mapping tools, which then be used by TALE for network merging. In the merged network, TALE pulls in interactions present in both species (orange lines) along with some interactions that are present in either of the species (purple or green lines).