Coordinated modular functionality and prognostic potential of a heart failure biomarker-driven interaction network

Azuaje et al. BMC Systems Biology 2010, 4:60 http://www.biomedcentral.com/1752-0509/4/60 RESEARCH ARTICLE Open Access Coordinated modular functionality and prognostic potential of a heart failure biomarker-driven interaction network Research article Francisco Azuaje*1, Yvan Devaux1 and Daniel R Wagner1,2 Abstract Background: The identification of potentially relevant biomarkers and a deeper understanding of molecular mechanisms related to heart failure (HF) development can be enhanced by the implementation of biological networkbased analyses. To support these efforts, here we report a global network of protein-protein interactions (PPIs) relevant to HF, which was characterized through integrative bioinformatic analyses of multiple sources of "omic" information. Results: We found that the structural and functional architecture of this PPI network is highly modular. These network modules can be assigned to specialized processes, specific cellular regions and their functional roles tend to partially overlap. Our results suggest that HF biomarkers may be defined as key coordinators of intra- and inter-module communication. Putative biomarkers can, in general, be distinguished as "information traffic" mediators within this network. The top high traffic proteins are encoded by genes that are not highly differentially expressed across HF and non-HF patients. Nevertheless, we present evidence that the integration of expression patterns from high traffic genes may support accurate prediction of HF. We quantitatively demonstrate that intra- and inter-module functional activity may be controlled by a family of transcription factors known to be associated with the prevention of hypertrophy. Conclusion: The systems-driven analysis reported here provides the basis for the identification of potentially novel biomarkers and understanding HF-related mechanisms in a more comprehensive and integrated way. Background Heart failure (HF) is a clinical syndrome that results from cardiac disease. HF can be characterized as the heart's inability to pump enough blood to meet physiological requirements. HF may be caused by cardiac injury (e.g. failure after myocardial infarction) or by non-ischemic diseases (e.g. dilated cardiomyopathy). Independently of the etiology, HF is known to be the by-product of a largescale, dynamic interplay of proteins, hormones and metabolites. These interactions are in turn brought about and controlled by a diversity of genes and molecular pathways responsible for different processes, which range from inflammation trough extracellular-matrix remodeling to angiogenesis. This motivates the development of approaches to the systematic, integrated analysis of protein interactions related to HF. * Correspondence: 1 Laboratory of Cardiovascular Research, Centre de Recherche Public - Santé, L- 1150, Luxembourg Full list of author information is available at the end of the article Many questions connected to the elucidation of the complex molecular mechanisms spurring the emergence, progression and repair of cardiac malfunction remain to be answered. The increasing amounts of information about accepted and putative HF biomarkers and therapeutic targets, as well as of annotated datasets of proteinprotein interactions (PPIs) in humans, offer new opportunities to understand HF within a systems biology framework [1]. Advances in high-throughput technologies for the quantitative assessment of different "omic" information variables are fostering a more comprehensive, systemslevel view of the PPIs involved in different physiological and pathological conditions. Over the past few years, larger amounts of experimentally-validated human PPIs [1-3] have been made available via public or proprietary Web-based information resources. Advances in this area have traditionally concentrated on the analysis of largescale, global PPI networks (i.e., interactomes) in a small number of model organisms and more recently in © 2010 Azuaje et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Azuaje et al. BMC Systems Biology 2010, 4:60 http://www.biomedcentral.com/1752-0509/4/60 humans. For instance, using experimentally-validated or expert-annotated interactomes, researchers have shown how the structure and composition of PPI networks can be linked to specific biological processes, properties and clinical outcomes [4-6]. Furthermore, investigations have demonstrated how such information can be meaningfully correlated with observations and predictions at different "omic" information levels, e.g. genomic variation [6], gene expression [7,8] and standard functional annotations [9]. Major steps forward in this area have been: a) the capacity to link clusters of highly-connected proteins (commonly referred to as "modules") within these networks and specific biological processes [4,10] and b) the capacity to detect potential biomarkers, therapeutic targets or critical functional components using network topology features [5,11]. The majority of these contributions have focused on the investigation of large-scale networks that are not specific to diseases or phenotypes. Furthermore, network-based approaches have not been sufficiently investigated in the area of HF research. The potential of network-based analyses in the cardiovascular area has been previously reported in dilated cardiomyopathy (DCM) investigations [8,12]. Zhu et al. [12] integrated public gene expression data with a layered PPI network that was organised into four functional compartments: extracellular, plasma membrane, cytoplasm and nucleus. This allowed them to identify the Janus family tyrosine kinase-signal transducer and activator of transcription (Jak-STAT) signaling pathway as a potential key driver of DCM development. Despite the potential limitations related to knowledge incompleteness and uncertainty in the network inference process, the characterization of complex biological phenomena on the basis of functional modular architectures and topological parameters present us with new opportunities to improve our understanding of the evolution, operation and possible re-engineering of these systems [6,13,14]. Here we report the analysis of a PPI network in the context of human HF and in relation to diverse, complementary resources of "omic" information. This analysis aimed to characterize potential functional and structural patterns and associations, which may explain fundamental molecular mechanisms underlying HF, as well as the role of biomarkers, from a systems biology standpoint. The practical utility and potential biomedical relevance of the outcomes of this research are two-fold. First, the products of this research can be seen as a disease-specific knowledge refer (...truncated)