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

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 network-based 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.

Figure 1

Integrative analysis of "omic" information in the context of a HF PPI network: Overview of main analytical phases implemented in this research. Novel relationships refer to new associations between proteins and specific processes and cellular localizations, and between functional modules and specific transcriptional regulatory mechanisms.

Figure 2

Schematic representations of PPI networks related to HF. A. Core HF PPI network. B. Degree distribution of the core network. In A, nodes and edges represent proteins and interactions respectively. Nodes located near the center represent highly-connected nodes, or nodes at the intersection between different node-node pathways. In B, the relationship between node degree and the observed frequency is plotted on a logarithmic scale (ln) for this network.

Figure 3

Functional landscape and module-oriented architecture of the HF PPI network. Lines linking the modules represent inter-module interactions (independently of the number of individual PPIs). A. Functional characterization based on module-specific involvement in different biological processes. B. Functional characterization based on module-specific associations with cellular localizations.

Figure 4

Characterization of major network communication properties. A. Relationship between node degree and traffic. B. A 3D contour plot of the communication and connectivity structure of the HF network. In A. a line is fitted to the data to highlight the linear relationship between the variables. In B. the black squares represent network proteins plotted against their corresponding degree values, and the colour-coded regions reflect the traffic levels. Colour regions and contours were fitted according to a distance weighted least squares procedure. The higher the position of a protein on the plot, the larger its number of connections and traffic level. Fibronectin 1 (FN1), integrin beta 1 (ITGB1) and platelet-derived growth factor receptor beta (PDGFRB) are the top three communication "hotpots" with the highest degree and traffic values in this network.

Figure 5

Regulation of modules at the transcriptional level through the action of transcription factors with known strong associations with module members.