Proteomics: from single molecules to biological pathways

Abstract

The conventional reductionist approach to cardiovascular research investigates individual candidate factors or linear signalling pathways but ignores more complex interactions in biological systems. The advent of molecular profiling technologies that focus on a global characterization of whole complements allows an exploration of the interconnectivity of pathways during pathophysiologically relevant processes, but has brought about the issue of statistical analysis and data integration. Proteins identified by differential expression as well as those in protein-protein interaction networks identified through experiments and through computational modelling techniques can be used as an initial starting point for functional analyses. In combination with other '-omics' technologies, such as transcriptomics and metabolomics, proteomics explores different aspects of disease, and the different pillars of observations facilitate the data integration in disease-specific networks. Ultimately, a systems biology approach may advance our understanding of cardiovascular disease processes at a 'biological pathway' instead of a 'single molecule' level and accelerate progress towards disease-modifying interventions.

Figure 1

Proteomic approaches. Protein extracts can either be fractionated at the protein level prior to digestion or after protein digestion at the peptide level. In DIGE, the protein extracts are labelled with different fluorescent dyes before they are separated by 2-DE. For SILAC, cells are metabolically labelled in culture by incorporation of heavy or light amino acids. Alternatively, labelling is performed at the peptide level, using iTRAQ or TMT isobaric tags. Peptides are then analysed by MS/MS. 2-DE, two-dimensional gel electrophoresis; DIGE, difference gel electrophoresis; 1-DE, one-dimensional gel electrophoresis; SILAC, stable isotope labelling with amino acids in cell culture; AA, amino acid; iTRAQ, isobaric tag for relative and absolute quantitation; TMT, tandem mass tag.

Figure 2

Gel-based proteomics. Separation of the murine cardiac proteome by DIGE on different immobilized pH gradients: pH 3–10 NL (A) and pH 4–7 (B). The white box highlights the better resolution of the same area on the narrow pH gradient. (C) Mw distribution of six extracellular glycoproteins by SDS–PAGE. The characteristic ‘laddering’ in abdominal aortic aneurysms (AAA) compared with normal aortic tissue (CON) is indicative of proteolysis. Differences in spectral counts are color-coded (red high, blue low). (D) Incubation of healthy aortic tissues with matrix metalloproteinases-12 (MMP-12) induced a similar fragmentation pattern of fibronectin as observed in AAA. In comparison, degradation by matrix metalloproteinases-9 (MMP-9) was less pronounced (reproduced with permission from Didangelos et al.).

Figure 3

Computational approaches in proteomics. Bioinformatics has become an essential part of the proteomic workflow to comprehensively analyse and visualize global changes in proteins as biological networks.

Figure 4

Protein identification. The visualization of ECM proteins, and the corresponding correlation networks, identified by proteomics in the secretome of two murine cell types: (A) aortic SMC and (B) CF. Correlation networks have been thresholded at a correlation coefficient of >0.90. (C) Number of ECM proteins identified. Number of nodes (proteins) (D) and links (correlations) (E) found in each cell type, as well as those in common.

Figure 5

Metabolomics. A comparison of control and hibernating murine hearts by high-resolution magic-angle-spinning ¹H-magnetic resonance spectroscopy (HRMAS ¹H-MRS) analysis from solid hearts (A) and ¹H-nuclear magnetic resonance spectroscopy (¹H-NMR) of cardiac tissue extracts (B, reproduced with permission from Mayr et al.). Both techniques showed consistent changes in metabolites, i.e. the ratio of glutamate, lactate, and taurine in hibernating compared with control hearts as determined by HRMAS ¹H-MRS was 0.81, 1.09, and 0.79, respectively, which is in good agreement with the measurements of 0.68, 1.13, and 0.72 for the same metabolites by ¹H-NMR. HRMAS ¹H-MRS provides a means for measuring metabolites in intact hearts ex vivo. ¹H-NMR of tissue extracts offers better resolution and allows the identification of more metabolites than HRMAS ¹H-MRS spectra obtained from solid tissue.