Systems biology approaches and pathway tools for investigating cardiovascular disease

Abstract

Systems biology aims to understand the nonlinear interactions of multiple biomolecular components that characterize a living organism. One important aspect of systems biology approaches is to identify the biological pathways or networks that connect the differing elements of a system, and examine how they evolve with temporal and environmental changes. The utility of this method becomes clear when applied to multifactorial diseases with complex etiologies, such as inflammatory-related diseases, herein exemplified by atherosclerosis. In this paper, the initial studies in this discipline are reviewed and examined within the context of the development of the field. In addition, several different software tools are briefly described and a novel application for the KEGG database suite called KegArray is presented. This tool is designed for mapping the results of high-throughput omics studies, including transcriptomics, proteomics and metabolomics data, onto interactive KEGG metabolic pathways. The utility of KegArray is demonstrated using a combined transcriptomics and lipidomics dataset from a published study designed to examine the potential of cholesterol in the diet to influence the inflammatory component in the development of atherosclerosis. These data were mapped onto the KEGG PATHWAY database, with a low cholesterol diet affecting 60 distinct biochemical pathways and a high cholesterol exposure affecting 76 biochemical pathways. A total of 77 pathways were differentially affected between low and high cholesterol diets. The KEGG pathways "Biosynthesis of unsaturated fatty acids" and "Sphingolipid metabolism" evidenced multiple changes in gene/lipid levels between low and high cholesterol treatment, and are discussed in detail. Taken together, this paper provides a brief introduction to systems biology and the applications of pathway mapping to the study of cardiovascular disease, as well as a summary of available tools. Current limitations and future visions of this emerging field are discussed, with the conclusion that combining knowledge from biological pathways and high-throughput omics data will move clinical medicine one step further to individualize medical diagnosis and treatment.

Questing for Circadian Dependence in ST-Segment–Elevation Acute Myocardial Infarction

“…13 Nevertheless, we stress the importance of pursuing systematic studies that clarify the latent pathways behind these phenomena. Modern systems biology offers advanced computational tools to analyze nonlinear molecular patterns, 14,15 and we believe that to assess the intriguing observation that circadian rhythms can affect the infarct size, future study design should not only include larger samples but also aim to untangle the molecular time-dynamic mechanisms underlying such a relation. …”

The Use of High-Throughput Technologies to Investigate Vascular Inflammation and Atherosclerosis

“…115 Furthermore, 1 example of how lipids may affect pathway signaling has been nicely discussed by Wheelock et al, who revealed 77 biochemical pathways to be differentially affected between low and high fat cholesterol diet in mice, by combining liver transcriptomics and lipidomics data and mapping them onto a metabolic pathway database. 116 System biology approaches like those introduced by Wheelock et al 116 are very helpful to process high-throughput data, which is further discussed below. In addition, changes in lipid profiles, including phospholipids, ceramides, sphingomyelin, cerebrosides, cholesterol, and their oxidized derivatives, may also affect or be indicative of ischemic stroke, as indicated by Fonteh and Fisher 117 and by Fonteh et al 118 Protein microarrays are an emerging tool, competing with traditional gel-based methods (eg, 2-DE), offering a higher reproducibility and the possibility of high-throughput approaches.…”

Clinical Implementation of Metabolomics

“…The critical step is the construction of models from the raw dataset of transcriptomics, proteomics, and metabolomics. This may be achieved by using different mathematical techniques ranging from simple Pearson correlations to the use of ordinary differential equations (Wheelock et al, 2009). Through this modeling, fundamental concepts in the understanding of biological systems like robustness, modularity, emergence, etc.…”

“…A systems biology approach does not focus on identifying a single target or mechanism of an observed phenotype. Instead it seeks to identify the biological networks or pathways that connect the differing elements of a system (Wheelock et al, 2009). When a shift in equilibrium is observed in a disease, such as altered metabolic fluxes or enzymatic activities, it can be elucidated that those components of the network that are associated with the observed shift are characteristic and potentially descriptive of the disease, and that they accordingly represent potential targets for intervention.…”