An overview of cardiac systems biology

Robust coordination of cardiac functions from gene co-expression reveals a versatile combinatorial transcriptional control

The necessary overall coordination of cardiac cellular functions is little known at the mRNA level. Focusing on energy production and cardiac contraction, we analyzed microarray data from heart tissue obtained in groups of mice and rats in normal conditions and with a left ventricular dysfunction. In each group and for each function, we identified genes positively or negatively correlated with numerous genes of the function, which were called coordinated or inversely coordinated with the function. The genes coordinated with energy production or cardiac contraction showed the coupling of these functions in all groups. Among coordinated or inversely coordinated genes common to the two functions, we proposed a fair number of transcriptional regulators as potential determinants of the energy production and cardiac contraction coupling. Although this coupling was constant across the groups and unveiled a stable gene core, the combinations of transcriptional regulators were very different between the groups, including one half that has never been linked to heart function. These results highlighted the stable coordination of energy production or cardiac contraction at the mRNA level, and the combinatorial and versatile nature of potential transcriptional regulation. In addition, this work unveiled new transcriptional regulators potentially involved in normal or altered cardiac functional coupling.

Cardiovascular translational medicine (III). Genomics and proteomics in heart failure research

Heart failure is a complex syndrome and is one of the main causes of morbidity and mortality in developed countries. Despite considerable research effort in recent years, heart failure prevention and treatment strategies still suffer significant limitations. New theoretical and technical approaches are, therefore, required. It is in this context that the "omic" sciences have a role to play in heart failure. The incorporation of "omic" methodologies into the study of human disease has substantially changed biological approaches to disease and has given an enormous impetus to the search for new disease mechanisms, as well as for novel biomarkers and therapeutic targets. The application of genomics, proteomics and metabonomics to heart failure research could increase our understanding of the origin and development of the different processes contributing to this syndrome, thereby enabling the establishment of specific diagnostic profiles and therapeutic templates that could help improve the poor prognosis associated with heart failure. This brief review contains a short description of the fundamental principles of the "omic" sciences and an evaluation of how these new techniques are currently contributing to research into human heart failure. The focus is mainly on the analysis of gene expression microarrays in the field of genomics and on studies using two-dimensional electrophoresis with mass spectrometry in the area of proteomics.

Genome-wide expression patterns in physiological cardiac hypertrophy

Background

Genome-wide expression patterns in physiological cardiac hypertrophy. Co-expression patterns in physiological cardiac hypertrophy

Results

In this study, the first large-scale analysis of publicly available genome-wide expression data of several in vivo murine models of physiological LVH was carried out using network analysis. On evaluating 3 million gene co-expression patterns across 141 relevant microarray experiments, it was found that physiological adaptation is an evolutionarily conserved processes involving preservation of the function of cytochrome c oxidase, induction of autophagy compatible with cell survival, and coordinated regulation of angiogenesis.

Conclusion

This analysis not only identifies known biological pathways involved in physiological LVH, but also offers novel insights into the molecular basis of this phenotype by identifying key networks of co-expressed genes, as well as their topological and functional properties, using relevant high-quality microarray experiments and network inference.

Systems biology approaches and pathway tools for investigating cardiovascular disease

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.

A framework for evolutionary systems biology

BACKGROUND

Many difficult problems in evolutionary genomics are related to mutations that have weak effects on fitness, as the consequences of mutations with large effects are often simple to predict. Current systems biology has accumulated much data on mutations with large effects and can predict the properties of knockout mutants in some systems. However experimental methods are too insensitive to observe small effects.

RESULTS

Here I propose a novel framework that brings together evolutionary theory and current systems biology approaches in order to quantify small effects of mutations and their epistatic interactions in silico. Central to this approach is the definition of fitness correlates that can be computed in some current systems biology models employing the rigorous algorithms that are at the core of much work in computational systems biology. The framework exploits synergies between the realism of such models and the need to understand real systems in evolutionary theory. This framework can address many longstanding topics in evolutionary biology by defining various 'levels' of the adaptive landscape. Addressed topics include the distribution of mutational effects on fitness, as well as the nature of advantageous mutations, epistasis and robustness. Combining corresponding parameter estimates with population genetics models raises the possibility of testing evolutionary hypotheses at a new level of realism.

CONCLUSION

EvoSysBio is expected to lead to a more detailed understanding of the fundamental principles of life by combining knowledge about well-known biological systems from several disciplines. This will benefit both evolutionary theory and current systems biology. Understanding robustness by analysing distributions of mutational effects and epistasis is pivotal for drug design, cancer research, responsible genetic engineering in synthetic biology and many other practical applications.