Cardiovascular Research (2008) 79, 218–227 Review doi:10.1093/cvr/cvn061 Genomics in cardiac metabolism Jane-Lise Samuel1,2, Marcus C. Schaub3, Michael Zaugg4, Mamas Mamas5, Warwick B. Dunn6, and Bernard Swynghedauw1,2* 1U689 INSERM, Hoˆpital Lariboisie`re, 41 Bd de la Chapelle, 75475 Paris Cedex 10, France; 2Universite´ Denis Diderot, 3 4 Paris Cedex 7, France; Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland; Institute of Downloaded from https://academic.oup.com/cardiovascres/article-abstract/79/2/218/270179 by guest on 22 November 2018 Anaesthesiology, University Hospital Zurich, Zurich, Switzerland; 5Department of Cardiology, Manchester Royal Infirmary, Manchester, UK; and 6The Manchester Centre for Integrative Systems Biology, University of Manchester, Manchester, UK Received 5 December 2007; revised 3 February 2008; accepted 15 February 2008; online publish-ahead-of-print 7 March 2008 Time for primary review: 15 days KEYWORDS Cell biology is in transition from reductionism to a more integrated science. Large-scale analysis of Cardiac metabolism; genome structure, gene expression, and metabolites are new technologies available for studying Genome-wide analysis; cardiac metabolism in diseases known to modify cardiac function. These technologies have several Metabolomics; limitations and this review aims both to assess and take a critical look at some important results Transcriptomics obtained in genomics restricted to molecular genetics, transcriptomics and metabolomics of cardiac metabolism in pathophysiological processes known to alter myocardial function. Therefore, our goal was to delineate new signalling pathways and new areas of research from the vast amount of data already published on genomics as applied to cardiac metabolism in diseases such as coronary heart disease, heart failure, and ischaemic reperfusion. 1. Introduction study of gene expression of either transcripts or proteins. Gene expression is a short-term approach and is based on This review aims both to assess and critically review the two main techniques, namely microarrays analysis and pro- main results obtained in genomics of cardiac metabolism teomics.1 Two different aspects of transcriptomics have by the end of 2007. Although genomics have been differen- been developed herein: (a) modifications observed during tially defined, we use the most widely accepted one, which 1 the time course of a chronic disease of the heart and (b) was utilized by Gibson and Muse. Genomics covers the pre- and post-conditioning and changes induced by short- overall structure or expression of our genetic inheritance term metabolic interventions (such as anaesthesia) that including molecular genetics, transcriptomics, proteomics, confer cardioprotection. (iii) The final chapter is on metabo- and metabolomics. lomics that aims to quantify still more rapid modifications in The present work aims to delineate new signalling path- metabolic compounds, but again on a genome-wide scale ways and new areas of research from the vast amount of with the potential of medical applications. data already published on genomics as applied to cardiac It is worthy to note that with regards to the importance of metabolism in diseases, such as coronary heart disease the research area, we have selected studies, which were the (CHD) and heart failure (HF). This review was clearly most informative in the field of cardiology. limited by the fact that both the new molecular genetics, It has to be underscored that the majority of the studies based on genome-wide association studies (GWAS) and reported herein, imply a technical approach, which is both metabolomics are still in their infancy, at least in the car- costly and frequently requires large groups of investigators, diovascular (CV) field. In contrast, the science of transcrip- patient cohorts (for GWAS) and multidisciplinary approaches tomics in the CV arena is more mature and had been (for metabolomics). Briefly, GWAS is principally based on developed in more detail. high-density genotyping arrays that combine the power of From the growing flow of new data, a selection was made association studies with the systematic nature of genome- to illustrate better the potential of such a global approach. wide research. Transcriptomics is mainly based on micro- (i) Genetics has presently reached a new era based on GWAS. array analysis, i.e. a high-throughput method for screening GWAS is, in principle, more liable to identify low-effect a collection of microscopic DNA spots attached to a solid genes operative in pathological pathways and disease sus- 2 surface and to measure the expression levels of large ceptibility in common diseases. (ii) Transcriptomics is the numbers of genes in different samples simultaneously. Pro- teomics commonly utilized two-dimensional polyacrylamide * Corresponding author. Tel: þ33 1 5321 6760; fax: þ33153216739. gel electrophoresis to separate proteins, but there are also E-mail address: [email protected] Published on behalf of the European Society of Cardiology. All rights reserved. & The Author 2008. For permissions please email: [email protected]. Genomics in cardiac metabolism 219 many other technical approaches available including protein (i) Diseases with phenotypic variance mainly due to microarrays. Finally, metabolomics is mainly a multidisci- genetic factors such as type 1 diabetes. GWAS docu- plinary approach based on the combination of pyrolysis mented at least a dozen genes strongly associated and different spectrometry. with type 1 diabetes (HLA class genes, insulin gene, CTLA4 locus, PTPN22, and the IFIH1 region) that mostly belong to the immune system which are the 2. Are genome-wide association studies ready most important targets for research on type 1 diabetes. for common diseases? (ii) On the other hand, diseases such as arterial hyperten- Medical genetics not only involves the identification of one sion and hyperlipidaemias remained poorly associated specific gene associated with a severe risk of having a to simple genetic factors. Despite a considerable given disease, but also provides information on associations hope, genetic influence is weak and there are few Downloaded from https://academic.oup.com/cardiovascres/article-abstract/79/2/218/270179 by guest on 22 November 2018 of gene variants, each providing a moderate risk.3–6 GWAS is risk alleles of large size effects and GWAS has been based on (i) the availability of dense genotyping chips made unable to identify with certainty susceptibility genes of modest effect size and we need, in the least, geno- with single-nucleotide polymorphisms (SNPs) (100 000– 12 500 000, and recently, one million) covering most of the typic resources of increased density. Long-term genome unequally; (ii) the growing resources of the Inter- averages of low-density lipoprotein cholesterol, high- national HapMap consortium (2007) which documents density lipoprotein cholesterol, triglycerides, and linkage disequilibrium (LD—a non-random association blood pressure are highly heritable. Nevertheless, between alleles in a population due to their tendency to there are no significant associations that could help be co-inherited because of reduced recombination further research. between them. Haplotype is the combination of alleles at (iii) Type 2 diabetes, obesity, and atherosclerosis are in an neighbouring SNPs. Haplotypes blocks are the apparent hap- intermediary position. GWAS has succeeded in detect- lotype structures of recombining portions of the genome in ing new loci of interest, which were strongly and repro- which blocks of consecutive co-inherited alleles are separ- ducibly linked to the phenotype. Several new loci and ated by short boundary regions), and is a public resource genes of interest have now been identified: of common SNPs capturing most of the common genome sequence variability. In the human genome, there are (a) The association of the 9p21 locus with CHD has been 3.2 billion base pairs and approximately 15 million SNPs, found by every GWAS published so far which indicating that kits using 500 000 SNPs should cover ,0.2% suggests, at least, that this association is widely dis- of the genome.4 The second generation human haplotype tributed. The locus contains two cyclin-dependent map now covers over 3.1 million SNPs.7 Nevertheless, the kinase inhibitors, which regulate cell cycles. Inter- statistical association among groups of SNPs, i.e. haplotype estingly, the cell cycle pathway, including cyclins, blocks, suggests that the identification of a few of the SNPs was also over-represented in a large-scale gene within the blocks can unambiguously identify all associated expression study,which analysed pathways involved SNPs without the need to measure them directly. Recent in atherogenesis using a modular approach. For studies have shown that the human genome is organized these authors, their data suggest that smooth into a succession of ancestrally conserved distinct haplotype muscle de-differentiation is a key determinant in 13 blocks. On the basis of this assumption, it is assumed that a atherogenesis, which is new and unexpected. 500 000 SNP scan should cover approximately 90% of the (b) FHS has uncovered unexpected genetic associ- genome.8 This chapter on genetics has been deliberately ations with various markers of arterial stiffness, limited to GWAS and aims both to take a critical look at including large arteries calcium content and the the main results obtained by the end of 2007 in