http://genomemedicine.com The most accessed research articles published by Genome Medicine 2012-05-01T00:00:00Z .{no abstract} http://genomemedicine.com/content/4/4/40 Timothy Veenstra Genome Medicine 2012, null:40 2012-04-30T00:00:00Z doi:10.1186/gm339 Metabolomics: the final frontier? Timothy Veenstra introduces Genome Medicine's special issue on metabolomics of disease, and discusses the impact and opportunities presented by metabolomics, the final piece of the omics puzzle. /content/figures/gm339-toc.gif Genome Medicine 1756-994X ${item.volume} 40 2012-04-30T00:00:00Z XML Background: Altered networks of gene regulation underlie many complex conditions including cancer. Inferring gene regulatory networks from high-throughput microarray expression data is a fundamental but challenging task in computational systems biology and its translation to genomic medicine. Although diverse computational and statistical approaches have been brought to bear on the gene regulatory network inference problem, their relative strengths and disadvantages remain poorly understood, largely because comparative analyses usually consider only small subsets of methods, use only synthetic data, and/or fail to adopt a common measure of inference quality. Methods: We report a comprehensive comparative evaluation of nine state-of-the art gene regulatory network inference methods encompassing the main algorithmic approaches (mutual information, correlation, partial correlation, random forests, support vector machines) using 38 simulated datasets and empirical serous papillary ovarian adenocarcinoma expression-microarray data. We then apply the best-performing method to infer normal and cancer networks. We assess the druggability of the proteins encoded by our predicted target genes using the CancerResource and PharmGKB webtools and databases. Results: We observe large differences in the accuracy with which these methods predict the underlying gene regulatory network depending on features of the data, network size, topology, experiment type, and parameter settings. Applying the best-performing method (the supervised method SIRENE) to the serous papillary ovarian adenocarcinoma dataset, we infer and rank regulatory interactions, some previously reported and others novel. For selected novel interactions we propose testable mechanistic models linking gene regulation to cancer. Using network analysis and visualisation we uncover cross-regulation of angiogenesis-specific genes through three key transcription factors in normal and cancer conditions. Druggabilty analysis of proteins encoded by the 10 highest-confidence target genes, and by 15 genes with differential regulation in normal and cancer conditions, reveals 75% to be potential drug targets. Conclusions: Our study represents a concrete application of gene regulatory network inference to ovarian cancer, demonstrating the complete cycle of computational systems biology research, from genome-scale data analysis via network inference, evaluation of methods, to the generation of novel testable hypotheses, their prioritisation for experimental validation, and discovery of potential drug targets. http://genomemedicine.com/content/4/5/41 Piyush Madhamshettiwar Stefan Maetschke Melissa Davis Antonio Reverter Mark Ragan Genome Medicine 2012, null:41 2012-05-01T00:00:00Z doi:10.1186/gm340 /content/figures/gm340-toc.gif Genome Medicine 1756-994X ${item.volume} 41 2012-05-01T00:00:00Z PDF Genome-wide association studies (GWAS) analyze the genetic component of a phenotype or the etiology of a disease. Despite the success of many GWAS, little progress has been made in uncovering the underlying mechanisms for many diseases. The use of metabolomics as a readout of molecular phenotypes has enabled the discovery of previously undetected associations between diseases and signaling and metabolic pathways. In addition, combining GWAS and metabolomic information allows the simultaneous analysis of the genetic and environmental impacts on homeostasis. Most success has been seen in metabolic diseases such as diabetes, obesity and dyslipidemia. Recently, associations between loci such as FADS1, ELOVL2 or SLC16A9 and lipid concentrations have been explained by GWAS with metabolomics. Combining GWAS with metabolomics (mGWAS) provides the robust and quantitative information required for the development of specific diagnostics and targeted drugs. This review discusses the limitations of GWAS and presents examples of how metabolomics can overcome these limitations with the focus on metabolic diseases. http://genomemedicine.com/content/4/4/34 Jerzy Adamski Genome Medicine 2012, null:34 2012-04-30T00:00:00Z doi:10.1186/gm333 Overcoming the limits of GWAS Jerzy Adamski discusses the limitations of genome-wide association studies and presents examples of how metabolomics can overcome these limitations, focusing on metabolic diseases. /content/figures/gm333-toc.gif Genome Medicine 1756-994X ${item.volume} 34 2012-04-30T00:00:00Z XML Increasingly sophisticated measurement technologies have allowed the fields of metabolomics and genomics to identify, in parallel, risk factors of disease; predict drug metabolism; and study metabolic and genetic diversity in large human populations. Yet the complementarity of these fields and the utility of studying genes and metabolites together is belied by the frequent separate, parallel applications of genomic and metabolomic analysis. Early attempts at identifying co-variation and interaction between genetic variants and downstream metabolic changes, including metabolic profiling of human Mendelian diseases and quantitative trait locus mapping of individual metabolite concentrations, have recently been extended by new experimental designs that search for a large number of gene-metabolite associations. These approaches, including metabolomic quantitiative trait locus mapping and metabolomic genome-wide association studies, involve the concurrent collection of both genomic and metabolomic data and a subsequent search for statistical associations between genetic polymorphisms and metabolite concentrations across a broad range of genes and metabolites. These new data-fusion techniques will have important consequences in functional genomics, microbial metagenomics and disease modeling, the early results and implications of which are reviewed. http://genomemedicine.com/content/4/4/30 Steven Robinette Elaine Holmes Jeremy Nicholson Marc Dumas Genome Medicine 2012, null:30 2012-04-30T00:00:00Z doi:10.1186/gm329 Fusing metabolomics and genomics Robinette, Holmes, Nicholson and Dumas review data-fusion techniques using metabonomics, metabolomics and genomics, and the implications for functional genomics, metagenomics and disease modeling. /content/figures/gm329-toc.gif Genome Medicine 1756-994X ${item.volume} 30 2012-04-30T00:00:00Z XML The peroxisome proliferator-activated receptors (PPARs) are a set of three nuclear hormone receptors that together play a key role in regulating metabolism, particularly the switch between the fed and fasted state and the metabolic pathways involving fatty-acid oxidation and lipid metabolism. In addition, they have a number of important developmental and regulatory roles outside metabolism. The PPARs are also potent targets for treating type II diabetes, dyslipidemia and obesity, although a number of individual agonists have also been linked to unwanted side effects, and there is a complex relationship between the PPARs and the development of cancer. This review examines the part that metabolomics, including lipidomics, has played in elucidating the roles PPARs have in regulating systemic metabolism, as well as their role in aspects of drug-induced cancer and xenobiotic metabolism. These studies have defined the role PPARδ plays in regulating fatty-acid oxidation in adipose tissue and the interaction between aging and PPARα in the liver. The potential translational benefits of these approaches include widening the role of PPAR agonists and improved monitoring of drug efficacy. http://genomemedicine.com/content/4/4/32 Zsuzsanna Ament Mojgan Masoodi Julian Griffin Genome Medicine 2012, null:32 2012-04-30T00:00:00Z doi:10.1186/gm331 A metabolomic understanding of PPARs Julian Griffin and colleagues review the role metabolomics has played in elucidating the roles of peroxisome proliferator-activated receptors in systemic metabolism regulation. /content/figures/gm331-toc.gif Genome Medicine 1756-994X ${item.volume} 32 2012-04-30T00:00:00Z XML Breast cancer is the most common cancer in women worldwide, and the development of new technologies for better understanding of the molecular changes involved in breast cancer progression is essential. Metabolic changes precede overt phenotypic changes, because cellular regulation ultimately affects the use of small-molecule substrates for cell division, growth or environmental changes such as hypoxia. Differences in metabolism between normal cells and cancer cells have been identified. Because small alterations in enzyme concentrations or activities can cause large changes in overall metabolite levels, the metabolome can be regarded as the amplified output of a biological system. The metabolome coverage in human breast cancer tissues can be maximized by combining different technologies for metabolic profiling. Researchers are investigating alterations in the steady state concentrations of metabolites that reflect amplified changes in genetic control of metabolism. Metabolomic results can be used to classify breast cancer on the basis of tumor biology, to identify new prognostic and predictive markers and to discover new targets for future therapeutic interventions. Here, we examine recent results, including those from the European FP7 project METAcancer consortium, that show that integrated metabolomic analyses can provide information on the stage, subtype and grade of breast tumors and give mechanistic insights. We predict an intensified use of metabolomic screens in clinical and preclinical studies focusing on the onset and progression of tumor development. http://genomemedicine.com/content/4/4/37 Carsten Denkert Elmar Bucher Mika Hilvo Reza Salek Matej Oresic Jules Griffin Scarlet Brockmöller Frederick Klauschen Sibylle Loibl Dinesh Barupal Jan Budczies Kristiina Iljin Valentina Nekljudova Oliver Fiehn Genome Medicine 2012, null:37 2012-04-30T00:00:00Z doi:10.1186/gm336 Breast cancer metabolomics Denkert et al. review recent work showing that integrated metabolomic analyses can provide information on the stage, subtype and grade of breast tumors and give mechanistic insights into breast cancer. /content/figures/gm336-toc.gif Genome Medicine 1756-994X ${item.volume} 37 2012-04-30T00:00:00Z XML {no abstract} http://genomemedicine.com/content/4/4/36 Mika Ala-Korpela Antti Kangas Pasi Soininen Genome Medicine 2012, null:36 2012-04-30T00:00:00Z doi:10.1186/gm335 Quantitative high-throughput metabolomics Mika Ala-Korpela and colleagues advocate the use of multiple metabolomic markers to get a detailed, quantitative picture of disease etiology, particularly for complex diseases. /content/figures/gm335-toc.gif Genome Medicine 1756-994X ${item.volume} 36 2012-04-30T00:00:00Z XML The number of metabolites identified in human cerebrospinal fluid (CSF) has steadily increased over the past 5 years, and in this issue of Genome Medicine David Wishart and colleagues provide a comprehensive update that brings the number of metabolites listed in the CSF metabolome database to 476 compounds. There is now a need for an integrative metabolome-proteome CSF database to maximize the impact of this achievement in biomedical research. Only by such efforts can we hope to unravel the complexity of molecular pathophysiological processes. http://genomemedicine.com/content/4/4/39 Emanuel Schwarz E Fuller Torrey Paul Guest Sabine Bahn Genome Medicine 2012, null:39 2012-04-30T00:00:00Z doi:10.1186/gm338 Sabine Bahn and colleagues highlight an article in Genome Medicine that provides a comprehensive update to the CSF metabolome database, and discuss the need for an integrative metabolome-proteome database. /content/figures/gm338-toc.gif Genome Medicine 1756-994X ${item.volume} 39 2012-04-30T00:00:00Z XML Changes in gene expression that reset a cell program from a normal to a diseased state involve multiple genetic circuitries, creating a characteristic signature of gene expression that defines the cell's unique identity. Such signatures have been demonstrated to classify subtypes of breast cancers. Because DNA methylation is critical in programming gene expression, a change in methylation from a normal to diseased state should be similarly reflected in a signature of DNA methylation that involves multiple gene pathways. Whole-genome approaches have recently been used with different levels of success to delineate breast-cancer-specific DNA methylation signatures, and to test whether they can classify breast cancer and whether they could be associated with specific clinical outcomes. Recent work suggests that DNA methylation signatures will extend our ability to classify breast cancer and predict outcome beyond what is currently possible. DNA methylation is a robust biomarker, vastly more stable than RNA or proteins, and is therefore a promising target for the development of new approaches for diagnosis and prognosis of breast cancer and other diseases. Here, I review the scientific basis for using DNA methylation signatures in breast cancer classification and prognosis. I discuss the role of DNA methylation in normal gene regulation, the aberrations in DNA methylation in cancer, and candidate-gene and whole-genome approaches to classify breast cancer subtypes using DNA methylation markers. http://genomemedicine.com/content/4/3/26 Moshe Szyf Genome Medicine 2012, null:26 2012-03-30T00:00:00Z doi:10.1186/gm325 DNA methylation signatures in breast cancer Moshe Szyf reviews recent progress in identifying DNA methylation in breast cancer genomes, and discusses the prospects for classifying subtypes and predicting outcome using DNA methylation markers. /content/figures/gm325-toc.gif Genome Medicine 1756-994X ${item.volume} 26 2012-03-30T00:00:00Z XML Human diseases can be caused by complex mechanisms involving aberrations in numerous proteins and pathways. With recent advances in genomics, elucidating the molecular basis of disease on a personalized level has become an attainable goal. In many cases, relevant molecular targets will be identified for which approved drugs already exist, and the potential repositioning of these drugs to a new indication can be investigated. Repositioning is an accelerated route for drug discovery because existing drugs have established clinical and pharmacokinetic data. Personalized medicine and repositioning both aim to improve the productivity of current drug discovery pipelines, which expend enormous time and cost to develop new drugs, only to have them fail in clinical trials because of lack of efficacy or toxicity. Here, we discuss the current state of research in these two fields, focusing on recent large-scale efforts to systematically find repositioning candidates and elucidate individual disease mechanisms in cancer. We also discuss scenarios in which personalized drug repositioning could be particularly rewarding, such as for diseases that are rare or have specific mutations, as well as current challenges in this field. With an increasing number of drugs being approved for rare cancer subtypes, personalized medicine and repositioning approaches are poised to significantly alter the way we diagnose diseases, infer treatments and develop new drugs. http://genomemedicine.com/content/4/3/27 Yvonne Li Steven Jones Genome Medicine 2012, null:27 2012-03-30T00:00:00Z doi:10.1186/gm326 Drug repositioning for personalized medicine Steven Jones and Yvonne Li review how drug discovery can be made more efficient by repositioning of approved drugs to treat new diseases and by identifying patients who will benefit from such treatments. /content/figures/gm326-toc.gif Genome Medicine 1756-994X ${item.volume} 27 2012-03-30T00:00:00Z XML