http://genomemedicine.com The latest research articles published by Genome Medicine 2012-05-22T00:00:00Z A report on the Genomic Disorders 2012: Genomics of Rare Diseases meeting, Hinxton, UK, 21-23 March 2012. http://genomemedicine.com/content/4/43 Daniel MacArthur Genome Medicine 2012, null:43 2012-05-22T00:00:00Z doi:10.1186/gm342 Genomic Disorders 2012 Daniel MacArthur reports on the Genomic Disorders 2012: Genomics of Rare Diseases meeting, Hinxton, UK, March 21-23, 2012. /content/figures/gm342-toc.gif Genome Medicine 1756-994X ${item.volume} 43 2012-05-22T00:00:00Z XML Background: Presently, colorectal cancer (CRC) is staged preoperatively by radiographic tests, and postoperatively by pathological evaluation of available surgical specimens. However, present staging methods do not accurately identify occult metastases. This has a direct effect on clinical management. Early identification of metastases isolated to the liver may enable surgical resection, whereas more disseminated disease may be best treated with palliative chemotherapy. Methods: Sera from 103 patients with colorectal adenocarcinoma treated at the same tertiary cancer center were analyzed by proton nuclear magnetic resonance (1H NMR) spectroscopy and gas chromatography-mass spectroscopy (GC-MS). Metabolic profiling was done using both supervised pattern recognition and orthogonal partial least squares-discriminant analysis (O-PLS-DA) of the most significant metabolites, which enables comparison of the whole sample spectrum between groups. The metabolomic profiles generated from each platform were compared between the following groups: locoregional CRC (N=42); liver-only metastases (N=45); and extrahepatic metastases (N=25). Results: The serum metabolomic profile associated with locoregional CRC was distinct from that associated with liver-only metastases, based on 1H NMR spectroscopy (p=5.10x10-7) and GC-MS (p=1.79x10-7). Similarly, the serum metabolomic profile differed significantly between patients with liver-only metastases and with extrahepatic metastases. The change in metabolomic profile was most markedly demonstrated on GC-MS (p=4.75x10-5). Conclusions: In CRC, the serum metabolomic profile changes markedly with metastasis, and site of disease also appears to affect the pattern of circulating metabolites. This novel observation may have clinical utility in enhancing staging accuracy and selecting patients for surgical or medical management. Additional studies are required to determine the sensitivity of this approach to detect subtle or occult metastatic disease. http://genomemedicine.com/content/4/5/42 Farshad Farshidfar Aalim Weljie Karen Kopciuk W Buie Anthony MacLean Elijah Dixon Francis Sutherland Andrea Molckovsky Hans Vogel Oliver Bathe Genome Medicine 2012, null:42 2012-05-14T00:00:00Z doi:10.1186/gm341 Metabolomic staging of colorectal cancer Serum metabolomic profiles change markedly with metastasis of colorectal cancer, a finding that will have clinical utility in enhancing staging accuracy and selecting the right treatment for patients. /content/figures/gm341-toc.gif Genome Medicine 1756-994X ${item.volume} 42 2012-05-14T00:00:00Z PDF 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 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 {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 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 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 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 Background: Human cerebral spinal fluid (CSF) is known to be a rich source of small molecule biomarkers for neurological and neurodegenerative diseases. In 2007, we conducted a comprehensive metabolomic study and performed a detailed literature review on metabolites that could be detected (via metabolomics or other techniques) in CSF. A total of 308 detectable metabolites were identified, of which only 23% were shown to be routinely identifiable or quantifiable with the metabolomics technologies available at that time. The continuing advancement in analytical technologies along with the growing interest in CSF metabolomics has led us to re-visit the human CSF metabolome and to re-assess both its size and the level of coverage than can be achieved with today's technologies. Methods: We used five analytical platforms, including nuclear magnetic resonance (NMR), gas chromatography-mass spectrometry (GC-MS), liquid chromatography-mass spectrometry (LC-MS), direct flow injection-mass spectrometry (DFI-MS/MS) and inductively coupled plasma-mass spectrometry (ICP-MS) to perform quantitative metabolomics on multiple human CSF samples. This experimental work was complemented with an extensive literature review to acquire additional information on reported CSF compounds, their concentrations and their disease associations. Results: NMR, GC-MS and LC-MS methods allowed the identification and quantification of 70 CSF metabolites (as previously reported). DFI-MS/MS allowed the quantification of 78 metabolites (6 acylcarnitines, 13 amino acids, hexose, 42 phosphatidylcholines, 2 lyso-phosphatidylcholines and 14 sphingolipids), while ICP-MS provided quantitative results for 33 metal ions in CSF. Literature analysis led to the identification of 57 more metabolites. In total, 476 compounds have now been confirmed to exist in human CSF. Conclusions: The use of improved metabolomic and other analytical techniques has led to a 54% increase in the known size of the human CSF metabolome over the past 5 years. Commonly available metabolomic methods, when combined, can now routinely identify and quantify 36% of the 'detectable' human CSF metabolome. Our experimental works measured 78 new metabolites that, as per our knowledge, have not been reported to be present in human CSF. An updated CSF metabolome database containing the complete set of 476 human CSF compounds, their concentrations, related literature references and links to their known disease associations is freely available at the CSF metabolome database. http://genomemedicine.com/content/4/4/38 Rupasri Mandal An Chi Guo Kruti Chaudhary Philip Liu Faizath Yallou Edison Dong Farid Aziat David Wishart Genome Medicine 2012, null:38 2012-04-30T00:00:00Z doi:10.1186/gm337 Extending the human CSF metabolome A comprehensive and quantitative update of the human cerebrospinal fluid metabolome database is provided by multi-platform analysis of multiple CSF samples and an extensive literature review. /content/figures/gm337-toc.gif Genome Medicine 1756-994X ${item.volume} 38 2012-04-30T00:00:00Z XML Background: Metabolomics, the non-targeted interrogation of small molecules in a biological sample, is an ideal technology for identifying diagnostic biomarkers. Current tissue extraction protocols involve sample destruction, precluding additional uses of the tissue. This is particularly problematic for high value samples with limited availability, such as clinical tumor biopsies that require structural preservation to histologically diagnose and gauge cancer aggressiveness. To overcome this limitation and increase the amount of information obtained from patient biopsies, we developed and characterized a workflow to perform metabolomic analysis and histological evaluation on the same biopsy sample. Methods: Biopsies of ten human tissues (muscle, adrenal gland, colon, lung, pancreas, small intestine, spleen, stomach, prostate, kidney) were placed directly in a methanol solution to recover metabolites, precipitate proteins, and fix tissue. Following incubation, biopsies were removed from the solution and processed for histology. Kidney and prostate cancer tumor and benign biopsies were stained with hemotoxylin and eosin and prostate biopsies were subjected to PIN-4 immunohistochemistry. The methanolic extracts were analyzed for metabolites on GC/MS and LC/MS platforms. Raw mass spectrometry data files were automatically extracted using an informatics system that includes peak identification and metabolite identification software. Results: Metabolites across all major biochemical classes (amino acids, peptides, carbohydrates, lipids, nucleotides, cofactors, xenobiotics) were measured. The number (ranging from 260 in prostate to 340 in colon) and identity of metabolites were comparable to results obtained with the current method requiring 30 mg ground tissue. Comparing relative levels of metabolites, cancer tumor from benign kidney and prostate biopsies could be distinguished. Successful histopathological analysis of biopsies by chemical staining (hematoxylin, eosin) and antibody binding (PIN-4, in prostate) showed cellular architecture and immunoreactivity were retained. Conclusions: Concurrent metabolite extraction and histological analysis of intact biopsies is amenable to the clinical workflow. Methanol fixation effectively preserves a wide range of tissues and is compatible with chemical staining and immunohistochemistry. The method offers an opportunity to augment histopathological diagnosis and tumor classification with quantitative measures of biochemicals in the same tissue sample. Since certain biochemicals have been shown to correlate with disease aggressiveness, this method should prove valuable as an adjunct to differentiate cancer aggressiveness. http://genomemedicine.com/content/4/4/33 Meredith Brown Jonathan McDunn Phillip Gunst Elizabeth Smith Michael Milburn Dean Troyer Kay Lawton Genome Medicine 2012, null:33 2012-04-30T00:00:00Z doi:10.1186/gm332 Combining histology and metabolomics A clinical workflow for concurrent metabolite extraction and histological analysis of intact tumor biopsies allows augmentation of histopathological diagnosis with quantitative biochemical measures. /content/figures/gm332-toc.gif Genome Medicine 1756-994X ${item.volume} 33 2012-04-30T00:00:00Z XML