http://genomemedicine.com The latest research articles published by Genome Medicine 2012-05-14T00:00:00Z 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: The prevalence, and associated healthcare burden, of diabetes mellitus is increasing worldwide. Mortality and morbidity are associated with diabetic complications in multiple organs and tissues, including the eye, kidney and cardiovascular system, and new therapeutics to treat these complications are required urgently. Triethylenetetramine (TETA) is one such experimental therapeutic that acts to chelate excess copper (II) in diabetic tissues and reduce oxidative stress and cellular damage. Methods: Here we have performed two independent metabolomic studies of serum to assess the suitability of the streptozotocin (STZ)-induced rat model for studying diabetes and to define metabolite-related changes associated with TETA treatment. Ultraperformance liquid chromatography-mass spectrometry studies of serum from non-diabetic/untreated, non-diabetic/TETA-treated, STZ-induced diabetic/untreated and STZ-induced diabetic/TETA-treated rats were performed followed by univariate and multivariate analysis of data. Results: Multiple metabolic changes related to STZ-induced diabetes, some of which have been reported previously in other animal and human studies, were observed, including changes in amino acid, fatty acid, glycerophospholipid and bile acid metabolism. Correlation analysis suggested that treatment with TETA led to a reversal of diabetes-associated changes in bile acid, fatty acid, steroid, sphingolipid and glycerophospholipid metabolism and proteolysis. Conclusions: Metabolomic studies have shown that the STZ-induced rat model of diabetes is an appropriate model system to undertake research into diabetes and potential therapies as several metabolic changes observed in humans and other animal models were also observed in this study. Metabolomics has also identified several biological processes and metabolic pathways implicated in diabetic complications and reversed following treatment with the experimental therapeutic TETA. http://genomemedicine.com/content/4/4/35 Marta Ugarte Marie Brown Katherine Hollywood Garth Cooper Paul Bishop Warwick Dunn Genome Medicine 2012, null:35 2012-04-30T00:00:00Z doi:10.1186/gm334 Metabolic changes linked with TETA treatment Metabolomic analysis of serum samples in a streptozotocin-induced diabetes rat model reveals metabolic pathways affected by treatment with oral triethylenetetramine, a potential therapy for diabetes. /content/figures/gm334-toc.gif Genome Medicine 1756-994X ${item.volume} 35 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 Background: Endogenous estrogens and estrogen metabolites play an important role in the pathogenesis and development of human breast, endometrial, and ovarian cancers. Increasing evidence also supports their involvement in the development of certain lung, colon and prostate cancers. Methods: In this study we systemically surveyed endogenous estrogen and estrogen metabolite levels in each of the NCI-60 human tumor cell lines, which include human breast, central nerve system, colon, ovarian, prostate, kidney and non-small cell lung cancers, as well as melanomas and leukemia. The absolute abundances of these metabolites were measured using a liquid chromatography-tandem mass spectrometry method that has been previously utilized for biological fluids such as serum and urine. Results: Endogenous estrogens and estrogen metabolites were found in all NCI-60 human tumor cell lines and some were substantially elevated and exceeded the levels found in well known estrogen-dependent and estrogen receptor-positive tumor cells such as MCF-7 and T-47D. While estrogens were expected to be present at high levels in cell lines representing the female reproductive system (that is, breast and ovarian), other cell lines, such as leukemia and colon, also contained very high levels of these steroid hormones. The leukemia cell line RMPI-8226 contained the highest levels of estrone (182.06 pg/106 cells) and 17β-estradiol (753.45 pg/106 cells). In comparison, the ovarian cancer cell line with the highest levels of these estrogens contained only 19.79 and 139.32 pg/106 cells of estrone and 17β-estradiol, respectively. The highest levels of estrone and 17β-estradiol in breast cancer cell lines were only 8.45 and 87.37 pg/106 cells in BT-549 and T-47D cells, respectively. Conclusions: The data provided evidence for the presence of significant amounts of endogenous estrogens and estrogen metabolites in cell lines not commonly associated with these steroid hormones. This broad discovery of endogenous estrogens and estrogen metabolites in these cell lines suggest that several human tumors may be beneficially treated using endocrine therapy aimed at estrogen biosynthesis and estrogen-related signaling pathways. http://genomemedicine.com/content/4/4/31 Xia Xu Timothy Veenstra Genome Medicine 2012, null:31 2012-04-30T00:00:00Z doi:10.1186/gm330 Estrogen metabolites in NCI-60 cell lines Systematic analysis of estrogen metabolite levels in NCI-60 human tumor cell lines reveals significant elevation of metabolites in cancers that are not dependent on these steroid hormones. /content/figures/gm330-toc.gif Genome Medicine 1756-994X ${item.volume} 31 2012-04-30T00:00:00Z XML