Trichloroethylene Metabolite S-(1,2-Dichlorovinyl)-l-cysteine Stimulates Changes in Energy Metabolites and Amino Acids in the BeWo Human Placental Trophoblast Model during Syncytialization.

Syncytialization, the fusion of cytotrophoblasts into an epithelial barrier that constitutes the maternal-fetal interface, is a crucial event of placentation. This process is characterized by distinct changes to amino acid and energy metabolism. A metabolite of the industrial solvent trichloroethylene (TCE), S-(1,2-dichlorovinyl)-l-cysteine (DCVC), modifies energy metabolism and amino acid abundance in HTR-8/SVneo extravillous trophoblasts. In the current study, we investigated DCVC-induced changes to energy metabolism and amino acids during forskolin-stimulated syncytialization in BeWo cells, a human villous trophoblastic cell line that models syncytialization in vitro. BeWo cells were exposed to forskolin at 100 µM for 48 h to stimulate syncytialization. During syncytialization, BeWo cells were also treated with DCVC at 0 (control), 10, or 20 µM. Following treatment, the targeted metabolomics platform, "Tricarboxylic Acid Plus", was used to identify changes in energy metabolism and amino acids. DCVC treatment during syncytialization decreased oleic acid, aspartate, proline, uridine diphosphate (UDP), UDP-d-glucose, uridine monophosphate, and cytidine monophosphate relative to forskolin-only treatment controls, but did not increase any measured metabolite. Notable changes stimulated by syncytialization in the absence of DCVC included increased adenosine monophosphate and guanosine monophosphate, as well as decreased aspartate and glutamate. Pathway analysis revealed multiple pathways in amino acid and sugar metabolisms that were altered with forskolin-stimulated syncytialization alone and DCVC treatment during syncytialization. Analysis of ratios of metabolites within the pathways revealed that DCVC exposure during syncytialization changed metabolite ratios in the same or different direction compared to syncytialization alone. Building off our oleic acid findings, we found that extracellular matrix metalloproteinase-2, which is downstream in oleic acid signaling, underwent the same changes as oleic acid. Together, the metabolic changes stimulated by DCVC treatment during syncytialization suggest changes in energy metabolism and amino acid abundance as potential mechanisms by which DCVC could impact syncytialization and pregnancy.

Sustained Perturbation of Metabolism and Metabolic Subphenotypes Are Associated With Mortality and Protein Markers of the Host Response.

Perturbed host metabolism is increasingly recognized as a pillar of sepsis pathogenesis, yet the dynamic alterations in metabolism and its relationship to other components of the host response remain incompletely understood. We sought to identify the early host-metabolic response in patients with septic shock and to explore biophysiological phenotyping and differences in clinical outcomes among metabolic subgroups. DESIGN: We measured serum metabolites and proteins reflective of the host-immune and endothelial response in patients with septic shock. SETTING: We considered patients from the placebo arm of a completed phase II, randomized controlled trial conducted at 16 U.S. medical centers. Serum was collected at baseline (within 24 hr of the identification of septic shock), 24-hour, and 48-hour postenrollment. Linear mixed models were built to assess the early trajectory of protein analytes and metabolites stratified by 28-day mortality status. Unsupervised clustering of baseline metabolomics data was conducted to identify subgroups of patients. PATIENTS: Patients with vasopressor-dependent septic shock and moderate organ dysfunction that were enrolled in the placebo arm of a clinical trial. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Fifty-one metabolites and 10 protein analytes were measured longitudinally in 72 patients with septic shock. In the 30 patients (41.7%) who died prior to 28 days, systemic concentrations of acylcarnitines and interleukin (IL)-8 were elevated at baseline and persisted at T24 and T48 throughout early resuscitation. Concentrations of pyruvate, IL-6, tumor necrosis factor-α, and angiopoietin-2 decreased at a slower rate in patients who died. Two groups emerged from clustering of baseline metabolites. Group 1 was characterized by higher levels of acylcarnitines, greater organ dysfunction at baseline and postresuscitation (p < 0.05), and greater mortality over 1 year (p < 0.001). CONCLUSIONS: Among patients with septic shock, nonsurvivors exhibited a more profound and persistent dysregulation in protein analytes attributable to neutrophil activation and disruption of mitochondrial-related metabolism than survivors.

Trichloroethylene modifies energy metabolites in the amniotic fluid of Wistar rats.

Exposure to trichloroethylene (TCE), an industrial solvent, is associated with several adverse pregnancy outcomes in humans and decreased fetal weight in rats. However, effects of TCE on energy metabolites in amniotic fluid, which have associations with pregnancy outcomes, has not been published previously. In the current exploratory study, timed-pregnant Wistar rats were exposed to 480 mg TCE/kg/day via vanilla wafer or to vehicle (wafer) alone from gestational day (GD) 6-16. Amniotic fluid collected on GD 16 was analyzed for metabolites important in energy metabolism using short chain fatty acid and tricarboxylic acid plus platforms (N = 4 samples/sex/treatment). TCE decreased concentrations of the following metabolites in amniotic fluid for both fetal sexes: 6-phosphogluconate, guanosine diphosphate, adenosine diphosphate, adenosine triphosphate, and flavin adenine dinucleotide. TCE decreased fructose 1,6-bisphosphate and guanosine triphosphate concentrations in amniotic fluid of male but not female fetuses. Moreover, TCE decreased uridine diphosphate-D-glucuronate concentrations, and increased arginine and phosphocreatine concentrations, in amniotic fluid of female fetuses only. No metabolites were increased in amniotic fluid of male fetuses. Pathway analysis suggested that TCE altered folate biosynthesis and pentose phosphate pathway in both sexes. Using metabolite ratios to investigate changes within specific pathways, some ratio alterations, including those in arginine metabolism and phenylalanine metabolism, were detected in females only. Ratio analysis also suggested enzymes, including gluconokinase, as potential TCE targets. Together, results from this exploratory study suggest that TCE differentially modified energy metabolites in amniotic fluid based on sex. These findings may inform future studies of TCE reproductive toxicity.

Pharmacologic modulation of brain metabolism by valproic acid can induce a neuroprotective environment.

OBJECTIVE: Traumatic brain injury (TBI) is a leading cause of trauma-related morbidity and mortality. Valproic acid (VPA) has been shown to attenuate brain lesion size and swelling within the first few hours following TBI. Because injured neurons are sensitive to metabolic changes, we hypothesized that VPA treatment would alter the metabolic profile in the perilesional brain tissues to create a neuroprotective environment. METHODS: We subjected swine to combined TBI (12-mm cortical impact) and hemorrhagic shock (40% blood volume loss and 2 hours of hypotension) and randomized them to two groups (n = 5/group): (1) normal saline (NS; 3× hemorrhage volume) and (2) NS-VPA (NS, 3× hemorrhage volume; VPA, 150 mg/kg). After 6 hours, brains were harvested, and 100 mg of the perilesional tissue was used for metabolite extraction. Samples were analyzed using reversed-phase liquid chromatography-mass spectrometry in positive and negative ion modes, and data were analyzed using MetaboAnalyst software (McGill University, Quebec, Canada). RESULTS: In untargeted reversed-phase liquid chromatography-mass spectrometry analysis, we detected 3,750 and 1,955 metabolites in positive and negative ion modes, respectively. There were no significantly different metabolites in positive ion mode; however, 167 metabolite features were significantly different (p < 0.05) in the negative ion mode, which included VPA derivates. Pathway analysis showed that several pathways were affected in the treatment group, including the biosynthesis of unsaturated fatty acids (p = 0.001). Targeted amino acid analysis on glycolysis/tricarboxylic acid (TCA) cycle revealed that VPA treatment significantly decreased the levels of the excitotoxic amino acid serine (p = 0.001). CONCLUSION: Valproic acid can be detected in perilesional tissues in its metabolized form. It also induces metabolic changes in the brains within the first few hours following TBI to create a neuroprotective environment.

Glycolytic Enzymes Coalesce in G Bodies under Hypoxic Stress.

Glycolysis is upregulated under conditions such as hypoxia and high energy demand to promote cell proliferation, although the mechanism remains poorly understood. We find that hypoxia in Saccharomyces cerevisiae induces concentration of glycolytic enzymes, including the Pfk2p subunit of the rate-limiting phosphofructokinase, into a single, non-membrane-bound granule termed the "glycolytic body" or "G body." A yeast kinome screen identifies the yeast ortholog of AMP-activated protein kinase, Snf1p, as necessary for G-body formation. Many G-body components identified by proteomics are required for G-body integrity. Cells incapable of forming G bodies in hypoxia display abnormal cell division and produce inviable daughter cells. Conversely, cells with G bodies show increased glucose consumption and decreased levels of glycolytic intermediates. Importantly, G bodies form in human hepatocarcinoma cells in hypoxia. Together, our results suggest that G body formation is a conserved, adaptive response to increase glycolytic output during hypoxia or tumorigenesis.

Whole Blood Reveals More Metabolic Detail of the Human Metabolome than Serum as Measured by 1H-NMR Spectroscopy: Implications for Sepsis Metabolomics.

Serum is a common sample of convenience for metabolomics studies. Its processing time can be lengthy and may result in the loss of metabolites including those of red blood cells (RBCs). Unlike serum, whole blood (WB) is quickly processed, minimizing the influence of variable hemolysis while including RBC metabolites. To determine differences between serum and WB metabolomes, both sample types, collected from healthy volunteers, were assayed by H-NMR (proton nuclear magnetic resonance) spectroscopy. A total of 34 and 50 aqueous metabolites were quantified from serum and WB, respectively. Free hemoglobin (Hgb) levels in serum were measured, and the correlation between Hgb and metabolite concentrations was determined. Most metabolites detected in serum were at higher concentrations in WB with the exception of acetoacetate and propylene glycol. The 18 unique metabolites of WB included adenosine, AMP, ADP, and ATP, which are associated with RBC metabolism. The use of serum results in the underrepresentation of a number of metabolic pathways including branched-chain amino acid degradation and glycolysis and gluconeogenesis. The range of free Hgb in serum was 0.03 to 0.01 g/dL, and eight metabolites were associated (P ≤ 0.05) with free Hgb. The range of free Hgb in serum samples from 18 sepsis patients was 0.02 to 0.46 g/dL. Whole blood and serum have unique aqueous metabolite profiles, but the use of serum may introduce potential pathway bias. Use of WB for metabolomics may be particularly important for studies in diseases such as sepsis in which RBC metabolism is altered, and mechanical and sepsis-induced hemolysis contributes to variance in the metabolome.

Epithelial-mesenchymal transition-associated secretory phenotype predicts survival in lung cancer patients.

In cancer cells, the process of epithelial-mesenchymal transition (EMT) confers migratory and invasive capacity, resistance to apoptosis, drug resistance, evasion of host immune surveillance and tumor stem cell traits. Cells undergoing EMT may represent tumor cells with metastatic potential. Characterizing the EMT secretome may identify biomarkers to monitor EMT in tumor progression and provide a prognostic signature to predict patient survival. Utilizing a transforming growth factor-ß-induced cell culture model of EMT, we quantitatively profiled differentially secreted proteins, by GeLC-tandem mass spectrometry. Integrating with the corresponding transcriptome, we derived an EMT-associated secretory phenotype (EASP) comprising of proteins that were differentially upregulated both at protein and mRNA levels. Four independent primary tumor-derived gene expression data sets of lung cancers were used for survival analysis by the random survival forests (RSF) method. Analysis of 97-gene EASP expression in human lung adenocarcinoma tumors revealed strong positive correlations with lymph node metastasis, advanced tumor stage and histological grade. RSF analysis built on a training set (n = 442), including age, sex and stage as variables, stratified three independent lung cancer data sets into low-, medium- and high-risk groups with significant differences in overall survival. We further refined EASP to a 20 gene signature (rEASP) based on variable importance scores from RSF analysis. Similar to EASP, rEASP predicted survival of both adenocarcinoma and squamous carcinoma patients. More importantly, it predicted survival in the early-stage cancers. These results demonstrate that integrative analysis of the critical biological process of EMT provides mechanism-based and clinically relevant biomarkers with significant prognostic value.

LRpath analysis reveals common pathways dysregulated via DNA methylation across cancer types.

BACKGROUND: The relative contribution of epigenetic mechanisms to carcinogenesis is not well understood, including the extent to which epigenetic dysregulation and somatic mutations target similar genes and pathways. We hypothesize that during carcinogenesis, certain pathways or biological gene sets are commonly dysregulated via DNA methylation across cancer types. The ability of our logistic regression-based gene set enrichment method to implicate important biological pathways in high-throughput data is well established. RESULTS: We developed a web-based gene set enrichment application called LRpath with clustering functionality that allows for identification and comparison of pathway signatures across multiple studies. Here, we employed LRpath analysis to unravel the commonly altered pathways and other gene sets across ten cancer studies employing DNA methylation data profiled with the Illumina HumanMethylation27 BeadChip. We observed a surprising level of concordance in differential methylation across multiple cancer types. For example, among commonly hypomethylated groups, we identified immune-related functions, peptidase activity, and epidermis/keratinocyte development and differentiation. Commonly hypermethylated groups included homeobox and other DNA-binding genes, nervous system and embryonic development, and voltage-gated potassium channels. For many gene sets, we observed significant overlap in the specific subset of differentially methylated genes. Interestingly, fewer DNA repair genes were differentially methylated than expected by chance. CONCLUSIONS: Clustering analysis performed with LRpath revealed tightly clustered concepts enriched for differential methylation. Several well-known cancer-related pathways were significantly affected, while others were depleted in differential methylation. We conclude that DNA methylation changes in cancer tend to target a subset of the known cancer pathways affected by genetic aberrations.

Convergence of genetic influences in comorbidity.

BACKGROUND: Predisposition to complex diseases is explained in part by genetic variation, and complex diseases are frequently comorbid, consistent with pleiotropic genetic variation influencing comorbidity. Genome Wide Association (GWA) studies typically assess association between SNPs and a single-disease phenotype. Fisher meta-analysis combines evidence of association from single-disease GWA studies, assuming that each study is an independent test of the same hypothesis. The Rank Product (RP) method overcomes limitations posed by Fisher assumptions, though RP was not designed for GWA data. METHODS: We modified RP to accommodate GWA data, and we call it modRP. Using p-values output from GWA studies, we aggregate evidence for association between SNPs and related phenotypes. To assess significance, RP randomly samples the observed ranks to develop the null distribution of the RP statistic, and then places the observed RPs into the null distribution. ModRP eliminates the effect of linkage disequilibrium and controls for differences in power at tested SNPs, to meet RP assumptions in application to GWA data. RESULTS: After validating modRP based on both positive and negative control studies, we searched for pleiotropic influences on comorbid substance use disorders in a novel study, and found two SNPs to be significantly associated with comorbid cocaine, opium, and nicotine dependence. Placing these SNPs into biological context, we developed a protein network modeling the interaction of cocaine, nicotine, and opium with these variants. CONCLUSIONS: ModRP is a novel approach to identifying pleiotropic genetic influences on comorbid complex diseases. It can be used to assess association for related phenotypes where raw data is unavailable or inappropriate for analysis using other approaches. The method is conceptually simple and produces statistically significant, biologically relevant results.

Metab2MeSH: annotating compounds with medical subject headings.

SUMMARY: Progress in high-throughput genomic technologies has led to the development of a variety of resources that link genes to functional information contained in the biomedical literature. However, tools attempting to link small molecules to normal and diseased physiology and published data relevant to biologists and clinical investigators, are still lacking. With metabolomics rapidly emerging as a new omics field, the task of annotating small molecule metabolites becomes highly relevant. Our tool Metab2MeSH uses a statistical approach to reliably and automatically annotate compounds with concepts defined in Medical Subject Headings, and the National Library of Medicine's controlled vocabulary for biomedical concepts. These annotations provide links from compounds to biomedical literature and complement existing resources such as PubChem and the Human Metabolome Database.

Metscape 2 bioinformatics tool for the analysis and visualization of metabolomics and gene expression data.

MOTIVATION: Metabolomics is a rapidly evolving field that holds promise to provide insights into genotype-phenotype relationships in cancers, diabetes and other complex diseases. One of the major informatics challenges is providing tools that link metabolite data with other types of high-throughput molecular data (e.g. transcriptomics, proteomics), and incorporate prior knowledge of pathways and molecular interactions. RESULTS: We describe a new, substantially redesigned version of our tool Metscape that allows users to enter experimental data for metabolites, genes and pathways and display them in the context of relevant metabolic networks. Metscape 2 uses an internal relational database that integrates data from KEGG and EHMN databases. The new version of the tool allows users to identify enriched pathways from expression profiling data, build and analyze the networks of genes and metabolites, and visualize changes in the gene/metabolite data. We demonstrate the applications of Metscape to annotate molecular pathways for human and mouse metabolites implicated in the pathogenesis of sepsis-induced acute lung injury, for the analysis of gene expression and metabolite data from pancreatic ductal adenocarcinoma, and for identification of the candidate metabolites involved in cancer and inflammation. AVAILABILITY: Metscape is part of the National Institutes of Health-supported National Center for Integrative Biomedical Informatics (NCIBI) suite of tools, freely available at http://metscape.ncibi.org. It can be downloaded from http://cytoscape.org or installed via Cytoscape plugin manager. CONTACT: metscape-help@umich.edu; akarnovs@umich.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Metabolic consequences of sepsis-induced acute lung injury revealed by plasma ¹H-nuclear magnetic resonance quantitative metabolomics and computational analysis.

Metabolomics is an emerging component of systems biology that may be a viable strategy for the identification and validation of physiologically relevant biomarkers. Nuclear magnetic resonance (NMR) spectroscopy allows for establishing quantitative data sets for multiple endogenous metabolites without preconception. Sepsis-induced acute lung injury (ALI) is a complex and serious illness associated with high morbidity and mortality for which there is presently no effective pharmacotherapy. The goal of this study was to apply ¹H-NMR based quantitative metabolomics with subsequent computational analysis to begin working towards elucidating the plasma metabolic changes associated with sepsis-induced ALI. To this end, this pilot study generated quantitative data sets that revealed differences between patients with ALI and healthy subjects in the level of the following metabolites: total glutathione, adenosine, phosphatidylserine, and sphingomyelin. Moreover, myoinositol levels were associated with acute physiology scores (APS) (ρ = -0.53, P = 0.05, q = 0.25) and ventilator-free days (ρ = -0.73, P = 0.005, q = 0.01). There was also an association between total glutathione and APS (ρ = 0.56, P = 0.04, q = 0.25). Computational network analysis revealed a distinct metabolic pathway for each metabolite. In summary, this pilot study demonstrated the feasibility of plasma ¹H-NMR quantitative metabolomics because it yielded a physiologically relevant metabolite data set that distinguished sepsis-induced ALI from health. In addition, it justifies the continued study of this approach to determine whether sepsis-induced ALI has a distinct metabolic phenotype and whether there are predictive biomarkers of severity and outcome in these patients.

ConceptGen: a gene set enrichment and gene set relation mapping tool.

MOTIVATION: The elucidation of biological concepts enriched with differentially expressed genes has become an integral part of the analysis and interpretation of genomic data. Of additional importance is the ability to explore networks of relationships among previously defined biological concepts from diverse information sources, and to explore results visually from multiple perspectives. Accomplishing these tasks requires a unified framework for agglomeration of data from various genomic resources, novel visualizations, and user functionality. RESULTS: We have developed ConceptGen, a web-based gene set enrichment and gene set relation mapping tool that is streamlined and simple to use. ConceptGen offers over 20,000 concepts comprising 14 different types of biological knowledge, including data not currently available in any other gene set enrichment or gene set relation mapping tool. We demonstrate the functionalities of ConceptGen using gene expression data modeling TGF-beta-induced epithelial-mesenchymal transition and metabolomics data comparing metastatic versus localized prostate cancers.

Quantitative organellar proteomics analysis of rough endoplasmic reticulum from normal and acute pancreatitis rat pancreas.

The rough endoplasmic reticulum (RER) is a central organelle for synthesizing and processing digestive enzymes and alteration of ER functions may participate in the pathogenesis of acute pancreatitis (AP). To comprehensively characterize the normal and diseased RER subproteome, this study quantitatively compared the protein compositions of pancreatic RER between normal and AP animals using isobaric tags (iTRAQ) and 2D LC-MALDI-MS/MS. A total of 469 unique proteins were revealed from four independent experiments using two different AP models. These proteins belong to a large number of functional categories including ribosomal proteins, translocon subunits, chaperones, secretory proteins, and glyco- and lipid-processing enzymes. A total of 37 RER proteins (25 unique in arginine-induced, 6 unique in caerulein-induced and 6 common in both models of AP) showed significant changes during AP including translational regulators and digestive enzymes, whereas only mild changes were found in some ER chaperones. The six proteins common to both AP models included a decrease in pancreatic triacylglycerol lipase precursor, Erp27, and prolyl 4-hydroxylase beta polypeptide as well as a dramatic increase in fibrinogen alpha, beta and gamma chains. These results suggest that the early stages of AP involve changes of multiple RER proteins that may affect the synthesis and processing of digestive enzymes.

A cluster of novel serotonin receptor 3-like genes on human chromosome 3.

The ligand-gated ion channel family includes receptors for serotonin (5-hydroxytryptamine, 5-HT), acetylcholine, GABA, and glutamate. Drugs targeting subtypes of these receptors have proven useful for the treatment of various neuropsychiatric and neurological disorders. To identify new ligand-gated ion channels as potential therapeutic targets, drafts of human genome sequence were interrogated. Portions of four novel genes homologous to 5-HT(3A) and 5-HT(3B) receptors were identified within human sequence databases. We named the genes 5-HT(3C1)-5-HT(3C4). Radiation hybrid (RH) mapping localized these genes to chromosome 3q27-28. All four genes shared similar intron-exon organizations and predicted protein secondary structure with 5-HT(3A) and 5-HT(3B). Orthologous genes were detected by Southern blotting in several species including dog, cow, and chicken, but not in rodents, suggesting that these novel genes are not present in rodents or are very poorly conserved. Two of the novel genes are predicted to be pseudogenes, but two other genes are transcribed and spliced to form appropriate open reading frames. The 5-HT(3C1) transcript is expressed almost exclusively in small intestine and colon, suggesting a possible role in the serotonin-responsiveness of the gut.