Dinesh K. Barupal

Human gut microbiome adopts an alternative state following small bowel transplantation

Small bowel transplants provide an exceptional opportunity for long-term study of the microbial ecology of the human small bowel. The ileostomy created at time of transplant for ongoing monitoring of the allograft provides access to samples of ileal effluent and mucosal biopsies. In this study, we used qPCR to assay the bacterial population of the small bowel lumen of 17 small bowel transplant patients over time. Surprisingly, the posttransplant microbial community was found to be dominated by Lactobacilli and Enterobacteria, both typically facultative anaerobes. This represents an inversion of the normal community that is dominated instead by the strictly anaerobic Bacteroides and Clostridia. We found this inverted community also in patients with ileostomies who did not receive a transplant, suggesting that the ileostomy itself is the primary ecological determinant shaping the microbiota. After surgical closure of the ileostomy, the community reverted to the normal structure. Therefore, we hypothesized that the ileostomy allows oxygen into the otherwise anaerobic distal ileum, thus driving the transition from one microbial community structure to another. Supporting this hypothesis, metabolomic profiling of both communities demonstrated an enrichment for metabolites associated with aerobic respiration in samples from patients with open ileostomies. Viewed from an ecological perspective, the two communities constitute alternative stable states of the human ileum. That the small bowel appears to function normally despite these dramatic shifts suggests that its ecological resilience is greater than previously realized.

The Blood Exposome and Its Role in Discovering Causes of Disease

Background: Since 2001, researchers have examined the human genome (G) mainly to discover causes of disease, despite evidence that G explains relatively little risk. We posit that unexplained disease risks are caused by the exposome (E; representing all exposures) and G × E interactions. Thus, etiologic research has been hampered by scientists’ continuing reliance on low-tech methods to characterize E compared with high-tech omics for characterizing G. Objectives: Because exposures are inherently chemical in nature and arise from both endogenous and exogenous sources, blood specimens can be used to characterize exposomes. To explore the “blood exposome” and its connection to disease, we sought human blood concentrations of many chemicals, along with their sources, evidence of chronic-disease risks, and numbers of metabolic pathways. Methods: From the literature we obtained human blood concentrations of 1,561 small molecules and metals derived from foods, drugs, pollutants, and endogenous processes. We mapped chemical similarities after weighting by blood concentrations, disease-risk citations, and numbers of human metabolic pathways. Results: Blood concentrations spanned 11 orders of magnitude and were indistinguishable for endogenous and food chemicals and drugs, whereas those of pollutants were 1,000 times lower. Chemical similarities mapped by disease risks were equally distributed by source categories, but those mapped by metabolic pathways were dominated by endogenous molecules and essential nutrients. Conclusions: For studies of disease etiology, the complexity of human exposures motivates characterization of the blood exposome, which includes all biologically active chemicals. Because most small molecules in blood are not human metabolites, investigations of causal pathways should expand beyond the endogenous metabolome. Citation: Rappaport SM, Barupal DK, Wishart D, Vineis P, Scalbert A. 2014. The blood exposome and its role in discovering causes of disease. Environ Health Perspect 122:769–774; http://dx.doi.org/10.1289/ehp.1308015

MetaMapp: mapping and visualizing metabolomic data by integrating information from biochemical pathways and chemical and mass spectral similarity

BackgroundExposure to environmental tobacco smoke (ETS) leads to higher rates of pulmonary diseases and infections in children. To study the biochemical changes that may precede lung diseases, metabolomic effects on fetal and maternal lungs and plasma from rats exposed to ETS were compared to filtered air control animals. Genome- reconstructed metabolic pathways may be used to map and interpret dysregulation in metabolic networks. However, mass spectrometry-based non-targeted metabolomics datasets often comprise many metabolites for which links to enzymatic reactions have not yet been reported. Hence, network visualizations that rely on current biochemical databases are incomplete and also fail to visualize novel, structurally unidentified metabolites.ResultsWe present a novel approach to integrate biochemical pathway and chemical relationships to map all detected metabolites in network graphs (MetaMapp) using KEGG reactant pair database, Tanimoto chemical and NIST mass spectral similarity scores. In fetal and maternal lungs, and in maternal blood plasma from pregnant rats exposed to environmental tobacco smoke (ETS), 459 unique metabolites comprising 179 structurally identified compounds were detected by gas chromatography time of flight mass spectrometry (GC-TOF MS) and BinBase data processing. MetaMapp graphs in Cytoscape showed much clearer metabolic modularity and complete content visualization compared to conventional biochemical mapping approaches. Cytoscape visualization of differential statistics results using these graphs showed that overall, fetal lung metabolism was more impaired than lungs and blood metabolism in dams. Fetuses from ETS-exposed dams expressed lower lipid and nucleotide levels and higher amounts of energy metabolism intermediates than control animals, indicating lower biosynthetic rates of metabolites for cell division, structural proteins and lipids that are critical for in lung development.ConclusionsMetaMapp graphs efficiently visualizes mass spectrometry based metabolomics datasets as network graphs in Cytoscape, and highlights metabolic alterations that can be associated with higher rate of pulmonary diseases and infections in children prenatally exposed to ETS. The MetaMapp scripts can be accessed at http://metamapp.fiehnlab.ucdavis.edu.

The volatile compound BinBase mass spectral database

BackgroundVolatile compounds comprise diverse chemical groups with wide-ranging sources and functions. These compounds originate from major pathways of secondary metabolism in many organisms and play essential roles in chemical ecology in both plant and animal kingdoms. In past decades, sampling methods and instrumentation for the analysis of complex volatile mixtures have improved; however, design and implementation of database tools to process and store the complex datasets have lagged behind.DescriptionThe volatile compound BinBase (vocBinBase) is an automated peak annotation and database system developed for the analysis of GC-TOF-MS data derived from complex volatile mixtures. The vocBinBase DB is an extension of the previously reported metabolite BinBase software developed to track and identify derivatized metabolites. The BinBase algorithm uses deconvoluted spectra and peak metadata (retention index, unique ion, spectral similarity, peak signal-to-noise ratio, and peak purity) from the Leco ChromaTOF software, and annotates peaks using a multi-tiered filtering system with stringent thresholds. The vocBinBase algorithm assigns the identity of compounds existing in the database. Volatile compound assignments are supported by the Adams mass spectral-retention index library, which contains over 2,000 plant-derived volatile compounds. Novel molecules that are not found within vocBinBase are automatically added using strict mass spectral and experimental criteria. Users obtain fully annotated data sheets with quantitative information for all volatile compounds for studies that may consist of thousands of chromatograms. The vocBinBase database may also be queried across different studies, comprising currently 1,537 unique mass spectra generated from 1.7 million deconvoluted mass spectra of 3,435 samples (18 species). Mass spectra with retention indices and volatile profiles are available as free download under the CC-BY agreement (http://vocbinbase.fiehnlab.ucdavis.edu).ConclusionsThe BinBase database algorithms have been successfully modified to allow for tracking and identification of volatile compounds in complex mixtures. The database is capable of annotating large datasets (hundreds to thousands of samples) and is well-suited for between-study comparisons such as chemotaxonomy investigations. This novel volatile compound database tool is applicable to research fields spanning chemical ecology to human health. The BinBase source code is freely available at http://binbase.sourceforge.net/ under the LGPL 2.0 license agreement.

System response of metabolic networks in Chlamydomonas reinhardtii to total available ammonium

Drastic alterations in macronutrients are known to cause large changes in biochemistry and gene expression in the photosynthetic alga Chlamydomonas reinhardtii. However, metabolomic and proteomic responses to subtle reductions in macronutrients have not yet been studied. When ammonium levels were reduced by 25–100% compared with control cultures, ammonium uptake and growth rates were not affected at 25% or 50% nitrogen-reduction for 28 h. However, primary metabolism and enzyme expression showed remarkable changes at acute conditions (4 h and 10 h after ammonium reduction) compared with chronic conditions (18 h and 28 h time points). Responses of 145 identified metabolites were quantified using gas chromatography-time of flight mass spectrometry; 495 proteins (including 187 enzymes) were monitored using liquid chromatography-ion trap mass spectrometry with label-free spectral counting. Stress response and carbon assimilation processes (Calvin cycle, acetate uptake and chlorophyll biosynthesis) were altered first, in addition to increase in enzyme contents for lipid biosynthesis and accumulation of short chain free fatty acids. Nitrogen/carbon balance metabolism was found changed only under chronic conditions, for example in the citric acid cycle and amino acid metabolism. Metabolism in Chlamydomonas readily responds to total available media nitrogen with temporal increases in short-chain free fatty acids and turnover of internal proteins, long before nitrogen resources are depleted.

Metabolomics of human breast cancer: new approaches for tumor typing and biomarker discovery

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.

Polyphenol metabolome in human urine and its association with intake of polyphenol-rich foods across European countries

BACKGROUND An improved understanding of the contribution of the diet to health and disease risks requires accurate assessments of dietary exposure in nutritional epidemiologic studies. The use of dietary biomarkers may improve the accuracy of estimates. OBJECTIVE We applied a metabolomic approach in a large cohort study to identify novel biomarkers of intake for a selection of polyphenol-containing foods. The large chemical diversity of polyphenols and their wide distribution over many foods make them ideal biomarker candidates for such foods. DESIGN Metabolic profiles were measured with the use of high-resolution mass spectrometry in 24-h urine samples from 481 subjects from the large European Prospective Investigation on Cancer and Nutrition cohort. Peak intensities were correlated to acute and habitual dietary intakes of 6 polyphenol-rich foods (coffee, tea, red wine, citrus fruit, apples and pears, and chocolate products) measured with the use of 24-h dietary recalls and food-frequency questionnaires, respectively. RESULTS Correlation (r > 0.3, P < 0.01 after correction for multiple testing) and discriminant [pcorr (1) > 0.3, VIP > 1.5] analyses showed that >2000 mass spectral features from urine metabolic profiles were significantly associated with the consumption of the 6 selected foods. More than 80 polyphenol metabolites associated with the consumption of the selected foods could be identified, and large differences in their concentrations reflecting individual food intakes were observed within and between 4 European countries. Receiver operating characteristic curves showed that 5 polyphenol metabolites, which are characteristic of 5 of the 6 selected foods, had a high predicting ability of food intake. CONCLUSION Highly diverse food-derived metabolites (the so-called food metabolome) can be characterized in human biospecimens through this powerful metabolomic approach and screened to identify novel biomarkers for dietary exposures, which are ultimately essential to better understand the role of the diet in the cause of chronic diseases.

Extending Biochemical Databases by Metabolomic Surveys

Metabolomics can map the large metabolic diversity in species, organs, or cell types. In addition to gains in enzyme specificity, many enzymes have retained substrate and reaction promiscuity. Enzyme promiscuity and the large number of enzymes with unknown enzyme function may explain the presence of a plethora of unidentified compounds in metabolomic studies. Cataloguing the identity and differential abundance of all detectable metabolites in metabolomic repositories may detail which compounds and pathways contribute to vital biological functions. The current status in metabolic databases is reviewed concomitant with tools to map and visualize the metabolome.

Comparative metabolomics of estrogen receptor positive and estrogen receptor negative breast cancer: alterations in glutamine and beta-alanine metabolism

UNLABELLED Molecular subtyping of breast cancer is necessary for therapy selection and mandatory for all breast cancer patients. Metabolic alterations are considered a hallmark of cancer and several metabolic drugs are currently being investigated in clinical trials. However, the dependence of metabolic alterations on breast cancer subtypes has not been investigated on -omics scale. Thus, 204 estrogen receptor positive (ER+) and 67 estrogen receptor negative (ER-) breast cancer tissues were investigated using GC-TOFMS based metabolomics. 19 metabolites were detected as altered in a predefined training set (2/3 of tumors) and could be validated in a predefined validation set (1/3 of tumors). The metabolite changes included increases in beta-alanine, 2-hydroyglutarate, glutamate, xanthine and decreases in glutamine in the ER- subtype. Beta-alanine demonstrated the strongest change between ER- and ER+ breast cancer (fold change=2.4, p=1.5E-20). In a correlation analysis with genome-wide expression data in a subcohort of 154 tumors, we found a strong negative correlation (Spearman R=-0.62) between beta-alanine and 4-aminobutyrate aminotransferase (ABAT). Immunohistological analysis confirmed down-regulation of the ABAT protein in ER- breast cancer. In a Kaplan-Meier analysis of a large external expression data set, the ABAT transcript was demonstrated to be a positive prognostic marker for breast cancer (HR=0.6, p=3.2E-15). BIOLOGICAL SIGNIFICANCE It is well-known for more than a decade that breast cancer exhibits distinct gene expression patterns depending on the molecular subtype defined by estrogen receptor (ER) and HER2 status. Here, we show that breast cancer exhibits distinct metabolomics patterns depending on ER status. Our observation supports the current view of ER+ breast cancer and ER- breast as different diseases requiring different treatment strategies. Metabolic drugs for cancer including glutaminase inhibitors are currently under development and tested in clinical trials. We found glutamate enriched and glutamine reduced in ER- breast cancer compared to ER+ breast cancer and compared to normal breast tissues. Thus, metabolomics analysis highlights the ER- subtype as a preferential target for glutaminase inhibitors. For the first time, we report on a regulation of beta-alanine catabolism in cancer. In breast cancer, ABAT transcript expression was variable and correlated with ER status. Low ABAT transcript expression was associated with low ABAT protein expression and high beta-alanine concentration. In a large external microarray cohort, low ABAT expression shortened recurrence-free survival in breast cancer, ER+ breast cancer and ER- breast cancer.

A new metabolomic workflow for early detection of Alzheimer's disease.

Alzheimers disease (AD) is the most prevalent cause of dementia among older people. Although AD probably starts 20-30 years before first clinical symptoms become noticeable, nowadays it cannot be diagnosed accurately in its early stages. In this work, we present a new MS-based metabolomic approach based on the use of ultra-high performance liquid chromatography-time-of-flight mass spectrometry (UHPLC-TOF MS) to investigate cerebrospinal fluid (CSF) samples from patients with different AD stages. With the aim to obtain wide metabolome coverage two different chromatographic separation modes, namely reversed phase (RP) and hydrophilic interaction chromatography (HILIC), were used. RP/UHPLC-MS and HILIC/UHPLC-MS methods were optimized and applied to analyze CSF samples from 75 patients related to AD progression. Significant metabolic differences in CSF samples from subjects with different cognitive status related to AD progression were detected using this methodology, obtaining a group of potential biomarkers together with a classification model by means of a multivariate statistical analysis. The proposed model predicted the development of AD with an accuracy of 98.7% and specificity and sensitivity values above of 95%.