Rustem Shaykhutdinov

HMDB: a knowledgebase for the human metabolome

The Human Metabolome Database (HMDB, http://www.hmdb.ca) is a richly annotated resource that is designed to address the broad needs of biochemists, clinical chemists, physicians, medical geneticists, nutritionists and members of the metabolomics community. Since its first release in 2007, the HMDB has been used to facilitate the research for nearly 100 published studies in metabolomics, clinical biochemistry and systems biology. The most recent release of HMDB (version 2.0) has been significantly expanded and enhanced over the previous release (version 1.0). In particular, the number of fully annotated metabolite entries has grown from 2180 to more than 6800 (a 300% increase), while the number of metabolites with biofluid or tissue concentration data has grown by a factor of five (from 883 to 4413). Similarly, the number of purified compounds with reference to NMR, LC-MS and GC-MS spectra has more than doubled (from 380 to more than 790 compounds). In addition to this significant expansion in database size, many new database searching tools and new data content has been added or enhanced. These include better algorithms for spectral searching and matching, more powerful chemical substructure searches, faster text searching software, as well as dedicated pathway searching tools and customized, clickable metabolic maps. Changes to the user-interface have also been implemented to accommodate future expansion and to make database navigation much easier. These improvements should make the HMDB much more useful to a much wider community of users.

Quantitative Metabolomic Profiling of Serum, Plasma, and Urine by 1H NMR Spectroscopy Discriminates between Patients with Inflammatory Bowel Disease and Healthy Individuals

Serologic biomarkers for inflammatory bowel disease (IBD) have yielded variable differentiating ability. Quantitative analysis of a large number of metabolites is a promising method to detect IBD biomarkers. Human subjects with active Crohn’s disease (CD) and active ulcerative colitis (UC) were identified, and serum, plasma, and urine specimens were obtained. We characterized 44 serum, 37 plasma, and 71 urine metabolites by use of 1H NMR spectroscopy and “targeted analysis” to differentiate between diseased and non-diseased individuals, as well as between the CD and UC cohorts. We used multiblock principal component analysis and hierarchical OPLS-DA for comparing several blocks derived from the same “objects” (e.g., subject) to examine differences in metabolites. In serum and plasma of IBD patients, methanol, mannose, formate, 3-methyl-2-oxovalerate, and amino acids such as isoleucine were the metabolites most prominently increased, whereas in urine, maximal increases were observed for mannitol, allantoin, xylose, and carnitine. Both serum and plasma of UC and CD patients showed significant decreases in urea and citrate, whereas in urine, decreases were observed, among others, for betaine and hippurate. Quantitative metabolomic profiling of serum, plasma, and urine discriminates between healthy and IBD subjects. However, our results show that the metabolic differences between the CD and UC cohorts are less pronounced.

Feasibility of Identifying Pancreatic Cancer based on Serum Metabolomics

Background: We postulated that the abundance of various metabolites in blood would facilitate the diagnosis of pancreatic and biliary lesions, which could potentially prevent unnecessary surgery. Methods: Serum samples from patients with benign hepatobiliary disease (n = 43) and from patients with pancreatic cancer (n = 56) were examined by 1H NMR spectroscopy to quantify 58 unique metabolites. Data were analyzed by “targeted profiling” followed by 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. Results: The metabolomic profile of patients with pancreatic cancer was significantly different from that of patients with benign disease (AUROC, area under the ROC curve, = 0.8372). Overt diabetes mellitus (DM) was identified as a possible confounding factor in the pancreatic cancer group. Thus, diabetics were excluded from further analysis. In this more homogeneous pancreatic cancer group, compared with benign cases, serum concentrations of glutamate and glucose were most elevated on multivariate analysis. In benign cases, creatine and glutamine were most abundant. To examine the usefulness of this test, a comparison was made to age- and gender-matched controls with benign lesions that mimic cancer, controlling also for presence of jaundice and diabetes (n = 14 per group). The metabolic profile in patients with pancreatic cancer remained distinguishable from patients with benign pancreatic lesions (AUROC = 0.8308). Conclusions: The serum metabolomic profile may be useful for distinguishing benign from malignant pancreatic lesions. Impact: Further studies will be required to study the effects of jaundice and diabetes. A more comprehensive metabolomic profile will be evaluated using mass spectrometry. Cancer Epidemiol Biomarkers Prev; 20(1); 140–7. ©2010 AACR.

Quality Assessment of Ginseng by 1H NMR Metabolite Fingerprinting and Profiling Analysis

Metabolite profiling and fingerprint analysis by (1)H NMR spectroscopy were used to identify potential biomarkers capable of distinguishing different ginseng species, varieties, and commercial products with the aim of establishing quality control code protocol based on biochemical phenotype. Principal component (PC) analyses of (1)H NMR spectra reliably discriminated between the various ginseng samples, demonstrating the potential utility of metabolomics in the natural health products industry. Four Asian ginseng varieties separated along the PC1 and PC2 axes, and four different Korean ginseng products were divided into two groups by PC1. A strong separation was also revealed between Asian ginseng (Panax ginseng) and American ginseng (Panax quinquefolius). Glutamine, arginine, sucrose, malate, and myo-inositol were the major metabolites in ginseng samples tested in this study. Combined metabolite fingerprinting and profiling suggested that several compounds including glucose, fumarate, and various amino acids could serve as biomarkers for quality assurance in ginseng.

Differences in Metabolism between the Biofilm and Planktonic Response to Metal Stress

Bacterial biofilms are known to withstand the effects of toxic metals better than planktonic cultures of the same species. This phenomenon has been attributed to many features of the sessile lifestyle not present in free-swimming populations, but the contribution of intracellular metabolism has not been previously examined. Here, we use a combined GC-MS and (1)H NMR metabolomic approach to quantify whole-cell metabolism in biofilm and planktonic cultures of the multimetal resistant bacterium Pseudomonas fluorescens exposed to copper ions. Metabolic changes in response to metal exposure were found to be significantly different in biofilms compared to planktonic cultures. Planktonic metabolism indicated an oxidative stress response that was characterized by changes to the TCA cycle, glycolysis, pyruvate and nicotinate and niacotinamide metabolism. Similar metabolic changes were not observed in biofilms, which were instead dominated by shifts in exopolysaccharide related metabolism suggesting that metal stress in biofilms induces a protective response rather than the reactive changes observed for the planktonic cells. From these results, we conclude that differential metabolic shifts play a role in biofilm-specific multimetal resistance and tolerance. An altered metabolic response to metal toxicity represents a novel addition to a growing list of biofilm-specific mechanisms to resist environmental stress.

Probiotic Bacteria Produce Conjugated Linoleic Acid Locally in the Gut That Targets Macrophage PPAR γ to Suppress Colitis

Background Inflammatory bowel disease (IBD) therapies are modestly successful and associated with significant side effects. Thus, the investigation of novel approaches to prevent colitis is important. Probiotic bacteria can produce immunoregulatory metabolites in vitro such as conjugated linoleic acid (CLA), a polyunsaturated fatty acid with potent anti-inflammatory effects. This study aimed to investigate the cellular and molecular mechanisms underlying the anti-inflammatory efficacy of probiotic bacteria using a mouse model of colitis. Methodology/Principal Findings The immune modulatory mechanisms of VSL#3 probiotic bacteria and CLA were investigated in a mouse model of DSS colitis. Colonic specimens were collected for histopathology, gene expression and flow cytometry analyses. Immune cell subsets in the mesenteric lymph nodes (MLN), spleen, blood and colonic lamina propria cells were phenotypically and functionally characterized. Fecal samples and colonic contents were collected to determine the effect of VSL#3 and CLA on gut microbial diversity and CLA production. CLA and VSL#3 treatment ameliorated colitis and decreased colonic bacterial diversity, a finding that correlated with decreased gut pathology. Colonic CLA concentrations were increased in response to probiotic bacterial treatment, but without systemic distribution in blood. VSL#3 and CLA decreased macrophage accumulation in the MLN of mice with DSS colitis. The loss of PPAR γ in myeloid cells abrogated the protective effect of probiotic bacteria and CLA in mice with DSS colitis. Conclusions/Significance Probiotic bacteria modulate gut microbial diversity and favor local production of CLA in the colon that targets myeloid cell PPAR γ to suppress colitis.

A Global Metabolic Shift Is Linked to Salmonella Multicellular Development

Bacteria can elaborate complex patterns of development that are dictated by temporally ordered patterns of gene expression, typically under the control of a master regulatory pathway. For some processes, such as biofilm development, regulators that initiate the process have been identified but subsequent phenotypic changes such as stress tolerance do not seem to be under the control of these same regulators. A hallmark feature of biofilms is growth within a self-produced extracellular matrix. In this study we used metabolomics to compare Salmonella cells in rdar colony biofilms to isogenic csgD deletion mutants that do not produce an extracellular matrix. The two populations show distinct metabolite profiles. Even though CsgD controls only extracellular matrix production, metabolite signatures associated with cellular adaptations associated with stress tolerances were present in the wild type but not the mutant cells. To further explore these differences we examine the temporal gene expression of genes implicated in biofilm development and stress adaptations. In wild type cells, genes involved in a metabolic shift to gluconeogenesis and various stress-resistance pathways exhibited an ordered expression profile timed with multicellular development even though they are not CsgD regulated. In csgD mutant cells, the ordered expression was lost. We conclude that the induction of these pathways results from production of, and growth within, a self produced matrix rather than elaboration of a defined genetic program. These results predict that common physiological properties of biofilms are induced independently of regulatory pathways that initiate biofilm formation.

Satiety Hormone and Metabolomic Response to an Intermittent High Energy Diet Differs in Rats Consuming Long-Term Diets High in Protein or Prebiotic Fiber

Large differences in the composition of diet between early development and adulthood can have detrimental effects on obesity risk. We examined the effects of an intermittent high fat/sucrose diet (HFS) on satiety hormone and serum metabolite response in disparate diets. Wistar rat pups were fed control (C), high prebiotic fiber (HF) or high protein (HP) diets (weaning to 16 weeks), HFS diet challenged (6 weeks), and finally reverted to their respective C, HF, or HP diet (4 weeks). At conclusion, measurement of body composition and satiety hormones was accompanied by 1H NMR metabolic profiles in fasted and postprandial states. Metabolomic profiling predicted dietary source with >90% accuracy. The HF group was characterized by lowest body weight and body fat (P < 0.05) and increased satiety hormone levels (glucagon-like peptide 1 and peptide-YY). Regularized modeling confirmed that the HF diet is associated with higher gut hormone secretion that could reflect the known effects of prebiotics on gut microbiota and their fementative end products, the short chain fatty acids. Rats reared on a HF diet appear to experience fewer adverse effects from an intermittent high fat diet in adulthood when rematched to their postnatal diet. Metabolite profiles associated with the diets provide a distinct biochemical signature of their effects.

Quantitative analysis of metabolite concentrations in human urine samples using 13C{1H} NMR spectroscopy

Targeted profiling is a library-based method of using mathematically modeled reference spectra for quantification of metabolite concentrations in NMR mixture analysis. Metabolomics studies of biofluids, such as urine, represent a highly complex problem in this area, and for this reason targeted profiling of 1H NMR spectra can be hampered. A number of the issues relating to 1H NMR spectroscopy can be overcome using 13C{1H} NMR spectroscopy. In this work, a 13C{1H} NMR database was created using Chenomx NMR Suite, incorporating 120 metabolites. The 13C{1H} NMR database was standardized through the analysis of a series of metabolite solutions containing varying concentrations of 19 distinct metabolites, where the metabolite concentrations were varied across a range of values including biological ranges. Subsequently, the NMR spectra of urine samples were collected using 13C{1H} NMR spectroscopy and profiled using the 13C{1H} NMR library. In total, about 30 metabolites were conclusively identified and quantified in the urine samples using 13C{1H} NMR targeted profiling. The proton decoupling and larger spectral window provided easier identification and more accurate quantification for specific classes of metabolites, such as sugars and amino acids with overlap in the aliphatic region of the 1H NMR spectrum. We discuss potential application areas in which 13C{1H} NMR targeted profiling may be superior to 1H NMR targeted profiling.

Metabolomic profiling in cattle experimentally infected with Mycobacterium avium subsp. paratuberculosis.

The sensitivity of current diagnostics for Johnes disease, a slow, progressing enteritis in ruminants caused by Mycobacterium avium subsp. paratuberculosis (MAP), is too low to reliably detect all infected animals in the subclinical stage. The objective was to identify individual metabolites or metabolite profiles that could be used as biomarkers of early MAP infection in ruminants. In a monthly follow-up for 17 months, calves infected at 2 weeks of age were compared with aged-matched controls. Sera from all animals were analyzed by 1H nuclear magnetic resonance spectrometry. Spectra were acquired, processed, and quantified for analysis. The concentration of many metabolites changed over time in all calves, but some metabolites only changed over time in either infected or non-infected groups and the change in others was impacted by the infection. Hierarchical multivariate statistical analysis achieved best separation between groups between 300 and 400 days after infection. Therefore, a cross-sectional comparison between 1-year-old calves experimentally infected at various ages with either a high- or a low-dose and age-matched non-infected controls was performed. Orthogonal Projection to Latent Structures Discriminant Analysis (OPLS DA) yielded distinct separation of non-infected from infected cattle, regardless of dose and time (3, 6, 9 or 12 months) after infection. Receiver Operating Curves demonstrated that constructed models were high quality. Increased isobutyrate in the infected cattle was the most important agreement between the longitudinal and cross-sectional analysis. In general, high- and low-dose cattle responded similarly to infection. Differences in acetone, citrate, glycerol and iso-butyrate concentrations indicated energy shortages and increased fat metabolism in infected cattle, whereas changes in urea and several amino acids (AA), including the branched chain AA, indicated increased protein turnover. In conclusion, metabolomics was a sensitive method for detecting MAP infection much sooner than with current diagnostic methods, with individual metabolites significantly distinguishing infected from non-infected cattle.