HMDB 5.0: the Human Metabolome Database for 2022.

The Human Metabolome Database or HMDB (https://hmdb.ca) has been providing comprehensive reference information about human metabolites and their associated biological, physiological and chemical properties since 2007. Over the past 15 years, the HMDB has grown and evolved significantly to meet the needs of the metabolomics community and respond to continuing changes in internet and computing technology. This year's update, HMDB 5.0, brings a number of important improvements and upgrades to the database. These should make the HMDB more useful and more appealing to a larger cross-section of users. In particular, these improvements include: (i) a significant increase in the number of metabolite entries (from 114 100 to 217 920 compounds); (ii) enhancements to the quality and depth of metabolite descriptions; (iii) the addition of new structure, spectral and pathway visualization tools; (iv) the inclusion of many new and much more accurately predicted spectral data sets, including predicted NMR spectra, more accurately predicted MS spectra, predicted retention indices and predicted collision cross section data and (v) enhancements to the HMDB's search functions to facilitate better compound identification. Many other minor improvements and updates to the content, the interface, and general performance of the HMDB website have also been made. Overall, we believe these upgrades and updates should greatly enhance the HMDB's ease of use and its potential applications not only in human metabolomics but also in exposomics, lipidomics, nutritional science, biochemistry and clinical chemistry.

From dysbiosis to neuropathologies: Toxic effects of glyphosate in zebrafish.

Glyphosate, a globally prevalent herbicide known for its selective inhibition of the shikimate pathway in plants, is now implicated in physiological effects on humans and animals, probably due to its impacts in their gut microbiomes which possess the shikimate pathway. In this study, we investigate the effects of environmentally relevant concentrations of glyphosate on the gut microbiota, neurotransmitter levels, and anxiety in zebrafish. Our findings demonstrate that glyphosate exposure leads to dysbiosis in the zebrafish gut, alterations in central and peripheral serotonin levels, increased dopamine levels in the brain, and notable changes in anxiety and social behavior. While the dysbiosis can be attributed to glyphosate's antimicrobial properties, the observed effects on neurotransmitter levels leading to the reported induction of oxidative stress in the brain indicate a novel and significant mode of action for glyphosate, namely the impairment of the microbiome-gut-axis. While further investigations are necessary to determine the relevance of this mechanism in humans, our findings shed light on the potential explanation for the contradictory reports on the safety of glyphosate for consumers.

Automated identification and quantification of metabolites in human fecal extracts by nuclear magnetic resonance spectroscopy.

We report the development of a software program, called MagMet-F, that automates the processing and quantification of 1D 1 H NMR of human fecal extracts. To optimize the program, we identified 82 potential fecal metabolites using 1D 1 H NMR of six human fecal extracts using manual profiling and a literature review of known fecal metabolites. We acquired pure versions of those metabolites and then acquired their 1D 1 H NMR spectra at 700 MHz to generate a fecal metabolite spectral library for MagMet-F. The fitting of these metabolites by MagMet-F was iteratively optimized to replicate manual profiling. We validated MagMet-F's automated profiling using a test set of six fecal extracts. It correctly identified 80% of the compounds and quantified those within <20% of the values determined by manual profiling using Chenomx. We also compared MagMet-F's profiling performance to two other open-access NMR profiling tools, Bayesil and Batman. MagMet-F outperformed both. Bayesil repeatedly overestimated metabolite concentrations by 10% to 40% while Batman was unable to properly quantify any compounds and took 10-20× longer. We have implemented MagMet-F as a freely accessible web server to enable automated, fast and convenient 1D 1 H NMR spectral profiling of fecal samples. MagMet-F is available at https://www.magmet.ca.

MagMet: A fully automated web server for targeted nuclear magnetic resonance metabolomics of plasma and serum.

Nuclear magnetic resonance (NMR) spectral analysis of biofluids can be a time-consuming process, requiring the expertise of a trained operator. With NMR becoming increasingly popular in the field of metabolomics, there is a growing need to change this paradigm and to automate the process. Here we introduce MagMet, an online web server, that automates the processing and quantification of 1D 1 H NMR spectra from biofluids-specifically, human serum/plasma metabolites, including those associated with inborn errors of metabolism (IEM). MagMet uses a highly efficient data processing procedure that performs automatic Fourier Transformation, phase correction, baseline optimization, chemical shift referencing, water signal removal, and peak picking/peak alignment. MagMet then uses the peak positions, linewidth information, and J-couplings from its own specially prepared standard metabolite reference spectral NMR library of 85 serum/plasma compounds to identify and quantify compounds from experimentally acquired NMR spectra of serum/plasma. MagMet employs linewidth adjustment for more consistent quantification of metabolites from higher field instruments and incorporates a highly efficient data processing procedure for more rapid and accurate detection and quantification of metabolites. This optimized algorithm allows the MagMet webserver to quickly detect and quantify 58 serum/plasma metabolites in 2.6 min per spectrum (when processing a dataset of 50-100 spectra). MagMet's performance was also assessed using spectra collected from defined mixtures (simulating other biofluids), with >100 previously measured plasma spectra, and from spiked serum/plasma samples simulating known IEMs. In all cases, MagMet performed with precision and accuracy matching the performance of human spectral profiling experts. MagMet is available at http://magmet.ca.

From Birth to Overweight and Atopic Disease: Multiple and Common Pathways of the Infant Gut Microbiome.

BACKGROUND & AIMS: Few studies, even those with cohort designs, test the mediating effects of infant gut microbes and metabolites on the onset of disease. We undertook such a study. METHODS: Using structural equation modeling path analysis, we tested directional relationships between first pregnancy, birth mode, prolonged labor and breastfeeding; infant gut microbiota, metabolites, and IgA; and childhood body mass index and atopy in 1667 infants. RESULTS: After both cesarean birth and prolonged labor with a first pregnancy, a higher Enterobacteriaceae/Bacteroidaceae ratio at 3 months was the dominant path to overweight; higher Enterobacteriaceae/Bacteroidaceae ratios and Clostridioides difficile colonization at 12 months were the main pathway to atopic sensitization. Depletion of Bifidobacterium after prolonged labor was a secondary pathway to overweight. Influenced by C difficile colonization at 3 months, metabolites propionate and formate were secondary pathways to child outcomes, with a key finding that formate was at the intersection of several paths. CONCLUSIONS: Pathways from cesarean section and first pregnancy to child overweight and atopy share many common mediators of the infant gut microbiome, notably C difficile colonization.

Childhood body mass index and associations with infant gut metabolites and secretory IgA: findings from a prospective cohort study.

BACKGROUND/OBJECTIVES: Differences in gut microbiota, metabolites and immune markers have been observed between individuals with and without obesity. Our study determined the temporal association between infant fecal gut metabolites, sIgA and body mass index (BMI) z score of preschool children, independent of pre/postnatal factors. SUBJECTS/METHODS: The study includes a subset of 647 infants from the CHILD Cohort Study (recruited between January 1, 2009, and December 31, 2012). Fecal metabolites and sIgA were measured at 3-4 months of age, and age and sex adjusted BMI z scores at 1 and 3 years of age. Associations between the metabolites, IgA, and child BMI z scores at age 1 and 3 years were tested using linear regression adjusted for pre/postnatal factors (breastfeeding, birthweight-for-gestational age, birthmode and IAP, solid food introduction). RESULTS: Mean BMI z score for all infants was 0.34 (SD 1.16) at 1 year (N = 647) and 0.71 (SD 1.06) at 3 years (N = 573). High fecal formate in infancy was associated with a significantly lower BMI z score (adjusted mean difference -0.23 (95% CI -0.42, -0.04)) and high butyrate was associated with a higher BMI z score (adjusted mean difference 0.21 (95% CI 0.01, 0.41)) at age 3 years only. The influence of formate and butyrate on BMI z score at age 3 were seen only in those that were not exclusively breastfed at stool sample collection (adjusted mean difference for high formate/EBF- group: -0.33 (95%CI -0.55, -0.10) and 0.25 (95% CI 0.02, 0.47) for high butyrate/EBF- group). No associations were seen between sIgA and BMI z score at age 1 or 3 years in adjusted regression models. CONCLUSION AND RELEVANCE: Differences in fecal metabolite levels in early infancy were associated with childhood BMI. This study identifies an important area of future research in understanding the pathogenesis of obesity.

Comprehensive Targeted Metabolomic Assay for Urine Analysis.

Among all the human biological fluids used for disease biomarker discovery or clinical chemistry, urine stands out. It can be collected easily and noninvasively, it is readily available in large volumes, it is typically free from protein contamination, and it is chemically complex-reflecting a wide range of physiological states and functions. However, the comprehensive metabolomic analysis of urine has been somewhat less studied compared to blood. Indeed, most published metabolomic assays are specifically optimized for serum or plasma. In an effort to improve this situation, we have developed a comprehensive, quantitative MS-based assay for urine analysis. The assay robustly detects and quantifies 142 urinary metabolites including 28 amino acids and derivatives, 17 organic acids, 22 biogenic amines and derivatives, 40 acylcarnitines, 34 lipids, and glucose/hexose, among which 67 metabolites are absolutely quantified and 75 metabolites are semiquantified. All the analysis methods in this assay are based on liquid chromatography-tandem mass spectrometry (LC-MS/MS) using both positive and negative-mode multiple reaction monitoring (MRM). The recovery rates of spiked urine samples at three different concentration levels, that is, low, medium and high, are in the range of 80% to 120% with satisfactory precision values of less than 20%. This targeted metabolomic assay has been successfully applied to the analysis of large numbers of human urine samples, with results closely matching those reported in the literature as well as those obtained from orthogonal analysis via NMR spectroscopy. Moreover, the assay was specifically developed in a 96-well plate format, which enables automated, high-throughput sample analysis. The assay has already been used to analyze more than 1800 urine samples in our laboratory since early 2019.

Serum metabolomics identifies metabolite panels that differentiate lame dairy cows from healthy ones.

INTRODUCTION: Although much is known about lameness application of metabolomics technologies to better understanding its etiology and pathogenesis is of utmost interest. OBJECTIVES: The objective of this study was to investigate serum metabolite alterations in pre-lame, lame and post-lame dairy cows in order to identify potential screening serum metabolite biomarkers for lameness and better understand its pathobiology. METHODS: A combination of direct injection and tandem mass spectrometry (DI-MS/MS) with a reverse-phase liquid chromatography and tandem mass spectrometry (LC-MS/MS) analysis was performed in the serum of six cases of lameness and 20 healthy control cows (CON) at - 8 and - 4 weeks prepartum, at lameness diagnosis week, and at + 4 and + 8 weeks postpartum. RESULTS: Data indicated that pre-lame, lame, and post-lame cows experienced altered concentrations of multiple metabolites. It is interesting to note that throughout the 16-weeks of the study, 7 serum metabolites [e.g., diacyl-phosphatidylcholine (PC aa) C30:0, phosphatidylcholine acyl-alkyl (PC ae) C40:2, sphingomyelin (SM) (OH) C14:1, SM C18:0, isoleucine (Ile), leucine (Leu), and lysine (Lys)] differentiated CON cows from the lame ones. Furthermore, 4 metabolic pathways (i.e., Lys degradation, biotin metabolism, tryptophan (Trp) metabolism, and valine [(Val)-Leu-Ile degradation) were altered in cows with lameness during the onset and progression of the disease. CONCLUSION: Multiple metabolite and pathway alterations were identified in the serum of pre-lame, lame, and post-lame cows that through light into the pathobiology of the disease and that can be used as potential biomarker sets that can predict the risk of lameness in dairy cows.

Workshop report: Toward the development of a human whole stool reference material for metabolomic and metagenomic gut microbiome measurements.

INTRODUCTION: To date, there has been little effort to develop standards for metabolome-based gut microbiome measurements despite the significant efforts toward standard development for DNA-based microbiome measurements. OBJECTIVES: The National Institute of Standards and Technology (NIST), The BioCollective (TBC), and the North America Branch of the International Life Sciences Institute (ILSI North America) are collaborating to extend NIST's efforts to develop a Human Whole Stool Reference Material for the purpose of method harmonization and eventual quality control. METHODS: The reference material will be rationally designed for adequate quality assurance and quality control (QA/QC) for underlying measurements in the study of the impact of diet and nutrition on functional aspects of the host gut microbiome and relationships of those functions to health. To identify which metabolites deserve priority in their value assignment, NIST, TBC, and ILSI North America jointly conducted a workshop on September 12, 2019 at the NIST campus in Gaithersburg, Maryland. The objective of the workshop was to identify metabolites for which evidence indicates relevance to health and disease and to decide on the appropriate course of action to develop a fit-for-purpose reference material. RESULTS: This document represents the consensus opinions of workshop participants and co-authors of this manuscript, and provides additional supporting information. In addition to developing general criteria for metabolite selection and a preliminary list of proposed metabolites, this paper describes some of the strengths and limitations of this initiative given the current state of microbiome research. CONCLUSIONS: Given the rapidly evolving nature of gut microbiome science and the current state of knowledge, an RM (as opposed to a CRM) measured for multiple metabolites is appropriate at this stage. As the science evolves, the RM can evolve to match the needs of the research community. Ultimately, the stool RM may exist in sequential versions. Beneficial to this evolution will be a clear line of communication between NIST and the stakeholder community to ensure alignment with current scientific understanding and community needs.

Gut Microenvironment and Bacterial Invasion in Paediatric Inflammatory Bowel Diseases.

OBJECTIVES: Host-microbial relationship is disrupted in inflammatory bowel diseases (IBD). We hypothesized that altered gut luminal microenvironment can impact microbial virulence in IBD, leading to disruption of homeostasis and disease. We investigated the relationship between gut microenvironment and microbial virulence. METHODS: Intestinal aspirates were collected from 10 non-IBD controls, 9 Crohn disease, and 10 ulcerative colitis paediatric patients during endoscopy. In vitro invasion of bacteria isolated from the duodenum and terminal ileum (TI) was quantified using gentamicin protection assays. Intestinal epithelial cells were infected in vitro by known Escherichia coli strains with patient intestinal aspirates added. Nuclear magnetic resonance spectroscopy (NMR) analysis was conducted on intestinal aspirates to identify metabolites associated with invasion; these metabolites were then introduced to the infection model. RESULTS: There was no difference in in vitro invasion of bacteria obtained from intestinal aspirates of non-IBD and IBD patients. Incubation of laboratory E coli strains with TI aspirates from IBD patients increased their invasion into epithelial cells in vitro. NMR analysis revealed intestinal metabolites that correlated with bacterial invasion; succinate present in the intestinal aspirates correlated positively, whereas acetate and formate related negatively with invasion. Addition of exogenous succinate increased invasion of E coli in vitro. CONCLUSIONS: Alterations in the gut microenvironment in IBD can affect bacterial invasion. Succinate is associated with increased bacterial invasion and can alter bacterial virulence in IBD. This highlights the interaction between specific metabolites and bacteria that could be instrumental in propagating or suppressing inflammation in paediatric IBD patients.

Exome Sequencing and the Management of Neurometabolic Disorders.

BACKGROUND: Whole-exome sequencing has transformed gene discovery and diagnosis in rare diseases. Translation into disease-modifying treatments is challenging, particularly for intellectual developmental disorder. However, the exception is inborn errors of metabolism, since many of these disorders are responsive to therapy that targets pathophysiological features at the molecular or cellular level. METHODS: To uncover the genetic basis of potentially treatable inborn errors of metabolism, we combined deep clinical phenotyping (the comprehensive characterization of the discrete components of a patient's clinical and biochemical phenotype) with whole-exome sequencing analysis through a semiautomated bioinformatics pipeline in consecutively enrolled patients with intellectual developmental disorder and unexplained metabolic phenotypes. RESULTS: We performed whole-exome sequencing on samples obtained from 47 probands. Of these patients, 6 were excluded, including 1 who withdrew from the study. The remaining 41 probands had been born to predominantly nonconsanguineous parents of European descent. In 37 probands, we identified variants in 2 genes newly implicated in disease, 9 candidate genes, 22 known genes with newly identified phenotypes, and 9 genes with expected phenotypes; in most of the genes, the variants were classified as either pathogenic or probably pathogenic. Complex phenotypes of patients in five families were explained by coexisting monogenic conditions. We obtained a diagnosis in 28 of 41 probands (68%) who were evaluated. A test of a targeted intervention was performed in 18 patients (44%). CONCLUSIONS: Deep phenotyping and whole-exome sequencing in 41 probands with intellectual developmental disorder and unexplained metabolic abnormalities led to a diagnosis in 68%, the identification of 11 candidate genes newly implicated in neurometabolic disease, and a change in treatment beyond genetic counseling in 44%. (Funded by BC Children's Hospital Foundation and others.).

International Ring Trial of a High Resolution Targeted Metabolomics and Lipidomics Platform for Serum and Plasma Analysis.

A challenge facing metabolomics in the analysis of large human cohorts is the cross-laboratory comparability of quantitative metabolomics measurements. In this study, 14 laboratories analyzed various blood specimens using a common experimental protocol provided with the Biocrates AbsoluteIDQ p400HR kit, to quantify up to 408 metabolites. The specimens included human plasma and serum from male and female donors, mouse and rat plasma, as well as NIST SRM 1950 reference plasma. The metabolite classes covered range from polar (e.g., amino acids and biogenic amines) to nonpolar (e.g., diacyl- and triacyl-glycerols), and they span 11 common metabolite classes. The manuscript describes a strict system suitability testing (SST) criteria used to evaluate each laboratory's readiness to perform the assay, and provides the SST Skyline documents for public dissemination. The study found approximately 250 metabolites were routinely quantified in the sample types tested, using Orbitrap instruments. Interlaboratory variance for the NIST SRM-1950 has a median of 10% for amino acids, 24% for biogenic amines, 38% for acylcarnitines, 25% for glycerolipids, 23% for glycerophospholipids, 16% for cholesteryl esters, 15% for sphingolipids, and 9% for hexoses. Comparing to consensus values for NIST SRM-1950, nearly 80% of comparable analytes demonstrated bias of <50% from the reference value. The findings of this study result in recommendations of best practices for system suitability, quality control, and calibration. We demonstrate that with appropriate controls, high-resolution metabolomics can provide accurate results with good precision across laboratories, and the p400HR therefore is a reliable approach for generating consistent and comparable metabolomics data.

The role of the Human Metabolome Database in inborn errors of metabolism.

Metabolomics holds considerable promise to advance our understanding of human disease, including our understanding of inborn errors of metabolism (IEM). The application of metabolomics in IEM research has already led to the discovery of several novel IEMs and the identification of novel IEM biomarkers. However, with hundreds of known IEMs and more than 700 associated IEM metabolites, it is becoming increasingly challenging for clinical researchers to keep track of IEMs, their associated metabolites, and their corresponding metabolic mechanisms. Furthermore, when using metabolomics to assist in IEM biomarker discovery or even in IEM diagnosis, it is becoming much more difficult to properly identify metabolites from the complex NMR and MS spectra collected from IEM patients. To that end, comprehensive, open access metabolite databases that provide up-to-date referential information about metabolites, metabolic pathways, normal/abnormal metabolite concentrations, and reference NMR or MS spectra for compound identification are essential. Over the last few years, a number of compound databases, including the Human Metabolome Database (HMDB), have been developed to address these challenges. First described in 2007, the HMDB is now the world's largest and most comprehensive metabolomic resource for human metabolic studies. The latest release of the HMDB contains 114,100 metabolite entries (with 247 being relevant to IEMs), thousands of metabolite concentrations (with 600 being relevant to IEMs), and ~33,000 metabolic and disease-associated pathways (with 202 being relevant to IEMs). Here we provide a summary of the HMDB and offer some guidance on how it can be used in metabolomic studies of IEMs.

Evolution of renal function and urinary biomarker indicators of inflammation on serial kidney biopsies in pediatric kidney transplant recipients with and without rejection.

Urinary CXCL10 and metabolites are biomarkers independently associated with TCMR. We sought to test whether these biomarkers fluctuate in association with histological severity of TCMR over short time frames. Forty-nine pairs of renal biopsies obtained 1-3 months apart from 40 pediatric renal transplant recipients were each scored for TCMR acuity score (i + t; Banff criteria). Urinary CXCL10:Cr and TCMR MDS were obtained at each biopsy and were tested for association with changes between biopsies in acuity, estimated GFR (ΔeGFR), and 12-month ΔeGFR. Sequential biopsies were obtained 1.8 ± 0.8 months apart. Biopsy 1 was usually obtained under protocol (75%), and 62% percent had evidence of TCMR. Using each biopsy pair for comparison, ΔeGFR did not predict change in acuity. By contrast, change in acuity was significantly correlated with change in urinary CXCL10:Cr (ρ 0.45, P = .003) and MDS (ρ 0.29, P = .04) between biopsies. The 12-month ΔeGFR was not predicted by TCMR acuity or CXCL10:Cr at Biopsy 2; however, an inverse correlation was seen with urinary MDS (ρ -0.35; P = .02). Changes in eGFR correlate poorly with evolving TCMR acuity on histology. Urinary biomarkers may be superior for non-invasive monitoring of rejection, including histological response to therapy, and may be prognostic for medium-term function.

GC-MS Metabolomics Identifies Metabolite Alterations That Precede Subclinical Mastitis in the Blood of Transition Dairy Cows.

The objectives of this study were to determine alterations in the serum metabolites related to amino acid (AA), carbohydrate, and lipid metabolism in transition dairy cows before diagnosis of subclinical mastitis (SCM), during, and after diagnosis of disease. A subclinical mastitis case was determined as a cow having somatic cell count (SCC) > 200 000/mL of milk for two or more consecutive reports. Blood samples were collected from 100 Holstein dairy cows at five time points at -8 and -4 weeks before parturition, at the week of SCM diagnosis, and +4 and +8 weeks after parturition. Twenty healthy control cows (CON) and six cows that were diagnosed with SCM were selected for serum analysis with GC-MS. At -8 weeks a total of 13 metabolites were significantly altered in SCM cows. In addition, at the week of SCM diagnosis 17 metabolites were altered in these cows. Four weeks after parturition 10 metabolites were altered in SCM cows and at +8 weeks 11 metabolites were found to be different between the two groups. Valine (Val), serine (Ser), tyrosine (Tyr), and phenylalanine (Phe) had very good predictive abilities for SCM and could be used at -8 weeks and -4 weeks before calving. Combination of Val, isoleucine (Ile), Ser, and proline (Pro) can be used as diagnostic biomarkers of SCM during early stages of lactation at +4 to +8 weeks after parturition. In conclusion, SCM is preceded and followed by alteration in AA metabolism.

Targeted Metabolic Profiling of Post-Mortem Brain from Infants Who Died from Sudden Infant Death Syndrome.

Currently little is known about the underlying pathophysiology associated with SIDS, and no objective biomarkers exist for the accurate identification of those at greatest risk of dying from SIDS. Using targeted metabolomics, we aim to profile the medulla oblongata of infants who have died from SIDS (n = 16) and directly compare their biochemical profile with age matched controls. Combining data acquired using 1H NMR and targeted DI-LC-MS/MS, we have identified fatty acid oxidation as a pivotal biochemical pathway perturbed in the brains of those infants who have from SIDS (p = 0.0016). Further we have identified a potential central biomarker with an AUC (95% CI) = 0.933 (0.845-1.000) having high sensitivity (0.933) and specificity (0.875) values for discriminating between control and SIDS brains. This is the first reported study to use targeted metabolomics for the study of PM brain from infants who have died from SIDS. We have identified pathways associated with the disease and central biomarkers for early screening/diagnosis.

Salt Segregation and Sample Cleanup on Perfluoro-Coated Nanostructured Surfaces for Laser Desorption Ionization Mass Spectrometry of Biofluid Samples.

We present a surface assisted laser desorption ionization (SALDI) technique, coupled with fluorocarbon coating, to achieve selective segregation of ionic and/or hydrophilic analytes from background biofluid electrolytes for quantiatve mass spectrometric analysis. By controlling the contact angle of (1H,1H,2H,2H-perfluorooctyl) trichlorosilane or (1H,1H,2H,2H-perfluorooctyl) dimethylchlorosilane to a specific range (105-120°), background electrolytes can be made to segregate from hydrophilic analytes during a drying step on the surface of a highly nanoporous thin film. Nanoporous silicon films were prepared using glancing angle deposition (GLAD) thin film technology, then coated with fluorcarbon. This desalting method directly separates highly polar, ionic metabolites, such as amino acids, from salty biofluids such as aritificial cerebrospinal fluid (aCSF) and serum. Derivatization, extraction and rinsing steps are not required to separate the analytes from the bioelectrolytes. With on-chip desalting, the limit of quantitation for histidine spiked in aCSF is ∼1 µM, and calibration curves with internal standards can achieve a precision of 1-9% within a 1 to 50 µM range. Five highly polar organic acids in serum were successfully quantified, and the SALDI-MS results obtained on the desalted serum sample spots show both good reproducibility and compare well to results from NMR and liquid chromatography-mass spectrometry. Putative identification of a total of 32 metabolites was accomplished in blood using time-of-flight MS with perfluoro coated Si-GLAD SALDI, by comparison to tabulated data.

Metabolomic prediction of endometrial cancer.

INTRODUCTION: Endometrial cancer (EC) is associated with metabolic disturbances including obesity, diabetes and metabolic syndrome. Identifying metabolite biomarkers for EC detection has a crucial role in reducing morbidity and mortality. OBJECTIVE: To determine whether metabolomic based biomarkers can detect EC overall and early-stage EC. METHODS: We performed NMR and mass spectrometry based metabolomic analyses of serum in EC cases versus controls. A total of 46 early-stage (FIGO stages I-II) and 10 late-stage (FIGO stages III-IV) EC cases constituted the study group. A total of 60 unaffected control samples were used. Patients and controls were divided randomly into a discovery group (n = 69) and an independent validation group (n = 47). Predictive algorithms based on biomarkers and demographic characteristics were generated using logistic regression analysis. RESULTS: A total of 181 metabolites were evaluated. Extensive changes in metabolite levels were noted in the EC versus the control group. The combination of C14:2, phosphatidylcholine with acyl-alkyl residue sum C38:1 (PCae C38:1) and 3-hydroxybutyric acid had an area under the receiver operating characteristics curve (AUC) (95% CI) = 0.826 (0.706-0.946) and a sensitivity = 82.6%, and specificity = 70.8% for EC overall. For early EC prediction: BMI, C14:2 and PC ae C40:1 had an AUC (95% CI) = 0.819 (0.689-0.95) and a sensitivity = 72.2% and specificity = 79.2% in the validation group. CONCLUSIONS: EC is characterized by significant perturbations in important cellular metabolites. Metabolites accurately detected early-stage EC cases and EC overall which could lead to the development of non-invasive biomarkers for earlier detection of EC and for monitoring disease recurrence.

Metabolome analysis of 20 taxonomically related benzylisoquinoline alkaloid-producing plants.

BACKGROUND: Recent progress toward the elucidation of benzylisoquinoline alkaloid (BIA) metabolism has focused on a small number of model plant species. Current understanding of BIA metabolism in plants such as opium poppy, which accumulates important pharmacological agents such as codeine and morphine, has relied on a combination of genomics and metabolomics to facilitate gene discovery. Metabolomics studies provide important insight into the primary biochemical networks underpinning specialized metabolism, and serve as a key resource for metabolic engineering, gene discovery, and elucidation of governing regulatory mechanisms. Beyond model plants, few broad-scope metabolomics reports are available for the vast number of plant species known to produce an estimated 2500 structurally diverse BIAs, many of which exhibit promising medicinal properties. RESULTS: We applied a multi-platform approach incorporating four different analytical methods to examine 20 non-model, BIA-accumulating plant species. Plants representing four families in the Ranunculales were chosen based on reported BIA content, taxonomic distribution and importance in modern/traditional medicine. One-dimensional (1)H NMR-based profiling quantified 91 metabolites and revealed significant species- and tissue-specific variation in sugar, amino acid and organic acid content. Mono- and disaccharide sugars were generally lower in roots and rhizomes compared with stems, and a variety of metabolites distinguished callus tissue from intact plant organs. Direct flow infusion tandem mass spectrometry provided a broad survey of 110 lipid derivatives including phosphatidylcholines and acylcarnitines, and high-performance liquid chromatography coupled with UV detection quantified 15 phenolic compounds including flavonoids, benzoic acid derivatives and hydroxycinnamic acids. Ultra-performance liquid chromatography coupled with high-resolution Fourier transform mass spectrometry generated extensive mass lists for all species, which were mined for metabolites putatively corresponding to BIAs. Different alkaloids profiles, including both ubiquitous and potentially rare compounds, were observed. CONCLUSIONS: Extensive metabolite profiling combining multiple analytical platforms enabled a more complete picture of overall metabolism occurring in selected plant species. This study represents the first time a metabolomics approach has been applied to most of these species, despite their importance in modern and traditional medicine. Coupled with genomics data, these metabolomics resources serve as a key resource for the investigation of BIA biosynthesis in non-model plant species.

ECMDB: the E. coli Metabolome Database.

The Escherichia coli Metabolome Database (ECMDB, http://www.ecmdb.ca) is a comprehensively annotated metabolomic database containing detailed information about the metabolome of E. coli (K-12). Modelled closely on the Human and Yeast Metabolome Databases, the ECMDB contains >2600 metabolites with links to ∼1500 different genes and proteins, including enzymes and transporters. The information in the ECMDB has been collected from dozens of textbooks, journal articles and electronic databases. Each metabolite entry in the ECMDB contains an average of 75 separate data fields, including comprehensive compound descriptions, names and synonyms, chemical taxonomy, compound structural and physicochemical data, bacterial growth conditions and substrates, reactions, pathway information, enzyme data, gene/protein sequence data and numerous hyperlinks to images, references and other public databases. The ECMDB also includes an extensive collection of intracellular metabolite concentration data compiled from our own work as well as other published metabolomic studies. This information is further supplemented with thousands of fully assigned reference nuclear magnetic resonance and mass spectrometry spectra obtained from pure E. coli metabolites that we (and others) have collected. Extensive searching, relational querying and data browsing tools are also provided that support text, chemical structure, spectral, molecular weight and gene/protein sequence queries. Because of E. coli's importance as a model organism for biologists and as a biofactory for industry, we believe this kind of database could have considerable appeal not only to metabolomics researchers but also to molecular biologists, systems biologists and individuals in the biotechnology industry.