Untargeted Metabolomics Data Research Articles

Depletion by Hemodialysis of the Antioxidant Ergothioneine

Background: Hemodialysis may excessively remove valuable solutes. Untargeted metabolomics data from a prior study suggested that ergothioneine was depleted in the plasma of hemodialysis subjects. Ergothioneine is a dietary-derived solute with antioxidant properties. The presence of a highly specific ergothioneine uptake transporter suggests that it is valuable. Ergothioneine levels are high in tissues susceptible to oxidative stress, particularly erythrocytes. We compared erythrocyte and plasma ergothioneine levels in subjects receiving hemodialysis to those in subjects with advanced chronic kidney disease (CKD) and subjects without known kidney disease (controls). We further examined the extent to which indiscriminate removal by hemodialysis could contribute to ergothioneine depletion. Methods: Liquid chromatography tandem mass spectrometry with stable isotope dilution was used to measure the erythrocyte and plasma levels of ergothioneine in 12 control, 12 CKD, and 11 hemodialysis subjects. We also measured the urinary excretion of ergothioneine in control and CKD subjects, and the dialytic removal of ergothioneine in hemodialysis subjects. Results: Erythrocyte ergothioneine levels were markedly reduced in CKD and hemodialysis subjects. Erythrocyte levels in CKD subjects were on average 24% of the levels in control subjects and were even lower in hemodialysis subjects, averaging 8% of control subjects. Plasma ergothioneine levels were also reduced in CKD and hemodialysis subjects but to a lesser extent than the erythrocyte levels. Kidney tubular reabsorption of ergothioneine was avid. In contrast, hemodialysis cleared ergothioneine at a rate of 146±36 ml/min so that removal of ergothioneine by hemodialysis greatly exceeded the amount excreted in the urine in both CKD and control subjects. Conclusions: Ergothioneine, a potentially valuable antioxidant, is severely depleted in people maintained on hemodialysis. Future studies are required to assess the consequences of ergothioneine depletion.

Immunomodulatory metabolites in IgE-mediated food allergy and oral immunotherapy outcomes based on metabolomic profiling.

The immunometabolic mechanisms underlying variable responses to oral immunotherapy (OIT) in patients with IgE-mediated food allergy are unknown. To identify novel pathways associated with tolerance in food allergy, we used metabolomic profiling to find pathways important for food allergy in multiethnic cohorts and responses to OIT. Untargeted plasma metabolomics data were generated from the VDAART healthy infant cohort (N = 384), a Costa Rican cohort of children with asthma (N = 1040), and a peanut OIT trial (N = 20) evaluating sustained unresponsiveness (SU, protection that lasts after therapy) versus transient desensitization (TD, protection that ends immediately afterward). Generalized linear regression modeling and pathway enrichment analysis identified metabolites associated with food allergy and OIT outcomes. Compared with unaffected children, those with food allergy were more likely to have metabolomic profiles with altered histidines and increased bile acids. Eicosanoids (e.g., arachidonic acid derivatives) (q = 2.4 × 10-20) and linoleic acid derivatives (q = 3.8 × 10-5) pathways decreased over time on OIT. Comparing SU versus TD revealed differing concentrations of bile acids (q = 4.1 × 10-8), eicosanoids (q = 7.9 × 10-7), and histidine pathways (q = .015). In particular, the bile acid lithocholate (4.97 [1.93, 16.14], p = .0027), the eicosanoid leukotriene B4 (3.21 [1.38, 8.38], p = .01), and the histidine metabolite urocanic acid (22.13 [3.98, 194.67], p = .0015) were higher in SU. We observed distinct profiles of bile acids, histidines, and eicosanoids that vary among patients with food allergy, over time on OIT and between SU and TD. Participants with SU had higher levels of metabolites such as lithocholate and urocanic acid, which have immunomodulatory roles in key T-cell subsets, suggesting potential mechanisms of tolerance in immunotherapy.

Met4DX: A Unified and Versatile Data Processing Tool for Multidimensional Untargeted Metabolomics Data.

Liquid chromatography-mass spectrometry (LC-MS) is a powerful tool in untargeted metabolomics, enabling the high-sensitivity and high-specificity characterization of metabolites. The integration of ion mobility (IM) with LC-MS, known as LC-IM-MS, enhances the analytical depth, facilitating more comprehensive metabolite profiling. However, the complexity of data generated by these technologies presents significant challenges in data processing. Addressing these challenges, we developed Met4DX, a unified and versatile software tool for processing both 3D and 4D untargeted metabolomics data. Met4DX incorporates a new MS1-oriented peak detection approach coupled with our bottom-up assembly algorithm, enabling highly sensitive and comprehensive peak detection in untargeted metabolomics data. Additionally, Met4DX employs a uniform quantification strategy to enhance the precision of peak integration across different samples. The software provides a user-friendly interface that simplifies data processing with default parameter sets, consolidating peak detection, alignment, quantification, and other procedures into a single streamlined workflow. Together, Met4DX offers a comprehensive solution for multidimensional metabolomics data processing, transforming raw data from diverse MS instruments into a final feature table containing quantification and identification results. We postulate Met4DX facilitates metabolite discovery in biological samples by deciphering the complex untargeted metabolomics data. Met4DX is freely available on the Internet (https://met4dx.zhulab.cn/).

Empirically establishing drug exposure records directly from untargeted metabolomics data.

Despite extensive efforts, extracting information on medication exposure from clinical records remains challenging. To complement this approach, we developed the tandem mass spectrometry (MS/MS) based GNPS Drug Library. This resource integrates MS/MS data for drugs and their metabolites/analogs with controlled vocabularies on exposure sources, pharmacologic classes, therapeutic indications, and mechanisms of action. It enables direct analysis of drug exposure and metabolism from untargeted metabolomics data independent of clinical records. Our library facilitates stratification of individuals in clinical studies based on the empirically detected medications, exemplified by drug-dependent microbiota-derived N -acyl lipid changes in a human immunodeficiency virus cohort. The GNPS Drug Library holds potential for broader applications in drug discovery and precision medicine.

Detection of Inverse Stable Isotopic Labeling in Untargeted Metabolomic Data.

Stable isotopic labeling is a powerful tool for determining the biosynthetic origin of metabolites and for discovering natural products that incorporate precursors of interest. When isotopically substituted precursors are not available commercially or synthetically, inverse stable isotopic labeling (InverSIL) is a useful alternative. With InverSIL, an organism is grown on an isotopically substituted medium and then fed precursors of natural isotopic abundance which can be tracked by mass spectrometry, thereby bypassing issues with precursor availability. Currently, there is no automated way to identify precursor incorporation in untargeted metabolomic data using InverSIL without specifying an expected change in the mass-to-charge ratio of metabolites that have incorporated the precursor. This makes it difficult to identify unknown natural products that may incorporate portions of precursors of interest using new biochemistry or to rapidly identify incorporation of multiple precursors into different metabolites simultaneously. To address this, we developed a new, robust workflow for the automated identification of inverse labeling in untargeted metabolomic data. We then use this method to identify metabolites that incorporate para-aminobenzoic acid and different portions of l-methionine, including in the same sample, and in the process discover the likely biosynthetic origin for the C-7 and C-9 methyl groups of the pterin portion of dephosphotetrahydromethanopterin, a C1 transfer coenzyme used by methylotrophic bacteria. This workflow can be applied in the future to streamline the use of the versatile InverSIL approach for natural product and metabolism research.

Combined LC-MS/MS feature grouping, statistical prioritization, and interactive networking in msFeaST.

Computational metabolomics workflows have revolutionized the untargeted metabolomics field. However, the organization and prioritization of metabolite features remains a laborious process. Organizing metabolomics data is often done through mass fragmentation-based spectral similarity grouping, resulting in feature sets that also represent an intuitive and scientifically meaningful first stage of analysis in untargeted metabolomics. Exploiting such feature sets, feature-set testing has emerged as an approach that is widely used in genomics and targeted metabolomics pathway enrichment analyses. It allows for formally combining groupings with statistical testing into more meaningful pathway enrichment conclusions. Here, we present msFeaST (mass spectral Feature Set Testing), a feature-set testing and visualization workflow for LC-MS/MS untargeted metabolomics data. Feature-set testing involves statistically assessing differential abundance patterns for groups of features across experimental conditions. We developed msFeaST to make use of spectral similarity-based feature groupings generated using k-medoids clustering, where the resulting clusters serve as a proxy for grouping structurally similar features with potential biosynthesis pathway relationships. Spectral clustering done in this way allows for feature group-wise statistical testing using the globaltest package, which provides high power to detect small concordant effects via joint modeling and reduced multiplicity adjustment penalties. Hence, msFeaST provides interactive integration of the semi-quantitative experimental information with mass-spectral structural similarity information, enhancing the prioritization of features and feature sets during exploratory data analysis. The msFeaST workflow is freely available through https://github.com/kevinmildau/msFeaST and built to work on MacOS and Linux systems.

Integration of multi-omics reveals the important role of the BBS10 gene in reproduction.

Blood samples are easily obtained from sheep. Therefore, blood analysis can be a convenient method for evaluating reproductive traits in sheep by detecting genetic and metabolic changes in the ovary. By combining 167 RNA sequencing (RNA-seq) data and 60 untargeted metabolomics data, this study analyzed the relationship between genes and metabolites in the ovary and blood. The conjoint KEGG enrichment analysis enriched glutathione metabolic pathways both in the ovary and blood. This finding provides an explanation for possible glutathione metabolic processes in the ovary with metabolite exchange in the blood. The metabolite-gene-disease interaction network revealed a correlation between the expression of certain Bardet-Biedl syndrome (BBS) family genes in the ovary and blood. This indicates that BBS family genes, such as BBS10 in sheep blood, could be a potential biomarker for BBS. We investigated the relationship between BBS10 gene expression in the ovary and lambing numbers using whole-genome sequencing data from 450 ewes. Our findings suggest that g.112314188C > G may lead to decreased litter size in ewes carrying the FecB gene. These SNPs could be potential molecular markers for breeding sheep.

MetaboLink: A web application for Streamlined Processing and Analysis of Large-Scale Untargeted Metabolomics Data.

The post-processing and analysis of large-scale untargeted metabolomics data face significant challenges due to the intricate nature of correction, filtration, imputation, and normalization steps. Manual execution across various applications often leads to inefficiencies, human-induced errors, and inconsistencies within the workflow. Addressing these issues, we introduce MetaboLink, a novel web application designed to process LC-MS metabolomics datasets combining established methodologies and offering flexibility and ease of implementation. It offers visualization options for data interpretation, an interface for statistical testing, and integration with PolySTest for further tests and with VSClust for clustering analysis. Fully functional tool is publicly available at https://computproteomics.bmb.sdu.dk/Metabolomics/. The source code is available at https://github.com/anitamnd/MetaboLink and a detailed description of the app can be found at https://github.com/anitamnd/MetaboLink/wiki. A tutorial video can be found at https://youtu.be/ZM6j10S6Z8Q. Supplementary data are available at Bioinformatics online.

Optimal transport for automatic alignment of untargeted metabolomic data.

Untargeted metabolomic profiling through liquid chromatography-mass spectrometry (LC-MS) measures a vast array of metabolites within biospecimens, advancing drug development, disease diagnosis, and risk prediction. However, the low throughput of LC-MS poses a major challenge for biomarker discovery, annotation, and experimental comparison, necessitating the merging of multiple datasets. Current data pooling methods encounter practical limitations due to their vulnerability to data variations and hyperparameter dependence. Here, we introduce GromovMatcher, a flexible and user-friendly algorithm that automatically combines LC-MS datasets using optimal transport. By capitalizing on feature intensity correlation structures, GromovMatcher delivers superior alignment accuracy and robustness compared to existing approaches. This algorithm scales to thousands of features requiring minimal hyperparameter tuning. Manually curated datasets for validating alignment algorithms are limited in the field of untargeted metabolomics, and hence we develop a dataset split procedure to generate pairs of validation datasets to test the alignments produced by GromovMatcher and other methods. Applying our method to experimental patient studies of liver and pancreatic cancer, we discover shared metabolic features related to patient alcohol intake, demonstrating how GromovMatcher facilitates the search for biomarkers associated with lifestyle risk factors linked to several cancer types.

Optimal transport for automatic alignment of untargeted metabolomic data

Untargeted metabolomic profiling through liquid chromatography-mass spectrometry (LC-MS) measures a vast array of metabolites within biospecimens, advancing drug development, disease diagnosis, and risk prediction. However, the low throughput of LC-MS poses a major challenge for biomarker discovery, annotation, and experimental comparison, necessitating the merging of multiple datasets. Current data pooling methods encounter practical limitations due to their vulnerability to data variations and hyperparameter dependence. Here, we introduce GromovMatcher, a flexible and user-friendly algorithm that automatically combines LC-MS datasets using optimal transport. By capitalizing on feature intensity correlation structures, GromovMatcher delivers superior alignment accuracy and robustness compared to existing approaches. This algorithm scales to thousands of features requiring minimal hyperparameter tuning. Manually curated datasets for validating alignment algorithms are limited in the field of untargeted metabolomics, and hence we develop a dataset split procedure to generate pairs of validation datasets to test the alignments produced by GromovMatcher and other methods. Applying our method to experimental patient studies of liver and pancreatic cancer, we discover shared metabolic features related to patient alcohol intake, demonstrating how GromovMatcher facilitates the search for biomarkers associated with lifestyle risk factors linked to several cancer types.

Metabolomics of IgE-Mediated Food Allergy and Oral Immunotherapy Outcomes based on Metabolomic Profiling.

The immunometabolic mechanisms underlying variable responses to oral immunotherapy (OIT) in patients with IgE-mediated food allergy are unknown. To identify novel pathways associated with tolerance in food allergy, we used metabolomic profiling to find pathways important for food allergy in multi-ethnic cohorts and responses to OIT. Untargeted plasma metabolomics data were generated from the VDAART healthy infant cohort (N=384), a Costa Rican cohort of children with asthma (N=1040), and a peanut OIT trial (N=20) evaluating sustained unresponsiveness (SU, protection that lasts after therapy) versus transient desensitization (TD, protection that ends immediately afterwards). Generalized linear regression modeling and pathway enrichment analysis identified metabolites associated with food allergy and OIT outcomes. Compared with unaffected children, those with food allergy were more likely to have metabolomic profiles with altered histidines and increased bile acids. Eicosanoids (e.g., arachidonic acid derivatives) (q=2.4×10 -20 ) and linoleic acid derivatives (q=3.8×10 -5 ) pathways decreased over time on OIT. Comparing SU versus TD revealed differing concentrations of bile acids (q=4.1×10 -8 ), eicosanoids (q=7.9×10 -7 ), and histidine pathways (q=0.015). In particular, the bile acid lithocholate (4.97[1.93,16.14], p=0.0027), the eicosanoid leukotriene B4 (3.21[1.38,8.38], p=0.01), and the histidine metabolite urocanic acid (22.13[3.98,194.67], p=0.0015) were higher in SU. We observed distinct profiles of bile acids, histidines, and eicosanoids that vary among patients with food allergy, over time on OIT and between SU and TD. Participants with SU had higher levels of metabolites such as lithocholate and urocanic acid, which have immunomodulatory roles in key T-cell subsets, suggesting potential mechanisms of tolerance in immunotherapy. - Compared with unaffected controls, children with food allergy demonstrated higher levels of bile acids and distinct histidine/urocanic acid profiles, suggesting a potential role of these metabolites in food allergy. - In participants receiving oral immunotherapy for food allergy, those who were able to maintain tolerance-even after stopping therapyhad lower overall levels of bile acid and histidine metabolites, with the exception of lithocholic acid and urocanic acid, two metabolites that have roles in T cell differentiation that may increase the likelihood of remission in immunotherapy. This is the first study of plasma metabolomic profiles of responses to OIT in individuals with IgE-mediated food allergy. Identification of immunomodulatory metabolites in allergic tolerance may help identify mechanisms of tolerance and guide future therapeutic development.

WED-525 Hepatocellular carcinoma risk associated with circulating metabolites of gut microbiome origin assessed by targeted mining of high-resolution untargeted metabolomics data

WED-525 Hepatocellular carcinoma risk associated with circulating metabolites of gut microbiome origin assessed by targeted mining of high-resolution untargeted metabolomics data

Offline Two-Dimensional Liquid Chromatography-Mass Spectrometry for Deep Annotation of the Fecal Metabolome Following Fecal Microbiota Transplantation.

Biological interpretation of untargeted LC-MS-based metabolomics data depends on accurate compound identification, but current techniques fall short of identifying most features that can be detected. The human fecal metabolome is complex, variable, incompletely annotated, and serves as an ideal matrix to evaluate novel compound identification methods. We devised an experimental strategy for compound annotation using multidimensional chromatography and semiautomated feature alignment and applied these methods to study the fecal metabolome in the context of fecal microbiota transplantation (FMT) for recurrent C.difficile infection. Pooled fecal samples were fractionated using semipreparative liquid chromatography and analyzed by an orthogonal LC-MS/MS method. The resulting spectra were searched against commercial, public, and local spectral libraries, and annotations were vetted using retention time alignment and prediction. Multidimensional chromatography yielded more than a 2-fold improvement in identified compounds compared to conventional LC-MS/MS and successfully identified several rare and previously unreported compounds, including novel fatty-acid conjugated bile acid species. Using an automated software-based feature alignment strategy, most metabolites identified by the new approach could be matched to features that were detected but not identified in single-dimensional LC-MS/MS data. Overall, our approach represents a powerful strategy to enhance compound identification and biological insight from untargeted metabolomics data.

NP3 MS Workflow: An Open-Source Software System to Empower Natural Product-Based Drug Discovery Using Untargeted Metabolomics.

Natural products (or specialized metabolites) are historically the main source of new drugs. However, the current drug discovery pipelines require miniaturization and speeds that are incompatible with traditional natural product research methods, especially in the early stages of the research. This article introduces the NP3 MS Workflow, a robust open-source software system for liquid chromatography-tandem mass spectrometry (LC-MS/MS) untargeted metabolomic data processing and analysis, designed to rank bioactive natural products directly from complex mixtures of compounds, such as bioactive biota samples. NP3 MS Workflow allows minimal user intervention as well as customization of each step of LC-MS/MS data processing, with diagnostic statistics to allow interpretation and optimization of LC-MS/MS data processing by the user. NP3 MS Workflow adds improved computing of the MS2 spectra in an LC-MS/MS data set and provides tools for automatic [M + H]+ ion deconvolution using fragmentation rules; chemical structural annotation against MS2 databases; and relative quantification of the precursor ions for bioactivity correlation scoring. The software will be presented with case studies and comparisons with equivalent tools currently available. NP3 MS Workflow shows a robust and useful approach to select bioactive natural products from complex mixtures, improving the set of tools available for untargeted metabolomics. It can be easily integrated into natural product-based drug-discovery pipelines and to other fields of research at the interface of chemistry and biology.

Comparison of maternal venous blood metabolomics collected as dried blood spots, dried blood microsamplers, and plasma for integrative environmental health research

Use of capillary blood devices for exposome research can deepen our understanding of the intricate relationship between environment and health, and open up new avenues for preventive and personalized medicine, particularly for vulnerable populations. While the potential of these whole blood devices to accurately measure chemicals and metabolites has been demonstrated, how untargeted metabolomics data from these samplers can be integrated with previous and ongoing environmental health studies that have used conventional blood collection approaches is not yet clear. Therefore, we performed a comprehensive comparison between relative-quantitative metabolite profiles measured in venous blood collected with dried whole blood microsamplers (DBM), dried whole blood spots (DBS), and plasma from 54 mothers in an ethnically diverse population. We determined that a majority of the 309 chemicals and metabolites showed similar median intensity rank, moderate correlation, and moderate agreement between participant-quantiled intraclass correlation coefficients (ICCs) for pair-wise comparisons among the three biomatrices. In particular, whole blood sample types, DBM and DBS, were in highest agreement across metabolite comparison metrics, followed by metabolites measured in DBM and plasma, and then metabolites measured in DBS and plasma. We provide descriptive characteristics and measurement summaries as a reference database. This includes unique metabolites that were particularly concordant or discordant in pairwise comparisons. Our results demonstrate that the range of metabolites from untargeted metabolomics data collected with DBM, DBS, and plasma provides biologically relevant information for use in independent exposome investigations. However, before meta-analysis with combined datasets are performed, robust statistical approaches that integrate untargeted metabolomics data collected on different blood matrices need to be developed.

Multiomics analysis to explore blood metabolite biomarkers in an Alzheimer’s Disease Neuroimaging Initiative cohort

Alzheimer's disease (AD) is a neurodegenerative disease that commonly causes dementia. Identifying biomarkers for the early detection of AD is an emerging need, as brain dysfunction begins two decades before the onset of clinical symptoms. To this end, we reanalyzed untargeted metabolomic mass spectrometry data from 905 patients enrolled in the AD Neuroimaging Initiative (ADNI) cohort using MS-DIAL, with 1,304,633 spectra of 39,108 unique biomolecules. Metabolic profiles of 93 hydrophilic metabolites were determined. Additionally, we integrated targeted lipidomic data (4873 samples from 1524 patients) to explore candidate biomarkers for predicting progressive mild cognitive impairment (pMCI) in patients diagnosed with AD within two years using the baseline metabolome. Patients with lower ergothioneine levels had a 12% higher rate of AD progression with the significance of P = 0.012 (Wald test). Furthermore, an increase in ganglioside (GM3) and decrease in plasmalogen lipids, many of which are associated with apolipoprotein E polymorphism, were confirmed in AD patients, and the higher levels of lysophosphatidylcholine (18:1) and GM3 d18:1/20:0 showed 19% and 17% higher rates of AD progression, respectively (Wald test: P = 3.9 × 10–8 and 4.3 × 10–7). Palmitoleamide, oleamide, diacylglycerols, and ether lipids were also identified as significantly altered metabolites at baseline in patients with pMCI. The integrated analysis of metabolites and genomics data showed that combining information on metabolites and genotypes enhances the predictive performance of AD progression, suggesting that metabolomics is essential to complement genomic data. In conclusion, the reanalysis of multiomics data provides new insights to detect early development of AD pathology and to partially understand metabolic changes in age-related onset of AD.

The association of infant urinary adrenal steroids with the risk of childhood asthma development

BackgroundAdrenal steroids play important roles in early-life development. However, our understanding of the effects of perinatal adrenal steroids on the development of childhood asthma is incomplete. ObjectiveTo evaluate the associations between early-life adrenal steroid levels and childhood asthma. MethodsParticipants included the Infant Susceptibility to Pulmonary Infections and Asthma following Respiratory Syncytial Virus Exposure birth cohort children with untargeted urinary metabolomics data measured during early infancy (n = 264) and nasal immune mediator data measured concurrently at age 2 to 6 months (n = 76). A total of 11 adrenal steroid compounds were identified using untargeted metabolomics and 6 asthma-relevant nasal immune mediators from multiplex assays were a priori selected. Current asthma at ages 5 and 6 years was ascertained using validated questionnaires. Associations were tested using logistic and linear regression with confounders adjustment. ResultsPregnenetriol disulfate (adjusted odds ratio [aOR] = 0.20, 95% CI = 0.06-0.68) and 3a,21-dihydroxy-5b-pregnane-11,20-dione-21-glucuronide (aOR = 0.34, 95% CI = 0.14-0.75) were inversely associated with childhood asthma at 5 and 6 years after multiple testing adjustment. There was a significant interaction effect of pregnanediol-3-glucuronide by biological sex assigned at birth (aOR = 0.11, 95% CI = 0.02-0.51, for those with female sex) on childhood asthma. Pregnenetriol disulfate was inversely associated with granulocyte-macrophage colony-stimulating factor (β = −0.45, q-value = 0.05). 3a,21-dihydroxy-5b-pregnane-11,20-dione 21-glucuronide was inversely associated with interleukin [IL]-4 (β = −0.29, q-value = 0.02), IL-5 (β = −0.35, q-value = 0.006), IL-13 (β = −0.26, q-value = 0.02), granulocyte-macrophage colony-stimulating factor (β = −0.35, q-value = 0.006), and fibroblast growth factor-β (β = −0.24, q-value = 0.01) after multiple testing adjustment. ConclusionThe inverse association between adrenal steroids downstream of progesterone and 17-hydroxypregnenolone and the odds of childhood asthma and nasopharyngeal type 2 immune biomarkers suggest that increased early-life adrenal steroids may suppress type 2 inflammation and protect against the development of childhood asthma.

Bayesian functional analysis for untargeted metabolomics data with matching uncertainty and small sample sizes.

Untargeted metabolomics based on liquid chromatography-mass spectrometry technology is quickly gaining widespread application, given its ability to depict the global metabolic pattern in biological samples. However, the data are noisy and plagued by the lack of clear identity of data features measured from samples. Multiple potential matchings exist between data features and known metabolites, while the truth can only be one-to-one matches. Some existing methods attempt to reduce the matching uncertainty, but are far from being able to remove the uncertainty for most features. The existence of the uncertainty causes major difficulty in downstream functional analysis. To address these issues, we develop a novel approach for Bayesian Analysis of Untargeted Metabolomics data (BAUM) to integrate previously separate tasks into a single framework, including matching uncertainty inference, metabolite selection and functional analysis. By incorporating the knowledge graph between variables and using relatively simple assumptions, BAUM can analyze datasets with small sample sizes. By allowing different confidence levels of feature-metabolite matching, the method is applicable to datasets in which feature identities are partially known. Simulation studies demonstrate that, compared with other existing methods, BAUM achieves better accuracy in selecting important metabolites that tend to be functionally consistent and assigning confidence scores to feature-metabolite matches. We analyze a COVID-19 metabolomics dataset and a mouse brain metabolomics dataset using BAUM. Even with a very small sample size of 16 mice per group, BAUM is robust and stable. It finds pathways that conform to existing knowledge, as well as novel pathways that are biologically plausible.

Meta-metabolomic responses of river biofilms to cobalt exposure and use of dose-response model trends as an indicator of effects

The response of the meta-metabolome is rarely used to characterize the effects of contaminants on a whole community. Here, the meta-metabolomic fingerprints of biofilms were examined after 1, 3 and 7 days of exposure to five concentrations of cobalt (from background concentration to 1 × 10−5 M) in aquatic microcosms. The untargeted metabolomic data were processed using the DRomics tool to build dose-response models and to calculate benchmark-doses. This approach made it possible to use 100% of the chemical signal instead of being limited to the very few annotated metabolites (7%). These benchmark-doses were further aggregated into an empirical cumulative density function. A trend analysis of the untargeted meta-metabolomic feature dose-response curves after 7 days of exposure suggested the presence of a concentration range inducing defense responses between 1.7 × 10−9 and 2.7 × 10−6 M, and of a concentration range inducing damage responses from 2.7 × 10−6 M and above. This distinction was in good agreement with changes in the other biological parameters studied (biomass and chlorophyll content). This study demonstrated that the molecular defense and damage responses can be related to contaminant concentrations and represents a promising approach for environmental risk assessment of metals.

Urinary metabolic characterization of advanced tuberculous meningitis cases in a South African paediatric population.

Tuberculous meningitis (TBM) is a severe form of tuberculosis with high neuro-morbidity and mortality, especially among the paediatric population (aged ≤12years). Little is known of the associated metabolic changes. This study aimed to identify characteristic metabolic markers that differentiate severe cases of paediatric TBM from controls, through non-invasive urine collection. Urine samples selected for this study were from two paediatric groups. Group 1: controls (n = 44): children without meningitis, no neurological symptoms and from the same geographical region as group 2. Group 2: TBM cases (n = 13): collected from paediatric patients that were admitted to Tygerberg Hospital in South Africa on the suspicion of TBM, mostly severely ill; with a later confirmation of TBM. Untargeted 1H NMR-based metabolomics data of urine were generated, followed by statistical analyses via MetaboAnalyst (v5.0), and the identification of important metabolites. Twenty nine urinary metabolites were identified as characteristic of advanced TBM and categorized in terms of six dysregulated metabolic pathways: 1) upregulated tryptophan catabolism linked to an altered vitamin B metabolism; 2) perturbation of amino acid metabolism; 3) increased energy production-metabolic burst; 4) disrupted gut microbiota metabolism; 5) ketoacidosis; 6) increased nitrogen excretion. We also provide original biological insights into this biosignature of urinary metabolites that can be used to characterize paediatric TBM patients in a South African cohort.