MetabNet: An R Package for Metabolic Association Analysis of High-Resolution Metabolomics Data

Gastric Cancer and Intestinal Metaplasia: Differential Metabolic Landscapes and New Pathways to Diagnosis

Gastric cancer (GC) is the fifth most common cause of cancer-related death worldwide. Early detection is crucial for improving survival rates and treatment outcomes. However, accurate GC-specific biomarkers remain unknown. This study aimed to identify the metabolic differences between intestinal metaplasia (IM) and GC to determine the pathways involved in GC. A metabolic analysis of IM and tissue samples from 37 patients with GC was conducted using ultra-performance liquid chromatography with tandem mass spectrometry. Overall, 665 and 278 significant features were identified in the aqueous and 278 organic phases, respectively, using false discovery rate analysis, which controls the expected proportion of false positives among the significant results. sPLS-DA revealed a clear separation between IM and GC samples. Steroid hormone biosynthesis, tryptophan metabolism, purine metabolism, and arginine and proline metabolism were the most significantly altered pathways. The intensity of 11 metabolites, including N1, N2-diacetylspermine, creatine riboside, and N-formylkynurenine, showed significant elevation in more advanced GC. Based on pathway enrichment analysis and cancer stage-specific alterations, we identified six potential candidates as diagnostic biomarkers: aldosterone, N-formylkynurenine, guanosine triphosphate, arginine, S-adenosylmethioninamine, and creatine riboside. These metabolic differences between IM and GC provide valuable insights into gastric carcinogenesis. Further validation is needed to develop noninvasive diagnostic tools and targeted therapies to improve the outcomes of patients with GC.

Trypanosoma brucei: Metabolomics for analysis of cellular metabolism and drug discovery

BACKGROUND

Trypanosoma brucei is the causative agent of Human African Trypanosomiasis (also known as sleeping sickness), a disease causing serious neurological disorders and fatal if left untreated. Due to its lethal pathogenicity, a variety of treatments have been developed over the years, but which have some important limitations such as acute toxicity and parasite resistance. Metabolomics is an innovative tool used to better understand the parasite's cellular metabolism, and identify new potential targets, modes of action and resistance mechanisms. The metabolomic approach is mainly associated with robust analytical techniques, such as NMR and Mass Spectrometry. Applying these tools to the trypanosome parasite is, thus, useful for providing new insights into the sleeping sickness pathology and guidance towards innovative treatments.

AIM OF REVIEW

The present review aims to comprehensively describe the T. brucei biology and identify targets for new or commercialized antitrypanosomal drugs. Recent metabolomic applications to provide a deeper knowledge about the mechanisms of action of drugs or potential drugs against T. brucei are highlighted. Additionally, the advantages of metabolomics, alone or combined with other methods, are discussed.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Compared to other parasites, only few studies employing metabolomics have to date been reported on Trypanosoma brucei. Published metabolic studies, treatments and modes of action are discussed. The main interest is to evaluate the metabolomics contribution to the understanding of T. brucei's metabolism.

Overview of Metabolomic Analysis and the Integration with Multi-Omics for Economic Traits in Cattle

Metabolomics has been applied to measure the dynamic metabolic responses, to understand the systematic biological networks, to reveal the potential genetic architecture, etc., for human diseases and livestock traits. For example, the current published results include the detected relevant candidate metabolites, identified metabolic pathways, potential systematic networks, etc., for different cattle traits that can be applied for further metabolomic and integrated omics studies. Therefore, summarizing the applications of metabolomics for economic traits is required in cattle. We here provide a comprehensive review about metabolomic analysis and its integration with other omics in five aspects: (1) characterization of the metabolomic profile of cattle; (2) metabolomic applications in cattle; (3) integrated metabolomic analysis with other omics; (4) methods and tools in metabolomic analysis; and (5) further potentialities. The review aims to investigate the existing metabolomic studies by highlighting the results in cattle, integrated with other omics studies, to understand the metabolic mechanisms underlying the economic traits and to provide useful information for further research and practical breeding programs in cattle.

Intra-Ramanome Correlation Analysis Unveils Metabolite Conversion Network from an Isogenic Population of Cells

To reveal the dynamic features of cellular systems, such as the correlation among phenotypes, a time or condition series set of samples is typically required. Here, we propose intra-ramanome correlation analysis (IRCA) to achieve this goal from just one snapshot of an isogenic population, via pairwise correlation among the cells of the thousands of Raman peaks in single-cell Raman spectra (SCRS), i.e., by taking advantage of the intrinsic metabolic heterogeneity among individual cells. For example, IRCA of Chlamydomonas reinhardtii under nitrogen depletion revealed metabolite conversions at each time point plus their temporal dynamics, such as protein-to-starch conversion followed by starch-to-triacylglycerol (TAG) conversion, and conversion of membrane lipids to TAG. Such among-cell correlations in SCRS vanished when the starch-biosynthesis pathway was knocked out yet were fully restored by genetic complementation. Extension of IRCA to 64 microalgal, fungal, and bacterial ramanomes suggests the IRCA-derived metabolite conversion network as an intrinsic metabolic signature of isogenic cellular population that is reliable, species-resolved, and state-sensitive. The high-throughput, low cost, excellent scalability, and general extendibility of IRCA suggest its broad applications.

Plasma high-resolution metabolomics identifies linoleic acid and linked metabolic pathways associated with bone mineral density

BACKGROUND & AIMS

There is a considerable degree of variation in bone mineral density (BMD) within populations. Use of plasma metabolomics may provide insight into established and novel determinants of BMD variance, such as nutrition and gut microbiome composition, to inform future prevention and treatment strategies for loss of BMD. Using high-resolution metabolomics (HRM), we examined low-molecular weight plasma metabolites and nutrition-related metabolic pathways associated with BMD.

METHODS

This cross-sectional study included 179 adults (mean age 49.5 ± 10.3 yr, 64% female). Fasting plasma was analyzed using ultra-high-resolution mass spectrometry with liquid chromatography. Whole body and spine BMD were assessed by dual energy X-ray absorptiometry and expressed as BMD (g/cm2) or Z-scores. Multiple linear regression, pathway enrichment, and module analyses were used to determine key plasma metabolic features associated with bone density.

RESULTS

Of 10,210 total detected metabolic features, whole body BMD Z-score was associated with 710 metabolites, which were significantly enriched in seven metabolic pathways, including linoleic acid, fatty acid activation and biosynthesis, and glycerophospholipid metabolism. Spine BMD was associated with 970 metabolites, significantly enriched in pro-inflammatory pathways involved in prostaglandin formation and linoleic acid metabolism. In module analyses, tryptophan- and polyamine-derived metabolites formed a network that was significantly associated with spine BMD, supporting a link with the gut microbiome.

CONCLUSIONS

Plasma HRM provides comprehensive information relevant to nutrition and components of the microbiome that influence bone health. This data supports pro-inflammatory fatty acids and the gut microbiome as novel regulators of postnatal bone remodeling.

Omics Integration for Mitochondria Systems Biology

Significance: Elucidation of the central importance of mitophagy in homeostasis of cells and organisms emphasizes that mitochondrial functions extend far beyond short-term needs for energy production. In mitochondria systems biology, the mitochondrial genome, proteome, and metabolome operate as a functional network in coordination of cell activities. Organization occurs through subnetworks that are interconnected by membrane potential, transport activities, allosteric and cooperative interactions, redox signaling mechanisms, rheostatic control by post-translational modifications, and metal ion homeostasis. These subnetworks enable use of varied energy precursors, defense against environmental stressors, and macromolecular rewiring to titrate energy production, biosynthesis, and detoxification according to cell-specific needs. Rewiring mechanisms, termed mitochondrial reprogramming, enhance fitness to respond to metabolic resources and challenges from the environment. Maladaptive responses can cause cell death. Maladaptive rewiring can cause disease. In cancer, adaptive rewiring can interfere with effective treatment. Recent Advances: Many recent advances have been facilitated by the development of new omics tools, which create opportunities to use data-driven analysis of omics data to address these complex adaptive and maladaptive mechanisms of mitochondrial reprogramming in human disease. Critical Issues: Application of omics integration to model systems reveals a critical role for metal ion homeostasis broadly impacting mitochondrial reprogramming. Importantly, data show that trans-omics associations are more robust and biologically relevant than single omics associations. Future Directions: Application of omics integration to mitophagy research creates new opportunities to link the complex, interactive functions of mitochondrial form and function in mitochondria systems biology.

The metaRbolomics Toolbox in Bioconductor and beyond

Metabolomics aims to measure and characterise the complex composition of metabolites in a biological system. Metabolomics studies involve sophisticated analytical techniques such as mass spectrometry and nuclear magnetic resonance spectroscopy, and generate large amounts of high-dimensional and complex experimental data. Open source processing and analysis tools are of major interest in light of innovative, open and reproducible science. The scientific community has developed a wide range of open source software, providing freely available advanced processing and analysis approaches. The programming and statistics environment R has emerged as one of the most popular environments to process and analyse Metabolomics datasets. A major benefit of such an environment is the possibility of connecting different tools into more complex workflows. Combining reusable data processing R scripts with the experimental data thus allows for open, reproducible research. This review provides an extensive overview of existing packages in R for different steps in a typical computational metabolomics workflow, including data processing, biostatistics, metabolite annotation and identification, and biochemical network and pathway analysis. Multifunctional workflows, possible user interfaces and integration into workflow management systems are also reviewed. In total, this review summarises more than two hundred metabolomics specific packages primarily available on CRAN, Bioconductor and GitHub.

Advances and challenges in development of precision psychiatry through clinical metabolomics on mood and psychotic disorders

Metabolomics is defined as the study of the global metabolite profile in a system under a given set of conditions. The objective of this review is to comprehensively assess the literature on metabolomics in mood disorders and schizophrenia and provide data for mental health researchers about the challenges and potentials of metabolomics. The majority of studies in metabolomics in Psychiatry uses peripheral blood or urine. The most widely used analytical techniques in metabolomics research are nuclear magnetic resonance (NMR) and mass spectrometry (MS). They are multiparametric and provide extensive structural and conformational information on multiple chemical classes. NMR is useful in untargeted analysis, which focuses on biosignatures or 'metabolic fingerprints' of illnesses. MS targeted metabolomics approach focuses on the identification and quantification of selected metabolites known to be involved in a particular metabolic pathway. The available studies of metabolomics in Schizophrenia, Bipolar Disorder and Major Depressive Disorder suggest a potential in investigating metabolic pathways involved in these diseases' pathophysiology and response to treatment, as well as its potential in biomarkers identification.

Metabolomics of childhood exposure to perfluoroalkyl substances: a cross-sectional study

INTRODUCTION

Exposure to perfluoroalkyl substances (PFAS), synthetic and persistent chemicals used in commercial and industrial processes, are associated with cardiometabolic dysfunction and related risk factors including reduced birth weight, excess adiposity, and dyslipidemia. Identifying the metabolic changes induced by PFAS exposure could enhance our understanding of biological pathways underlying PFAS toxicity.

OBJECTIVE

To identify metabolic alterations associated with serum concentrations of four PFAS in children using a metabolome-wide association study.

METHODS

We performed untargeted metabolomic profiling by liquid chromatography with ultra-high-resolution mass spectrometry, and separately quantified serum concentrations of perfluorooctanoic acid, perfluorooctanesulfonic acid, perfluorononanoic acid, and perfluorohexanesulfonic acid (PFHxS) for 114 8-year old children from Cincinnati, OH. We evaluated associations between each serum PFAS concentration and 16,097 metabolic features using linear regression adjusted for child age, sex, and race with a false discovery rate < 20%. We annotated PFAS-associated metabolites and conducted pathway enrichment analyses.

RESULTS

Serum PFAS concentrations were associated with metabolic features annotated primarily as lipids and dietary factors. Biological pathways associated with all four PFAS included arginine, proline, aspartate, asparagine, and butanoate metabolism.

CONCLUSIONS

In this cross-sectional study, childhood serum PFAS concentrations were correlated with metabolic pathways related to energy production and catabolism. Future studies should determine whether these pathways mediate associations between PFAS exposure and childhood cardiometabolic health.

Metabolomic assessment of exposure to near-highway ultrafine particles

Exposure to traffic-related air pollutants has been associated with increased risk of adverse cardiopulmonary outcomes and mortality; however, the biochemical pathways linking exposure to disease are not known. To delineate biological response mechanisms associated with exposure to near-highway ultrafine particles (UFP), we used untargeted high-resolution metabolomics to profile plasma from 59 participants enrolled in the Community Assessment of Freeway Exposure and Health (CAFEH) study. Metabolic variations associated with UFP exposure were assessed using a cross-sectional study design based upon low (mean 16,000 particles/cm3) and high (mean 24,000 particles/cm3) annual average UFP exposures. In comparing quantified metabolites, we identified five metabolites that were differentially expressed between low and high exposures, including arginine, aspartic acid, glutamine, cystine and methionine sulfoxide. Analysis of the metabolome identified 316 m/z features associated with UFP, which were consistent with increased lipid peroxidation, endogenous inhibitors of nitric oxide and vehicle exhaust exposure biomarkers. Network correlation analysis and metabolic pathway enrichment identified 38 pathways and included variations related to inflammation, endothelial function and mitochondrial bioenergetics. Taken together, these results suggest UFP exposure is associated with a complex series of metabolic variations related to antioxidant pathways, in vivo generation of reactive oxygen species and processes critical to endothelial function.

Multigenerational metabolic profiling in the Michigan PBB registry

Although polychlorinated biphenyls and polybrominated biphenyls are no longer manufactured the United States, biomonitoring in human populations show that exposure to these pollutants persist in human tissues. The objective of this study was to identify metabolic variations associated with exposure to 2,2'4,4',5,5'-hexabromobiphenyl (PBB-153) and 2,2'4,4',5,5'-hexachlorobiphenyl (PCB-153) in two generations of participants enrolled in the Michigan PBB Registry (http://pbbregistry.emory.edu/). Untargeted, high-resolution metabolomic profiling of plasma collected from 156 individuals was completed using liquid chromatography with high-resolution mass spectrometry. PBB-153 and PCB-153 levels were measured in the same individuals using targeted gas chromatography-tandem mass spectrometry and tested for dose-dependent correlation with the metabolome. Biological response to these exposures were evaluated using identified endogenous metabolites and pathway enrichment. When compared to lipid-adjusted concentrations for adults in the National Health and Nutrition Examination Survey (NHANES) for years 2003-2004, PCB-153 levels were consistent with similarly aged individuals, whereas PBB-153 concentrations were elevated (p<0.0001) in participants enrolled in the Michigan PBB Registry. Metabolic alterations were correlated with PBB-153 and PCB-153 in both generations of participants, and included changes in pathways related to catecholamine metabolism, cellular respiration, essential fatty acids, lipids and polyamine metabolism. These pathways were consistent with pathophysiological changes observed in neurodegenerative disease and included previously identified metabolomic markers of Parkinson's disease. To determine if the metabolic alterations detected in this study are replicated other cohorts, we evaluated correlation of PBB-153 and PCB-153 with plasma fatty acids measured in NHANES. Both pollutants showed similar associations with fatty acids previously linked to PCB exposure. Thus, the results from this study show metabolic alterations correlated with PBB-153 and PCB-153 exposure can be detected in human populations and are consistent with health outcomes previously reported in epidemiological and mechanistic studies.

The metabolome: A key measure for exposome research in epidemiology

PURPOSE OF REVIEW

Application of omics to study human health has created a new era of opportunities for epidemiology research. However, approaches to characterize exogenous health triggers have largely not leveraged advances in analytical platforms and big data. In this review, we highlight the exposome, which is defined as the cumulative measure of exposure and biological responses across a lifetime as a cornerstone for new epidemiology approaches to study complex and preventable human diseases.

RECENT FINDINGS

While no universal approach exists to measure the entirety of the exposome, use of high-resolution mass spectrometry methods provide distinct advantages over traditional biomonitoring and have provided key advances necessary for exposome research. Application to different study designs and recommendations for combining exposome data with novel data analytic frameworks to study complex interactions of multiple stressors are also discussed.

SUMMARY

Even though challenges still need to be addressed, advances in methods to characterize the exposome provide exciting new opportunities for epidemiology to support fundamental discoveries to improve public health.

Metabolome-wide association study of anti-epileptic drug treatment during pregnancy

Pregnant women with epilepsy (PWWE) require continuous anti-epileptic drug (AED) treatment to avoid risk to themselves and fetal risks secondary to maternal seizures, resulting in prolonged AED exposure to the developing embryo and fetus. The objectives of this study were to determine whether high-resolution metabolomics is able to link the metabolite profile of PWWE receiving lamotrigine or levetiracetam for seizure control to associated pharmacodynamic (PD) biological responses. Untargeted metabolomic analysis of plasma obtained from 82 PWWE was completed using high-resolution mass spectrometry. Biological alterations due to lamotrigine or levetiracetam monotherapy were determined by a metabolome-wide association study that compared patients taking either drug to those who did not require AED treatment. Metabolic changes associated with AED use were then evaluated by testing for drug-dose associated metabolic variations and pathway enrichment. AED therapy resulted in drug-associated metabolic profiles recognizable within maternal plasma. Both the parent compounds and major metabolites were detected, and each AED was correlated with other metabolic features and pathways. Changes in metabolites and metabolic pathways important to maternal health and linked to fetal neurodevelopment were detected for both drugs, including changes in one‑carbon metabolism, neurotransmitter biosynthesis and steroid metabolism. In addition, decreased levels of 5-methyltetrahydrofolate and tetrahydrofolate were detected in women taking lamotrigine, which is consistent with recent findings showing increased risk of autism spectrum disorder traits in PWWE using AED. These results represent a first step in development of pharmacometabolomic framework with potential to detect adverse AED-related metabolic changes during pregnancy.

Grape and Wine Metabolomics to Develop New Insights Using Untargeted and Targeted Approaches

Chemical analysis of grape juice and wine has been performed for over 50 years in a targeted manner to determine a limited number of compounds using Gas Chromatography, Mass-Spectrometry (GC-MS) and High Pressure Liquid Chromatography (HPLC). Therefore, it only allowed the determination of metabolites that are present in high concentration, including major sugars, amino acids and some important carboxylic acids. Thus, the roles of many significant but less concentrated metabolites during wine making process are still not known. This is where metabolomics shows its enormous potential, mainly because of its capability in analyzing over 1000 metabolites in a single run due to the recent advancements of high resolution and sensitive analytical instruments. Metabolomics has predominantly been adopted by many wine scientists as a hypothesis-generating tool in an unbiased and non-targeted way to address various issues, including characterization of geographical origin (terroir) and wine yeast metabolic traits, determination of biomarkers for aroma compounds, and the monitoring of growth developments of grape vines and grapes. The aim of this review is to explore the published literature that made use of both targeted and untargeted metabolomics to study grapes and wines and also the fermentation process. In addition, insights are also provided into many other possible avenues where metabolomics shows tremendous potential as a question-driven approach in grape and wine research.

Challenges and emergent solutions for LC-MS/MS based untargeted metabolomics in diseases

In the past decade, advances in liquid chromatography-mass spectrometry (LC-MS) have revolutionized untargeted metabolomics analyses. By mining metabolomes more deeply, researchers are now primed to uncover key metabolites and their associations with diseases. The employment of untargeted metabolomics has led to new biomarker discoveries and a better mechanistic understanding of diseases with applications in precision medicine. However, many major pertinent challenges remain. First, compound identification has been poor, and left an overwhelming number of unidentified peaks. Second, partial, incomplete metabolomes persist due to factors such as limitations in mass spectrometry data acquisition speeds, wide-range of metabolites concentrations, and cellular/tissue/temporal-specific expression changes that confound our understanding of metabolite perturbations. Third, to contextualize metabolites in pathways and biology is difficult because many metabolites partake in multiple pathways, have yet to be described species specificity, or possess unannotated or more-complex functions that are not easily characterized through metabolomics analyses. From a translational perspective, information related to novel metabolite biomarkers, metabolic pathways, and drug targets might be sparser than they should be. Thankfully, significant progress has been made and novel solutions are emerging, achieved through sustained academic and industrial community efforts in terms of hardware, computational, and experimental approaches. Given the rapidly growing utility of metabolomics, this review will offer new perspectives, increase awareness of the major challenges in LC-MS metabolomics that will significantly benefit the metabolomics community and also the broader the biomedical community metabolomics aspire to serve.

Putrescine as indicator of manganese neurotoxicity: Dose-response study in human SH-SY5Y cells

Disrupted polyamine metabolism with elevated putrescine is associated with neuronal dysfunction. Manganese (Mn) is an essential nutrient that causes neurotoxicity in excess, but methods to evaluate biochemical responses to high Mn are limited. No information is available on dose-response effects of Mn on putrescine abundance and related polyamine metabolism. The present research was to test the hypothesis that Mn causes putrescine accumulation over a physiologically adequate to toxic concentration range in a neuronal cell line. We used human SH-SY5Y neuroblastoma cells treated with MnCl2 under conditions that resulted in cell death or no cell death after 48 h. Putrescine and other metabolites were analyzed by liquid chromatography-ultra high-resolution mass spectrometry. Putrescine-related pathway changes were identified with metabolome-wide association study (MWAS). Results show that Mn caused a dose-dependent increase in putrescine over a non-toxic to toxic concentration range. MWAS of putrescine showed positive correlations with the polyamine metabolite N8-acetylspermidine, methionine-related precursors, and arginine-associated urea cycle metabolites, while putrescine was negatively correlated with γ-aminobutyric acid (GABA)-related and succinate-related metabolites (P < 0.001, FDR < 0.01). These data suggest that measurement of putrescine and correlated metabolites may be useful to study effects of Mn intake in the high adequate to UL range.

Selenium Supplementation Alters Hepatic Energy and Fatty Acid Metabolism in Mice

Background

Human and animal studies have raised concerns that supplemental selenium can increase the risk of metabolic disorders, but underlying mechanisms are unclear.

Objective

We used an integrated transcriptome and metabolome analysis of liver to test for functional pathway and network responses to supplemental selenium in mice.

Methods

Male mice (8-wk-old, C57BL/6J) fed a standard diet (0.41 ppm Se) were given selenium (Na2SeO4, 20 μmol/L) or vehicle (drinking water) for 16 wk. Livers were analyzed for selenium concentration, activity of selenoproteins, reduced glutathione (GSH) redox state, gene expression, and high-resolution metabolomics. Transcriptomic and nontargeted metabolomic data were analyzed with biostatistics, bioinformatics, pathway enrichment analysis, and combined transcriptome-metabolome-wide association study (TMWAS).

Results

Mice supplemented with selenium had greater body mass gain from baseline to 16 wk (55% ± 5%) compared with controls (40% ± 3%) (P < 0.05); however, no difference was observed in liver selenium content, selenoenzyme transcripts, or enzyme activity. Selenium was higher in the heart, kidney, and urine of mice supplemented with selenium. Gene enrichment analysis showed that supplemental selenium altered pathways of lipid and energy metabolism. Integrated transcriptome and metabolome network analysis showed 2 major gene-metabolite clusters, 1 centered on the transcript for the bidirectional glucose transporter 2 (Glut2) and the other centered on the transcripts for carnitine-palmitoyl transferase 2 (Cpt2) and acetyl-CoA acyltransferase (Acaa1). Pathway analysis showed that highly associated metabolites (P < 0.05) were enriched in fatty acid metabolism and bile acid biosynthesis, including acylcarnitines, triglycerides and glycerophospholipids, long-chain acyl-coenzyme As, phosphatidylcholines, and sterols. TMWAS of body weight gain confirmed changes in the same pathways.

Conclusions

Supplemental selenium in mice alters hepatic fatty acid and energy metabolism and causes increases in body mass. A lack of effect on hepatic selenium content suggests that signaling involves an extrahepatic mechanism.

Selenium supplementation prevents metabolic and transcriptomic responses to cadmium in mouse lung

BACKGROUND

The protective effect of selenium (Se) on cadmium (Cd) toxicity is well documented, but underlying mechanisms are unclear.

METHODS

Male mice fed standard diet were given Cd (CdCl₂, 18 μmol/L) in drinking water with or without Se (Na₂SeO_4, 20 μmol/L) for 16 weeks. Lungs were analyzed for Cd concentration, transcriptomics and metabolomics. Data were analyzed with biostatistics, bioinformatics, pathway enrichment analysis, and combined transcriptome-metabolome-wide association study.

RESULTS

Mice treated with Cd had higher lung Cd content (1.7 ± 0.4 pmol/mg protein) than control mice (0.8 ± 0.3 pmol/mg protein) or mice treated with Cd and Se (0.4 ± 0.1 pmol/mg protein). Gene set enrichment analysis of transcriptomics data showed that Se prevented Cd effects on inflammatory and myogenesis genes and diminished Cd effects on several other pathways. Similarly, Se prevented Cd-disrupted metabolic pathways in amino acid metabolism and urea cycle. Integrated transcriptome and metabolome network analysis showed that Cd treatment had a network structure with fewer gene-metabolite clusters compared to control. Centrality measurements showed that Se counteracted changes in a group of Cd-responsive genes including Zdhhc11, (protein-cysteine S-palmitoyltransferase), Ighg1 (immunoglobulin heavy constant gamma-1) and associated changes in metabolite concentrations.

CONCLUSION

Co-administration of Se with Cd prevented Cd increase in lung and prevented Cd-associated pathway and network responses of the transcriptome and metabolome. Se protection against Cd toxicity in lung involves complex systems responses.

GENERAL SIGNIFICANCE

Environmental Cd stimulates proinflammatory and profibrotic signaling. The present results indicate that dietary or supplemental Se could be useful to mitigate Cd toxicity.

High-Resolution Metabolomics Assessment of Military Personnel: Evaluating Analytical Strategies for Chemical Detection

OBJECTIVE

The aim of this study was to maximize detection of serum metabolites with high-resolution metabolomics (HRM).

METHODS

Department of Defense Serum Repository (DoDSR) samples were analyzed using ultrahigh resolution mass spectrometry with three complementary chromatographic phases and four ionization modes. Chemical coverage was evaluated by number of ions detected and accurate mass matches to a human metabolomics database.

RESULTS

Individual HRM platforms provided accurate mass matches for up to 58% of the KEGG metabolite database. Combining two analytical methods increased matches to 72% and included metabolites in most major human metabolic pathways and chemical classes. Detection and feature quality varied by analytical configuration.

CONCLUSIONS

Dual chromatography HRM with positive and negative electrospray ionization provides an effective generalized method for metabolic assessment of military personnel.

Metabolic Pathways and Networks Associated With Tobacco Use in Military Personnel

OBJECTIVE

The aim of this study is to use high-resolution metabolomics (HRM) to identify metabolic pathways and networks associated with tobacco use in military personnel.

METHODS

Four hundred deidentified samples obtained from the Department of Defense Serum Repository were classified as tobacco users or nonusers according to cotinine content. HRM and bioinformatic methods were used to determine pathways and networks associated with classification.

RESULTS

Eighty individuals were classified as tobacco users compared with 320 nonusers on the basis of cotinine levels at least 10 ng/mL. Alterations in lipid and xenobiotic metabolism, and diverse effects on amino acid, sialic acid, and purine and pyrimidine metabolism were observed. Importantly, network analysis showed broad effects on metabolic associations not simply linked to well-defined pathways.

CONCLUSIONS

Tobacco use has complex metabolic effects that must be considered in evaluation of deployment-associated environmental exposures in military personnel.

Pilot Metabolome-Wide Association Study of Benzo(a)pyrene in Serum From Military Personnel

OBJECTIVE

A pilot study was conducted to test the feasibility of using Department of Defense Serum Repository (DoDSR) samples to study health and exposure-related effects.

METHODS

Thirty unidentified human serum samples were obtained from the DoDSR and analyzed for normal serum metabolites with high-resolution mass spectrometry and serum levels of free benzo(a)pyrene (BaP) by gas chromatography-mass spectrometry. Metabolic associations with BaP were determined using a metabolome-wide association study (MWAS) and metabolic pathway enrichment.

RESULTS

The serum analysis detected normal ranges of glucose, selected amino acids, fatty acids, and creatinine. Free BaP was detected in a broad concentration range. MWAS of BaP showed associations with lipids, fatty acids, and sulfur amino acid metabolic pathways.

CONCLUSION

The results show that the DoDSR samples are of sufficient quality for chemical profiling of DoD personnel.

Sparse network modeling and metscape-based visualization methods for the analysis of large-scale metabolomics data

Motivation

Recent technological advances in mass spectrometry, development of richer mass spectral libraries and data processing tools have enabled large scale metabolic profiling. Biological interpretation of metabolomics studies heavily relies on knowledge-based tools that contain information about metabolic pathways. Incomplete coverage of different areas of metabolism and lack of information about non-canonical connections between metabolites limits the scope of applications of such tools. Furthermore, the presence of a large number of unknown features, which cannot be readily identified, but nonetheless can represent bona fide compounds, also considerably complicates biological interpretation of the data.

Results

Leveraging recent developments in the statistical analysis of high-dimensional data, we developed a new Debiased Sparse Partial Correlation algorithm (DSPC) for estimating partial correlation networks and implemented it as a Java-based CorrelationCalculator program. We also introduce a new version of our previously developed tool Metscape that enables building and visualization of correlation networks. We demonstrate the utility of these tools by constructing biologically relevant networks and in aiding identification of unknown compounds.

Availability and Implementation

http://metscape.med.umich.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Annotation: A Computational Solution for Streamlining Metabolomics Analysis

Metabolite identification is still considered an imposing bottleneck in liquid chromatography mass spectrometry (LC/MS) untargeted metabolomics. The identification workflow usually begins with detecting relevant LC/MS peaks via peak-picking algorithms and retrieving putative identities based on accurate mass searching. However, accurate mass search alone provides poor evidence for metabolite identification. For this reason, computational annotation is used to reveal the underlying metabolites monoisotopic masses, improving putative identification in addition to confirmation with tandem mass spectrometry. This review examines LC/MS data from a computational and analytical perspective, focusing on the occurrence of neutral losses and in-source fragments, to understand the challenges in computational annotation methodologies. Herein, we examine the state-of-the-art strategies for computational annotation including: (i) peak grouping or full scan (MS1) pseudo-spectra extraction, i.e., clustering all mass spectral signals stemming from each metabolite; (ii) annotation using ion adduction and mass distance among ion peaks; (iii) incorporation of biological knowledge such as biotransformations or pathways; (iv) tandem MS data; and (v) metabolite retention time calibration, usually achieved by prediction from molecular descriptors. Advantages and pitfalls of each of these strategies are discussed, as well as expected future trends in computational annotation.

Plasma metabolomics reveals membrane lipids, aspartate/asparagine and nucleotide metabolism pathway differences associated with chloroquine resistance in Plasmodium vivax malaria

Background

Chloroquine (CQ) is the main anti-schizontocidal drug used in the treatment of uncomplicated malaria caused by Plasmodium vivax. Chloroquine resistant P. vivax (PvCR) malaria in the Western Pacific region, Asia and in the Americas indicates a need for biomarkers of resistance to improve therapy and enhance understanding of the mechanisms associated with PvCR. In this study, we compared plasma metabolic profiles of P. vivax malaria patients with PvCR and chloroquine sensitive parasites before treatment to identify potential molecular markers of chloroquine resistance.

Methods

An untargeted high-resolution metabolomics analysis was performed on plasma samples collected in a malaria clinic in Manaus, Brazil. Male and female patients with Plasmodium vivax were included (n = 46); samples were collected before CQ treatment and followed for 28 days to determine PvCR, defined as the recurrence of parasitemia with detectable plasma concentrations of CQ ≥100 ng/dL. Differentially expressed metabolic features between CQ-Resistant (CQ-R) and CQ-Sensitive (CQ-S) patients were identified using partial least squares discriminant analysis and linear regression after adjusting for covariates and multiple testing correction. Pathway enrichment analysis was performed using Mummichog.

Results

Linear regression and PLS-DA methods yielded 69 discriminatory features between CQ-R and CQ-S groups, with 10-fold cross-validation classification accuracy of 89.6% using a SVM classifier. Pathway enrichment analysis showed significant enrichment (p<0.05) of glycerophospholipid metabolism, glycosphingolipid metabolism, aspartate and asparagine metabolism, purine and pyrimidine metabolism, and xenobiotics metabolism. Glycerophosphocholines levels were significantly lower in the CQ-R group as compared to CQ-S patients and also to independent control samples.

Conclusions

The results show differences in lipid, amino acids, and nucleotide metabolism pathways in the plasma of CQ-R versus CQ-S patients prior to antimalarial treatment. Metabolomics phenotyping of P. vivax samples from patients with well-defined clinical CQ-resistance is promising for the development of new tools to understand the biological process and to identify potential biomarkers of PvCR.

From chromatogram to analyte to metabolite. How to pick horses for courses from the massive web resources for mass spectral plant metabolomics

The grand challenge currently facing metabolomics is the expansion of the coverage of the metabolome from a minor percentage of the metabolic complement of the cell toward the level of coverage afforded by other post-genomic technologies such as transcriptomics and proteomics. In plants, this problem is exacerbated by the sheer diversity of chemicals that constitute the metabolome, with the number of metabolites in the plant kingdom generally considered to be in excess of 200 000. In this review, we focus on web resources that can be exploited in order to improve analyte and ultimately metabolite identification and quantification. There is a wide range of available software that not only aids in this but also in the related area of peak alignment; however, for the uninitiated, choosing which program to use is a daunting task. For this reason, we provide an overview of the pros and cons of the software as well as comments regarding the level of programing skills required to effectively exploit their basic functions. In addition, the torrent of available genome and transcriptome sequences that followed the advent of next-generation sequencing has opened up further valuable resources for metabolite identification. All things considered, we posit that only via a continued communal sharing of information such as that deposited in the databases described within the article are we likely to be able to make significant headway toward improving our coverage of the plant metabolome.

Metabolomics of fescue toxicosis in grazing beef steers

Fescue toxicosis (FT) results from consumption of tall fescue (Lolium arundinaceum) infected with an endophyte (Epichloë coenophiala) that produces ergot alkaloids (EA), which are considered key etiological agents of FT. Decreased weight gains, hormonal imbalance, circulating cholesterol disruption, and decreased volatile fatty acid absorption suggest toxic (E+) fescue-induced metabolic perturbations. Employing untargeted high-resolution metabolomics (HRM) to analyze E+ grazing-induced plasma and urine metabolome changes, fescue-naïve Angus steers were placed on E+ or non-toxic (Max-Q) fescue pastures and plasma and urine were sampled before, 1, 2, 14, and 28 days after pasture assignment. Plasma and urine catecholamines and urinary EA concentrations were also measured. In E+ steers, urinary EA appeared early and peaked at 14 days. 13,090 urinary and 20,908 plasma HRM features were detected; the most significant effects were observed earlier (2 days) in the urine and later (≥14 days) in the plasma. Alongside EA metabolite detection, tryptophan and lipid metabolism disruption were among the main consequences of E+ consumption. The E+ grazing-associated metabolic pathways and signatures described herein may accelerate development of novel early FT detection and treatment strategies.

Western diet induces colonic nitrergic myenteric neuropathy and dysmotility in mice via saturated fatty acid- and lipopolysaccharide-induced TLR4 signalling

KEY POINTS

A high-fat diet (60% kcal from fat) is associated with motility disorders inducing constipation and loss of nitrergic myenteric neurons in the proximal colon. Gut microbiota dysbiosis, which occurs in response to HFD, contributes to endotoxaemia. High levels of lipopolysaccharide lead to apoptosis in cultured myenteric neurons that express Toll-like receptor 4 (TLR4). Consumption of a Western diet (WD) (35% kcal from fat) for 6 weeks leads to gut microbiota dysbiosis associated with altered bacterial metabolites and increased levels of plasma free fatty acids. These disorders precede the nitrergic myenteric cell loss observed in the proximal colon. Mice lacking TLR4 did not exhibit WD-induced myenteric cell loss and dysmotility. Lipopolysaccharide-induced in vitro enteric neurodegeneration requires the presence of palmitate and may be a result of enhanced NO production. The present study highlights the critical role of plasma saturated free fatty acids that are abundant in the WD with respect to driving enteric neuropathy and colonic dysmotility.

ABSTRACT

The consumption of a high-fat diet (HFD) is associated with myenteric neurodegeneration, which in turn is associated with delayed colonic transit and constipation. We examined the hypothesis that an inherent increase in plasma free fatty acids (FFA) in the HFD together with an HFD-induced alteration in gut microbiota contributes to the pathophysiology of these disorders. C57BL/6 mice were fed a Western diet (WD) (35% kcal from fat enriched in palmitate) or a purified regular diet (16.9% kcal from fat) for 3, 6, 9 and 12 weeks. Gut microbiota dysbiosis was investigated by fecal lipopolysaccharide (LPS) measurement and metabolomics (linear trap quadrupole-Fourier transform mass spectrometer) analysis. Plasma FFA and LPS levels were assessed, in addition to colonic and ileal nitrergic myenteric neuron quantifications and motility. Compared to regular diet-fed control mice, WD-fed mice gained significantly more weight without blood glucose alteration. Dysbiosis was exhibited after 6 weeks of feeding, as reflected by increased fecal LPS and bacterial metabolites and concomitant higher plasma FFA. The numbers of nitrergic myenteric neurons were reduced in the proximal colon after 9 and 12 weeks of WD and this was also associated with delayed colonic transit. WD-fed Toll-like receptor 4 (TLR4)^-/- mice did not exhibit myenteric cell loss or dysmotility. Finally, LPS (0.5-2 ng·ml^-1 ) and palmitate (20 and 30 μm) acted synergistically to induce neuronal cell death in vitro, which was prevented by the nitric oxide synthase inhibitor NG-nitro-l-arginine methyl ester. In conclusion, WD-feeding results in increased levels of FFA and microbiota that, even in absence of hyperglycaemia or overt endotoxaemia, synergistically induce TLR4-mediated neurodegeneration and dysmotility.

xMSannotator: An R Package for Network-Based Annotation of High-Resolution Metabolomics Data

Improved analytical technologies and data extraction algorithms enable detection of >10 000 reproducible signals by liquid chromatography-high-resolution mass spectrometry, creating a bottleneck in chemical identification. In principle, measurement of more than one million chemicals would be possible if algorithms were available to facilitate utilization of the raw mass spectrometry data, especially low-abundance metabolites. Here we describe an automated computational framework to annotate ions for possible chemical identity using a multistage clustering algorithm in which metabolic pathway associations are used along with intensity profiles, retention time characteristics, mass defect, and isotope/adduct patterns. The algorithm uses high-resolution mass spectrometry data for a series of samples with common properties and publicly available chemical, metabolic, and environmental databases to assign confidence levels to annotation results. Evaluation results show that the algorithm achieves an F1-measure of 0.8 for a data set with known targets and is more robust than previously reported results for cases when database size is much greater than the actual number of metabolites. MS/MS evaluation of a set of randomly selected 210 metabolites annotated using xMSannotator in an untargeted metabolomics human data set shows that 80% of features with high or medium confidence scores have ion dissociation patterns consistent with the xMSannotator annotation. The algorithm has been incorporated into an R package, xMSannotator, which includes utilities for querying local or online databases such as ChemSpider, KEGG, HMDB, T3DB, and LipidMaps.

Computational Metabolomics: A Framework for the Million Metabolome

"Sola dosis facit venenum." These words of Paracelsus, "the dose makes the poison", can lead to a cavalier attitude concerning potential toxicities of the vast array of low abundance environmental chemicals to which humans are exposed. Exposome research teaches that 80-85% of human disease is linked to environmental exposures. The human exposome is estimated to include >400,000 environmental chemicals, most of which are uncharacterized with regard to human health. In fact, mass spectrometry measures >200,000 m/z features (ions) in microliter volumes derived from human samples; most are unidentified. This crystallizes a grand challenge for chemical research in toxicology: to develop reliable and affordable analytical methods to understand health impacts of the extensive human chemical experience. To this end, there appears to be no choice but to abandon the limitations of measuring one chemical at a time. The present review looks at progress in computational metabolomics to provide probability-based annotation linking ions to known chemicals and serve as a foundation for unambiguous designation of unidentified ions for toxicologic study. We review methods to characterize ions in terms of accurate mass m/z, chromatographic retention time, correlation of adduct, isotopic and fragment forms, association with metabolic pathways and measurement of collision-induced dissociation products, collision cross section, and chirality. Such information can support a largely unambiguous system for documenting unidentified ions in environmental surveillance and human biomonitoring. Assembly of this data would provide a resource to characterize and understand health risks of the array of low-abundance chemicals to which humans are exposed.

High-resolution metabolomics of occupational exposure to trichloroethylene

Background: Occupational exposure to trichloroethylene (TCE) has been linked to adverse health outcomes including non-Hodgkin’s lymphoma and kidney and liver cancer; however, TCE’s mode of action for development of these diseases in humans is not well understood.

Methods: Non-targeted metabolomics analysis of plasma obtained from 80 TCE-exposed workers [full shift exposure range of 0.4 to 230 parts-per-million of air (ppm_a)] and 95 matched controls were completed by ultra-high resolution mass spectrometry. Biological response to TCE exposure was determined using a metabolome-wide association study (MWAS) framework, with metabolic changes and plasma TCE metabolites evaluated by dose-response and pathway enrichment. Biological perturbations were then linked to immunological, renal and exposure molecular markers measured in the same population.

Results: Metabolic features associated with TCE exposure included known TCE metabolites, unidentifiable chlorinated compounds and endogenous metabolites. Exposure resulted in a systemic response in endogenous metabolism, including disruption in purine catabolism and decreases in sulphur amino acid and bile acid biosynthesis pathways. Metabolite associations with TCE exposure included uric acid (β = 0.13, P-value = 3.6 × 10⁻⁵), glutamine (β = 0.08, P-value = 0.0013), cystine (β = 0.75, P-value = 0.0022), methylthioadenosine (β = −1.6, P-value = 0.0043), taurine (β = −2.4, P-value = 0.0011) and chenodeoxycholic acid (β = −1.3, P-value = 0.0039), which are consistent with known toxic effects of TCE, including immunosuppression, hepatotoxicity and nephrotoxicity. Correlation with additional exposure markers and physiological endpoints supported known disease associations.

Conclusions: High-resolution metabolomics correlates measured occupational exposure to internal dose and metabolic response, providing insight into molecular mechanisms of exposure-related disease aetiology.

Deployment-Associated Exposure Surveillance With High-Resolution Metabolomics

OBJECTIVE

The aim of this study was to assess the suitability of high-resolution metabolomics (HRM) for measure of internal exposure and effect biomarkers from deployment-related environmental hazards.

METHODS

HRM provides extensive coverage of metabolism and data relevant to a broad spectrum of environmental exposures. This review briefly describes the analytic platform, workflow, and recent applications of HRM as a prototype environmental exposure surveillance system.

RESULTS

Building upon techniques available for contemporary occupational medicine and exposure sciences, HRM methods are able to integrate external exposures, internal body burden of environmental agents, and relevant biological responses with health outcomes.

CONCLUSIONS

Systematic analysis of existing Department of Defense Serum Repository samples will provide a high-quality, cross-sectional reference dataset for deployment-associated exposures while at the same time establishing a foundation for precision medicine.

Sequencing the exposome: A call to action

The exposome is a complement to the genome that includes non-genetic causes of disease. Multiple definitions are available, with salient points being global inclusion of exposures and behaviors, and cumulative integration of associated biologic responses. As such, the concept is both refreshingly simple and dauntingly complex. This article reviews high-resolution metabolomics (HRM) as an affordable approach to routinely analyze samples for a broad spectrum of environmental chemicals and biologic responses. HRM has been successfully used in multiple exposome research paradigms and is suitable to implement in a prototype universal exposure surveillance system. Development of such a structure for systematic monitoring of environmental exposures is an important step toward sequencing the exposome because it builds upon successes of exposure science, naturally connects external exposure to body burden and partitions the exposome into workable components. Practical results would be repositories of quantitative data on chemicals according to geography and biology. This would support new opportunities for environmental health analysis and predictive modeling. Complementary approaches to hasten development of exposome theory and associated biologic response networks could include experimental studies with model systems, analysis of archival samples from longitudinal studies with outcome data and study of relatively short-lived animals, such as household pets (dogs and cats) and non-human primates (common marmoset). International investment and cooperation to sequence the human exposome will advance scientific knowledge and also provide an important foundation to control adverse environmental exposures to sustain healthy living spaces and improve prediction and management of disease.