Predicting network activity from high throughput metabolomics

Unveiling metabolic remodeling in mucopolysaccharidosis type III through integrative metabolomics and pathway analysis

BACKGROUND

Metabolomics represent a valuable tool to recover biological information using body fluids and may help to characterize pathophysiological mechanisms of the studied disease. This approach has not been widely used to explore inherited metabolic diseases. This study investigates mucopolysaccharidosis type III (MPS III). A thorough and holistic understanding of metabolic remodeling in MPS III may allow the development, improvement and personalization of patient care.

METHODS

We applied both targeted and untargeted metabolomics to urine samples obtained from a French cohort of 49 patients, consisting of 13 MPS IIIA, 16 MPS IIIB, 13 MPS IIIC, and 7 MPS IIID, along with 66 controls. The analytical strategy is based on ultra-high-performance liquid chromatography combined with ion mobility and high-resolution mass spectrometry. Twenty-four amino acids have been assessed using tandem mass spectrometry combined with liquid chromatography. Multivariate data modeling has been used for discriminant metabolite selection. Pathway analysis has been performed to retrieve metabolic pathways impairments.

RESULTS

Data analysis revealed distinct biochemical profiles. These metabolic patterns, particularly those related to the amino acid metabolisms, allowed the different studied groups to be distinguished. Pathway analysis unveiled major amino acid pathways impairments in MPS III mainly arginine-proline metabolism and urea cycle metabolism.

CONCLUSION

This represents one of the first metabolomics-based investigations of MPS III. These results may shed light on MPS III pathophysiology and could help to set more targeted studies to infer the biomarkers of the affected pathways, which is crucial for rare conditions such as MPS III.

An Integrated, High-Throughput Strategy for Multiomic Systems Level Analysis

Proteomics, metabolomics, and transcriptomics generate comprehensive data sets, and current biocomputational capabilities allow their efficient integration for systems biology analysis. Published multiomics studies cover methodological advances as well as applications to biological questions. However, few studies have focused on the development of a high-throughput, unified sample preparation approach to complement high-throughput omic analytics. This report details the automation, benchmarking, and application of a strategy for transcriptomic, proteomic, and metabolomic analyses from a common sample. The approach, sample preparation for multi-omics technologies (SPOT), provides equivalent performance to typical individual omic preparation methods but greatly enhances throughput and minimizes the resources required for multiomic experiments. SPOT was applied to a multiomics time course experiment for zinc-treated HL-60 cells. The data reveal Zn effects on NRF2 antioxidant and NFkappaB signaling. High-throughput approaches such as these are critical for the acquisition of temporally resolved, multicondition, large multiomic data sets such as those necessary to assess complex clinical and biological concerns. Ultimately, this type of approach will provide an expanded understanding of challenging scientific questions across many fields.

The potential of metabolic profiling for vaccine development

Recent technological advances have provided deeper insights into the role of small molecules in biological processes. Metabolic profiling has thus entered the arena of -omics studies and rapidly proven its value both as stand-alone and as complement to other more advanced approaches, notably transcriptomics. Here we describe the potential of metabolic profiling for vaccinology embedded in the context of infection and immunity. This discussion is preceded by a description of the relevant technical and analytical tools for biological interpretation of metabolic data. Although not as widely applied as other -omics technologies, we believe that metabolic profiling can make important contributions to the better understanding of mechanisms underlying vaccine-induced responses and their effects on the prevention of infection or disease.

Autonomous Multimodal Metabolomics Data Integration for Comprehensive Pathway Analysis and Systems Biology

Comprehensive metabolomic data can be achieved using multiple orthogonal separation and mass spectrometry (MS) analytical techniques. However, drawing biologically relevant conclusions from this data and combining it with additional layers of information collected by other omic technologies present a significant bioinformatic challenge. To address this, a data processing approach was designed to automate the comprehensive prediction of dysregulated metabolic pathways/networks from multiple data sources. The platform autonomously integrates multiple MS-based metabolomics data types without constraints due to different sample preparation/extraction, chromatographic separation, or MS detection method. This multimodal analysis streamlines the extraction of biological information from the metabolomics data as well as the contextualization within proteomics and transcriptomics data sets. As a proof of concept, this multimodal analysis approach was applied to a colorectal cancer (CRC) study, in which complementary liquid chromatography-mass spectrometry (LC-MS) data were combined with proteomic and transcriptomic data. Our approach provided a highly resolved overview of colon cancer metabolic dysregulation, with an average 17% increase of detected dysregulated metabolites per pathway and an increase in metabolic pathway prediction confidence. Moreover, 95% of the altered metabolic pathways matched with the dysregulated genes and proteins, providing additional validation at a systems level. The analysis platform is currently available via the XCMS Online ( XCMSOnline.scripps.edu ).

Global untargeted serum metabolomic analyses nominate metabolic pathways responsive to loss of expression of the orphan metallo β-lactamase, MBLAC1

The C. elegans gene swip-10 encodes an orphan metallo β-lactamase that genetic studies indicate is vital for limiting neuronal excitability and viability. Sequence analysis indicates that the mammalian gene Mblac1 is the likely ortholog of swip-10, with greatest sequence identity localized to the encoded protein's single metallo β-lactamase domain. The substrate for the SWIP-10 protein remains unknown and to date no functional roles have been ascribed to MBLAC1, though we have shown that the protein binds the neuroprotective β-lactam antibiotic, ceftriaxone. To gain insight into the functional role of MBLAC1 in vivo, we used CRISPR/Cas9 methods to disrupt N-terminal coding sequences of the mouse Mblac1 gene, resulting in a complete loss of protein expression in viable, homozygous knockout (KO) animals. Using serum from both WT and KO mice, we performed global, untargeted metabolomic analyses, resolving small molecules via hydrophilic interaction chromatography (HILIC) based ultra-performance liquid chromatography, coupled to mass spectrometry (UPLC-MS/MS). Unsupervised principal component analysis reliably segregated the metabolomes of MBLAC1 KO and WT mice, with 92 features subsequently nominated as significantly different by ANOVA, and for which we made tentative and putative metabolite assignments. Bioinformatic analyses of these molecules nominate validated pathways subserving bile acid biosynthesis and linoleate metabolism, networks known to be responsive to metabolic and oxidative stress. Our findings lead to hypotheses that can guide future targeted studies seeking to identify the substrate for MBLAC1 and how substrate hydrolysis supports the neuroprotective actions of ceftriaxone.

Sorting nexin 10 controls mTOR activation through regulating amino-acid metabolism in colorectal cancer

Amino-acid metabolism plays a vital role in mammalian target of rapamycin (mTOR) signaling, which is the pivot in colorectal cancer (CRC). Upregulated chaperone-mediated autophagy (CMA) activity contributes to the regulation of metabolism in cancer cells. Previously, we found that sorting nexin 10 (SNX10) is a critical regulator in CMA activation. Here we investigated the role of SNX10 in regulating amino-acid metabolism and mTOR signaling pathway activation, as well as the impact on the tumor progression of mouse CRC. Our results showed that SNX10 deficiency promoted colorectal tumorigenesis in male FVB mice and CRC cell proliferation and survival. Metabolic pathway analysis of gas chromatography–mass spectrometry (GC-MS) data revealed unique changes of amino-acid metabolism by SNX10 deficiency. In HCT116 cells, SNX10 knockout resulted in the increase of CMA and mTOR activation, which could be abolished by chloroquine treatment or reversed by SNX10 overexpression. By small RNA interference (siRNA), we found that the activation of mTOR was dependent on lysosomal-associated membrane protein type-2A (LAMP-2A), which is a limiting factor of CMA. Similar results were also found in Caco-2 and SW480 cells. Ultra-high-performance liquid chromatography–quadrupole time of flight (UHPLC-QTOF) and GC-MS-based untargeted metabolomics revealed that 10 amino-acid metabolism in SNX10-deficient cells were significantly upregulated, which could be restored by LAMP-2A siRNA. All of these amino acids were previously reported to be involved in mTOR activation. In conclusion, this work revealed that SNX10 controls mTOR activation through regulating CMA-dependent amino-acid metabolism, which provides potential target and strategy for treating CRC.

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.

Untargeted metabolomic on urine samples after α-lipoic acid and/or eicosapentaenoic acid supplementation in healthy overweight/obese women

Background

Eicosapentaenoic acid (EPA) and α-lipoic acid (α-LA) have been investigated for their beneficial effects on obesity and cardiovascular risk factors. In the current research, the goal was to evaluate metabolomic changes following the dietary supplementation of these two lipids, alone or combined in healthy overweight/obese sedentary women following an energy-restricted diet. For this purpose, an untargeted metabolomics approach was conducted on urine samples using liquid chromatography coupled with time of flight mass spectrometry (HPLC-TOF-MS).

Methods

This is a short-term double blind placebo-controlled study with a parallel nutritional design that lasted 10 weeks. Participants were assigned to one of the 4 experimental groups [Control, EPA (1.3 g/d), α-LA (0.3 g/d) and EPA+α-LA (1.3 g/d + 0.3 g/d)]. All intervention groups followed an energy-restricted diet of 30% less than total energy expenditure. Clinically relevant biochemical measurements were analyzed. Urine samples (24 h) were collected at baseline and after 10 weeks. Untargeted metabolomic analysis on urine samples was carried out, and principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) were performed for the pattern recognition and characteristic metabolites identification.

Results

Urine samples were scattered in the PCA scores plots in response to the supplementation with α-LA. Totally, 28 putative discriminant metabolites in positive ionization, and 6 in negative ionization were identified among groups clearly differentiated according to the α-LA administration. Remarkably is the presence of an ascorbate intermediate metabolite (one of the isomers of trihydroxy-dioxohexanoate, or dihydroxy–oxohexanedionate) in the groups supplemented with α-LA. This fact might be associated with antioxidant properties of both α-LA and ascorbic acid. Correlations between phenotypical parameters and putative metabolites of provided additional information on whether there is a direct or inverse relationship between them. Especially interesting are the negative correlation between ascorbate intermediate metabolite and asymmetric dimethylarginine (ADMA) and the positive one between superoxide dismutase (SOD) and α-LA supplementation.

Conclusions

This metabolomic approach supports that the beneficial effects of α-LA administration on body weight reduction may be partly explained by the antioxidant properties of this organosulfur carboxylic acid mediated by isomers of trihydroxy-dioxohexanoate, or dihydroxy–oxohexanedionate.

Trial registration

Clinicaltrials.gov NCT01138774.

Electronic supplementary material

The online version of this article (10.1186/s12944-018-0750-4) contains supplementary material, which is available to authorized users.

Engineered microfluidic bioreactor for examining the three-dimensional breast tumor microenvironment

The interaction of cancer cells with the stromal cells and matrix in the tumor microenvironment plays a key role in progression to metastasis. A better understanding of the mechanisms underlying these interactions would aid in developing new therapeutic approaches to inhibit this progression. Here, we describe the fabrication of a simple microfluidic bioreactor capable of recapitulating the three-dimensional breast tumor microenvironment. Cancer cell spheroids, fibroblasts, and endothelial cells co-cultured in this device create a robust microenvironment suitable for studying in real time the migration of cancer cells along matrix structures laid down by fibroblasts within the 3D tumor microenvironment. This system allows for ready evaluation of response to targeted therapy.

Advances in metabolome information retrieval: turning chemistry into biology. Part II: biological information recovery

This work reports the second part of a review intending to give the state of the art of major metabolic phenotyping strategies. It particularly deals with inherent advantages and limits regarding data analysis issues and biological information retrieval tools along with translational challenges. This Part starts with introducing the main data preprocessing strategies of the different metabolomics data. Then, it describes the main data analysis techniques including univariate and multivariate aspects. It also addresses the challenges related to metabolite annotation and characterization. Finally, functional analysis including pathway and network strategies are discussed. The last section of this review is devoted to practical considerations and current challenges and pathways to bring metabolomics into clinical environments.

Multiomics tools for the diagnosis and treatment of rare neurological disease

Conventional workup of rare neurological disease is frequently hampered by diagnostic delay or lack of diagnosis. While biomarkers have been established for many neurometabolic disorders, improved methods are required for diagnosis of previously unidentified or underreported causes of rare neurological disease. This would result in a higher diagnostic yield and increased patient numbers required for interventional studies. Recent studies using next-generation sequencing and metabolomics have led to identification of novel disease-causing genes and biomarkers. This combined approach can assist in overcoming challenges associated with analyzing and interpreting the large amount of data obtained from each technique. In particular, metabolomics can support the pathogenicity of sequence variants in genes encoding enzymes or transporters involved in metabolic pathways. Moreover, metabolomics can show the broader perturbation caused by inborn errors of metabolism and identify a metabolic fingerprint of metabolic disorders. As such, using "omics" has great potential to meet the current needs for improved diagnosis and elucidation of rare neurological disease.

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.

Integration of genomics and metabolomics for prioritization of rare disease variants: a 2018 literature review

Many inborn errors of metabolism (IEMs) are amenable to treatment; therefore, early diagnosis and treatment is imperative. Despite recent advances, the genetic basis of many metabolic phenotypes remains unknown. For discovery purposes, whole exome sequencing (WES) variant prioritization coupled with clinical and bioinformatics expertise is the primary method used to identify novel disease-causing variants; however, causation is often difficult to establish due to the number of plausible variants. Integrated analysis of untargeted metabolomics (UM) and WES or whole genome sequencing (WGS) data is a promising systematic approach for identifying disease-causing variants. In this review, we provide a literature-based overview of UM methods utilizing liquid chromatography mass spectrometry (LC-MS), and assess approaches to integrating WES/WGS and LC-MS UM data for the discovery and prioritization of variants causing IEMs. To embed this integrated -omics approach in the clinic, expansion of gene-metabolite annotations and metabolomic feature-to-metabolite mapping methods are needed.

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.

Integrative metabolomics and transcriptomics signatures of clinical tolerance to Plasmodium vivax reveal activation of innate cell immunity and T cell signaling

Almost invariably, humans become ill during primary infections with malaria parasites which is a pathology associated with oxidative stress and perturbations in metabolism. Importantly, repetitive exposure to Plasmodium results in asymptomatic infections, which is a condition defined as clinical tolerance. Integration of transcriptomics and metabolomics data provides a powerful way to investigate complex disease processes involving oxidative stress, energy metabolism and immune cell activation. We used metabolomics and transcriptomics to investigate the different clinical outcomes in a P. vivax controlled human malaria infection trial. At baseline, the naïve and semi-immune subjects differed in the expression of interferon related genes, neutrophil and B cell signatures that progressed with distinct kinetics after infection. Metabolomics data indicated differences in amino acid pathways and lipid metabolism between the two groups. Top pathways during the course of infection included methionine and cysteine metabolism, fatty acid metabolism and urea cycle. There is also evidence for the activation of lipoxygenase, cyclooxygenase and non-specific lipid peroxidation products in the semi-immune group. The integration of transcriptomics and metabolomics revealed concerted molecular events triggered by the infection, notably involving platelet activation, innate immunity and T cell signaling. Additional experiment confirmed that the metabolites associated with platelet activation genes were indeed enriched in the platelet metabolome.

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Plasmodium vivax infection induces significant change in blood metabolomics.

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Naïve and semi-immune subjects exhibit different molecular profiles.

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Network integration of metabolites/genes hinges on innate activation, chemokines and T cell signaling.

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Involvement of platelet activation is confirmed by platelet metabolomics.

Developing Multipollutant Exposure Indicators of Traffic Pollution: The Dorm Room Inhalation to Vehicle Emissions (DRIVE) Study

Introduction

The Dorm Room Inhalation to Vehicle Emissions (DRIVE2) study was conducted to measure traditional single-pollutant and novel multipollutant traffic indicators along a complete emission-to-exposure pathway. The overarching goal of the study was to evaluate the suitability of these indicators for use as primary traffic exposure metrics in panel-based and small-cohort epidemiological studies.

Methods

Intensive field sampling was conducted on the campus of the Georgia Institute of Technology (GIT) between September 2014 and January 2015 at 8 monitoring sites (2 indoors and 6 outdoors) ranging from 5 m to 2.3 km from the busiest and most congested highway artery in Atlanta. In addition, 54 GIT students living in one of two dormitories either near (20 m) or far (1.4 km) from the highway were recruited to conduct personal exposure sampling and weekly biomonitoring. The pollutants measured were selected to provide information about the heterogeneous particulate and gaseous composition of primary traffic emissions, including the traditional traffic-related species (e.g., carbon monoxide [CO], nitrogen dioxide [NO2], nitric oxide [NO], fine particulate matter [PM2.5], and black carbon [BC]), and of secondary species (e.g., ozone [O3] and sulfate as well as organic carbon [OC], which is both primary and secondary) from traffic and other sources. Along with these pollutants, we also measured two multipollutant traffic indicators: integrated mobile source indicators (IMSIs) and fine particulate matter oxidative potential (FPMOP). IMSIs are derived from elemental carbon (EC), CO, and nitrogen oxide (NOx) concentrations, along with the fractions of these species emitted by gasoline and diesel vehicles, to construct integrated estimates of gasoline and diesel vehicle impacts. Our FPMOP indicator was based on an acellular assay involving the depletion of dithiothreitol (DTT), considering both water-soluble and insoluble components (referred to as FPMOPtotal-DTT). In addition, a limited assessment of 18 low-cost sensors was added to the study to supplement the four original aims.

Results

Pollutant levels measured during the study showed a low impact by this highway hotspot source on its surrounding vicinity. These findings are broadly consistent with results from other studies throughout North America showing decreased relative contributions to urban air pollution from primary traffic emissions. We view these reductions as an indication of a changing near-road environment, facilitated by the effectiveness of mobile source emission controls. Many of the primary pollutant species, including NO, CO, and BC, decreased to near background levels by 20 to 30 m from the highway source. Patterns of correlation among the sites also varied by pollutant and time of day. NO2 exhibited spatial trends that differed from those of the other single-pollutant primary traffic indicators. We believe this was caused by kinetic limitations in the photochemical chemistry, associated with primary emission reductions, required to convert the NO-dominant primary NOx, emitted from automobiles, to NO2. This finding provides some indication of limitations in the use of NO2 as a primary traffic exposure indicator in panel-based health effect studies. Roadside monitoring of NO, CO, and BC tended to be more strongly correlated with sites, both near and far from the road, during morning rush hour periods and often weakly to moderately correlated during other time periods of the day. This pattern was likely associated with diurnal changes in mixing and chemistry and their impact on spatial heterogeneity across the campus. Among our candidate multipollutant primary traffic indicators, we report several key findings related to the use of oxidative potential (OP)-based indicators. Although earlier studies have reported elevated levels of FPMOP in direct exhaust emissions, we found that atmospheric processing further enhanced FPMOPtotal-DTT, likely associated with the oxidation of primary polycyclic aromatic hydrocarbons (PAHs) to quinones and hydroxyquinones and with the oxidization and water solubility of metals. This has important implications in terms both of the utility of FPMOPtotal-DTT as a marker for exhaust emissions and of the importance of atmospheric processing of particulate matter (PM) being tied to potential health outcomes. The results from the personal exposure monitoring also point to the complexity and diversity of the spatiotemporal variability patterns among the study monitoring sites and the importance of accounting for location and spatial mobility when estimating exposures in panel-based and small-cohort studies. This was most clearly demonstrated with the personal BC measurements, where ambient roadside monitoring was shown to be a poor surrogate for exposures to BC. Alternative surrogates, including ambient and indoor BC at the participants' respective dorms, were more strongly associated with personal BC, and knowledge of the participants' mean proximity to the highway was also shown to explain a substantial level of the variability in corresponding personal exposures to both BC and NO2. In addition, untargeted metabolomic indicators measured in plasma and saliva, which represent emerging methods for measuring exposure, were used to extract approximately 20,000 and 30,000 features from plasma and saliva, respectively. Using hydrophilic interaction liquid chromatography (HILIC) in the positive ion mode, we identified 221 plasma features that differed significantly between the two dorm cohorts. The bimodal distribution of these features in the HILIC column was highly idiosyncratic; one peak consisted of features with elevated intensities for participants living in the near dorm; the other consisted of features with elevated intensities for participants in the far dorm. Both peaks were characterized by relatively short retention times, indicative of the hydrophobicity of the identified features. The results from the metabolomics analyses provide a strong basis for continuing this work toward specific chemical validation of putative biomarkers of traffic-related pollution. Finally, the study had a supplemental aim of examining the performance of 18 low-cost CO, NO, NO2, O3, and PM2.5 pollutant sensors. These were colocated alongside the other study monitors and assessed for their ability to capture temporal trends observed by the reference monitoring instrumentation. Generally, we found the performance of the low-cost gas-phase sensors to be promising after extensive calibration; the uncalibrated measurements alone, however, would likely not have led to reliable results. The low-cost PM sensors we evaluated had poor accuracy, although PM sensor technology is evolving quickly and warrants future attention.

Conclusions

An immediate implication of the changing near-road environment is that future studies aimed at characterizing hotspots related to mobile sources and their impacts on health will need to consider multiple approaches for characterizing spatial gradients and exposures. Specifically and most directly, the mobile source contributions to ambient concentrations of single-pollutant indicators of traffic exposure are not as distinguishable to the degree that they have been in the past. Collectively, the study suggests that characterizing exposures to traffic-related pollutants, which is already difficult, will become more difficult because of the reduction in traffic-related emissions. Additional multi-tiered approaches should be considered along with traditional measurements, including the use of alternative OP measures beyond those based on DTT assays, metabolomics, low-cost sensors, and air quality modeling.

Propagating annotations of molecular networks using in silico fragmentation

The annotation of small molecules is one of the most challenging and important steps in untargeted mass spectrometry analysis, as most of our biological interpretations rely on structural annotations. Molecular networking has emerged as a structured way to organize and mine data from untargeted tandem mass spectrometry (MS/MS) experiments and has been widely applied to propagate annotations. However, propagation is done through manual inspection of MS/MS spectra connected in the spectral networks and is only possible when a reference library spectrum is available. One of the alternative approaches used to annotate an unknown fragmentation mass spectrum is through the use of in silico predictions. One of the challenges of in silico annotation is the uncertainty around the correct structure among the predicted candidate lists. Here we show how molecular networking can be used to improve the accuracy of in silico predictions through propagation of structural annotations, even when there is no match to a MS/MS spectrum in spectral libraries. This is accomplished through creating a network consensus of re-ranked structural candidates using the molecular network topology and structural similarity to improve in silico annotations. The Network Annotation Propagation (NAP) tool is accessible through the GNPS web-platform https://gnps.ucsd.edu/ProteoSAFe/static/gnps-theoretical.jsp.

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.

Human Suction Blister Fluid Composition Determined Using High-Resolution Metabolomics

Interstitial fluid (ISF) surrounds the cells and tissues of the body. Since ISF has molecular components similar to plasma, as well as compounds produced locally in tissues, it may be a valuable source of biomarkers for diagnostics and monitoring. However, there has not been a comprehensive study to determine the metabolite composition of ISF and to compare it to plasma. In this study, the metabolome of suction blister fluid (SBF), which largely consists of ISF, collected from 10 human volunteers was analyzed using untargeted high-resolution metabolomics (HRM). A wide range of metabolites were detected in SBF, including amino acids, lipids, nucleotides, and compounds of exogenous origin. Various systemic and skin-derived metabolite biomarkers were elevated or found uniquely in SBF, and many other metabolites of clinical and physiological significance were well correlated between SBF and plasma. In sum, using untargeted HRM profiling, this study shows that SBF can be a valuable source of information about metabolites relevant to human health.

Cord Blood Metabolic Signatures of Birth Weight: A Population-Based Study

Birth weight is an important indicator of maternal and fetal health and a predictor of health in later life. However, the determinants of variance in birth weight are still poorly understood. We aimed to identify the biological pathways, which may be perturbed by environmental exposures, that are important in determining birth weight. We applied untargeted mass-spectrometry-based metabolomics to 481 cord blood samples collected at delivery in four birth cohorts from across Europe: ENVIRONAGE (Belgium), INMA (Spain), Piccolipiu (Italy), and Rhea (Greece). We performed a metabolome-wide association scan for birth weight on over 4000 metabolic features, controlling the false discovery rate at 5%. Annotation of compounds was conducted through reference to authentic standards. We identified 68 metabolites significantly associated with birth weight, including vitamin A, progesterone, docosahexaenoic acid, indolelactic acid, and multiple acylcarnitines and phosphatidylcholines. We observed enrichment (p < 0.05) of the tryptophan metabolism, prostaglandin formation, C21-steroid hormone signaling, carnitine shuttle, and glycerophospholipid metabolism pathways. Vitamin A was associated with both maternal smoking and birth weight, suggesting a mediation pathway. Our findings shed new light on the pathways central to fetal growth and will have implications for antenatal and perinatal care and potentially for health in later life.

Integrative analysis of transcriptomic and metabolomic data via sparse canonical correlation analysis with incorporation of biological information

Integrative analysis of high dimensional omics data is becoming increasingly popular. At the same time, incorporating known functional relationships among variables in analysis of omics data has been shown to help elucidate underlying mechanisms for complex diseases. In this article, our goal is to assess association between transcriptomic and metabolomic data from a Predictive Health Institute (PHI) study that includes healthy adults at a high risk of developing cardiovascular diseases. Adopting a strategy that is both data-driven and knowledge-based, we develop statistical methods for sparse canonical correlation analysis (CCA) with incorporation of known biological information. Our proposed methods use prior network structural information among genes and among metabolites to guide selection of relevant genes and metabolites in sparse CCA, providing insight on the molecular underpinning of cardiovascular disease. Our simulations demonstrate that the structured sparse CCA methods outperform several existing sparse CCA methods in selecting relevant genes and metabolites when structural information is informative and are robust to mis-specified structural information. Our analysis of the PHI study reveals that a number of gene and metabolic pathways including some known to be associated with cardiovascular diseases are enriched in the set of genes and metabolites selected by our proposed approach.

High-resolution mass spectrometry of skin mucus for monitoring physiological impacts and contaminant biotransformation products in fathead minnows exposed to wastewater effluent

High-resolution mass spectrometry is advantageous for monitoring physiological impacts and contaminant biotransformation products in fish exposed to complex wastewater effluent. We evaluated this technique using skin mucus from male and female fathead minnows (Pimephales promelas) exposed to control water or treated wastewater effluent at 5, 20, and 100% levels for 21 d, using an on-site, flow-through system providing real-time exposure. Both sex-specific and non-sex-specific responses were observed in the mucus metabolome, the latter suggesting the induction of general compensatory pathways for xenobiotic exposures. Altogether, 85 statistically significant treatment-dependent metabolite changes were observed out of the 310 total endogenous metabolites that were detected (156 of the 310 were annotated). Partial least squares-regression models revealed strong covariances between the mucus metabolomes and up-regulated hepatic messenger ribonucleic acid (mRNA) transcripts reported previously for these same fish. These regression models suggest that mucus metabolomic changes reflected, in part, processes by which the fish biotransformed xenobiotics in the effluent. In keeping with this observation, we detected a phase II transformation product of bisphenol A in the skin mucus of male fish. Collectively, these findings demonstrate the utility of mucus as a minimally invasive matrix for simultaneously assessing exposures and effects of environmentally relevant mixtures of contaminants. Environ Toxicol Chem 2018;37:788-796. Published 2017 Wiley Periodicals Inc. on behalf of SETAC. This article is a US government work and, as such, is in the public domain in the United States of America.

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.

Accelerated, untargeted metabolomics analysis of cutaneous T-cell lymphoma reveals metabolic shifts in plasma and tumor adjacent skins of xenograft mice

Cutaneous T-cell lymphoma (CTCL) is a heterogeneous group of skin-homing T-cell neoplasms. Clinical management is stage based but diagnosis and prognosis could be extremely challenging. The presented study aims to explore the metabolic profiling of CTCL by an accelerated untargeted metabolomics data analysis tool "Mummichog" to facilitate the discoveries of potential biomarkers for clinical early stage diagnosis, prognosis, and treatments in CTCL. Ultra high-performance liquid chromatography-quadrupole time-of-flight-based untargeted metabolomics were conducted on the skin and plasma of CTCL mice. It showed that the metabolism of skin changed greatly versus control samples in the development of CTCL. Increased l-glutamate and decreased adenosine monophosphate were the most essential metabolic features of CTCL tumor and tumor adjacent skins. Unique metabolism changes in tumor adjacent non-involved skin tissues (ANIT) occurred in the progress of carcinogenesis, including upregulated cytidine-5'-triphosphate, aberrant biosynthesis of prostaglandins, pyrimidine, mevalonate pathway, and tryptophan degradation. Sharply elevated 5-phospho-α-d-ribose 1-diphosphate (PRPP) marked the final state of tumor in CTCL. In the plasma, systematic shifts in corticosterone, sphingolipid, and ceramide metabolism were found. These uncovered aberrant metabolites and metabolic pathways suggested that the metabolic reprogramming of PRPP in tumor tissues may cause the disturbance of cytidine and uridine metabolic homeostasis in ANIT. Accumulative cytidine-5'-triphosphate in ANIT may exert positive feedback on the PRPP level and leads to CTCL further development. In addition, the accelerated data analysis tool "Mummichog" showed good practicability and can be widely used in high-resolution liquid chromatography mass spectrometry-based untargeted metabolomics.

Beyond genomics: understanding exposotypes through metabolomics

BACKGROUND

Over the past 20 years, advances in genomic technology have enabled unparalleled access to the information contained within the human genome. However, the multiple genetic variants associated with various diseases typically account for only a small fraction of the disease risk. This may be due to the multifactorial nature of disease mechanisms, the strong impact of the environment, and the complexity of gene-environment interactions. Metabolomics is the quantification of small molecules produced by metabolic processes within a biological sample. Metabolomics datasets contain a wealth of information that reflect the disease state and are consequent to both genetic variation and environment. Thus, metabolomics is being widely adopted for epidemiologic research to identify disease risk traits. In this review, we discuss the evolution and challenges of metabolomics in epidemiologic research, particularly for assessing environmental exposures and providing insights into gene-environment interactions, and mechanism of biological impact.

MAIN TEXT

Metabolomics can be used to measure the complex global modulating effect that an exposure event has on an individual phenotype. Combining information derived from all levels of protein synthesis and subsequent enzymatic action on metabolite production can reveal the individual exposotype. We discuss some of the methodological and statistical challenges in dealing with this type of high-dimensional data, such as the impact of study design, analytical biases, and biological variance. We show examples of disease risk inference from metabolic traits using metabolome-wide association studies. We also evaluate how these studies may drive precision medicine approaches, and pharmacogenomics, which have up to now been inefficient. Finally, we discuss how to promote transparency and open science to improve reproducibility and credibility in metabolomics.

CONCLUSIONS

Comparison of exposotypes at the human population level may help understanding how environmental exposures affect biology at the systems level to determine cause, effect, and susceptibilities. Juxtaposition and integration of genomics and metabolomics information may offer additional insights. Clinical utility of this information for single individuals and populations has yet to be routinely demonstrated, but hopefully, recent advances to improve the robustness of large-scale metabolomics will facilitate clinical translation.

Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling

Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies.

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.

Yale school of public health symposium on lifetime exposures and human health: the exposome; summary and future reflections

The exposome is defined as "the totality of environmental exposures encountered from birth to death" and was developed to address the need for comprehensive environmental exposure assessment to better understand disease etiology. Due to the complexity of the exposome, significant efforts have been made to develop technologies for longitudinal, internal and external exposure monitoring, and bioinformatics to integrate and analyze datasets generated. Our objectives were to bring together leaders in the field of exposomics, at a recent Symposium on "Lifetime Exposures and Human Health: The Exposome," held at Yale School of Public Health. Our aim was to highlight the most recent technological advancements for measurement of the exposome, bioinformatics development, current limitations, and future needs in environmental health. In the discussions, an emphasis was placed on moving away from a one-chemical one-health outcome model toward a new paradigm of monitoring the totality of exposures that individuals may experience over their lifetime. This is critical to better understand the underlying biological impact on human health, particularly during windows of susceptibility. Recent advancements in metabolomics and bioinformatics are driving the field forward in biomonitoring and understanding the biological impact, and the technological and logistical challenges involved in the analyses were highlighted. In conclusion, further developments and support are needed for large-scale biomonitoring and management of big data, standardization for exposure and data analyses, bioinformatics tools for co-exposure or mixture analyses, and methods for data sharing.

Biologically Consistent Annotation of Metabolomics Data

Annotation of metabolites remains a major challenge in liquid chromatography-mass spectrometry (LC-MS) based untargeted metabolomics. The current gold standard for metabolite identification is to match the detected feature with an authentic standard analyzed on the same equipment and using the same method as the experimental samples. However, there are substantial practical challenges in applying this approach to large data sets. One widely used annotation approach is to search spectral libraries in reference databases for matching metabolites; however, this approach is limited by the incomplete coverage of these libraries. An alternative computational approach is to match the detected features to candidate chemical structures based on their mass and predicted fragmentation pattern. Unfortunately, both of these approaches can match multiple identities with a single feature. Another issue is that annotations from different tools often disagree. This paper presents a novel LC-MS data annotation method, termed Biologically Consistent Annotation (BioCAn), that combines the results from database searches and in silico fragmentation analyses and places these results into a relevant biological context for the sample as captured by a metabolic model. We demonstrate the utility of this approach through an analysis of CHO cell samples. The performance of BioCAn is evaluated against several currently available annotation tools, and the accuracy of BioCAn annotations is verified using high-purity analytical standards.

New Strategies and Challenges in Lung Proteomics and Metabolomics. An Official American Thoracic Society Workshop Report

This document presents the proceedings from the workshop entitled, "New Strategies and Challenges in Lung Proteomics and Metabolomics" held February 4th-5th, 2016, in Denver, Colorado. It was sponsored by the National Heart Lung Blood Institute, the American Thoracic Society, the Colorado Biological Mass Spectrometry Society, and National Jewish Health. The goal of this workshop was to convene, for the first time, relevant experts in lung proteomics and metabolomics to discuss and overcome specific challenges in these fields that are unique to the lung. The main objectives of this workshop were to identify, review, and/or understand: (1) emerging technologies in metabolomics and proteomics as applied to the study of the lung; (2) the unique composition and challenges of lung-specific biological specimens for metabolomic and proteomic analysis; (3) the diverse informatics approaches and databases unique to metabolomics and proteomics, with special emphasis on the lung; (4) integrative platforms across genetic and genomic databases that can be applied to lung-related metabolomic and proteomic studies; and (5) the clinical applications of proteomics and metabolomics. The major findings and conclusions of this workshop are summarized at the end of the report, and outline the progress and challenges that face these rapidly advancing fields.

Metabolome-wide association study of peripheral parasitemia in Plasmodium vivax malaria

BACKGROUND

Plasmodium vivax is one of the leading causes of malaria worldwide. Infections with this parasite cause diverse clinical manifestations, and recent studies revealed that infections with P. vivax can result in severe and fatal disease. Despite these facts, biological traits of the host response and parasite metabolism during P. vivax malaria are still largely underexplored. Parasitemia is clearly related to progression and severity of malaria caused by P. falciparum, however the effects of parasitemia during infections with P. vivax are not well understood.

RESULTS

We conducted an exploratory study using a high-resolution metabolomics platform that uncovered significant associations between parasitemia levels and plasma metabolites from 150 patients with P. vivax malaria. Most plasma metabolites were inversely associated with higher levels of parasitemia. Top predicted metabolites are implicated into pathways of heme and lipid metabolism, which include biliverdin, bilirubin, palmitoylcarnitine, stearoylcarnitine, phosphocholine, glycerophosphocholine, oleic acid and omega-carboxy-trinor-leukotriene B4.

CONCLUSIONS

The abundance of several plasma metabolites varies according to the levels of parasitemia in patients with P. vivax malaria. Moreover, our data suggest that the host response and/or parasite survival might be affected by metabolites involved in the degradation of heme and metabolism of several lipids. Importantly, these data highlight metabolic pathways that may serve as targets for the development of new antimalarial compounds.

Metabolomic Modularity Analysis (MMA) to Quantify Human Liver Perfusion Dynamics

Large-scale -omics data are now ubiquitously utilized to capture and interpret global responses to perturbations in biological systems, such as the impact of disease states on cells, tissues, and whole organs. Metabolomics data, in particular, are difficult to interpret for providing physiological insight because predefined biochemical pathways used for analysis are inherently biased and fail to capture more complex network interactions that span multiple canonical pathways. In this study, we introduce a nov-el approach coined Metabolomic Modularity Analysis (MMA) as a graph-based algorithm to systematically identify metabolic modules of reactions enriched with metabolites flagged to be statistically significant. A defining feature of the algorithm is its ability to determine modularity that highlights interactions between reactions mediated by the production and consumption of cofactors and other hub metabolites. As a case study, we evaluated the metabolic dynamics of discarded human livers using time-course metabolomics data and MMA to identify modules that explain the observed physiological changes leading to liver recovery during subnormothermic machine perfusion (SNMP). MMA was performed on a large scale liver-specific human metabolic network that was weighted based on metabolomics data and identified cofactor-mediated modules that would not have been discovered by traditional metabolic pathway analyses.

Commensal Propionibacterium strain UF1 mitigates intestinal inflammation via Th17 cell regulation

Consumption of human breast milk (HBM) attenuates the incidence of necrotizing enterocolitis (NEC), which remains a leading and intractable cause of mortality in preterm infants. Here, we report that this diminution correlates with alterations in the gut microbiota, particularly enrichment of Propionibacterium species. Transfaunation of microbiota from HBM-fed preterm infants or a newly identified and cultured Propionibacterium strain, P. UF1, to germfree mice conferred protection against pathogen infection and correlated with profound increases in intestinal Th17 cells. The induction of Th17 cells was dependent on bacterial dihydrolipoamide acetyltransferase (DlaT), a major protein expressed on the P. UF1 surface layer (S-layer). Binding of P. UF1 to its cognate receptor, SIGNR1, on dendritic cells resulted in the regulation of intestinal phagocytes. Importantly, transfer of P. UF1 profoundly mitigated induced NEC-like injury in neonatal mice. Together, these results mechanistically elucidate the protective effects of HBM and P. UF1-induced immunoregulation, which safeguard against proinflammatory diseases, including NEC.

The best models of metabolism

Biochemical systems are among of the oldest application areas of mathematical modeling. Spanning a time period of over one hundred years, the repertoire of options for structuring a model and for formulating reactions has been constantly growing, and yet, it is still unclear whether or to what degree some models are better than others and how the modeler is to choose among them. In fact, the variety of options has become overwhelming and difficult to maneuver for novices and experts alike. This review outlines the metabolic model design process and discusses the numerous choices for modeling frameworks and mathematical representations. It tries to be inclusive, even though it cannot be complete, and introduces the various modeling options in a manner that is as unbiased as that is feasible. However, the review does end with personal recommendations for the choices of default models. WIREs Syst Biol Med 2017, 9:e1391. doi: 10.1002/wsbm.1391 For further resources related to this article, please visit the WIREs website.

Metabolomics guided pathway analysis reveals link between cancer metastasis, cholesterol sulfate, and phospholipids

Background

Cancer cells that enter the metastatic cascade require traits that allow them to survive within the circulation and colonize distant organ sites. As disseminating cancer cells adapt to their changing microenvironments, they also modify their metabolism and metabolite production.

Methods

A mouse xenograft model of spontaneous tumor metastasis was used to determine the metabolic rewiring that occurs between primary cancers and their metastases. An “autonomous” mass spectrometry-based untargeted metabolomic workflow with integrative metabolic pathway analysis revealed a number of differentially regulated metabolites in primary mammary fat pad (MFP) tumors compared to microdissected paired lung metastases. The study was further extended to analyze metabolites in paired normal tissues which determined the potential influence of metabolites from the microenvironment.

Results

Metabolomic analysis revealed that multiple metabolites were increased in metastases, including cholesterol sulfate and phospholipids (phosphatidylglycerols and phosphatidylethanolamine). Metabolite analysis of normal lung tissue in the mouse model also revealed increased levels of these metabolites compared to tissues from normal MFP and primary MFP tumors, indicating potential extracellular uptake by cancer cells in lung metastases. These results indicate a potential functional importance of cholesterol sulfate and phospholipids in propagating metastasis. In addition, metabolites involved in DNA/RNA synthesis and the TCA cycle were decreased in lung metastases compared to primary MFP tumors.

Conclusions

Using an integrated metabolomic workflow, this study identified a link between cholesterol sulfate and phospholipids, metabolic characteristics of the metastatic niche, and the capacity of tumor cells to colonize distant sites.

Electronic supplementary material

The online version of this article (10.1186/s40170-017-0171-2) contains supplementary material, which is available to authorized users.

Urinary metabolic phenotyping of mucopolysaccharidosis type I combining untargeted and targeted strategies with data modeling

BACKGROUND

Application of metabolic phenotyping could expand the pathophysiological knowledge of mucopolysaccharidoses (MPS) and may reveal the comprehensive metabolic impairments in MPS. However, few studies applied this approach to MPS.

METHODS

We applied targeted and untargeted metabolic profiling in urine samples obtained from a French cohort comprising 19 MPS I and 15 MPS I treated patients along with 66 controls. For that purpose, we used ultra-high-performance liquid chromatography combined with ion mobility and high-resolution mass spectrometry following a protocol designed for large-scale metabolomics studies regarding robustness and reproducibility. Furthermore, 24 amino acids have been quantified using liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). Keratan sulfate, Heparan sulfate and Dermatan sulfate concentrations have also been measured using an LC-MS/MS method. Univariate and multivariate data analyses have been used to select discriminant metabolites. The mummichog algorithm has been used for pathway analysis.

RESULTS

The studied groups yielded distinct biochemical phenotypes using multivariate data analysis. Univariate statistics also revealed metabolites that differentiated the groups. Specifically, metabolites related to the amino acid metabolism. Pathway analysis revealed that several major amino acid pathways were dysregulated in MPS. Comparison of targeted and untargeted metabolomics data with in silico results yielded arginine, proline and glutathione metabolisms being the most affected.

CONCLUSION

This study is one of the first metabolic phenotyping studies of MPS I. The findings might help to generate new hypotheses about MPS pathophysiology and to develop further targeted studies of a smaller number of potentially key metabolites.

Low-level maternal exposure to nicotine associates with significant metabolic perturbations in second-trimester amniotic fluid

Decades of public health research have documented that smoking in pregnancy poses significant health risks to both mother and child. More recent studies have shown that even passive maternal exposure to secondhand smoke associates with negative birth outcomes. However, the mechanisms linking exposure to outcomes have remained obscure. As a first step toward defining the metabolic consequence of low-level nicotine exposure on fetal development, we conducted an untargeted metabolomic analysis of 81 paired samples of maternal serum and amniotic fluid collected from karyotypically normal pregnancies in the second trimester. By comparing the m/z and retention times of our mass spectral features with confirmed standards, we identified cotinine, a nicotine derivative, and used the calculated cotinine concentrations to classify our maternal serum samples into exposure groups using previously defined cut-offs. We found that cotinine levels consistent with low-level maternal exposure to nicotine associated with distinct metabolic perturbations, particularly in amniotic fluid. In fact, the metabolic effects in amniotic fluid of ostensibly low-level exposed mothers showed greater overlap with perturbations previously observed in the sera of adult smokers than did the perturbations observed in the corresponding maternal sera. Dysregulated fetal pathways included aspartate and asparagine metabolism, pyrimidine metabolism, and metabolism of other amino acids. We also observed a strong negative association between level of maternal serum cotinine and acetylated polyamines in the amniotic fluid. Combined, these results confirm that low-level maternal nicotine exposure, indicated by a maternal serum cotinine level of 2-10ng/mL, is associated with striking metabolic consequences in the fetal compartment, and that the affected pathways overlap those perturbed in the sera of adult smokers.

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.

mTOR regulates metabolic adaptation of APCs in the lung and controls the outcome of allergic inflammation

Antigen-presenting cells (APCs) occupy diverse anatomical tissues, but their tissue-restricted homeostasis remains poorly understood. Here, working with mouse models of inflammation, we found that mechanistic target of rapamycin (mTOR)-dependent metabolic adaptation was required at discrete locations. mTOR was dispensable for dendritic cell (DC) homeostasis in secondary lymphoid tissues but necessary to regulate cellular metabolism and accumulation of CD103⁺ DCs and alveolar macrophages in lung. Moreover, while numbers of mTOR-deficient lung CD11b⁺ DCs were not changed, they were metabolically reprogrammed to skew allergic inflammation from eosinophilic T helper cell 2 (T_H2) to neutrophilic T_H17 polarity. The mechanism for this change was independent of translational control but dependent on inflammatory DCs, which produced interleukin-23 and increased fatty acid oxidation. mTOR therefore mediates metabolic adaptation of APCs in distinct tissues, influencing the immunological character of allergic inflammation.

Stepwise inference of likely dynamic flux distributions from metabolic time series data

MOTIVATION

Most metabolic pathways contain more reactions than metabolites and therefore have a wide stoichiometric matrix that corresponds to infinitely many possible flux distributions that are perfectly compatible with the dynamics of the metabolites in a given dataset. This under-determinedness poses a challenge for the quantitative characterization of flux distributions from time series data and thus for the design of adequate, predictive models. Here we propose a method that reduces the degrees of freedom in a stepwise manner and leads to a dynamic flux distribution that is, in a statistical sense, likely to be close to the true distribution.

RESULTS

We applied the proposed method to the lignin biosynthesis pathway in switchgrass. The system consists of 16 metabolites and 23 enzymatic reactions. It has seven degrees of freedom and therefore admits a large space of dynamic flux distributions that all fit a set of metabolic time series data equally well. The proposed method reduces this space in a systematic and biologically reasonable manner and converges to a likely dynamic flux distribution in just a few iterations. The estimated solution and the true flux distribution, which is known in this case, show excellent agreement and thereby lend support to the method.

AVAILABILITY AND IMPLEMENTATION

The computational model was implemented in MATLAB (version R2014a, The MathWorks, Natick, MA). The source code is available at https://github.gatech.edu/VoitLab/Stepwise-Inference-of-Likely-Dynamic-Flux-Distributions and www.bst.bme.gatech.edu/research.php .

CONTACT

mojdeh@gatech.edu or eberhard.voit@bme.gatech.edu.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Longitudinal development of the gut microbiome and metabolome in preterm neonates with late onset sepsis and healthy controls

BACKGROUND

Late onset sepsis (LOS) in preterm infants is associated with considerable morbidity and mortality. While studies have implicated gut bacteria in the aetiology of the disease, functional analysis and mechanistic insights are generally lacking. We performed temporal bacterial (n = 613) and metabolomic (n = 63) profiling on extensively sampled stool from 7 infants with LOS and 28 matched healthy (no LOS or NEC) controls.

RESULTS

The bacteria isolated in diagnostic blood culture usually corresponded to the dominant bacterial genera in the gut microbiome. Longitudinal changes were monitored based on preterm gut community types (PGCTs), where control infants had an increased number of PGCTs compared to LOS infants (P = 0.011). PGCT 6, characterised by Bifidobacteria dominance, was only present in control infants. Metabolite profiles differed between LOS and control infants at diagnosis and 7 days later, but not 7 days prior to diagnosis. Bifidobacteria was positively correlated with control metabolites, including raffinose, sucrose, and acetic acid.

CONCLUSIONS

Using multi-omic analysis, we show that the gut microbiome is involved in the pathogenesis of LOS. While the causative agent of LOS varies, it is usually abundant in the gut. Bifidobacteria dominance was associated with control infants, and the presence of this organism may directly protect, or act as a marker for protection, against gut epithelial translocation. While the metabolomic data is preliminary, the findings support that gut development and protection in preterm infants is associated with increased in prebiotic oligosaccharides (e.g. raffinose) and the growth of beneficial bacteria (e.g. Bifidobacterium).

The impact of ambient air pollution on the human blood metabolome

BACKGROUND

Biological perturbations caused by air pollution might be reflected in the compounds present in blood originating from air pollutants and endogenous metabolites influenced by air pollution (defined here as part of the blood metabolome). We aimed to assess the perturbation of the blood metabolome in response to short term exposure to air pollution.

METHODS

We exposed 31 healthy volunteers to ambient air pollution for 5h. We measured exposure to particulate matter, particle number concentrations, absorbance, elemental/organic carbon, trace metals, secondary inorganic components, endotoxin content, gaseous pollutants, and particulate matter oxidative potential. We collected blood from the participants 2h before and 2 and 18h after exposure. We employed untargeted metabolite profiling to monitor 3873 metabolic features in 493 blood samples from these volunteers. We assessed lung function using spirometry and six acute phase proteins in peripheral blood. We assessed the association of the metabolic features with the measured air pollutants and with health markers that we previously observed to be associated with air pollution in this study.

RESULTS

We observed 89 robust associations between air pollutants and metabolic features two hours after exposure and 118 robust associations 18h after exposure. Some of the metabolic features that were associated with air pollutants were also associated with acute health effects, especially changes in forced expiratory volume in 1s. We successfully identified tyrosine, guanosine, and hypoxanthine among the associated features. Bioinformatics approach Mummichog predicted enriched pathway activity in eight pathways, among which tyrosine metabolism.

CONCLUSIONS

This study demonstrates for the first time the application of untargeted metabolite profiling to assess the impact of air pollution on the blood metabolome.

Metabolic network segmentation: A probabilistic graphical modeling approach to identify the sites and sequential order of metabolic regulation from non-targeted metabolomics data

In recent years, the number of large-scale metabolomics studies on various cellular processes in different organisms has increased drastically. However, it remains a major challenge to perform a systematic identification of mechanistic regulatory events that mediate the observed changes in metabolite levels, due to complex interdependencies within metabolic networks. We present the metabolic network segmentation (MNS) algorithm, a probabilistic graphical modeling approach that enables genome-scale, automated prediction of regulated metabolic reactions from differential or serial metabolomics data. The algorithm sections the metabolic network into modules of metabolites with consistent changes. Metabolic reactions that connect different modules are the most likely sites of metabolic regulation. In contrast to most state-of-the-art methods, the MNS algorithm is independent of arbitrary pathway definitions, and its probabilistic nature facilitates assessments of noisy and incomplete measurements. With serial (i.e., time-resolved) data, the MNS algorithm also indicates the sequential order of metabolic regulation. We demonstrated the power and flexibility of the MNS algorithm with three, realistic case studies with bacterial and human cells. Thus, this approach enables the identification of mechanistic regulatory events from large-scale metabolomics data, and contributes to the understanding of metabolic processes and their interplay with cellular signaling and regulation processes.

Reciprocal crosstalk between metabolism and cellular signaling pathways plays a crucial role in cellular decision-making. In recent years, this premise has motivated several metabolomics studies that aimed to gain a mechanistic understanding of metabolic phenotypes. However, due to complex interactions within metabolic networks, it remains a challenge to infer mechanisms that underlie metabolome changes. We present the metabolic network segmentation approach, a novel method that aimed to identify the sites and sequential order of metabolic regulatory events, based merely on steady state or dynamic metabolomics data. This method employs probabilistic graphical models to partition the entire metabolic network into modules with correlated metabolites. It identifies fractures between modules (i.e., reactions that connect non-correlated metabolites) as sites of regulation. We performed validation and benchmark analyses in hundreds of E. coli knockout mutants deficient in enzymes and transcription factors. Moreover, we verified the capability of our method for identifying the sequential order of metabolic regulatory events by testing it on fibroblasts exposed to oxidative stress. Our metabolic network segmentation algorithm is widely applicable, and thus, it will enhance our mechanistic understanding of metabolic phenotypes and their connections to cellular signaling and regulatory processes.