ArticleLiterature Review

Computational Metabolomics: A Framework for the Million Metabolome

September 2016
Chemical Research in Toxicology 29(12)

September 2016
29(12)

DOI:10.1021/acs.chemrestox.6b00179

Authors:

Karan Uppal

Emory University

Douglas Walker

Tufts University

Ken H. Liu

Emory University

Show all 6 authorsHide

“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 crystalizes 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 article reviews 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.

PISTACHIo (PreemptIon of diSrupTive behAvior in CHIldren): real-time monitoring of sleep and behavior of children 3–7 years old receiving parent–child interaction therapy augment with artificial intelligence — the study protocol, pilot study

Article

Full-text available

Feb 2023

Background Emotional behavior problems (EBP) are the most common and persistent mental health issues in early childhood. Early intervention programs are crucial in helping children with EBP. Parent–child interaction therapy (PCIT) is an evidence-based therapy designed to address personal difficulties of parent–child dyads as well as reduce externalizing behaviors. In clinical practice, parents consistently struggle to provide accurate characterizations of EBP symptoms (number, timing of tantrums, precipitating events) even from the week before in their young children. The main aim of the study is to evaluate feasibility of the use of smartwatches in children aged 3–7 years with EBP. Methods This randomized double-blind controlled study aims to recruit a total of 100 participants, consisting of 50 children aged 3–7 years with an EBP measure rated above the clinically significant range (T-score ≥ 60) (Eyberg Child Behavior Inventory-ECBI; Eyberg & Pincus, 1999) and their parents who are at least 18 years old. Participants are randomly assigned to the artificial intelligence-PCIT group (AI-PCIT) or the PCIT-sham biometric group. Outcome parameters include weekly ECBI and Pediatric Sleep Questionnaire (PSQ) as well as Child Behavior Checklist (CBCL) obtained weeks 1, 6, and 12 of the study. Two smartphone applications (Garmin connect and mEMA) and a wearable Garmin smartwatch are used collect the data to monitor step count, sleep, heart rate, and activity intensity. In the AI-PCIT group, the mEMA application will allow for the ecological momentary assessment (EMA) and will send behavioral alerts to the parent. Discussion Real-time predictive technologies to engage patients rely on daily commitment on behalf of the participant and recurrent frequent smartphone notifications. Ecological momentary assessment (EMA) provides a way to digitally phenotype in-the-moment behavior and functioning of the parent–child dyad. One of the study’s goals is to determine if AI-PCIT outcomes are superior in comparison with standard PCIT. Overall, we believe that the PISTACHIo study will also be able to determine tolerability of smartwatches in children aged 3–7 with EBP and could participate in a fundamental shift from the traditional way of assessing and treating EBP to a more individualized treatment plan based on real-time information about the child’s behavior. Trial registration The ongoing clinical trial study protocol conforms to the international Consolidated Standards of Reporting Trials (CONSORT) guidelines and is registered in clinicaltrials.gov (ID: NCT05077722), an international clinical trial registry.

Metabolomic Profiling in Children with Celiac Disease: Beyond the Gluten-Free Diet

Article

Full-text available

Jun 2023

Celiac disease (CD) is included in the group of complex or multifactorial diseases, i.e., those caused by the interaction of genetic and environmental factors. Despite a growing understanding of the pathophysiological mechanisms of the disease, diagnosis is still often delayed and there are no effective biomarkers for early diagnosis. The only current treatment, a gluten-free diet (GFD), can alleviate symptoms and restore intestinal villi, but its cellular effects remain poorly understood. To gain a comprehensive understanding of CD's progression, it is crucial to advance knowledge across various scientific disciplines and explore what transpires after disease onset. Metabolomics studies hold particular significance in unravelling the complexities of multifactorial and multisystemic disorders, where environmental factors play a significant role in disease manifestation and progression. By analyzing metabolites, we can gain insights into the reasons behind CD's occurrence, as well as better comprehend the impact of treatment initiation on patients. In this review, we present a collection of articles that showcase the latest breakthroughs in the field of metabolomics in pediatric CD, with the aim of trying to identify CD biomarkers for both early diagnosis and treatment monitoring. These advancements shed light on the potential of metabolomic analysis in enhancing our understanding of the disease and improving diagnostic and therapeutic strategies. More studies need to be designed to cover metabolic profiles in subjects at risk of developing the disease, as well as those analyzing biomarkers for follow-up treatment with a GFD.

Integrating ion mobility into comprehensive multidimensional metabolomics workflows: critical considerations

Article

Full-text available

Dec 2022
METABOLOMICS

Background Ion mobility (IM) separation capabilities are now widely available to researchers through several commercial vendors and are now being adopted into many metabolomics workflows. The added peak capacity that ion mobility offers with minimal compromise to other analytical figures-of-merit has provided real benefits to sensitivity and structural selectivity and have allowed more specific metabolite annotations to be assigned in untargeted workflows. One of the greatest promises of contemporary IM-enabled instrumentation is the capability of operating multiple analytical dimensions inline with minimal sample volumes, which has the potential to address many grand challenges currently faced in the omics fields. However, comprehensive operation of multidimensional mass spectrometry comes with its own inherent challenges that, beyond operational complexity, may not be immediately obvious to practitioners of these techniques.Aim of reviewIn this review, we outline the strengths and considerations for incorporating IM analysis in metabolomics workflows and provide a critical but forward-looking perspective on the contemporary challenges and prospects associated with interpreting IM data into chemical knowledge.Key scientific concepts of reviewWe outline a strategy for unifying IM-derived collision cross section (CCS) measurements obtained from different IM techniques and discuss the emerging field of high resolution ion mobility (HRIM) that is poised to address many of the contemporary challenges associated with ion mobility metabolomics. Whereas the LC step limits the throughput of comprehensive LC-IM-MS, the higher peak capacity of HRIM can allow fast LC gradients or rapid sample cleanup via solid-phase extraction (SPE) to be utilized, significantly improving the sample throughput.

Metabolomics profiling in acute liver transplant rejection in a pediatric population

Article

Full-text available

Nov 2022

Pediatric liver transplantation rejection affects 20% of children. Currently, liver biopsy, expensive and invasive, is the best method of diagnosis. Discovery and validation of clinical biomarkers from blood or other biospecimens would improve clinical care. For this study, stored plasma samples were utilized from two cross-sectional cohorts of liver transplant patients at Children’s Healthcare of Atlanta. High resolution metabolic profiling was completed using established methods. Children with (n = 18) or without (n = 25) acute cellular rejection were included in the analysis (n = 43 total). The mean age of these racially diverse cohorts ranged from 12.6 years in the rejection group and 13.6 years in the no rejection group. Linear regression provided 510 significantly differentiating metabolites between groups, and OPLS-DA showed 145 metabolites with VIP > 2. A total of 95 overlapping significant metabolites between OPLS-DA and linear regression analyses were detected. Pathway analysis (p < 0.05) showed bile acid biosynthesis and tryptophan metabolism as the top two differentiating pathways. Network analysis also identified tryptophan and clustered with liver enzymes and steroid use. We conclude metabolic profiling of plasma from children with acute liver transplant rejection demonstrates > 500 significant metabolites. This result suggests that development of a non-invasive biomarker-based test is possible for rejection screening.

A nested case‐control study of untargeted plasma metabolomics and lung cancer among never‐smoking women within the prospective Shanghai Women's Health Study

Article

Full-text available

Apr 2024
INT J CANCER

The etiology of lung cancer in never‐smokers remains elusive, despite 15% of lung cancer cases in men and 53% in women worldwide being unrelated to smoking. Here, we aimed to enhance our understanding of lung cancer pathogenesis among never‐smokers using untargeted metabolomics. This nested case‐control study included 395 never‐smoking women who developed lung cancer and 395 matched never‐smoking cancer‐free women from the prospective Shanghai Women's Health Study with 15,353 metabolic features quantified in pre‐diagnostic plasma using liquid chromatography high‐resolution mass spectrometry. Recognizing that metabolites often correlate and seldom act independently in biological processes, we utilized a weighted correlation network analysis to agnostically construct 28 network modules of correlated metabolites. Using conditional logistic regression models, we assessed the associations for both metabolic network modules and individual metabolic features with lung cancer, accounting for multiple testing using a false discovery rate (FDR) < 0.20. We identified a network module of 121 features inversely associated with all lung cancer (p = .001, FDR = 0.028) and lung adenocarcinoma (p = .002, FDR = 0.056), where lyso‐glycerophospholipids played a key role driving these associations. Another module of 440 features was inversely associated with lung adenocarcinoma (p = .014, FDR = 0.196). Individual metabolites within these network modules were enriched in biological pathways linked to oxidative stress, and energy metabolism. These pathways have been implicated in previous metabolomics studies involving populations exposed to known lung cancer risk factors such as traffic‐related air pollution and polycyclic aromatic hydrocarbons. Our results suggest that untargeted plasma metabolomics could provide novel insights into the etiology and risk factors of lung cancer among never‐smokers.

Olive mill solid waste induces beneficial mushroom-specialized metabolite diversity: a computational metabolomics study

Preprint

Feb 2024

Hericium erinaceus and Pleurotus eryngii are edible and medicinal mushrooms grown commercially in many countries around the world. In nature, H. erinaceus grows on old or dead trunks of hardwood trees. P. eryngii grows on the roots of Apiaceae plants. To exploit their beneficial properties, these mushrooms have been grown indoors using mushroom substrates mainly consisting of dry wood chips, straw, and cereals originating from forest maintenance, agriculture, and industry wastes, respectively. Additional supplements such as olive mill solid waste are added to the substrate to support mushroom development. However, the impact of substrate additives on the edible mushroom metabolic content has not been assessed so far. We examined the effect of adding to the substrate different proportions of olive mill solid waste on the metabolic profiles of the fruiting body (FB) and mycelium of H. erinaceus and P. eryngii mushrooms. We used computational metabolomics methods to analyze the untargeted metabolomics data obtained from Q-Exactive Plus high-resolution LC-MS/MS data. In general, the methanolic extracts of H. erinaceus FB and mycelium were more highly enriched with specialized metabolites than those of P. eryngii . Interestingly, olive mill solid waste increased some of the unique metabolites related to the beneficial hericenone family in the H. erinaceus FB and several erinacerin metabolites from the mycelium. At the same time, the additive decreased the toxic enniatin metabolite abundance. Altogether, we demonstrate how a change in substrate composition affects the mushroom’s specialized metabolome and can induce beneficial mushroom metabolite diversity. This highlights the importance of including metabolomics strategies to investigate new sustainable growth options for edible mushrooms and other natural foods.

Real-Time Exposure to 3D-Printing Emissions Elicits Metabolic and Pro-Inflammatory Responses in Human Airway Epithelial Cells

Article

Full-text available

Jan 2024

Three-dimensional (3D) printer usage in household and school settings has raised health concerns regarding chemical and particle emission exposures during operation. Although the composition of 3D printer emissions varies depending on printer settings and materials, little is known about the impact that emissions from different filament types may have on respiratory health and underlying cellular mechanisms. In this study, we used an in vitro exposure chamber system to deliver emissions from two popular 3D-printing filament types, acrylonitrile butadiene styrene (ABS) and polylactic acid (PLA), directly to human small airway epithelial cells (SAEC) cultured in an air–liquid interface during 3D printer operation. Using a scanning mobility particle sizer (SMPS) and an optical particle sizer (OPS), we monitored 3D printer particulate matter (PM) emissions in terms of their particle size distribution, concentrations, and calculated deposited doses. Elemental composition of ABS and PLA emissions was assessed using scanning electron microscopy coupled with energy dispersive X-ray spectroscopy (SEM-EDX). Finally, we compared the effects of emission exposure on cell viability, inflammation, and metabolism in SAEC. Our results reveal that, although ABS filaments emitted a higher total concentration of particles and PLA filaments emitted a higher concentration of smaller particles, SAEC were exposed to similar deposited doses of particles for each filament type. Conversely, ABS and PLA emissions had distinct elemental compositions, which were likely responsible for differential effects on SAEC viability, oxidative stress, release of inflammatory mediators, and changes in cellular metabolism. Specifically, while ABS- and PLA-emitted particles both reduced cellular viability and total glutathione levels in SAEC, ABS emissions had a significantly greater effect on glutathione relative to PLA emissions. Additionally, pro-inflammatory cytokines including IL-1β, MMP-9, and RANTES were significantly increased due to ABS emissions exposure. While IL-6 and IL-8 were stimulated in both exposure scenarios, VEGF was exclusively increased due to PLA emissions exposures. Notably, ABS emissions induced metabolic perturbation on amino acids and energy metabolism, as well as redox-regulated pathways including arginine, methionine, cysteine, and vitamin B3 metabolism, whereas PLA emissions exposures caused fatty acid and carnitine dysregulation. Taken together, these results advance our mechanistic understanding of 3D-printer-emissions-induced respiratory toxicity and highlight the role that filament emission properties may play in mediating different respiratory outcomes.

Metabolic reprograming and increased inflammation by cadmium exposure following early-life respiratory syncytial virus infection-the involvement of protein S-palmitoylation

Article

Nov 2023
TOXICOL SCI

Early-life respiratory syncytial virus (RSV) infection (eRSV) is one of the leading causes of serious pulmonary disease in children. eRSV is associated with higher risk of developing asthma and compromised lung function later in life. Cadmium (Cd) is a toxic metal, widely present in the environment and in food. We recently showed that eRSV re-programs metabolism and potentiates Cd toxicity in the lung, and our transcriptome-metabolome-wide study showed strong associations between S-palmitoyl transferase expression and Cd-stimulated lung inflammation and fibrosis signaling. Limited information is available on the mechanism by which eRSV re-programs metabolism and potentiates Cd toxicity in the lung. In the current study, we used a mouse model to examine the role of protein S-palmitoylation (Pr-S-Pal) in low dose Cd-elevated lung metabolic disruption and inflammation following eRSV. Mice exposed to eRSV were later treated with Cd (3.3 mg CdCl2/L) in drinking water for 6 weeks (RSV+Cd). The role of Pr-S-Pal was studied using a palmitoyl transferase inhibitor, 2-bromopalmitate (BP, 10 µM). Inflammatory marker analysis showed that cytokines, chemokines and inflammatory cells were highest in the RSV+Cd group, and BP decreased inflammatory markers. Lung metabolomics analysis showed that pathways including phenylalanine, tyrosine and tryptophan, phosphatidylinositol and sphingolipid were altered across treatments. BP antagonized metabolic disruption of sphingolipid and glycosaminoglycan metabolism by RSV+Cd, consistent with BP effect on inflammatory markers. This study shows that Cd exposure following eRSV has a significant impact on subsequent inflammatory response and lung metabolism, which is mediated by Pr-S-Pal, and warrants future research for a therapeutic target.

Linking Air Pollution Exposure to Blood-Based Metabolic Features in a Community-Based Aging Cohort with and without Dementia

Article

Full-text available

Nov 2023
J ALZHEIMERS DIS

Background: Long-term exposure to air pollution has been associated with changes in levels of metabolites measured in the peripheral blood. However, most research has been conducted in ethnically homogenous, young or middle-aged populations. Objective: To study the relationship between the plasma metabolome and long-term exposure to three air pollutants: particulate matter (PM) less than 2.5μm in aerodynamic diameter (PM2.5), PM less than 10μm in aerodynamic diameter (PM10), and nitrogen dioxide (NO2) in an ethnically diverse, older population. Methods: Plasma metabolomic profiles of 107 participants of the Washington Heights and Inwood Community Aging Project in New York City, collected from 1995–2015, including non-Hispanic white, Caribbean Hispanic, and non-Hispanic Black older adults were used. We estimated the association between each metabolic feature and predicted annual mean exposure to the air pollutants using three approaches: 1) A metabolome wide association study framework; 2) Feature selection using elastic net regression; and 3) A multivariate approach using partial-least squares discriminant analysis. Results: 79 features associated with exposure to PM2.5 but none associated with PM10 or NO2. PM2.5 exposure was associated with altered amino acid metabolism, energy production, and oxidative stress response, pathways also associated with Alzheimer’s disease. Three metabolites were associated with PM2.5 exposure through all three approaches: cysteinylglycine disulfide, a diglyceride, and a dicarboxylic acid. The relationship between several features and PM2.5 exposure was modified by diet and metabolic diseases. Conclusions: These relationships uncover the mechanisms through which PM2.5 exposure can lead to altered metabolic outcomes in an older population.

Multiomics tools for improved atherosclerotic cardiovascular disease management

Article

Full-text available

Oct 2023
TRENDS MOL MED

Multiomics studies offer accurate preventive and therapeutic strategies for atherosclerotic cardiovascular disease (ASCVD) beyond traditional risk factors. By using artificial intelligence (AI) and machine learning (ML) approaches, it is possible to integrate multiple ‘omics and clinical data sets into tools that can be utilized for the development of personalized diagnostic and therapeutic approaches. However, currently multiple challenges in data quality, integration, and privacy still need to be addressed. In this opinion, we emphasize that joined efforts, exemplified by the AtheroNET COST Action, have a pivotal role in overcoming the challenges to advance multiomics approaches in ASCVD research, with the aim to foster more precise and effective patient care.

EXPOSOME RESEARCH: DEFINITION, SCOPE AND THE NEED FOR MULTIDISCIPLINARY RESEARCH

Article

Aug 2023
FARMACIA

CAROLINA NEGREI

Third Trimester Serum Amino Acid Metabolism is Associated with Maternal Breast Cancer Diagnosed within 15 years of Pregnancy

Preprint

Full-text available

Aug 2023

A prospective metabolome-wide association study revealed widespread amino acid limitation in late pregnancy is associated with early onset breast cancer. Archival third trimester pregnancy serum samples from 172 women who subsequently were diagnosed with breast cancer within 38 years after pregnancy were compared to 351 women without breast cancer. No individual metabolite differed after false discovery rate adjustment, indicating that individual metabolites are unlikely to be useful for classification or prediction. Despite this, pathway enrichment analysis showed that amino acid pathways, including lysine, arginine, proline, aspartate, asparagine, alanine, tyrosine, tryptophan, histidine, branched-chain amino acid and urea cycle, were enriched among metabolites that differed at raw p < 0.05. Several of these pathways previously were linked to breast carcinogen exposures, including dichlorodiphenyltrichloroethane and perfluorinated alkyl substances. Network analyses showed that amino acids correlated with parity and the ratio of estriol to estrone and estradiol known risk factors for breast cancer in this cohort. Overall, amino acid associations were stronger for early onset breast cancer, defined here as occurring within the first 15 years following pregnancy. Although results must be interpreted cautiously, lower amino acid concentrations for histidine, threonine and proline, and stronger associations for tryptophan, histidine, and lysine pathways with breast cancer within 15 years, suggests that amino acid limitations during late pregnancy contribute to metabolic reprogramming that is causally related to early onset breast cancer. Environmental chemical effects on nutrient sensing could account for these effects through known oncogenic mechanisms linked to nutrient stress.

Association between Neuroligin-1 polymorphism and plasma glutamine levels in individuals with autism spectrum disorder

Article

Full-text available

Aug 2023

Background: Unravelling the relationships between candidate genes and autism spectrum disorder (ASD) phenotypes remains an outstanding challenge. Endophenotypes, defined as inheritable, measurable quantitative traits, might provide intermediary links between genetic risk factors and multifaceted ASD phenotypes. In this study, we sought to determine whether plasma metabolite levels could serve as endophenotypes in individuals with ASD and their family members. Methods: We employed an untargeted, high-resolution metabolomics platform to analyse 14,342 features across 1099 plasma samples. These samples were collected from probands and their family members participating in the Autism Genetic Resource Exchange (AGRE) (N = 658), compared with neurotypical individuals enrolled in the PrecisionLink Health Discovery (PLHD) program at Boston Children's Hospital (N = 441). We conducted a metabolite quantitative trait loci (mQTL) analysis using whole-genome genotyping data from each cohort in AGRE and PLHD, aiming to prioritize significant mQTL and metabolite pairs that were exclusively observed in AGRE. Findings: Within the AGRE group, we identified 54 significant associations between genotypes and metabolite levels (P < 5.27 × 10-11), 44 of which were not observed in the PLHD group. Plasma glutamine levels were found to be associated with variants in the NLGN1 gene, a gene that encodes post-synaptic cell-adhesion molecules in excitatory neurons. This association was not detected in the PLHD group. Notably, a significant negative correlation between plasma glutamine and glutamate levels was observed in the AGRE group, but not in the PLHD group. Furthermore, plasma glutamine levels showed a negative correlation with the severity of restrictive and repetitive behaviours (RRB) in ASD, although no direct association was observed between RRB severity and the NLGN1 genotype. Interpretation: Our findings suggest that plasma glutamine levels could potentially serve as an endophenotype, thus establishing a link between the genetic risk associated with NLGN1 and the severity of RRB in ASD. This identified association could facilitate the development of novel therapeutic targets, assist in selecting specific cohorts for clinical trials, and provide insights into target symptoms for future ASD treatment strategies. Funding: This work was supported by the National Institute of Health (grant numbers: R01MH107205, U01TR002623, R24OD024622, OT2OD032720, and R01NS129188) and the PrecisionLink Biobank for Health Discovery at Boston Children's Hospital.

Comparison of Internal Cluster Validation Indices in the Aspect of the Number of Clusters Evaluation

Conference Paper

Full-text available

Dec 2022

Ali Şenol

Clustering algorithms are very efficient approaches on datasets for which we have no class labels. Besides, clustering is also commonly used for evaluating datasets in pre-processing stage of machine learning algorithms. However, having an idea about the number of clusters is also required in many applications. Uncertainty about the number of clusters is an important problem for partitioning-based clustering algorithms like k-means. On the other hand, internal cluster validation indices evaluate the quality of clusters according to the similarity among the data. The internal cluster validation indices aim to maximize both the similarity among data in the same cluster and the dissimilarity among the data in the different clusters. In this study, we used Silhouette Index (SI), Dunn Index (DI), Davies-Bouldin (DB), Calinski-Harabasz (CH), and VIASCKDE indices to estimate the number of clusters by using k-means on various datasets. According to the experimental studies, VIASCKDE was the most successful among the others in the aspect of clustering quality while VIASCKDE, SI, and DI were the most successful ones to determine the number of clusters.

Comparison of Internal Cluster Validation Indices in the Aspect of the number of Clusters Evaluation

Conference Paper

Full-text available

Dec 2022

Ali Şenol

A pilot metabolomic study of drug interaction with the immune response to seasonal influenza vaccination

Article

Full-text available

Jun 2023

Many human diseases, including metabolic diseases, are intertwined with the immune system. The understanding of how the human immune system interacts with pharmaceutical drugs is still limited, and epidemiological studies only start to emerge. As the metabolomics technology matures, both drug metabolites and biological responses can be measured in the same global profiling data. Therefore, a new opportunity presents itself to study the interactions between pharmaceutical drugs and immune system in the high-resolution mass spectrometry data. We report here a double-blinded pilot study of seasonal influenza vaccination, where half of the participants received daily metformin administration. Global metabolomics was measured in the plasma samples at six timepoints. Metformin signatures were successfully identified in the metabolomics data. Statistically significant metabolite features were found both for the vaccination effect and for the drug-vaccine interactions. This study demonstrates the concept of using metabolomics to investigate drug interaction with the immune response in human samples directly at molecular levels.

A State-of-the-Science Review on High-Resolution Metabolomics Application in Air Pollution Health Research: Current Progress, Analytical Challenges, and Recommendations for Future Direction

Article

May 2023
ENVIRON HEALTH PERSP

Background: Understanding the mechanistic basis of air pollution toxicity is dependent on accurately characterizing both exposure and biological responses. Untargeted metabolomics, an analysis of small-molecule metabolic phenotypes, may offer improved estimation of exposures and corresponding health responses to complex environmental mixtures such as air pollution. The field remains nascent, however, with questions concerning the coherence and generalizability of findings across studies, study designs and analytical platforms. Objectives: We aimed to review the state of air pollution research from studies using untargeted high-resolution metabolomics (HRM), highlight the areas of concordance and dissimilarity in methodological approaches and reported findings, and discuss a path forward for future use of this analytical platform in air pollution research. Methods: We conducted a state-of-the-science review to a) summarize recent research of air pollution studies using untargeted metabolomics and b) identify gaps in the peer-reviewed literature and opportunities for addressing these gaps in future designs. We screened articles published within Pubmed and Web of Science between 1 January 2005 and 31 March 2022. Two reviewers independently screened 2,065 abstracts, with discrepancies resolved by a third reviewer. Results: We identified 47 articles that applied untargeted metabolomics on serum, plasma, whole blood, urine, saliva, or other biospecimens to investigate the impact of air pollution exposures on the human metabolome. Eight hundred sixteen unique features confirmed with level-1 or -2 evidence were reported to be associated with at least one or more air pollutants. Hypoxanthine, histidine, serine, aspartate, and glutamate were among the 35 metabolites consistently exhibiting associations with multiple air pollutants in at least 5 independent studies. Oxidative stress and inflammation-related pathways-including glycerophospholipid metabolism, pyrimidine metabolism, methionine and cysteine metabolism, tyrosine metabolism, and tryptophan metabolism-were the most commonly perturbed pathways reported in >70% of studies. More than 80% of the reported features were not chemically annotated, limiting the interpretability and generalizability of the findings. Conclusions: Numerous investigations have demonstrated the feasibility of using untargeted metabolomics as a platform linking exposure to internal dose and biological response. Our review of the 47 existing untargeted HRM-air pollution studies points to an underlying coherence and consistency across a range of sample analytical quantitation methods, extraction algorithms, and statistical modeling approaches. Future directions should focus on validation of these findings via hypothesis-driven protocols and technical advances in metabolic annotation and quantification. https://doi.org/10.1289/EHP11851.

Antagonistic Interactions in Mitochondria ROS Signaling Responses to Manganese

Article

Full-text available

Mar 2023

Antagonistic interaction refers to opposing beneficial and adverse signaling by a single agent. Understanding opposing signaling is important because pathologic outcomes can result from adverse causative agents or the failure of beneficial mechanisms. To test for opposing responses at a systems level, we used a transcriptome–metabolome-wide association study (TMWAS) with the rationale that metabolite changes provide a phenotypic readout of gene expression, and gene expression provides a phenotypic readout of signaling metabolites. We incorporated measures of mitochondrial oxidative stress (mtOx) and oxygen consumption rate (mtOCR) with TMWAS of cells with varied manganese (Mn) concentration and found that adverse neuroinflammatory signaling and fatty acid metabolism were connected to mtOx, while beneficial ion transport and neurotransmitter metabolism were connected to mtOCR. Each community contained opposing transcriptome–metabolome interactions, which were linked to biologic functions. The results show that antagonistic interaction is a generalized cell systems response to mitochondrial ROS signaling.

Metabolome × Microbiome Changes Associated with a Diet-Induced Reduction in Hepatic Fat among Adolescent Boys

Article

Full-text available

Mar 2023

Dietary sugar reduction is one therapeutic strategy for improving nonalcoholic fatty liver disease (NAFLD), and the underlying mechanisms for this effect warrant further investigation. Here, we employed metabolomics and metagenomics to examine systemic biological adaptations associated with dietary sugar restriction and (subsequent) hepatic fat reductions in youth with NAFLD. Data/samples were from a randomized controlled trial in adolescent boys (11–16 years, mean ± SD: 13.0 ± 1.9 years) with biopsy-proven NAFLD who were either provided a low free-sugar diet (LFSD) (n = 20) or consumed their usual diet (n = 20) for 8 weeks. Plasma metabolomics was performed on samples from all 40 participants by coupling hydrophilic interaction liquid chromatography (HILIC) and C18 chromatography with mass spectrometry. In a sub-sample (n = 8 LFSD group and n = 10 usual diet group), 16S ribosomal RNA (rRNA) sequencing was performed on stool to examine changes in microbial composition/diversity. The diet treatment was associated with differential expression of 419 HILIC and 205 C18 metabolite features (p < 0.05), which were enriched in amino acid pathways, including methionine/cysteine and serine/glycine/alanine metabolism (p < 0.05), and lipid pathways, including omega-3 and linoleate metabolism (p < 0.05). Quantified metabolites that were differentially changed in the LFSD group, compared to usual diet group, and representative of these enriched metabolic pathways included increased serine (p = 0.001), glycine (p = 0.004), 2-aminobutyric acid (p = 0.012), and 3-hydroxybutyric acid (p = 0.005), and decreased linolenic acid (p = 0.006). Microbiome changes included an increase in richness at the phylum level and changes in a few genera within Firmicutes. In conclusion, the LFSD treatment, compared to usual diet, was associated with metabolome and microbiome changes that may reflect biological mechanisms linking dietary sugar restriction to a therapeutic decrease in hepatic fat. Studies are needed to validate our findings and test the utility of these “omics” changes as response biomarkers.

A metabolome-wide case-control study of african american breast cancer patients

Article

Full-text available

Feb 2023
BMC CANCER

Background Breast cancer survivors face long-term sequelae compared to the general population, suggesting altered metabolic profiles after breast cancer. We used metabolomics approaches to investigate the metabolic differences between breast cancer patients and women in the general population, aiming to elaborate metabolic changes among breast cancer patients and identify potential targets for clinical interventions to mitigate long-term sequelae. Methods Serum samples were retrieved from 125 breast cancer cases recruited from the Chicago Multiethnic Epidemiologic Breast Cancer Cohort (ChiMEC), and 125 healthy controls selected from Chicago Multiethnic Prevention and Surveillance Study (COMPASS). We used liquid chromatography-high resolution mass spectrometry to obtain untargeted metabolic profiles and partial least squares discriminant analysis (PLS-DA) combined with fold change to select metabolic features associated with breast cancer. Pathway analyses were conducted using Mummichog to identify differentially enriched metabolic pathways among cancer patients. As potential confounders we included age, marital status, tobacco smoking, alcohol drinking, type 2 diabetes, and area deprivation index in our model. Random effects of residence for intercept was also included in the model. We further conducted subgroup analysis by treatment timing (chemotherapy/radiotherapy/surgery), lymph node status, and cancer stages. Results The entire study participants were African American. The average ages were 57.1 for cases and 58.0 for controls. We extracted 15,829 features in total, among which 507 features were eventually selected by our criteria. Pathway enrichment analysis of these 507 features identified three differentially enriched metabolic pathways related to prostaglandin, leukotriene, and glycerophospholipid. The three pathways demonstrated inconsistent patterns. Metabolic features in the prostaglandin and leukotriene pathways exhibited increased abundances among cancer patients. In contrast, metabolic intensity in the glycerolphospholipid pathway was deregulated among cancer patients. Subgroup analysis yielded consistent results. However, changes in these pathways were strengthened when only using cases with positive lymph nodes, and attenuated when only using cases with stage I disease. Conclusion Breast cancer in African American women is associated with increase in serum metabolites involved in prostaglandin and leukotriene pathways, but with decrease in serum metabolites in glycerolphospholipid pathway. Positive lymph nodes and advanced cancer stage may strengthen changes in these pathways.

Study on the Relationship between Selenium and Cadmium in Diseased Human Lungs

Article

Full-text available

Feb 2023

Cadmium (Cd) is a toxic environmental metal that interacts with selenium (Se) and contributes to many lung diseases. Humans have widespread exposures to Cd through diet and cigarette smoking, and studies in rodent models show that Se can protect against Cd toxicities. We sought to identify whether an antagonistic relationship existed between Se and Cd burdens and determine whether this relationship may associate with metabolic variation within human lungs. We performed metabolomics of 31 human lungs, including 25 with end-stage lung disease due to idiopathic pulmonary fibrosis, cystic fibrosis, chronic obstructive lung disease (COPD)/emphysema and other causes, and 6 non-diseased lungs. Results showed pathway associations with Cd including amino acid, lipid and energy-related pathways. Metabolic pathways varying with Se had considerable overlap with these pathways. Hierarchical cluster analysis (HCA) of individuals according to metabolites associated with Cd showed partial separation of disease types, with COPD/emphysema in the cluster with highest Cd, and non-diseased lungs in the cluster with the lowest Cd. When compared to HCA of metabolites associated with Se, the results showed that the cluster containing COPD/emphysema had the lowest Se, and the non-diseased lungs had the highest Se. A greater number of pathway associations occurred for Cd to Se ratio than either Cd or Se alone, indicating that metabolic patterns were more dependent on Cd to Se ratio than on either alone. Network analysis of interactions of Cd and Se showed network centrality was associated with pathways linked to polyunsaturated fatty acids involved in inflammatory signaling. Overall, the data show that metabolic pathway responses in human lung vary with Cd and Se in a pattern suggesting that Se is antagonistic to Cd toxicity in humans.

Metabolomic changes associated with chronic arsenic exposure in a Bangladeshi population

Article

Feb 2023
CHEMOSPHERE

Chronic exposure to arsenic (As) remains a global public health concern and our understanding of the biological mechanisms underlying the adverse effects of As exposure remains incomplete. Here, we used a high-resolution metabolomics approach to examine how As affects metabolic pathways in humans. We selected 60 non-smoking adults from the Folic Acid and Creatine Trial (FACT). Inorganic (AsIII, AsV) and organic (monomethylarsonous acid [MMAs], dimethylarsinous Acid [DMAs]) As species were measured in blood and urine collected at baseline and at 12 weeks. Plasma metabolome profiles were measured using untargeted high-resolution mass spectrometry. Associations of blood and urinary As with 170 confirmed metabolites and >26,000 untargeted spectral features were modeled using a metabolome-wide association study (MWAS) approach. Models were adjusted for age, sex, visit, and BMI and corrected for false discovery rate (FDR). In the MWAS screening of confirmed metabolites, 17 were associated with ≥1 blood As species (FDR<0.05), including fatty acids, neurotransmitter metabolites, and amino acids. These results were consistent across blood As species and between blood and urine As. Untargeted MWAS identified 423 spectral features associated with ≥1 blood As species. Unlike the confirmed metabolites, untargeted model results were not consistent across As species, with AsV and DMAs showing distinct association patterns. Mummichog pathway analysis revealed 12 enriched metabolic pathways that overlapped with the 17 identified metabolites, including one carbon metabolism, tricarboxylic acid cycle, fatty acid metabolism, and purine metabolism. Exposure to As may affect numerous essential pathways that underlie the well-characterized associations of As with multiple chronic diseases.

When cancer drug resistance meets metabolomics (bulk, single-cell and/or spatial): Progress, potential, and perspective

Article

Full-text available

Jan 2023

Resistance to drug treatment is a critical barrier in cancer therapy. There is an unmet need to explore cancer hallmarks that can be targeted to overcome this resistance for therapeutic gain. Over time, metabolic reprogramming has been recognised as one hallmark that can be used to prevent therapeutic resistance. With the advent of metabolomics, targeting metabolic alterations in cancer cells and host patients represents an emerging therapeutic strategy for overcoming cancer drug resistance. Driven by technological and methodological advances in mass spectrometry imaging, spatial metabolomics involves the profiling of all the metabolites (metabolomics) so that the spatial information is captured bona fide within the sample. Spatial metabolomics offers an opportunity to demonstrate the drug-resistant tumor profile with metabolic heterogeneity, and also poses a data-mining challenge to reveal meaningful insights from high-dimensional spatial information. In this review, we discuss the latest progress, with the focus on currently available bulk, single-cell and spatial metabolomics technologies and their successful applications in pre-clinical and translational studies on cancer drug resistance. We provide a summary of metabolic mechanisms underlying cancer drug resistance from different aspects; these include the Warburg effect, altered amino acid/lipid/drug metabolism, generation of drug-resistant cancer stem cells, and immunosuppressive metabolism. Furthermore, we propose solutions describing how to overcome cancer drug resistance; these include early detection during cancer initiation, monitoring of clinical drug response, novel anticancer drug and target metabolism, immunotherapy, and the emergence of spatial metabolomics. We conclude by describing the perspectives on how spatial omics approaches (integrating spatial metabolomics) could be further developed to improve the management of drug resistance in cancer patients.

Comprehensive characterization of putative genetic influences on plasma metabolome in a pediatric cohort

Article

Full-text available

Dec 2022
Hum Genom

Abstract Background The human exposome is composed of diverse metabolites and small chemical compounds originated from endogenous and exogenous sources, respectively. Genetic and environmental factors influence metabolite levels, while the extent of genetic contributions across metabolic pathways is not yet known. Untargeted profiling of human metabolome using high-resolution mass spectrometry (HRMS) combined with genome-wide genotyping allows comprehensive identification of genetically influenced metabolites. As such previous studies of adults discovered and replicated genotype–metabotype associations. However, these associations have not been characterized in children. Results We conducted the largest genome by metabolome-wide association study to date of children (N = 441) using 619,688 common genetic variants and 14,342 features measured by HRMS. Narrow-sense heritability (h 2) estimates of plasma metabolite concentrations using genomic relatedness matrix restricted maximum likelihood (GREML) method showed a bimodal distribution with high h 2 (> 0.8) for 15.9% of features and low h 2 (

Toward building mass spectrometry-based metabolomics and lipidomics atlases for biological and clinical research

Article

Full-text available

Nov 2022
TRAC-TREND ANAL CHEM

Current metabolomics and lipidomics studies are limited in the number of examined matrices, the breadth and scope of methods, reporting the number of metabolites, and data sharing. Here, we discuss the concept of metabolomics and lipidomics atlases that characterize the quantitative distribution and relationships of metabolites in biological matrices and serve as a resource for future studies. Combined sample extraction is recommended to screen the metabolome and lipidome comprehensively. A multiplatform mass spectrometry-based approach with methods for each fraction should follow to separate and detect metabolites differing in their physicochemical properties. Since many known metabolites are detected through untargeted analysis, routine use of multiple internal standards for quantification is advised. This approach provides semiquantitative data and delivers molar concentrations for selected polar metabolites and lipids. An interactive web tool to query metabolites, generate statistical models, visualize data, and download the results should be developed to access generated data easily.

Mechanistic insights into the health benefits of fish-oil supplementation against fine particulate matter air pollution: a randomized controlled trial

Article

Full-text available

Oct 2022
ENVIRON HEALTH-GLOB

Background Dietary fish-oil supplementation might attenuate the associations between fine particulate matter (PM2.5) and subclinical biomarkers. However, the molecular mechanisms remain to be elucidated. This study aimed to explore the molecular mechanisms of fish-oil supplementation against the PM2.5-induced health effects. Methods We conducted a randomized, double-blinded, and placebo-controlled trial among healthy college students in Shanghai, China, from September 2017 to January 2018. A total of 70 participants from the Fenglin campus of Fudan University were included. We randomly assigned participants to either supplementation of 2.5-gram fish oil (n = 35) or sunflower-seed oil (placebo) (n = 35) per day and conducted four rounds of health measurements in the last two months of the trial. As a post hoc exploratory study, the present untargeted metabolomics analysis used remaining blood samples collected in the previous trial and applied a Metabolome-Wide Association Study framework to compare the effects of PM2.5 on the metabolic profile between the sunflower-seed oil and fish oil groups. Results A total of 65 participants completed the trial (34 of the fish oil group and 31 of the sunflower-seed oil group). On average, ambient PM2.5 concentration on the day of health measurements was 34.9 µg/m³ in the sunflower-seed oil group and 34.5 µg/m³ in the fish oil group, respectively. A total of 3833 metabolites were significantly associated with PM2.5 in the sunflower-seed oil group and 1757 in the fish oil group. Of these, 1752 metabolites showed significant between-group differences. The identified differential metabolites included arachidonic acid derivatives, omega-3 fatty acids, omega-6 fatty acids, and omega-9 fatty acids that were related to unsaturated fatty acid metabolism, which plays a role in the inflammatory responses. Conclusion This trial suggests fish-oil supplementation could mitigate the PM2.5-induced inflammatory responses via modulating fatty acid metabolism, providing biological plausibility for the health benefits of fish-oil supplementation against PM2.5 exposure. Trial registration This study is registered at ClinicalTrails.gov (NCT03255187).

Metabolomics Tools in Antiviral Research

Chapter

May 2024

Direct-acting antivirals (DAAs) and therapeutics must achieve two goals to effectively assist with disease mitigation. They must reduce viral replication and dissemination as well as reduce tissue damage caused by viral infection and/or the resulting immune response. They are generally “chemically modified mimics” of naturally occurring small molecules/metabolites that are intermediates or products of active biochemical pathways in the cell. However, as opposed to the naturally occurring metabolites, in a disease state, DAAs and therapeutics will divert biochemical pathways toward a more desirable outcome. Mass spectrometry (MS)-based metabolomics can detect and quantify small-molecule metabolites in cells, organs, or biofluids and provide a library of molecules that can be developed into novel chemical inhibitors or activators. However, measuring the metabolome requires consideration of various analytical challenges. Expansive chemical diversity inhibits the development of a catch-all metabolomics workflow, so various workflows must be used to measure specific molecular classes within the metabolome. A wide range of metabolite concentrations requires measurement technologies with a high dynamic range, and the temporal nature of metabolic intermediates and reactivity of certain molecular classes necessitates care with sample handling. Additionally, structural characterization and identification of metabolites are difficult due to the chemical and structural heterogeneity of the metabolome. Thus, metabolomics discovery experiments require careful consideration of study design and choice of analytical technologies with follow-up experiments for confident metabolite identification. In this chapter, we focus on the criteria that need to be considered when designing a metabolomics experiment such that quality and interpretability of data are maximized.

Exposomics in practice: Multidisciplinary perspectives on environmental health and risk assessment

Article

Apr 2024

Implementation of multiomic mass spectrometry approaches for the evaluation of human health following environmental exposure

Article

Full-text available

Apr 2024

This review introduces advancements in multiomic mass spectrometry which revolutionized our knowledge of complex biological processes across scientific disciplines, exposure scenarios, and diseases, benefiting diagnostic and treatment strategies.

Metabolic alterations and mitochondrial dysfunction in human airway BEAS-2B cells exposed to vanadium pentoxide

Article

Mar 2024
TOXICOLOGY

Proceedings of International Conference on Engineering, Science and Technology 2023

Book

Full-text available

Dec 2023

Proceedings of International Conference on Engineering, Science and Technology 2023 Editors Mack Shelley, Valarie Akerson, Sabri Turgut Volume 1, Pages 1-228 Proceedings of International Conference on Engineering, Science and Technology © 2023 Published by the ISTES Organization ISBN: 978-1-952092-49-7 Editors: Mack Shelley, Valarie Akerson, & Sabri Turgut Articles: 1-21 Conference: International Conference on Engineering, Science and Technology (IConEST) Dates: October 19-22, 2023 Location: Las Vegas, United States Conference Chair(s): Prof.Dr. Valarie Akerson, Indiana University, United States Prof. Dr. Mack Shelley, Iowa State University, United States Dr. Elizabeth (Betsy) Kersey, University of Northern Colorado, United States

New advances in exposomics-analysis methods and research paradigms based on chromatography-mass spectrometry

Article

Feb 2024
Chin J Chrom

Metabolomic signatures of ideal cardiovascular health in black adults

Article

Full-text available

Jan 2024

Plasma metabolomics profiling is an emerging methodology to identify metabolic pathways underlying cardiovascular health (CVH). The objective of this study was to define metabolomic profiles underlying CVH in a cohort of Black adults, a population that is understudied but suffers from disparate levels of CVD risk factors. The Morehouse-Emory Cardiovascular (MECA) Center for Health Equity study cohort consisted of 375 Black adults (age 53 ± 10, 39% male) without known CVD. CVH was determined by the AHA Life’s Simple 7 (LS7) score, calculated from measured blood pressure, body mass index (BMI), fasting blood glucose and total cholesterol, and self-reported physical activity, diet, and smoking. Plasma metabolites were assessed using untargeted high-resolution metabolomics profiling. A metabolome wide association study (MWAS) identified metabolites associated with LS7 score after adjusting for age and sex. Using Mummichog software, metabolic pathways that were significantly enriched in metabolites associated with LS7 score were identified. Metabolites representative of these pathways were compared across clinical domains of LS7 score and then developed into a metabolomics risk score for prediction of CVH. We identified novel metabolomic signatures and pathways associated with CVH in a cohort of Black adults without known CVD. Representative and highly prevalent metabolites from these pathways included glutamine, glutamate, urate, tyrosine and alanine, the concentrations of which varied with BMI, fasting glucose, and blood pressure levels. When assessed in conjunction, these metabolites were independent predictors of CVH. One SD increase in the novel metabolomics risk score was associated with a 0.88 higher LS7 score, which translates to a 10.4% lower incident CVD risk. We identified novel metabolomic signatures of ideal CVH in a cohort of Black Americans, showing that a core group of metabolites central to nitrogen balance, bioenergetics, gluconeogenesis, and nucleotide synthesis were associated with CVH in this population.

Cheminformatics and artificial intelligence for accelerating agrochemical discovery

Article

Full-text available

Nov 2023

The global cost-benefit analysis of pesticide use during the last 30 years has been characterized by a significant increase during the period from 1990 to 2007 followed by a decline. This observation can be attributed to several factors including, but not limited to, pest resistance, lack of novelty with respect to modes of action or classes of chemistry, and regulatory action. Due to current and projected increases of the global population, it is evident that the demand for food, and consequently, the usage of pesticides to improve yields will increase. Addressing these challenges and needs while promoting new crop protection agents through an increasingly stringent regulatory landscape requires the development and integration of infrastructures for innovative, cost- and time-effective discovery and development of novel and sustainable molecules. Significant advances in artificial intelligence (AI) and cheminformatics over the last two decades have improved the decision-making power of research scientists in the discovery of bioactive molecules. AI- and cheminformatics-driven molecule discovery offers the opportunity of moving experiments from the greenhouse to a virtual environment where thousands to billions of molecules can be investigated at a rapid pace, providing unbiased hypothesis for lead generation, optimization, and effective suggestions for compound synthesis and testing. To date, this is illustrated to a far lesser extent in the publicly available agrochemical research literature compared to drug discovery. In this review, we provide an overview of the crop protection discovery pipeline and how traditional, cheminformatics, and AI technologies can help to address the needs and challenges of agrochemical discovery towards rapidly developing novel and more sustainable products.

Associations between metals and metabolomic profiles related to diabetes among adults in a rural region

Article

Dec 2023
ENVIRON RES

High-Resolution Plasma Metabolomics and Thiamine Status in Critically Ill Adult Patients

Preprint

Full-text available

Nov 2023

Background and Aim Thiamine (Vitamin B1) is an essential micronutrient and a co-factor for metabolic functions related to energy metabolism. We determined the association between whole blood thiamine pyrophosphate (TPP) concentrations and plasma metabolites using high resolution metabolomics in critically ill patients. Methods Cross-sectional study performed in Erciyes University Hospital, Kayseri, Turkey and Emory University, Atlanta, GA, USA. Participants were ≥ 18 years of age, with an expected length of ICU stay longer than 48 hours, receiving furosemide therapy for at least 6 months before ICU admission. Results Blood for TPP and metabolomics was obtained on the day of ICU admission. Whole blood TPP concentrations were measured using high-performance liquid chromatography (HPLC). Liquid chromatography/mass spectrometry was used for plasma high-resolution metabolomics. Data was analyzed using regression analysis of TPP levels against all plasma metabolomic features in metabolome-wide association studies. We also compared metabolomic features from patients in the highest TPP concentration tertile to patients in the lowest TPP tertile as a secondary analysis. We enrolled 76 participants with a median age of 69 (range, 62.5–79.5) years. Specific metabolic pathways associated with whole blood TPP levels, using both regression and tertile analysis, included pentose phosphate, fructose and mannose, branched chain amino acid, arginine and proline, linoleate, and butanoate pathways. Conclusions Plasma high-resolution metabolomics analysis showed that whole blood TPP concentrations are significantly associated with metabolites and metabolic pathways linked to the metabolism of energy, amino acids, lipids, and the gut microbiome in adult critically ill patients.

Changing epidemiology of colorectal cancer — birth cohort effects and emerging risk factors

Article

Sep 2023

Incidence and mortality of colorectal cancer (CRC) are increasing worldwide, suggesting broad changes in the epidemiology of CRC. In this Review, we discuss the changes that are becoming evident, including trends in CRC incidence and mortality by age and birth cohort, and consider the contributions of early-life exposures and emerging risk factors to these changes. Importantly, incidence of CRC has increased among people born since the early 1950s in nearly all regions of the world. These so-called birth cohort effects imply the involvement of factors that influence the earliest stages of carcinogenesis and have effects across the life course. Accumulating evidence supports the idea that early-life exposures are important risk factors for CRC, including exposures during fetal development, childhood, adolescence and young adulthood. Environmental chemicals could also have a role because the introduction of many in the 1950s and 1960s coincides with increasing incidence of CRC among people born during those years. To reverse the expected increases in the global burden of CRC, participation in average-risk screening programmes needs to be increased by scaling up and implementing evidence-based screening strategies, and emerging risk factors responsible for these increases need to be identified.

Association of metabolic dysfunction with cognitive decline and Alzheimer's disease: A review of metabolomic evidence

Article

Aug 2023

The discovery of new biomarkers that can distinguish Alzheimer's disease (AD) from mild cognitive impairment (MCI) in the early stages will help to provide new diagnostic and therapeutic strategies and slow the transition from MCI to AD. Patients with AD may present with a concomitant metabolic disorder, such as diabetes, obesity, and dyslipidemia, as a risk factor for AD that may be involved in the onset of both AD pathology and cognitive impairment. Therefore, metabolite profiling, or metabolomics, can be very useful in diagnosing AD, developing new therapeutic targets, and evaluating both the course of treatment and the clinical course of the disease. In addition, studying the relationship between nutritional behavior and AD requires investigation of the role of conditions such as obesity, hypertension, dyslipidemia, and elevated glucose level. Based on this literature review, nutritional recommendations, including weight loss by reducing calorie and cholesterol intake and omega-3 fatty acid supplementation can prevent cognitive decline and dementia in the elderly. The underlying metabolic causes of the pathology and cognitive decline caused by AD and MCI are not well understood. In this review article, metabolomics biomarkers for diagnosis of AD and MCI and metabolic risk factors for cognitive decline in AD were evaluated.

Combining Experimental with Computational Infrared and Mass Spectra for High-Throughput Nontargeted Chemical Structure Identification

Article

Aug 2023

The inability to identify the structures of most metabolites detected in environmental or biological samples limits the utility of nontargeted metabolomics. The most widely used analytical approaches combine mass spectrometry and machine learning methods to rank candidate structures contained in large chemical databases. Given the large chemical space typically searched, the use of additional orthogonal data may improve the identification rates and reliability. Here, we present results of combining experimental and computational mass and IR spectral data for high-throughput nontargeted chemical structure identification. Experimental MS/MS and gas-phase IR data for 148 test compounds were obtained from NIST. Candidate structures for each of the test compounds were obtained from PubChem (mean = 4444 candidate structures per test compound). Our workflow used CSI:FingerID to initially score and rank the candidate structures. The top 1000 ranked candidates were subsequently used for IR spectra prediction, scoring, and ranking using density functional theory (DFT-IR). Final ranking of the candidates was based on a composite score calculated as the average of the CSI:FingerID and DFT-IR rankings. This approach resulted in the correct identification of 88 of the 148 test compounds (59%). 129 of the 148 test compounds (87%) were ranked within the top 20 candidates. These identification rates are the highest yet reported when candidate structures are used from PubChem. Combining experimental and computational MS/MS and IR spectral data is a potentially powerful option for prioritizing candidates for final structure verification.

Exposome Epidemiology for Suspect Environmental Chemical Exposures during Pregnancy Linked to Subsequent Breast Cancer Diagnosis

Article

Full-text available

Jul 2023
ENVIRON INT

Breast cancer is now the most common cancer globally, accounting for 12% of all new annual cancer cases worldwide. Despite epidemiologic studies having established a number of risk factors, knowledge of chemical exposure risks is limited to a relatively small number of chemicals. In this exposome research study, we used non-targeted, high-resolution mass spectrometry of pregnancy cohort biospecimens in the Child Health and Development Studies to test for associations with breast cancer identified via the California Cancer Registry. Second and third trimester archival samples were analyzed from 182 women who subsequently developed breast cancer and 384 randomly selected women who did not develop breast cancer. Environmental chemicals were annotated with the Toxin and Toxin-Target Database for chemical signals that were higher in breast cancer cases and used with an exposome epidemiology analytic framework to identify suspect chemicals and associated metabolic networks. Network and pathway enrichment analyses showed consistent linkage in both second and third trimesters to inflammation pathways, including linoleate, arachidonic acid and prostaglandins, and identified new suspect environmental chemicals associated with breast cancer, i.e., an N-substituted piperidine insecticide and a common commercial product, 2,4-dinitrophenol, linked to variations in amino acid and nucleotide pathways in second trimester and benzo[a]carbazole and a benzoate derivative linked to glycan and amino sugar metabolism in third trimester. The results identify new suspect environmental chemical risk factors for breast cancer and provide an exposome epidemiology framework for discovery of suspect environmental chemicals and potential mechanistic associations with breast cancer.

A pilot study to characterize substrate utilization during the fasted state using plasma high-resolution metabolomics

Article

Jul 2023
NUTRITION

Objectives: High-resolution metabolomics enables global assessment of metabolites and molecular pathways underlying physiologic processes, including substrate utilization during the fasted state. The clinical index for substrate utilization, respiratory exchange ratio (RER), is measured via indirect calorimetry. The aim of this pilot study was to use metabolomics to identify metabolic pathways and plasma metabolites associated with substrate utilization in healthy, fasted adults. Methods: This cross-sectional study included 33 adults (mean age 27.7 ± 4.9 y, mean body mass index 24.8 ± 4 kg/m2). Participants underwent indirect calorimetry to determine resting RER after an overnight fast. Untargeted metabolomics was performed on fasted plasma samples using dual-column liquid chromatography and ultra-high-resolution mass spectrometry. Linear regression and pathway enrichment analyses identified pathways and metabolites associated with substrate utilization measured with indirect calorimetry. Results: RER was significantly associated with 1389 metabolites enriched within 13 metabolic pathways (P < 0.05). Lipid-related findings included general pathways, such as fatty acid activation, and specific pathways, such as C21-steroid hormone biosynthesis and metabolism, butyrate metabolism, and carnitine shuttle. Amino acid pathways included those central to metabolism, such as glucogenic amino acids, and pathways needed to maintain reduction-oxidation reactions, such as methionine and cysteine metabolism. Galactose and pyrimidine metabolism were also associated with RER (all P < 0.05). Conclusions: The fasting plasma metabolome reflects the diverse macronutrient pathways involved in carbohydrate, amino acid, and lipid metabolism during the fasted state in healthy adults. Future studies should consider the utility of metabolomics to profile individual nutrient requirements and compare findings reported here to clinical populations.

Sample preparation strategies for mass spectrometry analysis in human exposome research: Current status and future perspectives

Article

Jun 2023
TRAC-TREND ANAL CHEM

Low-dose vanadium pentoxide perturbed lung metabolism associated with inflammation and fibrosis signaling in male animal and in vitro models

Article

Jun 2023
AM J PHYSIOL-LUNG C

Vanadium is available as a dietary supplement and also is known to be toxic if inhaled, yet little information is available concerning effects of vanadium on mammalian metabolism when at concentrations found in food and water. Vanadium pentoxide (V+5) is representative of the most common dietary and environmental exposures, and prior research shows low-dose V+5 exposure causes oxidative stress measured by glutathione oxidation and protein S-glutathionylation. We examined the metabolic impact of V+5 at relevant dietary and environmental doses (0.01, 0.1, 1 ppm for 24 h) in human lung fibroblasts (HLF) and male C57BL/6J mice (0.02, 0.2, 2 ppm in drinking water for 7 months). Untargeted metabolomics using liquid chromatography-high-resolution mass spectrometry (LC-HRMS) showed that V+5 induced significant metabolic perturbations in both HLF cells and mouse lungs. We noted 30% of the significantly altered pathways in HLF cells, including pyrimidines and aminosugars, fatty acids, mitochondrial and redox pathways, showed similar dose-dependent patterns in mouse lung tissues. Alterations in lipid metabolism included leukotrienes and prostaglandins involved in inflammatory signaling, which have been associated with the pathogenesis of idiopathic pulmonary fibrosis (IPF) and other disease processes. Elevated hydroxyproline levels and excessive collagen deposition were also present in lungs from V+5-treated mice. Taken together, these results show that oxidative stress from environmental V+5, ingested at low levels, could alter metabolism to contribute to common human lung diseases.

Respiratory Effects of Traffic-Related Air Pollution: A Randomized, Crossover Analysis of Lung Function, Airway Metabolome, and Biomarkers of Airway Injury

Article

May 2023
ENVIRON HEALTH PERSP

Background: Exposure to traffic-related air pollution (TRAP) has been associated with increased risks of respiratory diseases, but the biological mechanisms are not yet fully elucidated. Objectives: Our aim was to evaluate the respiratory responses and explore potential biological mechanisms of TRAP exposure in a randomized crossover trial. Methods: We conducted a randomized crossover trial in 56 healthy adults. Each participant was exposed to high- and low-TRAP exposure sessions by walking in a park and down a road with high traffic volume for 4 h in random order. Respiratory symptoms and lung function, including forced expiratory volume in the first second (FEV1), forced vital capacity (FVC), the ratio of FEV1 to FVC, and maximal mid-expiratory flow (MMEF), were measured before and after each exposure session. Markers of 8-isoprostane, tumor necrosis factor-α (TNF-α), and ezrin in exhaled breath condensate (EBC), and surfactant proteins D (SP-D) in serum were also measured. We used linear mixed-effects models to estimate the associations, adjusted for age, sex, body mass index, meteorological condition, and batch (only for biomarkers). Liquid chromatography-mass spectrometry was used to profile the EBC metabolome. Untargeted metabolome-wide association study (MWAS) analysis and pathway enrichment analysis using mummichog were performed to identify critical metabolomic features and pathways associated with TRAP exposure. Results: Participants had two to three times higher exposure to traffic-related air pollutants except for fine particulate matter while walking along the road compared with in the park. Compared with the low-TRAP exposure at the park, high-TRAP exposure at the road was associated with a higher score of respiratory symptoms [2.615 (95% CI: 0.605, 4.626), p=1.2×10-2] and relatively lower lung function indicators [-0.075L (95% CI: -0.138, -0.012), p=2.1×10-2] for FEV1 and -0.190L/s (95% CI: -0.351, -0.029; p=2.4×10-2) for MMEF]. Exposure to TRAP was significantly associated with changes in some, but not all, biomarkers, particularly with a 0.494-ng/mL (95% CI: 0.297, 0.691; p=9.5×10-6) increase for serum SP-D and a 0.123-ng/mL (95% CI: -0.208, -0.037; p=7.2×10-3) decrease for EBC ezrin. Untargeted MWAS analysis revealed that elevated TRAP exposure was significantly associated with perturbations in 23 and 32 metabolic pathways under positive- and negative-ion modes, respectively. These pathways were most related to inflammatory response, oxidative stress, and energy use metabolism. Conclusions: This study suggests that TRAP exposure might lead to lung function impairment and respiratory symptoms. Possible underlying mechanisms include lung epithelial injury, inflammation, oxidative stress, and energy metabolism disorders. https://doi.org/10.1289/EHP11139.

Precision medicine in complex diseases-Molecular subgrouping for improved prediction and treatment stratification

Article

Apr 2023
J INTERN MED

Complex diseases are caused by a combination of genetic, lifestyle, and environmental factors and comprise common noncommunicable diseases, including allergies, cardiovascular disease, and psychiatric and metabolic disorders. More than 25% of Europeans suffer from a complex disease, and together these diseases account for 70% of all deaths. The use of genomic, molecular, or imaging data to develop accurate diagnostic tools for treatment recommendations and preventive strategies, and for disease prognosis and prediction, is an important step toward precision medicine. However, for complex diseases, precision medicine is associated with several challenges. There is a significant heterogeneity between patients of a specific disease-both with regards to symptoms and underlying causal mechanisms-and the number of underlying genetic and nongenetic risk factors is often high. Here, we summarize precision medicine approaches for complex diseases and highlight the current breakthroughs as well as the challenges. We conclude that genomic-based precision medicine has been used mainly for patients with highly penetrant monogenic disease forms, such as cardiomyopathies. However, for most complex diseases-including psychiatric disorders and allergies-available polygenic risk scores are more probabilistic than deterministic and have not yet been validated for clinical utility. However, subclassifying patients of a specific disease into discrete homogenous subtypes based on molecular or phenotypic data is a promising strategy for improving diagnosis, prediction, treatment, prevention, and prognosis. The availability of high-throughput molecular technologies, together with large collections of health data and novel data-driven approaches, offers promise toward improved individual health through precision medicine.

Metabolic pathways altered by air pollutant exposure in association with lipid profiles in young adults

Article

Apr 2023

Mounting evidence suggests that air pollution influences lipid metabolism and dyslipidemia. However, the metabolic mechanisms linking air pollutant exposure and altered lipid metabolism is not established. In year 2014-2018, we conducted a cross-sectional study on 136 young adults in southern California, and assessed lipid profiles (triglycerides, total cholesterol, high-density lipoprotein (HDL)-cholesterol, low-density lipoprotein (LDL)-cholesterol, very-low-density lipoprotein (VLDL)-cholesterol), and untargeted serum metabolomics using liquid chromatography-high-resolution mass spectrometry, and one-month and one-year averaged exposures to NO2, O3, PM2.5 and PM10 air pollutants at residential addresses. A metabolome-wide association analysis was conducted to identify metabolomic features associated with each air pollutant. Mummichog pathway enrichment analysis was used to assess altered metabolic pathways. Principal component analysis (PCA) was further conducted to summarize 35 metabolites with confirmed chemical identity. Lastly, linear regression models were used to analyze the associations of metabolomic PC scores with each air pollutant exposure and lipid profile outcome. In total, 9309 metabolomic features were extracted, with 3275 features significantly associated with exposure to one-month or one-year averaged NO2, O3, PM2.5 and PM10 (p < 0.05). Metabolic pathways associated with air pollutants included fatty acid, steroid hormone biosynthesis, tryptophan, and tyrosine metabolism. PCA of 35 metabolites identified three main PCs which together explained 44.4% of the variance, representing free fatty acids and oxidative byproducts, amino acids and organic acids. Linear regression indicated that the free fatty acids and oxidative byproducts-related PC score was associated with air pollutant exposure and outcomes of total cholesterol and LDL-cholesterol (p < 0.05). This study suggests that exposure to NO2, O3, PM2.5 and PM10 contributes to increased level of circulating free fatty acids, likely through increased adipose lipolysis, stress hormone and response to oxidative stress pathways. These alterations were associated with dysregulation of lipid profiles and potentially could contribute to dyslipidemia and other cardiometabolic disorders.

Untargeted, High-Resolution Metabolomics in Pediatric Eosinophilic Esophagitis

Article

Dec 2022
J PEDIATR GASTR NUTR

Background/objectives: Eosinophilic esophagitis (EoE) is an inflammatory disease of unclear etiology. The aim of this study was to use untargeted plasma metabolomics to identify metabolic pathway alterations associated with EoE to better understand the pathophysiology. Methods: This prospective, case-control study included 72 children, aged 1-17 years, undergoing clinically indicated upper endoscopy (14 diagnosed with EoE and 58 controls). Fasting plasma samples were analyzed for metabolomics by high-resolution dual-chromatography mass spectrometry. Analysis was performed on sex-matched groups at a 2:1 ratio. Significant differences among the plasma metabolite features between children with and without EoE were determined using multivariate regression analysis and were annotated with a network-based algorithm. Subsequent pathway enrichment analysis was performed. Results: Patients with EoE had a higher proportion of atopic disease (85.7% vs. 50% p-value p=0.019) and any allergies (100% vs. 57.1% p-value=0.0005). Analysis of the dual chromatography features resulted in a total of 918 metabolites that differentiated EoE and controls. Glycerophospholipid metabolism was significantly enriched with the greatest number of differentiating metabolites and overall pathway enrichment (p < 0.01). Multiple amino and fatty acid pathways including linoleic acid were also enriched, as well as pyridoxine metabolism (p<0.01). Conclusions: In this pilot study, we found differences in metabolites involved in glycerophospholipid and inflammation pathways in pediatric patients with EoE using untargeted metabolomics, as well as overlap with amino acid metabolome alterations found in atopic disease.

Linking air pollution exposure to blood-based metabolic features in a community-based aging cohort

Preprint

Full-text available

Dec 2022

Long-term exposure to air pollution has been associated with changes in levels of several metabolites measured in the peripheral blood. However, most work has been conducted in ethnically homogenous populations. We studied the relationship between the plasma metabolome and long-term exposure to three air pollutants: particulate matter (PM) less than 2.5 µm in aero diameter (PM 2.5 ), PM less than 10 µm in aero diameter (PM 10 ) and nitrogen dioxide (NO 2 ) among 107 participants of the Washington Heights and Inwood Community Aging Project (WHICAP) in New York City. Plasma metabolomic profiles were generated using untargeted liquid chromatography coupled with high-resolution mass spectrometry. We estimated the association between each metabolic feature and predicted annual mean exposure to the air pollutants using three approaches: 1. A metabolome wide association study (MWAS) framework; 2. Feature selection using elastic net regression; and 3. A multivariate approach using partial least squares discriminant analysis. Additionally, we identified the pathways enriched by metabolic features associated with exposure through pathway analysis. The samples were collected from 1995 – 2015 and included non-Hispanic white, Caribbean Hispanic, and non-Hispanic Black older adults. Through the MWAS, we found 79 features associated with exposure to PM 2.5 (false discovery rate at 5%) but none associated with PM 10 or NO 2 . Pathway analysis revealed that PM 2.5 exposure was associated with altered amino acid metabolism, energy production, and oxidative stress response. Six features were found to be associated with PM 2.5 exposure through all three approaches, annotated as: cysteinylglycine disulfide, a diglyceride, and a dicarboxylic acid. Additionally, we found that the relationship between several features and PM 2.5 exposure was modified by diet and metabolic diseases. These signals, identified in a neighborhood-representative older population, could help understand the mechanisms through which PM 2.5 exposure can lead to altered metabolic outcomes in an older population. HIGHLIGHTS Long-term exposure to PM 2.5 is associated with altered plasma metabolic features in an aging population These associations are modified by a dementia diagnosis, history of diabetes, APOE-ε4 allele, and diet Pathways related to energy production, amino acid metabolism, and redox homeostasis are associated with exposure to PM 2.5 GRAPHICAL ABSTRACT

Metabolomics of V2O5 nanoparticles and V2O5 nanofibers in human airway epithelial BEAS-2B cells

Article

Nov 2022
TOXICOL APPL PHARM

Vanadium is a toxic metal listed by the IARC as possibly carcinogenic to humans. Manufactured nanosize vanadium pentoxide (V2O5) materials are used in a wide range of industrial sectors and recently have been developed as nanomedicine for cancer therapeutics, yet limited information is available to evaluate relevant nanotoxicity. In this study we used high-resolution metabolomics to assess effects of two V2O5 nanomaterials, nanoparticles and nanofibers, at exposure levels (0.01, 0.1, and 1 ppm) that did not cause cell death (i.e., non-cytotoxic) in a human airway epithelial cell line, BEAS-2B. As prepared, V2O5 nanofiber exhibited a fibrous morphology, with a width approximately 63 ± 12 nm and length in average 420 ± 70 nm; whereas, V2O5 nanoparticles showed a typical particle morphology with a size 36 ± 2 nm. Both V2O5 nanoparticles and nanofibers had dose-response effects on aminosugar, amino acid, fatty acid, carnitine, niacin and nucleotide metabolism. Differential effects of the particles and fibers included dibasic acid, glycosphingolipid and glycerophospholipid pathway associations with V2O5 nanoparticles, and cholesterol and sialic acid metabolism associations with V2O5 nanofibers. Examination by transmission electron microscopy provided evidence for mitochondrial stress and increased lysosome fusion by both nanomaterials, and these data were supported by effects on mitochondrial membrane potential and lysosomal activity. The results showed that non-cytotoxic exposures to V2O5 nanomaterials impact major metabolic pathways previously associated with human lung diseases and suggest that toxico-metabolomics may be useful to evaluate health risks from V2O5 nanomaterials.

Cancer Epidemiology

Chapter

Oct 2022

Overview This article gives a straightforward introduction to cancer epidemiology and is organized as follows. We first provide US data on cancer cases, deaths, and survival. These data, typically derived from national registries and surveys from scientific organizations devoted to the cause, for example, surveillance by the National Cancer Institute's Surveillance, Epidemiology, and End Results Program (SEER) and the International Agency for Research on Cancer (IARC), form the basis for policies centered around cancer control. We then discuss epidemiological methods the practicing oncologist may encounter in research or literature, followed by a tour of common (e.g., tobacco) and emerging (e.g., energy balance) causes of cancer. We follow this by describing molecular epidemiology, a subfield rejuvenated by molecular technologies to inform components of environmental factors and risks associated with the host, such as genetics, transcriptomics, metabolomics, and the microbiome. Finally, we discuss future directions, the potential and current importance of epidemiology.

Metabolic Pathways and Networks Associated With Tobacco Use in Military Personnel

Article

Full-text available

Aug 2016

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.

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

Article

Full-text available

Aug 2016

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.

Biomonitoring in the Era of the Exposome

Article

Full-text available

Jul 2016

Background: The term "exposome" was coined in 2005 to underscore the importance of the environment to human health and bring research efforts in line with those on the human genome. The ability to characterize environmental exposures through biomonitoring is key to exposome research efforts. Objectives: Our objective was to describe why traditional and non-traditional (exposomic) biomonitoring are both critical in studies aiming to capture the exposome and make recommendations on how to transition exposure research toward exposomic approaches. We describe the biomonitoring needs of exposome research and approaches and recommendations that will help fill the gaps in the current science. Discussion: Traditional and exposomic biomonitoring approaches have key advantages and disadvantages for assessing exposure. Exposomic approaches differ from traditional biomonitoring methods in that they can include all exposures of potential health significance, whether from endogenous or exogenous sources. Issues of sample availability and quality, identification of unknown analytes, capture of non-persistent chemicals, integration of methods and statistical assessment of increasingly complex datasets remain as challenges that must continue to be addressed. Conclusions: To understand the complexity of exposures faced across the lifespan, traditional and nontraditional biomonitoring methods should both be used. Through hybrid approaches and integration of emerging techniques, biomonitoring strategies can be maximized in research to define the exposome.

Ion Mobility Spectrometry-Mass Spectrometry (IMS-MS) for on- and off-line analysis of atmospheric gas and aerosol species

Article

Full-text available

Apr 2016

Measurement techniques that provide molecular-level information are needed to elucidate the multi-phase processes that produce secondary organic aerosol (SOA) species in the atmosphere. Here we demonstrate the application of ion mobility spectrometry-mass spectrometry (IMS-MS) to the simultaneous characterization of the elemental composition and molecular structures of organic species in the gas and particulate phases. Molecular ions of gas-phase organic species are measured online with IMS-MS after ionization with a custom build nitrate chemical ionization (CI) source. This CI-IMS-MS technique is used to obtain time-resolved measurements (5 min) of highly oxidized organic molecules during the 2013 Southern Oxidant and Aerosol Study (SOAS) ambient field campaign in the forested SE US. The ambient IMS-MS signals are consistent with laboratory IMS-MS spectra obtained from single-component carboxylic acids and multicomponent mixtures of isoprene and monoterpene oxidation products. Mass-mobility correlations in the 2-dimensional IMS-MS space provide a means of identifying ions with similar molecular structures within complex mass spectra and are used to separate and identify monoterpene oxidation products in the ambient data that are produced from different chemical pathways. Water-soluble organic carbon (WSOC) constituents of fine aerosol particles that are not resolvable with standard analytical separation methods, such as liquid chromatography (LC), are shown to be separable with IMS-MS coupled to an electrospray ionization (ESI) source. The capability to use ion mobility to differentiate between isomers is demonstrated for organosulfates derived from the reactive uptake of isomers of isoprene epoxydiols (IEPOX) onto wet acidic sulfate aerosol. Controlled fragmentation of precursor ions by collisional dissociation (CID) in the transfer region between the IMS and the MS is used to validate MS peak assignments, elucidate structures of oligomers, and confirm the presence of the organosulfate functional group.

Development and Evaluation of a Parallel Reaction Monitoring Strategy for Large-Scale Targeted Metabolomics Quantification

Article

Full-text available

Mar 2016

Recent advances in mass spectrometers which have yielded higher resolution and faster scanning speeds have expanded their application in metabolomics of diverse diseases. Using a quadrupole-Orbitrap LC-MS system, we developed an efficient large-scale quantitative method targeting 237 metabolites involved in various metabolic pathways using scheduled, parallel reaction monitoring (PRM). We assessed the dynamic range, linearity, reproducibility and system suitability of the PRM assay by measuring concentration curves, biological samples and clinical serum samples. The quantification performances of PRM and MS1-based assays in Q-Exactive were compared, and the MRM assay in QTRAP 6500 was also compared. The PRM assay monitoring 237 polar metabolites showed greater reproducibility and quantitative accuracy than MS1-based quantification, and also showed greater flexibility in post-acquisition assay refinement than the MRM assay in QTRAP 6500. We present a workflow for convenient PRM data processing using Skyline software which is free of charge. In this study we have established a reliable PRM methodology on a quadrupole-Orbitrap platform for evaluation of large-scale targeted metabolomics, which provides a new choice for basic and clinical metabolomics study.

MetFrag relaunched: Incorporating strategies beyond in silico fragmentation

Article

Full-text available

Jan 2016

Background: The in silico fragmenter MetFrag, launched in 2010, was one of the first approaches combining compound database searching and fragmentation prediction for small molecule identification from tandem mass spectrometry data. Since then many new approaches have evolved, as has MetFrag itself. This article details the latest developments to MetFrag and its use in small molecule identification since the original publication. Results: MetFrag has gone through algorithmic and scoring refinements. New features include the retrieval of reference, data source and patent information via ChemSpider and PubChem web services, as well as InChIKey filtering to reduce candidate redundancy due to stereoisomerism. Candidates can be filtered or scored differently based on criteria like occurence of certain elements and/or substructures prior to fragmentation, or presence in so-called "suspect lists". Retention time information can now be calculated either within MetFrag with a sufficient amount of user-provided retention times, or incorporated separately as "user-defined scores" to be included in candidate ranking. The changes to MetFrag were evaluated on the original dataset as well as a dataset of 473 merged high resolution tandem mass spectra (HR-MS/MS) and compared with another open source in silico fragmenter, CFM-ID. Using HR-MS/MS information only, MetFrag2.2 and CFM-ID had 30 and 43 Top 1 ranks, respectively, using PubChem as a database. Including reference and retention information in MetFrag2.2 improved this to 420 and 336 Top 1 ranks with ChemSpider and PubChem (89 and 71 %), respectively, and even up to 343 Top 1 ranks (PubChem) when combining with CFM-ID. The optimal parameters and weights were verified using three additional datasets of 824 merged HR-MS/MS spectra in total. Further examples are given to demonstrate flexibility of the enhanced features. Conclusions: In many cases additional information is available from the experimental context to add to small molecule identification, which is especially useful where the mass spectrum alone is not sufficient for candidate selection from a large number of candidates. The results achieved with MetFrag2.2 clearly show the benefit of considering this additional information. The new functions greatly enhance the chance of identification success and have been incorporated into a command line interface in a flexible way designed to be integrated into high throughput workflows. Feedback on the command line version of MetFrag2.2 available at http://c-ruttkies.github.io/MetFrag/ is welcome.

Correlation of the lung microbiota with metabolic profiles in bronchoalveolar lavage fluid in HIV infection

Article

Full-text available

Jan 2016

Background: While 16S ribosomal RNA (rRNA) sequencing has been used to characterize the lung's bacterial microbiota in human immunodeficiency virus (HIV)-infected individuals, taxonomic studies provide limited information on bacterial function and impact on the host. Metabolic profiles can provide functional information on host-microbe interactions in the lungs. We investigated the relationship between the respiratory microbiota and metabolic profiles in the bronchoalveolar lavage fluid of HIV-infected and HIV-uninfected outpatients. Results: Targeted sequencing of the 16S rRNA gene was used to analyze the bacterial community structure and liquid chromatography-high-resolution mass spectrometry was used to detect features in bronchoalveolar lavage fluid. Global integration of all metabolic features with microbial species was done using sparse partial least squares regression. Thirty-nine HIV-infected subjects and 20 HIV-uninfected controls without acute respiratory symptoms were enrolled. Twelve mass-to-charge ratio (m/z) features from C18 analysis were significantly different between HIV-infected individuals and controls (false discovery rate (FDR) = 0.2); another 79 features were identified by network analysis. Further metabolite analysis demonstrated that four features were significantly overrepresented in the bronchoalveolar lavage (BAL) fluid of HIV-infected individuals compared to HIV-uninfected, including cystine, two complex carbohydrates, and 3,5-dibromo-L-tyrosine. There were 231 m/z features significantly associated with peripheral blood CD4 cell counts identified using sparse partial least squares regression (sPLS) at a variable importance on projection (VIP) threshold of 2. Twenty-five percent of these 91 m/z features were associated with various microbial species. Bacteria from families Caulobacteraceae, Staphylococcaceae, Nocardioidaceae, and genus Streptococcus were associated with the greatest number of features. Glycerophospholipid and lineolate pathways correlated with these bacteria. Conclusions: In bronchoalveolar lavage fluid, specific metabolic profiles correlated with bacterial organisms known to play a role in the pathogenesis of pneumonia in HIV-infected individuals. These findings suggest that microbial communities and their interactions with the host may have functional metabolic impact in the lung.

Microbial, host and xenobiotic diversity in the cystic fibrosis sputum metabolome

Article

Full-text available

Dec 2015
ISME J

Cystic fibrosis (CF) lungs are filled with thick mucus that obstructs airways and facilitates chronic infections. Pseudomonas aeruginosa is a significant pathogen of this disease that produces a variety of toxic small molecules. We used molecular networking-based metabolomics to investigate the chemistry of CF sputa and assess how the microbial molecules detected reflect the microbiome and clinical culture history of the patients. Metabolites detected included xenobiotics, P. aeruginosa specialized metabolites and host sphingolipids. The clinical culture and microbiome profiles did not correspond to the detection of P. aeruginosa metabolites in the same samples. The P. aeruginosa molecules that were detected in sputum did not match those from laboratory cultures. The pseudomonas quinolone signal (PQS) was readily detectable from cultured strains, but absent from sputum, even when its precursor molecules were present. The lack of PQS production in vivo is potentially due to the chemical nature of the CF lung environment, indicating that culture-based studies of this pathogen may not explain its behavior in the lung. The most differentially abundant molecules between CF and non-CF sputum were sphingolipids, including sphingomyelins, ceramides and lactosylceramide. As these highly abundant molecules contain the inflammatory mediator ceramide, they may have a significant role in CF hyperinflammation. This study demonstrates that the chemical makeup of CF sputum is a complex milieu of microbial, host and xenobiotic molecules. Detection of a bacterium by clinical culturing and 16S rRNA gene profiling do not necessarily reflect the active production of metabolites from that bacterium in a sputum sample.

Sequencing the exposome: A call to action

Article

Full-text available

Nov 2015

Dean Jones

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.

High resolution mass spectrometry for structural identification of metabolites in metabolomics

Article

Full-text available

Nov 2015
METABOLOMICS

High resolution mass spectrometry (HRMS) is increasingly used to produce metabolomics data. Thanks to its high mass resolution and mass measurement accuracy, it is also very useful for metabolite identification. Nevertheless, a rigorous methodology is required. This manuscript describes different steps involved in the structural elucidation of metabolites and demonstrates the utility of HRMS for such purpose. After a brief overview of HRMS performances in terms of mass measurement accuracy, peak resolution, isotopic clusters/patterns and the instrumentation used, the first section is devoted to the data processing generally performed to reduce the data set size. Based on the mass accuracy measurements, different post-acquisition data processing procedures have been developed for complex mixture analysis and can be used in metabolomics. The second section describes protocols used to process putative metabolite annotations or identifications with HRMS data, based on elemental composition determined from accurately measured m/z value and mass spectral databases. Non-classical approaches are also proposed for tentative structure elucidation of unknown metabolites. Finally, limitations of the proposed workflow for metabolite structure elucidation are discussed and possible improvements are proposed.

Mass spectral databases for LC/MS- and GC/MS-based metabolomics: State of the field and future prospects

Article

Full-text available

Nov 2015
TRAC-TREND ANAL CHEM

At present, mass spectrometry (MS)-based metabolomics has been widely used to obtain new insights into human, plant, and microbial biochemistry; drug and biomarker discovery; nutrition research; and food control. Despite the high research interest, identifying and characterizing the structure of metabolites has become a major drawback for converting raw MS data into biological knowledge. Comprehensive and well-annotated MS-based spectral databases play a key role in serving this purpose via the formation of metabolite annotations. The main characteristics of the mass spectral databases currently used in MS-based metabolomics are reviewed in this study, underlining their advantages and limitations. In addition, the overlap of compounds with MSn (n ≥ 2) spectra from authentic chemical standards in most public and commercial databases has been calculated for the first time. Finally, future prospects of mass spectral databases are discussed in terms of the needs posed by novel applications and instrumental advancements.

Blood transcriptomics and metabolomics for personalized medicine

Article

Full-text available

Oct 2015

Molecular analysis of blood samples is pivotal to clinical diagnosis, and has been intensively investigated since the rise of systems biology. Recent developments have opened new opportunities to utilize transcriptomics and metabolomics for personalized and precision medicine. Efforts from human immunology have infused into this area exquisite characterizations of subpopulations of blood cells. It is now possible to infer from blood transcriptomics, with fine accuracy, the contribution of immune activation and of cell subpopulations. In parallel, high-resolution mass spectrometry has brought revolutionary analytical capability, detecting > 10,000 metabolites, together with environmental exposure, dietary intake, microbial activity and pharmaceutical drugs. Thus, the re-examination of blood chemicals by metabolomics is in order. Transcriptomics and metabolomics can be integrated to provide a more comprehensive understanding of the human biological states. We will review these new data and methods, and discuss how they can contribute to personalized medicine.

Illuminating the dark matter in metabolomics

Article

Full-text available

Oct 2015

Despite the over 100-y history of mass spectrometry, it remains challenging to link the large volume of known chemical structures to the data obtained with mass spectrometers. Presently, only 1.8% of spectra in an untargeted metabolomics experiment can be annotated. This means that the vast majority of information collected by metabolomics is “dark matter,” chemical signatures that remain uncharacterized (Fig. 1). For a genomic comparison, 80% of predicted genes in the Escherichia coli genome are known. In a bacteriophage metagenome, a well-known frontier of biological dark matter, the amount of known genes is 1–30%, depending on the sample (1). Thus, one could argue that we know more about the genetics of uncultured phage than we do about the chemistry within our own bodies. Much of the chemical dark matter may include known structures, but they remain undiscovered because the reference spectra are not available in mass spectrometry databases. The only way to overcome this challenge is through the development of computational solutions. In PNAS, Duhrkop et al. describe the development of such a computational tool, called CSI (compound structure identification):FingerID (2). The tool is designed to aid in the annotation of chemistries that can be observed by mass spectrometry. CSI:FingerID uses fragmentation trees to connect tandem MS (MS/MS) data to chemical structures found in public chemistry databases. Tools such as this can allow metabolomics with mass spectrometry to become as commonly used and scientifically productive as sequencing technologies have in the field of genomics.

Genome-wide association study identifies novel genetic variants contributing to variation in blood metabolite levels

Article

Full-text available

Jun 2015

Metabolites are small molecules involved in cellular metabolism, which can be detected in biological samples using metabolomic techniques. Here we present the results of genome-wide association and meta-analyses for variation in the blood serum levels of 129 metabolites as measured by the Biocrates metabolomic platform. In a discovery sample of 7,478 individuals of European descent, we find 4,068 genome- and metabolome-wide significant (Z-test, P<1.09 × 10(-9)) associations between single-nucleotide polymorphisms (SNPs) and metabolites, involving 59 independent SNPs and 85 metabolites. Five of the fifty-nine independent SNPs are new for serum metabolite levels, and were followed-up for replication in an independent sample (N=1,182). The novel SNPs are located in or near genes encoding metabolite transporter proteins or enzymes (SLC22A16, ARG1, AGPS and ACSL1) that have demonstrated biomedical or pharmaceutical importance. The further characterization of genetic influences on metabolic phenotypes is important for progress in biological and medical research.

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

Article

Full-text available

Jun 2015

Liquid-chromatography high-resolution mass spectrometry provides capability to measure >40,000 ions derived from metabolites in biologic samples. This presents challenges to confirm identities of known chemicals and delineate potential metabolic pathway associations of unidentified chemicals. We provide an R package for metabolic network analysis, MetabNet, to perform targeted metabolome-wide association study of specific metabolites to facilitate detection of their related metabolic pathways and network structures.

MS-DIAL: Data-independent MS/MS deconvolution for comprehensive metabolome analysis

Article

Full-text available

May 2015
Br J Pharmacol

Data-independent acquisition (DIA) in liquid chromatography (LC) coupled to tandem mass spectrometry (MS/MS) provides comprehensive untargeted acquisition of molecular data. We provide an open-source software pipeline, which we call MS-DIAL, for DIA-based identification and quantification of small molecules by mass spectral deconvolution. For a reversed-phase LC-MS/MS analysis of nine algal strains, MS-DIAL using an enriched LipidBlast library identified 1,023 lipid compounds, highlighting the chemotaxonomic relationships between the algal strains.

Linking molecular models with ion mobility experiments. Illustration with a rigid nucleic acid structure: Ion mobility calculations and experiments

Article

Full-text available

May 2015
J MASS SPECTROM

Ion mobility spectrometry experiments allow the mass spectrometrist to determine an ion's rotationally averaged collision cross section ΩEXP . Molecular modelling is used to visualize what ion three-dimensional structure(s) is(are) compatible with the experiment. The collision cross sections of candidate molecular models have to be calculated, and the resulting ΩCALC are compared with the experimental data. Researchers who want to apply this strategy to a new type of molecule face many questions: (1) What experimental error is associated with ΩEXP determination, and how to estimate it (in particular when using a calibration for traveling wave ion guides)? (2) How to generate plausible 3D models in the gas phase? (3) Different collision cross section calculation models exist, which have been developed for other analytes than mine. Which one(s) can I apply to my systems? To apply ion mobility spectrometry to nucleic acid structural characterization, we explored each of these questions using a rigid structure which we know is preserved in the gas phase: the tetramolecular G-quadruplex [dTGGGGT]4 , and we will present these detailed investigation in this tutorial. © 2015 The Authors. Journal of Mass Spectrometry published by John Wiley & Sons Ltd. © 2015 The Authors. Journal of Mass Spectrometry published by John Wiley & Sons Ltd.

IPO: A tool for automated optimization of XCMS parameters

Article

Full-text available

Apr 2015
BMC BIOINFORMATICS

Background: Untargeted metabolomics generates a huge amount of data. Software packages for automated data processing are crucial to successfully process these data. A variety of such software packages exist, but the outcome of data processing strongly depends on algorithm parameter settings. If they are not carefully chosen, suboptimal parameter settings can easily lead to biased results. Therefore, parameter settings also require optimization. Several parameter optimization approaches have already been proposed, but a software package for parameter optimization which is free of intricate experimental labeling steps, fast and widely applicable is still missing. Results: We implemented the software package IPO ('Isotopologue Parameter Optimization') which is fast and free of labeling steps, and applicable to data from different kinds of samples and data from different methods of liquid chromatography - high resolution mass spectrometry and data from different instruments. IPO optimizes XCMS peak picking parameters by using natural, stable (13)C isotopic peaks to calculate a peak picking score. Retention time correction is optimized by minimizing relative retention time differences within peak groups. Grouping parameters are optimized by maximizing the number of peak groups that show one peak from each injection of a pooled sample. The different parameter settings are achieved by design of experiments, and the resulting scores are evaluated using response surface models. IPO was tested on three different data sets, each consisting of a training set and test set. IPO resulted in an increase of reliable groups (146% - 361%), a decrease of non-reliable groups (3% - 8%) and a decrease of the retention time deviation to one third. Conclusions: IPO was successfully applied to data derived from liquid chromatography coupled to high resolution mass spectrometry from three studies with different sample types and different chromatographic methods and devices. We were also able to show the potential of IPO to increase the reliability of metabolomics data. The source code is implemented in R, tested on Linux and Windows and it is freely available for download at https://github.com/glibiseller/IPO . The training sets and test sets can be downloaded from https://health.joanneum.at/IPO .

Comprehensive analytical strategy for biomonitoring of pesticides in urine by liquid chromatography-Orbitrap high resolution masss pectrometry

Article

Full-text available

Nov 2014
J CHROMATOGR A

MetMSLine: An automated and fully integrated pipeline for rapid processing of high-resolution LC-MS metabolomic datasets

Article

Full-text available

Oct 2014

MetMSLine represents a complete collection of functions in the R programming language as an accessible GUI for biomarker discovery in large-scale liquid-chromatography high-resolution mass spectral datasets from acquisition through to final metabolite identification forming a backend to output from any peak-picking software such as XCMS. MetMSLine automatically creates subdirectories, data tables and relevant figures at the following steps: (i) signal smoothing, normalization, filtration and noise transformation (PreProc.QC.LSC.R); (ii) PCA and automatic outlier removal (Auto.PCA.R); (iii) automatic regression, biomarker selection, hierarchical clustering and cluster ion/artefact identification (Auto.MV.Regress.R); (iv) Biomarker—MS/MS fragmentation spectra matching and fragment/neutral loss annotation (Auto.MS.MS.match.R) and (v) semi-targeted metabolite identification based on a list of theoretical masses obtained from public databases (DBAnnotate.R). Availability and implementation: All source code and suggested parameters are available in an un-encapsulated layout on http://wmbedmands.github.io/MetMSLine/. Readme files and a synthetic dataset of both X-variables (simulated LC–MS data), Y-variables (simulated continuous variables) and metabolite theoretical masses are also available on our GitHub repository. Contact: ScalbertA@iarc.fr

Autophagy is essential for effector CD8(+) T cell survival and memory formation

Article

Full-text available

Nov 2014

The importance of autophagy in the generation of memory CD8(+) T cells in vivo is not well defined. We report here that autophagy was dynamically regulated in virus-specific CD8(+) T cells during acute infection of mice with lymphocytic choriomeningitis virus. In contrast to the current paradigm, autophagy decreased in activated proliferating effector CD8(+) T cells and was then upregulated when the cells stopped dividing just before the contraction phase. Consistent with those findings, deletion of the gene encoding either of the autophagy-related molecules Atg5 or Atg7 had little to no effect on the proliferation and function of effector cells, but these autophagy-deficient effector cells had survival defects that resulted in compromised formation of memory T cells. Our studies define when autophagy is needed during effector and memory differentiation and warrant reexamination of the relationship between T cell activation and autophagy.

Plasma Metabolomics in Human Pulmonary Tuberculosis Disease: A Pilot Study

Article

Full-text available

Oct 2014
PLOS ONE

We aimed to characterize metabolites during tuberculosis (TB) disease and identify new pathophysiologic pathways involved in infection as well as biomarkers of TB onset, progression and resolution. Such data may inform development of new anti-tuberculosis drugs. Plasma samples from adults with newly diagnosed pulmonary TB disease and their matched, asymptomatic, sputum culture-negative household contacts were analyzed using liquid chromatography high-resolution mass spectrometry (LC-MS) to identify metabolites. Statistical and bioinformatics methods were used to select accurate mass/charge (m/z) ions that were significantly different between the two groups at a false discovery rate (FDR) of q

MS2Analyzer: A Software for Small Molecule Substructure Annotations from Accurate Tandem Mass Spectra

Article

Full-text available

Sep 2014
ANAL CHEM

Systematic analysis and interpretation of the large number of tandem mass spectra (MS/MS) obtained in metabolomics experiments is a bottleneck in discovery-driven research. MS/MS mass spectral libraries are small compared to all known small molecule structures and are often not freely available. MS2Analyzer was therefore developed to enable user-defined searches of thousands of spectra for mass spectral features such as neutral losses, m/z differences, product and precursor ions from MS/MS spectra in MSP/MGF files. The software is freely available at http://fiehnlab.ucdavis.edu/projects/MS2Analyzer/. As the reference query set, 147 literature-reported neutral losses and their corresponding substructures were collected. This set was tested for accuracy of linking neutral loss analysis to substructure annotations using 19,329 accurate mass tandem mass spectra of structurally known compounds from the NIST11 MS/MS library. Validation studies showed that 92.1±6.4% of 13 typical neutral losses such as acetylations, cysteine conjugates or glycosylations are correct annotating the associated substructures, while the absence of mass spectra features does not necessarily imply the absence of such substructures. Use of this tool has been successfully demonstrated for complex lipids in microalgae.

Transcriptome-metabolome wide association study (TMWAS) of maneb and paraquat neurotoxicity reveals network level interactions in toxicologic mechanism

Article

Full-text available

Dec 2014

A combination of the herbicide paraquat (PQ) and fungicide maneb (MB) has been linked to Parkinson's disease. Previous studies show that this involves an additive toxicity with at least two different mechanisms. However, detailed understanding of mixtures is often difficult to elucidate because of the multiple ways by which toxic agents can interact. In the present study, we used a combination of transcriptomics and metabolomics to investigate mechanisms of toxicity of PQ and MB in a neuroblastoma cell line. Conditions were studied with concentrations of PQ and MB that each individually caused 20% cell death and together caused 50% cell death. Transcriptomic and metabolomic samples were collected at time points prior to significant cell death. Statistical and bioinformatic methods were applied to the resulting 30,869 transcripts and 1,358 metabolites. Results showed that MB significantly changed more transcripts and metabolites than PQ, and combined PQ + MB impacted more than MB alone. Transcriptome-metabolome-wide association study (TMWAS) showed that significantly changed transcripts and metabolites mapped to two network substructures, one associating with significant effects of MB and the other included features significantly associated with PQ + MB. The latter contained 4 clusters of genes and associated metabolites, with one containing genes for two cation transporters and a cation transporter regulatory protein also recognized as a pro-apoptotic protein. Other clusters included stress response genes and transporters linked to cytoprotective mechanisms. MB also had a significant network structure linked to cell proliferation. Together, the results show that the toxicologic mechanism of the combined neurotoxicity of PQ and MB involves network level interactions and that TMWAS provides an effective approach to investigate such complex mechanisms.

KEGG: Kyoto encyclopedia of genes and genomes

Article

Jan 2006
ARTIF INTELL

Data, information, knowledge and principle: Back to metabolism in KEGG

Article

Jan 2013

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

Article

Aug 2016

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.

Deployment-Associated Exposure Surveillance With High Resolution Metabolomics

Article

Aug 2016

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.

Population Screening for Biological and Environmental Properties of the Human Metabolic Phenotype

Chapter

Dec 2016

Adoption of metabolic screening into routine health care has the potential to support Personalized Medicine; however, before informed decisions can be made on the basis of metabolic phenotyping, heterogeneity and confounding factors will have to be understood. This can only be accomplished through a data-driven approach, particularly when considering dietary and environmental influences. Identification of how phenotypic and lifestyle traits contribute to definition of an individual’s metabolic phenotype is required for meaningful interpretation of metabolic associations. In this chapter, we discuss the feasibility of metabolomics as a tool for population screening and for defining the human metabolic phenotype. Inherent factors contributing to the metabolic phenotype, including circadian rhythm, gender, age, and disease status, are reviewed. Because of the all-encompassing influence of human–environment interactions, we also discuss how environmental factors affect the metabolic phenotype and the use of metabolomics for broad-spectrum chemical biomonitoring. We close with a case study identifying biological responses to insecticide exposure in a healthy population.

The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases

Article

Jan 2016

Hartmut Foerster

Amino Acid Metabolism is Altered in Adolescents with Nonalcoholic Fatty Liver Disease—An Untargeted, High Resolution Metabolomics Study

Article

Feb 2016

Objective: To conduct an untargeted, high resolution exploration of metabolic pathways that was altered in association with hepatic steatosis in adolescents. Study design: This prospective, case-control study included 39 Hispanic-American, obese adolescents aged 11-17 years evaluated for hepatic steatosis using magnetic resonance spectroscopy. Of these 39 individuals, 30 had hepatic steatosis ≥5% and 9 were matched controls with hepatic steatosis <5%. Fasting plasma samples were analyzed in triplicate using ultra-high resolution metabolomics on a Thermo Fisher Q Exactive mass spectrometry system, coupled with C18 reverse phase liquid chromatography. Differences in plasma metabolites between adolescents with and without nonalcoholic fatty liver disease (NAFLD) were determined by independent t tests and visualized using Manhattan plots. Untargeted pathway analyses using Mummichog were performed among the significant metabolites to identify pathways that were most dysregulated in NAFLD. Results: The metabolomics analysis yielded 9583 metabolites, and 7711 with 80% presence across all samples remained for statistical testing. Of these, 478 metabolites were associated with the presence of NAFLD compared with the matched controls. Pathway analysis revealed that along with lipid metabolism, several major amino acid pathways were dysregulated in NAFLD, with tyrosine metabolism being the most affected. Conclusions: Metabolic pathways of several amino acids are significantly disturbed in adolescents with elevated hepatic steatosis. This is a novel finding and suggests that these pathways may be integral in the mechanisms of NAFLD.

A longitudinal analysis of the effects of age on the blood plasma metabolome in the common marmoset, Callithrix jacchus

Article

Jan 2016

Primates tend to be long-lived for their size with humans being the longest lived of all primates. There are compelling reasons to understand the underlying age-related processes that shape human lifespan. But the very fact of our long lifespan that makes it so compelling, also makes it especially difficult to study. Thus, in studies of aging, researchers have turned to non-human primate models, including chimpanzees, baboons, and rhesus macaques. More recently, the common marmoset, Callithrix jacchus, has been recognized as a particularly valuable model in studies of aging, given its small size, ease of housing in captivity, and relatively short lifespan. However, little is known about the physiological changes that occur as marmosets age. To begin to fill in this gap, we utilized high sensitivity metabolomics to define the longitudinal biochemical changes associated with age in the common marmoset. We measured 2104 metabolites from blood plasma at three separate time points over a 17-month period, and we completed both a cross-sectional and longitudinal analysis of the metabolome. We discovered hundreds of metabolites associated with age and body weight in both male and female animals. Our longitudinal analysis identified age-associated metabolic pathways that were not found in our cross-sectional analysis. Pathways enriched for age-associated metabolites included tryptophan, nucleotide, and xenobiotic metabolism, suggesting these biochemical pathways might play an important role in the basic mechanisms of aging in primates. Moreover, we found that many metabolic pathways associated with age were sex specific. Our work illustrates the power of longitudinal approaches, even in a short time frame, to discover novel biochemical changes that occur with age.

Elucidation of Drug Metabolite Structural Isomers Using Molecular Modeling Coupled with Ion Mobility Mass Spectrometry

Article

Jan 2016
ANAL CHEM

Ion mobility mass spectrometry (IM-MS) in combination with molecular modelling offers the potential for small molecule structural isomer identification by measurement of their gas phase collision cross sections (CCSs). Successful application of this approach to drug metabolite identification would facilitate resource reduction, including animal usage, and may benefit other areas of pharmaceutical structural characterisation including impurity profiling and degradation chemistry. However, the conformational behaviour of drug molecules and their metabolites in the gas phase is poorly understood. Here the gas phase conformational space of drug and drug-like molecules has been investigated, as well as the influence of protonation and adduct formation on the conformations of drug metabolite structural isomers. The use of CCSs, measured from IM-MS and molecular modelling information, for the structural identification of drug metabolites has also been critically assessed. Detection of structural isomers of drug metabolites using IM-MS is demonstrated and, in addition, a molecular modelling approach has been developed offering rapid conformational searching and energy assessment of candidate structures which agree with experimental CCSs. Here it is illustrated that isomers must possess markedly dissimilar CCS values for structural differentiation - the existence and extent of CCS differences being ionization state and molecule dependent. The results present that IM-MS and molecular modelling can inform on the identity of drug metabolites and highlight the limitations of this approach in differentiating structural isomers.

Interpretation of Mass Spectra; An Introduction

Article

Jan 1993

Metabolome Analyses in Exposome Studies: Profiling Methods for a Vast Chemical Space

Article

Oct 2015
ARCH BIOCHEM BIOPHYS

Toby J Athersuch

Metabolic profiling (metabonomics/metabolomics) is now used routinely as a tool to provide information-rich datasets for biomarker discovery, prompting and augmenting detailed mechanistic studies. The experimental design and focus of any individual study will be reflected in the types of biomarkers that can be detected; toxicological studies will likely focus on markers of response to insult, whereas clinical case-control studies may yield diagnostic markers of disease. Population studies can make use of omics analyses, including metabonomics, to provide mechanistically-relevant markers that link environmental exposures to chronic disease endpoints. In this article, examples of how metabolic profiling has played a key role in molecular epidemiological analyses of chronic disease are presented, and how these reflect different aspects of the causal pathway. A commentary on the nature of metabolome analysis as a complex mixture analysis problem as opposed to a coded, sequence or template problem is provided, alongside an overview of current and future analytical platforms that are being applied to meet this analytical challenge. Epidemiological studies are an important nexus for integrating various measures of the human exposome, and the ubiquity, diversity and functions of small molecule metabolites, represent an important way to link individual exposures, genetics and phenotype.

Warpgroup: Increased Precision of Metabolomic Data Processing by Consensus Integration Bound Analysis

Article

Oct 2015
BIOINFORMATICS

Motivation: Current informatic techniques for processing raw, chromatography/mass spectrometry data break down under several common, non-ideal conditions. Importantly, hydrophilic liquid interaction chromatography (a key separation technology for metabolomics) produces data which are especially challenging to process. We identify three critical points of failure in current informatic workflows: compound specific drift, integration region variance, and naive missing value imputation. We implement the Warpgroup algorithm to address these challenges. Results: Warpgroup adds peak subregion detection, consensus integration bound detection, and intelligent missing value imputation steps to the conventional informatic workflow. When compared to the conventional workflow, Warpgroup made major improvements to the processed data. The coefficient of variation for replicate injections of a complex Escherichia Coli extract were halved (a reduction of 19%). Integration regions across samples were much more robust. Additionally, many signals lost by the conventional workflow were "rescued" by the Warpgroup refinement, thereby resulting in greater analyte coverage in the processed data. Availability and implementation: Warpgroup is an open source R package available on GitHub at github.com/nathaniel-mahieu/warpgroup. The package includes example data and XCMS compatibility wrappers for ease of use. Contact: nathaniel.mahieu@wustl.edu and gjpattij@wustl.edu SUPPLEMENTARY INFORMATION: Supplementary information is available online.

Searching molecular structure databases with tandem mass spectra using CSI:FingerID

Article

Sep 2015

Significance Untargeted metabolomics experiments usually rely on tandem MS (MS/MS) to identify the thousands of compounds in a biological sample. Today, the vast majority of metabolites remain unknown. Recently, several computational approaches were presented for searching molecular structure databases using MS/MS data. Here, we present CSI:FingerID, which combines fragmentation tree computation and machine learning. An in-depth evaluation on two large-scale datasets shows that our method can find 150% more correct identifications than the second-best search method. In comparison with the two runner-up methods, CSI:FingerID reaches 5.4-fold more unique identifications. We also present evaluations indicating that the performance of our method will further improve when more training data become available. CSI:FingerID is publicly available at www.csi-fingerid.org .

Untargeted Identification of Organo-bromine Compounds in Lake Sediments by Ultra-High Resolution Mass Spectrometry with the Data-Independent Precursor Isolation and Characteristic Fragment (DIPIC-Frag) Method

Article

Sep 2015
ANAL CHEM

While previous studies have found that unknown natural and synthetic organo-bromine compounds (NSOBCs) contributed more than 99% of the total organic bromine (Br) in the environment, there was no efficient method for untargeted screening to identify NSOBCs in environmental samples. A novel, untargeted method for identifying, based on ultra-high resolution mass spectrometry (UHRMS) with the Q Exactive instrument was developed. This method included a novel data-independent precursor isolation and characteristic fragment (DIPIC-Frag) procedure to identify NSOBCs. One hundred and eighty successive 5-m/z-wide windows were used to isolate precursor ions. This resulted in sufficient dynamic range and specificity to identify peaks of Br fragment ion for analysis. A total of 2520 peaks of NSOBC compounds containing Br were observed in sediments from Lake Michigan, USA. A new chemometric strategy which combined chromatographic profiles, isotope peaks, precursor isolation window information and intensities was used to identify precursor ions and chemical formulae for detected brominated compounds. Precursor ions for 2163 of the 2520 NSOBCs peaks (86%) were identified, and chemical formulae for 2071 NSOBCs peaks (82%) were determined. After exclusion of isotopic peaks, 1593 unique brominated compounds were identified and chemical formulas derived for each. Most of the compounds identified had not been previously reported and had intensities which were 100- to 1000-fold greater than the congeners of polybrominated diphenyl ethers (PBDEs). In extracts of sediments, these compounds exhibited variations in intensities (<103 to ~108), m/z values (170.9438 to 997.5217), retention times on a C18 column (1.1 to 29.52 min), and the number of Br atoms (1 to 8). Generally, compounds with greater m/z values had longer retention times and greater numbers of Br atoms. Three compounds were used in a proof-of-concept experiment to demonstrate that structures of some of the screened NSOBCs could be further predicted by combining database searching and high-resolution MS2 spectra.

Reference Standardization for Mass Spectrometry and High-Resolution Metabolomics Applications to Exposome Research

Article

Sep 2015
TOXICOL SCI

The exposome is the cumulative measure of environmental influences and associated biological responses throughout the lifespan, including exposures from the environment, diet, behavior, and endogenous processes. A major challenge for exposome research lies in the development of robust and affordable analytic procedures to measure the broad range of exposures and associated biologic impacts occurring over a lifetime. Biomonitoring is an established approach to evaluate internal body burden of environmental exposures, but use of biomonitoring for exposome research is often limited by the high costs associated with quantification of individual chemicals. High-resolution metabolomics (HRM) uses ultra-high resolution mass spectrometry with minimal sample preparation to support high-throughput relative quantification of thousands of environmental, dietary and microbial chemicals. HRM also measures metabolites in most endogenous metabolic pathways, thereby providing simultaneous measurement of biologic responses to environmental exposures. The present research examined quantification strategies to enhance the usefulness of HRM data for cumulative exposome research. The results provide a simple reference standardization protocol in which individual chemical concentrations in unknown samples are estimated by comparison to a concurrently analyzed, pooled reference sample with known chemical concentrations. The approach was tested using blinded analyses of amino acids in human samples and was found to be comparable to independent laboratory results based on surrogate standardization or internal standardization. Quantification was reproducible over a 13-month period and extrapolated to thousands of chemicals. The results show that reference standardization protocol provides an effective strategy that will enhance data collection for cumulative exposome research. In principle, the approach can be extended to other types of mass spectrometry and other analytical methods.

High resolution ion mobility-mass spectrometry for separation and identification of isomeric lipids

Article

Aug 2015
ANALYST

Lipidomics is a particularly difficult analytical challenge due to the number and importance of isomeric species that are known or postulated in biological samples. Current separation and identification techniques are too often insufficiently powerful, slow or ambiguous. High resolution, low field ion mobility coupled to mass spectrometry is shown here to have sufficient performance to represent a new alternative for lipidomics. For the first time, drift-tube ion mobility separation of lipid isomers that differ only in position of the acyl chain, position of the double bond or double bond geometry is demonstrated. Differences in collision cross sections of less than 1% are sufficient for baseline separation. The same level of performance is maintained in complex biological mixtures. More than 130 high-precision reduced mobility and collision cross section values were also determined for a range of lipids. Such data can be the basis of a new lipidomics workflow, as the appropriate libraries are developed.

Metabolome-Wide Association Study of Primary Open Angle Glaucoma

Article

Jul 2015

To determine if primary open-angle glaucoma (POAG) patients can be differentiated from controls based on metabolic characteristics. We used ultra-high resolution mass spectrometry with C18 liquid chromatography for metabolomic analysis on frozen plasma samples from 72 POAG patients and 72 controls. Metabolome-wide Spearman correlation was performed to select differentially expressed metabolites (DEM) correlated with POAG. We corrected P values for multiple testing using Benjamini and Hochberg false discovery rate (FDR). Hierarchical cluster analysis (HCA) was used to depict the relationship between participants and DEM. Differentially expressed metabolites were matched to the METLIN metabolomics database; both DEM and metabolites significantly correlating with DEM were analyzed using MetaboAnalyst to identify metabolic pathways altered in POAG. Of the 2440 m/z (mass/charge) features recovered after filtering, 41 differed between POAG cases and controls at FDR = 0.05. Hierarchical cluster analysis revealed these DEM to associate into eight clusters; three of these clusters contained the majority of the DEM and included palmitoylcarnitine, hydroxyergocalciferol, and high-resolution METLIN matches to sphingolipids, other vitamin D-related metabolites, and terpenes. MetaboAnalyst also indicated likely alteration in steroid biosynthesis pathways. Global ultrahigh resolution metabolomics emphasized the importance of altered lipid metabolism in POAG. The results suggest specific metabolic processes, such as those involving palmitoylcarnitine, sphingolipids, vitamin D-related compounds, and steroid precursors, may contribute to POAG status and merit more detailed study with targeted methods.

High-Resolution Metabolomics: Review of the Field and Implications for Nursing Science and the Study of Preterm Birth

Article

Jul 2015
BIOL RES NURS

Most complex health conditions do not have a single etiology but rather develop from exposure to multiple risk factors that interact to influence individual susceptibility. In this review, we discuss the emerging field of metabolomics as a means by which metabolic pathways underlying a disease etiology can be exposed and specific metabolites can be identified and linked, ultimately providing biomarkers for early detection of disease onset and new strategies for intervention. We present the theoretical foundation of metabolomics research, the current methods employed in its conduct, and the overlap of metabolomics research with other "omic" approaches. As an exemplar, we discuss the potential of metabolomics research in the context of deciphering the complex interactions of the maternal-fetal exposures that underlie the risk of preterm birth, a condition that accounts for substantial portions of infant morbidity and mortality and whose etiology and pathophysiology remain incompletely defined. We conclude by providing strategies for including metabolomics research in future nursing studies for the advancement of nursing science. © The Author(s) 2015.

Applications of ion-mobility mass spectrometry for lipid analysis

Article

Apr 2015
ANAL BIOANAL CHEM

The high chemical complexity of the lipidome is one of the major challenges in lipidomics research. Ion-mobility spectrometry (IMS), a gas-phase electrophoretic technique, makes possible the separation of ions in the gas phase according to their charge, shape, and size. IMS can be combined with mass spectrometry (MS), adding three major benefits to traditional lipidomic approaches. First, IMS-MS allows the determination of the collision cross section (CCS), a physicochemical measure related to the conformational structure of lipid ions. The CCS is used to improve the confidence of lipid identification. Second, IMS-MS provides a new set of hybrid fragmentation experiments. These experiments, which combine collision-induced dissociation with ion-mobility separation, improve the specificity of MS/MS-based approaches. Third, IMS-MS improves the peak capacity and signal-to-noise ratio of traditional analytical approaches. In doing so, it allows the separation of complex lipid extracts from interfering isobaric species. Developing in parallel with advances in instrumentation, informatics solutions enable analysts to process and exploit IMS-MS data for qualitative and quantitative applications. Here we review the current approaches for lipidomics research based on IMS-MS, including liquid chromatography-MS and direct-MS analyses of "shotgun" lipidomics and MS imaging.

Using fragmentation trees and mass spectral trees for identifying unknown compounds in metabolomics

Article

Apr 2015
TRAC-TREND ANAL CHEM

Identification of unknown metabolites is the bottleneck in advancing metabolomics, leaving interpretation of metabolomics results ambiguous. The chemical diversity of metabolism is vast, making structure identification arduous and time consuming. Currently, comprehensive analysis of mass spectra in metabolomics is limited to library matching, but tandem mass spectral libraries are small compared to the large number of compounds found in the biosphere, including xenobiotics. Resolving this bottleneck requires richer data acquisition and better computational tools. Multi-stage mass spectrometry (MSn) trees show promise to aid in this regard. Fragmentation trees explore the fragmentation process, generate fragmentation rules and aid in sub-structure identification, while mass spectral trees delineate the dependencies in multi-stage MS of collision-induced dissociations. This review covers advancements over the past 10 years as a tool for metabolite identification, including algorithms, software and databases used to build and to implement fragmentation trees and mass spectral annotations.

Avoiding Misannotation of In-Source Fragmentation Products as Cellular Metabolites in Liquid Chromatography–Mass Spectrometry-Based Metabolomics

Article

Jan 2015

Liquid chromatography-mass spectrometry (LC-MS) technology allows rapid quantitation of cellular metabolites, with metabolites identified by mass spectrometry and chromatographic retention time. Recently, with the development of rapid scanning high-resolution high accuracy mass spectrometers and the desire for high throughput screening, minimal or no chromatographic separation has become increasingly popular. When analyzing complex cellular extracts, however, the lack of chromatographic separation could potentially result in misannotation of structurally related metabolites. Here we show that, even using electrospray ionization, a soft ionization method, in-source fragmentation generates unwanted byproducts of identical mass to common metabolites. For example, nucleotide-triphosphates generate nucleotide-diphosphates and hexose-phosphates generate triose-phosphates. We evaluated yeast intracellular metabolite extracts and found more than 20 cases of in-source fragments that mimic common metabolites. Accordingly, chromatographic separation is required for accurate quantitation of many common cellular metabolites.

An Introduction to Separation Science

Book

Oct 1973

Bioinformatics: The Next Frontier of Metabolomics

Article

Nov 2014

Bioinformatic tools are required to carry out essential functions such as statistical analyses and database func-tionalities. Now, they are also needed for one of the most difficult tasks, helping researchers decide which metabo-lites are the most biologically meaningful. This can be achieved through aiding the identification process, reducing feature redundancy, putting forward better candidates for tandem mass spectrometry (MS/MS), speeding up or automating the workflow, deconvolving the feature list through meta-analysis or multigroup analysis, or using stable isotopes and pathway mapping. This review thus focuses on the most recent and innovative bioinformatic advancements for identifying metabolites.

Applying ‘Sequential Windowed Acquisition of All Theoretical Fragment Ion Mass Spectra’ (SWATH) for systematic toxicological analysis with liquid chromatography-high-resolution tandem mass spectrometry

Article

Nov 2014
ANAL BIOANAL CHEM

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has become an indispensable analytical technique in clinical and forensic toxicology for detection and identification of potentially toxic or harmful compounds. Particularly, non-target LC-MS/MS assays enable extensive and universal screening requested in systematic toxicological analysis. An integral part of the identification process is the generation of information-rich product ion spectra which can be searched against libraries of reference mass spectra. Usually, ‘data-dependent acquisition’ (DDA) strategies are applied for automated data acquisition. In this study, the ‘data-independent acquisition’ (DIA) method ‘Sequential Windowed Acquisition of All Theoretical Fragment Ion Mass Spectra’ (SWATH) was combined with LC-MS/MS on a quadrupole-quadrupole-time-of-flight (QqTOF) instrument for acquiring informative high-resolution tandem mass spectra. SWATH performs data-independent fragmentation of all precursor ions entering the mass spectrometer in 21m/z isolation windows. The whole m/z range of interest is covered by continuous stepping of the isolation window. This allows numerous repeat analyses of each window during the elution of a single chromatographic peak and results in a complete fragment ion map of the sample. Compounds and samples typically encountered in forensic casework were used to assess performance characteristics of LC-MS/MS with SWATH. Our experiments clearly revealed that SWATH is a sensitive and specific identification technique. SWATH is capable of identifying more compounds at lower concentration levels than DDA does. The dynamic range of SWATH was estimated to be three orders of magnitude. Furthermore, the >600,000 SWATH spectra matched led to only 408 incorrect calls (false positive rate = 0.06 %). Deconvolution of generated ion maps was found to be essential for unravelling the full identification power of LC-MS/MS with SWATH. With the available software, however, only semi-automated deconvolution was enabled, which rendered data interpretation a laborious and time-consuming process. Graphical Abstract High-resolution LC-MS/MS with SWATH represents a sensitive and specific compound identification tool that has vast potential to become a leading technique in systematic toxicological analysis. SWATH solves the problem of unused precursor ions often encountered with data-dependent acquisition methods by acquiring complete fragment ion maps of a sample

An Introduction to Separation Science

Book

Nov 1973

Creation of Libraries of Recurring Mass Spectra from Large Data Sets Assisted by a Dual-Column Workflow

Article

Sep 2014

An analytical methodology has been developed for extracting recurrent unidentified spectra (RUS) from large GC/MS data sets. Spectra were first extracted from original data files by the Automated Mass Spectral Deconvolution and Identification System (AMDIS)1 using settings designed to minimize spurious spectra, followed by searching the NIST library with all unidentified spectra. The spectra that could not be identified were then filtered to remove poorly deconvoluted data and clustered. The results were assumed to be unidentified components. This was tested by requiring each unidentified spectrum to be found in two chromatographic columns with slightly different stationary phases. This methodology has been applied to a large set of pediatric urine samples. A library of spectra and retention indices for derivatized urine components, both identified and recurrent unidentified has been created and is available for download.

Computational Metabolomics: A Framework for the Million Metabolome

Abstract

No full-text available

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