Urinary biomonitoring of subjects with different smoking habits. Part II: an untargeted metabolomic approach and the comparison with the targeted measurement of mercapturic acids

Metabolomic Plasma Profile of Chronic Obstructive Pulmonary Disease Patients

The analysis of blood metabolites may help identify individuals at risk of having COPD and offer insights into its underlying pathophysiology. This study aimed to identify COPD-related metabolic alterations and generate a biological signature potentially useful for screening purposes. Plasma metabolomic profiles from 91 COPD patients and 91 controls were obtained using complementary semi-targeted and untargeted LC-MS approaches. Univariate analysis identified metabolites with significant differences between groups, and enrichment analysis highlighted the most affected metabolic pathways. Multivariate analysis, including ROC curve assessment and machine learning algorithms, was applied to assess the discriminatory capacity of selected metabolites. After adjustment for major potential confounders, 56 metabolites showed significant differences between COPD patients and controls. The enrichment analysis revealed that COPD-associated metabolic alterations primarily involved lipid metabolism (especially fatty acids and acylcarnitines), followed by amino acid pathways and xenobiotics. A panel of 10 metabolites, mostly related to lipid metabolism, demonstrated high discriminatory performance for COPD (ROC-AUC: 0.916; 90.1% sensitivity and 89% specificity). These findings may contribute to improving screening strategies and a better understanding of COPD-related metabolic changes. However, our findings remain exploratory and should be interpreted with caution, needing further validation and mechanistic studies.

Simultaneous profiling of mercapturic acids, glucuronic acids, and sulfates in human urine

Humans are constantly exposed to both naturally-occurring and anthropogenic chemicals. Targeted mass spectrometry approaches are frequently used to measure a small panel of chemicals and their metabolites in environmental or biological matrices, but methods for comprehensive individual-level exposure assessment are limited. In this study, we applied an integrated library-guided analysis (ILGA) with ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS) to profile phase II metabolites, specifically mercapturic acids (MAs), glucuronic acids (GAs), and sulfates (SAs) in human urine samples (n = 844). We annotated 424 metabolites (146 MAs, 171 GAs, 107 SAs) by querying chromatographic features with in-house structural libraries, filtering against fragmentation patterns (common neutral loss and ion fragment), and comparing mass spectra with in-silico fragmentations and external spectral libraries. These metabolites were derived from over 200 putative parent compounds of exogenous and endogenous sources, such as dietary compounds, benzene/monocyclic substituted aromatics, pharmaceuticals, polycyclic aromatic hydrocarbons, bile acids/bile salts, and 4-hydroxyalkenals associated with lipid peroxidation process. Further, we performed statistical analyses on 214 metabolites found in more than 75% of samples to examine the association between metabolites and demographic characteristics using integrated network analysis, principal component analysis (PCA), and multivariable linear regression models. The network analysis revealed four distinct communities of 37 positively correlated metabolites, and the PCA (using the 37 metabolites) presented 4 principal components that meaningfully explained at least 80% of the variance in the data. The multivariable linear regression models showed some positive and negative associations between metabolite profiles and certain demographic variables (e.g., age, sex, race, education, income, and tobacco use).

The Metabolomic Profile of Microscopic Colitis Is Affected by Smoking but Not Histopathological Diagnosis, Clinical Course, Symptoms, or Treatment

Microscopic colitis (MC) is classified as collagenous colitis (CC) and lymphocytic colitis (LC). Genetic associations between CC and human leucocyte antigens (HLAs) have been found, with smoking being a predisposing external factor. Smoking has a great impact on metabolomics. The aim of this explorative study was to analyze global metabolomics in MC and to examine whether the metabolomic profile differed regarding the type and course of MC, the presence of IBS-like symptoms, treatment, and smoking habits. Of the 240 identified women with MC aged ≤73 years, 131 completed the study questionnaire; the Rome III questionnaire; and the Visual Analog Scale for Irritable Bowel Syndrome (VAS-IBS). Blood samples were analyzed by ultra-high-performance liquid chromatograph mass spectrometry (UHLC-MS/UHPLC-MSMS). The women, 63.1 (58.7-67.2) years old, were categorized based on CC (n = 76) and LC (n = 55); one episode or refractory MC; IBS-like symptoms or not; use of corticosteroids or not; and smoking habits. The only metabolomic differences found in the univariate model after adjustment for false discovery rate (FDR) were between smokers and non-smokers. Serotonin was markedly increased in smokers (p < 0.001). No clear patterns appeared when conducting a principal component analysis (PCA). No differences in the metabolomic profile were found depending on the type or clinical course of the disease, neither in the whole MC group nor in the subgroup analysis of CC.

Applications of biomarkers of exposure and biological effects in users of new generation tobacco and nicotine products: Tentative proposals

Despite efforts to eliminate smoking, more than 1 billion people worldwide continue to use combustible cigarettes through choice or inability to quit. With an associated 8 million deaths, the provision of noncombustible tobacco and nicotine products that smokers will accept to replace combustible cigarettes can lessen harm. However, most of these products have entered the market only in the past 20 years. Therefore, particularly for some smoking-related diseases, epidemiological studies to test harm reduction potential are only now becoming feasible. For cancer and chronic obstructive pulmonary disease, around two decades of data might be required. In this article, we discuss how the use of biomarkers might be applied to supplement epidemiological research for regulators. We further discuss how health providers and insurers can keep up with the rapid changes in biomarker research and recognize these reduced risks.

Comparison of urinary mercapturic acid excretions in users of various tobacco/nicotine products

Urinary mercapturic acids (MAs) are detoxification products for electrophiles occurring in the human body. They are suitable biomarkers of exposure to directly acting electrophilic chemicals or to chemicals which generate the electrophile during its metabolism. We determined the urinary excretion of 19 MAs in habitual users of combustible cigarettes (CCs), electronic cigarettes (ECs), heated tobacco products (HTPs), oral tobacco (OT), and nicotine replacement therapy (NRT) products, and nonusers (NUs) of any tobacco/nicotine products. The 19 MAs are assumed to be physiologically formed primarily from 15 toxicants with three of them belonging to IARC Group 1 (human carcinogen), seven to Group 2A (probable human carcinogen), four to Group 2B (possible human carcinogen), and one to Group 3 (not classifiable as carcinogen). Smoking (CC) was found to be associated with significantly elevated exposure to ethylene oxide (or ethylene), 1,3-butadiene, benzene, dimethylformamide, acrolein, acrylamide, styrene, propylene oxide, acrylonitrile, crotonaldehyde, and isoprene compared with the other user groups and NU. Users of HTPs revealed slight elevation in the MAs related to acrolein, acrylamide, and crotonaldehyde compared with the other non-CC groups. Vaping (EC) was not found to be associated with any of the MAs studied. In conclusion, the determination of urinary MAs is a useful tool for assessing the exposure to toxicants (mainly potential carcinogens) in users of various tobacco/nicotine products. Our data also give cause to clarify the role of vaping (EC) in urinary excretion of DHPMA (precursor: glycidol).

Update on metabolomic findings in COPD patients

COPD is a heterogeneous disorder that shows diverse clinical presentations (phenotypes and "treatable traits") and biological mechanisms (endotypes). This heterogeneity implies that to carry out a more personalised clinical management, it is necessary to classify each patient accurately. With this objective, and in addition to clinical features, it would be very useful to have well-defined biological markers. The search for these markers may either be done through more conventional laboratory and hypothesis-driven techniques or relatively blind high-throughput methods, with the omics approaches being suitable for the latter. Metabolomics is the science that studies biological processes through their metabolites, using various techniques such as gas and liquid chromatography, mass spectrometry and nuclear magnetic resonance. The most relevant metabolomics studies carried out in COPD highlight the importance of metabolites involved in pathways directly related to proteins (peptides and amino acids), nucleic acids (nitrogenous bases and nucleosides), and lipids and their derivatives (especially fatty acids, phospholipids, ceramides and eicosanoids). These findings indicate the relevance of inflammatory-immune processes, oxidative stress, increased catabolism and alterations in the energy production. However, some specific findings have also been reported for different COPD phenotypes, demographic characteristics of the patients, disease progression profiles, exacerbations, systemic manifestations and even diverse treatments. Unfortunately, the studies carried out to date have some limitations and shortcomings and there is still a need to define clear metabolomic profiles with clinical utility for the management of COPD and its implicit heterogeneity.

Global Profiling of Urinary Mercapturic Acids Using Integrated Library-Guided Analysis

Urinary mercapturic acids (MAs) are often used as biomarkers for monitoring human exposures to occupational and environmental xenobiotics. In this study, we developed an integrated library-guided analysis workflow using ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry. This method includes expanded assignment criteria and a curated library of 220 MAs and addresses the shortcomings of previous untargeted approaches. We employed this workflow to profile MAs in the urine of 70 participants─40 nonsmokers and 30 smokers. We found approximately 500 MA candidates in each urine sample, and 116 MAs from 63 precursors were putatively annotated. These include 25 previously unreported MAs derived mostly from alkenals and hydroxyalkenals. Levels of 68 MAs were comparable in nonsmokers and smokers, 2 MAs were higher in nonsmokers, and 46 MAs were elevated in smokers. These included MAs of polycyclic aromatic hydrocarbons and hydroxyalkenals and those derived from toxicants present in cigarette smoke (e.g., acrolein, 1,3-butadiene, isoprene, acrylamide, benzene, and toluene). Our workflow allowed profiling of known and unreported MAs from endogenous and environmental sources, and the levels of several MAs were increased in smokers. Our method can also be expanded and applied to other exposure-wide association studies.

Optimizing XCMS parameters for GC-MS metabolomics data processing: a case study

BACKGROUND AND AIMS

Optimizing metabolomics data processing parameters is a challenging and fundamental task to obtain reliable results. Automated tools have been developed to assist this optimization for LC-MS data. GC-MS data require substantial modifications in processing parameters, as the chromatographic profiles are more robust, with more symmetrical and Gaussian peaks. This work compared an automated XCMS parameter optimization using the Isotopologue Parameter Optimization (IPO) software with manual optimization of GC-MS metabolomics data. Additionally, the results were compared to online XCMS platform.

METHODS

GC-MS data from control and test groups of intracellular metabolites from Trypanosoma cruzi trypomastigotes were used. Optimizations were performed on the quality control (QC) samples.

RESULTS

The results in terms of the number of molecular features extracted, repeatability, missing values, and the search for significant metabolites showed the importance of optimizing the parameters for peak detection, alignment, and grouping, especially those related to peak width (fwhm, bw) and noise ratio (snthresh).

CONCLUSION

This is the first time that a systematic optimization using IPO has been performed on GC-MS data. The results demonstrate that there is no universal approach for optimization but automated tools are valuable at this stage of the metabolomics workflow. The online XCMS proves to be an interesting processing tool, helping, above all, in the choice of parameters as a starting point for adjustments and optimizations. Although the tools are easy to use, there is still a need for technical knowledge about the analytical methods and instruments used.

Development and Application of an LC-MS/MS Untargeted Exposomics Method with a Separated Pooled Quality Control Strategy

Pooled quality controls (QCs) are usually implemented within untargeted methods to improve the quality of datasets by removing features either not detected or not reproducible. However, this approach can be limiting in exposomics studies conducted on groups of exposed and nonexposed subjects, as compounds present at low levels only in exposed subjects can be diluted and thus not detected in the pooled QC. The aim of this work is to develop and apply an untargeted workflow for human biomonitoring in urine samples, implementing a novel separated approach for preparing pooled quality controls. An LC-MS/MS workflow was developed and applied to a case study of smoking and non-smoking subjects. Three different pooled quality controls were prepared: mixing an aliquot from every sample (QC-T), only from non-smokers (QC-NS), and only from smokers (QC-S). The feature tables were filtered using QC-T (T-feature list), QC-S, and QC-NS, separately. The last two feature lists were merged (SNS-feature list). A higher number of features was obtained with the SNS-feature list than the T-feature list, resulting in identification of a higher number of biologically significant compounds. The separated pooled QC strategy implemented can improve the nontargeted human biomonitoring for groups of exposed and nonexposed subjects.

Analytical aspects of meet-in-metabolite analysis for molecular pathway reconstitution from exposure to adverse outcome

To explore the etiology of diseases is one of the major goals in epidemiological study. Meet-in-metabolite analysis reconstitutes biomonitoring-based adverse outcome (AO) pathways from environmental exposure to a disease, in which the chemical exposome-related metabolism responses are transmitted to incur the AO-related metabolism phenotypes. However, the ongoing data-dependent acquisition of non-targeted biomonitoring by high-resolution mass spectrometry (HRMS) is biased against the low abundance molecules, which forms the major of molecular internal exposome, i.e., the totality of trace levels of environmental pollutants and/or their metabolites in human samples. The recent development of data-independent acquisition protocols for HRMS screening has opened new opportunities to enhance unbiased measurement of the extremely low abundance molecules, which can encompass a wide range of analytes and has been applied in metabolomics, DNA, and protein adductomics. In addition, computational MS for small molecules is urgently required for the top-down exposome databases. Although a holistic analysis of the exposome and endogenous metabolites is plausible, multiple and flexible strategies, instead of "putting one thing above all" are proposed.

Toxicology of flavoring- and cannabis-containing e-liquids used in electronic delivery systems

Electronic cigarettes (e-cigarettes) were introduced in the United States in 2007 and by 2014 they were the most popular tobacco product amongst youth and had overtaken use of regular tobacco cigarettes. E-cigarettes are used to aerosolize a liquid (e-liquid) that the user inhales. Flavorings in e-liquids is a primary reason for youth to initiate use of e-cigarettes. Evidence is growing in the scientific literature that inhalation of some flavorings is not without risk of harm. In this review, 67 original articles (primarily cellular in vitro) on the toxicity of flavored e-liquids were identified in the PubMed and Scopus databases and evaluated critically. At least 65 individual flavoring ingredients in e-liquids or aerosols from e-cigarettes induced toxicity in the respiratory tract, cardiovascular and circulatory systems, skeletal system, and skin. Cinnamaldehyde was most frequently reported to be cytotoxic, followed by vanillin, menthol, ethyl maltol, ethyl vanillin, benzaldehyde and linalool. Additionally, modern e-cigarettes can be modified to aerosolize cannabis as dried plant material or a concentrated extract. The U.S. experienced an outbreak of lung injuries, termed e-cigarette, or vaping, product use-associated lung injury (EVALI) that began in 2019; among 2,022 hospitalized patients who had data on substance use (as of January 14, 2020), 82% reported using a delta-9-tetrahydrocannabinol (main psychoactive component in cannabis) containing e-cigarette, or vaping, product. Our literature search identified 33 articles related to EVALI. Vitamin E acetate, a diluent and thickening agent in cannabis-based products, was strongly linked to the EVALI outbreak in epidemiologic and laboratory studies; however, e-liquid chemistry is highly complex, and more than one mechanism of lung injury, ingredient, or thermal breakdown product may be responsible for toxicity. More research is needed, particularly with regard to e-cigarettes (generation, power settings, etc.), e-liquids (composition, bulk or vaped form), modeled systems (cell type, culture type, and dosimetry metrics), biological monitoring, secondhand exposures and contact with residues that contain nicotine and flavorings, and causative agents and mechanisms of EVALI toxicity.