Exposome最新文献

{"title":"An actionable annotation scoring framework for gas chromatography-high-resolution mass spectrometry.","authors":"Jeremy P Koelmel, Hongyu Xie, Elliott J Price, Elizabeth Z Lin, Katherine E Manz, Paul Stelben, Matthew K Paige, Stefano Papazian, Joseph Okeme, Dean P Jones, Dinesh Barupal, John A Bowden, Pawel Rostkowski, Kurt D Pennell, Vladimir Nikiforov, Thanh Wang, Xin Hu, Yunjia Lai, Gary W Miller, Douglas I Walker, Jonathan W Martin, Krystal J Godri Pollitt","doi":"10.1093/exposome/osac007","DOIUrl":"10.1093/exposome/osac007","url":null,"abstract":"<p><p>Omics-based technologies have enabled comprehensive characterization of our exposure to environmental chemicals (chemical exposome) as well as assessment of the corresponding biological responses at the molecular level (eg, metabolome, lipidome, proteome, and genome). By systematically measuring personal exposures and linking these stimuli to biological perturbations, researchers can determine specific chemical exposures of concern, identify mechanisms and biomarkers of toxicity, and design interventions to reduce exposures. However, further advancement of metabolomics and exposomics approaches is limited by a lack of standardization and approaches for assigning confidence to chemical annotations. While a wealth of chemical data is generated by gas chromatography high-resolution mass spectrometry (GC-HRMS), incorporating GC-HRMS data into an annotation framework and communicating confidence in these assignments is challenging. It is essential to be able to compare chemical data for exposomics studies across platforms to build upon prior knowledge and advance the technology. Here, we discuss the major pieces of evidence provided by common GC-HRMS workflows, including retention time and retention index, electron ionization, positive chemical ionization, electron capture negative ionization, and atmospheric pressure chemical ionization spectral matching, molecular ion, accurate mass, isotopic patterns, database occurrence, and occurrence in blanks. We then provide a qualitative framework for incorporating these various lines of evidence for communicating confidence in GC-HRMS data by adapting the Schymanski scoring schema developed for reporting confidence levels by liquid chromatography HRMS (LC-HRMS). Validation of our framework is presented using standards spiked in plasma, and confident annotations in outdoor and indoor air samples, showing a false-positive rate of 12% for suspect screening for chemical identifications assigned as Level 2 (when structurally similar isomers are not considered false positives). This framework is easily adaptable to various workflows and provides a concise means to communicate confidence in annotations. Further validation, refinements, and adoption of this framework will ideally lead to harmonization across the field, helping to improve the quality and interpretability of compound annotations obtained in GC-HRMS.</p>","PeriodicalId":73005,"journal":{"name":"Exposome","volume":"2 1","pages":"osac007"},"PeriodicalIF":0.0,"publicationDate":"2022-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9719826/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10741160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interactive Software for Visualization of Non-Targeted Mass Spectrometry Data—FluoroMatch Visualizer","authors":"J. Koelmel, Paul Stelben, David Godri, Jiarong Qi, C. McDonough, David A. Dukes, Juan J. Aristizabal-Henao, John A. Bowden, Sandi Sternberg, Emma Rennie, K. Pollitt","doi":"10.1093/exposome/osac006","DOIUrl":"https://doi.org/10.1093/exposome/osac006","url":null,"abstract":"\u0000 There are thousands of different per- and polyfluoroalkyl substances (PFAS) in everyday products and in the environment. Discerning the abundance and diversity of PFAS is essential for understanding sources, fate, exposure routes, and the associated health impacts of PFAS. While comprehensive detection of PFAS requires use of non-targeted mass spectrometry, data-processing is time intensive and prone to error. While automated approaches can compile all mass spectrometric evidence (e.g., retention time, isotopic pattern, fragmentation, and accurate mass) and provide ranking or scoring metrics for annotations, confident assignment of structure often still requires extensive manual review of the data. To aid this process, we present FluoroMatch Visualizer which was developed to provide interactive visualizations which include normalized mass defect plots, retention time versus accurate mass plots, MS/MS fragmentation spectra, and tables of annotations and meta-data. All graphs and tables are interactive and have cross-filtering such that when a user selects a feature, all other visuals highlight the feature of interest. Several filtering options have been integrated into this novel data visualization tool, specifically with the capability to filter by PFAS chemical series, fragment(s), assignment confidence, and MS/MS file(s). FluoroMatch Visualizer is part of FluoroMatch Suite, which consists of FluoroMatch Modular, FluoroMatch Flow, and FluoroMatch Generator. FluoroMatch Visualizer enables annotations to be extensively validated, increasing annotation confidence. The resulting visualizations and datasets can be shared online in an interactive format for community based PFAS discovery. FluoroMatch visualizer holds potential to promote harmonization of non-targeted data-processing and interpretation throughout the PFAS scientific community.","PeriodicalId":73005,"journal":{"name":"Exposome","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46688749","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nature versus nurture-on the origins of a specious argument.","authors":"Robert O Wright","doi":"10.1093/exposome/osac005","DOIUrl":"https://doi.org/10.1093/exposome/osac005","url":null,"abstract":"<p><p>The concept of heritability parses out genetic and environmental causes of diseases and does not fit the underlying biology of complex diseases that arise from interactions among genetics and environment. Exposomics places environment on a similar scale as genomics and allows for more modern research approaches that estimate time-varying genome by exposome interactions. By addressing the biological underpinnings of disease comprehensively, we will find the \"missing heritability\" which is not solely based on genetic variation but is instead driven by time, life stage, and geographic variability in our exposome as it interacts with our genome.</p>","PeriodicalId":73005,"journal":{"name":"Exposome","volume":" ","pages":"osac005"},"PeriodicalIF":0.0,"publicationDate":"2022-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9366178/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40696450","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of occupations susceptible to high exposure and risk associated with multiple toxicants in an observational study: National Health and Nutrition Examination Survey 1999-2014.","authors":"Vy Kim Nguyen, Justin Colacino, Chirag J Patel, Maureen Sartor, Olivier Jolliet","doi":"10.1093/exposome/osac004","DOIUrl":"10.1093/exposome/osac004","url":null,"abstract":"<p><p>Occupational exposures to toxicants are estimated to cause over 370 000 premature deaths annually. The risks due to multiple workplace chemical exposures and those occupations most susceptible to the resulting health effects remain poorly characterized. The aim of this study is to identify occupations with elevated toxicant biomarker concentrations and increased health risk associated with toxicant exposures in a diverse working US population. For this observational study of 51 008 participants, we used data from the 1999-2014 National Health and Nutrition Examination Survey. We characterized differences in chemical exposures by occupational group for 131 chemicals by applying a series of generalized linear models with the outcome as biomarker concentrations and the main predictor as the occupational groups, adjusting for age, sex, race/ethnicity, poverty income ratio, study period, and biomarker of tobacco use. For each occupational group, we calculated percentages of participants with chemical biomarker levels exceeding acceptable health-based guidelines. Blue-collar workers from \"Construction,\" \"Professional, Scientific, Technical Services,\" \"Real Estate, Rental, Leasing,\" \"Manufacturing,\" and \"Wholesale Trade\" have higher biomarker levels of toxicants such as several heavy metals, acrylamide, glycideamide, and several volatile organic compounds (VOCs) compared with their white-collar counterparts. Moreover, blue-collar workers from these industries have toxicant concentrations exceeding acceptable levels: arsenic (16%-58%), lead (1%-3%), cadmium (1%-11%), glycideamide (3%-6%), and VOCs (1%-33%). Blue-collar workers have higher toxicant levels relative to their white-collar counterparts, often exceeding acceptable levels associated with noncancer effects. Our findings identify multiple occupations to prioritize for targeted interventions and health policies to monitor and reduce toxicant exposures.</p>","PeriodicalId":73005,"journal":{"name":"Exposome","volume":"2 1","pages":"osac004"},"PeriodicalIF":0.0,"publicationDate":"2022-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9266352/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9247257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An exposomic framework to uncover environmental drivers of aging.","authors":"Vrinda Kalia, Daniel W Belsky, Andrea A Baccarelli, Gary W Miller","doi":"10.1093/exposome/osac002","DOIUrl":"10.1093/exposome/osac002","url":null,"abstract":"<p><p>The exposome, the environmental complement of the genome, is an omics level characterization of an individual's exposures. There is growing interest in uncovering the role of the environment in human health using an exposomic framework that provides a systematic and unbiased analysis of the non-genetic drivers of health and disease. Many environmental toxicants are associated with molecular hallmarks of aging. An exposomic framework has potential to advance understanding of these associations and how modifications to the environment can promote healthy aging in the population. However, few studies have used this framework to study biological aging. We provide an overview of approaches and challenges in using an exposomic framework to investigate environmental drivers of aging. While capturing exposures over a life course is a daunting and expensive task, the use of historical data can be a practical way to approach this research.</p>","PeriodicalId":73005,"journal":{"name":"Exposome","volume":" ","pages":"osac002"},"PeriodicalIF":0.0,"publicationDate":"2022-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8917275/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41858506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Chemical contact tracing for exposomics","authors":"Ken H. Liu","doi":"10.1093/exposome/osac001","DOIUrl":"https://doi.org/10.1093/exposome/osac001","url":null,"abstract":"\u0000 Human health and disease reflects a complex interplay between the genome and the exposome. High-resolution mass spectrometry (HRMS) based metabolomics routinely measures thousands of endogenous, dietary and xenobiotic chemicals. However, confident identification of exposure-related chemicals remains a challenge as a significant portion of chemical signals detected in metabolomics analyses remain uncharacterized. Illuminating the “dark matter” of the exposome cannot be accomplished efficiently if the prevailing approach depends on the use of purified authentic standards that are not readily accessible for most laboratories. An alternative approach involves chemical exposure “contact tracing” analogous to contact tracing used to track the spread of infectious disease. For transmissible diseases, contact tracing identifies sets of potentially infected individuals that are linked by close contact to a confirmed positive case. Similarly, chemical exposures can be identified by establishing sets of xenobiotic metabolites that are linked to the original exposure via enzymatic biotransformation. Here, we provide a commentary on how incorporating enzyme-based strategies for chemical contact tracing enables -omics scale characterization of chemical exposures to further illuminate the “dark matter” of the exposome.","PeriodicalId":73005,"journal":{"name":"Exposome","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47882566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling environment through a general exposome factor in two independent adolescent cohorts.","authors":"Tyler M Moore,&nbsp;Elina Visoki,&nbsp;Stirling T Argabright,&nbsp;Grace E Didomenico,&nbsp;Ingrid Sotelo,&nbsp;Jeremy D Wortzel,&nbsp;Areebah Naeem,&nbsp;Ruben C Gur,&nbsp;Raquel E Gur,&nbsp;Varun Warrier,&nbsp;Sinan Guloksuz,&nbsp;Ran Barzilay","doi":"10.1093/exposome/osac010","DOIUrl":"https://doi.org/10.1093/exposome/osac010","url":null,"abstract":"<p><p>Exposures to perinatal, familial, social, and physical environmental stimuli can have substantial effects on human development. We aimed to generate a single measure that capture's the complex network structure of the environment (ie, exposome) using multi-level data (participant's report, parent report, and geocoded measures) of environmental exposures (primarily from the psychosocial environment) in two independent adolescent cohorts: The Adolescent Brain Cognitive Development Study (ABCD Study, <i>N</i> = 11 235; mean age, 10.9 years; 47.7% females) and an age- and sex-matched sample from the Philadelphia Neurodevelopmental Cohort (PNC, <i>N</i> = 4993). We conducted a series of data-driven iterative factor analyses and bifactor modeling in the ABCD Study, reducing dimensionality from 348 variables tapping to environment to six orthogonal exposome subfactors and a general (adverse) exposome factor. The general exposome factor was associated with overall psychopathology (<i>B</i> = 0.28, 95% CI, 0.26-0.3) and key health-related outcomes: obesity (odds ratio [OR] , 1.4; 95% CI, 1.3-1.5) and advanced pubertal development (OR, 1.3; 95% CI, 1.2-1.5). A similar approach in PNC reduced dimensionality of environment from 29 variables to 4 exposome subfactors and a general exposome factor. PNC analyses yielded consistent associations of the general exposome factor with psychopathology (<i>B</i> = 0.15; 95% CI, 0.13-0.17), obesity (OR, 1.4; 95% CI, 1.3-1.6), and advanced pubertal development (OR, 1.3; 95% CI, 1-1.6). In both cohorts, inclusion of exposome factors greatly increased variance explained in overall psychopathology compared with models relying solely on demographics and parental education (from <4% to >38% in ABCD; from <4% to >18.5% in PNC). Findings suggest that a general exposome factor capturing multi-level environmental exposures can be derived and can consistently explain variance in youth's mental and general health.</p>","PeriodicalId":73005,"journal":{"name":"Exposome","volume":"2 1","pages":"osac010"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9798749/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9462732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editor-in-Chief response to “FAIR-ifying the Exposome Journal: templates for chemical structures and transformations”","authors":"G. Miller","doi":"10.1093/exposome/osac003","DOIUrl":"https://doi.org/10.1093/exposome/osac003","url":null,"abstract":"","PeriodicalId":73005,"journal":{"name":"Exposome","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45119680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Historical Exposomics And High Resolution Mass Spectrometry","authors":"Dagny Aurich, Owen Miles, Emma L. Schymanski","doi":"10.1093/exposome/osab007","DOIUrl":"https://doi.org/10.1093/exposome/osab007","url":null,"abstract":"\u0000 Awareness of the exposome and its influence on health has increased in the last decade. As past exposures can cause changes in human health many years later, delving into the past is relevant for both diagnostic and prevention purposes, but remains a challenging task. Lifestyle, diet and socioeconomic information of the past should be well documented and compatible with modern data science methods. While chemical analysis nowadays makes use of High Resolution Mass Spectrometry (HR-MS) for highly sensitive and comprehensive coverage of samples plus retrospective analysis, these data archives are in the very early stages. Since past measurements are often only available for a limited set of chemicals, adding to this knowledge requires careful selection of sample types and sampling sites, which may not always be available. The choice of analytes and analytical methods should be suitable for the study question—which is not always clear in advance in exposomics. Data interpretation and the use of appropriate databases are indispensable for a proper exposure assessment, and as databases and knowledge grow, re-analysis of physically or digitally archived samples could enable “continuous monitoring” efforts. This review focusses on the chemical analytical approaches necessary to capture the complexity of the historical exposome. Various sample types, analytes as well as analyses and data interpretation methods are discussed in relation to chemical exposures, while the connection to health remains in focus. It ends with perspectives and challenges in assessing the historical exposome, discussing how we can “learn from the past” to build a better future.","PeriodicalId":73005,"journal":{"name":"Exposome","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46596575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FAIR-ifying the Exposome Journal: Templates for Chemical Structures and Transformations","authors":"Emma L. Schymanski, Evan E. Bolton","doi":"10.1093/exposome/osab006","DOIUrl":"https://doi.org/10.1093/exposome/osab006","url":null,"abstract":"\u0000 The exposome, the totality of lifetime exposures, is a new and highly complex paradigm for health and disease. Tackling this challenge requires an effort well beyond single individuals or laboratories, where every piece of the puzzle will be vital. The launch of this new Exposome journal coincides with the evolution of the exposome through its teenage years and into a growing maturity in an increasingly open and FAIR (findable, accessible, interoperable, reusable) world. This letter discusses how both authors and the Exposome journal alike can help increase the FAIRness of the chemical structural information and the associated metadata in the journal, aiming to capture more details about the chemistry of exposomics. The proposed chemical structure template can serve as an interoperable supplementary format that is made accessible through the website and more findable by linking the DOI of this data file to the article DOI metadata, supporting further reuse. An additional Transformations template provides authors with a means to connect predecessor (parent, substrate) molecules to successor (transformation product, metabolite) molecules and thus provide FAIR connections between observed (i.e., experimental) chemical exposures and biological responses, to help improve the public knowledgebase on exposome-related transformations. These connections are vital to extend current biochemical knowledge and to fulfil the current Exposome definition of “the cumulative measure of environmental influences and associated biological responses throughout the lifespan including exposures from the environment, diet, behaviour, and endogenous processes”.","PeriodicalId":73005,"journal":{"name":"Exposome","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46075507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}