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Journal articles on the topic 'Exposomics'

Author: Grafiati

Published: 25 May 2024

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1

Neagu, Anca-Narcisa, Taniya Jayaweera, Lilian Corrice, Kaya Johnson, and Costel Darie. "Breast Cancer Exposomics." Life 14, no. 3 (March 18, 2024): 402. http://dx.doi.org/10.3390/life14030402.

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We are exposed to a mixture of environmental man-made and natural xenobiotics. We experience a wide spectrum of environmental exposure in our lifetime, including the effects of xenobiotics on gametogenesis and gametes that undergo fertilization as the starting point of individual development and, moreover, in utero exposure, which can itself cause the first somatic or germline mutation necessary for breast cancer (BC) initiation. Most xenobiotics are metabolized or/and bioaccumulate and biomagnify in our tissues and cells, including breast tissues, so the xenobiotic metabolism plays an important role in BC initiation and progression. Many considerations necessitate a more valuable explanation regarding the molecular mechanisms of action of xenobiotics which act as genotoxic and epigenetic carcinogens. Thus, exposomics and the exposome concept are based on the diversity and range of exposures to physical factors, synthetic chemicals, dietary components, and psychosocial stressors, as well as their associated biologic processes and molecular pathways. Existing evidence for BC risk (BCR) suggests that food-borne chemical carcinogens, air pollution, ionizing radiation, and socioeconomic status are closely related to breast carcinogenesis. The aim of this review was to depict the dynamics and kinetics of several xenobiotics involved in BC development, emphasizing the role of new omics fields related to BC exposomics, such as environmental toxicogenomics, epigenomics and interactomics, metagenomics, nutrigenomics, nutriproteomics, and nutrimiRomics. We are mainly focused on food and nutrition, as well as endocrine-disrupting chemicals (EDCs), involved in BC development. Overall, cell and tissue accumulation and xenobiotic metabolism or biotransformation can lead to modifications in breast tissue composition and breast cell morphology, DNA damage and genomic instability, epimutations, RNA-mediated and extracellular vesicle effects, aberrant blood methylation, stimulation of epithelial–mesenchymal transition (EMT), disruption of cell–cell junctions, reorganization of the actin cytoskeleton, metabolic reprogramming, and overexpression of mesenchymal genes. Moreover, the metabolism of xenobiotics into BC cells impacts almost all known carcinogenic pathways. Conversely, in our food, there are many bioactive compounds with anti-cancer potential, exerting pro-apoptotic roles, inhibiting cell cycle progression and proliferation, migration, invasion, DNA damage, and cell stress conditions. We can conclude that exposomics has a high potential to demonstrate how environmental exposure to xenobiotics acts as a double-edged sword, promoting or suppressing tumorigenesis in BC.

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Casella, V., M. Franzini, M. T. Rocca, A. Pogliaghi, N. Fiscante, L. Raso, and F. Sapio. "CUSTOMIZED WEBGIS SOLUTIONS FOR EXPOSOMICS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 22, 2020): 1431–38. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-1431-2020.

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Abstract. Exposomics is a science aiming at quantifying the effects on human health of all the factors influencing it, but genetic ones. They include environment, food, mobility habits and cultural factors. The percentage of the world’s population living in the urban areas is projected to increase in the next decades. Rising industrialization, urbanization and heterogeneity are leading to new challenges for public health and quality of life in the population. The prevalence of conditions such as asthma and cardiovascular diseases is increasing due to a change in lifestyle and air quality. This enlightens the necessity of targeted interventions to increase citizens’ quality of life and decrease their health risks. Within the EU H2020 PULSE project, a multi-technological system to assist the population in the prevention and treatment of asthma and type 2 diabetes has been developed. The system created in PULSE features several parts, such as a personal App for the citizens, a set of air quality sensors, a WebGIS and dashboards for the public health operators. Citizens are directly involved in an exchange paradigm in which they send their own data and receive feedbacks and suggestions about their health in return. The WebGIS is a very distinguishing element of the PULSE technology and the paper illustrates its main functionalities focusing on the distinguishing and innovative features developed.

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Choi, Hyunok, Mark T. McAuley, and David A. Lawrence. "Prenatal exposures and exposomics of asthma." AIMS Environmental Science 2, no. 1 (2015): 87–109. http://dx.doi.org/10.3934/environsci.2015.1.87.

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4

Jobst, Karl J., and Krystal Godri Pollitt. "Editorial overview: Exposomics, emerging exposures and analytical challenges." Current Opinion in Environmental Science & Health 15 (June 2020): A1—A3. http://dx.doi.org/10.1016/j.coesh.2020.08.001.

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5

Cooke, Marcus S., Chiung-Wen Hu, Yuan-Jhe Chang, and Mu-Rong Chao. "Urinary DNA adductomics – A novel approach for exposomics." Environment International 121 (December 2018): 1033–38. http://dx.doi.org/10.1016/j.envint.2018.10.041.

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6

Fan, Jung-wei, Jianrong Li, and Yves A. Lussier. "Semantic Modeling for Exposomics with Exploratory Evaluation in Clinical Context." Journal of Healthcare Engineering 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/3818302.

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Exposome is a critical dimension in the precision medicine paradigm. Effective representation of exposomics knowledge is instrumental to melding nongenetic factors into data analytics for clinical research. There is still limited work in (1) modeling exposome entities and relations with proper integration to mainstream ontologies and (2) systematically studying their presence in clinical context. Through selected ontological relations, we developed a template-driven approach to identifying exposome concepts from the Unified Medical Language System (UMLS). The derived concepts were evaluated in terms of literature coverage and the ability to assist in annotating clinical text. The generated semantic model represents rich domain knowledge about exposure events (454 pairs of relations between exposure and outcome). Additionally, a list of 5667 disorder concepts with microbial etiology was created for inferred pathogen exposures. The model consistently covered about 90% of PubMed literature on exposure-induced iatrogenic diseases over 10 years (2001–2010). The model contributed to the efficiency of exposome annotation in clinical text by filtering out 78% of irrelevant machine annotations. Analysis into 50 annotated discharge summaries helped advance our understanding of the exposome information in clinical text. This pilot study demonstrated feasibility of semiautomatically developing a useful semantic resource for exposomics.

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7

Vineis, P., M. Chadeau-Hyam, H. Gmuender, J. Gulliver, Z. Herceg, J. Kleinjans, M. Kogevinas, et al. "The exposome in practice: Design of the EXPOsOMICS project." International Journal of Hygiene and Environmental Health 220, no. 2 (March 2017): 142–51. http://dx.doi.org/10.1016/j.ijheh.2016.08.001.

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8

Schramm, Karl-Werner, Jingxian Wang, Yonghong Bi, Cedrique Temoka, Gerd Pfister, Bernhard Henkelmann, and Hagen Scherb. "Chemical- and effect-oriented exposomics: Three Gorges Reservoir (TGR)." Environmental Science and Pollution Research 20, no. 10 (November 22, 2012): 7057–62. http://dx.doi.org/10.1007/s11356-012-1319-9.

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9

Smith, Martyn T., Rosemarie de la Rosa, and Sarah I. Daniels. "Using exposomics to assess cumulative risks and promote health." Environmental and Molecular Mutagenesis 56, no. 9 (October 17, 2015): 715–23. http://dx.doi.org/10.1002/em.21985.

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10

McKeon, Thomas P., Vicky Tam, Wei-Ting Hwang, Paul Wileyto, Karen Glanz, and Trevor M. Penning. "Abstract PR06: Geocoding and integrating multiple environmental exposomics sources: Assessing population hazard to lung carcinogens in 421 zip codes of a cancer center catchment area." Cancer Epidemiology, Biomarkers & Prevention 29, no. 9_Supplement (September 1, 2020): PR06. http://dx.doi.org/10.1158/1538-7755.modpop19-pr06.

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Abstract To assess risk factors that contribute to lung cancer burden in the Abramson Cancer Center (ACC) catchment area, we integrated geospatial data of exposure to pollutants from publicly available EPA and NASA datasets. The study area covers the 421 zip codes that make up the 12 counties of the catchment area from which most of the ACC patients come. The counties include 5 that surround Philadelphia, 6 in New Jersey, and 1 in Delaware. Environmental exposure data, sourced from US-EPA Air Quality System (AQS) Data Mart, were focused on air pollutants since air pollution is recognized by the International Agency on Cancer (IARC) as a Group 1 human carcinogen. Exposomics data included: hourly, daily, and annual (1980 -2018) PM2.5, PM10, NO2; Hazardous Air Pollutants (HAPS); Volatile Organic Compounds (VOCs); (Air Quality Index) AQI; NONOxNOy monitoring; and annual Toxic Release Inventory (TRI) air emissions by chemical classifier and point source (1987 -2017). Annual NASA satellite-derived grids were incorporated for PM2.5 (1998-2016; 1 km resolution) and NOx (1997 - 2012; 10 km resolution). ESRI’s ArcGIS was used to develop programming scripts to automate the process of data integration, geocoding, and classifying chemical parameters by (1) status as a lung carcinogen with sufficient evidence of lung carcinogenesis; (2) status as one of the priority 16 EPA polycyclic aromatic hydrocarbons, as a surrogate marker of exposure to carcinogens; (3) status in the IARC rankings for Cancer Group; (4) status as a component of diesel exhaust; and (5) status as a VOC. 1-km search radius kernel density grids were generated for each air pollutant. We sliced the density estimates into ordinal rankings ranging from “10 = high” to “1 = low.” A hazard index may be generated by summing data layers of cumulative environmental exposomics in a process called map algebra. Spatial sorting and merging of exposome releases by facility, year, chemical and zip code concentration allow for addressing “low-hanging fruit” through summary statistics. Although the focus of this investigation is on lung cancer, the utility of the methodology may be applied to probe exposures related to other cancers. Incorporating more years or larger geographic areas of study may make exploring the risk of exposure possible for less prevalent cancers. In future studies, we are conducting statistical analysis to determine whether geocoded exposure data predict lung cancer risks in those vulnerable zip codes using electronic health record data of geolocations of lung cancer patients. This novel approach will help determine whether geocoded exposomics data are associated with cancer incidence. The hazard index was used to identify zip codes that are the most vulnerable to carcinogen exposure. Zip codes 19720, 19061, 08066, 08027, 19153, and 19145 scored highest on the hazard index based on cumulative exposure. (Supported by P30-CA-016520 and P30-ES013508.) This abstract is also being presented as Poster A08. Citation Format: Thomas P. McKeon, Vicky Tam, Wei-Ting Hwang, Paul Wileyto, Karen Glanz, Trevor M. Penning. Geocoding and integrating multiple environmental exposomics sources: Assessing population hazard to lung carcinogens in 421 zip codes of a cancer center catchment area [abstract]. In: Proceedings of the AACR Special Conference on Modernizing Population Sciences in the Digital Age; 2019 Feb 19-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(9 Suppl):Abstract nr PR06.

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11

Pero-Gascon, Roger, Lieselot Y. Hemeryck, Giulia Poma, Gwen Falony, Tim S. Nawrot, Jeroen Raes, Lynn Vanhaecke, Marthe De Boevre, Adrian Covaci, and Sarah De Saeger. "FLEXiGUT: Rationale for exposomics associations with chronic low-grade gut inflammation." Environment International 158 (January 2022): 106906. http://dx.doi.org/10.1016/j.envint.2021.106906.

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12

Ljoncheva, Milka, Tomaž Stepišnik, Sašo Džeroski, and Tina Kosjek. "Cheminformatics in MS-based environmental exposomics: Current achievements and future directions." Trends in Environmental Analytical Chemistry 28 (December 2020): e00099. http://dx.doi.org/10.1016/j.teac.2020.e00099.

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13

Holland, Nina. "Future of environmental research in the age of epigenomics and exposomics." Reviews on Environmental Health 32, no. 1-2 (March 1, 2017): 45–54. http://dx.doi.org/10.1515/reveh-2016-0032.

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Abstract Environmental research and public health in the 21st century face serious challenges such as increased air pollution and global warming, widespread use of potentially harmful chemicals including pesticides, plasticizers, and other endocrine disruptors, and radical changes in nutrition and lifestyle typical of modern societies. In particular, exposure to environmental and occupational toxicants may contribute to the occurrence of adverse birth outcomes, neurodevelopmental deficits, and increased risk of cancer and other multifactorial diseases such as diabetes and asthma. Rapidly evolving methodologies of exposure assessment and the conceptual framework of the Exposome, first introduced in 2005, are new frontiers of environmental research. Metabolomics and adductomics provide remarkable opportunities for a better understanding of exposure and prediction of potential adverse health outcomes. Metabolomics, the study of metabolism at whole-body level, involves assessment of the total repertoire of small molecules present in a biological sample, shedding light on interactions between gene expression, protein expression, and the environment. Advances in genomics, transcriptomics, and epigenomics are generating multidimensional structures of biomarkers of effect and susceptibility, increasingly important for the understanding of molecular mechanisms and the emergence of personalized medicine. Epigenetic mechanisms, particularly DNA methylation and miRNA expression, attract increasing attention as potential links between the genetic and environmental determinants of health and disease. Unlike genetics, epigenetic mechanisms could be reversible and an understanding of their role may lead to better protection of susceptible populations and improved public health.

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14

Wood, Katie, Nikhita Damaraju, Callan Krevanko, Abebe G. Aberra, Patricia Cirone, Bruce Duncan, and Elaine M. Faustman. "Exposomics in practice: Multidisciplinary perspectives on environmental health and risk assessment." Integrated Environmental Assessment and Management 20, no. 3 (April 19, 2024): 891–93. http://dx.doi.org/10.1002/ieam.4926.

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15

Petrick, Lauren M., and Noam Shomron. "AI/ML-driven advances in untargeted metabolomics and exposomics for biomedical applications." Cell Reports Physical Science 3, no. 7 (July 2022): 100978. http://dx.doi.org/10.1016/j.xcrp.2022.100978.

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16

Katemauswa, Mitchelle, Ekram Hossain, Zongyuan Liu, Mahbobeh Lesani, Adwaita R. Parab, Danya A. Dean, and Laura-Isobel McCall. "Enabling Quantitative Analysis of Surface Small Molecules for Exposomics and Behavioral Studies." Journal of the American Society for Mass Spectrometry 33, no. 3 (January 27, 2022): 412–19. http://dx.doi.org/10.1021/jasms.1c00263.

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17

Canali, Stefano. "Big Data, epistemology and causality: Knowledge in and knowledge out in EXPOsOMICS." Big Data & Society 3, no. 2 (September 22, 2016): 205395171666953. http://dx.doi.org/10.1177/2053951716669530.

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18

Coughlin, S. S., and A. Dawson. "Ethical, Legal and Social Issues in Exposomics: A Call for Research Investment." Public Health Ethics 7, no. 3 (October 8, 2014): 207–10. http://dx.doi.org/10.1093/phe/phu031.

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19

Barupal, Dinesh Kumar, Priyanka Mahajan, Sadjad Fakouri-Baygi, Robert O. Wright, Manish Arora, and Susan L. Teitelbaum. "CCDB: A database for exploring inter-chemical correlations in metabolomics and exposomics datasets." Environment International 164 (June 2022): 107240. http://dx.doi.org/10.1016/j.envint.2022.107240.

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20

Holden, Emily. "Understanding the importance of exposomics in everyday life: an interview with Emily Holden." Future Science OA 6, no. 10 (December 1, 2020): FSO621. http://dx.doi.org/10.2144/fsoa-2020-0125.

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21

Misra, Biswapriya B. "Metabolomics Tools to Study Links Between Pollution and Human Health: an Exposomics Perspective." Current Pollution Reports 5, no. 3 (May 22, 2019): 93–111. http://dx.doi.org/10.1007/s40726-019-00109-4.

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22

Pala, D., L. Annovazzi-Lodi, R. Bellazzi, N. Fiscante, M. Franzini, C. Larizza, A. Pogliaghi, et al. "THE KEY ROLE OF GEOGRAPHIC INFORMATION IN EXPOSOMICS: THE EXAMPLE OF THE H2020 PULSE PROJECT." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2020 (August 24, 2020): 283–89. http://dx.doi.org/10.5194/isprs-archives-xliii-b4-2020-283-2020.

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Abstract. Exposomics is a novel concept that indicates the combination of all the external factors we are exposed to throughout our entire life, as the environment we live in, our lifestyle and behavior are able to have a notable influence on our health. The quantity and typology of environmental factors we are exposed to are clearly dependent on the geographical location of each individual, e.g. some areas are more polluted that others and even the social characteristics of a certain place can have an effect on the way we behave, exposing us to different levels of risk of developing certain diseases or exacerbating existing ones. In this context, the PULSE project, briefly described in this paper, is building an advanced system to identify the effect of a complex set of environmental and social exposures in the big cities, that represent the most complicated environment from this point of view, and mitigate health risk related to common diseases such as asthma, type 2 diabetes and cardiovascular diseases. This system is composed by several parts, most of which apply advanced spatial analytics and geographic information-based tools to estimate health risk in a precise way, providing both citizens and public health officers with tools to monitor it. This paper summarizes the work performed in the project using these analytics, and quickly describes some of the tools in which geographic information has been applied in the most innovative way.

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23

Jamin, Emilien L., Nathalie Bonvallot, Marie Tremblay-Franco, Jean-Pierre Cravedi, Cécile Chevrier, Sylvaine Cordier, and Laurent Debrauwer. "Untargeted profiling of pesticide metabolites by LC–HRMS: an exposomics tool for human exposure evaluation." Analytical and Bioanalytical Chemistry 406, no. 4 (July 28, 2013): 1149–61. http://dx.doi.org/10.1007/s00216-013-7136-2.

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24

Milman, B. L., and I. К. Zhurkovich. "DOMAIN DELINEATION OF AN EMERGING FIELD OF INTERDISCIPLINARY RESEARCH BY SCIENTOMETRICS. THE EXAMPLE OF EXPOSOMICS." Научно-техническая информация Серия 2 Информационные процессы и системы, no. 3 (2023): 20–26. http://dx.doi.org/10.36535/0548-0027-2023-03-3.

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25

Milman, B. L., and I. K. Zhurkovich. "Domain Delineation of an Emerging Field of Interdisciplinary Research Using Scientometric Methods: Case Study of Exposomics." Automatic Documentation and Mathematical Linguistics 57, no. 2 (April 2023): 104–10. http://dx.doi.org/10.3103/s0005105523020024.

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26

Lamoreaux, Janelle. "Beyond the Egg and the Sperm?: How Science Has Revised a Romance through Reproductomics." Science, Technology, & Human Values 47, no. 6 (October 25, 2022): 1180–204. http://dx.doi.org/10.1177/01622439221123943.

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Social scientists have shown that scientific characterizations of the egg and the sperm are shaped by gender stereotypes and cultural values. How have such characterizations been transformed by a recent embrace of -omics, when studies of reproduction increasingly go beyond genomics to incorporate proteomics, transcriptomics, exposomics, and other -omics perspectives? Scientists studying reproduction and analyzing eggs, sperm, and embryos are in some ways reimagining the roles, identities, and functions of gametes as fundamentally shaped by other molecular entities and environments. Such relational understandings of substances and processes, however, continue to operate through a teleology that often conscripts more nuanced -omics reflection into familiar genomic visions of sex and reproduction. While ideas of the gene as an alienable object may be unraveling, -omics efforts to go beyond the egg and the sperm are frequently constricted by an understanding of reproduction that remains tied to individualized bodies and by a genomically infused interpretation of the gamete as life itself.

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27

Sun, Jiachen, Runcheng Fang, Hua Wang, De-Xiang Xu, Jing Yang, Xiaochen Huang, Daniel Cozzolino, Mingliang Fang, and Yichao Huang. "A review of environmental metabolism disrupting chemicals and effect biomarkers associating disease risks: Where exposomics meets metabolomics." Environment International 158 (January 2022): 106941. http://dx.doi.org/10.1016/j.envint.2021.106941.

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28

Chaker, Jade, David Møbjerg Kristensen, Thorhallur Ingi Halldorsson, Sjurdur Frodi Olsen, Christine Monfort, Cécile Chevrier, Bernard Jégou, and Arthur David. "Comprehensive Evaluation of Blood Plasma and Serum Sample Preparations for HRMS-Based Chemical Exposomics: Overlaps and Specificities." Analytical Chemistry 94, no. 2 (January 5, 2022): 866–74. http://dx.doi.org/10.1021/acs.analchem.1c03638.

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29

Marchiandi, Jaye, Mark P. Green, Sonia Dagnino, Tarun Anumol, and Bradley O. Clarke. "Characterising the effects of per- and polyfluoroalkyl substances (PFASs) on health and disease: An opportunity for exposomics?" Current Opinion in Environmental Science & Health 15 (June 2020): 39–48. http://dx.doi.org/10.1016/j.coesh.2020.05.006.

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30

Carli, Fabrizia, Demetrio Ciociaro, and Amalia Gastaldelli. "Assessment of Exposure to Di-(2-ethylhexyl) Phthalate (DEHP) Metabolites and Bisphenol A (BPA) and Its Importance for the Prevention of Cardiometabolic Diseases." Metabolites 12, no. 2 (February 10, 2022): 167. http://dx.doi.org/10.3390/metabo12020167.

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Exposomics analyses have highlighted the importance of biomonitoring of human exposure to pollutants, even non-persistent, for the prevention of non-communicable diseases such as obesity, diabetes, non-alcoholic fatty liver disease, atherosclerosis, and cardiovascular diseases. Phthalates and bisphenol A (BPA) are endocrine disrupting chemicals (EDCs) widely used in industry and in a large range of daily life products that increase the risk of endocrine and cardiometabolic diseases especially if the exposure starts during childhood. Thus, biomonitoring of exposure to these compounds is important not only in adulthood but also in childhood. This was the goal of the LIFE-PERSUADED project that measured the exposure to phthalates (DEHP metabolites, MEHP, MEHHP, MEOHP) and BPA in Italian mother–children couples of different ages. In this paper we describe the method that was set up for the LIFE PERSUADED project and validated during the proficiency test (ICI/EQUAS) showing that accurate determination of urinary phthalates and BPA can be achieved starting from small sample size (0.5 mL) using two MS techniques applied in cascade on the same deconjugated matrix.

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31

Frigerio, Gianfranco, Camilla Moruzzi, Rosa Mercadante, Emma L. Schymanski, and Silvia Fustinoni. "Development and Application of an LC-MS/MS Untargeted Exposomics Method with a Separated Pooled Quality Control Strategy." Molecules 27, no. 8 (April 16, 2022): 2580. http://dx.doi.org/10.3390/molecules27082580.

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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.

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32

Guo, Jian, Sam Shen, Min Liu, Chenjingyi Wang, Brian Low, Ying Chen, Yaxi Hu, et al. "JPA: Joint Metabolic Feature Extraction Increases the Depth of Chemical Coverage for LC-MS-Based Metabolomics and Exposomics." Metabolites 12, no. 3 (February 26, 2022): 212. http://dx.doi.org/10.3390/metabo12030212.

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Extracting metabolic features from liquid chromatography-mass spectrometry (LC-MS) data has been a long-standing bioinformatic challenge in untargeted metabolomics. Conventional feature extraction algorithms fail to recognize features with low signal intensities, poor chromatographic peak shapes, or those that do not fit the parameter settings. This problem also poses a challenge for MS-based exposome studies, as low-abundant metabolic or exposomic features cannot be automatically recognized from raw data. To address this data processing challenge, we developed an R package, JPA (short for Joint Metabolomic Data Processing and Annotation), to comprehensively extract metabolic features from raw LC-MS data. JPA performs feature extraction by combining a conventional peak picking algorithm and strategies for (1) recognizing features with bad peak shapes but that have tandem mass spectra (MS2) and (2) picking up features from a user-defined targeted list. The performance of JPA in global metabolomics was demonstrated using serial diluted urine samples, in which JPA was able to rescue an average of 25% of metabolic features that were missed by the conventional peak picking algorithm due to dilution. More importantly, the chromatographic peak shapes, analytical accuracy, and precision of the rescued metabolic features were all evaluated. Furthermore, owing to its sensitive feature extraction, JPA was able to achieve a limit of detection (LOD) that was up to thousands of folds lower when automatically processing metabolomics data of a serial diluted metabolite standard mixture analyzed in HILIC(−) and RP(+) modes. Finally, the performance of JPA in exposome research was validated using a mixture of 250 drugs and 255 pesticides at environmentally relevant levels. JPA detected an average of 2.3-fold more exposure compounds than conventional peak picking only.

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Longo, Valentina, Angiola Forleo, Antonio Vincenzo Radogna, Pietro Siciliano, Tiziana Notari, Sebastiana Pappalardo, Marina Piscopo, Luigi Montano, and Simonetta Capone. "A novel human biomonitoring study by semiconductor gas sensors in Exposomics: investigation of health risk in contaminated sites." Environmental Pollution 304 (July 2022): 119119. http://dx.doi.org/10.1016/j.envpol.2022.119119.

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Gautam, Yadu, Elisabet Johansson, and Tesfaye B. Mersha. "Multi-Omics Profiling Approach to Asthma: An Evolving Paradigm." Journal of Personalized Medicine 12, no. 1 (January 7, 2022): 66. http://dx.doi.org/10.3390/jpm12010066.

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Asthma is a complex multifactorial and heterogeneous respiratory disease. Although genetics is a strong risk factor of asthma, external and internal exposures and their interactions with genetic factors also play important roles in the pathophysiology of asthma. Over the past decades, the application of high-throughput omics approaches has emerged and been applied to the field of asthma research for screening biomarkers such as genes, transcript, proteins, and metabolites in an unbiased fashion. Leveraging large-scale studies representative of diverse population-based omics data and integrating with clinical data has led to better profiling of asthma risk. Yet, to date, no omic-driven endotypes have been translated into clinical practice and management of asthma. In this article, we provide an overview of the current status of omics studies of asthma, namely, genomics, transcriptomics, epigenomics, proteomics, exposomics, and metabolomics. The current development of the multi-omics integrations of asthma is also briefly discussed. Biomarker discovery following multi-omics profiling could be challenging but useful for better disease phenotyping and endotyping that can translate into advances in asthma management and clinical care, ultimately leading to successful precision medicine approaches.

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Sajed, Tanvir, Zinat Sayeeda, Brian L. Lee, Mark Berjanskii, Fei Wang, Vasuk Gautam, and David S. Wishart. "Accurate Prediction of 1H NMR Chemical Shifts of Small Molecules Using Machine Learning." Metabolites 14, no. 5 (May 19, 2024): 290. http://dx.doi.org/10.3390/metabo14050290.

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NMR is widely considered the gold standard for organic compound structure determination. As such, NMR is routinely used in organic compound identification, drug metabolite characterization, natural product discovery, and the deconvolution of metabolite mixtures in biofluids (metabolomics and exposomics). In many cases, compound identification by NMR is achieved by matching measured NMR spectra to experimentally collected NMR spectral reference libraries. Unfortunately, the number of available experimental NMR reference spectra, especially for metabolomics, medical diagnostics, or drug-related studies, is quite small. This experimental gap could be filled by predicting NMR chemical shifts for known compounds using computational methods such as machine learning (ML). Here, we describe how a deep learning algorithm that is trained on a high-quality, “solvent-aware” experimental dataset can be used to predict 1H chemical shifts more accurately than any other known method. The new program, called PROSPRE (PROton Shift PREdictor) can accurately (mean absolute error of <0.10 ppm) predict 1H chemical shifts in water (at neutral pH), chloroform, dimethyl sulfoxide, and methanol from a user-submitted chemical structure. PROSPRE (pronounced “prosper”) has also been used to predict 1H chemical shifts for >600,000 molecules in many popular metabolomic, drug, and natural product databases.

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Neagu, Anca-Narcisa, Pathea Bruno, Kaya R. Johnson, Gabriella Ballestas, and Costel C. Darie. "Biological Basis of Breast Cancer-Related Disparities in Precision Oncology Era." International Journal of Molecular Sciences 25, no. 7 (April 8, 2024): 4113. http://dx.doi.org/10.3390/ijms25074113.

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Precision oncology is based on deep knowledge of the molecular profile of tumors, allowing for more accurate and personalized therapy for specific groups of patients who are different in disease susceptibility as well as treatment response. Thus, onco-breastomics is able to discover novel biomarkers that have been found to have racial and ethnic differences, among other types of disparities such as chronological or biological age-, sex/gender- or environmental-related ones. Usually, evidence suggests that breast cancer (BC) disparities are due to ethnicity, aging rate, socioeconomic position, environmental or chemical exposures, psycho-social stressors, comorbidities, Western lifestyle, poverty and rurality, or organizational and health care system factors or access. The aim of this review was to deepen the understanding of BC-related disparities, mainly from a biomedical perspective, which includes genomic-based differences, disparities in breast tumor biology and developmental biology, differences in breast tumors’ immune and metabolic landscapes, ecological factors involved in these disparities as well as microbiomics- and metagenomics-based disparities in BC. We can conclude that onco-breastomics, in principle, based on genomics, proteomics, epigenomics, hormonomics, metabolomics and exposomics data, is able to characterize the multiple biological processes and molecular pathways involved in BC disparities, clarifying the differences in incidence, mortality and treatment response for different groups of BC patients.

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North, Michelle L., Musawir Ahmed, Sepehr Salehi, Kavindi Jayasinghe, Manisha Tilak, Joyce Wu, Cheol-Heon Jeong, Greg J. Evans, and Chung-Wai Chow. "Exposomics-based Analysis of Environmental Factors Associated with Forced Expiratory Volume in 1 Second at 6 Months Post Lung Transplantation." Annals of the American Thoracic Society 15, Supplement_2 (April 2018): S122. http://dx.doi.org/10.1513/annalsats.201707-543mg.

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Trinh, Joanne, Emma L. Schymanski, Semra Smajic, Meike Kasten, Esther Sammler, and Anne Grünewald. "Molecular mechanisms defining penetrance of LRRK2-associated Parkinson’s disease." Medizinische Genetik 34, no. 2 (June 1, 2022): 103–16. http://dx.doi.org/10.1515/medgen-2022-2127.

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Abstract Mutations in Leucine-rich repeat kinase 2 (LRRK2) are the most frequent cause of dominantly inherited Parkinson’s disease (PD). LRRK2 mutations, among which p.G2019S is the most frequent, are inherited with reduced penetrance. Interestingly, the disease risk associated with LRRK2 G2019S can vary dramatically depending on the ethnic background of the carrier. While this would suggest a genetic component in the definition of LRRK2-PD penetrance, only few variants have been shown to modify the age at onset of patients harbouring LRRK2 mutations, and the exact cellular pathways controlling the transition from a healthy to a diseased state currently remain elusive. In light of this knowledge gap, recent studies also explored environmental and lifestyle factors as potential modifiers of LRRK2-PD. In this article, we (i) describe the clinical characteristics of LRRK2 mutation carriers, (ii) review known genes linked to LRRK2-PD onset and (iii) summarize the cellular functions of LRRK2 with particular emphasis on potential penetrance-related molecular mechanisms. This section covers LRRK2’s involvement in Rab GTPase and immune signalling as well as in the regulation of mitochondrial homeostasis and dynamics. Additionally, we explored the literature with regard to (iv) lifestyle and (v) environmental factors that may influence the penetrance of LRRK2 mutations, with a view towards further exposomics studies. Finally, based on this comprehensive overview, we propose potential future in vivo, in vitro and in silico studies that could provide a better understanding of the processes triggering PD in individuals with LRRK2 mutations.

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Wishart, David S., AnChi Guo, Eponine Oler, Fei Wang, Afia Anjum, Harrison Peters, Raynard Dizon, et al. "HMDB 5.0: the Human Metabolome Database for 2022." Nucleic Acids Research 50, no. D1 (November 19, 2021): D622—D631. http://dx.doi.org/10.1093/nar/gkab1062.

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Abstract The Human Metabolome Database or HMDB (https://hmdb.ca) has been providing comprehensive reference information about human metabolites and their associated biological, physiological and chemical properties since 2007. Over the past 15 years, the HMDB has grown and evolved significantly to meet the needs of the metabolomics community and respond to continuing changes in internet and computing technology. This year's update, HMDB 5.0, brings a number of important improvements and upgrades to the database. These should make the HMDB more useful and more appealing to a larger cross-section of users. In particular, these improvements include: (i) a significant increase in the number of metabolite entries (from 114 100 to 217 920 compounds); (ii) enhancements to the quality and depth of metabolite descriptions; (iii) the addition of new structure, spectral and pathway visualization tools; (iv) the inclusion of many new and much more accurately predicted spectral data sets, including predicted NMR spectra, more accurately predicted MS spectra, predicted retention indices and predicted collision cross section data and (v) enhancements to the HMDB’s search functions to facilitate better compound identification. Many other minor improvements and updates to the content, the interface, and general performance of the HMDB website have also been made. Overall, we believe these upgrades and updates should greatly enhance the HMDB’s ease of use and its potential applications not only in human metabolomics but also in exposomics, lipidomics, nutritional science, biochemistry and clinical chemistry.

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Misra, Biswapriya B. "Advances in high resolution GC-MS technology: a focus on the application of GC-Orbitrap-MS in metabolomics and exposomics for FAIR practices." Analytical Methods 13, no. 20 (2021): 2265–82. http://dx.doi.org/10.1039/d1ay00173f.

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Gas chromatography-mass spectrometry (GC-MS) is a complementary analytical platform to LC-MS and NMR for capturing volatiles, non-polar and (derivatized) polar metabolites and exposures from a diverse array of matrixes.

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Ananthakrishnan, Ashwin N., Kostantinos Gerasimidis, Shuk-Mei Ho, Emeran Mayer, Jennifer Pollock, Shefali Soni, Gary D. Wu, et al. "Challenges in IBD Research 2024: Environmental Triggers." Inflammatory Bowel Diseases 30, Supplement_2 (May 1, 2024): S19—S29. http://dx.doi.org/10.1093/ibd/izae085.

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Abstract Environmental factors play an important role in inflammatory bowel diseases (IBD; Crohn’s disease, [CD], ulcerative colitis [UC]). As part of the Crohn’s & Colitis Challenges 2024 agenda, the Environmental Triggers workgroup summarized the progress made in the field of environmental impact on IBD since the last Challenges cycle in this document. The workgroup identified 4 unmet gaps in this content area pertaining to 4 broad categories: (1) Epidemiology; (2) Exposomics and environmental measurement; (3) Biologic mechanisms; and (4) Interventions and Implementation. Within epidemiology, the biggest unmet gaps were in the study of environmental factors in understudied populations including racial and ethnic minority groups and in populations witnessing rapid rise in disease incidence globally. The workgroup also identified a lack of robust knowledge of how environmental factors may impact difference stages of the disease and for different disease-related end points. Leveraging existing cohorts and targeted new prospective studies were felt to be an important need for the field. The workgroup identified the limitations of traditional questionnaire-based assessment of environmental exposure and placed high priority on the identification of measurable biomarkers that can quantify cross-sectional and longitudinal environmental exposure. This would, in turn, allow for identifying the biologic mechanisms of influence of environmental factors on IBD and understand the heterogeneity in effect of such influences. Finally, the working group emphasized the importance of generating high-quality data on effective environmental modification on an individual and societal level, and the importance of scalable and sustainable methods to deliver such changes.

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Bezrodny, S. L., S. G. Mardanly, A. M. Zatevalov, V. V. Pomazanov, and E. R. Mekhtiyev. "Estimation of the state of the microbiome in the elderly with impairments of carbohydrate and lipid exchange by the method of microbiome-associated exposomics." Microbiology Independent Research Journal (MIR Journal) 9, no. 1 (March 24, 2022): 9–17. http://dx.doi.org/10.18527/2500-2236-2022-9-1-9-17.

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A biochemical analysis of blood and a study of the chemical composition of the blood samples of 163 people 45–90 years old with type 2 diabetes mellitus and dyslipidemia was conducted. The concentrations of the following compounds in the blood were determined: fatty acids, aldehydes, and styrenes of microbial origin as well as the levels of glucose triglycerides, cholesterol, and lipids of low and high density. The chemical composition of blood was determined by gas chromatography-mass spectrometry. The concentrations of fatty acids, aldehydes, and styrenes were used to calculate the total molar concentration of chemical compounds, the concentration of octadecene aldehyde (18a), the total concentration of hydroxy acids of derivatives of the hydroxyl residue of lipid A of bacterial endotoxin (3OH-FA), and the grouped total concentrations of chemical compounds of microbial origin, which determine the representation of the main four phylotypes of the human microbiome: Actinobacteria, Bacteroidetes, Proteobacteria, and Firmicutes.As a result of the study, data were obtained on an increase in the total concentration of chemical compounds, the concentration of octadecene aldehyde, and the concentration of 3OH-FA in violation of carbohydrate metabolism by the type of diabetes mellitus type 2. There was a decrease in the representation of Bacteroidetes in violation of carbohydrate metabolism and a decrease in the representation of Proteobacteria, Firmicutes in violation of carbohydrate and lipid metabolism, and an increase in the representation of Actinobacteria in violation of lipid metabolism, including those combined with a violation of carbohydrate metabolism.In patients with type 2 diabetes mellitus and in the control group, there was an inverse correlation between the presence of Firmicutes and blood glucose levels. In the type 2 diabetes mellitus group, there was a direct correlation between the representation of Bacteroidetes and the level of triglycerides in the blood.In patients with lipid metabolism disorders, we did not reveal statistically significant changes in the concentrations of microbial markers in the blood of patients nor statistically significant correlations between the biochemical blood parameters and the representation of microbiome phylotypes.

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Bhimaraju, Hari, Nitish Nag, Vaibhav Pandey, and Ramesh Jain. "Understanding “Atmosome”, the Personal Atmospheric Exposome: Comprehensive Approach." JMIR Biomedical Engineering 6, no. 4 (November 23, 2021): e28920. http://dx.doi.org/10.2196/28920.

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Background Modern environmental health research extensively focuses on outdoor air pollutants and their effects on public health. However, research on monitoring and enhancing individual indoor air quality is lacking. The field of exposomics encompasses the totality of human environmental exposures and its effects on health. A subset of this exposome deals with atmospheric exposure, termed the “atmosome.” The atmosome plays a pivotal role in health and has significant effects on DNA, metabolism, skin integrity, and lung health. Objective The aim of this work is to develop a low-cost, comprehensive measurement system for collecting and analyzing atmosomic factors. The research explores the significance of the atmosome in personalized and preventive care for public health. Methods An internet of things microcontroller-based system is introduced and demonstrated. The system collects real-time indoor air quality data and posts it to the cloud for immediate access. Results The experimental results yield air quality measurements with an accuracy of 90% when compared with precalibrated commercial devices and demonstrate a direct correlation between lifestyle and air quality. Conclusions Quantifying the individual atmosome is a monumental step in advancing personalized health, medical research, and epidemiological research. The 2 main goals in this work are to present the atmosome as a measurable concept and to demonstrate how to implement it using low-cost electronics. By enabling atmosome measurements at a communal scale, this work also opens up potential new directions for public health research. Researchers will now have the data to model the impact of indoor air pollutants on the health of individuals, communities, and specific demographics, leading to novel approaches for predicting and preventing diseases.

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Schmitt, Charles P., Jeanette A. Stingone, Arcot Rajasekar, Yuxia Cui, Xiuxia Du, Chris Duncan, Michelle Heacock, et al. "A roadmap to advance exposomics through federation of data." Exposome, November 14, 2023. http://dx.doi.org/10.1093/exposome/osad010.

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Abstract The scale of the human exposome, which covers all environmental exposures encountered from conception to death, presents major challenges in managing, sharing, and integrating a myriad of relevant data types and available data sets for the benefit of exposomics research and public health. By addressing these challenges, the exposomics research community will be able to greatly expand on its ability to aggregate study data for new discoveries, construct and update novel exposomics data sets for building artificial intelligence and machine learning-based models, rapidly survey emerging issues, and advance the application of data-driven science. The diversity of the field, which spans multiple subfields of science disciplines and different environmental contexts, necessitates adopting data federation approaches to bridge between numerous geographically and administratively separated data resources that have varying usage, privacy, access, analysis, and discoverability capabilities and constraints. This paper presents use cases, challenges, opportunities, and recommendations for the exposomics community to establish and mature a federated exposomics data ecosystem.

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Chang, Le, Jessica Ewald, Fiona Hui, Stéphane Bayen, and Jianguo Xia. "A Data-Centric perspective on exposomics data analysis." Exposome, April 24, 2024. http://dx.doi.org/10.1093/exposome/osae005.

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Abstract Exposomics represents a systematic approach to investigate the etiology of diseases by formally integrating individuals’ entire environmental exposures and associated biological responses into the traditional genotype-phenotype framework. The field is largely enabled by various omics technologies which offer practical means to comprehensively measure key components in exposomics. The bottleneck in exposomics has gradually shifted from data collection to data analysis. Effective and easy-to-use bioinformatics tools and computational workflows are urgently needed to help obtain robust associations and to derive actionable insights from the observational, heterogenous, and multi-omics datasets collected in exposomics studies. This data-centric perspective starts with an overview of the main components and common analysis workflows in exposomics. We then introduce six computational approaches that have proven effective in addressing some key analytical challenges, including linear modeling with covariate adjustment, dimensionality reduction for covariance detection, neural networks for identification of complex interactions, network visual analytics for organizing and interpreting multi-omics results, Mendelian randomization for causal inference, and cause-effect validation by coupling effect-directed analysis with dose-response assessment. Finally, we present a series of well-designed web-based tools, and briefly discuss how they can be used for exposomics data analysis.

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Aurich, Dagny, Aida Horaniet Ibanez, Christophe Hissler, Simon Kreipl, Laurent Pfister, Emma L. Schymanski, and Andreas Fickers. "Historical Exposomics: A Manifesto." Exposome, August 18, 2023. http://dx.doi.org/10.1093/exposome/osad007.

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Abstract The exposome complements information captured in the genome by covering all external influences and internal (biological) responses of a human being from conception onwards. Such a paradigm goes beyond a single scientific discipline and instead requires a truly interdisciplinary approach. The concept of “historical exposomics” could help bridge the gap between “nature” and “nurture” using both natural and social archives to capture the influence of humans on earth (the Anthropocene) in an interdisciplinary manner. The LuxTIME project served as a test bed for an interdisciplinary exploration of the historical exposome, focusing on the Belval area located in the Minett region in southern Luxembourg. This area evolved from a source of mineral water to steel production through to the current campus for research and development. This article explores the various possibilities of natural and social archives that were considered in creating the historical exposome of Belval and reflects upon possibilities and limitations of the current approaches in assessing the exposome using purely a natural science approach. Issues surrounding significance, visualization, and availability of material suitable to form natural archives are discussed in a critical manner. The “Minett Stories” are presented as a way of creating new historical narratives to support exposome research. New research perspectives on the history of the Anthropocene were opened by investigating the causal relationships between factual evidence and narrative evidence stemming from historical sources. The concept of historical exposome presented here may thus offer a useful conceptual framework for studying the Anthropocene in a truly interdisciplinary fashion.

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Baygi, Sadjad Fakouri, and Dinesh Kumar Barupal. "IDSL_MINT: a deep learning framework to predict molecular fingerprints from mass spectra." Journal of Cheminformatics 16, no. 1 (January 18, 2024). http://dx.doi.org/10.1186/s13321-024-00804-5.

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AbstractThe majority of tandem mass spectrometry (MS/MS) spectra in untargeted metabolomics and exposomics studies lack any annotation. Our deep learning framework, Integrated Data Science Laboratory for Metabolomics and Exposomics—Mass INTerpreter (IDSL_MINT) can translate MS/MS spectra into molecular fingerprint descriptors. IDSL_MINT allows users to leverage the power of the transformer model for mass spectrometry data, similar to the large language models. Models are trained on user-provided reference MS/MS libraries via any customizable molecular fingerprint descriptors. IDSL_MINT was benchmarked using the LipidMaps database and improved the annotation rate of a test study for MS/MS spectra that were not originally annotated using existing mass spectral libraries. IDSL_MINT may improve the overall annotation rates in untargeted metabolomics and exposomics studies. The IDSL_MINT framework and tutorials are available in the GitHub repository at https://github.com/idslme/IDSL_MINT.Scientific contribution statement.Structural annotation of MS/MS spectra from untargeted metabolomics and exposomics datasets is a major bottleneck in gaining new biological insights. Machine learning models to convert spectra into molecular fingerprints can help in the annotation process. Here, we present IDSL_MINT, a new, easy-to-use and customizable deep-learning framework to train and utilize new models to predict molecular fingerprints from spectra for the compound annotation workflows.

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Miller, Gary W., Vrinda Kalia, Yunjia Lai, Lawrence S. Honig, Richard Mayeux, Badri N. Vardarajan, Rafael A. Lantigua, Annie J. Lee, Jennifer J. Manly, and Renu Nandakumar. "Exposomics for Characterization of Environmental Drivers of AD." Alzheimer's & Dementia 19, S23 (December 2023). http://dx.doi.org/10.1002/alz.077827.

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AbstractIn order to provide a comprehensive evaluation of the non‐genetic factors involved in the development of Alzheimer’s disease and related disorders it is necessary to develop a systematic process to capture the range of environmental and social influences. Exposomics has emerged as an approach to do this. Using high‐resolution mass spectrometry and geospatial techniques it is possible to evaluate thousands of external factors (chemical, nutritional, social, environmental) and their corresponding impact on biology. In this presentation, we will describe the steps we are taking to build the required infrastructure. The first aspect is to improve the identification of exogenous and endogenous chemical features by expansion of our high‐resolution mass spectrometry capabilities (automated liquid handling with ThermoFisher Orbitrap‐based technology: HFX, LC‐Exploris 240, GC‐Exploris 240, and IQX Tribrid Mass Spectrometer). We are in the process of analyzing thousands of samples from WHICAP, EFIGA, and RANN using our combined LC/GC Orbitrap platform, to identify significant associations with disease traits (existing and next generation biomarkers, pathology, imaging, clinical features). Initial results from these studies will be presented to demonstrate feasibility of the approach. We have also initiated a series of pilot studies for other AD cohorts, which cover a range of populations with diverse ethnicity, disease stage, and age. These studies have extensive clinical phenotyping and deep molecular phenotyping. The addition of exposomics will leverage these existing studies to uncover novel environmental contributors to AD. We also also developing a platform to distribute workflows via an online EXCEL AD Community Dashboard to other ADRCs and AD research groups interested in incorporating exposomics into their studies. We will also provide guidance on quality control materials, including providing standards as needed. The Community Dashboard would also compile information on ongoing AD studies that are incorporating exposomics, as well as relevant publications. Various tools will be developed to encourage the utilization of this new platform such as user manuals, training programs (in‐person, online) exposomics bootcamps, and scholar exchange through on‐site visits to laboratories. We will deploy an academic research consultancy that will help other interested laboratories to establish the exposomics workflow.

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Liu, Ken. "Chemical contact tracing for exposomics." Exposome 1, no. 1 (January 1, 2021). http://dx.doi.org/10.1093/exposome/osac001.

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Abstract 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 remains 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.

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Vitale, Chiara Maria, Elliott J. Price, Gary W. Miller, Arthur David, Jean-Philippe Antignac, Robert Barouki, and Jana Klánová. "Analytical strategies for chemical exposomics: exploring limits and feasibility." Exposome, September 20, 2021. http://dx.doi.org/10.1093/exposome/osab003.

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Abstract Tackling the challenges of chemical exposomics will require the implementation of diverse analytical strategies and technological advancements. Herein, high-resolution mass spectrometry-based methods applied in current chemical exposome studies have been surveyed and are shown to be limited. Notably, liquid chromatography separations almost exclusively employ reversed-phase C18 columns using water-methanol gradients with formic acid additive, whilst gas chromatography is underexploited in the field at this stage. A systematic evaluation of strategies applied in related disciplines (i.e. metabolomics, proteomics, multi-residue trace analysis) was undertaken to provide practical guidance for the development of chemical exposomics. The approaches were assessed on the basis of their costs (i.e. capital expenditure, overhead and maintenance fees, expertise required, consumables) and potential benefits (i.e. improvements to sensitivity, coverage, reproducibility, throughput, ease of use) to prioritize those with promise for chemical exposomics application. Alongside a need for technological investments (e.g. advanced hardware updates), numerous low cost strategies showed high potential benefits (e.g. different column phases, enhanced sample fractionation) and are feasible for rapid adoption.

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