ExposomeX: Integrative Exposomic Platform Expediates Discovery of “Exposure-Biology-Disease” Nexus

Wang, Bin; Lan, Xi; Zhang, Guohuan; Ren, Mengyuan; Feng, Yanqiu; Gao, Ning; Lin, Weinan; Jiangtulu, Bahabake; Liu, Zhijian; Shao, Xuqiang; Su, Shu; Wang, Yuting; Wang, Han; Zhao, Fanrong; Peng, Bo; Ji, Xiaotong; Chen, Xiao‐Jia; Nian, Min; Fang, Mingliang

doi:10.1101/2022.11.23.517586

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…Systematically analyzing these chemicals and cross-comparing them with human studies can reveal more chemicals for mixture study. Our newly developed exposome platform, ExposomeX (Preprint), 49 integrates all these databases, offering another useful tool for identifying chemical mixtures related to health outcomes.…”

Section: ■ Introductionmentioning

confidence: 99%

Toxicological Study of Human Exposure to Mixtures of Chemicals: Challenges and Approaches

Nian,

Braun,

Escher

et al. 2024

Environ. Sci. Technol. Lett.

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Humans are exposed to highly diverse mixtures of complex and often unknown compositions, which may adversely impact their health. There is a lack of guidance on how to design toxicological studies of chemical mixtures. This paper aims to evaluate the weaknesses and strengths of chemical mixture studies and to propose the practical design of mixture effect studies including chemical selection, dosing considerations, and determination of toxicological mechanisms of mixtures. The design of mixture effect studies should be optimized to yield relevant information on real-life mixture effects (1) by selecting appropriate chemicals based on the association between chemical effects and diseases using information from epidemiological studies and "chemical-disease" associations from toxicological databases and/or animal studies; (2) by testing mixture ratios and concentrations derived from biomonitoring data; (3) by evolving from binary mixtures and those with a low number of components to complex and diverse mixtures with as many components as practically possible; (4) by applying multiomics and high-throughput screening approaches to increase the number of testable individual chemicals and mixture scenarios; and (5) by implementing computational mixture models in the experiment planning phase and to interpret the outcomes to examine the toxicological mechanisms of "multiple exposure-multiple targets". We urge to move mixture investigation from answering academic questions such as mixture interaction models at high effect and concentration levels to mixture scenarios that are relevant for today's human exposure, i.e., low concentration, low effect but high complexity mixtures.

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Section: ■ Introductionmentioning

confidence: 99%

Toxicological Study of Human Exposure to Mixtures of Chemicals: Challenges and Approaches

Nian,

Braun,

Escher

et al. 2024

Environ. Sci. Technol. Lett.

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Using Machine Learning to Construct the Blood–Follicle Distribution Models of Various Trace Elements and Explore the Transport-Related Pathways with Multiomics Data

Zhang,

Lin,

Gao

et al. 2024

Environ. Sci. Technol.

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Permeabilities of various trace elements (TEs) through the blood−follicle barrier (BFB) play an important role in oocyte development. However, it has not been comprehensively described as well as its involved biological pathways. Our study aimed to construct a blood−follicle distribution model of the concerned TEs and explore their related biological pathways. We finally included a total of 168 women from a cohort of in vitro fertilization-embryo transfer conducted in two reproductive centers in Beijing City and Shandong Province, China. The concentrations of 35 TEs in both serum and follicular fluid (FF) samples from the 168 women were measured, as well as the multiomics features of the metabolome, lipidome, and proteome in both plasma and FF samples. Multiomics features associated with the transfer efficiencies of TEs through the BFB were selected by using an elastic net model and further utilized for pathway analysis. Various machine learning (ML) models were built to predict the concentrations of TEs in FF. Overall, there are 21 TEs that exhibited three types of consistent BFB distribution characteristics between Beijing and Shandong centers. Among them, the concentrations of arsenic, manganese, nickel, tin, and bismuth in FF were higher than those in the serum with transfer efficiencies of 1.19−4.38, while a reverse trend was observed for the 15 TEs with transfer efficiencies of 0.076−0.905, e.g., mercury, germanium, selenium, antimony, and titanium. Lastly, cadmium was evenly distributed in the two compartments with transfer efficiencies of 0.998−1.056. Multiomics analysis showed that the enrichment of TEs was associated with the synthesis and action of steroid hormones and the glucose metabolism. Random forest model can provide the most accurate predictions of the concentrations of TEs in FF among the concerned ML models. In conclusion, the selective permeability through the BFB for various TEs may be significantly regulated by the steroid hormones and the glucose metabolism. Also, the concentrations of some TEs in FF can be well predicted by their serum levels with a random forest model.

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ExposomeX: Integrative Exposomic Platform Expediates Discovery of “Exposure-Biology-Disease” Nexus

Cited by 2 publications

References 18 publications

Toxicological Study of Human Exposure to Mixtures of Chemicals: Challenges and Approaches

Toxicological Study of Human Exposure to Mixtures of Chemicals: Challenges and Approaches

Using Machine Learning to Construct the Blood–Follicle Distribution Models of Various Trace Elements and Explore the Transport-Related Pathways with Multiomics Data

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