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

Guohuan Zhang; Weinan Lin; Ning Gao; Changxin Lan; Mengyuan Ren; Lailai Yan; Bo Pan; Jia Xu; Bin Han; Ligang Hu; Yuanchen Chen; Tianxiang Wu; Lili Zhuang; Qun Lu; Bin Wang; Mingliang Fang

doi:10.1021/acs.est.3c10904

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

Environ Sci Technol. 2024 May 7;58(18):7743-7757. doi: 10.1021/acs.est.3c10904. Epub 2024 Apr 23.

Authors

Guohuan Zhang^{1

2

3

4}, Weinan Lin^{1

2

3

4}, Ning Gao^{1

2

3

4}, Changxin Lan^{1

2

3

4}, Mengyuan Ren^{1

2

3

4}, Lailai Yan⁵, Bo Pan⁶, Jia Xu⁷, Bin Han⁷, Ligang Hu⁸, Yuanchen Chen⁹, Tianxiang Wu^{1

2

3

4}, Lili Zhuang¹⁰, Qun Lu^{11

12}, Bin Wang^{1

2

3

4

13}, Mingliang Fang¹⁴

Affiliations

¹ Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, P. R. China.
² Institute of Reproductive and Child Health, School of Public Health, Peking University, Beijing 100191, P. R. China.
³ Key Laboratory of Reproductive Health, National Health and Family Planning Commission of the People's Republic of China, Beijing 100191, P. R. China.
⁴ Key Laboratory of Epidemiology of Major Diseases (Peking University), Ministry of Education, Beijing 100191, P. R. China.
⁵ Department of Laboratorial Science and Technology, School of Public Health, Peking University, Beijing 100191, P. R. China.
⁶ Yunnan Provincial Key Lab of Soil Carbon Sequestration and Pollution Control, Faculty of Environmental Science & Engineering, Kunming University of Science & Technology, Kunming 650500, P. R. China.
⁷ State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, P. R. China.
⁸ State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Science, Chinese Academy of Sciences, Beijing 100085, P. R. China.
⁹ Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province, College of Environment, Zhejiang University of Technology, Hangzhou, Zhejiang 310032, P. R. China.
¹⁰ Reproductive Medicine Centre, Yuhuangding Hospital of Yantai, Affiliated Hospital of Qingdao University, Yantai 264000, P. R. China.
¹¹ Medical Center for Human Reproduction, Beijing Chao-Yang Hospital, Capital Medical University, Beijing 100020, P.R China.
¹² Center of Reproductive Medicine, Peking University People's Hospital, Beijing 100044, P. R. China.
¹³ Laboratory for Earth Surface Processes, College of Urban and Environmental Science, Peking University, Beijing 100871, China.
¹⁴ Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, P. R. China.

PMID: 38652822
DOI: 10.1021/acs.est.3c10904

Abstract

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.

Keywords: blood–follicle barrier; machine learning; multiomics; trace elements.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

China
Female
Follicular Fluid / chemistry
Follicular Fluid / metabolism
Humans
Machine Learning*
Multiomics
Trace Elements* / metabolism

Substances

Trace Elements