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

Journal: Environmental science & technology
Published Date: Apr 23, 2024

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 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.

Authors

  • Guohuan Zhang
    Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, P. R. China.
  • Weinan Lin
    Department of Materials Science & Engineering, National University of Singapore, Singapore, 117575, Singapore.
  • Ning Gao
    Department of Chemistry & Biochemistry, University of Texas at El Paso, Texas, USA.
  • Changxin Lan
    Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, P. R. China.
  • Mengyuan Ren
    Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, P. R. China.
  • Lailai Yan
    Center of Medical & Health Analysis, School of Public Health, Peking University, Beijing, China.
  • Bo Pan
    State Key Laboratory of Robotics and System, Harbin Institute of Technology, China.
  • Jia Xu
    Key Laboratory of Flexible Electronics (KLOFE) & Institute of Advanced Materials (IAM), Jiangsu National Synergetic Innovation Center for Advanced Materials (SICAM), Nanjing Tech University (Nanjing Tech), 30 South Puzhu Road, Nanjing, 211816, P.R. China.
  • Bin Han
    2 Department of Radiation Oncology, Stanford University, Stanford, CA, USA.
  • Ligang Hu
    Institute of Environment and Health, Jianghan University, Wuhan 430056, China.
  • Yuanchen Chen
    Beijing Advanced Innovation Center for Food Nutrition and Human Health, College of Environment, Zhejiang University of Technology, Hangzhou 310032, China.
  • Tianxiang Wu
    State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, China.
  • Lili Zhuang
    Reproductive Medicine Centre, Yuhuangding Hospital of Yantai, Affiliated Hospital of Qingdao University, Yantai 264000, P. R. China.
  • Qun Lu
    Internal Medicine, The Affiliated Wuxi People's Hospital of Nanjing Medical University, 214000 Wuxi, China.
  • Bin Wang
    State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling 712100, China; New South Wales Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga 2650, Australia. Electronic address: bin.a.wang@dpi.nsw.gov.au.
  • Mingliang Fang
    School of Civil and Environmental Engineering, Nanyang Technological University , 639798 Singapore.

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