Machine learning based prediction by PlantCdMiner and experimental validation of cadmium-responsive genes in plants.

Journal: Journal of hazardous materials
Published Date:

Abstract

Plants have evolved diverse adaptive mechanisms to sense and respond to environmental stimuli such as cadmium stress. The regulation of gene expression plays a critical role in plant responses to abiotic stress. However, homologous genes from different plant species or even different genotypes within the same species often show divergent responses to stress, and sequence homology does not necessarily imply functional similarity. Therefore, current homology alignment approaches to predicting transcriptional response to the specific stress have inherent limitations. In this study, we trained supervised classification models using the Random Forest algorithm to predict cadmium-responsive genes based on gene sequence features in Arabidopsis thaliana, Avicennia marina, Hordeum vulgare, and Nicotiana tabacum. Our models successfully predicted transcriptional response to cadmium stress both within and across species. The results suggested that transcriptome data from well-studied species can be used to predict cadmium-responsive genes in other species lacking such data. Cis-regulatory elements analysis further revealed that MYB TFs play essential roles in cadmium stress responses. Additionally, we experimentally confirmed that the MYB TF Am06526 activates the expression of AmPCR2 using yeast one-hybrid and dual-luciferase reporter assays. Finally, we developed PlantCdMiner (https://jasonxu.shinyapps.io/PlantCdMiner/), a web-based tool that enables users to predict cadmium-responsive genes and visualize cis-regulatory elements based on genomic features using machine learning.

Authors

  • Chaoqun Xu
    Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, Xiamen 361102, China.
  • Ling Sun
  • Lu-Dan Zhang
    Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, Xiamen 361102, China.
  • Ze-Jun Guo
    Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, Xiamen 361102, China.
  • Ji-Cheng Wang
    Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, Xiamen 361104, China.
  • Li-Han Zhuang
    Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, Xiamen 361104, China.
  • Dong-Na Ma
    Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, Xiamen 361104, China; National Engineering Research Center of Cereal Fermentation and Food Biomanufacturing, School of Food Science and Technology, Jiangnan University, Wuxi, Jiangsu 214122, China.
  • Ling-Yu Song
    Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, Xiamen 361102, China.
  • Jing Li
    Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China.
  • Qian-Su Ding
    Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, Xiamen 361104, China.
  • Han-Chen Tang
    Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, Xiamen 361104, China.
  • Hai-Lei Zheng
    Key Laboratory of the Ministry of Education for Coastal and Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, Xiamen 361102, China.