Using Network-Based Machine Learning to Predict Transcription Factors Involved in Drought Resistance
Document Type
Article
Publication Date
6-2021
Keywords
rice; oryza; drought; transcription factor; gene regulatory network; machine learning; abiotic stress; R shiny
Abstract
Gene regulatory networks underpin stress response pathways in plants. However, parsing these networks to prioritize key genes underlying a particular trait is challenging. Here, we have built the Gene Regulation and Association Network (GRAiN) of rice (Oryza sativa). GRAiN is an interactive query-based web-platform that allows users to study functional relationships between transcription factors (TFs) and genetic modules underlying abiotic-stress responses. We built GRAiN by applying a combination of different network inference algorithms to publicly available gene expression data. We propose a supervised machine learning framework that complements GRAiN in prioritizing genes that regulate stress signal transduction and modulate gene expression under drought conditions. Our framework converts intricate network connectivity patterns of 2160 TFs into a single drought score. We observed that TFs with the highest drought scores define the functional, structural, and evolutionary characteristics of drought resistance in rice. Our approach accurately predicted the function of OsbHLH148 TF, which we validated using in vitro protein-DNA binding assays and mRNA sequencing loss-of-function mutants grown under control and drought stress conditions. Our network and the complementary machine learning strategy lends itself to predicting key regulatory genes underlying other agricultural traits and will assist in the genetic engineering of desirable rice varieties.
Citation
Gupta, C., Ramegowda, V., Basu, S., & Pereira, A. (2021). Using Network-Based Machine Learning to Predict Transcription Factors Involved in Drought Resistance. Frontiers in Genetics, 12, 652189. https://doi.org/10.3389/fgene.2021.652189
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
