Leaf Transcriptome Analysis of Broomcorn Millet Uncovers Key Genes and Pathways in Response to Sporisorium destruens

Abstract

Broomcorn millet (Panicum miliaceum L.) affected by smut (caused by the pathogen Sporisorium destruens) has reduced production yields and quality. Determining the tolerance of broomcorn millet varieties is essential for smut control. This study focuses on the differences in the phenotypes, physiological characteristics, and transcriptomes of resistant and susceptible broomcorn millet varieties under Sporisorium destruens stress. In diseased broomcorn millet, the plant height and stem diameter were reduced, while the number of nodes increased. After infection, the activities of superoxide dismutase and peroxidase decreased, and malondialdehyde and relative chlorophyll content (SPAD) decreased. Transcriptome analysis showed 514 and 5452 differentially expressed genes (DEGs) in the resistant and susceptible varieties, respectively. The Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of DEGs showed that pathways related to plant disease resistance, such as phenylpropanoid biosynthesis, plant–pathogen interaction, and plant hormone signal transduction, were significantly enriched. In addition, the transcriptome changes of cluster leaves and normal leaves in diseased broomcorn millet were analysed. Gene ontology and KEGG enrichment analyses indicated that photosynthesis played an important role in both varieties. These findings lay a foundation for future research on the molecular mechanism of the interaction between broomcorn millet and Sporisorium destruens.

Milletdb: a multi‐omics database to accelerate the research of functional genomics and molecular breeding of millets

“…Raw transcriptomic data of other millets was sourced from the SRA database (Bandyopadhyay et al ., 2020 ; Bennetzen et al ., 2012 ; Cannarozzi et al ., 2014 ; Fang et al ., 2019 ; Guo et al ., 2017 ; Hatakeyama et al ., 2017 ; Jin et al ., 2021 ; Lai et al ., 2021 ; Pan et al ., 2020 ; Qin et al ., 2020 ; Ramadoss, 2014 ; Shen et al ., 2020 ; Sun et al ., 2022 ; Wang et al ., 2020 , 2021 ; Watson‐Lazowski et al ., 2019 ; Wu et al ., 2022 ; Yan et al ., 2021 , 2022 , 2023 ; Yu et al ., 2020 ; Yuan et al ., 2021 , 2022 ; Zhang et al ., 2022 ; Zou et al ., 2019 ) (Table S1 ). Fastq (Version 0.11.9) was used for raw data filtering.…”