Functional characterization of cotton genes responsive to Verticillium dahliae through bioinformatics and reverse genetics strategies.

Abstract

Verticillium wilt causes dramatic cotton yield loss in China. Although some genes or biological processes involved in the interaction between cotton and Verticillium dahliae have been identified, the molecular mechanism of cotton resistance to this disease is still poorly understood. The basic innate immune response for defence is somewhat conserved among plant species to defend themselves in complex environments, which makes it possible to characterize genes involved in cotton immunity based on information from model plants. With the availability of Arabidopsis databases, a data-mining strategy accompanied by virus-induced gene silencing (VIGS) and heterologous expression were adopted in cotton and tobacco, respectively, for global screening and gene function characterization. A total of 232 Arabidopsis genes putatively involved in basic innate immunity were screened as candidate genes, and bioinformatic analysis suggested a role of these genes in the immune response. In total, 38 homologous genes from cotton were singled out to characterize their response to V. dahliae and methyl jasmonate treatment through quantitative real-time PCR. The results revealed that 24 genes were differentially regulated by pathogen inoculation, and most of these genes responded to both Verticillium infection and jasmonic acid stimuli. Furthermore, the efficiency of the strategy was illustrated by the functional identification of six candidate genes via heterologous expression in tobacco or a knock-down approach using VIGS in cotton. Functional categorization of these 24 differentially expressed genes as well as functional analysis suggest that reactive oxygen species, salicylic acid- and jasmonic acid-signalling pathways are involved in the cotton disease resistance response to V. dahliae. Our data demonstrate how information from model plants can allow the rapid translation of information into non-model species without complete genome sequencing, via high-throughput screening and functional identification of target genes based on data-mining and VIGS.