Transcriptome Profiling to Identify Genes Involved in Mesosulfuron-Methyl Resistance in .

Non-target-site resistance (NTSR) to herbicides is a worldwide concern for weed control. However, as the dominant NTSR mechanism in weeds, metabolic resistance is not yet well-characterized at the genetic level. For this study, we have identified a shortawn foxtail ( Sobol.) population displaying both TSR and NTSR to mesosulfuron-methyl and fenoxaprop--ethyl, yet the molecular basis for this NTSR remains unclear. To investigate the mechanisms of metabolic resistance, an RNA-Seq transcriptome analysis was used to find candidate genes that may confer metabolic resistance to the herbicide mesosulfuron-methyl in this plant population. The RNA-Seq libraries generated 831,846,736 clean reads. The transcriptome assembly yielded 95,479 unigenes (averaging 944 bp in length) that were assigned putative annotations. Among these, a total of 29,889 unigenes were assigned to 67 GO terms that contained three main categories, and 14,246 unigenes assigned to 32 predicted KEGG metabolic pathways. Global gene expression was measured using the reads generated from the untreated control (CK), water-only control (WCK), and mesosulfuron-methyl treatment (T) of R and susceptible (S). Contigs that showed expression differences between mesosulfuron-methyl-treated R and S biotypes, and between mesosulfuron-methyl-treated, water-treated and untreated R plants were selected for further quantitative real-time PCR (qRT-PCR) validation analyses. Seventeen contigs were consistently highly expressed in the resistant plants, including four cytochrome P450 monooxygenase (CytP450) genes, two glutathione S-transferase (GST) genes, two glucosyltransferase (GT) genes, two ATP-binding cassette (ABC) transporter genes, and seven additional contigs with functional annotations related to oxidation, hydrolysis, and plant stress physiology. These 17 contigs could serve as major candidate genes for contributing to metabolic mesosulfuron-methyl resistance; hence they deserve further functional study. This is the first large-scale transcriptome-sequencing study to identify NTSR genes in that uses the Illumina platform. This work demonstrates that NTSR is likely driven by the differences in the expression patterns of a set of genes. The assembled transcriptome data presented here provide a valuable resource for biology, and should facilitate the study of herbicide resistance at the molecular level in this and other weed species.