Transcriptome landscape of a bacterial pathogen under plant immunity
Friday, 2018/03/30 | 07:51:51
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Tatsuya Nobori, André C. Velásquez, Jingni Wu, Brian H. Kvitko, James M. Kremer, Yiming Wang, Sheng Yang He and Kenichi Tsuda PNAS March 27, 2018. 115 (13) E3055-E3064; SignificancePlants have evolved a powerful innate immune system to defend against microbial pathogens. Despite extensive studies, how plant immunity ultimately inhibits bacterial pathogen growth is largely unknown, due to difficulties in profiling bacterial responses in planta. In this study, we established two methods for in planta bacterial transcriptome analysis using RNA sequencing. By analyzing 27 combinations of plant immunity mutants and Pseudomonas syringae strains, we succeeded in the identification of specific bacterial transcriptomic signatures that are influenced by plant immune activation. In addition, we found that overexpression of an immune-responsive P. syringae sigma factor gene involved in iron regulation could partially counter bacterial growth restriction during plant immunity. This study illuminates the enigmatic mechanisms of bacterial growth inhibition by plant immunity. AbstractPlant pathogens can cause serious diseases that impact global agriculture. The plant innate immunity, when fully activated, can halt pathogen growth in plants. Despite extensive studies into the molecular and genetic bases of plant immunity against pathogens, the influence of plant immunity in global pathogen metabolism to restrict pathogen growth is poorly understood. Here, we developed RNA sequencing pipelines for analyzing bacterial transcriptomes in planta and determined high-resolution transcriptome patterns of the foliar bacterial pathogen Pseudomonas syringae in Arabidopsis thaliana with a total of 27 combinations of plant immunity mutants and bacterial strains. Bacterial transcriptomes were analyzed at 6 h post infection to capture early effects of plant immunity on bacterial processes and to avoid secondary effects caused by different bacterial population densities in planta. We identified specific “immune-responsive” bacterial genes and processes, including those that are activated in susceptible plants and suppressed by plant immune activation. Expression patterns of immune-responsive bacterial genes at the early time point were tightly linked to later bacterial growth levels in different host genotypes. Moreover, we found that a bacterial iron acquisition pathway is commonly suppressed by multiple plant immune-signaling pathways. Overexpression of a P. syringae sigma factor gene involved in iron regulation and other processes partially countered bacterial growth restriction during the plant immune response triggered by AvrRpt2. Collectively, this study defines the effects of plant immunity on the transcriptome of a bacterial pathogen and sheds light on the enigmatic mechanisms of bacterial growth inhibition during the plant immune response.
See http://www.pnas.org/content/115/13/E3055
Fig. 1. Establishment of in planta Pto transcriptome analysis. (A) Workflow of in planta bacterial transcriptome analysis based on bacterial isolation (see Materials and Methods for further information). (B) Workflow of in plantabacterial transcriptome analysis based on selective depletion of plant-derived transcripts (see Materials and Methods for further information). (C) The ratio of sequenced reads mapped on the bacterial (Bac) CDS, bacterial noncoding sequence, A. thaliana (Plant) genome, and sequence reads that mapped to neither the Pto nor the A. thaliana genome (Else) in all samples, including samples without bacterial enrichment and in vitro samples. (D) Validation of RNA-seq data by RT-qPCR. Four-week-old A. thaliana leaves were pretreated with 1 μM flg22 or water (Mock) 1 d before infection with Pto (OD600 = 0.5) and were harvested at 6 hpi. The samples were split into two. One sample was subjected to direct RNA extraction followed by RT-qPCR analysis, and the other was subjected to bacterial enrichment followed by RNA-seq. RT-qPCR results were normalized with the Pto 16S or gyrA expression (mean ± SEM; n = 4 biological replicates from four independent experiments). RNA-seq data were processed as described in Materials and Methods (mean log2 count per million ± SEM; n = 4 biological replicates from four independent experiments). Pearson correlation coefficients (R2) are shown. (E) Comparison of log2 fold changes in Pto gene expression in flg22-pretreated plants and mock-pretreated plants based on RNA-seq data independently obtained by two different approaches in two different laboratories: The method based on bacterial isolation from infected plants [Max Planck Institute Cologne (MPI), x axis] and on bacterial mRNA enrichment using customized oligonucleotides to remove abundant plant RNA without bacterial isolation [Michigan State University (U), y axis]. The Pearson correlation coefficient is shown. See Materials and Methods for detailed experimental procedures. |
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