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Large-scale transcriptome comparison of sunflower genes responsive to Verticillium dahliae

Overview of attention for article published in BMC Genomics, January 2017
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Title
Large-scale transcriptome comparison of sunflower genes responsive to Verticillium dahliae
Published in
BMC Genomics, January 2017
DOI 10.1186/s12864-016-3386-7
Pubmed ID
Authors

Shuchun Guo, Yongchun Zuo, Yanfang Zhang, Chengyan Wu, Wenxia Su, Wen Jin, Haifeng Yu, Yulin An, Qianzhong Li

Abstract

Sunflower Verticillium wilt (SVW) is a vascular disease caused by root infection with Verticillium dahliae (V. dahlia). It is a serious threat to the yield and quality of sunflower. However, chemical and agronomic measures for controlling this disease are not effective. The selection of more resistant genotypes is a desirable strategy to reduce contamination. A deeper knowledge of the molecular mechanisms and genetic basis underlying sunflower Verticillium wilt is necessary to accelerate breeding progress. An RNA-Seq approach was used to perform global transcriptome profiling on the roots of resistant (S18) and susceptible (P77) sunflower genotypes infected with V. dahlia. Different pairwise transcriptome comparisons were examined over a time course (6, 12 and 24 h, and 2, 3, 5 and 10 d post inoculation). In RD, SD and D datasets, 1231 genes were associated with SVW resistance in a genotype-common transcriptional pattern. Moreover, 759 and 511 genes were directly related to SVW resistance in the resistant and susceptible genotypes, respectively, in a genotype-specific transcriptional pattern. Most of the genes were demonstrated to participate in plant defense responses; these genes included peroxidase (POD), glutathione peroxidase, aquaporin PIP, chitinase, L-ascorbate oxidase, and LRR receptors. For the up-regulated genotype-specific differentially expressed genes (DEGs) in the resistant genotype, higher average fold-changes were observed in the resistant genotype compared to those in the susceptible genotype. An inverse effect was observed in the down-regulated genotype-specific DEGs in the resistant genotype. KEGG analyses showed that 98, 112 and 52 genes were classified into plant hormone signal transduction, plant-pathogen interaction and flavonoid biosynthesis categories, respectively. Many of these genes, such as CNGC, RBOH, FLS2, JAZ, MYC2 NPR1 and TGA, regulate crucial points in defense-related pathway and may contribute to V. dahliae resistance in sunflower. The transcriptome profiling results provided a clearer understanding of the transcripts associated with the crosstalk between sunflower and V. dahliae. The results identified several differentially expressed unigenes involved in the hyper sensitive response (HR) and the salicylic acid (SA)/jasmonic acid (JA)-mediated signal transduction pathway for resistance against V. dahliae. These results are useful for screening resistant sunflower genotypes.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 52 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 13 25%
Researcher 11 21%
Student > Master 6 12%
Student > Bachelor 5 10%
Student > Doctoral Student 4 8%
Other 8 15%
Unknown 5 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 32 62%
Biochemistry, Genetics and Molecular Biology 6 12%
Environmental Science 2 4%
Engineering 2 4%
Nursing and Health Professions 1 2%
Other 1 2%
Unknown 8 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 January 2017.
All research outputs
#20,382,391
of 22,931,367 outputs
Outputs from BMC Genomics
#9,303
of 10,676 outputs
Outputs of similar age
#355,357
of 420,293 outputs
Outputs of similar age from BMC Genomics
#175
of 228 outputs
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