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Large-scale identification of wheat genes resistant to cereal cyst nematode Heterodera avenae using comparative transcriptomic analysis

Overview of attention for article published in BMC Genomics, October 2015
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  • Above-average Attention Score compared to outputs of the same age and source (64th percentile)

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Title
Large-scale identification of wheat genes resistant to cereal cyst nematode Heterodera avenae using comparative transcriptomic analysis
Published in
BMC Genomics, October 2015
DOI 10.1186/s12864-015-2037-8
Pubmed ID
Authors

Ling-An Kong, Du-Qing Wu, Wen-Kun Huang, Huan Peng, Gao-Feng Wang, Jiang-Kuan Cui, Shi-Ming Liu, Zhi-Gang Li, Jun Yang, De-Liang Peng

Abstract

Cereal cyst nematode Heterodera avenae, an important soil-borne pathogen in wheat, causes numerous annual yield losses worldwide, and use of resistant cultivars is the best strategy for control. However, target genes are not readily available for breeding resistant cultivars. Therefore, comparative transcriptomic analyses were performed to identify more applicable resistance genes for cultivar breeding. The developing nematodes within roots were stained with acid fuchsin solution. Transcriptome assemblies and redundancy filteration were obtained by Trinity, TGI Clustering Tool and BLASTN, respectively. Gene Ontology annotation was yielded by Blast2GO program, and metabolic pathways of transcripts were analyzed by Path_finder. The ROS levels were determined by luminol-chemiluminescence assay. The transcriptional gene expression profiles were obtained by quantitative RT-PCR. The RNA-sequencing was performed using an incompatible wheat cultivar VP1620 and a compatible control cultivar WEN19 infected with H. avenae at 24 h, 3 d and 8 d. Infection assays showed that VP1620 failed to block penetration of H. avenae but disturbed the transition of developmental stages, leading to a significant reduction in cyst formation. Two types of expression profiles were established to predict candidate resistance genes after developing a novel strategy to generate clean RNA-seq data by removing the transcripts of H. avenae within the raw data before assembly. Using the uncoordinated expression profiles with transcript abundance as a standard, 424 candidate resistance genes were identified, including 302 overlapping genes and 122 VP1620-specific genes. Genes with similar expression patterns were further classified according to the scales of changed transcript abundances, and 182 genes were rescued as supplementary candidate resistance genes. Functional characterizations revealed that diverse defense-related pathways were responsible for wheat resistance against H. avenae. Moreover, phospholipase was involved in many defense-related pathways and localized in the connection position. Furthermore, strong bursts of reactive oxygen species (ROS) within VP1620 roots infected with H. avenae were induced at 24 h and 3 d, and eight ROS-producing genes were significantly upregulated, including three class III peroxidase and five lipoxygenase genes. Large-scale identification of wheat resistance genes were processed by comparative transcriptomic analysis. Functional characterization showed that phospholipases associated with ROS production played vital roles in early defense responses to H. avenae via involvement in diverse defense-related pathways as a hub switch. This study is the first to investigate the early defense responses of wheat against H. avenae, not only provides applicable candidate resistance genes for breeding novel wheat cultivars, but also enables a better understanding of the defense mechanisms of wheat against H. avenae.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 44 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 25%
Student > Ph. D. Student 9 20%
Student > Master 6 14%
Student > Bachelor 3 7%
Unspecified 2 5%
Other 4 9%
Unknown 9 20%
Readers by discipline Count As %
Agricultural and Biological Sciences 25 57%
Biochemistry, Genetics and Molecular Biology 6 14%
Unspecified 2 5%
Business, Management and Accounting 1 2%
Social Sciences 1 2%
Other 0 0%
Unknown 9 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 02 July 2016.
All research outputs
#7,156,795
of 22,830,751 outputs
Outputs from BMC Genomics
#3,383
of 10,655 outputs
Outputs of similar age
#89,276
of 280,050 outputs
Outputs of similar age from BMC Genomics
#129
of 375 outputs
Altmetric has tracked 22,830,751 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 10,655 research outputs from this source. They receive a mean Attention Score of 4.7. This one has gotten more attention than average, scoring higher than 67% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 280,050 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.
We're also able to compare this research output to 375 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 64% of its contemporaries.