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The prediction of a pathogenesis-related secretome of Puccinia helianthi through high-throughput transcriptome analysis

Overview of attention for article published in BMC Bioinformatics, March 2017
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Title
The prediction of a pathogenesis-related secretome of Puccinia helianthi through high-throughput transcriptome analysis
Published in
BMC Bioinformatics, March 2017
DOI 10.1186/s12859-017-1577-0
Pubmed ID
Authors

Lan Jing, Dandan Guo, Wenjie Hu, Xiaofan Niu

Abstract

Many plant pathogen secretory proteins are known to be elicitors or pathogenic factors,which play an important role in the host-pathogen interaction process. Bioinformatics approaches make possible the large scale prediction and analysis of secretory proteins from the Puccinia helianthi transcriptome. The internet-based software SignalP v4.1, TargetP v1.01, Big-PI predictor, TMHMM v2.0 and ProtComp v9.0 were utilized to predict the signal peptides and the signal peptide-dependent secreted proteins among the 35,286 ORFs of the P. helianthi transcriptome. 908 ORFs (accounting for 2.6% of the total proteins) were identified as putative secretory proteins containing signal peptides. The length of the majority of proteins ranged from 51 to 300 amino acids (aa), while the signal peptides were from 18 to 20 aa long. Signal peptidase I (SpI) cleavage sites were found in 463 of these putative secretory signal peptides. 55 proteins contained the lipoprotein signal peptide recognition site of signal peptidase II (SpII). Out of 908 secretory proteins, 581 (63.8%) have functions related to signal recognition and transduction, metabolism, transport and catabolism. Additionally, 143 putative secretory proteins were categorized into 27 functional groups based on Gene Ontology terms, including 14 groups in biological process, seven in cellular component, and six in molecular function. Gene ontology analysis of the secretory proteins revealed an enrichment of hydrolase activity. Pathway associations were established for 82 (9.0%) secretory proteins. A number of cell wall degrading enzymes and three homologous proteins specific to Phytophthora sojae effectors were also identified, which may be involved in the pathogenicity of the sunflower rust pathogen. This investigation proposes a new approach for identifying elicitors and pathogenic factors. The eventual identification and characterization of 908 extracellularly secreted proteins will advance our understanding of the molecular mechanisms of interactions between sunflower and rust pathogen and will enhance our ability to intervene in disease states.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Unknown 40 98%

Demographic breakdown

Readers by professional status Count As %
Researcher 7 17%
Student > Ph. D. Student 7 17%
Student > Master 6 15%
Student > Bachelor 4 10%
Student > Postgraduate 3 7%
Other 4 10%
Unknown 10 24%
Readers by discipline Count As %
Agricultural and Biological Sciences 16 39%
Biochemistry, Genetics and Molecular Biology 6 15%
Computer Science 3 7%
Medicine and Dentistry 2 5%
Arts and Humanities 1 2%
Other 3 7%
Unknown 10 24%

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 30 August 2017.
All research outputs
#10,506,670
of 13,187,495 outputs
Outputs from BMC Bioinformatics
#4,095
of 4,956 outputs
Outputs of similar age
#188,329
of 257,157 outputs
Outputs of similar age from BMC Bioinformatics
#16
of 22 outputs
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