Proteomic and Phosphoproteomic Analysis in Tobacco Mosaic Virus-Infected Tobacco (Nicotiana tabacum)

Tobacco mosaic virus (TMV) is a common source of biological stress that significantly affects plant growth and development. It is also useful as a model in studies designed to clarify the mechanisms involved in plant viral disease. Plant responses to abiotic stress were recently reported to be regulated by complex mechanisms at the post-translational modification (PTM) level. Protein phosphorylation is one of the most widespread and major PTMs in organisms. Using immobilized metal ion affinity chromatography (IMAC) enrichment, high-pH C18 chromatography fraction, and high-accuracy mass spectrometry (MS), a set of proteins and phosphopeptides in both TMV-infected tobacco and control tobacco were identified. A total of 4905 proteins and 3998 phosphopeptides with 3063 phosphorylation sites were identified. These 3998 phosphopeptides were assigned to 1311 phosphoproteins, as some proteins carried multiple phosphorylation sites. Among them, 530 proteins and 337 phosphopeptides corresponding to 277 phosphoproteins differed between the two groups. There were 43 upregulated phosphoproteins, including phosphoglycerate kinase, pyruvate phosphate dikinase, protein phosphatase 2C, and serine/threonine protein kinase. To the best of our knowledge, this is the first phosphoproteomic analysis of leaves from a tobacco cultivar, K326. The results of this study advance our understanding of tobacco development and TMV action at the protein phosphorylation level.

Download Full-text

Fast detection of tobacco mosaic virus infected tobacco using laser-induced breakdown spectroscopy

Scientific Reports ◽

10.1038/srep44551 ◽

2017 ◽

Vol 7 (1) ◽

Cited By ~ 18

Author(s):

Jiyu Peng ◽

Kunlin Song ◽

Hongyan Zhu ◽

Wenwen Kong ◽

Fei Liu ◽

...

Keyword(s):

Tobacco Mosaic Virus ◽

Mosaic Virus ◽

Laser Induced Breakdown Spectroscopy ◽

Fast Detection ◽

Breakdown Spectroscopy ◽

Laser Induced Breakdown ◽

Infected Tobacco

Download Full-text

Computational prediction and analysis of species-specific fungi phosphorylation via feature optimization strategy

Briefings in Bioinformatics ◽

10.1093/bib/bby122 ◽

2018 ◽

Vol 21 (2) ◽

pp. 595-608 ◽

Cited By ~ 4

Author(s):

Man Cao ◽

Guodong Chen ◽

Jialin Yu ◽

Shaoping Shi

Keyword(s):

Protein Phosphorylation ◽

Computational Prediction ◽

Optimization Method ◽

Phosphorylation Sites ◽

Optimization Strategy ◽

Post Translational Modification ◽

Tyrosine Residues ◽

Identification Of Fungi ◽

Feature Optimization ◽

Species Specific

Abstract Protein phosphorylation is a reversible and ubiquitous post-translational modification that primarily occurs at serine, threonine and tyrosine residues and regulates a variety of biological processes. In this paper, we first briefly summarized the current progresses in computational prediction of eukaryotic protein phosphorylation sites, which mainly focused on animals and plants, especially on human, with a less extent on fungi. Since the number of identified fungi phosphorylation sites has greatly increased in a wide variety of organisms and their roles in pathological physiology still remain largely unknown, more attention has been paid on the identification of fungi-specific phosphorylation. Here, experimental fungi phosphorylation sites data were collected and most of the sites were classified into different types to be encoded with various features and trained via a two-step feature optimization method. A novel method for prediction of species-specific fungi phosphorylation-PreSSFP was developed, which can identify fungi phosphorylation in seven species for specific serine, threonine and tyrosine residues (http://computbiol.ncu.edu.cn/PreSSFP). Meanwhile, we critically evaluated the performance of PreSSFP and compared it with other existing tools. The satisfying results showed that PreSSFP is a robust predictor. Feature analyses exhibited that there have some significant differences among seven species. The species-specific prediction via two-step feature optimization method to mine important features for training could considerably improve the prediction performance. We anticipate that our study provides a new lead for future computational analysis of fungi phosphorylation.

Download Full-text