Prediction and functional analysis of the sweet orange protein-protein interaction network
Identifieur interne : 000957 ( Main/Merge ); précédent : 000956; suivant : 000958Prediction and functional analysis of the sweet orange protein-protein interaction network
Auteurs : Yu-Duan Ding [République populaire de Chine] ; Ji-Wei Chang [République populaire de Chine] ; Jing Guo [République populaire de Chine] ; Dijun Chen [République populaire de Chine] ; Sen Li [République populaire de Chine] ; Qiang Xu [République populaire de Chine] ; Xiu-Xin Deng [République populaire de Chine] ; Yun-Jiang Cheng [République populaire de Chine] ; Ling-Ling Chen [République populaire de Chine]Source :
- BMC Plant Biology [ 1471-2229 ] ; 2014.
English descriptors
- KwdEn :
- MESH :
- chemical , metabolism : Plant Growth Regulators.
- genetics : Citrus sinensis.
- metabolism : Citrus sinensis.
- Genome, Plant, Protein Interaction Maps, Sequence Analysis, RNA.
Abstract
Sweet orange (
In this analysis, we employed ortholog identification and domain combination methods to predict the protein-protein interaction (PPI) network for sweet orange. The K-nearest neighbors (KNN) classification method was used to verify and filter the network. The final predicted PPI network, CitrusNet, contained 8,195 proteins with 124,491 interactions. The quality of CitrusNet was evaluated using gene ontology (GO) and Mapman annotations, which confirmed the reliability of the network. In addition, we calculated the expression difference of interacting genes (
Gene expression in CitrusNet has significant modular features. Target of rapamycin (TOR) protein served as the central node of the hormone-signaling sub-network. All evidence supported the idea that TOR can integrate various hormone signals and affect plant growth. CitrusNet provides valuable resources for the study of biological functions in sweet orange.
Url:
DOI: 10.1186/s12870-014-0213-7
PubMed: 25091279
PubMed Central: 4236729
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<term>Plant Growth Regulators (metabolism)</term>
<term>Protein Interaction Maps</term>
<term>Sequence Analysis, RNA</term>
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p>Sweet orange (<italic>Citrus sinensis</italic>
) is one of the most important fruits world-wide. Because it is a woody plant with a long growth cycle, genetic studies of sweet orange are lagging behind those of other species.</p>
</sec>
<sec><title>Results</title>
<p>In this analysis, we employed ortholog identification and domain combination methods to predict the protein-protein interaction (PPI) network for sweet orange. The K-nearest neighbors (KNN) classification method was used to verify and filter the network. The final predicted PPI network, CitrusNet, contained 8,195 proteins with 124,491 interactions. The quality of CitrusNet was evaluated using gene ontology (GO) and Mapman annotations, which confirmed the reliability of the network. In addition, we calculated the expression difference of interacting genes (<italic>EDI</italic>
) in CitrusNet using RNA-seq data from four sweet orange tissues, and also analyzed the <italic>EDI</italic>
distribution and variation in different sub-networks.</p>
</sec>
<sec><title>Conclusions</title>
<p>Gene expression in CitrusNet has significant modular features. Target of rapamycin (TOR) protein served as the central node of the hormone-signaling sub-network. All evidence supported the idea that TOR can integrate various hormone signals and affect plant growth. CitrusNet provides valuable resources for the study of biological functions in sweet orange.</p>
</sec>
</div>
</front>
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