Abstract
BackgroundSweet orange (Citrus sinensis) 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.ResultsIn 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 (EDI) in CitrusNet using RNA-seq data from four sweet orange tissues, and also analyzed the EDI distribution and variation in different sub-networks.ConclusionsGene 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.
Highlights
Sweet orange (Citrus sinensis) is one of the most important fruits world-wide
Topological properties of the protein-protein interaction (PPI) network Based on a combination of ortholog- and domain-based prediction methods, we obtained 146,056 PPIs, and assessed and filtered all the predicted PPIs by K-nearest neighbors (KNN)
We predicted 159 protein complexes in sweet orange using orthologs of the yeast protein complexes and employed them to assess CitrusNet; the results revealed that protein complexes had relatively tight connections
Summary
Sweet orange (Citrus sinensis) 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. Understanding the interactions between proteins is an important goal of systems biology, and such knowledge can provide crucial insights into protein function and molecular mechanisms. Various experimental technologies, such as affinity purification mass spectrometry (AP-MS) [1], the yeast two-hybrid (Y2H) system [2], and protein arrays [3,4,5] have been applied to detection of genome-wide PPIs in many model species, including Homo sapiens [6], Drosophila melanogaster [7], Saccharomyces cerevisiae [8], and Caenorhabditis elegans [9]. Most experimental methods have condition- or method-specific features; the data obtained by various methods sometimes exhibit minimal overlap even within the same species
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