Exploring Tomato Gene Functions Based on Coexpression Modules Using Graph Clustering and Differential Coexpression Approaches      

Abstract

Gene-to-gene coexpression analysis provides fundamental information and is a promising approach for predicting unknown gene functions in plants. We investigated various associations in the gene expression of tomato (Solanum lycopersicum) to predict unknown gene functions in an unbiased manner. We obtained more than 300 microarrays from publicly available databases and our own hybridizations, and here, we present tomato coexpression networks and coexpression modules. The topological characteristics of the networks were highly heterogenous. We extracted 465 total coexpression modules from the data set by graph clustering, which allows users to divide a graph effectively into a set of clusters. Of these, 88% were assigned systematically by Gene Ontology terms. Our approaches revealed functional modules in the tomato transcriptome data; the predominant functions of coexpression modules were biologically relevant. We also investigated differential coexpression among data sets consisting of leaf, fruit, and root samples to gain further insights into the tomato transcriptome. We now demonstrate that (1) duplicated genes, as well as metabolic genes, exhibit a small but significant number of differential coexpressions, and (2) a reversal of gene coexpression occurred in two metabolic pathways involved in lycopene and flavonoid biosynthesis. Independent experimental verification of the findings for six selected genes was done using quantitative real-time polymerase chain reaction. Our findings suggest that differential coexpression may assist in the investigation of key regulatory steps in metabolic pathways. The approaches and results reported here will be useful to prioritize candidate genes for further functional genomics studies of tomato metabolism.

Integrated bioinformatics to decipher the ascorbic acid metabolic network in tomato

“…An increasing number of studies, based on the concept of the guilt by association (Quackenbush 2003), have shown the versatility of co-expression analysis. Gene co-expression analysis has been applied to identify and characterize various biological interactions, and has helped to predict gene function and analyze complex physiological traits in many plants, such as Arabidopsis (Movahedi et al 2011), tomato (Ozaki et al 2010Fukushima et al 2012), maize and rice (Ficklin and Feltus 2011). In this study, the survey of the co-expressed genes in different tissues and developmental stages of tomato fruit provided an overview on the groups of pivotal genes associated with the AsA metabolism.…”

Phenotypic Analyses of Arabidopsis T-DNA Insertion Lines and Expression Profiling Reveal That Multiple L-Type Lectin Receptor Kinases Are Involved in Plant Immunity

“…Expression profiling analysis has been widely used to predict potential biological functions of genes (Fukushima et al 2012;Li et al 2006). Here, we explored the transcriptome-tophenotype relations by comparing LecRK expression profiles retrieved from publicly available microarray data with the mutant phenotypes obtained in this study (Fig.…”

“…Genes that exhibit similar expression patterns are often expected to be involved in the same biological processes (Eisen et al 1998;Fukushima et al 2012). We computed coexpression profiles of all LecRK in leaves inoculated with P. infestans and DC3000 over time.…”

The MORPH Algorithm: Ranking Candidate Genes for Membership in Arabidopsis and Tomato Pathways

“…Only data sets that passed quality control test by their original authors were chosen. Normalized matrices were retrieved from Mintz-Oron et al (2008), Adato et al (2009), andFukushima et al (2012). These matrices included 32 profiles (after uniting replicates and dividing by the control) from fruit tissues (the fruit data set) and 21 profiles (after uniting replicates and dividing by control) of root and leaf tissues (the root and leaf data set).…”