Annotation of gene function in citrus using gene expression information and co-expression networks
© Wong et al.; licensee BioMed Central Ltd. 2014
Received: 11 April 2014
Accepted: 30 June 2014
Published: 15 July 2014
The genus Citrus encompasses major cultivated plants such as sweet orange, mandarin, lemon and grapefruit, among the world’s most economically important fruit crops. With increasing volumes of transcriptomics data available for these species, Gene Co-expression Network (GCN) analysis is a viable option for predicting gene function at a genome-wide scale. GCN analysis is based on a “guilt-by-association” principle whereby genes encoding proteins involved in similar and/or related biological processes may exhibit similar expression patterns across diverse sets of experimental conditions. While bioinformatics resources such as GCN analysis are widely available for efficient gene function prediction in model plant species including Arabidopsis, soybean and rice, in citrus these tools are not yet developed.
We have constructed a comprehensive GCN for citrus inferred from 297 publicly available Affymetrix Genechip Citrus Genome microarray datasets, providing gene co-expression relationships at a genome-wide scale (33,000 transcripts). The comprehensive citrus GCN consists of a global GCN (condition-independent) and four condition-dependent GCNs that survey the sweet orange species only, all citrus fruit tissues, all citrus leaf tissues, or stress-exposed plants. All of these GCNs are clustered using genome-wide, gene-centric (guide) and graph clustering algorithms for flexibility of gene function prediction. For each putative cluster, gene ontology (GO) enrichment and gene expression specificity analyses were performed to enhance gene function, expression and regulation pattern prediction. The guide-gene approach was used to infer novel roles of genes involved in disease susceptibility and vitamin C metabolism, and graph-clustering approaches were used to investigate isoprenoid/phenylpropanoid metabolism in citrus peel, and citric acid catabolism via the GABA shunt in citrus fruit.
Integration of citrus gene co-expression networks, functional enrichment analysis and gene expression information provide opportunities to infer gene function in citrus. We present a publicly accessible tool, Network Inference for Citrus Co-Expression (NICCE, http://citrus.adelaide.edu.au/nicce/home.aspx), for the gene co-expression analysis in citrus.
The genus Citrus of the plant family Rutaceae contains some of the world’s most economically important fruit crops. Major cultivated Citrus plants include C. sinensis (sweet orange), C. reticulata (mandarin), C. limon (lemon) and C. paradisi (grapefruit). Citrus species contributed to a global production of 131 million tons of fruit harvested over 8.7 million hectares in 2011 (FAOSTAT, 2013), and are primarily utilised for juice making and fresh fruit consumption. Citrus fruits contain a rich combination of nutrients important for the promotion of good health, such as simple sugars, dietary fibres, vitamins (vitamin B and C), minerals (calcium, magnesium and potassium) and bioactive phytochemicals (carotenoids, flavonoids and limonoids) . The metabolic pathways by which many of these compounds are made in plants are widely known, however the genes responsible for encoding proteins of these pathways in citrus fruits remain largely undetermined.
The sequencing of plant genomes to uncover their genes, and the application of high throughput expression technologies (e.g. DNA microarray and RNA sequencing) to profile these genes, have produced large datasets of gene information and genome-scale transcriptomic data that have facilitated our understanding of many biological processes. Recently, the draft genome of sweet orange revealed that this species is highly heterozygous, with 29,445 predicted protein-coding genes out of 44,387 predicted transcripts. Of these, a total of 23,804 protein-coding genes were classified into 14,348 gene families, while the rest have been annotated as ‘hypothetical’ or ‘unknown function’ proteins . Comprehensive transcriptome sequencing has also revealed insights into the molecular mechanisms underpinning key traits important for citrus fruit biology, such as vitamin C metabolism, regulation of fruit ripening and identification of disease resistance genes . Taken together, these pieces of information form an invaluable resource for understanding molecular plant-pathogen interactions, abiotic stress tolerance and improvement of economically and agronomically important traits in citrus plants. However, despite recent efforts in sequencing the sweet orange genome, the majority of genes encoded in the genome remain uncharacterised, while sequencing efforts of other citrus genomes are still in progress .
One promising approach to improve our understanding of how these genes may function in sweet orange and related citrus plants is through Gene Co-expression Analysis (GCA). Accumulation of publicly available, genome-wide gene expression data from DNA microarrays in plants has proved useful for defining correlated expression patterns between genes using pairwise similarity metrics such as Pearson’s correlation coefficient, r, and subsequent genome-scale reconstruction of gene co-expression networks (GCN) [4, 5]. Genes are usually represented as ‘nodes’, whilst the lines linking individual nodes, or ‘edges’, represent pairwise relationships between nodes. A collection of densely connected nodes represents a ‘cluster’ and the entire collection of nodes, edges and clusters forms the co-expression ‘network’. Often, co-expressed genes within a cluster are expected to be functionally related to genes with a similar expression pattern. This ‘guilt-by-association’ approach has become a powerful tool for transcriptional regulatory inference and understanding the evolution of transcript expression within and between plants [6, 7]. Although ‘condition-independent’ GCA is common practice in plant GCA, integrating all available expression data regardless of tissue source or experimental procedure, several examples of ‘condition-dependent’ GCA have also been successfully employed to infer functions of genes in relation to conditions of interest (i.e. particular developmental stages, tissue types or stress conditions) [8–10].
To detect functional clusters (or modules) within the gene co-expression network, graph clustering and guide (or seed) gene based techniques have been successfully applied. The latter approach often requires a priori knowledge on function of the guide gene(s) and considers the node vicinity network of the given guide genes (i.e. genes within a defined distance, n from the specified guide gene) [9, 11, 12]. Alternatively, graph clustering algorithms such as Markov Cluster Algorithm (MCL) , Heuristic Cluster Chiseling Algorithm (HCCA)  and weighted correlation network analysis (WCGNA)  have been widely used to partition the complex gene co-expression network of plants in to defined functional clusters.
With emphasis on fruit crops such as sweet orange, grapevine and tomato, the application of RNA-sequencing has paved the way for transcriptome analysis of fruit crops in recent years in various stress, development and environment settings [16–22]. For the purpose of GCA, a comprehensive catalogue of experimental conditions from RNA-seq studies is still incomplete. Nevertheless, historical microarray data have provided a basis for genome-wide co-expression studies in these fruit crops [8, 9, 23, 24]. Notably, a condition-dependent GCA coupled with a guide gene search approach was performed to identify clusters involved in biotic stress responses in citrus , while a combination of condition –dependent and –independent, as well as guide gene and clustering based approaches were applied to provide novel insights into grapevine berry development, photosynthesis and flavonoid metabolism .
Genome-wide transcript analysis studies in citrus plants including various citrus species (primarily sweet orange), tissue types and stress experiments have been widely performed on the Affymetrix Genechip Citrus Genome Array, which represents roughly 70% of the transcriptome (based on the sweet orange genome). Although these studies were mainly based on understanding a specific biological process, integration of these heterogeneous datasets for GCA can provide a functional basis for hypothesis-driven gene discovery in citrus. Here, we present a global (condition-independent) and four manually assigned (condition-dependent) GCNs of citrus inferred from 297 publicly available Affymetrix Citrus Genome Array datasets. Using genome-wide guide and graph clustering of GCNs, systematic assessments of clusters were performed using a combination of GO enrichment analysis, gene expression information and literature searches.
Results and discussion
General overview - Identification of biologically relevant clusters in citrus
Summary of citrus co-expression network features in this study
No of nodes
No of edges
No of clusters
k = 100
k = 100
k = 100
k = 100
k = 100
I = 1.2
I = 1.3
I = 1.2
I = 1.3
I = 1.3
Novel roles of Lateral organ boundaries 1 (LOB1) in citrus
Guide gene co-expression analysis using LOB1 (Cit.35190.1.S1_at and Cit.37210.1.S1_at)
Enriched GO BP
Oxidative phophorylation (1.80E-02/2.20E-02)
Cell wall organisation (2.73E-05/6.45E-04)
DNA replication (1.83E-09/ NA)
Pigment accumulation (4.52E-04/ NA) phenylpropanoid metabolic process (2.17E-02/ NA)
Vitamin C metabolism in citrus
Guide gene co-expression analysis using GME (Cit.23640.1.S1_s_at, Cit.7984.1.S1_s_at, Cit.7984.1.S1_at)
Enriched GO BP
L-ascorbic acid biosynthesis (3.00E-03/ 1.90E-02/ 7.05E-05)
Response to hormone stimulus (6.00E-03/ 2.00E-03/ 1.10E-02)
Electron transport chain (6.00E-03/ 3.40E-02/ 7.05E-05)
Cell wall modification (NA/ 1.60E-03/ 1.30E-03) (NA/ 7.60E-04/ 3.39E-05)
L-ascorbic acid recycling
L-ascorbic acid recycling
Citrus peel isoprenoid and phenylpropanoid metabolism
Summary of gene ontology terms enriched of citrus MCL cluster 14
# in input
aromatic compound biosynthesis
carboxylic acid biosynthesis
fatty acid biosynthesis
acetyl-CoA catabolic process
ethylene mediated signaling pathway
chlorophyll catabolic process
ATP citrate synthase activity
transferase activity, transferring acyl groups
farnesyl-diphosphate farnesyltransferase activity
magnesium ion binding
transition metal ion binding
Inspection of the cluster expression specificity index showed that a large fraction of genes (>70%) was specifically expressed in fruit peels (flavedo) of sweet oranges and grapefruit, but to a lesser extent in whole fruits of lemon (>50%) and with low expression specificity in leaf, flower and root tissues (Figure 2D, Additional file 3: Table S4). Significantly connected clusters 210 and 147 shared functional commonalities (enriched in secondary metabolism) as well as being enriched in other closely related biological functions such as pyruvate metabolism, glycolysis, response to oxidative stress, cytokinin biosynthesis and flower development (Additional file 3: Table S5). Overall, Citrus cluster 14 showed significant co-expression between genes involved in terpenoid and phenylpropanoid pathways, with dominant expression profiles in citrus fruit peels. This suggests that a complex regulatory network exists, underpinning the composition of secondary metabolites correlated with colour development, synthesis of phenylpropanoid derivatives and essential oil as seen in developing citrus fruits . Functional evaluation of the various interesting nodes will provide the next step in the novel discovery of pathway members and regulators.
Citric Acid Catabolism and the GABA Shunt
Citric acid is the predominant organic acid of citrus fruits. Differences in concentration of this acid in acidic and ‘acidless’ or ‘sweet’ citrus fruit species  may be due to regulation of citric acid catabolism . The catabolism of citric acid in citrus fruits has been linked to the GABA-shunt, whereby (i) citric acid is converted to α-ketoglutaric acid via aconitase and isocitrate dehydrogenase activities (ii) α-ketoglutaric acid is converted to glutamic acid via aspartate aminotransferase or alanine aminotransferase, (iii) glutamic acid is converted to γ-aminobutyric acid (GABA) via glutamate decarboxylase, (iv) GABA is converted to succinic semialdehyde by GABA aminotransferase and (v) succinic semialdehyde is converted to succinate by succinate semialdehyde dehydrogenase, and fed back into the TCA cycle . The proposed purpose of this shunt in citrus fruits is to reduce the effect of high citric acid concentrations on the pH of the fruit cell cytosol, as the biosynthesis of GABA consumes protons in the cytosol .
Summary of gene ontology terms enriched of fruit MCL cluster 102 (A) and 11 (B)
# in input
glutathione dehydrogenase (ascorbate) activity
oxidoreductase activity, acting on sulfur group of donors, quinone or similar compound as acceptor
glutathione disulfide oxidoreductase activity
peptide disulfide oxidoreductase activity
response to heat
coenzyme M metabolism
coenzyme M biosynthesis
proteolysis involved in cellular protein catabolism
cellular protein catabolism
response to stress
negative regulation of DNA metabolism
negative regulation of DNA recombination
cellular macromolecule catabolism
regulation of DNA recombination
cellular protein metabolism
Meanwhile, fruit-specific cluster 11 (Figure 3B) contained 213 nodes, including a GABA aminotransferase gene, which converts GABA to succinic semialdehyde in preparation for re-entry of succinate to the TCA cycle, three putative genes of glycolysis (aldolase, enolase, glucose-6-phosphate isomerase), five putative genes of the mitochondrial electron chain (two cytochrome c oxidases and three NADH:ubiquinone reductases), and 39 genes putatively involved in regulating gene transcription and protein translation, post-translational modification and degradation (Additional file 3: Table S8). Interestingly, this cluster was enriched for heat stress (FDR 0.0205) (Table 5B) among other gene ontologies, with putative heat shock proteins being represented by eighteen genes. Therefore these TCA cycle, GABA shunt and mETC genes may be responsive to environmental stresses. There was also a putative calmodulin gene (Cit.14580.1.S1_at) and a gene putatively involved in cellular Ca2+ sensing (Cit.12067.1.S1_s_at), suggesting the involvement of calcium in the regulation of these pathways under stress conditions, based on the highly co-expressed genes within their sub-network (Additional file 3: Table S9). Fruit-specific cluster 11, home of the predicted GABA aminotransferase gene discussed above, also contained probesets that were likely to encode ten metallothionein proteins, two manganese superoxide dismutases and two ascorbate peroxidases, all of which are involved in ROS scavenging. This cluster also contained a senescence-associated gene with a likely role in oxidative stress tolerance and genes that putatively encoded two glycolate oxidases, which produce H2O2 (Additional file 3: Table S8). Overall, the exploration of co-expression patterns between genes involved in citric acid catabolism and the GABA shunt in the context of fruit-specific MCL clusters has highlighted relationships between putative genes of these pathways and genes involved in oxidative stress responses, with specific expression in fruit vesicles in various sweet orange cultivars including Navel, Valencia and Hamlin (Figure 3C; Additional file 3: Table S10 and S11). These genes should be examined further for their roles in determining citric acid concentration in the fruits of different citrus species and in response to abiotic stress.
Summary and future directions
We have provided a comprehensive framework for GCN inference applicable to the citrus genera, and show that meaningful co-expression relationships can be obtained in these clusters. The relevant genes and clusters were supported by the co-expression network structure, functional enrichment of co-expressed genes, gene expression specificity and literature information. For this study, we include examples of genes and clusters that are biologically relevant and are of importance to the citrus industry. We also describe NICCE (Network Inference for Citrus Co-Expression, http://citrus.adelaide.edu.au/nicce/home.aspx), a user-friendly web portal equipped with comprehensive tools for citrus researchers to rapidly mine and interpret interesting co-expression relationships of genes and clusters.
Raw expression data and pre-processing
A total of 297 publicly available Affymetrix Genechip Citrus Genome microarray datasets measuring the transcriptional activity of approximately 33,000 transcripts (~70% transcriptome coverage) were retrieved from the Gene Expression Omnibus, NCBI. Raw CEL files were processed using RMAExpress (http://rmaexpress.bmbolstad.com/) using the default settings to compute robust multi-array average (RMA) expression values. A total of 18 potential outlier arrays that failed the probe-level, model-based quality assessment were discarded, retaining 279 arrays for further analysis. Control probesets were also removed prior to co-expression network analysis. Generalised condition-independent co-expression network analyses for citrus species were constructed using all 279 arrays. Several condition-dependent co-expression networks were also constructed separately based on their associated meta-data (classified according to their subspecies, tissue samples and experimental conditions). Finally, gene co-expression networks were generated for the generalised, sub-species-, tissue- and stress- specific datasets by applying the procedure below.
Rank calculation and co-expression network construction
Using Pearson’s correlation coefficient (r) as a metric of similarity between expression values, correlation matrices were first calculated. The r values for all co-expressed gene pairs were transformed into ranks in ascending order of r for each probeset. Highest reciprocal rank (HRR) values between pair-wise probesets were calculated using formulas (1) HRR(A,B) = [max(rank(A,B), rank(B, A))] where rank(A,B) is the transformed rank of gene B according to gene A’s co-expression list and vice versa for rank(B,A) . HRR are used as an index of gene co-expression and in the construction of the aforementioned gene co-expression networks. The significance of HRR for each individual network was estimated based on 100 permutations as per . Genome-scale, gene-centric co-expression clusters were created by considering each gene as a ‘seed’ or ‘guide’ and all genes within the top 100 HRR for a given gene as individual clusters. This resulted in a total of 30,217 clusters (i.e. the number of probesets represented on the array), sharing potentially overlapping co-expressed genes at a genome-wide scale. Graph clustering was performed using Markov Cluster Algorithm (MCL)  using MCL version 12–068 (http://micans.org/mcl/) with varying inflation values, I, between 1.1 and 2.0, and different HRR cut-offs to identify functional clusters. HRR networks were generated using different cut-offs, where weights of 0.2, 0.067, 0.04, 0.028 and 0.022 were given cut-off HRR scores of 10, 20, 30, 40 and 50 respectively for performance evaluation. Predicted clusters with fewer than 3 probesets are often biologically meaningless and were removed.
Evaluation of functional enrichment, cluster characteristics and clustering performance
Assessment of gene ontology (GO) term overrepresentation within a cluster was performed using BiNGO . The statistical significance for all GO biological process (BP), molecular function (MF) and cellular component (CC) terms within a cluster were evaluated using the hypergeometric distribution-adjusted Benjamini & Hochberg false discovery rate (FDR) for multiple hypothesis correction. GO annotation terms were considered significant if the corrected P-value (FDR) < 0.05 and if there were at least 2 genes associated with the same annotation. Evaluation of clustering performance using MCL at various I values was determined by calculating the fraction of modules enriched with one annotation at FDR < 0.05 (expressed as specificity) and the fraction of annotations enriched in at least one module at FDR < 0.05 (expressed as sensitivity), having at least 2 genes associated with the enriched annotation . The specificity and sensitivity values were then summarised as a functional enrichment score, the F-measure, calculated as the harmonic mean between specificity and sensitivity [(2 × Specificity × Sensitivity/(Specificity + Sensitivity)]. Probeset expression specificity was calculated according to  by standardising gene expression values within, and then between microarray assays. A gene was considered well and specifically expressed in the corresponding experimental condition when the probeset expression specificity index values were > 1 and > 5, respectively. Similarly, the cluster cumulative expression specificity index (cESI) was defined as the fraction of cluster members specifically expressed in a particular tissue or condition (and across all arrays) with an expression specificity index above 1, according to .
Annotation of genes and visualisation of network
Previous annotations of the Citrus probesets based on homology and orthology searches against the Arabidopsis genome and NCBI best blasts hit were downloaded from Zheng and Zhao  and CitrusPLEX , and merged into a single annotation table containing reference ID (Ref_ID), reference source (Ref_Source), reference description (Ref_Desc), E-value and percentage identity (where applicable). An update to include mappings against the latest sweet orange genome annotation was also performed using local BLAST (ncbi-blast-2.2.29+) downloaded from NCBI website (ftp://ftp.ncbi.nlm.nih.gov/blast/executables/blast+/LATEST/). Briefly, consensus sequences representing each probesets (total of 30, 217) were mapped to the latest sweet orange genome annotation using BLASTx with the default setting except the E-value and identity cut-off were 1e-20 and 40% respectively. The best blast hit for this search was considered a representative sweet orange gene identifier of the underlying probesets in the array. Additionally, electronically-inferred GO assignments of the Citrus probesets using the Blast2GO pipelines  were downloaded from AgriGO download centre (http://bioinfo.cau.edu.cn/agriGO/download.php) prior to GO enrichment analysis in BinGO . Visualisation of nodes and edge attributes were performed using a combination of features introduced in Cytoscape 2.8  and CytoscapeWeb .
We gratefully acknowledge the Citrus research community for the provision of various microarray data in the public domain. DCJW is supported by a postgraduate research scholarship from the University of Adelaide.
- Liu Y, Heying E, Tanumihardjo SA: History, global distribution, and nutritional importance of citrus fruits. Comprehensive Reviews in Food Science and Food Safety. 2012, 11 (6): 530-545.View ArticleGoogle Scholar
- Xu Q, Chen LL, Ruan X, Chen D, Zhu A, Chen C, Bertrand D, Jiao WB, Hao BH, Lyon MP, Chen J, Gao S, Xing F, Lan H, Chang JW, Ge X, Lei Y, Hu Q, Miao Y, Wang L, Xiao S, Biswas MK, Zeng W, Guo F, Cao H, Yang X, Xu XW, Cheng YJ, Xu J, Liu JH, et al: The draft genome of sweet orange (Citrus sinensis). Nat Genet. 2013, 45 (1): 59-66. doi:10.1038/ng.2472. Epub 2012 Nov 25View ArticlePubMedGoogle Scholar
- Gmitter F, Chen C, Machado MA, Souza AA, Ollitrault P, Froehlicher Y, Shimizu T: Citrus genomics. Tree Genetics & Genomes. 2012, 8 (3): 611-626.View ArticleGoogle Scholar
- Usadel B, Obayashi T, Mutwil M, Giorgi FM, Bassel GW, Tanimoto M, Chow A, Steinhauser D, Persson S, Provart NJ: Co-expression tools for plant biology: opportunities for hypothesis generation and caveats. Plant Cell Environ. 2009, 32 (12): 1633-1651.View ArticlePubMedGoogle Scholar
- López-Kleine L, Leal L, López C: Biostatistical approaches for the reconstruction of gene co-expression networks based on transcriptomic data. Brief Funct Genomics. 2013, 12 (5): 457-467.View ArticlePubMedGoogle Scholar
- Saito K, Hirai MY, Yonekura-Sakakibara K: Decoding genes with coexpression networks and metabolomics – ‘majority report by precogs’. Trends Plant Sci. 2008, 13 (1): 36-43.View ArticlePubMedGoogle Scholar
- Aoki K, Ogata Y, Shibata D: Approaches for extracting practical information from gene co-expression networks in plant biology. Plant and Cell Physiology. 2007, 48 (3): 381-390.View ArticlePubMedGoogle Scholar
- Fukushima A, Nishizawa T, Hayakumo M, Hikosaka S, Saito K, Goto E, Kusano M: Exploring tomato gene functions based on coexpression modules using graph clustering and differential coexpression approaches. Plant Physiol. 2012, 158 (4): 1487-1502.PubMed CentralView ArticlePubMedGoogle Scholar
- Wong DCJ, Sweetman C, Drew DP, Ford CM: VTCdb: a gene co-expression database for the crop species Vitis vinifera (grapevine). BMC Genomics. 2013, 14 (1): 882-PubMed CentralView ArticlePubMedGoogle Scholar
- Childs KL, Davidson RM, Buell CR: Gene coexpression network analysis as a source of functional annotation for rice genes. PLoS One. 2011, 6 (7): e22196-PubMed CentralView ArticlePubMedGoogle Scholar
- Obayashi T, Hayashi S, Saeki M, Ohta H, Kinoshita K: ATTED-II provides coexpressed gene networks for Arabidopsis. Nucleic Acids Res. 2009, 37 (suppl 1): D987-D991.PubMed CentralView ArticlePubMedGoogle Scholar
- Sato Y, Namiki N, Takehisa H, Kamatsuki K, Minami H, Ikawa H, Ohyanagi H, Sugimoto K, Itoh J-I, Antonio BA, Nagamura Y: RiceFREND: a platform for retrieving coexpressed gene networks in rice. Nucleic Acids Res. 2013, 41 (D1): D1214-D1221.PubMed CentralView ArticlePubMedGoogle Scholar
- Enright AJ, Van Dongen S, Ouzounis CA: An efficient algorithm for large-scale detection of protein families. Nucleic Acids Res. 2002, 30 (7): 1575-1584.PubMed CentralView ArticlePubMedGoogle Scholar
- Mutwil M, Usadel B, Schütte M, Loraine A, Ebenhöh O, Persson S: Assembly of an interactive correlation network for the Arabidopsis genome using a novel heuristic clustering algorithm. Plant Physiol. 2010, 152 (1): 29-43.PubMed CentralView ArticlePubMedGoogle Scholar
- Langfelder P, Horvath S: WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics. 2008, 9 (1): 559-PubMed CentralView ArticlePubMedGoogle Scholar
- Zouari I, Salvioli A, Chialva M, Novero M, Miozzi L, Tenore G, Bagnaresi P, Bonfante P: From root to fruit: RNA-Seq analysis shows that arbuscular mycorrhizal symbiosis may affect tomato fruit metabolism. BMC Genomics. 2014, 15 (1): 221-PubMed CentralView ArticlePubMedGoogle Scholar
- Pereira A, Carazzolle M, Abe V, de Oliveira M, Domingues M, Silva J, Cernadas R, Benedetti C: Identification of putative TAL effector targets of the citrus canker pathogens shows functional convergence underlying disease development and defense response. BMC Genomics. 2014, 15 (1): 157-PubMed CentralView ArticlePubMedGoogle Scholar
- Wang Y, Tao X, Tang X-M, Xiao L, Sun J-l, Yan X-F, Li D, Deng H-Y, Ma X-R: Comparative transcriptome analysis of tomato (Solanum lycopersicum) in response to exogenous abscisic acid. BMC Genomics. 2013, 14 (1): 841-PubMed CentralView ArticlePubMedGoogle Scholar
- Rodrigues C, de Souza A, Takita M, Kishi L, Machado M: RNA-Seq analysis of Citrus reticulata in the early stages of Xylella fastidiosa infection reveals auxin-related genes as a defense response. BMC Genomics. 2013, 14 (1): 676-PubMed CentralView ArticlePubMedGoogle Scholar
- Yu K, Xu Q, Da X, Guo F, Ding Y, Deng X: Transcriptome changes during fruit development and ripening of sweet orange (Citrus sinensis). BMC Genomics. 2012, 13 (1): 10-PubMed CentralView ArticlePubMedGoogle Scholar
- Sweetman C, Wong DCJ, Ford CM, Drew DP: Transcriptome analysis at four developmental stages of grape berry (Vitis vinifera cv. Shiraz) provides insights into regulated and coordinated gene expression. BMC Genomics. 2012, 13 (1): 691-PubMed CentralView ArticlePubMedGoogle Scholar
- Perazzolli M, Moretto M, Fontana P, Ferrarini A, Velasco R, Moser C, Delledonne M, Pertot I: Downy mildew resistance induced by Trichoderma harzianum T39 in susceptible grapevines partially mimics transcriptional changes of resistant genotypes. BMC Genomics. 2012, 13 (1): 660-PubMed CentralView ArticlePubMedGoogle Scholar
- Zheng Z-L, Zhao Y: Transcriptome comparison and gene coexpression network analysis provide a systems view of citrus response to 'Candidatus Liberibacter asiaticus' infection. BMC Genomics. 2013, 14 (1): 27-PubMed CentralView ArticlePubMedGoogle Scholar
- Ozaki S, Ogata Y, Suda K, Kurabayashi A, Suzuki T, Yamamoto N, Iijima Y, Tsugane T, Fujii T, Konishi C, Inai S, Bunsupa S, Yamazaki M, Shibata D, Aoki K: Coexpression analysis of tomato genes and experimental verification of coordinated expression of genes found in a functionally enriched coexpression module. DNA Res. 2010, 17 (2): 105-116.PubMed CentralView ArticlePubMedGoogle Scholar
- Wang J, Chen D, Lei Y, Chang J-W, Hao B-H, Xing F, Li S, Xu Q, Deng X-X, Chen L-L: Citrus sinensis Annotation Project (CAP): A comprehensive database for sweet orange genome. PLoS One. 2014, 9 (1): e87723-PubMed CentralView ArticlePubMedGoogle Scholar
- Obayashi T, Kinoshita K: Rank of correlation coefficient as a comparable measure for biological significance of gene coexpression. DNA Res. 2009, 16 (5): 249-260.PubMed CentralView ArticlePubMedGoogle Scholar
- Mutwil M, Klie S, Tohge T, Giorgi FM, Wilkins O, Campbell MM, Fernie AR, Usadel B, Nikoloski Z, Persson S: PlaNet: Combined sequence and expression comparisons across plant networks derived from seven species. The Plant Cell Online. 2011, 23 (3): 895-910.View ArticleGoogle Scholar
- Obayashi T, Kinoshita K: Coexpression landscape in ATTED-II: usage of gene list and gene network for various types of pathways. J Plant Res. 2010, 123 (3): 311-319.View ArticlePubMedGoogle Scholar
- Vlasblom J, Wodak S: Markov clustering versus affinity propagation for the partitioning of protein interaction graphs. BMC Bioinformatics. 2009, 10 (1): 99-PubMed CentralView ArticlePubMedGoogle Scholar
- Brohee S, van Helden J: Evaluation of clustering algorithms for protein-protein interaction networks. BMC Bioinformatics. 2006, 7 (1): 488-PubMed CentralView ArticlePubMedGoogle Scholar
- Netotea S, Sundell D, Street N, Hvidsten T: ComPlEx: conservation and divergence of co-expression networks in A. thaliana, Populus and O. sativa. BMC Genomics. 2014, 15 (1): 106-PubMed CentralView ArticlePubMedGoogle Scholar
- Cernadas RA, Camillo LR, Benedetti CE: Transcriptional analysis of the sweet orange interaction with the citrus canker pathogens Xanthomonas axonopodis pv. citri and Xanthomonas axonopodis pv. aurantifolii. Mol Plant Pathol. 2008, 9 (5): 609-631.View ArticlePubMedGoogle Scholar
- Li Z, Zou L, Ye G, Xiong L, Ji Z, Zakria M, Hong N, Wang G, Chen G: A Potential disease susceptibility gene CsLOB of citrus is targeted by a major virulence effector PthA of Xanthomonas citri subsp. citri. Mol Plant. 2014, 7 (5): 912-915.View ArticlePubMedGoogle Scholar
- Hu Y, Zhang J, Jia H, Sosso D, Li T, Frommer WB, Yang B, White FF, Wang N, Jones JB: Lateral organ boundaries 1 is a disease susceptibility gene for citrus bacterial canker disease. Proc Natl Acad Sci. 2014, 111 (4): E521-E529.PubMed CentralView ArticlePubMedGoogle Scholar
- Bao Z, Yang H, Hua J: Perturbation of cell cycle regulation triggers plant immune response via activation of disease resistance genes. Proc Natl Acad Sci. 2013, 110 (6): 2407-2412.PubMed CentralView ArticlePubMedGoogle Scholar
- Hamdoun S, Liu Z, Gill M, Yao N, Lu H: Dynamics of defense responses and cell fate change during Arabidopsis Pseudomonas syringae interactions. PLoS One. 2013, 8 (12): e83219-PubMed CentralView ArticlePubMedGoogle Scholar
- Gest N, Gautier H, Stevens R: Ascorbate as seen through plant evolution: the rise of a successful molecule?. J Exp Bot. 2013, 64 (1): 33-53.View ArticlePubMedGoogle Scholar
- Valpuesta V, Botella MA: Biosynthesis of L-ascorbic acid in plants: new pathways for an old antioxidant. Trends Plant Sci. 2004, 9 (12): 573-577.View ArticlePubMedGoogle Scholar
- Gilbert L, Alhagdow M, Nunes-Nesi A, Quemener B, Guillon F, Bouchet B, Faurobert M, Gouble B, Page D, Garcia V, Petit J, Stevens R, Causse M, Fernie AR, Lahaye M, Rothan C, Baldet P: GDP-D-mannose 3,5-epimerase (GME) plays a key role at the intersection of ascorbate and non-cellulosic cell-wall biosynthesis in tomato. Plant J. 2009, 60 (3): 499-508.View ArticlePubMedGoogle Scholar
- Voxeur A, Gilbert L, Rihouey C, Driouich A, Rothan C, Baldet P, Lerouge P: Silencing of the GDP- D -mannose 3,5-Epimerase affects the structure and cross-linking of the pectic polysaccharide rhamnogalacturonan II and plant growth in tomato. Journal of Biological Chemistry. 2011, 286 (10): 8014-8020.PubMed CentralView ArticlePubMedGoogle Scholar
- Smirnoff N: Ascorbate biosynthesis and function in photoprotection. Philos Trans R Soc Lond B Biol Sci. 2000, 355 (1402): 1455-1464.PubMed CentralView ArticlePubMedGoogle Scholar
- Lima-Silva V, Rosado A, Amorim-Silva V, Munoz-Merida A, Pons C, Bombarely A, Trelles O, Fernandez-Munoz R, Granell A, Valpuesta V, Botella MÁ: Genetic and genome-wide transcriptomic analyses identify co-regulation of oxidative response and hormone transcript abundance with vitamin C content in tomato fruit. BMC Genomics. 2012, 13 (1): 187-PubMed CentralView ArticlePubMedGoogle Scholar
- Cruz-Rus E, Amaya I, Sánchez-Sevilla JF, Botella MA, Valpuesta V: Regulation of L-ascorbic acid content in strawberry fruits. J Exp Bot. 2011, 62 (12): 4191-4201.PubMed CentralView ArticlePubMedGoogle Scholar
- Melino V, Soole K, Ford C: Ascorbate metabolism and the developmental demand for tartaric and oxalic acids in ripening grape berries. BMC Plant Biol. 2009, 9 (1): 145-PubMed CentralView ArticlePubMedGoogle Scholar
- Pollier J, Moses T, Gonzalez-Guzman M, De Geyter N, Lippens S, Bossche RV, Marhavy P, Kremer A, Morreel K, Guerin CJ, Tava A, Oleszek W, Thevelein JM, Campos N, Goormachtig S, Goossens A: The protein quality control system manages plant defence compound synthesis. Nature. 2013, 504 (7478): 148-152.View ArticlePubMedGoogle Scholar
- Oñate-Sánchez L, Singh KB: Identification of Arabidopsis ethylene-responsive element binding factors with distinct induction kinetics after pathogen infection. Plant Physiol. 2002, 128 (4): 1313-1322.PubMed CentralView ArticlePubMedGoogle Scholar
- S-j L, Park J, Lee M, Yu J-h, Kim S: Isolation and functional characterization of CE1 binding proteins. BMC Plant Biol. 2010, 10 (1): 277-View ArticleGoogle Scholar
- Voo SS, Grimes HD, Lange BM: Assessing the biosynthetic capabilities of secretory glands in citrus peel. Plant Physiol. 2012, 159 (1): 81-94.PubMed CentralView ArticlePubMedGoogle Scholar
- Albertini M-V, Carcouet E, Pailly O, Gambotti C, Luro F, Berti L: Changes in organic acids and sugars during early stages of development of acidic and acidless citrus fruit. J Agric Food Chem. 2006, 54 (21): 8335-8339.View ArticlePubMedGoogle Scholar
- Shimada T, Nakano R, Shulaev V, Sadka A, Blumwald E: Vacuolar citrate/H + symporter of citrus juice cells. Planta. 2006, 224 (2): 472-480.View ArticlePubMedGoogle Scholar
- Cercós M, Soler G, Iglesias D, Gadea J, Forment J, Talón M: Global analysis of gene expression during development and ripening of citrus fruit flesh. A proposed mechanism for citric acid utilization. Plant Mol Biol. 2006, 62 (4–5): 513-527.View ArticlePubMedGoogle Scholar
- Bouché N, Fromm H: GABA in plants: just a metabolite?. Trends Plant Sci. 2004, 9 (3): 110-115.View ArticlePubMedGoogle Scholar
- Sun X, Zhu A, Liu S, Sheng L, Ma Q, Zhang L, Nishawy EME, Zeng Y, Xu J, Ma Z, Cheng Y, Deng X: Integration of metabolomics and subcellular organelle expression microarray to increase understanding the organic acid changes in post-harvest citrus fruit. J Integr Plant Biol. 2013, 55 (11): 1038-1053.View ArticlePubMedGoogle Scholar
- Martinelli F, Uratsu SL, Albrecht U, Reagan RL, Phu ML, Britton M, Buffalo V, Fass J, Leicht E, Zhao W, Lin D, D'Souza R, Davis CE, Bowman KD, Dandekar AM: Transcriptome profiling of citrus fruit response to Huanglongbing disease. PLoS One. 2012, 7 (5): e38039-PubMed CentralView ArticlePubMedGoogle Scholar
- Wu J, Xu Z, Zhang Y, Chai L, Yi H, Deng X: An integrative analysis of the transcriptome and proteome of the pulp of a spontaneous late-ripening sweet orange mutant and its wild type improves our understanding of fruit ripening in citrus. J Exp Bot. 2014, 65 (6): 1651-1671.PubMed CentralView ArticlePubMedGoogle Scholar
- Wang Z, Gerstein M, Snyder M: RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009, 10 (1): 57-63.PubMed CentralView ArticlePubMedGoogle Scholar
- Lopes CT, Franz M, Kazi F, Donaldson SL, Morris Q, Bader GD: Cytoscape Web: an interactive web-based network browser. Bioinformatics. 2010, 26 (18): 2347-2348.PubMed CentralView ArticlePubMedGoogle Scholar
- Maere S, Heymans K, Kuiper M: BiNGO: a Cytoscape plugin to assess overrepresentation of gene ontology categories in biological networks. Bioinformatics. 2005, 21 (16): 3448-3449.View ArticlePubMedGoogle Scholar
- Wang J, Li M, Deng Y, Pan Y: Recent advances in clustering methods for protein interaction networks. BMC Genomics. 2010, 11 (Suppl 3): S10-View ArticleGoogle Scholar
- Ogata Y, Suzuki H, Sakurai N, Shibata D: CoP: a database for characterizing co-expressed gene modules with biological information in plants. Bioinformatics. 2010, 26 (9): 1267-1268.View ArticlePubMedGoogle Scholar
- Dash S, Van Hemert J, Hong L, Wise RP, Dickerson JA: PLEXdb: gene expression resources for plants and plant pathogens. Nucleic Acids Res. 2012, 40 (D1): D1194-D1201.PubMed CentralView ArticlePubMedGoogle Scholar
- Conesa A, Götz S, García-Gómez JM, Terol J, Talón M, Robles M: Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics. 2005, 21 (18): 3674-3676.View ArticlePubMedGoogle Scholar
- Smoot ME, Ono K, Ruscheinski J, Wang P-L, Ideker T: Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics. 2011, 27 (3): 431-432.PubMed CentralView ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.