VTCdb: a gene co-expression database for the crop species Vitis vinifera (grapevine)
© Wong et al.; licensee BioMed Central Ltd. 2013
Received: 25 March 2013
Accepted: 29 November 2013
Published: 16 December 2013
Gene expression datasets in model plants such as Arabidopsis have contributed to our understanding of gene function and how a single underlying biological process can be governed by a diverse network of genes. The accumulation of publicly available microarray data encompassing a wide range of biological and environmental conditions has enabled the development of additional capabilities including gene co-expression analysis (GCA). GCA is based on the understanding that genes encoding proteins involved in similar and/or related biological processes may exhibit comparable expression patterns over a range of experimental conditions, developmental stages and tissues. We present an open access database for the investigation of gene co-expression networks within the cultivated grapevine, Vitis vinifera.
The new gene co-expression database, VTCdb (http://vtcdb.adelaide.edu.au/Home.aspx), offers an online platform for transcriptional regulatory inference in the cultivated grapevine. Using condition-independent and condition-dependent approaches, grapevine co-expression networks were constructed using the latest publicly available microarray datasets from diverse experimental series, utilising the Affymetrix Vitis vinifera GeneChip (16 K) and the NimbleGen Grape Whole-genome microarray chip (29 K), thus making it possible to profile approximately 29,000 genes (95% of the predicted grapevine transcriptome). Applications available with the online platform include the use of gene names, probesets, modules or biological processes to query the co-expression networks, with the option to choose between Affymetrix or Nimblegen datasets and between multiple co-expression measures. Alternatively, the user can browse existing network modules using interactive network visualisation and analysis via CytoscapeWeb. To demonstrate the utility of the database, we present examples from three fundamental biological processes (berry development, photosynthesis and flavonoid biosynthesis) whereby the recovered sub-networks reconfirm established plant gene functions and also identify novel associations.
Together, we present valuable insights into grapevine transcriptional regulation by developing network models applicable to researchers in their prioritisation of gene candidates, for on-going study of biological processes related to grapevine development, metabolism and stress responses.
The cultivated grapevine Vitis vinifera is one of the most highly valued horticultural crops in the world, and amongst the earliest domesticated fruit crops in human history. The global production of grapes in 2011 was 70 million tonnes, harvested over approximately 7 million hectares of land, making the grapevine the most widely cultivated fruit species . Quality attributes of grapes, including aroma, flavour, colour and texture characteristics, have a profound impact on the fruit and wine and therefore on the value of the crop itself. An in-depth understanding of gene expression and the regulation of metabolic pathways controlling various aspects of grapevine development and berry metabolism could provide insights into the genetic factors influencing fruit quality and ultimately inform future vineyard germplasm and cultural practices.
Functional genomics studies in plants have contributed to a systems-level understanding of how genes function and how an underlying biological process is governed by the cooperation of a set of genes. Genome sequencing of two grapevine cultivars [2, 3] and successive improvements on genome assembly and prediction [4–6] have been invaluable for gene discovery, while application of high throughput technologies such as microarrays has enabled large-scale transcriptional analysis in grapevine. During the pre-genomic period, sequences selected from Genbank, expressed sequence tags and the NCBI RefSeq grapevine transcripts were the main sources for the design and annotation of the grapevine 16 K Affymetrix Genechip (http://www.affymetrix.com), with approximately one third of the transcriptome represented on the array based on the 12X v1 gene annotation . The grapevine 29 K Nimblegen whole-genome array, (http://www.nimblegen.com), which represents approximately 29,000 genes (>95% of the predicted transcriptome) is the most well-developed of the grapevine microarray platforms. To date, microarray studies feature numerous experiments, including different stages of berry development for various cultivars [7, 8], a range of grapevine tissues [9, 10] and the application of various biotic and abiotic stressors [11, 12]. A survey from gene expression data repositories including the Gene Expression Omnibus  and Arrayexpress  revealed that a large number of expression datasets have been generated from plants, especially Arabidopsis thaliana, Glycine max (soybean) and Oryza sativa (rice), and these also involve diverse experimental conditions. Although these gene expression datasets have been primarily generated within a particular experimental context, the accumulation of large numbers of expression profiles has offered additional capabilities. These include comparative genomics between plant species, screening and functional assignment of gene candidates, the discovery of novel DNA motifs, and the dissection of regulatory networks. One technique that has proved invaluable in this role is that of gene co-expression analysis (GCA).
GCA is based on the notion that genes involved in similar or related processes may exhibit similar expression patterns over a range of experimental conditions [15, 16]. This “guilt by association” principle has been initially applied to gain insights into co-expressed gene modules within an organism [17, 18], to assign novel gene functions previously not ascribed to any biological processes, and to understand the evolution of gene expression and diversity across species and kingdoms [19, 20]. A ‘condition-independent’ GCA derived from a large dataset compiled from various experimental conditions has been adopted in many studies for convenience while providing a global overview of gene-to-gene relationships [21–23]. However, drawbacks to the condition-independent approach include the complexity of drawing biological insights and the potential loss of co-expression relationships due to variation between the numerous experimental conditions. Alternatively, the ‘condition-dependent’ approach, which draws upon GCA derived from smaller and predefined datasets (conditions), provides an additional opportunity to test specific hypotheses or to gain biological insights in an underlying condition [24, 25]. However, when too few sample datasets are chosen, noise inherent in the microarray data will also affect the results obtained from ‘condition-dependent’ GCA. Nevertheless, both ‘condition-independent’ and ‘condition-dependent’ approaches have proven useful in many co-expression studies in plants .
Within a co-expression network, genes and similarity relationships (commonly represented by correlation coefficients) can be visualised as “nodes” and “edges” respectively. The connection of two nodes by an edge indicates a similar expression profile of the nodes according to a particular similarity metric. For a given set of genes, the collection of these nodes and edges forms a network. Visualisation of the co-expression network enables the identification and description of densely connected gene clusters, referred to as modules, and an assessment of biological relevance can be achieved by investigating the functions of genes within each module [15, 26]. Many graph clustering algorithms have been developed with the aim of extracting functional modules comprising densely connected groups of nodes (representing co-expressed genes). Such algorithms can be classified as density-based and local search algorithms, hierarchical clustering, and other optimization-based algorithms . In addition to the model plant Arabidopsis, these algorithms have also been applied to study co-expression networks in important crop species such as rice, barley and soybean [21, 28], with databases developed to store inferred modules and provide a user-friendly resource for plant biologists. Examples of outcomes reported using the “guilt by association” principle include the identification of genes involved in cellulose biosynthesis  and transcription factors (TFs) involved in glucosinolate regulation in Arabidopsis.
In the present study, over 800 publicly available microarray datasets related to the V. vinifera L. transcriptome were selected to construct global co-expression networks (GCNs), consisting of 463 datasets from the Nimblegen whole-genome arrays and 403 datasets from the 16 K Affymetrix Genechip arrays. A combination of correlation rank transformation and graph-clustering approaches was used. With particular emphasis on the GCN constructed using the Nimblegen whole-genome array, we demonstrate the utility of this V. vinifera GCN using selected examples where we confirm well-characterised biochemical pathways, and infer potential novel gene functions and processes. A dedicated grapevine gene co-expression database, named VTCdb (http://vtcdb.adelaide.edu.au/Home.aspx), equipped with functional enrichment and visualisation capabilities, has been made available to the public to query and browse the associated GCN.
Construction and content
Data acquisition and processing
Publicly available grapevine 29 K NimbleGen whole-genome (http://www.nimblegen.com) and 16 K Affymetrix Genechip (http://www.affymetrix.com) microarray datasets were retrieved from Gene Expression Omnibus , Arrayexpress  and PLEXdb . Summaries of the experiments and associated metadata are given in Additional files 1 and 2, detailing the 481 (Nimblegen) and 451 (Affymetrix) arrays (containing approximately 29,000 and 16,000 probesets respectively) that were used for subsequent analysis. Raw intensity data from both platforms were separately background-adjusted, quantile-normalised and summarised using the RMA method in R (http://www.r-project.org) using the ‘oligo’ package . Potential outlier arrays were removed by visual inspection of raw perfect match data and iteratively discarding arrays that failed the quality control test (where expression values deviated significantly from the relative log expression and the normalised unscaled standard error) leaving 463 (Nimblegen) and 403 (Affymetrix) arrays for subsequent analysis (see Additional file 2). A survey of the underlying experimental conditions represented by the arrays can be assembled into a general category (‘All’ datasets) covering a broad range of treatments and plant development stages such as tissue development, stress and vineyard management strategies. Additionally, two condition-specific datasets were established for the 29 K Nimblegen datasets, one for berry-related tissues and treatments only, and one for stress-related processes (biotic and abiotic) across the whole vine (Additional file 2). The number of arrays corresponding to arrays for ‘All’ , ‘Berry’ and ‘Stress’ datasets are 463, 305 and 59, respectively. Together, this provided a broad basis for inferring both condition- independent and dependent gene co-expression relationships in grapevine. Separate GCNs were generated for all, berry- and stress-related datasets by applying the procedure below.
Gene co-expression network construction
Correlations between all mapped probesets were calculated using Pearson’s correlation coefficient (PCC) and Spearman’s correlation coefficient (SCC) as measures of similarity between expression profiles. Additionally, the mutual co-expression relationships between all gene pairs were calculated (without applying any cut-offs) by first transforming raw correlation values (PCC and SCC) into highest reciprocal ranks (HRR)  and mutual ranks (MR) . Rank-based networks are robust and offer advantages over correlation-based networks [34, 35]. Such approaches have been frequently applied to retain weak but significant co-expression relationships and circumvent the unequal distribution of gene correlations for some genes when applying a fixed similarity threshold. This index of co-expression (HRR and MR) serves as a basis for ranking co-expressed genes when using a ‘guide gene’ approach to query the network. In this study, we focussed our attention on the mutual co-expression relationships derived from PCC values for simplicity, and because the Gene ontology (GO) prediction performance of transformed ranks from PCC and SCC values were similar . An estimation of the statistical significance of mutual co-expression ranks  showed that HRR and MR values ≤ 350 and 200 respectively were significant (P < 0.01), and therefore these were applied as a generalised threshold for obtaining biological relevant relationships in grapevine.
Graph clustering and meta-network construction
To identify modules of densely connected nodes, the Heuristic Cluster Chiselling Algorithm (HCCA)  and Markov clustering (MCL)  techniques were applied. With an input network of HRR ≤ 30, we first assigned weights of 0.2, 0.067, and 0.04 to HRR scores of 10, 20 and 30, respectively. Parameters were adjusted to a desired step size of 3 and cluster size between 40 and 400 for HCCA and an inflation value of 1.2 for MCL using Python 2.7.3 (http://www.python.org) and MCL version 12–068 (http://micans.org/mcl/), respectively. To depict the relationships between modules generated, we first calculated the total edge weights shared between any two connected modules and assigned an empirically derived statistical significance (P-value) by permutation test according to . The various grapevine meta-networks were constructed with edges connecting modules at a significance of P < 0.01.
Functional enrichment and expression specificity analysis
To assist with the categorisation of co-expressed genes and partitioned modules according to their potential function or processes, we assessed the modules for enrichment primarily for GO terms in R (http://www.r-project.org) using the ‘gProfileR’ package  to interface g:profiler (http://gprofiler.at.mt.ut.ee/gprofiler/). Enrichment for GO terms was validated using the hypergeometric distribution adjusted by set count sizes (SCS) for multiple hypothesis correction. SCS threshold considers the hierarchal structure of GO in an underlying organism and prioritises truly significant results (while removing enriched false positive GO terms) . GO terms were considered significant if the adjusted P-values (SCS) < 0.05 and there were at least two genes associated with the same annotation. Network representation of GO terms was prepared using GO-module webserver . Expression specificities of individual probesets and modules were determined using the Std2Gx procedure . Expression specificity index values > 1 and > 5 indicates the gene is well and specifically expressed in the corresponding experimental condition respectively, as compared with other genes and array samples. Expression specificity of modules is expressed as the percentage of module members specifically expressed in a particular tissue/condition (and across all arrays) with an expression specificity index above 1.
Gene annotations and network visualization
The latest grapevine gene and probeset annotations based the 12X v1 prediction were obtained from Vitisnet [4, 39] and mappings for probesets containing functional annotation and categorization of predicted genes (chromosome location, predicted function, subcellular localization, orthology and pathway level information) were used. CytoscapeWeb  was used to visualise nodes and edges and their attributes.
VTCdb web interface and content
Under the single guide gene query, when a gene identifier (i.e. VIT_ code) is used as a query using the ‘CoexQuery’ field, the user can select the various predefined conditions (‘All’ , ‘Berry’ and ‘Stress’) followed by the preferred co-expression measure (‘HRR’ , ‘MR’ and ‘CORR’) (Figure 1B). Users will be re-directed to the co-expressed genes result page, ‘CoexQuery result’ for the chosen gene, with the chosen dataset and co-expression measure.
To demonstrate the applicability and robustness of the VTCdb web server for co-expression studies, we present some examples for the use of VTCdb query tools in the analysis of well-characterised biological processes and highlight gene co-expression networks that may be of biological interest in future grapevine research.
Example application I: grapevine berry development
Grapevine fruit development can be broken into 3 phases by chronological sequence: berry formation, veraison and berry ripening, reviewed in . Each of these involves specific changes in gene expression, biochemical, compositional and physiological properties of the berry. For example, processes involving cell wall reorganization are crucial during periods of rapid cell division and elongation (during berry formation) and softening (during berry ripening). Accumulating evidence suggests that the involvement of various activities of grapevine expansins (among others) are crucial in regulating cell wall expansion and enlargement during berry development [42–44]. To provide additional insights into the transcriptional regulation of cell wall metabolism during grapevine development, co-expression analysis using a grapevine bHLH TF, grapevine CEB1, which is known to regulate grape berry development  was performed. In this example, the respective unique code for grapevine CEB1 (VIT_01s0244g00010) was input into the ‘CoexQuery’ field and selected ‘all’ and ‘HRR’ for the preferred predefined datasets and co-expression measure options, respectively (Figure 1B) or by using the keyword query ‘CEB1’ (Figure 1C) and choosing ‘all’ under the ‘CoexQuery’ column in the ‘keyword query’ results page. A total of 266 genes were indicated to be co-expressed with grapevine CEB1, with average HRR and PCC values of 167 and 0.73 respectively. Among others, genes encoding enzymes involved in auxin signalling (SAUR9, VIT_04s0023g03230; ARF2_2, VIT_01s0244g00150; TPR1, VIT_04s0008g06400), cell wall metabolism (EXPA11, VIT_18s0001g01130) and various classes of TF (bHLH, ERF, MYB) were highly co-expressed with grapevine VvCEB1 (Additional file 3: Table S1). In agreement with the co-expression results, experimental evidence has shown that overexpression of VvCEB1 in grapevine embryos is able to stimulate cell expansion via control of Auxin/IAA TFs, SAUR and cell wall modification genes . Interestingly, among the transcripts tested, EXPA11 (VIT_18s0001g01130, XM_002285855.1 in their study) was the most up-regulated (> 1000 fold) in VvCEB1-overexpressing grape embryos compared to control . In the co-expressed genes results, EXPA11 (VIT_18s0001g01130) was highly co-expressed (top 6) with grapevine VvCEB1 in ‘all’ conditions while the HRR (top 1) were improved in berry-related datasets (Figure 3C). As an alternative, selecting other predefined conditions to understand the molecular mechanisms of query genes in a specific context could also be carried out. Nevertheless, using ‘all’ datasets is sufficient for most applications. Below the list of co-expressed genes, GO enrichment analysis of the whole co-expressed gene lists (HRR <350, P < 0.01) revealed that terms such as GO:0006355, regulation of transcription DNA-dependent (5.06e-05); GO:0009699, phenylpropanoid biosynthetic process (3.86e-06); GO:0009813, flavonoid biosynthetic process (8.57e-04) and GO:0009745, sucrose mediated signalling (3.95e-04) were highly enriched (Figure 3D; Additional file 3: Table S2). This data suggests the potential involvement of additional TFs, phenylpropanoid (shikimate) pathway genes and sucrose metabolism in regulating the cell expansion during berry development. Additionally, the highly interactive expression specificity chart showed that the grapevine VvCEB1 gene was expressed specifically in berry-related tissues with highest specificity during veraison onwards (Figure 3E; Additional file 3: Table S3). Taken together, the co-expression results largely confirm results from previous studies and strengthen the putative role of VvCEB1 in controlling berry growth while providing additional clues into the complex molecular mechanisms of VvCEB1 [i.e. potential targets (direct/indirect), expression specificity and enriched pathways].
Example application II: photosynthesis and phenylpropanoid metabolism
The regulation of genes associated with photosynthesis and flavonoid metabolism displayed conserved co-expression network structures at the gene and module level across nine different plant species [21, 28]. Both MCL and HCCA were able to partition the grapevine co-expression network efficiently and into biologically relevant modules in which genes involved in shared biological processes were successfully recovered. Thus, the co-expression analysis (both condition-independent and -dependent) performed here largely confirms previous work while revealing new putative roles for uncharacterised grapevine genes, and demonstrates the utility of the grapevine co-expression network generated in this study.
Comparison to similar co-expression studies and future developments
Currently, two other broad plant co-expression databases include grapevine microarray data [23, 28]. Compared with these, VTCdb has a species-specific focus on grapevine and offers additional advantages including (1) greater transcriptome coverage for GCA, encompassing over 29,000 genes (>95% of the predicted genome, according to the12X v1 grape gene annotation), (2) flexibility to perform GCA based on either the 29 K Nimblegen array or the extensively utilised 16 K Affymetrix Genechip, (3) the option to choose between ‘condition-independent’ and ‘condition-dependent’ GCA, (4) the option to explore grapevine functional modules inferred from various graph clustering approaches and (5) the provision of web-based tools to enhance the functional interpretation from GCA (i.e. functional enrichment analysis, expression patterns across a wide range of experimental conditions/treatments and network visualisation). We note that despite having thorough transcriptome coverage, this study can only provide a glimpse into ‘condition-specific’ GCA in grapevine. Arrays of experimental conditions encompassing berry tissues and berry developmental series as well as limited stress conditions and management treatments were sufficiently represented in the public domain. A comprehensive catalogue for datasets encompassing additional stress, hormone and tissue datasets is still needed to fine-tune and facilitate the discovery of novel co-expression relationships based on condition-specific circumstances. To this end, biannual updates of the database will be conducted when new microarray experiments are published or sufficient arrays from other platforms becomes available for GCA. Nevertheless, users of VTCdb are able to perform GCA using datasets from the 16 K Affymetrix Genechip, which encompass a greater variety of experimental conditions (e.g. drought, salinity, heat and pathogen attack) than the Nimblegen array, albeit at the cost of transcriptome coverage. We have demonstrated that the co-expression relationships obtained using grapevine berry development, photosynthesis and flavonoid pathway-related genes were robust and could be used to identify novel transcriptional regulatory mechanisms, supported by combined network and functional analysis in plants [49, 51, 54]. These are examples of how VTCdb can be utilised to infer gene function. The predicted modules using graph clustering were of high biological relevance and may offer new biological insights into many uncharacterised genes within these modules. Due to the large proportion of uncharacterised genes within the grapevine genome, functional annotation on the basis of gene co-expression analysis and expression patterns will provide an additional tool toward gene discovery. Therefore, VTCdb offers a one-stop online platform for GCA for the grapevine research community.
Gene co-expression analysis of the grapevine transcriptome and the creation of an online tool to interrogate this data, provide a vital step towards uncovering additional relationships using publicly available grapevine microarray data. This meta-analysis approach has facilitated the comprehensive annotation of functions to unknown genes and the discovery of functional modules in grapevines. With the rising trend of transcriptional analyses using RNA-sequencing in grapevine [55–57] and on-going improvement of the methods required to process these data for GCA [58, 59], the prospect for GCA using grapevine RNA-sequencing data will become feasible in the future. Nevertheless, for the purposes of reverse-engineering gene co-expression networks, microarrays are currently better suited in this goal . We envisage the utility and potential of VTCdb (http://vtcdb.adelaide.edu.au/home.aspx) to provide further valuable information in hypothesis-driven studies and to aid grapevine researchers in their prioritisation of gene candidates for further study towards the understanding of biological processes related to many aspects of grapevine development and metabolism.
Availability and requirements
All results discussed within this study and additional tools to query pre-constructed networks, perform additional gene co-expression, expression meta-analysis and annotation searches are available freely at VTCdb (http://vtcdb.adelaide.edu.au/home.aspx). VTCdb supports all major web-browsers, preferably Google Chrome or Mozilla Firefox for visualization and performance purposes.
Vitis Transcriptomics and co-expression database
Gene co-expression analysis
Gene co-expression network
Pearson’s correlation coefficient
Spearman’s correlation coefficient
Highest reciprocal rank
Set count sizes
Heuristic cluster chiselling algorithm
We gratefully acknowledge the grapevine research community for the provision of various microarray data in the public domain and the anonymous referees for providing us with constructive comments and suggestions. This work was part-supported by Australia's grape growers and winemakers through their investment body, the Grape and Wine Research and Development Corporation, with matching funds from the Australian Government (project UA 10/01). DCJW is supported by a postgraduate research scholarship from the University of Adelaide. DPD received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement 275422, which supported a Marie Curie International Outgoing Fellowship.
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