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. 2009 Dec 21;4(12):e8365. doi: 10.1371/journal.pone.0008365

VitisNet: “Omics” Integration through Grapevine Molecular Networks

Jérôme Grimplet 1, Grant R Cramer 2, Julie A Dickerson 3, Kathy Mathiason 1, John Van Hemert 3, Anne Y Fennell 1,*
Editor: Nicholas James Provart4
PMCID: PMC2791446  PMID: 20027228

Abstract

Background

Genomic data release for the grapevine has increased exponentially in the last five years. The Vitis vinifera genome has been sequenced and Vitis EST, transcriptomic, proteomic, and metabolomic tools and data sets continue to be developed. The next critical challenge is to provide biological meaning to this tremendous amount of data by annotating genes and integrating them within their biological context. We have developed and validated a system of Grapevine Molecular Networks (VitisNet).

Methodology/Principal Findings

The sequences from the Vitis vinifera (cv. Pinot Noir PN40024) genome sequencing project and ESTs from the Vitis genus have been paired and the 39,424 resulting unique sequences have been manually annotated. Among these, 13,145 genes have been assigned to 219 networks. The pathway sets include 88 “Metabolic”, 15 “Genetic Information Processing”, 12 “Environmental Information Processing”, 3 “Cellular Processes”, 21 “Transport”, and 80 “Transcription Factors”. The quantitative data is loaded onto molecular networks, allowing the simultaneous visualization of changes in the transcriptome, proteome, and metabolome for a given experiment.

Conclusions/Significance

VitisNet uses manually annotated networks in SBML or XML format, enabling the integration of large datasets, streamlining biological functional processing, and improving the understanding of dynamic processes in systems biology experiments. VitisNet is grounded in the Vitis vinifera genome (currently at 8x coverage) and can be readily updated with subsequent updates of the genome or biochemical discoveries. The molecular network files can be dynamically searched by pathway name or individual genes, proteins, or metabolites through the MetNet Pathway database and web-portal at http://metnet3.vrac.iastate.edu/. All VitisNet files including the manual annotation of the grape genome encompassing pathway names, individual genes, their genome identifier, and chromosome location can be accessed and downloaded from the VitisNet tab at http://vitis-dormancy.sdstate.org.

Introduction

During the pre-genomics era, gene function was established through a reductionist approach [1] where organism physiology was understood by breaking components into pieces, studying them, and then putting them back together to see the larger picture. With the emergence of genome sequencing, organisms are now seen as complex interactive systems. Systems biology, adapted from the general system theory [2] and the living system theory [3], intends to explain biological phenomena utilizing a systemic view of the objects' relationships rather than their simple composition [4]. Integrative functional genomics combines the molecular components (transcripts, proteins, and metabolites) of an organism and incorporates them into functional networks or models designed to describe the dynamic activities of that organism. While many of the functions of individual parts are unknown or not well defined, their biological role can sometimes be inferred through association with other known parts, providing a better understanding of the biological system as a whole. On a system-wide scale the description requires three levels of information [5], [6]: (1) identification of the components (structural annotation) and characterization of their identity (functional annotation); (2) identification of molecules that interact with each component, which leads to the reconstruction of a biochemical reaction network; and (3) characterization of the behaviors of the transcripts, proteins, and metabolites under various conditions. Integration of the three levels of information into a coherent framework (or canvas) provides a powerful approach to tackle the difficult problem of extracting systems-wide behavior from the component interactions.

The most developed examples of application of this approach can be found in prokaryotes, because of their small genomes [7], [8]. For example, in E. coli, 92% of the gene product functions have been experimentally verified. Genome-scale models (GEMs) have been used for metabolic engineering to systematically manipulate E. coli strains to overproduce lycopene, lactic acid, ethanol, succinate, amino acids, and many other products including hydrogen and vanillin. New biological discoveries of open reading frames (ORF) can be made by focusing on the gaps in the unknown portions of the Omic maps, using the genomic responses of different genotypes under different conditions to determine the probable gene candidates that fill knowledge gaps. GEMs have been widely used to characterize and understand physiological responses to environmental conditions such as abiotic and biotic stresses. This has been particularly useful in the identification of resistance mechanisms that can be established in new strains.

Such global analyses have become possible with the development of high throughput genomics technologies in both the field of nucleic acid sequencing and quantitative data acquisition. Over the last 20 years, expressed tag sequencing (EST) [9] has been widely utilized for gene discovery and genome characterization. EST data are stored in comprehensive databases such as UniGene [10] or the DFCI Gene Indices [11]. Recently, cheaper and faster Next-Gen sequencing technologies have emerged such as 454 [12] or Illumina [13]. Recently, cheaper and faster Next-Gen sequencing technologies have emerged such as 454 [12] or Illumina [13]. In parallel, methods have been developed for quantitative data acquisition: microarrays are used to quantitatively assess the transcriptome [14]. Two dimensional-gels have routinely been used for proteome studies [15]. Recently, however, gel-free technologies have emerged such as ICAT [16] or iTRAQ [17]. Metabolome studies are performed with a variety of tools such as gas chromatography or high performance liquid chromatography for separation and mass spectrometry and nuclear magnetic resonance for the identification and quantification of the metabolites [18].

Genomics resources for Vitis vinifera and related species have proliferated rapidly within the last several years, including EST sequencing [19], [20], [21] to whole genome sequencing [22], [23] and integrated genetic maps [24]. These resources have permitted large-scale mRNA expression profiling studies of gene expression profiles during berry development using cDNA or oligonucleotide microarrays [25], [26]. A high-density, Affymetrix GeneChip® Vitis vinifera (Grape) Genome Array containing approximately one-third of the expected gene content of the V. vinifera genome with some bias towards leaf and berry tissues was developed, leading to numerous publications [27], [28], [29], [30], [31], [32], [33]. Under the encouragement of the international grape community, the microarray data for several of these experiments has been centralized and can be accessed at PLEXdb (http://www.plexdb.org) [34]. Six additional microarray datasets using cDNA, oligo, or Affymetrix arrays are available through Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/sites/entrez?db=geo) and citations for publications are also linked to these public data sets [33], [35], [36]. Proteomics resources have also emerged recently. Most of these studies use 2-D gel analysis and focus either on berry metabolism [37], [38] or abiotic stress resistance [39], [40], [41] or both [42]. Recently high resolution techniques, such as iTRAQ, have also been applied to grape [43]. Metabolomics studies for grape are still rudimentary; however, several works have presented simultaneous analysis of about 50 to 120 compounds [28], [30], [42], [44]. To date, only two studies present the transcriptomic, proteomic, and metabolomic analyses on the same material, one in berry tissues [29], [42] and the other on abiotic stress in shoots [40], [28].

Information from structural and functional genomics must be combined with detailed biochemical reaction networks to further our understanding of biological function and incorporate the knowledge into cultural practice. While a considerable amount of effort has been put into resolving the structural information (level 1) and “Omics” characterization of individual groups of transcripts, proteins or metabolites (level 3), relatively few biochemical reaction networks (level 2) have been constructed in grapevines or other plant systems. While pathway databases exist at the KEGG (http://www.genome.jp/kegg/pathway.html) or AraCyc [45], they are limited to metabolic pathways. In contrast, MetNet (http://metnet3.vrac.iastate.edu/) stores both metabolic and regulatory interactions for Arabidopsis and soybean [46].

In order to contextualize the molecular structure and a metric representing their behavior, we have developed a model of the molecular networks present in grapevines (VitisNet). This resource allows visualization of the dynamic interactions in the transcriptome, proteome, and metabolome within known molecular networks (for example, metabolic or signaling pathways). Integrating transcripts with protein and metabolite profiles in a comprehensive molecular map enables the researcher to elucidate different biochemical responses of grapevines to developmental and environmental cues.

Results and Discussion

A Set of 39,424 Unique Sequences Defined

The set of unique genes was not restricted to the Pinot Noir genome sequences, as an extensive amount of data have been produced on other V. vinifera cultivars and other Vitis species. The V. vinifera EST database contains only a very small fraction of Pinot Noir sequences (1.8% or 6,385/353,688), whereas Cabernet Sauvignon (half of the EST sequences), Chardonnay, Thompson Seedless, Muscat de Hambourg, and Perlette each have at least two times the number of Pinot Noir sequences. In addition, a significant amount of ESTs have been produced for other Vitis species. It is expected that a significant amount of transcript sequences are cultivar and species specific and may not be represented within the Pinot Noir PN40024 genome. A set of 39,424 unique sequences were defined after the matching of the genomic sequences and the transcripts (Figure 1). Only 36.4% of these sequences (14,330) were found in both the genomic sequences and the transcripts. In the set of unique sequences, the genomic sequences were conserved over transcript sequences because they should be the full length gene, whereas there is less certainty for the transcript. In some cases, several supposedly unique transcript sequences matched a single gene, mainly because they matched different regions of the gene. A total of 652 unique sequences corresponded to previously published grapevine sequences (Table S1).

Figure 1. Overview of the unique set assembly and results of the annotation procedure.

Figure 1

Box sizes are relative to respective number of genes inside.

The set that was found only in the genomic sequences included 40.8% (16,104) of the unique sequences. This means that so far there is no proof that these sequences are actually transcribed. Finally 22.8% (8990) of the unique sequences were found only as transcripts. This set could include cultivar or species specific genes absent in the Pinot Noir genome or genes not yet extracted from the genome. However as 73% (6553) of these unique sequences were not homologous to sequences from other organisms, it is likely that most of them corresponded to short sequences or contained mostly UTR regions so that a BLAST analysis could not be conducted against the genome sequences encoding for their putative proteins. These sequences were of interest because many of them were placed on the highly popular Affymetrix GeneChip® Vitis vinifera (Grape) Genome Array. There were 3208 sequences amongst the 11,734 non-redundant sequences in the Affymetrix chip that did not present a match in the genome.

Half of the Matched Sequences Were Assigned to Molecular Networks

Seventy percent (27,680) of the unique genes matched a previously described Vitis cDNA or protein or a sequence from another organism. The remaining 11,744 sequences were Vitis-specific and a function could not be assigned. This number rose to 83% when only genes from the genome sequences were used. This gene set was divided into two groups, a group that could not be assigned to molecular networks and a group that could be assigned. The group that was not assigned to molecular networks consisted of 14,535 genes (52.5%) that covered a wide range of functional descriptions. At one extreme, the sequences (1,817) presented a completely unknown function. At the other extreme, an identifier was attributed to unmapped sequences (1,578). An identifier was assigned because an EC or KO number could be attributed to these sequences or an Arabidopsis homolog had an identifier; however, they couldn't be placed on the networks. In between the unknown and EC/KO identity, the description of the function ranged from sequences containing a poorly described domain, a general enzymatic activity, or to a well-documented gene.

The second subset of the matched genes (13,145 sequences, 47.5%), which were homologous to proteins with a known function, was assigned to the molecular networks. The 13,145 genes present in the networks were classified into 6 main overlapping categories (Table 1- 6): Metabolism (5442 sequences), Genetic Information Processing (1249 sequences), Environmental Information Processing (1305 sequences), Cellular Processes (1121 sequences), Transport (3523 sequences), and Transcription Factors (2423 sequences). The complete annotation of the genes and relevant information for each is presented in Table S1. The references used for annotating genes and for developing pathways not found in KEGG are presented in Text S1.

Table 1. List of Metabolic Pathways.

VVID Network Name #gen #pro #met VVID Network Name #gen #pro #met
1.1 Carbohydrate Metabolism
10010 Glycolysis / Gluconeogenesis 192 28 28 10530 Aminosugars metabolism 90 9 11
10020 Citrate cycle (TCA cycle) 74 17 21 10520 Nucleotide sugars met. 60 16 18
10030 Pentose phosphate pathway 83 17 21 10620 Pyruvate metabolism 197 27 19
10040 Pentose/glucuron. interconv. 57 11 14 10630 Glyoxyl., dicarboxyl. met. 90 18 19
10051 Fructose and mannose met. 108 21 21 10640 Propanoate metabolism 73 9 12
10052 Galactose metabolism 155 17 27 10650 Butanoate metabolism 85 18 22
10053 Ascorbate and aldarate met. 40 9 9 10562 Inositol phosphate met. 131 18 20
10500 Starch and sucrose met. 337 43 34
1.2 Energy Metabolism
10190 Oxidative phosphorylation 343 101 7 10720 Red. Carb. cyc. (CO2 fix.) 41 10 14
10195 Photosynthesis 173 52 10680 Methane metabolism 130 9 11
10196 Photosynthesis - antenna prot. 27 11 10910 Nitrogen metabolism 112 22 19
10710 Carbon fixation 140 21 20 10920 Sulfur metabolism 46 12 12
1.3 Lipid Metabolism
10061 Fatty acid biosynthesis 76 13 36 10561 Glycerolipid met. 146 19 18
10062 Fatty acid elongation in mitoc. 25 7 29 10564 Glycerophospholipid met. 140 29 32
10071 Fatty acid metabolism 94 17 40 10565 Ether lipid metabolism 57 8 9
10072 Synth. / degr. of ketone bodies 18 3 4 10600 Sphingolipid metabolism 67 13 15
10100 Biosynthesis of steroids 142 47 74 10592 alpha-Linolenic acid met. 104 14 29
10140 C21-Steroid hormone met. 20 6 14 11040 Biosynth. unsat. fatty ac. 42 14 27
1.4 Nucleotide Metabolism
10230 Purine metabolism 151 48 62 10240 Pyrimidine metabolism 109 35 46
1.5 Amino Acid Metabolism
10251 Glutamate metabolism 93 28 25 10330 Arginine and proline met. 54 17 23
10252 Alanine and aspartate met. 109 23 24 10340 Histidine metabolism 70 16 19
10260 Gly, ser and thr met. 110 30 38 10350 Tyrosine metabolism 149 25 39
10271 Methionine metabolism 124 33 48 10360 Phenylalanine metabolism 212 15 14
10272 Cysteine metabolism 78 17 25 10380 Tryptophan metabolism 20 6 7
10280 Val, leu and Ile degr. 85 18 34 10400 Phe, tyr and try biosynth. 144 30 35
10290 Val, leu and Ile biosynth. 60 13 26 10220 Urea cyc., met. amino grp 120 31 41
10300 Lysine biosynthesis 82 17 22
1.6 Met. of Other Amino Acids
10410 beta-Alanine met. 60 12 13 10460 Cyanoamino acid met. 35 8 16
10450 Selenoamino acid met. 69 15 17 10480 Glutathione met. 127 35 16
1.7 Glycan Biosynth. And Met.
10510 N-Glycan biosynthesis 50 19 21 10563 GPI-anchor biosynthesis 21 12 14
10511 N-Glycan degradation 67 8 10602 Glycosphingolip. biosynth. 15 7 16
10540 Lipopolysac. Biosynth. 12 10 13 11030 Glycan struct. biosynth. 1 88 26 49
10550 Peptidoglycan biosynth. 18 3 15
1.8 Met. of Cofactors and Vit.
10730 Thiamine metabolism 21 12 20 10780 Biotin metabolism 12 6 8
10740 Riboflavin metabolism 63 12 15 10790 Folate biosynthesis 39 18 24
10750 Vitamin B6 metabolism 23 7 13 10670 One carbon pool by folate 42 15 9
10760 Nicotinate, nicotinamide met 30 12 13 10860 Porph. and chloroph. met. 67 31 39
10770 Pantothenate, CoA biosynth. 44 15 19 10130 Ubiquinone biosynthesis 31 17 25
1.9 Biosynth. of Secondary Met.
10900 Terpenoid biosynthesis 182 18 24 10941 Flavonoid biosynthesis 183 25 52
10904 Diterpenoid biosynthesis 72 18 37 10942 Anthocyanin biosynthesis 59 8 18
10902 Monoterpenoid biosynth. 192 24 37 10943 Isoflavonoid biosynthesis 63 7 17
10908 Zeatin biosynthesis 52 10 20 10950 Alkaloid biosynthesis I 65 17 23
10906 Carotenoid biosynth. 40 19 33 10311 Penicillin/cephalosp. bioS. 14 4 5
10905 Brassinosteroid biosynth. 19 7 24 11002 Auxin biosynthesis 98 18 12
10940 Phenylpropanoid biosynth. 220 21 44 11012 IBA metabolism 14 11 5
1.10 Other
11000 Single reactions 162 15 38

VVID: VitisNet identification number; #gen: number of genes in network; #pro: number of proteins in network; #met: number of metabolites in network.

Table 2. List of Genetic Information Processing Networks.

VVID Network Name #gen #pro VVID Network Name #gen #pro #met
2.1 Transcription
23020 RNA polymerase 85 32 23022 Basal transcription factors 55 20
2.2 Translation
23010 Ribosome 473 147 20970 Aminoacyl-tRNA biosynthesis 128 22 64
2.3 Folding, Sorting Degr.
23060 Protein export 36 16 24120 Ubiquitin mediated proteolysis 158 65 4
24130 SNARE int. in ves. transport 63 22 24140 Regulation of autophagy 48 15 5
23050 Proteasome 58 48
2.4 Replication and Repair
23030 DNA replication 60 38 23430 Mismatch repair 37 19
23410 Base excision repair 31 21 23440 Homologous recombination 39 19
23420 Nucleotide excision repair 53 36 23450 Non-homologous end-joining 14 8

VVID: VitisNet identification number; #gen: number of genes in network; #pro: number of proteins in network; #met: number of metabolites in network.

Table 3. List of Environmental Information Processing Networks.

VVID Network Name #gen #pro #met VVID Network Name #gen #pro #met
3.1 Signal Transduction
34020 Calcium signaling 142 27 22 34150 mTOR signaling 28 17
34070 Phosphatidylinositol sign. syst. 98 13 17
3.2 Hormone Signaling
30001 ABA signaling 102 56 11 30008 Ethylene signaling 248 101 3
30003 Auxin signaling 262 103 2 30010 Gibberellin signaling 31 14 1
30005 Brassinosteroids signaling 30 13 2 30011 Jasmonate signaling 86 36 4
30007 Cytokinin signaling 70 42 2
3.3 Plant-Specific Signaling
34710 Circadian rhythm 94 48 30009 Flower development 185

VVID: VitisNet identification number; #gen: number of genes in network; #pro: number of proteins in network; #met: number of metabolites in network.

Table 4. List of Cellular Processes Networks.

VVID Network Name #gen #pro #met
4.1 Cell Motility
44810 Regulation of actin cytoskeleton 360 114 1
4.2 Cell Growth and Death
44110 Cell cycle 315 192
4.3 Cell Wall
40006 Cell wall 448 53 11

VVID: VitisNet identification number; #gen: number of genes in network; #pro: number of proteins in network; #met: number of metabolites in network.

Table 5. List of Transport Networks.

VVID Network Name #gen #pro #met. VVID Network Name #gen #pro
5.1 Membrane Transport
52010 ABC transporters 283 87
5.2 Hormone Transport
50004 Auxin transport 57 23 2
5.3 Transport System
50110 Protein coat 157 83 50112 Nuclear pore complex 72 26
50111 tethering factors 100 65 50113 Thylakoid targeting pathway 62 15
5.4 Transporter Catalog
50101 Channels and pores 391 131 50124 Porters categories 30 to 64 155 69
50104 Group translocators 39 4 50125 Porters categories 66 to 94 215 50
50105 Transport electron carriers 89 38 50131 Prim. active transp. cat. A2-A4 200 44
50108 Accessory fact. Inv. in transp. 173 11 50132 Prim. active transp. cat. A5-A8 184 69
50109 Incomp. charact. transp. syst. 332 101 50133 Prim. act. transp. cat. A9-A18 191 71
50121 Porters categories 1 to 6 187 85 50134 Primary. active transp. Cat. D1 164 39
50122 Porters categories 7 to 17 242 49 50135 Prim. active transp. Cat. D3-E2 125 43
50123 Porters categories 18 to 29 204 46

VVID: VitisNet identification number; #gen: number of genes in network; #pro: number of proteins in network; #met: number of metabolites in network.

Table 6. List of Transcription Factors Networks.

VVID Network Name #gen #pro VVID Network Name #gen #pro VVID Network Name #gen #pro
60001 ABI3VP1 26 26 60028 FHA 19 19 60055 SBP 22 22
60002 Alfin 8 8 60029 G2-like 39 39 60056 SET PCG 52 52
60003 AP2 EREBP 139 139 60030 GeBP 7 7 60057 Sigma70-like 8 8
60004 ARF 27 27 60031 GIF 4 4 60058 SNF2 44 44
60005 ARID 11 11 60032 GRAS 53 53 60059 SRS 5 5
60006 ARR-B 15 15 60033 GRF 14 14 60060 TAZ 7 7
60007 AS2 42 42 60034 HB 93 93 60061 TCP 20 20
60008 AUXIAA 28 28 60035 HMG 16 16 60062 Trihelix 36 36
60009 BBR 5 5 60036 HRT 1 1 60063 TUB 17 17
60010 BES1 7 7 60037 HSF 23 23 60064 ULT 1 1
60011 BHLH 146 146 60038 Jumonji 27 27 60065 VOZ 2 2
60012 BZIP 66 66 60039 LFY 1 1 60066 WRKY 69 69
60013 BHSH 1 1 60040 LIM 15 15 60067 zf-MYND 4 4
60014 C2C2-CO 15 15 60041 LUG 7 7 60068 zf-HD 15 15
60015 C2C2-DOF 26 26 60042 MADS 71 71 60069 ZIM 14 14
60016 C2C2-GATA 20 20 60043 MBF1 5 5 60070 Orph_CCT 9 9
60017 C2H2 117 116 60044 MYB 176 176 60071 Orph_FAR-RED 53 53
60018 C3H 79 79 60045 MYB rel. 59 59 60072 Orph_Resp_reg 14 14
60019 C2C2-YABBY 7 7 60046 NAC 86 86 60073 Orph_zf-b_box 14 14
60020 CAMTA 6 6 60047 PBF-2like 2 2 60074 Orph_zf-SWIM 9 9
60021 CCAAT 30 30 60048 PHD 71 71 60075 Other BSD 8 8
60022 CPP 7 7 60049 PLATZ 11 11 60076 Other GTF 7 7
60023 CSD 3 3 60050 PsARR-B 8 8 60077 Other zf-AN1 14 14
60024 DBP 3 3 60051 RB 2 2 60078 Other zf-C3HC4 244 244
60025 DDT 8 8 60052 RWP-RK 10 10 60079 Other zf-DHHC 24 24
60026 E2F-DP 9 9 60053 S1Fa-like 3 3 60080 Other zf 32 32
60027 EIL 4 4 60054 SAP 1 1

VVID: VitisNet identification number; #gen: number of genes in network; #pro: number of proteins in network.

Construction of 219 Networks

The networks were constructed with the CellDesigner software. This software has the benefit of being able to save the networks in the SBML (System Biology Markup Language) format. This format is highly portable into a variety of software packages, including Cytoscape, which was used here for data visualization of molecular expression. The networks were constructed with four main families of nodes (gene, transcripts, proteins, and metabolites) represented by specific shapes and colors in CellDesigner (Figure 2) and by shape only in Cytoscape (Figure 3; color was used to visualize abundance). In VitisNet, some extra node styles can be used in the networks for additional categories (phenotypes, phylogenic tree node, etc.). Edge styles represented different types of reactions, and they were specified by shape in CellDesigner and color in Cytoscape; Text S2 has a legend that summarizes the node and edge styles used in VitisNet in Cytoscape. Five digit IDs were assigned to the networks (Table S2). The first digit refers to the network category (metabolic pathway etc.), and the last four digits refer to the KEGG pathway number (if it existed in KEGG).

Figure 2. Citrate cycle pathway visualized using CellDesigner.

Figure 2

Symbols represent different molecules or reactions, i.e. blue rectangle: gene; green parallelogram: transcript; orange round rectangle: protein; and yellow ellipse: metabolite. Edges with a circle at the tip: catalysis (A). Edges with Delta at the tip: metabolic reaction (B). Edges with dash-dot-dot-dash: transcription (C). Edges with dash-dot-dash: translation (D). Insert box at the upper right represents a zoom-in of an area of the network showing the different molecule types.

Figure 3. Flavonoid biosynthesis pathway and tissue-specific molecule abundance visualized using Cytoscape.

Figure 3

Parallelogram: transcript; rectangle: protein; ellipse: metabolite; triangle: reaction node. Blue edges with circle at the tip: catalysis. Black edges with Delta at the tip: metabolic reaction. Turquoise edges: translation. Transcript node in bold: existence of an Affymetrix (Vitis Vinifera (Grape) Genome Array) probeset. Red: over abundant in seed; magenta: over abundant in skin; green: over abundant in pulp; orange: over abundant in seed and skin. Insert box at the upper right represents a zoom-in of an area of the network showing the different molecule types.

Metabolic pathways (1)

Metabolic pathways are the most common type of pathway that can be found for plants in several online databases such as KEGG or PlantCyc (http://www.plantcyc.org/). These networks (Table 1) represented metabolic reactions known to occur in grapevines. With the software package KEGG2SBML, it was easy to import the metabolic pathways from KEGG. The KEGG pathways were limited when they were used; they only showed metabolites and proteins involved in reactions and included reactions that may not occur in plants. Therefore, additional information and symbols representing the missing grape genes and transcripts were added to the networks in VitisNet described in this paper. Reactions in KEGG without a putative grape protein identified and for which no evidence for their presence in plants could be found in the literature were removed. Finally, reactions in grapevines that were absent in KEGG were manually added to the networks. The total number of items in the 88 grape metabolic pathways constructed included: 7,854 genes and transcripts, 1,631 proteins, and 1,998 metabolites. Some of these items were present in more than one network.

Genetic information processing (2)

The category “Genetic Information Processing” (Table 2) corresponds to housekeeping mechanisms that are present and highly conserved in all eukaryotes. These networks were present on the KEGG website but in a different format than the metabolic networks; therefore exportation with KEGG2SBML was not possible. These networks were represented by a picture of a specific modus operandi, with every involved protein listed at the side rather than in a diagram of the enzymatic reactions. In VitisNet, we have tried to represent these pictures interactively. Where this was not possible, the networks were presented as lists of genes, transcripts, and proteins. The total number of items in the 15 “Genetic Information Processing” networks included 1,338 genes and transcripts, 527 proteins, and 71 metabolites.

Environmental information processing (3)

The category “Environmental Information Processing” (Table 3) represents signal processes that occur in the grapevine. The networks belonging to “Signal Transduction” are highly variable amongst species but they are well documented for Arabidopsis in KEGG and were constructed using the Arabidopsis data. The networks for hormone signaling and plant-specific signaling were reconstructed from the literature. To the best of our knowledge, these networks could not be found in any other pathway databases. These networks are particularly valuable for the plant community since hormonal signaling is an important subject in many plant physiology studies. The total number of items in the 12 “Environmental Information Processing” networks included 1,373 genes and transcripts, 563 proteins, and 63 metabolites.

Cellular processes (4)

These networks for the “Cellular Processes” category (Table 4) were named from the KEGG pathways; however the KEGG pathways were not related to the molecular events occurring in plants. Although a small portion of the pathways were derived from KEGG, most components of the networks were constructed from information collected from the literature. The total number of items in the 3 “Cellular process” networks included 1,123 genes and transcripts, 359 proteins, and 12 metabolites.

Transport (5)

The networks for Hormone Transport (5.2) and Transport Systems were constructed from the literature (Table 5). The networks in “Transporters Catalog” present the classification of the putative grape transporters according to the transporter classification (TC) system. This classification was formally adopted by the International Union of Biochemistry and Molecular Biology (IUBMB) in June 2001 and is the international standard for the classification of transporters. In VitisNet, molecules designating a transporter were linked to their corresponding category. The total number of items in the 21 “Transport” networks included 3,622 genes and transcripts, 1,149 proteins, and 1 metabolite.

Transcription factors (6)

These networks presented the classification of the grape putative transcription factors (Table 6). The classification used here was a customized version of two plant transcription factor databases that contained a total of 80 families. The PlantTFDB [47] contained 64 families and the PlnTFDB [48] contained 68 families. Most of the families (58) were present in the two databases, although two families were exclusive to PlantTFDB and eight were exclusive to PlnTFDB. In addition, 12 families were exclusive to the grapevine transcription factors. Representatives of five of these families were present in the plntfdb under the family named “orphans” and we chose to break this group into distinct families. The seven other families identified were proteins that contain a domain found in BTF2-like transcription factors, Synapse associated proteins and DOS2-like proteins (BSD, [49]), the Global Transcription Factor group (GTF), and subfamilies of zinc finger proteins. The transcription factor families were presented as a phylogenetic tree, which allowed subfamilies to be grouped together. The total number of items in the 80 “Transcription factors” networks included 2,423 genes, transcripts, and proteins.

Omics Data Can Be Visualized on the Networks

Annotation of the genes and construction of VitisNet has filled a major gap in precise descriptive and quantitative tools for grapevine systems biology. The next challenge is the integration of the data. The molecular networks were built to allow simultaneous visualization of transcripts, proteins, and metabolites. Their respective abundance under various conditions can be visualized through the Cytoscape software.

Several methods exist to correlate and integrate transcript, protein, and metabolite profiles. For example molecular abundance profiles were linked with Pearson [50], [51] and Spearman [52] correlation coefficients, the BL-SOM method [53], [54] and the O2PLS method [55]. The O2PLS method enables the determination of the effect of each variable, in a multivariable experiment, on the co-expression of molecules. More recently the O2PLS method has been developed further to integrate all three molecular profiles (transcripts, proteins, and metabolites) [56].

In most of these statistical studies, data were visualized by representing molecules by nodes and the correlation by edges. Subsequently, selected pathways were drawn manually for biological phenomenon highlighted by the correlations of molecular abundance. In the visualization of “omics” data in VitisNet, edges represented biological processes and nodes represented molecules, as in classical presentations of pathways. Molecular abundance was represented by color changes of the nodes and biological phenomenon could be visualized automatically. As an illustration of the methodology used in VitisNet to provide visualization of “omics” data, datasets from a study of the differential transcript, protein, and metabolite abundance measured in three berry tissues [29], [42] was uploaded into the molecular maps. For consistency, proteins and metabolites [42] were clustered with the same methods used for clustering the transcripts [29] and the same color scheme was used, (green = molecules over-abundant in pulp, purple = molecules over-abundant in the skin, and orange = molecules over-abundant in seed [29]). The flavonoid biosynthesis pathway (Figure 3) presented here was more complex than previous representations of the pathway in [29] and [42]. Here it was further customized from the total flavonoid biosynthesis pathway in VitisNet by removing the gene nodes for easier visualization. As these studies have illustrated, molecules involved in the flavonoid biosynthesis pathway are slightly more abundant in skin than seed and clearly more abundant in both skin and seed than in the pulp. Transcriptomic results from Affymetrix GeneChip® Vitis vinifera (Grape) Genome Array were used here, but data from any microarray platform can be uploaded onto the networks. For example, Table S1 contains data for mapping the cDNA array used in a grape bud chilling requirement fulfillment study [35]. The integration of the berry tissues “omic” data on all the pathways was divided into higher level pathway categories; the Cytoscape session files, molecular networks and a tutorial (Text S2) can be accessed and downloaded at the VitisNet tab at vitis-dormancy.sdstate.org. All molecular network files are also available for browsing or downloading at MetNet (http://metnet3.vrac.iastate.edu/)

Conclusion

An exhaustive coverage of the network of grapevine molecules has been developed. It presents an easy, fast, and comprehensive method for simultaneous integration and visualization of “omics” data. These molecular networks provide biological value for both grapevine researchers and the rest of the plant scientist community. The following attributes are provided: (i) original plant-specific pathways within VitisNet, (ii) the possibility to create a mapping file of genes from other plants, and (iii) the ability to customize the schematics for new or species-specific reactions. In the future, in cooperation with the scientific community's curation of gene annotations, we are planning to release new networks and update existing networks with emerging data (ie. miRNA) at MetNet (http://metnet3.vrac.iastate.edu/) and VitisNet (http://vitis-dormancy.sdstate.org/pathways.cfm).

Materials and Methods

Definition of a Unique Set of Genes

The 30,434 DNA sequences encoding for putative proteins from the Vitis vinifera (c.v Pinot noir PN40024) genome [23] were matched to EST sequences from Vitis vinifera and other Vitis species. The V. vinifera sequences originated from the 5.0 release of the DFCI grape index (http://compbio.dfci.harvard.edu/tgi/cgi-bin/tgi/gimain.pl?gudb=grape) which contained 34,134 unique sequences. The set of non-vinifera sequences contained a total of 26,589 redundant ESTs obtained from the NCBI website. This set included sequences from the following species: V. shuttleworthii (10,704 sequences), hybrid cultivars (6,542 sequences), V. arizonica x rupestris (5,421 sequences), V. aestivalis (2,101 sequences), and V. riparia (1,821 sequences). A BLAST analysis of the sequences from the V. vinifera EST set and the non-vinifera EST set (Megablast, p > 95, e-value<1e-15) was conducted against the genomic sequences. Sequences not identified in the genome were added to the genomic sequences to constitute the unique sequences set. The 1395 mRNAs corresponding to grapevine protein sequences registered in UniProt and not belonging to one of the two genome sequencing projects were manually retrieved and BLAST analyzed (blastn e-value <1 e-15) against the unique sequences set.

Gene Annotation

During the first steps of annotation, a batch BLAST analysis (blastx, e-value<1e-10) of unique sequences was conducted against several relevant databases, including the Arabidopsis and rice genomes and the Viridiplantae protein sequences in NCBI. For each gene, the ten best significant matches in each database were conserved and reviewed for defining the most likely annotation. Particular attention was paid to using identical nomenclature for genes with the same function. A BLAST analysis of the genes that had at least one significant match containing a putative function was conducted against the KEGG database (http://www.genome.jp/kegg/) for defining an enzyme commission (EC) number or a KEGG Orthology (KO) number. For genes not identified in this screen, the EC number of genes suspected to encode for a protein with enzymatic function was identified by browsing enzyme nomenclature databases (such as Expasy (http://www.expasy.org/enzyme/) or BRENDA (http://www.brenda-enzymes.org/)). A BLAST analysis (blastx, e-value<1e-10) of the unique set was conducted against the Transport Classification Database (TCDB) (http://www.tcdb.org/) and the genes matching sequences from that database were again manually reviewed and assigned to a category from the Transport Classification System [57].

BLAST analysis (blastx, e-value<1e-10) of the unique set was conducted against two plant transcription factor databases, PlantTFDB, (http://planttfdb.cbi.pku.edu.cn/) [47] and PlnTFDB (http://plntfdb.bio.uni-potsdam.de/v2.0/) [48]. InterPro domains obtained for the grape sequences from the UniProt website were also used for the classification of transcription factors. The transcription factors were then grouped into families.

Where molecular interactions were identified in the literature, the gene function was browsed to identify the Vitis gene potentially involved. The genes described in the literature were validated by BLAST against the unique set of Vitis sequences to correctly identify any potential homolog that was previously mislabeled.

A short identifier was defined for genes that were present on the networks but did not have a previously defined EC number or a KO. For most of these, that identifier corresponded to the one commonly used for their Arabidopsis homolog in their Entrez webpage (http://www.ncbi.nlm.nih.gov/sites/entrez?db=gene). For genes without an Arabidopsis homolog with a clear identifier, a unique identifier was created that was consistent with the gene function.

Network Construction

Metabolic pathways (1)

KEGG metabolic pathways were downloaded from the KEGG website and converted into SBML files with the KEGG2SBML software package [58]. Grape genes and transcripts were manually added to the networks and linked to their corresponding proteins with the CellDesigner software package [59]. Plant- or grape-specific reactions that were not present in KEGG but were described in the literature were added manually.

Genetic information processing (2), signal transduction (3.1), and ABC transporters (5.2)

KEGG pathways were manually reconstructed with CellDesigner using the SBML format, and then grape genes and transcripts were manually added to the networks and linked to their corresponding proteins. Plant- or grape-specific processes that were not present in KEGG but were described in the literature were manually added.

Hormones signaling (3.2), plant-specific signaling (3.3), cellular processes (4), hormone transport (5.2), and transport system (5.3)

Networks were manually constructed from the literature with CellDesigner using the SBML format, and then grape genes and transcripts were manually added to the networks and linked to their corresponding proteins.

Transport catalog (5.4)

Networks were manually constructed with CellDesigner using the SBML format. Grape genes and transcripts matching transporter proteins from any other organisms were manually added to the networks and linked to their corresponding proteins. Proteins were linked to an object class representing a transporter subcategory from the TCdb.

Transcription (6)

Networks were manually constructed with CellDesigner using the SBML format. Grape genes and transcripts matching transcription factors from other species were manually added to the networks and linked to their corresponding proteins. For each transcription factor family, a phylogenetic tree was constructed based on protein alignment generated with the neighbor-joining method using ClustalW. The transcription factors were then grouped according to the phylogenetic tree. Distances are not related to respective phylogenic distances. All the relevant bibliography for the construction of literature-based pathways is included in Table S2 and Text S1.

Expression Profiling

Affymetrix probesets were matched to the genome using the same process as that used between the genome sequences and EST sequences. The tentative contigs from the DFCI Grape Gene Index (http://compbio.dfci.harvard.edu/tgi/cgi-bin/tgi/gimain.pl?gudb=grape), that contain the ESTs that were used as templates for the Affymetrix probesets, were BLAST analyzed against the genome sequences (Megablast, p>95, e-value<1e-15).

Transcriptomic data were retrieved from Grimplet et al. [29]. Proteomics and metabolomics data were retrieved from Grimplet et al. [42]. All molecules with differential abundance were grouped into 12 clusters presented by Grimplet et al. [29] according to their abundance in the three berry tissues. Data were visualized using VitisNet with the Cytoscape software [60] (see Text S2 for a tutorial on the complete procedure).

Supporting Information

Table S1

The complete grape gene annotation based on the 8X assembly (Jaillon et al., 2007) of transcript sequences. Unique Gene: Genoscope ID (Jaillon et al., 2007) is used if a genome sequence has been identified, otherwise VVGI 5 TC (Tentative Consensus sequences) number or EST GenBank ID is used. Unique transcript: VVGI 5 TC number or EST GenBank ID is used if a transcript has been identified, otherwise the Genoscope ID is used. Function: tentative functional annotation. Network ID: the identifier that is used in the networks. Network or simplified category: list of the networks where the genes appear, otherwise a short description of the biological role. In Network: the gene is present in at least one network. Probeset: probeset ID for the Affymetrix GeneChip® Vitis vinifera (Grape) Genome Array. Best Arabidopsis match: best matched hit in Arabidopsis putative proteins. InterPro domain ID: list of the domains detected from InterPro (Hunter et al., 2009). Gene Ontology ID: list of the identified GO terms. Gene Ontology description: description of the GO term (The Gene Ontology Consortium, 2009). Accession UniProt: UniProt ID for the genome sequences (Apweiler et al., 2004). Accession UniProt for published grapevine protein: UniProt ID for grapevine proteins individually published apart of the genome sequencing. EST probeset: EST from which the probeset was designed. IASMA gene: ID from the heterozygote Vitis genome (Velasco et al., 2007). Chromosome position: position of the gene on chromosome retrieved from Gramene.org. Other Vitis: presence in non-vinifera Vitis species. cDNA array: ID used in the cDNA array from Mathiason et al., (2009). Other TC from VVGI5: list of other TC from the DFCI matching the gene. Other probesets: other Affymetrix probesets matching the gene.

(10.28 MB XLS)

Table S2

List of pathways constructed from bibliographic data and the corresponding journal articles used.

(0.03 MB DOC)

Text S1

References for supporting material.

(0.06 MB DOC)

Text S2

Tutorial for Using VitisNet, a database for the grapevine molecular networks.

(3.51 MB DOC)

Acknowledgments

The authors wish to thank Wei Ma and Wendy Cradduck for the http://vitis-dormancy.sdstate.org website design, Kim Victor for curation of the SBML networks source code, and Yves Sucaet and Eve S. Wurtele for the MetNet interface API.

Footnotes

Competing Interests: The authors have declared that no competing interests exist.

Funding: This work was support by a grant from the National Science Foundation Plant Genome Program (DBI- 0604755) to Anne Y. Fennell, Grant R. Cramer, Julie A. Dickerson and Karen Schlauch. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Supplementary Materials

Table S1

The complete grape gene annotation based on the 8X assembly (Jaillon et al., 2007) of transcript sequences. Unique Gene: Genoscope ID (Jaillon et al., 2007) is used if a genome sequence has been identified, otherwise VVGI 5 TC (Tentative Consensus sequences) number or EST GenBank ID is used. Unique transcript: VVGI 5 TC number or EST GenBank ID is used if a transcript has been identified, otherwise the Genoscope ID is used. Function: tentative functional annotation. Network ID: the identifier that is used in the networks. Network or simplified category: list of the networks where the genes appear, otherwise a short description of the biological role. In Network: the gene is present in at least one network. Probeset: probeset ID for the Affymetrix GeneChip® Vitis vinifera (Grape) Genome Array. Best Arabidopsis match: best matched hit in Arabidopsis putative proteins. InterPro domain ID: list of the domains detected from InterPro (Hunter et al., 2009). Gene Ontology ID: list of the identified GO terms. Gene Ontology description: description of the GO term (The Gene Ontology Consortium, 2009). Accession UniProt: UniProt ID for the genome sequences (Apweiler et al., 2004). Accession UniProt for published grapevine protein: UniProt ID for grapevine proteins individually published apart of the genome sequencing. EST probeset: EST from which the probeset was designed. IASMA gene: ID from the heterozygote Vitis genome (Velasco et al., 2007). Chromosome position: position of the gene on chromosome retrieved from Gramene.org. Other Vitis: presence in non-vinifera Vitis species. cDNA array: ID used in the cDNA array from Mathiason et al., (2009). Other TC from VVGI5: list of other TC from the DFCI matching the gene. Other probesets: other Affymetrix probesets matching the gene.

(10.28 MB XLS)

Table S2

List of pathways constructed from bibliographic data and the corresponding journal articles used.

(0.03 MB DOC)

Text S1

References for supporting material.

(0.06 MB DOC)

Text S2

Tutorial for Using VitisNet, a database for the grapevine molecular networks.

(3.51 MB DOC)


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