Open Access

A predicted protein interactome for rice

  • Chai-Ling Ho1,
  • Yingzhou Wu2,
  • Hong-bin Shen3,
  • Nicholas J Provart4 and
  • Matt Geisler5Email author
Rice20125:15

https://doi.org/10.1186/1939-8433-5-15

Received: 7 June 2012

Accepted: 13 June 2012

Published: 2 July 2012

Abstract

Background

Protein-protein interactions (PPIs) create the steps in signaling and regulatory networks central to most fundamental biological processes. It is possible to predict these interactions by making use of experimentally determined orthologous interactions in other species.

Results

In this study, prediction of PPIs in rice was carried out by the interolog method of mapping deduced orthologous genes to protein interactions supported by experimental evidence from reference organisms. We predicted 37112 interactions for 4567 rice proteins, including 1671 predicted self interactions (homo-interactions) and 35441 predicted interactions between different proteins (hetero-interactions). These matched 168 of 675 experimentally-determined interactions in rice. Interacting proteins were significantly more co-expressed than expected by chance, which is typical of experimentally-determined interactomes. The rice interacting proteins were divided topologically into 981 free ends (proteins with single interactions), 499 pipes (proteins with two interactions) and 3087 hubs of different sizes ranging from three to more than 100 interactions.

Conclusions

This predicted rice interactome extends known pathways and improves functional annotation of unknown rice proteins and networks in rice, and is easily explored with software tools presented here.

Background

Protein-protein interactions (PPIs) are essential for many fundamental biological processes. With the advent of high-throughput approaches, genome-wide networks of PPIs have been generated in Saccharomyces cerevisiae (Uetz et al., 2000; Miller et al., 2005; Gandhi et al., 2006), Drosophila melanogaster (Giot et al., 2003), Caenorhabditis elegans (Li et al., 2004), Homo sapiens (Rual et al., 2005) and other organisms. Recently, a large scale map of 6200 PPIs was completed for Arabidopsis (Arabidopsis interactome mapping consortium, 2011). Another medium-scale yeast two-hybrid screen on proteins involved in the two-component signaling pathway of Arabdidopsis thaliana has revealed 160 interactions of which 136 were novel (Dortay et al., 2008). Networks of rice genes associated with stress response, seed development and cell cycle mediated by cyclin were built from the results generated from yeast two hybrids (Cooper et al., 2003a,b). In addition, a rice kinase-protein interaction map of 116 representative rice kinases and their interacting proteins was generated from the results of yeast two hybrids (Ding et al., 2009).

Prediction of PPIs is made possible in organisms lacking experimental determination of PPIs using the PPI networks established in reference organisms. In this approach, orthologous genes are deduced using prediction algorithms and mapped to protein interactions supported by experimental evidence from reference organisms retrieved from publicly available databases such as Biomolecular Interaction Network Database (BIND; Bader et al., 2001), Molecular Interaction Database (MINT; Zanzoni et al., 2002; Ceol et al., 2009), Munich Information Center for Protein Sequences (MIPS; Pagel et al., 2005), Database of Interacting Proteins (DIP; Salwinski et al., 2004), IntAct (http://www.ebi.ac.uk/intact; Aranda et al., 2010) and Biological General Repository for Interaction Data sets (BioGRID; Breitkreutz et al., 2008). Using this approach, a predicted interactome of Arabidopsis thaliana was made consisting 1159 high confidence, 5913 medium confidence and 12907 low confidence interactions. This was established using a confidence scoring based method on the number of different data sets in which the interaction was recorded, the number of different types of experiments supported the interactions, and the number of species in which the interaction was discovered (Geisler-Lee et al., 2007). In addition, the data on subcellular localization and co-expression of interacting proteins were integrated into the deduction of PPIs to strengthen the confidence of the resulting predicted interactome. The predicted interactome in Arabidopsis revealed that many of the most highly conserved proteins were also the most highly connected hubs involved in important signaling complexes, and revealed the preservation of original functions of nuclear-located pathways in non-photosynthetic reference organisms in the chloroplasts of higher plants post endosymbiosis (Geisler-Lee et al., 2007). The Arabidopsis predicted interactome has enabled researchers to fruitfully generate and test network and protein interaction hypotheses (e.g. Liu and Howell 2010, Gu et al. 2008).

In this study, a similar approach was used to predict the interactome of rice with the aim to expand the current understanding of PPIs in monocot based on our predicted interactome. A second goal is to provide a tool that leads to useful hypothesis generation.

Results and discussion

Predicted rice interactions

In this study, a rice protein-protein interaction network was predicted based on the universality of conserved protein function among different organisms. This was undertaken with the assumption that evolutionarily conserved orthologous proteins are likely to retain their interactions with other similarly conserved proteins. Using ortholog prediction algorithm, 13070 rice genes (23% of rice genome) had an ortholog that matched at least one of the eleven reference organisms (Arabidopsis thaliana, Homo sapiens, Mus musculus, Rattus norvegicus, Drosophila melanogaster, Caenorhabditis elegans, Saccharomyces cerevisiae, Schizosaccharomyces pombe, Escherichia coli, Bacillus subtilis, Helicobacter pylori). A confidence value (CV, see Methods) was calculated to estimate the strength of experimental support for each predicted interaction. We have identified 37112 predicted interactions for 4567 rice proteins (Additional file 1: Table S1), whereby 1671 are predicted self interactions and 35441 are interactions between different proteins. These interactions were classified into 2904 interactions with high confidence (CV > 10), 11152 interactions with medium confidence (CV between 2 and 10) and 23056 interactions with low confidence (CV = 1) (Figure 1). Table 1 lists the twenty protein interactions with the highest confidence, owing to repeatability using different experimental techniques and species. Among these interactions are DNA repair pathways involving RAD50, 51, 54, MCM2, 5, 6, UVH1, MRE11, and others; Cell cycle control pathways with CDC2, cyclin dependent kinases, SNF1-related protein kinase; and the transcription initiation complex. While many of these interactions have been well studied in human and yeast, equivalent studies are lacking for most of these in rice. All genes in Table 1 have been functionally annotated in rice only by sequence homology, often only to the level of general gene family and not their specific role. By adding predicted interactions, the exact role for each protein in the network can be more specifically hypothesized.
Figure 1

Confidence Values of predicted rice protein-protein interactions. Confidence value was calculated from the overall supporting evidence with a multiplier for interactions found by different experimental methodologies and found in different reference species. Most predicted interactions were of low confidence (1), but 11152 interactions had medium and 2904 had high confidence levels. 37112 unique interactions involving 4567 proteins were predicted which included 1671 self interactions and 35441 hetero-interactions.

Table 1

Twenty rice protein interactions with the highest confidence

Locus A*

Locus B*

Protein A

Protein B

CV

Os11g40150

Os11g40150

DNA repair protein RAD51 homolog A

DNA repair protein RAD51 homolog A

2080

Os03g53960

Os10g28040

transcriptional adaptor

general control of amino acid synthesis protein 5-like 2

2048

Os01g72880

Os02g37920

MUTL protein homolog 1

ATP binding protein

2040

Os02g30800

Os05g06840

DNA polymerase family B, exonuclease domain containing protein, expressed

DNA polymerase epsilon subunit 2

1404

Os07g02350

Os10g41520

casein kinase II subunit alpha-2

casein kinase II subunit beta-4

1368

Os03g63940

Os05g45420

protein kinase AKINbetagamma-2

SNF1-related protein kinase catalytic alpha subunit KIN10

1281

Os02g29464

Os04g54340

DNA repair protein RAD50

double-strand break repair protein MRE11

1254

Os02g38340

Os08g28190

actin-like protein 3

actin-like protein 2

1155

Os04g43300

Os05g43610

ATBRCA1

ATBRCA1

1040

Os06g43790

Os12g39070

HAF01

TATA-binding protein 2

1026

Os02g52510

Os11g40150

DNA repair protein RAD54-like, putative

DNA repair protein RAD51 homolog A

952

Os03g46650

Os05g26890

guanine nucleotide-binding protein beta subunit

guanine nucleotide-binding protein alpha-1 subunit

936

Os11g29380

Os12g37400

DNA replication licensing factor Mcm2

PROLIFERA protein

800

Os03g01100

Os10g37490

DNA repair endonuclease UVH1

mating-type switching protein swi10

783

Os01g08540

Os05g19270

DNA mismatch repair protein MSH6-2

DNA mismatch repair protein MSH2

774

Os01g32750

Os03g29470

TBP-associated 59 kDa subunit protein

transcription initiation factor TFIID subunit 9B

756

Os01g64820

Os12g13950

DNA polymerase alpha catalytic subunit

DNA polymerase alpha subunit B

720

Os01g07110

Os11g40150

BRCA2 repeat family protein

DNA repair protein RAD51 homolog A

714

Os06g08770

Os07g08170

ruvB-like 2

ruvB-like 1

651

Os03g02680

Os03g05300

cell division control protein 2 homolog 1

cyclin-dependent kinases regulatory subunit

648

*rice locus identifiers are also written as e.g. LOC_Os11g40150.

Conservation in interactions

The same protein interactions were found in many species, despite the incompleteness of the experimentally-determined interactomes (Wiles et al. 2010). The same pathway interactions for DNA repair is found in 5 other eukaryotic species, including the pathway components RAD51, DNA replication licensing factors MCM2,5,6 and PROLIFERA; DNA polymerase subunits and actin-like proteins (Table 2). These are also interactions with the highest confidence (with CV more than 400). We identified 793 interactions that were present in at least two different species and orthologous genes were found in rice. Proteins in rice with highest connectivity (number of different interactions) were not always the most evolutionarily conserved, which differs from the predicted Arabidopsis interactome (Geisler-Lee et al., 2007). Three of the most conserved protein interactions in rice between DNA polymerase catalytic subunit and DNA polymerase subunit B, small nuclear ribonucleoproteins SmD2 and F and self-interaction of UTP-glucose-1-phosphate uridyltransferase were also among the most conserved in Arabidopsis (Geisler-Lee et al., 2007). The other highly conserved interactions between the two species are different (Table 2). More rice orthologs (13,070) were identified in comparison to Arabidopsis (10,776) possibly owing to a larger genome in rice, and there was an increased pool of reference species.
Table 2

Twenty most conserved rice protein interactions

Locus A*

Locus B*

Protein A

Protein B

Species

CV

Os11g40150

Os11g40150

DNA repair protein RAD51 homolog A

DNA repair protein RAD51 homolog A

5

2080

Os02g55410

Os11g29380

DNA replication licensing factor MCM5

DNA replication licensing factor Mcm2

5

560

Os01g64820

Os12g13950

DNA polymerase alpha catalytic subunit

DNA polymerase alpha subunit B

5

720

Os02g38340

Os08g28190

actin-like protein 3

actin-like protein 2

5

1155

Os07g22400

Os12g13950

DNA primase large subunit

DNA polymerase alpha subunit B

5

450

Os01g36390

Os11g29380

DNA replication licensing factor mcm4

DNA replication licensing factor Mcm2

5

420

Os11g29380

Os12g37400

DNA replication licensing factor Mcm2

PROLIFERA protein

5

800

Os05g14590

Os11g29380

DNA replication licensing factor MCM6

DNA replication licensing factor Mcm2

5

595

Os01g72880

Os02g37920

MUTL protein homolog 1

ATP binding protein

4

2040

Os09g38030

Os09g38030

UTP--glucose-1-phosphate uridylyltransferase

UTP--glucose-1-phosphate uridylyltransferase

4

176

Os05g24970

Os11g43620

small nuclear ribonucleoprotein Sm D2

small nuclear ribonucleoprotein F

4

272

Os01g71990

Os01g71990

pyrroline-5-carboxylate reductase

pyrroline-5-carboxylate reductase

4

144

Os05g39850

Os11g29380

DNA replication licensing factor MCM3

DNA replication licensing factor Mcm2

4

476

Os02g55410

Os05g39850

DNA replication licensing factor MCM5

DNA replication licensing factor MCM3

4

448

Os07g02350

Os10g41520

casein kinase II subunit alpha-2

casein kinase II subunit beta-4

4

1368

Os03g53960

Os10g28040

transcriptional adaptor

general control of amino acid synthesis protein 5-like 2

4

2048

Os06g43790

Os06g44030

HAF01

transcription initiation factor TFIID subunit 5

4

580

Os02g52510

Os11g40150

DNA repair protein RAD54-like

DNA repair protein RAD51 homolog A

4

952

Os01g53600

Os09g33930

farnesyltransferase beta subunit

farnesyltransferase/geranylgeranyltransferase type IA

4

456

Os05g32310

Os08g08040

SAD1

LSM7 homolog, U6 small nuclear RNA associated

4

224

*rice locus identifiers are also written as e.g. LOC_Os11g40150.

Experimental verification of predicted rice interactions

A gold standard of 675 experimentally confirmed interactions was abstracted from IntAct (http://www.ebi.ac.uk/intact/; Additional file 2: Table S2). Of these 675 experimentally-determined interactions, there were 168 interactions that were also predicted by our method. This is a very high overlap, with a significance p-value of less than 10-99 when compared to a chance overlap with an equal number of random protein pairs. This is especially significant when considering not all experimentally-determined interactions were also evolutionarily conserved, and thus not easily detectable by interacting orthologs. A PubMed ID was assigned to each of the gold standard interactions in our data sets. The remaining experimentally-determined interactions were loaded into the Rice Interactions Viewer (RIV) database.

Interactome topology

The rice interacting proteins were divided into free ends (981 proteins with single interactions), pipes (499 proteins with two interactions) and hubs of different sizes ranging from three to more than 100 interactions. The largest class of proteins is that of medium hubs (between 11 to 50 interactions). The interacting proteins had an average of 14–15 partners, which is comparable to Drosophila (9 interacting partners) and Arabidopsis (11 interacting partners) and smaller than in yeast (22 interacting partners). Table 3 lists the most highly connected rice protein interaction hubs, including heat shock protein 81–1, elongation factor 1-alpha, vacuolar ATP synthase subunit B isoform 1. Among these proteins, ubiquitin-conjugating enzyme E2, ubiquitin-like protein SMT3, fibrallarin-2 were also found among the twenty most highly connected Arabidopsis interaction hubs (Geisler-Lee et al., 2007).
Table 3

Twenty most highly connected rice protein interaction hubs

Locus

Protein Description

Edges

Os08g39140

heat shock protein 81-1

686

Os03g08050

elongation factor 1-alpha

410

Os06g37180

vacuolar ATP synthase subunit B isoform 1

298

Os01g38970

carbamoyl-phosphate synthase large chain

276

Os07g08330

60 S ribosomal protein L4

272

Os01g62840

mannose-1-phosphate guanyltransferase

245

Os01g73310

actin-1

234

Os01g62244

ubiquitin-conjugating enzyme E2 7

224

Os10g32550

chaperonin CPN60-1, mitochondrial precursor

214

Os11g04070

60 S acidic ribosomal protein P0

197

Os07g31370

ras-related protein Rab-6A

187

Os07g43360

histone acetyltransferase MYST1

182

Os05g38530

heat shock cognate 70 kDa protein

180

Os03g45920

tubulin beta-8 chain

170

Os09g38020

histone H4

169

Os06g38470

histone deacetylase

168

Os11g14220

tubulin alpha-3 chain

167

Os01g68940

ubiquitin-like protein SMT3

167

Os05g08360

fibrillarin-2

162

Os03g13970

26 S proteasome non-ATPase regulatory subunit 4

161

Protein domain enrichment in rice interologs

Protein-Protein interactions are often mediated by protein interaction domains (PIDs) which bind to other domains (domain-domain interactions), or short conserved amino acid motifs in the partner protein. PIDs that interact most frequently among the rice protein pairs (interologs) were determined using annotation by PFAM domain models (see methods). In rice, the most enriched domains among the interactors included known interaction domains PF00400 (WD40), PF00069 (protein kinase), PF00270 (DEAD), and PF02985 (HEAT). Looking at domain-domain interactions amongst interacting pairs, domain pairs (with one domain in each interacting protein) with extremely high fold of enrichment (vs. random pairing) were often otherwise rare domains among the protein interactions. Among the twenty most frequent PFAM interacting pairs were 8 self interactions between homodomains and 12 interactions between different PFAM domains. Of these 14 (70%) were enriched more than 2 fold, with the highest fold of enrichment (134 fold) for interaction between the homodomains of the proteasome (PF00227; Table 4). The proteasome is a proteinase complex involved in an ATP/ubiquitin-dependent non-lysosomal proteolytic pathway which liberates the cell of misfolded or damaged proteins and controls the level of certain regulatory proteins (Goldberg and Rock, 1992; Hilt and Wolf, 1996; Rivett, 1993; Wilk, 1993). Since its function is crucial and is composed of about 28 distinct subunits that form a highly ordered structure in eukaryotes, it is not surprising that this PFAM domain shows the highest fold of enrichment in the predicted rice interactions. These interactions form a distinct subnetwork of highly conserved interactions.
Table 4

Twenty most frequent domain pairs between interacting partners

Domain IDs in interacting pairs

Domain names

Number of interactions observed

Fold enrichment vs. random

PF00400 PF00400

WD40 : WD40

166

2.4280

PF00271 PF00270

Helicase C : DEAD

131

8.0228

PF00069 PF00069

Pkinase : Pkinase

120

0.0383

PF00227 PF00227

Proteasome : Proteasome

114

133.8151

PF00076 PF00076

rrm :rrm

112

1.2412

PF01486 PF00319

K-box : SRF-TF

104

30.0497

PF00271 PF00271

Helicase_C : Helicase_C

101

3.5858

PF00400 PF00271

WD40 : Helicase_C

99

2.2560

PF00271 PF00076

Helicase C: rrm

94

1.8646

PF01423 PF01423

Sm : Sm

91

116.3077

PF07714 PF00069

Pkinase_Tyr : Pkinase

91

0.0473

PF00270 PF00076

DEAD : rrm

74

2.5320

PF00400 PF00069

WD40 : Pkinase

70

0.1512

PF00400 PF00076

WD40 : rrm

70

0.8912

PF00069 PF00036

Pkinase : efhand

70

0.1994

PF02985 PF00400

HEAT : WD40

65

2.5236

PF00400 PF00118

WD40 : cpn60_TCP1

62

8.8624

PF00004 PF00004

AAA : AAA

60

3.4780

PF00400 PF00270

WD40 : DEAD

59

2.3192

PF00270 PF00270

DEAD : DEAD

58

6.1273

Predicted rice interactome subnetworks

In this study, we present a predicted interactome from rice that is useful for hypothesis generation towards better understanding of protein-protein interactions in rice and also other monocotyledonous plants. We have constructed a predicted rice MADS network consisting of 19 rice MADS-box family members which was determined partially by experiments in rice (Figure 2) which has been expanded using interologs from Arabidopsis, (de Folter et al. 2005) indicated by blue lines in Figure 2. Although these genes are plant specific, the degree of interconnectivity among MADS box genes is not surprising as many proteins form functional complexes consisting of homodimers or heterodimers (Pelaz et al., 2000; Theissen and Melzer, 2007) that have diverse roles. In addition, predicted interactions have connected several rice MADS proteins to proteins that do not belong to the MADS-box family, e.g. LEUNIG (LUG), SEUSS (SEU), and PROLIFERA and more than 10 other proteins involved in diverse functions including DNA metabolism (RuvB-like proteins; Gorynia et al., 2006), initiation of transcription TATA binding protein protein associated factors (TAFs) and a general transcription factor, TFIID (Cler et al., 2009), cell cycle progression (mannose-1-phosphate guanyltransferase, Donoso et al., 2005), and cell division (cell division control protein 45). Both LUG and SEU are known transcription co-repressors that form a co-repressor complex with MADS box dimers, APETALA1 (AP1) AP1- AGAMOUS-LIKE 24 (AGL24) and AP1-SHORT VEGETATIVE PHASE (SVP) to repress AGAMOUS (AG) gene in flowers (Gregis et al., 2006). SEU has been demonstrated to interact with AP1 and SEPALLATA3 (SEP3) to bridge the interaction between AP1/SEP3 and LUG in Arabidopsis (Sridhar et al., 2006) resulting in transcription repression during flower development. OsMADS14 and OsMADS8 which are connected to SEU here, could be the rice orthologs for AP1 and SEP, while OsMADS13 could be a rice ortholog for AGL11 (Arora et al., 2007) which was reported to be preferentially expressed in ovule (Rounsley et al. 1995) and carpel (Yung et al., 1999). OsMADS13 was also connected to PROLIFERA (PRL) which encodes a DNA replication licensing factor Mcm7. In Arabidopsis, PRL was demonstrated to be expressed in dividing cells in the palisade layer of the leaf, founder cells of initiating flower primordial, and central cell nucleus of mature mega gametophytes (Springer et al., 2000) whereas PROLIFERA protein was reported during G(1) phase of the cell cycle. The interaction network involving PROLIFERA was further expanded through interologs in other eukaryotes (Figure 3a), showing the relationship between the MADS box network to conserved network of cell division and cell cycle regulators.
Figure 2

MADS network in rice expanded by predicted interactions . The core interactions between rice MADS box proteins were experimentally determined by de Folter et al. 2005(Circles: rice proteins, Blue edges: experimentally determined interactions). This network is expanded by predicted interactions (Red edges), and several experimentally determined interactions were also predicted (Green edges).

Figure 3

Subnetworks of predicted rice interactions. Subnetworks were constructed by identifying protein that interact with a single protein (a: PROLIFERA), or interactions involving proteins with a defined domain and their interacting neighbors (b: MAP kinase, c: protein phosphatase 2 C isoform 4).

The predicted rice interactome is also useful for the functional annotation of expressed proteins with unknown functions through their associations with known proteins, or known pathways in the predicted network. Subnetworks of interacting proteins were constructed around MAP kinase signaling proteins identified by homology (Figure 3b) and the known protein phosphatase OSPP2C4, (Figure 3c). These subnetworks contained many expected interactions but also included both metabolic enzymes (e.g. acyl co-enzyme A synthase in Figure 3c) and regulators (e.g. serine/threonine phosphatase 2A regulatory subunits), as well as unexpected connections between metabolic enzymes, signaling and ribosomal proteins.

Coexpression of interologs

Proteins which physically interact are often expressed at the same time in the same tissue (Narayanan et al. 2010). This is possibly due to common transcription factors controlling individual members of a protein complex or pathway, a common network motif known as a single input module (Alon, 2007). Especially in metabolism, co-expressed enzymes utilize “just in time” mechanisms of regulation so that they do not waste energy producing idle proteins (Zaslaver et al., 2004). Thus mRNA levels of the transcripts for interacting proteins also frequently co-vary, as do levels of transcripts for proteins that are part of a signaling chain or enzymes in a metabolic pathway that is coordinately stimulated or suppressed. A network of gene co-expression can itself be informative in pathway reconstruction. As a result, if a pair of proteins is positively coexpressed, the confidence level to predict the pair interacts is generally high (Geisler-Lee et al. 2007). The co-expression level of each interolog is measured by Pearson Correlation Coefficient (PCC) which is computed from available rice Affymetrix microarray data which includes 165 data sets in total from diverse tissues and treatments (Additional file 3: Table S3). In comparison to the randomly generated gene pairs (see methods), our predicted interologs exhibited a strong and statistically significant trend of coexpression (Figure 4). Predicted interologs with low coexpression were also found in the analysis. Not all proteins with physical interactions are co-expressed, as may be the case with rate limiting steps or proteins regulated at the post-transcriptional level. One protein can be constitutively expressed while the other interacting partner is expressed under certain conditions only, especially where a pathway branches into two downstream paths. Protein relocation to another compartment or even another tissue is also a possible cause. Collectively, these are called “limiting factor” based regulation mechanisms. Thus lack of expression correlation does not necessarily imply that proteins do not interact. Indeed the combination of co-expression and predicted physical interaction reveals the regulation mechanisms involved in that pathway as either “just in time”, or “limiting factor” based. Overall, however, a positive correlation was found between the interolog coexpression (PCC) and the interolog confidence level (CV), indicating that “just in time” regulation is more common.
Figure 4

Co-expression of interologs. a, The PCC for 37,112 predicted interaction pairs was calculated and plotted as the number of pairs in each Pearson correlation coefficient range (blue curve). The PCC calculation is also performed for 37,112 randomly selected pairs of rice genes from within our interactome (red curve), from the whole rice genes (green curve), or from whole rice genes such that the topology of the random network was the as that of our predicted interactome in terms of node degree distribution (purple curve). b, The interolog CV was plotted against the PCC for each pair (blue dots). The red dots are the interologs whose CV is larger than 10.

Subcellular localization of interologs

In a general sense, for a pair of proteins to physically interact, each protein must be located at same or adjacent subcellular compartments either permanently or transiently. Thus it is foreseeable that interacting proteins are more likely to have the same or adjacent subcellular localization. We have performed subcellular localization enrichment analysis to validate this trend. Unlike the similar approach for Arabidopsis where extensive subcellular localization information can be retrieved from SUBA (Geisler-Lee et al. 2007), comprehensive experimentally derived subcellular localization data for rice are not available. Fortunately, many computational methods are available for predicting protein subcellular localization. Some machine learning-based methods that use sequence-derived features to predict localization have reached remarkable levels of performance. Therefore, we adopted one of these computational methods named Plant-mPloc (http://www.csbio.sjtu.edu.cn/bioinf/plant-multi/; Chou and Shen, 2010) to predict the subcellular localization of each protein in our interactome. Since Plant-mPloc is able to cover 12 distinct compartments, 78 distinct compartment pairs were used for analysis. Figure 5 shows the observed number of interologs of each possible subcellular compartment pairs. P values associated with the deviation of these numbers from a random interactome network with the same properties (see “Material and Methods”) were used to evaluate the statistical significance of enrichment or depletion. Most of the statistically significant enriched compartment pairs are those paired with same compartment or adjacent compartments such as ER and Golgi (Figure 5). A significant number of cytosolic localized proteins are predicted to interact with nuclear and mitochondrial localized proteins, indicating possible import/export regulation and dual localized proteins such as transcription factors. No statistically significant depletion was found in this analysis. The results differ slightly from what we observed in the case of the predicted interactome for Arabidopsis (Geisler-Lee et al., 2007), in part due to the large number of proteins in our interactome that have multiple predicted subcellular localizations while in the Arabidopsis approach single subcellular localization was assigned to each protein through winner-takes-all strategy. We were unable to use the same strategy since Plant-mPloc is our only source for subcellular localization determination. In our analysis, one predicted interaction is scored as belonging to several compartment pairs thereby increasing the number of interologs in these compartment pairs, which could cause no depletion. Additionally, the accuracy of the Plant-mPloc prediction must also be considered. Nevertheless, a clear tendency that interologs are more likely enriched in the compartment pairs paired with same or adjacent compartment can be found from our analysis.
Figure 5

Co-localization of interologs. Analysis of all interaction protein pairs in which both partners were assigned to a subcellular compartment. Compartment pairs that showed enriched or depleted numbers of interactions (compared to chance) are color coded.

Rice interactome visualization

A predicted interactome is usually stored in a table with columns and rows. However, in essence it is a network which consists of nodes and edges, which is better visualized as a graph. The Rice Interactions Viewer (RIV; Figure 6), which was developed based on the infrastructure of Arabidopsis Interactions Viewer (AIV), is such a web–based interactome network visualization tool that allows users to perform customized query and analysis (Geisler-Lee et al. 2007). Besides the replacement of the AIV data set with the rice interolog data set described here and the amendment of GUI text, some redundant code was also corrected for the purpose of functional expansion. One major improvement on RIV is the integration of CytoscapeWeb (Lopes et al., 2010; http://cytoscapeweb.cytoscape.org/). By using the graph definition language GraphML, the color of each protein (node) is painted differently based on its predicted subcellular localization (Figure 6a). Alternatively, nodes can be colored according to its expression level in a specific tissue/condition (Figure 6b), based on rice gene expression data sets stored in the Bio-Array Resource for Plant Biology (Toufighi et al., 2005). For each edge which connects a pair of proteins, the RIV is able to show different color based on the coexpression value (PCC). As well the relative thickness corresponds to its Confidence Value (CV). Moreover, a simple mouse click on each protein (node) is able to show its ID, annotation and predicted subcellular localization.
Figure 6

Visualizing the rice interactome by RIV. a, nodes are colored according to its subcellular localization, edges are colored according to its coexpression correlation and the line thickness correlates to CV. The popup message is the ID, annotation and subcellular localization of the clicked node. b, same as a except the nodes color represent the expression level in “Seeding Root” tissue.

Conclusions

How to use the predicted interactome

A rice interactome has been predicted based on conservation of protein interactions across species over the course of evolution. Each interaction has been assigned a confidence level (CV) as an internal quality control. Assignments for subcellular localization and coexpression can be used to further validate the predicted interaction. Coexpression analysis has shown that interacting proteins tend to have similar expression profiles, and tend to be localized to the same or adjacent compartment. By inputting rice proteins of interest, the Rice Interactions Viewer (Figure 6) can be used to display the results of a query of our predicted interactome in a tablular format and CytoscapeWeb network graph format. In table format output, for the interologs confirmed by published literature, users can link to the paper by clicking the assigned PubMedID. Users can also sort any column easily by clicking the column header. This function is very useful for users who want to check the highest coexpression or confidence level in the output of interologs. This predicted interactome is not without limits however, and some caution should be observed before concluding on any positive and especially negative results (interactions expected but not found). Firstly, the interactions predicted are based on orthology between rice and other eukaroytes with experimentally-determined interactomes. Other than about 4000 interactions in Arabidopsis, no other plant has a large number of experimentally-determined interactions to draw from. With the possible exception of yeast (S. cerevisiae), none of the experimental data sets from reference organisms are complete. The interactions contained in this data set are of evolutionarily conserved proteins and pathways. However as the experimental data are based on high throughput studies, the predicted rice interactome does not represent only well studied interactions like the proteosome, ribosome and base transcriptional machinery: there are still some surprising connections and a number of unknown proteins within the data set as well.

Note added in revision: we expanded the number of predictions by incorporating Braun et al.’s Arabidopsis Interactome data set (Arabidopsis Interactome Mapping 2011) and also from an Arabidopsis G-protein network (Klopffleisch et al. 2011). A predicted rice interactome by Gu et al. (2011) was published while this manuscript was under review. While Gu et al. used similar methodologies, our study had significantly more reference species and thus has improved resolution of conserved interactions.

Methods

Prediction of orthologs

Peptide sequences from rice (Oryza sativa subsp. japonica), Arabidopsis thaliana, Homo sapiens, Mus musculus, Rattus norvegicus, Drosophila melanogaster, Caenorhabditis elegans, Saccharomyces cerevisiae, Schizosaccharomyces pombe, Escherichia coli, Bacillus subtilis, Helicobacter pylori were retrieved from TAIR (http://www.arabidopsis.org; Swarbreck et al., 2008), ENSEMBL (http://www.ensembl.org/index.html; Flicek et al., 2010) for the prediction of orthologs using INPARANOID 3.0 (inparanoid.cgb.ki.se, O'Brien et al. 2005) at BLOSUM 80 for rice-A. thaliana, BLOSUM 80 and 62 for rice- H. sapiens, M. musculus, R. norvegicus and S. cerevisiae; BLOSUM 62 for D. melanogaster, C. elegans, Sch. pombe and BLOSUM 45 for E. coli, B. subtilis and H. pylori. Only ortholog pairs that had a score of 100% confidence (From INPARANOID 3.0) were retrieved for the prediction of rice interactome.

Mapping of rice orthologs to reference interactomes

Interactome databases were obtained from BioGRID (version 47; http://www.thebiogrid.org; Breitkreutz et al., 2008), DIP (October 2008 release; dip.doe.mbi.ucla.edu; Salwinski et al., 2004); and IntAct (downloaded December 5, 2009, http://www.ebi.ac.uk/intact; Aranda et al., 2010), respectively. Rice orthologs were mapped onto interactome data using a cross-reference tables assembled from identifiers retrieved from ENSEMBL, The Arabidopsis Information Resource (TAIR; Swarbreck et al., 2008) and The Institute for Genomic Research (JCVI; http://www.jcvi.org; formerly TIGR). A predicted interactome was recorded in an Excel spreadsheet when both interacting proteins in a reference species had orthologs in rice. Duplicates of the same interactions were removed from different interactome data sets and from different reference species to generate unique interacting protein pairs.

Calculation of the confidence value (CV) for experimental support

The confidence value (CV) for experimental support of a predicted interaction was calculated using the following formula, CV = N x E x S; where N is the total number of data sets in which the interaction was recorded, E is the number of different experimental methods from which an interaction was inferred, and S is the number of reference species from which the same interaction was recorded. The higher the CV, the more likely an interaction is to be conserved in multiple species and to be more convincing as it is supported by different experimental methods. The interactions were classified according to their CV to three groups: low confidence (CV = 1), medium confidence (CV = 2 to 10) and high confidence (CV > 10).

Mapping of rice orthologs to Gene Ontology (GO)

Gene Ontology (GO) terms for rice were retrieved from the GO site (Revision 1.52 validated 30 August 2008; http://geneontology.org). The top 7 enriched GOSlim terms for molecular functions (MF) and cellular component (CC), respectively; were used as node features in the visualization of protein network using Cytoscape 2.6 (Lopes et al., 2010; chianti.ucsd.edu).

Mapping of rice orthologs to PFAM domains

The PFAM data of rice proteins were retrieved from Rice Genome Annotation Project from Michigan State University, USA (rice.plantbiology.msu.edu/). Some of the proteins involved in the interactome may possess more than one PFAM domain, thus all possible pairs of PFAM domains of the interacting protein pairs were queried using MySql (Additional file 4: Table S4). The fold enrichment (F) was calculated as the observed number of PFAM pairs in the rice predicted interactions (O) divided by the expected number of PFAM pairs in the rice predicted interactions (E), where E is the total number of PFAM pairs in the rice predicted interactions x (frequency of PFAM domain of protein A x frequency of PFAM domain of protein B found in the total number of unique interacting proteins).

Subcellular localization prediction and enrichment analysis

The subcellular localization of 67,393 rice proteins was predicted by Plant-mPLoc which covers 12 distinct cellular compartments: chloroplast, nucleus, cytoplasm, mitochondrion, cell wall, cell membrane, peroxisome, endoplasm reticulum, golgi apparatus, vacuole, plastid and extracellular. Due to the existence of splice variants and the nature of protein sorting and trafficking, 8,970 proteins have multiple predicted subcellular localizations. To assess the statistical significance of the enrichment of different subcellular compartment pairs in the predicted rice interactome, we used a randomization algorithm. The P value for the observed numbers of interologs is calculated using a Poisson
P ( n ab ) = { j = 0 n ab n ab j e ( n a b ¯ ) j ! n ab < n ab ¯ ( d e p l a t i o n ) j = n ab n ab j e ( n a b ¯ ) j ! n ab < n ab ¯ ( e n r i c h m e m t )
(1)

distribution:

Here n ab is the observed number of interologs in our predict interactome where one protein is in compartment a and the other in b. n ab is given by:
n ab = j i < j ( C ia C jb O R C ib C ja ) e i j
(2)
  • C ia  = 1 or 0, whether protein i is in compartment a or not

  • C ib  = 1 or 0, whether protein i is in compartment b or not

  • e ij  = 1 or 0, whether protein i and protein j interacts or not

Here n ab is the expected number of interologs with one protein in location a and the other in location b for the ensemble of random protein networks that maintain the following properties as the predicted network: the annotation of proteins in their subcellular localization, the degree (kk) of each protein (the number of proteins that interact with it), and the total number of interacting pairs (EE), n ab is given by
n ab ¯ = j i < j ( C ia C jb O R C ib C ja ) e ij ¯ e ij ¯ = k i k j ( 2 E + k 1 k j ) a n d E = 1 2 i k i
(3)
  • C ia  = 1 or 0, whether protein i is in compartment a or not

  • C ib  = 1 or 0, whether protein i is in compartment b or not

  • k i ,k j  = the node degree of protein(node) i,j

  • eij = the probability of protein(node) i and protein(node) j interacts

  • E = total number of interactions(edges)

The P values are finally subject to a multiple-test correction P(multi) = 1 − (1−P)m where for enrichment m equals the number of ab pairs with at least one observed interolog and for depletion m equals the number of ab pairs possible in the ensemble of random networks.

Coexpression analysis

Coexpression between interacting proteins was determined using the Pearson correlation coefficient
r = 1 N i = 1. N X i X ¯ σ x Y i Y ¯ σ y
(4)

Where N is the number of expression samples, X is the expression level for gene X in ith sample, and Y is the expression level for gene Y in the ith sample (−1≤ r ≤1).

High positive r values indicate a correlation of expression patterns, while low negative r values indicated anti-correlation. A large multi data set compendium consisting of 165 diverse data sets (see Additional file 3: Table S3) was used to generate an accurate representation of gene expression. To determine whether the interolog coexpression distribution was enriched in pairs that exhibited high correlation coefficients, we performed a two-sample Kolomogorov-Smirnov test on the interolog and random distributions. Random distributions were generated by randomly generating protein pairs within the protein collection of our predicted interactome, from any of proteins in whole rice genome, or from any protein in whole rice genome such that the topology of this random set matched that of our predicted interactome in terms of degree distribution.

Declarations

Acknowledgements

C.-L. Ho would like to acknowledge the funding of Fulbright Visiting Scholar Grant from the Department of State of U.S.A. and Universiti Putra Malaysia; and the facilities provided by the Department of Plant Biology, Southern Illinois University Carbondale for her research.

Authors’ Affiliations

(1)
Faculty of Biotechnology and Biomolecular Sciences, Universiti Putra Malaysia
(2)
Department of Cell & Systems Biology/Centre for the Analysis of Genome Evolution and Function, University of Toronto
(3)
Department of Automation, Shanghai Jiao Tong University
(4)
Department of Cell & Systems Biology/Centre for the Analysis of Genome Evolution and Function, University of Toronto
(5)
Department of Plant Biology, Southern Illinois University Carbondale

References

  1. Alon U: Network motifs: theory and experimental approaches. Nat Rev Genet 2007, 8: 450–461. 10.1038/nrg2102View ArticlePubMedGoogle Scholar
  2. Arabidopsis Interactome Mapping Consortium: Evidence for network evolution in an Arabidopsis interactome map. Science 2011, 333: 601–607.PubMed CentralView ArticleGoogle Scholar
  3. Aranda B, Achuthan P, Alam-Faruque Y, Armean I, Bridge A, Derow C, Feuermann M, Ghanbarian AT, Kerrien S, Khadake J, Kerssemakers J, Leroy C, Menden M, Michaut M, Montecchi-Palazzi L, Neuhauser SN, Orchard S, Perreau V, Roechert B, van Eijk K, Hermjakob H: The IntAct molecular interaction database in 2010. Nucleic Acids Res 2010, 38: D525-D531. 10.1093/nar/gkp878PubMed CentralView ArticlePubMedGoogle Scholar
  4. Arora R, Agarwal P, Ray S, Singh A, Singh V, Tyagi A, Kapoor S: MADS-box gene family in rice: genome-wide identification, organization and expression profiling during reproductive development and stress. BMC Genomics 2007, 8: 242. 10.1186/1471-2164-8-242PubMed CentralView ArticlePubMedGoogle Scholar
  5. Bader GD, Donaldson I, Wolting C, Ouellette BFF, Pawson T, Hogue CWV: BIND - The biomolecular interaction network database. Nucleic Acids Res 2001, 29: 242–245. 10.1093/nar/29.1.242PubMed CentralView ArticlePubMedGoogle Scholar
  6. Arabidopsis Interactome Mapping C: Evidence for network evolution in an Arabidopsis interactome map. Science 2011, 333: 601–607.View ArticleGoogle Scholar
  7. Breitkreutz BJ, Stark C, Reguly T, Boucher L, Breitkreutz A, Livstone M, Oughtred R, Lackner DH, Bahler J, Wood V, Dolinski K, Tyers M: The BioGRID interaction database: 2008 update. Nucleic Acids Res 2008, 36: D637-D640.PubMed CentralView ArticlePubMedGoogle Scholar
  8. Ceol A, Chatr Aryamontri A, Licata L, Peluso D, Briganti L, Perfetto L, Castagnoli L, Cesareni G: MINT, the molecular interaction database. Nucleic Acids Res 2009, 38: D532-D539.PubMed CentralView ArticlePubMedGoogle Scholar
  9. Chou KC, Shen HB: Plant-mPLoc: a top-down strategy to augment the power for predicting plant protein subcellular localization. PLoS One 2010, 5: e9931. 10.1371PubMed CentralView ArticlePubMedGoogle Scholar
  10. Cler E, Papai G, Schultz P, Davidson I: Recent advances in understanding the structure and function of general transcription factor TFIID. Cell Mol Life Sci 2009, 66: 2123–2134. 10.1007/s00018-009-0009-3View ArticlePubMedGoogle Scholar
  11. Cooper B, Clarke JD, Budworth P, Kreps J, Hutchison D, Park S, Guimil S, Dunn M, Luginbuhl P, Ellero C, Goff SA, Glazebrook J: A network of rice genes associated with stress response and seed development. Proc Natl Acad Sci USA 2003, 100: 4945–4950. 10.1073/pnas.0737574100PubMed CentralView ArticlePubMedGoogle Scholar
  12. Cooper B, Hutchison D, Park S, Guimil S, Luginbuhl P, Ellero C, Goff SA, Glazebrook J: Identification of rice (Oryza sativa) proteins linked to the cyclin-mediated regulation of the cell cycle. Plant Mol Biol 2003, 53: 273–279.View ArticlePubMedGoogle Scholar
  13. de Folter S, Immink RGH, Kieffer M, Parenicova L, Henz SR, Weigel D, Busscher M, Kooiker M, Colombo L, Kater MM, Davies B, Angenent GC: Comprehensive interaction map of the Arabidopsis MADS box transcription factors. Plant Cell 2005, 17: 1424–1433. 10.1105/tpc.105.031831PubMed CentralView ArticlePubMedGoogle Scholar
  14. Ding XD, Richter T, Chen M, Fujii H, Seo YS, Xie MT, Zheng XW, Kanrar S, Stevenson RA, Dardick C, Li Y, Jiang H, Zhang Y, Yu FH, Bartley LE, Chern M, Bart R, Chen XH, Zhu LH, Farmerie WG, Gribskov M, Zhu JK, Fromm ME, Ronald PC, Song WY: A Rice kinase-protein interaction map. Plant Physiol 2009, 149: 1478–1492. 10.1104/pp.108.128298PubMed CentralView ArticlePubMedGoogle Scholar
  15. Donoso I, Munoz-Centeno MC, Sanchez-Duran MA, Flores A, Daga RR, Guevara CM, Bejarano ER: Mpg1, a fission yeast protein required for proper septum structure, is involved in cell cycle progression through cell-size checkpoint. Mol Genet Genomics 2005, 274: 155–167. 10.1007/s00438-005-0005-8View ArticlePubMedGoogle Scholar
  16. Dortay H, Gruhn N, Pfeifer A, Schwerdtner M, Schmuälling T, Heyl A: Toward an Interaction Map of the two-component signaling pathway of Arabidopsis thaliana. J Proteome Res 2008, 7: 3649–3660. 10.1021/pr0703831View ArticlePubMedGoogle Scholar
  17. Flicek P, Aken BL, Ballester B, Beal K, Bragin E, Brent S, Chen Y, Clapham P, Coates G, Fairley S, Fitzgerald S, Fernandez-Banet J, Gordon L, Graf S, Haider S, Hammond M, Howe K, Jenkinson A, Johnson N, Kahari A, Keefe D, Keenan S, Kinsella R, Kokocinski F, Koscielny G, Kulesha E, Lawson D, Longden I, Massingham T, McLaren W, Megy K, Overduin B, Pritchard B, Rios D, Ruffier M, Schuster M, Slater G, Smedley D, Spudich G, Tang YA, Trevanion S, Vilella A, Vogel J, White S, Wilder SP, Zadissa A, Birney E, Cunningham F, Dunham I, Durbin R, Fernandez-Suarez XM, Herrero J, Hubbard TJP, Parker A, Proctor G, Smith J, Searle SMJ: Ensembl's 10th year. Nucleic Acids Res 2010, 38: D557-D562. 10.1093/nar/gkp972PubMed CentralView ArticlePubMedGoogle Scholar
  18. Gandhi TKB, Zhong J, Mathivanan S, Karthick L, Chandrika KN, Mohan SS, Sharma S, Pinkert S, Nagaraju S, Periaswamy B, Mishra G, Nandakumar K, Shen BY, Deshpande N, Nayak R, Sarker M, Boeke JD, Parmigiani G, Schultz J, Bader JS, Pandey A: Analysis of the human protein interactome and comparison with yeast, worm and fly interaction datasets. Nat Genet 2006, 38: 285–293. 10.1038/ng1747View ArticlePubMedGoogle Scholar
  19. Geisler-Lee J, O'Toole N, Ammar R, Provart NJ, Millar AH, Geisler M: A predicted interactome for Arabidopsis. Plant Physiol 2007, 145: 317–329. 10.1104/pp.107.103465PubMed CentralView ArticlePubMedGoogle Scholar
  20. Giot L, Bader JS, Brouwer C, Chaudhuri A, Kuang B, Li Y, Hao YL, Ooi CE, Godwin B, Vitols E, Vijayadamodar G, Pochart P, Machineni H, Welsh M, Kong Y, Zerhusen B, Malcolm R, Varrone Z, Collis A, Minto M, Burgess S, McDaniel L, Stimpson E, Spriggs F, Williams J, Neurath K, Ioime N, Agee M, Voss E, Furtak K, Renzulli R, Aanensen N, Carrolla S, Bickelhaupt E, Lazovatsky Y, DaSilva A, Zhong J, Stanyon CA, Finley RL, White KP, Braverman M, Jarvie T, Gold S, Leach M, Knight J, Shimkets RA, McKenna MP, Chant J, Rothberg JM: A protein interaction map of Drosophila melanogaster. Science 2003, 302: 1727–1736. 10.1126/science.1090289View ArticlePubMedGoogle Scholar
  21. Goldberg AL, Rock KL: Proteolysis, proteasomes and antigen presentation. Nature 1992, 357: 375–379. 10.1038/357375a0View ArticlePubMedGoogle Scholar
  22. Gorynia S, Matias PM, Goncalves S, Coelho R, Lopes G, Thomaz M, Huber M, Haendler B, Donner P, Carrondo MA: Expression, purification, crystallization and preliminary X-ray analysis of the human RuvB-like protein RuvBL1. Acta Crystallogr Sect F Struct Biol Cryst Commun 2006, 62: 61–66. 10.1107/S1744309105041400PubMed CentralView ArticlePubMedGoogle Scholar
  23. Gregis V, Sessa A, Colombo L, Kater MM: AGL24, SHORT VEGETATIVE PHASE, and APETALA1 redundantly control AGAMOUS during early stages of flower development in Arabidopsis. Plant Cell 2006, 18: 1373–1382. 10.1105/tpc.106.041798PubMed CentralView ArticlePubMedGoogle Scholar
  24. Gu Y, Deng Z, Paredez AR, DeBolt S, Wang ZH, Somerville C: Prefoldin 6 is required for normal microtubule dynamics and organization in Arabidopsis. PNAS 2008,105(46):18064–18069. 10.1073/pnas.0808652105PubMed CentralView ArticlePubMedGoogle Scholar
  25. Gu H, Zhu P, Jiao Y, Meng Y, Chen M: PRIN: a predicted rice interactome network. BMC Bioinformatics 2011, 12: 161. 10.1186/1471-2105-12-161PubMed CentralView ArticlePubMedGoogle Scholar
  26. Hilt W, Wolf DH: Proteasomes: destruction as a programme. Trends Biochem Sci 1996, 21: 96–102.View ArticlePubMedGoogle Scholar
  27. Klopffleisch K, Phan N, Augustin K, Bayne RS, Booker KS, Botella JR, Carpita NC, Carr T, Chen J-G, Cooke TR, Frick-Cheng A, Friedman EJ, Fulk B, Hahn MG, Jiang K, Jorda L, Kruppe L, Liu C, Lorek J, McCann MC, Molina A, Moriyama EN, Mukhtar MS, Mudgil Y, Pattathil S, Schwarz J, Seta S, Tan M, Temp U, Trusov Y, Urano D, Welter B, Yang J, Panstruga R, Uhrig JF, Jones AM: Arabidopsis G-protein interactome reveals connections to cell wall carbohydrates and morphogenesis. Mol Syst Biol 2011,7(Article number):532.PubMed CentralPubMedGoogle Scholar
  28. Li SM, Armstrong CM, Bertin N, Ge H, Milstein S, Boxem M, Vidalain PO, Han JDJ, Chesneau A, Hao T, Goldberg DS, Li N, Martinez M, Rual JF, Lamesch P, Xu L, Tewari M, Wong SL, Zhang LV, Berriz GF, Jacotot L, Vaglio P, Reboul J, Hirozane-Kishikawa T, Li QR, Gabel HW, Elewa A, Baumgartner B, Rose DJ, Yu HY, Bosak S, Sequerra R, Fraser A, Mango SE, Saxton WM, Strome S, van den Heuvel S, Piano F, Vandenhaute J, Sardet C, Gerstein M, Doucette-Stamm L, Gunsalus KC, Harper JW, Cusick ME, Roth FP, Hill DE, Vidal M: A map of the interactome network of the metazoan C-elegans. Science 2004, 303: 540–543. 10.1126/science.1091403PubMed CentralView ArticlePubMedGoogle Scholar
  29. Liu JX, Howell SH: bZIP28 and NF-Y Transcription factors are activated by ER stress and assemble into a transcriptional complex to regulate stress response genes in Arabidopsis. Plant Cell 2010, 22: 782–796. 10.1105/tpc.109.072173PubMed CentralView ArticlePubMedGoogle Scholar
  30. 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. 10.1093/bioinformatics/btq430PubMed CentralView ArticlePubMedGoogle Scholar
  31. Miller JP, Lo RS, Ben-Hur A, Desmarais C, Stagljar I, Noble WS, Fields S: Large-scale identification of yeast integral membrane protein interactions. Proc Natl Acad Sci USA 2005, 102: 12123–12128. 10.1073/pnas.0505482102PubMed CentralView ArticlePubMedGoogle Scholar
  32. Narayanan M, Vetta A, Schadt EE, Zhu J: Simultaneous clustering of multiple gene expression and physical interaction datasets. PLoS Comput Biol 2010, 6: 4.View ArticleGoogle Scholar
  33. O'Brien KP, Remm M, Sonnhammer ELL: Inparanoid: a comprehensive database of eukaryotic orthologs. Nucleic Acids Res 2005, 33: D476-D480.PubMed CentralView ArticlePubMedGoogle Scholar
  34. Pagel P, Kovac S, Oesterheld M, Brauner B, Dunger-Kaltenbach I, Frishman G, Montrone C, Mark P, Stumpflen V, Mewes HW, Ruepp A, Frishman D: The MIPS mammalian protein-protein interaction database. Bioinformatics 2005, 21: 832–834. 10.1093/bioinformatics/bti115View ArticlePubMedGoogle Scholar
  35. Pelaz S, Ditta GS, Baumann E, Wisman E, Yanofsky MF: B and C floral organ identity functions require SEPALLATA MADS-box genes. Nature 2000, 405: 200–203. 10.1038/35012103View ArticlePubMedGoogle Scholar
  36. Rivett AJ: Proteasomes - Multicatalytic Proteinase Complexes. Biochem J 1993, 291: 1–10.PubMed CentralView ArticlePubMedGoogle Scholar
  37. Rounsley SD, Ditta GS, Yanofsky MF: Diverse roles for mads box genes in Arabidopsis development. Plant Cell 1995, 7: 1259–1269.PubMed CentralView ArticlePubMedGoogle Scholar
  38. Rual JF, Venkatesan K, Hao T, Hirozane-Kishikawa T, Dricot A, Li N, Berriz GF, Gibbons FD, Dreze M, Ayivi-Guedehoussou N, Klitgord N, Simon C, Boxem M, Milstein S, Rosenberg J, Goldberg DS, Zhang LV, Wong SL, Franklin G, Li SM, Albala JS, Lim JH, Fraughton C, Llamosas E, Cevik S, Bex C, Lamesch P, Sikorski RS, Vandenhaute J, Zoghbi HY, Smolyar A, Bosak S, Sequerra R, Doucette-Stamm L, Cusick ME, Hill DE, Roth FP, Vidal M: Towards a proteome-scale map of the human protein-protein interaction network. Nature 2005, 437: 1173–1178. 10.1038/nature04209View ArticlePubMedGoogle Scholar
  39. Salwinski L, Miller CS, Smith AJ, Pettit FK, Bowie JU, Eisenberg D: The database of interacting proteins: 2004 update. Nucleic Acids Res 2004, 32: D449-D451. 10.1093/nar/gkh086PubMed CentralView ArticlePubMedGoogle Scholar
  40. Springer PS, Holding DR, Groover A, Yordan C, Martienssen RA: The essential Mcm7 protein PROLIFERA is localized to the nucleus of dividing cells during the G(1) phase and is required maternally for early Arabidopsis development. Development 2000, 127: 1815–1822.PubMedGoogle Scholar
  41. Sridhar VV, Surendrarao A, Liu ZC: APETALA1 and SEPALLATA3 interact with SEUSS to mediate transcription repression during flower development (vol 133, pg 3159, 2001). Development 2006, 133: 3496.View ArticleGoogle Scholar
  42. Swarbreck D, Wilks C, Lamesch P, Berardini TZ, Garcia-Hernandez M, Foerster H, Li D, Meyer T, Muller R, Ploetz L, Radenbaugh A, Singh S, Swing V, Tissier C, Zhang P, Huala E: The Arabidopsis information resource (TAIR): gene structure and function annotation. Nucleic Acids Res 2008, 36: D1009-D1014.PubMed CentralView ArticlePubMedGoogle Scholar
  43. Theissen G, Melzer R: Molecular mechanisms underlying origin and diversification of the angiosperm flower. Ann Bot 2007, 100: 603–619. 10.1093/aob/mcm143PubMed CentralView ArticlePubMedGoogle Scholar
  44. Toufighi K, Brady SM, Austin R, Ly E, Provart NJ: The botany array resource: e-Northerns, expression angling, and promoter analyses. Plant J 2005, 43: 153–163. 10.1111/j.1365-313X.2005.02437.xView ArticlePubMedGoogle Scholar
  45. Uetz P, Giot L, Cagney G, Mansfield TA, Judson RS, Knight JR, Lockshon D, Narayan V, Srinivasan M, Pochart P, Qureshi-Emili A, Li Y, Godwin B, Conover D, Kalbfleisch T, Vijayadamodar G, Yang MJ, Johnston M, Fields S, Rothberg JM: A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature 2000, 403: 623–627. 10.1038/35001009View ArticlePubMedGoogle Scholar
  46. Wiles AM, Doderer M, Ruan JH, Gu TT, Ravi D, Blackman B, Bishop AJR: Building and analyzing protein interactome networks by cross-species comparisons. BMC Syst Biol 2010, 4: 36. 10.1186/1752-0509-4-36PubMed CentralView ArticlePubMedGoogle Scholar
  47. Wilk S: Proteasomes - multicatalytic proteinase complexes. Enzyme protein 1993, 47: 187–188.PubMedGoogle Scholar
  48. Yung MH, Schaffer R, Putterill J: Identification of genes expressed during early Arabidopsis carpel development by mRNA differential display: characterisation of ATCEL2, a novel endo-1,4-beta-D-glucanase gene. Plant J 1999, 17: 203–208. 10.1046/j.1365-313X.1999.00359.xView ArticlePubMedGoogle Scholar
  49. Zanzoni A, Montecchi-Palazzi L, Quondam M, Ausiello G, Helmer-Citterich M, Cesareni G: MINT: a molecular interaction database. FEBS Lett 2002, 513: 135–140. 10.1016/S0014-5793(01)03293-8View ArticlePubMedGoogle Scholar
  50. Zaslaver A, Mayo AE, Rosenberg R, Bashkin P, Sberro H, Tsalyuk M, Surette MG, Alon U: Just-in-time transcription program in metabolic pathways. Nat Genet 2004, 36: 486–491. 10.1038/ng1348View ArticlePubMedGoogle Scholar

Copyright

© Ho et al.; licensee Springer. 2012

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/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.