Unraveling Transcriptional Control in Arabidopsis Using ... · Bioinformatics Unraveling Transcriptional Control in Arabidopsis Using cis-Regulatory Elements and Coexpression Networks1[C][W]

Bioinformatics

Unraveling Transcriptional Control in Arabidopsis Usingcis-Regulatory Elements and Coexpression Networks1[C][W]

Klaas Vandepoele, Mauricio Quimbaya, Tine Casneuf2, Lieven De Veylder, and Yves Van de Peer*

Department of Plant Systems Biology, Flanders Institute for Biotechnology, B–9052 Ghent, Belgium; andDepartment of Plant Biotechnology and Genetics, Ghent University, B–9052 Ghent, Belgium

Analysis of gene expression data generated by high-throughput microarray transcript profiling experiments has demonstratedthat genes with an overall similar expression pattern are often enriched for similar functions. This guilt-by-associationprinciple can be applied to define modular gene programs, identify cis-regulatory elements, or predict gene functions forunknown genes based on their coexpression neighborhood. We evaluated the potential to use Gene Ontology (GO) enrichmentof a gene’s coexpression neighborhood as a tool to predict its function but found overall low sensitivity scores (13%–34%). Thisindicates that for many functional categories, coexpression alone performs poorly to infer known biological gene functions.However, integration of cis-regulatory elements shows that 46% of the gene coexpression neighborhoods are enriched for oneor more motifs, providing a valuable complementary source to functionally annotate genes. Through the integration ofcoexpression data, GO annotations, and a set of known cis-regulatory elements combined with a novel set of evolutionarilyconserved plant motifs, we could link many genes and motifs to specific biological functions. Application of our coexpressionframework extended with cis-regulatory element analysis on transcriptome data from the cell cycle-related transcription factorOBP1 yielded several coexpressed modules associated with specific cis-regulatory elements. Moreover, our analysis stronglysuggests a feed-forward regulatory interaction between OBP1 and the E2F pathway. The ATCOECIS resource (http://bioinformatics.psb.ugent.be/ATCOECIS/) makes it possible to query coexpression data and GO and cis-regulatory elementannotations and to submit user-defined gene sets for motif analysis, providing an access point to unravel the regulatory codeunderlying transcriptional control in Arabidopsis (Arabidopsis thaliana).

The rapid accumulation of genome-wide data de-scribing both genome sequences and functional prop-erties of genes facilitates the development of systemsbiology approaches. Especially the application of mi-croarray experiments for several model organismsnow provides us with detailed catalogs of condition-dependent transcriptional activity during develop-ment, in different organs, cell types, or in response tovarious endo- or exogenous stimuli (Birnbaum et al.,2003; Schmid et al., 2005). In plants, transcriptionalregulation is mediated by a large number (.1,500)of transcription factors (TFs) controlling the expres-

sion of tens or hundreds of target genes in various,sometimes intertwined, signal transduction cascades(Wellmer and Riechmann, 2005). Whereas the similar-ity in gene expression patterns can be used to infermodular gene programs (or regulatory networks), theintegration of expression and sequence data makes itpossible to identify cis-regulatory elements, the func-tional elements responsible for the timing and locationof transcriptional activity (Haberer et al., 2006; Maet al., 2007). TF-binding sites (or DNA sequence mo-tifs, referred to as motifs) are the functional elementsthat determine the timing and location of transcrip-tional activity. Also, the identification of differentiallyexpressed genes in response to a treatment/stimulusor in a transgenic overexpression/knockout experi-ment can identify new target genes and provide in-sights into the underlying regulatory interactions(Vandepoele et al., 2005; Zhang et al., 2005).

Systematic computational analysis of DNA motifsillustrated the presence of TATA boxes as well as Ypatches characterizing a large fraction of plant corepromoters (Yamamoto et al., 2007). Other motifs havebeen described showing strong position- and/orstrand-dependent localization, and a subset of thesecorrespond to known cis-regulatory elements (Molinaand Grotewold, 2005; Obayashi et al., 2007; Yamamotoet al., 2007). Through the combination of motif map-ping data on Arabidopsis (Arabidopsis thaliana) pro-moters with gene expression patterns, Walther et al.(2007) found a positive correlation between multi-

1 This workwas supported by the Interuniversity Attraction PolesProgramme (IUAP VI/25 and VI/33), initiated by the Belgian State,Science Policy Office. K.V. and L.D.V. are postdoctoral fellows of theResearch Foundation-Flanders.

2 Present address: Ortho Biotech Oncology Research and Devel-opment, a division of Janssen Pharmaceutica, Turnhoutseweg 30,B–2340 Beerse, Belgium.

* Corresponding author; e-mail [email protected] author responsible for the distribution of materials integral to

the findings presented in this article in accordance with the policydescribed in the Instructions for Authors (www.plantphysiol.org) is:Yves Van de Peer ([email protected]).

[C] Some figures in this article are displayed in color online but inblack and white in the print edition.

[W] The online version of this article contains Web-only data.www.plantphysiol.org/cgi/doi/10.1104/pp.109.136028

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stimuli response genes and cis-element density in up-stream regions. Studies focusing on the combinatorialnature of transcriptional control have identified severalexamples of cooperative elements (or cis-regulatorymodules) driving time-of-day-specific expression pat-terns or regulating genes involved in processes such asphotosynthesis or protein biosynthesis (Vandepoeleet al., 2006; Michael et al., 2008). Interestingly, evolu-tionary analysis suggests that these regulatory modulesare conserved between species belonging to differentplant families (Kim et al., 2006).

The exploitation of the idea that correlated expres-sion implies a biological relevant relationship resultedin the development of several meta-analysis tools thatinfer Arabidopsis gene functions using a guilt-by-association principle, such as ACT (Jen et al., 2006),ATTED-II (Obayashi et al., 2007), and CressExpress(Srinivasasainagendra et al., 2008). In general, thesemethods determine, for a gene of interest, a set ofcoexpressed genes, while significant functional anno-tations in the gene’s coexpression neighborhood areused to draw new biological hypotheses. The GeneOntology (GO) or AraCyc functional annotation sys-tems in combination with a statistical test are mostlyused to determine functional enrichment. While gen-erally coexpression networks cover all correlated ex-pression patterns between genes within an expressioncompendium, detailed analysis of the topology ornode-to-node relationships within the network pro-vides an overview of the organization and complexityof transcriptional regulation. For example, Perssonet al. (2005) nicely illustrated the existence of severalcoexpression clusters corresponding to functionalmodules involved in primary and secondary cellwall formation. Similarly, Ma et al. (2007) identifiedseveral highly connected subclusters in an Arabidop-sis gene network grouping genes related to biochem-ical pathways and cold stress. Besides the genecoexpression networks within one organism, the com-parison of expression data between different speciesusing orthologous genes makes it possible to identifyevolutionarily conserved regulatory programs as wellas species-specific adaptations in response to changesin lifestyle or environmental conditions (Stuart et al.,2003).

Although these examples demonstrate the potentialof coexpression-based meta-analysis, our current un-derstanding of the relationship between regulatoryelements and the observed expression states in differ-ent developmental stages, tissues, or treatments re-mains limited. The main objectives of this study were(1) to analyze the properties and the functional pre-dictive power of coexpression networks in Arabidop-sis, (2) to extend coexpression frameworks withinformation about cis-regulatory elements to function-ally annotate genes, (3) to apply GO and motif enrich-ment analysis to dissect cell cycle regulatory controlusing publicly available transcriptome data, and (4) tostudy the organization of cis-regulatory elements inArabidopsis promoters.

RESULTS

The Biological Significance of Expression Similarity

Starting from a set of 322 Affymetrix ATH1 micro-array slides retrieved from various publicly availablesources, data normalization and averaging of repli-cates resulted in a nonredundant expression data set of129 experiments (see “Materials andMethods”). Usinga custom-made chip description file (CDF) groupingonly non-cross-hybridizing probes in probe sets(Casneuf et al., 2007), the expression patterns of 19,937genes could be monitored. Although it does not coverall annotated protein-coding genes in Arabidopsis, theCDF file has the advantage that it can reliably measureand discriminate between the expression of both copiesof duplicated gene pairs with valid probe sets (over-coming potential cross-hybridization caused by highsequence similarity).

To verify the guilt-by-association relationship be-tween expression similarity and similarity in gene func-tion for predefined functional sets of genes grouped inGO categories, we first quantified their level of expres-sion similarity using the expression coherence (EC). ECis a measure for the amount of expression similaritywithin a set of genes, ranging between zero and one andis high for sets of genes that converge into one or a fewtight coexpression clusters (Pilpel et al., 2001). As shownin Figure 1A, for many GO categories the EC is higherthan expected by chance. For Biological Process andCellular Component, approximately 41% and 74% of allcategories have EC values higher than expected bychance, respectively, whereas for Molecular Function,36% of the GO categories show elevated coexpressionlevels. Also for genes annotated in biochemical path-ways through AraCyc, 33% of all categories show ECvalues higher than random (Fig. 1B). The highest ECvalues for GO Biological Process cover categories in-volved in photosynthesis (EC = 0.60, 124 genes), por-phyrin biosynthesis (EC = 0.35, 45 genes), ribosomebiogenesis and assembly (EC = 0.44, 114 genes), tetra-terpenoid biosynthesis (EC = 0.31, 21 genes), and starchmetabolism (EC = 0.19, 27 genes). For the AraCycpathways, the categories “glucosinolate biosynthesisfrom Trp,” “photosynthesis, light reaction,” “carotenoidbiosynthesis,” and “urea cycle” all have EC values of.50%. Nevertheless, since most functional categorieshave only low EC values (Supplemental Table S1), theseresults indicate that genes within a functional categorydo not completely correspond to transcriptional mod-ules and suggest that several coexpression subgroupsmight exist for genes annotated in the same functionalcategory.Therefore, anunsupervisedapproachbasedonclustering of expression profiles should offer a betterstrategy to identify transcriptional modules comparedto predefined functional categories. Since the AraCycpathways only group a small number of genes (perpathway) andmanypathwaysare alsopresent in theGOannotation, we only retained GO categories (containing25 or more genes) for further analysis.

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Construction of Arabidopsis Coexpression Networks

Before building a gene coexpression network, ex-pression similarities between gene pairs were calcu-lated using the Pearson correlation coefficient (PCC).To determine valid coexpressed genes, we appliedthree similarity thresholds (PCC higher than 0.63, 0.72,and 0.83) corresponding to the 90th, 95th, and 99thpercentile of a background PCC distribution contain-ing nearly half a million gene pairs sampled from 1,000randomly selected genes. Subsequently, all gene-genecoexpression relationships with PCC values above aselected threshold were grouped resulting in threenetworks (hereafter referred to as ATH90, ATH95, andATH99) for which the estimated amount of false-positive coexpressed gene pairs is 10%, 5%, and 1%,respectively. These networks can be represented asundirected graphs where genes (or nodes) are con-nected by edges if they are coexpressed. Our approachto initially build multiple networks with differentexpression similarity constraints is motivated by thefact that it is difficult a priori to define an optimalthreshold to capture biological knowledge from thenetwork. Therefore, in a first evaluation experiment,we estimated the biological knowledge captured in thethree networks delineated with different similaritythresholds.We used a guide gene clustering method to group

genes with similar expression patterns followed by agene set enrichment analysis (Wolfe et al., 2005; Aokiet al., 2007). For each query gene, guide gene clustersgroup all coexpressed genes, resulting, on a genome-wide scale, in potentially overlapping clusters. GOenrichment analysis was then applied to functionallyannotate coexpression clusters (Fig. 2) and to assessthe predictive power of the three networks to recoverknown functional annotations. For each gene belong-ing to a GO category i, we determined if the functionalGO enrichment in its coexpression guide cluster (orneighborhood) could predict the correct function.Likewise, using sets of randomly selected genes notannotated with GO category i, we estimated the num-ber of false positive predictions. The assessment of the

prediction power using this approach aims at estimat-ing the optimal size of a gene’s coexpression neigh-borhood to retrieve relevant GO enrichments and toassociate unknown genes to specific biological pro-cesses. Although GO function predictions for somenegative genes might correspond to false negatives(i.e. a correctly predicted functional gene associationnot yet described in the current GO annotations), appli-cation of an iterative random sampling proceduremakes it possible to compare the false-positive rates

Figure 1. EC scores for genes func-tionally annotated using GO (A)and AraCyc (B). BP, MF, and CCrefer to Biological Process, Molec-ular Function, and Cellular Com-ponent categories, and the numberof categories is indicated in paren-theses. [See online article for colorversion of this figure.]

Figure 2. Functional enrichment of GO and cis-regulatory elementannotation for guide gene cluster AT5G59220. Lines indicate coex-pression relationships, and colored circles show the functional anno-tation for the individual genes. Enrichment analysis is performed usingthe hypergeometric distribution and Bonferroni correction for multiplehypotheses testing. Results are shown for the ATH95 benchmarkcoexpression network. [See online article for color version of thisfigure.]

cis-Elements and Coexpression Networks in Arabidopsis

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between different GO categories and for different sim-ilarity thresholds (see “Materials and Methods”). Basedon a subset of 50 different GO categories (18 BiologicalProcess, 16 Molecular Function, and 16 Cellular Com-ponents categories, covering in total 11,838 genes), weobserved that the positive predictive value (PPV), orprecision rate, referring to the proportion of genes witha functional prediction being correctly predicted, is thehighest for the ATH90 and ATH95 networks (0.93 and0.92, respectively; Table I; Fig. 3). Complementary to thePPV, the sensitivity (SN), or recall, measures the pro-portion of actual positives (i.e. known functional anno-tations) that are correctly identified as such. Althoughfor GO Biological Process the ATH90 and ATH95networks again have the highest average SN (Fig. 3),their actual values (SN = 0.39; see Table I) indicate thatmany known biological annotations cannot be inferredfrom a gene’s coexpression neighborhood. Assessingthe functional predictive power of a recently publishedArabidopsis gene network (Ma et al., 2007) based on agraphical Gaussian model (called ATHGGM in Table I)reveals that the limited number of genes in this net-work, together with its sparse nature (the mediannumber of coexpressed genes is 4), is responsible forlow PPV and SN values. Since the ATHGGM networkaims to discover regulatory interactions, the low pre-diction scores are not surprising and suggest that itcaptures complementary information compared to co-expression networks. Although for someArabidopsis co-expression platforms, such as ATTED-II, genome-widedata about coexpressed genes are available (Obayashiet al., 2007), the absence of a predefined coexpressionneighborhood for each gene makes it practically impos-sible to systematically evaluate and compare the predic-tive power of other meta-analysis tools.

Comparing the SN scores over the different ontol-ogies in this benchmark experiment shows that, whenrequiring that at least half of the known annotationsare recovered, approximately 22%, 25%, and 56% ofthe GO Biological Process, Molecular Function, andCellular Component categories are retained, respec-tively (see series ATH95 in Fig. 3, A–C). Whereas thePPV and SN values both confirm that the ATH90 andATH95 coexpression networks are better able to infergene functions than the ATH99 network, we selectedthe ATH95 expression similarity threshold for further

analysis because the functional enrichment folds arehigher than those from the ATH90 network (medianenrichment fold of 3.97 and 3.24, respectively).

Properties of the Arabidopsis Coexpression Network

After reconstructing the ATH95 network using all19,937 measurable genes present on the ATH1 micro-array, the final network covers 19,064 genes with amedian number of 548 coexpressed genes. When com-paring the average connectivity per GO category, weobserved that genes involved in processes, such asrRNA metabolism, histone modification, amino acidactivation, photosynthesis, or DNA repair, all have.1,500 coexpression partners (top 5% of connectivitydistribution). In contrast, several categories involvedin “response to” (response to water, reactive oxygenspecies, brassinosteroid stimulus, extracellular stimu-lus, high light intensity, and hyperosmotic salinityresponse) show low average connectivity values (low-est 10% of distribution; Supplemental Table S2). Thisfinding does not indicate that response-to genes areless coexpressed than the general housekeeping func-tions described above. Rather, it suggests that the latterform large coexpression modules compared to thestress-related coexpression modules.

Whereas the average SN (covering all GO cate-gories) of functional predictive power for the completecoexpression network is low (SN = 0.19 with averagePPV of 0.92), several examples of GO categories withgood SN scores can be found (Supplemental Table S3).These include photosynthesis (0.80), ribosome biogen-esis and assembly (0.70), tRNA metabolism (0.64),starch metabolism (0.59), and amino acid activation(0.58). In contrast, very general GO categories receivelow PPV scores due to the large number of putativefalse positive predictions (e.g. PPV Biological Processterm metabolism = 0.035; PPV Molecular Functionterm catalytic activity = 0.16). Although comparingaverage connectivity with SN per GO category sug-gests that primarily genes with large coexpressionneighborhoods yield good prediction SN, plottingboth variables against each other (Supplemental Fig.S1) reveals that also many small coexpression neigh-borhoods provide good predictive power. Examples ofGO categories with small coexpression neighborhoods

Table I. Properties of the different coexpression networks

Network

Name

No. of

GenesNo. of Edges

Median No. of

Edges per Gene

(Lower and

Upper Quartile)

No. of (%) Genes

with GO BP

Enrichment

Average PPV

for GO

Categories

Average SN

for GO

Categories

%

ATH90a 19,716 13,580,283 1,625 (341; 2,988) 18,668 (94%) 93 39ATH95a 18,861 6,765,135 650 (88; 1,590) 16,663 (84%) 92 39ATH99a 14,187 1,504,781 91 (11; 449) 9,566 (50%) 88 33ATHGGM 6,653 25,106 4 (2; 8) 1,232 (19%) 85 9

aBased on 50 GO categories covering 11,838 guide genes.

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but high prediction SN include response to hydrogenperoxide, starch metabolism, and response to highlight intensity (average connectivity , 600 and SN $0.38). Examples of GO categories with low SN scoresbut high connectivity (e.g. protein ubiquitination,meiosis, protein targeting, and posttranscriptionalgene silencing) suggest that the primary regulationof these genes is not at the transcriptional level,explaining the bad prediction scores.

Identification of cis-Regulatory Elements and Integrationwith Coexpression Clusters

Complementary to functional enrichment using GO,we also mapped putative cis-regulatory elements onall genes and calculated motif enrichment for thedifferent gene coexpression clusters. Whereas knownplant cis-regulatory elements were retrieved fromPLACE (Higo et al., 1999) and AGRIS (Palaniswamyet al., 2006), a complementary set of elements wasidentified using the network-level conservation princi-ple,which applies a systems-level constraint (Elementoand Tavazoie, 2005). Briefly, this method exploits thewell-established notion that each TF regulates the ex-pression of many genes in the genome and that theconservation of global gene expression between tworelated species requires that most of these targetsmaintain their regulation. In practice, this assumptionis tested for each candidate motif by determining itspresence in the upstream regions of two related speciesand by calculating the significance of conservation overorthologous genes (see “Materials and Methods”).Whereas the same principle of evolutionary conserva-tion is also applied in phylogenetic footprintingmethods to identify TF-binding sites, it is importantto note that here the conservation of several targets inthe regulatory network is evaluated simultaneouslyand that aligned noncoding DNA sequences are not

required. This is in contrast with standard footprintingapproaches, which only use sequence conservation inupstream regions on a gene-by-gene basis to detectfunctional DNAmotifs. Using motif conservation overorthologous genes between Arabidopsis and poplar(Populus trichocarpa), 866 nonredundant 8-mer motifswith significant Network-level Conservation Scores(NCS; P value , 0.05) were identified. Comparingthese NCS motifs with the known cis-regulatory ele-ments fromPLACEandAGRIS revealed that 63% (544/866) match described elements. Reversely, 24% of theknown motifs show significant evolutionary conserva-tion when applying the network-level conservationprinciple, suggesting that some of these motifs mightbe too stringently defined to show cross-species con-servation or represent species-specific regulatory ele-ments. Plotting the NCS values for the remaining 322NCS motifs not matching known motifs revealed thatthey have similar conservation scores (interquantilevalues 13.91–15.21–17.63) compared to the known mo-tifs (interquantile values 13.81–15.33–17.99). This com-parison indicates that both sets of motifs (i.e. NCSmotifs matching and not matching known motifs) areequally well conserved between Arabidopsis and pop-lar at a genome-wide level and that the newmotifs canbe considered as putative cis-regulatory elements.

Although the network-level conservation methodprovides an elegant way to uncover candidate cis-regulatory elements, identifying individual biologicalfunctional motif instances on promoter sequencesremains problematic. Especially the short and some-times degenerate nature of these 8-mers (or TF-bindingsites in general) yields a large fraction of false-positivemotif matches. Therefore, for NCS motifs, we onlyconsidered Arabidopsis instances showing evolution-ary conservation in one or more orthologous poplarpromoters. This filtering step yielded overall higherenrichment values when validating motif instances

Figure 3. Functional predictive powerfor three benchmark coexpressionnetworks built using different ex-pression similarity thresholds (ATH90,ATH95, and ATH99). A to C showcumulative SN scores for a subsetof GO categories, and D showsoverall cumulative SN scores.





using GO (Table II). In contrast, for known experimen-tally defined plant motifs from PLACE and AGRIS, allmotif instances on Arabidopsis promoters were re-tained for further analysis. Although these databasessometimes report highly similar motifs that might beconsidered as redundant entries, we observed thatin several cases motif variants, when performinggenome-wide mappings, yielded sets of target genesshowing different GO enrichment. For example, whenconsidering the Gbox-related motifs CACGTG,ACACGTG, CACGTGTA, and CACGTGGC, we ob-served that the first two show GO enrichment toresponse to cold, the last motif variant toward photo-synthesis and starch metabolism, and that the thirdmotif with TA suffix does not show any significantenrichment to any of these GO terms. Also, of thesefour motifs, only ACACGTG shows enrichmenttoward response to abscisic acid (ABA) stimulus(P value , 0.017), although the more degenerateACGTGKC PLACEmotif shows a stronger associationwith ABA-responsive genes (P value , 1.1e-04). Sincethese examples confirm the biological relevance ofmotif variants (Geisler et al., 2006), for all PLACE andAGRIS elements motif variants were maintained.

Performing motif enrichment using the completeATH95 network reveals that 46% of the genes have oneor more significant motifs in their coexpression neigh-borhood. In total, 762 of the 866 NCS motifs (or 88%)and 249 of the known 721 motifs (35%) were found tobe enriched. An overview of the 10 most frequentlyenriched NCS motifs, together with their biologicalrole determined using GO enrichment, is shown inTable II. All 10 motifs correspond with well-describedplant cis-regulatory elements. Examples of frequentmotifs include the TELO and UP1 motif driving theexpression of ribosomal genes, the Ibox and Gboxpresent in genes involved in photosynthesis and stressresponse, the ABA-responsive element, the E2F motifregulating DNA replication genes, and the M-specificactivator (MSA) element responsible for M-phasespecificity during the cell cycle. For each motif, thefull set of putative target genes, including GO enrich-

ments, can be found online (http://bioinformatics.psb.ugent.be/ATCOECIS/).

Dissecting the Cell Cycle Regulatory Network UsingE2Fa and OBP1 Target Genes

To test the applicability of our approach to unravelbiological coexpression networks and infer regulatorylogic, we used data from a detailed TF overexpressionexperiment studying cell cycle control in Arabidopsis.Based on transcriptome analysis of OBP1 overexpres-sion lines, Skirycz et al. (2008) recently identified thatthis DNAbindingwith one finger (DOF) TF is involvedin cell cycle initiation. To identify cis-regulatory ele-ments and predict new regulatory interactions, wecombined expression data reporting oscillating tran-scripts in synchronized Arabidopsis cell suspensions(Menges et al., 2003) with clustering, GO, and motifenrichment analysis. For the 632 genes up-regulated byOBP1, a significant enrichment of the correspondingcis-regulatory element TAAAG is observed (Table III).Partitioning the genes using phase expression duringcell division reveals that 69% of the DOF up-regulatedgenes with periodic expression peak at M-phase. Thisexpression pattern is clearly reflected in the motifanalysis with the MSA element being 11-fold enriched(GACCGTTN; P value, 6.64e-30).

The genes repressed by OBP1 show GO enrichmentfor cell wall modification and response to biotic stim-ulus. To study the underlying regulatory control, weapplied the CASTclustering algorithm (Ben-Dor et al.,1999) on our full expression matrix and analyzed thesecoexpression clusters containing five or more DOFdown-regulated genes. Advantages of CASTclusteringover more classical algorithms, such as hierarchical orK-means clustering, are that only two parameters haveto be specified (the affinity measure, here defined asPCC$ 0.72 and the minimal number of genes within acluster, here set to 5) and that it independently deter-mines the total number of clusters and whether a genebelongs to a cluster. In addition, only genes are groupedin a cluster if they all show a minimal expression

Table II. GO enrichment for the 10 most frequent cis-elements enriched in ATH95 gene coexpression neighborhoodsa

Motif No. of Genes Known MotifGO Category Enrichment

GO Label GO Description P Value Foldb

AAACCCTA 2,524 TELO GO:0042254 Ribosome biogenesis and assembly 5.84E-53 7.67 (3.85)CTTATCCN 1,794 Ibox GO:0015979 Photosynthesis 2.18E-87 11.88 (3.15)GGCCCANN 1,601 UP1 GO:0042254 Ribosome biogenesis and assembly 6.08E-68 11.93 (2.68)GCCACGTN 1,475 Gbox GO:0015979 Photosynthesis 1.95E-86 13.55 (n.e.)GCGGGAAN 1,303 E2F GO:0006260 DNA replication 8.89E-26 8.80 (9.40)GACCGTTN 930 MSA GO:0007018 Microtubule-based movement 9.83E-12 9.82 (n.e.)AANGTCAA 389 Wbox GO:0050832 Defense response to fungi 6.22E-08 13.00 (1.50)CNGATCNA 382 AGMOTIFNTMYB2 GO:0048193 Golgi vesicle transport 1.22E-08 17.60 (n.e.)NCGTGTCN 328 ABA-responsive element GO:0009737 Response to ABA stimulus 1.04E-07 6.84 (2.00)CATGCANN 284 RYREPEATBNNAPA GO:0048316 Lipid transport 9.99E-04 8.10 (1.84)

aSimilar motifs with the same GO enrichment trends are not shown. bEnrichment values considering all motif matches on Arabidopsispromoters (i.e. both conserved and nonconserved) are shown in parentheses. n.e., No enrichment.

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similarity with all other genes present in that cluster,yielding global nonoverlapping gene clusters withhomogeneous expression patterns. The largest clustercovers 164 of the 842 down-regulated genes and isstrongly enriched for photosynthesis and the Ibox(CTTATCCN). Additionally, five smaller clusters werefound all showing stress or defense response, of whichtwo also showed motif enrichment. The first clustercontains 25 genes with strong shoot osmotic stressresponse in the expression data and is enriched forANCATGTG (MYCATRD22), a dehydration-responsiveelement. The second cluster contains 11 genes mainlyexpressed in leaf, enriched for GO category “systemicacquired resistance” and motif ACGTCATAGA(LS7ATPR1), a salicylic acid-inducible element involvedin systemically inducible plant defense responses(Despres et al., 2000). Whereas the down-regulation ofseveral stress-responsive regulons coincides with thenegative link between stress and cell proliferation, thedown-regulation of the photosynthetic machinery is inagreement with the lack of Rubisco expression inmeristems (Fleming et al., 1996).The observation that 38 DOF up-regulated genes

peak during S-phase are enriched for the E2F motif(5-fold for GCGGGAAN; P value , 9.97e-06) sug-gested a link between OBP1 and E2F, a well-studiedregulator controlling the activation of genes requiredfor cell cycle progression and DNA replication(Vandepoele et al., 2005). Therefore, we comparedthese DOF target genes and a set of putative E2Ftarget genes that were also identified through micro-array analysis on E2Fa/DPa-overexpressing plants(Vandepoele et al., 2005). Comparing the up-regulatedgenes from the E2Fa and OBP1 experiments revealedthat a significant number of 65 genes are shared

between both overexpression lines (Table III, data setDOF/E2F_UP). Although this set of genes does notshow enrichment for the TAAAG DOF motif, 74% ofthese genes have a WTTSSCSS E2F-binding site intheir promoter. Together with the observation that theTF E2Fa is up-regulated byOBP1, these results suggesta feed-forward mechanism between both regulators.Our hypothesis that the E2Fa TF is a downstreamOBP1 target is in agreement with the observation thatOBP1 is involved in cell cycle initiation in response todevelopmental and environmental signals (Skiryczet al., 2008). Similarly, the strong enrichment of theMSA element in the DOF target genes showing astrong M-phase peak expression suggests that otherfactors are involved in the signaling between OBP1and the activation of these mitotic cell cycle genes.

The Organization of cis-Regulatory Elements inArabidopsis Promoters

Complementary to the enrichment analysis of genecoexpression neighborhoods to gain novel insightsinto gene functions, summarizing all motif instancesover all target genes provides a global view on motiforganization in Arabidopsis. Enrichment analysis ofcis-regulatory elements over all GO categories yieldedseveral examples of strong associations between mo-tifs and biological processes (Fig. 4; for complete lists,see Supplemental Figs. S2 and S3 or the ATCOECISWeb site). Similar to the motif enrichment analysisusing gene coexpression neighborhoods, we foundmore NCS motifs enriched over one or more GOcategories compared to known plant motifs (50% and10%, respectively). Moreover, combining genes fromdifferent GO categories with conserved motif in-

Table III. Regulatory analysis of E2Fa/OBP1 target genes

Features: OBP1_UP OBP1_DOWN E2Fa_UP E2Fa_DOWN OBP1/E2Fa_UP

No. of Genes: 632 842 412 220 65a

Cell cycle-modulated expressionG1 9 13 2 3 2S 38 52 70 13 12G2 4 19 0 1 0M 114 3 3 0 3

Enrichment GOb

GO:0006260 DNA replication 6.6x (2.83E-06) 24.2x (8.59E-36) 55.9x (4.90E-19)GO:0007017 microtubule-based process 9.9x (2.61E-13)GO:0042545 cell wall modification 4.3x (3.52E-03)GO:0009607 response to biotic stimulus 2.5x (5.87E-09)

Enrichment cis-regulatory elementsc

GCGGGAAN (E2F) 3% (9.97E-06) 15% (4.19E-65) 26% (1.11E-20)WTTSSCSS (E2F PLACE) 55% (3.16E-37) 74% (4.72E-14)TAAAG (DOF PLACE) 94% (4.45E-04)GACCGTTN (MSA) 7% (6.64E-30)CTTATCCN (Ibox) 4% (4.56E-05)CCATGTGN (MYCATERD1) 3% (1.95E-04)ANCACATG (MYCATRD22) 6% (2.02E-07)

aOverlap significantly larger than expected by chance (P value , 3.50E-27). bEnrichment fold (P value). cFraction of genes with motif (Pvalue).





stances reveals the existence of specific and global cis-regulatory elements. Whereas more than three-quarters(327/430) of all NCS motifs are only enriched in lessthan five GO categories, the remaining 103 motifs areenriched inmultiple (between 5 and 45) GO categories.Examples of global cis-regulatory elements enriched in15 or more categories are the TELO motif, the Ibox, theE2F motif, and the AGATCTNN motif (SupplementalFig. S2). Also, we found two other (sets of) motifs,CTATATAN and CT-dinucleotide motifs, showingstrong position and strand specificity (i.e. close to thestart codon of the gene and on the same strand of thetranscribed gene) resembling TATA and Y patch corepromoter motifs, respectively. In agreement withYamamoto et al. (2007), the Y path (e.g. ACAGAGNGor CNTCTCTC) is preferentially located closer to thetranscription start site than the TATA motif (Supple-mental Table S4). Examples of specific motifs consist ofthe heat shock element GAANNTTC found to beenriched in “response to heat” genes, a DRE-like motifGNNGACCA enriched in red light signaling genes,and ANGAAAGA enriched in cytokinin-mediatedsignaling genes. When comparing motif position bia-ses (Supplemental Table S4), we found that 40% of theglobal cis-regulatory elements show a preferential

promoter location compared to 12% for the specificelements. This tendency for the former to be prefer-entially located close to the transcription start siteconfirms their role as core promoter elements. Al-though this strong positional bias of CT-dinucleotidemotifs confirms their putative function as core ele-ments, several GO categories were found enriched forthe presence of conserved CT-dinucleotide motifs,suggesting a biological role for these low complexitymotifs. Examples include kinase regulatory activity(70% of genes have CNTCTCTC), microtubule motoractivity (46% of genes have CTCTNCNC), cell wallbiosynthesis (45% of genes have CNTCTCTC), andGolgi membrane localization (52% of genes haveGNCTCTCN). Besides positional biases for individualmotifs, in a set of ribosome biogenesis genes, we founda clear and strict promoter organization when com-paring TELO and UP1 motif instances. On a genome-wide scale, both motifs are significantly enriched inribosomal genes and in 92% of the genes containingboth motifs the TELO motif is located more upstreamthan the UP1 motif (Supplemental Fig. S4). This ob-servation confirms the existence of cis-regulatorymotifsin plants with clear organizational constraints (Kimet al., 2006).

Figure 4. Examples of cis-regulatory motifs showing significant associations with one or more GO categories. GO motifnetworks reveal for different GO categories the fraction of genes having the motif in their promoter (P value , 0.05 using thehypergeometric distribution). The line thickness reflects the motif coverage per GO category and varies from 6% to 70%. Knownmotifs from AGRIS or PLACE are indicated in italics.

Vandepoele et al.




DISCUSSION

The aim of our study was to investigate the appli-cability of coexpression networks to infer functionalinformation for Arabidopsis genes. For a large fractionof genes with similar functional annotation, eitherusing GO categories or AraCyc pathways, elevatedcoexpression levels were found using the EC measure(Wei et al., 2006). Although many of these functionalcategories only partially correspond to transcriptionalmodules, the clustering of expression profiles using agene-centric approach provides a practical startingpoint to study the coexpression neighborhood of acertain gene. Whereas enrichment analysis using GOconfirms the “guilt-by-association” principle for manygenes (Horan et al., 2008), our benchmark experimentquantifying the predictive power of coexpression net-works to infer known functional annotations revealsthat for a majority of biological processes, manyknown GO associations cannot be deduced from thecoexpression network. Although more advanced com-putational classification systems trained for a specificbiological process can partially solve this problem (Liet al., 2006), the integration of information about cis-regulatory elements provides an alternative approachto further characterize gene functions.By combining known plant motifs and a new set of

evolutionarily conservedmotifs,we could annotate 9,117coexpression neighborhoods with one or more motif.Compared to the Pathway-Level Coexpressionmethodimplemented in CressExpress (Srinivasasainagendraet al., 2008), which selects relevant genes if they arecoexpressedwithmultiple query genes, the applicationof a statistical test for enrichment of functional annota-tion provides a robust and complementary method toidentify new genes involved in different biologicalprocesses. Similar GO enrichment tools are also avail-able in Arabidopsis coexpression tools, such as ACT(Jen et al., 2006), ATTED-II (Obayashi et al., 2007), andPlant Gene Expression Database (Horan et al., 2008).Clearly, the annotation of enriched cis-regulatory ele-ments in guide gene clusters provides additionalinformation compared to existing coexpression toolsfor Arabidopsis, such as ACT (Jen et al., 2006), Cress-Express (Srinivasasainagendra et al., 2008), and thePlant Gene Expression Database (Horan et al., 2008).For a set of 866 putative cis-regulatory elementsidentified using the network-level evolutionary con-servation principle, we found that 88% of them aresignificantly enriched in one or more coexpressionneighborhoods and that half of these NCS motifs areenriched in one ormoreGOcategory. Since 37%of thesemotifs do not match any known plant cis-regulatoryelement, the detailed information about conservedmotif instances provides a valuable resource to furtherenlarge our knowledge about transcriptional control inplants. Whereas the ATTED-II coexpression databasealso provides information about cis-regulatory ele-ments, only 7-bp words are used to predict functionalelements using the CEG method (i.e. correlation be-

tween gene expression and a defined gene group(Obayashi et al., 2007). The presence of both knownand NCS-based cis-regulatory elements in ATCOECISoffers a complementary set of tools to analyze coex-pression gene sets. It contains a diverse set of simpleand intuitive search functions that makes it possible toretrieve information about GO and motif enrichmentfor the gene coexpression neighborhoods described inthis study. In addition, user-defined gene sets gener-ated using clustering of dedicated expression data orchromatin immunoprecipitation experiments can beprocessed to identify motifs overrepresented in thetarget genes. Although some tools (e.g. ACT) provideclique finders to extract sets of genes showing consis-tent coexpression, so farwewereunable to obtain betterresults when systematically comparing cliques withother clustering algorithms using GO and motif en-richment (K. Vandepoele, unpublished data).

To demonstrate the utility of our framework to detectnew regulatory interactions,weusedpublicly availabletranscriptome data of OBP1 overexpression lines. ThisDOF TF was recently identified as a regulator integrat-ing developmental signals and cell cycle initiation(Skirycz et al., 2008). Starting from differentially ex-pressed genes, the clustering of expression data andsubsequent motif analysis identified five different TF-binding sites that could be linked with different modesof DOF regulation. Whereas the TAAAG DOF motifand the MSA element were found to be enriched inmany up-regulated genes (Table III), clustering of thedown-regulated genes yielded three coexpression clus-ters with motif enrichment. The largest cluster mainlycontained photosynthesis genes having an Ibox intheir promoter, and the two smaller clusters, showingstress and defense response, were enriched for theMYCATRD22 dehydration-responsive element andLS7ATPR1, an element involved in systemically induc-ible plant defense responses, respectively. The presenceof several DOF up-regulated genes involved in DNAreplication with S-phase peak expression in synchro-nized Arabidopsis cell suspensions (Menges et al.,2003) suggests a link between OBP1 and the E2F path-way. Indeed, comparison of E2F target geneswith theseDOF targets showeda significant overlapof 65genes, ofwhich 74% have aWTTSSCSS E2F-binding site in theirpromoter (Table III). Our hypothesis that a regulatoryinteraction exists between theOBP1 and the E2Fa TF issupported by the fact that the latter is also up-regulatedin the OBP1 overexpression line. We speculate thatOBP1, linking developmental and environmental sig-nals with cell cycle initiation, might regulate severalTFs controlling the progression through the differentphases of the cell cycle.

As reported in this analysis, the SN of coexpressionfunctional prediction systems varies largely for genesinvolved in different biological processes. Also, theselected set of microarray experiments, together withthe applieddistancemeasures and similarity thresholds,will have a great influence on the biological relevance ofpredictedgene functions (Yeunget al., 2004).Although it





has been shown that coexpression is relatively stablewhen using .100 arrays (Vandepoele et al., 2006; Aokiet al., 2007), the availability of relevant microarrayexperiments to infer regulatorynetworks for a biologicalprocess of interest undoubtedly will increase the reso-lution and prediction accuracy of meta-analysis plat-forms. Whereas the association of different global andspecific regulatory elements with different GO cate-gories provides a first glimpse on the regulatory logicembedded in plant promoters, the application of biclus-tering methods on a genome-wide scale can providemore detailed insights into the combinatorial nature oftranscriptional control in plants. Similarly, the applica-tion of coexpression neighborhood analysis in a multi-species phylogenomic framework using orthologousgene relationships will make it possible to maximallyexploit evolutionary conservation and enrichment anal-ysis for gene function inference.

MATERIALS AND METHODS

Expression Data

A total of 322 (483 3 AtGenExpress Development and Tissue slides + 6832 AtGenExpress Stress slides + 42 Birnbaum Root slides) Affymetrix ATH1

microarray slides monitoring the transcriptional activity of approximately

20,000 Arabidopsis (Arabidopsis thaliana) genes in different tissues and under

different experimental conditions were retrieved from the Nottingham

Arabidopsis Stock Centre (http://arabidopsis.info/). Raw data were back-

ground corrected and normalized using RMA (Irizarry et al., 2003) and a

custom-made CDF. This high-quality CDF file was built using selected

reporter probes that have perfect sequence identity with a single target

transcript. Reporters that hybridized with one mismatch to another gene’s

transcript were filtered out, as well as reverse complementary matching

reporters and reporters that hybridized multiple times on the genomic

sequence. The latter was done to remove reporters that match unannotated

sequences. We included probe sets in this study only if they consisted of at

least eight reporters, which resulted in 19,937 unique probe sets (Casneuf

et al., 2007). Note that these stringent criteria used to construct the CDF file

make it possible to reliably measure expression values for duplicated genes

(i.e. free from cross-hybridization between paralogs showing high sequence

similarity).The mean intensity value was calculated for the replicated slides.

As a result, 129 experiments measuring the expression for 19,937 genes were

retained for further analysis yielding an expression matrix with approxi-

mately 2.5 million data points (Supplemental Table S5).

EC and Clustering

The EC, which is a measure for the amount of expression similarity within

a set of genes, was calculated as described by Pilpel et al. (2001). EC reports the

fraction of gene pairs per GO category that show elevated coexpression. Here,

the PCC was used as a measure for similarity between expression profiles.

Based on the similarity between expression profiles for 1,000 random genes

(approximately 1,000*999*0.5 gene pairs), a PCC threshold of 0.72 correspond-

ing with the 95th percentile of this random distribution was used to detect

significantly coexpressed genes. To calculate the random EC for GO cate-

gories, random gene sets were sampled with the same size as the category

under investigation.

To create guide gene clusters, we selected for each gene all coexpression

partners showing a PCC higher than or equal to a defined threshold. Only

guide gene clusters containing 10 or more genes were retained. Three PCC

thresholds were evaluated corresponding with the 90th, 95th, and 99th

percentile of the random background distribution. Note that guide gene

clusters can overlap with each other because each gene present on the ATH1

microarray is initially selected as a guide gene. CAST clusters were identified

as described by Vandepoele et al. (2006).

GO Functional Annotation

GO associations for Arabidopsis proteins were retrieved from The Arabi-

dopsis Information Resource (www.arabidopsis.org; Swarbreck et al., 2008).

The assignments of genes to the original GO categories were extended to

include parental terms (i.e. a gene assigned to a given category was automat-

ically assigned to all the parent categories as well) using the Perl GO::Parser

and GO::Node modules. All GO categories containing ,25 genes were

discarded from further analyses. Enrichment values were calculated as the

ratio of the relative occurrence in a set of genes to the relative occurrence in the

genome. The statistical significance of the functional enrichment within gene

sets was evaluated using the hypergeometric distribution adjusted by the

Bonferroni correction for multiple hypotheses testing. Corrected P values ,0.05 were considered as significant. GO motif networks were drawn using

Cytoscape (Cline et al., 2007).

Evaluation of Functional Predictive Power

The functional predictivepower of a gene’s coexpressionneighborhoodwas

determinedby calculating the SN [SN=TP/(TP+FN)] and thePPVorprecision

rate [PPV=TP/(TP+FP)]. For each guide gene i, all significantGOenrichments

found in the set of coexpressed genes were considered as GO predictions for

gene i. True positives (TPs) are actual positive examples predicted as positives,

false negatives (FNs) are actual positive examples predicted as negatives, and

false positives (FPs) are actual negative examples predicted as positives. AsGO

annotations are far from complete, we estimated the number of false predic-

tions using a random sampling approach. Starting from all j positive genes

annotatedwith a particularGO term, FPwas estimated by randomly selecting j

negative genes and counting how much positive predictions were made. This

procedure was repeated 100 times, and FP for a given GO term was calculated

as the average fraction of positive predictions.

cis-Regulatory Element Analysis

Starting from all possible 8-mers (generated using the five-letter alphabet

A,C,G,T,N), we applied the NCS to determine evolutionarily conserved motifs

present in the upstream sequences of Arabidopsis genes. This evolutionary

filter is used to discriminate between potentially functional and false motifs

and applies a systems-level constraint to identify putative cis-regulatory

elements (Elemento and Tavazoie, 2005; Vandepoele et al., 2006). The method

exploits the well-established notion that each TF regulates the expression of

many genes in the genome and that the conservation of global gene expression

between two related species requires that most of these targets maintain their

regulation. In practice, this assumption is tested for each candidate motif by

determining its presence in the upstream regions of two related species (here

Arabidopsis and poplar [Populus trichocarpa]) and by calculating the signifi-

cance of conservation over orthologous genes. Orthologous groups were

identified through protein clustering using OrthoMCL (Li et al., 2003). Starting

from an all-against-all BLASTP sequence similarity search using the full

proteomes of Arabidopsis (26,541 proteins) and P. trichocarpa (45,554 proteins),

11,707 orthologous clusters were defined, covering 18,088 Arabidopsis

and 22,760 poplar genes. These orthologous groups contain inparalogous

genes (i.e. genes duplicated after the divergence between Arabidopsis and

P. trichocarpa) and thus offer a more realistic representation of orthology

compared to, for example, reciprocal best hit approaches. Motif mapping was

done using dna-pattern (RSA tools; van Helden et al., 2000) and was restricted

to the first 1,000 bp upstream from the translation start site or to a shorter

region if the adjacent upstream gene is located within a distance smaller than

1,000 bp. Starting from all 193,584 8-mers, the top 5% motifs with the highest

NCS values (NCS score . 12.48) were selected and similar motifs were

grouped. We measured the similarity between two motifs as the PCC of their

corresponding position weight matrix. Note that all NCS motifs are repre-

sented by consensus sequences and that the transformation to position weight

matrices was only done for internal motif processing. Each motif of length w

was represented using a single vector, by concatenating the rows of its matrix

(obtaining a vector of length 4*w). Subsequently, the PCC between every

alignment of twomotifs was calculated, as they are scanned past each other, in

both strands (Kreiman, 2004; Xie et al., 2005). Then, all motifs with a PCC .0.75 were considered as similar, and only the motif with the highest NCS value

was retained using its consensus sequence. This resulted in a set of 866

nonredundant motifs that were used for further analysis.

Vandepoele et al.




To calculate motif enrichment for clusters, only Arabidopsis NCS motif

matches conserved in one or more orthologous poplar gene were retained.

Significance levels were calculated using the hypergeometric distribution

adjusted by the Bonferroni correction for multiple hypotheses testing (using

the number of evaluated motifs). Corrected P values , 0.05 were considered

as significant.

Supplemental Data

The following materials are available in the online version of this article.

Supplemental Figure S1. Correlation between average connectivity and

prediction SN per GO category.

Supplemental Figure S2. Enrichment of a subset of NCS motifs over GO

categories.

Supplemental Figure S3. Enrichment of known AGRIS and PLACE motifs

over GO categories.

Supplemental Figure S4. Genome-wide positional biases of TELO and

UP1 motifs in genes enriched for ribosome biogenesis.

Supplemental Table S1. EC values for different GO and AraCyc

categories.

Supplemental Table S2. Coexpression network properties per GO

category.

Supplemental Table S3. Predictive power scores for different GO

categories based on the full ATH95 network.

Supplemental Table S4. Position and strand biases of conserved NCS

motif instances.

Supplemental Table S5. Microarray experiments in expression compen-

dium.

ACKNOWLEDGMENT

We thank Mattias de Hollander for helpful discussions and technical

assistance with the analysis of cis-regulatory elements.

Received January 22, 2009; accepted April 2, 2009; published April 8, 2009.

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