Infection, Genetics and Evolution 12 (2012) 987–996

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Infection, Genetics and Evolution

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Genes under positive selection in a model plant pathogenic fungus, Botrytis

Gabriela Aguileta a,b,c, Juliette Lengelle c, Hélène Chiapello c, Tatiana Giraud a,b, Muriel Viaud d,Elisabeth Fournier f, François Rodolphe c, Sylvain Marthey c, Aurélie Ducasse d, Annie Gendrault c,Julie Poulain e, Patrick Wincker e, Lilian Gout d,g,⇑a Ecologie, Systématique et Evolution, Université Paris-Sud UMR8079, F-91405 Orsay Cedex, Franceb Ecologie, Systématique et Evolution, CNRS UMR8079, 91405 Orsay Cedex, Francec MIG – INRA UR1077, Domaine de Vilvert, 78352 Jouy en Josas Cedex, Franced BIOGER – INRA UR1290, Avenue Lucien Brétignières, 78850 Thiverval-Grignon, Francee Genoscope – Centre National de Séquençage, UMR CNRS 8030, 2 Gaston Crémieux, CP 5706, 91507 Evry, Francef BGPI – INRA UMR385, TA A 54/K, Campus International de Baillarguet, 34398 Montpellier Cedex 5, Franceg BIOGER – AgroParisTech UR1290, 75231 Paris Cedex 05, Paris, France

a r t i c l e i n f o

Article history:Received 6 December 2011Received in revised form 15 February 2012Accepted 23 February 2012Available online 3 March 2012

Keywords:BotrytisMolecular evolutionFungiNatural selectionCoevolution

1567-1348/$ - see front matter � 2012 Elsevier B.V. Adoi:10.1016/j.meegid.2012.02.012

⇑ Corresponding author at: BIOGER – INRA UR129078850 Thiverval-Grignon, France. Tel.: +33 1 30 81 54

E-mail addresses: gout@versailles.inra.fr, lilian.gou

a b s t r a c t

The rapid evolution of particular genes is essential for the adaptation of pathogens to new hosts and newenvironments. Powerful methods have been developed for detecting targets of selection in the genome.Here we used divergence data to compare genes among four closely related fungal pathogens adapted todifferent hosts to elucidate the functions putatively involved in adaptive processes. For this goal, ESTswere sequenced in the specialist fungal pathogens Botrytis tulipae and Botrytis ficariarum, and comparedwith genome sequences of Botrytis cinerea and Sclerotinia sclerotiorum, responsible for diseases on over200 plant species. A maximum likelihood-based analysis of 642 predicted orthologs detected 21 genesshowing footprints of positive selection. These results were validated by resequencing nine of these genesin additional Botrytis species, showing they have also been rapidly evolving in other related species.Twenty of the 21 genes had not previously been identified as pathogenicity factors in B. cinerea, but somehad functions related to plant–fungus interactions. The putative functions were involved in respiratoryand energy metabolism, protein and RNA metabolism, signal transduction or virulence, similarly to whatwas detected in previous studies using the same approach in other pathogens. Mutants of B. cinerea weregenerated for four of these genes as a first attempt to elucidate their functions.

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1. Introduction

There is considerable interest in finding the genes that underliethe capacity of pathogens to evolve and adapt to new habitats andhosts, especially in the case of devastating emergent diseases(Archie et al., 2009). Global trade and the worldwide transport ofgoods and people increasingly contribute to the spread of patho-gens to new habitats and potential hosts (Lebarbenchon et al.,2008). Fungi are among the most devastating plant pathogens,causing important losses in agriculture, decimating natural popu-lations, and they have been associated with several cases of emer-gent plant diseases in new hosts and novel environments(Anderson et al., 2004; Desprez-Loustau et al., 2007; Blausteinand Johnson, 2010). In this context, it is highly desirable to deter-mine the genes that evolved rapidly, in order to understand hownew diseases originated.

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, Avenue Lucien Brétignières,34; fax: +33 1 30 81 53 06.

t@agroparistech.fr (L. Gout).

Recent studies have indeed shown that the rapid evolution ofparticular genes can play a significant role in favoring the capacityof a pathogen to adapt to a new environment or to infect new hostspecies (Matute et al., 2008; Sacristán and García-Arenal, 2008; deCrecy et al., 2009). Pinpointing the relevant genes in the lab is oftena daunting and costly task. A faster and cheaper alternative, in theabsence of a priori candidates for genes involved in pathogenicity,is to look for evidence of genes evolving rapidly or that have beensubject to positive selection. For a pathogen, advantageous substi-tutions that contribute to the capacity of infecting a host or adapt-ing a new environment will in principle be rapidly fixed in thepopulation. Genes showing footprints of positive selection canadditionally be involved in the arms race with their host (Aguiletaet al., 2009) and these are also interesting genes to be identified.Such a blind, computational approach has already identified thegenes and gene functions likely involved in the origin of new path-ogenic species, specialized on closely related plant hosts (Aguiletaet al., 2010).

The genus Botrytis Persoon (Ascomycota, Leotiomycetes, Sordar-iaceae) encompasses 22 pathogenic species with different host

988 G. Aguileta et al. / Infection, Genetics and Evolution 12 (2012) 987–996

ranges. All Botrytis species are necrotrophic pathogens, meaningthat they feed only on dead tissues; they therefore possess the abil-ity to kill the attacked plant cells and to develop on dead tissues.The genus is separated into two clades (Staats et al., 2005), onewith four species attacking only Eudicot plants, the other includingthe 18 remaining species, found mainly on Monocot plants. TheEudicot clade includes Botrytis cinerea (teleomorph Botryotiniafuckeliana), the agent of gray mold on numerous plants (>200), thathas recently been separated into two cryptic species (Fournieret al., 2005; Walker et al., 2011). The wide variety of symptomson different organs and plants suggests that the B. cinerea genomepossesses a large ‘‘arsenal of weapons’’ to attack its hosts (Choqueret al., 2007; Williamson et al., 2007). Apart from the gray moldagent, most Botrytis species have a narrow host range, restrictedto few or a single species. For example, in the clade associated withMonocot hosts, Botrytis tulipae attacks several Tulipae spp., andBotrytis ficariarum has been described only on Ficaria verna. Theclosely related species Sclerotinia sclerotiorum is also necrotrophicpathogen with a wide host range (>400; Bolton et al., 2006).Searching for rapidly evolving genes between these four speciesmay therefore help to elucidate the genetic basis of host rangedetermination and adaptation to different hosts or environments,or genes involved in the rapid coevolution with their host plants.B. cinerea is also one of the most studied necrotrophic fungi, forwhich numerous reverse genetic and transcriptomic tools areavailable (van Kan, 2006; Choquer et al., 2007), making it possibleto test the functions of particular candidate genes.

In this study, we used a genome-wide approach to compare asmany orthologs as possible from the species B. cinerea, B. tulipae,B. ficariarum and S. sclerotiorum, four closely related fungal phyto-pathogens adapted to different hosts and with narrow or wide hostranges. The goal was to find genes evolving under positive selec-tion that might thus be involved in host range determination andadaptation to the host. Rapidly evolving genes may alternativelybe involved in coevolution with the current hosts (Aguileta et al.,2009) or genetic incompatibilities between species (Presgraveset al., 2003), which are also important phenomena to understandin pathogens. For this goal, EST libraries were sequenced for B. tuli-pae and B. ficariarum, and compared with full genome sequences ofB. cinerea and S. sclerotiorum (Amselem et al., 2011). The best can-didate genes were sequenced in additional species of the Botrytisgenus to validate the evolutionary pressure. Finally, we also at-tempted to validate using functional genetics that these candidategenes were involved in pathogenicity and adaptation to the host,by disrupting them in one B. cinerea strain and testing mutantsin host plant-infection studies.

2. Experimental procedures

2.1. Strains, culture conditions, RNA isolation, cDNA libraryconstruction and sequencing

The B. cinerea T4 strain was isolated in a glasshouse of tomatoes(Lycopersicum esculentum) in Avignon, France (Levis et al., 1997).The B. ficariarum CBS17663 and the B. tulipae Bt9901 strains wereobtained from culture collections of micro-organisms of the Cen-traalbureau voor Schimmelcultures (CBS, Utrecht, The Nether-lands) and of the Applied Plant Research (PPO) organization(Lisse, the Netherlands), respectively. The origins of B. ficariarumand B. tulipae strains were described previously (Staats et al.,2005). These isolates were routinely grown on solid NY medium(2 g l�1 malt extract, 2 g l�1 yeast extract, 15 g l�1 agar) at 21 �Cwith 16 h daylight a day.

The genomic sequence of B. cinerea T4 isolate was obtained fromtheURGI(http://urgi.versailles.inra.fr/index.php/urgi/Species/Botrytis)

and that of S. sclerotiorum were obtained from the Broad Institute(http://www.broadinstitute.org/annotation/genome/sclerotinia_sclerotiorum/MultiHome.html). Two EST libraries were obtained for B.ficariarum CBS17663 isolate and B. tulipae Bt9901 isolate. For RNAextraction, mycelia and spores of 4-day-old cultures were harvestedby flooding the plate with 1 ml of sterile dH2O, inoculated into100 ml of liquid NY medium and grown at 21 �C for 48 h with aeration(shaking incubator, 120 r.p.m.). For each culture, the fungal cell masswas then filtered through a sterile Nylon membrane, washed once withsterile dH2O and transferred to 100 ml of V8-based medium. This richplant-based medium was also supplemented with 20% homogenizedleavesof tulip(Tulipa spp.) orbuttercup(F.verna),whichareknownhostplants of B. tulipae and B. ficariarum, respectively. Such enrichment inhost plant compounds was successfully used in previous proteomicstudies to induce the expression of B. cinerea genes related to the infec-tion (Espino et al., 2010; Shah et al., 2009). After 2 h incubation at 21 �C,fungal tissues were harvested by filtration, frozen in liquid nitrogen andstored at �80 �C until RNA extraction. Total RNA was extracted frommycelial samples ground in liquid nitrogen with TRIzol (Invitrogen) fol-lowing the manufacturer’s instructions. Thereafter, cDNA was synthe-sized from 2 lg of total RNA and cDNA libraries were prepared by usingthe Creator SMART cDNA Library Construction kit (ClonTech), accord-ing to the manufacturers’ instructions. For each Botrytis species, thecDNA library was robotically arrayed and 20,000 clones were submit-ted for systematic 50 end sequencing performed by Genoscope (CEAEvry, France) following standard protocols in order to identify a sampleof genes having orthologues in the four species.

2.2. Sequence cleaning, assembly and annotation

Sequences are available in Genbank (accessions numbersFO084242 to FO113725). Raw sequence data were cleaned fromvector and adaptor sequences. Contaminating plasmid sequenceswere removed from the analyses. The SURF (SeqUence Repositoryand Feature detection) package (Lannuccelli, 2005) was used forsequence base-calling, cleaning, and for detection of any contami-nation in putative inserts. This analysis involved three stepsincluding the use of PHRED (Ewing and Green, 1998; Ewinget al., 1998), which detected bad quality regions, of RepeatMasker,which masked low complexity regions, and of Crossmatch, whichfound putative contaminated sequences of the UNIVEC databaseand single nucleotide repetitions. Only sequences with a SURFscore over 20 on at least 100 bp were released in the EST divisionof the EMBL-EBI Nucleotide Sequence Database.

ESTs were aligned and assembled into contigs using the CAP3software (Huang and Madan, 1999) when the criterion of a mini-mum identity of 95% over 50 bp was met. When an EST could notbe assembled with others in a contig, it was retained as a ‘‘singlet’’.The contigs and the singlets should thus correspond to sequences ofunique genes, and will be called hereafter ‘‘unisequences’’.

For the annotation of the predicted ESTs, the consensus se-quences of the contigs and the sequences of the singlets werecompared to B. cinerea sequences in the Genbank database andin the Uniprot database (The Uniprot Consortium, 2007) usingthe tBLASTx and the BLASTx algorithms (Altschul et al., 1997).Unisequences showing significant similarity (E-value 6 10�4) todatabase entries were annotated using their most significantmatch. Unisequences were also classified into gene ontologyfunctional categories (http://www.geneontology.org) based onBLAST similarities to known genes of the NCBI nr (non-redun-dant) protein database and using the Blast2GO annotation tool(Conesa et al., 2005).

The EST data obtained for B. ficariarum and B. tulipae were inte-grated into a database called FunyEST, which is part of the FunyBaseresource (Marthey et al., 2008) and freely accessible through theweb site http://genome.jouy.inra.fr/funybase. FunyEST structure

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and content is similar to one developed for the MICROBASE re-source, previously described in Aguileta et al. (2009) and dedicatedto Microbotryum violaceum EST analysis. The FunyEST database in-cludes information on EST sequences, contigs, annotations, geneontology functional categories and search programs to comparesimilarities of any sequence against the database.

2.3. Unisequence CDS predictions and clustering

The pipeline of the Prot4EST software (Wasmuth and Blaxter,2004) was used to predict unisequence CDS positions and to trans-late coding regions into protein sequences. In a first step, all puta-tive ribosomal sequences in the dataset were identified through aBLASTn search against the rRNA sequence database (RibosomalDatabase II) and the sequences whose BLASTn E-value P 1e-65were discarded. The second and third steps use the BLASTx algo-rithm to detect any similarity between unisequences and se-quences from both the mitochondrial protein database (NCBI ftpsite) and the Uniprot database (The Uniprot Consortium, 2007).Unisequences showing a significant BLAST result (i.e. cutoff ofe-08) against the mitochondrial database were annotated as mito-chondrial genes to be translated subsequently with the relevantgenetic code. We removed all sequences corresponding to trans-posable elements, as well as those containing internal stop codons.Sequences that matched the Uniprot-Swissprot database signifi-cantly (i.e. cutoff of e-08) were considered as CDS and a HSP tilepath was constructed. This means that Prot4EST then considersthat the nascent translation of these sequences can be extendedat either end in the same reading frame. Only sequences thatyielded no sequence similarity were then submitted to the fourthstep of the pipeline which aims at identifying coding regions usinghidden Markov models implemented in the ESTscan software (Iseliet al., 1999). For this step, a transition matrix was created from thegenome ORF sequences of B. cinerea and CDS sequences of B. tulipaeand Botrytis elliptica, obtained from the EMBL. Predicted polypep-tides satisfying a given length threshold criteria (CDS of at least30 codons in length and covering at least 10% of the input se-quence) then underwent the extension process (like for HSP tiling).In a fifth step, the DECODER program (Fukunishi and Hayashizaki,2001) is used to predict the CDS and polypeptide translations forthe remaining sequences. DECODER exploits the quality scores ofthe sequences produced from base calling software (such as PHREDused in the SURF package) and additional text base information(such as optimal codon usage). DECODER computes a likelihoodscore for each possible CDS, and the one with the lowest score ischosen as the correct CDS. Finally, a last attempt is performed toprovide a putative polypeptide translation based on the longeststring of amino acids uninterrupted by stop codons from a six-frame translation of the sequence. In spite of all the cautionexerted at the previously described stages for predicting CDSs,potential problems could arise from the incorporation of an intronthat does not destroy the reading frame. In this case, the predictedCDS would contain a region of neutral sequence that could biasanalyses of selective pressure. This phenomenon however is unli-kely to be a problem here as gaps were removed from alignments.

Clustering of unisequence CDSs into groups of orthologsincluded three main steps for which we employed Ortho-MCL (Liet al., 2003) and custom-made Perl scripts. The first step detectedthe single-copy unisequences from each CDS library. To do this, eachlibrary was aligned against itself by using a BLASTn algorithm. AllCDS sequences having exactly one significant hit (e-value < 1e-10)were considered as single-copy unisequences (thus avoiding hiddenparalogy) and were kept for the detection of orthologs among thefour libraries, using a derivative of the Best Bidirectional Hit for nsequences. The single-copy unisequences from all four librarieswere combined in a single file and aligned against themselves using

the BLASTn algorithm. All single-copy unisequences with a hit(e-value < 0.1) with a unisequence of another library were consid-ered to have an ortholog in the corresponding species and weretherefore kept for the last step. The last script compiled alignmentresults and built clusters of putative orthologs, including foursequences, each of them belonging to a different species.

2.4. Orthologous gene alignment, filtering and sorting by alignmentlength

The predicted protein unisequences of orthologs were alignedusing T-coffee (Notredame et al., 2000) with default settings. Thecorresponding nucleotide alignments were performed by using theprotein alignments as guide, as implemented in the tranalign pro-gram of the EMBOSS package (http://embossgui.sourceforge.net/demo/manual/tranalign.html). In order to keep only reliable align-ments, which are crucial for the subsequent detection of selection,the alignments were then filtered using different criteria. First, werequired a level of protein sequence identity of at least 70% for allalignments of putative orthologs. Second, the alignments werepost-processed to remove gaps and keep only unambiguouslyaligned blocks of sequence. This step was performed using Gblocks(Castresana, 2000) with the maximum number of non-conservedpositions set to 8, and the minimal block size set to 5 (for all otherparameters, default settings were used). Finally, we used the lengthof the final alignments to classify the resulting ortholog clusters forsubsequent analysis: clusters with at least 300 nucleotides wereanalyzed individually.

2.5. Detection of positive selection

Positive selection was tested using the CODEML program of thePAML4 package (Yang, 2007). Selective pressure was measuredby using the nonsynonymous/synonymous substitution rate ratio(dN/dS), also referred to as x. An x < 1 suggests purifying selection,x = 1 is consistent with neutral evolution, and x > 1 is indicativeof positive selection (Yang and Bielawski, 2000). Nested codonmodels implementing the x ratio can be compared by means ofa likelihood ratio test (LRT) (Anisimova et al., 2001). We used thenull model M1a, which assumes two site classes with 1 > x > 0,and x1 = 1, which therefore implicitly supposed that no site isunder positive selection, and compared it with the alternativemodel M2a, which adds an extra class of sites that allows x to takevalues >1. We also compared the null model M7, which assumes abeta distribution of x across sites, with the alternative model M8,which adds an extra class of sites to M7 where x can take values>1. Thereby positive selection can be detected if a model allowingfor positive selection is significantly more likely (as estimated bythe LRT) than a null model without positive selection.

2.6. Functional annotation

In order to assign functional annotation to cluster orthologsexhibiting evidence for positive selection, we first used the avail-able FunyEST annotation of individual unisequences contained ineach cluster of B. ficariarum and B. tulipae. For B. cinerea and S. scle-rotiorum BLAST searches against their whole genomes at NCBI wereconducted. As described previously, this annotation was obtainedfrom the BLASTp best hit and the corresponding GO terms.

We then performed three complementary analyses in order tocollect maximal functional information contained in the clustersof interest. In a first step, we looked for all possible motifs, signaland domains in the individual sequences using the Interproscansoftware (assuming default settings) of the Interpro database(Mulder et al., 2007). In the second and third steps, we tried toidentify distant homologs for each individual sequences using

990 G. Aguileta et al. / Infection, Genetics and Evolution 12 (2012) 987–996

two complementary methods: (1) Interproscan with default set-tings using the two EST banks of B. tulipae and B. ficariarum andthe two genomic banks of B. cinerea and S. sclerotiorum. Differentdatabases were searched: Pfam (Finn et al., 2010) and Panther(Thomas et al., 2003) for motif search, Superfamily (Gough, 2002)for protein family assignment, CATH (Cuff et al., 2011) for 3D struc-ture, SignalP (Bendtsen et al., 2004) for signal peptides and an-chors, and Tmhmm (Krogh et al., 2001) for transmembranedomain identification. (2) PSI-BLAST (Position-Specific IteratedBLAST) algorithm (Altschul et al., 1997), which is an efficient meth-od to detect weak but biologically relevant sequence similaritiesagainst the Uniprot database.

2.7. Gene inactivation of candidate genes

Four candidate genes from the blind in silico approach were inac-tivated by the construction of Knock-Out (KO) cassettes and proto-plast transformation. The hygromycin resistance gene hph wasamplified from the plasmids pCB1004 (Carroll et al., 1994) orpCSN44 (Colot et al., 2006) (primers in Table S1). The pairs of prim-ers 5F/5R and 3F/3R were used to amplify regions of about 1 kb in 50

and in 30 of each of the target genes (Table S1). Then, KO cassettesconsisting of the 50 region of gene, the resistance gene and the 30 re-gion of gene were generated by using two alternative strategies.Either the KO cassettes were generated by using the double-jointPCR strategy described by Yu et al. (2004) or the KO cassettes weregenerated by homologous recombination in yeast as described inColot et al. (2006). The Gene Knock-out kit was obtained from theFungal Genetic Stock Centre (http://www.fgsc.net/clones.html).

Protoplasts from B05.10 were prepared and transformed asdescribed previously (Levis et al., 1997) using 2 lg of linear DNA.Transformed protoplasts were plated in molten osmoticallystabilized medium agar containing 100 lg ml�1 hygromycin(Invitrogen). Transformants were selected after 6–8 days at 23 �C,sub-cultured twice on selective media and single spore cultureswere made to get genetically pure transformants. The screeningsfor gene inactivation event were done by PCR using the primerverif-5 located upstream the 50 flanking region and Hyg-F locatedinside the hph gene.

2.8. Infection assays

Infection assays of B. cinerea WT strain and mutants were doneon French bean (Phaseolus vulgaris cv. Caruso) and tomato leaves(Solanum lycopersicum cv. Moneymaker) by inoculating detachedleaves with young unsporulating mycelium or conidial suspensionsfrom cultures on NY. Bean leaves were harvested from 2 weeks-oldplants and placed in a transparent plastic box lined with tissuemoistened with sterile water. Leaves were inoculated with1.8 mm diameter-plugs of 3 days old mycelium. Alternatively, con-idia were collected from 10 days-old plates and suspended in su-crose phosphate buffer (10 mM sucrose, 10 mM KH2PO4) to a finalconcentration of 105 conidia per ml. Droplets of 10 ll were appliedto the leaves. Storage boxes containing inoculated leaves were incu-bated in a growth cabinet at 21 �C with 16 h daylight. Disease devel-opment on leaves was recorded daily as radial spread from theinoculation point to the lesion margin. Pathogenicity assays onleaves were repeated three times using at least five leaves per assay.

3. Results

3.1. Sequence analysis: gene finding, assembly and clustering

We obtained 15,014 ESTs for B. ficariarum and 19,006 ESTs for B.tulipae after the cleaning step. Next, unisequences (i.e., contigs or

singlets corresponding to sequences of unique genes) were re-trieved and assembled for each species, which resulted in 2855and 3715 unisequences for B. ficariarum and B. tulipae, respectively.The following step involved the prediction of coding sequences(CDS) for each unisequence to obtain the coding frame requiredfor detecting synonymous and nonsynonymous substitutions.After carefully filtering incorrect results, a total of 2798 and 3661CDSs were predicted for B. ficariarum and B. tulipae, respectively.Finally, ortholog detection from comparisons between the twoEST libraries of species B. ficariarum and B. tulipae with the twoavailable genomes of B. cinerea and S. sclerotiorum yielded 979clusters of orthologs, including four sequences from the four spe-cies, all of which were at least 300 nucleotides long, the minimallength for avoiding stochastic sampling problems during the detec-tion of positive selection. These 979 alignments were verified fortheir quality and a total of 642 ortholog clusters were further ana-lyzed to detect signals of positive selection.

3.2. Detection of positive selection

Positive selection is detected when a model of evolution allow-ing for positive selection appears significantly more likely than anull model without positive selection, as indicated by likelihoodratio tests (LRTs). The individual analysis of the 642 ortholog clus-ters detected 21 genes as likely candidates to be subjected to posi-tive selection, as revealed by the significant LRTs comparingmodels M1a vs M2a and M7 vs M8. Table 1 presents the parameterestimates of each model and Table 2 shows the results of the twoLRTs performed. The orthologs found to be under selection are:B152, B161, B194, B248, B24, B266, B387, B398, B402, B417,B431, B541, B549, B57, B605, B695, B814, B821, B907, B967 (LRTsof M7–M8, P < 0.05) and B897, for which the LRT comparing M7and M8 was marginally significant (P = 0.054).

3.3. Functional annotation of the putative genes detected as beingunder positive selection

We performed the functional annotation of the 21 genes in atwo-step approach. In a first step, we assigned functional catego-ries to genes using both the BLASTp best hit obtained for each ofthe coding sequences included in the clusters (Table S2). The 21genes were classified into eight GO (Gene Ontology) categoriesaccording to their function: (1) regulation of gene expression [1cluster], (2) respiratory and energy metabolism [6 clusters], (3)protein degradation [3 clusters], (4) protein folding [1 cluster],(5) cellular development [3 clusters], (6) cell wall modification [1cluster], (7) binding [1 cluster] and (8) unknown function [5 clus-ters]. We compared the proportions of genes in the different GOclasses in the whole set of 642 orthologs and in the 21 genes underpositive selection. Some GO classes appeared to include a higherproportion of genes under positive selection, as compared to thewhole set of orthologs (especially the ‘‘molecular function class’’).However, the differences between the two distributions were notsignificant (Chi2 test, data not shown).

We performed a second step of annotation, by using three com-plementary methods on each sequence of the 21 genes. First, motifand domain predictions were obtained by using the Interpro data-base (Table S3). These genes under positive selection and the list of642 orthologous genes were also analyzed by TargetP in order todetect signal peptides indicating their subcellular localization.Interestingly, 33% of the genes under positive selection show a sig-nal peptide suggesting that they are secreted or localized in themembrane. This corresponds to a 2-fold enrichment compared tothe 642 genes analyzed in this study (13% have a predicted signalpeptide) and to the putative secreted and membrane genes of thewhole genome (15% have a predicted signal peptide; Amselem

Table 1Parameter estimates of site-specific CODEML analyses.

Cluster M1a (neutral) M2a (selection) M7 (beta) M8 (beta&w)

�lnL Parameter estimates �lnL Parameter estimates �lnL Parameterestimates

�lnL Parameter estimates

B152 1309.21 p0 = 0.5 (p1 = 0.5), w0 = 0.0, w1 = 1.0 1275.62 p0 = 0.43, p1 = 0.35 (p2 = 0.22), w0 = 0.0 (w1 = 1.0),w2 = 623.98

1309.21 p = 0.005,q = 0.005

1275.81 p0 = 0.79, (p1 = 0.22), p = 0.005, q = 0.007.0,w = 577.84

B161 1984.61 p0 = 0.91 (p1 = 0.09), w0 = 0.006,w1 = 1.0

1980.82 p0 = 0.92, p1 = 0.07 (p2 = 0.007), w0 = 0.07 (w1 = 1.0),w2 = 35.11

1985.50 p = 0.17, q = 0.93 1980.37 p0 = 0.99 (p1 = 0.007), p = 0.26, q = 1.77,w = 31.11

B194 641.61 p0 = 0.88 (p1 = 0.12), w0 = 0.0,w1 = 1.0

637.72 p0 = 0.99, p1 = 0.0 (p2 = 0.007), w0 = 0.11 (w1 = 1.0),w2 = 52.45

641.65 p = 0.005, q = 0.05 637.75 p0 = 0.99 (p1 = 0.007), p = 0.16, q = 1.2,w = 53.16

B248 2130.09 p0 = 0.99 (p1 = 0.01), w0 = 0.0,w1 = 1.0

2124.03 p0 = 0.99, p1 = 0.0 (p2 = 0.007), w0 = 0.0 (w1 = 1.0),w2 = 10.04

2136.79 p = 0.01, q = 0.23 2124.03 p0 = 0.99 (p1 = 0.007), p = 0.005, q = 7.66,w = 10.04

B24 970.69 p0 = 0.91 (p1 = 0.09), w0 = 0.0,w1 = 1.0

967.45 p0 = 0.95, p1 = 0.0 (p2 = 0.05), w0 = 0.01 (w1 = 1.0),w2 = 3.56

970.78 p = 0.005, q = 0.05 967.45 p0 = 0.95 (p1 = 0.05), p = 1.41, q = 99.0, w = 3.57

B266 1669.13 p0 = 0.92 (p1 = 0.08), w0 = 0.06,w1 = 1.0

1666.68 p0 = 0.95, p1 = 0.05 (p2 = 0.005), w0 = 0.08 (w1 = 1.0),w2 = 12.94

1669.78 p = 0.1, q = 0.59 1666.60 p0 = 0.995 (p1 = 0.005), p = 0.3, q = 2.15,w = 12.54

B387 630.35 p0 = 0.89 (p1 = 0.11), w0 = 0.0,w1 = 1.0

626.47 p0 = 0.89, p1 = 0.09 (p2 = 0.02), w0 = 0.0 (w1 = 1.0),w2 = 12.9

630.39 p = 0.005, q = 0.05 626.45 p0 = 0.98 (p1 = 0.02), p = 0.01, q = 0.16,w = 11.89

B398 969.15 p0 = 0.95 (p1 = 0.05), w0 = 0.0,w1 = 1.0

967.69 p0 = 0.98, p1 = 0.0 (p2 = 0.01), w0 = 0.01 (w1 = 1.0),w2 = 4.72

970.70 p = 0.01, q = 0.16 967.69 p0 = 0.98 (p1 = 0.02), p = 1.42, q = 99.0, w = 4.72

B402 882.05 p0 = 0.95 (p1 = 0.05), w0 = 0.0,w1 = 1.0

874.83 p0 = 0.99, p1 = 0.0 (p2 = 0.005), w0 = 0.03 (w1 = 1.0),w2 = 157.35

882.95 p = 0.005, q = 0.05 874.83 p0 = 0.99 (p1 = 0.005), p = 3.31, q = 99.0,w = 157.95

B417 923.88 p0 = 0.73 (p1 = 0.27), w0 = 0.06,w1 = 1.0

923.88 p0 = 0.73, p1 = 0.2 (p2 = 0.07), w0 = 0.06 (w1 = 1.0),w2 = 1.0

923.72 p = 0.22, q = 0.59 919.49 p0 = 0.98 (p1 = 0.02), p = 0.27, q = 0.82,w = 226.8

B431 1847.67 p0 = 0.9 (p1 = 0.1), w0 = 0.04,w1 = 1.0

1842.76 p0 = 0.92, p1 = 0.06 (p2 = 0.02), w0 = 0.05 (w1 = 1.0),w2 = 8.78

1850.05 p = 0.1, q = 0.57 1842.59 p0 = 0.97 (p1 = 0.03), p = 0.25, q = 2.34, w = 7.97

B541 1001.61 p0 = 0.97 (p1 = 0.03), w0 = 0.04,w1 = 1.0

999.89 p0 = 0.98, p1 = 0.01 (p2 = 0.009), w0 = 0.04 (w1 = 1.0),w2 = 10.91

1002.27 p = 0.14, q = 1.82 999.34 p0 = 0.99 (p1 = 0.008), p = 0.38, q = 6.61,w = 10.88

B549 707.01 p0 = 0.79 (p1 = 0.21), w0 = 0.0,w1 = 1.0

703.27 p0 = 0.84, p1 = 0.0 (p2 = 0.16), w0 = 0.0 (w1 = 1.0),w2 = 3.16

708.30 p = 0.005, q = 0.01 703.27 p0 = 0.84 (p1 = 0.16), p = 0.005, q = 99.0, w = 3.16

B57 1037.83 p0 = 0.94 (p1 = 0.06), w0 = 0.003,w1 = 1.0

1034.67 p0 = 0.98, p1 = 0.0 (p2 = 0.02), w0 = 0.02 (w1 = 1.0),w2 = 8.03

1038.47 p = 0.006, q = 0.1 1034.68 p0 = 0.98 (p1 = 0.02), p = 2.48, q = 99.0, w = 8.04

B605 857.20 p0 = 0.95 (p1 = 0.05), w0 = 0.0,w1 = 1.0

855.22 p0 = 0.96, p1 = 0.0 (p2 = 0.04), w0 = 0.0 (w1 = 1.0),w2 = 2.72

858.53 p = 0.005, q = 0.05 855.22 p0 = 0.96 (p1 = 0.04), p = 0.005, q = 99.0, w = 2.72

B695 1024.51 p0 = 0.97 (p1 = 0.03), w0 = 0.0,w1 = 1.0

1016.32 p0 = 0.98, p1 = 0.01 (p2 = 0.01), w0 = 0.0 (w1 = 1.0),w2 = 29.11

1028.40 p = 0.005, q = 0.05 1016.90 p0 = 0.98 (p1 = 0.02), p = 0.005, q = 0.16,w = 13.69

B814 1017.53 p0 = 0.88 (p1 = 0.12), w0 = 0.0,w1 = 1.0

1013.81 p0 = 0.88, p1 = 0.12 (p2 = 0.008), w0 = 0.0 (w1 = 1.0),w2 = 19.03

1017.73 p = 0.005, q = 0.05 1013.88 p0 = 0.99, (p1 = 0.01), p = 0.005, q = 0.05,w = 14.56

B821 1433.53 p0 = 0.85 (p1 = 0.15), w0 = 0.0,w1 = 1.0

1430.64 p0 = 0.91, p1 = 0.0 (p2 = 0.09), w0 = 0.02 (w1 = 1.0),w2 = 3.05

1434.74 p = 0.005, q = 0.03 1430.65 p0 = 0.91 (p1 = 0.09), p = 2.59, q = 99.0, w = 3.05

B897 1014.15 p0 = 0.78 (p1 = 0.22), w0 = 0.03,w1 = 1.0

1012.01 p0 = 0.78, p1 = 0.20 (p2 = 0.02), w0 = 0.03 (w1 = 1.0),w2 = 6.98

1013.68 p = 0.10, q = 0.37 1010.85 p0 = 0.98 (p1 = 0.02), p = 0.15, q = 0.65, w = 5.9

B907 695.28 p0 = 0.79 (p1 = 0.21), w0 = 0.08,w1 = 1.0

691.44 p0 = 0.81, p1 = 0.17 (p2 = 0.01), w0 = 0.11 (w1 = 1.0),w2 = 18.65

695.69 p = 0.14, q = 0.35 691.39 p0 = 0.99 (p1 = 0.01), p = 0.33, q = 1.01, w = 18.27

B967 882.33 p0 = 0.98 (p1 = 0.02), w0 = 0.0,w1 = 1.0

879.68 p0 = 0.99, p1 = 0.0 (p2 = 0.006), w0 = 0.005 (w1 = 1.0),w2 = 99.75

885.13 p = 0.01, q = 0.24 879.70 p0 = 0.99 (p1 = 0.006), p = 0.005, q = 0.13,w = 99.86

G.A

guiletaet

al./Infection,Genetics

andEvolution

12(2012)

987–996

991

Table 2Likelihood ratio tests of codeml site-specific analyses.

Cluster M1a vs M2a M7 vs M8

2d d.f. P-value 2d d.f. P-value

B152 67.187 2 <0.001 66.813 2 <0.001B161 7.576 2 0.023 10.264 2 0.006B194 7.782 2 0.020 7.795 2 0.020B24 6.495 2 0.039 6.642 2 0.036B248 12.129 2 0.002 25.538 2 <0.001B266 4.892 2 0.087 6.358 2 0.042B387 7.759 2 0.021 7.883 2 0.019B398 2.922 2 0.232 6.018 2 0.049B402 14.445 2 0.001 16.243 2 <0.001B417 0.000 2 1.000 8.463 2 0.015B431 9.817 2 0.007 14.929 2 0.001B541 3.436 2 0.179 5.861 2 0.053B549 7.469 2 0.024 10.055 2 0.007B57 6.347 2 0.042 7.582 2 0.023B605 3.951 2 0.139 6.606 2 0.037B695 16.391 2 <0.001 23.007 2 <0.001B814 7.441 2 0.024 7.699 2 0.021B821 5.774 2 0.056 8.185 2 0.017B897 4.566 2 0.102 5.813 2 0.055B907 7.679 2 0.022 8.599 2 0.014B967 5.299 2 0.071 10.868 2 0.004

992 G. Aguileta et al. / Infection, Genetics and Evolution 12 (2012) 987–996

et al., 2011). Among the genes that are under positive selection andpredicted to be secreted, two have experimental evidence forsecretion as their products were identified in the secretome of B.cinerea on plant containing media (B431 and B897; Espino et al.,2010). Then, using PSI-BLAST comparisons (Table S4), allowingdetecting weak but biologically relevant sequence similarities withthe Uniprot database, annotations were found for three genes:B266, B417 and B907. Finally, CATH search, to look for compatiblefold in the PDB structure database, resulted in annotations for 10genes: B161, B248, B24, B398, B431, B541, B549, B57, B695, B821(Table S5). Overall, owing to the use of multiple annotation meth-ods, the total 21 clusters of putatively interesting orthologs couldbe annotated. Among the putative functions of the genes showingsignificant signal of positive selection, many could be related tointeraction with the host plants (see Section 4).

Among the 21 genes exhibiting evidence of positive selection,B417 and B907 were identified as Leotiomycetes specific followingan orthology analysis made on nine sequenced fungal genomes(Amselem et al., 2011). This list of genes under positive selectionwas also compared to the list of B. cinerea genes that are over-ex-pressed during sunflower leaves infection (Amselem et al., 2011).Only the B161 gene, which encodes a putative dehydrogenase ispresent in both lists.

3.4. Validation of selected candidate genes showing signals of positiveselection by sequencing in additional Botrytis species

On the basis of their putative function, their elevated x ratio,their secretion, their expression during plant infection and theirlineage specificity (Amselem et al., 2011; Espino et al., 2010), nineout of the 21 genes showing signals of positive selection were se-lected (B161, B24, B266, B417, B431, B57, B897, B907, B967). Thesegenes were sequenced in 12 additional species of Botrytis special-ized on different host plants (Table 3) in order to validate that theyhave indeed evolved under positive selection during the diversifi-cation of the Botrytis genus. We designed primers in flanking re-gions containing conserved sequenced across B. cinerea and S.sclerotiorum to amplify and determine the complete sequence ofthe nine genes in the 12 Botrytis species. Most of these genes couldbe specifically amplified and sequenced in a number of speciesranging from 7 to 12 (B161, B24, B431, B57, B897, B967) but the

sequence of the B417 gene could be determined only in five ofthe 12 selected species (Table 3). Analysis using the CODEML pro-gram (Yang, 2007) of alignments containing sequences of all spe-cies (the four initial species plus the additional Botrytis species)detected sites under positive selection in all but the B24 geneand LRTs were significant for all these eight genes (Table 4). Tohave an independent sample, analysis of alignments containingonly sequences of the additional Botrytis species (without the fourinitial species) further confirmed that seven of these genes (B161,B417, B431, B57, B897, B907 and B967; Table 4) evolved under po-sitive selection, with very similar parameter estimates. The ex-tended alignments enabled detection of additional sites evolvingunder positive selection in five of the genes (B161, B417, B431,B897 and B907) (Table 4). For the B266 gene, footprints of positiveselection were identified only when the sequence of the closelyrelated Sclerotinia species was included in the alignment. Theseresults provide strong support for the validity of our approach todetect genes under positive selection without a priori candidates,showing that candidate genes under selection also show signatureof positive selection in other closely related pathogen species.

3.5. Experimental validation of the blind in silico approach fordetecting genes under positive selection

Among the 21 genes showing significant signal of positive selec-tion, four genes (B897, B417, B431 and B161), for which the signa-ture of positive selection was validated in other closely relatedBotrytis species, were chosen for functional characterization by re-verse genetics on the basis of their putative function, their elevatedx ratio and/or their level of expression during plant infection.These candidate genes were inactivated in order to test whetherthey were involved in aggressiveness of B. cinerea. Gene replace-ment cassettes conferring resistance to hygromycin were con-structed (see Section 2 and Table S1), and protoplasts of thestrain B05.10 were transformed with the K.O. cassettes. Protoplastregeneration and further purification on selective media led to theisolation of 10 B897, 4 B417, 11 B431, and 10 B161 and transfor-mants. The expected gene replacement events were screened byPCR. Using one primer located upstream of the 50 region of the tar-get gene and one primer located inside the hph (see Section 2), weselected 3 B897D, 3 B417D, 3 B431D and 3 B161D null mutants.

On synthetic media, all mutants obtained for the four candidategenes had similar growth and conidiation rates as the WT strain(data not shown). Aggressiveness of the mutants on both hostswas not significantly different from that of the WT (data notshown). Therefore, the genes B161, B431 and B897 and B417 donot appear to be essential for the infectious process of the B05.10strain on the two standard host plants commonly tested in vitroto check B. cinerea pathogenicity.

4. Discussion

In this study, we used a genome-wide approach based on thecomparative analysis of complete genome sequences of B. cinereaand S. sclerotiorum and newly generated EST datasets for B. tulipaeand B. ficariarum to identify rapidly evolving orthologous genesthat might be involved in host range determination, adaptationand coevolution with host plants in the Botrytis genus. This evolu-tionary genomics approach provided a list of 21 genes evolving un-der positive selection that thus represent new candidate genes inBotrytis, which could be pathogenicity factors. So far, pathogenicitygenes in B. cinerea were identified either by a ‘‘candidate gene’’strategy (Choquer et al., 2007), by random mutagenesis (Giesbertet al., 2012) or by profiling fungal gene expression during hostplant infection (Gioti et al, 2006). This latter approach has greatly

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G. Aguileta et al. / Infection, Genetics and Evolution 12 (2012) 987–996 993

enhanced our understanding of fungal phytopathogenicity of B.cinerea and other plant pathogens. The analysis of gene expressionduring infection has indeed led to the identification of a number offungal genes, that other methods have failed to uncover, related topathogenicity and adaptation to the environment in planta or in-volved in the mechanisms underlying adaptation to plants andcontrol of host ranges (Gioti et al., 2006; Wise et al., 2007; Genin,2010). However, genes involved in these functions are not neces-sarily all overexpressed during infection. Interestingly, 20 of the21 genes identified in this study as evolving under positive selec-tion had not been identified as overexpressed during infection ofsunflower by B. cinerea (Amselem et al., 2011). Also, 19 of themare conserved among filamentous fungi while two (B417 andB907) appear specific to the Leotiomycetes (Amselem et al.,2011). So far, only the B897 gene was known to act as a phytotoxinin B. cinerea and other fungal plant pathogens (Frías et al., 2011).The known role of the B897 protein in pathogenicity confirms thatthe evolutionary approach we used is valuable for the identifica-tion of fungal determinants of the host/pathogen interaction.Among the 20 remaining genes, some have functions that are re-lated to plant–fungus interactions.

4.1. Putative functions of genes under positive selection in Botrytis

The functional features of the 21 genes detected as havingevolved under positive selection were analyzed to gain additionalinsights into the evolutionary pressures acting on Botrytis species.For most of the genes (17 out of 21), putative functions can be pos-tulated on the basis of their structural, biochemical, or physiologi-cal characteristics (Tables S1–S3). Compared with the set of 642orthologs analyzed, this list of 21 proteins is also enriched in pre-dicted secreted proteins that are more likely involved in plant/pathogen interactions and may contribute to the infection processof their host plant by Botrytis species. These 21 candidate geneshave a wide variety of predicted functions and include genesknown to be involved in various aspects of the respiratory and en-ergy metabolism, protein and RNA metabolism, signal transductionor virulence. Several genes under positive selection have putativefunctions that can be related to pathogenicity or recognition ofthe pathogen by the host plant.

The B431 gene encodes a putative pectin methylesterase (PME),which hydrolyzes pectin, the major component of plant cell wall,and is involved in maceration and soft-rotting of plant tissue by fun-gal pathogens. B. cinerea secretes numerous pectinolytic enzymes(Amselem et al., 2011), including two PMEs and many polygalactu-ronases, to promote cell wall degradation. The other PME, BcPME1was shown to be involved in the infection process (Valette-Colletet al., 2003). It may be expected that cell wall degrading enzymesevolve under positive selection as they lie at the interface betweenthe host and the pathogen. For instance, the B. cinerea endopolyga-lacturonase BcPG1, known as both a virulence factor (ten Have et al.,1998) and as an elicitor of defense responses in grape (Poinssotet al., 2003), was recently shown to be under positive selection(Cettul et al., 2008).

The B897 gene encodes a protein with a conserved domainrelated to the fungal cerato-platanin phytotoxic proteins. Cerato-platanin proteins are involved in the interaction with the host-plantand induce both cell necrosis and phytoalexin synthesis, which isone of the first plant defense-related events (Pazzagli, 1999; Scalaet al., 2004). Frías et al. (2011) have shown that this protein (thatthey named BcSpl1) is involved in virulence on tobacco leaves andis able to induce necrosis. Interestingly, the two sites found hereto be under positive selection in the BcSpl1/B897 protein were situ-ated precisely in the region (residues 59 and 99, Table 4) with necro-sis-inducing activity on tobacco leaves.

Table 4Predicted positively selected sites identified in nine genes evolving under positive selection in the initial Botrytis and Sclerotinia species and in the additional Botrytis species.

B161 B24 B266 B417 B431 B57 B897 B907 B967

Gene length in B. cinerea 1050 1116 1068 375 984 594 414 1596 597Length of alignment used for the initial analysis 915 483 855 345 816 559 384 324 467Sites detected under positive selection in the initial species 170 Aa 20 K**

151 I*

10 S* 102 A* 88 E*

263 E*

264 S**

269 Q*

34 A*

114 T*

58 K** 496 F** 118 T*

Length of alignment used for the validation 990 1113 1056 381 930 591 441 1260 555Sites detected under positive selection inAll species 170 A**

288 S**

335 A*

(20 K*)b 10 S* 54 G*

102 A*

47 T*

88 E**

263 E*

264 S**

269 Q**

310 A*

34 A** 58 K**

99 G**

20 P*

262 T*

397 E**

435 A*496 F**

118 T*

Additional species only 288 S** (–)c –c (54 G*) 21 S*

47 T*

88 E**

237 K*

264 S**

310 A*

107 R* 58 K**

99 G**

262 T**

397 E**

435 A*

496 F**

118 T*

Botrytis species 288 S** (–)c –c 54 G**

102 A*

88 E**

237 K*

264 S**

310 A*

107 R*

114 T*

58 K**

99 G**

262 T**

397 E**

118 T*

a Bayes Empirical Bayes (BEB) analysis of sites under positive selection.b Significant sites detected by BEB analysis and associated with non-significant M7–M8 likelihood ratio tests are indicated within brackets.c –, no site.

* P > 95%.** P > 99%.

994 G. Aguileta et al. / Infection, Genetics and Evolution 12 (2012) 987–996

The B161 gene encoding a putative oxydoreductase is a putativeortholog to RED1, a gene involved in the T-Toxin synthesis in Coch-liobolus heterostrophus, the agent of Southern Corn Leaf Blight(Inderbitzin et al., 2010). The T-toxin polyketide is a determinantof high virulence to maize carrying Texas male sterile cytoplasm.As in C. heterostrophus, B161 gene is localized at a genomic locustogether with a polyketide synthase encoding gene and othergenes possibly involved in the synthesis of secondary metabolites.Interestingly, B161 was identified as overexpressed during sun-flower infection (Amselem et al., 2011).

Several genes (B387, B398, B605 and B967) were annotated asplasma membrane vacuolar type H+-ATPases, which are protonpumps playing a key role in the physiology of fungi. These vacuolarATPases control essential functions such as nutrient uptake, osmoticbalance, ion homeostasis, and stress tolerance (Portillo 2000) andwere shown to be essential for the growth of Saccharomyces cerevi-siae in stressful environmental conditions, including alkaline condi-tions (Finnigan et al., 2011). Genes putatively involved in the abilityto survive environmental stresses, such as those found in the hostplants, were also well represented. B248 and B821 were similar toenzymes (6-phosphogluconate dehydrogenase and transaldolase,respectively) involved in the pentose phosphate pathway, whichis critical for the ability of fungi to resist and adapt to oxidativestress (Juhnke et al., 1996). B24 and B541 have putative roles in pro-tein folding or protein catabolism and may rapidly initiate proteinproduction upon infection. For instance, B24 is a predicted cyclophi-lin. Another B. cinerea cyclophilin has previously been shown to playa critical role in plant tissues colonization (Viaud et al., 2003).

Other genes identified as under positive selection are involved insignal transduction pathways (B695, GTP-binding nuclear proteinGSP1/Ran) and in transcription (B402, ribosomal protein S8E;B814, 40S ribosomal protein S7). Signatures of positive selectionfound in these latter genes indicate that a plasticity in gene expres-sion may also have an important role in adaptation of organisms toenvironmental changes, along with variability in gene coding se-quences, as reported in a number of studies on adaptive responses

of species to their environment (Roelofs et al., 2010). Furthermore,recent studies have shown that stress-related genes are particularlyprone to tuned expression (Lopez-Maury et al., 2008).

4.2. Functional experiment

To investigate whether genes detected using a blind approach asevolving under positive selection do have functions potentiallyimportant in the pathogenesis of Botrytis species, we made reversegenetics analysis of four of the detected genes (B431, B897, B161,and B417). Four mutants of B. cinerea were generated, in which thesegenes were inactive, and then assayed for defects in pathogenicityon bean and tomato leaves. Overall, the null mutant generated forthe four genes were not significantly affected in virulence and noparticular phenotype was observed in infection assays. Howeveronly two hosts (bean and tomato) were used, and in vitro conditionsmay not reflect natural conditions. This may also suggest that B. cine-rea has evolved a number of backup biological processes, such asfunctional redundancy and compensatory processes, in order to pro-tect infection process from being impaired. For instance, the absenceof significant virulence defect in the B431 mutant may be due to thefunctional redundancy of these enzymes, as two putative pectinmethylesterases were identified in the genome of B. cinerea (Amse-lem et al., 2011). These genes under positive selection thereforeremain interesting candidates to be involved in host adaptation.Alternatively, they may be involved in coevolution with theircurrent host, or in genetic conflicts, and may cause genetic incom-patibilities between species as a consequence of their rapid evolu-tion (Presgraves et al., 2003). Further experiments on the mutantsgenerated in this study and on gene expression should reveal thefunctions involved.

4.3. Comparison with previous studies on other pathogens

It is remarkable that previous studies using the same approachof scans of genes under positive selection on different pathogens

G. Aguileta et al. / Infection, Genetics and Evolution 12 (2012) 987–996 995

have detected genes with similar functions. Li et al. (2009) com-pared three strains of the baker’s yeast S. cerevisiae, one of thembeing the commonly used s288c lab strain extracted from a rottenfig about 70 years ago, the pathogenic strain YJM789 from a patientwith pneumonia, and the wild strain RM11-1a, isolated from avineyard and used in labs since 1996. As outgroup they used thegenomes of Saccharomyces paradoxus and Saccharomyces mikatae.The different yeast species and strains used correspond to differentlife-styles and environments. In another study, Aguileta et al.(2010) analyzed four species of Microbotryum, each specializedpathogen of a Caryophyllaceae plant species. In all these cases,even if the number and identity of the genes under positive selec-tion are not strictly the same, the common functions annotatedprovide an important clue as to which are the most relevant mech-anisms and cellular components that are rapidly evolving andmaybe associated with infection or specialization. In the compari-son of S. cerevisiae strains, of the 76 orthologs under positive selec-tion six were related to cell wall, four to metal ion transport, threewere transmembrane proteins, and two were cellular bud mem-brane; the rest were not annotated or corresponded to unknownfunctions (Li et al., 2009). In the case of Microbotryum, of the 42genes subject to positive selection, six were transmembrane pro-teins involved in transport activity, four were related to proteinand RNA metabolism, two to protein folding, two to respirationand energy metabolism, one was a secreted protein involved inextracellular communication, and one was related to cell regula-tion (Aguileta et al., 2010). Finally, in our study on four Botrytis/Sclerotinia species, the 21 positively selected genes were annotatedas follows: six were related to protein and RNA metabolism, threewere transmembrane proteins with transporter activity, six partic-ipate in respiration and energy metabolism, two were secretedproteins possibly acting in extracellular communication, one in-volved in cell signaling, and two in protein folding. As in the otherstudies, the remaining orthologous clusters were either not anno-tated or with unknown functions. The fraction of genes detectedas evolving under positive selection in the three studies (76/3300[2.3%] in S. cerevisiae; 42/372 [11.2%] in Microbotryum; and 23/642 [3.6%] in Botrytis sp.) differed, but the species sampling andthe type of data varied among studies (either cDNA libraries orwhole genome sequences), being thus not directly comparable.Therefore, they should not be taken as estimates of the proportionof positively selected genes across the genome.

In all cases, a large fraction of the annotated genes under posi-tive selection are transmembrane proteins, putative secreted pro-teins or proteins located in the cell wall that presumablyparticipate in transporter activities and establish communicationwith the host (host recognition) cell and the external environment.As Li and colleagues (2009) also pointed out, positive selection ap-pears to respond to external, environmental conditions. In the con-text of emerging fungal diseases, this is all the more relevant asrapid adaptation to new hosts is generally observed. Also, functionsrelated to rapid cellular growth appear to evolve under positiveselection among these pathogen species, indicating that part ofthe resources is allocated to growing invading tissues. Althoughin smaller numbers, proteins involved in respiration under stress-ful conditions, cell signaling and regulation as well as protein fold-ing appear as being under positive selection in all the studiedcases. Overall, the same relevant functions have been found inthe present work and in the studies of Li and colleagues (2009)and Aguileta et al. (2010), adding support to their involvement inhost adaptation, coevolution and/or genetic conflicts.

Acknowledgments

We are grateful to the INRA MIGALE bioinformatics platform(http://migale.jouy.inra.fr) for providing help and computational

resources. We would also like to acknowledge Adeline Simon, Guil-laume Morgant and Pascal Le Pêcheur (INRA BIOGER, Grignon) fortheir help in gene annotation, transformation and pathogenicitytests, respectively. This work was funded by the Grants ANR-06-BLAN-0201 and ANR 07-BDIV-003, by a post-doctoral Grant fromthe French Ile-de-France Region, and by the ‘Consortium Nationalde Recherche en Génomique’ for sequencing the cDNA libraries.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, inthe online version, at doi:10.1016/j.meegid.2012.02.012.

References

Aguileta, G., Lengelle, J., Marthey, S., Chiapello, H., Rodolphe, F., Gendrault, A.,Yockteng, R., Vercken, E., Devier, B., Fontaine, M.C., Wincker, P., Dossat, C.,Cruaud, C., Couloux, A., Giraud, T., 2010. Finding candidate genes under positiveselection in non-model species: examples of genes involved in hostspecialization in pathogens. Mol. Ecol. 19, 292–306.

Aguileta, G., Refrégier, G., Yockteng, R., Fournier, E., Giraud, T., 2009. Rapidlyevolving genes in pathogens: methods for detecting positive selection andexamples among fungi, bacteria, viruses and protists. Infect. Genet. Evol. 9, 656–670.

Altschul, S.F., Madden, T.L., Schäffer, A.A., Zhang, J., Zhang, Z., Miller, W., Lipman, D.J.,1997. Gapped BLAST and PSI-BLAST: a new generation of protein databasesearch programs. Nucleic Acids Res. 25, 3389–3402.

Amselem, J., Cuomo, C.A., Van Kan, J.A.L., Viaud, M., Benito, E.P., Couloux, A., et al.,2011. Genomic analysis of the necrotrophic fungal pathogens Sclerotiniasclerotiorum and Botrytis cinerea. PLoS Genet. 7, e1002230.

Anderson, P.K., Cunningham, A.A., Patel, N.G., Morales, F.J., Epstein, P.R., Daszak, P.,2004. Emerging infectious diseases of plants: pathogen pollution, climatechange and agrotechnology drivers. Trends Ecol. Evol. 19, 535–544.

Anisimova, M., Bielawski, J.P., Yang, Z., 2001. Accuracy and power of the likelihoodratio test in detecting adaptive molecular evolution. Mol. Biol. Evol. 18, 1585–1592.

Archie, E.A., Luikart, G., Ezenwa, V.O., 2009. Infecting epidemiology with genetics: anew frontier in disease ecology. Trends Ecol. Evol. 24, 21–30.

Bendtsen, J.D., Nielsen, H., von Heijne, G., Brunak, S., 2004. Improved prediction ofsignal peptides: SignalP 3.0. J. Mol. Biol. 340, 783–795.

Blaustein, A.R., Johnson, P.T.J., 2010. Conservation biology: when an infection turnslethal. Nature 465, 881–882.

Bolton, M.D., Thomma, B.P.H.J., Nelson, B.D., 2006. Sclerotinia sclerotiorum (Lib.) deBary: biology and molecular traits of a cosmopolitan pathogen. Mol. PlantPathol. 7, 1–16.

Carroll, B.J., Sweigard, J.A., Valent, B., 1994. Improved vectors for selecting resistanceto hygromycin. Fungal Genet. Newsl. 41, 22.

Castresana, J., 2000. Selection of conserved blocks from multiple alignments fortheir use in phylogenetic analysis. Mol. Biol. Evol. 17, 540–552.

Cettul, E., Rekab, D., Locci, R., Firrao, G., 2008. Evolutionary analysis ofendopolygalacturonase-encoding genes of Botrytis cinerea. Mol. Plant Pathol.9, 675–685.

Choquer, M., Fournier, E., Kunz, C., Levis, C., Pradier, J.-M., Simon, A., Viaud, M., 2007.Botrytis cinerea virulence factors: new insights into a necrotrophic andpolyphageous pathogen. FEMS Microbiol. Lett. 277, 1–10.

Colot, H.V., Park, G., Turner, G.E., Ringelberg, C., Crew, C.M., Litvinkova, L., Weiss,R.L., Borkovich, K.A., Dunlap, J.C., 2006. A high-throughput gene knockoutprocedure for Neurospora reveals functions for multiple transcription factors.Proc. Natl. Acad. Sci. USA 103, 10352–10357.

Conesa, A., Götz, S., García-Gómez, J.M., Terol, J., Talón, M., Robles, M., 2005.Blast2GO: a universal tool for annotation, visualization and analysis infunctional genomics research. Bioinformatics 21, 3674–3676.

de Crecy, E., Jaronski, S., Lyons, B., Lyons, T.J., Keyhani, N.O., 2009. Directed evolutionof a filamentous fungus for thermotolerance. BMC Biotechnol. 9, 74.

Cuff, A.L., Sillitoe, I., Lewis, T., Clegg, A.B., Rentzsch, R., Furnham, N., Pellegrini-Calace, M., Jones, D., Thornton, J., Orengo, C.A., 2011. Extending CATH:increasing coverage of the protein structure universe and linking structurewith function. Nucleic Acids Res. 39, D420–D426.

Desprez-Loustau, M.-L., Robin, C., Buée, M., Courtecuisse, R., Garbaye, J., Suffert, F.,Sache, I., Rizzo, D.M., 2007. The fungal dimension of biological invasions. TrendsEcol. Evol. 22, 472–480.

Espino, J.J., Gutiérrez-Sánchez, G., Brito, N., Shah, P., Orlando, R., González, C., 2010.The Botrytis cinerea early secretome. Proteomics 10, 3020–3034.

Ewing, B., Green, P., 1998. Base-calling of automated sequencer traces using Phred.II. Error probabilities. Genome Res. 8, 186–194.

Ewing, B., Hillier, L., Wendl, M.C., Green, P., 1998. Base-calling of automatedsequencer traces using Phred. I. Accuracy assessment. Genome Res. 8, 175–185.

Finn, R.D., Mistry, J., Tate, J., Coggill, P., Heger, A., Pollington, J.E., Gavin, O.L.,Gunasekaran, P., Ceric, G., Forslund, K., Holm, L., Sonnhammer, E.L.L., Eddy, S.R.,Bateman, A., 2010. The Pfam protein families database. Nucleic Acids Res. 38,D211–D222.

996 G. Aguileta et al. / Infection, Genetics and Evolution 12 (2012) 987–996

Finnigan, G.C., Ryan, M., Stevens, T.H., 2011. A genome-wide enhancer screenimplicates sphingolipid composition in vacuolar ATPase function inSaccharomyces cerevisiae. Genetics 187, 771–783.

Fournier, E., Giraud, T., Albertini, C., Brygoo, Y., 2005. Partition of the Botrytis cinereacomplex in France using multiple gene genealogies. Mycologia 97, 1251–1267.

Frías, M., González, C., Brito, N., 2011. BcSpl1, a cerato-platanin family protein,contributes to Botrytis cinerea virulence and elicits the hypersensitive responsein the host. The New Phytol. 192, 483–495.

Fukunishi, Y., Hayashizaki, Y., 2001. Amino acid translation program for full-lengthcDNA sequences with frameshift errors. Physiol. Genomics 5, 81–87.

Genin, S., 2010. Molecular traits controlling host range and adaptation to plants inRalstonia solanacearum. The New Phytol. 187, 920–928.

Giesbert, S., Schumacher, J., Kupas, V., Espino, J., Segmüller, N., Haeuser-Hahn, I.,Schreier, P.H., Tudzynski, P., 2012. Identification of pathogenesis-associatedgenes by T-DNA-mediated insertional mutagenesis in Botrytis cinerea: a type 2Aphosphoprotein phosphatase and an SPT3 transcription factor have significantimpact on virulence. Mol. Plant Microbe Interact. 25, 481–495.

Gioti, A., Simon, A., Pêcheur, P.Le., Giraud, C., Pradier, J.M., Viaud, M., Levis, C., 2006.Expression profiling of Botrytis cinerea genes identifies three patterns of up-regulation in planta and an FKBP12 protein affecting pathogenicity. J. Mol. Biol.358, 372–386.

Gough, J., 2002. SUPERFAMILY: HMMs representing all proteins of known structure.SCOP sequence searches, alignments and genome assignments. Nucleic AcidsRes. 30, 268–272.

ten Have, A., Mulder, W., Visser, J., van Kan, J.A., 1998. The endopolygalacturonasegene Bcpg1 is required for full virulence of Botrytis cinerea. Mol. Plant MicrobeInteract. 11, 1009–1016.

Huang, X., Madan, A., 1999. CAP3: a DNA sequence assembly program. Genome Res.9, 868–877.

Inderbitzin, P., Asvarak, T., Turgeon, B.G., 2010. Six new genes required forproduction of T-toxin, a polyketide determinant of high virulence ofCochliobolus heterostrophus to maize. Mol. Plant Microbe Interact. 23, 458–472.

Iseli, C., Jongeneel, C.V., Bucher, P., 1999. ESTScan: a program for detecting,evaluating, and reconstructing potential coding regions in EST sequences. Proc.Int. Conf. Intell. Syst. Mol. Biol., 138–148.

Juhnke, H., Krems, B., Kötter, P., Entian, K.D., 1996. Mutants that show increasedsensitivity to hydrogen peroxide reveal an important role for the pentosephosphate pathway in protection of yeast against oxidative stress. Mol. Gen.Genet. 252, 456–464.

van Kan, J.A.L., 2006. Licensed to kill: the lifestyle of a necrotrophic plant pathogen.Trends Plant Sci. 11, 247–253.

Krogh, A., Larsson, B., von Heijne, G., Sonnhammer, E.L., 2001. Predictingtransmembrane protein topology with a hidden Markov model: application tocomplete genomes. J. Mol. Biol. 305, 567–580.

Lannuccelli, E., 2005. SURF (SeqUence Repository and Feature detection) package.Lebarbenchon, C., Brown, S.P., Poulin, R., Gauthier-Clerc, M., Thomas, F., 2008.

Evolution of pathogens in a man-made world. Mol. Ecol. 17, 475–484.Levis, C., Fortini, D., Brygoo, Y., 1997. Transformation of Botrytis cinerea with the

nitrate reductase gene (niaD) shows a high frequency of homologousrecombination. Curr. Genet. 32, 157–162.

Li, Y.-D., Liang, H., Gu, Z., Lin, Z., Guan, W., Zhou, L., Li, Y.-Q., Li, W.-H., 2009.Detecting positive selection in the budding yeast genome. J. Evol. Biol. 22,2430–2437.

Li, L., Stoeckert, C.J., Roos, D.S., 2003. OrthoMCL: identification of ortholog groups foreukaryotic genomes. Genome Res. 13, 2178–2189.

Lopez-Maury, L., Marguerat, S., Bahler, J., 2008. Tuning gene expression to changingenvironments: from rapid responses to evolutionary adaptation. Nat. Rev.Genet. 9, 583–593.

Marthey, S., Aguileta, G., Rodolphe, F., Gendrault, A., Giraud, T., Fournier, E., Lopez-Villavicencio, M., Gautier, A., Lebrun, M.-H., Chiapello, H., 2008. FUNYBASE: aFUNgal phYlogenomic dataBASE. BMC Bioinformatics 9, 456.

Matute, D.R., Quesada-Ocampo, L.M., Rauscher, J.T., McEwen, J.G., 2008. Evidence forpositive selection in putative virulence factors within the Paracoccidioides

brasiliensis species complex. In: Taylor, J.W. (Ed.), PLoS Negl. Trop. Dis., vol. 2, p.e296.

Mulder, N.J., Apweiler, R., Attwood, T.K., Bairoch, A., Bateman, A., Binns, D., et al.,2007. New developments in the InterPro database. Nucleic Acids Res. 35, D224–D228.

Notredame, C., Higgins, D.G., Heringa, J., 2000. T-Coffee: a novel method for fast andaccurate multiple sequence alignment. J. Mol. Biol. 302, 205–217.

Pazzagli, L., 1999. Purification, characterization, and amino acid sequence of Cerato-platanin, a new phytotoxic protein from Ceratocystis fimbriata f. sp. platani. J.Biol. Chem. 274, 24959–24964.

Poinssot, B., Vandelle, E., Bentéjac, M., Adrian, M., Levis, C., Brygoo, Y., Garin, J.,Sicilia, F., Coutos-Thévenot, P., Pugin, A., 2003. The endopolygalacturonase 1from Botrytis cinerea activates grapevine defense reactions unrelated to itsenzymatic activity. Mol. Plant Microbe Interact. 16, 553–564.

Portillo, F., 2000. Regulation of plasma membrane H+-ATPase in fungi and plants.Biochim. Biophys. Acta Biomembr. 1469, 31–42.

Presgraves, D.C., Balagopalan, L., Abmayr, S.M., Orr, H.A., 2003. Adaptive evolutiondrives divergence of a hybrid inviability gene between two species ofDrosophila. Nature 423, 715–719.

Roelofs, D., Morgan, J., Stürzenbaum, S., 2010. The significance of genome-widetranscriptional regulation in the evolution of stress tolerance. Evol. Ecol. 24,527–539.

Sacristán, S., García-Arenal, F., 2008. The evolution of virulence and pathogenicity inplant pathogen populations. Mol. Plant Pathol. 9, 369–384.

Scala, A., Pazzagli, L., Comparini, C., Santini, A., Tegli, S., Cappugi, G., 2004. Cerato-platanin, an early-produced protein by Ceratocystis fimbriata f. sp. platani, elicitsphytoalexin synthesis in host and non-host plants. J. Plant Pathol. 86, 27–33.

Shah, P., Atwood, J.A., Orlando, R., El Mubarek, H., Podila, G.K., Davis, M.R., 2009.Comparative proteomic analysis of Botrytis cinerea secretome. J. Proteome Res.8, 1123–1130.

Staats, M., van Baarlen, P., Van Kan, J.A.L., 2005. Molecular phylogeny of the plantpathogenic genus Botrytis and the evolution of host specificity. Mol. Biol. Evol.22, 333–346.

The UniProt Consortium, 2007. The Universal Protein Resource (UniProt). NucleicAcids Res. 35, D193–D197.

Thomas, P.D., Campbell, M.J., Kejariwal, A., Mi, H., Karlak, B., Daverman, R., Diemer,K., Muruganujan, A., Narechania, A., 2003. PANTHER: a library of proteinfamilies and subfamilies indexed by function. Genome Res. 13, 2129–2141.

Valette-Collet, O., Cimerman, A., Reignault, P., Levis, C., Boccara, M., 2003.Disruption of Botrytis cinerea pectin methylesterase gene Bcpme1 reducesvirulence on several host plants. Mol. Plant Microbe Interact. 16, 360–367.

Viaud, M., Brunet-Simon, A., Brygoo, Y., Pradier, J.-M., Levis, C., 2003. Cyclophilin Aand calcineurin functions investigated by gene inactivation, cyclosporin Ainhibition and cDNA arrays approaches in the phytopathogenic fungus Botrytiscinerea. Mol. Microbiol. 50, 1451–1465.

Walker, A.-S., Gautier, A., Confais, J., Martinho, D., Viaud, M., Pêcheur, P.Le., Dupont,J., Fournier, E., 2011. Botrytis pseudocinerea, a new cryptic species causing greymould in French vineyards in sympatry with Botrytis cinerea. Phytopathology101, 1433–1445.

Wasmuth, J.D., Blaxter, M.L., 2004. Prot4EST: translating expressed sequence tagsfrom neglected genomes. BMC Bioinformatics 5, 187.

Williamson, B., Tudzynski, B., Tudzynski, P., van Kan, J.A.L., 2007. Botrytis cinerea:the cause of grey mould disease. Mol. Plant Pathol. 8, 561–580.

Wise, R.P., Moscou, M.J., Bogdanove, A.J., Whitham, S.A., 2007. Transcript profiling inhost–pathogen interactions. Annu. Rev. Phytopathol. 45, 329–369.

Yang, Z., 2007. PAML 4: phylogenetic analysis by maximum likelihood. Mol. Biol.Evol. 24, 1586–1591.

Yang, Z., Bielawski, J.P., 2000. Statistical methods for detecting molecularadaptation. Trends Ecol. Evol. 15, 496–503.

Yu, J.-H., Hamari, Z., Han, K.-H., Seo, J.-A., Reyes-Domínguez, Y., Scazzocchio, C.,2004. Double-joint PCR: a PCR-based molecular tool for gene manipulations infilamentous fungi. Fungal Genet. Biol. 41, 973–981.