Sclerotinia sclerotiorum and Botrytis cinerea are closely related necrotrophic plant pathogenic fungi notable for their wide host ranges and environmental persistence. These attributes have made these species models for understanding the complexity of necrotrophic, broad host-range pathogenicity. Despite their similarities, the two species differ in mating behaviour and the ability to produce asexual spores. We have sequenced the genomes of one strain of S. sclerotiorum and two strains of B. cinerea. The comparative analysis of these genomes relative to one another and to other sequenced fungal genomes is provided here. Their 38–39 Mb genomes include 11,860–14,270 predicted genes, which share 83% amino acid identity on average between the two species. We have mapped the S. sclerotiorum assembly to 16 chromosomes and found large-scale co-linearity with the B. cinerea genomes. Seven percent of the S. sclerotiorum genome comprises transposable elements compared to <1% of B. cinerea. The arsenal of genes associated with necrotrophic processes is similar between the species, including genes involved in plant cell wall degradation and oxalic acid production. Analysis of secondary metabolism gene clusters revealed an expansion in number and diversity of B. cinerea–specific secondary metabolites relative to S. sclerotiorum. The potential diversity in secondary metabolism might be involved in adaptation to specific ecological niches. Comparative genome analysis revealed the basis of differing sexual mating compatibility systems between S. sclerotiorum and B. cinerea. The organization of the mating-type loci differs, and their structures provide evidence for the evolution of heterothallism from homothallism. These data shed light on the evolutionary and mechanistic bases of the genetically complex traits of necrotrophic pathogenicity and sexual mating. This resource should facilitate the functional studies designed to better understand what makes these fungi such successful and persistent pathogens of agronomic crops.
Phytopathogenic fungi have evolved a wide range of strategies to infect and colonize plants through both convergent and divergent adaptations. This is reflected in the occurrence of species within common evolutionary branches with widely diverse pathogenic lifestyles, ranging from obligate biotrophs to necrotrophs, and from host-specific to broad host range pathogens. Operationally, necrotrophs have been defined as pathogens that derive nutrients from killed host cells, biotrophs as pathogens that derive nutrients from living tissues and hemibiotrophs as pathogens that derive nutrients from a combination of feeding from living and killed host cells, respectively. The mechanisms that drive these adaptations remain largely enigmatic. Among the few pathogens considered to be exemplary necrotrophs are the white mold fungus Sclerotinia sclerotiorum (Lib.) de Bary and the taxonomically closely related grey mold fungus Botrytis cinerea Pers. Fr. [teleomorph Botryotinia fuckeliana (de Bary) Whetzel]. Both fungi have considerably broader host ranges (>400 and >200 species, respectively) than most plant pathogens and each causes multi-millions of US dollars in pre- and postharvest crop losses world wide [1], [2]. Necrotrophs secrete an array of cell wall-degrading enzymes and toxins, which led to their reputation as relatively less adapted as compared to biotrophic fungi, which manipulate host physiology to obtain their nutrients from living tissues. Biotrophs are widely accepted to intimately interact and co-evolve with their hosts. Recent studies have, however, revealed that interactions between necrotrophs and their host plants are considerably more complex and subtle than previously appreciated. Some necrotrophs secrete effector proteins which are internalised by host cells and interact with the host in a gene-for-gene relationship to initiate disease, albeit in an inverse manner compared to biotrophs [3]. In the case of S. sclerotiorum and B. cinerea, the active modulation of the host redox status and the subversion of host (programmed) cell death pathways by the pathogen appear to be crucial for disease to develop [4]–[8]. The availability of molecular tools has considerably advanced our understanding of the infection strategies and pathogenic development of S. sclerotiorum and B. cinerea, yet only very few absolutely critical virulence determinants have been identified by candidate gene approaches [9]. Their ability to infect different plant species and tissues under a wide range of environmental conditions, as well as their ability to produce sclerotia that survive in the soil for many years, contribute to the persistent and widespread nature of these pathogens (Figure 1). The melanized sclerotium plays a central role in the lifecycle of both fungi by germinating either vegetatively for local colonization or carpogenically to initiate the sexual cycle including the production of apothecia from which ascospores are released (Figure 1). Although S. sclerotiorum and B. cinerea share many developmental and physiological features, important differences exist in their regulation and potential for sporulation. Dispersal of both species is via air-borne spores. S. sclerotiorum exclusively produces ascospores and not conidia (asexual spores). In contrast, B. cinerea, although capable of producing ascospores, is dispersed predominantly via conidia. Furthermore the regulation of sexual sporulation is quite different, S. sclerotiorum being homothallic (self–fertile) [1] and B. cinerea heterothallic (requiring a sexual partner of opposite mating type) [2]. These differences in mitotic and meiotic sporulation impact not only the life histories of these fungi but also their epidemiology and the disease control methods employed towards each. The characteristics of S. sclerotiorum and B. cinerea pathogenicity and development stand in stark contrast to their fellow Leotiomycete powdery mildew fungi (e. g. Blumeria, Erisyphe, Podosphaera) which are obligate biotrophs often restricted at the species level to a single host genus. The recent description of genome sequences of two powdery mildew species [10] and two phylogenetically distant, restricted host range necrotrophs (Phaeosphaeria nodorum [11], Pyrenophora teres f. teres [12]) provides the opportunity to assess whether genomic features can be identified that are common to broad host range necrotrophs such as B. cinerea and S. sclerotiorum, yet distinct from other plant pathogenic fungi. Here we describe and compare the genome sequence assemblies and annotations for S. sclerotiorum and for two strains of B. cinerea. The comparative genome analyses of these two phytopathogenic fungi to each other, to a closely related powdery mildew and to distantly related necrotrophs offer insight into common genes underlying development and pathogenesis in S. sclerotiorum and B. cinerea, as well as genes that condition specific features of their pathogenic success.
S. sclerotiorum and B. cinerea are now the only fully sequenced species in the order Helotiales and with the obligate biotroph, Blumeria graminis, in the class Leotiomycetes of the Pezizomycotina, the largest subphylum of Ascomycota [10], [13], [14]. Within the Pezizomycotina, Leotiomycetes are most closely related to the sister lineage Sordariomycetes, and more distantly to the Eurofotiomycetes and the Dothideomycetes [14]–[16]. A phylogeny based on 82 completed fungal genomes anchors a well-supported and highly divergent Helotiales lineage including S. sclerotiorum and B. cinerea [17]. The order is, however, far too large and heterogeneous to be characterized by S. sclerotiorum and B. cinerea alone. Additional species are needed to increase the phylogenetic resolution. We constructed a five-locus phylogeny rooted with Blumeria graminis, that includes two loci not previously used for this taxon sample, G3PDH and HSP60. This analysis confirms that the Sclerotiniaceae, Sclerotinia and Botrytis are closely related but distinct, monophyletic evolutionary lineages (Figure 2). This analysis also confirms that “Sclerotinia” homoeocarpa, an important pathogen of turf with morphology and etiology quite distinct from that of Sclerotinia, is not a Sclerotinia and should be reassigned to a genus in the family Rutstroemiaceae pending a reassessment of related species and generic limits. The Sclerotiniaceae includes obligate and facultative biotrophs, such as Myriosclerotinia species, as well as necrotrophs, as exemplified by Sclerotinia and Botrytis. Botrytis is divided in two sub-lineages as previously described [18]; one lineage is associated with both eudicots and monocots and the other with eudicots only. The strongly supported lineage with species of Sclerotinia on one branch, also includes the asexual Sclerotium cepivorum, an important pathogen of Allium, and a representative of the genus Dumontinia associated with wild plants such as Anemone (Ranunculaceae). Wang et al. [13] suggest that the ancestors of the lineages representing the Sclerotiniaceae and Rutstroemiaceae were associated with conifers, inferring a radiation of the Sclerotiniaceae and Rutstroemiaceae in association with the emergence and diversification of angiosperms. Co-evolution of Botrytis with host species has been investigated but evidence is inconclusive [18]; evidence would be concordant phylogenies between symbiont/pathogen species and host species, as demonstrated in Monilinia [19]. Estimates of divergence times in the phylogeny would require a molecular clock model that could be violated if some lineages have undergone accelerated evolution, as in a radiation event. Such estimates are inexact, especially when not calibrated, e. g. , by fossil evidence. Comparative genomics of S. sclerotiorum and B. cinerea has revealed an expected high degree of sequence identity and synteny, however, we observed several striking differences in gene content between these plant pathogens. The first difference was in the content of transposable elements. The data suggest a recent burst of transposition in the S. sclerotiorum genome relative to B. cinerea. The two species differ in the regulation of sexual reproduction and this is fully explained from the sequence and organization of the MAT loci. Features of these loci provide evidence for an evolution of heterothallism from homothallism. The failure of S. sclerotiorum to produce conidiospores could not be explained from the gene content, as all key conidiation genes seemed to be present and potentially functional. The genome sequences further revealed a striking difference in the amount and types of potential secondary metabolites. The botrydial toxin biosynthetic cluster and the phytohormone abscisic acid biosynthetic genes were unique to B. cinerea. The precise chemical nature and biological function of most of these metabolites remains to be determined to understand whether they contribute to the adaptation of the two species to different ecological niches. The content of CAZyme-encoding genes revealed a preference of S. sclerotiorum and B. cinerea for pectin as nutrient source. This is in agreement with the observation that S. sclerotiorum and B. cinerea are pathogens of dicots and preferentially grow on aerial plant tissues that are rich in pectin. With respect to pathogenicity determinants, there are no unique features that could be identified as ‘silver bullets’, which distinguish these aggressive pathogens from other pathogenic and non-pathogenic fungi. Comparison with two other necrotrophic fungi revealed only few (if any) genes shared among necrotrophs and absent in fungi with other trophic lifestyles (i. e saprotrophs or hemi-biotrophs). These findings point to the multigenic, variable, and sophisticated nature of necrotrophic pathogenicity whose nuance may only be revealed through systematic functional analysis of candidate activities and regulators. We suggest that the specific regulation of networks of the available suite of genes is key to pathogenic success for S. sclerotiorum and B. cinerea.
The sequenced strain of S. sclerotiorum ‘1980’ is available from the Fungal Genetics Stock Center (http: //www. fgsc. net/). Plasmid (4-kb and 10-kb inserts) and Fosmid (40-kb inserts) libraries were generated with randomly sheared and size-selected DNA. Plasmid and Fosmid inserts were sequenced from both ends to generate paired reads. Sequence reads (total of 507,621) were filtered for quality, vector and other contamination, and the resulting 476,001 reads were assembled with the Arachne assembler [20]. A consensus sequence was determined from an average of approximately 9. 1-fold sequence depth (7. 8 depth in Q20 bases). The assembly (GenBank accession AAGT01000000) totals 38. 3 Mb and consists of 36 scaffolds with an N50 of 1. 6 Mb. The scaffolds consist of 679 sequence contigs with a N50 of 123 kb; at least half of all bases in the assembly fall within at least the size of the N50 scaffold or contig. The contigs total 38. 0 Mb, so only 0. 8% of the assembly is represented by contig gaps. This assembly contains 98. 7% of bases with Q40 quality or greater. The sequence of B. cinerea strain B05. 10 was generated by Syngenta AG using four size selected shotgun libraries. Sequence reads were filtered for quality, vector and other contamination, and the resulting 291,603 reads were assembled using the Arachne assembler. A consensus sequence was generated from an average of approximately 4. 5 fold sequence depth (3. 9-fold depth in Q20 bases). The assembly (GenBank accession AAID00000000) totals 42. 3 Mb and consists of 4,534 contigs with a N50 of 16. 4 kb, which are ordered and oriented by paired end clone reads within 588 scaffolds, with a N50 of 257 kb. The sequence of B. cinerea strain T4 (Genbank accessions FQ790245 to FQ790362) was determined and assembled by Genoscope, Evry, France. The genome was sequenced using a whole genome shotgun (WGS) strategy, by generating plasmid (3 kb and 10 kb inserts) and BAC (∼50 kb inserts) libraries. Clones from these libraries were end-sequenced to generate paired reads. Sequence reads from plasmid inserts (573,705, representing 10. 5× coverage) were assembled with Arachne [20], resulting in 3,054 contigs. Contigs were ordered and oriented into scaffolds using paired sequences from BACs (35,668 reads). The gap lengths were estimated using linked clones. 118 scaffolds larger than 20 kb with gap sizes lower than 10%, were used for annotation. These 118 scaffolds (39. 5 Mb, N50 of 562 kb, Table 1) contain 2,281 contigs (37. 9 Mb, N50 of 35 kb). This assembly contains 98. 7% of bases with Q40 quality or greater. An optical map, a type of physical map, was created for S. sclerotiorum strain 1980 by OpGen. The restriction enzyme BsiWI was used to create ordered fragments and the resulting map contained 16 optical contigs. These likely correspond to complete chromosomes, in agreement with an estimated chromosome number of 16 using pulsed-field gel electrophoresis [21]. The assembly was compared to the optical map using an in silico digest of the assembly (http: //www. broadinstitute. org/annotation/genome/sclerotinia_sclerotiorum/MultiHome. html). This comparison suggested that supercontig 4 contained an unsupported join in the assembly, between contigs 132 and 133; the assembly was updated to break scaffold 4 between these contigs, and a revised version of the assembly was submitted to Genbank. No other major disagreements were found. A total map size of approximately 38. 5 Mb was estimated by calculating the ratio between the assembly fragments and map units. The assembly supercontigs cover 99. 4% of the optical map (contigs cover 98. 6%) (Figure S1). Only two supercontigs, 35 and 36, were not anchored to the optical map; these supercontigs are 39 kb and 16 kb respectively, and do not contain any BsiWI restriction site. Supercontig 35 consists of arrayed ribosomal DNA repeats. The optical contigs appear to correspond to complete chromosomes, as they end with a flush set of DNA molecules (data not shown). Additionally, reads containing telomeric repeat arrays of (TTAGGG) are linked to the ends of 26 of the 32 chromosome ends, and are present at the end of two additional scaffolds (Figure S1). At least two scaffolds are mapped to each optical contig, suggesting that centromeres could lie in these unassembled gaps. 68 individual progeny from a cross between strains T4 (isolated from tomato) and 32 (isolated from Vitis vinifera) were kindly provided by Caroline Kunz (Université Pierre et Marie Curie, Paris). Around 400 microsatellite markers were designed from the genomic sequences of the B05. 10 and T4 strains by using the “GRAMENE” software [133]. In addition, a set of 144 SNP (Single Nucleotide Polymorphism) markers were identified through the comparison of the B05. 10 and T4 genome sequences and genotyped in the progeny using the SNPlex genotyping assay (Applied Biosystems). The segregation of markers among the progeny was analysed using MAPMAKER software [134] set at min LOD3 and max Distance at 37. For S. sclerotiorum and B. cinerea B05. 10, gene structures were predicted using a combination of FGENESH and GENEID. The version of GENEID used for these calls was 1. 2a. FGENESH is unversioned. As FGENESH uses a statistical model of gene structure that requires training on each organism for accurate prediction, Softberry trained FGENESH on S. sclerotiorum sequences. GENEID is an ab initio gene caller and was run with the default parameters after being trained on a set of 542 S. sclerotiorum genes that were manually curated based on EST and protein alignments. These S. sclerotiorum trained gene models were also used for B. cinerea B05. 10 without additional training. The results from these two gene callers were combined in the following manner. Both FGENESH and GENEID were run on the entire genomic sequences of S. sclerotiorum and B. cinerea (B05. 10) to provide an initial set of predicted genes. This resulted in a set containing 12,961 FGENESH predictions and 14,711 GENEID predictions for S. sclerotiorum, and 13,864 FGENESH predictions and 16,907 GENEID predictions for B. cinerea. Next, gene predictions less than 30aa (90 nt) were removed, and any gene prediction less than 50aa (150 nt) was removed, unless it was overlapped by a prediction from a different program, BLAST evidence, a HMMER PFAM domain, or an EST alignment. Applying these criteria removed 2,166 S. sclerotiorum and 2,373 B. cinerea genes from consideration. We manually annotated 1,141 S. sclerotiorum and 751 B. cinerea genes. FGENESH and GENEID predictions were clustered based on overlapping exons, requiring strand consistency. For each locus, a gene structure was chosen based on BLAST similarity to other proteins (requiring ≥60% average identity and ≥80% query coverage), and selecting the gene prediction from the program with best overall agreement to EST splice sites (GENEID performed better than FGENESH by this metric). The initial gene sets contained 14,522 genes for S. sclerotiorum and 16,448 genes for B. cinerea. The accuracy of the gene set was evaluated by comparing to EST sequences. For S. sclerotiorum, a set of 75,468 ESTs that were sequenced as part of this project or available in Genbank, were aligned to the genome using BLAT. EST alignments were clustered by combining all overlapping ESTs. Each of the resulting 7,400 EST clusters was compared to any overlapping predicted genes. In cases where multiple overlapping ESTs suggest different gene structures, the EST that most closely matched the gene structure was used. Roughly one-third of genes (5,192) have some overlap with an EST cluster; of these, 75% show no splice site disagreements. A small number of predicted genes appear partial in S. sclerotiorum: 4 are missing a start codon, 6 are missing a stop codon, and 98 span contigs. Possible missed annotations include ESTs with no overlapping gene; a total of 559 clusters are at least 200 bases from an annotated gene, and of these 88 contain canonical splice signals. After the automated annotation was complete, ESTs from additional libraries were generated and aligned to the genome. In total, we sequenced ESTs from 8 libraries, generating 96,700 quality filtered sequences (Table S30). These ESTs align to 7,942 genes. For B. cinerea (B05. 10), a set of 9,207 ESTs were aligned to the genome using BLAT. EST alignments were clustered by combining all overlapping ESTs into a cluster. Each of the resulting 2,349 EST clusters was compared to overlapping predicted genes. In cases where multiple overlapping ESTs suggest different gene structures, the EST that most closely matched the gene structure was used. Roughly one-eighth of genes (2,012) have some overlap with an EST cluster; of these, 82% show no splice site disagreements. A small number of predicted genes appear partial in B. cinerea (B05. 10): 67 are missing a start codon, 153 are missing a stop codon, and 444 span contigs. Possible missed annotations include ESTs with no overlapping gene; a total of 132 clusters are at least 200 bases from an annotated gene, and of these only 4 contain canonical splice signals. S. sclerotiorum and B. cinerea (B05. 10) gene calls were further evaluated to remove transposable elements and other dubious calls. Genes with at least 100 bases of overlap with repeats called by RepeatMasker or BLAST similarity against a transposon-related protein database, or containing a PFAM repeat domain but not a PFAM non-repeat domain, were flagged as dubious gene calls. Also, genes that have 5 or more BLAST hits at ≥95% identity to the same genome and have no supporting ESTs, BLAST hits or non-repeat PFAM domains, were flagged as dubious. To identify poorly supported gene calls, two additional gene predictors, GeneMark and SNAP, were run on both genomes. Genes less than 100 amino acids that were only supported by a single gene predictor (comparing GENEID, FGENESH, GeneMark, and SNAP) and without EST, BLAST, or PFAM domain support were flagged as dubious genes. Additionally, genes with two predictions that do not share the same reading frame were flagged. In total, all these methods flagged 2,662 S. sclerotiorum genes and 2,784 B. cinerea (B05. 10) genes as dubious. All predicted genes were used to query the PFAM set of hidden Markov models using HMMER (http: //hmmer. janelia. org) and the public protein databases using BLASTP. Transfer RNAs were identified using the tRNAScan-SE program. For B. cinerea T4, the automated gene prediction was performed using the URGI genomic annotation platform including pipelines, databases and interfaces, developed for fungi (http: //urgi. versailles. inra. fr/). Gene prediction was performed using Eugene pipeline version 3 [135]. The gene models predicted by EuGene rely on a combination of several in silico evidences (ab initio and similarity). Ab initio gene prediction softwares are Eugene_IMM [136] (probabilistic models discriminating coding from non coding sequences), SpliceMachine [137] (prediction of CDS start sites and intron splicing sites) and FGENESH (http: //linux1. softberry. com/berry. phtml) (ab initio gene predictor). Several similarity methods were used to identify genes such as BLASTN and Sim4 against B. cinerea ESTs, as well as BLASTX against Uniprot and fungal protein databases. The different results were used by Eugene to predict final gene models. The three ab initio gene prediction softwares were trained using a set of manually annotated genes from B. cinerea. FGENESH was trained by Softberry (Boston, USA), while EuGene-IMM and SpliceMachine were trained at URGI using a set of 305 genomic/full-coding cDNA pairs. One third of the set was used for training ab initio softwares, one third to optimize the parameters of EuGene and the last third to calculate the accuracy of EuGene. We finally obtained for exons and genes, a sensitivity of 97. 8 and 92. 1 and a specificity of 97. 5 and 92. 1, respectively. Genes with a size smaller than 100 nucleotides were automatically filtered out by EuGene. EuGene finally predicted 16360 genes in the B. cinerea T4 genome. In addition, 434 tRNA genes were predicted by tRNAscan-SE [138]. Genome assemblies were aligned using MUMmer [139]. The B. cinerea genomes were aligned with nucmer, selecting only 1∶1 matches (−mum), and alignments were processed with delta-filter to select 1∶1 local mapping of the reference to query. A total of 35. 5 Mb of each could be aligned, or 94% of the B. cinerea T4 genome and 91% of the B. cinerea B05. 10 genome. A total of 98,744 insertion/deletion positions and 175,009 SNPs were identified from the nucmer alignments using the show-snps program. This suggests that the overall rate of difference between the two genomes in aligned regions is 1 SNP every 203 bases and 1 insertion/deletion every 360 bases. S. sclerotiorum and B. cinerea are too distant to align well at the nucleotide level globally; only a total of 7. 4 Mb can be aligned at 85. 0% identity. Therefore the B. cinerea genomes were aligned to S. sclerotiorum using promer, selecting only 1∶1 matches (−mum), and alignments were processed with delta-filter to select 1∶1 local mapping of the reference to query. The promer alignments cover 17. 4 Mb sharing 74. 3% identity for S. sclerotiorum-B. cinerea T4 and 17. 2 Mb sharing 74. 6% identity for S. sclerotiorum-B. cinerea B05. 10. To identify larger syntenic regions between genomes, we first identified orthologs using BLASTP as well as OrthoMCL. Using BLASTP (1e-5, softmasked), S. sclerotiorum shares 8,609 best bi-directional (bbd) BLAST hits with B. cinerea B05. 10 or 8,601 with B. cinerea T4; 8,088 S. sclerotiorum proteins have matches in both genomes. About 1,300 additional proteins in S. sclerotiorum have BLAST matches that are not best bidirectional. Using OrthoMCL to cluster the three genomes, which uses BLASTP similarity for clustering, there are a total of 12,095 protein families, of which 8,079 are in all three genomes and nearly all of these (7,677) are single copy. Either raw BLAST data or ortholog sets were then used to find syntenic regions of at least four genes between genomes using DAGchainer [27], and parsed using accessory perl scripts. Syntenic regions between the two B. cinerea genomes include 11,422 paired genes and cover 36. 9 Mb in 249 regions (average size 148 kb). Syntenic regions between S. sclerotiorum and B. cinerea T4 include 7,752 paired genes and cover 27. 7 Mb in 571 regions (average size 49 kb). Syntenic regions between S. sclerotiorum and B. cinerea B05. 10 include 7,702 paired genes and cover 27. 9 Mb in 602 regions (average size 46 kb). Segmental duplication blocks were identified by comparing each genome against itself, using DAGchainer [27]. Transposable Elements (TEs) were detected and annotated using the REPET pipeline [30], [31] http: //urgi. versailles. inra. fr/Tools/REPET) for detection (TEdenovo) and annotation (TEannot) of transposons in eukaryotic genomes. The TEdenovo pipeline detects TE copies, groups them into families and defines the consensus sequence for each family containing at least 3 copies. The TEannot pipeline annotates TEs using the consensus sequences library. Manual annotation of the consensus sequences allowed their grouping in TE super-families corresponding to a transposon with a defined full-length copy (with both LTRs or ITRs and full ORFs when available) and classified as classI or classII TE. In addition, the genome of B. cinerea T4 was searched using TBLASTN for sequences similar to either the transposase of B. cinerea mariner element Flipper [140] or the reverse transcriptase from Boty1 [141]. Sequences with similarities to TE-encoded proteins were grouped and aligned to identify or reconstruct the corresponding full-length TE. Manually identified TEs were compared to the consensus sequences obtained from REPET. Automated functional annotation of S. sclerotiorum and B. cinerea proteins was performed using protein sequences deduced from all gene models automatically predicted. The pipeline uses protein domain identifier InterProScan [142] which runs a set of methods including pattern matching and motif recognition. In addition, we used an automated assignment against protein domains databases such as CDD [143], KEG [144] and KOG [145]. Sub-cellular targeting signals as well as transmembrane domains were predicted using SignalP, TargetP and TMHMM [146]. Orthologs in S. sclerotiorum were determined based on best bi-directional BLAST hits. Overall, 72% of B. cinerea T4 predicted genes (11767 of 16360) encode proteins having either functional information and/or an ortholog in S. sclerotiorum. This percentage increases to 82% taking into account only reliable genes. Four types of evidence were used to support gene calls, based on bioinformatics or experimental criteria (Figure S3). The first criterium was based on identification of functional domains (e. g. InterProScan, CDD) or topology/targeting domains (e. g. SignalP, TargetP, TMHMM) (see M7). Genes with at least one positive hit (functional or topology/targeting domain) were considered as supported by protein domain evidence. The second criterium was based on identification of orthology to genes in other fungi using OrthoMCL (see M9). The third criterium relied on expression data using ESTs (existence of at least one EST for a gene) (see M10). The fourth criterium relied on expression data using hybridization signals on a whole-genome oligonucleotide Nimblegen microarray (see M11). Among the 16,360 predicted genes in B. cinerea T4,13,555 (83%) genes are supported by at least 1 type of evidence and 6,462 (39. 5%) genes are supported by all 4 types of evidence. Among the 16,448 predicted genes in B. cinerea B05. 10,13,922 (85%) genes are supported by at least 1 type of evidence and 6,319 (38. 5%) genes are supported by all 4 types of evidence. Among the 14,522 predicted genes in S. sclerotiorum, 12,283 (85%) genes are supported by at least 1 type of evidence and 4,121 (28%) genes are supported by all 4 types of evidence. Orthologs between S. sclerotiorum, B. cinerea, and other ascomycete fungi were identified using OrthoMCL version 1. 4 [147]. The Ascomycete genomes included in the analysis were B. graminis (22_02_11; blugen. org), P. nodorum (20110506 from Richard Oliver; Broad Institute), P. teres f. teres (PRJNA50389; Genbank), G. zeae (FG3; Broad Institute), M. oryzae (MG8; Broad Institute), N. crassa (NC10; Broad Institute), and A. niger (AspGD). Each set of proteins was blasted against itself and other proteomes with an e value of 1e-5. An inflation parameter of 1. 5 was used for Markov Clustering with MCL. 12,120 B. cinerea (T4) proteins, 12,260 B. cinerea (B05. 10) proteins, and 9,930 S. sclerotiorum proteins were identified as members of gene families; of these about 1,500 were conserved only in the two species (Table S8). OrthoMCL families enriched in S. sclerotiorum and B. cinerea were identified based on a hypergeometric distribution (p-value computed by phyper function in R; q-value computed by p. adjust in R), however significantly enriched families (q-value<0. 05) included only repetitive elements or families specific to only these species. To identify functions specific to these lineage specific proteins, we mapped GO terms to the protein sets of S. sclerotiorum and B. cinerea (B05. 10) using Blast2GO, and computed enrichment using Fisher' s Test exact test. To identify functions enriched in S. sclerotinia and B. cinerea proteins, we identified PFAM domains in each of the above genomes, and computed enrichment or depletion in subsets of species. Protein domains from PFAM release 25 (ftp: //ftp. sanger. ac. uk/pub/databases/Pfam) were assigned to proteins in each genome using hmmsearch from hmmer3 (http: //hmmer. janelia. org/software), requiring an Evalue cutoff of 1e-5. For each genome, the total number of proteins with each type of domain was computed; a protein with multiple domains of the same type was counted only once. To identify domains enriched in subsets of genomes, significant differences were identified by computing the p-value for each domain based on a hypergeometric distribution (phyper function in R), and computing q-values to correct for multiple testing (p. adjust, fdr). Domains significantly enriched in the S. sclerotiorum and B. cinerea genomes (Table S10) were filtered to remove those found in transposable elements (PF03221, Tc5 transposase DNA binding domain; PF00078, Reverse transcriptase; PF00665, Integrase core domain; PF03184, DDE superfamily endonuclease; PF03732, Retrotransposon gag protein; PF00075, RNase H; PF05225, helix-turn-helix, Psq domain). Domains significantly depleted in the S. sclerotiorum and B. cinerea genomes (Table S11) were filtered to list those that are conserved in at least 5 species. For S. sclerotiorum, a total of 96,700 filtered EST sequenced were generated from eight cDNA libraries, prepared from mRNA from developing sclerotia, developing apothecia following 55 h light exposure, mycelia at pH 7, infected Brassica or infected tomato, two infection cushion samples, and mycelium exposed to oxidative stress (Table S30). ESTs were aligned to the S. sclerotiorum genome using BLAST and compared to predicted gene structures. Fourteen cDNA libraries of various B. cinerea isolates (T4, B05. 10, SAS56×SAS405, ATCC 58025) and stages of development have been prepared and sequenced in various laboratories (Table S31). Some of them were publicly available [148], [149] (library BcA1 and AL11), others were sequenced in the framework of the B. cinerea sequencing project by Genoscope (http: //www. genoscope. cns. fr/, libraries PD0A*) while others are private (Bayer Crop Sciences/P. Tudzynski). From these 14 cDNA libraries (Table S31), 78,755 bacterial clones were obtained and sequenced once or twice (5′ end; 3′ end), leading to 83,117 ESTs (1 or 2 ESTs per clone). Eighty six percent of ESTs, (71,238 ESTs, 67,625 clones, Figure S10) were successfully clustered on the B. cinerea T4 genome and assembled using Phrap [http: //www. phrap. org/] to finally obtain 9,667 EST contigs. As some genes were lying in different contigs, the 9,667 contigs were assembled in 9,004 unisequences. In order to get an estimate of the expression of genes corresponding to the unisequences, we calculated the raw_clone_nr sum (clone number in all libraries), the raw_clone_nr (clone number in the current library), the percent_clone (100* raw_clone_nr/clone_nr in the current library), and the norm_percent (percent_clone normalized by the percent_clones in all libraries = 100 * (percent_clone) /sum (percent_clone) in all libraries). Eighteen percent of unisequences without any corresponding genes automatically predicted (mostly due to gap in the genome sequence) were used to design oligos for the Nimblegen microarrays. A Nimblegen 1-plex array was designed using 21,200 B. cinerea gene models corresponding to 19,454 ORFs either identified in T4 or B05. 10 genomes (12,071 T4 ORFs shared between T4 and B05. 10; 93 B05. 10 ORFs shared between T4 and B05. 10; 4,072 T4 ORFs specific to T4; 3,218 B05. 10 ORFs specific to B05. 10), 12 experimental genes and 1,734 additional EST unisequences. Nine probes per sequence were defined, leading to 169,347 probes representing 20,889 genes and covering 20,916 provided gene models (19,222 ORFs, 12 experimental genes and 1,682 ESTs). In addition, 16,871 random probes (negative controls) were designed. Two copies of each probe were placed on the array. A second Nimblegen 1-plex array was designed using 15,026 S. sclerotiorum gene models corresponding to 14,522 ORFs and 504 additional EST unisequences. Thirteen probes per sequence were defined, leading to 190,130 probes representing 14,801 genes and covering 14,858 provided gene models (14,360 ORFs and 498 ESTs). In addition, 9,047 random probes (negative controls) were designed. Two copies of each probe were placed on the array. Expression of fungal genes was studied during infection on sunflower cotyledon, and compared with in vitro expression. The experimental conditions were: (i) Infection of sunflower cotyledons by mycelial plugs of B. cinerea (B05. 10) and S. sclerotiorum at 2 days after inoculation (100% of the surface area was infected), (ii) mycelial cultures grown in vitro (malt agar) for each fungal strain, and (iii) non-infected sunflower cotyledons. RNAs were extracted from 3 biological replicates, labelled and hybridized to arrays. 2 or 3 biological replicates per experimental condition were exploitable. Data were analysed using ANAIS methods [150]. Probe hybridization signals were normalized using the quantile function, and summed for each gene. Genes were considered as expressed when the signal was above the defined background (1. 5 fold the 95th percentile of random probes hybridization signals), in all the biological replicates of at least one experimental condition. The identification of differentially expressed genes was performed using an ANOVA test. To deal with multiple testings, the ANOVA p-values were further submitted to Bonferroni correction. Transcripts with a corrected p-value<0. 05 and more than 2. 0 fold change in transcript level were considered as significantly differentially expressed. Details of experiments, raw values and lists of differentially expressed genes with associated normalized values are available at http: //urgi. versailles. inra. fr/Data/Transcriptome. Complete proteome sequences were downloaded from NCBI or JGI and subjected to Merops Batch BLAST analysis [151]. Sequences from S. sclerotiorum and B. cinerea identified by Merops as putative peptidases were scrutinised based on the presence of active site and ligand residues as well as e values (cut off 1e-5). Additional BLAST analysis was performed at NCBI in order to confirm or reject suspicious hits. We excluded from the analysis the peptidases of which the activity is restricted to an autocatalytic activation of the precursor protein. We also excluded putative peptidases with problematic function inference such as the S9 and S33 proteases. The peptidases of S. sclerotiorum and B. cinerea were subjected to SignalP analysis [152]. Analyses of the other fungal peptidases were restricted to the families of which at least one sequence was predicted to correspond to a secreted peptidase and performed as described. The best protein models of the S. sclerotiorum and B. cinerea genomes were subject to expert analysis using the CAZy database (www. cazy. org) annotation pipeline [153]. Each model was compared by BLAST [154] to libraries of known catalytic and carbohydrate-binding modules derived from the CAZy database and from previously analyzed genomes. Each identified protein model was subject to modular analysis compring BLAST [154] and HMMer [155] analysis against CAZy-derived libraries and HMM profiles, respectively, followed by human curation. Later, the quality of each identified model was manually evaluated and an expert functional annotation was proposed by comparison against characterized enzymes from CAZy. The approaches are described in more detail elsewhere [153]. Finally, comparative analysis was performed against other fungal genomes using the same principles as described before [156]. Assemblies and annotations were submitted to GenBank under the following accession numbers: AAGT01000000 (S. sclerotiorum), AAID00000000 (B. cinerea B05. 10), FQ790245 to FQ790362 (B. cinerea T4).
Sclerotinia sclerotiorum and Botrytis cinerea are notorious plant pathogenic fungi with very wide host ranges. They cause vast economic damage during crop cultivation as well as in harvested produce. These fungi are typical examples of necrotrophs: they first kill host plant cells and then colonize the dead tissue. The genome sequences of the two fungi were determined in order to examine commonalities in structure and content and in order to find unique features that may distinguish them from other pathogenic fungi and from saprotrophic fungi. The genomes show high sequence identity and a similar arrangement of genes. S. sclerotiorum and B. cinerea differ in their regulation of sexual reproduction, and the genetic basis and its evolution could be explained from the genome sequence. The genome sequence revealed a striking difference in the number and diversity of secondary metabolism gene clusters, which may be involved in the adaptation to different ecological niches. Altogether, there were no unique features in the genomes of S. sclerotiorum and B. cinerea that could be identified as “silver bullets, ” which distinguish these aggressive pathogens from other pathogenic and non-pathogenic fungi. These findings reinforce the quantitative, multigenic nature of necrotrophic pathogenesis.
genome sequencing
genome expression analysis
functional genomics
genome evolution
biology
genomics
comparative genomics
genetics and genomics
Genomic Analysis of the Necrotrophic Fungal Pathogens Sclerotinia sclerotiorum and Botrytis cinerea