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Genome reconstruction in Cynara cardunculus taxa gains access to chromosome-scale DNA variation


The genome sequence of globe artichoke (Cynara cardunculus L. var. scolymus, 2n = 2x = 34) is now available for use. A survey of C. cardunculus genetic resources is essential for understanding the evolution of the species, carrying out genetic studies and for application of breeding strategies. We report on the resequencing analyses (~35×) of four globe artichoke genotypes, representative of the core varietal types, as well as a genotype of the related taxa cultivated cardoon. The genomes were reconstructed at a chromosomal scale and structurally/functionally annotated. Gene prediction indicated a similar number of genes, while distinctive variations in miRNAs and resistance gene analogues (RGAs) were detected. Overall, 23,5 M SNP/indel were discovered (range 6,34 M –14,50 M). The impact of some missense SNPs on the biological functions of genes involved in the biosynthesis of phenylpropanoid and sesquiterpene lactone secondary metabolites was predicted. The identified variants contribute to infer on globe artichoke domestication of the different varietal types, and represent key tools for dissecting the path from sequence variation to phenotype. The new genomic sequences are fully searchable through independent Jbrowse interfaces (, which allow the analysis of collinearity and the discovery of genomic variants, thus representing a one-stop resource for C. cardunculus genomics.


The genus Cynara, a member of Asteraceae family (a.k.a. Compositae), contains eight species and four subspecies, all native to the Mediterranean basin1. The members of the species Cynara cardunculus L. (2n = 2x = 34) are globe artichoke (var. scolymus (L.) Fiori), cultivated cardoon (var. altilis DC.) and the wild cardoon (var. sylvestris (Lamk) Fiori). The three C. cardunculus botanical varietas are fully cross-compatible with one another, and their F1 hybrids are fully fertile. This, together with phenotype data2 and inferences based on genetic markers3, 4 suggest that both the globe artichoke and cultivated cardoon were domesticated from wild cardoons and it is likely that their domestication occurred in the island of Sicily5. Globe artichoke was anthropogenically selected for the production of immature inflorescences (heads or capitula), and cultivated cardoon for its fleshy stalks4.

The three C. cardunculus taxa are also exploited for the production of a number of nutraceutically and pharmaceutically active compounds6,7,8,9,10,11 such as phenylpropanoids (mono- and di- caffeoylquinic acids) and sesquiterpene lactones, the latter being responsible for their characteristic bitter taste. Cultivated cardoon in particular represents a source of both lignocellulosic biomass12,13,14 and seed oil for edible and biofuel uses15,16,17,18.

After Italy, the next biggest globe artichoke producers are Egypt and Spain (FAO19 data, 2013), but its cultivation has spread to the Near East (Turkey and Iran), North Africa (Algeria, and Tunisia), South America (Argentina, Chile and Peru), United States (mainly in California) and China. Italy also harbors the richest primary cultivated gene-pool, which as a rule is classified on the basis of capitulum traits20,21,22 into: i) ‘Spinosi’: with long sharp spines on bracts and leaves; ii) ‘Violetti’: with medium-sized, green-violet-colored capitula and fleshy thorns; iii) ‘Romaneschi’: with spherical or sub-spherical green capitula and; iv) ‘Catanesi’: with relatively small, elongated capitula with more or less marked violet streaks.

Recently, we published the sequence of the globe artichoke genome23 ( Its assembly, comprising 13,588 scaffolds covering 725 of the 1,084 Mb genome, was generated using 133-fold Illumina sequencing data and encodes 26,889 predicted genes. A new highly saturated linkage map was also constructed, which represents a big step forward from the genetic maps we previously developed24,25,26,27.

In the present work we report on the re-sequencing analysis of four globe artichoke genotypes (SP, C3, VS and VT) representative of the core varietal types in cultivation and also a genotype of cultivated cardoon, A41. By combining iterative mapping and reference guided-assembly, the five genomes were reconstructed and annotated; miRNA loci as well the number, position and phylogenetic relationships of putative resistance gene analogues RGAs were identified. Finally, SNPs/indels among the five genotypes were highlighted versus the reference globe artichoke genome, and a functional SNP analysys was carried out on the metabolic pathways of phenylpropanoids and sesquiterpene lactones.


Genome assembly and reconstruction

Genome sequencing of six C. cardunculus genotypes (Table 1) yielded 1.4 billion raw pair-end reads with an average length of 100 bp (Table 2). The latter were reduced to 1.27 billion (94%) after filtering/trimming for high quality reads, corresponding to a total of 395.7 Gbp available for the assembly procedures. The sequence depth of coverage ranged from 24.6× (A41) to 45.3× (SP), being 35.3× on average (Table 2). A genome reconstruction method was adopted based on a combination of iterative read mapping against the globe artichoke reference together with de novo assembly. De novo assembly was performed using k-mers that allowed achieving the best assembly for each genotype. The values k = 68 and k = 66 were selected for A41 and C3 respectively, with k = 65 for SP, VS and VT. The contig-based assemblies was then used as a basis for the genome reconstructions of the five genotypes (Table 2).

Table 1 Details of the Cynara cardunculus genotypes studied.
Table 2 Genomics statistics.

Genome annotation and OrthoMCL analysis

Gene prediction was conducted for the five re-sequenced genotypes and a fresh gene prediction was also performed for the reference genome (2 C), which increased the gene number from 26,88923 to 28,310. The six genomic sequences, together with their structural and functional annotations are available at, and they are fully searchable through six independent Jbrowse28 interfaces. Comparative analysis revealed a similar number of genes for all the genotypes under study (Fig. 1, Table 2), with 2 C and VS having the highest (28,310) and lowest (27,121) number of genes (AED ≤ 0.5) respectively. Orthology between genes was also assessed via a reciprocal best-hit analysis (File S1).

Figure 1

Feature content analysis in the sequenced genomes of C. cardunculus. (a) Heads of the six analyzed genotypes (REF, VT, SP, VS, A41, C3. (b) Relative number of predicted genes, miRNAs loci and total RGAs loci. (c) Distribution of orthologous gene families in 2 C (reference), VT, SP, VS, A41 and C3 genotypes, calculated using OrthoMCL29.

OrthoMCL29 clustered 161,855 sequences from the six genotypes into 24,417 gene families (excluding singletons) (Fig. 1 and Table S1). A total of 18,826 gene families (containing 138,098 genes) were in common between the six genotypes, while 426 (with 2145 genes) were shared between the five globe artichoke genotypes. All in all, just 11 gene families (24 genes) were absent from the reference genome while 1 (2 genes) was unique to VS and SP. The non-spiny genotypes (VS, VT and C3) shared 12 gene families (including 36 genes), with the ‘pigmented’ types VS and VT sharing 159 gene families (including 318 genes). It was found that 149 gene families (449 genes) were specific to the oblong capitula types (SP, VS and VT). Lastly, the number of sequences not falling into any cluster (singletons) ranged from 149 (in the reference 2C) to 999 (in the cultivated cardoon A41).

A search for enrichment analyses (SEACOMPARE30) for genes shared between the five artichoke genotypes (Table S2), revealed significant enrichment for some GO31 terms. The top ranked enrichments observed for processes were GO:0006412 (translation) and GO:0010467 (gene expression), the latter missing in C3 genotype. With respect to functions (F), enrichments were observed in GO:0005198 (structural molecule activity) and GO:0003735 (structural constituent of ribosome). For components (C), enrichments were present for GO:0005840 (ribosome) and GO:0030529 (ribonucleoprotein) complexes. InterProScan32 analyses highlighted about 80% of the predicted proteins with at least one IPR domain (Table 2). The top 20 SUPERFAMILY33 domains are listed in Table S3 and the most abundant domain was SSF52540 (P-loop containing nucleoside triphosphate hydrolase) which is involved in several UniPathways, including chlorophyll or coenzyme A biosynthesis. The second most abundant SUPERFAMILY ID was SSF56112 (protein Kinase-like domain), which includes proteins acting on signaling and regulatory processes in the eukaryotic cell, followed by SSF52058 (Leucine-rich repeat domain, L domain-like) and SSF48371 (Armadillo-type fold), which are involved, inter alia, in defense response and translation factor activity respectively.

Detection of Presence/Absence variants (PAVs)

Based on gene coordinates, the five datasets were inspected for PAVs and 346 putative PAV genes (Figure S1) was uncovered. The frequency of PAVs varied among the genomes ranging from 241 in SP and VS to 225 and 261 in C3 and VT respectively. Among the 251 shared PAVs, 173 (50.0%) were absent in all the genotypes, while 30 (8.7%) were absent in 2 genotypes and 14 (4.1%) in three genotypes. A total of 87 (25.1%) of the detected PAV genes were absent only in one genotype (17 in SP, 14 in VS, 4 in C3, 27 in VT and 25 in A41). The PAV genes exclusively present in just one accession were 17 in A41, 1 in SP, 4 in C3, 1 in VS and 2 in VT (Figure S1, Table S4). Functional information for candidate specific absent and present PAV genes was evaluated (Supplementary data) and a GO enrichment analysis conducted for PAV genes (17) present in the genotype A41 (Table S5).

Prediction and annotation of miRNA

From a search against miRBase34, 21 high confidence database, species-specific miRNAs were predicted and used for further analyses (File S2). The total number of predicted non-redundant miRNAs varied from 51 (within 74 VS genome regions) and 143 (in 241 genome regions of the reference 2 C), belonging to 32 (for SP) or up to 45 (2 C) miRNA families (Fig. 1 and Table S6). The Tapir hybrid35 search for target genes of the identified miRNA in the six genotypes, revealed between 307 (VS) and 1167 (2 C) putative miRNA: mRNA duplexes. Almost 90% of genes encoding predicted target transcripts have functional InterPro annotations. The total number of miRNA families involved in miRNA: mRNA interactions varied according to the genotype, ranging from 20 in VS to 45 in 2 C and C3 (Fig. 1 and Table S7). Although the main families involved in miRNA: mRNA duplex formation were generally genotype-specific (Table S8), miRNA172 was the top ranked family for all the genotypes (varying between 83 for VS and 283 for C3), with the exception of SP where no miRNA 172 were identified. Putative miRNA-target gene enrichment analysis for each genotype revealed significant enrichment for some GO terms (Supplementary data, and File S3). The REVIGO36 summarization of enriched terms for biological process, cellular component and molecular function, obtained by removing redundant GO terms, are reported in Figure S2, S3 and S4. Finally, comparisons of GO term enrichment (AGRIGO SEACOMPARE30) applied to the five genotypes showed that just one GO term (GO:0005634: nucleus) was shared among all the genotypes (Table S9). As previously reported23, miRNAs are predicted to target known transcription factors related to plant development, morphology and flowering time. Examples include miR160 and ARF (Auxin Response Factor: absent in VS), miR156 and SQUAMOSA promoter binding-like proteins, miR164 and NAC-like proteins (absent in VT and SP), miR172 and AP2-like proteins (absent in SP) and miR171 and GRAS-like proteins (absent in VS).

Resistance genes

A total of 682 proteins in 2 C, 654 in A41, 643 in VS, 675 in C3, 647 in SP and 640 in VT (Fig. 1), showed homology (at p < 1e-60) with 1,605 out of the 2,680 Arabidopsis unique RGA proteins. The Hmmer37, 38 search of HMMs for key resistance genes motifs revealed that in all genotypes, about 89% of the RGAs identified by Blastp against Arabidopsis unique RGA proteins contained at least one HMM domain (File S4). The majority of RGAs (Table 3) belonged to the RLK family (170 in VT up to 180 in C3), followed by proteins containing P-kinase and TM domains (other-KTM), ranging from 146 in A41 to 157 in 2 C, and RLP, with 65 in both VS and SP up to 86 in 2 C). In addition, a few RGAs containing at least one NB-ARC domain were identified (Table 3).

Table 3 Classification of RGAs identified in the six genotypes.

The number of RLK, RLP, NB and other-KTM genes detected in each chromosome of the six genotypes in study is shown in Figure S5. In all the genotypes most RGAs were found on chromosome 1, followed by chromosomes 2, 13, 10 and 5. Chromosomes 6, 7, 14 and 16 (with the exclusion of C3) were devoid of RLP genes, while chromosomes 3, 6, 8 (with the exclusion of C3), 11, 12, and 14 were without NB genes. Distinct clusters of RGAs were identified for three of the main classes: RLK, RLP, NB and other-KTM (Table S10). In particular, chromosome 10 was found to harbor two RGA clusters: one of genes with an NB domain in a region of two Mb (16–18 Mb) and one of other-KTM genes at 10–11 Mb. No clusters of RLK genes were identified. RGAs were found to be quite uniformly distributed along the 17 chromosomes, except for the clustering of RLP genes on ch.1 (0–3 Mb) and ch.11 (1–3 Mb), of NB genes on ch.13 (40–41 Mb), and of other-KTM genes on ch.10 (10–11 MB). Alignments of the amino acid sequences and subsequent RAxML39 analyses allowed the generation of phylogenetic trees for each of the RGA classes in the study. As expected, each resistance gene and its orthologs clustered together, although in some taxa one or more orthologs were absent (File S5). In particular for the most numerous RGA families, RLK and RLP, taxa missing at least one RGA from at least one genotype were 46 (24%) and 45 (50%) respectively (Fig. 2 light blue). In addition, 2 clusters for both RLKs and RLPs contained at least one duplicated gene from one genotype (Fig. 2 orange), while clusters with at least one missing gene from a genotype and a duplicated gene from another genotype amounted to 1 for RLK and 4 for RLP (Fig. 2, highlighted in purple).

Figure 2

RLP and RLK resistance genes dendrogram of the six C. cardunculus genotypes. Taxa missing one or more RGA gene from at least one genotype are highlighted in light blue; clusters with one or more duplicated genes in one genotype are highlighted in orange, while clusters with at least one gene missing from one genotype and also containing at least one duplicated gene from one genotype are highlighted in purple.

SNP/indel discovery and heterozygosity estimation

To identify large-scale polymorphisms of sequenced accessions, reads were aligned against the globe artichoke reference genome (2 C). The mapping rate across different accessions varied from 95.8% to 97.6%, for an average of 96.5%. The whole SNP/indel set contained 23,450,539 entries (File S6), with 815,853 in 2 C rising to 14,495,680 in VS (Table 4). In the sequenced globe artichoke genotypes, similar SNP/Indel numbers were detected in SP and VS (ca 14.4 M) with slightly fewer in C3 and VT (ca 12.8 M), while in the cultivated cardoon A41 SNP/Indels halved to about 6 M. Among the globe artichoke genotypes the highest SNP frequency was found in VS (1/54 bp) with the highest indel frequency in SP (1/122 bp). Table S11 reports heterozygosity levels estimated across the six genotypes. All the genotypes, except the reference (2 C) which has been specifically bred by repeated cycles of selfing to attain a very low level of residual heterozygosity (Fig. 3a, track B), showed SNP-dense regions dispersed genome-wide (Figure S6). However, many regions with a low frequency of heterozygous SNPs (Fig. 3) and carrying SNPs fixed in the homozygous state (Figure S6) were observed. Some of them were genotype-specific (Fig. 3a) and occurred in gene-dense regions: chromosomes 4, 8, 11, 15 in SP; ch.10, 13 in VS; ch.5, 12, 17 in VT; ch.11 in A41. Some others are in common between genotypes (ch.1 in VS, C3 and VT). Interestingly, in chromosome 7 a wide region carrying SNPs mainly in the homozygous state was observed in both the SP and VT genotypes. Genetic relatedness among the six genotypes was assessed based on the whole SNP set (23 M) and also the SNPs detected in coding sequences (Fig. 3b). In both cases the reference genotype (2 C) and the cultivated cardoon (A41) clustered at a high level of genetic differentiation from the four globe artichoke genomes. Small differences were detected in the relationships among the latter, as the whole genome SNP analysis revealed a higher similarity between VT and C3, and a significant genetic differentiation between spiny (SP) and non-spiny (VS, VT and C3) globe artichoke types, while the coding SNPs highlighted a higher-similarity VT/SP cluster.

Table 4 Statistics (SNP, Indel and SNP/indel) of the analyzed genotypes.
Figure 3

Representation of the existing genetic variability among the C. cardunculus genotypes analyzed. (a) Circos diagram depicting gene and heterozygous SNP densities; from the outer circle to the inner circle: A) Heat map of gene density in the reference genome; 1 M histograms representing the density of heterozygous SNPs for the Reference (track B) A41 (track C), SP (track D), VS (track E), C3 (track F), VT (track G). (b) UPGMA-based dendrograms of the six genotypes taking into account 23 M SNPs and (c) 500 k coding SNPs.

Variants annotation

The analysis of functional variants between the reference and the resequenced genotypes was performed using the SnpEff40 suite on both heterozygous and homozygous SNP/indel sets. The observed fraction of coding SNPs (500 k, exon-based) was as little as 2% of the 23 M detected SNPs. Two thirds of the total homozygous variants were located outside the gene space (intergenic region: 57.6%; intronic region: 9.3%) with only a small fraction contained within coding sequences (1.41%, Fig. 4). In SP a total of 6.2 M mutations were detected, 5,290,997 were observed as intergenic (Table S12), while 113,099 were in the annotated gene space. VS and VT showed a similar number of intergenic mutations, while variants annotated in CDS were 96,437 and 108,712 respectively. In C3, 3,719,864 intergenic SNPs were found, with 89,177 SNPs located in genic space. Finally, A41 (intergenic mutations: 1,470,424) contained 47,438 SNPs in CDS. About 98% of the variants were classified as modifiers. The fraction of moderate variants ranged from 0.58% to 0.8% according to accessions, and those having low effect from 0.75% to 1.05%. The high effect variants were the smallest class, with 1,464 to 3,761 mutations depending on the specific genotype (Table S12). Among the homozygous variations detected in coding sequences, 53.20% led to synonymous and 46.19% to non-synonymous amino acid changes, while 0.62% gave rise to non-sense mutations (Table S13). In respect to the heterozygous SNP/indels, the highest number of variants were mutations located in the intergenic and intronic regions (65.0% and 8.1%), while only 1.35% occurred in genic space (CDS, Fig. 4, Table S12). The high effect variants represented only 0.048% of total mutations. The fraction of mutations with low effect varied between 0.65% and 0.86%, while moderate variants accounted for between 0.6% and 0.77%. About 98% of the total mutations in all the resequenced genotypes were classified as modifier variants. With regard to the variations detected in coding sequences, 49.60% were annotated as non-synonymous mutations. Synonymous mutations amounted to 49.35% of total variants and only 1.05% were classified as non-sense mutation (Table S13).

Figure 4

SNP categorization of coding and non-coding variants. SNP effect by type and region for homozygous (a) and heterozygous (b) regions. SNP effect by functional class for homozygous (c) and heterozygous (d) regions.

Analysis of variants impact on CQAs and SLs gene classes

The possible impact of variants localized in genes responsible for caffeoylquinic acids (CQAs) and sesquiterpene lactones (SLs) biosynthesis were analyzed. Almost all investigated genes showed variants, with SLs showing a higher number of variants (24.5 SNP/gene) compared to CQAs (5.5 SNP/gene, Fig. 5). The genes with non-synonymous variants potentially giving rise to high-effect impacts are reported in Table S14. Of the 33 non-synonymous variants belonging to the CQAs related genes, 10 (30.3%) showed a predicted deleterious effect in coding sequences when translated as amino acid substitution. These were located in all the polymorphic genes (Fig. 5). In the SL group, which contained 417 non-synonymous variants, about 83 (19.9%) were deleterious. All the coding SNPs were in the heterozygous state and affected 14 of the 17 genes (Fig. 5). In the CQAs pathway, one significant deleterious mutation, which creates a premature stop-codon, was heterozygous in the C4H gene (in SP, Table S14). No relevant homozygous deleterious variations were identified in the SL gene class. However, many deleterious variants in the heterozygous state were found in the germacrene A synthase (GAS) gene, which mutated exclusively in the four globe artichoke genotypes, and not in the cultivated cardoon (A41, Table S14). The impact of coding SNPs on protein genetic diversity among the analyzed genotypes is shown using family based phylogenetic trees (Fig. 5).

Figure 5

Coding SNPs data in CQAs and SLs pathways. (a) Number of genes, polymorphic genes, variants and high deleterious impact SNPs in CQA- (light blue) and SL- (orange) related genes. Phylogenetic trees of proteins belonging to CQA-related transferase genes from the six genotypes analyzed (b) and to SL-related p450 genes from the same accessions (c).


Among the species belonging to the Asteraceae family, Cynara cardunculus has remained for a long time relatively unexplored compared to other species such as sunflower and lettuce, for which genomes extensive investigations have been performed. Following our recent release of the first globe artichoke genome sequence23, we performed the first WGR (at ~35× coverage) of five C. cardunculus genotypes, four representatives of the core globe artichoke varietal types at present in cultivation and one of the related and inter-fertile taxa cultivated cardoon. By the use of a genome reconstruction strategy based on iterative mapping and reference-guided assembly41, the five genomes were assembled and reconstructed at the chromosome scale. Our approach proved to be less demanding in terms of sequencing depth and multiple libraries construction compared to a de novo assembly. The sizes of the reconstructed genomes were comparable to that of the reference genome, with an average percentage of reconstruction close to 98.9%, but smaller compared to the estimated genome size of C. cardunculus (1,084 Mbp). This is likely due to the absence in the reference genome assembly of some repetitive sections, though it included about 95% of the gene space23. Indeed, by performing a new gene prediction of the globe artichoke reference sequence following the application of a more stringent AED threshold, we found 28,310 predicted genes, which corresponds to a 5% increase over the 26,889 previously predicted.

As reported for other species42, globe artichoke and cultivated cardoon suffer major losses from numerous diseases, as a result of many years of cultivation and selection mainly focused on desirable commercial traits at the expense of disease resistance. Plants have developed effective mechanisms to recognize and respond to infections caused by pathogens, among these the RGAs43,44,45 play a key role. In the five reconstructed genomes in study, as well as in the reference genome, we computationally mined and characterized RGAs on the basis of their significant structural features and conserved domains.

The RGAs identified here represent on average about 2% of the total number of predicted genes for all the genotypes. The majority of them were located preferentially on six chromosomes: 1, 2, 3, 5, 10 and 13, suggesting their specialization in resistance pathways. Their location assists development of a high-density genome-wide RGA genetic map for the species, which is pivotal for designing diagnostic markers and identifying quantitative trait loci (QTL) or markers associated with plant disease resistance. Cynara cardunculus RGAs mainly fall into RLK, RLP and other-KTM (i.e. genes with a P-kinase and a TM domain) classes (Table 3), while NBS (such as TNL, CNL, and TN) were poorly represented. Previous studies report that TN (TIR-NBS) are the largest group of resistance genes in both Arabidopsis thaliana (64%)46 and Brassica rapa (64%)47, but they are rare in Oryza sativa (1%)48 and Sorghum bicolor (1%)49 and absent in Brachypodium distachyon and Zea mays 50, suggesting their specificity for dicotyledons51. Kim et al.52, in a survey of RGA genes based on UNIGENE analyses, highlighted that some Asterids, such as Solanaceae, contain functional TNLs, whereas others do not. The same authors identified only 19 and 13 full length CNLs in the two Asteraceae, sunflower and lettuce, but no full length TNLs, and concluded that the latter are relatively poorly distributed or have been species (or family) specifically lost in Asterids. In line with our results, Christopoulu et al.53 recently identified several RGA classes in lettuce (one of the closest species to C. cardunculus), with the RLK class most represented. Other NBS-LRR like RGAs were found to vary widely in number (for a summary, see Sekhwal et al.54). Finally, it has been reported that NBS-LRR genes underwent gene expansion after speciation in Arabidopsis 46, 55, rice56, corn57, Populus and grape vine58. From our results, it appears that C. cardunculus RGAs belong almost exclusively to the RLK/RLP families, while a few NBS RGAs were identified. Our results confirm the hypothesis of Kim et al.52 of a species-specific evolution of TNLs in Asterids. Interestingly, several putative RGAs were identified and showed missing domains compared to the ‘canonical’ NBS, RLP and RLK families, in line with reports by several authors59,60,61,62,63,64,65,66,67,68. It was also reported that maintaining many NBS/resistance genes has potential fitness costs69, 70 and it has been suggested that plants use microRNAs to regulate NBS gene expression71,72,73,74,75. Indeed, we found that 9 (in VS) to 41 (in 2 C) identified RGAs (by Blastp approach) were putatively targeted by a miRNA, suggesting that this mechanism could also be present in C. cardunculus.

The number of identified miRNAs varied across the different genotypes, with the highest in 2 C and the lowest in VS. The variable number of identified miRNAs might also arise from SNPs present in some miRNA loci hampering their identification in some genotypes. In addition, as previously reported23, conserved miRNAs, such as miR162 and miR482, were not identified possibly because of their loss in the lines’ genomes and/or because of genomic loci missing in the assembly. Most of the conserved miRNAs detected in the six genomes were predicted to target well known biological processes; this confirms, on the basis of a more comprehensive data set, what has been previously reported23, 76. The comparison of GO term enrichments for the five newly reconstructed genomes revealed that GO terms related to binding and transcription were shared among the C. cardunculus genotypes, suggesting an involvement of miRNAs in transcription factors regulation. However, the process GO terms lignin and phenylpropanoids were enriched in 2 C, A41 and C3. This suggests their involvement in the regulation of genes involved in lignin biosynthesis and the flavonol pathway in these three genotypes, although this hypothesis should be furtherly investigated (e.g.: small RNA and degradome sequencing).

The comparison of the five reconstructed C. cardunculus sequences with the reference genome led to the discovery of more than 23 million SNP/indels, heterogeneously distributed across all the genotypes and their chromosomes revealing traces of breeding efforts. SNP frequency ranged from about 1/54 bp (VS) to 1/122 bp (A41), while indels frequency varied between about 1/630 bp (SP) to 1/1,634 bp (A41). In a previous study77, focused on RAD-tag marker development in globe artichoke, we estimated a SNP frequency of one SNP per 179 bp and one indel every 5000 bp, values which differ significantly from the those presented here (Table 4). This discrepancy may be attributed to the RAD-tag protocol we applied, which was based on the use of one methylation sensitive enzyme, thus the obtained metrics mainly refer to the un-transcribed portion of the genome.

As expected the globe artichoke reference genome (2 C) showed the lowest heterozygosity (0.11%, a frequency of 1/893 bp), which is comparable to that found in inbred species78. This reflects its breeding history as 2 C is the result of three cycles of self-fertilization. In contrast, according to Portis et al.26, 79, the four globe artichoke varietal types showed a high level of heterozygosity, as their heterozygous SNPs ranged from 7.4 M in VT to 9.3 M in VS (Table S11). Of these, the globe artichoke VT, the only genotype to be seed propagated, contained the lowest number of heterozygous SNPs (7,4 M); this is likely due to breeding practices aimed at decreasing its heterozygosity in order to stabilize its commercially-important attributes. Analogously, in the seed-propagated cultivated cardoon A41, a relatively low number (4,2 M) of heterozygous SNP was found. Many regions were observed with a low frequency of heterozygous SNPs (Fig. 3a) and carrying SNPs fixed in the homozygous state (Figure S6).

In a previous study, we identified Sicily Island, in the South of Italy, as a possible primary center of globe artichoke domestication. Following characterization of 24 landraces collected from small-holdings, through a combination of morphological traits and PCR-based markers, we recognized intermediary spiny forms in the domestication process5. The presence/absence of spines has been considered a key trait to understand the origin of the material at present in cultivation. Barbieri80 hypothesized that the spiny types (Spinosi) were selected first, followed by the violet types (Violetti), which possess less spiny heads, and finally by the non-spiny Romaneschi and Catanesi. However, both UPGMA analyses, performed on the whole set of detected SNPs/Indels and on the ones detected in coding regions, did not cluster separately the spiny and non-spiny types. This seems more consistent with the pattern of evolution proposed by Lanteri et al.4 according to which the Spinosi and the Violetti, the latter characterized by capitula with bracts harbouring fleshy thorns, evolved side by side with the non-spiny Catanesi and Romaneschi. Since domestication implies the intensification of pressure for traits relevant to farming conditions, and may have had a greater impact on genes than on intergenic regions, our hypothesis appears to be confirmed by the presence SNPs fixed in the homozygous state occurring in gene-dense regions in a genotype-specific fashion (Fig. 3a), which is attributable to a signature of the domestication process addressed to fix distinctive traits in the different varietal types.

The species Cynara cardunculus has interesting applications in pharmacology, since the leaves and heads represent natural sources of bio-active compounds such as mono- and di-caffeoylquinic acids and sesquiterpene lactones, with several medicinal properties. In previous studies we isolated, functionally characterized both ‘in vitro’ and in vivo’ and mapped the genes involved in their bio-synthetic pathway81,82,83,84. Our functional annotation of SNPs revealed thousands of coding polymorphisms (Table S12, Fig. 4) and the analysis of variants of genes related to caffeoylquinic acid (CQA) and sesquiterpene lactone (SL) biosynthesis displayed different outcomes. In the CQA pathway, except for one case, non-relevant deleterious SNPs were found. Conversely, in the SL pathway, although in heterozygosis, 54 deleterious mutations were found in 12 out of the 17 SL-related genes. This was confirmed by the remarkably lower diversity detected in CQA- compared to SL-related proteins (Fig. 5b). Many deleterious mutations were located in GAS, a key gene in the biosynthesis of SLs, of which cynaropicrin is the major representative in C. cardunculus 83. The latter was found to accumulate in leaf tissues while its concentration is lower in inflorescence bracts of globe artichokes85, 86; the same results were also confirmed in cultivated cardoon leaves87. Cynaropicrin acts in leaves as an antifeedant88 but is also responsible for the bitter taste of globe artichokes, thus the deleterious mutation in GAS gene detected in globe artichoke genotypes, but not in cultivated cardoon, might be the result of domestic breeding aimed at reducing bitterness in the globe artichoke edible capitula.

Next-generation sequencing is rapidly expanding our knowledge of genetic variation in many crops. The availability of the globe artichoke reference genome and the resequencing of four globe artichoke genotypes, representative of the germplasm in cultivation, has provided clues about their domestication processes as well as a first comprehensive identification of the genetic diversity of the Cynara cardunculus cultivated forms. For years, we have studied the progeny of the cross between the globe artichoke Romanesco with both globe artichoke and cultivated cardoon, with the goal to develop molecular maps on the basis of the two-way pseudo test cross strategy24, 25, and we have identified several QTLs for a number of capitula traits26, 89, 90. Based on our resequencing effort, a set of SNPs regularly spaced along the chromosomes was identified and annotated. This resource will be fruitful for the identification of regions responsible for the QTL, and lays the groundwork for a new phase of globe artichoke genomics to further the understanding of the genetic basis of agronomical important traits and for selective breeding. Beyond, our marker catalogue provides a highly valuable resource in terms of polymorphism, and allows foreseeing the future through the detection of fine haplotypes and imputation of SNPs on large accessions panels. Our results also provide key information for further functional, as well as comparative, genomics studies with other important crops such as sunflower, lettuce and chicory within the Asteraceae family.

Materials and Methods

Plant materials and DNA extraction

Six C. cardunculus genotypes (Table 1) were considered in the analysis; they included one cultivated cardoon (‘A41’ - Altilis 41), and five globe artichokes: ‘2 C’ (reference genome), VS (‘Violetto di Sicilia’), VT (‘Violetto di Toscana’), C3 (‘Romanesco C3’) and SP (‘Spinoso di Palermo’). SP belongs to the ‘Spinosi’ type, characterized by long sharp spines on bracts and leaves. VT belongs to the ‘Violetti’ type, produces medium-sized, green-violet-colored capitula harbouring fleshy thorns and it is seed-propagated. C3 belongs to the ‘Romaneschi’ type, and its capitula are spherical, green and non-spiny. VS belongs to the ‘Catanesi’ type, yielding non spiny elongated capitula with more or less marked violet streaks. 2 C is a Brazilian breeding line result of three cycles of selfing and characterized by a reduced level of heterozygosity23. A41 is a selected genotype of cultivated cardoon25 producing small non-spiny capitula and it is seed-propagated.

All of them derived from propagated clones grown in a field trial location at University of Catania (Italy), during the 2014–2015 growing season. For the genotypes VS, VT, SP, total genomic DNA was extracted from fresh leaves of each genotype, using DNA Mini Plant kit (Qiagen). RNAse A was used to remove RNA contamination. DNA quality was checked by 1% (w/v) agarose gel electrophoresis, and its quantity was assessed by Qubit 2.0 (Life Technologies, Carlsbad, CA, USA) based on Qubit dsDNA HS Assay (Life Science). Raw sequence data for the genotypes A41, 2 C, and C3 were obtained from NCBI (Table 2)23.

Genome Sequencing

A total amount of one μg DNA was sonicated using 30″/90″ on/off time for 7 cycles with Bioruptor UCD-300 TS instrument (Diagenode, Belgium) to obtain 350 bp long fragments. End-repair and A-tailing procedures followed standard Illumina protocols, except PCR-free barcoded adapters (Biooscientific, Austin, TX, USA), used during the ligation step, and five conclusive enrichment PCR cycles were carried out. Negative selection of 100–150 bp fragments was performed with 0.8 X AMpure XP beads (Beckman Coulter, Inc., (Brea, California)). Quality control (QC) of libraries was performed with Bioanalyzer 2100 instrument (Agilent, Inc., Santa Clara, CA, USA) using High Sensitivity DNA kit and an accurate quantification was made using qPCR with Library Quantification kit (Kapa Biosystem, USA). Library were then pooled and diluted to a final concentration of 10 nM. Sequencing were performed with Illumina NextSeq500 sequencer (Illumina Inc., San Diego, CA, USA) and 150 bp paired-end sequences were generated. Raw reads were analyzed with Scythe ( for filtering out contaminant substrings and Sickle (, which allows to remove reads with poor quality ends (Q < 30).

Genome reconstruction, gene prediction and annotation

For the genome reconstruction, a combination of iterative read mapping against the globe artichoke reference and de novo assembly was adopted. De novo assembly was performed with ABySS 1.9.0 assembler91 using k-mers which allowed to achieve the best assembly. Genome reconstruction of each varietal type was performed submitting the Abyss assembled sequences to IMR/DENOM41 (ver. 0.4.1; pipeline using default parameters, adopting the globe artichoke genome 2 C (LEKV00000000.1) as a guide. Quality metrics for the assembled genomes were calculated with ( Gene prediction was performed using reiterative runs of the Maker-P suite92. HMM models from Augustus93 and SNAP94 ab initio gene prediction algorithms previously developed23 were combined with proteins and ESTs alignments as supporting evidence. All predicted gene models were filtered to retain only those with an AED ≤ 0.5. Gene function was assigned to predicted genes using BlastN95 and Swissprot96 database, using default parameters, with the exception of sequence E value = 1 e−5. Predicted protein sequences were functionally annotated using InterproScan32 (ver. 5.18–57.0) against all the available databases (ProSiteProfiles-20.11997, PANTHER-10.098, Coils-2.2.199, PIRSF-3.01100, Hamap-201511.02101, Pfam-29.0102, ProSitePatterns-20.11997, SUPERFAMILY-1.7533, ProDom-2006.1103, SMART-7.1104, Gene3D-3.5.0105 and TIGRFAM-15.0106). If InterProScan32 results were available, the domain name, IPR codes and GO terms31 were extracted and appended to the description line. The 6 sequences, together with their structural and functional annotation, are available through a JBrowse28 interface at The proteomes from the six genotypes were clustered using OrthoMCL29 version 2.0.9, a Venn diagram of the 6 proteomes was constructed with Interactivenn107 and AGRIGO30 cross comparison of SEA (SEACOMPARE) was used to identify common and different enrichment GO terms for specific gene clusters.

Identification and characterization of PAV genes

Samtools108 was used to generate a text file containing the number of illumina reads that mapped at each gene location on the reference genome. The number of reads that mapped at each gene location for every globe artichoke varieties were normalized by the total number of reads mapping the whole reference genome for each four globe artichoke varieties. These figures were calculated as follows: samtools view -c -F 4 -q 1 mapping_file.sorted.bam. As approach to identify putative PAV genes, all genes with less than 6 mapped reads from at least 1 varietal type and more than 29 mapped reads from at least another varietal type were selected. A Venn diagram of the shared/exclusive PAV genes was depicted using InteractiVenn107. Obtained list of candidate PAV genes were described/GO-categorised using the here produced functional annotation and through a blastP95 analysis (TAIR10109). GO enrichments in artichoke selected genes were calculated with AmiGO2110 web service and Panther111.

miRNA annotation

The MIReNA112 software was used for the identification of high confidence miRNA-coding sequences (miRBase release 2134: high confidence database) in each pseudomolecule and CH0 of all the six genotypes in study. An homology search was conducted with known miRNAs from an array of 13 species (plants and algae), including: Solanum lycopersicum, Solanum tuberosum, Nicotiana tabacum, Vitis vinifera, Arabidopsis thaliana, Oryza sativa, Populus trichocarpa, Medicago trunculata, Zea mays, Picea abies, Triticum aestivum, Physcomitrella patens, Chlamydomonas reinhardtii. MIReNA was run with default parameters and the maximum number of allowed mismatches between known miRNAs and putative miRNAs was set to 10. For each genotypes, miRNA sequences identified were named based on the miRNA family with the addition of the name of the genotype (2 C, A41, VS, VT, C3 and SP). The Tapir35 standalone software was applied to identify the targets of the identified eggplant miRNA on the predicted CDSs. In particular, we applied the Tapir hybrid function, which is based on the RNA hybrid search engine113, an algorithm conceived to determine, with high accuracy, miRNA:mRNA duplexes. Results were parsed with hybrid_parser function using default parameters. GO term enrichment of target sequences for each line was carried out with AGRIGO30 and REVIGO36 to find out a representative subset of the GO terms previously identified with the Interproscan32 pipeline (medium similarity) and to visualize results. The size of the circles have been adjusted to reflect the p-value. AGRIGO30 cross comparison of SEA (SEACOMPARE) was used to identify common and different enrichment GO terms between the genotypes showing GO terms enrichment.

Resistance genes analogs (RGA) identification and classification

Candidates genes were identified by means of a Blastp95 analysis against the Plant Resistance Genes database114 ( Protein sequences of 2860 Arabidopsis RGAs (from RGDB) were used to perform BlastP searches against all the six proteomes. For each genotype, positive hits (p < 1e-60) were validated via HMMER37,38 v3 software, searching against PFAM102 Hidden Markov Models (HMMs: available at using a cutoff E-value of 1e-10. HMMs domains were chosen based on their known involvement in plant resistance against pathogens and included: NB-ARC (PF00931), TIR (PF01582), LRR (PF00560, PF07723, PF007725, PF08263, PF12799, PF13306, PF13516, PF13855 and PF14580), Pkinase (PF00069), ABC transporter (PF00005), and WD40 (PF00400). The CC motifs were predicted with EMBOSS pepcoil115, while TM domains were predicted using both TMHMM 2.0116 and SCAMPI117. Resistance genes classification was based as followed: acronym containing the same domains from Christoupolou et al.53 were used. For missing domains combination, the acronyms were generated by adding to ‘other’ the following letters: L (LRR), N (NB-ARC), TM (TM), T (TIR), C (CC), K (Pkinase), and A (ABC transporter).

RGAs alignment and phylogenetic tree estimation

To identify the RGAs evolution within and among the 6 lines in study, we divided the RGAs identified following Hmmer analyses in 6 separated groups: RLP (Receptor Like Proteins), RLK (Receptor like Kinases), NB (proteins containing at least one NB-ARC domain), LRR (containing only LRR domains), ABC (containing at least one ABC domain) and other-KTM (containing a Kinase and a TM domains and not belonging to RLK/RLP group). The multiple alignments were performed using MAFFT118 v7.294b with the following parameters:–ep 0–thread 22–reorder–maxiterate 1000–genafpair. Phylogenetic trees were inferred with RAxML39 v.8.2.8 using the Maximum Likelihood method based on the GAMMAPROTILGF method (LG matrix with GAMMA model of heterogeneity, empirical amino acid frequency and estimate of proportion of invariable site). The tree with the highest log-likelihood was selected and combined with the bootstrap output to print support values assigned to nodes. For bootstrap analyses, RAxML39 v.8.2.8 was used, with fast bootstrapping119 and outMRE option. FigTree 1.4.2120 was used to display all the six dendrograms in cladogram format. To identify the number of RGAs per chromosome across all the 6 genotypes, as well as the presence of clusters, coordinates of the genes belonging to the classes RLK, RLP, NB (containing at least a NB domain), and other-KTM were extracted and BEDTools121 intersected using genome windows of 1 Mb to count the number of genes falling into these regions.

SNP calling

Reads were mapped onto globe artichoke genome reference using Burrows-Wheeler Aligner program (BWA)122 and ‘mem’ command with default parameters. The BAM files were processed and adapted for SNP calling program with Samtools108 mpileup using default parameters with the exclusion of minimum mapping quality equal to 25 and filtering ambiguous read mapping. Results were filtered taking into account two parameters: the SNPs call quality and depth. SNPs having mapping quality lower than 20 were removed. In addition, we set as lower limit of mapping depth a value of eight and the upper limit was set to 450. Relationships among the genotypes were computed using: i) whole genome and ii) coding (within exons) SNP/indel datasets. Genetic distances were computed based on the two datasets (R package SNPRelate) and dendrogram was computed and drawn using R Graphic-package123. The chromosomal locations of SNP densities were visualized in CIRCOS ideograms using the software package from

SNP annotation

Identified variants were analyzed using SNPeff40 to predict their effect on the set of gene models of globe artichoke. We investigated the role of missense and non-sense mutations in both homozygous/heterozygous states, evaluating in which region the variations are found. The effect of each SNP/indel was classified according to SNPeff software into four classes: (1)”modifier”, for the variants located outside the genes, in non-transcribed regions or introns; (2) “low effect” for variants in coding regions which do not change the amino acid sequence; (3) “moderate” effect for variants which change the amino acid sequence and (4) “high effect” for variants which modify splice sites, stop or start codons (loss or gain). CDS non-synonymous variants belonging to well characterized pathway and classified as missense, stop codon gained and frameshift effect were also submitted to Provean (Protein Variation Effect Analyzer algorithm)124 analysis, in order to understand if an amino acid substitution has an impact on the biological protein functions. Provean predicts the functional impact for all classes of protein sequence variation such a single amino acid substitution, insertion, deletion and multiple substitution. The score threshold used was set to −2.5. Non-synonymous variations causing deleterious effects are evaluated in homozygous and heterozygous state for the negative impact on protein functionality.

Accession codes

Sequence reads have been deposited in NCBI sequence read archive (SRA) under the number SRP055806. A JBrowse28 interface, to access genomic data and related annotation, is available at


  1. 1.

    Wiklund, A. The genus Cynara L. (Asteraceae-Cardueae). Bot. J. Linn. Soc. 109, 75–123 (1992).

    Article  Google Scholar 

  2. 2.

    Foury, C. Quelques aspects du développement de l′artichaut (Cynara scolymus L.) issu de semences; analyse plus particulière de la floraison en conditions naturelles. (Paris: VI University Orsay, 1987).

  3. 3.

    Acquadro, A., Portis, E., Albertini, E. & Lanteri, S. M-AFLP-based protocol for microsatellite loci isolation in Cynara cardunculus L. (Asteraceae). Mol. Ecol. Notes 5, 272–274 (2005).

    CAS  Article  Google Scholar 

  4. 4.

    Lanteri, S. et al. Amplified fragment length polymorphism for genetic diversity assessment in globe artichoke. TAG Theor. Appl. Genet. 108, 1534–1544 (2004).

    CAS  PubMed  Article  Google Scholar 

  5. 5.

    Mauro, R. et al. Genetic diversity of globe artichoke landraces from Sicilian small-holdings: implications for evolution and domestication of the species. Conserv. Genet. 10, 431–440 (2009).

    Article  Google Scholar 

  6. 6.

    Gebhardt, R. Inhibition of cholesterol biosynthesis in primary cultured rat hepatocytes by artichoke (Cynara scolymus L.) extracts. J. Pharmacol. Exp. Ther. 286, 1122–8 (1998).

    CAS  PubMed  Google Scholar 

  7. 7.

    Gebhardt, R. Antioxidative and protective properties of extracts from leaves of the artichoke (Cynara scolymus L.) against hydroperoxide-induced oxidative stress in cultured rat hepatocytes. Toxicol. Appl. Pharmacol. 144, 279–286 (1997).

    CAS  PubMed  Article  Google Scholar 

  8. 8.

    Ammerer, D. I. K., Arle, R. E. C. & Chieber, A. N. S. Identification and quantification of caffeoylquinic acids and flavonoids from artichoke (Cynara scolymus L.) heads, juice, and pomace by HPLC-DAD-ESI / MS n. 4090–4096 (2004).

  9. 9.

    Lattanzio, V., Kroon, P. A., Linsalata, V. & Cardinali, A. Globe artichoke: A functional food and source of nutraceutical ingredients. J. Funct. Foods 1, 131–144 (2009).

    CAS  Article  Google Scholar 

  10. 10.

    Pandino, G., Lombardo, S. & Mauromicale, G. Mineral profile in globe artichoke as affected by genotype, head part and environment. J. Sci. Food Agric. 91, 302–8 (2011).

    CAS  PubMed  Article  Google Scholar 

  11. 11.

    Shimoda, H. et al. Anti-Hyperlipidemic sesquiterpenes and new sesquiterpene glycosides from the leaves of artichoke (Cynara scolymus L.): structure requirement and mode of action. Bioorganic & Medicinal Chemistry Letters 13 (2003).

  12. 12.

    Mauromicale, G., Sortino, O., Pesce, G. R., Agnello, M. & Mauro, R. P. Suitability of cultivated and wild cardoon as a sustainable bioenergy crop for low input cultivation in low quality Mediterranean soils. Ind. Crops Prod. 57, 82–89 (2014).

    Article  Google Scholar 

  13. 13.

    Ierna, A. & Mauromicale, G. Cynara cardunculus L. genotypes as a crop for energy purposes in a Mediterranean environment. Biomass and Bioenergy 34, 754–760 (2010).

    Article  Google Scholar 

  14. 14.

    Portis, E. et al. Potentiality of Cynara cardunculus L. as energy crop. J. Biotechnol. 150, 165–166 (2010).

    Article  Google Scholar 

  15. 15.

    González, J. F. et al. Combustion optimisation of biomass residue pellets for domestic heating with a mural boiler. Biomass and Bioenergy 27, 145–154 (2004).

    Article  CAS  Google Scholar 

  16. 16.

    Encinar, J. M., Gonzalez, J. F., Rodriguez, J. J. & Tajedor, A. Biodiesels fuel from veg-etable oils: transesterification of Cynara cardunculus L. oils with ethanol. EnergyFuels 443–450 (2002).

  17. 17.

    Lapuerta, M., Armas, O., Ballesteros, R. & Fernández, J. Diesel emissions from biofuels derived from Spanish potential vegetable oils. Fuel 84, 773–780 (2005).

    CAS  Article  Google Scholar 

  18. 18.

    Maccarone, E. et al. Possible alternative utilization of Cynara spp.: II. Chemical characterization of their grain oil. Ind. Crops Prod. 10, 229–237 (1999).

    CAS  Article  Google Scholar 

  19. 19.


  20. 20.

    Dellacecca, V., Magnifico, V., Marzi, V., Porceddu, E. & Mugnozza, G. T. In Atti 2° Congresso Internazionale di Studi sul carciofo 199–315. (Edizioni Minerva Medica, Turin, Italy).

  21. 21.

    Porceddu, E., Dellacecca, V. & Bianco, V. V. In Atti 2° Congresso Internazionale di Studi sul carciofo 2, 1105–1119 (Edizioni Minerva Medica, Turin, Italy, 1976).

  22. 22.

    Vanella, B., Porceddu, E. & De Pace, C. Applicazioni di metodi di analisi numerica per il miglioramento genetico del carciofo. Atti 3rd Congr Int Di Stud. sul Carciofo. Laterza, Bari 797–807 (1981).

  23. 23.

    Scaglione, D. et al. The genome sequence of the outbreeding globe artichoke constructed de novo incorporating a phase-aware low-pass sequencing strategy of F1 progeny. Sci. Rep. 6, 19427 (2016).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  24. 24.

    Lanteri, S. et al. A first linkage map of globe artichoke (Cynara cardunculus var. scolymus L.) based on AFLP, S-SAP, M-AFLP and microsatellite markers. Theor. Appl. Genet. 112, 1532–1542 (2006).

    CAS  PubMed  Article  Google Scholar 

  25. 25.

    Portis, E. et al. Construction of a reference molecular linkage map of globe artichoke (Cynara cardunculus var. scolymus). Theor. Appl. Genet. 120, 59–70 (2009).

    CAS  PubMed  Article  Google Scholar 

  26. 26.

    Portis, E. et al. Genetic mapping and identification of QTL for earliness in the globe artichoke/cultivated cardoon complex. BMC Res. Notes 5, 252 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  27. 27.

    Sonnante, G. et al. Genetic map of artichoke × wild cardoon: toward a consensus map for Cynara cardunculus. Theor. Appl. Genet. 123, 1215–1229 (2011).

    PubMed  Article  Google Scholar 

  28. 28.

    Skinner, M. E., Uzilov, A. V., Stein, L. D., Mungall, C. J. & Holmes, I. H. JBrowse: a next-generation genome browser. Genome Res. 19, 1630–8 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  29. 29.

    Li, L., Stoeckert, C. J. & Roos, D. S. OrthoMCL: identification of ortholog groups for eukaryotic genomes. Genome Res. 13, 2178–89 (2003).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  30. 30.

    Du, Z., Zhou, X., Ling, Y., Zhang, Z. & Su, Z. agriGO: a GO analysis toolkit for the agricultural community. Nucleic Acids Res 38, W64–70 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  31. 31.

    The Gene Ontology Consortium. Gene Ontology Consortium: going forward. Nucleic Acids Res. 43, D1049–1056 (2014).

  32. 32.

    Jones, P. et al. InterProScan 5: genome-scale protein function classification. Bioinformatics 30, 1236–40 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  33. 33.

    de Lima Morais, D. A. et al. SUPERFAMILY 1.75 including a domain-centric gene ontology method. Nucleic Acids Res. 39, D427–34 (2011).

    PubMed  Article  CAS  Google Scholar 

  34. 34.

    Kozomara, A. & Griffiths-Jones, S. miRBase: integrating microRNA annotation and deep-sequencing data. Nucleic Acids Res. 39, D152–7 (2011).

    CAS  PubMed  Article  Google Scholar 

  35. 35.

    Bonnet, E., He, Y., Billiau, K. & Van de Peer, Y. TAPIR, a web server for the prediction of plant microRNA targets, including target mimics. Bioinformatics 26, 1566–8 (2010).

    CAS  PubMed  Article  Google Scholar 

  36. 36.

    Supek, F., Bošnjak, M., Škunca, N. & Šmuc, T. REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS One 6, e21800 (2011).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. 37.

    hmmer. at

  38. 38.

    Eddy, S. R. Accelerated Profile HMM Searches. PLoS Comput. Biol. 7, e1002195 (2011).

    ADS  MathSciNet  CAS  PubMed  PubMed Central  Article  Google Scholar 

  39. 39.

    Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–3 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  40. 40.

    Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff. (2012).

  41. 41.

    Gan, X. et al. Multiple reference genomes and transcriptomes for Arabidopsis thaliana. Nature 477, 419–423 (2011).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  42. 42.

    Guo, S. et al. The draft genome of watermelon (Citrullus lanatus) and resequencing of 20 diverse accessions. Nat. Genet. 45, 51–8 (2013).

    CAS  PubMed  Article  Google Scholar 

  43. 43.

    Hulbert, S. H., Webb, C. A., Smith, S. M. & Sun, Q. Resistance gene complexes: Evolution and Utilization. Annu. Rev. Phytopathol. 39, 285–312 (2001).

    CAS  PubMed  Article  Google Scholar 

  44. 44.

    Takken, F. L. & Goverse, A. How to build a pathogen detector: structural basis of NB-LRR function. Curr. Opin. Plant Biol. 15, 375–384 (2012).

    CAS  PubMed  Article  Google Scholar 

  45. 45.

    Monaghan, J. & Zipfel, C. Plant pattern recognition receptor complexes at the plasma membrane. Curr. Opin. Plant Biol. 15, 349–357 (2012).

    CAS  PubMed  Article  Google Scholar 

  46. 46.

    Meyers, B. C., Kozik, A., Griego, A., Kuang, H. & Michelmore, R. W. Genome-Wide Analysis of NBS-LRR-Encoding Genes in Arabidopsis. Plant cell online 15, 809–834 (2003).

    CAS  Article  Google Scholar 

  47. 47.

    Mun, J.-H., Yu, H.-J., Park, S. & Park, B.-S. Genome-wide identification of NBS-encoding resistance genes in Brassica rapa. Mol. Genet. Genomics 282, 617–631 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. 48.

    Monosi, B., Wisser, R. J., Pennill, L. & Hulbert, S. H. Full-genome analysis of resistance gene homologues in rice. Theor. Appl. Genet. 109, 1434–1447 (2004).

    CAS  PubMed  Article  Google Scholar 

  49. 49.

    Paterson, A. H. et al. The Sorghum bicolor genome and the diversification of grasses. Nature 457, 551–556 (2009).

    ADS  CAS  PubMed  Article  Google Scholar 

  50. 50.

    Li, J. et al. Unique evolutionary pattern of numbers of gramineous NBS–LRR genes. Mol. Genet. Genomics 283, 427–438 (2010).

    CAS  PubMed  Article  Google Scholar 

  51. 51.

    Perazzolli, M. et al. Characterization of Resistance Gene Analogues (RGAs) in apple (Malus × domestica Borkh.) and their evolutionary history of the Rosaceae family. PLoS One 9, e83844 (2014).

    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

  52. 52.

    Kim, J. et al. A genome-wide comparison of NB-LRR type of resistance gene analogs (RGA) in the plant kingdom. Mol. Cells 33, 385–92 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  53. 53.

    Christopoulou, M. et al. Genome-wide architecture of disease resistance genes in lettuce. G3; Genes|Genomes|Genetics 5, 2655–2669 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  54. 54.

    Sekhwal, K. M. et al. Disease Resistance Gene Analogs (RGAs) in plants. International Journal of Molecular Sciences 16 (2015).

  55. 55.

    Ding, J., Zhang, W., Jing, Z., Chen, J.-Q. & Tian, D. Unique pattern of R-gene variation within populations in Arabidopsis. Mol. Genet. Genomics 277, 619–629 (2007).

    CAS  PubMed  Article  Google Scholar 

  56. 56.

    Zhou, T. et al. Genome-wide identification of NBS genes in japonica rice reveals significant expansion of divergent non-TIR NBS-LRR genes. Mol. Genet. Genomics 271, 402–415 (2004).

    CAS  PubMed  Article  Google Scholar 

  57. 57.

    Lai, J. et al. Genome-wide patterns of genetic variation among elite maize inbred lines. Nat. Genet. 42, 1027–1030 (2010).

    CAS  PubMed  Article  Google Scholar 

  58. 58.

    Yang, S., Zhang, X., Yue, J.-X., Tian, D. & Chen, J.-Q. Recent duplications dominate NBS-encoding gene expansion in two woody species. Mol. Genet. Genomics 280, 187–198 (2008).

    CAS  PubMed  Article  Google Scholar 

  59. 59.

    Brandwagt, B. F., Kneppers, T. J. A., Nijkamp, H. J. J. & Hille, J. Overexpression of the tomato Asc-1 gene mediates high insensitivity to AAL toxins and fumonisin b1 in tomato hairy roots and confers resistance to Alternaria alternata f. sp. lycopersici in Nicotiana umbratica plants. (2007).

  60. 60.

    Büschges, R. et al. The barley Mlo gene: a novel control element of plant pathogen resistance. Cell 88, 695–705 (1997).

    PubMed  Article  Google Scholar 

  61. 61.

    Cao, A. et al. Serine/threonine kinase gene Stpk-V, a key member of powdery mildew resistance gene Pm21, confers powdery mildew resistance in wheat. Proc. Natl. Acad. Sci. 108, 7727–7732 (2011).

    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

  62. 62.

    Gururani, M. A. et al. Plant disease resistance genes: Current status and future directions. Physiol. Mol. Plant Pathol. 78, 51–65 (2012).

    CAS  Article  Google Scholar 

  63. 63.

    Panstruga, R. Discovery of novel conserved peptide domains by ortholog comparison within plant multi-protein families. Plant Mol. Biol. 59, 485–500 (2005).

    CAS  PubMed  Article  Google Scholar 

  64. 64.

    Schmitz-Linneweber, C. & Small, I. Pentatricopeptide repeat proteins: a socket set for organelle gene expression. Trends Plant Sci. 13, 663–670 (2008).

    CAS  PubMed  Article  Google Scholar 

  65. 65.

    Wang, W. et al. A Comprehensive mutational analysis of the Arabidopsis resistance protein RPW8.2 reveals key amino acids for defense activation and protein targeting. Plant Cell 25, 4242–4261 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  66. 66.

    Wretblad, S., Bohman, S. & Dixelius, C. Overexpression of a Brassica nigra cDNA gives enhanced resistance to Leptosphaeria maculans in B. napus. (2007).

  67. 67.

    Xiao, S. et al. Broad-spectrum mildew resistance in Arabidopsis thaliana mediated by RPW8. Science 291, 118–20 (2001).

    ADS  CAS  PubMed  Article  Google Scholar 

  68. 68.

    Xiao, S. et al. Origin and maintenance of a broad-spectrum disease resistance locus in Arabidopsis. Mol. Biol. Evol. 21, 1661–1672 (2004).

    CAS  PubMed  Article  Google Scholar 

  69. 69.

    Orgil, U., Araki, H., Tangchaiburana, S., Berkey, R. & Xiao, S. Intraspecific genetic variations, fitness cost and benefit of RPW8, a disease resistance locus in Arabidopsis thaliana. Genetics 176, 2317–33 (2007).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  70. 70.

    Tian, D., Traw, M. B., Chen, J. Q., Kreitman, M. & Bergelson, J. Fitness costs of R-gene-mediated resistance in Arabidopsis thaliana. Nature 423, 74–77 (2003).

    ADS  CAS  PubMed  Article  Google Scholar 

  71. 71.

    Eckardt, N. A. A microRNA cascade in plant defense. Plant Cell 24, 840 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  72. 72.

    Fei, Q., Xia, R. & Meyers, B. C. Phased, secondary, small interfering RNAs in posttranscriptional regulatory networks. Plant Cell 25, 2400–15 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  73. 73.

    Kallman, T., Chen, J., Gyllenstrand, N. & Lagercrantz, U. A significant fraction of 21-nucleotide small RNA originates from phased degradation of resistance genes in several perennial species. PLANT Physiol. 162, 741–754 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  74. 74.

    Shivaprasad, P. V. et al. A microRNA superfamily regulates nucleotide binding site-leucine-rich repeats and other mRNAs. Plant Cell 24, 859–74 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  75. 75.

    Zhai, J. et al. MicroRNAs as master regulators of the plant NB-LRR defense gene family via the production of phased, trans-acting siRNAs. Genes Dev. 25, 2540–2553 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  76. 76.

    Denoeud, F. et al. The coffee genome provides insight into the convergent evolution of caffeine biosynthesis. Science 345, 1181–4 (2014).

    ADS  CAS  PubMed  Article  Google Scholar 

  77. 77.

    Scaglione, D. et al. RAD tag sequencing as a source of SNP markers in Cynara cardunculus L. BMC Genomics 13, 3 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  78. 78.

    Delledonne, M. et al. A high quality eggplant (Solanum melongena L.) genome draft allows the mapping of phenotypic and metabolic QTLs. In The 12th Solanaceae Conference (2015).

  79. 79.

    Portis, E., Mauromicale, G., Barchi, L., Mauro, R. & Lanteri, S. Population structure and genetic variation in autochthonous globe artichoke germplasm from Sicily Island. Plant Sci. 168, 1591–1598 (2005).

    CAS  Article  Google Scholar 

  80. 80.

    Barbieri, R. Osservazioni sulla biologia del carciofo‘ Spinoso Sardo’. (Gallizzi, 1959).

  81. 81.

    Comino, C. et al. The isolation and mapping of a novel hydroxycinnamoyltransferase in the globe artichoke chlorogenic acid pathway. BMC Plant Biol. 9, 30 (2009).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  82. 82.

    Moglia, A. et al. Genome-wide identification of BAHD acyltransferases and in vivo characterization of HQT-like enzymes involved in caffeoylquinic acid synthesis in globe artichoke. Front. Plant Sci. 7, 1424 (2016).

    PubMed  PubMed Central  Article  Google Scholar 

  83. 83.

    Eljounaidi, K. et al. Cytochrome P450s from Cynara cardunculus L. CYP71AV9 and CYP71BL5, catalyze distinct hydroxylations in the sesquiterpene lactone biosynthetic pathway. Plant Sci. 223, 59–68 (2014).

    CAS  PubMed  Article  Google Scholar 

  84. 84.

    Eljounaidi, K. et al. Accumulation of cynaropicrin in globe artichoke and localization of enzymes involved in its biosynthesis. Plant Sci. 239, 128–136 (2015).

    CAS  PubMed  Article  Google Scholar 

  85. 85.

    Menin, B. et al. Genetic mapping and characterization of the globe artichoke (+)-germacrene A synthase gene, encoding the first dedicated enzyme for biosynthesis of the bitter sesquiterpene lactone cynaropicrin. Plant Sci. 190, 1–8 (2012).

    CAS  PubMed  Article  Google Scholar 

  86. 86.

    Schneider, G. & Thiele, K. Die verteilung des bitterstoffes cynaropicrin in der artischocke. Planta Med. 26, 174–183 (1974).

    CAS  Article  Google Scholar 

  87. 87.

    Ramos, P. A. B. et al. Lipophilic extracts of Cynara cardunculus L. var. altilis (DC): a source of valuable bioactive terpenic compounds. J. Agric. Food Chem. 61, 8420–8429 (2013).

    CAS  PubMed  Article  Google Scholar 

  88. 88.

    Chadwick, M., Trewin, H., Gawthrop, F. & Wagstaff, C. Sesquiterpenoids lactones: benefits to plants and people. Int. J. Mol. Sci. 14, 12780–12805 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  89. 89.

    Portis, E. et al. Mapping yield-associated QTL in globe artichoke. Mol. Breed. 34, 615–630 (2014).

    Article  Google Scholar 

  90. 90.

    Portis, E. et al. The inheritance of bract pigmentation and fleshy thorns on the globe artichoke capitulum. Euphytica 206, 523–531 (2015).

    Article  Google Scholar 

  91. 91.

    Simpson, J. T. et al. ABySS: a parallel assembler for short read sequence data. Genome Res. 19, 1117–23 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  92. 92.

    Campbell, M. S. et al. MAKER-P: a tool kit for the rapid creation, management, and quality control of plant genome annotations. Plant Physiol. 164, 513–24 (2014).

    CAS  PubMed  Article  Google Scholar 

  93. 93.

    Stanke, M. et al. AUGUSTUS: ab initio prediction of alternative transcripts. Nucleic Acids Res. 34, W435–9 (2006).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  94. 94.

    Bromberg, Y. & Rost, B. SNAP: predict effect of non-synonymous polymorphisms on function. Nucleic Acids Res. 35, 3823–35 (2007).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  95. 95.

    Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–10 (1990).

    CAS  PubMed  Article  Google Scholar 

  96. 96.

    The UniProt Consortium. UniProt: a hub for protein information. Nucleic Acids Res. 43, D204–212 (2014).

  97. 97.

    Sigrist, C. J. A. et al. New and continuing developments at PROSITE. Nucleic Acids Res 41, D344–7 (2013).

    CAS  PubMed  Article  Google Scholar 

  98. 98.

    Mi, H., Muruganujan, A. & Thomas, P. D. PANTHER in 2013: modeling the evolution of gene function, and other gene attributes, in the context of phylogenetic trees. Nucleic Acids Res. 41, D377–86 (2013).

    CAS  PubMed  Article  Google Scholar 

  99. 99.

    Lupas, A., Van Dyke, M. & Stock, J. Predicting coiled coils from protein sequences. Science 252, 1162–4 (1991).

    ADS  CAS  PubMed  Article  Google Scholar 

  100. 100.

    Wu, C. H. et al. PIRSF: family classification system at the Protein Information Resource. Nucleic Acids Res 32, D112–4 (2004).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  101. 101.

    Pedruzzi, I. et al. HAMAP in 2015: updates to the protein family classification and annotation system. Nucleic Acids Res 43, D1064–70 (2015).

    CAS  PubMed  Article  Google Scholar 

  102. 102.

    Punta, M. et al. The Pfam protein families database. Nucleic Acids Res 40, D290–301 (2012).

    CAS  PubMed  Article  Google Scholar 

  103. 103.

    Bru, C. et al. The ProDom database of protein domain families: more emphasis on 3D. Nucleic Acids Res 33, D212–5 (2005).

    CAS  PubMed  Article  Google Scholar 

  104. 104.

    Letunic, I., Doerks, T. & Bork, P. SMART 7: recent updates to the protein domain annotation resource. Nucleic Acids Res. 40, D302–5 (2012).

    CAS  PubMed  Article  Google Scholar 

  105. 105.

    Lees, J. et al. Gene3D: a domain-based resource for comparative genomics, functional annotation and protein network analysis. Nucleic Acids Res. 40, D465–71 (2012).

    CAS  PubMed  Article  Google Scholar 

  106. 106.

    Haft, D. H. et al. TIGRFAMs and genome properties in 2013. Nucleic Acids Res. 41, D387–95 (2013).

    CAS  PubMed  Article  Google Scholar 

  107. 107.

    Heberle, H. et al. InteractiVenn: a web-based tool for the analysis of sets through Venn diagrams. BMC Bioinformatics 16, 169 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  108. 108.

    Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–9 (2009).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  109. 109.

    Lamesch, P. et al. The Arabidopsis Information Resource (TAIR): improved gene annotation and new tools. Nucleic Acids Res. 40, D1202–10 (2012).

    CAS  PubMed  Article  Google Scholar 

  110. 110.

    Carbon, S. et al. AmiGO: online access to ontology and annotation data. Bioinformatics 25, 288–9 (2009).

    CAS  PubMed  Article  Google Scholar 

  111. 111.

    Mi, H., Poudel, S., Muruganujan, A., Casagrande, J. T. & Thomas, P. D. PANTHER version 10: expanded protein families and functions, and analysis tools. Nucleic Acids Res. 44, D336–42 (2016).

    CAS  PubMed  Article  Google Scholar 

  112. 112.

    Mathelier, A. & Carbone, A. MIReNA: finding microRNAs with high accuracy and no learning at genome scale and from deep sequencing data. Bioinformatics 26, 2226–34 (2010).

    CAS  PubMed  Article  Google Scholar 

  113. 113.

    Kruger, J. & Rehmsmeier, M. RNAhybrid: microRNA target prediction easy, fast and flexible. Nucleic Acids Res. 34, W451–W454 (2006).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  114. 114.

    Sanseverino, W. et al. PRGdb: a bioinformatics platform for plant resistance gene analysis. Nucleic Acids Res. 38, D814–21 (2010).

    CAS  PubMed  Article  Google Scholar 

  115. 115.

    Rice, P., Longden, I. & Bleasby, A. EMBOSS: the european molecular biology open software suite. Trends in Genetics 16, 276–277 (2000).

    CAS  PubMed  Article  Google Scholar 

  116. 116.

    Krogh, A., Larsson, B., von Heijne, G. & Sonnhammer, E. L. Predicting transmembrane protein topology with a hidden markov model: application to complete genomes. J. Mol. Biol. 305, 567–580 (2001).

    CAS  PubMed  Article  Google Scholar 

  117. 117.

    Peters, C., Tsirigos, K. D., Shu, N. & Elofsson, A. Improved topology prediction using the terminal hydrophobic helices rule. Bioinformatics 32, 1158–1162 (2016).

    CAS  PubMed  Article  Google Scholar 

  118. 118.

    Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–80 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  119. 119.

    Stamatakis, A., Hoover, P. & Rougemont, J. A rapid bootstrap algorithm for the RAxML Web servers. Syst. Biol. 57, 758–71 (2008).

    PubMed  Article  Google Scholar 

  120. 120.

    FigTree. at

  121. 121.

    Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–2 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  122. 122.

    Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–60 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  123. 123.

    R Development Core Team. R Software. R: A Language and Environment for Statistical Computing (2016).

  124. 124.

    Choi, Y. & Chan, A. P. PROVEAN web server: a tool to predict the functional effect of amino acid substitutions and indels. Bioinformatics 31, 2745–7 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

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This study was supported by the MIUR (Italian Ministry of Instruction, University and Research).

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G.Mau. and D.V. selected/provided plant materials and maintained all living materials. G.Man. performed in field plant sampling, DNA extraction and sequencing/assembling experiments. A.A. performed the sequencing experiments, conducted the genome assembly/reconstruction, setup SNP mining pipelines, analyzed/interpreted data. L.B. performed functional annotation, gene prediction analyses, deployed the jbrowse interface, conducted miRNAs and RGA analysis. A.A. and L.B. drafted the paper and curated the supplementary materials. E.P., A.A. and S.L. planned the study, contributed to the data interpretation, and revised the manuscript. All the authors discussed the results and commented on the manuscript.

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Correspondence to Ezio Portis.

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Acquadro, A., Barchi, L., Portis, E. et al. Genome reconstruction in Cynara cardunculus taxa gains access to chromosome-scale DNA variation. Sci Rep 7, 5617 (2017).

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