Abstract
The plants of the Papaveraceae family are used for ornamental purposes because of their varicolored flowers, and are known as medicinal crops. Some species of poppy are used in foods such as salads or sorbets, utilizing the seeds, leaves, pedicels, and petals. There are several morphological similarities among the species of this family, which make it difficult to distinguish the seeds of different species or identify opium poppies. The family is known to contain about 100 species. The leaves of Iceland poppy (Papaver nudicaule) cultivars with five different flower colors (white, yellow, pink, orange, and scarlet) were sequenced to obtain transcriptome data. Sequencing was done on plants in three different developmental growth stages (leaf rosette, branching and elongation of internodes, and blossom and seed formation). Systematic bioinformatics analysis was conducted to identify single nucleotide polymorphisms (SNPs) unique to the five Papaver nudicaule cultivars and two other Papaver species (Papaver rhoeas and Papaver somniferum). A 739-Mb reference transcriptome (94.6% BUSCO completeness score) from a 566-Gb RNA-sequencing (RNA-Seq) dataset was obtained. Likewise, 18 significant SNPs were identified to authenticate the three species and five cultivars of Papaver. This study will facilitate future Papaver research, including evaluation of the results for more detailed characterization.
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Key words: Papaveraceae, Alkaloid, Transcriptome, Reference, SNP
INTRODUCTION
The opium poppy (
Papaver somniferum and
Papaver setigerum) is known for its medicinal properties, which are related to its secondary metabolite content.
Papaver is a group of medicinal plant species known to produce therapeutically useful alkaloids such as the narcotic analgesics codeine, morphine, thebaine, and papaverine (
Wijekoon and Facchini 2012;
Singh et al. 2014). The plant’s important secondary metabolites, benzylisoquinoline alkaloids (BIAs), are essential in the pharmaceutical field, and poppies remain the sole commercial source of the compounds (
Labanca et al. 2018). The alkaloids of the opium poppy are precursors of heroin and other narcotic drugs, ensuring that the plant continues to flourish in several regions throughout the world. These alkaloids are produced in a few plants, such as
Papaver somniferum and
Papaver setigerum, and the growth of those particular species is continuously monitored by the United Nations for reasons pertaining to drug trafficking and abuse (
Choe et al. 2011;
Oh et al. 2018a). To improve the beneficial elements while reducing the harmful elements of plant materials, many studies have attempted to engineer changes in the alkaloid metabolism of
Papaver species (
Larkin et al. 2007;
Hagel and Facchini 2013;
Diamond and Desgagné-Penix 2016). These works emphasized the potential for biosynthetic platforms to support the synthesis of alkaloid compounds, which can advance alkaloid-based drug discovery and development (
Li et al. 2018). In addition, microbial transformation with an autonomous system can enhance the production of all opioids (
Galanie et al. 2015) or individual drugs such as noscapine. Due to some technical issues, such as difficulties with iden-tification,
Papaver plant materials (
Papaver rhoeas and
Papaver somniferum) and their products, commonly known as opium poppies, are extensively used for different purposes including narcotic production and as ornamental plants (
Oh et al. 2018b). Accordingly,
Papaver species have many important uses, which give rise to extensive production and trade. The poppy seeds are edible and other plant materials are used as a source of edible oils. The seeds themselves also provide good-quality oil. It is sometimes combined with controlled and non-controlled
Papaver material contaminants from other
Papaver species, which contain narcotic alkaloids (Knutsen
et al. 2018). High-sensitivity molecular markers are needed to identify medicinal plants to avoid confusion regarding the nature of poppy by-products (
Chester et al. 2016;
Hakeem et al. 2017). In the absence of discriminating factors such as morphology (
Sarkar et al. 2015), chemicals and drugs are used as quantifiable markers to identify
Papaver species (
Choe et al. 2011;
Oh et al. 2018b). Compared to other morphological and metabolite markers, DNA markers, particularly single nucleotide polymorphism (SNP) markers, are more suitable for the authentication of
Papaver plant materials (
Ganie et al. 2015). A few studies have attempted to apply metabolite quantification standards to identify
Papaver species (
Choe et al. 2011;
Oh et al. 2018b) or evaluate the morphological characteristics of the plants to select those with the highest alkaloid content (
Zhou et al. 2018). Another study used DNA barcodes with limited organelle genomes (
Zhang et al. 2015). However, the genome and transcriptome-wide approaches to
Papaver species identification have yielded no clear molecular markers since there is currently a paucity of relevant genetic datasets (
Kim et al. 2018).
Papaver nudicaule, the Iceland poppy, is native to the subpolar regions of Asia, North America, Europe, the mountains of central Asia, and the temperate regions of China.
Papaver nudicaule is a hardy but short-lived perennial that is also grown as a biennial ornamental plant. This
Papaver species encompasses different cultivars with different flower colors. The cultivars come in shades of scarlet/red, orange, pink, yellow, and white. In general, wild
Papaver species have white or yellow flowers (
Fabergé 1942;
Huang et al. 2016). Indeed, an interesting characteristic of
Papaver species, in general, is their wide array of flower colors and shapes, which make the flowers very attractive to many pollinators (
Hicks et al. 2016;
Oh et al. 2018a). Most
de novo plant projects have attempted to shed light on the molecular mechanisms underlying the differences between the cultivars and facilitate further characterization to address a variety of problems (
Hakeem et al. 2017). The genetics of
P. nudicaule have been studied, particularly with respect to flower color (
Fabergé 1942). For example, a molecule within
Papaver nudicaulins has been identified as a marker of the yellow pigment in the flowers. It is a carbohydrate with different isoforms in different
Papaver nudicaulins cultivars, accounting for the color variation in different
Papaver flowers. However, the biosynthetic pathway responsible for the production of this molecule remains uncharacterized in the
Papaver family. Furthermore, other secondary metabolites, such as antho-cyanin (blue, purple, and red), carotenoids (orange, yellow, and red), and alkaloids (yellow), are also responsible for different color pigmentation (
Tatsis et al. 2013;
Dudek et al. 2016). Access to genetic datasets would allow researchers to address several questions related to
Papaver development, including the issue of flower color variation. In the present study, through polymorphism analysis, we differentiated the species and varieties of poppies. This was done via analysis of the reference genome of one opium poppy species and the whole transcript. Finally, we selected 18 candidate SNPs that may be markers of different cultivars.
MATERIALS AND METHODS
Plant materials
Five
Papaver nudicaule varieties with different flower colors, i.e., white, yellow, pink, orange, and scarlet, were individually grown in multiple pots under natural light conditions inside a glasshouse by the National Institute of Agricultural Science (NAS, RDA, Republic of Korea). The growth conditions (45% relative humidity and 26°C) were maintained for three months. For mRNA sequencing, leaf samples were obtained at three developmental stages. The three stages were leaf rosette (30 days after germination (DAG)), elongation and branching (60 DAG), and blossom and seed formation (90 DAG). Three
Papaver species,
P. rhoeas (NS and RS),
P. nudicaule (NW, NO, NP, NY, and NS), and
P. somniferum (PS), were sampled. The samples collected for transcriptome analysis were immediately frozen in liquid nitrogen and stored at ‒70℃. For each species, the experiments were repeated in triplicate under the same conditions. Phenotypic differences among the plants, i.e., differences in flower color, leaf morphology, and the visual appearance of the plant with flowers, are shown in
Fig. 1.
We divided the plants into group A (NW, NO, NP, NY, and NS) and group B (NS, RS, and PS) for the comparison between species and cultivars. Preparation of the complete sequence library and sequencing experiments of the indi-vidual samples were performed using the Illumina Hi-Seq 4000 system according to the manufacturer’s protocols. A pooled assembly was generated in this study from short reads. The results of two previous studies (
Kim et al. 2018;
Oh et al. 2018a) and the recently published
Papaver somniferum genome (
Guo et al. 2018) were also used in the current experiments. All raw reads from each sample under-went pre-processing to remove contamination, adapter sequences, and low-quality reads using Trimmomatic v0.36 (
Bolger et al. 2014). For each species, the pre-processed reads were assembled using the Trinity v2.2.0 program (
Haas et al. 2013). To obtain the pooled assembly, transcripts from three different transcriptomes (i.e., Trinity short read assemblies, PacBio high-quality assembled and polished consensus reads, and genome transcripts) were subjected to CD-HIT-EST v5.6 similarity-based clustering to obtain the non-redundant contigs for all individual assemblies. After assembly, the following filtering criteria were applied to reduce assembly artifacts and obtain the full-length transcripts: transcripts per kilobase million (TPM) ≥ 0.3, read counts ≥ 5, peptide length ≥ 75, and full-length status obtained from TransDecoder (
https:// github.com/TransDecoder/TransDecoder). The selected transcriptomes were annotated with BLAST2GO v.4 (
Conesa and Götz 2008) using the Plant UniProt database (updated February 2018), with an e-value of ≥ 0.05 and a minimum of 20 sequences, using the other default values. Ultimately, the complete-ness of the reference transcriptome was assessed by the BUSCO method using the Embryophyta (ODB9, creation date: February 13, 2016) core gene dataset (
Waterhouse et al. 2018). The other multifamily gene predictions and classifications (i.e., secondary metabolite biosynthesis, cytochromes, and carbohydrate-active enzymes (CAZY)) were accom-plished with CD-HIT v4.6 (
Li and Godzik 2006) with respect to the sequence similarity-based clustering method. The reference datasets for the multifamily genes and their structures were obtained from the cytochrome P450 engineering database (
Gricman et al. 2015), the CAZY database (
Lombard et al. 2014), and the KEGG pathway database (
Kim et al. 2018).
The pre-processed short-read sequences were mapped to the previously assembled transcriptomes and the
Papaver somniferum reference genome (
Guo et al. 2018) using Bowtie2 (
Langmead and Salzberg 2012). To optimize small insertion and deletion artifacts, the reads were re-mapped to the reference with Indel Realigner and the base pair quality scores (QUAL) were calibrated using count covariates and the table recalibration functions in the genome analysis toolkit (GATK-v.3.5) (
McCormick et al. 2015) following best practices. The variants for the individual samples were called by unified genotyper with variant call format (GVCF) and filters (normalized quality score (NQS) ≥ 2 and mapping quality (MQ) ≥ 40) were used to obtain high-quality SNPs. Finally, the SNPs were annotated using SnpEff-v.4.2 (
Cingolani et al. 2012), the missing genotypes were imputed by Beagle-v.4.1 (
Browning and Browning 2016), and the linkage disequilibrium (LD) score was constructed (
Chang et al. 2015). To select materials for principal component analysis (PCA), the species were classified by differences in genetic variation, and the varieties were classified by color. The GATK 4.0 analysis tool downloaded from GitHub was used for this task. Variant calling was used to obtain high-quality SNPs. PCA was performed and visualized using the PCA analysis tool in R package.
Statistical analyses were performed with SigmaPlot 10.0 software (Systat Software, Inc., San Jose, CA, USA).
Dataset
The complete sequences generated in this study were submitted to the Genbank sequence read archive (SRA) under bio-project ID PRJNA476004.
RESULTS
De novo assemblies and variant calling
Overall, 308 Gb of short-read bases were sequenced from 15 libraries from five different
Papaver nudicaule cultivars (i.e., white, yellow, pink, orange, and scarlet), for three growth stages (i.e., 30, 60, and 90 days) of the developmental growth cycle (
Fig. 1). A comparison between groups A and B identified 57,734 and 8,343 unique transcripts, respectively. The two groups of transcripts were used for the SNP analysis (
Fig. 2). In combination with data from three other
Papaver species, a total of 566 Gb of RNA-seq short-read sequences were used in this study for the complete systematic bioinformatics analysis, as illustrated in
Fig. 2. In total, 739 Mb of representative transcriptome data were obtained from the pooled assembly, from the transcripts annotated in
Papaver somniferum and the other assembled transcripts from previous tran-scriptomes (
Table 1). A completeness BUSCO score of 94.6% was obtained for the representative transcriptome and 80% of all short reads from all of the RNA-Seq libraries were mapped to the reference transcriptome, which together indicated that the quality of the transcriptome was high and the model was nearly complete. The tran-scriptome results were due to the inclusion of genome transcripts and PacBio long-read sequences in the pooled assembly. However, another factor that forced us to use pooled assembly was the low proportion of short reads that were mapped to the representative genome. A total of 107,475 (14.2%) transcripts were expressed in > 30% of the samples under the conditions used for LD analysis. The mapping rate was configured to extend the relationship between transcripts to allow for the detailed functional characterization of overlooked transcripts (
Supplementary Fig. S1).
Overall SNP variant calls were conducted to identify the significant SNPs among
Papaver nudicaule and between
Papaver species (
Fig. 2). Here the
Papaver somniferum reference genome (
Guo et al. 2018) and the previously assembled transcriptomes were individually used as references to obtain 635 significant markers from the genome and transcriptome data (
Supplementary Table S1). Mutations found through comparisons between the species (NS, PS, and RS) were identified at 11,551 loci in 2,737 transcripts. Meanwhile, mutations found by comparing the genomes of the plants with different flower colors (NY, NO, NW, NP, and NS) were identified at 21,058 loci in 7,372 transcripts (
Fig. 3). Finally, 18 transcripts were identified using various statistically significant criteria in the SNP calling protocols and selected as significant SNP markers, which could be used to clearly differentiate between cultivars based on genetic differences (
Table 2). It was also used for PCA analysis to distinguish flower colors between the cultivars and varieties. We performed PCA analysis to determine whether it was possible to distinguish between species and color using only 18 SNPs in the secondary metabolite that have a great effect on the differences between flower colors and varieties. The results confirmed that it was possible not only to distinguish between species but also to distinguish between flower colors, which were difficult to distinguish between NP and NS (
Fig. 4). In conclusion, the results of this study have laid the foundation for the development of SNPs as markers in the opium poppy.
DISCUSSION
To date, some markers have been developed to distinguish between different species of opium poppies and non-opium ornamental poppies, but more reliable markers using SNPs are currently not available (
Danert 1958;
Dittbrenner et al. 2007;
Chester et al. 2016;
Hakeem et al. 2017). Given this status, distinguishing between interspecific and intraspecific seeds is difficult (
Zhang et al. 2015). Continuous research on SNP markers is required to differentiate poppy species (
Labanca et al. 2018). This study identified interspecies and intraspecies SNPs using the transcripts produced in this study (
Table 1) and those in the recently published
Papaver somniferum genome (
Guo et al. 2018). We used the PacBio and Illumina technology for the transcriptome sequencing of
Papaver species and cultivars. Transcriptome sequencing is important for the identification of large numbers of informative SNPs. Thus, the leaf transcripts obtained from different developmental stages of five cultivars/varieties of
Papaver nudicaule, along with three other
Papaver species (
Papaver nudicaule,
Papaver rhoeas, and
Papaver somniferum) were sequenced and profiled (
Table 1,
Fig. 1). We also divided the species and cultivars into group A and group B for comparison (
Fig. 2). After confirming the transcripts of the two groups, the significant SNPs of each species and cultivar were selected by comparing the different samples (significance level log2FC ≥ 1 and
P < 0.01). The pooled assembly from poppy species provided detailed profiles of the SNPs specific to the species and cultivars/varieties that were studied (
Table 2). Also, variant calling was performed to select transcripts for use in PCA analysis (
Fig. 3). Finally, 18 SNPs were selected as involved in the generation of secondary metabolites, which could be used to classify five
Papaver nudicaule varieties and three other poppy species (
Papaver nudicaule,
Papaver rhoeas, and
Papaver somniferum). We performed PCA analysis to determine whether they distinguished between species and flower colors. PCA analysis performed using the 18 SNPs showed that it was possible not only to distinguish between species but also to distinguish between flower colors, which were difficult to distinguish between NP and NS (
Fig. 4). Finally, we laid the basis for the development of SNPs as markers for poppies by selecting 18 SNPs present in genes involved in the generation of secondary metabolites that have a great effect on the differences in flower color and poppy varieties. These markers require further validation with a large number of samples to confirm their signi-ficance and resistance to technological artifacts. This study also supports the
Papaver research community by providing a systematic bioinformatics analysis of the transcriptome of
Papaver leaves, which included annotation of the secondary metabolite biosynthetic pathways and multifamily genes (carbohydrate-active enzymes and cytochrome) mainly associated with plant secondary metabolites (e.g., ginsenoside biosynthesis in
Panax ginseng (
Devi et al. 2011;
Labanca et al. 2018;
Yang et al. 2018) according to the newly published
Papaver somniferum genome (
Guo et al. 2018). In conclusion, we identified and sequenced SNPs that were useful in interspecific and intraspecific differentiation between varieties. The results of this study are organized in simple files, which will facilitate further characterizations of
Papaver spp. In addition, the SNP markers identified from interspecific and intraspecific differences are expected to contribute to the mapping, genotyping, plant breeding, functional, comparative genomics, and industrialization of
Papaver spp.
Supplementary Materials
ACKNOWLEDGEMENTS
This research was conducted with the support of the Cooperative Research Program for Agriculture Science and Technology Development (Project No. PJ01184702), Rural Development Administration, Republic of Korea.
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CONFLICT OF INTEREST
The authors have declared that no competing interests exist.
Fig. 1Morphological illustrations of Papaver nudicaule cultivars along with two other Papaver species. From the left, the species are Papaver somniferum (PS), Papaver rhoeas (NS and RS), and Papaver nudicaule (NW, NS, NO, NP, and NY). SNP profiles were generated in two modules, i.e., Papaver nudicaule cultivar-specific (yellow dotted lines) and different Papaver species-specific SNPs (red dotted lines). *DAG: days after germination (30, 60, and 90).
Fig. 2The complete workflow of the systematic bioinformatics analysis, which was used to obtain SNP profiles in this study. Eight individual assemblies along with the Papaver somniferum reference genome were included in the comparative genome analysis. The results from PacBio (gray dotted lines) and the RNA libraries used for the variant calls (orange dotted lines) are indicated. Multifamily gene databases were referenced (CYPED: Cytochrome P450 Engineering Database; KEGG: Kyoto Encyclopedia of Genes and Genomes; CAZY: Carbohydrate Active EnZyme).
Fig. 3Comparison of variant calls between species and cultivars.
Fig. 4PCA plots of the selected SNPs significantly different between the given cultivars.
Table 1Summary of the transcriptome generated in this study.
Table 1
|
Descriptions |
Values |
|
Total Papaver species and cultivars |
8 |
|
Sequencing technologies |
PacBio, Illumina |
|
Total RNA-Seq libraries |
72 (8 speceis × 3 DAG × 3 Rep)*
|
|
Total size of RNA-Seq (Gb) |
566 |
|
Total transcripts |
756,856 |
|
Total size of transcriptome (bp) |
739,286,006 |
|
Minimum base length |
75 |
|
Maximum base length |
20463 |
|
Average |
976.79 |
|
N50 |
1876 |
|
BUSCO completeness |
94.60% |
Table 2Information on SNPs that may serve as specific markers of species and cultivars (log2FC ≥ 1 and P ≤ 0.01).
Table 2
|
Specific marker in |
SNP ID |
Variant type |
Minor allele |
Major allele |
NO vs. REST* |
NW vs. REST |
NP vs. REST |
NS vs. REST |
NY vs. REST |
PS vs. REST |
RS vs. REST |
|
Papaver somniferum
|
chr1:14440360 |
synonymous_variant |
T |
C |
NA |
NA |
NA |
NA |
NA |
5.09E-09 |
0.05652 |
|
Papaver somniferum
|
chr1:63408681 |
missense_variant |
C |
A |
NA |
NA |
NA |
NA |
NA |
5.09E-09 |
0.05652 |
|
Papaver rhoeas
|
chr1:50543171 |
synonymous_variant |
A |
G |
NA |
NA |
NA |
NA |
NA |
0.000533 |
4.49E-27 |
|
Papaver rhoeas
|
chr1:50543171 |
synonymous_variant |
A |
G |
NA |
NA |
NA |
NA |
NA |
0.000533 |
4.49E-27 |
|
Papaver rhoeas
|
chr1:50543180 |
synonymous_variant |
T |
C |
NA |
NA |
NA |
NA |
NA |
0.000533 |
4.49E-27 |
|
Papaver rhoeas
|
chr1:50543180 |
synonymous_variant |
T |
C |
NA |
NA |
NA |
NA |
NA |
0.000533 |
4.49E-27 |
|
Papaver nudicaule
|
chr11:106698721 |
missense_variant |
T |
A |
0.1355 |
0.1084 |
0.1084 |
4.27E-10 |
0.1084 |
0.005217 |
0.2716 |
|
Papaver nudicaule
|
chr5:141967375 |
upstream_gene_variant |
G |
A |
0.1004 |
0.1835 |
0.1004 |
4.18E-12 |
0.0241 |
0.00062 |
0.02911 |
|
Papaver nudicaule
|
chr5:141967375 |
upstream_gene_variant |
G |
A |
0.1004 |
0.1835 |
0.1004 |
4.18E-12 |
0.0241 |
0.00062 |
0.02911 |
|
Papaver nudicaule
|
chr1:100614208 |
missense_variant |
T |
C |
0.09464*
|
0.07218 |
0.07218 |
0.3179 |
5.90E-10 |
0.001417 |
0.2279 |
|
Papaver nudicaule
|
chr10:40112008 |
missense_variant |
T |
G |
5.62E-08 |
0.008627 |
0.0456 |
0.4568 |
0.8548 |
1.20E-05 |
0.08619 |
|
Papaver nudicaule
|
chr5:215123172 |
missense_variant |
A |
G |
4.92E-09 |
0.07218 |
0.07218 |
0.8417 |
0.07218 |
0.001417 |
0.2279 |
|
Papaver nudicaule
|
chr6:2099249 |
synonymous_variant |
G |
C |
0.04679 |
0.7934 |
1.35E-10 |
0.03231 |
0.03231 |
0.000195 |
0.1631 |
|
Papaver nudicaule
|
chr6:58448114 |
5_prime_UTR_premature_start_codon_gain_variant |
A |
C |
0.07889 |
1.97E-13 |
0.05873 |
0.05873 |
0.05873 |
0.001527 |
0.1929 |
|
Papaver nudicaule
|
chr7:161966122 |
synonymous_variant |
G |
A |
0.6802 |
0.1781 |
0.03854 |
0.03854 |
4.13E-09 |
0.000277 |
0.1695 |
|
Papaver nudicaule
|
chr7:242821166 |
synonymous_variant |
A |
G |
0.1355 |
0.1084 |
4.27E-10 |
0.1084 |
0.1084 |
0.2878 |
0.08486 |
|
Papaver nudicaule
|
chr7:242821166 |
synonymous_variant |
A |
G |
0.1355 |
0.1084 |
4.27E-10 |
0.1084 |
0.1084 |
0.2878 |
0.08486 |
|
Papaver nudicaule
|
unplaced-scaffold_158:2741063 |
synonymous_variant |
T |
C |
0.09464 |
2.71E-12 |
0.07218 |
0.07218 |
0.07218 |
0.8978 |
0.09378 |
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