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Improved pokeweed genome assembly and early gene expression changes in response to jasmonic acid

BMC Plant Biology volume 24, Article number: 801 (2024) Cite this article

Abstract

Background

Jasmonic acid (JA) is a phytohormone involved in regulating responses to biotic and abiotic stress. Although the JA pathway is well characterized in model plants such as Arabidopsis thaliana, less is known about many non-model plants. Phytolacca americana (pokeweed) is native to eastern North Americana and is resilient to environmental stress. The goal of this study was to produce a publicly available pokeweed genome assembly and annotations and use this resource to determine how early response to JA changes gene expression, with particular focus on genes involved in defense.

Results

We assembled the pokeweed genome de novo from approximately 30 Gb of PacBio Hifi long reads and achieved an NG50 of ~ 13.2 Mb and a minimum 93.9% complete BUSCO score for gene annotations. With this reference, we investigated the early changes in pokeweed gene expression following JA treatment. Approximately 5,100 genes were differentially expressed during the 0–6 h time course with almost equal number of genes with increased and decreased transcript levels. Cluster and gene ontology analyses indicated the downregulation of genes associated with photosynthesis and upregulation of genes involved in hormone signaling and defense. We identified orthologues of key transcription factors and constructed the first JA gene response network integrated with our transcriptomic data from orthologues of Arabidopsis genes. We discovered that pokeweed did not use leaf senescence as a means of reallocating resources during stress; rather, most secondary metabolite synthesis genes were constitutively expressed, suggesting that pokeweed directs its resources for survival over the long term. In addition, pokeweed synthesizes several RNA N-glycosylases hypothesized to function in defense, each with unique expression profiles in response to JA.

Conclusions

Our investigation of the early response of pokeweed to JA illustrates patterns of gene expression involved in defence and stress tolerance. Pokeweed provides insight into the defense mechanisms of plants beyond those observed in research models and crops, and further study may yield novel approaches to improving the resilience of plants to environmental changes. Our assembled pokeweed genome is the first within the taxonomic family Phytolaccaceae to be publicly available for continued research.

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Background

Jasmonic acid (JA) is a phytohormone involved in regulating plant development and adaptation to various environmental conditions by mediating the balance between growth and defense [1, 2]. JA is involved in response to biotic and abiotic stresses such as drought, salt, pathogens, and insect herbivory [3, 4]. In the absence of JA, activated transcription of genes in the JA pathway is repressed by binding of JASMONATE-ZIM DOMAIN (JAZ) proteins to transcription factors bound to their target promoters. When JA is present, it is converted to its bioactive form (+)-7-iso-JA-isoleucine (JA-Ile) which binds to the CORONATINE INSENSITIVE 1 (COI1) receptor [5] prompting formation of the SKP1-CULLIN1-F-box-type (SCF) E3 ubiquitin ligase complex (SCFCOI1) complex. This complex ubiquitinates the JAZ proteins marking them for degradation [6]. The ubiquitin-mediated degradation of the JAZ proteins [7] de-represses previously bound transcription factors allowing for downstream gene expression. JAZs have been shown to repress several stress-responsive transcription factors [8,9,10]. Most importantly, JAZs repress the MYCs, of which MYC2 is considered the master regulator for the JA pathway [5]. MYC2 regulates many genes involved in growth and development, biotic and abiotic stress response [11] and does so by forming a variety of regulatory complexes; there are at least 100 proteins that can interact with MYC2 [12].

Though the JA response pathway is well characterized in Arabidopsis, less is known about many non-model plants. For example, pokeweed (Phytolacca americanaL.) is native to eastern North America and is a member of the taxonomic order Caryophyllales. The plant is recognized as a heavy metal hyperaccumulator [13,14,15] and is broadly resistant to many pathogens [16,17,18]. Resistance is attributed to the presence of pokeweed antiviral protein (PAP), an RNA N-glycosylase that depurinates the sarcin/ricin loop of 28 S rRNA. Damage to rRNA limits pathogen spread either by inhibiting translation needed for pathogen replication or signaling a defense response [19, 20]. These enzymes are synthesized by 21 flowering plant orders, with many plants maintaining several different isoform genes [21]. For example, six genes of protein-encoding isoforms of PAP have been partially characterized with varying responses to JA [22].

Our previous study showed that the transcriptome of pokeweed is responsive to JA 24 h after treatment [22,23,24]. However, the gene expression patterns in pokeweed within the first six hours following JA exposure, or the early JA response, remains unknown. Furthermore, there are currently no publicly available genome assemblies in the taxonomic family Phytolaccaceae, which hinders further genomics research in pokeweed and other closely related species. The goal of this study was to produce a quality publicly available pokeweed genome assembly and annotations and use this resource to determine how the early JA response changes gene expression, with particular focus on genes involved in defense.

Results

Genome assembly and annotation quality

The pokeweed genome was assembled de novo from approximately 30 Gb of PacBio Hifi long reads using hifiasm and represents the first publicly available genome assembly for this non-model plant. The quality of this assembly was assessed with several metrics. Genomescope2 was used to predict the read error rate, the percentage of genome homozygosity, the haploid genome length, and the percentage of repeat content. BUSCO scores were generated using the orthologue databases ‘eukaryota_odb10’ and ‘eudicots_odb10’ to determine what percentage of the highly conserved coding sequences in the two respective taxonomic groups was present in the genome assembly.

The initial profile generated from the long reads indicated that the genome of our pokeweed sample was 99.8% homozygous which, given that pokeweed is a tetraploid plant [25], lends confidence to the quality of the final assembly. The BUSCO scores of the genome assembly were high, with 100% complete BUSCOs using the ‘eukaryota_odb10’ database, and 97.4% complete BUSCOs using the ‘eudicots_odb10’ database (Table 1). While the total assembled genome length changed very little from the previous published assembly with short-read data only [22], the contiguity of the assembly improved greatly as did BUSCO scores which were originally 84.3%. The current assembly has a total of 1,058 contigs, with ~ 98% of all identified genes (33,410 of 34,107) present on the largest 100 contigs. The N50 of this assembly is ~ 14,000,000 bp, and its longest contig is ~ 51,000,000 bp. (Table 2).

Several attempts were made to annotate the genome assembly using BRAKER, MAKER, and a combination of the two with different evidence sources. The transcript evidence was the 0–6 h RNA-seq JA time-course samples described in this study, sequences previously published by our lab [22], and sequences downloaded from NCBI (PRJEB21674, [26]; PRJNA649785, [27]; PRJNA623405, [28]; PRJNA669370; PRJNA384358). For protein data, the ‘plants’ database from OrthoDB [29] and all proteins, both canonical and isoforms, available for the taxonomic order Caryophyllales in the UniProt database were used ( [30], retrieved November 2022). The BUSCO results from the annotation produced by MAKER were lower quality than the BRAKER annotation attempts with an average of 22% fragmented or missing BUSCOs between both databases searched. This was unexpected as the only input sources were the BRAKER annotations and the Stringtie transcriptome assembly, and both had higher BUSCO scores. The BRAKER-only version had the highest BUSCO score (93.9% complete BUSCOs with the ‘eudicots_odb10’ database and 96.5% with the ‘eukaryota_odb10’ database) and therefore, was selected as the final annotation set (Supplementary Figure S1). These BUSCO scores exceed our original BUSCO annotation score of 75.8% and comparison between the two genome assemblies [22] shows that gene lengths increased in the current assembly, observed mostly for the introns and total mRNAs (Supplementary Figure S2). Intron length may have increased because of less collapse and/or fragmentation at repetitive sequences and total mRNA length increased because of greater number of exons per mRNA. We attribute these improvements to greater contiguity of the genome assembly afforded by long read data, which contributed to improved annotations. The genome assembly is available on NCBI under the accession GCA_040581565.1 and genome annotations are listed in gff3 format in Supplementary Data S1. Additionally, the Gene Ontology (GO) annotation file that accompanies the structural annotations and was used for GO analysis of the RNA-Seq data is available in Supplementary Data S2. It is worth noting that about one third of genes have no assigned GO annotations despite multiple attempted strategies to reduce this number. We anticipate that this is due to bias in the reference databases used to generate the annotations against non-model plants, as there has been limited physicochemical characterization of proteins from such plants.

Table 1 Quality scores for the primary haplotype-resolved genome assembly using the programs Genomescope2 and BUSCO with two different databases from OrthoDB (eukaryota_odb10 and eudicots_odb10)
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Table 2 QUAST output comparing the pokeweed genome assembly based on short reads alone to the assembly based on long reads alone. The short-read assembly is from Neller et al., (2019) [22], and all parameters were identical
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Time-course RNA-Seq, cluster analysis, and GO analysis in pokeweed

Our previous study showed significant changes in the pokeweed transcriptome following a 24-hour treatment of JA [22]. To identify the early response of pokeweed to JA, an RNA-Seq time-course experiment was performed for six hours comparing plants sprayed with JA to the control plants sprayed with dilute ethanol. All differential expression analysis results including log2 fold change (logFC) and false discovery rate (FDR) for each contrast calculated are available in Supplementary Data S3. A total of 5133 genes were differentially expressed in at least one time point throughout the study, and of these a similar number had an increase as had a decrease in expression level at a particular time point compared to controls, with an increasing number of differentially expressed genes (FDR < 0.01) over time. To further investigate this dataset, the genes identified as differentially expressed in at least one time point were grouped by cluster analysis and their functional characteristics were explored by GO analysis to identify their enriched terms (Fig. 1A). We chose four clusters based on best cluster validity indices (described in Methods) which also allowed for overall patterns in gene expression change to be observed. There were approximately 2600 genes in cluster 1, 1850 genes in cluster 2, 300 genes in cluster 3, and 350 genes in cluster 4. The clusters that each gene belongs to are available in Supplementary Data S3. Genes within cluster 1 tended to decrease in relative transcript abundance, genes of cluster 2 and 3 were increased, and the genes of cluster 4 fluctuated between decreased and increased transcript abundance (Fig. 1B, C). The genes in cluster 1 tended to function in photosynthesis (e.g. chlorophyll biosynthesis, chloroplast organization, response to blue light) and cell division (e.g. regulation of mitotic spindle organization, mitotic spindle assembly, regulation of meristem structural organization) suggestive of growth. Likewise, cluster 4, whose genes fluctuated between decreased and increased relative transcript abundance, though rarely with a logFC larger than +/- 1, tended to involve DNA replication and repair (e.g. DNA strand elongation, DNA replication initiation, double-strand break repair). By comparison, cluster 2, which had a greater change in relative transcript abundance over the six hours and in a pattern inverse to that of cluster 1, contained terms related to metabolic changes during biotic and abiotic stress response (e.g. spermine metabolism, inositol metabolism, sulfur compound biosynthesis) and jasmonic acid (e.g. oxylipin biosynthesis, lipid oxidation). The genes of cluster 3, which showed an increasing logFC until four hours after treatment and then decreased to a mean close to zero, had GO terms enriched in a variety of specific defense responses (e.g. chitin catabolic process, killing cells of another organism, alkaloid biosynthesis). Though most genes with increasing relative transcript levels also had a larger fold change over time, a smaller subset, those of cluster 3, initially increased at a higher rate than cluster 2 following JA application but declined at the four-hour mark. Therefore, there was a coordinated response to JA such that photosynthesis genes were generally decreased in transcript level whereas genes involved in stress response were increased with groups of genes more specific to defense following different expression patterns.

Fig. 1
figure 1

Cluster analysis and subsequent GO analysis of differentially expressed genes in pokeweed during the JA treatment time-course. Hierarchical Euclidean clusters, with four clusters as the target, were generated with the R package dtwclust. Each cluster is represented by a colour identified in the legend. (A) Top significantly differentially enriched GO terms per cluster (p value less than 0.01 using the ‘elim’, ‘weight’, and classic methods among the top 25 differentially expressed GO terms). X-axis represents the percentage of each GO term present per cluster compared to all instances of that term, and the y-axis indicates each enriched GO term. (B) Bar graph specifying the number of genes represented per cluster. (C) Ribbon plot of the mean logFC of each cluster, flanked by the interquartile range. Genes were selected as differentially expressed if they had an FDR < 0.01 in any contrast

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To further visualize the distribution and numbers of differentially expressed genes over time, an upset plot was generated in R. The left bar graph shows the number of genes with positive or negative relative expression levels at a single time point and the top graph indicates how many of those genes fall within different categories based on their patterns of expression over time (Fig. 2). Of the top 20 sub-groups representing 95% of all differentially expressed genes, many discrete patterns of gene expression were observed in response to JA. The largest group (853 genes) was not differentially expressed at one and two hours but showed decreased expression levels at four and six hours compared to controls and GO analysis indicated that this group of genes primarily functioned in photosynthesis. The second largest group (615 genes) was similarly not differentially expressed at one and two hours but was more highly expressed at four and six hours with enriched GO terms related to metabolic changes during biotic and abiotic stress response. The third and fourth groups, comprising 1017 genes together, were both not differentially expressed at one and two hours but showed a negative change in expression levels at four and six hours, respectively. These genes were associated with GO terms involved in chloroplast organization and cell division. In contrast, group five (398 genes) was not differentially expressed at one hour but had increased relative transcript levels at two hours and remained at a higher expression level for the remainder of the time course. These genes were enriched in GO terms associated with JA metabolism. These five groups comprised approximately 50% of the differentially expressed genes and illustrated a time-dependent response to JA. Specifically, most genes tended to alter expression levels at the four-hour time point except for those genes primarily involved in JA metabolism. Examination of the remaining groups of genes indicated that specific GO terms were repeated for sets of genes with different expression patterns. For example, genes involved in stress response were prevalent in group 2, as described above, but were also concentrated in groups 9, 15 and 17, but unlike group 2 genes, genes within groups 15 and 17 had higher relative transcript levels at two and one hour respectively. Group 9 genes showed higher expression levels for the entire time course. Despite differences in logFC, genes from these defense-related groups never showed decreased expression levels. Cell division and DNA replication were also controlled by groups of genes with different expression patterns. Specifically, these genes had lower relative transcript levels in group 4 at six hours as stated above but had lower expression earlier at two hours for genes within groups 10 and 12. Similarly, genes involved in photosynthesis and chlorophyll biosynthesis were consistently decreased in transcript level compared to controls but were distributed among groups 1, 3 and 7, each with different expression patterns. Therefore, these processes were controlled by groups of genes at different timepoints following JA treatment. This granular examination of changes in levels of differentially expressed genes indicates that transcript levels of genes involved in JA metabolism were increased early, within the first hour, whereas most genes were differentially expressed at later time points. Those involved in photosynthesis and growth were decreased in transcript level whereas transcripts of genes involved in adaptation to stress were increased over time. All differentially enriched GO terms, along with their GO IDs, respective groups, and statistical metrics are available in Supplementary Data S4.

Fig. 2
figure 2

Upset plot of the number of genes in various combinations of differential expression groups over time (top 20 largest). All time points are specified in hours (h). “Decreased” refers to gene transcript levels that have a FC < -1.5 and an FDR < 0.01 for the time point specified. “Increased” refers to gene transcript levels that have a FC > 1.5 and an FDR < 0.01 for the time point specified. “Not_DE” refers to genes that are not differentially expressed (have an FDR > = 0.01) for the time point specified. Genes not differentially expressed (FDR < 0.01) in all four contrasts were not included.

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Expression of PAP genes during JA time course

We also examined the differential expression of the PAP genes during the JA time course. Our previous work indicated that six protein-coding PAP genes were responsive to JA to varying degrees following a 24-hour application of the hormone [22]. Our current analysis showed that five of these genes, specifically PAP-I, PAP-II, PAPα, PAP-S1 and novel PAP were increased in transcript level compared to controls at one or more timepoints following JA treatment (Fig. 3). PAP-S2 was not detected at this early stage but was expressed at the 24-hour mark in our previous study. Each PAP gene showed a unique pattern of expression over time, suggesting that they may have different functions in response to stress. PAPα and PAP-S1 transcripts were identified in group 2 of the upset plot (Fig. 2) with genes enriched in GO terms for metabolic changes during biotic and abiotic stress response. PAP-I and PAP-II were located in group 5 along with genes involved in JA metabolism. Novel PAP was the least responsive to JA, which was consistent with our previous 24-hour single timepoint of JA treatment [22]. In the current study, novel PAP was identified in group 14 of the upset plot together with genes involved in response to salt stress. While relative transcript levels of novel PAP were only increased at two hours and otherwise were not differentially expressed, PAP-I and PAP-II transcript levels were increased at two hours and remained at higher levels. PAPα and PAP-S1 expression levels were increased at four hours and also remained high. Curiously, relative transcript abundance for the majority of PAP isoform genes (PAP-I, PAP-II, PAPS-I) were decreased between zero and one hour, indicating a delay with regards to transcript level increase. To validate these changes in PAP isoform expression, we conducted RT-qPCR and quantified fold change in transcript levels relative to two internal control genes. Supplementary Figure S3 illustrates a similar pattern of expression for each PAP isoform in response to JA treatment. In addition to fold change over time, we also examined counts per million as a measure of transcript abundance of each isoform. PAP-I was expressed at the highest level relative to the other isoforms (Supplementary Table T1). Taken together, these data show that pokeweed maintains genes encoding defense proteins with different patterns and levels of expression in response to JA.

Differential expression of genes involved in secondary metabolite synthesis

An interesting observation resulting from our analysis was that most genes involved in the synthesis of secondary metabolites were not differentially expressed during the JA time course (Fig. 4A). The three main classes of plant secondary metabolites, nitrogen-sulfur compounds, phenolic compounds and terpenoids, each contain compounds involved in plant defense among other roles [31, 32]. Of the 434 genes we identified involved in secondary metabolite synthesis in pokeweed, 293 were not differentially expressed within 6 h of JA treatment. The remaining 141 genes that were differentially expressed represented 28% of the nitrogen-sulfur compounds, 30% of the phenolic compounds and 36% of the terpenoids. One subgroup of each class was not identified among the differentially expressed genes; specifically, we did not identify genes involved in phytoalexin, phenolic or glycoside synthesis. Examination of expression patterns during the JA time course showed that transcript levels of the majority of differentially expressed genes were increased (blue lines) rather than decreased (red lines) (Fig. 4B). Purple lines represent those genes with transcript levels that were both increased and decreased relative to controls during the time course. However, among the nitrogen-sulfur containing compounds, expression levels of the majority of genes involved in the synthesis of alkaloids were decreased. Within the differentially expressed phenolic compounds group, transcript abundance of genes involved in flavonoid synthesis represented the largest proportion and among these, the levels of most were also increased by JA. With regards to differentially expressed terpenoids, genes involved in the production of terpenes were most prevalent and relative transcript levels of the majority of these were also increased during the time course.

Fig. 3
figure 3

The expression pattern of each differentially expressed gene identified as a ribosome inactivating protein by InterProScan5. The y-axis represents the logFC of the contrast between JA and Et at each of the four time points in comparison to time 0, the x-axis denotes the time points in hours, and the coloured lines represent the transcripts of genes encoding RIPs

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Fig. 4
figure 4

Number of pokeweed genes involved in secondary metabolite production and their expression patterns throughout the JA time course. GO terms relating to secondary metabolite biosynthesis were identified by keyword searches from the dataset of GO identifiers and their associated terms from the GO.db package, and then all pokeweed genes containing at least one of these terms were separated into another table for plotting. (A) Bar graph of the number of secondary metabolite biosynthesis-related genes, separated into columns based on whether they were differentially expressed or not in pokeweed, and colour-coded based on their product. (B) Line graphs of the logFC pattern of each gene involved in secondary metabolite production; the line colour represents genes with transcript levels that are only decreased (red), only increased (blue), or both decreased and increased (purple) during the time course

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JA gene regulatory network

Many of the downstream effects known to be associated with JA application were reflected in the GO analysis results (Fig. 1A). Likewise, many of the products of key genes involved in the JA response pathway likely have similar physiological effects in pokeweed. To investigate the expression patterns in pokeweed, a gene regulatory pathway was constructed based on known JA response pathways in Arabidopsis [4, 11, 33]. Orthologues of these key Arabidopsis genes were identified in pokeweed using a blastp search [34]. Only those unique transcripts with the highest percent identity were selected and all those with a minimum E-value of 1e-50, minimum bit score of 50, minimum alignment length of 150, and minimum percent identity of 50 were excluded from consideration. The unfiltered blast results, along with associated Araport11 IDs for each sequence used in the blast search, are available in Supplementary Data S5. Arrows in Fig. 5 represent instances where one gene, or complex of genes, positively regulates another, and the lines ending in a semicircle are instances where one gene negatively regulates another; the white boxes contain the Arabidopsis gene names and the grey boxes contain their associated functions. Alongside this representation of the pathway are heatmaps of the logFC values of pokeweed orthologues of these genes, identified with blastp, at the one-, two-, four-, and six-hour time points, respectively. Among genes with multiple orthologues, only those that were differentially expressed are shown, and in cases where no orthologues were differentially expressed the associated heatmap for that gene depicts a logFC of 0 at all time points (Fig. 5).

The expression patterns observed in pokeweed for the genes in the JA response pathway were similar in many ways to what has been documented in other plants. MYCs are master regulators of the JA pathway [35] and MYC4 expression was increased at all time points relative to controls. Aside from this, none of the upstream transcriptional regulators that are de-repressed by JAZs were differentially expressed. We searched for orthologues for JAZ1 and JAZ5, which are known to repress upstream transcription factors [8,9,10], but despite almost all meeting other blastp filtering criteria, none of the 16 hits had a percent identity higher than ~ 35% (Supplementary Data S5). Additionally, there were other cases where transcript levels of some orthologues of upstream transcription factors were increased while others for the same gene were strongly decreased, such as ANAC019 and ANAC055 (Fig. 5). In Arabidopsis, MYCs repress EIN3, preventing the positive regulation of HLS1 [36] and this pattern is reflected in pokeweed as the EIN3 orthologue was not differentially expressed while the HLS1 orthologue showed decreased expression.

Downstream of these factors, many of their targets were regulated as they would be in Arabidopsis. Relative transcript levels of orthologues associated with cold stress response (CBF1), JA biosynthesis and catabolism (JOX2, LOX2, AOC1, OPR3, JAR1, AOS, CYP94B1) and terpenoid biosynthesis (TPS11) were increased while orthologues associated with salicylic acid (SA) biosynthesis (ICS1) were decreased. In other cases, expression of some orthologues was increased as expected, such as APK2, MAM1 and BCAT4 involved in glucosinolate biosynthesis and NYC1 involved in chlorophyll degradation. Other orthologues, although expected to be increased in transcript abundance, were not differentially expressed or were decreased. For example, transcript levels of genes related to chlorophyll degradation (NYE1, PAO), glucosinolate biosynthesis (BAT5, IPMDH1), drought response (ERD1), leaf senescence (SAG29) and auxin biosynthesis (YUC8) were either decreased or not differentially expressed. Likewise CAT2, involved in metabolism of reactive oxygen species, is normally negatively regulated in Arabidopsis but in pokeweed its expression was increased at both the one and four hour time points (Fig. 5). In sum, orthologues of key genes in the JA response pathway were identified in pokeweed and factors regulating these genes were conserved between pokeweed and Arabidopsis. Some differences existed between the two plants however, with regards to expression levels of genes that control leaf senescence and the extent of secondary metabolite production.

Fig. 5
figure 5

Known gene regulatory network of Arabidopsis in response to JA with the differential expression patterns of identified pokeweed orthologues. The Araport11 Arabidopsis amino acid sequences for the genes shown were downloaded from TAIR (arabidopsis.org; March 1, 2023) and used as the query sequences in a blastp search against the translated gene sequences from the pokeweed annotations. Genes were considered orthologues if they had an e-value less than 0.01 and percent identity greater than 50. Coloured bars represent the logFC values of each orthologue at the time points 1, 2, 4, and 6 h (left to right)

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Discussion

Even though genomic studies have been conducted in pokeweed and its relatives, our work is the first to share a genome assembly. Yang et al. (2018) documented the complete assembly of the Phytolacca insularis chloroplast sequence [37], but did not make the sequence publicly available. Our first genome assembly in pokeweed contributed greatly to our understanding of the regulation and function of pokeweed genes [22]. However, because the pokeweed genome is more than 50% repetitive regions (Table 1) it was difficult to assemble with short-read data and the result was highly fragmented and therefore was not made publicly available [22]. The use of Pacbio Hifi long read data allowed for the current, and substantially more complete, genome assembly and represents the first publicly available genome assembly for pokeweed. It will serve as an ongoing resource for future genomics research in pokeweed and other closely related species, as it is the only genome assembly in the taxonomic family Phytolaccaceae. Presently the species with a public genome assembly that is most closely related to pokeweed, as they share a taxonomic order, is Beta vulgaris [38].

Our pokeweed genome assembly and annotation supported the investigation of early changes in gene expression following JA treatment. We identified orthologues of key transcription factors known to control downstream expression of genes and built the first JA gene response network for pokeweed. Based on the differential expression of genes and their patterns of expression during the time course, we discovered how pokeweed responds to JA and what genes may be responsible for its resiliency to stress.

Balance between growth and stress response

Plants under stress generally sacrifice growth to increase defense [39, 40] and GO analysis of differentially expressed genes in pokeweed agree with these published data. Genes with decreased expression in pokeweed were enriched in terms indicating inhibition of photosynthesis and growth (Fig. 2A). Similar trends have been found in other species. Following application of JA, growth was reduced in the perennials Chelidonium majus [41], Hypericum perforatum [42], and Scrophularia striata [43]. With regards to enriched terms associated with increased expression of genes in pokeweed, they centered on responses to stress, response to hormones, and secondary metabolite biosynthesis (Fig. 2A). Defense-related GO terms were also upregulated, such as flavonoid and terpene biosynthesis genes, in white pine Picea abies with application of MeJA [44]. In Arabidopsis, cluster analysis of differentially expressed genes upon application of MeJA revealed that GO terms such as secondary metabolism, response to wounding, and response to JA were among the upregulated clusters [40]. Therefore, pokeweed responds to stress by balancing regulation of defense genes with growth and development.

Plants also have means of recycling the products of chlorophyll degradation into useful lipids [45] and nitrogen can be re-mobilized from senescing stems and leaves into seeds with high efficiency [46], both of which are beneficial during stress to divert resources to secondary metabolite synthesis and other defense responses. However, leaf senescence and chlorophyll degradation, which are triggered by JA in Arabidopsis [47], appeared to be less regulated by JA in pokeweed. Specifically, none of the top differentially enriched GO terms involved chlorophyll degradation or senescence (Fig. 2A), and orthologues of two primary genes involved in leaf senescence from Arabidopsis did not match their expected expression patterns in pokeweed (Fig. 5). In Arabidopsis, SAG29 is a plasma membrane-localized MtN3 protein that accelerates leaf senescence when overexpressed [48] but transcript levels of its orthologue in pokeweed were decreased relative to controls throughout the time course. Meanwhile, CAT2 converts the reactive oxygen species H2O2 into water and oxygen [49] which is beneficial for responding to oxidative stress; however, when CAT2 is inhibited by MYCs, leaf senescence can be promoted due to the accumulation of H2O2 [50]. Pokeweed showed changing expression of this gene with upregulation seen at one and four hours, and downregulation at two and six hours (Fig. 5). Together these results suggest that pokeweed does not rely on leaf senescence as a defense strategy. In tandem with this observation, expression of only one of the three main genes involved in chlorophyll degradation, NYC1, was increased as expected in pokeweed; the other two, NYE1 and PAO, were not differentially expressed. NYE1 is a Mg-dechelatase involved in chlorophyll degradation [51], and PAO is involved in converting chlorophyll to colorless nonfluorescent chlorophyll catabolites [52]. In contrast, NYC1 specifically degrades chlorophyll b, which is beneficial for improving the plant’s light-harvesting capability under high-light conditions [53]. Therefore, it may be that pokeweed is sufficiently able to fine-tune chlorophyll use without resorting to more generalized chlorophyll degradation and deals with reactive oxygen species as needed through intermittent expression of CAT2 to prevent leaf senescence during stress.

JA gene network and hormone cross talk

This paper represents the first time that the JA signalling pathway, and its primary associated genes, have been elucidated in pokeweed, and their expression patterns aligned well with their orthologues in Arabidopsis in some cases but less so in others. For instance, the expression patterns of orthologues in pokeweed consistently aligned with what has been observed in Arabidopsis regarding metabolism of plant hormones JA and SA, whose signaling antagonism in Arabidopsis has been well established [4, 54]. In accordance with this, there was increase in expression of one of the orthologues of SAGT1 in the first hour, which converts SA to an inactive SA glucose conjugate [55], and decrease of ICS1 transcript levels, which generates the precursor to SA isochorismate [56]. Correspondingly, expression of genes involved in JA metabolism were among the most strongly increased in the JA pathway (Fig. 5). AOS is part of the oxylipin pathway, along with lipoxygenase 2 (LOX2) and allene oxide cyclase (AOC1), that is involved in producing JA [47, 57, 58]. OPR3 is then involved in JA synthesis by reducing a certain double bond of the cyclopentenone moiety in 12-oxophytodienoic acid [59]. Finally, JAR1 is a jasmonate: amino acid synthetase which is involved in generating jasmonoyl-L-isoleucine (JA-Ile) [60]. Therefore, each major step of the JA biosynthesis pathway was upregulated in pokeweed, from converting lipids to JA precursors, to forming JA itself, and finally converting JA to its active form JA-Ile. With regards to attenuating this JA biosynthesis, JOX2 catalyzes the oxidation of JA to 12OH-JA [61], and CYP94B1 is a cytochrome P450 family protein that metabolizes JA-Ile, causing its levels to decrease [62]. These expression patterns agree with the GO analysis results (Fig. 1A, cluster 2) as terms related to jasmonic acid signaling and biosynthesis were enriched among genes with increased transcript levels. This gene pathway indicates that application of JA triggers decreased expression of genes involved in SA accumulation and increased expression of genes involved in JA accumulation and bioactivity.

Typically, there is considerable cross talk between hormone signalling pathways [2, 63]; however, expression of orthologues relating to the biosynthesis of the hormones ethylene and auxin did not, with the exception of ACS1, tend to be increased in pokeweed. Auxin is involved in leaf development [64] while ethylene is involved in growth [65], reproduction, and stress responses [66] including leaf senescence [67]. JA and ethylene act synergistically by both regulating key genes involved in the ethylene response pathway [4]. EIN3 is an ethylene-responsive gene involved in increasing salt stress tolerance, reducing reactive oxygen species accumulation [68], and inhibiting leaf growth [65] and leaf senescence [67]. Its pokeweed orthologue was not differentially expressed, possibly due to inhibition by MYCs [69]. This observation is consistent with the lack of upregulation of primary genes related to senescence.

Genes involved in secondary metabolism

An important response of plants to stress is the production of secondary metabolites. For example, MeJA induces the biosynthesis of a variety of flavonoids and phenylethanoid glycosides in Scrophularia striata [43], monoterpenes, sesquiterpenes, and green leaf volatiles in Polygonum minus [70], benzophenanthridine alkaloids in Chelidonium majus [41], and isoflavonoids in Phaseolus vulgaris, Glycine max, and Vigna radiata [71]. Likewise, after JA application 24 different phenylpropanoids and naphtodianthrones were differentially enriched in Hypericum perforatum [42] and six phenolics were detected Fagopyrum esculentum [72]. Glucosinolates are also required for innate immune response in Arabidopsis [73] indicating their more long-term defense capabilities. While relative transcript levels of most orthologues related to terpenoid biosynthesis were increased as anticipated in pokeweed, expression of genes involved in glucosinolate biosynthesis was inconsistent. Among the orthologues with increased expression in pokeweed, APK2 is involved in generating sulfated glucosinolates [74], while MAM1 and BCAT2 are involved in producing glucosinolates from methionine [75, 76]. In contrast, BAT5 was not differentially expressed and IPMDH1 transcript levels were decreased after the one-hour time point (Fig. 5). IPMDH1 and BAT5 are both involved in aliphatic glucosinolate biosynthesis [69, 77]. Genes with GO terms related to indole glucosinolate biosynthesis were enriched in cluster 3, and genes containing GO terms related to the biosynthesis of terpenes and their precursors were enriched in cluster 2 (Fig. 1A) indicating that pokeweed is synthesizing glucosinolates, and other secondary metabolites, with other genes. It may be that pokeweed produces a different repertoire of secondary metabolites in response to stress which requires a different set of genes. For example, glucosinolates are known to be produced in pokeweed [78], pokeberrygenin is a triterpene identified in pokeweed [79] and phytolaccasaponin B, E and G are the major saponins of pokeweed [80]. Approximately 32% of genes containing a GO term related to nitrogen-sulfur compounds, phenolic compounds and terpenoids were differentially expressed in at least one time point during the time course (Fig. 4). This level of response to JA suggests that pokeweed may produce a variety of defense-related secondary metabolites, and that in many cases their synthesis may be constitutive rather than produced only in response to stress.

Methods

Raising plants, sampling and genome sequencing

Pokeweed (Phytolacca americana L.) seeds were provided by Prof. Nilgun Tumer, Rutgers University, New Jersey, USA. Pokeweed was formally identified by Carolus Linnaeus. A specimen of pokeweed has been deposited in the Chrysler Herbarium of Rutgers University, New Brunswick Campus, New Jersey, USA, Catalog #: 02319887, Record ID: dba0d469-1149-4200-886d-37d85c7b710c. The herbarium’s specimens are housed in the online database accessible at the Mid-Atlantic Herbaria Consortium.

Pokeweed plants were grown to the 4–5 leaf stage in growth chambers with a 14-hour light/10-hour darkness cycle; chamber lighting was comprised of 75% fluorescent and 25% incandescent bulbs (180 µE/m2/s). Temperature was held at 24 °C and 21 °C during the light and darkness periods, respectively, and relative humidity was approximately 50%. Plants were watered every 2–3 days and fertilized weekly with NPK 20:20:20 fertilizer.

At the 4–5 leaf stage, leaves were harvested, the mid vein removed, and remaining tissue frozen in liquid N2. Frozen tissue was shipped on dry ice to Histogenetics (Ossining, NY). They isolated genomic DNA, then performed HiFi standard library preparation with a 15-20 kb insert, sequencing with a 30 h movie time on a PacBio Sequel II machine, quality checking, adapter trimming, and HiFi read generation. The sequencing depth was ~ 25x and the read error rate was 0.1%.

Time-course sample preparation

For the JA time course, 45 pokeweed plants at the four-leaf stage were sprayed with 0.5 mM JA dissolved in 0.5% ethanol (Et). For the control group, another 45 plants were sprayed with 0.5% Et alone. Leaves were harvested and frozen in liquid nitrogen at time points zero, one, two, four, and six hours. To reduce the biological variability within budgetary limitations, two approaches were used. Firstly, three biological replicates were taken per treatment and per time point, and secondly, within each replicate equal proportions of mRNA from three independent plants were pooled, totaling nine plants per time point and per treatment (Supplementary Figure S4).

Isolation of total RNA and sequencing

Frozen leaf tissue was ground in liquid N2 with mortar and pestle into a fine powder and total RNA was extracted using organic solvents. Briefly, leaf powder was suspended and vortexed in 1:1 ratio of aqueous buffer and phenol: chloroform: isoamyl alcohol (25:24:1) equilibrated at pH 5.5. Phases were separated by centrifugation and extraction was repeated once with phenol: chloroform: isoamyl alcohol (25:24:1) and once with chloroform alone. RNA was precipitated in 70% isopropanol, resuspended in water and treated with DNaseI to digest contaminating gDNA. Samples were re-extracted with phenol: chloroform: isoamyl alcohol (25:24:1) and the RNA was precipitated from the aqueous phase in 0.3 M NaOAc and 70% ethanol. Following centrifugation, RNA was dissolved in water and quantified using a nanodrop spectrophotometer.

RNA samples were sent to the Centre for Applied Genomics (Toronto, ON) for sequencing. RNA quality was first assessed with a bioanalyzer and mRNA was isolated from each total RNA sample using NEB poly(A) mRNA magnetic isolation module with the NEB Ultra II Directional RNA kit which uses oligo dT beads to capture the mRNA transcripts that have a polyA tail for sequencing. A total of 30 samples were sequenced on a NovaSeq S4 flowcell PE 2 × 150 bp at a depth of ~ 67–83 million reads per sample.

Genome assembly

Detailed commands and software versions used in this section are available on GitHub (https://github.com/kd-lab/Genome_Assembly_Annotation). The quality of the Hifi reads was checked with FastQC [81] and found to be of sufficient quality to proceed without additional adapter trimming. Prior to assembly, a k-mer profile was generated with meryl [82] with a kmer size of 21, and genomescope2 was used to infer genome properties [83]. These results indicated that purging duplicates would be an unnecessary step as this genome was 99.8% homozygous, but that chromosome-level assembly was unlikely because ~ 55% of the genome length consisted of repeats. To reduce misassemblies because pokeweed is a tetraploid, a haplotype-resolved assembly was produced with hifiasm [84, 85]. The alternate assembly was very short and was therefore not used further. The quality of the assembly was checked with BUSCO [86], QUAST [87] using the estimated reference size calculated with genomescope2 [83], and Merqury [88] using the output from meryl [82] generated previously.

Structural gene annotation

Detailed commands and software versions used in this section are available on GitHub (https://github.com/kd-lab/Genome_Assembly_Annotation). Repeats were identified with RepeatModeler2 [89] then masked with RepeatMasker [90]. This repeat-masked genome assembly file was used as input for all subsequent annotation steps.

Genome annotation was performed with BRAKER2 [91] in two separate runs with default settings, one with protein data only and one with mRNA data only, and then the two were merged into one annotation file as per the recommendations by the BRAKER2 developers. The unaligned protein data used were the ‘plants’ database from OrthoDB [29] and all proteins, both canonical and isoforms, available for the taxonomic order Caryophyllales in the UniProt database ( [30], retrieved November 2022). The RNA-seq data were from the zero-six hour JA time-course samples described in this study, sequences previously published by our lab [22], and sequences downloaded from NCBI (PRJEB21674, [26]; PRJNA649785, [27]; PRJNA623405, [28]; PRJNA669370; PRJNA384358). All sequences were spot-checked for quality with FastQC [81] and any datasets with poor quality reads were trimmed with Trimmomatic [92] in paired-end mode. The reads were aligned to the reference genome with STAR [93]. These files were then sorted with samtools sort using default settings and indexed with samtools index [94].

The program used to merge the two BRAKER2 annotation files was TSEBRA [95] with default settings. The quality of the annotation was calculated with BUSCO [86]. Additional annotation files were generated with MAKER (v3.01.04; [96]) with various settings, and in combinations with the BRAKER2 annotations, but the quality was found to be poorer than the BRAKER2-based annotations so were not used (Supplementary Figure S1).

Functional gene annotation

Detailed commands and software versions used in this section are available on GitHub (https://github.com/kd-lab/Genome_Assembly_Annotation). The annotation files were converted to transcript sequence files with gffread [97] using default settings and then translated to amino acid sequences with transeq [98]. These amino acid fasta sequence files were used as input for the functional annotation prediction program InterProScan5 [99]. Additionally, blastp [34] was used to identify all high-quality hits for each sequence within the SwissProt database [30] and their associated GO annotations were downloaded from the UniProt database. The gene ontology annotations produced by both of these methods were concatenated, duplicate and erroneous annotations within each gene removed, and the result exported as a GO annotation file with a custom R script. This GO annotation file is available in Supplementary Data S2.

RNA-Seq and differential expression analysis

Detailed commands and software versions used in this section are available on GitHub (https://github.com/kd-lab/Genome_Assembly_Annotation). The RNA-seq JA time course reads, previously trimmed with Trimmomatic [92], were aligned to the reference genome one sample at a time with STAR [93]. The read alignments were sorted by read name and then position using default settings with samtools sort [94]. Read counting was done with htseq [100]; the raw counts are available in Supplementary Data S6.

Normalization and differential expression analysis from the counts produced by htseq [100] were performed with edgeR [101]. To reduce false positives, genes with a read count less than 30 were excluded from the analysis, and the trimmed mean of M-values (TMM) method was used for normalization [102]. Raw tagwise estimates, mean dispersion estimates across all genes, and the fitted value of the mean-dispersion trend were calculated using both a linear model and generalized linear model (GLM). To examine likelihood of false discoveries during differential expression analysis for each model, the gene wise biological coefficient of variation (BCV) was plotted against gene abundance for each; the GLM-based approach estimated a slightly larger BCV than the approach based on a linear model so for this reason, and because GLM models are generally more robust than linear models [103], the GLM-based method was chosen for subsequent analyses. The counts per million (CPM) for each sample are available in Supplementary Data S7. To estimate the differences between replicates and between treatments as a measure of quality control, a multi-dimensional scaling (MDS) plot was generated which showed, as expected, that there was little difference between biological replicates and increasing difference between treatment groups over the time-course (Supplementary Figure S5). Four contrasts were conducted using the formula (JAxh-JA0h)-(Etxh-Et0h) where ‘JA’ is the jasmonic acid treated group, ‘Et’ is the control group, ‘h’ represents hours, and ‘x’ are the time points 1, 2, 4, and 6. These contrasts were made with the ‘glmTreat’ function in edgeR with a minimum fold change (FC) of 1.5. The criteria used to define differentially expressed genes were those that had a false discovery rate (FDR) less than 0.01 in any of the four contrasts.

Reverse transcription and quantitative PCR (RT-qPCR) validation of PAP isoform transcript levels

Reverse transcription (RT) was performed with MashUp reverse transcriptase [104] to generate cDNA using 0.5 µg of total pokeweed RNA and gene specific RT primers (Supplementary Table 2). RNA-seq data were examined to identify candidate internal control genes that were stably expressed under both JA and Et stress treatments. PolyA-binding protein (transcript ID# anno1.g17908.t1) and Dynamin (transcript ID# anno1.g23978.t1) were found to be suitable internal controls for RT-qPCR. Each reaction (final volume of 20 µL) for RT-qPCR analysis was carried out in a QIAGEN RotorGene Q thermocycler by combining 1 µL of cDNA as template, 0.3 µM forward primer, 0.3 µM reverse primer, and 10 µL of GB-Amp 2X SYBR Green qPCR Master Mix (Cat #P2092, GeneBio Systems). The following conditions were used for all qPCRs: polymerase activation for 10 min at 95 °C, followed by amplification and quantification cycles repeated 40 times, alternating between denaturation (95 °C; 15 s) and combined annealing/extension (58–66 °C, 45 s) (see Supplementary Table 2 for Tm for each primer pair). The relative fold change in gene expression was quantified using the ΔΔCt method [105] for three independent biological replicates.

Cluster analysis, GO analysis, and differential expression visualizations

All analyses were performed in R and all code and package versions are available on GitHub (https://github.com/kd-lab/Genome_Assembly_Annotation). To get an overview of the number of genes conforming to different upregulation and downregulation patterns over the time-course, an upset plot was generated in R with the UpSetR package [106]. To ensure that the data conformed to the assumptions of the clustering algorithms, and that outlier genes with unusually high or low fold changes would not impact the effectiveness of clustering, the logFC values from edgeR were centered and scaled using the base R function ‘scale’ [107]. Multiple cluster analyses were then performed using various combinations of clustering methods, cluster sizes, distance metrics, and control methods in the package dtwclust [108]. The cluster validity indices (CVIs) Silhouette index [109], Calinski-Harabasz index, Dunn index, COP index, Davies-Bouldin index [110], Modified Davies-Bouldin index [111], and Score Function [112] were calculated for each cluster analysis strategy and the one with best index/score in multiple CVIs was selected; in this case, hierarchical clustering with Euclidean distance, four clusters, and ‘centroid’ control performed the best. With the ggplot2 package [113], the number of genes per cluster was plotted as a bar graph, and the average logFC per cluster over time, including interquartile range, was plotted as a ribbon graph.

GO analysis was performed independently on each group of the upset plot and each of the four clusters from the cluster analysis. The GO annotation file generated during functional gene annotation was loaded into R and GO analysis was performed with topGO [114] using Fisher’s exact test with the algorithms ‘classic’, ‘elim’, and ‘weight’, excluding GO terms with less than eight annotations in the whole genome. The top 25 enriched terms in each cluster were initially generated, but only those with a p-value of less than 0.01 from all three algorithms were considered enriched within a cluster (Supplementary Data S8). The enriched GO terms resulting from the four clusters were plotted as a stacked bar graph with the ggplot2 package [113], while the GO analysis results from the Upset plot groups are presented as a table in Supplementary Data 4.

Genes identified as ‘ribosome inactivating proteins’ by InterProScan5 [99] were selected and their sequences were compared to known ribosome inactivating proteins with blastp and visually compared through multiple sequence alignment to identify their common name. To determine the expression changes of RIPs over time, all those genes what were differentially expressed in at least one time point were visualized on a line graph.

A list of 1318 GO terms associated with the secondary metabolite groups ‘nitrogen-sulfur compounds’ (sub-groups ‘alkaloids’, ‘glucosinolates’, and ‘phytoalexins’), ‘phenolic compounds’ (sub-groups ‘anthocyanins’, ‘coumarins’, ‘flavonoids’, ‘lignins/lignans’, and ‘phenolics’), and ‘terpenoids’ (sub-groups ‘carotenoids’, ‘glycosides’, ‘saponins’, ‘sterols’, and ‘terpenes’) was determined from review of literature. All pokeweed genes containing at least one of these GO terms was selected, and the expression patterns of these genes were determined with a bar plot and with a series of line graphs split by group and sub-group.

JA Network

Detailed commands, package versions, and R code used in this section are available on GitHub (https://github.com/kd-lab/Genome_Assembly_Annotation). To investigate the early pokeweed JA pathway based on research from Arabidopsis, a detailed JA pathway map was assembled from several published reviews in Arabidopsis [4, 11, 33]. To obtain the amino acid sequences associated with these genes, the Araport11 representative gene model protein fasta file [115] was downloaded from The Arabidopsis Information Resource (TAIR), (https://www.arabidopsis.org/download/index auto.jsp? dir=%2Fdownload_files%2FSequences%2FAraport11_blastsets, March 1, 2023), and the sequences for the genes of interest were selected in R. These sequences were used as the query for blastp [34] against the translated gene models from our pokeweed annotations with a minimum E-value of 1e-50, minimum bit score of 50, minimum alignment length of 150, and minimum percent identity of 50. Candidate orthologues were further refined by removing duplicate pokeweed hits leaving only the one with the highest percent identity and bit score and selecting at most ten hits per Arabidopsis gene. The logFC values of these selected orthologues were plotted as heatmaps with pheatmap [116] and this information was added to the JA pathway map. The blast results for each Arabidopsis gene and the associated logFC values for each hit are available in Supplementary Data S5.

Conclusions

The early time course of gene expression changes in response to JA illustrated that pokeweed responds to stress by favouring defense over growth. However, this shift did not come at the expense of leaf senescence or chlorophyll degradation. Rather, most secondary metabolites were constitutively expressed in pokeweed, suggesting that it allocates resources for survival over the long term. In addition, isoforms of pokeweed antiviral protein (PAP) were differentially regulated by JA, suggesting a coordinated response of these defense proteins during stress. Understanding the resiliency of non-model plants such as pokeweed can provide novel approaches to improve crop survival during environmental changes.

Data availability

The pokeweed genome assembly and all raw sequence data have been submitted to the DDBJ/ENA/GenBank databases under bioproject number PRJNA974046. This Whole Genome Shotgun project has been deposited at DDBJ/ENA/GenBank under the accession JBEVOY000000000 and the version described in this paper is version 1. The pokeweed genome assembly accession number is GCA_040581565.1. The genome annotations are listed in Supplementary Data S1 and are publicly available on the Hudak Lab webpage as a gff3 file at https://hudak.lab.yorku.ca/pokeweed-genome/. All other data are included in the paper and its supplementary files.

Abbreviations

BUSCO:

benchmarking universal single-copy orthologs

CPM:

counter per million

FDR:

false discovery rate

GO:

Gene Ontology

PAP:

pokeweed antiviral protein

JA:

jasmonic acid

MeJA:

methyl jasmonate

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Acknowledgements

Not applicable.

Funding

This work was supported by a Discovery Grant to K.A.H. from the Natural Sciences and Engineering Research Council of Canada, and a Canada Graduate Scholarship – Master’s (CGS M) to K.D.

Author information

Authors and Affiliations

  1. Department of Biology, York University, 4700 Keele St, Toronto, ON, M3J 1P3, Canada

    Kyra Dougherty, Tanya Prashar & Katalin A. Hudak

Authors
  1. Kyra Dougherty
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  2. Tanya Prashar
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  3. Katalin A. Hudak
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Contributions

KD and KAH designed the project. KD performed bioinformatic analyses and drafted the manuscript. TP performed RT-qPCR validations and wrote associated method. KD and KAH edited the manuscript. All authors approved the final version prior to submission.

Corresponding author

Correspondence to Katalin A. Hudak.

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Dougherty, K., Prashar, T. & Hudak, K.A. Improved pokeweed genome assembly and early gene expression changes in response to jasmonic acid. BMC Plant Biol 24, 801 (2024). https://doi.org/10.1186/s12870-024-05446-1

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  • DOI: https://doi.org/10.1186/s12870-024-05446-1

Keywords

BMC Plant Biology

ISSN: 1471-2229

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