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BSA-seq and transcriptome analyses reveal candidate gene associated with petiole color in papaya (Carica papaya L.)

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  • Papaya (Carica papaya L.) is an important tropical species popular for highly nutritious fruit as well as medicinal value. In addition, non-commercial cultivation of papaya trees has resulted in dual-purpose cultivars grown for both fruit and ornamental value in residential areas. Petiole color is a key ornamental trait in papaya that varies amongst cultivars depending on anthocyanin accumulation resulting in purple or green pigmentation. Although inherited as a simple trait, genetic characterization and genomic loci responsible for the purple petiole color in papaya is unknown. In this study, F1 and F2 populations generated from two breeding lines PR-2043 (green petiole) and T5-2562 (purple petiole) were used to evaluate the inheritance patterns of petiole color as well as determine genetic loci and genes involved in petiole pigmentation in papaya through bulk segregant analysis (BSA) and transcriptome sequencing. The segregation of purple petiole color followed a single dominant gene inheritance model (3:1). BSA-seq analysis indicated key genes influencing petiole color are mainly located in chromosome 1 (0.01 to 5.96 Mb) of the papaya genome. Four major genes, including CHS, MYB20, MYB315-like, and MYB75-like within this region exhibited significant differential expression in a comparison between purple and green petiole papaya plants. A relatively high abundance of CHS transcripts was observed in purple petioles and may signify a major involvement in regulating anthocyanins accumulation in papaya petioles. The findings of this study facilitate the future efforts of breeding papaya cultivars with higher economical value in residential landscapes.
  • Grapevines are among the most widely grown and economically important fruit crops globally. Grapes are used not only for wine making and juice, but also are consumed fresh and as dried fruit[1]. Additionally, grapes have been increasingly recognized as an important source of resveratrol (trans-3, 5,4'-trihydroxystilbene), a non-flavonoid stilbenoid polyphenol that in grapevine may act as a phytoalexin. In humans, it has been widely reported that dietary resveratrol has beneficial impacts on various aspects of health[2, 3]. Because of the potential value of resveratrol both to grapevine physiology and human medicine, resveratrol biosynthesis and its regulation has become an important avenue of research. Similar to other stilbenoids, resveratrol synthesis utilizes key enzymes of the phenylpropanoid pathway including phenylalanine ammonia lyase (PAL), cinnamate 4-hydroxylase (C4H), and 4-coumarate-CoA ligase (4CL). In the final steps, stilbene synthase (STS), a type II polyketide synthase, produces trans-resveratrol from p-coumaroyl-CoA and malonyl-CoA, while chalcone synthase (CHS) synthesizes flavonoids from the same substrates[4, 5]. Moreover, trans-resveratrol is a precursor for other stilbenoids such as cis-resveratrol, trans-piceid, cis-piceid, ε-viniferin and δ-viniferin[6]. It has been reported that stilbenoid biosynthesis pathways are targets of artificial selection during grapevine domestication[7] and resveratrol accumulates in various structures in response to both biotic and abiotic stresses[812]. This stress-related resveratrol synthesis is mediated, at least partialy, through the regulation of members of the STS gene family. Various transcription factors (TFs) participating in regulating STS genes in grapevine have been reported. For instance, MYB14 and MYB15[13, 14] and WRKY24[15] directly bind to the promoters of specific STS genes to activate transcription. VvWRKY8 physically interacts with VvMYB14 to repress VvSTS15/21 expression[16], whereas VqERF114 from Vitis quinquangularis accession 'Danfeng-2' promotes expression of four STS genes by interacting with VqMYB35 and binding directly to cis-elements in their promoters[17]. Aided by the release of the first V. vinifera reference genome assembly[18], genomic and transcriptional studies have revealed some of the main molecular mechanisms involved in fruit ripening[1924] and stilbenoid accumulation[8, 25] in various grapevine cultivars. Recently, it has been reported that a root restriction treatment greatly promoted the accumulation of trans-resveratrol, phenolic acid, flavonol and anthocyanin in 'Summer Black' (Vitis vinifera × Vitis labrusca) berry development during ripening[12]. However, most of studies mainly focus on a certain grape variety, not to investigate potential distinctions in resveratrol biosynthesis among different Vitis genotypes. In this study, we analyzed the resveratrol content in seven grapevine accessions and three berry structures, at three stages of fruit development. We found that the fruits of two wild, Chinese grapevines, Vitis amurensis 'Tonghua-3' and Vitis davidii 'Tangwei' showed significant difference in resveratrol content during development. These were targeted for transcriptional profiling to gain insight into the molecular aspects underlying this difference. This work provides a theoretical basis for subsequent systematic studies of genes participating in resveratrol biosynthesis and their regulation. Further, the results should be useful in the development of grapevine cultivars exploiting the genetic resources of wild grapevines. For each of the seven cultivars, we analyzed resveratrol content in the skin, pulp, and seed at three stages of development: Green hard (G), véraison (V), and ripe (R) (Table 1). In general, we observed the highest accumulation in skins at the R stage (0.43−2.99 µg g−1 FW). Lesser amounts were found in the pulp (0.03−0.36 µg g−1 FW) and seed (0.05−0.40 µg g−1 FW) at R, and in the skin at the G (0.12−0.34 µg g−1 FW) or V stages (0.17−1.49 µg g−1 FW). In all three fruit structures, trans-resveratrol showed an increasing trend with development, and this was most obvious in the skin. It is worth noting that trans-resveratrol was not detectable in the skin of 'Tangwei' at the G or V stage, but had accumulated to 2.42 µg g−1 FW by the R stage. The highest amount of extractable trans-resveratrol (2.99 µg g−1 FW) was found in 'Tonghua-3' skin at the R stage.
    Table 1.  Resveratrol concentrations in the skin, pulp and seed of berries from different grapevine genotypes at green hard, véraison and ripe stages.
    StructuresSpeciesAccessions or cultivarsContent of trans-resveratrol (μg g−1 FW)
    Green hardVéraisonRipe
    SkinV. davidiiTangweindnd2.415 ± 0.220
    V. amurensisTonghua-30.216 ± 0.0410.656 ± 0.0432.988 ± 0.221
    Shuangyou0.233 ± 0.0620.313 ± 0.0172.882 ± 0.052
    V. amurensis × V. ViniferaBeibinghong0.336 ± 0.0761.486 ± 0.1771.665 ± 0.100
    V. viniferaRed Global0.252 ± 0.0510.458 ± 0.0571.050 ± 0.129
    Thompson seedless0.120 ± 0.0251.770 ± 0.0320.431 ± 0.006
    V. vinifera × V. labruscaJumeigui0.122 ± 0.0160.170 ± 0.0210.708 ± 0.135
    PulpV. davidiiTangwei0.062 ± 0.0060.088 ± 0.009nd
    V. amurensisTonghua-30.151 ± 0.0660.324 ±0.1040.032 ± 0.004
    Shuangyou0.053 ± 0.0080.126 ± 0.0440.041 ± 0.017
    V. amurensis × V. ViniferaBeibinghong0.057 ± 0.0140.495 ± 0.0680.087 ± 0.021
    V. viniferaRed Global0.059 ± 0.0180.159 ± 0.0130.027 ± 0.004
    Thompson seedless0.112 ± 0.0160.059 ± 0.020nd
    V. vinifera × V. labruscaJumeigui0.072 ± 0.0100.063 ± 0.0170.359 ± 0.023
    SeedV. davidiiTangwei0.096 ± 0.0140.169 ± 0.0280.049 ± 0.006
    V. amurensisTonghua-30.044 ± 0.0040.221 ± 0.0240.113 ± 0.027
    Shuangyound0.063 ± 0.0210.116 ± 0.017
    V. amurensis × V. ViniferaBeibinghongnd0.077 ± 0.0030.400 ± 0.098
    V. viniferaRed Global0.035 ± 0.0230.142 ± 0.0360.199 ± 0.009
    Thompson seedless
    V. vinifera × V. labruscaJumeigui0.077 ± 0.0250.017 ± 0.0040.284 ± 0.021
    'nd' indicates not detected in samples, and '−' shows no samples are collected due to abortion.
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    To gain insight into gene expression patterns influencing resveratrol biosynthesis in 'Tangwei' and 'Tonghua-3', we profiled the transcriptomes of developing berries at G, V, and R stages, using sequencing libraries representing three biological replicates from each cultivar and stage. A total of 142.49 Gb clean data were obtained with an average of 7.92 Gb per replicate, with average base Q30 > 92.5%. Depending on the sample, between 80.47%−88.86% of reads aligned to the V. vinifera reference genome (Supplemental Table S1), and of these, 78.18%−86.66% mapped to unique positions. After transcript assembly, a total of 23,649 and 23,557 unigenes were identified as expressed in 'Tangwei' and 'Tonghua-3', respectively. Additionally, 1,751 novel transcripts were identified (Supplemental Table S2), and among these, 1,443 could be assigned a potential function by homology. Interestingly, the total number of expressed genes gradually decreased from the G to R stage in 'Tangwei', but increased in 'Tonghua-3'. About 80% of the annotated genes showed fragments per kilobase of transcript per million fragments mapped (FPKM) values > 0.5 in all samples, and of these genes, about 40% showed FPKM values between 10 and 100 (Fig. 1a). Correlation coefficients and principal component analysis of the samples based on FPKM indicated that the biological replicates for each cultivar and stage showed similar properties, indicating that the transcriptome data was reliable for further analyses (Fig. 1b & c).
    Figure 1.  Properties of transcriptome data of 'Tangwei' (TW) and 'Tonghua-3' (TH) berry at green hard (G), véraison (V), and ripe (R) stages. (a) Total numbers of expressed genes with fragments per kilobase of transcript per million fragments mapped (FPKM) values; (b) Heatmap of the sample correlation analysis; (c) Principal component analysis (PCA) showing clustering pattern among TW and TH at G, V and R samples based on global gene expression profiles.
    By comparing the transcriptomes of 'Tangwei' and 'Tonghua-3' at the G, V and R stages, we identified 6,770, 3,353 and 6,699 differentially expressed genes (DEGs), respectively (Fig. 2a). Of these genes, 1,134 were differentially expressed between the two cultivars at all three stages (Fig. 2b). We also compared transcriptional profiles between two adjacent developmental stages (G vs V; V vs R) for each cultivar. Between G and V, we identified 1,761 DEGs that were up-regulated and 2,691 DEGs that were down-regulated in 'Tangwei', and 1,836 and 1,154 DEGs that were up-regulated or down-regulated, respectively, in 'Tonghua-3'. Between V and R, a total of 1,761 DEGs were up-regulated and 1,122 DEGs were down-regulated in 'Tangwei', whereas 2,774 DEGs and 1,287 were up-regulated or down-regulated, respectively, in 'Tonghua-3' (Fig. 2c). Among the 16,822 DEGs between the two cultivars at G, V, and R (Fig. 2a), a total of 4,570, 2,284 and 4,597 had gene ontology (GO) annotations and could be further classified to over 60 functional subcategories. The most significantly represented GO terms between the two cultivars at all three stages were response to metabolic process, catalytic activity, binding, cellular process, single-organism process, cell, cell part and biological regulation (Fig. 2d).
    Figure 2.  Analysis of differentially expressed genes (DEGs) at the green hard (G), véraison (V), and ripe (R) stages in 'Tangwei' (TW) and 'Tonghua-3' (TH). (a) Number of DEGs and (b) numbers of overlapping DEGs between 'Tangwei' and 'Tonghua-3' at G, V and R; (c) Overlap among DEGs between G and V, and V and R, for 'Tangwei' and 'Tonghua-3'; (d) Gene ontology (GO) functional categorization of DEGs.
    We also identified 57 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways that were enriched, of which 32, 28, and 31 were enriched at the G, V, and R stages, respectively. Seven of the KEGG pathways were enriched at all three developmental stages: photosynthesis-antenna proteins (ko00196); glycine, serine and threonine metabolism (ko00260); glycolysis/gluconeogenesis (ko00010); carbon metabolism (ko01200); fatty acid degradation (ko00071); cysteine and methionine metabolism (ko00270); and valine, leucine and isoleucine degradation (ko00280) (Supplemental Tables S3S5). Furthermore, we found that the predominant KEGG pathways were distinct for each developmental stage. For example, phenylpropanoid biosynthesis (ko00940) was enriched only at the R stage. Overall, the GO and KEGG pathway enrichment analysis showed that the DEGs in 'Tangwei' and 'Tonghua-3' were enriched for multiple biological processes during the three stages of fruit development. We then analyzed the expression of genes with potential functions in resveratrol and flavonoid biosynthesis between the two cultivars and three developmental stages (Fig. 3 and Supplemental Table S6). We identified 30 STSs, 13 PALs, two C4Hs and nine 4CLs that were differentially expressed during at least one of the stages of fruit development between 'Tangwei' and 'Tonghua-3'. Interestingly, all of the STS genes showed increasing expression with development in both 'Tangwei' and 'Tonghua-3'. In addition, the expression levels of STS, C4H and 4CL genes at V and R were significantly higher in 'Tonghua-3' than in 'Tangwei'. Moreover, 25 RESVERATROL GLUCOSYLTRANSFERASE (RSGT), 27 LACCASE (LAC) and 21 O-METHYLTRANSFERASE (OMT) DEGs were identified, and most of these showed relatively high expression at the G and V stages in 'Tangwei' or R in 'Tonghua-3'. It is worth noting that the expression of the DEGs related to flavonoid biosynthesis, including CHS, FLAVONOL SYNTHASE (FLS), FLAVONOID 3′-HYDROXYLASE (F3'H), DIHYDROFLAVONOL 4-REDUCTASE (DFR), ANTHOCYANIDIN REDUCTASE (ANR) and LEUCOANTHOCYANIDIN REDUCTASE (LAR) were generally higher in 'Tangwei' than in 'Tonghua-3'at G stage.
    Figure 3.  Expression of differentially expressed genes (DEGs) associated with phenylalanine metabolism. TW, 'Tangwei'; TH, 'Tonghua-3'. PAL, PHENYLALANINE AMMONIA LYASE; C4H, CINNAMATE 4-HYDROXYLASE; 4CL, 4-COUMARATE-COA LIGASE; STS, STILBENE SYNTHASE; RSGT, RESVERATROL GLUCOSYLTRANSFERASE; OMT, O-METHYLTRANSFERASE; LAC, LACCASE; CHS, CHALCONE SYNTHASE; CHI, CHALCONE ISOMERASE; F3H, FLAVANONE 3-HYDROXYLASE; FLS, FLAVONOL SYNTHASE; F3'H, FLAVONOID 3′-HYDROXYLASE; DFR, DIHYDROFLAVONOL 4-REDUCTASE; LAR, LEUCOANTHOCYANIDIN REDUCTASE; ANR, ANTHOCYANIDIN REDUCTASE; LDOX, LEUCOANTHOCYANIDIN DIOXYGENASE; UFGT, UDP-GLUCOSE: FLAVONOID 3-O-GLUCOSYLTRANSFERASE.
    Among all DEGs identified in this study, 757 encoded potential TFs, and these represented 57 TF families. The most highly represented of these were the AP2/ERF, bHLH, NAC, WRKY, bZIP, HB-HD-ZIP and MYB families with a total of 76 DEGs (Fig. 4a). We found that the number of downregulated TF genes was greater than upregulated TF genes at G and V, and 48 were differentially expressed between the two cultivars at all three stages (Fig. 4b). Several members of the ERF, MYB, WRKY and bHLH families showed a strong increase in expression at the R stage (Fig. 4c). In addition, most of the TF genes showed > 2-fold higher expression in 'Tonghua-3' than in 'Tangwei' at the R stage. In particular, a few members, such as ERF11 (VIT_07s0141g00690), MYB105 (VIT_01s0026g02600), and WRKY70 (VIT_13s0067g03140), showed > 100-fold higher expression in 'Tonghua-3' (Fig. 4d).
    Figure 4.  Differentially expressed transcription factor (TF) genes. (a) The number of differentially expressed genes (DEGs) in different TF families; (b) Number of differentially expressed TF genes, numbers of overlapping differentially expressed TF genes, and (c) categorization of expression fold change (FC) for members of eight TF families between 'Tangwei' and 'Tonghua-3' at green hard (G), véraison (V), and ripe (R) stages; (d) Heatmap expression profiles of the three most strongly differentially expressed TF genes from each of eight TF families.
    We constructed a gene co-expression network using the weighted gene co-expression network analysis (WGCNA) package, which uses a systems biology approach focused on understanding networks rather than individual genes. In the network, 17 distinct modules (hereafter referred to by color as portrayed in Fig. 5a), with module sizes ranging from 91 (antiquewhite4) to 1,917 (magenta) were identified (Supplemental Table S7). Of these, three modules (ivory, orange and blue) were significantly correlated with resveratrol content, cultivar ('Tonghua-3'), and developmental stage (R). The blue module showed the strongest correlation with resveratrol content (cor = 0.6, p-value = 0.008) (Fig. 5b). KEGG enrichment analysis was carried out to further analyze the genes in these three modules. Genes in the ivory module were significantly enriched for phenylalanine metabolism (ko00360), stilbenoid, diarylheptanoid and gingerol biosynthesis (ko00945), and flavonoid biosynthesis (ko00941), whereas the most highly enriched terms of the blue and orange modules were plant-pathogen interaction (ko04626), plant hormone signal transduction (ko04075) and circadian rhythm-plant (ko04712) (Supplemental Fig. S1). Additionally, a total of 36 genes encoding TFs including in 15 ERFs, 10 WRKYs, six bHLHs, two MYBs, one MADs-box, one HSF and one TRY were identified as co-expressed with one or more STSs in these three modules (Fig. 5c and Supplemental Table S8), suggesting that these TFs may participate in the STS regulatory network.
    Figure 5.  Results of weighted gene co-expression network analysis (WGCNA). (a) Hierarchical clustering tree indicating co-expression modules; (b) Module-trait relationship. Each row represents a module eigengene, and each column represents a trait. The corresponding correlation and p-value are indicated within each module. Res, resveratrol; TW, 'Tangwei'; TH, 'Tonghua-3'; (c) Transcription factors and stilbene synthase gene co-expression networks in the orange, blue and ivory modules.
    To assess the reliability of the RNA-seq data, 12 genes determined to be differentially expressed by RNA-seq were randomly selected for analysis of expression via real-time quantitative PCR (RT-qPCR). This set comprised two PALs, two 4CLs, two STSs, two WRKYs, two LACs, one OMT, and MYB14. In general, these RT-qPCR results strongly confirmed the RNA-seq-derived expression patterns during fruit development in the two cultivars. The correlation coefficients between RT-qPCR and RNA-seq were > 0.6, except for LAC (VIT_02s0154g00080) (Fig. 6).
    Figure 6.  Comparison of the expression patterns of 12 randomly selected differentially expressed genes by RT-qPCR (real-time quantitative PCR) and RNA-seq. R-values are correlation coefficients between RT-qPCR and RNA-seq. FPKM, fragments per kilobase of transcript per million fragments mapped; TW, 'Tangwei'; TH, 'Tonghua-3'; G, green hard; V, véraison; R, ripe.
    Grapevines are among the most important horticultural crops worldwide[26], and recently have been the focus of studies on the biosynthesis of resveratrol. Resveratrol content has previously been found to vary depending on cultivar as well as environmental stresses[27]. In a study of 120 grape germplasm cultivars during two consecutive years, the extractable amounts of resveratrol in berry skin were significantly higher in seeded cultivars than in seedless ones, and were higher in both berry skin and seeds in wine grapes relative to table grapes[28]. Moreover, it was reported that total resveratrol content constantly increased from véraison to complete maturity, and ultraviolet-C (UV-C) irradiation significantly stimulated the accumulation of resveratrol of berry during six different development stages in 'Beihong' (V. vinifera × V. amurensis)[9]. Intriguingly, a recent study reported that bud sport could lead to earlier accumulation of trans-resveratrol in the grape berries of 'Summer Black' and its bud sport 'Nantaihutezao' from the véraison to ripe stages[29]. In the present study, resveratrol concentrations in seven accessions were determined by high performance liquid chromatography (HPLC) in the seed, pulp and skin at three developmental stages (G, V and R). Resveratrol content was higher in berry skins than in pulp or seeds, and were higher in the wild Chinese accessions compared with the domesticated cultivars. The highest resveratrol content (2.99 µg g−1 FW) was found in berry skins of 'Tonghua-3' at the R stage (Table 1). This is consistent with a recent study of 50 wild Chinese accessions and 45 cultivars, which reported that resveratrol was significantly higher in berry skins than in leaves[30]. However, we did not detect trans-resveratrol in the skins of 'Tangwei' during the G or V stages (Table 1). To explore the reason for the difference in resveratrol content between 'Tangwei' and 'Tonghua-3', as well as the regulation mechanism of resveratrol synthesis and accumulation during berry development, we used transcriptional profiling to compare gene expression between these two accessions at the G, V, and R stages. After sequence read alignment and transcript assembly, 23,649 and 23,557 unigenes were documented in 'Tangwei' and 'Tonghua-3', respectively. As anticipated, due to the small number of structures sampled, this was less than that (26,346) annotated in the V. vinifera reference genome[18]. Depending on the sample, 80.47%−88.86% of sequence reads aligned to a single genomic location (Supplemental Table S1); this is similar to the alignment rate of 85% observed in a previous study of berry development in Vitis vinifera[19]. Additionally, 1751 novel transcripts were excavated (Supplemental Table S2) after being compared with the V. vinifera reference genome annotation information[18, 31]. A similar result was also reported in a previous study when transcriptome analysis was performed to explore the underlying mechanism of cold stress between Chinese wild Vitis amurensis and Vitis vinifera[32]. We speculate that these novel transcripts are potentially attributable to unfinished V. vinifera reference genome sequence (For example: quality and depth of sequencing) or species-specific difference between Vitis vinifera and other Vitis. In our study, the distribution of genes based on expression level revealed an inverse trend from G, V to R between 'Tangwei' and 'Tonghua-3' (Fig. 1). Furthermore, analysis of DEGs suggested that various cellular processes including metabolic process and catalytic activity were altered between the two cultivars at all three stages (Fig. 2 and Supplemental Table S3S5). These results are consistent with a previous report that a large number of DEGs and 100 functional subcategories were identified in 'Tonghua-3' grape berries after exposure to UV-C radiation[8]. Resveratrol biosynthesis in grapevine is dependent on the function of STSs, which compete with the flavonoid branch in the phenylalanine metabolic pathway. Among the DEGs detected in this investigation, genes directly involved in the resveratrol synthesis pathway, STSs, C4Hs and 4CLs, were expressed to significantly higher levels in 'Tonghua-3' than in 'Tangwei' during V and R. On the other hand, DEGs representing the flavonoid biosynthesis pathway were upregulated in 'Tangwei', but downregulated in 'Tonghua-3' (Fig. 3 and Supplemental Table S6). These expression differences may contribute to the difference in resveratrol content between the two cultivars at these stages. We note that 'Tangwei' and 'Tonghua-3' are from two highly diverged species with different genetic backgrounds. There might be some unknown genetic differences between the two genomes, resulting in more than 60 functional subcategories being enriched (Fig. 2d) and the expression levels of genes with putative roles in resveratrol biosynthesis being significantly higher in 'Tonghua-3' than in 'Tangwei' during V and R (Fig. 3). A previous proteomic study also reported that the expression profiles of several enzymes in the phenylalanine metabolism pathway showed significant differences between V. quinquangularis accession 'Danfeng-2' and V. vinifera cv. 'Cabernet Sauvignon' at the véraison and ripening stages[33]. In addition, genes such as RSGT, OMT and LAC involved in the production of derivatized products of resveratrol were mostly present at the G and V stages of 'Tangwei', potentially resulting in limited resveratrol accumulation. However, we found that most of these also revealed relatively high expression at R in 'Tonghua-3' (Fig. 3). Despite this situation, which does not seem to be conducive for the accumulation of resveratrol, it still showed the highest content (Table 1). It has been reported that overexpression of two grapevine peroxidase VlPRX21 and VlPRX35 genes from Vitis labruscana in Arabidopsis may be involved in regulating stilbene synthesis[34], and a VqBGH40a belonging to β-glycoside hydrolase family 1 in Chinese wild Vitis quinquangularis can hydrolyze trans-piceid to enhance trans-resveratrol content[35]. However, most studies mainly focus on several TFs that participate in regulation of STS gene expression, including ERFs, MYBs and WRKYs[13, 15, 17]. For example, VvWRKY18 activated the transcription of VvSTS1 and VvSTS2 by directly binding the W-box elements within the specific promoters and resulting in the enhancement of stilbene phytoalexin biosynthesis[36]. VqWRKY53 promotes expression of VqSTS32 and VqSTS41 through participation in a transcriptional regulatory complex with the R2R3-MYB TFs VqMYB14 and VqMYB15[37]. VqMYB154 can activate VqSTS9/32/42 expression by directly binding to the L5-box and AC-box motifs in their promoters to improve the accumulation of stilbenes[38]. In this study, we found a total of 757 TF-encoding genes among the DEGs, including representatives of the MYB, AP2/ERF, bHLH, NAC, WRKY, bZIP and HB-HD-ZIP families. The most populous family was MYB, representing 76 DEGs at G, V and R between 'Tangwei' and 'Tonghua-3' (Fig. 4). A recent report indicated that MYB14, MYB15 and MYB13, a third uncharacterized member of Subgroup 2 (S2), could bind to 30 out of 47 STS family genes. Moreover, all three MYBs could also bind to several PAL, C4H and 4CL genes, in addition to shikimate pathway genes, the WRKY03 stilbenoid co-regulator and resveratrol-modifying gene[39]. VqbZIP1 from Vitis quinquangularis has been shown to promote the expression of VqSTS6, VqSTS16 and VqSTS20 by interacting with VqSnRK2.4 and VqSnRK2.6[40]. In the present study, we found that a gene encoding a bZIP-type TF (VIT_12s0034g00110) was down-regulated in 'Tangwei', but up-regulated in 'Tonghua-3', at G, V and R (Fig. 4). We also identified 36 TFs that were co-expressed with 17 STSs using WGCNA analysis, suggesting that these TFs may regulate STS gene expression (Fig. 5 and Supplemental Table S8). Among these, a STS (VIT_16s0100g00880) was together co-expressed with MYB14 (VIT_07s0005g03340) and WRKY24 (VIT_06s0004g07500) that had been identified as regulators of STS gene expression[13, 15]. A previous report also indicated that a bHLH TF (VIT_11s0016g02070) had a high level of co-expression with STSs and MYB14/15[15]. In the current study, six bHLH TFs were identified as being co-expressed with one or more STSs and MYB14 (Fig. 5 and Supplemental Table S8). However, further work needs to be done to determine the potential role of these TFs that could directly target STS genes or indirectly regulate stilbene biosynthesis by formation protein complexes with MYB or others. Taken together, these results identify a small group of TFs that may play important roles in resveratrol biosynthesis in grapevine. In summary, we documented the trans-resveratrol content of seven grapevine accessions by HPLC and performed transcriptional analysis of the grape berry in two accessions with distinct patterns of resveratrol accumulation during berry development. We found that the expression levels of genes with putative roles in resveratrol biosynthesis were significantly higher in 'Tonghua-3' than in 'Tangwei' during V and R, consistent with the difference in resveratrol accumulation between these accessions. Moreover, several genes encoding TFs including MYBs, WRKYs, ERFs, bHLHs and bZIPs were implicated as regulators of resveratrol biosynthesis. The results from this study provide insights into the mechanism of different resveratrol accumulation in various grapevine accessions. V. davidii 'Tangwei', V. amurensis × V. Vinifera 'Beibinghong'; V. amurensis 'Tonghua-3' and 'Shuangyou'; V. vinifera × V. labrusca 'Jumeigui'; V. vinifera 'Red Globe' and 'Thompson Seedless' were maintained in the grapevine germplasm resource at Northwest A&F University, Yangling, Shaanxi, China (34°20' N, 108°24' E). Fruit was collected at the G, V, and R stages, as judged by skin and seed color and soluble solid content. Each biological replicate comprised three fruit clusters randomly chosen from three plants at each stage. About 40−50 representative berries were separated into skin, pulp, and seed, and immediately frozen in liquid nitrogen and stored at −80 °C. Resveratrol extraction was carried out as previously reported[8]. Quantitative analysis of resveratrol content was done using a Waters 600E-2487 HPLC system (Waters Corporation, Milford, MA, USA) equipped with an Agilent ZORBAX SB-C18 column (5 µm, 4.6 × 250 mm). Resveratrol was identified by co-elution with a resveratrol standard, and quantified using a standard curve. Each sample was performed with three biological replicates. Three biological replicates of each stage (G, V and R) from whole berries of 'Tangwei' and 'Tonghua-3' were used for all RNA-Seq experiments. Total RNA was extracted from 18 samples using the E.Z.N.A. Plant RNA Kit (Omega Bio-tek, Norcross, GA, USA). For each sample, sequencing libraries were constructed from 1 μg RNA using the NEBNext UltraTM RNA Library Prep Kit for Illumina (New England Biolabs, Ipswich, MA, USA). The library preparations were sequenced on an Illumina HiSeq2500 platform (Illumina, San Diego, CA, USA) at Biomarker Technologies Co., Ltd. (Beijing, China). Raw sequence reads were filtered to remove low-quality reads, and then mapped to the V. vinifera 12X reference genome[18, 31] using TopHat v.2.1.0[41]. The mapped reads were assembled into transcript models using Stringtie v2.0.4[42]. Transcript abundance and gene expression levels were estimated as FPKM[43]. The formula is as follows:
    FPKM =cDNA FragmentsMapped Fragments(Millions)× Transcript Length (kb)
    Biological replicates were evaluated using Pearson's Correlation Coefficient[44] and principal component analysis. DEGs were identified using the DEGSeq R package v1.12.0[45]. A false discovery rate (FDR) threshold was used to adjust the raw P values for multiple testing[46]. Genes with a fold change of ≥ 2 and FDR < 0.05 were assigned as DEGs. GO and KEGG enrichment analyses of DEGs were performed using GOseq R packages v1.24.0[47] and KOBAS v2.0.12[48], respectively. Co-expression networks were constructed based on FPKM values ≥ 1 and coefficient of variation ≥ 0.5 using the WGCNA R package v1.47[49]. The adjacency matrix was generated with a soft thresholding power of 16. Then, a topological overlap matrix (TOM) was constructed using the adjacency matrix, and the dissimilarity TOM was used to construct the hierarchy dendrogram. Modules containing at least 30 genes were detected and merged using the Dynamic Tree Cut algorithm with a cutoff value of 0.25[50]. The co-expression networks were visualized using Cytoscape v3.7.2[51]. RT-qPCR was carried out using the SYBR Green Kit (Takara Biotechnology, Beijing, China) and the Step OnePlus Real-Time PCR System (Applied Biosystems, Foster, CA, USA). Gene-specific primers were designed using Primer Premier 5.0 software (PREMIER Biosoft International, Palo Alto, CA, USA). Cycling parameters were 95 °C for 30 s, 42 cycles of 95 °C for 5 s, and 60 °C for 30 s. The grapevine ACTIN1 (GenBank Accession no. AY680701) gene was used as an internal control. Each reaction was performed in triplicate for each of the three biological replicates. Relative expression levels of the selected genes were calculated using the 2−ΔΔCᴛ method[52]. Primer sequences are listed in Supplemental Table S9. This research was supported by the National Key Research and Development Program of China (2019YFD1001401) and the National Natural Science Foundation of China (31872071 and U1903107).
  • The authors declare that they have no conflict of interest.
  • Supplementary Table S1 The primers for candidate genes in papaya for qPCR.
    Supplementary Table S2 The expression level of DEGs relating to anthocyanins biosynthesis.
    Supplementary Table S3 The GO annotation of DEGs located in chromosome 1 QTL region.
    Supplementary Table S4 The NCBI blast annotation of CHS, MYB20, MYB75-like and MYB315-like.
    Supplementary Fig. S1 The differential expression heatmap of CHS, MYB20, MYB75-like and MYB315-like between purple and green petioles.
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  • Cite this article

    Chen S, Michael VN, Brewer S, Chambers A, Wu X. 2025. BSA-seq and transcriptome analyses reveal candidate gene associated with petiole color in papaya (Carica papaya L.). Ornamental Plant Research 5: e002 doi: 10.48130/opr-0024-0032
    Chen S, Michael VN, Brewer S, Chambers A, Wu X. 2025. BSA-seq and transcriptome analyses reveal candidate gene associated with petiole color in papaya (Carica papaya L.). Ornamental Plant Research 5: e002 doi: 10.48130/opr-0024-0032

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BSA-seq and transcriptome analyses reveal candidate gene associated with petiole color in papaya (Carica papaya L.)

Ornamental Plant Research  5 Article number: e002  (2025)  |  Cite this article

Abstract: Papaya (Carica papaya L.) is an important tropical species popular for highly nutritious fruit as well as medicinal value. In addition, non-commercial cultivation of papaya trees has resulted in dual-purpose cultivars grown for both fruit and ornamental value in residential areas. Petiole color is a key ornamental trait in papaya that varies amongst cultivars depending on anthocyanin accumulation resulting in purple or green pigmentation. Although inherited as a simple trait, genetic characterization and genomic loci responsible for the purple petiole color in papaya is unknown. In this study, F1 and F2 populations generated from two breeding lines PR-2043 (green petiole) and T5-2562 (purple petiole) were used to evaluate the inheritance patterns of petiole color as well as determine genetic loci and genes involved in petiole pigmentation in papaya through bulk segregant analysis (BSA) and transcriptome sequencing. The segregation of purple petiole color followed a single dominant gene inheritance model (3:1). BSA-seq analysis indicated key genes influencing petiole color are mainly located in chromosome 1 (0.01 to 5.96 Mb) of the papaya genome. Four major genes, including CHS, MYB20, MYB315-like, and MYB75-like within this region exhibited significant differential expression in a comparison between purple and green petiole papaya plants. A relatively high abundance of CHS transcripts was observed in purple petioles and may signify a major involvement in regulating anthocyanins accumulation in papaya petioles. The findings of this study facilitate the future efforts of breeding papaya cultivars with higher economical value in residential landscapes.

    • Papaya (Carica papaya L., Caricaceae) is a widely cultivated fruit crop in tropical and subtropical regions around the world[1]. Papaya has economic and cultural importance due to its high yield, nutritional value, and medicinal properties[24]. The fruit is consumed when mature as a fresh fruit or when immature as a vegetable; processed products are also produced from it. The worldwide production of papaya in 2022 was estimated to be 13,822,328 metric tons according to the Food and Agriculture Organization Corporate Statistical Database (www.fao.org/faostat/en/#data/QCL). In addition to its value as a food crop, the striking leaf shape and distinctive plant architecture of papaya allow this plant to be used as an ornamental plant in residential landscapes.

      Petiole color is an important ornamental trait in papaya. The common petiole color is green; however, there is a purple color form. The accumulation of purple pigmentation on the petiole, combined with the green lamina gives the papaya plant a unique appearance, reminiscent of a purple and green umbrella. These two distinct petiole phenotypes were first described in 1938, where purple petiole was found to be dominant over non-purple stem color[5]. Subsequent genetic analysis showed that the inheritance of stem color was fairly associated with flower colors, and loosely linked to sex type[5]. Folorunso observed that the petiole color in papaya exhibited an equal segregation ratio of 1:1 (purple : green) among the offspring that resulted from open-pollinated crosses between female and hermaphrodite papaya parents with purple petioles[6]. Interestingly, the study also revealed that the purple petiole color co-segregated with the pigment color of petals, peduncle, fruit rind, and fruit stalk[6], suggesting a genetic linkage or shared regulatory pathway controlling the pigmentation across these tissues.

      In papaya, the accumulation of purple pigment is primarily attributed to a buildup of anthocyanin, which imparts the red to blue hues commonly observed in various plant tissues[7]. Anthocyanins are water-soluble natural pigments that belong to the flavonoid group, and are widely distributed across angiosperms[8]. The presence of anthocyanins is often associated with various biological functions in plants. They play a crucial role in attracting pollinators and seed dispersers, thus affecting plant reproduction rates[9]. Anthocyanins also enhance plant resilience by protecting against a range of biotic and abiotic stresses, including protection against UV light exposure[10]. Additionally, anthocyanins have been acknowledged for their antioxidant/anticarcinogenic properties and health-promoting effects in the prevention of heart disease, cardiovascular disease and cancer[11,12]. Given their importance, the pathways governing anthocyanin biosynthesis, degradation, and regulation have been extensively studied[13,14]. The biosynthesis of anthocyanins is primarily controlled by two gene groups: structural genes and regulatory genes[15]. Structural genes include those involved in the phenylpropanoid pathway, such as phenylalanine ammonia-lyase (PAL), cinnamate 4-hydroxylase (C4H), 4-coumarate:coenzyme A ligase (4CL)[16,17], which are responsible for the initial steps in the biosynthesis of flavonoids, and the genes in the flavonoid biosynthetic pathway, such as chalcone synthase (CHS), chalcone isomerase (CHI), flavanone 3-hydroxylase (F3H), 3'-hydroxylase (F3'H), flavonoid 3',5'-hydroxylase (F3'5'H), dihydroflavonol 4-reductase (DFR), leucoanthocyanidin dioxygenase (LDOX), anthocyanidin synthase (ANS), and flavonoid 3-O-glucosyltransferase (UFGT), which are active downstream of anthocyanin biosynthesis[18,19]. Regulatory genes usually influence the pattern and intensity of anthocyanin biosynthesis by controlling the expression of these structural genes. A 'MBW complex' has been widely recognized as a major regulator consisting of R2R3-MYB, basic helix–loop–helix (bHLH), and WD40 proteins[13,2022]. These proteins can act as either activators or repressors in controlling the accumulation of anthocyanins in plants[2325].

      QTL-Seq is a highly efficient approach for rapid identification of genetic loci associated with traits of interest, offering a significant advantage over the more time-consuming and costly conventional QTL analysis methods[26]. This technique integrates bulked-segregant analysis (BSA), an elegant method to rapidly identify the specific genomic region by analyzing two bulked DNA pools consisting of F2 progeny with contrasting phenotypes using next-generation sequencing[26,27]. By comparing two bulked DNA pools representing contrasting phenotypes, the candidate genomic regions or genes are identified via the distribution of single nucleotide polymorphisms (SNPs). In addition to QTL-seq, transcriptome analysis has gained recognition as a reliable strategy for discovering genes associated with specific traits. By examining the expression patterns of genes across different tissues or stages, transcriptome analysis can provide valuable insight into the molecular mechanisms underlying phenotypic variation[28].The combination of QTL-Seq and transcriptome studies has been widely applied to identify genes associated with target traits in different plant species[2931].

      Despite two previous studies on papaya petiole color[5,6], little follow-up work has been done. However, understanding the genetic mechanism that governs petiole color in papaya is not only crucial for unraveling the fundamental biology of this trait but also has significant potential for its practical application in breeding programs. By elucidating the genetic basis of purple pigmentation in petioles, breeders could develop papaya varieties with higher aesthetic and commercial appeal. It also can provide insight into the introduction of purple pigmentation into other tissues and can contribute to the development of novel ornamental or fruit qualities, thereby increasing the economic value of papaya, optimizing plant appeal to consumers and expanding their marketability. In the present study, a joint approach combining BSA-Seq and transcriptome analysis were employed to investigate the genetic basis of petiole color in papaya. By integrating these two methods, the aim is to pinpoint specific genomic regions and the genes responsible for regulating pigmentation of petiole color in papaya. The results from this study will contribute to a deep understanding of how pigmentation is regulated in papaya, and expands the economic value of papaya through breeding new cultivars with both ornamental and fruit traits.

    • Two breeding lines, PR-2043 with green petioles and T5-2562 with purple petioles, were developed by crossing transgenic lines X17-2 with 'Tainung No. 5', and 'Puerto Rico-65' respectively[32,33], and maintained at the Tropical Research and Education Center, University of Florida, Homestead, FL, USA. PR-2043 and T5-2562 were crossed to generate an F1 population, and eight F1 of these plants were transplanted to the field. A hermaphrodite purple petiole F1 papaya plant was selfed to generate the F2 segregating population. The F2 seeds were soaked in water overnight and subsequently immersed in 2.5 mM gibberellic acid for 30 min before sowing in April 2020. These pre-treated seeds were planted in a mixture of 1:1 Promix BX mycorrhiza and perlite. Each 38-cell tray was top-dressed with Osmocote 14-14-14 fertilizer. Seedlings were maintained in the greenhouse and watered as necessary. Phenotyping of the F2 seedlings was carried out in the greenhouse three months after sowing and further confirmed in the field two months later. The petiole color was visually categorized as 'green' or 'purple', and the purple became more visible with plant growth. Chi-square analysis was conducted to assess the segregation ratio of petiole color in the F2 population.

    • The total genomic DNA was extracted from young leaves of individual F2 lines and the two parents (T5-2562 and PR-2590) following a CTAB method[34]. A total of 25 DNA samples representing each petiole color phenotype were pooled together into two DNA bulks for sequencing. Four sequencing libraries were constructed by shearing DNA into short fragments, repairing the ends, and making poly-A-tailed fragments before ligation with Illumina adapters. After size selection, quantified libraries were pooled and sequenced using a 150 bp paired-end program on Illumina HiSeq X10 platform (Novogene, Beijing, China).

      Quality control of the raw sequencing reads was first determined by FastQC[35]. To ensure high-confidence variant calling, the adapters were trimmed using BBDuk[36]. The processed reads were then used to create the consensus sequences of both T5-2562 and PR-2590 by aligning to the 'SunUp' reference genome[37]. Read alignment of both F2 bulked pools were assessed by BWA software[38], and SAMtools[39]. Picard tools were used to mark duplicate and index bam files of F2 bulked pools and each parent's consensus sequences. GenomeAnalysisToolkit (GATK) was used to perform variant calling[40]. SNPs and indels were filtered by GATK VariantFiltration function with parameters QD < 2.0 || FS > 60.0 || MQ < 40.0 || MQRankSum < −12.5 || ReadPosRankSum < −8.0. Low-quality SNPs were removed from the final output, which were subsequently used for QTL analysis with the 'QTLseqr' R package[41]. The confidence intervals were determined using 10,000 simulations of the QTL-seq method as described previously[26]. The 95% (p < 0.05) confidence interval was set to consider that the genomic loci showing statistical significance[41].

    • The epidermal and cortex layers were collected from the papaya petiole of mature (18 months-old) PR-2043 and T5-2562 plants for the transcriptome study (< 1 mm thick). Green petioles were collected from PR-2043 and petioles in the process of turning from green to purple were collected from T5-2562. The freshly harvested tissues were flash-frozen in liquid nitrogen and then ground into fine powder for RNA extraction, two technical replications were processed for each sample. A total of 100 mg of tissue was processed with 1 mL TRIzol reagent, followed by washing with 70% ethanol and resuspension in 50 μL of DEPC-treated water. RNA-free Dnase (Qiagen, Hilden, Germany) and Rneasy PowerClean Pro Cleanup Kit (Qiagen, Hilden, Germany) were applied for further purification. The NovaSeq6000 platform was used to perform the sequencing.

      Quality control and adapter removal of the raw sequence data were processed as described above. Clean reads were then mapped to the papaya reference genome using HISAT2 (--dta) before counting mapped transcripts with featureCount software following default parameters[42,43]. Genes that were differently expressed between the petioles of PR-2043 and T5-2562 were identified and quantified using DESeq2 with normalization[44]. Transcripts with a |log2(fold change)| > 2 were considered as differentially expressed genes and annotated by Blast2Go[45]. The candidate genes were identified from DEGs according to their function and false discovery rate (FDR) correction (> 0.05). The expression of candidate genes was visualized in a heatmap plotted by R Package 'Pheatmap'[46].

    • To verify candidate gene expression in green and purple petioles, PR-2240 and T5-2562 were used respectively. PR-2240 is a green-petiole line genetically associated with PR-2043. The purple and green epidermal and cortex layers of petioles from mature (18 months old) T5-2562 and PR-2240 plants were collected (< 1 mm thick) freshly and frozen using liquid nitrogen, respectively. Then, high-quality RNA was extracted from collected epidermal and cortex layers by using E.Z.N.A. Plant RNA Kit (Omega Bio-tek, GA, USA). The quantity of the RNA was determined by Qubit4 (Thermo Fisher, MA, USA). The RNA samples were aliquoted to uniform concentration (744 ng/μL) and reverse transcribed into cDNA using amfiRivert Sensi cDNA Master Mix (GenDEPOT, TX, USA). The CDS nucleotide sequences of each candidate gene and Primer3 were used to develop the primer pairs for CHS, and MYB20 (Supplementary Table S1). Three biological and technical replicates were processed for each candidate gene using the housekeeping gene actin as a control. The qPCR reaction was performed in QuantStudio3 (Applied Biosystems, CA, US) in a 10 μL reaction containing 5 μL 2X PowerUp™ SYBR™ Green Master Mix (Applied Biosystems), 0.4 μM of forward and reverse primer, 3.2 μL Nuclease Free water, and 1 μL cDNA. The mixture was initially held at 50 °C for 2 min and 95 °C for 2 min, incubated at 95 °C for 15 s, followed by 40 cycles at 55 °C for 15 s, and 72 °C for 1 min. The melt curve was set at 95 °C for 15 s, 60 °C for 1 min, and 95 °C for 1 s. The 2−ΔΔCᴛ method was used to analyze the relative changes in gene expression[47].

    • To investigate the inheritance of petiole color, an F2 population was developed. By crossing PR-2043 (green petiole) × T5-2562 (purple petiole, Fig. 1a), eight F1 plants were generated and all with purple petiole. A single fruit from one of the F1 plants was used to produce the F2 seedlings used in this study. In this study, the petiole color was evaluated three months after the seed germination and then the stability of the petiole color was confirmed two months later. Of the total 280 F2 seedlings, 223 were observed to have purple petioles, and 57 had green petioles (Fig. 1b), and the purple pigmentation was observed to become more noticeable as the plant grew. The purple-to-green color segregated at a 3:1 ratio in the F2 population. The Chi-square statistic and p-value were 3.219 and 0.0728 respectively (Fig. 1c), indicating that purple petioles in papaya follow a single dominant gene inheritance model.

      Figure 1. 

      Phenotypes of papaya petioles. (a) Petiole color of PR-2043 and T5-2562 parents. (b) Purple and green petioles of papaya F2 population. (c) Segregation of petiole color in the F2 population.

    • BSA-Seq analysis was used to examine the nucleotide diversity between F2 progenies with purple petioles (F2P) and green petioles (F2G) to characterize the genomic regions responsible for papaya purple petiole color. A total of 21.4 Gb (61.02 × depth) and 29.2 Gb (83.36 × depth) sequence reads (150 bp pair end) were generated for F2P and F2G bulks using whole genome sequence (Table 1). A total of 22.4 Gb (63.87 × depth) and 29.2 Gb (83.23 × depth) raw sequence reads was generated for T5-2562 and PR-2590, respectively. Consensus genomes of each parent were constructed by using papaya 'SunUp' genome as a reference. Subsequently, SNPs calling was carried out by comparing two F2 bulks and three genomes. The short reads of F2P and F2G bulks were aligned to the two parental consensus genomes and to the 'SunUp' genome, which yielded three sets of allelic segregation with 927,513, 518,567, and 1,423,583 SNPs, respectively. SNPs with low mapping rate (< 40%) were removed from the dataset, which yielded a total of 443,996 SNPs from purple parent, 235,895 SNPs from a green parent, and 687,084 SNPs from the reference genome for QTL mapping. At a 95% confidence interval, two QTL regions (189,558−1,368,545 bp and 2,739,922−3,777,906 bp) were identified on chromosome 1 of the reference genome, two QTLs were identified on chromosome 1 of the PR-2590 consensus sequence, spanning 621,177−1,791,321 bp and 3,799,705−5,554,073 bp and one QTL was identified on chromosome 1 of the T5-2562 consensus sequence (13,715−5,961,552 bp (Fig. 2). The QTL regions consistently overlapped across the same region in chromosome 1 of all three genomes with peak QTL SNPs supported by 99% confidence levels. Genome annotation identified a total of 653 genes located in the overlapping QTL region (13,715−5,961,552 bp).

      Table 1.  Sequencing information of parental lines and two bulks.

      Sample Raw reads Raw data Sequencing
      depth
      Effective (%) GC (%)
      T5-2562 149366802 22.4 63.87 99.12 37.36
      PR-2590 194642908 29.2 83.23 98.40 37.26
      F2P 142694068 21.4 61.02 98.03 37.05
      F2G 194944976 29.2 83.36 98.29 36.89

      Figure 2. 

      QTL regions associated with papaya petiole color in three genomes, (a) SunUp, (b) PR-2590, and (c) T5-2562.

    • A total of 2,145 differentially expressed genes (DEGs) (|log2fold change| > 2) were identified through the transcriptome profiling of PR-2043 and from T5-2562. The GO analysis found that most DEGs were involved in various molecular functions, including small molecular binding, and organic cyclic compound binding transferase activity. Nine DEGs were involved in flavonoid biosynthetic pathways including CHI, DFR, CHS, UFGT, and flavanol synthase. Thirty-five and 17 DEGs were identified as putative MYB and bHLH transcription factors, respectively (Supplementary Table S2).

    • The BSA-seq and transcriptome analysis identified a total of 67 genes within the QTL region on chromosome 1 that were differentially expressed between the green and purple petiole color papayas. Of them, the functional annotation identified 32 genes that acted on several biological processes, including the regulation of DNA-templated transcription, fruit ripening, methylation, and glutamine metabolism. Eleven of these play a role in molecular function, such as methyltransferase activity and nucleic acid binding. The remaining genes have a function in cellular components, including membrane, plasma membrane, and plasmodesma (Supplementary Table S3). Notably, four genes including chalcone synthase CHS, MYB315-like, MYB20, and MYB75-like, were associated with anthocyanin biosynthesis and regulation (Fig. 3a, Supplementary Fig. S1, Supplementary Table S4). CHS was highly expressed in purple petioles as compared to green petioles, suggesting CHS might play a key role in anthocyanin accumulation of papaya petioles.

      Figure 3. 

      Candidate genes associated with anthocyanin accumulation in papaya petiole. (a) The statistical information of candidate genes expression in different material. (b) The expression level validation of CHS and MYB20 in purple and green papaya petiole by q-PCR.

      The RNA was extracted from the epidermic layer of the petiole of T5-2562 and PR-2240 to determine the expression level of CHS and MYB20 using qPCR. The qPCR results showed that the expression of CHS and MYB20 in purple petioles were both more highly expressed than that of the green petioles (Fig. 3b). The qPCR expression pattern of CHS was consistent with the RNA-seq results, whereas MYB20 showed a contradictory pattern (Fig. 3a & b; Supplementary Fig. S1). Segregation analysis, transcriptome data, and qPCR validation suggest that the MYB20 may be involved in other biological functions during petiole development, but it is not associated with petiole color in papaya.

    • Anthocyanins, water-soluble pigments generated by the phenylpropanoid pathway, contribute many pink, purple, and blue hues in plants. Anthocyanins are not only natural dyes with brilliant colors but also edible consumption that benefit heart, eye, metabolic, and cognitive health in humans[12]. The accumulation of anthocyanins contributes to pigment diversity in distinct species pigment variation. It is very common in floral tissues[4850], and vegetative tissues[51,52]. While this within-species pigment variation is rare in displaying contrasting fruit color, like grapes[53], apples[54], and cherries[55].

      Papaya is an economically and culturally important crop in the tropical areas of the world. Ornamental traits such as petiole color, leaf shape, and growth habit are value-added traits in papaya for homeowners and landscapers. In papaya, anthocyanin accumulation only appears in a few phenotypes, specifically in the epidermis of the petiole, stem, fruit stem, and leaf vein. Additionally, the purple pigmentation in the petiole was observed to become more pronounced as the papaya plant grows[6]. The present genetic study revealed that the purple phenotype is dominant over the green in papaya and follows a single dominant inheritance pattern, which is consistent with the previous hypothesis of anthocyanin accumulation in papaya[5,6]. In other species including tomatoes[56], sweet cherries[55], and blood oranges[57], a single dominant gene has also been implicated as controlling contrasting anthocyanin phenotypes. In other cases, species such as purple cabbage, however, anthocyanin accumulation is regulated by a transcription repressor[58]. Although the evidence strongly supports purple as a dominant trait in papaya petioles, the prevalence of green petiole papayas in nature remains an enigma that demands more investigation. There is evidence indicating that the inheritance of purple pigmentation in papaya stem is loosely linked to sex type[5]. Therefore, one hypothesis is that the gene governing anthocyanin accumulation in papaya was subject to human selection based on sex types during cultivation.

      The anthocyanins biosynthetic pathway is downstream of the flavonoid pathway and includes structural genes such as CHS, CHI, F3H, F3'H, F3'5'H, DFR, LDOX, ANS, and UFGT[18,19]. CHI, DFR, CHS, and UFGT were found to be expressed differentially between purple and green petioles. Among these genes, CHS was the only differentially expressed gene that was also located in the QTL region identified by QTL-seq analysis. The flavonoid pathway begins when CHS mediates the synthesis of naringenin chalcone[14,15]. Several reports have indicated a positive correlation between CHS gene expression and anthocyanin content[59,60]. RNA-Seq and qPCR both verified the expression level of CHS in purple petiole is higher than that of green petiole in PR-2043 and PR-2240 compared to T5-2562, strongly suggesting that anthocyanin accumulation in papaya petiole is influenced by elevated CHS expression. MYB transcription factors have also been identified as a crucial group regulating anthocyanin biosynthesis either by acting independently on other structural genes or combining into MBW complexes with bHLH and WD40 proteins to regulate late pathway genes[61]. Contrasting anthocyanin accumulation phenotypes are often caused by mutations within the coding sequence of MYB factors, as in Chinese bayberry[62], or in the promoter region, e.g. in cauliflower[63]. In this study, a total of 35 and 17 MYB and bHLH transcripts, respectively, were detected as DEGs from the RNA-Seq analysis. Three of them lie within the QTL region identified through QTL-seq analysis. However, inconclusive expression patterns were observed in different papaya cultivars with green petioles through qPCR and RNA-Seq, suggesting further research is required to characterize the role of MYB20 in anthocyanin accumulation in papaya petioles.

      Anthocyanin-rich foods, such as eggplant and blueberry are popular in the market. High-anthocyanin varieties have been developed to meet the demand for diverse and nutrient-rich produce, like blood orange, red cabbage, etc. It has been reported that the purple color pigmentation in papaya has pleiotropic effects, which is also noticed in the fruit rind, fruit stalk, and peduncle[6]. Elucidation of the genetics governing purple pigmentation in this study will not only give insight into developing the different phenotypes of papaya to explore its ornamental value but also facilitate future efforts to breed the anthocyanin-rich papaya fruits. Furthermore, the genetic mechanism behind anthocyanin accumulation in vegetative tissues can have future applications. For example, anthocyanin accumulation genes can be transformed into plants that are driven by a papaya fruit-specific promoter, to potentially develop the anthocyanin-rich fruits. CRISPR technology can also be applied to papaya for seedling selection with sex types by using anthocyanin accumulation gene as an indicator, which would greatly benefit commercial papaya growers.

      • This work was supported in part by the U.S. Department of Agriculture Hatch project FLA-TRC-006217.

      • The authors confirm contribution to the paper as follows: study concept and design: Chambers A, Wu X; population development and phenotyping: Brewer S, Chen S; data analysis: Chen S, Brewer S, Michael VN; manuscript preparation: Chen S, Brewer S, Michael VN, Chambers A, Wu X; manuscript revision: Chen S, Michael VN, Wu X. All authors reviewed the results and approved the final version of the manuscript.

      • The data generated is available in the Gene Expression Omnibus (GEO), NCBI, via accession number GSE269737.

      • The authors declare that they have no conflict of interest.

      • Copyright: © 2025 by the author(s). Published by Maximum Academic Press, Fayetteville, GA. This article is an open access article distributed under Creative Commons Attribution License (CC BY 4.0), visit https://creativecommons.org/licenses/by/4.0/.
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    Chen S, Michael VN, Brewer S, Chambers A, Wu X. 2025. BSA-seq and transcriptome analyses reveal candidate gene associated with petiole color in papaya (Carica papaya L.). Ornamental Plant Research 5: e002 doi: 10.48130/opr-0024-0032
    Chen S, Michael VN, Brewer S, Chambers A, Wu X. 2025. BSA-seq and transcriptome analyses reveal candidate gene associated with petiole color in papaya (Carica papaya L.). Ornamental Plant Research 5: e002 doi: 10.48130/opr-0024-0032

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  • Table 1.  Resveratrol concentrations in the skin, pulp and seed of berries from different grapevine genotypes at green hard, véraison and ripe stages.
    StructuresSpeciesAccessions or cultivarsContent of trans-resveratrol (μg g−1 FW)
    Green hardVéraisonRipe
    SkinV. davidiiTangweindnd2.415 ± 0.220
    V. amurensisTonghua-30.216 ± 0.0410.656 ± 0.0432.988 ± 0.221
    Shuangyou0.233 ± 0.0620.313 ± 0.0172.882 ± 0.052
    V. amurensis × V. ViniferaBeibinghong0.336 ± 0.0761.486 ± 0.1771.665 ± 0.100
    V. viniferaRed Global0.252 ± 0.0510.458 ± 0.0571.050 ± 0.129
    Thompson seedless0.120 ± 0.0251.770 ± 0.0320.431 ± 0.006
    V. vinifera × V. labruscaJumeigui0.122 ± 0.0160.170 ± 0.0210.708 ± 0.135
    PulpV. davidiiTangwei0.062 ± 0.0060.088 ± 0.009nd
    V. amurensisTonghua-30.151 ± 0.0660.324 ±0.1040.032 ± 0.004
    Shuangyou0.053 ± 0.0080.126 ± 0.0440.041 ± 0.017
    V. amurensis × V. ViniferaBeibinghong0.057 ± 0.0140.495 ± 0.0680.087 ± 0.021
    V. viniferaRed Global0.059 ± 0.0180.159 ± 0.0130.027 ± 0.004
    Thompson seedless0.112 ± 0.0160.059 ± 0.020nd
    V. vinifera × V. labruscaJumeigui0.072 ± 0.0100.063 ± 0.0170.359 ± 0.023
    SeedV. davidiiTangwei0.096 ± 0.0140.169 ± 0.0280.049 ± 0.006
    V. amurensisTonghua-30.044 ± 0.0040.221 ± 0.0240.113 ± 0.027
    Shuangyound0.063 ± 0.0210.116 ± 0.017
    V. amurensis × V. ViniferaBeibinghongnd0.077 ± 0.0030.400 ± 0.098
    V. viniferaRed Global0.035 ± 0.0230.142 ± 0.0360.199 ± 0.009
    Thompson seedless
    V. vinifera × V. labruscaJumeigui0.077 ± 0.0250.017 ± 0.0040.284 ± 0.021
    'nd' indicates not detected in samples, and '−' shows no samples are collected due to abortion.
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