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Article

sRNA Sequencing of Dahlia Bicolor Petals Revealed the Post-Transcriptional Regulation of Anthocyanin Biosynthetic Pathway

by
Jiuchun Zou
1,
Xiaoshuang Wu
1,
Shuyan Li
1,
Mengqing Liu
1,
Yuyu Chen
2,
Haoran Wang
3,* and
Xue Tao
2,*
1
Guangling College, Yangzhou University, Yangzhou 225009, China
2
Department of Resource and Environment, Moutai Institute, Renhuai 564500, China
3
Jiangsu Key Laboratory for the Research and Utilization of Plant Resources, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing Botanical Garden Memorial Sun Yat-Sen, Nanjing 210014, China
*
Authors to whom correspondence should be addressed.
Agronomy 2025, 15(2), 495; https://doi.org/10.3390/agronomy15020495
Submission received: 23 December 2024 / Revised: 2 February 2025 / Accepted: 17 February 2025 / Published: 18 February 2025
(This article belongs to the Section Plant-Crop Biology and Biochemistry)
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Abstract

:
Garden dahlias (Dahlia pinnata) are popular for their rich flower color variations that have produced many typical bicolor cultivars. Previous studies on the anthocyanin biosynthetic pathway (ABP) observed that the miR156-SPL9 module contributes to the formation of white tips on dahlia petals by repressing the MYB-bHLH-WDR complex. In this study, we further detected the potential post-transcriptional regulation involved in the bicolor petal formation by the small RNA sequencing of red bases and white tips. Compared with red bases, 89 differentially expressed miRNAs and 6349 target genes were identified. And 78 up-regulated miRNAs with their 249 down-regulated target genes were involved in the formation process of white petal tips. The target genes of differentially expressed miRNAs significantly enriched in the ABPs and miRNAs of six conserved families (MIR 156, 164, 167, 169, 482 and 6114) targeted to four transcription factor families (ARF, HD-ZIP, SBP and NAC) were involved in the post-transcriptional gene silencing (PTGS) of the ABP. Transcription sequencing and quantitative reverse transcription PCR analysis demonstrated that the MIR167-ARF8 module and the MIR6114-ANL2 module were the candidate regulators of the inactive ABP in the white tips by depressing the transcription of multiple structure genes. The findings gave new insights into the post-transcriptional regulation of the ABP and would be valuable for further studies of the PTGS mechanisms of bicolor petal formation.

1. Introduction

Dahlias are tuberous-rooted ornamental plants of Asteraceae. Garden dahlias are widely cultivated worldwide for cut flowers and potted flowers [1,2,3]. Along with the spread to Europe, Africa and Asia of Dahlia pinnata (syn. Dahlia variabilis), which originates from Mexico [4], the hybridization and introgression of dahlias have produced tens of thousands of cultivars. Many cultivars of these garden dahlias spontaneously formatted bicolor petals with ivory [2], yellow [1], red, purple [5] and black [6] in the petal base, and white in the petal tips, improving the ornamental value. D. pinnata cultivars are autoallooctoploids (2N = 8X = 64) [3] with a large genome size (3.93 Gb) resulting from Gypsy transposable elements [7]. Previous studies and our recent transcriptome analysis proved that the white tips of dahlia ray florets resulted from the lack of anthocyanins (cyanidin and pelargonidin), which are rich in petal bases [2,8,9]. Our recent study found that the proanthocyanidin content in the white tips are also much lower than that of red bases, though its biosynthetic gene anthocyanidin reductase has the same expression level [9]. This is consistent with the observation of other flavonoid derivatives that accumulate weakly in the white-color tips of bicolor or star-type dahlias [2,10,11]. Plant anthocyanins and flavonoids come from the p-coumaroyl-CoA produced by phenylalanine ammonia-lyase, cinnamate 4-hydroxylase and 4-coumarate-CoA ligase coding by the PAL-C4H-4CL genes, induced in the general phenylpropanoid pathway. Then, the flavonoid biosynthetic pathway generates flavanonol (dihydrokaempferol, dihydromyricetin of petal bases) using the early biosynthetic genes (EBGs), i.e., chalcone synthase (CHS), chalcone isomerase (CHI) and flavanone 3-hydroxylase (F3H) [12], and then anthocyanidins by the later biosynthetic genes (LBGs), i.e., dihydroflavonol reductase (DFR) and anthocyanin synthase (ANS). Most anthocyanidins are modified as anthocyanins by modification enzymes (anthocyanidin 3-O-glucosyltransferase, GT/BZ1 and anthocyanidin 3-O-glucoside 6‘-O-acyltransferase, 3AT). Together, PAL-C4H-4CL genes, EBGs, LBGs and the modification enzyme genes make up the anthocyanin biosynthetic pathway (ABP) of dahlias [8,9,11,13].
The well-known plant ABP activator, the MYB-bHLH-WD40 protein complex (MBW complex), consists of three kinds of transcription factors (TFs), MYB (SG5 or SG6 R2R3-MYB, TT2, PAP1, PAP2, MYB113 and MYB114), bHLH (TT8, GL3 and EGL3) and WD40 protein (TTG1), and is indispensable for the activation of LBGs in plants, including dahlias [14,15]. The insertion mutation of the bHLH gene DvIVS regulating DvCHS1, DvF3H, DvDFR and DvANS contributes to the lack of anthocyanin [8,16] and the down-regulation of DpMYB1 observed in white tips [9]. Three R2R3-MYBs (MYB11, MYB12 and MYB111) control anthocyanin biosynthesis via activating EBGs [14], while other ABP regulators like WRKY10 of apple [17], NAC [18], LBD [19], SPL9 [20], TCP3 [21], MYBL2 [22] or CPC (R3-MYBs) [23] and zinc finger protein TT1 [24] regulate LBGs by competitive interaction with MBW components. Most of their homologs in dahlias are not differentially expressed in the white tips of dahlias except SPL9 targeted by miR156 [9]. In addition to the conserved miR156-SPL model integrating anthocyanin accumulation with vegetative-phase transitions [25], the miR858-MYB module is involved in ABP regulation. The light-responsive expression of miR858 positively regulates anthocyanin biosynthesis by degrading the ABP depressor MYBL2 in Arabidopsis [26] and VvMYB114 of grape [27]. While in apples, miR858 negatively regulates anthocyanin biosynthesis by targeting McMYB9/McMYBPA1 in the fruit flesh [28]. Several miRNAs identified by small RNA (sRNA) sequencing studies [29,30] have emerged as playing key roles in anthocyanin accumulation, and their transcriptional regulation of ABPs deserves more attention while their target genes are still unexplored. For example, miR399d positively regulates the anthocyanin biosynthesis of apple leaves due to Pi deficiency along with the activation of McMYB10, McCHS and McANS [31]. The photomorphogenesis factor miR408 leads to increased anthocyanin accumulation in Arabidopsis seedlings [32]. Additionally, TAS4-siRNA81(−) in Arabidopsis negatively regulates anthocyanin biosynthesis by targeting ABP compounds PAP1, PAP2 and MYB113 [33]. And miRNA-encoded micropeptide miPEP166c was recently proved to be involved in plant anthocyanin biosynthesis [34].
The bicolor petal phenotype has attracted particular interest due to its striking appearance, which results from complex interactions between various genetic factors [35]. The low abundance of LBGs and EBGs was observed in white tips, while the expression profile analysis of MBW complex-related genes could not fully explain the low abundance of EBGs [9]. The post-transcriptional gene silencing (PTGS) of CHS may contribute to the low abundance of EBGs in polychromatic dahlias [2,8]. Recent studies have highlighted the role of miRNA-TF regulatory modules in modulating the anthocyanin biosynthesis pathway. However, the specific mechanisms by which these modules influence the bicolor phenotype remain poorly understood. This study aimed to provide new insights into how miRNA-TF modules contribute to the regulation of anthocyanin synthesis and patterning in dahlias, with particular focus on the bicolor phenotype.

2. Materials and Methods

2.1. Plant Materials

The cultivar D. pinnata ‘LiRen’ (Figure 1a) is a popular bicolor cut-flower grown in the greenhouse of YanXi Flowers Co., Ltd. (Yangzhou, Jiangsu, China) and was used in our recent study [9]. The red bases (R) and white tips (W) of ray florets (Figure 1b) were collected from one-year-old seedings for RNA extraction, with 3 duplications. Total RNA extraction was performed using an RNAprep Pure Plant Kit (DP441, Tiangen, Beijing, China). The high-quality genome of dahlias assembled recently was downloaded as a reference sequence for this study [7].

2.2. Construction and Sequencing of sRNA Libraries

The construction and sequencing of 4 sRNA libraries (R1, R2, W1 and W2) were performed using the TruSeq Small RNA Sample Prep Kits (Illumina, San Diego, CA, USA) and NovaSeq 6000 platform (Illumina, San Diego, CA, USA) through an outsourcing service of Biomarker Biotechnology Co., Ltd. (Beijing, China). The SR-adaptors ligated sRNAs ranging from 140 to 160 nt were reverse transcribed as cDNA libraries and raw reads were detected by libraries sequencing using SE50 model.

2.3. Identification and Quantification of miRNAs of Dahlias

We followed the workflow of the One Step sRNAminer program of sRNAminer v1.1.2 [36]: (1) adapter trimming, seq collapsing and data cleaning, which obtained the clean reads; (2) read mapping miRNA and bowtie to sRNAminer, which mapped the clean reads to the dahlia reference genome; and (3) miRNA identification no align and miRNA abundance classification identified conserved and novel miRNAs using the annotation criteria for plant microRNA [37] and quantified the abundance of miRNAs using Transcripts Per 10 Million (TP10M) by read counts of each miRNA, which were divided by all counts of the library then multiplied by 10E7. For the predicted novel miRNAs, we further checked read mapping on the second structure of miRNA precursors (pre-miRNAs) using the IGV-sRNA software (windows-x64_0_3_3) [36], which can visualize the bam file generated from the read mapping miRNA step.
Differentially expressed unigene (DEU) identification was performed using the DEseq2 software version 1.46 (https://github.com/thelovelab/DESeq2, accessed on 20 January 2024) with the threshold of ‘q-value < 0.01’ and ‘|log2(fold change)| > 1’, referring to other omics studies [38,39].

2.4. Identification of Target Genes and Differentially Expressed miRNAs (DEMs)

All coding sequences of reference genomes and all identified miRNAs were submitted to psRNATarget Schema V2 [40] with default parameters for target gene prediction (all parameters from https://www.zhaolab.org/psRNATarget/analysis?function=3, accessed on 25 January 2024). The read counts of 4 sRNA libraries counted by sRNAminer were compared by DEseq2 [41] with the adjusted p-value (padj, Supporting Information, Table S5) threshold of 0.05 for DEM identification. The target genes were annotated by the GhostKOALA webtool (https://www.kegg.jp/ghostkoala/, accessed on 26 January 2024) of KEGG for the KEGG enrichment analysis of target genes of DEMs.

2.5. Construction for the Post-Transcriptional Regulation Network of ABPs

The expression profiles of ABP genes and MBW-related genes in dahlia petals have been established in a previous RNA-seq study [9]. These candidate genes targeted by miRNAs were checked for expression correlation first. Then, the TF binding sites on promotors of dahlia ABP genes (2000 bp upstream) were predicted using the webtool Plant Transcriptional Regulatory Map (https://plantregmap.gao-lab.org/binding_site_prediction.php, accessed on 27 January 2024) with p-value cutoff 1e-4 [38]. The predicted Arabidopsis TFs binding to promotors were mapped to dahlia homologous TFs by The Best ID Converter tool of TBtools v2 [42]. Then, TFs-ABP pairs targeted by miRNAs were collected for construction for the post-transcriptional regulation network of dahlia petals.

2.6. Validation of Expression Levels of miRNA-TF Pairs

The negative expression correlations of miRNA-TF pairs in W and R were validated by qRT-PCR. The reverse transcription of total RNA and qRT-PCR on Viia 7 Real Time PCR System (ABI, New York, NY, USA) with EvaGreen Mix (Biotium, Fremont, CA, USA) was the same as a previous study. The cycle thresholds (CTs) of miRNAs and TFs were normalized as relative expression levels using the 2−∆∆Ct method, which deducts CTs of reference genes (Actin primers: 5-GCTTATGTTGGTGATGAAG-3 and 5-CCCTGTTAGCCTTAGGATT-3) and control groups (W) from CTs of R. All the primers of 6 miRNAs and 10 target genes are listed in Table S10.

3. Results

3.1. Identification of miRNA and Target Genes of Dahlias

Four sRNA libraries generated a total of 114,521,778 raw reads to produce 57,260,724 clean reads (49.99% of raw reads, 14.3 million per library) after junk reads filtration and adaptor trimming. The data pre-processing program of sRNAminer obtained 19,493,385 unique reads by merging redundant reads, obtained 16,041,981 valid reads by filtering out noncoding RNA reads (rRNA, tRNA, snRNA, snoRNA and others) and organelle reads (Supporting Information, Table S1). About 88.96–89.41% of valid reads were mapped to the reference genome of dahlias. Based on the mapping reads and plant miRNA identification criteria [43], 328 conserved miRNAs of 27 families and 62 novel miRNAs of 25 families (Figure 2A, Supporting Information, Tables S2 and S4) were identified from 390 MIR loci across all chromosomes of the dahlia genome (Figure 2B). The secondary structures of precursors of novel miRNAs were checked manually and all predicted pre-miRNAs could form a stable stem-loop structure, which is critical for miRNA processing. Figure S1 illustrates eight precursors randomly selected from novel miRNAs.
The PsRNATarget webtool predicted that 14.37% (26,150) of all 181,915 genes in the reference genome would be targeted by miRNAs and only 977 target genes would be transcription factors (TFs) (Supporting Information, Table S3). The conserved MIR families targeted more genes than novel MIR families (Figure 2A, Supporting Information, Table S4). In particular, MIR159 and MIR169 targeted over 1000 genes, which was much higher than the target gene number of other MIR families.
MIR alleles with high colinearity were observed in the 16 homologous chromosomes of each sub-genome of dahlia (Figure 2B,C). Chr9 and Chr11 contained the least MIR loci (Figure 2B) since only one MIR was located on Chr9 of sub-genome A4 or Chr11 of sub-genome A2. There are more members in conserved MIR families (2–46 members) than in novel MIR families (1–4 members) (Figure 2A). For example, seven miRNA members located on six chromosomes of each sub-genome composed the MIR396 family with twenty-eight members, while most novel MIR families with one member were identified as nonhomologous loci (Figure 2C, red labels). And candidate-missing MIR loci (Figure 2C, question marks) or the lowly conserved MIR loci (Figure 2C, color labels) of corresponding homologous chromosomes (Figure 2C) contributed to the non-tetraploid member number of some MIR families.

3.2. MiRNA Expression Profiles of Dahlia Petals

The read counts were normalized as TP10M to quantify the abundance of miRNAs (Supporting Information, Table S5). The alleles produced the same pre-miRNA (two SNP at most) with the same abundance as the miR166 family profile (Figure 3A). All miR166 target genes, including 12 groups of miR166 alleles located on an unplaced scaffold, generated six kinds of expression patterns (Figure 3A). To reduce redundancy, each miRNA allele of sub-genome A1 (Figure 3C) and miRNAs of three other sub-genomes whose alleles were missing in A1 (Figure 3B) generated the expression profiles of dahlia petals. The expression profiles clustered into three groups, high abundance miRNAs, low abundance miRNAs and moderate abundance miRNAs (Figure 3C). The correlation coefficients of miRNA expression profiles indicated that the biological duplications of each group were highly coincident (Figure 3D). The density distribution profiles of W with positive skewness indicated that average miRNA abundance was much higher than R with negative skewness (Figure 3E).

3.3. DEMs Identification and Enrichment Analysis

Further, the differential expression analysis identified 89 DEMs in comparison of white petal tips and red petal bases (W vs. R) (Supporting Information, Table S5). A total of 6349 genes were targeted by 79 DEMs and these target genes were significantly enriched in six KEGG pathways including map04075: plant hormone signal transduction; map00062: fatty acid elongation; map01040: biosynthesis of unsaturated fatty acids; map00232: caffeine metabolism; and two autophagy-related pathways that may be involved in the morphogenesis of petals (Figure 4). While map00941: flavonoid biosynthesis contains the main steps of the ABP, it was not significantly enriched. Combining the expression profiles of differentially expressed genes (DEGs) from our previous RNA-seq data [9] (Supporting Information, Table S6), 78 up-regulated miRNAs of the MIR156/159/160/164/167/168/169/482/6114/6118 families targeted 47 up-regulated genes and 249 down-regulated genes, while 11 down-regulated miRNAs of MIR169/1408/N11 families targeted 15 down-regulated genes. Together, the up-regulated miRNAs and significant down-regulation of target genes in W indicated that PTGS induced by miRNA plays a role in the formation process of white petal tips.

3.4. Analysis of the Post-Transcriptional Regulation Network Involved in ABP

A total of 36 structure genes were identified as ABP genes by KO annotation and all of them were significantly down-regulated in W (Supporting Information, Table S8). Previously reported PTGS of two DvCHS loci were also observed in four CHS loci including three alleles (h2tg000072l.g15533.t1, h1tg000148l.g126527.t1 and h2tg000010l.g155409.t1) and another locus (h2tg000044l.g928.t1) based on read mapping illustration of hc-siRNA (Figure 5A). Additionally, hc-siRNA loci dominated by 23 nt-sRNAs were also observed in three alleles (h2tg000130l.g42910.t1, h2tg000221l.g72127.t1 and h1tg000145l.g125960.t1) of all eight PAL loci (Supporting Information, Figure S2). Interestingly, one CHS gene (h2tg000041l.g180976.t1) and two PAL genes (h2tg000168l.g59867.t1 and h2tg000182l.g64659.t1) were predicted as target genes and the three loci did not generate hc-siRNAs. Unlike its hc-siRNA-generating allele (h2tg000044l.g928.t1) with a total of 14 SNPs of coding sequences, h2tg000041l.g180976.t1 was predicted to inhibit translation through miRN18′s binding to the MRE (‘5- uuguucggguuugggccgggG-3′) while the fourteenth SNP of h2tg000044l.g928.t1 (‘5- uuguucggguuugggccgggC-3′) should block the binding of miRN18. But the stable abundance of miRN18 in R and W (Supporting Information, Table S8) was unable to induce the PTGS in W. Similar, two PAL genes were targeted by stably expressed miR399 members.
Since the miRNAs should not contribute to the PTGS by targeting structure genes, we checked the ABP regulator genes according to the MBW complex identified in our previous study [9]. Two PAP1 homologs (MYB TF, h2tg000115l.g38125.t1 and h2tg000050l.g4664.t1) and one TT2-like homolog (bHLH TF and h2tg000173l.g61699.t1) were targeted by MIR398 and MIR858, respectively. But all members of MIR398 and MIR858 were slightly down-regulated in W with a non-significant Q-value. Whether their slight down-regulation contributed to the PTGS of MBW needs to be verified in the future. In addition to MBWs, other TFs could play roles in ABP gene regulation. The binding site scan tool predicted that 612 Arabidopsis TFs would target promotors of dahlia ABP genes (Supporting Information, Table S8). From the 346 dahlia TFs targeted by DEMs, 4/612 Arabidopsis TFs hit 198 TFs. And eleven DEGs including four ARF (ARF8, AT5G37020) homologs, three HD-ZIP (ANL2, AT4G00730) homologs, one SBP (SPL15, AT3G57920) homolog and three NAC (NAC4, AT5G07680) homologs (Supporting Information, Table S8) were selected as ABP regulator candidates. The eleven ABP regulator candidates were targeted by several members of six conserved miRNA families (MIR 156, 164, 167, 169, 482 and 6114). These miRNA families induced the post-transcription regulation network of the ABP and was established by the corresponding regulator genes (Figure 5B, Supporting Information, Tables S8 and S9) including the MIR156-SPL5, MIR482-SPL5, MIR164-NAC4, MIR167-ARF8 and MIR6114-ANL2 modules.
The MIR156/MIR482-SPL modules regulated genes of PAL, C4H and all EBGs. Both the MIR164-NAC module and the MIR6114-ANL2 module regulated the genes of PAL, C4H, CHS and DFR of LBGs. The MIR167-ARF module regulated genes of all steps of the ABP except GT/BZ1 (Figure 5B). Validation based on a qRT-PCR of representative miRNA members (Supporting Information, Table S10) proved that all miRNAs (miR156, 164, 167, 169, 482 and 6114) showed similar expression patterns corresponding to the expression profiles of sRNA-seq (Figure 5C–H). Further, the expression patterns of 11 target genes indicated that the positive expression correlation of miR169-ARFs, miR482-SPL15, miR156-SPL15 and miR164-NAC4 does not conform to the silencing model. The MIR167-ARF8 module and the MIR6114-ANL2 module with negative expression correlation could play key roles in the PTGS of the ABP.

4. Discussion

Previous observation of 18–32 nt sRNAs matching to two pairs of alleles DvCHS1-1, -2 and DvCHS2-1, -2 indicated the potential PTGS of CHS loci in the white parts of dahlia petals [2]. Our study found the significant enrichment of 23–24 nt siRNAs located on three alleles of CHS loci whose two exons produced more siRNA in W than in R, while the independent CHS locus (h2tg000044l.g928.t1) with a no sRNA reads mapping to the first exon produced more siRNA in the second exon in W. Similarly, the enrichment of siRNAs with no length bias was observed in three alleles of PAL loci. The sRNA-reads covered more parts of three alleles in W than in R, though the sRNA reads were similar. This indicated that differential levels of siRNA-induced silencing in specific CHS and PAL loci contributed to the PTGS of the ABP (Figure 6).
Though the siRNA-induced PTGS was not identified in other ABP genes, miRNA-induced PTGS was predicted in ABP depressors. But all members of MIR398 and MIR858 were slightly down-regulated in W with non-significant Q-value. SPL15/SPL9 of the same branch are partially redundant in plant transition from juvenile stage to adult stage as the conserved targets of miR156 [44]. In the transition process, SPL9 is a negative regulator of LBGs by interfering with MBW formation [20] including the dahlia miR156-SPL9 module involved in the formation of white petals [9]. Though the GTAC boxes binding SPL9 are indirectly recruited to the DFR promotor, SPL9 and SPL15 showed opposite expression pattern with DFR in all tissues [20]. And in the turnip mosaic virus infection Arabidopsis, anthocyanin accumulation is suppressed by down-regulated LBGs along with up-regulated SPL15 [45]. It is suggested that the up-regulation of the SPL15 of dahlias is involved in white petal tips by depressing EBGs (Figure 6). Similarly, the up-regulation of NAC4 in W, which could be a negative regulator of the ABP, is also involved in bicolor dahlia formation, since repressing anthocyanin biosynthesis have been observed in several NACs including AtNAC032 [46] and JUB1/ANAC042 [47]. The co-upregulation of MIR156/482-SPL15 and MIR164-NAC4 is confusing for the miRNA-induced PTGS. Though the miRNAs-induced up-regulation of targets is found in nuclear activating miRNA (NamiRNA), NamiRNA-induced positive regulation has been observed in plants [48].
ABP activators are mostly MBW compounds or other positive regulation TFs. For MBW compounds, though the MIR858-MYB module plays a key role in plant ABP activation [26], MIR398-PAP1s and MIR858-TT2-like were not discussed here due to the stable expression of MIR858 and MIR858 in dahlia petals. ANL2, a homolog of plant epidermal cell fate decider GLABRA2 (GL2), promoted anthocyanin accumulation and distribution in subepidermal cells [49]. The up-regulation of MIR6114 and down-regulation ANL2 in W in this study indicated that miR6114-induced PTGS of ANL2 may contribute to white tip formation. By the way, MBW-induced GL2 provided a feedback loop of the ABP by transcriptional depressing of MBW compounds and decided epidermal cell fate through the protein interactions of MBW [50,51], while the regulation mechanism of ANL2 in still undetermined. ARF8 promotes the expression of ANS and DFR through interaction with the WD40 protein TTG2, which has positive effects on anthocyanin production and tobacco flower color [52]. Anyway, ABP depressor ARFs were also identified, like ARF13, which repressed anthocyanin accumulation in apples and peaches through interaction with MYB10 and targeting DFR [53,54]. Together, potential roles of MIR167-ARF8 and MIR6114-ANL2 on bicolor dahlias are emerging and the molecular mechanisms need to be illustrated by further genetic analysis (Figure 6).
The bicolor petal phenotype is a multifactorial trait that results from complex pigmentation patterns [55]. Our study highlights the significant role of miRNA-TF regulatory modules in regulating anthocyanin biosynthesis, which reduces the pigment patterning in petal tips, which could contribute to the formation of ‘white–red’ dahlias. Similar findings have been reported in bicolor lilies through the miR828-mediated regulation of MYB12 inhibiting anthocyanin biosynthesis [56]. In addition to miR828, miR156, 399, 778 and 858 also regulate anthocyanin biosynthesis in plant reproductive organs [57]. Though miR167 and miR6114 have not been proved to take part in other plant anthocyanin biosynthesis regulation, their target genes ARF8 and ANL2 are closely related to pigment patterning. These miRNAs, together with our findings, suggest that miRNA-TF modules could be key to understanding underlying complex flower color traits across multiple species.

5. Conclusions

Our study of miRNA profiles of Dahlia pinnata petals provided the post-transcriptional regulation network of the ABP. In particular, we observed that miRNA-TF modules may interact in a way that regulates anthocyanin synthesis to affect pigmentation patterns. The MIR167-ARF8 module and the MIR6114-ANL2 module are the potential key regulators of the inactive ABP and this advances our understanding of how PTGS influences anthocyanin biosynthesis, contributing specifically to the unique bicolor phenotype. These findings could have significant implications for the development of novel breeding strategies aimed at manipulating flower color in ornamental plants.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/agronomy15020495/s1: Table S1: Type and percentage of reads of sRNA libraries; Table S2: Prediction of mature miRNAs and miRNA precursors with genome location by sRNAminer; Table S3: Target gene prediction by psRNATarget webtool; Table S4: Number of miRNAs and targets of MIR families; Table S5: Expression profiles and differential expression of miRNAs in petals; Table S6: Expression profiles of DEGs in petals. Table S7: KEGG annotation of ABP genes in dahlia genome; Table S8: Prediction of TF binding sites on promotors of dahlia ABP genes; Table S9: Prediction of miRNA-TF module regulating ABP genes; Table S10: miRNA-TF modules involved in petal color and primers. Figure S1: stem-loop structures of precursors eight novel miRNAs. Figure S2: 23 nt-sRNA mapping on a representative phenylalanine ammonia-lyase loci (h2tg000221l.g72127.t1).

Author Contributions

Conceptualization, J.Z. and X.T.; software, X.W. and S.L.; resources, J.Z., Y.C. and X.T.; data curation, S.L. and M.L.; writing—original draft preparation, J.Z., X.W. and X.T.; writing—review and editing, J.Z., X.T. and H.W.; visualization, Y.C., M.L. and H.W.; project administration, J.Z. and H.W.; funding acquisition, X.T., H.W. and J.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the natural science research project of higher education institutions in Jiangsu Province, grant number 24KJD220002, the philosophy and social science research project of higher education institutions in Jiangsu Province, grant number 2023SJYB2082, the Technology Research and Development Program of ZunYi, grant number HZ [2022]162 and Guizhou provincial science and technology projects, grant number QKHJC[2021]282.

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Materials. Further enquiries can be directed to the corresponding authors.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

All abbreviations involved in this study: 3AT, anthocyanidin 3-O-glucoside 6″-O-acyltransferase; 4CL, 4-coumarate-CoA ligase; ABP, anthocyanin biosynthetic pathway; ANR, anthocyanidin reductase; ANS, anthocyanidin synthase; C4H, cinnamate 4-hydroxylase; CHI, chalcone isomerase; CHS, chalcone synthase; DEMs, differentially expressed miRNAs; DFR, dihydroflavonol reductase; EBGs, early biosynthetic genes; F3H, flavonoid 3-hydroxylase; F3′H, flavonoid 3′-hydroxylase; FLS, flavonol synthase; FNS, flavone synthase; GT/BZ1, anthocyanidin 3-O-glucosyltransferase; LBGs, later biosynthetic genes; MBW, MYB-bHLH-WD40 protein complex; MRE, miRNA response elements; PAL, phenylalanine ammonia-lyase; PTGS, post-transcriptional gene silencing; TFs, transcription factors; and TP10M, Reads Per 10 Million.

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Figure 1. Flowers of Dahlia pinnata (a) and red bases and white tips of bicolor petals (b).
Figure 1. Flowers of Dahlia pinnata (a) and red bases and white tips of bicolor petals (b).
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Figure 2. Identification of miRNAs in dahlia petals. (A) Predicted miRNA number in each miRNA family and target gene number of each gene family. (B) Number of miRNA loci in 16 groups of homologous chromosomes of the dahlia genome. Each column corresponds to the locus number of sub-genomes A1, A2, A3 and A4 from top to bottom. (C) MiRNA loci on chromosomes of sub-genomes A1, A2, A3 and A4. The assumed missing miRNA alleles are labeled with question marks. For each group of alleles with two missing alleles, at least is colored with the same color to highlight the low conserved MIR loci.
Figure 2. Identification of miRNAs in dahlia petals. (A) Predicted miRNA number in each miRNA family and target gene number of each gene family. (B) Number of miRNA loci in 16 groups of homologous chromosomes of the dahlia genome. Each column corresponds to the locus number of sub-genomes A1, A2, A3 and A4 from top to bottom. (C) MiRNA loci on chromosomes of sub-genomes A1, A2, A3 and A4. The assumed missing miRNA alleles are labeled with question marks. For each group of alleles with two missing alleles, at least is colored with the same color to highlight the low conserved MIR loci.
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Figure 3. MiRNA expression profile analysis of white tips (W) and red bases (R) dahlia petals. (A) The heatmap of the MIR166 family grouped by alleles in sub-genomes A1, A2, A3 and A4. TP10M values are listed in the corresponding color rectangles. (B,C) The heatmap of non-redundant miRNA including alleles in A1 sub-genome (C) and other sub-genomes (B). The high, middle, and low abundance groups of miRNA profiles are clustered. The miRNA families are represented by coloring boxes. (D) Correlation coefficients of TP10M in four libraries of W and R represented by coloring boxes. (E) Density distributions profiles of logarithm TP10M in four libraries of W and R.
Figure 3. MiRNA expression profile analysis of white tips (W) and red bases (R) dahlia petals. (A) The heatmap of the MIR166 family grouped by alleles in sub-genomes A1, A2, A3 and A4. TP10M values are listed in the corresponding color rectangles. (B,C) The heatmap of non-redundant miRNA including alleles in A1 sub-genome (C) and other sub-genomes (B). The high, middle, and low abundance groups of miRNA profiles are clustered. The miRNA families are represented by coloring boxes. (D) Correlation coefficients of TP10M in four libraries of W and R represented by coloring boxes. (E) Density distributions profiles of logarithm TP10M in four libraries of W and R.
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Figure 4. KEGG enrichment analysis of target genes of differentially expressed miRNAs. Gene number and enrichment significance are indicated by color and area of circles. Rich factor is the percentage of enriched gene in the background gene of each pathway.
Figure 4. KEGG enrichment analysis of target genes of differentially expressed miRNAs. Gene number and enrichment significance are indicated by color and area of circles. Rich factor is the percentage of enriched gene in the background gene of each pathway.
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Figure 5. MiRNAs and target genes involved in the anthocyanin biosynthetic pathway (ABP) of dahlia petals. (A) Comparison of sRNA reads mapping on 1507 bp of chalcone synthase geneloci between white tips (W1) and red bases (R1). Read depth of each nucleotide is represented by gray bar while reads mapping by coloring line. Characteristic peaks of 23 nt phase siRNA are illustrated by purple bars in panel of W1. (B) Sankey diagram of miRNAs transcription factor ABP genes. (B–G) Validation of expression levels of TFs targeted by miR167 (C), miR169 (D), miR6114 (E), miR156 (F), miR482 (G) and miR164 (H), compared with TP10M of sRNA sequencing.
Figure 5. MiRNAs and target genes involved in the anthocyanin biosynthetic pathway (ABP) of dahlia petals. (A) Comparison of sRNA reads mapping on 1507 bp of chalcone synthase geneloci between white tips (W1) and red bases (R1). Read depth of each nucleotide is represented by gray bar while reads mapping by coloring line. Characteristic peaks of 23 nt phase siRNA are illustrated by purple bars in panel of W1. (B) Sankey diagram of miRNAs transcription factor ABP genes. (B–G) Validation of expression levels of TFs targeted by miR167 (C), miR169 (D), miR6114 (E), miR156 (F), miR482 (G) and miR164 (H), compared with TP10M of sRNA sequencing.
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Figure 6. A putative roadmap of post-transcription regulation on anthocyanin biosynthesis of bicolor dahlia. The coloration of compounds in petals corresponds to the background colors of the rectangles. All siRNA loci, positive or negative regulators of the pathway, are marked with color labels as per the legend. Promotion and inhibition are indicated with arrows and T-lines, respectively. Gene name list: 3AT, anthocyanidin 3-O-glucoside 6″-O-acyltransferase; 4CL, 4-coumarate-CoA ligase; ANS, anthocyanidin synthase; C4H, cinnamate 4-hydroxylase; CHI, chalcone isomerase; CHS, chalcone synthase; DFR, dihydroflavonol reductase; F3H, flavonoid 3-hydroxylase; F3′H, flavonoid 3′-hydroxylase; GT, anthocyanidin 3-O-glucosyltransferase; and PAL, phenylalanine ammonia-lyase.
Figure 6. A putative roadmap of post-transcription regulation on anthocyanin biosynthesis of bicolor dahlia. The coloration of compounds in petals corresponds to the background colors of the rectangles. All siRNA loci, positive or negative regulators of the pathway, are marked with color labels as per the legend. Promotion and inhibition are indicated with arrows and T-lines, respectively. Gene name list: 3AT, anthocyanidin 3-O-glucoside 6″-O-acyltransferase; 4CL, 4-coumarate-CoA ligase; ANS, anthocyanidin synthase; C4H, cinnamate 4-hydroxylase; CHI, chalcone isomerase; CHS, chalcone synthase; DFR, dihydroflavonol reductase; F3H, flavonoid 3-hydroxylase; F3′H, flavonoid 3′-hydroxylase; GT, anthocyanidin 3-O-glucosyltransferase; and PAL, phenylalanine ammonia-lyase.
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Zou, J.; Wu, X.; Li, S.; Liu, M.; Chen, Y.; Wang, H.; Tao, X. sRNA Sequencing of Dahlia Bicolor Petals Revealed the Post-Transcriptional Regulation of Anthocyanin Biosynthetic Pathway. Agronomy 2025, 15, 495. https://doi.org/10.3390/agronomy15020495

AMA Style

Zou J, Wu X, Li S, Liu M, Chen Y, Wang H, Tao X. sRNA Sequencing of Dahlia Bicolor Petals Revealed the Post-Transcriptional Regulation of Anthocyanin Biosynthetic Pathway. Agronomy. 2025; 15(2):495. https://doi.org/10.3390/agronomy15020495

Chicago/Turabian Style

Zou, Jiuchun, Xiaoshuang Wu, Shuyan Li, Mengqing Liu, Yuyu Chen, Haoran Wang, and Xue Tao. 2025. "sRNA Sequencing of Dahlia Bicolor Petals Revealed the Post-Transcriptional Regulation of Anthocyanin Biosynthetic Pathway" Agronomy 15, no. 2: 495. https://doi.org/10.3390/agronomy15020495

APA Style

Zou, J., Wu, X., Li, S., Liu, M., Chen, Y., Wang, H., & Tao, X. (2025). sRNA Sequencing of Dahlia Bicolor Petals Revealed the Post-Transcriptional Regulation of Anthocyanin Biosynthetic Pathway. Agronomy, 15(2), 495. https://doi.org/10.3390/agronomy15020495

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