Multi-Omics Insights into Grapevine Ecodormancy to Budburst Transition: Interplay of Gene Expression, miRNA Regulation, and DNA Methylation

doi:10.21203/rs.3.rs-4421410/v1

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Multi-Omics Insights into Grapevine Ecodormancy to Budburst Transition: Interplay of Gene Expression, miRNA Regulation, and DNA Methylation

https://doi.org/10.21203/rs.3.rs-4421410/v1

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In grapevine, the transition from a dormant bud to budburst is a critical developmental process related to vegetative and reproductive growth. We generated a time series analysis (five sampling time points) and used transcriptome, small RNA, and whole-genome bisulfite sequencing to characterize this transition. Ecodormant buds took an average of 17 days to budburst. Transcriptome analysis identified a total of 7002 differentially expressed genes across all sampling times and revealed that the brassinosteroid metabolism and the linoleic acid metabolism pathways are upregulated and downregulated respectively across all time points. Gene expression cluster analysis identified the activation of the photosynthesis pathway and photosynthesis related genes during this transition. miRNA expression analysis identified a steady increase in expression of two miRNAs, miR159a and miR159b during the transition from dormancy to budburst. Our analysis suggests that these two miRNAs promote budburst by repressing the expression of auxin responsive genes. Finally, a continued increase in CG methylation levels was observed during the dormancy to budburst transition. Differential methylation analysis, considering dormant buds as the control stage, yielded 6354 differentially methylated regions across the genome. Two glucosidase genes exhibited increases in promoter methylation and a corresponding decrease in gene expression in our analysis. This study provides a multi-omics view of grapevine bud transition from dormancy to bud burst and unveils the interacting genetic and epigenetic networks regulating this process.

Grapevine (Vitis vinifera ssp. vinifera) is an economically important fruit crop, well adapted to grow in wide range of climatic conditions [1], which is mainly grown for wine production. Throughout the 2021/2022 season, global grapevine production amounted to 25.62 million metric tons [2]. In the United States alone, which ranks fourth in wine production by volume, wine, grapes, and grape products contributed $276 billion to the economy in 2022 [3]. Grapevine importance goes beyond economics. Since its genome sequence was released, grapevine has become a model crop for fruit genetics studies and is one of the most widely studied fruit crops among non-climacteric fruit crops [4, 5].

During unfavourable conditions, plants temporarily suspend growth using a set of molecular, physiological, and morphological processes, collectively called dormancy. Dormancy is common survival strategy of plants, especially in temperate woody perennials. Based on the source of environmental signals that induce dormancy, it is categorized into three classes: paradormancy (PD), endodormancy (ED), and ecodormancy (ECD) [6]. PD, also called summer dormancy, is the temporary dormancy which precedes ED in woody plants. PD is mainly controlled by dominant bud growth i.e., apical dominance [7] In ED, low temperature stimulates internal bud signals, which enable buds to become tolerant to low temperature. ED restricts new growth until the fulfillment of the chilling requirement [8]. ECD is last stage of dormancy where buds resume new growth after getting the correct environmental signal. This resumption of new growth after ECD is often called budburst or bud break [9].

Bud dormancy and budburst are important agronomical traits as they control vegetative growth. Budburst is the stage when dormant plants initiate new growth from their dormant buds, signalling the end of dormancy, and the beginning of the new growing season [10]. Timing of budburst helps to synchronize flowering, fruit set, and harvest. Much of the supply of grapevine fruits is reliant on the successful timing of budburst [11]. Phytohormones, xylem pressure, tissue oxygen status, callose deposition, photoperiod, and soil temperature are already reported to be involved in this phase transition [12–14].

Dormancy to budburst phase transition is a complex process, with many physiological, molecular, and biochemical changes occurring during the process. Little is known about the molecular processes underlying this transition in grapevine. Previous reports using next-generation sequencing in sweet cherry predicted a complex array of signaling pathways, including cold responsive genes, abscisic acid (ABA)-responsive genes and DORMANCY ASSOCIATED MADS-box genes involved in bud dormancy and budburst [15]. Transcriptome analysis of hazelnut buds during the ECD and bud burst stages has shown that the phenylpropanoid and phytohormone biosynthesis pathway genes are significantly enriched during budburst, indicating that such pathways play critical roles in regulating bud burst [16]. This study further reported that bud burst involves the interaction between plant growth inhibiting substances such as, abscisic acid and other plant hormones including auxin, gibberellins, and ethylene [16]. A recent study in grapevine (cultivar Merlot) identified the influence of ABA-signaling in the bud dormancy (winter bud) to budburst (spring bud) transition. This research has also emphasized the significance of tissue oxygen levels and the metabolic activity of buds during this transitional phase [17].

Our research study was planned to investigate the molecular processes involved in bud ecodormancy to budburst (partial bud opening) transition in grapevine (Vitis vinifera ssp. vinifera cv. Cabernet Sauvignon). RNA sequencing, whole-genome bisulphite sequencing and small RNA sequencing were performed to study changes in protein coding and small RNA gene expression, and DNA methylation at five time points during budburst. Findings of our analysis will contribute to an understanding how such molecular processes interact to regulate the transition between bud dormancy and bud burst.

Plant material

V. vinifera ssp. vinifera cv. Cabernet Sauvignon plants were vegetatively propagated using callused cuttings (CC) as described previously [18], from approximately 60 cm canes collected from endodormant vines planted at the UC-Davis experimental vineyard during the Fall of 2019 and stored at 4°C for three months to break endodormancy. In May 2020, 15 canes were randomly selected and trimmed to sections containing 3 nodes (approximately 20cm), the lower end was dipped in rooting gel (IBA, 3mg/litre; clonex growth technology) for 2 seconds and placed in 24 cm pots containing 1:1 prolite-promix mixture and placed in a mist bed. During the experiment, soil temperature and soil moisture were monitored using soil sensor (WatchDog 1000 Series Micro Stations - External Sensors-3685WD1). The soil temperature throughout the experiment fluctuated between 27-33^oC, whereas soil moisture was 3–5%.

The 15 CC were randomly assigned to 5 groups, with each group containing 3 replicates. Each group was used for plant material collection at one of the five time points selected for this study. In all cases, only the top end bud was collected, and plant material was collected only once from each CC at the same time of the day (11am in the morning). The initial time point, ST0 hereafter, was defined as the day canes were taken from cold storage, so buds were sampled before cuttings were treated with IBA and planted. Such resting buds prior to planting were considered as ecodormant and considered the control to all other time points. ST1 to ST4 time points were determined according to the modified Eichhorn-Lorenz System (E-L) [19]. The number of days since ecodormant canes were taken from cold storage (i.e., DSCS) was recorded for each time point. ST1 (5 DSCS): Swelling observed on the top bud; ST2 (9 DSCS): Bud enlargement and bud scales opening; ST3 (11 DSCS): bud turn woolly; ST4 (17 DSCS): Leaf green tip visible, first clear indication of budburst (Fig. 1). Bud samples were immediately frozen in liquid nitrogen and stored at -80^oC until nucleic acid extraction.

Nucleic acids extraction

MPbio Fast DNA & RNA extraction kit was used for DNA and RNA from collected buds following the manufacturer’s instructions with a minor modification, i.e., the volume of 1 M DTT was also increased to 170% of the extraction kit specified amount. DNA and RNA were quantified, and quality checked using the High Sensitivity DNA or RNA Kits for Fragment Analyzer (Agilent Technologies). Only RNA samples with a RQN above 7 were kept for further use. All extracted samples were kept at -80°C until further use.

Gene expression analysis

Total RNA extracted from bud samples was used for the expression analysis of protein coding (PCG) and long non-coding RNA genes.

Sequencing libraries for PCG expression analysis were prepared by NovoGene USA. In brief, mRNA from total RNA was enriched using oligo (dT) beads. cDNA was synthesized from the purified mRNA using random hexamer primers, after which second-strand synthesis buffer (Illumina) that included dNTPs, RNase H and DNA polymerase I added to initiate the second-strand synthesis. The double-stranded cDNA library was subjected to size selection (150 bp) and PCR enriched. Libraries were sequenced using a NovaSeq 6000 (Illumina) (2X150bp) paired end run.

Raw sequencing reads were subjected to quality control by FastQC (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Sequencing adaptors and poor-quality reads were filtered using trimmomatic [20] After series QC analysis, RNA-Seq reads were mapped to the grapevine reference genome PN40024.V2 [21] using HISAT2 default parameters [22]. The python framework HTSeq (version 0.6.1) [23] was used to count the number of reads mapped to each gene. Individual gene expression levels were calculated in the form of Transcripts Per Million (TPM) for all 15 samples from their binary sequence alignment files (BAM) using the TPM calculator package [24]. Gene expression results were used for the identification of clusters using k mean clustering [25] and clust v1.8.4 [26]. Principal component analysis of all 15 samples was performed using PGSEA package in iDEP.96. The count normalisation i.e., count per million (CPM) and differential gene expression were performed using DESeq2. Genes showing a fold change in expression ≥ 2 or ≤ − 2 and an FDR-corrected p values < 0.01 were deemed as differentially expressed between ST0 and ST1-4 samples. Pathway enrichment analysis was performed using KEGG pathway analysis tool using plugins of iDEP.96 web application [27].

Small RNA libraries were generated in house using GenXPro low-input small RNA library preparation kit (GenXPro GmbH, Frankfurt, Germany) following manufacturer’s instructions. Individually indexed libraries were equimolarly pooled and sequenced using an Illumina HiSeq 150bp PE run. Quality control and adaptor trimming of raw small RNA reads were conducted using FastQC and Cutadapt respectively. miRDeep2 pipeline was used to identify and quantify known microRNAs [28]. The target genes of miRNAs were searched using online web server psRNATarget [29].

Whole-genome bisulphite sequencing (WGBS).

WGBS libraries were prepared by NovoGene USA (California). In brief, after a quality check, genomic DNA was spiked with unmethylated Lambda DNA and fragmented to 200–400 bp was performed using a Covaris S220 focussed ultrasonicator. Next, libraries were prepared using Accel-NGS Methyl-Seq DNA Library Kit for Illumina, (Swift biosciences) following the manufacturer's instructions. Libraries were equimolarly pooled and sequenced using Nova Seq 6000, using a paired end (150bp) run. After sequencing, adaptor sequences, and low-quality reads were removed using Adapter Removal V2 software.

The bisulphite-converted version of grapevine reference genome (PN40024) was performed using Bismarck. Sequencing read mapping and methylation calling were performed using Bowtie2 and Bismark respectively [30, 31]. Samtools (http://www.htslib.org/doc/samtools-reference.html) with --coverage and --depth option was used to calculate sequencing coverage and depth. Differentially methylated regions (DMRs) were identified using R package methylKit [32]. The tilling window for DMRs were set at 1000kb. The regions showing difference in methylation ≥ 25% or ≤ -25% and q-value ≤ 0.01 were considered as significant DMRs.

RNA Sequencing

Transcriptome sequencing yielded an average of 46 million reads per sample after quality filtering. The average percentage of mappable reads per sample after de-multiplexing was 90.26%, ranging from 84.33–92.85% (Supplementary file S1A). Analysis of mapping results yielded sequencing data for 26,678 genes out of the 30,661 genes annotated for this version of the V. vinifera genome (Supplementary file S2).

Principal component analysis (PCA) (Fig. 2A) grouped samples by sampling time point, except for one of the replicates from ST2. Samples were further grouped into three differentiated clusters. PC1 (33% of total variability) separated the cluster containing ecodormant samples (ST0) from two clusters containing ST1-ST4 samples, while PC2 (18% of total variability) separated ST1 and ST2 samples from ST3 and ST4 samples. The cluster occupying the top-right quadrant contained samples in the early stages of budbreak, i.e., all three samples from time point ST1 and two out of three samples from ST2. The cluster occupying the bottom-right quadrant contained samples in the late stages of budbreak, i.e., all three samples from time point ST3 and ST4 plus the remaining sample from ST2 (Fig. 2A).

Differential gene expression analysis considering ST0 as a control yielded in total 3512 unique differentially expressed protein coding genes. Of these, 11 DEGs were upregulated in one or more sampling times and downregulated in the rest. Of the remaining 3501 DEGs, 1151 were upregulated and 2350 downregulated in one or more time point, 1590 genes were unique DEGs to a single time point, and 1444 and 467 were down or upregulated in two or more time points respectively. Finally, 72 and 461 DEGs were found to be up-regulated or down-regulated respectively in all time points (Fig. 2B-C) (See Supplementary Fig. 1A and Supplementary File S3A-D for DEGs identified for each of the four comparisons and Supplementary File S3E for DEGs shared by more than one time point). Pathway analysis of up-regulated or down-regulated genes during all time points, showed that the former encodes for proteins involved in the brassinosteroid biosynthesis pathway, while the later encode for proteins involved in the linoleic acid metabolism pathway (Fig. 2B-C). Further analysis indicates that the MYB transcription factor family contains the largest number of transcription factor genes among the commonly downregulated genes (Fig. 2D). Differential expression of 142 non-coding RNAs (116 upregulated and 26 downregulated) was also observed. Non-coding RNAs were mainly snoRNAs, snRNAs and SRP-RNAs (Supplementary Fig. 1B, Supplementary File S4A-D).

Gene Cluster Analysis

TPM calculated from BAM files were used for gene expression clustering analyses (Supplementary file S2). K-mean clustering analysis generated four gene clusters (Clusters A-D hereafter) (Fig. 3; Supplementary File S5), showing distinct expression pattern during the time course of bud break. Cluster A contained 735 genes associated with cell wall biogenesis and plant hormone signalling, which exhibited higher transcript abundance during bud ecodormancy (ST0) and a subsequent abrupt decrease in the following stages of budbreak. Cluster B contained 340 genes which expression increased in the earlier stages of budbreak (ST1-2) followed by a decrease in expression in later stages (ST3-4) and which function is linked to response to oxidative stress and temperature stimulus. Genes in Cluster C (306), on the contrary showed an increase in expression on the later stages of budbreak (ST3-4) and their function is associated to transport and flavonoid biosynthesis. Finally, Cluster D contained 619 genes, which had minimal transcript accumulation during bud ecodormancy (ST0) followed by a steady increase in expression reaching a maximum transcript accumulation during bud burst (ST4). Functional annotation of cluster D discovered activation of photosynthesis and photosynthesis related genes (Fig. 3).

Gene clustering analysis using ‘clust’ identified thirteen clusters (C0 to C12) containing 14012 genes (Supplementary Fig. 2; Supplementary File S6). Of these, clusters C0 and C7, which contained the largest number of genes (C0:3210 genes, and C7:3399 genes) and showed a gradual increase (C0) or decrease (C7) in gene expression during the bud dormancy to bud burst transition, were selected for more detailed analysis. Analysis of gene functions and pathways associated with these two clusters, identified that cluster C0 (which expression is positively associated with the transition from bud dormancy to budburst) was significantly associated with photosynthesis, especially with genes associated with photosystem I and photosystem II protein complexes (Fig. 4). While C7 main associated function was ribosome, (Supplementary Fig. 3).

micro-RNA analysis.

The total small RNA sequencing raw read counts varied between 7 and 33 million reads, with average of 23 million reads per sample (Supplementary file S1B). After quality check and filtering, 83% raw reads were processed for miRNA identification. A total of 577 miRNA species were identified from our sequencing results (Supplementary File S7). Of these, only miRNA159a and miRNA159b showed sufficient sequencing depth for further analysis. Both miRNA159a and miRNA159b exhibited a steady increase in expression from the bud ecodormancy (ST0) to the bud burst (ST4) transition (Fig. 5; Supplementary File S7). Search for their potential target genes in cluster C7 (3399 genes) yielded a total of 136 potential targets (Supplementary file S8). Biological function of these target genes identified plant hormone signalling pathways as the most significant pathway we identified increased transcript abundance of auxin induced genes.

DNA methylation

Whole genome methylome sequencing yielded a total of 1.49 billion reads. Bisulfite conversion efficiency showed on average 99.72% unmethylated cytosines converted to uracils. Average genome coverage was 80.2%, ranging from 78.66–82.01%, and the average sequencing depth reached was 9%, ranging from 7.1-12.48% (Supplementary file S1C). The average percentage of mappable reads per sample after de-multiplexing was 30.43%, ranging from 25-32.90%. The average percentage of methylated cytosines (mCs) varied between 47.40% and 51.20% mCs at CG sites, 20.40–22.20% at CHGs, and 4.3–6.7% at CHHs (H = A, T or C). Analysis of global levels of DNA methylation showed a significant increase in DNA methylation in the CG and CHG contexts in the transition from eco-dormant to breaking buds (Percentage of methylated CGs and CHGs at ST0 = 47.96 and 20.9% respectively and at ST4 = 50.5 and 21.9%), while the CHH context did not show any trend (Fig. 6A).

Differential methylation analysis considering ST0 as the control stage and ST1, ST2, ST2 and ST4 as treatments yielded 6354 differentially methylated regions (DMRs), among them 3078 regions were hypomethylated (hyper-DMR) and 3276 were hypomethylated (hypo-DMR) (Fig. 6B and Supplementary file S9A-D). Annotation and regional analysis exhibited DMRs were higher in intergenic regions (35 to 38%) compared to promoters (26–28%), exons (21 to 25%) and introns (12 to 14%) (Fig. 6C).

Gene expression and DNA methylation integration.

For methylation and gene-expression integration analysis, we selected differentially expressed genes and differently methylated regions of budburst stage (ST4) compared to control (ST0). Integration of differentially methylated regions with differentially expressed genes discovered 27 genes (8 promoters and 19 gene bodies) that show hypermethylation and downregulation, 9 genes 3 promoters and 6 gene bodies) that show hyper-methylation and up-regulation, 26 genes (3 promoters and 23 gene bodies) exhibited hypo-methylation and down-regulation, and 7 genes (3 promoters and 4 gene bodies) exhibited hypo-methylation and up-regulation. Glucosidase 12, and genes involved in ABA response, and photosystem A/B genes showed hyper-methylation in promoter region and downregulation in gene expression. A gene encoding a pyrophosphokinase and two uncharacterized genes exhibited hypomethylation in promoter region and down-regulation in gene expression. In case of gene body methylation, genes like members of the families of sucrose synthase, kinesin, ethylene responsive, photosystem-I subunit, kinase and so on exhibited hypo-methylation in the gene body and down-regulation in gene expression. Genes encoding epimerase and AAA-ATPase showed hyper-methylation in the gene body and up-regulation (Fig. 7).

Budburst following dormancy is complex and a multifactor driven phenomenon in perennial plants. In grapevine, the molecular aspects of bud dormancy and budburst have been examined using a transcriptome approach [33–36]. The current study expands on previous work in grapevine to include a description of global gene and small RNA expression and DNA methylation pattern from same tissues to study the programmed mechanism during bud ecodormancy to bud burst transition.

The PCA analysis using transcriptome datasets represented gradual development in buds from bud dormancy to bud burst. For tree species, photoperiod, air temperature and relative water content are key drivers of budburst [37]. The resting or dormant buds contains photosynthetically inactive proplastids. During the transition of these dormant buds to budburst these become functional, and photosynthesis related genes get activated [38, 39]. In our analysis, we observed activation of genes associated with photosynthesis, mainly genes that encode photosystem I and photosystem II components. Photosystem I and photosystem II are protein complexes that contain the pigments necessary to harvest light and use light energy to catalyse photosynthesis [40]. Our gene clustering analyses identified photosynthesis as a significant pathway altered during budburst which gets activated during bud ecodormancy to bud burst transition. Expression of 14 different PSI and PSII related genes were found in KEGG pathway analysis. Two of the photosystems I and photosystem A/B genes showed promoter and gene body methylation and changes in their expression, suggesting that epigenetic regulation might play an important role in activating photosynthesis related genes in grapevine, and that the activation of such genes would be indicative of the transition from ecodormancy to budburst. In K- mean cluster analysis, we also observed the higher expression of plasma membrane associated genes in ST3 and ST4 as compared to ecodormant stage (Fig. 3). Different studies have documented the role of plasma membrane (PM) in dormancy release. Throughout the dormancy PM remains in semi-dehydrated state. After getting external and internal signals the PM gets hydrated and activated [41, 42]. This activated PM is involved in bud development during budburst. C7 cluster which shows decrease in gene expression during transition encode for ribosome pathway. Since, ribosomes control mRNA translation into proteins, our findings suggest that there might be a steady decrease in protein synthesis during the bud dormancy to budburst transition.

In plants, a major function of miR159 is to enable normal growth by silencing R2R3-MYB transcription factors [43]. The miR159-MYB pathway in plants has been implicated in several different functions. In our small RNA sequencing data analysis, we observed steady increase in miR159a and miR159b from bud ecodormancy to budburst transition. This is consistent with the possibility that these two miRNAs positively regulate the budburst initiation. We observed the silencing of two auxin responsive genes and R2R3-MYB transcription factor gene by these miR159s. According to previous reports, auxin promotes the bud dormancy in woody plants [44]. Our study, suggest that miR159a and miRNA159b could be silencing auxin responsive genes to overcome bud ecodormancy and eventually initiating budburst. Through our study we report, the miRNA159-Auxin responsive gene pathway as a positive regulator of budburst and negative regulator of bud dormancy in grapevine. We also observed the expression of the long encoding RNAs such as snoRNAs, snRNAs and SnoRNAs. In peach, the regulatory role of snRNAs in ecodormancy has been reported [45]

During resumption of budburst most of the bud cells undergo cell division and cell elongation [46]. These processes are mainly controlled by plant hormones. Brassinosteroids are a class of plant hormones reported to promote cell division [47]. Our differential gene expression analysis discovered brassinosteroid biosynthesis as a common upregulated pathway across all stages. A study in peach has reported that an early bud-break transcription factor gene 1 is greatly influenced by brassinosteroid metabolism [48]. Taken together, brassinosteroid acts positively in promoting budburst, our RNA sequencing results well support previous hypothesis, however detailed analysis is needed to prove the function of brassinosteoid pathway in budburst. Interestingly, the linoleic acid metabolism pathway was down-regulated during the ecodormancy to budburst transition. Linoleic acid in rapeseeds represses expression of gene regulators responsible for maintaining bud dormancy, resulting in bud growth [49]. Linolenic acid initiates jasmonic acid biosynthesis in plants [50]. In Japanese apricot, jasmomic acid, auxin and abscisic acid response genes are involved in seasonal bud dormancy release [51]. Linoleic acid is possibly responsible for bud dormancy release. Since our study reports down-regulation of genes with products involved in linoleic acid metabolism, there might be linoleic acid catabolism than anabolism.

The observed consistent increase in CG and CHG methylation from bud ecodormancy stage (ST0) to budburst stage (ST4), suggests changes at epigenetic level during bud ecodormancy to bud burst transition. Several studies have indicated that plant hormones such as ABA, GA, auxin and cytokinin guide bud dormancy and bud burst [52–54]. Auxin promotes bud dormancy and suppress the budburst in plants [55]. Our integrated DNA methylation and gene expression analysis found the hypermethylation and reduced gene expression of promoter of ABA responsive gene and hypo-methylation and increased transcript abundance of ethylene responsive genes. Two of the auxin responsive genes has also been reported as targets of miR159 in this study. Previous study in oak, has discussed activation of auxin, gibberellin and brassinosteroid responses during ecodormancy to swelling bud and budburst transition [56].

Breakdown of sugar promotes bud dormancy release and budburst [57]. Sugar accumulation in dormant bud is often connected with freezing tolerance, whereas mobilization and metabolism of this reserved sugar is often related with dormancy release [58]. In oak transcriptome analyses, stimulation of sucrose metabolism related genes was observed during ecodormancy to swelling bud transition [56]. In our DNA methylation and gene expression integrated analysis three different glucosidase genes exhibited promoter and gene body methylation and alteration in their gene expression at same time. Glucosidases are enzymes responsible for breakdown of polysaccharides into their monomers.

A schematic model depicting the molecular regulators of bud ecodormancy to budburst transition grapevine is shown in Fig. 8. We propose that the activation of the photosynthesis and the brassinosteoid biosynthesis pathways, MYB transcription factor, microRNAs 159a and 159b and phosphokinase genes positively regulated this transition. Whereas, Linoleic acid biosynthesis and ribosome pathways, in conjunction with Auxin induced genes and glucosidase genes found are repressors of budburst in grapevine.

Our study provides several novel insights into genetic and epigenetic regulation of bud ecodormancy to budburst transition in grapevine. Multi-omics analysis reveals key changes in gene expression, microRNAs, and DNA methylation during this transition. Our results enable proposition of budburst regulating networks, such as miRNA159-Auxin responsive factors, plant hormone signalling through brassinosteroid, auxin and ABA, activation of photosynthesis and breakdown of sugar through glucosidases and so on. At molecular level, all these networks could interact and respond to external factors to regulate budburst. The novel ideas and results presented in our study, will be further explored to better understand the regulation of budburst at molecular level.

PD: Paradormancy.

ED: Endodormancy.

ECD: Ecodormancy.

ABA: abscisic acid.

CC: callused cuttings.

ST: Sampling time.

E-L: Eichhorn-Lorenz System.

TPM: Transcripts Per Million.

BAM: binary sequence alignment files.

CPM: Count per million.

DEGs: Differentially expressed genes

KEGG: Kyoto Encyclopedia of Genes and Genomes

DMRs: Differentially methylated regions

TFs: Transcription factors

mCs: Methylated cytosines.

CHG or CHH: (C: Cytosine, where H correspond to A, T or C, G: Guanine)

PM: plasma membrane.

Acknowledgements

We thank Prof. Dario Cantù for the donation of plant material from UC-Davis experimental vineyard.

Author contributions

C.L. R.G., and H.S. designed the experiment. H.S., T.R., and L.A. Methodology, bioinformatics analysis, visualization, and result interpretation. H.S. -Writing manuscript draft. C.L. R.G., and S.P. Supervision, resources, manuscript revision & editing. C.L. R.G., and S.P., Funding acquisition, Resources, Supervision.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors have no conflict of interest to declare.

Funding:

This study was supported by the National Institute of Food and Agriculture, AFRI Competitive Grant Program Accession number 1018617, the National Institute of Food and Agriculture, United States Department of Agriculture, Hatch Program accession number 1020852.

Data availability.

The raw sequencing data were deposited to National Centre for Biotechnology Information (NCBI)- Sequence Read Archive (SRA) database. The bioproject accession number for RNA sequencing reads - PRJNA954365, small RNA sequencing reads - PRJNA955780 and Whole genome bisulfite sequencing reads- PRJNA956264.

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No competing interests reported.

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Multi-Omics Insights into Grapevine Ecodormancy to Budburst Transition: Interplay of Gene Expression, miRNA Regulation, and DNA Methylation

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Abstract

Figures

Introduction

Materials and methods

Plant material

Nucleic acids extraction

Gene expression analysis

Results

RNA Sequencing

Gene Cluster Analysis

DNA methylation

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1