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. 2021 Mar 20;11(4):181. doi: 10.1007/s13205-021-02743-6

Transcriptome sequencing revealed the mechanism of promoting floret opening by exogenous methyl jasmonate in sorghum

Suifei Liu 1,2, Yongming He 1, Yongqi Fu 1, Xiaochun Zeng 1,3,✉
PMCID: PMC7981369  PMID: 33927972

Abstract

Flowering time is a critical trait reflecting the adaptation of plants to their environments. Our initial research has shown that exogenous methyl jasmonate (MeJA) significantly promoted the floret opening of sorghum. To better understand the mechanism of this phenomenon in sorghum, the comparative transcriptome analysis was performed. Transcriptomic analysis showed that the most number of differentially expressed genes was presented between control plants and plants treated with 2.0 mM exogenous MeJA in 2.5 h. A large number of differentially expressed genes were assigned to the subcategory of carbohydrate metabolism and lipid metabolism. The transcriptomic analysis of differentially expressed genes involved in glycolysis/gluconeogenesis and tricarboxylic acid cycle indicated a close relationship between carbohydrates metabolism and flowering. In addition, potassium uptake proteins and aquaporins also played important role in response to the exogenous MeJA in the flowering process. These results provide insights into the effect of MeJA on flowering time and explore the possible molecular mechanism of advancing the flowering period by spraying MeJA.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13205-021-02743-6.

Keywords: Sorghum, Methyl jasmonate, Comparative transcriptome, Carbohydrate metabolism

Introduction

Flowering time is a critical trait that reflects the adaptation of plants to their environments by tailoring vegetative and reproductive growth phases to local climatic effects. It is directly related to successful reproduction. A better understanding of plant flowering time is critical for building models that can predict the flowering time of particular genotypes under specific environmental conditions and for exploiting variation in flowering time to expand current cultivation zones.

Initiation of flowering is mainly controlled by endogenous and environmental conditions including photoperiod (light sensing and circadian rhythm), temperature, autonomous flowering, and plant hormonal pathways (Qin et al. 2018). The photoperiod mediates broad adaptations to particular climatic regions based on the day length. Long-day (LD) plants, such as Arabidopsis, only flower when the day length exceeds a threshold, while short-day (SD) plants, e.g., rice and Sorghum, only flower when the day length is below a threshold (Buckler et al. 2009). The temperature accurately restricts plants flowering to a specific time of the year when conditions for pollination and seed development are optimal. On the other hand, the autonomous flowering promotes flowering indirectly by creating favorable conditions that actively promote flowering, and the hormonal pathway controls the timing of flowering in relation to reproductive development. Recently, exogenous hormones, such as methyl jasmonate (MeJA), have been identified as among one of the most important environmental factors that can influence the onset of flowering (Niwa et al. 2018; Yu et al. 2018). For example, Yan et al. reported that the spraying of MeJA effectively promotes rice flowering (Zhi-Qiang et al. 2014). Similarly, Pak et al. found that exogenous MeJA promoted flowering when it was applied to the early flowering types of oilseed rape (Pak et al. 2009).

Sorghum bicolor (L.) Moench, a short-day plant originated from pan Africa, is traditionally grown in dry regions because of its ability to adapt the drought and heat stress. It is grown for grain, forage, sugar, and most recently for biomass, which makes sorghum an attractive crop. Because of its economic value and its ease of scoring, researchers focused on the genetic basis of flowering time in sorghum to achieve wide adaptation (Mace et al. 2013). Six major genes controlling flowering time in sorghum, including Ma_1-Ma_6, have been identified by researchers previously. The dominant allele of each gene delays flowering in long-day environments. As early as 1930s, Ma_1–Ma_4 were proved to control photoperiod-insensitive early maturity in sorghum (Sorghum Improvement and the Genetics of Growth) (Quinby and Karper 1945). Recessive loss-of-function alleles at the Ma loci convert sorghum from a short-day plant to a photoperiod-insensitive plant, which allows it to be grown in temperate latitudes. Ma_1 encodes a pseudoresponse regulator protein, PRR37 (Murphy et al. 2011). Additional maturity loci with great effects have been reported (Ma_5, Ma_6) (Quinby and Karper 1945). Each of these regions appears to contain multiple quantitative trait locus (QTL) for maturity and/or multiple functional alleles at each locus. Lower frequency introgressions outside these regions might contain additional QTL for flowering time (Barrero Farfan et al. 2012; Thurber et al. 2013). However, the understanding of the underlying genes and molecular mechanisms controlling flowering in sorghum is limited when compared to model systems such as Arabidopsis and rice. Thus, a comprehensive analysis of the underlying genes and gene regulation mechanisms in sorghum flowering would be helpful in the understanding of the underlying biological processes and will be beneficial for molecular breeding.

In recent years, the advent of next-generation sequencing has revolutionized genomic and transcriptomic approaches in the biology (He et al. 2018a, b). The sorghum genome was sequenced using the whole-genome shotgun approach in 2009, yielding an approximately 730-megabase assembled genome (Paterson et al. 2009). Subsequently, considerable efforts have been put in the transcriptome analysis of Sorghum. For example, cell wall biosynthesis gene regulatory networks in two high biomass energy genotypes and one grain sorghum genotype were characterized by RNA-seq analysis (Kiani 2014). Additionally, mechanisms of salt response in sorghum were studied through comparative transcriptome analysis (Cui et al. 2018). Although some flowering controlling genes have been identified, the transcriptional biological processes and molecular mechanisms of controlling flowering still remain unclear (C et al. 2018; Madugula et al. 2018). Our initial studies found that exogenous MeJA promoted the floret opening in sorghum. To better understand the molecular mechanisms by which exogenous MeJA promotes floret opening, RNA-seq was employed to analyze the responses and changes of gene expression under varied MeJA concentrations and exposure times.

Materials and methods

Plant growth conditions and treatments

The Dwarf8 variety of Sorghum bicolor (L.) Moench with dwarf and high yield characteristics was used in this study. MeJA was dissolved in a small amount of ethanol (0.5 ml) and then diluted with H2O to give the desired concentrations (0.5 mM and 2 mM). The excised spikelets were selected from the tops of plants with florets that were nearly opened. During the experiment, the excised spikelets were carefully sprayed with 0.5 mM and 2 mM MeJA. Control plants were sprayed with an equal volume of ethanol in H2O (Gao et al. 2004). Each treatment was conducted at least three times with three spikelets per treatment.

Extraction of α-amylase and determination of its activity

Sorghum spikelets were homogenized with an ice-cold solution of 100 mM HEPES–KOH (pH 7.5), which contained 1 mM EDTA, 5 mM MgCl2, 5 mM DTT, 10 mM NaHSO3, and 50 mM bovine serum albumin. After centrifugation of the homogenate at 15,000 g for 30 min, the supernatant was incubated with 3 mM CaCl2 at 75 °C for 15 min for the inactivation of β-amylase and α-glucosidase (Ozaki and Kato-Noguchi 2016). Then, α-amylase activity was assayed based on the amount of reducing sugar released according to the dinitrosalicylic acid (DNS) method as described previously (Miller 1959). One unit of amylase activity was defined as the amount of enzyme that released 1 μmol of reducing sugar equivalent to glucose per min under the assay conditions. Maltose was used as a standard solution with a concentration of 100 μg·ml−1. Three replications were performed at each time point in above experiments.

RNA extraction, Illumina sequencing, and quality controls

Total RNA of collected samples was extracted using the Trizol reagent (Invitrogen, USA) according to the manufacturer’s instructions. Three replications were performed at 1 h, 2.5 h, and 4.5 h after spraying with MeJA in above experiments. The RNA samples were treated with 10 units of DNase I (Fermentas, USA) for 30 min at 37 °C to remove genomic DNA, and mRNA was enriched with Oligo (dT) beads. The enriched mRNA was fragmented into short fragments and reverse transcripted into cDNA using random primers and M-MuLV Reverse Transcriptase (RNase H-). To select cDNA fragments of preferentially 240 bp in length, the cDNA fragments were purified with the AMPure XP system (Beckman Coulter, Beverly, USA). The ligation products were size selected with 3 μL of USER Enzyme (NEB, USA), adaptor-ligated cDNA at 37 °C for 15 min followed by 5 min at 95 °C before PCR. Then, PCR was performed with Phusion High-Fidelity DNA polymerase, Universal PCR primers, and Index (X) Primer. The cDNA library was sequenced from both 5′ and 3′ ends using the Illumina HiSeq2500. To obtain clean reads, reads were those reads containing adapter sequence or comprised of more than 10% unknown nucleotide base calls (N) using SOAPnuke (v1.5.2) (Chen et al. 2018). The sequencing data were deposited in the NCBI Sequencing Read Archive (SRA) database (Bioproject: PRJNA629423; BioSample: SAMN14775430 and SAMN14775431 for samples CK_1 and CK_2, SAMN14775456-SAMN14775462 for samples CK_3, CK_4, LM_1, LM_2, LM_3, HM_1, HM_2). CK_1, 2, 3, 4 indicate that plants were sprayed with H2O for 1 h, 2.5 h, 4.5 h, and 6.5 h; LM_1, 2, 3 indicate that plants were sprayed with 0.5 mM MeJA for 1 h, 2.5 h, and 4.5 h; HM_1, 2 indicate that plants were sprayed with 2.0 mM MeJA for 1 h and 2.5 h.

Alignment with reference genome and differentially expressed genes analysis

Sorghum genome sequences (Accession number: GCA_000003195, v3) were downloaded from NCBI (https://www.ncbi.nlm.nih.gov/genome/108?genome_assembly_id=321335, used at June 2018) as a reference (Paterson et al. 2009). Clean reads from each sample were mapped to the reference genome using TopHat2 (version 2.0.3.12). The reconstruction of transcripts was carried out with software Cufflinks, which together with (Trapnell et al. 2012). The relative abundances of transcripts were quantified by software RSEM. Gene expression levels were normalized using the FPKM (Fragments Per Kilobase of transcriptper Million mapped reads) method (Langmead and Salzberg 2012; Li and Dewey 2011).

To identify differentially expressed genes (DEGs) between two groups, the DESeq R package (v1.10.1) was used. DESeq provide statistical routines for determining differential expression in digital gene expression data using a model based on the negative binomial distribution. The resulting P values were adjusted using the Benjamini and Hochberg’s approach for controlling the false discovery rate (FDR). Genes with an adjusted P value < 0.05 found by DESeq were assigned as differentially expressed. The heatmap was drawn by pheatmap according to the gene expression in different samples. DEGs were then subjected to enrichment analysis of Gene Ontology (GO) functions and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways.

Functional annotation of DEGs

The GO framework for model biological systems uses three main categories (Huntley et al. 2015). All the DEGs were mapped to the GO databases (http://www.geneontology.org, used at June 2018) and then assessed for their enrichment in various categories by the GOseq (v1.40.0) R (v3.5.1) package based on the Wallenius non-central hyper-geometric distribution, which can adjust for gene length bias in DEGs (Young et al. 2010). GO terms with a FDR-corrected p value below 0.05 were considered to have significant enrichment.

KEGG is a database resource for understanding high-level functions within biological system based on molecular-level information, especially large-scale molecular datasets generated by genome sequencing and other high-throughput experimental technologies (http://www.genome.jp/kegg/, used at June 2018) (Minoru et al. 2008). KEGG enrichment analysis was carried out using KEGG Orthology-Based Annotation System (KOBAS, v2.0) (Xie et al. 2011). We used the phyper function in R to test the statistical enrichment of DEGs in KEGG pathways. Significance was determined using FDR-corrected p values.

Quantitative real-time PCR analysis

The expression levels of 15 genes that are involved in carbohydrate metabolism (glycolysis/gluconeogenesis pathway and tricarboxylic acid cycle) in Sorghum were validated by real-time RT-PCR analysis. Real-time RT-PCR was performed using a StepOnePlus real-time PCR system (Applied Biosystems, USA). 18S RNA served as the reference gene for normalization of the target gene expression. Serine/threonine-Protein Phosphatase (PP2A) served as the other reference gene to correct for variation of gene expression between samples (A et al. 2010; Palakolanu et al. 2016). The PCR thermal cycling conditions were as follows: 94 °C for 1 min, followed by 40 cycles of 95 °C for 15 s, 55 °C for 1 min and 72 °C for 5 min. Melting curve analyses of the amplification products were performed at the end of each PCR reaction to ensure that only specific products were amplified. Primers used to amplify the candidate genes are listed in Table S1. The comparative 2−ΔΔCT method was employed to calculate relative expression between samples (Cheng et al. 2016; Tang et al. 2019).

Results

Effect of MeJA on the floret opening

To investigate the effect of MeJA on floret opening, the number of hours it took for floret opening of the plants treated with varying doses of MeJA was recorded. As shown in Table 1, the exogenous MeJA significantly induced early floret opening. The phenotypic changes in control and treated sorghum are shown in Fig. S1. After the application of 0.5 mM MeJA to the spikelets, the number of hours it took for floret opening was reduced from 6.5 to 4.5 h in comparison with the control plants. When treated with concentrations of MeJA (2.0 mM), the plants began flowering at 2.5 h after treatment. The observation showed that the higher concentration of MeJA significantly promoted the floret opening of sorghum.

Table 1.

The number of hours it took for floret opening of the plants treated with MeJA in comparison with the plants treated with plain water (CK)

Sample MeJA level [mM] Handing time [h] Status of floret
CK_1 0 1.0 No
CK_2 0 2.5 No
CK_3 0 4.5 No
CK_4 0 6.5 Yes
LM_1 0.5 1.0 No
LM_2 0.5 2.5 No
LM_3 0.5 4.5 Yes
HM_1 2.0 1.0 No
HM_2 2.0 2.5 Yes
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Each treatment was carried out at least three times with three spikelets per treatment. CK_1, 2, 3, 4 indicate that plants were sprayed with H2O for 1 h, 2.5 h, 4.5 h, and 6.5 h; LM_1, 2, 3 indicate that plants were sprayed with 0.5 mM MeJA for 1 h, 2.5 h, and 4.5 h; HM_1, 2 indicate that plants were sprayed with 2.0 mM MeJA for 1 h and 2.5 h

α-Amylase activity before and after floret opening

As the previous studies reported, carbohydrate metabolism is closely related to the flowering (Santos et al. 2016; Winde et al. 2017). To preliminary understand the main carbohydrate metabolic alternations of sorghum before and after floret opening, we investigated the α-amylase activity. We found that the α-amylase activity of sorghum was increased significantly after floret opening (Fig. 1), which means that the content of maltose increased significantly. The results indicated a close correlation between carbohydrates metabolism and flowering.

Fig. 1.

Fig. 1

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The α-amylase activity of sorghum before and after floret opening. The five bars 1–5 on the x-axis represent the α-amylase activity of samples treated with 2.0 mM exogenous MeJA for 2.5 h, 0.5 mM exogenous MeJA for 4.5 h, 0.5 mM exogenous MeJA for 2.5 h, 0 mM exogenous MeJA for 2.5 h, and 0 mM exogenous MeJA for 4.5 h

Global analysis of transcriptome

To investigate the molecular mechanisms of exogenous MeJA to promote floret opening, transcriptome sequencing was employed to analyze the responses and changes of gene expression under different MeJA concentrations. Nine libraries were constructed from samples treated with different concentrations of MeJA containing three biological replicates. After removing reads containing adapters and more than 10% of unknown nucleotides, approximately 24 million clean reads per sample were obtained. The Q20 value of reads was all more than 96%, which indicated the high-quality of reads. When the clean reads were mapped to the whole reference genome sequences, it was found that at least 93% of the clean reads for each sample could be mapped to the genome and more than 86% of the clean reads for each sample could be mapped to the genome uniquely (Table S2). The results indicated that our transcriptome data were good enough for further analysis.

Identification of DEGs with the MeJA treatment

We further characterized the DEGs between samples with and without exogenous MeJA treatment. Figure 2a shows that the most number of DEGs was presented between samples after treating with water and 2.0 mM exogenous MeJA in 2.5 h, with 3456 up-regulated genes (accounting for 61% of all significant DEGs) and 2219 down-regulated genes (accounting for the remaining 39%). After 4.5 h, between samples treated with water and 0.5 mM exogenous MeJA, a total of 5146 DEGs were identified, with 3694 up-regulated genes (72%) and 1452 down-regulated genes (28%). However, 1 h after 0.5 mM and 2.0 mM MeJA treatment, there were fewer genes which were differentially expressed (136 and 1260). For these DEGs, 81 DEGs were commonly shared among five groups (Fig. 2b). The numbers of specific DEGs between 2.0 mM exogenous MeJA treatments in 2.5 h and the control (1817) as well as 0.5 mM MeJA treatment in 4.5 h and the control (1449) were remarkably greater than the specific DEGs of the other three groups. These results indicated that the complex transcriptional biological events were more evident as the MeJA exposure time and concentration increased. All commonly shared DEGs were further analyzed by KEGG pathway enrichment. KEGG pathway analysis showed that these DEGs were mainly enriched in the pathway of biosynthesis of secondary, plant hormone signal transduction, linoleic acid metabolism, and phenylpropanoid biosynthesis (Fig. S2).

Fig. 2.

Fig. 2

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Analysis of DEGs between samples with different concentrations of MeJA. a DEGs’ distribution between each two samples; b Venn diagram exhibiting the DEGs’ distribution in eight samples. CK_1, 2, 3: plants were sprayed with H2O for 1 h, 2.5 h, and 4.5 h, LM_1, 2, 3: plants were sprayed with 0.5 mM MeJA for 1 h, 2.5 h, and 4.5 h, HM_1, 2: plants were sprayed with 2.0 mM MeJA for 1 h and 2.5 h. VS: The comparisons of DEGs between two groups. Most number of DEGs was presented between samples after treating with water and 2.0 mM exogenous MeJA in 2.5 h

Functional classification using the KEGG pathway

These DEGs between samples without and with 2.0 mM exogenous MeJA for 2.5 h and 0.5 mM exogenous MeJA for 4.5 h treatment were annotated based on KEGG database. As shown in Fig. 3, these DEGs could be assigned to five main categories, including cellular processes, environmental information processing, genetic information processing, metabolism, and organismal systems. Among the five main categories, the largest category was metabolism. In the category, besides of the global and overview maps, the largest subcategory was carbohydrate metabolism, which contains 409 DEGs, followed by lipid metabolism (274) between samples without and with 2.0 mM exogenous MeJA for 2.5 h treatment. When referring to 0.5 mM exogenous MeJA treatment for 4.5 h, the similar results were observed for carbohydrate metabolism, which was the largest subcategory in the metabolism category. Furthermore, 307 and 309 DEGs between samples without and with 2.0 mM exogenous MeJA treatment for 2.5 h and 0.5 mM exogenous MeJA treatment for 4.5 h were assigned to the subcategory of signal transduction.

Fig. 3.

Fig. 3

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Pathway assignment based on KEGG annotation of DEGs. a KEGG annotation of DEGs between samples without and with 2.0 mM exogenous MeJA for 2.5 h treatment. b KEGG annotation of DEGs between samples without and with 0.5 mM exogenous MeJA for 4.5 h treatment. The P values we corrected for multiple testing to produce q values. The cutoff for the P values was set as 0.05

DEGs involved in glycolysis/gluconeogenesis pathway

As shown in Fig. 1, the amylase activity is increased after floret opening. Therefore, we analyzed the DEGs involved in glycolysis/gluconeogenesis pathway. Glycolysis is the process of converting glucose into pyruvate and generating small amounts of ATP (energy) and NADH (reducing power). Gluconeogenesis is a synthesis pathway of glucose from non-carbohydrate precursors, which is essentially a reversal of glycolysis with minor variations of alternative paths. A total of 34 genes encoding 16 enzymes in this pathway were expressed differentially between control and samples treated with different concentrations of MeJA (Fig. 4). When treated with 2.0 mM MeJA in 2.5 h, most of enzyme genes converting glucose into pyruvate were down-regulated, such as phosphoglucomutase (EC: 5.4.2.2), glucose-6-phosphate isomerase (5.3.1.9), phosphoglycerate mutase (5.4.2.12), andfructose-bisphosphate aldolase (4.1.2.13). Pyruvate is further converted to lactate or ethanol under oxygen free conditions. In this study, we found that the gene-encoding lactate dehydrogenase was not significantly different when treated with MeJA, while most of enzyme genes converting pyruvate to ethanol were highly expressed when treated with 2.0 mM MeJA in 2.5 h. To further confirm the reliability of DEGs obtained from the RNA-seq analysis, 15 genes involved in the carbohydrates were selected for qRT-PCR analysis. The results of qRT-PCR were corroborated those of transcriptome analysis (Fig. S3).

Fig. 4.

Fig. 4

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Genes of sorghum involved in glycolysis/gluconeogenesis pathway. Gene expression levels were calculated using the FPKM method. The bar at right represents relative expression values, thereby green color representing low-level expression, black shows medium-level expression, and red signifies high-level expression. Most of enzyme genes converting pyruvate to ethanol were highly expressed when treated with 2.0 mM MeJA in 2.5 h. The number of bars indicates the number of genes encoded the enzyme. CK_1, 2, 3: plants were sprayed with H2O for 1 h, 2.5 h, and 4.5 h, LM_1, 2, 3: plants were sprayed with 0.5 mM MeJA for 1 h, 2.5 h, and 4.5 h, HM_1, 2: plants were sprayed with 2.0 mM MeJA for 1 h and 2.5 h

DEGs involved in tricarboxylic acid cycle

In aerobic conditions, pyruvate is converted to CO2 and H2O through tricarboxylic acid cycle, also known as citrate cycle (TCA cycle), which is an important aerobic pathway for the final steps of the oxidation of carbohydrates and fatty acids. Before TCA cycle, pyruvate is first converted to acetyl-coenzymeA under multienzyme complex including pyruvate dehydrogenase (EC: 1.2.4.1). In this study, the gene-encoding pyruvate dehydrogenase was down-regulated when treated with 0.5 mM MeJA in 4.5 h (Fig. 5). Then, the two-carbon acetyl group in acetyl-CoA is transferred to the four-carbon compound of oxaloacetate to form the six-carbon compound of citrate. In a series of reactions, two carbons in citrate are oxidized to CO2 and the reaction pathway supplies NADH for use in the oxidative phosphorylation and other metabolic processes. In this pathway, the genes encoding aconitate hydratase (EC: 4.2.1.3), citrate synthase (2.3.3.1), and malate dehydrogenase (1.1.1.37) were highly expressed when treated with 0.5 mM MeJA in 4.5 h and treated with 2.0 mM MeJA in 2.5 h. Other enzymes in this pathway, such as isocitrate dehydrogenase (1.1.1.42) and succinate dehydrogenase (1.3.5.1), were up-regulated when treated with 2.0 mM MeJA in 2.5 h. The detailed EC number information of DEGs in this pathway is showed in Table S3.

Fig. 5.

Fig. 5

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Genes involved in tricarboxylic acid cycle of sorghum. Gene expression levels were calculated using the FPKM method. The bar at right represents relative expression values, thereby green color representing low-level expression, black shows medium-level expression, and red signifies high-level expression. Genes involved in the tricarboxylic acid cycle were mostly highly expressed when treated with 2.0 mM MeJA in 2.5 h. The number of bars indicates the number of genes encoded the enzyme. CK_1, 2, 3: plants were sprayed with H2O for 1 h, 2.5 h, and 4.5 h, LM_1, 2, 3: plants were sprayed with 0.5 mM MeJA for 1 h, 2.5 h, and 4.5 h, HM_1, 2: plants were sprayed with 2.0 mM MeJA for 1 h and 2.5 h

Genes related to KUP system potassium uptake

The osmolarity changes in flowers are believed to be the main driving force for flower opening and this is thought to be involved into the metabolism and flux of ions. Therefore, we identified genes related to KUP system potassium uptake and analyzed their expression patterns in sorghum treated with MeJA. A total of 28 genes related to KUP system potassium uptake performed significant difference between control and samples treated with MeJA. As expected, most genes related to KUP system potassium uptake were highly expressed in samples treated with 0.5 mM MeJA in 4.5 h and 2.0 mM MeJA in 2.5 h, such as gene 8061597, 8,065,633, 110,437,595, and 110,429,937 (Fig. 6). In the early stage of treating with MeJA (LM_1 and HM_1), these genes were expressed lower compared with samples treating with MeJA longer (HM_2 and LM_3), except gene 110435639. The results suggest that potassium uptake protein could be induced for MeJA treatments of long duration, which may play a key role in the flowering process of sorghum.

Fig. 6.

Fig. 6

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Transcriptional changes of genes related to KUP system potassium uptake. Gene expression levels were calculated using the FPKM method. The bar at right represents relative expression values, thereby blue color representing low-level expression, white shows medium-level expression, and red signifies high-level expression. CK_1, 2, 3: plants were sprayed with H2O for 1 h, 2.5 h, and 4.5 h, LM_1, 2, 3: plants were sprayed with 0.5 mM MeJA for 1 h, 2.5 h, and 4.5 h, HM_1, 2: plants were sprayed with 2.0 mM MeJA for 1 h and 2.5 h

Identification and expression analysis of aquaporin

Plants during flowering period are very sensitive to water stress, which would directly affect floret opening. Water supplies are determined by aquaporin genes expression and its activities. To investigate the reason why higher concentrations of MeJA could promote the floret opening of sorghum, the expression levels of aquaporin genes were studied in sorghum treated with MeJA. There were 36 aquaporin genes which showed significant difference between control and samples treated with MeJA (Fig. 7). Most aquaporin genes were highly expressed in samples treated with 0.5 mM MeJA in 4.5 h and 2.0 mM MeJA in 2.5 h. The trend of aquaporin genes expression level is quite similar to that of genes related to KUP system potassium uptake. It is that the expression level of these genes was increasing with the MeJA treating time.

Fig. 7.

Fig. 7

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Express patterns of aquaporin genes in sorghum after treating with MeJA. Gene expression levels were calculated using the FPKM method. The bar at right represents relative expression values, thereby blue color representing low-level expression, white shows medium-level expression, and red signifies high-level expression. CK_1, 2, 3: plants were sprayed with H2O for 1 h, 2.5 h, and 4.5 h, LM_1, 2, 3: plants were sprayed with 0.5 mM MeJA for 1 h, 2.5 h, and 4.5 h, HM_1, 2: plants were sprayed with 2.0 mM MeJA for 1 h and 2.5 h

Discussion

Flowering time is a major determinant of the adaptation of plants to their environments by tailoring vegetative and reproductive growth phases to local climatic effects in sorghum and other cereal crops. The relationship of MeJA and floret opening was reported in many studies previously. For example, our earlier investigation indicated that floret opening of excised spikelets in sorghum was significantly stimulated by immersing into 2 mM (MeJA) solution and Salicylic acid (SA) could abolish the effect of MeJA on the opening of spikelets (Gao et al. 2004). However, in some other studies, they reported that JA biosynthetic enzymes are transiently activated in pedicellate spikelets of multiseeded (msd) Sorghum bicolor mutants that all spikelets are fertile and set grain (Dampanaboina et al. 2019; Gladman et al. 2019; Jiao et al. 2018). In our study, higher exogenous MeJA significantly promoted the floret opening in sorghum (Table 1). Regardless, MeJA plays a role in floret opening in sorghum. To better understand the molecular mechanisms by which exogenous MeJA promotes floret opening, RNA-seq was employed in this study to analyze the responses and changes of gene expression under varied MeJA concentrations and exposure times.

Based on KEGG annotation of DEGs between samples without and with 2.0 mM exogenous MeJA treatment for 2.5 h and 0.5 mM exogenous MeJA treatment for 4.5 h, large amounts of DEGs were assigned to the subcategory of carbohydrate metabolism and lipid metabolism (Fig. 3). Additionally, through analyzing the DEGs involved in glycolysis/gluconeogenesis pathway, we found that most genes that convert glucose into pyruvate were down-regulated, while most genes that convert pyruvate to ethanol were highly expressed when treated with 2.0 mM MeJA in 2.5 h (Fig. 4). Furthermore, genes involved in the tricarboxylic acid cycle were mostly highly expressed when treated with 2.0 mM MeJA in 2.5 h (Fig. 5). TCA cycle is an important aerobic pathway for the final steps of the oxidation of carbohydrates and fatty acids. These results, combined with the observed change in amylase activity, indicated a close correlation between carbohydrate metabolism and flowering. Indeed, the important role of carbohydrate metabolism in flowering has been observed in multiple species (Santos et al. 2016; Winde et al. 2017). Wang et al. indicated that non-structural carbohydrates in petals, including accumulation, and hydrolysis, change dramatically during the development and senescence of florets in gentian (Wang et al. 2016). In Arabidopsis, high concentrations of sucrose were shown to delay flowering, while low levels of sucrose (1%) promoted flowering (Ohto et al. 2001). Mukherjee et al. reported that the altered expression patterns of genes involved in glycolysis/gluconeogenesis pathway caused early flowering in a late maturing native indica rice cultivar (Mukherjee et al. 2012). Interestingly, genes involved in glycolysis/gluconeogenesis pathway which caused early flowering in rice are known to be gibberlin regulated (Asad et al. 2006). These studies were reconcile with our results that DEGs involved in glycolysis/gluconeogenesis pathway which regulated by MeJA might lead to early flowering. The reason may be that the transition from vegetative to generative phase demanded additional energy flow from carbohydrates metabolism.

In addition, potassium uptake proteins were also reported to be important in the regulation of the flowering process in many plant species (Lu et al. 2011). For example, knocking-out of two potassium uptake proteins in Arabidopsis, NHX1 and NHX2, resulted in a significant change in intravacuolar K + and then influenced flowering (Bassil et al. 2011). A recent study interfering OsCHX14, a gene that encodes potassium uptake protein in rice, revealed that OsCHX14 plays an important role in regulating the opening and closure of rice flowers, as regulated by the JA signaling pathway (Yi et al. 2016). In this study, most genes related to KUP system potassium uptake were highly expressed in samples treated with 0.5 mM MeJA in 4.5 h and 2.0 mM MeJA in 2.5 h, which promoted sorghum flowering. The above data suggest that potassium uptake proteins are important in regulation of the flowering process.

Furthermore, the process of floret opening involves water uptake by petal cells, in which aquaporins serve as the channels for water transport across biological membranes (Ariani and Gepts 2015). Experimental evidence has showed that the expression level of a TIP-type aquaporin gene from rose was maintained at a high level during rapid flower opening, and decreased when the flowers were fully opened (Xue et al. 2009). Azad et al. also indicated that phosphorylation of plasma membrane aquaporin regulates temperature-dependent opening of tulip petals (Kalam et al. 2008). In rose, a plasma membrane aquaporin (PIP) gene, Rh-PIP2, is involved in ethylene-regulated petal expansion (Ma et al. 2008). In the present study, most aquaporin genes were highly expressed in samples treated with 0.5 mM MeJA in 4.5 h and 2.0 mM MeJA in 2.5 h, which promote flowering of sorghum. In the early stage of treating with MeJA (LM_1 and HM_1), these genes were expressed lower compared with samples treating with MeJA longer (Fig. 6). Thus, aquaporins were confirmed to play a crucial role in the process of floret opening.

In summary, the degree of effects of MeJA treatment depended on MeJA dosage and exposure time. Transcriptome sequencing analysis revealed that genes involved in carbohydrate metabolism, potassium uptake, and aquaporins showed significant differential expression between control samples and samples treated with different concentrations of exogenous MeJA. These results provide insight into the effect of MeJA on flowering time and elucidate the possible molecular mechanisms that promote flowering by exogenous MeJA application.

Supplementary Information

Below is the link to the electronic supplementary material.

13205_2021_2743_MOESM1_ESM.xlsx (11.3KB, xlsx)

Supplementary file 1 (XLSX 11 KB) Table S1 qRT-PCR primers used in this study.

13205_2021_2743_MOESM2_ESM.xlsx (11.5KB, xlsx)

Supplementary file 2 (XLSX 11 KB) Table S2 Summary of the sequencing data of sorghum under exogenous methyl jasmonate.

13205_2021_2743_MOESM3_ESM.xlsx (10.8KB, xlsx)

Supplementary file 3 (XLSX 11 KB) Table S3 Differential genes involved in Glycolysis / Gluconeogenesis pathway and Tricarboxylic acid cycle in sorghum.

13205_2021_2743_MOESM4_ESM.tif (48.7MB, tif)

Supplementary file 4 (TIF 49889 KB) Fig. S1 The phenotypic changes in control and treated sorghum. A: control; B: treated sorghum.

13205_2021_2743_MOESM5_ESM.tif (17MB, tif)

Supplementary file 5 (TIF 17433 KB) Fig. S2 KEGG pathway enrichment of commonly shared DEGs.

13205_2021_2743_MOESM6_ESM.tif (52.9MB, tif)

Supplementary file 6 (TIF 54189 KB) Fig. S3 Expression profiles of 15 selected genes in control and treated sorghum determined by qRT-PCR. The bars represent the average (±SE) of biological repeats.

Acknowledgements

The authors thank Gongbo Lv for critical reading of this manuscript.

Authors' contributions

Conceptualization, S.L. and X.Z.; methodology, Y.H., Y.F., and X.Z.; validation, S.L. and X.Z.; writing—original draft preparation, S.L. and X.Z.; supervision, X.Z.; project administration, X.Z.; funding acquisition, S.L. and X.Z.

Funding

This research was funded by National Natural Science Foundation of China (31360297), “Gan Po Excellence 555 Project”of Jiangxi Province and the Science and Technology Support Program of Jiangxi Province (20111BBF60009).

Availability of data and materials

The sequencing data were deposited in the NCBI Sequencing Read Archive (SRA) database (Bioproject: PRJNA629423; BioSample: SAMN14775430 and SAMN14775431 for samples CK_1 and CK_2, SAMN14775456-SAMN14775462 for samples CK_3, CK_4, LM_1, LM_2, LM_3, HM_1, HM_2).

Declarations

Ethics approval and consent to participate

Not applicable.

Competing interests

The authors declare that they have no conflict of interest in the publication.

References

  1. Ariani A, Gepts P. Genome-wide identification and characterization of aquaporin gene family in common bean (Phaseolus vulgaris L.) Mol Genet Genomics. 2015;290:1771–1785. doi: 10.1007/s00438-015-1038-2. [DOI] [PubMed] [Google Scholar]
  2. Asad J, Hidemitsu N, Hirokazu H, Hiroaki I, Hiroshi M, Setsuko K. Gibberellin regulates mitochondrial pyruvate dehydrogenase activity in rice. Plant Cell Physiol. 2006;47:244–253. doi: 10.1093/pcp/pci241. [DOI] [PubMed] [Google Scholar]
  3. Barrero Farfan ID, Bergsma BR, Johal G, Tuinstra MR. A stable allele in sorghum and a molecular marker to facilitate selection. Crop Sci. 2012;52:2063. doi: 10.2135/cropsci2011.12.0631. [DOI] [Google Scholar]
  4. Bassil E, et al. The Arabidopsis Na+/H+ antiporters NHX1 and NHX2 control vacuolar pH and K+ homeostasis to regulate growth, flower development, and reproduction. Plant Cell. 2011;23:3482–3497. doi: 10.1105/tpc.111.089581. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Buckler ES, et al. The genetic architecture of maize flowering time. Science. 2009;325:714–718. doi: 10.1126/science.1174276. [DOI] [PubMed] [Google Scholar]
  6. Chen Y, et al. SOAPnuke: a mapreduce acceleration supported software for integrated quality control and preprocessing of high-throughput sequencing data. Giga Sci. 2018;7:1–6. doi: 10.1093/gigascience/gix120. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Cheng XJ, et al. Transcriptome analysis confers a complex disease resistance network in wild rice Oryza meyeriana against Xanthomonas oryzae pv. oryzae. Sci Rep. 2016;6:38215. doi: 10.1038/srep38215. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Cui J, Ren G, Qiao H, Xiang X, Huang L, Chang J. Comparative transcriptome analysis of seedling stage of two sorghum cultivars under salt stress. J Plant Growth Regul. 2018;37:986–998. doi: 10.1007/s00344-018-9796-9. [DOI] [Google Scholar]
  9. Dampanaboina L, et al. Sorghum MSD3 encodes an ω-3 fatty acid desaturase that increases grain number by reducing jasmonic acid levels. Int J Mol Sci. 2019;20:5359. doi: 10.3390/ijms20215359. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Gao X, Zeng X, Xia K, Yoshihara T, Zhou X. Interactive effects of methyl jasmonate and salicylic acid on floret opening in spikelets of Sorghum. Plant Growth Regul. 2004;43:269–273. doi: 10.1023/B:GROW.0000046004.77424.1d. [DOI] [Google Scholar]
  11. Gladman N, Jiao Y, Lee YK, Zhang L, Xin Z. Fertility of pedicellate spikelets in sorghum is controlled by a jasmonic acid regulatory module. Int J Mol Sci. 2019;20:4951. doi: 10.3390/ijms20194951. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. He B, Hu Z, Ma L, Li H, Ai M, Han J, Zeng B. Transcriptome analysis of different growth stages of Aspergillus oryzae reveals dynamic changes of distinct classes of genes during growth. BMC Microbiol. 2018;18:12. doi: 10.1186/s12866-018-1158-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. He B, Ma L, Hu Z, Li H, Zeng B. Deep sequencing analysis of transcriptomes in Aspergillus oryzae in response to salinity stress. Appl Microbiol Biotechnol. 2018;102:1–10. doi: 10.1007/s00253-017-8535-7. [DOI] [PubMed] [Google Scholar]
  14. Huntley RP, Tony S, Prudence MM, Aleksandra S, Carlos B, Martin MJ, Claire OD. The GOA database: gene ontology annotation updates for 2015. Nuclc Acids Res. 2015;43:1057–1063. doi: 10.1093/nar/gku1113. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Jiao Y, Lee YK, Gladman N, Chopra R, Xin Z. MSD1 regulates pedicellate spikelet fertility in sorghum through the jasmonic acid pathway. Nat Commun. 2018;9:822. doi: 10.1038/s41467-018-03238-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Kalam AA, Maki K, Yoshihiro S, Takahiro I, Hitoshi S. Characterization of four plasma membrane aquaporins in tulip petals: a putative homolog is regulated by phosphorylation. Plant Cell Physiol. 2008;49:1196–1208. doi: 10.1093/pcp/pcn095. [DOI] [PubMed] [Google Scholar]
  17. Kiani M (2014) Transcriptome analysis of cell wall related genes in high biomass energy sorghum plant animal genome
  18. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat Methods. 2012;9:357–359. doi: 10.1038/nmeth.1923. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinform. 2011;12:323. doi: 10.1186/1471-2105-12-323. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Lu Y, Chanroj S, Zulkifli L, Johnson MA, Uozumi N, Sze CH. Pollen tubes lacking a pair of K transporters fail to target ovules in arabidopsis. Plant Cell. 2011;23:81–93. doi: 10.1105/tpc.110.080499. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Ma N, et al. Rh-PIP2;1, a rose aquaporin gene, is involved in ethylene-regulated petal expansion. Plant Physiol. 2008;148:894–907. doi: 10.1104/pp.108.120154. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Madugula P, Uttam AG, Tonapi VA, Ragimasalawada M, Snowdon R. Fine mapping of Rf2, a major locus controlling pollen fertility restoration in sorghum A1 cytoplasm, encodes a PPR gene and its validation through expression analysis. Plant Breeding. 2018;137:148–161. doi: 10.1111/pbr.12569. [DOI] [Google Scholar]
  23. Miller GL. Use of dinitrosalicylic acid reagent for determination of reducing sugar. Anal Biochem. 1959;31:426–428. [Google Scholar]
  24. Minoru K, et al. KEGG for linking genomes to life and the environment. Nucleic Acids Res. 2008;36:480–484. doi: 10.1093/nar/gkm882. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Mukherjee R, Gayen S, Chakraborty A, Bhattacharyya J, Maiti MK, Basu A, Sen SK. Double-stranded RNA-mediated downregulation of gene expression to shorten maturation time of a late maturing native rice cultivar, Badshahbhog. Crop Sci. 2012;52:1743–1753. doi: 10.2135/cropsci2011.07.0352. [DOI] [Google Scholar]
  26. Murphy RL, et al. Coincident light and clock regulation of pseudoresponse regulator protein 37 (PRR37) controls photoperiodic flowering in sorghum. Proc Natl Acad Sci. 2011;108:16469–16474. doi: 10.1073/pnas.1106212108. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Niwa T, Suzuki T, Takebayashi Y, Ishiguro R, Higashiyama T, Sakakibara H, Ishiguro S. Jasmonic acid facilitates flower opening and floral organ development through the upregulated expression of SlMYB21 transcription factor in tomato. Biosci Biotechnol Biochem. 2018;82:292–303. doi: 10.1080/09168451.2017.1422107. [DOI] [PubMed] [Google Scholar]
  28. Ohto MA, Onai K, Furukawa Y, Aoki E, Nakamura AK. Effects of sugar on vegetative development and floral transition in arabidopsis. Plant Physiol. 2001;127:252–261. doi: 10.1104/pp.127.1.252. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Ozaki Y, Kato-Noguchi H. Effects of benzoxazinoids in wheat residues may inhibit the germination, growth and gibberellin-induced α-amylase activity in rice. Acta Physiol Plant. 2016;38:24. doi: 10.1007/s11738-015-2041-2. [DOI] [Google Scholar]
  30. Pak H, et al. The effect of exogenous methyl jasmonate on the flowering time, floral organ morphology, and transcript levels of a group of genes implicated in the development of oilseed rape flowers (Brassica napus L.) Planta. 2009;231:79–91. doi: 10.1007/s00425-009-1029-9. [DOI] [PubMed] [Google Scholar]
  31. Palakolanu SR, Dumbala SR, Kaliamoorthy S, Pooja BM, Vincent V, Sharma KK. Evaluation of sorghum [Sorghum bicolor (L.)] reference genes in various tissues and under abiotic stress conditions for quantitative real-time PCR data normalization. Front Plant Sci. 2016;7:529. doi: 10.3389/fpls.2016.00529. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Paterson AH, et al. The Sorghum bicolor genome and the diversification of grasses. Nature. 2009;457:551–556. doi: 10.1038/nature07723. [DOI] [PubMed] [Google Scholar]
  33. Quinby JR, Karper RE. The inheritance of three genes that influence time of floral initiation and maturity date in milo 1. Agron J. 1945;37:916–936. doi: 10.2134/agronj1945.00021962003700110006x. [DOI] [Google Scholar]
  34. Santos MNDS, Mapeli AM, Tolentino MM. Carbohydrate metabolism in floral structures of Lilium pumilum in different development stages. Ciencia Rural. 2016;46:7–10. [Google Scholar]
  35. Tang W, Tu Y, Cheng X, Zhang L, He B. Genome-wide identification and expression profile of the MADS-box gene family in Erigeron breviscapus. PLoS ONE. 2019;14:e0226599. doi: 10.1371/journal.pone.0226599. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Thurber CS, Ma JM, Higgins RH, Brown PJ. Retrospective genomic analysis of sorghum adaptation to temperate-zone grain production. Genome Biol. 2013;14:R68. doi: 10.1186/gb-2013-14-6-r68. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Trapnell CRA, Goff L, Pertea G, Kim D, Kelley DR. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat Protoc. 2012;7:562–578. doi: 10.1038/nprot.2012.016. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Wang Y, Funnell KA, Eason JR, Morgan ER, Woolley DJ. Osmotic regulation via carbohydrate metabolism drives petal expansion and floret opening in gentian 'Showtime Spotlight'. Sci Hortic. 2016;211:19–25. doi: 10.1016/j.scienta.2016.08.004. [DOI] [Google Scholar]
  39. Winde J, Andersen UB, Kjaer KH, Pagter M. Variation in freezing tolerance, water content and carbohydrate metabolism of floral buds during deacclimation of contrasting blackcurrant cultivars. Acta Physiol Plant. 2017;39:201. doi: 10.1007/s11738-017-2503-9. [DOI] [Google Scholar]
  40. Xie C, et al. KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases. Nucleic Acids Res. 2011;39:W316–W322. doi: 10.1093/nar/gkr483%JNucleicAcidsResearch. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Xue J, Yang F, Gao J. Isolation of Rh-TIP1;1, an aquaporin gene and its expression in rose flowers in response to ethylene and water deficit. Postharvest Biol Technol. 2009;51:407–413. doi: 10.1016/j.postharvbio.2008.08.011. [DOI] [Google Scholar]
  42. Young MD, Wakefield MJ, Smyth GK, Oshlack A. Gene ontology analysis for RNA-seq: accounting for selection bias. Genome Biol. 2010;11:R14–R14. doi: 10.1186/gb-2010-11-2-r14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Yu X, Chen G, Tang B, Zhang J, Zhou S, Hu Z. The Jasmonate ZIM-domain protein gene SlJAZ2 regulates plant morphology and accelerates flower initiation in Solanum lycopersicum plants. Plant Sci Int J Exp Plant Biol. 2018;267:65. doi: 10.1016/j.plantsci.2017.11.008. [DOI] [PubMed] [Google Scholar]
  44. Zhi-Qiang Y, Hai XU, Zuo-Bin MA, Dong-Chang G, Zheng-Jin XU. Differential response of floret opening to exo-methyl jasmonate between Subsp. Indica and Subsp. Japonica in Rice Scientia Agricultura Sinica. 2014;47:2529–2540. [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

13205_2021_2743_MOESM1_ESM.xlsx (11.3KB, xlsx)

Supplementary file 1 (XLSX 11 KB) Table S1 qRT-PCR primers used in this study.

13205_2021_2743_MOESM2_ESM.xlsx (11.5KB, xlsx)

Supplementary file 2 (XLSX 11 KB) Table S2 Summary of the sequencing data of sorghum under exogenous methyl jasmonate.

13205_2021_2743_MOESM3_ESM.xlsx (10.8KB, xlsx)

Supplementary file 3 (XLSX 11 KB) Table S3 Differential genes involved in Glycolysis / Gluconeogenesis pathway and Tricarboxylic acid cycle in sorghum.

13205_2021_2743_MOESM4_ESM.tif (48.7MB, tif)

Supplementary file 4 (TIF 49889 KB) Fig. S1 The phenotypic changes in control and treated sorghum. A: control; B: treated sorghum.

13205_2021_2743_MOESM5_ESM.tif (17MB, tif)

Supplementary file 5 (TIF 17433 KB) Fig. S2 KEGG pathway enrichment of commonly shared DEGs.

13205_2021_2743_MOESM6_ESM.tif (52.9MB, tif)

Supplementary file 6 (TIF 54189 KB) Fig. S3 Expression profiles of 15 selected genes in control and treated sorghum determined by qRT-PCR. The bars represent the average (±SE) of biological repeats.

Data Availability Statement

The sequencing data were deposited in the NCBI Sequencing Read Archive (SRA) database (Bioproject: PRJNA629423; BioSample: SAMN14775430 and SAMN14775431 for samples CK_1 and CK_2, SAMN14775456-SAMN14775462 for samples CK_3, CK_4, LM_1, LM_2, LM_3, HM_1, HM_2).

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