Introduction
MicroRNAs (miRNAs) are a class of short
endogenous non-coding small RNAs (sRNAs) with a length of 20–24 nucleotides (nt) in eukaryotic organisms (Tian et al. 2014). They had been generally recognized as crucial regulators of gene
expression through negatively regulating their target genes expression at transcriptional or post-transcriptional
levels (Kang et al. 2014;
Li et al. 2016a; Ma et al. 2019). In plants, a large number of studies had indicated that miRNAs had vital roles in many
biological processes including the development of roots, shoots, leaves and
flowers, secondary substance metabolism, biotic and abiotic
stress tolerance and so on (Bulgakov and Avramenko 2015; D’Ario et al. 2017; Ma et al. 2019; Hu et al. 2019; Zhang et al.
2019).
Seeds of crop plants were not only a unit of reproduction, but also as the primary storage organs for nutrients such as starch, lipids,
and proteins (Huang et al. 2017). Therefore,
the successful development of
seeds would directly determine seed crop yield and seed quality and the
successful racial continuation of crop plants. Plant seed
development was an extremely complex and dynamic process, which
involved in the expression regulation of numerous
genes at transcriptional and post-transcriptional
levels (Samad et al. 2017; Savadi 2017; Li et al.
2019a). Currently, the
increasing body of evidence showed that miRNAs played a key regulatory role in plant seed
development. Using high-throughput sequencing
approach, thousands of miRNAs had
been identified in the developing
seed of multiple crop plants, of which many miRNAs were specially or differentially expressed in the developing
seed, indicating the
significance of miRNAs in seed development (Li et al. 2015; Wang et al. 2015, 2016; Bai et al.
2017; Wei et al. 2018; Yu et al. 2019; DeBoer et al. 2019). In fact, some miRNAs had been functionally demonstrated to regulate the seed development, with focus on in rice. For example, OsmiR397 and OsmiR408 positively regulated
the grain size and weight by down-regulating their targets OsLAC
and OsUCL8
expression (Zhang et al. 2013,
2017a), respectively. Interestingly, over-expression miR408 of Arabidopsis
and tobacco also increased the seed size, suggesting that miR408 had a conserved function in the determination of seed size
in seed plants (Pan et al. 2018).
Furthermore, OsmiR398 and OsmiR159 were also revealed that positively regulated
rice grain size using short tandem target mimic (STTM) technology (Zhang et al. 2017b; Peng et al.
2018). On the contrary, OsmiR156 and OsmiR396 targeted OsSPL14 and OsGRF4, respectively, and negatively regulated the grain size (Jiao et al.
2010; Duan et al.
2015; Li et al. 2016b). Likely,
OsmiR1432 and OsmiR167 were also demonstrated to play a negative regulatory role in grain size using STTM technology (Peng et al. 2018; Zhao et al. 2019). In addition, a few of other miRNAs were demonstrated to regulate the nutrients accumulation during seed
development. For example, OsmiR160
positively regulated the
starch accumulation and affected grain size through negative regulating its target OsARF18
expression (Huang et al. 2016).
Overexpression EgmiR5179 of oil palm (Elaeis guineensis Jacq.) in Arabidopsis increased the content of seeds oil and linolenic acid (Gao et al. 2019),
indicating that EgmiR5179 played a positive regulatory role in oil and linolenic
acid accumulation. Similarly, in Camelina sativa, CsmiR167A had positive and negative effects on the biosynthesis of linolenic acid and α-linolenic acid, respectively (Na et al. 2019).
Common buckwheat (Fagopyrum
esculentum) is an important pseudocereal crop
that widely cultivated in the mountainous areas of Asia due to its rich seed
nutrients such as starch, protein, fatty acid,
vitamins, minerals, flavonoids, and dietary fibers (Yasui
et al. 2016; Li et al. 2019b). Therefore, it is very important to insight into the molecular mechanisms of common buckwheat seed development,
as it will contribute to the seed improvement of common
buckwheat especially nutrient accumulation and seed size. So far, several
studies have been performed to investigate the gene expression profile of the
developing common buckwheat seeds using RNA-Seq (Gao et al. 2017;
Shi et al. 2017; Li et al. 2019b). However,
as far as we know, miRNAs in common buckwheat have not been identified and
characterized, and miRNA-mediated regulation in developing
common buckwheat seeds needs to be explored.
In this study, genome-wide identification of miRNAs was
conducted in developing common buckwheat seeds using high-throughput sequencing
technology. Through comprehensive analysis, the conserved, new, and
differentially expressed miRNAs were identified. Furthermore, the differentially expressed miRNAs and its target genes were analyzed,
and the candidate miRNAs that involved in seed development including plant hormone signal
transduction and seed size were also identified based on their target genes annotation. These results extend our knowledge for miRNAs participated in the seed development of common buckwheat.
Materials and Methods
Plant materials and samples collection
Common
buckwheat cultivar “Chitian No. 1” was planted in the
experimental field of the Guizhou Normal University (Guiyang, China; Lat.
26˚62' N, 106˚72' E, Alt. 1168 m) on 20 March 2018. The field
management, seed samples collection was performed as previously
described (Li et al. 2019b). Seeds
were harvested at the 8th, 14th and 21st days after
pollination (Li et al. 2019b).
Construction and sequencing of small RNA libraries
Total RNA
was isolated from three samples using the TaKaRa MiniBEST Plant RNA Extraction Kit (TaKaRa,
Dalian, China) based on the manufacturer’s instructions. RNA samples quality
was examined with a NanoDrop 2000c spectrophotometer
(NanoDrop, Wilmington, DE, USA) and 1.2% agarose gel
electrophoresis, respectively. Small RNA libraries were constructed according
to the previous description by (Xu et al. 2018) and subjected
to high-throughput sequencing using Illumina SE50 system at Biomarker
Technologies Co., Ltd. (Beijing, China).
Small RNA (sRNA) sequencing data analysis
Clean
reads were generated by removing adaptor sequences and low-quality reads in raw
data. Then, the obtained clean reads were used to search on Silva database, GtRNAdb database, Rfam 11.0
database and Repbase database to filter the rRNA,
tRNA, snRNA and snoRNA to product the unannotated clean reads that containing
miRNA using the Bowtie software according to
default parameters (Langmead et al.
2009). The unannotated clean reads were further mapped on the common buckwheat
reference genome (ftp://ftp.kazusa.or.jp/pub/buckwheat/) (Yasui et al. 2016; Li et al. 2019b) using the Bowtie software. After that, these
perfectly matched clean reads were blasted against in miRBase
v. 21.0 (http://www.mirbase.org/index.shtml) to identify the known miRNAs, and
the rest unmapped sequences were used to predict novel miRNAs using miRdeep2
program with score ≤ 5 (Zhang et al. 2015).
Identification of differential expression miRNAs during
common buckwheat seed development
To screen
differentially
expressed miRNAs in the course of common buckwheat seed development, the
expression level of miRNAs in each sample was normalized to TPM using miRNA
counts. Then, the
significantly differential expression miRNAs were identified by using the IDEG6
software with |log2(fold change) | ≥ 1 and P
≤ 0.05 as the threshold (Romualdi et al. 2003).
Prediction and analysis of miRNA target genes
The TargetFinder software was used to predict the potential
miRNAs target genes with default parameters (Allen et al. 2005). Furthermore, the predicted
target genes were further function annotated in Blast2GO 5.0 software
(http://www.blast2go.org/), and search on the KEGG pathway database to
elucidate the pathways, respectively.
qRT-PCR analysis of miRNAs and their targets
To verify
the sequencing data, 12 miRNAs and 4 corresponding target
TFs of among 4 miRNAs were selected to perform qRT-PCR
analysis. The RNA samples were used for qRT-PCR from
these samples that used for small RNA libraries construction. RT-qPCR was
conducted on a CFX96 Real-time System (BIO-RAD, U.S.A.) using SYBR® Premix Ex TaqTM II Kit (TaKaRa, Dalian, China) and
Mir-X miRNA qRT-PCR TB Green® Kit (TaKaRa, Dalian, China), respectively. For miRNAs, the
mature miRNA sequences were used to design the forward primers and the adapter sequences from
the cDNA synthesis
kit (TaKaRa, Dalian, China) were used as the reverse
primers. The U6 snRNA and Actin7 of common buckwheat were used as
the references for miRNAs and their target genes, respectively. Three
biological replicates with three technical replicates were performed for each
sample. The relative expression level of each miRNA or target gene was
calculated by the 2−ΔΔCt method. All primers used
for qRT-PCR were listed in Table S1.
RLM-5′ RACE
The 5′-RLM-RACE RNA
method was used to examine the miRNA-directed cleavage
for their predicted target genes in vivo. The 5′-RLM-RACE was carried out
as described previously (DeBoer et al. 2019). The primers were listed in
Table S2.
Results
sRNA sequencing from developing common buckwheat seeds
Nine sRNA
libraries derived from three different developmental stages
common buckwheat seeds were constructed and sequenced. Upon sequencing, a total of 103,446,033,
57,491,562, and 54,022,652 reads, on average, were obtained for each sample,
respectively (Table 1). After removing the adaptor
sequences, low-quality reads and reads with lengths < 18 or > 30, a total
of 10,412,548 to 44,809,759 clean reads were obtained for each library (Table
1). As shown in Fig. 1, most sRNA sequences in the nine
libraries were 21–24 nt
long. Among them, 24 nt
sRNAs were the most abundant sRNAs in all samples, followed by 23 and 21 nt.
In addition, the number of 21–24 nt sRNAs was dynamic
change during the seed development, with a decreased
trend (Fig. 1). Among these clean reads, 16.93, 16.11, 17.18, 30.85,
21.30, 26.67, 52.28, 55.48 and 51.07% reads for each
library were annotated as rRNA, tRNA, snRNA, snoRNA, and Repbase (Table 1). Furthermore, a total of 49.15, 49.22, 50.01, 46.75,
45.89, 47.23, 49.41, 48.59 and 47.34% unannotated
sRNA for each library were mapped to the common
buckwheat reference genome (Table 1).
Conserved miRNAs in developing common
buckwheat seeds
A total of
96 conserved miRNAs, which divided into 24 families, were identified
(Table S3). Among these families, 3 families (miR156, miR166, and miR319) contained ≥ 10 members, 17 families contained 2 to 7 members, and 4 families (miR159, miR394,
miR828, and miR858) only contained 1 member (Fig. 2a). The lengths of these conserved miRNAs were 19 to 22 nt,
of which 22 nt contained the largest number of miRNAs (52) (Fig. 2b). Among these conserved
miRNAs, about 60% miRNAs (57) were lowly expressed
with < 100 read counts in each library (Table S3). Other 17 and 22 miRNAs
were expressed at a high (read counts > 1000 at least in one sample) and moderate level (100 ≤ read counts ≤
1000) (Table S3), respectively. The fes-miR159-3p had the
highest expression in all 96 conserved miRNAs, with the read counts ranging from 12,912 to 48,871 for each library (Table S3).
Novel miRNAs in developing common buckwheat seeds
To further
identify novel miRNAs in common buckwheat seeds, miRdeep2 program was utilized. As a result, a total of
151 novel miRNAs were predicted (Table S4). Of these, 57
miRNAs were divided into 36 currently known miRNA families while the
remained 94 miRNAs did not show
similarity with any known family in the
miRNA database. The lengths of these
novel miRNAs were 18 nt
(1), 20 nt (1), 21 nt (53),
22 nt (19), 23 nt (6), and
24 nt (71), and the minimum free energy (MFE)
distributed between -113.2 kcal
moL-1 to -10.6 kcal moL-1 (Table S4). In addition, the precursor
lengths of these novel miRNAs
ranged from 64 to 250 nt (Table
S4). Among these novel miRNAs, most
miRNAs were moderately or highly expressed in the developmental common buckwheat seeds. The fes_novel_miR48 displayed the highest expression with the read
counts
ranging from 46,207 to 78,975 for each library (Table S4).
Prediction and functional annotation of the miRNAs
targets
Table 1: Statistical
analysis of sequencing reads in nine sRNA libraries
in common buckwheat seeds
Notes:
S1, S2, and S3 represent the seed samples at three different developmental
stages (pre-filling stage, filling stage, and initial maturity stage), and -1,
-2, and -3 stand for three replicates of each sample
%201115-1124_files/image002.jpg)
Fig. 1: Read
length distribution of small RNAs. S1,
S2, and S3 represent the seed samples at three different developmental stages
(pre-filling stage, filling stage, and initial maturity stage), and -1, -2, and
-3 stand for three replicates of each sample
Target
prediction is a good way to understand the functions of miRNAs. Using the TargetFinder software, 15,403 potential target
genes of 242 miRNAs were
successfully predicted. Of which, 96 conserved miRNAs
targeted 8997 genes, and 146
novel miRNAs targeted 6862
genes. All predicted target genes were further annotated
in eight databases.
As a result, 3,348, 4,305, 1,209, 7,879, 7,760, 5,674, 9,861, and 10552 target
genes were annotated in CGO, GO, KEGG, KOG, Pfam, Swissprot, eggNOG, and NR databases, respectively. The
target genes from GO annotations were
further subjected functional classification (biological process, cellular
component, and molecular function). The 4,305 GO annotated target genes were
assigned to 967 unique biological processes, 188
unique cellular components, and 566
unique molecular functions (Table
S5). The top 15 GO groups of these three categories were represented in Fig. 3. As shown in Fig. 3, a
numerous of target genes involved in DNA metabolic process (GO:0006259), DNA integration (GO:0015074), nucleus
(GO:0005634), membrane (GO:0016020), binding (GO:0005488), and nucleic acid
binding (GO:0003676) (Fig. 3 and Table S5). In addition, based on the NR functional
annotation results, 260
transcription factors (TFs) from 34 TF families were
identified as the potential target genes of 141 miRNAs (including
79 conserved and 62 novel
miRNAs (Table S6). The top five largest TF families were MYB (48), MYB-related
(20), FAR1 (18), C2H2 (15), GRAS (15), bHLH (13), NAC (13), and SBP (13) (Table S6).
Differentially expressed miRNAs during common buckwheat
seed development
The
expression abundance of these identified miRNAs were normalized
to TPM. A good correlation existed between any two biological
replicates of each sample (Table S7). Based on a pairwise comparison
of three samples, a total of 49 miRNAs shown significantly differentially
expressed during common buckwheat seed development (Fig. 4 and Table S8). When
compared S1 to S2, 25 miRNAs,
including 10 up-regulated (7 conserved and 3 novel miRNAs) and 15
down-regulated (13 conserved and 2 novel miRNAs), were identified (Fig. 5 and
Table S8). In comparison S2 vs S3, 9 miRNAs (2 conserved and 7 novel miRNAs) and 6 conserved miRNAs were up- or down-regulated, respectively (Fig. 5 and Table S8).
For S1 vs S3 comparison, 29 miRNAs were differentially expressed with 13
up-regulated (4 conserved and 9 novel miRNAs) and 16
down-regulated (11 conserved and 5 novel miRNAs) (Fig. 5 and
Table S8). In addition, 8 (fes-miR166b-3p, fes-miR166g-3p, fes-miR395a-3p,
fes-miR395b-3p, fes-miR396f-3p,
fes-miR398c-3p, fes-miR530b-5p, fes_novel_miR6), 2 (fes-miR397a-5p and
fes-miR166j-3p), and 8 (fes-miR164a-5p, fes-miR164b-5p, fes-miR164c-5p,
fes_novel_miR85, fes_novel_miR98, fes_novel_miR106, fes_novel_miR114,
fes_novel_miR130) miRNAs were
existed both in S1 vs. S2 and S1 vs. S3, S1 vs. S2 and S2 vs. S3, and
S2 vs. S3 and S1 vs. S3, respectively (Fig. 5 and Table S8). Lastly, 1 miRNA
(fes_novel_miR48) contained in all
three comparisons, which displayed up-trend during common buckwheat seed
development (Fig. 5 and Table S8). Notably,
the expressions of most miRNAs were
changed in only one stage while the
expressions of fes-miR166j-3p,
fes-miR397a-5p, and fes_novel_miR48 were changed in all developmental stages (Fig. 4 and Table S8).
%201115-1124_files/image004.jpg)
Fig. 2: The number of miRNA
family members (a) and the length
distribution of conserved miRNAs (b)
%201115-1124_files/image006.jpg)
Fig. 3: GO annotations of the predicted target genes of identified miRNAs in common buckwheat seed
%201115-1124_files/image008.jpg)
Fig. 4: Heat map diagrams of the differentially
expressed miRNAs in the different developmental stages of common buckwheat seed. MeV 4.9.0
software was used to construct the heat map based on the Log2(TPM)
value of miRNAs. S1, S2, and S3 represent the seed
samples at three different developmental stages (pre-filling stage, filling
stage, and initial maturity stage), and -1, -2, and -3 stand for three
replicates of each sample
%201115-1124_files/image010.jpg)
Fig. 5: Numbers
of differentially expressed miRNAs in different
comparison groups during common buckwheat seed development.
(a) Numbers of up- and down
regulated miRNAs in different comparison groups. (b) Venn diagram for differentially
expressed miRNAs in different comparison groups. S1,
S2, and S3 stand for the seed samples at pre-filling stage, filling stage, and
initial maturity stage, respectively.
%201115-1124_files/image012.jpg)
Fig. 6: GO enrichment of the
differentially expressed miRNAs target genes
Functional prediction of the differentially
expressed miRNAs
To provide
insight into the
potential regulatory functions of these differentially expressed miRNAs,
target
genes of them were searched in the
total predicted target genes. As a result, these 49 differentially expressed
miRNAs were found that have 5845 potential target genes (Table S9).
Of these, fes_novel_miR95 and
fes_novel_miR143 had the smallest number (1) and the largest number (937) target genes, respectively. All the other miRNAs had multiple target genes. In addition, the same
target gene could be targeted by multiple miRNAs (Table S9).
Based on the GO enrichment result, these target genes
were enriched into 36 functional groups, including 19 biological process
categories, 12 cellular component categories, and 14 molecular function
categories (Fig. 6).
For biological process category, the ‘metabolic process’,
‘cellular process’ and ‘single-organism process’ have the top three gene
numbers (Fig. 6). Within cellular component category, the ‘cell part’, ‘cell’,
and ‘organelle’ were the greatest abundance terms (Fig. 6).
Under the molecular function category, ‘binding’, ‘catalytic activity’, and
‘transporter activity’ represented the terms with the highest gene numbers
(Fig. 6).
KEGG
pathway analysis revealed 141 target genes were significantly enriched into 44 pathways (Table S9).
All these enriched pathways could be further classified into the organismal
systems category, metabolism category, cellular
process category, environmental information processing category, and genetic
information processing category. The metabolism category possessed the maximum
number of enriched pathways (22) and target
genes (67), of which pathways
major involved in phenylpropanoid biosynthesis (ko00940), amino sugar and
nucleotide sugar metabolism (ko00520), amino acids biosynthesis (ko01230), and purine metabolism
(ko00230). Furthermore, in environmental information processing category, 8
target genes from 7 miRNAs were
significantly enriched in the “plant
hormone signal transduction” pathway (Table S9). In this pathway, four miRNAs, fes-miR160c-3p,
fes_novel_miR96,
fes_novel_miR130, and fes_novel_miR143, targeted ARF, AUX,
IAA, and SAUR genes, respectively, to respond to the indole-3-acetic acid (IAA) signal (Fig. 7). Two miRNAs, fes-miR397a-5p and fes-miR164a-5p were involved in abscisic acid (ABA) and ethylene (ET)
pathway though targeting SnRK2 and CTR1
genes (Fig. 7), respectively. Three miRNAs,
fes-miR390a-5p,
fes-miR390b-5p, and fes-miR156j-5p targeted BRI1
and CYCD3 genes, respectively, and were involved in brassinosteroid (BR) signal
(Fig. 7).
In addition, based on the NR functional annotation result and homologous
query of target genes, it was found that four miRNAs, fes-miR156j-5p, fes-miR396a-5p,
fes-miR530b-5p, fes_novel_miR48, targeted the
orthologs of 4 known seed size-related AtIKU2,
OsGRF4, OsGIF1, and AtANT
genes, respectively (Li et al. 2019a). These findings indicated that the four miRNAs might play a crucial role
in control the common buckwheat seed size.
qRT-PCR validation of differentially expressed miRNAs
To
validate the reliability of miRNA sequencing data, 8 differentially expressed miRNAs (4 conserved
and 4 novel miRNAs) were further analyzed by qRT-PCR.
As shown in Fig. 8a, except for
fes_novel_miR85, all the examined miRNAs displayed similar expression patterns
between the qRT-PCR results and sequencing data, indicating that the RNA-seq was accurate and reliable.
To further verify the expression relationship
between miRNAs and their targets, 4
differentially expressed miRNAs (fes-miR160c-3p, fes-miR164a-5p,
fes-miR395a-3p,
fes-miR397a-5p)
and their 4 predicted TF target genes (ARF, NAC, bHLH, and MYB) were subjected to qRT-PCR analyzed. As a result, the 4 miRNAs showed strong
negative correlations with their potential
target genes (Fig. 8b), indicating that these miRNAs may play important regulatory roles in common buckwheat seed development though
negatively regulating these
target TF expressions.
miRNA target validation
%201115-1124_files/image014.jpg)
Fig. 7: KEGG pathways related to plant hormone signal
transduction targeted by differentially expressed miRNAs.
Target genes
labeled red represented the corresponding miRNAs were
up-regulated; Target genes labeled green represented the corresponding miRNAs were down-regulated; Target genes labeled purple
represented the corresponding miRNAs were up- or
down-regulated
%201115-1124_files/image016.jpg)
Fig. 8: Validation differentially
expressed miRNAs and their target genes
expression in developing common buckwheat seed using qRT-PCR.
(a) Validation the expression of 8 differentially expressed miRNAs between small RNA sequencing and qRT-PCR. (b) Determination the expression of 4 differentially expressed miRNAs and their 4 target TFs during common buckwheat seed
development. U6 snRNA
and Actin7 as a standard for miRNA and target genes, respectively
To further validate the cleavage sites in target
transcripts, 4 target TFs (ARF, NAC, bHLH, and MYB)
from previous qRT-PCR results were selected to
perform 5′ RLM-RACE analysis. As results, 4 target TFs could be cleavage
by 4 corresponding miRNAs (fes-miR160c-3p, fes-miR164a-5p, fes-miR395a-3p,
fes-miR397a-5p), and the cleavage sites were mapped between the 10th
and 11th nucleotide upstream of the miRNA 5′end (Fig. 9).
%201115-1124_files/image018.png)
Fig. 9: Mapping of the mRNA cleavage sites by 5′
RLM-RACE. Arrows indicated
miRNAs cleavage sites. The numbers above sequences
indicated the detected cleavage site of independent clones
Discussion
Plant seed development was an extremely complex and
dynamic process, which was involved
in the expression regulation of numerous genes (Samad et al. 2017; Savadi 2017; Li et al.
2019a). Previous studies had indicated
that miRNAs played crucial
regulatory roles in seed development of many plants through negatively regulating its
target genes expression (Wei et al. 2017). Nevertheless, miRNAs for seed development had not been well-explored in common buckwheat. In this
study, we used the high-throughput sequencing
technology to insight into the miRNA profiles and identified miRNAs that might
be involved in common buckwheat seed development.
In total, 247 miRNAs (96 conserved and 151 novel) were first identified in common buckwheat. Of these,
242 miRNAs were predicted to target 15,403 genes. Among the 242 miRNAs, 141 miRNAs were found to
target 260 TFs from 34 TF families, which were similar to previous reports that plant miRNAs
perfected targeting transcription factors to
perform potent functions in many developmental processes (Li and Zhang 2016). This suggested that the expression changes of TFs mediated by miRNAs were a conserved regulation network
for different developmental processes in different species. According to
bioinformatics analysis, we found that most conserved and novel miRNAs were
lowly expressed, suggesting that they might have some roles in common buckwheat seed development.
Notably, fes-miR159-3p was the highest expressed conserved miRNA in developing seeds at
all the three developmental stages. In rice,
miR159 was also found displayed higher expression in developing seed (Peng et al. 2014) and it was demonstrated that positively regulated grain
length and width in rice (Jiao et al.
2010; Peng et al. 2018). Similarly,
in tomato, miR159 also displayed higher
expression in developing fruit and played a vital regulatory role in fruit development (Silva et al. 2017). These indicated that
fes-miR159-3p might also have a key regulatory role in the development of tartary
buckwheat seed, and miR159 regulated seed or fruit development was conserved in different plants. Similarly, fes_novel_miR48
was the highest expressed novel miRNA in developing common
buckwheat seeds. Target gene prediction found that fes_novel_miR48 targeted
four GRAS TFs, including two orthologs
of
Arabidopsis SCL3 which as a direct target gene of DELLAs and
positively responding to GA-signaling (Cui et al.
2007). Furthermore, in rice, a GRAS gene was demonstrated regulating seed development (Sun et al.
2013). These indicated that fes_novel_miR48 might also play a crucial regulatory role in common buckwheat seed development through
GRAS mediating GA-signaling.
Generally, miRNAs expression pattern is closely related to its
function. In
multiple crop plants, many miRNAs had been found that differentially expressed in the developing seed (Li et al. 2015; Wang
et al. 2015, 2016; Bai et al. 2017; Wei et al. 2018; Yu et al.
2019; DeBoer et al. 2019), implying
they had key
regulatory roles for
seed development. In our study, 49 miRNAs (31 conserved and 18
novel), were identified as differentially expressed miRNAs during
common buckwheat seed development. Notably, among the conserved miRNAs, most of
them (miR156, miR160, miR164, miR166, miR168, miR169, miR390,
miR395, miR396, miR397, miR398, miR399, and miR408) were also found that
differentially expressed in other seed crops such as peanut (Ma et al.
2018), rice (Peng et al. 2014), wheat (Li et al. 2015), Brassica napus (Wei et al.
2018), maize (Li et al. 2016a). These findings indicated that these conserved miRNAs might have important and conserved regulatory roles in different plants seed development. It was well known that hormone signaling
played a key role
in seed development (Li et al.
2019a). In our study, based on KEGG enrichment analysis of the target genes of
differentially expressed miRNAs, 4 miRNAs, including fes-miR160c-3p (targeting
the ARF), fes_novel_miR96
(targeting AUX1), fes_novel_miR130 (targeting
IAA), and fes_novel_miR143 (targeting
SAUR), were found to be related to
auxin signaling. In barley (Bai et al.
2017) and peanut (Ma et al. 2018), miR160 was also found to
target ARF TFs gene and displayed
increasing expression during seed development. Furthermore, in rice, OsmiR160 had been demonstrated that positively regulated the
grain development through negatively regulating its target OsARF18
expression (Huang et al. 2016). In our study, we observed
the expression level of fes-miR160c-3p and its target ARF were up- and
down-regulated during common buckwheat seed development, respectively, through qRT-PCR analysis. These indicated that miR160 mediated ARF expression was a conserved auxin regulation mechanism in
different plants seed development. In addition, fes-miR397a-5p (targeting SnRK2), fes-miR164a-5p (targeting CTR1), and fes-miR390a-5p (targeting BRI1), fes-miR390b-5p (targeting BRI1) and fes-miR156j-5 (targeting CYCD3) were found involved in ABA, ET,
and BRs signaling. Together, these results suggested that miRNAs might have a significant effect on the regulation of
common buckwheat seed development by effecting different hormone signaling
pathways. To date, there were over 100 genes had been
demonstrated controlled the plant
seed size (Li et al. 2019a). In our
study, four miRNAs, fes-miR156j-5p,
fes-miR396a-5p, fes-miR530b-5p, fes_novel_miR48, were found to target the
orthologs of 4 known seed size-related AtIKU2, OsGRF4, OsGIF1, and AtANT genes, respectively. In Arabidopsis, AtIKU2 had been demonstrated that positively regulated
embryo and endosperm development (Luo et
al. 2005). Interestingly, in our study, fes-miR156j-5p was lowly expressed in S1 and S2 periods (the key periods of embryo
and endosperm development) and highly expressed in S3, suggesting that it might negatively regulate the embryo and endosperm
development of common buckwheat seed through regulating its target gene IKU2 expression. Notably, fes-miR396a-5p targeted the orthologs of OsGRF4. In rice, OsmiR396 was found to target OsGRF4 and negatively regulated the grain size (Duan
et al. 2015; Li et al. 2016b). This indicated that fes-miR396a-5p might also negatively regulate the seed size of common buckwheat, and miR396 might have a conserved role in regulating seed size in
different plants. In
addition, the expression negative correlation and cleavage sites between 4
differentially expressed miRNAs and their target TFs (ARF, NAC, bHLH and MYB) were confirmed by qRT-PCR and 5′ RLM-RACE, respectively, suggesting
that these 4 miRNAs and their corresponding target TFs might play crucial
regulation roles in common buckwheat seed development. Together, our results
indicated that the complex regulatory mechanism of miRNAs for common buckwheat seed
development. However, the detailed roles and mechanism underlying specific
miRNAs involved in seed development remained to be investigated.
Conclusion
A total of
247 miRNAs, including 96 conserved and 151 novel miRNAs,
were first identified in common buckwheat. 15,403 target genes were predicted,
8,997 for 96 conserved miRNAs and 6862 for146 novel miRNAs. 49 miRNAs,
including 31 conserved and 18 novel miRNAs, displayed significantly up- or
down-regulated during common buckwheat seed development. Some differentially
expressed miRNAs that involved in hormone signaling and seed size were
identified based on KEGG and NR annotation. The accumulations of some
differentially expressed miRNAs and their corresponding target genes were
determined in developing common buckwheat seed by qRT-PCR
assays. In addition, the cleavage sites between 4
differentially expressed miRNAs and their target TFs were confirmed by 5′
RLM-RACE. As the first study to identify miRNA in common buckwheat, it not
only provided insights
into the miRNA participated in common buckwheat seed development, but also
laid the
foundation for further miRNA study in common buckwheat.
Acknowledgements
This
research was supported by the National Natural Science Foundation of
China-Project of Karst Science Research Center of Guizhou
Provincial People's Government (U1812401), the National Natural Science
Foundation of China (31701494, 31860408), the Guizhou Provincial Science and
Technology Foundation (QianKeHeJiChu [2019]1235 and QianKeHeJiChu [2016]1107), the Earmarked Fund for
construction of the Key Laboratory for Conservation and Innovation of Buckwheat
Germplasm in Guizhou (QianJiaoHe KY Zi [2017]002),
the Training Program of Guizhou Normal University (QianKeHePingTaiRenCai
[2017]5726), and the Initial Fund for Doctor Research in Guizhou Normal
University (11904/0517051).
Author
Contributions
Hongyou
Li designed and participated in the study, analyzed the data and wrote the
manuscript. Xiaoqian Sun, Chao Ma, and Hengling Meng participated in experiments. Qingfu Chen revised the manuscript.
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