Open Access

Identification of novel microRNAs in the Verticillium wilt-resistant upland cotton variety KV-1 by high-throughput sequencing

  • Xiaohong He2,
  • Quan Sun2,
  • Huaizhong Jiang2,
  • Xiaoyan Zhu2,
  • Jianchuan Mo1,
  • Lu Long1,
  • Liuxin Xiang2,
  • Yongfang Xie2,
  • Yuzhen Shi3,
  • Youlu Yuan3Email author and
  • Yingfan Cai1, 2Email author
SpringerPlus20143:564

https://doi.org/10.1186/2193-1801-3-564

Received: 6 September 2014

Accepted: 16 September 2014

Published: 27 September 2014

Abstract

Plant microRNAs (miRNAs) play essential roles in the post-transcriptional regulation of gene expression during development, flowering, plant growth, metabolism, and stress responses. Verticillium wilt is one of the vascular disease in plants, which is caused by the Verticillium dahlia and leads to yellowing, wilting, lodging, damage to the vascular tissue, and death in cotton plants. Upland cotton varieties KV-1 have shown resistance to Verticillium wilt in multiple levels. However, the knowledge regarding the post-transcriptional regulation of the resistance is limited. Here two novel small RNA (sRNA) libraries were constructed from the seedlings of upland cotton variety KV-1, which is highly resistant to Verticillium wilts and inoculated with the V991 and D07038 Verticillium dahliae (V. dahliae) of different virulence strains. Thirty-seven novel miRNAs were identified after sequencing these two libraries by the Illumina Solexa system. According to sequence homology analysis, potential target genes of these miRNAs were predicted. With no more than three sequence mismatches between the novel miRNAs and the potential target mRNAs, we predicted 49 target mRNAs for 24 of the novel miRNAs.

These target mRNAs corresponded to genes were found to be involved in plant–pathogen interactions, endocytosis, the mitogen-activated protein kinase (MAPK) signaling pathway, and the biosynthesis of isoquinoline alkaloid, terpenoid backbone, primary bile acid and secondary metabolites. Our results showed that some of these miRNAs and their relative gene are involved in resistance to Verticillium wilts. The identification and characterization of miRNAs from upland cotton could help further studies on the miRNA regulatory mechanisms of resistance to Verticillium wilt.

Keywords

Gossypium hirsutum KV-1 Verticillium dahliae Kleb Deep sequencingMicroRNAs

Introduction

Cotton is a fiber crop with worldwide economic importance. It is vulnerable to some diseases such as Fusarium wilt and Verticillium wilt, which caused by the phytopathogenic fungus V. dahliae Kleb and could have severe detrimental effects on the cotton in China, the Americas, and Mediterranean regions (Cai et al.2009; Gao et al.2010). The soil-borne fungal pathogen has displayed extraordinary genetic plasticity and broad-hosts rang in diverse ecological niches (Fradin & Thomma2006). They invade the vascular tissue by invading cortex of root via wounds or epidermal cells to cross the endodermis. The plant could show leaf vein browning, wilting, yellowing, vascular discoloration, defoliation and death after conidia are produced in large numbers and migrate via the xylem to the aerial part of the plants (Klosterman et al.2009; BejaranoAlcazar et al.1997). Consequently, Verticillium wilt is often referred to as the ‘cancer of cotton crops’. Therefore, the control against Verticillium wilt is necessary. However, it is also particularly difficult. The V. dahliae strains D07038 and V991 were used in this study and are moderately virulent and virulent, respectively. At the peak of infection, the defoliating type V991 strain has a disease incidence of 99.10% and a disease index of 81.98 relative to uninfected control plants (Fan & Jiang2006). The moderately toxic and defoliating wild-type D07038 strain was obtained from infected Gossypium hirsutum, plant and had a mean disease index of 46.1%.

Cotton varieties are assigned to five disease-resistance classes based on relative disease index scores. Varieties with relative disease index scores of 0.0 are designated immune (I); scores between 0.1 and 10.0 indicate high resistance (HR); varieties with scores between 10.1 and 20.0 are resistant (R); scores between 20.1 and 35.0 indicate tolerant (T) varieties; and varieties with scores greater than 35.0 are susceptible (S) (Zhang et al.2012a). The cotton variety Zhongzhimian KV-1 (KV-1) was developed at Institute of Plant Protection, Chinese Academy of Agricultural Sciences (Beijing, China) and shows stable high resistance to Verticillium wilt (Zhao et al.2009) with no significant differences between relative disease indices over three consecutive years. In this study we identify the novel miRNAs by high-throughput sequencing applying KV-1 highly resistant to Verticillium wilt.

MicroRNAs are small (~21 nt), single strain and non-coding RNAs which play key role in plant developmental and responses to stress. Precursor stem-loop secondary structures are characteristic features of miRNAs and are conserved across species (Bartel2004; Carrington & Ambros2003). miRNAs are transcribed from DNA. However, those miRNAs are not translated into protein. They regulate functions of other genes at the level of protein synthesis. To date, 4,933 miRNAs which are from 53 eudicotyledon species had been identified and submitted to miRBase (miRBase Release 20.0, http://www.mirbase.org/). A total of 78 precursors and 80 mature Gossypium hirsutum miRNAs have been deposited in miRBase. Previous studies have focused primarily on miRNA expression in ovules and during fiber development. Knowledge of miRNA regulatory mechanisms associated with G. hirsutum resistant to Verticillium wilt is very limited. The miRNAs, miR1321-1334, were predicted and identified in cotton using bioinformatics and the Solexa sequencing method (Yin et al.2012a). Identification of novel miRNAs provides us an important genomic resource to investigate of the miRNAs regulatory mechanism of the resistance to wilt disease in upland cotton.

This study aimed to identify novel miRNAs and their potential target genes, which specifically expressed in the Verticillium wilt-resistant upland cotton variety KV-1. So two independent small RNA (sRNA) libraries were constructed from whole KV-1 seedlings inoculated with the V991 and D07038 V. dahliae strains; the libraries were then sequenced using the Illumina Solexa system. Then the selected upland cotton miRNAs were confirmed by quantitative real time PCR (qRT-PCR).

The potential target genes of these novel miRNAs were predicted by transcriptome sequencing in G. hirsutum. We found some of these targets could function as new transcripts involved in plant-pathogen interactions, which will facilitate identification of candidate genes in G. hirsutum for resistance to V. dehliae.

Materials and methods

Fungal strains and inoculums preparation

The wild-type pathogenic V. dehliae V991 strain are used for inoculations, which was isolated from an infected upland cotton plant. Compared with the intermediately aggressive D07038 strain obtained from the Cotton Research Institute of the Chinese Academy of Agricultural Sciences, V991 is highly toxic, defoliant and more virulent. For conidial production, V991 and D07038 were subcultured from potato dextrose agar plates onto Czapek’s medium and incubated at 26°C for 7 days. Fungal cultures were filtered through sterile gauze to removal the mycelium. The concentrations of the conidial suspensions were counted under a microscope using a hemocytometer and adjusted to 1.0 × 107conidia/ml with sterile distilled water (Zhang et al.2012b).

Plant culture and treatment

To investigate the mechanism of disease resistance in the cotton variety Zhongzhimian KV-1, we infected seedlings with the defoliating V. dahliae strains D07038 (moderate virulence) and V991 (high virulence). Seeds were treated with 98% H2SO4 to remove the surface fuzz, and then soaked in 70% ethanol for 5 min and in 10% H2O2 for 1 h to sterilize the surface, followed by three rinses with sterile water. Surface-sterilised seeds were germinated at 26°C on plates containing sterilised water and filter paper. The seedlings were grown under supplemental light outside the laboratory for 2 days and then transferred to an autoclaved mixture of vermiculite and peat (1:1 v/v). Cotton seedlings were inoculated using a root dip method. The V. dahliae strains D07038 and V991 were cultured for 7 days and used separately to infect roots of 21-day-old seedlings, at which point the seedlings had grown one to two euphylla. Roots were infected with V. dahliae inoculums of 107 CFU/mL (10 mL) fungal suspensions and incubated for ~1–2 days at 26°C and ~80% humidity. The seedlings were treated for 24 and 48 h with D07038 as the control group or with V991 as the experimental group. Total RNAs from seedlings were harvested 24 and 48 h after inoculation and pooled in a 1:1 ratio. The whole seedling was chosen as the experimental material because the pathogenic fungus directly infects cotton roots in soil and enters the vasculature through the cortical cells, resulting in disease in the stems and leaves (Cai et al.2009).

Construction and sequencing of sRNA libraries

Two sRNA libraries were constructed using the procedure of Kwak et al. (Kwak et al.2009). Total RNA was isolated from G. hirsutum seedlings using a Plant RNA Kit (Watson Biotech, Shanghai, China) according to the manufacturer’s instructions. All the RNA samples were quantified and equalised to guarantee equal amounts of RNA from each treatment. The quality of RNA samples was evaluated using an Agilent 2100 Bioanalyzer (Agilent, Waldbronn, Germany). All the RNA samples were separated by 15% urea denaturing polyacrylamide gel electrophoresis and the 18–30 nucleotide sRNA bands were excised and extracted with 0.4 M NaCl over night at 4°C. The isolated sRNAs were converted into DNA by reverse transcription PCR (RT-PCR) after being ligated sequentially to 5′ and 3′ adapters. The DNAs were confirmed by sequencing with Solexa sequencing technology (BGI, Shenzhen, China).

Bioinformatics analysis

Raw sequences were processed using the SOAP 2.0 software (BGI, Shenzhen, China) as reported previously (Li et al.2008). Without the vector sequences, the sequences longer than 17 nt were considered for further analyses. Excluding rRNA, scRNA, snoRNA, snRNA, tRNA and the sequences containing a polyA tail, the other sequences were compared with G. hirsutum non-coding RNA sequences in the NCBI GenBank and Rfam 10.1 databases. To identify conserved miRNAs in upland cotton, the unique small RNA sequences were selected to carry out BLASTN search in the miRBase. Conserved miRNAs require perfect match. The novel miRNAs are predicted according to the characteristic hairpin structure of microRNA precursors by the RNA-folding software Mireap (http://sourceforge.net/projects/mireap/), which could provide the information about the secondary structures, the minimum free energies and the Dicer cleavage sites of the unidentified sRNA tags. We used the G. raimondii genome sequences (ftp://ftp.ncbi.nih.gov/pub/TraceDB/gossypium_raimondii/) as an miRNA positioning reference sequence. Only mature miRNA sequences were considered, which locatein the stem region of the stem-loop structure and range between 20–22 nt as well as a minimal folding free energy index (MFEI) greater than 0.85. After the selected sequences by the Mireap were folded into the secondary structure, a novel miRNA was selected when it was at 5′end arm of the stem of a perfect stem-loop structure, and another sequence was at 3′end arm and then the small RNA was consisted as a novel miRNA from cotton.

The targets of these novel microRNAs were predicted through BLAST analysis of transcriptome sequencing data from KV-1 plants infected with V. dahliae Kleb. Target gene predictions followed the methods reported by Allen et al. (Allen et al.2005). Sequences less than 4 nt mismatches relative to the query miRNA sequences were chosen manually. We allowed one mismatch in the region complementary to nucleotide positions 2–12 of the miRNA except the mismatch at a predicted cleavage site at positions 10 or 11. We also allowed three additional mismatches between positions 12 and 22 except more than two continuous mismatches. The G:U base pair was treated as a mismatch with a value of 0.5.

Confirmation of predicted miRNAs by qRT-PCR

To confirm miRNA expression, we analyzed seven conserved miRNAs by RNA-tailing and primer-extension qRT-PCR. Small RNA was separated from seedlings by the RNAiso for sRNA reagent (TaKaRa, Dalian, China). According to the manufacturer’s protocols (New England Biolabs, Beijing, China), the treated sRNA (1.5 μg) was polyadenylated by polyA polymerase (PAP) at 37°C for 30 min of the Poly(A) Tailing Kit (New England Biolabs). The RNAs were dissolved in diethylpyrocarbonate (DEPC)-treated water and reverse-transcribed with 200 U M-MLV reverse transcriptase (RNase Hˉ) and 2 μL 10 pmol poly(T) adapter after phenol-chloroform extraction and ethanol precipitation (Table 1) according to the reverse transcriptase M-MLV manufacturer’s instructions (TaKaRa) (Shi & Chiang2005). Here G. hirsutum 5S ribosomal RNA (rRNA, GenBank: U32085.1) worked as the internal reference gene of PCR quantization. A 3′ adapter primer worked as the reverse primer for the miRNAs and the 5S rRNA. The sequence of the forward primer was selected by the entire test miRNA sequence. Reverse transcription reactions were performed at 50°C for 1 h, then inactivated at 75°C for 15 min. The qRT-PCR was carried out with SYBR Premix Ex Taq II (TaKaRa, Dalian, China) on a IQ™5 real-time PCR detection system (Bio-Rad). Each PCR included 0.5 μL template cDNA, approximately 100 pg sRNA, 5 μL 2× SYBR Green II PCR master mix, and 0.5 μL 10 pmol the forward and reverse primers (Table 1) in 10 μL reaction system. All reactions were performed in triplicate for each sample. All reactions used the following thermal cycling profile: an initial step at 95°C for 60 s, followed by 45 cycles of amplification (95°C for 20 s, 58°C for 20 s, 72°C for 30 s) with a termination reaction at 4°C. Relative miRNA expression levels were calculated by the method of Livak and Schmittgen (Livak & Schmittgen2001).
Table 1

Primers Used in this Study

Name

Sequence(5′ → 3′)

miR5266

CTGGGGGACTGTCTGGGGC

miR5562

TGTGGAGTCTTTTGCATGAAG

miR3444a-5p

TTGGGAGCTCGATGAGATCG

miR2867-5p

TGTGCCATCCCACACATC

miR1148

CCAACGTGCAGGGGGACA

miR1423a-5p

AGGCAACTACACGTTGGGCG

miR952a

AACAGAGCATGCCGTTGGT

Ghr5S RU

GGATCCCATCAGAACTCCAC

Ghr5S RL

ACGAGGACTTCCCAGAAGGT

Poly(T) adapter

GCTGTCAACGATACGCTACGTAACGGCATGACAGTG(T)22V

Reverse primer

GCTGTCAACGATACGCTACGTAACG

*V = A, G, C;

Results

Analysis of sequences from libraries of small RNAs

Totally 2 small RNA, namely sRNA libraries were constructed and sequenced to identify sRNAs from G. hirsutum L. seedlings infected with either of two strains of V. dahliae. The V. dahliae strains D07038 and V991 were cultured separately for 7 days on infected 21-day-old KV-1 cotton seedlings. Then roots were infected with the cultured D07038 or V991 as control and experimental treatments, respectively. Two sRNA cDNA libraries were generated from pooled total RNA isolated 24 and 48 h after inoculation for each treatment. Each library was sequenced by a Solexa/Illumina analyser and generated 14,491,706 primary reads for KV-1 plants infected with D07038, and 14,543,279 primary reads for KV-1 plants infected with V991 (Table 2).
Table 2

Statistics of small RNA sequences from two upland cotton libraries generated from KV-1 seedlings infected with the D07038 or V991 strain

Category

KV-1_D07038

KV-1_V991

 

Sequences

Unique sequences

Sequences

Unique sequences

Clean reads

14,156,228

4,348,363

14,186,947

4,597,927

miRNA

3,689,747

34,177

2,956,044

27,783

rRNA

745,970

79,074

1,549,145

145,979

snRNA

8,674

2,586

12,067

3,710

snoRNA

2,518

953

4,612

1,429

tRNA

232,045

13,306

778,699

40,648

Unannotated

9,477,274

4,608,248

8,886,380

4,378,378

Approximately 75–90% of the sRNAs in the libraries were 20–24 nt in length, and 21 and 24 nt are the main size groups (Figure 1). This distribution of sizes was similar to the other plants species and indicates that cotton miRNAs are primarily processed by DCL1 (Wang et al.2007). Most of the sRNAs in the libraries were 24 nt long and were 38.2% and 32.3% of the total number of sequences from the D07038 and V991 libraries, respectively. Small RNAs that were 21 nt long made up 32.3% and 25.2% of the D07038 and V991 libraries, respectively, and 22 nt sRNAs made up 9.8% and 9.5% of the D07038 and V991 libraries, respectively. This observation pointed out could reflect the complexity of the cotton genome because 24 nt sRNAs are reported to regulate the heterochromatin modification of the genomes with many repetitive sequences (Vazquez2006). The sRNAs identified by deep sequencing represented almost all RNA classes. By comparing with the sequences to those in databases, most of our sRNAs were assigned to the different classes. The sequences that could not be assigned were used to perform the prediction of novel miRNAs by Mireap software.
Figure 1

The size distribution of the small RNAs in upland cotton seedling KV-1_D07038 and KV-1_V991 libraries.

Thirty-four miRNAs were screened out from 31 families. We studied the miRNAs from known families and found they showed significant difference. The abundance of individual miRNAs in the libraries varied drastically in our dataset (Table 3). Some miRNAs were represented only a few times while others existed in hundreds of thousands of copies, which indicated that the cotton seedlings contained a large and diverse collection of sRNAs. Among them, ghr-miR394 and ghr-miR2950 miRNAs showed significant differences between treatment and control groups. These observations indicate that different members could have different expression levels from the same miRNA family. Probably expression is specific to different tissues or specific to different developmental stages (Chi et al.2011). The differential expression of a few conserved miRNAs in the two samples is not more than two times, means that several families involved in the development regulation of upland cotton seedlings.
Table 3

Expression levels of upland cotton miRNA families showed by Solexa sequencing

Family

KV-1_D07038 expressed

KV-1_V991 expressed

fold-change*

Family

KV-1_D07038 expressed

KV-1_V991 expressed

Fold-change*

ghr-miR156

678465

372468

-0.87

ghr-miR398

6

2

-1.59

ghr-miR162

587

727

0.31

ghr-miR399

23

26

0.17

ghr-miR164

17299

26429

0.61

ghr-miR479

135

195

0.53

ghr-miR166

147030

125066

-0.24

ghr-miR482

1093

1101

0.01

ghr-miR167

967

2049

1.08

ghr-miR827

4191

3246

-0.37

ghr-miR172

1

2

1.00

ghr-miR2948

6453

2260

-1.52

ghr-miR390

35883

18183

-0.98

ghr-miR2949

2528

3049

0.27

ghr-miR393

18

30

0.73

ghr-miR2950

6806

1028

-2.73

ghr-miR394

121

10

-3.60

ghr-miR3476

20464

16352

-0.33

ghr-miR396

3666

3444

-0.09

    

*Compare the known miRNA expression level in two samples .The procedures are shown as following:

(1) Calibration of the transcript expression per million (TPM) by normalizing the expression of micoRNA in control and treatment samples.

Normalization formula: Normalized expression = Actual miRNA count/Total count of clean reads*1000000.

(2) Calculation of fold-change based on the normalized expression: Fold_change = log2 (KV-1_V991-std /KV-1_D07038 -std).

Identification of conserved miRNAs from upland cotton

We raked through sequence homology analysis for known microRNAs in the two sRNA libraries with a requirement of at least 18 nt in length with a maximum of three mismatches compared to miRNAs in miRBase17. The abundance of specific miRNAs varied greatly between different miRNA families and between members within families. The depth of sequencing in our study was successfully identified 284 significant genes out of 443 differences expressed miRNA families of conservative miRNAs (Additional file1). Among the miRNAs identified by comparing resistance to V991 and D07038 in KV-1 seedlings, 119 miRNAs were differentially up-regulated and 165 miRNAs were differentially down-regulated in cotton seedlings. As shown in Additional file1, miR156, miR2911, miR2916, and miR172 were high expression in two sRNA libraries but a few conserved miRNAs (for example, miR161, miR163, and miR416) and most of the non-conserved miRNAs were found to have either very low expression. We also found some miRNAs such as miR5266, miR2867, miR3437, miR2086, and miR5652 were expressed preferentially corresponding to resistance to Verticillium wilt in KV-1 cotton. The results indicate that the expression of miRNAs was significantly affected by infection with V. dahliae, which suggested that miRNAs could be participate in the regulation of gene expression as part of the resistance to Verticillium wilt infection in the KV-1 variety.

Identification of novel miRNAs in upland cotton

Many researchers have identified novel miRNAs in numerous plant species by high-throughput sequencing means based on small RNA. They have find sRNA to a genomic location based on genomic DNA sequences of the species to predict secondary structure characteristic of an miRNA precursor. As described by Reinhart et al. (Reinhart et al.2002), the lowest-energy structures sRNA is identified as new miRNAs. A total of 78 miRNA precursors and 80 mature miRNAs from G. hirsutum are listed in miRbase release 20 and they belong to 51 miRNA families respectively. We screened the sRNA sequences generated in this study against the Rfam database to remove all known non-coding RNAs, which including scRNA, rRNAs, snoRNAs, tRNAs, piRNA, et al. For the identification of novel cotton miRNAs using the Mireap software, we used the G. raimondii genome sequence as an miRNA positioning reference sequence. We predicted 1141 miRNA candidate genes from the two sRNA libraries.

For 37 of the genes (Table 4), complementary miRNA* species (Meyers et al.2008) with MFEI values higher than 0.85 were present in the libraries providing an indication of precise excision from the stem-loop precursor, which has recently been proposed as a primary criterion for the confident identification of an miRNA. The presence of microRNA* sequences is very important criterion because it indicates the release of the microRNA duplex from the predicted fold-back structure (Lu et al.2006; Rajagopalan et al.2006; Fahlgren et al.2007), which were predicted using the genome sequence flanking the cloned sRNAs for all 37 of the novel microRNAs (Additional file2) and microRNA precursors varied in length from 69 to 345 nt. Putative hairpin structures are shown in Additional file3 with the novel miRNA candidates highlighted in blue. The predicted hairpins had MFEI ranging from 0.86 to 1.64 and negative folding free energies ranging from -117.6 to -33.8 kcal·mol-1. These values were much lower than the reported folding free energies of tRNA (-27.5 kcal·mol-1) and rRNA (-33 kcal·mol-1) (Bonnet et al.2004).
Table 4

Identified of novel miRNAs from upland cotton

miRNA_id

Mature miRNA sequence(5′→3′)

LM(nt)

MFEIs

ghr-miR8156-5p

AAACUAUUCCUGGCUGAUUCG

21

1.00

ghr-miR7513-3p

AAUCAGCCAGGAAUCGUUUGA

21

0.97

ghr-miR8157-5p

AAGGCAAAGGAAGAAAAAGAGUG

23

1.08

ghr-miR8158-3p

AAGGGAGAACCUAGAUUCAUU

21

1.21

ghr-miR8159-5p

AAUGGAGGAGUUGGAAAGAUU

21

1.23

ghr-miR8160-5p

ACAGCUUUAGAAAUCAUCCCU

21

1.64

ghr-miR8161-3p

GUGGAUUAAAAUUUUGGUUGG

21

1.05

ghr-miR8162-5p

ACUUGCCUGCAUCUUUCAAAGA

22

0.87

ghr-miR8163-3p

GGUUGCUUACUUCUCUUCUGU

21

1.10

ghr-miR8164-3p

CCUAAUAAGGAUGAUGUCUCA

21

1.03

ghr-miR8165-3p

UCCAUAUUUCACUAUCUCUUA

21

1.31

ghr-miR8166-3p

CACAGGGACAAUACCUUCUAC

21

1.04

ghr-miR8167-5p

AGCUUUAGAAAUCAUCCCUU

20

1.54

ghr-miR7495a-3p

UUACUUUAGAUGUCUCCUUCA

21

0.87

ghr-miR8168-5p

AUUCAAACACAACACAGUGCA

21

0.97

ghr-miR7508-5p

CAAGAAAAGAAGUCGGGAGAG

21

0.94

ghr-miR8169-5p

CGGACUCUCAAACAGUGGAGGUA

23

0.90

ghr-miR8170-3p

CAAAUGAGUUAGGCGAGAGGU

21

1.12

ghr-miR8171-3p

UCGGGGCUUUAGCGGCGUUUUUA

23

1.12

ghr-miR8172-5p

GACGGGUGAUGGAAGUUUUUGG

22

0.96

ghr-miR8173-5p

GGAAUGGAGGAGUUGGAAAGA

21

1.40

ghr-miR8174-3p

ACAGCUUUAGAAAUCAUCCCU

21

1.00

ghr-miR8175-3p

AUGAGCUAGAAGUUGGAACUC

21

1.21

ghr-miR8176-5p

UAAGUGAAGAAAGAGGUAGGUU

22

1.16

ghr-miR8177-5p

UCAUGGUCUUUAGCGGUGUUU

21

1.34

ghr-miR8178-3p

AUGAGCUAGAAGUUGGAACUC

21

1.24

ghr-miR8179-5p

UCGGACUGGAUUUGUUGACAA

21

0.91

ghr-miR8180-5p

UGAACUUGGUAACUAUUCCCAC

22

1.16

ghr-miR8181-5p

UGAGUGGAGUUAGGAGACAAA

21

0.92

ghr-miR8182-3p

UCGCUUCCCUAAUUUGGACGA

21

0.86

ghr-miR8183-3p

GCAUCAGAGGACUCAGGCAGGU

22

1.07

ghr-miR8184-5p

UGGCACGGCUCAAUCAAAUUA

21

0.98

ghr-miR8185-5p

UUAGAAGUUAGAUUGCAUUUUG

22

1.43

ghr-miR8186-3p

UCUAACGUGUAGGGACUAAUU

21

1.22

ghr-miR8187-5p

UUAUGAUCUUUAGCGGCGUUU

21

1.38

ghr-miR8188-5p

UUGCAUGACACUACUUUAAAU

21

1.06

ghr-miR8189-3p

GUGUUUCGCGCGUGGACGACG

21

1.26

Identification of miRNA targets in G. hirsutum

Identification of putative miRNA targets is an effective approach for identifying the functions of miRNAs. Plant miRNAs typically have higher sequence complementarity with their target mRNAs than do animal miRNAs (Wang et al.2004). We search for potential silenced target genes by the novel miRNAs isolated from cotton base on transcriptome sequencing data generated for KV-1 cotton infected with the D07038 and V991 V. dahliae strains as described in the Materials and Methods section. We were predicting 49 putative targets (Additional file4) for 24 novel microRNA candidates. No potential targets were identified for 13 of the novel miRNAs.

To have a deeper understand functions of microRNA, we subjected the identified target genes to gene ontology (GO) analysis (http://www.geneontology.org), a method for identifying relevant miRNA–gene regulatory networks based on biological processes and molecular functions (Ashburner & Bergman2005). KEGG is the major public pathway-related database (Elliott et al.2008) and it was also used to estimate the potential functions of predicted target gene candidates of the novel miRNAs. Analysis of regulatory networks and biochemical pathways help understand the biological functions of the miRNA targets. Some miRNA families had multiple target genes, which were usually different function with homologous sequences. For these target genes, binding sites of the putative miRNA were often located in the highly conserved regions. It is notable that two miRNAs, ghr-miR8156-3p and ghr-miR8170-3p, potentially target disease resistance genes such as mitogen-activated protein kinase kinase kinase 17, Nbs-lrr resistance protein, and plant viral-response family proteins. These targets include genes involved in plant–pathogen interactions, endocytosis, terpenoid backbone biosynthesis, isoquinoline alkaloid biosynthesis, primary bile acid biosynthesis, the MAPK signalling pathway, and biosynthesis of secondary metabolites. Our results may help advance understanding of the mechanism of resistance to Verticillium wilts in the upland cotton variety KV-1.

Quantitative real time-PCR and data analyses

Sequence counts were normalized between the datasets to account of different magnitudes raw counts. To accurate validate the deep sequencing miRNA data, we examined the correlation between normalized sequencing counts and gene expression as determined by poly (A) qRT-PCR. The expression patterns of some genes in the KV-1 plants infected by D07038 or V991 were conforming to their sequencing results. Some of the miRNA families, such as miR5266, miR5562, miR3444, and miR1148, were highly expressed, whereas miR2867, miR1423, and miR952 had relatively low level expression. A number of miRNAs were preferentially expressed in KV-1 plants infected by D07038 (e.g., miR1148) and others were preferentially expressed in KV-1 plants infected by V991 (such as miR3444) (Figure 2). There was no significant difference in miRNA levels between the Solexa and qRT-PCR data sets except for miR952 (Figure 3). We concluded that this difference was primarily due to the limitations associated with using qRT-PCR technology. The miRNA expression profiling by deep sequencing and qRT-PCR provided valuable information, however, the additional effort required to validate the data makes this approach less attractive for quantifying the effects of miRNA expression on target genes.
Figure 2

Relative expression of seven miRNAs in different virulence Verticillium wilts of Upland cotton.

Figure 3

Expression confirmation of miRNAs in Gossypium hirsutum derived from high throughput sequencing. Differentially expressed miRNAs expression detected by poly(A) qRT-PCR.

Discussions

The development of high-throughput sequencing methods have greatly accelerated the identification of miRNAs in species without or with fully sequenced genomes (Peláez et al.2012). The newly identified cotton miRNAs belonging to known miRNA families exhibited a wide range of characteristics. Previous studies have focused primarily on miRNA expression in ovules, during fiber development, and with respect to stress responses (Khan Barozai et al.2008; Pang et al.2009; Yin et al.2012b). For example, miR172, miR2948, miR2949, miR2950, miR3476, miR399, miR479, and miR827 were identified during the development of ovule and fiber in upland cotton (Pang et al.2009). In addition to miRNAs related to cotton fiber development, some miRNAs have been showed to play most vital role in response to growth stages and growth conditions. For example, ghr-miR482 regulates NBS-LRR defence genes by suppressing the miRNA-mediated gene-silencing pathway in cotton during fungal pathogen infection in cotton (Zhu et al.2013). Liu Yang also reported that bra-miR1885 the targets protein-coding disease-resistance genes of the TIR-NBS-LRR class (Yang et al.2013). 14 novel cotton miRNAs (miR1321 to miR1334) were identified from the G. barbadense L. variety ‘Hai-7124’, a Verticillium-tolerant cultivar, and the G. hirsutum L. variety ‘Yimian-11’, a Verticillium-sensitive cultivar, after roots were mock-infected and infected with Verticillium (Yin et al.2012a). There have been no reports on the potential involvement of miRNAs in mechanisms of resistance to different Verticillium wilts in upland cotton varieties. Plenty of plant miRNAs have been submitted to miRBase and their biological functions have been investigated using the Illumina Genome Analyzer. Our deep sequencing data includes conserved and novel miRNAs from upland cotton based on the G. raimondii genome sequence. We identified 443 conserved and 37 non-conserved miRNAs from our cotton sRNA data, and also analysed the miRNAs which differentially expressed against the two V. dahliae strains used. Moreover, we predicted 49 potential targets of novel miRNAs to take part in plant–pathogen interactions. Our results show that miR8163, miR8165, miR8170, miR8175, and miR8178 are involved in defence responses through relevant regulatory networks, but the underlying mechanisms remain to be elucidated. Consistent with the control group, miR8163, miR8165, miR8170 and miR8175 showed significantly low level expression in our study. Similarly, because the expression level of NBS-LRR defence genes and plant viral-response family proteins were up-regulated, we can conclude that the new miRNA may involve in the regulation of the cotton in response to Verticillium wilt. The identification of these novel microRNAs and their potential target genes improves our understanding of the mechanisms regulating defense responses. We used qRT-PCR analysis to confirm the predicted known miRNAs and found that most of the expressed miRNA shave Verticillium wilt resistance characteristics. Their specific expression could provide important information about how they function. Our future work will focus on their roles in upland KV-1 cotton resistance to Verticillium wilts. This study also provides a glimpse of the abundance and diversity of sRNAs in upland cotton.

Declarations

Acknowledgments

This work was supported by grants from the National Natural Science Foundation of China (No. 31071461), Chongqing Municipal Commission of Education (No. KJ130520, KJ130502, KJ130510, and KJ050511), Natural Sciences Foundation of Chongqing (cstc2012jjA80037).

Authors’ Affiliations

(1)
State Key Laboratory of Cotton Biology, Henan Key Laboratory of Plant Stress Biology, College of Life Sciences, Henan University
(2)
College of Bioinformation, Chongqing University of Posts and Telecommunications
(3)
State Key Laboratory of Cotton Biology, Cotton Institute of the Chinese Academy of Agricultural Sciences, Key Laboratory of Cotton Genetic Improvement, Ministry of Agriculture

References

  1. Allen E, Xie Z, Gustafson AM, Carrington JC: microRNA-directed phasing during trans-acting siRNA biogenesis in plants. Cell 2005, 121: 207-221. doi: 10.1016/j.cell.2005.04.004 [PubMed] [Cross Ref]View ArticleGoogle Scholar
  2. Ashburner M, Bergman CM: Drosophila melanogaster: a case study of a model genomic sequence and its consequences. Genome Res 2005, 15: 1661-1667. doi: 10.1101/gr.3726705 [PubMed] [Cross Ref]View ArticleGoogle Scholar
  3. Bartel DP: MicroRNAs: genomics, biogenesis, mechanism, and function. Cell 2004, 116: 281-297. doi: 10.1016/S0092-8674(04)00045-5 [PubMed] [Cross Ref]View ArticleGoogle Scholar
  4. BejaranoAlcazar J, BlancoLopez MA, MeleroVara JM, JimenezDiaz RM: The influence of verticillium wilt epidemics on cotton yield in southern Spain. Plant Pathol 1997, 46: 168-178. doi: 10.1046/j.1365-3059.1997.d01-221.x [Cross Ref]View ArticleGoogle Scholar
  5. Bonnet E, Wuyts J, Rouze P, Van de Peer Y: Evidence that microRNA precursors, unlike other non-coding RNAs, have lower folding free energies than random sequences. Bioinformatics 2004, 20: 2911-2917. doi: 10.1093/bioinformatics/bth374 [PubMed] [Cross Ref]View ArticleGoogle Scholar
  6. Cai YF, He XH, Mo JC, Sun Q, Yang JP, Liu JG: Molecular research and genetic engineering of resistance to Verticillium wilt in cotton: A review. Afr J Biotechnol 2009, 8: 7363-7372. doi: 10.5897/AJB2009.000-9571 [Cross Ref]Google Scholar
  7. Carrington JC, Ambros V: Role of microRNAs in plant and animal development. Science 2003, 301: 336-338. doi: 10.1126/science.1085242 [PubMed] [Cross Ref]View ArticleGoogle Scholar
  8. Chi XY, Yang QL, Chen XP, Wang JY, Pan LJ, Chen MN, Yang Z, He YA, Liang XQ, Yu SL: Identification and characterization of microRNAs from peanut (Arachis hypogaea L.) by high-throughput sequencing. PLoS One 2011, 6: e27530. doi: 10.1371/journal.pone.0027530 [PMC free article] [PubMed] [Cross Ref]View ArticleGoogle Scholar
  9. Elliott B, Kirac M, Cakmak A, Yavas G, Mayes S, Cheng E, Wang Y, Gupta C, Ozsoyoglu G, Meral Ozsoyoglu Z: PathCase: pathways database system. Bioinformatics 2008, 24: 2526-2533. doi: 10.1093/bioinformatics/btn459 [PubMed] [Cross Ref]View ArticleGoogle Scholar
  10. Fahlgren N, Howell MD, Kasschau KD, Chapman EJ, Sullivan CM, Cumbie JS, Givan SA, Law TF, Grant SR, Dangl JL, Carrington JC: High-throughput sequencing of arabidopsis microRNAs: evidence for frequent birth and death of Mirna genes. PLoS One 2007, 2: e219. doi: 10.1371/journal.pone.0000219 [PMC free article] [PubMed] [Cross Ref]View ArticleGoogle Scholar
  11. Fan J, Jiang JK: A new strain of China's first transgenic cotton of resistance to verticillium wilts. Sci Times 2006. [Cross Ref] (In Chinese)http://www.most.gov.cn/gnwkjdt/200608/t20060831_35734.htmGoogle Scholar
  12. Fradin EF, Thomma BP: Physiology and molecular aspects of Verticillium wilt diseases caused by V. dahliae and V. albo - atrum . Mol Plant Pathol 2006, 7: 71-86. doi: 10.1111/j.1364-3703.2006.00323.x [PubMed] [Cross Ref]View ArticleGoogle Scholar
  13. Gao F, Zhou BJ, Li GY, Jia PS, Li H, Zhao YL, Zhao P, Xia GX, Guo HS: A Glutamic Acid-Rich Protein Identified in Verticillium dahliae from an Insertional Mutagenesis Affects Microsclerotial Formation and Pathogenicity. PLoS One 2010, 5: e15319. doi: 10.1371/journal.pone.0015319 [PMC free article] [PubMed] [Cross Ref] 10.1371/journal.pone.0015319View ArticleGoogle Scholar
  14. Khan Barozai MY, Irfan M, Yousaf R, Ali I, Qaisar U, Maqbool A, Zahoor M, Rashid B, Hussnain T, Riazuddin S: Identification of micro-RNAs in cotton. Plant Physiol Biochem 2008, 46: 739-751. doi: 10.1016/j.plaphy.2008.05.009 [PubMed] [Cross Ref]View ArticleGoogle Scholar
  15. Klosterman SJ, Atallah ZK, Vallad GE, Subbarao KV: Diversity, pathogenicity, and management of Verticillium species. Annu Rev Phytopathol 2009, 47: 39-62. doi: 10.1146/annurev-phyto-080508-081748 [PubMed] [Cross Ref]View ArticleGoogle Scholar
  16. Kwak PB, Wang QQ, Chen XS, Qiu CX, Yang ZM: Enrichment of a set of microRNAs during the cotton fiber development. BMC Genomics 2009, 10: 457. doi: 10.1186/1471-2164-10-457 [PMC free article] [PubMed] [Cross Ref]View ArticleGoogle Scholar
  17. Li RQ, Li YR, Kristiansen K, Wang J: SOAP: short oligonucleotide alignment program. Bioinformatics 2008, 24: 713-714. doi: 10.1093/bioinformatics/btn025 [PubMed] [Cross Ref]View ArticleGoogle Scholar
  18. Livak KJ, Schmittgen TD: Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods 2001, 25(4):402-408. doi: 10.1006/meth.2001.1262 [PubMed] [Cross Ref]View ArticleGoogle Scholar
  19. Lu C, Kulkarni K, Souret FF, Muthuvalliappan R, Tej SS, Poethig RS, Henderson IR, Jacobsen SE, Wang WZ, Green PJ, Meyers BC: Micro-RNAs and other small RNAs enriched in the Arabidopsis RNA-dependent RNA polymerase-2 mutant. Genome Res 2006, 16: 1276-1288. doi: 10.1101/gr.5530106 [PMC free article] [PubMed] [Cross Ref]View ArticleGoogle Scholar
  20. Meyers BC, Axtell MJ, Bartel B, Bartel DP, Baulcombe D, Bowman JL, Cao XF, Carrington JC, Chen XM, Green PJ, Jones SG, Jacobsen SE, Mallory AC, Martienssen RA, Poethig RS, Qi YJ, Vaucheret H, Voinnet O, Watanabe YC, Weigel D, Zhui JK: Criteria for annotation of plant MicroRNAs. Plant Cell 2008, 20: 3186-3190. doi: 10.1105/tpc.108.064311 [PMC free article] [PubMed] [Cross Ref]View ArticleGoogle Scholar
  21. Pang M, Woodward AW, Agarwal V, Guan X, Ha M, Ramachandran V, Chen X, Triplett BA, Stelly DM, Chen ZJ: Genome-wide analysis reveals rapid and dynamic changes in miRNA and siRNA sequence and expression during ovule and fiber development in allotetraploid cotton ( Gossypium hirsutum L.). Genome Biol 2009, 10: R122. doi: 10.1186/gb-2009-10-11-r122 [PMC free article] [PubMed] [Cross Ref]View ArticleGoogle Scholar
  22. Peláez P, Trejo MS, Iñiguez LP, Estrada-Navarrete G, Covarrubias AA, Reyes JL, Sanchez F: Identification and characterization of microRNAs in phaseolus vulgaris by high-throughput sequencing. BMC Genomics 2012, 13: 83. doi: 10.1186/1471-2164-13-83 [PMC free article] [PubMed] [Cross Ref]View ArticleGoogle Scholar
  23. Rajagopalan R, Vaucheret H, Trejo J, Bartel DP: A diverse and evolutionarily fluid set of microRNAs in Arabidopsis thaliana. Genes Dev 2006, 20: 3407-3425. doi: 10.1101/gad.1476406 [PMC free article] [PubMed] [Cross Ref]View ArticleGoogle Scholar
  24. Reinhart BJ, Weinstein EG, Rhoades MW, Bartel B, Bartel DP: MicroRNAs in plants. Genes Dev 2002, 16: 1616-1626. doi: 10.1101/gad.1004402 [PMC free article] [PubMed] [Cross Ref]View ArticleGoogle Scholar
  25. Shi R, Chiang VL: Facile means for quantifying microRNA expression by real-time PCR. Biotechniques 2005, 39: 519-25. doi: 10.2144/000112010 [PubMed] [Cross Ref]View ArticleGoogle Scholar
  26. Vazquez F: Arabidopsis endogenous small RNAs: highways and byways. Trends Plant Sci 2006, 11: 460-468. doi: 10.1016/j.tplants.2006.07.006 [PubMed] [Cross Ref]View ArticleGoogle Scholar
  27. Wang XJ, Reyes JL, Chua NH, Gaasterland T: Prediction and identification of Arabidopsis thaliana microRNAs and their mRNA targets. Genome Biol 2004, 5: R65. doi: 10.1186/gb-2004-5-9-r65 [PMC free article] [PubMed] [Cross Ref]View ArticleGoogle Scholar
  28. Wang L, Wang MB, Tu JX, Helliwell CA, Waterhouse PM, Dennis ES, Fu TD, Fan YL: Cloning and characterization of microRNAs from Brassica napus. FEBS Lett 2007, 581(20):3848-3856. doi: 10.1016/j.febslet.2007.07.010 [PubMed] [Cross Ref]View ArticleGoogle Scholar
  29. Yang L, Jue D, Li W, Zhang R, Chen M, Yang Q: Identification of MiRNA from eggplant (Solanum melongena L.) by small RNA deep sequencing and their response to Verticillium dahliae infection. PLoS One 2013, 8(8):e72840. doi: 10.1371/journal.pone.0072840 [PMC free article] [PubMed] [Cross Ref]View ArticleGoogle Scholar
  30. Yin ZJ, Li Y, Han XL, Shen FF: Genome-wide profiling of mirnas and other small non-coding rnas in the Verticillium dahliae–inoculated cotton roots. PLoS One 2012, 7: e35765. doi: 10.1371/journal.pone.0035765 [PMC free article] [PubMed] [Cross Ref]View ArticleGoogle Scholar
  31. Yin Z, Li Y, Yu J, Liu Y, Li C, Han X, Shen F: Difference in miRNA expression profiles between two cotton cultivars with distinct salt sensitivity. Mol Biol Rep 2012, 39: 4961-4970. doi: 10.1007/s11033-011-1292-2 [PubMed] [Cross Ref]View ArticleGoogle Scholar
  32. Zhang WW, Zhao L, Wang SZ, Jiang TF, Qi FJ, Jian GL: Stability analysis on Verticillium Wilt resistance of ten cotton cultivars in disease nursery. China Cotton 2012, 39: 20-22. doi: 10.3969/j.issn.1000-632X.2012.05.006 [Cross Ref] (In Chinese)Google Scholar
  33. Zhang WW, Jian GL, Jiang TF, Wang SZ, Qi FJ, Xu SC: Cotton gene expression profiles in resistant Gossypium hirsutum cv. Zhongzhimian KV1 responding to Verticillium dahliae strain V991 infection. Mol Biol Rep 2012, 39: 9765-74. doi: 10.1007/s11033-012-1842-2 [PubMed] [Cross Ref]View ArticleGoogle Scholar
  34. Zhao L, Wang SZ, QI FJ, Zhang WW, Jian GL: Isolation, characterization and phylogenetic analysis of the resistance geneanalogues (RGAs) in upland cotton ( Gossypium hirsutum L.) Zhongzhimian KV-1. Cotton Science 2009, 21: 462-467. [Cross Ref] (In Chinese)Google Scholar
  35. Zhu QH, Fan L, Liu Y, Xu H, Llewellyn D, Wilson I: miR482 regulation of NBS-LRR defense genes during fungal pathogen infection in cotton. PLoS One 2013, 8(12):e84390. doi: 10.1371/journal.pone.0084390 [PMC free article] [PubMed] [Cross Ref]View ArticleGoogle Scholar

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© He et al.; licensee Springer. 2014

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