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PlantaAn International Journal of PlantBiology ISSN 0032-0935Volume 240Number 6 Planta (2014) 240:1287-1298DOI 10.1007/s00425-014-2153-8

Genome-wide fungal stress responsivemiRNA expression in wheat

Behçet Inal, Mine Türktaş, Hakan Eren,Emre Ilhan, Sezer Okay, Mehmet Atak,Mustafa Erayman & Turgay Unver

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ORIGINAL ARTICLE

Genome-wide fungal stress responsive miRNA expression in wheat

Behcet Inal • Mine Turktas • Hakan Eren •

Emre Ilhan • Sezer Okay • Mehmet Atak •

Mustafa Erayman • Turgay Unver

Received: 20 June 2014 / Accepted: 12 August 2014 / Published online: 26 August 2014

� Springer-Verlag Berlin Heidelberg 2014

Abstract MicroRNAs (miRNAs) are small non-coding

class of RNAs. They were identified in many plants with

their diverse regulatory roles in several cellular and met-

abolic processes. A number of miRNAs were involved in

biotic and abiotic stress responses. Here, fungal stress

responsive wheat miRNAs were analyzed by using miR-

NA-microarray strategy. Two different fungi (Fusarium

culmorum and Bipolaris sorokiniana) were inoculated on

resistant and sensitive wheat cultivars. A total of 87 dif-

ferentially regulated miRNAs were detected in the

8 9 15 K array including all of the available plant miR-

NAs. Using bioinformatics tools, the target transcripts of

responsive miRNAs were predicted, and related biological

processes and mechanisms were assessed. A number of the

miRNAs such as miR2592s, miR869.1, miR169b were

highly differentially regulated showing more than 200-fold

change upon fungal-inoculation. Some of the miRNAs

were identified as fungal-inoculation responsive for the first

time. The analyses showed that some of the differentially

regulated miRNAs targeted resistance-related genes such

as LRR, glucuronosyl transferase, peroxidase and Pto

kinase. The comparison of the two miRNA-microarray

analyses indicated that fungal-responsive wheat miRNAs

were differentially regulated in pathogen- and cultivar-

specific manners.

Keywords Fungal stress � Microarray � Micro RNA �Wheat

Abbreviations

B Resistant wheat cv. Bezostaja

BC Bezostaja control

BI Bezostaja inoculated

EST Expressed sequence tag

M Susceptible wheat cv. Mizrak

MC Mizrak control

MI Mizrak inoculated

Introduction

Wheat (Triticum aestivum L.) is one of the major crops

grown throughout the world (Tanaka et al. 2013) covering

17 % of all the cultivated land, and supplying ca. 55 % of

the carbohydrates consumed by humans (Han et al. 2013;

Meng et al. 2013). By the year of 2050, it is proposed that

the need for wheat will increase with 60 %; albeit the

production will decrease with 29 % due to the environ-

mental stress factors (Manickavelu et al. 2012). Fusarium

culmorum, a ubiquitous soil-borne fungus, is one of the

biotic stress agents in small-grain cereals, particularly in

wheat and barley. It causes foot- and root-rot, a disease

B. Inal and H. Eren authors have equally contributed.

Electronic supplementary material The online version of thisarticle (doi:10.1007/s00425-014-2153-8) contains supplementarymaterial, which is available to authorized users.

B. Inal � M. Turktas � S. Okay � T. Unver (&)

Department of Biology, Faculty of Science, Cankiri Karatekin

University, 18100 Cankiri, Turkey

e-mail: turgayunver@gmail.com; turgayunver@karatekin.edu.tr

H. Eren � E. Ilhan � M. Erayman

Department of Biology, Faculty of Arts and Science, Mustafa

Kemal University, Hatay, Turkey

M. Atak

Department of Field Crops, Faculty of Agriculture, Mustafa

Kemal University, Hatay, Turkey

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DOI 10.1007/s00425-014-2153-8

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designated as Fusarium head blight (FHB), resulting in a

significant reduction in crop yield and quality as well as

contamination of the grain with mycotoxins (Scherm et al.

2013) such as trichothecenes leading to considerable

human and animal health risks (Kammoun et al. 2010).

Another biotic stress agent in wheat is Bipolaris sorokini-

ana Sacc. Shoemaker [teleomorph Cochliobolus sativus Ito

and Kuribayash (Drechs.)], a fungal phytopathogen with a

short biotrophic phase followed by successful tissue

infection in the necrotrophic growth phase, causing spot

blotch, root-rot, leaf-spot disease, black point, seedling and

head blight in cereal crops (Nizam et al. 2012; Zhang et al.

2012).

MicroRNAs (miRNAs) are an important class of

endogenous, single-stranded, non-coding, small RNAs

(sRNAs) regulating the gene expression at the post-tran-

scriptional levels in eukaryotes and viruses (Eldem et al.

2013; Han et al. 2013). Plant miRNAs are short (20–22 bp)

RNAs produced from imperfectly complementary (stem-

loop) RNA precursors (Meng et al. 2013). The genes

coding for miRNAs are transcribed by RNA polymerase II

or III as pri-miRNAs which are processed by a dicer-like

enzyme (DCL1) to form pre-miRNAs, and further cleaved

by DCL1 into a miRNA:miRNA* duplex. These molecules

are methylated by the RNA methylase HUA ENHANCER

1. Eventually, the mature miRNA is incorporated into a

ribonucleoprotein complex known as RNA-induced

silencing complex (RISC) (Eldem et al. 2013; Han et al.

2013).

Currently, 24,521 miRNA sequences from various

organisms are deposited in the publicly available miRNA

database (miRbase, Release 20.0, June 2013, http://www.

mirbase.org). Among 7,385 mature plant miRNAs pre-

sented in miRbase, 42 of them belong to bread wheat,

while 756 and 713 miRNAs are found for Medicago

truncatula and rice, respectively.

Considering the large genome size of the bread wheat

(Brenchley et al. 2012), a number of wheat miRNAs are

expected to be increased by the further studies. The prop-

erties of the miRNAs, such as their genome location can be

found via next-generation survey sequencing. Tanaka et al.

(2013) reported that some miRNAs are located in repeat

regions of wheat chromosome 6B and 1,805 out of 2,906

miRNA genes were identified in a DNA transposon-Mari-

ner. Deep-sequencing of sRNAs in developing grains

revealed 605 miRNAs representing 540 families in wheat

(Meng et al. 2013). Moreover, in silico analyses can also be

used for miRNA prediction (Pandey et al. 2013). Mani-

ckavelu et al. (2012) analyzed ca. 1 million ESTs and

found that wheat miRNA target sequences had maximum

homology with rice followed by maize and M. truncatula.

Similarly, Han et al. (2013) analyzed wheat ESTs, and

identified 48 new miRNAs belonging to 20 miRNA fami-

lies. Involvement of miRNAs in stress response was

reported by a number of studies. Wang et al. (2013)

reported that miR159, miR167a, and miR171 were signif-

icantly up-regulated in wheat upon UV-B radiation.

Additionally, miR164 was shown to be targeted a novel

NAC transcription factor from the NAM subfamily, nega-

tively regulating resistance of wheat to stripe rust (Feng

et al. 2013b).

The involvement of miRNAs in biotic stress response in

wheat has been exhibited in recent studies. Tae-miR159

targeted TaMYB3 (Feng et al. 2013a), tae-164 targeted a

NAC transcription factor, TaNAC21/22 (Feng et al. 2014),

and tae-miR408 targeted a chemocyanin-like protein gene

(TaCLP1) (Feng et al. 2013b). Additionally, miR167,

miR171, miR444, miR1129 and miR1138 were determined

as players in resistance of wheat against stripe rust (Gupta

et al. 2012). Moreover, 24 miRNAs were identified as

being responsive to powdery mildew infection in wheat via

Solexa high-throughput sequencing Xin et al. (2010).

The aim of this study was to identify the fungal-stress

responsive miRNAs in wheat. The miRNA expression

profiling of fungus inoculated resistant cultivars and sus-

ceptible cultivars were analyzed by miRNA-microarray

approach. Using bioinformatics tools, the target transcripts

of responsive miRNAs were predicted, and related bio-

logical processes and mechanisms were evaluated.

Materials and methods

Plant materials, growth conditions and pathogen

inoculation

In this study, two hexaploid bread wheat Triticum aestivum

L. cultivars with different levels of resistance response

against B. sorokiniana and F. culmorum were used. The

seeds were kindly provided by Research Institute of Field

Crops (TARM) in Ankara, Turkey. Two replicates of the

susceptible cv. Mizrak (M), and resistant cv. Bezostaja

(B) were used for fungal-inoculations. The seeds were

sterilized with 0.35 % (w/v) ethyl methane sulphonate

(EMS). They were planted in sterile peat, sand, and soil

mixed in a ratio of 1:1:1 by weight, and were kept for

8 days at climate cabinet under 24 �C 16/8 h light/dark

periods. Leaves and stems of four-week-old wheat were

individually sprayed with a suspension of 1 9 105 ml-1 B.

sorokiniana and F. culmorum spores. Plants sprayed with

sterile water were used as mock inoculation. 48 h after

inoculation (hai), leaves were harvested, and stored in

-80 �C until use. After 2 weeks, the plants were observed

for pathogen growth and resistance symptoms.

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RNA extraction, labeling, hybridization and washing

Total RNA was extracted from the leaves of two biological

replicates of pathogen and mock inoculated samples.

TRIzol reagent (Invitrogen) was used for the RNA isolation

according to the manufacturer’s instructions. The integrity

of RNA was evaluated on 1.5 % agarose gel. The amount

of the RNA was determined by NanoDrop 2000c spectro-

photometer (ThermoFisherScientific).

Two separate microRNA-Microarray analyses were

performed for the two different pathogen treatments. One

array slide was hybridized with two replicates of each M

and B cultivars inoculated with B. sorokiniana and their

controls, while the other slide was used for the corre-

sponding samples inoculated with F. culmorum and the

controls. The whole experimental steps for the two arrays

were performed simultaneously.

A total of 100 ng RNA from each sample were labeled

with cyanin-3 (Cy3). Labeling and hybridization steps were

performed according to the miRNA-microarray System

with miRNA Complete Labeling and Hyb Kit (Agilent

Technologies). The arrays were incubated at 55 �C for 20 h

in Agilent hybridization oven rotating at 20 rpm. After

hybridization, washing process was started immediately as

described in the manual. The images of hybridized

microarrays were acquired with MS 200 Microarray

Scanner System (NimbleGen). Scanning of images was

achieved with 5 lm resolution in a 61 9 21.6 mm scan

area, and 532 nm TIFF images were generated.

miRNA-microarray design strategy

The 8 9 15 K custom miRNA-microarrays (Agilent

Technologies) were designed according to manufacturer’s

protocol. To obtain a broad representation of miRNAs, a

total of 11,861 plant miRNAs obtained from the latest

miRBase v. 20 (http://www.mirbase.org, released June,

2013), and PMRD (Plant microRNA Database) http://

bioinformatics.cau.edu.cn/PMRD databases were used for

the probe design. Each miRNA was printed in 20 replicates

on the array. Including internal controls and replicated

probes, the custom 8 9 15 K array contained 237,580

probes, which were derived from all of the available plant

miRNAs in the current databases.

Data processing

To calculate the signal intensities of the spots, the TIFF

images were extracted using Feature Extraction v. 9. 5

(Agilent Technologies). For expression analysis, mean

value of the each probe intensity was used. The raw probe

signals were processed for background-correction with a

median polish algorithm (Irizarry et al. 2003), and were

normalized by quantile normalization method (Bolstad

et al. 2003). The signal intensities were transformed to

log2-ratio data. To obtain the miRNA expression mea-

surement, GeneSpringGX v. 11.5.1 (Agilent Technologies)

software was used. For one-color experiments, P values of

the quantile tests, and the expression ratios between the

two sets were computed. The probes with expression value

more than 1.45-fold and P B 0.05 between the two data

sets were designated as differentially expressed.

Target transcript prediction and onthology analysis

To identify the targets of the differentially expressed

miRNAs, psRNAtarget (http://plantgrn.noble.org/psRNA

Target/), a plant miRNA target finder software web tool,

was used. The parameters were set to T. aestivum unigene

library (v. 12, released on 2010_04_18) to BLAST the

target sites against miRNA sequences.

To annotate the target genes responsible to miRNAs

which were significantly differentially expressed, Blast to

Gene Ontology (Blast2GO) http://www.blast2go.com/

b2glaunch tool was used against the National Center for

Biotechnology Information (NCBI) database.

Validation of miRNA expression

To verify the miRNA-microarray data, 9 miRNAs were

selected based on differential expression patterns and

miRNA expression levels were measured using quantita-

tive RT-PCR (qRT-PCR). To make miRNA quantification

by qRT-PCR, 1,500 ng of RNA was used as template

(Yanik et al. 2013). Stem-loop reverse transcription reac-

tion was carried out in a total volume of 10 ll containing

0.4 mM of dNTP mix, 1 lM of the stem-loop reverse-

transcription (RT) primer. The mixture was incubated at

65 �C for 5 min, and then put on ice for 2 min. Then, 19

first-strand buffer, 0.01 M dithiothreitol (DTT), 0.4 U

RNAseOUT (Invitrogen) and 50 U SuperScriptIII (Invit-

rogen) were added to the mixture. The RT reaction was

achieved as follows: 30 min at 16 �C; and 60 cycles (30 �C

for 30 s, 42 �C for 30 s and 50 �C for 1 s). The RT reac-

tion was ended with 85 �C for 5 min. cDNA synthesis of

PCR control samples including no-RT primer, and no-RNA

template were also prepared (Varkonyi-Gasic et al. 2007;

Unver et al. 2010a).

The real-time qRT-PCR was performed using a SYBR

Green I Master mix from Roche on the LightCycler480 II

Real-Time PCR (Roche). The reaction mix was prepared in

a final volume of 20 ll containing 19 Master mix, 1 pmol

of the forward and reverse primers. Specifically designed

forward primers for each individual miRNA and a uni-

versal reverse-primer (50-GTGCAGGGTCCGAGGT-30)were used for qRT-PCR reactions (Yanik et al. 2013).

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Sequences of the primers used in the qRT-PCR were listed

in suppl. Table S1. The qRT-PCR conditions were setup as

follows: initial denaturation at 95 �C for 5 min, followed

by 50 cycles at 95 �C for 10 s, 55 �C for 20 s, and 72 �C

for 10 s. The melting curves were adjusted at 95 �C for 5 s

and 55 �C for 1 min, and then cooled to 40 �C for 30 s. All

reactions were repeated as triplicates.

Expression analysis of miRNA targeted transcripts

To quantify the expression level of target transcripts of the

selected stress responsive miRNAs, qRT-PCR analyses

were performed. The reverse transcription reactions of the

target genes were performed with the Fermentas First-

Strand cDNA Synthesis kit (ThermoFisher Scientific) as

mentioned in Turktas et al. (2013). PCR primers specific to

target genes were designed by using Primer3Plus software

v. 2.3.3 (http://primer3plus.com) (Untergasser et al. 2012).

The quantification was also performed using 18SrRNA as

internal reference (GenBank ID): AF147501; forward pri-

mer: 50-GTGACGGGTGACGGAGAATT-30; reverse pri-

mer: 50-GACACTAATGCGCCCGGTAT-30 (Unver et al.

2010b; Eldem et al. 2012; Turktas et al. 2013). Relative

expression levels of the target genes were calculated using

the 2-DDCt method (Schmittgen and Livak 2008).

Results

To identify F. culmorum and B. sorokiniana stress

responsive miRNAs in wheat, two separate 8 9 15 K

custom miRNA-microarray assays were conducted. Com-

parative analyses of the two array datasets were performed

to evaluate how the miRNA patterns are affected by dif-

ferent pathogens. The data obtained from the both miRNA-

microarray experiments were analyzed based on three

comparisons: (1) Bezostaja control (BC) vs Bezostaja

inoculated (BI), (2) Mizrak control (MC) vs Mizrak inoc-

ulated (MI) and (3) Bezostaja inoculated (BI) vs Mizrak

inoculated (MI).

Fig. 1 Venn diagram showing the numbers of differentially

expressed miRNAs in the two miRNA-Microarray experiments

Table 1 Fusarium culmorum pathogen responsive miRNAs in two

wheat cultivars

miRNA_Name Fold change

BC vs BI MC vs MI BI vs MI

ath-miR159b 1.00 -1.50 -1.19

ath-miR2933a -1.35 2.04 1.29

ath-miR395b -2.29 -1.19 -1.06

ath-miR447c-3p 60.83 1.03 1.05

ath-miR869.1 216.31 1.03 1.05

bdi-miR159 1.26 -2.33 -1.25

cre-miR1168.1 1.57 19.47 1.05

cre-miR1169-3p 3.14 -1.66 26.82

cre-miR916 -82.44 -225.75 -2.82

csi-miR535 -1.56 -1.08 -1.07

gma-miR156aa -1.22 1.67 -1.14

gma-miR169j-3p 41.44 1.03 1.05

gma-miR171 k-5p -1.66 1.55 1.07

gma-miR2119 -3.19 -1.40 1.09

gma-miR390b-5p 1.22 19.18 -2.56

gma-miR4402 1.21 28.23 -3.42

gma-miR4409 66.50 1.03 1.05

gma-miR4997 -1.23 1.74 1.07

gma-miR5674a 28.23 1.03 1.05

gma-miR5783 -8.23 2.21 1.50

gma-miR6300 1.24 -1.89 -1.47

hbr-miR408a 1.13 2.24 1.39

hbr-miR6170 -1.14 1.59 -1.03

hvu-miR6180 -1.64 1.10 -1.04

mdm-miR169e 78.94 1.03 1.05

mdm-miR319c 3.29 83.49 1.05

mdm-miR3627a 1.63 -1.07 -1.06

mtr-miR2592 s 251.90 1.03 1.05

mtr-miR2657a 117.41 1.03 1.05

mtr-miR2679a -1.08 1.61 -1.09

mtr-miR5208a 155.79 1.03 1.05

mtr-miR5263 -1.94 -1.14 -1.01

mtr-miR5560-3p -1.83 -1.16 1.18

mtr-miR5562-3p 1.23 7.52 -11.77

osa-miR1423b -1.31 -2.50 1.09

osa-miR1427 1.20 57.64 1.05

osa-miR1439 4.31 3.73 -19.69

osa-miR159f 1.43 -2.09 -1.29

osa-miR1869 4.07 -22.82 -60.00

osa-miR2093-5p -1.52 -1.41 1.03

osa-miR2867-3p 1.58 1.06 -19.21

osa-miR2918 37.03 1.03 1.05

osa-miR2928 35.77 1.03 1.05

osa-miR319a-3p -1.48 -1.10 1.25

osa-miR390-5p 1.52 1.05 -20.80

osa-miR5338 158.65 1.03 1.05

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Fungal attack responsive miRNA expression patterns

Upon both fungal-inoculations, a total of 87 differentially

expressed wheat miRNAs were identified (Fig. 1). Out of 87

miRNAs, 66 were differentially expressed in wheat upon F.

culmorum pathogen inoculation. The remaining 21 miRNAs

were detected as B. sorokiniana pathogen-responsive.

Additionally, 8 of the 87 miRNAs (athmiR869.1, cre-

miR1169-3p, mtr-miR2592 s-3p, osa-miR1427, osa-

miR319a-3p.2-3p, ptc-miR169b-3p, vvi-miR3624-5p,

miR482e) were found to be common in both miRNA-

microarray analyses.

Identification F. culmorum responsive miRNAs

in wheat

A total of 59,430 probes produced detectable signals in the

array were hybridized with F. culmorum treated plants and

their controls. Although a large number of probes gener-

ated signals, 66 miRNAs were found to be differentially

expressed in at least one comparison between the samples

(Fig. 2; Table 1). These 66 miRNAs were also detected on

16 different plant species including 15 Oryza sativa, 11

Glycine max, 5 Arabidopsis thaliana, 5 Populus tricho-

carpa miRNAs.

The cluster analysis of the signals revealed that the

inoculated samples were grouped together which were

close to susceptible mock inoculation, while resistant mock

inoculation was clustered in a distant branch (Fig. 2).

Out of 66, 9 miRNAs (miR156, miR159, miR169,

miR171, miR319, miR390, miR396, miR408, miR535)

belong to conserved miRNA families. While 19 miRNAs

were found to be up-regulated, 29 miRNAs were detected

as down-regulated in BI compared to BC. On the other

hand, comparison between MI and MC revealed that

expression of 13 miRNAs were found to be induced, and

16 miRNAs were reduced.

The results indicated that some of the differentially

expressed miRNAs showed reverse expression patterns

between the two cultivars. Among the 19 up-regulated

miRNAs in BI vs BC comparison, eight of them (ath-

miR2933a, gma-miR171 k-5p, gma-miR5783, hvu-

miR6180, osa-miR5819, peu-miR2911, ptc-miR474b, vvi-

miR3626-3p) were found to be down-regulated in MI vs

MC comparison. Similarly, ath-miR159b, bdi-miR159, cre-

miR1169-3p, gma-miR6300, osa-miR159f, osa-miR1869,

ptc-miR482c-5p were identified as down-regulated in BI vs

BC library; however, they were observed as up-regulated in

MI vs MC (Table 1).

Additionally, irrelevant from resistance nature of the

cultivars, three miRNAs (cre-miR916, osa-miR5510, ptc-

miR916e-3p) were found to be up-regulated in inoculated

samples compared to control samples (Table 1).

To identify cultivar specific expression, we compared

miRNA patterns of BI vs MI. We found that vvi-miR3626-

3p and cre-miR1169-3p were 166 and 27-fold overex-

pressed, respectively, in BI. On the other hand, eight

miRNAs were measured as highly down-regulated in BI

(Table 1). Interestingly, 30 % of those 66 miRNAs were

highly down-regulated in BI compared to BC, while no

expression difference was observed for those miRNAs

comparing MI vs MC.

Identification B. sorokiniana responsive miRNAs

in wheat

The second microarray slide was subjected to the small

RNAs of B. sorokiniana and mock-inoculated samples.

Similar to the microarray experiment performed using F.

culmorum, 25 % of the all probes produced detectable

signals. The inoculated and control samples were clearly

clustered into different branches (Fig. 3).

The results showed that a total of 21 miRNAs were

differentially expressed between the samples (Table 2).

These 21 miRNAs belong to 11 different plant species

including A. thaliana (2), Chlamydomonas reinhardtii (1),

Table 1 continued

miRNA_Name Fold change

BC vs BI MC vs MI BI vs MI

osa-miR5510 -1.77 -1.67 -1.18

osa-miR5819 -1.56 1.19 -1.02

osa-miR5832 91.42 1.03 1.05

peu-miR2911 -19.92 1.01 1.66

ppt-miR1057 -1.52 -1.02 -1.31

ppt-miR901 20.78 1.76 -56.82

ptc-miR169b-3p 212.16 1.03 1.05

ptc-miR396e-3p -2.46 -1.47 1.07

ptc-miR474b -1.68 1.06 1.14

ptc-miR482c-5p 1.20 -19.50 -18.95

ptc-miR6436 -1.06 -112.18 -109.02

sbi-miR5565d 96.92 1.03 1.05

sbi-miR5565e 43.84 1.03 1.05

sbi-miR6228-5p 94.15 1.03 1.05

sbi-miR6229-5p -1.09 -1.70 1.16

vvi-miR3624-5p 98.31 1.03 1.05

vvi-miR3626-3p -166.78 1.03 166.15

zma-miR169a-3p 137.70 1.03 1.05

zma-miR169r-3p 33.50 1.03 1.05

zma-miR396a-3p -1.97 -1.12 1.04

Fold changes between the samples based on log2 were represented.

The signals were considered as differentially expressed with P B 0.05

and 1.45-fold change values

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G. max (2), M. truncatula (2), O. sativa (7), Physcomitrella

patens (1), P. trichocarpa (2), Salvia sclarea (1), Sorghum

bicolor (1), Vitis vinifera (1), and Zea mays (1) miRNAs.

The identified miRNAs were found to be the members of

four conserved miRNA families such as miR164, miR169,

miR319, and miR398.

Comparison between BI and BC showed that seven

miRNAs were identified as up-regulated, while 12 miR-

NAs were observed as down-regulated in the fungal-inoc-

ulated sample. On the other hand, fewer numbers of

miRNAs were found to be differentially regulated in sus-

ceptible cultivar M than those found in B. Five miRNAs

were found to be down-regulated, and seven miRNAs were

identified as up-regulated in MI vs MC comparison.

The analyses showed that majority of the miRNAs

represent similar expression patterns in both cultivars. For

instance, osa-miR1427, osa-miR319a-3p, osa-miR528-5p,

osa-miR5525, ssl-miR398, and zma-miR164 g-3p miRNAs

showed high induction in response to fungal attack in both

resistant and susceptible cultivars (Table 2).

On the other hand, ath-miR771, ath-miR869.1, mtr-

miR2592 s, mtr-miR2634, osa-miR6253, ptc-miR169b-3p,

ptc-miR477d-3p, and vvi-miR3624-5p expressions were

found to be down-regulated in BI compared to BC. How-

ever, downregulation of those miRNAs were observed in

MI compared to MC, while their expression differences

were not significant.

To identify cultivar-specific expression, we compared

miRNA patterns of BI vs MI. The gma-miR482e showed

35 fold higher expression in MI compared to BI. On the

other hand, compared to MI, BI had 4.6-fold upregulation

of ssl-miR398 (Table 2).

Target gene analysis of the miRNAs

Using bioinformatic tools, 546 potential target genes were

predicted as 60 F. culmorum responsive miRNAs, and any

potential targets were observed for 6 miRNAs (suppl.

Table S2). The discovered target genes were subjected to

gene ontology (GO) analyses to interpret their biological

functions and cellular organizations. The BLAST analysis

showed that the target genes were involved in a broad

spectrum of processes (Fig. 4). Most of the genes were

involved in metabolic process, protein metabolism, regu-

lation of biological quality, and response to stimulus. The

rest were responsible for cellular process, carbohydrate

metabolism, gene expression, developmental process,

transport, signal transduction, oxidation–reduction process,

RNA metabolic process, reproductive process and other

categories.

Fig. 2 The heat map of F. culmorum responsive miRNAs in wheat miRNA-Microarray assay. Normalized signal intensities of 66 differentially

expressed miRNAs were illustrated in color scheme ranged from red to blue. The signal intensities decreases from red to blue color

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We found a total of 155 transcripts targeted by 18 dif-

ferently expressed B. sorokiniana pathogen responsive

miRNAs (suppl. Table 3). The analyses showed that the

identified miRNAs target numerous transcripts with diverse

functions (Fig. 5). The targets were involved in response to

stimulus which was followed by transport, response to

stress, carbohydrate metabolism, cellular process, protein

metabolism, gene expression, oxidation–reduction process,

peroxidase reaction and other processes. The majority of

the miRNAs were involved in regulation of numerous

target genes. Among the responsive miRNA, ssl-miR398

targets the highest number of genes with 101 transcripts,

while ath-miR869 targets only one transcript (probable

glucuronoxylan glucuronosyltransferase f8h-like).

Validation of miRNA-microarray data

To validate the results of the two miRNA-microarrays,

expression of the selected nine fungal-responsive miRNAs

and their target transcripts were measured by qRT-PCR

(Fig. 6). Out of nine, five miRNAs (bdi-miR159, osa-

miR1869, ppt-miR901, ptc-miR169b-3p, and ptc-

miR482c-5p) were selected among F. culmorum respon-

sive miRNAs, and the remaining four miRNAs (cre-

miR1169-3p, osa-miR319a-3p, ssl-miR398, and zma-

miR164 g-3p) were selected from B. sorokiniana respon-

sive miRNAs. Expression profiles of most of the selected

miRNAs were comparable with the results of the micro-

arrays. Through miRNA-microarray measurement, ppt-

miR901 expression was found to be down-regulated upon

fungal-inoculation in the both cultivars, similarly, the

expression repression was also confirmed by qRT-PCR

analysis. Moreover, the expression of osa-miR319 was

detected as relatively high expressed in MI the BI in both

assays. Additionally, ssl-miR398 was expressed higher

upon inoculation in both cultivars which was validated by

qRT-PCR (Fig. 6).

The expected negative correlation between the expres-

sion pattern of the eight selected miRNAs and the target

transcripts were successfully confirmed by qRT-PCR

(Fig. 6). However, we detected an unexpected result only

for target transcript of miR398.

Discussion

In recent years, using different approaches, several studies

were conducted to understand functions of miRNAs in

stress tolerance in plants (Li et al. 2011; Zhou et al. 2012;

Chen et al. 2012). Being a high-throughput system,

Fig. 3 The heat map of B. sorokiniana responsive miRNAs in wheat miRNA-Microarray assay. Normalized signal intensities of 21 differentially

expressed miRNAs were illustrated in color scheme ranged from red to blue. The signal intensities decreases from red to blue color

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miRNA-arrays have an important and comprehensive role

in the identification of miRNAs responsive to different

stress factors. Although functions of plant miRNAs in

virus-plant interactions has been investigated, information

of miRNAs induced by fungal or bacterial pathogens is

limited (Zhao et al. 2012). In literature, there are only two

reports about fungal-responsive wheat miRNAs (Xin et al.

2010; Gupta et al. 2012). The current study aimed to

identify the wheat miRNAs involved in response to two

different fungi attacks. Comparing the results of two dis-

tinct wheat pathogens, F. culmorum and B. sorokiniana,

this study enabled us to identify how different wheat plants

responded to two different fungi.

Up to date, few numbers of miRNAs were arrayed in the

majority of the microarray based studies. Here, all the

known plant miRNAs were used to obtain more compre-

hensive data set. To our knowledge, this is the broadest

range miRNA-microarray study performed in wheat. A

previous study indicated the miRNAs can be transferred

from one species to another (Liang et al. 2013). Similarly, a

quarter of the probes originated from different species also

produced signals in our array study.

Here, two disease causing fungi were inoculated on two

wheat cultivars having different resistance levels. A num-

ber of miRNAs were found to be differentially regulated in

both assays. In which 66 and 21 miRNAs were observed as

responsive to F. culmorum and B. sorokiniana, respec-

tively. Two different miRNA expression profiles showed

that miRNA responses were significantly affected by

genetic background of the cultivars as well as fungal

agents.

Expression analysis revealed that fungal-inoculation

itself causes miRNA response in wheat. Heat-map illus-

trated a correlation between stressed and unstressed sam-

ples (Figs. 2, 3). Differentially expressed miRNAs detected

in B and M cultivars were clustered together, while the

signals obtained from control samples were grouped into a

different branch.

Comparing inoculated and control samples of resistant

cultivar, among 66 F. culmorum responsive miRNAs,

expression of 21 miRNAs was significantly reduced after

inoculation in the range between 20 and 251 folds. How-

ever, this pattern was not observed in susceptible cultivars

upon pathogen attack (Table 1). A similar expression

profile was also observed among the B. sorokiniana

responsive miRNAs. Among 21 differentially expressed

miRNAs, six miRNAs were down-regulated only in the

infected resistant B cultivar relative to the susceptible M

cultivar upon pathogen inoculation (Table 2). Ontology

analysis of the transcripts targeted by these miRNAs

revealed that some of the targets were included in disease

resistance processes (suppl. Tables S2, S3). Previous

studies were reported that a number of those target genes

were highly expressed in plants upon pathogen stress (DIaz

et al. 2002; Wagacha and Muthomi 2007; Xiao et al. 2013;

Gupta et al. 2014). The main reason behind the miRNA

regulation on fungal disease response in resistant cultivars

may be associated with the following hypothesis: Upon

fungal-inoculation, repression of these miRNAs causes

relatively higher accumulation of target transcripts. In the

end, resistant plant response against the pathogen attack.

Based on the ontology analysis, some of the miRNAs

have not been reported as stress responsive, while some

were known to be involved in fungal stress (Zhao et al.

2012). Roles of miR156, miR159, miR169, miR164,

miR319, and miR398 in response to fungal stress were

identified in pine, poplar, and wheat (Xin et al. 2010).

These results pointed that conserved miRNAs have vital

roles in pathogen defense mechanism in wheat. It was

reviewed that these miRNAs were also found to be

responsive to various abiotic stresses (Eldem et al. 2013).

However, the expression profiles of those miRNAs were

not fully consistent with each other indicating a species and

infection-specific miRNA regulation. Specifically, con-

flicting results were reported about the expression of

Table 2 B. sorokiniana pathogen-responsive miRNAs in two wheat

cultivars

miRNA_Name Fold change

BC vs BI MC vs MI BI vs MI

ath-miR771 1.70 1.17 -1.10

ath-miR869,1 129.71 1.04 -1.02

cre-miR1169-3p 274.31 10.44 -5.23

gma-miR4398 -1.45 -1.00 -1.79

gma-miR482e 2.02 -41.84 -34.90

mtr-miR2592 s 151.14 1.04 -1.02

mtr-miR2634 119.45 1.04 -1.02

osa-miR1427 -8.97 -32.38 -1.88

osa-miR1881 158.49 40.26 -2.56

osa-miR319a-3p,2-3p -9.51 -30.28 -1.66

osa-miR528-5p -2.18 -1.56 1.52

osa-miR5525 -2.03 -31.14 -1.06

osa-miR5800 -1.23 1.04 1.47

osa-miR6253 1.74 1.23 -1.78

ppt-miR894 1.37 1.58 1.16

ptc-miR169b-3p 102.68 1.04 -1.02

ptc-miR477d-3p 1.58 1.19 -1.17

sbi-miR169r-5p 2.12 2.80 1.51

ssl-miR398 -145.98 -29.78 4.64

vvi-miR3624-5p 58.70 1.04 -1.02

zma-miR164 g-3p -16.86 -49.34 1.31

Fold changes between the samples based on log2 were represented.

The signals were considered as differentially expressed with P B 0.05

and 1.45-fold change values

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miR169 family members against pathogen stress. miR169

was found to be down-regulated in soybean in response to

pathogen stress (Subramanian et al. 2008), while its

expression was induced in poplar against fungal attack

(Gupta et al. 2014). miR169 family members were signif-

icantly down-regulated in inoculated wheat cultivars.

Fig. 4 Gene ontology terms of biological function and molecular processes of F. culmorum responsive miRNA target transcripts in wheat

Fig. 5 Gene ontology terms of biological function and molecular processes of B. sorokiniana responsive miRNA target transcripts in wheat

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miR169 potentially targets a number of transcripts

including Pto kinase 1 which is a well-known resistance

gene (suppl. Table S2). Therefore, its expression level is

expected to be higher in inoculated samples compared to

controls. Since all these studies presented in silico miR169

target transcript prediction, it is not possible to identify

whether the same transcripts were targeted by miR169,

detailed target gene analysis is required to better under-

stand the exact reason of miR169 suppression upon fungal-

inoculation. Similarly, expression level of ath-miR869.1

was down-regulated in the infected wheat. The target

analysis showed that glucuronosyltransferase (GT)

(TC457607), which has a novel role in plant defense to

fungal pathogen, was regulated by ath-miR869.1 (suppl.

Table S2). GT was found to be participated in resistance

against deoxynivalenol (DON) which is a mycotoxin pro-

duced by fungal of the genus Fusarium (Bowles et al.

2005). Consequently, the higher level of GT in infected

plant is required to provide resistance.

To further evaluate miRNA regulation upon fungal

attack, we also analyzed miRNAs that showed opposite

expression in B and M cultivars. F. culmorum inoculation

caused downregulation of osa-159f, cre-1169-3p, and osa-

1869 miRNAs in BI, but induction of those in MI com-

pared to mock inoculations. Based on the target analysis,

those miRNAs were predicted to regulate SQUAMOSA

promoter binding proteins (SBP) family TFs, leucine-rich

repeat receptor, and copine family. It was reported that

expression of the target genes were induced upon pathogen

attack (Jambunathan et al. 2001; Kulcheski et al. 2011;

Eldem et al. 2013). Similarly, gma-miR482e was identified

as up-regulated in B. sorokiniana infected susceptible M

cultivar, but down-regulated in treated resistant B cultivar.

It was reported that a nucleotide-binding site leucine-rich

repeat (NBS-LRR) protein, targeted by miR482, was sup-

pressed in tomato when inoculated with Pseudomonas

syringe (DC3000) (Gupta et al. 2014). Furthermore, it was

identified that miR482bd-3p was down-regulated in a

susceptible soybean cultivar, which was treated with

Phakopsora pachyrhizi pathogen (Kulcheski et al. 2011).

In another study, expression of several ghr-miR482 family

members were reduced when infected with Verticillium

dahliae which caused upregulation of their NBS-LRR

targets (Zhu et al. 2013). Since a resistant cultivar needs to

synthesize more defense proteins than a susceptible culti-

var does, higher expression levels of such target genes in B

cultivar was expected.

On the other hand, three miRNAs (gma- miR5783, gma-

miR171 k-5p, ath- miR2933) were expressed more in F.

culmorum treated resistant B cultivar compared to its

Fig. 6 qRT-PCR validation of selected miRNAs and their target transcripts. The histograms show the relative values for the quantified

expressions of the miRNAs and the target genes. The analyses were performed as triplicates, and the error bars were indicated on each column

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control, while the opposite pattern was observed in sus-

ceptible M cultivar. A similar pattern was also observed for

gma-miR4398 upon B. sorokiniana infection. Previous

studies identified miR171 as adaptive responsive to stress

(Zhou et al. 2007; Liu et al. 2008). It was described that

conserved miR171 family was found to be down-regulated

in response to powdery mildew in wheat (Xin et al. 2010;

Khraiwesh et al. 2012). Although the two cultivars pre-

sented different expression profiles in our study, miR171

might take role in fungal pathogen stress. Peroxiredoxin

(Prx) (BE418197), predicted target of gma-miR5783,

functions as peroxidase which plays various functions

including response to pathogen infections (Rouhier et al.

2004). Recently, it was reported that WRKY transcription

factor was found to be the target of miR4398 (Zhang et al.

2014), and WRKYs in sunflower were identified as defense

response in biotic stress (Giacomelli et al. 2010).

Comparing the results of the two fungal-inoculation

miRNA expression assays, we found that eight miRNAs

were differentially regulated against both inoculations.

Besides, five of them showed the same expression profiles

in the two analyses. Although different pathogen caused

specific stress response, a common stress resistance

mechanism might also be considered based on this

outcome.

The overall analyses indicated that fungal-responsive

wheat miRNAs were differentially regulated in a pathogen

specific manner. In addition to inoculation-based miRNA

expression pattern, cultivar-related miRNA regulation

against pathogen response was also observed. This sug-

gested that such genetic variation among cultivars could be

amenable to selection for disease resistance in terms of

miRNAs expression upon fungal stresses. Some of the

miRNAs were identified as fungal-inoculation responsive

for the first time. Coupled with the genetic variation among

cultivars, understanding the genetic mechanisms of fungal

agents will enable us to better deal with the disease in

wheat.

Acknowledgments The study was funded by TUBITAK with Grant

Number: 113O546.

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