Abstract
Background
Sugarcane smut caused by Sporisorium scitamineum leads to a significant
reduction in cane yield and sucrose content. MicroRNAs (miRNAs) play an
important role in regulating plant responses to biotic stress. The
present study was the first to use two sugarcane genotypes, YA05-179
(smut-resistant) and ROC22 (smut-susceptible), to identify
differentially expressed miRNAs in sugarcane challenged with S.
scitamineum by using high-throughput sequencing.
Results
The predicted target gene number corresponding to known differentially
expressed miRNAs in YA05-179 was less than that in ROC22, however most
of them were in common. Expression of differential miRNAs under S.
scitamineum challenge was mostly downregulated, with similar trends in
the two varieties. Gene ontology (GO) analysis showed that the target
gene classification of known miRNAs was similar to that of the newly
identified miRNAs. These were mainly associated with cellular processes
and metabolic processes in the biological process category, as well as
combination and catalytic activity in the molecular function category.
Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment
analysis revealed that these predicted target genes involved in a
series of physiological and biochemical pathways or disease
resistance-related physiological metabolism and signal transduction
pathways, suggesting that the molecular interaction mechanism between
sugarcane and S. scitamineum was a complex network system. These
findings also showed certain predicted target genes of miR5671,
miR5054, miR5783, miR5221, and miR6478 play roles in the
mitogen-activated protein kinase (MAPK) signaling pathway, plant
hormone signal transduction, and plant-pathogen interaction.
Quantitative real-time PCR (qRT-PCR) analysis showed that majority of
the known miRNAs and its predicted target genes followed a negatively
regulated mode. Seven out of eight predicted target genes showed
identical expression after 12 h treatment and reached the highest
degree of matching at 48 h, indicating that the regulatory role of
miRNAs on the target genes in sugarcane was maximized at 48 h after S.
scitamineum challenge.
Conclusions
Taken together, our findings serve as evidence for the association of
miRNA expression with the molecular mechanism underlying the
pathogenesis of sugarcane smut, particularly on the significance of
miRNA levels in relation to the cultivation of smut-resistant sugarcane
varieties.
Electronic supplementary material
The online version of this article (doi:10.1186/s12864-017-3716-4)
contains supplementary material, which is available to authorized
users.
Keywords: Saccharum spp., Sporisorium scitamineum, High-throughput
sequencing, miRNA, Function prediction of target genes, qRT-PCR
Background
Sugarcane is the most important sugar crop in China, accounting for 92%
of the total sugar production in the country. To date, sugarcane smut
caused by Sporisorium scitamineum widely occurs in sugarcane fields
worldwide and has become one of the most difficult fungal diseases to
control [[43]1, [44]2]. Due to poor resistance to sugarcane smut,
several prevalent sugarcane varieties in China such as NCO310, F134,
and CP73-351 have become vulnerable to smut disease, thereby resulting
in major losses in cane yield and sugar and thereby have been
eliminated in succession [[45]2]. The cultivation of smut-resistant
sugarcane varieties is considered as the most cost-effective approach
in controlling this disease [[46]3]. Hence, in-depth studies of disease
resistance mechanisms are imperative for the prevention and control of
sugarcane smut.
Extensive studies on the mechanism of interaction between sugarcane and
S. scitamineum have been conducted in the past few years
[[47]4–[48]16]. Heinzeetal et al. [[49]4] used suppression subtractive
hybridization (SSH) to obtain two full-length genes that were
potentially important for the interaction between sugarcane and S.
scitamineum, which in turn could facilitate in the evaluation of the
two molecular markers for sugarcane smut resistant varieties. Borrás et
al. [[50]5] used cDNA-amplified fragment length polymorphism
(cDNA-AFLP) technology to study the differential gene expression of
sugarcane after the development of smut disease and screened 62
differentially expressed genes, including 52 upregulated genes and ten
downregulated genes. Among these 52 upregulated genes, 19 were directly
related to biological functions such as defense and signal
transmission. Que et al. [[51]6] used two-dimensional gel
electrophoresis (2DE), matrix-assisted laser desorption/ionization
time-of-flight mass spectrometry (MALDI-TOF MS), and quantitative PCR
to comprehensively analyze the molecular responses, including
transcription and protein expression in relation to the interaction
between sugarcane and S. scitamineum. By RNA sequencing and isobaric
tags for relative and absolute quantitation (iTRAQ), the gene and
protein expression profiles of sugarcane in response to the infection
of S. scitamineum were constructed, and its relationship with the
mechanism of interaction between sugarcane and S. scitamineum was
identified [[52]7]. In 2015, Su et al. [[53]8] developed a rapid and
visual loop-mediated isothermal amplification (LAMP) for the detection
of S. scitamineum in sugarcane. This assay was nearly 100 times more
sensitive than conventional PCR for detection of this pathogen [[54]8].
Su et al. also obtained three members of the β-1,3-glucanase gene
family (ScGluA1, ScGluD1 and ScGluD2) [[55]9, [56]10], one catalase
gene (ScCAT1) [[57]11] and ten chitinase family genes [[58]12], which
are associated with the pathogenicity of sugarcane smut. Wu et al.
[[59]13] used Solexa high-throughput sequencing for differential gene
expression profiling of sugarcane after infection with sugarcane smut
and 2,015 differentially expressed sequence tags (ESTs) were screened,
including three upregulated ESTs that were related to the
mitogen-activated protein kinase (MAPK) signaling pathways. In
addition, our group was the first to report the whole-genome sequence
of S. scitamineum and to comprehensively describe the pathogenesis of
sugarcane smut [[60]14], and two other groups, Taniguti et al. [[61]15]
and Dutheil et al. [[62]16], followed suit. It is the fourth smut
fungal species subjected to whole-genome sequencing, following Ustilago
maydis and S. reilianum in maize [[63]17, [64]18] and Ustilago hordei
in barley [[65]19]. The aforementioned findings have promoted research
on the molecular response involved in the interaction between sugarcane
and smut fungus. However, no mechanistic analysis of microRNA (miRNA)
differential expression and functional analysis of the potential target
gene of sugarcane under S. scitamineum challenge has been conducted to
date.
miRNAs are a class of non-coding small RNA (sRNA) of unequal lengths
that ranging from 20 to 25 nt [[66]20–[67]22]. These have important
biological functions, and its mediated post-transcriptional gene
regulation is an extremely important sRNA regulatory pathway in in vivo
models [[68]20–[69]22]. In 1993, Lee et al. [[70]23] first described a
miRNA in Caenorhabditis elegans by showing that an sRNA fragment that
was complementary to the lin-14 genomic sequence was a lin-14
transcript, which could negatively regulate lin-14 expression. In 2000,
Reinhar et al. [[71]24] found a gene, let-7, in C. elegans with a
similar function as that of lin-14, further confirming that
miRNA-mediated transcriptional regulation commonly but not
coincidentally existed in organisms. Ruvkun et al. [[72]25] in 2004
described a novel post-transcriptional gene expression regulatory
mechanism that involved miRNA target genes. Since then, a large number
of novel miRNAs have been reported in various plant and animal species,
including human [[73]26], mouse [[74]27], Drosophila [[75]28],
nematodes, and Arabidopsis [[76]29].
Previous studies have shown that miRNAs do not only influence the
growth and development of organisms by regulating transcription
factors, but also degrade target gene mRNAs or stop the target gene
translation to change the cellular behavior of plants through numerous
physiological pathways such as protein hydrolysis, metabolism, and ion
transport [[77]30], as well as signal transduction pathways
[[78]31–[79]33]. Compared to miRNAs in animals, studies on plant miRNAs
have lagged behind and only begun in 2002 [[80]34]. Napoli et al.
[[81]35] confirmed the presence of miRNAs and small interfering RNAs
(siRNAs), including its mechanism of interaction. They also explained
the specific mechanism underlying co-suppression during the synthesis
of flavonols and anthocyanins in plants [[82]35]. A subsequent study
showed that miRNAs played important biological functions in plants,
wherein they are not only involved in various physiological and
biochemical processes and regulate normal growth and development of
organisms by controlling the expression of transcription factors
[[83]30–[84]33], but also had a regulatory role in abiotic or biotic
stresses [[85]36, [86]37]. Jones et al. [[87]38] showed that plants
subjected to drought, cold, and high salinity stress induced changes in
the expression of miR319c, miR393, miR395, miR397b, and miR402. For
example, the ATP sulfurylase 1 gene (APS1), a target gene of miR395,
significantly decreased under low sulfate stress. Patade et al.
[[88]39] revealed that sugarcane miR159 plays an important role in its
response to high salinity stress. Lu et al. [[89]40] obtained 48 miRNA
sequences from poplars whose target genes were associated with growth
and development, stress responses, anti-virus infection, and also other
life-related processes. A previous study showed correlations between
miRNA and virus-induced illness, as well as virus-mediated gene
silencing [[90]41]. Kasschau et al. [[91]42] demonstrated that
overexpression of the helper component-proteinase (Hc-Pro) gene in
Arabidopsis plants significantly reduced miR171 expression and further
increased target gene expression of miR171, thereby resulting in
miR171-related developmental deficiency in plants.
This study focused on the interaction of two different sugarcane
genotypes YA05-179 (smut resistant) and ROC22 (smut susceptible)
inoculated with S. scitamineum 48 h by conducting differential miRNA
expression analysis and quantitative real-time PCR (qRT-PCR) validation
to identify and analyze the expression patterns of miRNA in sugarcane
during S. scitamineum challenge, as well as functional analysis to
predict its target genes. Moreover, the present study provides evidence
on the role of miRNA against S. scitamineum challenge in sugarcane to
further broaden and strengthen our understanding of the molecular
mechanism underlying the response of sugarcane against smut disease,
thereby providing a theoretical basis for the cultivation of
smut-resistant sugarcane varieties.
Methods
Plant growth and stress treatment
Smut spores were collected from the host variety of ROC22, which were
propagated at the Key Laboratory of Sugarcane Biology and Genetic
Breeding, the Ministry of Agriculture/Fujian Agriculture and Forestry
University (Fuzhou, China). The smut spores were placed in a paper bag,
air-dried, and subsequently stored in a sealed container at 4 °C until
use. The sugarcane varieties used in the experiments, ROC22
(smut-susceptible genotype) and YA05-179 (smut-resistant genotype),
were provided by the Key Laboratory of Sugarcane Biology and Genetic
Breeding, Ministry of Agriculture/Fujian Agriculture and Forestry
University. Healthy and uniform sugarcane (from the 4^th to 7^th nodes
counting from the basal node) were collected and cut into single-bud
setts, followed by soaking in clean running water for 1 d. The treated
materials were then incubated at 28 °C and cultivated in a moisturizer
until the sugarcane buds grew to 1–2-cm in length, followed by needle
puncture inoculation of smut spores suspension (5 × 10^6 spores/mL,
with 0.01% volume ratio of Tween-20) into the sugarcane buds. The
control groups were injected with sterile water (with 0.01% volume
ratio of Tween-20). After treatment, the sugarcane buds of the two
groups were continuously incubated at 28 °C with conditions of 12 h
light and 12 h dark photoperiods [[92]8]. To minimize biological
variance, three sugarcane buds were collected at each time point (i.e.,
0, 12, 48, and 96 h) after inoculation and mixed well, followed by snap
freezing in liquid nitrogen and storing at −80 °C until use.
RNA isolation and sequencing
Total RNA was extracted from the ROC22 and YA05-179 sugarcanes in the
treated and control groups using TRIzol^TM (Invitrogen, Carlsbad, CA,
USA). RNA integrity was determined by 1% agarose gel electrophoresis.
The concentration of total RNA was determined by using an Agilent
Bio-analyzer 2100 system (Agilent Technologies, Santa Clara, CA, USA).
The samples of ROC22 and YA05-179 inoculated with sterile water and
smut spores for 48 h, namely, RCK (control group of ROC 22), RT
(treatment group of ROC22), YACK (control group of YA05-179), and YAT
(treatment group of YA05-179) were used for the construction of sRNA
libraries (Beijing Genomics Institute, Beijing, China). sRNA fragments
(18–30 nt) collected from 15% PAGE were each connected to 5′ end and 3′
end adapters, followed by reverse transcription and PCR analysis and
were then sequenced using a HiSeq2000 sequencing system.
Sequencing data processing and analysis
The adapters at both ends of the 50-nt fragments obtained from the
high-throughput sequencing were detached, followed by removal of
contaminating sequences and low-quality reads to obtain clean reads.
Then, statistical analysis of sequence length distribution, as well as
common and specific sequences among samples was performed. The length
of sRNA ranged from 18 to 30 nt, with miRNAs normally 21–22 nt in
length, siRNA 21–24 nt, and Piwi-interacting RNA (piRNA) within the
range of 28–30 nt [[93]43]. The peak value representing the quantity of
different lengths of sRNAs was either 21 nt or 24 nt in length for
plant samples, whereas the peak value of sRNAs of animal samples was
22 nt [[94]43]. Classification and annotation of clean sequences were
performed to generate information on each component and expression
levels of each sample. Using GenBank ([95]http://www.ncbi.nlm.nih.gov/)
and Rfam 10.1 ([96]http://rfam.sanger.ac.uk) databases comparison,
non-coding RNAs such as ribosomal RNA (rRNA), transfer RNA (tRNA),
small nuclear RNA (snRNA), snoRNA, and signal recognition particle RNA
(srpRNA) were identified and removed. The remaining sRNAs were
subjected to a Basic Local Alignment Search Tool (BLASTn) search with
no more than two mismatches against miRBase 18.0 database
([97]http://www.mirbase.org/) to identify known miRNA in the samples
[[98]44, [99]45]. sRNA annotation followed a priority rule for
classification to avoid redundancy: non-coding RNA (in which
GenBank > Rfam) > known miRNA > repeat [[100]46]. Due to the lack of
genome information for sugarcane, the sequences that did not match
known miRNAs were mapped to the Sugarcane_Unigene database (65,852
unigenes) established by our previous transcriptome analysis in ROC22
and YA05-179 post-S. scitamineum infection for 24, 48 and 120 h
[[101]47] and the sugarcane EST in GenBank and the S. scitamineum
genome database [[102]14] to identify potentially novel miRNA
candidates. Mireap ([103]http://sourceforge.net/projects/mireap/) with
default parameters was used to predict the sRNAs without annotation and
prepare the secondary structure of the novel miRNA.
Differential expression of S. scitamineum-responsive miRNAs
To understand the differential expression of sugarcane miRNA after S.
scitamineum challenge, the observed frequencies of unique sequences
were normalized to the reads per million (RPM) data. If the original
miRNA expression in a library was zero, the normalized read count of
this miRNA was adjusted to 0.01 in the library for further calculation
[[104]48–[105]50]. We performed statistical analysis of known miRNAs,
as well as novel miRNAs to identify significant differences in the
expression between the treatment and control groups (comparisons: RT
vs. RCK or YAT vs. YACK). Then we used the graphs of log[2]-ratio and
scatterplot to compare the expression level of miRNAs expressed by both
groups. The specific procedures are as follows: (1) treatment and
control groups were normalized to the same orders of magnitude.
Formula: Normalized expression level = miRNA expression level/total
expression level of the sample × normalized magnitude; (2) Normalized
results were used to calculate the fold change and P-value, as well as
for graph preparation. The formula for calculating fold change was as
follows: Fold change = log[2] (treatment vs. control). The P-value was
calculated based on the following equation [[106]51]:
[MATH: pyx=N2N1<
/mn>yx+y!x!y!1+N2N1x+y+1Cy≤yminx=∑y=0
y≤yminpyxDy≥ymaxx=∑y≥y
max∞pyx :MATH]
where x means control, y means treatment, N [1] means the normalized
expression of a miRNA in the control library, and N [2] means the
normalized expression of the same miRNA in the treatment library. C and
D are used to estimate the confidence intervals [y [min], y [max]] in
regards to a specific P-value.
The value of fold change >1 or < −1 and P-value <0.05 were used as
criteria in screening for miRNAs that were significantly differentially
expressed between samples. In addition, the present study performed
cluster analysis of differentially expressed miRNAs in the two
sugarcane varieties, i.e., cluster analysis was performed to identify
differentially expressed known and novel miRNAs in the treated
sugarcane samples after S. scitamineum challenge and the control
samples inoculated with sterile water [[107]52].
miRNA target gene prediction and functional analysis
With reference to the Sugarcane_Unigene database (65,852 unigenes)
[[108]47] and the sugarcane EST in GenBank, the present study used the
psRNATarget online software
([109]http://plantgrn.noble.org/psRNATarget/) to predict the target
genes of the known miRNAs and the novel miRNAs. The specific prediction
standards were based on Allen et al. [[110]53] and Schwab et al.
[[111]54]. Then, these predicted target genes of differentially
expressed miRNAs in RT/RCK and YAT/YACK were subjected to Gene Ontology
(GO, [112]http://www.geneontology.org/) enrichment and Kyoto
Encyclopedia of Genes and Genomes (KEGG,
[113]http://www.genome.jp/kegg/) pathway analyses [[114]55].
Validation of miRNAs and its predicted target genes by qRT-PCR analysis
qRT-PCR was performed to determine the expression levels of 20
differentially expressed miRNAs. These 20 miRNAs included 16 known
miRNAs and 4 novel miRNAs. Primer 5.0 software was used to design
upstream primers (Additional file [115]1: Table S1), whereas downstream
primers were derived from the Uni-miR qPCR Kit purchased from Takara
(Dalian, China). The internal reference was 5S rRNA [[116]56]. The
sugarcane buds from ROC22 and YA05-179 inoculated with distilled water
and S. scitamineum at 0 and 48 h were used for qRT-PCR samples. Reverse
transcription was conducted using the One Step PrimeScript® miRNA cDNA
Synthesis Kit (Perfect Real Time) (Takara, China), following the
manufacturer’s instructions. Polyadenylation reaction was used to
detect miRNA expression. The 2^−ΔΔCt method [[117]57] was used to
calculate the miRNA expression levels of ROC22 and YA05-179 at 48 h
after S. scitamineum infection. Moreover, qRT-PCR was used to determine
the expression patterns of the 12 miRNAs in ROC22 and YA05-179 at 0,
12, 48, and 96 h after S. scitamineum challenge.
Beacon Designer 7.0 software was used to design the quantification
primers of the 23 randomly selected miRNA target genes (Additional file
[118]2: Table S2). qRT-PCR was used to analyze the expression patterns
of the predicted target genes in ROC22 and YA05-179 at 0, 12, 48, and
96 h after S. scitamineum challenge. The glyceraldehyde-3-phosphate
dehydrogenase (GAPDH) gene was used as internal reference [[119]58].
Reverse transcription was performed using the PrimeScript^TM RT-PCR Kit
(Takara, Dalian, China), following the manufacturer’s instructions. For
qRT-PCR expression analysis, the total volume of reaction system was
20 μL, which included 10 μL SYBR Premix Ex Taq ^TM II (2×) (Takara),
0.8 μL each of the upstream and downstream primers (10 μM), 1 μL of the
cDNA template, and 7.4 μL of ddH[2]O. The reaction conditions were
50 °C for 2 min and 95 °C for 60 s; followed by 40 cycles of 95 °C for
15 s and 60 °C for 60 s; and 95 °C for 15 s; 60 °C for 60 s; 95 °C for
15 s. All assays were performed in triplicate. At the end of
amplification, the 2^−ΔΔCt method [[120]57] was used to calculate the
results of qRT-PCR analysis. Statistical analysis was conducted using
the Data Processing System (DPS) v7.05 software (China). Data were
expressed as the mean ± standard error (SE). Significance (P-value
<0.05) was calculated using one-way Analysis of Variance (ANOVA)
followed by Duncan’s new multiple range test.
Results
Categories and size distribution of sRNAs in sugarcane after challenging with
S. scitamineum
Sequence impurities generated from sRNA sequencing generally refers to
contaminated sequences such as non-insert fragments, fragments without
3′ adapters or fragments with only 5′ adapters, reads containing polyA
sequences, and sequences with lengths <18 nt [[121]59]. In the present
study, the four samples, RCK, RT, YACK, and YAT, underwent sequencing
and all obtained 3 × 10^8 high-quality 18–30 nt clean reads, in which
36,396,588, 27,812,972, 27,464,468, and 28,290,231 reads were from each
library, respectively (Additional file [122]3: Table S3). Additional
file [123]4: Figure S1 shows that the tested sRNA sequence length of
the four samples were mainly within the range of 20–24 nt, a typical
size of Dicer-derived sRNAs, in which, majority of the reads were 24 nt
in length, and accounted for 45.09, 54.26, 55.80, and 59.24% of the
read in RACK, RT, YACK, and YAT, respectively. The proportions of
20–22 nt sRNAs in the treated sugarcane samples of ROC22 and YA05-179
after S. scitamineum challenge were relatively less than that observed
in the controls, whereas the proportion of 23 nt–24 nt sRNAs in the
treated sugarcane samples of ROC22 and YA05-179 after S. scitamineum
challenge were relatively higher than that of the controls. These
findings indicated that S. scitamineum induces changes in the
expression pattern of sRNAs in both sugarcane genotypes.
Comparison of the sRNA libraries of the S. scitamineum-treated and the
control groups (RT vs. RCK and YAT vs. YACK) indicated that only less
than 15% of the unique reads were shared by the two sRNA libraries.
Figure [124]1a and [125]b show that the common sequences between RT and
RCK accounted for 64.47% of the total reads and 13.27% of the unique
reads, respectively. Figure [126]1c and [127]d show that the common
sequences between YAT and YACK accounted for 58.23% of the total reads
and 12.88% of the unique reads. The observation of unique reads with
broad specificity suggests that these may be related to the resistant
phenotype of sugarcane to S. scitamineum.
Fig. 1.
Fig. 1
[128]Open in a new tab
The common and specific sRNAs in ROC22 and YA05-179 after Sporisorium
scitamineum challenge. a Summary of total sRNAs between RT and RCK. b
Summary of unique sRNAs between RT and RCK. c Summary of total sRNAs
between YAT and YACK. d Summary of unique sRNAs between YAT and YACK.
RCK and YACK: ROC22 and YA05-179 under sterile water stress after 48 h,
respectively; RT and YAT: ROC22 and YA05-179 under S. scitamineum
stress after 48 h, respectively
The sRNAs obtained in the present study were concentrated within the
sense and antisense strands of rRNA and sense strand of tRNA
(Additional file [129]5: Table S4). Matching of the sRNA sequences of
small cytoplasmic RNA (scRNA), rRNA, snRNA, small nucleolar RNA
(snoRNA), and tRNA obtained from the GenBank and Rfam 10.1 databases
were then performed (Additional file [130]6: Table S5 and Additional
file [131]7: Table S6). Table [132]1 shows the annotated of sRNAs.
Among the unique sequences, 62,110 (0.46%), 56,796 (0.44%), 58,020
(0.45%), and 60,639 (0.42%) were identified to be similar to known
miRNAs in RCK, RT, YACK, and YAT after searching the miRBase,
respectively. Other types of unique sequences, including rRNA, snRNA,
snoRNA, srpRNA, and tRNA were also detected in the four libraries.
However, there were 13,132,245 (98.13%), 12,686,933 (98.77%),
12,816,045 (98.61%), and 14,130,110 (98.89%) unique sequences in RCK,
RT, YACK, and YAT that could not be annotated, respectively, suggesting
these sRNAs may be unique to sugarcane.
Table 1.
Annotation and classification of the small RNAs in the four libraries
Category RCK RT YACK YAT
Unique Total Unique Total Unique Total Unique Total
Total reads 13,382,890 36,396,588 12,844,706 27,812,972 12,996,601
27,464,468 14,288,057 28,290,231
miRNA reads 62,110 3,842,212 56,796 2,769,304 58,020 2,117,246 60,639
1,973,307
rRNA reads 149,095 4,535,331 80,458 1,347,015 97,711 1,924,796 76,758
1,102,526
repeat reads 526 1,214 489 1,114 424 1,067 443 1,153
snRNA reads 6,390 25,149 4,072 11,579 4,800 12,774 4,897 12,309
snoRNA reads 4,381 13,333 2,596 5,793 2,676 6,200 2,545 5,375
srpRNA reads 2 2 0 0 0 0 0 0
tRNA reads 28,141 1,578,262 13,362 475,165 16,925 588,689 12,665
323,280
Unannotaed sRNA reads 13,132,245 26,401,085 12,686,933 23,203,004
12,816,045 22,813,696 14,130,110 24,870,281
[133]Open in a new tab
RCK and YACK: ROC22 and YA05-179 under sterile water stress after 48 h,
respectively; RT and YAT: ROC22 and YA05-179 under Sporisorium
scitamineum stress after 48 h, respectively
Identification of known and novel miRNAs
miRNA prediction was performed according to the formation and
biometrics of miRNAs [[134]60]. In the present study, the sequences of
the miRNA precursors and mature miRNA in the four sugarcane samples in
the unique sRNAs and miRBase databases were compared. BLAST analysis
identified a total of 264, 263, 260, and 262 known miRNAs in RCK, RT,
YACK, and YAT, respectively. The Mireap software was used to compare
and screen the unannotated sequences (unann) using sugarcane ESTs from
GenBank and transcriptome database of sugarcane in response to smut
infection (65,852 unigenes) to predict novel miRNAs. Our results showed
that there were 137, 140, 111, and 119 novel miRNAs in RCK, RT, YACK,
and YAT, respectively. All of the miRNAs obtained by sequencing were
mapped to the S. scitamineum genome database reported by Que et al.
[[135]14]. The result showed that none of the miRNA sequences was
mapped to S. scitamineum genome sequence, suggesting there was no
contaminated S. scitamineum reads in the identified sugarcane miRNAs.
Additional file [136]8: Figure S2A shows that the base distribution of
the leading sites of novel miRNAs in the four samples were very
similar. For example, the leading base of novel miRNAs of 20 nt in
length was C, and majority of the leading base of novel miRNAs of 21
and 22 nt lengths was U. The distribution and proportion of the four
bases in the leading sites of novel miRNAs of 23 nt in length were
similar. The base distribution of novel miRNAs at different sites was
identical in the four samples (Additional file [137]8: Figure S2B). In
addition, the 11th nucleotide of the candidate miRNA sequences from all
samples was generally A.
S. scitamineum-responsive miRNAs
Transcriptional regulation in plants mainly relies on differentially
expressed miRNAs [[138]61]. In the present study, scatter plot analysis
(Fig. [139]2a and b) showed that most differentially expressed known
miRNAs in ROC22 and YA05-179 after S. scitamineum challenge was
equally-expressed miRNAs. The number of upregulated miRNAs was less
than that of downregulated miRNAs. Under the screening criteria of
log[2]-ratio >1 or < −1 and P-value <0.01, nine significantly
upregulated and 26 significantly downregulated miRNAs (Additional file
[140]9: Table S7) were detected among the 231 differentially expressed
known miRNAs in the RT/RCK comparison; and nine significantly
upregulated and two significantly downregulated miRNAs (Additional file
[141]10: Table S8) were observed among the 208 differentially expressed
known miRNAs in the YAT/YACK comparison. The value of differential
fluctuation of differentially expressed known miRNAs in ROC22 and
YA05-179 was up to −4.61- (miR5242) and −2.15-fold (miR5152-3p),
respectively.
Fig. 2.
Fig. 2
[142]Open in a new tab
Expression of miRNAs in ROC22 and YA05-179 after Sporisorium
scitamineum challenge. a The differences of known miRNAs expression
between RT and RCK. b The differences of known miRNAs expression
between YAT and YACK. c The differences of novel miRNAs expression
between RT and RCK. d The differences of novel miRNAs expression
between YAT and YACK. The scatter plot of differentially expressed
miRNAs (control: X-axis, treatment: Y-axis). The X and Y show the
expression level of miRNAs in the two strains respectively. Red points
mean miRNAs with log[2]-ratio >1; Blue points mean miRNAs with
−1 ≤ log[2]-ratio ≤1; Green points mean miRNAs with log[2]-ratio <1.
Ratio = normalized expression of the treatment/normalized expression of
the control. RCK and YACK: ROC22 and YA05-179 under sterile water
stress after 48 h, respectively; RT and YAT: ROC22 and YA05-179 under
S. scitamineum stress after 48 h, respectively
The number of differentially expressed novel miRNAs was lower than that
of known differentially expressed miRNAs. Most of the differentially
expressed novel miRNAs were equally expressed miRNAs (Fig. [143]2c and
d). RT/RCK comparison identified nine significantly upregulated and
eight significantly downregulated miRNAs (P-value <0.01) among the 48
differentially expressed novel miRNAs (Additional file [144]11: Table
S9). In the YAT/YACK comparison, two significantly upregulated and four
significantly downregulated miRNAs (P-value <0.01) were detected among
the 41 differentially expressed novel miRNAs (Additional file [145]12:
Table S10). Interestingly, the differentially expressed novel miRNAs
with highly significant differential expression levels (P-value <0.01)
were specifically expressed in both sugarcane varieties. The
differential fluctuation of differentially expressed novel miRNAs in
ROC22 and YA05-179 was up to −11.46- (novel_mir_11) and 9.65-fold
(novel_mir_187), respectively.
Although differences in miRNA expression levels were significant, the
expression trends (either up- or downregulated) of differentially
expressed known miRNAs in both sugarcane varieties (RT/RCK and
YAT/YACK) were generally similar (Fig. [146]3a). The specific
regulatory modes of differentially expressed known miRNAs could be
divided into four categories: significantly upregulated, upregulated in
trace amounts, downregulated in trace amounts, and significantly
downregulated. Most of the differentially expressed known miRNAs were
categorized as significantly downregulated. Data analysis showed that
the eight known miRNAs, miR1077-3p, miR262, miR5212-3p, miR5020a,
miR1144a.1, miR5657, miR6149-3p, and miR536 were specifically expressed
in YA05-179, but not in ROC22. Figure [147]3b shows that differentially
expressed novel miRNAs were mostly detected in a single variety of
sugarcane, but not in both sugarcane varieties.
Fig. 3.
Fig. 3
[148]Open in a new tab
Hierarchical clustering of differentially expressed known (a) and novel
(b) miRNAs in ROC22 and YA05-179 after Sporisorium scitamineum
challenge. R-0 and YA-0: ROC22 and YA05-179 under sterile water stress
after 48 h, respectively; R-48 and YA-48: ROC22 and YA05-179 under S.
scitamineum stress after 48 h, respectively
Prediction of target genes of miRNAs in sugarcane
In the present study, the targeted genes of the known miRNAs, and novel
miRNAs were subjected to prediction. The results indicated that most of
the miRNAs had approximately ten target genes. Some miRNAs even had
>100 target genes. According to the results of target gene prediction
of known miRNAs (Additional file [149]13: Table S11), 32 differentially
expressed known miRNAs of RT/RCK had 814 target genes, and ten
differentially expressed known miRNAs of YAT/YACK had 127 target genes.
Target gene prediction of novel miRNAs (Additional file [150]14: Table
S12) showed that the 15 differentially expressed miRNAs of RT/RCK had
457 target genes, and the six differentially expressed miRNAs of
YAT/YACK were associated with 1,754 target genes. Additional file
[151]14: Table S12 presents the prediction results for partial target
genes of differentially expressed miRNAs. Analysis indicated that the
function of these unannotated miRNA target genes were unknown or poorly
characterized, thereby suggesting the possibility of new roles for
these miRNAs in sugarcane in response to S. scitamineum.
GO analysis of the predicted target genes
GO enrichment analysis was conducted for the predicted target genes of
differentially expressed miRNAs in RT/RCK and YAT/YACK. Additional file
[152]15: Figure S3 and Additional file [153]16: Figure S4 demonstrate
the GO classification of the predicted target genes of known and novel
miRNAs associated with biological processes, cellular components, and
molecular functions. The main GO classification of target genes of
differentially expressed known miRNAs and novel miRNAs in YAT/YACK and
RT/RCK was similar, which demonstrated that the differentially
expressed miRNAs of the two sugarcane varieties after S. scitamineum
challenge mainly targeted genes that were associated with cellular
processes and metabolic processes. The predicted target genes were
mainly associated with cell and organelle components. The molecular
functions of the predicted target genes were mainly related to binding
and catalytic activities.
KEGG analysis of the predicted target genes
KEGG pathway enrichment analysis identifies the most important
physiological metabolic pathways and signal transduction pathways of
candidate target genes [[154]12]. In RT/RCK, the predicted target genes
of known miRNAs had 13 pathways that were enriched (P <0.05), and ten
pathways that were significantly enriched (P <0.01) (Additional file
[155]17: Table S13). In YAT/YACK, the predicted target genes of known
miRNAs had four pathways that were enriched (P <0.05) and three
pathways that were significantly enriched (P <0.01) (Additional file
[156]18: Table S14), including pathogenic Escherichia coli infection,
phagosome, and other types of O-glycan biosynthesis. In RT/RCK, the
predicted target genes of novel miRNAs had 13 pathways that were
enriched (P <0.05) and 11 pathways that were significantly enriched (P
<0.01) (Additional file [157]19: Table S15). In YAT/YACK, the predicted
target genes of novel miRNAs showed 13 pathways that were enriched (P
<0.05) and five pathways that were significantly enriched (P <0.01)
(Additional file [158]20: Table S16).
The significantly enriched KEGG pathway of the predicted target genes
in sugarcane after S. scitamineum challenge (Fig. [159]4) could be
divided into five types, including stress response pathway (i.e.,
plant-pathogen interaction, apoptosis, pathogenic Escherichia coli
infection, phagosome, cutin, suberine and wax biosynthesis, and
peroxisome), hormone and signal transduction pathways (i.e., calcium
signaling pathway, MAPK signaling pathway, plant hormone signal
transduction, zeatin biosynthesis, and brassinosteroid biosynthesis),
metabolic pathway (i.e., stilbenoid, diarylheptanoid and gingerol
biosynthesis, polycyclic aromatic hydrocarbon degradation,
phenylalanine metabolism, bisphenol degradation, glucosinolate
biosynthesis, other types of O-glycan biosynthesis, N-Glycan
biosynthesis, and pantothenate and CoA biosynthesis), transcription and
protein synthesis pathways (i.e., RNA polymerase, RNA degradation, mRNA
surveillance pathway, RNA transport, and aminoacyl-tRNA biosynthesis),
and cell division pathway (i.e., cell cycle, DNA replication, meiosis,
nucleotide excision repair, homologous recombination, mismatch repair,
base excision repair, and non-homologous end-joining). In general,
miRNA target gene enriched pathways of both sugarcane varieties were
mostly identical, but not in the number of target genes and the degree
of significant enrichment in the pathways.
Fig. 4.
Fig. 4
[160]Open in a new tab
The significantly enriched KEGG pathway (P-value <0.05) of the
predicted target genes. The genes were targeted by differently
expressed known and novel miRNAs in RT/RCK (a and c) and YAT/YACK (b
and d), respectively. RCK and YACK: ROC22 and YA05-179 under sterile
water stress after 48 h, respectively; RT and YAT: ROC22 and YA05-179
under S. scitamineum stress after 48 h, respectively. Number of target
genes with pathway annotation was shown in the bar charts
Validation of miRNAs by qRT-PCR analysis
In the present study, 12 candidate differentially expressed known
miRNAs with different expression levels, including miR394a, miR408-3p,
miR397-3p, miR7545, miR5066, miR5261, miR948, miR5059, miR5783,
miR5077, miR6300, and miR894, as well as four candidate differentially
expressed novel miRNAs, including novel_mir_133, novel_mir_99,
novel_mir_32, and novel_mir_58 were screened and further analyzed via
qRT-PCR to verify their expression. Among the 16 miRNAs, miR408-3p,
novel_mir_133, novel_mir_99, novel_mir_32, and novel_mir_58 were
specifically expressed in a single sugarcane variety. As shown in
Fig. [161]5, except for four miRNAs, including miR5261 and miR397-3p in
YA05-179, as well as miR5077 and novel_mir_99 in ROC22, the miRNA
expression patterns (up- or downregulated) as measured by quantitative
analysis and the sequencing results of the miRNAs were generally
similar.
Fig. 5.
Fig. 5
[162]Open in a new tab
qRT-PCR validation of 16 randomly selected differentially expressed
miRNAs identified by small RNA sequencing. a miRNAs expressed in
YA05-17. b miRNAs expressed in ROC22. Sugarcane buds of ROC22 and
YA05-179 post inoculation with sterile water or Sporisorium scitamineum
for 48 h were used as qRT-PCR samples. The data of qRT-PCR were
normalized to the 5S rRNA expression level and represented as means of
three replicates (n = 3) ± standard error
In addition, we performed qRT-PCR analysis of the 12 candidate
differentially expressed miRNAs at different time points (0, 12, 48,
and 96 h) after S. scitamineum challenge. As shown in the line charts
of Fig. [163]6, seven miRNAs, including miR394a, miR7545, miR894,
miR397-3p, miR5261, miR5783, and miR948 were determined to be expressed
in both sugarcane varieties, YA05-179 and ROC22. A specific expression
pattern for miR5261 was observed, which involved a drastic upregulation
in the sugarcane smut-resistant genotype, YA05-179, after inoculation
of S. scitamineum. Its expression reached 1,000-fold at 48 h after S.
scitamineum challenge and declined to 0-fold at 96 h. On the other
hand, in the smut-susceptible variety, ROC22, miR5261 expression
drastically increased after S. scitamineum challenge and reached
11-fold at 12 h post-inoculation, followed by a decline at 48 h and
remained unchanged at 96 h. Among the seven miRNAs, the expression
patterns of six miRNAs, except for miR5261, could be generally divided
into two types: first, opposite expression patterns in YA05-179 and
ROC22 (Fig. [164]6a), and second, identical expression pattern in
YA05-179 and ROC22, which referred to similar relative expression
regulatory trends of miRNAs in the two sugarcane varieties and at
different treatment time points after S. scitamineum challenge
(Fig. [165]6b). There were four miRNAs, which included miR394a, miR894,
miR7545, and miR397-3p that demonstrated the first type of expression
pattern, whereas miRNAs involved in the second expression pattern
consisted of miR948 and miR5783. However, miR5783 was upregulated in
YA05-179, but upregulated and subsequently downregulated in ROC22. On
the other hand, both miR948 and miR5783 were upregulated within 12 h
after S. scitamineum challenge, which was followed by its
downregulation. In addition, the remaining five miRNAs (i.e.,
miR408-3p, novel_mir_133, novel_mir_99, novel_mir_58, and novel_mir_32)
were only expressed in a single variety (Fig. [166]6c). Among these,
miR408-3p, novel_mir_133, novel_mir_99, and novel_mir_58 were
specifically expressed in ROC22. miR408-3p was markedly increased at
12 h and subsequently downregulated from 48 to 96 h after S.
scitamineum challenge. Novel_mir_133 was downregulated at 12, 48, and
96 h compared to control. Novel_mir_99 and novel_mir_58 showing an
overall downregulation, followed by a slight upregulation at 12 and
96 h. Novel_mir_32 in YA05-179, demonstrated an
“upregulated-downregulated” specific expression pattern.
Fig. 6.
Fig. 6
[167]Open in a new tab
Expression patterns of selected 12 miRNAs (line charts) and 15
predicted target genes (bar charts) in ROC22 and YA05-179 at 0, 12, 48,
and 96 h after Sporisorium scitamineum challenge. Gene expression
levels were assessed by qRT-PCR. The right and left y axis represented
the relative expression of miRNA and its predicted target gene,
respectively. The relative expression levels of miRNAs and their
predicted target genes were normalized to the 5S rRNA and
glyceraldehyde-3-phosphate dehydrogenase (GAPDH) expression levels,
respectively. Each bar represented as means of three replicates
(n = 3) ± standard error. Different lowercase letters indicate a
significant difference, as determined by Duncan’s new multiple range
test (P-value <0.05). The names of miRNA and corresponding targeted
gene were listed in the top of each panel. Five out of twelve miRNAs,
including miR408-3p, novel_mir_32, novel_mir_133, novel_mir_99, and
novel_mir_58, were expressed specifically in ROC22 or YA05-179 after S.
scitamineum challenge
Validation of the predicted target genes by qRT-PCR analysis
qRT-PCR analysis of 15 predicted target genes of differentially
expressed known and novel miRNAs in different sugarcane varieties was
conducted (Fig. [168]6) at different time points (i.e., 0–96 h) after
S. scitamineum challenge. Despite the specific expression of predicted
target gene [169]CF573748 of differentially expressed known miRNA
miR408-3p (Fig. [170]6c), the predicted target genes (i.e.,
[171]CF570081, [172]CF570940, [173]CF575522, and [174]CF576305) of four
novel miRNAs (Fig. [175]6c) were detected in a single sugarcane
variety, and the remaining predicted target genes were expressed in
both sugarcane varieties (Fig. [176]6a and b).
Figure [177]6 shows the results of 11 predicted target genes of eight
candidate differentially expressed known miRNAs. The expression of
predicted target gene [178]CF573748 in ROC22 was generally upregulated.
The other predicted target genes were divided into two categories based
on its expression patterns: (i) Significantly upregulated expression in
ROC22, which was mostly higher than that in the upregulated expression
in YA05-179. The observed upregulation in YA05-179 was gradual or
mostly downregulated at 12 h after S. scitamineum challenge compared to
that at 0 h of S. scitamineum challenge. This category included
[179]CF574674, Sugarcane_Unigene_BMK.105, [180]CF577228, [181]AA961317,
[182]CF569707, and [183]GT757759. (ii) Significantly and generally
upregulated expression in YA05-179 compared to that at 0 h of S.
scitamineum challenge, whereas it was first downregulated, followed by
moderate upregulation in ROC22 after S. scitamineum challenge. This
category included [184]CF573424, [185]CF573595, [186]CF577206, and
[187]CF569809. Figure [188]6c shows that the overall expression of the
predicted target gene, [189]CF570081, of novel miRNA novel_mir_32 in
YA05-179 was upregulated, whereas that of the three predicted target
genes, i.e., [190]CF570940, [191]CF575522, and [192]CF576305 in ROC22
was initially downregulated, and subsequently upregulated with an
extended duration of S. scitamineum challenge.
Discussion
Identification and annotation of miRNAs and high-throughput sequencing of
sugarcane sRNAs
YA05-179 is a BC4 generation of sugarcane and Eranthus arundinacus with
high smut-resistance, whereas ROC22 is a highly smut-susceptible
variety. These two sugarcane varieties have completely opposite
disease-resistance performance and thus are good models for gene
regulation during sugarcane smut infection. A previous study has shown
that under natural stress, the disease symptoms of sugarcane smut
usually occur two to four months after the dissemination of pathogens
[[193]62], with the phenocritical period significantly later than the
gene regulation period. To maintain a consistent physiological and
biochemical condition, the present study used the artificial
inoculation with 5 × 10^6 spores/mL smut spore suspension [[194]63]. Su
et al. [[195]8] showed that the logarithmic growth period of the
pathogens was at 48 h after the artificial inoculation of sugarcane
smut pathogens, and the stationary phase was at 96 h post-inoculation,
suggesting that the critical period for physiological and biochemical
reactions in sugarcane in response to the smut disease was the first
48 h after inoculation.
Solexa sequencing technology, HiSeq sequencing system, 454 sequencing,
and sequencing by oligonucleotide ligation and detection (SOLiD)
sequencing technology [[196]64], which are relatively mature and
optimized and have been widely used in the analysis of plant miRNA
expression profiles [[197]65], the screening of differentially
expressed miRNAs under biotic and abiotic stresses [[198]66], the
identification of miRNAs associated with growth and development
[[199]67], and the discovery of novel miRNAs with new functions
[[200]68]. In the present study, we used HiSeq sequencing system to
identify differentially expressed miRNAs in sugarcane after challenging
with the pathogen for smut disease. The number of clean reads obtained
from each RCK, RT, YACK, and YAT contained <1% of contaminated
sequences and therefore, 99% of the sequences were clean reads.
Statistical data of analyses of the sequence length and the common and
specific sequences showed that the sequencing quality of each sample
was relatively high, with good overall consistency among different
samples.
The genetic background of sugarcane is complicated, and genome analysis
of sugarcane is currently in its preliminary stages [[201]69]. ESTs are
only a part of the annotation results in the database, suggesting that
most of the unknown sequences have not been characterized. Sequences
detected from the four sugarcane samples were not directly matched with
the miRNA sequences in the miRBase database. For the prediction of
novel miRNAs, we combined our sequencing data with the
Sugarcane_Unigene sequences of the sugarcane transcriptome database,
which was provided by our laboratory. The sequencing results of the
present study were comparable to the entries in the sugarcane
transcriptome database [[202]47] and the sugarcane EST in GenBank,
thereby indicating that our findings were reliable. Statistical
analysis of base distribution of novel miRNAs demonstrated that the
most common base of novel miRNAs of 21 and 22 nt in length was U. In
addition, nucleotide 11 of candidate miRNA sequences of the four
samples was A. A previous study revealed that plant miRNAs mostly act
via the degradation of target mRNAs [[203]70]. The cleavage of this
degradation occurred very precisely at nucleotide 10 or 11 of the
matched miRNAs [[204]70]. In addition, miRNAs bound to the argonaut
(AGO) protein later complementarily bound to the mRNA 3′ untranslated
region of the target gene, thereby cleaving or degrading the target
gene [[205]71]. In the present study, the 5′ end of 21- and 22-nt novel
miRNA sequences preferentially harbored the U base, indicating that
these novel miRNA sequences might play an important role in regulating
gene expression after smut pathogen challenge. In addition, nucleotide
11 of candidate miRNA sequences in the four samples tended to be A,
which was consistent with the bias of cleavage sites of degradation, as
well as the statistical results of the base, thereby suggesting that
the prediction method used in the present study was feasible. The above
results showed that the quality of the measured data was high, thus
meeting the requirements for subsequent analysis.
Screening and analysis of differentially expressed miRNAs by using qRT-PCR
High-throughput sequencing generates massive data on miRNAs. Therefore,
the present study exclusively focused on screening of differentially
expressed miRNAs after S. scitamineum challenge. The present study
identified individual differences in miRNA expression in the two
sugarcane varieties, YA05-179 and ROC22, after the challenge of
sugarcane smut pathogen, as well as differences between the two
varieties. In terms of individual differences of each variety after S.
scitamineum challenge, ROC22 had 231 differentially expressed miRNAs,
which included 35 significantly expressed miRNAs (|log[2]-ratio| >1, P
<0.01), and the read count of miR2199, miR2916, miR5077, miR5813,
miR6300, and miR894 was >10,000. Among the 208 differentially expressed
miRNAs screened in YA05-179, 11 miRNAs showed significant individual
differences in miRNA expression, of which the read count of miR894 was
also >10,000. In terms of differences between two sugarcane varieties,
when ROC22 and YA05-179 were subjected to smut disease, the
differentially expressed miRNAs screened from the individual known
miRNAs were mostly the same. However, miR397-3p (−1.54-fold) was
upregulated in YA05-179 only, although not very high. Eight known
miRNAs, including miR1077-3p, miR262, miR5212-3p, miR5020a, miR1144a.1,
miR5657, miR6149-3p, and miR536, were specifically expressed in the
smut resistant-genotype, YA05-179. Cluster analysis of expression
patterns of known miRNAs in the two sugarcane varieties showed that
most miRNAs changed in the same way in the two varieties following
treatment (either upregulated in both or downregulated in both), with
only differences found in the degree of the changes. Some regulatory
modes of the miRNAs were inconsistent, although not significant. The
present study identified 48 novel miRNAs that were differentially
expressed in ROC22, of which 16 were upregulated. Forty-one
differentially expressed novel miRNAs were detected in YA05-179, of
which six were upregulated. The differentially expressed miRNAs with
significant expression levels in both control and treated groups were
almost specifically expressed in a single sugarcane variety, with
differentially expressed novel miRNAs determined to be the majority. No
novel miRNA was differentially expressed in both sugarcane varieties.
In addition, to ensure the reliability of expression profiling data,
miRNAs obtained from high-throughput sequencing should be verified in
plant tissues, commonly by qRT-PCR [[206]72]. The present study
validated the 27 expression profiles correspond to 16 differentially
expressed miRNAs in ROC22 and YA05-179 (Fig. [207]5). Except for the
unmatched expression level and sequencing results of miR5261 and
miR397-3p in YA05-179 and miR5077 and novel_mir_99 in ROC22, the
expression patterns (up- or downregulated) of the remaining miRNAs and
their corresponding sequencing results were consistent, suggesting that
the sequencing results of the present study were reliable. Whereas the
minor discrepancy between the qRT-PCR expression levels and sequencing
results may possibly be due to different software and algorithms used
in processing the vast amount of data generated from high-throughput
sequencing [[208]73, [209]74]. Overall, the results indicated that the
high-throughput sequencing was a powerful tool for discovering novel
and differentially expressed miRNAs in sugarcane after S. scitamineum
challenge, and it is also anticipated that more replicates in further
work should help to ensure the accuracy of the sequencing results.
Previous studies have demonstrated that the regulatory networks of the
target genes of plant miRNAs in response to the environmental stress
are centrally located, thereby affecting a variety of regulatory
signals [[210]67, [211]75]. In the present study, we focused on the
possible regulatory roles of differentially expressed known and novel
miRNAs in sugarcane after smut pathogen challenge. Seven miRNAs,
including miR894, miR394a, miR7545, miR397-3p, miR5261, miR5783, and
miR948, were co-expressed in both sugarcane varieties after the
inoculation of S. scitamineum and showed different expression patterns.
This study also identified a specifically expressed miRNA, namely,
miR5261, in YA05-179 and ROC22, with highly significant fold changes in
the expression and a relatively wide range of differential fluctuations
in expression levels. Five miRNAs, including miR408-3p, novel_mir_32,
novel_mir_133, novel_mir_99, and novel_mir_58 were only detected in a
single sugarcane variety after S. scitamineum inoculation. However, the
specific roles of differentially expressed miRNAs on the target genes
in resisting S. scitamineum after sugarcane smut infection remain
unclear and need to be further investigated.
Prediction and expression analysis of target genes of differentially
expressed miRNAs
Sugarcane is a highly heterozygous allopolyploid and aneuploid crop
[[212]69]. The present study performed a comprehensive analysis of
existing transcriptome sequences and our experimental sequencing
results. However, because whole-genome sequencing of sugarcane has not
been completed [[213]69], majority of the sRNA sequences during miRNA
annotation remained unknown. The results of the present study indicated
that although the number of predicted target genes of the whole miRNAs
in the YA05-179 and ROC22 varieties was relatively equivalent, that of
differentially expressed known miRNAs in YA05-179 (127) was less than
that observed in ROC22 (814). On the other hand, the predicted target
gene number of differentially expressed novel miRNAs in YA05-179 (1754)
was significantly higher than that in ROC22 (457). The number of
differentially expressed known miRNAs in YA05-179 (10) after S.
scitamineum challenge was slightly lower than that in ROC22 (32).
Predictive analysis of differentially expressed novel miRNA candidates
in both sugarcane varieties demonstrated that only a few novel miRNA
candidates were co-expressed in both sugarcane varieties. On the other
hand, most of the novel miRNA candidates were specifically expressed in
a single variety and majority of these were upregulated. Prediction
results of the above target genes indicated that novel miRNAs screened
from YA05-179 were more significant in the evaluation of the molecular
mechanism underlying the interaction between sugarcane and S.
scitamineum.
Functional analysis of target genes of miRNAs is the most direct
approach in studying the function of miRNAs [[214]20]. Bioinformatics
analysis can effectively predict the target genes of miRNAs, including
its functions [[215]76]. In the present study, we focused on
identifying the regulatory role of differentially expressed miRNAs,
with functional analysis and annotation of potential target genes from
the GO and KEGG pathways. In the three GO categories, namely,
biological processes, cellular components, and molecular functions, the
classifications of predicted target genes of the differentially
expressed novel miRNAs in both sugarcane varieties were similar to the
sequencing data on the predicted target genes of differentially
expressed known miRNAs. The target gene distribution in the biological
processes was mainly associated with cellular processes and metabolic
processes. KEGG pathway enrichment analysis demonstrated that the
predicted target genes of differentially expressed miRNAs participated
in a series of biochemical pathways or disease resistance-related
physiological, metabolic, and signal transduction pathways such as
plant-pathogen interaction, peroxisome, apoptosis, phagosome, cutin,
suberine and wax biosynthesis, plant hormone signal transduction, MAPK
signaling pathway, calcium signaling pathway, zeatin biosynthesis, and
brassinosteroid biosynthesis. Although the predicted target genes of
the identified differentially expressed miRNAs from different sugarcane
varieties were not the same, its response processes almost covered
every aspect throughout the life course, and its metabolic regulatory
pathways were identical. Previous studies have also shown that the
molecular mechanism of the interaction between sugarcane and S.
scitamineum was regulated by multigenic network systems, and the
pathogen of sugarcane smut also activated a variety of smut-resistance
metabolic pathways [[216]77, [217]78]. In addition, our findings
prompted us to speculate that miRNAs post-transcriptionally regulate
mRNAs, which are consistent with the results of previous studies
[[218]77, [219]78]. Upregulation of miRNAs may result in the
degradation of target genes or the downregulation of miRNAs may promote
the overexpression of target genes, thereby changing a number of
metabolic or signal transduction pathways.
Expression analysis of several predicted target genes using qRT-PCR
Prediction and functional analysis of the target genes of
differentially expressed miRNAs are efficient approaches in studying
the functions of miRNAs [[220]20]. In the present study, we used
qRT-PCR to evaluate the correlation between miRNAs and its predicted
target genes and analyzed the expression patterns of the target genes
of differentially expressed miRNAs in the same sugarcane variety at
different time points after S. scitamineum inoculation. In total, 15
predicted target genes of 12 miRNAs were detected, including 11
predicted target genes of eight known miRNAs and four predicted target
genes of four novel miRNAs (Fig. [221]6). We combined the miRNA
expression patterns and the target gene expression patterns to analyze
the quantitative expression of target genes at four different time
points and its corresponding miRNA expression.
qRT-PCR analysis (Fig. [222]6) showed that except for the inconsistent
expression between the predicted target gene, [223]CF569707, and its
corresponding miRNA, miR5783, ten out of 11 predicted target genes of
known miRNAs had basically identical negatively regulated mode after
12 h and reached the highest degree of matching at 48 h. In particular,
the expression patterns of three corresponding target genes of miR948
were identical, suggesting that the expression of these genes was
highly consistent with that of the miRNAs. These results also indicated
that the regulatory effect of the corresponding genes of miRNAs after
S. scitamineum challenge was maximized at 48 h after post-inoculation.
However, this is a negative regulatory effect. The above results also
showed that the expression enrichment prediction of known miRNAs and
its predicted target genes in the present study was reliable. Our
prediction of biochemical, metabolic, or signal transduction pathways
associated with the miRNAs and its predicted target genes might provide
novel insights on future anti-smut research studies. In addition, the
negatively regulated role in quantitative expression between the novel
miRNAs and its predicted target genes was not extremely high. The
expression of [224]CF570081 and CF57094 was anti-correlated with miRNA
results at 48 and 96 h but the expression of [225]CF575522 and
[226]CF576305 was not. It is possible that these potential target genes
are regulated by more than one miRNAs at the translational level
[[227]79]. Meanwhile, due to the lacking of relevant research and also
the lack of sugarcane genomic information, many of the novel miRNAs
identified in the present study are being reported for the first time.
These miRNAs do represent a portion of novel miRNAs involved with smut
pathogen challenge, however it needs more samples or sequencing
coverage to extract more reliable and functional novel miRNAs. Further
improvement in methods and strategies may be necessary for the
prediction and functional analysis of novel miRNAs, such as mismatches
of nucleotide sequences of miRNA, which may cause errors in gene
targeting [[228]60].
Analyses of predicted target gene functions and smut resistance-related
metabolic pathways
A previous study demonstrated that in response to pathogen infection,
the appearance, physiological, and biochemical changes in host
cultivars are ultimately caused by the disruptions at the molecular
level [[229]80]. Disease-susceptible varieties show relatively slow and
weak responses and signals to infection, whereas the responses of
signals of disease-resistant varieties are relatively rapid and strong
[[230]80]. For these reasons, the disease-resistant plant varieties
could combat most of the damages inflicted by the pathogen, as well as
prevent its proliferation and further spread [[231]80]. In the present
study, the predicted target genes of differentially expressed miRNAs in
ROC22 and YA05-179 were determined to participate in several metabolic
pathways after S. scitamineum challenge. Notably, three possible
pathways associated with smut pathogen stress, including plant-pathogen
interaction pathway, MAPK signalling pathway, and plant hormone signal
transduction, were chosen for further analysis. The expression profiles
of five crucial miRNAs and eight of their predicted target genes in
YA05-179 and ROC22 after S. scitamineum challenge for 48 h were
confirmed by qRT-PCR (Fig. [232]7).
Fig. 7.
Fig. 7
[233]Open in a new tab
A proposed regulatory network of partial miRNAs in sugarcane after
Sporisorium scitamineum challenge. The gene expression profiles of five
differentially expressed miRNAs, including miR5671, miR5054, miR5783,
miR5221, and miR6478, as well as eight of their target genes, including
RPM1 (Sugarcane_Unigene_BMK.42342), CDPK (Sugarcane_Unigene_BMK.34960),
PEX5 ([234]CA223872), PKA ([235]CA133877), HSP72
(Sugarcane_Unigene_BMK.31740), ABF (Sugarcane_Unigene_BMK.68798), AUX1
([236]CA105497), and CTR1 (Sugarcane_Unigene_BMK.73145), were validated
by qRT-PCR. The sugarcane buds from ROC22 and YA05-179 inoculated with
distilled water and S. scitamineum at 0 and 48 h were used for qRT-PCR
samples. The expression profiles of the miRNAs were normalized to the
5S rRNA expression level and represented as means of three replicates
(n = 3) ± standard error. The expression profiles of the target genes
were normalized to the glyceraldehyde-3-phosphate dehydrogenase (GAPDH)
expression level and represented as means of three replicates
(n = 3) ± standard error. Different lowercase letters indicate a
significant difference, as determined by Duncan’s new multiple range
test (P-value <0.05). RPM1, effector-triggered immune receptor; CDPK,
calmodulin-independent protein kinase; PEX5, peroxisome 5; PKA, protein
kinase A; HSP72, heat shock protein 72; ABF, ABA-responsive
element-binding protein; AUX1, auxin transporter; CTR1, constitutive
triple-response 1
MAPK signaling pathway in plants
When plant cells are exposed to environmental stress and receive
hormonal signals, MAPK cascade pathway was activated to induce the
expression of specific transcription factors in the nucleus and
functional gene that would, in turn, activate other protein kinases in
the cytoplasm to ultimately trigger the plant cell to elicit
physiological and biochemical responses [[237]81]. A previous study has
shown that when rice crops were infected by Pyricularia grisea, several
genes belonging to the MAPK pathway were upregulated [[238]82]. In the
present study, the predicted target genes of some miRNAs participated
in the MAPK cascade pathway. As reported, expression of the protein
kinase A (PKA) gene significantly inhibits the activities of several
members of the MAPK family, thereby blocking the MAPK signaling pathway
[[239]83]. Wheareas, the target PKA gene ([240]CA133877) of miR5783 was
significantly upregulated (1.39-) in the smut-resistant genotype
YA05-179, but downregulated (0.44-) in the smut-susceptible genotype
ROC22 (Fig. [241]7). Similarly, miR5783 was also significantly
upregulated (1.52-) in YA05-179 and downregulated (0.95-) in ROC22 but
not at significant level. We speculated that miR5783 might positively
regulate PKA expression, thereby regulating related genes in the MAPK
pathway. Heat shock protein 72 (HSP72) is a chaperone protein that
plays an important regulatory role in the cells of various organisms
[[242]84]. During adverse conditions such as high temperature,
endotoxin, and oxidative stress, organisms stimulate and induce the
synthesis of HSP72 [[243]84]. In the present study, miR5221 was
significantly downregulated (0.51-) in YA05-179 and slightly
upregulated (1.11-) in ROC22 which was not significant. Expression
level of the predicted target gene of miR5221, HSP72
(Sugarcane_Unigene_BMK.31740), was significantly increased (1.44-) in
YA05-179 and significantly decreased (0.40-) in ROC22, suggesting that
miR5221 may play a negative regulation role in HSP72 expression to
combat smut infestation.
The signal transduction pathways of plant hormones
Plant hormones play an important role in the response to environmental
stresses [[244]85]. The most common plant hormones include auxin
(namely indole-3-acetic acid, IAA), cytokinin (CK), abscisic acid
(ABA), gibberellin acid (GA), ethylene (ET), brassinosteroid (BR),
salicylic acid (SA), jasmonic acid (JA), and polyamines. A previous
study has shown that plant hormones play a critical role in plant
defenses against pathogens [[245]86]. During growth and development,
plants generate regulatory defense responses to abiotic stresses, which
include the endogenous hormones, SA, ET, and JA. During abiotic stress
in plants, ABA interacts with the SA, JA, and ET-regulated signal
transduction pathways and negatively regulates the resistance of
abiotic stress in plants [[246]87]. IAA induces the transient
expression of some genes in response to abiotic stress [[247]88]. AUX1
(auxin transporter) is an auxin influx carrier protein that plays a
role in cellular transport in the plant root system [[248]89].
Overexpression of AUX1 accelerates the transportation of IAA, which
regulates the expression of a stress-related gene, C-repeat binding
factor (CBF) [[249]90]. In the present study, the predicted target
genes of the differentially expressed miRNAs participate in a variety
of hormone signaling pathways, thereby suggesting that these
miRNA-regulated target genes play critical roles in signal transduction
pathways involving plant hormones. Among these miRNAs, miR5221 targeted
the AUX1 gene and negatively regulate its expression in both sugarcane
genotypes. In Fig. [250]7, AUX1 ([251]CA105497) was significantly
upregulated (3.27-) in YA05-179 and downregulated (0.37-) in ROC22.
Constitutive triple-response 1 (CTR1) is a gene involved in the ET
signal transduction pathway. A previous study showed that the amino
terminus of CTR1 could combine with the ET receptor to form a complex,
which negatively regulates the ET response [[252]91]. Our analysis
indicated that the expression of miR6478 was obviously repressed
(0.20-) in YA05-179 but remained stable (1.14-) in ROC22. The CTR1 gene
(Sugarcane_Unigene_BMK.73145), one of the predicted target genes of
miR6478, was significantly upregulated (1.43-) in YA05-179 and only
slightly downregulated (0.82-) in ROC22. This result indicated that
miR6478 might negatively regulate CTR1 expression and affected the ET
signal transduction pathway. ABA pathway has been reported to be a
negative regulator of plant disease resistance [[253]92]. ABF is a
binding factor of ABA-responsive element-binding protein (AREB)
[[254]93]. AREB/ABF transcription factors are ABA-responsive
element-binding proteins that regulating the expression of ABA-related
genes [[255]93]. In the present study, miR5783 targeted the ABF gene
(Sugarcane_Unigene_BMK.68798) and slightly downregulated (0.81- and
0.88-) it in both YA05-179 and ROC22 however the level was not
significant. This finding suggested that miR5783 might play a negative
regulation role in ABF in YA05-179, but a positive regulation role in
ABF in ROC22. Based on the above findings, we speculated that the
upregulated and downregulated expression of differentially expressed
miRNAs reflect the close relationship of the auxin signal transduction
pathway, ABA signal transduction pathway, and ET signal transduction
pathway in sugarcane during S. scitamineum infection.
Pathway of plant-pathogen interaction
In plant-pathogen interaction, plants have a variety of defense
mechanisms against different pathogens [[256]94], including
hypersensitive responses (HR), changes in enzymatic activities, and the
accumulation of defense proteins [[257]95]. A previous study has
indicated that catalase plays an important role in plant defense
response, stress response, and in regulating redox balance in cells
[[258]96]. In the present study, miR5054 was downregulated (0.64-) in
YA05-179 and significantly upregulated (3.83-) in ROC22. The expression
of one miR5054 targeted gene, peroxisome 5 (PEX5, [259]CA223872), was
significantly increased (1.51-) in YA05-179 and nearly remained stable
(1.12-) in ROC22. Calcium-dependent and calmodulin-independent protein
kinase (CDPK) is a key protein gene that involved in the mechanism of
disease resistance in plants [[260]97]. Overexpression of CDPK triggers
the primary reaction of active cell necrosis. In addition, hydrogen
peroxide also activates CDPK and enhances the expression of the CDPK
gene [[261]98]. In the present study, CDPK gene
(Sugarcane_Unigene_BMK.34960), one of the predicted target genes of
miR5054, was significantly upregulated (1.67-) in YA05-179 and
downregulated (0.34-) in ROC22, revealing an opposite expression trend
compared to that of miR5054 in both sugarcane genotypes. These results
revealed that there might be a negatively regulated mode between
miR5054 and PEX5 or CDPK gene. The formation of HR depends on the
interaction between plant disease resistance gene products and the
avirulence (Avr) gene product of the corresponding pathogen [[262]99].
RPM1 (effector-triggered immune receptor) is a type of R-gene-mediated
plant disease resistance, whose overexpression induces HR in plants
[[263]99]. In the present study, miR5671 was determined to target RPM1.
We found that the expression of RPM1 (Sugarcane_Unigene_BMK.42342) was
significantly increased (1.53- and 2.54-) in YA05-179 and ROC22 after
S. scitamineum challenge. Meanwhile, miR5671 was significantly
downregulated (0.65- and 0.65) in both sugarcane genotypes, suggesting
the negative regulation of RPM1 in sugarcane smut resistance.
Conclusions
This is the first study that has employed high-throughput sequencing
technology to identify and establish the expression profiles of various
sugarcane miRNAs that are associated with S. scitamineum challenge. The
post-transcriptional miRNA regulatory mechanism in the compatible and
incompatible interactions between sugarcane and S. scitamineum was then
systemically evaluated, which enriched and deepened our knowledge in
the molecular mechanism underlying sugarcane resistance to smut
disease. The present study has presented various regulatory pathways
that are affected by smut infection, as well as generated a regulatory
network of some miRNAs in sugarcane post S. scitamineum infection
(Fig. [264]7).
Additional files
[265]Additional file 1: Table S1.^ (20.9KB, docx)
The forward primers of qRT-PCR performed to validate the 20 selected
differentially expressed miRNAs. (DOCX 20 kb)
[266]Additional file 2: Table S2.^ (41KB, doc)
The primers of qRT-PCR performed to validate 23 selected miRNA target
genes. (DOC 41 kb)
[267]Additional file 3: Table S3.^ (40KB, doc)
The filtering results of high-through sequencing data in the four
libraries. (DOC 40 kb)
[268]Additional file 4:Figure S1.^ (35.9KB, tif)
Length distribution of the unique sRNA sequences in the four libraries.
RCK and YACK: ROC22 and YA05-179 under sterile water stress after 48 h,
respectively; RT and YAT: ROC22 and YA05-179 under Sporisorium
scitamineum stress after 48 h, respectively. (TIF 35 kb)
[269]Additional file 5: Table S4.^ (69KB, doc)
The statistics of types and total number of repetitive sequence of the
sRNAs in the four libraries. (DOC 69 kb)
[270]Additional file 6: Table S5.^ (30KB, doc)
The matching results of sRNAs among non-coding RNAs in the four
libraries by Genbank search. (DOC 30 kb)
[271]Additional file 7: Table S6.^ (31KB, doc)
The matching results of sRNAs among non-coding RNAs in the four
libraries by Rfam search. (DOC 31 kb)
[272]Additional file 8: Figure S2.^ (279KB, zip)
The distribution of first nucleotide bias (A) and the nucleotide bias
at each position (B) of the novel miRNAs in the four libraries. (A)
Each color in the figure showed the miRNA tags whose first base was a
certain base. Height of bar was proportional to the frequency of the
corresponding base at the given length from 20 to 23 nt. (B) Each color
in the figure showed the miRNA tags whose certain base was a certain
base. Height of bar was proportional to the frequency of the
corresponding base at the given position from 1 to 23 nt. RCK and YACK:
ROC22 and YA05-179 under sterile water stress after 48 h, respectively;
RT and YAT: ROC22 and YA05-179 under Sporisorium scitamineum stress
after 48 h, respectively. (ZIP 279 kb)
[273]Additional file 9: Table S7.^ (70KB, doc)
The significantly differentially expressed known miRNAs in the RT/RCK.
(DOC 70 kb)
[274]Additional file 10: Table S8.^ (37KB, doc)
The significantly differentially expressed known miRNAs in the
YAT/YACK. (DOC 37 kb)
[275]Additional file 11:Table S9.^ (42.5KB, doc)
The significantly differentially expressed novel miRNAs in the RT/RCK.
(DOC 42 kb)
[276]Additional file 12: Table S10.^ (32KB, doc)
The significantly differentially expressed novel miRNAs in the
YAT/YACK. (DOC 32 kb)
[277]Additional file 13: Table S11.^ (30KB, doc)
Prediction of target genes of known and novel miRNAs. (DOC 30 kb)
[278]Additional file 14: Table S12.^ (41.5KB, doc)
The prediction results for partial target genes of differentially
expressed miRNAs. (DOC 41 kb)
[279]Additional file 15: Figure S3.^ (3.7MB, tif)
GO categories and distribution of known miRNAs targets in RT/RCK (A)
and YAT/YACK (B), respectively. RCK and YACK: ROC22 and YA05-179 under
sterile water stress after 48 h, respectively; RT and YAT: ROC22 and
YA05-179 under Sporisorium scitamineum stress after 48 h, respectively.
(TIF 3774 kb)
[280]Additional file 16: Figure S4.^ (1.6MB, tif)
GO categories and distribution of novel miRNAs targets in RT/RCK (A)
and YAT/YACK (B), respectively. RCK and YACK: ROC22 and YA05-179 under
sterile water stress after 48 h, respectively; RT and YAT: ROC22 and
YA05-179 under Sporisorium scitamineum stress after 48 h, respectively.
(TIF 1606 kb)
[281]Additional file 17: Table S13.^ (58KB, doc)
KEGG analysis of predicted target genes of known miRNAs in RT/RCK. (DOC
58 kb)
[282]Additional file 18: Table S14.^ (34.5KB, doc)
KEGG analysis of predicted target genes of known miRNAs in YAT/YACK.
(DOC 34 kb)
[283]Additional file 19: Table S15.^ (57KB, doc)
KEGG analysis of predicted target genes of novel miRNAs in RT/RCK. (DOC
57 kb)
[284]Additional file 20: Table S16.^ (58KB, doc)
KEGG analysis of predicted target genes of novel miRNAs in YAT/YACK.
(DOC 58 kb)
Acknowledgements