Abstract
Plutella xylostella, a global key pest, is one of the major
lepidopteran pests of cruciferous vegetables owing to its strong
ability of resistance development to a wide range of insecticides.
Destruxin A, a mycotoxin of the entomopathogenic fungus, Metarhizium
anisopliae, has broad-spectrum insecticidal effects and has been used
as an alternative control strategy to reduce harmful effects of
insecticides. However, microRNA (miRNA)-regulated reactions against
destruxin A have not been elucidated yet. Therefore, here, to identify
immunity-related miRNAs, we constructed four small RNA libraries from
destruxin A-injected larvae of P. xylostella at three different time
courses (2, 4, and 6 h) with a control, and sequenced by Illumina. Our
results showed that totally 187 known and 44 novel miRNAs were
identified in four libraries by bioinformatic analysis. Interestingly,
among differentially expressed known miRNAs, some conserved miRNAs,
such as miR-263, miR-279, miR-306, miR-2a, and miR-308, predicted to be
involved in regulating immunity-related genes, were also identified.
Worthy to mention, miR-306 and miR-279 were also listed as common
abundantly expressed miRNA in all treatments. The Kyoto Encyclopedia of
Genes and Genomes pathway analysis also indicated that differentially
expressed miRNAs were involved in several immunity-related signaling
pathways, including toll signaling pathway, IMD signaling pathway,
JAK–STAT signaling pathway, and cell adhesion molecules signaling
pathway. To the best of our knowledge, this is the first comprehensive
report of destruxin A-responsive immunity-related miRNAs in
P. xylostella. Our findings will improve in understanding the role of
destruxin A-responsive miRNAs in the host immune system and would be
useful to develop biological control strategies for controlling P.
xylostella.
Keywords: microRNAs, destruxin A, immunity, innate, Plutella
xylostella, differential expression analysis
Introduction
In nature, with the passage of time, a number of new mechanisms of host
defenses have been developed in the arms race of host–pathogen. Animals
face pathogen challenge with innate and adaptive immune system whereas,
invertebrates, unlike mammals, do not have an adaptive immune system,
but instead they rely on a sophisticated innate immune system for
defense against invading microbes ([41]1). The innate immune system of
insects is comprised of two main components, cellular and humoral
immune responses ([42]2). The former relies majorly on the action of
hemocytes in the phagocytosis of pathogens ([43]3), while the latter
refers to the process of melanization with phenoloxidases ([44]4) and
synthesis of immune effector molecules ([45]5). Upon microorganism
invasion, the immune system reactions are initiated by the recognition
proteins, including peptidoglycan recognition proteins, β-1,3-glucan
recognition proteins (βGRPs), galectins, C-type lectins, and scavenger
receptors ([46]6). The recognition step then leads to amplification of
signals by serine proteases and triggers the activation of immune
signaling pathways followed by induction of antimicrobial peptides to
clear the infection ([47]7, [48]8).
MicroRNAs (miRNAs), a class of small RNAs (sRNAs), are reported to play
a pivotal role in the gene expression regulation at the level of
posttranscription in many organisms. Since the discovery of the first
miRNA in Caenorhabditis elegans ([49]9), many miRNAs have been
identified in plants, animals, and insects ([50]10), and our knowledge
of miRNA interaction with targets is persistently increasing and
developing with the passage of time. In insects, miRNAs have been
demonstrated to be key mediators in various physiological processes,
including embryonic development ([51]11), apoptosis ([52]12),
morphogenesis ([53]13), and cell differentiation ([54]14), and a recent
report demonstrated that miRNAs may also play crucial roles in the
regulation of innate immunity ([55]15).
The entomopathogenic fungi, such as Metarhizium anisopliae and
Beauveria bassiana, are considered as an environmentally friendly
approach for the control of insect pests. During pathogenesis, these
entomopathogenic fungi secrete virulence factors to accelerate the
death of infected host ([56]16). Destruxins, the virulence factors of
fungi, have been reported to exhibit high toxicity to various insect
species when ingested or injected ([57]17–[58]19) and inhibit V-type
ATPase hydrolytic activity, prompt oxidative stress, and affects the
Ca^2+ channel in muscle cells ([59]20–[60]22). Additionally, destruxins
play a pivotal role in the immune system of insects, such as Drosophila
melanogaster; innate immune response was suppressed by destruxin A
following the inhibition of antimicrobial peptides ([61]16), whereas
the immune system of Bombyx mori was induced in response to destruxin A
([62]23). Although recently, the number of studies reporting
immunity-related genes in Plutella xylostella is growing
([63]24–[64]27), thanks to the availability of P. xylostella genome,
however, information regarding miRNA-regulated reactions in
insect–pathogen interactions is still in its infancy and, to the best
of our knowledge, there is no information available on miRNA-regulated
reactions against the secondary metabolites of fungi, especially
destruxin A.
Keeping in view the importance of miRNAs in insect–pathogen
interaction, as exhibited by previous reports, we hypothesized that the
network of miRNA-guided gene expression regulation might play a pivotal
role in the control of innate immunity in destruxin A-infected P.
xylostella. Furthermore, we also intended to find out that how P.
xylostella miRNAs respond to destruxin A at different time courses of
infection and whether the immune system of P. xylostella has developed
novel miRNAs to combat the infection of destruxin A. To address these
questions, a high-throughput sequencing Illumina and real-time
quantitative PCR (RT-qPCR) were carried out to identify unique, novel,
and immune-responsive miRNAs in P. xylostella infected with destruxin A
at three different time courses (2, 4, and 6 h).
Materials and Methods
Rearing of Insects and Destruxin A Preparation
A susceptible P. xylostella strain was obtained from the Engineering
Research Centre of Biological Control, Ministry of Education, South
China Agricultural University, China and was kept insecticide-free for
10 generations. P. xylostella were maintained at 25 ± 1°C, 65% relative
humidity, and a 14:10 h (light: dark) photoperiod. Destruxin A was
extracted from M. anisopliae strain MaQ-10. The purity of destruxin A
was evaluated by high-performance liquid chromatography. Finally,
destruxin A was diluted in phosphate buffered saline (PBS, PH 7.4).
Destruxin A Injection, sRNA Library Construction, and Sequencing
To inject destruxin A into the susceptible fourth instar larvae of P.
xylostella, a stock solution of destruxin A (200 μg/mL) was prepared,
and then 2 μL of that solution was injected to each larva. The control
group was treated with PBS. A number of 30 larvae were collected from
each treatment (2, 4, and 6 h post-injection) and control following
instant freezing in liquid nitrogen. Trizol Total RNA Isolation Kit
(Takara, Japan) was used to extract total RNA following manufacturer’s
instructions. The concentrations of RNA were assessed using Nanodrop
(Bio-Rad, USA) and its integrity was determined on Agilent 2100
Bioanalyzer (Agilent, USA).
Furthermore, RNAs were firstly ligated with 3′ adapter and after size
fraction, ligated to 5′ adapter. The sRNA fractions were then used for
reverse transcription following PCR. The final ligation PCR products,
after purification, were sequenced using Illumina Genome Analyzer (San
Diego, CA, USA) at the Beijing Genomics Institute (BGI, Shenzhen,
China).
Bioinformatics of Destruxin A-Responsive sRNA Sequences
To obtain clean reads from raw data reads having low-quality, 5′ primer
contaminants, without 3′ primers and insert tag, and sequences fewer
than 18 nucleotides (nt), were filtered out. To analyze the
distribution, the final clean reads of the four libraries were mapped
to P. xylostella genome (GCA_000330985.1) using Bowtie program
([65]28). All the remaining clean sequences were annotated into
different classes to remove rRNA, scRNA, snoRNA, snRNA, and tRNA using
Rfam database. Finally, the unannotated clean sequences were used to
predict novel miRNAs using the miRDeep2 software.
Differential Expression Analysis of Destruxin A-Responsive miRNAs
The expression of miRNAs was compared between treatment and control to
identify differentially expressed miRNAs. First, the expression of
miRNA in the four libraries was normalized to transcripts per million.
If the normalized expression of the miRNA was 0, it was modified to
0.01 to enable calculation. If the normalized expression of the miRNA
was less than 1 in all libraries, it was ignored to compare for low
expression. The normalization formula was
[MATH: Normalized expression=actual
miRNA count/total count of clean
reads×106.
:MATH]
The normalized data were then used to calculate fold-change values and
P-values, and a scatter plot of the fold-change values was generated.
Fold-change was calculated as
[MATH:
Fold-change=log2(treatment/control
). :MATH]
The P-value was calculated by the following equation:
[MATH:
p(x|y)=(N2N1)y(x+y)<
/mrow>!x!y!(1+
N2N1)(<
mrow>x+y+1)C
(y≤y
min|x)=∑y=0<
mrow>y≤yminp(y
|x)D(y≥ymax|x)
=∑y≥ymax∞p(y|x), :MATH]
where x represents sRNA total clean reads in the control, y represents
total clean reads in the treatment, N[1] represents the normalized
expression of a miRNA in library control, and N[2] represents the
normalized expression of the same miRNA in library treatment. The
corrected P-value corresponds to differential gene expression test
using Bonferroni method ([66]29).
Genome-Wide Target Prediction for Destruxin A-Responsive miRNAs
To predict and analyze potential targets of differentially expressed
miRNAs, three different software, including RNAhybrid ([67]30), miRanda
([68]31), and TargetScan ([69]32) were used following the principles of
target prediction as described previously elsewhere ([70]33, [71]34).
To increase the level of confidence and get more reliable results, we
selected only those binding sites that were predicted by all three
software.
Gene Ontology (GO) Enrichment and Kyoto Encyclopedia of Genes and Genomes
(KEGG) Pathway Analysis of Predicted Targets of Destruxin A-Response miRNAs
The genome database of P. xylostella was used as the background to
determine GO terms enriched within the predicted targets dataset using
hypergeometric test and a corrected P-value (≤0.05) as a threshold in
order to find out significantly enriched terms. Finally, KEGG pathway
enrichment analysis was performed to identify significantly enriched
pathways within the predicted targets datasets compared with the genome
database using hypergeometric test and a corrected P-value (≤0.05) as a
threshold.
Verification of Destruxin A-Responsive miRNAs by RT-qPCR
Real-time quantitative PCR is the method of choice for analyzing
expression of genes and to confirm the results of RNA-Sequencing
([72]35). The RT-qPCR analysis was conducted to ensure the expression
levels of miRNAs displayed by Illumina sequencing results and 10
differentially expressed miRNAs were selected from the comparison of
control vs. treatments. Total RNA was extracted from each sample as
described earlier. The RNA sample (1 μg) was treated with DNaseI
(Fermentas, Glen Burnie, MD, USA) following manufacturer’s protocol and
then complementary DNA was synthesized using M-MLV reverse
transcriptase (Promega, USA). The RT-qPCR was carried out on a Bio-Rad
iQ2 optical system (Bio-Rad) using SsoFast EvaGreen Supermix (Bio-Rad,
Hercules, CA, USA) following the manufacturer’s guidelines. The
amplification cycling parameters were: 95°C for 30 s, 40 cycles of 95°C
for 5 s, and 55°C for 10 s with a dissociation curve generated from
65–95°C to ensure the purity of PCR products ([73]36). The U6 snRNA was
used as an internal control for normalization and the relative
expression of genes was calculated using the 2^−ΔΔCT method ([74]37).
Each experiment was replicated in triplicate.
Statistical Analysis
Statistical analyses of the present study were performed using SPSS
software (version 22.0; IBM Corp., Armonk, NY, USA). The differences
between treatments were compared using Student’s t-test or one-way
analysis of variance followed by Tukey’s test for multiple comparisons
at the following significance levels: lowercase letters at P < 0.05 and
capital case letters at P < 0.01. All results are expressed as
means ± SEM.
Results
Construction of sRNA Libraries
Four sRNA libraries were constructed for control, 2, 4, and 6 h, and
11,586,075, 15,391,118, 12,893,039, and 13,876,345 high-quality reads
were obtained, respectively. The low-quality sequences, reads with 5′
contaminants and without a 3′ primer or insert tag, and reads shorter
than 18 nt, were eliminated; subsequently, 11,492,082 (99.19%),
15,048,896 (97.78%), 12,519,758 (97.1%), and 13,777,285 (99.29%) clean
reads in the control, 2, 4, and 6 h were obtained for further analysis,
respectively (Table S1 in Supplementary Material). The sRNA length
distribution of the four libraries exhibited that most of the sRNAs
ranged from 18 to 30 nt with 26 to 28 nt sRNAs being most abundant
following 22 and 29 nt (Figure [75]1). Among the total and unique clean
reads, 21,944,702 and 39,83,00 sRNAs between control and 2 h;
20,268,898 and 3,22,472 sRNAs between control and 4 h; 20,334,789 and
3,80,546 sRNAs between 6 h and control; 23,648,245 and 4,36,969 sRNAs
between 2 and 4 h; 24,227,618 and 5,33,391 sRNAs between 6 and 2 h; and
22,256,011 and 4,35,747 sRNAs were common between 6 and 4 h,
respectively (Figure S1 in Supplementary Material).
Figure 1.
Figure 1
[76]Open in a new tab
Size distribution of destruxin A-responsive small RNAs (sRNAs) in the
libraries of Plutella xylostella. Different colors represent different
libraries. x-Axis represents sRNA length distribution and y-axis
represents frequency percentage.
Genome Mapping and sRNA Annotation
Of the clean reads, 7,359,318, 8,346,568, 6,685,514, and 7,855,270
reads from control, 2, 4, and 6 h accounted for 64.04, 55.46, 53.4, and
57.02%, respectively, and were mapped to the genome of P. xylostella
(GCA_000330985.1) (Table S2 in Supplementary Material). The annotation
of sRNAs was carried out by following priority rule of rRNA, etc.;
(GenBank > Rfam) > known miRNA > repeat > exon > intron ([77]38). The
clean reads were categorized into miRNA, rRNA, snRNA, snoRNA, tRNA, and
unannotated. The composition of the sRNA classes in each library is
displayed in Figure S2 in Supplementary Material.
Identification of Destruxin A-Responsive Known miRNAs
The bioinformatic analysis was carried out to identify destruxin
A-responsive known miRNAs in P. xylostella. Although miRNAs are among
the most intensively studied molecules since last two decades, deciding
what is and what is not a miRNA has been difficult and it has been
argued that miRBase is riddled with false positives (sequences that are
not derived from bona fide miRNA genes) ([78]39–[79]41). Recently,
Etebari and Asgari ([80]42) re-annotated miRNAs of P. xylostella and
after removing previously reported low confidence precursor miRNAs
(pre-miRNAs), finally, they confirmed 114 highly confident pre-miRNAs
of P. xylostella following strict criteria. Therefore, in the present
study, after successful mapping of clean reads against P. xylostella
genome, the mapped miRNA sequences were matched to miRNAs reported by
Etebari and Asgari ([81]42). Our analysis initially identified, based
on sequence similarity, in total, 187 mature miRNAs. Then, precursor
sequences of these mature miRNAs were aligned to those reported by
Etebari and Asgari ([82]42), and 99 highly confident pre-miRNAs were
confirmed, which produced 167 of 187 mature miRNAs. Our analysis
indicated that pre-miRNA sequences of the remaining 20 conserved miRNAs
were not detectable in the current assembly of P. xylostella genome. To
obtain more reliable results, we removed those known miRNAs with read
count < 10 in all libraries, and, finally, remaining 124 known miRNAs
with precursor sequences (Table S3 in Supplementary Material), and 17
miRNAs without precursor sequences (Table S4 in Supplementary Material)
were retained for further analysis. The remaining sequences that were
not matched to conserved miRNAs were used to predict novel miRNAs by
using the miRDeep2 program. The most abundant miRNA was pxy-miR-1-3p
following pxy-miR-184-3p, pxy-let7-5p, and pxy-miR-31-5p. The top 10
most highly expressed miRNAs in the four libraries of P. xylostella are
presented in Figure [83]2.
Figure 2.
Figure 2
[84]Open in a new tab
A time course of destruxin A-responsive top 10 abundantly expressed
microRNA (miRNAs) in Plutella xylostella. Average expression value of
all time courses is presented here. TPM, transcripts per million.
Identification of Destruxin A-Responsive Novel miRNAs
The novel miRNAs with predicted precursor and secondary structures were
identified by using miRDeep2 software ([85]43), and, in total, 44
potential novel miRNAs from all the libraries were identified (Table S5
in Supplementary Material) following the standard criteria of novel
miRNA prediction with miRDeep score > 1, randfold P-value < 0.05, and
MFE < -19 kcal/mol. The most abundant miRNA was pxy-novel-mir-1
following pxy-novel-mir-38, and pxy-novel-mir-3 (Table S5 in
Supplementary Material).
Identification of Destruxin A-Responsive Differentially Expressed miRNAs
The differential expression analysis of known miRNAs among the
treatments exhibited that 26, 53, and 24 miRNAs were identified as
differentially expressed between control and 2, 4, and 6 h,
respectively (Figure [86]3). Of these, 12, 48, and 18 miRNAs were
upregulated while 14, 5, and 6 miRNAs were downregulated, respectively
(Table S6 in Supplementary Material).
Figure 3.
[87]Figure 3
[88]Open in a new tab
Volcano plots of destruxin A-responsive differentially expressed
microRNA (miRNAs) in Plutella xylostella. The volcano plots represent
differentially expressed miRNAs at different time courses (2, 4, and
6 h) compared to control.
The novel miRNA differential expression analysis indicated that 16, 17,
and 18 miRNAs were identified as differentially expressed between
control and 2, 4, and 6 h, respectively (Figure [89]3). Of these, 10,
12, and 11 miRNAs were upregulated whereas 6, 5, and 7 were
downregulated, respectively (Table S7 in Supplementary Material).
Overall, most of the differentially expressed miRNAs were upregulated
in all the treatments.
Target Prediction of Destruxin A-Responsive Known and Novel miRNAs
The annotation of known and novel miRNA targets is necessary for
defining their roles in response to destruxin A treatment. All the
annotated genes of P. xylostella were screened using three different
algorithms (RNAhybrid, miRanda, and TargetScan) to predict the
potential binding sites for destruxin A-responsive miRNAs of P.
xylostella. Our target prediction results identified 27,073 common
spots between RNAhybrid and TargetScan, 26,989 between RNAhybrid and
miRanda, and 28,183 between TargetScan and miRanda. When the target
prediction results of all three software were combined, 26,874 common
spots were detected and selected for further analysis (Figure [90]4).
Figure 4.
Figure 4
[91]Open in a new tab
Target prediction of potential target genes of destruxin A-responsive
microRNAs (miRNAs) in all libraries of Plutella xylostella. Venn
diagram shows the number of miRNA targets and their overlapping spots
predicted by the three programs (RNAhybrid, miRanda, and TargetScan).
GO Enrichment and KEGG Pathway Analysis of Destruxin A-Responsive miRNA
Target Genes
The GO enrichment analysis was carried out to gain knowledge of the
potential function of each putative target gene. The GO enrichment
analysis exhibited that cellular process and metabolic process, cell
and cell part, and catalytic activity and binding were the most
enriched categories in the biological process, cellular component, and
molecular function, respectively (Figure [92]5). The KEGG
classification system categorized destruxin A-responsive miRNA target
genes into different groups. In the gene repertoire of 2, 4, and 6 h,
the top five enriched groups among KEGG categories included signal
transduction, cancers, digestive system, immune system, and transport
and catabolism (Figure [93]6).
Figure 5.
[94]Figure 5
[95]Open in a new tab
Gene ontology (GO) annotation of target genes of destruxin A-responsive
microRNA in all libraries of Plutella xylostella. The abscissa is the
GO annotation and the ordinate left is the gene number.
Figure 6.
[96]Figure 6
[97]Open in a new tab
Kyoto Encyclopedia of Genes and Genomes (KEGG) classification of target
genes of destruxin A-responsive microRNAs in all libraries of Plutella
xylostella. The abscissa is the KEGG classification and the ordinate
left is the gene number.
Moreover, KEGG pathway analysis of predicted target genes was carried
out to identify potential pathways regulated by destruxin A-responsive
differentially expressed miRNAs. The results exhibited that target
genes were enriched in cell adhesion and focal adhesion (Figures S3 and
S4 in Supplementary Material), as well as several immunity-related
signaling pathways, including toll signaling pathway, IMD signaling
pathway, and JAK–STAT signaling pathway (Figure [98]7 and Figure S5 in
Supplementary Material).
Figure 7.
[99]Figure 7
[100]Open in a new tab
Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway map for Toll and
IMD signaling pathway. The red boxes indicate target genes of destruxin
A-responsive microRNAs (miRNAs) in the particular pathways.
Validation of Differentially Expressed miRNAs by RT-qPCR
To validate sRNA sequencing results, 10 randomly selected
differentially expressed miRNAs among control, 2, 4, and 6 h were
analyzed by RT-qPCR (Figure [101]8). The results exhibited that the
trend of expression level for the selected miRNAs showed a little
discrepancy to that of RNA-Seq analysis, which might be due to the
differences in the sensitivity, specificity, and algorithm between the
two techniques.
Figure 8.
[102]Figure 8
[103]Open in a new tab
Expression of significantly differentially expressed microRNA (miRNAs)
at different time courses after destruxin A injection. Each vertical
bar represents the mean ± SEM (n = 3) for various time courses.
Statistically significant differences in different groups are indicated
by different letters (lowercase letters at P < 0.05 and capital case
letters at P < 0.01).
Discussion
The diamondback moth, P. xylostella, has become the major lepidopteran
pest of Brassica owing to its strong ability of resistance development
to a wide range of insecticides. In the present scenario, there is a
need to develop novel biological control methods, to reduce harmful
effects of insecticides, as alternative control ([104]44). The
entomopathogenic fungi such as M. anisopliae, B. bassiana, and Isaria
fumosorosea have gained an increased attention for controlling insect
pests as they are considered to offer an environmentally friendly
alternative to insecticides ([105]45). The secondary metabolites of
fungi, such as destruxins, are reported to have substantial
insecticidal activity ([106]46–[107]48) and can destroy the immune
system of insects ([108]16, [109]23). miRNAs, the key regulators of
gene expression at the posttranscriptional level, play a pivotal role
in host–pathogen interaction. In recent years, the number of miRNAs
isolated from insect species is increasing as new ones are deposited in
the databases. Although the role of miRNAs in insect development is
well understood, there is limited information available about the role
of miRNAs in insect–pathogen interaction. Therefore, the present study
aimed to acquire the immunity-related miRNAs in P. xylostella treated
with destruxin A at three-time courses (2, 4, and 6 h). For this
purpose, four sRNA libraries were constructed and sequenced using the
high-throughput Illumina sequencing resulting in a total of 187 known
and 44 novel miRNAs identified in the four libraries.
The sRNAs are classified into miRNAs, small interfering RNAs, and
piwi-interacting RNAs (piRNAs) according to their size ([110]49). Our
length distribution results showed two peaks; one at 22 nt and the
second at 28 nt, indicating to typical miRNAs and piRNAs. The piRNAs
are commonly found in sRNA libraries of insects ([111]50–[112]52) and
function as sequence-specific silencers in many organisms ([113]53).
Some conserved miRNAs, such as miR-1, let-7, miR-10, and miR-306,
showed abundant expression in the four libraries indicating that these
miRNAs play vital regulatory roles in P. xylostella. These miRNAs were
also discovered to be abundantly expressed in sRNA libraries of other
insects ([114]52, [115]54); however, a low copy number of miR-1, a
conserved miRNA, was detected after parasitization in a previous report
([116]55). Moreover, it is worth mentioning that the expression levels
of miR-2755, miR-184, and miR-281 were also abundant in all the
treatments indicating that these miRNAs might play a crucial role in P.
xylostella immunity against microorganisms.
The differential expression analysis indicated that in total, 26, 53,
and 24 known miRNAs were differentially expressed after treatment of P.
xylostella with destruxin A at 2, 4, and 6 h, respectively. It is worth
mentioning, of these, some conserved miRNAs such as miR-263, miR-279,
miR-306, miR-2a, and miR-308 also showed differential expression in the
present study. Previous studies reported that these miRNAs (miR-263,
miR-279, miR-306, miR-2a, and miR-308) were also differentially
expressed after treatment with pathogens indicating that these miRNAs
play important role in the regulation of immunity-related genes in
insects ([117]52, [118]55, [119]56). Notably, miR-263 has been shown to
regulate immunity-related signal transduction in Galleria mellonella by
affecting the gene expression of tumor necrosis factor receptor
superfamily ([120]56). In addition, miR-263 was hypothesized to be
involved in the regulation of signal modulation in Manduca sexta by
affecting the gene expression of serine protease inhibitors ([121]52).
However, in the present study, a lower expression level, less than
onefold, was observed. Similar to our findings, miR-279 was also
upregulated when P. xylostella was parasitized with Diadegma
semiclausum indicating that it plays an important role in the immune
response of P. xylostella against parasitoids and microorganisms
([122]55). Interestingly, pxy-miR-306 not only showed differential
expression after treatment with destruxin A but was also listed in the
top 10 highly expressed miRNAs in our study. It suggests that miR-306
family might be involved in the regulation of immunity-related genes.
Previously, it has been found that miR-306 family is associated with
Cry1Ab resistance in Ostrinia furnacallis ([123]57). Of note,
pxy-miR-308 was commonly differentially expressed in all the treatments
indicating that it might play a vital role in immunity of P. xylostella
against destruxin A by regulating immunity-related genes. According to
a previous report, Mse-miR-308 was supposed to be involved in
extracellular signal transduction and melanization ([124]52).
To better understand the function of each putative target gene, GO
annotation and KEGG pathway analysis were performed. According to GO
annotation, the differentially expressed target genes were mainly
classified in a cellular process, cell, catalytic activity, and
metabolic process (Figure [125]5). Previously, similar GO annotation of
differentially expressed target genes was obtained in O. furnacallis in
response to Bacillus thuringiensis and Wolbachia-responsive miRNAs in
Tetranychus urticae ([126]57, [127]58).
It is of interest that KEGG pathway annotation of target genes resulted
in the identification of several immunity-related signaling pathways,
including toll signaling pathway, IMD signaling pathway, JAK–STAT
signaling pathway, and cell adhesion molecules pathway. The toll
signaling pathway is a crucial pathway in the innate immunity
([128]59), and in insects, it is responsible for fungi and
Gram-positive bacteria recognition ([129]60). The presence of miRNA
target genes in toll signaling pathway in the current study suggests
that some miRNAs regulate innate immunity against the entomopathogenic
fungi via toll pathway. Previous studies on the model insect D.
melanogaster have also indicated the role of toll pathway in immunity
against fungi and pointed out that toll-mutant flies were highly
susceptible to fungi ([130]61).
Interestingly, the cell adhesion molecules pathway was highly enriched
in the current study. Cell adhesion molecules, glycoproteins, are
expressed on the surface of a cell and are reported to play a vital
role in biological processes, including immune response ([131]62).
These cell adhesion molecules are categorized as integrin family,
selectins, immunoglobulin superfamily, and cadherins. The
immunoglobulin superfamily proteins are reported to play an important
role in facilitating specific interactions with particular pathogens
([132]63). Taken together, the current study clearly presents patterns
of differentially expressed miRNAs in P. xylostella treated with
destruxin A at different time courses.
Conclusion
Concluding our findings, the present study adopted high-throughput sRNA
sequencing to systematically screen out destruxin A-responsive
immunity-related miRNAs in P. xylostella. According to our information,
this is the first study about immunity-related miRNA profiles of P.
xylostella in response to pathogens especially on secondary metabolites
of entomopathogenic fungi like destruxin A. In the current study,
several miRNAs that may regulate immunity through their targets and
related pathways were identified. Among them, miR-263, miR-279,
miR-306, miR-2a, and miR-308 are worthy to mention as these showed
differential expression at different time courses and have also been
predicted to regulate immunity in the other insects. Our findings will
provide a strong foundation for further functional studies of miRNAs
regulating immunity-related target genes of P. xylostella in response
to microorganisms.
Ethics Statement
Our work confirms to the legal requirements of the country in which it
was carried out.
Author Contributions
Conceived and designed the experiments: FJ, MS, and XiaoxX. Performed
the experiments: MS, JX, and XiaoxX. Analyzed the data: MS, XiaojX, JY,
XZ, and JX. Contributed reagents/materials/analysis tools: SL and QH.
Wrote the manuscript: MS and XiaoxX. Revised the manuscript: FJ and YX.
Conflict of Interest Statement
The authors declare that the research was conducted in the absence of
any commercial or financial relationships that could be construed as a
potential conflict of interest.
Acknowledgments