Comparative transcriptome analysis reveals significant metabolic alterations in eri-silkworm (Samia cynthia ricini) haemolymph in response to 1-deoxynojirimycin
Comparative transcriptome analysis reveals significant metabolic alterations in eri- silkworm (Samia cynthia ricini) haemolymph in response to 1-deoxynojirimycin
Shang-Zhi Zhang 1 2 3
Hai-Zhong Yu 1 2 3
Ming-Jie Deng 0 1 2 3
Yan Ma 1 2 3
Dong-Qiong Fei 1 2 3
Jie Wang 1 2 3
Zhen Li 1 2 3
Yan Meng 1 2 3
Jia-Ping Xu 1 2 3
☯ These authors contributed equally to this work. 1 3
jiapingxu@ 1 3
0 Analytical and Testing Center of Wenzhou Medical University , Wenzhou, Zhejiang , People's Republic of China
1 National Science Foundation of China (No. 31472148) (
2 School of Life Sciences, Anhui Agricultural University , Hefei, Anhui , People's Republic of China
3 Editor: Erjun Ling, Institute of Plant Physiology and Ecology Shanghai Institutes for Biological Sciences , CHINA
Samia cynthia ricini (Lepidoptera: Saturniidae) is an important commercial silk-producing insect; however, in contrast to the silkworm, mulberry leaves are toxic to this insect because the leaves contain the component 1-deoxynojirimycin (DNJ). A transcriptomic analysis of eri-silkworm haemolymph was conducted to examine the genes related to different metabolic pathways and to elucidate the molecular mechanism underlying eri-silkworm haemolymph responses to DNJ. Eight hundred sixty-five differentially expressed genes (DEGs) were identified, among which 577 DEGs were up-regulated and 288 DEGs were down-regulated in the 2% DNJ group compared to control (ddH2O) after 12h. Based on the results of the functional analysis, these DEGs were associated with ribosomes, glycolysis, N-glycan biosynthesis, and oxidative phosphorylation. In particular, according to the KEGG analysis, 138 DEGs were involved in energy metabolism, glycometabolism and lipid metabolism, and the changes in the expression of nine DEGs were confirmed by reverse transcription quantitative PCR (RT-qPCR). Thus, DNJ induced significant metabolic alterations in eri-silkworm haemolymph. These results will lay the foundation for research into the toxic effects of DNJ on eri-silkworm as a model and provide a reference for the exploitation of new drugs in humans.
Data Availability Statement: All relevant data are
within the paper and its Supporting Information
Mulberry trees are perennial woody plants that have great economic importance because their
leaves are used to feed the silkworm [
]. Mulberry leaves also contain a large number of
traditional Chinese herbal medicines. One of the main active compounds in mulberry latex is the
natural 1-deoxynojirimycin (DNJ) [
]. DNJ is a D-glucose analogue with promising biological
activity in vivo, contains an NH group in place of the oxygen atom of the pyranose ring and is
also distributed in other plants, including Hyacinthus orientalis, Commelina communis and
Adenophora triphylla var. japonica [
]. Recently, DNJ has been reported to have a significant
effect on improving diabetic conditions by inhibiting the activity of α-glucosidase. Therefore,
it has gained extensive attention for potential use as a medical food to control postprandial
blood glucose levels [
]. Additionally, DNJ and its derivatives have also shown potential
antiviral activity and the ability to inhibit tumours and hypolipidaemia [7±9].
Samia cynthia ricini (Lepidoptera: Saturniidae; Bombycoidea) is a commercial
silk-producing insect originating from India, China and Japan [
]. Eri-silkworm are mainly reared on
castor leaves rather than mulberry leaves because mulberry leaves contain large amounts of a
56-kDa defence protein designated mulatexin (MLX56) and alkaloids, such as DNJ, in
addition to the latex. These active substances are lethal to S. c.ricini, Mamestra brassicae Linnaeus,
and several other herbivorous insects [
]. Eri-silkworm larvae have been applied to detect
and assess the level of plant defence against herbivorous animals. Thus, eri-silkworm
represents a good pharmacological model for researching the mechanism of action of DNJ [
Over the past few decades, technological innovations have enabled researchers to construct
an overview of the changes at the transcriptional level in insects following xenobiotic
stimulation, including insecticides and viruses [
]. During the feeding stage, insects store glycogen
and triglycerides as energy reserves in the fat body. Trehalose and diglycerides are released
from the fat body into the haemolymph to meet the insect's energy demands . Therefore,
we utilized the fifth-instar eri-silkworm haemolymph to research the toxic effects of DNJ using
a transcriptomic analysis. As shown in our previous study, DNJ has a positive impact on
reverse glycometabolism by modulating glycometabolism and inhibiting glucogenesis and
energy metabolism in the fourth-instar eri-silkworm haemolymph [
]. In the eri-silkworm
midgut, DNJ not only exerts a potent negative effect on energy metabolism and
glycometabolism but also modulates lipid metabolism [
]. However, the metabolic mechanism underlying
the fifth-instar eri-silkworm response to DNJ is unclear, particularly at the transcriptional
Transcriptomics were applied to examine the differences in transcript levels in S. c.ricini
haemolymph in response to DNJ and to reveal the toxic effects of DNJ on S. c.ricini at the
transcriptional level. A large number of DEGs were identified that are involved in
glycometabolism, lipid metabolism and energy metabolism in eri-silkworm. This study will lay the
foundation for a better understanding of the effects of DNJ on glycometabolism, lipid
metabolism and energy metabolism and provide a reference for further studies of drug targets using
Materials and methods
Eri-silkworm rearing conditions and experimental design
S. c.ricini larvae were provided by the Sericultural Research Institute of Chinese Academy of
Agricultural Sciences, Zhenjiang. The larvae were reared on fresh castor leaves at 24 ± 1ÊC
with 75% humidity and a 12:12 L:D photoperiod. The newly exuviated fifth-instar larvae were
randomly selected, divided into two groups, and then fed 5 μL of 2% DNJ (J&K Chemicals,
China) or ddH2O. The larval haemolymph was collected from control and treatment groups
after 12h. A small amount of thiourea and TRIzol (Invitrogen, Grand Island, NY, USA) was
added to the haemolymph and immediately stored at -80ÊC until further use.
Total RNA was extracted from eri-silkworm haemolymph (control and 2% DNJ-treated) using
TRIzol (Invitrogen), according to the manufacturer's protocol. The A260/A280 ratios and the
concentrations were examined using a Qubit RNA Kit and a Qubit 2.0 Fluorometer (Life
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Technologies, CA, USA). Ultimately, RNA integrity was assessed using the RNA Nano 6000
Assay Kit and an Agilent 2100 Bioanalyzer (Agilent, Palo Alto, CA, USA) and was confirmed
using 1% agarose gel electrophoresis.
Library preparation and Illumina sequencing
Fragment interruption, cDNA synthesis, the addition of adapters, PCR amplification and
Illumina sequencing were performed by Beijing Novogene Bioinformatics Technology Co., Ltd.
(Beijing, China). The sequencing libraries were constructed using a NEBNext1 Ultra™ RNA
Library Prep Kit for Illumina1 (NEB, Ipswich, MA, USA), according to the manufacturer's
recommendations, and index codes were added to attribute sequences to each sample. The
quality of these libraries was assessed with an Agilent 2100 Bioanalyzer system. Index-coded
samples were clustered with a cBot Cluster Generation System using a TruSeq PE Cluster
Kitv3-cBot-HS (Illumina, San Diego, CA, USA), according to the manufacturer's instructions.
The libraries were sequenced using an Illumina HiSeq™ 2000 platform, and 100-bp paired-end
reads were generated. The fastq format raw reads were processed using in-house Perl scripts.
Clean reads were obtained by removing reads containing adapters or poly-N sequences and
removing low quality reads from the raw data. Q20 (the percentage of bases with a Phred
value > 20), Q30 (the percentage of bases with a Phred value > 30), and the GC (base G and
C) content of the clean data were calculated. All downstream analyses were performed based
on clean and high quality data.
Reads assembly and functional annotation
The left files (read 1 files) from all samples were pooled into one large left.fq file. The right files
(read 2 files) were pooled into one large right.fq file. Transcriptome assembly was
accomplished based on the left.fq and right.fq files using Trinity [
]. The min_kmer_cov was set to
2 and all other parameters were set to default values. Gene function was annotated based on
the following databases: Nr (NCBI non-redundant protein sequences), KOG/COG (Clusters
of Orthologous Groups of proteins), Swiss-Prot (a manually annotated and reviewed protein
sequence database), KO (KEGG Orthologue database), and GO (Gene Ontology). All searches
were performed with an E-value < 10−5. Fragments per kilobase of transcript per million
fragments mapped (FPKM) were calculated to represent the expression level of the unigenes.
Identification and analysis of DEGs
DEGs were identified using the DESeq R package (1.10.1). DESeq includes statistical routines
for determining DEGs based on the negative binomial distribution. The resulting P-values
were adjusted using the Benjamini and Hochberg approach for controlling the false discovery
rate. Genes were designated as differentially expressed when the adjusted p-value was < 0.05
and |log2 (fold change)| > 0. GO and Kyoto Encyclopedia of Genes and Genomes (KEGG)
enrichment analyses were conducted using the GOseq R packages and KOBAS software [
Reverse transcription quantitative PCR (RT-qPCR) analysis
The relative expression levels of 11 randomly selected DEGs were confirmed by RT-qPCR to
validate the reliability of the transcriptome data. Additionally, 9 genes related to
glycometabolism, lipid metabolism and energy metabolism were validated. The primers are listed in S1
Table. Total RNA was extracted from the haemolymph of the control and treatment groups
using TRIzol reagent. The concentration of each RNA sample was adjusted to 1 μg/μL with
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nuclease-free water and total RNA was reverse transcribed in a 20-μL reaction system using
the PrimeScript™ RT Reagent Kit with gDNA Eraser (TaKaRa, Dalian, China). RT-qPCR was
conducted in a 25-μL reaction mixture containing 12.5 μL of SYBR Premix Ex Taq (TaKaRa).
PCR amplification was performed in triplicate wells. S.c.ricini β-actin (ScActin) was used as a
reference gene. The thermal cycling profile consisted of an initial denaturation step at 95ÊC for
30 s and 40 cycles of 95ÊC for 5 s and 60ÊC for 30 s. The reactions were performed in 96-well
plates with a Multicolor Real-time PCR Detection System (Bio-Rad, Hercules, CA, USA).
Relative expression levels were calculated using the 2−ΔΔCt method according to a previously
reported protocol [
]. Three biological replicates were performed for each sample, and each
biological replicate included three technical replicates. The statistical analysis was conducted
using ANOVA and the LSD post hoc test using SPSS (p < 0.01).
Illumina sequencing, reads assembly and functional annotation
RNA samples from the haemolymph of the two groups (2% DNJ-treated and
ddH2Otreated) were sequenced on an Illumina HiSeq™ 2000 platform; three biological replicates and
three technical replicates were included. We generated 46,811,398, 57,836,584, 48,797,240,
44,655,898, 42,290,810 and 42,821,388 raw reads from the 2% DNJ-treated and control groups,
respectively. After stringent quality assessment and data filtering using the Trinity de novo
assembly programme, 45,386,222, 56,099,312, 47,216,414, 43,437,882, 41,263,002, 41,636,180
clean reads were obtained. The Q20 (sequencing error rate < 1%) and Q30 (sequencing error
rate < 0.1%) were greater than 96.04% and 90.83%, and the GC contents were 45.8%, 45.72%,
46.42%, 45.23%, 46.43% and 46.65%, respectively (S2 Table). All short-read sequences were
assembled into 86,319 transcripts and 73,296 unigenes (S3 Table). Thus, the quality and
accuracy of the sequencing data were sufficient for further analysis. Using a blastx programme with
a cutoff E-value of 10−5, 73,296 unigenes were annotated to different protein databases,
including the Nr, KEGG, KOG, GO and Swiss-Prot databases (Fig 1A). Based on an analysis of the
Fig 1. Characteristics of the homology search of Illumina sequences against the Nr database. (A) Venn diagram of unigenes
annotated by Blastx against five protein databases. (B) The species distribution is shown as a percentage of the total homologous sequences
with an E-value of at least 1.0E-5. (C) E-value distribution of BLAST hits for each unique sequence with a cut-off E-value 1.0E-5. (D)
Similarity distribution of the top BLAST hits for each sequence.
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Venn diagram, 19,846 unigenes had significant matches in the Nr database and 14,639
unigenes had matches in the Swiss-Prot database. Additionally, 14,436, 20,083 and 9,448 unigenes
were annotated in GO, KEGG and KOG, respectively. A total of 2,413 unigenes were
annotated only in the GO database, 223 unigenes were assigned only in Swiss-Prot and 3,952 and
18 unigenes were annotated only by Nr and KOG, respectively. In addition, 3,752 unigenes
were assigned to a homologue in all five databases (Fig 1A). The species, E-value and similarity
distribution were analysed by evaluating the matched unigenes from the BLASTX results
returned from the Nr protein database. Regarding the species distribution, the highest
percentage of unigenes was matched to Bombyx mori (39.3%), followed by Danaus plexippus
(10.9%) and Plutella xylostella (8.7%) (Fig 1B). The E-value distribution of the best hits against
the nr database showed that 55.4% of the sequences displayed significant homology (E-value <
1.0E-45), and the E-values of most of the unigenes ranged from 1.0E-15 to 1.0E-45 (Fig 1C). On
the other hand, according to the similarity distribution, 26% of the unigenes exhibited
significant homology greater than 95%, and only 7.0% of the sequences displayed homology less than
60% (Fig 1D).
RT-qPCR validation of differentially expressed transcripts
The relative expression levels of 11 randomly selected genes were analysed by RT-qPCR to validate
the reliability of the transcriptome sequencing data (Fig 2A). The trends in the RT-qPCR data
were consistent with the transcriptome data. A linear regression analysis of the correlation
between RT-qPCR and RNA-Seq data showed an R2 (goodness of fit) value of 0.917 and
corresponding slope of 1.306 (Fig 2B), suggesting a strong positive correlation between the RT-qPCR and
transcriptome data. Therefore, the transcriptome data were suitable for further analysis.
Identification of DEGs in response to DNJ
In this study, differentially expressed genes between control and treated groups were defined
using adjusted p-values. Nine hundred fifty-six DEGs were identified in the 2% DNJ-treated
group compared with the control group, of which 577 DEGs were up-regulated and 388 DEGs
were down-regulated (Fig 3A). We performed hierarchical clustering of all DEGs based on the
log10 (RPKM+1) values of the two groups to determine the expression patterns of the
identified genes (Fig 3B). DNJ significantly altered the transcriptional profile of the DEGs. The
expression of a greater number of genes was up-regulated than down-regulated in the
DNJtreated group compared with the control group.
Fig 2. Correlation between gene expression ratios obtained from the transcriptome data and RT-qPCR data. (A)
Expression ratios (FPKM fold change) obtained from transcriptome data (blue) and RT-qPCR data (red). (B) Lineage
analysis between the transcriptome and RT-qPCR data. The ratios obtained by RT-qPCR (Y-axis) were plotted against
the ratios obtained by RNA-Seq (X-axis).
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Fig 3. Identification and hierarchical cluster analysis of differentially expressed genes. (A) Scatter diagram for each
gene. The blue, red and green points represent no difference in expression, up-regulated and down-regulated unigenes,
respectively. (B) Hierarchical clustering of DEGs between the control and 2% DNJ-treated groups. Columns indicate
different samples. Rows represent different DEGs. Blue bands indicate a low gene expression level, and red bands
indicate a high gene expression level.
Functional annotation of DEGs
GO and KEGG enrichment analyses were performed to further analyse the functions of the
DEGs. The GO project provides structured and controlled vocabularies and classifications for
the annotation of genes that cover several domains of molecular and cellular biology [
Seven hundred eight unigenes were assigned into three main GO categories: cellular
component, molecular function and biological process. The GO analysis of the up-regulated and
down-regulated DEGs is shown in Fig 4. For the biological process category, the up-regulated
genes were mainly involved in cellular component biogenesis, ribosome biogenesis,
ribonucleoprotein complex biogenesis and carbohydrate metabolic process, and down-regulated
Fig 4. GO categories of the differentially expressed genes (DEGs). The annotated DEGs were classified into the cellular
component, molecular function and biological process categories by WEGO according to the GO terms. Red and blue bars
indicate up-regulated and down-regulated DEGs, respectively.
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Fig 5. KEGG enrichment analysis of DEGs. Scatter diagram of KEGG pathways. The X-axis indicates the enrichment
factor. The Y-axis indicates different pathways. (A) Up-regulated DGEs. (B) Down-regulated DGEs.
genes were involved in anatomical structure development. For the cellular component
category, up-regulated genes were assigned into the cytoplasmic part, followed by the
ribonucleoprotein complex, and the down-regulated genes were assigned to the extracellular region. For
the molecular function category, the up-regulated genes were mainly related to structural
molecule activity, followed by structural constituent of ribosome, and the down-regulated genes
were associated with peptidase activity.
The KEGG pathway analysis provides classifications that are valuable for studying the
complex biological functions of genes [
]. According to the KEGG pathway enrichment analysis,
the up-regulated DEGs were significantly enriched in six pathways, including ribosomes, the
endoplasmic reticulum and protein processing, glycolysis, N-glycan biosynthesis, starch and
sucrose metabolism and biosynthesis of amino acids (Fig 5A). The down-regulated DGEs were
significantly enriched oxidative phosphorylation and complement and coagulation cascades,
fructose and mannose metabolism (Fig 5B).
Analysis of DEGs related to glycometabolism, lipid metabolism and energy metabolism
In this study, based on results of GO annotation and KEGG pathway enrichment analysis, the
DEGs were mainly enriched in glycometabolism, lipid metabolism and energy metabolism
(Fig 6 and S4 Table). Eighty-one DEGs related to energy metabolism were identified, of which
43 DEGs were up-regulated and 38 were down-regulated in the 2% DNJ-treated group
compared with the control. Thirty-seven DEGs related to glycometabolism were identified, of
which 26 DEGs were up-regulated and 11 DEGs were down-regulated in the 2% DNJ-treated
group compared with the control. Additionally, 20 DEGs were associated with lipid
metabolism, of which 15 DEGs were up-regulated and 5 DEGs were down-regulated in the 2%
DNJtreated group compared with the control. Nine genes were selected for further analysis of their
expression patterns using RT-qPCR (Table 1 and Fig 7). Three genes were related to lipid
metabolism, including 4-aminobutyrate aminotransferase (GABAT), eye-specific diacylglycerol
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Fig 6. Expression patterns of DEGs related to glycometabolism, lipid metabolism and energy metabolism in the
2% DNJ-treated group and control group. Each row represents a different gene, with green and red indicating low
and high levels of gene expression, respectively. C: control, T: 2% DNJ treatment. The numbers 1, 2 and 3 represent the
three biological replicates.
kinase isoform X3 (DGK), and aldose reductase-like (AR). Based on the RT-qPCR results,
GABAT and AR were down-regulated, and DGK was up-regulated in the 2% DNJ-treated
group compared with the control. Three genes were associated with glycometabolism,
including UDP-glucosyltransferase precursor (UGT), mannosyl-oligosaccharide glucosidase (MOGS),
and glucosidase II alpha-subunit (GII alpha). According to the RT-qPCR analysis, these three
genes were up-regulated in the 2% DNJ-treated group compared with the control. On the
hand, 3 genes were involved in energy metabolism, including multidrug resistance protein
homologue 49-like (MRP49), proto-oncogene tyrosine-protein kinase ROS isoform X1 (ROS), and
multidrug resistance protein 1A (MRP1A). The RT-qPCR analysis indicated that MRP-49 and
ROS were up-regulated, and MRP1A was down-regulated in the 2% DNJ-treated group
compared with the control. These results of the RT-qPCR analysis are consistent with the
2% DNJ FPKM
Fig 7. RT-qPCR analysis of the expression patterns of nine genes in eri-silkworm haemolymph. The different colours
represent different groups; blue and red represent the control 2% DNJ-treated groups, respectively ( p < 0.05, p < 0.01).
transcriptome sequencing data. Thus, DNJ caused significant alterations in lipid metabolism,
glycometabolism and energy metabolism in eri-silkworm haemolymph.
As an alkaloid from mulberry, DNJ shows substantial inhibitory activity towards α-glucosidase
in vitro, which may be beneficial for suppressing abnormally high blood glucose levels [
our previous study, we used eri-silkworm as a model to study the toxic actions of DNJ via
metabonomics, and DNJ modulated glycometabolism and inhibit glucogenesis and energy
]. DNJ is reported to exert a significant inhibitory effect on the activity of
αglucosidase. However, the overall effects of DNJ on glycometabolism, energy metabolism and
lipid metabolism in the eri-silkworm are unclear. Moreover, genes involved in
glycometabolism, lipid metabolism and energy metabolism have not been researched at the transcriptional
level. In the present study, transcriptome sequencing was performed on fifth-instar
haemolymph samples from two groups (2% DNJ-treated and control) at 12h after treatment and
73,296 unigenes were obtained (S3 Table). Based on adjusted p-values, 965 DEGs were
identified. To our surprise, a greater number of up-regulated DEGs (577) was identified than
downregulated DEGs (288) (Fig 3A). DNJ stimulation promoted the up-regulation of gene
expression in S. c.ricini. Many xenobiotics also induce the up-regulation of host gene expression. For
example, Hou et al.  employed transcriptomics to screen the DEGs of silkworm larvae
during an early response to Beauveria bassiana, and 960 DEGs were up-regulated and 470 DEGs
were down-regulated. According to the results of the KEGG pathway enrichment analysis,
upregulated genes were significantly enriched in four pathways, including ribosome, glycolysis/
gluconeogenesis, N-glycan biosynthesis and starch and sucrose metabolism (Fig 5A).
Downregulated DEGs were significantly enriched in oxidative phosphorylation and fructose and
mannose metabolism (Fig 5B).
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Glycometabolism plays an important role in the physiological balance of living organisms
]. As shown in our previous studies, the glycometabolism pathway was impaired in the
haemolymph and midgut of fourth-instar larvae after oral administration of DNJ [
]. In this
study, based on the KEGG database analyses, the up-regulated genes related to
glycometabolism were mainly enriched in N-glycan biosynthesis, glycolysis and gluconeogenesis, starch
and sucrose metabolism, and the down-regulated genes associated with glycometabolism were
mainly enriched in fructose and mannose metabolism. In eukaryotes, the attachment and
subsequent modification of N-glycans affect the folding of glycoproteins and regulate their
]. MOGS and GII alpha were assigned to N-glycan biosynthesis, and their
relative expression levels were up-regulated in response to DNJ (Fig 7). MOGS (also known as
glucosidase I) is expressed in the endoplasmic reticulum and is involved in trimming
N-glycans; it was the first enzyme to be identified in the pathway for processing N-linked
]. GII alpha belongs to glycoside hydrolase family 31 (GH31), which has similar
functions as MOGS and is involved in trimming N-glycans [
]. As a recognized inhibitor of
α-glucosidase, DNJ also inhibits MOGS and GII alpha activities [32±34]. However, the relative
expression levels of the two genes showed an increasing trend in the 2% DNJ-treated group
compared with the control. We speculated that the eri-silkworm might a produces stress
response when glycosidase activity was inhibited by DNJ.UGT was enriched in the starch and
sucrose metabolism pathway, and its expression was up-regulated in the 2% DNJ-treated
group. UGTs are membrane-bound proteins that are mainly located on the luminal side of the
endoplasmic reticulum (ER) in animals [
]. UGTs catalyse the conjugation of a range of
diverse small lipophilic compounds with sugars to produce glycosides, playing an important
role in the detoxification of xenobiotics and the regulation of endobiotics in insects [35±38].
Thus, UGT might be involved in the detoxification of DNJ and delaying the toxic effect.
Lipids are one of the three nutrients involved in energy storage, participate in cell membrane
structure, hormone synthesis and vitamin storage. Lipid metabolism in insects is similar to
mammals, comprising lipid absorption, transport, storage, and mobilization processes [
An insect that consumes a high sugar or high fat food for a long period will display
disturbances in fat metabolism and lipotoxicity in organs [
]. DNJ has been shown to modulate
lipid metabolism and prevent hyperlipidaemia [
]. In the present study, most of the
genes involved in lipid metabolism were up-regulated after treatment with DNJ. These genes
were related to fatty acid biosynthetic process, sphingolipid, glycerophospholipid and
glycerolipid metabolism. According to the KEGG analysis, DGK and AR were enriched in
glycerolipid metabolism. Diacylglycerol kinase isoforms regulate signal transduction and lipid
metabolism and have divergent functional roles in distinct tissues . AR is an
NADPHdependent reductase that is the first rate-limiting enzyme of the polyol pathway in glucose
metabolism and is implicated in the pathogenesis of secondary diabetic complications [
the last few decades, this enzyme has been used as a target to prevent cellular inflammatory
events . Based on the transcriptome sequencing and RT-qPCR results, DGK was
up-regulated and AR was down-regulated in the 2% DNJ-treated group compared with the control.
We speculated that DNJ modulated lipid metabolism by influencing the expression levels of
AR and DGK in eri-silkworm. GABAT is a dimeric homopolymer that catalyses the first step
in the conversion of the central inhibitory neurotransmitter gamma-amino butyric acid
(GABA) to succinic acid [
]. As shown in our previous study, succinate levels were reduced
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in the haemolymph of fourth-instar eri-silkworm in response to the DNJ treatment [
DNJ inhibited the expression of the GABAT gene to influence succinate production.
Energy metabolism is an important metabolic pathway in organisms and is the foundation of
biological growth, development and life. In the present study, we focused on the genes related
to oxidative phosphorylation, ATP binding, carbohydrate digestion and absorption pathway.
Eighty-one DEGs related to energy metabolism were identified from transcriptome database.
Oxidative phosphorylation is the metabolic pathway cells use enzymes to oxidize nutrients,
thereby releasing the energy used to regenerate ATP [
]. In our previous study, DNJ had a
significant effect on the TCA cycle in eri-silkworm. For example, DNJ reduced the fumarate
and succinate levels in the metabolic pathway [
]. Most genes related to the oxidative
phosphorylation pathway were down-regulated, indicating that DNJ might inhibit gene expression
by modulating the TCA cycle. MRP49, ROS, and MRP1A are mainly related to the ATP
binding pathway. The multidrug resistance protein is an ATP-binding cassette (ABC) transporter
that is divided into eight subfamilies (from ABC-A to ABC-H) and transports a series of
substrates across cellular membranes [
]. MDR1A (also known as p-glycoprotein) is a transport
protein with a wide substrate specificity that belongs to the ATP-binding cassette protein
]. It plays an important role in drug excretion and is located in the apical membrane of
cells in the intestine, kidney and liver to protect tissues from toxic xenobiotics and endogenous
metabolites. In addition, it can affect the uptake and distribution of many clinically important
]. MRP49 and MRP1A belong to the ABC family; therefore, we postulated that these
two genes were involved in protecting the eri-silkworm haemolymph from DNJ toxicity.
In the present study, transcriptome sequencing was employed in eri-silkworm haemolymph for
the first time to investigate the changes in genes related to glycometabolism, lipid metabolism
and energy metabolism after the insects were fed DNJ. Our comprehensive analysis revealed
effects on the three main metabolic pathways at the transcriptional level. Eight hundred
sixtyfive DEGs were identified, of which 138 DEGs were involved in energy metabolism,
glycometabolism and lipid metabolism. Based on these results, DNJ influences gene expression levels to
modulate glycometabolism, lipid metabolism and energy metabolism. These findings lay the
foundation for obtaining a better understanding of the toxic effects of DNJ on eri-silkworm as a
model and provide a reference for the exploitation of new drugs for humans.
S1 Table. Primers used for RT-qPCR to validate DEGs.
S2 Table. Summary of the sequence assembly obtained after Illumina sequencing.
S3 Table. Length frequency distribution of transcripts and unigenes.
S4 Table. Identification of genes related to glycometabolism, lipid metabolism and energy metabolism.
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This work was also supported by the National Science Foundation of China (No. 31472148)
and the International Science & Technology Cooperation Plan of Anhui Province
Conceptualization: Jia-Ping Xu.
Data curation: Ming-Jie Deng.
Formal analysis: Shang-Zhi Zhang, Hai-Zhong Yu, Ming-Jie Deng, Yan Ma.
Funding acquisition: Yan Meng, Jia-Ping Xu.
Investigation: Shang-Zhi Zhang, Hai-Zhong Yu, Yan Ma, Dong-Qiong Fei, Jie Wang, Zhen
Methodology: Jia-Ping Xu.
Project administration: Jia-Ping Xu.
Resources: Ming-Jie Deng, Yan Meng.
Software: Ming-Jie Deng, Dong-Qiong Fei, Jie Wang.
Supervision: Jia-Ping Xu.
Validation: Jia-Ping Xu.
Visualization: Shang-Zhi Zhang, Hai-Zhong Yu.
Writing ± original draft: Shang-Zhi Zhang, Hai-Zhong Yu.
Writing ± review & editing: Jia-Ping Xu.
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