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Transcriptome-Wide Identification of Differentially Expressed Genes in Chinese Oak Silkworm Antheraea pernyi in Response to Lead Challenge Zhao-Zhe Xin, Qiu-Ning Liu, Yu Liu, Dai-Zhen Zhang, Zheng-Fei Wang, HuaBin Zhang, Bao-Ming Ge, Chun-Lin Zhou, Xin-Yue Chai, and Bo-Ping Tang J. Agric. Food Chem., Just Accepted Manuscript • DOI: 10.1021/acs.jafc.7b03391 • Publication Date (Web): 27 Sep 2017 Downloaded from http://pubs.acs.org on September 28, 2017

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Journal of Agricultural and Food Chemistry

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Transcriptome-Wide Identification of Differentially Expressed

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Genes in Chinese Oak Silkworm Antheraea pernyi in Response

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to Lead Challenge

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Zhao-Zhe Xin1, Qiu-Ning Liu1, *, Yu Liu, Dai-Zhen Zhang, Zheng-Fei Wang, Hua-Bin

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Zhang, Bao-Ming Ge, Chun-Lin Zhou, Xin-Yue Chai, Bo-Ping Tang*

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Jiangsu Key Laboratory for Bioresources of Saline Soils, Jiangsu Synthetic

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Innovation Center for Coastal Bio-agriculture, Jiangsu Provincial Key Laboratory of

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Coastal Wetland Bioresources and Environmental Protection, School of Ocean and

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Biological Engineering, Yancheng Teachers University, Yancheng, 224007, PR China.

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* Corresponding author: Bo-Ping Tang and Qiu-Ning Liu

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Tel/fax: +86 515 88233991

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E-mail: [email protected] (Bo-Ping Tang), [email protected], [email protected] (Qiu-Ning Liu)

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Address: Jiangsu Key Laboratory for Bioresources of Saline Soils, Jiangsu Synthetic

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Innovation Center for Coastal Bio-agriculture, Jiangsu Provincial Key Laboratory of

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Coastal Wetland Bioresources and Environmental Protection, School of Ocean and

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Biological Engineering, Yancheng Teachers University, Yancheng 224007, PR China.

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These authors contribute to this work equally.

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ACS Paragon Plus Environment


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ABSTRACT

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Antheraea pernyi is a commercially-cultivated silk moth and a source of insect food

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with high-quality protein. Insects suffer oxidative stress on exposure to heavy metals,

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and reactive oxygen species are cleared by antioxidant enzymes. To gain better

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understanding of the antioxidant defense system of A. pernyi, we analyzed

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transcriptomes of pupae after stimulation with lead and phosphate-buffered saline

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(control). In total, 72,367 unigenes were identified. Gene ontology analysis revealed

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these DEGs were in 20 biological process subcategories, 19 cellular component

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subcategories and 18 molecular function subcategories. Clusters of orthologous

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groups of proteins annotation placed a total of 528 DEGs into 25 categories. Kyoto

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Encyclopedia of Genes and Genomes enrichment analysis identified antioxidant

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defense pathways, including “Peroxisome” and “Glutathione metabolism”, which are

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reported for the first time in A. pernyi. Our study enriches A. pernyi transcriptome

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databases and provides insight into the heavy metals responses of antioxidant systems

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of this insect fat bodies.

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KEYWORDS: Antheraea pernyi, silkworm, heavy metals, lead, antioxidant defense

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system, transcriptome

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INTRODUCTION

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Heavy metal pollution is one of the most serious ecological problem on account

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of burning of mining and fossil fuels, the chaos of urban garbage, the overuse of

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fertilizers.1 Heavy metals such as arsenic, cadmium, chromium and lead are very

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common in the soil and water, which could result in growth inhibition and toxicity in

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insects parts.2 The changes in lifestyles and environments make it easier for insects to

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be exposed to heavy metals pollution. Exposure to heavy metals can promote the

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generation of reactive oxygen species (ROS) in insects which is known to induce

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oxidative stress.3 Therefore, insects have evolved antioxidant defense systems,

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including antioxidant enzymes. Insects can clear the ROS through the antioxidant

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enzymes to avoid oxidative damage. The general consensus is that insect larvae

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contain catalase (CAT), superoxide dismutase (SOD), glutathione reductase (GR) and

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glutathione S transferase (GSTs) activities.4-7 There have been a number of published

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studies about antioxidant enzymes present in the species of lepidopteran insect

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larvae.8-10 However, there are very few reports regarding the heavy metals responses

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of antioxidant systems in the Antheraea pernyi fat bodies. In addition, transcriptome

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data gives us a better understanding of insect immune mechanisms.11,12

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A. pernyi is among the best-known species of wild silkworms. It is widely cultured

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in China, India and Korea for silk production.13 A. pernyi has been used as an

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experimental model insect to study breeding technique, development, metabolism

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regulation, and immune response.14-23 It is also used as a bioreactor to produce

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recombinant proteins.24 A. pernyi has four life-cycle stages: egg, larva, pupa and adult.

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Recently, it is mainly used as a source of insect food (its larva, pupa and adult) in

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China.25 The pupae of A. pernyi are regarded as a source of high-quality protein-rich

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food containing all the essential amino acids.26 The larvae of A. pernyi are raised on

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oak leaves in the field. Thus, the growth and development of larva are highly

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susceptible to environmental conditions, including heavy metal pollution, foliage,

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climate, and disease.

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In this study, transcriptome sequencing libraries were constructed from the fat

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bodies of A. pernyi from a lead-treated group and a phosphate-buffered saline (PBS)

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control group. The differentially expressed genes (DEGs) were analyzed and

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identified in different functional databases. The results enrich the information in A.

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pernyi gene databases and provide insights into the potential antioxidant mechanisms

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of A. pernyi.

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MATERIALS AND METHODS

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Experimental Insects and Challenge

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A. pernyi were originally obtained from the Sericultural Research Institute of

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Henan and reared on the leaves of oak until pupation. The pupae were collected and

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kept at room temperature. Three individuals were used for lead challenge. Fat bodies

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was used for experiment materials. Fat bodies were collected at 24 h through

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dissection. In order to study the influencing mechanism of lead on A. pernyi, the

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pupae of diapause were chosen to be injected with 10 μl Pb(NO3)2 for lead exposure.

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The concentration of lead was 20 mg/l. The control pupae were injected with 10 μl

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PBS solution at 24 h.

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RNA Preparation and Library Construction

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RNA was extracted using Trizol (Invitrogen, USA), based on the manufacturer’s

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instructions. RNA degradation and contamination were monitored on 1% agarose gels.

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RNA purity was checked using a NanoPhotometer® spectrophotometer (Implen,

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Westlake Village, CA, USA). RNA concentration was measured using the Qubit®

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RNA Assay Kit in a Qubit® 2.0 Fluorometer (Life Technologies, Carlsbad, CA, USA).

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RNA integrity was assessed using the RNA Nano 6000 Assay Kit and the Agilent

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Bioanalyzer 2100 system (Agilent Technologies, Santa Clara, CA, USA).

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A total of 3 μg RNA per sample were used as input material for RNA sample

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preparation. Sequencing libraries were generated using the NEBNext® Ultra™ RNA

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Library Prep Kit for Illumina® (New England Biolabs (NEB), Ipswich, MA, USA)

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following the manufacturer’s recommendations, and index codes were added to

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attribute sequences to each sample. Briefly, mRNA was purified from total RNA using

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poly-T oligo-attached magnetic beads. Fragmentation was carried out using divalent

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cations at elevated temperature in NEBNext First Strand Synthesis Reaction Buffer

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(5×). First strand cDNA was synthesized using random hexamer primers and

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M-MuLV Reverse Transcriptase (RNase H). Second strand cDNA synthesis was

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subsequently performed using DNA Polymerase I and RNase H. Remaining

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overhangs were converted into blunt ends via exonuclease/polymerase activities. After

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adenylation of 3ʹ - ends of DNA fragments, NEBNext Adaptors with hairpin loop

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structure were ligated in preparation for hybridization. To select cDNA fragments of

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preferentially 150–200 bp in length, the library fragments were purified with the

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AMPure XP system (Beckman Coulter, Beverly, MA, USA). Then 3 μl

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Uracil-Specific Excision Reagent Enzyme (NEB) was applied to size-selected,

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adaptor-ligated cDNA at 37°C for 15 min, followed by incubation for 5 min at 95°C

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before PCR. PCR was performed with Phusion High-Fidelity DNA polymerase,

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universal PCR primers and Index (X) Primer. Finally, PCR products were purified and

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library quality was assessed.

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Transcriptome Assembly, Annotation and Function Enrichment

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Raw data (raw reads) in FASTQ format were firstly processed through in-house

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Perl scripts. In this step, clean data (clean reads) were obtained by removing reads

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containing adapter, reads containing poly-N and low-quality reads from the raw data.

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At the same time, Q20, Q30, G+C-content and sequence duplication level of the clean

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data were calculated. All downstream analyses were based on clean data with high

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quality. The left files (read1 files) from all libraries/samples were pooled into one big

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left.fq file, and right files (read2 files) into one big right.fq file. Transcriptome

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assembly was accomplished based on the left.fq and right.fq using Trinity software.27

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Gene function was annotated based on the following databases: NCBI non-redundant

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protein sequences (NR); Protein family (Pfam); Clusters of Orthologous Groups of

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proteins (KOG/COG); Swiss-Prot (a manually annotated and reviewed protein

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sequence database);28 Kyoto Encyclopedia of Genes and Genomes (KEGG);29 and

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Gene Ontology (GO).30

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Quantification of Gene Expression Levels and Differential Expression Analysis

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Gene expression levels were estimated by RSEM31 for each sample. Differential

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expression analysis of two conditions/groups was performed using the DESeq R

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package. DESeq provides statistical routines for determining differential expression in

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digital gene expression data using a model based on negative binomial distribution.

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The resulting P-values were adjusted using the Benjamini and Hochberg approach for

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controlling the false discovery rate (FDR). Genes with an adjusted P-value < 0.05

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found by DESeq were assigned as differentially expressed.

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GO and KEGG Pathway Enrichment Analysis

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GO enrichment analysis of the DEGs was implemented by the topGO R package

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based on the Kolmogorov-Smirnov test. KEGG32 is a database resource for

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understanding high-level functions of biological systems, such as the cell, the

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organism and the ecosystem, from molecular-level information, especially large-scale

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molecular datasets generated by genome sequencing and other high-throughput

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experimental technologies (http://www.genome.jp/kegg/). We used KOBAS33

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software to test the statistical enrichment of DEGs in KEGG pathways.

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RESULTS AND DISCUSSION

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Assembly and Splicing

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A total of 34,909,205 clean reads were obtained from lead-treated samples and

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25,795,510 clean reads from the control (PBS-treated) samples, giving rise to total

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clean base numbers of 10,351,801,068 and 7,662,720,366, respectively. Q30

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was >92%, and the G+C content was approximately 44% in lead-treated samples and

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43% in PBS-treated samples (Table 1). Using Trinity software, 106,751 transcripts

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were generated with average length 953.20 bp and N50 length 1,779 bp; 72,367

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unigenes were identified with mean length 738.57 bp and N50 length 1,336bp.

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Among these unigenes, 45,461 (62.82%) were in the range 200–500 bp, 13,202

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(18.24%) were 500–1000 bp, 7,870 (10.88%) were 1–2 kbp, and 5,833 (8.06%)

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unigenes were longer than 2 kbp (Table 2). These results show that the data are

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high-quality and the unigenes can be used for annotation analysis.

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Functional Annotation and Classification

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To obtain comprehensive gene function information, we used seven databases to

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annotate the unigenes, including NR,34 Swiss-Prot, GO, COG,35 KOG,36 eggNOG37

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and KEGG. In total, 26,426 unigenes were annotated as follows: 22,620 in NR

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(85.60%); 10,001 in KEGG (37.85%); 9,002 in COG (34.06%); 12,123 in Swiss-Prot

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(45.88%); 23,914 in eggNOG (90.49%); 17,204 in Pfam (65.10%); 12,602 in GO

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(47.69%); and 14,961 in KOG (56.61%) (Table 3).

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Identification and Analysis of DEGs

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Based on the differential expression analysis, 1,609 significant DEGs ((|log2(fold

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change)|) > 2, FDR < 0.01) were identified between the lead-challenged and PBS

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control groups, including 807 upregulated and 802 downregulated unigenes (Fig. 1).

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The number of DEGs identified in different functional databases were: 369 in COG,

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692 in GO, 458 in KEGG, 757 in KOG, 900 in Pfam, 694 in Swiss-Prot, 1,126 in

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eggNOG, and 1,168 in NR (Table 4).

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Enrichment Analysis of DEGs

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The functions of the predicted unigenes were classified in KOG, GO and KEGG.

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All DEGs were classified into three GO categories by topGO,38 including "Biological

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Process" (BP), "Cellular Component" (CC) and "Molecular Function" (MF),

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comprised of 20, 19 and 18 subcategories respectively. Among the various categories

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of BP, the top three by frequency were “metabolic process” (350), “cellular process”

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(326) and “single-organism process” (284). Some BP terms were closely related to

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immune response, for example “immune system process” (22). Within the CC

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category, “cell” (179), “cell part” (179) and “organelle” (134) were the dominant

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groups. In the MF classes, “catalytic activity” (371), “binding” (327) and “transporter

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activity” (55) constituted the top three clusters by frequency (Fig. 2 and Table 5).

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COG annotation showed that a total of 528 DEGs could be classified into 25

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categories. Among them, the classification "General function prediction only (R)"

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represented the largest group (95, 18.00%), followed by "Replication, recombination

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and repair (L)" (45, 8.52%) and "Carbohydrate transport and metabolism (G)" (44,

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8.33%). Six DEGs (1.14%) were related to the category "Defense mechanisms (V)".

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In addition, the function of six DEGs was unknown (Fig. 3 and Table 6).

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All KEGG annotation pathways are shown in Fig. 4. Four pathways were

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connected with cellular processes, five were related to environmental information

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processing, and six were associated with genetic information processing. Thirty-five

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pathways were relevant to metabolism. The top 20 KEGG pathways are shown in

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Table 7, which lists the number of DEGs and the total number of genes in each

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pathway. Among them, “Phagosome”, “Endocytosis”, “Peroxisome” and “Glutathione

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metabolism” are related to immune responses and antioxidant defence.39-41

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Peroxisomes contains CAT, SOD and peroxidase, which are antioxidant enzymes. To

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avoid oxidative damage, insects clear reactive oxygen species (ROS) using

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antioxidant enzymes. Glutathione will arise in the course of glutathione metabolism,

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which is a mechanism for removing toxins.5 Classifications associated with genetic

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information42 processing were “Ribosome”, “Ubiquitin mediated proteolysis”,

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“Protein processing in endoplasmic reticulum” and “RNA transport”. “FoxO signaling

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pathway” is associated with signal transduction. Signal transduction is activated by

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complex protein-protein interactions between ligands, receptors and kinases, which

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play an important role in regulating a variety of physiological and metabolic

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processes.43-46 “Biosynthesis of amino acids”, “Carbon metabolism”, “Fatty acid

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metabolism”, “Arginine and proline metabolism”, “Glycolysis/Gluconeogenesis”,

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“Oxidative phosphorylation”, “Glycerolipid metabolism”, “Pentose and glucuronate

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interconversions”, “Purine metabolism”, “Glycine, serine and threonine metabolism”,

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and “Starch and sucrose metabolism” were connected with metabolism, which

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controls production, maintenance and destruction of biomolecules and energy

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balance.47-50

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Pathways Related to Antioxidant Defense

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Figure 5 shows the peroxisome pathway; this pathway is associated with the

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antioxidant system. In this pathway, peroxin-14 (PEX14), ATP-binding cassette,

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subfamily D (ALD), member 3 (PMP70), phytanoyl-CoA Hydroxylase (PHYH),

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acyl-CoA oxidase (ACOX), ATP-binding cassette, subfamily D (ALD), member 2

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(ABCD) and xanthine dehydrogenase (XDH) are associated with the upregulated

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genes. The alcohol-forming fatty acyl-CoA reductase (FAR) protein is associated with

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the upregulated and downregulated genes. The main function of peroxisome is fatty

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acid-oxidation, ether phospholipid biosynthesis and antioxidation system. In

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antioxidation system, peroxisomes perform important functions, including hydrogen

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peroxide metabolism and glutathione metabolism. CAT and SOD were generated in

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the process of hydrogen peroxide metabolism. SODs convert O2− into H2O2. CATs

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and peroxidases convert H2O2 into H2O. Thus, these enzymes work in concert. Two

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major types of SOD, copper-zinc SOD (CuZnSOD) and manganese SOD (MnSOD),

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are found in mammalian cells. CuZnSOD is found in the nucleus and cytosol,

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MnSOD is localized in mitochondria. CAT is localized to the peroxisomes and

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cytosol.51 peroxisomes are also connected with glutathione metabolism. Glutathione S

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transferase Kappa 1 (GSTK1) was generated in the process of glutathione metabolism.

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GSTK1 is a kind of protein enzyme, belonging to GST gene family. Figure 6 shows

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that pyrimidodiazepine synthase (1.5.4.1) and ornithine decarboxylase (4.1.1.17) are

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associated with the upregulated genes; The aminopeptidase N (3.4.11.2) and cytosol

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aminopeptidase (LAP3) are associated with the downregulated genes; The glutathione

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S-transferase (2.5.1.18) is associated with the upregulated and downregulated genes.

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Among molecular antioxidants, reduced/oxidized glutathione (GSH/GSSG) plays a

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crucial role in direct scavenging of radiation-induced ROS or as cofactor for GR and

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peroxidase in the glutathione metabolism pathway.52 Glutathione peroxidase (GSH-Px)

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can catalyze GSH to GSSG. In conjunction with NADPH, glutathione reductase can

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catalyze GSSG to GSH (Fig. 6). GSH can adjust the synthesis of protein and

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ribonucleotide and is related to the antioxidant capacity of the body.53 GST can be

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considered a primary antioxidant enzyme as it can also remove hydroperoxides from

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cells.51 Molecular and enzymatic antioxidant systems are crucial in determining the

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survival of insects after heavy metal exposure. Some pathways of antioxidant defense

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identified here are reported for the first time in A. pernyi. Our results suggest that A.

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pernyi fat bodies have a strong antioxidant system that protects against heavy metal

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pollution.

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AUTHOR INFORMATION

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Corresponding Author

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B.P.T. and Q.N.L., E-mail: [email protected] (B.P.T.), [email protected],

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[email protected] (Q.N.L.), Tel/fax: +86 515 88233991

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Funding

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This work was supported by the National Natural Science Foundation of China

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(31640074 and 31672267), the Natural Science Foundation of Jiangsu Province

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(BK20160444), the Natural Science Research General Program of Jiangsu Provincial

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Higher Education Institutions (15KJB240002, 16KJA180008 and 12KJA180009), the

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Special Guide Fund Project of Agricultural Science and Technology Innovation of

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Yancheng city (YKN2014022), the Jiangsu Provincial Key Laboratory of Coastal

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Wetland

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JLCBE14006), and the Jiangsu Provincial Key Laboratory for Bioresources of Saline

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Soils (JKLBS2014013 and JKLBS2015004).

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Notes

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The authors declare no competing interests.

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Table 1. The quality of clean reads.

423

Sample

Clean Reads

Clean bases

GC (%)

Q30 (%)

PBS

25,795,510

7,662,720,366

43.12%

93.98%

Lead

34,909,205

10,351,801,068

43.95%

92.21%

Table 2. The distribution of splicing length.

424

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Page 20 of 32

Length Range

Transcript

Unigene

200-300

31,361(29.38%)

28,027(38.73%)

300-500

23,174(21.71%)

17,434(24.09%)

500-1000

21,887(20.50%)

13,202(18.24%)

1000-2000

16,671(15.62%)

7,870(10.88%)

2000+

13,656(12.79%)

5,833(8.06%)

Total Number

106,751

72,367

Total Length

101,755,331

53,447,744

N50 Length

1,779

1,336

Mean Length

953.20

738.57

425 426

Table 3. Summary statistics of the A. pernyi transcriptome annotation.

427 Annotated databases

Annotated_Number

Percentage (%)

9,002 12,602 10,001 14,961 17,204 12,123 23,914 22,620 26,426

34.06 47.69 37.85 56.61 65.10 45.88 90.49 85.60 100

COG_Annotation GO_Annotation KEGG_Annotation KOG_Annotation Pfam_Annotation Swissprot_Annotation eggNOG_Annotation NR_Annotation All_Annotated

300

Transcriptome-Wide Identification of Differentially Expressed Genes in

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