Figures
Abstract
Background
Chrysopa pallens (Rambur) are the most important natural enemies and predators of various agricultural pests. Understanding the sophisticated olfactory system in insect antennae is crucial for studying the physiological bases of olfaction and also could lead to effective applications of C. pallens in integrated pest management. However no transcriptome information is available for Neuroptera, and sequence data for C. pallens are scarce, so obtaining more sequence data is a priority for researchers on this species.
Results
To facilitate identifying sets of genes involved in olfaction, a normalized transcriptome of C. pallens was sequenced. A total of 104,603 contigs were obtained and assembled into 10,662 clusters and 39,734 singletons; 20,524 were annotated based on BLASTX analyses. A large number of candidate chemosensory genes were identified, including 14 odorant-binding proteins (OBPs), 22 chemosensory proteins (CSPs), 16 ionotropic receptors, 14 odorant receptors, and genes potentially involved in olfactory modulation. To better understand the OBPs, CSPs and cytochrome P450s, phylogenetic trees were constructed. In addition, 10 digital gene expression libraries of different tissues were constructed and gene expression profiles were compared among different tissues in males and females.
Conclusions
Our results provide a basis for exploring the mechanisms of chemoreception in C. pallens, as well as other insects. The evolutionary analyses in our study provide new insights into the differentiation and evolution of insect OBPs and CSPs. Our study provided large-scale sequence information for further studies in C. pallens.
Citation: Li Z-Q, Zhang S, Ma Y, Luo J-Y, Wang C-Y, Lv L-M, et al. (2013) First Transcriptome and Digital Gene Expression Analysis in Neuroptera with an Emphasis on Chemoreception Genes in Chrysopa pallens (Rambur). PLoS ONE 8(6): e67151. https://doi.org/10.1371/journal.pone.0067151
Editor: Walter S. Leal, University of California Davis, United States of America
Received: February 26, 2013; Accepted: May 14, 2013; Published: June 27, 2013
Copyright: © 2013 Li et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by grants from the Ministry of Agricultural (2011ZX08011-002), the Ministry of Science and Technology (2012BAD19B05), and the National Natural Science Foundation of China (31071978). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors declare that they have no competing interests.
Background
A sophisticated olfactory system is a key physiological trait for insect survival and reproduction. Olfaction is crucial to many behaviors, including feeding, mating, toxin avoidance, and negative taxis [1]. Previous studies have shown that the major olfactory-related proteins in insects are odorant-binding proteins (OBPs), odorant receptors (ORs), ionotropic receptors (IRs), sensory neuron membrane proteins (SNMPs) and odorant-degrading enzyme (ODEs) [2]. The process of insect olfaction can be generalized as having several main steps. First, odorants reaching the pore tubules enter a sensillum and are bound and solubilized by OBPs; then, the OBPs are transported through the sensillar lymph that fills the cavity around the dendrites, where they finally activate membrane-bound ORs [2]. After OR activation, the odorant will be rapidly removed by ODEs to restore the sensitivity of the sensory neuron [3], [4].
In addition, some other proteins are also thought to be involved in insect olfaction. Soluble chemosensory proteins (CSPs) are also found in abundance within the sensillar lymph. Similar to OBPs, CSPs are thought to affect insect chemoreception by enhancing the solubility of semiochemicals and delivering them to the chemosensory receptors [5]. SNMPs, located in the dendritic membranes of pheromone sensitive neurons [4], [6], appear to trigger ligand delivery to the olfactory receptor [7]. To maintain olfactory system sensitivity, ODEs then remove the odorant. Carboxylesterases (CXEs) and aldehyde oxidases (AOXs), which degrade ester and aldehyde pheromones [8], [9], were the first identified ODEs in insects [10], [11]. Additionally, P450s play important roles in many physiological functions in insects, such as signal molecule metabolism, adaptation to host plants, and insecticide resistance. However, some reports have revealed that P450s may be involved in odorant processing in antennae [12]–[14].
Most Neuroptera are natural enemies of agroecosystem pests. The lacewing Chrysopa pallens (Rambur) (Neuroptera: Chrysopidae) are the most important natural predators of various pests such as aphids, coccids, thrips, mites, and caterpillars [15]. The larvae of C. pallens are called aphid-lions because each can eat more than 800 aphids during its larval stage. C. pallens are voracious predators that adapt well to different agroecosystems [16]–[18], so they are very useful auxiliaries in integrated pest management (IPM). Mass production and release of C. pallens in fields is economically unfeasible compared with recruitment of existing individuals in the environment. Based on the feeding habits of adult lacewings, several plant-derived compounds were suggested as attractants [19]–[22]. Recently, individual aphid sex pheromone compounds (enantiomers of nepetalactol and nepetalactone) were shown to attract predatory males of Chrysopa spp. and can potentially be used to enhance biological control of aphids [23]. Furthermore, field attraction of Chrysopa lacewings to (1R, 4aS, 7S, 7aR)-nepetalactol has been reported [24], [25].
To enhance the effectiveness of C. pallens in IPM via tools such as the “push-pull” strategy, in which combinations of repellent and attractive stimuli are used to alter the populations of insect pests and natural enemies [26], research about olfactory behavior, including hunting, mating, and oviposition, is essential. Discovery of chemoreception genes is the primary step for exploring the mechanism underlying insect olfactory behavior. Unfortunately, sequence data from C. pallens is scarce. Therefore, more sequence information and transcriptome analyses could serve as valuable molecular resources leading ultimately to more effective use of C. pallens in pest control and contributing to the understanding of co-evolution between C. pallens and its prey.
Deep sequencing data can provide extensive information about genomes and gene expression profiling via next-generation high-throughput techniques. For example, Illumina technology have proven to be an efficient and low cost method to find genes in many insects, such as Spodoptera littoralis, Manduca sexta, Helicoverpa armigera, Apis cerana cerana, Cnaphalocrosis medinalis, and Nilaparvata lugens [27]–[33]. High-throughput sequencing has been successfully used to identify olfactory genes in Lepidoptera [30], [34]–[36], but no transcriptome information has been reported from Neuroptera.
Given the lack of sequence information for C. pallens, we constructed a whole-body cDNA library and obtained 50,396 distinct unigenes by Illumina RNA sequencing and sequence assembly. In total, 14 OBPs, 22 CSPs, 60 P450s, 16 IRs, 14 ORs, 2 SNMPs, 54 CXEs and 21 AOXs were obtained. Furthermore, 10 digital gene expression (DGE) libraries were constructed and the gene expression profiles were compared among different tissues.
Results
Illumina Sequencing and Sequence Assembly
A library of C. pallens was constructed by Illumina sequencing. A total of 58,580,430 raw reads were obtained from the sample. After removing low-quality, adaptor, and contaminating sequences, 54,331,274 clean reads (Q20 = 98.87%) with an accumulated length of 4,889,814,660 nucleotides (nt) were generated and assembled into 104,603 contigs with an N50 length of 567 nt. Using paired-ends reads, these contigs were further assembled into 50,396 distinct unigenes, including 10,662 clusters and 39,734 singletons, with a mean length of 722 nt and N50 of 1142 bp (Table 1). The size distribution indicated that the 10,107 unigenes were longer than 1000 bp (Fig. 1).
All unigene sizes were calculated. The X-axis shows the length (nt) of unigenes. The Y-axis shows the number of unigenes.
Gene Identification and Functional Annotation
For annotation, all of 50,396 distinct unigenes longer than 200 bp were analysised by searching against NR, Swiss-Prot, KEGG, COG, and GO databases using BLASTX with a cut-off e-value of 10−5. A total of 21,644 unigenes (42.96%) returned a BLAST result; 20,524, 16,967, 14,938, 8,154, and 6,209 were annotated by NR, Swiss-Prot, KEGG, COG, and GO, respectively. A much higher cut-off e-value of 10−20 were also used to search against NR, Swiss-Prot, KEGG and COG databases by BLASTX, and 15584, 12051, 10109 and 3774 unigenes were annotated, respectively.
GO annotation was used to classify the function of the C. pallens unigenes. Among the distinct unigenes, 6,209 (12.33%) corresponded to at least one GO term. A total of 14,790, 8,609, and 5,706 unigenes were classified as being involved in the categories of biological process, cellular component, and molecular function, respectively. Within biological process, cellular process and metabolic process represented the most abundant GO terms. Most unigenes that corresponded to cellular component were involved in cell and cell part. Binding and catalytic activity were most prevalent in of molecular function (Fig. 2).
The X-axis shows three categories and their subcategories. The Y-axis shows the percentage of sequences. The analysis level was 3.
To further evaluate the functions of the unigenes, COG annotation was used. A total of 8,154 of the unigenes were distributed into 25 COG classifications. Among these, the cluster “general function prediction only” was the biggest group. The clusters “replication, recombination and repair”, “transcription” and “translation, ribosomal structure and biogenesis” were also enriched (Fig. S1). The species distribution of unigenes was annotated with the NR protein database. The C. pallens sequences revealed substantial (35.34%) matches with Tribolium castaneum (Fig. 3).
Unigenes were searched against the NR protein database using BLASTX with a cutoff e-value <10−5 and the proportions of each species (represented by different colors) graphed. Species with proportions of more than 1% are shown.
We identified 14 candidate OBPs, 22 candidate CSPs, 60 candidate P450s, 16 candidate IRs, 14 candidate ORs, 2 candidate SNMPs, 54 candidate CXEs, and 21 candidate AOXs with BLASTX and BLASTN (Table S1).
Additionally, 36 putative chemoreception genes, including 14 OBPs and 22 CSPs, were used to confirm transcriptome assemblies by sequencing their PCR products. The sequences obtained from positive clones had ≥99% identities at the nucleic acid level with corresponding sequences from the transcriptome, indicating that unigene assemblies were adequate.
Sequence Alignment and Phylogenetic Analyses
OBP genes.
All unigenes were searched by BLASTX, 14 distinct unigenes encoding OBPs were identified from the C. pallens transcriptome. Based on sequence analysis, 10 sequences contained a full length open reading frame with a predicted signal peptide sequence (Fig. 4A). These 14 genes were used to construct a phylogenetic tree with other OBPs from five species (Fig. 5). The tree revealed that all the candidate OBP sequences were distributed among orthologous groups. Three of them showed higher homology to ApisOBPs.
(A): Alignment of amino acid sequences of the candidate OBPs. (B): Alignment of amino acid sequences of the candidate CSPs. Predicted signal peptides are boxed, and the conserved cysteines are highlighted in blue. Sequences which contained a full length open reading frame were labeled by red line.
Tcas: Tribolium castaneum, Dmel: Drosophila melanogaster, Bmor: Bombyx mori, Apis: Acyrthosiphon pisum, Amel: Apis mellifera. The C. pallens unigenes were labeled with ‘CL’ (clusters) and ‘U’ (unigenes). CL-X: CL-contigX.
CSP genes.
Sequence annotation led to the identification of 22 different candidate CSPs. Fifteen were predicted to have full lengths with signal peptides by sequence analysis (Fig. 4B). These 22 candidate CSPs were phylogenetically analyzed with data from five other species. Most CSPs from the same species formed monophyletic groups, unlike the OBPs. Nevertheless, eight CSPs representing five of the six species clustered together with a bootstrap percentage >50 (Fig. 6).
Tcas: Tribolium castaneum, Dmel: Drosophila melanogaster, Bmor: Bombyx mori, Apis: Acyrthosiphon pisum, Amel: Apis mellifera. The C. pallens unigenes were labeled with ‘CL’ (clusters) and ‘U’ (unigenes). CL-X: CL-contigX. Arrow means 8 CSPs representing five of the six species clustered together with a bootstrap percentage >50.
P450 genes.
The 134 putative P450 sequences in the C. pallens transcriptome assembly were identified based on similarity to known insect P450s. Forty-seven P450s (containing 28 full-length sequences) more than 220 amino acids in length were used to construct a phylogenetic tree with 82 B. mori P450s (Fig. 7). The 47 predicted P450 belonged to four major clades of P450s [37], namely the CYP2, CYP3, CYP4, and mitochondrial clades. Most belonged to CYP3 (22 candidates) and CYP4 (26).
The C. pallens unigenes were labeled with ‘CL’ (clusters) and ‘U’ (unigenes). Bmor: B. mori. CL-X: CL-contigX.
DGE Library Sequencing
Ten DGE libraries of C. pallens including, female antennae (FA), female heads without antennae (FH), female legs (FL), female wings (FW), female thoraxes and abdomens (FB), male antennae (MA), male heads without antennae (MH), male legs (ML), male wings (MW), and male thoraxes and abdomen (MB), were constructed to investigate the expression profiles of the unigenes in these tissues. In total, 5.76, 5.65, 6.25, 5.82, 5.80, 5.61, 5.28, 5.27, 5.80, and 5.63 million raw tags were generated in each library. The percentage of clean tags among the raw tags was more than 95% in each library (Fig. S2), and the percentages of clean tags that could be mapped to unigenes ranged from 69.05–79.15% (Table 2).
To evaluate the DGE data, we analyzed the distribution of clean tags in each library. Genes with more than 100 copies constituted more than 77.94% of the clean tags but less than 7.04% of the genes (Figs. S3 and S4). In contrast, genes with low expression levels (fewer than five copies) represented many distinct clean tags in each library.
Differentially Expressed Genes between Tissues
In this study, pair-wise comparison of each tissue within each sex against all the other tissues, respectively, was used to determine the gene expression. Compared with other tissues of each sex, between 439 and 1030 unigenes (females, 439–1030; males, 552–1026) were up-regulated in antennae (Table 3, Fig. 8). Among the 10 most up-regulated unigenes in these eight comparisons (after duplicates were removed), only 11 genes were up-regulated: seven olfactory-related genes (OBPs and CSPs), a luciferase, a P450, an alpha-amylase, and an extradiol ring-cleavage dioxygenase (Table S2). According to the GO classification, most of the gene sets that were up-regulated were related to transport. Most of the olfactory and circadian genes were up-regulated in antennae, the center of olfaction.
(A) Numbers of up- and down-regulated unigenes in each pair-wise comparison of tissues. (B) Numbers of up- and down-regulated unigenes compared between female and male tissues. FA: female antennae, FH: female heads without antennae, FL: female legs, FW: female wings, FB: female thoraxes and abdomens, MA: male antennae, MH: male heads without antennae, ML: male legs, MW: male wings, MB: male thoraxes and abdomen.
When body (thorax and abdomen) tissues were compared with other tissues of each sex, between 547 and 3367 (females, 2152–3367; males, 547–1177) unigenes were up-regulated (Table 3, Fig. 8). Among the top 10 up-regulated unigenes in the comparisons, 11 genes, including a eupolytin, a vitellogenin receptor, two enteropeptidases, two hemolymph lipopolysaccharide-binding proteins, two chymotrypsins, a lysozyme, and three cellular FABPs were identified (Table S3). Most of the up-regulated genes were involved in the GO metabolic process. Most of the genes related to metabolic process and digestion were more abundant in the body.
In the comparative analysis between the heads and other tissues of each sex, a range from 658 to 1532 (females, 726–1532; males, 658–1163) unigenes were up-regulated (Table 3, Fig. 8). Fifteen genes were identified from the top 10 up-regulated unigenes in the comparisons: a serine proteinase inhibitor, three glucose dehydrogenases, a flagelliform silk protein, a calmodulin-binding protein, two lysozymes, a chymotrypsin, a venom allergen, an ultraviolet-sensitive opsin, two cuticular proteins, a P450, and a transient receptor potential channel (Table S4). Base on the GO functional classifications, most gene sets were correlated to gene expression and transport.
In legs, as compared with other tissues of each sex, as few as 208 and as many as 1005 (females, 208–775; males, 321–1005) unigenes had significantly higher expressions (Table 3, Fig. 8). Among the top 10 up-regulated unigenes in the comparisons, 25 unigenes were identified: three glucose dehydrogenases, a 4-hydroxyphenylpyruvate dioxygenase, a serine proteinase inhibitor, a trimeric intracellular cation channel, a CSP, two cuticular proteins, a facilitated trehalose transporter, two pro-phenol oxidases, a long chain fatty acids protein, a calcium-independent phospholipase, an ionotropic glutamate receptor, a P450, a vitellogenin, a allantoicase, a ultraviolet-sensitive opsin, a 1,6-bisphosphate aldolase, a progestin and adipo Q receptor, a chitin deacetylase, a cellular FABP-like protein, and a glutamate receptor interacting protein (Table S5). Most of the unigene sets enriched in GO process were involved in metabolic processes. Some of these up-regulated genes were related to the respiratory electron transport chain, which may be involved in providing energy for movement.
Differences in the gene-expression profiles between wings and other tissues of each sex, from 608 to 1442 (females, 608–859; males, 664–1442) unigenes were notably up-regulated (Table 3, Fig. 8). An analysis of the top 10 up-regulated unigenes in the comparisons found that nine unigenes had defined functions, including two P450s, one nose resistant to fluoxetine protein, two cuticular proteins, a lysozyme, a polyprotein, a glucose dehydrogenase, and an extradiol ring-cleavage dioxygenase (Table S6). Most of up-regulated unigenes were related to gene expression and metabolic process in females and to transport and metabolic process in males.
Equivalent tissues were also compared between females and males (Table. 3, Fig. 8). Comparing MA and FA, two signal transduction genes, calmodulin-binding protein and G-protein coupled receptor, were up-regulated in the FA library, and an OBP gene was up-regulated in MA. Two CSPs gene were up-regulated in FW relative to MW (Table S7 and S8).
Quantitative Real-time PCR Validation
According to the DGE data, most of the OBPs and all of OR were abundant mainly in antennae. Among the 14 OBP genes, all had more than 10 TPMs (transcripts per million clean tags). We examined the relative expressions of these 14 OBP genes. The qRT-PCR results for these genes were consistent with the DGE results (Fig. 9).
(A) Gene expression analysis of 14 OBP genes for which DGE data were available. Blue, DGE data; red, qRT-PCR results. (B) Gene expression analysis of four OR genes. The relative expression levels were normalized by GTP-binding protein (U15543) and ribosomal protein (U29931) Error bars represent standard error.
By comparing the tissues between females and males, three (CL961-1, U6888, and U81231) had antennal specific expressions and seven (CL979-2, U15134, U15188, U21061, U66755, and U79506) were enriched in antennae. U29002 and U29658showed broad expression profiles with higher expression levels in legs. Interesting, two OBPs displayed head enrichment.
Discussion
C. pallens is one of the most important natural enemies of insect pests in China. Until now, no transcriptome information has been available for Neuroptera and sequence data for C. pallens were scarce, so more data was a research priority for investigating gene function in this species. In this study, a reference transcriptome was completed, yielding 4.89 Gb of transcriptome data and 56.85 Mb of gene expression tags, using next-generation sequencing technology. To our knowledge, this is the first report of whole transcriptome and DGE profile information for C. pallens and could serve as a valuable resource leading ultimately to more effective application of this insect in pest control.
Transcriptome de novo assembly was carried out with short reads because of the lack of C. pallens genome sequences. In this study, the N50 of the unigenes was 1,142 bp, much higher than in other studies [27], [32], suggesting high quality sequencing and assembly. In the transcriptome annotation, only 40.73% of 50,396 unigenes had matches in the NR database, and 12.32% could be annotated to one or more GO terms, indicating that large numbers of the unigenes were either non-coding or specific to C. pallens.
Using BLASTX annotation of the C. pallens transcriptome, we found that C. pallens shared more similarity with T. castaneum than with the other species examined, with a significant percentage (35.34%) of highest-similarity sequences. There were 28,690 protein sequences for Anopheles gambiae and 27,406 for T. castaneum, much more than for the other species. Nevertheless, only 4.37% of the A. gambiae were most homologous, much lower than the value for T. castaneum. The similarity between C. pallens and T. castaneum transcriptomes reflected the relatively close phylogenetic relationship between their orders (Neuroptera and Coleoptera).
To analyze the gene expression information, 10 DGE libraries of different tissues were constructed. Most of the differentially-expressed genes were up-regulated in FB. The majority of up-regulated genes were involved in metabolic processes, suggesting that the thorax and abdomen have enhanced metabolic activities, perhaps because these tagmata are the most important sites of digestion, circulation, and reproduction. Compared with other tissues, the period, timeless, and clock genes, which are involved in circadian rhythms, were more up-regulated in heads, antennae and, particularly, in wings, which are the most important organs for circadian rhythms in C. pallens [38]–[40].
The insect olfactory system is a highly specific and sensitive chemical detector essential for feeding, mating, and finding oviposition sites. To better understand the sophisticated olfactory system of C. pallens, 66 putative chemoreception genes(including OBPs, CSPs, ORs, IRs, and SNMPs) were identified from our transcriptome. This number is more than the numbers found in Cnaphalocrosis medinalis (22) [41] and Cotesia vestalis (28) [42], but less than that of Spodoptera littoralis (82) [30], Manduca sexta (94) [43] and Helicoverpa armigera (99) [34].
OBPs are thought to aid in capture and transport of odorants and pheromones to chemoreceptors [44], [45], which were discovered in the early 1980s in the giant moth Antheraea polyphemus [10]. On the basis of BLASTX results, all 14 candidate OBPs were classified into two groups: 12 OBPs and 2 PBPs. Base on the OBP sequences, where only the cysteine motif defines the relatedness, insect OBPs have been further grouped into: Classic OBPs (with 6 conserved cysteines), Plus-C OBPs (with more than six conserved cysteines) and Minus-C (with only four conserved cysteines) [1], [46]–[48]. According to DGE and qRT-PCR results (Fig 9 and Table S1), OBPs and ORs were abundant mainly in antennae, consistent with studies on OBPs and ORs in other insects 46,49. Three of 14 OBPs, which had antennae-specific expressions, may play important roles in pheromone detection. Interestingly, two putative OBPs (U29002 and U29658) were expressed at very high levels in legs, and two OBPs showed head enrichment. This high expression might suggest the unique functions in chemoreception.
As other soluble secreted proteins, CSPs are also found in the sensillum lymph, but their roles in olfaction remain elusive. Insect CSPs were also known as OS-D-like proteins [48] or sensory appendage proteins (SAPs) [35] before being named CSPs [47]. In our study, most of the CSPs showed broad expression profiles which were consistent with previous studies on these genes in other insects [50]–[52]. In brief, antennae-enriched OBPs and CSPs may be involved in C. pallens identifying and binding volatile from pests, compatriots or pest-damaged plants.
Antennae-restricted expression is a useful criterion to identify genes involved in specific olfactory functions, including ODEs. Because the sequences of these transcripts were so short in the transcriptome, most have not been detected by DGE. Interestingly, 12 of the 60 P450s were clearly enriched in or restricted to antennae, suggesting an ODE function. In addition, CL147-6 and U30217 were related to the CYP4A subfamily of P450s (Fig. 7). Several genes in this family are olfaction specific and also expressed in olfactory sensilla [53].
To better understand the OBPs and CSPs, phylogenetic trees of each of these genes were constructed. Three antennae enriched candidate OBPs showed higher homology to A. pisum OBPs (Fig. 5 and Fig. 9). Because aphids are the major prey of C. pallens, the higher homology of candidate OBPs between C. pallens and A. pisum OBPs may play an important role in hunting. In the CSPs phylogenetic tree, most of the CSPs from the same species formed a clade. Nevertheless, eight CSPs from five of the six species clustered together (Fig. 6). This high conservation of CSPs may indicate the multiple copies formed after these insect orders diverged from one another and that these proteins may have crucial functions. Competitive fluorescence binding assays, fluorescent in situ hybridization, RNAi, and behavioral bioassays may be used to characterize these olfaction-related genes in further studies [54], [55].
The DGE results were consistent with the major functions of different tissues, most of the olfaction genes were up-regulated in male and female antennae, the center of olfaction. The antennae-enriched P450s may play roles in digesting poisonous odors to protect the sensitivity of sensory neurons [53], [56]. Additionally, three insecticide-resistance-related genes, including two P450s, were enriched in wings, indicating that wings may play an important role in insecticide resistance. Some genes related to energy generation and transmission, three glucose dehydrogenases, an ionotropic glutamate receptor, and a facilitated trehalose transporter, were up-regulated in legs, consistent with their important function in movement.
Conclusions
Using next-generation sequencing technology, we provided large-scale sequence information for C. pallens and identified 14 OBPs, 22 CSPs, 60 P450s, 16 IRs, 14 ORs, 2 SNMPs, 54 CXEs, and 21 AOXs. This large number of insect chemosensory genes will provide the basis for functional studies. Together with the evolutionary analysis, the results provide new insights into the differentiation and evolution of insect OBPs and CSPs, as well as data for further studies on non-olfaction genes in C. pallens and other Neuropterans. Understanding how structure, function, and gene expression interact in the olfactory systems of predatory insects can inform the development of new semiochemical tools that will improve biological control in sustainable agriculture.
Methods
Insects
The C. pallens used in this experiment were provided by the Plant Protect & Environment Protect Research Institute, Beijing Academy of Agriculture and Forestry Sciences, China. Experimental insects were the offspring of a single female and reared in the laboratory on Acyrthosiphon pisum. Rearing conditions were 25±1°C, a 14∶10 h light/dark (L:D) photoperiod, and 65±5% relative humidity (RH). Pupae were kept in separate cages for eclosion, after which adults were supplied with A. pisum. Thirty 3-day-old virgin females and males, respectively, were used to collect female antennae (FA), heads (FH), wings (FW), legs (FL) and thoraxes and abdomens (FB) and male antennae (MA), heads (MH), wings (MW), legs (ML) and thoraxes and abdomens (MB). Tissue samples were kept at −80°C until RNA isolation.
cDNA Library Preparation and Illumina Sequencing for Transcriptomes
Total RNA was extracted by SV Total Isolation System (Promega, Madison, WI, USA) following the manufacturer’s instructions. cDNA library construction and Illumina sequencing of the samples were performed at Beijing Genomics Institute – Shenzhen, Shenzhen, China [57]. Briefly, oligo(dT) beads were used to isolate poly (A) mRNA from 20 µg of pooled total RNA(FA : FH : FW : FL : FB : MA : MH : MW : ML : MB at 5∶1:1∶1:1∶5:1∶1:1∶1 ratio). To interrupt mRNA into short fragments, fragmentation buffer and divalent cations were used at 94°C for 5 min. Using these short fragments as templates, random hexamer-primers were used to synthesize first-strand cDNA. Second-strand cDNA was generated using buffer, dNTPs, RNAseH, and DNA polymerase I. After end-repair and ligation of adaptors, the products were amplified by PCR and purified with QIAquick PCR extraction kit (Qiagen, Venlo, Netherlands) and resolved with EB buffer for end reparation and adding poly (A). Then, the short fragments connected to sequencing adapters and detected by agarose gel electrophoresis were selected as templates for PCR amplification and sequencing using Illumina HiSeq™ 2000 (San Diego, CA, USA).
Assembly and Function Annotation
Transcriptome de novo assembly was carried out with the short-read assembly program Trinity [58], which generated two classes of unigenes: clusters (prefix CL) and singletons (prefix unigene). Finally, unigenes larger than 150 bp were first aligned by BLASTX to protein databases including Nr, Swiss-Prot, KEGG and COG (e-value<10−5) and by BLASTN to the NCBI nucleotide databases (Nt; e-value <10−5) to retrieve proteins with the highest sequence similarity with the given unigenes along with their protein functional annotations.
To validate the assembly results, 14 OBPs and 22 CSPs were confirmed by end-to-end RT-PCR using specific primers designed using Primer Premier 5.0 (Table S9). The PCR products were purified using the Wizard® SV Gel and PCR Clean-Up System (Promega) and then subcloned into a T/A plasmid using the pEASY-T3 cloning vector system (TransGene, Beijing, China) following manufacturer’s instructions. In this study, the clean read and computationally assembled sequences were submitted and available from the NCBI/SRA data base and NCBI/TSA repository (SRA experiment accession number: SRX219870, TSA accession number: GAGF01000001 - GAGF01050315, BioProject accession number: PRJNA186574).
Sequence Alignment and Phylogenetic Analysis
The amino acid sequence alignment of the candidate OBPs and CSPs were performed using CLUSTALX 2.0 [59] and then arranged by Jalview 2.4.0 b2 [60]. The 14 OBP, 22 CSP, and 47 P450 conceptually-translated sequences from the C. pallens transcriptome, along with OBPs, CSPs, and P450s from other insect species, were used to construct three phylogenetic trees based on the amino sequences. The OBP data set contained OBPs from five other insect species (46 from T. castaneum, 46 from Drosophila melanogaster, 43 from Bombyx mori, 13 from Acyrthosiphon pisum, and 27 from Apis mellifera) (Table S10). The CSP data set contained CSPs from five other insect species (20 from T. castaneum, 19 from D. melanogaster, 16 from B. mori, 6 from A. pisum, and 6 from A. mellifera) (Table S11). The signal peptide predicted by SignalIP 4.0 (http://www.cbs.dtu.dk/services/SignalP/) was deleted from the amino acid sequences of OBPs and CSPs before phylogenetic analyses. The P450 data set contained 82 P450 sequences from B. mori [14]. Amino acid sequences for each protein were aligned using ClustalX 1.83 [61]. Neighbor-joining trees were produced using MEGA5 [62] with Poisson correction of distances, and 1000 neighbor-joining bootstrap replicates were performed.
DGE Library Preparation and Sequencing
Six µg total RNA were extracted from FA, FH, FL, FW, FB, MA, MH, ML, MW, and MB as described above, and DGE library preparation and sequencing followed previously-described protocols [32]. mRNA were purified by using Oligo (dT) magnetic beads adsorption, 5′ and 3′ ends of tags were generated by endonuclease MmeI, NlaIII and DpnII (Table S12). Illumina adaptors(sense: 5′ACACTCTTTCCCTACACGACGCTCTTCCGATC3′ and 5′GATCGGAAGAGCGGTTCAGCAGGAATGCCGAG3′) were ligated to the sticky 5′ and 3′ ends of tags, respectively. At last, the short fragments connected to sequencing adapters were selected as templates for PCR amplification and sequencing using Illumina HiSeq™ 2000.
Analysis and Gene Expression Annotation of DGE Tags
DGE tag mapping followed previously-described protocols [32]. The raw sequences were filtered to clean tags, and were mapped to the transcriptome, used as reference sequences containing all the possible clean tags containing CATG and 17 bases length sequences of the reference gene sequences. The number of unambiguous clean tags for each gene was calculated and then normalized to TPM (number of transcripts per million clean tags) [63], [64].
Screening of Differentially Expressed Genes
A rigorous algorithm was developed to identify differentially expressed genes (DEGs) between the different tissues of C. pallens, referring to the method described previously [65]. P Value corresponds to differential gene expression test. False discovery rate (FDR) was used to determine the threshold of P value in multiple tests and analysis through manipulating the FDR value. We use FDR ≤0.001 and the absolute value of log2 ratio ≥1 as the threshold to judge the significance of gene expression difference [66].
GO Functional Enrichment Analysis for DGEs
In gene expression profiling analysis, GO enrichment analysis of functional significance followed previously-described protocols [32]: applies hypergeometric test to map all differentially expressed genes to terms in GO database, looking for significantly enriched GO terms in DEGs comparing to the genome background. The calculating formula is:
In this equation, N is the number of all genes with GO annotation; n is the number of DEGs in N; M is the number of all genes that are annotated to the certain GO terms; m is the number of DEGs in M. For GO enrichment analysis, all of the P values were calculated with Bonferroni correction. We selected a corrected P value ≤0.05 as a threshold to determine significant enrichment of the gene sets.
Quantitative Real-time PCR Validation
Total RNA was extracted as described above. The concentration of each RNA sample was standardized to 0.5 µg/µL for reverse transcription. cDNAs were synthesized using Reverse Transcription System (Promega) according to the manufacturer’s protocol. The results were normalized by the internal controls GTP-binding protein (U15543) and ribosomal protein (U29931). qRT-PCR primers were designed based on the nucleotide sequences of the chosen unigenes using Beacon Designer 7.7. Primers and unigenes used in qRT-PCR are listed Table S13. qRT-PCR reactions were run in triplicate (technical repeats) with three independent biological replicates. The quantitative validation was analyzed by a relative quantitative method (2–△△CT) [67].
Supporting Information
Figure S1.
COG classification of Chrysopa pallens unigenes. This figure shows the COG classifications of the unigene BLASTX results against the COG database. The X-axis shows the function class of unigenes. The Y-axis shows the number of unigenes.
https://doi.org/10.1371/journal.pone.0067151.s001
(TIF)
Figure S2.
Different components of the raw tags in each Chrysopa pallens tissue sample. The percentages of tags containing Ns, only adaptors, a tag copy number <2, and clean tags are shown. FA: female antennae, FH: female heads without antennae, FL: female legs, FW: female wings, FB: female thoraxes and abdomens, MA: male antennae, MH: male heads without antennae, ML: male legs, MW: male wings, MB: male thoraxes and abdomen.
https://doi.org/10.1371/journal.pone.0067151.s002
(TIF)
Figure S3.
Distribution of total and distinct clean tags in each female Chrysopa pallens sample. Numbers in square brackets indicate the copy number ranges for each tag category. Data in parentheses indicate the numbers and percentages of each category of tags. (A) Distribution of total clean tags. (B) Distribution of distinct clean tags.
https://doi.org/10.1371/journal.pone.0067151.s003
(TIF)
Figure S4.
Distribution of total and distinct clean tags in each male Chrysopa pallens sample. Numbers in square brackets indicate copy number ranges for each tag category. Data in parentheses indicate the numbers and percentages of each category of tags. (A) Distribution of total clean tags. (B) Distribution of distinct clean tags. FA: female antennae, FH: female heads without antennae, FL: female legs, FW: female wings, FB: female thoraxes and abdomens, MA: male antennae, MH: male heads without antennae, ML: male legs, MW: male wings, MB: male thoraxes and abdomen.
https://doi.org/10.1371/journal.pone.0067151.s004
(TIF)
Table S1.
BLASTX results and digital gene expression profiles of candidate OBPs, CSPs, P450s, IRs, ORs, CXEs and AOXs from Chrysopa pallens. Information includes gene ID in this transcriptome, open reading frame length, gene name, accession number, species, E-value, identity to other proteins, and number of transcripts per million clean tags (TPM).
https://doi.org/10.1371/journal.pone.0067151.s005
(XLSX)
Table S2.
Top 10 most up-regulated unigenes in antennae compared with other tissues, including gene name, expression ratio, GO process, and annotation result.
https://doi.org/10.1371/journal.pone.0067151.s006
(XLSX)
Table S3.
Top 10 most up-regulated unigenes in the thorax and abdomen compared with other tissues, including gene name, expression ratio, GO process, and annotation result.
https://doi.org/10.1371/journal.pone.0067151.s007
(XLSX)
Table S4.
Top 10 most up-regulated unigenes in heads compared with other tissues, including gene name, expression ratio, GO process, and annotation result.
https://doi.org/10.1371/journal.pone.0067151.s008
(XLSX)
Table S5.
Top 10 most up-regulated unigenes in legs compared with other tissues, including gene name, expression ratio, GO process, and annotation result.
https://doi.org/10.1371/journal.pone.0067151.s009
(XLSX)
Table S6.
Top 10 most up-regulated unigenes in wings compared with other tissues, including gene name, expression ratio, GO process, and annotation result.
https://doi.org/10.1371/journal.pone.0067151.s010
(XLSX)
Table S7.
Top 10 most up-regulated unigenes in male compared with female tissues, including gene name, expression ratio, GO process and annotation result.
https://doi.org/10.1371/journal.pone.0067151.s011
(XLSX)
Table S8.
Top 10 most up-regulated unigenes in female compared with male tissues, including gene name, expression ratio, GO process and annotation result.
https://doi.org/10.1371/journal.pone.0067151.s012
(XLSX)
Table S9.
Primers and unigenes used in end-to-end RT-PCR validation, including unigene names and primer sequences.
https://doi.org/10.1371/journal.pone.0067151.s013
(XLSX)
Table S10.
Odorant-binding proteins used in phylogenetic tree construction, including protein name and GenBank accession number.
https://doi.org/10.1371/journal.pone.0067151.s014
(DOCX)
Table S11.
Chemosensory proteins used in phylogenetic tree construction, including protein name and GenBank accession number.
https://doi.org/10.1371/journal.pone.0067151.s015
(DOCX)
Table S12.
Recognition sites of several endonucleases on cDNA in the sample prepared for the construction of DGE libraries.
https://doi.org/10.1371/journal.pone.0067151.s016
(DOCX)
Table S13.
Primers and unigenes used in quantitative real-time PCR validation, including unigene names and primer sequences.
https://doi.org/10.1371/journal.pone.0067151.s017
(XLSX)
Acknowledgments
We thank Yan-Nan Zhang (Nanjing Agricultural University, China) for useful discussion; Na Li (Institute of Cotton Research, Chinese Academy of Agricultural Sciences) for help with experiments; and Fan Zhang (Plant Protect & Environment Protect Research Institute, Beijing Academy of Agriculture and Forestry Sciences, China) for providing insects.
Author Contributions
Conceived and designed the experiments: ZL SD JC. Performed the experiments: ZL. Analyzed the data: SZ YM JL LL CW. Contributed reagents/materials/analysis tools: ZL SZ SD JC. Wrote the paper: ZL.
References
- 1.
Zhou JJ (2010) Odorant-binding proteins in insects. Vitamins and Hormones. 2010/09/14 ed. 241–272.
- 2. Leal WS (2012) Odorant reception in insects: roles of receptors,binding proteins, and degrading enzymes. Annual Review of Entomology 58: 373–463.
- 3.
Vogt RG (2004) Molecular Basis of Pheromone Detection in Insects.
- 4.
Vogt RG (2003) Biochemical diversity of odor detection: OBPs, ODEs and SNMPs. Insect Pheromone Biochemistry and Molecular Biology. 391–445.
- 5. Jacquin-Joly E, Vogt RG, Francois MC, Nagnan-Le Meillour P (2001) Functional and expression pattern analysis of chemosensory proteins expressed in antennae and pheromonal gland of Mamestra brassicae. Chemical Senses 26: 833–844.
- 6. Rogers ME, Krieger J, Vogt RG (2001) Antennal SNMPs (sensory neuron membrane proteins) of Lepidoptera define a unique family of invertebrate CD36-like proteins. J Neurobiol 49: 47–61.
- 7. Benton R, Vannice KS, Vosshall LB (2007) An essential role for a CD36-related receptor in pheromone detection in Drosophila. Nature 450: 289–293.
- 8. Ishida Y, Leal WS (2008) Chiral discrimination of the Japanese beetle sex pheromone and a behavioral antagonist by a pheromone-degrading enzyme. Proc Natl Acad Sci U S A 105: 9076–9080.
- 9. Durand N, Carot-Sans G, Bozzolan F, Rosell G, Siaussat D, et al. (2011) Degradation of pheromone and plant volatile components by a same odorant-degrading enzyme in the cotton leafworm, Spodoptera littoralis. PLoS One 6: e29147.
- 10. Vogt RG, Riddiford LM (1981) Pheromone binding and inactivation by moth antennae. Nature 293: 161–163.
- 11. Rybczynski R, Reagan J, Lerner MR (1989) A pheromone-degrading aldehyde oxidase in the antennae of the moth Manduca sexta. J Neurosci 9: 1341–1353.
- 12. Wojtasek H, Leal WS (1999) Degradation of an alkaloid pheromone from the pale-brown chafer, Phyllopertha diversa (Coleoptera: Scarabaeidae), by an insect olfactory cytochrome P450. FEBS Lett 458: 333–336.
- 13. Maïbèche-Coisne M, Merlin C, Francois MC, Queguiner I, Porcheron P, et al. (2004) Putative odorant-degrading esterase cDNA from the moth Mamestra brassicae: cloning and expression patterns in male and female antennae. Chem Senses 29: 381–390.
- 14. Ai J, Zhu Y, Duan J, Yu Q, Zhang G, et al. (2011) Genome-wide analysis of cytochrome P450 monooxygenase genes in the silkworm, Bombyx mori. Gene 480: 42–50.
- 15. Winterton S, de Freitas S (2006) Molecular phylogeny of the green lacewings (Neuroptera : Chrysopidae). Australian Journal of Entomology 45: 235–243.
- 16. Athhan R, Kaydan B, Ozgokce MS (2004) Feeding activity and life history characteristics of the generalist predator, Chrysoperla carnea (Neuroptera : Chrysopidae) at different prey densities. Journal of Pest Science 77: 17–21.
- 17. Medina P, Smagge G, Budia F, Tirry L, Vinuela E (2003) Toxicity and absorption of azadirachtin, diflubenzuron, pyriproxyfen, and tebufenozide after topical application in predatory larvae of Chrysoperla carnea (Neuroptera : Chrysopidae). Environmental Entomology 32: 196–203.
- 18. New TR (2002) Prospects for extending the use of Australian lacewings in biological control. Acta Zoologica Academiae Scientiarum Hungaricae 48: 209–216.
- 19. Junwei Zhu AAC, Obrycki JJ, Boo KS, Baker TC (1999) Olfactory Reactions of the Twelve-Spotted Lady Beetle, Coleomegilla maculata and the Green Lacewing, Chrysoperla carnea to Semiochemicals Released from Their Prey and Host Plant: Electroantennogram and Behavioral Responses. Journal of Chemical Ecology 25: 1163–1177.
- 20. Fint HMS, S S, Walters S (1979) Caryophyllene: an Attractant for the Green Lacewing. Environmental Entomology 8: 1123–1125.
- 21. Zhu J, Obrycki JJ, Ochieng SA, Baker TC, Pickett JA, et al. (2005) Attraction of two lacewing species to volatiles produced by host plants and aphid prey. Naturwissenschaften 92: 277–281.
- 22. Toth M, Szentkiralyi F, Vuts J, Letardi A, Tabilio MR, et al. (2009) Optimization of a phenylacetaldehyde-based attractant for common green lacewings (Chrysoperla carnea s.l.). J Chem Ecol 35: 449–458.
- 23. Koczor S, Szentkiralyi F, Birkett MA, Pickett JA, Voigt E, et al. (2010) Attraction of Chrysoperla carnea complex and Chrysopa spp. lacewings (Neuroptera: Chrysopidae) to aphid sex pheromone components and a synthetic blend of floral compounds in Hungary. Pest Manag Sci 66: 1374–1379.
- 24. Boo KS, Chung IB, Han KS, Pickett JA, Wadhams LJ (1998) Response of The Lacewing Chrysopa cognata to Pheromones of its Aphid Prey. Journal of Chemical Ecology 24: 631–643.
- 25. Zhang QH, Chauhan KR, Erbe EF, Vellore AR, Aldrich JR (2004) Semiochemistry of the goldeneyed lacewing Chrysopa oculata: attraction of males to a male-produced pheromone. J Chem Ecol 30: 1849–1870.
- 26. Cook SM, Khan ZR, Pickett JA (2007) The use of push-pull strategies in integrated pest management. Annu Rev Entomol 52: 375–400.
- 27. Wang ZL, Liu TT, Huang ZY, Wu XB, Yan WY, et al. (2012) Transcriptome Analysis of the Asian Honey Bee Apis cerana cerana. PLoS One 7: e47954.
- 28. Ferreiro MJ, Rodriguez-Ezpeleta N, Perez C, Hackenberg M, Aransay AM, et al. (2012) Whole transcriptome analysis of a reversible neurodegenerative process in Drosophila reveals potential neuroprotective genes. BMC Genomics 13: 483.
- 29. Doroszuk A, Jonker MJ, Pul N, Breit TM, Zwaan BJ (2012) Transcriptome analysis of a long-lived natural Drosophila variant: a prominent role of stress- and reproduction-genes in lifespan extension. BMC Genomics 13: 167.
- 30. Legeai F, Malpel S, Montagne N, Monsempes C, Cousserans F, et al. (2011) An Expressed Sequence Tag collection from the male antennae of the Noctuid moth Spodoptera littoralis: a resource for olfactory and pheromone detection research. BMC Genomics 12: 86.
- 31. Etebari K, Palfreyman RW, Schlipalius D, Nielsen LK, Glatz RV, et al. (2011) Deep sequencing-based transcriptome analysis of Plutella xylostella larvae parasitized by Diadegma semiclausum. BMC Genomics 12: 446.
- 32. Xue J, Bao YY, Li BL, Cheng YB, Peng ZY, et al. (2010) Transcriptome analysis of the brown planthopper Nilaparvata lugens. PLoS One 5: e14233.
- 33. David JP, Coissac E, Melodelima C, Poupardin R, Riaz MA, et al. (2010) Transcriptome response to pollutants and insecticides in the dengue vector Aedes aegypti using next-generation sequencing technology. BMC Genomics 11: 216.
- 34. Liu Y, Gu S, Zhang Y, Guo Y, Wang G (2012) Candidate Olfaction Genes Identified within the Helicoverpa armigera Antennal Transcriptome. PLoS One 7: e48260.
- 35. Robertson HM, Martos R, Sears CR, Todres EZ, Walden KK, et al. (1999) Diversity of odourant binding proteins revealed by an expressed sequence tag project on male Manduca sexta moth antennae. Insect Molecular Biology 8: 501–518.
- 36. Jordan MD, Stanley D, Marshall SD, De Silva D, Crowhurst RN, et al. (2008) Expressed sequence tags and proteomics of antennae from the tortricid moth, Epiphyas postvittana. Insect Mol Biol 17: 361–373.
- 37. Feyereisen R (2006) Evolution of insect P450. Biochem Soc Trans 34: 1252–1255.
- 38. Merlin C, Lucas P, Rochat D, Francois MC, Maibeche-Coisne M, et al. (2007) An antennal circadian clock and circadian rhythms in peripheral pheromone reception in the moth Spodoptera littoralis. J Biol Rhythms 22: 502–514.
- 39. Ito C, Goto SG, Shiga S, Tomioka K, Numata H (2008) Peripheral circadian clock for the cuticle deposition rhythm in Drosophila melanogaster. Proc Natl Acad Sci U S A 105: 8446–8451.
- 40. Plautz JD, Kaneko M, Hall JC, Kay SA (1997) Independent photoreceptive circadian clocks throughout Drosophila. Science 278: 1632–1635.
- 41. Li SW, Yang H, Liu YF, Liao QR, Du J, et al. (2012) Transcriptome and Gene Expression Analysis of the Rice Leaf Folder, Cnaphalocrosis medinalis. PLoS One 7: e47401.
- 42. Nishimura O, Brillada C, Yazawa S, Maffei ME, Arimura G (2012) Transcriptome Pyrosequencing of the Parasitoid Wasp Cotesia vestalis: Genes Involved in the Antennal Odorant-Sensory System. PLoS One 7: e50664.
- 43. Grosse-Wilde E, Kuebler LS, Bucks S, Vogel H, Wicher D, et al. (2011) Antennal transcriptome of Manduca sexta. Proceedings of the National Academy of Sciences of the United States of America 108: 7449–7454.
- 44. Pelosi P, Zhou JJ, Ban LP, Calvello M (2006) Soluble proteins in insect chemical communication. Cellular and Molecular Life Sciences 63: 1658–1676.
- 45. Wanner KW, Willis LG, Theilmann DA, Isman MB, Feng Q, et al. (2004) Analysis of the insect os-d-like gene family. J Chem Ecol 30: 889–911.
- 46. Gong DP, Zhang HJ, Zhao P, Xia QY, Xiang ZH (2009) The odorant binding protein gene family from the genome of silkworm, Bombyx mori. BMC Genomics 10: 332.
- 47. Angeli S, Ceron F, Scaloni A, Monti M, Monteforti G, et al. (1999) Purification, structural characterization, cloning and immunocytochemical localization of chemoreception proteins from Schistocerca gregaria. European Journal of Biochemistry 262: 745–754.
- 48. McKenna MP, Hekmat-Scafe DS, Gaines P, Carlson JR (1994) Putative Drosophila pheromone-binding proteins expressed in a subregion of the olfactory system. Journal of Biological Chemistry 269: 16340–16347.
- 49. Forêt S, Maleszka R (2006) Function and evolution of a gene family encoding odorant binding-like proteins in a social insect, the honey bee (Apis mellifera). Genome Research 16: 1404–1413.
- 50. Gong DP, Zhang HJ, Zhao P, Lin Y, Xia QY, et al. (2007) Identification and expression pattern of the chemosensory protein gene family in the silkworm, Bombyx mori. Insect Biochemistry and Molecular Biology 37: 266–277.
- 51. Liu X, Luo Q, Zhong G, Rizwan-Ul-Haq M, Hu M (2010) Molecular Characterization and Expression Pattern of Four Chemosensory Proteins from Diamondback Moth, Plutella xylostella (Lepidoptera: Plutellidae). J Biochem 148: 189–200.
- 52. Gu SH, Wang SY, Zhang XY, Ji P, Liu JT, et al. (2012) Functional characterizations of chemosensory proteins of the alfalfa plant bug Adelphocoris lineolatus indicate their involvement in host recognition. PLoS One 7: e42871.
- 53. Maibeche-Coisne M, Jacquin-Joly E, Francois MC, Nagnan-Le Meillour P (2002) cDNA cloning of biotransformation enzymes belonging to the cytochrome P450 family in the antennae of the noctuid moth Mamestra brassicae. Insect Mol Biol 11: 273–281.
- 54. He P, Zhang J, Liu NY, Zhang YN, Yang K, et al. (2011) Distinct expression profiles and different functions of odorant binding proteins in Nilaparvata lugens Stal. PLoS One 6: e28921.
- 55. Sun YL, Huang LQ, Pelosi P, Wang CZ (2012) Expression in antennae and reproductive organs suggests a dual role of an odorant-binding protein in two sibling Helicoverpa species. PLoS One 7: e30040.
- 56. Pottier MA, Bozzolan F, Chertemps T, Jacquin-Joly E, Lalouette L, et al. (2012) Cytochrome P450s and cytochrome P450 reductase in the olfactory organ of the cotton leafworm Spodoptera littoralis. Insect Mol Biol 21: 568–580.
- 57. Zhang GJ, Guo GW, Hu XD, Zhang Y, Li QY, et al. (2010) Deep RNA sequencing at single base-pair resolution reveals high complexity of the rice transcriptome. Genome Research 20: 646–654.
- 58. Grabherr MG, Haas BJ, Yassour M, Levin JZ, Thompson DA, et al. (2011) Full-length transcriptome assembly from RNA-Seq data without a reference genome. Nature Biotechnology 29: 644–U130.
- 59. Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan PA, et al. (2007) Clustal W and Clustal X version 2.0. Bioinformatics 23: 2947–2948.
- 60. Waterhouse AM, Procter JB, Martin DM, Clamp M, Barton GJ (2009) Jalview Version 2–a multiple sequence alignment editor and analysis workbench. Bioinformatics 25: 1189–1191.
- 61. Thompson JD, Gibson TJ, Plewniak F, Jeanmougin F, Higgins DG (1997) The CLUSTAL_X windows interface: flexible strategies for multiple sequence alignment aided by quality analysis tools. Nucleic Acids Res 25: 4876–4882.
- 62. Tamura K, Peterson D, Peterson N, Stecher G, Nei M, et al. (2011) MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Mol Biol Evol 28: 2731–2739.
- 63.
Hoen PAC, Ariyurek Y, Thygesen HH, Vreugdenhil E, Vossen RHAM, et al.. (2008) Deep sequencing-based expression analysis shows major advances in robustness, resolution and inter-lab portability over five microarray platforms. Nucleic Acids Research 36.
- 64. Morrissy AS, Morin RD, Delaney A, Zeng T, McDonald H, et al. (2009) Next-generation tag sequencing for cancer gene expression profiling. Genome Research 19: 1825–1835.
- 65. Audic S, Claverie JM (1997) The significance of digital gene expression profiles. Genome Research 7: 986–995.
- 66. Benjamini Y, Yekutieli D (2001) The control of the false discovery rate in multiple testing under dependency. Annals of Statistics 29: 1165–1188.
- 67. Pfaffl MW (2001) A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Research 29: e45.