Advertisement

BMC Genomics

, 19:970 | Cite as

Identification of putative fecundity-related gustatory receptor genes in the brown planthopper Nilaparvata lugens

  • Kui Kang
  • Pan Yang
  • Li-E Chen
  • Rui Pang
  • Lu-Jun Yu
  • Wen-Wu Zhou
  • Zeng-Rong Zhu
  • Wen-Qing Zhang
Open Access
Research article
  • 245 Downloads
Part of the following topical collections:
  1. Multicellular invertebrate genomics

Abstract

Background

The insect gustatory system plays a central role in the regulation of multiple physiological behaviors and the co-evolution between insects and their hosts. The gustatory receptors (Gr) are important to allow insects to sense their environment. It is critical to the selection of foods, mates and oviposition sites of insects. In this study, the Gr family genes of the brown planthopper (BPH) Nilaparvata lugens Stål (Hemiptera: Delphacidae) were identified and analyzed, and their potential relationship to the fecundity of BPH was explored by RNA interference (RNAi).

Results

We identified 32 putative Gr genes by analyzing transcriptome and genome data from BPH. Most of these Gr proteins have the typical structure of seven transmembrane domains. The BPH Gr genes (NlGrs) were expressed in virtually all tissues and stages, whilst higher transcript accumulations were found in adult stages and in the midguts of females. Based on the phylogenic analysis, we classified NlGrs into five potential categories, including 2 sugar receptors, 2 Gr43a-like receptors, 7 CO2 receptors, 5 bitter receptors and 13 NlGrs with unknown functions. Moreover, we found that 10 NlGrs have at least two alternative splicing variants, and obtained alternative splicing isoforms of 5 NlGrs. Finally, RNAi of 29 NlGrs showed that 27 of them are related to the transcript levels of two fecundity related genes vitellogenin and vitellogenin receptor.

Conclusions

We found 32 Gr genes in BPH, among which at least 27 are required for normal expression of fecundity markers of this insect pest. These findings provide the basis for the functional study of Grs and the exploration of potential genes involved in the monophagous character of BPH.

Keywords

Nilaparvata lugens Gustatory receptor Alternative splicing Fecundity 

Abbreviations

AKT

Protein kinase B

AMPK

Adenosine 5′-monophosphate (AMP)-activated protein kinase

BPH

The brown planthopper

cDNA

Complementary DNA

DGE

Digital gene expression

dsRNA

Double-stranded RNA

Gr

Gustatory receptor

GTF

Gene transfer format

JTT

Jones-Taylor-Tornton

NCBI

National Center for Biotechnology Information

Or

Odorant receptor

ORF

Open reading frame

PCR

Polymerase chain reaction

qRT-PCR

Quantitative real time RT-PCR

RACE

Rapid-amplification of cDNA ends

RNAi

RNA interference

SE

Standard error

tBLASTn

Translated basic local alignment search tool

Vg

Vitellogenin

VgR

Vitellogenin receptor

Background

Insects interact with their environment primarily through a sensitive chemosensory system that can detect and discriminate a diverse array of chemicals. This system plays critical roles in the survival and reproductive success of insects, mediating their behavioral responses to food, mates, and oviposition sites [1, 2]. Gustatory receptors (Grs), members of the chemosensory superfamily are mainly distributed in the chemosensory organs. They are responsible for distinguishing CO2 and non-volatile chemicals including the nutrients and toxins [3].

Insect Grs, with a typical seven transmembrane structure, were first identified in the Drosophila melanogaster genome based on a bioinformatic approach [4]. Further studies found that there are 68 gustatory receptor proteins in D. melanogaster, encoded by 60 gustatory receptor genes through alternative splicing [5, 6, 7]. Most gustatory receptor proteins are extraordinarily divergent, sharing only 8–12% sequence identity at the amino acid level. Some of this divergence could improve the diversity of Grs’ responses to ligands [7]. Based on the known ligands to which they respond, Grs were grouped into sugar receptors, CO2 receptors, Gr43a-like receptors, bitter receptors, sex pheromone receptors, and unknown receptors [8, 9, 10, 11, 12, 13]. With the development of genome sequencing in insects, insect Gr genes are identified in an increasing number of additional species: Anopheles gambiae has 52 Gr genes encoding 76 Gr proteins [14], and Aedes aegypti has 79 Gr genes encoding 114 Gr proteins [15]. Bombyx mori and Tribolium castaneum have 65 and 220 Gr genes, respectively [16, 17]. Helicoverpa armigera showed the second highest number (197) of Gr genes among all insect species studied [18].

Alternative pre-mRNA splicing greatly expands the proteome diversity within species by creating different combinations of exons from the same genomic loci [19], and has been most extensively studied in D. melanogaster [20]. Among sixty D. melanogaster Grs, three genes have alternatively spliced transcripts. Gr23a and Gr39a encoded two and four predicted proteins, respectively [4], and Gr28b encodes five predicted proteins [7]. Gr39a is a multifunctional receptor set, and alternative splicing is the mechanism for the generation of molecular forms responsible for different functions, such as pheromonal perception and host plant selection [21]. Gr28b.d, one alternatively spliced transcript of Gr28b, encoded a potential warmth sensor (Gr28B(D)) [22]. T. castaneum Gr214 is a massive alternatively spliced locus with 30 alternative long 5′ exons spliced into three shared 3′ exons encoding the C terminus [16]. The alternative splicing pattern of gustatory receptor has also been found in other insect species, such as A. aegypti [15] and B. mori [17]. However, the function of these alternatively spliced forms remains unclear.

The brown planthopper (BPH) Nilaparvata lugens (Hemiptera: Delphacidae) is one of the most devastating insect pests of rice [23]. Vitellogenin (Vg) and vitellogenin receptor (VgR) were always used as molecular markers of fecundity [24].Gustatory receptors may play an important role in the interaction of BPH and rice by detecting chemical compounds in rice. In this study, we identified 32 Grs in BPH based on transcriptome and genome data. Then we analyzed the alternative splicing patterns of all NlGrs, and found that 10 of them had alternative splicing events; further studies showed that alternative splicing could alter the protein structure of some NlGrs. Finally, we employed the RNA interference (RNAi) experiments and found that 27 NlGrs are required for normal expression of fecundity markers of this insect pest.

Results

Transcriptome sequencing and sequence assembly

We performed next-generation sequencing on a cDNA library constructed from the adult head, leg and midgut of BPH. All clean reads were aligned to the reference transcripts (generated in our laboratory, unpublished) and genome [25] of BPH, and identified approximately 47,000 genes with an N50 length of 2300 bp (see Additional file 1: Table S1). The mapping rate was approximately 75%, except in the midgut (57.9%), which contains a large number of enteric microorganisms (see Additional file 1: Table S1).

Annotation and identification of gr genes

To identify as many gustatory receptors as possible, three strategies were used. Firstly, we obtained 33 and 10 potential Gr sequences from the transcriptome data and genome data, respectively, using reciprocal hit of tBLASTn. 36 potential Gr sequences were identified after combining these sequences. The length of them was between 216 bp and 3921 bp, and most sequences were shorter than 1000 bp (see Additional file 2: Table S2). We then used pfam to build the second structure model of GR, PF02949, PF08395, and PF06151 and searched for the target sequences in the protein database. Using this method, we obtained 20 potential GR protein sequences, and 3 were identified as olfactory receptors via blast in NCBI; 13 were covered with previously potential sequences. After removing the sequence whose predicted protein length are less than 100 amino acids, only one new potential GR sequence was found, XLOC_018226:3–81 (see Additional file 3: Table S3). Finally, using a conserved sequences search, we obtained 21 potential GR protein sequences, all of which were covered with previously sequences. In summary, we obtained 37 potential Gr sequences using bioinformatic analyses.

To further determine whether these genes are Gr genes, we used 3′ rapid amplification of cDNA ends (3’ RACE) and 5’ RACE to obtain longer fragments. After sequencing, the result was tested by using BLAST and compared with the database of National Center for Biotechnology Information. The results showed that 6 potential sequences were not Grs and 4 were identical to each other. In conclusion, we identified 27 Grs in BPH. By combining the sequences with 10 previously reported gustatory receptors [25], a total of 32 NlGrs were identified. Detailed information of the identified and cloned Grs in BPH is available (Table 1).
Table 1

Nucleotide, amino acid, scaffold distribution, alternative splice variant and other information for the identified NlGrs

GENE

TMHMM

TMPred

HMMTOP

AA

Nucleic acids(bp)

Scaffold

Predicted alternative

splice variant number

NlGr1

8

8

8

436

1554

KN153269.1

0

NlGr2

7

8

9

461

1717

KN153837.1

2

NlGr3

7

8

8

440

1631

KN153837.1

0

NlGr4

4

4

4

265

798

KN153837.1

0

NlGr5

6

7

7

425

1670

KN152938.1

0

NlGr6

5

5

5

488

2188

KN152682.1

10

NlGr7

8

8

8

413

1605

KN153186.1

0

NlGr8

5

6

6

357

2176

KN153141.1

10

NlGr9

4

6

4

400

3216

 

10

NlGr10

7

8

8

471

2121

KN151984.1

2

NlGr11

7

7

8

466

1342

KN152173.1

0

NlGr12

6

6

6

339a

1785

KN152706.1

0

NlGr13

8

8

7

401

1625

KN152706.1

0

NlGr14

6

6

7

339

1536

KN152506.1

0

NlGr15

4

4

6

321

1604

KN152468.1

2

NlGr16

4

8

6

383

1149

KN152007.1

0

NlGr17

7

8

9

520

1561

KN152004.1

4

NlGr18

3

7

6

374

1308

KN153751.1

3

NlGr19

3

3

3

218a

1730

KN152376.1

4

NlGr20

6

7

7

449

2062

KN152775.1

2

NlGr21

7

6

8

352

1435

KN154548.1

0

NlGr22

6

6

5

370

1110

KN154035.1

0

NlGr23

7

7

6

398

1537

KN153338.1

2

NlGr24

7

7

7

379

1141

KN152091.1

0

NlGr25

3

3

3

218

1580

KN152147.1

0

NlGr26

5

5

5

429

1750

KN152989.1

3

NlGr27

0

0

0

0b

212

 

0

NlGr28

6

6

6

357

1071

KN154570.1

2

NlGr29

6

6

6

373

2032

KN152059.1

0

NlGr30

0

0

0

0b

416

 

0

NlGr31

3

3

3

153

1926

KN152885.1

0

NlGr32

0

0

0

131

394

KN154495.1

0

aHas more than one ORF regions, and the longest ORF was used for protein translation

bORF is incomplete

Phylogenetic analysis

The phylogenetic analysis result revealed that NlGr1–7 are potential members of the CO2 receptor subfamily (Fig. 1); NlGr8 and NlGr9 are potential members of the Gr43a-like (Drosophila) receptor subfamily [13]. NlGr10 and NlGr11 are potential members of the insect sugar receptor subfamily [26, 27]. NlGr12–NlGr14 are orthologs to the DmGr66a protein which is responsible for the recognition of various bitter compounds [3]. In addition, NlGr15-NlGr16 might belong to the narrowly tuned Grs required for some bitter compounds, as they are in the same branch as DmGr93a or DmGr33a [28]. Moreover, NlGr17- NlGr18 have a simple orthologous relationships with DmGr77a and other DmGrs, whilst their functions remain unclear [29]. The remaining 11 NlGrs do not have confident phylogenetic relationships with Grs from other insects (Fig. 1).
Fig. 1

Phylogenetic relationship of 29 Nilaparvata lugens gustatory receptors (NlGrs) and five published insect gustatory receptors. The reference sequence includes 68 Drosophila melanogaster Grs (DmGrs), 65 Bombyx mori Grs (BmGrs), 13 Apis mellifera Grs (AmGrs), 58 Aedes aegypti Grs (AgGrs), 62 Tribolium castaneum Grs (TcasGrs). DmGrs and NlGrs are highlighted with blue and red letters, respectively. The maximum-likelihood tree was calculated using default settings and the Jones-Taylor-Tornton (JTT) model with partial deletions and 2000 bootstrap replications

Expression profiles of NlGr genes

qRT-PCR was used to investigate the expression pattern of the Gr genes in different tissues and developmental stages of BPH. Across the developmental stages, some Grs have a higher transcript accumulation in 4th or 5th day old brachypterous female adult than other developmental stages, such as NlGr6, NlGr9, NlGr10, NlGr14, NlGr18, NlGr19, NlGr16 and NlGr25 (Fig. 2). Five Grs, including NlGr1, NlGr12, NlGr15, NlGr17, and NlGr20, have a higher mRNA expression in nymph stages (Fig. 2). In particular, NlGr12 was only detected in nymph stages, and NlGr29 showed the highest expression in 3rd instars nymphs. Moreover, all NlGr genes were found in the heads, legs, midgut, fat body or female ovaries (Fig. 3). 21 NlGr genes were expressed in multiple tissues. For example, among them, NlGr24 and NlGr32 were mainly detected in 3 tissues: the midgut, fat body and female ovaries. NlGr16 and NlGr21 were expressed in the midgut and fat body. Conversely, 11 NlGr genes were mainly expressed in specific tissues. For example, NlGr5, NlGr11, NlGr18, NlGr23, NlGr25, NlGr28, NlGr30, and NlGr31 were most highly expressed in the midgut. NlGr9 and NlGr14 were mainly expressed in the fat body. And NlGr15 was mainly expressed in the adult heads (Fig. 3). By analyzing the DEG data, the expression profiles of NlGrs in head, midgut and legs were similar to the qRT-PCR results.
Fig. 2

The expression pattern of NlGrs in different developmental stage. NlGrs expression at various stages including 1st-instar nymph (1st), 2nd-instar nymph (2nd), 3rd-instar nymph (3rd), 4th-instar nymph (4th), 5th-instar nymph (5th), and 1-day- to 5-day-old female adults (1d-5d); mRNA levels of 1st-instar nymphs were used as controls. Different color fonts represent different predicted subfamilies: red: CO2 receptor subfamily; green: Gr43a-like receptor subfamily; blue: sugar receptor subfamily; orange: bitter receptor subfamily; black: unknown functions receptor. All mRNA levels are normalized by the β-actin mRNA levels, and data are shown as the mean (n = 3)

Fig. 3

Tissue specific expression pattern of NlGrs. Expression of NlGrs in various female tissues including the heads (He), legs (Le), midgut (Mg), fat body (Fb) and ovaries (Ov); mRNA levels of legs were used as controls. Different color fonts represent different predicted subfamilies: red: CO2 receptor subfamily; green: Gr43a-like receptor subfamily; blue: sugar receptor subfamily; orange: bitter receptor subfamily; black: unknown functions receptor. All mRNA levels are normalized by the β-actin mRNA levels. Data are shown as the mean (n = 3)

Alternative splicing analysis of NlGrs

By assembling the transcriptome, a total of 70,361 transcripts were obtained. Among them, 49,930 transcripts were found to contain multiple exons, and 27,880 alternative splicing events were identified. Among the 32 NlGr genes, 13 genes have potential alternative splicing events (Table 1). We verified all of the predicted alternative splicing events for NlGrs by PCR, and found that 10 of them had at least two splicing variants (Table 2). By comparison with the genome of BPH, we obtained alternative splicing forms of five NlGrs: NlGr1, NlGr8, NlGr10, NlGr21, and NlGr23 (Figs. 4 and 5).
Table 2

Clone of alternatively splicing of NlGrs in Nilaparvata lugens

Gene

Type

Predicted number of alternative splice variants

Verified number of alternative splice variants

NlGr1

CO2 receptor

0

2

NlGr8

Gr43a-like receptor

10

10

NlGr10

Sugar receptor

2

2

NlGr12

Bitter receptor

0

2

NlGr19

Unknown

4

3

NlGr21

Unknown

0

2

NlGr23

Unknown

2

2

NlGr25

Unknown

0

3

NlGr26

Unknown

3

2

NlGr31

Unknown

0

3

Fig. 4

Alternatively spliced pattern of five NlGrs. The data were verified by PCR with genomic DNA and cDNA as templetes. Bars represent exon sequence; lines represent intron sequences. Blue lines and black lines represent different splicing modes. Red lines in bars represent transmembrance domain. Splicing patterns include cassette exon skipping (NlGr1, NlGr21), alternative first exons (NlGr10), and alternative 5ʹ splice site (NlGr23). The histograms showed the different isoform relative usage from the RNA-seq data, blue bars represent blue line spliceosome

Fig. 5

Ten alternatively spliced patterns of NlGr8. Bars represent exon sequence, and lines represent intron sequence. The data were verified by PCR with genomic DNA and cDNA. Red lines in bars represent transmembrance domain. Splicing patterns include cassette exon skipping, alternative 3ʹ splice site, alternative last exons and mutually exclusive cassettes. The histograms showed the different isoform relative usage from the RNA-seq data, different color bars represent different spliceosome

NlGr10, a putative sugar receptor, had an alternative first exon event, and was found to have two splicing variants: NlGr10a, which encodes 470 amino acids; and NlGr10b, which encodes 458 amino acids. NlGr1 and NlGr21 showed cassette exon skipping events, and were found to have two splicing variants, respectively. Another unknown receptor, NlGr23 had an alternative 5′ splice site, and the splice site was located in its second exon. NlGr8, with ten exons and ten splicing forms, had the most complex splicing events among all the NlGrs (Fig. 5). The alternative splicing may change the protein structure of these 5 NlGrs. For example, one splicing variants of NlGr23 lost the second and third transmembrane domains, and the NlGr10 splice variants generated by the alternative first exon and encoded an N-terminal altered NlGR10 protein.

Effects of dsRNA injection on NlVg and NlVgR expression

To further investigate the function of the NlGr genes in BPH, RNAi experiments were performed. Gene silencing of the target genes was achieved by dsRNA injection (Fig. 6a, see Additional file 4: Table S4), except for three NlGrs (NlGr27, NlGr30 and NlGr32), whose nucleotide sequences were less than 450 bp and were too short for primer design. We found three patterns of NlVg and NlVgR mRNA levels in BPH after silencing the 29 NlGrs (Fig. 6b). In the first type, the transcript of NlVg and NlVgR was decreased when BPH was injected with dsNlGr. The genes with this pattern included 17 NlGrs: NlGr1, NlGr3, NlGr5, NlGr6, NlGr7, NlGr8, NlGr10, NlGr12, NlGr13, NlGr14, NlGr15, NlGr17, NlGr18, NlGr20, NlGr22, NlGr25, and NlGr29. In the second type, the transcript of NlVg was increased, while it was decreased for NlVgR, when BPH was injected with dsNlGr19, dsNlGr28 or dsNlGr31. And the third type, either NlVg or NlVgR had changed mRNA level after injection. For example, the NlVgR expression was decreased when treated with dsNlGr2, dsNlGr9 or dsNlGr11, whilst no significant change in NlVg mRNA level were found; and the NlVgR, but not NlVg, showed increased mRNA level when BPH was treated with dsNlGr4 or dsNlGr16 (Fig. 6b). Furthermore, NlGr23 and NlGr26 did not appeare to have significant effect on the expression of NlVgR, while the NlVg expression was significantly decreased or increased at 72 h after with the injection of them, respectively. In summary, except for NlGr21 and NlGr24, the other tested 27 NlGrs affected the transcript accumulation of NlVg or NlVgR. These results implied that NlGrs may play an important role in modulating NlVg or NlVgR expression in BPH. Since Vg and VgR were always used as molecular markers of fecundity [24], it is likely that these 27 NlGrs are required for normal expression of fecundity markers.
Fig. 6

Knockdown the expression of NlGrs in BPH and its effect on NlVg and NlVgR transcript accumulation. a: mRNA level of target genes; b: mRNA level of vitellogenin (Vg) and vitellogenin receptor (VgR) was detected by qRT-PCR; mRNA levels of females treated with dsGFP were used as controls. Different color fonts represent different predicted subfamilies: red: CO2 receptor subfamily; green: Gr43a-like receptor subfamily; blue: sugar receptor subfamily; orange: bitter receptor subfamily; black: unknown functions receptor. All mRNA levels are normalized by the β-actin mRNA levels and data are shown as the mean (n = 3)

NlGr25 response to resistant rice varieties

qRT-PCR was used to measure the mRNA levels of NlGr25 from the whole body fed on the sensitive rice variety (TN1) and resistance rice varieties (IR26, IR36, IR56). The results showed that NlGr25 transcript levels were significantly increased at 3 h, 6 h and 12 h post fed on IR26 compared to fed on TN1. Similar results were observed for IR56. Whereas, feeding on IR36 didn’t influence expression of NlGr25 in BPH (Fig. 7).
Fig. 7

Expression of NlGr25 induced by various rice cultivars. NlGr25 mRNA abundance was measured by quantitative real-time PCR. Total RNA was extracted from BPH at 3, 6, 12 and 24 h after feeding from different rice cultivars. TN1: Taichung Native 1, a BPH susceptible variety; IR36: International Rice 36, a BPH-resistant variety contains resistance gene bph2; IR56: International Rice 56, a BPH-resistant variety contains resistance gene Bph3. Data are shown as the means ± SE of three replicates. Significant differences between two treatments are marked with an asterisk (t-test), *, at the 0.05 level; **, at the 0.01 level

Discussion

We identified 32 Gr genes in BPH by combining transcriptome and genome data [25]. Insect Gr families appear to be adapted to their ecological niche. For example, the honeybee Apis mellifera has an expansion of the olfactory receptor gene family (163 Ors) and only 10 Grs [30]. It has been hypothesized that bees have a limited need for Grs in plant secondary metabolite discrimination due to their typical foraging and social behavior [30]. Helicoverpa armigera has three times as many Grs (197) than Ors (64), which may be linked to this species’ capacity as a successful generalist, as the expansion presumably broadens the range of plant secondary metabolites detected by this species [18]. The number of NlGr was less than that in the aphid Acyrthosiphon pisum (72 Grs) [31], which is also a hemipteran insect. This may be because BPH is a monophagous insect that only feeds on rice, possibly explaining the reduced number of Gr genes. A. pisum is a type of omnivorous agricultural pest, and more Grs may allow it to recognize more secondary or primary metabolites from different host plants. Based on phylogenetic analysis, we classified 29 NlGrs into five categories: sugar receptors, Gr43a-like receptors, CO2 receptors, bitter receptors and Grs with unknown functions. No loss of NlGr function shows that BPH Grs may be able to detect and discriminate large amount of metabolites in rice. Rapid changes in the “bitter” receptor repertoire have been described in Drosophila flies, apparently concomitant with changes in their plant hosts [32].

Transcriptome and qRT-PCR approaches were used to analyze the expression profile of NlGr genes in different tissues and at different stages, and the results showed that NlGrs was expressed almost in all tissues and stages. It is similar to the observations in Nasonia vitripennis whose Grs are most likely expressed in diverse chemosensory tissues, such as mouthparts, legs, and the ovipositor, as well as the digestive tract [33]. In other insects, such as T. castaneum, the Grs are mainly expressed in the labium and maxillae, femur, tibia, and tarsus of the adult prolegs [34]. In Aedge aegypti, the gustatory sensilla are located in stereotyped positions on the labella and tarsi of the legs [35], and there is also specific expression of Grs in these tissues [36]. DmGr genes are found in abdominal tissues and the taste organs, including the labellum, pharyngeal organs, and tarsi [37, 38]. Since BPH is a piercing sucking insect, the midgut is the main tissue used to digest food, absorb nutrients and remove toxic substances. NlGr25 was most highly expressed in the midgut and in 3th day female adults, we propose that that it might affect behaviors of BPH by responding to metabolite(s) in rice. Similarly, 12 DmGrs were expressed in midgut enteroendocrine cells, implying that the Grs have chemosensory roles in the intestine to regulate physiological functions, such as food uptake, nutrient absorption, or sugar homeostasis [38]. Some DmGrs have a higher mRNA level in the nymph stage, indicated that they play a role in the development of insects, as previously reported in Drosophila [39].

Alternative splicing is increasingly of interest in the study of evolution and adaptation of insects, as it provides the means for rapid functional innovations via very economical genomic changes [20]. However, to date, alternative splicing phenomenon has not been well studied for gustatory receptor family. In D. melanogaster, only three Gr genes (Gr23a, Gr39a and Gr28b) were found to have alternative splicing events [7], and alternative splicing in three genes brings the total number of Grs from 71 to 81 in Drosophila suzukii [40]. In BPH, 10 of 32 NlGrs have alternative splicing events, a higher percentage than that in Drosophila, which may be caused by the factor of the low number of Gr genes. Insect Grs often have a seven transmembrane structure [4], and the alternative splicing can change the transmembrane structure or the number units spanning the membrane and may greatly alter the function of the GR protein. The DmGr39a gene produces four isoforms through alternative splicing of various 5′-most exons [7]. The seventh transmembrane domain and C-terminus are encoded by the last three exons, which are shared by the four isoforms, while the N-terminus and the first six transmembrane domains, which are unique to each isoform, are encoded by four alternative 5′ exons [21]. The RNAi of DmGr39a reduced courtship levels toward females in Drosophila, whilst it is not clear whether each isoform has a different function [21]. NlGr10 showed alternative first exon events, and the two isoforms both exhibited an entire seventh transmembrane domain. However, these two isoforms responded to different sugars (unpublished data). As a Gr43a-like receptor, NlGr8 was verified to have as many as ten splicing forms. Its homologous gene DmGr43a plays a critical role in sensing internal fructose levels in the fly brain and is both necessary and sufficient to sense hemolymph fructose and promote feeding in hungry flies, while suppresses feeding in satiated flies [41]. Thus, we propose that NlGr8 may play important roles in recognizing carbohydrates in rice phloem juice.

Gustatory receptors can affect insect behavior, such as feeding, oviposition or mating, by recognizing non-volatile plant secondary substances [3, 42]. The butterfly, Papilio xuthus, uses a gustatory receptor PxutGr1 to select its host during oviposition [42]. DmGr33a is required for proper oviposition to avoid ovicidal coumarin-laced food in D. melanogaster [3]. DmGr66a in the ventral cibarial sensory organ (VCSO) mediates the egg-laying attraction to lobeline and in neurons on the legs mediates positional aversion [43]. Functional and behavioral studies showed that other Gr genes (Dm8a, Dm93a, Dm98b) responded to bitter compounds to induce avoidance responses, such as caffeine, umbelliferone, L-canavanine [28, 44]. Our results showed that NlGr can be induced by resistant rice varieties, suggesting that they may recognize some resistant or toxic metabolites in resistant rice, and subsequently affect BPH feeding behavior and oviposition behavior. And our previous study confirmed that NlGr11, a sugar receptor, accelerated the fecundity of BPH through the AMPK- and AKT-mediated signaling pathways [45].

Conclusion

In conclusion, we discovered that N. lugens has 32 Gr genes, most of which belong to the bitter receptor and an unknown function clade. Ten of these Gr genes have two or more alternative splicing variants. The RNAi results showed that 27 NlGrs are required for normal expression of fecundity markers. These findings contribute to the understanding of the interactions between BPH and rice.

Methods

Insects

A N. lugens laboratory strain was originally obtained from Guangdong Academy of Agricultural Sciences (GDAAS; Guangdong, China), and this strain was reared in a continuous laboratory culture on BPH-susceptible rice plants (Huang Hua Zhan, bought from GDAAS). The insects were maintained in the laboratory at 26 ± 2 °C with 80 ± 10% humidity and a light-dark cycle of L16:D8h.

Sample preparation

A total of 100 3-day-old brachypterous females were used for tissue dissection. The following tissues were used for RNAseq: head, midgut and legs. The fat body and ovary were also used for the gene expression profiling. Different development stages of BPH, from the first nymph to five days after emergence, were collected. Total RNA was isolated using the TRIzol method (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s protocol. The samples were treated with DNase, and their RNA contents were quantified using a microvolume spectrophotometer (NanoDrop 2000, Thermo Fisher Scientific, Waltham, MA, USA).

DGE analysis

The cDNA libraries were constructed and sequenced by the Beijing Genomics Institute (BGI, Shenzhen, China) on the Illumina sequencing platform (HiSeq™ 2000), producing 100-bp pair-end reads (SRA accession: PRJNA504931). All clean reads were aligned to the reference transcript (generated in our laboratory, unpublished) of N. lugens using TopHat [46].

Gene annotation

We used three methods to identify the BPH gustatory receptor by assembled transcripts. Firstly, we download all the insect gustatory receptor gene family from National Center for Biotechnology Information (https://www.ncbi.nlm.nih.gov/) as a database, and the basic local alignment was used to search the DGE data of head, midgut and legs, respectively. Then tBLASTn searches against the NCBI database to confirm that the obtained sequences belong to the chemosensory family. In addition, the secondary structure model of the gustatory receptor by pfam (http://pfam.xfam.org/) and HMMER v3.1b2 (http://www.hmmer.org/) were used to search the target sequences in the protein database translated from the DEG database. Finally, using the gustatory receptor C-terminal conserved sequences (hh(G/A/S) (A/S)hhTYhhhhhQF) as a template, tBLASTn searches were performed in DGE data. Using these three methods in combination, we obtained all of the gustatory receptors in BPH.

Cloning of NlGr genes

The cDNA from various tissues, including head, midgut, fat body, ovary and whole body, was used as a template to clone the cDNA sequence of NlGrs. 3’ RACE and 5’ RACE PCR was further performed to get the complete cDNA sequences of Grs using a SMARTer® RACE 5′/3′ kit (Takara Bio) according to the manufactures’ manuals. PCR products were purified using a Gel Extraction Kit (OMEGA Bio-Tek, USA), cloned into the pEASY® Blunt vector (TRANSGEN BIOTECH, Beijing, China) and then sequenced by IGE Biotechnology, Ltd. (Guangzhou, China). Then tBLASTn searches against the NCBI database to confirm that the cloning sequences belong to the Gr family. The primers used for gene clone are listed in supplementary file (see Additional file 5: Table S5).

Phylogenetic analysis

To investigate the expanding types of 29 BPH Grs, we selected 266 published insect Grs as the reference source. Among these 266 Grs, 68 were from Drosophila melanogaster (DmGrs), 65 were from Bombyx mori (BmGrs), 13 were from Apis mellifera (AmGrs), 58 were from Aedes aegypti (AgGrs), and 62 were from Tribolium castaneum (TcasGrs). Amino acid sequences were used for phylogenetic analysis in MEGA 6.0 [47]. A maximum-likelihood tree was calculated using the default settings and the Jones-Taylor-Tornton (JTT) with Freqs. (+F) model with partial deletions and 2000 bootstrap replications was applied in the calculation. The software TMpred (https://embnet.vital-it.ch/software/TMPRED_form.html), HMMTOP (http://www.enzim.hu/hmmtop/), and TMHMM (http://www.cbs.dtu.dk/services/ TMHMM/) were used for the prediction of transmembrane domains.

Expression profiles of NlGr genes

Total RNA was collected from the ovaries, midgut, fat body, legs and heads of third-day brachypterous female adults for tissue-specific expression profiles. RNA from the first to fifth instar nymph stages and brachypterous female adults (1–5 days old) were isolated for developmental expression profiles. The total RNA was extracted based on the method described above. For the gene transcription assay, 1 μg RNA was used for first-strand cDNA synthesis using a PrimeScript™ 1st Strand cDNA Synthesis Kit (Takara Bio, Kyoto, Japan). The mRNA level was detected by quantitative real-time PCR (qRT-PCR), the primers are listed in supplementary file (see Additional file 5: Table S5).

Prediction and verification of alternative splicing

To identify alternative splicing events in BPH, we generated a relatively complete set of transcripts based on all available transcriptomes with different developmental stages, sexes and phenotypes (see Additional file 6: Table S6), following a computational pipeline presented in supplementary file (see Additional file 7: Figure S1). Firstly, the reads of each RNA-seq dataset were mapped separately to the reference genome using TopHat [46]. The alignment files produced by TopHat were loaded into Cufflinks to generate a transcriptome assembly for each dataset [48]. These assembled transcripts were combined into a general transcriptome assembly using the Cuffmerge module, and the alternative splicing events were identified according to the GTF file from Cuffmerge. Considering the high false positives of the alternative splicing events predicted from single-exon genes, we removed transcripts with single exon. After identification, cDNA was used as the template to clone the predicted isoforms sequence, and then cloned the NlGrs into the genome. The primers used for verification are listed in supplementary file (see Additional file 8: Table S7).

Herbivory treatments

The resistant rice varieties (IR26, IR36, IR56) and susceptible rice variety TN1 (Taichuang native 1) (used as a control) were provided by the International Rice Research Institute (IRRI, Los Banos, the Philippines). Thirty one-day old brachypterous BPH females starved for at least 3 h prior to the experiment were put on each rice variety. Five individuals were sampled for qRT-PCR at each time point of 3 h, 6 h, 24 h and 24 h post feeding on rice. There were three replications for each treatment and sampling time point. All the collected samples were placed in liquid nitrogen immediately, and then stored at − 80 °C for qRT-PCR.

RNAi experiment and quantitative real-time PCR analysis

The dsRNA was produced using the T7 RiboMAX™ Express RNAi System (Promega, USA). For each NlGr, thirty newly emerged virgin females were used for the injection experiment. 50-nanoliter of dsRNA (4 μg/μl) was injected into the side of the abdomen of BPH using 3.5 Drummond needles and the NARISHIGE IM-31 (Nikon, Tokyo, Japan). Five individuals that survived for 48 and 72 h after injection were randomly collected for RNA extraction. GFP gene (DQ389577) was used as a control dsRNA. Each treatment was performed in triplicate. qRT-PCR also used a previously described method [49]. The primers used for dsRNA preparation and real-time PCR are listed in Table S5.

Statistical analysis

For statistical analysis of the qRT-PCR results, the relative expression were calculated by 2−△△Ct values as previously described [50]. The mRNA levels of target genes were normalized relative to the β-actin mRNA levels, and all of the results are expressed as the means + SE; the differences between two groups were analyzed using t-tests. The differences between multiple groups were analyzed using one-way analysis of variance followed by Duncan’s multiple range test for multiple comparisons. Differences were considered to be significant at P < 0.05 and to be very significant at P < 0.01.

Notes

Acknowledgements

We thank Dr. Wei-Wen Chen, Dr. Yi Dong and Master’s student Yong-Jin Cai (State Key Laboratory of Biocontrol and School of Life Sciences, Sun Yat-sen University) for their help in the RNA interference.

Funding

This work was funded by the National Natural Science Foundation of China (U1401212), the main support in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript, and the China Postdoctoral Science Foundation (2017 M612808), the support in RNAi experiment and quantitative real-time PCR analysis.

Availability of data and materials

Raw data for this project have been submitted to the Sequence Read Archive (SRA) database of the National Center for Biotechnology Information (Accession number: PRJNA504931), the SRA records will be accessible with the following link: https://www.ncbi.nlm.nih.gov/bioproject/PRJNA504931/. The phylogenetic data was shown in TreeBASE database (Submission ID: 23701) with the following accession URL: http://purl.org/phylo/treebase/phylows/study/TB2:S23701

Authors’ contributions

WQZ made substantial contributions to conception and design, and given final approval of the version to be published. RP, LJY, WWZ and ZRZ analyzed and interpreted the sequence data. Verification of selected genes and expression profiles investigation were performed by PY and LEC, with contributions from KK. Alternative splicing analysis was performed by KK and PY. Phylogenetic analysis and RNAi analysis was performed by KK, PY and LEC. KK drafted the manuscript and given final approval of the version to be published. WWZ and ZRZ were involved in drafting the manuscript for important intellectual content. All authors read and approved the final manuscript.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary material

12864_2018_5391_MOESM1_ESM.xlsx (9 kb)
Additional file 1: Table S1. Summary of Nilaparvata lugens transcriptome assembly. (XLSX 8 kb)
12864_2018_5391_MOESM2_ESM.xlsx (10 kb)
Additional file 2: Table S2. Screening for putative gustatory receptors of Nilaparvata lugens by tBLASTn. (XLSX 10 kb)
12864_2018_5391_MOESM3_ESM.xlsx (10 kb)
Additional file 3: Table S3. Prediction of potential gustatory receptors of Nilaparvata lugens by HMMER. (XLSX 9 kb)
12864_2018_5391_MOESM4_ESM.xlsx (12 kb)
Additional file 4: Table S4. Knockdown the expression of NlGrs in BPH and its effect on NlVg and NlVgR transcript accumulation. (XLSX 12 kb)
12864_2018_5391_MOESM5_ESM.xlsx (12 kb)
Additional file 5: Table S5. The primers used in this study. (XLSX 11 kb)
12864_2018_5391_MOESM6_ESM.xlsx (10 kb)
Additional file 6: Table S6. The data source for alternative splicing prediction. (XLSX 10 kb)
12864_2018_5391_MOESM7_ESM.jpg (65 kb)
Additional file 7: Figure S1. Computational pipeline for identifying alternative splicing events in N. lugens from RNA-seq data. (JPG 64 kb)
12864_2018_5391_MOESM8_ESM.xlsx (9 kb)
Additional file 8: Table S7. The primers used in alternative splicing verification. (XLSX 9 kb)

References

  1. 1.
    Hallem EA, Dahanukar A, Carlson JR. Insect odor and taste receptors. Annu Rev Entomol. 2006;51(10):113–35.CrossRefGoogle Scholar
  2. 2.
    Engsontia P, Sanderson AP, Cobb M, Walden KKO, Robertson HM, Brown S. The red flour beetle's large nose: an expanded odorant receptor gene family in Tribolium castaneum. Insect Biochem Mol Biol. 2008;38(4):387–97.CrossRefGoogle Scholar
  3. 3.
    Poudel S, Lee Y. Gustatory receptors required for avoiding the toxic compound coumarin in Drosophila melanogaster. Mol Cells. 2016;39(4):310–5.CrossRefGoogle Scholar
  4. 4.
    Clyne PJ, Warr CG, Carlson JR. Candidate taste receptors in Drosophila. Science. 2000;287(5459):1830–4.CrossRefGoogle Scholar
  5. 5.
    Dunipace L, Meister S, McNealy C, Amrein H. Spatially restricted expression of candidate taste receptors in the Drosophila gustatory system. Curr Biol. 2001;11(11):822–35.CrossRefGoogle Scholar
  6. 6.
    Scott K, Brady R, Cravchik A, Morozov P, Rzhetsky A, Zuker C, Axel R. A chemosensory gene family encoding candidate gustatory and olfactory receptors in Drosophila. Cell. 2001;104(5):661–73.CrossRefGoogle Scholar
  7. 7.
    Robertson HM, Warr CG, Carlson JR. Molecular evolution of the insect chemoreceptor gene superfamily in Drosophila melanogaster. Proc Natl Acad Sci U S A. 2003;100:14537–42.CrossRefGoogle Scholar
  8. 8.
    Bray S, Amrein H. A putative Drosophila pheromone receptor expressed in male-specific taste neurons is required for efficient courtship. Neuron. 2003;39(6):1019–29.CrossRefGoogle Scholar
  9. 9.
    Chyb S, Dahanukar A, Wickens A, Carlson JR. Drosophila Gr5a encodes a taste receptor tuned to trehalose. Proc Natl Acad Sci U S A. 2003;100:14526–30.CrossRefGoogle Scholar
  10. 10.
    Thorne N, Chromey C, Bray S, Amrein H. Taste perception and coding in Drosophila. Curr Biol. 2004;14(12):1065–79.CrossRefGoogle Scholar
  11. 11.
    Moon SJ, Koettgen M, Jiao Y, Xu H, Montell C. A taste receptor required for the caffeine response in vivo. Curr Biol. 2006;16(18):1812–7.CrossRefGoogle Scholar
  12. 12.
    Jones WD, Cayirlioglu P, Kadow IG, Vosshall LB. Two chemosensory receptors together mediate carbon dioxide detection in Drosophila. Nature. 2007;445(7123):86–90.CrossRefGoogle Scholar
  13. 13.
    Sato K, Tanaka K, Touhara K. Sugar-regulated cation channel formed by an insect gustatory receptor. Proc Natl Acad Sci U S A. 2011;108(28):11680–5.CrossRefGoogle Scholar
  14. 14.
    Hill CA, Fox AN, Pitts RJ, Kent LB, Tan PL, Chrystal MA, Cravchik A, Collins FH, Robertson HM, Zwiebel LJ. G protein coupled receptors in Anopheles gambiae. Science. 2002;298(5591):176–8.CrossRefGoogle Scholar
  15. 15.
    Kent LB, Walden KKO, Robertson HM. The gr family of candidate gustatory and olfactory receptors in the yellow-fever mosquito Aedes aegypti. Chem Senses. 2008;33(1):79–93.CrossRefGoogle Scholar
  16. 16.
    Richards S, Gibbs RA, Weinstock GM, Brown SJ, Denell R, Beeman RW, Gibbs R, Bucher G, Friedrich M, Grimmelikhuijzen CJP, et al. The genome of the model beetle and pest Tribolium castaneum. Nature. 2008;452(7190):949–55.CrossRefGoogle Scholar
  17. 17.
    Wanner KW, Robertson HM. The gustatory receptor family in the silkworm moth Bombyx mori is characterized by a large expansion of a single lineage of putative bitter receptors. Insect Mol Biol. 2008;17(6):621–9.CrossRefGoogle Scholar
  18. 18.
    Xu W, Papanicolaou A, Zhang HJ, Anderson A. Expansion of a bitter taste receptor family in a polyphagous insect herbivore. Sci Rep. 2016;6:23666.CrossRefGoogle Scholar
  19. 19.
    Nilsen TW, Graveley BR. Expansion of the eukaryotic proteome by alternative splicing. Nature. 2010;463(7280):457–63.CrossRefGoogle Scholar
  20. 20.
    Gibilisco L, Zhou Q, Mahajan S, Bachtrog D. Alternative splicing within and between Drosophila species, sexes, tissues, and developmental stages. PLoS Genet. 2016;12(12):e1006464.CrossRefGoogle Scholar
  21. 21.
    Watanabe K, Toba G, Koganezawa M, Yamamoto D. Gr39a, a highly diversified gustatory receptor in Drosophila, has a role in sexual behavior. Behav Genet. 2011;41(5):746–53.CrossRefGoogle Scholar
  22. 22.
    Ni L, Bronk P, Chang EC, Lowell AM, Flam JO, Panzano VC, Theobald DL, Griffith LC, Garrity PA. A gustatory receptor paralogue controls rapid warmth avoidance in Drosophila. Nature. 2013;500(7464):580–4.CrossRefGoogle Scholar
  23. 23.
    Qiu J, He Y, Zhang JQ, Kang K, Li TC, Zhang WQ. Discovery and functional identification of fecundity-related genes in the brown planthopper by large-scale RNA interference. Insect Mol Biol. 2016;25(6):724–33.CrossRefGoogle Scholar
  24. 24.
    Zhai YF, Sun ZX, Zhang JQ, Kang K, Chen J, Zhang WQ. Activation of the TOR signalling pathway by glutamine regulates insect fecundity. Sci Rep. 2015;5:10694.CrossRefGoogle Scholar
  25. 25.
    Xue J, Zhou X, Zhang C-X, Yu LL, Fan HW, Wang Z, Xu HJ, Xi Y, Zhu ZR, Zhou WW, et al. Genomes of the rice pest brown planthopper and its endosymbionts reveal complex complementary contributions for host adaptation. Genome Biol. 2014;15(12):521.CrossRefGoogle Scholar
  26. 26.
    Dahanukar A, Foster K, van Naters W, Carlson JR. A gr receptor is required for response to the sugar trehalose in taste neurons of Drosophila. Nat Neurosci. 2001;4(12):1182–6.CrossRefGoogle Scholar
  27. 27.
    Slone J, Daniels J, Amrein H. Sugar receptors in Drosophila. Curr Biol. 2007;17(20):1809–16.CrossRefGoogle Scholar
  28. 28.
    Lee Y, Kang MJ, Shim J, Cheong CU, Moon SJ, Montell C. Gustatory receptors required for avoiding the insecticide L-Canavanine. J Neurosc. 2012;32(4):1429–35.CrossRefGoogle Scholar
  29. 29.
    Shim J, Lee Y, Jeong YT, Kim Y, Lee MG, Montell C, Moon SJ. The full repertoire of Drosophila gustatory receptors for detecting an aversive compound. Nat Commun. 2015;6:8867.CrossRefGoogle Scholar
  30. 30.
    Robertson HM, Wanner KW. The chemoreceptor superfamily in the honey bee, Apis mellifera. expansion of the odorant, but not gustatory, receptor family. Genome Res. 2006;16(11):1395–403.CrossRefGoogle Scholar
  31. 31.
    Smadja C, Shi P, Butlin RK, Robertson HM. Large gene family expansions and adaptive evolution for odorant and gustatory receptors in the pea aphid, Acyrthosiphon pisum. Mol Biol Evol. 2009;26(9):2073–86.CrossRefGoogle Scholar
  32. 32.
    McBride CS, Arguello JR. Five Drosophila genomes reveal nonneutral evolution and the signature of host specialization in the chemoreceptor superfamily. Genetics. 2007;177(3):1395–416.CrossRefGoogle Scholar
  33. 33.
    Robertson HM, Gadau J, Wanner KW. The insect chemoreceptor superfamily of the parasitoid jewel wasp Nasonia vitripennis. Insect Mol Biol. 2010;19:121–36.CrossRefGoogle Scholar
  34. 34.
    Abdel-Latief M. A family of chemoreceptors in Tribolium castaneum (Tenebrionidae: Coleoptera). PLoS One. 2007;2(12):e1319.CrossRefGoogle Scholar
  35. 35.
    Lee RM, Craig DA. Fine structure of the sense organs on the labella and labium of the mosquito Aedes aegypti (L.). Open Entomol J. 2009;3(1):7–17.CrossRefGoogle Scholar
  36. 36.
    Sparks JT, Vinyard BT, Dickens JC. Gustatory receptor expression in the labella and tarsi of Aedes aegypti. Insect Biochem Mol Biol. 2013;43(12):1161–71.CrossRefGoogle Scholar
  37. 37.
    Thorne N, Amrein H. Atypical expression of Drosophila gustatory receptor genes in sensory and central neurons. J Comp Neurol. 2008;506(4):548–68.CrossRefGoogle Scholar
  38. 38.
    Park JH, Kwon JY. A systematic analysis of Drosophila gustatory receptor gene expression in abdominal neurons which project to the central nervous system. Mol Cells. 2011;32(4):375–81.CrossRefGoogle Scholar
  39. 39.
    Saina M, Busengdal H, Sinigaglia C, Petrone L, Oliveri P, Rentzsch F, Benton R. A cnidarian homologue of an insect gustatory receptor functions in developmental body patterning. Nat Commun. 2015;6:6243.CrossRefGoogle Scholar
  40. 40.
    Hickner PV, Rivaldi CL, Johnson CM, Siddappaji M, Raster GJ, Syed Z. The making of a pest: insights from the evolution of chemosensory receptor families in a pestiferous and invasive fly, Drosophila suzukii. BMC Genomics. 2016;17(1):648.CrossRefGoogle Scholar
  41. 41.
    Miyamoto T, Slone J, Song X, Amrein H. A fructose receptor functions as a nutrient sensor in the Drosophila brain. Cell. 2012;151(5):1113–25.CrossRefGoogle Scholar
  42. 42.
    Ozaki K, Ryuda M, Yamada A, Utoguchi A, Ishimoto H, Calas D, Marion-Poll F, Tanimura T, Yoshikawa H. A gustatory receptor involved in host plant recognition for oviposition of a swallowtail butterfly. Nat Commun. 2011;2:542.Google Scholar
  43. 43.
    Joseph RM, Heberlein U. Tissue-specific activation of a single gustatory receptor produces opposing behavioral responses in Drosophila. Genetics. 2012;192(2):521–32.CrossRefGoogle Scholar
  44. 44.
    Poudel S, Kim Y, Kim YT, Lee Y. Gustatory receptors required for sensing umbelliferone in Drosophila melanogaster. Insect Biochem Mol Biol. 2015;66:110–8.CrossRefGoogle Scholar
  45. 45.
    Chen WW, Kang K, Yang P, Zhang WQ. Identification of a sugar gustatory receptor and its effect on fecundity of the brown planthopper Nilaparvata lugens. Insect Sci. 2017.  https://doi.org/10.1111/1744-7917.12562.
  46. 46.
    Trapnell C, Pachter L, Salzberg SL. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics. 2009;25(9):1105–11.CrossRefGoogle Scholar
  47. 47.
    Tamura K, Stecher G, Peterson D, Filipski A, Kumar S. MEGA6: molecular evolutionary genetics analysis version 6.0. Mol Biol Evol. 2013;30(12):2725–9.CrossRefGoogle Scholar
  48. 48.
    Trapnell C, Roberts A, Goff L, Pertea G, Kim D, Kelley DR, Pimentel H, Salzberg SL, Rinn JL, Pachter L. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and cufflinks. Nat Protocols. 2012;7(3):562–78.CrossRefGoogle Scholar
  49. 49.
    Kang K, Yang P, Pang R, Yue L, Zhang WQ. Cycle affects imidacloprid efficiency by mediating cytochrome P450 expression in the brown planthopper Nilaparvata lugens. Insect Mol Biol. 2017;26(5):522–9.CrossRefGoogle Scholar
  50. 50.
    Livak KJ, Schmittgen TD. Analysis of relative gene expression data using real-time quantitative PCR and the 2-△△CT method. Methods. 2001;25(4):402–8.CrossRefGoogle Scholar

Copyright information

© The Author(s). 2018

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors and Affiliations

  1. 1.State Key Laboratory of Biocontrol and School of Life SciencesSun Yat-sen UniversityGuangzhouChina
  2. 2.State Key Laboratory of Rice Biology, Institute of Insect SciencesZhejiang UniversityHangzhouChina

Personalised recommendations