http://orcid.org/0000-0002-5298-6954
Figures
Abstract
Amrasca biguttula biguttula (Ishida) commonly known as cotton leafhopper is a severe pest of cotton and okra. Not much is known on this insect at molecular level due to lack of genomic and transcriptomic data. To prepare for functional genomic studies in this insect, we evaluated 15 common housekeeping genes (Tub, B-Tub, EF alpha, GADPH, UbiCF, RP13, Ubiq, G3PD, VATPase, Actin, 18s, 28s, TATA, ETF, SOD and Cytolytic actin) during different developmental stages and under starvation stress. We selected early (1st and 2nd), late (3rd and 4th) stage nymphs and adults for identification of stable housekeeping genes using geNorm, NormFinder, BestKeeper and RefFinder software. Based on the different algorithms, RP13 and VATPase are identified as the most suitable reference genes for quantification of gene expression by reverse transcriptase quantitative PCR (RT-qPCR). Based on RefFinder which comprehended the results of three algorithms, RP13 in adults, Tubulin (Tub) in late nymphs, 28S in early nymph and UbiCF under starvation stress were identified as the most stable genes. We also developed methods for feeding double-stranded RNA (dsRNA) incorporated in the diet. Feeding dsRNA targeting Snf7, IAP, AQP1, and VATPase caused 56.17–77.12% knockdown of targeted genes compared to control and 16 to 48% mortality of treated insects when compared to control.
Citation: Singh S, Gupta M, Pandher S, Kaur G, Rathore P, Palli SR (2018) Selection of housekeeping genes and demonstration of RNAi in cotton leafhopper, Amrasca biguttula biguttula (Ishida). PLoS ONE 13(1): e0191116. https://doi.org/10.1371/journal.pone.0191116
Editor: Yulin Gao, Chinese Academy of Agricultural Sciences Institute of Plant Protection, CHINA
Received: September 23, 2017; Accepted: December 28, 2017; Published: January 12, 2018
This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: The authors received no specific funds for this work. The internal funding of Punjab Agricultural University supported this research.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Gene expression studies are indispensable for molecular biology research. The knowledge on gene expression helps to better understand its regulation and functions. The availability of huge sequence data in the form of transcriptomes and genomes of several organisms can be utilized to understand the transcription of the gene(s) [1]. However, these studies have become more accurate and robust after the development of gene expression quantitation method, reverse transcriptase quantitative PCR (RT-qPCR). This method is highly sensitive, reproducible and accurate to a level that it can identify even minute variations, which are frequently undetected. The RT-qPCR data are influenced by many factors, which include quality and quantity of the starting material, RNA extraction, cDNA synthesis, and other laboratory procedures. Even pipetting errors and reverse transcription efficiency can influence the Ct values significantly[2,3]. Thus normalization is a prerequisite in gene expression studies as it limits variability by comparing target gene expression with housekeeping genes (HKGs). Normalization is based on the assumption that the expression of the HKGs is stable across various biotic and abiotic stresses and treatments. Recent research indicates that a condition-specific reference gene needs to be identified for accurate measurements of gene expression[4]. Likewise, it is also evident that a single reference gene is not desirable for the wider experimental regime[5,6]. The use of a single reference gene can generate up to a 20-fold error in the expression data [7]. In most of the expression studies, actin is taken as a universal HKG [8] or the HKGs validated for certain systems are being directly applied to other without an appropriate validation of their stability in that particular system. To address this, different software programs have been developed to choose HKGs that are most suited for normalization[7–10]. Thus, it is necessary to choose the most suitable genes for normalization from a panel of candidate genes in a dedicated set of biological samples from a particular organism. Although RT-qPCR has been widely used for detecting gene expression in insects but there is yet no suitable HKG and stable gene quantification system for the cotton leafhopper. In addition, there is no report of RNAi (RNA interference) in this insect. We demonstrated feeding RNAi in this insect for few genes such as Aquaporins (AQPs), inhibitor-of-apoptosis (IAP), VATPases and Snf7. Aquaporins (AQPs) belong to the family of the major intrinsic proteins (MIP), which are integral membrane channel proteins in most living organisms and facilitate mass transfer of water and sometimes other substrates across cell membranes [11–14]. In insects, the inhibitor-of-apoptosis (IAP) proteins are a family of conserved survival factors that determine cell fate during development, stress, tumorigenesis, and infection by regulating the phenomenon of apoptosis [15–19]. VATPases are highly conserved and ubiquitous proton pumps which acidify specific organelles such as lysosomes, endosomes or secretory vesicles in every eukaryotic cell. These are present in the plasma membrane of different animal cell types where they are involved either in membrane energization or pH homeostasis [20–22]. Snf7 functions as a component of the ESCRT (Endosomal Sorting Complex Required for Transport) pathway which plays an essential part in cellular housekeeping by internalization, transport, sorting and lysosomal degradation of transmembrane proteins [23]. It has been shown to be involved in sorting of transmembrane proteins either through recycling to plasma membrane or routed to lysosomal degradation through the endosomal-autophagic pathway in many organisms [24–28].
Amrasca biguttula biguttula (Ishida) (Hemiptera: Cicadellidae), commonly known as cotton leafhopper/jassid, is among the most economically important sucking pests of cotton. Leafhopper infested tender leaves become small, crinkled, yellow and the margin of the leaves starts curling downwards. In the case of severe infestation, leaves get a bronze or brick red color which is typical “hopperburn” symptom. The leaves dry up and are shed, and the growth of the plant is retarded. Apart from drying of leaves, the punctures of leafhoppers induce the shortening of the internodes, which contribute to reduced plant vigor and yield [29]. The pest has the potential of causing 25–45% loss in seed cotton yield [30,31]. Besides cotton, it also infests many crops in malvaceous and solanaceous families. The mainstay of leafhopper control shifted to neonicotinoids after the development of resistance against most of the pyrethroids and organophosphates [32]. Bt-cotton seeds treated with neonicotinoids protect against cotton leafhopper till 40–50 days after sowing. However, there have been reports of the leafhoppers showing a high level of resistance up to 5,000-fold to imidacloprid and other neonicotinoid insecticides introduced barely decade ago in central India [33]. Besides, the usage of synthetic insecticides is also associated with a number of environmental issues such as insecticide residues in soil and water, and effects on non-targets, etc. The ill effects of pesticides can only be addressed through eco-friendly methods of pest control, but on contrary Bt-toxins (sole candidates of insect- resistant transgenics) are either not available or less effective against sap-sucking pests such as aphids, leafhoppers and whitefly. The present—day RNAi technology has the potential of developing novel insect control strategies. The method relies on feeding/injecting sequence-specific double-stranded RNA (dsRNA) targeted towards downregulation or knockdown of essential genes for causing mortality. The dsRNA targeting such essential genes can help in developing new generation insect resistant transgenic plants. Ironically, in spite of attaining serious pest status on cotton, the molecular genetics of this insect has remained unexplored with information available only on a single gene (mtCOI-taxonomic importance) in the NCBI database. To initiate molecular studies related to gene expression, RNAi, and other future functional genomics studies, selection of right HKGs is a prerequisite. Hence, the studies first time report the suitable HKGs for different developmental stages and under starvation stress in cotton leafhopper. In addition downregulation of targeted genes associated with different physiological functions through dsRNA feeding has been successfully demonstrated in this insect.
Materials and methods
Selection of genes and primer design
The selection of genes for identifying stable reference genes in Amrasca biguttula biguttula (Cotton leafhopper) was based on previous studies in other insects [34–36]. As per information available, we chose 15 commonly used reference genes such as Tub (Tubulin), β-Tub (Beta Tubulin), EF alpha (elongation factor alpha), GADPH (Glyceraldehyde 3-phosphate dehydrogenase), UbiCF (ubiquitin-conjugating factor), RP13 (ribosomal protein 13), Ubiq (ubiquitin), VATPase (V-type adenosine triphosphatase), Actin, 18s (ribosomal protein 18), 28s (ribosomal protein 28), TATA (TATA-binding protein), ETF (electron transfer flavoprotein), SOD (superoxide dismutase) and Cytolytic actin as shown in Table 1. The sequences of these genes were retrieved from our transcriptome sequence data (unpublished) followed by a BLASTX search of each sequence in NCBI database for re-confirmation of their annotation. The primers were designed using Primer3 [37] software to amplify 100–150 bp region of the respective gene (Table 1). The sequences of all the selected genes have been submitted to NCBI database and are available with Accessions MF101761-MF101776.
Rearing of insect
A. biguttula insects were reared on Gossypium hirsutum variety Ganganagar ageti in the walk-in environmental chamber at 65–70% RH, 14:10 h light and dark photoperiod and 27± 2°C. The plants of the variety were changed from time to time for continuous fresh food supply to the insect and availability of its culture throughout the study period.
dsRNA synthesis and feeding
To test RNAi in A. biguttula biguttula, we chose AQP (aquaporin), IAP, (inhibitor of apoptosis) VATPase and Snf7 (vacuolar sorting protein) genes shown in Table 2. Gene specific primers were designed from the transcriptome sequences using Primer3 open source software using RNA sequences [37]. The dsRNA primers were also designed using Primer3.However, an additional T7 promoter sequence was added to 5’ end of both reverse and forward primers as given in Table 2. These primers were used to amplify the template for dsRNA using cDNA which was synthesized using PrimeScript™ 1st strand cDNA Synthesis Kit (Clontech Takara) as per manufacturer’s protocol. Amplified fragments of genes were purified using nucleospin PCR cleanup (Macherey-Nagel Nucleospin Gel and PCR Cleanup) and used for the synthesis of dsRNA with T7 RiboMAX™ Express RNAi System (Promega) following manufacturer’s protocol. The dsRNA was quantified using BioSpectrometer® basic (Eppendorf). The 500ng/μl of dsAQP, dsIAP, dsVATPase, dsSnf7, and dsGFP was mixed with diet [38] as described in Table 3. Three biological replicates of five insects each were released in a 1.5 ml tube (1/4th cut from the bottom side and covered with muslin cloth) with diet incorporated dsRNA between two layers of stretched parafilm. The insects were observed 48 h post release and the mortality was compared to dsGFP treated insects. Live insects were collected in Tri-Reagent® (Sigma-Aldrich), frozen in liquid nitrogen and processed for RNA extraction.
RNA extraction and cDNA synthesis
For validation of housekeeping genes, insects were categorized into different developmental stages, i.e., early nymph (I and II), late nymph (III and IV) and adult. Three biological replicates of each developmental stage were separated and pooled, i.e., early nymphs 25–30 individuals per pool, late nymphs 15–20 per pool and adult hopper five insects per pool. For expression under starvation stress, late stage nymphs (III and IV) were starved for four h in empty ventilated boxes, and four individuals each were pooled in three different tubes. The total RNA was isolated from each developmental stage, starved leafhopper and dsRNA fed insects using Tri Reagent® (Sigma-Aldrich) as per manufacturer’s protocol. Isolated RNA was given DNase treatment to remove DNA contamination. The RNA was quantified and checked for its quality and quantity on Eppendorf BioSpectrometer® basic. The total RNA (2μg) was used for first-strand cDNA preparation using PrimeScript™ 1st strand cDNA Synthesis Kit (Clontech Takara) as per manufacturer’s protocol.
RT-qPCR analysis
qPCR analysis used SYBR® Premix Ex Taq™ II (TliRNase H Plus) (Clontech) with the LightCycler®96 (Roche Molecular Systems, Inc.). PCR reactions were performed in triplicates in 10μl volumes using 1μl of 1:10 diluted cDNA, 0.2 ul of 10mM of gene-specific primers per reaction. For absolute quantification, three 10-fold serial dilutions were performed to ensure that the cDNA synthesis reagents did not impair PCR efficiency. Thermal cycling conditions constituted initial denaturation of 95°C for the 30s followed by 40 cycles of 95°C of 5s and 60°C for 10s. Melting curve analysis was done to ensure the specificity and consistency of the amplified product. RT-qPCR efficiency was measured for each gene with the slope of linear regression model and standard curves. Amplification efficiency and correlation coefficients for each primer pair were calculated as described in LightCycler®96 users guide. In RNAi studies, the relative expression of genes in biological samples was estimated using the ΔΔCt method [39] normalized with RP13 as HKG and compared with dsGFP.
Stability and statistical analysis for reference genes
We chose four algorithms to determine the stability of genes, i.e., geNorm [7], NormFinder [9], Bestkeeper [10] and RefFinder [40]. The qPCR obtained Ct values from the Lightcycler software (Roche) were transferred to MS-excel for the calculation of linear relative values by comparative Ct method (keeping lowest relative quantity for each gene as 1). These linear relative quantities were used as input data for further analysis of gene stability with geNorm and NormFinder. geNorm calculates the expression stability score (M) by averaging the mean pairwise variation of each HKG. Lower ‘M’ value indicates the stability of the gene, so the HKGs showing M value > 0.5 were not considered for further normalization studies. NormFinder calculates expression stability of the genes within a group and between the groups. It also determines standard deviation (SD) through advanced analysis. BestKeeper is also a freely available algorithm that directly utilizes the Ct value obtained from the software. It evaluates standard deviation, p-values, index and correlation coefficient of each gene to elucidate the most stable gene. The gene with a lower SD value could serve as a better reference gene. The final assessment was made on the basis of geometric mean calculated for each gene using RefFinder (http://leonxie.esy.es/RefFinder/?type=reference), a web-based tool which uses all three algorithms along with ΔΔCt method for stability analysis of genes. This web-based tool is user-friendly and allows to evaluate and screen the candidate reference genes directly on the basis of Ct values across the samples efficiently. It makes a comprehensive analysis of data obtained from all the algorithms and ranks the candidate genes in decreasing order of their stability. On the grounds of rankings from each program, an appropriate weight is assigned to each candidate gene, and the geometric mean of their weights is calculated for the net final ranking.
Results
Verification of expression of selected genes
Fifteen candidate genes based on their significance in the biological processes were tested for their expression. These included the structure-related genes, Tub, B-Tub, Cytolytic Actin and Actin; ribosomal and protein function genes, EF alpha, 18s, 28s, RP13, TATA, ETF; metabolism-related genes, UbiCF, Ubiq, GAPDH, VATPase and SOD. The initial screening of all targeted genes was done by PCR and the amplified products were checked by agarose gel electrophoresis. These analyses verified that all the targeted genes were expressed in A. biguttula biguttula (data not shown). In order to calculate the amplification efficiency for all fifteen candidate genes, the three-point standard curve was obtained using the Lightcycler software following the PCR amplification with the known concentration of cDNA template. All genes except Ubiq and CytoActin (78.04% and 62.32% respectively) showed significant amplification efficiency (Table 4). So these two genes were eliminated and not taken into account for further analysis. The correlation coefficient (R2) for all genes ranged between 0.92~1.00 (Table 4). Melt-curve analysis was also performed for amplification specificity, and all genes displayed a single peak. Purified qPCR products were sequenced, and the sequences matched 100% with the target sequences.
Expression profiles of genes
Relative expression levels were determined using RT-qPCR. The Ct values of the candidate reference genes ranged from 22.3 (B-Tub) to 32.24 (Ubiq). The expression of Ubiq gene was low (29.13~32.50) in all developmental stages tested. In starved individuals, the expression of CytoActin and Ubiq was considerably low, showing higher Ct values as compared to the normal samples. The genes RP13, VATPase and β-Tub, showed Ct values ranging between 22~25 indicating higher expression levels. There was not much difference in Ct values across various stages for each gene except for GADPH, which ranged between 22.69~31.06 (Fig 1).
Each data point represents the Mean±S. D. of Ct values for three biological replications in each treatment.
BestKeeper analysis
BestKeeper reveals the best genes on the basis of SD value for each candidate reference gene from raw Ct values. An SD > 1 manifests that the diversification in the expression of a gene within a sample of the same origin is high, thus indicating instability in the expression of that gene. Our data signified that expression was not so stable for many genes, i.e., UbiCF, GAPDH, 28s, and SOD in adult samples as it has shown variations across the samples along with high SD values. Similarly, in late nymph, instability was observed in the genes such as UbiCF, GAPDH, RP13, VATPase and EF based on high SD value (Table 5). In adult hopper, 18s was the most stable gene with lowest SD value of 0.34. Similarly, in late nymph 28s (SD = 0.1), early nymph Tub (SD = 0.08) and under starvation stress TATA (SD = 0.06) were the most stable genes. Ranking of all genes in decreasing order of their stability across all the stages is presented in S1 Table and Fig 2.
geNorm analysis
Two parameters such as M (expression stability value) and V (pairwise variation) are determined by this program. The gene with the highest M value is considered least stable and vice versa. In early nymphal stage, RP13 and 28s were the most stable genes with M value of 0.005. Similarly, in late nymphs, Tub and TATA appeared as most stable genes with M value 0.05, while in adult hopper M value of 0.2 revealed RP13 and VATPase as the most stable genes. Under starvation stress GAPDH and EF were the most stable genes with M value is 0.04 (Fig 3).
NormFinder analysis
Based on this program 18S was identified as best gene among all the samples showing stability value ~ 0.102. NormFinder also determines the best combination of two HKGs which can be used simultaneously in a single expression studies. Based on results, the combination of 18s and TATA was highly stable (stability value ~ 0.079). Under starvation stress, UbiCF appeared to be the most stable gene with a stability value of 0.034. The rankings of genes on the basis of their stability values are presented in Table 5 and graphically in Fig 4.
RefFinder analysis
RefFinder assembles the output of all three algorithms described earlier along with ΔΔCt to assign final rankings for all the genes. It interprets the stability of genes by ranking of their geometric mean (Table 6), as well as allocates overall values to the genes across different stages (Table 7). These analyses showed that RP13, TUB, 28s and UbiCF were the most stable genes in the adult, late stage, early stage nymph and starvation stress, respectively. However, RefFinder also revealed RP13, VATPase, 18s in the adult; 28s, RP13, TUB in early nymph; TUB, TATA, Actin in late nymph and UbiCF, B-Tub, G3PD in starvation stress as the most stable genes. Based on overall analysis of all developmental stages, RP13 was identified as the best stable gene across all stages (Table 7). Thus, this gene was used as HKG for relative expression analysis in dsRNA feeding assays.
Bioassay with gene-specific dsRNA:
The target gene knockdown was confirmed by feeding the late stage nymphs with different dsRNAs using membrane feeding assay (Fig 5). Initial experiments using different doses of dsRNA showed that 500 ng/μl of dsRNA in diet caused knockdown of respective genes when compared to that in dsGFP fed control insects at 48 h post feeding. All the targeted genes showed a significant reduction (p≤ 0.05) in their mRNA levels compared to that in dsGFP fed control insects. The dsRNA feeding caused 4.37, 2.99, 2.49 and 2.28 fold reduction in the levels of Snf7, IAP, AQP and VATPase mRNA, respectively compared to that in control insects (Fig 6). The percent corrected mortality calculated using Abbott’s formula [41] varied between 16–48%; highest in dsSnf7 (48%) fed insects followed by dsAQP (27%), dsVATPase (20%) and dsIAP (16.7%) (Table 8); (S2 Table).
The dsRNA was synthesized for RNAi studies in cotton leafhopper using diet bioassay.
Expression of candidate genes in A. biguttula biguttula fed with dsRNA containing liquid diet by membrane feeding assay (A) Abb IAP (Inhibitor of Apoptosis) knockdown, (B) Abb AQP (Aquaporin) knockdown, (C) VATPase knockdown, (D) Snf7 (Multivesicular protein) knockdown. The mRNA levels of each gene have been normalized with ribosomal protein (RP13) as a reference gene. The error bars represent the standard deviation (n = 3) and * represents significant differences in mRNA levels compared to that in control—fed on dsGFP (P ≤ 0.05, Student’s t-test).
Discussion
In recent years, it has become clear that selection of an appropriate reference gene or genes is the basic requirement for the success of RT-qPCR [42]. However, in the most of the previously reported expression studies with insects, actin has been used as a universal HKG across species as well as across stages of insects [8,43]. Simultaneously, its use as an internal control has been contradictory in many cases [44,45]. Various studies have already been accomplished by identifying HKGs in different species and tissues. Recently, numerous reference genes have been testified under different conditions in stinkbug (Bansal et al, 2016), western flower thrips [46], honey bee [47], fly [48], silkworm [49], moths [50] and beetles [51]. The analysis of reference genes in cotton leafhopper across different developmental stages showed variation in expression as reported in several other insects [52]. These genes have significant involvement in ubiquitous cellular and biological processes, therefore cannot be used as single normalizer in expression studies. In order to improve the efficiency and accuracy of qRT-PCR, we need to validate various internal genes for their expression. RT-qPCR studies revealed variability in the expression of reference genes across samples inferring that a universal reference gene cannot be used for all species or all experimental conditions [53]. Thus validation of reference HKGs is obligatory to gene expression studies wherein a candidate reference gene should have amplification efficiency similar to the target gene.
In the current study, we evaluated fifteen candidate HKGs of cotton leafhopper using multiple statistical models (Bestkeeper, geNorm, NormFinder, and RefFinder) across different developmental stages and starvation stress for identification of reference genes as suggested by [54]. Interestingly, results obtained from different algorithms were highly variable. As each program is using the unique algorithm, incongruities are to be anticipated. For instance, the BestKeeper takes into account the InVar (intrinsic variance), SD (standard deviation) and P-values, all of which contribute to the BestKeeper vs. Pearson correlation coefficient value. These compounding factors result in the obvious differences in outcomes. To integrate the results of all three algorithms, we further used RefFinder for comprehensive ranking of the genes. RefFinder abdicated the ambiguity in the results and helped in the compilation of three algorithms as shown in Table 7.
Our results suggested that the RP13 gene was the most stable gene across all the developmental stages tested. The RP13 gene encodes a ribosomal protein that is a component of the 60S subunit, which is involved in the translation initiation [55]. Various ribosomal proteins genes have been evaluated as reference genes for RT-qPCR in many insects, and these genes have been reported to show the most stable expression in Tetranychus cinnabarinus (RPS18:[56]), Apis mellifera (RPS18: [47]), Rhodnius prolixus (RPS18: [57]), Cimex lectularius (RPL18: [58]), and Schistocerca gregaria (RP49: [59]). In late and early stage nymphs, Tub and 28s were the most stable genes, respectively. Previous studies also demonstrated 28s rRNA to be the stable and suitable gene for internal control in various organisms ([47, 60–63]. Tubulin has also been most commonly used as a reference gene in various expression studies [64–66]. These belong to the family of eukaryotic structural genes that form microtubules, fundamental components of the cytoskeleton that mediate cell division, shape, motility, and intracellular trafficking [67]. Nevertheless, tubulin is reported to be the most variable gene in plant hoppers [68]. Under starvation stress, UbiCF has come out to be the most stable gene in a comprehensive analysis. Ubiquitin is the founding member of a family of structurally conserved proteins that regulate a host of processes (protein degradation, DNA repair, signal transduction and transcription regulation by endocytosis) in insect cells [69–71]. Various genes coding for ubiquitin proteins such as ubiquitin conjugation factor (Bansal et al, 2016), polyubiquitin [68] and ubiquitin [64] evaluated in different insect species have shown stable expression as HKGs. Earlier reports concluded that it was difficult to identify most stable reference gene across various developmental stages in an organism [72–73]. We set our parameters under three algorithms (i.e. Bestkeeper, geNorm, Normfinder) to find the stable HKGs across developmental stages and starvation stress. However, the results obtained from individual algorithm were variable. Thus, we used RefFinder to comprehend the results of three algorithms and assign a ranking to the genes based on their stability under each and across (overall ranking) developmental stages and starvation stress. The overall ranking concludes RP13, VATPase and TUB as the most stable genes which could be helpful in the expression studies involving starvation stress and mixture of individuals from different life stages of leafhopper. Finally it may be concluded that top two-three genes could be selected and validated to elucidate the appropriate reference gene(s) under a particular set of experimental conditions for future gene expression studies in this insect.
Feeding of dsRNA for causing RNAi has been demonstrated in a few hemipteran insects including Halyomorpha halys [36]; Bemisia tabaci [74]; Acyrthosiphon pisum [75]; Bactericera cockerelli [76]; Nilaparvata lugens [77]; Sogatella furcifera [78]; Laodelphax striatellus [79]. Many insects in this order have also shown good RNAi response through injection of dsRNA [80–81]. In planta expressed dsRNA too has been successful in harnessing RNAi response in few of the hemipteran insects [82–87]. Gene silencing has been successfully demonstrated for many hemipteran insects through the oral delivery of synthetic dsRNAs dissolved in sucrose via artificial membrane [84,76,88]. The slightly similar and modified procedure has been used for delivering dsRNA in cotton leafhopper in the current study. Robust RNAi efficiency has been demonstrated in cotton leafhopper through the feeding of dsSnf7. Snf7 belongs to the ESCRT (Endosomal Sorting Complex Required for Transport)–III complex, the ESCRT pathway is a key regulator of biological processes important for eukaryotic cell growth and survival [89]. Feeding of dsRNA targeting western corn rootworm, Snf7 homolog in larvae caused severe stunting after five days of exposure followed by the death of the larvae [23]. The highest mortality was observed with the feeding of dsSnf7 to nymphs of cotton leafhopper. This may be correlated to 77.12% (4.3 fold) decline in the expression of the targeted gene compared to the GFP control. Aquaporin water channels have been implicated in mediating the mass transfer of water in a various physiological processes. Hemipteran water-specific aquaporins have been reported in the gut of phloem-feeding leafhopper Cicadella viridis [90] and B. tabaci [91]. The expression of the Aquaporin 1 gene was reduced to 70% at six days post-feeding and caused 84% mortality in B. tabaci adults fed on 20 μg/ml AQP1dsRNA in 20% sucrose (Vyas et al, 2017). The reports showed no mortality in A. pisum fed on one μg/μl dsApAQP in the diet [92]. However, there was a two-fold reduction in the ApAQP expression compared to that in control. Thus, it is evident that the concentration of dsRNA might be a key factor for causing significant RNAi effect. In our experiments, dsRNA at 500 ng /μl diet caused 66.59% (2.99-fold) reduction in AbbAQP mRNA levels compared to that in dsGFP-fed control insects. However the percent corrected mortality caused by dsAbbAQP feeding was low compared with dsAbbSnf7 feeding. The mortality observed with the feeding of dsAbbIAP and dsAbbVATPase was also low. Nevertheless, there was a significant reduction of 59.87 and 56.17% in the mRNA levels of both the genes in the dsRNA fed insects. In some of the hemipteran insects, the downregulation of VATPase has resulted in a significant mortality as compared to the control [76,74,86,93]. RNAi mediated screening of 290 genes of western corn rootworm (D. virgifera virgifera) revealed that only 125 genes were able to cause larval mortality [94]. In lepidopteran insects, out of 130 genes, only 49 genes (38%) were silenced more than 50% whereas 18 genes (14%) showed lesser silencing while 62 (48%) genes did not show silencing at all [95]. In insects, Hyalophora cecropia, Antheraea pernyi and Manduca sexta even ten ng dsRNA per mg of insect biomass was sufficient to elicit high levels of target gene silencing [96–98]. Similarly, silencing of target genes in coleopteran beetles could be achieved with small quantities of dsRNA [99]. Contrastingly, very high concentration (100 μg) of dsRNA per mg of insect biomass was required to silence target gene of Antheraea mylitta [100].The length of dsRNA is also one of the critical parameters that determine dsRNA uptake and RNAi efficiency [101–102]. To conclude, the RNAi efficiency in cotton leafhopper with different genes showed a differential response. Thus exhaustive studies in future may be helpful to better understand the RNAi and its core machinery in this insect. Overall, the current study has identified stable reference genes across various developmental stages and starvation stress in A. biguttula biguttula. Considered in concert, using different software algorithms and comprehensive analysis results, we suggest that RP13 and VATPase are the most suitable HKGs for gene expression studies across developmental stages. RefFinder concluded RP13, VATPase, 18s in adult, 28s, RP13, TUB in early stage nymph, TUB, TATA, Actin in late- stage nymph and UbiCF, B-Tub, G3PD in starvation stress as the top three stables genes. However, considering the individual developmental stage RP13, Tub and 28S are best suited for adult, late, and early nymphal stages, respectively. In addition, UBiCF is most stable HKG under starvation stress. This study also reports for the first time successful gene silencing through RNAi in A. biguttula biguttula.
Supporting information
S1 Table. Descriptive analysis of all candidate reference genes by calculating standard deviation and p-values by Bestkeeper.
https://doi.org/10.1371/journal.pone.0191116.s001
(DOC)
S2 Table. Mortality calculated in bioassays with dsRNA of different genes in comparison to dsGFP.
https://doi.org/10.1371/journal.pone.0191116.s002
(DOCX)
References
- 1. Heid CA, Stevens J, Livak KJ, Williams PM. Real time quantitative PCR. Genome Res. 1996; 6: 986–994 pmid:8908518
- 2. Bustin SA, Benes V, Nolan T, Pfaffl MW. Quantitative real-time RT-PCR—a perspective. J Mol Endocrinol. 2005; 34: 597–601. pmid:15956331
- 3. Yeung AT, Holloway BP, Adams PS, Shipley GL. Evaluation of dual-labeled fluorescent DNA probe purity versus performance in real-time PCR. BioTechniques. 2004; 36: 266–270, 272, 274–265 pmid:14989091
- 4. Ponton F, Chapuis MP, Pernice M, Sword GA, Simpson SJ. Evaluation of potential reference genes for reverse transcription-qPCR studies of physiological responses in Drosophila melanogaster. J Insect Physiol. 2011; 57: 840–850. pmid:21435341
- 5. Chandna R, Augustine R, Bisht NC. Evaluation of candidate reference genes for gene expression normalization in Brassica juncea using real time quantitative RT-PCR. PLoS One. 2012; 7(5): e36918. pmid:22606308
- 6. Koramutla MK, Aminedi R, Bhattacharya R. Comprehensive evaluation of candidate reference genes for qRT-PCR studies of gene expression in mustard aphid, Lipaphis erysimi (Kalt). Sci Rep. 2016; 6: 25883. pmid:27165720
- 7. Vandesompele J, De PK, Pattyn F, Poppe B, Van RN, et al. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002; 3: RESEARCH0034. pmid:12184808
- 8. Li Z, Yang L, Wang J, Shi W, Pawar RA, Liu Y, et al. beta-Actin is a useful internal control for tissue-specific gene expression studies using quantitative real-time PCR in the half-smooth tongue sole Cynoglossus semilaevis challenged with LPS or Vibrio anguillarum. Fish Shellfish Immunol. 2010; 29: 89–93. pmid:20227507
- 9. Andersen CL, Jensen JL, Orntoft TF. Normalization of real-time quantitative reverse transcription-PCR data: A model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Res. 2004; 64: 5245–5250. pmid:15289330
- 10. Pfaffl MW, Tichopad A, Prgomet C, Neuvians TP. Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper–Excel-based tool using pairwise correlations. Biotechnol Lett. 2004; 26: 509–515. pmid:15127793
- 11. Benga G. Water channel proteins (later called aquaporins) and relatives: past, present, and future. IUBMB Life. 2009; 61: 112–133. pmid:19165894
- 12. Campbell EM, Ball A, Hoppler S, Bowman AS. Invertebrate aquaporins: a review. J Comp Physiol. 2008; 178: 935–955.
- 13. Heymann JB, Engel A. Aquaporins: phylogeny, structure, and physiology of water channels. News Physiol. Sci. 1999; 14: 187–193. pmid:11390849
- 14. Yool AJ. Functional domains of aquaporin-1: keys to physiology, and targetsfor drug discovery. Curr Pharm Des. 2007; 13: 3212–3221. pmid:18045170
- 15. Orme M, Meier P. Inhibitor of apoptosis proteins in Drosophila: gatekeepers of death. Apoptosis. 2009; 14: 950–960. pmid:19495985
- 16. Srinivasula SM, Ashwell JD. IAPs: what's in a name? Mol Cell. 2008; 30: 123–135. pmid:18439892
- 17. Rumble JM, Duckett CS. Diverse functions within the IAP family. J Cell Sci. 2008; 121: 3505–3507. pmid:18946021
- 18. Gyrd-Hansen M, Meier P. IAPs: from caspase inhibitors to modulators of NF-κB, inflammation, and cancer. Nat Rev Cancer. 2010; 10: 561–574. pmid:20651737
- 19. O'Riordan MX, Bauler LD, Scott FL, Duckett CS. Inhibitor of apoptosis proteins in eukaryotic evolution and development: a model of thematic conservation. Dev Cell. 2008; 15: 497–508. pmid:18854135
- 20. Nishi T and Forgac M. The vacuolar (H+)-ATPases-nature's most versatile proton pumps. Nat. Rev. Mol. Cell. Biol. 2002; 3: 94–103. pmid:11836511
- 21. Wieczorek H., Brown D., Grinstein S., Ehrenfeld J. and Harvey W. R. Animal plasma membrane energization by proton-motive V-ATPases. Bioessays. 1999; 21: 637–648. pmid:10440860
- 22. Beyenbach KW and Wieczorek H. The V-type H+ ATPase: molecular structure and function, physiological roles and regulation. J Exp Biol. 2006; 209: 577–589. pmid:16449553
- 23. Ramaseshadri P, Segers G, Flannagan R, Wiggins E, Clinton W, Ilagan O et al. Physiological and cellular responses caused by RNAi- mediated suppression of snf7 orthologue in western corn rootworm (Diabrotica virgifera virgifera) larvae. Cotterill S, ed. P.one. 2013; 8(1): e54270.
- 24. Kim DW, Sung H, Shin D, Shen H, Ahnn J, Lee SK et al. Differential physiological roles of ESCRT complexes in Caenorhabditis elegans. Mol Cells. 2011; 31: 585–592 pmid:21688204
- 25. Teis D, Saksena S, Emr SD. Ordered assembly of the ESCRT-III complex on endosomes is required to sequester cargo during MVB formation. Dev Cell. 2008; 15: 578–589 pmid:18854142
- 26. Rusten TE, Simonsen A. ESCRT functions in autophagy and associated disease. Cell Cycle. 2008; 7: 1166–1172 pmid:18418046
- 27. Lee JA, Gao FB. Roles of ESCRT in autophagy-associated neurodegeneration. Autophagy. 2008; 4: 230–232 pmid:18094607
- 28. Vaccari T, Rusten TE, Menut L, Nezis IP, Brech A, Stenmark H, et al. Comparative analysis of ESCRT-I, ESCRT-II and ESCRT-III function in Drosophila by efficient isolation of ESCRT mutants. J Cell Sci. 2009; 122: 2413–2423 pmid:19571114
- 29. Thirasack S. Yield losses assessment due to pests on cotton in Lao PDR. Kasetsart J Nat Sci. 2001; 35: 271–283.
- 30. Dhawan AK and Sidhu AS. Assessment of losses due to attack of cotton jassid on hirsutum cotton. Indian J Plant Prot. 1986; 14: 45–50.
- 31. Bhat A, Soomro A and Mailah GH. Evaluation of some cotton varieties with known genetic markers for their resistance/tolerance against sucking and bollworm complex.Turkye Butki Manpur Degisi. 1986; 6: 3–14.
- 32. Ahmad M, Arif MI and Ahmad Z. Detection of resistance to pyrethroids in field populations of cotton jassid (Homoptera: Cicadellidae) from Pakistan. J Econ Entomol. 1999; 92: 1246–1250
- 33.
Kranthi S, Kranthi KR, Kumar R, Udikeri DSS, Rao GMVP. Emerging and key insect pests on Bt cotton—their identification, taxonomy, genetic diversity and management. Book of paper; World Cotton Research Conference-5, Mumbai, India, 2011. pp. 281–286. Cited 7–11 November, 2011
- 34. Zhu X, Yuan M, Shakeel M, Zhang Y, Wang S, Wang X et al. Selection and evaluation of reference genes for expression analysis using qRT-PCR in the beet armyworm Spodoptera exigua (Hübner) (Lepidoptera: Noctuidae). PLoS One. 2014; 9: e84730. pmid:24454743
- 35. Rodrigues TH, Chitvan K, Haichuan W, Natalie M, Danielle CC, Valicente FH et al. Validation of reference housekeeping genes for gene expression studies in western corn rootworm (Diabrotica virgifera virgifera). PLoS ONE 2014; 9: e109825. pmid:25356627
- 36. Bansal R, Priyanka M, Yuting C, Praveen M, Chaoyang Z, Michel A, et al. Quantitative RT-PCR gene evaluation and RNA interference in the brown marmorated stink bug. PLoS One. 2016; 11: e0152730 pmid:27144586
- 37. Untergasser A, Cutcutache I, Koressaar T, Ye J, Faircloth BC, Remm M, et al. Primer3—new capabilities and interfaces. Nucleic Acids Res. 2014; 40: e115.
- 38. Koyama K. Artificial rearing and nutritional physiology of planthoppers and leafhoppers (Hemiptera: Delphacidae and Deltocephalidae) on a holidic diet. JARQ. 1988; 22:1
- 39. Schmittgen TD, Livak KJ. Analyzing real-time PCR data by the comparative C(T) method. Nat Protoc. 2008; 3: 1101–8. pmid:18546601
- 40.
(http://leonxie.esy.es/RefFinder/?type=reference).
- 41. Abbott WS. A method of computing the effectiveness of an insecticide. J. Econ. Entomol. 1925; 18: 265–267.
- 42. Bas A, Forsberg G, Hammarström S, Hammarström ML. Utility of the housekeeping genes 18S rRNA, beta-actin and glyceraldehyde-3-phosphate-dehydrogenase for normalization in real-time quantitative reverse transcriptase-polymerase chain reaction analysis of gene expression in human T lymphocytes. Scand J Immunol. 2004; 59: 566–73 pmid:15182252
- 43. Kreuzer KA, Lass U, Landt O, Nitsche A, Laser J, Ellerbrok H, et al. Highly sensitive and specific fluorescence reverse transcription-PCR assay for the pseudogene-free detection of beta-actin transcripts as quantitative reference. ClinChem.1999; 45: 297–300.
- 44. Foss DL, Baarsch MJ, Murtaugh MP. Regulation of hypoxanthine phosphoribosyltransferase, glyceraldehyde-3-phosphate dehydrogenase and beta-actin mRNA expression in porcine immune cells and tissues. Anim Biotechnol. 1998; 9: 67–78. pmid:9676236
- 45. Spanakis E. Problems related to the interpretation of autoradiographic data on gene expression using common constitutive transcripts as controls. Nucleic Acids Res. 1993; 21: 3809–3819. pmid:8367299
- 46. Zheng YT, Li HB, Lu MX, Du YZ. Evaluation and validation of reference genes for qRT-PCR normalization in Frankliniella occidentalis (Thysanoptera:Thripidae). PLoS One. 2014; 9: e111369. pmid:25356721
- 47. Scharlaken B, de Graaf DC, Goossens K, Brunain M, Peelman LJ, et al. Reference gene selection for insect expression studies using quantitative real-time PCR: the head of the honeybee, Apis mellifera, after a bacterial challenge. J Insect Sci. 2008; 8: 33.
- 48. Shen GM, Jiang HB, Wang X and Wang JJ. Evaluation of endogenous references for gene expression profiling in different tissues of the oriental fruit fly Bactrocera dorsalis (Diptera: Tephritidae). BMC Mol Biol. 2010; 11:76 pmid:20923571
- 49. Peng R, Zhai Y, Ding H, Di T, Zhang T, Li B, et al. Analysis of reference gene expression for real-time PCR based on relative quantitation and dual spike-in strategy in the silkworm Bombyx mori Acta Biochim Biophys Sin. 2012; 44: 614–622 pmid:22623504
- 50. Liu G, Qiu X, Cao L, Zhang Y, Zhan Z, Han R. Evaluation of reference genes for reverse transcription quantitative PCR studies of physiological responses in the ghost moth, Thitarodes armoricanus (Lepidoptera, Hepialidae). PLoS One. 2016; 11: e0159060 pmid:27392023
- 51. Pan H, Yang X, Siegfried BD, & Zhou XA. Comprehensive selection of reference genes for RT-qPCR analysis in a predatory lady beetle, Hippodamia convergens (Coleoptera: Coccinellidae). PLoS ONE. 2015; 10: e0125868. pmid:25915640
- 52. Lu Y, Yuan M, Gao X, Kang T, Zhan S, Wan H, et al. Identification and validation of reference genes for gene expression analysis using quantitative PCR in Spodoptera litura (lepidoptera: Noctuidae). PLoS One. 2013; 8: e68059. pmid:23874494
- 53. Bustin SA, Benes V, Garson JA, Hellemans J, Huggett J, Kubista M, et al. The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem. 2009; 55: 611–22. pmid:19246619
- 54. Araujo R, Santos A, Pinto F, Gontijo N, Lehane M, Pereira MH. RNA interference of the salivary gland nitrophorin 2 in the triatomine bug Rhodnius prolixus (hemiptera: Reduviidae) by dsRNA ingestion or injection. Insect Biochem Mol Biol. 2006; 36: 683–693.
- 55. Li B, Nierras C R, Warner J R. Transcriptional elements involved in the repression of ribosomal protein synthesis. Mol Cell Biol. 1999; 19: 5393–5404. pmid:10409730
- 56. Sun W, Jin Y, He L, Lu WC, Li M. Suitable reference gene selection for the different strains and developmental stages of the carmine spider mite, Tetranychus cinnabarinus, using quantitative real-time PCR. J Insect Sci. 2010; 10: 208 pmid:21265619
- 57. Majerowicz D, Alves-Bezerra M, Logullo R, Fonseca-de-Souza AL, Meyer- Fernandes JR, Braz GR, et al. Looking for reference genes for real-time quantitative PCR experiments in Rhodnius prolixus (Hemiptera: Reduviidae) Insect Mol Biol. 2011; 20: 713–722. pmid:21929722
- 58. Mamidala P, Rajarapu SP, Jones SC, Mittapalli O. Identification andvalidation of reference genes for quantitative real-time polymerse chain reaction in Cimex lectularius. J Med Entomol. 2011; 48: 947–951 pmid:21845960
- 59. Van HMB, Van WP, Temmerman L, Van SS, Vuerinckx K, Huybrechts R, et al. Identification and validation of housekeeping genes in brains of the desert locust Schistocerca gregaria under different developmental conditions. BMC Mol Biol. 2009; 10: 56–65. pmid:19508726
- 60. Bagnall NH, Kotze AC. Evaluation of reference genes for real-time PCR quantification of gene expression in the Australian sheep blowfly, Lucilia cuprina. Med Vet Entomol. 2010; 24: 176–181. pmid:20604863
- 61. Zhang S, An S, Li Z, Wua F, Yanga Q, Liu Y, et al. Identification and validation of reference genes fornormalization of gene expression analysis using qRT-PCR in Helicoverpa armigera (Lepidoptera: Noctuidae). Gene. 2015; 555: 393–402. pmid:25447918
- 62. Wu Y, Zhai Y, Huang M. The expression stability analysis of commonly used reference genes and research on the expression regulation of silk protein related genes in Bombyx mori. Chinese J Cell Biol. 2013; 35: 423–431
- 63. Xue JL, Tamer ZS, Colin MT, Cheng XW. Strategy of the use of 28S rRNA as a housekeeping genein real-time quantitative PCR analysis of gene transcription ininsect cells infected by viruses. J Virol Met. 2010; 163: 210–215.
- 64. Huis R, Hawkins S and Neutelings G. Selection of reference genes for quantitative gene expression normalization in flax (Linum usitatissimum L.) BMC Plant Biol. 2010; 10: 71 pmid:20403198
- 65. Kozera B, Rapacz M. Reference genes in real-time PCR. J App Gen. 2013; 54: 391–406.
- 66. Wang L. The transcriptome sequencing and diapause related gene-cloning research of Bactrocera minax. Henan Institute of Science and Technology. 2013; 53–59
- 67. Nielsen MG, Gadagkar SR and Gutzwiller L. Tubulin evolution in insects: gene duplication and subfunctionalization provide specialized isoforms in a functionally constrained gene family. BMC Evol Biol. 2010; 10:113 pmid:20423510
- 68. Maroniche AG, Sagadín M, Mongelli VC, Truol GA, Vas MD. Reference gene selection for gene expression studies using RT-qPCR in virus-infected planthoppers.Virol J. 2011; 8: 308 pmid:21679431
- 69. Christensen AH, Sharrok RA, Quail PH. Maize polyubiquitin genes: structure, thermal perturbation of expression and transcript splicing, and promoter activity following transfer to protoplasts by electroporation. Plant Mol Biol. 1992; 18: 675–689. pmid:1313711
- 70. Chan YL, Suzuki K, Wool IG. The carboxyl extensions of two rat ubiquitin fusion proteins are ribosomal proteins S27a and L40. Biochem Bioph Res Co. 1995; 215: 682–690.
- 71. Okazaki K, Okayama H, Niwa O. The polyubiquitin gene is essential for meiosis in fission yeast. Exp Cell Res. 2000; 254: 143–152. pmid:10623474
- 72. Thellin O, Zorzi W, Lakaye B, De BB, Coumans B, Hennen G, et al. Housekeeping genes as internal standards: use and limits. J Biotechnol. 1999; 75: 291–295 pmid:10617337
- 73. Haberhausen G, Pinsl J, Kuhn CC, Markert-Hahn C: Comparative study of different standardization concepts in quantitative competitive reverse transcription-PCR assays. J Clin Microbiol 1998; 36: 628–633. pmid:9508285
- 74. Upadhyay SK, Chandrashekar K, Thakur N, Verma PC, Borgio JF, Singh PK, et al. RNA interference for the control of whiteflies (Bemisia tabaci) by oral route. J Biosci. 2011; 36: 153–161. pmid:21451256
- 75. Whyard S, Singh AD, Wong S. Ingested double-stranded RNAs can act as species-specific insecticides. Insect Biochem Mol Biol. 2009; 39: 824–832. pmid:19815067
- 76. Wuriyanghan H, Rosa C, Falk BW. Oral delivery of double stranded RNAs and siRNAs induces RNAi effects in the potato/tomato psyllid, Bactericerca cockerelli. PLoS One. 2011; 6: e27736. pmid:22110747
- 77. Ge LQ, Huang LJ, Yang GQ, Song QS, Stanley D, Gurr GM, et al. Molecular basis for insecticide-enhanced thermotolerance in the brown planthopper Nilaparvata lugens Stal (Hemiptera: Delphacidae). Mol Ecol. 2013; 22: 5624–5634. pmid:24303791
- 78. Yang Y, Wan PJ, Hu XX, Li GQ. RNAi mediated knockdown of the ryanodine receptor gene decreases chlorantraniliprole susceptibility in Sogatella furcifera. Pest Biochem Physiol. 2014; 108: 58–65.
- 79. Wan PJ, Jia S, Li N, Fan JM, Li GQ. RNA interference depletion of the halloween gene disembodied implies its potential application for management of planthopper Sogatella furcifera and Laodelphax striatellus. PLoS One. 2014; 9: e86675. pmid:24489765
- 80. Christiaens O and Smagghe G. The challenge of RNAi-mediated control of hemipterans. Curr Opin Insect Sci. 2014; 6: 15–21
- 81. Wang K, Peng Y, Pu J, Fu Wi, Wang J, Han Z. Variation in RNAi efficacy among insect species is attributable to dsRNA degradation in vivo. Insect Biochem Mol Biol. 2016; 77: 1–9 pmid:27449967
- 82. Zha W, Peng X, Chen R, Du B, Zhu L, He G. Knockdown of midgut genes by dsRNA-transgenic plant-mediated RNA interference in the hemipteran insect Nilaparvata lugens. PLoS One PLoS One. 2011; 6: e20504. pmid:21655219
- 83. Pitino M, Coleman AD, Maffei ME, Ridout CJ, Hogenhout SA. Silencing of aphid genes by dsRNA feeding from plants. PLoS ONE. 2011; 6: e25709. pmid:21998682
- 84. Mao J, Zeng F. Plant-mediated RNAi of a gap gene-enhanced tobacco tolerance against the Myzus persicae. Transgenic Res. 2014; 23: 145–152. pmid:23949691
- 85. Hajeri S, Killiny N, El-Mohtar C, Dawson WO, Gowda S. Citrus tristeza virus-based RNAi in citrus plants induces gene silencing in Diaphorina citri, a phloem-sap sucking insect vector of citrus greening disease (Huanglongbing). J Biotechnol. 2014; 176: 42–49. pmid:24572372
- 86. Thakur N, Upadhyay SK, Verma PC, Chandrashekar K, Tuli R, Singh PK. Enhanced whitefly resistance in transgenic tobacco plants expressing double stranded RNA of v-ATPase A gene. PLoS One. 2014; 9: e87235. pmid:24595215
- 87. Vyas M, Raza A, Ali M Y, Ashraf M A, Mansoor S, Shahid AA, et al. Knock down of whitefly gut gene expression and mortality by orally delivered gut gene-specific dsRNAs. PLoS One. 2017; 12: e0168921. pmid:28045942
- 88. Lu ZC, Wan FH. Using double-stranded RNA to explore the role of heat shock protein genes in heat tolerance in Bemisia tabaci (Gennadius). J Exp Biol. 2011; 214: 764–9. pmid:21307062
- 89. Slagsvold T, Pattni K, Malerod L, Stenmark H. Endosomal and non-endosomal functions of ESCRT proteins. Trends Cell Biol. 2006; 16: 317–326. pmid:16716591
- 90. LeCaherec F, Deschamps S, Delamarche C, Pellerin I, Bonnec G, Guillam MT, et al Molecular cloning and characterization of an insect aquaporin–Functional comparison with aquaporin 1. Eur J Biochem. 1996; 241: 707–715 pmid:8944756
- 91. Mathew LG, Campbell EM, Yool AJ, Fabrick JA. Identification and characterization of functional aquaporin water channel protein from alimentary tract of whitefly, Bemisia tabaci. Insect Biochem Mol Biol. 2011; 41: 178–190 pmid:21146609
- 92. Shakesby AJ, Wallace IS, Isaacs HV, Pritchard J, Roberts DM, Douglas AE, et al. A water-specific aquaporin involved in aphid osmoregulation. Insect Biochem Mol Biol. 2009; 39: 1–10 pmid:18983920
- 93. Ghanim M, Kontsedalov S, Czosnek H. Tissue-specific gene silencing by RNA interference in the whitefly Bemisia tabaci (Gennadius). Insect Biochem Mol Biol. 2007; 37: 732–738. pmid:17550829
- 94. Baum JA, Bogaert T, Clinton W, Heck GR., Feldmann P, Ilagan O, et al, Control of coleopteran insect pests through RNA interference. Nature Biotechnol. 2007; 25: 1322–1326
- 95. Terenius O, Papanicolaou A, Garbutt JS, Eleftherianos I, Huvenne H, Sriramana K, et al. RNA interference in Lepidoptera: An overview of successful and unsuccessful studies and implications for experimental design. J Insect Phys. 2011; 57: 231–245.
- 96. Bettencourt R, Terenius O, Faye I. Hemolin gene silencing by ds-RNA injected into Cecropia pupae is lethal to next generation embryos. Insect Mol Biol. 2002; 11: 267–271. pmid:12000646
- 97. Hirai M, Terenius O, Faye I. Baculovirus and dsRNA induce hemolin, but no antibacterial activity, in Antheraea pernyi. Insect Mol Biol. 2004; 13; 399–405.
- 98. Terenius O, Bettencourt R, Lee SY, Li W, Soderhall K, Faye I. RNA interference of hemolin causes depletion of phenoloxidase activity in Hyalophora cecropia. Dev Comp Immunol. 2007; 31: 571–575. pmid:17129606
- 99. Palli SR. RNA interference in Colorado potato beetle: steps toward development of dsRNA as a commercial insecticide. Curr Opin Insect Sci. 2014; 6: 1–8. pmid:26705514
- 100. Gandhe AS, John SH, Nagaraju J. Noduler, a novel immune up-regulated protein mediates nodulation response in insects. J Immunol. 2007; 179: 6943–6951. pmid:17982085
- 101. Huvenne H, Smagghe G. Mechanisms of dsRNA uptake in insects and potential of RNAi for pest control: a review. J Insect Physiol. 2010; 56: 227–35 pmid:19837076
- 102. Garbutt JS, Reynolds SE. Induction of RNA interference genes by double-stranded RNA; implications for susceptibility to RNA interference. Insect Biochem Mol Biol. 2012; 42: 621–8. pmid:22634162