Literature DB >> 23308130

Reference gene selection for qRT-PCR analysis in the sweetpotato whitefly, Bemisia tabaci (Hemiptera: Aleyrodidae).

Rumei Li1, Wen Xie, Shaoli Wang, Qingjun Wu, Nina Yang, Xin Yang, Huipeng Pan, Xiaomao Zhou, Lianyang Bai, Baoyun Xu, Xuguo Zhou, Youjun Zhang.   

Abstract

BACKGROUND: Accurate evaluation of gene expression requires normalization relative to the expression of reliable reference genes. Expression levels of "classical" reference genes can differ, however, across experimental conditions. Although quantitative real-time PCR (qRT-PCR) has been used extensively to decipher gene function in the sweetpotato whitefly Bemisia tabaci, a world-wide pest in many agricultural systems, the stability of its reference genes has rarely been validated.
RESULTS: In this study, 15 candidate reference genes from B. tabaci were evaluated using two Excel-based algorithms geNorm and Normfinder under a diverse set of biotic and abiotic conditions. At least two reference genes were selected to normalize gene expressions in B. tabaci under experimental conditions. Specifically, for biotic conditions including host plant, acquisition of a plant virus, developmental stage, tissue (body region of the adult), and whitefly biotype, ribosomal protein L29 was the most stable reference gene. In contrast, the expression of elongation factor 1 alpha, peptidylprolyl isomerase A, NADH dehydrogenase, succinate dehydrogenase complex subunit A and heat shock protein 40 were consistently stable across various abiotic conditions including photoperiod, temperature, and insecticide susceptibility.
CONCLUSION: Our finding is the first step toward establishing a standardized quantitative real-time PCR procedure following the MIQE (Minimum Information for publication of Quantitative real time PCR Experiments) guideline in an agriculturally important insect pest, and provides a solid foundation for future RNA interference based functional study in B. tabaci.

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Year:  2013        PMID: 23308130      PMCID: PMC3540095          DOI: 10.1371/journal.pone.0053006

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


Introduction

In recent years, quantitative real-time PCR (qRT-PCR) has been widely utilized for gene expression analysis because of its sensitivity, accuracy, reproducibility, and most importantly, quantitativeness [1]–[4]. There is no argument that qRT-PCR is a powerful tool for gene expression analysis, data analysis, and subsequent interpretation. However, interpretation can be challenging due to variation caused by pipetting error and different extraction techniques, transcription and amplification efficiencies among different samples [2], [5]–[7]. Therefore, controlling for internal differences and reducing errors between samples requires the use of reliable reference genes for normalization in gene expression analysis [8]. Traditionally, housekeeping genes including 18S ribosomal RNA, glyceraldehyde-3-phosphate dehydrogenase, elongation factor, ubiquitin-conjugating enzyme, alpha microtubules protein, and beta microtubule protein have been used extensively as endogenous controls for the normalization of qRT-PCR data, but the expression levels of these reference genes can differ under environmental conditions [9]. Based on previous studies, it is evident that the existence of a single universal reference gene suited for all experimental conditions is highly unlikely [5], [10]–[12]. Therefore, selection of reliable reference genes that are consistently expressed under specific experimental conditions is critical for the interpretation of qRT-PCR results. Currently, two Excel-based software tools including geNorm (http://medgen.ugent.be/~jvdesomp/genorm/) [10] and Normfinder (http://www.mdl.dk/publications normfinder.htm) [13], are widely used for evaluating the performance of reference genes. The geNorm program was used to calculate the mean pair-wise variation between an individual gene and all other tested candidate reference genes and the results were shown as expression stability (M). Normfinder is an algorithm for estimation of reference genes among a set of candidates. It ranks the candidate genes based on their expression stability. The sweetpotato whitefly Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) is one of the most destructive insect pests worldwide [14]–[15]. This whitefly damages many crops by direct feeding and by vectoring 114 plant viruses [16]. Bemisia tabaci has long been thought to comprise morphologically indistinguishable biotypes that often differ in host range, fecundity, insecticide resistance, transmission competency for begomoviruses, and the symbionts they harbor [14], [16], [17]. Recent studies suggest that most of these biotypes represent genetically distinct cryptic species [18]–[19], among which the B biotype of the Middle East-Minor Asia 1 and the Q biotype of the Mediterranean group are the most invasive and destructive [20]. Although B. tabaci was first recorded in China in the late 1940s, crop damage caused by this insect did not become serious until the introduction of the B biotype in the 1990s [21]. The Q biotype of B. tabaci was first detected in Yunnan Province, China in 2003 [22]. Since then, the Q biotype has gradually displaced the established B populations and has become the dominant B. tabaci in most of China [23]. To examine the temporal and spatial changes of gene expression in B. tabaci, β-actin and α-tubulin are the most frequently used endogenous reference genes in qRT-PCR analyses [24]–[27]. These genes were selected without the companion validation study to evaluate their suitability under specific experimental conditions. Previous studies have demonstrated that the expression of β-actin can be significantly influenced by tissue type [12]. Bustin and his colleagues proposed a MIQE guideline (Minimum Information for publication of Quantitative real time PCR Experiments) [28] to standardize qRT-PCR analysis; reference gene selection is an integral part of their recommendations. In this study, 15 housekeeping genes from a parallel B. tabaci transcriptome study [29] were selected as candidate reference genes. The overall goal of this research is to develop a standardized qRT-PCR analysis in B. tabaci following the MIQE guideline. Specifically, we evaluate the stability and performance of the above mentioned candidate reference genes under different experimental conditions including five biotic factors (host, acquisition of a plant virus, developmental stage, tissue, and whitefly biotype) and three abiotic factors (photoperiod, temperature, and insecticide exposure). The choice and number of reference genes needed under various conditions are investigated and recommended.

Materials and Methods

Ethics Statement

Bemisia tabaci B biotype strains used in this study were initially collected in the field at Beijing in 2000, and have been maintained in a greenhouse at the Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences. The species in the genus Aleyrodidaeare common agricultural pests and are not included in the “List of Protected Animals in China”. No specific permits were required for the described field studies.

Candidate Reference Genes

Housekeeping genes from a previous B. tabaci transcriptomic study [29] were selected as candidate reference genes including β-actin (Actin), 18S rRNA (18S), heat shock protein (HSP20, HSP40, HSP70, HSP90), γ-tubulin, 60S ribosomal protein L29 (RPL29), succinate dehydrogenase complex subunit A (SDHA), flavoprotein, glyceraldehyde phosphate dehydrogenase (GAPDH), elongation factor 1 alpha (EF-1α), peptidylprolyl isomeraseA (PPIA), NADH dehydrogenase (NADH), Myosin light chain (Myosin L), and adenosine triphosphate enzyme (ATPase). Primer 5.0 (http://www.premierbiosoft.com/) was used to design primers for qRT-PCR analysis. The validity of these candidate reference genes were evaluated under selected biotic and abiotic conditions described in the following sections.

Biotic Conditions

Host plant

Bemisia tabaci B biotype was maintained on three different host plants including cabbage, tomato, and cucumber [30]. A total of 180 3-day-old adults were collected, snap frozen in liquid nitrogen, and stored at −80°C before qRT-PCR analysis.

Acquisition of a plant virus

Tomato plants infected with Tomato yellow leaf curl virus (TYLCV) were obtained by Agrobacterium tumefaciens-mediated inoculation using a cloned TYLCV genome (GenBank accession ID: AM282874) [31]. Plants were inoculated with the virus at the 3-true-leaf stage. Viral infection of tomato plants was confirmed by the development of characteristic leaf curl symptoms and was further validated by molecular analysis [32]. Viruliferous B. tabaci were obtained by caging non-viruliferous B. tabaci adults with TYLCV-infected tomato plant for a 72 h acquisition access period [33]. Non-viruliferous B. tabaci were obtained by caging non-viruliferous B. tabaci adults with healthy tomato plants for 72 h. A total of 180 3-day-old adult whiteflies from both virus-infected and virus-free tomato plants, respectively, were snap frozen and stored as described earlier.

Developmental stage

Three developmental stages (egg, pupa, and adult) were collected from B. tabaci B biotype maintained on healthy cabbage plants. A total of 900 eggs, 900 pupae, and 300 adults were snap frozen and stored as described earlier.

Tissue

A dissection needle and a stereo microscope (Leica, DFC425) were used to obtain three body regions (head, thorax, and abdomen) from 3-day-old B. tabaci adults (TH-S). These sections were dissected from adults reared on cabbage plants; snap frozen, and stored as described earlier.

Whitefly biotype

Bemisia tabaci B and Q biotype strains were collected from Beijing, China in 2000 and 2008, respectively, and have been maintained in a greenhouse at the Institute of Vegetables and Flowers, Chinese Academy of Agricultural Sciences [30].

Abiotic Conditions

Photoperiod

A total of 200 3-day-old B. tabaci adults were placed into nine screen cages, respectively, and provisioned with cabbage plants at the 5 to 7-true-leaf stage. These cages were kept in growth chambers (27±0.5°C, 60±10% RH) with photoperiods (L/D) of 24∶0, 0∶24, and 14∶10, respectively. After 96 h, B. tabaci adults were snap frozen and stored as described earlier.

Temperature

A total of 720 3-day-old B. tabaci adults (80 whiteflies×9 replications) were collected from cabbage plant and placed individually into 30 ml specimen tubes. The tubes were then placed in climatic chambers at 4.0, 25.0, and 37.5°C, respectively. After 1 h, the live adults were snap frozen and stored as described earlier.

Insecticide susceptibility

Thiamethoxam susceptible (TH-S) and resistant (TH-R) B. tabaci strains were established from the same populations described previously [34]. Before sample collection, a leaf-dip bioassay [34] was conducted to confirm that the resistance factor [LC50 (TH-R)/LC50 (TH-S)] was over 70-fold. A total of 180 adults from both TH-S and TH-R were collected, snap frozen, and stored as described earlier.

Total RNA Extraction and cDNA Synthesis

Total RNA was extracted with a Trizol reagent (Invitrogen, Carlsbad, CA, USA). RNA was quantified by measuring the absorbance at 260 nm with a Nano Vue UV/Vis spectrophotometer (GE Healthcare). The purity of RNA was assessed at an absorbance ratio of OD260/280 and OD260/230, and the integrity was checked with 1% agarose gel electrophoresis. Then, 1 µg of RNA was used to synthesize the first-strand cDNA using the PrimeScript®RT reagent Kit (Takara Bio, Tokyo, Japan) with gDNA Eraser (Perfect Real Time, TaKara, Shiga, Japan) according to the manufacturer's protocol. The synthesized cDNA was stored at −20°C.

Quantitative Real-time PCR analysis

Quantitative Real-time PCR (qRT-PCR) was performed on an ABI 7500 real-time system. The cDNA of each sample representing one biological replicate was diluted to a working concentration of 17 ng/µl for the qRT-PCR analysis. The melt temperature was 60°C and product contained between 80 and 200 base pairs (Table 1). The 25 µl reaction system contained 1 µl of diluted cDNA, 11.25 µl of SYBR® Green Real-time PCR Master Mix (TIANGEN, Corp, Beijing, China), and 0.5 µl of each primer. The cycling parameters were as follows: 95°C for 3 min followed by 40 cycles of 95°C for 30 s, 60°C for 30 s, and 72°C for 35 s. A 3-fold serial dilution of cDNA was used to construct a standard curve to determine the PCR efficiency that would be used to convert the quantification cycles (Ct-values) into the relative quantities (relative gene expressions).
Table 1

Primers used for qRT-PCR analysis.

GeneAccession NumberPrimer sequences (5′to 3′)1 Amplicon (bp)Tm (°C)2 E (%)3 R 2 4
HSP20 EU934239F-AAGAAGTCAGCGTGAAAGTCGR-GTACCTCCTAGTGAAAGATCGG 1076099.50.9978
HSP40 EE597535F-AGATGAGGCTCATGATGGTCAAR-TGAGAAGCGCATTGCATTGT 8160109.40.9992
HSP70 EU934240F-AGCACTCCGGCGTCTACGR-CGAACCTGGCACGGGACAC 13460109.60.9944
HSP90 EU934241F-ATCGCCAAATCTGGAACTAAAGCR-GTGTTTTGAGACGACTGTGACGGTG 13560100.90.9951
PPIA JU470456F-ATGTTTTGGGCTTTGGTCR-CGTTGCCATCTGAATGAAATAC 1486096.90.9988
EF-1α EE600682F-TAGCCTTGTGCCAATTTCCGR-CCTTCAGCATTACCGTCC 11060103.90.998
SDHA JU470457F-GCGACTGATTCTTCTCCTGCR-TGGTGCCAACAGATTAGGTGC 1416092.40.9986
NADH JU470455F-ATAGTTGGCTGTAGAACCAGAGTGR-ACACGAAGGGAAGAGCACATA 966093.50.9973
γ- tubulin JU470458F-CCACAATCCATGCAAATCR-CCGAAATGGCCTCTGCTA 1176075.30.9832
Myosin L EE597481F-TTTCAGACGAGGATGTCGCAR-CGTCATAGATTTCGAACGCG 8160108.01.0000
RPL29 EE596314F-TCGGAAAATTACCGTGAGR-GAACTTGTGATCTACTCCTCTCGTG 14460101.30.9909
ATPase JU470453F-AGAGCGAGTGTTTGGGTGR-GACGGCGATTCGAGAAGG 1386098.90.9994
18S U20401F-CGGCTACCACATCCAAGGAAR-GCTGGAATTACCGCGGCT 1876099.50.9987
Actin AF071908F-TCTTCCAGCCATCCTTCTTGR-CGGTGATTTCCTTCTGCATT 1746095.00.9973
GAPDH JU470454F-GGACACGGAAAGCCATACCAGR-ACCACCGCTACCCAAAAGACC 1666077.00.9943

: F, forward primer; R, reverse primer;

: Tm, Annealing temperature;

: E, Efficiency;

: R 2, Coefficient of determination.

: F, forward primer; R, reverse primer; : Tm, Annealing temperature; : E, Efficiency; : R 2, Coefficient of determination.

Data Analysis

Expression of reference genes was evaluated with two web-based analysis tools: geNorm and NormFinder. geNorm was used to calculate the M stability value as the mean pairwise variation between an individual gene and all other tested candidate genes. The lower the M value, the more stable the reference genes. The value of Vn/Vn+1 indicates the pairwise variation between two sequential normalization factors and determines the optimal number of reference genes required for accurate normalization. A value below 0.15 indicates that an additional reference gene will not significantly improve normalization. Normfinder evaluates the overall variation of the candidate reference genes under the experimental conditions and estimates the variation between and within groups. For each candidate gene, Normfinder provides a stability value that is a direct and rapid measurement of expression variation. This stability value enables the user to estimate the systematic error introduced when selecting a suitable reference gene.

Results

Expression profiles of candidate reference genes

For each reference gene, a dissociation curve with a single-peak ensured that the primer sets amplified a unique PCR product ranging from 81 to 187 bp. The PCR efficiency was consistently high for candidate reference genes except γ-tubulin (75.3%) and GAPDH (77.0%) (Table 1). The raw Ct values ranged from 11.63 (18S) to 31.11 (γ-tubulin) with different host plants; from 12.02 (18S) to 31.25 (HSP70) with different photoperiods; from 11.85 (18S) to 30.37 (γ-tubulin) with different temperatures; from 11.49 (18S) to 30.86 (HSP70) for non-viruliferous and viruliferous adults; from 12.44 (18S) to 29.58 (γ-tubulin) in thiamethoxam-resistant and -susceptible adults; from 12.17 (18S) to 29.58 (γ-tubulin) for different developmental stages; from 9.15 (18S) to 33.13 (γ-tubulin) for different tissues; and from 12.56 (18S) to 30.71 (γ-tubulin) for B and Q biotype adults.

Stability of candidate reference gene expression

geNorm

The geNorm program was used to calculate the average expression stability values (M stability values) and to plot the influence of different factors using pairwise comparisons. The least stable genes have the highest M values and were successively excluded. The program also indicated the minimum number of reference genes for accurate normalization in B. tabaci by the pairwise variation value. Values (V2/3) under 0.15 shows that no additional genes are required for the normalization (Figure S1 and S2). For different hosts, reference genes with M values<0.5 are ranked (from highest to lowest stability) in the order of PPIA+EF1-α > HSP90 > HSP40 > RPL29 (Figure 1). For virus status (with or without TYLCV), RPL29, HSP90, and HSP40 are the most suited reference genes. For developmental stage, the ranking of reference gene stability among those with M values < 0.5 is HSP90+NADH > 18S > γ-tubulin > RPL29 > EF1-α > HSP20 > HSP40 > SDHA. For different B. tabaci tissues, HSP20, HSP40, HSP90, PPIA, RPL29, and EF1-α are relatively stable. For whitefly biotype, reference genes with M values < 0.5 are ranked (from highest to lowest stability) in the order of HSP40+NADH > SDHA > HSP90 > EF1-α > ATPase > PPIA > γ-tubulin > RPL29 > HSP20. Based on data obtained with five biotic factors, the ideal reference genes according to geNorm are RPL29, HSP40, and HSP90.
Figure 1

Reference genes selected by geNorm under various biotic conditions.

The expression stability measure (M) is the mean of the stability values of the remaining genes. The least stable genes have the highest M values. The genes listed here are considered stable based on a cutoff M value of less than 0.5. Each circle with a distinct color represents a different set of biotic condition. Genes located within one circle are stable under a specific biotic condition, and genes shared with multiple circles are stable across those conditions.

Reference genes selected by geNorm under various biotic conditions.

The expression stability measure (M) is the mean of the stability values of the remaining genes. The least stable genes have the highest M values. The genes listed here are considered stable based on a cutoff M value of less than 0.5. Each circle with a distinct color represents a different set of biotic condition. Genes located within one circle are stable under a specific biotic condition, and genes shared with multiple circles are stable across those conditions. For photoperiod, the M values are <0.5 for all candidate reference genes (Figure 2). For temperature, reference genes with M values <0.5 are ranked (from highest to lowest stability) in the order of EF1-α+NADH > SDHA > RPL29 > PPIA > HSP40 > ATPase > 18S > GAPDH > γ-tubulin. For pesticide resistance, reference genes with M values<0.5 are ranked (from highest to lowest stability) in the order of PPIA+NADH>HSP20>HSP40>HSP90>18S>EF1-α>SDHA. Based on data obtained from three abiotic factors, the ideal reference genes are EF1-α, PPIA, NADH, SDHA, and HSP40.
Figure 2

Reference genes selected by geNorm under various abiotic conditions.

The expression stability measure (M) is the mean of the stability values of the remaining genes. The least stable genes have the highest M values. The genes listed here are considered stable based on a cutoff M value of less than 0.5. Each circle with a distinct color represents a different set of biotic condition. Genes located within one circle are stable under a specific abiotic condition, and genes shared with multiple circles are stable across those conditions.

Reference genes selected by geNorm under various abiotic conditions.

The expression stability measure (M) is the mean of the stability values of the remaining genes. The least stable genes have the highest M values. The genes listed here are considered stable based on a cutoff M value of less than 0.5. Each circle with a distinct color represents a different set of biotic condition. Genes located within one circle are stable under a specific abiotic condition, and genes shared with multiple circles are stable across those conditions.

Normfinder

Normfinder indicated that RPL29, GAPDH, and NADH are the most stable reference genes for host plants, tissues, biotypes, respectively (Table 2). Specifically, for developmental stages and viruliferous/non-viruliferous B. tabaci, SDHA is the most stable reference gene. A similar trend is observed under selected abiotic factors, in which HSP20, HSP40, and EF1-α are ranked as the most stable reference genes for photoperiod, temperature, and insecticide susceptibility, respectively (Table 3).
Table 2

Ranking of candidate reference genes in response to biotic factors.

RankHostTYLCVDevelopmental stagesTissueBiotype
GeneSV1 GeneSVGeneSVGeneSVGeneSV
1 RPL29 0.197 SDHA 0.207 SDHA 0.212 GAPDH 0.116 NADH 0.150
2 HSP90 0.318 RPL29 0.394 HSP90 0.332 EF-1α 0.203 HSP40 0.172
3 SDHA 0.351 HSP90 0.394 EF-1α 0.359 RPL29 0.295 HSP90 0.178
4 NADH 0.418 γ-tubulin 0.457 RPL29 0.378 HSP70 0.323 RPL29 0.263
5 EF-1α 0.457 18s 0.502 NADH 0.393 ATPase 0.340 EF-1α 0.344
6 PPIA 0.491 HSP40 0.592 HSP20 0.525 SDHA 0.363 ATPase 0.425
7 ATPase 0.568 PPIA 0.593 HSP40 0.599 HSP20 0.559 HSP20 0.489
8 HSP40 0.604 NADH 0.633 18s 0.605 HSP40 0.675 γ-tubulin 0.536
9 γ-tubulin 0.638 HSP20 0.672 ATPase 0.605 HSP90 0.807 PPIA 0.585
10 GAPDH 0.672 EF-1α 0.797 GAPDH 0.706 PPIA 0.858 Myosin L 0.812
11 HSP20 0.681 ATPase 0.875 PPIA 0.759 Actin 0.979 18s 0.841
12 18s 0.710 HSP70 1.221 Myosin L 0.901 NADH 1.244 Actin 0.945
13 Actin 1.186 Actin 1.233 γ-tubulin 0.936 18s 1.318 GAPDH 1.125
14 Myosin L 1.216 Myosin L 1.310 Actin 1.101 Myosin L 1.512 SDHA 1.455
15 HSP70 1.240 GAPDH 1.587 HSP70 1.252 γ-tubulin 2.121 HSP70 1.563

: Stability Value was evaluated by Normfinder.

Table 3

Ranking of candidate reference genes in response to abiotic factors.

RankPhotoperiodTemperatureInsecticide Susceptibility1
GeneSV2 GeneSVGeneSV
1 HSP90 0.069 HSP40 0.263 EF-1α 0.162
2 HSP20 0.093 HSP90 0.353 ATPase 0.269
3 ATPase 0.162 EF-1α 0.375 SDHA 0.279
4 HSP40 0.181 PPIA 0.425 HSP90 0.335
5 RPL29 0.244 NADH 0.429 PPIA 0.364
6 HSP70 0.262 SDHA 0.451 HSP20 0.447
7 EF-1α 0.292 RPL29 0.473 RPL29 0.453
8 SDHA 0.293 γ-tubulin 0.569 18s 0.502
9 NADH 0.306 18s 0.587 Actin 0.514
10 γ-tubulin 0.341 ATPase 0.589 NADH 0.590
11 PPIA 0.379 GAPDH 0.684 HSP40 0.607
12 Actin 0.385 Myosin L 0.917 γ-tubulin 0.680
13 GAPDH 0.463 Actin 1.306 HSP70 0.767
14 Myosin L 0.605 HSP20 1.804 GAPDH 1.461
15 18s 0.893 HSP70 3.981 Myosin L 1.484

: Thiamethoxam-resistant and -susceptible whiteflies.

: Stability Value was evaluated by Normfinder.

: Stability Value was evaluated by Normfinder. : Thiamethoxam-resistant and -susceptible whiteflies. : Stability Value was evaluated by Normfinder.

Discussion

Because it is highly sensitive, specific, accurate, and reproducible, qRT-PCR is, in many ways, superior to conventional methods (northern hybridization and semi-quantitative PCR), and has become an essential tool for gene expression analysis [13], [28], [35]–[39]. qRT-PCR analysis, however, is influenced greatly by the selection of reference genes [10], [40]–[41]. The endogenous reference genes should be stable across different experimental treatments; otherwise, a variable reference gene can compromise the qRT-PCR analysis by introducing artificial changes or masking true changes in target gene expression. Some commonly used reference genes can vary substantially in response to specific experimental conditions [11], [42]–[43]. In this study, we used two Excel-based algorithms geNorm and Normfinder to evaluate the stability of 15 candidate reference genes in B. tabaci in response to five biotic factors (host, virus, stage, tissue, and biotype) and three abiotic factors (photoperiod, temperature, and insecticide susceptibility). A major conclusion of this study is that many of the candidate genes in B. tabaci should not be used as the default reference genes because their expression is highly variable under certain conditions. Our results indicate that the stability of reference gene expression must be validated for each experimental condition under investigation. The ranking of these reference genes differs somewhat for geNorm and Normfinder, because these programs have different algorithms and different sensitivities toward co-regulated reference genes. Despite the discrepancies, both programs identified a similar set of reference genes suited for the respective experimental conditions. The ideal reference genes in response to biotic factors were RPL29, HSP40, and HSP90 according to geNorm and RPL29 based on Normfinder, respectively. Combing these results, RPL29 is a consensus reference gene that is reliable across a range of biotic conditions (Table 4), and this is consistent with the performance of the other ribosomal protein L32, a widely used single normaliser in gene expression studies [11], [43]–[47]. Despite subtle ranking differences between geNorm and Normfinder, the ideal reference genes in response to abiotic factors were determined to be EF1-α, PPIA, NADH, SDHA, and HSP40 (Table 4). EF-1α has rarely been used as a normaliser in the past but has recently been selected as a suitable reference gene in salmon [48], humans [47], [49], Orthoptera [46], [50], and Hymenoptera [43]. PPIA was considered sufficiently stable for normalization in this study, which is consistent with a previous report in human cervical tissues [47].
Table 4

Recommended reference genes for various experimental conditions.

Experimental ConditionsRecommended Reference Genes
Biotic Factors
Host HSP90 RPL29 EF-1α
TYLCV HSP90 RPL29
Developmental stages NADH HSP90 RPL29
Tissue RPL29 EF-1α
Biotype NADH HSP90 EF-1α
Abiotic Factors
Photoperiod HSP40 HSP90 PPIA
Temperature EF-1α NADH SDHA
Thiamethoxam susceptibility PPIA EF-1α HSP20
Another conclusion of our study is that some genes that have been consistently used for the normalization study showed high levels of variation in response to certain treatments. Previously, 18S has been considered an ideal reference gene because the expression level of rRNA appears to vary considerably less than mRNA [51]. In this study, the raw Ct values of 18S ranged from 9.92 to 15.94 depending on insect body region and host plants, suggesting that the expression of 18S can be highly variable and consequently, it could not be used as a reference gene under certain experimental conditions. This result is consistent with some earlier studies on 18S RNA [11], [42]. Another commonly used reference gene, actin, encodes a major component of the protein scaffold that supports the cell and determines its shape. The expression of actin is moderately abundant in most cell types, and actin has been used extensively as a reference gene in B. tabaci and in many other insects including the desert locust [46], European honey bee [45], and two species of Collembola [52]. In our study actin was not stable among different tissues (body regions) and hosts; disqualifying actin as a suitable reference gene under these conditions. In recent years, more researchers have adopted a multiple reference gene approach to analyze gene expression [38], [53]. Our results demonstrated that the expression of several reference genes from B. tabaci were consistently stable across selected experimental conditions. However, the best-suited reference genes can be different in response to diverse biotic and abiotic factors (Table 4). Our finding is the very first step toward establishing a standardized qRT-PCR procedure following the MIQE (Minimal Information required for Publication of Quantitative Real-Time PCR) guideline in an agriculturally important insect pest. More importantly, this study provides a solid foundation for future RNAi-based functional study in B. tabaci. Optimal number of reference genes required for accurate normalization of gene expression under biotic conditions. Based on geNorm analysis, average pairwise variations are calculated between the normalization factors NFn and NFn+1 to indicate whether inclusion of an extra reference gene increases the stability of the normalization factor. Values<0.15 indicate that additional genes are not required for the normalization of gene expression. (TIFF) Click here for additional data file. Optimal number of reference genes required for accurate normalization of gene expression under abiotic conditions. Based on geNorm analysis, average pairwise variations are calculated between the normalization factors NFn and NFn+1 to indicate whether inclusion of an extra reference gene adds to the stability of the normalization factor. Values<0.15 indicate that additional genes are not required for the normalization of gene expression. (TIFF) Click here for additional data file.
  45 in total

1.  Further spread of and domination by Bemisia tabaci (Hemiptera: Aleyrodidae) biotype Q on field crops in China.

Authors:  Huipeng Pan; Dong Chu; Daqing Ge; Shaoli Wang; Qingjun Wu; Wen Xie; Xiaoguo Jiao; Baiming Liu; Xin Yang; Nina Yang; Qi Su; Baoyun Xu; Youjun Zhang
Journal:  J Econ Entomol       Date:  2011-06       Impact factor: 2.381

2.  Induction effects of host plants on insecticide susceptibility and detoxification enzymes of Bemisia tabaci (Hemiptera: Aleyrodidae).

Authors:  Wen Xie; Shaoli Wang; Qingjun Wu; Yuntao Feng; Huipeng Pan; Xiaoguo Jiao; Long Zhou; Xin Yang; Wei Fu; Haiyuan Teng; Baoyun Xu; Youjun Zhang
Journal:  Pest Manag Sci       Date:  2010-10-28       Impact factor: 4.845

3.  Evidence for transovarial transmission of tomato yellow leaf curl virus by its vector, the whitefly Bemisia tabaci.

Authors:  M Ghanim; S Morin; M Zeidan; H Czosnek
Journal:  Virology       Date:  1998-01-20       Impact factor: 3.616

Review 4.  Absolute quantification of mRNA using real-time reverse transcription polymerase chain reaction assays.

Authors:  S A Bustin
Journal:  J Mol Endocrinol       Date:  2000-10       Impact factor: 5.098

5.  Selection of reference genes for real-time polymerase chain reaction analysis in tissues from Bombus terrestris and Bombus lucorum of different ages.

Authors:  Darina Hornáková; Petra Matousková; Jirí Kindl; Irena Valterová; Iva Pichová
Journal:  Anal Biochem       Date:  2009-09-12       Impact factor: 3.365

6.  Susceptibility to insecticides in the Q biotype of Bemisia tabaci is correlated with bacterial symbiont densities.

Authors:  Murad Ghanim; Svetlana Kontsedalov
Journal:  Pest Manag Sci       Date:  2009-09       Impact factor: 4.845

7.  Thermotolerance and gene expression following heat stress in the whitefly Bemisia tabaci B and Q biotypes.

Authors:  Assaf Mahadav; Svetlana Kontsedalov; Henryk Czosnek; Murad Ghanim
Journal:  Insect Biochem Mol Biol       Date:  2009-08-14       Impact factor: 4.714

8.  Assessment and validation of a suite of reverse transcription-quantitative PCR reference genes for analyses of density-dependent behavioural plasticity in the Australian plague locust.

Authors:  Marie-Pierre Chapuis; Donya Tohidi-Esfahani; Tim Dodgson; Laurence Blondin; Fleur Ponton; Darron Cullen; Stephen J Simpson; Gregory A Sword
Journal:  BMC Mol Biol       Date:  2011-02-16       Impact factor: 2.946

9.  Evaluation of potential reference genes in real-time RT-PCR studies of Atlantic salmon.

Authors:  Pål A Olsvik; Kai K Lie; Ann-Elise O Jordal; Tom O Nilsen; Ivar Hordvik
Journal:  BMC Mol Biol       Date:  2005-11-17       Impact factor: 2.946

10.  Selection of reference genes for quantitative real-time PCR in a rat asphyxial cardiac arrest model.

Authors:  Kristina Langnaese; Robin John; Hannes Schweizer; Uwe Ebmeyer; Gerburg Keilhoff
Journal:  BMC Mol Biol       Date:  2008-05-28       Impact factor: 2.946

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  74 in total

1.  Evaluation of potential internal references for quantitative real-time RT-PCR normalization of gene expression in red drum (Sciaenops ocellatus).

Authors:  Bo-Guang Sun; Yong-Hua Hu
Journal:  Fish Physiol Biochem       Date:  2015-03-06       Impact factor: 2.794

2.  The autophagy pathway participates in resistance to tomato yellow leaf curl virus infection in whiteflies.

Authors:  Lan-Lan Wang; Xin-Ru Wang; Xue-Mei Wei; Huang Huang; Jian-Xiang Wu; Xue-Xin Chen; Shu-Sheng Liu; Xiao-Wei Wang
Journal:  Autophagy       Date:  2016-06-16       Impact factor: 16.016

3.  Validation of suitable reference genes for gene expression analysis in the halophyte Salicornia europaea by real-time quantitative PCR.

Authors:  Xinlong Xiao; Jinbiao Ma; Junru Wang; Xiaomeng Wu; Pengbo Li; Yinan Yao
Journal:  Front Plant Sci       Date:  2015-01-21       Impact factor: 5.753

4.  Temperature and Development Impacts on Housekeeping Gene Expression in Cowpea Aphid, Aphis craccivora (Hemiptera: Aphidiae).

Authors:  Chunxiao Yang; Huipeng Pan; Yong Liu; Xuguo Zhou
Journal:  PLoS One       Date:  2015-06-19       Impact factor: 3.240

5.  Selection of Reference Genes for RT-qPCR Analysis in the Monarch Butterfly, Danaus plexippus (L.), a Migrating Bio-Indicator.

Authors:  Huipeng Pan; Xiaowei Yang; Keith Bidne; Richard L Hellmich; Blair D Siegfried; Xuguo Zhou
Journal:  PLoS One       Date:  2015-06-01       Impact factor: 3.240

6.  Reference gene selection for quantitative real-time PCR normalization in larvae of three species of Grapholitini (Lepidoptera: Tortricidae).

Authors:  Jaryd A Ridgeway; Alicia E Timm
Journal:  PLoS One       Date:  2015-06-01       Impact factor: 3.240

7.  Selection of reference genes for RT-qPCR analysis in a predatory biological control agent, Coleomegilla maculata (Coleoptera: Coccinellidae).

Authors:  Chunxiao Yang; Huipeng Pan; Jeffrey Edward Noland; Deyong Zhang; Zhanhong Zhang; Yong Liu; Xuguo Zhou
Journal:  Sci Rep       Date:  2015-12-10       Impact factor: 4.379

8.  A Comprehensive Selection of Reference Genes for RT-qPCR Analysis in a Predatory Lady Beetle, Hippodamia convergens (Coleoptera: Coccinellidae).

Authors:  Huipeng Pan; Xiaowei Yang; Blair D Siegfried; Xuguo Zhou
Journal:  PLoS One       Date:  2015-04-27       Impact factor: 3.240

9.  Transcriptomic dissection of sexual differences in Bemisia tabaci, an invasive agricultural pest worldwide.

Authors:  Wen Xie; Litao Guo; Xiaoguo Jiao; Nina Yang; Xin Yang; Qingjun Wu; Shaoli Wang; Xuguo Zhou; Youjun Zhang
Journal:  Sci Rep       Date:  2014-02-14       Impact factor: 4.379

10.  CircMTO1 inhibits ox-LDL-stimulated vascular smooth muscle cell proliferation and migration via regulating the miR-182-5p/RASA1 axis.

Authors:  Ningning Ji; Yu Wang; Xinyan Gong; Shimao Ni; Hui Zhang
Journal:  Mol Med       Date:  2021-07-08       Impact factor: 6.354

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