Literature DB >> 29426915

Reference gene selection for RT-qPCR analysis in Harmonia axyridis, a global invasive lady beetle.

Xiaowei Yang1,2, Huipeng Pan1,3, Ling Yuan4, Xuguo Zhou5.   

Abstract

Harmonia axyridis is a voracious predator, a biological control agent, and one of the world most invasive insect species. The advent of next-generation sequencing platforms has propelled entomological research into the genomics and post-genomics era. Real-time quantitative PCR (RT-qPCR), a primary tool for gene expression analysis, is a core technique governs the genomic research. The selection of internal reference genes, however, can significantly impact the interpretation of RT-qPCR results. The overall goal of this study is to identify the reference genes in the highly invasive H. axyridis. Our central hypothesis is that the suitable reference genes for RT-qPCR analysis can be selected from housekeeping genes. To test this hypothesis, the stability of nine housekeeping genes, including 18S, 28S, ACTB, ATP1A1, GAPDH, HSP70, HSP90, RP49, and ATP6V1A, were investigated under both biotic (developmental time, tissue and sex), and abiotic (temperature, photoperiod, in vivo RNAi) conditions. Gene expression profiles were analyzed by geNorm, Normfinder, BestKeeper, and the ΔCt method. Our combined results recommend a specific set of reference genes for each experimental condition. With the recent influx of genomic information for H. axyridis, this study lays the foundation for an in-depth omics dissection of biological invasion in this emerging model.

Entities:  

Mesh:

Year:  2018        PMID: 29426915      PMCID: PMC5807316          DOI: 10.1038/s41598-018-20612-w

Source DB:  PubMed          Journal:  Sci Rep        ISSN: 2045-2322            Impact factor:   4.379


Introduction

The multicolored Asian lady beetle, Harmonia axyridis (Coleoptera: Coccinellidae), a generalist predator, preys on aphids and scale insects on crops and other plants[1]. Harmonia axyridis is native to central and eastern Asian. To exploit its ecosystem services, numerous releases were attempted in North America and Europe, as early as 1916[2,3]. Due to its broad range of preys and incredible consumption rate, H. axyridis indeed has been used to control aphids[4-6] and other sap-sucking arthropod pests[7,8]. However, the worldwide propagation of H. axyridis threatens the indigenous lady beetles and other non-target species[9-11]. Considered as “the most invasive ladybird on Earth”, the role of H. axyridis has shifted from a global biological control agent to an invasive alien species[12]. Multiple factors contribute to this transition. Predation of eggs and larvae of other lady beetle species is one of the reasons which leads to the decline of native species[13,14]. A higher level of resistance to infection is the other major reason to benefit its competition in the field[15-17]. The molecular basis of this resistance, however, is poorly understood. Double-stranded RNA (dsRNA) can induce sequence-specific posttranscriptional gene silencing in many organisms, i.e., RNA interference (RNAi)[18,19]. RNAi can not only investigate gene functions in vivo or in vitro, but also offers a novel approach with a brand new mode-of-action to control arthropod pests[20-24]. With a recent influx of genomic information for H. axyridis, there is an increasing need for the development of genetic tools to functionally interpret the sequencing data[20,24-26]. Real-time quantitative PCR (RT-qPCR) has been used primarily for gene expression quantification[27-29]. RT-qPCR analysis is highly sensitive, and its accuracy can be affected by RNA quantity, transcription efficiency, amplification efficiency and experimental procedures between samples. To avoid biases, normalization of gene expression is an essential step[30]. The most common practice is to compare a target gene expression with an internal reference gene[31]. Housekeeping genes, such as beta-actin (ACTB), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), and translation elongation factor 1-alpha (EF1A)[32,33] have been used extensively for RT-qPCR analysis. However, under any given experimental condition, the expression of these commonly used reference genes may vary substantially[34-37]. A systematic and customized study for each tested species is recommended for identifying appropriate reference genes[38,39]. The overall goal of this study is to identify candidate reference genes in the highly invasive H. axyridis. Our objective is to determine the suitable reference genes for RT-qPCR analysis in H. axyridis from selected housekeeping genes, an array of constitutively expressed genes maintaining the basic cellular functions in an organism. We evaluated the stability of nine housekeeping genes under selected biotic and abiotic conditions, respectively. The candidate genes include 18S ribosomal RNA(18S), 28S ribosomal RNA (28S), Na+/K+-ATPase subunit alpha 1 (ATP1A1), heat shock protein 70 (HSP70), heat shock protein 90 (HSP90), ribosomal protein 49 (RP49), V-ATPase subunit A (ATP6V1A), ACTB and GAPDH from H. axyridis. All these housekeeping genes have been used empirically as the reference genes for RT-qPCR analyses in other organisms, especially in insects. The specific environmental conditions range from biotic (developmental stage, tissue type, and sex) to abiotic treatments (temperature, photoperiod, and in vivo RNAi). As a result, a specific set of reference genes is recommended for each given condition.

Results

RT-qPCR analysis

For each candidate reference gene, a single amplicon was produced, as detected by agarose gel electrophoresis analysis and the melting curve analysis. Nonspecific bands were not found, and a single peak was observed in the melting curve analysis. A standard curve was generated for each gene, using a five-fold serial dilution of the pooled cDNA. Efficiency of RT-qPCR ranged between 90 and 110% (Table 1), which is considered standard[40]. Ct values of the nine candidate reference genes ranged from 8 to 27, covering all the experimental conditions (Fig. 1). While the vast majority of Ct values were found between 17 and 26, 18S was the most abundant transcript. ATP1A1, VATP6V1A, and RP49 were the least abundant candidate reference genes.
Table 1

Primer sequence, amplicon length and RT-qPCR analysis of candidate reference genes and a target gene.

GenesAccession NumberPrimer SequenceAmplicon Length(bp)PCR EfficiencyRegression Coefficient
Candidate reference gene
  18SGU073689.1AAGACGGACAGAAGCGAAAG1001.0290.9999
GGTTAGAACTAGGGCGGTATCT
  28SFJ621330.1ACCCGAAAGATGGTGAACTATG1011.0250.9995
CCAGTTCCGACGATCGATTT
  ACTBMF785104ACCCATCTACGAAGGTTATGC1221.0050.9962
CGGTGGTGGTGAAAGAGTAA
  ATP1A1AY303371.1CCGTAACTGGTGATGGTGTT1111.0660.981
GGATCATATCTGCCGCTTGT
  GAPDHMF785103TGACTACAGTTCACGCAACC1401.0600.9754
GATGACTTTGGTTACAGCCTTTG
  HSP70EF668009.1CCAAAGACAGGCTACCAAAGA1010.9820.9989
TGTCCAAACCGTAGGCAATAG
  HSP90FJ501962.1CGCCTTCCAAGCAGAAATTG1351.0780.9847
GTGAGAGACTGGTAACGGATTT
  RP49AB552923.1GCCGTTTCAAGGGACAGTAT840.9720.998
TGAATCCAGTAGGAAGCATGTG
  ATP6V1AMF785105GAGTTGGGTCCTGGTATTATGG1261.0930.9989
AGTTCTGGACAAACAAGGTACA
Target gene
  TPSFJ501960.1CATACTATAATGGTGCGTGTAATG1440.9430.9985
ATTTAAGGGCTTTGATTGTGC
Figure 1

Ct value of candidate reference genes in H. axyridis. The Ct values of candidate reference genes in all tested samples were documented. The dot indicates the maximum or minimum value of replicated samples, while whiskers indicate the standard error of the mean.

Primer sequence, amplicon length and RT-qPCR analysis of candidate reference genes and a target gene. Ct value of candidate reference genes in H. axyridis. The Ct values of candidate reference genes in all tested samples were documented. The dot indicates the maximum or minimum value of replicated samples, while whiskers indicate the standard error of the mean.

Stability of candidate reference genes under biotic conditions

For different developmental stages, geNorm ranked the stability from high to low as 18S = HSP70, 28S, ATP6V1A, ATP1A1, ACTB, HSP90, GAPDH, and RP49. Normfinder provided a ranking as 18S, HSP70, ATP6V1A, 28S, ATP1A1, ACTB, HSP90, GAPDH, and RP49. Bestkeeper offered a list as follows: 18S, HSP70, 28S, ATP1A1, GAPDH, HSP90, ACTB, ATP6V1A, and RP49 (Table 2). The best set of reference genes was recommended in Table 2. Integrating the results from all four programs, RefFinder identified the consensus top three candidates, 18S, HSP70 and 28S, across different developmental stages. 18S was the most stable gene, while RP49 was the least stable candidate (Table 2, Fig. 2A).
Table 2

Stability of candidate reference genes in response to biotic conditions.

Biotic ConditionsCandidateGenes geNorm Normfinder BestKeeper ΔCt Recommendation
StabilityRankingStabilityRankingStabilityRankingStabilityRanking
Development stage 18S 0.67410.59610.5411.21 18S, HSP70, 28S
28S 0.85530.92940.8231.334
ACTB 1.2161.0961.0371.466
ATP1A1 1.17450.99750.9341.45
ATP6V1A 1.07840.86131.0981.333
GAPDH 1.3481.20180.9351.558
HSP70 0.67410.7520.7621.252
HSP90 1.26871.15770.9961.57
RP49 1.40791.33391.1291.649
Tissue 18S 0.13810.45550.1410.733 28S, 18S, RP49
28S 0.13810.43640.1720.721
ACTB 0.7380.93980.9881.098
ATP1A1 0.57960.38130.4550.775
ATP6V1A 0.87191.27291.291.369
GAPDH 0.52650.56860.6670.826
HSP70 0.45140.37320.4240.744
HSP90 0.63770.59270.5560.877
RP49 0.35130.35610.3630.732
Sex 18S 0.60450.63460.3220.856 HSP90, RP49
28S 0.67360.82180.4640.958
ACTB 0.74680.60150.6980.825
ATP1A1 0.82190.99991.0291.089
ATP6V1A 0.36830.42440.5860.724
GAPDH 0.70670.76870.3530.937
HSP70 0.19710.37230.670.73
HSP90 0.45340.23110.1810.672
RP49 0.19710.28320.4950.671
Figure 2

Stability of candidate reference gene expression under biotic and abiotic experimental conditions. (A) Development stage, (B) Tissue, (C) Sex, (D) Biotic factors, (E) Temperature, (F) Photoperiod, (G) In vivo RNAi, and (H) Abiotic factors. A lower Geomean value suggests stable expression.

Stability of candidate reference genes in response to biotic conditions. Stability of candidate reference gene expression under biotic and abiotic experimental conditions. (A) Development stage, (B) Tissue, (C) Sex, (D) Biotic factors, (E) Temperature, (F) Photoperiod, (G) In vivo RNAi, and (H) Abiotic factors. A lower Geomean value suggests stable expression. For different tissues, the consensus top three candidates were 28S, 18S and RP49 according to RefFinder (Table 2, Fig. 2B). Specifically, 28S and ATP6V1A were the most and the least stable genes, respectively. For different sexes, the top three most stable candidates in both sexes were HSP90, RP40, and HSP70 according to RefFinder (Table 2, Fig. 2C). HSP90 and ATP1A1 were the most and the least stable genes, respectively. Based on the comprehensive ranking of RefFinder, the most to the least stable candidate reference genes under the biotic conditions was: 18S, 28S, ATP1A1, ACTB, HSP70, ATP6V1A, GAPDH, RP49, and HSP90 (Table 2; Fig. 2D).

Stability of candidate reference genes under abiotic conditions

According to RefFinder, the consensus top three candidate reference genes under different temperature regime were 18S, 28S and GAPDH (Table 3, Fig. 2E). Specifically, 18S and ATP6V1A was the most and least stable candidate, respectively. For different photoperiods, the top three candidates were 18S, 28S and HSP90 (Table 3, Fig. 2F), in which 18S and RP49 was the most and the least stable candidates, respectively. For in vivo RNAi experiments, the top three candidates were RP49, ATP1A1, and 28S (Table 3, Fig. 2G), in which RP49 and HSP90 was the most and the least stable candidates, respectively. Based on the comprehensive ranking of RefFinder, the most to the least stable candidate reference genes under the abiotic conditions was: 18S, 28S, GAPDH, HSP90, ATP6V1A, ACTB, ATP1A1, HSP70, and RP49 (Table 3; Fig. 2H).
Table 3

Stability of candidate reference genes in response to abiotic conditions.

Biotic ConditionsCandidateGenes geNorm Normfinder BestKeeper ΔCt Recommendation
StabilityRankingStabilityRankingStabilityRankingStabilityRanking
Temperature 18S 0.28710.27610.1920.551 18S, 28S, GAPDH
28S 0.28710.32230.1410.552
ACTB 0.3530.43850.2730.634
ATP1A1 0.39640.53570.3440.676
ATP6V1A 0.64890.68390.5770.819
GAPDH 0.42950.28520.3850.563
HSP70 0.60380.63580.6590.778
HSP90 0.49460.42440.5260.635
RP49 0.55270.50260.5880.687
Photoperiod 18S 0.2810.1710.1510.651 18S, 28S, HSP90
28S 0.2810.3540.2720.693
ACTB 0.55860.67160.6880.866
ATP1A1 0.69580.71480.5560.948
ATP6V1A 0.52150.59250.6270.85
GAPDH 0.48640.34430.4140.74
HSP70 0.62670.68870.4250.97
HSP90 0.43630.28820.3830.682
RP49 0.84191.25790.9391.359
In vivo RNAi 18S 0.30350.28460.1820.446 RP49, ATP1A1
28S 0.28340.24640.1110.413
ACTB 0.36570.53780.230.598
ATP1A1 0.22710.20130.2340.42
ATP6V1A 0.40680.39570.5180.527
GAPDH 0.22710.27150.3550.445
HSP70 0.32560.17520.3870.424
HSP90 0.48990.74490.7990.789
RP49 0.26330.10710.3760.391
Stability of candidate reference genes in response to abiotic conditions.

Recommended reference genes

For repeatable and consistent results, multiple normalizers (≥2 reference genes) are required for RT-qPCR analysis. GeNorm analysis evaluated all pairwise variations under each experimental conditions (Fig. 3). According to Vandesompele et al.[31], a Vn/Vn + 1 cutoff value of 0.15 means the addition of n + 1 reference gene is not necessary, i.e., the first n references genes are sufficient to normalize qRT-PCR results. The optimal number of reference genes was recommended in Tables 2 and 3, respectively, for biotic and abiotic conditions. Specifically, for different developmental stages, the recommended reference genes were 18S, HSP70, and 28S. For different tissues, the recommendation was 28S, 18S, and RP49. For different sexes, the recommendation was HSP90 and RP49. For different temperature treatments, the recommendation was 18S, 28S, and GAPDH. For different photoperiods, the recommendation was 18S, 28S, and HSP90. Finally, for in vivo RNAi, the best combination was RP49 and ATP1A1.
Figure 3

Optimal number of reference genes required for accurate normalization of gene expression. Based on geNorm analysis, average pairwise variations are calculated between the normalization factors NFn and NFn + 1. Values less than 0.15 indicate that n + 1 genes are not required for the normalization of gene expression.

Optimal number of reference genes required for accurate normalization of gene expression. Based on geNorm analysis, average pairwise variations are calculated between the normalization factors NFn and NFn + 1. Values less than 0.15 indicate that n + 1 genes are not required for the normalization of gene expression.

Validation of selected reference genes

The expression of TPS, a target gene, was evaluated to validate the recommended reference genes under different temperature treatments. Using the most stable reference gene 18S (NF 1), the top two stable reference genes 18S and 28S (NF 1–2), and the top three stable reference genes, 18S, 28S, and GAPDH (NF 1–3) for normalization, TPS expression profiles were similar throughout all three temperature regimes (Fig. 4). In comparison, when ATP6V1A, the least stable candidate (NF 9), was used as the reference gene, TPS expression patterns were inconsistent across different temperature treatments. Specifically, TPS expression was numerically higher at 10 °C, and lower at 22 and 30 °C (Fig. 4).
Figure 4

Validation of the recommended reference gene(s). Expression profiles of TPS under different temperature treatments were investigated using different normalization factors. Bars represent the means ± standard error of three biological replicates.

Validation of the recommended reference gene(s). Expression profiles of TPS under different temperature treatments were investigated using different normalization factors. Bars represent the means ± standard error of three biological replicates.

Discussion

RT-qPCR has been used extensively for quantification of mRNA expression and is a primary tool for genetic research. Although multiple factors, such as RNA extraction, storage, cDNA synthesis, and handling of materials and reagents, can affect the RT-qPCR analysis, a reliable reference gene (set) to overcome confounding variations in an empirical dataset is of particular importance. Normalization by internal controls is an integral part of the quantification process. A single or multiple stably expressed reference genes are required for the normalization process to achieve accurate and reliable results. Each candidate reference gene should be evaluated under specific experimental conditions to ensure a constant level of expression[35]. Following the “Minimum Information for Publication of Quantitative Real-Time PCR Experiments” (MIQE) guideline[41], reference gene selection study has been carried out for many insect species[34,42,43], and has become a routine practice to standardize RT-qPCR analysis. Due to different algorithms, stability ranking derived from the four analytical tools can vary. For example, when H. axyridis was injected with dsRNAs (in vivo RNAi), 28S was rated as the best reference gene by BestKeeper, RP49 was considered as the most stable by Normfinder as well as ΔCT method, whereas ATP1A1 and GAPDH were the top choice by geNorm. Despite some discrepancies in individual rankings, RP49 and ATP1A1 were consistently exhibited a higher level of stability than the rest of the candidates projected by all four algorithms (Table 3), suggesting the importance of (1) using a comprehensive analysis to interpret the dataset and (2) adopting the multiple instead of a single normalizer for RT-qPCR analysis. In recent years, researchers have been more receptive to use multiple reference genes to replace a single normalizer in RT-qPCR analysis[44]. The optimal number of reference genes is typically determined by geNorm. In this study, three reference genes for recommended for different developmental stages (18S, HSP70, and 28S), tissues (28S, 18S, and RP49), temperatures (18S, 28S and GAPDH), and photoperiods (18S, 28S and HSP90), while two reference genes were required for the reliable normalization in different sexes (HSP90 and RP49), and in vivo RNAi (RP49 and ATP1A1). Our combined results are, in part, consistent with previous studies of other Coccinellidae predatory species (Table 4), especially for ribosome RNAs (rRNAs).
Table 4

Recommended reference genes for RT-qPCR Analysis in Coleoptera.

SpeciesBiotic ConditionsAbiotic ConditionsOthers
Dev. Stage*TissueSexTemperaturePhotoperiodRNAi
Coccinellidae
  Harmonia axyridis (this study) 18S, HSP70, 28S 28S, 18S, Rp49/RpL32 HSP90, Rp49/RpL32, HSP70 18S, 28S, GAPDH 18S, 28S, HSP90 Rp49/RpL32, ATP1A1, 28S
  Hippodamia convergens[45] 28S, EF1A, CypA GAPDH, 28S, CypA GAPDH, CypA, 28S EF1A, 28S, ATP6V1A CypA, GAPDH, ATP6V1A CpyA, Actin, GAPDH
  Coleomegilla maculate[46] ATP6V1A, RPS18, EF1A NA** 16S, HSP70, RpS18 18S, TUBA, 12S NA 18S, 16S, 12S
  Coccinella septempunctata[47] 16S, 28S, NADH 28S, 16S, 18S NANANA ACTB, TUBA, EF1A
Chrysomelidae
  Diabrotica virgifera virgifera[57] ACTB, EF1A, RpS9 EF1A, GAPDH, TUBB NANANA RpS9, EF1A, GAPDH EF1A, GAPDH, TUBB (Bt)
  Leptinotarsa decemlineata[58] RP18, ARF1, RP4 RP18, ARF1, RP4 NANANANARP18, RP4, ARF1 (Insecticide)
  Galeruca daurica[59] SDHA, Rp49/RpL32, GST SDHA, TUBA, Rp49/RpL32 ACTB, TUBA, SDHA SDHA, TUBA, ACTB NANASDHA, TUBA, GAPDH (Diapause)
Cerambycidae
  Anoplophora glabripennis[60]NARp49/RpL32, GAPDH, SDF (Adults)NANANANAGAPDH, UBQ, Rp49/RpL32 (Larvae)
Tenebrionidae
  Tribolium castaneum[61,62]NANANANANANARPS3, RPS18, RPL13a (Fungus)RpL13A, RpS3, ACTB (UV)
Meloidae
  Mylabris cichorii[63]NANA TAF5, UBE3A, RPL22e (Male) NANANA UBE3A, RPL22e, TAF5 (Female)

*Developmental stages.

**Not Applicable. Please note that the abbreviation of gene names may differ among the cited references.

Recommended reference genes for RT-qPCR Analysis in Coleoptera. *Developmental stages. **Not Applicable. Please note that the abbreviation of gene names may differ among the cited references. Not surprisingly, rRNAs (e.g., 18S and 28S) were consistently stably expressed throughout the vast majority of biotic and abiotic conditions among the four Coccinellidae species, including H. axyridis, Hippodamia convergens[45], Coleomegilla maculate[46], and Coccinella septempunctata[47]. The over-representation of rRNAs in the total RNA pool (>80%), however, can potentially mask the subtle changes of the target gene expression[48]. Therefore, customized reference gene study is still a prerequisite for standardized RT-qPCR analysis in predatory lady beetles. A large body of works has demonstrated that there are no “universal” reference genes applicable for all cell and tissue types and various experimental conditions[49]. As a major structural protein, Actin has been used extensively as the internal control without any validation. In this study, however, Actin was one of the least stable candidates under both biotic and abiotic conditions, except the temperature treatment, which is consistent with the other three Coccinellidae species[45-47]. This study not only provides a standardized procedure for the quantification of gene expression, but also lays a foundation for the genomics and functional genomics dissection of H. axyridis, an emerging model in invasion biology[50].

Materials and Methods

Insects

Harmonia axyridis was originally collected from the University of Kentucky North Farm (38°07′N, 84°30′W). Harmonia axyridis colony was maintained at 23 ± 1 °C, 12 L:12D photoperiod, 50% relative humidity, and provisioned with pea aphids and sugar water for more than two months. Pea aphid clones were a gift from Dr. John Obrycki (University of Kentucky) and maintained on seedlings of fava beans in a glasshouse.

Experimental conditions

Biotic conditions

The developmental stages include eggs (N = 15), four larval instars (N = 5 for each instar, respectively), pupae (N = 1), and adults (one male and one female). Sex of adult beetles was determined by the presence or absence of the male genitalia. Tissues, including head, midgut, and carcass, were dissected from the fourth instar larvae (N = 5).

Abiotic conditions

To examine the effects of temperature, third instars were exposed to 10, 22, and 30 °C for 3 hours. For photoperiod, third-instar larvae were treated with a series of light and dark regime of 16 L:8D, 12 L:12D, and 8 L:16D for two days. For in vivo RNAi, H. axyridis ATP6V1A was the intended molecular target. Specifically, 280 ng of dsRNAs (56 nl, 5 μg/μl), derived from H. axyridis ATP6V1A (HA-dsRNA) and a plant gene, β-glucuronidase (GUS-dsRNA), were injected into the abdomen of third instars (N = 5). GUS-dsRNA is an exogenous control for the unintended silencing effects, and H2O is the vehicle control for the delivery agent of dsRNAs. Samples were collected on day-3 for RT-qPCR analysis.

Total RNA extraction and reverse transcription

Total RNA was extracted separately from each developmental stage, including eggs (N = 15), pupa (N = 1), and adult (N = 1) for each sex. For other experiments involving larvae, five individuals were pooled as one sample. Each experiment was repeated three times independently. Samples were preserved in 1.5 ml centrifuge tubes and snap frozen immediately in liquid nitrogen before storage at −80 °C. Total RNA was extracted using TRIzol® (Invitrogen, Carlsbad, CA) following the manufacturer’s instructions. Each sample of 2.0 μg RNA was reverse transcribed with random primers using the M-MLV reverse transcription kit (Invitrogen, Carlsbad, CA) according to the manufacturer’s recommendations.

Primer design and cloning of candidate reference genes

Primers for 18S, 28S, ATP1A1, HSP70, HSP90, and RP49 (Table 1) were designed based on their respective sequences from NCBI (http://www.ncbi.nlm.nih.gov/). Degenerate primers for ACTB, GAPDH, ATP6V1A were designed using CODEHOP (http://blocks.fhcrc.org/codehop.html). PCR amplifications were performed in 50 μl reactions containing 10 μl 5 × PCR Buffer (Mg2+ Plus), 1 μl dNTP mix (10 mM of each nucleotide), 5 μl of each primer (10 μM each), 0.25 μl of Go Taq (5 u/μl) (Promega, Madison, WI) and 25 ng first-strand cDNA. The PCR parameters were as follows: one cycle of 94 °C for 3 min; 35 cycles of 94 °C for 30s, 55 °C for 1 min and 72 °C for 1 min; a final cycle of 72 °C for 10 min. PCR products were purified and cloned into the pCR™4-TOPO® vector (Invitrogen, Carlsbad, CA) for sequencing confirmation. The primers for the target gene, TPS, were obtained from a previous work[51].

Quantitative real-time PCR (RT-qPCR)

Gene-specific primers (Table 1) were used in PCR reactions (20 μl) containing 7.0 μl water, 10.0 μl 2 × SYBR Green MasterMix (BioRad, Hercules, CA), 1.0 μl each specific primer (10 μM), and 10 ng first-strand cDNA. The RT-qPCR program included an initial denaturation for 3 min at 95 °C followed by 40 cycles of denaturation at 95 °C for 10 s, annealing for 30 s at 55 °C, and extension for 30 s at 72 °C. For melting curve analysis, a dissociation step cycle (55 °C for 10 s, and then 0.5 °C for 10 s until 95 °C) was added. Three technical replicates were analyzed for each biological replicate. Reactions were performed in a MyiQ Single Color Real-Time PCR Detection System (BioRad). The existence of one peak in melting curve analysis was used to confirm gene-specific amplification and to rule out non-specific amplification and primer-dimer generation. The RT-qPCR was determined for each gene using slope analysis with a linear regression model. Relative standard curves for the transcripts were generated with a serial dilution of cDNA. The corresponding RT-qPCR efficiencies (E) was calculated according to the equation:

Stability of gene expression

The stability of the nine candidate reference genes were evaluated using RefFinder (http://www.leonxie.com/referencegene.php), a web-based analysis tool which integrates all four major computational programs, including geNorm[31], NormFinder[52], BestKeeper[53], and the comparative ΔCt method[54]. geNorm calculates an expression stability value (M) for each gene and a pair-wise comparison. NormFinder ranks the set of candidate genes based on their expression stability in the given sample set. BestKeeper considers the Ct values of all candidate reference genes, to calculate standard deviation and coefficient of variation. ΔCt approach directly compares relative expression of ‘pairs of genes’ within each sample. Then, RefFinder assigned an appropriate weight of the four methods to an individual gene and calculated the geometric mean of their weights for the overall final ranking. Trehalose-6-phosphate synthase (TPS), the intermediate of trehalose, is a key component in insect energy metabolism and resilience[25,51,55]. The stability of candidate reference genes was evaluated using TPS as the target gene. TPS expression levels under different temperature treatments were calculated based on selected sets of candidate reference genes. Two separate normalization factors (NFs) have been computed based on (1) the geometric mean of the genes with the lowest Geomean values (as determined by RefFinder), and (2) a single normalizer with the lowest or highest Geomean value. Relative expression of TPS in different samples was calculated using the 2−ΔΔCt method[56].
  45 in total

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6.  Invasive harlequin ladybird carries biological weapons against native competitors.

Authors:  Andreas Vilcinskas; Kilian Stoecker; Henrike Schmidtberg; Christian R Röhrich; Heiko Vogel
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Authors:  Fred R Musser; Anthony M Shelton
Journal:  J Econ Entomol       Date:  2003-02       Impact factor: 2.381

8.  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

9.  Validation of reference genes for expression analysis by quantitative real-time PCR in Leptinotarsa decemlineata (Say).

Authors:  Xiao-Qin Shi; Wen-Chao Guo; Pin-Jun Wan; Li-Tao Zhou; Xiang-Liang Ren; Tursun Ahmat; Kai-Yun Fu; Guo-Qing Li
Journal:  BMC Res Notes       Date:  2013-03-13

10.  Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes.

Authors:  Jo Vandesompele; Katleen De Preter; Filip Pattyn; Bruce Poppe; Nadine Van Roy; Anne De Paepe; Frank Speleman
Journal:  Genome Biol       Date:  2002-06-18       Impact factor: 13.583

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  22 in total

1.  Involvement of glucose transporter 4 in ovarian development and reproductive maturation of Harmonia axyridis (Coleoptera: Coccinellidae).

Authors:  Yan Li; Sha-Sha Wang; Su Wang; Shi-Gui Wang; Bin Tang; Fang Liu
Journal:  Insect Sci       Date:  2021-10-29       Impact factor: 3.605

2.  Identification and Validation of Reference Genes for Quantitative Gene Expression Analysis in Ophraella communa.

Authors:  Yan Zhang; Jiqiang Chen; Guangmei Chen; Chao Ma; Hongsong Chen; Xuyuan Gao; Zhenqi Tian; Shaowei Cui; Zhenya Tian; Jianying Guo; Fanghao Wan; Zhongshi Zhou
Journal:  Front Physiol       Date:  2020-05-07       Impact factor: 4.566

3.  Evaluation of reference genes for real-time quantitative PCR analysis in southern corn rootworm, Diabrotica undecimpunctata howardi (Barber).

Authors:  Saumik Basu; Adriano E Pereira; Daniele H Pinheiro; Haichuan Wang; Arnubio Valencia-Jiménez; Blair D Siegfried; Joe Louis; Xuguo 'Joe' Zhou; Ana Maria Vélez
Journal:  Sci Rep       Date:  2019-07-24       Impact factor: 4.379

4.  Reference gene selection for quantitative gene expression analysis in black soldier fly (Hermetia illucens).

Authors:  Zhenghui Gao; Wenhui Deng; Fen Zhu
Journal:  PLoS One       Date:  2019-08-16       Impact factor: 3.240

5.  Development of a universal endogenous qPCR control for eukaryotic DNA samples.

Authors:  Cecilia Mittelberger; Lisa Obkircher; Vicky Oberkofler; Alan Ianeselli; Christine Kerschbamer; Andreas Gallmetzer; Yazmid Reyes-Dominguez; Thomas Letschka; Katrin Janik
Journal:  Plant Methods       Date:  2020-04-16       Impact factor: 4.993

6.  Selection of Reference Genes for the Normalization of RT-qPCR Data in Gene Expression Studies in Insects: A Systematic Review.

Authors:  Jing Lü; Chunxiao Yang; Youjun Zhang; Huipeng Pan
Journal:  Front Physiol       Date:  2018-11-06       Impact factor: 4.566

7.  Selection and Validation of Reference Genes for RT-qPCR Analysis of the Ladybird Beetle Henosepilachna vigintioctomaculata.

Authors:  Jing Lü; Shimin Chen; Mujuan Guo; Cuiyi Ye; Baoli Qiu; Jianhui Wu; Chunxiao Yang; Huipeng Pan
Journal:  Front Physiol       Date:  2018-11-14       Impact factor: 4.566

8.  Selection of appropriate reference genes for RT-qPCR analysis in Propylea japonica (Coleoptera: Coccinellidae).

Authors:  Jing Lü; Shimin Chen; Mujuan Guo; Cuiyi Ye; Baoli Qiu; Chunxiao Yang; Huipeng Pan
Journal:  PLoS One       Date:  2018-11-27       Impact factor: 3.240

9.  Identification and validation of potential reference gene for effective dsRNA knockdown analysis in Chilo partellus.

Authors:  Olawale Samuel Adeyinka; Bushra Tabassum; Idrees Ahmad Nasir; Iqra Yousaf; Imtiaz Ahmad Sajid; Khurram Shehzad; Anicet Batcho; Tayyab Husnain
Journal:  Sci Rep       Date:  2019-09-20       Impact factor: 4.379

10.  Evaluation and Validation of Reference Genes for Quantitative Real-Time PCR in Helopeltis theivora Waterhouse (Hemiptera: Miridae).

Authors:  Zheng Wang; Qianqian Meng; Xi Zhu; Shiwei Sun; Shengfeng Gao; Yafeng Gou; Aiqin Liu
Journal:  Sci Rep       Date:  2019-09-16       Impact factor: 4.379

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