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Article

Screening and Validation of Stable Reference Genes for qRT-PCR Analysis in Epicauta gorhami (Coleoptera: Meloidae)

by
Guofeng Yang
1,†,
Xuetao Yu
1,†,
Yan Zhang
1,
Jinhua Luo
2,
Xiaofei Li
1,
Li Zhu
1,
Huanhuan Zhang
3,
Lin Jin
4,
Gang Wu
1,
Xiaohong Yan
1,* and
Chenhui Shen
1,*
1
Key Laboratory of Agricultural Genetically Modified Organisms Traceability, Oil Crops Research Institute of Chinese Academy of Agricultural Science/Supervision and Test Center (Wuhan) for Plant Ecological Environment Safety, Ministry of Agriculture and Rural Affairs, Wuhan 430062, China
2
Enshi Tujia and Miao Autonomous Prefecture Academy of Agricultural Sciences, Enshi 445000, China
3
Institute of Vegetable, Tibet Academy of Agricultural and Animal Husbandry Sciences, Lhasa 850032, China
4
Department of Entomology, College of Plant Protection, Nanjing Agricultural University, Nanjing 210095, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Insects 2024, 15(12), 942; https://doi.org/10.3390/insects15120942
Submission received: 26 October 2024 / Revised: 22 November 2024 / Accepted: 28 November 2024 / Published: 29 November 2024
(This article belongs to the Special Issue Research on Insect Molecular Biology)
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Simple Summary

The reverse transcription-quantitative polymerase chain reaction (qRT-PCR) is often employed to examine the gene expressions under diverse treatments. Screening optimal reference genes are necessary for obtaining reliable expression results of qRT-PCR. Here, the stability of 10 selected reference genes in Epicauta gorhami was assessed under three conditions (adult ages, tissues/organs and temperatures). These findings displayed that the best suitable reference genes were as follows: SOD and RPS18 for different adult ages and various temperatures and RPS18 and RPS28 for adult tissues. The relative expression patterns of uridine diphosphate (UDP)-N-acetylglucosamine-pyrophosphorylase (EgUAP) in diverse adult tissues was employed to verify the results. Our study will lay a vital basis for future functional gene expressions in E. gorhami.

Abstract

Epicauta gorhami is a hypermetamorphic insect that mainly forage soybeans during the adult stage. However, the lack of appropriate references hinders our studying of the gene function in E. gorhami. In this study, referring to five computational tools (Ct value, geNorm, NormFinder, BestKeeper and RefFinder), the stability of 10 housekeeping genes (GAPDH, ACT, RPL4, RPL27, α-TUB, RPS18, EF1α, RPS28, RPL13 and SOD) was assessed by qRT-PCR under three different conditions (adult ages, tissues/organs and temperatures). The findings suggested that SOD and RPS18 were the most ideal references for examine gene transcripts among diverse adult ages and at various temperatures; a pair of RPS18 and RPS28 was the most reliable genes to assess gene expressions in diverse adult tissues. Finally, the relative expression levels of EgUAP were computed after normalization RPS18 and RPS28 with across diverse adult tissues. As expected, EgUAP expression was abundant in the foregut, trachea and antenna and scarce in the midgut, hindgut and epidermis. These findings will lay a solid foundation for analyzing the gene expression of E. gorhami.

1. Introduction

The bean blister beetle, Epicauta gorhami (Coleoptera: Meloidae) is a hypermetamorphic insect that can forage soybeans (Glycine max) during the adult stage [1,2,3,4]. It poses potential threats to soybean production, which is a crucial oilseed, protein and biodiesel crop [5,6]. The adults of this species mainly feed on leaves of G. max [1,2,3,4]. Previous studies on E. gorhami have traditionally placed a strong emphasis on diapause [1,3], seasonal adaptation [2], mitogenomic sequencing [7] and the phylogenetic relationship [4,7]. However, there is currently no research on gene functions in E. gorhami. In order to further explore molecular mechanisms of target genes, it is imperative to screen the suitable reference genes.
The reverse transcription-quantitative polymerase chain reaction (qRT-PCR) has advantages of high sensitivity, specificity and accuracy, which is widely employed to analyze the target gene transcripts [8,9]. For accurately measuring the mRNA levels of target genes by qRT-PCR, selecting the most optimal references to conduct normalization is essential [10,11]. If unstable reference genes are employed, the results of gene transcripts will be inaccurate [9,10]. Hence, selecting appropriate reference genes should be assessed under various experimental conditions [12,13,14].
As usual, housekeeping genes (HKGs), such as actin (ACT), ribosomal protein (RPL and RPS) and elongation factor 1α (EF1α), are widely employed as references in insect species, due to relative stable expression levels. However, the stability of internal genes may not always constant under diverse experimental treatments [14,15]. In the potato ladybird Henosepilachna vigintioctomaculata, RPL6 and RPL13 are the suitable references in different developmental stages, whereas RPS18 and EF1α are more optimal among diverse tissues [14]. In the sugarcane stem borer Chilo sacchariphagus, β-ACT and RPL7 show the best stable expression in different tissues, whereas EF1α and SDHA exhibit stability between sexes [15]. Hence, evaluating reference genes at different backgrounds is necessary.
Currently, important advances have been made in the study of reference genes selection in numerous insect species, such as Mylabris sibirica (RPL6 and RPL13) [12], Phthorimaea operculella (EF1α and RPL13) [13], H. vigintioctomaculata (RPS18 and RPL13) [14] and C. sacchariphagus (β-ACT and RPL7 across various tissues and at distinct temperatures; EF1α and SDHA between sexes) [15]. To sum up, each experimental condition needs at least two reference genes to measure the target gene transcripts in insect species.
In this paper, we selected 10 potential reference genes from the full-length transcriptome of E. gorhami adults (unpublished data), which were ACT, EF1α, GAPDH (glyceraldehyde-3-phosphate dehydrogenase), α-TUB (α-tubulin), RPL4, RPL13, RPL27, RPS18, RPS28 and SOD (superoxide dismutase). The stability of these genes was assessed at diverse adult ages, across different adult tissues and at various temperatures of E. gorhami. In addition, the expression pattern of E. gorhami uridine diphosphate (UDP)-N-acetylglucosamine (UDP-GlcNAc) pyrophosphorylase (EgUAP) across different adult tissues was used to evaluate the results. These findings will lay a vital basis for further research on gene functions in E. gorhami.

2. Materials and Methods

2.1. Insect Rearing and Treatment Methods

E. gorhami adults used in this study were obtained in August 2024 from G. max plants in Enshi Tujia and Miao Autonomous Prefecture, Hubei Province, China (coordinates: 30°19′43″ N, 109°38′48″ E). The adults were fed under suitable conditions (25 ± 2 °C, 16 h:8 h photoperiod and 50–60% relative humidity) in the insectary for one week.
Adult ages: Four newly emerged adults (two males and two females) were sampled for one biological replicate, every day for a continuous period of 3 days (1-day-old adults, 2-day-old adults and 3-day-old adults). Three biological replicates were conducted.
Adult tissues: These tissues (foregut, midgut, hindgut, antenna, trachea and epidermis) were collected from the 5-day-old adults. Ten adults (5 males and 5 females) were dissected for one biological replicate. Three biological replicates were conducted.
Temperature: The newly emerged adults were reared at three temperatures (4 °C, 25 °C and 37 °C) for 6 h. Four newly emerged adults (2 males and 2 females) were treated for one biological replicate. Three biological replicates were prepared.

2.2. Total RNA Isolation and cDNA Synthesis

Total RNA of each sample was extracted by the TRIzol reagent (YiFeiXue Tech, Nanjing, China), referring to the manufacturer’s instructions. The RNA’s purity and concentration were detected by a NanoDrop ND-1000 spectrophotometer. Subsequently, 1000 ng of total RNA was used to synthesize cDNA using the HiScript III RT SuperMix with gDNA wiper (Vazyme Biotech Co., Ltd., Nanjing, China). The reaction mixtures were subjected to incubation at 37 °C for 15 min, followed by 85 °C for 5 s. The resultant cDNA samples were diluted 3-fold for the pursuant PCR and qRT-PCR.

2.3. Selection of Candidate References and Primer Design

Based on the full-length transcriptome of E. gorhami adults, the BioEdit software (version 7.1.3.0) was employed to conduct a TBLASTN search by using the amino acid sequence of ten candidate reference genes as the queries from M. sibirica [12] and P. operculella [13]. This resulted in the identification of ten reference genes in E. gorhami. These genes included EF1α, ACT, RPL4, α-TUB, RPL13, RPL27, RPS18, GAPDH, RPS28 and SOD, all of which are commonly utilized as reference genes in other insects [16].
The correctness of the reference gene sequences was substantiated by polymerase chain reaction (PCR) using primers in Table S1. Primer pairs were designed to verify the complete open reading frames of reference genes by Primer Premier 5.0 software (version 5.00), based on the RT-PCR primer design principles [15]. After the amplicons of RT-PCR were confirmed by Sanger sequencing, the sequences of amplicons were uploaded to GenBank, with the accession numbers of PQ497541-PQ497541 (Table S1).

2.4. Quantitative Real-Time PCR (qRT-PCR)

The primers for qRT-PCR were performed by Primer3web version 4.1.0 (https://primer3.ut.ee/, accessed on 12 September 2024) and are presented in Table 1. The products of qRT-PCR were verified by Sanger sequencing. The qRT-PCR experiment was executed by a CFX96 Real-Time System (Bio-Rad Laboratories, Hercules, CA, USA) and ChamQ Universal SYBR qPCR Master Mix (Vazyme Biotech Co., Ltd.). Each reaction consisted of a final volume of 20 μL, containing 10 μL of 2 ChamQ Universal SYBR qPCR Master Mix, 0.4 μL of reverse primer (10 μM), 1 μL of cDNA template and 8.2 μL of RNase-free water. The thermocycling procedure consisted of an initial step of 95 °C for 30 s, followed by 40 cycles of 95 °C for 5 s and annealing at 60 °C for 34 s. After the reaction, a melting curve analysis was conducted for one cycle of 95 °C for 15 s, 60 °C for 60 s and 95 °C for 1 s, which was used to confirm the specificity of the amplified product. The amplification efficiency (E) was computed using a series of 3-fold dilutions of a cDNA template, referring to the equation E = (10[−1/slope] − 1) × 100% [17]. All experiments were conducted in triplicate.

2.5. Assessment of Reference Gene Stability

The raw Ct values were required for the BioRadCFXManager software (version 3.1.1517.0823). The stability of 10 reference genes under different backgrounds were assessed using a range of algorithms, including BestKeeper [18], Normfinder [19], geNorm [10] and the ΔCt method [20]. In addition, the suitable number of reference genes under different conditions was confirmed by pairwise variation (Vn/n + 1), required for the GeNorm method. In general, a Vn/n + 1 value below the threshold of 0.15 suggests that the starting n reference genes are suitable to normalize gene expression. Finally, the RefFinder [21,22] was applied to analyze the comprehensive ranking of each experimental condition.

2.6. Validation of Selected Reference Genes in Diverse Adult Tissues

The transcription level of uridine diphosphate (UDP)-N-acetylglucosamine-pyrophosphorylase (UAP, GenBank: PQ497551) in E. gorhami was utilized to assess the stability of selected reference genes across various adult tissues. The primer sequence of UAP was as follows: Forward: CGCTACAACGTAACGCCATC, Reverse: CCCCACAATCGCTACGTTTC. The relative transcription level of EgUAP in adult tissues were computed by the 2−∆∆Ct method [23], based on RPS18 and RPS28. The SPSS Statistics 29 software (version 29.0.2.0) package was then used to evaluate the variance in expression levels of EgUAP among different adult tissues through one-way ANOVA analysis followed by Tukey’s test for multiple comparisons.

3. Results

3.1. Selection of Candidate Reference Genes

According to the full-length transcriptome of E. gorhami, we applied the BioEdit software (version 7.1.3.0) to perform a TBLASTN search by using the amino acid sequence of ten candidate reference genes as the queries from M. sibirica [12] and P. operculella [13]. This enabled the identification of ten cDNAs, which we named ACT, GAPDH, RPL4, EF1α, RPL13, RPL27, RPS18, RPS28, α-TUB and SOD in E. gorhami. We used the Primer Premier 5.0 software to design the primers, with the lengths of 18–22 bp. After the amplification round and then Sanger sequencing, the sequencing results of amplicons were uploaded to GenBank under these accession numbers: PQ497541-PQ497541 (Table S1).
The Primer3web version 4.1.0 (https://primer3.ut.ee/, accessed on 12 September 2024) was used to design the qRT-PCR primers, with the amplification product length ranging from 80 bp to 250 bp. The products of qRT-PCR were verified by sequencing. The melting curve analysis confirmed the specificities of each primer pair for qRT-PCR. As expected, the efficiency (E) of 10 primer pairs were between 95.50% (EF1α) and 102.73% (α-TUB), with the correlation coefficients (R2) varying from 0.993 to 0.999 (Table 1). These results demonstrated that efficiency of primers reached the standards of traditional qRT-PCR [24].

3.2. Expression Levels of Reference Genes

The threshold-cycle (Ct) of ten reference genes under various testing conditions were presented (Figure 1, Table S2). Among diverse adult ages, SOD exhibited smaller gene expression variations, whereas GAPDH and EF1α demonstrated greater fluctuations (Figure 1A). Across different adult tissues and under different temperature treatments, the expression fluctuations were lower in RPS18 and RPS28 and higher in ACT and GAPDH (Figure 1B,C). A combination of above results indicated that the expression difference was small in all reference genes except for ACT and GAPDH (Figure 1D).

3.3. Stability of the Ten HKGs Among Adult Ages

Based on average expression stability (M-values) and pairwise variation (V-values), the geNorm algorithm evaluates gene stability. These results exhibited that ACT, RPS18 and SOD were the most suitable, with M-values below 0.20 (Figure 2A, Table 2). Moreover, V-values suggested that V2/3 to V9/10 values were below 0.15, showing that two diverse references were equal for assessing the gene transcript among adult ages (Figure 2B).
The NormFinder algorithm indicated that the stable ranking of ten reference genes from the most to the least stable were as follows: SOD, RPS18, ACT, RPL13, RPL4, RPS28, RPL27, α-TUB, EF1α and GAPDH, with the p-value of 0.091, 0.143, 0.172, 0.243, 0.268, 0.329, 0.458, 0.651, 0.849 and 0.954, respectively (Figure 2C, Table 2). The BestKeeper analysis showed that RPL4, RPS28 and SOD were the top three stable genes (Figure 2D, Table 2).
Referring to the results of RefFinder, the ranking of 10 reference genes at adult ages were as follows: SOD > RPS18 > ACT > RPL4 > RPS28 > RPL13 > EF1α > RPL27 > α-TUB > GAPDH (Figure 5A). Therefore, the combination of SOD and RPS18 are the most appropriate for gene transcript analysis by qRT-PCR among various adult ages (Table 3).

3.4. Stability of the Ten HKGs Across Various Adult Tissues

The geNorm results displayed that RPS28, EF1α, RPL4 and RPS18 were the top four stable genes (Figure 3A, Table 2). V-values data demonstrated that all values of from V2/3 to V9/10 were below 0.15, showing that two reference genes in various adult tissues were suitable (Figure 3B).
According to the NormFinder data, the rankings of reference genes were as follows: RPS18 > RPS28 > RPL13 > RPL4 > EF1α > RPL27 > α-TUB > SOD > ACT > GAPDH (Figure 3C, Table 2). Moreover, the p-values of all reference genes were less than 1.0 (Figure 3C, Table 2). The BestKeeper results indicated that RPS18 and RPS28 were the most stable, with the SD values of 0.700 and 0.914, respectively (Figure 3D, Table 2). Furthermore, the SD values of ACT, GADPH, α-TUB and RPL4 were above 1.0 (Figure 3D, Table 2).
According to RefFinder results, the comprehensive ranking order were as follows: RPS28 > RPS18 > EF1α > RPL27 > RPL13 > RPL4 > SOD > α-TUB > ACT > GAPDH (Figure 5B). Therefore, the pair of RPS18 and RPS28 is the most appropriate for qRT-PCR data normalization among diverse adult tissues (Table 3).

3.5. Stability of the Ten HKGs Under Diverse Temperature Conditions

The geNorm results indicated that RPS18, SOD and RPL13 were the steadiest references in diverse temperatures, with M-values of 0.123, 0.123 and 0.188, respectively (Figure 4A, Table 2). Moreover, V-values data displayed that all values were below 0.15, showing that two reference genes under various temperature treatments were suitable (Figure 4B).
The NormFinder results manifested that the steady rankings were RPS28, RPS18, SOD, EF1α, α-TUB, RPL13, RPL4, RPL27, GAPDH and ACT (Figure 4C, Table 2). BestKeeper data uncovered that the SD values of these genes were less than 1.0, except for ACT (Figure 4D, Table 2).
Refer to the results of RefFinder, the stability orders were as follows: RPS18 > SOD > RPS28 > RPL13 > RPL27 > RPL4 > EF1α > α-TUB > GAPDH > ACT (Figure 5C). To sum up, RPS18 and SOD are the best reference gene pair to evaluate the gene transcript under diverse temperature conditions (Table 3). When combining the three diverse treatments together, the RefFinder results showed that the stability ranking in sequence were RPS18, RPL13, RPS28, RPL4, RPL27, EF1α, SOD, α-TUB, GAPDH and ACT (Figure 5D).

3.6. Validation of the Selected Reference Genes

To estimate the performance of the recommended reference genes, the relative mRNA level of EgUAP in various adult tissues was measured using the most stable references (RPS18 and RPS28) by the 2−∆∆Ct method [23]. Our results indicated that the transcription level of EgUAP was high in the foregut, trachea and antenna and low in the midgut, hindgut and epidermis of E. gorhami (Figure 6).

4. Discussion

In this study, the expression stabilities of ten selected reference genes in E. gorhami were estimated to perform qRT-PCR analysis normalization, laying a vital basis for future study of gene functions. Notably, we evaluated the stability of these genes under three different conditions (adult ages, adult tissues/organs and temperatures) for the first time in E. gorhami. These findings suggested that SOD and RPS18 were the most ideal reference combination to measure gene transcription levels among different adult ages (Figure 1, Figure 2 and Figure 5 and Table 2 and Table 3) and at various temperatures (Figure 1, Figure 4 and Figure 5 and Table 2 and Table 3); RPS18 and RPS28 was the most reliable genes to assess gene expressions under diverse adult tissues (Figure 1, Figure 3 and Figure 5 and Table 2 and Table 3).
Here, we used four programs (geNorm, Normfinder, Bestkeeper and ∆CT) to examine the suitability of reference genes. The findings suggest that different programs’ results under different experimental conditions present diverse most stable reference genes (Figure 1, Figure 2, Figure 3 and Figure 4). For example, in different adult tissues, the geNorm method showed EF1α and RPS28 were the most stable reference genes, and the Normfinder and ∆CT methods identified RPS18 as the top-ranking reference gene, while the Bestkeeper ranked RPL27 as the most stable reference gene (Figure 1 and Figure 3, Table 2). These differences may be due to diverse statistical algorithms of each method, which can be found in the publications [10,18,19,20]. Under such a condition, RefFinder, a web-based tool is widely used to compute the geometric mean of the rankings obtained by the different algorithms, which solves the limitations of operating one single program [21,22].
Ribosomal proteins play a vital function on ribosome assembly, which bind to four ribosomal RNAs (rRNAs) to constitute the ribosomes [25]. Similar to our findings, ribosomal proteins are selected as the most optimal reference genes in insect species, such as Coleopterans M. sibirica (RPL6 and RPL13) [12], Leptinotarsa decemlineata (RP18 and RP4) [26], Phaedon brassicae (RPL32 and RPL19) [27], Henosepilachna vigintioctopunctata (RPL13 and RPS18) [16], Tribolium castaneum (RPS6, RPL13a, RPS3 and RPL18) [28], Ips sexdentatus (RPS3) [29] and H. vigintioctomaculata (RPS18 and RPL13) [14]; Hemipterans Psammotettix striatus (RPLP2) [30], Rhopalosiphum padi (RPL13, RPS6 and RPS18) [31], Aphis glycines (RPS9) [32], Ferrisia gilli (RPS8, RPL40 and RPL7) [33], Diaphorina citri (RPL7) [34] and Dichelops melacanthus (RPL9 and RPS23) [35]; Hymenopterans Anastatus japonicus (RPL13 and RPS6) [36]; Lepidopterans Mythimna loreyi (RPL10, RPL27 and RPS3) [37], Plutella xylostella (RPS13 and RPS23) [38], Spodoptera litura (RPS13 and RPLP0) [39], P. operculella (RPL13) [13], Bombyx mori (RPS7) [40] and Helicoverpa armigera (RPS15 and RPL27) [41]; Dipterans Aphidoletes aphidimyza (RPL8 and RPS3) [42], Exorista sorbillans (RP49) [43] and Chlorops oryzae (RPS15) [44]; Orthopteran Locusta migratoria (RPL32) [45] and Thysanopterans Megalurothrips usitatus (RPL30) [46] and Frankliniella occidentalis (RPL32) [47].
At different adult ages and temperatures, SOD was verified to be the most reliable gene (Figure 1, Figure 2, Figure 4 and Figure 5 and Table 2 and Table 3). Superoxide dismutase (SOD) is a crucial antioxidant enzyme, catalyzing the conversion of reactive oxygen species into oxygen and hydrogen peroxide [48]. Consistent with our data, SOD is recommended as the most appropriate reference gene in Spodoptera frugiperda [49], Thrips tabaci [50], Riptortus pedestris [51] and Spodoptera exigua [52].
In addition, the BestKeeper analysis data indicated that the SD values of α-TUB, EF1α, GAPDH and ACT were greater than 1.0 (Table 2), showing that these genes were unaccommodated as reference genes to perform qRT-PCR normalization. Similar results have been verified in other insects, such as P. operculella [13], Ophraella communa [53], A. aphidimyza [42], Hippodamia convergens [54], H. vigintioctomaculata [14], Colaphellus bowringi [55], H. vigintioctopunctata [16] and M. sibirica [12].
To further validate the accuracy of RPS18 and RPS28 in qRT-PCR normalization in E. gorhami, we examined the relative transcript level of EgUAP in diverse adult tissues. Our results exhibited that EgUAP expression was high in the foregut, trachea and antenna and low in the midgut, hindgut and epidermis (Figure 6). The expression pattern is consistent with the fact that UAP catalyzes the formation of UDP-GlcNAc, which is the precursor of the production of chitin in ectodermally derived epidermal cells, foregut, hindgut and trachea [56,57,58].
Overall, it is essential to screen and verify the most suitable references to guarantee the accuracy of gene expression. This study would offer a solid basis for further molecular functions of target genes in E. gorhami.

5. Conclusions

Ten potential reference genes in E. gorhami were assessed for accurate qRT-PCR analysis of E. gorhami under three different treatments. These results demonstrated that the steadiest reference genes were as follows: SOD and RPS18 for different adult ages and various temperatures and RPS18 and RPS28 for adult tissues. This study is the first one to establish the qRT-PCR normalization analyses in E. gorhami, facilitating further research on gene functions of E. gorhami.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/insects15120942/s1, Table S1: A list of primers used for RT-PCR of the genes and Table S2: The overall threshold cycle (Ct) values under different experimental conditions.

Author Contributions

Experiments were designed by G.Y., X.Y. (Xuetao Yu), Y.Z., G.W., X.Y. (Xiaohong Yan) and C.S.; Figures designed and created by G.Y., X.Y. (Xuetao Yu), Y.Z., J.L., G.W., X.Y. (Xiaohong Yan) and C.S.; Statistical analyses were implemented by G.Y., X.Y. (Xuetao Yu), Y.Z., J.L., X.L., L.Z., H.Z., L.J., G.W., X.Y. (Xiaohong Yan) and C.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the National Major Special Project of Breeding for Genetically Modified Organisms in China (No. 2016ZX08012-005) and the Innovation Project of Chinese Academy of Agricultural Sciences (No. 2060302-049-091).

Data Availability Statement

The data presented in this study are included in the article and Supplementary Materials; further inquiries can be directed to the corresponding authors.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Terao, M.; Hirose, Y.; Shintani, Y. Effects of temperature and photoperiod on termination of pseudopupal diapause in the bean blister beetle, Epicauta gorhami. J. Insect Physiol. 2012, 58, 737–742. [Google Scholar] [CrossRef] [PubMed]
  2. Shintani, Y.; Terao, M.; Tanaka, S. Adaptive significance of precocious pupation in the bean blister beetle, Epicauta gorhami (Coleoptera: Meloidae), a hypermetamorphic insect. J. Insect Physiol. 2017, 99, 107–112. [Google Scholar] [CrossRef] [PubMed]
  3. Terao, M.; Tokuda, M.; Shintani, Y. Geographic variation in photoperiodic response for induction of pseudopupal diapause in Epicauta gorhami (Coleoptera: Meloidae). Environ. Entomol. 2021, 50, 1145–1150. [Google Scholar] [CrossRef] [PubMed]
  4. Zhou, Z.; Liu, Y.; Chen, X. Structural features and phylogenetic implications of three new mitochondrial genomes of blister beetles (Coleoptera: Meloidae). J. Insect Sci. 2021, 21, 19. [Google Scholar] [CrossRef] [PubMed]
  5. Hina, A.; Razzaq, M.K.; Abbasi, A.; Shehzad, M.B.; Arshad, M.; Sanaullah, T.; Arshad, K.; Raza, G.; Ali, H.M.; Hayat, F.; et al. Genomic blueprints of soybean (Glycine max) pathogen resistance: Revealing the key genes for sustainable agriculture. Funct. Plant Biol. 2024, 51, FP23295. [Google Scholar] [CrossRef] [PubMed]
  6. Vianna, G.R.; Cunha, N.B.; Rech, E.L. Soybean seed protein storage vacuoles for expression of recombinant molecules. Curr. Opin. Plant Biol. 2023, 71, 102331. [Google Scholar] [CrossRef] [PubMed]
  7. Du, C.; Zhang, L.; Lu, T.; Ma, J.; Zeng, C.; Yue, B.; Zhang, X. Mitochondrial genomes of blister beetles (Coleoptera, Meloidae) and two large intergenic spacers in Hycleus genera. BMC Genom. 2017, 18, 698. [Google Scholar] [CrossRef]
  8. Jozefczuk, J.; Adjaye, J. Quantitative real-time PCR-based analysis of gene expression. Methods Enzymol. 2011, 500, 99–109. [Google Scholar]
  9. Derveaux, S.; Vandesompele, J.; Hellemans, J. How to do successful gene expression analysis using real-time PCR. Methods 2010, 50, 227–230. [Google Scholar] [CrossRef]
  10. Vandesompele, J.; De Preter, K.; Pattyn, F.; Poppe, B.; Van Roy, N.; De Paepe, A.; Speleman, F. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002, 3, Research0034. [Google Scholar] [CrossRef]
  11. Valasek, M.A.; Repa, J.J. The power of real-time PCR. Adv. Physiol. Educ. 2005, 29, 151–159. [Google Scholar] [CrossRef] [PubMed]
  12. Shen, C.H.; Tang, M.; Li, X.F.; Zhu, L.; Li, W.; Deng, P.; Zhai, Q.; Wu, G.; Yan, X.H. Evaluation of reference genes for quantitative expression analysis in Mylabris sibirica (Coleoptera, Meloidae). Front. Physiol. 2024, 15, 1345836. [Google Scholar] [CrossRef] [PubMed]
  13. Shen, C.H.; Peng, L.J.; Zhang, Y.X.; Zeng, H.R.; Yu, H.F.; Jin, L.; Li, G.Q. Reference genes for expression analyses by qRT-PCR in Phthorimaea operculella (Lepidoptera: Gelechiidae). Insects 2022, 13, 140. [Google Scholar] [CrossRef] [PubMed]
  14. Zhang, Y.-X.; Tan, Q.; Shen, C.-H.; Wu, J.-J.; Wu, Y.-K.; Li, W.-Z.; Jin, L.; Li, G.-Q. Reference gene selection for transcriptional profiling by RT-qPCR in the 28-spotted larger potato ladybird. J. Asia Pac. Entomol. 2022, 25, 101900. [Google Scholar] [CrossRef]
  15. Wang, Z.; Shang, X.; Wei, J.; Tian, X.; Liu, Y.; Zhang, G. Evaluation and validation of reference genes for gene expression analysis using qRT-PCR in the sugarcane stem borer Chilo sacchariphagus (Lepidoptera: Pyralidae). Insects 2024, 15, 594. [Google Scholar] [CrossRef]
  16. Lü, J.; Yang, C.; Zhang, Y.; Pan, H. Selection of reference genes for the normalization of RT-qPCR data in gene expression studies in insects: A systematic review. Front. Physiol. 2018, 9, 1560. [Google Scholar] [CrossRef]
  17. Pfaffl, M.W. A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res. 2001, 29, e45. [Google Scholar] [CrossRef]
  18. Pfaffl, M.W.; Tichopad, A.; Prgomet, C.; Neuvians, T.P. Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper--Excel-based tool using pair-wise correlations. Biotechnol. Lett. 2004, 26, 509–515. [Google Scholar] [CrossRef]
  19. Andersen, C.L.; Jensen, J.L.; Ørntoft, T.F. 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. [Google Scholar] [CrossRef]
  20. Silver, N.; Best, S.; Jiang, J.; Thein, S.L. Selection of housekeeping genes for gene expression studies in human reticulocytes using real-time PCR. BMC Mol. Biol. 2006, 7, 33. [Google Scholar] [CrossRef]
  21. Xie, F.; Xiao, P.; Chen, D.; Xu, L.; Zhang, B. miRDeepFinder: A miRNA analysis tool for deep sequencing of plant small RNAs. Plant Mol. Biol. 2012, 80, 75–84. [Google Scholar] [CrossRef] [PubMed]
  22. Xie, F.; Wang, J.; Zhang, B. RefFinder: A web-based tool for comprehensively analyzing and identifying reference genes. Funct. Integr. Genom. 2023, 23, 125. [Google Scholar] [CrossRef] [PubMed]
  23. Livak, K.J.; Schmittgen, T.D. Analysis of relative gene expression data using real-time quantitative PCR and the 2−ΔΔCT method. Methods 2001, 25, 402–408. [Google Scholar] [CrossRef]
  24. Bustin, S.A.; Benes, V.; Garson, J.A.; Hellemans, J.; Huggett, J.; Kubista, M.; Mueller, R.; Nolan, T.; Pfaffl, M.W.; Shipley, G.L.; et al. The MIQE guidelines: Minimum information for publication of quantitative real-time PCR experiments. Clin. Chem. 2009, 55, 611–622. [Google Scholar] [CrossRef]
  25. Landry-Voyer, A.M.; Mir Hassani, Z.; Avino, M.; Bachand, F. Ribosomal protein uS5 and friends: Protein-protein interactions involved in ribosome assembly and beyond. Biomolecules 2023, 13, 853. [Google Scholar] [CrossRef] [PubMed]
  26. Shi, X.Q.; Guo, W.C.; Wan, P.J.; Zhou, L.T.; Ren, X.L.; Ahmat, T.; Fu, K.Y.; Li, G.Q. Validation of reference genes for expression analysis by quantitative real-time PCR in Leptinotarsa decemlineata (Say). BMC Res. Notes 2013, 6, 93. [Google Scholar] [CrossRef] [PubMed]
  27. Ma, L.; Jiang, T.; Liu, X.; Xiao, H.; Peng, Y.; Zhang, W. Evaluation of candidate reference genes for gene expression analysis in the brassica leaf beetle, Phaedon brassicae (Coleoptera: Chrysomelidae). PLoS ONE 2021, 16, e0251920. [Google Scholar] [CrossRef]
  28. Toutges, M.J.; Hartzer, K.; Lord, J.; Oppert, B. Evaluation of reference genes for quantitative polymerase chain reaction across life cycle stages and tissue types of Tribolium castaneum. J. Agric. Food Chem. 2010, 58, 8948–8951. [Google Scholar] [CrossRef]
  29. Sellamuthu, G.; Amin, S.; Bílý, J.; Synek, J.; Modlinger, R.; Sen, M.K.; Chakraborty, A.; Roy, A. Reference gene selection for normalizing gene expression in Ips sexdentatus (Coleoptera: Curculionidae: Scolytinae) under different experimental conditions. Front. Physiol. 2021, 12, 752768. [Google Scholar] [CrossRef]
  30. Yuan, F.; Xie, Z.; Li, Z.; Lian, P.; Wei, C. Screening of reference genes for gene expression study in different tissues from the transcriptome data of the vector leafhopper Psammotettix striatus. Gene 2024, 927, 148696. [Google Scholar] [CrossRef]
  31. Li, M.; Li, X.; Wang, C.; Li, Q.; Zhu, S.; Zhang, Y.; Li, X.; Yang, F.; Zhu, X. Selection and validation of reference genes for qRT-PCR analysis of Rhopalosiphum padi (Hemiptera: Aphididae). Front. Physiol. 2021, 12, 663338. [Google Scholar] [CrossRef] [PubMed]
  32. Bansal, R.; Mamidala, P.; Mian, M.A.; Mittapalli, O.; Michel, A.P. Validation of reference genes for gene expression studies in Aphis glycines (Hemiptera: Aphididae). J. Econ. Entomol. 2012, 105, 1432–1438. [Google Scholar] [CrossRef] [PubMed]
  33. Bansal, R.; Haviland, D.R.; Hunter, W.B. Selection and validation of reference genes for quantifying gene expression in the Gill’s mealybug. J. Econ. Entomol. 2023, 116, 2166–2172. [Google Scholar] [CrossRef] [PubMed]
  34. Bassan, M.M.; Angelotti-Mendonc, A.J.; Alves, G.R.; Yamamoto, P.T.; Moura, O.F.F.A.A. Selection of reference genes for expression studies in Diaphorina citri (Hemiptera: Liviidae). J. Econ. Entomol. 2017, 110, 2623–2629. [Google Scholar] [CrossRef] [PubMed]
  35. Pinheiro, D.H.; Moreira, R.O.; Leite, N.A.; Redoan, A.C.; Xavier, A.D.S.; Barros, B.A.; Carneiro, N.P. Suitable reference genes for RT-qPCR analysis in Dichelops melacanthus (Hemiptera: Pentatomidae). Mol. Biol. Rep. 2020, 47, 4989–5000. [Google Scholar] [CrossRef]
  36. Liu, Z.; Xiao, J.; Xia, Y.; Wu, Q.; Zhao, C.; Li, D. Selection and validation of reference genes for RT-qPCR-based analyses of Anastatus japonicus Ashmead (Hymenoptera: Helicopteridae). Front. Physiol. 2022, 13, 1046204. [Google Scholar] [CrossRef] [PubMed]
  37. Wang, L.; Yang, C.; Liu, Q.; Zhang, X.; Mei, X.; Zhang, T.; Ning, J. Validation and evaluation of reference genes for quantitative real-time PCR analysis in Mythimna loreyi (Lepidoptera: Noctuidae). Insects 2024, 15, 185. [Google Scholar] [CrossRef]
  38. Fu, W.; Xie, W.; Zhang, Z.; Wang, S.; Wu, Q.; Liu, Y.; Zhou, X.; Zhou, X.; Zhang, Y. Exploring valid reference genes for quantitative real-time PCR analysis in Plutella xylostella (Lepidoptera: Plutellidae). Int. J. Biol. Sci. 2013, 9, 792–802. [Google Scholar] [CrossRef]
  39. Wu, S.; Luo, Y.; Zeng, Z.; Yu, Y.; Zhang, S.; Hu, Y.; Chen, L. Determination of internal controls for quantitative gene expression of Spodoptera litura under microbial pesticide stress. Sci. Rep. 2024, 14, 6143. [Google Scholar] [CrossRef]
  40. Dalai, M.; Jagota, A. Identification of specific reference gene for normalization of RT-qPCR data in rhythmic gene expression studies of the effect of developmental hormone antagonist in postembryonic development in Bombyx mori. Front. Insect Sci. 2024, 4, 1362473. [Google Scholar] [CrossRef]
  41. Zhang, S.; An, S.; Li, Z.; Wu, F.; Yang, Q.; Liu, Y.; Cao, J.; Zhang, H.; Zhang, Q.; Liu, X. Identification and validation of reference genes for normalization of gene expression analysis using qRT-PCR in Helicoverpa armigera (Lepidoptera: Noctuidae). Gene 2015, 555, 393–402. [Google Scholar] [CrossRef] [PubMed]
  42. Shen, X.X.; Zhang, G.Q.; Zhao, Y.X.; Zhu, X.X.; Yu, X.F.; Yang, M.F.; Zhang, F. Selection and validation of optimal reference genes for RT-qPCR analyses in Aphidoletes aphidimyza Rondani (Diptera: Cecidomyiidae). Front. Physiol. 2023, 14, 1277942. [Google Scholar] [CrossRef] [PubMed]
  43. Yang, J.; Jiang, Z.; Xu, Q.; Liu, X.; Dai, M.; Li, B.; Wei, J. Evaluation of suitable reference genes for expression analysis using quantitative real-time polymerase chain reaction in the parasitoid Exorista sorbillans (Diptera: Tachinidae). Arch. Insect Biochem. Physiol. 2023, 113, e22009. [Google Scholar] [CrossRef]
  44. Tian, P.; Qiu, L.; Zhou, A.; Chen, G.; He, H.; Ding, W.; Li, Y. Evaluation of appropriate reference genes For investigating gene expression in Chlorops oryzae (Diptera: Chloropidae). J. Econ. Entomol. 2019, 112, 2207–2214. [Google Scholar] [CrossRef]
  45. Yang, Q.; Li, Z.; Cao, J.; Zhang, S.; Zhang, H.; Wu, X.; Zhang, Q.; Liu, X. Selection and assessment of reference genes for quantitative PCR normalization in migratory locust Locusta migratoria (Orthoptera: Acrididae). PLoS ONE 2014, 9, e98164. [Google Scholar] [CrossRef] [PubMed]
  46. Hou, Q.; Yuan, L.; Jin, H.; Yan, H.; Li, F.; Wu, S. Identification and validation of reference genes for normalization of gene expression analysis using qRT-PCR in Megalurothrips usitatus (thysanoptera: Thripidae). Front. Physiol. 2023, 14, 1161680. [Google Scholar] [CrossRef] [PubMed]
  47. Zheng, Y.T.; Li, H.B.; Lu, M.X.; Du, Y.Z. Evaluation and validation of reference genes for qRT-PCR normalization in Frankliniella occidentalis (Thysanoptera: Thripidae). PLoS ONE 2014, 9, e111369. [Google Scholar] [CrossRef]
  48. Yamamoto, K.; Yamaguchi, M. Characterization of a novel superoxide dismutase in Nilaparvata lugens. Arch. Insect Biochem. Physiol. 2022, 109, e21862. [Google Scholar] [CrossRef]
  49. Han, S.; Qin, Q.; Wang, D.; Zhou, Y.; He, Y. Selection and evaluation of reference genes for qRT-PCR in Spodoptera frugiperda (Lepidoptera: Noctuidae). Insects 2021, 12, 902. [Google Scholar] [CrossRef]
  50. Singh, S.; Gupta, M.; Pandher, S.; Kaur, G.; Goel, N.; Rathore, P.; Palli, S.R. RNA sequencing, selection of reference genes and demonstration of feeding RNAi in Thrips tabaci (Lind.) (Thysanoptera: Thripidae). BMC Mol. Biol. 2019, 20, 6. [Google Scholar] [CrossRef] [PubMed]
  51. Wang, L.; Liu, Q.; Guo, P.; Gao, Z.; Chen, D.; Zhang, T.; Ning, J. Evaluation of reference genes for quantitative real-time PCR analysis in the bean bug, Riptortus pedestris (Hemiptera: Alydidae). Insects 2023, 14, 906. [Google Scholar] [CrossRef] [PubMed]
  52. Zhu, X.; Yuan, M.; Shakeel, M.; Zhang, Y.; Wang, S.; Wang, X.; Zhan, S.; Kang, T.; Li, J. 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. [Google Scholar] [CrossRef] [PubMed]
  53. Zhang, Y.; Chen, J.; Chen, G.; Ma, C.; Chen, H.; Gao, X.; Tian, Z.; Cui, S.; Tian, Z.; Guo, J.; et al. Identification and validation of reference genes for quantitative gene expression analysis in Ophraella communa. Front. Physiol. 2020, 11, 355. [Google Scholar] [CrossRef] [PubMed]
  54. Yang, C.; Preisser, E.L.; Zhang, H.; Liu, Y.; Dai, L.; Pan, H.; Zhou, X. Selection of reference genes for RT-qPCR analysis in Coccinella septempunctata to assess un-intended effects of RNAi transgenic plants. Front. Plant. Sci. 2016, 7, 1672. [Google Scholar]
  55. Tan, Q.Q.; Zhu, L.; Li, Y.; Liu, W.; Ma, W.H.; Lei, C.L.; Wang, X.P. A de novo transcriptome and valid reference genes for quantitative real-time PCR in Colaphellus bowringi. PLoS ONE 2015, 10, e0118693. [Google Scholar] [CrossRef] [PubMed]
  56. Jiang, L.H.; Mu, L.L.; Jin, L.; Anjum, A.A.; Li, G.Q. Silencing uridine diphosphate N-acetylglucosamine pyrophosphorylase gene impairs larval development in Henosepilachna vigintioctopunctata. Pest. Manag. Sci. 2022, 78, 3894–3902. [Google Scholar] [CrossRef]
  57. Zhang, Z.; Song, X.; Hu, H.; Wang, D.; Chen, J.; Ma, Y.; Ma, X.; Ren, X.; Ma, Y. Knockdown of UDP-N-acetylglucosamine pyrophosphorylase and chitin synthase A increases the insecticidal efficiency of Lufenuron to Spodoptera exigua. Pestic. Biochem. Physiol. 2022, 186, 105178. [Google Scholar] [CrossRef]
  58. Wang, Z.; Long, G.Y.; Zhou, C.; Jin, D.C.; Yang, H.; Yang, W.J. Molecular characterization of UDP-N-acetylglucosamine pyrophosphorylase and its role in the growth and development of the white-backed planthopper Sogatella furcifera (Hemiptera: Delphacidae). Genes 2022, 13, 1340. [Google Scholar] [CrossRef]
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Figure 1. Stability rankings of the ten candidate reference genes in Epicauta gorhami computed by the ΔCt method in different samples. The stability values are showed from the least (left) to the most stable gene (right). Abbreviation: ACT, actin; SOD, superoxide dismutase; α-TUB, α-tubulin; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; EF1α, elongation factor 1α; RPL4, RPL13, RPL27, RPS18 and RPS28, ribosomal protein. The abbreviations are exactly the same as Figures 2–5.
Figure 1. Stability rankings of the ten candidate reference genes in Epicauta gorhami computed by the ΔCt method in different samples. The stability values are showed from the least (left) to the most stable gene (right). Abbreviation: ACT, actin; SOD, superoxide dismutase; α-TUB, α-tubulin; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; EF1α, elongation factor 1α; RPL4, RPL13, RPL27, RPS18 and RPS28, ribosomal protein. The abbreviations are exactly the same as Figures 2–5.
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Figure 2. Stability rankings of the ten candidate reference genes in Epicauta gorhami computed by geNorm, NormFinder and BestKeeper at various adult ages. Diverse ages of Epicauta gorhami adults were sampled (collected on the first to third days of the newly emerged adults). The stability values are showed from the least stable (left) to the most stable gene (right). (A,B) geNorm, (C) NormFinder and (D) BestKeeper.
Figure 2. Stability rankings of the ten candidate reference genes in Epicauta gorhami computed by geNorm, NormFinder and BestKeeper at various adult ages. Diverse ages of Epicauta gorhami adults were sampled (collected on the first to third days of the newly emerged adults). The stability values are showed from the least stable (left) to the most stable gene (right). (A,B) geNorm, (C) NormFinder and (D) BestKeeper.
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Figure 3. Stability rankings of the ten candidate reference genes in Epicauta gorhami calculated by geNorm, NormFinder and BestKeeper among diverse adult tissues. Foregut, midgut, hindgut, epidermis, trachea and antenna were dissected from the 5-day-old adults. The stability values are showed from the least stable (left) to the most stable gene (right). (A,B) geNorm, (C) NormFinder and (D) BestKeeper.
Figure 3. Stability rankings of the ten candidate reference genes in Epicauta gorhami calculated by geNorm, NormFinder and BestKeeper among diverse adult tissues. Foregut, midgut, hindgut, epidermis, trachea and antenna were dissected from the 5-day-old adults. The stability values are showed from the least stable (left) to the most stable gene (right). (A,B) geNorm, (C) NormFinder and (D) BestKeeper.
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Figure 4. Stability rankings of the ten candidate reference genes in Epicauta gorhami assessed by geNorm, NormFinder and BestKeeper at different temperatures. The newly emerged adults reared for 6 h under three temperatures (4 °C, 25 °C and 37 °C) were collected. The stability values are showed from the least stable (left) to the most stable gene (right). (A,B) geNorm, (C) NormFinder and (D) BestKeeper.
Figure 4. Stability rankings of the ten candidate reference genes in Epicauta gorhami assessed by geNorm, NormFinder and BestKeeper at different temperatures. The newly emerged adults reared for 6 h under three temperatures (4 °C, 25 °C and 37 °C) were collected. The stability values are showed from the least stable (left) to the most stable gene (right). (A,B) geNorm, (C) NormFinder and (D) BestKeeper.
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Figure 5. Stability rankings of the ten candidate reference genes in Epicauta gorhami computed by the Geomean method of RefFinder in different samples. The stability values are showed from the most stable (left) to the least stable gene (right). (A) adult ages, (B) adult tissues, (C) temperature and (D) all samples.
Figure 5. Stability rankings of the ten candidate reference genes in Epicauta gorhami computed by the Geomean method of RefFinder in different samples. The stability values are showed from the most stable (left) to the least stable gene (right). (A) adult ages, (B) adult tissues, (C) temperature and (D) all samples.
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Figure 6. Relative expression level of EgUAP among diverse adult tissues of Epicauta gorhami. The relative gene expression level of EgUAP in the foregut (FG), midgut (MG), hindgut (HG), epidermis (EP), trachea (TR) and antenna (AN) was normalized to the most suitable reference genes (RPS18 and RPS28) by the 2−∆∆Ct method. The relative transcripts are the ratios of copy numbers in different adult tissues relative to the epidermis, which is set at 1. The values are means + SE. Different letters indicate significant differences in gene expression among different tissues (p < 0.05).
Figure 6. Relative expression level of EgUAP among diverse adult tissues of Epicauta gorhami. The relative gene expression level of EgUAP in the foregut (FG), midgut (MG), hindgut (HG), epidermis (EP), trachea (TR) and antenna (AN) was normalized to the most suitable reference genes (RPS18 and RPS28) by the 2−∆∆Ct method. The relative transcripts are the ratios of copy numbers in different adult tissues relative to the epidermis, which is set at 1. The values are means + SE. Different letters indicate significant differences in gene expression among different tissues (p < 0.05).
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Table 1. Primers of 10 candidate house-keeping genes used in qRT-PCR.
Table 1. Primers of 10 candidate house-keeping genes used in qRT-PCR.
GenePrimer Sequences (5′to 3′)Length (bp)SlopeR2Efficiency (%)
EF1αF-ATCATTGACGCACCTGGACA99−3.4350.99995.50
R-ACCAGTACCAGCAGCAACAA
GAPDHF-ACAGTACATGCCACCACAGC180−3.3510.99998.82
R-TGGTACACGGAAGGCCATAC
RPL4F-TCGATGAACCACCGTCAACC80−3.3080.993100.60
R-CGACCGGTACCCCATGATTC
RPL13F-AAGCCGCCGGTATTAACAGC172−3.3180.998100.16
R-TCACCAGGACGCAACTTCTT
RPL27F-TCGTATTGGTCTTGGCAGGC112−3.3730.99998.03
R-TCAATGCCGGCAACAATAGC
SODF-AGTTGTCCATGCTGATCCGG95−3.320.998100.08
R-TAACACCACAGGCCAAACGT
ACTF-TACGTGTGGCACCTGAAGAA169−3.2840.997101.60
R-CCAGTTGTACGACCAGAAGCA
α-TUBF-ATGGGCACGTCTTGATCACA174−3.2580.999102.73
R-TCATTTTCACCTTCGCCCGA
RPS18F-AGGTGTTGGTCGTCGTTACTC210−3.3230.99999.96
R-TCTAAGGTAGCCGATGTGAGC
RPS28F-GGTGAGCAAAACCGTCAGATC90−3.3470.99798.97
R-TGCTTCACGTTCAGACTCCA
Note: ACT, actin; SOD, superoxide dismutase; α-TUB, α-tubulin; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; EF1α, elongation factor 1α; RPL4, RPL13, RPL27, RPS18 and RPS28, ribosomal protein.
Table 2. Stability rankings of the ten reference genes under diverse experimental backgrounds.
Table 2. Stability rankings of the ten reference genes under diverse experimental backgrounds.
ConditionsCRGs *geNormNormfinderBestKeeperΔCt
M-ValueRankp-ValueRankStabilityRankStabilityRank
Adult agesEF1α0.54080.84991.14940.9319
GADPH0.63690.954101.32581.02410
RPL40.31350.26850.92010.5435
RPL130.22130.24341.23660.5004
RPL270.35160.45871.34490.6197
SOD0.18420.09111.12130.4431
ACT0.16610.17231.24970.4693
α-TUB0.44470.65181.589100.7828
RPS180.16610.14321.20950.4762
RPS280.28440.32961.03320.5576
Adult tissuesEF1α0.23910.38450.82820.6215
GADPH0.74190.933101.32691.05510
RPL40.27720.34741.05170.6144
RPL130.37240.22030.98960.6083
RPL270.41450.49660.70010.7106
SOD0.56370.81580.97140.9448
ACT0.66380.88991.389101.0129
α-TUB0.46860.53671.08580.7447
RPS180.31830.14610.98750.5491
RPS280.23910.20120.91430.5522
Temperature treatmentEF1α0.34660.33440.5860.5325
GADPH0.47480.54790.97690.6759
RPL40.24630.44670.35730.5496
RPL130.18820.39560.31720.5224
RPL270.26140.46880.27810.5557
SOD0.12310.21730.40740.4313
ACT0.55690.847101.265100.88610
α-TUB0.41270.39250.89980.5708
RPS180.12310.15520.42950.4151
RPS280.30750.13110.64670.4302
∗ Candidate reference gene.
Table 3. The most optimal reference genes in E. gorhami for various experimental backgrounds.
Table 3. The most optimal reference genes in E. gorhami for various experimental backgrounds.
Experimental ConditionsThe Recommended Reference Genes
Adult agesSODRPS18
Adult tissuesRPS28RPS18
TemperatureSODRPS18
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Yang, G.; Yu, X.; Zhang, Y.; Luo, J.; Li, X.; Zhu, L.; Zhang, H.; Jin, L.; Wu, G.; Yan, X.; et al. Screening and Validation of Stable Reference Genes for qRT-PCR Analysis in Epicauta gorhami (Coleoptera: Meloidae). Insects 2024, 15, 942. https://doi.org/10.3390/insects15120942

AMA Style

Yang G, Yu X, Zhang Y, Luo J, Li X, Zhu L, Zhang H, Jin L, Wu G, Yan X, et al. Screening and Validation of Stable Reference Genes for qRT-PCR Analysis in Epicauta gorhami (Coleoptera: Meloidae). Insects. 2024; 15(12):942. https://doi.org/10.3390/insects15120942

Chicago/Turabian Style

Yang, Guofeng, Xuetao Yu, Yan Zhang, Jinhua Luo, Xiaofei Li, Li Zhu, Huanhuan Zhang, Lin Jin, Gang Wu, Xiaohong Yan, and et al. 2024. "Screening and Validation of Stable Reference Genes for qRT-PCR Analysis in Epicauta gorhami (Coleoptera: Meloidae)" Insects 15, no. 12: 942. https://doi.org/10.3390/insects15120942

APA Style

Yang, G., Yu, X., Zhang, Y., Luo, J., Li, X., Zhu, L., Zhang, H., Jin, L., Wu, G., Yan, X., & Shen, C. (2024). Screening and Validation of Stable Reference Genes for qRT-PCR Analysis in Epicauta gorhami (Coleoptera: Meloidae). Insects, 15(12), 942. https://doi.org/10.3390/insects15120942

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