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Article

Selection and Validation of Reliable Reference Genes for qRT-PCR Normalization of Bursaphelenchus xylophilus from Different Temperature Conditions and Developmental Stages

by
Yajing Wu
1,2,3,†,
Jiao Zhou
2,†,
Yaning Liu
2,
Yutong Gu
2,
Hongxia Zhang
2,
Faheem Ahmad
4,
Guochang Wang
3,* and
Lili Ren
1,5,*
1
Science and Technology Research Center of China Customs, Beijing 100026, China
2
State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
3
School of Resources and Environment, Henan Institute of Science and Technology, Xinxiang 453003, China
4
Department of Biosciences, COMSATS University Islamabad (CUI), Park Road, Tarlai Kalan, Islamabad 45550 ICT, Pakistan
5
Institute of Inspection Technology and Equipment, Chinese Academy of Inspection and Quarantine, Beijing 100029, China
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Appl. Sci. 2022, 12(6), 2880; https://0-doi-org.brum.beds.ac.uk/10.3390/app12062880
Submission received: 23 January 2022 / Revised: 20 February 2022 / Accepted: 3 March 2022 / Published: 11 March 2022
(This article belongs to the Special Issue Application of Semiochemicals and Molecular Signals in Pest Management)
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Abstract

:
Quantitative reverse transcription polymerase chain reaction (qRT-PCR) is a powerful technique for studying gene expression. The key to quantitative accuracy depends on the stability of the reference genes used for data normalization under different experimental conditions. Pine wood nematode (PWN, Bursaphelenchus xylophilus) is the causal agent of the devastating pine wilt disease (PWD). Extensive and prompt research is needed to understand the molecular mechanism of PWD, but identification of the reference PWN genes for standardized qRT-PCR has not been reported yet. We have analyzed eight candidate reference genes of PWN across different temperature conditions and developmental stages. Delta Ct method, GeNorm, NormFinder, BestKeeper, and RefFinder algorithms were used to evaluate the stability of expression of these genes. Finally, we use heat shock protein 90 (HSP90) in different temperatures and arginine kinase gene (AK) in different developmental stages to confirm the stability of these genes. UBCE and EF1γ were most stable across different temperature treatments, whereas EF1γ and Actin were most stable across different developmental stages. In general, these results indicate that EF1γ is the most stable gene for qRT-PCR under different conditions. The systematic analysis of qRT-PCR reference gene selection will be helpful for future functional analysis and exploration of B. xylophilus genetic resources.

1. Introduction

Pine wilt disease (PWD) is a globally quarantined conifer disease and poses a serious threat to forest safety and ecosystem stability in parts of Asia and Europe [1,2]. In China, the disease is caused by an invasive species, the pine wood nematode (PWN), Bursaphelenchus xylophilus. The destructiveness of PWN is usually closely related to its native vector beetles and ophiostomatoid fungal species [3]. PWN feeds on the epithelial tissue of the host tree and on ophiostomatoid (blue stain) fungi. The fungi reproduce on the damaged tree host [4,5]. PWN undergoes four propagative larval stages (L1–L4) from eclosion to reproduction [6]. However, under adverse conditions, such as low food availability and extreme temperatures, PWN will enter a dispersal stage and molt from L2 into third stage dispersal juveniles (LIII) [7]. These LIII are attracted by cues from their insect vector, Monochamus alternatus, and gather around the beetles’ pupal chambers [2,5]. In synchrony with the suitable developmental stages of the vector beetles, i.e., late pupal or early adult phase, PWN molds into fourth juvenile (LIV) dispersal stage and enters the beetle’s tracheal system, inside of which the nematodes are transported to new pine hosts [2,5]. Because the pathogenesis of B. xylophilus and the virulence of PWD are complex and interact strongly with the vector beetle, associated fungi and prevailing environmental conditions, detailed study of B. xylophilus is necessary for the development of new PWD control methods.
To manage this destructive disease, extensive efforts have been made to control PWN, chemical control being one of the most common approaches. However, the indiscriminate use of pesticides has not only posed environmental hazards, but also lead to the emergence of pesticide resistance in PWN [8]. Developing environmentally friendly pest control methods, such as RNAi or nematophagous fungal formulations, are necessary alternative solutions. Considering the economic importance and the urgency of developing effective and efficient control strategies, further ecological, physiological, and molecular biological research on B. xylophilus has been extensively conducted over the past few decades [9,10,11,12,13,14]. However, further studies involving various aspects of the molecular mechanism of PWN, including development, immunity, energy metabolism, reproduction, chemoreception, and other biological process are required [15,16,17,18]. To achieve these research goals, analysis of the regulation and expression patterns of functional genes is essential.
Quantitative reverse transcription polymerase chain reaction (qRT-PCR) has been widely used for gene expression analysis owing to its high specificity, rapidity, reproducibility, and low cost [19,20,21]. An indispensable factor for the qRT-PCR method is to select a gene with a stable expression and use it as a reference to reduce the differences between samples through analysis and calculation to ensure the confidence of the final result [22]. Common internal reference genes generally include elongation factor (EF), actin (ACT), 18S rRNA, etc. [23,24,25]. However, the stability of the reference gene is affected by various factors such as temperature, development stage, and tissue type. Previous studies have shown that when test conditions change, the expression of a reference gene is not always constant across different cell types and physiological states [26,27,28]. Therefore, screening appropriate internal reference genes under different conditions is the basis for accurately judging qRT-PCR test results. Although qPCR has been widely used in B. xylophilus studies, the most stable internal reference genes across different development stages and temperatures have still not been identified.
Therefore, to find the most stable and appropriate reference gene, we have evaluated eight commonly used candidate reference genes for B. xylophilus, including elongation factor-1γ (EF1γ), actin (ACT), β-tubulin (βTUB), ubiquitin conjugating enzyme (UBCE), ubiquitin (UBQ), histone H2A (HIS), 18S ribosomal RNA (18S rRNA), and peroxisomal membrane protein (PMP). To obtain reference genes for qPCR, several statistical algorithms, such as the delta Ct method, BestKeeper, geNorm, NormFinder, and RefFinder, have been established to identify reference genes with stable expression levels [29,30,31,32,33]. We used these five statistical tools to investigate the expression of these eight PWN internal reference genes in different development stages and temperature treatments.

2. Materials and Methods

2.1. Collection of Reproductive Juveniles Nematode

B. xylophilus was isolated from wilted pine trees or M. alternatus from the Zhejiang Province, China. The PWN were reared on Botrytis cinerea growing on PDA plates at 25 °C in laboratory. After eight days of culturing, B. xylophilus were isolated using a modified Baermann funnel technique and stored for two hours at 25 °C [34]. The collected nematodes were cleaned by sucrose flotation and phosphate-buffered saline Tween 20 (PBST) [35]. The washed isolates of nematodes were poured into a glass dish and allowed to stand for half an hour to collect eggs.
When the remaining nematode eggs settled at the base of the dish, the excess liquid was drained off. PBST containing streptomycin sulfate and chloramphenicol were added. The glass dishes were incubated for 24 h at 25 °C to hatch the eggs and obtain L2 larvae. The L2 nematodes were cultured on B. cinerea-PDA culture plates and incubated for 18 h at 25 °C to get L3 nematodes. The earlier prepared L2 nematode cultures were incubated for 42 h to allow them to develop into fourth-stage reproductive juveniles (L4). L2 nematodes as collected earlier, were inoculated to B. cinerea cultures and kept for 72 h to obtain adult nematodes. All the collected reproductive juveniles were frozen in liquid nitrogen immediately and stored at −80 °C.

2.2. Collection of Dispersal Juveniles

Pine wood infested with PWD was cut into 3–5 cm thin strips, and the nematodes were separated by the Baermann funnel technique [36]. Non-LIII nematodes were removed during microscopic observations. To further purify the isolates, sucrose centrifugation-flotation method was used to remove fungal hyphae and impurities [37]. These purified supernatants containing LIII nematodes were frozen in liquid nitrogen and stored at −80 °C for later use.
The newly eclosed M. alternatus beetles were dissected and placed in a 35 mm Petri dish with sterilized ddH2O for 30 min. After half an hour, a large number of LIV nematodes were clearly visible floating in the solution. These nematodes were isolated and cleaned before freezing in liquid nitrogen and stored at −80 °C.

2.3. Collection of Nematodes Treated with Different Temperatures

To study the effect of different temperatures, randomly collected nematodes from B. cinerea culture plates were isolated and cleaned as described earlier. These cultures were exposed to 4 °C, 10 °C, 15 °C, 20 °C, 25 °C, 30 °C or 35 °C for 48 h. Another dish of the random culture of nematodes was incubated at 50 °C for 20 min and then shifted at 25 °C for the next 24 h. Each temperature treatment was replicated thrice and after incubation all samples were stored in liquid nitrogen at −80 °C.

2.4. Total RNA Isolation and cDNA Synthesis

Total RNA from each sample was extracted using Trizol reagent (Invitrogen, Carlsbad, CA, USA) according to the manufacturer’s instructions. RNA integrity was confirmed by 1% agarose gel electrophoresis and RNA concentration was determined using a NanoDrop 2000 C spectrophotometer (Thermo Fisher Scientific, West Palm Beach, FL, USA). cDNAs for qRT-PCR were synthesized from 2 μg of total RNA using the PrimescriptTM RT reagent Kit with genomic DNA eraser (Perfect Real Time) (Tiangen, Beijing, China), according to the manufacturer’s instructions.

2.5. Selection of Candidate Reference Genes and qPCR Primer Design

Eight candidate reference genes previously reported to be suitable to transcript normalization in other organisms under different experimental conditions were selected. The sequences of candidate reference genes were obtained from the transcriptome de novo assembly sequences of B. xylophilus [38]. Primers were designed using Primer Premier 6 software (Premier Biosoft International, Palo Alto, CA, USA) according to the SYBR Green Master Mix (Tiangen, Beijing, China) manufacturer’s instructions.

2.6. Quantitative Reverse Transcription-Polymerase Chain Reaction

qRT-PCR was carried out using the StepOne Plus Real-Time PCR System (Applied Biosystems, Foster City, CA, USA) in which amplification, detection, and analysis steps were combined. Reactions were performed using the SYBR Green Master Mix (Tiangen, Beijing, China) in a 20 μL reaction volume, containing 10 μL SYBR Green Master Mix, 0.4 μL 10 pmol/L of each primer (Table 1) and 2 μL diluted cDNA. The following program parameters were used for all amplifications: 95 °C for 30 s followed by 40 cycles at 95 °C for 5 s each, 60 °C for 30 s, 7 °C for 30 s, and finally one cycle at 95 °C for 15 s, 60 °C for 1 min, 95 °C for 15 s, and 60 °C for 15 s to generate dissociation curves [39]. The amplification efficiency (E) was calculated from standard curves, according to the SYBR Green Master Mix manufacturer’s instructions. All assays were performed using three biological replicates, each consisting of technical triplicates and a non-template control [39].

2.7. Assessing Expression Stability of Reference Genes

The stability of expression of the candidate reference genes were estimated and ranked using four different statistical algorithms, geNorm, NormFinder, BestKeeper, and the delta Ct method, and a web-based analysis tool, RefFinder. GeNorm evaluates the most stable reference genes and determines the optimal number of reference genes required for normalization [31]. When the expression stability value (MV) is less than 1.5, the expression of this gene is stable [40]. NormFinder ranks candidate reference genes by calculating their stability value (SV) [32]. BestKeeper calculates the stability of the candidate genes based on the standard deviation (SD) and coefficient of variation (CV): the smaller the SD and CV values, the more suitable these genes are to be used as references [30]. The comparative delta Ct method calculates the delta Ct of the candidate reference genes, and smaller ΔCt means more stable gene expression [29]. Finally, we comprehensively ranked the candidate reference genes based on the geomean (GM) values of the above results from the four different statistical algorithms, using the web-based analysis tool RefFinder [33].

2.8. Validation of Reference Genes

To confirm the stability of expression of the selected reference genes, verification experiments were carried out with samples from different temperatures and developmental stages. For testing the expression stability across different developmental stages, we quantified the relative expression of AK, which have been shown to be differentially expressed at different developmental stages [41,42]. For assessing the stability across different temperatures, we quantified the relative expression of the thermotolerance-related gene HSP90, which is essential for the survival of B. xylophilus [43]. Normalization of the two target genes (AK, HSP90) was conducted using the most stable gene combinations (EF1γ/Actin or UCBE/EF1γ) and the least stable combinations (UBQ/18S rRNA or EF1γ/PMP-2) as determined by geNorm and RefFinder. Relative quantification of these two target genes was calculated using the 2−ΔΔCt method. The primers for validation of selected reference genes were as follows: Bx-AK-F, 5′-CATCTCCGAATCATCTCCAT-3′ and Bx-AK-R, 5′-GCCTTGAACTTGTCCTTATC-3′; Bx-HSP90-F, 5′-GAAGGTCATCAGGAAGAACT-3′ and Bx-HSP90-R, 5′-TGTGGTAACGGAGGAACT-3′. Statistical analysis was performed by SPSS 22.0 (SPSS Inc., Chicago, IL, USA) using a Student’s t-test for independent samples.

3. Results

3.1. Performance of qRT-PCR Primers

In order to determine the availability of reference genes, we first verified the specificity and efficiency of their primers. The 2.0% agarose gel image showed that the PCR amplification of each primer pair had a single expected size (Figure 1a), and the melting curves showed a single peak (Figure 1b and Figure S1). The primer efficiency (E) was between 92.86% and 105.15%, and the determination coefficient (R2) values were between 0.9843 and 0.9961 (Table 1). These comprehensive results showed that the designed primers could accurately amplify the candidate reference genes.

3.2. Expression Profiles of Candidate Reference Genes

In order to obtain an overall representation of primer variability, we examined the expression profiles of the candidate reference genes under different experimental conditions (Figure 2). All Ct values of the candidate reference genes were between 12 and 28, indicating that the eight reference genes had high expression levels under different experimental conditions. TUB had average Ct values >25, followed by PMP-2, HIS, UBCE, EF1γ, UBQ, Actin, and 18S rRNA with average Ct values ranging between 14–25 (Figure 2). However, the degree of expression of some reference genes clearly varied depending on the experimental conditions. For example, Actin varied less (~2 cycles) between temperature treatments than across developmental stages (>5 cycles).

3.3. Stability of the Candidate Reference Genes

To further evaluate the expression stability of the eight candidate reference genes under two experimental conditions (temperature and developmental stage), the ΔCt method, BestKeeper, NormFinder, and geNorm were employed. RefFinder was used to calculate an overall stability ranking.
For different temperature treatments, the analysis of the result of the ΔCt method and NormFinder clearly showed that UBCE, PMP-2 and TUB were the most stable reference genes (Table 2). Based on BestKeeper, HIS and UBQ were the most stable reference genes, whereas, that Actin and 18S rRNA were relatively unstable (Table 2). Based on the results from geNorm, all eight candidate genes were suitable for reference genes (MV < 1.5), Actin and EF1γ being the most stable genes (Figure 3a). Based on the results from all four statistical algorithms, EF1γ and UBCE were the most unstable reference genes. The stability ranking of genes from highest to lowest stability by RefFinder was UBCE > EF1γ > TUB > PMP-2 > Actin > HIS > UBQ > 18S rRNA (Figure 4a). Pairwise variation values of the expression stability of the candidate reference genes calculated by geNorm was V-value < 0.15 at V2/3, indicating that only two references genes were required to normalize the target gene data (Figure 5). Thus, the best combination of reference genes for temperature treatments of B. xylophilus was UBCE and EF1γ.
For the different developmental stages, the ΔCt method and NormFinder showed that EF1γ and UBCE were the most stable genes, whereas UBQ and 18S rRNA exhibited the greatest variation (Table 2). Based on BestKeeper, PMP-2 and Actin were the most stable reference genes, whereas, that UBQ and 18S rRNA were relatively unstable (Table 2). Additionally, GeNorm calculated the lowest M value for the Actin/TUB pair, suggesting that they are the most stable reference genes (Figure 3b). RefFinder ranked the genes from most to least stable as follows: EF1γ > Actin > TUB > UBCE > PMP-2 > HIS > UBQ > 18S rRNA (Figure 4b). The geNorm pairwise variation analysis also revealed that the first V-value < 0.15 emerged at V2/3 (Figure 5). Thus, the best combination of reference genes for developmental stages samples of B. xylophilus was EF1γ and Actin.

3.4. Validation of Selected Reference Genes in B. xylophilus

To validate the reference genes selected in B. xylophilus under different experimental conditions, the expression of HSP90 and AK were checked under different temperature treatments and developmental stages. The proposed stable gene combinations (UBCE/EF1γ or EF1γ/Actin, respectively) (Figure 6) and the least stable combinations (UBQ/18S rRNA, respectively) (Figure 7) were used to normalize the expression levels of the two target genes.
The expression patterns of HSP90 under different temperature treatments were inconsistent when normalized with the two most stable or most unstable reference genes. HSP90 expression was found to be 3.45-fold (p < 0.05) higher at 50 °C than at 25 °C when the proposed gene combination (UBCE/EF1γ) was used (Figure 6a). When the most unstable combination (UBQ/18S rRNA) was used, HSP90 expression was only 2.0-fold (p < 0.05) higher at 50 °C than in the controls under the same conditions (Figure 6b).
At different developmental stages, AK expression was sharply up-regulated at the L4 and adult stages. However, when AK expression at the adult stage was normalized according to the two most stable reference genes it was 695 times higher compared to the L3 stage, but when normalized according to the two most unstable reference genes, its expression level dropped to be 215 times higher compared to the L3 stage (Figure 7).

4. Discussion

B. xylophilus is a serious pest of pine trees causing PWD, which has been considered as the most destructive forest disease in China in recent years. Due to the significant economic losses caused by the PWD, many in-depth studies are being initiated to understand the disease cycle. The genetic basis is essential to study the adaptations of dispersal PWN to its host, and the interspecific communication between propagative PWN and its associated partners, and the potential role of this communication in promoting pathogenicity and invasiveness of PWN. Most of these studies involve qRT-PCR technique to quantify the expression of target genes efficiently and accurately. This technique relies on the stable expression of reference genes to eliminate the impact of differences in RNA quality, reverse transcription efficiency, and human factors. Hence, the selection of the most appropriate and stable reference genes is the basis of qRT-PCR success [44]. Because the expression of reference genes of interest is influenced by experimental conditions [45,46,47], the screening of reference genes is particularly necessary for quantitative genetic studies. Based on the transcriptome data of pine wood nematodes, we have screened candidate reference genes of B. xylophilus suitable for studying the effects of temperature variation and developmental stages.
The suitable reference genes vary according to species, developmental stages, and temperature used, indicating that there is no absolute universality between the homologous reference genes of different insect species [48,49,50,51]. In order to overcome this limitation, some studies have screened internal reference genes of insects in different conditions, such as selecting reference genes in different tissues and geographical populations of Nilaparvata lugens [52] or screening reference genes in Helicoverpa armigera at different temperatures [53]. Our current study is the first one to evaluate different internal reference genes of B. xylophilus in varying developmental stages and temperatures, and to report their relative stability.
The stability of the eight reference genes selected varied based on the chosen analysis method (Table 2). It is difficult to identify reference genes which are generally stable across these four algorithms, as each program has its own strengths and appropriate application conditions [29,54,55]. When compared across different temperature treatments, the results of geNorm were distinct from those of delta Ct, BestKeeper, and NormFinder (Table 2). Nonetheless, there was also partial consistency. As shown in Table 2, the stable combination of reference genes for temperature treatments was UBCE and EF1γ, whereas UBQ and 18S rRNA were the most unstable reference genes based on all four statistical algorithms. In the analysis of the developmental stages, the ΔCt method and NormFinder represented that EF1γ and UBCE were the most stable genes. Based on BestKeeper, PMP-2 and Actin were the appropriate reference genes (Table 2).
Therefore, we suggest in order to obtain a reliable indication of the stability of the target gene, a comprehensive ranking should be made based on the results of different methods. In this study, in order to overcome the differences in the results of different algorithms and obtain the final ranking, the web-based analysis tool RefFinder was used. RefFinder obtained comprehensive overall ranking based on the Geomean of the ranking values from the above-mentioned algorithms [33]. However, one disadvantage of RefFinder is that the results of these algorithms are not weighted according to the unavailability of their cut-offs and appropriate weights [56].
Based on the results from the comprehensive analysis using the five algorithms, UBCE and EF1γ, EF1γ and Actin are pairs of the reliable combination. These findings will facilitate the molecular mechanisms conferring functional and development genomics studies of the nematode in future studies.
Furthermore, according to the results of gene expression studies, we found that traditional reference genes are not always stable and consistent with earlier studies. Many studies have shown that some common reference genes have different expression stabilities under different experimental conditions [48,52,57,58,59]. Our current study further emphasizes the shortcomings of simple use of common reference genes without a comprehensive evaluation (Table 2; Figure 4). For example, the commonly used internal reference gene ACT has unstable expression under different developmental stages and was especially unstable in different temperature treatments. Similarly, UBCE gene expression is stable under different temperature treatments, but unstable at different developmental stages (Table 2; Figure 2). These data indicate that under different conditions, the expression levels of traditional reference genes may vary significantly, highlighting that all reference genes should be validated.

5. Conclusions

Under a series of experimental conditions, we identified suitable and reliable reference genes for normalizing gene expression in B. xylophilus (PWN), the causal agent of the PWD. In conclusion, UBCE and EF1γ can give stable gene expressions under temperatures, whereas EF1γ and Actin are reliable reference genes across different developmental stages. This study identified several suitable internal reference genes for the standardized qRT-PCR analysis of PWN, which will contribute to further explorations of molecular mechanism and functional genomics of pine wood nematode with pest control implications.

Supplementary Materials

The following supporting information can be downloaded at: https://0-www-mdpi-com.brum.beds.ac.uk/article/10.3390/app12062880/s1. Figure S1: Standard curves of eight candidate reference genes.

Author Contributions

Conceptualization, G.W. and L.R.; Methodology, Y.W., J.Z., Y.L., Y.G., H.Z. and F.A.; Formal analysis, Y.W. and J.Z.; Investigation, Y.W. and H.Z.; Resources, Y.W., J.Z., G.W. and L.R.; Data curation, Y.W. and J.Z.; Writing—original draft preparation, Y.W. and J.Z.; Writing—review and editing, Y.W., J.Z., Y.L., Y.G., H.Z., F.A., G.W. and L.R.; Supervision, G.W. and L.R.; Funding acquisition, G.W. and L.R. All authors have read and agreed to the published version of the manuscript.

Funding

The Scientific Research Program of the Chinese Academy of Inspection and Quarantine 2020Jk030 and 2019JK028. The State Key Laboratory of Integrated Management of Pest Insects and Rodents (Chinese IPM2111). The Natural Science Foundation of China (31970466). National Key Research and Development Project (2019YFC1200504, 2021YFC2600100).

Conflicts of Interest

The authors declare no competing interest.

References

  1. Rodrigues, J.M. A Worldwide Threat to Forest Ecosystems. In Pine Wilt Disease; Mota, M.M., Vieira, P., Eds.; Springer: Dordrechet, The Netherlands, 2008; pp. 5–14. [Google Scholar]
  2. Zhao, L.; Mota, M.; Vieira, P.; Butcher, R.A.; Sun, J. Interspecific Communication between Inewood Nematode, Its Insect Vector, and Associated Microbes. Trends Parasitol. 2014, 30, 299–308. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Zhao, L.; Mota, M.; Vieira, P.; Butcher, R.A.; Sun, J. A Native Fungal Symbiont Facilitates the Prevalence and Development of an Invasive Pathogen-native Vector Symbiosis. Ecol. Ecol. 2013, 94, 2817–2826. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Maehara, N.; Futai, K. Effect of Fungal Interactions on the Numbers of the Pinewood Nematode, Bursaphelenchus xylophilus (Nematoda: Aphelenchoididae), Carried by the Japanese Pine Sawyer, Monochamus alternatus (Coleoptera: Cerambycidae). Fundam. Appl. Nematol. 1997, 20, 611–617. [Google Scholar]
  5. Zhao, B.G.; Tao, J.; Ju, Y.W.; Wang, P.K.; Ye, J.L. The Role of Wood-inhabiting Bacteria in Pine Wilt Disease. J. Nematol. 2011, 43, 129–134. [Google Scholar] [PubMed]
  6. Zhao, L.; Zhang, X.; Wei, Y.; Zhou, J.; Zhang, W.; Qin, P.; Chinta, S.; Kong, X.; Liu, Y.; Yu, H.; et al. Ascarosides Coordinate the Dispersal of a Plant-parasitic Nematode with the Metamorphosis of Its Vector Beetle. Nat. Commun. 2016, 7, 8. [Google Scholar] [CrossRef] [Green Version]
  7. Tomminen, J.; Halik, S.; Bergdahl, D.R. Incubation-Temperature and Time Effects on Life Stages of Bursaphelenchus Xylophilus in Wood Chips. J. Nematol. 1991, 23, 477–484. [Google Scholar]
  8. Cha, D.J.; Kim, J.; Kim, D.S. Nematicidal Activities of Three Naphthoquinones against the Pine Wood Nematode, Bursaphelenchus xylophilus. Molecules 2019, 24, 3634. [Google Scholar] [CrossRef] [Green Version]
  9. de la Fuente, B.; Saura, S.; Beck, P.S.A. Predicting the Spread of an Invasive Tree Pest: The Pine Wood Nematode in Southern Europe. J. Appl. Ecol. 2018, 55, 2374–2385. [Google Scholar] [CrossRef]
  10. Firmino, P.N.; Calvao, T.; Ayres, M.P.; Pimentel, C.S. Monochamus galloprovincialis and Bursaphelenchus xylophilus Life History in an Area Severely Affected by Pine Wilt Disease: Implications for Forest Management. For. Ecol. Manag. 2017, 389, 105–115. [Google Scholar] [CrossRef]
  11. Huang, X.; Hu, L.J.; Wu, X.Q. Identification of a Novel Effector BxSapB3 that Enhances the Virulence of Pine Wood Nematode Bursaphelenchus xylophilus. Acta Biochim. Biophys. Sin. 2019, 51, 1071–1078. [Google Scholar] [CrossRef]
  12. Qiu, X.; Yang, L.; Ye, J.; Wang, W.; Zhao, T.; Hu, H.; Zhou, G. Silencing of Cyp-33C9 Gene Affects the Reproduction and Pathogenicity of the Pine Wood Nematode, Bursaphelenchus xylophilus. Int. J. Mol. Sci. 2019, 20, 4520. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  13. Li, Y.; Meng, F.; Deng, X.; Wang, X.; Feng, Y.; Zhang, W.; Pan, L.; Zhang, X. Comparative Transcriptome Analysis of the Pinewood Nematode Bursaphelenchus xylophilus Reveals the Molecular Mechanism Underlying Its Defense Response to Host-Derived -pinene. Int. J. Mol. Sci. 2019, 20, 911. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Espada, M.; Silva, A.C.; Akker, S.E.V.D.; Cock, P.; Mota, M.; Jones, J.T. Identification and Characterization of Parasitism Genes from the Pinewood Nematode Bursaphelenchus xylophilus Reveals a Multilayered Detoxification Strategy. Mol. Plant Pathol. 2016, 17, 286–295. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  15. He, L.X.; Wu, X.Q.; Xue, Q.; Qiu, X.W. Effects of Endobacterium (Stenotrophomonas maltophilia) on Pathogenesis-Related Gene Expression of Pine Wood Nematode (Bursaphelenchus xylophilus) and Pine Wilt Disease. Int. J. Mol. Sci. 2016, 17, 778. [Google Scholar] [CrossRef] [Green Version]
  16. Ma, H.B.; Lu, Q.; Liang, J.; Zhang, X.Y. Functional Analysis of the Cellulose Gene of the Pine Wood Nematode, Bursaphelenchus xylophilus, Using RNA Interference. Genet. Mol. Res. 2011, 10, 1931–1941. [Google Scholar] [CrossRef]
  17. Qiu, X.W.; Wu, X.Q.; Huang, L.; Ye, J.R. Influence of Bxpel1 Gene Silencing by dsRNA Interference on the Development and Pathogenicity of the Pine Wood Nematode, Bursaphelenchus xylophilus. Int. J. Mol. Sci. 2016, 17, 125. [Google Scholar] [CrossRef] [Green Version]
  18. Tang, J.; Ma, R.; Zhu, N.; Guo, K.; Guo, Y.; Bai, L.; Yu, H.; Hu, J.; Zhang, X. Bxy-fuca Encoding Alpha-L-fucosidase Plays Crucial Roles in Development and Reproduction of the Pathogenic Pinewood Nematode, Bursaphelenchus xylophilus. Pest Manag. Sci. 2020, 76, 205–214. [Google Scholar] [CrossRef]
  19. Bustin, S.A. Developments in Real-Time PCR Research and Molecular Diagnostics. Expert Rev. Mol. Diagn. 2010, 10, 713–715. [Google Scholar] [CrossRef]
  20. Jozefczuk, J.; Adjaye, J. Methods in Systems Biology. In Methods in Enzymology; Jameson, D., Verma, M., Westerhoff, H.V., Eds.; Academic Press: San Diego, CA, USA, 2011; Volume 500, pp. 99–109. [Google Scholar]
  21. Singh, C.; Roy-Chowdhuri, S. Methods in Molecular Biology. In Clinical Applications of PCR, 3rd ed.; Luthra, R., Singh, R.R., Patel, K.P., Eds.; Humana Press: Totowa, NJ, USA, 2016; Volume 1392, pp. 161–176. [Google Scholar]
  22. Wen-Kai, W.U.; Cheng-Qian, L.I.U.; Zhi-Gang, Z.; Shan, L.U. The Selection of Reference Genes in Chlamydomonas reinhardtii P.A. Dangeard by Real-Time Quantitative PCR. Plant Physiol. Commun. 2009, 45, 667–672. [Google Scholar]
  23. Condeelis, J. Elongation-Factor 1-Alpha, Translation and the Cytoskeleton. Trends Biochem. Sci. 1995, 20, 169–170. [Google Scholar] [CrossRef]
  24. Thellin, O.; Zorzi, W.; Lakaye, B.; De Borman, B.; Coumans, B.; Hennen, G.; Grisar, T.; Igout, A.; Heinen, E. Housekeeping Genes as Internal Standards: Use andLimits. J. Biotechnol. 1999, 75, 291–295. [Google Scholar] [CrossRef] [Green Version]
  25. Lu, Y.; Yuan, M.; Gao, X.; Kang, T.; Zhan, S.; Wan, H.; Li, J. Identification and Validation of Reference Genes for Gene Expression Analysis Using Quantitative PCR in Spodoptera litura (Lepidoptera: Noctuidae). PLoS ONE 2013, 8, e68059. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  26. De Jonge, H.J.M.; Fehrmann, R.; De Bont, E.S.J.M.; Hofstra, R.; Gerbens, F.; Kamps, W.A.; de Vries, E.; Van Der Zee, A.G.J.; Meerman, G.J.T.; Ter Elst, A. Evidence Based Selection of Housekeeping Genes. PLoS ONE 2007, 2, e898. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  27. Sun, H.-F.; Meng, Y.-P.; Cui, G.-M.; Cao, Q.-F.; Li, J.; Liang, A.-H. Selection of Housekeeping Genes for Gene Expression Studies on the Development of Fruit Bearing Shoots in Chinese Jujube (Ziziphus jujube Mill.). Mol. Biol. Rep. 2009, 36, 2183–2190. [Google Scholar] [CrossRef]
  28. Huis, R.; Hawkins, S.; Neutelings, G. Selection of Reference Genes for Quantitative Gene Expression Normalization in Flax (Linum usitatissimum L.). BMC Plant Biol. 2010, 10, 14. [Google Scholar] [CrossRef] [Green Version]
  29. 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, 9. [Google Scholar] [CrossRef] [Green Version]
  30. 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]
  31. Vandesompele, J.; De Preter, K.; Pattyn, F.; Poppe, B.; Van Roy, N.; De Paepe, A.; Speleman, F. Accurate Nnormalization of Real-Time Quantitative RT-PCR Data by Geometric Averaging of Multiple Internal Control Genes. Genome Biol. 2002, 3, research0034.1. [Google Scholar] [CrossRef] [Green Version]
  32. Andersen, C.L.; Jensen, J.L.; Orntoft, 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] [Green Version]
  33. 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]
  34. Viglierchio, D.R.; Schmitt, R.V. On the Methodology of Nematode Extraction from Field Samples-baermann Funnel Modifications. J. Nematol. 1983, 15, 438–444. [Google Scholar] [PubMed]
  35. Kikuchi, T.; Cotton, J.A.; Dalzell, J.J.; Hasegawa, K.; Kanzaki, N.; McVeigh, P.; Takanashi, T.; Tsai, I.J.; Assefa, S.A.; Cock, P.J.; et al. Genomic Insights into the Origin of Parasitism in the Emerging Plant Pathogen Bursaphelenchus xylophilus. PLoS Pathog. 2011, 7, e1002219. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  36. Baermann, G. A Simple Method for the Detection of Ankylostomum (Nematode) Larvae in Soil Tests. In Mededelingen uit het Geneeskundig Laboratorium te Weltevreden; Javasche Boekhandel & Drukkerij: Batavia, IL, USA, 1917; pp. 41–47. [Google Scholar]
  37. Freckman, D.W.; Mankau, R.; Ferris, H. Nematode Community Structure in Desert Soils-Nematode Recovery. J. Nematol. 1975, 7, 343–346. [Google Scholar] [PubMed]
  38. Zhou, L.; Chen, F.; Pan, H.; Ye, J.; Dong, X.; Li, C.; Lin, F. Identifying Virulence-Associated Genes Using Transcriptomic and Proteomic Association Analyses of the Plant Parasitic Nematode Bursaphelenchus mucronatus. Int. J. Mol. Sci. 2016, 17, 1492. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  39. Zhou, L.F.; Chen, F.M.; Ye, J.R.; Pan, H.Y. Selection of Reliable Reference Genes for RT-qPCR Analysis of Bursaphelenchus mucronatus Gene Expression from Different Habitats and Developmental Stages. Front. Genet. 2018, 9, 11. [Google Scholar] [CrossRef] [Green Version]
  40. Hellemans, J.; Mortier, G.; De Paepe, A.; Speleman, F.; Vandesompele, J. qBase Relative Quantification Framework and Software for Management and Automated Analysis of Real-Time Quantitative PCR Data. Genome Biol. 2007, 8, 14. [Google Scholar] [CrossRef] [Green Version]
  41. Kulathunga, D.G.R.S.; Wickramasinghe, S.; Rajapakse, R.P.V.J.; Yatawara, L.; Jayaweera, W.R.; Agatsuma, T. Immunolocalization of Arginine Kinase (AK) in Toxocara canis, Toxocara vitulorum, and Ascaris lumbricoides. Parasitol. Res. 2012, 111, 663–671. [Google Scholar] [CrossRef]
  42. Qi, X.-L.; Su, X.-F.; Lu, G.-Q.; Liu, C.-X.; Liang, G.-M.; Cheng, H.-M. The effect of Silencing Arginine Kinase by RNAi on the Larval Development of Helicoverpa armigera. Bull. Entomol. Res. 2015, 105, 555–565. [Google Scholar] [CrossRef]
  43. Wang, F.; Wang, Z.Y.; Li, D.L.; Chen, Q.L. Identification and Characterization of a Bursaphelenchus xylophilus (Aphelenchida: Aphelenchoididae) Thermotolerance-Related Gene: Bx-HSP90. Int. J. Mol. Sci. 2012, 13, 8819–8833. [Google Scholar] [CrossRef]
  44. Hunter, T.; Garrels, J.I. Characterization of Messenger-Rnas for Alpha-Actin, Beta-Actin and Gamma-Actin. Cell 1977, 12, 767–781. [Google Scholar] [CrossRef]
  45. Liang, P.; Guo, Y.; Zhou, X.; Gao, X. Expression Profiling in Bemisia tabaci under Insecticide Treatment: Indicating the Necessity for Custom Reference Gene Selection. PLoS ONE 2014, 9, e87514. [Google Scholar] [CrossRef] [PubMed]
  46. Yang, C.; Pan, H.; Liu, Y.; Zhou, X. Selection of Reference Genes for Expression Analysis Using Quantitative Real-Time PCR in the Pea Aphid, Acyrthosiphon pisum (Harris) (Hemiptera, Aphidiae). PLoS ONE 2014, 9, e110454. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  47. Ladror, D.T.; Frey, B.L.; Scalf, M.; Levenstein, M.E.; Artymiuk, J.M.; Smith, L.M. Methylation of Yeast Ribosomal Protein S2 is Elevated During Stationary Phase Growth Conditions. Biochem. Biophys. Res. Commun. 2014, 445, 535–541. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  48. Lü, J.; Chen, S.; Guo, M.; Ye, C.; Qiu, B.; Wu, J.; Yang, C.; Pan, H. Selection and Validation of Reference Genes for RT-qPCR Analysis of the Ladybird Beetle Henosepilachna vigintioctopunctata. Front. Physiol. 2019, 10, 981. [Google Scholar] [CrossRef] [PubMed]
  49. Wang, Z.; Meng, Q.; Zhu, X.; Sun, S.; Gao, S.; Gou, Y.; Liu, A. Evaluation and Validation of Reference Genes for Quantitative Real-Time PCR in Helopeltis theivora Waterhouse (Hemiptera: Miridae). Sci. Rep. 2019, 9, 10. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  50. Dzaki, N.; Ramli, K.N.; Azlan, A.; Ishak, I.H.; Azzam, G. Evaluation of Reference Genes at Different Developmental Stages for QuantitativeReal-Time PCR in Aedes aegypti. Sci. Rep. 2017, 7, srep43618. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  51. Zaros, L.G.; Coutinho, L.; Sider, L.H.; de Medeiros, H.R.; Das Neves, M.R.M.; Benvenuti, C.L.; Navarro, A.M.D.C.; Vieira, L.D.S. Evaluation of Reference Genes for Real-Time PCR Studies of Brazilian Somalis Sheep Infected by Gastrointestinal Nematodes. Genet. Mol. Biol. 2010, 33, 486–490. [Google Scholar] [CrossRef]
  52. Yuan, M.; Lu, Y.; Zhu, X.; Wan, H.; Shakeel, M.; Zhan, S.; Jin, B.-R.; Li, J. Selection and Evaluation of Potential Reference Genes for Gene Expression Analysis in the Brown Planthopper, Nilaparvata lugens (Hemiptera: Delphacidae) Using Reverse-Transcription Quantitative PCR. PLoS ONE 2014, 9, e86503. [Google Scholar] [CrossRef] [Green Version]
  53. Shakeel, M.; Zhu, X.; Kang, T.H.; Wan, H.; Li, J.H. Selection and Evaluation of Reference Genes for Quantitative Gene Expression Studies in Cotton Bollworm, Helicoverpa armigera (Lepidoptera: Noctuidae). J. Asia-Pac. Entomol. 2015, 18, 123–130. [Google Scholar] [CrossRef]
  54. Robledo, D.; Hernández-Urcera, J.; Cal, R.M.; Pardo, B.G.; Sánchez, L.; Martínez, P.; Viñas, A. Analysis of qPCR Reference Gene Stability Determination Methods and a Practical Approach for Efficiency Calculation on a Turbot (Scophthalmus maximus) gonad dataset. BMC Genom. 2014, 15, 648. [Google Scholar] [CrossRef] [Green Version]
  55. Nakayama, T.; Okada, N.; Yoshikawa, M.; Asaka, D.; Kuboki, A.; Kojima, H.; Tanaka, Y.; Haruna, S.-I. Assessment of Suitable Reference Genes for RT-qPCR Studies in Chronic Rhinosinusitis. Sci. Rep. 2018, 8, 9. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  56. Wu, H.; Taki, F.A.; Zhang, Y.; Dobbins, D.L.; Pan, X. Evaluation and Identification of Reliable Reference Genes for Toxicological Study in Caenorhabditis elegans. Mol. Biol. Rep. 2014, 41, 3445–3455. [Google Scholar] [CrossRef] [PubMed]
  57. Li, R.; Xie, W.; Wang, S.; Wu, Q.; Yang, N.; Yang, X.; Pan, H.; Zhou, X.; Bai, L.; Xu, B.; et al. Reference Gene Selection for qRT-PCR Analysis in the Sweetpotato Whitefly, Bemisia tabaci (Hemiptera: Aleyrodidae). PLoS ONE 2013, 8, e53006. [Google Scholar] [CrossRef] [PubMed]
  58. Bansal, R.; Mittapelly, P.; Chen, Y.; Mamidala, P.; Zhao, C.; Michel, A. Quantitative RT-PCR Gene Evaluation and RNA Interference in the Brown Marmorated Stink Bug. PLoS ONE 2016, 11, e0152730. [Google Scholar] [CrossRef]
  59. Luo, J.; Ma, C.; Li, Z.; Zhu, B.-Q.; Zhang, J.; Lei, C.-L.; Jin, S.-X.; Hull, J.J.; Chen, L.-Z. Assessment of Suitable Reference Genes for qRT-PCR Analysis in Adelphocoris suturalis. J. Integr. Agric. 2018, 17, 2745–2757. [Google Scholar] [CrossRef]
Applsci 12 02880 g001a 550Applsci 12 02880 g001b 550
Figure 1. Amplification specificity of primers in RT-PCR and qRT-PCR. (a) Single amplicon of the desired size for each candidate reference gene (M represents marker) visualized on a 2% agarose gel and (b) melting curves of the eight candidate reference genes.
Figure 1. Amplification specificity of primers in RT-PCR and qRT-PCR. (a) Single amplicon of the desired size for each candidate reference gene (M represents marker) visualized on a 2% agarose gel and (b) melting curves of the eight candidate reference genes.
Applsci 12 02880 g001aApplsci 12 02880 g001b
Applsci 12 02880 g002 550
Figure 2. Expression profiles of the eight candidate reference genes in different experimental conditions. (a) Different temperature treatment; (b) Different developmental stages. The expression levels of the candidate reference genes are shown as Ct values. The box represents the 75th to 25th percentiles and the line in the box represents the median. The whiskers on each box represent the minimum and maximum Ct values.
Figure 2. Expression profiles of the eight candidate reference genes in different experimental conditions. (a) Different temperature treatment; (b) Different developmental stages. The expression levels of the candidate reference genes are shown as Ct values. The box represents the 75th to 25th percentiles and the line in the box represents the median. The whiskers on each box represent the minimum and maximum Ct values.
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Applsci 12 02880 g003 550
Figure 3. Average expression stability values (MV) of the candidate reference genes calculated by geNorm at (a) different temperature treatments and (b) different developmental stages.
Figure 3. Average expression stability values (MV) of the candidate reference genes calculated by geNorm at (a) different temperature treatments and (b) different developmental stages.
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Applsci 12 02880 g004 550
Figure 4. Expression stability of the candidate reference genes determined by RefFinder analysis. The average expression stability of the reference genes was calculated by the Geomean method of RefFinder. A lower Geomean value indicates more stable expression. (a) Different temperature treatments and (b) different developmental stages.
Figure 4. Expression stability of the candidate reference genes determined by RefFinder analysis. The average expression stability of the reference genes was calculated by the Geomean method of RefFinder. A lower Geomean value indicates more stable expression. (a) Different temperature treatments and (b) different developmental stages.
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Applsci 12 02880 g005 550
Figure 5. Pairwise variations (V) of the expression stability of the candidate reference genes calculated by geNorm in two experimental sets. Pairwise variations (Vn/n + 1) were analyzed to determine the optimal number of the reference genes for normalizing qRT-PCR data.
Figure 5. Pairwise variations (V) of the expression stability of the candidate reference genes calculated by geNorm in two experimental sets. Pairwise variations (Vn/n + 1) were analyzed to determine the optimal number of the reference genes for normalizing qRT-PCR data.
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Applsci 12 02880 g006 550
Figure 6. Relative expression levels of HSP90 under different temperature treatments. The relative mRNA expression levels of HSP90 were normalized according to the most suitable ((a), UBCE and EF1γ) and the least suitable ((b), UBQ and 18S rRNA) reference genes, respectively. Bars show the means and SEs. Different letters indicate significant differences (p < 0.05, one-way analysis of variance followed by LSD Multiple Comparison).
Figure 6. Relative expression levels of HSP90 under different temperature treatments. The relative mRNA expression levels of HSP90 were normalized according to the most suitable ((a), UBCE and EF1γ) and the least suitable ((b), UBQ and 18S rRNA) reference genes, respectively. Bars show the means and SEs. Different letters indicate significant differences (p < 0.05, one-way analysis of variance followed by LSD Multiple Comparison).
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Figure 7. Relative expression levels of AK in different developmental stages. The relative mRNA expression levels of AK were normalized according to the most suitable ((a), EF1γ and Actin) and the least suitable ((b), UBQ and 18S rRNA) reference genes, respectively. Bars show the means and SEs. Different letters indicate significant differences (p < 0.05, one-way analysis of variance followed by LSD Multiple Comparison).
Figure 7. Relative expression levels of AK in different developmental stages. The relative mRNA expression levels of AK were normalized according to the most suitable ((a), EF1γ and Actin) and the least suitable ((b), UBQ and 18S rRNA) reference genes, respectively. Bars show the means and SEs. Different letters indicate significant differences (p < 0.05, one-way analysis of variance followed by LSD Multiple Comparison).
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Table
Table 1. Primers for candidate reference genes investigated in B. xylophilus by qRT-PCR.
Table 1. Primers for candidate reference genes investigated in B. xylophilus by qRT-PCR.
Gene SymbolPrimer Sequence(5′ to 3′)Amplicon Length (bp)E%R2
ActinF:GCTGCCTCCTCTTCTTCCCTC
R:GGAGTTGTAGGTGGTCTCGTGGATA		15699.470.9908
TUBF:TGTGATTGTCTTCAAGGCTTCC
R:GATTCTGTCTGGGTATTCTTCTCG		88102.520.9936
UBCEF:ACCGCCTGGGACTTTGTATC
R:TTGGACAGGAACCGAATGG		99104.670.9843
PMP-2F:CTCTACTCGCAGATTACCAAACC
R:CTGGCAGTCGCTGAAACAA		14392.860.9902
HISF:GTGGGTCGTCTGCACCGTATCCT
R:TTGTCACGAGCGGCGTTTCC		140103.660.9892
EF1γF:GTTTCTGTCGCATTGAACCTCC
R:CGATGGTCAAGAAATAACGGGTAA		100101.370.9863
18S rRNAF:TCAGGGAACATAGGAGGC
R:TCAAGCGAGGAGGAGAAT		172105.150.9961
UBQF:AGCACGGTATAGTTACAGATTGG
R:GCAGAGGATTCAGAGGAGC		12995.390.9863
E, qRT-PCR efficiency; R2, Determination coefficient of the qPCR reaction; F, forward primers; R, reverse primers; TUB, tubulin; UBCE, ubiquitin conjugating enzyme; PMP-2, peroxisomal membrane protein-2; HIS, histone; EF1γ, elongation factor-1γ; 18S rRNA, 18S ribosomal RNA; UBQ, ubiquitin.
Table
Table 2. Stability of eight candidate reference genes expression in B. xylophilus under different experimental conditions.
Table 2. Stability of eight candidate reference genes expression in B. xylophilus under different experimental conditions.
ΔCtBestkeeperNormFindergeNorm
Experimental ConditionsCRGsStabilityRankStabilityRankStabilityRankStabilityRank
Temperature treatmentActin0.45951.17370.32650.2491
TUB0.41530.98530.18320.3285
UBCE0.40111.08040.16010.3104
PMP-20.40241.15660.18930.2893
HIS0.63760.63110.56680.4888
EF1γ0.43921.13150.27440.2491
18S rRNA0.56081.31580.48160.3686
UBQ0.58770.70220.49570.4387
Developmental stagesActin0.43330.36320.30640.1091
TUB0.45040.37330.33850.1091
UBCE0.38420.68750.08820.2545
PMP-20.48460.30410.39660.1243
HIS0.45050.81360.21130.3056
EF1γ0.38010.56640.08810.2044
18S rRNA0.85180.97370.79380.5148
UBQ0.67071.09880.57970.4027
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Wu, Y.; Zhou, J.; Liu, Y.; Gu, Y.; Zhang, H.; Ahmad, F.; Wang, G.; Ren, L. Selection and Validation of Reliable Reference Genes for qRT-PCR Normalization of Bursaphelenchus xylophilus from Different Temperature Conditions and Developmental Stages. Appl. Sci. 2022, 12, 2880. https://0-doi-org.brum.beds.ac.uk/10.3390/app12062880

AMA Style

Wu Y, Zhou J, Liu Y, Gu Y, Zhang H, Ahmad F, Wang G, Ren L. Selection and Validation of Reliable Reference Genes for qRT-PCR Normalization of Bursaphelenchus xylophilus from Different Temperature Conditions and Developmental Stages. Applied Sciences. 2022; 12(6):2880. https://0-doi-org.brum.beds.ac.uk/10.3390/app12062880

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Wu, Yajing, Jiao Zhou, Yaning Liu, Yutong Gu, Hongxia Zhang, Faheem Ahmad, Guochang Wang, and Lili Ren. 2022. "Selection and Validation of Reliable Reference Genes for qRT-PCR Normalization of Bursaphelenchus xylophilus from Different Temperature Conditions and Developmental Stages" Applied Sciences 12, no. 6: 2880. https://0-doi-org.brum.beds.ac.uk/10.3390/app12062880

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