http://orcid.org/0000-0002-6287-6812
Figures
Abstract
The northern root-knot nematode (Meloidogyne hapla) is a critical pathogen with a wide host range. Quantitative real-time polymerase chain reaction (qRT-PCR) has been used to elucidate gene expression and function of M. hapla. Suitable reference genes are required to ensure accurate results of qRT-PCR for normalising gene expression. Eleven candidate reference genes of M. hapla were selected to evaluate gene expression stability under different conditions. The stability of candidate reference genes was ranked using RefFinder analysis, and the optimal number of reference genes was recommended with geNorm. Notably, the most stable reference genes were SDHA, Mdh, and RpS6 for all samples; SDHA and RpS6 were particularly stable during development stage treatments, whereas Mdh and RpS6 were appropriate for temperature and inorganic compound treatments. In contrast, the least stable reference genes were Actin1 during development stages and all other treatments, GAPDH for temperature treatments, and α-Tub for inorganic compound treatments. One target gene, Mh-Hsp90, was used to verify the selection of reference genes, results showed Mdh and RpS6 could be used as suitable reference genes for M. hapla, and Mdh plus RpS6 were better. Our finding contributes to further work on gene transcription analysis in M. hapla.
Citation: Wu X, Yu H, Yang R, Zhou Y, Zhu X, Wang Y, et al. (2019) Evaluation of suitable reference genes for gene expression analysis in the northern root-knot nematode, Meloidogyne hapla. PLoS ONE 14(6): e0218610. https://doi.org/10.1371/journal.pone.0218610
Editor: Haitao Shi, Hainan University, CHINA
Received: April 3, 2019; Accepted: June 5, 2019; Published: June 19, 2019
Copyright: © 2019 Wu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: The raw data for this paper has been uploaded to Figshare: Raw data of all treatments (DOI: 10.6084/m9.figshare.8246066), Raw data of target gene (DOI: 10.6084/m9.figshare.8246063), Raw data of standard curve (DOI: 10.6084/m9.figshare.8246054).
Funding: This work was supported by National Natural Science Foundation of China (grant number: 31471748), Major Fund for National Natural Science Foundation of China (grant number: 31330063), and Mount Tianzhu Scholar Funding Scheme, Special Fund for Agro-scientific Research in the Public Interest (grant number: 201103018).
Competing interests: The authors have declared that no competing interests exist.
Introduction
Quantitative real-time polymerase chain reaction (qRT-PCR) is an important conventional method for measuring gene expression in molecular biology applications and has several advantages, including high sensitivity, wide dynamic range, and low cost [1–4]. However, experimental error can be caused by poor quality and low concentrations of RNA and cDNA [5–10]. In order to reduce error and achieve reliable results, reference genes, called housekeeping genes, are essential for normalising gene expression [11].
Several common reference genes, including arginine kinase (AK), Actin 1 (Actin1), elongation factor 1 alpha (EF1-α), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), malate dehydrogenase (Mdh), transcriptional activator protein PUR (Pur), ribosomal protein S6 (RpS6), transcription initiation factor (TAF), alpha tubulin (α-Tub), polyubiquitin (Ubp), and succinate dehydrogenase flavoprotein subunit (SDHA), have been identified and used for normalisation of gene expression by qRT-PCR in various organisms [12–17]. An appropriate reference gene shows similar expression levels under different treatments [13]. However, no absolute reference genes in plants or animals or under different experimental treatments have been reported [8, 18–20]. Therefore, identifying the stability of reference genes is a crucial step for qRT-PCR analysis of gene expression. Some specialised analysis software, e.g. geNorm [21], Normfinder [22], and Bestkeeper [23], have been used to evaluate the stability of candidate reference genes under various experimental treatments.
The northern root-knot nematode (Meloidogyne hapla) is a sedentary-biotrophic parasite that feeds on plant roots and induces galling. The plant damages caused by nematodes influence the root system and reduce crop yields or quality [24–26], consequently causing severe economic losses in temperature cropping regions [27, 28]. The whole genome sequence of M. hapla is available, and the size (54 Mb) of the M. hapla genome is smaller than that (184 Mb) of M. incognita [29, 30]. Moreover, the proteome of M. hapla has been annotated [31], and the MhTTL2 and Mh265 genes, which are related to parasitism, have been identified [32]. M. hapla has a moderate cold tolerance [26] and is sensitive to some inorganic compounds [33]. However, the adaptation strategies of M. hapla remain unknown. Gene expression analysis by qRT-PCR is an important method that monitors expression of certain candidate genes in response to cold temperature and inorganic compound stress in M. hapla, which might lend insight into mechanisms of nematodes adaptation to certain stress environmental. However, no comprehensive studies of appropriate reference genes in M. hapla have been performed.
Accordingly, in this study, eleven candidate reference genes in M. hapla were evaluated to determine their stability for normalisation of gene expression under different experimental treatments (development stages, temperature, and inorganic compounds). The target gene Mh-Hsp90 was used to identify the selection of reference genes.
Materials and methods
Nematode culture and treatments
The northern root-knot nematode M. hapla was maintained on susceptible tomatoes (L-402) in a greenhouse as described by Forge and MacGuidwin [34]. The eggs were extracted from tomato roots [26]. Second stage juveniles (J2) were collected 48 hr after egg hatching. Females were picked from diseased roots for the experiment.
Development stage treatments.
M. hapla eggs, J2 and females were transferred to 1.5-mL Eppendorf tubes and centrifuged. Pellets weighing approximately 20 mg were collected, immediately frozen in liquid nitrogen and stored at −80°C for analysis.
Temperature treatments.
Approximately 20 mg of centrifuged J2 was collected as described above, transferred to a 30 mm diameter Petri dish containing 4 mL sterile water. The samples were respectively exposed to a low temperature (4°C) for 12 hr, preferred temperature (25°C) for 12 hr, and high temperatures (38°C and 40°C) using a programmable cooling device (TEMI990, Shanghai, China) in a temperature-control chamber. The temperature was initially set at 34°C and then increased in increments of 0.5°C/min to 38°C and 40°C; samples were held for 30 min at the high temperatures. The samples were then cooled to 34°C by decreasing the temperature in increments of 0.5°C/min. After removal of the samples from the temperature chamber, the liquid supernatant was discarded, and the pellets were immediately frozen in liquid nitrogen and stored at −80°C.
Inorganic compound treatments.
Approximately 20 mg J2 was collected in a 1.5 ml tube and 500 uL inorganic compound (6 mM NH4HCO3, 0.77 mM FeCl3·6H2O, 0.16 mM CuCl2·2H2O, and 0.16 mM CuSO4·5H2O) was added to each tube [33]. The samples were incubated at 25°C under dark conditions for 24 hr, rinsed for five times with RNase-free water, frozen, and stored at −80°C. Three independent biological replicates were evaluated for each treatment.
Total RNA isolation and cDNA synthesis
Total RNA was extracted using a MiniBEST Universal RNA Extraction Kit (TaKaRa, Dalian, China) according to the manufacturer’s instructions. The concentration and purity were determined twice for each RNA sample by NanoVue, and samples with an A260/A280 ratio between 1.9 and 2.2 were used for cDNA synthesis. Five hundred nanogram of RNA was reverse-transcribed into cDNA in a final volume of 10 μL using a PrimeScript RT Master Mix (TaKaRa). The cDNA was serially diluted 10-fold (10×, 102×, 103×, 104×, and 105× dilutions) to assess the amplification efficiency (E%) of primers and correlation coefficients (R2) or 5-fold for qPCR analysis.
The primer design and qRT-PCR method
The eleven candidate reference genes were AK, Actin1, EF1-α, GAPDH, Mdh, Pur, RpS6, TAF, α-Tub, Ubp, and SDHA. The EST sequences of candidate genes and the mRNA sequences of the target gene (Mh-Hsp90) were obtained from the GenBank database. Twelve pairs of specific primers were designed by Primer Premier 5 according to the design parameters of qPCR primers with 55–65°C melting temperature, 18–23 bp primer length, 30–55% GC content and 90–260 bp product length.
qPCR was performed using SYBR Premix Ex Taq II (TaKaRa) following the manufacturer’s protocol on a Bio-Rad CFX-96 real-time PCR system (Bio-Rad, Hercules, CA, USA). Each 10-μL qPCR mixture included 5 μL SYBR Premix Ex Taq II, 1 μL diluted cDNA, 0.4 μL of each primer (10 μM), and 3.2 μL ddH2O. The reaction conditions were as follows: 95°C for 30 s, followed by 40 cycles of 95°C for 5 s and 60°C for 30 s, and a final melt curve from 65°C to 95°C with a 0.5°C increment. Each treatment included three technical and biological replicates.
Data analysis
The amplification efficiency of primers was calculated using the formula: %E = (10[-1/slope] − 1) × 100% [35], and the correlation coefficient (R2 > 0.99) was obtained from the standard curve of a 10-fold dilution template.
Four data analysis methods, the ΔCq method, geNorm [21], Normfinder [22], and Bestkeeper [23], and the website tool RefFinder were used to evaluate the best reference genes. The data were directly analysed with BestKeeper, but converted into relative quantities for geNorm and Normfinder via the formula 2-ΔCq, ΔCq = the corresponding Cq value − minimum Cq [36]. The geNorm program calculated the expression stability value (M) for each gene, and the pairwise variation (Vn / Vn + 1) to find the optimal number of reference genes. Using stability values, each candidate gene was estimated with NormFinder software. The lowest M-value indicated the highest stability. The gene with the highest stability was ranked with BestKeeper based on the standard deviation value and coefficient of variation value. Finally, a web-based analysis tool RefFinder was used to comprehensively estimate the best candidate genes from each program [37–39].
Analysis of reference gene validation
The expression level of the heat-shock protein 90 gene (Hsp90) involved in the regulation of environmental stress, is increased in nematodes under abiotic stresses, such as heat shock stress and inorganic compound stress [40, 41]. Therefore, the expression levels of Hsp90 in M. hapla (Mh-Hsp90) were evaluated to validate the identified reference genes. The qRT-PCR primer pairs for Mh-Hsp90 were 5’- TTGCTAAATCTGGCACGAAGG -3’ (forward) and 5’- ATGAAGGAACCACCAGCAGA -3’ (reverse).
Results
Primer amplification efficiency and specificity of candidate reference genes
The eleven candidate reference genes with accession numbers, primer pair sequences, annealing temperatures, PCR product lengths, the Tm of products, amplification efficiencies (E%), and correlation coefficients (R2) are listed in Table 1. The E% values for the eleven candidate reference genes ranged from 93.6% to 106.7%, and the R2 values reached 0.99 (Table 1). A single peak in the melting curve showed specific amplification of all primers (Fig 1).
A: Actin1, B: AK, C: EF1-α, D: GAPDH, E: Mdh, F: Pur, G: RpS6, H: TAF, I: α-Tub, J: Ubp, K: SDHA, L: Mh-Hsp 90.
Cq value analysis of candidate reference genes
Cq values indicate the expression levels of reference genes. The distributions of all Cq values for all samples are shown in Fig 2. The Cq values ranged from 14.91 to 29.34 for the eleven candidate reference genes, and the mean values for Actin1 and TAF were 18.59 and 25.7, respectively. Low Cq values indicate high expression levels. Among the eleven reference genes, Actin1 showed high expression, whereas TAF showed low expression.
The open circle inside the boxes represents the median. The black bold lines present the 25th and 75th percentiles. The thin lines indicate the 5th and 95th percentiles.
Expression stability of candidate reference genes under different treatments
Five methods (ΔCq method, geNorm, NormFinder, BestKeeper, and RefFinder) were used to evaluated the stability of eleven candidate reference genes. Each reference gene was subjected to ten treatments, and the stability of them analysed individually (Table 2). In addition, these ten treatments were divide into four groups for more comprehensive analysis: “Development stage (egg, J2 and female), Temperature treatments (4, 25, 38 and 40°C), Inorganic compound treatments (CuSO4·5H2O, FeCl3·6H2O, CuCl2·2H2O, and NH4HCO3), and All treatments (composed of all the treatments sets). The ranks of the eleven genes for groups were calculated and shown in Table 2.
Development stage treatments.
Mdh was the stable gene used by ΔCq method, geNorm and RefFingder in egg treatments; The stable gene was Actin 1 in female treatments and α-Tub in J2 treatments through ΔCq method, NormFinder and RefFingder. For the groups, SDHA, RpS6 and Mdh was identified as the stable gene by the ΔCq method and Normfinder; Mdh and SDHA were identified as the most stable genes by geNorm; and Ubp, EF1-α, and α-Tub were identified as the most stable genes by BestKeeper. Therefore, combining all four rankings by RefFinder, SDHA, RpS6, and Mdh were considered the most stable genes, and α-Tub, AK, and Actin1 were considered the least stable genes (Table 2). The optimal number of reference genes defined by geNorm shown in Fig 3. The V2/3 values was less than 0.15 among egg, female, J2and development stage treatments. Therefore, the best reference gene combination was Mdh and TAF, GAPDH and SDHA, GAPDH and TAF, Mdh and SDHA, respectively.
Pairwise variations (Vn/n+1) were calculated using the geNorm program to determine the optimal number of reference genes for normalization. A value of Vn/n+1 below 0.15 denoted that additional reference genes were not necessary for improving normalization.
Temperature treatments.
Pur and Mdh were the most stable gene in 4°C treatment by ΔCq method, NormFinder, BestKeeper and RefFingder. Mdh was the most stable gene in 38 and 40°C treatments by ΔCq method, NormFinder, and RefFingder; For the temperature treatments, EF1-α and α-Tub were identified as the least stable genes by the other three analysis programs except BestKeeper. Mdh and RpS6 were the most stable genes identified by the ΔCq method and geNorm analysis, and Pur, AK, and Mdh showed stable expression in Normfinder analysis. In contrast, α-Tub, Ubp, and EF1-α were identified as stable genes using BestKeeper analysis. Taken together, these results suggested that Mdh and RpS6, were the most stable genes, whereas EF1-α, and GAPDH were the least stable genes by RefFinder (Table 2). The V2/3 with values lower than 0.15 calculated by geNorm shown in Fig 3 demonstrated that Mdh and RpS6, AK and Actin 1, AK and RpS6, Mdh and RpS6 were sufficient for normalisation among 4°C, 38°C, 40°C and temperature treatments.
Inorganic compound treatments.
Pur and Actin 1 was identified as the most stable gene in CuSO4·5H2O treatments by ΔCq method, NormFinder, and RefFingder; The most stable gene in FeCl3·6H2O treatments was RpS6 used ΔCq method, geNorm, NormFind and RefFingder; TAF was the most stable gene in CuCl2·2H2O and NH4HCO3 treatments through ΔCq method, geNorm and RefFingderh. The four analysis programs, except for BestKeeper, found that RpS6 was the most stable gene, and EF1-α and α-Tub were the least stable genes. Combining ranking demonstrated that RpS6 and Mdh were the most stable genes, whereas Ubp, and α-Tub were the least stable genes (Table 2). For geNorm, the V2/3 values was below 0.15 shown that Mdh and RpS6, Pur and RpS6, GAPDH and TAF, TAF and SDHA, Mdh and RpS6 were sufficient for normalisation within CuSO4·5H2O, FeCl3·6H2O, CuCl2·2H2O, NH4HCO3 and inorganic compound treatments (Fig 3).
All treatments.
Based on the comprehensive ranking, SDHA, Mdh, and RpS6 were the most stable genes, as evaluated under all treatments, with geometric mean ranking values of 1.63, 2.45, and 3.08, respectively. In contrast, α-Tub, AK, and Actin1 were the least stable genes, with values of 7.95, 8.30, and 8.43, respectively (Table 2). V2/3 values of less than 0.15 indicated that two reference genes, SDHA and Mdh, were sufficient for normalisation in all treatments (Fig 3).
Validation of stable reference genes
The stable genes Mdh and RpS6, the unstable gene GAPDH and Ubp, and the combined group of Mdh + RpS6 were selected to normalise the expression of Mh-Hsp90 under temperature (40°C and 4°C) and inorganic compound (CuSO4·5H2O) treatments. Generally, the use of multiple reference genes presents more accurate normalization of the gene expression. Results showed in Fig 4, the relative expression levels of Mh-Hsp90 were similar when normalised using Mdh, RpS6, and Mdh + RpS6, but different when normalised using GADPH and Ubp.
Mdh, RpS6, GAPDH, Ubp and the group of Mdh + RpS6 were used for normalization of Mh-Hsp 90 gene expression. The bars are means ± standard errors of three technical and biological replicates.
Discussion
qRT-PCR is a powerful technique with high sensitivity and specificity and enables gene expression analysis within a large dynamic range [1, 42, 43]. The amplification efficiency (E%) of reference genes should be similar to that of the target gene, which are essential for improving the accuracy of gene expression. Additionally, an optimal reference gene should show moderate and stable expression levels in all test samples. In our study, the amplification efficiencies (E%) of stable candidate reference genes were similar to that of Mh-Hsp90 on the same treatments. Moreover, except for Actin1, α-Tub, and TAF, all other genes showed moderate Cq values, and all candidate reference gene were specifically amplified. The values of candidate reference genes were slight difference between programs (geNorm, NormFinder, Bestkeeper) under different treatments, that caused by the difference of algorithms employed [22]. Therefore, it is better to evaluate reference gene using multiple methods, and then determined the suitable reference genes with the geometric mean of comprehensive ranking for all programs which generated by RefFinder.
The stable reference genes have been identified in Caenorhabditis elegans that were tba-1, Y45F10D.4 and pmp-3 for studying nanoparticle-induced genetic response [44], cdc-42, pmp-3 and Y45F10D.4 for normalizing 5 sod expression levels [45]. In this paper, the Mdh and RpS6 were reliable gene in M. hapla, consistent with the RPS15 in Helicoverpa armigera [37], RPS4 in turbot [46], RpS6 in Macrobrachium olfersii [47] for different developmental stages, and similar with the RPS20 for Sesamia inferens [7], RPS15 and RPS27 for H. armigera [37] under different temperature treatments. EF1-α, and GAPDH were not good reference genes in M. hapla for temperature stress, while EF1-α was useful reference gene for parsley [39]. According to these findings, there were no absolute reference genes for different species and treatments. While, the qRT-PCR relied on accurate normalization of stable reference genes. Therefore, the stability of reference genes should be validated for different experimental condition before use.
Inorganic compounds affect the survival of J2 and the hatch rate of egg-masses for M. hapla [33]. The responses of M. hapla to inorganic compound stress are still unknown. Analysis of gene expression will enable researchers to study the effects of inorganic compound stresses on M. hapla. In this study, we identified suitable reference genes (RpS6 and Mdh) for normalising gene expression. These findings are expected to facilitate further analyses of the mechanisms of inorganic compound stress in M. hapla.
In conclusions, this work validated that RpS6, Mdh, SDHA and Pur could be used as suitable reference genes for normalising qRT-PCR data in M. hapla under different treatments, and the combination of RpS6 + Mdh were better. This study provides a basis for future studies of gene function in M. hapla.
Acknowledgments
The authors would like to thank Congli Wang (Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences) for providing some of the root knot nematode materials used in this study and for helpful comments on the manuscript.
References
- 1. Heid CA, Stevens J, Livak KJ, Williams PM. Real time quantitative PCR. Genome Res. 1996;6(10):986–94. pmid:8908518.
- 2. VanGuilder HD, Vrana KE, Freeman WM. Twenty-five years of quantitative PCR for gene expression analysis. Biotechniques. 2008;44(5):619–26. pmid:18474036.
- 3. Bustin SA, Benes V, Nolan T, Pfaffl MW. Quantitative real-time RT-PCR—a perspective. J Mol Endocrinol. 2005;34(3):597–601. pmid:15956331.
- 4. Kubista M, Andrade JM, Bengtsson M, Forootan A, Jonak J, Lind K, et al. The real-time polymerase chain reaction. Mol Aspects Med. 2006;27(2–3):95–125. pmid:16460794.
- 5. Bustin SA, Beaulieu JF, Huggett J, Jaggi R, Kibenge FS, Olsvik PA, et al. MIQE precis: Practical implementation of minimum standard guidelines for fluorescence-based quantitative real-time PCR experiments. BMC Mol Biol. 2010;11:74. pmid:20858237.
- 6. Bustin SA, Benes V, Garson JA, Hellemans J, Huggett J, Kubista M, et al. The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem. 2009;55(4):611–22. pmid:19246619.
- 7. Sun M, Lu MX, Tang XT, Du YZ. Exploring valid reference genes for quantitative real-time PCR analysis in Sesamia inferens (Lepidoptera: Noctuidae). PLoS One. 2015;10(1):e0115979. pmid:25585250.
- 8. Derveaux S, Vandesompele J, Hellemans J. How to do successful gene expression analysis using real-time PCR. Methods. 2010;50(4):227–30. pmid:19969088
- 9. Bustin SA, Nolan T. Pitfalls of quantitative real-time reverse-transcription polymerase chain reaction. J Biomol Tech. 2004;15(3):155–66. pmid:15331581.
- 10. Bustin S. Quantification of mRNA using real-time reverse transcription PCR (RT-PCR): trends and problems. Journal of molecular endocrinology. 2002;29(1):23–39. pmid:12200227
- 11. Huggett J, Dheda K, Bustin S, Zumla A. Real-time RT-PCR normalisation; strategies and considerations. Genes Immun. 2005;6(4):279–84. pmid:15815687.
- 12. Su Y, He WB, Wang J, Li JM, Liu SS, Wang XW. Selection of endogenous reference genes for gene expression analysis in the Mediterranean species of the Bemisia tabaci (Hemiptera: Aleyrodidae) complex. J Econ Entomol. 2013;106(3):1446–55. pmid:23865213.
- 13. Kozera B, Rapacz M. Reference genes in real-time PCR. J Appl Genet. 2013;54(4):391–406. pmid:24078518.
- 14. Lu Y, Yuan M, Gao X, Kang T, Zhan S, Wan H, et al. Identification and validation of reference genes for gene expression analysis using quantitative PCR in Spodoptera litura (Lepidoptera: Noctuidae). PloS one. 2013;8(7):e68059. pmid:23874494
- 15. Yuan M, Lu Y, Zhu X, Wan H, Shakeel M, Zhan S, et al. 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(1):e86503. pmid:24466124
- 16. Lord JC, Hartzer K, Toutges M, Oppert B. Evaluation of quantitative PCR reference genes for gene expression studies in Tribolium castaneum after fungal challenge. J Microbiol Methods. 2010;80(2):219–21. pmid:20026205.
- 17. Reid KE, Olsson N, Schlosser J, Peng F, Lund ST. An optimized grapevine RNA isolation procedure and statistical determination of reference genes for real-time RT-PCR during berry development. BMC Plant Biol. 2006;6:27. pmid:17105665.
- 18. Warrington JA, Nair A, Mahadevappa M, Tsyganskaya M. Comparison of human adult and fetal expression and identification of 535 housekeeping/maintenance genes. Physiol Genomics. 2000;2(3):143–7. pmid:11015593.
- 19. Schmittgen TD, Zakrajsek BA. Effect of experimental treatment on housekeeping gene expression: validation by real-time, quantitative RT-PCR. J Biochem Biophys Methods. 2000;46(1–2):69–81. pmid:11086195.
- 20. Kim BR, Nam HY, Kim SU, Kim SI, Chang YJ. Normalization of reverse transcription quantitative-PCR with housekeeping genes in rice. Biotechnol Lett. 2003;25(21):1869–72. pmid:14677714.
- 21. Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, et al. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome biology. 2002;3(7):research0034.1.
- 22. Andersen CL, Jensen JL, Ørntoft TF. Normalization of Real-Time Quantitative Reverse Transcription-PCR Data: A Model-Based Variance Estimation Approach to Identify Genes Suited for Normalization, Applied to Bladder and Colon Cancer Data Sets. Cancer Research. 2004;64:5245–50. pmid:15289330
- 23. Pfaffl MW, Tichopad A, Prgomet C, Neuvians TP. Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper–Excel-based tool using pair-wise correlations. Biotechnology letters. 2004;26(6):509–15. pmid:15127793
- 24.
Taylor A, Sasser J. Biology, identification and control of root-knot nematodes. North Carolina State University Graphics. 1978:35–42.
- 25.
Perry RN, Moens M, Starr JL. Root-knot nematodes: CABI; 2009. 1–17 p.
- 26. Wu X, Zhu X, Wang Y, Liu X, Chen L, Duan Y. The cold tolerance of the northern root-knot nematode, Meloidogyne hapla. PLoS One. 2018;13(1):e0190531. pmid:29293608.
- 27. Koenning S, Overstreet C, Noling J, Donald P, Becker J, Fortnum B. Survey of crop losses in response to phytoparasitic nematodes in the United States for 1994. J Nematol. 1999;31(4S):587–618. pmid:19270925
- 28.
Taylor AL, Sasser JN, Nelson L. Relationship of climate and soil characteristics to geographical distribution of Meloidogyne species in agricultural soils: North Carolina State University/AID; 1982. 1–8 p.
- 29. Opperman CH, Bird DM, Williamson VM, Rokhsar DS, Burke M, Cohn J, et al. Sequence and genetic map of Meloidogyne hapla: A compact nematode genome for plant parasitism. Proc Natl Acad Sci U S A. 2008;105(39):14802–7. pmid:18809916.
- 30. Blanc-Mathieu R, Perfus-Barbeoch L, Aury J-M, Da Rocha M, Gouzy J, Sallet E, et al. Hybridization and polyploidy enable genomic plasticity without sex in the most devastating plant-parasitic nematodes. PLoS genetics. 2017;13(6):e1006777. pmid:28594822
- 31. Mbeunkui F, Scholl EH, Opperman CH, Goshe MB, Bird DM. Proteomic and bioinformatic analysis of the root-knot nematode Meloidogyne hapla: the basis for plant parasitism. J Proteome Res. 2010;9(10):5370–81. pmid:20804128.
- 32. Gleason C, Polzin F, Habash SS, Zhang L, Utermark J, Grundler FM, et al. Identification of two meloidogyne hapla genes and an investigation of their roles in the plant-nematode interaction. Mol Plant Microbe In. 2017;30(2):101–12.
- 33. Wu X, Li C, Zhou Y, Chen L, Duan Y. Effects of Inorganic Compounds on Survival of Meloidogyne hapla Juveniles. Northern Horticulture. 2017;24:53–9.
- 34. Forge T, MacGuidwin A. Cold hardening of Meloidogyne hapla second-stage juveniles. J Nematol. 1990;22(1):101. pmid:19287695
- 35. Radonić A, Thulke S, Mackay IM, Landt O, Siegert W, Nitsche A. Guideline to reference gene selection for quantitative real-time PCR. Biochemical and biophysical research communications. 2004;313(4):856–62. pmid:14706621.
- 36. Tian C, Jiang Q, Wang F, Wang GL, Xu ZS, Xiong AS. Selection of Suitable Reference Genes for qPCR Normalization under Abiotic Stresses and Hormone Stimuli in Carrot Leaves. Plos One. 2015;10(2). pmid:25658122
- 37. Zhang SD, An SH, Li Z, Wu FM, Yang QP, Liu YC, et al. Identification and validation of reference genes for normalization of gene expression analysis using qRT-PCR in Helicoverpa armigera (Lepidoptera: Noctuidae). Gene. 2015;555(2):393–402. pmid:25447918
- 38. Li MY, Wang F, Jiang Q, Wang GL, Tan C, Xiong AS. Validation and Comparison of Reference Genes for qPCR Normalization of Celery (Apium graveolens) at Different Development Stages. Frontiers in Plant Science. 2016;7. pmid:27014330
- 39. Li MY, Song X, Wang F, Xiong AS. Suitable Reference Genes for Accurate Gene Expression Analysis in Parsley (Petroselinum crispum) for Abiotic Stresses and Hormone Stimuli. Front Plant Sci. 2016;7:1481. pmid:27746803.
- 40. Bai C, Duan Y, Chen L, Liu Y, Zheng Y, Wang Y, et al. Gene cloning and gene expression of Hsp90 from Meloidogyne incognita under the temperature and heavy metal stress. International Journal of Agriculture & Biology. 2014;16(3):451–60.
- 41. De LF, Di VM, Fanelli E, Reyes A, Greco N, De GC. Characterization of the heat shock protein 90 gene in the plant parasitic nematode Meloidogyne artiellia and its expression as related to different developmental stages and temperature. Gene. 2009;440(1):16–22.
- 42. Haller F, Kulle B, Schwager S, Gunawan B, von Heydebreck A, Sultmann H, et al. Equivalence test in quantitative reverse transcription polymerase chain reaction: confirmation of reference genes suitable for normalization. Anal Biochem. 2004;335(1):1–9. pmid:15519565.
- 43. Ransbotyn V, Reusch TB. Housekeeping gene selection for quantitative real-time PCR assays in the seagrass Zostera marina subjected to heat stress. Limnology and Oceanography: Methods. 2006;4(10):367–73.
- 44. Zhang Y, Chen D, Smith MA, Zhang B, Pan X. Selection of reliable reference genes in Caenorhabditis elegans for analysis of nanotoxicity. PLoS One. 2012;7(3):e31849. pmid:22438870.
- 45. Hoogewijs D, Houthoofd K, Matthijssens F, Vandesompele J, Vanfleteren JR. Selection and validation of a set of reliable reference genes for quantitative sod gene expression analysis in C. elegans. BMC Mol Biol. 2008;9:9. pmid:18211699.
- 46. Robledo D, Hernandez-Urcera J, Cal RM, Pardo BG, Sanchez L, Martinez P, et al. Analysis of qPCR reference gene stability determination methods and a practical approach for efficiency calculation on a turbot (Scophthalmus maximus) gonad dataset. Bmc Genomics. 2014;15.
- 47. Jaramillo ML, Ammar D, Quispe RL, Guzman F, Margis R, Nazari EM, et al. Identification and evaluation of reference genes for expression studies by RT-qPCR during embryonic development of the emerging model organism, Macrobrachium olfersii. Gene. 2017;598:97–106. pmid:27825774.