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

Zheng, Wenxu; Meng, Qianqian; Zhu, Xuemei; Sun, Shiwei; Gao, Shengfeng; Gou, Yan-You; Liu, Aiqin

doi:10.1038/s41598-019-49479-1

Cited by 26 publications

(21 citation statements)

References 51 publications

(69 reference statements)

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“…Consistent with observations in previous studies 7 , 12 , 13 , the three algorithms, NormFinder, BestKeeper and geNorm, produced different results when ranking gene stability in the present study; therefore, the combined use of all algorithms would ensure more credible results 13 . Considering the cutoff values in each algorithm, most of the seven candidate genes could be deemed acceptable for use as reference genes when analysing gene expression in different conditions in honey bees.…”

Section: Discussionsupporting

confidence: 90%

“…Given the importance of accurate normalisation in qRT-PCR assays, reference genes have been identified and validated in various insect species 8,13,14 . According to previous studies of the honey bee, widely used reference genes were validated at different developmental stages 6 , in the brains after a bacterial challenge 15 , different ages and social roles 7,10,16 .…”

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confidence: 99%

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Reference gene selection for qRT-PCR analysis of season- and tissue-specific gene expression profiles in the honey bee Apis mellifera

Jeon

Moon

Kim

et al. 2020

Sci Rep

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Honey bees are both important pollinators and model insects due to their highly developed sociality and colony management. To better understand the molecular mechanisms underlying honey bee colony management, it is important to investigate the expression of genes putatively involved in colony physiology. Although quantitative real-time PCR (qRT-PCR) can be used to quantify the relative expression of target genes, internal reference genes (which are stably expressed across different conditions) must first be identified to ensure accurate normalisation of target genes. To identify reliable reference genes in honey bee (Apis mellifera) colonies, therefore, we evaluated seven candidate genes (ACT , EIF, EF1, RPN2, RPS5, RPS18 and GAPDH) in samples collected from three honey bee tissue types (head, thorax and abdomen) across all four seasons using three analysis programmes (NormFinder, BestKeeper and geNorm). Subsequently, we validated various normalisation methods using each of the seven reference genes and a combination of multiple genes by calculating the expression of catalase (CAT). Although the genes ranked as the most stable gene were slightly different on conditions and analysis methods, our results suggest that RPS5, RPS18 and GAPDH represent optimal honey bee reference genes for target gene normalisation in qRT-PCR analysis of various honey bee tissue samples collected across seasons. The Western honey bee, Apis mellifera L., plays an important role as a pollinator 1. In addition, the honey bee is considered to be a key model insect due to its relatively complex behaviours, including sociality, labour division and colony management 2. Previous studies have demonstrated that endocrine system status and gene expression are important factors for flexible honey bee colony management, which involves colonies seasonally regulating their labour division and population dynamics 3-5. In order to extend our understanding of the molecular mechanisms that underlie the regulation of honey bee colony physiology, information on the physiological functions of the genes putatively associated with colony management can be determined by analysing their expression profiles among different seasons and honey bee tissues 6,7. In quantitative real-time PCR (qRT-PCR), gene-specific mRNA (or cDNA) is quantified; this method has been used extensively because of its relative speed, sensitivity, replicability and accuracy 8,9. Therefore, qRT-PCR would be an ideal method for analysing the expression patterns of honey bee genes putatively involved in the plasticity of colony molecular physiology in samples collected across different seasons and tissues. However, because qRT-PCR results are highly sensitive to the initial amount of RNA content in the amplification reaction, the interpretation of target gene expression levels among various conditions would result in appreciable errors without the use of a reliable internal standard 7-10. Therefore, prior to analysing target gene expression levels among conditions, reference genes are requir...

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Section: Discussionsupporting

confidence: 90%

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confidence: 99%

Reference gene selection for qRT-PCR analysis of season- and tissue-specific gene expression profiles in the honey bee Apis mellifera

Jeon

Moon

Kim

et al. 2020

Sci Rep

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“…However, gapdh and rps 18 were suggested to be the optimal reference genes for RT-qPCR-based under the labor-specific gene expression, bacterial infected and developmental gene expression in Western honey bee as shown in Table 2 ( Scharlaken et al, 2008 ; Reim et al, 2013 ; Moon et al, 2018a ). Previous studies have shown that some ribosome-associated genes have been used as stable internal reference genes for quantitative analysis ( Wang et al, 2019 ). For instance, Freitas et al (2019) found that rpl 32, rps 5, and rps 18 were identified as suitable reference genes for the different tissues of stingless bees.…”

Section: Discussionmentioning

confidence: 99%

Screening and Validation of Reference Genes for RT-qPCR Under Different Honey Bee Viral Infections and dsRNA Treatment

Deng¹,

Zhao²,

Yang³

et al. 2020

Front. Microbiol.

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Honey bee viruses are one of the most important pathogens that have contributed to the decrease in honey bee colony health. To analyze the infection dynamics of honey bee viruses, quantification of viral gene expression by RT-qPCR is necessary. However, suitable reference genes have not been reported from viral and RNAi studies of honey bee. Here, we evaluated the expression of 11 common reference genes (ache2, rps18, β-actin, tbp, tif, rpl32, gadph, ubc, α-tubulin, rpl14, and rpsa) from Apis mellifera (Am) and Apis cerana (Ac) under Israeli acute paralysis virus (IAPV), chronic bee paralysis virus (CBPV), and Chinese sacbrood virus (CSBV) infection as well as dsRNA-PGRP-SA treatment, and we confirmed their validation by evaluating the levels of the defensin 1 and prophenoloxidase (ppo) genes during viral infection. Our results showed that the expression of selected genes varied under different viral infections. ache2, rps18, β-actin, tbp, and tif can be used to normalize expression levels in Apis mellifera under IAPV infection, while the combination of actin and tif is suitable for CBPV-infected experiments. The combination of rpl14, tif, rpsa, ubc, and ache2 as well as more reference genes is suitable for CSBV treatment in Apis cerana. Rpl14, tif, rps18, ubc, and α-tubulin were the most stable reference genes under dsRNA treatment in Apis mellifera. Furthermore, the geNorm and NormFinder algorithms showed that tif was the best suitable reference gene for these four treatments. This study screened and validated suitable reference genes for the quantification of viral levels in honey bee, as well as for RNAi experiments.

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“…The comparison of the gene expression patterns across different experimental or biological conditions can provide valuable insights into the regulation of the metabolic pathways that help cells withstand or adapt to environmental change. In this context, RT-qPCR is one of the most commonly used approaches to measure gene expression levels owing to its accuracy, sensitivity, specificity, precision, real-time detection of the reaction progress, dynamic range, and the speed of analysis [ 19 – 22 ]. However, a range of factors can significantly diminish the accuracy and reliability of RT-qPCR data, such as quality and quantity of the mRNA templates, enzymatic efficiency of the reverse transcription and PCR amplification reaction, and differences between cells or tissues in the overall transcriptional activity and mRNA half-life [ 23 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

“…Several statistical algorithms have been developed to identify the most suitable reference genes under a given set of experimental or biological conditions in order to avoid artifacts due to normalization, and include geNorm [ 27 ], NormFinder [ 28 ], BestKeeper [ 29 ], the comparative Δ C T method [ 30 ], and the web-based program RefFinder [ 31 ]. These tools have been carried out on a broad range of species, including whole organisms, specific tissues or cell types from animals [ 19 – 21 , 32 , 33 ], plants [ 34 – 37 ], and fungi [ 38 , 39 ]. Selection of reference genes has also been addressed in macroalgae [ 40 – 42 ] as well as in specific microalgae species, including the diatoms Phaeodactylum tricornutum [ 43 ], Pseudo-nitzschia multristrata and P .…”

Section: Introductionmentioning

confidence: 99%

Selection and validation of reference genes for quantitative real-time PCR in the green microalgae Tetraselmis chui

Torres¹,

Lama²,

Mantecón³

et al. 2021

PLoS ONE

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Quantitative real-time reverse transcription PCR (RT-qPCR) is a highly sensitive technique that can be applied to analyze how genes are modulated by culture conditions, but identification of appropriate reference genes for normalization is a critical factor to be considered. For this reason, the expression stability of 18 candidate reference genes was evaluated for the green microalgae Tetraselmis chui using the widely employed algorithms geNorm, NormFinder, BestKeeper, the comparative ΔCT method, and RefFinder. Microalgae samples were collected from large scale outdoor photobioreactors during the growing phase (OUT_GP), and during the semi-continuous phase at different times of the day (OUT_DC). Samples from standard indoor cultures under highly controlled conditions (IND) were also collected to complement the other data. Different rankings for the candidate reference genes were obtained depending on the culture conditions and the algorithm employed. After comparison of the achieved ranks with the different methods, the references genes selected for samples from specific culture conditions were ALD and EFL in OUT_GP, RPL32 and UBCE in OUT_DC, and cdkA and UBCE in IND. Moreover, the genes EFL and cdkA or EFL and UBCE appeared as appropriate combinations for pools generated from all samples (ALL). Examination in the OUT_DC cultures of genes encoding the large and small subunits of ADP-glucose pyrophosphorylase (AGPL and AGPS, respectively) confirmed the reliability of the identified reference genes, RPL32 and UBCE. The present study represents a useful contribution for studies of gene expression in T. chui, and also represents the first step to set-up an RT-qPCR platform for quality control of T. chui biomass production in industrial facilities.

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

Cited by 26 publications

References 51 publications

Reference gene selection for qRT-PCR analysis of season- and tissue-specific gene expression profiles in the honey bee Apis mellifera

Reference gene selection for qRT-PCR analysis of season- and tissue-specific gene expression profiles in the honey bee Apis mellifera

Screening and Validation of Reference Genes for RT-qPCR Under Different Honey Bee Viral Infections and dsRNA Treatment

Selection and validation of reference genes for quantitative real-time PCR in the green microalgae Tetraselmis chui

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