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      Evaluation of Optimal Reference Genes for qRT-PCR Analysis in Hyphantria cunea (Drury)

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      Insects
      MDPI AG

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          Abstract

          The relative quantification of gene expression is mainly achieved through reverse transcription-quantitative PCR (qRT-PCR); however, its reliability and precision rely on proper data normalization using one or more optimal reference genes. Hyphantria cunea (Drury) has been an invasive pest of forest trees, ornamental plants, and fruit trees in China for many years. Currently, the molecular physiological role of reference genes in H. cunea is unclear, which hinders functional gene study. Therefore, eight common reference genes, RPS26, RPL13, UBI, AK, RPS15, EIF4A, β-actin, α-tub, were selected to evaluate levels of gene expression stability when subjected to varied experimental conditions, including developmental stage and gender, different tissues, larvae reared on different hosts and different larval density. The geNorm, BestKeeper, ΔCt method, and NormFinder statistical algorithms were used to normalize gene transcription data. Furthermore, the stability/suitability of these candidates was ranked overall by RefFinder. This study provides a comprehensive evaluation of reference genes in H. cunea and could help select reference genes for other Lepidoptera species.

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          Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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            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.

            Accurate normalization is an absolute prerequisite for correct measurement of gene expression. For quantitative real-time reverse transcription-PCR (RT-PCR), the most commonly used normalization strategy involves standardization to a single constitutively expressed control gene. However, in recent years, it has become clear that no single gene is constitutively expressed in all cell types and under all experimental conditions, implying that the expression stability of the intended control gene has to be verified before each experiment. We outline a novel, innovative, and robust strategy to identify stably expressed genes among a set of candidate normalization genes. The strategy is rooted in a mathematical model of gene expression that enables estimation not only of the overall variation of the candidate normalization genes but also of the variation between sample subgroups of the sample set. Notably, the strategy provides a direct measure for the estimated expression variation, enabling the user to evaluate the systematic error introduced when using the gene. In a side-by-side comparison with a previously published strategy, our model-based approach performed in a more robust manner and showed less sensitivity toward coregulation of the candidate normalization genes. We used the model-based strategy to identify genes suited to normalize quantitative RT-PCR data from colon cancer and bladder cancer. These genes are UBC, GAPD, and TPT1 for the colon and HSPCB, TEGT, and ATP5B for the bladder. The presented strategy can be applied to evaluate the suitability of any normalization gene candidate in any kind of experimental design and should allow more reliable normalization of RT-PCR data.
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              Selection of housekeeping genes for gene expression studies in human reticulocytes using real-time PCR

              Background Control genes, which are often referred to as housekeeping genes, are frequently used to normalise mRNA levels between different samples. However, the expression level of these genes may vary among tissues or cells and may change under certain circumstances. Thus, the selection of housekeeping genes is critical for gene expression studies. To address this issue, 7 candidate housekeeping genes including several commonly used ones were investigated in isolated human reticulocytes. For this, a simple ΔCt approach was employed by comparing relative expression of 'pairs of genes' within each sample. On this basis, stability of the candidate housekeeping genes was ranked according to repeatability of the gene expression differences among 31 samples. Results Initial screening of the expression pattern demonstrated that 1 of the 7 genes was expressed at very low levels in reticulocytes and was excluded from further analysis. The range of expression stability of the other 6 genes was (from most stable to least stable): GAPDH (glyceraldehyde 3-phosphate dehydrogenase), SDHA (succinate dehydrogenase), HPRT1 (hypoxanthine phosphoribosyl transferase 1), HBS1L (HBS1-like protein) and AHSP (alpha haemoglobin stabilising protein), followed by B2M (beta-2-microglobulin). Conclusion Using this simple approach, GAPDH was found to be the most suitable housekeeping gene for expression studies in reticulocytes while the commonly used B2M should be avoided.
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                Author and article information

                Journal
                Insects
                Insects
                MDPI AG
                2075-4450
                January 2022
                January 14 2022
                : 13
                : 1
                : 97
                Article
                10.3390/insects13010097
                d636e34c-ac5a-40d6-b124-6908856976ef
                © 2022

                https://creativecommons.org/licenses/by/4.0/

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