Identification of Suitable Reference Genes for Gene Expression Normalization in Jatropha curcas L During Development and Under Stress Conditions Using Real Time Quantitative PCR

Rocha, Antônio José; Maranhão, Paulo A.C.; Silva, Rafaela Oliveira; Pohl, Simone; Fonteles, Cristiane Sá Roriz

doi:10.1590/1678-4324-2016150396

Cited by 6 publications

(7 citation statements)

References 20 publications

(29 reference statements)

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“…In addition, the current study showed that Actb, one of the most commonly used reference genes in qPCR-based analyses, was only identified as a stable reference gene in the kidney, echoing a previous report using a mouse model of obesity [30]. Overall, like many previous studies [6,7,22], we suggest using no fewer than two reference genes for normalization, which results in more reliable gene expression analysis data.…”

Section: Discussionsupporting

confidence: 85%

“…In recent years, several research groups have developed algorithms, including GeNorm [15], BestKeeper [16], NormFinder [17], the ΔCt approach [18], and RefFinder [19], to identify the most stable reference gene(s) in a set of samples. Using these algorithms, the stability of various reference genes under specific experimental conditions has been evaluated in many studies of animals and plants [21][22][23]. Although all five algorithms are suitable, RefFinder and GeNorm are considered the best algorithms for evaluation of the most stable reference gene(s) because RefFinder yields more accurate results and GeNorm determines the optimal number of reference genes needed for normalization of qPCR data.…”

Section: Discussionmentioning

confidence: 99%

“…Although all five algorithms are suitable, RefFinder and GeNorm are considered the best algorithms for evaluation of the most stable reference gene(s) because RefFinder yields more accurate results and GeNorm determines the optimal number of reference genes needed for normalization of qPCR data. The other three algorithms are not comprehensive enough or will only identify one stable reference gene, as has been covered by previous studies and reviews related to the evaluation of stable reference gene(s) in various organisms [6,[22][23][24][25].…”

Section: Discussionmentioning

confidence: 99%

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Evaluation of Reference Genes for Quantitative PCR in Four Tissues from Rabbits with Hypercholesterolaemia

Zhang

Wen

et al. 2019

Braz. arch. biol. technol.

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Rabbit with hypercholesterolaemia is an important model for studying cholesterol metabolism disease. This study aimed to evaluate the expression stability of nine reference genes for quantitative PCR (qPCR) analysis in adrenal gland, liver, spleen, and kidney tissue from rabbits with hypercholesterolaemia. In total, 30 male Harbin Large White (HLW) rabbits were fed a normal feed (n = 15) or a high cholesterol feed (n = 15) for 8 weeks to induce hypercholesterolaemia. Nine reference genes were verified by qPCR using cDNA extracted from rabbit tissue samples. For qPCR analysis, reference genes were evaluated using the RefFinder and GeNorm algorithms. Overall, seven rabbits with hypercholesterolaemia were identified based on body weight and total cholesterol measurements. Combining the results of the RefFinder and GeNorm algorithms, the most stable reference genes were hypoxanthine phosphoribosyltransferase 1 (Hprt1) and eukaryotic translation elongation factor 1 alpha 1 (Eef1a1) in the adrenal gland, β-2-microglobulin (B2m) and glyceraldehyde-3-phosphate dehydrogenase (Gapdh) in the liver, tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein zeta (Ywhaz) and Gapdh in the spleen, and peptidylprolyl isomerase (Ppia), β-actin (Actb), succinate dehydrogenase complex subunit A flavoprotein (Sdha), and B2m in the kidney. Taken together, our results confirmed that Hprt1 and Eef1a1, B2m and Gapdh, Ywhaz and Gapdh, and Ppia, Actb, Sdha, and B2m were the best reference genes for qPCR analyses in adrenal gland, liver, spleen, and kidney tissue, respectively, of rabbits with hypercholesterolaemia.

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Section: Discussionsupporting

confidence: 85%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Evaluation of Reference Genes for Quantitative PCR in Four Tissues from Rabbits with Hypercholesterolaemia

Zhang

Wen

et al. 2019

Braz. arch. biol. technol.

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“…Designing specific primers and adopting appropriate probes are crucial requirements for amplification efficiency, specificity and fluorescence in qPCR assays. The primer should be designed in junction exon-exon of genic sequence to avoid amplification of contaminant genomic DNA, amplifying specifically the target cDNA sequence [6]. The primer efficiency might be analyzed employing serial dilutions or standard curves, defining the ideal primer concentration and/or assessing the reaction efficiency.…”

Section: Primer Design and Probe Considerationsmentioning

confidence: 99%

“…UCP -Ubiquitin conjugation protein; Ciclofilin; ACT11 -actin-11; Tα2 -tubulin alpha-2; EF1-α -elongation factor 1-alpha; PP2A2 -protein phosphatase 2A-2; PUB3 -polyubiquitin-3; GAPDH -glyceraldehyde-3-phosphate dehydrogenase; Tβ2 -tubulin beta-2. Adapted with permission from Rocha et al[6].…”

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confidence: 99%

Guidelines for Successful Quantitative Gene Expression in Real- Time qPCR Assays

Rocha¹,

Miranda²,

Sousa³

et al. 2016

Polymerase Chain Reaction for Biomedical Applications

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This chapter was developed to provide some important guidelines for studies with quantitative PCR (qPCR) using either dyes or probes, citing several essential components necessary for a good PCR assay. The efficiency and specificity of quantitative PCR (qPCR) depend on several parameters related to mRNA quantification that must be controlled to avoid mistakes in data interpretation. Avoiding contamination with proteins, carbohydrate and phenolic compounds during RNA extraction and purification processes will improve RNA quality and provide reliable results. Specific primers and sensible probes are also crucial to intensify efficiency, specificity and fluorescence. Other parameters such as the optimization of primer concentrations and efficiency primer curves must be done. During gene-expression profile quantification, qPCR assays using reference genes are required to normalize the target gene expression data. These reference genes are checked for stability to identify the most stable genes among a group of candidate genes that will be used to normalize the qPCR data, using programs such as geNorm, BestKeeper and NormFinder. Additionally, the choice of appropriate reference genes for a specific experimental condition is fundamental. The main aim of this chapter is to provide guidelines and highlight precautions to obtain a successful qPCR assays.

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Cloning and gene expression analysis of two cDNA of cysteine proteinase genes involved in programmed cell death in the inner integument from developing seeds of Jatropha curcas L

Rocha

Pohl

Fonteles

2018

Gene Expression Patterns

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Identification of Suitable Reference Genes for Gene Expression Normalization in Jatropha curcas L During Development and Under Stress Conditions Using Real Time Quantitative PCR

Cited by 6 publications

References 20 publications

Evaluation of Reference Genes for Quantitative PCR in Four Tissues from Rabbits with Hypercholesterolaemia

Evaluation of Reference Genes for Quantitative PCR in Four Tissues from Rabbits with Hypercholesterolaemia

Guidelines for Successful Quantitative Gene Expression in Real- Time qPCR Assays

Cloning and gene expression analysis of two cDNA of cysteine proteinase genes involved in programmed cell death in the inner integument from developing seeds of Jatropha curcas L

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