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The dilution effect and the importance of selecting the right internal control genes for RT-qPCR: a paradigmatic approach in fetal sheep.

Xu H, Bionaz M, Sloboda DM, Ehrlich L, Li S, Newnham JP, Dudenhausen JW, Henrich W, Plagemann A, Challis JR, Braun T - BMC Res Notes (2015)

Bottom Line: Our interest in prenatal glucocorticoid (GC) effects on fetal growth has resulted in our investigation of suitable ICGs relevant in this model.In order to account for confounding effects on the geNorm analysis due to co-regulation among ICGs tested, network analysis was performed using Ingenuity Pathway Analysis software.Raw RT-qPCR data of all the tested ICGs was significantly reduced across gestation.

View Article: PubMed Central - PubMed

Affiliation: Departments of Obstetrics and Division of Experimental Obstetrics, Charité - University Berlin, Augustenburger Platz 1, Berlin, Germany. xuhuaisheng@gmail.com.

ABSTRACT

Background: The key to understanding changes in gene expression levels using reverse transcription real-time quantitative polymerase chain reaction (RT-qPCR) relies on the ability to rationalize the technique using internal control genes (ICGs). However, the use of ICGs has become increasingly problematic given that any genes, including housekeeping genes, thought to be stable across different tissue types, ages and treatment protocols, can be regulated at transcriptomic level. Our interest in prenatal glucocorticoid (GC) effects on fetal growth has resulted in our investigation of suitable ICGs relevant in this model. The usefulness of RNA18S, ACTB, HPRT1, RPLP0, PPIA and TUBB as ICGs was analyzed according to effects of early dexamethasone (DEX) treatment, gender, and gestational age by two approaches: (1) the classical approach where raw (i.e., not normalized) RT-qPCR data of tested ICGs were statistically analyzed and the best ICG selected based on absence of any significant effect; (2) used of published algorithms. For the latter the geNorm Visual Basic application was mainly used, but data were also analyzed by Normfinder and Bestkeeper. In order to account for confounding effects on the geNorm analysis due to co-regulation among ICGs tested, network analysis was performed using Ingenuity Pathway Analysis software. The expression of RNA18S, the most abundant transcript, and correlation of ICGs with RNA18S, total RNA, and liver-specific genes were also performed to assess potential dilution effect of raw RT-qPCR data. The effect of the two approaches used to select the best ICG(s) was compared by normalization of NR3C1 (glucocorticoid receptor) mRNA expression, as an example for a target gene.

Results: Raw RT-qPCR data of all the tested ICGs was significantly reduced across gestation. TUBB was the only ICG that was affected by DEX treatment. Using approach (1) all tested ICGs would have been rejected because they would initially appear as not reliable for normalization. However, geNorm analysis (approach 2) of the ICGs indicated that the geometrical mean of PPIA, HPRT1, RNA18S and RPLPO can be considered a reliable approach for normalization of target genes in both control and DEX treated groups. Different subset of ICGs were tested for normalization of NR3C1 expression and, despite the overall pattern of the mean was not extremely different, the statistical analysis uncovered a significant influence of the use of different normalization approaches on the expression of the target gene. We observed a decrease of total RNA through gestation, a lower decrease in raw RT-qPCR data of the two rRNA measured compared to ICGs, and a positive correlation between raw RT-qPCR data of ICGs and total RNA. Based on the same amount of total RNA to performed RT-qPCR analysis, those data indicated that other mRNA might have had a large increase in expression and, as consequence, had artificially diluted the stably expressed genes, such as ICGs. This point was demonstrated by a significant negative correlation of raw RT-qPCR data between ICGs and liver-specific genes.

Conclusion: The study confirmed the necessity of assessing multiple ICGs using algorithms in order to obtain a reliable normalization of RT-qPCR data. Our data indicated that the use of the geometrical mean of PPIA, HPRT1, RNA18S and RPLPO can provide a reliable normalization for the proposed study. Furthermore, the dilution effect observed support the unreliability of the classical approach to test ICGs. Finally, the observed change in the composition of RNA species through time reveals the limitation of the use of ICGs to normalize RT-qPCR data, especially if absolute quantification is required.

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Related in: MedlinePlus

Average expression stability values of tested potential internal control genes (ICGs). Average expression stability values (M) of remaining ICGs and determination of the optimal number of genes for normalization performed by geNorm, measured in n = 106 fetal sheep liver samples. The x axis indicates the ranking of the ICGs from least (left) to most (right) stable. The pairwise variation indicates the increase in normalization factor reliability by adding additional less stable ICGs.
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Fig2: Average expression stability values of tested potential internal control genes (ICGs). Average expression stability values (M) of remaining ICGs and determination of the optimal number of genes for normalization performed by geNorm, measured in n = 106 fetal sheep liver samples. The x axis indicates the ranking of the ICGs from least (left) to most (right) stable. The pairwise variation indicates the increase in normalization factor reliability by adding additional less stable ICGs.

Mentions: The raw RT-qPCR data of the 6 tested ICGs were analyzed with geNorm, Bestkeeper, and Normfinder. The rank from the most to the least reliable ICG was similar between the three algorithms (Table 1), with PPIA being one of the two most reliable ICGs among the ones tested. In geNorm the lowest M value indicates ICGs with the most stable expression. In the overall analyses, stepwise elimination of successive genes showed that PPIA and HPRT1 were the most stable ICGs across gestation followed by RNA18S and RPLPO (Figure 2). The determination of the optimal number of ICGs for normalization is performed by geNorm by calculating the pairwise variation (V-value) of adding the subsequent more reliable gene. A V-value below 0.15, cut-off reported by Vandesompele et al. [2], was obtained by adding the 5th more reliable gene (V4/5); however, the addition of the 4th more reliable gene (i.e., V3/4) had a V-value of 0.157, which is similar to V4/5 (Figure 2). Based on this observation and based also on practicality, we deemed that the use of 4 most reliable ICGs among the one tested, that is PPIA, HPRT1, RNA18S and RPLPO, can provide a trustworthy normalization factor.Table 1


The dilution effect and the importance of selecting the right internal control genes for RT-qPCR: a paradigmatic approach in fetal sheep.

Xu H, Bionaz M, Sloboda DM, Ehrlich L, Li S, Newnham JP, Dudenhausen JW, Henrich W, Plagemann A, Challis JR, Braun T - BMC Res Notes (2015)

Average expression stability values of tested potential internal control genes (ICGs). Average expression stability values (M) of remaining ICGs and determination of the optimal number of genes for normalization performed by geNorm, measured in n = 106 fetal sheep liver samples. The x axis indicates the ranking of the ICGs from least (left) to most (right) stable. The pairwise variation indicates the increase in normalization factor reliability by adding additional less stable ICGs.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4352295&req=5

Fig2: Average expression stability values of tested potential internal control genes (ICGs). Average expression stability values (M) of remaining ICGs and determination of the optimal number of genes for normalization performed by geNorm, measured in n = 106 fetal sheep liver samples. The x axis indicates the ranking of the ICGs from least (left) to most (right) stable. The pairwise variation indicates the increase in normalization factor reliability by adding additional less stable ICGs.
Mentions: The raw RT-qPCR data of the 6 tested ICGs were analyzed with geNorm, Bestkeeper, and Normfinder. The rank from the most to the least reliable ICG was similar between the three algorithms (Table 1), with PPIA being one of the two most reliable ICGs among the ones tested. In geNorm the lowest M value indicates ICGs with the most stable expression. In the overall analyses, stepwise elimination of successive genes showed that PPIA and HPRT1 were the most stable ICGs across gestation followed by RNA18S and RPLPO (Figure 2). The determination of the optimal number of ICGs for normalization is performed by geNorm by calculating the pairwise variation (V-value) of adding the subsequent more reliable gene. A V-value below 0.15, cut-off reported by Vandesompele et al. [2], was obtained by adding the 5th more reliable gene (V4/5); however, the addition of the 4th more reliable gene (i.e., V3/4) had a V-value of 0.157, which is similar to V4/5 (Figure 2). Based on this observation and based also on practicality, we deemed that the use of 4 most reliable ICGs among the one tested, that is PPIA, HPRT1, RNA18S and RPLPO, can provide a trustworthy normalization factor.Table 1

Bottom Line: Our interest in prenatal glucocorticoid (GC) effects on fetal growth has resulted in our investigation of suitable ICGs relevant in this model.In order to account for confounding effects on the geNorm analysis due to co-regulation among ICGs tested, network analysis was performed using Ingenuity Pathway Analysis software.Raw RT-qPCR data of all the tested ICGs was significantly reduced across gestation.

View Article: PubMed Central - PubMed

Affiliation: Departments of Obstetrics and Division of Experimental Obstetrics, Charité - University Berlin, Augustenburger Platz 1, Berlin, Germany. xuhuaisheng@gmail.com.

ABSTRACT

Background: The key to understanding changes in gene expression levels using reverse transcription real-time quantitative polymerase chain reaction (RT-qPCR) relies on the ability to rationalize the technique using internal control genes (ICGs). However, the use of ICGs has become increasingly problematic given that any genes, including housekeeping genes, thought to be stable across different tissue types, ages and treatment protocols, can be regulated at transcriptomic level. Our interest in prenatal glucocorticoid (GC) effects on fetal growth has resulted in our investigation of suitable ICGs relevant in this model. The usefulness of RNA18S, ACTB, HPRT1, RPLP0, PPIA and TUBB as ICGs was analyzed according to effects of early dexamethasone (DEX) treatment, gender, and gestational age by two approaches: (1) the classical approach where raw (i.e., not normalized) RT-qPCR data of tested ICGs were statistically analyzed and the best ICG selected based on absence of any significant effect; (2) used of published algorithms. For the latter the geNorm Visual Basic application was mainly used, but data were also analyzed by Normfinder and Bestkeeper. In order to account for confounding effects on the geNorm analysis due to co-regulation among ICGs tested, network analysis was performed using Ingenuity Pathway Analysis software. The expression of RNA18S, the most abundant transcript, and correlation of ICGs with RNA18S, total RNA, and liver-specific genes were also performed to assess potential dilution effect of raw RT-qPCR data. The effect of the two approaches used to select the best ICG(s) was compared by normalization of NR3C1 (glucocorticoid receptor) mRNA expression, as an example for a target gene.

Results: Raw RT-qPCR data of all the tested ICGs was significantly reduced across gestation. TUBB was the only ICG that was affected by DEX treatment. Using approach (1) all tested ICGs would have been rejected because they would initially appear as not reliable for normalization. However, geNorm analysis (approach 2) of the ICGs indicated that the geometrical mean of PPIA, HPRT1, RNA18S and RPLPO can be considered a reliable approach for normalization of target genes in both control and DEX treated groups. Different subset of ICGs were tested for normalization of NR3C1 expression and, despite the overall pattern of the mean was not extremely different, the statistical analysis uncovered a significant influence of the use of different normalization approaches on the expression of the target gene. We observed a decrease of total RNA through gestation, a lower decrease in raw RT-qPCR data of the two rRNA measured compared to ICGs, and a positive correlation between raw RT-qPCR data of ICGs and total RNA. Based on the same amount of total RNA to performed RT-qPCR analysis, those data indicated that other mRNA might have had a large increase in expression and, as consequence, had artificially diluted the stably expressed genes, such as ICGs. This point was demonstrated by a significant negative correlation of raw RT-qPCR data between ICGs and liver-specific genes.

Conclusion: The study confirmed the necessity of assessing multiple ICGs using algorithms in order to obtain a reliable normalization of RT-qPCR data. Our data indicated that the use of the geometrical mean of PPIA, HPRT1, RNA18S and RPLPO can provide a reliable normalization for the proposed study. Furthermore, the dilution effect observed support the unreliability of the classical approach to test ICGs. Finally, the observed change in the composition of RNA species through time reveals the limitation of the use of ICGs to normalize RT-qPCR data, especially if absolute quantification is required.

Show MeSH
Related in: MedlinePlus