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

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• 作者：Huaisheng Xu (1) (2)
  Massimo Bionaz (3)
  Deborah M Sloboda (4)
  Loreen Ehrlich (1)
  Shaofu Li (5)
  John P Newnham (5)
  Joachim W Dudenhausen (1)
  Wolfgang Henrich (1)
  Andreas Plagemann (1)
  John RG Challis (6) (7)
  Thorsten Braun (1)
  
  1. Departments of Obstetrics and Division of Experimental Obstetrics ; Charit茅 - University Berlin ; Augustenburger Platz 1 ; Berlin ; Germany
  2. Departments of Obstetrics and Gynecology ; Linyi People鈥檚 Hospital ; Shandong ; China
  3. Animal and Rangeland Sciences ; Oregon State University ; Corvallis ; USA
  4. Departments of Biochemistry and Biomedical Sciences ; Obstetrics & Gynecology and Pediatrics ; McMaster University ; Hamilton ; Canada
  5. School of Women鈥檚 and Infants鈥?Health ; King Edward Memorial Hospital ; The University of Western Australia ; and Women and Infants Research Foundation of Western Australia ; Perth ; Australia
  6. Departments of Physiology ; Obstetrics and Gynecology ; University of Toronto ; Toronto ; Canada
  7. Faculty of Health Sciences ; Simon Fraser University ; Vancouver ; Canada
  
• 关键词：Dexamethasone ; Sheep ; Fetal liver ; geNorm ; Co ; regulation ; Internal control gene ; RNA18S ; RPLP0 ; HPRT1 ; PPIA ; TUBB ; ACTB ; RNA28S
• 刊名：BMC Research Notes
• 出版年：2015
• 出版时间：December 2015
• 年：2015
• 卷：8
• 期：1
• 全文大小：1,827 KB
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• 刊物主题：Biomedicine general; Medicine/Public Health, general; Life Sciences, general;
• 出版者：BioMed Central
• ISSN：1756-0500

文摘

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.