大鼠肝纤维化模型中sdha、hprt、prl3d1和hes1基因表达的稳定性

E. I. Lebedeva, A. S. Babenko, A. Shchastniy
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摘要

到目前为止,还没有一组通用的参考基因来规范实时聚合酶链反应数据。许多聚焦于特定目的的内参基因选择的研究往往未能阐述合适的选择策略。在许多此类研究中,由于成本高或其他原因,选择内参基因的阶段被忽略了。因此,数据的归一化是使用基因进行的,这些基因先前在其他(有时是完全不同的)实验条件下显示了它们的有效性。在这项工作中,我们的目标是研究几种基因mRNA表达水平的变化,其中一些基因通常用于标准化RT-PCR数据。作为特殊条件,采用硫乙酰胺大鼠肝纤维化模型。在我们的实验中,当考虑整个纤维形成过程时,我们发现最优的内参基因是hes1和sdha。然而,当关注纤维化的特定阶段时,应根据稳定性指标选择一对基因。在纤维形成初期,可以使用sdha和hprt。当目标基因的平均Cq值约为29个周期(如hes1)时,hes1基因适合作为内参基因。Hes1在26-29及30以上靶基因的Cq范围内工作时应谨慎使用,误差可能增大。根据同样的原理,sdha基因的最佳Cq值为27,尽管24-27的Cq范围也是可以接受的。同时,在Cq大于28的范围内工作时,使用sdha可能会增加计算误差。
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Stability of the sdha, hprt, prl3d1 and hes1 Gene Expression in a Rat Liver Fibrosis Model
So far, no versatile set of reference genes for normalizing real-time polymerase chain reaction data has been identified. Numerous studies focusing the selection of reference genes for specific purposes frequently fail to elaborate a suitable selection strategy. In a number of such studies, the stage of selecting reference genes is ignored due to either its high cost or other reasons. As a result, the normalization of data is carried out using genes, which have previously shown their effectiveness under other, sometimes completely different, experimental conditions. In this work, we aim to study variations in the level of mRNA expression of several genes, some of which are commonly used to normalize RT-PCR data. As special conditions, modeling of rat liver fibrosis with thioacetamide was used.In our experiment, when considering the process of fibrogenesis as a whole, the optimal reference genes were found to be hes1 and sdha. However, when focusing on specific stages of fibrosis, a pair of genes should be selected depending on the stability indicators. At the initial fibrogenesis stages, sdha and hprt can be used. The hes1 gene is suitable as a reference gene, when the average Cq value of the target genes is approximately 29 cycles (as in hes1). Hes1 should be used with care when working in the Cq ranges of target genes of 26–29 and above 30, since the error is likely to increase. Following the same principle, the optimum Cq value for the sdha gene was observed to be 27, although the Cq range of 24–27 is also acceptable. At the same time, when working in the Cq range of above 28, the use of sdha may be associated with an increase in calculation errors.
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