Identification of cell-specific patterns of reference gene stability in quantitative reverse-transcriptase polymerase chain reaction studies of embryonic, placental and neural stem models of prenatal ethanol exposure

Identification of the transcriptional networks disrupted by prenatal ethanol exposure remains a core requirement to better understanding the molecular mechanisms of alcohol-induced teratogenesis. In this regard, quantitative reverse-transcriptase polymerase chain reaction (qPCR) has emerged as an essential technique in our efforts to characterize alterations in gene expression brought on by exposure to alcohol.

However, many publications continue to report the utilization of inappropriate methods of qPCR normalization, and for many in vitro models, no consistent set of empirically tested normalization controls have been identified. 

In the present study, we sought to identify a group of candidate reference genes for use within studies of alcohol exposed embryonic, placental, and neurosphere stem cells under both conditions maintaining stemness as well as throughout in vitro differentiation. To this end, we surveyed the recent literature and compiled a short list of fourteen candidate genes commonly used as normalization controls in qPCR studies of gene expression. This list included: Actb, B2m, Gapdh, Gusb, H2afz, Hk2, Hmbs, Hprt, Mrpl1, Pgk1, Ppia, Sdha, Tbp, and Ywhaz. 

From these studies, we find no single candidate gene was consistently refractory to the influence of alcohol nor completely stable throughout in vitro differentiation.

Accordingly, we propose normalizing qPCR measurements to the geometric mean C_T values obtained for three independent reference mRNAs as a reliable method to accurately interpret qPCR data and assess alterations in gene expression within alcohol treated cultures. Highlighting the importance of careful and empirical reference gene selection, the commonly used reference gene Actb was often amongst the least stable candidate genes tested. In fact, it would not serve as a valid normalization control in many cases. 

Data presented here will aid in the design of future experiments using stem cells to study the transcriptional processes driving differentiation, and model the developmental impact of teratogens.


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