ACT1; the reference genes identified as the most stable genes based on the scores, which were calculated using both NormFinder and geNorm results, was used for determining the expression profile of MEP2. MEP2 displayed a fluctuating expression profile in response to ammonium perturbation when the data was normal- ized using ACT1; a commonly used reference gene or using the most stable gene FBA1 along with ACT1. In contrast, the relative expression of MEP2 decreased gradually following the impulse and remained repressed during the time course of the experiment as expected when the four most stable reference genes (FBA1, TDH3, CCW12 and ACT1) were used for normalization (Figure 4-a). These results clearly demonstrated that using a commonly preferred reference gene without verification would yield to misleading expression profiles. The FBA1-TDH3-CCW12-ACT1 reference gene set was further analyzed in order to investigate the effect of having additional genes in the set or the effect of excluding one or more of the members of the set. The contribution of the fifth most stable reference genes in determining the expression profile of MEP2 was investigated. The results indicated that the inclusion of CDC19 in the reference gene set did not yield any improvements in the determination of the expression profile of MEP2 displaying an unexpected increase in gene expression at the 8th minute (Figure 4- b). Therefore a pool consisting of at most four reference genes, as also suggested by geNorm, was determined to be sufficient. The observation that the inclusion of the more genes in the reference set did not provide any improvement led to the thinking that the reference gene set would be further reduced in size as it was in Case Study I. For this purpose, the number of reference genes included in the set was reduced by one each time starting from the least stable gene in the set. The exclusion of CCW12 or CCW12 and ACT1 from the reference gene set resulted in an unexpected increase in gene expression during a period from approximately the 32nd minute until the 2nd hour post impulse. A further exclusion of TDH3 from the set, utilizing FBA1 as the sole reference gene, caused a fluctuating expression profile for MEP2 (Figure 4-c). Two of the most commonly used housekeeping genes ACT1 and TDH3 were included in the reference gene set. A further analysis was conducted to investigate whether using the geometric average of the Ct values for these two genes would yield a non-fluctuating and decreasing expression profile for MEP2. However, it was observed that an increase in MEP2 expression during a period from approximately the 32nd minute until the 2nd hour post impulse was present indicating that FBA1 and CCW12 were both essential in acquiring a smooth expression profile (Figure S11). Determination of the optimum number of reference genes for normalization The geNorm algorithm provides the contribution of each additional stable gene into the reference gene set in terms of the pairwise variation between the two sequential normalization factors [5]. The developers suggest using a threshold of 0.15 in the determination of the optimal number of reference genes required for normalization. geNorm suggested using a different number of reference genes varying between 3 and 6 for each pool in the case of glucose perturbation (Table S6 and Figure S6). This result necessitated a human-mediated evaluation of the expression profiles of the gene of interest obtained by different sets of reference genes. The results presented in this study indicated that the utilization of a set comprised of four reference genes with stable and uncorrelated expression profiles would be sufficient for this case. On the other hand, in the case of ammonium perturbation, the analysis using geNorm revealed that the optimal Yeast Reference Genes in Dynamic RT-qPCR Analysis number of reference genes was 4 for each pool (Table S6 and Figure S9). Further analysis also demonstrated that neither increasing nor decreasing the number of reference genes improved the expression profile obtained for MEP2. Therefore, the number of reference genes suitable for real-time RT-qPCR data normalization would also vary on a case specific manner and a human-mediated evaluation of the obtained results to design real-time RT-qPCR experiments may provide reduc- tions in the number of experiments, thus reducing the unnecessary costs. Stability of the expression profile of the reference gene sets along a dynamic experimental condition In order to demonstrate the stability of the reference genes that were identified in each case study, an external control gene would be required in the analysis such that the expression of these reference genes were shown to be constant along a dynamic experimental condition. 18S rRNA was believed to be a suitable choice of selection as reference. 18S rRNA was frequently reported among the candidate reference genes that were used in real-time RT-qPCR analysis [10,11,19,20,21,22,23,24]. With its low turnover rate and large abundance, 18S rRNA was reported as an attractive choice as a reference gene [19]. However, the results of real-time RT-qPCR analysis indicated that 18S rRNA was the least stable candidate based on the rankings identified by both NormFinder and geNorm for both cases investigated in the present study (Figure S12). This outcome led to the investigation of the raw Ct values as a measure of stability as reported previously [25]. The Ct values were analysed for 18S rRNA along with the Ct values for two stable genes; TPI1 and CDC19 for Case Study I and FBA1 and TDH3 for Case Study II, the least stable gene for each case; ADH1 for Case Study I and RPS26A for Case Study II, as well as the geometric average of the Ct values for the reference gene set that was determined to be suitable for each case that was presented above. The results indicated that the response by 18S rRNA fluctuated the most together with the genes identified as the least stable (Figure S13). Indeed other studies also reported that 18S rRNA was not a preferable choice as a reference gene due to several factors including its high abundance introducing human errors during dilution [19] and its regulation by RNA polymerase I rather than RNA polymerase II, which regulated the expression of mRNA in the cell [26]. Because of these concerns the use of 18S rRNA as reference was not preferred in several studies [20,22,27,28,29]. In fact this issue of the evaluation of the expression stability of a candidate was addressed as a circular problem in several studies [8,30]. In order to overcome this problem the data was normalized across average of the Ct values of all genes at each time point. Although this might introduce a bias as a result of the overall trend captured in the data, it should be noted that at times when the commonly used reference genes fail to display a stable expression under various experimental conditions, this strategy would be undertaken for data specific validation of reference genes [31,32]. The variation in the expression profile of the reference gene sets was the lowest in each case study in comparison to the variations in the expression profiles of the genes whose Ct value profiles were investigated in Figure S13. The expression profile of the gene set was more stable across time than the expression profiles of its individual members and the least stable profiles were observed for the gene that was identified as the least stable in each case as well as for 18S rRNA [Figure S14]. PLoS ONE | www.plosone.org 7 June 2012 | Volume 7 | Issue 6 | e38351