Optimisation of region-specific reference gene selection and relative gene expression analysis methods for pre-clinical trials of Huntington's disease

Abstract

Background: Transcriptional dysregulation is an early, key pathogenic mechanism in Huntington's disease (HD). Therefore, gene expression analyses have biomarker potential for measuring therapeutic efficacy in pre-clinical trials, particularly those aimed at correcting gene expression abnormalities. Housekeeping genes are commonly used as endogenous references in gene expression studies. However, a systematic study comparing the suitability of candidate reference genes for use in HD mouse models has not been performed. To remedy this situation, 12 housekeeping genes were examined to identify suitable reference genes for use in expression assays.

Results: We found that commonly used reference genes are dysregulated at later time points in the R6/2 mouse model of HD. Therefore, in order to reliably measure gene expression changes for use as pre-clinical trial biomarkers, we set out to identify suitable reference genes for use in R6/2 mice. The expression of potential reference genes was examined in striatum, cortex and cerebellum from 15 week old R6/2 and matched wild-type littermates. Expression levels of candidate reference genes varied according to genotype and brain region. GeNorm software was used to identify the three most stably expressed genes for each brain region. Relative quantification methods using the geometric mean of three reference genes for normalisation enables accurate determination of gene expression levels in wild-type and R6/2 mouse brain regions.

Conclusion: Our study has identified a reproducible, reliable method by which we able to accurately determine the relative expression level of target genes in specific brain regions, thus increasing the potential of gene expression analysis as a biomarker in HD pre-clinical trials.

Figure 1

Progressive gene dysregulation in R6/2 mice over time. (A) Raw crossing threshold data for Actb, Grin1 and Ubc in 12 and 15 week old R6/2 transgenic mice and wild-type littermates in striatum shows an increase in crossing threshold data for both Actb and Grin1 but not Ubc, despite carefully controlling for RNA amounts. (B) Similar data as for (A), except that RNA extracts are from cerebellum. 12 week old wild-type (WT) = white bars, 12 week old R6/2 mice = stripes, 15 week old WT = dots, 15 week old R6/2 = solid fill bars. Error bars are S.E.M. (n = 10), * p < 0.05.

Figure 2

GeNorm analyses to identify optimal reference genes in the striatum. (A) Raw crossing threshold (C_t) data for a panel of 12 potential references from the geNorm kit in wild-type (open bars) and R6/2 (filled bars) mice. (B) Raw C_t data was subjected to analysis with the geNorm applet which automatically calculates the gene-stability measure M, which is an average pairwise variation of a particular gene with all other control genes. Therefore, genes with the lowest M value have the most stable expression, in this case across genotypes (i.e. comparing wild-type and R6/2 mice). Expression stability is plotted for each of the potential reference genes, progressing from the least stable genes with a higher M value to the most stable genes with a lower M value. (C) In order to measure expression levels accurately, normalization by multiple housekeeping genes is optimal. The graph illustrates the levels of variation in average reference gene stability with the sequential addition of each reference gene to the equation, starting with the three most stably expressed genes on the left with the inclusion of a 4^th gene and so on, moving to the right. This measure is known as pairwise variation (V), the values of which are indicated above each bar, with a score of <0.15 as a target.

Figure 3

Comparison of normalisation strategy. Relative expression analyses using either a single reference gene with low stability between wild-type (WT, open bars) and R6/2 (filled bars) cortex such as Actb, a single reference gene with high stability between the two genotypes such as calnexin or the geometric mean of three highly stable reference genes. P-values above the R6/2 bars show that while a single, stably expressed gene (p = 0.0016) is a better calibrator than a gene with low expression stability between the two genotypes (p = 0.0926), calculating the geometric mean of three stably expressed genes is a far superior normalisation strategy (p = 0.0006). Error bars are S.E.M (n = 6).

Figure 4

Bdnf promoter-specific expression analyses in R6/2 cortex. Relative expression ratios to the geometric mean of three housekeeping genes for Bdnf promoter-specific transcripts in R6/2 (filled bars) and littermate wild-type controls (open bars). Data from 4, 8, 12 and 15 weeks (4 w, 8 w, 12 w and 15 w) represents the pathogenic time course from pre-symptomatic stages through to late symptomatic stages for Bdnf promoter-specific transcripts (A) I, (B) IIa, (C) IIb, (D) IIc, (E) III, (F) IV, (G) V and (H) coding exon VIII, according to Liu et al., 2006 [39]. Error bars are S.E.M (n = 8), * p < 0.05, ** p < 0.01, *** p < 0.001.

Figure 5

Power calculations determine the optimal Bdnf assays for use as preclnical assessment tools. (A) Shows the relative expression level of each Bdnf promoter-specific transcript expressed as a ratio of R6/2: wild-type from a pre-symptomatic time point (4 weeks, 4 w) to early symptomatic (8 w) and late symptomatic time points (12 and 15 w). Therefore, each bar represents the relative amount of down-regulation of each transcript in R6/2 mouse cortex compared to wild-type. Significant differences between R6/2 and wild-type mice for a specific transcript at the ages indicated are represented with asterisks below the relevant bar. * p < 0.05, ** p < 0.01, *** p < 0.001. Promoter-specific transcript nomenclature is according to Liu et al., 2006 [39]. We performed power calculations (shown is 80% power of detection of improvement at p > 0.05) as previously described [40] for BDNF promoters, with promoters I (B), IIa (C), V (D) and VIII (E) being the best powered to detect potential modulation of Bdnf expression levels by genetic or pharmacological approaches (data not shown for remaining promoters). The dotted line illustrates the number of mice needed in order to be able to detect a 30% improvement with 80% confidence.