Identification and validation of condition-specific candidate reference genes for accurate RT-qPCR normalization in acute and chronic methamphetamine-exposed cynomolgus monkeys.

Reverse transcription quantitative real-time PCR (RT-qPCR) is widely used to quantify gene expression, but its accuracy depends on appropriate normalization using stable reference genes (RGs). Because methamphetamine (METH) exposure induces widespread transcriptional changes, conventional housekeeping genes may not remain stable under these conditions. However, condition-specific RGs have not been systematically evaluated in METH-exposed nonhuman primate models. We evaluated transcriptome-derived candidate RGs together with four commonly used RGs (GAPDH, ACTB, RPS5, and YWHAZ) in blood and tissue samples obtained from acute and chronic METH-exposed cynomolgus monkeys representing multiple age groups. Expression stability was assessed using geNorm, NormFinder, and BestKeeper, and the results were integrated using geometric mean ranking. The impact of RG selection on target-gene quantification was further examined by analyzing the expression of FOSL2, JUN, and NR4A1. The stability rankings of candidate RGs differed across age-stratified groups, exposure paradigms, and sample types. No single gene exhibited consistently stable expression across all experimental conditions. In contrast, the traditionally used RGs generally ranked poorly in most sample groups. Normalization using the most stable and least stable RGs produced different expression patterns of FOSL2 and JUN in acute blood samples, while NR4A1 and JUN expression in chronic blood samples was evaluated using the selected RGs. This study provides condition-specific candidate reference genes for RT-qPCR normalization in acute and chronic METH-exposed cynomolgus monkeys. Rather than identifying universally stable housekeeping genes, our findings demonstrate that reference-gene stability should be empirically validated for each experimental context. These findings provide a practical framework for improving the reliability and reproducibility of gene expression analyses in METH exposure studies.