Herbicide-resistant weeds pose a considerable threat to agriculture, but their resistance mechanisms are poorly understood. Differential gene expression analysis of a weed subjected to herbicide treatment is a key step toward more mechanistic studies. Such an analysis, often involving quantitative real-time PCR (qPCR), requires suitable reference genes as internal controls. In this study, we identified optimal reference genes in the noxious weed, Japanese foxtail. This weed has evolved resistance to acetyl-coenzyme A carboxylase (ACCase) inhibitors. We analyzed the stability of eight commonly used candidate reference genes (glyceraldehyde-3-phosphate dehydrogenase [GAPDH]; ubiquitin [UBQ]; capsine phosphatase [CAP]; beta-tubulin [TUB]; eukaryotic initiation factor 4a [EIF4A]; elongation factor-1 alpha [EF1]; 18S ribosomal RNA [18S]; 25S ribosomal RNA [25S]) from root, stem, and leaf tissue of plants that were either resistant or sensitive to ACCase inhibitors, with or without herbicide stress, using qPCR. The results were further ranked and analyzed using geNorm, NormFinder, and BestKeeper software. These analyses identified EF1 and UBQ in roots, EF1, TUB, CAP, and 18S in stems, and EF1, GAPDH, and 18S in leaves as suitable references for qPCR normalization. We have identified a set of reference genes that can be used to study herbicide resistance mechanisms in Japanese foxtail.

Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) is an important technology to analyse gene expression levels during plant development or in response to different treatments. An important requirement to measure gene expression levels accurately is a properly validated set of reference genes. In this context, we analysed the potential use of 17 candidate reference genes across a diverse set of samples, including several tissues, different stages and environmental conditions, encompassing seed germination and seedling growth in Ricinus communis L. These genes were tested by RT-qPCR and ranked according to the stability of their expression using two different approaches: GeNorm and NormFinder. GeNorm and Normfinder indicated that ACT, POB and PP2AA1 comprise the optimal combination for normalization of gene expression data in inter-tissue (heterogeneous sample panel) studies. We also describe the optimal combination of reference genes for a subset of root, endosperm and cotyledon samples. In general, the most stable genes suggested by GeNorm are very consistent with those indicated by NormFinder, which highlights the strength of the selection of reference genes in our study. We also validated the selected reference genes by normalizing the expression levels of three target genes involved in energy metabolism with the reference genes suggested by GeNorm and NormFinder. The approach used in this study to identify stably expressed genes, and thus potential reference genes, was applied successfully for R. communis and it provides important guidelines for RT-qPCR studies in seeds and seedlings for other species (especially in those cases where extensive microarray data are not available).