Meiosis is a specialized type of cell division occurring in sexually reproducing organisms to gen... more Meiosis is a specialized type of cell division occurring in sexually reproducing organisms to generate haploid cells known as gametes. In flowering plants, male gametes are produced in anthers, being encased in pollen grains. Understanding the genetic regulation of meiosis key events such as chromosome recognition and pairing, synapsis and recombination, is needed to manipulate chromosome associations for breeding purposes, particularly in important cereal crops like wheat. Reverse transcription-quantitative PCR (RT-qPCR) is widely used to analyse gene expression and to validate the results obtained by other transcriptomic analyses, like RNA-seq. Selection and validation of appropriate reference genes for RT-qPCR normalization is essential to obtain reproducible and accurate expression data. In this work, twelve candidate reference genes were evaluated using the mainstream algorithms geNorm, Normfinder, BestKeeper and ΔCt, then ranked from most to least suitable for normalization with RefFinder. Different sets of reference genes were recommended to normalize gene expression data in anther meiosis of bread and durum wheat, their corresponding genotypes in the absence of the Ph1 locus and for comparative studies among wheat genotypes. Comparisons between meiotic (anthers) and somatic (leaves and roots) wheat tissues were also carried out. To the best of our knowledge, our study provides the first comprehensive list of reference genes for robust RT-qPCR normalization to study differentially expressed genes during male meiosis in wheat in a breeding framework. The study of biological processes usually involves gene expression analyses and quantification. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) is the most widely used technique nowadays to analyse gene expression due to factors like cost-effectiveness, specificity and sensitivity 1. However, to achieve accurate and reliable results, sample-to-sample variation and experimental error need to be controlled by making use of normalization strategies 2,3. The most common and effective method for RT-qPCR normalization is the use of reference genes (RGs), often referred as control genes or housekeeping genes, as internal controls. RGs need to be validated for a given experimental setup, since there are no universal RGs suitable for every tissue or experimental condition as the vast scientific literature on this topic proves. Validation is a critical step, since the use of random, putative or unvalidated RGs introduces significant biases in results 1,4. The main premise for RGs is that their expression remains unchanged or relatively invariant in the experimental context under study, which is not always the case in practice 1. Thus, the available validation methods perform a selection based on the expression stability of the candidate RGs. That is to say, the least variable genes are the most stably expressed and the most suitable for normalization. Among the available RG methods, the most frequently used are geNorm 3 , or its updated version, qBase 5 , BestKeeper 2 and Normfinder 6. geNorm performs pairwise comparisons, calculating the gene stability value (M) as the mean standard deviation of the log-transformed expression ratios for every candidate RG. Moreover, given that several RGs must be used for accurate normalization, geNorm calculates the pairwise variation (V n/n+1) on the normalization factor (NF n /NF n+1) resulting from the inclusion of additional RGs, in order to estimate the optimal number of RGs needed for normalization. However, because the method also top ranks the candidate RGs with high similarity in their expression profiles, it is vulnerable to recommend co-regulated RGs 6. Another validation algorithm, NormFinder, uses a model-based approach to calculate a stability value that ranks RGs according to their intra-and inter-group variation, thus
Meiosis is a specialized type of cell division occurring in sexually reproducing organisms to gen... more Meiosis is a specialized type of cell division occurring in sexually reproducing organisms to generate haploid cells known as gametes. In flowering plants, male gametes are produced in anthers, being encased in pollen grains. Understanding the genetic regulation of meiosis key events such as chromosome recognition and pairing, synapsis and recombination, is needed to manipulate chromosome associations for breeding purposes, particularly in important cereal crops like wheat. Reverse transcription-quantitative PCR (RT-qPCR) is widely used to analyse gene expression and to validate the results obtained by other transcriptomic analyses, like RNA-seq. Selection and validation of appropriate reference genes for RT-qPCR normalization is essential to obtain reproducible and accurate expression data. In this work, twelve candidate reference genes were evaluated using the mainstream algorithms geNorm, Normfinder, BestKeeper and ΔCt, then ranked from most to least suitable for normalization with RefFinder. Different sets of reference genes were recommended to normalize gene expression data in anther meiosis of bread and durum wheat, their corresponding genotypes in the absence of the Ph1 locus and for comparative studies among wheat genotypes. Comparisons between meiotic (anthers) and somatic (leaves and roots) wheat tissues were also carried out. To the best of our knowledge, our study provides the first comprehensive list of reference genes for robust RT-qPCR normalization to study differentially expressed genes during male meiosis in wheat in a breeding framework. The study of biological processes usually involves gene expression analyses and quantification. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) is the most widely used technique nowadays to analyse gene expression due to factors like cost-effectiveness, specificity and sensitivity 1. However, to achieve accurate and reliable results, sample-to-sample variation and experimental error need to be controlled by making use of normalization strategies 2,3. The most common and effective method for RT-qPCR normalization is the use of reference genes (RGs), often referred as control genes or housekeeping genes, as internal controls. RGs need to be validated for a given experimental setup, since there are no universal RGs suitable for every tissue or experimental condition as the vast scientific literature on this topic proves. Validation is a critical step, since the use of random, putative or unvalidated RGs introduces significant biases in results 1,4. The main premise for RGs is that their expression remains unchanged or relatively invariant in the experimental context under study, which is not always the case in practice 1. Thus, the available validation methods perform a selection based on the expression stability of the candidate RGs. That is to say, the least variable genes are the most stably expressed and the most suitable for normalization. Among the available RG methods, the most frequently used are geNorm 3 , or its updated version, qBase 5 , BestKeeper 2 and Normfinder 6. geNorm performs pairwise comparisons, calculating the gene stability value (M) as the mean standard deviation of the log-transformed expression ratios for every candidate RG. Moreover, given that several RGs must be used for accurate normalization, geNorm calculates the pairwise variation (V n/n+1) on the normalization factor (NF n /NF n+1) resulting from the inclusion of additional RGs, in order to estimate the optimal number of RGs needed for normalization. However, because the method also top ranks the candidate RGs with high similarity in their expression profiles, it is vulnerable to recommend co-regulated RGs 6. Another validation algorithm, NormFinder, uses a model-based approach to calculate a stability value that ranks RGs according to their intra-and inter-group variation, thus
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Papers by Miguel Aguilar