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Selection of reference genes for normalization of microRNA expression in sugarcane stalks during its interaction with Colletotrichum falcatum.

ABSTRACT: The microRNAs role in various cellular and metabolic functions is gaining more limelight in line with second-generation NGS technology. For the validation of candidate miRNA genes, the quantitative real-time PCR is the widely trusted and efficient method to follow. Sugarcane miRNAs are less explored in sugarcane defense response during their interaction with Colletotrichum falcatum inciting red rot. Further, for RT-qPCR experiments involving sugarcane miRNA expression studies, a stable internal reference gene is required. Hence, we have taken a study involving 20 candidate genes to identify stable expressing reference genes using NormFinder, geNorm, BestKeeper, and deltaCt statistical algorithms. The candidate reference genes included miRNAs and protein-coding genes. The results indicated that there is a variation in ranking among the algorithms. We found miR1862c as the stably expressed miRNA reference gene among the candidates and miR444b.2 along miR1862c formed the best reference gene pair combination, which can be used in the experiments aiming to explore sugarcane miRNAs in the defense mechanism against C. falcatum. The stable miRNA reference gene was further validated with other lesser stable reference gene candidates to assess the effect of stable reference genes during normalization. The present study evaluating the sugarcane miRNAs as reference genes for normalizing RT-qPCR expression data involving miRNAs during sugarcane × C. falcatum interaction is the first of its kind. Further, this systematic approach can be followed to assess the reference gene in various experimental conditions involving sugarcane miRNAs.

SUBMITTER: Nandakumar M

PROVIDER: S-EPMC7806679 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundNormalization in real-time qRT-PCR is necessary to compensate for experimental variation. A popular normalization strategy employs reference gene(s), which may introduce additional variability into normalized expression levels due to innate variation (between tissues, individuals, etc). To minimize this innate variability, multiple reference genes are used. Current methods of selecting reference genes make an assumption of independence in their innate variation. This assumption is not always justified, which may lead to selecting a suboptimal set of reference genes.ResultsWe propose a robust approach for selecting optimal subset(s) of reference genes with the smallest variance of the corresponding normalizing factors. The normalizing factor variance estimates are based on the estimated unstructured covariance matrix of all available candidate reference genes, adjusting for all possible correlations. Robustness is achieved through bootstrapping all candidate reference gene data and obtaining the bootstrap upper confidence limits for the variances of the log-transformed normalizing factors. The selection of the reference gene subset is optimized with respect to one of the following criteria: (A) to minimize the variability of the normalizing factor; (B) to minimize the number of reference genes with acceptable upper limit on variability of the normalizing factor, (C) to minimize the average rank of the variance of the normalizing factor. The proposed approach evaluates all gene subsets of various sizes rather than ranking individual reference genes by their stability, as in the previous work. In two publicly available data sets and one new data set, our approach identified subset(s) of reference genes with smaller empirical variance of the normalizing factor than in subsets identified using previously published methods. A small simulation study indicated an advantage of the proposed approach in terms of sensitivity to identify the true optimal reference subset in the presence of even modest, especially negative correlation among the candidate reference genes.ConclusionsThe proposed approach performs comprehensive and robust evaluation of the variability of normalizing factors based on all possible subsets of candidate reference genes. The results of this evaluation provide flexibility to choose from important criteria for selecting the optimal subset(s) of reference genes, unless one subset meets all the criteria. This approach identifies gene subset(s) with smaller variability of normalizing factors than current standard approaches, particularly if there is some nontrivial innate correlation among the candidate genes.

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Selection of reference genes for normalization of microRNA expression in sugarcane stalks during its interaction with Colletotrichum falcatum.

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