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Selection of reference genes for quantitative PCR: identifying reference genes for airway epithelial cells exposed to Pseudomonas aeruginosa.

ABSTRACT: Most quantitative PCR (qPCR) experiments report differential expression relative to the expression of one or more reference genes. Therefore, when experimental conditions alter reference gene expression, qPCR results may be compromised. Little is known about the magnitude of this problem in practice. We found that reference gene responses are common and hard to predict and that their stability should be demonstrated in each experiment. Our reanalysis of 15 airway epithelia microarray data sets retrieved from the National Center for Biotechnology Information (NCBI) identified no common reference gene that was reliable in all 15 studies. Reanalysis of published RNA sequencing (RNA-seq) data in which human bronchial epithelial cells (HBEC) were exposed to Pseudomonas aeruginosa revealed that minor experimental details, including bacterial strain, may alter reference gene responses. Direct measurement of 32 TaqMan reference genes in primary cultures of HBEC exposed to P. aeruginosa (strain PA14) demonstrated that choosing an unstable reference gene could make it impossible to observe statistically significant changes in IL8 gene expression. We found that reference gene instability is a general phenomenon and not limited to studies of airway epithelial cells. In a diverse compendium of 986 human microarray experiments retrieved from the NCBI, reference genes were differentially expressed in 42% of studies. Experimentally induced changes in reference gene expression ranged from 21% to 212%. These results highlight the importance of identifying adequate reference genes for each experimental system and documenting their response to treatment in each experiment. This will enhance experimental rigor and reproducibility in qPCR studies.

SUBMITTER: Hampton TH

PROVIDER: S-EPMC7473940 | 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 quantitative PCR: identifying reference genes for airway epithelial cells exposed to Pseudomonas aeruginosa.

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