Optimal Reference Gene Selection for Expression Studies in Human Reticulocytes

Abstract

Reference genes are indispensable for normalizing mRNA levels across samples in real-time quantitative PCR. Their expression levels vary under different experimental conditions and because of several inherent characteristics. Appropriate reference gene selection is thus critical for gene-expression studies. This study aimed at selecting optimal reference genes for gene-expression analysis of reticulocytes and at validating them in hereditary spherocytosis (HS) and β-thalassemia intermedia (βTI) patients. Seven reference genes (PGK1, MPP1, HPRT1, ACTB, GAPDH, RN18S1, and SDHA) were selected because of published reports. Real-time quantitative PCR was performed on reticulocytes in 20 healthy volunteers, 15 HS patients, and 10 βTI patients. Threshold cycle values were compared with fold-change method and RefFinder software. The stable reference genes recommended by RefFinder were validated with SLC4A1 and flow cytometric eosin-5'-maleimide binding assay values in HS patients and HBG2 and high performance liquid chromatography-derived percentage of hemoglobin F in βTI. Comprehensive ranking predicted MPP1 and GAPDH as optimal reference genes for reticulocytes that were not affected in HS and βTI. This was further confirmed on validation with eosin-5'-maleimide results and percentage of hemoglobin F in HS and βTI patients, respectively. Hence, MPP1 and GAPDH are good reference genes for reticulocyte expression studies compared with ACTB and RN18S1, the two most commonly used reference genes.

Selection and Validation of Appropriate Reference Genes for Quantitative RT-PCR Analysis in Rubia yunnanensis Diels Based on Transcriptome Data

“…Fortunately, BestKeeper still showed some conformance in the top five most stable reference genes with geNorm and Normfinder (Figure 4; Tables 2 and 3). Finally, a widely used web-based tool RefFinder was used to obtain a consensus stability ranking of each candidate gene under the given conditions according to the geometric mean of the attributed weights of each gene [45,47]. For RefFinder analysis, hnRNP, RPL5, TBP, and EF-1α were ranked as the top four most stable reference genes similar with the results from geNorm, Normfinder, and BestKeeper.…”

“…Indeed, the 15 candidate reference genes showed a relatively wide range of expression profiles in our study, confirming again that no single gene could be used for normalization in all the samples of different tissues and various experimental conditions, similar to the results in Euscaphis konishii [11], Pennisetum glaucum [37], Bixa orellana [42], and Klebsiella pneumoniae [43]. Owning to no reports on reference genes of R. yunnanensis, 15 candidate reference genes including 10 general housekeeping genes (GAPDH, eIF, EF-1α, UBCE, RPL5, TBP, TUB, ACT2, MDH, and PEPC) and 5 novel genes (SAND, PP2A, PTBP2, hnRNP, and F-box) were selected from the R. yunnanensis transcriptome database for evaluation according to some literatures [44][45][46]. RT-qPCR is considered the most common method for gene expression analyses based on its high sensitivity and specificity.…”

Deciphering molecular heterogeneity of Indian families with hereditary spherocytosis using targeted next‐generation sequencing: First South Asian study

“…Relative mRNA quantification for the erythrocyte membrane genes was performed to estimate the relative changes in mRNA levels on a LightCycler 480 Instrument II using Light Cycler FastStart SYBR Green Mastermix (Roche Molecular Systems, Inc., Pleasanton, CA, USA). GAPDH and MPP1 were used as reference genes and data was analyzed using delta‐delta Ct (ΔΔCt) method (Aggarwal et al , ).…”

Identification of optimal endogenous reference RNAs for RT-qPCR normalization in hindgut of rat models with anorectal malformations

“…In fact, the reference genes can be affected by various factors (Harris, Reeves & Phillips, 2009; Mughal et al, 2018). For instance, diseases or experimental treatments can alter the expression stability of the reference genes (Aggarwal et al, 2018; Giulietti et al, 2001; Neerukonda et al, 2016). Physical or chemical factors can also influence the variability of the reference genes (Giulietti et al, 2001; Mahanty et al, 2017; Svingen et al, 2015; Yuanyuan et al, 2015).…”

“…Based on the above conditions, some researchers have suggested that normalization against a reference gene should not be acceptable unless it has been clearly confirmed that the reference gene is invariantly expressed in the experimental and control conditions (Aggarwal et al, 2018; Bustin et al, 2009). In order to obtain reliable results in RT-qPCR, systematic validation of reference genes is essential (Guenin et al, 2009).…”