Transcriptome-based selection and validation of optimal house-keeping genes for skin research in goats (Capra hircus)

Abstract

Background: In quantitative real-time polymerase chain reaction (qRT-PCR) experiments, accurate and reliable target gene expression results are dependent on optimal amplification of housekeeping genes (HKGs). RNA-seq technology offers a novel approach to detect new HKGs with improved stability. Goat (Capra hircus) is an economically important livestock species and plays an indispensable role in the world animal fiber and meat industry. Unfortunately, uniform and reliable HKGs for skin research have not been identified in goat. Therefore, this study seeks to identify a set of stable HKGs for the skin tissue of C. hircus using high-throughput sequencing technology. Results: Based on the transcriptome dataset of 39 goat skin tissue samples, 8 genes (SRP68, NCBP3, RRAGA, EIF4H, CTBP2, PTPRA, CNBP, and EEF2) with relatively stable expression levels were identified and selected as new candidate HKGs. Commonly used HKGs including SDHA and YWHAZ from a previous study, and 2 conventional genes (ACTB and GAPDH) were also examined. Four different experimental variables: (1) different development stages, (2) hair follicle cycle stages, (3) breeds, and (4) sampling sites were used for determination and validation. Four algorithms (geNorm, NormFinder, BestKeeper, and ΔCt method) and a comprehensive algorithm (ComprFinder, developed inhouse) were used to assess the stability of each HKG. It was shown that NCBP3 + SDHA + PTPRA were more stably expressed than previously used genes in all conditions analysis, and that this combination was effective at normalizing target gene expression. Moreover, a new algorithm for comprehensive analysis, ComprFinder, was developed and released. Conclusion: This study presents the first list of candidate HKGs for C. hircus skin tissues based on an RNA-seq dataset. We propose that the NCBP3 + SDHA + PTPRA combination could be regarded as a triplet set of HKGs in skin molecular biology experiments in C. hircus and other closely related species. In addition, we also encourage researchers who perform candidate HKG evaluations and who require comprehensive analysis to adopt our new algorithm, ComprFinder.

Transcriptome-Based Selection and Validation of Reference Genes for Gene Expression Analysis of Alicyclobacillus acidoterrestris Under Acid Stress

“…A total of eight candidate novel RGs were selected from the transcriptome data of A. acidoterrestris using the criteria including high expression levels (RPKM ≥ 10), and a low variability with CV ≤ 20%, MFC < 2.5, and DPM < 0.2 (Figure 2). Multiple studies have shown that transcriptomic data is a reliable source for exploring suitable RGs under specific experimental conditions, and similar criteria have been used for RGs screening in other species (Gao et al, 2018;Zhang et al, 2020;Xing et al, 2021). For A. acidoterrestris, all current studies on the functional gene expression used 16s rRNA as RG to normalize RT-qPCR data, but the stability of 16s rRNA has not been evaluated under specific conditions (Jiao et al, 2015;Feng et al, 2019;Zhao et al, 2021).…”

“…As reported in the MIQE guidelines, normalization based on a single RG is not acceptable, and the optimal number of RGs must be experimentally determined (Bustin et al, 2009). Moreover, many studies indicated that the normalization with multiple RGs was necessary for the accurate assessment of gene expression under specific conditions (Noti et al, 2015;Zhang et al, 2020). The geNorm algorithm is usually used to determine the optimal number of RGs based on the pairwise variation (V n/n+1 ) (Vandesompele et al, 2002).…”

“…A total of 2010 genes had RPKM values ≥ 10, accounting for 73.06% of all 2,751 genes (blue area, Figure 2A). Meanwhile, the most promising RGs had low expression variability, namely, the lowest CV, MFC, and DPM values (Gao et al, 2018;Zhang et al, 2020). As shown in Figure 2B, 33 genes had a CV value ≤ 20% (1.20% of 2751 genes, yellow area).…”

“…The coefficient of variation (CV,%) and maximum fold change (MFC) were calculated using Microsoft Excel based on the RPKM value of every gene in each sample. The CV is defined as the ratio of the standard deviation (SD) to the mean of the FPKM of all samples for one gene, while the MFC is defined as the fold change between the largest and smallest RPKM values within all samples (Zhang et al, 2020). Meanwhile, the dispersion measure (DPM) was calculated on the pattern gene finder (PaGeFinder) website 1 (Pan et al, 2012).…”

“…Therefore, a systematic research on stably expressed RGs of A. acidoterrestris under stress conditions is required. The RNA-seq technique provides not only a method to determine gene expression at the transcriptome level but also a novel approach for RGs prediction, which has been successfully applied in multiple species (Alexander et al, 2012;He et al, 2019;Zhang et al, 2020;Jureckova et al, 2021). Therefore, it was hypothesized that the novel and reliable RGs for investigating stress responses in A. acidoterrestris could be predicted using transcriptome data.…”