Reference gene validation for gene expression normalization in canine osteosarcoma: a geNorm algorithm approach

BackgroundAppropriate reference genes are critical to accurately quantifying relative gene expression in research and clinical applications. Numerous efforts have been made to select the most stable reference gene(s), but a consensus has yet to be achieved. In this report, we propose an in silico reference gene validation method, iRGvalid, that can be used as a universal tool to validate the reference genes recommended from different resources so as to identify the best ones without a need for any wet lab validation tests.MethodsiRGvalid takes advantage of high throughput gene expression data and is built on a double-normalization strategy. First, the expression level of each individual gene is normalized against the total gene expression level of each sample, followed by a target gene normalization to the candidate reference gene(s). Linear regression analysis is then performed between the pre- and post- normalized target gene across the whole sample set to evaluate the stability of the reference gene(s), which is positively associated with the Pearson correlation coefficient, Rt. The higher the Rt value, the more stable the reference gene. We applied iRGvalid to 14 candidate reference genes to validate and identify the most stable reference genes in four cancer types: lung adenocarcinoma, breast cancer, colon adenocarcinoma, and nasopharyngeal cancer. The stability of the reference gene is evaluated both individually and in groups of all possible combinations.ResultsHighly stable reference genes resulted in high Rt values regardless of the target gene used. The highest stability was achieved with a specific combination of 3 to 6 reference genes. A few genes were among the best reference genes across the cancer types studied here.ConclusioniRGvalid provides an easy and robust method to validate and identify the most stable reference gene or genes from a pool of candidate reference genes. The inclusivity of large expression data sets as well as the direct comparison of candidate reference genes makes it possible to identify reference genes with universal quality. This method can be used in any other gene expression studies when large cohorts of expression data are available.

Download Full-text

Reference gene validation for qRT-PCR based gene expression studies in different developmental stages and under biotic stress in apple

Scientia Horticulturae ◽

10.1016/j.scienta.2015.10.025 ◽

2015 ◽

Vol 197 ◽

pp. 597-606 ◽

Cited By ~ 15

Author(s):

Gulshan Kumar ◽

Anil Kumar Singh

Keyword(s):

Gene Expression ◽

Reference Gene ◽

Biotic Stress ◽

Developmental Stages ◽

Qrt Pcr ◽

Expression Studies ◽

Reference Gene Validation ◽

Gene Expression Studies

Download Full-text

Reference gene validation for quantification of gene expression during ovarian development of turbot (Scophthalmus maximus)

Scientific Reports ◽

10.1038/s41598-020-57633-3 ◽

2020 ◽

Vol 10 (1) ◽

Cited By ~ 2

Author(s):

Yunhong Gao ◽

Yuntao Gao ◽

Bin Huang ◽

Zhen Meng ◽

Yudong Jia

Keyword(s):

Gene Expression ◽

Reference Gene ◽

Ovarian Development ◽

Scophthalmus Maximus ◽

Reference Gene Validation ◽

Turbot Scophthalmus Maximus

Download Full-text

Reference gene validation for quantification of gene expression during final oocyte maturation induced by diethylstilbestrol and di-(2-ethylhexyl)-phthalate in common carp

Journal of Environmental Sciences ◽

10.1016/j.jes.2015.12.030 ◽

2016 ◽

Vol 46 ◽

pp. 47-54 ◽

Cited By ~ 2

Author(s):

Yanyan Shi ◽

Jie Lu ◽

Yilei Wang ◽

Shuhong Wang

Keyword(s):

Gene Expression ◽

Reference Gene ◽

Common Carp ◽

Oocyte Maturation ◽

Final Oocyte Maturation ◽

Reference Gene Validation ◽

Ethylhexyl Phthalate

Download Full-text

Optimizations for identifying reference genes in bone and cartilage bioengineering

BMC Biotechnology ◽

10.1186/s12896-021-00685-8 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Fei Xiong ◽

Xiangyun Cheng ◽

Chao Zhang ◽

Roland Manfred Klar ◽

Tao He

Keyword(s):

Gene Expression ◽

Skeletal Muscle ◽

Adipose Tissue ◽

Real Time ◽

Reference Gene ◽

Muscle Tissue ◽

Reference Genes ◽

Target Genes ◽

The Stability ◽

And Cartilage

Abstract Background Reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) remains one of the best-established techniques to assess gene expression patterns. However, appropriate reference gene(s) selection remains a critical and challenging subject in which inappropriate reference gene selction can distort results leading to false interpretations. To date, mixed opinions still exist in how to choose the most optimal reference gene sets in accodrance to the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guideline. Therefore, the purpose of this study was to investigate which schemes were the most feasible for the identification of reference genes in a bone and cartilage bioengineering experimental setting. In this study, rat bone mesenchymal stem cells (rBMSCs), skeletal muscle tissue and adipose tissue were utilized, undergoing either chondrogenic or osteogenic induction, to investigate the optimal reference gene set identification scheme that would subsequently ensure stable and accurate interpretation of gene expression in bone and cartilage bioengineering. Results The stability and pairwise variance of eight candidate reference genes were analyzed using geNorm. The V0.15- vs. Vmin-based normalization scheme in rBMSCs had no significant effect on the eventual normalization of target genes. In terms of the muscle tissue, the results of the correlation of NF values between the V0.15 and Vmin schemes and the variance of target genes expression levels generated by these two schemes showed that different schemes do indeed have a significant effect on the eventual normalization of target genes. Three selection schemes were adopted in terms of the adipose tissue, including the three optimal reference genes (Opt3), V0.20 and Vmin schemes, and the analysis of NF values with eventual normalization of target genes showed that the different selection schemes also have a significant effect on the eventual normalization of target genes. Conclusions Based on these results, the proposed cut-off value of Vn/n + 1 under 0.15, according to the geNorm algorithm, should be considered with caution. For cell only experiments, at least rBMSCs, a Vn/n + 1 under 0.15 is sufficient in RT-qPCR studies. However, when using certain tissue types such as skeletal muscle and adipose tissue the minimum Vn/n + 1 should be used instead as this provides a far superior mode of generating accurate gene expression results. We thus recommended that when the stability and variation of a candidate reference genes in a specific study is unclear the minimum Vn/n + 1 should always be used as this ensures the best and most accurate gene expression value is achieved during RT-qPCR assays.

Download Full-text

Current Practices for Reference Gene Selection in RT-qPCR of Aspergillus: Outlook and Recommendations for the Future

Genes ◽

10.3390/genes12070960 ◽

2021 ◽

Vol 12 (7) ◽

pp. 960

Author(s):

Meagan Archer ◽

Jianping Xu

Keyword(s):

Gene Expression ◽

Reference Gene ◽

Reference Genes ◽

Gene Selection ◽

Quantitative Polymerase Chain Reaction ◽

Specific Method ◽

Experimental Conditions ◽

Reference Gene Selection ◽

Physiological Processes ◽

The Stability

Aspergillus is a genus of filamentous fungi with vast geographic and ecological distributions. Species within this genus are clinically, agriculturally and biotechnologically relevant, leading to increasing interest in elucidating gene expression dynamics of key metabolic and physiological processes. Reverse-transcription quantitative Polymerase Chain Reaction (RT-qPCR) is a sensitive and specific method of quantifying gene expression. A crucial step for comparing RT-qPCR results between strains and experimental conditions is normalisation to experimentally validated reference gene(s). In this review, we provide a critical analysis of current reference gene selection and validation practices for RT-qPCR gene expression analyses of Aspergillus. Of 90 primary research articles obtained through our PubMed query, 17 experimentally validated the reference gene(s) used. Twenty reference genes were used across the 90 studies, with beta-tubulin being the most used reference gene, followed by actin, 18S rRNA and glyceraldehyde 3-phosphate dehydrogenase. Sixteen of the 90 studies used multiple reference genes for normalisation. Failing to experimentally validate the stability of reference genes can lead to conflicting results, as was the case for four studies. Overall, our review highlights the need to experimentally validate reference genes in RT-qPCR studies of Aspergillus.

Download Full-text

Identification and selection of reference genes for gene expression analysis by quantitative real-time PCR in Suaeda glauca’s response to salinity

Scientific Reports ◽

10.1038/s41598-021-88151-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Meng Wang ◽

Tingting Ren ◽

Prince Marowa ◽

Haina Du ◽

Zongchang Xu

Keyword(s):

Gene Expression ◽

Real Time ◽

Reference Gene ◽

Reference Genes ◽

Suitable Reference Gene ◽

Normalize Gene Expression ◽

Suaeda Glauca ◽

Suitable Reference ◽

Germinating Seeds ◽

Different Tissues

AbstractQuantitative real-time polymerase chain reaction (qPCR) using a stable reference gene is widely used for gene expression research. Suaeda glauca L. is a succulent halophyte and medicinal plant that is extensively used for phytoremediation and extraction of medicinal compounds. It thrives under high-salt conditions, which promote the accumulation of high-value secondary metabolites. However, a suitable reference gene has not been identified for gene expression standardization in S. glauca under saline conditions. Here, 10 candidate reference genes, ACT7, ACT11, CCD1, TUA5, UPL1, PP2A, DREB1D, V-H+-ATPase, MPK6, and PHT4;5, were selected from S. glauca transcriptome data. Five statistical algorithms (ΔCq, geNorm, NormFinder, BestKeeper, and RefFinder) were applied to determine the expression stabilities of these genes in 72 samples at different salt concentrations in different tissues. PP2A and TUA5 were the most stable reference genes in different tissues and salt treatments, whereas DREB1D was the least stable. The two reference genes were sufficient to normalize gene expression across all sample sets. The suitability of identified reference genes was validated with MYB and AP2 in germinating seeds of S. glauca exposed to different NaCl concentrations. Our study provides a foundational framework for standardizing qPCR analyses, enabling accurate gene expression profiling in S. glauca.

Download Full-text