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

Quantitative 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.

Isolation of Housekeeping Genes on Durik-durik (Syzygium sp)

Housekeeping gene is a gene expressed with a fixed level and in abundant amounts under various conditions. After validation, the housekeeping gene can be used as an internal control to normalize gene expression data. This study reports the isolation of several housekeeping genes in Durik-durik plant (Syzygium sp). This plant material in form of fresh leaves from Durik-durik plants are taken from Kajuik Lake, Riau Province. The next stage is total DNA isolation, polymerase chain reaction, electrophoresis, sequencing and data analysis using bioinformatic tools. The isolated housekeeping genes included 18S rRNA, actin, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), Beta-tubulin and Ubiquitin with sizes of 400 bp, 679 bp, 1134 bp, 836 bp, 1167 bp and 2155 bp, respectively. In addition to 18S rRNA, the five housekeeping genes are the first reported from the genus Syzygium and referable to isolate housekeeping genes in other species in this genus. The six housekeeping genes can be used as internal controls on Durik-durik plants after validation.

NormExpression: an R package to normalize gene expression data using evaluated methods

Data normalization is a crucial step in the gene expression analysis as it ensures the validity of its downstream analyses. Although many metrics have been designed to evaluate the current normalization methods, the different metrics yield inconsistent results. In this study, we designed a new metric named Area Under normalized CV threshold Curve (AUCVC) and applied it with another metric mSCC to evaluate 14 commonly used normalization methods, achieving consistency in our evaluation results using both bulk RNA-seq and scRNA-seq data from the same library construction protocol. This consistency has validated the underlying theory that a sucessiful normalization method simultaneously maximizes the number of uniform genes and minimizes the correlation between the expression profiles of gene pairs. This consistency can also be used to analyze the quality of gene expression data. The gene expression data, normalization methods and evaluation metrics used in this study have been included in an R package named NormExpression. NormExpression provides a framework and a fast and simple way for researchers to evaluate methods (particularly some data-driven methods or their own methods) and then select a best one for data normalization in the gene expression analysis.

Identification of optimal reference genes for examination of gene expression in different tissues of fetal yaks

Reverse transcription quantitative real-time PCR (RT-qPCR) is widely used to study the relative abundance of mRNA transcripts because of its sensitivity and reliable quantification. However, the reliability of the interpretation of expression data is influenced by several complex factors, including RNA quality, transcription activity, and PCR efficiency, among others. To avoid experimental errors arising from potential variation, the selection of appropriate reference genes to normalize gene expression is essential. In this study, 10 commonly used reference genes – ACTB, B2M, HPRT1, GAPDH, 18SrRNA, 28SrRNA, PPIA, UBE2D2, SDHA, and TBP – were selected as candidate reference genes for six fetal tissues (heart, liver, spleen, lung, kidney, and forehead skin) of yak (Bos grunniens). The transcription stability of the candidate reference genes was evaluated using geNorm, NormFinder, and BestKeeper. The results showed that the combination of TBP and ACTB provided high-quality data for further study. In contrast, the commonly used reference genes 28SrRNA, SDHA, GAPDH, and B2M should not be used for endogenous controls because of their unstable expression in this study. The reference genes that could be used in future gene expression studies in yaks were indentified.

Coordinate Transcriptomic and Metabolomic Effects of the Insulin Sensitizer Rosiglitazone on Fundamental Metabolic Pathways in Liver, Soleus Muscle, and Adipose Tissue in Diabetic db/db Mice

Rosiglitazone (RSG), developed for the treatment of type 2 diabetes mellitus, is known to have potent effects on carbohydrate and lipid metabolism leading to the improvement of insulin sensitivity in target tissues. To further assess the capacity of RSG to normalize gene expression in insulin-sensitive tissues, we compared groups of 18-day-treated db/db mice with increasing oral doses of RSG (10, 30, and 100 mg/kg/d) with untreated non-diabetic littermates (db/+). For this aim, transcriptional changes were measured in liver, inguinal adipose tissue (IAT) and soleus muscle using microarrays and real-time PCR. In parallel, targeted metabolomic assessment of lipids (triglycerides (TGs) and free fatty acids (FFAs)) in plasma and tissues was performed by UPLC-MS methods. Multivariate analyses revealed a relationship between the differential gene expressions in liver and liver trioleate content and between blood glucose levels and a combination of differentially expressed genes measured in liver, IAT, and muscle. In summary, we have integrated gene expression and targeted metabolomic data to present a comprehensive overview of RSG-induced changes in a diabetes mouse model and improved the molecular understanding of how RSG ameliorates diabetes through its effect on the major insulin-sensitive tissues.