Identification and validation of reference genes for RT-qPCR analysis in fetal rat pancreas

Dai, Yongguo; Kou, Hongchao; Xia-zhen, Guo; Gong, Zheng; Liu, Heze; Liu, Yi; Wang, Hui; Guo, Yu

doi:10.1016/j.reprotox.2021.09.009

Cited by 6 publications

(3 citation statements)

References 41 publications

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“…Two highly connected subgraphs are obtained as shown in Figure 3, which include the following 14 genes: ITG1, HSPA5, ACTB, ATP5F1B, CCT5, GAPDH, ARPC1A, GNA12, ARFGAP1, INS, CDC42, BSG, ARFGAP3, and ARF1. Among these 14 genes, it has recently been reported that ARFGAP1 is a predictive factor for pancreatic cancer [38], and GAPDH has been used as a reference gene in fetal rat pancreas analysis [39]. In Figure S2.4 in Supplementary Additional File 2, only the androgen receptor (AR) transcription factor was reported by Metascape.…”

Section: Resultsmentioning

confidence: 99%

A new machine learning based computational framework identifies therapeutic targets and unveils influential genes in pancreatic islet cells

Turki

Taguchi

2022

Preprint

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Pancreatic islets comprise a group of cells that produce hormones regulating blood glucose levels. Particularly, the alpha and beta islet cells produce glucagon and insulin to stabilize blood glucose. When beta islet cells are dysfunctional, insulin is not secreted, inducing a glucose metabolic disorder. Identifying effective therapeutic targets against the disease is a complicated task and is yet not conclusive. To close the wide gap between understanding the molecular mechanism of pancreatic islet cells and providing effective therapeutic targets, we present a computational framework to identify potential therapeutic targets against pancreatic disorders. First, we downloaded three transcriptome expression profiling datasets pertaining to pancreatic islet cells (GSE87375, GSE79457, GSE110154) from the Gene Expression Omnibus database. For each dataset, we extracted expression profiles for two cell types. We then provided the extracted expression profile along with the cell types to our proposed constrained optimization problem of a support vector machine and to other existing methods, selecting important genes from the expression profiles. Finally, we performed (1) an evaluation from a classification perspective showing the superiority of our methods against the baseline; and (2) an enrichment analysis showing that our methods achieved better outcomes. Results for the three datasets included 44 unique genes and 10 unique transcription factors (SP1, HDAC1, EGR1, E2F1, AR, STAT6, RELA, SP3, NFKB1, and ESR1) that have been reported related to pancreatic islet functions, diseases, and therapeutic targets.

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Section: Resultsmentioning

confidence: 99%

A new machine learning based computational framework identifies therapeutic targets and unveils influential genes in pancreatic islet cells

Turki

Taguchi

2022

Preprint

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“…Moreover, Dai et al ( 27 ) found that YWHAZ ranked high in the search for reference genes of fetal mice, which is suitable to be used in the study together with other reference genes. YWHAZ is considered the most stable reference gene in sheep leukocytes ( 28 ) and hypothalamic-pituitary-gonadal axis tissues of sows ( 29 ), which can serve for accurate and repeatable qPCR data analysis.…”

Section: Discussionmentioning

confidence: 99%

Identification of stable reference genes for quantitative gene expression analysis in the duodenum of meat-type ducks

et al. 2023

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Quantitative polymerase chain reaction (qPCR) is an important method to detect gene expression at the molecular level. The selection of appropriate housekeeping genes is the key to accurately calculating the expression level of target genes and conducting gene function studies. In this study, the expression of eight candidate reference genes, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), beta-actin (β-actin), 18S ribosomal RNA (18S rRNA), hydroxymethylbilane synthase (HMBS), hypoxanthine phosphoribosyltransferase 1 (HPRT1), TATA box binding protein (TBP), ribosomal protein L13 (RPL13), and tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein (YWHAZ), in the duodenal epithelial tissue of 42-day-old meat-type ducks were detected using qPCR. Furthermore, their expression stability was analyzed using the geNorm, NormFinder, and BestKeeper programs. The results indicated that HMBS and YWHAZ were the most stably expressed genes. All three programs indicated that the expression of 18S rRNA was the least stable, making it unsuitable for the study of gene expression in meat-type duck tissues. This study provides stable reference genes for gene expression analysis and contributes to further studies on the gene function of meat-type ducks.

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“…We used GAPDH as a housekeeping gene because it is useful when the study aims to compare gene expression between tissues because its expression is abundant and less variable among tissues 46 . GAPDH has relatively stable expression in the liver 47 , kidney 48 , pancreas 49 , heart 50 , brain 51 , adipose tissue 47 , and intestine 52 in rodents. In addition, its expression remains relatively stable in the tissues of rodents with T2D 53 .…”

Section: Assessment Of Sialin Mrna Expressionmentioning

confidence: 99%

Altered sialin mRNA gene expression in type 2 diabetic male Wistar rats: implications for nitric oxide deficiency

Yousefzadeh

Jeddi

Zarkesh

et al. 2023

Sci Rep

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Nitrate therapy has been suggested to boost nitric oxide (NO) levels in type 2 diabetes (T2D); however, little is known about nitrate transport across the membranes. This study aimed to assess changes in the mRNA expression of sialin, as a nitrate transporter, in the main tissues of rats with T2D. Rats were divided into two groups (n = 6/group): Control and T2D. A high-fat diet combined with a low dose of streptozotocin (STZ, 30 mg/kg) was used to induce T2D. At month 6, samples from the main tissues of rats were used to measure the mRNA expression of sialin and levels of NO metabolites. Rats with T2D had lower nitrate levels in the soleus muscle (66%), lung (48%), kidney (43%), aorta (30%), adrenal gland (58%), epididymal adipose tissue (eAT) (61%), and heart (37%) and had lower nitrite levels in the pancreas (47%), kidney (42%), aorta (33%), liver (28%), eAT (34%), and heart (32%). The order of sialin gene expression in control rats was: soleus muscle > kidney > pancreas > lung > liver > adrenal gland > brain > eAT > intestine > stomach > aorta > heart. Compared to controls, rats with T2D had higher sialin mRNA expressions in the stomach (2.1), eAT (2.0), adrenal gland (1.7), liver (8.9), and soleus muscle (3.4), and lower sialin expression in the intestine (0.56), pancreas (0.42), and kidney (0.44), all P values < 0.05. These findings indicate altered sialin mRNA expression in the main tissues of male T2D rats and may have implications for future NO-based treatment of T2D.

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Identification and validation of reference genes for RT-qPCR analysis in fetal rat pancreas

Cited by 6 publications

References 41 publications

A new machine learning based computational framework identifies therapeutic targets and unveils influential genes in pancreatic islet cells

A new machine learning based computational framework identifies therapeutic targets and unveils influential genes in pancreatic islet cells

Identification of stable reference genes for quantitative gene expression analysis in the duodenum of meat-type ducks

Altered sialin mRNA gene expression in type 2 diabetic male Wistar rats: implications for nitric oxide deficiency

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