Validation of Housekeeping Genes as Reference for Reverse-Transcription-qPCR Analysis in Busulfan-Injured Microvascular Endothelial Cells.
Wen JuAlhaji Osman SmithTiantian SunPingping ZhaoYan JiangLu LiuTing ZhangKunming QiJianlin QiaoKailin XuLingyu ZengPublished in: BioMed research international (2018)
Endothelial cells (ECs) could express some important cytokines and signal molecules which play a key role in normal hematopoiesis and repopulation. Busulfan-induced vascular endothelial injury is an important feature after hematopoietic stem cell transplantation (HSCT). But the molecular mechanism of how the injured ECs affect hematopoietic reconstruction is still unknown. It is possibly through modulation of the change of some gene expression. RT-qPCR is one of the most popular methods used to accurately determine gene expression levels, based on stable reference gene (RG) selection from housekeeping genes. So our aim is to select stable RGs for more accurate measures of mRNA levels during Busulfan-induced vascular endothelial injury. In this study, 14 RGs were selected to investigate their expression stability in ECs during 72 hours of EC injury treated with Busulfan. Our results revealed extreme variation in RG stability compared by five statistical algorithms. ywhaz and alas1 were recognized as the two idlest RGs on account of the final ranking, while the two most usually used RGs (gapdh and actb) were not the most stable RGs. Next, these data were verified by testing signalling pathway genes ctnnb1, robo4, and notch1 based on the above four genes ywha, alas1, gapdh, and actb. It shows that the normalization of mRNA expression data using unstable RGs greatly affects gene fold change, which means the reliability of the biological conclusions is questionable. Based on the best RGs used, we also found that robo4 is significantly overexpressed in Busulfan-impaired ECs. In conclusion, our data reaffirms the importance of RGs selection for the valid analysis of gene expression in Busulfan-impaired ECs. And it also provides very useful guidance and basis for more accurate differential expression gene screening and future expanding biomolecule study of different drugs such as cyclophosphamide and fludarabine-injured ECs.
Keyphrases
- gene expression
- endothelial cells
- genome wide
- genome wide identification
- high glucose
- dna methylation
- allogeneic hematopoietic stem cell transplantation
- genome wide analysis
- transcription factor
- machine learning
- electronic health record
- copy number
- big data
- diabetic rats
- poor prognosis
- drug induced
- acute myeloid leukemia
- low dose
- deep learning
- cell proliferation
- climate change
- binding protein
- vascular endothelial growth factor
- high dose
- data analysis
- acute lymphoblastic leukemia
- single cell
- oxidative stress
- artificial intelligence
- hematopoietic stem cell