Comprehensive transcriptome analysis of reference genes for fruit development ofEuscaphis konishii

Abstract

Background. Quantitativereal-time reverse transcriptase polymerase chain reaction is the common method to quantify relative gene expression. Normalizating using reliable genes is critical in correctly interpreting expression data from qRT-PCR. Euscaphis konishii is a medicinal plant with a long history in China, which has various chemical compounds in fruit. However, there is no report describing the selection of reference genes in fruit development of Euscaphis konishii. Methods. We selected eight candidate reference genes based on RNA-seq database analysis, and ranked expression stability using statistical algorithms GeNorm, NormFinder, BestKeeper and ReFinder. Finally, The nine genes related to the anthocyanin synthesis pathway of Euscaphis konishii were used to verify the suitability of reference gene. Results. The results showed that the stability of EkUBC23, EkCYP38 and EkGAPDH2 was better, and the low expression reference genes (EkUBC23 and EkCYP38) were favourable for quantifying low expression target genes, while the high expression reference gene (EkGAPDH2) was beneficial for quantifying high expression genes. In this study, we present the suitable reference genes for fruit development of Euscaphis konishii based on transcriptome data, our study will contribute to further studies in molecular biology and gene function on Euscaphis konishii and other closely related species. S-Q. 2020. Comprehensive transcriptome analysis of reference genes for fruit development of Euscaphis konishii. PeerJ 8:e8474 http://doi.. Coker JS, Davies E. 2004. Selection of candidate housekeeping controls in tomato plants using EST data. Biotechniques 35(4):740-749. Czechowski T, Stitt M, Altmann T, Udvardi MK, Scheible WR. 2005. Genome-wide identification and testing of superior reference genes for transcript normalization in Arabidopsis. Plant Physiology 139(1):5-17 DOI 10.1104/pp.105.063743. Derveaux S, Vandesompele J, Hellemans J. 2010. How to do successful gene expression analysis using real-time PCR. Methods 50(4):Regev A. 2011. Full length transcriptome assembly from RNA seq data without a reference genome. Nature Biotechnology Italic 29:644-652 DOI 10.1038/nbt.1883. Guo XJ, Chen LN, Yang HQ. 2018. Reference gene selection for quantitative real-time PCR in studying culm shape development of Dendrocalamus sinicus. Forest Research 31(02):120-125 SQ. 2019. Protective effect of the total triterpenes of Euscaphis konishii Hayata pericarp on bacillus Calmette-Guérin plus lipopolysaccharide-induced liver injury. LK. 2014. Reference gene selection for real-time quantitative PCR normalization in switchgrass (Panicum virgatum L.) root tissue. Journal of Agricultural Biotechnology 22(1):55-63. Kimmy AS, Patrick PE, Joshua RP. 2017. A whole-transcriptome approach to evaluating reference genes for quantitative gene expression studies: a case study in mimulus. G3 (Bethesda) 7(4):

Genome-Wide Identification and Co-Expression Analysis of ARF and IAA Family Genes in Euscaphis konishii: Potential Regulators of Triterpenoids and Anthocyanin Biosynthesis

“…We then used publicly available RNA-seq datasets to analyze the expression patterns of EkIAA and EkARF genes at four fruit developmental stages, including green, turning red fruit, and red-winged pericarp stages, as well as in branches and leaves ( Yang et al, 2020 ); the results are summarized as heatmaps in Figure 6 . EkIAA gene family members showed varying expression patterns.…”

Determination of reliable reference genes for gene expression studies in Chinese chive (Allium tuberosum) based on the transcriptome profiling

“…Therefore, according to the FPKM values, researchers can examine the expression stability using the mean expression value, standard deviation, coefficient of variation (CV), and normalized median absolute deviation (NMAD). The CV reflects the extent of variability in relation to the mean of the expression levels, and it was successfully used to identify the best RGs from the transcriptome dataset 33 – 35 . However, the CV is simply calculated based on the means, which is less susceptible to the deviations caused by the outliers.…”