Selection and Validation of Reference Genes for Quantitative RT-PCR Analysis in Fisch. L

Overview

Journal Plants (Basel)

Date 2021 Jan 20

PMID 33467497

Citations 4

Authors

Sihao Hou

Tiantian Zhao

Dan Yang

Qing Li

Lisong Liang

Guixi Wang

QingHua Ma

Affiliations

Soon will be listed here.

Abstract

(1) Background: the species of have sporophytic type of self-incompatibility. Several genes related to recognition reaction between pollen and stigma have been identified in hazelnuts. To better understand the self-incompatibility (SI) response, we screened the suitable reference genes by using quantitative real-time reverse transcription PCR (qRT-PCR) analysis in hazelnut for the first time. (2) Methods: the major cultivar "" was used as material. A total of 12 candidate genes were identified and their expression profiles were compared among different tissues and in response to various treatments (different times after self- and cross-pollination) by RT-qPCR. The expression stability of these 12 candidate reference genes was evaluated using geNorm, NormFinder, BestKeeper, Delta Ct, and RefFinder programs. (3) Results: the comprehensive ranking of RefFinder indicated that , and were the most suitable reference genes. According to the stability analysis of 12 candidate reference genes for each sample group based on four software packages, and were most stable in different times after self-pollination and 4 h after self- and cross-pollination, respectively. To further validate the suitability of the reference genes identified in this study, , which the expression profiles in have been reported, was quantified by using and as reference genes. (4) Conclusions: our study of reference genes selection in hazelnut shows that the two reference genes, and , are suitable for the evaluation of gene expression, and can be used for the analysis of pollen-pistil interaction in . The results supply a reliable foundation for accurate gene quantifications in species, which will facilitate the studies related to the reproductive biology in

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