Ten flower samples were respectively collected from five male and five female E. ulmoides individual trees growing on the campus of Northwest A&F University, Yangling, China (34°16′56″N, 108°04′27″E) in April 2021 for quantitative Real-Time PCR (qRT-PCR) analysis. The collected male and female flowers was about to open. Because there are no sepal and petal in either male or female flowers (Wang and Zhang, 2017 (link); Wuyun et al., 2018 (link); Zhu, 2019 ), only stamens and pistils were isolated and immediately immersed into liquid nitrogen before stored at −80°C until use. Total RNA extraction and cDNA synthesization were performed using RNeasy Plant Mini Kit (74904, Qiagen, German) and SuperScript™ IV VILO™ Master Mix (Thermo Fisher, United States of) respectively following the manufacturer’s instructions.
qRT-PCR analysis was conducted according to the manuals of Hieff qRT-PCR SYBR Green Master Mix (YEASEN, Shanghai, China) on a LightCycler 480 II Real-Time PCR Platform (Roche, Germany). The reaction system (20 μL) consisted of 10.0 μL of qRT-PCR Mix, 1.0 μL cDNA, 0.4 μL forward primer, 0.4 μL reverse primer, and 8.2 μL of ddH2O. EuGAPDH gene was used as internal control for data normalization (Liu et al., 2018 (link)). 2−ΔΔCT method was applied to calculate the relative gene expression of 14 A/B/C/D/E-class EuMADS genes (Livak and Schmittgen, 2001 (link)). Primers used for EuMADS and EuGAPDH genes (Supplementary Table S6) were designed using the online program Primer3 (http://bioinfo.ut.ee/primer3-0.4.0/primer3/). Five biological replicates and two technical replicates were carried on for each gene. Differences of gene expression level between male and female flowers were analyzed via ANOVA followed by Student’s t-test in SPSS software (v.24, IBM).
The identified 14 A/B/C/D/E-class EuMADS genes were also functionally annotated by the online BLAST in NCBI database (https://blast.ncbi.nlm.nih.gov/Blast.cgi). The most similar genes in model plants (Arabidopsis, snapdragon/petunia) of flower development studies (Theißen et al., 2016 (link); Irish, 2017 (link); Ruelens et al., 2017 (link)) were screened as orthologous genes to predict gene function.
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