Comprehensive transcriptome analysis of reference genes for fruit development of Euscaphis konishii

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.

Genes with expression levels lower than 5 FPKM in tanscriptomes would make poor qPCR 118 references due to the difficulties in detecting and quantifying their expression. (Kimmy AS,119 Patrick PE& Joshua RP, 2017). After their removal, a total of 75,048 genes in E. konishii were 120 evaluated. We calculated the mean expression value, standard deviation, and coefficients of 121 variation based on the raw RNA-seq data. Base on the requirements CV ≤ 0.2 and FC ≤ 0.2, 122 1,131 genes (Table S1) were obtained by removing overabundant genes with low expression 123 levels. Finally, we selected the eight candidate genes according to genome annotation of each, 124 which was assigned base on the best mach of the alignments using Blast to NR, TrEMBL and 125 KEGG databases. The selecting steps of candidate reference genes were shown in Fig S1. In 126 order to ensure the accuracy of the reference gene predictions, the coding sequences of eight 127 selected genes was used as queries for BLAST orderly through the TAIR database 128 (http://www.arabidopsis.org/). The sequences with the highest homology with Arabidopsis were 129 shown in Table 1.

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Here we obtained the nucleotide sequences of reference genes from our laboratory. The 131 nucleotide sequences of candidatate reference genes were appended in Table S2. According to 132 the nucleotide sequence of candidate reference genes and the design principle of qRT-PCR, 133 forward and reverse primers of all candidate reference genes were designed using Primer 134 Premier 6.0 software and were synthesized by Jinweizhi Biotechnology Co., LTD (Suzhou, 135 China) and purified by PAGE. The primer sequences, amplicon size, anneal temperature were 136 shown in Table 2.    The instrument calculated the Cq value of each sample based on the qRT-PCR experiment. 159 We can analyse the stability of candidate reference genes using GeNorm, NormFinder, and 160 BestKeeper software. Finally, ReFinder was used to calculate the comprehensive ranking of the 161 stability of candidate reference genes based on analysis results.

Validation of the candidate reference genes 163
Anthocyanin is the key factor affecting fruit colouration as an important plant pigment. In 164 general, fruit colouration is closely related to the content and proportion of anthocyanin. To 165 verify the results of our experiments, the expressions of nine genes related to the anthocyanin 166 synthesis pathway were calculated with the reference genes selected (Fig. S5). Moreover, the 167 EkSS3 gene related to glycometabolism pathway was also used to validate the reliability of 168 reference genes. The details of primer were shown in Table 3. The qRT-PCR experimental 169 method was the same as described above, and the relative expression level was calculated by the 170 2 −ΔΔct method. Experimental data from three biological replicates were analysed using analysis of 171 variance (ANOVA), followed by Student's t-test (P<0.05). According to selecting process (Fig. S1), eight candidate reference genes were chosen to 184 perform the gene normalization studies. The sequences of these candidate reference gene were 185 used to design the qRT-PCR primers. The details of eight reference genes were lised in Table 1. 186 The gene models of eight candidate reference refered to the homologous genes of Arabidopsis, 187 and the details were shown in Figure S2.
188 Candidate reference genes for RT-PCR amplification 189 PCR results of the 8 candidate internal reference genes are shown in Figure S4 with a single 190 band, no primer dimer and nonspecific amplification, which could be used for subsequent qRT-191 PCR analysis. We sequenced the PCR amplification products of eight genes and blast the 192 correctness of the PCR amplified fragments (Table S3).  As shown in Figure S3, the melting curve of the 8 candidate reference genes at different 200 developmental stages only had a single main peak, and the amplification curves between the 201 repeated samples had high repeatability.   Manuscript to be reviewed 238 EkUBC23 was used to normalize the expression due to its greater stability. Relative expression 239 levels were normalized using the low expression reference gene (EkUBC23) and high expression 240 reference gene (EkGAPDH2). The results showed that there were some differences between the 241 expression levels of 9 genes related to the anthocyanin synthesis pathway and transcriptome 242 sequencing (Figure 4). When EkUBC23 was used as the reference gene, the results had no 243 significant difference. When EkGAPDH2 was used for normalizing low expression target genes 244 [c57877.graph_c0( DFR), c59825.graph_c0(CHS) and c69862.graph_c1(UFGT)], the results 245 were different from the expression of transcriptome. However, EkGAPDH2 has better accuracy 246 than EkUBC23 when it was used for normalizing high expression target genes 247 [c72659.graph_c0(CHS) and c72737.graph_c0(CHI)]. Therefore, we suggest that the stability 248 and expression of reference genes should be considered as important selection conditions. 249 To ensure the reliabilty of result, We chosen EkSS3 gene related to glycometabolism pathway 250 as target gene. Relative expression levels were normalized using two most stable reference genes 251 (EkGAPDH2 and EkUBC23). As shown in Figure S6, when normalized using EkGAPDH2 and 252 EkUBC23 as reference gene respectively, the results had no significant difference. Compared 253 with the green stage, EkSS3 expression was clearly downregulated at turning stage and red stage.  Many studies have shown that it was quicker and more efficient to screen appropriate 259 reference genes based on transcriptome data. In addition, primers designed according to the 260 transcriptome sequencing of the material itself are more stable than those using other materials. 261 Selection of the reference gene based on transcriptome data has been done in many plant species, 262 such as Corylus (Yang D et al., 2017), Sedum alfredii (Sang J et al., 2013) and Dendrocalamus 263 sinicus [Guo XJ et al., 2018], and so on. In the present study, we selected 3 stable reference 264 genes (EkUBC23, EkCYP38 and EkGAPDH2) based on transcriptome data of E. konishii 265 pericarp in three different developmental stages. This result validates and complements the 266 results of reference gene screening by Liang. (Liang WX et al., 2018b).

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The expression of reference genes was related to organ type, developmental stages and 268 external environmental conditions. ACT was commonly used as a reference gene. For example, it