JIN Chaofan, SONG Weihao, WANG Mengya, QI Jie, ZHANG Quanqi, 2), and HE Yan, *

Transcriptome-Wide Identification and Validation of Reference Genes in Black Rockfish ()

JIN Chaofan1), SONG Weihao1), WANG Mengya1), QI Jie1), ZHANG Quanqi1), 2), and HE Yan1), *

1),,,266003,2),,266237,

The quantitative real-time reverse transcription PCR (qRT-PCR) is a widely used technique to analyze gene expression levels. Selecting a suitable reference gene is a crucial step to obtain an accurate result in qRT-PCR. However, most previous studies on fishes adopted reference genes that were commonly used in mammals without validation. In this study, we utilized 89 transcriptome datasets covering early developmental stages and different adult tissues, and carried out transcriptome-wide identification and validation of reference genes in. Finally, 121 candidate reference genes were identified based on four criteria. Eight candidates (,,,,,,, and) and four commonly used reference genes in mammals (,,,) were selected for validation via qRT-PCR and four statistical analysis methods (delta-Ct, Best- Keeper, geNorm, and NormFinder). The results indicated that when the black rockfish are cultured in a general condition, the eight candidate reference genes are more stable than traditional reference genes in mammals, and,,andarethe best reference genes in rockfish. This is the first study to conduct transcriptome-wide identification and validation of reference genes for quantitative RT-PCR in the black rockfish, and lay an important foundation for gene expression analysis in teleost.

reference genes; black rockfish; transcriptome-wide; CV value; qRT-PCR

1 Introduction

The quantitative real-time reverse transcription PCR (qRT- PCR) is a popularized technique for quantification of re- lative mRNA levels due to the advantage of its low cost, specificity, sensitivity and large dynamic range (Huggett., 2005). In qRT-PCR, the expression levels of target genes are usually normalized to the endogenous controls, also called reference genes. The purpose of using ref- erence genes is to remove or reduce the variations in the quantity and quality of total RNA among samples, thus making it possible for accurate comparison of transcript abundance of a gene of interest among various samples (Dheda., 2004; Zhu., 2008). Selecting an ideal re- ference gene that showing constant expression across all samples is an essential step for normalization and quanti- tative analysis in qRT-PCR. Though a suitable reference gene is very important, many studies are using commonly used reference genes without validation in qRT-PCR as- says. Some commonly used reference genes were confirm- ed to be unsuitable in some conditions (Zhu., 2008). For example,, a widely used reference gene in human, has been reported to be an unsuitable reference ge-ne in some other species (Long., 2010; Ghani., 2013), and so are many other traditionally reference genes, such asand(Olsvik., 2005).

There have been many emerging studies to identify and validate reliable reference genes with various methods fol- lowing the development of biotechnology. For example, serial analysis of gene expression, microarray analysis, and expressed tag sequences have been widely used for ref- erence genes identification in human and other mammals (Velculescu., 1999;Zhu., 2008; Eisenberg and Levanon 2013); however, few similar studies have been conducted in other species. In recent years, RNA-seq has become a useful experimental tool to identify the genes with stable expression patterns. Many studies were carriedout utilizing RNA-seq for reference genes selection not only in model organisms, but also in many non-model or- ganisms, like kiwi, seaweed, and scallop (Gao., 2018;Liu., 2018; Li., 2019).

The black rockfish (, hereafter denoted as ‘rockfish’) is a good model of teleost to study the adap- tion to viviparity (He., 2019). Some studies have been carried out focusing on the genes related to development, growth, immunity, and reproduction in this group of fish(Ma., 2014;Kugapreethan., 2017; Kim and Cho,2019). Genes,, andare usually adopted as the endogenous control of qRT-PCR in rock- fish studies. A previous study of our lab compared the sta- bilities of several commonly used reference genes (,,,,,,, and) and defined,andas the most reliable three ones(Liman., 2013). However, some inconsistent results are still observed even we used themost stable reference gene in rockfish studies. The most stable reference gene usually is selected by the results in adult tissue, it is not confirmed whether it is still stable during early developmental stages. It is the best way to study as many samples as possible to select the reference genes. The availability of a chromosome-level genome and a large set of transcriptomes of rockfish (He., 2019) provide valuable resources to conduct the transcriptome- wide analysis of suitable reference genes selection.

In this study, we analyzed 89 transcriptome datasets, and 121 candidate reference genes were identified based on four criteria. The expression stabilities of eight candi- dates and four commonly used genes were validated by qRT-PCR and four statistical methods. The results reveal- ed that candidates we recommended are more stable than traditionally used reference genes. This will benefit fur- ther gene quantification analysis of the rockfish and other teleost species.

2 Materials and Methods

2.1 Sample Collection

All adult rockfish in this study were obtained from the fish hatchery of Qingdao Beibao Aquatic Co., Ltd., Shan- dong, China. They were transported to our laboratory and maintained in aerated seawater for acclimation. Then, they were sacrificed and the following tissues were dissected: heart, liver, spleen, kidney, brain, gill, intestine, testis and ovary. All samples were frozen in liquid nitrogen andstored at −80℃ for further RNA extraction. Each tissue was collected in triplicate or more.

Embryos at six different stages (1-cell, 32-cell, blastula, gastrula, somite and pre-hatching) were collected as des- cribed in our previous study (He., 2019). Then, they were stored at −80℃ for further analyses.

2.2 RNA-Seq Datasets

Totally 89 RNA-libraries, including 63 from different tis-sues and 26 from six developmental stages (1-cell, 32-cell, blastula, gastrula, somite and pre-hatching), were selected with three biological replicates or more.In each gene 100bp from each end was sequenced using BGI-500 platform. Then, the transcriptome data were processed and the raw counts were converted into TPM (Transcripts per million) values using the RNA-seq by Expectation Maximization software package (Li and Dewey, 2011).

2.3 Identification of Reference Genes Using Transcriptome Data

Modified methods as described byLi. (2019)from the method of Eisenberg and Levanon (2013) were used to select reference genes among all tissues and at different early developmental stages of rockfish. Briefly, four cri- teria utilized to screen reference genes were as follows: 1) expression could be detected in all tissues and develop- mental stages of embryo;2) low dispersion degree over alltissues and developmental stages by requiring log2(TPM)<1;3) no log2(TPM) differed from the mean log2(TPM) by two or more in each tissue and developmental stage regarding expression level;4)mean log2(TPM)>6, where 6 wasthe median of log2(TPM) values of all genes in all tissues and developmental stages. As described in Li.(2019), the stability of reference genes was further evalu- ated according to the CV value (stdev/mean). Based on the criteria mentioned above, mean log2(TPM)>6 and disper- sion degree log2(TPM)<1, the CV values of reference genes we identified are always less than 0.17.

2.4 RNA Extraction and cDNA Synthesis

Total RNA was extracted using Trizol reagent(Invitro- gen) according to the manufacturer’s protocol. After RNA purification, the integrity and concentration of RNA ex- tracted was assessed1.5% agarose gel electrophoresis and determined by a Nano photometer. The cDNA was synthesized with Reverse Transcriptase M-MLV kit (Ta- kara) and the final volume was 20mL. The cDNA pro- ducts were stored at −20℃ for further analyses.

2.5 Primer Design and qRT-PCR

In total, we selected ten candidate reference genes from transcriptome data, and four commonly used reference ge- nes for further qRT-PCR validation. Primers of these genes were designed by Primer 5.0 software. The criteria of pri- mer design were as follows: amplicon lengths of 80–200bp, annealing temperature of 60–63℃, primer lengths of 18–25bp, and GC content of 40%–60%. The information of primers is listed in Table 1. All primers amplification efficiencies were determined by the standard curve gen- erated from two-fold dilution series of cDNA concentra- tion (0.625–20ngmL−1).

qRT-PCR was conducted in a 384-well plate using a Light Cycler 480 RT-PCR system (Roche Molecular Bio- chemical, Mannheim, Germany). Each reaction contained 2mL cDNA(5ngmL−1), 10mL Light Cycler 480 SYBR Green I Master, 0.5mL of each primer and 7mL ddH2O. Three biological replicates were required for each sample, and three technical replicates were performed for each biological replicate. RT-PCR cycling conditions were 95℃ for 5min, followed by 45 cycles at 95℃for 15s, and 61℃for 45s. The specificity of each primer was determined by melting curve and gel electrophoresis.

2.6 qRT-PCR Data Analysis

The stabilities of reference genes determined in our study were evaluated by ReFinder (https://www.heartcure.com.au/reffinder/) (Liu., 2014), which integrates four commonly used statistical approaches: geNorm, NormFin- der, Bestkeeper, and comparative delt-Ct method (Vande- sompele., 2002; Andersen., 2004; Silver., 2006). Then the geometric mean was calculated to guarantee a comprehensive analysis (Chen., 2011).

Table 1 List of primers used for qRT-PCR analysis

3 Results

3.1 Selection of the Reference Genes from the Transcriptome Data of Rockfish

We identified rockfish candidate reference genes among 24094 transcripts from 89 RNA-seq datasets, which are available at CNSA (CNGB Nucleotide Sequence Archive) under the accession ID CNP0000222. Firstly, we obtained 8357 (34.7%), 439 (2%), 242 (1%) genes respectively, ac- cording to the first three criteria (Fig.1). Then, we figured out the median log2(TPM) value among genes that met the first three criteria was approximately six. Finally, the fourth criteria were applied to screen genes with an aver- age log2(TPM)>6, resulting in 121 candidate reference genes for all tissues and early developmental stages inrockfish (Fig.1). As shown in Table 2, the 15 most stable genes have mean log2(TPM) values ranging from 6.33 to 11.00, and the lowest CV values vary from 0.069 to 0.093.

Fig.1 The number of genes that met our fourth criteria. (I) TPM>0; (II) standard-deviation log2(TPM)<1; (III) no log2 (TPM) differed from the mean log2 (TPM) by two or more; (IV) mean log2 (TPM)>6.

Table 2 The top 15 candidate reference genes identified from the 89 transcriptome datasets of S. schlegeli

3.2 RNA-seq Analysis of Candidate and Commonly Used Reference Genes Expression Level

To validate the candidate reference genes we identified, eight genes were chosen from the top 15 candidates and four commonly used were selected to compare their ex- pression profiles. The eight candidates include,,,,,,and, the four commonly used reference genes include,,and(Table 3).

Consideringlog2(TPM) values, candidate reference geneshave lower variances than commonly used reference genes as shown in Fig.2. For example,is a widely used reference genes in mammals, while itslog2(TPM) values (ranging from 2 to 14) exhibit high variances inrockfish tissues and developmental stages. Additionally,andalso show relatively high variances. In contrast to these commonly used reference genes, candidates identi- fied from the transcriptome datasets have smaller vari- ances in log2(TPM) values among different tissues and de- velopmental stages. Our transcriptome datasets suggest the candidate reference genes identified are more stable than commonly used reference genes in rockfish tissues and early developmental stages.

Table 3 Detailed information on the 8 selected candidate reference genes and the 4 commonly used reference genes

Fig.2 Evaluation of the reference gene candidates and com- monly used reference genes based on RNA-seq analysis. A boxplot showing the log2 (TPM) values of the 8 candi- date reference genes and 4 commonly used reference ge- nes among tissues and developmental stages.

3.3 qRT-PCR Validation and Stability Analysis

To validate the results from our transcriptome datasets, we conducted the qRT-PCR of twelve genes and four me- thods (comparative delta-Ct, geNorm, Bestkeeper, and Norm- Finder) were used for further expression stability evalua- tion. We used the Pearson coefficient to determine the cor- relation between Ct values and log2(TPM) values, and there is a negative correlation between these two values (=−0.636,<0.01).

As shown in Fig.3, the Ct values of each gene in dif- ferent tissues and developmental stages were determined. Among commonly used reference genes,displaysthe highest variation of Ct values (17–32), and(Ct 21–29) also expresses variably in different tissues and developmental stages.andhave relative sta- ble Ct values. In contrast to commonly used genes, most candidates have smaller variances than commonly used re- ference genes. For example,(Ct 22–25),(25–27),(24–27) display very low expression vari- ations. According to the results of qRT-PCR, Ct values of most candidates are relatively more stable than commonly used reference genes, consisting with our transcriptome data.

Fig.3 Evaluation of the reference genes based on qRT- PCR analysis. A boxplot shows the Ct values of the 8 can- didate reference genes and 4 commonly used reference genes in different tissues and at different earlydevelopmental stages.

After having summarized the qRT-PCR results, it’s ne- cessary to compare the expression stability between the candidates and commonly used reference genes by the sta- tistical methods for determining the optimal reference ge- nes in further normalization. Here, we applied the four sta- tistical methods for stability analysis: delta-Ct, Best-Kee- per, geNorm and NormFinder (Fig.4).

Fig.4 Expression stability of the twelve genes based on qRT-PCR experiments. The stability was evaluated based on geNorm, NormFinder and comparative delta-Ct analyses of the qRT-PCR data.

According to the delta-Ct method,was identi- fied as the most stable reference gene with the highest ranking value.,,/were most sta- ble genes by the BestKeeper, NormFinder, geNorm me- thods respectively. In consistent with our observation of CV values and Ct Values,andshowed the lowest ranking values among twelve genes by four me- thods. Although the stability rankings of these genesare variable by different methods, the candidates selected fromtranscriptome data tend to have higher ranking values com- pared to the commonly used reference genes. The result of stability analysis with different statistical analysis me- thods further confirmed the reliability of candidates se- lected based on transcriptome data.

4 Discussion

The inappropriate reference genes for normalizing tran- scription levels can cause systematic errors in qRT-PCR analysis, which can cause wrong interpretation of results. Thus selecting optimal reference genes is very important in gene quantitative analysis under different experimental conditions. The same reference genes can have differentexpression stability in different species, especially between mammals and lower vertebrates like fish. For example,has been widely identified and used as a house- keeping gene in mammals (Sun., 2012; Niu., 2016), but it has a significant expression variation in At- lantic halibut and zebrafish (Tang., 2007; Fernandes., 2008). However, most reference genes for qRT- PCR in fishes are selected from commonly used reference genes in mammals, which can lead to unnecessary errors in gene quantitative analysis. With the wide application of omics in molecular biology, transcriptomic data have been becoming a reliable source for the selection of optimal re- ference genes for gene quantitative analysis (Yang., 2014; Fu., 2015;Gao., 2018).

In this study, we modified a method developed by Ei- senberg and Levanon (2013) to select suitable reference genes in rockfish. The reasonability and advantage of the method wereintroduced in detail in a previous study(Li., 2019). We aimed to screen reference genes with high stability among different tissues and early develop- mental stages, so we integrated the transcriptome data of different adult tissues and developmental stages. With four criteria, only 121 genes with CV values<1 were selected from all 24094 genes, suggesting the method we used is very stringent. Furthermore, we analyzed the transcriptomedata of tissues and early developmental stages respec- tively by four criteria. Interestingly, 375 and 752 genes were obtained respectively, suggesting a significant difference in gene expression patterns between tissues and develop- mental stages, which is similar to some other studies (Car- lyle., 1996; Jorgensen., 2006; Liu., 2014). After comparing two gene sets obtained from tissues and developmental stages, we obtained a core set of 122 genes,and most of them are overlapped with the candidate re- ference genes from 121 gene sets, confirming the strin- gency and reliability of the methods we applied.

Top 15 candidate reference genes with the lowest CV values are all annotated proteins, and most of them have been widely studied in model organisms. For example,has been identified as a reliable reference gene in some organisms (Mosley and HogenEsch, 2017;Wang., 2017;Zhao., 2018). From these 15 genes, 8 genes were se- lected for further study to detect the reliability of our me- thod.

The results of RNA-seq analysis of eight top candidates and four commonly used reference geneswere validated by qRT-PCR, and four statistical methods were used to evaluate their expression stabilities. The stability ranking values of most reference genes obtained from log2(TPM) values and Ct values are consistent in samples used for qRT-PCR and RNA-seq. Furthermore, candidate referencegenes showed higher stabilities than those commonly used reference genes.andare the least stable genes among twelve genes, which is consistent with a pre- vious study in rockfish,and they have been widely des- cribed to be unsuitable for gene quantitative analysis in some species (Jorgensen., 2006; De Jonge., 2007;Du., 2013).

Intriguingly,gets a medium ranking value by qRT- PCR, showing it is more stable than the other three com- monly used reference genes. However,in the process of screening reference genes based on four criteria,wasfiltered out during the second step for screening genes with low dispersion degree over all tissues and developmental stages (log2(TPM)<1) (The standard deviation value<1). The CV ofis 0.131. Considering these two results, we don’t thinkis a perfect reference gene in rock- fish, since it is not like the candidates we recommended,such as,,and, which showthe high stability ranking values evaluated both from RNA- seq and qRT-PCR. In non-model organisms, especially in teleosts, the reference genes used for normalization in qRT-PCR experiments usually are from mammals, like,and. Their unstable expressions un- der different conditions may cause misleading results du- ring normalization. The reference genes identified in this study can be widely applied in teleost studies. The results of this study are from the black rockfish cultured under general conditions. When the fish are cultured in an envi- ronment with different stresses, whether the expressions of these genes are still stable need to be studied carefully.

5 Conclusions

In this study, 89 transcriptome datasets of rockfish wereemployed to identify reference genes, and 121 candidate re- ference genes were selected from the transcriptomes based on four criteria. Combing RNA-seq and qRT-PCR assays, the expression stabilities of candidates we identified were higher than commonly used reference genes.,,andmight be the best reference genes in rockfish. Our studies provide valuable resources for further gene expression analysis in teleost.

Abbreviations

METAP2, Methionine aminopeptidase 2; BTF3L4, Tran-scription factor BTF3 homolog 4; EIF5A1, Eukaryotic tran- slation initiation factor 5A-1; TCTP, Translationally-con- trolled tumor protein; UBC, Ubiquitin C; PAIRB, Plasmi- nogen activator inhibitor 1 RNA-binding protein; RAB10, Ras-related protein Rab-10; DLD, Dihydrolipoamide de- hydrogenase; TUBA, α-Tubulin; RPL17, Ribosomal pro- tein L17; ACTB, β-actin; GAPDH, Glyceraldehyde-3- phosphate dehydrogenase; qRT-PCR, Quantitative reverse transcription PCR; TPM, Transcripts per million.

Acknowledgement

This study was supported by the National Key R&D Program of China (No. 2018YFD0900101).

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August 26, 2020;

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© Ocean University of China, Science Press and Springer-Verlag GmbH Germany 2021

* Correspondence author. E-mail: yanhe@ouc.edu.cn

(Edited by Qiu Yantao)