Skip to main content
Download PDF

Identification and validation of the reference genes in the echiuran worm Urechis unicinctus based on transcriptome data

BMC Genomics volume 24, Article number: 248 (2023) Cite this article

Abstract

Background

Real-time quantitative PCR (RT-qPCR) is a crucial and widely used method for gene expression analysis. Selecting suitable reference genes is extremely important for the accuracy of RT-qPCR results. Commonly used reference genes are not always stable in various organisms or under different environmental conditions. With the increasing application of high-throughput sequencing, transcriptome analysis has become an effective method for identifying novel stable reference genes.

Results

In this study, we identified candidate reference genes based on transcriptome data covering embryos and larvae of early development, normal adult tissues, and the hindgut under sulfide stress using the coefficient of variation (CV) method in the echiuran Urechis unicinctus, resulting in 6834 (15.82%), 7110 (16.85%) and 13880 (35.87%) candidate reference genes, respectively. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses revealed that the candidate reference genes were significantly enriched in cellular metabolic process, protein metabolic process and ribosome in early development and normal adult tissues as well as in cellular localization and endocytosis in the hindgut under sulfide stress. Subsequently, ten genes including five new candidate reference genes and five commonly used reference genes, were validated by RT-qPCR. The expression stability of the ten genes was analyzed using four methods (geNorm, NormFinder, BestKeeper, and ∆Ct). The comprehensive results indicated that the new candidate reference genes were more stable than most commonly used reference genes. The commonly used ACTB was the most unstable gene. The candidate reference genes STX12, EHMT1, and LYAG were the most stable genes in early development, normal adult tissues, and hindgut under sulfide stress, respectively. The log2(TPM) of the transcriptome data was significantly negatively correlated with the Ct values of RT-qPCR (Ct =  − 0.5405 log2(TPM) + 34.51), which made it possible to estimate the Ct value before RT-qPCR using transcriptome data.

Conclusion

Our study is the first to select reference genes for RT-qPCR from transcriptome data in Echiura and provides important information for future gene expression studies in U. unicinctus.

Peer Review reports

Background

Real-time quantitative PCR (RT-qPCR) is the most widely used technique for relative gene quantification because of its good repeatability, high sensitivity, strong specificity, high throughput, simplicity, speed, and low cost [1,2,3]. However, the biological variability of initial materials and the technical factors involved in sample preparation, such as the quantity of cDNA, RNA extraction, RNA integrity, and storage conditions will inevitably affect the accuracy of RT-qPCR [4,5,6]. Therefore, normalization is necessary to correct for variations in template quantity. Reference genes are used for the normalization of gene expression because of the stability of expression levels among different tissues, different developmental stages, or under various treatments [5].

In general, constitutively expressed housekeeping genes are used as reference genes, such as actin, elongation factor, glyceraldehyde-3-phosphate dehydrogenase, ribosomal RNA, translation initiation factor, tubulin, and ubiquitin [7,8,9]. However, many studies have shown that some of these genes are not always stable and their expression levels vary greatly under specific experimental conditions [6, 10, 11]. This is especially true for non-model organisms, which currently lag behind well-characterized model organisms in terms of genomic resources and empirically tested reference genes [4, 6, 10, 11]. Moreover, recent studies have shown that it is impossible to totally normalize gene expression data from all sample types using a single gene [6, 10]. Therefore, two or more reference genes are desirable to improve the reliability and accuracy of the RT-qPCR results. With the increasing application of high-throughput sequencing, RNA-seq has provided a new strategy for identifying new highly stable reference genes from transcriptome data. Heretofore, identification of many novel reference genes has been performed based on transcriptome data in various organisms [10,11,12,13,14,15,16,17,18,19,20,21,22].

The Echiura worm Urechis unicinctus, a typical benthic species living in intertidal sediments, is widely distributed in Russia, Korean Peninsula, Japan and China [23]. U. unicinctus possesses high economic value because of great edible value and potential medical value [23]. U. unicinctus is also mostly used to study gametogenesis [24], development [25,26,27,28,29,30,31], evolution [32, 33], and sulfide metabolism [34] because of its characteristics such as a large number of eggs laid, high fertilization rate, biphasic life cycle, and high sulfide tolerance ability. Recently, the use of U. unicinctus in evo-devo studies has generated many breakthrough [32, 33]. Evolutionary transcriptome analysis of the trochophores of U. unicinctus and other metazoan animals reveal an adult-first evolutionary scenario with a single metazoan larval intercalation [32]. Hox-mediated body plan diversification is an important developmental process [35]. In U. unicinctus, the expression of Hox genes exhibits a subcluster-based whole-cluster spatio-temporal collinearity pattern, suggesting that Hox subcluster play an important role in spatio-temporal collinearity pattern in invertebrates [33]. On the other hand, as a species living in the intertidal zone, U. unicinctus can tolerate, metabolize and utilize environmental sulfide and is considered a model species for sulfide adaptation [34, 36,37,38,39,40,41,42,43,44,45].

At present, gene expression analysis by RT-qPCR has been widely performed in U. unicinctus, using commonly used reference genes, such as ATPase [29,30,31, 33, 46] and β-actin [34, 36, 40, 41, 43, 47,48,49,50,51]. Previous studies have identified some reference genes, but have generally focused on some traditionally used genes, such as EF-1-α, TBP, TUB, eIF3, and ATPase [52, 53]. Suitable reference genes are crucial for verifying the expression profiles of related genes for future studies in U. unicinctus. RNA-Seq, which can provide a large amount of gene transcription information, is a better method for reference gene screening [10,11,12,13,14,15,16,17,18,19,20,21,22]. In addition to classical housekeeping genes, transcriptome data analysis provides an opportunity to identify novel and more stable reference genes. In recent years, a large amount of transcriptome data of U. unicinctus has been published [32, 45, 46, 54], which provides a new strategy for selecting housekeeping genes or reference genes in U. unicinctus.

In this study, we systematically screened reference genes by analyzing transcriptome data including early development, normal adult tissues, and the hindgut under sulfide stress in U. unicinctus. Candidate reference genes were selected from the three datasets. Moreover, the correlation between the Ct of RT-qPCR and transcripts per million (TPM) of the transcriptome data was investigated. Our findings identified novel stable reference genes from transcriptome data and contributed to the accurate quantification of gene expression in U. unicinctus.

Results and discussion

Identification of the candidate reference genes from transcriptome data

In this study, we systematically screened reference genes based on transcriptome datasets from early developmental embryos and larvae, normal adult tissues, and the hindgut under sulfide stress using the coefficient of variation (CV) method in U. unicinctus. The CV method is simple to use for candidate reference gene selection. Moreover, compared with other methods such as the fold change method, the CV method can quantify expression variability in a way in which genes can be ranked and directly compared, which has previously been used to identify novel reference genes from transcriptome data in plant species such as the monkeyflower genera Mimulus luteus, Polygonum cuspidatum, apple, and Lycium barbarum L [12, 16, 21, 55] and animals such as Mizuhopecten yessoensis, and silkworm Bombyx mori [17, 18]. Although both reads/fragments per kilobase per million (RPKM or FPKM) and TPM can be used to measure gene expression levels, RPKM and FPKM may not be applicable to the comparison of gene expression levels because of the differences sequencing depth between samples. TPM is more suitable for comparison of expression levels among samples [11, 56]. First, we excluded genes with low expression levels for easy detection in RT-qPCR assays and adopted a minimum mean log2(TPM) cut-off of 5 as a criterion for gene expression levels. Second, to ensure that the reference genes had low variance, a standard deviation (SD) log2(TPM) value of less than 1 was required. So a 0.2 CV cut-off was applied to further identify reference genes, which has been recommended in previous studies [11]. Based on these criteria, we identified 6834 (15.82%), 7110 (16.85%) and 13880 (35.87%) candidate reference genes from 43209 genes of early developmental embryos and larvae, 42191 genes of different normal adult tissues, and 38690 genes of the hindgut under sulfide stress. The number of candidate reference genes for early development was lowest. This result was expected because gene expression levels can change dramatically in a short time during early development [11]. Further, the expression levels of the candidate reference genes were analyzed. The results indicated that the median log2(TPM) values of the candidate reference genes were 14.162 in the early developmental stages, 15.389 in normal adult tissues and 16.317 in hindgut under sulfide stress (Fig. 1A). The ten most stable genes with the lowest CV values in early developmental embryos and larvae, different normal adult tissues, and hindgut under sulfide stress are listed in Table 1. The mean log2(TPM) and CV values of the ten most stable genes in the early development stages ranged from 14.317 to 14.329 and 0.0157 to 0.0203, respectively. All genes were annotated, of which six encoded proteins (FXRD1, IF2M, STX12, PTCD3, TCF25, and TFG) related to gene transcription, translation, protein transport and assembly, and three encoded proteins (CAPR1, NSMA, PDCD6, and HBS1L) related to cell growth, proliferation and apoptosis. As for the ten most stable genes in normal adult tissues, their mean log2(TPM) and CV values ranged from 15.524 to 15.532 and 0.0093 to 0.0138, respectively. Nine genes were annotated, of which six were encoded proteins (UBE4A, OGT1, GGA3, EHMT1, TRA2B, and PRP39) related to protein modification, transport and mRNA splicing. The ten most stable genes in the hindgut under sulfide stress showed mean log2(TPM) values ranged from 16.3457 to 16.3463 and CV values ranging from 0.0018 to 0.0032. Nine genes were annotated, of which three (CNOT1, RBM26, and HTR5B) encoded proteins related to post-transcriptional regulation.

Fig. 1
figure 1

The selected candidate reference genes in early development, normal adult tissues, and the hindgut under sulfide stress. A Boxplot exhibiting the log2TPM values of candidate reference genes that met the criteria. B Venn diagram showing the relationships between candidate reference genes under three conditions. Pink, early development; blue, normal adult tissues; green, hindgut under sulfide stress

Full size image
Table 1 The information of the top 10 candidate reference genes in the early development, normal adult tissues and hindgut under sulfide stress of the U. unicinctus
Full size table

Functional enrichment analysis of candidate reference genes

To further analyze the relationships of candidate reference genes in early development, normal adult tissues, and the hindgut under sulfide stress, we compared the three candidate reference gene datasets. As shown in Fig. 1B, 4079 genes were shared in three candidate reference gene datasets, 4184 genes were shared in early development and normal adult tissues, 5790 genes were shared in early development and hindgut under sulfide stress, and 6615 genes were shared in normal adult tissues and hindgut under sulfide stress. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of the three candidate reference gene datasets were then performed (Table 2). In early development, GO enrichment analysis showed that the candidate reference genes were mainly enriched in biological process (BP) terms associated with cellular protein metabolic process and macromolecule metabolic process, and in molecular function (MF) terms related to binding. These genes were also enriched in cellular component (CC) terms associated with intracellular and cell. KEGG pathway enrichment analysis indicated ribosome was the most significant pathway, followed by proteasome. In different adult tissues, GO enrichment analysis showed that the candidate reference genes were mainly enriched in biological process (BP) terms associated with cellular protein metabolic process and intracellular transport, and in molecular function (MF) terms related to binding. These genes were also enriched in cellular component (CC) terms associated with intracellular and cell. KEGG pathway enrichment analysis indicated ribosome was the most significant pathway, followed by oxidative phosphorylation. In the hindgut under sulfide stress, GO enrichment analysis showed that the candidate reference genes were mainly enriched in biological process (BP) terms associated with protein phosphorylation and in molecular function (MF) terms related to protein binding. These genes were also enriched in cellular component (CC) terms associated with intracellular, cell and organelle. KEGG pathway enrichment analysis revealed diabetic cardiomyopathy was the most significant pathway, followed by the oxidative phosphorylation. In summary, the results of GO and KEGG enrichment analysis in the hindgut under sulfide stress were different from those in early development and different normal adult tissues, with the proportion of specific candidate reference genes belonging to the hindgut under sulfide stress being the largest, suggesting that the hindgut under sulfide stress, early developmental embryos and larvae, and different adult tissues may focus on diverse biological processes or pathways. Therefore, we need to screen for optimal reference genes in early development, different normal adult tissues, and the hindgut under sulfide stress.

Table 2 GO and KEGG pathways enriched in the candidate reference genes
Full size table

Validation of candidate and commonly used reference genes expression stability by RT-qPCR assay

Five candidates and five commonly used reference genes were selected for validation and comparison of the expression stability in this study (Additional file 1: Table S1). Five of the top ten candidate reference genes were chosen for RT-qPCR in each case, as shown in Table 1. The five candidate reference genes were FXRD1, CAPR1, NSMA, IF2M, and STX12 in early development; UBE4A, OGT1, EHMT1, GGA3, and TRA2B in normal adult tissues and CNOT1, CLH1, EXOC6, LYAG, and TRFM in the hindgut under sulfide stress. The five commonly used reference genes were ATPase B, TBP, eIF3, ACTB, and GAPDH (Table 3).

Table 3 Detailed information on the five commonly used reference genes in the early development, normal adult tissues, and the hindgut under sulfide stress of the U. unicinctus
Full size table

Boxplots were constructed to present the expression levels of five candidate reference genes and five commonly used reference genes under all three conditions (Fig. 2). As shown in Fig. 2, Table 1, and Table 3, the novel candidate reference genes possessed higher stability than the commonly used reference genes in all three conditions. The variances in the commonly used reference genes were different among three cases. Three of the five commonly used reference genes, ACTB, GAPDH, and TBP are unstable during early development. ACTB was the most unstable reference gene in early development and normal adult tissues. GAPDH have the highest variance in the hindgut under sulfide stress, followed by TBP.

Fig. 2
figure 2

The boxplots that show the log2TPM values of the five commonly used reference genes and five candidate reference genes in early development (A), normal adult tissues (B) and hindgut under sulfide stress

Full size image

To further examine the results of transcriptome analysis, RT-qPCR experiments were carried out, and the expression level constancy of ten reference genes in different cases was assessed by four data processing methods (geNorm, NormFinder, BestKeeper, and ∆Ct) [57,58,59,60]. Despite a slight difference in the samples used between transcriptome data and RT-qPCR, the results of transcriptome analysis and RT-qPCR assay are very similar, which suggests that candidate reference genes have higher stability than most of the commonly used genes. As shown in Fig. 3A, during early development, syntaxin-12 (STX12) was the most stable gene, which is a member of the syntaxin family localized to the endosome [61]. The syntaxin family belongs to the t-SNARE subfamily of the SNARE superfamily and is involved in vesicle trafficking [62, 63]. STX12 is widely expressed and potentially participates in a common trafficking event that occurs in every cell [64,65,66], which explains why STX12 showed stable expression levels during early development. The most stable reference gene in different adult tissues was euchromatic histone-lysine N-methyltransferase 1 (EHMT1) (Fig. 3B). EHMT1 and euchromatic histone-lysine N-methyltransferase 2 (EHMT2) are highly homologous and generate functional heterodimeric complexes that are mainly responsible for mono- and dimethylation of histone H3 lysine 9 (H3K9) in euchromatin [67]. EHMT1/EHMT2 is essential for maintaining the normal methylation patterns of H3K9 and plays a central role in the epigenetic control of euchromatin, which is vital for normal cell function. They are universally expressed and associated with many biological processes [67,68,69,70,71]. EHMT1 is also required for normal levels of DNA methylation in facultative heterochromatin [72]. The stable expression levels of EHMT1 and its key role in cells enabled its use as a reference gene in normal adult tissues. As to hindgut under sulfide stress, the most stable gene was lysosomal alpha-glucosidase (LYAG) (Fig. 3C), which is a retaining exo-glucosidase catalyzing the production of glucose from glycogen in lysosomes [73, 74]. LYAG is extremely important for the degradation of glycogen in lysosomes [75]. This defect can cause the substrate to accumulate in almost all body tissues [76]. Alpha-glucosidases have weak specificity and a given substrate is not strictly connected to a single type of protein [77]. The expression levels of LYAG were not affected by sulfide treatment. Therefore, STX12, EHMT1 and LYAG can be selected as reference genes to normalize the results of the RT-qPCR assay in early development, normal adult tissues, and hindgut after sulfide stress in U. unicinctus, respectively.

Fig. 3
figure 3

Expression stability of the ten genes in early development (A), normal adult tissues (B), and hindgut under sulfide stress (C). Stability was estimated using geNorm, NormFinder, BestKeeper, and ∆Ct analyses of the RT-qPCR data. The genes were ranked in descending order of expression stability from left to right after comprehensive analysis

Full size image

Compared to the novel screened candidate reference genes, most of the commonly used reference genes had lower comprehensive ranking values. During early development, ACTB was the most variable, followed by TBP, which is consistent with the transcriptome data of the two genes with high CV values (Fig. 3A, Table 3). Similarly, in different normal adult tissues, ACTB had the lowest comprehensive analysis ranking values of RT-qPCR and the highest variance, which was not included in the candidate reference gene list. TBP and ATPase B were more stable with relatively high rankings of stability by RT-qPCR compared with other traditional reference genes (Fig. 3B), which is expected because the two genes are candidate reference genes that pass the criteria and have lower CV values in five commonly used reference genes (Table 3). The results of the comprehensive analysis in early development and normal adult tissues are in accordance with those of previous studies in U. unicinctus [52]. During sulfide stress in the hindgut, ACTB was the most unstable in the comprehensive RT-qPCR results (Fig. 3C). However, there was also some inconsistency between the results of RT-qPCR and transcriptome data analysis. For example, GAPDH, which ranked second after STX12 in the RT-qPCR results during early development (Fig. 3A), was not included in the candidate reference gene list with a 1.23 of SD log2(TPM) value by transcriptome data analysis. We deduced that the inconsistent phenomenon may result from the difference in the samples between the transcriptome data and RT-qPCR.

Relationship of gene expression level between transcriptome data and RT-qPCR

Previous studies have suggested that there is a high correlation between RNA-Seq data and Ct value of RT-qPCR [11]. Therefore, we assessed the relationship between the TPM values of the transcriptome data and RT-qPCR data. As shown in Fig. 4, there was a significant negative correlation between log2(TPM) and Ct values (R2 = 0.0453, P < 0.0001), with the formula Ct =  − 0.5405 log2(TPM) + 34.51. This formula will contribute to the estimation the Ct value based on transcriptome data without executing the RT-qPCR assay, and will be conducive to our further research.

Fig. 4
figure 4

The correlation of gene expression between log2TPM of transcriptome data and Ct values of RT-qPCR

Full size image

Conclusions

In this study, we identified candidate reference genes for embryos and larvae of early developmental stages, normal adult tissues, and the hindgut under sulfide stress based on transcriptome data from U. unicinctus. We then validated of the candidate reference genes by RT-qPCR using four methods (geNorm, NormFinder, BestKeeper, and ∆Ct) and compared the stability between the candidate reference genes and commonly used reference genes. The results showed that STX12, EHMT1, and LYAG are the most stable candidate reference genes in early development, normal adult tissues, and the hindgut under sulfide stress, respectively. Our study indicates that transcriptome analysis approaches have great potential to discover novel stable reference genes and will contribute to future gene expression level research in U. unicinctus.

Materials and methods

Animals materials and treatments

Adult U. unicinctus were collected from the intertidal zone along the coast of Yantai city, China. They were maintained in aerated seawater (19℃, pH8.0, salinity 30 PSU) and raised with Chaetoceros muelleri, Chlorella vulgaris, and Platymonas helgolandica.

We selected three healthy adult worms and dissected six tissues, including the body wall, coelomic fluid, foregut, mid-gut, hindgut and anal sac from each individual in phosphate-buffered saline (PBS, pH7.4). After dissection, the tissues were immediately frozen in liquid nitrogen and stored at -80℃.

Sexually mature individuals were selected and dissected to acquire mature ova and sperm from nephridia (gonoducts) during the spawning season. Sperm and ova were then mixed for artificial insemination at ratio of 10:1. Fertilized eggs were reared in filtered seawater (FSW) (17℃, pH 7.9, salinity 30). Embryos and larvae from ten developmental stages, including early cells (EC, 2 cells, 4 cells, and 8 cells), multiple cell (MC, generally more than 32 cells), blastulae (BL), gastrulae (GA), early trochophore larva (ET, 1 d post fertilization, dpf), mid-trochophore larva (MT, 2 dpf), late-trochophore larva (LT, 25 dpf), early segmentation larva (ES, 30 dpf), segmentation larvae (SL, 35 dpf) and worm-shaped larvae (WL, 42 dpf), were collected, frozen immediately in liquid nitrogen and then stored at -80℃ for total RNA extraction. Three biological replicates were prepared for each developmental stage.

The experimental system and sulfide treatment were conducted as described previously [45]. We prepared three aquariums containing 30 L of seawater and sealed them with a cling film. Six individuals were randomly selected for placement in each aquarium. The sulfide concentration in seawater was maintained at 50 μM (equivalent to moderately polluted sediment that U. unicinctus can live normally) by adding the sulfide stock solution (10 mM Na2S, pH 8.0) at 2 h interval, and detected by the methylene blue method [41]. The hindguts of three individuals from each aquarium were dissected 0 (control), 6, 24, and 48 h after sulfide treatment. The hindgut was immediately frozen in liquid nitrogen and stored at − 80 °C.

Transcriptome datasets

Transcriptome data of U. unicinctus embryos and larvae at various developmental periods were obtained from the NCBI Sequence Read Archive (SRA) database under the accession numbers PRJNA485379 and PRJNA394029, mainly including the following stages, EC: Early cells; MC: Multicellular; BL: Blastula; GA: Gastrula; ET: Early-trochophore; MT: Mid-trochophore; LT: Late-trochophore; SL: Segmentation larva and WL: Worm-shaped larva. The transcriptome data of the normal adult tissues were also obtained from the NCBI SRA database under the accession number PRJNA917787, which mainly included the body wall, coelomic fluid, foregut, mid-gut, hindgut, and anal sac. Transcriptome data of the sulfide stress hindgut samples (50 μM for 0, 6, 24, and 48 h) were also obtained from the NCBI SRA database under the accession number PRJNA752504.

Identification of reference genes based on transcriptome data

Reference genes for RT-qPCR were selected using the coefficient of variation (CV) method as previously described [11]. TPM values were used to measure gene expression levels and averaged for subsequent analyses of biological replicates. Firs, genes with log2(TPM) values less than or equal to 5 were excluded, because these low-expression genes would lead to poor RT-qPCR results, which makes it difficult to detect and quantify their expression. CV values were calculated using the formula CV = standard deviation (SD) of log2(TPM) / average log2(TPM) (mean). Calculations for the mean, SD, and CV were implemented in Microsoft Excel. Candidate reference genes with low variances were required, with SD values lower than 1. Therefore, a CV cut-off of 0.2 for stable genes was adopted, which was the cut-off for stable expression across heterogeneous genes.

Functional enrichment analysis

To further understand the functions of the selected candidate reference genes, Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed. The Swiss-Prot Blast results for all genes and the results of reference genes were imported into the online software OmicShare Tools (https://www.omicshare.com/tools/home/index/index.html), and the GO and KEGG enrichment analysis was completed using the Bioinformatics Cloud Tool Platform [78,79,80].

RNA isolation and cDNA synthesis

Total RNA was extracted from the stored different samples using TRIzol reagent (Invitrogen, Carlsbad, CA, USA), according to the manufacturer’s instructions. The RNA quality was assessed using NanoDrop 2000 (Thermo Scientific, Wilmington, DE, USA) and agarose gel electrophoresis. Then, the cDNA template was prepared using a PrimeScript™ RT reagent kit with gDNA Eraser (TaKaRa, Dalian, China) following the manufacturing’s instruction, and diluted with distilled water (1:10) for subsequent experiments.

Validation of the reference gene expression stability by RT-qPCR assay

Ten genes, consisting of five novel candidates and five commonly used reference genes, were chosen for RT-qPCR validation in early development, normal adult tissues, and the hindgut under sulfide stress. Primers were designed using Primer Primier software (5.0) and the primer sequences are listed in Additional file 1: Table S1.

RT-qPCR was performed on Light Cycler 480 system (Roche, Basel, Switzerland) using SYBR Premix Ex TaqTM (TaKaRa, Dalian, China). All reactions were carried out with three sample replicates and three technical replicates, and all RT-qPCR assays were validated in compliance with “MIQE guidelines” [81].

Four statistical approaches, geNorm (https://genorm.cmgg.be/) [57], NormFinder (http://moma.dk/) [58], BestKeeper (www.gene-quantification.com/bestkeeper.html) [59], and ∆Ct method [60], were applied to estimate the expression stability of the reference genes. The final ranking of gene expression stability was determined by calculating the geometric mean values of the results acquired using the four approaches.

Statistical analysis

Statistical analysis of the correlation between FPKM and Ct values was performed using one-way analysis of variance (one-way ANOVA). Statistical significance was set at P < 0.05.

Availability of data and materials

The datasets analysed during the current study are available in the NCBI SRA repository (PRJNA485379, PRJNA394029, PRJNA752504 and PRJNA917787).

Abbreviations

RT-qPCR:

Reverse transcription quantitative PCR

GAPDH:

Glyceraldehyde-3-phosphate dehydrogenase

EF-1-α:

Elongation factor 1 alpha

TBP:

TATA-box-binding protein

TUB:

Beta-tublin

eIF3:

Eukaryotic initiation factors 3

ATPase B:

Mitochondrial adenosine triphosphate synthase subunit b

ACTB:

Beta-actin

FPKM:

Fragments per kilobase million

TPM:

Transcripts per million

RPKM:

Reads per kilobase million

PBS:

Phosphate buffer saline

FSW:

Filtered sea water

EC:

Early cells

MC:

Multiple cell

BL:

Blastulae

GA:

Gastrulae

ET:

Early-trochophore larva

MT:

Mid-trochophore larvae

LT:

Late-trochophore larvae

ES:

Early-segmentation larvae

SL:

Segmentation larvae

WL:

Worm-shaped larvae

CV:

Coefficient of variation

GO:

Gene Ontology

KEGG:

Kyoto Encyclopedia of Genes and Genomes

FXRD1:

FAD-dependent oxidoreductase domain-containing protein 1

CAPR1:

Caprin-1

NSMA:

Neutral sphingomyelinase

IF2M:

Translation initiation factor IF-2

STX12:

Syntaxin-12

UBE4A:

Ubiquitin conjugation factor E4 A

OGT1:

UDP-N-acetylglucosamine–peptide N-acetylglucosaminyltransferase 110 kDa subunit

GGA3:

ADP-ribosylation factor-binding protein

EHMT1:

Histone-lysine N-methyltransferase

TRA2B:

Transformer-2 protein homolog beta

CNOT1:

CCR4-NOT transcription complex subunit 1

CLH1:

Clathrin heavy chain 1

EXOC6:

Exocyst complex component 6

LYAG:

Lysosomal alpha-glucosidase

TRFM:

Melanotransferrin

References

  1. Bustin SA, Benes V, Nolan T, Pfaffl MW. Quantitative real-time RT-PCR–a perspective. J Mol Endocrinol. 2005;34(3):597–601.

    Article  CAS  PubMed  Google Scholar 

  2. Kubista M, Andrade JM, Bengtsson M, Forootan A, Jonák J, Lind K, Sindelka R, Sjöback R, Sjögreen B, Strömbom L, et al. The real-time polymerase chain reaction. Mol Aspects Med. 2006;27(2):95–125.

    Article  CAS  PubMed  Google Scholar 

  3. Green MR, Sambrook J. Quantification of RNA by Real-Time Reverse Transcription-Polymerase Chain Reaction (RT-PCR). Cold Spring Harb Protoc. 2018;2018(10):847-56.

  4. Li Z, Li X, Zhang Q, Yuan L, Zhou X. Reference gene selection for transcriptional profiling in Cryptocercus punctulatus, an evolutionary link between Isoptera and Blattodea. Sci Rep. 2020;10(1):22169.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Harshitha R, Arunraj DR. Real-time quantitative PCR: A tool for absolute and relative quantification. Biochem Mol Biol Educ. 2021;49(5):800–12.

    Article  CAS  PubMed  Google Scholar 

  6. Zhang Y, Zhang Z, Ren M, Liu X, Zhou X, Yang J. Selection of Reference Genes for RT-qPCR Analysis in the Hawthorn Spider Mite, Amphitetranychus viennensis (Acarina: Tetranychidae), Under Acaricide Treatments. J Econ Entomol. 2022;115(2):662–70.

    Article  CAS  PubMed  Google Scholar 

  7. da Conceição BL, Gonçalves BÔP, Coelho PL, da Silva Filho AL, Silva LM. Identification of best housekeeping genes for the normalization of RT-qPCR in human cell lines. Acta Histochem. 2022;124(1):151821.

    Article  Google Scholar 

  8. Li J, Fu N, Ren L, Luo Y. Identification and Validation of Reference Genes for Gene Expression Analysis in Monochamus saltuarius Under Bursaphelenchus xylophilus Treatment. Front Physiol. 2022;13:882792.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Song J, Cho J, Park J, Hwang JH. Identification and validation of stable reference genes for quantitative real time PCR in different minipig tissues at developmental stages. BMC Genomics. 2022;23(1):585.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Gao D, Kong F, Sun P, Bi G, Mao Y. Transcriptome-wide identification of optimal reference genes for expression analysis of Pyropia yezoensis responses to abiotic stress. BMC Genomics. 2018;19(1):251.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Li Y, Zhang L, Li R, Zhang M, Li Y, Wang H, Wang S, Bao Z. Systematic identification and validation of the reference genes from 60 RNA-Seq libraries in the scallop Mizuhopecten yessoensis. BMC Genomics. 2019;20(1):288.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Wang X, Wu Z, Bao W, Hu H, Chen M, Chai T, Wang H. Identification and evaluation of reference genes for quantitative real-time PCR analysis in Polygonum cuspidatum based on transcriptome data. BMC Plant Biol. 2019;19(1):498.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Yi S, Lin Q, Zhang X, Wang J, Miao Y, Tan N. Selection and Validation of Appropriate Reference Genes for Quantitative RT-PCR Analysis in Rubia yunnanensis Diels Based on Transcriptome Data. Biomed Res Int. 2020;2020:5824841.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Czechowski T, Stitt M, Altmann T, Udvardi MK, Scheible W-R. Genome-Wide Identification and Testing of Superior Reference Genes for Transcript Normalization in Arabidopsis. Plant Physiol. 2005;139(1):5–17.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Gabrielsson BG, Olofsson LE, Sjögren A, Jernås M, Elander A, Lönn M, Rudemo M, Carlsson LM. Evaluation of reference genes for studies of gene expression in human adipose tissue. Obes Res. 2005;13(4):649–52.

    Article  PubMed  Google Scholar 

  16. Gong L, Yang Y, Chen Y, Shi J, Song Y, Zhang H. LbCML38 and LbRH52, two reference genes derived from RNA-Seq data suitable for assessing gene expression in Lycium barbarum L. Sci Rep. 2016;6(1):37031.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Guo H, Jiang L, Xia Q. Selection of reference genes for analysis of stress-responsive genes after challenge with viruses and temperature changes in the silkworm Bombyx mori. Mol Genet Genomics. 2016;291(2):999–1004.

    Article  CAS  PubMed  Google Scholar 

  18. Hu Y, Xie S, Yao J. Identification of Novel Reference Genes Suitable for qRT-PCR Normalization with Respect to the Zebrafish Developmental Stage. PLoS ONE. 2016;11(2):e0149277.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Kudo T, Sasaki Y, Terashima S, Matsuda-Imai N, Takano T, Saito M, Kanno M, Ozaki S, Suwabe K, Suzuki G, et al. Identification of reference genes for quantitative expression analysis using large-scale RNA-seq data of Arabidopsis thaliana and model crop plants. Genes Genet Syst. 2016;91(2):111–25.

    Article  CAS  PubMed  Google Scholar 

  20. Vieira A, Cabral A, Fino J, Azinheira HG, Loureiro A, Talhinhas P, Pires AS, Varzea V, Moncada P, Oliveira H, et al. Comparative Validation of Conventional and RNA-Seq Data-Derived Reference Genes for qPCR Expression Studies of Colletotrichum kahawae. PLoS ONE. 2016;11(3):e0150651.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Zhou Z, Cong P, Tian Y, Zhu Y. Using RNA-seq data to select reference genes for normalizing gene expression in apple roots. PLoS ONE. 2017;12(9):e0185288.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Mughal BB, Leemans M, Spirhanzlova P, Demeneix B, Fini J-B. Reference gene identification and validation for quantitative real-time PCR studies in developing Xenopus laevis. Sci Rep. 2018;8(1):496.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Liu F, Li X, Ji Y, Liu C, Sun T, Zhao Y, Nicolae CG, Dediu L. THE BIOLOGICAL CHARACTERISTICS AND UTILIZATION OF Urechis unicinctus. AgroLife Sci J. 2019;8(1):146–52.

    Google Scholar 

  24. Tan X, Wang YC, Sun QY, Peng A, Chen DY, Tang YZ. Effects of MAP kinase pathway and other factors on meiosis of Urechis unicinctus eggs. Mol Reprod Dev. 2005;71(1):67–76.

    Article  CAS  PubMed  Google Scholar 

  25. Qin Z, Zhang Y, Mu H, Zhang Z, Qiu JW. The sperm proteome of the echiuran Urechis unicinctus (Annelida, Echiura). Proteomics. 2018;18(16):1800107.

    Article  Google Scholar 

  26. Han Y-H, Ryu K-B, Medina Jiménez BI, Kim J, Lee H-Y, Cho S-J. Muscular Development in Urechis unicinctus (Echiura, Annelida). Int J Mol Sci. 2020;21(7):2306.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Fujiwara A, Tazawa E, Hino A, Asami K, Yasumasu I. Respiration in Eggs of the Echiuroid, Urechis unicinctus, Before and After Fertilization: echiuroid eggs/fertilization/respiration/redox dyes/uncoupler of oxidative phosphorylation. Dev Growth Differ. 1986;28(5):431–42.

    Article  CAS  Google Scholar 

  28. Kojima MK. On the vitally stainable granules in the egg of the echiuroid Urechis unicinctus. Embryologia. 1959;4(3):211–8.

    Article  Google Scholar 

  29. Hou X, Qin Z, Wei M, Fu Z, Liu R, Lu L, Bai S, Ma Y, Zhang Z. Identification of the neuropeptide precursor genes potentially involved in the larval settlement in the Echiuran worm Urechis unicinctus. BMC Genomics. 2020;21(1):892.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Bai S, Fan S, Liu D, Zhang Z, Zhang Z. Identification and expression analysis of receptors that mediate MIP regulating larval settlement in Urechis unicinctus. Comp Biochem Physiol B: Biochem Mol Biol. 2022;260:110732.

    Article  CAS  PubMed  Google Scholar 

  31. Lu L, Zhang Z, Zheng Q, Chen Z, Bai S, Zhang Z. Expression Characteristics and Potential Function of Neuropeptide MIP in Larval Settlement of the Echiuran Worm Urechis unicinctus. J Ocean Univ China. 2022;21(4):977–86.

    Article  CAS  Google Scholar 

  32. Wang J, Zhang L, Lian S, Qin Z, Zhu X, Dai X, Huang Z, Ke C, Zhou Z, Wei J, Liu P, Hu N, Zeng Q, Dong B, Dong Y, Kong D, Zhang Z, Liu S, Xia Y, Li Y, Zhao L, Xing Q, Huang X, Hu X, Bao Z, Wang S. Evolutionary transcriptomics of metazoan biphasic life cycle supports a single intercalation origin of metazoan larvae. Nat Ecol Evol. 2020;4(5):725–36.

    Article  PubMed  Google Scholar 

  33. Wei M, Qin Z, Kong D, Liu D, Zheng Q, Bai S, Zhang Z, Ma Y. Echiuran Hox genes provide new insights into the correspondence between Hox subcluster organization and collinearity pattern. Proc Biol Sci. 1982;2022(289):20220705.

    Google Scholar 

  34. Ma YB, Zhang ZF, Shao MY, Kang KH, Tan Z, Li JL. Sulfide:quinone oxidoreductase from echiuran worm Urechis unicinctus. Mar Biotechnol. 2011;13(1):93–107.

    Article  CAS  Google Scholar 

  35. Zandvakili A, Gebelein B. Mechanisms of Specificity for Hox Factor Activity. J Dev Biol. 2016;4(2):16.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Ma Y-B, Zhang Z-F, Shao M-Y, Kang K-H, Shi X-L, Dong Y-P, Li J-L. Response of sulfide: quinone oxidoreductase to sulfide exposure in the echiuran worm Urechis unicinctus. Mar Biotechnol. 2012;14(2):245–51.

    Article  CAS  Google Scholar 

  37. Ma Y-B, Zhang Z-F, Shao M-Y, Kang K-H, Zhang L-T, Shi X-L, Dong Y-P. Function of the anal sacs and mid-gut in mitochondrial sulphide metabolism in the echiuran worm Urechis unicinctus. Mar Biol Res. 2012;8(10):1026–31.

    Article  Google Scholar 

  38. Zhang L, Liu X, Liu J, Zhang Z. Characteristics and function of sulfur dioxygenase in echiuran worm Urechis unicinctus. PLoS ONE. 2013;8(12):e81885.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Liu X, Qin Z, Li X, Ma X, Gao B, Zhang Z. NF1, Sp1 and HSF1 are synergistically involved in sulfide-induced sqr activation in echiuran worm Urechis unicinctus. Aquat Toxicol. 2016;175:232–40.

    Article  CAS  PubMed  Google Scholar 

  40. Liu X, Zhang Z, Ma X, Li X, Zhou D, Gao B, Bai Y. Sulfide exposure results in enhanced sqr transcription through upregulating the expression and activation of HSF1 in echiuran worm Urechis unicinctus. Aquat Toxicol. 2016;170:229–39.

    Article  CAS  PubMed  Google Scholar 

  41. Zhang L, Liu X, Qin Z, Liu J, Zhang Z. Expression characteristics of sulfur dioxygenase and its function adaption to sulfide in echiuran worm Urechis unicinctus. Gene. 2016;593(2):334–41.

    Article  CAS  PubMed  Google Scholar 

  42. Li X, Liu X, Qin Z, Wei M, Hou X, Zhang T, Zhang Z. A novel transcription factor Rwdd1 and its SUMOylation inhibit the expression of sqr, a key gene of mitochondrial sulfide metabolism in Urechis unicinctus. Aquat Toxicol. 2018;204:180–9.

    Article  CAS  PubMed  Google Scholar 

  43. Zhang L, Zhang Z. The response of sulfur dioxygenase to sulfide in the body wall of Urechis unincinctus. Peer J. 2019;7:e6544.

    Article  PubMed  PubMed Central  Google Scholar 

  44. Zhang T, Qin Z, Liu D, Wei M, Fu Z, Wang Q, Ma Y, Zhang Z. A novel transcription factor MRPS27 up-regulates the expression of sqr, a key gene of mitochondrial sulfide metabolism in echiuran worm Urechis unicinctus. Comp Biochem Physiol C: Toxicol Pharmacol. 2021;243:108997.

    CAS  PubMed  Google Scholar 

  45. Liu D, Qin Z, Wei M, Kong D, Zheng Q, Bai S, Lin S, Zhang Z, Ma Y. Genome-Wide Analyses of Heat Shock Protein Superfamily Provide New Insights on Adaptation to Sulfide-Rich Environments in Urechis unicinctus (Annelida, Echiura). Int J Mol Sci. 2022;23(5):2715.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Hou X, Wei M, Li Q, Zhang T, Zhou D, Kong D, Xie Y, Qin Z, Zhang Z. Transcriptome Analysis of Larval Segment Formation and Secondary Loss in the Echiuran Worm Urechis unicinctus. Int J Mol Sci. 2019;20(8):1806.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Liu X, Zhang L, Zhang Z, Ma X, Liu J. Transcriptional response to sulfide in the Echiuran Worm Urechis unicinctus by digital gene expression analysis. BMC Genomics. 2015;16:829.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Ma X, Liu X, Zhou D, Bai Y, Gao B, Zhang Z, Qin Z. The NF-κB pathway participates in the response to sulfide stress in Urechis unicinctus. Fish Shellfish Immunol. 2016;58:229–38.

    Article  CAS  PubMed  Google Scholar 

  49. Shi X, Shao M, Zhang L, Ma Y, Zhang Z. Screening of genes related to sulfide metabolism in Urechis unicinctus (Echiura, Urechidae) using suppression subtractive hybridization and cDNA microarray analysis. Comp Biochem Physiol D: Genomics Proteomics. 2012;7(3):254–9.

    CAS  PubMed  Google Scholar 

  50. Huang J, Zhang L, Li J, Shi X, Zhang Z. Proposed function of alternative oxidase in mitochondrial sulphide oxidation detoxification in the Echiuran worm Urechis unicinctus. J Mar Biolog. 2013;93(8):2145–54.

    Article  CAS  Google Scholar 

  51. Oh HY, Kim CH, Go HJ, Park NG. Isolation of an invertebrate-type lysozyme from the nephridia of the echiura, Urechis unicinctus, and its recombinant production and activities. Fish Shellfish Immunol. 2018;79:351–62.

    Article  CAS  PubMed  Google Scholar 

  52. Bai Y, Zhou D, Wei M, Xie Y, Gao B, Qin Z, Zhang Z. Identification of reference genes for normalizing quantitative real-time PCR in Urechis unicinctus. J Ocean Univ China. 2018;17(3):614–22.

    Article  CAS  Google Scholar 

  53. Wei M, Lu L, Wang Q, Kong D, Zhang T, Qin Z, Zhang Z. Evaluation of suitable reference genes for normalization of RT-qPCR in Echiura (Urechis unicinctus) during developmental process. Russ J Mar Biol. 2019;45(6):464–9.

    Article  Google Scholar 

  54. Park C, Han YH, Lee SG, Ry KB, Oh J, Kern EMA, Park JK, Cho SJ. The developmental transcriptome atlas of the spoon worm Urechis unicinctus (Echiurida: Annelida). Gigascience. 2018;7(3):1–7.

    Article  CAS  PubMed  Google Scholar 

  55. Stanton KA, Edger PP, Puzey JR, Kinser T, Cheng P, Vernon DM, Forsthoefel NR, Cooley AM. A Whole-Transcriptome Approach to Evaluating Reference Genes for Quantitative Gene Expression Studies: A Case Study in Mimulus. G3 (Bethesda). 2017;7(4):1085–95.

    Article  CAS  PubMed  Google Scholar 

  56. Dos Santos KCG, Desgagné-Penix I, Germain H. Custom selected reference genes outperform pre-defined reference genes in transcriptomic analysis. BMC Genomics. 2020;21(1):35.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, Speleman F. Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002;3(7):Research0034.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Andersen CL, Jensen JL, Ørntoft TF. Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Can Res. 2004;64(15):5245–50.

    Article  CAS  Google Scholar 

  59. Pfaffl MW, Tichopad A, Prgomet C, Neuvians TP. Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper–Excel-based tool using pair-wise correlations. Biotech Lett. 2004;26(6):509–15.

    Article  CAS  Google Scholar 

  60. Silver N, Best S, Jiang J, Thein SL. Selection of housekeeping genes for gene expression studies in human reticulocytes using real-time PCR. BMC Mol Biol. 2006;7:33.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Tang BL, Tan AE, Lim LK, Lee SS, Low DY, Hong W. Syntaxin 12, a member of the syntaxin family localized to the endosome. J Biol Chem. 1998;273(12):6944–50.

    Article  CAS  PubMed  Google Scholar 

  62. Minakami R, Kato A, Sugiyama H. Interaction of Vesl-1L/Homer 1c with syntaxin 13. Biochem Biophys Res Commun. 2000;272(2):466–71.

    Article  CAS  PubMed  Google Scholar 

  63. Goh CS, Cohen FE. Co-evolutionary analysis reveals insights into protein-protein interactions. J Mol Biol. 2002;324(1):177–92.

    Article  CAS  PubMed  Google Scholar 

  64. Subramaniam VN, Loh E, Horstmann H, Habermann A, Xu Y, Coe J, Griffiths G, Hong W. Preferential association of syntaxin 8 with the early endosome. J Cell Sci. 2000;113(6):997–1008.

    Article  CAS  PubMed  Google Scholar 

  65. Das J. SNARE Complex-Associated Proteins and Alcohol. Alcohol Clin Exp Res. 2020;44(1):7–18.

    Article  PubMed  Google Scholar 

  66. Prekeris R, Klumperman J, Chen YA, Scheller RH. Syntaxin 13 mediates cycling of plasma membrane proteins via tubulovesicular recycling endosomes. J Cell Biol. 1998;143(4):957–71.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Battisti V, Pontis J, Boyarchuk E, Fritsch L, Robin P, Ait-Si-Ali S, Joliot V. Unexpected Distinct Roles of the Related Histone H3 Lysine 9 Methyltransferases G9a and G9a-Like Protein in Myoblasts. J Mol Biol. 2016;428(11):2329–43.

    Article  CAS  PubMed  Google Scholar 

  68. Pless O, Kowenz-Leutz E, Knoblich M, Lausen J, Beyermann M, Walsh MJ, Leutz A. G9a-mediated lysine methylation alters the function of CCAAT/enhancer-binding protein-beta. J Biol Chem. 2008;283(39):26357–63.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Karl M, Sommer C, Gabriel CH, Hecklau K, Venzke M, Hennig AF, Radbruch A, Selbach M, Baumgrass R. Recruitment of Histone Methyltransferase Ehmt1 to Foxp3 TSDR Counteracts Differentiation of Induced Regulatory T Cells. J Mol Biol. 2019;431(19):3606–25.

    Article  CAS  PubMed  Google Scholar 

  70. Kerchner KM, Mou TC, Sun Y, Rusnac DV, Sprang SR, Briknarová K. The structure of the cysteine-rich region from human histone-lysine N-methyltransferase EHMT2 (G9a). J Struct Biol-X. 2021;5:100050.

    CAS  PubMed  PubMed Central  Google Scholar 

  71. Pareek C, Michno J, Smoczynski R, Tyburski J, Golebiewski M, Piechocki K, Wimmers K. Identification of predicted genes expressed differentially in pituitary gland tissue of young growing bulls revealed by the cDNA-AFLP technique. Czeh J Anim Sci. 2013;58:147–58.

    Article  CAS  Google Scholar 

  72. Collins R, Cheng X. A case study in cross-talk: the histone lysine methyltransferases G9a and GLP. Nucleic Acids Res. 2010;38(11):3503–11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Kato A, Nakagome I, Hata M, Nash RJ, Fleet GWJ, Natori Y, Yoshimura Y, Adachi I, Hirono S. Strategy for Designing Selective Lysosomal Acid α-Glucosidase Inhibitors: Binding Orientation and Influence on Selectivity. Molecules. 2020;25(12):2843.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Hamura R, Shirai Y, Shimada Y, Saito N, Taniai T, Horiuchi T, Takada N, Kanegae Y, Ikegami T, Ohashi T, Yanaga K. Suppression of lysosomal acid alpha-glucosidase impacts the modulation of transcription factor EB translocation in pancreatic cancer. Cancer Sci. 2021;112(6):2335–48.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Hoefsloot LH, Hoogeveen-Westerveld M, Kroos M, Van Beeumen J, Reuser AJ, Oostra B. Primary structure and processing of lysosomal alpha-glucosidase; homology with the intestinal sucrase-isomaltase complex. EMBO J. 1988;7(6):1697–704.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Cagin U, Puzzo F, Gomez MJ, Moya-Nilges M, Sellier P, Abad C, Van Wittenberghe L, Daniele N, Guerchet N, Gjata B, Collaud F, Charles S, Sola MS, Boyer O, Krijnse-locker J, Ronzitti G, Colella P, Mingozzi F. Rescue of Advanced Pompe Disease in Mice with Hepatic Expression of Secretable Acid α-Glucosidase. Mol Ther. 2020;28(9):2056–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Le Chevalier P, Sellos D, Van Wormhoudt A. Molecular cloning of a cDNA encoding alpha-glucosidase in the digestive gland of the shrimp, Litopenaeus vannamei. Cell Mol Life Sci. 2000;57:1135–43.

    Article  PubMed  Google Scholar 

  78. Kanehisa M, Goto S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Resarch. 2000;28:27–30.

    Article  CAS  Google Scholar 

  79. Kanehisa M. Toward understanding the origin and evolution of cellular organisms. Protein Sci. 2019;28:1947–51.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Kanehisa M, Furumichi M, Sato Y, Kawashima M, Ishiguro-Watanabe M. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res. 2023;51:D587–92.

    Article  PubMed  Google Scholar 

  81. Bustin SA, Benes V, Garson JA, Hellemans J, Huggett J, Kubista M, Mueller R, Nolan T, Pfaffl MW, Shipley GL, Vandesompele J, Wittwer CT. The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. Clin Chem. 2009;55(4):611–22.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We would like to thank the anonymous reviewers for their kind and helpful comments on the original manuscript.

Funding

This work was supported by National Natural Science Foundation of China (32170373, 42176122), key research & development plan of Hainan province (ZDYF2021XDNY180), Shandong province science outstanding Youth Fund (ZR2020YQ20), China Postdoctoral Science Foundation (2020M680095) and Qingdao postdoctoral application research project.

Author information

Authors and Affiliations

  1. Ministry of Education Key Laboratory of Marine Genetics and Breeding, College of Marine Life Sciences, Ocean University of China, Qingdao, China

    Jiao Chen, Yunjian Wang, Danwen Liu, Yao Jin, Xixi Li, Yuhang Deng, Boya Wang, Zhifeng Zhang & Yubin Ma

  2. Key Laboratory of Tropical Aquatic Germplasm of Hainan Province, Sanya Oceanographic Institution, Ocean University of China, Sanya, China

    Zhi Yang & Zhifeng Zhang

Authors
  1. Jiao Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yunjian Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhi Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Danwen Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yao Jin
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xixi Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yuhang Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Boya Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Zhifeng Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Yubin Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The work presented here was a collaborative effort among all the authors. MYB formulated the research topic. MYB, CJ, WYJ, YZ, LDW, JY, LXX, DYH, WBY and ZZF analyzed data and interpreted the results. MYB and CJ wrote the manuscript. MYB and CJ revised the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Yubin Ma.

Ethics declarations

Ethics approval and consent to participate

All animal care and use procedures were approved by the Committee of the Ethics of Animal Experiments of the Ocean University of China (Identification code: 2020018, on 25 June 2020), and were performed according to the Chinese Guidelines for the Care and Use of Laboratory Animals (GB/T 35892‐2018). We declare that this study is reported in accordance with ARRIVE guidelines.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1:

  Table S1. Information of primers of reference genes in RT-qPCR validation.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, J., Wang, Y., Yang, Z. et al. Identification and validation of the reference genes in the echiuran worm Urechis unicinctus based on transcriptome data. BMC Genomics 24, 248 (2023). https://doi.org/10.1186/s12864-023-09358-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12864-023-09358-6

Keywords

BMC Genomics

ISSN: 1471-2164

Contact us