Digital gene expression analysis of Takifugu rubripes brain after acute hypoxia exposure using next-generation sequencing

    Digital gene expression analysis of Takifugu rubripes brain after acute hypoxia exposure using next-generation sequencing Jie-Lan Jiang, Ming-Guang Mao, Hui-Qian L¨u, Shi-Hui Wen, Meng-Lei Sun, Rui-ting Liu, Zhi-Qiang Jiang PII: DOI: Reference:

S1744-117X(17)30042-4 doi:10.1016/j.cbd.2017.05.003 CBD 458

To appear in:

Comparative Biochemistry and Physiology - Part D: Genomics and Proteomics

Received date: Revised date: Accepted date:

9 December 2016 7 March 2017 27 May 2017

Please cite this article as: Jiang, Jie-Lan, Mao, Ming-Guang, L¨ u, Hui-Qian, Wen, ShiHui, Sun, Meng-Lei, Liu, Rui-ting, Jiang, Zhi-Qiang, Digital gene expression analysis of Takifugu rubripes brain after acute hypoxia exposure using next-generation sequencing, Comparative Biochemistry and Physiology - Part D: Genomics and Proteomics (2017), doi:10.1016/j.cbd.2017.05.003

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ACCEPTED MANUSCRIPT Digital gene expression analysis of Takifugu rubripes brain after acute hypoxia exposure using next-generation sequencing

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Jie-Lan Jiang1, Ming-Guang Mao1, Hui-Qian Lü, Shi-Hui Wen, Meng-Lei Sun, Rui-ting Liu, Zhi-Qiang Jiang* Key Laboratory of Mariculture & Stock Enhancement in North China's Sea, Ministry of Agriculture, Key

Life Science, Dalian Ocean University, Dalian 116023, China

1 These authors contributed equally to the study

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laboratory of fish applied biology and aquaculture in North China, Liaoning Province, College of Fisheries and

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Running title: DGE analysis of Takifugu rubripes brain after hypoxia exposure

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ms. has 17 pages, 3 figures, 4 tables

*Corresponding author: Zhi-Qiang Jiang

[email protected]

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Tel: +86 041184762875; Fax: +86 041184762871

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Abstract The adverse effects of hypoxia are confined to biochemical, physiological, developmental and behavioral processes, especially injury of the brain. In this study, a subset of genes in the brain of Takifugu rubripes were analyzed using digital gene expression (DGE) profiles and next-generation sequencing after acute hypoxia. Among 32 differentially expressed genes, 29 were up-regulated and 3 were down-regulated following hypoxia exposure. Using Gene Ontology analysis, it was found that transcription and translation, metabolism, and the stress response were affected by exposure to hypoxia. KEGG analysis revealed that the neuroactive ligand-receptor interaction pathway was significantly enriched in hypoxia-exposed T. rubripes. To further confirm the differential expression of genes, quantitative real-time PCR was performed to test six candidate genes, with the following five genes exhibiting the same expression patterns as the sequencing results: Proto-oncogene c-fos, Kruppel-like factor 2, immediate early response 2, proopiomelanocortin A and rhodopsin. This work is the first to identify and annotate genes in T. rubripes affected by hypoxia stress. This investigation provides data for understanding the molecular mechanism of fish adaptation to hypoxia and provides a reference for rationally setting dissolved oxygen levels in aquaculture. Key words: hypoxia; brain; Takifugu rubripes; digital gene expression profiles

1. Introduction Takifugu rubripes is an economically important fish in China. Since wild resources have recently declined, artificial breeding is increasing and has become a prosperous industry. In

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northern China, cultured T. rubripes spend half a year indoors for overwintering every year and are moved back to outdoor cages or ponds until June or July the next year. Some farmers currently breed T. rubripes indoors during the whole culturing process. Under indoor breeding conditions, a high-density of breeding, sudden power failures, or other accidents sometimes lead to hypoxia. Hypoxia not only inhibits oxidative phosphorylation but also affects many other aerobic reactions. The adverse effects of hypoxia are confined to biochemical, physiological, developmental and behavioral processes, including respiration, metabolism, growth, locomotion and reproduction (Heath, 1995). The underlying mechanism of fish responses to hypoxia has become a subject of great concern in recent years. Numerous studies have indicated that the ability of fishes to survive under hypoxia derives from a core triad of adaptations: decreasing energy costs, maintaining major protein expression, and increasing antioxidant defenses of the gill respiratory surface area (Bickler and Buck, 2007; Richard et al., 2007). In addition, air-breathing behavior and ionic regulation have also been found to change when fish are subjected to hypoxia stress (Sollid and Nilsson, 2006; Richard et al., 2007; Wood et al., 2009). Hypoxia exposure could conceivably alter expression patterns of select genes, which in turn may alter the expression patterns of many other genes as part of a regulatory cascade aimed at ameliorating pathological effects (Zhang et al., 2009). Gracey et al. (2001) profiled gene expression responses in Gillichthys mirabilis that experienced hypoxia using a cDNA microarray. The identified hypoxia responsive genes including those related to the following processes: (1) ATP metabolism; (2) amino acid metabolism; (3) protein translation; (4) iron metabolism; (5) locomotion and contraction; and (6) antigrowth and proliferation. Ton et al. (2003) found that genes encoding the skeletal and cardiac contractile proteins and cell cycle regulatory genes were suppressed in zebrafish embryos under hypoxic stress. Expression of Bcl-2 genes in channel catfish was influenced after hypoxia exposure (Yuan et al., 2016). Hsp70 and HIF-1α mRNA expression levels increased significantly in the blood, liver, gills, and kidneys of Larimichthys crocea following exposure to hypoxia, which may play important roles in protecting fish against oxidative damage (Wang at al., 2017). Tse et al. (2015) found that hypoxia induced miR-210, leading to anti-apoptosis in ovarian follicular cells in marine medaka, Oryzias melastigma. The finding that MicroRNA is involved in fish responses to hypoxia reveals a new direction to explore the mechanism of fish adaptation to hypoxia. Hypoxia causes severe injury to the brain. Acute hypoxia leads to acute hypoxic encephalopathy with a series of nervous and mental disorders as symptoms. The underlying mechanism is complicated and may include: (1) change of cerebral blood flow, (2) dysfunction of energy metabolism, (3) generation of neurotoxic materials, and (4) neuronal cell apoptosis. To date, a great amount of attention has been paid to investigating the effects and toxicity mechanisms of hypoxia in mammalian systems (Petkova et al., 2000; Khedr et al., 2000; Rofstad, 2000). Our understanding of hypoxic impacts in fish is still relatively poor. Although efforts have been made in recent decades (Zhang et al., 2009; Zhang et al., 2012; Baptista et al., 2016; Tse et al., 2016), hypoxia response mechanisms in fish still need to be further documented. Whole genome sequencing of T. rubripes was completed in 2002. Since then, T. rubripes has attracted great attention and has been used as a model organism in comparative genomics research. Nevertheless, there are currently no studies regarding the hypoxia response of T. rubripes. In this study, we analyzed genes that were differentially expressed in the T. rubripes brain after acute hypoxia using digital gene expression (DGE) profiles, and differential gene expression was further

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validated by quantitative real-time PCR (qPCR). This work is the first to identify and annotate genes in T. rubripes affected by hypoxia stress. This investigation provides data for understanding the molecular mechanism of fish adaptation to hypoxia and provides a reference for rationally setting dissolved oxygen levels in aquaculture. However, this is a preliminary first step in identifying the mechanisms related to hypoxia in fish, and further studies will be needed to probe the detailed mechanisms concerning functions of particular genes.

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2. Materials and methods

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2.1 Fish maintenance and challenge T. rubripes with body weights of 32-42 g were obtained from Dalian days industrial co., LTD, Liaoning Province, China. After transporting to the lab, fish were maintained in 1000-L tanks at the key laboratory of Mariculture & Stock Enhancement in North’s China’s Sea, Ministry of Agriculture for at least 7 days. Fish were fed with fresh bait daily. Water was held at 18.9±1.0C, salinity 31.5±0.5 and with 6.7-7.0 mg L−1 dissolved oxygen (DO). At the beginning of the experiment, fish were separated randomly into two groups. One group was reared under hypoxic condition while the other was kept in water with 6.7-7.0 mg L−1 DO as a control group. In the hypoxic group, the containers were sealed to prevent air entering and to create a hypoxic condition. Both groups were kept in identical 16-liter aquaria with 3 fish apiece. The experiments were carried out with 4 replications. According to the results of a pre-experiment, the handling time was set to be 3 h. After 3 h, T. rubripes were anesthetized and dissected, and their brains were immediately frozen in liquid nitrogen and transferred to a -80C freezer and stored until analysis. Samples from the hypoxic and control groups were designated HypBr and NorBr, respectively. During the experiment, water from both groups was sampled at 1 h, 2 h and 3 h for DO detection.

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2.2 RNA extraction, Library preparation and DGE sequencing Total RNAs were extracted from the brains using the Tripure (Roche) reagent. The quality and quantity of total RNA were analyzed using a NanoPhotometer® spectrophotometer (IMPLEN, CA, USA), Qubit® RNA Assay Kit in a Qubit® 2.0 Flurometer (Life Technologies, CA, USA), RNA Nano 6000 Assay Kit and gel electrophoresis. To eliminate individual differences, an equal amount of high-quality RNA from three different individuals were combined (Chen et al., 2015), using 3 μg of total RNA for each library. Sequencing libraries were generated using the NEBNext® Ultra™ RNA Library Prep Kit for Illumina® (NEB, USA) following the manufacturer’s recommendations, and unique indexes were added to adapters to demultiplex the sequence data into samples. Library fragments were purified with the AMPure XP system (Beckman Coulter, Beverly, USA). Then, 3 μL of USER Enzyme (NEB, USA) was used with size-selected, adaptor-ligated cDNA at 37°C for 15 min followed by 5 min at 95 °C before PCR. PCR was performed with Phusion High-Fidelity DNA polymerase, Universal PCR primers and an Index (X) Primer. Finally, PCR products were purified (AMPure XP system) and library quality was assessed on an Agilent Bioanalyzer 2100. After cluster generation, libraries were sequenced on an Illumina Hiseq 2000. Raw data (raw reads) in fastq format were first processed through in-house Perl scripts and clean data (clean reads) were obtained. At the same time, the Q20, Q30, GC-content and sequence

ACCEPTED MANUSCRIPT duplication level of the clean data were calculated. All downstream analyses were based on high-quality clean data. Differential expression analysis of two samples was performed using the DEGseq (2010) R

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package. P values were adjusted using q value. The criteria Q value<0.005 & |log2（fold change）

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|>1 were used to set the threshold for significant differential expression. Gene Ontology (GO) enrichment analysis of the differentially expressed genes (DEGs) was

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performed using the GOseq R package and Wallenius non-central hyper-geometric distribution， which can adjust for the gene length bias in DEGs. The software KOBAS was used to test for statistical enrichment of differential expression genes in KEGG pathways.

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2.3 Quantitative real-time PCR For quantitative real-time PCR (qPCR), 36 T. rubripes with body weights of 32-42 g were divided randomly into two groups; 18 fish were reared under hypoxic conditions (HG), and 18 were kept in water with 6.7-7.0 mg L−1 DO as a control group (CG). At 1 h, 2 h, and 3 h, three fish were sampled from the HG and CG for qPCR. Total RNAs were extracted from the brains using the Tripure (Roche) reagent. The quality and quantity of total RNA were analyzed using a NanoPhotometer® spectrophotometer (IMPLEN, CA, USA) and gel electrophoresis. High-quality RNAs were then used in qPCR. qPCR was carried out using a 7500 Real-Time PCR System (Applied Biosystem), with SYBR Green as the fluorescent dye, according to the manufacturer’s protocol. Six candidate genes, including Proto-oncogene c-fos, immediate early response 2 (IER2), Kruppel-like factor 2 (KLF2), rhodopsin, proopiomelanocortin A (POMCA), and carnosine dipeptidase 1 (CNDP1) were used for validation. Two commonly used reference genes, glyceraldehyde 3-phosphate dehydrogenase (GAPDH) and actin, were tested to evaluate their stability as endogenous control genes. Primers were designed using Primer Premier 5.0 and are listed in Table 1. Reaction conditions were as follows: 95°C for 1 min, followed by 40 cycles at 94°C for 15 s and 60°C for 1 min. All the reactions were performed in biological triplicate, and samples were normalized using the endogenous control gene. The results were expressed as the mean ± SD of the relative fold of the endogenous control gene in each experiment.

3. Results The DO levels of the hypoxic and control groups are shown in Figure 1. The DO level decreased dramatically for 1 h and maintained low levels through 3 h in the hypoxia group. 3.1 Sequencing of DGE library To obtain an overview of the effects of hypoxia on Takifugu rubripes, we used paired-end Illumina sequencing of the cDNA library that was generated by combing an equal amount of RNA isolated from the control and hypoxia groups. After purification and quality checks, the NorBr group had 12,299,089 raw reads, of which 12,147,937 were clean reads with 11,024,116 uniquely mapping to genes. The HypBr group had 12,178,140 raw reads, of which 12,075,182 were clean reads with 10,927,117 mapping uniquely to genes (Table 2). These results showed that most of the sequencing data (over 90%) was meaningful and indicated our sequencing data were reliable and could be used for further analysis. After mapping these unique clean reads to specific genes and

ACCEPTED MANUSCRIPT analyzing gene expression, we found that 29 genes were up-regulated and 3 genes were down-regulated in HypBr compared to NorBr (Figure 2). The genes differentially expressed after exposure to hypoxia are listed in Table 3.

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3.2 GO classification Using GO analysis, we classified these genes based on enriched GO-terms in the cellular component, molecular function, and biological process ontologies. In total, 32 unigenes with BLAST matches to known proteins were assigned to GO classes with a total of 320 functional terms. The assignments to biological processes made up the majority (200; 62.5%) followed by molecular functions (93; 29.1%) and cellular component (27; 8.4%). Under the category of biological processes, transcription and translation, cell metabolism and stress response were prominently represented, indicating that some important biological processes were involved in the response to hypoxia in T. rubripes. These observations are similar to those found in previous studies (Gracey et al., 2001; Ton et al., 2003; Ju et al., 2007). Under the classification of molecular functions, binding was the largest category. Other categories, such as hormone activity and cyclase activity, for example, were less well represented. For cellular components, extracellular region was the largest category, whereas few unigenes were assigned to cell and cell part, for example.

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3.3 Functional classification by KEGG To identify biological pathways that were active in T. rubripes during exposure to hypoxia, 20 genes were mapped to the reference canonical pathways in KEGG. In total, 12 KEGG pathways were assigned. The pathways with the maximal representation by Unigene were Neuroactive ligand-receptor interaction (5 proteins), Jak-STAT signaling pathway (3 proteins), and MAPK signaling pathway (3 proteins). The pathways that were significantly different in HypBr compared to NorBr are listed in Table 4. We found that 3 pathways were significantly different between HypBr and NorBr (P-value < 0.05); however, only 1 of these, neuroactive ligand-receptor interaction, remained significant following Q-value correction. 3.4 Confirmation of differential gene expression by qPCR GAPDH and actin were first tested for their stability as candidate endogenous controls for quantitative gene analysis. The expression of GAPDH between the hypoxia and control groups showed more stability than that of actin. Therefore, we chose GAPDH as the endogenous control for gene qPCR. The results indicated that transcripts of four selected genes were sharply increased by 3 h of hypoxia exposure (Figure 3, P<0.05). hypoxia obviously increased expression of c-fos (4.0-fold, Figure 3A), IER2 (5.1-fold, Figure 3B), KLF2 (2.1-fold, Figure 3C), and POMCA (2.3-fold, Figure 3D). In contrast, the genes encoding rhodopsin (0.1-fold, Figure 3E) and CNDP1 (0.5-fold, Figure 3F) exhibited down-regulation after 3 h of hypoxia exposure. 4. Discussion Hypoxia is a pervasive environmental stimulus that can alter gene expression. Genes responding to hypoxia have been identified not only in bacteria and yeast but also in mammalians (Royer et al., 2000; Strzyz 2016; Rauen et al., 2016). Although the effects of hypoxia have been studied extensively, the effects of hypoxia in the model organism T. rubripes remain unclear. In this study, the effects of 3 h of hypoxia on T. rubripes were investigated.

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Functional annotation of the genes that were differentially expressed between our HypBr and NorBr DGE libraries revealed biological processes that may characterize the response of T. rubripes to hypoxia exposure. Compared with the control group, 29 genes were up-regulated and 3 genes were down-regulated in the hypoxic group. Therefore, our results indicate that acute hypoxia causes substantial up-regulation, rather than down-regulation, of T. rubripes genes and that many of these genes are related to transcription and translation. Based on GO enrichment analysis, many of the differentially expressed genes were involved in regulation of transcription, metabolic processes, and response to stress. We identified the genes involved in these three functions, which represent candidates for genes involved in the hypoxia exposure response: c-fos, IE2, KLF2, CNDP1, POMCA and rhodopsin. qPCR was performed to confirm the validity of the genes identified by DGE. qPCR results showed that, of the six selected genes, five (i.e., c-fos, IER2, KLF2, POMCA and rhodopsin) displayed the same expression patterns as DGE sequencing. Therefore, the authenticity of the sequencing results was confirmed. Transcriptional factors, such as c-fos, IE2 and KLF2, were up-regulated in HypBr. c-fos and IE2 are members of immediate early response genes (IERGs), which are a family of inducible genes that respond within a short time after the occurrence of a stimulus (Cullinan et al., 1995; Chen et al., 2013; Moriya et al., 2016). c-fos is normally found in the central nervous system, but it is highly conserved and its expression level is very low and hard to detect. When subject to hypoxia, light stimulation, mechanical stimulation and pain stimulation, and so on, c-fos will be activated and the mRNA transcript will enter the cytoplasm and be translated into fos protein. The fos protein subsequently form a heterodimer complex with the Jun protein which is encoded by the proto-oncogene c-jun via the leucine zipper. The heterodimer complex then binds to the target genes and activates transcriptional activity, and plays the role of a third messenger (Herrera and Robertson 1996; Chen et al., 2013). Through the above process, extracellular stimulation signals are converted to signals that regulate gene expression within the nucleus. IER2 can be induced by a variety of cytokines, but its function hasn’t been clearly understood (Moriya et al., 2016). As an early response gene, it participates in cell apoptosis and cerebral ischemia injuries (Schneider et al., 2004). After cerebral ischemia, the expression of IER2 is up-regulated, and it acts as a pro-apoptotic factor in the injury process after cerebral ischemia (Schneider et al., 2004). Since cerebral ischemia is usually accompanied by cerebral hypoxia, whether the role of IER2 during hypoxia is the same as in cerebral ischemia needs to be further studied. KLF2 is a member of the Sp/Kruppel-like factor family, which is a subclass of the zinc finger family of transcriptional regulators implicated in the regulation of cellular growth and differentiation (Feinberg et al., 2004; Atkins and Jain, 2007). KLF2 expression has been found to be acutely induced by hypoxia in endothelial cells (Kawanami et al., 2009). Adenoviral overexpression of KLF2 inhibited hypoxia-induced expression of HIF-1α and its target genes, while knockdown of KLF2 up-regulated the expression of HIF-1α and its targets (Kawanami et al., 2009). Genes associated with metabolic processes, such as CNDP1, were also affected by hypoxia exposure. CNDP1 is the rate-limiting enzyme of carnosine hydrolyzing into β-Alanine and L-leucine. Prior studies have shown that low CNDP1 levels are linked to diabetic nephropathy and several forms of cancer (Qundos et al., 2014; Guo et al., 2016), but there is no research relating CNDP1 to hypoxia exposure. DGE sequencing revealed that hypoxia induced CNDP1 expression, but results from qPCR indicated that CNDP1 expression was down-regulated during 3 h of hypoxia exposure. It has been reported that carnosine is able to inactivate reactive oxygen species

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(ROS), scavenge free radicals, and act as an anti-oxidant in vivo (Guiotto et al., 2005). It’s well known that hypoxia leads to an accumulation of ROS and free radicals in a variety of organisms (Guzy et al., 2005; Liu et al., 2016; Yang et al., 2016). The down-regulation of CNDP1, which leads to carnosine accumulation, may be due to the role of carnosine as an anti-oxidant. Genes participating in response to stress, including proopiomelanocortin A and rhodopsin, were differentially expressed between HypBr and NorBr. POMCA is the precursor of adrenocorticotropic hormone, β-endorphin, α-melanocyte stimulating hormone and some other hormones (Cao et al., 2011). Studies have suggested that the POMCA system is related to the stress response (Shiomi and Akil, 1982; Mei et al., 2000). Shiomi and Akil (1982) found that repeated foot shock not only increased POMCA mRNA level in the anterior pituitary gland but also led to an increase in incorporation of labeled amino acid into POMCA and a shortening of POMCA half-life. Mei et al. (2000) suggested that hypoxia gave rise to a release of POMCA-derived peptides in the pituitary, which decreased the level of β-endorphin in the pituitary but increased the level of β-endorphin and ACTH in blood. According to the above studies, we can deduce that POMCA biosynthesis is activated by acute exposure to stress, such as hypoxia, and that the maturation of POMCA to B-LPH and β-endorphin is accelerated. Rhodopsin (also known as visual purple) is a light-sensitive receptor protein involved in visual phototransduction. Studies have shown that hypoxia inhibits rhodopsin regeneration. A KEGG pathway named the neuroactive ligand-receptor interaction pathway was significantly enriched and thus apparently active in hypoxia-exposed T. rubripes. Changes in the neuroactive ligand-receptor interaction pathway suggest that hypoxia could influence genes that are involved in neurodevelopment in T. rubripes. Taken together, our analyses allow us to produce a preliminary description of the possible mechanism of hypoxia exposure in T. rubripes, and we propose that it may be a multiple-step process as follows. When T. rubripes are exposed to hypoxia, expression of transcriptional factors apparently increases with the purpose of regulating expression of other genes to adapt to hypoxia exposure. Additionally, metabolic processes are delayed with the aim of regulating metabolism to avoid further damage and to store resources that may be necessary for stress defense. Despite these defense mechanisms, it is likely that T. rubripes cannot fully escape the effects of hypoxia. For example, the development of the nervous system was disrupted by hypoxia exposure. However, due to the small size of the fish, the whole brain was used in this study, which may have influenced the results. We acknowledge that the hypoxia-response mechanism described here is preliminary, and further research will be necessary to validate our hypotheses. Acknowledgements This work was supported by the general project of the Education Department of Liaoning Province (L2015076), the National Natural Science Foundation of China (NSFC-31302202), the Public Science and Technology Research Funds Project of Ocean of the State Oceanic Administration of the People's Republic of China (201405003), the opening foundation of Key Laboratory of Mariculture & Stock Enhancement in North China's Sea, Ministry of Agriculture (2015-MSENC-KF-04), and the talents introduction project of Dalian Ocean University (HDYJ201501). Any fieldwork in this study complied with the current laws of China, where it was performed.

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Nat. Rev. Mol. Cell Biol. 17(10), 610. Ton, C., Stamatiou, D., Liew, C.C., 2003. Gene expression profile of zebrafish exposed to hypoxia during development. Physiol. Genomics. 13, 97-106. Tse, C.K., Li, J.W., Chan, T.F., Wu, S.S., Lai, K.P., 2015. Hypoxia induces mir-210, leading to anti-apoptosis in ovarian follicular cells of marine medaka oryzias melastigma. Aquat. Toxicol. 165, 189-196. Tse, A.C., Li, J.W., Wang, S.Y., Chan, T.F., Lai, K.P., Wu, R.S., 2016. Hypoxia alters testicular functions of marine medaka through microRNAs regulation. Aquatic Toxicology 180, 266-273. Wang, Q.F., Shen, W.L., Hou, C.C., Liu, C., Wu, X.F., Zhu, J.Q., 2017. Physiological responses and changes in gene expression in the large yellow croaker larimichthys crocea following exposure to hypoxia. Chemosphere 169, 418-427. Wood, C.M., Iftikar, F.I., Scott, G.R., Boeck, G.D., Sloman, K.A., Matey, V., Domingos, F.X.V., Duarte, R.M., Almeida-Val, V.M.F., Val, A.L., 2009. Regulation of gill transcellular permeability and renal function during acute hypoxia in the Amazonian Oscar (Astronotus ocellatus): new angles to the osmorespiratory compromise. J. Exp. Biol. 212, 1949-1964. Yang, B., Xu, Y., Hu, Y., Luo, Y., Lu, X., Tsui, C.K., Lu, L., Liang ,X., 2016. Madecassic Acid protects against hypoxia-induced oxidative stress in retinal microvascular endothelial cells via ROS-mediated endoplasmic reticulum stress. Biomed. pharmacother. 84, 845-852. Yuan, Z., Liu, S., Yao, J., Zeng, Q., Tan, S., Liu, Z., 2016. Expression of bcl-2 genes in channel catfish after bacterial infection and hypoxia stress. Dev. Comp. Immunol. 65, 79-90. Zhang, Z., Ju, Z., Wells, M.C., Walter, R.B., 2009. Genomic approaches in the identification of hypoxia biomarkers in model fish species. J. Exp. Mar. Biol. Ecol. 381(Suppl 1), S180-S187. Zhang, Z., Wells, M.C., Boswell, M.G., Beldorth, I., Kirk, L.M., Wang, Y., Wang, S., Savage, M., Walter, R.B., Booth, R.E., 2012. Identification of robust hypoxia biomarker candidates from fin of medaka (Oryzias latipes). Comp. Biochem. Physiol. C 155, 11–17.

ACCEPTED MANUSCRIPT Legends Figure 1. Dissolved oxygen levels during the experiment.

T

Figure 2. Cluster analysis of differentially expressed genes

AC

CE P

TE

D

MA

NU

SC R

IP

Figure 3. Validation of differential gene expressions using quantitative real-time PCR. Y-axis: the raw relative quantification. The glyceraldehyde 3-phosphate dehydrogenase (GAPDH) gene was used as the endogenous control. Bars indicate Standard Deviation. Significant differences of gene expression between the hypoxia-exposed group and the control group are indicated with asterisks (*P<0.05). Significantly increased expression was found for Proto-oncogene c-Fos (A), immediate early response 2 (B), Kruppel-like factor 2 (C), and proopiomelanocortin A (D) at 3 h. By contrast, the gene encoding rhodopsin (E) and carnosine dipeptidase 1 (F) mRNA showed a clear down-regulation at 3 h.

ACCEPTED MANUSCRIPT

Hypoxia group Control group

T

7

IP

6

SC R

5 4 3 2 1 0 0

1

NU

Dissolve oxygen level (mg/L)

Figure 1

2

AC

CE P

TE

D

MA

Exposure time (h)

3

ACCEPTED MANUSCRIPT

AC

CE P

TE

D

MA

NU

SC R

IP

T

Figure 2

ACCEPTED MANUSCRIPT

4 2 0 Ctr

1h

2h

3h

D

14 12 10 8 6 4

*

2

*

0 Ctr

1h

2h

4 2 0 Ctr

2h

E

14 12 10 8 6

2 0

1h

* 2h

Exposure time

D TE

3h

*

Ctr

3h

Exposure time

CE P

1h

Exposure time

4

C

14 12 10 8

T

*

6

16 16

AC

Relative quantity of proopiomelanocortin a

Exposure time

8

16

6

IP

*

10

Relative quantity of krupple-like factor 2

6

12

3h

Relative quantity of carnosine dipeptidase 1

8

B

14

*

4 2

*

0

SC R

*

10

Relative quantity of rhodopsin

Relative quantity of c-fos

12

16

NU

A

14

MA

16

Relative quantity of immediate early response 2

Figure 3

16

Ctr

1h

2h

3h

Exposure time

F

14 12 10 8 6 4 2

*

0 Ctr

1h

* 2h

Exposure time

* 3h

ACCEPTED MANUSCRIPT

Table 1. Primers used for quantitative real-time PCR. “F”: forward primers; “R”: reverse primers. Primer sequence

SC R

immediate early response 2

F:5’ TGCAGGAAACCGATCAGTTG 3’ R: 5’ TTTTGCAGATGGGCTGGT 3’ F: 5’ TTCATCGTCCAGCCTATCGTT 3’ R: 5’ AACCCAGCGGAACAAACC3’ F: 5’ TAAAGTTCATGGGCTGGCA 3’ R: 5’ TGATGTCCTTCGAGCAACCA 3’ F: 5’ AGCTTCACCTTTGGTCGATCT 3’ R: 5’ TTGATTCTGGGTTTGATCTCC 3’ F: 5’ CAGCTCAGATGGTCCCAACA 3’ R: 5’ CAGAATGGGTCCTGTGTGGCT 3’ F: 5’ TCCAGATTACGTCCTCACCTT 3’ R: 5’ ACGAGGAGTGGAAAATGTACC 3’ F: 5’ AGAAGCCTGCCAAGTACGAC 3’ R: 5’ CAACCTGGTGCTCCGTGTAT 3’ F: 5’AACACCACACATTTCTCATACAC3’ R: 5’TTAACTAGCCAAAAACATTACCG3’

IP

Proto-oncogene c-Fos

Kruppel-like factor 2

carnosine dipeptidase 1

CE P

TE

glyceraldehyde-3-phosphate dehydrogenase actin

D

proopiomelanocortin A

MA

NU

rhodopsin

AC

PCR product size (bp)

T

Gene name

120 93 129 99 90 110 95 145

ACCEPTED MANUSCRIPT Table 2. Sequence data quality control results Raw

Clean

Clean

Error

name

reads

reads

bases

rate(%)

NorBr

12,299,089

12,147,937

0.61G

HypBr

12,178,140

12,075,182

0.6G

Q30(%)

0.01

98.49

95.30

49.94

0.01

98.46

50.25

95.25

IP SC R NU MA D TE CE P AC

GC

Q20(%)

T

Sample

content(%)

ACCEPTED MANUSCRIPT Table 3. Genes differentially expressed after acute hypoxia exposure in the brain of Takifugu rubripes

ENSTRUG00000007449

1.34E-05 3.44E-18

AC

ENSTRUG00000015503 ENSTRUG00000017592 ENSTRUG00000018232 ENSTRUG00000019222 ENSTRUG00000019224 ENSTRUG00000019230 Down-regulated: ENSTRUG00000002242 ENSTRUG00000003400 ENSTRUG00000010617

IP

T

3.19E-08 2.80E-21

2.47E-07

5.74E-08 1.28E-21 3.00E-10 7.61E-45 3.46E-19 5.56E-27

2.35E-05 1.83E-18 1.61E-07 1.87E-41 3.97E-16 9.56E-24

7.04E-19

7.12E-16

6.63E-08 2.29E-08 1.86E-46 1.14E-15 0 6.52E-16 3.97E-12 2.01E-06 6.48E-19 1.67E-21 1.11E-44 5.35E-23 1.02E-09

2.65E-05 1.01E-05 5.34E-43 8.93E-13 0 5.34E-13 2.63E-09 0.00066335 6.96E-16 2.21E-18 2.39E-41 8.37E-20 4.88E-07

1.60E-10

9.16E-08

3.69E-06

0.001133

1.55E-16

1.40E-13

2.00E-268 0 5.92E-10

1.15E-264 0 2.99E-07

lysozyme g-like S-arrestin-like

1.18E-60 2.86E-08

5.07E-57 1.23E-05

rhodopsin

7.68E-12

4.72E-09

SC R

4.75E-10

CE P

ENSTRUG00000007551 ENSTRUG00000007985 ENSTRUG00000008210 ENSTRUG00000008379 ENSTRUG00000009747 ENSTRUG00000010065 ENSTRUG00000010071 ENSTRUG00000010441 ENSTRUG00000010579 ENSTRUG00000012177 ENSTRUG00000013520 ENSTRUG00000014181 ENSTRUG00000014820

soluble guanylate cyclase 88E-like growth hormone 1 insulin-like growth factor binding protein 1a cAMP responsive element modulator a soluble guanylate cyclase 88E-like tuftelin 1a prolactin glycoprotein hormones alpha chain dual specificity phosphatase 1 CCAAT/enhancer binding protein (C/EBP), beta CCAAT/enhancer-binding protein delta TSC22 domain family, member 3 somatolactin alpha early growth response protein 1-B-like proopiomelanocortin A Kruppel-like factor 2 salt-inducible kinase 1 immediate early response 2 Proto-oncogene c-Fos nose resistant to fluoxetine protein 6-like carnosine dipeptidase 1 zinc finger, NFX1-type containing 1 phosphodiesterase 9A nuclear receptor subfamily 4, group A, member 1 ribosomal RNA processing protein 1 homolog A thyroid stimulating hormone, beta subunit 12S ribosomal RNA gene mitochondrial DNA mitochondrial DNA

NU

ENSTRUG00000004097 ENSTRUG00000004373 ENSTRUG00000005348 ENSTRUG00000005607 ENSTRUG00000005818 ENSTRUG00000007159

Q-value

MA

ENSTRUG00000002524

P-value

D

Up-regulated: ENSTRUG00000001102 ENSTRUG00000002048

Gene description

TE

Gene_id

ACCEPTED MANUSCRIPT Table 4. List of KEGG pathways active in T. rubripes during hypoxia exposure, identified using pathway enrichment analysis. P-valuea

Neuroactive ligand-receptor interaction Jak-STAT signaling pathway MAPK signaling pathway

5 (25%) 3 (15%) 3 (15%)

2.24E-04 1.18E-03 1.42E-02

Q-valueb

T

DGE with pathway annotation (20)

SC R

IP

Pathway

3.47E-02 9.17E-02 7.32E-01

AC

CE P

TE

D

MA

NU

a: P-values ≤0.05 are significantly different from control. b: Q-values ≤0.05 are significantly enriched in DGE.