Comparative Transcriptomic Analysis of the Brain in Takifugu Rubripes Shows Its Tolerance to Acute Hypoxia

Comparative Transcriptomic Analysis of the Brain in Takifugu Rubripes Shows Its Tolerance to Acute Hypoxia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comparative Transcriptomic Analysis of the Brain in Takifugu Rubripes Shows Its Tolerance to Acute Hypoxia Mingxiu Bao, Fengqin Shang, Fujun Liu, Ziwen Hu, Shengnan Wang, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-542334/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Hypoxia is reduced levels of oxygen. Especially in water, due to the complex environment, hypoxic situations often occur. Although fish can survive in low-oxygen waters, this survival ability depends on a complete set of coping mechanisms such as oxygen perception and gene-protein interaction regulation. The research on this mechanism is very meaningful. The present study was undertaken to examine the short-term effects of hypoxia on the brain in Takifugu rubripes . We sequenced the transcriptomes of the brain in T. rubripes to studied their response mechanism to acute hypoxia. Total 167 genes with adjusted P values<0.05 were differentially expressed in the brain of T. rubripes exposed to acute hypoxia. However, hif1a , the master transcriptional regulator of the adaptive response to hypoxia, was not significantly regulated, which indicated that the T. rubripes brain might prevent the HIF-1 signaling pathway. Then Gene Ontology and KEGG Enrichment Analysis were carried out. The results indicated that hypoxia could cause metabolic and neurological changes, showing the clues of their adaptation to acute hypoxia. Overall, the sequenced transcriptomes of the brain in T. rubripes showed small changes under acute hypoxia. As the most complex and important organ, the brain of T. rubripes might be able to create a self-protection mechanism to resist or reduce damage caused by acute hypoxia stress. General Biochemistry Physiology Acute hypoxia Brain Transcriptome Takifugu rubripes Gene expression Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Introduction Hypoxia is reduced levels of oxygen. The existence of factors, such as reduced air pressure and poor gas exchange in nature, will cause the occurrence of a hypoxic environment, such as in plateaus( Qiu et al. 2012 ), aquatic environments( Jackson and Ultsch 2010 ) , and underground tunnels( Avivi et al. 2005 ). The animals and plants living in a hypoxic environment have corresponding coping mechanisms in physiology, biochemistry, and behavior. Plants use substances other than oxygen as terminal electron acceptors, and the redox potential in the rhizosphere is reduced. Through a series of physiological and biochemical metabolic reactions, plants can adapt to or alleviate the damage caused by hypoxia stress( Schmidt et al. 2018 ). Animals have different ways of adapting to different levels of hypoxia: in mild or moderate hypoxic environments, they adjust to the hypoxic environment through the adjustment of the overall horizontal compensation mechanism, such as faster breathing rates, increased pulmonary ventilation, alveolar-blood, and accelerating blood-tissue gas diffusion and strengthening the permanent expansion of capillaries, etc.( Avivi et al. 1999 ; Liu et al. 2001 ; Scott 2011 ; Wan et al. 2013 ). In severe hypoxic environments, adaptation through overall compensation alone is far from satisfying the body's energy needs, and more importantly, through the adjustment of cell metabolism and the induction of many anti-hypoxic factors adapt to the hypoxic environment at the molecular level. For example, intracellular free calcium increases during hypoxia, then it can activate calcium-related signaling pathways and regulate the increase in transcription of some hypoxia-sensitive genes ( Millhorn et al. 1997 ). Hif-1 binds to hypoxia-responsive elements (HRE) on its target genes, triggering downstream target genes such as epo , inducible nitric oxide synthase ( inos ), vegf , and other genes. Transcription affects physiological and pathological processes such as erythropoiesis, angiogenesis, apoptosis, and proliferation, and causes the body to produce a series of adaptive responses to hypoxia ( Bruzzi et al. 1997 ; Erkan et al. 2007 ). In addition to the hypoxia caused by the reduction of the oxygen content in the environment, many physiological and pathological processes of higher animals also have the phenomenon of hypoxia. For example, in the early stages of mammalian embryonic development, a hypoxic environment is created in the womb. This hypoxic microenvironment is essential for embryo development ( Simon and Keith 2008 ). Whereas aquatic species (such as fish) are particularly vulnerable to hypoxic conditions, the hypoxic state of aquatic systems usually means that the saturation of dissolved oxygen (DO) is between 0-30% ( Pelster and Egg 2018 ; Wu et al. 2003 ). The DO concentration in fish ponds depends generally on many factors including photosynthesis of phytoplankton, respiration of aquatic organisms, and/or the diffusion of atmospheric O 2 , O 2 partial pressure, water temperature, and salinity. In captivity, fish always face repetitive and chronic stress situations (e.g., confinement, crowding, handling, variable water quality including hypoxia) from which they cannot escape. Across a broad range of fishes, hypoxia can cause direct mortality but more commonly results in various sublethal effects, such as behavioral and physiological stress( Abdel-Tawwab et al. 2019 ). Hypoxia has been shown to retard growth in bivalves, polychaetes, and fish ( Shang and Wu 2004 ). It also can induce apoptosis( Arend et al. 2011 ). Fish have developed various adaptation strategies in the long-term evolution process, and their tolerance to hypoxia is very different ( Bickler and Buck 2007 ). Studies have shown that fish could initiate special biological processes and molecular mechanisms in low-oxygen environments, and could more efficiently store and use oxygen through the regulation of key gene expression ( Rimoldi et al. 2012 ). Salmo salar could induce vascular endothelial growth factor expression through hypoxia to promote angiogenesis and increase the body's oxygen supply ( Vuori et al. 2004 ). In zebrafish ( Danio rerio ) embryos, gene expression changed in response to hypoxia. Hif-1a played an important role in regulating the formation of neural crest and the central nervous system ( Ton et al. 2003 ). Although there are many studies on molecular mechanisms related to hypoxia, the molecular basis of these adaptations has not been fully understood ( Xia et al. 2018 ). The transcriptome is the sum of all the RNA expressed by a cell or tissue at a specific period. By detecting the expression difference of the transcriptome in different periods and tissues, the gene expression regulation status can be dynamically analyzed ( Wilhelm et al. 2008 ). It is widely used to study the molecular mechanisms of many different species under specific conditions. The transcriptome analysis of Carassius auratus showed that the expression of glycolytic pathway-related genes in fish exposed to hypoxia for a long time will be significantly enhanced ( Liao et al. 2013 ). The brain is an oxygen-sensitive organ whose functions are highly susceptible to low oxygen levels ( Rahman and Thomas 2015 ). Also, other studies indicate that the crucian carp may be relying on neurotransmitters and neuromodulators to suppress its CNS energy use under hypoxic conditions ( Nilsson and Renshaw 2004 ). Under hypoxic conditions, the expression of hypoxia-inducible factors in the brain tissue of sturgeon increased significantly ( Pelster and Egg 2018 ). The T. rubripes genome sequencing work was completed in 2002. It was found that the T. rubripes genome is about 400Mb, which is only one-seventh the size of the human genome. It is the smallest genome of known vertebrates and the number of genes is similar to humans( Brenner et al. 1993 ; Kai et al. 2011 ) , so it has been extensively studied as a model organism. We have carried out a simple experiment of acute hypoxia tolerance of T. rubripes , which showed that few differentially expressed genes were found ( Jiang et al. 2017 ). In this study, we set four O 2 concentration to detect the transcriptome changes in the brain of T. rubripes and aimed to explore the mechanisms of the brain in T. rubripes responses to acute hypoxic stress. Materials And Methods Experimental animals and acute hypoxia exposure Animal experiments were approved by the Animal Care and Use committee at Dalian Ocean University. Healthy T. rubripes with body weights of 461.75±40.66g were obtained from Dalian Tianzheng Industrial Co., in Dalian, Liaoning, China. Forty fish were randomly selected from the stock and divided equally into four groups kept in 380L tanks with a flow-through seawater supply. The temperature of the water was 19±0.5˚C. Sodium sulfite was used to regulate the DO which was detected by dissolved oxygen meter (Hanna HI9146-04, Romania). The DO of the four tanks (four groups) were maintained at 5.4±0.05 mg O 2 /L (ppm5.4), 4±0.05 mg O 2 /L (ppm4), 2±0.05 mg O 2 /L (ppm2) and 0±0.05mg O 2 /L (ppm0). The fish treated by the DO of 5.4±0.05 mg O 2 /L (ppm5.4) was considered as control because the DO was same as that in the stock. After 6 hours of treatment, fish were anaesthetized with 80 mg/L concentration of tricaine methane sulfonate (MS-222, Sigma) and then dissected. Brain was obtained and snap-frozen in liquid nitrogen, and then stored at −80℃. Then total RNA was extracted from the brain for the construction of RNA library and qPCR. Transcript profiling and sequencing (RNAseq) Total RNA was quantified by the Qubit® RNA Analysis Kit in Qubit® 2.0 (Life Technologies, CA, USA), and its integrity was checked using an Agilent 2100 Bioanalyzer (Agilent Technologies, USA). Twenty RNA (cDNA) libraries (5 biological replicates of each group, including ppm5.4, ppm4, ppm2, and ppm0) were constructed for RNA sequencing and bioinformatics analysis. The library was then sequenced by Novaseq 6000 (Illumina, USA). Processing of RNAseq data Raw data were firstly qualified by FastQC (www.bioinformatics.babraham.ac.uk/projects/fastqc/). Clean data (clean reads) were obtained after removing read with containing adapter, ploy-N and low-quality bases by Trimmomatic v0.38 ( Bolger et al. 2014 ). All the downstream analyses were based on clean data. Reference genome ( assembly fTakRub1.2 , www.ncbi.nlm.nih.gov/genome/63) were downloaded from the genome database of NCBI (The National Center for Biotechnology Information). Paired-end clean reads were mapped to the indexed reference genome using Hisat2 v2.1.0 ( Kim et al. 2015 ). The results of mapping were also qualified by FastQC. HTSeq v0.11.2 was used to count the reads mapped to each gene ( Anders et al. 2015 ). Differential gene expression analysis was performed by an R package DESeq2 v3.10 ( Love et al. 2014 ). Principal component analysis (PCA) was performed using an in-house python script (Supplementary Information File S12). Obtained DEGs were annotated by KOBAS v3.0 ( Xie et al. 2011 ), and subsequently subjected to GO functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis by an R package clusterProfiler v3.18.1 ( Yu et al. 2012 ). The software Cytoscape v3.7.1 was used to map the enriched gene and KEGG signaling pathway network interactions ( Shannon et al. 2003 ). Several R packages (ggplot2, pheatmap, venn) were used to draw graphics for the visualization of analyzed data (cran.r-project.org/). Validation of DEGs by q RT-PCR Total RNA was extracted from the brain samples using RNAprep pure tissue kit (Tiangen, China). First-strand cDNA was synthesized from 1μg of total RNA using a PrimeScript™ RT Master Mix (Takara, Japan). qPCR was performed using TransStart® Top Green qPCR SuperMix (Transgen, China) on an ABI StepOnePlus TM Real-Time PCR System (Life Technologies, USA). The primers for each gene are listed in Supplementary Information Table S7. The gene expression levels were evaluated relative to the expression level of β-actin using the 2 −ΔΔCT method ( Livak and Schmittgen 2001 ). The correlation between normalized counts from RNAseq and the relative expression from qPCR was calculated. Results Construction of RNA libraries, quality control of raw data Total 20 libraries were constructed and sequenced from 20 samples in four treatment groups. The average size of insert fragments in these libraries was 275-315 bp (Supplementary Information Table S1). After quality control, clean data from 20 libraries was retained for next step (Supplementary Information Table S2). Total 102.75 Gb clean nucleotides and 685.06 million clean reads were obtained finally. Quality control showed that more than 95% and 89% reads of each sample had the quality scores of Q20 and Q30 (nucleotides with a quality value larger than 20 and 30), respectively. These results indicated our sequencing data were reliable and could be used for further analysis. All clean data was submitted to SRA databases in NCBI (BioProject: PRJNA645780). Mapping and counting reads Obtained reads with high quality were mapped to reference genome (Supplementary Information Table S3). Total and unique mapping rates of all samples were more than 92% and 89% respectively. Reads mapped to positive chains of genome were almost equal to that mapped to negative chains. According to the structure of the reference genome, mapped reads were mainly positioned in exons (77-81.5%) (Supplementary Information Table S4). Analysis of mapping results by FastQC showed that the level of duplication in mapped reads was about 15-21% from all samples (Supplementary Information Table S5). These results showed a good mapping quality for further analysis. Then mapped reads in exons were counted by HTSeq (Supplementary Information Table S6). Analysis of differentially expressed genes (DEGs) The counted reads by HTSeq were loaded by DESeq2 for differential expression analysis. The results showed that 167 genes were differentially expressed among the different treated groups with adjusted P value <0.05 (Supplementary Information Table S8). But only 19 DEGs with adjusted P value =1 were obtained (Fig. 1). The comparison group with the highest number of DEGs was ppm0Vsppm5.4, and the three comparison groups with the large significant differential fold change of DEGs were ppm0Vsppm2, ppm2Vsppm5.4, and ppm2Vsppm4. (Fig. 2). The numbers of DEGs in different comparison groups were showed in (Fig. 3). There were 118 DEGs between control group (ppm5.4) and treated group with the lowest level of DO (ppm0). Between group ppm5.4 and ppm2, 56 DEGs were identified. There only 5 DEGs between the group ppm0 and ppm2. But no DEGs were identified between the group ppm5.4 and ppm4. Transformed count data (Supplementary Information Table S13) was extracted by DESeq2 using the method of variance stabilizing transformation in order to visualization, including clustering, heatmap and principal components analysis (Fig.3, 4, 5). All 20 samples were clustered into two main branches by the 167 DEGs (Fig. 4a, 5). The group ppm0 and ppm2 were clustered into one clade, ppm5.4 and ppm4 were clustered into another. These results indicated that the level of DO might make more serious effects on the brain of T. rubripes when the concentration of DO was less than 4 mg O 2 /L. In heatmap, 167 DEGs were mainly clustered into 3 clades based on their expression levels (Fig. 4a). About a quarter of the DEGs exhibited high expression levels, suggesting that they could play important roles when T. rubripes was under the condition of low DO. Interestingly, range of DEGs fold change was small. There were only 19 DEGs with a fold change of more than 2 (Fig. 4b), and among these 19 DEGs, 16 DEGs were found between the control ppm5.4 and the treatment ppm0 (Fig. 1), which was the largest difference among the comparison groups, followed by when the oxygen concentration was ppm5.4 and ppm2 (Fig. 1). These results indicated that the transcriptomes of brain in T. rubripes changed little during acute hypoxia treatment. Annotation and function analysis of DEGs The GO classification system grouped the DEGs into three main categories: biological process, cellular component, and molecular function (Fig. 6). The possible roles of DEGs were investigated by GO enrichment analysis. The enrichment analysis showed that most of the DEGs were significantly enriched to the category of biological processes (Fig. 6a). For example, the three terms ("response to steroid hormone", "response to hypoxia", and "response to decreased oxygen levels") were enriched significantly enriched with a high number of DEGs. The next significant enrichment was in the category of molecular function (Fig. 6b), and the top two enriched terms with a high number of DEGs were "dioxygenase activity" and "RNA polymerase II−specific DNA−binding transcription factor binding". However, for the category of cellular components, we found that the number of DEGs enriched was small and insignificant (Fig. 6c). Interestingly, we clustered all these enriched terms and found that they could be well clustered into eight major functional categories (Fig. 7). Among them, most of the significantly enriched terms were clustered into two functional categories, decreased oxygen levels hypoxia and corticosteroid corticosterone death glucocorticoid. KEGG is a database resource containing many metabolic pathways and their relationships ( Kanehisa and Goto 2000 ). In our study,167 DEGs were grouped into 187 known pathways and the largest group was MAPK signaling pathway, containing 10 DEGs, followed by PI3K-Akt signaling pathway(9), Axon guidance(6), IL-17 signaling pathway (5), HIF-1 signaling pathway(5). Among them, the MAPK signaling pathway enrichment was most significant (Fig. 8). Interestingly, through the network diagram we found that MAPK signaling pathway and PI3K-Akt signaling pathway had the highest degree and connected the most genes（Fig. 9）. For genes, fos , jun , rxra , vegfa , and flt1 had the highest degree and connected the most network pathways. Metabolism-related pathways and genes also clustered well together. In addition, five genes were found to be enriched in HIF-1 signaling pathways related to hypoxia stress: LOC101071669( egln2 ), flt1, LOC101079462(hk2), LOC101063282(slc2a1) , and vegfa. Among those HIF-1 signaling pathway-related genes, as the DO concentration decreased, all five enriched genes were up-regulated, while hif1a did not change significantly. Validation of DEGs by q RT-PCR To validate our Illumina sequencing results, 6 genes were selected for q RT-PCR analysis. As shown in（Fig. 10）, although the relative expression levels were not completely consistent, the expression patterns of these genes identified by qRT-PCR were similar to those obtained in RNA-Seq analysis, which indicated the expression data from RNAseq was reliable. Discussion Maintaining homeostasis is a key function of the brain, and homeostasis includes blood oxygen levels( van der Velpen et al. 2017 ). Hypoxia-inducible factor 1α ( hif-1α ) and vascular endothelial growth factor ( vegf ) are activated by hypoxia, which then leads to a disruption of the total blood-brain barrier (BBB), resulting in brain edema ( Lafuente et al. 2016 ; Mohaddes et al. 2017 ). In mammals, hypoxia is a trigger stimulus for vascular remodeling, altered cell permeability, and angiogenesis ( Zimna and Kurpisz 2015 ). Vegf has been proven to be an important determinant of angiogenesis and plays an important role in hypoxic-ischemic brain injury, functioning to promote angiogenesis and neuroprotection ( Cao et al. 2018 ). Vegf could stimulate axonal growth and improve neuronal cell survival. Under pathological conditions, vegf has a protective effect on the central nervous system ( Plaschke et al. 2008 ). Vascular endothelial growth factor-α ( vegfa ) is a pro-angiogenic member of the vascular endothelial growth factor ( vegf ) family ( Ferrara et al. 2003 ). Here, we showed that vegfa was up-regulated with decreasing DO concentration. It participates in the AGE-RAGE signaling pathway. In addition, egr1 , a gene of the EGR family involved in the AGE-RAGE signaling pathway, showed a decreasing trend. Early growth response 1 ( egr1 ) is considered to be a transcription factor sensitive to ischemia and hypoxia. In the brain tissue after ischemic stroke, the mRNA level of egr1 was significantly increased, while egr1 was overexpressed induced post-stroke inflammatory response and led to secondary brain damage after cerebral infarction ( Tureyen et al. 2008 ). The main mechanism of the AGE-RAGE signaling pathway is that the interaction between AGE and RAGE causes an oxidative stress response, which in turn promotes the increase in diacylglycerol (DAG) synthesis and activates PKC. Activation of PKC increases the expression of vascular endothelial growth factor ( vegf ), transforming growth factor-β ( tgf-β ), endothelin-1, and prostaglandin, which proliferates the extracellular matrix, leading to vasomotor dysfunction and capillaries changes in permeability ( Ishii et al. 1998 ; Toth et al. 2008 ). In our study, under hypoxic stress, vegfa expression was up-regulated and egr1 was down-regulated. It is shown that the brain of T. rubripes might create hypoxia tolerance to resist brain damage caused by hypoxia stress. HIF-1 signaling pathway is a critical pathway during hypoxia. vegfa not only mediates the AGE-RAGE signaling pathway but also mediates the HIF-1 signaling pathway. Hypoxia-inducible factor 1 ( hif1 ) is a transcription factor expressed in all metazoans and consists of hif-1 alpha and hif-1 beta subunits. Under hypoxic conditions, hif1 regulates the transcription of hundreds of genes in a cell-type-specific manner and was a major regulator in the HIF-1 signaling pathway ( Semenza 2007 ) . In our study, genes enriched for the HIF-1 signaling pathway: egln2 , flt1 , sl2a1 , vegfa and hk2 were all up-regulated. However, hif1 (and hif1an ) did not show a significant change during hypoxia in our results. In Eurasian perch, hif1 a mRNA levels were upregulated after acute severe hypoxia exposure in the brain and liver, but not in muscle tissue, whereas significant changes were detected in muscle, but not in the brain and liver after chronic moderate hypoxia exposure ( Rimoldi et al. 2012 ). Ndubuizu et al. demonstrated that despite the lack of hif1 activation, the relative mRNA levels of vegf in the aged cortex significantly increased ( Ndubuizu et al. 2010 ). Fong et al. demonstrated that the knockout of hif1 α in colon cancer cells had no effect on angiogenesis ( Fong 2008 ). Liu et al. demonstrated that the large yellow croaker ( Larimichthys crocea ) has low hypoxia tolerance compared with other fish species, and the mRNA levels of hif1α in its brain did not change markedly under hypoxic conditions ( Liu et al. 2018 ), which was consistent with our results. Hif1 α is specific for the hypoxia response, and its degradation mediated by three enzymes egln1 , egln2 , and egln3 ( Zhang et al. 2019 ) . Tcf7l2 positively regulated aerobic glycolysis by suppressing Egl-9 family hypoxia inducible factor 2 ( egln2 ), leading to the upregulation of hif1 α ( Xiang et al. 2018 ). Egln2 can hydroxylate foxo3a on two specific prolyl residues in vitro and in vivo. Hydroxylation of these sites prevents the binding of USP9x deubiquitinase, thereby promoting the proteasomal degradation of foxo3a ( Zheng et al. 2014 ). Flt-1 ( vegfr-1 ) and kdr ( vegfr-2 ) were two highly homologous tyrosine kinase receptors of vegf , which can synergize with vegf to promote angiogenesis ( Gille et al. 2000 ). Solute Carrier Family 2 Member 1 ( slc2a1 ) was the most important energy carrier of the brain: present at the blood-brain barrier and assures the energy-independent, facilitative transport of glucose into the brain( Klepper et al. 1999 ).The hyperpolarization has been proposed to occur via stimulation of Na + /K + ATPase pumps caused by a glucokinase ( gck ) induced rise of ATP levels within neurons, leading to inhibition of neuronal activity( De Backer et al. 2016 ). In summary, hif1 -mediated gene expression may be related to hypoxia-induced tolerance ( Jones and Bergeron 2001 ), or it may be related to the different stresses of different tissues on hypoxia stimulation. Hif1 was inhibited in the T. rubripes brain to prevent brain damage and activate related genes of angiogenesis, promotes blood vessel growth, maintains normal blood vessel density, and was protected from damage caused by ischemia and hypoxia. At the same time, hypoxic stress also promoted the brain to reduce energy consumption. In our research, we observed that when T. rubripes faced with hypoxia, they stopped swimming, laid on the bottom of the tank, and maintained their balance, with only slight swings in the fins. We speculated that the T. rubripes had a certain ability to tolerate hypoxia, and it can be made tolerant to hypoxia by changing its activity mode. The central nervous system of vertebrates controls the body's cognitive functions and autonomous motor activities( Paridaen and Huttner 2014 ). Axon guidance represents a key stage in the formation of neuronal networks ( Negishi et al. 2005 ). Axons were guided by a variety of guidance factors and these guidance cues are read by growth cone receptors, and signal transduction pathways downstream of these receptors converge onto the Rho GTPases to elicit changes in the cytoskeletal organization that determine which way the growth cone will turn ( Govek et al. 2005 ). Recent work in the D. rerio has shown that developmental hypoxic injury disrupts pathfinding of forebrain neurons in D. rerio , leading to errors in which commissural axons fail to cross the midline. EphrinB2a acts as a ligand for one of the receptor tyrosine kinases (RTK) of the epha3 , epha4 , or ephb4 families, which in turn sets off an intracellular signaling cascade in the RTK-expressing cell ( Stevenson et al. 2012 ). Hypoxia stimulated the uptake of 5-bromo-2'-deoxyuridine (BrdU) and reduced cell death Coincident with these proliferative changes, both hif1-α and phospho(p)-AKT were increased while ephb3 expression was decreased ( Baumann et al. 2013 ). These reports are consistent with our results. Our results showed that the expression of ephb3, ntng1 and rnd1 was down-regulated with decreasing DO concentration, while epha4, sema5b and nck2 appeared to be up-regulated. Among the down-regulated genes, rnd1 was a small signal transduction G protein and a member of the rnd subgroup of the Rho family of GTPases( Ridley 2006 ). It contributes to the regulation of the actin cytoskeleton in response to extracellular growth factors( Nobes et al. 1998 ). Ntng1 serves as an axonal guidance cue during vertebrate nervous system development( Nakashiba et al. 2000 ). Among the up-regulated genes, sema5b regulates the development and maintenance of synapse size and number in hippocampal neurons ( O'Connor et al. 2009 ). Nck2 adaptor proteins were involved in signaling pathways mediating proliferation, cytoskeleton organization, and integrated stress response ( Labelle-Cote et al. 2011 ). From this, we can speculate that the brain of T. rubripes may be affected by acute hypoxic stress. However, the brain responded promptly by inhibiting the expression of hif1, reducing the damage caused by hypoxic stress and repairing the damaged nerves in time. This may also be the reason why the brain of T. rubripes is able to tolerate hypoxia. Hypoxia induces bhlhe40 expression independent of hif1α but through a novel p53-dependent signaling pathway, and inhibition of bhlhe40 or p53 may facilitate muscle regeneration after ischemic injuries ( Wang et al. 2015 ). Studies have shown that prenatal hypoxia in mice can cause continuous changes in circadian rhythms in born mice ( Joseph et al. 2002 ). Hypoxia significantly reduced clock, cry2 , and per3 in GF and cry1 , cry2 , and per3 in PDLF ( Janjic et al. 2017 ). Hif-2α increased the expression levels of clock, bmal1 , per1 , cry1 , cry2 , and cki ɛ , and decreased the expression levels of per2 and per3 ( Yu et al. 2015 ) . The negative circadian regulator cry1 was a negative regulator of hif1a ( Dimova et al. 2019 ). Per3 was one of the primary components of circadian clock system. It was found to play a pivotal role in corticogenesis via regulation of excitatory neuron migration and synaptic network formation ( Noda et al. 2019 ). In our study, the expression levels of both genes cry1 and bhlhe40 showed a decreasing trend due to acute hypoxic stress. Hypoxia has caused brain damage in T. rubripes to some extent, thus causing circadian rhythm disturbance in T. rubripes . In conclusion, the brain of T. rubripes has a certain tolerance to hypoxia, but hypoxia also causes damage to it. Under hypoxic stress, the brain of T. rubripes struggles to maintain its central system from damage. Choose to maintain only basic life activities to resist the effects of hypoxic stress, such as stopping swimming to reduce oxygen consumption. In addition, through the KEGG network interaction map we found that metabolism-related pathways and genes could be well clustered together, including fatty acid metabolism, phosphate metabolism, nitrogen metabolism, bile secretion, and steroid hormone synthesis. Acl3 encodes a protein that is an isozyme of the long-chain fatty acid coenzyme A ligase family and plays a key role in lipid biosynthesis and fatty acid degradation. And this isozyme is highly expressed in the brain. Furthermore, acadl , the gene encoding LCAD (acyl coenzyme A dehydrogenase), has been shown to consume less energy in LCAD-deficient mice and also suffers from hypothermia, which can be explained by the fact that the reduced rate of fatty acid oxidation correlates with a reduced ability to generate heat( Diekman et al. 2014 ). From this, we can speculate that regulation of energy metabolism may be another effective way for T. rubripes to cope with hypoxic stress. Conclusion The purpose of this study was to explore the mechanisms of the brain in T. rubripes responses to acute hypoxic stress. The transcriptomes of their brain were sequenced and showed small changes under acute hypoxia. We also found that the circadian rhythm, neurodevelopment, and energy metabolism were affected to some extent under acute hypoxic stress. In addition, acute hypoxia also affected the HIF-1 signaling pathway and AGE-RAGE signaling pathway, probably promoting increased cerebral blood flow. Finally, our results indicated that the brain of T. rubripes was able to adapt to acute hypoxia and showed high tolerance to acute hypoxia. Declarations Funding This work was supported by the National Key R&D Program of China (2018YFD0900301-10) and the China Agriculture Research System (CARS-47). Conflicts of interest/Competing interests The authors declare that they have no competing interests. Data Availability All sequencing data were submitted to the Sequence Read Archive (SRA) public database in NCBI (www.ncbi.nlm.nih.gov/), under the accession code PRJNA645780. All other data included in this study are available upon request by contact with the corresponding author (Yang Liu). Code availability Python code for PCA was included in Supplementary Information File S12. Other codes used in this study are available upon request by contact with the corresponding author (Yang Liu). Authors' contributions. M. Bao performed the experiments and sampling, initially analyzed the results, and drafted the manuscript. F. Shang further analyzed the experimental results, made graphs and revised the paper. M. Bao and F. Shang contributed equally to this work. F. Liu, Z. Hu, S. Wang, X. Yang, Y. Yu, H. Zhang, C. Jiang, and J. Jiang participated in the experiment and sampling. Y. Liu and X. Wang wrote and reviewed the manuscript. All authors reviewed and approved the final manuscript. Ethics approval Animal experiments were approved by the Animal Care and Use committee at Dalian Ocean University. Consent to participate All names in the author list have been involved in various stages of experimentation or writing. Consent for publication All authors agreed to submit the paper for publication in the Journal of Fish Physiology and Biochemistry. Acknowledgements We are very grateful to Dalian Tianzheng Industrial Co. for providing experimental materials and sites. References Abdel-Tawwab M, Monier MN, Hoseinifar SH, Faggio C (2019) Fish response to hypoxia stress: growth, physiological, and immunological biomarkers Fish. Physiol Biochem 45:997–1013. doi: 10.1007/s10695-019-00614-9 Anders S, Pyl PT, Huber W (2015) HTSeq–a Python framework to work with high-throughput. sequencing data Bioinformatics 31:166–169. doi: 10.1093/bioinformatics/btu638 Arend KK et al (2011) Seasonal and interannual effects of hypoxia on fish habitat quality in central Lake. 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Genes Dev 28:1429–1444. doi: 10.1101/gad.242131.114 Zimna A, Kurpisz M (2015) Hypoxia-Inducible Factor-1 in Physiological and Pathophysiological Angiogenesis: Applications and Therapies Biomed Res Int 2015:549412 doi: 10.1155/2015/549412 Supplementary Files SupplementaryInformationS1S11S13.xls Supplementary Information Table S1 Size of library insert fragments. Supplementary Information Table S2 Summary of QC for sequencing data. Supplementary Information Table S3 Summary of mapping results. Supplementary Information Table S4 Distribution of aligned reads in Takifugu rubripes genomes. Supplementary Information Table S5 Quality control of sequencing after mapping to references. Supplementary Information Table S6 Summary of the reads in exons of Takifugu rubripes counted by HTSeq. Supplementary Information Table S7 Primers for quantitative real-time PCR. Supplementary Information Table S8 List of DEGs. Basic information of 167 differentially expressed genes identified under acute hypoxic stress with adjusted P value <0.05. Supplementary Information Table S9 Results of the enrichment analysis of the KEGG pathway. Corresponding symbols of Entrez IDs was were listed in Supplementary Information Table S11. Supplementary Information Table S10 Results of GO enrichment analysis. Supplementary Information Table S11 Gene symbols and their corresponding Entrez ID. Supplementary Information File S12 The Python code used to draw the PCA's 3D diagram. Supplementary Information Table S13 Normalized expression data of DEGs. Normalized data was calculated by the function varianceStabilizingTransformation() in DESeq2. SupplementaryInformationS12pythonPCA3Dplot.py.txt Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 24 May, 2021 Reviewers invited by journal 23 May, 2021 Editor invited by journal 20 May, 2021 Editor assigned by journal 19 May, 2021 First submitted to journal 19 May, 2021 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-542334","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":28772455,"identity":"20d31764-178e-4eeb-8f9f-6b8663e7f9f8","order_by":0,"name":"Mingxiu Bao","email":"","orcid":"","institution":"Dalian Ocean University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mingxiu","middleName":"","lastName":"Bao","suffix":""},{"id":28772456,"identity":"633b26ab-0824-465d-aff4-7abb5bd01fed","order_by":1,"name":"Fengqin Shang","email":"","orcid":"","institution":"Dalian Ocean 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09:27:56","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-542334/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-542334/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":9561752,"identity":"7dadaefa-398e-4d4b-a044-a40de089352f","added_by":"auto","created_at":"2021-05-25 16:01:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":162891,"visible":true,"origin":"","legend":"Volcano plot of differentially expressed genes. DEGs with adjusted P value \u003c0.05 and |log2FoldChange|\u003e=1 were chosen for volcano plot in each comparison group. Red points indicated the up-regulated DEGs, and green points indicated down-regulated DEGs.","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-542334/v1/f2c2b402929fac91e8da88c5.png"},{"id":9561405,"identity":"0ff5288a-a509-4bfb-86e4-024681c19eaf","added_by":"auto","created_at":"2021-05-25 15:58:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":127669,"visible":true,"origin":"","legend":"Boxplot and density of the foldchange of differentially expressed genes in each comparison group. The horizontal coordinate indicates the comparison groups between two different oxygen concentrations, and the vertical coordinate indicates the log2(foldchange). log2FoldChange\u003e0 indicates the genes were up-regulated, and log2FoldChange\u003c0 indicates the genes were down-regulated.","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-542334/v1/5996eac8d7001ef04a6a0dd5.png"},{"id":9561748,"identity":"cb873210-f412-4c3e-853b-5af8375c580f","added_by":"auto","created_at":"2021-05-25 16:01:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":160713,"visible":true,"origin":"","legend":"Venn diagram of differentially expressed genes. The comparison groups between two different oxygen concentrations are indicated by different colors. Arabic numbers indicate the number of differentially expressed genes.","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-542334/v1/369cddb051034728a512c9ff.png"},{"id":9562109,"identity":"1562a5ae-1f79-4408-8bd4-0a92ccd8718b","added_by":"auto","created_at":"2021-05-25 16:04:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":324012,"visible":true,"origin":"","legend":"Heatmap of differentially expressed genes. a: DEGs with adjusted P value \u003c0.05 were selected for heatmap. b: DEGs with adjusted P value \u003c0.05 and |log2FoldChange|\u003e=1 were selected for heatmap. Heatmap was drawn based on the normalized expression data (Supplementary Information Table S13). 20 samples were marked under the heatmap. ","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-542334/v1/87284a5191136e6ebae33a77.png"},{"id":9562110,"identity":"45876f51-339f-4929-b656-4568bbf58c09","added_by":"auto","created_at":"2021-05-25 16:04:47","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":158721,"visible":true,"origin":"","legend":"Principal component analysis (PCA) based on the normalized expression data of differentially expressed genes. All 20 samples were shaded by different light blue ellipses indicating the different treatment groups.","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-542334/v1/e82ca9e83c1776005b30efda.png"},{"id":9561413,"identity":"20a2657a-884f-489b-8c10-b17562383ec0","added_by":"auto","created_at":"2021-05-25 15:58:47","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":869119,"visible":true,"origin":"","legend":"GO enrichment analysis of differentially expressed genes. a: biological processes, b: cellular components, c: molecular functions.","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-542334/v1/030faf4691a40674e40c9b33.png"},{"id":9561414,"identity":"b1fe277d-285c-461a-b0bc-e38c0fa53f03","added_by":"auto","created_at":"2021-05-25 15:58:47","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1720395,"visible":true,"origin":"","legend":"Functional grouping network diagram for GO enrichment analysis. The annotated GO terms were plotted in a network diagram, and the ellipses of different colors in the diagram represented clustered functional groups. Each point represented a GO term.","description":"","filename":"floatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-542334/v1/4dd5ea302f88707b69afc33b.png"},{"id":9561747,"identity":"4aafac6c-d537-4ed0-b68f-b61de5fb7f1c","added_by":"auto","created_at":"2021-05-25 16:01:46","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":272791,"visible":true,"origin":"","legend":"The top 50 enriched KEGG pathways. ","description":"","filename":"floatimage11.png","url":"https://assets-eu.researchsquare.com/files/rs-542334/v1/a83a176785b0a489698c6d41.png"},{"id":9561412,"identity":"bc438843-088e-46b3-9eaf-d9bee628acd7","added_by":"auto","created_at":"2021-05-25 15:58:47","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":230854,"visible":true,"origin":"","legend":"Network of Enriched KEGG pathways and related genes. Red points represented the DEGs with up-regulated trend, green points represented the DEGs with up-regulated trend, and gray points represented enriched KEGG signaling pathway. Enriched metabolism-related pathways were shaded by a gray ellipse. Annotations of KEGG pathway was in Supplementary Information Table S9.","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-542334/v1/a9f408593996bde006040732.png"},{"id":9562108,"identity":"37c847e6-2fe9-40e1-8626-c96f91f75169","added_by":"auto","created_at":"2021-05-25 16:04:47","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":217280,"visible":true,"origin":"","legend":"Quantitative real-time PCR verification. The gene expression levels were evaluated relative to the expression level of β-actin using the 2−ΔΔCT method. The correlation between normalized counts from RNAseq and the relative expression from qPCR was calculated.","description":"","filename":"floatimage13.png","url":"https://assets-eu.researchsquare.com/files/rs-542334/v1/2572777036c4e71503c9eb4e.png"},{"id":13694903,"identity":"6b128240-c5cd-4ea6-8374-26e415382044","added_by":"auto","created_at":"2021-09-17 12:54:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3448870,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-542334/v1/a2acec6f-8ee5-4335-9191-32511d41ce01.pdf"},{"id":9561750,"identity":"1a75ca13-799d-4e27-98a1-f7d7d1343cec","added_by":"auto","created_at":"2021-05-25 16:01:47","extension":"xls","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":245248,"visible":true,"origin":"","legend":"Supplementary Information Table S1 Size of library insert fragments. \nSupplementary Information Table S2 Summary of QC for sequencing data. \nSupplementary Information Table S3 Summary of mapping results.\nSupplementary Information Table S4 Distribution of aligned reads in Takifugu rubripes genomes. \nSupplementary Information Table S5 Quality control of sequencing after mapping to references. \nSupplementary Information Table S6 Summary of the reads in exons of Takifugu rubripes counted by HTSeq. \nSupplementary Information Table S7 Primers for quantitative real-time PCR. \nSupplementary Information Table S8 List of DEGs. Basic information of 167 differentially expressed genes identified under acute hypoxic stress with adjusted P value \u003c0.05.\nSupplementary Information Table S9 Results of the enrichment analysis of the KEGG pathway. Corresponding symbols of Entrez IDs was were listed in Supplementary Information Table S11.\nSupplementary Information Table S10 Results of GO enrichment analysis. \nSupplementary Information Table S11 Gene symbols and their corresponding Entrez ID.\nSupplementary Information File S12 The Python code used to draw the PCA's 3D diagram.\nSupplementary Information Table S13 Normalized expression data of DEGs. Normalized data was calculated by the function varianceStabilizingTransformation() in DESeq2.","description":"","filename":"SupplementaryInformationS1S11S13.xls","url":"https://assets-eu.researchsquare.com/files/rs-542334/v1/2d35578ef145dc4b621fabda.xls"},{"id":9561403,"identity":"8cc77716-4f62-4aff-ad08-387b14876946","added_by":"auto","created_at":"2021-05-25 15:58:46","extension":"txt","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":3868,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformationS12pythonPCA3Dplot.py.txt","url":"https://assets-eu.researchsquare.com/files/rs-542334/v1/5fa23caa98b4245cc6407ef7.txt"}],"financialInterests":"","formattedTitle":"\u003cp\u003eComparative Transcriptomic Analysis of the Brain in Takifugu Rubripes Shows Its Tolerance to Acute Hypoxia\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHypoxia is reduced levels of oxygen. The existence of factors, such as reduced air pressure and poor gas exchange in nature, will cause the occurrence of a hypoxic environment, such as in plateaus(\u003ca href=\"#_ENREF_49\"\u003eQiu et al. 2012\u003c/a\u003e), aquatic environments(\u003ca href=\"#_ENREF_21\"\u003eJackson and Ultsch 2010\u003c/a\u003e) , and underground tunnels(\u003ca href=\"#_ENREF_4\"\u003eAvivi et al. 2005\u003c/a\u003e). The animals and plants living in a hypoxic environment have corresponding coping mechanisms in physiology, biochemistry, and behavior. Plants use substances other than oxygen as terminal electron acceptors, and the redox potential in the rhizosphere is reduced. Through a series of physiological and biochemical metabolic reactions, plants can adapt to or alleviate the damage caused by hypoxia stress(\u003ca href=\"#_ENREF_53\"\u003eSchmidt et al. 2018\u003c/a\u003e). Animals have different ways of adapting to different levels of hypoxia: in mild or moderate hypoxic environments, they adjust to the hypoxic environment through the adjustment of the overall horizontal compensation mechanism, such as faster breathing rates, increased pulmonary ventilation, alveolar-blood, and accelerating blood-tissue gas diffusion and strengthening the permanent expansion of capillaries, etc.(\u003ca href=\"#_ENREF_5\"\u003eAvivi et al. 1999\u003c/a\u003e; \u003ca href=\"#_ENREF_34\"\u003eLiu et al. 2001\u003c/a\u003e; \u003ca href=\"#_ENREF_54\"\u003eScott 2011\u003c/a\u003e; \u003ca href=\"#_ENREF_65\"\u003eWan et al. 2013\u003c/a\u003e). In severe hypoxic environments, adaptation through overall compensation alone is far from satisfying the body's energy needs, and more importantly, through the adjustment of cell metabolism and the induction of many anti-hypoxic factors adapt to the hypoxic environment at the molecular level. For example, intracellular free calcium increases during hypoxia, then it can activate calcium-related signaling pathways and regulate the increase in transcription of some hypoxia-sensitive genes (\u003ca href=\"#_ENREF_37\"\u003eMillhorn et al. 1997\u003c/a\u003e). \u003cem\u003eHif-1\u003c/em\u003e binds to hypoxia-responsive elements (HRE) on its target genes, triggering downstream target genes such as\u0026nbsp;\u003cem\u003eepo\u003c/em\u003e, inducible nitric oxide synthase (\u003cem\u003einos\u003c/em\u003e),\u0026nbsp;\u003cem\u003evegf\u003c/em\u003e, and other genes. Transcription affects physiological and pathological processes such as erythropoiesis, angiogenesis, apoptosis, and proliferation, and causes the body to produce a series of adaptive responses to hypoxia (\u003ca href=\"#_ENREF_10\"\u003eBruzzi et al. 1997\u003c/a\u003e; \u003ca href=\"#_ENREF_15\"\u003eErkan et al. 2007\u003c/a\u003e). In addition to the hypoxia caused by the reduction of the oxygen content in the environment, many physiological and pathological processes of higher animals also have the phenomenon of hypoxia. For example, in the early stages of mammalian embryonic development, a hypoxic environment is created in the womb. This hypoxic microenvironment is essential for embryo development (\u003ca href=\"#_ENREF_58\"\u003eSimon and Keith 2008\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eWhereas aquatic species (such as fish) are particularly vulnerable to hypoxic conditions, the hypoxic state of aquatic systems usually means that the saturation of dissolved oxygen (DO) is between 0-30% (\u003ca href=\"#_ENREF_47\"\u003ePelster and Egg 2018\u003c/a\u003e; \u003ca href=\"#_ENREF_68\"\u003eWu et al. 2003\u003c/a\u003e). The DO concentration in fish ponds depends generally on many factors including photosynthesis of phytoplankton, respiration of aquatic organisms, and/or the diffusion of atmospheric O\u003csub\u003e2\u003c/sub\u003e, O\u003csub\u003e2\u003c/sub\u003e partial pressure, water temperature, and salinity. In captivity, fish always face repetitive and chronic stress situations (e.g., confinement, crowding, handling, variable water quality including hypoxia) from which they cannot escape. Across a broad range of fishes, hypoxia can cause direct mortality but more commonly results in various sublethal effects, such as behavioral and physiological stress(\u003ca href=\"#_ENREF_1\"\u003eAbdel-Tawwab et al. 2019\u003c/a\u003e). Hypoxia has been shown to retard growth in bivalves, polychaetes, and fish (\u003ca href=\"#_ENREF_56\"\u003eShang and Wu 2004\u003c/a\u003e). It also can induce apoptosis(\u003ca href=\"#_ENREF_3\"\u003eArend et al. 2011\u003c/a\u003e). Fish have developed various adaptation strategies in the long-term evolution process, and their tolerance to hypoxia is very different (\u003ca href=\"#_ENREF_7\"\u003eBickler and Buck 2007\u003c/a\u003e). Studies have shown that fish could initiate special biological processes and molecular mechanisms in low-oxygen environments, and could more efficiently store and use oxygen through the regulation of key gene expression (\u003ca href=\"#_ENREF_52\"\u003eRimoldi et al. 2012\u003c/a\u003e). \u003cem\u003eSalmo salar\u003c/em\u003e\u0026nbsp;could induce vascular endothelial growth factor expression through hypoxia to promote angiogenesis and increase the body's oxygen supply (\u003ca href=\"#_ENREF_64\"\u003eVuori et al. 2004\u003c/a\u003e). In zebrafish (\u003cem\u003eDanio rerio\u003c/em\u003e) embryos, gene expression changed in response to hypoxia. \u003cem\u003eHif-1a\u003c/em\u003e played an important role in regulating the formation of neural crest and the central nervous system (\u003ca href=\"#_ENREF_60\"\u003eTon et al. 2003\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eAlthough there are many studies on molecular mechanisms related to hypoxia, the molecular basis of these adaptations has not been fully understood (\u003ca href=\"#_ENREF_69\"\u003eXia et al. 2018\u003c/a\u003e). The transcriptome is the sum of all the RNA expressed by a cell or tissue at a specific period. By detecting the expression difference of the transcriptome in different periods and tissues, the gene expression regulation status can be dynamically analyzed (\u003ca href=\"#_ENREF_67\"\u003eWilhelm et al. 2008\u003c/a\u003e). It is widely used to study the molecular mechanisms of many different species under specific conditions. The transcriptome analysis of \u003cem\u003eCarassius auratus\u003c/em\u003e\u0026nbsp;showed that the expression of glycolytic pathway-related genes in fish exposed to hypoxia for a long time will be significantly enhanced (\u003ca href=\"#_ENREF_32\"\u003eLiao et al. 2013\u003c/a\u003e). The brain is an oxygen-sensitive organ whose functions are highly susceptible to low oxygen levels (\u003ca href=\"#_ENREF_50\"\u003eRahman and Thomas 2015\u003c/a\u003e). Also, other studies indicate that the \u003cem\u003ecrucian carp\u003c/em\u003e may be relying on neurotransmitters and neuromodulators to suppress its CNS energy use under hypoxic conditions (\u003ca href=\"#_ENREF_42\"\u003eNilsson and Renshaw 2004\u003c/a\u003e). Under hypoxic conditions, the expression of hypoxia-inducible factors in the brain tissue of sturgeon increased significantly (\u003ca href=\"#_ENREF_47\"\u003ePelster and Egg 2018\u003c/a\u003e).\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eT. rubripes\u003c/em\u003e genome sequencing work was completed in 2002. It was found that the \u003cem\u003eT. rubripes\u003c/em\u003e genome is about 400Mb, which is only one-seventh the size of the human genome. It is the smallest genome of known vertebrates and the number of genes is similar to humans(\u003ca href=\"#_ENREF_9\"\u003eBrenner et al. 1993\u003c/a\u003e; \u003ca href=\"#_ENREF_26\"\u003eKai et al. 2011\u003c/a\u003e) , so it has been extensively studied as a model organism. We have carried out a simple experiment of acute hypoxia tolerance of \u003cem\u003eT. rubripes\u003c/em\u003e, which showed that few differentially expressed genes were found (\u003ca href=\"#_ENREF_23\"\u003eJiang et al. 2017\u003c/a\u003e). In this study, we set four O\u003csub\u003e2\u003c/sub\u003e concentration to detect the transcriptome changes in the brain of \u003cem\u003eT. rubripes \u003c/em\u003eand aimed to explore the mechanisms of the brain in \u003cem\u003eT. rubripes\u003c/em\u003e responses to acute hypoxic stress.\u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003eExperimental animals and acute hypoxia exposure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnimal experiments were approved by the Animal Care and Use committee at Dalian Ocean University. Healthy\u003cem\u003e\u0026nbsp;T. rubripes\u003c/em\u003e\u0026nbsp;with body weights of 461.75\u0026plusmn;40.66g were obtained from Dalian Tianzheng Industrial Co., in Dalian, Liaoning, China. Forty fish were randomly selected from the stock and divided equally into four groups kept in 380L tanks with a flow-through seawater supply. The temperature of the water was 19\u0026plusmn;0.5˚C. Sodium sulfite was used to regulate the DO which was detected by dissolved oxygen meter (Hanna HI9146-04, Romania). The DO of the four tanks (four groups) were maintained at 5.4\u0026plusmn;0.05 mg O\u003csub\u003e2\u003c/sub\u003e/L (ppm5.4), 4\u0026plusmn;0.05 mg O\u003csub\u003e2\u003c/sub\u003e/L (ppm4), 2\u0026plusmn;0.05 mg O\u003csub\u003e2\u003c/sub\u003e/L (ppm2) and 0\u0026plusmn;0.05mg O\u003csub\u003e2\u003c/sub\u003e/L (ppm0). The fish treated by the DO of 5.4\u0026plusmn;0.05 mg O\u003csub\u003e2\u003c/sub\u003e/L (ppm5.4) was considered as control because the DO was same as that in the stock. After 6 hours of treatment, fish were anaesthetized with 80 mg/L concentration of tricaine methane sulfonate (MS-222, Sigma) and then dissected. Brain was obtained and snap-frozen in liquid nitrogen, and then stored at \u0026minus;80℃. Then total RNA was extracted from the brain for the construction of RNA library and qPCR.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTranscript profiling and sequencing (RNAseq)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal RNA was quantified by the Qubit\u0026reg; RNA Analysis Kit in Qubit\u0026reg; 2.0 (Life Technologies, CA, USA), and its integrity was checked using an Agilent 2100 Bioanalyzer (Agilent Technologies, USA). Twenty RNA (cDNA) libraries (5 biological replicates of each group, including ppm5.4, ppm4, ppm2, and ppm0) were constructed for RNA sequencing and bioinformatics analysis. The library was then sequenced by Novaseq 6000 (Illumina, USA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProcessing of RNAseq data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaw data were firstly qualified by FastQC (www.bioinformatics.babraham.ac.uk/projects/fastqc/). Clean data (clean reads) were obtained after removing read with containing adapter, ploy-N and low-quality bases by Trimmomatic v0.38 (\u003ca href=\"#_ENREF_8\"\u003eBolger et al. 2014\u003c/a\u003e). All the downstream analyses were based on clean data. Reference genome (\u003cstrong\u003eassembly fTakRub1.2\u003c/strong\u003e, www.ncbi.nlm.nih.gov/genome/63) were downloaded from the genome database of NCBI (The National Center for Biotechnology Information). Paired-end clean reads were mapped to the indexed reference genome using Hisat2 v2.1.0 (\u003ca href=\"#_ENREF_28\"\u003eKim et al. 2015\u003c/a\u003e). The results of mapping were also qualified by FastQC. HTSeq v0.11.2 was used to count the reads mapped to each gene (\u003ca href=\"#_ENREF_2\"\u003eAnders et al. 2015\u003c/a\u003e). Differential gene expression analysis was performed by an R package DESeq2 v3.10 (\u003ca href=\"#_ENREF_36\"\u003eLove et al. 2014\u003c/a\u003e). Principal component analysis (PCA) was performed using an in-house python script (Supplementary Information File S12). Obtained DEGs were annotated by KOBAS v3.0 (\u003ca href=\"#_ENREF_71\"\u003eXie et al. 2011\u003c/a\u003e), and subsequently subjected to GO functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis by an R package clusterProfiler v3.18.1 (\u003ca href=\"#_ENREF_73\"\u003eYu et al. 2012\u003c/a\u003e). The software Cytoscape v3.7.1 was used to map the enriched gene and KEGG signaling pathway network interactions (\u003ca href=\"#_ENREF_57\"\u003eShannon et al. 2003\u003c/a\u003e). Several R packages (ggplot2, pheatmap, venn) were used to draw graphics for the visualization of analyzed data (cran.r-project.org/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation of DEGs by q RT-PCR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal RNA was extracted from the brain samples using RNAprep pure tissue kit (Tiangen, China). First-strand cDNA was synthesized from 1\u0026mu;g of total RNA using a PrimeScript\u0026trade; RT Master Mix (Takara, Japan). qPCR was performed using TransStart\u0026reg; Top Green qPCR SuperMix (Transgen, China) on an ABI StepOnePlus\u003csup\u003eTM\u003c/sup\u003e Real-Time PCR System (Life Technologies, USA). The primers for each gene are listed in Supplementary Information Table S7. The gene expression levels were evaluated relative to the expression level of \u0026beta;-actin using the 2\u003csup\u003e\u0026minus;\u0026Delta;\u0026Delta;CT\u003c/sup\u003e method (\u003ca href=\"#_ENREF_35\"\u003eLivak and Schmittgen 2001\u003c/a\u003e). The correlation between normalized counts from RNAseq and the relative expression from qPCR was calculated.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eConstruction of RNA libraries, quality control of raw data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal 20 libraries were constructed and sequenced from 20 samples in four treatment groups. The average size of insert fragments in these libraries was 275-315 bp (Supplementary Information Table S1). After quality control, clean data from 20 libraries was retained for next step (Supplementary Information Table S2). Total 102.75 Gb clean nucleotides and 685.06 million clean reads were obtained finally. Quality control showed that more than 95% and 89% reads of each sample had the quality scores of Q20 and Q30 (nucleotides with a quality value larger than 20 and 30), respectively. These results indicated our sequencing data were reliable and could be used for further analysis. All clean data was submitted to SRA databases in NCBI (BioProject: PRJNA645780).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMapping and counting reads\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eObtained reads with high quality were mapped to reference genome (Supplementary Information Table S3). Total and unique mapping rates of all samples were more than 92% and 89% respectively. Reads mapped to positive chains of genome were almost equal to that mapped to negative chains. According to the structure of the reference genome, mapped reads were mainly positioned in exons (77-81.5%) (Supplementary Information Table S4). Analysis of mapping results by FastQC showed that the level of duplication in mapped reads was about 15-21% from all samples (Supplementary Information Table S5). These results showed a good mapping quality for further analysis. Then mapped reads in exons were counted by HTSeq (Supplementary Information Table S6).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnalysis of differentially expressed genes (DEGs)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe counted reads by HTSeq were loaded by DESeq2 for differential expression analysis. The results showed that 167 genes were differentially expressed among the different treated groups with adjusted P value \u0026lt;0.05 (Supplementary Information Table S8). But only 19 DEGs with adjusted P value \u0026lt;0.05 and |log2FoldChange|\u0026gt;=1 were obtained (Fig. 1). The comparison group with the highest number of DEGs was ppm0Vsppm5.4, and the three comparison groups with the large significant differential fold change of DEGs were ppm0Vsppm2, ppm2Vsppm5.4, and ppm2Vsppm4. (Fig. 2). The numbers of DEGs in different comparison groups were showed in (Fig. 3). There were 118 DEGs between control group (ppm5.4) and treated group with the lowest level of DO (ppm0). Between group ppm5.4 and ppm2, 56 DEGs were identified. There only 5 DEGs between the group ppm0 and ppm2. But no DEGs were identified between the group ppm5.4 and ppm4.\u003c/p\u003e\n\u003cp\u003eTransformed count data (Supplementary Information Table S13) was extracted by DESeq2 using the method of variance stabilizing transformation in order to visualization, including clustering, heatmap and principal components analysis (Fig.3, 4, 5). All 20 samples were clustered into two main branches by the 167 DEGs (Fig. 4a, 5). The group ppm0 and ppm2 were clustered into one clade, ppm5.4 and ppm4 were clustered into another. These results indicated that the level of DO might make more serious effects on the brain of \u003cem\u003eT. rubripes when the concentration of DO was less than \u003c/em\u003e4 mg O\u003csub\u003e2\u003c/sub\u003e/L. In heatmap, 167 DEGs were mainly clustered into 3 clades based on their expression levels (Fig. 4a). About a quarter of the DEGs exhibited high expression levels, suggesting that they could play important roles when \u003cem\u003eT. rubripes\u003c/em\u003e was under the condition of low DO. Interestingly, range of DEGs fold change was small. There were only 19 DEGs with a fold change of more than 2 (Fig. 4b), and among these 19 DEGs, 16 DEGs were found between the control ppm5.4 and the treatment ppm0 (Fig. 1), which was the largest difference among the comparison groups, followed by when the oxygen concentration was ppm5.4 and ppm2 (Fig. 1). These results indicated that the transcriptomes of brain in \u003cem\u003eT. rubripes\u003c/em\u003e changed little during acute hypoxia treatment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnnotation and function analysis of DEGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe GO classification system grouped the DEGs into three main categories: biological process, cellular component, and molecular function (Fig. 6). The possible roles of DEGs were investigated by GO enrichment analysis. The enrichment analysis showed that most of the DEGs were significantly enriched to the category of biological processes (Fig. 6a). For example, the three terms (\"response to steroid hormone\", \"response to hypoxia\", and \"response to decreased oxygen levels\") were enriched significantly enriched with a high number of DEGs. The next significant enrichment was in the category of molecular function (Fig. 6b), and the top two enriched terms with a high number of DEGs were \"dioxygenase activity\" and \"RNA polymerase II\u0026minus;specific DNA\u0026minus;binding transcription factor binding\". However, for the category of cellular components, we found that the number of DEGs enriched was small and insignificant (Fig. 6c). Interestingly, we clustered all these enriched terms and found that they could be well clustered into eight major functional categories (Fig. 7). Among them, most of the significantly enriched terms were clustered into two functional categories, decreased oxygen levels hypoxia and corticosteroid corticosterone death glucocorticoid.\u003c/p\u003e\n\u003cp\u003eKEGG is a database resource containing many metabolic pathways and their relationships (\u003ca href=\"#_ENREF_27\"\u003eKanehisa and Goto 2000\u003c/a\u003e). In our study,167 DEGs were grouped into 187 known pathways and the largest group was MAPK signaling pathway, containing 10 DEGs, followed by PI3K-Akt signaling pathway(9), Axon guidance(6), IL-17 signaling pathway (5), HIF-1 signaling pathway(5). Among them, the MAPK signaling pathway enrichment was most significant (Fig. 8). Interestingly, through the network diagram we found that MAPK signaling pathway and PI3K-Akt signaling pathway had the highest degree and connected the most genes（Fig. 9）. For genes,\u003cem\u003e fos\u003c/em\u003e, \u003cem\u003ejun\u003c/em\u003e, \u003cem\u003erxra\u003c/em\u003e, \u003cem\u003evegfa\u003c/em\u003e, and \u003cem\u003eflt1\u003c/em\u003e had the highest degree and connected the most network pathways. Metabolism-related pathways and genes also clustered well together. In addition, five genes were found to be enriched in HIF-1 signaling pathways related to hypoxia stress: \u003cem\u003eLOC101071669(\u003cem\u003eegln2\u003c/em\u003e),\u0026nbsp;flt1, LOC101079462(hk2), LOC101063282(slc2a1)\u003c/em\u003e, and\u003cem\u003e vegfa.\u003c/em\u003e Among those HIF-1 signaling pathway-related genes, as the DO concentration decreased,\u0026nbsp;\u003cem\u003eall five \u003c/em\u003eenriched genes were up-regulated, while\u0026nbsp;\u003cem\u003ehif1a\u003c/em\u003e\u0026nbsp;did not change significantly.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation of DEGs by q RT-PCR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo validate our Illumina sequencing results, 6 genes were selected for q RT-PCR analysis. As shown in（Fig. 10）, although the relative expression levels were not completely consistent, the expression patterns of these genes identified by qRT-PCR were similar to those obtained in RNA-Seq analysis, which indicated the expression data from RNAseq was reliable.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eMaintaining homeostasis is a key function of the brain, and homeostasis includes blood oxygen levels(\u003ca href=\"#_ENREF_63\"\u003evan der Velpen et al. 2017\u003c/a\u003e). Hypoxia-inducible factor 1\u0026alpha; (\u003cem\u003ehif-1\u0026alpha;\u003c/em\u003e) and vascular endothelial growth factor (\u003cem\u003evegf\u003c/em\u003e) are activated by hypoxia, which then leads to a disruption of the total blood-brain barrier (BBB), resulting in brain edema (\u003ca href=\"#_ENREF_31\"\u003eLafuente et al. 2016\u003c/a\u003e; \u003ca href=\"#_ENREF_38\"\u003eMohaddes et al. 2017\u003c/a\u003e). In mammals, hypoxia is a trigger stimulus for vascular remodeling, altered cell permeability, and angiogenesis (\u003ca href=\"#_ENREF_76\"\u003eZimna and Kurpisz 2015\u003c/a\u003e).\u0026nbsp;\u003cem\u003eVegf\u003c/em\u003e\u0026nbsp;has been proven to be an important determinant of angiogenesis and plays an important role in hypoxic-ischemic brain injury, functioning to promote angiogenesis and neuroprotection (\u003ca href=\"#_ENREF_11\"\u003eCao et al. 2018\u003c/a\u003e). \u003cem\u003eVegf\u003c/em\u003e could stimulate axonal growth and improve neuronal cell survival. Under pathological conditions, \u003cem\u003evegf\u003c/em\u003e has a protective effect on the central nervous system (\u003ca href=\"#_ENREF_48\"\u003ePlaschke et al. 2008\u003c/a\u003e). Vascular endothelial growth factor-\u0026alpha; (\u003cem\u003evegfa\u003c/em\u003e) is a pro-angiogenic member of the vascular endothelial growth factor (\u003cem\u003evegf\u003c/em\u003e) family (\u003ca href=\"#_ENREF_16\"\u003eFerrara et al. 2003\u003c/a\u003e). Here, we showed that \u003cem\u003evegfa\u003c/em\u003e was up-regulated with decreasing DO concentration. It participates in the AGE-RAGE signaling pathway. In addition, \u003cem\u003eegr1\u003c/em\u003e, a gene of the EGR family involved in the AGE-RAGE signaling pathway, showed a decreasing trend. Early growth response 1 (\u003cem\u003eegr1\u003c/em\u003e) is considered to be a transcription factor sensitive to ischemia and hypoxia. In the brain tissue after ischemic stroke, the mRNA level of\u0026nbsp;\u003cem\u003eegr1\u003c/em\u003e\u0026nbsp;was significantly increased, while\u0026nbsp;\u003cem\u003eegr1\u003c/em\u003e\u0026nbsp;was overexpressed induced post-stroke inflammatory response and led to secondary brain damage after cerebral infarction (\u003ca href=\"#_ENREF_62\"\u003eTureyen et al. 2008\u003c/a\u003e). The main mechanism of the AGE-RAGE signaling pathway is that the interaction between AGE and RAGE causes an oxidative stress response, which in turn promotes the increase in diacylglycerol (DAG) synthesis and activates PKC. Activation of PKC increases the expression of vascular endothelial growth factor (\u003cem\u003evegf\u003c/em\u003e), transforming growth factor-\u0026beta; (\u003cem\u003etgf-\u0026beta;\u003c/em\u003e), endothelin-1, and prostaglandin, which proliferates the extracellular matrix, leading to vasomotor dysfunction and capillaries changes in permeability (\u003ca href=\"#_ENREF_20\"\u003eIshii et al. 1998\u003c/a\u003e; \u003ca href=\"#_ENREF_61\"\u003eToth et al. 2008\u003c/a\u003e). In our study, under hypoxic stress,\u0026nbsp;\u003cem\u003evegfa\u003c/em\u003e\u0026nbsp;expression was up-regulated and\u0026nbsp;\u003cem\u003eegr1\u003c/em\u003e\u0026nbsp;was down-regulated. It is shown that the brain of\u0026nbsp;\u003cem\u003eT. rubripes\u0026nbsp;might \u003c/em\u003ecreate hypoxia tolerance to resist brain damage caused by hypoxia stress.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHIF-1 signaling pathway is a critical pathway during hypoxia.\u0026nbsp;\u003cem\u003evegfa\u003c/em\u003e\u0026nbsp;not only mediates the AGE-RAGE signaling pathway but also mediates the HIF-1 signaling pathway. Hypoxia-inducible factor 1 (\u003cem\u003ehif1\u003c/em\u003e) is a transcription factor expressed in all metazoans and consists of\u0026nbsp;\u003cem\u003ehif-1\u003c/em\u003ealpha and\u0026nbsp;\u003cem\u003ehif-1\u003c/em\u003ebeta subunits. Under hypoxic conditions,\u0026nbsp;\u003cem\u003ehif1\u003c/em\u003e\u0026nbsp;regulates the transcription of hundreds of genes in a cell-type-specific manner and was a major regulator in the HIF-1 signaling pathway (\u003ca href=\"#_ENREF_55\"\u003eSemenza 2007\u003c/a\u003e) . In our study, genes enriched for the HIF-1 signaling pathway: \u003cem\u003eegln2\u003c/em\u003e, \u003cem\u003eflt1\u003c/em\u003e, \u003cem\u003esl2a1\u003c/em\u003e, \u003cem\u003evegfa\u003c/em\u003e and hk2\u0026nbsp;were all up-regulated. However,\u0026nbsp;\u003cem\u003ehif1\u003c/em\u003e\u0026nbsp;(and\u0026nbsp;\u003cem\u003ehif1an\u003c/em\u003e) did not show a significant change during hypoxia in our results. In Eurasian perch,\u0026nbsp;\u003cem\u003ehif1\u003c/em\u003ea mRNA levels were upregulated after acute severe hypoxia exposure in the brain and liver, but not in muscle tissue, whereas significant changes were detected in muscle, but not in the brain and liver after chronic moderate hypoxia exposure (\u003ca href=\"#_ENREF_52\"\u003eRimoldi et al. 2012\u003c/a\u003e). Ndubuizu et al. demonstrated that despite the lack of\u0026nbsp;\u003cem\u003ehif1\u003c/em\u003e\u0026nbsp;activation, the relative mRNA levels of\u0026nbsp;\u003cem\u003evegf\u003c/em\u003e\u0026nbsp;in the aged cortex significantly increased (\u003ca href=\"#_ENREF_40\"\u003eNdubuizu et al. 2010\u003c/a\u003e). Fong et al. demonstrated that the knockout of\u0026nbsp;\u003cem\u003ehif1\u003c/em\u003e\u0026alpha; in colon cancer cells had no effect on angiogenesis (\u003ca href=\"#_ENREF_17\"\u003eFong 2008\u003c/a\u003e). Liu et al. demonstrated that the large yellow croaker (\u003cem\u003eLarimichthys crocea\u003c/em\u003e) has low hypoxia tolerance compared with other fish species, and the mRNA levels of \u003cem\u003ehif1\u0026alpha;\u003c/em\u003e in its brain did not change markedly under hypoxic conditions (\u003ca href=\"#_ENREF_33\"\u003eLiu et al. 2018\u003c/a\u003e), which was consistent with our results.\u0026nbsp;\u003cem\u003eHif1\u003c/em\u003e\u0026alpha; is specific for the hypoxia response, and its degradation mediated by three enzymes\u0026nbsp;\u003cem\u003eegln1\u003c/em\u003e,\u0026nbsp;\u003cem\u003eegln2\u003c/em\u003e, and\u0026nbsp;\u003cem\u003eegln3\u0026nbsp;\u003c/em\u003e\u003cem\u003e(\u003c/em\u003e\u003ca href=\"#_ENREF_74\"\u003e\u003cem\u003eZhang et al. 2019\u003c/em\u003e\u003c/a\u003e\u003cem\u003e)\u003c/em\u003e.\u0026nbsp;\u003cem\u003eTcf7l2\u003c/em\u003e\u0026nbsp;positively regulated aerobic glycolysis by suppressing Egl-9 family hypoxia inducible factor 2 (\u003cem\u003eegln2\u003c/em\u003e), leading to the upregulation of \u003cem\u003ehif1\u003c/em\u003e\u0026alpha; (\u003ca href=\"#_ENREF_70\"\u003eXiang et al. 2018\u003c/a\u003e).\u0026nbsp;\u003cem\u003eEgln2\u003c/em\u003e can hydroxylate\u0026nbsp;\u003cem\u003efoxo3a\u003c/em\u003e\u0026nbsp;on two specific prolyl residues in vitro and in vivo. Hydroxylation of these sites prevents the binding of USP9x deubiquitinase, thereby promoting the proteasomal degradation of\u0026nbsp;\u003cem\u003efoxo3a\u003c/em\u003e\u0026nbsp;(\u003ca href=\"#_ENREF_75\"\u003eZheng et al. 2014\u003c/a\u003e). Flt-1 (\u003cem\u003evegfr-1\u003c/em\u003e) and\u0026nbsp;\u003cem\u003ekdr\u003c/em\u003e\u0026nbsp;(\u003cem\u003evegfr-2\u003c/em\u003e) were two highly homologous tyrosine kinase receptors of \u003cem\u003evegf\u003c/em\u003e, which can synergize with\u0026nbsp;\u003cem\u003evegf\u003c/em\u003e\u0026nbsp;to promote angiogenesis (\u003ca href=\"#_ENREF_18\"\u003eGille et al. 2000\u003c/a\u003e). Solute Carrier Family 2 Member 1 (\u003cem\u003eslc2a1\u003c/em\u003e) was the most important energy carrier of the brain: present at the blood-brain barrier and assures the energy-independent, facilitative transport of glucose into the brain(\u003ca href=\"#_ENREF_29\"\u003eKlepper et al. 1999\u003c/a\u003e).The hyperpolarization has been proposed to occur via stimulation of Na\u003csup\u003e+\u003c/sup\u003e/K\u003csup\u003e+\u003c/sup\u003e ATPase pumps caused by a glucokinase (\u003cem\u003egck\u003c/em\u003e) induced rise of ATP levels within neurons, leading to inhibition of neuronal activity(\u003ca href=\"#_ENREF_12\"\u003eDe Backer et al. 2016\u003c/a\u003e). In summary,\u0026nbsp;\u003cem\u003ehif1\u003c/em\u003e-mediated gene expression may be related to hypoxia-induced tolerance (\u003ca href=\"#_ENREF_24\"\u003eJones and Bergeron 2001\u003c/a\u003e), or it may be related to the different stresses of different tissues on hypoxia stimulation.\u0026nbsp;\u003cem\u003eHif1\u003c/em\u003e\u0026nbsp;was inhibited in the \u003cem\u003eT. rubripes\u003c/em\u003e brain to prevent brain damage and activate related genes of angiogenesis, promotes blood vessel growth, maintains normal blood vessel density, and was protected from damage caused by ischemia and hypoxia. At the same time, hypoxic stress also promoted the brain to reduce energy consumption.\u003c/p\u003e\n\u003cp\u003eIn our research, we observed that when \u003cem\u003eT. rubripes\u003c/em\u003e faced with hypoxia, they stopped swimming, laid on the bottom of the tank, and maintained their balance, with only slight swings in the fins. We speculated that the \u003cem\u003eT. rubripes\u003c/em\u003e had a certain ability to tolerate hypoxia, and it can be made tolerant to hypoxia by changing its activity mode. The central nervous system of vertebrates controls the body's cognitive functions and autonomous motor activities(\u003ca href=\"#_ENREF_46\"\u003eParidaen and Huttner 2014\u003c/a\u003e). Axon guidance represents a key stage in the formation of neuronal networks (\u003ca href=\"#_ENREF_41\"\u003eNegishi et al. 2005\u003c/a\u003e). Axons were guided by a variety of guidance factors and these guidance cues are read by growth cone receptors, and signal transduction pathways downstream of these receptors converge onto the Rho GTPases to elicit changes in the cytoskeletal organization that determine which way the growth cone will turn (\u003ca href=\"#_ENREF_19\"\u003eGovek et al. 2005\u003c/a\u003e). Recent work in the \u003cem\u003eD. rerio\u003c/em\u003e has shown that developmental hypoxic injury disrupts pathfinding of forebrain neurons in \u003cem\u003eD. rerio\u003c/em\u003e, leading to errors in which commissural axons fail to cross the midline.\u0026nbsp;\u003cem\u003eEphrinB2a\u003c/em\u003e\u0026nbsp;acts as a ligand for one of the receptor tyrosine kinases (RTK) of the\u0026nbsp;\u003cem\u003eepha3\u003c/em\u003e,\u0026nbsp;\u003cem\u003eepha4\u003c/em\u003e, or\u0026nbsp;\u003cem\u003eephb4\u003c/em\u003e\u0026nbsp;families, which in turn sets off an intracellular signaling cascade in the RTK-expressing cell (\u003ca href=\"#_ENREF_59\"\u003eStevenson et al. 2012\u003c/a\u003e). Hypoxia stimulated the uptake of 5-bromo-2'-deoxyuridine (BrdU) and reduced cell death Coincident with these proliferative changes, both hif1-\u0026alpha; and phospho(p)-AKT were increased while\u0026nbsp;\u003cem\u003eephb3\u003c/em\u003e\u0026nbsp;expression was decreased (\u003ca href=\"#_ENREF_6\"\u003eBaumann et al. 2013\u003c/a\u003e). These reports are consistent with our results. Our results showed that the expression of ephb3, ntng1 and rnd1 was down-regulated with decreasing DO concentration, while epha4, sema5b and nck2 appeared to be up-regulated. Among the down-regulated genes, rnd1 was a small signal transduction G protein and a member of the rnd subgroup of the Rho family of GTPases(\u003ca href=\"#_ENREF_51\"\u003eRidley 2006\u003c/a\u003e). It contributes to the regulation of the actin cytoskeleton in response to extracellular growth factors(\u003ca href=\"#_ENREF_43\"\u003eNobes et al. 1998\u003c/a\u003e). Ntng1 serves as an axonal guidance cue during vertebrate nervous system development(\u003ca href=\"#_ENREF_39\"\u003eNakashiba et al. 2000\u003c/a\u003e). Among the up-regulated genes,\u0026nbsp;\u003cem\u003esema5b\u003c/em\u003e\u0026nbsp;regulates the development and maintenance of synapse size and number in hippocampal neurons (\u003ca href=\"#_ENREF_45\"\u003eO'Connor et al. 2009\u003c/a\u003e).\u0026nbsp;\u003cem\u003eNck2\u003c/em\u003e\u0026nbsp;adaptor proteins were involved in signaling pathways mediating proliferation, cytoskeleton organization, and integrated stress response (\u003ca href=\"#_ENREF_30\"\u003eLabelle-Cote et al. 2011\u003c/a\u003e). From this, we can speculate that the brain of \u003cem\u003eT. rubripes\u003c/em\u003e may be affected by acute hypoxic stress. However, the brain responded promptly by inhibiting the expression of hif1, reducing the damage caused by hypoxic stress and repairing the damaged nerves in time. This may also be the reason why the brain of \u003cem\u003eT. rubripes\u003c/em\u003e is able to tolerate hypoxia.\u003c/p\u003e\n\u003cp\u003eHypoxia induces\u0026nbsp;\u003cem\u003ebhlhe40\u003c/em\u003e\u0026nbsp;expression independent of\u0026nbsp;\u003cem\u003ehif1\u0026alpha;\u003c/em\u003e\u0026nbsp;but through a novel p53-dependent signaling pathway, and inhibition of\u0026nbsp;\u003cem\u003ebhlhe40\u003c/em\u003e\u0026nbsp;or p53 may facilitate muscle regeneration after ischemic injuries (\u003ca href=\"#_ENREF_66\"\u003eWang et al. 2015\u003c/a\u003e). Studies have shown that prenatal hypoxia in mice can cause continuous changes in circadian rhythms in born mice (\u003ca href=\"#_ENREF_25\"\u003eJoseph et al. 2002\u003c/a\u003e). Hypoxia significantly reduced clock,\u0026nbsp;\u003cem\u003ecry2\u003c/em\u003e, and\u0026nbsp;\u003cem\u003eper3\u003c/em\u003e\u0026nbsp;in GF and\u0026nbsp;\u003cem\u003ecry1\u003c/em\u003e,\u0026nbsp;\u003cem\u003ecry2\u003c/em\u003e, and\u003cem\u003e\u0026nbsp;per3\u003c/em\u003e\u0026nbsp;in PDLF (\u003ca href=\"#_ENREF_22\"\u003eJanjic et al. 2017\u003c/a\u003e). Hif-2\u0026alpha; increased the expression levels of clock,\u0026nbsp;\u003cem\u003ebmal1\u003c/em\u003e,\u003cem\u003e\u0026nbsp;per1\u003c/em\u003e,\u0026nbsp;\u003cem\u003ecry1\u003c/em\u003e,\u0026nbsp;\u003cem\u003ecry2\u003c/em\u003e, and\u0026nbsp;\u003cem\u003ecki\u003c/em\u003e\u003cem\u003eɛ\u003c/em\u003e, and decreased the expression levels of\u0026nbsp;\u003cem\u003eper2\u003c/em\u003e\u0026nbsp;and\u0026nbsp;\u003cem\u003eper3 \u003c/em\u003e\u003cem\u003e(\u003c/em\u003e\u003ca href=\"#_ENREF_72\"\u003e\u003cem\u003eYu et al. 2015\u003c/em\u003e\u003c/a\u003e\u003cem\u003e)\u003c/em\u003e. The negative circadian regulator\u0026nbsp;\u003cem\u003ecry1\u003c/em\u003e\u0026nbsp;was a negative regulator of \u003cem\u003ehif1a\u003c/em\u003e (\u003ca href=\"#_ENREF_14\"\u003eDimova et al. 2019\u003c/a\u003e).\u003cem\u003e\u0026nbsp;Per3\u003c/em\u003e\u0026nbsp;was one of the primary components of circadian clock system. It was found to play a pivotal role in corticogenesis via regulation of excitatory neuron migration and synaptic network formation (\u003ca href=\"#_ENREF_44\"\u003eNoda et al. 2019\u003c/a\u003e). In our study, the expression levels of both genes cry1 and bhlhe40 showed a decreasing trend due to acute hypoxic stress. Hypoxia has caused brain damage in \u003cem\u003eT. rubripes\u003c/em\u003e to some extent, thus causing circadian rhythm disturbance in \u003cem\u003eT. rubripes\u003c/em\u003e. In conclusion, the brain of \u003cem\u003eT. rubripes\u003c/em\u003e has a certain tolerance to hypoxia, but hypoxia also causes damage to it. Under hypoxic stress, the brain of \u003cem\u003eT. rubripes\u003c/em\u003e struggles to maintain its central system from damage. Choose to maintain only basic life activities to resist the effects of hypoxic stress, such as stopping swimming to reduce oxygen consumption.\u003c/p\u003e\n\u003cp\u003eIn addition, through the KEGG network interaction map we found that metabolism-related pathways and genes could be well clustered together, including fatty acid metabolism, phosphate metabolism, nitrogen metabolism, bile secretion, and steroid hormone synthesis. \u003cem\u003eAcl3\u003c/em\u003e encodes a protein that is an isozyme of the long-chain fatty acid coenzyme A ligase family and plays a key role in lipid biosynthesis and fatty acid degradation. And this isozyme is highly expressed in the brain. Furthermore, \u003cem\u003eacadl\u003c/em\u003e, the gene encoding LCAD (acyl coenzyme A dehydrogenase), has been shown to consume less energy in LCAD-deficient mice and also suffers from hypothermia, which can be explained by the fact that the reduced rate of fatty acid oxidation correlates with a reduced ability to generate heat(\u003ca href=\"#_ENREF_13\"\u003eDiekman et al. 2014\u003c/a\u003e). From this, we can speculate that regulation of energy metabolism may be another effective way for \u003cem\u003eT. rubripes\u003c/em\u003e to cope with hypoxic stress.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe purpose of this study was to explore the mechanisms of the brain in \u003cem\u003eT. rubripes\u003c/em\u003e responses to acute hypoxic stress. The transcriptomes of their brain were sequenced and showed small changes under acute hypoxia. We also found that the circadian rhythm, neurodevelopment, and energy metabolism were affected to some extent under acute hypoxic stress. In addition, acute hypoxia also affected the HIF-1 signaling pathway and AGE-RAGE signaling pathway, probably promoting increased cerebral blood flow. Finally, our results indicated that the brain of \u003cem\u003eT. rubripes\u003c/em\u003e was able to adapt to acute hypoxia and showed high tolerance to acute hypoxia.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the National Key R\u0026amp;D Program of China (2018YFD0900301-10) and the China Agriculture Research System (CARS-47).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest/Competing interests \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll sequencing data were submitted to the Sequence Read Archive (SRA) public database in NCBI (www.ncbi.nlm.nih.gov/), under the accession code PRJNA645780. All other data included in this study are available upon request by contact with the corresponding author (Yang Liu).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePython code for PCA was included in Supplementary Information File S12. Other codes used in this study are available upon request by contact with the corresponding author (Yang Liu).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM. Bao performed the experiments and sampling, initially analyzed the results, and drafted the manuscript. F. Shang further analyzed the experimental results, made graphs and revised the paper. M. Bao and F. Shang contributed equally to this work. F. Liu, Z. Hu, S. Wang, X. Yang, Y. Yu, H. Zhang, C. Jiang, and J. Jiang participated in the experiment and sampling. Y. Liu and X. Wang wrote and reviewed the manuscript. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnimal experiments were approved by the Animal Care and Use committee at Dalian Ocean University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll names in the author list have been involved in various stages of experimentation or writing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors agreed to submit the paper for publication in the Journal of Fish Physiology and Biochemistry.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are very grateful to Dalian Tianzheng Industrial Co. for providing experimental materials and sites.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdel-Tawwab M, Monier MN, Hoseinifar SH, Faggio C (2019) Fish response to hypoxia stress: growth, physiological, and immunological biomarkers Fish. 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Genes Dev 28:1429\u0026ndash;1444. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1101/gad.242131.114\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZimna A, Kurpisz M (2015) Hypoxia-Inducible Factor-1 in Physiological and Pathophysiological Angiogenesis: Applications and Therapies Biomed Res Int 2015:549412 doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1155/2015/549412\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"fish-physiology-and-biochemistry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"fish","sideBox":"Learn more about [Fish Physiology and Biochemistry](https://www.springer.com/journal/10695)","snPcode":"10695","submissionUrl":"https://submission.nature.com/new-submission/10695/3","title":"Fish Physiology and Biochemistry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Acute hypoxia, Brain, Transcriptome, Takifugu rubripes, Gene expression","lastPublishedDoi":"10.21203/rs.3.rs-542334/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-542334/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHypoxia is reduced levels of oxygen. Especially in water, due to the complex environment, hypoxic situations often occur. Although fish can survive in low-oxygen waters, this survival ability depends on a complete set of coping mechanisms such as oxygen perception and gene-protein interaction regulation. The research on this mechanism is very meaningful. The present study was undertaken to examine the short-term effects of hypoxia on the brain in\u0026nbsp;\u003cem\u003eTakifugu rubripes\u003c/em\u003e. We sequenced the transcriptomes of the brain in\u0026nbsp;\u003cem\u003eT. rubripes\u003c/em\u003e\u0026nbsp;to studied their response mechanism to acute hypoxia. Total 167 genes with adjusted P values\u0026lt;0.05 were differentially expressed in the brain of\u0026nbsp;\u003cem\u003eT. rubripes\u003c/em\u003e\u0026nbsp;exposed to acute hypoxia. However, \u003cem\u003ehif1a\u003c/em\u003e, the master transcriptional regulator of the adaptive response to hypoxia, was not significantly regulated, which indicated that the\u0026nbsp;\u003cem\u003eT. rubripes\u003c/em\u003e\u0026nbsp;brain might prevent the HIF-1 signaling pathway.\u0026nbsp;Then Gene Ontology and KEGG Enrichment Analysis were carried out. The results indicated that hypoxia could cause metabolic and neurological changes, showing the clues of their adaptation to acute hypoxia. Overall, the sequenced transcriptomes of the brain in \u003cem\u003eT. rubripes\u003c/em\u003e\u0026nbsp;showed small changes under acute hypoxia. As the most complex and important organ, the brain of\u0026nbsp;\u003cem\u003eT. rubripes\u003c/em\u003e\u0026nbsp;might be able to create a self-protection mechanism to resist or reduce damage caused by acute hypoxia stress.\u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Comparative Transcriptomic Analysis of the Brain in Takifugu Rubripes Shows Its Tolerance to Acute Hypoxia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-05-25 15:58:44","doi":"10.21203/rs.3.rs-542334/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2021-05-24T04:13:00+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-05-23T10:59:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"Fish Physiology and Biochemistry","date":"2021-05-20T10:01:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-05-20T02:25:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Fish Physiology and Biochemistry","date":"2021-05-19T04:55:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"fish-physiology-and-biochemistry","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"fish","sideBox":"Learn more about [Fish Physiology and Biochemistry](https://www.springer.com/journal/10695)","snPcode":"10695","submissionUrl":"https://submission.nature.com/new-submission/10695/3","title":"Fish Physiology and Biochemistry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"ff6f7b21-e6ab-4aa9-a81e-d5acca83a46b","owner":[],"postedDate":"May 25th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":4568690,"name":"General Biochemistry"},{"id":4568691,"name":"Physiology"}],"tags":[],"updatedAt":"2021-08-21T03:21:18+00:00","versionOfRecord":[],"versionCreatedAt":"2021-05-25 15:58:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-542334","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-542334","identity":"rs-542334","version":["v1"]},"buildId":"rHA-KDH7Qsr4HCuvH75dn","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}