1 Introduction

In the wild and aquaculture conditions, marine fish expose to variable environmental stresses (Fu et al., 2014).In recent years, fish adaptation and response to several important external stresses, such as temperature (Smith et al., 2013), salinity (Larsen et al., 2012) and hypoxia(Ao et al., 2015), were investigated, providing us the knowledge of molecular and physiological changes of fish upon they meet these stresses. The sequencing-based investigations demonstrated that these stresses to aquatic organisms can affect physiological functions, including metabolism, immunity, growth and development (Schartl et al., 2013). In fish aquaculture pond, light could influence circadian rhythms of spawning and feeding, which has important effects on breeding and growth (Blanco-Vives, and Sánchez-Vázquez, 2009). Additionally, we noticed that light intensity (e.g., light or dark) can cause fish coloration changes.

The large yellow croaker, Larimichthys crocea, is a temperate-water migratory fish that belongs to the order of Perciformes and the family of Sciaenidae (Xiao et al.,2015). It is a commercially important marine species in China and East Asian countries owing to its high nutrition and delicate flavor. In recent decades, L. crocea aquaculture ranks the top in production among all net-caged marine fish species in China (Liu et al., 2014; Liu et al.,2013). The skin color correlates with price of the fish; the price of yellow individuals is about 3 times higher than yellowish ones. Up to date, the basic researches around the genetic improvement of the growth and disease resistance of L. crocea have been performed (Ye et al., 2014;Mu et al., 2010; Dong et al., 2016). L. crocea exhibits behavioral and physiological sensitivities to sound, lights and other environments (Su et al., 2007; Zhou et al.,2011). According to the previous reports, L. crocea can quickly and robustly respond to hypoxia in its brain (Gu and Xu, 2011). A lot of mucus would be secreted from the skin when it is exposed to air (Su et al., 2007). Meanwhile, the skin changes color when the light is dim. These traits make L. crocea a suitable experimental animal to investigate the molecular response of teleost to environmental changes. Although the proteomic changes in blood and mucus of hypoxia-treated L. crocea have been reported (Ao et al., 2015; Gu et al., 2011), the skin transcriptome profile of this species in light stress is largely unknown.

With the advances of high-throughput next-generation sequencing (NGS) technologies, RNA-seq is particularly suitable for quantifying transcript abundance in complex conditions. To study gene expression profile and identify possible driver genes in response to light stress, two groups of large yellow croakers were separately exposed to light for 0.5 h and 2 h; two groups were respectively exposed to dark for 0.5 h and 2 h; two groups were firstly exposed to dark for 0.5 h and 2 h and then treated with light for 5 min; while the group without being exposed to light or light was control. We sequenced and analyzed the transcriptome of the abdominal skin of L. crocea. All differently expressed genes (DEGs) were grouped in GO enrichments and KEGG pathways for functional annotation. Our findings indicated that the density of chromatophores changes induced by light might lead to skin coloration change in L. crocea, while genes with steady expression trends involved in the systematic and complicated molecular signal pathways could play a key role in responding to the light stress.

2 Materials and Methods

2.1 Ethics Statement

All experimental procedures were conducted in conformity with institutional guidelines for the care and use of laboratory animals, and protocols were approved by the Animal Care and Use Committee of Fisheries College of Jimei University.

2.2 Sample Collection and Stressing Experiment

Totally 140 L. crocea (90–100 g) individuals were collected from a mariculture farm in Ningde, Fujian Province, China. They were acclimated in aerated seawater at about 23℃ for seven days. The pilot experiment indicated that the skin coloration of L. crocea could change from yellowish to golden yellow when they were exposed to the dark for 0.5 h, and remain stable golden if being kept in dark. However, the coloration would rapidly recover to normal when it was transferred to light condition (Fig.1).According to that, stress experiments were conducted with the following methods. All 140 selected individuals were transferred to laboratory and separated equally into seven groups, 20 each. For control (0 h), abdominal skins including epidermis and dermis (approximate size of 1 cm2) were directly collected, and immediately frozen in liquid nitrogen. And the other six groups were respectively stressed for 0.5 h and 2 h in a tank with three treatments: exposed to daylight lamp (2500 Lm) (light groups);covered with a light-tight cloth in dark (dark groups); and firstly cultured in dark and then recovered in light for 5 min (recovery groups). Before skin collections, all organisms were rightly dipped with anesthetic. Then the scales were removed with tweezers and the muscle adhering to the skin was wiped off using a scalpel. A total of 140 abdominal skin samples were dissected, directly frozen in liquid nitrogen, and stored in a −80℃ freezer for RNA collection.

高校为了推进工匠精神与思想政治教育两者相互融合、相互促进，就必须为大学生的社会实践提供基地的保障，为社会实践活动创造条件。保障工匠精神的实践锻炼长期稳定能够保证实践活动的系统化、经常化、制度化，有利于形成科学、规范的实践教育体系。对基地进行长期有效的运营，建立完善的系统的机制有利于避免“走马观花”“蜻蜓点水”式的实践结果，保证实践锻炼能够循序渐进、扎实稳定的开展，从而使工匠精神能被学生更好地接受与运用。

Fig.1 The abdominal skin coloration of L. crocea changed in treatments of control (0 h), 0.5 h in dark, 2 h in dark and recovery from dark to light.

2.3 RNA Extraction and Sequencing

DEGs that were identified in the comparisons of 0 h–0.5 h and 0 h–2 h were used for GO enrichment analysis.In the 0 h–0.5 h comparison, 610 of 758 (80.5%) unigenes were classified into three major functional categories (i.e.,biological process, cellular component and molecular functions), and 237 subcategory terms, including 160(67.5%) BP categories, 30 (12.7%) CC categories and 47(19.8%) MF categories. In the 0 h–2 h comparison, 1523 of 1912 (79.7%) unigenes were annotated into 287 subcategories, including 191 (66.5%) BP categories, 41 (14.3%)CC categories and 55 (19.2%) MF categories. The detailed annotations for each category were depicted in Fig.5. Cellular process (GO:0009987) and metabolic process (GO:0008152) were the most enriched components out of BP terms; organelle (GO:0043226) and macromolecular complex (GO:0032991) were the most common terms in CC terms; while binding (GO:0005488) and cellular part (GO:0044464) were the top two terms in MF category. Within each of the three categories, some genes were assigned to subcategories of immune response (GO:0002376), responding to stimulus (GO:0050896) and structural molecule activity (GO:0005198). These immune-relevant and structure-sensitive genes might play key roles in responding to stress duration and other environment stresses.

2.4 Gene Expression Estimation and Differentially Expressed Gene Analysis

The further sample comparisons of light with dark in same duration investigated 377 (0.5 h) and 513 (2 h)DEGs, respectively (Fig.4). And 47 DEGs were shared in the above-mentioned two comparisons, which likely caused by the light or dark treatment.

庭前会议制度虽然有其先天的优越性，但是它毕竟是第一次出现在我国的法律制度内，所以在法条的规定上还存在一些缺陷，有待进一步修改和完善。

首先要严格按要求订立合同，明确各方权利义务，使其有效约束和规范各方行动，确保合同各项规定得到认真遵守。要严格审核工程设计变更，确保符合要求，防止因设计变更而增加成本。对可能出现的索赔，也要按合同规定处理。注意工程索赔的正当要求，维护自身权益，尽量降低损失，妥善处理可能出现的索赔。一旦出现索赔，也要按合同规定和法律要求进行索赔处理，防止出现不必要损失，确保PPP投资型项目效益提升。

2.5 qRT-PCR Validation

Furthermore, the light and dark samples of identical duration (0.5 h or 2 h) were compared to develop DEGs just corresponding to light or dark. As shown in Table 5,enrichments of 47 shared DEGs were mainly composed of cytoskeletal protein (e.g., cytoskeleton (GO:0005856)),muscle cell development (GO:0055001), skeletal muscle tissue development (GO:0007519) and regulation of actin cytoskeleton (ko04810)) and pigmentation (e.g.,eye pigmentation (GO:0048069) and developmental pigmentation (GO:0048066)). Therefore, we speculate that the reason for the coloration change of abdominal skin might be that dark stress can cause the cytoskeletal change, thus leads to the change in the density of chromatophores.

Table 1 Primers for amplification of selected differently expressed genes (DGE) and internal control genes

Annotation Forward (5’-3’) Reverse (5’-3’)Activating transcription factor 3 (ATF3) CCAGCGAGGAAGCGAAACA AAGTCGTCCAGGGTGAGCGT Calreticulin 3b (CALR3b) AGTTCTACGGAGATGCTGAGGC TGGATGACCAGGGGCTTTC Heat shock 70kDa protein 5 (HSPA5) AGGCTCATTGGAGATGCTGC GGACTTTGGGTTTTCCGCTAT Heat shock protein 8 (HSPA8) GATTTCTTCAACGGCAGGGAG TTTAGCGGGGATGGTGGTG Claudin e (CLDNE) ATCGTTGCTGCCTTTGGG CAGACGGGGATAAGGATGAGA Actin, alpha, cardiac muscle 1 (ACTC1) GAACCCCAAAGCCAACAGG TCACCAGAGTCCAGCACGATAC Leukocyte cell-derived chemotaxin 2 (LECT2) AGCCAGACGGCAAACCAGA CGTAGAACAACTTGAAGCACAGAC Major histocompability complex I (MHC-I) TTTGTTGCTGTTGGGATAGTTG TCCAGTCCTGTTTCGGTTCTT Ribonucleotide reductase M1 (RRM1) TCTACGGCTGGAAGTTGGG TCTCCTCCTCGCTCTTGGTT

3 Results

3.1 Raw Sequencing Reads and Quality Statistics

To understand the change of gene expression in response to light stress, the seven libraries (7 groups) constructed from skin of L. crocea were sequenced in the HiSeq 2000 platform (Illumina). As shown in Table 2, a total of 129.6 million of raw reads were generated, and an average of 15.3, 18.5 and 19.6 million reads were obtained from libraries of the control (0 h), 0.5 h duration and 2 h duration group, respectively. After reads filtering,a total of 128.9 million (99.5% of raw reads) clean reads were obtained. The percentages of Q30 bases are more than 95% for all samples, ensuring good results of the following reads alignment and analysis. We noticed thatthe clean reads with an average length of 90 bp (<100 bp)because of the end-trimming of low-quality bases. Raw sequencing data were deposited in the NCBI Sequence Read Archive (SRA) under Project Accession PRJNA-303096.

Table 2 Statistic summary of the transcriptomic data generated from L. crocea

ment Library Raw reads Clean reads Average reads length (bp) Q30 (%) GC (%)Control (0 h) Control 15273653 15192467 89.71 95.21 48.0 0.5 h_light 15536378 15457027 90.12 95.14 49.5 0.5 h-treatment 0.5 h_dark 18893105 18797645 90.10 95.19 48.5 0.5 h_recovery 21133364 21046245 90.09 95.82 49.0 2 h_light 21377309 21275133 90.48 95.33 49.0 2 h-treatment 2 h_dark 19972523 19891992 90.50 95.87 49.0 2 h_recovery 17385944 17304477 90.16 95.79 49.0

3.2 Sample Clustering and Differential Expression Genes Analysis

The high-quality reads were aligned to the draft reference genome of the large yellow croaker, while 48.07%reads were mapped and 40.09% reads were uniquely mapped, and a total of 95913 transcripts were assembled.For gene expression clusters, samples with the same duration time (0.5 h or 2 h) rather than same treatment (light,dark and recovery) were grouped together (Fig.2), which suggested that stress durations (0.5 h and 2 h) rather than light or dark were the principal stress factor.

Fig.2 Gene expression correlation analysis of L. crocea under the light stress. The color scheme represents for correlation coefficients among samples.

To identify key genes responding to duration stress,differentially expressed genes were analyzed between the control and 0.5 h-treatment group (0 h–0.5 h), as well as between the control and 2 h-treatment group (0 h–2 h). For the treatment duration comparisons, 758 (0 h–0.5 h) and 1912 (0 h–2 h) DEGs were identified. Among the DEGs identified with Cuffdiff, 397 genes were up-regulated and 361 genes were down-regulated in the 0 h–0.5 h comparison, and 1016 genes were up-regulated and 896 genes were down-regulated in the 0 h–2 h comparison (Fig.3(B)).More genes were up-regulated, suggesting a large number of genes were activated in the response of duration stress.These genes, showed different expression levels after stress, may play important roles in physiological processes associated with light duration stress. As shown in Fig.3(A), 496 (0 h–0.5 h) and 1515 (0 h–2 h) DEGs were detected by edgeR. A total of 241 and 995 DEGs were detected by both methods in the comparisons of 0 h–0.5 h and 0 h–2 h, respectively. To evaluate the consistence of the two methods, Fisher exact test (Upton, 1992) was performed to calculate the significance of the number of the shared DEGs. We found that the shared DEGs identified by Cuffdiff and edgeR were extremely significant for both comparisons (P < 2×10−6 in both cases), indicating that the DEGs identified in the two programs were generally consistent with each other. With genome sequence and gene structure information (exon and intron coordinate) in Cuffdiff, alternative splicing and exon usage analysis can be conducted (Hooper, 2014). Therefore,DEGs detected by Cuffdiff were chosen for further annotations and pathway analysis.

Fig.3 DEGs identification during the light stress. (A) Venn diagram of DEGs detected by Cuffdiff and edgeR. Intersections of circles indicate stress-responsive DEGs among different time point comparisons or different methods. (B)The number of up-/down-regulated DEGs identified by Cuffdiff.

Fig.4 Venn plots of DEGs in comparisons between light and dark with same duration (0.5 h and 2 h).

To retain high-quality reads for gene expression estimation, raw reads were trimmed with HTQC toolkit(Yang et al., 2013) by removing four kinds of low-quality reads: 1) adaptor contaminated sequences; 2) with unknown bases (‘N’) greater than 5%; 3) with five continuous low-quality bases at the 3’-end (Q-score < 20); 4)shorter than 50 bp after trimming. The filtered high-quality reads were mapped to L. crocea genome (Ao et al.,2015) using Tophat software with default parameters(Trapnell et al., 2012). To normalize and estimate the abundance each transcript, FPKM (Fragment Per Kilo base per Million) scores were calculated with Cufflinks(Tu et al., 2012). We compared treatment groups with control and light groups with dark groups to identify DEGs induced by stress duration and light stress using Cuffdiff software (Trapnell et al., 2013). The thresholds of significantly different expression were set at P-value <0.05 and fold change (FC) > 2. Meanwhile, edgeR (Robinson et al., 2010) was also used to identify DEGs based on previously reported gene annotation of L. crocea (Ao et al., 2015). WEGO (Ye et al., 2006) was adopted in Gene Ontology (GO) annotation analysis of the DEGs.Furthermore, enrichment analyses in GO terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were performed with custom scripts.

3.3 GO and KEGG Pathway Enrichment Analysis

Total RNA was extracted from the abdominal skin of each fish each group with Trizol Reagent (Invitrogen, CA,USA), and then pooled together with equal amounts each group. Purified RNA samples were detected with 260 nm/280 nm ranging from 2.0 to 2.2 and RIN ranging from 7.4 to 8.4 using Nanodrop ND-1000 spectrophotometer(LabTech, Ringmer, UK) and Agilent 2100 Bioanalyzer(Agilent Technologies, Palo Alto, CA), respectively. The high-quality RNA samples were used to construct RNA libraries using the TruSeq RNA Sample Prep Kit (Illumina, CA, USA). The libraries were sequenced on the HiSeq 2000 platform (Illumina, CA, USA) with 100 bp sequenced from both ends (paired-end).

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Fig.5 Function of DEGs in L. crocea in response to stress based on Gene Ontology distribution. GO subcategories are on the X-axis. Y-axis represents the percentage of genes in total DEGs. Red color represents the comparison between the control sample and 0.5 h-stress samples. Blue color represents the comparison between the control sample and 2 h-stress samples.

Nine out of twelve genes related with immune systems(e.g., ATF3, LECT2, CALR3b, HSPA5, HSPA8, CLDNE and MHC-I), regulation of actin cytoskeleton (e.g.,ACTC1) and glutathione metabolism (e.g., RRM1) were successfully validated by qRT-PCR. As shown in Fig.7,most of the qRT-PCR results agreed with the transcriptomic analysis, except for ACTC1 (actin, alpha, cardiac muscle 1). The comparisons of gene expression changes between RNA-seq and qRT-PCR supported the reliability of the method we employed for the estimation of gene expression.

Twelve genes at different expression levels related to functional biological process were selected in qRT-PCR validation. As a result, 9 out of 12 genes (75%) were successfully amplified. These 9 candidate genes were quantified, and the libraries of 0.5 h and 2 h treatments were compared with that of control (0 h), respectively. Total RNA were extracted from abdominal skin with Trizol Reagent (Invitrogen, Carlsbad, CA) according to the manufacturer’s instructions. The first strand cDNA was synthesized using GoScriptTM Reverse Transcription Systerm kit(Promega) following the manufacturer’s protocol. The qRT-PCR was performed on StepOnePlus thermocyler(Applied Biosysterms) using SuperReal PreMix Plus SYBR Green I (Tiangen Biotech, Beijing, China). The primers of target genes used in qRT-PCR were designed using Primer 5.0 software and listed in Table 1. β-actin,served as the internal control, was amplified with β-actin-F and R. The amplifications were carried out in a total volume of 20 µL containing 6.8 µL of 1:100 diluted cDNA,10 µL 2× SuperReal PreMix Plus SYBR Green I, 2 µL 50× ROX Reference Dye and 0.6 µL of each primer (10µmol µL–1). The amplification program was as follows:95℃ for 15 min, 40 cycles with 95℃ for 15 s and 60℃ for 1 min, followed by a dissociation stage to verify the specificity of PCR products. All samples were tested in quadruplicate. The relative expression of each target gene was determined by comparative threshold cycle method (2–ΔΔCt).

Table 3 Significant GO enrichments with significantly annotated DEGs for comparisons of the control and 0.5 h-treatment group (0 h–0.5 h) and the control and 2h-treatment group (0 h–2 h)

Notes: Counts indicate DEGs clustered into the GO terms; sizes indicate all unigenes associated with the GO terms. FDR represents for false discovery rate.

Comparison GO ID GO term description Counts Sizes FDR GO:0060536 Cartilage morphogenesis 7 14 0.000 0 h?0.5 h GO:0060788 Ectodermal placode formation 6 17 0.010 GO:0048708 Astrocyte differentiation 3 3 0.015 GO:0043049 Otic placode formation 5 12 0.017 GO:0030916 Otic vesicle formation 5 14 0.039 GO:0042981 Regulation of apoptotic process 18 164 0.046 GO:0043067 Regulation of programmed cell death 18 166 0.050 GO:0003779 Actin binding 21 215 0.015 GO:0008092 Cytoskeletal protein binding 29 367 0.040 GO:0043436 Oxoacid metabolic process 70 361 0.009 GO:0019752 Carboxylic acid metabolic process 68 353 0.014 GO:0043207 Response to external biotic stimulus 22 76 0.017 GO:0007049 Cell cycle 62 316 0.017 GO:0000188 Inactivation of MAPK activity 6 8 0.020 GO:0043407 Negative regulation of MAP kinase activity 7 11 0.022 GO:0042074 Cell migration involved in gastrulation 19 64 0.036 0 h?2 h GO:0006575 Cellular modified amino acid metabolic process 18 60 0.046 GO:0005856 Cytoskeleton 100 603 0.005 GO:0000796 Condensin complex 4 4 0.008 GO:0043292 Contractile fiber 11 29 0.010 GO:0015629 Actin cytoskeleton 34 170 0.038 GO:0005509 Calcium ion binding 130 788 0.005 GO:0016755 Transferase activity, transferring amino-acyl groups 11 29 0.022

Table 4 The top 10 KEGG pathways with significantly annotated DEGs for the 0 h–0.5 h comparison group and the 0 h–2 h comparison group

Comparison KEGG ID Pathway description Counts Sizes FDR ko04380 Osteoclast differentiation 18 151 0.005 ko04145 Phagosome 0 h–0.5 h 20 182 0.007 ko05130 Pathogenic Escherichia coli infection 11 69 0.007 ko04670 Leukocyte transendothelial migration 19 187 0.022 ko05412 Arrhythmogenic right ventricular cardiomyopathy 14 131 0.050 ko04142 Lysosome 15 146 0.050 ko04912 GnRH signaling pathway 13 128 0.108 ko00480 Glutathione metabolism 6 41 0.148 ko03030 DNA replication 6 42 0.153 9 83 0.167 ko04670 Leukocyte transendothelial migration 44 187 0.002 ko04380 Osteoclast differentiation 35 151 0.012 ko00480 Glutathione metabolism 13 41 0.047 ko04612 Antigen processing and presentation 17 61 0.047 ko04141 Protein processing in endoplasmic reticulum 40 197 0.050 0 h–2 h ko00330 Arginine and proline metabolism 15 55 0.096 ko00051 Fructose and mannose metabolism 16 63 0.148 ko03030 DNA replication 12 42 0.148 ko04110 Cell cycle 29 143 0.217 ko04145 Phagosome 35 182 0.236 ko05131 Shigellosis

Table 5 Significant GO and KEGG enrichments of the 47 shared DEGs identified from comparisons between 0.5 h-light and 0.5h-dark group and between 2 h-light and 2-dark

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3.4 Gene Expression Trend with Light Duration

在φTHNS,auth中，理想状态下首先A发送THNS1，B接收THNS1；接着B发送THNS2，S接收THNS2；然后S发送THNS3,A接收THNS3，最后A发送THNS4，B接收THSN4。由于篇幅限制，本研究只给出关键步骤PCL分析。根据THNS,auth，有如下过程：

3.5 Validation of Gene Expression Levels

In addition, several function categories were signifycantly enriched among DEGs. For the stress duration analysis, significantly enriched DEGs of 0 h–0.5 h comparison were mainly related to cytoskeletal protein, immunity and cell apoptosis. Cytoskeletal protein related categories were detected in GO enrichments including cartilage morphogenesis (GO:0060536), actin binding(GO:0003779) and cytoskeletal protein binding (GO:0008092) (Table 3). The immune-relevant items were identified in KEGG metabolism pathways including phagosome, leukocyte transendothelial migration, lysosome,pathogenic Escherichia coli infection and Shigellosis(Table 4). Regulation of apoptotic process (GO:0042981)and DNA replication can be classified as cell apoptosis.Compared with duration of 0 h–0.5 h, most items of DEGs indentified in 0 h–2 h duration comparison were identical except for energy metabolism, which was identified in GO enrichments (e.g., oxoacid metabolic process (GO:0043436) and cellular modified amino acid metabolic process (GO:0006575)) and KEGG pathway (e.g., Fructose and mannose metabolism). The identical categories of cytoskeletal protein might be resulted from light or dark stress. Many immunity and cell apoptosis related categories were enriched can aid to avoid inflammatory injury in fish. Energy metabolism category specially found in 2 h stress duration can help the fish maintain energy balance during the stress processing. Besides, other metabolisms such as glutathione metabolism, arrhythmogenic right ventricular cardiomyopathy and GnRH signaling pathway were also identified in the enrichments.

Our experiments were designed to identify DEGs under light stress for different durations, which provided us a good chance to analyze gene expression trends associated with the stress duration. We clustered DEGs by their normalized expression levels (FPKM values) in Fig.6. All regulated genes were grouped into 14 clusters by their expression profiles, as shown by color bars besides the gene expression heat map (Fig.6). Eight out of fourteen expression trends were highlighted as subsets from ‘a’ to‘h’. Regular gene expression trends including increasing and descending, were observed in the majority of the clusters. Detailed GO annotation analysis suggested that the genes clustered by expression also enriched in similar biological functions. The most significant enriched pathways in the increasing trends of b, c, f and g were related to fatty acid metabolic process, response to lipid, inactivation of MAPK activity, and intermediate filament cytoskeleton organization, respectively; while the functions of genes in the descending trends of d and e were enriched in response to external stimulus and oxidoreductase activity, respectively. The regular trend of transcript abundances revealed systematic and complicated regulatory cascades in responding to light stress.

4 Discussion

4.1 DEG Analysis and Gene Expression Trends for the Stress Duration

According to clustering, the RNA-seq successfully grouped the samples under identical stress responses by gene expression profiles. Nevertheless, similar experimental treatments (light, dark or recovery) were not clustered together as expected, indicating that light and dark treatments are not the principal stress factors.Fig.3(A) identifies 521 shared DEGs in two comparisons,in spite of different light durations. In addition to the large number of shared DEGs, many functional GO terms and KEGG pathways were both enriched in two cases(Tables 3 and 4). Table 4 and Fig.8 show that osteoclast differentiation pathway was enriched in two comparisons.Previous studies reported that osteoclast differentiation plays an important role in response to hypoxic stress in mice (Fukuoka et al., 2005), suggesting teleost share similar molecular response to hypoxic stress in terms of osteoclast differentiation. There were more specific DEGs and enriched function in the 0 h–2 h comparison group,indicating that the molecular response of the large yellow croaker to the light duration is a continuous regulation progress. Additionally, the gene expression clusters for DEGs revealed regular increasing/decreasing gene expression trends for stress resistance (Fig.6). The genes with identical trends may be regulated by similar genes or pathways.Therefore, the gene expression trends illuminated the finetuned regulation networks underlying the stress.

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Fig.6 Heat map and subclusters expression change of experimental L. crocea samples. The color scheme in the heat map represents gene expression measured by FPKM. Eight clusters surrounding the heat map show the representative gene expression trends. The most significantly enriched pathway for each clusters are: (a) regulation of synaptic transmission,(b) fatty acid metabolic process, (c) response to lipid, (d) response to external stimulus, (e) oxidoreductase activity, (f) inactivation of MAPK activity, (g) intermediate filament cytoskeleton organization, and (h) mitotic cell cycle.

Fig.7 Comparison of relative fold changes from RNA-seq and qRT-PCR in the 0 h–0.5 h and 0 h–2 h studies. The transcript expression levels of the selected genes are normalized to that of the β-actin gene. ATF3: activating transcription factor 3;CALR3b: calreticulin 3b; HSPA5: heat shock 70 kDa protein 5; HSPA8: heat shock protein 8; CLDNE: claudin e; ACTC1:actin, alpha, cardiac muscle 1; LECT2: leukocyte cell-derived chemotaxin 2; MHC-I:major histocompability complex I;RRM1: ribonucleotide reductase M1.

Fig.8 DEGs list involved in osteoclast differentiation pathway (K04380). Blue, red and green color represents for specific DEGs in the 0 h–0.5 h comparison, 0 h–2 h comparison and both comparisons, respectively.

4.2 Important Biological Genes and Pathways for the Light Stress

For pathway enrichment analysis of DEGs in duration comparisons, immunity, metabolism and actin were most frequently represented categories. For immunity category,response to external biotic stimulus, leukocytes transmitgrating, phagosome and antigen processing and presentation subcategories were largely enriched and represented in both comparisons. Up-regulated gene of activating transcription factor 3 (ATF3) and down-regulated gene of leucocyte cell-derived chemotaxin-2 (LECT2)were involved in pathway of responding to external biotic stimulus. ATF3 was shown to have a crucial role in the coordinate gene expression induced by eukaryotic initiation factor 2 (eIF2), and occurs early in response to a more diverse set of environmental stress (Jiang et al.,2004). Similarly, LECT2 was a key gene for cell growth,differentiation and autoimmune (Li et al., 2008). It was involved in response pathways to external biotic stimulus.The activation of phagosome leads to rapid killing and degradation of bacteria, thus preventing escape of bacteria to the cytosol (Herskovits et al., 2007). In a similar manner, MAP kinases (MAPKs) play significant roles in the immune system by regulating key cellular events including cell migration and phagocytosis (Crean et al.,2002; Huang et al., 2009). It was reported in previous studies that the hypothalamic-pituitary-adrenal (HPA)axis could prevent cerebral inflammation by the suppression of ET-1 (endothelin-1) and ADM (adrenomedullin)(Nadeau and Rivest, 2003; Sorrells and Sapolsky, 2007;Hayashi et al., 2004; Takahashi et al., 2003), and the HPA axis is mainly activated by ET-1/ADM and IL-6/TNF-α in hypoxia (Ao et al., 2015). RNA-seq analysis indicated that the expression of inflammatory cytokine genes (IL-6/TNF-α) was significantly regulated to protect individuals from cerebral inflammatory injury. Energy metabolism and cellular differentiation were the two major subclasses of metabolism analysis. Energy metabolism related pathways such as oxoacid metabolic process and carboxylic acid metabolic process were obviously detected after 2 h duration stress. In the previous ischemic/hypoxic brain studies, energy metabolism was considered to play a key role in protecting the organs from the consequences of energy deprivation (Schurr, 2002). And a broad range of functions in energy metabolism were enriched in KEGG pathway, such as arginine and proline metabolism, fructose and mannose metabolism. This led to a speculation that the regulation on energy metabolism would keep energy supplies under hypoxia. Prior report suggests that neuro-endocrine-immune regulatory networks help this fish avoid cerebral inflammatory injury and maintain energy balance under hypoxia stress (Ao et al., 2015). Oxidoreductase activity (Fig.6e) and related genes TRα (hyroid hormone receptor α) were significantly down-regulated during the stress treatment, which might help to reduce cellular oxygen consumption during the process of stressing. Broad immune-relevant genes and metabolism identified in duration stress indicated a welldeveloped immunity system of this species responding to external stress.

In addition, DEGs identified between light and dark exhibit significant enrichments in cytoskeletal proteins and pigmentation. Cytoskeletal proteins are indispensable for mechano-transduction processes, since cytoskeletal network facilitates the translocation of signal molecules from the focal adhesion site to the cytoplasm (Shyy and Chien, 2002). The ACTC1 (actin, alpha, cardiac muscle 1)gene expresses in strained muscle and encodes cardiac muscle alpha actin, which can function in the maintenance of cytoskeleton, cell motility and muscle contraction (Bertola et al., 2008). Muscle cell development relevant gene of Myosin-7B (MYH7B) was involved in muscle contraction (Desjardins etal., 2002). We also noticed that the developmental pigmentation (GO:0048066) and regulation of retinoic acid biosynthetic process (GO:1900052) were enriched. Thus, we speculated that the fish firstly perceived changes of light intensity using eye,then led to cytoskeleton construction induced by the skeletal muscle cell development can cause the contraction of pigment cell, and eventually promoted skin coloration changes. This can partially explain the abdominal skin coloration change from yellowish to golden yellow when they were transferred from light to dark. Further investigation was still needed to validate this speculation.

5 Conclusions

This study provided the gene expression profiles responding to the light and dark stress with two different durations. The results revealed that many genes involved in immune responses and energy metabolism, such as ATF3, LECT2, ADM, IL-6/INF-α and TRα, were signifycantly influenced by the stress durations. These genes and involved pathways are crucial to avoid cerebral inflamematory injury and to maintain energy balance of the stressed large yellow croakers. The identified DEGs related to those biological functions offer us systematic knowledge to understand gene expression trends and regulations responding to stress in L. crocea. Additionally,light and dark comparisons suggest that the skin coloration change is likely attributed to changes in the density of chromatophores according to DEGs annotations. The gene expression trends identified in this study will facilitate the gene network analysis in the future. In a summery,the transcriptome profile analysis would provide valuable information for future studies on coloration change and broad stress response in teleosts.

Acknowledgements

This work was supported by grants from the National Natural Science Foundation of China (No. U1205122),Key projects of the Xiamen Southern Ocean Research Center (14GZY70NF34), the Natural Science Foundation of Fujian Province (No. 2015J05069), ‘Li ShangDa’Foundation and Innovation Research Team Foundation of Jimei University (No. 2010A02). The authors thank Wanbo Li and Edna Falcis Salcedo as they revised the manuscript and gave a lot of helpful suggestions.

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