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1 49 2 Vol.49, No OCEANOLOGIA ET LIMNOLOGIA SINICA Mar., 2018 (Lateolabrax japonicus) * 1, ( ; ; ) (Lateolabrax japonicus), Illumina HiSeq TM2000 (0, 0.1, 1.0, 10.0, 100μg/L) Trinity, clean reads , 53716, 135, , BLAST, Swiss-prot Nr Pfam KEGG KOG GO KEGG 6 141, KOG ,,,,,, ; ; ; Q955; S949; X55 doi: /hyhz (Chlorophenols, CPs), (U.S. Environmental Protection Agency, 1992;, 2009;, 2009), (, 2011),, (do Nascimento et al, 2004; Kot-Wasik et al, 2004; Kawaguchi et al, 2005;, 2006;, 2009) (, 2001;, 2006) (, 2001; Liu et al, 2006),, (2009) (Gobiocypris rarus) SOD GSH HSP70, (2009), (2003) (Carassius auratus), (Lockhart et al, 2000),, * ( ), ;, 2013C11027,,, @qq.com :,,, xiaojunyan@hotmail.com : , :

2 2 : (Lateolabrax japonicus) 353 (Huang et al, 2012; Bougas et al, 2013; Guo et al, 2013; Elran et al, 2014; Hook et al, 2014; Hussainzada et al, 2014;, 2016), (, 2015) (Lateolabrax japonicus),,, (, 2016),,,,,, 1 1.1, 7d,, 2 (, ),, 1m 3, 24h 20, ph 7.8±0.7,, 1, 368±15g 1.2 0μg/L (CVST0) 0.1μg/L (CVST0.1) 1.0μg/L (CVST1.0) 10μg/L (CVST10) 100μg/L (CVST100), 3, 15, 100mL,, 1, 1/2,, 5,,,, 4h, 80, 1.3 RNA RNA, Illumina bp N 5% (Clean Data) Q20 Q30 GC, Trinity 1.5 NCBI Nr Swiss-Prot KEGG pathway Pfam KOG, BLAST, unigenes cdna Illumina, 6.00G Trinity, clean reads , 53716, N bp 71258, N bp Genes GC Transcripts GC, 2.2, PCA 1, 2,,, 表 1 花鲈肝脏转录组测序组装质量统计 Tab.1 Assembly quality of L. japonicas liver transcriptome GC GC% GC% N

354 49, 40.20% Swiss-prot 21459 unigenes, 39.95%; Nr 26464 unigenes, 49.27%; Pfam 18896 unigenes, 35.18%; KEGG 12403 unigenes, 23.09%; KOG 17262 unigenes, 32.14%; GO 19159 unigenes, 35.67% Fig.

3 354 49, 40.20% Swiss-prot unigenes, 39.95%; Nr unigenes, 49.27%; Pfam unigenes, 35.18%; KEGG unigenes, 23.09%; KOG unigenes, 32.14%; GO unigenes, 35.67% Fig.1 1 PCA Results of PCA for different samples 3 Fig.3 The Venn diagrams of differentially-expressed genes of L. japonicus 2 Fig.2 Length distribution of genes and transcripts 2.3 3, , 4 135, GO genes, 5, e 1e 10, ( 2) BLAST E-value, unigenes GO (biological process) (cellular component) (molecular function) unigenes 25 DNA (transcription, DNA-dependent), DNA (regulation of transcription, DNA-dependent); 15 (integral to membrane) (cytoplasm), (nucleus) (plasma membrane); 10 ATP (ATP binding) (zinc ion binding) (protein binding) DNA (DNA binding) (metal ion binding), GO 33%( 4) 2.5 KEGG KEGG unigenes ( 5), unigenes 141, 6 : 1000 unigenes (Signal transduction), 1410 (9.25%), (Immune

4 2 : (Lateolabrax japonicus) 355 表 2 Unigenes 在不同数据库中的注释 Tab.2 Annotation of unigenes in different databases Swiss-prot Nr Pfam KEGG KOG GO % 39.95% 49.27% 35.18% 23.09% 32.14% 35.67% Fig.4 4 Unigenes GO Gene ontology (GO) functional categories of unigenes system), 1090 (7.15%); unigenes (cell communication), 963 (6.32%), (transport and catabolism), 950 (6.23%), (Endocrine system) 706 (4.63%), (cancers), 697 (4.57%), (Carbohydrate metabolism), 672 (4.41%), (neurodegenerative disease), 637 (4.18%), (infectious disease), 635 (4.17%), (cell growth and death), 559 (3.67%), (signaling molecules and interaction), 554 (3.64%), (Amino acid metabolism), 521 (3.42%); 100 unigenes (sensory system), 85 (0.56%), (environmental adaptation), 70 (0.46%), (metabolism of terpenoids and polyketides), 69 (0.45%), (biosynthesis of other secondary metabolites), 77 (0.50%); unigenes KOG, unigenes KOG, ( 6) (Signal transduction mechanisms) (general function prediction only), 2600 (15.69%) 2595 (15.66%), (Posttranslational modification, protein turnover, chaperones) 1392 (8.40%), (Transcription) 1137 (6.86%),, (Intracellular trafficking, secretion, and vesicular transport) 997 (6.02%), (Cytoskeleton) RNA (RNA processing and modification), (Translation, ribosomal structure and biogenesis) (Carbohydrate transport and metabolism) (Cell cycle control, cell division, chromosome partitioning) 748 (4.51%) 653 (3.94%)

5 356 海图5 Fig.5 与湖沼 49 卷 Unigenes 的 KO 功能分类 KO functional categories of unigens 图6 Fig.6 洋 Unigenes 的 KOG 分类 Clusters of orthologous groups classification 652 条(3.93%) 505 条(3.05%) 497 条(3.00%) 此外, 的 15.12%, 其次是重复 7 次和重复 5 次, 分别有 1195 功能未知的(Function unknown)的有 1185 条(7.15%) 个和 1101 个, 占 SSR 总数的 11.40%和 10.50% SSR 其他 unigenes 数量均少于 500 条, 其中细胞运动(cell 的长度影响其多态性高低的重要因素, Temnykh 等 motility)的数量最少, 仅有 34 条(0.21%) (2001)研究表明 SSR 长度小于 12bp 的多态性极低, 2.7 SSR 多态性分析由表 3 可见, 花鲈肝脏转录组 SSR 种类较丰富, 因此本文将 SSR 长度低于 12bp 的剔除经过筛选花以重复 6 次的频率最高, 有 1586 个, 占总 SSR 总数其中长度在 12 20bp 之间的有 8256 条(占 82.97%), 鲈 SSR 的总数为 9951 条, 其长度范围为 bp:

6 2 : (Lateolabrax japonicus) bp 1695 ( 17.03%), 20bp SSR SSR 10486, SSR, 4176, 39.82%; SSR, 3993, 37.51%; SSR 1992, 19.00%; SSR 234, 2.23%; SSR 95 56, 0.91% 0.53% 表 3 花鲈肝脏转录组中 Unigenes 的 SSR 分析结果 Tab.3 Summary of simple sequence repeats (SSRs) of unigenes in L. japonicas liver transcriptome (%) (%) ,, (, 1995), (, 2005) (2009),,,, SOD, GSH HSP70 (, 2009),,, (, 2003), PCP, SOD, GSH, MDA,,, (2005), (LDH),, 135, ,

7 358 49,, (Brachydanio rerio) , 106,, p53, (Hussainzada et al, 2014) (Perca flavescens), 106, (Bougas et al, 2013), 6.0G, Trinity clean, , , PCA,, ,, (Cadigan et al, 1997;, 2005; Pilon et al, 2006;, 2009), GO,, DNA,, ATP DNA, KEGG,, MAPK (466 ), (238 ) Wnt (259 ), (181 ) TGF-β (171 ) MAPK AC UhpC ; ATP C / (CaM )II C,,,, ATP Ca 2+ ATP Ca 2+, Ca 2+, Ca 2+ (, 2012), ATP,,, Wnt, (, 2009),, Wnt fzd2 fzd10 gsk3b axin2 PLC-γ, (FGF) (RTK), RTK PLC-γ, (, 2005; Pilon et al, 2006;, 2009),, Wnt, (Cadigan et al, 1997),, Wnt,, 4,, Trinity, , ,,,,,,,,,, ATP., 28(7): ,,, SOD GSH HSP70., 4(3):

8 2 : (Lateolabrax japonicus) 359,, 祎, , 25(5): ,,, , 25(6): ,,, , 19(1): 40 42,,, , 22(5): ,,, Wnt., 4(4): ,,, , 35(2): ,,, , 24(3): ,, 芃, , 21(2): ,,, , (2): 20 23,,, , 39(4): ,,, , 4(6): ,,, , 4(6): , Fgf17b Mkp3. :,,, , 9(5): ,,, , 11(6): Bougas B, Normandeau E, Pierron F et al, How does exposure to nickel and cadmium affect the transcriptome of yellow perch (Perca flavescens) results from a 1000 candidate-gene microarray. Aquatic Toxicology, : Cadigan K M, Nusse R, Wnt signaling: a common theme in animal development. Genes & Development, 11(24): do Nascimento N R, Nicola S M C, Rezende M O O et al, Pollution by hexachlorobenzene and pentachlorophenol in the coastal plain of São Paulo state, Brazil. Geoderma, 121(3 4): Elran R, Raam M, Kraus R et al, Early and late response of Nematostella vectensis transcriptome to heavy metals. Molecular Ecology, 23(19): Guo H, Ye C X, Wang A L et al, Trascriptome analysis of the Pacific white shrimp Litopenaeus vannamei exposed to nitrite by RNA-seq. Fish & Shellfish Immunology, 35(6): Hook S E, Osborn H L, Gissi F et al, RNA-Seq analysis of the toxicant-induced transcriptome of the marine diatom, Ceratoneis closterium. Marine Genomics, 16: Huang Q S, Dong S J, Fang C et al, Deep sequencing-based transcriptome profiling analysis of Oryzias melastigma exposed to PFOS. Aquatic Toxicology, : Hussainzada N, Lewis J A, Baer C E et al, Whole adult organism transcriptional profiling of acute metal exposures in male Zebrafish. BMC Pharmacology and Toxicology, 15(1): 15 Kawaguchi M, Ishii Y, Sakui N et al, Stir bar sorptive extraction with in situ derivatization and thermal desorption-gas chromatography-mass spectrometry for determination of chlorophenols in water and body fluid samples. Analytica Chimica Acta, 533(1): Kot-Wasik A, Dębska J, Namieśnik J, Monitoring of organic pollutants in coastal waters of the Gulf of Gdańsk, Southern Baltic. Marine Pollution Bulletin, 49(3): Liu Y, Wen B, Shan X Q, Determination of pentachlorophenol in wastewater irrigated soils and incubated earthworms. Talanta, 69(5): Lockhart D J, Winzeler E A, Genomics, gene expression and DNA arrays. Nature, 405(6788): Pilon N, Oh K, Sylvestre J R et al, Cdx4 is a direct target of the canonical Wnt pathway. Developmental Biology, 289(1): Temnykh S, DeClerck G, Lukashova A et al, Computational and experimental analysis of microsatellites in rice (Oryza sativa L.): frequency, length variation, transposon associations, and genetic marker potential. Genome Research, 11(8): U.S. Environmental Protection Agency, Guidelines for exposure assessment. Federal Register, 57(104):

9 TRANSCRIPTOMIC PROFILES OF LATEOLABRAX JAPONICUS LIVER INJURED BY PENTACHLOROPHENOL STRESS ZHENG Dan 1, 2, JIAO Hai-Feng 2, YAN Xiao-Jun 1, ZHANG Le 3, RUAN Song-Lin 3 (1. School of Marine Science, Ningbo University, Ningbo , China; 2. Ningbo Academy of Oceanology and Fishery, Ningbo , China; 3. Hangzhou Academy of Agricultural Sciences, Hangzhou , China) Abstract To analyze the toxic mechanism of Lateolabrax japonicus liver injury after exposure to different concentrations of pentachlorophenol (0, 0.1, 1.0, 10.0, and 100μg/L), we conducted the transcriptome sequencing and analysis by Illumina HiSeq TM2000. A total of clean reads and genes were determined by software Trinity. By comparing the four stress groups with the control, the number of common differentially expressed genes was 135, and the number of specific differentially expressed genes was from 127 to 819. These unigenes were partially matched with known proteins in the National Center for Biotechnology Information (NCBI) database, as determined by BLAST search, with 21459, 26464, 18896, 12403, 17262, and unigenes assigned to Swiss-Prot, Nr database, Pfam database, KEGG, KOG and Gene Ontology (GO), respectively. The KEGG pathway analysis indicated that differentially expressed genes were enriched in 141 pathways of six categories. These differentially expressed genes were enriched in the signaling pathways that associated with environmental information process and immune system, and as well as in the signal endocrine system, cell communication, transport and catabolic processes. The result of KOG database prediction and classification indicates that genes are enriched in the 25 categories. About these genes, the function of general functional predictions and signal transduction mechanisms accounted for the largest proportion. The pathways involved in post-translational modification, protein conversion, and molecular chaperone pathways also accounted for a large proportion. Overall, the signal transduction system of L. japonicus was first responded after pentachlorophenol stress, resulting in the body disorder. Key words Lateolabrax japonicus; transcriptome; pentachlorophenol; differentially expressed genes

Bioinformatics for Microbiology

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