文章摘要
李文文,徐革锋,黄天晴,谷伟,刘恩慧,王高超,潘玉财,周金鑫,姜再胜,王炳谦.基于转录组测序筛选乌苏里白鲑肌肉生长关键候选基因研究.渔业科学进展,2024,45(3):87-100
基于转录组测序筛选乌苏里白鲑肌肉生长关键候选基因研究
Screening muscle growth-related genes of Coregonus ussurinsis Berg based on transcriptome sequencing
投稿时间:2022-12-05  修订日期:2023-02-09
DOI:
中文关键词: 乌苏里白鲑  转录组测序  差异表达基因  肌肉生长
英文关键词: Coregonus ussurinsis Berg  RNA-seq  Differentially expressed genes  Muscle growth
基金项目:
作者单位
李文文 上海海洋大学水产与生命学院 上海 201306冷水性鱼类产业技术创新战略联盟 中国水产科学研究院 黑龙江水产研究所 黑龙江 哈尔滨 150070 
徐革锋 冷水性鱼类产业技术创新战略联盟 中国水产科学研究院 黑龙江水产研究所 黑龙江 哈尔滨 150070 
黄天晴 冷水性鱼类产业技术创新战略联盟 中国水产科学研究院 黑龙江水产研究所 黑龙江 哈尔滨 150071 
谷伟 冷水性鱼类产业技术创新战略联盟 中国水产科学研究院 黑龙江水产研究所 黑龙江 哈尔滨 150072 
刘恩慧 冷水性鱼类产业技术创新战略联盟 中国水产科学研究院 黑龙江水产研究所 黑龙江 哈尔滨 150073 
王高超 冷水性鱼类产业技术创新战略联盟 中国水产科学研究院 黑龙江水产研究所 黑龙江 哈尔滨 150074 
潘玉财 冷水性鱼类产业技术创新战略联盟 中国水产科学研究院 黑龙江水产研究所 黑龙江 哈尔滨 150075 
周金鑫 冷水性鱼类产业技术创新战略联盟 中国水产科学研究院 黑龙江水产研究所 黑龙江 哈尔滨 150076 
姜再胜 安徽省桐城市水产服务中心 安徽 桐城 231400 
王炳谦 冷水性鱼类产业技术创新战略联盟 中国水产科学研究院 黑龙江水产研究所 黑龙江 哈尔滨 150070 
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中文摘要:
      为了挖掘调控乌苏里白鲑(Coregonus ussurinsis Berg)肌肉生长的关键候选基因,本研究对不同生长速度乌苏里白鲑的肌肉组织进行转录组测序,以期为乌苏里白鲑群体选育提供基础数据。首先,在同等条件下(从混合池中)随机选择乌苏里白鲑F2个体进行实验分组(快长组和慢长组)。接着分别从快长组[体重为(219.20±38.66) g]和慢长组[体重为(74.30±17.86) g]中随机选取10尾样本,取其背部肌肉进行转录组测序。以FDR (false discovery rate)<0.05且|log2(FC)|>1 (FC, fold change)为条件筛选差异表达基因并对其进行GO (gene ontology)和KEGG (Kyoto encyclopedia of genes and genomes)富集分析,并通过qPCR验证转录组数据的准确性。转录组测序结果显示,共筛选出2 211个差异表达基因,与慢长组相比,快长组中583个差异基因表达上调及1 628个基因表达下调。GO功能注释结果显示,差异基因主要参与细胞过程和结合过程,差异基因显著富集到251条KEGG通路(P<0.05),其中,MAPK信号通路(MAPK signaling pathway)、PI3K-Akt信号通路(PI3K-Akt signaling pathway)、紧密连接(tight junction)、胰岛素信号通路(insulin signaling pathway)、糖酵解/糖异生(glycolysis/gluconeogenesis)和PPAR信号通路(PPAR signaling pathway)参与细胞生长。之后结合功能注释结果和KEGG,鉴定出肌浆/内质网钙ATP酶基因atp2a1和atp2a2、葡萄糖-6-磷酸脱氢酶基因g6pc、生长因子结合蛋白1基因igfbp1以及肌球蛋白重链基因myh1、myh4、myh6、myh7、myh9和myh13等可能与肌肉生长密切相关的基因。本研究共筛选出10个可能与乌苏里白鲑肌肉生长相关的关键候选基因,为今后乌苏里白鲑分子标记辅助育种提供了基础数据。
英文摘要:
      Coregonus ussurinsis Berg is a rare cold-water fish found in Heilongjiang Province, which has high nutritional and economical value. The growth traits of fish are critical breeding target traits, and improving the growth efficiency of cultured fishes has always been a major issue for researchers. As an endangered fish, very limited research has been conducted on C. ussurinsis, and studies on its growth and development are still lacking. Therefore, investigating gene expression in C. ussurinsis muscles would significantly contribute to our understanding of their muscle development. RNA-Seq was used to find and study the specific genes and pathways of muscle development under different conditions. Recently, transcriptome sequencing has been applied to diverse animal populations, aiding in the selection of candidate genes related to important traits by comparing the global gene expression profiles between different animal populations with specific characteristics. This study aims to understand the genetic basis of muscle development in C. ussurinsis at the transcriptome level and to provide new insights into growth and development. To explore the molecular regulation mechanism of growth traits of C. ussurinsis, F2 individuals of C. ussurinsis were randomly selected from the mixed pool for test grouping (fast-growing group and slow-growing group). The dorsal muscle tissue was clipped from 10 fast-growing individuals (219.20±38.66 g, weight) and 10 slow-growing individuals (74.30±17.86 g, weight) for transcriptome sequencing to construct six cDNA libraries. High-throughput sequencing from Illumina NovaSeq 6000 and bioinformatics was used to determine the abundances and characteristics of transcripts. The differentially expressed genes were screened with FDR (false discovery rate)<0.05 and |log2FoldChange|>1; the functions of these differentially expressed genes (DEGs) were annotated and analyzed by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) database to identify the genes and genetic pathways related to the development of muscle in C. ussurinsis. Moreover, to verify the sequencing results, real-time fluorescence quantitative PCR (qRT-PCR) was used to detect the expression levels of DEGs. The results showed that the correlation coefficients of all the samples used for transcriptome sequencing were above 0.83, indicating high correlation between the samples and experimental reliability. Transcriptome sequencing results showed that a total of 295 605 738 raw reads were assembled from the six cDNA libraries, and 283 133 612 clean reads were obtained after quality control. Q20 and Q30 sequences accounted for above 97.80% and 93.90%, respectively, and the content of GC bases accounted for more than 49.1% of the total bases. Through comparison with the genome using EdgeR software, which was used to analyze the differences in gene expression, 2 211 DEGs were preliminarily obtained from muscle, including 659 novel genes. Compared with the slow-growing group, 583 differential genes were up-regulated, and 1 628 differential genes were down-regulated in the fast-growing group. Function enrichment analysis found that the DEGs participated in 3 620 GO terms. Among them, 2 457 biological processes were primarily involved in cellular and metabolic processes; there were 782 molecular functions, primarily involved in binding function and catalytic activity processes, and 381 cellular components, primarily involved in cell and cell component processes. The enrichment analysis of the KEGG pathway found that a total of 251 signal pathways were obtained, among which 73 were significantly enriched (P<0.05). Among them, the up-regulated DEGs were mainly involved in glycolysis/gluconeogenesis, methane metabolism, and biosynthesis of amino acids, while the down-regulated DEGs were mainly involved in osteoclast differentiation, IL-17 signaling pathway, and biosynthesis of amino acids. The genes related to muscle growth were significantly (P<0.05) enriched in the MAPK signaling pathway, PI3K-Akt signaling pathway, tight junction, insulin signaling pathway, glycolysis/gluconeogenesis, and PPAR signaling pathway. These pathways might be closely related to muscle growth. Combined with the GO functional annotation, the KEGG pathway enrichment, and the annotation results, 31 potential growth-related candidate genes were preliminarily screened. Protein-protein interaction networks were used to further analyze the relationship between these differential genes. It was found that atp2a2, atp2a1, g6pc, igfbp1, myh1, myh4, myh6, myh7, myh9, and myh13 might be closely related to muscle growth regulation, and these 10 genes can be used as crucial candidate genes for the growth regulation of C. ussurinsis. The qRT-PCR validation of 10 randomly selected differential genes showed consistent gene expression trends with the transcriptome sequencing results, which indicated that the results obtained by transcriptome sequencing in this study were accurate. A total of 10 growth-related essential candidate genes were screened in this study; these genes affect the growth of C. ussurinsis by regulating their expression levels in muscle tissue. These results provide vital information for the further understanding of the molecular basis and marker-assisted breeding of the growth regulation of C. ussurinsis.
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