• 首页关于本刊期刊订阅编委会作者指南过刊浏览
陈思梦,贾婷,王政昆,朱万龙.2021.大理和勐腊中缅树鼩血清及 肝差异代谢物的比较.动物学杂志,56(5):729-745.
大理和勐腊中缅树鼩血清及 肝差异代谢物的比较
Comparison of Differential Metabolites in Serum and Liver of Tupaia belangeri from Dali and Mengla Area
投稿时间:2021-01-22  修订日期:2021-07-01
DOI:10.13859/j.cjz.202105010
中文关键词:  中缅树鼩  血清    差异代谢物  代谢通路
英文关键词:Tupaia belangeri  Serum  Liver  Differential metabolites  Metabolic pathways
基金项目:国家自然科学基金项目(No. 31660121),云南省中青学术和技术带头人后备人才项目(No. 2019HB013),云南省万人计划青年拔尖人才项目(No. YNWR-QNRC-2019-047)
作者单位E-mail
陈思梦 云南省高校西南山地生态系统动植物生态适应进化及保护重点实验室云南师范大学生命科学学院 生物能源持续开发利用教育部工程研究中心云南省生物质能与环境生物技术重点实验室 昆明 650500 CHENSIMENG205520@163.com 
贾婷 云南经济管理学院 昆明 650106 monica_8209@163.com 
王政昆 云南省高校西南山地生态系统动植物生态适应进化及保护重点实验室云南师范大学生命科学学院 生物能源持续开发利用教育部工程研究中心云南省生物质能与环境生物技术重点实验室 昆明 650500 wangzk_123@163.com 
朱万龙 云南省高校西南山地生态系统动植物生态适应进化及保护重点实验室云南师范大学生命科学学院 生物能源持续开发利用教育部工程研究中心云南省生物质能与环境生物技术重点实验室 昆明 650500 zwl_8307@163.com 
摘要点击次数: 92
全文下载次数: 77
中文摘要:
      为探究不同海拔地区中缅树鼩(Tupaia belangeri)血清和肝的差异代谢物及代谢通路变化,本研究采集大理(高海拔)和勐腊(低海拔)中缅树鼩分别12只和8只的血清和肝,采用非靶向代谢组气相色谱-质谱联用检测技术测定代谢物。结果表明,高海拔种群和低海拔种群相比,血清中一共有36种代谢物差异显著,其中,柠檬酸、葡萄糖、胆固醇等共32种代谢物的浓度上调,N-乙酰谷氨酸、葵酸、庚酸和对羟基苯甲酸这4种代谢物的浓度下调;高海拔种群肝差异代谢物和低海拔肝相比,一共有18种代谢物浓度差异显著,其中,苹果酸、核糖、葡萄糖等共10种差异代谢物浓度上调,谷氨酰胺、乙醇酸、硬脂酸等共8种差异代谢物浓度下调。高海拔种群与低海拔种群相比,血清中一共有76条代谢通路活性得分差异显著,其中,69条活性得分上调,7条活性得分下调;肝中一共有75条代谢通路活性得分差异显著,其中,43条活性得分上调,32条活性得分下调。以上结果说明,中缅树鼩在面对不同环境时,会调节不同组织中不同代谢通路(如三羧酸循环、糖酵解、脂类代谢和氨基酸代谢)的代谢物含量来适应环境,且血清比肝对环境变化更敏感。
英文摘要:
      To explore the effects of different altitudes on changes of different metabolites and metabolic pathways the sera and livers of T. belangeri from Dali (high-altitude) and Mongla (low-altitude) were collected. The metabolites were analyzed by using the non-targeted metabonomics gas chromatography-mass spectrometry (GC-MS). The original data were preprocessed by software XCMS (www. bioconductor.org/), converting the original GC-MS data into common data format (CDF) format. The XCMS program was used for peak identification, peak filtering and peak alignment to determine the parameters of XCMS (Fig. 1). A metabolite tree map was constructed based on the euclidean distance between samples, and clustering of samples was performed by a clustering algorithm (Fig. 2). Then the processed data were imported into SIMCA-P software (Umetrics, Umea, Sweden), and multivariate statistical analysis was carried out. Hierarchical cluster analysis (HCA) was used to analyze the metabolite thermograms with the heatmap function in the R package (Fig. 3, 4). The correlation analysis of metabolites was carried out for significance statistical test, and the statistical test method was the COR. TEST function in R language package (Fig. 9, 10). Metabolic pathways were assigned to metabolites based on Kyoto encyclopedia of genes and genomes (KEGG, http://www.genome.jp/kegg/), and Pathway Activity Profiling (PAPi) was used to compare the relative activities of different metabolic pathways in different groups (Appendix 1, 2). All analyses were performed using the R package. Differential metabolites were screened by One-way ANOVA analysis (P < 0.05) and ploidy change Log2 value (fold change > 1.5 or fold change < 0.667) (Fig. 5‐8). The results showed that there were 36 different metabolites in serum of the high-altitude population compared to the low-altitude population (Fig. 3), among which 32 were up-regulated (citric acid, glucose, cholesterol, et al) and 4 were down-regulated (N-acetylglutamic acid, decanoic acid, 4-hdroxybutyric acid, et al.). There were 18 metabolites showing significant difference in the high-altitude population compared to the low-altitude population (Fig. 4), among which 10 were up-regulated (malic acid, ribose, glucose, et al.) and 8 were down-regulated (glutamine, glycolic acid, octadecanoic acid, et al.). Compared with the serum metabolic pathways at low-altitude, there were 76 metabolic pathways with significantly different activity scores in high-altitude population (Appendix 1), among which 69 were up-regulated and 7 were down-regulated. There were 75 metabolic pathways with significantly different activity scores in the high-altitude population compared with the low-altitude population (Appendix 2), among which 43 were up-regulated and 32 were down-regulated. All of the above results suggest that T. belangeri would adjust the metabolites of different metabolic pathways (including tricarboxylic acid cycle, glycolysis, lipid metabolism and amino acid metabolism) in different tissues to adapt to different environments, and serum is more sensitive to environmental changes than the liver.
附件
查看全文  查看/发表评论  下载PDF阅读器