干旱区研究

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2023 , Vol. 40 >Issue 4: 670 - 680

DOI: https://doi.org/10.13866/j.azr.2023.04.16

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天山乌鲁木齐河源1号冰川表层雪微生物多样性分析

  • 张丽娟 ,
  • 杜瀚 ,
  • 贠丰泽 ,
  • 马应辉 ,
  • 张新强 ,
  • 阿瓦古丽·图尔荪 ,
  • 马正海
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  • 1.新疆大学生命科学与技术学院，新疆生物资源基因工程重点实验室，新疆 乌鲁木齐 830046
    2.中国科学院新疆天山冰川观测试验站，新疆 乌鲁木齐 830035
张丽娟（1997-），女，硕士研究生，主要从事微生物学研究. E-mail: lijuancheung@outlook.com

收稿日期: 2022-04-24

  修回日期: 2023-02-09

  网络出版日期: 2023-04-28

基金资助

国家自然科学基金项目(41761096)

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Analysis of the microbial diversity of the surface snow from Glacier No. 1 at the headwaters of Urumqi River, Tianshan Mountains

  • Lijuan ZHANG ,
  • Han DU ,
  • Fengze YUN ,
  • Yinghui MA ,
  • Xinqiang ZHANG ,
  • TUERSUN Awaguli ,
  • Zhenghai MA
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  • 1. Xinjiang Key Laboratory of Biological Resources and Genetic Engineering, College of Life Science and Technology, Xinjiang University, Urumqi 830046, Xinjiang, China
    2. Tianshan Glaciological Station, Chinese Academy of Sciences, Xinjiang, Urumqi 830035, Xinjiang, China

Received date: 2022-04-24

  Revised date: 2023-02-09

  Online published: 2023-04-28

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摘要

为探究天山乌鲁木齐河源1号冰川（简称“乌源1号冰川”）积雪微生物群落特征及其与气候环境的关系，采集该区域2021年春季（4月）海拔3549 m处（TSX1）以及夏季（6月）海拔3770 m处（TSX2）和海拔3800 m处（TSX3）表层雪样，针对细菌16S rDNA V3-V4区、古菌16S rDNA V4-V5区和真菌ITS2区分别进行高通量测序，分析雪样中细菌、古菌和真菌的多样性。结果表明：（1） 乌源1号冰川表层雪微生物多样性具有季节性差异，细菌多样性春季较高夏季较低，而真菌多样性则相反。（2） 在物种组成上，细菌优势门为Proteobacteria（58.13%~89.10%）和Bacteroidetes（4.24%~40.74%），优势属为Flavobacterium（2.32%~33.64%）和Polaromonas（0.01%~24.72%）；古菌优势门为Thaumarchaeota（38.10%~97.55%），其次为Nanoarchaeaeota（0%~61.90%）和Euryarchaeota（0%~2.82%）；真菌优势门为Ascomycota（7.06%~88.43%）和Monoblepharidomycota（36.21%~40.78%），优势属为Aspergillus（0.16%~81.04%）和Rhodotorula（0.02%~8.05%）。（3） 网络互作分析表明，微生物网络互作以正相关连接为主（97.3%），负相关连接仅占2.7%，互作关系趋于合作关系。（4） 乌源1号冰川表层雪中具有丰富的微生物，微生物群落的季节变化反映了微生物对不同季节大气环流的响应。

关键词： 乌鲁木齐河源1号冰川; 天山; 表层雪; 微生物多样性; 网络互作分析

本文引用格式

张丽娟 , 杜瀚 , 贠丰泽 , 马应辉 , 张新强 , 阿瓦古丽·图尔荪 , 马正海 . 天山乌鲁木齐河源1号冰川表层雪微生物多样性分析[J]. 干旱区研究, 2023 , 40(4) : 670 -680 . DOI: 10.13866/j.azr.2023.04.16

Abstract

To examine the characteristics of microbial communities in the surface snow samples from the Glacier No. 1 at the headwaters of Urumqi River, Tianshan Mountains (henceforth referred to as “Urumqi Glacier No. 1”) and their relationship with climate and environment, surface snow samples were collected at an altitude of 3549 m in the spring (April, TSX1), and 3770 m (TSX2) and 3800 m (TSX3) in the summer (June) in the region in 2021. The V3-V4 region of 16S rDNA of bacteria, the V4-V5 region of 16S rDNA of archaea, and the ITS2 region of fungi were amplified by polymerase chain reaction (PCR). The products were then subjected to high-throughput sequencing, after which the microbial diversity was analyzed. The results showed that the microbial diversity of the surface snow samples from the Urumqi Glacier No. 1 differed in spring and summer, with the bacterial diversity being higher in spring and lower in summer, while the fungal diversity showed the opposite pattern. Proteobacteria (58.13%-89.10%) and Bacteroidetes (4.24%-40.74%) were the dominant bacteria at the phylum level, while Flavobacterium (2.32%-33.64%) and Polaromonas (0.01%-24.72%) were the dominant bacteria at the genus level. Thaumarchaeota (38.10%-97.55%) was the dominant archaea in the three samples, followed by Nanoarchaeaeota (0%-61.90%) and Euryarchaeota (0%-2.82%). Ascomycota (7.06%-88.43%) and Monoblepharidomycota (36.21%-40.78%) were the dominant fungi at the phylum level, and Aspergillus (0.16%-81.04%) and Rhodotorula (0.02%-8.05%) were the dominant fungi at the genus level. Network interaction analysis showed that the microbial network interaction was dominated by the positive correlation connection (97.3%), and the negative correlation connection accounted for 2.7%, and the interactive relationship tended to be cooperative. In summary, the surface snow microbiota of the Urumqi Glacier No.1 was highly diverse, and the seasonal variation in the microbial community reflects the response of microorganisms to atmospheric circulation in different seasons.

Key words： Urumqi Glacier No.1; Tianshan; surface snow; microbial diversity; network interaction analysis

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