Microbial profiling of the East Siberian Sea sediments using 16S rRNA gene and metagenome sequencing.

IF 5.8 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Jehyun Jeon, Yerin Park, Dong-Hun Lee, Ji-Hoon Kim, Young Keun Jin, Jong Kuk Hong, Yung Mi Lee
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引用次数: 0

Abstract

The Arctic Ocean is experiencing significant global warming, leading to reduced sea-ice cover, submarine permafrost thawing, and increased river discharge. The East Siberian Sea (ESS) undergoes more significant terrestrial inflow from coastal erosion and river runoff than other Arctic seas. Despite extensive research on environmental changes, microbial communities and their functions in the ESS, which are closely related to environmental conditions, remain largely unexplored. Here, we investigated microbial communities in ESS surface sediments spanning latitudes from 73°N to 77°N using 16S rRNA amplicon sequencing, and reconstructed 211 metagenome-assembled genomes (MAGs) using shotgun metagenome sequencing. Taxonomic analysis identified 209 bacterial MAGs, with the predominant phyla Pseudomonadota (n = 82), Actinobacteriota (n = 38), Desulfobacterota (n = 23), along with 2 archaeal MAGs of Thermoproteota. Notably, 86% of the MAGs (n = 183) could not be classified into known species, indicating the potential presence of novel and unidentified microorganisms in the ESS. This dataset provides invaluable information on the microbial diversity and ecological functions in the rapidly changing ESS.

利用16S rRNA基因和宏基因组测序研究东西伯利亚海沉积物微生物谱。
北冰洋正在经历显著的全球变暖,导致海冰覆盖减少,海底永久冻土融化,河流流量增加。东西伯利亚海(ESS)经历了比其他北极海域更显著的海岸侵蚀和河流径流的陆地流入。尽管对环境变化的研究非常广泛,但与环境条件密切相关的微生物群落及其在ESS中的功能仍未得到充分的探索。本研究利用16S rRNA扩增子测序技术研究了分布在73°N至77°N纬度的ESS表层沉积物中的微生物群落,并利用鸟枪宏基因组测序技术重建了211个宏基因组组装基因组(MAGs)。分类分析共鉴定出209个细菌MAGs,其中优势门为假单胞菌门(82个)、放线菌门(38个)、脱硫菌门(23个),古细菌MAGs为热变形门2个。值得注意的是,86%的mag (n = 183)无法归类为已知物种,这表明ESS中可能存在未知的新型微生物。该数据集提供了在快速变化的ESS中微生物多样性和生态功能的宝贵信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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