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一个经过精心策划的 214K 个宏基因组数据集,用于全球抗生素耐药组的特征描述。

A curated data resource of 214K metagenomes for characterization of the global antimicrobial resistome.

机构信息

Research Group for Genomic Epidemiology, Technical University of Denmark, Kongens Lyngby, Denmark.

出版信息

PLoS Biol. 2022 Sep 6;20(9):e3001792. doi: 10.1371/journal.pbio.3001792. eCollection 2022 Sep.

DOI:10.1371/journal.pbio.3001792
PMID:36067158
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9447899/
Abstract

The growing threat of antimicrobial resistance (AMR) calls for new epidemiological surveillance methods, as well as a deeper understanding of how antimicrobial resistance genes (ARGs) have been transmitted around the world. The large pool of sequencing data available in public repositories provides an excellent resource for monitoring the temporal and spatial dissemination of AMR in different ecological settings. However, only a limited number of research groups globally have the computational resources to analyze such data. We retrieved 442 Tbp of sequencing reads from 214,095 metagenomic samples from the European Nucleotide Archive (ENA) and aligned them using a uniform approach against ARGs and 16S/18S rRNA genes. Here, we present the results of this extensive computational analysis and share the counts of reads aligned. Over 6.76∙108 read fragments were assigned to ARGs and 3.21∙109 to rRNA genes, where we observed distinct differences in both the abundance of ARGs and the link between microbiome and resistome compositions across various sampling types. This collection is another step towards establishing global surveillance of AMR and can serve as a resource for further research into the environmental spread and dynamic changes of ARGs.

摘要

抗微生物药物耐药性(AMR)的威胁日益严重,需要新的流行病学监测方法,以及更深入地了解抗微生物药物耐药基因(ARGs)如何在全球传播。公共存储库中提供的大量测序数据为监测不同生态环境中 AMR 的时间和空间传播提供了极好的资源。然而,全球只有有限数量的研究小组拥有分析此类数据的计算资源。我们从欧洲核苷酸档案库(ENA)中检索了 214095 个宏基因组样本的 442 Tbp 测序读数,并使用统一的方法对 ARGs 和 16S/18S rRNA 基因进行了对齐。在这里,我们展示了这项广泛的计算分析的结果,并分享了对齐的读数计数。超过 6.76×108 个读段被分配给 ARGs,3.21×109 个读段被分配给 rRNA 基因,我们观察到不同采样类型的 ARG 丰度和微生物组与抗微生物组组成之间的联系存在明显差异。该数据集是建立全球 AMR 监测的又一步,可作为进一步研究 ARGs 环境传播和动态变化的资源。

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本文引用的文献

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mSystems. 2022 Apr 26;7(2):e0010522. doi: 10.1128/msystems.00105-22. Epub 2022 Mar 28.
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Assessment of global health risk of antibiotic resistance genes.抗生素抗性基因的全球健康风险评估。
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GISAID's Role in Pandemic Response.全球流感共享数据库(GISAID)在大流行应对中的作用。
对南非污水进行宏基因组学分析以监测抗微生物药物耐药性。
PLoS One. 2024 Aug 26;19(8):e0309409. doi: 10.1371/journal.pone.0309409. eCollection 2024.
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Utilizing co-abundances of antimicrobial resistance genes to identify potential co-selection in the resistome.利用抗菌药物耐药基因的共同丰度来识别耐药组中潜在的共同选择。
Microbiol Spectr. 2024 Jul 2;12(7):e0410823. doi: 10.1128/spectrum.04108-23. Epub 2024 Jun 4.
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ARGprofiler-a pipeline for large-scale analysis of antimicrobial resistance genes and their flanking regions in metagenomic datasets.ARGprofiler-一个用于大规模分析宏基因组数据集中抗菌药物耐药基因及其侧翼区域的管道。
Bioinformatics. 2024 Mar 4;40(3). doi: 10.1093/bioinformatics/btae086.
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The antimicrobial resistance crisis needs action now.抗菌药物耐药性危机亟待行动。
PLoS Biol. 2022 Nov 23;20(11):e3001918. doi: 10.1371/journal.pbio.3001918. eCollection 2022 Nov.
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