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功能宏基因组学作为从自然环境中鉴定新抗生素抗性基因的工具

Functional Metagenomics as a Tool for Identification of New Antibiotic Resistance Genes from Natural Environments.

作者信息

Dos Santos Débora Farage Knupp, Istvan Paula, Quirino Betania Ferraz, Kruger Ricardo Henrique

机构信息

Departamento de Biologia Celular, Universidade de Brasília, Brasília, DF, Brazil.

Embrapa-Agroenergia, Brasília, DF, Brazil.

出版信息

Microb Ecol. 2017 Feb;73(2):479-491. doi: 10.1007/s00248-016-0866-x. Epub 2016 Oct 5.

Abstract

Antibiotic resistance has become a major concern for human and animal health, as therapeutic alternatives to treat multidrug-resistant microorganisms are rapidly dwindling. The problem is compounded by low investment in antibiotic research and lack of new effective antimicrobial drugs on the market. Exploring environmental antibiotic resistance genes (ARGs) will help us to better understand bacterial resistance mechanisms, which may be the key to identifying new drug targets. Because most environment-associated microorganisms are not yet cultivable, culture-independent techniques are essential to determine which organisms are present in a given environmental sample and allow the assessment and utilization of the genetic wealth they represent. Metagenomics represents a powerful tool to achieve these goals using sequence-based and functional-based approaches. Functional metagenomic approaches are particularly well suited to the identification new ARGs from natural environments because, unlike sequence-based approaches, they do not require previous knowledge of these genes. This review discusses functional metagenomics-based ARG research and describes new possibilities for surveying the resistome in environmental samples.

摘要

抗生素耐药性已成为人类和动物健康的主要担忧,因为治疗多重耐药微生物的治疗替代方案正在迅速减少。抗生素研究投资不足以及市场上缺乏新的有效抗菌药物使问题更加复杂。探索环境抗生素耐药基因(ARGs)将有助于我们更好地理解细菌耐药机制,这可能是识别新药物靶点的关键。由于大多数与环境相关的微生物尚未可培养,不依赖培养的技术对于确定给定环境样本中存在哪些生物体并评估和利用它们所代表的遗传财富至关重要。宏基因组学是使用基于序列和基于功能的方法实现这些目标的强大工具。功能宏基因组学方法特别适合从自然环境中鉴定新的ARGs,因为与基于序列的方法不同,它们不需要对这些基因有先验知识。本文综述了基于功能宏基因组学的ARGs研究,并描述了在环境样本中检测耐药基因组的新可能性。

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