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将细菌身份与生态系统过程联系起来:“组学”分析能否不仅仅是其各部分的总和?

Linking bacterial identities and ecosystem processes: can 'omic' analyses be more than the sum of their parts?

机构信息

Microbial Ecology Program, Division of Biological Sciences, The University of Montana, Missoula, MT 59812-1006, USA.

出版信息

FEMS Microbiol Ecol. 2011 Jan;75(1):2-16. doi: 10.1111/j.1574-6941.2010.00938.x.

DOI:10.1111/j.1574-6941.2010.00938.x
PMID:20662931
Abstract

A major goal in microbial ecology is to link specific microbial populations to environmental processes (e.g. biogeochemical transformations). The cultivation and characterization of isolates using genetic, biochemical and physiological tests provided direct links between organisms and their activities, but did not provide an understanding of the process networks in situ. Cultivation-independent molecular techniques have extended capabilities in this regard, and yet, for two decades, the focus has been on monitoring microbial community diversity and population dynamics by means of rRNA gene abundances or rRNA molecules. However, these approaches are not always well suited for establishing metabolic activity or microbial roles in ecosystem function. The current approaches, microbial community metagenomic and metatranscriptomic techniques, have been developed as other ways to study microbial assemblages, giving rise to exponentially increasing collections of information from numerous environments. This review considers some advantages and limitations of nucleic acid-based 'omic' approaches and discusses the potential for the integration of multiple molecular or computational techniques for a more effective assessment of links between specific microbial populations and ecosystem processes in situ. Establishing such connections will enhance the predictive power regarding ecosystem response to parameters or perturbations, and will bring us closer to integrating microbial data into ecosystem- and global-scale process measurements and models.

摘要

微生物生态学的一个主要目标是将特定的微生物种群与环境过程(例如生物地球化学转化)联系起来。通过遗传、生化和生理测试对分离物进行培养和表征,为生物与其活动之间提供了直接联系,但并没有了解原位过程网络。非培养依赖性的分子技术在这方面扩展了能力,但在过去的二十年中,重点一直是通过 rRNA 基因丰度或 rRNA 分子监测微生物群落多样性和种群动态。然而,这些方法并不总是适合于确定代谢活性或微生物在生态系统功能中的作用。当前的方法,微生物群落宏基因组和宏转录组技术,已经被开发为研究微生物组合的其他方法,从众多环境中产生了指数增长的信息集合。本综述考虑了基于核酸的“组学”方法的一些优点和局限性,并讨论了整合多种分子或计算技术的潜力,以更有效地评估特定微生物种群与原位生态系统过程之间的联系。建立这种联系将增强对生态系统对参数或干扰的反应的预测能力,并使我们更接近于将微生物数据整合到生态系统和全球尺度的过程测量和模型中。

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