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利用竞争性聚合酶链反应对氨氧化细菌进行定量分析。

Quantitative analysis of ammonia oxidising bacteria using competitive PCR.

作者信息

Phillips CJ, Paul EA, Prosser JI

机构信息

Department of Molecular and Cell Biology, Institute of Medical Sciences, Aberdeen University, Foresterhill, Aberdeen, UK

出版信息

FEMS Microbiol Ecol. 2000 Apr 1;32(2):167-175. doi: 10.1111/j.1574-6941.2000.tb00710.x.

DOI:10.1111/j.1574-6941.2000.tb00710.x
PMID:10817869
Abstract

Culture-based methods for enumeration, such as most probable number (MPN) methodologies, have proved inefficient due to difficulties in the isolation and cultivation of ammonia oxidising bacteria in the laboratory. Biases are associated with the isolation of bacteria in selective media and organisms cultivated in the laboratory may not be truly representative of those in the environment. In this study, we developed a competitive PCR (cPCR)-based method based on the amplification of 16S rRNA genes specific for the beta-subgroup proteobacterial ammonia oxidising bacteria for enumeration of these organisms. Populations in both agricultural soils and estuarine sediments were quantified by traditional MPN and by cPCR. The numbers of ammonia oxidisers for both sample types were significantly underestimated by conventional MPN and were 1-3 orders of magnitude lower than those obtained by cPCR. Higher numbers of ammonia oxidisers found in fertilised plots in agricultural soils by the cPCR technique were not observed in MPN estimates. It was necessary to construct a separate standard curve for each sample type as differences in DNA extraction, quantity and purity had a significant bearing on the ease of PCR of both competitor and target DNA.

摘要

基于培养的计数方法,如最大可能数(MPN)方法,由于在实验室中分离和培养氨氧化细菌存在困难,已被证明效率低下。偏差与在选择性培养基中分离细菌有关,并且在实验室中培养的生物体可能并非环境中生物体的真实代表。在本研究中,我们基于对β-亚群变形菌纲氨氧化细菌特异性的16S rRNA基因扩增,开发了一种基于竞争PCR(cPCR)的方法来计数这些生物体。通过传统的MPN和cPCR对农业土壤和河口沉积物中的种群进行了定量。两种样品类型中氨氧化菌的数量都被传统MPN显著低估,并且比通过cPCR获得的数量低1 - 3个数量级。通过cPCR技术在农业土壤施肥地块中发现的较高数量的氨氧化菌在MPN估计中未被观察到。由于DNA提取、数量和纯度的差异对竞争DNA和靶DNA的PCR难易程度有显著影响,因此有必要为每种样品类型构建单独的标准曲线。

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