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利用单一碳源利用模式追踪细菌群落差异的一维度量方法。

One-dimensional metric for tracking bacterial community divergence using sole carbon source utilization patterns.

作者信息

Weber Kela P, Legge Raymond L

机构信息

Department of Chemical Engineering, University of Waterloo, 200 University Avenue W., Waterloo, ON N2L3G1, Canada.

出版信息

J Microbiol Methods. 2009 Oct;79(1):55-61. doi: 10.1016/j.mimet.2009.07.020. Epub 2009 Aug 6.

Abstract

Community level physiological profiling (CLPP) has become a popular method to characterize and track changes in heterotrophic bacterial communities. Although the CLPP method is a straight forward laboratory protocol which yields large amounts of functional information, the amount of data obtained can become overwhelming and often requires some type of multivariate analysis method for ordination and interpretation. Multivariate analysis can be challenging and requires a significant statistics background along with an understanding of the inferences and biases each multivariate analysis method incurs. This paper presents and evaluates a new approach to analyzing sole carbon source utilization data. A method is described which provides a one-dimensional metric derived from standard CLPP data (BIOLOG EcoPlate data). The one-dimensional community metric was derived using normalized Euclidean distances and shifts in the carbon source utilization patterns. The one-dimensional community metric did not provide all of the information of classical approaches such as principle component analysis (PCA) or guild grouping analysis; however, it was found to be more easily implemented and interpreted when analyzing the plate data. Validation of this approach is demonstrated using data acquired to track the divergence of bacterial communities in wetland mesocosm systems after an experimentally controlled disturbance. If the objective is to investigate community shifts over time the one-dimensional community divergence metric can be a useful tool.

摘要

群落水平生理特征分析(CLPP)已成为表征和追踪异养细菌群落变化的常用方法。尽管CLPP方法是一种简单直接的实验室方案,能产生大量功能信息,但所获得的数据量可能变得庞大,通常需要某种多变量分析方法进行排序和解释。多变量分析具有挑战性,需要扎实的统计学背景以及对每种多变量分析方法所涉及的推断和偏差的理解。本文介绍并评估了一种分析单一碳源利用数据的新方法。描述了一种从标准CLPP数据(BIOLOG生态板数据)中得出一维指标的方法。该一维群落指标是使用归一化欧几里得距离和碳源利用模式的变化得出的。该一维群落指标并未提供主成分分析(PCA)或功能组分组分析等经典方法的所有信息;然而,在分析平板数据时,发现它更易于实施和解释。通过在实验控制干扰后追踪湿地中宇宙系统中细菌群落的分化所获取的数据,证明了该方法的有效性。如果目标是研究群落随时间的变化,那么一维群落分化指标可能是一个有用的工具。

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