Dollhopf S.L., Hashsham S.A., Tiedje J.M.
Center for Microbial Ecology, Michigan State University, East Lansing, MI 48824, USA.
Microb Ecol. 2001 Dec;42(4):495-505. doi: 10.1007/s00248-001-0027-7.
Interpreting the large amount of data generated by rapid profiling techniques, such as T-RFLP, DGGE, and DNA arrays, is a difficult problem facing microbial ecologists. This study compares the ability of two very different ordination methods, principal component analysis (PCA) and self-organizing map neural networks (SOMs), to analyze 16S-DNA terminal restriction-fragment length polymorphism (T-RFLP) profiles from microbial communities in glucose-fed methanogenic bioreactors during startup and changes in operational parameters. Our goal was not only to identify which samples were similar, but also to decipher community dynamics and describe specific phylotypes, i.e., phylogenetically similar organisms, that behaved similarly in different reactors. Fifteen samples were taken over 56 volume changes from each of two bioreactors inoculated from river sediment (S2) and anaerobic digester sludge (M3) and from a well-established control reactor (R1). PCA of bacterial T-RFLP profiles indicated that both the S2 and M3 communities changed rapidly during the first nine volume changes, and then became relatively stable. PCA also showed that an HRT of 8 or 6 days had no effect on either reactor communtity, while an HRT of 2 days changed community structure significantly in both reactors. The SOM clustered the terminal restriction fragments according to when each fragment was most abundant in a reactor community, resulting in four clearly discernible groups. Thirteen fragments behaved similarly in both reactors, eight of which composed a significant proportion of the microbial community as judged by the relative abundance of the fragment in the T-RFLP profiles. Six Bacteria terminal restriction fragments shared between the two communities matched cloned 16S rDNA sequences from the reactors related to Spirochaeta, Aminobacterium, Thermotoga, and Clostridium species. Convergence also occurred within the acetoclastic methanogen community, resulting in a predominance of Methanosarcina siciliae-related organisms. The results demonstrate that both PCA and SOM analysis are useful in the analysis of T-RFLP data; however, the SOM was better at resolving patterns in more complex and variable data than PCA ordination.
解读由快速分析技术（如末端限制性片段长度多态性分析（T-RFLP）、变性梯度凝胶电泳（DGGE）和DNA阵列）产生的大量数据，是微生物生态学家面临的一个难题。本研究比较了两种截然不同的排序方法——主成分分析（PCA）和自组织映射神经网络（SOM）——分析葡萄糖喂养的产甲烷生物反应器在启动阶段以及运行参数变化期间微生物群落16S-DNA末端限制性片段长度多态性（T-RFLP）图谱的能力。我们的目标不仅是确定哪些样本相似，还要解读群落动态并描述特定的系统发育型，即在不同反应器中表现相似的系统发育相似的生物体。从两个分别接种河流沉积物（S2）和厌氧消化池污泥（M3）的生物反应器以及一个成熟的对照反应器（R1）中，在56次体积变化过程中采集了15个样本。细菌T-RFLP图谱的PCA分析表明，S2和M3群落在最初的9次体积变化过程中变化迅速，然后变得相对稳定。PCA还表明，8天或6天的水力停留时间（HRT）对任何一个反应器群落都没有影响，而2天的HRT显著改变了两个反应器的群落结构。SOM根据每个片段在反应器群落中最丰富的时间对末端限制性片段进行聚类，结果形成了四个清晰可辨的组。13个片段在两个反应器中的表现相似，根据T-RFLP图谱中片段的相对丰度判断，其中8个片段在微生物群落中占很大比例。两个群落共有的6个细菌末端限制性片段与来自反应器的与螺旋体属、氨基杆菌属、嗜热栖热菌属和梭菌属相关的克隆16S rDNA序列相匹配。在乙酸营养型产甲烷菌群落中也出现了趋同现象，导致与西西里甲烷八叠球菌相关的生物体占主导地位。结果表明，PCA和SOM分析在T-RFLP数据分析中都很有用；然而，与PCA排序相比，SOM在解析更复杂和多变的数据模式方面表现更好。
