Rudi K, Zimonja M, Trosvik P, Naes T
MATFORSK Norwegian Food Research Institute, Osloveien 1, NO-1430 As, Norway.
Int J Food Microbiol. 2007 Nov 30;120(1-2):95-9. doi: 10.1016/j.ijfoodmicro.2007.06.004. Epub 2007 Jun 12.
Understanding dynamic processes and diversity in microbial communities is of key importance for combating pathogens and for stimulating beneficial bacteria. We have addressed these challenges utilising multivariate statistics for analyses of microbial community structures. We based our microbial community analyses on 16S rRNA gene data. This gene is by far the most widely applied genetic marker for phylogenetic and microbial community studies. Both probe and clone library data were analysed. We analysed the clone library data using a newly developed coordinate-based phylogenetic approach. By using coordinates, we avoid both DNA sequence alignments and the need for definition of operational taxonomic units (OTUs). The basic principle is to transform the sequence data to frequencies of multimers (short sequences of n=2 to 6), and then to use principal component analyses (PCA) for data compression into an orthogonal coordinate space. We used our coordinate method for global 16S rRNA gene analyses of prokaryotes. When comparing microbial communities, it is often important to determine the relationship between the microflora and knowledge about the samples analysed. We used partial least square regression (PLSR) to relate physical/chemical properties to microbial community composition. This was done by analysing both probe and clone library data using the effect of modified atmosphere packaging (MAP) on fish microflora as an example. We are currently investigating approaches to describe dynamic microbial community interactions. Our ultimate goal is to understand and model the main dynamic interactions in complete microbial communities.
了解微生物群落中的动态过程和多样性对于对抗病原体和促进有益细菌生长至关重要。我们利用多元统计分析微生物群落结构来应对这些挑战。我们基于16S rRNA基因数据进行微生物群落分析。到目前为止，该基因是系统发育和微生物群落研究中应用最广泛的遗传标记。我们对探针和克隆文库数据都进行了分析。我们使用一种新开发的基于坐标的系统发育方法分析克隆文库数据。通过使用坐标，我们既避免了DNA序列比对，也无需定义操作分类单元（OTU）。其基本原理是将序列数据转换为多聚体（n = 2至6的短序列）的频率，然后使用主成分分析（PCA）将数据压缩到正交坐标空间中。我们使用这种坐标方法对原核生物进行全球16S rRNA基因分析。在比较微生物群落时，确定微生物区系与所分析样本知识之间的关系通常很重要。我们使用偏最小二乘回归（PLSR）将物理/化学性质与微生物群落组成联系起来。这是以分析气调包装（MAP）对鱼类微生物区系的影响为例，对探针和克隆文库数据进行分析来完成的。我们目前正在研究描述动态微生物群落相互作用的方法。我们的最终目标是了解完整微生物群落中的主要动态相互作用并建立模型。
