The community ecology of microbial molecules.
The community ecology of microbial molecules.
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微生物分子的群落生态学。
DOI：
10.1007/s10886-014-0528-8
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发表时间：
2014
影响因子：
2.3
通讯作者：
Alexandrov,Theodore
中科院分区：
文献类型：
--
作者：
Quinn,RobertA;Alexandrov,Theodore
群落生态学研究生物的聚集以及这些聚集在时间和空间上的动态。在过去的十年中，微生物群落生态学通过采用高通量方法（如16 S rDNA测序和宏基因组学）得到了极大的发展。这使得分析多微生物群落（定义为多个到数千个微生物物种的集合）在其自然栖息地，并提高了我们对这些社区的结构，以及它们如何在扰动期间变化的理解。多微生物群落和它们产生的分子总是相互联系的。专门的代谢物已被证明是微生物群落结构和功能的主要介质（Dorrestein等人，2014）。尽管有这样的理解，微生物群落的代谢组学并没有像基于测序的方法那样受到关注，即使使用质谱的高通量方法也同样可用，并且这两种方法产生的数据之间存在相似之处。在微生物生态学中，微生物群落在“操作分类单位”（OTU）的水平上进行研究，每个单位代表进化分歧的单位，与术语“物种”松散相关。高通量核酸测序方法产生了对一起构成微生物群落的数百至数千个OTU的丰度进行定量的数据。多种方法可用于比对DNA序列以比较和定量个别OTU及其携带的基因，例如BLAST（http：//blast. ncbi. nlm。nih。政府/爆炸。CGI）和序列组装算法。基于非靶向质谱的代谢组学类似于基于测序的组学，因为数据包含数千个变量的丰度，但在代谢组学中，变量是代谢物。然而，类似于分配序列相似性的算法对于代谢组学数据的分析是有限的。与基于序列比对的方法相比，这阻碍了进展。然而，最近已经开发了新的算法来对分子相似性进行评分并建立类似于OTU的化学关系（Barupal等人，2012; Watrous等人，2012）。例如，分子网络可以通过计算比较单个MS/MS光谱来可视化分子相关性，以构建关系网络（Watrous等人，2012）。这种方法是一种在大数据集中识别独特和相关分子的新方法，类似于用于识别序列相似性的生物信息学工具。非靶向代谢组学的新兴领域可以从结合来自群落生态学的数十年历史的统计方法和旨在识别MS数据中独特和相关分子的新算法中受益匪浅，这种方法在基于OTU种群动力学的微生物群落生态学领域非常成功。生态统计数据，包括香农和辛普森多样性指数和稀疏曲线等措施，在分析化学物种方面可能特别有用。将这些方法应用到代谢组学中，有助于我们了解分子物种的多样性，并将多微生物群落中的微生物多样性与分子多样性联系起来。微生物群落直接受其环境化学的影响，而微生物群落本身也直接影响其环境化学。生态指数可以解决的问题包括：微生物多样性的驱动因素是否影响分子多样性？分子多样性是如何...
Community ecology is the study of assemblages of organisms and the dynamics of those assemblages in time and space. In the last decade, microbial community ecology has greatly advanced by employing high-throughput approaches, such as 16S rDNA sequencing and metagenomics. This has enabled the analysis of polymicrobial communities (defined as assemblages of multiple to thousands of microbial species) in their natural habitats and enhanced our understanding of the structure of these communities and how they change during perturbations. Polymicrobial communities and the molecules they produce are invariably linked. Specialized metabolites have been shown to function as major mediators of the structure and function of microbial communities (Dorrestein et al. 2014). Despite this understanding the metabolomics of microbial communities has not received as much attention as sequencingbased approaches, even though high-throughput methods using mass spectrometry are similarly available and there are parallels between the data generated in these two approaches. In microbial ecology, communities of microorganisms are studied on the level of ‘operational taxonomic units’(OTUs), each representing a unit of evolutionary divergence, loosely associated with the term “species”. High-throughput nucleic acid sequencing methods produce data quantifying the abundances of hundreds to thousands of OTUs that, together, constitute a microbial community. Multiple methods are available for aligning DNA sequences for the comparison and quantification of individual OTUs and the genes they carry, such as BLAST (http://blast. ncbi. nlm. nih. gov/Blast. cgi) and sequence assembly algorithms. Untargeted mass spectrometry-based metabolomics is similar to sequencing based-omics in that the data contains the abundances of thousands of variables, but in metabolomics, the variables are metabolites. However, algorithms analogous to assigning sequence similarity are limited for the analysis of metabolomics data. This has hindered progress compared with methods based on sequence alignments. Recently though, novel algorithms have been developed for scoring molecular similarity and building chemical relationships analogous to those of OTUs (Barupal et al. 2012; Watrous et al. 2012). Molecular networking, for example, can visualize molecular relatedness by computationally comparing individual MS/MS spectra to build relationship networks (Watrous et al. 2012). This approach is a novel means of identifying unique and related molecules in large data sets, and is similar to the bioinformatics tools used to identify sequence similarity. The nascent field of untargeted metabolomics could benefit greatly from combining decades-old statistical approaches from community ecology and novel algorithms designed to identify unique and related molecules in MS data, an approach that has been highly successful in the field of microbial community ecology based on OTU population dynamics. Ecological statistics, including measures such as the Shannon and Simpson indices of diversity and rarefaction curves, may be particularly useful in the analysis of chemical species. Applying these methods in metabolomics can help us to understand the diversity of molecular species and relate the microbial diversity in polymicrobial communities to the molecular diversity. Microbial communities are directly influenced by, and themselves directly influence, their environmental chemistry. Questions that can be addressed with ecological indices include: do the well-described drivers of microbial diversity affect molecular diversity? How does molecular diversity …