Zhu Chengsheng, Delmont Tom O, Vogel Timothy M, Bromberg Yana
Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey, United States of America.
Environmental Microbial Genomics, Laboratoire Ampere, École Centrale de Lyon, Université de Lyon, Ecully, France.
PLoS Comput Biol. 2015 Aug 28;11(8):e1004472. doi: 10.1371/journal.pcbi.1004472. eCollection 2015 Aug.
Correctly identifying nearest "neighbors" of a given microorganism is important in industrial and clinical applications where close relationships imply similar treatment. Microbial classification based on similarity of physiological and genetic organism traits (polyphasic similarity) is experimentally difficult and, arguably, subjective. Evolutionary relatedness, inferred from phylogenetic markers, facilitates classification but does not guarantee functional identity between members of the same taxon or lack of similarity between different taxa. Using over thirteen hundred sequenced bacterial genomes, we built a novel function-based microorganism classification scheme, functional-repertoire similarity-based organism network (FuSiON; flattened to fusion). Our scheme is phenetic, based on a network of quantitatively defined organism relationships across the known prokaryotic space. It correlates significantly with the current taxonomy, but the observed discrepancies reveal both (1) the inconsistency of functional diversity levels among different taxa and (2) an (unsurprising) bias towards prioritizing, for classification purposes, relatively minor traits of particular interest to humans. Our dynamic network-based organism classification is independent of the arbitrary pairwise organism similarity cut-offs traditionally applied to establish taxonomic identity. Instead, it reveals natural, functionally defined organism groupings and is thus robust in handling organism diversity. Additionally, fusion can use organism meta-data to highlight the specific environmental factors that drive microbial diversification. Our approach provides a complementary view to cladistic assignments and holds important clues for further exploration of microbial lifestyles. Fusion is a more practical fit for biomedical, industrial, and ecological applications, as many of these rely on understanding the functional capabilities of the microbes in their environment and are less concerned with phylogenetic descent.
在工业和临床应用中，正确识别给定微生物的最近“邻居”非常重要，因为密切的关系意味着相似的治疗方法。基于生理和遗传生物特征相似性（多相相似性）的微生物分类在实验上很困难，而且可以说是主观的。从系统发育标记推断出的进化相关性有助于分类，但不能保证同一分类单元成员之间的功能一致性，也不能保证不同分类单元之间缺乏相似性。我们使用了一千三百多个已测序的细菌基因组，构建了一种基于功能的新型微生物分类方案，即基于功能库相似性的生物网络（FuSiON；简化为fusion）。我们的方案是表型的，基于跨越已知原核生物空间的定量定义的生物关系网络。它与当前的分类法有显著相关性，但观察到的差异揭示了两点：（1）不同分类单元之间功能多样性水平不一致；（2）（不出所料）在分类时偏向于优先考虑人类特别感兴趣的相对次要的特征。我们基于动态网络的生物分类独立于传统上用于确定分类身份的任意成对生物相似性阈值。相反，它揭示了自然的、基于功能定义的生物分组，因此在处理生物多样性方面具有稳健性。此外，fusion可以使用生物元数据来突出驱动微生物多样化的特定环境因素。我们的方法为分支分类提供了一个补充视角，并为进一步探索微生物生活方式提供了重要线索。Fusion更适合生物医学、工业和生态应用，因为其中许多应用依赖于了解微生物在其环境中的功能能力，而不太关注系统发育谱系。
