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Functional Basis of Microorganism Classification
PLoS Comput Biol, 11 (8): e1004472+ (28.08.2015)
DOI: 10.1371/journal.pcbi.1004472
Аннотация
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. Taxonomic classification of microorganisms according to similarity is important for industrial and clinical applications where close relationships imply similar uses and/or treatments. Current microbial taxonomy is phylogeny-guided, i.e., the organisms are grouped based on their evolutionary relationships, defined by vertical inheritance of genetic information from mother to daughter cells. Microbes, however, are capable of horizontal gene transfer (HGT). Thus, the current taxonomic assignments cannot guarantee genome-encoded molecular functional similarity; i.e. two microbes of the same taxonomic group inhabiting different environments may be very different—just as your cousin may be more different from you than your unrelated best friend. Our work establishes a computational framework for comparison of microorganisms based on their molecular functionality. In our functional-repertoire similarity-based organism network (FuSiON; flattened to fusion) representation, organisms can be consistently assigned to groups based on a quantitative measure of their functional similarities. Our approach highlights the specific environmental factor(s) that explain the functional differences between groups of microorganism. Fusion is a more practical choice for biomedical, industrial, and ecological applications, as many of these rely on understanding the functional capabilities of the microbes in their environment.
