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2015 ; 11
(8
): e1004472
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Functional Basis of Microorganism Classification
#MMPMID26317871
Zhu C
; Delmont TO
; Vogel TM
; Bromberg Y
PLoS Comput Biol
2015[Aug]; 11
(8
): e1004472
PMID26317871
show ga
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.
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