Functional Basis of Microorganism Classification

Abstract

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.

Microbial energy and matter transformation in agricultural soils

“…Large-scale (1374 bacterial genomes) distance-based network analyses comparing functional traits predicted from annotated genomes support strong clustering of shared function in specific phyla, such as Firmicutes, Chlamydia and Acidobacteria (Zhu et al, 2015). Ecological coherence at high taxonomic rank has been used to describe ecological groupings of soil bacterial taxa, with the relative abundance of 'copiotrophic' Beta-Proteobacteria and Bacteroides linked to increased rates of soil C mineralisation and 'oligotrophic' Acidobacteria linked to decreased rates of soil C mineralisation .…”

“…These include: a) phyla do not always demonstrate the same ecological behaviour between studies, for example Actinobacteria and Firmicutes have also been reported to have no relationship with C mineralisation ; b) specialised functions, such as lignin catabolism and denitrification, can be widely dispersed across phyla and so ecological coherence is function specific (Goldfarb et al, 2011;Schimel and Schaeffer, 2012); c) high levels of functional diversity can be present within a phylum, such as the Gamma, Alpha and Beta-Proteobacterial classes (Zhu et al, 2015); and d) our understanding of the distribution of functional traits related to C and nutrient turnover remains quite poor. This is evident from the recent discoveries of aerobic methane oxidation in Verrucomicrobia and the importance of aerobic ammonia oxidation by Archaea, which were functions historically thought specific to Proteobacteria (Dunfield et al, 2007;Prosser and Nicol, 2012), and non-photosynthetic Cyanobacteria (Soo et al, 2014).…”

Computational Approaches to Study Microbes and Microbiomes

“…Recent work 31 has shown the advantages of using microbial genome-guided predictions as proxies for functional comparisons. Microbial functional comparisons, informed by individual organisms' environmental preferences, highlight specific genes and functions responsible for particular environmental adaptations ( e.g .…”

“…functional studies of cyanobacteria clades identify sigma factors potentially responsible for salt tolerance). 31 However, despite significant recent efforts 32,33 , only a third of the microbial genes (for which sequences are available) are explicitly functionally annotated, 31 and high-throughput experiments exploring temporal relationships between gene expressions are missing for the vast majority of (already fully sequenced) microorganisms, and annotations of molecular pathways are limited. Additionally, any available experimental tests only reflect a portion of overall bacterial functionality, with nearly three hundred tests only accessing 5–20% of the total functional potential.…”

Snow Microbiome Functional Analyses Reveal Novel Microbial Metabolism of Complex Organic Compounds

“…Studies have been carried out in a wide variety of environments including the human gut [25][26][27], groundwater [28], acid mine drainage [29], beach sand [30], etc., and identified potential diagnostic, therapeutic, or bioremediation targets. With ample data, comparative analysis of meta-genomes/transcriptomes under different conditions highlights the key microbial members and functions that result from and/or contribute to niche differences [31,32]. Comparative meta-genomes/transcriptomes analysis have not been commonly applied to cold environment samples.…”