Abstract
BackgroundThe microbial communities populating human and natural environments have been extensively characterized with shotgun metagenomics, which provides an in-depth representation of the microbial diversity within a sample. Microbes thriving in urban environments may be crucially important for human health, but have received less attention than those of other environments. Ongoing efforts started to target urban microbiomes at a large scale, but the most recent computational methods to profile these metagenomes have never been applied in this context. It is thus currently unclear whether such methods, that have proven successful at distinguishing even closely related strains in human microbiomes, are also effective in urban settings for tasks such as cultivation-free pathogen detection and microbial surveillance. Here, we aimed at a) testing the currently available metagenomic profiling tools on urban metagenomics; b) characterizing the organisms in urban environment at the resolution of single strain and c) discussing the biological insights that can be inferred from such methods.ResultsWe applied three complementary methods on the 1614 metagenomes of the CAMDA 2017 challenge. With MetaMLST we identified 121 known sequence-types from 15 species of clinical relevance. For instance, we identified several Acinetobacter strains that were close to the nosocomial opportunistic pathogen A. nosocomialis. With StrainPhlAn, a generalized version of the MetaMLST approach, we inferred the phylogenetic structure of Pseudomonas stutzeri strains and suggested that the strain-level heterogeneity in environmental samples is higher than in the human microbiome. Finally, we also probed the functional potential of the different strains with PanPhlAn. We further showed that SNV-based and pangenome-based profiling provide complementary information that can be combined to investigate the evolutionary trajectories of microbes and to identify specific genetic determinants of virulence and antibiotic resistances within closely related strains.ConclusionWe show that strain-level methods developed primarily for the analysis of human microbiomes can be effective for city-associated microbiomes. In fact, (opportunistic) pathogens can be tracked and monitored across many hundreds of urban metagenomes. However, while more effort is needed to profile strains of currently uncharacterized species, this work poses the basis for high-resolution analyses of microbiomes sampled in city and mass transportation environments.ReviewersThis article was reviewed by Alexandra Bettina Graf, Daniel Huson and Trevor Cickovski.
Highlights
The microbial communities populating human and natural environments have been extensively characterized with shotgun metagenomics, which provides an in-depth representation of the microbial diversity within a sample
We applied three strain-level computational profiling approaches for metagenomic data (MetaMLST [35], StrainPhlAn [34], PanPhlAn [33]) to a total of 1614 environmental samples collected across the urban environment of three cities in the United States: New York [13], Boston [12], and Sacramento
The metagenomes were analyzed in the framework of the CAMDA 2017 Challenge conference and are referred to as the “MetaSUB data set” which includes the unpublished data of the Sacramento urban environment
Summary
The microbial communities populating human and natural environments have been extensively characterized with shotgun metagenomics, which provides an in-depth representation of the microbial diversity within a sample. Ongoing efforts started to target urban microbiomes at a large scale, but the most recent computational methods to profile these metagenomes have never been applied in this context It is currently unclear whether such methods, that have proven successful at distinguishing even closely related strains in human microbiomes, are effective in urban settings for tasks such as cultivation-free pathogen detection and microbial surveillance. Endeavors like the MetaSUB Project have started to characterize the composition of the microbial inhabitants of urban environments [25], but the increasing effort in sampling and metagenomic sequencing from these environments has to be paralleled with either the development or adaptation of computational tools able to fully exploit this urban metagenomic data
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