In Situ Replication Rates for Uncultivated Bacteria in Microbial Communities

Brown, Christopher T.; Olm, Matthew R.; Thomas, Brian C.; Banfield, Jillian F.

doi:10.1101/057992

Cited by 3 publications

(5 citation statements)

References 43 publications

(94 reference statements)

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“…For example, clade 1 is associated with six metagenomes of the upper troposphere and clade 2 is associated with three metagenomes of thiocyanate bioreactor communities (see Table S2 ). In the case of clade 2, we conclude that D. acidovorans was native to the sample because the population was growing rapidly, on the basis of differential coverage at the origin and terminus of replication (peak-to-trough ratios [PTRs]) (full range, 1.5 to 1.9) ( 27 , 28 ) (see Table S4 ). In contrast, bacteria of the contaminant clade (clade 5) had consistently low growth rates, based on PTRs between 1.4 and 1.1 ( Fig.…”

Section: Resultsmentioning

confidence: 99%

The Source and Evolutionary History of a Microbial Contaminant Identified Through Soil Metagenomic Analysis

Olm

Butterfield

Copeland

et al. 2017

mBio

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In this study, strain-resolved metagenomics was used to solve a mystery. A 6.4-Mbp complete closed genome was recovered from a soil metagenome and found to be astonishingly similar to that of Delftia acidovorans SPH-1, which was isolated in Germany a decade ago. It was suspected that this organism was not native to the soil sample because it lacked the diversity that is characteristic of other soil organisms; this suspicion was confirmed when PCR testing failed to detect the bacterium in the original soil samples. D. acidovorans was also identified in 16 previously published metagenomes from multiple environments, but detailed-scale single nucleotide polymorphism analysis grouped these into five distinct clades. All of the strains indicated as contaminants fell into one clade. Fragment length anomalies were identified in paired reads mapping to the contaminant clade genotypes only. This finding was used to establish that the DNA was present in specific size selection reagents used during sequencing. Ultimately, the source of the contaminant was identified as bacterial biofilms growing in tubing. On the basis of direct measurement of the rate of fixation of mutations across the period of time in which contamination was occurring, we estimated the time of separation of the contaminant strain from the genomically sequenced ancestral population within a factor of 2. This research serves as a case study of high-resolution microbial forensics and strain tracking accomplished through metagenomics-based comparative genomics. The specific case reported here is unusual in that the study was conducted in the background of a soil metagenome and the conclusions were confirmed by independent methods.

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Section: Resultsmentioning

confidence: 99%

The Source and Evolutionary History of a Microbial Contaminant Identified Through Soil Metagenomic Analysis

Olm

Butterfield

Copeland

et al. 2017

mBio

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“…A previous analysis revealed that antibiotic administration is associated with elevated replication rates of gut organisms, which was hypothesized to be due to high resource availability after elimination of antibiotic susceptible strains 36 . We show here that a sample's mean replication index in the days following antibiotic treatment is positively correlated with total resistance gene content (p < 0.05) (Fig 3A).…”

Section: Major Facilitator Superfamily Pumps Are Associated With Incrmentioning

confidence: 99%

“…We show here that a sample's mean replication index in the days following antibiotic treatment is positively correlated with total resistance gene content (p < 0.05) (Fig 3A). The replication index, or iRep value 36 , is influenced by the fraction of cells in the population that are replicating. Our results suggest that the elevated iRep values after antibiotic treatment are likely not solely explained by resource availability, but rather appear to be influenced by the organisms' resistance gene sets.…”

Section: Major Facilitator Superfamily Pumps Are Associated With Incrmentioning

confidence: 99%

“…In these infants, organisms carrying genes for major facilitator superfamily (MFS) transporters have significantly higher iRep values than those that do not have MFS genes (p < 5 × 10 -5 ) ( Fig 3B). As there are known differences in median iRep values among phyla 36 , the comparison was repeated within each phylum that contained members with and without MFS genes. The trend of higher iRep values for organisms with MFS was most apparent in Firmicutes (p < 5 × 10 -4 ) (Fig 3B).…”

Section: Major Facilitator Superfamily Pumps Are Associated With Incrmentioning

confidence: 99%

“…Next, reads from each sample were mapped to its representative genome list using bowtie2 56 default parameters. iRep 36 was run on the resulting mapping files using default parameters and without GC correction. Only values that passed iRep's default filtering and were < 3 were considered for analysis.…”

Section: Irep Calculationmentioning

confidence: 99%

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Machine learning leveraging genomes from metagenomes identifies influential antibiotic resistance genes in the infant gut microbiome

Rahman

Olm

Morowitz

et al. 2017

Preprint

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Antibiotic resistance in pathogens is extensively studied, yet little is known about how antibiotic resistance genes of typical gut bacteria influence microbiome dynamics. Here, we leverage genomes from metagenomes to investigate how genes of the premature infant gut resistome correspond to the ability of bacteria to survive under certain environmental and clinical conditions. We find that formula feeding impacts the resistome. Random forest models corroborated by statistical tests revealed that the gut resistome of formula-fed infants is enriched in class D beta-lactamase genes.Interestingly, Clostridium difficile strains harboring this gene are at higher abundance in formula-fed infants compared to C. difficile lacking this gene. Organisms with genes for major facilitator superfamily drug efflux pumps have faster replication rates under all conditions, even in the absence of antibiotic therapy. Using a machine learning approach, we identified genes that are predictive of an organism's direction of change in relative abundance after administration of vancomycin and cephalosporin antibiotics. The most accurate results were obtained by reducing annotated genomic data into five principal components classified by boosted decision trees. Among the genes involved in predicting if an organism increased in relative abundance after treatment are those that encode for subclass B2 beta-lactamases and transcriptional regulators of vancomycin resistance. This demonstrates that machine learning applied to genome-resolved metagenomics data can identify key genes for survival after antibiotics and predict how organisms in the gut microbiome will respond to antibiotic administration.. CC-BY 4.0 International license It is made available under a was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint (which . http://dx.doi.org/10.1101/185348 doi: bioRxiv preprint first posted online 3 ImportanceThe process of reconstructing genomes from environmental sequence data (genomeresolved metagenomics) allows for unique insight into microbial systems. We apply this technique to investigate how the antibiotic resistance genes of bacteria affect their ability to flourish in the gut under various conditions. Our analysis reveals that strain-level selection in formula-fed infants drives enrichment of beta-lactamase genes in the gut resistome. Using genomes from metagenomes, we built a machine learning model to predict how organisms in the gut microbial community respond to perturbation by antibiotics. This may eventually have clinical and industrial applications.

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Capturing the genetic makeup of the active microbiome in situ

2017

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More than any other technology, nucleic acid sequencing has enabled microbial ecology studies to be complemented with the data volumes necessary to capture the extent of microbial diversity and dynamics in a wide range of environments. In order to truly understand and predict environmental processes, however, the distinction between active, inactive and dead microbial cells is critical. Also, experimental designs need to be sensitive toward varying population complexity and activity, and temporal as well as spatial scales of process rates. There are a number of approaches, including single-cell techniques, which were designed to study in situ microbial activity and that have been successively coupled to nucleic acid sequencing. The exciting new discoveries regarding in situ microbial activity provide evidence that future microbial ecology studies will indispensably rely on techniques that specifically capture members of the microbiome active in the environment. Herein, we review those currently used activity-based approaches that can be directly linked to shotgun nucleic acid sequencing, evaluate their relevance to ecology studies, and discuss future directions.

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In Situ Replication Rates for Uncultivated Bacteria in Microbial Communities

Cited by 3 publications

References 43 publications

The Source and Evolutionary History of a Microbial Contaminant Identified Through Soil Metagenomic Analysis

The Source and Evolutionary History of a Microbial Contaminant Identified Through Soil Metagenomic Analysis

Machine learning leveraging genomes from metagenomes identifies influential antibiotic resistance genes in the infant gut microbiome

Capturing the genetic makeup of the active microbiome in situ

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