Matthew R Olm

Measurement of bacterial replication rates in microbial communities

Culture-independent microbiome studies have increased our understanding of the complexity and metabolic potential of microbial communities. However, to understand the contribution of individual microbiome members to community functions, it is important to determine which bacteria are actively replicating. We developed an algorithm, iRep, that uses draft-quality genome sequences and single time-point metagenome sequencing to infer microbial population replication rates. The algorithm calculates an index of replication (iRep) based on the sequencing coverage trend that results from bi-directional genome replication from a single origin of replication. We apply this method to show that microbial replication rates increase after antibiotic administration in human infants. We also show that uncultivated, groundwater-associated, Candidate Phyla Radiation bacteria only rarely replicate quickly in subsurface communities undergoing substantial changes in geochemistry. Our method can be applied to any genome-resolved microbiome study to track organism responses to varying conditions, identify actively growing populations and measure replication rates for use in modeling studies.

Identical bacterial populations colonize premature infant gut, skin, and oral microbiomes and exhibit different in situ growth rates

The initial microbiome impacts the health and future development of premature infants. Methodological limitations have led to gaps in our understanding of the habitat range and subpopulation complexity of founding strains, as well as how different body sites support microbial growth. Here, we used metagenomics to reconstruct genomes of strains that colonized the skin, mouth, and gut of two hospitalized premature infants during the first month of life. Seven bacterial populations, considered to be identical given whole-genome average nucleotide identity of >99.9%, colonized multiple body sites, yet none were shared between infants. Gut-associated Citrobacter koseri genomes harbored 47 polymorphic sites that we used to define 10 subpopulations, one of which appeared in the gut after 1 wk but did not spread to other body sites. Differential genome coverage was used to measure bacterial population replication rates in situ. In all cases where the same bacterial population was detected in multiple body sites, replication rates were faster in mouth and skin compared to the gut. The ability of identical strains to colonize multiple body sites underscores the habit flexibility of initial colonists, whereas differences in microbial replication rates between body sites suggest differences in host control and/or resource availability. Population genomic analyses revealed microdiversity within bacterial populations, implying initial inoculation by multiple individual cells with distinct genotypes. Overall, however, the overlap of strains across body sites implies that the premature infant microbiome can exhibit very low microbial diversity.

dRep: a tool for fast and accurate genomic comparisons that enables improved genome recovery from metagenomes through de-replication

The number of microbial genomes sequenced each year is expanding rapidly, in part due to genome-resolved metagenomic studies that routinely recover hundreds of draft-quality genomes. Rapid algorithms have been developed to comprehensively compare large genome sets, but they are not accurate with draft-quality genomes. Here we present dRep, a program that reduces the computational time for pairwise genome comparisons by sequentially applying a fast, inaccurate estimation of genome distance, and a slow, accurate measure of average nucleotide identity. dRep achieves a 28 × increase in speed with perfect recall and precision when benchmarked against previously developed algorithms. We demonstrate the use of dRep for genome recovery from time-series datasets. Each metagenome was assembled separately, and dRep was used to identify groups of essentially identical genomes and select the best genome from each replicate set. This resulted in recovery of significantly more and higher-quality genomes compared to the set recovered using co-assembly.

Strain-resolved analysis of hospital rooms and infants reveals overlap between the human and room microbiome

Preterm infants exhibit different microbiome colonization patterns relative to full-term infants, and it is speculated that the hospital room environment may contribute to infant microbiome development. Here, we present a genome-resolved metagenomic study of microbial genotypes from the gastrointestinal tracts of infants and from the neonatal intensive care unit (NICU) room environment. Some strains detected in hospitalized infants also occur in sinks and on surfaces, and belong to species such as Staphylococcus epidermidis, Enterococcus faecalis, Pseudomonas aeruginosa, and Klebsiella pneumoniae, which are frequently implicated in nosocomial infection and preterm infant gut colonization. Of the 15 K. pneumoniae strains detected in the study, four were detected in both infant gut and room samples. Time series experiments showed that nearly all strains associated with infant gut colonization can be detected in the room after, and often before, detection in the gut. Thus, we conclude that a component of premature infant gut colonization is the cycle of microbial exchange between the room and the occupant.It is thought that the hospital environment may contribute to infant microbiome development. Here, Brooks et al. present a genome-resolved metagenomic study of microbial genotypes from the infant gut and from neonatal intensive care unit rooms, showing that some strains are found in both infants and rooms.

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

ABSTRACT 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. IMPORTANCE It is often important to determine the source of a microbial strain. Examples include tracking a bacterium linked to a disease epidemic, contaminating the food supply, or used in bioterrorism. Strain identification and tracking are generally approached by using cultivation-based or relatively nonspecific gene fingerprinting methods. Genomic methods have the ability to distinguish strains, but this approach typically has been restricted to isolates or relatively low-complexity communities. We demonstrate that strain-resolved metagenomics can be applied to extremely complex soil samples. We genotypically defined a soil-associated bacterium and identified it as a contaminant. By linking together snapshots of the bacterial genome over time, it was possible to estimate how long the contaminant had been diverging from a likely source population. The results are congruent with the derivation of the bacterium from a strain isolated in Germany and sequenced a decade ago and highlight the utility of metagenomics in strain tracking. It is often important to determine the source of a microbial strain. Examples include tracking a bacterium linked to a disease epidemic, contaminating the food supply, or used in bioterrorism. Strain identification and tracking are generally approached by using cultivation-based or relatively nonspecific gene fingerprinting methods. Genomic methods have the ability to distinguish strains, but this approach typically has been restricted to isolates or relatively low-complexity communities. We demonstrate that strain-resolved metagenomics can be applied to extremely complex soil samples. We genotypically defined a soil-associated bacterium and identified it as a contaminant. By linking together snapshots of the bacterial genome over time, it was possible to estimate how long the contaminant had been diverging from a likely source population. The results are congruent with the derivation of the bacterium from a strain isolated in Germany and sequenced a decade ago and highlight the utility of metagenomics in strain tracking.

Machine Learning Leveraging Genomes from Metagenomes Identifies Influential Antibiotic Resistance Genes in the Infant Gut Microbiome

The process of reconstructing genomes from environmental sequence data (genome-resolved metagenomics) allows 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 applications. ABSTRACT Antibiotic resistance in pathogens is extensively studied, and yet little is known about how antibiotic resistance genes of typical gut bacteria influence microbiome dynamics. Here, we leveraged 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 found 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 than C. difficile strains lacking this gene. Organisms with genes for major facilitator superfamily drug efflux pumps have higher 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 to five principal components classified by boosted decision trees. Among the genes involved in predicting whether an organism increased in relative abundance after treatment are those that encode 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 treatment and predict how organisms in the gut microbiome will respond to antibiotic administration. IMPORTANCE The process of reconstructing genomes from environmental sequence data (genome-resolved metagenomics) allows 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 applications.

Hydrogen-based metabolism - An ancestral trait in lineages sibling to the Cyanobacteria

The metabolic machinery from which microbial aerobic respiration evolved is tightly linked to the origins of oxygenic Cyanobacteria (Oxyphotobacteria). Even though the majority of Oxyphotobacteria are photoautotrophs and can use carbohydrates with oxygen (O2) as the electron acceptor, all are fermenters under dark anoxic conditions. Studies suggest that the ancestor of Oxyphotobacteria may have used hydrogen (H2) as an electron donor and that two types of NiFe hydrogenases are essential for its oxidation. Melainabacteria and Sericytochromatia, close phylogenetic neighbors to Oxyphotobacteria comprise fermentative and aerobic representatives, or organisms capable of both. Margulisbacteria (candidate divisions RBX-1 and ZB3) and Saganbacteria (candidate division WOR-1), a novel cluster of bacteria phylogenetically related to Melainabacteria, Sericytochromatia and Oxyphotobacteria may further constrain the metabolic platform in which oxygenic photosynthesis and aerobic respiration arose. Here, we predict the metabolisms of Margulisbacteria and Saganbacteria from new and published metagenome-assembled genomes (MAGs) and single amplified genomes (SAGs), and compare them to their phylogenetic neighbors. Sediment-associated Margulisbacteria are predicted to have a fermentation-based metabolism featuring a variety of hydrogenases, a nitrogenase for nitrogen (N2) fixation, and electron bifurcating complexes involved in cycling of ferredoxin and NAD(P)H. Overall, the genomic features suggest the capacity for metabolic fine-tuning under strictly anoxic conditions. In contrast, the genomes of Margulisbacteria from the ocean ecosystem encode an electron transport chain that supports aerobic growth. Similarly, some Saganbacteria genomes encode various hydrogenases, and others may have the ability to use O2 under certain conditions via a putative novel type of heme copper O2 reductase. Like Melainabacteria and Sericytochromatia, Margulisbacteria and Saganbacteria have diverse energy metabolisms capable of fermentation, and aerobic or anaerobic respiration. In summary, our findings support the hypothesis that the ancestor of these groups was an anaerobe in which fermentation and H2 metabolism were central metabolic features. Our genomic data also suggests that contemporary lineages sibling to the Oxyphotobacteria may have acquired the ability to use O2 as a terminal electron acceptor under certain environmental conditions.

dRep: A tool for fast and accurate genome de-replication that enables tracking of microbial genotypes and improved genome recovery from metagenomes

The number of microbial genomes sequenced each year is expanding rapidly, in part due to genome-resolved metagenomic studies that routinely recover hundreds of draft-quality genomes. Rapid algorithms have been developed to comprehensively compare large genome sets, but they are not accurate with draft-quality genomes. Here we present dRep, a program that sequentially applies a fast, inaccurate estimation of genome distance and a slow but accurate measure of average nucleotide identity to reduce the computational time for pair-wise genome set comparisons by orders of magnitude. We demonstrate its use in a study where we separately assembled each metagenome from time series datasets. Groups of essentially identical genomes were identified with dRep, and the best genome from each set was selected. This resulted in recovery of significantly more and higher-quality genomes compared to the set recovered using the typical co-assembly method. Documentation is available at http://drep.readthedocs.io/en/master/ and source code is available at https://github.com/MrOlm/drep.

In situ replication rates for uncultivated bacteria in microbial communities

Culture-independent microbiome studies have revolutionized our understanding of the complexity and metabolic potential of microbial communities, but information about in situ growth rates has been lacking. Here, we show that bacterial replication rates can be determined using genome-resolved metagenomics without requirement for complete genome sequences. In human infants, we detected elevated microbial replication rates following administration of antibiotics, and bacterial growth rate anomalies prior to the onset of necrotizing enterocolitis. We studied microorganisms in subsurface communities and determined that a diverse group of groundwater-associated bacteria typically exhibit slow growth rates, despite significant changes in geochemical conditions. All microbiome studies will be advanced by measurements of replication rates that can identify actively growing populations, track organism responses to changing conditions, and provide growth rate information needed for modeling.

Microbial diversity and metabolic potential in cyanotoxin producing cyanobacterial mats throughout a river network

Microbial mats formed by Cyanobacteria of the genus Phormidium produce the neurotoxin anatoxin-a that has been linked to animal deaths. Blooms of planktonic Cyanobacteria have long been of concern in lakes, but recognition of potential harmful impacts of riverine benthic cyanobacterial mats is more recent. Consequently little is known about the diversity of the biosynthetic capacities of cyanobacterial species and associated microbes in mats throughout river networks. Here we performed metagenomic sequencing for 22 Phormidium-dominated microbial mats collected across the Eel River network in Northern California to investigate cyanobacterial and co-occurring microbial assemblage diversity, probe their metabolic potential and evaluate their capacities for toxin production. We genomically defined four Cyanobacterial species clusters that occur throughout the river network, three of which have not been described previously. From the genomes of seven strains from one species group we describe the first anatoxin-a operon from the genus Phormidium. Community composition within the mat appears to be associated with the presence of Cyanobacteria capable of producing anatoxin-a. Bacteroidetes, Proteobacteria, and novel Verrucomicrobia dominated the microbial assemblages. Interestingly, some mats also contained organisms from candidate phyla such as Canditatus Kapabacteria, as well as Absconditabacteria (SR1), Parcubacteria (OD1) and Peregrinibacteria (PER) within the Candidate Phyla Radiation. Oxygenic photosynthesis and carbon respiration were the most common metabolisms detected in mats but other metabolic capacities include aerobic anoxygenic photosynthesis, sulfur compound oxidation and breakdown of urea. The results reveal the diversity of metabolisms fueling the growth of mats, and a relationship between microbial assemblage composition and the distribution of anatoxin-a producing cyanobacteria within freshwater Phormidium mats in river networks.