Biology subjects

Olm, M. R.

Publications and source records attributed to Olm, M. R..

7 recordsLinked to original sources

bioRxiv2018

Megaphage infect Prevotella and variants are widespread in gut microbiomes

Bacteriophage (phage) dramatically shape microbial community composition, redistribute nutrients via host lysis, and drive evolution through horizontal gene transfer. Despite their importance, much remains to be learned about phage in the human microbiome. We investigated gut microbiomes of humans from Bangladesh and Tanzania, two African baboon social groups, and Danish pigs, and report that many contain phage belonging to a clade with genomes >540 kb in length, the largest yet reported in the human microbiome and close to the maximum size ever reported for phage. We refer to these as Lak phage. CRISPR spacer targeting indicates that the Lak phage infect bacteria of the genus Prevotella. We manually curated to completion 15 distinct Lak phage genomes recovered from metagenomes. The genomes display several interesting features, including use of an alternative genetic code, large intergenic regions that are highly expressed, and up to 35 putative tRNAs, some of which contain enigmatic introns. Different individuals have distinct phage genotypes, and shifts in variant frequencies over consecutive sampling days reflect changes in relative abundance of phage sub-populations. Recent homologous recombination has resulted in extensive genome admixture of nine baboon Lak phage populations. We infer that Lak phage are widespread in gut communities that contain Prevotella species, especially in individuals in the developing world, and conclude that megaphage, with fascinating and underexplored biology, may be common but largely overlooked components of human and animal gut microbiomes.

microbiology↗

bioRxiv2018

Strain-level overlap between infant and hospital fungal microbiomes revealed through de novo assembly of eukaryotic genomes from metagenomes

Eukaryotes are a leading cause of nosocomial infections in neonates, but their diversity and population heterogeneity are rarely investigated. This has led to an incomplete understanding of eukaryotic strains that colonize infants and of the neonatal intensive care unit (NICU) as a possible source of these strains. Analysis of 1,174 time-series metagenomes from 161 premature infants revealed fungal colonization of 13 infants, primarily in the first two weeks of life. Nearly all 24 NICU samples contained eukaryotes, and the most diverse communities were in NICU sinks. Five of fourteen newly-assembled eukaryotic genomes derived from genomically undescribed species. Purpureocillium lilacinum genomes from infant and NICU samples shared 99.999% average nucleotide identity, highlighting the potential of hospital-associated fungi to colonize hospitalized infants. We quantified zygosity and within-population variation associated with the diploid eukaryotes, and thus defined the genetic reservoirs of eukaryotes in room environments and infants.

microbiology↗

bioRxiv2018

The developing premature infant gut microbiome is a major factor shaping the microbiome of neonatal intensive care unit rooms

1.1BackgroundThe neonatal intensive care unit (NICU) contains a unique cohort of patients with underdeveloped immune systems and nascent microbiome communities. Patients often spend several months in the same room and it has been previously shown that the gut microbiomes of these infants often resemble the microbes found in the NICU. Little is known, however, about the identity, persistence and absolute abundance of NICU room-associated bacteria over long stretches of time. Here we couple droplet digital PCR (ddPCR), 16S rRNA gene surveys, and recently published metagenomics data from infant gut samples to infer the extent to which the NICU microbiome is shaped by its room occupants.\n\nResultsOver 2,832 swabs, wipes, and air samples were collected from sixteen private-style NICU rooms housing very low birthweight (<1,500 g), premature (<31 weeks gestation) infants. For each infant, room samples were collected daily, Monday through Friday, for one month. The first samples from the first infant and last samples from the last infant were collected 383 days apart. Twenty-two NICU locations spanning room surfaces, hands, electronics, sink basins, and air were collected. Results point to an incredibly simple room community where 5-10 taxa, mostly skin associated, account for over 50% of 16S reads. Biomass estimates reveal 4-5 orders of magnitude difference between the least to the most dense microbial communities, air and sink basins, respectively. Biomass trends from bioaerosol samples and petri dish dust collectors suggest occupancy to be a main driver of suspended biological particles within the NICU. Using a machine learning algorithm to classify the origin of room samples, we show that each room has a unique microbial fingerprint. Several important taxa driving this model were dominant gut colonizers of infants housed within each room.\n\nConclusionsDespite regular cleaning of hospital surfaces, bacterial biomass was detectable at varying densities. A room specific microbiome signature was detected, suggesting microbes seeding NICU surfaces are sourced from reservoirs within the room and that these reservoirs contain actively dividing cells. Collectively, the data suggests that hospitalized infants, in combination with their caregivers, shape the microbiome of NICU rooms.

microbiology↗

bioRxiv2018

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.

microbiology↗

bioRxiv2017

Hospitalized premature infants are colonized by related bacterial strains with distinct proteomic profiles

During the first weeks of life, microbial colonization of the gut impacts human immune system maturation and other developmental processes. In premature infants, aberrant colonization has been implicated in the onset of necrotizing enterocolitis (NEC), a life-threatening intestinal disease. To study the premature infant gut colonization process, genome-resolved metagenomics was conducted on 343 fecal samples collected during the first three months of life from 35 premature infants housed in a neonatal intensive care unit, 14 of which developed NEC, and metaproteomic measurements were made on 87 samples. Microbial community composition and proteomic profiles remained relatively stable on the time scale of a week, but the proteome was more variable. Although genetically similar organisms colonized many infants, most infants were colonized by distinct strains with metabolic profiles that could be distinguished using metaproteomics. Microbiome composition correlated with infant, antibiotics administration, and NEC diagnosis. Communities were found to cluster into seven primary types, and community type switched within infants, sometimes multiple times. Interestingly, some communities sampled from the same infant at subsequent time points clustered with those of other infants. In some cases, switches preceded onset of NEC; however, no species or community type could account for NEC across the majority of infants. In addition to a correlation of protein abundances with organism replication rates, we found that organism proteomes correlated with overall community composition. Thus, this genome-resolved proteomics study demonstrates that the contributions of individual organisms to microbiome development depend on microbial community context.\n\nImportance\n\nHumans are colonized by microbes at birth, a process that is important to health and development. However, much remains to be known about the fine-scale microbial dynamics that occur during the colonization period. We conducted a genome-resolved study of microbial community composition, replication rates, and proteomes during the first three months of life of both healthy and sick premature infants. Infants were found to be colonized by similar microbes, but each underwent a distinct colonization trajectory.\n\nInterestingly, related microbes colonizing different infants were found to have distinct proteomes, indicating that microbiome function is not only driven by which organisms are present, but also largely depends on microbial responses to the unique set of physiological conditions in the infant gut.

microbiology↗

bioRxiv2017

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

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 organisms 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.\n\nImportanceThe process of reconstructing genomes from environmental sequence data (genome-resolved 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.

microbiology↗

bioRxiv2017

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.

bioinformatics↗