ABSTRACT
The reconstruction of complete microbial metabolic pathways using 'omics data from environmental samples remains challenging. Computational pipelines for pathway reconstruction that utilize machine learning methods to predict the presence or absence of KEGG modules in incomplete genomes are lacking. Here, we present MetaPathPredict, a software tool that incorporates machine learning models to predict the presence of complete KEGG modules within bacterial genomic datasets. Using gene annotation data and information from the KEGG module database, MetaPathPredict employs deep learning models to predict the presence of KEGG modules in a genome. MetaPathPredict can be used as a command line tool or as a Python module, and both options are designed to be run locally or on a compute cluster. Benchmarks show that MetaPathPredict makes robust predictions of KEGG module presence within highly incomplete genomes.
Subject(s)
Genome, Bacterial , Metabolic Networks and Pathways , Software , Metabolic Networks and Pathways/genetics , Computational Biology/methods , Machine Learning , Bacteria/genetics , Bacteria/metabolism , Bacteria/classificationABSTRACT
Plasmids are mobile genetic elements known to carry secondary metabolic genes that affect the fitness and survival of microbes in the environment. Well-studied cases of plasmid-encoded secondary metabolic genes in marine habitats include toxin/antitoxin and antibiotic biosynthesis/resistance genes. Here, we examine metagenome-assembled genomes (MAGs) from the permanently-stratified water column of the Cariaco Basin for integrated plasmids that encode biosynthetic gene clusters of secondary metabolites (smBGCs). We identify 16 plasmid-borne smBGCs in MAGs associated primarily with Planctomycetota and Pseudomonadota that encode terpene-synthesizing genes, and genes for production of ribosomal and non-ribosomal peptides. These identified genes encode for secondary metabolites that are mainly antimicrobial agents, and hence, their uptake via plasmids may increase the competitive advantage of those host taxa that acquire them. The ecological and evolutionary significance of smBGCs carried by prokaryotes in oxygen-depleted water columns is yet to be fully elucidated.
ABSTRACT
Previous studies of microbial communities in subseafloor sediments reported that microbial abundance and diversity decrease with sediment depth and age, and microbes dominating at depth tend to be a subset of the local seafloor community. However, the existence of geographically widespread, subsurface-adapted specialists is also possible. Here, we use metagenomic and metatranscriptomic analyses of the hydrothermally heated, sediment layers of Guaymas Basin (Gulf of California, Mexico) to examine the distribution and activity patterns of bacteria and archaea along thermal, geochemical and cell count gradients. We find that the composition and distribution of metagenome-assembled genomes (MAGs), dominated by numerous lineages of Chloroflexota and Thermoproteota, correlate with biogeochemical parameters as long as temperatures remain moderate, but downcore increasing temperatures beyond ca. 45 ºC override other factors. Consistently, MAG size and diversity decrease with increasing temperature, indicating a downcore winnowing of the subsurface biosphere. By contrast, specific archaeal MAGs within the Thermoproteota and Hadarchaeota increase in relative abundance and in recruitment of transcriptome reads towards deeper, hotter sediments, marking the transition towards a specialized deep, hot biosphere.
Subject(s)
Archaea , Crenarchaeota , Archaea/genetics , Metagenome/genetics , Geologic Sediments/chemistry , Phylogeny , Bacteria/genetics , RNA, Ribosomal, 16SABSTRACT
Secondary metabolites play essential roles in ecological interactions and nutrient acquisition, and are of interest for their potential uses in medicine and biotechnology. Genome mining for biosynthetic gene clusters (BGCs) can be used for the discovery of new compounds. Here, we use metagenomics and metatranscriptomics to analyze BGCs in free-living and particle-associated microbial communities through the stratified water column of the Cariaco Basin, Venezuela. We recovered 565 bacterial and archaeal metagenome-assembled genomes (MAGs) and identified 1154 diverse BGCs. We show that differences in water redox potential and microbial lifestyle (particle-associated vs. free-living) are associated with variations in the predicted composition and production of secondary metabolites. Our results indicate that microbes, including understudied clades such as Planctomycetota, potentially produce a wide range of secondary metabolites in these anoxic/euxinic waters.
Subject(s)
Microbiota , Seawater , Seawater/microbiology , Bacteria/metabolism , Metagenome , Microbiota/genetics , Water/metabolismABSTRACT
BACKGROUND: The 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. RESULTS: Over 2832 swabs, wipes, and air samples were collected from 16 private-style NICU rooms housing very low birth weight (< 1500 g), premature (< 31 weeks' gestation) infants. For each infant, room samples were collected daily, Monday through Friday, for 1 month. The first samples from the first infant and the 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 the amplicon reads. Biomass estimates reveal four to five 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. CONCLUSIONS: Despite 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.
Subject(s)
Bacteria/classification , Bacteria/isolation & purification , Gastrointestinal Microbiome/genetics , Gastrointestinal Tract/microbiology , Intensive Care Units, Neonatal , Skin/microbiology , Aerosols , Bacteria/genetics , Base Sequence , Dust , Feces/microbiology , Humans , Infant, Newborn , Infant, Premature , Infant, Very Low Birth Weight , Premature Birth , RNA, Ribosomal, 16S/genetics , Sequence Analysis, DNAABSTRACT
An enormous diversity of previously unknown bacteria and archaea has been discovered recently, yet their functional capacities and distributions in the terrestrial subsurface remain uncertain. Here, we continually sampled a CO2-driven geyser (Colorado Plateau, Utah, USA) over its 5-day eruption cycle to test the hypothesis that stratified, sandstone-hosted aquifers sampled over three phases of the eruption cycle have microbial communities that differ both in membership and function. Genome-resolved metagenomics, single-cell genomics and geochemical analyses confirmed this hypothesis and linked microorganisms to groundwater compositions from different depths. Autotrophic Candidatus "Altiarchaeum sp." and phylogenetically deep-branching nanoarchaea dominate the deepest groundwater. A nanoarchaeon with limited metabolic capacity is inferred to be a potential symbiont of the Ca. "Altiarchaeum". Candidate Phyla Radiation bacteria are also present in the deepest groundwater and they are relatively abundant in water from intermediate depths. During the recovery phase of the geyser, microaerophilic Fe- and S-oxidizers have high in situ genome replication rates. Autotrophic Sulfurimonas sustained by aerobic sulfide oxidation and with the capacity for N2 fixation dominate the shallow aquifer. Overall, 104 different phylum-level lineages are present in water from these subsurface environments, with uncultivated archaea and bacteria partitioned to the deeper subsurface.