RESUMO
OBJECTIVES: Indonesia's location at the convergence of multiple tectonic plates results in a unique geomorphological feature with abundant hot springs. This study pioneers the metagenomic exploration of Indonesian hot springs, harbouring unique life forms despite high temperatures. The microbial community of hot springs is taxonomically versatile and biotechnologically valuable. 16s rRNA amplicon sequencing of the metagenome is a viable option for the microbiome investigation. This study utilized Oxford Nanopore's long-read 16 S rRNA sequencing for enhanced species identification, improved detection of rare members, and a more detailed community composition profile. DATA DESCRIPTION: Water samples were taken from three hot springs of the Bali, Indonesia (i) Angseri, 8.362503 S, 115.133452 E; (ii) Banjar, 8.210270 S, 114.967063 E; and (iii) Batur, 8.228806 S, 115.404829 E. BioLit Genomic DNA Extraction Kit (SRL, Mumbai, India) was used to isolate DNA from water samples. The quantity and quality of the DNA were determined using a NanoDrop™ spectrophotometer and a Qubit fluorometer (Thermo Fisher Scientific, USA). The library was created using Oxford Nanopore Technology kits, and the sequencing was done using Oxford Nanopore's GridION platform. All sequencing data was obtained in FASTQ files and filtered using NanoFilt software. This dataset is valuable for searching novel bacteria diversity and their existence.
Assuntos
Fontes Termais , Sequenciamento por Nanoporos , RNA Ribossômico 16S , Fontes Termais/microbiologia , Indonésia , RNA Ribossômico 16S/genética , Sequenciamento por Nanoporos/métodos , Microbiota/genética , Bactérias/genética , Bactérias/isolamento & purificação , Bactérias/classificação , Metagenoma/genética , Metagenômica/métodos , Microbiologia da Água , Filogenia , DNA Bacteriano/genética , DNA Bacteriano/análise , Análise de Sequência de DNA/métodosRESUMO
MOTIVATION: Functional profiling of metagenomic samples is essential to decipher the functional capabilities of microbial communities. Traditional and more widely used functional profilers in the context of metagenomics rely on aligning reads against a known reference database. However, aligning sequencing reads against a large and fast-growing database is computationally expensive. In general, k-mer-based sketching techniques have been successfully used in metagenomics to address this bottleneck, notably in taxonomic profiling. In this work, we describe leveraging FracMinHash (implemented in sourmash, a publicly available software), a k-mer-sketching algorithm, to obtain functional profiles of metagenome samples. RESULTS: We show how pieces of the sourmash software (and the resulting FracMinHash sketches) can be put together in a pipeline to functionally profile a metagenomic sample. We named our pipeline fmh-funprofiler. We report that the functional profiles obtained using this pipeline demonstrate comparable completeness and better purity compared to the profiles obtained using other alignment-based methods when applied to simulated metagenomic data. We also report that fmh-funprofiler is 39-99× faster in wall-clock time, and consumes up to 40-55× less memory. Coupled with the KEGG database, this method not only replicates fundamental biological insights but also highlights novel signals from the Human Microbiome Project datasets. AVAILABILITY AND IMPLEMENTATION: This fast and lightweight metagenomic functional profiler is freely available and can be accessed here: https://github.com/KoslickiLab/fmh-funprofiler. All scripts of the analyses we present in this manuscript can be found on GitHub.
Assuntos
Algoritmos , Metagenoma , Metagenômica , Software , Metagenômica/métodos , Metagenoma/genética , Humanos , Microbiota/genética , Bases de Dados GenéticasRESUMO
Leaf litter decomposition, a crucial component of the global carbon cycle, relies on the pivotal role played by microorganisms. However, despite their ecological importance, leaf-litter-decomposing microorganism taxonomic and functional diversity needs additional study. This study explores the taxonomic composition, dynamics, and functional role of microbial communities that decompose leaf litter of forest-forming tree species in two ecologically unique regions of Europe. Twenty-nine microbial metagenomes isolated from the leaf litter of eight forest-forming species of woody plants were investigated by Illumina technology using read- and assembly-based approaches of sequences analysis. The taxonomic structure of the microbial community varies depending on the stage of litter decomposition; however, the community's core is formed by Pseudomonas, Sphingomonas, Stenotrophomonas, and Pedobacter genera of Bacteria and by Aureobasidium, Penicillium, Venturia genera of Fungi. A comparative analysis of the taxonomic structure and composition of the microbial communities revealed that in both regions, seasonal changes in structure take place; however, there is no clear pattern in its dynamics. Functional gene analysis of MAGs revealed numerous metabolic profiles associated with leaf litter degradation. This highlights the diverse metabolic capabilities of microbial communities and their implications for ecosystem processes, including the production of volatile organic compounds (VOCs) during organic matter decomposition. This study provides important advances in understanding of ecosystem processes and the carbon cycle, underscoring the need to unravel the intricacies of microbial communities within these contexts.
Assuntos
Florestas , Microbiota , Folhas de Planta , Estações do Ano , Folhas de Planta/microbiologia , Folhas de Planta/metabolismo , Microbiota/genética , Microbiota/fisiologia , Bactérias/genética , Bactérias/classificação , Bactérias/metabolismo , Fungos/genética , Fungos/classificação , Fungos/metabolismo , Fungos/isolamento & purificação , Sequenciamento Completo do Genoma , Metagenoma/genética , Árvores/microbiologiaRESUMO
The human microbiome emerges as a promising reservoir for diagnostic markers and therapeutics. Since host-associated microbiomes at various body sites differ and diseases do not occur in isolation, a comprehensive analysis strategy highlighting the full potential of microbiomes should include diverse specimen types and various diseases. To ensure robust data quality and comparability across specimen types and diseases, we employ standardized protocols to generate sequencing data from 1931 prospectively collected specimens, including from saliva, plaque, skin, throat, eye, and stool, with an average sequencing depth of 5.3 gigabases. Collected from 515 patients, these samples yield an average of 3.7 metagenomes per patient. Our results suggest significant microbial variations across diseases and specimen types, including unexpected anatomical sites. We identify 583 unexplored species-level genome bins (SGBs) of which 189 are significantly disease-associated. Of note, the existence of microbial resistance genes in one specimen was indicative of the same resistance genes in other specimens of the same patient. Annotated and previously undescribed SGBs collectively harbor 28,315 potential biosynthetic gene clusters (BGCs), with 1050 significant correlations to diseases. Our combinatorial approach identifies distinct SGBs and BGCs, emphasizing the value of pan-body pan-disease microbiomics as a source for diagnostic and therapeutic strategies.
Assuntos
Metagenoma , Metagenômica , Microbiota , Humanos , Microbiota/genética , Metagenoma/genética , Metagenômica/métodos , Bactérias/genética , Bactérias/isolamento & purificação , Bactérias/classificação , Fezes/microbiologia , Masculino , Feminino , Família Multigênica , Saliva/microbiologia , AdultoRESUMO
The spread of antibiotic resistance genes (ARGs) poses a substantial threat to human health. Phage-mediated transduction could exacerbate ARG transmission. While several case studies exist, it is yet unclear to what extent phages encode and mobilize ARGs at the global scale and whether human impacts play a role in this across different habitats. Here, we combine 38,605 bacterial genomes, 1432 metagenomes, and 1186 metatranscriptomes across 12 contrasting habitats to explore the distribution of prophages and their cargo ARGs in natural and human-impacted environments. Worldwide, we observe a significant increase in the abundance, diversity, and activity of prophage-encoded ARGs in human-impacted habitats linked with relatively higher risk of past antibiotic exposure. This effect was driven by phage-encoded cargo ARGs that could be mobilized to provide increased resistance in heterologous E. coli host for a subset of analyzed strains. Our findings suggest that human activities have altered bacteria-phage interactions, enriching ARGs in prophages and making ARGs more mobile across habitats globally.
Assuntos
Antibacterianos , Bactérias , Farmacorresistência Bacteriana , Prófagos , Prófagos/genética , Humanos , Farmacorresistência Bacteriana/genética , Antibacterianos/farmacologia , Bactérias/genética , Bactérias/virologia , Bactérias/efeitos dos fármacos , Genoma Bacteriano/genética , Metagenoma/genética , Ecossistema , Escherichia coli/genética , Escherichia coli/virologia , Escherichia coli/efeitos dos fármacos , Resistência Microbiana a Medicamentos/genética , Genes BacterianosRESUMO
For taxonomy based classification of metagenomics assembled contigs, current methods use sequence similarity to identify their most likely taxonomy. However, in the related field of metagenomic binning, contigs are routinely clustered using information from both the contig sequences and their abundance. We introduce Taxometer, a neural network based method that improves the annotations and estimates the quality of any taxonomic classifier using contig abundance profiles and tetra-nucleotide frequencies. We apply Taxometer to five short-read CAMI2 datasets and find that it increases the average share of correct species-level contig annotations of the MMSeqs2 tool from 66.6% to 86.2%. Additionally, it reduce the share of wrong species-level annotations in the CAMI2 Rhizosphere dataset by an average of two-fold for Metabuli, Centrifuge, and Kraken2. Futhermore, we use Taxometer for benchmarking taxonomic classifiers on two complex long-read metagenomics data sets where ground truth is not known. Taxometer is available as open-source software and can enhance any taxonomic annotation of metagenomic contigs.
Assuntos
Metagenômica , Software , Metagenômica/métodos , Redes Neurais de Computação , Classificação/métodos , Metagenoma/genética , Algoritmos , Mapeamento de Sequências Contíguas/métodos , RizosferaRESUMO
The lower respiratory tract (LRT) microbiome impacts human health, especially among critically ill patients. However, comprehensive characterizations of the LRT microbiome remain challenging due to low microbial mass and host contamination. We develop a chelex100-based low-biomass microbial-enrichment method (CMEM) that enables deep metagenomic profiling of LRT samples to recover near-complete microbial genomes. We apply the method to 453 longitudinal LRT samples from 157 intensive care unit (ICU) patients in three geographically distant hospitals. We recover 120 high-quality metagenome-assembled genomes (MAGs) and associated plasmids without culturing. We detect divergent longitudinal microbiome dynamics and hospital-specific dominant opportunistic pathogens and resistomes in pneumonia patients. Diagnosed pneumonia and the ICU stay duration were associated with the abundance of specific antibiotic-resistance genes (ARGs). Moreover, CMEM can serve as a robust tool for genome-resolved analyses. MAG-based analyses reveal strain-specific resistome and virulome among opportunistic pathogen strains. Evolutionary analyses discover increased mobilome in prevailing opportunistic pathogens, highly conserved plasmids, and new recombination hotspots associated with conjugative elements and prophages. Integrative analysis with epidemiological data reveals frequent putative inter-patient strain transmissions in ICUs. In summary, we present a genome-resolved functional, transmission, and evolutionary landscape of the LRT microbiota in critically ill patients.
Assuntos
Estado Terminal , Unidades de Terapia Intensiva , Metagenoma , Microbiota , Humanos , Microbiota/genética , Metagenoma/genética , Metagenômica/métodos , Estudos Longitudinais , Masculino , Feminino , Plasmídeos/genética , Genoma Bacteriano/genética , Sistema Respiratório/microbiologia , Idoso , Pessoa de Meia-Idade , Bactérias/genética , Bactérias/classificação , Bactérias/isolamento & purificação , Pneumonia/microbiologia , Evolução MolecularRESUMO
Microbial communities exhibit intricate interactions underpinned by metabolic dependencies. To elucidate these dependencies, we present a workflow utilizing random matrix theory on metagenome-assembled genomes to construct co-occurrence and metabolic complementarity networks. We apply this approach to a temperature gradient hot spring, unraveling the interplay between thermal stress and metabolic cooperation. Our analysis reveals an increase in the frequency of metabolic interactions with rising temperatures. Amino acids, coenzyme A derivatives, and carbohydrates emerge as key exchange metabolites, forming the foundation for syntrophic dependencies, in which commensalistic interactions take a greater proportion than mutualistic ones. These metabolic exchanges are most prevalent between phylogenetically distant species, especially archaea-bacteria collaborations, as a crucial adaptation to harsh environments. Furthermore, we identify a significant positive correlation between basal metabolite exchange and genome size disparity, potentially signifying a means for streamlined genomes to leverage cooperation with metabolically richer partners. This phenomenon is also confirmed by another composting system which has a similar wide range of temperature fluctuations. Our workflow provides a feasible way to decipher the metabolic complementarity mechanisms underlying microbial interactions, and our findings suggested environmental stress regulates the cooperative strategies of thermophiles, while these dependencies have been potentially hardwired into their genomes during co-evolutions.
Assuntos
Archaea , Bactérias , Redes e Vias Metabólicas , Metagenoma , Microbiota , Redes e Vias Metabólicas/genética , Archaea/genética , Archaea/metabolismo , Bactérias/metabolismo , Bactérias/genética , Bactérias/classificação , Metagenoma/genética , Fontes Termais/microbiologia , Filogenia , Interações Microbianas , Temperatura AltaRESUMO
Characterization of microbial community metabolic output is crucial to understanding their functions. Construction of genome-scale metabolic models from metagenome-assembled genomes (MAG) has enabled prediction of metabolite production by microbial communities, yet little is known about their accuracy. Here, we examined the performance of two approaches for metabolite prediction from metagenomes, one that is MAG-guided and another that is taxonomic reference-guided. We applied both on shotgun metagenomics data from human and environmental samples, and validated findings in the human samples using untargeted metabolomics. We found that in human samples, where taxonomic profiling is optimized and reference genomes are readily available, when number of input taxa was normalized, the reference-guided approach predicted more metabolites than the MAG-guided approach. The two approaches showed significant overlap but each identified metabolites not predicted in the other. Pathway enrichment analyses identified significant differences in inferences derived from data based on the approach, highlighting the need for caution in interpretation. In environmental samples, when the number of input taxa was normalized, the reference-guided approach predicted more metabolites than the MAG-guided approach for total metabolites in both sample types and non-redundant metabolites in seawater samples. Nonetheless, as was observed for the human samples, the approaches overlapped substantially but also predicted metabolites not observed in the other. Our findings report on utility of a complementary input to genome-scale metabolic model construction that is less computationally intensive forgoing MAG assembly and refinement, and that can be applied on shallow shotgun sequencing where MAGs cannot be generated.IMPORTANCELittle is known about the accuracy of genome-scale metabolic models (GEMs) of microbial communities despite their influence on inferring community metabolic outputs and culture conditions. The performance of GEMs for metabolite prediction from metagenomes was assessed by applying two approaches on shotgun metagenomics data from human and environmental samples, and validating findings in the human samples using untargeted metabolomics. The performance of the approach was found to be dependent on sample type, but collectively, the reference-guided approach predicted more metabolites than the MAG-guided approach. Despite the differences, the predictions from the approaches overlapped substantially but each identified metabolites not predicted in the other. We found significant differences in biological inferences based on the approach, with some examples of uniquely enriched pathways in one group being invalidated when using the alternative approach, highlighting the need for caution in interpretation of GEMs.
Assuntos
Metabolômica , Metagenômica , Microbiota , Humanos , Metagenômica/métodos , Metabolômica/métodos , Microbiota/genética , Metagenoma/genéticaRESUMO
Although long-read sequencing has enabled obtaining high-quality and complete genomes from metagenomes, many challenges still remain to completely decompose a metagenome into its constituent prokaryotic and viral genomes. This study focuses on decomposing an estuarine metagenome to obtain a more accurate estimate of microbial diversity. To achieve this, we developed a new bead-based DNA extraction method, a novel bin refinement method, and obtained 150 Gbp of Nanopore sequencing. We estimate that there are ~500 bacterial and archaeal species in our sample and obtained 68 high-quality bins (>90% complete, <5% contamination, ≤5 contigs, contig length of >100 kbp, and all ribosomal and tRNA genes). We also obtained many contigs of picoeukaryotes, environmental DNA of larger eukaryotes such as mammals, and complete mitochondrial and chloroplast genomes and detected ~40,000 viral populations. Our analysis indicates that there are only a few strains that comprise most of the species abundances. IMPORTANCE: Ocean and estuarine microbiomes play critical roles in global element cycling and ecosystem function. Despite the importance of these microbial communities, many species still have not been cultured in the lab. Environmental sequencing is the primary way the function and population dynamics of these communities can be studied. Long-read sequencing provides an avenue to overcome limitations of short-read technologies to obtain complete microbial genomes but comes with its own technical challenges, such as needed sequencing depth and obtaining high-quality DNA. We present here new sampling and bioinformatics methods to attempt decomposing an estuarine microbiome into its constituent genomes. Our results suggest there are only a few strains that comprise most of the species abundances from viruses to picoeukaryotes, and to fully decompose a metagenome of this diversity requires 1 Tbp of long-read sequencing. We anticipate that as long-read sequencing technologies continue to improve, less sequencing will be needed.
Assuntos
Estuários , Metagenômica , Microbiota , Vírus , Microbiota/genética , Metagenômica/métodos , São Francisco , Vírus/genética , Vírus/classificação , Vírus/isolamento & purificação , Metagenoma/genética , Bactérias/genética , Bactérias/classificação , Archaea/genética , Archaea/virologia , Eucariotos/genética , Genoma Viral/genéticaRESUMO
BACKGROUND: Construction of co-occurrence networks in metagenomic data often employs correlation to infer pairwise relationships between microbes. However, biological systems are complex and often display qualities non-linear in nature. Therefore, the reliance on correlation alone may overlook important relationships and fail to capture the full breadth of intricacies presented in underlying interaction networks. It is of interest to incorporate metrics that are not only robust in detecting linear relationships, but non-linear ones as well. RESULTS: In this paper, we explore the use of various mutual information (MI) estimation approaches for quantifying pairwise relationships in biological data and compare their performances against two traditional measures-Pearson's correlation coefficient, r, and Spearman's rank correlation coefficient, ρ. Metrics are tested on both simulated data designed to mimic pairwise relationships that may be found in ecological systems and real data from a previous study on C. diff infection. The results demonstrate that, in the case of asymmetric relationships, mutual information estimators can provide better detection ability than Pearson's or Spearman's correlation coefficients. Specifically, we find that these estimators have elevated performances in the detection of exploitative relationships, demonstrating the potential benefit of including them in future metagenomic studies. CONCLUSIONS: Mutual information (MI) can uncover complex pairwise relationships in biological data that may be missed by traditional measures of association. The inclusion of such relationships when constructing co-occurrence networks can result in a more comprehensive analysis than the use of correlation alone.
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Metagenômica , Metagenômica/métodos , Algoritmos , Metagenoma/genéticaRESUMO
BACKGROUND: Current methods for comparing metagenomes, derived from whole-genome sequencing reads, include top-down metrics or parametric models such as metagenome-diversity, and bottom-up, non-parametric, model-free machine learning approaches like Naïve Bayes for k-mer-profiling. However, both types are limited in their ability to effectively and comprehensively identify and catalogue unique or enriched metagenomic genes, a critical task in comparative metagenomics. This challenge is significant and complex due to its NP-hard nature, which means computational time grows exponentially, or even faster, with the problem size, rendering it impractical for even the fastest supercomputers without heuristic approximation algorithms. METHOD: In this study, we introduce a new framework, MC (Metagenome-Comparison), designed to exhaustively detect and catalogue unique or enriched metagenomic genes (MGs) and their derivatives, including metagenome functional gene clusters (MFGC), or more generally, the operational metagenomic unit (OMU) that can be considered the counterpart of the OTU (operational taxonomic unit) from amplicon sequencing reads. The MC is essentially a heuristic search algorithm guided by pairs of new metrics (termed MG-specificity or OMU-specificity, MG-specificity diversity or OMU-specificity diversity). It is further constrained by statistical significance (P-value) implemented as a pair of statistical tests. RESULTS: We evaluated the MC using large metagenomic datasets related to obesity, diabetes, and IBD, and found that the proportions of unique and enriched metagenomic genes ranged from 0.001% to 0.08 % and 0.08%-0.82 % respectively, and less than 10 % for the MFGC. CONCLUSION: The MC provides a robust method for comparing metagenomes at various scales, from baseline MGs to various function/pathway clusters of metagenomes, collectively termed OMUs.
Assuntos
Metagenoma , Metagenômica , Humanos , Metagenômica/métodos , Metagenoma/genética , Sequenciamento Completo do Genoma/métodos , AlgoritmosRESUMO
Background: Tracking the spread of antibiotic resistant bacteria is critical to reduce global morbidity and mortality associated with human and animal infections. There is a need to understand the role that wild animals in maintenance and transfer of antibiotic resistance genes (ARGs). Methods: This study used metagenomics to identify and compare the abundance of bacterial species and ARGs detected in the gut microbiomes from sympatric humans and wild mouse lemurs in a forest-dominated, roadless region of Madagascar near Ranomafana National Park. We examined the contribution of human geographic location toward differences in ARG abundance and compared the genomic similarity of ARGs between host source microbiomes. Results: Alpha and beta diversity of species and ARGs between host sources were distinct but maintained a similar number of detectable ARG alleles. Humans were differentially more abundant for four distinct tetracycline resistance-associated genes compared to lemurs. There was no significant difference in human ARG diversity from different locations. Human and lemur microbiomes shared 14 distinct ARGs with highly conserved in nucleotide identity. Synteny of ARG-associated assemblies revealed a distinct multidrug-resistant gene cassette carrying dfrA1 and aadA1 present in human and lemur microbiomes without evidence of geographic overlap, suggesting that these resistance genes could be widespread in this ecosystem. Further investigation into intermediary processes that maintain drug-resistant bacteria in wildlife settings is needed.
Assuntos
Microbioma Gastrointestinal , Metagenoma , Animais , Madagáscar , Humanos , Metagenoma/genética , Microbioma Gastrointestinal/genética , Simpatria , População Rural , Metagenômica , Bactérias/genética , Bactérias/efeitos dos fármacos , Farmacorresistência Bacteriana/genética , Genes Bacterianos , Cheirogaleidae/genética , Cheirogaleidae/microbiologiaRESUMO
Oil reservoirs, being one of the significant subsurface repositories of energy and carbon, host diverse microbial communities affecting energy production and carbon emissions. Viruses play crucial roles in the ecology of microbiomes, however, their distribution and ecological significance in oil reservoirs remain undetermined. Here, we assemble a catalogue encompassing viral and prokaryotic genomes sourced from oil reservoirs. The catalogue comprises 7229 prokaryotic genomes and 3,886 viral Operational Taxonomic Units (vOTUs) from 182 oil reservoir metagenomes. The results show that viruses are widely distributed in oil reservoirs, and 85% vOTUs in oil reservoir are detected in less than 10% of the samples, highlighting the heterogeneous nature of viral communities within oil reservoirs. Through combined microcosm enrichment experiments and bioinformatics analysis, we validate the ecological roles of viruses in regulating the community structure of sulfate reducing microorganisms, primarily through a virulent lifestyle. Taken together, this study uncovers a rich diversity of viruses and their ecological functions within oil reservoirs, offering a comprehensive understanding of the role of viral communities in the biogeochemical cycles of the deep biosphere.
Assuntos
Biodiversidade , Metagenoma , Campos de Petróleo e Gás , Vírus , Campos de Petróleo e Gás/virologia , Campos de Petróleo e Gás/microbiologia , Vírus/genética , Vírus/classificação , Vírus/isolamento & purificação , Metagenoma/genética , Microbiota/genética , Genoma Viral/genética , Filogenia , Bactérias/genética , Bactérias/classificação , Bactérias/isolamento & purificação , MetagenômicaRESUMO
Cold seeps in the deep sea are closely linked to energy exploration as well as global climate change. The alkane-dominated chemical energy-driven model makes cold seeps an oasis of deep-sea life, showcasing an unparalleled reservoir of microbial genetic diversity. Here, by analyzing 113 metagenomes collected from 14 global sites across 5 cold seep types, we present a comprehensive Cold Seep Microbiomic Database (CSMD) to archive the genomic and functional diversity of cold seep microbiomes. The CSMD includes over 49 million non-redundant genes and 3175 metagenome-assembled genomes, which represent 1895 species spanning 105 phyla. In addition, beta diversity analysis indicates that both the sampling site and cold seep type have a substantial impact on the prokaryotic microbiome community composition. Heterotrophic and anaerobic metabolisms are prevalent in microbial communities, accompanied by considerable mixotrophs and facultative anaerobes, highlighting the versatile metabolic potential in cold seeps. Furthermore, secondary metabolic gene cluster analysis indicates that at least 98.81% of the sequences potentially encode novel natural products, with ribosomally synthesized and post-translationally modified peptides being the predominant type widely distributed in archaea and bacteria. Overall, the CSMD represents a valuable resource that would enhance the understanding and utilization of global cold seep microbiomes.
Assuntos
Archaea , Metagenoma , Microbiota , Metagenoma/genética , Archaea/genética , Archaea/metabolismo , Archaea/classificação , Microbiota/genética , Bactérias/genética , Bactérias/classificação , Bactérias/metabolismo , Produtos Biológicos/metabolismo , Temperatura Baixa , Filogenia , Água do Mar/microbiologia , Metagenômica/métodos , BiodiversidadeRESUMO
Nucleocytoplasmic large DNA viruses (NCLDVs; also called giant viruses), constituting the phylum Nucleocytoviricota, can infect a wide range of eukaryotes and exchange genetic material with not only their hosts but also prokaryotes and phages. A few NCLDVs were reported to encode genes conferring resistance to betalactam, trimethoprim, or pyrimethamine, suggesting that they are potential vehicles for the transmission of antibiotic resistance genes (ARGs) in the biome. However, the incidence of ARGs across the phylum Nucleocytoviricota, their evolutionary characteristics, their dissemination potential, and their association with virulence factors remain unexplored. Here, we systematically investigated ARGs of 1416 NCLDV genomes including those of almost all currently available cultured isolates and high-quality metagenome-assembled genomes from diverse habitats across the globe. We reveal that 39.5% of them carry ARGs, which is approximately 37 times higher than that for phage genomes. A total of 12 ARG types are encoded by NCLDVs. Phylogenies of the three most abundant NCLDV-encoded ARGs hint that NCLDVs acquire ARGs from not only eukaryotes but also prokaryotes and phages. Two NCLDV-encoded trimethoprim resistance genes are demonstrated to confer trimethoprim resistance in Escherichia coli. The presence of ARGs in NCLDV genomes is significantly correlated with mobile genetic elements and virulence factors.
Assuntos
Genoma Viral , Vírus Gigantes , Filogenia , Vírus Gigantes/genética , Genoma Viral/genética , Resistência Microbiana a Medicamentos/genética , Bacteriófagos/genética , Bacteriófagos/isolamento & purificação , Antibacterianos/farmacologia , Metagenoma/genética , Transferência Genética Horizontal , Trimetoprima/farmacologia , Farmacorresistência Bacteriana/genéticaRESUMO
Small open reading frames (smORFs) shorter than 100 codons are widespread and perform essential roles in microorganisms, where they encode proteins active in several cell functions, including signal pathways, stress response, and antibacterial activities. However, the ecology, distribution and role of small proteins in the global microbiome remain unknown. Here, we construct a global microbial smORFs catalog (GMSC) derived from 63,410 publicly available metagenomes across 75 distinct habitats and 87,920 high-quality isolate genomes. GMSC contains 965 million non-redundant smORFs with comprehensive annotations. We find that archaea harbor more smORFs proportionally than bacteria. We moreover provide a tool called GMSC-mapper to identify and annotate small proteins from microbial (meta)genomes. Overall, this publicly-available resource demonstrates the immense and underexplored diversity of small proteins.
Assuntos
Archaea , Bactérias , Metagenoma , Microbiota , Fases de Leitura Aberta , Microbiota/genética , Fases de Leitura Aberta/genética , Bactérias/genética , Bactérias/classificação , Bactérias/metabolismo , Metagenoma/genética , Archaea/genética , Archaea/metabolismo , Archaea/classificação , Anotação de Sequência Molecular , Proteínas de Bactérias/genética , Proteínas de Bactérias/metabolismoRESUMO
Sewage metagenomics has risen to prominence in urban population surveillance of pathogens and antimicrobial resistance (AMR). Unknown species with similarity to known genomes cause database bias in reference-based metagenomics. To improve surveillance, we seek to recover sewage genomes and develop a quantification and correlation workflow for these genomes and AMR over time. We use longitudinal sewage sampling in seven treatment plants from five major European cities to explore the utility of catch-all sequencing of these population-level samples. Using metagenomic assembly methods, we recover 2332 metagenome-assembled genomes (MAGs) from prokaryotic species, 1334 of which were previously undescribed. These genomes account for ~69% of sequenced DNA and provide insight into sewage microbial dynamics. Rotterdam (Netherlands) and Copenhagen (Denmark) show strong seasonal microbial community shifts, while Bologna, Rome, (Italy) and Budapest (Hungary) have occasional blooms of Pseudomonas-dominated communities, accounting for up to ~95% of sample DNA. Seasonal shifts and blooms present challenges for effective sewage surveillance. We find that bacteria of known shared origin, like human gut microbiota, form communities, suggesting the potential for source-attributing novel species and their ARGs through network community analysis. This could significantly improve AMR tracking in urban environments.
Assuntos
Bactérias , Metagenoma , Metagenômica , Microbiota , Estações do Ano , Esgotos , Esgotos/microbiologia , Metagenômica/métodos , Humanos , Microbiota/genética , Bactérias/genética , Bactérias/classificação , Bactérias/isolamento & purificação , Metagenoma/genética , Europa (Continente)RESUMO
Metagenomic sequencing has advanced our understanding of biogeochemical processes by providing an unprecedented view into the microbial composition of different ecosystems. While the amount of metagenomic data has grown rapidly, simple-to-use methods to analyze and compare across studies have lagged behind. Thus, tools expressing the metabolic traits of a community are needed to broaden the utility of existing data. Gene abundance profiles are a relatively low-dimensional embedding of a metagenome's functional potential and are, thus, tractable for comparison across many samples. Here, we compare the abundance of KEGG Ortholog Groups (KOs) from 6,539 metagenomes from the Joint Genome Institute's Integrated Microbial Genomes and Metagenomes (JGI IMG/M) database. We find that samples cluster into terrestrial, aquatic, and anaerobic ecosystems with marker KOs reflecting adaptations to these environments. For instance, functional clusters were differentiated by the metabolism of antibiotics, photosynthesis, methanogenesis, and surprisingly GC content. Using this functional gene approach, we reveal the broad-scale patterns shaping microbial communities and demonstrate the utility of ortholog abundance profiles for representing a rapidly expanding body of metagenomic data. IMPORTANCE: Metagenomics, or the sequencing of DNA from complex microbiomes, provides a view into the microbial composition of different environments. Metagenome databases were created to compile sequencing data across studies, but it remains challenging to compare and gain insight from these large data sets. Consequently, there is a need to develop accessible approaches to extract knowledge across metagenomes. The abundance of different orthologs (i.e., genes that perform a similar function across species) provides a simplified representation of a metagenome's metabolic potential that can easily be compared with others. In this study, we cluster the ortholog abundance profiles of thousands of metagenomes from diverse environments and uncover the traits that distinguish them. This work provides a simple to use framework for functional comparison and advances our understanding of how the environment shapes microbial communities.
Assuntos
Metagenoma , Metagenômica , Metagenômica/métodos , Metagenoma/genética , Ecossistema , Análise por Conglomerados , Microbiota/genéticaRESUMO
Constructing metagenome-assembled genomes (MAGs) from complex metagenomic samples involves a series of bioinformatics operations, each requiring deep bioinformatics knowledge. Here, we present a protocol for constructing MAGs and conducting functional profiling to address biological questions. We describe steps for system configuration, data downloads, read processing, removal of human DNA contamination, metagenomic assembly, and statistical quality assessment of the final assembly. Additionally, we detail procedures for the construction and refinement of MAGs, as well as the functional profiling of MAGs.