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The ISME Journal Jan 2021Growth rates are central to understanding microbial interactions and community dynamics. Metagenomic growth estimators have been developed, specifically codon usage bias...
Growth rates are central to understanding microbial interactions and community dynamics. Metagenomic growth estimators have been developed, specifically codon usage bias (CUB) for maximum growth rates and "peak-to-trough ratio" (PTR) for in situ rates. Both were originally tested with pure cultures, but natural populations are more heterogeneous, especially in individual cell histories pertinent to PTR. To test these methods, we compared predictors with observed growth rates of freshly collected marine prokaryotes in unamended seawater. We prefiltered and diluted samples to remove grazers and greatly reduce virus infection, so net growth approximated gross growth. We sampled over 44 h for abundances and metagenomes, generating 101 metagenome-assembled genomes (MAGs), including Actinobacteria, Verrucomicrobia, SAR406, MGII archaea, etc. We tracked each MAG population by cell-abundance-normalized read recruitment, finding growth rates of 0 to 5.99 per day, the first reported rates for several groups, and used these rates as benchmarks. PTR, calculated by three methods, rarely correlated to growth (r ~-0.26-0.08), except for rapidly growing γ-Proteobacteria (r ~0.63-0.92), while CUB correlated moderately well to observed maximum growth rates (r = 0.57). This suggests that current PTR approaches poorly predict actual growth of most marine bacterial populations, but maximum growth rates can be approximated from genomic characteristics.
Topics: Archaea; Bacteria; Benchmarking; Metagenome; Metagenomics
PubMed: 32939027
DOI: 10.1038/s41396-020-00773-1 -
Nucleic Acids Research Aug 2022Genome binning has been essential for characterization of bacteria, archaea, and even eukaryotes from metagenomes. Yet, few approaches exist for viruses. We developed...
Genome binning has been essential for characterization of bacteria, archaea, and even eukaryotes from metagenomes. Yet, few approaches exist for viruses. We developed vRhyme, a fast and precise software for construction of viral metagenome-assembled genomes (vMAGs). vRhyme utilizes single- or multi-sample coverage effect size comparisons between scaffolds and employs supervised machine learning to identify nucleotide feature similarities, which are compiled into iterations of weighted networks and refined bins. To refine bins, vRhyme utilizes unique features of viral genomes, namely a protein redundancy scoring mechanism based on the observation that viruses seldom encode redundant genes. Using simulated viromes, we displayed superior performance of vRhyme compared to available binning tools in constructing more complete and uncontaminated vMAGs. When applied to 10,601 viral scaffolds from human skin, vRhyme advanced our understanding of resident viruses, highlighted by identification of a Herelleviridae vMAG comprised of 22 scaffolds, and another vMAG encoding a nitrate reductase metabolic gene, representing near-complete genomes post-binning. vRhyme will enable a convention of binning uncultivated viral genomes and has the potential to transform metagenome-based viral ecology.
Topics: Genome, Viral; High-Throughput Nucleotide Sequencing; Humans; Metagenome; Metagenomics; Sequence Analysis, DNA; Software
PubMed: 35544285
DOI: 10.1093/nar/gkac341 -
Chinese Medical Journal Oct 2022
Topics: Humans; Virome; Bacteriophages; Feces; Metagenome; Metagenomics
PubMed: 36583859
DOI: 10.1097/CM9.0000000000002382 -
Sheng Wu Gong Cheng Xue Bao = Chinese... Dec 2020Virome is the collective term for the viral collection or viral metagenomes that are distributed in various environments. Viruses can be found in bodies of water,...
Virome is the collective term for the viral collection or viral metagenomes that are distributed in various environments. Viruses can be found in bodies of water, glaciers, plants, animals, and even some viruses, which are classified as eukaryotes, prokaryotes and subviruses. Viruses play very important role in maintaining environmental homeostasis and ecosystem balance, and are especially closely related to human health. In recent years, with the advancement of sequencing technology and data analysis, we are able to gain more insights into the virome and explore its potential role in the ecological niche by metagenomic sequencing. A large amount of viral data have been obtained from glaciers, oceans, and various plants and animals, and numerous unknown viruses have been discovered. Virome has been studied mainly through metagenomic data mining, as well as virus-like particles separation and enrichment. To date, several different methods for viral isolation and enrichment exist, and numerous bioinformatic analyses of the virome have been performed. However, there is a lack of specific and complete reviews on the enrichment and data analysis methods for the virome. Thus, our review will summarize viral isolation and enrichment methods and data analysis, and present some of the landmark research conducted by the enrichment method, to provide a reference for researchers of interest and further advance the field of virome research.
Topics: Animals; Humans; Metagenome; Metagenomics; Microbiota; Virome; Viruses
PubMed: 33398955
DOI: 10.13345/j.cjb.200372 -
Progress in Molecular Biology and... 2022Recent advances in sequencing technologies, experimental protocols and approaches in data generation and analysis have enabled us to investigate the human microbiome at...
Recent advances in sequencing technologies, experimental protocols and approaches in data generation and analysis have enabled us to investigate the human microbiome at an unprecedented level of resolution. The current chapter aims to provide an understanding of the different computational and bioinformatic strategies adopted to answer the different questions of a typical microbiome investigation and how the upstream DNA sequencing methodologies can affect this. The chapter enlist the state-of-the-art in metagenomic data analysis along with the available strategies to perform an integrated investigation of the human microbiome along with other data layers.
Topics: Humans; High-Throughput Nucleotide Sequencing; Microbiota; Metagenome; Metagenomics; Computational Biology
PubMed: 36270681
DOI: 10.1016/bs.pmbts.2022.06.027 -
International Microbiology : the... Nov 2021The study of the food microbiome has gained considerable interest in recent years, mainly due to the wide range of applications that can be derived from the analysis of... (Review)
Review
The study of the food microbiome has gained considerable interest in recent years, mainly due to the wide range of applications that can be derived from the analysis of metagenomes. Among these applications, it is worth mentioning the possibility of using metagenomic analyses to determine food authenticity, to assess the microbiological safety of foods thanks to the detection and tracking of pathogens, antibiotic resistance genes and other undesirable traits, as well to identify the microorganisms responsible for food processing defects. Metataxonomics and metagenomics are currently the gold standard methodologies to explore the full potential of metagenomes in the food industry. However, there are still a number of challenges that must be solved in order to implement these methods routinely in food chain monitoring, and for the regulatory agencies to take them into account in their opinions. These challenges include the difficulties of analysing foods and food-related environments with a low microbial load, the lack of validated bioinformatics pipelines adapted to food microbiomes and the difficulty of assessing the viability of the detected microorganisms. This review summarizes the methods of microbiome analysis that have been used, so far, in foods and food-related environments, with a specific focus on those involving Next-Generation Sequencing technologies.
Topics: Drug Resistance, Microbial; Food Industry; Metagenome; Metagenomics; Microbiota
PubMed: 34686940
DOI: 10.1007/s10123-021-00215-8 -
Genes Oct 2022The recent increase in publicly available metagenomic datasets with geospatial metadata has made it possible to determine location-specific, microbial fingerprints from...
The recent increase in publicly available metagenomic datasets with geospatial metadata has made it possible to determine location-specific, microbial fingerprints from around the world. Such fingerprints can be useful for comparing microbial niches for environmental research, as well as for applications within forensic science and public health. To determine the regional specificity for environmental metagenomes, we examined 4305 shotgun-sequenced samples from the MetaSUB Consortium dataset-the most extensive public collection of urban microbiomes, spanning 60 different cities, 30 countries, and 6 continents. We were able to identify city-specific microbial fingerprints using supervised machine learning (SML) on the taxonomic classifications, and we also compared the performance of ten SML classifiers. We then further evaluated the five algorithms with the highest accuracy, with the city and continental accuracy ranging from 85-89% to 90-94%, respectively. Thereafter, we used these results to develop Cassandra, a random-forest-based classifier that identifies bioindicator species to aid in fingerprinting and can infer higher-order microbial interactions at each site. We further tested the Cassandra algorithm on the Tara Oceans dataset, the largest collection of marine-based microbial genomes, where it classified the oceanic sample locations with 83% accuracy. These results and code show the utility of SML methods and Cassandra to identify bioindicator species across both oceanic and urban environments, which can help guide ongoing efforts in biotracing, environmental monitoring, and microbial forensics (MF).
Topics: Metagenomics; Metagenome; Microbiota; Supervised Machine Learning; Cities
PubMed: 36292799
DOI: 10.3390/genes13101914 -
Current Opinion in Microbiology Dec 2022While they are the most abundant biological entities on the planet, the role of bacteriophages (phages) in the microbiome remains enigmatic and understudied. With a rise... (Review)
Review
While they are the most abundant biological entities on the planet, the role of bacteriophages (phages) in the microbiome remains enigmatic and understudied. With a rise in the number of metagenomics studies and the publication of highly efficient phage mining programmes, we now have extensive data on the genomic and taxonomic diversity of (mainly) DNA bacteriophages in a wide range of environments. In addition, the higher throughput and quality of sequencing is allowing for strain-level reconstructions of phage genomes from metagenomes. These factors will ultimately help us to understand the role these phages play as part of specific microbial communities, enabling the tracking of individual virus genomes through space and time. Using lessons learned from the latest metagenomic studies, we focus on two explicit aspects of the role bacteriophages play within the microbiome, their ecological role in structuring bacterial populations, and their contribution to microbiome functioning by encoding auxiliary metabolism genes.
Topics: Humans; Bacteriophages; Metagenomics; Metagenome; Genome, Viral; Bacteria
PubMed: 36347213
DOI: 10.1016/j.mib.2022.102229 -
Clinical Microbiology and Infection :... Sep 2022The diagnosis of bacterial infections continues to rely on culture, a slow process in which antibiotic susceptibility profiles of potential pathogens are made available... (Review)
Review
BACKGROUND
The diagnosis of bacterial infections continues to rely on culture, a slow process in which antibiotic susceptibility profiles of potential pathogens are made available to clinicians 48 hours after sampling, at best. Recently, clinical metagenomics, the metagenomic sequencing of samples with the purpose of identifying microorganisms and determining their susceptibility to antimicrobials, has emerged as a potential diagnostic tool that could prove faster than culture. Clinical metagenomics indeed has the potential to detect antibiotic resistance genes (ARGs) and mutations associated with resistance. Nevertheless, many challenges have yet to be overcome in order to make rapid phenotypic inference of antibiotic susceptibility from metagenomic data a reality.
OBJECTIVES
The objective of this narrative review is to discuss the challenges underlying the phenotypic inference of antibiotic susceptibility from metagenomic data.
SOURCES
We conducted a narrative review using published articles available in the National Center for Biotechnology Information PubMed database.
CONTENT
We review the current ARG databases with a specific emphasis on those which now provide associations with phenotypic data. Next, we discuss the bioinformatic tools designed to identify ARGs in metagenomes. We then report on the performance of phenotypic inference from genomic data and the issue predicting the expression of ARGs. Finally, we address the challenge of linking an ARG to this host.
IMPLICATIONS
Significant improvements have recently been made in associating ARG and phenotype, and the inference of susceptibility from genomic data has been demonstrated in pathogenic bacteria such as Staphylococci and Enterobacterales. Resistance involving gene expression is more challenging however, and inferring susceptibility from species such as Pseudomonas aeruginosa remains difficult. Future research directions include the consideration of gene expression via RNA sequencing and machine learning.
Topics: Anti-Bacterial Agents; Drug Resistance, Microbial; Genes, Bacterial; Metagenome; Metagenomics
PubMed: 35551982
DOI: 10.1016/j.cmi.2022.04.017 -
Bioresource Technology Feb 2022Traditionally, lipid-producing microorganisms have been obtained via conventional bioprospecting based on isolation and screening techniques, demanding time and effort.... (Review)
Review
Traditionally, lipid-producing microorganisms have been obtained via conventional bioprospecting based on isolation and screening techniques, demanding time and effort. Thus, high-throughput sequencing combined with conventional microbiological approaches has emerged as an advanced and rapid strategy for recovering novel oleaginous microorganisms from target environments. This review highlights recent developments in lipid-producing microorganism bioprospecting, following (i) from traditional cultivation techniques to state-of-the-art metagenomics approaches; (ii) related topics on workflow, next-generation sequencing platforms, and knowledge bioinformatics; and (iii) biotechnological potential of the production of docosahexaenoic acid (DHA) by Aurantiochytrium limacinum, arachidonic acid (ARA) by Mortierella alpina and biodiesel by Rhodosporidium toruloides. These three species have been shown to be highly promising and studied in research articles, patents and commercialized products. Trends, innovations and future perspectives of these microorganisms are also addressed. Thus, these microbial lipids allow the development of food, feed and biofuels as alternative solutions to animal and vegetable oils.
Topics: Animals; Arachidonic Acid; Biofuels; Bioprospecting; Metagenome; Metagenomics
PubMed: 34863851
DOI: 10.1016/j.biortech.2021.126455