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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 -
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 -
Chinese Medical Journal Oct 2022
Topics: Humans; Virome; Bacteriophages; Feces; Metagenome; Metagenomics
PubMed: 36583859
DOI: 10.1097/CM9.0000000000002382 -
Frontiers in Cellular and Infection... 2021Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can cause gastrointestinal symptoms in the patients, but the role of gut microbiota in SARS-CoV-2...
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection can cause gastrointestinal symptoms in the patients, but the role of gut microbiota in SARS-CoV-2 infection remains unclear. Thus, in this study, we aim to investigate whether SARS-CoV-2 infection affects the composition and function of gut microbiota. In this study, we demonstrated for the first time that significant shifts in microbiome composition and function were appeared in both SARS-CoV-2-infected asymptomatic and symptomatic cases. The relative abundance of was significantly increased, whereas the levels of was remarkably reduced in SARS-CoV-2-infected cases. There was one bacterial species, displayed the difference between patients and asymptomatic cases. On the genus level, Tyzzerella was the key species that remarkably increased in both symptomatic and asymptomatic cases. Analyses of genome annotations further revealed SARS-CoV-2 infection resulted in the significant 'functional dysbiosis' of gut microbiota, including metabolic pathway, regulatory pathway and biosynthesis of secondary metabolites etc. We also identified potential metagenomic markers to discriminate SARS-CoV-2-infected symptomatic and asymptomatic cases from healthy controls. These findings together suggest gut microbiota is of possible etiological and diagnostic importance for SARS-CoV-2 infection.
Topics: COVID-19; Dysbiosis; Humans; Metagenome; Metagenomics; SARS-CoV-2
PubMed: 34926314
DOI: 10.3389/fcimb.2021.706970 -
NPJ Biofilms and Microbiomes Apr 2021Investigation of the microbial ecology of terrestrial, aquatic and atmospheric ecosystems requires specific sampling and analytical technologies, owing to vastly...
Investigation of the microbial ecology of terrestrial, aquatic and atmospheric ecosystems requires specific sampling and analytical technologies, owing to vastly different biomass densities typically encountered. In particular, the ultra-low biomass nature of air presents an inherent analytical challenge that is confounded by temporal fluctuations in community structure. Our ultra-low biomass pipeline advances the field of bioaerosol research by significantly reducing sampling times from days/weeks/months to minutes/hours, while maintaining the ability to perform species-level identification through direct metagenomic sequencing. The study further addresses all experimental factors contributing to analysis outcome, such as amassment, storage and extraction, as well as factors that impact on nucleic acid analysis. Quantity and quality of nucleic acid extracts from each optimisation step are evaluated using fluorometry, qPCR and sequencing. Both metagenomics and marker gene amplification-based (16S and ITS) sequencing are assessed with regard to their taxonomic resolution and inter-comparability. The pipeline is robust across a wide range of climatic settings, ranging from arctic to desert to tropical environments. Ultimately, the pipeline can be adapted to environmental settings, such as dust and surfaces, which also require ultra-low biomass analytics.
Topics: Air Microbiology; Biomass; Ecosystem; Environmental Microbiology; Environmental Monitoring; Metagenome; Metagenomics; Microbiota; Soil Microbiology; Water Microbiology
PubMed: 33863892
DOI: 10.1038/s41522-021-00209-4 -
Bioinformatics (Oxford, England) Dec 2022Recovery of metagenome-assembled genomes (MAGs) from shotgun metagenomic data is an important task for the comprehensive analysis of microbial communities from variable...
MOTIVATION
Recovery of metagenome-assembled genomes (MAGs) from shotgun metagenomic data is an important task for the comprehensive analysis of microbial communities from variable sources. Single binning tools differ in their ability to leverage specific aspects in MAG reconstruction, the use of ensemble binning refinement tools is often time consuming and computational demand increases with community complexity. We introduce MAGScoT, a fast, lightweight and accurate implementation for the reconstruction of highest-quality MAGs from the output of multiple genome-binning tools.
RESULTS
MAGScoT outperforms popular bin-refinement solutions in terms of quality and quantity of MAGs as well as computation time and resource consumption.
AVAILABILITY AND IMPLEMENTATION
MAGScoT is available via GitHub (https://github.com/ikmb/MAGScoT) and as an easy-to-use Docker container (https://hub.docker.com/repository/docker/ikmb/magscot).
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
Topics: Algorithms; Metagenomics; Metagenome; Microbiota
PubMed: 36264141
DOI: 10.1093/bioinformatics/btac694 -
Microbial Biotechnology Jan 2024The human microbiome plays a crucial role in maintaining health, with advances in high-throughput sequencing technology and reduced sequencing costs triggering a surge... (Review)
Review
The human microbiome plays a crucial role in maintaining health, with advances in high-throughput sequencing technology and reduced sequencing costs triggering a surge in microbiome research. Microbiome studies generally incorporate five key phases: design, sampling, sequencing, analysis, and reporting, with sequencing strategy being a crucial step offering numerous options. Present mainstream sequencing strategies include Amplicon sequencing, Metagenomic Next-Generation Sequencing (mNGS), and Targeted Next-Generation Sequencing (tNGS). Two innovative technologies recently emerged, namely MobiMicrobe high-throughput microbial single-cell genome sequencing technology and 2bRAD-M simplified metagenomic sequencing technology, compensate for the limitations of mainstream technologies, each boasting unique core strengths. This paper reviews the basic principles and processes of these three mainstream and two novel microbiological technologies, aiding readers in understanding the benefits and drawbacks of different technologies, thereby guiding the selection of the most suitable method for their research endeavours.
Topics: Humans; Microbiota; Metagenome; High-Throughput Nucleotide Sequencing; Metagenomics; Technology
PubMed: 37929823
DOI: 10.1111/1751-7915.14364 -
BMC Bioinformatics May 2022FragGeneScan is currently the most accurate and popular tool for gene prediction in short and error-prone reads, but its execution speed is insufficient for use on...
BACKGROUND
FragGeneScan is currently the most accurate and popular tool for gene prediction in short and error-prone reads, but its execution speed is insufficient for use on larger data sets. The parallelization which should have addressed this is inefficient. Its alternative implementation FragGeneScan+ is faster, but introduced a number of bugs related to memory management, race conditions and even output accuracy.
RESULTS
This paper introduces FragGeneScanRs, a faster Rust implementation of the FragGeneScan gene prediction model. Its command line interface is backward compatible and adds extra features for more flexible usage. Its output is equivalent to the original FragGeneScan implementation.
CONCLUSIONS
Compared to the current C implementation, shotgun metagenomic reads are processed up to 22 times faster using a single thread, with better scaling for multithreaded execution. The Rust code of FragGeneScanRs is freely available from GitHub under the GPL-3.0 license with instructions for installation, usage and other documentation ( https://github.com/unipept/FragGeneScanRs ).
Topics: Algorithms; Metagenome; Metagenomics; Software
PubMed: 35643462
DOI: 10.1186/s12859-022-04736-5 -
Bioinformatics (Oxford, England) May 2021The microbes that live in an environment can be identified from the combined genomic material, also referred to as the metagenome. Sequencing a metagenome can result in...
MOTIVATION
The microbes that live in an environment can be identified from the combined genomic material, also referred to as the metagenome. Sequencing a metagenome can result in large volumes of sequencing reads. A promising approach to reduce the size of metagenomic datasets is by clustering reads into groups based on their overlaps. Clustering reads are valuable to facilitate downstream analyses, including computationally intensive strain-aware assembly. As current read clustering approaches cannot handle the large datasets arising from high-throughput metagenome sequencing, a novel read clustering approach is needed. In this article, we propose OGRE, an Overlap Graph-based Read clustEring procedure for high-throughput sequencing data, with a focus on shotgun metagenomes.
RESULTS
We show that for small datasets OGRE outperforms other read binners in terms of the number of species included in a cluster, also referred to as cluster purity, and the fraction of all reads that is placed in one of the clusters. Furthermore, OGRE is able to process metagenomic datasets that are too large for other read binners into clusters with high cluster purity.
CONCLUSION
OGRE is the only method that can successfully cluster reads in species-specific clusters for large metagenomic datasets without running into computation time- or memory issues.
AVAILABILITYAND IMPLEMENTATION
Code is made available on Github (https://github.com/Marleen1/OGRE).
SUPPLEMENTARY INFORMATION
Supplementary data are available at Bioinformatics online.
Topics: Algorithms; Cluster Analysis; High-Throughput Nucleotide Sequencing; Metagenome; Metagenomics; Sequence Analysis, DNA; Software
PubMed: 32871010
DOI: 10.1093/bioinformatics/btaa760 -
Microbiological Research Jul 2022Reference genomes are essential for analyzing the metabolic and functional potentials of microbiomes. However, microbial genome resources are limited because most of... (Review)
Review
Reference genomes are essential for analyzing the metabolic and functional potentials of microbiomes. However, microbial genome resources are limited because most of microorganisms are difficult to culture. Genome binning is a culture-independent approach that can recover a vast number of microbial genomes from short-read high throughput shotgun metagenomic sequencing data. In this review, we summarize methods commonly used for reconstructing metagenome-assembled genomes (MAGs) to provide a reference for researchers to choose propriate software programs among the numerous and complicated tools and pipelines that are available for these analyses. In addition, we discuss application prospects, challenges, and opportunities for recovering MAGs from metagenomic sequencing data.
Topics: High-Throughput Nucleotide Sequencing; Metagenome; Metagenomics; Microbiota
PubMed: 35430490
DOI: 10.1016/j.micres.2022.127023