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Viruses Jun 2023Blood transfusion safety is an essential element of public health. Current blood screening strategies rely on targeted techniques that could miss unknown or unexpected...
Blood transfusion safety is an essential element of public health. Current blood screening strategies rely on targeted techniques that could miss unknown or unexpected pathogens. Recent studies have demonstrated the presence of a viral community (virobiota/virome) in the blood of healthy individuals. Here, we characterized the blood virome in patients frequently exposed to blood transfusion by using Illumina metagenomic sequencing. The virome of these patients was compared to viruses present in healthy blood donors. A total number of 155 beta-thalassemia, 149 hemodialysis, and 100 healthy blood donors were pooled with five samples per pool. Members of the and family were most frequently observed. Interestingly, samples of healthy blood donors harbored traces of potentially pathogenic viruses, including adeno-, rota-, and Merkel cell polyomavirus. Viruses of the family were most abundant in the blood of hemodialysis patients and displayed a higher anellovirus richness. Pegiviruses () were only observed in patient populations. An overall trend of higher eukaryotic read abundance in both patient groups was observed. This might be associated with increased exposure through blood transfusion. Overall, the findings in this study demonstrated the presence of various viruses in the blood of Iranian multiple-transfused patients and healthy blood donors.
Topics: Humans; Iran; Virome; Viruses; Anelloviridae; Metagenome; Metagenomics
PubMed: 37515113
DOI: 10.3390/v15071425 -
NPJ Biofilms and Microbiomes Aug 2022Dogs have a key role in law enforcement and military work, and research with the goal of improving working dog performance is ongoing. While there have been intriguing...
Dogs have a key role in law enforcement and military work, and research with the goal of improving working dog performance is ongoing. While there have been intriguing studies from lab animal models showing a potential connection between the gut microbiome and behavior or mental health there is a dearth of studies investigating the microbiome-behavior relationship in working dogs. The overall objective of this study was to characterize the microbiota of working dogs and to determine if the composition of the microbiota is associated with behavioral and performance outcomes. Freshly passed stools from each working canine (Total n = 134) were collected and subject to shotgun metagenomic sequencing using Illumina technology. Behavior, performance, and demographic metadata were collected. Descriptive statistics and prediction models of behavioral/phenotypic outcomes using gradient boosting classification based on Xgboost were used to study associations between the microbiome and outcomes. Regarding machine learning methodology, only microbiome features were used for training and predictors were estimated in cross-validation. Microbiome markers were statistically associated with motivation, aggression, cowardice/hesitation, sociability, obedience to one trainer vs many, and body condition score (BCS). When prediction models were developed based on machine learning, moderate predictive power was observed for motivation, sociability, and gastrointestinal issues. Findings from this study suggest potential gut microbiome markers of performance and could potentially advance care for working canines.
Topics: Animals; Dogs; Gastrointestinal Microbiome; Metagenome; Metagenomics; Microbiota; Working Dogs
PubMed: 35995802
DOI: 10.1038/s41522-022-00329-5 -
Microbiome Dec 2022The accurate and comprehensive analyses of genome-resolved metagenomics largely depend on the reconstruction of reference-quality (complete and high-quality) genomes...
BACKGROUND
The accurate and comprehensive analyses of genome-resolved metagenomics largely depend on the reconstruction of reference-quality (complete and high-quality) genomes from diverse microbiomes. Closing gaps in draft genomes have been approaching with the inclusion of Nanopore long reads; however, genome quality improvement requires extensive and time-consuming high-accuracy short-read polishing.
RESULTS
Here, we introduce NanoPhase, an open-source tool to reconstruct reference-quality genomes from complex metagenomes using only Nanopore long reads. Using Kit 9 and Q20+ chemistries, we first evaluated the feasibility of NanoPhase using a ZymoBIOMICS gut microbiome standard (including 21 strains), then sequenced the complex activated sludge microbiome and reconstructed 275 MAGs with median completeness of ~ 90%. As a result, NanoPhase improved the MAG contiguity (median MAG N50: 735 Kb, 44-86X compared to conventional short-read-based methods) while maintaining high accuracy, allowing for a full and accurate investigation of target microbiomes. Additionally, leveraging these high-contiguity reference-quality genomes, we identified 165 prophages within 111 MAGs, with 5 as active prophages, indicating the prophage was a neglected source of genetic diversity within microbial populations and influencer in shaping microbial composition in the activated sludge microbiome.
CONCLUSIONS
Our results demonstrated that NanoPhase enables reference-quality genome reconstruction from complex metagenomes directly using only Nanopore long reads. Furthermore, besides the 16S rRNA genes and biosynthetic gene clusters, the generated high-accuracy and high-contiguity MAGs improved the host identification of critical mobile genetic elements, e.g., prophage, serving as a genomic blueprint to investigate the microbial potential and ecology in the activated sludge ecosystem. Video Abstract.
Topics: Metagenome; Metagenomics; Nanopores; RNA, Ribosomal, 16S; Sewage; Microbiota; Prophages
PubMed: 36457010
DOI: 10.1186/s40168-022-01415-8 -
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 -
Viruses Nov 2022Viral metagenomics has revolutionized our understanding for identification of unknown or poorly characterized viruses. For that reason, metagenomic studies gave been... (Review)
Review
Viral metagenomics has revolutionized our understanding for identification of unknown or poorly characterized viruses. For that reason, metagenomic studies gave been largely applied for virus discovery in a wide variety of clinical samples, including blood specimens. The emerging blood-transmitted virus infections represent important problem for public health, and the emergence of HIV in the 1980s is an example for the vulnerability of Blood Donation systems to such infections. When viral metagenomics is applied to blood samples, it can give a complete overview of the viral nucleic acid abundance, also named "blood virome". Detailed characterization of the blood virome of healthy donors could identify unknown (emerging) viral genomes that might be assumed as hypothetic transfusion threats. However, it is impossible only by application of viral metagenomics to assign that one viral agent could impact blood transfusion. That said, this is a complex issue and will depend on the ability of the infectious agent to cause clinically important infection in blood recipients, the viral stability in blood derivatives and the presence of infectious viruses in blood, making possible its transmission by transfusion. This brief review summarizes information regarding the blood donor virome and some important challenges for use of viral metagenomics in hemotherapy for identification of transfusion-transmitted viruses.
Topics: Humans; Metagenomics; Transfusion Medicine; Viruses; Virus Diseases; Metagenome; Genome, Viral
PubMed: 36366546
DOI: 10.3390/v14112448 -
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 -
International Journal of Molecular... Dec 2023Our understanding of the symbiotic relationship between the microbiota and its host has constantly evolved since our understanding that the "self" was not only defined... (Review)
Review
Our understanding of the symbiotic relationship between the microbiota and its host has constantly evolved since our understanding that the "self" was not only defined by our genetic patrimony but also by the genomes of bugs living in us. The first culture-based methods highlighted the important functions of the microbiota. However, these methods had strong limitations and did not allow for a full understanding of the complex relationships that occur at the interface between the microbiota and the host. The recent development of metagenomic approaches has been a groundbreaking step towards this understanding. Its use has provided new insights and perspectives. In the present chapter, we will describe the advances of functional metagenomics to decipher food-microbiota and host-microbiota interactions. This powerful high-throughput approach allows for the assessment of the microbiota as a whole (including non-cultured bacteria) and enabled the discovery of new signaling pathways and functions involved in the crosstalk between food, the gut microbiota and its host. We will present the pipeline and highlight the most important studies that helped to develop the field. To conclude, we will emphasize the most recent developments and hot topics in functional metagenomics.
Topics: Humans; Host Microbial Interactions; Microbiota; Gastrointestinal Microbiome; Metagenomics; Metagenome
PubMed: 38139456
DOI: 10.3390/ijms242417630 -
Applied and Environmental Microbiology Aug 2020Many biological contaminants are disseminated through water, and their occurrence has potential detrimental impacts on public and environmental health. Conventional... (Review)
Review
Many biological contaminants are disseminated through water, and their occurrence has potential detrimental impacts on public and environmental health. Conventional monitoring tools rely on cultivation and are not robust in addressing modern water quality concerns. This review proposes metagenomics as a means to provide a rapid, nontargeted assessment of biological contaminants in water. When further coupled with appropriate methods (e.g., quantitative PCR and flow cytometry) and bioinformatic tools, metagenomics can provide information concerning both the abundance and diversity of biological contaminants in reclaimed waters. Further correlation between the metagenomic-derived data of selected contaminants and the measurable parameters of water quality can also aid in devising strategies to alleviate undesirable water quality. Here, we review metagenomic approaches (i.e., both sequencing platforms and bioinformatic tools) and studies that demonstrated their use for reclaimed-water quality monitoring. We also provide recommendations on areas of improvement that will allow metagenomics to significantly impact how the water industry performs reclaimed-water quality monitoring in the future.
Topics: Environmental Monitoring; Metagenome; Metagenomics; Waste Disposal, Fluid; Water Quality
PubMed: 32503906
DOI: 10.1128/AEM.00724-20 -
Applied and Environmental Microbiology Jun 2023Surveillance for early disease detection is crucial to reduce the threat of plant diseases to food security. Metagenomic sequencing and taxonomic classification have...
Surveillance for early disease detection is crucial to reduce the threat of plant diseases to food security. Metagenomic sequencing and taxonomic classification have recently been used to detect and identify plant pathogens. However, for an emerging pathogen, its genome may not be similar enough to any public genome to permit reference-based tools to identify infected samples. Also, in the case of point-of care diagnosis in the field, database access may be limited. Therefore, here we explore reference-free detection of plant pathogens using metagenomic sequencing and machine learning (ML). We used long-read metagenomes from healthy and infected plants as our model system and constructed k-mer frequency tables to test eight different ML models. The accuracy in classifying individual reads as coming from a healthy or infected metagenome were compared. Of all models, random forest (RF) had the best combination of short run-time and high accuracy (over 0.90) using tomato metagenomes. We further evaluated the RF model with a different tomato sample infected with the same pathogen or a different pathogen and a grapevine sample infected with a grapevine pathogen and achieved similar performances. ML models can thus learn features to successfully perform reference-free detection of plant diseases whereby a model trained with one pathogen-host system can also be used to detect different pathogens on different hosts. Potential and challenges of applying ML to metagenomics in plant disease detection are discussed. Climate change may lead to the emergence of novel plant diseases caused by yet unknown pathogens. Surveillance for emerging plant diseases is crucial to reduce their threat to food security. However, conventional genomic based methods require knowledge of existing plant pathogens and cannot be applied to detecting newly emerged pathogens. In this work, we explored reference-free, meta-genomic sequencing-based disease detection using machine learning. By sequencing the genomes of all microbial species extracted from an infected plant sample, we were able to train machine learning models to accurately classify individual sequencing reads as coming from a healthy or an infected plant sample. This method has the potential to be integrated into a generic pipeline for a meta-genomic based plant disease surveillance approach but also has limitations that still need to be overcome.
Topics: Metagenome; Metagenomics; Machine Learning; Chromosome Mapping; Plant Diseases; High-Throughput Nucleotide Sequencing
PubMed: 37184398
DOI: 10.1128/aem.00260-23 -
STAR Protocols Sep 2022Homology-based search is commonly used to uncover mobile genetic elements (MGEs) from metagenomes, but it heavily relies on reference genomes in the database. Here we...
Homology-based search is commonly used to uncover mobile genetic elements (MGEs) from metagenomes, but it heavily relies on reference genomes in the database. Here we introduce a protocol to extract CRISPR-targeted sequences from the assembled human gut metagenomic sequences without using a reference database. We describe the assembling of metagenome contigs, the extraction of CRISPR direct repeats and spacers, the discovery of protospacers, and the extraction of protospacer-enriched regions using the graph-based approach. This protocol could extract numerous characterized/uncharacterized MGEs. For complete details on the use and execution of this protocol, please refer to Sugimoto et al. (2021).
Topics: Base Sequence; Clustered Regularly Interspaced Short Palindromic Repeats; Humans; Metagenome; Metagenomics
PubMed: 35780428
DOI: 10.1016/j.xpro.2022.101525