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Journal of Virological Methods Oct 2023The ability of viral metagenomic Next-Generation Sequencing (mNGS) to unbiasedly detect nucleic acids in a clinical sample is a powerful tool for advanced diagnosis of...
The ability of viral metagenomic Next-Generation Sequencing (mNGS) to unbiasedly detect nucleic acids in a clinical sample is a powerful tool for advanced diagnosis of viral infections. When clinical symptoms do not provide a clear differential diagnosis, extensive laboratory testing with virus-specific PCR and serology can be replaced by a single viral mNGS analysis. However, widespread diagnostic use of viral mNGS is thus far limited by long sample-to-result times, as most protocols rely on Illumina sequencing, which provides high and accurate sequencing output but is time-consuming and expensive. Here, we describe the development of an mNGS protocol based on the more cost-effective Nanopore Flongle sequencing with decreased turnaround time and lower, yet sufficient sequencing output to provide sensitive virus detection. Sample preparation (6 h) and sequencing (2 h) times are substantially reduced compared to Illumina mNGS and allow detection of DNA/RNA viruses at low input (up to 33-38 cycle threshold of specific qPCR). Although Flongles yield lower sequencing output, direct comparison with Illumina mNGS on diverse clinical samples showed similar results. Collectively, the novel Nanopore mNGS approach is specifically tailored for use in clinical diagnostics and provides a rapid and cost-effective mNGS strategy for individual testing of severe cases.
Topics: Humans; Metagenomics; Nanopores; Virus Diseases; Viruses; RNA Viruses; DNA Viruses; High-Throughput Nucleotide Sequencing; Sensitivity and Specificity
PubMed: 37516367
DOI: 10.1016/j.jviromet.2023.114784 -
PeerJ 2024Bacteriophages are bacterial viruses that are distributed throughout the environment. Lytic phages and prophages in saliva, oral mucosa, and dental plaque interact with... (Review)
Review
Bacteriophages are bacterial viruses that are distributed throughout the environment. Lytic phages and prophages in saliva, oral mucosa, and dental plaque interact with the oral microbiota and can change biofilm formation. The interactions between phages and bacteria can be considered a portion of oral metagenomics. The metagenomic profile of the oral microbiome indicates various bacteria. Indeed, there are various phages against these bacteria in the oral cavity. However, some other phages, like phages against Absconditabacteria, Chlamydiae, or Chloroflexi, have not been identified in the oral cavity. This review gives an overview of oral bacteriophage and used for metagenomics. Metagenomics of these phages deals with multi-drug-resistant bacterial plaques (biofilms) in oral cavities and oral infection. Hence, dentists and pharmacologists should know this metagenomic profile to cope with predental and dental infectious diseases.
Topics: Bacteriophages; Microbiota; Metagenome; Prophages; Mouth; Bacteria
PubMed: 38406289
DOI: 10.7717/peerj.16947 -
Communications Biology Oct 2023Assembly of reads from metagenomic samples is a hard problem, often resulting in highly fragmented genome assemblies. Metagenomic binning allows us to reconstruct...
Assembly of reads from metagenomic samples is a hard problem, often resulting in highly fragmented genome assemblies. Metagenomic binning allows us to reconstruct genomes by re-grouping the sequences by their organism of origin, thus representing a crucial processing step when exploring the biological diversity of metagenomic samples. Here we present Adversarial Autoencoders for Metagenomics Binning (AAMB), an ensemble deep learning approach that integrates sequence co-abundances and tetranucleotide frequencies into a common denoised space that enables precise clustering of sequences into microbial genomes. When benchmarked, AAMB presented similar or better results compared with the state-of-the-art reference-free binner VAMB, reconstructing ~7% more near-complete (NC) genomes across simulated and real data. In addition, genomes reconstructed using AAMB had higher completeness and greater taxonomic diversity compared with VAMB. Finally, we implemented a pipeline Integrating VAMB and AAMB that enabled improved binning, recovering 20% and 29% more simulated and real NC genomes, respectively, compared to VAMB, with moderate additional runtime.
Topics: Metagenome; Genome, Microbial; Metagenomics; Cluster Analysis; Benchmarking
PubMed: 37865678
DOI: 10.1038/s42003-023-05452-3 -
Microbiome Oct 2023With the emergence of metagenomic data, multiple links between the gut microbiome and the host health have been shown. Deciphering these complex interactions require...
BACKGROUND
With the emergence of metagenomic data, multiple links between the gut microbiome and the host health have been shown. Deciphering these complex interactions require evolved analysis methods focusing on the microbial ecosystem functions. Despite the fact that host or diet-derived fibres are the most abundant nutrients available in the gut, the presence of distinct functional traits regarding fibre and mucin hydrolysis, fermentation and hydrogenotrophic processes has never been investigated.
RESULTS
After manually selecting 91 KEGG orthologies and 33 glycoside hydrolases further aggregated in 101 functional descriptors representative of fibre and mucin degradation pathways in the gut microbiome, we used nonnegative matrix factorization to mine metagenomic datasets. Four distinct metabolic profiles were further identified on a training set of 1153 samples, thoroughly validated on a large database of 2571 unseen samples from 5 external metagenomic cohorts and confirmed with metatranscriptomic data. Profiles 1 and 2 are the main contributors to the fibre-degradation-related metagenome: they present contrasted involvement in fibre degradation and sugar metabolism and are differentially linked to dysbiosis, metabolic disease and inflammation. Profile 1 takes over Profile 2 in healthy samples, and unbalance of these profiles characterize dysbiotic samples. Furthermore, high fibre diet favours a healthy balance between profiles 1 and profile 2. Profile 3 takes over profile 2 during Crohn's disease, inducing functional reorientations towards unusual metabolism such as fucose and H2S degradation or propionate, acetone and butanediol production. Profile 4 gathers under-represented functions, like methanogenesis. Two taxonomic makes up of the profiles were investigated, using either the covariation of 203 prevalent genomes or metagenomic species, both providing consistent results in line with their functional characteristics. This taxonomic characterization showed that profiles 1 and 2 were respectively mainly composed of bacteria from the phyla Bacteroidetes and Firmicutes while profile 3 is representative of Proteobacteria and profile 4 of methanogens.
CONCLUSIONS
Integrating anaerobic microbiology knowledge with statistical learning can narrow down the metagenomic analysis to investigate functional profiles. Applying this approach to fibre degradation in the gut ended with 4 distinct functional profiles that can be easily monitored as markers of diet, dysbiosis, inflammation and disease. Video Abstract.
Topics: Humans; Gastrointestinal Microbiome; Mucins; Dysbiosis; Microbiota; Metagenome; Dietary Fiber; Inflammation; Metagenomics
PubMed: 37858269
DOI: 10.1186/s40168-023-01667-y -
Gut Microbes Dec 2023Microbial signatures show remarkable potentials in predicting colorectal cancer (CRC). This study aimed to evaluate the diagnostic powers of multimodal microbial...
Microbial signatures show remarkable potentials in predicting colorectal cancer (CRC). This study aimed to evaluate the diagnostic powers of multimodal microbial signatures, multi-kingdom species, genes, and single-nucleotide variants (SNVs) for detecting precancerous adenomas. We performed cross-cohort analyses on whole metagenome sequencing data of 750 samples via xMarkerFinder to identify adenoma-associated microbial multimodal signatures. Our data revealed that fungal species outperformed species from other kingdoms with an area under the ROC curve (AUC) of 0.71 in distinguishing adenomas from controls. The microbial SNVs, including dark SNVs with synonymous mutations, displayed the strongest diagnostic capability with an AUC value of 0.89, sensitivity of 0.79, specificity of 0.85, and Matthews correlation coefficient (MCC) of 0.74. SNV biomarkers also exhibited outstanding performances in three independent validation cohorts (AUCs = 0.83, 0.82, 0.76; sensitivity = 1.0, 0.72, 0.93; specificity = 0.67, 0.81, 0.67, MCCs = 0.69, 0.83, 0.72) with high disease specificity for adenoma. In further support of the above results, functional analyses revealed more frequent inter-kingdom associations between bacteria and fungi, and abnormalities in quorum sensing, purine and butanoate metabolism in adenoma, which were validated in a newly recruited cohort via qRT-PCR. Therefore, these data extend our understanding of adenoma-associated multimodal alterations in the gut microbiome and provide a rationale of microbial SNVs for the early detection of CRC.
Topics: Colorectal Neoplasms; Polymorphism, Single Nucleotide; Early Detection of Cancer; Metagenomics; Precancerous Conditions; Adenoma; Metagenome; Gastrointestinal Microbiome; Genetic Markers; Feces; Humans; Fungi; Bacteria; Archaea; Viruses; Cohort Studies
PubMed: 37635357
DOI: 10.1080/19490976.2023.2245562 -
Briefings in Bioinformatics Sep 2023Microbial genome recovery from metagenomes can further explain microbial ecosystem structures, functions and dynamics. Thus, this study developed the Additional...
Microbial genome recovery from metagenomes can further explain microbial ecosystem structures, functions and dynamics. Thus, this study developed the Additional Clustering Refiner (ACR) to enhance high-purity prokaryotic and eukaryotic metagenome-assembled genome (MAGs) recovery. ACR refines low-quality MAGs by subjecting them to iterative k-means clustering predicated on contig abundance and increasing bin purity through validated universal marker genes. Synthetic and real-world metagenomic datasets, including short- and long-read sequences, evaluated ACR's effectiveness. The results demonstrated improved MAG purity and a significant increase in high- and medium-quality MAG recovery rates. In addition, ACR seamlessly integrates with various binning algorithms, augmenting their strengths without modifying core features. Furthermore, its multiple sequencing technology compatibilities expand its applicability. By efficiently recovering high-quality prokaryotic and eukaryotic genomes, ACR is a promising tool for deepening our understanding of microbial communities through genome-centric metagenomics.
Topics: Metagenome; Eukaryota; Microbiota; Algorithms; Metagenomics; Cluster Analysis
PubMed: 37889119
DOI: 10.1093/bib/bbad381 -
Current Microbiology Sep 2023Advances in metagenomics analysis with the advent of next-generation sequencing have extended our knowledge of microbial communities as compared to conventional... (Review)
Review
Advances in metagenomics analysis with the advent of next-generation sequencing have extended our knowledge of microbial communities as compared to conventional techniques providing advanced approach to identify novel and uncultivable microorganisms based on their genetic information derived from a particular environment. Shotgun metagenomics involves investigating the DNA of the entire community without the requirement of PCR amplification. It provides access to study all genes present in the sample. On the other hand, amplicon sequencing targets taxonomically important marker genes, the analysis of which is restricted to previously known DNA sequences. While sequence-based metagenomics is used to analyze DNA sequences directly from the environment without the requirement of library construction and with limited identification of novel genes and products that can be complemented by functional genomics, function-based metagenomics requires fragmentation and cloning of extracted metagenome DNA in a suitable host with subsequent functional screening and sequencing clone for detection of a novel gene. Although advances were made in metagenomics, different challenges arise. This review provides insight into advances in the metagenomic approaches combined with next-generation sequencing, their recent applications highlighting the emerging ones, such as in astrobiology, forensic sciences, and SARS-CoV-2 infection diagnosis, and the challenges associated. This review further discusses the different types of metagenomics and outlines advancements in bioinformatics tools and their significance in the analysis of metagenomic datasets.
Topics: Humans; Metagenome; Metagenomics; COVID-19; SARS-CoV-2; Genomics
PubMed: 37733134
DOI: 10.1007/s00284-023-03451-5 -
Microbial Genomics Apr 2024The ever-decreasing cost of sequencing and the growing potential applications of metagenomics have led to an unprecedented surge in data generation. One of the most... (Review)
Review
The ever-decreasing cost of sequencing and the growing potential applications of metagenomics have led to an unprecedented surge in data generation. One of the most prevalent applications of metagenomics is the study of microbial environments, such as the human gut. The gut microbiome plays a crucial role in human health, providing vital information for patient diagnosis and prognosis. However, analysing metagenomic data remains challenging due to several factors, including reference catalogues, sparsity and compositionality. Deep learning (DL) enables novel and promising approaches that complement state-of-the-art microbiome pipelines. DL-based methods can address almost all aspects of microbiome analysis, including novel pathogen detection, sequence classification, patient stratification and disease prediction. Beyond generating predictive models, a key aspect of these methods is also their interpretability. This article reviews DL approaches in metagenomics, including convolutional networks, autoencoders and attention-based models. These methods aggregate contextualized data and pave the way for improved patient care and a better understanding of the microbiome's key role in our health.
Topics: Humans; Deep Learning; Microbiota; Metagenome; Gastrointestinal Microbiome; Metagenomics
PubMed: 38630611
DOI: 10.1099/mgen.0.001231 -
Scientific Data Aug 2023The great threat of microbes carried by ballast water calls for figuring out the species composition of the ballast-tank microbial community, where the dark, cold, and...
The great threat of microbes carried by ballast water calls for figuring out the species composition of the ballast-tank microbial community, where the dark, cold, and anoxic tank environment might select special taxa. In this study, we reconstructed 103 metagenome-assembled genomes (MAGs), including 102 bacteria and one archaea, from four vessels on international voyages. Of these MAGs, 60 were 'near complete' (completeness >90%), 34 were >80% complete, and nine were >75% complete. Phylogenomic analysis revealed that over 70% (n = 74) of these MAGs represented new taxa at different taxonomical levels, including one order, three families, 12 genera, and 58 species. The species composition of these MAGs was most consistent with the previous reports, with the most abundant phyla being Proteobacteria (n = 69), Bacteroidota (n = 17), and Actinobacteriota (n = 7). These draft genomes provided novel data on species diversity and function in the ballast-tank microbial community, which will facilitate ballast water and sediments management.
Topics: Archaea; Bacteria; Genome, Microbial; Metagenome; Metagenomics; Microbiota
PubMed: 37563185
DOI: 10.1038/s41597-023-02447-x -
Cell Host & Microbe Jun 2024How infants acquire their gut microbial communities and the various factors influencing these dynamics remain unclear. In this issue of Cell Host & Microbe, Selma-Royo...
How infants acquire their gut microbial communities and the various factors influencing these dynamics remain unclear. In this issue of Cell Host & Microbe, Selma-Royo et al. and Dubois et al. use shotgun metagenomic sequencing to understand the transmission of microbes from parents to infants and delve into factors modifying this process.
Topics: Humans; Gastrointestinal Microbiome; Metagenomics; Infant
PubMed: 38870905
DOI: 10.1016/j.chom.2024.05.012