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Microbial Biotechnology Jan 2024Building models is essential for understanding the functions and dynamics of microbial communities. Metabolic models built on genome-scale metabolic network... (Review)
Review
Building models is essential for understanding the functions and dynamics of microbial communities. Metabolic models built on genome-scale metabolic network reconstructions (GENREs) are especially relevant as a means to decipher the complex interactions occurring among species. Model reconstruction increasingly relies on metagenomics, which permits direct characterisation of naturally occurring communities that may contain organisms that cannot be isolated or cultured. In this review, we provide an overview of the field of metabolic modelling and its increasing reliance on and synergy with metagenomics and bioinformatics. We survey the means of assigning functions and reconstructing metabolic networks from (meta-)genomes, and present the variety and mathematical fundamentals of metabolic models that foster the understanding of microbial dynamics. We emphasise the characterisation of interactions and the scaling of model construction to large communities, two important bottlenecks in the applicability of these models. We give an overview of the current state of the art in metagenome sequencing and bioinformatics analysis, focusing on the reconstruction of genomes in microbial communities. Metagenomics benefits tremendously from third-generation sequencing, and we discuss the opportunities of long-read sequencing, strain-level characterisation and eukaryotic metagenomics. We aim at providing algorithmic and mathematical support, together with tool and application resources, that permit bridging the gap between metagenomics and metabolic modelling.
Topics: Metagenome; Microbiota; Metagenomics; Sequence Analysis, DNA; Computational Biology
PubMed: 38243750
DOI: 10.1111/1751-7915.14396 -
Nature Reviews. Genetics Apr 2024
Topics: Sequence Analysis, DNA; Metagenomics
PubMed: 38366032
DOI: 10.1038/s41576-024-00708-y -
Viruses Nov 2023Huge phages have genomes larger than 200 kilobases, which are particularly interesting for their genetic inventory and evolution. We screened 165 wastewater metagenomes...
Huge phages have genomes larger than 200 kilobases, which are particularly interesting for their genetic inventory and evolution. We screened 165 wastewater metagenomes for the presence of viral sequences. After identifying over 600 potential huge phage genomes, we reduced the dataset using manual curation by excluding viral contigs that did not contain viral protein-coding genes or consisted of concatemers of several small phage genomes. This dataset showed seven fully annotated huge phage genomes. The phages grouped into distinct phylogenetic clades, likely forming new genera and families. A phylogenomic analysis between our huge phages and phages with smaller genomes, i.e., less than 200 kb, supported the hypothesis that huge phages have undergone convergent evolution. The genomes contained typical phage protein-coding genes, sequential gene cassettes for metabolic pathways, and complete inventories of tRNA genes covering all standard and rare amino acids. Our study showed a pipeline for huge phage analyses that may lead to new enzymes for therapeutic or biotechnological applications.
Topics: Bacteriophages; Metagenome; Wastewater; Phylogeny; Genome, Viral
PubMed: 38140571
DOI: 10.3390/v15122330 -
Frontiers in Cellular and Infection... 2023As a common central nervous system infection in newborns, neonatal bacterial meningitis (NBM) can seriously affect their health and growth. However, although metagenomic...
As a common central nervous system infection in newborns, neonatal bacterial meningitis (NBM) can seriously affect their health and growth. However, although metagenomic approaches are being applied in clinical diagnostic practice, there are some limitations for whole metagenome sequencing and amplicon sequencing in handling low microbial biomass samples. Through a newly developed ultra-sensitive metagenomic sequencing method named 2bRAD-M, we investigated the microbial signatures of central nervous system infections in neonates admitted to the neonatal intensive care unit. Particularly, we recruited a total of 23 neonates suspected of having NBM and collected their blood, cerebrospinal fluid, and skin samples for 2bRAD-M sequencing. Then we developed a novel decontamination method (Reads Level Decontamination, RLD) for 2bRAD-M by which we efficiently denoised the sequencing data and found some potential biomarkers that have significantly different relative abundance between 12 patients that were diagnosed as NBM and 11 Non-NBM based on their cerebrospinal fluid (CSF) examination results. Specifically, we discovered 11 and 8 potential biomarkers for NBM in blood and CSF separately and further identified 16 and 35 microbial species that highly correlated with the physiological indicators in blood and CSF. Our study not only provide microbiological evidence to aid in the diagnosis of NBM but also demonstrated the application of an ultra-sensitive metagenomic sequencing method in pathogenesis study.
Topics: Infant, Newborn; Humans; Meningitis, Bacterial; Biomass; Hospitalization; Metagenome; Metagenomics
PubMed: 37674578
DOI: 10.3389/fcimb.2023.1169101 -
Genome Biology Apr 2024In the metagenomic assembly of a microbial community, abundant species are often thought to assemble well given their deeper sequencing coverage. This conjuncture is...
BACKGROUND
In the metagenomic assembly of a microbial community, abundant species are often thought to assemble well given their deeper sequencing coverage. This conjuncture is rarely tested or evaluated in practice. We often do not know how many abundant species are missing and do not have an approach to recover them.
RESULTS
Here, we propose k-mer based and 16S RNA based methods to measure the completeness of metagenome assembly. We show that even with PacBio high-fidelity (HiFi) reads, abundant species are often not assembled, as high strain diversity may lead to fragmented contigs. We develop a novel reference-free algorithm to recover abundant metagenome-assembled genomes (MAGs) by identifying circular assembly subgraphs. Complemented with a reference-free genome binning heuristics based on dimension reduction, the proposed method rescues many abundant species that would be missing with existing methods and produces competitive results compared to those state-of-the-art binners in terms of total number of near-complete genome bins.
CONCLUSIONS
Our work emphasizes the importance of metagenome completeness, which has often been overlooked. Our algorithm generates more circular MAGs and moves a step closer to the complete representation of microbial communities.
Topics: Metagenome; Microbiota; Algorithms; Bacteria; Metagenomics
PubMed: 38605401
DOI: 10.1186/s13059-024-03234-6 -
World Journal of Microbiology &... May 2024The rapid industrial revolution significantly increased heavy metal pollution, becoming a major global environmental concern. This pollution is considered as one of the... (Review)
Review
The rapid industrial revolution significantly increased heavy metal pollution, becoming a major global environmental concern. This pollution is considered as one of the most harmful and toxic threats to all environmental components (air, soil, water, animals, and plants until reaching to human). Therefore, scientists try to find a promising and eco-friendly technique to solve this problem i.e., bacterial bioremediation. Various heavy metal resistance mechanisms were reported. Omics technologies can significantly improve our understanding of heavy metal resistant bacteria and their communities. They are a potent tool for investigating the adaptation processes of microbes in severe conditions. These omics methods provide unique benefits for investigating metabolic alterations, microbial diversity, and mechanisms of resistance of individual strains or communities to harsh conditions. Starting with genome sequencing which provides us with complete and comprehensive insight into the resistance mechanism of heavy metal resistant bacteria. Moreover, genome sequencing facilitates the opportunities to identify specific metal resistance genes, operons, and regulatory elements in the genomes of individual bacteria, understand the genetic mechanisms and variations responsible for heavy metal resistance within and between bacterial species in addition to the transcriptome, proteome that obtain the real expressed genes. Moreover, at the community level, metagenome, meta transcriptome and meta proteome participate in understanding the microbial interactive network potentially novel metabolic pathways, enzymes and gene species can all be found using these methods. This review presents the state of the art and anticipated developments in the use of omics technologies in the investigation of microbes used for heavy metal bioremediation.
Topics: Metals, Heavy; Bacteria; Biodegradation, Environmental; Genome, Bacterial; Proteomics; Transcriptome; Metagenomics; Metagenome; Genomics; Drug Resistance, Bacterial
PubMed: 38709343
DOI: 10.1007/s11274-024-04005-y -
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 -
BMC Microbiology Apr 2024Obesity is a metabolic disorder closely associated with profound alterations in gut microbial composition. However, the dynamics of species composition and functional... (Meta-Analysis)
Meta-Analysis
Obesity is a metabolic disorder closely associated with profound alterations in gut microbial composition. However, the dynamics of species composition and functional changes in the gut microbiome in obesity remain to be comprehensively investigated. In this study, we conducted a meta-analysis of metagenomic sequencing data from both obese and non-obese individuals across multiple cohorts, totaling 1351 fecal metagenomes. Our results demonstrate a significant decrease in both the richness and diversity of the gut bacteriome and virome in obese patients. We identified 38 bacterial species including Eubacterium sp. CAG:274, Ruminococcus gnavus, Eubacterium eligens and Akkermansia muciniphila, and 1 archaeal species, Methanobrevibacter smithii, that were significantly altered in obesity. Additionally, we observed altered abundance of five viral families: Mesyanzhinovviridae, Chaseviridae, Salasmaviridae, Drexlerviridae, and Casjensviridae. Functional analysis of the gut microbiome indicated distinct signatures associated to obesity and identified Ruminococcus gnavus as the primary driver for function enrichment in obesity, and Methanobrevibacter smithii, Akkermansia muciniphila, Ruminococcus bicirculans, and Eubacterium siraeum as functional drivers in the healthy control group. Additionally, our results suggest that antibiotic resistance genes and bacterial virulence factors may influence the development of obesity. Finally, we demonstrated that gut vOTUs achieved a diagnostic accuracy with an optimal area under the curve of 0.766 for distinguishing obesity from healthy controls. Our findings offer comprehensive and generalizable insights into the gut bacteriome and virome features associated with obesity, with the potential to guide the development of microbiome-based diagnostics.
Topics: Humans; Gastrointestinal Microbiome; Metagenome; Obesity; Bacteria; Feces; Clostridiales; Akkermansia
PubMed: 38580930
DOI: 10.1186/s12866-024-03278-5 -
Current Opinion in Microbiology Oct 2023Novel antibiotics are in constant demand to combat a global increase in antibiotic-resistant infections. Bacterial natural products have been a long-standing source of... (Review)
Review
Novel antibiotics are in constant demand to combat a global increase in antibiotic-resistant infections. Bacterial natural products have been a long-standing source of antibiotic compounds, and metagenomic mining of environmental DNA (eDNA) has increasingly provided new antibiotic leads. The metagenomic small-molecule discovery pipeline can be divided into three main steps: surveying eDNA, retrieving a sequence of interest, and accessing the encoded natural product. Improvements in sequencing technology, bioinformatic algorithms, and methods for converting biosynthetic gene clusters into small molecules are steadily increasing our ability to discover metagenomically encoded antibiotics. We predict that, over the next decade, ongoing technological improvements will dramatically increase the rate at which antibiotics are discovered from metagenomes.
Topics: Anti-Bacterial Agents; DNA, Environmental; Bacteria; Metagenomics; Biological Products; Multigene Family
PubMed: 37327680
DOI: 10.1016/j.mib.2023.102335 -
MSystems Jan 2024Inflammatory bowel disease (IBD) is characterized by complex etiology and a disrupted colonic ecosystem. We provide a framework for the analysis of multi-omic data,...
Inflammatory bowel disease (IBD) is characterized by complex etiology and a disrupted colonic ecosystem. We provide a framework for the analysis of multi-omic data, which we apply to study the gut ecosystem in IBD. Specifically, we train and validate models using data on the metagenome, metatranscriptome, virome, and metabolome from the Human Microbiome Project 2 IBD multi-omic database, with 1,785 repeated samples from 130 individuals (103 cases and 27 controls). After splitting the participants into training and testing groups, we used mixed-effects least absolute shrinkage and selection operator regression to select features for each omic. These features, with demographic covariates, were used to generate separate single-omic prediction scores. All four single-omic scores were then combined into a final regression to assess the relative importance of the individual omics and the predictive benefits when considered together. We identified several species, pathways, and metabolites known to be associated with IBD risk, and we explored the connections between data sets. Individually, metabolomic and viromic scores were more predictive than metagenomics or metatranscriptomics, and when all four scores were combined, we predicted disease diagnosis with a Nagelkerke's of 0.46 and an area under the curve of 0.80 (95% confidence interval: 0.63, 0.98). Our work supports that some single-omic models for complex traits are more predictive than others, that incorporating multiple omic data sets may improve prediction, and that each omic data type provides a combination of unique and redundant information. This modeling framework can be extended to other complex traits and multi-omic data sets.IMPORTANCEComplex traits are characterized by many biological and environmental factors, such that multi-omic data sets are well-positioned to help us understand their underlying etiologies. We applied a prediction framework across multiple omics (metagenomics, metatranscriptomics, metabolomics, and viromics) from the gut ecosystem to predict inflammatory bowel disease (IBD) diagnosis. The predicted scores from our models highlighted key features and allowed us to compare the relative utility of each omic data set in single-omic versus multi-omic models. Our results emphasized the importance of metabolomics and viromics over metagenomics and metatranscriptomics for predicting IBD status. The greater predictive capability of metabolomics and viromics is likely because these omics serve as markers of lifestyle factors such as diet. This study provides a modeling framework for multi-omic data, and our results show the utility of combining multiple omic data types to disentangle complex disease etiologies and biological signatures.
Topics: Humans; Inflammatory Bowel Diseases; Metagenomics; Phenotype; Microbiota; Risk Factors
PubMed: 38095449
DOI: 10.1128/msystems.00677-23