Targeted metagenomics of active microbial populations with stable-isotope probing

The ability to explore microbial diversity and function has been enhanced by novel experimental and computational tools. The incorporation of stable isotopes into microbial biomass enables the recovery of labeled nucleic acids from active microorganisms, despite their initial abundance and culturability. Combining stable-isotope probing (SIP) with metagenomics provides access to genomes from microorganisms involved in metabolic processes of interest. Studies using metagenomic analysis on DNA obtained from DNA-SIP incubations can be ideal for the recovery of novel enzymes for biotechnology applications, including biodegradation, biotransformation, and biosynthesis. This chapter introduces metagenomic and DNA-SIP methodologies, highlights biotechnology-focused studies that combine these approaches, and provides perspectives on future uses of these methods as analysis tools for applied and environmental microbiology

Bacteria are important dimethylsulfoniopropionate producers in marine aphotic and high-pressure environments

Dimethylsulfoniopropionate (DMSP) is an important marine osmolyte. Aphotic environments are only recently being considered as potential contributors to global DMSP production. Here, our Mariana Trench study reveals a typical seawater DMSP/dimethylsulfide (DMS) profile, with highest concentrations in the euphotic zone and decreased but consistent levels below. The genetic potential for bacterial DMSP synthesis via the dsyB gene and its transcription is greater in the deep ocean, and is highest in the sediment.s DMSP catabolic potential is present throughout the trench waters, but is less prominent below 8000 m, perhaps indicating a preference to store DMSP in the deep for stress protection. Deep ocean bacterial isolates show enhanced DMSP production under increased hydrostatic pressure. Furthermore, bacterial dsyB mutants are less tolerant of deep ocean pressures than wild-type strains. Thus, we propose a physiological function for DMSP in hydrostatic pressure protection, and that bacteria are key DMSP producers in deep seawater and sediment

Nevrological manifestations in patiens with West Nile fever

Гарячка Західного Нілу (ГЗН) є найпоширенішим зооантропонозним трансмісивним «комариним» захворюванням у групі природно осередкових інфекцій. Вірус ГЗН належить до роду Flavivirus родини Flaviviridae, поширений на усіх континентах за винятком Антарктиди. В Європі основним видом комарів, які передають вірус ГЗН людям, є Culex pipiens. Інфікування описане також при вертикальній передачі від матері до дитини та парентеральним шляхом. За філогенетичними властивостями вірусу розрізняють кілька генетичних груп – генотипів, деякі з яких містять підгрупи, що обумовлює нерівномірний територіальний розподіл та тяжкість клінічної маніфестації ГЗН

Recovering complete and draft population genomes from metagenome datasets

Assembly of metagenomic sequence data into microbial genomes is of fundamental value to improving our understanding of microbial ecology and metabolism by elucidating the functional potential of hard-to-culture microorganisms. Here, we provide a synthesis of available methods to bin metagenomic contigs into species-level groups and highlight how genetic diversity, sequencing depth, and coverage influence binning success. Despite the computational cost on application to deeply sequenced complex metagenomes (e.g., soil), covarying patterns of contig coverage across multiple datasets significantly improves the binning process. We also discuss and compare current genome validation methods and reveal how these methods tackle the problem of chimeric genome bins i.e., sequences from multiple species. Finally, we explore how population genome assembly can be used to uncover biogeographic trends and to characterize the effect of in situ functional constraints on the genome-wide evolution

Multiple Data Analyses and Statistical Approaches for Analyzing Data from Metagenomic Studies and Clinical Trials

Metagenomics, also known as environmental genomics, is the study of the genomic content of a sample of organisms (microbes) obtained from a common habitat. Metagenomics and other “omics” disciplines have captured the attention of researchers for several decades. The effect of microbes in our body is a relevant concern for health studies. There are plenty of studies using metagenomics which examine microorganisms that inhabit niches in the human body, sometimes causing disease, and are often correlated with multiple treatment conditions. No matter from which environment it comes, the analyses are often aimed at determining either the presence or absence of specific species of interest in a given metagenome or comparing the biological diversity and the functional activity of a wider range of microorganisms within their communities. The importance increases for comparison within different environments such as multiple patients with different conditions, multiple drugs, and multiple time points of same treatment or same patient. Thus, no matter how many hypotheses we have, we need a good understanding of genomics, bioinformatics, and statistics to work together to analyze and interpret these datasets in a meaningful way. This chapter provides an overview of different data analyses and statistical approaches (with example scenarios) to analyze metagenomics samples from different medical projects or clinical trials

Critical Assessment of Metagenome Interpretation:A benchmark of metagenomics software

International audienceIn metagenome analysis, computational methods for assembly, taxonomic profilingand binning are key components facilitating downstream biological datainterpretation. However, a lack of consensus about benchmarking datasets andevaluation metrics complicates proper performance assessment. The CriticalAssessment of Metagenome Interpretation (CAMI) challenge has engaged the globaldeveloper community to benchmark their programs on datasets of unprecedentedcomplexity and realism. Benchmark metagenomes were generated from newlysequenced ~700 microorganisms and ~600 novel viruses and plasmids, includinggenomes with varying degrees of relatedness to each other and to publicly availableones and representing common experimental setups. Across all datasets, assemblyand genome binning programs performed well for species represented by individualgenomes, while performance was substantially affected by the presence of relatedstrains. Taxonomic profiling and binning programs were proficient at high taxonomicranks, with a notable performance decrease below the family level. Parametersettings substantially impacted performances, underscoring the importance ofprogram reproducibility. While highlighting current challenges in computationalmetagenomics, the CAMI results provide a roadmap for software selection to answerspecific research questions

ICoVeR - an interactive visualization tool for verification and refinement of metagenomic bins.

BACKGROUND: Recent advances in high-throughput sequencing allow for much deeper exploitation of natural and engineered microbial communities, and to unravel so-called "microbial dark matter" (microbes that until now have evaded cultivation). Metagenomic analyses result in a large number of genomic fragments (contigs) that need to be grouped (binned) in order to reconstruct draft microbial genomes. While several contig binning algorithms have been developed in the past 2 years, they often lack consensus. Furthermore, these software tools typically lack a provision for the visualization of data and bin characteristics. RESULTS: We present ICoVeR, the Interactive Contig-bin Verification and Refinement tool, which allows the visualization of genome bins. More specifically, ICoVeR allows curation of bin assignments based on multiple binning algorithms. Its visualization window is composed of two connected and interactive main views, including a parallel coordinates view and a dimensionality reduction plot. To demonstrate ICoVeR's utility, we used it to refine disparate genome bins automatically generated using MetaBAT, CONCOCT and MyCC for an anaerobic digestion metagenomic (AD microbiome) dataset. Out of 31 refined genome bins, 23 were characterized with higher completeness and lower contamination in comparison to their respective, automatically generated, genome bins. Additionally, to benchmark ICoVeR against a previously validated dataset, we used Sharon's dataset representing an infant gut metagenome. CONCLUSIONS: ICoVeR is an open source software package that allows curation of disparate genome bins generated with automatic binning algorithms. It is freely available under the GPLv3 license at https://git.list.lu/eScience/ICoVeR . The data management and analytical functions of ICoVeR are implemented in R, therefore the software can be easily installed on any system for which R is available. Installation and usage guide together with the example files ready to be visualized are also provided via the project wiki. ICoVeR running instance preloaded with AD microbiome and Sharon's datasets can be accessed via the website