Bacteria
Ontology highlight
ABSTRACT:
PROVIDER: PRJDB18532 | ENA |
REPOSITORIES: ENA
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Project description:During an intracellular bacterial infection, the host cell and the infecting pathogen interact through a progressive series of events that may result in many distinct outcomes. To understand the specific strategies our immune system employs to manage attack by diverse pathogens, we sought to identify the unique and the core host and pathogen interactions that occur during infection: We compared in molecular detail the pathways induced across infection by seven diverse bacterial species that constitute many of the main human pathogens: Staphylococcus aureus, Listeria monocytogenes, Enterococcus faecalis, Group B Streptococcus, Yersinia pseudotuberculosis, Shigella flexneri and Salmonella enterica. We infected primary human macrophages with each species and used scRNA-Seq to generate a comprehensive dataset of gene expression profiles during bacterial infection. Examining the expression profiles of the infected macrophages across the pathogens, we discovered different modules of infection representing different states through which the infection progresses. The early module captures intra-cellular activity such as lysosome and degranulation, followed by type I IFN signaling, from which results in a cell death module, with a last mode of inflammatory response through response to IL-1. Comparing these modules across the pathogens, we found that their dynamics differ, with some modules active in all species and others which are present in some, but not all pathogens. Our work defines the hallmarks of host-pathogen interactions by identifying recurring properties of infection that can provide insight into diagnostics and therapeutic timing.
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Project description:Multiomics of faecal samples collected from individuals in families with multiple cases of type 1 diabetes mellitus (T1DM) over 3 or 4 months. Metagenomic and metatranscriptomic sequencing and metaproteomics were carried out, as well as whole human genome sequencing. Phenotypic data is available.
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2016-08-22 | PXD003791 | Pride
Project description:Staphylococcus aureus is a high-priority pathogen causing severe infections with high morbidity and mortality worldwide. Many S. aureus strains are methicillin-resistant (MRSA) or even multi-drug resistant. It is one of the most successful and prominent modern pathogens. An effective fight against S. aureus infections requires novel targets for antimicrobial and antistaphylococcal therapies. Recent advances in whole-genome sequencing and high-throughput techniques facilitate the generation of genome-scale metabolic models (GEMs). Among the multiple applications of GEMs is drug-targeting in pathogens. Hence, comprehensive and predictive metabolic reconstructions of S. aureus could facilitate the identification of novel targets for antimicrobial therapies. This review aims at giving an overview of all available GEMs of multiple S. aureus strains. We downloaded all 114 available GEMs of S. aureus for further analysis. The scope of each model was evaluated, including the number of reactions, metabolites, and genes.Furthermore, all models were quality-controlled using Mᴇᴍᴏᴛᴇ, an open-source application with standardized metabolic tests. Growth capabilities and model similarities were examined. This review should lead as a guide for choosing the appropriate GEM for a given research question. With the information about the availability, the format, and the strengths and potentials of each model, one can either choose an existing model or combine several models to create models with even higher predictive values. This facilitates model-driven discoveries of novel antimicrobial targets to fight multi-drug resistant S. aureus strains.
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