Ontology highlight
ABSTRACT:
PROVIDER: PRJNA368917 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| Other | ||||
| SRR1369610_1.fastq.gz | Fastqsanger.gz | |||
| SRR1369610_2.fastq.gz | Fastqsanger.gz | |||
| SRR1369620_1.fastq.gz | Fastqsanger.gz | |||
| SRR1369620_2.fastq.gz | Fastqsanger.gz |
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Project description:An Integrated Approach to Understanding Host-Pathogen Interactions (SuperSeries GSE94274)
| PRJNA385820 | ENA
Project description:To advance our understanding of cellular host-pathogen interactions, technologies that facilitate the co-capture of both host and pathogen spatial transcriptome information are needed. Here, we present an approach to simultaneously capture host and pathogen spatial gene expression information from the same formalin-fixed paraffin embedded (FFPE) tissue section using the spatial transcriptomics technology. We applied the method to COVID-19 patient lung samples and enabled the dual detection of human and SARS-CoV-2 transcriptomes at 55 µm resolution. We validated our spatial detection of SARS-CoV-2 and identified an average specificity of 94.92% in comparison to RNAScope and 82.20% in comparison to in situ sequencing (ISS). COVID-19 tissues showed an upregulation of host immune response, such as increased expression of inflammatory cytokines, lymphocyte and fibroblast markers. Our colocalization analysis revealed that SARS-CoV-2+ spots presented shifts in host RNA metabolism, autophagy, NFκB, and interferon response pathways. Future applications of our approach will enable new insights into host response to pathogen infection through the simultaneous, unbiased detection of two transcriptomes.
| EGAS00001006186 | EGA
Project description:Co-localization of host and pathogen spatial transcriptome information can improve our understanding on how inflammatory environments modulate pathogen virulence in tissues and vice versa. Here, we demonstrate for the first time that bacterial probes can be used to simultaneously identify host-pathogen mutual interactions in formalin-fixed, paraffin-embedded (FFPE) tissues using spatial transcriptomics technology. With properly designed probes targeting pathogens of interest, our approach could provide an improved definition of infection processes by detecting pathogen and host genes expression simultaneously, spatially distributed, and unbiased.
2024-09-18 | GSE242471 | GEO
Project description:This is the genome scale metabolic reconstruction of the human alveloar macrophage, iAB-AMØ-1410, described in the article:
Insight into human alveolar macrophage and M. tuberculosis interactions via metabolic reconstructions.
Bordbar A, Lewis NE, Schellenberger J, Palsson BØ, Jamshidi N. Mol Syst Biol.
2010 Oct 19;6:422. PMID: 20959820
, DOI: 10.1038/msb.2010.68
Abstract:
Metabolic coupling of Mycobacterium tuberculosis to its host is foundational to its pathogenesis. Computational genome-scale metabolic models have shown utility in integrating -omic as well as physiologic data for systemic, mechanistic analysis of metabolism. To date, integrative analysis of host-pathogen interactions using in silico mass-balanced, genome-scale models has not been performed. We, therefore, constructed a cell-specific alveolar macrophage model, iAB-AMØ-1410, from the global human metabolic reconstruction, Recon 1. The model successfully predicted experimentally verified ATP and nitric oxide production rates in macrophages. This model was then integrated with an M. tuberculosis H37Rv model, iNJ661, to build an integrated host-pathogen genome-scale reconstruction, iAB-AMØ-1410-Mt-661. The integrated host-pathogen network enables simulation of the metabolic changes during infection. The resulting reaction activity and gene essentiality targets of the integrated model represent an altered infectious state. High-throughput data from infected macrophages were mapped onto the host-pathogen network and were able to describe three distinct pathological states. Integrated host-pathogen reconstructions thus form a foundation upon which understanding the biology and pathophysiology of infections can be developed.
This model was downloaded from the supplementary materials ( link
) to the article. To make this file valid SBML the units of all parameters where changed from mmole per gDW per hour to mmole per hour and the empty reactions with the ids R_EX_retpalm_LPAREN_e_RPAREN_ were removed. The model can be used eg. fpr FBA with the COBRA toolbox
, amongst others. This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication
for more information. In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.
2005-01-01 | MODEL1011090001 | BioModels
Project description:This is the joined genome scale reconstruction of both Mycobacterium tuberculosis and the human alveloar macrophage metabolism, iAB-AMØ-1410-Mt-661, described in the article:
Insight into human alveolar macrophage and M. tuberculosis interactions via metabolic reconstructions.
Bordbar A, Lewis NE, Schellenberger J, Palsson BØ, Jamshidi N. Mol Syst Biol.
2010 Oct 19;6:422. PMID: 20959820
, DOI: 10.1038/msb.2010.68
Abstract:
Metabolic coupling of Mycobacterium tuberculosis to its host is foundational to its pathogenesis. Computational genome-scale metabolic models have shown utility in integrating -omic as well as physiologic data for systemic, mechanistic analysis of metabolism. To date, integrative analysis of host-pathogen interactions using in silico mass-balanced, genome-scale models has not been performed. We, therefore, constructed a cell-specific alveolar macrophage model, iAB-AMØ-1410, from the global human metabolic reconstruction, Recon 1. The model successfully predicted experimentally verified ATP and nitric oxide production rates in macrophages. This model was then integrated with an M. tuberculosis H37Rv model, iNJ661, to build an integrated host-pathogen genome-scale reconstruction, iAB-AMØ-1410-Mt-661. The integrated host-pathogen network enables simulation of the metabolic changes during infection. The resulting reaction activity and gene essentiality targets of the integrated model represent an altered infectious state. High-throughput data from infected macrophages were mapped onto the host-pathogen network and were able to describe three distinct pathological states. Integrated host-pathogen reconstructions thus form a foundation upon which understanding the biology and pathophysiology of infections can be developed.
This model was downloaded from the supplementary materials ( link
) to the article. To make this file valid SBML the units of all parameters where changed from mmole per gDW per hour to mmole per hour. The model can be used eg. fpr FBA with the COBRA toolbox
, amongst others. This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2011 The BioModels.net Team.
To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication
for more information. In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not..
To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.
2005-01-01 | MODEL1011090002 | BioModels
Project description:To holistically unravel the complexity of pathogen-host interactions within infected tissues we leverage a dual spatial transcriptomic approach that, for the first time, simultaneously captures the expression of Pseudomonas aeruginosa genes alongside the entire host transcriptome in a model of ocular infection. This innovative method reveals differential pathogen and host-specific gene expression patterns across specific anatomical regions generating a unified transcriptional map of infection. By integrating these data, we developed a predictive ridge regression model trained on images from infected tissues.
2024-12-27 | GSE285269 | GEO
Project description:The initial interactions between a host cell and a pathogen can dictate disease outcomes and are important targets for host-directed therapies. Mycobacterium abscessus (Mab) is a highly antibiotic resistant, rapidly growing non-tuberculous mycobacterium that infects patients with chronic lung diseases, like cystic fibrosis. Mab can infect host immune cells, such as macrophages, which contribute to its pathogenesis. However, our understanding of host genes that modulate the initial host-Mab interactions remains unclear. Here, we developed a functional genetic approach by coupling a Mab fluorescent reporter with a genome-wide knockout library in murine macrophages. We used this approach to conduct a forward genetic screen to uncover host genes that contribute to the uptake of Mab by macrophages. We identified known regulators of phagocytosis, such as the integrin Itgb2, and uncovered a key requirement for glycosaminoglycan (sGAG) synthesis for macrophages to efficiently bind to Mab. CRISPR-Cas9 targeting of three key sGAG biosynthesis regulators, UGDH, B4GALT7 and B3GAT3 resulted in reduced uptake of both smooth and rough Mab variants by macrophages. Mechanistic studies suggest that sGAGs function upstream of pathogen engulfment and are required for uptake of Mab but not Escherichia coli or latex beads. Further investigation found that the loss of sGAGs reduces the surface expression, but not the mRNA expression, of key integrins, suggesting a key role for sGAGs in modulating surface receptor availability. Together these studies globally defined and characterized important regulators of macrophage-Mab interactions and are a first step better understand important host genes that contribute to Mab pathogenesis and disease.
2023-01-13 | GSE221413 | GEO
Project description:The deposited microarray data were generated in a study that integrated the gene expression profiles and metabolic responses of Caco2 cells incubated with Bifidobacterium infantis subsp. infantis and Salmonella enterica subsp. enterica sv. Typhimurium. The aim of this study was to investigate the interaction of B. infantis, S. Typhimurium, and host cells (Caco2) in the course of infection to understand the molecular mechanics of probiotic-pathogen-host interactions.
2024-05-08 | GSE266880 | GEO
Project description:Phytophthora cinnamomi is a devastating soil-borne oomycete with a very broad host range however there remains a major gap in the understanding of plant resistance responses to the pathogen, furthermore, necrotrophic plant-pathogen interactions, particularly those of root pathogens, remain poorly understood. Zea mays exhibits non-host resistance to the pathogen and has been well characterised as a model species. Using the maize Affymetrix GeneChip array we conducted genome-wide gene expression profiling to elucidate the defence genes and pathways which are induced in the root tissue of a resistant plant species to the pathogen.
2011-03-02 | GSE27626 | GEO