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  1. Article ; Online: COVID-19 Disease Map, building a computational repository of SARS-CoV-2 virus-host interaction mechanisms

    Dräger, Andreas

    2020  

    Keywords 500 ; covid19
    Language English
    Publisher Nature Publishing Group
    Publishing country de
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Genome-scale model of

    Leonidou, Nantia / Ostyn, Lisa / Coenye, Tom / Crabbé, Aurélie / Dräger, Andreas

    Microbiology spectrum

    2024  , Page(s) e0400623

    Abstract: Cystic fibrosis (CF), an inherited genetic disorder caused by mutations in the cystic fibrosis transmembrane conductance regulator gene, results in sticky and thick mucosal fluids. This environment facilitates the colonization of various microorganisms, ... ...

    Abstract Cystic fibrosis (CF), an inherited genetic disorder caused by mutations in the cystic fibrosis transmembrane conductance regulator gene, results in sticky and thick mucosal fluids. This environment facilitates the colonization of various microorganisms, some of which can cause acute and chronic lung infections, while others may positively impact the disease.
    Language English
    Publishing date 2024-04-23
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2807133-5
    ISSN 2165-0497 ; 2165-0497
    ISSN (online) 2165-0497
    ISSN 2165-0497
    DOI 10.1128/spectrum.04006-23
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Genome-scale model of Pseudomonas aeruginosa metabolism unveils virulence and drug potentiation.

    Dahal, Sanjeev / Renz, Alina / Dräger, Andreas / Yang, Laurence

    Communications biology

    2023  Volume 6, Issue 1, Page(s) 165

    Abstract: Pseudomonas aeruginosa is one of the leading causes of hospital-acquired infections. To decipher the metabolic mechanisms associated with virulence and antibiotic resistance, we have developed an updated genome-scale model (GEM) of P. aeruginosa. The ... ...

    Abstract Pseudomonas aeruginosa is one of the leading causes of hospital-acquired infections. To decipher the metabolic mechanisms associated with virulence and antibiotic resistance, we have developed an updated genome-scale model (GEM) of P. aeruginosa. The model (iSD1509) is an extensively curated, three-compartment, and mass-and-charge balanced BiGG model containing 1509 genes, the largest gene content for any P. aeruginosa GEM to date. It is the most accurate with prediction accuracies as high as 92.4% (gene essentiality) and 93.5% (substrate utilization). In iSD1509, we newly added a recently discovered pathway for ubiquinone-9 biosynthesis which is required for anaerobic growth. We used a modified iSD1509 to demonstrate the role of virulence factor (phenazines) in the pathogen survival within biofilm/oxygen-limited condition. Further, the model can mechanistically explain the overproduction of a drug susceptibility biomarker in the P. aeruginosa mutants. Finally, we use iSD1509 to demonstrate the drug potentiation by metabolite supplementation, and elucidate the mechanisms behind the phenotype, which agree with experimental results.
    MeSH term(s) Virulence/genetics ; Pseudomonas aeruginosa/genetics ; Pseudomonas aeruginosa/metabolism ; Drug Synergism ; Virulence Factors/genetics ; Virulence Factors/metabolism ; Biofilms
    Chemical Substances Virulence Factors
    Language English
    Publishing date 2023-02-10
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 2399-3642
    ISSN (online) 2399-3642
    DOI 10.1038/s42003-023-04540-8
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  4. Article ; Online: New workflow predicts drug targets against SARS-CoV-2 via metabolic changes in infected cells.

    Leonidou, Nantia / Renz, Alina / Mostolizadeh, Reihaneh / Dräger, Andreas

    PLoS computational biology

    2023  Volume 19, Issue 3, Page(s) e1010903

    Abstract: COVID-19 is one of the deadliest respiratory diseases, and its emergence caught the pharmaceutical industry off guard. While vaccines have been rapidly developed, treatment options for infected people remain scarce, and COVID-19 poses a substantial ... ...

    Abstract COVID-19 is one of the deadliest respiratory diseases, and its emergence caught the pharmaceutical industry off guard. While vaccines have been rapidly developed, treatment options for infected people remain scarce, and COVID-19 poses a substantial global threat. This study presents a novel workflow to predict robust druggable targets against emerging RNA viruses using metabolic networks and information of the viral structure and its genome sequence. For this purpose, we implemented pymCADRE and PREDICATE to create tissue-specific metabolic models, construct viral biomass functions and predict host-based antiviral targets from more than one genome. We observed that pymCADRE reduces the computational time of flux variability analysis for internal optimizations. We applied these tools to create a new metabolic network of primary bronchial epithelial cells infected with SARS-CoV-2 and identified enzymatic reactions with inhibitory effects. The most promising reported targets were from the purine metabolism, while targeting the pyrimidine and carbohydrate metabolisms seemed to be promising approaches to enhance viral inhibition. Finally, we computationally tested the robustness of our targets in all known variants of concern, verifying our targets' inhibitory effects. Since laboratory tests are time-consuming and involve complex readouts to track processes, our workflow focuses on metabolic fluxes within infected cells and is applicable for rapid hypothesis-driven identification of potentially exploitable antivirals concerning various viruses and host cell types.
    MeSH term(s) Humans ; SARS-CoV-2/genetics ; COVID-19 ; Workflow ; Antiviral Agents/pharmacology ; Antiviral Agents/therapeutic use ; Epithelial Cells
    Chemical Substances Antiviral Agents
    Language English
    Publishing date 2023-03-23
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2193340-6
    ISSN 1553-7358 ; 1553-734X
    ISSN (online) 1553-7358
    ISSN 1553-734X
    DOI 10.1371/journal.pcbi.1010903
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  5. Article ; Online: Hierarchical modelling of microbial communities.

    Glöckler, Manuel / Dräger, Andreas / Mostolizadeh, Reihaneh

    Bioinformatics (Oxford, England)

    2023  Volume 39, Issue 1

    Abstract: Summary: The human body harbours a plethora of microbes that play a fundamental role in the well-being of the host. Still, the contribution of many microorganisms to human health remains undiscovered. To understand the composition of their communities, ... ...

    Abstract Summary: The human body harbours a plethora of microbes that play a fundamental role in the well-being of the host. Still, the contribution of many microorganisms to human health remains undiscovered. To understand the composition of their communities, the accurate genome-scale metabolic network models of participating microorganisms are integrated to construct a community that mimics the normal bacterial flora of humans. So far, tools for modelling the communities have transformed the community into various optimization problems and model compositions. Therefore, any knockout or modification of each submodel (each species) necessitates the up-to-date creation of the community to incorporate rebuildings. To solve this complexity, we refer to the context of SBML in a hierarchical model composition, wherein each species's genome-scale metabolic model is imported as a submodel in another model. Hence, the community is a model composed of submodels defined in separate files. We combine all these files upon parsing to a so-called 'flattened' model, i.e., a comprehensive and valid SBML file of the entire community that COBRApy can parse for further processing. The hierarchical model facilitates the analysis of the whole community irrespective of any changes in the individual submodels.
    Availability and implementation: The module is freely available at https://github.com/manuelgloeckler/ncmw.
    MeSH term(s) Humans ; Software ; Genome ; Microbiota ; Metabolic Networks and Pathways ; Bacteria
    Language English
    Publishing date 2023-03-01
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1422668-6
    ISSN 1367-4811 ; 1367-4803
    ISSN (online) 1367-4811
    ISSN 1367-4803
    DOI 10.1093/bioinformatics/btad040
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    Zs.A 2374: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  6. Article ; Online: SBOannotator: a Python tool for the automated assignment of systems biology ontology terms.

    Leonidou, Nantia / Fritze, Elisabeth / Renz, Alina / Dräger, Andreas

    Bioinformatics (Oxford, England)

    2023  Volume 39, Issue 7

    Abstract: Motivation: The number and size of computational models in biology have drastically increased over the past years and continue to grow. Modeled networks are becoming more complex, and reconstructing them from the beginning in an exchangeable and ... ...

    Abstract Motivation: The number and size of computational models in biology have drastically increased over the past years and continue to grow. Modeled networks are becoming more complex, and reconstructing them from the beginning in an exchangeable and reproducible manner is challenging. Using precisely defined ontologies enables the encoding of field-specific knowledge and the association of disparate data types. In computational modeling, the medium for representing domain knowledge is the set of orthogonal structured controlled vocabularies named Systems Biology Ontology (SBO). The SBO terms enable modelers to explicitly define and describe model entities, including their roles and characteristics.
    Results: Here, we present the first standalone tool that automatically assigns SBO terms to multiple entities of a given SBML model, named the SBOannotator. The main focus lies on the reactions, as the correct assignment of precise SBO annotations requires their extensive classification. Our implementation does not consider only top-level terms but examines the functionality of the underlying enzymes to allocate precise and highly specific ontology terms to biochemical reactions. Transport reactions are examined separately and are classified based on the mechanism of molecule transport. Pseudo-reactions that serve modeling purposes are given reasonable terms to distinguish between biomass production and the import or export of metabolites. Finally, other model entities, such as metabolites and genes, are annotated with appropriate terms. Including SBO annotations in the models will enhance the reproducibility, usability, and analysis of biochemical networks.
    Availability and implementation: SBOannotator is freely available from https://github.com/draeger-lab/SBOannotator/.
    MeSH term(s) Systems Biology ; Computational Biology ; Reproducibility of Results ; Biological Ontologies ; Computer Simulation ; Gene Ontology
    Language English
    Publishing date 2023-07-14
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1422668-6
    ISSN 1367-4811 ; 1367-4803
    ISSN (online) 1367-4811
    ISSN 1367-4803
    DOI 10.1093/bioinformatics/btad437
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    In stock of ZB MED Cologne/Königswinter

    Zs.A 2374: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  7. Article ; Online: Curating and comparing 114 strain-specific genome-scale metabolic models of Staphylococcus aureus.

    Renz, Alina / Dräger, Andreas

    NPJ systems biology and applications

    2021  Volume 7, Issue 1, Page(s) 30

    Abstract: 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 ... ...

    Abstract 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 MEMOTE, 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.
    MeSH term(s) Anti-Bacterial Agents/pharmacology ; Humans ; Methicillin-Resistant Staphylococcus aureus/genetics ; Staphylococcal Infections/drug therapy ; Staphylococcus aureus/genetics ; Whole Genome Sequencing
    Chemical Substances Anti-Bacterial Agents
    Language English
    Publishing date 2021-06-29
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Review
    ISSN 2056-7189
    ISSN (online) 2056-7189
    DOI 10.1038/s41540-021-00188-4
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Book ; Thesis: Noradrenerge Beeinflussung der Androgenrezeptorkonzentration in der ventralen Prostata der Ratte

    Dräger, Andreas

    1991  

    Author's details vorgelegt von Andreas Dräger
    Size 62 S. : graph. Darst.
    Document type Book ; Thesis
    Thesis / German Habilitation thesis Tübingen, Univ., Diss., 1991
    HBZ-ID HT003885698
    Database Catalogue ZB MED Medicine, Health

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    Shelf markLoan statusLocation
    91 D 4960 order for loan Magazin

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  9. Article ; Online: NCMW: A Python Package to Analyze Metabolic Interactions in the Nasal Microbiome.

    Glöckler, Manuel / Dräger, Andreas / Mostolizadeh, Reihaneh

    Frontiers in bioinformatics

    2022  Volume 2, Page(s) 827024

    Abstract: The human upper respiratory tract is the reservoir of a diverse community of commensals and potential pathogens (pathobionts), ... ...

    Abstract The human upper respiratory tract is the reservoir of a diverse community of commensals and potential pathogens (pathobionts), including
    Language English
    Publishing date 2022-02-25
    Publishing country Switzerland
    Document type Journal Article
    ISSN 2673-7647
    ISSN (online) 2673-7647
    DOI 10.3389/fbinf.2022.827024
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  10. Article: Computational Model Informs Effective Control Interventions against

    Mostolizadeh, Reihaneh / Dräger, Andreas

    Biology

    2020  Volume 9, Issue 12

    Abstract: The complex interplay between pathogens, host factors, and the integrity and composition of the endogenous microbiome determine the course and outcome of gastrointestinal infections. The model ... ...

    Abstract The complex interplay between pathogens, host factors, and the integrity and composition of the endogenous microbiome determine the course and outcome of gastrointestinal infections. The model organism
    Language English
    Publishing date 2020-11-30
    Publishing country Switzerland
    Document type Journal Article
    ZDB-ID 2661517-4
    ISSN 2079-7737
    ISSN 2079-7737
    DOI 10.3390/biology9120431
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