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

    Sanjeev Dahal / Alina Renz / Andreas Dräger / Laurence Yang

    Communications Biology, Vol 6, Iss 1, Pp 1-

    2023  Band 10

    Abstract: An updated genome-scale model of Pseudomonas aeruginosa explains the metabolic pathways leading to drug resistance and provides a computational platform to design experiments targeting P. aeruginosa metabolism. ...

    Abstract An updated genome-scale model of Pseudomonas aeruginosa explains the metabolic pathways leading to drug resistance and provides a computational platform to design experiments targeting P. aeruginosa metabolism.
    Schlagwörter Biology (General) ; QH301-705.5
    Sprache Englisch
    Erscheinungsdatum 2023-02-01T00:00:00Z
    Verlag Nature Portfolio
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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

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

    Communications biology

    2023  Band 6, Heft 1, Seite(n) 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-Begriff(e) Virulence/genetics ; Pseudomonas aeruginosa/genetics ; Pseudomonas aeruginosa/metabolism ; Drug Synergism ; Virulence Factors/genetics ; Virulence Factors/metabolism ; Biofilms
    Chemische Substanzen Virulence Factors
    Sprache Englisch
    Erscheinungsdatum 2023-02-10
    Erscheinungsland England
    Dokumenttyp Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 2399-3642
    ISSN (online) 2399-3642
    DOI 10.1038/s42003-023-04540-8
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  3. Artikel ; 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  Band 19, Heft 3, Seite(n) 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-Begriff(e) Humans ; SARS-CoV-2/genetics ; COVID-19 ; Workflow ; Antiviral Agents/pharmacology ; Antiviral Agents/therapeutic use ; Epithelial Cells
    Chemische Substanzen Antiviral Agents
    Sprache Englisch
    Erscheinungsdatum 2023-03-23
    Erscheinungsland United States
    Dokumenttyp 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
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  4. Artikel ; 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  Band 39, Heft 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-Begriff(e) Systems Biology ; Computational Biology ; Reproducibility of Results ; Biological Ontologies ; Computer Simulation ; Gene Ontology
    Sprache Englisch
    Erscheinungsdatum 2023-07-14
    Erscheinungsland England
    Dokumenttyp 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
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  5. Artikel ; Online: New workflow predicts drug targets against SARS-CoV-2 via metabolic changes in infected cells.

    Nantia Leonidou / Alina Renz / Reihaneh Mostolizadeh / Andreas Dräger

    PLoS Computational Biology, Vol 19, Iss 3, p e

    2023  Band 1010903

    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.
    Schlagwörter Biology (General) ; QH301-705.5
    Thema/Rubrik (Code) 570
    Sprache Englisch
    Erscheinungsdatum 2023-03-01T00:00:00Z
    Verlag Public Library of Science (PLoS)
    Dokumenttyp Artikel ; Online
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  6. Artikel ; Online: New workflow predicts drug targets against SARS-CoV-2 via metabolic changes in infected cells

    Nantia Leonidou / Alina Renz / Reihaneh Mostolizadeh / Andreas Dräger

    PLoS Computational Biology, Vol 19, Iss

    2023  Band 3

    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. Author summary The recently emerged human coronavirus SARS-CoV-2 spread worldwide, causing severe challenges in health care, the economy, and society. Developing new vaccines and therapies is essential to prevent the next pandemic efficiently. However, vaccines have the disadvantage of decreased immunity over time, while they lose their efficacy against subsequent mutations and variants. Hence, effective pandemic preparedness requires discovering broadly ...
    Schlagwörter Biology (General) ; QH301-705.5
    Thema/Rubrik (Code) 570
    Sprache Englisch
    Erscheinungsdatum 2023-03-01T00:00:00Z
    Verlag Public Library of Science (PLoS)
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  7. Artikel: First Genome-Scale Metabolic Model of

    Renz, Alina / Widerspick, Lina / Dräger, Andreas

    Metabolites

    2021  Band 11, Heft 4

    Abstract: Dolosigranulum ... ...

    Abstract Dolosigranulum pigrum
    Sprache Englisch
    Erscheinungsdatum 2021-04-09
    Erscheinungsland Switzerland
    Dokumenttyp Journal Article
    ZDB-ID 2662251-8
    ISSN 2218-1989
    ISSN 2218-1989
    DOI 10.3390/metabo11040232
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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

    Renz, Alina / Dräger, Andreas

    NPJ systems biology and applications

    2021  Band 7, Heft 1, Seite(n) 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-Begriff(e) Anti-Bacterial Agents/pharmacology ; Humans ; Methicillin-Resistant Staphylococcus aureus/genetics ; Staphylococcal Infections/drug therapy ; Staphylococcus aureus/genetics ; Whole Genome Sequencing
    Chemische Substanzen Anti-Bacterial Agents
    Sprache Englisch
    Erscheinungsdatum 2021-06-29
    Erscheinungsland England
    Dokumenttyp Journal Article ; Research Support, Non-U.S. Gov't ; Review
    ISSN 2056-7189
    ISSN (online) 2056-7189
    DOI 10.1038/s41540-021-00188-4
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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

    Alina Renz / Andreas Dräger

    npj Systems Biology and Applications, Vol 7, Iss 1, Pp 1-

    2021  Band 15

    Abstract: 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 ... ...

    Abstract 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.
    Schlagwörter Biology (General) ; QH301-705.5
    Thema/Rubrik (Code) 004
    Sprache Englisch
    Erscheinungsdatum 2021-06-01T00:00:00Z
    Verlag Nature Portfolio
    Dokumenttyp Artikel ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  10. Artikel ; Online: Fabrication and characterization of CMOS-compatible perforated micromembranes for biomedical applications

    Brechmann Noah / Michel Marvin / Bola Alina / Renz Franziska / Pickhinke Andreas / Seidl Karsten

    Current Directions in Biomedical Engineering, Vol 9, Iss 1, Pp 439-

    2023  Band 442

    Abstract: We report the optimized wafer-scale fabrication of microporous membranes for applications in the biomedical field such as cell filtration. Existing similar devices can mostly not be integrated on CMOS circuits or mass fabricated. Both is enabled here by ... ...

    Abstract We report the optimized wafer-scale fabrication of microporous membranes for applications in the biomedical field such as cell filtration. Existing similar devices can mostly not be integrated on CMOS circuits or mass fabricated. Both is enabled here by the exclusive use of scalable and low temperature microsystems technology methods on silicon wafers. The successfully manufactured devices are characterized with regard to the feasibility of an integrated clogging detection or captured cell counter. The results from electrochemical measurements across the chips match the calculations from a corresponding theoretical model well, verifying the described concept. Further electrical functionalities may thus be integrated into the micromembrane device in the future, equipping it for new applications and allowing a more efficient solution for existing tasks of similar devices.
    Schlagwörter micromembrane ; microperforation ; microfluidics ; filtration ; Medicine ; R
    Thema/Rubrik (Code) 620
    Sprache Englisch
    Erscheinungsdatum 2023-09-01T00:00:00Z
    Verlag De Gruyter
    Dokumenttyp Artikel ; Online
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