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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
Database	MEDical Literature Analysis and Retrieval System OnLINE

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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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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
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; 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 Volume 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.
Keywords	Biology (General) ; QH301-705.5
Language	English
Publishing date	2023-02-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; 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 Volume 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 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.
Keywords	Biology (General) ; QH301-705.5
Subject code	570
Language	English
Publishing date	2023-03-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: First Genome-Scale Metabolic Model of

Renz, Alina / Widerspick, Lina / Dräger, Andreas

Metabolites

2021 Volume 11, Issue 4

Abstract: Dolosigranulum ... ...

Abstract	Dolosigranulum pigrum
Language	English
Publishing date	2021-04-09
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662251-8
ISSN	2218-1989
ISSN	2218-1989
DOI	10.3390/metabo11040232
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; 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 Volume 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 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 ...
Keywords	Biology (General) ; QH301-705.5
Subject code	570
Language	English
Publishing date	2023-03-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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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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Article ; 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 Volume 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.
Keywords	Biology (General) ; QH301-705.5
Subject code	004
Language	English
Publishing date	2021-06-01T00:00:00Z
Publisher	Nature Portfolio
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Genome-Scale Metabolic Model of Infection with SARS-CoV-2 Mutants Confirms Guanylate Kinase as Robust Potential Antiviral Target.

Renz, Alina / Widerspick, Lina / Dräger, Andreas

Genes

2021 Volume 12, Issue 6

Abstract: The current SARS-CoV-2 pandemic is still threatening humankind. Despite first successes in vaccine development and approval, no antiviral treatment is available for COVID-19 patients. The success is further tarnished by the emergence and spreading of ... ...

Abstract	The current SARS-CoV-2 pandemic is still threatening humankind. Despite first successes in vaccine development and approval, no antiviral treatment is available for COVID-19 patients. The success is further tarnished by the emergence and spreading of mutation variants of SARS-CoV-2, for which some vaccines have lower efficacy. This highlights the urgent need for antiviral therapies even more. This article describes how the genome-scale metabolic model (GEM) of the host-virus interaction of human alveolar macrophages and SARS-CoV-2 was refined by incorporating the latest information about the virus's structural proteins and the mutant variants B.1.1.7, B.1.351, B.1.28, B.1.427/B.1.429, and B.1.617. We confirmed the initially identified guanylate kinase as a potential antiviral target with this refined model and identified further potential targets from the purine and pyrimidine metabolism. The model was further extended by incorporating the virus' lipid requirements. This opened new perspectives for potential antiviral targets in the altered lipid metabolism. Especially the phosphatidylcholine biosynthesis seems to play a pivotal role in viral replication. The guanylate kinase is even a robust target in all investigated mutation variants currently spreading worldwide. These new insights can guide laboratory experiments for the validation of identified potential antiviral targets. Only the combination of vaccines and antiviral therapies will effectively defeat this ongoing pandemic.
MeSH term(s)	Antiviral Agents/pharmacology ; Antiviral Agents/therapeutic use ; COVID-19/genetics ; COVID-19/metabolism ; COVID-19/virology ; Energy Metabolism ; Gene Knockdown Techniques ; Genome, Viral ; Guanylate Kinases/metabolism ; Host-Pathogen Interactions ; Humans ; Lipid Metabolism ; Macrophages/immunology ; Macrophages/metabolism ; Macrophages/virology ; Mutation ; SARS-CoV-2/drug effects ; SARS-CoV-2/genetics ; Viral Load ; Virus Replication ; COVID-19 Drug Treatment
Chemical Substances	Antiviral Agents ; Guanylate Kinases (EC 2.7.4.8)
Language	English
Publishing date	2021-05-24
Publishing country	Switzerland
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2527218-4
ISSN	2073-4425 ; 2073-4425
ISSN (online)	2073-4425
ISSN	2073-4425
DOI	10.3390/genes12060796
Database	MEDical Literature Analysis and Retrieval System OnLINE

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