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  1. 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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  2. 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
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. 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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