Pharma Focus America

New Workflow Predicts Drug Targets Against Sars-Cov-2 via Metabolic Changes in Infected Cells

Nantia Leonidou , Alina Renz, Reihaneh Mostolizadeh, Andreas Dräger

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.

Introduction

In a study published in October, 2007,, scientists studying coronaviruses characterized the situation in China as a ticking “time bomb” for a potential virus outbreak [1]. They had three strong indications to worry: the animal-related eating habits in southern China, the previous appearance of Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV)-like viruses in horseshoe bats, and the ability of coronaviruses to undergo recombination. Since the first major pandemic of the new millennium in 2002, over 4,000 publications on coronaviruses became available, giving insights and leading to the discovery of 36 SARS-related coronaviruses in humans and animals. Eighteen years later, the whole world experiences the realization of this prophecy with the emergence of the Coronavirus Disease 2019 (COVID-19) to be one of the deadliest respiratory disease pandemics since the “Spanish” influenza in 1918 [2]. Scientists globally try to understand the host’s immunopathological response, how the novel virus Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) adapts, and how it spreads.

Materials and Methods

More specifically, firstly, the production of precursor metabolites is checked. If this test fails, there is no need to check for model consistency with Flux Variability Analysis (FVA) or FASTCC (time-saving step). If the test leads to successful results, the set of inactive cores and non-cores is determined, and the algorithm moves on with the removal of reactions. Reactions with zero expression will be removed with their corresponding inactive core reactions if sufficiently more non-cores are pruned. On the other hand, if the reaction has expression evidence, pymCADRE only attempts to remove inactive non-cores

Results

Merimepodib has also been examined in the context of SARS-CoV-2 and has demonstrated in vitro suppression of viral inhibition [74]. Our methods reported the IMPD as a promising hit for therapies against all SARS-CoV-2 variants with 49.9% virus reduction. Together with merimepodib, DrugBank and BRENDA list ribavirin as an inhibitor with known pharmacological action. Several studies have postulated that ribavirin’s mechanism of action lies on various not mutually exclusive pathways [75]. Lastly, with our methods, we identified the purine-nucleoside phosphorylase (PUNP4) for which ganciclovir has known inhibitory effects (Table 3)

Discussion

Studying human metabolism guides the understanding of diverse diseases by determining the cells’ health. The existence of high-quality genome-scale reconstructions facilitates systems-based insights into metabolism. As complex organisms, humans embody multiple cell and tissue types, each with different functions and metabolisms, leading to the essential use of cell- or tissue-specific metabolic networks to enable the accurate prediction of the cells’ metabolic behavior. Here, we presented pymCADRE, a re-implementation of mCADRE [31] in Python that allows the reconstruction of tissue-specific human models based on human gene expression data and network topology information. Similar to the original mCADRE algorithm, pymCADRE consists of three parts: (1) ranking, (2) consistency check, and (3) pruning, enabling the user to choose between two optimization methods, FVA and FASTCC, to check for model consistency (S3 Fig).

Acknowledgments

The authors acknowledge the use of de.NBI cloud and the support by the High Performance and Cloud Computing Group at the Zentrum für Datenverarbeitung (ZDV) of the University of Tübingen and the Federal Ministry of Education and Research (BMBF) through grant № 031 A535A.

Citation: Leonidou N, Renz A, Mostolizadeh R, Dräger A (2023) New workflow predicts drug targets against SARS-CoV-2 via metabolic changes in infected cells. PLoS Comput Biol 19(3): e1010903. https://doi.org/10.1371/journal.pcbi.1010903

Editor: Pedro Mendes, University of Connecticut School of Medicine, UNITED STATES

Received: July 27, 2022; Accepted: January 30, 2023; Published: March 23, 2023

Copyright: © 2023 Leonidou et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The computational host-virus model Recon1-HBEC, as well as the source code of pymCADRE and PREDICATE, test scripts, and test dataset are available in a git repository at https://github.com/draeger-lab/pymCADRE/. Supplementary tables in Microsoft Excel format are available along with this article. Access the model at https://www.ebi.ac.uk/biomodels/MODEL2202240001.

Funding: This work was funded by Federal Ministry of Education and Research (BMBF) and the Baden-Württemberg Ministry of Science as part of the Excellence Strategy of the German Federal and State Governments within the project “identification of robust antiviral drug targets against SARS-CoV-2” as well as by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC 2124 – 390838134 and supported by the Cluster of Excellence ‘Controlling Microbes to Fight Infections’ (CMFI). R.M. and A.D are supported by the German Center for Infection Research (DZIF, doi: 10.13039/100009139) within the Deutsche Zentren der Gesundheitsforschung (BMBF-DZG, German Centers for Health Research of the BMBF), grant No 8020708703. The authors acknowledge the support by the Open Access Publishing Fund of the University of Tübingen (https://uni-tuebingen.de/en/216529). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors declare no conflict of interest.

ROUQETTE - Pharma Virtual LabSino Biological || Baculovirus - Insect Cell Expression Platform