Author(s):
Viana, Romeu ; Carreiro, Tiago ; Couceiro, Diogo ; Dias, Oscar ; Rocha, I. ; Teixeira, Miguel Cacho
Date: 2023
Persistent ID: https://hdl.handle.net/1822/93044
Origin: RepositóriUM - Universidade do Minho
Project/scholarship:
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04565%2F2020/PT;
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F04565%2F2020/PT;
info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04469%2F2020/PT;
info:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FBII-BIO%2F28216%2F2017/PT;
Subject(s): C. auris; Global stoichiometric model; gene essentiality; drug target; metabolic features
Description
Candida auris is an emerging human pathogen, associated with antifungal drug resistance and hospital candidiasis outbreaks. In this work, we present iRV973, the first reconstructed Genome-scale metabolic model (GSMM) for C. auris. The model was manually curated and experimentally validated, being able to accurately predict the specific growth rate of C. auris and the utilization of several sole carbon and nitrogen sources. The model was compared to GSMMs available for other pathogenic Candida species and exploited as a platform for cross-species comparison, aiming the analysis of their metabolic features and the identification of potential new antifungal targets common to the most prevalent pathogenic Candida species. From a metabolic point of view, we were able to identify unique enzymes in C. auris in comparison with other Candida species, which may represent unique metabolic features. Additionally, 50 enzymes were identified as potential drug targets, given their essentiality in conditions mimicking human serum, common to all four different Candida models analysed. These enzymes represent interesting drug targets for antifungal therapy, including some known targets of antifungal agents used in clinical practice, but also new potential drug targets without any human homolog or drug association in Candida species.
This work was supported by “Fundação para a Ciência e a Tecnologia” (FCT) (Contract PTDC/BII-BIO/28216/2017 and AEM PhD grant to RV). Funding received from project LISBOA-01- 0145-FEDER-022231-the BioData.pt Research Infrastructure is acknowledged. This work was further financed by national funds from FCT in the scope of the project UIDB/04565/2020 and UIDP/04565/2020 of the Research Unit Institute for Bioengineering and Biosciences—iBB, project UIDB/04469/2020 for the Centre of Biological Engineering —CEB, and the project LA/P/0140/2020 of the Associate Laboratory Institute for Health and Bioeconomy — i4HB.
info:eu-repo/semantics/publishedVersion
