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Screening of potential genes contributing to the macrocycle drug resistance of C. albicans via microarray analysis

The aim of the present study was to investigate the potential genes involved in drug resistance of Candida albicans (C. albicans) by performing microarray analysis. The gene expression profile of GSE65396 was downloaded from the Gene Expression Omnibus, including a control, 15-min and 45-min macrocy...

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Autores principales: Yang, Jing, Zhang, Wei, Sun, Jian, Xi, Zhiqin, Qiao, Zusha, Zhang, Jinyu, Wang, Yan, Ji, Ying, Feng, Wenli
Formato: Online Artículo Texto
Lenguaje:English
Publicado: D.A. Spandidos 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5865886/
https://www.ncbi.nlm.nih.gov/pubmed/28944888
http://dx.doi.org/10.3892/mmr.2017.7562
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author Yang, Jing
Zhang, Wei
Sun, Jian
Xi, Zhiqin
Qiao, Zusha
Zhang, Jinyu
Wang, Yan
Ji, Ying
Feng, Wenli
author_facet Yang, Jing
Zhang, Wei
Sun, Jian
Xi, Zhiqin
Qiao, Zusha
Zhang, Jinyu
Wang, Yan
Ji, Ying
Feng, Wenli
author_sort Yang, Jing
collection PubMed
description The aim of the present study was to investigate the potential genes involved in drug resistance of Candida albicans (C. albicans) by performing microarray analysis. The gene expression profile of GSE65396 was downloaded from the Gene Expression Omnibus, including a control, 15-min and 45-min macrocyclic compound RF59-treated group with three repeats for each. Following preprocessing using RAM, the differentially expressed genes (DEGs) were screened using the Limma package. Subsequently, the Kyoto Encyclopedia of Genes and Genomes pathways of these genes were analyzed using the Database for Annotation, Visualization and Integrated Discovery. Based on interactions estimated by the Search Tool for Retrieval of Interacting Gene, the protein-protein interaction (PPI) network was visualized using Cytoscape. Subnetwork analysis was performed using ReactomeFI. A total of 154 upregulated and 27 downregulated DEGs were identified in the 15-min treated group, compared with the control, and 235 upregulated and 233 downregulated DEGs were identified in the 45-min treated group, compared with the control. The upregulated DEGs were significantly enriched in the ribosome pathway. Based on the PPI network, PRP5, RCL1, NOP13, NOP4 and MRT4 were the top five nodes in the 15-min treated comparison. GIS2, URA3, NOP58, ELP3 and PLP7 were the top five nodes in the 45-min treated comparison, and its subnetwork was significantly enriched in the ribosome pathway. The macrocyclic compound RF59 had a notable effect on the ribosome and its associated pathways of C. albicans. RCL1, NOP4, MRT4, GIS2 and NOP58 may be important in RF59-resistance.
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spelling pubmed-58658862018-03-27 Screening of potential genes contributing to the macrocycle drug resistance of C. albicans via microarray analysis Yang, Jing Zhang, Wei Sun, Jian Xi, Zhiqin Qiao, Zusha Zhang, Jinyu Wang, Yan Ji, Ying Feng, Wenli Mol Med Rep Articles The aim of the present study was to investigate the potential genes involved in drug resistance of Candida albicans (C. albicans) by performing microarray analysis. The gene expression profile of GSE65396 was downloaded from the Gene Expression Omnibus, including a control, 15-min and 45-min macrocyclic compound RF59-treated group with three repeats for each. Following preprocessing using RAM, the differentially expressed genes (DEGs) were screened using the Limma package. Subsequently, the Kyoto Encyclopedia of Genes and Genomes pathways of these genes were analyzed using the Database for Annotation, Visualization and Integrated Discovery. Based on interactions estimated by the Search Tool for Retrieval of Interacting Gene, the protein-protein interaction (PPI) network was visualized using Cytoscape. Subnetwork analysis was performed using ReactomeFI. A total of 154 upregulated and 27 downregulated DEGs were identified in the 15-min treated group, compared with the control, and 235 upregulated and 233 downregulated DEGs were identified in the 45-min treated group, compared with the control. The upregulated DEGs were significantly enriched in the ribosome pathway. Based on the PPI network, PRP5, RCL1, NOP13, NOP4 and MRT4 were the top five nodes in the 15-min treated comparison. GIS2, URA3, NOP58, ELP3 and PLP7 were the top five nodes in the 45-min treated comparison, and its subnetwork was significantly enriched in the ribosome pathway. The macrocyclic compound RF59 had a notable effect on the ribosome and its associated pathways of C. albicans. RCL1, NOP4, MRT4, GIS2 and NOP58 may be important in RF59-resistance. D.A. Spandidos 2017-11 2017-09-20 /pmc/articles/PMC5865886/ /pubmed/28944888 http://dx.doi.org/10.3892/mmr.2017.7562 Text en Copyright: © Yang et al. This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.
spellingShingle Articles
Yang, Jing
Zhang, Wei
Sun, Jian
Xi, Zhiqin
Qiao, Zusha
Zhang, Jinyu
Wang, Yan
Ji, Ying
Feng, Wenli
Screening of potential genes contributing to the macrocycle drug resistance of C. albicans via microarray analysis
title Screening of potential genes contributing to the macrocycle drug resistance of C. albicans via microarray analysis
title_full Screening of potential genes contributing to the macrocycle drug resistance of C. albicans via microarray analysis
title_fullStr Screening of potential genes contributing to the macrocycle drug resistance of C. albicans via microarray analysis
title_full_unstemmed Screening of potential genes contributing to the macrocycle drug resistance of C. albicans via microarray analysis
title_short Screening of potential genes contributing to the macrocycle drug resistance of C. albicans via microarray analysis
title_sort screening of potential genes contributing to the macrocycle drug resistance of c. albicans via microarray analysis
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5865886/
https://www.ncbi.nlm.nih.gov/pubmed/28944888
http://dx.doi.org/10.3892/mmr.2017.7562
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