Coste, A. T. | bioRxiv Science

bioRxiv2023

Upc2-mediated mechanisms of azole resistance in Candida auris

Candida auris is an emerging yeast pathogen of major concern because of its ability to cause hospital outbreaks of invasive candidiasis and to develop resistance to antifungal drugs. A majority of C. auris isolates are resistant to fluconazole, a first-line treatment of invasive candidiasis. Mechanisms of azole resistance are multiple, including mutations in the target gene ERG11 and activation of the transcription factors Tac1b and Mrr1, which control the drug transporters Cdr1 and Mdr1, respectively. In this study, we investigated the role the transcription factor Upc2, which is known to regulate the ergosterol biosynthesis pathway and azole resistance in other Candida spp. Genetic deletion and hyperactivation of Upc2 by epitope tagging in C. auris resulted in drastic increased and decreased susceptibility to azoles, respectively. This effect was conserved in strains with genetic hyperactivation of Tac1b or Mrr1. Reverse transcription PCR analyses showed that Upc2 regulates ERG11 expression and also activates the Mrr1/Mdr1 pathway. We showed that upregulation of MDR1 by Upc2 could occur independently from Mrr1. The impact of UPC2 deletion on MDR1 expression and azole susceptibility in a hyperactive Mrr1 background was stronger than that of MRR1 deletion in a hyperactive Upc2 background. While Upc2 hyperactivation resulted in a significant increase of expression of TAC1b, CDR1 expression remained unchanged. Taken together, our results showed that Upc2 is crucial for azole resistance in C. auris, via regulation of the ergosterol biosynthesis pathway and activation of the Mrr1/Mdr1 pathway. Notably, Upc2 is a very potent and direct activator of Mdr1.

microbiology↗

bioRxiv2017

Gene set enrichment analysis using RNA-seq-based condition-specific metabolic networks

MotivationGenome-scale metabolic networks and transcriptomic data represent complementary sources of knowledge about an organisms metabolism, yet their integration to achieve biological insight remains challenging.\n\nResultsWe investigate here condition-specific series of metabolic sub-networks constructed by successively removing genes from a comprehensive network. The optimal order of gene removal is deduced from transcriptomic data. The sub-networks are evaluated via a fitness function, which estimates their degree of alteration. We then consider how a gene set, i.e. a group of genes contributing to a common biological function, is depleted in different series of sub-networks to detect the difference between experimental conditions. The method, named metaboGSE, is validated on public data for Yarrowia lipolytica and mouse. It is shown to produce GO terms of higher specificity compared to popular gene set enrichment methods like GSEA or topGO.\n\nAvailabilityThe metaboGSE R package is available at https://cran.r-project.org/web/packages/metaboGSE.

systems biology↗