Kirbizakis, E. | bioRxiv Science

bioRxiv2023

The Adr1 transcription factor directs regulation of the ergosterol pathway and azole resistance in C. albicans

Transcription factors play key roles in cellular regulation and are critical in the control of drug resistance in the fungal pathogen Candida albicans. We found that activation of the transcription factor C4_02500C_A (Adr1) conferred significant resistance against fluconazole. In Saccharomyces cerevisiae, Adr1 is a carbon-source-responsive zinc-finger transcription factor required for transcription of the glucose-repressed gene ADH1 and of genes required for ethanol, glycerol, and fatty acid utilization. Motif scanning of promoter elements suggests that Adr1 may be rewired in fungi and governs the ergosterol synthesis pathway in C. albicans. Because previous studies have identified the zinc-cluster transcription factor Upc2 as a regulator of the ergosterol pathway in both fungi, we examined the relationship of Adr1 and Upc2 in sterol biosynthesis in C. albicans. Phenotypic profiles of either ADR1 and UPC2 modulation in C. albicans showed differential growth in the presence of fluconazole; either adr1 or upc2 homozygous deletion results in sensitivity to the drug while their activation generates a fluconazole resistant strain. The rewiring from ergosterol synthesis to fatty acid metabolism involved all members of the Adr1 regulon except the alcohol dehydrogenase Adh1, which remains under Adr1 control in both circuits and may have been driven by the lifestyle of S. cerevisiae, which requires the ability to both tolerate and process high concentrations of ethanol.

genetics↗

bioRxiv2021

A deep learning approach to capture the essence of Candida albicans morphologies

We present deep learning-based approaches for exploring the complex array of morphologies exhibited by the opportunistic human pathogen C. albicans. Our system entitled Candescence automatically detects C. albicans cells from Differential Image Contrast microscopy, and labels each detected cell with one of nine vegetative, mating-competent or filamentous morphologies. The software is based upon a fully convolutional one-stage object detector and exploits a novel cumulative curriculum-based learning strategy that stratifies our images by difficulty from simple vegetative forms to more complex filamentous architectures. Candescence achieves very good performance on this difficult learning set which has substantial intermixing between the predicted classes. To capture the essence of each C. albicans morphology, we develop models using generative adversarial networks and identify subcomponents of the latent space which control technical variables, developmental trajectories or morphological switches. We envision Candescence as a community meeting point for quantitative explorations of C. albicans morphology.

microbiology↗