Sources of Antifungal Drugs

Vanreppelen, Giel; Wuyts, Jurgen; Dijck, Patrick Van; Vandecruys, Paul

doi:10.3390/jof9020171

Cited by 28 publications

(29 citation statements)

References 187 publications

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“…A quantitative study of the microbial life cycle is crucial to understand ecological and evolutionary processes that can be used to control parasites and pathogens [1][2][3][4] . However, defining the structure of a microbial life cycle traditionally relies on partial microscopy observations or genetic and theoretical evidence 5 .…”

Section: Introductionmentioning

confidence: 99%

Deep learning-driven imaging of cell division and cell growth across an entire eukaryotic life cycle

Ramakanth,

Kennedy,

Yalcinkaya

et al. 2024

Preprint

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The life cycle of biomedical and agriculturally relevant eukaryotic microorganisms involves complex transitions between proliferative and non-proliferative states such as dormancy, mating, meiosis, and cell division. New drugs, pesticides, and vaccines can be created by targeting specific life cycle stages of parasites and pathogens. However, defining the structure of a microbial life cycle often relies on partial observations that are theoretically assembled in an ideal life cycle path. To create a more quantitative approach to studying complete eukaryotic life cycles, we generated a deep learning-driven imaging framework to track microorganisms across sexually reproducing generations. Our approach combines microfluidic culturing, life cycle stage-specific segmentation of microscopy images using convolutional neural networks, and a novel cell tracking algorithm, FIEST, based on enhancing the overlap of single cell masks in consecutive images through deep learning video frame interpolation. As proof of principle, we used this approach to quantitatively image and compare cell growth and cell cycle regulation across the sexual life cycle of Saccharomyces cerevisiae. We developed a fluorescent reporter system based on a fluorescently labeled Whi5 protein, the yeast analog of mammalian Rb, and a new High-Cdk1 activity sensor, LiCHI, designed to report during DNA replication, mitosis, meiotic homologous recombination, meiosis I, and meiosis II. We found that cell growth preceded the exit from non-proliferative states such as mitotic G1, pre-meiotic G1, and the G0 spore state during germination. A decrease in the total cell concentration of Whi5 characterized the exit from non-proliferative states, which is consistent with a Whi5 dilution model. The nuclear accumulation of Whi5 was developmentally regulated, being at its highest during meiotic exit and spore formation. The temporal coordination of cell division and growth was not significantly different across three sexually reproducing generations. Our framework could be used to quantitatively characterize other single-cell eukaryotic life cycles that remain incompletely described. An off-the-shelf user interface Yeastvision provides free access to our image processing and single-cell tracking algorithms.

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Section: Introductionmentioning

confidence: 99%

Deep learning-driven imaging of cell division and cell growth across an entire eukaryotic life cycle

Ramakanth,

Kennedy,

Yalcinkaya

et al. 2024

Preprint

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“…In this regard, the main interest in the search for new antifungal solutions is shifting towards various natural, biologically active compounds. At this stage, plants are considered primarily as sources for the isolation of such substances [ 6 , 7 ]; however, their action is also not selective and, if used, can affect (inhibit) different metabolic and biochemical processes not only in fungal cells.…”

Section: Introductionmentioning

confidence: 99%

Enzymes with Lactonase Activity against Fungal Quorum Molecules as Effective Antifungals

Efremenko,

Aslanli,

Domnin

et al. 2024

Biomolecules

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Since the growing number of fungi resistant to the fungicides used is becoming a serious threat to human health, animals, and crops, there is a need to find other effective approaches in the eco-friendly suppression of fungal growth. One of the main mechanisms of the development of resistance in fungi, as well as in bacteria, to antimicrobial agents is quorum sensing (QS), in which various lactone-containing compounds participate as signaling molecules. This work aimed to study the effectiveness of action of enzymes exhibiting lactonase activity against fungal signaling molecules. For this, the molecular docking method was used to estimate the interactions between these enzymes and different lactone-containing QS molecules of fungi. The catalytic characteristics of enzymes such as lactonase AiiA, metallo-β-lactamase NDM-1, and organophosphate hydrolase His6-OPH, selected for wet experiments based on the results of computational modeling, were investigated. QS lactone-containing molecules (butyrolactone I and γ-heptalactone) were involved in the experiments as substrates. Further, the antifungal activity of the enzymes was evaluated against various fungal and yeast cells using bioluminescent ATP-metry. The efficient hydrolysis of γ-heptalactone by all three enzymes and butyrolactone I by His6-OPH was demonstrated for the first time. The high antifungal efficacy of action of AiiA and NDM-1 against most of the tested fungal cells was revealed.

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“…Among them, Candida auris , several Candida species and Aspergillus fumigatus , all drug-resistant fungal pathogens, feature on the CDC’s list of antimicrobial resistance threats [ 6 ]. Among other factors, the biological similarities between fungal and host cells make it difficult to design efficient antifungals with low toxicity [ 7 ]. Consequently, there are only five main classes of antifungals [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Cross-feeding affects the target of resistance evolution to an antifungal drug

Durand,

Jalbert-Ross,

Fijarczyk

et al. 2023

PLoS Genet

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Pathogenic fungi are a cause of growing concern. Developing an efficient and safe antifungal is challenging because of the similar biological properties of fungal and host cells. Consequently, there is an urgent need to better understand the mechanisms underlying antifungal resistance to prolong the efficacy of current molecules. A major step in this direction would be to be able to predict or even prevent the acquisition of resistance. We leverage the power of experimental evolution to quantify the diversity of paths to resistance to the antifungal 5-fluorocytosine (5-FC), commercially known as flucytosine. We generated hundreds of independent 5-FC resistant mutants derived from two genetic backgrounds from wild isolates of Saccharomyces cerevisiae. Through automated pin-spotting, whole-genome and amplicon sequencing, we identified the most likely causes of resistance for most strains. Approximately a third of all resistant mutants evolved resistance through a pleiotropic drug response, a potentially novel mechanism in response to 5-FC, marked by cross-resistance to fluconazole. These cross-resistant mutants are characterized by a loss of respiration and a strong tradeoff in drug-free media. For the majority of the remaining two thirds, resistance was acquired through loss-of-function mutations in FUR1, which encodes an important enzyme in the metabolism of 5-FC. We describe conditions in which mutations affecting this particular step of the metabolic pathway are favored over known resistance mutations affecting a step upstream, such as the well-known target cytosine deaminase encoded by FCY1. This observation suggests that ecological interactions may dictate the identity of resistance hotspots.

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Sources of Antifungal Drugs

Cited by 28 publications

References 187 publications

Deep learning-driven imaging of cell division and cell growth across an entire eukaryotic life cycle

Deep learning-driven imaging of cell division and cell growth across an entire eukaryotic life cycle

Enzymes with Lactonase Activity against Fungal Quorum Molecules as Effective Antifungals

Cross-feeding affects the target of resistance evolution to an antifungal drug

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