Biology subjects

Enserink, J.

Publications and source records attributed to Enserink, J..

4 recordsLinked to original sources

bioRxiv2023

Characterization of a selective, iron-chelating antifungal compound that disrupts fungal metabolism and synergizes with fluconazole

Fungal infections are a growing global health concern due to the limited number of available antifungal therapies as well as the emergence of fungi that are resistant to first-line antimicrobials, particularly azoles and echinocandins. Development of novel, selective antifungal therapies is challenging due to similarities between fungal and mammalian cells. An attractive source of potential antifungal treatments is provided by ecological niches co-inhabited by bacteria, fungi and multicellular organisms, where complex relationships between multiple organisms has resulted in evolvement of a wide variety of selective antimicrobials. Here, we characterized several analogs of the one such natural compound, Collismycin A. We show that NR-6226C has antifungal activity against several pathogenic Candida species, including C. albicans and C. glabrata, whereas it only has little toxicity against mammalian cells. Mechanistically, NR-6226C selectively chelates iron, which is a limiting factor for pathogenic fungi during infection. As a result, NR-6226C treatment causes severe mitochondrial dysfunction, leading to formation of reactive oxygen species, metabolic reprogramming and a severe reduction in ATP levels. Using an in vivo model for fungal infections, we show that NR-6226C significantly increases survival of Candida-infected Galleria mellonella larvae. Finally, our data indicate that NR-6226C synergizes strongly with fluconazole in inhibition of C. albicans. Taken together, NR-6226C is a promising antifungal compound that acts by chelating iron and disrupting mitochondrial functions. Importance statementDrug-resistant fungal infections are an emerging global threat, and pan-resistance to current antifungal therapies is an increasing problem. Clearly, there is a need for new antifungal drugs. In this study, we characterized a novel antifungal agent, the Collismycin analog NR-6226C. NR-6226C has a favorable toxicity profile for human cells, which is essential for further clinical development. We unraveled the mechanism of action of NR-6226C and found that it disrupts iron homeostasis and thereby depletes fungal cells of energy. Importantly, NR-6226C strongly potentiates the antifungal activity of fluconazole, thereby providing inroads for combination therapy that may reduce or prevent azole resistance. Thus, NR-6226C is a promising compound for further development into antifungal treatment.

microbiology↗

bioRxiv2022

Risk Stratification of Acute Myeloid Leukemia Using Ex Vivo Drug Sensitivity Profiling

Acute Myeloid Leukemia (AML) is a heterogeneous malignancy involving the clonal expansion of myeloid stem and progenitor cells in the bone marrow and peripheral blood. Most AML patients eligible for potentially curative treatment receive intensive chemotherapy. Risk stratification is used to optimize treatment intensity and transplant strategy, and is mainly based on cytogenetic screening for structural chromosomal alterations and targeted sequencing of a selection of common mutations. However, the forecasting accuracy of treatment response remains modest. Recently, ex vivo drug screening has gained traction for its potential in personalized treatment selection, as well as a tool for identifying and mapping patient groups based on relevant cancer dependencies. We systematically evaluated the use of drug sensitivity profiling for predicting patient survival and clinical response to chemotherapy in a cohort of AML patients. We compared computational methodologies for scoring drug efficacy and characterized tools to counter noise and batch-related confounders pervasive in high-throughput drug testing. We show that ex vivo drug sensitivity profiling is a robust and versatile approach to patient prognostics that comprehensively maps functional signatures of treatment response and disease progression. In conclusion, ex vivo drug profiling can accurately assess risk of individual AML patients and may guide clinical decision-making.

bioinformatics↗

bioRxiv2021

Kel1 is a phosphorylation-regulated noise suppressor of the pheromone signaling pathway

Mechanisms have evolved that allow cells to detect signals and generate an appropriate response. The accuracy of these responses relies on the ability of cells to discriminate between signal and noise. How cells filter noise in signaling pathways is not well understood. Here, we analyze noise suppression in the yeast pheromone signaling pathway and show that the poorly characterized protein Kel1 serves as a major noise suppressor and prevents cell death. At the molecular level, Kel1 prevents spontaneous activation of the pheromone response by inhibiting membrane recruitment of Ste5 and Far1. Only a hypophosphorylated form of Kel1 suppresses signaling, reduces noise and prevents pheromone-associated cell death, and our data indicate that the MAPK Fus3 contributes to Kel1 phosphorylation. Taken together, Kel1 serves as a phospho-regulated suppressor of the pheromone pathway to reduce noise, inhibit spontaneous activation of the pathway, regulate mating efficiency, and to prevent pheromone-associated cell death.

cell biology↗

bioRxiv2021

bayesynergy: flexible Bayesian modelling of synergistic interaction effects in in-vitro drug combination experiments

The effect of cancer therapies is often tested pre-clinically via in-vitro experiments, where the post-treatment viability of the cancer cell population is measured through assays estimating the number of viable cells. In this way, large libraries of compounds can be tested, comparing the efficacy of each treatment. Drug interaction studies focus on the quantification of the additional effect encountered when two drugs are combined, as opposed to using the treatments separately. In the bayesynergy R package, we implement a probabilistic approach for the description of the drug combination experiment, where the observed dose response curve is modelled as a sum of the expected response under a zero-interaction model and an additional interaction effect (synergistic or antagonistic). The interaction is modelled in a flexible manner, using a Gaussian process formulation. Since the proposed approach is based on a statistical model, it allows the natural inclusion of replicates, handles missing data and uneven concentration grids, and provides uncertainty quantification around the results. The model is implemented in the Stan programming language providing a computationally efficient sampler, a fast approximation of the posterior through variational inference, and features parallel processing for working with large drug combination screens.

bioinformatics↗