Biology subjects

Charlebois, D. A.

Publications and source records attributed to Charlebois, D. A..

9 recordsLinked to original sources

bioRxiv2026

Antifungal tolerance and resistance dynamics depend on infection load, drug treatment, and host innate immunity

Fungi contribute substantially to the global antimicrobial resistance crisis. Tolerance is a novel form of antifungal resistance in which fungal pathogens emerge, grow slowly, and survive antifungal drug treatment. We quantitatively model the population and evolutionary dynamics of a multidrug resistant pathogen Candidozyma auris (formerly Candida auris) infecting an invertebrate host (Galleria mellonella) with an innate immune system. We find that the establishment, dominance, and co-existence of tolerant and resistant subpopulations depend on infection load, drug-treatment, and innate host immunity. This study enhances our understanding of the population and evolutionary dynamics of pathogenic infections and provides a quantitative framework to predict antimicrobial resistance.

systems biology↗

bioRxiv2025

Diffusion-mediated quantification of dose-dependent antifungal drug tolerance

Antimicrobial resistance is a global health problem, with drug-resistant fungi posing major challenges for the treatment of immunocompromised patients. Antifungal tolerance is a recently discovered phenomenon whereby pathogenic fungi, including the multidrug-resistant pathogenic yeast Candidozyma auris (formerly Candida auris), grow slowly above minimum inhibitory drug concentrations. We combine physics-based spatiotemporal models with microbiology experiments to quantitatively investigate the emergence of tolerance to all three major classes of antifungal drugs in C. auris. Specifically, we combine Ficks second law solved using a finite difference method to simulate drug diffusion with data from experimental disk diffusion assays to determine the concentration ranges at which antifungal-tolerant colonies emerge. This biophysics study advances antimicrobial resistance research by providing a method to quantify antifungal tolerance and by demonstrating that antifungal tolerance is a strain-, drug-, and dose-dependent phenomenon in an emerging fungal pathogen.

biophysics↗

bioRxiv2025

Peering Inside the Black Box: Explainable AI to Interpret Advanced Computer Vision Fungal Pathogen Prediction

Antimicrobial resistance is a growing concern, with pathogenic fungi making a substantial contribution to untreatable life-threatening infections across the globe. Artificial intelligence is increasingly used in microbiology and antimicrobial resistance research, with promise to improve clinical diagnostics and infectious disease treatments. However, how AI models make predictions remains largely unknown, which is a major hurdle for human trust and regulatory approval. We trained convolutional neural networks (DenseNet121 and InceptionV3) and vision transformers (Swin Transformer-Tiny and Vision Transformer-Base 16) to quickly and accurately identify human fungal pathogens from microscopy images. Using explainable AI (Occlusion Sensitivity and Grad-CAM), we identified biologically relevant features (organelle, cell interior, cell wall, budding patterns/scars, and optical patterns) and irrelevant image features (background artifacts) high-performance computer vision models used to make predictions. These findings advance our understanding of how computer vision models make predictions on microbial pathogens and are anticipated to have profound implications for AI-based diagnostics.

microbiology↗

bioRxiv2024

A Novel Magnetic Field Device: Effects of Magnetic Fields on Planktonic Yeasts and Fungal Mats

Microorganisms evolved within the geomagnetic field and can be affected by magnetic field exposure. However, the mechanisms underlying many magnetic phenomena in microbes remain to be elucidated. We develop a 3D-printed magnetic field exposure device to perform experiments on microbes. This device is designed in AutoCAD, modeled in COMSOL, and validated using a Gaussmeter. Using the magnetic field exposure device, we perform static magnetic field experiments on different strains of the budding yeast Saccharomyces cerevisiae. We find that static magnetic field exposure slows the spatially-structured expansion of yeast mats that expands in two dimensions, but not yeast mats that expand in three dimensions, across the surface of semi-solid media. We also find that magnetic fields do not affect the growth of yeast cells in well-mixed liquid media. This study provides a novel device for magnetic field exposure experiments on microorganisms and advances our understanding of the effects of magnetic fields on fungi. Why it mattersMicroorganisms have evolved to function, survive, and reproduce in Earths magnetic field. However, the mechanisms underlying magnetic phenomena in microorganisms are unknown. This is especially true for fungi, which are important microorganisms for microbiological research, industrial application, and infectious disease. To elucidate mechanisms driving magnetic phenomena, we need devices to perform controlled experiments in a variety of conditions. We develop a 3D-printed magnetic field exposure device using computer-aided design, physics modeling software, and a magnetometer. Using this novel magnetic field device, we discover that magnetic fields can slow the growth of yeast on agar plates, but that magnetic fields do not affect the growth of yeast in liquid media.

biophysics↗

bioRxiv2023

Machine learning to identify clinically relevant Candida yeast species

BackgroundFungal infections, especially due to Candida species, are on the rise. Multi-drug resistant organism such as Candida auris are difficult and time consuming to identify accurately. Machine learning is increasingly being used in health care, especially in medical imaging. In this study, we evaluated the effectiveness of six convolutional neural networks (CNNs) to identify four clinically important Candida species. Materials and MethodsWet-mounted images were captured using bright field live-cell microscopy followed by separating single cells, budding cells, and cell group images which were then subjected to different machine learning algorithms (custom CNN, VGG16, ResNet50, InceptionV3, EfficientNetB0, and EfficientNetB7) to learn and predict Candida species. ResultsAmong the six algorithms tested, the InceptionV3 model performed best in predicting Candida species from microscopy images. All models performed poorly on raw images obtained directly from the microscope. The performance of all models increased when trained on single and budding cell images. The InceptionV3 model identified budding cells of C. albicans, C. auris, C. glabrata (Nakaseomyces glabrata), and C. haemulonii in 97.0%, 74.0%, 68.0%, and 66.0% cases, respectively. For single cells of C. albicans, C. auris, C. glabrata, and C. haemulonii InceptionV3 identified 97.0%, 73.0%, 69.0%, and 73.0% cases, respectively. The sensitivity and specificity of InceptionV3 were respectively 77.1% and 92.4%. ConclusionThis study provides proof of concept that microscopy images from wet mounted slides can be used to identify Candida yeast species using machine learning quickly and accurately.

microbiology↗

bioRxiv2022

Identification and Elimination of Antifungal Tolerance in Candida auris

Antimicrobial resistance is a global health crisis to which pathogenic fungi make a substantial contribution. The human fungal pathogen C. auris is of particular concern due to its rapid spread across the world and its evolution of multidrug resistance. Fluconazole failure in C. auris has been recently attributed to antifungal "tolerance". Tolerance is a phenomenon whereby a slow growing subpopulation of tolerant cells, which are genetically identical to susceptible cells, emerges during drug treatment. We use microbroth dilution and disk diffusion assays together with image analysis to investigate antifungal tolerance in C. auris to all three classes of antifungal drugs used to treat invasive candidiasis. We find that 1) C. auris is tolerant to several common fungistatic and fungicidal drugs, which in some cases can be visually detected after 24 hours, as well as after 48 hours, of antifungal drug exposure; 2) the tolerant phenotype reverts to the susceptible phenotype in C. auris; and 3) combining azole, polyene, and echinocandin antifungal drugs with the adjuvant chloroquine reduces or eliminates tolerance and resistance in patient-derived C. auris isolates. These results suggest that tolerance contributes to treatment failure in C. auris infections for a broad range of antifungal drugs and that antifungal adjuvants may improve treatment outcomes for patients infected with antifungal-tolerant or antifungal-resistant fungal pathogens.

microbiology↗

bioRxiv2021

Phenotypic Heterogeneity Facilitates Survival While Hindering the Evolution of Drug Resistance

Rising rates of resistance to antimicrobial drugs threatens the effective treatment of infections across the globe. Drug resistance has been established to emerge from non-genetic mechanisms, such as "persistence" in quiescent microbes and fluctuations in gene expression in actively replicating cells, as well as from genetic mutations. However, it is still unclear how non-genetic drug resistance affects the evolution of genetic drug resistance. We develop deterministic and stochastic population models that incorporate resource competition to quantitatively investigate the transition from non-genetic to genetic resistance during the exposure to static and cidal drugs. We find that non-genetic resistance facilitates the survival of cell populations during drug treatment, but that it hinders the development of genetic resistance due to the competition between the non-genetically and genetically resistant subpopulations. Non-genetic drug resistance in the presence of subpopulation competition is found to increase the first-appearance and fixation times of drug resistance mutations, while increasing the probability of mutation before population extinction during cidal drug treatment. Intense intraspecific competition during drug treatment leads to extinction of the susceptible and non-genetically resistant subpopulations. These findings advance our fundamental understanding of the evolution of drug resistance and may guide novel treatment strategies for patients with drug-resistant infections. SIGNIFICANCEDrug resistance is predicted to kill as many as 10 million people per year and cost over 100 trillion USD in cumulative lost production globally by 2050. To mitigate these socio-economic costs, we need to fundamentally understand the drug resistance process. We investigate the effect that different forms of resistance have on the evolution of drug resistance using mathematical modeling and computer simulations. We find that the presence of non-genetically drug-resistant cells (whose resistance is temporary and not encoded in a genetic mutation) allows the population to survive drug treatment, while competition between these subpopoulations simultaneously slows down the evolution of permanent genetic drug resistance and in some cases drives them extinct. These findings have important implications for advancing evolutionary theory and for developing effective "resistance-proof" treatments.

biophysics↗

bioRxiv2018

Backward evolution from gene network dynamics

Gene expression is controlled by regulator genes that together with effector genes form gene regulatory networks. How mutation in the genes comprising gene regulatory networks influences cell population dynamics has not been adequately investigated. In this study, we develop mathematical models to study how a mutation in a regulator gene that reaches the effector gene with a time delay affects short-term and long-term population growth. Using theory and experiment, we find a paradoxical outcome of evolution where a mutation in a regulator gene leads to an interaction between gene regulatory network and population dynamics, causing in certain cases a permanent decrease in population fitness in a constant environment.\n\nSignificance StatementThe properties of a cell are largely the products of its proteins, synthesized at rates depending on the regulation of protein coding genes. Single-cell measurements show that genetically identical cells can differ radically in their protein levels, partially due to the random production and degradation of proteins. It is currently unknown how mutants arise and spread in populations affected by such biological variability. We use computer simulations and evolution experiments to study how a mutant spreads in a population that carries a synthetic drug resistance gene network. Our results show for the first time a paradoxical outcome of evolution, where an initially beneficial mutation can interact with gene regulatory network dynamics and cause a permanent decrease in population fitness in the same environment.

evolutionary biology↗

bioRxiv2017

Functional effects of heating and cooling gene networks

Everyday existence and survival of most organisms requires coping with temperature changes, which involves gene regulatory networks both as subjects and agents of cellular protection. Yet, how temperature affects gene network function remains unclear, partly because natural gene networks are complex and incompletely characterized. Here, we study how heating and cooling affect the function of single genes and well-characterized synthetic gene circuits in Saccharomyces cerevisiae. We found nontrivial, nonmonotone temperature-dependent gene expression trends at non-growth-optimal temperatures. In addition, heating caused unusual bimodality in the negative-feedback gene circuit expression and shifts upward the bimodal regime for the positive feedback gene circuit. Mathematical models incorporating temperature-dependent growth rates and Arrhenius scaling of reaction rates captured the effects of cooling, but not those of heating. Molecular dynamics simulations revealed that heating alters the conformational dynamics and allows DNA-binding of the TetR transcriptional repressor, fully explaining the experimental results for the negative-feedback gene circuit. Overall, we uncover how temperature shifts may corrupt gene networks, which may aid future designs of temperature-robust synthetic gene circuits.

synthetic biology↗