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Fungi expand in space and time to form complex multicellular communities. The mechanisms by which they do so can vary dramatically and determine the life-history and dispersal traits of expanding populations. These traits influence deterministic and stochastic components of evolution, resulting in complex eco-evolutionary dynamics during colony expansion. We perform experiments on budding yeast strains genetically engineered to display rough-surface and smooth-surface phenotypes in colony-like structures called 'mats'. Previously, it was shown that the rough-surface strain has a competitive advantage over the smooth-surface strain when grown on semi-solid media. We experimentally observe the emergence and expansion of segments with a distinct smooth-surface phenotype during rough-surface mat development. We propose a trade-off between dispersal and local carrying capacity to explain the relative fitness of these two phenotypes. Using a modified stepping-stone model, we demonstrate that this trade-off gives the high-dispersing, rough-surface phenotype a competitive advantage from standing variation, but that it inhibits this phenotype's ability to invade a resident smooth-surface population via mutation. However, the trade-off improves the ability of the smooth-surface phenotype to invade in rough-surface mats, replicating the frequent emergence of smooth-surface segments in experiments. Together, these computational and experimental findings advance our understanding of the complex eco-evolutionary dynamics of fungal mat expansion.
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Evolução Biológica , Mutação , DemografiaRESUMO
Fungal infections, especially due to Candida species, are on the rise. Multi-drug resistant organisms 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. Wet-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. Among 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 77.1% and 92.4%, respectively. Overall, this study provides proof of the concept that microscopy images from wet-mounted slides can be used to identify Candida yeast species using machine learning quickly and accurately.
Fungal infections due to Candida yeasts are increasing worldwide. Existing methods to identify these pathogens are difficult and time consuming. We find that machine learning can identify Candida species from images quickly and accurately, improving the diagnosis of infectious fungal diseases.
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Antifúngicos , Candida , Animais , Filogenia , Antifúngicos/uso terapêutico , Candida glabrata , Candida auris , Aprendizado de MáquinaRESUMO
Non-genetic forms of antimicrobial (drug) resistance can result from cell-to-cell variability that is not encoded in the genetic material. Data from recent studies also suggest that non-genetic mechanisms can facilitate the development of genetic drug resistance. We speculate on how the interplay between non-genetic and genetic mechanisms may affect microbial adaptation and evolution during drug treatment. We argue that cellular heterogeneity arising from fluctuations in gene expression, epigenetic modifications, as well as genetic changes contribute to drug resistance at different timescales, and that the interplay between these mechanisms enhance pathogen resistance. Accordingly, developing a better understanding of the role of non-genetic mechanisms in drug resistance and how they interact with genetic mechanisms will enhance our ability to combat antimicrobial resistance. Also see the video abstract here: https://youtu.be/aefGpdh-bgU.
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Epigênese Genética , Heterogeneidade Genética , Resistência a Medicamentos , Epigênese Genética/genéticaRESUMO
Rising rates of resistance to antimicrobial drugs threaten the effective treatment of infections across the globe. Drug resistance has been established to emerge from non-genetic mechanisms as well as from genetic mechanisms. However, it is still unclear how non-genetic 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 while hindering the development of genetic resistance due to competition between the non-genetically and genetically resistant subpopulations. Non-genetic resistance in the presence of subpopulation competition increases the 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 susceptible and non-genetically resistant subpopulations. Alternating between drug and no drug conditions results in oscillatory population dynamics, increased resistance mutation fixation timescales, and reduced population survival. These findings advance our fundamental understanding of the evolution of resistance and may guide novel treatment strategies for patients with drug-resistant infections.
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Dinâmica Populacional , Resistência a Medicamentos/genética , Humanos , Mutação , ProbabilidadeRESUMO
Yeasts exist in communities that expand over space and time to form complex structures and patterns. We developed a lattice-based framework to perform spatial-temporal Monte Carlo simulations of budding yeast colonies exposed to different nutrient and magnetic field conditions. The budding patterns of haploid and diploid yeast cells were incorporated into the framework, as well as the filamentous growth that occurs in yeast colonies under nutrient limiting conditions. Simulation of the framework predicted that magnetic fields decrease colony growth rate, solidity, and roundness. Magnetic field simulations further predicted that colony elongation and boundary fluctuations increase in a nutrient- and ploidy-dependent manner. These in-silico predictions are an important step towards understanding the effects of the physico-chemical environment on microbial colonies and for informing bioelectromagnetic experiments on yeast colony biofilms and fungal pathogens.
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Most organisms must cope with temperature changes. This involves genes and gene networks both as subjects and agents of cellular protection, creating difficulties in understanding. Here, we study how heating and cooling affect expression of single genes and synthetic gene circuits in Saccharomyces cerevisiae We discovered that nonoptimal temperatures induce a cell fate choice between stress resistance and growth arrest. This creates dramatic gene expression bimodality in isogenic cell populations, as arrest abolishes gene expression. Multiscale models incorporating population dynamics, temperature-dependent growth rates, and Arrhenius scaling of reaction rates captured the effects of cooling, but not those of heating in resistant cells. Molecular-dynamics simulations revealed how heating alters the conformational dynamics of the TetR repressor, fully explaining the experimental observations. Overall, nonoptimal temperatures induce a cell fate decision and corrupt gene and gene network function in computationally predictable ways, which may aid future applications of engineered microbes in nonstandard temperatures.
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Adaptação Fisiológica/genética , Pontos de Checagem do Ciclo Celular/genética , Proteínas Fúngicas/genética , Regulação Fúngica da Expressão Gênica , Redes Reguladoras de Genes , Saccharomyces cerevisiae/genética , Temperatura Baixa , Proteínas Fúngicas/metabolismo , Genes Reporter , Proteínas de Fluorescência Verde/genética , Proteínas de Fluorescência Verde/metabolismo , Temperatura Alta , Proteínas Repressoras/genética , Proteínas Repressoras/metabolismo , Saccharomyces cerevisiae/metabolismo , Estresse Fisiológico , TermodinâmicaRESUMO
Quantitative modeling is quickly becoming an integral part of biology, due to the ability of mathematical models and computer simulations to generate insights and predict the behavior of living systems. Single-cell models can be incapable or misleading for inferring population dynamics, as they do not consider the interactions between cells via metabolites or physical contact, nor do they consider competition for limited resources such as nutrients or space. Here we examine methods that are commonly used to model and simulate cell populations. First, we cover simple models where analytic solutions are available, and then move on to more complex scenarios where computational methods are required. Overall, we present a summary of mathematical models used to describe cell population dynamics, which may aid future model development and highlights the importance of population modeling in biology.
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Fenômenos Fisiológicos Celulares , Modelos Biológicos , Algoritmos , Diferenciação Celular , Divisão Celular , Proliferação de Células , Cadeias de Markov , Método de Monte CarloRESUMO
Magnetic fields have been shown to affect sensing, migration, and navigation in living organisms. However, the effects of magnetic fields on microorganisms largely remain to be elucidated. We develop an open-source, 3D-printed magnetic field exposure device to perform experiments on well-mixed and spatially structured microbial populations. This device is designed in AutoCAD, modeled in COMSOL, and validated using a Gaussmeter and experiments on the budding yeast Saccharomyces cerevisiae. We find that static magnetic field exposure slows the spatially structured expansion of yeast mats that expand in two dimensions, but not yeast mats that expand in three dimensions, across the surface of semi-solid yeast extract-peptone-dextrose agar media. We also find that magnetic fields do not affect the growth of planktonic yeast cells in well-mixed liquid yeast extract-peptone-dextrose media. This study provides an adaptable device for performing controlled magnetic field experiments on microbes and advances our understanding of the effects of magnetic fields on fungi.
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Predicting evolution is a fundamental problem in biology with practical implications for treating antimicrobial resistance, which is a complex system-level phenomenon. In this perspective article, we explore the limits of predicting antimicrobial resistance evolution, quantitatively define the predictability and repeatability of microevolutionary processes, and speculate on how these quantities vary across temporal, biological, and complexity scales. The opportunities and challenges for predicting antimicrobial resistance in the context of systems biology are also discussed. Based on recent research, we conclude that the evolution of antimicrobial resistance can be predicted using a systems biology approach integrating quantitative models with multiscale data from microbial evolution experiments.
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Antibacterianos , Farmacorresistência Bacteriana , Antibacterianos/farmacologia , Farmacorresistência Bacteriana/genéticaRESUMO
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 detected after 24 h, as well as after 48 h, 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 in some cases 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.
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We show that the effect of stress on the reproductive fitness of noisy cell populations can be modeled as a first-passage time problem, and demonstrate that even relatively short-lived fluctuations in gene expression can ensure the long-term survival of a drug-resistant population. We examine how this effect contributes to the development of drug-resistant cancer cells, and demonstrate that permanent immunity can arise independently of mutations.
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Adaptação Fisiológica/efeitos dos fármacos , Adaptação Fisiológica/genética , Resistencia a Medicamentos Antineoplásicos/genética , Regulação da Expressão Gênica/efeitos dos fármacos , Modelos Biológicos , Mutação , Epigênese Genética/efeitos dos fármacos , Epigênese Genética/genética , Regulação da Expressão Gênica/genética , Aptidão Genética/efeitos dos fármacos , Aptidão Genética/genética , Mutação/efeitos dos fármacos , Estresse Fisiológico/efeitos dos fármacos , Estresse Fisiológico/genética , Fatores de TempoRESUMO
Antimicrobial resistance (AMR) is an emerging global health crisis that is undermining advances in modern medicine and, if unmitigated, threatens to kill 10 million people per year worldwide by 2050. Research over the last decade has demonstrated that the differences between genetically identical cells in the same environment can lead to drug resistance. Fluctuations in gene expression, modulated by gene regulatory networks, can lead to non-genetic heterogeneity that results in the fractional killing of microbial populations causing drug therapies to fail; this non-genetic drug resistance can enhance the probability of acquiring genetic drug resistance mutations. Mathematical models of gene networks can elucidate general principles underlying drug resistance, predict the evolution of resistance, and guide drug resistance experiments in the laboratory. Cells genetically engineered to carry synthetic gene networks regulating drug resistance genes allow for controlled, quantitative experiments on the role of non-genetic heterogeneity in the development of drug resistance. In this perspective article, we emphasize the contributions that mathematical, computational, and synthetic gene network models play in advancing our understanding of AMR to discover effective therapies against drug-resistant infections.
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A major challenge in biology is that genetically identical cells in the same environment can display gene expression stochasticity (noise), which contributes to bet-hedging, drug tolerance, and cell-fate switching. The magnitude and timescales of stochastic fluctuations can depend on the gene regulatory network. Currently, it is unclear how gene expression noise of specific networks impacts the evolution of drug resistance in mammalian cells. Answering this question requires adjusting network noise independently from mean expression. Here, we develop positive and negative feedback-based synthetic gene circuits to decouple noise from the mean for Puromycin resistance gene expression in Chinese Hamster Ovary cells. In low Puromycin concentrations, the high-noise, positive-feedback network delays long-term adaptation, whereas it facilitates adaptation under high Puromycin concentration. Accordingly, the low-noise, negative-feedback circuit can maintain resistance by acquiring mutations while the positive-feedback circuit remains mutation-free and regains drug sensitivity. These findings may have profound implications for chemotherapeutic inefficiency and cancer relapse.
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Antimetabólitos Antineoplásicos/farmacologia , Resistencia a Medicamentos Antineoplásicos/genética , Regulação da Expressão Gênica/efeitos dos fármacos , Redes Reguladoras de Genes/genética , Modelos Genéticos , Animais , Antimetabólitos Antineoplásicos/uso terapêutico , Células CHO , Simulação por Computador , Cricetulus , Relação Dose-Resposta a Droga , Resistencia a Medicamentos Antineoplásicos/efeitos dos fármacos , Retroalimentação Fisiológica , Regulação da Expressão Gênica/genética , Neoplasias/tratamento farmacológico , Neoplasias/patologia , Puromicina/farmacologia , Puromicina/uso terapêutico , Processos EstocásticosRESUMO
UNLABELLED: We present CellLine, a simulator of the dynamics of gene regulatory networks (GRN) in the cells of a lineage. From user-defined reactions and initial substance quantities, it generates cell lineages, i.e. genealogic pedigrees of cells related through mitotic division. Each cell's dynamics is driven by a delayed stochastic simulation algorithm (delayed SSA), allowing multiple time delayed reactions. The cells of the lineage can be individually subject to 'perturbations', such as gene deletion, duplication and mutation. External interventions, such as adding or removing a substance at a given moment, can be specified. Cell differentiation lineages, where differentiation is stochastically driven or externally induced, can be modeled as well. Finally, CellLine can generate and simulate the dynamics of multiple copies of any given cell of the lineage. As examples of CellLine use, we simulate the following systems: cell lineages containing a model of the P53-Mdm2 feedback loop, a differentiation lineage where each cell contains a 4 gene repressilator (a bistable circuit), a model of the differentiation of the cells of the retinal mosaic required for color vision in Drosophila melanogaster, where the differentiation pathway depends on one substance's concentration that is controlled by a stochastic process, and a 9 gene GRN to illustrate the advantage of using CellLine rather than simulating multiple independent cells, in cases where the cells of the lineage are dynamically correlated. AVAILABILITY: The CellLine program, instructions and examples are available at www.ucalgary.ca/~aribeiro/CellLine/CellLine.html
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Diferenciação Celular/fisiologia , Células Cultivadas/fisiologia , Evolução Molecular , Modelos Biológicos , Proteoma/metabolismo , Animais , Simulação por Computador , Humanos , Modelos Estatísticos , Processos EstocásticosRESUMO
Synthetic biologists aim to design biological systems for a variety of industrial and medical applications, ranging from biofuel to drug production. Synthetic gene circuits regulating efflux pump protein expression can achieve this by driving desired substrates such as biofuels, pharmaceuticals, or other chemicals out of the cell in a precisely controlled manner. However, efflux pumps may introduce implicit negative feedback by pumping out intracellular inducer molecules that control gene circuits, which then can alter gene circuit function. Therefore, synthetic gene circuits must be carefully designed and constructed for precise efflux control. Here, we provide protocols for quantitatively modeling and building synthetic gene constructs for efflux pump regulation.
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Redes Reguladoras de Genes/genética , Proteínas de Membrana Transportadoras/genética , Biocombustíveis , Genes Sintéticos/genética , Biologia Sintética/métodosRESUMO
Synthetic biology aims to design new biological systems for predefined purposes, such as the controlled secretion of biofuels, pharmaceuticals, or other chemicals. Synthetic gene circuits regulating an efflux pump from the ATP-binding cassette (ABC) protein family could achieve this. However, ABC efflux pumps can also drive out intracellular inducer molecules that control the gene circuits. This will introduce an implicit feedback that could alter gene circuit function in ways that are poorly understood. Here, we used two synthetic gene circuits inducible by tetracycline family molecules to regulate the expression of a yeast ABC pump (Pdr5p) that pumps out the inducer. Pdr5p altered the dose-responses of the original gene circuits substantially in Saccharomyces cerevisiae. While one aspect of the change could be attributed to the efflux pumping function of Pdr5p, another aspect remained unexplained. Quantitative modeling indicated that reduced regulator gene expression in addition to efflux pump function could fully explain the altered dose-responses. These predictions were validated experimentally. Overall, we highlight how efflux pumps can alter gene circuit dynamics and demonstrate the utility of mathematical modeling in understanding synthetic gene circuit function in new circumstances.
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Transportadores de Cassetes de Ligação de ATP/genética , Redes Reguladoras de Genes , Genes Sintéticos , Modelos Teóricos , Proteínas de Saccharomyces cerevisiae/genética , Saccharomyces cerevisiae/genética , Transportadores de Cassetes de Ligação de ATP/metabolismo , Relação Dose-Resposta a Droga , Doxiciclina/administração & dosagem , Doxiciclina/farmacologia , Regulação Fúngica da Expressão Gênica/efeitos dos fármacos , Proteínas de Fluorescência Verde/genética , Proteínas de Fluorescência Verde/metabolismo , Proteínas Recombinantes/genética , Proteínas Recombinantes/metabolismo , Saccharomyces cerevisiae/efeitos dos fármacos , Proteínas de Saccharomyces cerevisiae/metabolismo , Tetraciclina/farmacologiaRESUMO
Gene expression is a stochastic process that affects cellular and population fitness. Noise in gene expression can enhance fitness by increasing cell to cell variability as well as the time cells spend in favorable expression states. Using a stochastic model of gene expression together with a fitness function that incorporates the costs and benefits of gene expression in a stressful environment, we show that the fitness landscape is shaped by gene expression noise in more complex ways than previously anticipated. We find that mutations modulating the properties of expression noise enable cell populations to optimize their position on the fitness landscape. Additionally, we find that low levels of expression noise evolve under conditions where the fitness benefits of expression exceed the fitness costs, and that high levels of expression noise evolve when the expression costs exceed the fitness benefits. The results presented in this study expand our understanding of the interplay between stochastic gene expression and fitness in selective environments.
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Evolução Molecular , Expressão Gênica , Aptidão Genética , Modelos Genéticos , Fenômenos Fisiológicos Celulares/genética , Simulação por Computador , Mutação , Processos EstocásticosRESUMO
Fluctuations in gene expression give identical cells access to a spectrum of phenotypes that can serve as a transient, nongenetic basis for natural selection by temporarily increasing drug resistance. In this study, we demonstrate using mathematical modeling and simulation that certain gene regulatory network motifs, specifically coherent feedforward loop motifs, can facilitate the development of nongenetic resistance by increasing cell-to-cell variability and the time scale at which beneficial phenotypic states can be maintained. Our results highlight how regulatory network motifs enabling transient, nongenetic inheritance play an important role in defining reproductive fitness in adverse environments and provide a selective advantage subject to evolutionary pressure.