Biology subjects

Dunlop, M. J.

Publications and source records attributed to Dunlop, M. J..

12 recordsLinked to original sources

bioRxiv2024

Dynamic heterogeneity in an E. coli stress response regulon mediates gene activation and antimicrobial peptide tolerance

The bacterial stress response is an intricately regulated system that plays a critical role in cellular resistance to drug treatment. The complexity of this response is further complicated by cell-to-cell heterogeneity in the expression of bacterial stress response genes. These genes are often organized into networks comprising one or more transcriptional regulators that control expression of a suite of downstream genes. While the expression heterogeneity of many of these upstream regulators has been characterized, the way in which this variability affects the larger downstream stress response remains hard to predict, prompting two key questions. First, how does heterogeneity and expression noise in stress response regulators propagate to the diverse downstream genes in their regulons? Second, when expression levels vary, how do upstream and downstream genes act together to protect cells from stress? To address these questions, we focus on the transcription factor PhoP, a critical virulence regulator which coordinates pathogenicity in several gram-negative species. We use optogenetic stimulation to precisely control PhoP expression levels and utilize information theory to examine how variations in PhoP affect the downstream activation of genes in the PhoP regulon. We find that these downstream genes exhibit differences in mean expression level, sensitivity to increasing levels of PhoP, and signal transmission reliability. These response functions can also vary between individual cells, increasing heterogeneity in the population. We tie these variations to cell survival when bacteria are exposed to a clinically-relevant antimicrobial peptide, showing that while high expression of the PhoP-regulon gene pmrD provides a protective effect against Polymyxin B, cell survival chances are best determined by integrated dynamic PhoP and pmrD expression levels. Overall, we demonstrate that cell-to-cell and temporal heterogeneous expression of a stress response regulator can have clear consequences for enabling bacteria to survive stress.

systems biology↗

bioRxiv2024

Evaluating the predictive power of combined gene expression dynamics from single cells on antibiotic survival

Heteroresistance can allow otherwise drug-susceptible bacteria to survive and resume growth after antibiotic exposure. This temporary form of antibiotic tolerance can be caused by the upregulation of stress response genes or a decrease in cell growth rate. However, it is not clear how expression of multiple genes contributes to the tolerance phenotype. By using fluorescent reporters for stress related genes, we conducted real time measurements of expression prior to, during, and after antibiotic exposure. We first identified relationships between growth rate and reporter levels based on auto and cross correlation analysis, revealing consistent patterns where changes in growth rate were anticorrelated with fluorescence following a delay. We then used pairs of stress gene reporters and time lapse fluorescence microcopy to measure the growth rate and reporter levels in cells that survived or died following antibiotic exposure. Using these data, we asked whether combined information about reporter expression and growth rate could improve our ability to predict whether a cell would survive or die following antibiotic exposure. We developed a Bayesian inference model to predict how the combination of dual reporter expression levels and growth rate impact ciprofloxacin survival in Escherichia coli. We found clear evidence of the impact of growth rate and the gadX promoter activity on survival. Unexpectedly, our results also revealed examples where additional information from multiple genes decreased prediction accuracy, highlighting an important and underappreciated effect that can occur when integrating data from multiple simultaneous measurements.

systems biology↗

bioRxiv2024

Cell-TRACTR: A transformer-based model for end-to-end segmentation and tracking of cells

Deep learning-based methods for identifying and tracking cells within microscopy images have revolutionized the speed and throughput of data analysis. These methods for analyzing biological and medical data have capitalized on advances from the broader computer vision field. However, cell tracking can present unique challenges, with frequent cell division events and the need to track many objects with similar visual appearances complicating analysis. Existing architectures developed for cell tracking based on convolutional neural networks (CNNs) have tended to fall short in managing the spatial and global contextual dependencies that are crucial for tracking cells. To overcome these limitations, we introduce Cell-TRACTR (Transformer with Attention for Cell Tracking and Recognition), a novel deep learning model that uses a transformer-based architecture. The attention mechanism inherent in transformers facilitates long-range connections, effectively linking features across different spatial regions, which is critical for robust cell tracking. Cell-TRACTR operates in an end-to-end manner, simultaneously segmenting and tracking cells without the need for post-processing. Alongside this model, we introduce the Cell-HOTA metric, an extension of the Higher Order Tracking Accuracy (HOTA) metric that we adapted to assess cell division. Cell-HOTA differs from standard cell tracking metrics by offering a balanced and easily interpretable assessment of detection, association, and division accuracy. We test our Cell-TRACTR model on datasets of bacteria growing within a defined microfluidic geometry and mammalian cells growing freely in two dimensions. Our results demonstrate that Cell-TRACTR exhibits excellent performance in tracking and division accuracy compared to state-of-the-art algorithms, while also matching traditional benchmarks in detection accuracy. This work establishes a new framework for employing transformer-based models in cell segmentation and tracking. Author SummaryUnderstanding the growth, movement, and gene expression dynamics of individual cells is critical for studies in a wide range of areas, from antibiotic resistance to cancer. Monitoring individual cells can reveal unique insights that are obscured by population averages. Although modern microscopy techniques have vastly improved researchers ability to collect data, tracking individual cells over time remains a challenge, particularly due to complexities such as cell division and non-linear cell movements. To address this, we developed a new transformer-based model called Cell-TRACTR that can segment and track single cells without the need for post-processing. The strength of the transformer architecture lies in its attention mechanism, which integrates global context. Attention makes this model particularly well suited for tracking cells across a sequence of images. In addition to the Cell-TRACTR model, we introduce a new metric, Cell-HOTA, to evaluate tracking algorithms in terms of detection, association, and division accuracy. The metric breaks down performance into sub-metrics, helping researchers pinpoint the strengths and weaknesses of their tracking algorithm. When compared to state-of-the-art algorithms, Cell-TRACTR meets or exceeds many current benchmarks, offering excellent potential as a new tool for the analysis of series of images with single-cell resolution.

systems biology↗

bioRxiv2024

Unique growth and morphology properties of Clade 5 Clostridioides difficile strains revealed by single-cell time-lapse microscopy

Clostridioides difficile is a gastrointestinal pathogen of both humans and agricultural animals and thus a major One Health threat. The C. difficile species consists of five main clades, with Clade 5 currently undergoing speciation from Clades 1-4. Clade 5 strains are highly prevalent in agricultural animals and can cause zoonotic infections, suggesting that these strains have evolved phenotypes that distinguish them from Clade 1-4 strains. Here, we compare the growth properties of Clade 5 strains to those of Clade 1-4 strains using anaerobic time-lapse microscopy coupled with automated image analysis. Our analyses indicate that Clade 5 strains grow faster and are more likely to form long chains of cells than Clade 1-4 strains. Using comparative genomic and CRISPRi analyses, we show that the chaining phenotype of Clade 5 strains is driven by the orientation of the invertible cmr switch sequence, with chaining strains exhibiting a bias to the cmr-ON state. Interestingly, Clade 5 strains with a bias towards the cmr-ON state shifted to a largely cmr-OFF state during murine infection, suggesting that the cmr-OFF state is under positive selection during infection. Collectively, our data reveal that Clade 5 strains have distinct growth properties, which may allow them to inhabit diverse ecological niches. Author SummaryThe Clade 5 strains of the Clostridioides difficile species are so phylogenetically divergent that they almost meet the threshold of being a distinct species. Although these strains are ubiquitously isolated from agricultural and environmental settings and an important source of zoonotic and community-acquired infections, it is unclear whether they have distinct phenotypic properties that allow them to colonize diverse hosts or persist in the environment. By combining a novel anaerobic time-lapse microscopy method with automated image analysis, we discovered that Clade 5 strains grow faster than strains from other C. difficile clades and that they frequently form long chains. These chaining properties are driven by the environmentally responsive expression of a non-canonical signal transduction system, which our analyses suggest is selected against during murine infection. Collectively, our analyses reveal that Clade 5 strains have distinct growth properties that may promote their persistence in the environment.

microbiology↗

bioRxiv2023

Controlled protein activities with viral proteases, antiviral peptides, and antiviral drugs

Chemical control of protein activity is a powerful tool for scientific study, synthetic biology, and cell therapy; however, for broad use, effective chemical inducer systems must minimally crosstalk with endogenous processes and exhibit desirable drug delivery properties. Accordingly, the drug-controllable proteolytic activity of hepatitis C cis-protease NS3 and its associated antiviral drugs have been used to regulate protein activity and gene modulation. These tools advantageously exploit non-eukaryotic/prokaryotic proteins and clinically approved inhibitors. Here we expand the toolkit by utilizing catalytically inactive NS3 protease as a high affinity binder to genetically encoded, antiviral peptides. Through our approach, we create NS3-peptide complexes that can be displaced by FDA-approved drugs to modulate transcription, cell signaling, split-protein complementation. With our developed system, we discover a new mechanism to allosterically regulate Cre recombinase. Allosteric Cre regulation with NS3 ligands enables orthogonal recombination tools in eukaryotic cells and functions in divergent organisms to control prokaryotic recombinase activity.

synthetic biology↗

bioRxiv2023

Light inducible protein degradation in E. coli with LOVtag

Molecular tools for optogenetic control allow for spatial and temporal regulation of cell behavior. In particular, light controlled protein degradation is a valuable mechanism of regulation because it can be highly modular, used in tandem with other control mechanisms, and maintain functionality throughout growth phases. Here, we engineered LOVdeg, a tag that can be appended to a protein of interest for inducible degradation in Escherichia coli using blue light. We demonstrate the modularity of LOVdeg by using it to tag a range of proteins, including the LacI repressor, CRISPRa activator, and the AcrB efflux pump. Additionally, we demonstrate the utility of pairing the LOVdeg tag with existing optogenetic tools to enhance performance by developing a combined EL222 and LOVdeg system. Finally, we use the LOVdeg tag in a metabolic engineering application to demonstrate post-translational control of metabolism. Together, our results highlight the modularity and functionality of the LOVdeg tag system, and introduce a powerful new tool for bacterial optogenetics.

synthetic biology↗

bioRxiv2022

An Optogenetic Toolkit for Light-Inducible Antibiotic Resistance

Antibiotics are a key control mechanism for synthetic biology and microbiology. Resistance genes are used to select desired cells and regulate bacterial populations, however their use to-date has been largely static. Precise spatiotemporal control of antibiotic resistance could enable a wide variety of applications that require dynamic control of susceptibility and survival. Here, we use light-inducible Cre recombinase to activate expression of drug resistance genes in Escherichia coli. We demonstrate light-activated resistance to four antibiotics: carbenicillin, kanamycin, chloramphenicol, and tetracycline. Cells exposed to 465 nm blue light survive in the presence of lethal antibiotic concentrations, while those kept in the dark do not. To optimize resistance induction ranges, we characterize the impact of the promoter, ribosome binding site, and enzyme variant strength using chromosome and plasmid-based constructs. Using time-lapse microscopy, we further show resistance activation dynamics. These optogenetic drug resistance tools pave the way for spatiotemporal control of cell survival.

synthetic biology↗

bioRxiv2020

Cell-to-cell heterogeneity in Escherichia coli stress response originates from pulsatile expression and growth

Cell-to-cell heterogeneity in gene expression and growth can have critical functional consequences, such as determining whether individual bacteria survive or die following stress. Although phenotypic variability is well documented, the dynamics that underlie it are often unknown. This information is important because dramatically different outcomes can arise from gradual versus rapid changes in expression and growth. Using single-cell time-lapse microscopy, we measured the temporal expression of a suite of stress response reporters in Escherichia coli, while simultaneously monitoring growth rate. In conditions without stress, we found several examples of pulsatile expression. Single-cell growth rates were often anti-correlated with gene expression, with changes in growth preceding changes in expression. These expression and growth dynamics have functional consequences, which we demonstrate by measuring survival after challenging cells with the antibiotic ciprofloxacin. Our results suggest that fluctuations in gene expression and growth dynamics in stress response networks can have direct consequences for survival.

systems biology↗

bioRxiv2019

Light-Inducible Recombinases for Bacterial Optogenetics

Optogenetic tools can provide direct and programmable control of gene expression. Light-inducible recombinases, in particular, offer a powerful method for achieving precise spatiotemporal control of DNA modification. However, to-date this technology has been largely limited to eukaryotic systems. Here, we develop optogenetic recombinases for Escherichia coli which activate in response to blue light. Our approach uses a split recombinase coupled with photodimers, where blue light brings the split protein together to form a functional recombinase. We tested both Cre and Flp recombinases, Vivid and Magnet photodimers, and alternative protein split sites in our analysis. The optimal configuration, Opto-Cre-Vvd, exhibits strong blue light-responsive excision and low ambient light sensitivity. For this system we characterize the effect of light intensity and the temporal dynamics of light-induced recombination. These tools expand the microbial optogenetic toolbox, offering the potential for precise control of DNA excision with light-inducible recombinases in bacteria.

bioengineering↗

bioRxiv2018

Forecasting when cells die during antibiotic exposure using stochastic gene expression

Antibiotic killing does not occur at a single, precise time for all cells within a population. Variability in time to death can be caused by stochastic expression of genes, resulting in differences in endogenous stress-resistance levels between individual cells in a population. This variability can be part of a bet-hedging strategy where cells leverage noise to ensure a subset of the population can tolerate the drug, while decreasing the overall cost of expressing resistance genes. We asked whether single-cell differences in gene expression prior to antibiotic addition were related to cell survival times after antibiotic exposure for a range of genes of diverse function. We quantified the time to death of single cells under antibiotic exposure in combination with expression of reporters. For some reporters, the time to cell death had a strong relationship with the initial expression level of the genes. Reporters that could forecast cell fate included stress response genes, but also genes involved in a variety of other cellular processes like metabolism. Our results highlight the single-cell level non-uniformity of antibiotic killing and also provide examples of key genes where cell-to-cell variation in expression prior to antibiotic exposure is strongly linked to extended durations of antibiotic survival.

systems biology↗

bioRxiv2018

Salicylate increases fitness cost associated with MarA-mediated antibiotic resistance

Antibiotic resistance is generally associated with a fitness deficit resulting from the burden of producing and maintaining resistance machinery. This additional cost suggests that resistant bacteria will be outcompeted by susceptible bacteria in conditions without antibiotics. However, in practice this process is slow due in part to regulation that minimizes expression of these genes in the absence of antibiotics. This suggests that if it were possible to turn on their expression, the cost would increase, thereby accelerating removal of resistant strains. Experimental and theoretical studies have shown that environmental chemicals can change the fitness cost associated with resistance and therefore have a significant impact on population dynamics. MarA (multiple antibiotic resistance activator) is a clinically important regulator in Escherichia coli which activates downstream genes to increase resistance against multiple classes of antibiotics. Salicylate is an inducer of MarA which can be found in the environment and de-represses marAs expression. In this study, we sought to unravel the interplay between salicylate and the fitness cost of MarA-mediated antibiotic resistance. Using salicylate as a natural inducer of MarA, we found that a wide spectrum of concentrations can increase burden in resistant strains compared to susceptible strains. Induction resulted in rapid exclusion of resistant bacteria from mixed populations of antibiotic resistant and susceptible cells. A mathematical model captures the process and predicts its effect in various environmental conditions. Our work provides a quantitative understanding of salicylate exposure on the fitness of different MarA variants, and suggests that salicylate can lead to selection against MarA-mediated resistant strains. More generally, our findings show that natural inducers may serve to bias population membership and could impact antibiotic resistance and other important phenotypes.

systems biology↗

bioRxiv2018

Active degradation of a regulator controls coordination of downstream genes

Several key transcription factors have unusually short half-lives compared to other cellular proteins. Here, we explore the utility of active degradation in shaping how a master regulator coordinates its downstream targets. We focus our studies on the multi-antibiotic resistance activator MarA, which controls a variety of stress response genes in Escherichia coli. We modify its half-life either by knocking down the protease that targets it via CRISPRi or by engineering MarA to protect it from degradation. Our experimental, analytical, and computational results indicate that active degradation can impact both the rate of coordination and the maximum coordination that downstream genes can achieve. Trade-offs between these properties show that perfect information fidelity and instantaneous coordination cannot coexist.

systems biology↗