Biology subjects

Hardo, G.

Publications and source records attributed to Hardo, G..

5 recordsLinked to original sources

bioRxiv2026

STRESS HISTORY ESTABLISHES A TRANSIENT TOLERANT STATE THAT SHAPES ANTIBIOTIC SURVIVAL UPON RESUSCITATION

Antibiotic treatment failure, often driven by non-genetic mechanisms such as tolerance and persistence, remains a major global health challenge. {beta}-lactams, the most widely prescribed antibiotic class, are particularly compromised by tolerance in dormant, non-growing cells; yet, how these drugs act on cells resuscitating from dormancy remains poorly understood. Here, we investigate the resuscitation phase at an unprecedented scale using Hi-DFA (High-throughput Dynamic Fate Analyser), a single-cell microfluidic platform integrating time-lapse imaging with machine-learning-based image analysis for dynamic cell-fate tracking. We identify a distinct survival strategy: a significant fraction of resuscitating cells transiently slow their growth, facilitating survival upon {beta}-lactam exposure. This transiently tolerant phenotype is considerably less frequent in unstressed, exponentially growing cells, indicating that prior starvation history predisposes cells to this state. Using simulated in vitro pharmacokinetic treatment profiles, we show that suboptimal dosing selectively enriches for this transient tolerance state. A population dynamics model built from this single-cell antibiotic-response data suggests that these transient-tolerant cells, not typical starvation-triggered persisters, may be the primary drivers of rapid population regrowth post-treatment under clinically relevant conditions. Together, our findings define a distinct class of antibiotic survival shaped by stress history and treatment profile, offering a quantitative framework for optimising antibiotic dosing strategy.

systems biology↗

bioRxiv2024

Single-cell imaging of the lytic phage life cycle dissects the timing and noise in individual infection steps

When a lytic bacteriophage infects a bacterial cell, it commandeers the cells resources to replicate, ultimately causing cell lysis and the release of new virions. As phages function as obligate parasites, each stage of the infection process depends on the physiological parameters of the host cell. Given the inherent physiological variability within a population of genetically identical bacterial cells, we ask how the phage infection dynamic reflects such heterogeneity. Here, we introduce a timelapse imaging assay for investigating the dynamics of individual infection steps by a single T7 phage on a single bacterium. This high-throughput, time-resolved assay enables us to monitor the infection progression simultaneously in multiple cells, uncovering substantial heterogeneity at each step and revealing correlations between infection dynamics and the physiological state of the infected cell. Simulations of competing phage populations with different lysis time distributions reveal that heterogeneity in infection dynamics can significantly impact phage fitness, highlighting it as a potential evolutionary driver of phage-bacteria interactions.

microbiology↗

bioRxiv2023

Quantitative Microbiology with Microscopy: Effects of Projection and Diffraction

Time-resolved live-cell imaging using widefield microscopy is instrumental in quantitative microbiology research. It allows us to track and measure the size, shape, and content of individual microbial cells over time. However, the small size of microbial cells poses a significant challenge in interpreting image data, as it approaches the dimensions of the microscopes depth of field and experiences diffraction effects. As a result, 2D widefield images of microbial cells contain projected 3D information, blurred by the 3D point spread function. In this study, we employ computer simulations and targeted experiments to investigate the impact of diffraction and projection on our ability to quantify the size and content of microbial cells from 2D microscopic images. This study points to some new and often unconsidered artefacts resulting from the interplay of projection and diffraction effects, within the context of quantitative microbiology. These artefacts introduce substantial errors and biases in size and fluorescence quantification, and even single-molecule counting, making the elimination of these errors a complex task. Awareness of these artefacts is crucial for designing strategies to accurately interpret micrographs of microbes. To address this, we present new experimental designs and machine learning based analysis methods that account for these effects, resulting in accurate quantification of microbiological processes.

microbiology↗

bioRxiv2021

Accurate Segmentation of Bacterial Cells using Synthetic Training Data

We present a novel method of bacterial image segmentation using machine learning models trained with Synthetic Micrographs of Bacteria (SyMBac). SyMBac is a tool that allows for rapid, automatic creation of arbitrary amounts of training data, combining detailed models of cell growth, physical interactions, and microscope optics to create synthetic images which closely resemble real micrographs. The major advantages of our approach are: 1) synthetic training data can be generated virtually instantly, and on demand; 2) these synthetic images are accompanied by perfect ground truth positions of cells, meaning no data curation is required; 3) different biological conditions, imaging platforms, and imaging modalities can be rapidly simulated, meaning any change in ones experimental setup no longer requires the laborious process of manually generating new training data for each change. Our benchmarking results demonstrate that models trained on SyMBac data generate more accurate and precise cell masks than those trained on human annotated data, because the model learns the true position of the cell irrespective of imaging artefacts. Machine-learning models trained with SyMBac data are capable of analysing data from various imaging platforms and are robust to drastic changes in cell size and morphology.

microbiology↗

bioRxiv2019

Designing synthetic bacterial consortia for landfill leachate treatment based on community matrices and regression tree analysis

The performance of microbial communities exploited by industry are largely optimised by manipulating process parameters, such as flow rates, growth conditions, and reactor parameters. Conversely, the composition of microorganisms used are often viewed as a "black box". This is mostly due to the relatively high costs and technical expertise required to identify and quantify the microbial consortia, as well as limited tools to create functional assemblages. Unknown details about the interactions among species may impose a limit on how much microbial function can be optimised for industrial purposes. Here, a new workflow was developed for studying microbial consortia using high throughput, species and community specific measurements of growth rates and yields. Growth rate and yield among all single, pairwise, triple, quadruples, quintuple and sextuple combinations of six bacterial isolates on landfill leachate were evaluated. Additive, antagonistic (e.g. competitive) or synergistic (+/-) interactions can be inferred from the rate and yield data. We found that antagonistic interactions, which hinder growth and yield, were the dominant interaction type, with only a few synergistic interactions observed. Mixed effects models were used to investigate the relationship between interaction type and species richness (biodiversity). Community identity was found to be a more important factor in predicting yield determining interactions but not rate determining interactions. Species richness was a good predictor of rate determining interactions, with the most positive interactions happening at a low species richness. Regression tree analysis identified Lysinibacillus sp. as a keystone species, a genus previously associated with bioremediation. Its presence led to a drastic change in the function of the synthetic ecosystem, with both positive yield and rate determining interactions. We were able to infer interactions about specific pairs of species, and the competitive/synergistic tendencies of single species from only basic top-down growth measurements. In this way, we have demonstrated how factorial experiments using isolated microorganisms can be used to ultimately design synthetic consortia with desirable traits for industry. Funding statementThis research was funded by the EPSRC (Vacation Bursary Award), BBSRC (Impact Acceleration Award), Department of Chemical and Biological Engineering, The University of Sheffield (Summer Undergraduate Research Fellowship) and Viridor Ltd. Ethics statements(Authors are required to state the ethical considerations of their study in the manuscript, including for cases where the study was exempt from ethical approval procedures) Does the study presented in the manuscript involve human or animal subjects: No Data availability statementGenerated Statement: This manuscript contains previously unpublished data. The name of the repository and accession number are not available.

microbiology↗