Biology subjects

Abbott, K.

Publications and source records attributed to Abbott, K..

5 recordsLinked to original sources

bioRxiv2026

Population reweighting and reporter distortion obscure ribosome regulation during translation inhibition

Bacteria dynamically adjust their ribosome allocation as growth conditions change, but how this regulation unfolds within individual cells remains poorly understood. Established relationships between ribosome allocation and growth are largely derived from population measurements under steady-state conditions, which may not capture the trajectories followed by individual cells during perturbation. Differential growth can reweight heterogeneous cellular states over time, causing population-level measurements to diverge from the regulatory trajectories followed by individual cells. Here, we use lineage-resolved single-cell microscopy to follow growth and ribosomal reporter dynamics in Escherichia coli during translation inhibition without growth-dependent reweighting of the sampled lineages. Unexpectedly, fluorescent protein fusions to the ribosomal proteins S2 and L9 decrease in fluorescence following chloramphenicol treatment, whereas transcriptional reporters driven by the ribosomal promoters initially increase. The decrease in ribosomal fusion fluorescence persists across multiple fluorescent proteins and is reproduced during tetracycline treatment, indicating that it is not specific to a particular fluorophore, ribosomal protein or translation inhibitor. Although some fusion reporters retain a relationship with cellular growth within physiological states, this relationship is itself reporter-dependent. These results reveal two independent requirements for quantitative measurements of dynamic cellular responses: longitudinal sampling is needed to distinguish within-cell regulation from growth-dependent population reweighting, and reporter behaviour must be validated when the perturbation alters the processes generating the measured signal.

systems biology↗

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↗

bioRxiv2025

Nanoplasmonic SERS reveals previously uncharacterised indole derivative in E. coli metabolism

Tryptophanase (TnaA) is a promiscuous enzyme which regulates the production of amino acid-derived metabolites, essential for bacterial communication. TnaA is known to convert tryptophan into indole, an important signalling molecule influencing a range of behaviours - from bacterial quorum sensing to urinary tract infections (UTI) - but other conversion routes remain obscure. Here, we show that nanoplasmonic surface-enhanced Raman spectroscopy (SERS) combined with targeted genetic subtraction enables the label-free, cellular characterisation and structural identification of TnaA-derived metabolites directly from Escherichia coli culture solutions, without isolation or purification. We systematically profile the SERS spectra of wild-type and tnaA gene knockout strains (E. coli BW25113 and the uropathogenic 536) supplemented with each of the twenty amino acids, uncovering a previously unreported TnaA-dependent metabolic signature, I*, whose Raman fingerprint does not match free indole or any common indole derivative characterised by mass spectrometry. Using isotopomer-labelled substrates together with structural reference compounds, we perform label-free structural deduction of I* directly from SERS spectra. We find I*s structure is consistent with an intact indole ring carrying a C3 substituent, likely produced indirectly from intracellular tryptophan. These findings introduce a novel indole metabolite, raise new hypotheses regarding the biological role of TnaA-mediated byproducts in bacterial signalling and virulence, and demonstrate that nanoplasmonic SERS provides a powerful framework for probing enzyme activity and bioactive metabolite production directly from living bacterial cultures with nanomolar sensitivity and structural specificity.

microbiology↗

bioRxiv2020

Density Dependent Resource Budget Model for Alternate Bearing

Alternate bearing, seen in many types of plants, is the variable yield with a strongly biennial pattern. In this paper, we introduce a new model for alternate bearing behavior. Similar to the well-known Resource Budget Model, our model is based on the balance between photosynthesis (carbon accumulation) and reproduction processes. We consider two novel features with our model, 1) the existence of a finite capacity in the trees resource reservoir and 2) the possibility of having low (but non-zero) yield when the trees energy level is low. We achieve the former using a density dependent resource accumulation function, and the latter by removing the concept of the well-defined threshold used in the Resource Budget Model. At the level of an individual tree, our model has a stable two-cycle solution, which is suitable to model plants in which the alternate bearing behavior is pronounced. We incorporate environmental stochasticity by adding two uncorrelated noise terms to the parameters of the model associated with the carbon accumulation and reproduction processes. Furthermore, we examine the models behavior on a system of two coupled trees with direct coupling. Unlike the coupled Resource Budget Model, for which the only stable solution is the out-of-phase solution, our model with diffusive coupling has stable in-phase period-2 solutions. This suggests that our model might serve to explain spatial synchrony on a larger scale.

ecology↗

bioRxiv2017

Robustness of early warning signals for catastrophic and non-catastrophic transitions

Early warning signals (EWS) are statistical indicators that a rapid regime shift may be forthcoming. Their development has given ecologists hope of predicting rapid regime shifts before they occur. Accurate predictions, however, rely on the signals being appropriate to the system in question. Most of the EWS commonly applied in ecology have been studied in the context of one specific type of regime shift (the type brought on by a saddle-node bifurcation, at which one stable equilibrium point collides with an unstable equilibrium and disappears) under one particular perturbation scheme (temporally uncorrelated noise that perturbs the net population growth rate in a density independent way). Whether and when these EWS can be applied to other ecological situations remains relatively unknown, and certainly underappreciated. We study a range of models with different types of dynamical transitions (including rapid regime shifts) and several perturbation schemes (density-dependent uncorrelated or temporally-correlated noise) and test the ability of EWS to warn of an approaching transition. We also test the sensitivity of our results to the amount of available pre-transition data and various decisions that must be made in the analysis (i.e. the rolling window size and smoothing bandwidth used to compute the EWS). We find that EWS generally work well to signal an impending saddle-node bifurcation, regardless of the autocorrelation or intensity of the noise. However, EWS do not reliably appear as expected for other types of transition. EWS were often very sensitive to the length of the pre-transition time series analyzed, and usually less sensitive to other decisions. We conclude that the EWS perform well for saddle-node bifurcation in a range of noise environments, but different methods should be used to predict other types of regime shifts. As a consequence, knowledge of the mechanism behind a possible regime shift is needed before EWS can be used to predict it.

ecology↗