Biology subjects

Lopatkin, A. J.

Publications and source records attributed to Lopatkin, A. J..

8 recordsLinked to original sources

bioRxiv2026

Antibiotic inhibition uncouples growth from horizontal gene transfer

Conjugative plasmids often encode resistance to multiple antibiotics, allowing a single mobile genetic element to transfer across diverse selective environments. This co-selection is often invoked to explain the persistence and spread of multidrug resistance, with antibiotic resistance genes encoded on the same plasmid frequently treated as equivalent routes of plasmid maintenance. However, accumulating evidence suggests that different antibiotics can produce distinct co-selection outcomes. These differences have been interpreted through the lens of fitness and population-level selection, while the potential effects on plasmid transfer itself are largely unknown. Here, we show that different antibiotics generate significantly different transfer dynamics for the exact same multidrug-resistant plasmid. Surprisingly, much of this variation arises from continued conjugation after antibiotic exposure begins, whereby a transient residual conjugation window persists despite strong growth inhibition, allowing new transconjugants to continue forming for hours after treatment in an antibiotic dependent manner. A simple model for describing antibiotic effect on transfer competence of both donors and recipients quantitatively explains these antibiotic-specific dissemination dynamics. Moreover, different recipient genetic backgrounds exhibit distinct residual conjugation windows, demonstrating that the capacity for continued plasmid transfer during antibiotic exposure is both drug- and host-dependent. Together, our results show that antibiotics suppress plasmid dissemination on different timescales, creating drug-specific residual conjugation windows that are largely decoupled from growth inhibition. These findings identify conjugation dynamics as a previously overlooked contributor to co-selection outcomes and suggest that antibiotics that select for the same multidrug-resistant plasmid are not equivalent in their ability to promote plasmid persistence.

microbiology↗

bioRxiv2026

Plasmid-encoded host reprogramming promotes plasmid dissemination

Conjugative plasmids are major drivers of antibiotic resistance dissemination, yet how newly transferred plasmids establish in recipient cells remains poorly understood. Here we investigate YfjB, a previously uncharacterized conserved leading-region protein, which is zygotically induced immediately after plasmid entry and acts specifically during the earliest post-transfer stages. Multi-omics analyses reveal that YfjB reprograms host transcription, triggering extensive metabolic rewiring that compensates the transient fitness cost of plasmid acquisition. Structural analyses show that YfjB is a ParB-like protein containing a CTP-binding domain and a helix-turn-helix DNA-binding motif, linked to a previously uncharacterized dimerization module that forms a V-shaped clamp-like architecture compatible with DNA loading. Consistently, live-cell imaging reveals nucleoid-associated foci in transconjugants, and ChIP-seq identifies multiple chromosomal binding sites. We therefore rename the protein HerB (Host Expression Reprogrammer, ParB-like). More broadly, our findings reveal how mobile genetic elements facilitate their dissemination by transiently subverting host physiology.

microbiology↗

bioRxiv2025

A foundation model for microbial growth dynamics

Microbial growth dynamics contain rich information about microbial populations, which support applications from antibiotic testing to microbiome engineering. However, the high dimensionality of growth data and the scarcity of large, task-specific datasets have limited generalizable modeling analysis across systems. Here, we develop a foundation model for microbial growth dynamics. It is a large-scale, self-supervised representation model trained on [~]370,000 experimental and simulated growth curves spanning diverse microbial species, environmental conditions, and community contexts. The model learns lower-dimensional latent embeddings that capture essential dynamical features of raw growth data and enable accurate reconstruction of these data. The concise representations enhance predictive performance in diverse downstream applications. Using these embedding, we achieve few-shot learning for antibiotic classification and concentration prediction, accurate forecasting of simulated and experimental communities, and inference of total abundance from relative-abundance data. By extracting transferable representations from heterogeneous datasets, our model provides a general framework for analyzing and predicting microbial community dynamics from limited measurements.

synthetic biology↗

bioRxiv2025

A biphasic metabolism-β-lactamase production landscape governs the inoculum effect in β-lactamase-expressing Escherichia coli.

The inoculum effect (IE) reduces antibiotic efficacy in vitro and in clinical settings, yet remains difficult to predict. IE limits antibiotic efficacy but remains unpredictable, particularly in resistant bacteria, where resistance mechanisms and bacterial physiology interact in poorly understood ways. Using Escherichia coli expressing the NDM-1 {beta}-lactamase and clinical isolates, we quantified metabolism, growth rate, {beta}-lactamase expression, and IE across diverse growth environments. Across enzyme classes, antibiotics, and clinical isolates, we find that IE is governed by a conserved, biphasic dependence on metabolism normalized by growth rate. Mathematical modeling shows that this behavior reflects a tradeoff between {beta}lactamasemediated protection and metabolismpotentiated antibiotic lethality. These findings establish a predictive, physiology-based framework for IE in resistant bacteria and explain why resistance determinants alone fail to predict treatment outcomes across environments, including the clinic. Significance StatementAntibiotic efficacy depends on both bacterial resistance and physiology, yet these factors are rarely integrated when predicting treatment outcomes. One important consequence of their interaction is the inoculum effect (IE), in which antibiotic efficacy depends on the density of a resistant bacterial population. Here, we show that IE in {beta}lactamase-expressing bacteria is governed by a conserved physiological tradeoff between metabolism and growth. Across growth environments, antibiotics, inocula, and clinical isolates, IE is strongest at intermediate metabolic states, reflecting a balance between resistancemediated protection and metabolismpotentiated antibiotic lethality. This framework helps explain why resistance determinants alone are insufficient to account for IE and underscores the role of bacterial physiology in shaping antibiotic responses.

microbiology↗

bioRxiv2024

Purine and pyrimidine synthesis differently affect the strength of the inoculum effect for aminoglycoside and B-lactam antibiotics.

The inoculum effect has been observed for nearly all antibiotics and bacterial species. However, explanations accounting for its occurrence and strength are lacking. We previously found that growth productivity, which captures the relationship between [ATP] and growth, can account for the strength of the inoculum effect for bactericidal antibiotics. However, the molecular pathway(s) underlying this relationship, and therefore determining the inoculum effect, remain undiscovered. We show that nucleotide synthesis can determine the relationship between [ATP] and growth, and thus the strength of inoculum effect in an antibiotic class-dependent manner. Specifically, and separate from activity through the tricarboxylic acid cycle, we find that transcriptional activity of genes involved in purine and pyrimidine synthesis can predict the strength of the inoculum effect for {beta}-lactam and aminoglycosides antibiotics, respectively. Our work highlights the antibiotic class-specific effect of purine and pyrimidine synthesis on the severity of the inoculum effect and paves the way for intervention strategies to reduce the inoculum effect in the clinic.

microbiology↗

bioRxiv2022

Interactions between metabolism and growth can determine the co-existence of Staphylococcus aureus and Pseudomonas aeruginosa

Most bacteria exist and interact within polymicrobial communities. These interactions produce unique compounds, increased virulence and augmented antibiotic resistance. One community associated with negative healthcare outcomes consists of Pseudomonas aeruginosa and Staphylococcus aureus. When co-cultured, virulence factors secreted by P. aeruginosa reduce metabolism and growth in S. aureus. When grown in vitro this allows P. aeruginosa to drive S. aureus towards extinction. However, when found in vivo, both species can co-exist. Previous work has noted that this may due to altered gene expression or mutations. However, little is known about how the growth environment could influence co-existence of both species. Using a combination of mathematical modeling and experimentation, we show that changes to bacterial growth and metabolism caused by differences in the growth environment can determine final population composition. We found that changing the carbon source in growth medium affects the ratio of ATP to growth rate for both species, a metric we call absolute growth. We found that as a growth environment increases absolute growth for one species, that species will dominate the co-culture. This is due to interactions between growth, metabolism and metabolism altering virulence factors produced by P. aeruginosa. Finally, we show that the relationship between absolute growth and final population composition can be perturbed by altering the spatial structure in the community. Our results demonstrate that differences in growth environment can account for conflicting observations regarding the co-existence of these bacterial species in the literature, and may offer a novel mechanism to manipulate polymicrobial populations.

microbiology↗

bioRxiv2021

PHASE: A MATLAB Based Program for the Analysis of Drosophila Phase, Activity, and Sleep under Entrainment

The problem of entrainment is central to circadian biology. In this regard, Drosophila has been an important model system. Owing to the simplicity of its nervous system and the availability of powerful genetic tools, the system has shed significant light on the molecular and neural underpinnings of entrainment. However, much remains to be learned regarding the molecular and physiological mechanisms underlying this important phenomenon. Under cyclic light/dark conditions, Drosophila melanogaster displays crepuscular patterns of locomotor activity with one peak anticipating dawn and the other anticipating dusk. These peaks are characterized through an estimation of their phase relative to the environmental light cycle and the extent of their anticipation of light transitions. In Drosophila chronobiology, estimations of phases are often subjective, and anticipation indices vary significantly between studies. Though there is increasing interest in building flexible analysis software tools in the field, none incorporates objective measures of Drosophila activity peaks in combination with the analysis of fly activity/sleep in the same program. To this end, we have developed PHASE, a MATLAB-based program that is simple and easy to use and (i) supports the visualization and analysis of activity and sleep under entrainment, (ii) allows analysis of both activity and sleep parameters within user-defined windows within a diurnal cycle, (iii) uses a smoothing filter for the objective identification of peaks of activity (and therefore can be used to quantitatively characterize them), and (iv) offers a series of analyses for the assessment of behavioral anticipation of environmental transitions.

animal behavior and cognition↗

bioRxiv2018

Dissecting bacterial resistance and resilience in antibiotic responses

An essential property of microbial communities is the ability to survive a disturbance. Survival can be achieved through resistance, the ability to absorb effects of a disturbance without a significant change, or resilience, the ability to recover after being perturbed by a disturbance. These concepts have long been applied to the analysis of ecological systems, though their interpretations are often subject to debate. Here we show that this framework readily lends itself to the dissection of the bacterial response to antibiotic treatment, where both terms can be unambiguously defined. The ability to tolerate the antibiotic treatment in the short term corresponds to resistance, which primarily depends on traits associated with individual cells. In contrast, the ability to recover after being perturbed by an antibiotic corresponds to resilience, which primarily depends on traits associated with the population. This framework effectively reveals the phenotypic signatures of bacterial pathogens expressing extended spectrum {beta}-lactamases (ESBLs), when treated by a {beta}-lactam antibiotic. Our analysis has implications for optimizing treatment of these pathogens using a combination of a {beta}-lactam and a {beta}-lactamase (Bla) inhibitor. In particular, our results underscore the need to dynamically optimize combination treatments based on the quantitative features of the bacterial response to the antibiotic or the Bla inhibitor.

cell biology↗