Biology subjects

Bollenbach, T.

Publications and source records attributed to Bollenbach, T..

14 recordsLinked to original sources

bioRxiv2026

Restoring Antibiotic Sensitivity via Metabolic Pathway Manipulation in Pseudomonas aeruginosa

Stopping antimicrobial multidrug resistance (AMR) is essential to maintain the efficacy of current treatments and sustain modern medicine, but current control relies on a coordinated One Health approach involving stewardship, infection prevention and other control measures, without direct actionable mechanisms against resistance itself. Here, integrating proteomics and metabolomics with high-throughput Caenorhabditis elegans infection models, we show that unique synergy of biotin biosynthesis inhibition with metformin uncouples virulence from bacterial viability shutting down major Pseudomonas aeruginosa (PA) virulence mechanisms that impair host survival. Under directed evolution induced by central carbon metabolic stress, laboratory and clinical PA strain isolates acquire mutations in the MexAB-OprM multidrug efflux pump, leading to reversal of the AMR phenotype and restored susceptibility to multiple legacy and last-resort antibiotics such as meropenem. Our findings reveal a previously unexploited vulnerability in PA, its co-dependence on biotin biosynthesis and energy metabolism for virulence and resistance. This work establishes a conceptual framework in which metabolic co-targeting can abolish pathogenesis and drive reversal of antimicrobial resistance. It lays the foundation for first-in-class, non-antibiotic PA-selective anti-virulence and resistance-reversing therapies, key to extending the lifesaving duration of existing antibiotics.

microbiology↗

bioRxiv2026

A generalized growth law for translation- and transcription-targeting antibiotics captures drug interactions

Bacterial growth laws quantitatively connect intracellular resource allocation to growth rate, enabling accurate predictions of physiology and antibiotic responses. Yet these laws have been rigorously tested for only a handful of perturbations. Here, we show that the growth law linking ribosome levels to growth rate under translation-inhibiting antibiotics is not universal, but rather depends on the antibiotic's mechanism of action. Quantitative proteomics across finely resolved one- and two-dimensional antibiotic gradients showed that inhibitors of translocation elongation or peptide bond formation elicit the canonical rise in ribosome levels, consistent with the growth law. By contrast, antibiotics disrupting translation initiation or fidelity produced distinct responses without ribosome upregulation. The transcription inhibitor rifampicin even reduced ribosome abundance. Combining antibiotics with divergent ribosome responses revealed a generalized growth law, in which the individual responses to perturbations superimpose. Embedding this law in a mathematical model explains distinct drug interaction patterns observed between rifampicin and different translation inhibitors. A low-dimensional structure pervades the entire proteome, enabling prediction of responses to drug pairs based on single-drug measurements. Together, these findings broaden the scope of bacterial growth laws and provide new principles for predicting responses to antibiotic combinations.

systems biology↗

bioRxiv2026

A competition-protection balance explains the evolution of resistance within simple microbial communities

Stressful environments can pose a threat to microbial populations, but resistant individuals can emerge and avoid extinction. Adaptation to stress is classically studied in isolated microbial species, ignoring ecological interactions, a key component of natural ecosystems. A growing body of experimental work has shown that community context can affect resistance evolution due to a large variety of mechanisms. Here we set out to identify the minimal components needed to predict the likelihood of acquiring resistance in a focal species embedded within a simple community. To achieve this, we developed a mathematical model based on evolutionary rescue theory and validated it with two experimental systems: Escherichia coli evolving on exposure to the antibiotic nitrofurantoin alone or with one of 14 bacterial isolates from urinary tract infections, and Microbacterium liquefaciens evolving in ampicillin alone or with ampicillin-degrading Comamonas testosteroni. One key factor that emerged from our analyses - the relative strength of competition versus protection - could explain whether a focal species is more or less likely to evolve resistance in the presence of a partner species. While competition always hinders the emergence of resistance, protection can rescue the focal species in two ways: (i) ecological rescue, when the partner species completely removes the antibiotic and favors the survival of the susceptible population, or (ii) evolutionary rescue, when the partner only lowers antibiotic concentrations and favors the emergence of resistant variants, a previously overlooked evolutionary consequence of detoxification. Overall, by integrating theory and experiments, we propose a framework that clarifies how ecological interactions favor or hinder the evolution of resistance to antibiotics or potentially other stressors. SignificanceBacteria can rapidly adapt to resist stressors, such as antibiotics. While resistance evolution in single populations or species is well understood, it remains unclear how ecological interactions with other species influence this process. We develop a mathematical framework to predict what interactions should favor resistance evolution and validate it with two sets of experiments where bacteria adapt to antibiotics in small communities. Our work demonstrates that interactions with other species shape the probability of evolving resistance in a predictable way, determined by the balance between competition and protection against the stressor. By identifying the key factors that drive these dynamics, our work helps explain how bacteria adapt to environmental challenges within species-rich ecosystems.

microbiology↗

bioRxiv2025

Function-specific epistasis shapes evolutionary trajectories towards antibiotic resistance

Pre-existing mutations can influence subsequent evolution by constraining or opening evolutionary pathways through epistatic interactions. In some cases, global epistasis allows evolutionary pathways to be predicted from the fitness of the genetic background alone. In other cases, idiosyncratic epistasis makes evolution less predictable. Here, we show that the evolution of antibiotic resistance is highly repeatable, following a common path across most genetic backgrounds. However, a minority of genetic backgrounds exhibit evolutionary trajectories that significantly deviate from this common path. Rather than being predictable from global epistasis, these deviations are modulated by function-specific epistasis: perturbations to specific cellular functions lead to novel evolutionary trajectories towards resistance. Using tightly controlled robotic evolution experiments, we quantitatively analyzed resistance trajectories for three clinically relevant antibiotics across multiple genetic backgrounds, including hundreds of Escherichia coli gene-deletion strains and several clinical isolates from urinary-tract infections. We show that disrupting distinct sets of cellular functions alters evolutionary trajectories for specific drugs and across different drugs, and we identify genetic changes defining these alternative trajectories. Importantly, this function-specific epistasis often slows down resistance evolution. Some of these effects can also be induced by small-molecule inhibitors of the identified targets, suggesting that function-specific epistasis can be exploited to improve drug treatments.

evolutionary biology↗

bioRxiv2025

Ultrasensitive fitness costs arise from membrane proteome displacement under protein overexpression

Protein overexpression is crucial in many diseases and can lead to antibiotic resistance by increasing the expression of drug targets or resistance genes, such as membrane-localized efflux pumps. Using a high-throughput colony-imaging technique, we performed a quantitative genome-wide study of protein overexpression in Escherichia coli. Our results show that most membrane proteins impose steep overexpression costs, leading to an abrupt growth collapse beyond a critical expression threshold. By manipulating synthetic membrane proteins to target different translocation pathways, we ruled out saturation of these pathways as the cause of these fitness costs. Instead, we found that the growth collapse is driven by the displacement of endogenous membrane proteins, which we show directly using single-cell time-lapse imaging with fluorescently tagged membrane proteins in a microfluidic device. Displacement of as little as 10% of the membrane proteome trapped most bacteria in a non-growing state. Our findings enhance the quantitative understanding of expression costs, with implications for antibiotic resistance and the biotechnological production of membrane proteins.

systems biology↗

bioRxiv2024

Collective β-lactam resistance in Escherichia coli due to β-lactamase release upon cell death

Collective antibiotic resistance occurs when populations of bacteria survive antibiotic treatments that are lethal to individual bacteria, which affects the efficacy of drug therapies. An important mechanism of collective resistance against widely used {beta}-lactams is the production of drug-degrading {beta}-lactamases. Here, we integrate experiments with mathematical modeling to understand the collective survival of Escherichia coli challenged with cefotaxime. At near-lethal cefotaxime concentrations, we observe complex dynamics, involving initial biomass growth due to filamentation, followed by death, and subsequently growth recovery. We show that production of AmpC, a chromosomal {beta}-lactamase, is responsible for cefotaxime degradation, allowing the resumption of cell division in surviving filaments. The detoxification of the environment proceeds through CTX hydrolysis by AmpC in the periplasm of intact cells, as well as extracellularly after cell lysis. Our model predicts the recovery time from molecular parameters, and quantifies the relative roles of periplasmic and extracellular degradation for two strains of E. coli that differ in the degree of privatization of AmpC hydrolysis. Our findings suggest that {beta}-lactam survival of bacterial infections depends on a combination of intra- and extracellular {beta}-lactamase activity, which will likely vary among isolates.

microbiology↗

bioRxiv2024

Trace elements increase reproducibility of microbial growth

Trace elements are often omitted from chemically defined growth media. From established properties of trace elements, we deduce that this omission makes experiments unnecessarily sensitive to unavoidable contamination with trace elements. We confirm this experimentally by growing eleven bacterial strains in high replicate with and without supplementing trace elements, keeping all other conditions as fixed as possible to isolate the effect of trace elements. We find that supplementing trace elements considerably reduces variability of growth even in this benign scenario, and we argue that typical experimental setups exacerbate this. We discuss implications for the design and use of trace-element supplements, and in particular argue that their use should be standard practice, as they can reduce variability of almost all experiments using chemically defined media, taking a step towards greater precision and replicability in microbiology.

microbiology↗

bioRxiv2024

Extreme diversity of phage amplification rates and phage-antibiotic interactions revealed by PHORCE

Growth rate plays a fundamental role in microbiology and serves as an important proxy for fitness in evolution. While high-throughput measurements of bacterial growth rates are easily performed in any microbiology lab, similar methods are lacking for bacteriophages. This gap hinders systematic comparisons of important phage phenotypes, such as their amplification rate in bacterial populations and their bactericidal effect, across different phages and environmental conditions. Here, we show that the amplification rate of lytic phages can be quantified by analyzing bacterial population growth and collapse dynamics under phage predation using a parsimonious mathematical model - an approach termed PHORCE (Phage-Host Observation for Rate estimation from Collapse Events). We found that the resulting phage amplification rate captures the bactericidal effect independent of initial phage and bacterial population sizes and across different growth conditions. Using high-throughput PHORCE, we found that the amplification rates of Escherichia coli phages vary widely by more than three orders of magnitude. Furthermore, our approach suggests that phage-antibiotic interactions are predominantly determined by the antibiotic, and not by the phage. In particular, the ribosome-inhibiting antibiotic doxycycline generally showed antagonism with phage amplification, whereas the DNA-damaging antibiotic nitrofurantoin was synergistic. This framework provides a means to quantitatively characterize phage phenotypes and may facilitate future high-throughput phage screens for antibacterial applications.

microbiology↗

bioRxiv2021

Ribosomal intron-mediated switch governs yeast survival in starvation

Introns are universally present in the nuclear genomes of eukaryotes1. The budding yeast, an otherwise intron-poor species, preserves two sets of ribosomal protein (RP) genes differing primarily in their introns2-4. Despite recent findings on the role of RP introns under stress and starvation5-7, understanding the contribution of introns to ribosome regulation remains challenging. Here, combining isogrowth profiling8 with single-cell protein measurements9, we found that introns can mediate inducible phenotypic heterogeneity conferring a clear fitness advantage. Osmotic stress leads to bimodal expression of the small ribosomal subunit protein Rps22B, mediated by an intron in the 5 untranslated region of its transcript. The two resulting yeast subpopulations differ in their ability to cope with starvation. Low Rps22B protein levels resulted in prolonged survival under sustained starvation, while high Rps22B levels enabled cells to grow faster after transient starvation. Further, yeast growing at high sugar concentrations - similar to those in ripe grapes - exhibit bimodal Rps22B expression when approaching stationary phase. Differential intron-mediated regulation of RP genes thus provides a way to diversify the population when starvation looms in natural environments. Our findings reveal a new role for introns in inducing phenotypic heterogeneity in changing environments and suggest that duplicated RP genes in yeast contribute to resolving the evolutionary conflict between precise expression control and environmental responsiveness10.

systems biology↗

bioRxiv2020

Growth-mediated negative feedback shapes quantitative antibiotic response

Dose-response relationships are a general concept for quantitatively describing biological systems across multiple scales, from the molecular to the whole-cell level. A clinically relevant example is the bacterial growth response to antibiotics, which is routinely characterized by dose-response curves. The shape of the dose-response curve varies drastically between antibiotics and plays a key role for treatments, drug interactions, and resistance evolution. However, the mechanisms shaping the dose-response curve remain largely unclear. Here, we show in Escherichia coli that the distinctively shallow dose-response curve of the antibiotic trimethoprim is caused by a negative growth- mediated feedback loop: Trimethoprim slows growth, which in turn weakens the effect of this antibiotic. At the molecular level, this feedback is caused by the upregulation of the drug target dihydrofolate reductase (FolA/DHFR). We show that this upregulation is not a specific response to trimethoprim but follows a universal trend line that depends only on growth rate, irrespective of its cause. Rewiring the feedback loop alters the dose-response curve in a predictable manner, which we corroborate with a mathematical model. Our results indicate that growth-mediated feedback loops shape drug responses and could be exploited to design evolutionary traps that enable selection against drug resistance.

microbiology↗

bioRxiv2020

A Minimal Biophysical Model of Combined Antibiotic Action

Phenomenological relations such as Ohms or Fouriers law have a venerable history in physics, but are still scarce in biology. This situation restrains predictive theory. Here, we build on bacterial "growth laws," which capture physiological feedback between translation and cell growth, to construct a minimal biophysical model for the combined action of ribosome-targeting antibiotics. Our model predicts drug interactions like antagonism or synergy solely from responses to individual drugs. We systematically refine the model by including direct physical interactions of different drugs on the ribosome. In a limiting case, our model provides a mechanistic underpinning for recent predictions of higher-order interactions derived using entropy maximization. It further makes parameter-free predictions for combined drug effects on cells carrying resistance genes and for drugs that mimic poor nutrient environments. We show experimentally that resistance genes can drastically alter drug interactions in notable agreement with our theoretical predictions. While minimal, the model is readily adaptable and opens the door to predicting interactions of second and higher-order in a broad range of biological systems.

biophysics↗

bioRxiv2019

Mechanistic origin of drug interactions between translation-inhibiting antibiotics

Antibiotics that interfere with translation, when combined, interact in diverse and difficult-to-predict ways. Here, we demonstrate that these interactions can be accounted for by "translation bottlenecks": points in the translation cycle where antibiotics block ribosomal progression. To elucidate the underlying mechanisms of drug interactions between translation inhibitors, we generated translation bottlenecks genetically using inducible control of translation factors that regulate well-defined translation cycle steps. These perturbations accurately mimicked antibiotic action and their interactions, supporting that the interplay of different translation bottlenecks causes these interactions. We further showed that the kinetics of drug uptake and binding together with growth laws allows direct prediction of a large fraction of observed interactions, yet fails for suppression. Simultaneously varying two translation bottlenecks in the same cell revealed how the dense traffic of ribosomes and competition for translation factors results in previously unexplained suppression. This result highlights the importance of "continuous epistasis" in bacterial physiology.

systems biology↗

bioRxiv2019

Exploiting epistasis to perturb the evolution of antibiotic resistance

New ways of curbing the ability of bacteria to evolve spontaneous resistance could mitigate the looming antibiotic resistance crisis. Progress toward this goal requires a comprehensive understanding of the key factors that contribute to resistance evolvability. Here, we present a systematic approach to identify cellular functions that affect the evolvability of resistance. Using a robotic lab-evolution platform that keeps population size and selection pressure under tight control for hundreds of Escherichia coli populations evolving in parallel, we quantified the effects of a genome-wide selection of pre-existing gene deletions on resistance evolution. Initial resistance of strains with gene deletions differed by more than tenfold but converged toward a hard upper bound for resistance during the evolution experiment, reflecting a global pattern of diminishing returns epistasis. We identified specific cellular functions that drastically curtail the evolvability of resistance; beyond DNA repair, these include membrane transport, LPS biosynthesis, and chaperones. Perturbations of efflux pumps prevented resistance evolution completely or forced evolution on inferior mutational paths, not explored in the wild type. We show that strong negative epistasis generally underlies these phenomena. The identified functions provide new targets for adjuvants tailored to block evolutionary paths to resistance when combined with antibiotics.

evolutionary biology↗

bioRxiv2019

Axiomatic Ecology: Rules for Building Consistent Ecosystem Models

Many ecological studies employ general models that can feature an arbitrary number of populations. A critical requirement imposed on such models is clone consistency: If the individuals from two populations are indistinguishable, joining these populations into one shall not affect the outcome of the model. Otherwise a model produces different outcomes for the same scenario. Using functional analysis, we comprehensively characterize all clone-consistent models: We prove that they are necessarily composed from basic building blocks, namely linear combinations of parameters and abundances. These strong constraints enable a straightforward validation of model consistency or reveal implicit assumptions required to achieve it. We show that such implicit assumptions can considerably limit the applicability of models and the generality of results obtained with them. Moreover, our insights facilitate building new clone-consistent models, which we illustrate for a data-driven model of microbial communities. Finally, our insights point to new relevant forms of general models for theoretical ecology. Our framework thus provides a systematic way of comprehending ecological models, which can guide a wide range of studies.

ecology↗