Biology subjects

Zaman, L.

Publications and source records attributed to Zaman, L..

9 recordsLinked to original sources

bioRxiv2026

Cooperative antibiotic response in coupled biofilm and planktonic E. faecalis communities

Bacterial communities grow as dynamic populations that respond to their environments. A clinically relevant example is the inactivation of beta-lactam antibiotics by intracellular beta-lactamase in E. faecalis resistant strains. In these populations, resistant bacteria act as antibiotic sinks, detoxifying the environment and allowing sensitive bacteria to survive treatment through a cooperative interaction. In this work, we study strongly coupled planktonic and biofilm populations of mixed sensitive-resistant E. faecalis bacteria under antibiotic stress using fluorescent microscopy. The presence of resistant bacteria in the system benefits both resistant and sensitive cells, leading to mixed planktonic and biofilm populations at super-inhibitory drug concentrations. We show that a beta-lactam antibiotic with or without the addition of a beta-lactam inhibitor can lead to a population inversion effect, characterized by a non-monotonic relation between initial and final fractions of resistant bacteria. The effect is observed in both the planktonic and biofilm populations and is modulated by the total initial cell density. A well-mixed model with competition mediated by resource sharing and cooperation from global degradation of toxins predicts the experimentally observed behavior. These observations suggest underlying population-level mechanisms that are largely independent of biofilm spatial structure.

biophysics↗

bioRxiv2026

Minding the gap: collective determinants of multiscale structure across interacting bacterial colonies

A bacterial colony rarely exists in isolation - in natural habitats, colonies interact to form spatially structured communities across length and time scales. Eco-evolutionary feedbacks link these scales, such that structure at one level can influence another, yet the interplay between single- and multi-colony organization remains poorly understood. As a step toward addressing this, we develop a high-throughput platform to track population dynamics across spatially extended networks of colonies. A common structural feature observed at the multi-colony scale is the formation of a stable gap region between colonies, even when they are isogenic. Numerous studies observe similar patterns of behavior across species, with few resolving the underlying mechanism. Here, we ask: what are the minimal ingredients shaping this multi-colony structure? We focus on colonies of the opportunistic pathogen Enterococcus faecalis, a model organism for which this behavior has yet to be reported. By combining modeling and experiments, we show that both nutrient competition and direct growth inhibition control colony morphology and expansion of interacting colonies. We identify distinct regimes of gap formation, relating intra- and inter-colony spatial patterns to ecological interactions mediated at the cellular scale. Together, our results suggest that antagonism, even between isogenic populations through self-inhibition, is likely a common behavior of bacterial species in general.

ecology↗

bioRxiv2024

Relaxed selection can speed the evolution of complex adaptations

Natural selection drives adaptive evolution and removes deleterious mutations; these effects are countervailing when a complex adaptation requires mutations that are initially deleterious when they arise, but beneficial in combination. While many models of this dynamic consider how genetic drift or other influences can aid valley crossing by weakening selection, we lack a general, analytical treatment of when relaxed selection might speed this type of adaptation. Here we use simulation and analysis to show that relaxed selection is generally favorable for valley-crossing when adaptive pathways require more than a single deleterious step. We also demonstrate that spatial heterogeneity in selection pressures could, by relaxing selection, allow populations to cross valleys much more rapidly than expected. These results relate to several applications of evolutionary theory to complex systems ranging from host-pathogen evolution to search algorithms in computer science.

evolutionary biology↗

bioRxiv2023

Virus-Host Interactions Predictor (VHIP): machine learning approach to resolve microbial virus-host interaction networks

Viruses of microbes are ubiquitous biological entities that reprogram their hosts metabolisms during infection in order to produce viral progeny, impacting the ecology and evolution of microbiomes with broad implications for human and environmental health. Advances in genome sequencing have led to the discovery of millions of novel viruses and an appreciation for the great diversity of viruses on Earth. Yet, with knowledge of only "who is there?" we fall short in our ability to infer the impacts of viruses on microbes at population, community, and ecosystem-scales. To do this, we need a more explicit understanding "who do they infect?" Here, we developed a novel machine learning model (ML), Virus-Host Interaction Predictor (VHIP), to predict virus-host interactions (infection/non-infection) from input virus and host genomes. This ML model was trained and tested on a high-value manually curated set of 8849 virus-host pairs and their corresponding sequence data. The resulting dataset, Virus Host Range network (VHRnet), is core to VHIP functionality. Each data point that underlies the VHIP training and testing represents a lab-tested virus-host pair in VHRnet, from which features of coevolution were computed. VHIP departs from existing virus-host prediction models in its ability to predict multiple interactions rather than predicting a single most likely host or host clade. As a result, VHIP is the first virus-host range prediction tool able to reconstruct the complexity of virus-host networks in natural systems. VHIP has an 87.8% accuracy rate at predicting interactions between virus-host pairs at the species level and can be applied to novel viral and host population genomes reconstructed from metagenomic datasets. Through the reconstruction of complete virus-host networks from novel data, VHIP allows for the integration of multilayer network theory into microbial ecology and opens new opportunities to study ecological complexity in microbial systems. Author summaryThe ecology and evolution of microbial communities are deeply influenced by viruses. Metagenomics analysis, the non-targeted sequencing of community genomes, has led to the discovery of millions of novel viruses. Yet, through the sequencing process, only DNA sequences are recovered, begging the question: which microbial hosts do those novel viruses infect? To address this question, we developed a computational tool to allow researchers to predict virus-host interactions from such sequence data. The power of this tool is its use of a high-value, manually curated set of 8849 lab-verified virus-host pairs and their corresponding sequence data. For each pair, we computed signals of coevolution to use as the predictive features in a machine learning model designed to predict interactions between viruses and hosts. The resulting model, Virus-Host Interaction Predictor (VHIP), has an accuracy of 87.8% and can be applied to novel viral and host genomes reconstructed from metagenomic datasets. Because the model considers all possible virus-host pairs, it can resolve complete virus-host interaction networks and supports a new avenue to apply network thinking to viral ecology.

bioinformatics↗

bioRxiv2023

Fluctuating environments promote evolvability by shaping adaptive variation accessible to populations

Living systems are surprisingly effective at exploiting new opportunities, as evidenced by the rapid emergence of antimicrobial resistance and novel pathogens. How populations attain this level of evolvability and the various ways it aids their survival are major open questions with direct implications for human health. Here, we use digital evolution to show that particular kinds of environments facilitate the simultaneous evolution of high mutation rates and a distribution of mutational effects skewed towards beneficial phenotypes. The evolved mutational neighborhoods allow rapid adaptation to previously encountered environments, whereas higher mutation rates aid adaptation to completely new environmental conditions. By precisely tracking evolving lineages and the phenotypes of their mutants, we show that evolving populations localize on phenotypic boundaries between distinct regions of genotype space. Our results demonstrate how evolution shapes multiple determinants of evolvability concurrently, fine-tuning a populations adaptive responses to unpredictable or recurrent environmental shifts.

evolutionary biology↗

bioRxiv2022

Artificial selection methods from evolutionary computing show promise for directed evolution of microbes

Directed microbial evolution harnesses evolutionary processes in the laboratory to construct microorganisms with enhanced or novel functional traits. Attempting to direct evolutionary processes for applied goals is fundamental to evolutionary computation, which harnesses the principles of Darwinian evolution as a general purpose search engine for solutions to challenging computational problems. Despite their overlapping approaches, artificial selection methods from evolutionary computing are not commonly applied to living systems in the laboratory. In this work, we ask if parent selection algorithms--procedures for choosing promising progenitors--from evolutionary computation might be useful for directing the evolution of microbial populations when selecting for multiple functional traits. To do so, we introduce an agent-based model of directed microbial evolution, which we used to evaluate how well three selection algorithms from evolutionary computing (tournament selection, lexicase selection, and non-dominated elite selection) performed relative to methods commonly used in the laboratory (elite and top-10% selection). We found that multi-objective selection techniques from evolutionary computing (lexicase and non-dominated elite) generally outperformed the commonly used directed evolution approaches when selecting for multiple traits of interest. Our results motivate ongoing work transferring these multi-objective selection procedures into the laboratory. Additionally, our findings suggest that more sophisticated artificial selection methods from evolutionary computation should also be evaluated for use in directed microbial evolution.

evolutionary biology↗

bioRxiv2021

Evolution with private resources reverses some changes from long-term evolution with public resources

A population under selection to improve one trait may evolve a sub-optimal state for another trait due to tradeoffs and other evolutionary constraints. How this evolution affects the capacity of a population to adapt when conditions change to favor the second trait is an open question. We investigated this question using isolates from a lineage spanning 60,000 generations of the Long-Term Evolution Experiment (LTEE) with Escherichia coli, where cells have access to a shared pool of resources, and have evolved increased competitive ability and a concomitant reduction in numerical yield. Using media-in oil emulsions we shifted the focus of selection to numerical yield, where cells grew in isolated patches with private resources. We found that the time spent evolving under shared resources did not affect the ability to re-evolve toward higher numerical yield. The evolution of numerical yield commonly occurred through mutations in the phosphoenolpyruvate phosphotransferase system. These mutants exhibit slower uptake of glucose, making them poorer competitors for public resources, and produce smaller cells that release less carbon as overflow metabolites. Our results demonstrate that mutations that were not part of adaptation under one selective regime may enable access to ancestral phenotypes when selection changes to favor evolutionary reversion.

evolutionary biology↗

bioRxiv2021

Host-parasite coevolution promotes innovation through deformations in fitness landscapes

During the struggle for survival, populations occasionally evolve new functions that give them access to untapped ecological opportunities. Theory suggests that coevolution between species can promote the evolution of such innovations by deforming fitness landscapes in ways that open new adaptive pathways. We directly tested this idea by using high throughput gene editing-phenotyping technology (MAGE-Seq) to measure the fitness landscape of a virus, bacteriophage {lambda}, as it coevolved with its host, the bacterium Escherichia coli. Through computer simulations of {lambda}s evolution on the empirical fitness landscape, we showed that {lambda} was more likely to evolve to use a new receptor if it experienced a shift in its fitness landscape caused by coevolution. This result was further validated by additional laboratory experiments. This study provides direct evidence for the role of coevolution in driving evolutionary novelty and provides a quantitative framework for predicting evolution in coevolving ecological communities.

evolutionary biology↗

bioRxiv2017

Accelerating Evolutionary Hill Climbs in Parallel Turbidostats

Evolution has been used to address many engineering problems. Within the context of metabolic engineering and synthetic biology, directed evolution has natural appli-cations. However, most research concerning optimizing microbial evolution has been focused on library generation and screening, while accelerating evolutionary hill climbs and been largely ignored. Here, we develop a model to explore how population struc-ture can accelerate evolutionary hill climbs. We show that by adjusting the population size, environmental challenge, and meta-population dynamics that the rate of evolution can be accelerated in parallel turbidostats. Our analyses leads to two surprising results: small populations are favored over conventionally large microbial populations, and propagating modest fitness improvements is favored over propagating mutants with large beneficial mutations. When combined with rational design and other optimization techniques our theory can accelerate strain development for applications such as consolidated bioprocessing, and bioremidation systems.

synthetic biology↗