Biology subjects

Manhart, M.

Publications and source records attributed to Manhart, M..

18 recordsLinked to original sources

bioRxiv2026

Urea is a dynamic and functional component of mammalian breast milk

Urea is a constituent of human milk, yet its regulation and interactions with the infant gut microbiome remain unclear. Combining three human cohorts, a controlled mouse model, and complementary microbiome analyses, we show that milk urea increases across lactation, is conserved across mammals, and is not explained by circulating urea levels or maternal diet. Instead, we identify lactation-dependent remodeling of mammary urea-metabolism and transport pathways, including dynamic AQP3 localization in secretory epithelium, indicative of regulated transfer of circulating urea into breast milk. Alongside urea, maternal milk and stool provide complementary reservoirs of urea-metabolizing bacteria that help establish the infant "ureabiome", whose urea-utilization capacity is supported by metagenomics. This maternal-infant axis is context-dependent: infant formulas provide substantially higher urea levels than human milk, while functional assays show that urea supports both commensal and opportunistic bacteria in a concentration-dependent manner. Together, these findings define a maternal nitrogen axis through which lactation can shape the assembly and function of the infant gut microbiome.

microbiology↗

bioRxiv2026

Shifting resource limitation explains multiphasic patterns of density dependence

Several recent studies have presented sublinear density dependence as a universal phenomenon across species, driven by factors unrelated to resource limitation (e.g. predation or non-resource based inhibition). Through a combination of bacterial growth experiments and mathematical modelling, we instead show that regimes of sublinear density dependence readily emerge under sequential shifts in resource limitation, without the need to invoke other processes. We nevertheless predict that superlinear density dependence, driven by standard resource limitation, will still predominate at both high and low densities.

ecology↗

bioRxiv2026

Amino acid cross-feeding in E. coli globally and idiosyncratically alters mutant fitness

Cross-feeding between microbes is believed to be common in many natural ecosystems, especially between species that are auxotrophic for essential nutrients such as amino acids. Several recent studies have demonstrated that cross-feeding and other ecological interactions between microbes alter their evolution and the fitness of mutations. However, these studies focused on interactions between different species, where the nature of the interaction is complex due to the large genetic differences between species, making it difficult to directly attribute changes in mutant fitness to a specific interaction. To address this problem, we use a synthetic cross-feeding interaction between isoleucine and methionine auxotrophs of Escherichia coli, two commonly observed auxotrophies in bacterial genomes. Using a library of genome-wide knockout mutants from transposon insertions, we measure the fitness of these mutants in the presence and absence of cross-feeding. We find that cross-feeding either isoleucine or methionine globally shifts mutant fitness to become more beneficial on average by inducing strong positive selection on a few mutants and rescuing strongly deleterious mutants. However, cross-feeding also affects mutants idiosyncratically: the most beneficial mutants under cross-feeding are neutral or deleterious without cross-feeding, and cross-feeding isoleucine affects different genes than cross-feeding methionine does. We discover one spontaneous mutant, with especially dramatic idiosyncratic effects under isoleucine cross-feeding, that achieves this phenotype by becoming a partial threonine auxotroph and reversing its ancestral isoleucine auxotrophy. This work directly demonstrates the statistical patterns and possible mechanisms by which a common ecological interaction between microbes alters their mutant fitness.

microbiology↗

bioRxiv2026

A theoretical framework for how ecological interactions between microbes affect mutant fitness

The distribution of fitness effects (DFE) for spontaneous mutations characterizes both an organ-isms evolutionary potential as well as its genomic functions. The DFE of a genome depends on the specific environment in which it is measured, and for microbes a major feature of their environment is the presence of interactions with other species, such as competing for or cross-feeding nutrients. Several recent studies have empirically measured how the DFE of one microbial species changes in the presence of interactions with other species. However, the underlying mechanisms by which this happens, and the statistical patterns they are expected to produce, are unknown. Here we classify two types of statistical changes in the DFE: global changes to the DFE, such as to its mean or variance, and idiosyncratic changes in the fitness of individual mutants, summarized by the correlation of mutant fitness between environments. We first show that both types of effects occur in empirically measured DFEs across a wide range of species and interactions; idiosyncratic effects appear to have a maximum limit and constrain the size of global effects. We then show that a minimal model of an ecological interaction (competition for a single resource) is sufficient to generate both types of effects. Finally, we extend this model to arbitrary quantitative traits to reveal two general mechanisms of how interactions alter the DFE: 1) interactions can globally change fitness by altering the community growth rate, and 2) interactions can idiosyncratically change fitness of individual mutants by altering relative selection on different traits affected by those mutations.

evolutionary biology↗

bioRxiv2026

Plasmid transfer and loss probabilities interactively determine antibiotic resistance spread in surface-associated bacterial biomass

Plasmid transfer among bacteria is an evolutionary driver of the spread of antibiotic resistance. Surface-associated bacterial biomass is a hotspot for plasmid transfer due to the dense spatial packing of cells. Compared to plasmid dynamics within a single biomass unit, the determinants of plasmid transfer between discrete units are less understood. Yet, discrete units routinely physically collide with each other as they grow, such as in sparse biofilms. Here, we used individual-based modelling to quantitatively predict how the probabilities of plasmid transfer and loss affect plasmid spread between discrete bacterial colonies as they grow and physically collide into each other. We found that these factors have interactive effects on the extent of plasmid transfer and the subsequent proliferation of transconjugants. We further found that these effects are controlled by the spatial distance between the colliding colonies and the spatial positioning of plasmid-carrying cells along the collision boundary. We then experimentally tested our modelling predictions using strains of Pseudomonas stutzeri and Escherichia coli that can exchange an antibiotic resistance-encoding plasmid. Our study reveals that plasmid transfer between colliding biomass units is determined by the complex interplay between plasmid transfer, plasmid loss, and spatial constraints, expanding our understanding of plasmid dynamics.

microbiology↗

bioRxiv2025

Imbalance in gut microbial interactions as a marker of health and disease

Imbalances in the human gut microbiome (dysbioses) are linked to multiple diseases but remain poorly understood. Current biomarkers to identify dysbiosis are inconsistent and fail to capture the ecological mechanisms differentiating healthy from diseased microbiomes. We propose a general dysbiosis biomarker, inspired by phenomenology observed in a gut-microbiome theoretical model introduced here. The emergent communities show complex interaction networks and two distinct collective states, corresponding to healthy and dysbiotic microbiomes. Our robust metric for dysbiosis, by quantifying the balance between cooperation and competition, differentiates these states in both simulated and real datasets across diverse diseases. Moreover, it reveals that dysbiosis results from a shift toward greater cooperation in the community. Our metric further correlates with disease progression, highlighting its potential as a diagnostic tool.

microbiology↗

bioRxiv2024

Quantifying microbial fitness in high-throughput experiments

Few concepts are as central to evolution as is fitness, and yet the quantification of fitness is often ambiguous. In particular, high-throughput experiments to measure mutant fitness in microbes are increasingly common but vary widely in their definitions of fitness, which makes their results difficult to compare. What are the consequences of these different fitness statistics, and is there a best way to quantify fitness in a given context? Here we systematize the set of possible fitness statistics according to the following three choices: 1) the encoding of relative abundance (e.g., transforming by a log or logit function), 2) the time scale over which to measure the change in relative abundance, and 3) the choice of a reference subpopulation for calculating relative fitness in bulk competition experiments, such as those using DNA-barcoded mutants. We show that these different choices can lead to significantly different interpretations of mutant fitness, affecting the magnitude of fitness effects, the apparent presence of epistasis, and even the fitness ranking across mutants. This can confound predictions for evolutionary dynamics and gene functions. Altogether our results demonstrate the importance of consistent fitness definitions for reproducible results across experiments.

evolutionary biology↗

bioRxiv2023

Quantifying nutrient colimitation of microbial growth

Resource availability dictates how fast and how much microbial populations grow. Quantifying the relationship between microbial growth and resource concentrations makes it possible to promote, inhibit, and predict microbial activity. Microbes require many resources, including macronutrients (e.g., carbon and nitrogen), micronutrients (e.g., metals), and complex nutrients like vitamins and amino acids. When multiple resources are scarce, as occurs in nature, microbes may experience resource colimitation in which more than one resource limits growth simultaneously. Despite growing evidence for colimitation, the data is difficult to interpret and compare due to a lack of quantitative measures of colimitation and systematic tests of resource conditions. We hypothesize that microbes experience a continuum of nutrient limitation states and that nutrient colimitation is common in the laboratory and in nature. To address this, we develop a quantitative theory of resource colimitation that captures the range of possible limitation states and describes how they can change dynamically with resource conditions. We apply this approach to clonal populations of Escherichia coli to show that colimitation occurs in common laboratory conditions. We also show that growth rate and growth yield are colimited differently, reflecting their different underlying biology. Finally, we analyze environmental data to provide intuition for the continuum of limitation and colimitation conditions in nature. Altogether our results provide a quantitative framework for understanding and quantifying colimitation of microbes in biogeochemical, biotechnology, and human health contexts.

microbiology↗

bioRxiv2023

Physical contacts between sparse biofilms promote plasmid transfer and generate functional novelty

The horizontal transfer of plasmids is an important driver of microbial evolution, such as conferring antibiotic resistance (AR) to new genotypes. In biofilms, the abundance of cell-cell contacts promotes the frequent transfer of plasmids and their associated genes. In this study, we expand our knowledge about AR-encoding plasmids by investigating their transfer between discrete biofilms as the biofilms grow and physically collide with each other. Using an experimental system consisting of two fluorescently labelled Pseudomonas stutzeri strains and an Escherichia coli strain, we show that biofilm collisions promote plasmid transfer along the collision boundaries. The extent of plasmid transfer depends on the plasmid loss probability, the plasmid transfer probability, and the relative growth rates of plasmid-free and plasmid-carrying cells. We further show that the proliferation of plasmids after biofilm collision depends on the spatial positionings of plasmid-carrying cells along the collision boundary, thus establishing a link between the large-scale spatial distribution of discrete biofilms and the small-scale spatial arrangement of cells within individual biofilms. Our study reveals that plasmid transfer during biofilm collisions is determined by spatial factors operating at different organizational levels and length scales, expanding our understanding of the fate of plasmid-encoded traits in microbial communities.

microbiology↗

bioRxiv2022

Microbial population dynamics decouple nutrient affinity from environmental concentration

How the growth rate of a microbial population responds to the environmental availability of chemical nutrients and other resources is a fundamental question in microbiology. Models of this response, such as the widely-used Monod model, are generally characterized by a maximum growth rate and a half-saturation concentration of the resource. What values should we expect for these half-saturation concentrations, and how should they depend on the environmental concentration of the resource? We survey growth response data across a wide range of organisms and resources. We find that the half-saturation concentrations vary across orders of magnitude, even for the same organism and resource. To explain this variation, we develop an evolutionary model to show that demographic fluctuations (genetic drift) can constrain the adaptation of half-saturation concentrations. We find that this effect fundamentally differs depending on the type of population dynamics: populations undergoing periodic bottlenecks of fixed size will adapt their half-saturation concentration in proportion to the environmental resource concentration, but populations undergoing periodic dilutions of fixed size will evolve half-saturation concentrations that are largely decoupled from the environmental concentration. Our model not only provides testable predictions for laboratory evolution experiments, but it also reveals how an evolved half-saturation concentration may not reflect the organisms environment. In particular, this explains how organisms in resource-rich environments can still evolve fast growth at low resource concentrations. Altogether our results demonstrate the critical role of population dynamics in shaping fundamental ecological traits.

microbiology↗

bioRxiv2019

Evolutionary regain of lost gene circuit function

Evolutionary reversibility - the ability to regain a lost function - is an important problem both in evolutionary and synthetic biology, where repairing natural or synthetic systems broken by evolutionary processes may be valuable. Here, we use a synthetic positive-feedback (PF) gene circuit integrated into haploid Saccharomyces cerevisiae cells to test if the population can restore lost PF function. In previous evolution experiments, mutations in a gene eliminated the fitness costs of PF activation. Since PF activation also provides drug resistance, exposing such compromised or broken mutants to both drug and inducer should create selection pressure to regain drug resistance and possibly PF function. Indeed, evolving seven PF mutant strains in the presence of drug revealed three adaptation scenarios through genomic mutations outside of the PF circuit that elevate PF basal expression, possibly by affecting transcription, translation, degradation and other fundamental cell functions. Nonfunctional mutants gained drug resistance without ever developing high expression, while quasi-functional and dysfunctional PF mutants developed high expression which then diminished, although more slowly for dysfunctional mutants where revertant clones arose. These results highlight how intracellular context, such as the growth rate, can affect regulatory network dynamics and evolutionary dynamics, which has important consequences for understanding the evolution of drug resistance and developing future synthetic biology applications.\n\nSignificance StatementNatural or synthetic genetic modules can lose their function over long-term evolution if the function is costly. How populations can evolve to restore broken functions is poorly understood. To test the reversibility of evolutionary breakdown, we use yeast cell populations with a chromosomally integrated synthetic gene circuit. In previous evolution experiments the gene circuit lost its costly function through various mutations. By exposing such mutant populations to conditions where regaining gene circuit function would be beneficial we find adaptation scenarios with or without repairing lost gene circuit function. These results are important for drug resistance or future synthetic biology applications where loss and regain of function play a significant role.

systems biology↗

bioRxiv2019

Evolution of microbial growth traits under serial dilution

Selection of mutants in a microbial population depends on multiple cellular traits. In serial-dilution evolution experiments, three key traits are the lag time when transitioning from starvation to growth, the exponential growth rate, and the yield (number of cells per unit resource). Here we investigate how these traits evolve in laboratory evolution experiments using a minimal model of population dynamics, where the only interaction between cells is competition for a single limiting resource. We find that the fixation probability of a beneficial mutation depends on a linear combination of its growth rate and lag time relative to its immediate ancestor, even under clonal interference. The relative selective pressure on growth rate and lag time is set by the dilution factor; a larger dilution factor favors the adaptation of growth rate over the adaptation of lag time. The model shows that yield, however, is under no direct selection. We also show how the adaptation speeds of growth and lag depend on experimental parameters and the underlying supply of mutations. Finally, we investigate the evolution of covariation between these traits across populations, which reveals that the population growth rate and lag time can evolve a nonzero correlation even if mutations have uncorrelated effects on the two traits. Altogether these results provide useful guidance to future experiments on microbial evolution.

evolutionary biology↗

bioRxiv2019

Understanding the evolution of interspecies interactions in microbial communities

Microbial communities are complex multi-species assemblages that are characterized by a multitude of interspecies interactions, which can range from mutualism to competition. The overall sign and strength of interspecies interactions have important consequences for emergent community-level properties such as productivity and stability. It is not well understood whether and how interspecies interactions change over evolutionary timescales. Here, we review the empirical evidence that evolution is an important driver of microbial community properties and dynamics on timescales that have traditionally been regarded as purely ecological. Next, we briefly discuss different modelling approaches to study evolution of communities, emphasizing the similarities and differences between evolutionary and ecological perspectives. We then propose a simple conceptual model for the evolution of communities. Specifically, we propose that the evolution of interspecies interactions depends crucially on the spatial structure of the environment. We predict that in well-mixed environments, traits will be selected exclusively for their direct fitness effects, while in spatially structured environments, traits may also be selected for their indirect fitness effects. Selection of indirectly beneficial traits should result in an increase in interaction strength over time, while selection of directly beneficial traits should not have such a systematic effect. We tested our intuitions using a simple quantitative model and found support for our hypotheses. The next step will be to test these hypotheses experimentally and provide input for a more refined version of the model in turn, thus closing the scientific cycle of models and experiments.

ecology↗

bioRxiv2019

Inferring biophysical models of evolution from genome-wide patterns of codon usage

Frequencies of synonymous codons are typically non-uniform, despite the fact that such codons correspond to the same amino acid in the genetic code. This phenomenon, known as codon bias, is broadly believed to be due to a combination of factors including genetic drift, mutational biases, and selection for speed and accuracy of codon translation; however, quantitative modeling of codon bias has been elusive. We have developed a biophysical population genetics model which explains genome-wide codon frequencies observed across 20 organisms. We assume that codons evolve independently of each other under the influence of mutation and selection forces, and that the codon population has reached evolutionary steady state. Our model implements codon-level treatment of mutations with transition/transversion biases, and includes two contributions to codon fitness which describe codon translation speed and accuracy. Furthermore, our model includes wobble pairing - the possibility of codon-anticodon base pairing mismatches at the 3 nucleotide position of the codon. We find that the observed patterns of genome-wide codon usage are consistent with a strong selective penalty for mistranslated amino acids. Thus codons undergo purifying selection and their relative frequencies are affected in part by mutational robustness. We find that the dependence of codon fitness on translation speed is weaker on average compared to the strength of selection against mistranslation. Although no constraints on codon-anticodon pairing are imposed a priori, a reasonable hierarchy of pairing rates, which conforms to the wobble hypothesis and is consistent with available structural evidence, emerges spontaneously as a model prediction. Finally, treating the translation process explicitly in the context of a finite ribosomal pool has allowed us to estimate mutation rates per nucleotide directly from the coding sequences. Reminiscent of Drakes observation that mutation rates are inversely correlated with the genome size, we predict that mutation rates are inversely proportional to the number of genes. Overall, our approach offers a unified biophysical and population genetics framework for studying codon bias across all domains of life.

biophysics↗

bioRxiv2019

Chromosomal barcoding of E. coli populations reveals lineage diversity dynamics at high resolution

Evolutionary dynamics in large asexual populations is strongly influenced by multiple competing beneficial lineages, most of which segregate at very low frequencies. However, technical barriers to tracking a large number of these rare lineages have so far prevented a detailed elucidation of evolutionary dynamics in large bacterial populations. Here, we overcome this hurdle by developing a chromosomal barcoding technique that allows simultaneous tracking of [~]450,000 distinct lineages in E. coli. We used this technique to gather insights into the evolutionary dynamics of large (>107 cells) E. coli populations propagated for [~]420 generations in the presence of sub-inhibitory concentrations of common antibiotics. By deep sequencing the barcodes, we reconstructed trajectories of individual lineages at high frequency resolution (< 10-5). Using quantitative tools from ecology, we found that populations lost lineage diversity at distinct rates corresponding to their antibiotic regimen. Additionally, by quantifying the reproducibility of these dynamics across replicate populations, we found that some lineages had similar fates over independent experiments. Combined with an analysis of individual lineage trajectories, these results suggest how standing genetic variation and new mutations may contribute to adaptation to sub-inhibitory antibiotic levels. Altogether, our results demonstrate the power of high-resolution barcoding in studying the dynamics of bacterial evolution.

evolutionary biology↗

bioRxiv2018

Growth tradeoffs produce complex microbial communities without direct interactions

The relationship between the dynamics of a community and its constituent pairwise interactions is a fundamental problem in ecology. Higher-order ecological effects beyond pairwise interactions may be key to complex ecosystems, but mechanisms to produce these effects remain poorly understood. Here we show that higher-order effects can arise from variation in multiple microbial growth traits, such as lag times and growth rates, on a single limiting resource with no other interactions. These effects produce a range of ecological phenomena: an unlimited number of strains can exhibit multi stability and neutral coexistence, potentially with a single keystone strain; strains that coexist in pairs do not coexist all together; and the champion of all pairwise competitions may not dominate in a mixed community. Since variation in multiple growth traits is ubiquitous in microbial populations due to pleiotropy and non-genetic variation, our results indicate these higher-order effects may also be widespread, especially in laboratory ecology and evolution experiments.

ecology↗

bioRxiv2016

Tradeoffs between microbial growth phases lead to frequency-dependent and non-transitive selection

Mutations in a microbial population can increase the frequency of a genotype not only by increasing its exponential growth rate, but also by decreasing its lag time or adjusting the yield (resource efficiency). The contribution of multiple life-history traits to selection is a critical question for evolutionary biology as we seek to predict the evolutionary fates of mutations. Here we use a model of microbial growth to show there are two distinct components of selection corresponding to the growth and lag phases, while the yield modulates their relative importance. The model predicts rich population dynamics when there are tradeo[ff]s between phases: multiple strains can coexist or exhibit bistability due to frequency-dependent selection, and strains can engage in rock-paper-scissors interactions due to non-transitive selection. We characterize the environmental conditions and patterns of traits necessary to realize these phenomena, which we show to be readily accessible to experiments. Our results provide a theoretical framework for analyzing high-throughput measurements of microbial growth traits, especially interpreting the pleiotropy and correlations between traits across mutants. This work also highlights the need for more comprehensive measurements of selection in simple microbial systems, where the concept of an ordinary fitness landscape breaks down.

evolutionary biology↗

bioRxiv2016

Optimization of lag phase shapes the evolution of a bacterial enzyme

Mutations provide the variation that drives evolution, yet their effects on fitness remain poorly understood. Here we explore how mutations in the essential enzyme Adenylate Kinase (Adk) of E. coli affect multiple phases of population growth. We introduce a biophysical fitness landscape for these phases, showing how they depend on molecular and cellular properties of Adk. We find that Adk catalytic capacity in the cell (product of activity and abundance) is the major determinant of mutational fitness effects. We show that bacterial lag times are at a well-defined optimum with respect to Adks catalytic capacity, while exponential growth rates are only weakly affected by variation in Adk. Direct pairwise competitions between strains show how environmental conditions modulate the outcome of a competition where growth rates and lag times have a tradeoff, altogether shedding light on the multidimensional nature of fitness and its importance in the evolutionary optimization of enzymes.

evolutionary biology↗