Biology subjects

Lässig, M.

Publications and source records attributed to Lässig, M..

11 recordsLinked to original sources

bioRxiv2025

Detection of low pre-existing humoral immunity against influenza virus H5N1 clade 2.3.4.4b in unexposed individuals

The spill-over of Influenza A virus H5N1 clade 2.3.4.4b from cattle to humans highlights the risk of a human H5N1 pandemic. Given the impact of pre-existing immunity on the course and severity of viral infections, we comprehensively assessed the humoral immunity against the H5N1 A/Texas/37/2024 isolate in H5N1-naive individuals. To this end, we performed complementary binding and neutralization assays on 66 subjects and ranked activities among a panel of 76 influenza A virus isolates. We detected low but distinct cross-neutralizing titers against A/Texas/37/2024 with a 3.9 to 15.6-fold reduction compared to selected H1N1 or H3N2 strains. By cloning and evaluating 136 monoclonal antibodies from memory B cells, we identified potent A/Texas/37/2024-neutralizing monoclonal antibodies in five out of six investigated individuals. These antibodies cross-neutralize H1, compete with antibodies targeting the HA stem, and protect mice from lethal H5N1 challenge. Our findings demonstrate partial pre-existing humoral immunity to A/Texas/37/2024 in H5N1-naive individuals.

immunology↗

bioRxiv2024

Resource allocation in biochemically structured metabolic networks

Microbes tune their metabolism to environmental challenges by changing protein expression levels, metabolite concentrations, and reaction rates simultaneously. Here, we establish an analytical model for microbial resource allocation that integrates enzyme biochemistry and the global architecture of metabolic networks. We describe the production of protein biomass from external nutrients in pathways of Michaelis-Menten enzymes and compute the resource allocation that maximizes growth under constraints of mass conservation and metabolite dilution by cell growth. This model predicts generic patterns of growth-dependent microbial resource allocation to proteome and metabolome. In a nutrient-rich medium, optimal protein expression depends primarily on the biochemistry of individual synthesis steps, while metabolite concentrations and fluxes decrease along successive reactions in a metabolic pathway. Under nutrient limitation, individual protein expression levels change linearly with growth rate, the direction of change depending again on the enzymes biochemistry. Metabolite levels and fluxes show a stronger, nonlinear decline with growth rate. We identify a simple, metabolite-based regulatory logic by which cells can be tuned to near-optimal growth. Finally, our model predicts evolutionary stable states of metabolic networks, including local biochemical parameters and the global metabolite mass fraction, in tune with empirical data.

systems biology↗

bioRxiv2024

Concepts and methods for predicting viral evolution

The seasonal human influenza virus undergoes rapid evolution, leading to significant changes in circulating viral strains from year to year. These changes are typically driven by adaptive mutations, particularly in the antigenic epitopes, the regions of the viral surface protein haemagglutinin targeted by human antibodies. Here we describe a consistent set of methods for data-driven predictive analysis of viral evolution. Our pipeline integrates four types of data: (1) sequence data of viral isolates collected on a worldwide scale, (2) epidemiological data on incidences, (3) antigenic characterization of circulating viruses, and (4) intrinsic viral phenotypes. From the combined analysis of these data, we obtain estimates of relative fitness for circulating strains and predictions of clade frequencies for periods of up to one year. Furthermore, we obtain comparative estimates of protection against future viral populations for candidate vaccine strains, providing a basis for pre-emptive vaccine strain selection. Continuously updated predictions obtained from the prediction pipeline for influenza and SARS-CoV-2 are available on the website previr.app.

biophysics↗

bioRxiv2023

Immune recognition dynamics of a primary infection

The immune response to an acute primary infection is a coupled process of antigen proliferation, molecular recognition by naive B-cells, and their subsequent proliferation and antibody shedding. Here we show B-cells can efficiently recognise new antigens by a tuned kinetic proofreading mechanism, where the number of proofreading steps and the characteristic rate of each step are set by the complexity of the immune repertoire. This process produces potent, specific and fast recognition of antigens, maintaining a spectrum of genetically distinct B-cell lineages as input for affinity maturation. We show that the proliferation-recognition dynamics of a primary infection can me mapped onto a generalised Luria-Delbruck process, akin to the dynamics of the classic fluctuation experiment. We derive the resulting statistics of the activated immune repertoire: antigen binding affinity, expected size, and frequency of active B-cell clones are related by power laws. Their exponents depend on the antigen and B-cell proliferation rate, the number of proofreading steps, and the lineage density of the naive repertoire. Empirical data of mouse immune repertoires are found to be consistent with activation involving at least three proofreading steps. Our model predicts key clinical characteristics of acute infections. The primary immune response to a given antigen is strongly heterogeneous across individuals; few elite responders are distinguished by early activation of high-affinity clones. Conversely, ageing of the immune system, by reducing the density of naive clones, degrades potency and speed of pathogen recognition.

biophysics↗

bioRxiv2022

Vaccination shapes evolutionary trajectories of SARS-CoV-2

The large-scale evolution of the SARS-CoV-2 virus has been marked by rapid turnover of genetic clades. New variants show intrinsic changes, notably increased transmissibility, as well as anti-genic changes that reduce the cross-immunity induced by previous infections or vaccinations1-4. How this functional variation shapes the global evolutionary dynamics has remained unclear. Here we show that selection induced by vaccination impacts on the recent antigenic evolution of SARS-CoV-2; other relevant forces include intrinsic selection and antigenic selection induced by previous infections. We obtain these results from a fitness model with intrinsic and antigenic fitness components. To infer model parameters, we combine time-resolved sequence data5, epidemiological records6,7, and cross-neutralisation assays8-10. This model accurately captures the large-scale evolutionary dynamics of SARS-CoV-2 in multiple geographical regions. In particular, it quantifies how recent vaccinations and infections affect the speed of frequency shifts between viral variants. Our results show that timely neutralisation data can be harvested to identify hotspots of antigenic selection and to predict the impact of vaccination on viral evolution.

biophysics↗

bioRxiv2022

Two modes of evolution shape bacterial strain diversity in the gut for thousands of generations

How and at what pace bacteria evolve when colonizing healthy hosts remains unclear. Here, by monitoring evolution for more than six thousand generations in the mouse gut, we show that the successful colonization of an invader Escherichia coli depends on the diversity of the existing microbiota and the presence of a closely related strain. Following colonization, two modes of evolution were observed: one in which diversifying selection leads to long-term coexistence of ecotypes and a second in which directional selection propels selective sweeps. These modes can be quantitatively distinguished by the statistics of mutation trajectories. In our experiments, diversifying selection was marked by the emergence of metabolic mutations, and directional selection by acquisition of prophages, which bring their own benefits and costs. In both modes, we observed parallel evolution, with mutation accumulation rates comparable to those typically observed in vitro on similar time scales. Our results show that gut environments can rapidly generate diversifying selection and ecotype formation.

evolutionary biology↗

bioRxiv2021

Adaptive ratchets and the evolution of molecular complexity

Biological systems have evolved to amazingly complex states, yet we do not understand in general how evolution operates to generate increasing genetic and functional complexity. Molecular recognition sites are short genome segments or peptides binding a cognate recognition target of sufficient sequence similarity. Such sites are simple, ubiquitous modules of sequence information, cellular function, and evolution. Here we show that recognition sites, if coupled to a time-dependent target, can rapidly evolve to complex states with larger code length and smaller coding density than sites recognising a static target. The underlying fitness model contains selection for recognition, which depends on the sequence similarity between site and target, and a uniform cost per unit of code length. Site sequences are shown to evolve in a specific adaptive ratchet, which produces selection of different strength for code extensions and compressions. Ratchet evolution increases the adaptive width of evolved sites, accelerating the adaptation to moving targets and facilitating refinement and innovation of recognition functions. We apply these results to the recognition of fast-evolving antigens by the human immune system. Our analysis shows how molecular complexity can evolve as a collateral to selection for function in a dynamic environment.

biophysics↗

bioRxiv2020

Predicting trajectories and mechanisms of antibiotic resistance evolution

Bacteria evolve resistance to antibiotics by a multitude of mechanisms. A central, yet unsolved question is how resistance evolution affects cell growth at different drug levels. Here we develop a fitness model that predicts growth rates of common resistance mutants from their effects on cell metabolism. We map metabolic effects of resistance mutations in drug-free environments and under drug challenge; the resulting fitness trade-off defines a Pareto surface of resistance evolution. We predict evolutionary trajectories of dosage-dependent growth rates and resistance levels, as well as the prevalent resistance mechanism depending on drug and nutrient levels. These predictions are confirmed by empirical growth curves and genomic data of E. coli populations. Our results show that resistance evolution, by coupling major metabolic pathways, is strongly intertwined with systems biology and ecology of microbial populations.

evolutionary biology↗

bioRxiv2019

Eco-evolutionary control of pathogens

Control can alter the eco-evolutionary dynamics of a target pathogen in two ways, by changing its population size and by directed evolution of new functions. Here we develop a fitness model of eco-evolutionary control that specifies a minimum leverage for successful control against the intrinsic dynamics of the pathogen. We apply this model to pathogen control by molecular antibody-antigen binding with a tunable level of antibodies. By analytical solution, we obtain a phase diagram of optimal control and show that an error threshold separates regimes of successful and futile control. Our analysis identifies few, independently measurable fitness parameters that predict the outcome of control. We show that optimal control strategies depend on mutation rate and population size of the pathogen, and we discuss how monitoring and computational forecasting affect the efficiency of control. We argue that these results carry over to more general systems and are elements of an emerging eco-evolutionary control theory.

evolutionary biology↗

bioRxiv2018

Survival of the simplest: the cost of complexity in microbial evolution

The evolution of microbial and viral organisms often generates clonal interference, a mode of competition between genetic clades within a population. In this paper, we show that interference strongly constrains the genetic and phenotypic complexity of evolving systems. Our analysis uses biophysically grounded evolutionary models for an organisms quantitative molecular phenotypes, such as fold stability and enzymatic activity of genes. We find a generic mode of asexual evolution called phenotypic interference with strong implications for systems biology: it couples the stability and function of individual genes to the populations global speed of evolution. This mode occurs over a wide range of evolutionary parameters appropriate for microbial populations. It generates selection against genome complexity, because the fitness cost of mutations increases faster than linearly with the number of genes. Recombination can generate a distinct mode of sexual evolution that eliminates the superlinear cost. We show that positive selection can drive a transition from asexual to facultative sexual evolution, providing a specific, biophysically grounded scenario for the evolution of sex. In a broader context, our analysis suggests that the systems biology of microbial organisms is strongly intertwined with their mode of evolution.

evolutionary biology↗

bioRxiv2017

The asexual genome of Drosophila

The rate of recombination affects the mode of molecular evolution. In high-recombining sequence, the targets of selection are individual genetic loci; under low recombination, selection collectively acts on large, genetically linked genomic segments. Selection under linkage can induce clonal interference, a specific mode of evolution by competition of genetic clades within a population. This mode is well known in asexually evolving microbes, but has not been traced systematically in an obligate sexual organism. Here we show that the Drosophila genome is partitioned into two modes of evolution: a local interference regime with limited effects of genetic linkage, and an interference condensate with clonal competition. We map these modes by differences in mutation frequency spectra, and we show that the transition between them occurs at a threshold recombination rate that is predictable from genomic summary statistics. We find the interference condensate in segments of low-recombining sequence that are located primarily in chromosomal regions flanking the centromeres and cover about 20% of the Drosophila genome. Condensate regions have characteristics of asexual evolution that impact gene function: the efficacy of selection and the speed of evolution are lower and the genetic load is higher than in regions of local interference. Our results suggest that multicellular eukaryotes can harbor heterogeneous modes and tempi of evolution within one genome. We argue that this variation generates selection on genome architecture.\n\nAuthor SummaryThe Drosophila genome is an ideal system to study how the rate of recombination affects molecular evolution. It harbors a wide range of local recombination rates, and its high-recombining parts show broad signatures of adaptive evolution. The low-recombining parts, however, have remained dark genomic matter that has been omitted from most studies on the inference of selection. Here we show that these genomic regions evolve in a different way, which involves clonal competition and is akin to the evolution of asexual systems. This regime shows a lower efficacy of selection, a lower speed of evolution, and a higher genetic load than high-recombining regions. We argue these evolutionary differences have functional consequences: protein stability and protein expression are gene traits likely to be partially compromised by low recombination rates.

genetics↗