The initial immune response to an acute primary infection is a coupled process of antigen proliferation, molecular recognition by naive B cells, and their subsequent clonal expansion. This process contains a fundamental problem: the recognition of an exponentially time-dependent antigen signal. Here, we show that an efficient immune response must be stringently constrained to B-cell lineages with high antigen binding affinity. We propose a tuned proofreading mechanism for primary recognition of new antigens, where the molecular recognition machinery is adapted to the complexity of the immune repertoire. We show that this process produces potent, specific, and fast recognition of antigens, maintaining a spectrum of genetically distinct B-cell lineages as input for affinity maturation. Our analysis maps the proliferation-recognition dynamics of a primary infection to a generalized Luria-Delbrück process, akin to the dynamics of the classic fluctuation experiment. This map establishes a link between signal recognition dynamics and evolution. 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, which define the class of generalized Luria-Delbrück processes. Their exponents depend on the antigen and B-cell proliferation rate, the number of proofreading steps, and the lineage density of the naive repertoire. We extend the model to include spatiotemporal processes, including the diffusion-recognition dynamics of a vaccination. Empirical data of activated mouse immune repertoires are found to be consistent with activation involving about three proofreading steps. The model predicts key clinical characteristics of acute infections and vaccinations, including the emergence of elite neutralizers and the effects of immune aging. More broadly, our results establish infections and vaccinations as a new probe into the global architecture and functional principles of immune repertoires.

Nonequilibrium Antigen Recognition during Infections and Vaccinations

New paper on antigen recognition dynamics appeared in Phys. Rev. X

Matthijs Meijers, Denis Ruchnewitz, Jan Eberhardt, Malancha Karmakar, Marta Łuksza, Michael Lässig

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.

The manuscript will appear in Influenza Virus: Methods and Protocols, 2nd edition, Methods in Molecular Biology (MiMB), ed. M.J. Amorim and Y. Yamauchi, Springer Nature, in press.

Concepts and methods for predicting viral evolution

Concepts and methods for predicting viral evolution, Springer Nature

Matthijs Meijers, Denis Ruchnewitz, Jan Eberhardt, Marta Łuksza, Michael Lässig

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, and antigenic changes that reduce cross- immunity induced by previous infections or vaccinations. How this functional variation shapes global evolu- tion has remained unclear. Here, we establish a predictive fitness model for SARS-CoV-2 that integrates antigenic and intrinsic selection. 

The model is informed by tracking of time-resolved sequence data, epide- miological records, and cross-neutralization data of viral variants. Our inference shows that immune pres- sure, including contributions of vaccinations and previous infections, has become the dominant force driving the recent evolution of SARS-CoV-2. The fitness model can serve continued surveillance in two ways. First, it successfully predicts the short-term evolution of circulating strains and flags emerging variants likely to displace the previously predominant variant. Second, it predicts likely antigenic profiles of successful escape variants prior to their emergence.

Population immunity predicts evolutionary trajectories of SARS-CoV-2 – new paper in appeared in Cell

New paper on predicting the evolution of SARS-CoV-2 appeared in Cell

We are pleased to announce that we were awarded new grant within the CEIRR network. The grant will support collaboration between theory and experimental researchers at Previr and the lab of Adolfo Garcia Sastre at Mount Sinai, New York. The aim of the funded project is to enhance our understanding of the immune response to influenza vaccines.

The Centers of Excellence for Influenza Research and Response (CEIRR) Network is a multidisciplinary and collaborative research network funded by the National Institute of Allergy and Infectious Diseases (NIAID) to study the natural history, transmission, and pathogenesis of influenza and provide an international research infrastructure to address influenza outbreaks.