Report

Teaser, summary, work performed and final results

Periodic Reporting for period 1 - STRUGGLE (Statistical physics of immune-viral co-evolution)

Teaser

The immune system within each individual host destroys viruses, which manage to escape immunity on the global scale. Recent experiments show population-level responses of both immune repertoires and viruses, and a history dependence of their functional phenotypes. This...

Summary

The immune system within each individual host destroys viruses, which manage to escape immunity on the global scale. Recent experiments show population-level responses of both immune repertoires and viruses, and a history dependence of their functional phenotypes. This constrained long-term co-evolution of immune receptor and viral populations is a stochastic many-body problem occurring at many scales, in which the response emerges based on the past states of both the repertoire and viral populations. STRUGGLE infers the details of viral-immune receptor interactions from functional datasets to obtain a predictive statistical model of co-evolution between immune repertoires and viruses.
STRUGGLE covers the many scales of immune-virus interactions: from the molecular level, analyzing high-throughput mutational screens of libraries of antibodies binding a given antigen, through the population-level response of immune repertoires, analyzing next-generation sequencing of vaccine- stimulated whole repertoires, to the population level, modeling the long-term co-evolution of both repertoires and viruses.
STRUGGLE combines a statistical data analysis approach with cross-scale many-body physics to:
- build a molecular model for antigen-receptor binding;
- learn statistical models for repertoire-level response to viral antigen stimulation;
- validate dynamical models of interactions between antigen and immune receptors;
- theoretically evaluate the predictive power of the immune system and viruses; - and predict virus strains and immune responses based on past infections.
The outcomes of STRUGGLE include the quantitative characterization of the human T-cell response to flu vaccines, with implications for vaccination strategies, and the trout B-cell response to life-threatening rhabdoviruses, which aids vaccine design for fish, with wide use in agriculture. The statistical properties of the co-evolutionary process are needed for informed development of immunotherapies.

Work performed

The initial scientific goals of the project involved devising algorithms for identifying the response from human repertoires data. We have developed three methods that describe the response to different kinds of perturbations: strong acute infections such as yellow fever from time dependent data, auto-immune related conditions (diabetes, CMV) from population level data, and acute or individual conditions from single time-point blood samples using sequence similarity. We have developed a method for identifying selection pressure coming from HIV - immune system co-evolution directly from data, and we identified the diversity and overlap of response in trout, and humans based on available data. We have re-analyzed previously obtained antigen-antibody binding landscape data identifying signs of beneficial epistasis, and tried to include extinction into evolving populations. We developed efficient algorithms that are necessary for identifying response and shared clonotypes between individuals. We developed a statistical analysis that characterises the generation of both chains in T-cell receptors.

Final results

The proposed algorithms for identifying response propose methods for controlling noise in repertoire sequencing data, in a way that is specific to this kind of datasets. We have also proposed an efficient algorithm for identifying response in single time-point samples, without the need to cohort level analysis. Our work on antigen-antibody models develops a carefully controlled null model for noninteracting mutations, allowing us to reliably identify epistasis.  We find that epistatically interacting sites contribute substantially to binding. In addition to negative epistasis, we report a large amount of beneficial epistasis, enlarging the space of high-affinity antibodies as well as their mutational accessibility. 

We will continue to work towards the goals of the project.

Website & more info

More info: https://www.phys.ens.fr/.