Host-pathogen coevolution and the emergence of broadly neutralizing antibodies in chronic infections

Abstract

The vertebrate adaptive immune system provides a flexible and diverse set of molecules to neutralize pathogens. Yet, viruses such as HIV can cause chronic infections by evolving as quickly as the adaptive immune system, forming an evolutionary arms race. Here we introduce a mathematical framework to study the coevolutionary dynamics of antibodies with antigens within a host. We focus on changes in the binding interactions between the antibody and antigen populations, which result from the underlying stochastic evolution of genotype frequencies driven by mutation, selection, and drift. We identify the critical viral and immune parameters that determine the distribution of antibody-antigen binding affinities. We also identify definitive signatures of coevolution that measure the reciprocal response between antibodies and viruses, and we introduce experimentally measurable quantities that quantify the extent of adaptation during continual coevolution of the two opposing populations. Using this analytical framework, we infer rates of viral and immune adaptation based on time-shifted neutralization assays in two HIV-infected patients. Finally, we analyze competition between clonal lineages of antibodies and characterize the fate of a given lineage in terms of the state of the antibody and viral populations. In particular, we derive the conditions that favor the emergence of broadly neutralizing antibodies, which may be useful in designing a vaccine against HIV. IntroductionIt takes decades for humans to reproduce, but our pathogens can reproduce in less than a day. How can we coexist with pathogens whose potential to evolve is 10 4 -times faster than our own? In vertebrates, the answer lies in their adaptive immune system, which uses recombination, mutation, and selection to evolve a response on the same time-scale at which pathogens themselves evolve.One of the central actors in the adaptive immune system are B-cells, which recognize pathogens using highly diverse membrane-bound receptors. Naive B-cells are created by processes which generate extensive genetic diversity in their receptors via recombination, insertions and deletions, and hypermutations [1] which can potentially produce ∼ 10 18 variants in a human repertoire [2]. This estimate of potential lymphocyte diversity outnumbers the total population size of B-cells in humans, i.e., ∼ 10 10 [3,4]. During an infection, B-cells aggregate to form germinal centers, where they hypermutate at a rate of about ∼ 10 −3 per base pair per cell division over a region of 1-2 kilo base pairs [5]. The B-cell hypermutation rate is approximately 4 − 5 orders of magnitude larger than an average germline mutation rate per cell division in humans [6]. Mutated B-cells compete for survival and proliferation signals from helper T-cells, based on the B-cell receptor's binding to antigens. This form of natural selection is known as affinity maturation, and * Correspondence should be addressed to: Armita Nourmohammad (armitan@princeton.edu).† Authors with equal contribution it can incr...

The size of the immune repertoire of bacteria

“…For both vertebrates and bacteria, a major challenge is to characterize dynamics of the immune system as it chases a diverse pathogenic population that is itself evolving to evade detection by its hosts. This out-of-equilibrium process can last over an extended evolutionary period (54). Earlier work has attempted to account for such coevolutionary dynamics between prokaryotes with CRISPR and a few (typically one) species of phage (6,8,(16)(17)(18)(19).…”

Mechanisms underlying vaccination protocols that may optimally elicit broadly neutralizing antibodies against highly mutable pathogens

“…Upon vaccinating with one variant antigen, AM produces memory B cells that can be stimulated and can undergo further evolution upon vaccinating with a second variant antigen, and so on. Every time a new variant antigen is introduced, the environment in which the existing memory B cells evolved changes, and the memory B cell population is driven from one steady-state to another [22]. So, sequential immunization results in the evolution of B cells by AM in a time-varying environment.…”

Eco-evolutionary control of pathogens

“…WC (Θc + Θ b < 0) cannot elicit an evolved state (or triggers a reversal to the wild type). evolution in a host population, where the control amplitude is a quantitative trait peaked at its population mean value ζ(t) (12). In this case, the payoff function takes again the form of a free fitness; i.e.,…”

“…Fig. 2 shows the landscapes of [12] for ecological and evolutionary control. In both cases, the pathogen has two local fitness maxima (solid and dashed lines) with a rank order depending on the antibody dosage.…”

“…However, control is often compromised by escape evolution of the pathogen, highlighting the importance to factor pathogen evolution into control protocols (2,3). Promising evolutionary avenues include adaptive pathogen control and cancer therapy (4)(5)(6), vaccination, drug development and immunotherapy strategies based on evolutionary predictions (7-10), and controlled evolution of immune antibodies (11)(12)(13). However, we need quantitative relations between leverage and cost of control in order to generate optimization criteria and protocols that are comparable across systems.…”