Universität zu Köln

Meijers, M ORCID: 0000-0002-1876-8064 (2024). The antigenic evolution of fast-evolving viruses. PhD thesis, Universität zu Köln.

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Abstract

During antigenic evolution, a virus alters its presentation to the immune system, reducing the ability of the immune system to recognize and protect against the virus. Those viruses that most successfully escape from immune protection are selected for in the evolutionary process. These viruses are most likely to predominate in the future viral population. The selection pressures that shape this evolution are computable from models that use molecular input data on the interaction between the virus and the immune system. Given these selection pressures, future evolutionary trajectories of viruses can be predicted. These predictions help the timely identification of emerging variants and inform the most protective antigenic composition of vaccinations. Alternatively, given frequency trajectories of viral evolution, key parameters can be learned that describe the molecular interaction between the virus and the immune system. These parameters describe the effects of immune therapy while considering the evolutionary response of the virus explicitly. In this thesis, I discuss the antigenic evolution of three fast-evolving viruses. First, I analyze the in vivo antigenic escape evolution of HIV-1 from a broadly neutralizing antibody. In this analysis, I use data from clinical trials to infer key fitness parameters that determine escape evolution across multiple hosts. Second, I present an antigenic model to predict the evolutionary trajectories of SARS-CoV-2. The model is the first to use human antigenic data to predict viral evolution. In this work, I combine genetic, epidemiological, and antigenic data to model the population immunity that determines viral fitness. The fitness model can predict the short-term evolution of SARS-CoV-2, as well as predict the antigenic profile of future escape variants. Finally, I present a set of methods for the evolutionary analysis of influenza. It contains methods on evolutionary tracking, inference of selection, inference and tracking of population immunity, fitness modelling, and computation of vaccine protection. The focus is on influenza, but the methods are also relevant to other respiratory viruses, in particular SARS-CoV-2. Together, the work shows how the selection pressures that steer the antigenic evolution of viruses can be computed. Antigenic evolution is predictable. Fitness modelling of antigenic evolution can aid the design of better immune therapies against HIV-1 and improve the antigenic composition of vaccinations that protect people against respiratory viruses.

Item Type:	Thesis (PhD thesis)
Creators:	Creators Email ORCID ORCID Put Code Meijers, M mmeijers@uni-koeln.de orcid.org/0000-0002-1876-8064 UNSPECIFIED
URN:	urn:nbn:de:hbz:38-732526
Date:	22 July 2024
Language:	English
Faculty:	Faculty of Mathematics and Natural Sciences
Divisions:	Faculty of Mathematics and Natural Sciences > Department of Physics > Institut für Biologische Physik
Subjects:	Physics
Uncontrolled Keywords:	Keywords Language Biophysics; Evolution; Population immunity English
Date of oral exam:	11 July 2024
Referee:	Name Academic Title Lässig, Michael Prof. Dr. Krug, Joachim Prof. Dr. Greenbaum, Benjamin Prof. Dr.
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/73252