Periodic Reporting for period 3 - EVOLPROOF (Are HPV vaccines ‘evolution-proof’? Multilevel evolutionary ecology of human oncoviruses)
Reporting period: 2018-09-01 to 2020-02-29
Vaccination, together with antibiotics, have been instrumental in decreasing childhood mortality and increasing general life expectancy. As illustrated by the generalisation of antibiotic resistance, microbial evolution can threaten our ability to prevent and cure infections. Several examples also show that bacteria and viruses can evolve in response to vaccination. This evolution can render a vaccine less effective or, worse, select for more virulent parasites. In summary, society needs to be one step ahead of parasite evolution.
Our idea is to use concepts and tools from ecology and evolution to assess whether HPV vaccines are evolution-proof. This is done in two steps: first understand what happens inside a person infected by HPV and second use this knowledge to simulate the spread and evolution of the virus in the human population. Importantly, this work requires setting up a clinical study. Since currently most of the research on HPV is performed on chronic infections, we know extremely little about HPV infections occurring in young adults, even though these infections represent the vast majority of infections.
Our overall objective is to capture the ecological dynamics of HPV infections happening inside patients. Using mathematical models, we will parameterise and compare these models using clinical data and then use these results to simulate HPV epidemiology and evolution.
Second, we established an experimental lab. This meant importing existing protocols but also creating new ones. The most difficult step was to establish a protocol to count immune cells in cervical smears using flow cytometry technology.
Third, we built mathematical models to capture the interactions between HPV, epithelial cells, the vaginal microbes and the immune system. The first step was to describe the dynamics of a viral infection that is not systemic (contrarily to the ones that are considered by most models). We showed that accounting for the epithelial cell life cycle is essential to understand the infection develops. We also combined our mathematical models with experimental data from experimental cell cultures obtained by another lab to further demonstrate the usefulness of our framework. Our second step was to introduce more realism in the immune response into the model, especially the innate immune response. The third step is to focus more on the microbes of the vagina and study their ecological interactions.
Fourth, we begun to investigate questions at the between-host level using existing models. In particular, we focused on the epidemiological of coinfections that is the simultaneous infection of one person by two or more HPV types.
Importantly, we have also set up a network of collaborators and advisors on several aspects of the project such as microbial dynamics, clinical studies, immunity to HPV infections and flow cytometry. We have hosted workshops and are writing a proposal to coordinate a special issue for a journal.
Our clinical study is, to the best of our knowledge, one of the most ambitious clinical study aimed at understanding the within-host ecology and evolution of a human pathogen. The amount of information and data collected at each visit (virus quantity, immune cell and protein counts, microbial composition, circulating antibodies, etc.) is also unique in the context of HPV.
In the laboratory, we have extended a method to analyse cervical samples using a cell counting technology called flow cytometry, which allows us to count several kinds of immune cells using 10 different markers.
Here are some of the results we expect to obtain by the end of the project:
- Understand the dynamics and interactions of HPV acute infections in young adults (i.e. parameterising and comparing within-host models).
- Better understand of the role of stochasticity (i.e. ""chance"") in the persistence of HPV infections.
- Describe the human immune response to HPV acute infections and its role in clearing the infection.
- Better understand vaginal microbial dynamics by fitting models to data.
- Detect early signals of HPV clearance if any.
- Understand the interactions between the virus, microbiota and human genome in the context of HPV infections.
- Automatize immune cell counts data obtained from cervical samples using clustering algorithms
- Identify susceptibility factors to HPV infections if any.
- Nest models of within-host dynamics into population-level epidemiological models to predict HPV rapid evolution in response to vaccination.
- Model the evolution of HPV virulence.
- Better understand the potential effect of coinfections by different types on HPV evolution."