Using Multi-Locus Models to Understand Pathogen Evolution

Using multi-locus models to understand pathogen evolution

Sunetra Gupta Department of Zoology University of Oxford Can the epidemiological features of P. falciparum be explained by assuming that the parasite population consists of a set of distinct strains? Severe malarial anemia

Cerebral malaria

O’Meara et al. 2008 Lancet The life cycle of Plasmodium falciparum

From Protective hemoglobinopathies and Plasmodium falciparum transmission Geoffrey Pasvol Nature Genetics Volume: 42 , Pages: 284–285 Year published: (2010) Multi-locus models

part of the population immune to strain i

dzi = λi (1− zi )− µi zi a dt

part of the population immune to any strain sharing alleles with i x dw i 1 w w = ∑λ j ( − i )− µi i dt j

part of the population infectious for strain i

dyi = λi ((1− wi )+ (1−γ )(wi − zi ))−σ i yi dt part of the population immune to strain i

dzi = λi (1− zi )− µi zi dt

z ay

z ax

x z by

z bx part of the population immune to any strain sharing alleles with i

dw i 1 w w = ∑λ j ( − i )− µi i dt j

zay

zax a w x ax

zbx z ay z ay z ax z ax

z bx w z bx ax w ax z ax part of the population infectious for strain i

dyi = λi ((1− wi )+ (1−γ )(wi − zi ))−σ i yi dt Model dynamics under strong immune selection Strain frequency frequency Strain

Time Evolution of pathogen diversity

Immune Antigenic selection determinants

CO-CIRCULATING DISCRETE ANTIGENIC TYPES VR1-VR2 combinations 1989-1991

Martin Maiden

5 Number of isolates 21 10 19 17 18 22 7 0 12 VR1 Epitopes 4 13 1 27 9 26 25 24 15 28 del 16 2 10 VR2 Epitopes PorA VR combinations are non-random and non-overlapping Caroline Buckee

PNAS 2011 Meningococcal diversity

Housekeeping Transmission Genes factors Ro Virulence factors

Variable antigenic Outer determinants Membrane Proteins

Conserved determinant PNAS, 2008

Caroline Buckee Virulence factors

Housekeeping genes

Variable Immune antigenic determinants selection Non-overlapping associations between antigenic types and clonal Eleanor Watkins complexes (representing metabolic types) are characteristic of a number of other bacterial pathogens, including S. pneumoniae

Croucher et al (2013) Nature Genetics.

4.5 Pneumococcal protein conjugate 4 vaccines (PCV) can alter the genomic 3.5 3 all 19A

profile of non-vaccine serotypes, 2.5 19A cc695 potentially leading to an increase in 2 19A cc320 19A cc199 their transmissibility and virulence. 1.5 1

0.5

0 1999 2005 2006 2007 2008

Brueggemann et al (2007) PLoS Pathogens 3: e168. Evolution of pathogen diversity

Virulence factors

Housekeeping genes

Immune CO-CIRCULATING DISCRETE ANTIGENIC TYPES Antigenic (P. falciparum?, N. meningitidis) selection determinants

SEQUENTIALLY DOMINANT ANTIGENIC TYPES Can these principles also explain the population dynamics of influenza?

Mario Recker

The generation of influenza outbreaks by a Oliver Pybus network of host immune responses against a limited set of antigenic types

Recker M, Pybus OG, Nee S & Gupta S (2007) Proc Natl Acad Sci U S A. 104:7711-6. Sean Nee The epidemic behaviour of influenza is primarily Transmission determined by immune factors responses acting upon antigenic determinants of Virulence limited variability. factors

Immune Antigenic determinants selection

SEQUENTIALLY DOMINANT ANTIGENIC TYPES Successive emergence of unique antigenic types can occur within this simple framework in a manner that is independent of the mode and tempo of mutation

Time It is the shifting landscape of host immunity that determines when a new strain emerges rather than the mutational capabilities of the virus Epitopes differ in their degree of variability

Specific epitopes of high variability

Shared epitopes of intermediate variability Haemagglutinin Review articles

The product sk 0 specifies the efficacy of the group-specific virus population size very large. The critical transition occurs immune responses, such as antibodies or CTLs directed at when epitopes that are conserved between different virus strains. ru sk 0 The product pkD denotes strain-specific immune responses, D À : 5 pk ð Þ such as antibodies or CTLs directed at variable regions. The efficacy of these strain-specific responses depends on the Beyond this point, the total virus population grows antigenic diversity of the virus population. Equation 4 shows unboundedly. Equation (5) gives the diversity threshold. Once that increasing diversity (decreasing D) increases the total this threshold of viral diversity is exceeded, then the virus population size of the virus and hence drives disease pro- population escapes from control by the immune response and gression. The model has three distinct parameter regions, tends to arbitrarily high densities (Fig. 4). This process may be which correspond to three qualitatively different courses of interpreted as the development of immunodeficiency disease, infection. which is characterized by high virus counts and depletion of If ru>sk pk, there is no asymptomatic phase and the CD4 cells. During the asymptomatic phase, on the other 0þ þ virus population immediately replicates to high levels. In this hand, the diversity is increasing, but the immune system is case virus replication cannot be controlled by the combination able to control viral densities and to maintain CD4 cell levels Epitopes differ inof group-specific their and strain-specific degree responses. There of may bevariability(Fig. 4). no antigenic variation, but simply selection for the fastest The model describing the effect of antigenic escape growing virus strain. The immune system does not have time on disease progression has been illustrated in more detail to select for diversification. as an example of the more general principle that viral evo-

If sk 0>ru there is chronic infection, but no disease. In this lution in vivo can shift the dynamics between HIV and the KILLER case, the group-specific responses alone can control the immuneSpecific system over time, resulting in progression of the CELLS virus.This parameter region applies to non-pathogenic SIV epitopesdisease. of (18–21) infection. Thehigh evolutionary model of HIV disease progression The third, and most interesting, situation arises when the was controversial when introduced about 12 years ago(15) variability KILLER combined effects of group-specific and strain-specific im- and has remained so, but without warrant in our opi- CELLS mune responses are able to control the virus replication nion. A large number of experimental studies (reviewed in (of the individual strains), but the group-specific responses Ref. 17) have demonstrated the enormous potential of the alone are unable to do so. Mathematically this means that Sharedvirus to escape from any kind of selective pressure exerted by sk pk>ru>sk . If the virus diversity is low (D large) then CTL responses, antibody responses or drug treatment. As 0þ 0 invariant the total populationHIV-1 size is virus regulated to some equilibrium outlined by our theory, the necessary consequence is that the value (given by eq. 4). If viral diversity is high (D low) then epitopesviral population in any one patient will evolve away from control the denominator in eq. 4 becomes very small, and hence the by the immune system (or drug treatment) toward faster

Nowak et al, Science 1991

Figure 4. Evolution of antigenic diversity and HIV disease progression. During the initial acute phase of the infection, the virus population is relatively homogeneous. The immune system downregulates this initial viremia. During the asymptomatic phase of the infection, the virus evolves towards increased antigenic diversity. Once the diversity threshold has been crossed, the HIV-specific immunity collapses and the virus grows to high levels. More generally, there is not only evolution of increasing antigenic diversity but also faster virus replication and broader cell tropism. The evolutionary theory of HIV pathogenesis suggests that various selection pressures act on the virus and evolve a virus population that eventually can no longer be controlled by the immune responses.

BioEssays 24.12 1183 Epitopes also differ in the type of immune response they elicit

Neutralising antibodies Specific epitopes of high variability CD8+ T cells Shared invariant epitopes

…in both their quality and duration. A new model for the within-host dynamics of HIV-1

Neutralising antibodies Long-lived in Specific absence of antigen epitopes of high variability CD8+ T cells Shared Short-lived in epitopes of absence of HIV-1 virus low variability antigen

CD4+ T cells Breakdown of HIV-1 control is primarily due to loss of antibody induction rather than failure of cytotoxic CD8+ T cell responses

Paul Wikramaratna

Paul Klenerman

Oliver Pybus Chris Newbold Mario Recker Antigenic determinants can be virulence factors

var genes Transmission factors

Virulence factors

Cytoadherence Antigenic determinants

SEVERE MALARIA

Cerebral Malaria Severe Malarial Anaemia Diagrams showing processes that take place during the course of a simulation: (A) in a biting event, there is likeliness that multiple parasite genomes will be transmitted simultaneously.

Artzy-Randrup Y et al. eLife 2012;1:e00093 Characteristic time series demonstrating the emergence and maintenance of population structure under three levels of recombination probabilities, 0.001 (a), 0.01 (b) and 0.1 (c), while keeping all other parameters equal.

Artzy-Randrup Y et al. eLife 2012;1:e00093 Figure 1. Schematic illustrating the hierarchical organization of var domains, genes, and genome repertoires.

Buckee CO, Recker M (2012) Evolution of the Multi-Domain Structures of Virulence Genes in the Human Malaria Parasite, Plasmodium falciparum. PLoS Comput Biol 8(4): e1002451. doi:10.1371/journal.pcbi.1002451 http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002451 “Pung it all together”

Perhaps the most striking feature of eﬀorts to model evoluon in host-pathogen systems is the tendency to add genecs and/or ﬁtness funcons to an exisng ecological framework, or to add epidemiology to exisng genecal models. There may be value in aempng to develop models incorporang both populaon and evoluonary dynamics from the start.

G&D 1995: Genecs and Evoluon Group Acknowledgements

Mario Recker Martin Maiden Caroline Buckee Keith Jolley Eleanor Watkins Ian Feavers Paula Kriz Oliver Pybus Chris Newbold Sean Nee Bob Snow Neil Ferguson Kevin Marsh Roy Anderson Karen Day Robert May Brian Greenwood