Antigenic variation in malaria involves a highly structured switching pattern Mario Recker Department of Zoology, University of Oxford

Mathematical approach to (understand) malaria “the mathematical method of treatment is really nothing but the application of careful reasoning to the problems at issue.” Ross R, 1911. The Prevention of Malaria . London: John Murray. Sir Ronald Ross 20. Aug. 1897 ??? ? ? Macdonald G, 1957. The Epidemiology and Control of Malaria from McKenzie & Samba, AJTMH 2004

Most targets of protective immunity polymorphic surface proteins Development of immunity / effective vaccines hindered by extensive antigenic diversity: - mutation / recombination (genotypic change) - antigenic variation (no genotypic change) circumsporozoite protein (CSP) merozoite surface proteins (MSP) variant surface antigens (VSA) diversity Major multigene families: rif > 150 copies per genome stevor 30 copies per genome Pfmc-2TM 13 copies per genome var 60 copies per genome Scherf et al., Annu Rev Microbiol 2008 www.fda.gov/CbER/blood/malaria071206sk5.gif

Sequence diversity of var genes is immense! adapted from Gardner, M. et al., 2002, Kyes, S. et al., 2002 cumulative diversity of DBLa seqnuences pairwise sharing among DBLa seqnuences from Barry et al, PLoS Pathog. 2007

Antigenic variation in P.falciparum PfEMP1 (P. falciparum Erythrocyte Membrane Protein 1) embedded on surface of red cell causes severe disease through adherence to host cell receptors important immune target IE binding to endothelium IE binding to erythrocytes IE binding to dendritic cell EM by D. Ferguson, Oxford Univ.

Infected blood cells sequester in tissue capillaries EM by D. Ferguson, Oxford Univ.

(Molecular) Requirement for antigenic variation - every var gene recognised as part of a family - mechanism to limit expression to a single copy - activation coinciding with silencing of previously active gene - cellular memory to avoid ‘early’ repertoire exhaustion Result: succeeding waves of parasitaemia dominated by a single variant of PfEMP1 Scherf et al., Annu Rev Microbiol 2008 PfSir2: P.falciparum silent information regulator TPE: telomere position effect what orchestrates expression at population level?

- use mathematical models to create and test hypotheses - What orchestrates sequential dominance? - use mathematical models to create and test hypotheses - For example: differences in growth rates or probabilities in switch rates (e.g. Kosinski, 1980) differences in growth rates or probabilities in switch rates (e.g. Kosinski, 1980) differences in growth rates or probabilities in switch rates (e.g. Kosinski, 1980) differences in growth rates or probabilities in switch rates (e.g. Kosinski, 1980) differential susceptibilities assigned to variants expressing two surface proteins (e.g. Agur et al., 1989) differential susceptibilities assigned to variants expressing two surface proteins (e.g. Agur et al., 1989) differential susceptibilities assigned to variants expressing two surface proteins (e.g. Agur et al., 1989) differential susceptibilities assigned to variants expressing two surface proteins (e.g. Agur et al., 1989) modifications of switch rates by ‘natural selection’ (Frank, 1999) modifications of switch rates by ‘natural selection’ (Frank, 1999) modifications of switch rates by ‘natural selection’ (Frank, 1999) immunological interaction, e.g. cross-immunity (e.g. Recker et al, 2004) immunological interaction, e.g. cross-immunity (e.g. Recker et al, 2004)

Agglutinating antibody titer Increases in levels of antibodies to VSA expressed by heterologous isolates are transient and limited. 58 10 20 30 40 50 60 18 55 Percentage of infected red cells positive Agglutinating antibody titer time after infection

Model assumption: each variant comprises a unique major epitope which elicits variant specific, long-lived immune response a number of minor epitopes which elicits transient, cross-reactive immune response

The model m’>>m dynamics of variant i: intrinsic growth rate clearance by specific response clearance by cross-reactive response dynamics of specific response zi: immune response proportional to antigen decay rate m’>>m dynamics of transient, cross-reactive response, wi: transient immune response proportional to antigen variants with shared epitopes

Mathematical model without switching Recker et al, Nature 2004 Model suggested that parasite-host relationship has evolved to favour some short-lived immune responses that allow the parasite to persist and the host to survive

In vitro switching dynamics Horrocks et al. (PNAS, 2004) showed on and off rates for a given variant are dissimilar on and off rates vary dramatically among different variants var 1 var 2 var 3 time Abundance - rates appear to be intrinsic property of a particular gene - → could introduce a hierarchy of expression whereby stable variants are more prominently expressed, at least during the early phases of infection?

stable dominance of initial variant initial variant replaced To investigate the nature of var gene switching, generate transcription profiles for the entire repertoire in clonal parasite populations and measure the change in that profile over time stable dominance of initial variant 1st generation 2nd generation initial variant replaced

change over generations determined by:

- use mathematical model to determine most likely switching pathway - off rate switch bias on rate use iterative approach to find ‘best-fit’ switch matrix and off-rate vector

Switch matrix: Switch sequence: 1→2 → 4 → 3 →

Clone 3D7_AS2

Clone It_B2B

even for a stable clone…

Clone B10 Clone B12 Data provided by Dzwikowski, Frank & Deitsch

To test the validity of this prediction, examine the var transcript distribution in Clone 2 every few generations

protection of repertoire protection against immune attack Evolutionary conflict: protection of repertoire vs. protection against immune attack repertoire protection: immune evasion:

protection of repertoire protection against immune attack Evolutionary conflict: protection of repertoire vs. protection against immune attack Assume var gene repertoire as a network where - nodes = variants - edges = switch / transition probabilities Task: optimise network over two traits - pathlength (= repertoire protection) - robustness (= adaptability to selection pressure)

Clone 3D7_AS2

Clone It_B2B

Investigate effects of hierarchical switching for in vivo dynamics

naïve host highly structured switching results in (significantly?) increased length of infection.

naïve host highly structured switching results in (significantly?) increased length of infection. semi-immune host sms and lattice-type pathways far more flexible in overcoming pressure from pre-existing immune responses to help set up chronic infections.

Antigenic relationship between variants minor epitope 1 a b c d e u v minor epitope 2 x y z Switch sequence: (au) → (bu,av) → (cx) → (dx,cy) →…

Summary for pathogens with a limited antigenic pool, such as P. falciparum, tight control over variant expression is essential tightly ordered gene activation requires every subsequent variant to be able to evade current immune responses and therefore may be compromised by previous infections highly structured switching in P. falciparum has evolved as an evolutionary compromise between the protection of its limited antigenic repertoire and the flexibility to fully utilise this repertoire when needed

Acknowledgements Sunetra Gupta Sam Kinyanjui Caroline Buckee Pete Bull University of Oxford Department of Zoology Sunetra Gupta Caroline Buckee Robert Noble Sam Kinyanjui Pete Bull Kevin Marsh Chris Newbold Andrew Serazin Sue Kyes Zóe Christodoulou Robert Pinches