Antigenic variation in malaria involves a highly structured switching pattern

Antigenic variation in malaria involves

a highly structured switching pattern

Mario Recker

Department of Zoology, University of Oxford

Mathematical approach to (understand) malariaMathematical approach to (understand) malaria

Sir Ronald Ross20. Aug. 1897

“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.

Macdonald G, 1957. The Epidemiology and Control of Malaria

from McKenzie & Samba, AJTMH 2004

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www.fda.gov/CbER/blood/malaria071206sk5.gif

Most targets of Most targets of protectiveprotective immunity polymorphic surface immunity polymorphic surface

proteinsproteinsDevelopment of immunity / effective vaccines hindered by extensive antigenic diversity:

- mutation / recombination (genotypic change)

- antigenic variation (no genotypic change)

Major multigene families:

o rif > 150 copies per genomeo stevor 30 copies per genomeo Pfmc-2TM 13 copies per genome

o var 60 copies per genome

Scherf et al., Annu Rev Microbiol 2008

circumsporozoite protein (CSP)

merozoite surface proteins (MSP)

variant surface antigens (VSA)

diversity

Sequence diversity of var genes is immense!

from Barry et al, PLoS Pathog. 2007

cumulative diversity of DBL seqnuences pairwise sharing among DBL seqnuences

adapted from Gardner, M. et al., 2002, Kyes, S. et al., 2002

Antigenic variation in Antigenic variation in P.falciparumP.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

PfEMP1

var 1

t1

var 2

t2

var 3

t3

PfEMP1

var 1var 1

t1

var 2

t2

var 2

t2

var 3

t3

var 3var 3

t3

Days of infection

102

104

106

40 80 120 160 200

Nu

mb

er

of

para

sit

es

Days of infection

102

104

106

40 80 120 160 200

Nu

mb

er

of

para

sit

es

EM by D. Ferguson, Oxford Univ.

EM by D. Ferguson, Oxford Univ.

Infected blood cells sequester in tissue capillaries

succeeding waves of parasitaemia

dominated by a single variant of PfEMP1

(Molecular) Requirement for antigenic variation(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

PfEMP1

Infected RBC

var

n = 1

var

n = 1

var

n = ~59

var

n = ~59

var

n = ~59

var

n = ~59

ResultResult:

PfSir2: P.falciparum silent information regulator

TPE: telomere position effect

Scherf et al., Annu Rev Microbiol 2008

what orchestrates expression at population level?what orchestrates expression at population level?

What orchestrates sequential dominance?

- use mathematical models to create and test hypotheses -

• 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)

• modifications of switch rates by ‘natural selection’(Frank, 1999)

• immunological interaction, e.g. cross-immunity(e.g. Recker et al, 2004)

For example:






• immunological interaction, e.g. cross-immunity(e.g. Recker et al, 2004)




58

0

10

20

30

40

50

60

18

50

0

10

20

30

40

50

6055

Per

cen

tage

of

infe

cted

red

cel

ls p

osit

ive

Agg

luti

nat

ing

anti

bod

y ti

ter

Increases in levels of antibodies to VSA expressed by heterologous isolates

are transient and limited.

time after infection

Model assumption: each variant comprises

major epitope

minor epitopes

a V1 x

b V2 y

c V3 z

c V4 x

b Vn w

Var 1

Var 2

Var 3

Var 4

Var n

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

iii zy

dt

dz dynamics of specific response zi:

dynamics of transient, cross-reactive response, wi: ij

i wydt

dw''

iiiiii wzyzy

dt

dy' dynamics of variant i:

intrinsic growth rate clearance by specific response

clearance by cross-reactive

response

immune response proportional to antigen

transient immune response proportional to antigen variants with shared epitopes

’decay rate

0

1000

2000

3000

4000

5000

6000

7000

8000

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

infe

cti

on

len

gth

0

5E+10

1E+11

1.5E+11

2E+11

2.5E+11

3E+11

3.5E+11

contribution cross-reactive imm. response

pe

ak

pa

ras

itim

ia 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

Mathematical model Mathematical model withoutwithout switching switching

a

V1

x

b

V2

y

c

V3

z

b

Vn

wRecker et al, Nature 2004

In vitroIn vitro switching dynamics 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

Abu

ndan

ce

- 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?

1st generation 2nd generation

stable dominanceof initial variant

initial variantreplaced

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

on rate

off rate

switch bias

XX

X =

change over generations determined by:change over generations determined by:

off rate

low

high

t1 t2

switch bias

low

high

t1 t2

- use mathematical model to determine most likely switching pathway -

off rate switch bias on rate

nivvvij

tjjij

tii

ti ..1,11

,

n

3

2

1

0

0

0

1

3231

2321

11312

n

n

0

0

0

1

3231

2321

11312

n

n

n

3

2

1

use iterative approach to find ‘best-fit’ use iterative approach to find ‘best-fit’ switch matrixswitch matrix and and off-rate vectoroff-rate vector

Switch matrix:Switch matrix:

Switch sequence: 1→2 → 4 → 3 →

4

3

2

1

4321

4

3

2

1

4321

variant to

vari

ant

fro

m

Clone 3D7_AS2

Clone It_B2B

even for a stable clone…even for a stable clone…

Clone B10

Clone B12

Dat

a p

rovi

ded

by D

zwik

owsk

i, F

rank

& D

eits

ch

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

0

0.2

0.4

0.6

0.8

1

g20 g25 g30 g40 g48 g55 g60

rela

tive

ab

un

dan

ce

PFD0995c MAL7P1.56 PFE1640wPFA0005w PF10_0001 PFD0005w

Evolutionary conflict:Evolutionary conflict:

protection of repertoireprotection of repertoire

vs.vs.

protection against immune attackprotection against immune attack

repertoire protection:

immune evasion:

Evolutionary conflict:Evolutionary conflict:

Assume var gene repertoire as a network where

- nodes = variants

- edges = switch / transition probabilities

protection of repertoireprotection of repertoire

vs.vs.

protection against immune attackprotection against immune attack

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 Investigate effects of hierarchical switching for in vivo in vivo dynamicsdynamics

highly structured switching

results in (significantly?)

increased length of infection.

naïve host

highly structured switching

results in (significantly?)

increased length of infection.

sms and lattice-type pathways

far more flexible in overcoming

pressure from pre-existing

immune responses to help set

up chronic infections.

naïve host

semi-immune host

a b c d e

u

v

x

y

z

minor epitope 1minor epitope 1

min

or e

pito

pe 2

min

or e

pito

pe 2

Antigenic relationship between variantsAntigenic relationship between variants

Switch sequence: (au) → (bu,av) → (cx) → (dx,cy) →…

SummarySummary

• 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

AcknowledgementsAcknowledgements

• Sunetra Gupta

• Caroline Buckee

• Robert Noble

• Sam Kinyanjui

• Pete Bull

• Kevin Marsh

University of Oxford

Department of Zoology

• Chris Newbold

• Andrew Serazin

• Sue Kyes

• Zóe Christodoulou

• Robert Pinches

THE WEATHERALL INSTITUTEOF MOLECULAR MEDICINE

Antigenic variation in malaria involves a highly structured switching pattern

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