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.”
Sir Ronald Ross
20. Aug. 1897
Ross R, 1911. The Prevention of Malaria . London: John Murray.
???
?
?
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)
diversity
variant surface antigens (VSA)
www.fda.gov/CbER/blood/malaria071206sk5.gif
Major multigene families:
o rif
o stevor
o Pfmc-2TM
> 150 copies per genome
30 copies per genome
13 copies per genome
o var
60 copies per genome
Scherf et al., Annu Rev Microbiol 2008
Sequence diversity of var genes is immense!
adapted from Gardner, M. et al., 2002, Kyes, S. et al., 2002
cumulative diversity of DBLa seqnuences
from Barry et al, PLoS Pathog. 2007
pairwise sharing among DBLa seqnuences
Antigenic variation in P.falciparum
PfEMP1 (P. falciparum Erythrocyte Membrane Protein 1)
IE binding to endothelium
• embedded on surface of red cell
• causes severe disease through
adherence to host cell receptors
• important immune target
IE binding to erythrocytes
PfEMP1
t1
t2
Number of parasites
var 1
t3
var 2
var 3
106
IE binding to dendritic cell
104
102
40
80
120
Days of infection
160
200
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
PfEMP1
var
n=1
var
n = ~59
Infected RBC
Result:
Scherf et al., Annu Rev Microbiol 2008
succeeding waves of parasitaemia
dominated by a single variant of PfEMP1
PfSir2: P.falciparum silent information regulator
TPE: telomere position effect
what orchestrates expression at population level?
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)
•
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)
Increases in levels of antibodies to VSA expressed by heterologous isolates are
transient and limited.
60
50
55
50
30
20
10
0
60
58
50
40
30
20
10
0
time after infection
18
Agglutinating antibody titer
Percentage of infected red cells positive
40
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
major
epitope
minor
epitopes
Var 1
a
V1
x
Var 2
b
V2
y
Var 3
c
V3
z
Var 4
c
V4
x
Var n
b
Vn
w
The model
dyi
  yi  a zi yi  a ' zi wi
dt
dynamics of variant i:
intrinsic growth rate
dynamics of specific response zi:
clearance by
specific response
clearance by
cross-reactive
response
dzi
  yi   z i
dt
immune response
proportional to antigen
decay rate
’>>
dynamics of transient,
cross-reactive response, wi:
dwi
  '  y j   ' wi
dt
transient immune response proportional to
antigen variants with shared epitopes
Mathematical model without switching
Recker et al, Nature 2004
w
Vn
a
b
V1
V2
x
y
b
z
V3
8000
3.5E+11
7000
3E+11
6000
2.5E+11
5000
2E+11
4000
1.5E+11
3000
1E+11
2000
5E+10
1000
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
contribution cross-reactive imm. response
1
peak parasitimia
infection length
c
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
•on and off rates vary dramatically among
Abundance
dissimilar
var 1
var 2
var 3
different variants
time
- 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?
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:
X
off rate
t1
t2
switch bias
t1
=
on rate
t2
off rate
low
low
X
switch bias
high
high
- use mathematical model to determine most likely switching pathway -
vit 1  1  i vit    j  ji vjt  , i  1..n,
j i
off rate
 1 
 
 2 
 
 3
 
 
 n
switch bias
 0

  21

 31
 

 n1
12
0
 32
13  1n 

 23
 



on rate



0 
 0

  21

 31
 

 n1
12
0
 32
13  1n    1 
  
 23
   2 
  

3


use iterative approach to find ‘best-fit’
switch matrix and off-rate vector


0 
 
 
 
 n
Switch matrix:
variant to
variant from
1
2
3
4
1
2
3
4
Switch sequence: 1→2 → 4 → 3 →
Clone 3D7_AS2
Clone It_B2B
even for a stable clone…
Clone B12
Data provided by Dzwikowski, Frank & Deitsch
Clone B10
To test the validity of this prediction, examine the var
transcript distribution in Clone 2 every few generations
1
relative abundance
0.8
0.6
0.4
0.2
0
g20
g25
PFD0995c
PFA0005w
g30
g40
MAL7P1.56
PF10_0001
g48
g55
PFE1640w
PFD0005w
g60
Evolutionary conflict:
protection of repertoire
vs.
protection against immune attack
repertoire protection:
immune evasion:
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
minor epitope 2
u
v
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
University of Oxford
Department of Zoology
• Sunetra Gupta
• Caroline Buckee
• Robert Noble
THE WEATHERALL INSTITUTE
OF MOLECULAR MEDICINE
• Chris Newbold
• Andrew Serazin
• Sue Kyes
• Zóe Christodoulou
• Robert Pinches
• Sam Kinyanjui
• Pete Bull
• Kevin Marsh