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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:
• 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)
• 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)
• immunological interaction, e.g. cross-immunity(e.g. Recker et al, 2004)
• modifications of switch rates by ‘natural selection’(Frank, 1999)
• 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)
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