=1= Neutralising antibodies prevent PRRSv viremia rebound: evidence from a data-supported model ·...

– Natacha GO –[email protected]

NEUTRALISING ANTIBODIES PREVENT PRRSV

VIREMIA REBOUND:EVIDENCE FROM A DATA-SUPPORTED MODEL

PhD seminar, 10th April 2017

In collaboration withSuzanne TOUZEAU, ISA, INRA & BIOCORE, Inria, Sophia Antipolis, FranceCatherine BELLOC, BioEpAR, Oniris & INRA, Nantes, FranceAndrea WILSON, Div. Genetics and Genomics, Roslin Institute , Edinburgh, Scotland

mailto:[email protected]

INTRODUCTION MODEL RESULTS CONCLUSION

ContextPorcine Reproductive and Respiratory Syndrome virus (PRRSv):

– Targets the porcine antigen presenting cells (APC)

– Wide variability in virulence & susceptibility

– Numerous vaccines, but only partially protective

⇒ Need to better understand the immune response

Viral titer rebounds:

From PHGC data

Vir

al ti

ter

– Mechanisms unclear (immune response,viral mutation, re-exposure over infection)

– Issue for the infection control (vaccination,genetic selection, population dynamics)

⇒What immune mechanisms can cause rebounds ?

2


Linking immune response & infection dynamics

– Contrasted immune reponses can result in similar infection dynamics:

01

23

45

02

46

8

050

150

−20 0 20 40

01

23

45

−20 0 20 400

24

68

−20 0 20 40

050

150

Cytotoxic T-cellsViremia Pro-inf cytokines

strain:

strain:

●

experi

menta

l st

udie

s: -

Dia

z et

al. 2

01

2;

- W

eese

ndro

p e

t al.,

20

13

– Fragmented & partial view from the experimental studies– High variability & complex system

→ ORIGINAL MODEL: Integrative view of the within-host dynamics

3


(1) initiation of infection & immune response

naive target cells APCn(innate immunity)

APCm

V viral particles

APCi

viral particles decay

Innate responseearly & non-specific

Infection Excretion

Phagocytosis

viral particles multiplication

Exp

osur

e

4


(2) virus-immune response interactions

naive target cells APCn

natural killerscytotoxic lymphocytes

neutralising antibodies

NK

IgAPCm

V

CD8+

APCi


Innate responseearly & non-specific

Adaptive responsedelayed & specific

Neutralisation

Infection Excretion

Cytolysis

Phagocytosis

viral particles multiplication


infected cells decay

4


(3) immune response activation & regulations

CD4+APCm

APCi

B

APCn

NK

IgAPCm

V

CD8+

APCi

Neutralisation

Infection Excretion

Antigen presentation

Cytolysis

Phagocytosis

Cyt

ok

ines

Cyt

ok

ines

4


Model - overview

Integrative and detailed view of the within-host dynamics

ModelODE system describing the evolution over time of each

component of interest by the formalisation of the immune

mechanisms

Parametersfixed values ~ rate of immune mechanisms

Dynamics of:- PRRSv- Immune effectors- Antibodies- Cytokines

– evolution over time of PRRSv, innate and adaptive components

– deterministic system of 19 ODE involving 50 parameters

– mechanisms at between-cell scale, variability ∼ parameter values

5


Model - equations

example of viral dynamics:

APCm

V

APCi

Cyt

okin

es

APCn

Ig

V̇ =+ E(t)︸︷︷︸exposure

− ηT V (APCn + APCm) κ−() [1 + κ+()]︸︷︷︸

phagocytosis

− βT V (APCm + APCn) κ−() [1 + κ+()]︸︷︷︸

infection

+ eT APCi κ−()︸︷︷︸

excretion

− µadV V Ig︸︷︷︸

neutralisation

− µnatV V︸︷︷︸

natural decay

6


What prevents rebounds?

Data. 300 pigs (genomic variability), NVSL strain (high virulence)

0 10 20 30 40

110

102

103

104

105

106

107

0 10 20 30 40

110

102

103

104

105

106

107

uniphasic biphasic

Method.

(1) Model fitting

(2) Identify the underlying mechanisms (ex. of antibodies)

7



Data.

Method.

(1) Model fitting1. Selection of parameters: linked to between-host variability (14/50)2. Check if the model can generate the data3. Fitting method. 600 times the fitting process / profile:

Parameter set 1Parameter set 2 ...Parameter set s

Model

DataRejectedSelected

Viral titer 1Viral titer 2 ...Viral titer s

1

2

s

Start

End


7


What prevents rebounds?Data.

Method.

(1) Model fitting

Fitted viral titer:

0 10 20 30 40

01e

21e

41e

61e

8 peak

resolution

0 10 20 30 40

01e

21e

41e

61e

8 peakrebound (max)

rebound (min)

uniphasic biphasic

(2) Identify the underlying mechanisms (ex. of antibodies)7


What prevents rebounds?Data.

Method.

(1) Model fitting


Modelestimated parameters

(2 x 600 sets)synthesis of Ig

resulting dynamics(2 x 600 sets)

titer of Ig

cummulated flow(2 x 600 sets)

total # of neutralised viral particles:

0

20

21

42

0-20 >20

7



peak

Gro

ss fl

ow

rati

o/t

ot.

deacyIg

synth

esi

s

0-20 >20 0-20 >20

Modelestimated parametersuniphasic vs biphasic

resulting dynamicsuniphasic vs biphasic

cummulated flowsuniphasic vs biphasic

titer

(1) Rebounds due to a lack of antibodies synthesis

→ strength of neutralisation determines infection profiles?

8



peak

Gro

ss fl

ow

rati

o/t

ot.

deacyIg

synth

esi

s

0-20 >20 0-20 >20




titer

(2) Lower Ig titer despite similar synthesis !?!?!


8



peak

Gro

ss fl

ow

rati

o/t

ot.

deacyIg

synth

esi

s

0-20 >20 0-20 >20




titer

(3) Rebounders need more neutralising antibodies !!!


8



Influence of pomoting / inhibiting neutralisation efficiency on profile

Method: vary µadV Results: % of profile inversion

levels uniphasic biphasicL1 ×0.1 ×10↓ ↓ ↓L6 ×0.001 ×1000

levels uni→ bi bi→ uniL1↓ 0 85L6

⇓strength of neutralisation do not determines infection profile

... BUT ...high neutralisation can prevent rebounds !

8


ConclusionStrengths:

•Model: can reproduce the variability within and between PRRSv profiles

• Approach: powerfull to identify underlying mechanismsExpe.: only dynamics→ expensive & partial view• Results: new insights to explain PRRSv within-host dynamics→ prospects for infection control (vaccination & host genetic selection)

Limits:

• Only fitted on viral titer (1 model variable / 19)• No validation of fixed parameters & ranges

Prospects:

• Model simplification (Stefano’s method)• Exploration of other hypotheses (viral mutation, re-exposure)

9


Thank you!

CD4+APCm

APCi

B

APCn

NK

IgAPCm

V

CD8+

APCi

Neutralisation

Infection Excretion

Antigen presentation

Cytolysis

Phagocytosis

Cyt

ok

ines

Cyt

ok

ines

10

=1= Neutralising antibodies prevent PRRSv viremia rebound: evidence from a data-supported model ·...

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