New PRRS disease phenotypes as vaccine and genetic improvement targets - Dr. Andrea Wilson, Roslin Institute, from the 2017 North American PRRS/National Swine Improvement Federation Joint Meeting, December 1‐3, 2017, Chicago, Illinois, USA. More presentations at http://www.swinecast.com/2017-north-american-prrs-nsif-joint-meeting

1. New PRRS disease phenotypes
as vaccine & genetic
improvement targets
Andrea Doeschl-Wilson
Andrea.wilson@roslin.ed.ac.uk

2. Thanks to all collaborators & funders
Roslin Colleagues
• PhD students and post-docs in Wilson group
• Steve Bishop
External
• Jack Dekkers, A. Hess (ISU)
• H. Mulder, H. Rashidi (WUR, NL)
• I. Kyriazakis (Newcastle University, UK)
• S. Touzeau, C. Belloc (INRIA / INRA, France)
• P. Mathur (Topigs Norsvin, NL)
• G. Plastow (UAlberta), B. Kemp (PigGen), Canada
• J. Lunney (USDA), B. Rowland (KSU) & PHGC

3. -7 0 7 1
1
1
4
2
1
3
5
4
2
4 2
8
Acclimation
Weight
Blood,
Tempus (RNA)
Weight
Blood
Tempus
Weight
Blood
Tempus
Weight
Blood
Tempus
Weight
Blood
Tempus
Weight
Blood
Tempus
Weight
Blood
Tempus
TonsilsBlood
Tempus
Infection
Acute Infection
Rebound
Persistence
Data source for this talk
Viremia Weight
‘PHGC Nursery model’

4. Outline
• Viremia profiles, Tolerance & Infectivity as new disease
phenotypes
– What are they and why do they matter?
• What do the PHGC challenge data tell us about these?
1. A statistical model of PRRS viremia profiles
 New resistance & infectivity phenotypes
2. A mechanistic model of PRRS infection dynamics
 Understanding rebound
3. A random regression model
Is there genetic variation in tolerance of pigs to PRRS?

5. Desirable target trait for maintaining high individual & herd health & performance
Nr 1 target trait: Resistance
• Virus load is a good resistance
phenotype for PRRS
Resistance:
= ability to block pathogen entry
or restrict pathogen replication
High resistance corresponds to:
• Low pathogen burden
• High health and production
• Low risk of transmission

6. Much is known about genetic resistance of pigs to PRRS
Resistance quantified
as the area under
the curve from 0 to
21 dpi
‘The WUR SNP’

7. Tolerance:
= ability of a host to limit
the detrimental impact of
infection on health /
performance,
without affecting
pathogen burden per se
Desirable target trait to maintain high performance
in the face of constant exposure to infection
2. Target trait: Tolerance
Do pigs differ genetically in tolerance to PRRS?

8. Virus load
GrowthSame tolerance
Virus load
Growth
Different tolerance
High tolerance
Low tolerance
Tolerance is the slope of the reaction-norm of health /
performance with respect to change in pathogen load
How to measure tolerance?
Tolerance

9. Tolerance:
Ability to limit impact
of infection on health /
performance
Resistance:
Ability to block infection /
limit pathogen replication
Resilience:
Ability to maintain high health /
performance whilst exposed to
infectious pathogens

10. Different epidemiological
outcomes
• Tolerant hosts do not reduce
pathogen spread
• Only improvement of resistance
can lead to disease eradication
Different evolutionary outcomes
• Tolerance may be less pathogen-
strain specific:
• multiple pathogen protection
• may not drive pathogen co-evolution
Why distinguish between Resistance & Tolerance?

11. Infectivity:
= ability of an infected individual
to transmit the infection
• Many recent epidemic outbreaks
attributed to ‘super-spreaders’:
• 20% individuals responsible for
80% of transmissions
3. Target trait: Infectivity
• Early identification & removal of most infectious individuals would be a very
effective disease control
• It is not known to what extent infectivity is genetically controlled
• Infectivity cannot be directly measured, but can be inferred from disease data
• Can’t be inferred from challenge experiments

12. Message 1
• Resistance, tolerance & infectivity
are important host target traits for
genetic improvement
• Understanding the genetic control
& relationship between these traits
is important for effective disease
control

13. Making more of the PHGC data: modelling virus load dynamics
• Large variation in viremia profiles
• How to describe these?
• Viremia rebound: prolonged
infectivity?
• Is it common, predictable &
genetically controlled?
Viremia rebound

14. Virusload[RT-PCRlog10]
1 1
1
b c t
y a t e

Woods function:
1 1 2
1 2 0max(0, ( )b c bt
y a t e a t t e 
  
01 1 2 2 ( )
1 2 0max(0, ( ) )t tb c b ct
y a t e a t t e  
  
Extended Woods function:
An individual’s viremia profile is fully specified by 3 (7) parameters
Islam et al. Plos ONE 2013
The (extended) Woods function describes all viremia profiles

15. Woods function advantages
• Smooth continuous profiles rather than
noisy discrete data
• Objective classification of pigs into
rebounder or non-rebounder
• New phenotypes for genetic analyses
based on viremia profile characteristics:
• Rebound (yes / no)
• Peak viral load
• Time to peak
• Rate of viral load decline
VPeak1
Tpeak
VPeak2

16. Some results: the good news
Hess et al. GSE 2016
Quantitative comparison of profile
characteristics for 2 viral strains:
• Most viremia profile
characteristics are heritable
• The WUR resistance SNP
confers a more desirable
phenotype for most profile
characteristics
• Effect is strain dependent

17. Identification of new SNPs for viremia profile characteristics

18. • Rebound is a common
phenomenon
• But apparently
• not heritable
• not predictable (no significant
differences in viremia profiles
within the first 21 dpi)
Some results: the bad news
Number of individuals
Non-Rebound Rebound
683 (78%) 191(22%)
Islam et al. Plos ONE 2013

19. Message 2
Viremia profiles of PRRSV infected pigs
can be modelled by Woods functions
• Most profile characteristics are heritable &
favourably influenced by the WUR SNP
• Viremia rebound is undesirable, common
& apparently not under host genetic control

20. Hypotheses for viremia rebound:
1. Virus characteristics (e.g. emergence of escape mutants)
– Emergence of escape variants
– Latent in tissues & spontaneous release into blood
2. Environmental characteristics
– Re-infection
3. Differences in immune responsiveness of pigs
– Could potentially be modified by genetic selection or vaccines
What causes viremia rebound?

21. Adapted from Go et al., PloS One 2014
Can this model reproduce the PHGC viremia profiles?
Can we use it to identify causative mechanisms for rebound?
A mechanistic model of the immune response of pigs
to PRRSV

22. Input parameters
(50):
Baseline rates of
immune mechanisms
(individual immuno-
competence)
Mathematical
equations (19):
Describe the dynamic
interaction between
PRRSV & 18
(interacting) immune
mechanisms
Outputs (19):
• Viremia profile
• Immune profiles (18)
Modelling process
Pig α δ ω Φ
1 0.001 0.349 0.002 0.987
2 0..02 0.567 0.001 0/890
3 0.0004 0.345 0.002 0.987
4 0.0034 0.012 0.004 0.999
5 0.0023 0.675 0.007 0/.99

23. Woods viremia profiles from the PHGC nursery pigs
Step 1: Select subset of viremia profiles that
have similar profile characteristics within 3 wpi
Step 1: Selection of datasets for model fitting

24. Step 2: Fit model to viremia data
The model can reproduce the observed large variation
in viremia profiles for both types of profiles
Apply a mathematical search algorithm to identify input
parameter values that reproduce the viremia data profiles

25. Step 3: Identify candidate mechanisms for rebound
Stronger
immune
response
activation
Faster
depletion of
target cells
Predominant
orientation
towards
antiviral
response
Lower CTL
& nAB
response
But which of these are causative?
Non-rebound
Rebound

26. Step 4 Validation: How to prevent or trigger rebound?
A simulated knock-out experiment:
• Can we prevent rebound by altering a specific mechanism?
• Can we trigger rebound by modulating the mechanism in the
opposite direction?
• Boosting cytolysis or
virus neutralization
prevents rebound
• Weak virus
neutralization alone
does not cause
rebound
ApoptosisInfection NK
cytolysis
NeutralizationLc
cytolysis
Go et al., PloS Comp. Biol. Under Review

27. • The model demonstrates that rebound can be caused
by differences in host immune competence alone
 Preventable!!
• The model identified candidate immune mechanisms
that could cause or prevent rebound:
– (Target cell permissiveness, apoptosis of naïve target cells,
cytolysis of infected cells, virus neutralization)
 Vaccine or gene editing targets?
Modelling conclusions

28. Genetics of rebound revisited:
WUR SNP protects from rebound
Woods viremia profiles
Logistic regression with WUR SNP fitted as fixed effect:
‘WUR resistant’ pigs are 2.4 x less likely to
experience viremia rebound

29. • Resistance, Tolerance & Infectivity as new
disease phenotypes
– What are they and why do they matter?
• What do the PHGC challenge data tell us
about these?
1. Statistical modelling of viremia profiles
 New resistance & infectivity phenotypes
2. How to prevent viremia rebound? Evidence
from a mechanistic model of the immune
response of pigs to PRRSV
3. Is there genetic variation in tolerance of
pigs to PRRS, in addition to resistance?
Outline

30. PRRS as a case studyEvidence for phenotypic variation in PRRS tolerance
High tolerance
Low tolerance
• Is tolerance genetically controlled?
• What is the role of the WUR SNP on tolerance?

31. A: No genetic variation in
growth response to infection
Estimating genetic variation in tolerance
B: Genetic variation in growth
but not in tolerance
C: Genetic variation in
growth & tolerance
Statistical random regression sire model:
• Each line corresponds to one sire (54 sires)
• Estimated from measurements of at least 10
offspring per sire (~1300 pigs)

32. A: No genetic variation in
growth response to infection
B: Genetic variation in
growth but not tolerance
C: Genetic variation in
growth & tolerance
Model of best fit
True or statistical artefact?
Inconclusive evidence for genetic variation in tolerance
Lough et al. GSE 2017

33. A: No genetic variation in
growth response to infection
B: Genetic variation in
growth but not tolerance
C: Genetic variation in
growth & tolerance
True or statistical artefact?
Inconclusive evidence for genetic variation in tolerance
Lough et al. GSE 2017
Simulations show that measures of non-infected
relatives would provide accurate tolerance estimates

34. Message 4
• Estimating genetic effects for
tolerance is difficult
• Performance measures of
uninfected relatives would be
useful for estimating genetic
parameters of tolerance

35. Utilizing dynamic information
Split infection period into 3 distinct stages:
• Captures different sets of immune mechanisms
controlling resistance and tolerance
 stronger genetic signal
• 3 growth and virus load measures per individual
 greater statistical power

36. A: No genetic variation in
growth response to infection
B: Genetic variation in
growth but not tolerance
C: Genetic variation in
growth & tolerance
There is genetic variance in tolerance to PRRS
Lough et al., GSE 2017 & in prep.
• There is significant genetic variance in tolerance of pigs to PRRS
• WUR AB pigs are on avg. 1.6% more tolerant than AA pigs

37. Summary

38. Implications
• In principle, genetic selection of pigs with
desirable infection profiles & greater tolerance to
PRRS is possible
• In practice, this requires intense data collection
• Selecting on WUR genotype may
simultaneously improve resistance, tolerance
& prevent rebound
• In the future, we should assess the influence of
host genetics on disease spread & virus evolution
This requires new sets of data & models

39. Thank you for listening

41. • Pigs vary genetically in
resistance & tolerance to PRRS
• WUR resistance QTL also
confers differences in tolerance

42. • Resistance, tolerance & infectivity
are important host target traits for
genetic improvement
• Infectivity cannot be inferred from
challenge experiments

43. • Mathematical modelling of viremia
profiles characterises rebound &
shows that most profile characteristics
are under host genetic control
• The WUR resistance QTL has a
favourable effect on most viremia
profile characteristics

44. • Mechanistic models can identify
candidate immune mechanisms
underlying viremia rebound
• WUR resistance QTL appears to
reduce risk for rebound