Resilience and PRRS in a natural disease challenge model - Dr. Graham Plastow, University of Alberta, 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. Resilience and PRRS in a natural
disease challenge model
Graham Plastow
University of Alberta
Chicago, December 2nd 2017

2. Application of Genomics to Improve
Disease Resilience and Sustainability
in Pork Production
Project Leaders: Michael Dyck (U Alberta), John Harding
(U Saskatchewan), Bob Kemp (PigGen Canada)

3. Application of Genomics to Improve
Disease Resilience and Sustainability
in Pork Production
Project Leaders: Michael Dyck (U Alberta), John Harding
(U Saskatchewan), Bob Kemp (PigGen Canada)
Includes several components
Vaccine response
Salmonella challenge (Ab/Microbiome)
Nutrition & Gut Health (Microbiome)
PRRSV Pregnant Gilt Challenge e.g. Vet. Micro. 209: 114-123 (2017)
Organoid models
and Natural Challenge Model

4. A Natural Challenge Model
for Disease Resilience in Pigs
Jack Dekkers, John Harding, Frederic Fortin, PigGen
Canada, Michael Dyck, Graham Plastow
Andrea Wilson, Bonnie Mallard, Claire Rogel-Gaillard,
Ben Willing, Catherine Field
Austin Putz, Jiehan Lim, Ziqi Yang

5. What is Resilience?
• Definition of Resilience…..
– “the capacity to recover quickly from difficulties;
toughness.” Oxford Dictionary https://en.oxforddictionaries.com
• Disease Resilience…..
– combination of resistance and tolerance to
diseases….animal’s ability to remain productive
despite being disease-challenged
– The ability of pigs to respond to pathogens in
a way that minimizes the impact of disease

6. •Objectives
1. Establish a natural challenge model for grow-finish pigs
2. Determine the genetic basis of disease resilience in pigs
3. Develop predictors of resilience that can be obtained at a
young age in a high-health bio-secure environment
A Natural Challenge Model
for Disease Resilience in Pigs

7. • PRRS is the most significant disease but it is not only
PRRSV
• Numerous other pathogens are commonly isolated along
with PRRS virus from affected nursery or finishing pigs.
Other bacteria such as Streptococcus suis, Escherichia
coli, Salmonella cholerasuis, Haemophilus parasuis,
and Mycoplasma hyopneumoniae have been reported, as
well as viruses such as porcine respiratory coronavirus
and swine influenza virus.
Dee, Merck Veterinary Manual,
http://www.merckvetmanual.com/generalized-conditions/porcine-reproductive-and-
respiratory-syndrome/overview-of-porcine-reproductive-and-respiratory-syndrome
Motivation?

8. Health is the Biggest Opportunity for
Animal Genomics
 zoonoses (60% of EIDs)
Adapted from a slide by Dan Barreda The British Pig Association
http://www.britishpigs.org.uk/modern2.htm

9. High health
Lower health
Selection for genetic
potential for disease
resilience
Health & Growth
Performance
?
INTRODUCTION

10. • Established by introducing selected “seeder pigs” from
different commercial farms with first few batches
entering the model
• Continuous flow
• Each batch from a single company/herd
• All are LW*LR barrows from high health herds
• Weaned and transported to acclimation unit where
samples are collected prior to challenge
Natural Challenge (Pig & Sample Flow)

11. 60-75 LW/LR barrows/batch High
Health Multiplier Farm weaned - 21
days old
transported
Nursery 1 (2 weeks)
Quarantine and acclimatization
CDPQ Test Station (4 weeks)
Nursery
CDPQ Test Station (16 weeks)
Grower & finisher
transferred
Severity of disease challenge
Clean
Mild
challenge
Severe
challenge
Natural Challenge (Pig & Sample Flow)
Blood 1 – 26 day-old
Blood 3 – 56 days
(Week 2 post-exposure)
Blood 4 – 84 days
(Week 6 post-exposure)
Exposure to seeder
pigs at 42 days
DNA Samples -
Genotyping
660,000 SNPs
Target 3,300
pigs

12. N Cook, Alberta Agriculture & Forestry, Lacombe
J-P Laforest, Université Laval F Fortin, CDPQ, QC

13. Natural Challenge (Pig & Sample Flow)
60-75 barrows/batch from
High Health Multiplier Farm
Weaned at 21 days old
transported
CRSAD Building
(3 weeks)
Nursery I
D26 – Blood 1
D42 – Blood 2 &
exit Nursery I
CDPQ Test Station
(4 weeks)
Nursery II
D56 – Blood 3
CDPQ Test Station (16 weeks)
Grower & Finisher
D86 – Blood 4
D120B1-8/D166B9-on – Blood 5
transferred
Blood Samples collected
1 HIR, DRAA, CBC, WBT, Serology
2 HIR, Serology, microbiome
3 CBC, Nab, IgG, WBT, microbiome
4
CBC, IgG, WBT, Serology,
microbiome
5 Serology
6 WBT, Serology (sick & healthy)

14. Overview - Data
Quarantine
Nursery
Immune Response
•HIR
•DRAA
•CBC
•Blood Stimulation
•Phagocytosis
Blood Transcriptome
Blood Metabolome
Blood Proteome
Gut Microbiome
Natural
Challenge
Facility
Growth
Feed/ Water Intake
Mortality/Morbidity
Carcass/Ultrasound
Disease Diagnostics
Blood Transcriptome
Gut Microbiome
March 14, 2017 Midwest 2017 14
Deliverables
Phenotypic
Prediction
of
Resilience
Genetic
Prediction
of
Resilience
Trait:
• Parameters
• Architecture
• Mechanisms
Integrated statistical and
bioinformatic analyses
Putz, A.M., Harding, J.C.S., Fortin, F.,
Plastow, G., Dekkers, J.C.M. (2017) A
natural challenge model for disease
resilience in wean-to-finish pigs. ASAS
MidWest Meeting March 2017. J.
Anim. Sci 95: supplement2, p.17.
Sample
bank

15. Diseases
March 14, 2017 Midwest 2017 15
Adapted from Dr. John Harding

16. Lung lesion (Mycoplasma
hyopneumoniae, complicated
with PRRS & Streptococcus
suis)
Lung lesion
(chronic stage of
PRRS in grower)
Lung lesion (Haemophilus
parasuis in grower)
Types of lung lesion found in dead pigs with respiratory signs
NATURAL CHALLENGE
Jiehan Lim

17. Mortality Reasons %
Lameness 10
Coughing 11
Unthrifty 5
Diarrhea red 1
grey/brown 19
yellow 7
Dermatitis 4
CNS 3
Respiratory 31
Arthritis 5
Inappetance 2
Cyanosis 2
Example Growth Curves
# health
treatments
Blood
1
Blood
3
Blood
4
Disease exposure
Average Mortality at
78 ± 2.3 days

18. Predicting which Pigs are Resilient ?
Immune Response Measures
• Complete Blood Counts (UAlberta)
‒ CBC = Evaluates white blood cells, leukocytes, B cells,
monocytes and acute phase proteins in pigs
• High Immune Response Assay (UGuelph)
‒ HIR = Assesses adaptive immune response in pigs including:
antibody-mediated and cell-mediated immune responses
• Disease Resistance Assay for Animals (ULaval; USask)
‒ DRAA = Assess immune competence in pigs based on
proliferation of peripheral blood mononuclear cells in pigs
after stimulation by mitogens
‒ Total Immunoglobulin (IgG) & Natural antibodies (NAb) ELISA
• Blood Transcriptome (UAlberta)

19. Resilience Phenotype
• Treatments
• Disease diagnosis (qualitative)
• Mortality
• Growth
• Feed Intake
• Water Intake
• Blood Transcriptome (Blood 3 and 4)

20. Austin Putz
Example Feed Intake Curves
Poster
#63

21. Trait h2
Growth Nursery 0.45 + 0.07
Finish 0.26 + 0.07
Feed Intake 0.33 + 0.07
Gain/Feed 0.35 + 0.07
RFI 0.43 + 0.07
Treatments 0.14 + 0.07
Mortality Liability 0.26 + 0.07
Carcass Wt 0.31 + 0.07
Destron BF 0.52 + 0.08
Destron LD 0.40 + 0.08
Lean Yield 0.39 + 0.09
Trait Heritabilities

22. Daily Fluctuation in FI as a Measure of Resilience
Austin Putz
Trait
rg with
MSE of FI
Growth Finish -0.31 + 0.27
# Treatments 0.56 + 0.20
Mortality Liability 0.54 + 0.36
MSE of FI
h2 = 0.22+ 0.07

23. • A natural challenge model for characterization of
disease resilience in pigs has been successfully
established
Conclusions 1
• MSE of FI shows promise as a measure of
resilience.
• Heritable variation in growth, feed intake curves, mortality,
morbidity

24. 3300 YXLR WEANER PIGS
FROM HIGH-HEALTH HERDS
BATCHES OF 60-75 PIGS/3 WEEKS
650K SNP
genotypes
Trait Genetics
• Parameters
• Architecture
• Mechanisms
Genetic
Prediction
of Resilience
Phenotypic
Prediction
of Resilience
PROJECT
DELIVE-
RABLES
Potential BIOMARKERS
collected on
young healthy pigs
Immune Response
• High Immune Response
• In vitro immune tests
• Phagocytosis tests
Blood Transcriptome
Blood Metabolome
Blood Proteome
Gut Microbiome
Integrated
statistical and
bioinformatic
analyses
NATURAL CHALLENGE FACILITYQUARANTINE NURSERY
Continuous flow RESILIENCE PHENOTYPES
Mortality/Morbidity
Growth performance
Feed intake
Water intake
Disease diagnostics
Blood transcriptome
Gut Microbiome
Health treatments

25. •Example - Complete Blood Counts (CBC)

26. •Complete Blood Counts (CBC)
Number of treatments

27. Some initial
results
Difference in
neutrophil/
lymphocyte %
between R and S
Deads not
different
at Bleed 1
Neutrophil %
Lymphocyte %
abab
a bab

28. Resilient vs Susceptible
samples selected for qPCR
Class Count Bleed 1 Bleed 2 Bleed 3
Resistant Neutrophils 4.8 12.1 9.4
Susceptible Neutrophils 3.8 14.0 13.5
Resistant Lymphocytes 6.3 7.8 9.4
Susceptible Lymphocytes 7.1 8.0 7.4
Resistant N/L 0.77 1.57 1.12
Susceptible N/L 0.55 1.86 1.91

29. Using CBC for Prediction of Outcome
Cycle 1 (n=391) predicts Cycle 2 (n=397) including dead
Stepwise model accuracy 68.8%
Predicted Resilient pigs in Cycle 2
Actual Resilient 40% versus 29% in Predicted Susceptible
Grow 25g/day faster (all included)
Ave treatment rate 0.5x less
Mortality 0% versus 13%
Work in progress – results highly dependent on assumptions
and classification of resilient pigs in the training set.

30. Blood Transcriptome and Resilience
Differentially expressed
genes identified in three
different environments
“predicting outcome”
NC1
Genome dx.doi.org/10.1139/gen-2017-0074

31. • A natural challenge model for characterization of
disease resilience in pigs has been successfully
established
Conclusions 2
• Initial CBC shows initial promise as a predictor of
phenotypic resilience.
• Test different predictors with MSE of FI.
• Genetic analysis, GWAS (660k)……………….

32. Next?
• Complete data collection (3,300 from 2,000 today)
• Integrate analyses
• Explore blood transcriptome (3’RNAseq), metabolome
and proteome on 960 samples (14 batches) (USDA NIFA)
• Explore infectivity (Andrea Wilson, RI)
• The effect of environmental enrichment on the immune
response and measures of disease resilience and welfare
in pigs. (Yolande Seddon, U of SK)
• Other opportunities – e.g. pen-side diagnostics

33. 33
Thanks!

34. Partnerships:

35. UofA Team +