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Pneumococcal
genomics,
vaccines and
AMR
Bill Hanage
November 2021
whanage@hsph.harvard.edu
Acknowledgements
Taj Azarian Marc Lipsitch
Pamela Martinez
Forecasting the consequence of
vaccination
• Following the removal of vaccine
serotypes, non vaccine serotypes
increase in carriage (and disease,
depending on their virulence)
• Can we predict which will increase?
Vaccination in the South West US
• Carriage samples
straddling vaccination,
from Native American
communities (N=937)
• 35 “Sequence Clusters”
• Some are VT, some are
NVT and some are
mixed
• Vaccination did not
change carriage
prevalence
SC-01
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SC-04C
SC-05
SC-06A
SC-06B
SC-07
SC-08
SC-09
SC-10
SC-11
SC-12
SC-13
SC-14
SC-15
SC-16A
SC-16B
SC-17
SC-18
SC-19A
SC-19B
SC-20
SC-21
SC-22
SC-23
SC-24
SC-25
SC-26A
SC-26B
SC-03B
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Pre-vaccine
Post-vaccine
Vaccine
Non-vaccine
Sequence cluster
composition
Serotype
Epoch
Vaccine (VT)
Non-vaccine (NVT)
Mixed VT-NVT
Changes in the prevalence of sequence
clusters following vaccine
Why do some sequence clusters
increase more (or less) than expected?
Introducing the accessory genome
2996 genes
in common
E. coli K12
(commensal)
E. coli O157 H7
(enterohaemorrhagic)
E. coli from acute
pyelonephritis
1623 genes
1346 genes
204 genes
Only 39% of
genes present in
all three strains
Bacteria are Mr
Potato Heads
Welch et al. 2002 PNAS 99: 17020
585 genes
196 genes
514 genes
How does the accessory genome vary
between four different sample sites?
• Samples from MA (N=614)
• Southampton, UK (N=516)
• Nijmegen, the Netherlands (N=337)
• Maela camp, Thailand (N=3,085)
• Total of 4,127 isolates, falling into 73
Sequence clusters, and 1,731 accessory
COGs*
• *defined as present in 5%-95% of
isolates, to avoid dodgy sequence from
assembly errors and the like
Corander et al Nat Eco Evol 2017
Comparing Massachusetts and
Thailand
Corander et al Nat Eco Evol 2017
Corander et al Nat Eco Evol 2017
Comparing Massachusetts and Thailand
Population frequencies of accessory
loci in different places are highly
correlated
Corander et al Nat Eco Evol 2017
“Pickle plots”
Negative Frequency Dependent
Selection
• Form of balancing
selection
• Think about
surface antigens:
too common = too
much immunity
• Or bacteriophage
receptors, or many
other genes
Hastings Parasitology 2006
If NFDS structures the population, can
we use it to predict the impact of
removing vaccine types?
0
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0 0.2 0.4 0.6 0.8 1
SC-01
SC-02A
SC-02B
SC-03A
SC-04A
SC-04B
SC-04C
SC-05
SC-06A
SC-06B
SC-07
SC-08
SC-09
SC-10
SC-11
SC-12
SC-13
SC-14
SC-15
SC-16A
SC-16B
SC-17
SC-18
SC-19A
SC-19B
SC-20
SC-21
SC-22
SC-23
SC-24
SC-25
SC-26A
SC-26B
SC-03B
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)
Pre-vaccine
Post-vaccine
Vaccine
Non-vaccine
Sequence cluster
composition
Serotype
Epoch
Vaccine (VT)
Non-vaccine (NVT)
Mixed VT-NVT
The predicted fitness
Isolates in the same sequence cluster tend to have similar
accessory genome content (Croucher et al Nature
Comms 2014)
SC-01
SC-02A
SC-02B
SC-03A
SC-04A
SC-04B
SC-04C
SC-05
SC-06A
SC-06B
SC-07
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SC-11
SC-12
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SC-16B
SC-17
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SC-19B
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SC-26B
SC-03B
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)
Pre-vaccine
Post-vaccine
Vaccine
Non-vaccine
Sequence cluster
composition
Serotype
Epoch
Vaccine (VT)
Non-vaccine (NVT)
Mixed VT-NVT
Compute the consequences of
removing a Vaccine type SC
And its accessory loci
Case shown is removal of an SC making up 10% of the pre
vaccine population
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Pre
“Post”
SC-01
SC-02A
SC-02B
SC-03A
SC-04A
SC-04B
SC-04C
SC-05
SC-06A
SC-06B
SC-07
SC-08
SC-09
SC-10
SC-11
SC-12
SC-13
SC-14
SC-15
SC-16A
SC-16B
SC-17
SC-18
SC-19A
SC-19B
SC-20
SC-21
SC-22
SC-23
SC-24
SC-25
SC-26A
SC-26B
SC-03B
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(
S
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)
Pre-vaccine
Post-vaccine
Vaccine
Non-vaccine
Sequence cluster
composition
Serotype
Epoch
Vaccine (VT)
Non-vaccine (NVT)
Mixed VT-NVT
Consequence of removing all vaccine
types
More scatter
Pre
“Post”
0
0.1
0.2
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0.5
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0 0.2 0.4 0.6 0.8 1
SC-01
SC-02A
SC-02B
SC-03A
SC-04A
SC-04B
SC-04C
SC-05
SC-06A
SC-06B
SC-07
SC-08
SC-09
SC-10
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SC-12
SC-13
SC-14
SC-15
SC-16A
SC-16B
SC-17
SC-18
SC-19A
SC-19B
SC-20
SC-21
SC-22
SC-23
SC-24
SC-25
SC-26A
SC-26B
SC-03B
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Pre-vaccine
Post-vaccine
Vaccine
Non-vaccine
Sequence cluster
composition
Serotype
Epoch
Vaccine (VT)
Non-vaccine (NVT)
Mixed VT-NVT
Predicting the fitness of any SC
Identify the accessory genes that are in it
How far away are they from their pre-vaccine frequency?
Pre
“Post”
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 0.2 0.4 0.6 0.8 1
SC-01
SC-02A
SC-02B
SC-03A
SC-04A
SC-04B
SC-04C
SC-05
SC-06A
SC-06B
SC-07
SC-08
SC-09
SC-10
SC-11
SC-12
SC-13
SC-14
SC-15
SC-16A
SC-16B
SC-17
SC-18
SC-19A
SC-19B
SC-20
SC-21
SC-22
SC-23
SC-24
SC-25
SC-26A
SC-26B
SC-03B
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S
C
)
Pre-vaccine
Post-vaccine
Vaccine
Non-vaccine
Sequence cluster
composition
Serotype
Epoch
Vaccine (VT)
Non-vaccine (NVT)
Mixed VT-NVT
Combine to find the net fitness
Pre
“Post”
0
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0.8
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+ve
-ve net
In this case it’s positive
The replicator equation
Changes in the prevalence of sequence
clusters following vaccine
Predicted fitness compared with
prevalence change post vaccine
Two strains were not present
in the population at the first
time point
Their standardized predicted
fitness can nevertheless be
calculated
SC10 - 8.67
SC24 – 10.07
Predicted Fitness
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●
SC−20
SC−05
SC−18
−10
0
10
20
−0.04 0.00 0.04
Adjusted Prevalence Change
Predicted
Fitness
A
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−10
0
10
20
Decreased Increased
Adjusted Prevalence Change
Predicted
Fitness
B
Post−vaccine Equilibrium
●
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●
p−value: 0.26
Slope, 95% CI: 0.257, 1.08
Intercept, 95% CI: −0.005, 0.030
Chi−squared: 2.8
β = 0.68
SC−25
SC−18
0.000
0.025
0.050
0.075
0.100
0.125
0.000 0.025 0.050 0.075 0.100 0.1
Predicted Prevalence
(Accessory genome, nloci=2,371)
Actual
Prevalence
C
Quadratic programming
• What is the expected equilibrium?
• Not the same as the predicted fitness
• Quadratic programming can be used to
estimate the optimum frequencies of each SC
• Again, assuming the pre vaccine status quo is
a proxy for the selected frequency of each
gene
●
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Increased
ence Change
Post−vaccine Equilibrium Frequencies
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p−value: 0.26
Slope, 95% CI: 0.257, 1.08
Intercept, 95% CI: −0.005, 0.030
Chi−squared: 2.8
β = 0.68
SC−25
SC−18
0.000
0.025
0.050
0.075
0.100
0.125
0.000 0.025 0.050 0.075 0.100 0.125
Predicted Prevalence
(Accessory genome, nloci=2,371)
Actual
Prevalence
C
●
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● ●
●
●
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●
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●
●
●
−0.075
−0.050
−0.025
0.000
0.025
0.050
Decreased Increased
Actual Prevalence Change
Predicted
Prevalence
Change
D
Acknowledgments
Taj Azarian
Pamela Martinez
Brian Arnold
Marc Lipsitch
Alanna Callendrello
Jonathan Finkelstein
Jukka Corander
Nick Croucher
Ste Bentley
Lindsay Grant
Laura Hammitt
Raymond Reed
Mathuram Santosham
Robert Weatherholz
Kate O’Brien
2 postdocs available! whanage@hsph.harvard.edu
@BillHanage

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