Genomic epidemiology as translational research:
from basic science to public health
Sidney M. Bell, PhD (@sidneymbell)
UC Santa Cruz - Genomics Institute Seminar - August 2022
Impact of translating basic research to public health is
often faster and broader than translating to the clinic
https://www.hiv.gov/hiv-basics/overview/history/hiv-and-aids-timeline
1984
Warn against injection drug use
Offer sex education
Blood screening
1987
First high-sensitivity tests
First ART available
Virtuous cycle
Data generation
Problem identification / hypothesis generation
Methods development
Scientific findings
Application in public health practice
Genomic epidemiology
Using pathogen genomic sequences to understand the distribution and spread of infectious disease
Agenda
- Brief introduction to gen epi
- Gen epi research
- Dengue antigenic evolution
- Dengue antigenic evolution
- Gen epi practice
- COVIDTracker (2020 - 2021)
- Tool development (2021 - 2022)
- Future directions for the field
- Discussion and questions
Viruses evolve and spread on similar timescales
Grubaugh, Nature Micro, 2019
Outbreak spread
Sample some individuals
Determine phylogeny
Agenda
-
Brief introduction to gen epi
- Gen epi research
- Dengue antigenic evolution
- Dengue antigenic evolution
- Gen epi practice
- COVIDTracker (2020 - 2021)
- Tool development (2021 - 2022)
- Future directions for the field
- Discussion and questions
Dengue is a mosquito-borne virus
that kills 15,000 children annually
Four (uniform?) serotypes of dengue
DENV1
DENV3
DENV4
DENV2
Each serotype is genetically diverse
DENV1
DENV3
DENV4
DENV2
Lifelong protection
Temporary
cross-protection
Initial
response
Original antigenic sin drives
dengue case outcomes
After
1-3 yrs
Increased risk
Antigenic variation changes what the virus looks like to your immune system
Measles
Antigenically uniform
Flu
Antigenically
diverse
Lifelong protection
Temporary cross-protection
Antigenic variation is
why you need a new flu shot every year
Measles is
antigenically uniform
Flu is
antigenically diverse
DENV antigenic relationships
are poorly understood
Serotypes are genetically distinct
Clades are genetically distinct
Serotypes are antigenically distinct
Are clades antigenically distinct?
≠
?
≠
≠
Titers approximate pairwise
antigenic distance
Experimentally measure how well sera inhibits viral plaque formation
Sera produced by 1st infection
Test viruses
Dengue serotypes look antigenically diverse
NHP sera; 3 months post primary infection
= 2-fold change in PRNT50
Katzelnick et al, Science 2015
How does
dengue evolve
antigenically?
Dengue serotypes look antigenically diverse
Bell et al, eLife 2019
Antigenic distance between viruses i, j
Dij∝∑mdm
D_{ij} \propto \sum_{m}d_m
Model antigenic distance as the sum of mutation-specific antigenic effects
Mutations between i,j
Squared training error
L1 regularization
(i.e., most values are 0)
∝
\propto
Mapping genotype to phenotype identifies ~30 mutations with antigenic effects
Bell et al, eLife 2019
(95% CI 0.74–0.77)
(95% CI 0.77–0.79)
RMSE=0.75
RMSE=0.75
R2=0.78
R^2=0.78
Each serotype of dengue contains multiple
distinct antigenic phenotypes
Bell et al, eLife 2019
Sanofi vaccine trial efficacy
varies by genotype
DENV4-I
associated with
adverse events
DENV1
DENV3
DENV4
DENV2
Does antigenic diversity impact dengue population dynamics?
Serotypes cycle through populations
Genotypes
Bell et al, eLife 2019
Population susceptibility:
Previously circulating:
Population
immunity:
Clade growth:
Fitness based on antigenic distance from
standing population immunity
Does antigenic novelty contribute
to dengue fitness?
Relative frequency of j
Pi(t)∝∑jxj(t)∗Dij
P_i(t) \propto \sum_{j} x_{j}(t) * D_{ij}
Antigenic distance* between i and j
Lukzsa and Lassig, Nature, 2014
Antigenic fitness of i
* serotype level
or genotype level
Predict clade growth
based on antigenic fitness*
Lukzsa and Lassig, Nature, 2014
∝e(Pi(t)+dt)
\propto e^{(P_i(t) + dt)}
Growth rate @
next 5 years
* serotype level
or genotype level
Bell et al, eLife 2019
Fitness based on antigenic distance* from
standing population immunity
Bell et al, eLife 2019
Serotype-level antigenic fitness drives clade growth & decline
Bell et al, eLife 2019
Summary
- Dengue virus undergoes ongoing, slow
antigenic evolution within each serotype
- Antigenic novelty is a strong determinant
of serotype-level population dynamics
Agenda
-
Brief introduction to gen epi
-
Gen epi research
-
Dengue antigenic evolution
-
Dengue antigenic evolution
- Gen epi practice
- COVIDTracker (2020 - 2021)
- Tool development (2021 - 2022)
- Future directions for the field
- Discussion and questions
COVIDTracker
Applied genomic epidemiology for
local public health departments
2020 - 2021 Demonstrate value
2021 - 2022 Work ourselves out of a job
COVIDTracker
May 2020 - June 2021
COVIDTracker program scale
- 30% of counties in CA
- 13k sequences publicly released
- 100+ hours of small-group training
Applied gen epi delivered major local impact
-
Tracking introductions
County A caught transition from travel-associated (March/April '20) to community-based cases (July/August)
-
Ambiguous contact tracing
County B jail outbreak, overlaid with facility locations and transfer records
-
Related outbreaks
Shared genomes across skilled nursing facilities in County C identified shared staff as a major issue
-
...and dozens more
Work yourself out of a job
Ops
Four pillars of successful, applied gen epi
Building software to enable data management & bioinformatics
- PH4GE schema
relational database
- Intuitive UI
- Contextual data from GISAID
- Constantly updated integrations with Nextstrain, Nextclade, USHeR & Pangolin
Context is everything
Linking data is a huge challenge
| DOH ID | CZB ID | GISAID ID |
|---|---|---|
Queryable, version-controlled, single source of truth
ACTG...
ACTG...
ACTG...
Scripted, reproducible QC & disambiguation
Linking data is a huge challenge
Building local health departments' capacity for in-house sequencing & interpretation
Alli Black!
Hands-on training for sequencing, hundreds of hours of interpretation guidance
Pilot experiment: can we build software to automate basic interpretation?
- Tree traversals to examine topology & patristic distances
- Automated narrative interpretations for common epidemiological questions
- Visualizations germane to epidemiology
- Ability to overlay and filter on arbitrary epidemiological metadata
Agenda
-
Brief introduction to gen epi
-
Gen epi research
-
Dengue antigenic evolution
-
Dengue antigenic evolution
-
Gen epi practice
- COVIDTracker (2020 - 2021)
-
Tool development (2021 - 2022)
- Future directions for the field
- Discussion and questions
Scientific
- Topology-based clustering
- Inference of sampling density
- Viz & interpretation aids for
partially-sampled outbreaks
- Actionable metrics for interpreting genomic
surveillance data
Systemic
- Data management - linkage between patient data, epi data, and genomic data
- Sequencing & operations -
major equity issues
- Professional currency for academics doing capacity building & applied public health work
Needs in the field
Acknowledgements
Bedford lab, especially Trevor Bedford, Richard Neher, Alli Black, Gytis Dudas & Leah Katzelnick
NSF GRFP for funding dengue work
CZI + CZB Genomic Epi team, especially Patrick Ayscue, Alli Black, Shannon Axelrod, Tony Tung, Olivia Holmes, Josh Batson, David Dynerman, Amy Kistler, Dan Lu, Jack Kamm, Maira Phelps, Kirsty Ewing, Hana Zaydens, Colin Megill & Phoenix Logan
Questions?
@sidneymbell
https://sidneymbell.science
Genomic epidemiology as translational research: from basic science to public health Sidney M. Bell, PhD (@sidneymbell) UC Santa Cruz - Genomics Institute Seminar - August 2022
Genomic epidemiology as translational research
By Sidney Bell
Genomic epidemiology as translational research
UC Santa Cruz - Aug 2020
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