Phylodynamics and molecular evolution of SARS-CoV-2
Trevor Bedford (@trvrb)
Associate Professor, Fred Hutchinson Cancer Research Center
2 Dec 2021
Epidemics8
Slides at: bedford.io/talks
1. Real-time tracking of SARS-CoV-2 evolution
2. Emergence of variants of concern
3. Current circulation patterns
4. Assessing adaptive evolution
5. Variant transmission dynamics
6. Omicron variant
Real-time tracking of SARS-CoV-2 evolution
Over 5.5M SARS-CoV-2 genomes shared to GISAID and evolution tracked in real-time at nextstrain.org
Richard Neher,
Emma Hodcroft,
James Hadfield,
Thomas Sibley,
John Huddleston,
Ivan Aksamentov,
Moira Zuber,
Jover Lee,
Cassia Wagner,
Denisse Sequeira,
Cornelius Roemer,
Victor Lin,
Jennifer Chang
SARS-CoV-2 lineages establish globally in February and March
Limited early mutations like spike D614G spread globally during initial wave
Mutations in summer and fall 2020 were confined to regional dominance
Emergence of variants of concern
Repeated emergence of 484K and 501Y across the world
Emergence of Alpha (B.1.1.7) in the UK
Emergence of Beta (B.1.351) in the South Africa
Emergence of Gamma (P.1) in the Brazil
Working hypothesis of within-host evolution occurring during infection, driven by natural selection against host immunity
Rapid within-host evolution during persistent infection
484K and 501Y observed during this evolution
Current circulation patterns
Spread of VOC / VOI lineages across the world
Delta outcompeting other variants and seemed poised to sweep
Variants show excess mutations across the genome
But show most substantial excess in the S1 domain of spike
Assessing adaptive evolution
Rapid and parallel adaptive mutations in spike S1 drive clade success in SARS-CoV-2
Katie Kistler,
John Huddleston
Phylogeny of 10k genomes equitably sampled in space and time
Measure clade growth as a proxy for viral fitness
Clades with more S1 nonsynonymous mutations grow faster
S1 is quickly evolving and highly correlated with clade growth
dN/dS to root further highlights adaptive evolution
Rapid pace of adaptive evolution relative to H3N2 influenza
Mutations in S1 arising via within-host pressures result increase viral fitness and are enriched in the viral population by natural selection
Although selection has not been primarily for antigenic drift, observed level of adaptability suggests its potential
Variant transmission dynamics
Multiple approaches to modeling fitness differences between circulating variants
Multinomial logistic regression models work well on frequencies
However, frequency of a variant may rise while cases fall
SARS-CoV-2 variant dynamics across US states show consistent differences in transmission rates
Marlin Figgins
Estimation of variant-specific Rt through time using state-level data
State-level case counts are partitioned based on frequencies of sequenced cases
Differences in intrinsic Rt across variants, but all trending downwards
Consistent differences in variant-specific transmission rate across states
Consistent reductions in variant-specific Rt from vaccination
Future work would ideally tie together granular empirical estimates of viral fitness from frequency data together with mutational and phenotypic predictors to learn what is driving variant success
Omicron variant
Lineage B.1.1.539 / clade 21K / Omicron variant emerging from basal diversity
Long branch connecting closest sequenced viruses
Omicron viruses with huge excess of mutations in S1
But a slight paucity of divergence in the rest of the genome
Closing thoughts
Acknowledgements
SARS-CoV-2 genomic epi: Data producers from all over the world, GISAID and the Nextstrain team
Omicron: Tulio de Oliveira and team, Centre for Epidemic Response & Innovation, National Institute For Communicable Diseases in South Africa and the Botswana Harvard HIV Reference Laboratory
Bedford Lab:
John Huddleston,
James Hadfield,
Katie Kistler,
Louise Moncla,
Maya Lewinsohn,
Thomas Sibley,
Jover Lee,
Cassia Wagner,
Miguel Paredes,
Nicola Müller,
Marlin Figgins,
Denisse Sequeira,
Victor Lin,
Jennifer Chang
