Assessing SARS-CoV-2 variants early on

Richard Neher
Biozentrum & SIB, University of Basel

slides at neherlab.org/202104_ECDC.html

Similarities to influenza virus evolution

Klingen et al, Trends in MicroBio, 2018

Continuous back and forth between experimental and computational groups.

Weekly sequence submissions

Data we analyze are contributed by scientists from all over the world

Data are shared via and curated by GISAID

Challenges assessing novel variants

Frequency dynamics are skewed by sampling biases and varying incidence
Rare sequences might represent large outbreaks in poorly sampled regions
Sudden changes in sampling strategies might look like rapidly increasing frequencies
Focus on specific mutations in known VoCs might miss important others
Laboratory characterization takes time

Prioritization of variants for laboratory characterization.
Harmonized categorization of sequences as
- General surveillance
- Outbreak investigations
- Vaccine breakthroughs, reinfections, etc
- Travel associated cases
Support for denser sampling across the globe
(some places in Europe are still undersampled)

We need a sustainable and coordinated approach.

Avoid too much duplication of efforts
Maximize usefulness and usability of data and analyses
Establish exchange between groups with different expertise and stakeholders

Deep mutational scanning for RBD mutations that affect AB binding and ACE2 binding

By Tyler Starr, Allie Greaney, Jesse Bloom and colleagues
Systematic evaluation of all RBD mutations for escape and ACE2 binding
Monoclonals binding to class 1,2,3,4 epitopes
Convalescent and vaccine sera to map population escape
Escape maps of indivual sera and antibodies by Bloom lab
Escape calculator for retained binding by Jesse Bloom

Scoring all sequences for further characterization

Escape in distinct RBD epitope classes
RBD escape in convalescent/vaccine sera
NTD mutations
Other VoC associated mutations (nsp6 del)

ACE2 binding
Furin cleavage site mutations
Recent growth

Incorporation of scores into phylogenetic analysis

Acknowledgments

Trevor Bedford and his lab -- terrific collaboration since 2014

especially James Hadfield, Emma Hodcroft, Ivan Aksamentov, Moira Zuber, and Tom Sibley

Allie Greaney and Jesse Bloom for discussion of the dms data

Data we analyze are contributed by scientists from all over the world

Data are shared and curated by GISAID