Thread by @jbloom_lab

In this thread, I discuss what are candidates for the next mutational steps in evolution of #SARSCoV2 to evade neutralizing antibodies. TLDR: in addition to mutations at sites 452 & 486 in BA.4/5, watch for mutations at sites 346-348, 356, 444-446, & 468. (1/n)

As background, human CoVs evolve to erode antibody neutralization (

https://twitter.com/jbloom_lab/status/1339939739781066752

). As result, typical person infected with common-cold CoV every few years, which “updates” their immunity to newer strains until a few more years of viral evolution erodes it again (2/n)

As most people know, this process is ongoing for #SARSCoV2, with new variants continuing to erode neutralization by antibodies elicited by old strains (like one in vaccine), which contributes to increasing re-infections & vaccine breakthroughs (

https://twitter.com/trvrb/status/1511871593940545536

) (3/n)

Question I address here is which specific mutations are candidates for “next steps” in this antigenic evolution. I’ll focus only on spike’s receptor-binding domain (RBD), which is dominant but not exclusive target of neutralizing antibody response:

https://twitter.com/jbloom_lab/status/1382475942468087812

(4/n)

To prospectively identify antibody escape mutations, @tylernstarr @AllieGreaney developed deep mutational scanning to map all mutations that reduce binding (science.org/doi/10.1126/sc…). Below is map of mutations that escape one monoclonal antibody (LY-CoV555 = bamlanivimab) (5/n)

Of course, human antibody response to infection & vaccination is polyclonal. To understand escape from that, we need to identify how mutations affect the spectrum of different neutralizing antibodies generated by human immune system (6/n)

As simple example, consider mix of 3 antibodies shown below. Full escape from mix requires combining mutations that escape each antibody. Note mutations can be redundant if they escape same antibody (484 & 490) or synergistic if escape different antibodies (484 & 417). (7/n)

We previously formalized this idea into an antibody escape calculator, which leverages deep mutational scanning for a large set of antibodies to estimate effects of mutations on polyclonal serum: academic.oup.com/ve/article/8/1… (8/n)

Originally calculator used data for few dozen antibodies generated by @tylernstarr @AllieGreaney in our group, but recently Sunney Xie, Richard Cao, @facyanOvO et al at @PKU1898 generated HUGE set of data for ~1,500 antibodies!

https://twitter.com/fucyanOvO/status/1521218147235209216

(9/n)

Their data, which @facyanOvO generously posted on GitHub (github.com/jianfcpku/SARS…), now compose vast majority of info used by escape calculator. Such a large data set enables some pretty cool analyses. (10/n)

First, as has now been extensively described, antibodies elicited by early #SARSCoV2 (eg, current vaccine) that neutralize early strains (eg, Wuhan-Hu-1) strongly escaped by mutations at site 484 & also sites like 417, 346 & 446—all of which are mutated in some variants. (11/n)

Escape calculator also shows how Omicron BA.1 and BA.2 both have extensive escape from antibodies elicited by early #SARSCoV2, as is now well described. Importantly, it shows that 486 is site of largest escape from residual antibodies that still neutralize BA.1 / BA.2 (12/n)

In fact, using calculator we predicted back in Dec 2021 that site 486 was one to watch for future evolution (

https://twitter.com/jbloom_lab/status/1473089135288233993

). And just last month, @tuliodna reported it was mutated in BA.4/BA.5, which have largest antibody escape of any variants yet described. (13/n)

So what might be virus’s next steps in antigenic evolution? We can subset on just antibodies elicited by early (pre-Omicron) strains that still neutralize BA.2. In addition to mutations already in BA.4/BA.5, sites of possible future escape include 346, 444-446 & 499. (14/n)

But importantly, sites of escape in BA.2 somewhat different for antibodies elicited by early strains FOLLOWED by BA.1 breakthrough. Comparing below image to that in prior Tweet you can see some different peaks, such as at 347-348, 356 & 468. (15/n)

This divergence in effects of mutations between people +/- prior BA.1 breakthrough is because exposure history shapes immunity. We will increasingly see variation in how #SARSCoV2 mutations impact antibodies of different people, as for influenza: elifesciences.org/articles/49324 (16/n)

Finally, antibody-escape calculator is available at jbloomlab.github.io/SARS2_RBD_Ab_e… You can select antibodies by exposure history (including past infection w SARS-CoV-1) & by what strains they neutralize, & click on sites to see impacts of mutations. (17/n)

Also, some slides going into more detail on the material in this thread are at slides.com/jbloom/escape-… (18/n)

And really embarrassingly, I mis-tagged @fucyanOvO who shared so much of the great data analyzed here. Sorry... 😞

https://twitter.com/jbloom_lab/status/1527872381322022913

(19/n)