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Sep 10 31 tweets 10 min read
I wanted to summarize current knowledge about origins of the #SARSCoV2 Omicron variant.

(This 🧵 doesn’t have anything new for people following topic closely, but I still get many questions about this, so am recapping current knowledge.)
TLDR: there are now good reasons to favor explanation that Omicron largely evolved in chronic human infection(s), possibly w some compensatory evolution after re-entry into general human population.

No other proposed explanation look particularly convincing anymore.
To start, let’s review what was unusual about Omicron.

First, Omicron is on a very long branch from the rest of #SARSCoV2 phylogeny, indicating it has a lot of new mutations relative to anything before it (image below from…).
Second, for a virus of its “age”, Omicron has an excess of only one type of mutation: amino-acid mutations in spike.

Omicron has a “normal” number of synonymous mutations and non-spike amino-acid mutations for a virus that appeared when it did. See figure below:
Third, the excess amino-acid mutations in Omicron’s spike are concentrated in positions strongly targeted by human neutralizing antibodies. Many mutations in RBD, and within RBD they are at key antigenic positions (417, 446, 484, etc).
So Omicron emerged by process that fixed “normal” number of mutations for elapsed time in all of virus except spike, where there is strong enrichment of antibody-escape mutations.

Therefore, we can conclude there wasn’t elevated mutation rate, just elevated antibody selection.
To understand why this conclusion is justified, note that for *neutral* mutations, the rate of fixed mutations (substitutions) is just equal to the rate at which mutations appear:…
For a virus like #SARSCoV2, we expect many synonymous mutations to be roughly neutral. So if Omicron emerged by a process that elevated mutation rate, it would have excess synonymous mutations. But it doesn’t: it has normal number of synonymous mutations for virus of its age.
For mutations that aren’t neutral (like most amino-acid mutations), rate of fixed mutations depends on both mutation rate and selection on those mutations. If mutations are beneficial, then they will fix faster as viruses with those mutations will have a fitness advantage.
So Omicron evolved by process strongly favoring antibody-escape mutations in spike, but otherwise involving “typical” mutation rate & selection on non-spike proteins.

As most people reading this are probably already aware, we know of such process: chronic human infections.
It is now extensively documented that chronic infections (typically immunocompromised patients) impose strong selection for mutations in spike, probably because virus is exposed to sub-neutralizing antibody for extended time w/o transmission bottlenecks:
For instance, in 2020 @DrJLi described an immunocompromised patient whose virus acquired 10 amino-acid mutations in spike over a ~140-day chronic infection:
A study by @GuptaR_Lab likewise described a chronic infection with multiple spike amino-acid mutations, and presciently suggested such infections could contribute to emergence of future #SARSCoV2 variants:…
A study by @sigallab described extensive antibody escape via numerous amino-acid substitutions in spike in chronic infection of a HIV+ patient in South Africa:…
A nice meta-analysis by @SternLab summarizes across many cases how chronic infections often lead to strong selection for antibody-escape mutations in spike that have characteristics of variants of concern:…
Harm van Bakel & @VivianaSimonLab even caught the virus in the act, by identifying a highly mutated variant that appeared in a chronically infected patient and then transmitted to a few other people (although fortunately did not spread widely):…
So for these reasons, evolution in chronically infected humans is certainly *consistent* with mutation properties of Omicron. (Although Omicron does have more spike mutations than observed in any single chronic infection yet studied.)
Another more subtle aspect of Omicron’s evolution is also consistent with strong antigenic during a chronic infection: when a protein is strongly selected for some specific trait (say antibody escape), it often impairs other important biochemical properties (like stability).
This impairment can happen because antibody-escape mutations themselves may come at a cost to other biochemical properties, & also because deleterious mutations can hitchhike w beneficial ones if they are close enough in primary sequence to stay in linkage disequilibrium.
And in fact, we see clear evidence that region of strongest antigenic selection during Omicron’s evolution (the RBD) acquired deleterious mutations that have been getting “repaired” during more recent evolution of Omicron subvariants.
One way they are getting repaired is simply by reversion. Several mutations in early Omicron variants have repeatedly reverted mutations (eg, at site 493 in Omicron’s RBD). A reversion is an obvious way to repair a defect caused by a hitchhiking deleterious mutation.
In addition, the RBD of earliest Omicron variants (eg, BA.1) was notably destabilized, and later Omicron variants have been acquiring secondary mutations that repair this biochemical defect. See below from @yunlong_cao:
Probably the strong selection for antibody escape in Omicron’s evolution in a chronic infection impaired RBD stability, which in turn hurt transmissibility--and so this defect has been getting repaired during subsequent compensatory evolution in the general population.
Now I want to address other theories initially proposed for Omicron’s origins: (1) evolution in isolated human population, (2) jump from animal reservoir, (3) lab accident, (4) mutagenic agent like molnupiravir.

None of these explain the data well.
Evolution in isolated human population: (a) we don’t know of any populations sufficiently large to sustain extended transmission while remaining totally isolated; (b) evolution in isolated population can’t explain elevated rate of antibody-escape mutations in spike.
Jump from animal reservoir: there is no evidence that #SARSCoV2 picks up a dramatic excess of antibody-escape mutations in spike when it evolves in animals.

In fact, antigenic evolution is likely to be *slower* in animals than humans:…
Lab accident: while circumstantial evidence makes this a plausible theory for origins of #SARSCoV2 itself in Wuhan, this evidence doesn’t extrapolate to Omicron. South Africa (where Omicron identified) has plenty of HIV+ chronic infections.
Also, engineering of spike for antibody escape would not explain how Omicron also ended up with “normal” number of new mutations elsewhere in genome.
Additionally, this paper (…) arguing for lab origin of Omicron applies statistics that “neglect the effect of natural selection,” which is a poor assumption because Omicron’s spike clearly evolved under strong natural selection.
Finally, mutagenic drug such as molnupiravir would lead to excess mutations throughout genome, not just amino-acid mutations in spike.

Extended Fig 1 of this paper (…) shows example #SARSCoV2 hypermutation: lots of mutations everywhere. That's not Omicron.
Overall, I hope this thread recaps why chronic human infections are clearly best explanation for emergence of Omicron.

This doesn’t mean we shouldn’t keep the other possible explanations in mind as concerns for future, but they are unlikely to have contributed to Omicron.

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More from @jbloom_lab

Sep 1
In new study, we examine how target cell ACE2 expression affects #SARSCoV2 neutralization…

We find high-ACE2 cells emphasize role of RBD antibodies in serum & lower ACE2 cells reveal more neutralization by antibodies to other regions of spike.

🧵 below:
It’s generally accepted #SARSCoV2’s receptor-binding domain (RBD) is part of its spike that is most important for neutralization by serum antibodies elicited by vaccination and infection.
This conclusion is based largely on studies that remove just RBD-targeting antibodies from human sera (see below), and then test how much overall neutralization is reduced by removing the RBD antibodies.
Read 16 tweets
Aug 31
In new study led by @dbacsik, we show single influenza-infected cells produce wildly different numbers of viral progeny:…

Surprisingly, cells that transcribe the most viral mRNA are NOT ones that produce the most progeny virions.

Explanatory 🧵 below
First study of viral progeny production from single cells dates was by Max Delbruck, who in 1945 showed different bacteriophage-infected bacteria differ by over an order of magnitude in how many offspring virions they produce:…
Delbruck did his experiment by isolating single infected bacterial cells in small volume containers. Since 1945, several similar expts have been done for human viruses (… &…), and also found very heterogeneous progeny production.
Read 20 tweets
Aug 5
Fascinating paper on #SARSCoV2 aerosol shedding by @Jianyu16 @drkristenkc @Don_Milton et al

Interesting to see shedding increase for new variants as @drkristenkc describes in her 🧵 below.

But I'm more amazed by person-to-person variation in shedding regardless of viral variant
For instance, look at this plot which shows total aerosol shedding (y-axis) with points being different infected people.

For all viral variants, some people shed many orders of magnitude more virus in aerosols than others.
I was struck by same thing in @bencowling88's important paper on how masks reduce aerosol shedding:…

Yes, masks reduce droplet & aerosol shedding. But equally striking is how shedding varies by orders of magnitude from person-to-person even w/o masks.
Read 5 tweets
Jul 25
I've updated our #SARSCoV2 escape calculator to improve prediction of which future mutations will cause largest drop in neutralization.

In below 🧵 I summarize updates to calculator and the improved results with respect to potential future escape mutations in BA.2.* and BA.4/5.
Recall that the escape calculator works by aggregating deep mutational scanning measurements of which mutations escape ~1,500 individual antibodies to predict polyclonal antibody escape:…
The update to calculator is adoption of an excellent idea by @yunlong_cao @jianfcpku Sunney Xie et al () to weight antibodies by their potency (I've weighted by *log* IC50). This modestly changes results.
Read 8 tweets
Jul 14
Since I'm (allegedly 🙄) an expert in "predicting" viral evolution, I'm often asked below question (which is good question):

If we can predict some things about #SARSCoV2 evolution, why can't we use these predictions to keep vaccines ahead of evolution?

Here are my thoughts...
First, let's examine what we mean by "predicting." Here's one of my big successes: in Dec 2021, I predicted sites of future antibody escape in Omicron included 486 (in fact, it was predicted to be biggest site of escape):
Then in April 2022, @Tuliodna and coworkers identified new variants BA.4/BA.5 that had the F486V mutation:

Subsequent work showed that BA.4/BA.5 indeed have ~3-fold reduced neutralization (relative to BA.2), which is pretty substantial antibody escape.
Read 19 tweets
Jun 30
This new #SARSCoV2 Omicron subvariant (BA.2.75) flagged here by @PeacockFlu is worth tracking, as it has appreciable antigenic change relative to its parent BA.2. Key mutations: G446S & R493Q

Here is summary of what those mutations imply for antibody escape & ACE2 affinity (1/n)
As I discussed last month, G446S is at one of most potent sites of escape from antibodies elicited by current vaccines that still neutralize BA.2: So for immunity from vaccines or early infections, adding G446S to BA.2 will decrease neutralization (2/n)
However, G446S will have less effect on antibodies of people with prior BA.1 breakthrough infection: . Therefore, BA.2.75's antigenic advantage relative to BA.2 will be most pronounced in people who have NOT had BA.1 exposure. (3/n)
Read 13 tweets

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