I wanted to highlight this pre-print by David Ho’s group on the neutralizing antibody response to new (XBB.1.5-based) COVID vaccine booster, as it illustrates some points related to paradigm of updating SARS-CoV-2 vaccines to keep pace w viral evolution. biorxiv.org/content/10.110…
Recall original COVID vaccines worked very well against early SARS-CoV-2 strains
Unfortunately, virus has been evolving, so antibodies elicited by that vaccine don’t neutralize newer viral variants very well
So in fall 2022, new booster was made that mixed new (at time) BA.5 variant & original strain. Hope was to boost neutralization of new variants.
Unfortunately, only sort of worked. Titers did go up, but not a relatively greater increase for new variants.
The new pre-print by David Ho’s group linked at top of this thread reports what happens w new booster that was rolled out this fall (2023), which contains just XBB.1.5
Punchline is things look much better in terms of boosting neutralizing titers specifically to new variants
As figure below shows, XBB.1.5 booster strongly (~20-fold) increases neutralization of XBB.1.5 & XBB-descended viruses
Boost mostly specific to newer variants, as increases smaller for older variants like D614G or BA.5
Also, XBB.1.5 vaccine and XBB infection give similar boost
Therefore, new XBB.1.5 booster is much better than prior booster at specifically boosting titers to newer variants.
So at serological level, this new booster is better overcoming “imprinting” (but more on that concept below).
This is a good thing.
Why is XBB.1.5 booster better boosting titers to new variants?
Current data insufficient to be sure. “Imprinting” used to refer to both serological effects (as above) or activation of new vs pre-existing B-cells
We don’t yet know what is happening at B-cell level w new booster
But to list some hypotheses:
Hypothesis 1⃣: Maybe problem w 2022 bivalent BA.5 booster was simply that inclusion of original strain impeded response to new strain, and main improvement of new booster is not including older strain.
Hypothesis 2⃣: Maybe greater antigenic distance of XBB.1.5 helps overcome imprinting possibly by reducing epitope masking by pre-existing serum antibodies, a la this study by @victora_lab nature.com/articles/s4158…
Hypothesis 3⃣: Maybe XBB.1.5 mostly still boosting “imprinted” B-cells at cellular level, but greater antigenic distance or accumulation of Omicron exposures is causing more expression & affinity maturation of antibodies that neutralize newer variants ()
But regardless of cause, observation that updated booster is now strongly boosting neutralization to new variants is good news for overall strategy of regularly updating vaccine to keep pace with SARS-CoV-2 evolution.
This fact is important, because evolution has not stopped. XBB-descended variants are still dominant most places globally and in USA, but there are indications that could potentially change fairly soon ().
Descendants of highly divergent BA.2.86 variant like JN.1 growing rapidly (), and also now recombinants w BA.2.86 spike and other genomic regions from XBB-descended viruses. These BA.2.86-descended viruses have many spike mutations relative to XBB.1.5.
As shown by @yunlong_cao () and also the new David Ho pre-print, the BA.2.86 descendant JN.1 has the most neutralization escape of any variant, although it’s only modestly worse than newest XBB descended variants.
Fact XBB.1.5 booster still increases titers to JN.1 is good. But boost smaller than for XBB-descended variants, underscoring JN.1 is antigenically distinct
So there will certainly be more updated boosters in years to come; good to see evidence current one is working as hoped
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We made spike pseudovirus deep mutational scanning libraries of mutations across XBB.1.5 spike (as well as XBB.1.5 RBD and BA.2 spike libraries).
We used these libraries to measure how >9,000 mutations affected cell entry, ACE2 binding, and serum antibody escape.
To measure how mutations affected ACE2 binding, we leveraged approach previously used by David Ho & @yunlong_cao groups that is based on fact neutralization of spike-mediated entry by soluble ACE2 is proportional to ACE2 binding.
In new work led by Will Hannon, we have created tool for visualizing deep mutational scanning data on protein structures, such as to aid in understanding effects of viral mutations.
There have been many experimental papers on BA.2.86 over last month. This one by David Ho's group is one of most comprehensive:
I'm going to quickly summarize key points for people having hard time keeping up w all the recent BA.2.86 papers.biorxiv.org/content/10.110…
1⃣ Serum neutralization of BA.2.86 roughly comparable to XBB.1.5, & comparable or slightly better than newer XBB variants like EG.5.1
Similar results (within few-fold) reported by other groups too, eg @yunlong_cao @BenjMurrell @BarouchLab @sigallab @ShanLuLiu1 @SystemsVirology
2⃣ But BA.2.86 escapes somewhat different antibodies than XBB variants: BA.2.86 has more escape from SD1 & RBD class 2/3 Abs, but less escape from RBD class 1/4 Abs
So maybe BA.2.86 & XBB have slightly different immunological "niches"
It will be a few weeks before we finish & post pre-print for study, but we wanted to share data now for those interested in interpreting current SARS2 evolution.
Full analysis of the mutations is in these slides:
Analysis is based mostly on deep mutational scanning experiments
TLDR: lots of antigenic change, and some interesting RBD mutations (addition of N-linked glycan & deletion in receptor-binding motif)slides.com/jbloom/new_2nd…
First, to emphasize, only THREE sequences of variant identified so far. There is not currently evidence of wide transmission.
As this thread outlines, people who study SARS2 evolution may want to pay attention to features of this variant. Everyone else can ignore if they wish.
As background, common question in evolution is if some mutations have different effects in different homologs or conditions
Eg, our lab has measured how most mutations to Delta, BA.1 & BA.2 spike affect viral entry. Which mutations have different effects in these viral variants?
If deep mutational scanning experiments were perfect, we’d just compare the measured effects of each mutation in the different spikes to see if they were non-identical.
But in practice, experiments have noise: so how do we tell true biological differences from noise?