Took a look at the spike mutations in B.1.1.529 this evening, and colour coded them (details below)...there is...not much green.🧵
First the obviously bad stuff (red): nine mutations seen in previous VOCs. There's a lot of overlap already among VOCs (convergent evolution), but this variant has an unprecedented sampling from mutations previously seen in Alpha, Beta, Gamma and Delta separately.
In orange are three mutations that are probably meaningful biological changes for the virus, but not previously seen in VOCs. Two from VUI level lineages that likely had modest advantages over original virus, and E484A which is at a key site in the receptor binding domain.
Next eleven things seen rarely or never before (blue) that may be functional and just new to us, or may be a side-effect of whatever process led to so many mutations in this lineage (i.e. either neutral or mildly deleterious). Need more data on these.
In green is just D614G, which has been fixed in all SARS-CoV-2 since early 2020.
Finally, three shades of purple which are new (not in previous VOCs) but have some other data to suggest they may be functional. First is a deletion/substitution/insertion hotspot in the N terminal domain, that may be further remodelling the protein structure there.
Then there's a group of 4 nearby substitutions (3 in the space of 5 amino acids) that have not been seen before, but are so close together that I doubt its coincidence. They are also very close to the (previously conserved) binding site of sotrovimab, a therapeutic antibody.
Finally S477N and Q498R, predicted in an experimental evolution paper to substantially increase ACE2 binding together with N501Y, but only seen in the wild separately or rarely. Seeing this full combination now (along with everything else) is grim.
nature.com/articles/s4156…
There are also multiple (possibly funcitonal) mutations in genes other than spike: notably R203K and G204R in nucleocapsid, which were recently shown to be key in increasing transmissibility, and are present in all VOCs to date. science.org/doi/10.1126/sc…
So the mutation profile is bad (as @PeacockFlu and others have already pointed out). We don't yet know how they act together, or how a virus with so many changes will behave.
We need to learn more, fast. To end on a hopeful note, it's mind-blowing how quickly we've got this far. Kudos to @Tuliodna et al for getting this out, and setting the global scientific community onto experiments to answer more of these questions. Let's get to work.

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More from @jcbarret

19 Oct
Proportion of AY.4.2 (now on covid19.sanger.ac.uk) has been steadily increasing in England, which is a pattern that is quite different from other AY lineages. Several of them rose when there was still Alpha to displace, but none has had a consistent advantage vs other Delta.
And it has grown all over the place between mid-July (L) and now (R). AY.4 did a similar thing, but it was not displacing other Delta, and given that it hasn't spread through the world, likely just had some epidemiological luck.
AY.4.2 hasn't yet spread much outside the UK, so could be a fluke (no biological advantage), but given that it's apparent advantage is modest, it needs to really get established somewhere to overcome stochasticity of small numbers.
Read 6 tweets
18 Oct
Technical pre-print up from me and @theosanderson on why G142D is not the world's most recurrent mutation: medrxiv.org/content/10.110… A short 🧵 with highlights, and a tidbit about AY.4.2 to tempt you to read it.
People have noticed that S:G142D seems to appear and disappear on the Delta tree in a very weird fashion. @babarlelephant noted early on that this is due to an issue with the @NetworkArtic V3 primers, one of which is hit by a 6bp deletion in all Delta
In our paper we confirm that if you look not for presence/absence of 142D (e.g. by looking at text strings of mutations in GISAID), but at the actual sequence, all Delta is either D or N (missing data). Image
Read 8 tweets
19 Aug
Should I be worried about AY.3?

TLDR: No.

Longer answer about this lineage, the challenges of cataloguing the current diversity of #SARSCoV2, the miracle of global open science, and handy questions to ask yourself when you see a new variant report follows in a 🧵.
First off, summary and background on AY.3 and its cousins here:
AY.3 was designated by @PangoNetwork because it was growing in parts of the USA, apparently from a single (or small number of) introductions. This is done NOT because it is necessarily biologically more transmissible but to make it easier to refer to. github.com/cov-lineages/p…
Read 11 tweets
18 Aug
Paper on within-host diversity (credits in Gerry’s tweet) is from the first wave of the pandemic, but the conclusions are timely, as the world wonders how the virus evolved and will evolve. In particular, I think the mutation spectrum is relevant to some interesting questions 🧵
This figure shows relative mutation rate: each colour is a type of mutation like C to A, with the bars showing the context of the bases to the left and right; below the line is the reverse complement (e.g. G to T for blue).Red C>U (C>T in DNA) mutations are by far the most common
In the very early history of the virus in humans it split into the A and B lineages, which (figure from zenodo.org/record/5075888…) differ from each other at two positions, in each case one is C and the other is T. If you just look at these two positions, lineage A is TC and B is CT
Read 6 tweets
14 Jun
Monday always means new data at covid19.sanger.ac.uk, but this week also brings new features! Overall picture should come as no surprise: Delta variant growing in proportion and absolute numbers. In fact, next week is likely to be our highest ever count of one lineage🧵
First new feature is "fade areas by uncertainty", or "nebulosity view", as I like to call it. We fade the colouring of local authorities on proportion view to give a visual hint that you shouldn't over-interpret an area with 100% Delta if it only has a handful of sequences.
Related to this, we show confidence intervals on proportion tooltips and in the unstacked version of the proportion graph. Compare the following two local authorities with similar point estimates, but different amounts of data.
Read 5 tweets
8 Jun
The description of genomic analyses in this piece about the spread of the Delta variant in the UK has major inaccuracies, and hindsight bias. 🧵
theguardian.com/commentisfree/…
Early on, the author, @chrischirp, notes that Pakistan and Bangladesh were red-listed on 9 April, but India wasn't until 23 April. There is a reasonable criticism to be made here, but it has nothing to do with variants. Cases and % positivity were already on the rise in India
We know now that delay led to hundreds of importations of the Delta variant. The article says India "first reported a concerning new variant on 24 March...so why didn’t PHE immediately escalate Delta to a variant of concern in March?"
Read 11 tweets

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