What have we learned from analysing 200,000 SARS-CoV-2 genomes from genomic surveillance in England in the last 9 months?

These data provide important context for the current situation related to B.1.617.2.

Here’s a summary of the preprint. medrxiv.org/content/10.110…
@harald_voeh has developed a model that tracks 62 different lineages across 315 local authorities in England. His model estimates total and lineage-specific incidence and growth rates.
The model also calculates lineage-specific relative growth rates and provides a fairly accurate reconstruction of the epidemic and its many subepidemics across the nation between Sep '20 and Apr' 21. We also included a provisional analysis until 15 May '21 to track B.1.617.2
This allowed us to take a closer look at how B.1.1.7 spread during periods of lockdown and regionally tiered restrictions from Nov to Dec '20. The geographic and temporal correspondence between growth rates and restrictions is striking. Still, B.1.1.7 was simply too fast.
It was only the third national lockdown from Jan to Mar ‘21 (and an element of acquired and vaccine derived immunity) that stopped B.1.1.7.

Yet as a byproduct of the strong restrictions, almost all lineages present in Sep ‘20 were eliminated.

But some variants resisted.
Different E484K containing variants were repeatedly introduced (or emerged domestically) between Dec '20 and Mar ‘21. Yet they mostly caused short lived regional or local outbreaks and increased only very moderately over all.
But the situation changed in Apr ‘21 when B.1.617.2 was introduced and spread rapidly, reaching a national frequency of 40% on May 15 ‘21.
It was associated with a number of local outbreaks as in Bolton, but also spread to 227 LTLAs. We estimate it grew around 30-50% faster than B.1.1.7. since its introduction.
We don’t understand exactly why B.1.617.2 spread so rapidly. Three factors could have contributed: Transmissibility and/or immune (vaccine) escape, repeated introductions and demographic factors facilitating onwards transmission.
Regarding introductions and demographic factors it is instructive to study its sister lineage B.1.617.1, which would largely share these factors, but didn’t grow much and produced much smaller clades per introduction.
Recent data by PHE and other investigators points towards higher secondary attack rates and reduced vaccine efficiency, especially for single doses, yet some questions remain as summarised by co-lead @jcbarett
So far B.1.617.2’s relative increase was a combination of its own growth and B.1.1.7’s decline. But the equation might change in the future: Outbreaks may cede, circulation in the wider population increases, the effects of reopening and also further vaccination.
As we don’t have all the answers yet it becomes clear that further and rapid genomic surveillance is critical. @theosanderson and others have developed a website where these data can be explored. covid19.sanger.ac.uk/lineages/raw
A great thank you to everyone who has contributed to this work - this was an extremely collaborative project that couldn’t have been realised by any single individual. 🙏

• • •

Missing some Tweet in this thread? You can try to force a refresh
 

Keep Current with Moritz Gerstung

Moritz Gerstung Profile picture

Stay in touch and get notified when new unrolls are available from this author!

Read all threads

This Thread may be Removed Anytime!

PDF

Twitter may remove this content at anytime! Save it as PDF for later use!

Try unrolling a thread yourself!

how to unroll video
  1. Follow @ThreadReaderApp to mention us!

  2. From a Twitter thread mention us with a keyword "unroll"
@threadreaderapp unroll

Practice here first or read more on our help page!

More from @MoritzGerstung

17 Apr
Want to *see* how a tumour has evolved and grown? And also how different clones acquired characteristic transcriptional and histopathological features?

Hold on, that's magic.

No, it's our new preprint by @LucyYat47076319 and @MatsNilssonLab 1/9

biorxiv.org/content/10.110…
Jessica Svedlund developed a base-specific extension of the in situ sequencing protocol (BaSISS) to detect somatic mutations on a microscopy slide with fluorescently tagged padlock probes. 2/9
These signals are denoised and assembled into microscopic maps of subclonal growth using @LomakinAI's rigorous machine learning model. 3/9
Read 9 tweets
30 Dec 20
Did the new SARS-CoV-2 B.1.1.7 lineage spread during the English national lockdown? Rising numbers and estimated higher R value suggest so. Together with our colleagues from COG-UK we took a closer look. >> virological.org/t/lineage-spec…
Fitting lineage-agnostic daily PCR test and viral genome data from COG-UK to 382 local authorities we find evidence that B.1.1.7 has spread in a staggering 200/246 of affected LTLAs during the November lockdown (R>1) while at the same time other lineages contracted (R<1). >>
The evidence is therefore overwhelming that B.1.1.7 was repeatedly capable to proliferate under lockdown measures sufficient to suppress other SARS-CoV-2 lineages. B.1.1.7 spread was not an isolated event of general failure of viral containment (both R>1). >>
Read 10 tweets
27 Jul 20
Can you see mutations in cancer cells? Kind of.

We trained a neural network on 17k tumour slides with known genomics transcriptomics to assess how histopathology, molecular tumour characteristics and survival correspond. 1/8 nature.com/articles/s4301…
This analysis discovered histopathological patterns of 167 different mutations ranging from whole genome duplications to point mutations in cancer driver genes - about 1/4 mutations tested. 2/8
Further, around 40% of the transcriptome is correlated with histopathology reflecting tumour grade and composition. This is probably best illustrated at the example of infiltrating lymphocytes TILs, which can be identified and localised through their expression signature. 3/8
Read 9 tweets
1 May 20
Tired of C19 preprints? Read this: My student @NadezdaVolkova1 & collaborators completely took apart how mutational signatures are sculpted by DNA damage and repair. We grew and sequenced > 2700 worms from 53 repair KO's exposed to 11 mutagens. Phew. 1/7
nature.com/articles/s4146…
Analysing all different combinations, including wildtype and no treatment, allows to map the mutagenic contributions of damage and repair: 9/11 mutagens produce different mutational signatures depending on which repair pathways are acting. This involves 32/53 repair genes. 2/7
We can pin down which elements of a mutational signature are caused by which type of DNA alteration (the same mutagen often produces a variety) – and which repair pathway is involved in mending each type of lesion (usually many pathways operate jointly) 3/7
Read 8 tweets
26 Oct 19
Hello world. Here’s something interesting: @yufu0413 from my lab trained a deep convolutional neural net in cancer histopathology *and* genomics using 14M images from 17k H&E slides across 28 cancer types. The outcome is stunning. 1/5
biorxiv.org/cgi/content/sh… Image
The network can predict a good range of genomic alterations, including whole genome duplications. From H&E-images alone. 2/5 Image
It also finds a lot of associations in bulk transcriptome data, deconvolves the signal to find areas on each slide corresponding to molecular cell types such as tumour infiltrating lymphocytes. Entirely automated. 3/5 Image
Read 5 tweets
19 Oct 18
Here is what we learned recently about somatic evolution and cancer:
1. Mutations arise in virtually every tissue of our body, as a part of normal development and ageing doi.org/10.1101/416339. As a rule of thumb about 1 mutation is introduced at every cell division.
2. Somatic mutations shed light on the first cell divisions in life, informing us about early embryogenesis and how the cells in different parts of our bodies are related to each other via their shared mutations. dx.doi.org/10.1038/nature… dx.doi.org/10.1038/nature…
3. In some cases this allows one to draw detailed conclusions about the homeostatic dynamics of an adult organ, exemplified beautifully in recent work on normal blood by Henry Lee-Six, @scienceadvocacy and colleagues. doi.org/10.1038/s41586…
Read 8 tweets

Did Thread Reader help you today?

Support us! We are indie developers!


This site is made by just two indie developers on a laptop doing marketing, support and development! Read more about the story.

Become a Premium Member ($3/month or $30/year) and get exclusive features!

Become Premium

Too expensive? Make a small donation by buying us coffee ($5) or help with server cost ($10)

Donate via Paypal Become our Patreon

Thank you for your support!

Follow Us on Twitter!

:(