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). >>
Control of B.1.1.7 will therefore require stricter measures than applied during the November lockdown as noted and modelled by others. >> cmmid.github.io/topics/covid19…
As it emerges that B.1.1.7 has already spread to many other countries, everything possible should be done to contain it in its roots. Failure to stop this variant now will mean long and strict lockdowns one or two months later otherwise. >> theguardian.com/world/2020/dec…
Many thanks to @harald_voeh, @jcbarret, @imartincorena @CovidGenomicsUK and many other colleagues from @sangerinstitute and @emblebi for their hard work over the last weeks and months.
To address a few questions that came up. Some people wondered whether a lockdown would select for the new strain since it emerged during this period. In the weeks after lockdown was lifted, the spread of the new lineage was even faster than during lockdown. >>
Or put differently: if there had been no lockdown the relative proportions would about the same (this is defined by the fold change in R values), but there would be many more cases of both lineages. >>
Other people wondered about whether B.1.1.7 case numbers increased only in certain areas, possibly because of behavioural differences. Our analysis shows that B.1.1.7 case numbers increased approximately 3-5 fold during lockdown in nearly every LTLA. >>
In the South East this increase had a noticeable effect on case total because of the high B.1.1.7. prevalence (50% in some areas) at the beginning of lockdown. In other (most) areas with only a few % of B.1.1.7 this growth was masked by the decline of other lineages of ~50%.

• • •

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

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
10 Jul 18
Our work on predicting acute leukaemia based on blood sequencing is out in @nature. Essentially the risk of AML transformation can be determined 5-10yr prior to diagnosis from mutations in blood cells. >>
rdcu.be/2Muh
In this study, individuals who progressed to AML harboured more and slightly different pathogenic mutations -- and these affected more cells than in healthy individuals. The time scale is surprising as AML usually manifests rapidly. >>
Yet it is also a demonstration that somatic evolution and neoplastic transformation is common and takes place over long periods. The boundary between benign (ARCH) and malignant (AML) is somewhat blurry, but there is signal to quantify the risk of progression.
Read 6 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!