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Feb 10, 2023 23 tweets 13 min read Read on X
In a new paper led by @annasimsbiol, we find that influenza infections divide your throat into tiny territories, and ask:
why don’t these viruses want to be friends?

Paper doi.org/10.1371/journa…
Commentary doi.org/10.1371/journa…

A 🧵 An image showing two infected regions, one green and one mag
Some background: If two viruses get into the same cell, they can genes exchange (basically they can breed). This is really important for viral evolution. A dramatic example is when different strains of influenza A virus (IAVs) use coinfection to generate pandemic strains A schematic showing a green and magenta virus coinfecting a
Many viruses actively push back against coinfection, changing an infected cell until it becomes resistant to infection by related viruses. This effect is known as ‘superinfection exclusion’ (SIE) A schematic showing a cell infected by a green virus which,
Why might SIE be important?
In the lab it normally isn’t, as we set up experiments where cells are simultaneously
In a natural infection though – breath in, and the chances of two viruses entering your airway, finding the same cell, and entering it at the same moment are… slim
Presumably for influenza viruses (and many others), unrelated viruses first establish separate infections inside a host, then encounter each other when they spread through tissue, causing disease and onwards transmission.
How might SIE control these spreading infections?
Following the spread of an infection inside a living animal is not easy, so we started with a model: cell cultures, and modified IAVs that differed only in the colour of a fluorescent tag
We saw that for IAV SIE starts rapidly and early after a short window for coinfection A graph showing the proportion of cells infected with a seco
To observe spread we used every virologist’s favourite assay: the #PlaqueAssay! 🥳
When two growing plaques collide, different spreading viruses meet. We see that coinfection can only occur in a thin boundary of recently-infected cells. Beyond that point, SIE is fully effective Coinfection limited to a thin boundary of cells between a gr
What about interactions within a single, spreading, infected region?
To look at this we needed plaques that were a mixture of two viruses. As the mixed plaques spread, would coinfection be maintained, or would the plaques split into descendants of single viruses?
Any guesses? Two models of a spreading plaque, one in which coinfection b
Setting up mixed plaques required a bit of creativity
We coinfected cells simultaneously, dissociated them with trypsin, diluted them, then reseeded them onto new cells so that each coinfected cell was now a plaque forming unit
And we found… A schematic showing the cell seeding method described in the
… that they stayed well-mixed as they spread.
This wasn’t the result we expected, but it makes sense – each new cell in a growing plaque can receive plenty of both colours of virus before SIE kicks in A spreading plaque of green and magenta viruses, maintaining
This is all very well and good, but is it just a cell culture artefact?
Plaque assays are not lungs, after all.
So we infected mice with both viruses… A micrograph showing a thick section of a lung infected with
… and found that once lesions had grown there were clear boundaries between the two colours inside the same anatomical compartments.
So it seems SIE can impose its patterns inside a host, as well as in a dish Distinct regions of green and magenta infection in the same
What does this mean? 🤔
•Within a host, flu infections divide into a landscape of tiny territories
•Viruses can exchange genes within a territory, but ‘breeding’ between territories is unusual
•This is not a specific mechanism, and could also apply for many other viruses A landscape of green, magenta and mixed infections in the sa
Bonus things not in the paper but worth mentioning!

This project was triggered by two ideas:

(1) listening to #TWiV while lagging the loft, I heard about this cool paper and thought ‘I wonder if flu does this?’ (Spoiler: actually it probably doesn't)
pubmed.ncbi.nlm.nih.gov/30318351/
(2) after a seminar in which I made some careless comment about cells in a host obviously getting coinfected all the time, @Digs66768072 asked the best and most annoying question in science:

‘… but why do you think that’s true?’

(it was not and I was wrong)
Also worth mentioning was how great the submission process was

(I know! Let’s share examples of people doing this well)
This started with @npariente going out of her way at a conference to give @annasimsbiol advice on her first big project, including ‘think of a journal you could submit to, then aim one higher’

(then following up by e-mail to clarify that yes, she did actually mean @PLOSBiology)
It continued with exceptionally clear editorial requests during revision from @Paula_Jauregui_ and @MehleLab @PLOSBiology , who were direct and to the point about what they wanted if the paper was going to be accepted

I can’t begin to tell you how helpful (and uncommon) that is
Also thanks to the reviewers who made some suggestions that (much as it pains us to admit it) genuinely improved the paper

Some of these were from @anice_lowen. We know this because she signed her review (‘something of a power move there’ – Anna). Thanks Anice!
Thanks also to @anice_lowen and @LucasFerreri1 for their thought-provoking commentary, which does a great job of putting this work in a wider context
We found this really helpful to read ourselves when thinking about where to go next with this:
journals.plos.org/plosbiology/ar…
… and most of all thanks to lead author @annasimsbiol,

all of whose infections

are super

Follow up:

Thanks very much to @HelenPuttick for reporting on this for @thetimes [article behind paywall]!

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More from @CVRHutchinson

Jun 24
Some interesting reading on viruses in milk 🐄🦠
Recently we and others looked at the effects of pasteurisation on H5N1 in milk (very effective) and the risks of 'raw' milk (very risky).
But what about unpasteurised cheese ? The experiment sounded like microbiological chaos... A cheese board showing various soft cheeses
... and to be clear we haven't done it ourselves, partly because everyone in the lab looks horrified whenever I suggest it. But people have done it in the past for another bovine virus, foot and mouth disease: ...sciencedirect.com/science/articl…
... in this 1976 paper, John Blackwell reports experimentally infecting cows with FMDV and then setting out on a home cheese making project, eventually producing batches of virus-laden cheddar, camembert and mozarella...
Read 9 tweets
Jun 3
🚨New Pre-Print🚨
With #H5N1 #flu shed in cow’s milk in the USA, we worked with @cvrinfo, @roslininstitute, @Pirbright_Inst & @APHAgovuk to ask if pasteurisation would make it safe
🐮🦠🥛🌡️
TL;DR: pasteurisation’s effective, raw milk can be infectious
medrxiv.org/content/10.110…
There are lots of good reasons to pasteurise milk, which is done by heating at 63C for a long time or 72C quickly. But we hadn’t previously had to worry about influenza in milk
Would pasteurisation work here too? Fresian cows
We tested this in labs, where we could vary the times and temps carefully (you then extrapolate to actual pasteurisation equipment)
Using a panel of influenza viruses in milk we found that they were all effectively inactivated before reaching pasteurising times at either temp Two graphs showing multiple lines (for virus titre) dropping rapidly on heating at 63C and 72C
Read 11 tweets
Sep 15, 2022
🚨New pre-print!🚨
biorxiv.org/content/10.110…
Viruses are very small things with very big effects. During the #SARSCoV2 pandemic, tiny changes in molecular biology had huge impacts on people’s lives. How could we communicate about this clearly?
🦠#scicomm #sciart🦠
(1/9)
In a new study with @CVRinfo @GSofASimVis and @COGUK_ME, @sarahiannucci1 explains how she used animations and interactive visualisations to help explain the threat of #SARSCoV2 variants of concern (#VOCs; 2/9)
There were, of course, plenty of data out there, collected by groups like @COGUK_ME. But these sites were (quite reasonably) aimed at expert audiences.

Could the details of new SARS-CoV-2 variants be explained in a way that was accessible for the public? (3/9) Screenshot of the COG-UK/ME...
Read 9 tweets
Jun 9, 2022
In a new preprint led by @annasimsbiol, we ask:
Why Don’t These Viruses Want to be Friends?
A 🧵...
biorxiv.org/cgi/content/sh… An image showing two infected regions, one green and one mag
Coinfection is an important aspect of viral evolution. If two viruses can get into the same cell, they can undergo genetic exchange. A dramatic example of this is when different strains of influenza A virus (IAVs) use coinfection to generate novel pandemic strains A schematic showing a green and magenta virus coinfecting a
However, many viruses actively push back against coinfection. In a variety of ways, they change an infected cell until it becomes resistant to infection by related viruses. This effect is known as superinfection exclusion (SIE) A schematic showing a cell infected by a green virus which,
Read 17 tweets
Sep 21, 2021
A quick Tweetorial about our new paper with the #BoutellLab, which @CharmanMatthew led during his PhD @CVRinfo

Alternative title: ‘Why are virologists’ cells stupid and does this matter?’ 🧵 (1/N)
If you work with human influenza viruses in the lab you most likely grow them in MDCK cells, or possibly MDBK cells, or maybe A549 cells if you are fussy enough to want a cell line that comes from (a) the right organ system and (b) the right species of animal (2/N)
These cell lines are super-convenient – they grow forever, and it’s so much easier to grow influenza in them than in, say, human bronchial epithelial cells. Great, right? (3/N) Influenza does not like growing in primary lung cells
Read 11 tweets
Dec 31, 2020
Earlier this year, @Scient_Art collaborated with us to produce one of the first detailed 3D models of the #SARSCoV2 virus particle. To round off 2021 she's updated her model, and it looks great (1/N) Image
... the first model drew heavily on existing work on related viruses (SARS-CoV-1 and MHV). The updated model has an improved representation of the spike protein, building on the detailed model from @RommieAmaro's lab (pubs.acs.org/doi/10.1021/ac…) (2/N) Image
The spacing and flexing of the spikes was also updated based on cryoEM data, particularly from the labs of @BriggsGroup (disq.us/t/3r34r8p) and @drlisai (doi.org/10.1016/j.cell…) (3/N) Image
Read 7 tweets

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