#Phage are often only seen as bacterial predators, but they’re also major drivers of bacterial evolution through generalised transduction.

In this new preprint, we combine lab work & mathematical modelling to reveal the dynamics of this dual nature.

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biorxiv.org/content/10.110…
Generalised transduction happens when a replicating phage mistakenly packages non-phage DNA. The resulting transducing phage can inject this DNA in another bacteria, as shown in the Figure.

This is notably a powerful way to transfer plasmids, major vectors of #AMR genes.

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We co-cultured two single resistant strains of S. aureus (B_E and B_T) with 80alpha phage (P_L).

Here, horizontal gene transfer can only happen by generalised transduction, so if we detect double resistant bacteria (B_ET), we know that transduction happened.

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Results: we see double-resistant bacteria as early as 7h (red).

We also note that phage-bacteria dynamics vary depending on starting concentration of phage. For 10^3 and 10^4 phage, we see equilibriums after 8h, but for 10^5, the phage are still increasing after 16h…

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Here's where the modelling steps in. We developed & fitted models to the data to reveal the invisible underlying dynamics of phage predation & generalised transduction.
The Figure here shows the model diagram, including organisms above, but also transducing phage (P_E & P_T)

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The models suggest that these dynamics are due to a dependence of phage replication on bacterial growth rate. In other words, phage burst size decreases as bacteria reach stationary phase.

(I’ll let you read the paper to learn about density vs frequency-dependent models…)

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What about transduction then? Well, we can't easily count transducing phage in vitro, but remember that they're included in the model...

So we can use this to estimate parameters related to transduction - that's the strength of interdisciplinary work.

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We estimate that 1 per 10^8 new phage generated was a transducing phage carrying an #AMR gene, similar to previous estimates. That's often dismissed as too rare to be relevant.
Yet we've shown here that it's enough to consistently lead to double-resistant bacteria in just 7h

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Of course, new double-resistant bacteria (DRP) can also be generated by growth of existing DRP, rather than transduction

Again, using the model, we show that new DRP are always predominantly generated by transduction, further highlighting the importance of this process

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Implications for #phage predation: we’re adding to the growing body of evidence that phage replication depends on bacterial growth phase, with slower replication at stationary phase.

This should be considered for future experimental designs, but also phage therapy.

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Implications for #AMR: the contribution of transduction to the increasing global burden of AMR is currently underestimated, and hence opportunities for AMR control are potentially being missed.

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Massive thanks to coauthors @JacobWildfire @gupt_a_rya @jodi_lindsay @gmknght !

This is the 2nd paper from my PhD work, so it’s a big emotional moment! I look forward to hearing other people’s thoughts & reviewer comments on this 😁

12/12

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More from @QuentinLclrc

9 Jun
New open-access paper now published in @BMC_series #BMCHealthServRes, looking at hospital bed occupancy in England during the 1st #COVID19 wave

These results emphasise the importance of local knowledge in predicting bed occupancy & capacity planning

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bmchealthservres.biomedcentral.com/articles/10.11…
I previously wrote a thread going over the main results & implications of this work - head over there & read the paper for more details, here I will only cover a couple of key summary points

2/n
Most models predicting hosp. bed occupancy assume that patients receive care in only one bed type (eg general or critical), for an average length of stay

We suggest that this assumption + relying on national-level data is not appropriate for local bed occupancy predictions

3/n
Read 7 tweets
20 Nov 20
A few headlines popping up in the UK at the moment on "Supermarkets most common #COVID19 exposure location in England, data shows".

Yes, the data does show this, but don't be misled by incorrect interpretation!

1/n
What is this data?

It's the latest @PHE_uk surveillance report, which looks at 34,328 #COVID19 cases with a common exposure with at least 1 other case, over the period 9th - 15th November

assets.publishing.service.gov.uk/government/upl…

2/n
Here, "common exposure" means any location/event which at least 2 #COVID19 cases attended in the 2-7 days before testing positive.

If you rank these common exposures, you find supermarkets are the most often reported (18.3%).

But place of exposure ≠ place of infection !

3/n
Read 7 tweets
27 Oct 20
Currently a lot of discussions around #COVID19 superspreading events, so it's time for a thread to add some context and reply to common comments about this...
1/n
"*random setting* is not in the database! So it must safe!"

That's a risky conclusion to make. Not detecting transmission ≠ transmission didn't happen! There are a lot of biases that make it hard to identify some settings. We also have to remember that...
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... many setting have been closed / reduced visitors these past few months. Hard for transmission to happen in a setting when no-one's there! And, just based on that, risky to assume there won't be any transmission when people come back.
3/n
Read 9 tweets

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