New preprint in collaboration with @asantoslopez.bsky.social lab and led by Jorge Sastre. Combining experimental evolution and analyses of longitudinal samples from hospitalised patients, we unveiled a new mechanism of plasmid-mediated evolution beyond HGT 1/8
www.biorxiv.org/content/10.1...
Excited to share the first preprint from my lab, collaborating with the fantastic @sanmillan.bsky.social 's lab, led by the amazing Jorge Sastre. We've demonstrated how insertion sequences encoded in plasmids promote bacterial adaptation. This project has been incredibly fun!
* in 30 genes :)
Nice systematic study of all synonymous substitution in 30 E. coli finding that substantial fitness effects are common under strong selection
www.pnas.org/doi/10.1073/...
Sparsity of higher-order landscape interactions enables learning and prediction for microbiomes www.pnas.org/doi/10.1073/... #jcampubs
Announcing the Social Lives of Viruses 2024!
Do you study social evolution in viruses? Do you want to?
Join us in Puerto Rico this June for a 50-person meeting dedicated to viral sociality. All costs covered.
Apply here: forms.gle/sHTEcHJjryDh...
#ViroSky
#MicroSky
#EvoSky
#SocialViruses
Thank you for sharing!
Exiting (but difficult) task for the future is to find what makes some lineages better!
Our latest paper out today led by the incomparable Jukka Corander.
Using longitudinal antibiotic use and genomic data, we show that antibiotic use does not determine the success of E. coli lineages causing BSI.
www.sciencedirect.com/science/arti...
1/2
Pleiotropy is overated
Quantifying the strength of viral fitness tradeoffs between hosts: A meta-analysis of pleiotropic fitness effects www.biorxiv.org/content/10.1101/2023.12....
Excited to share our new preprint! w @jfriedman.bsky.social #Microsky#evosky
We tackle a simple question - How much of species evolution depends on the presence of other species?
Bottom line: Most evolutionary changes were robust across strains that evolved with different biotic partners 1/
A joint embedding of protein sequence and structure enables robust variant effect predictions www.biorxiv.org/content/10.1101/2023.12....
New call for postdoc fellowships from the 🇪🇸 govt.
My brilliant colleague Cristina Vieitez and I are happy to jointly sponsor candidates who'd like to join us in beautiful Salamanca & develop methods for characterizing and optimizing microbial consortia. More info ⬇️.
www.aei.gob.es/convocatoria....
Can we predict AMR evolution?? We sure can try! New study published today in PNAS
www.pnas.org/doi/10.1073/...
Ooops. Thank you for spotting! I will ask them to fix...
An amazing piece in @quantamagazine.bsky.social by Veronique Greenwood about our recent article. High accessibility of fitness landscapes explained in a very accessible way
… and an excellent companion article (to the repost 👇) from @quantamagazine.bsky.social. Super cool work! 🧪
Spurred on by ribosome profiling and other cool methods, translation elongation has grown into a whole field with new surprises all the time (and new relevance for vaccine mRNAs!). Here’s our latest on why synonymous codons aren’t all the same. (Thread) www.biorxiv.org/content/10.1...
Thank you! Nice to meet you. I really liked your eLife paper. doi.org/10.7554/eLif... It was very reassuring to see that 27Cys DHFR works.
No, we did not include mutational bias...
I am grateful to my collaborators @jaescudero.bsky.social
and Lucia Garcia-Pastor, my colleagues at the Anrdeas Wagner group. I am grateful to the reviewers for their helpful feedback and the Science production team for choosing a vivid illustration of a rugged landscape for the cover
To conclude, a high-dimensional landscape can be accessible to adaptive evolution, even when rugged. Future studies will show if other landscapes are similarly accessible. We hope this effort will both assist and inspire further theoretical work on fitness landscapes.
Interestingly, all high fitness peaks (27Asp and 27Glu) essentially share one enormous basin of attraction. This means that the evolving populations have access to multiple peaks, which makes it difficult to predict evolutionary outcomes.
It turned out that most genotypes have a large number of paths leading to high fitness peaks, and very few paths leading to low fitness peaks. Consequently, high peaks are evolutionary accessible to most genotypes (i.e. have large basins of attraction).
This result is robust against different population genetics assumptions
Given the presence of multiple low and high peaks, we considered this landscape as rugged and expected that adaptive evolution would be constrained. However, our simulations showed the opposite. High fitness peaks were reached in 77% of simulations via short adaptive paths
74 peaks have high fitness. All of them have negatively charged amino acids Asp or Glu at the conserved DHFR position 27. Otherwise, high fitness peaks have different amino acids at other amino acid positions
The resulting fitness landscape has >500 fitness peaks, most of which are low fitness peaks.
We took great care to validate our fitness measurements
We measured fitness at the presence of the antibiotic using a mass selection experiment and deep sequencing.