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Tim Yu

@timyu.bsky.social
128 followers 108 following 23 posts

Postdoc at MIT in the Lieberman lab (@contaminatedsci.bsky.social) thinking about microbial evolution. Previous: PhD with @jbloomlab.bsky.social.

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Reposted by Tim Yu
Bloom lab @jbloomlab.bsky.social · 17/09/2026
We have measured 47,851 neutralization titers of current human sera vs current influenza strains to inform vaccine update & analyses of viral evolution. New H1N1 strains (eg, D.3.1.1 + G155E) have reduced neutralization, as do some H3N2 subclade K descendants. www.biorxiv.org/content/10.6...
biorxiv.org
Near real-time data on the human neutralizing antibody landscape to influenza virus in summer of 2026 shows antigenic advance of H3N2 subclade K region D mutants and H1N1 D.3.1.1 Sa mutants
Human seasonal influenza evolves rapidly, necessitating twice yearly decisions about whether to update the strains in the vaccine. To help inform this decision, we have been using high-throughput sequ...
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Reposted by Tim Yu
Tami Lieberman @contaminatedsci.bsky.social · 08/09/2026
Two announcements regarding AccuSNV, which calls high precision SNVs across microbial genomes: Version 1.1 is now incredibly easy to install, run, and perform downstream analyses with (see image of typical output!). The manuscript was also published this summer in Genome Research! Links next...
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Reposted by Tim Yu
Guillaume Urtecho @gogurtecho.bsky.social · 02/09/2026
Excited to share the first paper from the Urtecho Lab! We built a framework to move fecal microbiota transplantation (FMT) from a black-box therapy to precision medicine for gut disorders 🧵 doi.org/10.1080/19490976.2026.2725403 #Microbiome. #OpenAccess
doi.org
Metagenome-scale modeling to assess microbiome metabolic complementarity for precision microbiota transplantation therapies
Fecal microbiota transplantation (FMT) holds therapeutic promise beyond recurrent Clostridioides difficile infection, but clinical outcomes remain unpredictable and donor-selection strategies remai...
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Reposted by Tim Yu
Tami Lieberman @contaminatedsci.bsky.social · 31/07/2026
The manuscript describing SimPhyNI is now published! www.microbiologyresearch.org/content/jour... A few updates since last post, including some method tweaks that improve performance and speed. Also even easier to run. Try it for your microbial GWAS or epistasis needs!
microbiologyresearch.org
High-precision binary trait association on phylogenetic trees
Traditional methods for identifying associations between genomic features and traits, or between pairs of genomic traits, struggle when applied to bacterial genomes. While several microbial genome-wid...
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Reposted by Tim Yu
Bloom lab @jbloomlab.bsky.social · 23/07/2026
In new study led by @bdadonaite.bsky.social, we show many influenza HAs (H5, H7, H9, H1, H2, H3) can use avian or human MHC-II to enter cells. We then use novel combo of deep mutational scanning & cryoEM to define how H5 HA binds to avian MHC-II. Preprint: doi.org/10.64898/202...
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Reposted by Tim Yu
William DeWitt @wsdewitt.github.io · 05/06/2026
This started in 2019 as daydreamy PhD student musings with Tatsuya Araki. We're as excited as ever about germinal centers as a platform for experimental evolution. Many thanks to PIs @victora.bsky.social @matsen.bsky.social, co-1st authors Ashni Vora and Tatsuya, and many other key collaborators!
cell.com
Replaying germinal center evolution on a quantified affinity landscape
Antibody affinity maturation results from a somatic evolutionary process that takes place in the germinal center. A “parallel replay” experiment on germinal center B cells reveals the evolutionary for...
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Reposted by Tim Yu
Bloom lab @jbloomlab.bsky.social · 20/02/2026
We have posted data providing real-time measurement of human neutralizing antibody landscape to seasonal influenza. Data explain spread of subclades K (H3N2) & D.3.1.1 (H1N1), identify subclade K subvariants w reduced neutralization, & can inform choice of strains for next vaccine.
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Tim Yu @timyu.bsky.social · 21/01/2026
Data, code, and interactive visualizations for comparing amino-acid preferences across H3, H5, and H7 available at: jbloomlab.github.io/ha-preferenc... Thanks to @jahn0.bsky.social for leading this with me, and also @bdadonaite.bsky.social, Caelan Radford, and @jbloomlab.bsky.social!
jbloomlab.github.io
Documentation of results rendered as of Tue Jan 6 19:25:41 2026
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Tim Yu @timyu.bsky.social · 21/01/2026
This also highlights limitation of using experimental measurements derived from a single genetic background for viral surveillance and vaccine immunogen design. Deep mutational scanning can be useful for predicting mutation effects in closely related variants, but less so across divergent homologs.
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Tim Yu @timyu.bsky.social · 21/01/2026
Overall, these results consistent with evolutionary contingency. Mutations can modify constraints at other sites, which snowballs over time.
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Tim Yu @timyu.bsky.social · 21/01/2026
One example is site 176. H5/H7 tolerate similar amino acids, but both are sharply diverged from H3 which only tolerates positively charged K. Structure shows how contacting sites form constrained hydrogen bond network in H3, but same sites have been rewired into hydrophobic environment in H5/H7.
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Tim Yu @timyu.bsky.social · 21/01/2026
What explains sites with divergent amino-acid preferences? We find that they tend to be buried in the protein and have biochemically distinct wildtype amino acids in the subtypes.
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Tim Yu @timyu.bsky.social · 21/01/2026
~50% of sites display significant divergence in amino-acid preferences between HAs. HA2 domain of H3/H7 is noticeably less divergent, consistent with higher amino-acid conservation.
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Tim Yu @timyu.bsky.social · 21/01/2026
We then compared the H7 measurements to previously generated data for H5 (journals.plos.org/plosbiology/...) and H3 (www.nature.com/articles/s41...). High divergence in amino-acid preferences = HA subtypes tolerate distinct amino acids (ex. site 86).
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Tim Yu @timyu.bsky.social · 21/01/2026
These data helpful for H7 vaccine immunogen design and viral surveillance. Explore the data interactively at: dms-vep.org/Flu_H7_Anhui...
dms-vep.org
Pseudovirus deep mutational scanning of H7 hemagglutinin (A/Anhui/1/2013)
Data and interactive figures for pseudovirus deep mutational scanning of influenza H7 HA
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Tim Yu @timyu.bsky.social · 21/01/2026
We first used pseudovirus deep mutational scanning to measure how all mutations to a recent H7 HA affect cell entry. This approach uses virions that can only undergo one round of cell entry and are therefore not capable of causing disease.
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Tim Yu @timyu.bsky.social · 21/01/2026
During protein evolution, mutation effects become less correlated as homologs diverge (www.science.org/doi/10.1126/...). For HA, we wondered how sequence divergence on a nearly fixed structural backbone affects tolerance to further mutations.
science.org
Epistatic drift causes gradual decay of predictability in protein evolution
The effect and fate of most mutations gradually become unpredictable as proteins evolve.
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Tim Yu @timyu.bsky.social · 21/01/2026
As background, there are at least 19 influenza A virus HA subtypes. Many subtypes are highly diverged at the sequence level (~40% amino-acid identity), but protein structure and cell entry function are highly conserved.
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Tim Yu @timyu.bsky.social · 21/01/2026
In new work by @jahn0.bsky.social and I in @jbloomlab.bsky.social, we investigate how sequence constraints differ across influenza HA subtypes. We find ~50% of sites in HA display substantially different amino-acid preferences across H3, H5, and H7. doi.org/10.64898/202...
doi.org
Influenza hemagglutinin subtypes have different sequence constraints despite sharing extremely similar structures
Hemagglutinins (HA) from different influenza A virus subtypes share as little as ∼40% amino acid identity, yet their protein structure and cell entry function are highly conserved. Here we examine the extent that sequence constraints on HA differ across three subtypes. To do this, we first use pseudovirus deep mutational scanning to measure how all amino-acid mutations to an H7 HA affect its cell entry function. We then compare these new measurements to previously described measurements of how all mutations to H3 and H5 HAs affect cell entry function. We find that ∼50% of HA sites display substantially diverged preferences for different amino acids across the HA subtypes. The sites with the most divergent amino-acid preferences tend to be buried and have biochemically distinct wildtype amino acids in the different HA subtypes. We provide an example of how rewiring the interactions among contacting residues has dramatically shifted which amino acids are tolerated at specific sites. Overall, our results show how proteins with the same structure and function can become subject to very different site-specific evolutionary constraints as their sequences diverge. ### Competing Interest Statement JDB consults for Apriori Bio, Invivyd, Pfizer, GSK, and the Vaccine Company. JDB and BD are inventors on Fred Hutch licensed patents related to the deep mutational scanning of viral proteins. National Institute of Allergy and Infectious Diseases, R01AI165821, 75N93021C00015 U.S. National Science Foundation, DGE-2140004 Howard Hughes Medical Institute, https://ror.org/006w34k90
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Tim Yu @timyu.bsky.social · 21/01/2026
Data, code, and interactive visualizations for comparing amino-acid preferences across H3, H5, and H7 available at: jbloomlab.github.io/ha-preferenc... Thanks to @jahn0.bsky.social for leading this with me, and also @bdadonaite.bsky.social, Caelan Radford, and @jbloomlab.bsky.social!
jbloomlab.github.io
Documentation of results rendered as of Tue Jan 6 19:25:41 2026
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Tim Yu @timyu.bsky.social · 21/01/2026
This also highlights limitation of using experimental measurements derived from a single genetic background for viral surveillance and vaccine immunogen design. Deep mutational scanning can be useful for predicting mutation effects in closely related variants, but less so across divergent homologs.
100
Tim Yu @timyu.bsky.social · 21/01/2026
Overall, these results consistent with evolutionary contingency. Mutations can modify constraints at other sites, which snowballs over time.
100
Tim Yu @timyu.bsky.social · 21/01/2026
One example is site 176. H5/H7 tolerate similar amino acids, but both are sharply diverged from H3 which only tolerates positively charged K. Structure shows how contacting sites form constrained hydrogen bond network in H3, but same sites have been rewired into hydrophobic environment in H5/H7.
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Tim Yu @timyu.bsky.social · 21/01/2026
What explains sites with divergent amino-acid preferences? We find that they tend to be buried in the protein and have biochemically distinct wildtype amino acids in the subtypes.
100
Tim Yu @timyu.bsky.social · 21/01/2026
~50% of sites display significant divergence in amino-acid preferences between HAs. HA2 domain of H3/H7 is noticeably less divergent, consistent with higher amino-acid conservation.
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Tim Yu @timyu.bsky.social · 21/01/2026
We then compared the H7 measurements to previously generated data for H5 (journals.plos.org/plosbiology/...) and H3 (www.nature.com/articles/s41...). High divergence in amino-acid preferences = HA subtypes tolerate distinct amino acids (ex. site 86).
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Tim Yu @timyu.bsky.social · 21/01/2026
These data helpful for H7 vaccine immunogen design and viral surveillance. Explore the data interactively at: dms-vep.org/Flu_H7_Anhui...
dms-vep.org
Pseudovirus deep mutational scanning of H7 hemagglutinin (A/Anhui/1/2013)
Data and interactive figures for pseudovirus deep mutational scanning of influenza H7 HA
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Tim Yu @timyu.bsky.social · 21/01/2026
We first used pseudovirus deep mutational scanning to measure how all mutations to a recent H7 HA affect cell entry. This approach uses virions that can only undergo one round of cell entry and are therefore not capable of causing disease.
100
Tim Yu @timyu.bsky.social · 21/01/2026
During protein evolution, mutation effects become less correlated as homologs diverge (www.science.org/doi/10.1126/...). For HA, we wondered how sequence divergence on a nearly fixed structural backbone affects tolerance to further mutations.
science.org
Epistatic drift causes gradual decay of predictability in protein evolution
The effect and fate of most mutations gradually become unpredictable as proteins evolve.
100
Tim Yu @timyu.bsky.social · 21/01/2026
As background, there are at least 19 influenza A virus HA subtypes. Many subtypes are highly diverged at the sequence level (~40% amino-acid identity), but protein structure and cell entry function are highly conserved.
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Reposted by Tim Yu
Bloom lab @jbloomlab.bsky.social · 27/05/2025
In new study led by @timyu.bsky.social, we measure how mutations to H3 flu HA affect cell entry, stability & antibody escape We find pleiotropic effects of mutations on these phenotypes shape evolution: epistasis alleviates cell-entry but not stability constraints www.biorxiv.org/content/10.1...
biorxiv.org
Pleiotropic mutational effects on function and stability constrain the antigenic evolution of influenza hemagglutinin
The evolution of human influenza virus hemagglutinin (HA) involves simultaneous selection to acquire antigenic mutations that escape population immunity while preserving protein function and stability...
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Reposted by Tim Yu
Bloom lab @jbloomlab.bsky.social · 12/03/2025
In study led by @ckikawa.bsky.social & Andrea Loes, we use new assay to measure ~10,000 neutralization titers to recent influenza strains & show titers correlate w evolutionary success of viral strains Similar data could help forecast evolution for vaccine selection www.biorxiv.org/content/10.1...
biorxiv.org
High-throughput neutralization measurements correlate strongly with evolutionary success of human influenza strains
Human influenza viruses rapidly acquire mutations in their hemagglutinin (HA) protein that erode neutralization by antibodies from prior exposures. Here, we use a sequencing-based assay to measure neu...
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