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Gerald Schwank Profile
Gerald Schwank

@schwanklab

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Associate Professor @UZH_en. We develop genome editing approaches for personalised treatment of rare diseases and cancer.

Zurich, Switzerland
Joined December 2017
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@schwanklab
Gerald Schwank
2 months
Led by @sharan_janjuha with @AcuitasTx @pardi_lab, our Nat Biomed Eng paper is out 🧬 We use in situ sequencing to read base & prime editing directly in tissues - from mouse brain (AAV) to macaque liver (RNA–LNP)-mapping lobules & redosing. Open access: https://t.co/uhohboUWnb
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@nicopmat
Nicolas Mathis
3 months
Excited to share our new protocol in #NatureProtocols! 🧬👨‍💻🎉 A comprehensive guide for using PRIDICT2.0 and ePRIDICT machine learning tools to design efficient #CRISPR-Cas9 prime editing experiments. 🧵 @UZH_Science @schwanklab @krauthammerlab https://t.co/xsIu9WFfHi
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@schwanklab
Gerald Schwank
6 months
Congrats to the entire team and collaborators @uzh @AcuitasTx @KispiZuerich @ETH @upenn. Step-by-step we are getting closer to clinical translation of prime editing to treat metabolic liver diseases!
@talasandris
András Tálas
6 months
Our paper describing an RNA-LNP prime editing strategy for treating phenylketonuria was published today in @natBME (1/7) https://t.co/Ls0SuAE49R @schwanklab @UZH_Science @UZH_en @KispiZuerich @ETH_en
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@MartinPacesa
Martin Pacesa
1 year
Have you ever wanted to design protein binders with ease? Today we present 𝑩𝒊𝒏𝒅𝑪𝒓𝒂𝒇𝒕, a user-friendly and open-source pipeline that allows to anyone to create protein binders de novo with high experimental success rates. @befcorreia @sokrypton https://t.co/IPhMFpRgHh
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@schwanklab
Gerald Schwank
1 year
Read the full study at https://t.co/9KuvCzNQlT and our media release at
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news.uzh.ch
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@schwanklab
Gerald Schwank
1 year
6) To apply TnpBmax in vivo we delivered it together with a PCSK9-targeting ωRNA via AAV9 into mice. This led to a robust PCSK9 knockout in the liver and a reduction in LDL-cholesterol levels
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@schwanklab
Gerald Schwank
1 year
5) ISDra2 TnpB requires a 5'-TTGAT TAM site to bind and cleave DNA. While this makes TnpB highly specific it also restricts its target range. Using structure-guided protein engineering we mutated amino acid 76 to altered the ISDra2 TnpB TAM preference from TTGAT to TTTAT or TTCAT
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@schwanklab
Gerald Schwank
1 year
4) Using this dataset, we developed TEEP, a machine-learning model to predict ωRNA efficiencies and help select potent ωRNAs. The model is freely available via https://t.co/bSXwU9D55e.
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@schwanklab
Gerald Schwank
1 year
3) We noticed that the activity of the TnpB system is strongly influenced by the ωRNA sequence that targets the DNA. This led us to screen over 10,000 target sites in cultured human cells to measure cleavage efficiency in high throughput.
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@schwanklab
Gerald Schwank
1 year
2) To enhance the editing efficiency of ISDra2 TnpB in mammalian cells, we optimized its DNA sequence to match human codon usage and rearranged NLS sequences for nuclear uptake. Our design resulted in a TnpBmax system with a 4.4-fold improvement in editing efficiency.
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@schwanklab
Gerald Schwank
1 year
1) TnpB is an RNA-guided endonuclease encoded on the IS200/IS605 transposon element and evolutionarily related to CRISPR-Cas12 proteins. A recent study from the Siksnys lab showed that ISDra2 TnpB can be programmed to induce targeted DNA breaks in human cells
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@schwanklab
Gerald Schwank
1 year
Excited to share our latest study! Today in @naturemethods the @schwanklab and @krauthammerlab report in @marquark et al. 'Effective genome editing with an enhanced ISDra2 TnpB system and deep learning-predicted ωRNAs' (1/6) https://t.co/lqDc5Kn2Xw
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nature.com
Nature Methods - This work introduces engineered TnpBmax proteins with enhanced efficiency and an expanded targeting range. By leveraging an extensive dataset on editing efficiencies, it also...
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@nicopmat
Nicolas Mathis
1 year
Our paper exploring the effect of chromatin on prime editing and diverse pegRNA designs was published today! 👨‍💻🧬🎉 https://t.co/hoygYAHbPH https://t.co/xvSlhTJvNV @UZH_Science @NatureBiotech @schwanklab @krauthammerlab @bvansteensellab 🧵
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@schwanklab
Gerald Schwank
1 year
If you want to know the outcome of your prime editing experiment before you even conducted the experiment, visit https://t.co/tEG0mcEIEo Congrats to Nicolas and many thanks to all collaborators @krauthammerlab @bvansteensellab
@NatureBiotech
Nature Biotechnology
1 year
Machine learning prediction of prime editing efficiency across diverse chromatin contexts https://t.co/pkUxOJdZ4E
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@schwanklab
Gerald Schwank
2 years
Join us at the Latsis Symposium on Genome and Transcriptome Engineering organized by @MartinJinek @randall_platt @jcornlab and @schwanklab, with lots of great speakers from academia and industry! 🧬 #Zurich #Switzerland June 13-14. Register at: https://t.co/Ch8w6g2pqt
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@krauthammerlab
Krauthammer Lab
2 years
🌟New Postdoc opportunity🌟 for developing machine learning methods in biomedicine and healthcare research is open in our lab! Apply 👉 https://t.co/IkVhBsKaK1 Please RT and share widely! #MachineLearning #healthcare #postdoc #ArtificialIntelligence #biomedicine #bioinformatics
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