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Effective genome editing with an enhanced ISDra2 TnpB system and deep learning-predicted ωRNAs

Nature Methods volume 21, pages 2084–2093 (2024)Cite this article

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Abstract

Transposon (IS200/IS605)-encoded TnpB proteins are predecessors of class 2 type V CRISPR effectors and have emerged as one of the most compact genome editors identified thus far. Here, we optimized the design of Deinococcus radiodurans (ISDra2) TnpB for application in mammalian cells (TnpBmax), leading to an average 4.4-fold improvement in editing. In addition, we developed variants mutated at position K76 that recognize alternative target-adjacent motifs (TAMs), expanding the targeting range of ISDra2 TnpB. We further generated an extensive dataset on TnpBmax editing efficiencies at 10,211 target sites. This enabled us to delineate rules for on-target and off-target editing and to devise a deep learning model, termed TnpB editing efficiency predictor (TEEP; https://www.tnpb.app), capable of predicting ISDra2 TnpB guiding RNA (ωRNA) activity with high performance (r > 0.8). Employing TEEP, we achieved editing efficiencies up to 75.3% in the murine liver and 65.9% in the murine brain after adeno-associated virus (AAV) vector delivery of TnpBmax. Overall, the set of tools presented in this study facilitates the application of TnpB as an ultracompact programmable endonuclease in research and therapeutics.

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Fig. 1: DNA cleavage and base editing in mammalian cells with TnpBmax.
Fig. 2: A target-matched library screen reveals principles for ωRNA guide design.
Fig. 3: Structure-guided rational engineering of TnpB to accept alternative TAMs.
Fig. 4: Machine learning accurately predicts ωRNA efficiency.
Fig. 5: In vivo genome editing with TnpBmax in the murine liver and brain.

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Data availability

All ωRNA and HTS primer sequences used for this study are provided in Supplementary Data 1. Deep amplicon sequencing data files are available from the National Center for Biotechnology Information’s Sequence Read Archive (accession PRJNA1019264). Plasmid sequences are provided at https://benchling.com/marquark7/f_/FOdfdV1v-tnpb/. Additionally, key plasmids from this work are available from Addgene. All data are freely accessible to the public.

Code availability

Computer code for the analysis of the pooled libraries is available at https://github.com/Schwank-Lab/tnpb. The code for training the machine learning models is available on GitHub (https://github.com/uzh-dqbm-cmi/Tnpb). In addition, we have developed a publicly available web application (https://go.tnpb.app or https://www.tnpb.app) for predicting TnpB ωRNA efficiencies using our trained models. HTS data were collected and demultiplexed by Illumina NovaSeq Control software version 1.7 and MiSeq Control software (versions 3.1 and 4.0). Pooled library analysis was performed using Python 3.9. Cutadapt (3.5) was used to trim sequencing reads. For characterization of indels and base edits at single sites (endogenous), CRISPResso2 (2.2.7) was used. For statistical analysis, SciPy (1.10.1) and Prism (9.0.0) were used.

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Acknowledgements

We thank the Functional Genomics Center Zurich for technical support and access to instruments at the University of Zurich and ETH Zürich, the mRNA platform at UZH–USZ and S. Pascolo, J. Frei and C. Wyss for the production and purification of RNA, the Viral Vector Facility of UZH and J.-C. Paterna and M. Rauch for production of AAVs, G. Andrieux for bioinformatic analysis of CAST-seq data and O. Melkonyan for HT-TAMDA analysis as well as J. Häberle and N. Rimann for measurements of blood LDL levels. We thank I. Querques, M. Jinek, M. Pacesa, L.-M. Koch, Lotti and members of the Schwank laboratory for valuable discussions, feedback and help throughout the study. This work was supported by the University Research Priority Programs ‘Human Reproduction Reloaded’ (to G.S.) and ‘ITINERARE’ (to G.S. and M. Krauthammer), the ProMedica Foundation (to G.S.), the Swiss National Science Foundation grant numbers 185293 and 214936 (to G.S.) and grant number 201184 (to M. Krauthammer), a UZH PhD fellowship (to T.R.), ETH PhD fellowships (to L.S. and K.F.M.) and the German Research Foundation (CRC 1597-A05 to T.C.).

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Author notes

  1. These authors contributed equally: Nicolas Mathis, Amina Mollaysa.

Authors and Affiliations

  1. Institute of Pharmacology and Toxicology, University of Zurich, Zürich, Switzerland

    Kim Fabiano Marquart, Nicolas Mathis, Saphira Müller, Lucas Kissling, Tanja Rothgangl, Lukas Schmidheini, Péter István Kulcsár, Tatjana Haenggi & Gerald Schwank

  2. Institute of Molecular Health Sciences, ETH Zürich, Zürich, Switzerland

    Kim Fabiano Marquart, Lukas Schmidheini, Mai Matsushita & Manfred Kopf

  3. Department of Quantitative Biomedicine, University of Zurich, Zürich, Switzerland

    Amina Mollaysa, Ahmed Allam & Michael Krauthammer

  4. Institute for Transfusion Medicine and Gene Therapy, Medical Center, University of Freiburg, Freiburg, Germany

    Masako M. Kaufmann & Toni Cathomen

  5. Spemann Graduate School of Biology and Medicine, University of Freiburg, Freiburg, Germany

    Masako M. Kaufmann

  6. Center for Chronic Immunodeficiency, Faculty of Medicine, University of Freiburg, Freiburg, Germany

    Toni Cathomen

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Contributions

K.F.M. performed numerous biological experiments throughout the study, analyzed data and prepared figures. N.M. performed bioinformatic analysis of all target-matched library experiments, prepared figures, curated data for the machine learning models and contributed to XGBoost model design. A.M. designed and developed machine learning models and implemented the web app for TEEP. S.M. prepared plasmids for TnpB and Fanzor and ωRNA expression, performed and analyzed endogenous DNA-editing experiments, conducted HT-TAMDA assays and performed western blotting experiments. L.K. and T.R. performed in vivo experiments, including intracerebroventricular and stereotactic injections and brain and hepatocyte isolation. L.S. prepared plasmids for ωRNA expression and conducted HT-TAMDA assays. P.I.K. performed and analyzed GUIDE-seq experiments. A.A. contributed to the design and development of machine learning models. M.M.K. performed CAST-seq experiments. M.M. assessed inflammation-linked cytokines. T.H. contributed to western blotting experiments. T.C., M. Kopf, M. Krauthammer and G.S. supervised the research and provided field-specific expertise. K.F.M. and G.S. designed the study and wrote the manuscript. All authors reviewed the manuscript.

Corresponding author

Correspondence to Gerald Schwank.

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Competing interests

K.F.M. and G.S. are co-inventors on a patent application filed by the University of Zurich relating to the work described in this paper. G.S. is an advisor to Prime Medicine. The other authors declare no competing interests.

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Nature Methods thanks the anonymous reviewers for their contribution to the peer review of this work. Primary Handling Editor: Lei Tang, in collaboration with the Nature Methods team. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Benchmarking of TnpB and Fanzor architectures in HEK293T cells.

(a) Schematic representation of experimental workflow and designs. NLS, nuclear localization sequence; BPNLS, bipartite NLS; SRAD, Serine-Arginine-Alanine-Aspartic acid; GS, Glycine-Serine; PuroR, Puromycin resistance; d, days; HTS, high-throughput sequencing; a codon-optimization and design from Xiang et al.11 and Saito et al.12 (b–d) Benchmarking of different architectures of ISDra2, ISAam1 and ISYmu1 TnpBs. Number of analyzed endogenous targets: ISDra2 TnpB, N = 7; ISAam1 TnpB, N = 7; ISYmu1 TnpB, N = 8. Each dot represents the mean of n = 3 independent biological replicates; the black bar represents the mean of all target sites tested for the respective design. Means were compared by two-tailed t-test. (e) Benchmarking of SpuFz1-v2 Fanzor embedded in various designs tested at one endogenous locus (B2M). Each bar represents the mean ± s.d. of n = 3 independent biological replicates and a two-tailed t-test was used to calculate variance. Indel frequencies are shown in Datafile S1.

Extended Data Fig. 2 High-throughput TAM determination assay (HT-TAMDA) of TnpBmax and variants thereof.

The log10 (rate constant) represents the mean of two replicates against two distinct target sequences.

Extended Data Fig. 3 Direct intracortical injection of scAAV-TnpB-Dnmt1.

a) Schematic representation of stereotactic scAAV injection. (b, c) TnpBmax mediated editing at the Dnmt1 locus determined by deep amplicon sequencing in separated brain regions of mice treated with 5.0 × 1013 vg/kg scAAV. CTX, cortex; BS, brain stem; Hipp, hippocampus; Hypo, hypothalamus; MB, midbrain; OB, olfactory bulb; ST, striatum; TM, thalamus; CTRL, control. Each dot represents data from one animal; bar represents the mean ± s.d. of n = 3 animals.

Extended Data Fig. 4 Detailed protocol for ωRNA cloning.

Step 1: Digest and purify the ωRNA acceptor plasmid with BbsI. Step 2: Perform ligation or Golden-Gate-Assembly of phosphorylated and annealed oligonucleotides into the digested pωRNA-acceptor.

Supplementary information

Supplementary Information (download PDF )

Supplementary Figs. 1–12 and Note 1.

Reporting Summary (download PDF )

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Supplementary Data 1 (download XLSX )

Supplementary dataset with DNA sequences, indel/editing rates and features for ML.

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Marquart, K.F., Mathis, N., Mollaysa, A. et al. Effective genome editing with an enhanced ISDra2 TnpB system and deep learning-predicted ωRNAs. Nat Methods 21, 2084–2093 (2024). https://doi.org/10.1038/s41592-024-02418-z

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