ArticleNucleic acids research2022
Engineered Sleeping Beauty transposase redirects transposon integration away from genes.
Article in Nucleic acids research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 27 citations in OpenAlex.
- XPRESSO: Rapid genetic engineering of human pluripotent stem cells for durable overexpression using a modular anti-silencing vector.Stem cell reports · 2025Article
- Genomic medicine in hepatology: mechanisms and liver treatment strategies.Molecular medicine (Cambridge, Mass.) · 2025Review
- Engineering of BZ transposase and transposon donor vector for enhanced efficiency and safety in gene delivery applications.Nucleic acids research · 2025Article
- Generation and GMP scale-up of human CAR-T cells using non-viralMolecular therapy. Methods & clinical development · 2025Article
- DNA binding and transposition activity of the Sleeping Beauty transposase: role of structural stability of the primary DNA-binding domain.Nucleic acids research · 2025Article
- A novel hyperactive variant of theMolecular therapy. Nucleic acids · 2024Article
- Non-viral targeted insertion of large payloads into T cells.Nature biomedical engineering · 2024Article
- Sleeping Beauty mRNA-LNP enables stable rAAV transgene expression in mouse and NHP hepatocytes and improves vector potency.Molecular therapy : the journal of the American Society of Gene Therapy · 2024Article
- Targeted gene delivery systems for T-cell engineering.Cellular oncology (Dordrecht, Netherlands) · 2024Review
- Auto-expansion ofMolecular therapy. Methods & clinical development · 2024Article
- A Naturally Active Spy Transposon Discovered from the Insect Genome of Colletes gigas as a Promising Novel Gene Transfer Tool.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Engineering of potent CAR NK cells using non-viral Sleeping Beauty transposition from minimalistic DNA vectors.Molecular therapy : the journal of the American Society of Gene Therapy · 2024Article
- Accelerating Diverse Cell-Based Therapies Through Scalable Design.Annual review of chemical and biomolecular engineering · 2024Review
- Mage transposon: a novel gene delivery system for mammalian cells.Nucleic acids research · 2024Article
- Article
- Review
- AAV-mediated delivery of a Sleeping Beauty transposon and an mRNA-encoded transposase for the engineering of therapeutic immune cells.Nature biomedical engineering · 2024Article
- Passer, a highly active transposon from a fish genome, as a potential new robust genetic manipulation tool.Nucleic acids research · 2023Article
Corrections and comments
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Authors and funding
6 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The Sleeping Beauty (SB) transposon system is a popular tool for genome engineering, but random integration into the genome carries a certain genotoxic risk in therapeutic applications. Here we investigate the role of amino acids H187, P247 and K248 in target site selection of the SB transposase. Structural modeling implicates these three amino acids located in positions analogous to amino acids with established functions in target site selection in retroviral integrases and transposases. Saturation mutagenesis of these residues in the SB transposase yielded variants with altered target site selection properties. Transposon integration profiling of several mutants reveals increased specificity of integrations into palindromic AT repeat target sequences in genomic regions characterized by high DNA bendability. The H187V and K248R mutants redirect integrations away from exons, transcriptional regulatory elements and nucleosomal DNA in the human genome, suggesting enhanced safety and thus utility of these SB variants in gene therapy applications.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.