Home > Publications database > Prediction and Validation of Hematopoietic Stem and Progenitor Cell Off-Target Editing in Transplanted Rhesus Macaques. > print

001     169364
005     20240229140924.0
024 7 _ |a 10.1016/j.ymthe.2021.06.016
|2 doi
024 7 _ |a pmid:34174439
|2 pmid
024 7 _ |a 1525-0016
|2 ISSN
024 7 _ |a 1525-0024
|2 ISSN
024 7 _ |a altmetric:108264778
|2 altmetric
037 _ _ |a DKFZ-2021-01444
041 _ _ |a English
082 _ _ |a 610
100 1 _ |a AlJanahi, Aisha A
|b 0
245 _ _ |a Prediction and Validation of Hematopoietic Stem and Progenitor Cell Off-Target Editing in Transplanted Rhesus Macaques.
260 _ _ |a New York, NY
|c 2022
|b Nature Publ. Group
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1642084111_474
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
500 _ _ |a 2022 Jan 5;30(1):209-222
520 _ _ |a The programmable nuclease technology CRISPR/Cas9 has revolutionized gene editing in the last decade. Due to the risk of off-target editing, accurate and sensitive methods for off-target characterization are crucial prior to applying CRISPR/Cas9 therapeutically. Here, we utilized a rhesus macaque model to compare the predictive values of CIRCLE-seq, an in vitro off-target prediction method, with in silico prediction (ISP) based solely on genomic sequence comparisons. We use AmpliSeq HD error-corrected sequencing to validate off-target sites predicted by CIRCLE-seq and ISP for a CD33 gRNA with thousands of off-target sites predicted by ISP and CIRCLE-seq. We found poor correlation between the sites predicted by the two methods. When almost 500 sites predicted by each method were analyzed by error-corrected sequencing of hematopoietic cells following transplantation, 19 off-target sites revealed insertion/deletion mutations. Of these sites, 8 were predicted by both methods, 8 by CIRCLE-seq only, and 3 by ISP only. The levels of cells with these off-target edits exhibited no expansion or abnormal behavior in vivo in animals followed for up to 2 years. In addition, we utilized an unbiased method termed CAST-Seq to search for translocations between the on-target site and off-target sites present in animals following transplantation, detecting one specific translocation that persisted in blood cells for at least one year following transplantation. In conclusion, neither CIRCLE-seq or ISP predicted all sites, and a combination of careful gRNA design, followed by screening for predicted off-target sites in target cells by multiple methods may be required for optimizing safety of clinical development.
536 _ _ |a 899 - ohne Topic (POF4-899)
|0 G:(DE-HGF)POF4-899
|c POF4-899
|f POF IV
|x 0
588 _ _ |a Dataset connected to CrossRef, PubMed, , Journals: inrepo01.inet.dkfz-heidelberg.de
700 1 _ |a Lazzarotto, Cicera R
|b 1
700 1 _ |a Chen, Shirley
|b 2
700 1 _ |a Shin, Tae-Hoon
|b 3
700 1 _ |a Cordes, Stefan
|b 4
700 1 _ |a Fan, Xing
|b 5
700 1 _ |a Jabara, Isabel
|b 6
700 1 _ |a Zhou, Yifan
|b 7
700 1 _ |a Young, David J
|b 8
700 1 _ |a Lee, Byung-Chul
|b 9
700 1 _ |a Yu, Kyung-Rok
|b 10
700 1 _ |a Li, Yuesheng
|b 11
700 1 _ |a Toms, Bradley
|b 12
700 1 _ |a Tunc, Ilker
|b 13
700 1 _ |a Hong, So Gun
|b 14
700 1 _ |a Truitt, Lauren L
|b 15
700 1 _ |a Klermund, Julia
|b 16
700 1 _ |a Andrieux, Geoffroy
|0 P:(DE-He78)a355989a6a384c85ecb759b528e07796
|b 17
|u dkfz
700 1 _ |a Kim, Miriam Y
|b 18
700 1 _ |a Cathomen, Toni
|b 19
700 1 _ |a Gill, Saar
|b 20
700 1 _ |a Tsai, Shengdar Q
|b 21
700 1 _ |a Dunbar, Cynthia E
|b 22
773 _ _ |a 10.1016/j.ymthe.2021.06.016
|g p. S1525001621003221
|0 PERI:(DE-600)2001818-6
|n 1
|p 209-222
|t Molecular therapy
|v 30
|y 2022
|x 1525-0016
909 C O |p VDB
|o oai:inrepo02.dkfz.de:169364
910 1 _ |a Deutsches Krebsforschungszentrum
|0 I:(DE-588b)2036810-0
|k DKFZ
|b 17
|6 P:(DE-He78)a355989a6a384c85ecb759b528e07796
913 1 _ |a DE-HGF
|b Programmungebundene Forschung
|l ohne Programm
|1 G:(DE-HGF)POF4-890
|0 G:(DE-HGF)POF4-899
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-800
|4 G:(DE-HGF)POF
|v ohne Topic
|x 0
913 0 _ |a DE-HGF
|b Programmungebundene Forschung
|l ohne Programm
|1 G:(DE-HGF)POF3-890
|0 G:(DE-HGF)POF3-899
|3 G:(DE-HGF)POF3
|2 G:(DE-HGF)POF3-800
|4 G:(DE-HGF)POF
|v ohne Topic
|x 0
914 1 _ |y 2021
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2021-02-04
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1190
|2 StatID
|b Biological Abstracts
|d 2021-02-04
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2021-02-04
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2022-11-08
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2022-11-08
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2022-11-08
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2022-11-08
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1050
|2 StatID
|b BIOSIS Previews
|d 2022-11-08
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1030
|2 StatID
|b Current Contents - Life Sciences
|d 2022-11-08
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b MOL THER : 2021
|d 2022-11-08
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0600
|2 StatID
|b Ebsco Academic Search
|d 2022-11-08
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b ASC
|d 2022-11-08
915 _ _ |a IF >= 10
|0 StatID:(DE-HGF)9910
|2 StatID
|b MOL THER : 2021
|d 2022-11-08
920 1 _ |0 I:(DE-He78)FR01-20160331
|k FR01
|l DKTK FR zentral
|x 0
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-He78)FR01-20160331
980 _ _ |a UNRESTRICTED

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21