CRISPR single base-editing: in silico predictions to variant clonal cell lines.

Dickson, K-A; Field, N; Blackman, T; Ma, Y; Xie, T; Kurangil, E; Idrees, S; Rathnayake, SNH; Mahbub, RM; Faiz, A; Marsh, DJ

CRISPR single base-editing: in silico predictions to variant clonal cell lines.

Dickson, K-A Field, N Blackman, T Ma, Y Xie, T Kurangil, E Idrees, S

Rathnayake, SNH Mahbub, RM Faiz, A

Marsh, DJ

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Publisher:: OXFORD UNIV PRESS
Publication Type:: Journal Article
Citation:: Hum Mol Genet, 2023, 32, (17), pp. 2704-2716
Issue Date:: 2023-08-26

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Field	Value	Language
dc.contributor.author	Dickson, K-A
dc.contributor.author	Field, N
dc.contributor.author	Blackman, T
dc.contributor.author	Ma, Y
dc.contributor.author	Xie, T
dc.contributor.author	Kurangil, E
dc.contributor.author	Idrees, S https://orcid.org/0000-0001-8512-7593
dc.contributor.author	Rathnayake, SNH
dc.contributor.author	Mahbub, RM
dc.contributor.author	Faiz, A https://orcid.org/0000-0003-1740-3538
dc.contributor.author	Marsh, DJ
dc.date.accessioned	2023-10-05T21:43:20Z
dc.date.available	2023-06-09
dc.date.available	2023-10-05T21:43:20Z
dc.date.issued	2023-08-26
dc.identifier.citation	Hum Mol Genet, 2023, 32, (17), pp. 2704-2716
dc.identifier.issn	0964-6906
dc.identifier.issn	1460-2083
dc.identifier.uri	http://hdl.handle.net/10453/172521
dc.description.abstract	Engineering single base edits using CRISPR technology including specific deaminases and single-guide RNA (sgRNA) is a rapidly evolving field. Different types of base edits can be constructed, with cytidine base editors (CBEs) facilitating transition of C-to-T variants, adenine base editors (ABEs) enabling transition of A-to-G variants, C-to-G transversion base editors (CGBEs) and recently adenine transversion editors (AYBE) that create A-to-C and A-to-T variants. The base-editing machine learning algorithm BE-Hive predicts which sgRNA and base editor combinations have the strongest likelihood of achieving desired base edits. We have used BE-Hive and TP53 mutation data from The Cancer Genome Atlas (TCGA) ovarian cancer cohort to predict which mutations can be engineered, or reverted to wild-type (WT) sequence, using CBEs, ABEs or CGBEs. We have developed and automated a ranking system to assist in selecting optimally designed sgRNA that considers the presence of a suitable protospacer adjacent motif (PAM), the frequency of predicted bystander edits, editing efficiency and target base change. We have generated single constructs containing ABE or CBE editing machinery, an sgRNA cloning backbone and an enhanced green fluorescent protein tag (EGFP), removing the need for co-transfection of multiple plasmids. We have tested our ranking system and new plasmid constructs to engineer the p53 mutants Y220C, R282W and R248Q into WT p53 cells and shown that these mutants cannot activate four p53 target genes, mimicking the behaviour of endogenous p53 mutations. This field will continue to rapidly progress, requiring new strategies such as we propose to ensure desired base-editing outcomes.
dc.format	Print
dc.language	eng
dc.publisher	OXFORD UNIV PRESS
dc.relation	http://purl.org/au-research/grants/nhmrc/APP2019296
dc.relation	http://purl.org/au-research/grants/nhmrc/GNT2019296
dc.relation.ispartof	Hum Mol Genet
dc.relation.isbasedon	10.1093/hmg/ddad105
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	06 Biological Sciences, 11 Medical and Health Sciences
dc.subject.classification	Genetics & Heredity
dc.subject.classification	3105 Genetics
dc.subject.mesh	Humans
dc.subject.mesh	Gene Editing
dc.subject.mesh	CRISPR-Cas Systems
dc.subject.mesh	RNA, Guide, CRISPR-Cas Systems
dc.subject.mesh	Tumor Suppressor Protein p53
dc.subject.mesh	Clustered Regularly Interspaced Short Palindromic Repeats
dc.subject.mesh	Cell Line
dc.subject.mesh	Adenine
dc.subject.mesh	Cytosine
dc.subject.mesh	Cell Line
dc.subject.mesh	Humans
dc.subject.mesh	Cytosine
dc.subject.mesh	Adenine
dc.subject.mesh	Tumor Suppressor Protein p53
dc.subject.mesh	CRISPR-Cas Systems
dc.subject.mesh	Clustered Regularly Interspaced Short Palindromic Repeats
dc.subject.mesh	Gene Editing
dc.subject.mesh	RNA, Guide, CRISPR-Cas Systems
dc.title	CRISPR single base-editing: in silico predictions to variant clonal cell lines.
dc.type	Journal Article
utslib.citation.volume	32
utslib.location.activity	England
utslib.for	06 Biological Sciences
utslib.for	11 Medical and Health Sciences
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (International)
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Life Sciences
pubs.organisational-group	/University of Technology Sydney/DVC (International)/Australia-China Relations Institute
pubs.organisational-group	/University of Technology Sydney/Centre for Health Technologies (CHT)
pubs.organisational-group	/University of Technology Sydney/Strength - CFI - Centre for Inflammation
utslib.copyright.status	closed_access	*
dc.date.updated	2023-10-05T21:43:19Z
pubs.issue	17
pubs.publication-status	Published
pubs.volume	32
utslib.citation.issue	17

Abstract:

Engineering single base edits using CRISPR technology including specific deaminases and single-guide RNA (sgRNA) is a rapidly evolving field. Different types of base edits can be constructed, with cytidine base editors (CBEs) facilitating transition of C-to-T variants, adenine base editors (ABEs) enabling transition of A-to-G variants, C-to-G transversion base editors (CGBEs) and recently adenine transversion editors (AYBE) that create A-to-C and A-to-T variants. The base-editing machine learning algorithm BE-Hive predicts which sgRNA and base editor combinations have the strongest likelihood of achieving desired base edits. We have used BE-Hive and TP53 mutation data from The Cancer Genome Atlas (TCGA) ovarian cancer cohort to predict which mutations can be engineered, or reverted to wild-type (WT) sequence, using CBEs, ABEs or CGBEs. We have developed and automated a ranking system to assist in selecting optimally designed sgRNA that considers the presence of a suitable protospacer adjacent motif (PAM), the frequency of predicted bystander edits, editing efficiency and target base change. We have generated single constructs containing ABE or CBE editing machinery, an sgRNA cloning backbone and an enhanced green fluorescent protein tag (EGFP), removing the need for co-transfection of multiple plasmids. We have tested our ranking system and new plasmid constructs to engineer the p53 mutants Y220C, R282W and R248Q into WT p53 cells and shown that these mutants cannot activate four p53 target genes, mimicking the behaviour of endogenous p53 mutations. This field will continue to rapidly progress, requiring new strategies such as we propose to ensure desired base-editing outcomes.

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http://hdl.handle.net/10453/172521