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Human genetic diversity alters off-target outcomes of therapeutic gene editing

Abstract

CRISPR gene editing holds great promise to modify DNA sequences in somatic cells to treat disease. However, standard computational and biochemical methods to predict off-target potential focus on reference genomes. We developed an efficient tool called CRISPRme that considers single-nucleotide polymorphism (SNP) and indel genetic variants to nominate and prioritize off-target sites. We tested the software with a BCL11A enhancer targeting guide RNA (gRNA) showing promise in clinical trials for sickle cell disease and β-thalassemia and found that the top candidate off-target is produced by an allele common in African-ancestry populations (MAF 4.5%) that introduces a protospacer adjacent motif (PAM) sequence. We validated that SpCas9 generates strictly allele-specific indels and pericentric inversions in CD34+ hematopoietic stem and progenitor cells (HSPCs), although high-fidelity Cas9 mitigates this off-target. This report illustrates how genetic variants should be considered as modifiers of gene editing outcomes. We expect that variant-aware off-target assessment will become integral to therapeutic genome editing evaluation and provide a powerful approach for comprehensive off-target nomination.

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Fig. 1: CRISPRme provides web-based analysis of CRISPR-Cas gene editing off-target potential reflecting population genetic diversity.
Fig. 2: CRISPRme provides analysis of off-target potential of CRISPR-Cas gene editing reflecting population and private genetic diversity.
Fig. 3: Allele-specific off-target editing by a BCL11A enhancer targeting gRNA in clinical trials associated with a common variant in African-ancestry populations.
Fig. 4: Allele-specific pericentric inversion following BCL11A enhancer editing due to off-target cleavage.
Fig. 5: CRISPRme illustrates prevalent off-target potential due to genetic variation.

Data availability

Sequencing data are deposited in the NCBI Sequence Read Archive database under accession number PRJNA733110. The data for 1000 G were downloaded from https://doi.org/10.12688/wellcomeopenres.15126.2. The data for the HGDP were downloaded from https://doi.org/10.1126/science.aay5012. The full CRISPRme results for NGG PAM gRNAs (including sg1617) are available at https://doi.org/10.5281/zenodo.7195706. Source data are provided with this paper.

Code availability

CRISPRme source code is available at https://github.com/pinellolab/crisprme and https://github.com/InfOmics/CRISPRme. The web app is available online at http://crisprme.di.univr.it. The versions of CRISPRme (1.8.8 and v1.7.7) used to generate the results presented in this manuscript have been deposited on Zenodo: https://doi.org/10.5281/zenodo.5047489. CRISPRitz v2.6.5 used to generate the data presented in this paper have been deposited to Zenodo: https://doi.org/10.5281/zenodo.7078220.

The scripts to generate the plots presented in the manuscript have been deposited to Zenodo: https://doi.org/10.5281/zenodo.7193131.

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Acknowledgements

L.P. received support from the National Institutes of Health (NIH) (R35 HG010717 and RM1 HG009490). D.E.B. was supported by the National Heart, Lung, and Blood Institute (OT2HL154984 and P01HL053749), Burroughs Wellcome Fund, Doris Duke Charitable Foundation and the St. Jude Children’s Research Hospital Collaborative Research Consortium. R.G received support from European Union’s ERA-NET JPCOFUND2 (JPND2019-466-037). We thank S. H. Orkin, G. Lettre, J. K. Joung, V. Pattanayak, K. Petri, A. H. Shen, E. Dirupo and F. Masillo for helpful input.

Author information

Authors and Affiliations

Authors

Contributions

S.C., L.Y.L., M.T., N.B., R.G. and L.P. created the software; J.Z., M.A.N., S.A.M., M.F.C., V.K., S.Q.T., M.A., S.A.W. and D.E.B. designed and conducted experiments; S.C., J.Z., L.Y.L., J.L., R.G., D.E.B. and L.P. performed data analysis; S.C., R.G., D.E.B. and L.P. conceived the work; S.C., J.Z., L.Y.L, R.G., D.E.B. and L.P. wrote the paper with input from all authors.

Corresponding authors

Correspondence to Rosalba Giugno, Daniel E. Bauer or Luca Pinello.

Ethics declarations

Competing interests

L.P. has financial interests in Edilytics, Excelsior Genomics and SeQure Dx. L.P.’s interests were reviewed and are managed by Massachusetts General Hospital and Partners HealthCare in accordance with their conflict of interest policies. The remaining authors declare no competing interests.

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Nature Genetics thanks Krishanu Saha and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Top 100 predicted off-target sites for BCL11A-1617 spacer by CFD score.

CRISPRme search as in Fig. 1. Candidate off-target sites within coding regions based on GENCODE annotations and ATAC-seq peaks in HSCs based on user-provided annotations (data from ref. 49) are highlighted.

Extended Data Fig. 2 Plots with rank ordered correlation between CFD and CRISTA reported targets.

Scatter plots show from left to right, the correlation of ranked targets, extracted by selecting top 10000 targets ordered by CFD and CRISTA score, respectively. The left plot shows the rank correlation of targets with 0 bulges (Pearson’s correlation: 0.57, p < 1e-10, Spearman’s correlation: 0.55, p < 1e-10), the center plot shows rank correlation of targets with 1 bulge (Pearson’s correlation: −0.16, p < 1 e-10, Spearman’s correlation: −0.33, p < 1 e-10) and the right plot shows the rank correlation of targets with 2 bulges (Pearson’s correlation: −0.55, p < 1e-10, Spearman’s correlation: −0.80, p < 1e-10). The correlation values and p-values(two-sided) were calculated using standard functions from the Python scipy library. The colors represent the lowest count of bulges for each target, because the two scoring methods may prioritize different alignments and thus different number of mismatches and bulges of the same genomic target.

Extended Data Fig. 3 HGDP superpopulation distribution plots.

HGDP variant off-targets with CFD ≥ 0.2 and increase in CFD of ≥0.1. Individual samples from each of the seven superpopulations were shuffled 100 times to calculate the mean and 95% confidence interval. First panel shows distribution within all 54 discrete populations, colored by superpopulation. Additional seven panels show distribution of discrete populations within each listed superpopulation.

Extended Data Fig. 4 Candidate transcript off-targets introduced by common genetic variants for non-CRISPR sequence-based RNA-targeting therapeutic strategies.

a) A common SNP (in blue) introduces a candidate CDS off-target site with 2 mismatches for the FDA-approved antisense oligo Nusinersen. b) Top 1000 candidate transcript off-targets ranked by mismatches and bulges for Nusinersen from a search performed with the 1000 G and HGDP genetic variant datasets. c) A common insertion variant (in red) introduces a candidate 3’UTR off-target site with 4 mismatches + bulges for the FDA-approved RNAi therapy Inclisiran. d) Top 1000 candidate transcript off-targets ranked by mismatches and bulges for Inclisiran from a search performed with the 1000 G and HGDP genetic variant datasets.

Supplementary information

Supplementary Information

Supplementary Figures 1–13, Tables 1–5, Notes 1–9 and Data 1–4. Supplementary figures, tables and notesare included in the flat file. Supplementary data files are separate (see below).

Reporting Summary

Supplementary Data 1

Top candidate off-targets in CRISPRme search results for sg1617 using hg38, 1000 G and HGDP data with up to six mismatches and two bulges (including the integrated_results, all_results_with_alternative_alignments, and private_targets files).

Supplementary Data 2

Top candidate off-targets in CRISPRme search results for other example gRNAs with NGG PAMs.

Supplementary Data 3

Top candidate off-targets in CRISPRme search results for example gRNAs with non-NGG PAMs.

Supplementary Data 4

Top candidate off-targets in CRISPRme search results for example non-CRISPR-based, RNA-targeting strategies (antisense oligo and RNA interference).

Source data

Source Data Fig. 1

Gel image.

Source Data Fig. 2

ddPCR image files.

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Cancellieri, S., Zeng, J., Lin, L.Y. et al. Human genetic diversity alters off-target outcomes of therapeutic gene editing. Nat Genet 55, 34–43 (2023). https://doi.org/10.1038/s41588-022-01257-y

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