A framework for individualized splice-switching oligonucleotide therapy.
Journal
Nature
ISSN: 1476-4687
Titre abrégé: Nature
Pays: England
ID NLM: 0410462
Informations de publication
Date de publication:
Jul 2023
Jul 2023
Historique:
received:
22
03
2022
accepted:
25
05
2023
medline:
28
7
2023
pubmed:
13
7
2023
entrez:
12
7
2023
Statut:
ppublish
Résumé
Splice-switching antisense oligonucleotides (ASOs) could be used to treat a subset of individuals with genetic diseases
Identifiants
pubmed: 37438524
doi: 10.1038/s41586-023-06277-0
pii: 10.1038/s41586-023-06277-0
pmc: PMC10371869
doi:
Substances chimiques
Oligonucleotides, Antisense
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
828-836Subventions
Organisme : NIA NIH HHS
ID : DP2 AG072437
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR000170
Pays : United States
Commentaires et corrections
Type : CommentIn
Informations de copyright
© 2023. The Author(s).
Références
Kim, J. et al. Patient-customized oligonucleotide therapy for a rare genetic disease. N. Engl. J. Med. 381, 1644–1652 (2019).
pubmed: 31597037
pmcid: 6961983
doi: 10.1056/NEJMoa1813279
Rothblum-Oviatt, C. et al. Ataxia telangiectasia: a review. Orphanet J. Rare Dis. 11, 159 (2016).
pubmed: 27884168
pmcid: 5123280
doi: 10.1186/s13023-016-0543-7
Verhagen, M. M. M. et al. Clinical spectrum of ataxia-telangiectasia in adulthood. Neurology 73, 430–437 (2009).
pubmed: 19535770
doi: 10.1212/WNL.0b013e3181af33bd
Kaufmann, P., Pariser, A. R. & Austin, C. From scientific discovery to treatments for rare diseases—the view from the National Center for Advancing Translational Sciences–Office of Rare Diseases Research. Orphanet J. Rare Dis. 13, 196 (2018).
pubmed: 30400963
pmcid: 6219030
doi: 10.1186/s13023-018-0936-x
Ferreira, C. R. The burden of rare diseases. Am. J. Med. Genet. A 179, 885–892 (2019).
pubmed: 30883013
doi: 10.1002/ajmg.a.61124
Woodcock, J. & Marks, P. Drug regulation in the era of individualized therapies. N. Engl. J. Med. 381, 1678–1680 (2019).
pubmed: 31597016
doi: 10.1056/NEJMe1911295
Shiloh, Y. ATM and related protein kinases: safeguarding genome integrity. Nat. Rev. Cancer 3, 155–168 (2003).
pubmed: 12612651
doi: 10.1038/nrc1011
Schon, K. et al. Genotype, extrapyramidal features, and severity of variant ataxia-telangiectasia. Ann. Neurol. 85, 170–180 (2019).
pubmed: 30549301
pmcid: 6590299
Gatti, R. & Perlman, S. in GeneReviews (eds Adam, M. P. et al) https://www.ncbi.nlm.nih.gov/books/NBK26468/ (University of Washington, 2016).
Dong, J.-Y., Fan, P.-D. & Frizzell, R. A. Quantitative analysis of the packaging capacity of recombinant adeno-associated virus. Hum. Gene Ther. 7, 2101–2112 (1996).
pubmed: 8934224
doi: 10.1089/hum.1996.7.17-2101
Ramos, D. M. et al. Age-dependent SMN expression in disease-relevant tissue and implications for SMA treatment. J. Clin. Invest. 129, 4817–4831 (2019).
pubmed: 31589162
pmcid: 6819103
doi: 10.1172/JCI124120
McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).
pubmed: 20644199
pmcid: 2928508
doi: 10.1101/gr.107524.110
Koboldt, D. C. et al. VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 22, 568–576 (2012).
pubmed: 22300766
pmcid: 3290792
doi: 10.1101/gr.129684.111
Kim, S. et al. Strelka2: fast and accurate calling of germline and somatic variants. Nat. Methods 15, 591–594 (2018).
pubmed: 30013048
doi: 10.1038/s41592-018-0051-x
Rausch, T. et al. DELLY: structural variant discovery by integrated paired-end and split-read analysis. Bioinformatics 28, i333–i339 (2012).
pubmed: 22962449
pmcid: 3436805
doi: 10.1093/bioinformatics/bts378
Ye, K., Schulz, M. H., Long, Q., Apweiler, R. & Ning, Z. Pindel: a pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short reads. Bioinformatics 25, 2865–2871 (2009).
pubmed: 19561018
pmcid: 2781750
doi: 10.1093/bioinformatics/btp394
Gardner, E. J. et al. The mobile element locator tool (MELT): population-scale mobile element discovery and biology. Genome Res. 27, 1916–1929 (2017).
pubmed: 28855259
pmcid: 5668948
doi: 10.1101/gr.218032.116
Chu, C. et al. Comprehensive identification of transposable element insertions using multiple sequencing technologies. Nat. Commun. 12, 3836 (2021).
pubmed: 34158502
pmcid: 8219666
doi: 10.1038/s41467-021-24041-8
Richards, S. et al. Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology. Genet. Med. 17, 405–424 (2015).
pubmed: 25741868
pmcid: 4544753
doi: 10.1038/gim.2015.30
Jaganathan, K. et al. Predicting splicing from primary sequence with deep learning. Cell 176, 535–548 (2019).
pubmed: 30661751
doi: 10.1016/j.cell.2018.12.015
Ioannidis, N. M. et al. REVEL: an ensemble method for predicting the pathogenicity of rare missense variants. Am. J. Hum. Genet. 99, 877–885 (2016).
pubmed: 27666373
pmcid: 5065685
doi: 10.1016/j.ajhg.2016.08.016
Smedley, D. et al. 100,000 Genomes pilot on rare-disease diagnosis in health care—preliminary report. N. Engl. J. Med. 385, 1868–1880 (2021).
pubmed: 34758253
doi: 10.1056/NEJMoa2035790
Martin, M. et al. WhatsHap: fast and accurate read-based phasing. Preprint at bioRxiv https://doi.org/10.1101/085050 (2016).
Yeo, G. & Burge, C. B. Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals. J. Comput. Biol. 11, 377–394 (2004).
pubmed: 15285897
doi: 10.1089/1066527041410418
Paggi, J. M. & Bejerano, G. A sequence-based, deep learning model accurately predicts RNA splicing branchpoints. RNA 24, 1647–1653 (2018).
pubmed: 30224349
pmcid: 6239175
doi: 10.1261/rna.066290.118
McConville, C. M. et al. Mutations associated with variant phenotypes in ataxia-telangiectasia. Am. J. Hum. Genet. 59, 320–330 (1996).
pubmed: 8755918
pmcid: 1914715
Perlman, S. L., Boder, E., Sedgewick, R. P. & Gatti, R. A. in Handbook of Clinical Neurology Vol. 103 (eds Subramony, S. H. & Dürr, A.) 307–332 (Elsevier, 2012).
Stankovic, T. et al. ATM mutations and phenotypes in ataxia-telangiectasia families in the british isles: expression of mutant ATM and the risk of leukemia, lymphoma, and breast cancer. Am. J. Hum. Genet. 62, 334–345 (1998).
pubmed: 9463314
pmcid: 1376883
doi: 10.1086/301706
Teraoka, S. N. et al. Splicing defects in the ataxia-telangiectasia gene, ATM: underlying mutations and consequences. Am. J. Hum. Genet. 64, 1617–1631 (1999).
pubmed: 10330348
pmcid: 1377904
doi: 10.1086/302418
Du, L., Pollard, J. M. & Gatti, R. A. Correction of prototypic ATM splicing mutations and aberrant ATM function with antisense morpholino oligonucleotides. Proc. Natl Acad. Sci. USA 104, 6007–6012 (2007).
pubmed: 17389389
pmcid: 1832221
doi: 10.1073/pnas.0608616104
Mallott, J. et al. Newborn screening for SCID identifies patients with ataxia telangiectasia. J. Clin. Immunol. 33, 540–549 (2013).
pubmed: 23264026
doi: 10.1007/s10875-012-9846-1
Menolfi, D. & Zha, S. ATM, DNA-PKcs and ATR: shaping development through the regulation of the DNA damage responses. Genome Instab. Dis. 1, 47–68 (2020).
doi: 10.1007/s42764-019-00003-9
Verhagen, M. M. M. et al. Presence of ATM protein and residual kinase activity correlates with the phenotype in ataxia-telangiectasia: a genotype-phenotype study. Hum. Mutat. 33, 561–571 (2012).
pubmed: 22213089
doi: 10.1002/humu.22016
Crawford, T. O. Ataxia telangiectasia. Semin. Pediatr. Neurol. 5, 287–294 (1998).
pubmed: 9874856
doi: 10.1016/S1071-9091(98)80007-7
Nissenkorn, A. & Ben-Zeev, B. in Handbook of Clinical Neurology Vol. 132 (eds Islam, M. P. & Roach, S.) 199–214 (Elsevier, 2015).
Jackson, T. J. et al. Longitudinal analysis of the neurological features of ataxia-telangiectasia. Dev. Med. Child Neurol. 58, 690–697 (2016).
pubmed: 26896183
doi: 10.1111/dmcn.13052
Cummings, B. B. et al. Improving genetic diagnosis in Mendelian disease with transcriptome sequencing. Sci. Transl. Med. 9, eaal5209 (2017).
pubmed: 28424332
pmcid: 5548421
doi: 10.1126/scitranslmed.aal5209
Nassisi, M. et al. Prevalence of ABCA4 deep-intronic variants and related phenotype in an unsolved “one-hit” cohort with Stargardt disease. Int. J. Mol. Sci. 20, 5053 (2019).
pubmed: 31614660
pmcid: 6829239
doi: 10.3390/ijms20205053
Del Pozo-Valero, M. et al. Genotype–phenotype correlations in a Spanish cohort of 506 families with biallelic ABCA4 pathogenic variants. Am. J. Ophthalmol. 219, 195–204 (2020).
pubmed: 32619608
doi: 10.1016/j.ajo.2020.06.027
Finkel, R. S. et al. Nusinersen versus sham control in infantile-onset spinal muscular atrophy. N. Engl. J. Med. 377, 1723–1732 (2017).
pubmed: 29091570
doi: 10.1056/NEJMoa1702752
Baranello, G. et al. Risdiplam in type 1 spinal muscular atrophy. N. Engl. J. Med. 384, 915–923 (2021).
pubmed: 33626251
doi: 10.1056/NEJMoa2009965
Slaugenhaupt, S. A. et al. Tissue-specific expression of a splicing mutation in the IKBKAP gene causes familial dysautonomia. Am. J. Hum. Genet. 68, 598–605 (2001).
pubmed: 11179008
pmcid: 1274473
doi: 10.1086/318810
Ajiro, M. et al. Therapeutic manipulation of IKBKAP mis-splicing with a small molecule to cure familial dysautonomia. Nat. Commun. 12, 4507 (2021).
pubmed: 34301951
pmcid: 8302731
doi: 10.1038/s41467-021-24705-5
Vockley, J. et al. Whole-genome sequencing holds the key to the success of gene-targeted therapies. Am. J. Med. Genet. 193, 19–29 (2023).
pubmed: 36453229
doi: 10.1002/ajmg.c.32017
The cost of getting personal. Nat. Med. 25, 1797 (2019).
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
pubmed: 19451168
pmcid: 2705234
doi: 10.1093/bioinformatics/btp324
Robinson, J. T., Thorvaldsdóttir, H., Wenger, A. M., Zehir, A. & Mesirov, J. P. Variant review with the integrative genomics viewer. Cancer Res. 77, e31–e34 (2017).
pubmed: 29092934
pmcid: 5678989
doi: 10.1158/0008-5472.CAN-17-0337
Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).
pubmed: 21653522
pmcid: 3137218
doi: 10.1093/bioinformatics/btr330
Manichaikul, A. et al. Robust relationship inference in genome-wide association studies. Bioinformatics 26, 2867–2873 (2010).
pubmed: 20926424
pmcid: 3025716
doi: 10.1093/bioinformatics/btq559
McLaren, W. et al. The Ensembl Variant Effect Predictor. Genome Biol 17, 122 (2016).
pubmed: 27268795
pmcid: 4893825
doi: 10.1186/s13059-016-0974-4
Karczewski, K. J. et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581, 434–443 (2020).
pubmed: 32461654
pmcid: 7334197
doi: 10.1038/s41586-020-2308-7
Taliun, D. et al. Sequencing of 53,831 diverse genomes from the NHLBI TOPMed Program. Nature 590, 290–299 (2021).
pubmed: 33568819
pmcid: 7875770
doi: 10.1038/s41586-021-03205-y
Lappalainen, I. et al. dbVar and DGVa: public archives for genomic structural variation. Nucleic Acids Res. 41, D936–D941 (2013).
pubmed: 23193291
doi: 10.1093/nar/gks1213
MacDonald, J. R., Ziman, R., Yuen, R. K. C., Feuk, L. & Scherer, S. W. The Database of Genomic Variants: a curated collection of structural variation in the human genome. Nucleic Acids Res. 42, D986–D992 (2014).
pubmed: 24174537
doi: 10.1093/nar/gkt958
Collins, R. L. et al. A structural variation reference for medical and population genetics. Nature 581, 444–451 (2020).
pubmed: 32461652
pmcid: 7334194
doi: 10.1038/s41586-020-2287-8
Landrum, M. J. et al. ClinVar: improving access to variant interpretations and supporting evidence. Nucleic Acids Res. 46, D1062–D1067 (2018).
pubmed: 29165669
doi: 10.1093/nar/gkx1153
Biesecker, L. G. & Harrison, S. M. The ACMG/AMP reputable source criteria for the interpretation of sequence variants. Genet. Med. 20, 1687–1688 (2018).
pubmed: 29543229
pmcid: 6709533
doi: 10.1038/gim.2018.42
Abou Tayoun, A. N. et al. Recommendations for interpreting the loss of function PVS1 ACMG/AMP variant criterion. Hum. Mutat. 39, 1517–1524 (2018).
pubmed: 30192042
pmcid: 6185798
doi: 10.1002/humu.23626
Riggs, E. R. et al. Technical standards for the interpretation and reporting of constitutional copy-number variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics (ACMG) and the Clinical Genome Resource (ClinGen). Genet. Med. 22, 245–257 (2020).
pubmed: 31690835
doi: 10.1038/s41436-019-0686-8
Gudmundsson, S. et al. Variant interpretation using population databases: lessons from gnomAD. Hum. Mutat. 43, 1012–1030 (2022).
pubmed: 34859531
doi: 10.1002/humu.24309
Kishore, S., Khanna, A. & Stamm, S. Rapid generation of splicing reporters with pSpliceExpress. Gene 427, 104–110 (2008).
pubmed: 18930792
pmcid: 2821805
doi: 10.1016/j.gene.2008.09.021
Rosenberg, A. B., Patwardhan, R. P., Shendure, J. & Seelig, G. Learning the sequence determinants of alternative splicing from millions of random sequences. Cell 163, 698–711 (2015).
pubmed: 26496609
doi: 10.1016/j.cell.2015.09.054
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
pubmed: 23104886
doi: 10.1093/bioinformatics/bts635
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
pubmed: 19505943
pmcid: 2723002
doi: 10.1093/bioinformatics/btp352