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Development of a CRISPR/Cas9-based therapy for Hutchinson–Gilford progeria syndrome

Nature Medicine volume 25, pages 423–426 (2019)Cite this article

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Abstract

CRISPR/Cas9-based therapies hold considerable promise for the treatment of genetic diseases. Among these, Hutchinson–Gilford progeria syndrome, caused by a point mutation in the LMNA gene, stands out as a potential candidate. Here, we explore the efficacy of a CRISPR/Cas9-based approach that reverts several alterations in Hutchinson–Gilford progeria syndrome cells and mice by introducing frameshift mutations in the LMNA gene.

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Fig. 1: CRISPR/Cas9 testing in HGPS cellular models.
Fig. 2: CRISPR/Cas9 delivery and phenotype amelioration in LmnaG609G/G609G mice.

Code availability

The in-house Perl scripts used for gene edition analysis can be freely downloaded at https://github.com/vqf/genotypes.

Data availability

The MiSeq data were deposited in the NCBI SRA (no. PRJNA505974). Other data from this study are available from the corresponding authors. Any materials that can be shared will be released via a Material Transfer Agreement.

References

  1. Hennekam, R. C. Am. J. Med. Genet. A. 140, 2603–2624 (2006).

    Article  Google Scholar 

  2. De Sandre-Giovannoli, A. et al. Science 300, 2055 (2003).

    Article  Google Scholar 

  3. Eriksson, M. et al. Nature 423, 293–298 (2003).

    Article  CAS  Google Scholar 

  4. Goldman, R.D. et al. Proc. Natl Acad. Sci. USA 101, 8963–8968 (2004).

    Article  CAS  Google Scholar 

  5. Osorio, F. G. et al. Sci. Transl. Med. 3, 106ra107 (2011).

    Article  Google Scholar 

  6. Fong, L. G. et al. J. Clin. Invest. 116, 743–752 (2006).

    Article  CAS  Google Scholar 

  7. Gordon, L. B., Rothman, F. G., Lopez-Otin, C. & Misteli, T. Cell 156, 400–407 (2014).

    Article  CAS  Google Scholar 

  8. Gordon, L. B. et al. Circulation 130, 27–34 (2014).

    Article  CAS  Google Scholar 

  9. Gordon, L. B. et al. JAMA 319, 1687–1695 (2018).

    Article  CAS  Google Scholar 

  10. Amoasii, L. et al. Sci. Transl. Med. 9, eaan8081 (2017).

  11. Yang, Y. et al. Nat. Biotechnol. 34, 334–338 (2016).

    Article  CAS  Google Scholar 

  12. Gao, X. et al. Nature 553, 217–221 (2018).

    Article  CAS  Google Scholar 

  13. Doudna, J. A. & Charpentier, E. Science 346, 1258096 (2014).

    Article  Google Scholar 

  14. de la Rosa, J. et al. Nat. Commun. 4, 2268 (2013).

    Article  Google Scholar 

  15. Ran, F. A. et al. Nature 520, 186–191 (2015).

    Article  CAS  Google Scholar 

  16. Scaffidi, P. & Misteli, T. Nat. Med. 11, 440–445 (2005).

    Article  CAS  Google Scholar 

  17. Bar, D. Z. et al. J. Med. Genet. 54, 212–216 (2017).

    Article  CAS  Google Scholar 

  18. Gaudelli, N. M. et al. Nature 551, 464–471 (2017).

    Article  CAS  Google Scholar 

  19. Beyret, E. et al. Nat. Med. https://doi.org/10.1038/s41591-019-0343-4 (2019).

  20. Li, H. & Durbin, R. Bioinformatics 26, 589–595 (2010).

    Article  Google Scholar 

  21. Li, H. et al. Bioinformatics 25, 2078–2079 (2009).

    Article  Google Scholar 

Download references

Acknowledgements

We thank G. Velasco, R. Villa-Bellosta, C. Bárcena, A.P. Ugalde and X.M. Caravia for helpful comments and advice, and R. Feijoo, A. Moyano, D.A. Puente and S.A. Miranda for excellent technical assistance. We also acknowledge the generous support by J.I. Cabrera and Associazione Italiana Progeria Sammy Basso and the contribution of Dr. Matthew Golding to the generation of the anti-murine progerin antibody. The Instituto Universitario de Oncología del Principado de Asturias is supported by Fundación Bancaria Caja de Ahorros de Asturias. J.M.P.F. is supported by Ministerio de Economía y Competitividad (MINECO/FEDER: No. SAF2015-64157-R) and Gobierno del Principado de Asturias. C.L.-O. is supported by grants from the European Research Council (ERC-2016-ADG, DeAge), Ministerio de Economía y Competitividad (MINECO/FEDER: Nos. SAF2014-52413-R and SAF2017-87655-R), Instituto de Salud Carlos III (RTICC) and Progeria Research Foundation (No. PRF2016-66). O.S.-F. is recipient of an FPU fellowship. A.R.F. is recipient of a Ramón y Cajal fellowship. The generation of progerin antibody was funded by the Wellcome Trust (No. 098291/Z/12/Z to S.N.).

Author information

Authors and Affiliations

  1. Departamento de Bioquímica y Biología Molecular, Facultad de Medicina, Instituto Universitario de Oncología del Principado de Asturias, Universidad de Oviedo, Oviedo, Spain

    Olaya Santiago-Fernández, Fernando G. Osorio, Víctor Quesada, Francisco Rodríguez, Sammy Basso, Daniel Maeso, Alicia R. Folgueras, José M. P. Freije & Carlos López-Otín

  2. Centro de Investigación Biomédica en Red de Cáncer, Madrid, Spain

    Víctor Quesada, José M. P. Freije & Carlos López-Otín

  3. William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK

    Loïc Rolas, Anna Barkaway & Sussan Nourshargh

Authors
  1. Olaya Santiago-Fernández
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  2. Fernando G. Osorio
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  3. Víctor Quesada
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  4. Francisco Rodríguez
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  5. Sammy Basso
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  6. Daniel Maeso
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  7. Loïc Rolas
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  8. Anna Barkaway
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  9. Sussan Nourshargh
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  10. Alicia R. Folgueras
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  11. José M. P. Freije
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  12. Carlos López-Otín
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Contributions

F.G.O., J.M.P.F. and C.L.-O. conceived and designed experiments. O.S.-F., F.G.O., V.Q., F.R., S.B., D.M. and A.R.F. performed experiments and analyzed data. L.R., A.B. and S.N. provided reagents. O.S.-F., F.G.O., A.R.F., J.M.P.F., and C.L.-O. wrote the manuscript. All authors revised the manuscript.

Corresponding authors

Correspondence to José M. P. Freije or Carlos López-Otín.

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The authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Representative capillary electrophoresis-based fragment analysis of sgRNA-control- and sgRNA-LCS1-transduced LmnaG609G/G609G mouse embryonic fibroblasts (n = 3 independent infections and MEF lines).

Red line and orange peaks correspond to size standards.

Extended Data Fig. 2 Representative capillary electrophoresis-based fragment analysis of sgRNA-control- and sgRNA-LCS1-transduced LMNAG608G/+ human fibroblasts (n = 3 independent infections).

Red line and orange peaks correspond to size standards.

Extended Data Fig. 3 Percentage of indels in LmnaG609G/G609G sgRNA-LC2-transduced male and female mouse tissues.

Data are mean ± s.e.m. (n = 5 tissues per group, except in sgRNA-LCS2-transduced female liver where n = 4; two-tailed Student’s t-test).

Source data

Extended Data Fig. 4 RT–qPCR analysis of progerin and lamin C in tissues from LmnaG609G/G609G sgRNA-control-transduced and LmnaG609G/G609G sgRNA-LCS2-transduced mice.

Data are mean ± s.e.m. (n = 4 tissues per group, except sgRNA-control-transduced liver and heart where n = 5; two-tailed Student’s t-test).

Source data

Extended Data Fig. 5

Progerin immunohistochemistry in lung, kidney and aorta from WT, LmnaG609G/G609G sgRNA-control-transduced and LmnaG609G/G609G sgRNA-LCS2-transduced mice (lung and kidney, n = 5 for WT and sgRNA-control-transduced mice and n = 4 for sgRNA-LCS2-transduced mice; aorta, n = 2 for WT and n = 3 for sgRNA-control- and sgRNA-LCS2-transduced LmnaG609G/G609G mice). Scale bar, 100 μm.

Extended Data Fig. 6

Kaplan–Meier survival plot of LmnaG609G/G609G male and female mice transduced with sgRNA-control (n = 6 males; n = 4 females) or sgRNA-LCS2 (n = 4 males; n = 6 females).

Source data

Extended Data Fig. 7 Progression of body weight of male and female mice transduced with sgRNA-control or sgRNA-LCS2, expressed as percentage of weight at 9 weeks.

Mean values ± s.e.m. are shown (for males, initial n = 9 sgRNA-control-transduced mice and n = 8 sgRNA-LCS2-transduced mice; for females, initial n = 6 mice per group; two-tailed Student’s t-test). Vertical arrow indicates the time point (3.5 months) at which the cohort destined for histological studies was sacrificed.

Source data

Extended Data Fig. 8

Images of three sex- and age-matched mice transduced with the sgRNA-LCS2 compared to sgRNA-control-transduced animals.

Extended Data Fig. 9 H&E staining of gastric mucosa from WT, LmnaG609G/G609G sgRNA-control-transduced and LmnaG609G/G609G sgRNA-LCS2-transduced mice.

The graph shows atrophy quantification according to a pathological score as described in Methods. Data are mean ± s.e.m. (n = 5 for WT and sgRNA-control-transduced mice; n = 3 for sgRNA-LCS2-transduced mice).

Supplementary information

Reporting Summary

Supplementary Video 1

LmnaG609G/G609G sgRNA-control-transduced (top) versus sgRNA-LCS2-transduced (bottom) mice. Both are 4-month-old littermates of the same gender.

Supplementary Tables

Supplementary Tables 1–4

Source data

Source data Fig. 1

Unprocessed Western Blots

Source data Fig. 1

Statistical Source Data

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Statistical Source Data

Extended Data Source Data Fig. 3

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Extended Data Source Data Fig. 7

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Santiago-Fernández, O., Osorio, F.G., Quesada, V. et al. Development of a CRISPR/Cas9-based therapy for Hutchinson–Gilford progeria syndrome. Nat Med 25, 423–426 (2019). https://doi.org/10.1038/s41591-018-0338-6

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