SNUPN deficiency causes a recessive muscular dystrophy due to RNA mis-splicing and ECM dysregulation.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
27 Feb 2024
27 Feb 2024
Historique:
received:
30
04
2023
accepted:
08
02
2024
medline:
28
2
2024
pubmed:
28
2
2024
entrez:
27
2
2024
Statut:
epublish
Résumé
SNURPORTIN-1, encoded by SNUPN, plays a central role in the nuclear import of spliceosomal small nuclear ribonucleoproteins. However, its physiological function remains unexplored. In this study, we investigate 18 children from 15 unrelated families who present with atypical muscular dystrophy and neurological defects. Nine hypomorphic SNUPN biallelic variants, predominantly clustered in the last coding exon, are ascertained to segregate with the disease. We demonstrate that mutant SPN1 failed to oligomerize leading to cytoplasmic aggregation in patients' primary fibroblasts and CRISPR/Cas9-mediated mutant cell lines. Additionally, mutant nuclei exhibit defective spliceosomal maturation and breakdown of Cajal bodies. Transcriptome analyses reveal splicing and mRNA expression dysregulation, particularly in sarcolemmal components, causing disruption of cytoskeletal organization in mutant cells and patient muscle tissues. Our findings establish SNUPN deficiency as the genetic etiology of a previously unrecognized subtype of muscular dystrophy and provide robust evidence of the role of SPN1 for muscle homeostasis.
Identifiants
pubmed: 38413582
doi: 10.1038/s41467-024-45933-5
pii: 10.1038/s41467-024-45933-5
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1758Investigateurs
Stephan Zuchner
(S)
Mustafa Tekin
(M)
Informations de copyright
© 2024. The Author(s).
Références
Mercuri, E. & Muntoni, F. Muscular dystrophies. Lancet 381, 845–860 (2013).
pubmed: 23465426
doi: 10.1016/S0140-6736(12)61897-2
Mercuri, E., Bönnemann, C. G. & Muntoni, F. Muscular dystrophies. Lancet 394, 2025–2038 (2019).
pubmed: 31789220
doi: 10.1016/S0140-6736(19)32910-1
Thompson, R. et al. Advances in the diagnosis of inherited neuromuscular diseases and implications for therapy development. Lancet Neurol. 19, 522–532 (2020).
pubmed: 32470424
doi: 10.1016/S1474-4422(20)30028-4
Cohen, E., Bonne, G., Rivier, F. & Hamroun, D. The 2022 version of the gene table of neuromuscular disorders (nuclear genome). Neuromuscul. Disord. 31, 1313–1357 (2021).
pubmed: 34930546
doi: 10.1016/j.nmd.2021.11.004
Monaco, A. P. et al. Detection of deletions spanning the Duchenne muscular dystrophy locus using a tightly linked DNA segment. Nature 316, 842–845 (1985).
pubmed: 2993910
doi: 10.1038/316842a0
Ryder, S. et al. The burden, epidemiology, costs and treatment for Duchenne muscular dystrophy: an evidence review. Orphanet J. Rare Dis. 12, 79 (2017).
pubmed: 28446219
pmcid: 5405509
doi: 10.1186/s13023-017-0631-3
Brook, J. D. et al. Molecular basis of myotonic dystrophy: expansion of a trinucleotide (CTG) repeat at the 3’ end of a transcript encoding a protein kinase family member. Cell 69, 385 (1992).
pubmed: 1568252
Mercuri, E. & Muntoni, F. The ever-expanding spectrum of congenital muscular dystrophies. Ann. Neurol. 72, 9–17 (2012).
pubmed: 22829265
doi: 10.1002/ana.23548
Zambon, A. A. & Muntoni, F. Congenital muscular dystrophies: what is new? Neuromuscul. Disord. 31, 931–942 (2021).
pubmed: 34470717
doi: 10.1016/j.nmd.2021.07.009
Angelini, C. L. G. M. D. Identification, description and classification. Acta Myol. 39, 207 (2020).
pubmed: 33458576
pmcid: 7783424
Iyadurai, S. J. P. & Kissel, J. T. The limb-girdle muscular dystrophies and the dystrophinopathies. Continuum 22, 1954–1977 (2016).
pubmed: 27922502
Mah, J. K. et al. A systematic review and meta-analysis on the epidemiology of the muscular dystrophies. Can. J. Neurol. Sci. 43, 163–177 (2016).
pubmed: 26786644
doi: 10.1017/cjn.2015.311
Barton, E. R., Pacak, C. A., Stoppel, W. L. & Kang, P. B. The ties that bind: functional clusters in limb-girdle muscular dystrophy. Skelet. Muscle 10, 22 (2020).
pubmed: 32727611
pmcid: 7389686
doi: 10.1186/s13395-020-00240-7
Georganopoulou, D. G. et al. A journey with LGMD: from protein abnormalities to patient impact. Protein J. 40, 466–488 (2021).
pubmed: 34110586
pmcid: 8190568
doi: 10.1007/s10930-021-10006-9
Lanfranco, M., Vassallo, N. & Cauchi, R. J. Spinal muscular atrophy: from defective chaperoning of snRNP assembly to neuromuscular dysfunction. Front. Mol. Biosci. 4, 41 (2017).
pubmed: 28642865
pmcid: 5463183
doi: 10.3389/fmolb.2017.00041
Pistoni, M., Ghigna, C. & Gabellini, D. Alternative splicing and muscular dystrophy. RNA Biol. 7, 441–452 (2010).
pubmed: 20603608
doi: 10.4161/rna.7.4.12258
Huber, J. et al. Snurportin1, an m3G-cap-specific nuclear import receptor with a novel domain structure. EMBO J. 17, 4114–4126 (1998).
pubmed: 9670026
pmcid: 1170744
doi: 10.1093/emboj/17.14.4114
Lott, K. & Cingolani, G. The importin β binding domain as a master regulator of nucleocytoplasmic transport. Biochim. Biophys. Acta 1813, 1578–1592 (2011).
pubmed: 21029753
doi: 10.1016/j.bbamcr.2010.10.012
Narayanan, U., Ospina, J. K. & Frey, M. R. SMN, the spinal muscular atrophy protein, forms a pre-import snRNP complex with snurportin1 and importin β. Hum. Mol. Genet. 11, 1785–95 (2002).
pubmed: 12095920
doi: 10.1093/hmg/11.15.1785
Raimer, A. C., Gray, K. M. & Matera, A. G. SMN - a chaperone for nuclear RNP social occasions? RNA Biol. 14, 701–711 (2017).
pubmed: 27648855
doi: 10.1080/15476286.2016.1236168
Staněk, D. Cajal bodies and snRNPs - friends with benefits. RNA Biol. 14, 671–679 (2017).
pubmed: 27627834
doi: 10.1080/15476286.2016.1231359
Strasser, A., Dickmanns, A., Lührmann, R. & Ficner, R. Structural basis for m3G-cap-mediated nuclear import of spliceosomal UsnRNPs by snurportin1. EMBO J. 24, 2235–2243 (2005).
pubmed: 15920472
pmcid: 1173142
doi: 10.1038/sj.emboj.7600701
Piecyk, K. et al. How to find the optimal partner—studies of snurportin 1 interactions with U snRNA 5′ TMG-cap analogues containing modified 2-amino group of 7-methylguanosine. Bioorg. Med. Chem. 23, 4660–4668 (2015).
pubmed: 26118337
doi: 10.1016/j.bmc.2015.05.054
Mitrousis, G., Olia, A. S., Walker-Kopp, N. & Cingolani, G. Molecular basis for the recognition of snurportin 1 by importin beta. J. Biol. Chem. 283, 7877–7884 (2008).
pubmed: 18187419
doi: 10.1074/jbc.M709093200
Goette, M., Stumpe, M. C., Ficner, R. & Grubmüller, H. Molecular determinants of snurportin 1 ligand affinity and structural response upon binding. Biophys. J. 97, 581–589 (2009).
pubmed: 19619473
pmcid: 2711343
doi: 10.1016/j.bpj.2009.04.049
Ospina, J. K. et al. Cross-talk between snurportin1 subdomains. Mol. Biol. Cell 16, 4660–4671 (2005).
pubmed: 16030253
pmcid: 1237072
doi: 10.1091/mbc.e05-04-0316
Cappi, C. et al. Whole-exome sequencing in obsessive-compulsive disorder identifies rare mutations in immunological and neurodevelopmental pathways. Transl. Psychiatry 6, e764 (2016).
pubmed: 27023170
pmcid: 4872454
doi: 10.1038/tp.2016.30
Sobreira, N., Schiettecatte, F., Valle, D. & Hamosh, A. GeneMatcher: a matching tool for connecting investigators with an interest in the same gene. Hum. Mutat. 36, 928–930 (2015).
pubmed: 26220891
pmcid: 4833888
doi: 10.1002/humu.22844
Trujillano, D. et al. Clinical exome sequencing: results from 2819 samples reflecting 1000 families. Eur. J. Hum. Genet. 25, 176–182 (2017).
pubmed: 27848944
doi: 10.1038/ejhg.2016.146
Lemm, I. et al. Ongoing U snRNP biogenesis is required for the integrity of Cajal bodies. Mol. Biol. Cell 17, 3221–3231 (2006).
pubmed: 16687569
pmcid: 1483051
doi: 10.1091/mbc.e06-03-0247
Shpargel, K. B. & Matera, A. G. Gemin proteins are required for efficient assembly of Sm-class ribonucleoproteins. Proc. Natl Acad. Sci. USA 102, 17372–17377 (2005).
pubmed: 16301532
pmcid: 1297697
doi: 10.1073/pnas.0508947102
Snider, P. et al. Periostin is required for maturation and extracellular matrix stabilization of noncardiomyocyte lineages of the heart. Circ. Res. 102, 752–760 (2008).
pubmed: 18296617
pmcid: 2754697
doi: 10.1161/CIRCRESAHA.107.159517
Jöbsis, G. J. et al. Type VI collagen mutations in Bethlem myopathy, an autosomal dominant myopathy with contractures. Nat. Genet. 14, 113–115 (1996).
pubmed: 8782832
doi: 10.1038/ng0996-113
Ryu, J. & Do Hee, L. Dual-specificity phosphatase 18 modulates the SUMOylation and aggregation of Ataxin-1. Biochem. Biophys. Res. Commun. 502, 389–396 (2018).
pubmed: 29852174
doi: 10.1016/j.bbrc.2018.05.178
Lin, H. et al. sFRP2 activates Wnt/β-catenin signaling in cardiac fibroblasts: differential roles in cell growth, energy metabolism, and extracellular matrix remodeling. Am. J. Physiol. Cell Physiol. 311, C710–C719 (2016).
pubmed: 27605451
pmcid: 5130588
doi: 10.1152/ajpcell.00137.2016
Wood, A. J. et al. RGD inhibition of itgb1 ameliorates laminin-α2-deficient zebrafish fibre pathology. Hum. Mol. Genet. 28, 1403–1413 (2019).
pubmed: 30566586
Fukai, Y. et al. Cleavage of β-dystroglycan occurs in sarcoglycan-deficient skeletal muscle without MMP-2 and MMP-9. Biochem. Biophys. Res. Commun. 492, 199–205 (2017).
pubmed: 28821434
doi: 10.1016/j.bbrc.2017.08.048
Huber, F., Boire, A., López, M. P. & Koenderink, G. H. Cytoskeletal crosstalk: when three different personalities team up. Curr. Opin. Cell Biol. 32, 39–47 (2015).
pubmed: 25460780
doi: 10.1016/j.ceb.2014.10.005
Machuca-Tzili, L., Brook, D. & Hilton-Jones, D. Clinical and molecular aspects of the myotonic dystrophies: a review. Muscle Nerve 32, 1–18 (2005).
pubmed: 15770660
doi: 10.1002/mus.20301
Fitzsimons, R. B. Facioscapulohumeral muscular dystrophy. Curr. Opin. Neurol. 12, 501–511 (1999).
pubmed: 10590886
doi: 10.1097/00019052-199910000-00003
Niba, E. T. E. et al. Stability and oligomerization of mutated SMN protein determine clinical severity of spinal muscular atrophy. Genes 13, 205 (2022).
pubmed: 35205250
pmcid: 8872419
doi: 10.3390/genes13020205
López-Martínez, A., Soblechero-Martín, P., de-la-Puente-Ovejero, L., Nogales-Gadea, G. & Arechavala-Gomeza, V. An overview of alternative splicing defects implicated in myotonic dystrophy type I. Genes 11, 1109 (2020).
pubmed: 32971903
pmcid: 7564762
doi: 10.3390/genes11091109
Vieira, N. M. et al. A defect in the RNA-processing protein HNRPDL causes limb-girdle muscular dystrophy 1G (LGMD1G). Hum. Mol. Genet. 23, 4103–4110 (2014).
pubmed: 24647604
doi: 10.1093/hmg/ddu127
Smith, L. R. & Barton, E. R. Regulation of fibrosis in muscular dystrophy. Matrix Biol. 68-69, 602–615 (2018).
pubmed: 29408413
pmcid: 6519730
doi: 10.1016/j.matbio.2018.01.014
Accorsi, A., Cramer, M. L. & Girgenrath, M. Fibrogenesis in LAMA2-related muscular dystrophy is a central tenet of disease etiology. Front. Mol. Neurosci. 13, 3 (2020).
pubmed: 32116541
pmcid: 7010923
doi: 10.3389/fnmol.2020.00003
Adzhubei, I. A. et al. A method and server for predicting damaging missense mutations. Nat. Methods 7, 248–249 (2010).
pubmed: 20354512
pmcid: 2855889
doi: 10.1038/nmeth0410-248
Kumar, P., Henikoff, S. & Ng, P. C. Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat. Protoc. 4, 1073–1081 (2009).
pubmed: 19561590
doi: 10.1038/nprot.2009.86
Schwarz, J. M., Rödelsperger, C., Schuelke, M. & Seelow, D. MutationTaster evaluates disease-causing potential of sequence alterations. Nat. Methods 7, 575–576 (2010).
pubmed: 20676075
doi: 10.1038/nmeth0810-575
Jagadeesh, K. A. et al. M-CAP eliminates a majority of variants of uncertain significance in clinical exomes at high sensitivity. Nat. Genet. 48, 1581–1586 (2016).
pubmed: 27776117
doi: 10.1038/ng.3703
Shihab, H. A. et al. An integrative approach to predicting the functional effects of non-coding and coding sequence variation. Bioinformatics 31, 1536–1543 (2015).
pubmed: 25583119
pmcid: 4426838
doi: 10.1093/bioinformatics/btv009
Reva, B., Antipin, Y. & Sander, C. Predicting the functional impact of protein mutations: application to cancer genomics. Nucleic Acids Res. 39, e118 (2011).
pubmed: 21727090
pmcid: 3177186
doi: 10.1093/nar/gkr407
Rentzsch, P., Witten, D., Cooper, G. M., Shendure, J. & Kircher, M. CADD: predicting the deleteriousness of variants throughout the human genome. Nucleic Acids Res. 47, D886–D894 (2019).
pubmed: 30371827
doi: 10.1093/nar/gky1016
Quang, D., Chen, Y. & Xie, X. DANN: a deep learning approach for annotating the pathogenicity of genetic variants. Bioinformatics 31, 761–763 (2015).
pubmed: 25338716
doi: 10.1093/bioinformatics/btu703
Quinodoz, M. et al. AutoMap is a high performance homozygosity mapping tool using next-generation sequencing data. Nat. Commun. 12, 518 (2021).
pubmed: 33483490
pmcid: 7822856
doi: 10.1038/s41467-020-20584-4
Vangipuram, M., Ting, D., Kim, S., Diaz, R. & Schüle, B. Skin punch biopsy explant culture for derivation of primary human fibroblasts. J. Vis. Exp. 77, e3779 (2013).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
pubmed: 23104886
doi: 10.1093/bioinformatics/bts635
Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010).
pubmed: 20979621
pmcid: 3218662
doi: 10.1186/gb-2010-11-10-r106
Shen, S. et al. rMATS: robust and flexible detection of differential alternative splicing from replicate RNA-Seq data. Proc. Natl Acad. Sci. USA. 111, E5593–601 (2014).
pubmed: 25480548
pmcid: 4280593
doi: 10.1073/pnas.1419161111
Ge, S. X., Son, E. W. & Yao, R. iDEP: an integrated web application for differential expression and pathway analysis of RNA-Seq data. BMC Bioinform. 19, 534 (2018).
doi: 10.1186/s12859-018-2486-6
Yang, J. et al. The I-TASSER Suite: protein structure and function prediction. Nat. Methods 12, 7–8 (2015).
pubmed: 25549265
pmcid: 4428668
doi: 10.1038/nmeth.3213
Roy, A., Kucukural, A. & Zhang, Y. I-TASSER: a unified platform for automated protein structure and function prediction. Nat. Protoc. 5, 725–738 (2010).
pubmed: 20360767
pmcid: 2849174
doi: 10.1038/nprot.2010.5
Zhang, Y. I-TASSER server for protein 3D structure prediction. BMC Bioinform. 9, 40 (2008).
doi: 10.1186/1471-2105-9-40
Monecke, T. et al. Crystal structure of the nuclear export receptor CRM1 in complex with Snurportin1 and RanGTP. Science 324, 1087–1091 (2009).
pubmed: 19389996
doi: 10.1126/science.1173388
Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021).
pubmed: 34265844
pmcid: 8371605
doi: 10.1038/s41586-021-03819-2
Mirdita, M. et al. ColabFold: making protein folding accessible to all. Nat. Methods 19, 679–682 (2022).
pubmed: 35637307
pmcid: 9184281
doi: 10.1038/s41592-022-01488-1
Kumar, P. & Woolfson, D. N. Socket2: a program for locating, visualizing and analyzing coiled-coil interfaces in protein structures. Bioinformatics 37, 4575–4577 (2021).
pubmed: 34498035
pmcid: 8652024
doi: 10.1093/bioinformatics/btab631