Single-nucleus RNA-seq identifies divergent populations of FSHD2 myotube nuclei

English version

Autoři: Shan Jiang ^aff001; Katherine Williams ^aff001; Xiangduo Kong ^aff003; Weihua Zeng ^aff001; Nam Viet Nguyen ^aff003; Xinyi Ma ^aff001; Rabi Tawil ^aff004; Kyoko Yokomori ^aff003; Ali Mortazavi ^aff001
Působiště autorů: Department of Developmental and Cell Biology, University of California Irvine, Irvine, California, United States of America ^aff001; Center for Complex Biological Systems, University of California Irvine, Irvine, California, United States of America ^aff002; Department of Biological Chemistry, School of Medicine, University of California Irvine, Irvine, California, United States of America ^aff003; Neuromuscular Disease Unit, Department of Neurology, University of Rochester Medical Center, Rochester, New York, United States of America ^aff004
Vyšlo v časopise: Single-nucleus RNA-seq identifies divergent populations of FSHD2 myotube nuclei. PLoS Genet 16(5): e32767. doi:10.1371/journal.pgen.1008754
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pgen.1008754

Souhrn

FSHD is characterized by the misexpression of DUX4 in skeletal muscle. Although DUX4 upregulation is thought to be the pathogenic cause of FSHD, DUX4 is lowly expressed in patient samples, and analysis of the consequences of DUX4 expression has largely relied on artificial overexpression. To better understand the native expression profile of DUX4 and its targets, we performed bulk RNA-seq on a 6-day differentiation time-course in primary FSHD2 patient myoblasts. We identify a set of 54 genes upregulated in FSHD2 cells, termed FSHD-induced genes. Using single-cell and single-nucleus RNA-seq on myoblasts and differentiated myotubes, respectively, we captured, for the first time, DUX4 expressed at the single-nucleus level in a native state. We identified two populations of FSHD myotube nuclei based on low or high enrichment of DUX4 and FSHD-induced genes (“FSHD-Lo” and “FSHD Hi”, respectively). FSHD-Hi myotube nuclei coexpress multiple DUX4 target genes including DUXA, LEUTX and ZSCAN4, and also upregulate cell cycle-related genes with significant enrichment of E2F target genes and p53 signaling activation. We found more FSHD-Hi nuclei than DUX4-positive nuclei, and confirmed with in situ RNA/protein detection that DUX4 transcribed in only one or two nuclei is sufficient for DUX4 protein to activate target genes across multiple nuclei within the same myotube. DUXA (the DUX4 paralog) is more widely expressed than DUX4, and depletion of DUXA suppressed the expression of LEUTX and ZSCAN4 in late, but not early, differentiation. The results suggest that the DUXA can take over the role of DUX4 to maintain target gene expression. These results provide a possible explanation as to why it is easier to detect DUX4 target genes than DUX4 itself in patient cells and raise the possibility of a self-sustaining network of gene dysregulation triggered by the limited DUX4 expression.

Klíčová slova:

Cell cycle and cell division – Cell differentiation – Gene expression – Gene regulation – Myoblasts – RNA sequencing – Transcription factors – Facioscapulohumeral muscular dystrophy

Zdroje

1. Tawil R, Van Der Maarel SM. Facioscapulohumeral muscular dystrophy. Muscle Nerve. 2006 Jul 1;34(1):1–15. doi: 10.1002/mus.20522 16508966

2. Zeng W, Chen YY, Newkirk DA, Wu B, Balog J, Kong X, et al. Genetic and Epigenetic Characteristics of FSHD-Associated 4q and 10q D4Z4 that are Distinct from Non-4q/10q D4Z4 Homologs. Hum Mutat. 2014;35(8):998–1010. doi: 10.1002/humu.22593 24838473

3. Young JM, Whiddon JL, Yao Z, Kasinathan B, Snider L, Geng LN, et al. DUX4 Binding to Retroelements Creates Promoters That Are Active in FSHD Muscle and Testis. PLoS Genet. 2013 Nov;9(11).

4. Geng LN, Yao Z, Snider L, Fong AP, Cech JN, Young JM, et al. DUX4 Activates Germline Genes, Retroelements, and Immune Mediators: Implications for Facioscapulohumeral Dystrophy. Dev Cell. 2012 Jan 17;22(1):38–51. doi: 10.1016/j.devcel.2011.11.013 22209328

5. Lemmers RJLF, Tawil R, Petek LM, Balog J, Block GJ, Santen GWE, et al. Digenic inheritance of an SMCHD1 mutation and an FSHD-permissive D4Z4 allele causes facioscapulohumeral muscular dystrophy type 2. Nat Genet. 2012 Dec;44(12):1370–4. doi: 10.1038/ng.2454 23143600

6. Sacconi S, Lemmers RJLF, Balog J, Van Der Vliet PJ, Lahaut P, Van Nieuwenhuizen MP, et al. The FSHD2 gene SMCHD1 Is a modifier of disease severity in families affected by FSHD1. Am J Hum Genet. 2013 Oct 3;93(4):744–51. doi: 10.1016/j.ajhg.2013.08.004 24075187

7. Larsen M, Rost S, El Hajj N, Ferbert A, Deschauer M, Walter MC, et al. Diagnostic approach for FSHD revisited: SMCHD1 mutations cause FSHD2 and act as modifiers of disease severity in FSHD1. Eur J Hum Genet. 2015 Jun 5;23(6):808–16. doi: 10.1038/ejhg.2014.191 25370034

8. Snider L, Geng LN, Lemmers RJLF, Kyba M, Ware CB, Nelson AM, et al. Facioscapulohumeral Dystrophy: Incomplete Suppression of a Retrotransposed Gene. Pearson CE, editor. PLoS Genet. 2010 Oct 28;6(10):e1001181. doi: 10.1371/journal.pgen.1001181 21060811

9. Lemmers RJLF, van der Vliet PJ, Klooster R, Sacconi S, Camaño P, Dauwerse JG, et al. A unifying genetic model for facioscapulohumeral muscular dystrophy. Science. 2010 Sep 24;329(5999):1650–3. doi: 10.1126/science.1189044 20724583

10. Himeda CL, Jones TI, Jones PL. Facioscapulohumeral muscular dystrophy as a model for epigenetic regulation and disease. Antioxid Redox Signal. 2015 Jun 1;22(16):1463–82. doi: 10.1089/ars.2014.6090 25336259

11. De Iaco A, Planet E, Coluccio A, Verp S, Duc J, Trono D. DUX-family transcription factors regulate zygotic genome activation in placental mammals. Nat Genet. 2017 Jun 1;49(6):941–5. doi: 10.1038/ng.3858 28459456

12. Hendrickson PG, Doráis JA, Grow EJ, Whiddon JL, Lim JW, Wike CL, et al. Conserved roles of mouse DUX and human DUX4 in activating cleavage-stage genes and MERVL/HERVL retrotransposons. Nat Genet. 2017 Jun 1;49(6):925–34. doi: 10.1038/ng.3844 28459457

13. Whiddon JL, Langford AT, Wong CJ, Zhong JW, Tapscott SJ. Conservation and innovation in the DUX4-family gene network. Nat Genet. 2017 Jun 1;49(6):935–40. doi: 10.1038/ng.3846 28459454

14. Bosnakovski D, Xu Z, Gang EJ, Galindo CL, Liu M, Simsek T, et al. An isogenetic myoblast expression screen identifies DUX4-mediated FSHD-associated molecular pathologies. EMBO J. 2008 Oct 22;27(20):2766–79. doi: 10.1038/emboj.2008.201 18833193

15. Vanderplanck C, Ansseau E, Charron S, Stricwant N, Tassin A, Laoudj-Chenivesse D, et al. The FSHD Atrophic Myotube Phenotype Is Caused by DUX4 Expression. Chadwick BP, editor. PLoS One. 2011 Oct 28;6(10):e26820. doi: 10.1371/journal.pone.0026820 22053214

16. Tassin A, Laoudj-Chenivesse D, Vanderplanck C, Barro M, Charron S, Ansseau E, et al. DUX4 expression in FSHD muscle cells: How could such a rare protein cause a myopathy? J Cell Mol Med. 2013 Jan;17(1):76–89. doi: 10.1111/j.1582-4934.2012.01647.x 23206257

17. Zeng W, De Greef JC, Chen YY, Chien R, Kong X, Gregson HC, et al. Specific loss of histone H3 lysine 9 trimethylation and HP1γ/cohesin binding at D4Z4 repeats is associated with facioscapulohumeral dystrophy (FSHD). PLoS Genet. 2009 Jul;5(7).

18. Van Overveld PGM, Lemmers RJFL, Sandkuijl LA, Enthoven L, Winokur ST, Bakels F, et al. Hypomethylation of D4Z4 in 4q-linked and non-4q-linked facioscapulohumeral muscular dystrophy. Nat Genet. 2003 Dec;35(4):315–7. doi: 10.1038/ng1262 14634647

19. Jansz N, Chen K, Murphy JM, Blewitt ME. The Epigenetic Regulator SMCHD1 in Development and Disease. Vol. 33, Trends in Genetics. Elsevier Ltd; 2017. p. 233–43.

20. Yao Z, Snider L, Balog J, Lemmers RJLF, Van Der Maarel SM, Tawil R, et al. DUX4-induced gene expression is the major molecular signature in FSHD skeletal muscle. Hum Mol Genet. 2014 Oct 15;23(20):5342–52. doi: 10.1093/hmg/ddu251 24861551

21. Zeng W, Jiang S, Kong X, El-Ali N, Ball AR, Ma CIH, et al. Single-nucleus RNA-seq of differentiating human myoblasts reveals the extent of fate heterogeneity. Nucleic Acids Res. 2016 Dec 1;44(21).

22. Rickard AM, Petek LM, Miller DG. Endogenous DUX4 expression in FSHD myotubes is sufficient to cause cell death and disrupts RNA splicing and cell migration pathways. Hum Mol Genet. 2015 Jun 5;24(20):5901–14. doi: 10.1093/hmg/ddv315 26246499

23. Conesa A, Nueda M. maSigPro: Significant Gene Expression Profile Differences in Time Course Gene Expression Data. 2017.

24. Jagannathan S, Shadle SC, Resnick R, Snider L, Tawil RN, van der Maarel SM, et al. Model systems of DUX4 expression recapitulate the transcriptional profile of FSHD cells. Hum Mol Genet. 2016 Aug 17;ddw271.

25. Leidenroth A, Hewitt JE. A family history of DUX4: phylogenetic analysis of DUXA, B, C and Duxbl reveals the ancestral DUX gene. BMC Evol Biol. 2010 Nov 26;10(1):364.

26. Banerji CRS, Panamarova M, Pruller J, Figeac N, Hebaishi H, Fidanis E, et al. Dynamic transcriptomic analysis reveals suppression of PGC1α/ERRα drives perturbed myogenesis in facioscapulohumeral muscular dystrophy. Hum Mol Genet. 2019;28(8).

27. Resnick R, Wong C-J, Hamm DC, Bennett SR, Skene PJ, Hake SB, et al. DUX4-Induced Histone Variants H3.X and H3.Y Mark DUX4 Target Genes for Expression. Cell Rep. 2019 Nov 12;29(7):1812–1820.e5. doi: 10.1016/j.celrep.2019.10.025 31722199

28. Knopp P, Krom YD, Banerji CRS, Panamarova M, Moyle LA, den Hamer B, et al. DUX4 induces a transcriptome more characteristic of a less-differentiated cell state and inhibits myogenesis. J Cell Sci. 2016 Oct 15;129(20):3816–31. doi: 10.1242/jcs.180372 27744317

29. van den Heuvel A, Mahfouz A, Kloet SL, Balog J, van Engelen BGM, Tawil R, et al. Single-cell RNA sequencing in facioscapulohumeral muscular dystrophy disease etiology and development. Hum Mol Genet. 2018 Nov 16;

30. Wallace LM, Garwick SE, Mei W, Belayew A, Coppee F, Ladner KJ, et al. DUX4, a candidate gene for facioscapulohumeral muscular dystrophy, causes p53-dependent myopathy in vivo. Ann Neurol. 2011 Mar 1;69(3):540–52. doi: 10.1002/ana.22275 21446026

31. Sadasivam S, DeCaprio JA. The DREAM complex: master coordinator of cell cycle-dependent gene expression. Nat Rev Cancer. 2013 Aug 11;13(8):585–95. doi: 10.1038/nrc3556 23842645

32. Kulakovskiy IV, Vorontsov IE, Yevshin IS, Sharipov RN, Fedorova AD, Rumynskiy EI, et al. HOCOMOCO: towards a complete collection of transcription factor binding models for human and mouse via large-scale ChIP-Seq analysis. Nucleic Acids Res. 2018 Jan 4;46(D1):D252–9. doi: 10.1093/nar/gkx1106 29140464

33. Saunders A, Huang X, Fidalgo M, Reimer MH, Faiola F, Ding J, et al. The SIN3A/HDAC Corepressor Complex Functionally Cooperates with NANOG to Promote Pluripotency. Cell Rep. 2017 Feb 14;18(7):1713–26. doi: 10.1016/j.celrep.2017.01.055 28199843

34. Campbell AE, Shadle SC, Jagannathan S, Lim J-W, Resnick R, Tawil R, et al. NuRD and CAF-1-mediated silencing of the D4Z4 array is modulated by DUX4-induced MBD3L proteins. Elife. 2018 Mar 13;7.

35. Fleming JD, Pavesi G, Benatti P, Imbriano C, Mantovani R, Struhl K. NF-Y coassociates with FOS at promoters, enhancers, repetitive elements, and inactive chromatin regions, and is stereo-positioned with growth-controlling transcription factors. Genome Res. 2013 Aug;23(8):1195–209. doi: 10.1101/gr.148080.112 23595228

36. Dubrez L. Regulation of E2F1 transcription factor by ubiquitin conjugation. Int J Mol Sci. 2017;18(10):1–9.

37. Feng Q, Snider L, Jagannathan S, Tawil R, van der Maarel SM, Tapscott SJ, et al. A feedback loop between nonsense-mediated decay and the retrogene DUX4 in facioscapulohumeral muscular dystrophy. Elife. 2015 Jan 7;2015(4).

38. Picelli S, Faridani OR, Björklund ÅK, Winberg G, Sagasser S, Sandberg R. Full-length RNA-seq from single cells using Smart-seq2. Nat Protoc. 2014;9(1):171–81. doi: 10.1038/nprot.2014.006 24385147

39. Library P, Data A. Illumina Bio-Rad SureCell TM WTA 3 ʹ Library Prep Kit for the ddSEQ TM System. 2017;(Pub. No. 1070-2016-014-C):5–8.

40. Kong X, Mohanty SK, Stephens J, Heale JT, Gomez-Godinez V, Shi LZ, et al. Comparative analysis of different laser systems to study cellular responses to DNA damage in mammalian cells. Nucleic Acids Res. 2009 May 1;37(9):e68–e68. doi: 10.1093/nar/gkp221 19357094

41. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013 Jan 1;29(1):15–21. doi: 10.1093/bioinformatics/bts635 23104886

42. Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics. 2011 Dec 4;12(1):323.

43. Robinson MD, McCarthy DJ, Smyth GK. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics. 2010 Jan 1;26(1):139–40. doi: 10.1093/bioinformatics/btp616 19910308

44. Romagnoli D, Boccalini G, Bonechi M, Biagioni C, Fassan P, Bertorelli R, et al. ddSeeker: a tool for processing Bio-Rad ddSEQ single cell RNA-seq data. BMC Genomics. 2018 Dec 24;19(1):960. doi: 10.1186/s12864-018-5249-x 30583719

45. Gu Z, Eils R, Schlesner M. Complex heatmaps reveal patterns and correlations in multidimensional genomic data. Bioinformatics. 2016 Sep 15;32(18):2847–9. doi: 10.1093/bioinformatics/btw313 27207943

46. Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018 May 2;36(5):411–20. doi: 10.1038/nbt.4096 29608179

47. Hafemeister C, Satija R. Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. bioRxiv. 2019 Mar 18;576827.

48. Zhou Y, Zhou B, Pache L, Chang M, Khodabakhshi AH, Tanaseichuk O, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun. 2019 Dec 3;10(1):1523. doi: 10.1038/s41467-019-09234-6 30944313

49. Jawaid W. enrichR: Provides an R Interface to “Enrichr.” R package version 2.1. 2019.

50. Hu H, Miao Y-R, Jia L-H, Yu Q-Y, Zhang Q, Guo A-Y. AnimalTFDB 3.0: a comprehensive resource for annotation and prediction of animal transcription factors. Nucleic Acids Res. 2019 Jan 8;47(D1):D33–8. doi: 10.1093/nar/gky822 30204897

51. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003 Nov 1;13(11):2498–504. doi: 10.1101/gr.1239303 14597658

52. Heinz S, Benner C, Spann N, Bertolino E, Lin YC, Laslo P, et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol Cell. 2010 May 28;38(4):576–89. doi: 10.1016/j.molcel.2010.05.004 20513432

53. Altschul SF, Wootton JC, Zaslavsky E, Yu Y-K. The Construction and Use of Log-Odds Substitution Scores for Multiple Sequence Alignment. Siepel A, editor. PLoS Comput Biol. 2010 Jul 15;6(7):e1000852. doi: 10.1371/journal.pcbi.1000852 20657661

Článek Accounting for long-range correlations in genome-wide simulations of large cohorts

Článek Rare protein-altering variants in ANGPTL7 lower intraocular pressure and protect against glaucoma

Článek Simultaneous SNP selection and adjustment for population structure in high dimensional prediction models

Článek Interplay between axonal Wnt5-Vang and dendritic Wnt5-Drl/Ryk signaling controls glomerular patterning in the Drosophila antennal lobe

Článek A new neuropeptide insect parathyroid hormone iPTH in the red flour beetle Tribolium castaneum

Článek An Out-of-Patagonia migration explains the worldwide diversity and distribution of Saccharomyces eubayanus lineages

Článek A cross-disorder PRS-pheWAS of 5 major psychiatric disorders in UK Biobank

Článek Depletion of Ric-8B leads to reduced mTORC2 activity

Článek A copy number variant is associated with a spectrum of pigmentation patterns in the rock pigeon (Columba livia)

Článek An osteocalcin-deficient mouse strain without endocrine abnormalities

Článek Osteocalcin is necessary for the alignment of apatite crystallites, but not glucose metabolism, testosterone synthesis, or muscle mass

Článek Beyond SNP heritability: Polygenicity and discoverability of phenotypes estimated with a univariate Gaussian mixture model