Quantitative analysis questions the role of MeCP2 as a global regulator of alternative splicing


Autoři: Kashyap Chhatbar aff001;  Justyna Cholewa-Waclaw aff002;  Ruth Shah aff002;  Adrian Bird aff002;  Guido Sanguinetti aff001
Působiště autorů: School of Informatics, University of Edinburgh, Edinburgh, United Kingdom aff001;  The Wellcome Centre for Cell Biology, University of Edinburgh, Edinburgh, United Kingdom aff002;  International School for Advanced Studies (SISSA), Trieste, Italy aff003
Vyšlo v časopise: Quantitative analysis questions the role of MeCP2 as a global regulator of alternative splicing. PLoS Genet 16(10): e32767. doi:10.1371/journal.pgen.1009087
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pgen.1009087

Souhrn

MeCP2 is an abundant protein in mature nerve cells, where it binds to DNA sequences containing methylated cytosine. Mutations in the MECP2 gene cause the severe neurological disorder Rett syndrome (RTT), provoking intensive study of the underlying molecular mechanisms. Multiple functions have been proposed, one of which involves a regulatory role in splicing. Here we leverage the recent availability of high-quality transcriptomic data sets to probe quantitatively the potential influence of MeCP2 on alternative splicing. Using a variety of machine learning approaches that can capture both linear and non-linear associations, we show that widely different levels of MeCP2 have a minimal effect on alternative splicing in three different systems. Alternative splicing was also apparently indifferent to developmental changes in DNA methylation levels. Our results suggest that regulation of splicing is not a major function of MeCP2. They also highlight the importance of multi-variate quantitative analyses in the formulation of biological hypotheses.

Klíčová slova:

Alternative splicing – Developmental neuroscience – DNA methylation – Gene expression – Gene regulation – Neurons – RNA sequencing – RNA splicing


Zdroje

1. Guy J, Cheval H, Selfridge J, Bird A. The Role of MeCP2 in the Brain. Annual Review of Cell and Developmental Biology. 2011;27(1):631–652.

2. Tillotson R, Bird A. The Molecular Basis of MeCP2 Function in the Brain. Journal of Molecular Biology. 2020;432(6):1602–1623. doi: 10.1016/j.jmb.2019.10.004

3. Lavery LA, Zoghbi HY. The distinct methylation landscape of maturing neurons and its role in Rett syndrome pathogenesis. Current Opinion in Neurobiology. 2019;59:180–188. doi: 10.1016/j.conb.2019.08.001

4. Amir RE, Veyver IBVd, Wan M, Tran CQ, Francke U, Zoghbi HY. Rett syndrome is caused by mutations in X-linked MECP2, encoding methyl-CpG-binding protein 2 Nature Genetics. 1999;23(2):185–188. doi: 10.1038/13810

5. Hendrich B, Bird A. Identification and Characterization of a Family of Mammalian Methyl-CpG Binding Proteins Molecular and Cellular Biology. 1998;18(11):6538–6547. doi: 10.1128/MCB.18.11.6538

6. Lagger S, Connelly JC, Schweikert G, Webb S, Selfridge J, Ramsahoye BH, et al. MeCP2 recognizes cytosine methylated tri-nucleotide and di-nucleotide sequences to tune transcription in the mammalian brain. PLOS Genetics. 2017;13(5):e1006793. doi: 10.1371/journal.pgen.1006793

7. Lister R, Mukamel EA, Nery JR, Urich M, Puddifoot CA, Johnson ND, et al. Global Epigenomic Reconfiguration During Mammalian Brain Development. Science. 2013;341(6146). doi: 10.1126/science.1237905

8. Varley KE, Gertz J, Bowling KM, Parker SL, Reddy TE, Pauli-Behn F, et al. Dynamic DNA methylation across diverse human cell lines and tissues. Genome Research. 2013;23(3):555–567. doi: 10.1101/gr.147942.112

9. Cholewa-Waclaw J, Shah R, Webb S, Chhatbar K, Ramsahoye B, Pusch O, et al. Quantitative modelling predicts the impact of DNA methylation on RNA polymerase II traffic. Proceedings of the National Academy of Sciences. 2019;116(30):14995–15000. doi: 10.1073/pnas.1903549116

10. Gabel HW, Kinde B, Stroud H, Gilbert CS, Harmin DA, Kastan NR, et al. Disruption of DNA-methylation-dependent long gene repression in Rett syndrome. Nature. 2015;522(7554):89–93. doi: 10.1038/nature14319

11. Kinde B, Wu DY, Greenberg ME, Gabel HW. DNA methylation in the gene body influences MeCP2-mediated gene repression. Proceedings of the National Academy of Sciences. 2016;113(52):15114–15119. doi: 10.1073/pnas.1618737114

12. Lyst MJ, Bird A. Rett syndrome: a complex disorder with simple roots. Nature Reviews Genetics. 2015;16(5):261–275. doi: 10.1038/nrg3897

13. Young JI, Hong EP, Castle JC, Crespo-Barreto J, Bowman AB, Rose MF, et al. Regulation of RNA splicing by the methylation-dependent transcriptional repressor methyl-CpG binding protein 2. Proceedings of the National Academy of Sciences. 2005;102(49):17551–17558. doi: 10.1073/pnas.0507856102

14. Wong JJL, Gao D, Nguyen TV, Kwok CT, van Geldermalsen M, Middleton R, et al. Intron retention is regulated by altered MeCP2-mediated splicing factor recruitment. Nature Communications. 2017;8(1):15134. doi: 10.1038/ncomms15134

15. Aslanzadeh V, Huang Y, Sanguinetti G, Beggs JD. Transcription rate strongly affects splicing fidelity and cotranscriptionality in budding yeast. Genome Research. 2018;28(2):203–213. doi: 10.1101/gr.225615.117

16. Lev Maor G, Yearim A, Ast G. The alternative role of DNA methylation in splicing regulation. Trends in Genetics. 2015;31(5):274–280. doi: 10.1016/j.tig.2015.03.002

17. Boxer LD, Renthal W, Greben AW, Whitwam T, Silberfeld A, Stroud H, et al. MeCP2 Represses the Rate of Transcriptional Initiation of Highly Methylated Long Genes Molecular Cell. 2020;77(2):294–309.e9. doi: 10.1016/j.molcel.2019.10.032

18. Yearim A, Gelfman S, Shayevitch R, Melcer S, Glaich O, Mallm JP, et al. HP1 Is Involved in Regulating the Global Impact of DNA Methylation on Alternative Splicing. Cell Reports. 2015;10(7):1122–1134. doi: 10.1016/j.celrep.2015.01.038

19. Domcke S, Bardet AF, Adrian Ginno P, Hartl D, Burger L, Schübeler D. Competition between DNA methylation and transcription factors determines binding of NRF1 Nature. 2015;528(7583):575–579. doi: 10.1038/nature16462

20. Stroud H, Su SC, Hrvatin S, Greben AW, Renthal W, Boxer LD, et al. Early-Life Gene Expression in Neurons Modulates Lasting Epigenetic States. Cell. 2017;171(5):1151–1164.e16. doi: 10.1016/j.cell.2017.09.047

21. He Y, Ecker JR. Non-CG Methylation in the Human Genome. Annual Review of Genomics and Human Genetics. 2015;16(1):55–77. doi: 10.1146/annurev-genom-090413-025437

22. Huang Y, Sanguinetti G. BRIE: transcriptome-wide splicing quantification in single cells. Genome Biology. 2017;18(1):123. doi: 10.1186/s13059-017-1248-5

23. Wong JL, Ritchie W, Ebner O, Selbach M, Wong JH, Huang Y, et al. Orchestrated Intron Retention Regulates Normal Granulocyte Differentiation. Cell. 2013;154(3):583–595. doi: 10.1016/j.cell.2013.06.052

24. Baralle FE, Giudice J. Alternative splicing as a regulator of development and tissue identity. Nature Reviews Molecular Cell Biology. 2017;18(7):437–451. doi: 10.1038/nrm.2017.27

25. Barash Y, Calarco JA, Gao W, Pan Q, Wang X, Shai O, et al. Deciphering the splicing code. Nature. 2010;465(7294):53–59. doi: 10.1038/nature09000

26. Xiong HY, Alipanahi B, Lee LJ, Bretschneider H, Merico D, Yuen RKC, et al. The human splicing code reveals new insights into the genetic determinants of disease Science. 2015;347(6218). doi: 10.1126/science.1254806

27. Katz Y, Wang ET, Airoldi EM, Burge CB. Analysis and design of RNA sequencing experiments for identifying isoform regulation. Nature Methods. 2010;7(12):1009–1015. doi: 10.1038/nmeth.1528

28. Ross PD, Guy J, Selfridge J, Kamal B, Bahey N, Tanner KE, et al. Exclusive expression of MeCP2 in the nervous system distinguishes between brain and peripheral Rett syndrome-like phenotypes Human Molecular Genetics. 2016;25(20):4389–4404. doi: 10.1093/hmg/ddw269

29. Kim D, Paggi JM, Park C, Bennett C, Salzberg SL. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nature Biotechnology. 2019;37(8):907–915. doi: 10.1038/s41587-019-0201-4

30. Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, Baren MJv, et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nature Biotechnology. 2010;28(5):511–515. doi: 10.1038/nbt.1621

31. Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30(7):923–930. doi: 10.1093/bioinformatics/btt656

32. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology. 2014;15(12):550. doi: 10.1186/s13059-014-0550-8

33. Kent WJ, Zweig AS, Barber G, Hinrichs AS, Karolchik D. BigWig and BigBed: enabling browsing of large distributed datasets. Bioinformatics. 2010;26(17):2204–2207. doi: 10.1093/bioinformatics/btq351

34. MacKay DJC. Bayesian Interpolation. Neural Computation. 1992;4(3):415–447. doi: 10.1162/neco.1992.4.3.415

35. Tipping ME. Sparse Bayesian Learning and the Relevance Vector Machine. Journal of Machine Learning Research. 2001;1(Jun):211–244.

36. Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research. 2011;12(85):2825–2830.

37. Breiman L. Random Forests. Machine Learning. 2001;45(1):5–32. doi: 10.1023/A:1010933404324


Článek vyšel v časopise

PLOS Genetics


2020 Číslo 10
Nejčtenější tento týden
Nejčtenější v tomto čísle
Kurzy

Zvyšte si kvalifikaci online z pohodlí domova

plice
INSIGHTS from European Respiratory Congress
nový kurz

Současné pohledy na riziko v parodontologii
Autoři: MUDr. Ladislav Korábek, CSc., MBA

Svět praktické medicíny 3/2024 (znalostní test z časopisu)

Kardiologické projevy hypereozinofilií
Autoři: prof. MUDr. Petr Němec, Ph.D.

Střevní příprava před kolonoskopií
Autoři: MUDr. Klára Kmochová, Ph.D.

Všechny kurzy
Kurzy Podcasty Doporučená témata Časopisy
Přihlášení
Zapomenuté heslo

Zadejte e-mailovou adresu, se kterou jste vytvářel(a) účet, budou Vám na ni zaslány informace k nastavení nového hesla.

Přihlášení

Nemáte účet?  Registrujte se