Evolutionary Constraint and Disease Associations of Post-Translational Modification Sites in Human Genomes

English version České info

Individual human genomes differ in numerous and infrequent small-scale changes such as single nucleotide variants. Understanding the biological role of variation and impact on phenotypes such as physical appearance or disease risk is an important challenge. We studied human variation of post-translational modification (PTM) sites spanning >11% of protein sequence. PTMs are chemical modifications of protein residues that extend protein functions and regulate many cellular processes. We found that PTM sites are specifically conserved among humans, indicating that these sequence regions are particularly important for human physiology. We confirm this observation by carefully studying other factors of genome variability, concluding that human PTM sites are broadly constrained in biological contexts. PTM sites are also significantly enriched in disease mutations, thus we can better understand disease genetics by analysing PTMs. We highlight 152 genes where disease mutations significantly accumulate in PTM regions, and integrate these with pharmacological information of PTM enzymes to predict new drug candidates to diseases. As an example, we propose a novel mechanism to PTPN11 mutations implicated in Noonan syndrome. This work aids understanding of the selective forces acting on protein-coding genome sequence and provides an integrative framework for predicting variant function in population and disease.

Vyšlo v časopise: Evolutionary Constraint and Disease Associations of Post-Translational Modification Sites in Human Genomes. PLoS Genet 11(1): e32767. doi:10.1371/journal.pgen.1004919
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1004919

Souhrn

Zdroje

1. Tennessen J.A., et al., Evolution and functional impact of rare coding variation from deep sequencing of human exomes. Science, 2012. 337(6090): p. 64–9. doi: 10.1126/science.1219240 22604720

2. 1000 Genomes Project Consortium, An integrated map of genetic variation from 1,092 human genomes. Nature, 2012. 491(7422): p. 56–65. doi: 10.1038/nature11632 23128226

3. MacArthur D.G., et al., Guidelines for investigating causality of sequence variants in human disease. Nature, 2014. 508(7497): p. 469–76. doi: 10.1038/nature13127 24759409

4. Gonzalez-Perez A., et al., Computational approaches to identify functional genetic variants in cancer genomes. Nat Methods, 2013. 10(8): p. 723–9. doi: 10.1038/nmeth.2562 23900255

5. Wang X., et al., Three-dimensional reconstruction of protein networks provides insight into human genetic disease. Nat Biotechnol, 2012. 30(2): p. 159–64. doi: 10.1038/nbt.2106 22252508

6. Pawson T., Protein modules and signalling networks. Nature, 1995. 373(6515): p. 573–80. 7531822

7. Lim W.A. and Pawson T., Phosphotyrosine signaling: evolving a new cellular communication system. Cell, 2010. 142(5): p. 661–7. doi: 10.1016/j.cell.2010.08.023 20813250

8. Pawson T. and Scott J.D., Protein phosphorylation in signaling—50 years and counting. Trends Biochem Sci, 2005. 30(6): p. 286–90.

9. Jenuwein T. and Allis C.D., Translating the histone code. Science, 2001. 293(5532): p. 1074–80.

10. Welchman R.L., Gordon C., and Mayer R.J., Ubiquitin and ubiquitin-like proteins as multifunctional signals. Nat Rev Mol Cell Biol, 2005. 6(8): p. 599–609. 16064136

11. Keshava Prasad T.S., et al., Human Protein Reference Database—2009 update. Nucleic Acids Res, 2009. 37(Database issue): p. D767–72. doi: 10.1093/nar/gkn892 18988627

12. Dinkel H., et al., Phospho.ELM: a database of phosphorylation sites—update 2011. Nucleic Acids Res, 2011. 39(Database issue): p. D261–7. doi: 10.1093/nar/gkq1104 21062810

13. Hornbeck P.V., et al., PhosphoSitePlus: a comprehensive resource for investigating the structure and function of experimentally determined post-translational modifications in man and mouse. Nucleic acids research, 2012. 40(Database issue): p. D261–70. doi: 10.1093/nar/gkr1122 22135298

14. Lukk M., et al., A global map of human gene expression. Nat Biotechnol, 2010. 28(4): p. 322–4.

15. Karolchik D., et al., The UCSC Genome Browser database: 2014 update. Nucleic Acids Res, 2014. 42(Database issue): p. D764–70. doi: 10.1093/nar/gkt1168 24270787

16. Ward J.J., et al., The DISOPRED server for the prediction of protein disorder. Bioinformatics, 2004. 20(13): p. 2138–9. 15044227

17. Howie B.N., Donnelly P., and Marchini J., A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS genetics, 2009. 5(6): p. e1000529. doi: 10.1371/journal.pgen.1000529 19543373

18. Stenson P.D., et al., The Human Gene Mutation Database: building a comprehensive mutation repository for clinical and molecular genetics, diagnostic testing and personalized genomic medicine. Hum Genet, 2014. 133(1): p. 1–9.

19. Reimand J. and Bader G.D., Systematic analysis of somatic mutations in phosphorylation signaling predicts novel cancer drivers. Mol Syst Biol, 2013. 9: p. 637. doi: 10.1038/msb.2012.68 23340843

20. Li, S., et al., Loss of post-translational modification sites in disease. Pac Symp Biocomput, 2010: p. 337–47.

21. Radivojac P., et al., Gain and loss of phosphorylation sites in human cancer. Bioinformatics, 2008. 24(16): p. i241–7. doi: 10.1093/bioinformatics/btn267 18689832

22. Siepel A., Pollard K.S., and Haussler D., New methods for detecting lineage-specific selection, in Proceedings of the 10th annual international conference on Research in Computational Molecular Biology2006, Springer-Verlag: Venice, Italy. p. 190–205.

23. Kumar P., Henikoff S., and Ng P.C., Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nature protocols, 2009. 4(7): p. 1073–81. doi: 10.1038/nprot.2009.86 19561590

24. Adzhubei I.A., et al., A method and server for predicting damaging missense mutations. Nat Methods, 2010. 7(4): p. 248–9. doi: 10.1038/nmeth0410-248 20354512

25. Chun S. and Fay J.C., Identification of deleterious mutations within three human genomes. Genome research, 2009. 19(9): p. 1553–61. doi: 10.1101/gr.092619.109 19602639

26. Schwarz J.M., et al., MutationTaster evaluates disease-causing potential of sequence alterations. Nat Methods, 2010. 7(8): p. 575–6. doi: 10.1038/nmeth0810-575 20676075

27. Kircher M., et al., A general framework for estimating the relative pathogenicity of human genetic variants. Nat Genet, 2014. 46(3): p. 310–5. doi: 10.1038/ng.2892 24487276

28. Reimand J., Wagih O., and Bader G.D., The mutational landscape of phosphorylation signaling in cancer. Sci Rep, 2013. 3: p. 2651. doi: 10.1038/srep02651 24089029

29. Iakoucheva L.M., et al., Intrinsic disorder in cell-signaling and cancer-associated proteins. J Mol Biol, 2002. 323(3): p. 573–84.

30. Buljan M., et al., Tissue-specific splicing of disordered segments that embed binding motifs rewires protein interaction networks. Mol Cell, 2012. 46(6): p. 871–83. doi: 10.1016/j.molcel.2012.05.039 22749400

31. Uhlen M., et al., Towards a knowledge-based Human Protein Atlas. Nat Biotechnol, 2010. 28(12): p. 1248–50. doi: 10.1038/nbt1210-1248 21139605

32. Brawand D., et al., The evolution of gene expression levels in mammalian organs. Nature, 2011. 478(7369): p. 343–8.

33. Knox K. and Baker J.C., Genomic evolution of the placenta using co-option and duplication and divergence. Genome Res, 2008. 18(5): p. 695–705. doi: 10.1101/gr.071407.107 18340042

34. Koivomagi M., et al., Cascades of multisite phosphorylation control Sic1 destruction at the onset of S phase. Nature, 2011. 480(7375): p. 128–31. doi: 10.1038/nature10560 21993622

35. Manning G., et al., The protein kinase complement of the human genome. Science, 2002. 298(5600): p. 1912–34. 12471243

36. Miller M.L., et al., Linear motif atlas for phosphorylation-dependent signaling. Sci Signal, 2008. 1(35): p. ra2. doi: 10.1126/scisignal.1159433 18765831

37. Miller M.L., et al., Linear motif atlas for phosphorylation-dependent signaling. Science signaling, 2008. 1(35): p. ra2. doi: 10.1126/scisignal.1159433 18765831

38. Higgins M.E., et al., CancerGenes: a gene selection resource for cancer genome projects. Nucleic Acids Res, 2007. 35(Database issue): p. D721–6. 17088289

39. Alonso A., et al., Hyperphosphorylation induces self-assembly of tau into tangles of paired helical filaments/straight filaments. Proc Natl Acad Sci U S A, 2001. 98(12): p. 6923–8. 11381127

40. Tartaglia M., et al., Mutations in PTPN11, encoding the protein tyrosine phosphatase SHP-2, cause Noonan syndrome. Nat Genet, 2001. 29(4): p. 465–8.

41. Keilhack H., et al., Diverse biochemical properties of Shp2 mutants. Implications for disease phenotypes. J Biol Chem, 2005. 280(35): p. 30984–93. 15987685

42. Rush J., et al., Immunoaffinity profiling of tyrosine phosphorylation in cancer cells. Nat Biotechnol, 2005. 23(1): p. 94–101. 15592455

43. Couture C., et al., Regulation of the Lck SH2 domain by tyrosine phosphorylation. J Biol Chem, 1996. 271(40): p. 24880–4. 8798764

44. Zhang J., Yang P.L., and Gray N.S., Targeting cancer with small molecule kinase inhibitors. Nat Rev Cancer, 2009. 9(1): p. 28–39.

45. Dokmanovic M., Clarke C., and Marks P.A., Histone deacetylase inhibitors: overview and perspectives. Mol Cancer Res, 2007. 5(10): p. 981–9. 17951399

46. Law V., et al., DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res, 2014. 42(Database issue): p. D1091–7.

47. Moses A.M., et al., Regulatory evolution in proteins by turnover and lineage-specific changes of cyclin-dependent kinase consensus sites. Proceedings of the National Academy of Sciences of the United States of America, 2007. 104(45): p. 17713–8. 17978194

48. Tan C.S., et al., Comparative analysis reveals conserved protein phosphorylation networks implicated in multiple diseases. Science signaling, 2009. 2(81): p. ra39. doi: 10.1126/scisignal.2000316 19638616

49. Schuster-Bockler B. and Lehner B., Chromatin organization is a major influence on regional mutation rates in human cancer cells. Nature, 2012. 488(7412): p. 504–7. doi: 10.1038/nature11273 22820252

50. Stergachis A.B., et al., Exonic transcription factor binding directs codon choice and affects protein evolution. Science, 2013. 342(6164): p. 1367–72. doi: 10.1126/science.1243490 24337295

51. Montecchi-Palazzi L., et al., The PSI-MOD community standard for representation of protein modification data. Nature biotechnology, 2008. 26(8): p. 864–6. doi: 10.1038/nbt0808-864 18688235

52. Mann M. and Jensen O.N., Proteomic analysis of post-translational modifications. Nature biotechnology, 2003. 21(3): p. 255–61.

53. Rapoport T.A., Protein translocation across the eukaryotic endoplasmic reticulum and bacterial plasma membranes. Nature, 2007. 450(7170): p. 663–9. 18046402

54. Khurana E., et al., Integrative annotation of variants from 1092 humans: application to cancer genomics. Science, 2013. 342(6154): p. 1235587. doi: 10.1126/science.1235587 24092746

55. Reimand J., Arak T., and Vilo J., g:Profiler—a web server for functional interpretation of gene lists (2011 update). Nucleic Acids Res, 2011. 39(Web Server issue): p. W307–15. doi: 10.1093/nar/gkr378 21646343

56. Li H., et al., The Sequence Alignment/Map format and SAMtools. Bioinformatics, 2009. 25(16): p. 2078–9. doi: 10.1093/bioinformatics/btp352 19505943

57. Wang K., Li M., and Hakonarson H., ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic acids research, 2010. 38(16): p. e164. doi: 10.1093/nar/gkq603 20601685

58. Liu X., Jian X., and Boerwinkle E., dbNSFP: a lightweight database of human nonsynonymous SNPs and their functional predictions. Human mutation, 2011. 32(8): p. 894–9. doi: 10.1002/humu.21517 21520341

59. Flicek P., et al., Ensembl 2014. Nucleic Acids Res, 2014. 42(Database issue): p. D749–55.

60. NCBI BLAST, BLAST substitution matrices, http://blast.ncbi.nlm.nih.gov/html/sub_matrix.html.

61. Ashburner M., et al., Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet, 2000. 25(1): p. 25–9. 10802651

62. Croft D., et al., The Reactome pathway knowledgebase. Nucleic Acids Res, 2014. 42(Database issue): p. D472–7.

63. Kanehisa M., et al., KEGG for integration and interpretation of large-scale molecular data sets. Nucleic Acids Res, 2012. 40(Database issue): p. D109–14. doi: 10.1093/nar/gkr988 22080510

64. Ruepp A., et al., CORUM: the comprehensive resource of mammalian protein complexes—2009. Nucleic Acids Res, 2010. 38(Database issue): p. D497–501.

65. Merico D., et al., Enrichment map: a network-based method for gene-set enrichment visualization and interpretation. PLoS One, 2010. 5(11): p. e13984. doi: 10.1371/journal.pone.0013984 21085593

66. Cline M.S., et al., Integration of biological networks and gene expression data using Cytoscape. Nat Protoc, 2007. 2(10): p. 2366–82. 17947979

67. Crooks G.E., et al., WebLogo: a sequence logo generator. Genome Res, 2004. 14(6): p. 1188–90.

68. Futreal P.A., et al., A census of human cancer genes. Nat Rev Cancer, 2004. 4(3): p. 177–83.

69. Vogelstein B. and Kinzler K.W., Cancer genes and the pathways they control. Nat Med, 2004. 10(8): p. 789–99.

70. Mitelman F., Recurrent chromosome aberrations in cancer. Mutat Res, 2000. 462(2–3): p. 247–53.

71. Hahn W.C. and Weinberg R.A., Modelling the molecular circuitry of cancer. Nat Rev Cancer, 2002. 2(5): p. 331–41.