Interactome of the Autoimmune Risk Protein ANKRD55

Nerea Ugidos

Jorge Mena

Sara Baquero

Iraide Alloza

Mikel Azkargorta

Felix Elortza

Koen Vandenbroeck

Click here for additional data file.^{(6.1M, docx)}

^{Neurogenomiks Group, Department of Neuroscience, University of the Basque Country (UPV/EHU), Leioa, Spain}

^{Achucarro Basque Center for Neuroscience, Leioa, Spain}

^{Proteomics Platform, CIC bioGUNE, CIBERehd, ProteoRed-ISCIII, Derio, Spain}

^{IKERBASQUE, Basque Foundation for Science, Bilbao, Spain}

Edited by: Susan Boackle, University of Colorado Denver, United States

Reviewed by: Maria I. Bokarewa, University of Gothenburg, Sweden; Jillian M. Richmond, University of Massachusetts Medical School, United States

*Correspondence: Koen Vandenbroeck gro.euqsabreki@kceorbnednav.k

This article was submitted to Autoimmune and Autoinflammatory Disorders, a section of the journal Frontiers in Immunology

†These authors shared first authorship

Edited by: Susan Boackle, University of Colorado Denver, United States

Reviewed by: Maria I. Bokarewa, University of Gothenburg, Sweden; Jillian M. Richmond, University of Massachusetts Medical School, United States

Received 2019 Mar 12; Accepted 2019 Aug 15.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

Abstract

The ankyrin repeat domain-55 (ANKRD55) gene contains intronic single nucleotide polymorphisms (SNPs) associated with risk to contract multiple sclerosis, rheumatoid arthritis or other autoimmune disorders. Risk alleles of these SNPs are associated with higher levels of ANKRD55 in CD4^{T cells. The biological function of ANKRD55 is unknown, but given that ankyrin repeat domains constitute one of the most common protein-protein interaction platforms in nature, it is likely to function in complex with other proteins. Thus, identification of its protein interactomes may provide clues. We identified ANKRD55 interactomes via recombinant overexpression in HEK293 or HeLa cells and mass spectrometry. One hundred forty-eight specifically interacting proteins were found in total protein extracts and 22 in extracts of sucrose gradient-purified nuclei. Bioinformatic analysis suggested that the ANKRD55-protein partners from total protein extracts were related to nucleotide and ATP binding, enriched in nuclear transport terms and associated with cell cycle and RNA, lipid and amino acid metabolism. The enrichment analysis of the ANKRD55-protein partners from nuclear extracts is related to sumoylation, RNA binding, processes associated with cell cycle, RNA transport, nucleotide and ATP binding. The interaction between overexpressed ANKRD55 isoform 001 and endogenous RPS3, the cohesins SMC1A and SMC3, CLTC, PRKDC, VIM, β-tubulin isoforms, and 14-3-3 isoforms were validated by western blot, reverse immunoprecipitaton and/or confocal microscopy. We also identified three phosphorylation sites in ANKRD55, with S436 exhibiting the highest score as likely 14-3-3 binding phosphosite. Our study suggests that ANKRD55 may exert function(s) in the formation or architecture of multiple protein complexes, and is regulated by (de)phosphorylation reactions. Based on interactome and subcellular localization analysis, ANKRD55 is likely transported into the nucleus by the classical nuclear import pathway and is involved in mitosis, probably via effects associated with mitotic spindle dynamics.}

Keywords: ANKRD55, ankyrin repeat, autoimmune, multiple sclerosis, rheumatoid arthritis

Abstract

^{http://interactome.baderlab.org/}

Funding. This work was supported by the following grants to KV: Grupos de Investigación (IT512-10, PPG17/44) and MINECO (SAF2016-74891R). NU is recipient of a predoctoral studentship from the Gobierno Vasco (Reference PRE-2013-1-891). CIC bioGUNE is accredited with the Severo Ochoa Excellence award by the Spanish Ministerio de Economía y Competitividad, MINECO (SEV-2016-0644).

Click here for additional data file.^{(6.1M, docx)}

References

1. Stahl EA, Raychaudhuri S, Remmers EF, Xie G, Eyre S, Thomson BP, et al. . Genome-wide association study meta-analysis identifies seven new rheumatoid arthritis risk loci. Nat Genet. (2010) 42:508–14. 10.1038/ng.582 ] [
2. Okada Y, Wu D, Trynka G, Raj T, Terao C, Ikari K, et al. . Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature. (2014) 506:376–81. 823 10.1038/nature12873 ] [
3. Eyre S, Bowes J, Diogo D, Lee A, Barton A, Martin P, et al. . High-density genetic mapping identifies new susceptibility loci for rheumatoid arthritis. Nat Genet. (2012) 44:1336–40. 10.1038/ng.2462 ] [
4. Alloza I, Otaegui D, de Lapuente AL, Antigüedad A, Varadé J, Núñez C, et al. . ANKRD55 and DHCR7 are novel multiple sclerosis risk loci. Genes Immun. (2012) 13:253–7. 10.1038/gene.2011.81 [] [[PubMed]
5. Lill CM, Schjeide BM, Graetz C, Liu T, Damotte V, Akkad DA. Genome-wide significant association of ANKRD55 rs6859219 and multiple sclerosis risk. J Med Genet. (2013) 50:140–3. 10.1136/jmedgenet-2012-101411 [] [[PubMed]
6. Beecham AH, Patsopoulos NA, Xifara DK, Davis MF, Kemppinen A, Cotsapas C, et al. . Analysis of immune-related loci identifies 48 new susceptibility variants for multiple sclerosis. Nat Genet. (2013) 45:1353–60. 10.1038/ng.2770 ] [
7. Jostins L, Ripke S, Weersma RK, Duerr RH, McGovern DP, Hui KY, et al. . Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature. (2012) 491:119–24. 10.1038/nature11582 ] [
8. Liu JZ, Van Sommeren S, Huang H, Ng SC, Alberts R, Takahashi A, et al. . Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nat Genet. (2015) 47:979–86. 10.1038/ng.3359 ] [
9. Fortune MD, Guo H, Burren O, Schofield E, Walker NM, Ban M, et al Statistical colocalization of genetic risk variants for related autoimmune diseases in the context of common controls. Nat Genet. (2015) 47:839–46. 10.1038/ng.3330 ] [[Google Scholar]
10. Hinks A, Cobb J, Marion MC, Prahalad S, Sudman M, Bowes J, et al. . Dense genotyping of immune-related disease regions identifies 14 new susceptibility loci for juvenile idiopathic arthritis. Nat Genet. (2013) 45:664–9. 10.1038/ng.2614 ] [
11. Zhernakova A, Stahl EA, Trynka G, Raychaudhuri S, Festen EA, Franke L, et al. . Meta-analysis of genome-wide association studies in celiac disease and rheumatoid arthritis identifies fourteen non-HLA shared loci. PLoS Genet. (2011) 7:e1002004. 10.1371/journal.pgen.1002004 ] [
12. Li L, Chen S, Wen X, Wang Q, Lv G, Li J, et al. . Positive association between ankrd55 polymorphism 7731626 and dermatomyositis/polymyositis with interstitial lung disease in Chinese han population. Biomed Res Int. (2017) 2017:2905987. 10.1155/2017/2905987 ] [
13. Houtman M, Ekholm L, Hesselberg E, Chemin K, Malmström V, Reed AM, et al. . T-cell transcriptomics from peripheral blood highlights differences between polymyositis and dermatomyositis patients. Arthritis Res Ther. (2018) 20:188. 10.1186/s13075-018-1688-7 ] [
14. Stein MB, Chen CY, Ursano RJ, Cai T, Gelernter J, Heeringa SG, et al. . Genome-wide association studies of posttraumatic stress disorder in 2 cohorts of US Army Soldiers. JAMA Psychiatry. (2016) 73:695–704. 10.1001/jamapsychiatry.2016.0350 ] [
15. Sherva R, Tripodis Y, Bennett DA, Chibnik LB, Crane PK, De Jager PL, et al. . Genome-wide association study of the rate of cognitive decline in Alzheimer's disease. Alzheimer's Dement. (2014) 10:45–52. 10.1016/j.jalz.2013.01.008 ] [
16. Morris AP, Voight BF, Teslovich TM, Ferreira T, Segrè A V, Steinthorsdottir V, et al. . Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat Genet. (2012) 44:981–90. 10.1038/ng.2383 ] [
17. Imamura M, Takahashi A, Yamauchi T, Hara K, Yasuda K, Grarup N, et al. . Genome-wide association studies in the Japanese population identify seven novel loci for type 2 diabetes. Nat Commun. (2016) 7:10531. 10.1038/ncomms10531 ] [
18. Lopez de Lapuente A, Feliú A, Ugidos N, Mecha M, Mena J, Astobiza I, et al. . Novel insights into the multiple sclerosis risk gene ANKRD55. J Immunol. (2016) 196:4553–65. 10.4049/jimmunol.1501205 [] [[PubMed]
19. James T, Lindén M, Morikawa H, Fernandes SJ, Ruhrmann S, Huss M, et al. . Impact of genetic risk loci for multiple sclerosis on expression of proximal genes in patients. Hum Mol Genet. (2018) 27:912–28. 10.1093/hmg/ddy001 [] [[PubMed]
20. Zhu Z, Zhang F, Hu H, Bakshi A, Robinson MR, Powell JE, et al. . Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat Genet. (2016) 48:481–7. 10.1038/ng.3538 [] [[PubMed]
21. Mosavi LK, Minor DL, Peng ZY. Consensus-derived structural determinants of the ankyrin repeat motif. Proc Natl Acad Sci USA. (2002) 99:16029–34. 10.1073/pnas.252537899 ] [
22. Mosavi LK, Cammett TJ, Desrosiers DC, Peng Z. The ankyrin repeat as molecular architecture for protein recognition. Protein Sci. (2004) 13:1435–48. 10.1110/ps.03554604 ] [
23. Bork P. Hundreds of ankyrin-like repeats in functionally diverse proteins: mobile modules that cross phyla horizontally?Proteins. (1993) 17:363–74. 10.1002/prot.340170405 [] [[PubMed]
24. Bennett V, Baines AJ. Spectrin and ankyrin-based pathways: metazoan inventions for integrating cells into tissues. Physiol Rev. (2001) 81:1353–92. 10.1152/physrev.2001.81.3.1353 [] [[PubMed]
25. Byeon IJL, Li J, Ericson K, Selby TL, Tevelev A, Kim HJ, et al. . Tumor suppressor p16INK4A: determination of solution structure and analyses of its interaction with cyclin-dependent kinase 4. Mol Cell. (1998) 1:421–31. 10.1016/S1097-2765(00)80042-8 [] [[PubMed]
26. Tevelev A, Byeon IJ, Selby T, Ericson K, Kim HJ, Kraynov V, et al. . Tumor suppressor p16INK4A: structural characterization of wild-type and mutant proteins by NMR and circular dichroism. Biochemistry. (1996) 35:9475–87. 10.1021/bi960211+ [] [[PubMed]
27. Li J, Mahajan A, Tsai MD. Ankyrin repeat: a unique motif mediating protein-protein interactions. Biochemistry. (2006) 45:15168–78. 10.1021/bi062188q [] [[PubMed]
28. Suzuki F, Goto M, Sawa C, Ito S, Watanabe H, Sawada JI, et al. . Functional interactions of transcription factor human GA-binding protein subunits. J Biol Chem. (1998) 273:29302–8. 10.1074/jbc.273.45.29302 [] [[PubMed]
29. Holden P, Horton W. Crude subcellular fractionation of cultured mammalian cell lines. BMC Res Notes. (2009) 2:243. 10.1186/1756-0500-2-243 ] [
30. Greenberg ME, Bender TP. Identification of newly transcribed RNA. Curr Protoc Mol Biol. (2007) 78:410.1–12. 10.1002/0471142727.mb0410s78 [] [[PubMed]
31. Blein-Nicolas M, Zivy M. Thousand and one ways to quantify and compare protein abundances in label-free bottom-up proteomics. Biochim Biophys Acta. (2016) 1864:883–95. 10.1016/j.bbapap.2016.02.019 [] [[PubMed]
32. Zybailov B, Mosley AL, Sardiu ME, Coleman MK, Florens L, Washburn MP. Statistical analysis of membrane proteome expression changes in Saccharomyces cerevisiae. J Proteome Res. (2006) 5:2339–47. 10.1021/pr060161n [] [[PubMed]
33. Rigbolt KTG, Prokhorova TA, Akimov V, Henningsen J, Johansen PT, Kratchmarova I, et al. . System-wide temporal characterization of the proteome and phosphoproteome of human embryonic stem cell differentiation. Sci Signal. (2011) 4:rs3. 10.1126/scisignal.2001570 [] [[PubMed]
34. Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. (2009) 4:44–57. 10.1038/nprot.2008.211 [] [[PubMed]
35. Hornbeck P V, Zhang B, Murray B, Kornhauser JM, Latham V, Skrzypek E. PhosphoSitePlus, 2014: Mutations, PTMs and recalibrations. Nucleic Acids Res. (2015) 43:D512–20. 10.1093/nar/gku1267 ] [
36. Ullah S, Lin S, Xu Y, Deng W, Ma L, Zhang Y, et al. . DbPAF: An integrative database of protein phosphorylation in animals and fungi. Sci Rep. (2016) 6:23534. 10.1038/srep23534 ] [
37. Madeira F, Tinti M, Murugesan G, Berrett E, Stafford M, Toth R, et al. . 14-3-3-Pred: improved methods to predict 14-3-3-binding phosphopeptides. Bioinformatics. (2015) 31:2276–83. 10.1093/bioinformatics/btv133 ] [
38. Yan J, Kurgan L. DRNApred, fast sequence-based method that accurately predicts and discriminates DNA-and RNA-binding residues. Nucleic Acids Res. (2017) 45:e84. 10.1093/nar/gkx059 ] [
39. Chen K, Mizianty MJ, Kurgan L. Prediction and analysis of nucleotide-binding residues using sequence and sequence-derived structural descriptors. Bioinformatics. (2012) 28:331–41. 10.1093/bioinformatics/btr657 [] [[PubMed]
40. Chatr-Aryamontri A, Breitkreutz BJ, Oughtred R, Boucher L, Heinicke S, Chen D, et al. . The BioGRID interaction database: 2015 update. Nucleic Acids Res. (2015) 43:D470–8. 10.1093/nar/gku1204 ] [
41. Szklarczyk D, Franceschini A, Wyder S, Forslund K, Heller D, Huerta-Cepas J, et al. . STRING v10: protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. (2015) 43:D447–52. 10.1093/nar/gku1003 ] [
42. Huttlin EL, Ting L, Bruckner RJ, Gebreab F, Gygi MP, Szpyt J, et al. . The BioPlex Network: a systematic exploration of the human interactome. Cell. (2015) 162:425–40. 10.1016/j.cell.2015.06.043 ] [
43. Huttlin EL, Bruckner RJ, Paulo JA, Cannon JR, Ting L, Baltier K, et al. . Architecture of the human interactome defines protein communities and disease networks. Nature. (2017) 545:505–9. 10.1038/nature22366 ] [
44. Cockman ME, Lancaster DE, Stolze IP, Hewitson KS, Mcdonough MA, Coleman ML, et al. . Posttranslational hydroxylation of ankyrin repeats in IkappaB proteins by the hypoxia-inducible factor (HIF) asparaginyl hydroxylase, factor inhibiting HIF (FIH). Proc Natl Acad Sci USA. (2006) 103:14767–72. 10.1073/pnas.0606877103 ] [
45. Drew K, Lee C, Huizar RL, Tu F, Borgeson B, McWhite CD, et al. . Integration of over 9,000 mass spectrometry experiments builds a global map of human protein complexes. Mol Syst Biol. (2017) 13:932. 10.15252/msb.20167490 ] [
46. Jiang JQ, Wu M. Predicting multiplex subcellular localization of proteins using protein-protein interaction network: a comparative study. BMC Bioinform. (2012) 13:S20. 10.1186/1471-2105-13-S10-S20 ] [
47. Lutz MI, Schwaiger C, Hochreiter B, Kovacs GG, Schmid JA. Novel approach for accurate tissue-based protein colocalization and proximity microscopy. Sci Rep. (2017) 7:2668. 10.1038/s41598-017-02735-8 ] [
48. Wilker E, Yaffe MB. 14-3-3 Proteins - A focus on cancer and human disease. J Mol Cell Cardiol. (2004) 37:633–42. 10.1016/j.yjmcc.2004.04.015 [] [[PubMed]
49. Yaffe MB. Master of all things phosphorylated. Biochem J. (2004) 379:e1–2. 10.1042/bj20040284 ] [
50. Wang B, Yang H, Liu YC, Jelinek T, Zhang L, Ruoslahti E, et al. . Isolation of high-affinity peptide antagonists of 14-3-3 proteins by phage display. Biochemistry. (1999) 38:12499–504. 10.1021/bi991353h [] [[PubMed]
51. Masters SC, Pederson KJ, Zhang L, Barbieri JT, Fu H. Interaction of 14-3-3 with a nonphosphorylated protein ligand, exoenzyme S of Pseudomonas aeruginosa. Biochemistry. (1999) 38:5216–21. 10.1021/bi982492m [] [[PubMed]
52. Hornbeck P V, Kornhauser JM, Tkachev S, Zhang B, Skrzypek E, Murray B, et al. . PhosphoSitePlus: a comprehensive resource for investigating the structure and function of experimentally determined post-translational modifications in man and mouse. Nucleic Acids Res. (2012) 40:261–70. 10.1093/nar/gkr1122 ] [
53. Song C, Ye M, Liu Z, Cheng H, Jiang X, Han G, et al. . Systematic analysis of protein phosphorylation networks from phosphoproteomic data. Mol Cell Proteomics. (2012) 11:1070–83. 10.1074/mcp.M111.012625 ] [
54. Zhou H, Di Palma S, Preisinger C, Peng M, Polat AN, Heck AJR, et al. . Toward a comprehensive characterization of a human cancer cell phosphoproteome. J Proteome Res. (2013) 12:260–71. 10.1021/pr300630k [] [[PubMed]
55. Weintz G, Olsen J V, Frühauf K, Niedzielska M, Amit I, Jantsch J, et al. . The phosphoproteome of toll-like receptor-activated macrophages. Mol Syst Biol. (2010) 6:371. 10.1038/msb.2010.29 ] [
56. Zanivan S, Gnad F, Wickström SA, Geiger T, Macek B, Cox J, et al. . Solid tumor proteome and phosphoproteome analysis by high resolution mass spectrometry. J Proteome Res. (2008) 7:5314–26. 10.1021/pr800599n [] [[PubMed]
57. Minard AY, Tan SX, Yang P, Fazakerley DJ, Domanova W, Parker BL, et al. . mTORC1 Is a major regulatory node in the FGF21 signaling network in Adipocytes. Cell Rep. (2016) 17:29–36. 10.1016/j.celrep.2016.08.086 ] [
58. Pinto SM, Nirujogi RS, Rojas PL, Patil AH, Manda SS, Subbannayya Y, et al Quantitative phosphoproteomic analysis of IL-33 mediated signaling. Proteomics. (2015) 14:532–444. 10.1002/pmic.201400303 ] [[Google Scholar]
59. Humphrey SJ, Yang G, Yang P, Fazakerley DJ, Stöckli J, Yang JY, et al. . Dynamic adipocyte phosphoproteome reveals that akt directly regulates mTORC2. Cell Metab. (2013) 17:1009–20. 10.1016/j.cmet.2013.04.010 ] [
60. Trost M, English L, Lemieux S, Courcelles M, Desjardins M, Thibault P. The phagosomal proteome in interferon-γ-activated macrophages. Immunity. (2009) 30:143–54. 10.1016/j.immuni.2008.11.006 [] [[PubMed]
61. Snider J, Kotlyar M, Saraon P, Yao Z, Jurisica I, Stagljar I. Fundamentals of protein interaction network mapping. Mol Syst Biol. (2015) 11:848. 10.15252/msb.20156351 ] [
62. Berg D, Holzmann C, Riess O. 14-3-3 proteins in the nervous system. Nat Rev Neurosci. (2003) 4:752–62 10.1038/nrn1197 [] [[PubMed]
63. Obsil T, Obsilova V. Structural basis of 14-3-3 protein functions. Semin Cell Dev Biol. (2011) 22:663–72. 10.1016/j.semcdb.2011.09.001 [] [[PubMed]
64. Zhou X, Liao WJ, Liao JM, Liao P, Lu H. Ribosomal proteins: functions beyond the ribosome. J Mol Cell Biol. (2015) 7:92–104. 10.1093/jmcb/mjv014 ] [
65. Lee SB, Kwon IS, Park J, Lee KH, Ahn Y, Lee C, et al. . Ribosomal protein S3, a new substrate of Akt, serves as a signal mediator between neuronal apoptosis and DNA repair. J Biol Chem. (2010) 285:29457–68. 10.1074/jbc.M110.131367 ] [
66. Wan F, Anderson DE, Barnitz RA, Snow A, Bidere N, Zheng L, et al. . Ribosomal Protein S3: a KH domain subunit in NF-κB complexes that mediates selective gene regulation. Cell. (2007) 131:927–39. 10.1016/j.cell.2007.10.009 [] [[PubMed]
67. Yang HJ, Youn H, Seong KM, Jin YW, Kim J, Youn B. Phosphorylation of ribosomal protein S3 and antiapoptotic TRAF2 protein mediates radioresistance in non-small cell lung cancer cells. J Biol Chem. (2013) 288:2965–75. 10.1074/jbc.M112.385989 ] [
68. Yadavilli S, Mayo LD, Higgins M, Lain S, Hegde V, Deutsch WA. Ribosomal protein S3: a multi-functional protein that interacts with both p53 and MDM2 through its KH domain. DNA Repair. (2009) 8:1215–24. 10.1016/j.dnarep.2009.07.003 ] [
69. Gao X, Hardwidge PR. Ribosomal protein S3: a multifunctional target of attaching/effacing bacterial pathogens. Front Microbiol. (2011) 2:1–6. 10.3389/fmicb.2011.00137 ] [
70. Tanaka T, Fuchs J, Loidl J, Nasmyth K. Cohesin ensures bipolar attachment of microtubules to sister centromeres and resists their precocious separation. Nat Cell Biol. (2000) 2:492–9. 10.1038/35019529 [] [[PubMed]
71. Jang CY, Kim HD, Zhang X, Chang JS, Kim J. Ribosomal protein S3 localizes on the mitotic spindle and functions as a microtubule associated protein in mitosis. Biochem Biophys Res. (2012) 429:57–62. 10.1016/j.bbrc.2012.10.093 [] [[PubMed]
72. Hetzer M, Gruss OJ, Mattaj IW. The Ran GTPase as a marker of chromosome position in spindle formation and nuclear envelope assembly. Nat Cell Biol. (2002) 4:E177–184 10.1038/ncb0702-e177 [] [[PubMed]
73. Zheng D, Ye S, Wang X, Zhang Y, Yan D, Cai X, et al. . Pre-RC protein MCM7 depletion promotes mitotic exit by inhibiting CDK1 activity. Sci Rep Springer US. (2017) 7:1–10. 10.1038/s41598-017-03148-3 ] [
74. Sachdev S, Hoffmann A, Hannink M. Nuclear localization of IkappaB alpha is mediated by the second ankyrin repeat: the IkappaB alpha ankyrin repeats define a novel class of cis-acting nuclear import sequences. Mol Cell Biol. (1998) 18:2524–34. 10.1128/MCB.18.5.2524 ] [
75. Lu M, Zak J, Chen S, Sanchez-Pulido L, Severson DT, Endicott J, et al. . A code for RanGDP binding in ankyrin repeats defines a nuclear import pathway. Cell. (2014) 157:1130–45. 10.1016/j.cell.2014.05.006 [] [[PubMed]