Transcriptome-wide transmission disequilibrium analysis identifies novel risk genes for autism spectrum disorder


Autoři: Kunling Huang aff001;  Yuchang Wu aff002;  Junha Shin aff002;  Ye Zheng aff003;  Alireza Fotuhi Siahpirani aff004;  Yupei Lin aff005;  Zheng Ni aff005;  Jiawen Chen aff005;  Jing You aff005;  Sunduz Keles aff001;  Daifeng Wang aff002;  Sushmita Roy aff002;  Qiongshi Lu aff001
Působiště autorů: Department of Statistics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America aff001;  Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America aff002;  Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America aff003;  Department of Computer Sciences, University of Wisconsin-Madison, Madison, Wisconsin, United States of America aff004;  University of Wisconsin-Madison, Madison, Wisconsin, United States of America aff005;  Waisman Center, University of Wisconsin-Madison, Madison, Wisconsin, United States of America aff006;  Center for Demography of Health and Aging, University of Wisconsin-Madison, Madison, Wisconsin, United States of America aff007
Vyšlo v časopise: Transcriptome-wide transmission disequilibrium analysis identifies novel risk genes for autism spectrum disorder. PLoS Genet 17(2): e1009309. doi:10.1371/journal.pgen.1009309
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pgen.1009309

Souhrn

Recent advances in consortium-scale genome-wide association studies (GWAS) have highlighted the involvement of common genetic variants in autism spectrum disorder (ASD), but our understanding of their etiologic roles, especially the interplay with rare variants, is incomplete. In this work, we introduce an analytical framework to quantify the transmission disequilibrium of genetically regulated gene expression from parents to offspring. We applied this framework to conduct a transcriptome-wide association study (TWAS) on 7,805 ASD proband-parent trios, and replicated our findings using 35,740 independent samples. We identified 31 associations at the transcriptome-wide significance level. In particular, we identified POU3F2 (p = 2.1E-7), a transcription factor mainly expressed in developmental brain. Gene targets regulated by POU3F2 showed a 2.7-fold enrichment for known ASD genes (p = 2.0E-5) and a 2.7-fold enrichment for loss-of-function de novo mutations in ASD probands (p = 7.1E-5). These results provide a novel connection between rare and common variants, whereby ASD genes affected by very rare mutations are regulated by an unlinked transcription factor affected by common genetic variations.

Klíčová slova:

Autism spectrum disorder – Gene expression – Genetic loci – Genome-wide association studies – Hippocampus – Medical risk factors – Metaanalysis – Transcriptome analysis


Zdroje

1. Eaton DK, Kann L, Kinchen S, Shanklin S, Flint KH, Hawkins J, et al. Youth risk behavior surveillance—United States, 2011. Morbidity and Mortality Weekly Report: Surveillance Summaries. 2012;61(4):1–162. 22673000

2. Association AP. Diagnostic and statistical manual of mental disorders. BMC Med. 2013;17:133–7. doi: 10.1016/j.comppsych.2012.06.001 22809622

3. O’Roak BJ, Deriziotis P, Lee C, Vives L, Schwartz JJ, Girirajan S, et al. Exome sequencing in sporadic autism spectrum disorders identifies severe de novo mutations. Nature genetics. 2011;43(6):585. doi: 10.1038/ng.835 21572417

4. Sanders SJ, Murtha MT, Gupta AR, Murdoch JD, Raubeson MJ, Willsey AJ, et al. De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature. 2012;485(7397):237–U124. doi: 10.1038/nature10945 WOS:000303799800041. 22495306

5. Iossifov I, O’Roak BJ, Sanders SJ, Ronemus M, Krumm N, Levy D, et al. The contribution of de novo coding mutations to autism spectrum disorder. Nature. 2014;515(7526):216–21. doi: 10.1038/nature13908 25363768.

6. Iossifov I, Ronemus M, Levy D, Wang ZH, Hakker I, Rosenbaum J, et al. De Novo Gene Disruptions in Children on the Autistic Spectrum. Neuron. 2012;74(2):285–99. doi: 10.1016/j.neuron.2012.04.009 WOS:000303361800011. 22542183

7. Krumm N, Turner TN, Baker C, Vives L, Mohajeri K, Witherspoon K, et al. Excess of rare, inherited truncating mutations in autism. Nature genetics. 2015;47(6):582. doi: 10.1038/ng.3303 25961944

8. Gaugler T, Klei L, Sanders SJ, Bodea CA, Goldberg AP, Lee AB, et al. Most genetic risk for autism resides with common variation. Nature genetics. 2014;46(8):881. doi: 10.1038/ng.3039 25038753

9. Weiner DJ, Wigdor EM, Ripke S, Walters RK, Kosmicki JA, Grove J, et al. Polygenic transmission disequilibrium confirms that common and rare variation act additively to create risk for autism spectrum disorders. Nature genetics. 2017;49(7):978. doi: 10.1038/ng.3863 28504703

10. Grove J, Ripke S, Als TD, Mattheisen M, Walters RK, Won H, et al. Identification of common genetic risk variants for autism spectrum disorder. Nature genetics. 2019;51(3):431. doi: 10.1038/s41588-019-0344-8 30804558

11. Gusev A, Ko A, Shi H, Bhatia G, Chung W, Penninx BWJH, et al. Integrative approaches for large-scale transcriptome-wide association studies. Nature genetics. 2016;48(3):245–52. doi: 10.1038/ng.3506 26854917

12. Hu Y, Li M, Lu Q, Weng H, Wang J, Zekavat SM, et al. A statistical framework for cross-tissue transcriptome-wide association analysis. Nature genetics. 2019;51(3):568–76. doi: 10.1038/s41588-019-0345-7 30804563

13. Gamazon ER, Wheeler HE, Shah KP, Mozaffari SV, Aquino-Michaels K, Carroll RJ, et al. A gene-based association method for mapping traits using reference transcriptome data. Nature Genetics. 2015;47:1091. doi: 10.1038/ng.3367 26258848

14. Wainberg M, Sinnott-Armstrong N, Mancuso N, Barbeira AN, Knowles DA, Golan D, et al. Opportunities and challenges for transcriptome-wide association studies. Nature Genetics. 2019;51(4):592–9. doi: 10.1038/s41588-019-0385-z 30926968

15. Hancock DB, Scott WK. Population-based case-control association studies. Curr Protoc Hum Genet. 2012;Chapter 1:Unit1.17. Epub 2012/07/13. doi: 10.1002/0471142905.hg0117s74 22786610.

16. Aguet F, Ardlie KG, Cummings BB, Gelfand ET, Getz G, Hadley K, et al. Genetic effects on gene expression across human tissues. Nature. 2017;550:204–13. doi: 10.1038/nature24277 29022597

17. Fromer M, Roussos P, Sieberts SK, Johnson JS, Kavanagh DH, Perumal TM, et al. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nature neuroscience. 2016;19(11):1442–53. doi: 10.1038/nn.4399 27668389

18. Cordell HJ, Clayton DG. A unified stepwise regression procedure for evaluating the relative effects of polymorphisms within a gene using case/control or family data: application to HLA in type 1 diabetes. Am J Hum Genet. 2002;70(1):124–41. Epub 2001/11/24. doi: 10.1086/338007 11719900; PubMed Central PMCID: PMC384883.

19. Yu Z, Deng L. Pseudosibship methods in the case-parents design. Stat Med. 2011;30(27):3236–51. Epub 2011/09/29. doi: 10.1002/sim.4397 21953439; PubMed Central PMCID: PMC3882162.

20. Self SG, Longton G, Kopecky KJ, Liang KY. On estimating HLA/disease association with application to a study of aplastic anemia. Biometrics. 1991;47(1):53–61. Epub 1991/03/01. 2049513.

21. Schaid DJ. General score tests for associations of genetic markers with disease using cases and their parents. Genetic Epidemiology. 1996;13(5):423–49. doi: 10.1002/(SICI)1098-2272(1996)13:5<423::AID-GEPI1>3.0.CO;2-3 8905391

22. Spielman RS, McGinnis RE, Ewens WJ. Transmission test for linkage disequilibrium: the insulin gene region and insulin-dependent diabetes mellitus (IDDM). Am J Hum Genet. 1993;52(3):506–16. Epub 1993/03/01. 8447318; PubMed Central PMCID: PMC1682161.

23. Gusev A, Mancuso N, Won H, Kousi M, Finucane HK, Reshef Y, et al. Transcriptome-wide association study of schizophrenia and chromatin activity yields mechanistic disease insights. Nature Genetics. 2018;50(4):538–48. doi: 10.1038/s41588-018-0092-1 29632383

24. Borgan O, Goldstein L, Langholz B. Methods for the analysis of sampled cohort data in the Cox proportional hazards model. The Annals of Statistics. 1995;23(5):1749–78.

25. Strunk D, Weber P, Röthlisberger B, Filges I. Autism and intellectual disability in a patient with two microdeletions in 6q16: a contiguous gene deletion syndrome? Molecular Cytogenetics. 2016;9(1):88. doi: 10.1186/s13039-016-0299-8 27980676

26. Schonemann MD, Ryan AK, Erkman L, McEvilly RJ, Bermingham J, Rosenfeld MG. POU domain factors in neural development. Vasopressin and Oxytocin: Springer; 1998. p. 39–53.

27. Chen C, Meng Q, Xia Y, Ding C, Wang L, Dai R, et al. The transcription factor POU3F2 regulates a gene coexpression network in brain tissue from patients with psychiatric disorders. Science translational medicine. 2018;10(472):eaat8178. doi: 10.1126/scitranslmed.aat8178 30545964

28. Mühleisen TW, Leber M, Schulze TG, Strohmaier J, Degenhardt F, Treutlein J, et al. Genome-wide association study reveals two new risk loci for bipolar disorder. Nature communications. 2014;5:3339. doi: 10.1038/ncomms4339 24618891

29. Hou L, Bergen SE, Akula N, Song J, Hultman CM, Landen M, et al. Genome-wide association study of 40,000 individuals identifies two novel loci associated with bipolar disorder. Human molecular genetics. 2016;25(15):3383–94. doi: 10.1093/hmg/ddw181 27329760

30. Pearl JR, Colantuoni C, Bergey DE, Funk CC, Shannon P, Basu B, et al. Genome-scale transcriptional regulatory network models of psychiatric and neurodegenerative disorders. Cell systems. 2019;8(2):122–35. e7. doi: 10.1016/j.cels.2019.01.002 30772379

31. Kasher PR, Schertz KE, Thomas M, Jackson A, Annunziata S, Ballesta-Martinez MJ, et al. Small 6q16. 1 deletions encompassing POU3F2 cause susceptibility to obesity and variable developmental delay with intellectual disability. The American Journal of Human Genetics. 2016;98(2):363–72. doi: 10.1016/j.ajhg.2015.12.014 26833329

32. Belinson H, Nakatani J, Babineau B, Birnbaum R, Ellegood J, Bershteyn M, et al. Prenatal β-catenin/Brn2/Tbr2 transcriptional cascade regulates adult social and stereotypic behaviors. Molecular psychiatry. 2016;21(10):1417. doi: 10.1038/mp.2015.207 26830142

33. Marchetto MC, Belinson H, Tian Y, Freitas BC, Fu C, Vadodaria K, et al. Altered proliferation and networks in neural cells derived from idiopathic autistic individuals. Molecular psychiatry. 2017;22(6):820. doi: 10.1038/mp.2016.95 27378147

34. Lei P, Ayton S, Finkelstein DI, Adlard PA, Masters CL, Bush AI. Tau protein: Relevance to Parkinson’s disease. The International Journal of Biochemistry & Cell Biology. 2010;42(11):1775–8. https://doi.org/10.1016/j.biocel.2010.07.016.

35. Spillantini MG, Van Swieten JC, Goedert M. Tau gene mutations in frontotemporal dementia and parkinsonism linked to chromosome 17 (FTDP-17). Neurogenetics. 2000;2(4):193–205. Epub 2000/09/13. doi: 10.1007/pl00022972 10983715.

36. Oshimori N, Ohsugi M, Yamamoto T. The Plk1 target Kizuna stabilizes mitotic centrosomes to ensure spindle bipolarity. Nature Cell Biology. 2006;8(10):1095–101. doi: 10.1038/ncb1474 16980960

37. Briscoe J, Sussel L, Serup P, Hartigan-O’Connor D, Jessell TM, Rubenstein JL, et al. Homeobox gene Nkx2.2 and specification of neuronal identity by graded Sonic hedgehog signalling. Nature. 1999;398(6728):622–7. Epub 1999/04/27. doi: 10.1038/19315 10217145.

38. Oien DB, Osterhaus GL, Latif SA, Pinkston JW, Fulks J, Johnson M, et al. MsrA knockout mouse exhibits abnormal behavior and brain dopamine levels. Free Radical Biology and Medicine. 2008;45(2):193–200. doi: 10.1016/j.freeradbiomed.2008.04.003 18466776

39. Pascual I, Larrayoz IM, Rodriguez IR. Retinoic acid regulates the human methionine sulfoxide reductase A (MSRA) gene via two distinct promoters. Genomics. 2009;93(1):62–71. doi: 10.1016/j.ygeno.2008.09.002 18845237

40. SFARI Gene scoring [Internet]. Available from: https://gene.sfari.org/about-gene-scoring/.

41. Won H, de La Torre-Ubieta L, Stein JL, Parikshak NN, Huang J, Opland CK, et al. Chromosome conformation elucidates regulatory relationships in developing human brain. Nature. 2016;538(7626):523. doi: 10.1038/nature19847 27760116

42. Li M, Santpere G, Imamura Kawasawa Y, Evgrafov OV, Gulden FO, Pochareddy S, et al. Integrative functional genomic analysis of human brain development and neuropsychiatric risks. Science. 2018;362(6420):eaat7615. doi: 10.1126/science.aat7615 30545854

43. Chasman D, Iyer N, Siahpirani AF, Silva ME, Lippmann E, McIntosh B, et al. Inferring Regulatory Programs Governing Region Specificity of Neuroepithelial Stem Cells during Early Hindbrain and Spinal Cord Development. Cell systems. 2019;9(2):167–86. e12. doi: 10.1016/j.cels.2019.05.012 31302154

44. Finucane HK, Bulik-Sullivan B, Gusev A, Trynka G, Reshef Y, Loh P-R, et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nature genetics. 2015. doi: 10.1038/ng.3404 26414678

45. Keys KL, Mak ACY, White MJ, Eckalbar WL, Dahl AW, Mefford J, et al. On the cross-population generalizability of gene expression prediction models. PLOS Genetics. 2020;16(8):e1008927. doi: 10.1371/journal.pgen.1008927 32797036

46. Werling DM, Brand H, An J-Y, Stone MR, Zhu L, Glessner JT, et al. An analytical framework for whole-genome sequence association studies and its implications for autism spectrum disorder. Nature Genetics. 2018;50(5):727–36. doi: 10.1038/s41588-018-0107-y 29700473

47. An J-Y, Lin K, Zhu L, Werling DM, Dong S, Brand H, et al. Genome-wide de novo risk score implicates promoter variation in autism spectrum disorder. Science (New York, NY). 2018;362(6420):eaat6576. doi: 10.1126/science.aat6576 30545852

48. Sanders SJ, He X, Willsey AJ, Ercan-Sencicek AG, Samocha KE, Cicek AE, et al. Insights into autism spectrum disorder genomic architecture and biology from 71 risk loci. Neuron. 2015;87(6):1215–33. doi: 10.1016/j.neuron.2015.09.016 26402605

49. Anney R, Klei L, Pinto D, Almeida J, Bacchelli E, Baird G, et al. Individual common variants exert weak effects on the risk for autism spectrum disorders. Human molecular genetics. 2012;21(21):4781–92. doi: 10.1093/hmg/dds301 22843504

50. Autism Genome Project (AGP) Consortium—Whole Genome Association Study of over 1,500 Parent-Offspring Trios—Stage I and II [Internet]. 2017. Available from: https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000267.v5.p2.

51. Simons Simplex Collection [Internet]. 2010. Available from: https://www.sfari.org/resource/simons-simplex-collection/.

52. Simons Foundation Powering Autism Research for Knowledge [Internet]. 2018. Available from: https://www.sfari.org/resource/spark/.

53. Feliciano P, Zhou X, Astrovskaya I, Turner TN, Wang T, Brueggeman L, et al. Exome sequencing of 457 autism families recruited online provides evidence for autism risk genes. NPJ Genomic Medicine. 2019;4(1):1–14. doi: 10.1038/s41525-019-0093-8 31452935

54. Feliciano P, Daniels AM, Snyder LG, Beaumont A, Camba A, Esler A, et al. SPARK: a US cohort of 50,000 families to accelerate autism research. Neuron. 2018;97(3):488–93. doi: 10.1016/j.neuron.2018.01.015 29420931

55. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. The American Journal of Human Genetics. 2007;81(3):559–75. doi: 10.1086/519795 17701901

56. Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis. The American Journal of Human Genetics. 2011;88(1):76–82. doi: 10.1016/j.ajhg.2010.11.011 21167468

57. Das S, Forer L, Schönherr S, Sidore C, Locke AE, Kwong A, et al. Next-generation genotype imputation service and methods. Nature genetics. 2016;48(10):1284. doi: 10.1038/ng.3656 27571263

58. Pedersen CB, Bybjerg-Grauholm J, Pedersen MG, Grove J, Agerbo E, Baekvad-Hansen M, et al. The iPSYCH2012 case–cohort sample: new directions for unravelling genetic and environmental architectures of severe mental disorders. Molecular psychiatry. 2018;23(1):6. doi: 10.1038/mp.2017.196 28924187

59. Euesden J, Lewis CM, O’reilly PF. PRSice: polygenic risk score software. Bioinformatics. 2014;31(9):1466–8. doi: 10.1093/bioinformatics/btu848 25550326

60. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society: Series B (Methodological). 1995;57(1):289–300. doi: 10.1111/j.2517-6161.1995.tb02031.x

61. Willer CJ, Li Y, Abecasis GR. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics. 2010;26(17):2190–1. Epub 2010/07/10. doi: 10.1093/bioinformatics/btq340 20616382; PubMed Central PMCID: PMC2922887.

62. Parikshak NN, Luo R, Zhang A, Won H, Lowe JK, Chandran V, et al. Integrative functional genomic analyses implicate specific molecular pathways and circuits in autism. Cell. 2013;155(5):1008–21. doi: 10.1016/j.cell.2013.10.031 24267887

63. BrainSpan Atlas of the Developing Human Brain [Internet]. Available from: http://www.brainspan.org/static/home.

64. Blake JA, Bult CJ, Kadin JA, Richardson JE, Eppig JT, Group MGD. The Mouse Genome Database (MGD): premier model organism resource for mammalian genomics and genetics. Nucleic acids research. 2010;39(suppl_1):D842–D8. doi: 10.1093/nar/gkq1008 21051359

65. Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, Fennell T, et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature. 2016;536(7616):285. doi: 10.1038/nature19057 27535533

66. The SPARK Gene List [Internet]. 2019. Available from: https://simonsfoundation.s3.amazonaws.com/share/SFARI/SPARK_Gene_List.pdf.

67. Genome-wide chromosomal conformation elucidates regulatory relationships in human brain development and disease [Internet]. 2016. Available from: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE77565.

68. Imakaev M, Fudenberg G, McCord RP, Naumova N, Goloborodko A, Lajoie BR, et al. Iterative correction of Hi-C data reveals hallmarks of chromosome organization. Nat Methods. 2012;9(10):999–1003. Epub 2012/09/04. doi: 10.1038/nmeth.2148 22941365; PubMed Central PMCID: PMC3816492.

69. Ay F, Bailey TL, Noble WS. Statistical confidence estimation for Hi-C data reveals regulatory chromatin contacts. Genome research. 2014;24(6):999–1011. doi: 10.1101/gr.160374.113 24501021

70. Kang HJ, Kawasawa YI, Cheng F, Zhu Y, Xu X, Li M, et al. Spatio-temporal transcriptome of the human brain. Nature. 2011;478(7370):483–9. doi: 10.1038/nature10523 22031440

71. Weirauch MT, Yang A, Albu M, Cote AG, Montenegro-Montero A, Drewe P, et al. Determination and inference of eukaryotic transcription factor sequence specificity. Cell. 2014;158(6):1431–43. doi: 10.1016/j.cell.2014.08.009 25215497

72. Wang J, Zhuang J, Iyer S, Lin X, Whitfield TW, Greven MC, et al. Sequence features and chromatin structure around the genomic regions bound by 119 human transcription factors. Genome research. 2012;22(9):1798–812. doi: 10.1101/gr.139105.112 22955990

73. Mathelier A, Fornes O, Arenillas DJ, Chen C-y, Denay G, Lee J, et al. JASPAR 2016: a major expansion and update of the open-access database of transcription factor binding profiles. Nucleic acids research. 2015;44(D1):D110–D5. doi: 10.1093/nar/gkv1176 26531826

74. Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, Heravi-Moussavi A, et al. Integrative analysis of 111 reference human epigenomes. Nature. 2015;518(7539):317–30. Epub 2015/02/20. doi: 10.1038/nature14248 25693563; PubMed Central PMCID: PMC4530010.

75. Sherwood RI, Hashimoto T, O’donnell CW, Lewis S, Barkal AA, Van Hoff JP, et al. Discovery of directional and nondirectional pioneer transcription factors by modeling DNase profile magnitude and shape. Nature biotechnology. 2014;32(2):171. doi: 10.1038/nbt.2798 24441470

76. Harrell FEJ. R Package Hmisc. 2020.

77. Samocha KE, Robinson EB, Sanders SJ, Stevens C, Sabo A, McGrath LM, et al. A framework for the interpretation of de novo mutation in human disease. Nature genetics. 2014;46(9):944. doi: 10.1038/ng.3050 25086666

78. Turner TN, Yi Q, Krumm N, Huddleston J, Hoekzema K, HA FS, et al. denovo-db: a compendium of human de novo variants. Nucleic acids research. 2017;45(D1):D804–d11. Epub 2016/12/03. doi: 10.1093/nar/gkw865 27907889; PubMed Central PMCID: PMC5210614.

79. Samocha KE, Kosmicki JA, Karczewski KJ, O’Donnell-Luria AH, Pierce-Hoffman E, MacArthur DG, et al. Regional missense constraint improves variant deleteriousness prediction. bioRxiv. 2017:148353. doi: 10.1101/148353

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


Článek vyšel v časopise

PLOS Genetics


2021 Číslo 2
Nejčtenější tento týden
Nejčtenější v tomto čísle
Kurzy

Zvyšte si kvalifikaci online z pohodlí domova

Důležitost adherence při depresivním onemocnění
nový kurz
Autoři: MUDr. Eliška Bartečková, Ph.D.

Koncepce osteologické péče pro gynekology a praktické lékaře
Autoři: MUDr. František Šenk

Sekvenční léčba schizofrenie
Autoři: MUDr. Jana Hořínková, Ph.D.

Hypertenze a hypercholesterolémie – synergický efekt léčby
Autoři: prof. MUDr. Hana Rosolová, DrSc.

Multidisciplinární zkušenosti u pacientů s diabetem
Autoři: Prof. MUDr. Martin Haluzík, DrSc., prof. MUDr. Vojtěch Melenovský, CSc., prof. MUDr. Vladimír Tesař, DrSc.

Všechny kurzy
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