Các biến thể mã hóa và không mã hóa trong EBF3 liên quan đến HADDS và tự kỷ đơn giản

Evin M. Padhi1, Tristan J. Hayeck2, Cheng Zhang3, Sumantra Chatterjee4, Brandon J. Mannion5, Marta Byrska-Bishop6, Marjolaine Willems7, Lucile Pinson7, Sylvia Redon8, Caroline Bénech8, Kévin Uguen8, Séverine Audebert‐Bellanger9, Cédric Le Maréchal8, Claude Férec8, Stéphanie Efthymiou10, Fatima Rahman11, Shazia Maqbool10, Reza Maroofian10, Henry Houlden10, Rajeeva Musunuri6, Giuseppe Narzisi6, Avinash Abhyankar6, Riana D. Hunter5, Jennifer A. Akiyama5, Lauren E. Fries4, Jeffrey K. Ng1, Elvisa Mehinovic1, Nicholas Stong12, Andrew S. Allen13, Diane E. Dickel5, Raphael Bernier14, David U. Gorkin15, L Pennacchio16, Michael C. Zody6, Tychele N. Turner1
1Department of Genetics, Washington University School of Medicine, 4523 Clayton Avenue, Campus Box 8232, St. Louis, MO, 63110, USA
2Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
3Center for Epigenomics, University of California San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA, 92093, USA
4Center for Human Genetics and Genomics, NYU School of Medicine, New York, NY, 10016, USA
5Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
6New York Genome Center, New York, NY 10013, USA
7University of Montpellier, département de Génétique, maladies rares médecine personnalisée, U 1298, CHU Montpellier, University of Montpellier, Montpellier, France
8CHU Brest, Inserm, Univ Brest, EFS,UMR 1078, GGB, F-29200, Brest, France
9Service de Génétique Médicale, CHRU de Brest, Brest, France
10Department of Neuromuscular disorders, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK
11Development and Behavioral Pediatrics Department, Institute of Child Health and Children Hospital, Lahore, Pakistan
12Institute for Genomic Medicine, Columbia University, New York, NY, 10027, USA
13Center for Statistical Genetics and Genomics, Duke University, Durham, NC, 27708, USA
14Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, 98195, USA
15Department of Biology, Emory University, Atlanta, GA 30322, USA
16U.S. Department of Energy Joint Genome Institute, Walnut Creek, CA 94598, USA

Tóm tắt

Tóm tắt Nền tảng Nghiên cứu trước đây về tự kỷ và các rối loạn phát triển thần kinh (NDDs) đã chỉ ra rằng các biến thể de novo mã hóa protein (DNVs) trong các gen cụ thể có một đóng góp quan trọng. Vai trò của các biến thể không mã hóa de novo đã được quan sát như là một sự gia tăng tổng thể của gánh nặng gen nhưng vẫn chưa được xác định đến các yếu tố chức năng riêng lẻ. Trong nghiên cứu này, chúng tôi đã đánh giá dữ liệu giải trình tự toàn bộ gen ở 2671 gia đình có người tự kỷ (tập hợp khám phá gồm 516 gia đình, tập hợp tái lập gồm 2155 gia đình). Chúng tôi tập trung vào các DNVs trong các enhancer có hoạt động in vivo được xác định trong não và đã xác định được một lượng dư thừa của các DNV trong một enhancer được gọi là hs737. Kết quả Chúng tôi đã điều chỉnh mô hình thống kê fitDNM để làm việc trong các vùng không mã hóa và thử nghiệm các enhancer cho sự dư thừa của các DNV trong các gia đình có người tự kỷ. Chúng tôi chỉ phát hiện được một enhancer (hs737) có ý nghĩa danh nghĩa trong tập hợp khám phá (p = 0.0172), tái lập (p = 2.5 × 10−3), và tập hợp kết hợp (p = 1.1 × 10−4). Mỗi cá thể có một DNV trong hs737 đều có những kiểu hình chung bao gồm là nam giới, chức năng nhận thức nguyên vẹn và giảm trương lực cơ hoặc chậm phát triển vận động. Đánh giá in vitro của chúng tôi về các DNV cho thấy tất cả chúng đều làm giảm hoạt động của enhancer trong một dòng tế bào thần kinh. Qua phân tích epigenome, chúng tôi phát hiện rằng hs737 là đặc hiệu cho não và nhắm đến gen yếu tố phiên mã EBF3 trong não thai nhi của con người. EBF3 có ý nghĩa toàn bộ gen đối với các DNVs mã hóa trong NDDs (missense p = 8.12 × 10−35, mất chức năng p = 2.26 × 10−13) và được biểu hiện rộng rãi trong cơ thể. Qua việc xác định các promoter được gắn bởi EBF3 trong các tế bào thần kinh, chúng tôi đã thấy sự gia tăng kết nối với các gen NDD (p = 7.43 × 10−6, OR = 1.87) tham gia vào việc điều chỉnh gen. Các cá thể có DNV mã hóa có mức độ nghiêm trọng về kiểu hình lớn hơn (giảm trương lực cơ, loạn vận động, và hội chứng chậm phát triển [HADDS]) so với những cá thể có DNV không mã hóa mà cũng có tự kỷ và giảm trương lực cơ. Kết luận Trong nghiên cứu này, chúng tôi xác định các DNV trong enhancer hs737 ở các cá thể có tự kỷ. Qua nhiều phương pháp, chúng tôi thấy hs737 nhắm đến gen EBF3 có ý nghĩa toàn bộ gen trong NDDs. Bằng việc đánh giá các biến thể không mã hóa và các gen mà chúng ảnh hưởng, chúng tôi bắt đầu hiểu rõ hơn tác động của chúng đối với các mạng lưới điều chỉnh gen trong NDDs.

Từ khóa


Tài liệu tham khảo

Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, Hunter DJ, et al. Finding the missing heritability of complex diseases. Nature. 2009;461(7265):747–53. https://doi.org/10.1038/nature08494.

Pollard KS, Hubisz MJ, Rosenbloom KR, Siepel A. Detection of nonneutral substitution rates on mammalian phylogenies. Genome Res. 2010;20(1):110–21. https://doi.org/10.1101/gr.097857.109.

Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alföldi J, Wang Q, et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature. 2020;581(7809):434–43. https://doi.org/10.1038/s41586-020-2308-7.

Moore JE, Purcaro MJ, Pratt HE, Epstein CB, Shoresh N, Adrian J, et al. Expanded encyclopaedias of DNA elements in the human and mouse genomes. Nature. 2020;583(7818):699–710. https://doi.org/10.1038/s41586-020-2493-4.

Grove J, Ripke S, Als TD, Mattheisen M, Walters RK, Won H, et al. Identification of common genetic risk variants for autism spectrum disorder. Nat Genet. 2019;51(3):431–44. https://doi.org/10.1038/s41588-019-0344-8.

Yang J, Manolio TA, Pasquale LR, Boerwinkle E, Caporaso N, Cunningham JM, et al. Genome partitioning of genetic variation for complex traits using common SNPs. Nat Genet. 2011;43(6):519–25. https://doi.org/10.1038/ng.823.

Maurano MT, Humbert R, Rynes E, Thurman RE, Haugen E, Wang H, et al. Systematic localization of common disease-associated variation in regulatory DNA. Science (New York, NY). 2012;337(6099):1190–5.

Turner TN, Coe BP, Dickel DE, Hoekzema K, Nelson BJ, Zody MC, et al. Genomic patterns of de novo mutation in simplex autism. Cell. 2017;171(3):710–722.e712.

An JY, 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).

Zhou J, Park C, Theesfeld C, Yuan Y, Sawicka K, Darnell J, et al. Whole-genome deep learning analysis reveals causal role of noncoding mutations in autism. bioRxiv. 2018.

Markenscoff-Papadimitriou E, Whalen S, Przytycki P, Thomas R, Binyameen F, Nowakowski TJ, et al. A chromatin accessibility atlas of the developing human telencephalon. Cell. 2020;182(3):754–769.e718.

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. https://doi.org/10.1038/nature13908.

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. Nat Genet. 2011;43(6):585–9. https://doi.org/10.1038/ng.835.

O'Roak BJ, Vives L, Girirajan S, Karakoc E, Krumm N, Coe BP, et al. Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Nature. 2012;485(7397):246–50. https://doi.org/10.1038/nature10989.

De Rubeis S, He X, Goldberg AP, Poultney CS, Samocha K, Cicek AE, et al. Synaptic, transcriptional and chromatin genes disrupted in autism. Nature. 2014;515(7526):209–15. https://doi.org/10.1038/nature13772.

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):237–41. https://doi.org/10.1038/nature10945.

Sandin S, Lichtenstein P, Kuja-Halkola R, Hultman C, Larsson H, Reichenberg A. The heritability of autism spectrum disorder. Jama. 2017;318(12):1182–4. https://doi.org/10.1001/jama.2017.12141.

Levy D, Ronemus M, Yamrom B, Lee YH, Leotta A, Kendall J, et al. Rare de novo and transmitted copy-number variation in autistic spectrum disorders. Neuron. 2011;70(5):886–97. https://doi.org/10.1016/j.neuron.2011.05.015.

Sebat J, Lakshmi B, Malhotra D, Troge J, Lese-Martin C, Walsh T, et al. Strong association of de novo copy number mutations with autism. Science (New York, NY). 2007;316(5823):445–9.

Sanders SJ, Ercan-Sencicek AG, Hus V, Luo R, Murtha MT, Moreno-De-Luca D, et al. Multiple recurrent de novo CNVs, including duplications of the 7q11.23 Williams syndrome region, are strongly associated with autism. Neuron. 2011;70(5):863–85. https://doi.org/10.1016/j.neuron.2011.05.002.

Girirajan S, Dennis MY, Baker C, Malig M, Coe BP, Campbell CD, et al. Refinement and discovery of new hotspots of copy-number variation associated with autism spectrum disorder. Am J Hum Genet. 2013;92(2):221–37. https://doi.org/10.1016/j.ajhg.2012.12.016.

Iossifov I, Ronemus M, Levy D, Wang Z, Hakker I, Rosenbaum J, et al. De novo gene disruptions in children on the autistic spectrum. Neuron. 2012;74(2):285–99. https://doi.org/10.1016/j.neuron.2012.04.009.

Neale BM, Kou Y, Liu L, Ma'ayan A, Samocha KE, Sabo A, et al. Patterns and rates of exonic de novo mutations in autism spectrum disorders. Nature. 2012;485(7397):242–5. https://doi.org/10.1038/nature11011.

Turner TN, Hormozdiari F, Duyzend MH, McClymont SA, Hook PW, Iossifov I, et al. Genome sequencing of autism-affected families reveals disruption of putative noncoding regulatory DNA. Am J Hum Genet. 2016;98(1):58–74. https://doi.org/10.1016/j.ajhg.2015.11.023.

Brandler WM, Antaki D, Gujral M, Kleiber ML, Whitney J, Maile MS, et al. Paternally inherited cis-regulatory structural variants are associated with autism. Science (New York, NY). 2018;360(6386):327–31.

Frazer KA, Pachter L, Poliakov A, Rubin EM, Dubchak I. VISTA: computational tools for comparative genomics. Nucleic Acids Res. 2004;32(Web Server issue):W273–9.

Visel A, Minovitsky S, Dubchak I, Pennacchio LA. VISTA Enhancer Browser--a database of tissue-specific human enhancers. Nucleic Acids Res. 2007;35(Database issue):D88–92. https://doi.org/10.1093/nar/gkl822.

Kvon EZ, Zhu Y, Kelman G, Novak CS, Plajzer-Frick I, Kato M, et al. Comprehensive in vivo interrogation reveals phenotypic impact of human enhancer variants. Cell. 2020;180(6):1262–1271.e1215.

Jiang Y, Han Y, Petrovski S, Owzar K, Goldstein DB, Allen AS. Incorporating functional information in tests of excess de novo mutational load. Am J Hum Genet. 2015;97(2):272–83. https://doi.org/10.1016/j.ajhg.2015.06.013.

Wilfert AB, Turner TN, Murali SC, Hsieh P, Sulovari A, Wang T, et al. Recent ultra-rare inherited mutations identify novel autism candidate risk genes. Nat Genet. in press.

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. Nat Genet. 2017;49(7):978–85. https://doi.org/10.1038/ng.3863.

Chatterjee S, Kapoor A, Akiyama JA, Auer DR, Lee D, Gabriel S, et al. Enhancer variants synergistically drive dysfunction of a gene regulatory network in Hirschsprung disease. Cell. 2016;167(2):355–368.e310.

Zhao J, Li D, Seo J, Allen AS, Gordân R. Quantifying the impact of non-coding variants on transcription factor-DNA binding. Res Comput Mol Biol. 2017;10229:336–52. https://doi.org/10.1007/978-3-319-56970-3_21.

Martin V, Zhao J, Afek A, Mielko Z, Gordân R. QBiC-Pred: quantitative predictions of transcription factor binding changes due to sequence variants. Nucleic Acids Res. 2019;47(W1):W127–w135. https://doi.org/10.1093/nar/gkz363.

Coe BP, Witherspoon K, Rosenfeld JA, van Bon BW, Vulto-van Silfhout AT, Bosco P, et al. Refining analyses of copy number variation identifies specific genes associated with developmental delay. Nat Genet. 2014;46(10):1063–71. https://doi.org/10.1038/ng.3092.

Cooper GM, Coe BP, Girirajan S, Rosenfeld JA, Vu TH, Baker C, et al. A copy number variation morbidity map of developmental delay. Nat Genet. 2011;43(9):838–46. https://doi.org/10.1038/ng.909.

Shen F, Kidd JM. Rapid, paralog-sensitive CNV analysis of 2457 human genomes using QuicK-mer2. Genes (Basel). 2020;11(2).

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. https://doi.org/10.1016/j.neuron.2015.09.016.

Byrska-Bishop M, Evani US, Zhao X, Basile AO, Abel HJ, Regier AA, et al. High coverage whole genome sequencing of the expanded 1000 Genomes Project cohort including 602 trios. bioRxiv. 2021; 2021.2002.2006.430068.

Collins RL, Brand H, Karczewski KJ, Zhao X, Alföldi J, Francioli LC, et al. A structural variation reference for medical and population genetics. Nature. 2020;581(7809):444–51. https://doi.org/10.1038/s41586-020-2287-8.

Reilly SK, Yin J, Ayoub AE, Emera D, Leng J, Cotney J, et al. Evolutionary genomics. Evolutionary changes in promoter and enhancer activity during human corticogenesis. Science (New York, NY). 2015;347(6226):1155–9.

Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM, et al. The human genome browser at UCSC. Genome Res. 2002;12(6):996–1006. https://doi.org/10.1101/gr.229102.

Gorkin DU, Barozzi I, Zhao Y, Zhang Y, Huang H, Lee AY, et al. An atlas of dynamic chromatin landscapes in mouse fetal development. Nature. 2020;583(7818):744–51. https://doi.org/10.1038/s41586-020-2093-3.

Ernst J, Kellis M. ChromHMM: automating chromatin-state discovery and characterization. Nat Methods. 2012;9(3):215–6. https://doi.org/10.1038/nmeth.1906.

Sloan CA, Chan ET, Davidson JM, Malladi VS, Strattan JS, Hitz BC, et al. ENCODE data at the ENCODE portal. Nucleic Acids Res. 2016;44(D1):D726–32. https://doi.org/10.1093/nar/gkv1160.

Dixon JR, Selvaraj S, Yue F, Kim A, Li Y, Shen Y, et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature. 2012;485(7398):376–80. https://doi.org/10.1038/nature11082.

Bonev B, Mendelson Cohen N, Szabo Q, Fritsch L, Papadopoulos GL, Lubling Y, et al. Multiscale 3D genome rewiring during mouse neural development. Cell. 2017;171(3):557–572.e524.

Durand NC, Shamim MS, Machol I, Rao SS, Huntley MH, Lander ES, et al. Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments. Cell Syst. 2016;3(1):95–8. https://doi.org/10.1016/j.cels.2016.07.002.

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):523–7. https://doi.org/10.1038/nature19847.

Coe BP, Stessman HAF, Sulovari A, Geisheker MR, Bakken TE, Lake AM, et al. Neurodevelopmental disease genes implicated by de novo mutation and copy number variation morbidity. Nat Genet. 2019;51(1):106–16. https://doi.org/10.1038/s41588-018-0288-4.

Kaplanis J, Samocha KE, Wiel L, Zhang Z, Arvai KJ, Eberhardt RY, et al. Evidence for 28 genetic disorders discovered by combining healthcare and research data. Nature. 2020;586(7831):757–62. https://doi.org/10.1038/s41586-020-2832-5.

Ware JS, Samocha KE, Homsy J, Daly MJ. Interpreting de novo variation in human disease using denovolyzeR. Current protocols in human genetics. 2015;87:7.25.21–15.

Sleven H, Welsh SJ, Yu J, Churchill MEA, Wright CF, Henderson A, et al. De novo mutations in EBF3 cause a neurodevelopmental syndrome. Am J Hum Genet. 2017;100(1):138–50. https://doi.org/10.1016/j.ajhg.2016.11.020.

Chao HT, Davids M, Burke E, Pappas JG, Rosenfeld JA, McCarty AJ, et al. A syndromic neurodevelopmental disorder caused by de novo variants in EBF3. Am J Hum Genet. 2017;100(1):128–37. https://doi.org/10.1016/j.ajhg.2016.11.018.

Harms FL, Girisha KM, Hardigan AA, Kortüm F, Shukla A, Alawi M, et al. Mutations in EBF3 disturb transcriptional profiles and cause intellectual disability, ataxia, and facial dysmorphism. Am J Hum Genet. 2017;100(1):117–27. https://doi.org/10.1016/j.ajhg.2016.11.012.

Hormozdiari F, Penn O, Borenstein E, Eichler EE. The discovery of integrated gene networks for autism and related disorders. Genome Res. 2015;25(1):142–54. https://doi.org/10.1101/gr.178855.114.

Fischbach GD, Lord C. The Simons Simplex Collection: a resource for identification of autism genetic risk factors. Neuron. 2010;68(2):192–5. https://doi.org/10.1016/j.neuron.2010.10.006.

Celestino-Soper PB, Shaw CA, Sanders SJ, Li J, Murtha MT, Ercan-Sencicek AG, et al. Use of array CGH to detect exonic copy number variants throughout the genome in autism families detects a novel deletion in TMLHE. Hum Mol Genet. 2011;20(22):4360–70. https://doi.org/10.1093/hmg/ddr363.

Satterstrom FK, Kosmicki JA, Wang J, Breen MS, De Rubeis S, An JY, et al. Large-scale exome sequencing study implicates both developmental and functional changes in the neurobiology of autism. Cell. 2020;180(3):568–584.e523.

Gaugler T, Klei L, Sanders SJ, Bodea CA, Goldberg AP, Lee AB, et al. Most genetic risk for autism resides with common variation. Nat Genet. 2014;46(8):881–5. https://doi.org/10.1038/ng.3039.

Bernier R, Golzio C, Xiong B, Stessman HA, Coe BP, Penn O, et al. Disruptive CHD8 mutations define a subtype of autism early in development. Cell. 2014;158(2):263–76. https://doi.org/10.1016/j.cell.2014.06.017.

Earl RK, Turner TN, Mefford HC, Hudac CM, Gerdts J, Eichler EE, et al. Clinical phenotype of ASD-associated DYRK1A haploinsufficiency. Molecular autism. 2017;8(1):54. https://doi.org/10.1186/s13229-017-0173-5.

Lonsdale J, Thomas J, Salvatore M, Phillips R, Lo E, Shad S, et al. The Genotype-Tissue Expression (GTEx) project. Nat Genet. 2013;45(6):580–5.

Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics (Oxford, England). 2010;26(6):841–2.

Turner TN, Wilfert AB, Bakken TE, Bernier RA, Pepper MR, Zhang Z, et al. Sex-based analysis of de novo variants in neurodevelopmental disorders. Am J Hum Genet. 2019;105(6):1274–85. https://doi.org/10.1016/j.ajhg.2019.11.003.

Huang da W, Sherman BT, Lempicki RA. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009;4(1):44–57.

Ren J, Wen L, Gao X, Jin C, Xue Y, Yao X. DOG 1.0: illustrator of protein domain structures. Cell Res. 2009;19(2):271–3. https://doi.org/10.1038/cr.2009.6.

Tokheim C, Bhattacharya R, Niknafs N, Gygax DM, Kim R, Ryan M, et al. Exome-scale discovery of hotspot mutation regions in human cancer using 3D protein structure. Cancer Res. 2016;76(13):3719–31. https://doi.org/10.1158/0008-5472.CAN-15-3190.

Niknafs N, Kim D, Kim R, Diekhans M, Ryan M, Stenson PD, et al. MuPIT interactive: webserver for mapping variant positions to annotated, interactive 3D structures. Hum Genet. 2013;132(11):1235–43. https://doi.org/10.1007/s00439-013-1325-0.