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Long E, Yin J, Shin JH, Li Y, Li B, Kane A, Patel H, Sun X, Wang C, Luong T, Xia J, Han Y, Byun J, Zhang T, Zhao W, Landi MT, Rothman N, Lan Q, Chang YS, Yu F, Amos CI, Shi J, Lee JG, Kim EY, Choi J. Context-aware single-cell multiomics approach identifies cell-type-specific lung cancer susceptibility genes. Nat Commun 2024; 15:7995. [PMID: 39266564 DOI: 10.1038/s41467-024-52356-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 09/03/2024] [Indexed: 09/14/2024] Open
Abstract
Genome-wide association studies (GWAS) identified over fifty loci associated with lung cancer risk. However, underlying mechanisms and target genes are largely unknown, as most risk-associated variants might regulate gene expression in a context-specific manner. Here, we generate a barcode-shared transcriptome and chromatin accessibility map of 117,911 human lung cells from age/sex-matched ever- and never-smokers to profile context-specific gene regulation. Identified candidate cis-regulatory elements (cCREs) are largely cell type-specific, with 37% detected in one cell type. Colocalization of lung cancer candidate causal variants (CCVs) with these cCREs combined with transcription factor footprinting prioritize the variants for 68% of the GWAS loci. CCV-colocalization and trait relevance score indicate that epithelial and immune cell categories, including rare cell types, contribute to lung cancer susceptibility the most. A multi-level cCRE-gene linking system identifies candidate susceptibility genes from 57% of the loci, where most loci display cell-category-specific target genes, suggesting context-specific susceptibility gene function.
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Affiliation(s)
- Erping Long
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jinhu Yin
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Ju Hye Shin
- Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Yuyan Li
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Bolun Li
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Alexander Kane
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Harsh Patel
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Xinti Sun
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Cong Wang
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Thong Luong
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Jun Xia
- Department of Biomedical Sciences, Creighton University, Omaha, NE, USA
| | - Younghun Han
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
| | - Jinyoung Byun
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
| | - Tongwu Zhang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Wei Zhao
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Maria Teresa Landi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Nathaniel Rothman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Qing Lan
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Yoon Soo Chang
- Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Fulong Yu
- Guangzhou National Laboratory, Guangzhou International Bio Island, Guangzhou, China
| | - Christopher I Amos
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
| | - Jianxin Shi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Jin Gu Lee
- Department of Thoracic and Cardiovascular Surgery, Yonsei University College of Medicine, Seoul, Republic of Korea.
| | - Eun Young Kim
- Department of Internal Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea.
| | - Jiyeon Choi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
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2
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Tervi A, Ramste M, Abner E, Cheng P, Lane JM, Maher M, Valliere J, Lammi V, Strausz S, Riikonen J, Nguyen T, Martyn GE, Sheth MU, Xia F, Docampo ML, Gu W, Esko T, Saxena R, Pirinen M, Palotie A, Ripatti S, Sinnott-Armstrong N, Daly M, Engreitz JM, Rabinovitch M, Heckman CA, Quertermous T, Jones SE, Ollila HM. Genetic and functional analysis of Raynaud's syndrome implicates loci in vasculature and immunity. CELL GENOMICS 2024; 4:100630. [PMID: 39142284 DOI: 10.1016/j.xgen.2024.100630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 04/25/2024] [Accepted: 07/14/2024] [Indexed: 08/16/2024]
Abstract
Raynaud's syndrome is a dysautonomia where exposure to cold causes vasoconstriction and hypoxia, particularly in the extremities. We performed meta-analysis in four cohorts and discovered eight loci (ADRA2A, IRX1, NOS3, ACVR2A, TMEM51, PCDH10-DT, HLA, and RAB6C) where ADRA2A, ACVR2A, NOS3, TMEM51, and IRX1 co-localized with expression quantitative trait loci (eQTLs), particularly in distal arteries. CRISPR gene editing further showed that ADRA2A and NOS3 loci modified gene expression and in situ RNAscope clarified the specificity of ADRA2A in small vessels and IRX1 around small capillaries in the skin. A functional contraction assay in the cold showed lower contraction in ADRA2A-deficient and higher contraction in ADRA2A-overexpressing smooth muscle cells. Overall, our study highlights the power of genome-wide association testing with functional follow-up as a method to understand complex diseases. The results indicate temperature-dependent adrenergic signaling through ADRA2A, effects at the microvasculature by IRX1, endothelial signaling by NOS3, and immune mechanisms by the HLA locus in Raynaud's syndrome.
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Affiliation(s)
- Anniina Tervi
- Institute for Molecular Medicine Finland, FIMM, Helsinki Institute of Life Science - HiLIFE, University of Helsinki, Helsinki, Finland.
| | - Markus Ramste
- Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Erik Abner
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Paul Cheng
- Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Jacqueline M Lane
- Division of Sleep and Circadian Disorders, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Matthew Maher
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jesse Valliere
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Vilma Lammi
- Institute for Molecular Medicine Finland, FIMM, Helsinki Institute of Life Science - HiLIFE, University of Helsinki, Helsinki, Finland
| | - Satu Strausz
- Institute for Molecular Medicine Finland, FIMM, Helsinki Institute of Life Science - HiLIFE, University of Helsinki, Helsinki, Finland
| | - Juha Riikonen
- Institute for Molecular Medicine Finland, FIMM, Helsinki Institute of Life Science - HiLIFE, University of Helsinki, Helsinki, Finland
| | - Trieu Nguyen
- Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Gabriella E Martyn
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA; Basic Science and Engineering Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
| | - Maya U Sheth
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA; Basic Science and Engineering Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
| | - Fan Xia
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA; Basic Science and Engineering Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA
| | - Mauro Lago Docampo
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA; Stanford Children's Health Betty Irene Moore Children's Heart Center, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Wenduo Gu
- Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Tõnu Esko
- Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Richa Saxena
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Matti Pirinen
- Institute for Molecular Medicine Finland, FIMM, Helsinki Institute of Life Science - HiLIFE, University of Helsinki, Helsinki, Finland; Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland; Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Aarno Palotie
- Institute for Molecular Medicine Finland, FIMM, Helsinki Institute of Life Science - HiLIFE, University of Helsinki, Helsinki, Finland; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland, FIMM, Helsinki Institute of Life Science - HiLIFE, University of Helsinki, Helsinki, Finland; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Public Health, Faculty of Medicine, University of Helsinki, Helsinki, Finland
| | - Nasa Sinnott-Armstrong
- Herbold Computational Biology Program, Public Health Sciences Division, Fred Hutch, Seattle, WA, USA
| | - Mark Daly
- Institute for Molecular Medicine Finland, FIMM, Helsinki Institute of Life Science - HiLIFE, University of Helsinki, Helsinki, Finland; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jesse M Engreitz
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA; Basic Science and Engineering Initiative, Stanford Children's Health, Betty Irene Moore Children's Heart Center, Stanford, CA, USA; The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Stanford Cardiovascular Institute, Stanford University, Stanford, CA, USA
| | - Marlene Rabinovitch
- Stanford Children's Health Betty Irene Moore Children's Heart Center, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Caroline A Heckman
- Institute for Molecular Medicine Finland, FIMM, Helsinki Institute of Life Science - HiLIFE, University of Helsinki, Helsinki, Finland
| | - Thomas Quertermous
- Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Samuel E Jones
- Institute for Molecular Medicine Finland, FIMM, Helsinki Institute of Life Science - HiLIFE, University of Helsinki, Helsinki, Finland
| | - Hanna M Ollila
- Institute for Molecular Medicine Finland, FIMM, Helsinki Institute of Life Science - HiLIFE, University of Helsinki, Helsinki, Finland; Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
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Engreitz JM, Lawson HA, Singh H, Starita LM, Hon GC, Carter H, Sahni N, Reddy TE, Lin X, Li Y, Munshi NV, Chahrour MH, Boyle AP, Hitz BC, Mortazavi A, Craven M, Mohlke KL, Pinello L, Wang T, Kundaje A, Yue F, Cody S, Farrell NP, Love MI, Muffley LA, Pazin MJ, Reese F, Van Buren E, Dey KK, Kircher M, Ma J, Radivojac P, Balliu B, Williams BA, Huangfu D, Park CY, Quertermous T, Das J, Calderwood MA, Fowler DM, Vidal M, Ferreira L, Mooney SD, Pejaver V, Zhao J, Gazal S, Koch E, Reilly SK, Sunyaev S, Carpenter AE, Buenrostro JD, Leslie CS, Savage RE, Giric S, Luo C, Plath K, Barrera A, Schubach M, Gschwind AR, Moore JE, Ahituv N, Yi SS, Hallgrimsdottir I, Gaulton KJ, Sakaue S, Booeshaghi S, Mattei E, Nair S, Pachter L, Wang AT, Shendure J, Agarwal V, Blair A, Chalkiadakis T, Chardon FM, Dash PM, Deng C, Hamazaki N, Keukeleire P, Kubo C, Lalanne JB, Maass T, Martin B, McDiarmid TA, Nobuhara M, Page NF, Regalado S, Sims J, Ushiki A, Best SM, Boyle G, Camp N, Casadei S, Da EY, Dawood M, Dawson SC, Fayer S, Hamm A, James RG, Jarvik GP, McEwen AE, Moore N, Pendyala S, Popp NA, Post M, Rubin AF, Smith NT, Stone J, Tejura M, Wang ZR, Wheelock MK, Woo I, Zapp BD, Amgalan D, Aradhana A, Arana SM, Bassik MC, Bauman JR, Bhattacharya A, Cai XS, Chen Z, Conley S, Deshpande S, Doughty BR, Du PP, Galante JA, Gifford C, Greenleaf WJ, Guo K, Gupta R, Isobe S, Jagoda E, Jain N, Jones H, Kang HY, Kim SH, Kim Y, Klemm S, Kundu R, Kundu S, Lago-Docampo M, Lee-Yow YC, Levin-Konigsberg R, Li DY, Lindenhofer D, Ma XR, Marinov GK, Martyn GE, McCreery CV, Metzl-Raz E, Monteiro JP, Montgomery MT, Mualim KS, Munger C, Munson G, Nguyen TC, Nguyen T, Palmisano BT, Pampari A, Rabinovitch M, Ramste M, Ray J, Roy KR, Rubio OM, Schaepe JM, Schnitzler G, Schreiber J, Sharma D, Sheth MU, Shi H, Singh V, Sinha R, Steinmetz LM, Tan J, Tan A, Tycko J, Valbuena RC, Amiri VVP, van Kooten MJFM, Vaughan-Jackson A, Venida A, Weldy CS, Worssam MD, Xia F, Yao D, Zeng T, Zhao Q, Zhou R, Chen ZS, Cimini BA, Coppin G, Coté AG, Haghighi M, Hao T, Hill DE, Lacoste J, Laval F, Reno C, Roth FP, Singh S, Spirohn-Fitzgerald K, Taipale M, Teelucksingh T, Tixhon M, Yadav A, Yang Z, Kraus WL, Armendariz DA, Dederich AE, Gogate A, El Hayek L, Goetsch SC, Kaur K, Kim HB, McCoy MK, Nzima MZ, Pinzón-Arteaga CA, Posner BA, Schmitz DA, Sivakumar S, Sundarrajan A, Wang L, Wang Y, Wu J, Xu L, Xu J, Yu L, Zhang Y, Zhao H, Zhou Q, Won H, Bell JL, Broadaway KA, Degner KN, Etheridge AS, Koller BH, Mah W, Mu W, Ritola KD, Rosen JD, Schoenrock SA, Sharp RA, Bauer D, Lettre G, Sherwood R, Becerra B, Blaine LJ, Che E, Francoeur MJ, Gibbs EN, Kim N, King EM, Kleinstiver BP, Lecluze E, Li Z, Patel ZM, Phan QV, Ryu J, Starr ML, Wu T, Gersbach CA, Crawford GE, Allen AS, Majoros WH, Iglesias N, Rai R, Venukuttan R, Li B, Anglen T, Bounds LR, Hamilton MC, Liu S, McCutcheon SR, McRoberts Amador CD, Reisman SJ, ter Weele MA, Bodle JC, Streff HL, Siklenka K, Strouse K, Bernstein BE, Babu J, Corona GB, Dong K, Duarte FM, Durand NC, Epstein CB, Fan K, Gaskell E, Hall AW, Ham AM, Knudson MK, Shoresh N, Wekhande S, White CM, Xi W, Satpathy AT, Corces MR, Chang SH, Chin IM, Gardner JM, Gardell ZA, Gutierrez JC, Johnson AW, Kampman L, Kasowski M, Lareau CA, Liu V, Ludwig LS, McGinnis CS, Menon S, Qualls A, Sandor K, Turner AW, Ye CJ, Yin Y, Zhang W, Wold BJ, Carilli M, Cheong D, Filibam G, Green K, Kawauchi S, Kim C, Liang H, Loving R, Luebbert L, MacGregor G, Merchan AG, Rebboah E, Rezaie N, Sakr J, Sullivan DK, Swarna N, Trout D, Upchurch S, Weber R, Castro CP, Chou E, Feng F, Guerra A, Huang Y, Jiang L, Liu J, Mills RE, Qian W, Qin T, Sartor MA, Sherpa RN, Wang J, Wang Y, Welch JD, Zhang Z, Zhao N, Mukherjee S, Page CD, Clarke S, Doty RW, Duan Y, Gordan R, Ko KY, Li S, Li B, Thomson A, Raychaudhuri S, Price A, Ali TA, Dey KK, Durvasula A, Kellis M, Iakoucheva LM, Kakati T, Chen Y, Benazouz M, Jain S, Zeiberg D, De Paolis Kaluza MC, Velyunskiy M, Gasch A, Huang K, Jin Y, Lu Q, Miao J, Ohtake M, Scopel E, Steiner RD, Sverchkov Y, Weng Z, Garber M, Fu Y, Haas N, Li X, Phalke N, Shan SC, Shedd N, Yu T, Zhang Y, Zhou H, Battle A, Jerby L, Kotler E, Kundu S, Marderstein AR, Montgomery SB, Nigam A, Padhi EM, Patel A, Pritchard J, Raine I, Ramalingam V, Rodrigues KB, Schreiber JM, Singhal A, Sinha R, Wang AT, Abundis M, Bisht D, Chakraborty T, Fan J, Hall DR, Rarani ZH, Jain AK, Kaundal B, Keshari S, McGrail D, Pease NA, Yi VF, Wu H, Kannan S, Song H, Cai J, Gao Z, Kurzion R, Leu JI, Li F, Liang D, Ming GL, Musunuru K, Qiu Q, Shi J, Su Y, Tishkoff S, Xie N, Yang Q, Yang W, Zhang H, Zhang Z, Beer MA, Hadjantonakis AK, Adeniyi S, Cho H, Cutler R, Glenn RA, Godovich D, Hu N, Jovanic S, Luo R, Oh JW, Razavi-Mohseni M, Shigaki D, Sidoli S, Vierbuchen T, Wang X, Williams B, Yan J, Yang D, Yang Y, Sander M, Gaulton KJ, Ren B, Bartosik W, Indralingam HS, Klie A, Mummey H, Okino ML, Wang G, Zemke NR, Zhang K, Zhu H, Zaitlen N, Ernst J, Langerman J, Li T, Sun Y, Rudensky AY, Periyakoil PK, Gao VR, Smith MH, Thomas NM, Donlin LT, Lakhanpal A, Southard KM, Ardy RC, Cherry JM, Gerstein MB, Andreeva K, Assis PR, Borsari B, Douglass E, Dong S, Gabdank I, Graham K, Jolanki O, Jou J, Kagda MS, Lee JW, Li M, Lin K, Miyasato SR, Rozowsky J, Small C, Spragins E, Tanaka FY, Whaling IM, Youngworth IA, Sloan CA, Belter E, Chen X, Chisholm RL, Dickson P, Fan C, Fulton L, Li D, Lindsay T, Luan Y, Luo Y, Lyu H, Ma X, Macias-Velasco J, Miga KH, Quaid K, Stitziel N, Stranger BE, Tomlinson C, Wang J, Zhang W, Zhang B, Zhao G, Zhuo X, Brennand K, Ciccia A, Hayward SB, Huang JW, Leuzzi G, Taglialatela A, Thakar T, Vaitsiankova A, Dey KK, Ali TA, Kim A, Grimes HL, Salomonis N, Gupta R, Fang S, Lee-Kim V, Heinig M, Losert C, Jones TR, Donnard E, Murphy M, Roberts E, Song S, Mostafavi S, Sasse A, Spiro A, Pennacchio LA, Kato M, Kosicki M, Mannion B, Slaven N, Visel A, Pollard KS, Drusinsky S, Whalen S, Ray J, Harten IA, Ho CH, Sanjana NE, Caragine C, Morris JA, Seruggia D, Kutschat AP, Wittibschlager S, Xu H, Fu R, He W, Zhang L, Osorio D, Bly Z, Calluori S, Gilchrist DA, Hutter CM, Morris SA, Samer EK. Deciphering the impact of genomic variation on function. Nature 2024; 633:47-57. [PMID: 39232149 DOI: 10.1038/s41586-024-07510-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 05/02/2024] [Indexed: 09/06/2024]
Abstract
Our genomes influence nearly every aspect of human biology-from molecular and cellular functions to phenotypes in health and disease. Studying the differences in DNA sequence between individuals (genomic variation) could reveal previously unknown mechanisms of human biology, uncover the basis of genetic predispositions to diseases, and guide the development of new diagnostic tools and therapeutic agents. Yet, understanding how genomic variation alters genome function to influence phenotype has proved challenging. To unlock these insights, we need a systematic and comprehensive catalogue of genome function and the molecular and cellular effects of genomic variants. Towards this goal, the Impact of Genomic Variation on Function (IGVF) Consortium will combine approaches in single-cell mapping, genomic perturbations and predictive modelling to investigate the relationships among genomic variation, genome function and phenotypes. IGVF will create maps across hundreds of cell types and states describing how coding variants alter protein activity, how noncoding variants change the regulation of gene expression, and how such effects connect through gene-regulatory and protein-interaction networks. These experimental data, computational predictions and accompanying standards and pipelines will be integrated into an open resource that will catalyse community efforts to explore how our genomes influence biology and disease across populations.
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Unger Avila P, Padvitski T, Leote AC, Chen H, Saez-Rodriguez J, Kann M, Beyer A. Gene regulatory networks in disease and ageing. Nat Rev Nephrol 2024; 20:616-633. [PMID: 38867109 DOI: 10.1038/s41581-024-00849-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/15/2024] [Indexed: 06/14/2024]
Abstract
The precise control of gene expression is required for the maintenance of cellular homeostasis and proper cellular function, and the declining control of gene expression with age is considered a major contributor to age-associated changes in cellular physiology and disease. The coordination of gene expression can be represented through models of the molecular interactions that govern gene expression levels, so-called gene regulatory networks. Gene regulatory networks can represent interactions that occur through signal transduction, those that involve regulatory transcription factors, or statistical models of gene-gene relationships based on the premise that certain sets of genes tend to be coexpressed across a range of conditions and cell types. Advances in experimental and computational technologies have enabled the inference of these networks on an unprecedented scale and at unprecedented precision. Here, we delineate different types of gene regulatory networks and their cell-biological interpretation. We describe methods for inferring such networks from large-scale, multi-omics datasets and present applications that have aided our understanding of cellular ageing and disease mechanisms.
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Affiliation(s)
- Paula Unger Avila
- Cluster of Excellence on Cellular Stress Responses in Aging-associated Diseases (CECAD), University of Cologne, Cologne, Germany
| | - Tsimafei Padvitski
- Cluster of Excellence on Cellular Stress Responses in Aging-associated Diseases (CECAD), University of Cologne, Cologne, Germany
| | - Ana Carolina Leote
- Cluster of Excellence on Cellular Stress Responses in Aging-associated Diseases (CECAD), University of Cologne, Cologne, Germany
| | - He Chen
- Cluster of Excellence on Cellular Stress Responses in Aging-associated Diseases (CECAD), University of Cologne, Cologne, Germany
- Department II of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Julio Saez-Rodriguez
- Faculty of Medicine and Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg University, Heidelberg, Germany
| | - Martin Kann
- Department II of Internal Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
- Center for Molecular Medicine Cologne, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Andreas Beyer
- Cluster of Excellence on Cellular Stress Responses in Aging-associated Diseases (CECAD), University of Cologne, Cologne, Germany.
- Center for Molecular Medicine Cologne, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.
- Institute for Genetics, Faculty of Mathematics and Natural Sciences, University of Cologne, Cologne, Germany.
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Tambets R, Kolde A, Kolberg P, Love MI, Alasoo K. Extensive co-regulation of neighboring genes complicates the use of eQTLs in target gene prioritization. HGG ADVANCES 2024; 5:100348. [PMID: 39210598 DOI: 10.1016/j.xhgg.2024.100348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 08/27/2024] [Accepted: 08/27/2024] [Indexed: 09/04/2024] Open
Abstract
Identifying causal genes underlying genome-wide association studies (GWASs) is a fundamental problem in human genetics. Although colocalization with gene expression quantitative trait loci (eQTLs) is often used to prioritize GWAS target genes, systematic benchmarking has been limited due to unavailability of large ground truth datasets. Here, we re-analyzed plasma protein QTL data from 3,301 individuals of the INTERVAL cohort together with 131 eQTL Catalog datasets. Focusing on variants located within or close to the affected protein identified 793 proteins with at least one cis-pQTL where we could assume that the most likely causal gene was the gene coding for the protein. We then benchmarked the ability of cis-eQTLs to recover these causal genes by comparing three Bayesian colocalization methods (coloc.susie, coloc.abf, and CLPP) and five Mendelian randomization (MR) approaches (three varieties of inverse-variance weighted MR, MR-RAPS, and MRLocus). We found that assigning fine-mapped pQTLs to their closest protein coding genes outperformed all colocalization methods regarding both precision (71.9%) and recall (76.9%). Furthermore, the colocalization method with the highest recall (coloc.susie - 46.3%) also had the lowest precision (45.1%). Combining evidence from multiple conditionally distinct colocalizing QTLs with MR increased precision to 81%, but this was accompanied by a large reduction in recall to 7.1%. Furthermore, the choice of the MR method greatly affected performance, with the standard inverse-variance-weighted MR often producing many false positives. Our results highlight that linking GWAS variants to target genes remains challenging with eQTL evidence alone, and prioritizing novel targets requires triangulation of evidence from multiple sources.
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Affiliation(s)
- Ralf Tambets
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Anastassia Kolde
- Institute of Genomics, University of Tartu, Tartu, Estonia; Institute of Mathematics and Statistics, University of Tartu, Tartu, Estonia
| | - Peep Kolberg
- Institute of Computer Science, University of Tartu, Tartu, Estonia
| | - Michael I Love
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kaur Alasoo
- Institute of Computer Science, University of Tartu, Tartu, Estonia.
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Li J, Wang Y, Wang Z, Wei Y, Diao P, Wu Y, Wang D, Jiang H, Wang Y, Cheng J. Super-Enhancer Driven LIF/LIFR-STAT3-SOX2 Regulatory Feedback Loop Promotes Cancer Stemness in Head and Neck Squamous Cell Carcinoma. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024:e2404476. [PMID: 39206755 DOI: 10.1002/advs.202404476] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2024] [Revised: 08/17/2024] [Indexed: 09/04/2024]
Abstract
Super-enhancers (SEs) have been recognized as key epigenetic regulators underlying cancer stemness and malignant traits by aberrant transcriptional control and promising therapeutic targets against human cancers. However, the SE landscape and their roles during head and neck squamous cell carcinoma (HNSCC) development especially in cancer stem cells (CSCs) maintenance remain underexplored yet. Here, we identify leukemia inhibitory factor (LIF)-SE as a representative oncogenic SE to activate LIF transcription in HNSCC. LIF secreted from cancer cells and cancer-associated fibroblasts promotes cancer stemness by driving SOX2 transcription in an autocrine/paracrine manner, respectively. Mechanistically, enhancer elements E1, 2, 4 within LIF-SE recruit SOX2/SMAD3/BRD4/EP300 to facilitate LIF transcription; LIF activates downstream LIFR-STAT3 signaling to drive SOX2 transcription, thus forming a previously unknown regulatory feedback loop (LIF-SE-LIF/LIFR-STAT3-SOX2) to maintain LIF overexpression and CSCs stemness. Clinically, increased LIF abundance in clinical samples correlate with malignant clinicopathological features and patient prognosis; higher LIF concentrations in presurgical plasma dramatically diminish following cancer eradication. Therapeutically, pharmacological targeting LIF-SE-LIF/LIFR-STAT3 significantly impairs tumor growth and reduces CSC subpopulations in xenograft and PDX models. Our findings reveal a hitherto uncharacterized LIF-SE-mediated auto-regulatory loop in regulating HNSCC stemness and highlight LIF as a novel noninvasive biomarker and potential therapeutic target for HNSCC.
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Affiliation(s)
- Jin Li
- Department of Oral and Maxillofacial Surgery, The Affiliated Stomatological Hospital of Nanjing Medical University, Jiangsu, 210029, China
- Jiangsu Key Laboratory of Oral Disease, Nanjing Medical University, Jiangsu, 210029, China
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Jiangsu, 210029, China
| | - Yuhan Wang
- Department of Oral and Maxillofacial Surgery, The Affiliated Stomatological Hospital of Nanjing Medical University, Jiangsu, 210029, China
- Jiangsu Key Laboratory of Oral Disease, Nanjing Medical University, Jiangsu, 210029, China
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Jiangsu, 210029, China
| | - Ziyu Wang
- Department of Oral and Maxillofacial Surgery, The Affiliated Stomatological Hospital of Nanjing Medical University, Jiangsu, 210029, China
- Jiangsu Key Laboratory of Oral Disease, Nanjing Medical University, Jiangsu, 210029, China
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Jiangsu, 210029, China
| | - Yuxiang Wei
- Jiangsu Key Laboratory of Oral Disease, Nanjing Medical University, Jiangsu, 210029, China
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Jiangsu, 210029, China
| | - Pengfei Diao
- Jiangsu Key Laboratory of Oral Disease, Nanjing Medical University, Jiangsu, 210029, China
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Jiangsu, 210029, China
| | - Yaping Wu
- Jiangsu Key Laboratory of Oral Disease, Nanjing Medical University, Jiangsu, 210029, China
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Jiangsu, 210029, China
| | - Dongmiao Wang
- Department of Oral and Maxillofacial Surgery, The Affiliated Stomatological Hospital of Nanjing Medical University, Jiangsu, 210029, China
| | - Hongbing Jiang
- Department of Oral and Maxillofacial Surgery, The Affiliated Stomatological Hospital of Nanjing Medical University, Jiangsu, 210029, China
- Jiangsu Key Laboratory of Oral Disease, Nanjing Medical University, Jiangsu, 210029, China
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Jiangsu, 210029, China
| | - Yanling Wang
- Department of Oral and Maxillofacial Surgery, The Affiliated Stomatological Hospital of Nanjing Medical University, Jiangsu, 210029, China
- Jiangsu Key Laboratory of Oral Disease, Nanjing Medical University, Jiangsu, 210029, China
| | - Jie Cheng
- Department of Oral and Maxillofacial Surgery, The Affiliated Stomatological Hospital of Nanjing Medical University, Jiangsu, 210029, China
- Jiangsu Key Laboratory of Oral Disease, Nanjing Medical University, Jiangsu, 210029, China
- Jiangsu Province Engineering Research Center of Stomatological Translational Medicine, Nanjing Medical University, Jiangsu, 210029, China
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7
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Bigdeli TB, Chatzinakos C, Bendl J, Barr PB, Venkatesh S, Gorman BR, Clarence T, Genovese G, Iyegbe CO, Peterson RE, Kolokotronis SO, Burstein D, Meyers JL, Li Y, Rajeevan N, Sayward F, Cheung KH, DeLisi LE, Kosten TR, Zhao H, Achtyes E, Buckley P, Malaspina D, Lehrer D, Rapaport MH, Braff DL, Pato MT, Fanous AH, Pato CN, Consortium P, Huang GD, Muralidhar S, Gaziano JM, Pyarajan S, Girdhar K, Lee D, Hoffman GE, Aslan M, Fullard JF, Voloudakis G, Harvey PD, Roussos P. Biological Insights from Schizophrenia-associated Loci in Ancestral Populations. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.08.27.24312631. [PMID: 39252912 PMCID: PMC11383513 DOI: 10.1101/2024.08.27.24312631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/11/2024]
Abstract
Large-scale genome-wide association studies of schizophrenia have uncovered hundreds of associated loci but with extremely limited representation of African diaspora populations. We surveyed electronic health records of 200,000 individuals of African ancestry in the Million Veteran and All of Us Research Programs, and, coupled with genotype-level data from four case-control studies, realized a combined sample size of 13,012 affected and 54,266 unaffected persons. Three genome-wide significant signals - near PLXNA4 , PMAIP1 , and TRPA1 - are the first to be independently identified in populations of predominantly African ancestry. Joint analyses of African, European, and East Asian ancestries across 86,981 cases and 303,771 controls, yielded 376 distinct autosomal loci, which were refined to 708 putatively causal variants via multi-ancestry fine-mapping. Utilizing single-cell functional genomic data from human brain tissue and two complementary approaches, transcriptome-wide association studies and enhancer-promoter contact mapping, we identified a consensus set of 94 genes across ancestries and pinpointed the specific cell types in which they act. We identified reproducible associations of schizophrenia polygenic risk scores with schizophrenia diagnoses and a range of other mental and physical health problems. Our study addresses a longstanding gap in the generalizability of research findings for schizophrenia across ancestral populations, underlining shared biological underpinnings of schizophrenia across global populations in the presence of broadly divergent risk allele frequencies.
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8
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Weinstock JS, Chaudhry SA, Ioannou M, Viskadourou M, Reventun P, Jakubek YA, Alexander Liggett L, Laurie C, Broome JG, Khan A, Taylor KD, Guo X, Peyser PA, Boerwinkle E, Chami N, Kenny EE, Loos RJ, Psaty BM, Russell TP, Brody JA, Yun JH, Cho MH, Vasan RS, Kardia SL, Smith JA, Raffield LM, Bidulescu A, O'Brien E, de Andrade M, Rotter JI, Rich SS, Tracy RP, Chen YDI, Gu CC, Hsiung CA, Kooperberg C, Haring B, Nassir R, Mathias R, Reiner A, Sankaran V, Lowenstein CJ, Blackwell TW, Abecasis GR, Smith AV, Kang HM, Natarajan P, Jaiswal S, Bick A, Post WS, Scheet P, Auer P, Karantanos T, Battle A, Arvanitis M. The Genetic Determinants and Genomic Consequences of Non-Leukemogenic Somatic Point Mutations. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.08.22.24312319. [PMID: 39228737 PMCID: PMC11370504 DOI: 10.1101/2024.08.22.24312319] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Abstract
Clonal hematopoiesis (CH) is defined by the expansion of a lineage of genetically identical cells in blood. Genetic lesions that confer a fitness advantage, such as point mutations or mosaic chromosomal alterations (mCAs) in genes associated with hematologic malignancy, are frequent mediators of CH. However, recent analyses of both single cell-derived colonies of hematopoietic cells and population sequencing cohorts have revealed CH frequently occurs in the absence of known driver genetic lesions. To characterize CH without known driver genetic lesions, we used 51,399 deeply sequenced whole genomes from the NHLBI TOPMed sequencing initiative to perform simultaneous germline and somatic mutation analyses among individuals without leukemogenic point mutations (LPM), which we term CH-LPMneg. We quantified CH by estimating the total mutation burden. Because estimating somatic mutation burden without a paired-tissue sample is challenging, we developed a novel statistical method, the Genomic and Epigenomic informed Mutation (GEM) rate, that uses external genomic and epigenomic data sources to distinguish artifactual signals from true somatic mutations. We performed a genome-wide association study of GEM to discover the germline determinants of CH-LPMneg. After fine-mapping and variant-to-gene analyses, we identified seven genes associated with CH-LPMneg ( TCL1A, TERT, SMC4, NRIP1, PRDM16 , MSRA , SCARB1 ), and one locus associated with a sex-associated mutation pathway ( SRGAP2C) . We performed a secondary analysis excluding individuals with mCAs, finding that the genetic architecture was largely unaffected by their inclusion. Functional analyses of SMC4 and NRIP1 implicated altered HSC self-renewal and proliferation as the primary mediator of mutation burden in blood. We then performed comprehensive multi-tissue transcriptomic analyses, finding that the expression levels of 404 genes are associated with GEM. Finally, we performed phenotypic association meta-analyses across four cohorts, finding that GEM is associated with increased white blood cell count and increased risk for incident peripheral artery disease, but is not significantly associated with incident stroke or coronary disease events. Overall, we develop GEM for quantifying mutation burden from WGS without a paired-tissue sample and use GEM to discover the genetic, genomic, and phenotypic correlates of CH-LPMneg.
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9
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Burton EA, Argenziano M, Cook K, Ridler M, Lu S, Su C, Manduchi E, Littleton SH, Leonard ME, Hodge KM, Wang LS, Schellenberg GD, Johnson ME, Pahl MC, Pippin JA, Wells AD, Anderson SA, Brown CD, Grant SFA, Chesi A. Variant-to-function mapping of late-onset Alzheimer's disease GWAS signals in human microglial cell models implicates RTFDC1 at the CASS4 locus. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.22.609230. [PMID: 39229212 PMCID: PMC11370593 DOI: 10.1101/2024.08.22.609230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Abstract
Late-onset Alzheimer's disease (LOAD) research has principally focused on neurons over the years due to their known role in the production of amyloid beta plaques and neurofibrillary tangles. In contrast, recent genomic studies of LOAD have implicated microglia as culprits of the prolonged inflammation exacerbating the neurodegeneration observed in patient brains. Indeed, recent LOAD genome-wide association studies (GWAS) have reported multiple loci near genes related to microglial function, including TREM2, ABI3, and CR1. However, GWAS alone cannot pinpoint underlying causal variants or effector genes at such loci, as most signals reside in non-coding regions of the genome and could presumably confer their influence frequently via long-range regulatory interactions. We elected to carry out a combination of ATAC-seq and high-resolution promoter-focused Capture-C in two human microglial cell models (iPSC-derived microglia and HMC3) in order to physically map interactions between LOAD GWAS-implicated candidate causal variants and their corresponding putative effector genes. Notably, we observed consistent evidence that rs6024870 at the GWAS CASS4 locus contacted the promoter of nearby gene, RTFDC1. We subsequently observed a directionallly consistent decrease in RTFDC1 expression with the the protective minor A allele of rs6024870 via both luciferase assays in HMC3 cells and expression studies in primary human microglia. Through CRISPR-Cas9-mediated deletion of the putative regulatory region harboring rs6024870 in HMC3 cells, we observed increased pro-inflammatory cytokine secretion and decreased DNA double strand break repair related, at least in part, to RTFDC1 expression levels. Our variant-to-function approach therefore reveals that the rs6024870-harboring regulatory element at the LOAD 'CASS4' GWAS locus influences both microglial inflammatory capacity and DNA damage resolution, along with cumulative evidence implicating RTFDC1 as a novel candidate effector gene.
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Affiliation(s)
- Elizabeth A Burton
- Center for Spatial and Functional Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Human Genetics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- CAMB Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Mariana Argenziano
- Center for Spatial and Functional Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Kieona Cook
- Center for Spatial and Functional Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Molly Ridler
- Center for Spatial and Functional Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Human Genetics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Sumei Lu
- Center for Spatial and Functional Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Human Genetics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Chun Su
- Center for Spatial and Functional Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Human Genetics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Elisabetta Manduchi
- Center for Spatial and Functional Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Human Genetics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Sheridan H Littleton
- Center for Spatial and Functional Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Human Genetics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- CAMB Graduate Group, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Michelle E Leonard
- Center for Spatial and Functional Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Human Genetics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | | | - Li-San Wang
- Department of Pathology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Gerard D Schellenberg
- Department of Pathology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Matthew E Johnson
- Center for Spatial and Functional Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Human Genetics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Matthew C Pahl
- Center for Spatial and Functional Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Human Genetics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - James A Pippin
- Center for Spatial and Functional Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Human Genetics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Andrew D Wells
- Center for Spatial and Functional Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Human Genetics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pathology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Stewart A Anderson
- Department of Child and Adolescent Psychiatry and Behavioral Services, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Christopher D Brown
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Struan F A Grant
- Center for Spatial and Functional Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Division of Human Genetics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Division Endocrinology and Diabetes, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Institute for Diabetes, Obesity and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Alessandra Chesi
- Center for Spatial and Functional Genomics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Department of Pathology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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10
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Rood JE, Hupalowska A, Regev A. Toward a foundation model of causal cell and tissue biology with a Perturbation Cell and Tissue Atlas. Cell 2024; 187:4520-4545. [PMID: 39178831 DOI: 10.1016/j.cell.2024.07.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2024] [Revised: 07/15/2024] [Accepted: 07/21/2024] [Indexed: 08/26/2024]
Abstract
Comprehensively charting the biologically causal circuits that govern the phenotypic space of human cells has often been viewed as an insurmountable challenge. However, in the last decade, a suite of interleaved experimental and computational technologies has arisen that is making this fundamental goal increasingly tractable. Pooled CRISPR-based perturbation screens with high-content molecular and/or image-based readouts are now enabling researchers to probe, map, and decipher genetically causal circuits at increasing scale. This scale is now eminently suitable for the deployment of artificial intelligence and machine learning (AI/ML) to both direct further experiments and to predict or generate information that was not-and sometimes cannot-be gathered experimentally. By combining and iterating those through experiments that are designed for inference, we now envision a Perturbation Cell Atlas as a generative causal foundation model to unify human cell biology.
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Affiliation(s)
| | | | - Aviv Regev
- Genentech, South San Francisco, CA, USA.
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11
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Johansen NJ, Kempynck N, Zemke NR, Somasundaram S, Winter SD, Hooper M, Dwivedi D, Lohia R, Wehbe F, Li B, Abaffyová D, Armand EJ, Man JD, Eksi EC, Hecker N, Hulselmans G, Konstantakos V, Mauduit D, Mich JK, Partel G, Daigle TL, Levi BP, Zhang K, Tanaka Y, Gillis J, Ting JT, Ben-Simon Y, Miller J, Ecker JR, Ren B, Aerts S, Lein ES, Tasic B, Bakken TE. Evaluating Methods for the Prediction of Cell Type-Specific Enhancers in the Mammalian Cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.21.609075. [PMID: 39229027 PMCID: PMC11370467 DOI: 10.1101/2024.08.21.609075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/05/2024]
Abstract
Identifying cell type-specific enhancers in the brain is critical to building genetic tools for investigating the mammalian brain. Computational methods for functional enhancer prediction have been proposed and validated in the fruit fly and not yet the mammalian brain. We organized the 'Brain Initiative Cell Census Network (BICCN) Challenge: Predicting Functional Cell Type-Specific Enhancers from Cross-Species Multi-Omics' to assess machine learning and feature-based methods designed to nominate enhancer DNA sequences to target cell types in the mouse cortex. Methods were evaluated based on in vivo validation data from hundreds of cortical cell type-specific enhancers that were previously packaged into individual AAV vectors and retro-orbitally injected into mice. We find that open chromatin was a key predictor of functional enhancers, and sequence models improved prediction of non-functional enhancers that can be deprioritized as opposed to pursued for in vivo testing. Sequence models also identified cell type-specific transcription factor codes that can guide designs of in silico enhancers. This community challenge establishes a benchmark for enhancer prioritization algorithms and reveals computational approaches and molecular information that are crucial for the identification of functional enhancers for mammalian cortical cell types. The results of this challenge bring us closer to understanding the complex gene regulatory landscape of the mammalian brain and help us design more efficient genetic tools and potential gene therapies for human neurological diseases.
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12
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Kimura Y, Ono Y, Katayama K, Imoto S. IVEA: an integrative variational Bayesian inference method for predicting enhancer-gene regulatory interactions. BIOINFORMATICS ADVANCES 2024; 4:vbae118. [PMID: 39193566 PMCID: PMC11349192 DOI: 10.1093/bioadv/vbae118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 06/26/2024] [Accepted: 08/18/2024] [Indexed: 08/29/2024]
Abstract
Motivation Enhancers play critical roles in cell-type-specific transcriptional control. Despite the identification of thousands of candidate enhancers, unravelling their regulatory relationships with their target genes remains challenging. Therefore, computational approaches are needed to accurately infer enhancer-gene regulatory relationships. Results In this study, we propose a new method, IVEA, that predicts enhancer-gene regulatory interactions by estimating promoter and enhancer activities. Its statistical model is based on the gene regulatory mechanism of transcriptional bursting, which is characterized by burst size and frequency controlled by promoters and enhancers, respectively. Using transcriptional readouts, chromatin accessibility, and chromatin contact data as inputs, promoter and enhancer activities were estimated using variational Bayesian inference, and the contribution of each enhancer-promoter pair to target gene transcription was calculated. Our analysis demonstrates that the proposed method can achieve high prediction accuracy and provide biologically relevant enhancer-gene regulatory interactions. Availability and implementation The IVEA code is available on GitHub at https://github.com/yasumasak/ivea. The publicly available datasets used in this study are described in Supplementary Table S4.
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Affiliation(s)
- Yasumasa Kimura
- DX Drug Discovery Department, Daiichi Sankyo RD Novare Co., Ltd., Edogawa-ku, Tokyo 134-8630, Japan
- Division of Health Medical Intelligence, Human Genome Center, Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo 108-8639, Japan
- Research Function Research Innovation Planning Department, Daiichi Sankyo Co., Ltd., Edogawa-ku, Tokyo 134-8630, Japan
| | - Yoshimasa Ono
- DX Drug Discovery Department, Daiichi Sankyo RD Novare Co., Ltd., Edogawa-ku, Tokyo 134-8630, Japan
| | - Kotoe Katayama
- Division of Health Medical Intelligence, Human Genome Center, Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo 108-8639, Japan
| | - Seiya Imoto
- Division of Health Medical Intelligence, Human Genome Center, Institute of Medical Science, The University of Tokyo, Minato-ku, Tokyo 108-8639, Japan
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13
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Shi C, Liu L, Hyeon C. Hi-C-guided many-polymer model to decipher 3D genome organization. Biophys J 2024; 123:2574-2583. [PMID: 38932457 PMCID: PMC11365109 DOI: 10.1016/j.bpj.2024.06.023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 05/27/2024] [Accepted: 06/21/2024] [Indexed: 06/28/2024] Open
Abstract
We propose a high-throughput chromosome conformation capture data-based many-polymer model that allows us to generate an ensemble of multi-scale genome structures. We demonstrate the efficacy of our model by validating the generated structures against experimental measurements and employ them to address key questions regarding genome organization. Our model first confirms a significant correlation between chromosome size and nuclear positioning. Specifically, smaller chromosomes are distributed at the core region, whereas larger chromosomes are at the periphery, interacting with the nuclear envelope. The spatial distribution of A- and B-type compartments, which is nontrivial to infer from the corresponding high-throughput chromosome conformation capture maps alone, can also be elucidated using our model, accounting for an issue such as the effect of chromatin-lamina interaction on the compartmentalization of conventional and inverted nuclei. In accordance with imaging data, the overall shape of the 3D genome structures generated from our model displays significant variation. As a case study, we apply our method to the yellow fever mosquito genome, finding that the predicted morphology displays, on average, a more globular shape than the previously suggested spindle-like organization and that our prediction better aligns with the fluorescence in situ hybridization data. Our model has great potential to be extended to investigate many outstanding issues concerning 3D genome organization.
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Affiliation(s)
- Chen Shi
- Key Laboratory of Optical Field Manipulation of Zhejiang Province, Department of Physics, Zhejiang Sci-Tech University, Hangzhou, China
| | - Lei Liu
- Key Laboratory of Optical Field Manipulation of Zhejiang Province, Department of Physics, Zhejiang Sci-Tech University, Hangzhou, China.
| | - Changbong Hyeon
- School of Computational Sciences, Korea Institute for Advanced Study, Seoul, Korea.
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14
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Wu B, Xu W, Wu K, Li Y, Hu M, Feng C, Zhu C, Zheng J, Cui X, Li J, Fan D, Zhang F, Liu Y, Chen J, Liu C, Li G, Qiu Q, Qu K, Wang W, Wang K. Single-cell analysis of the amphioxus hepatic caecum and vertebrate liver reveals genetic mechanisms of vertebrate liver evolution. Nat Ecol Evol 2024:10.1038/s41559-024-02510-9. [PMID: 39152328 DOI: 10.1038/s41559-024-02510-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2023] [Accepted: 07/19/2024] [Indexed: 08/19/2024]
Abstract
The evolution of the vertebrate liver is a prime example of the evolution of complex organs, yet the driving genetic factors behind it remain unknown. Here we study the evolutionary genetics of liver by comparing the amphioxus hepatic caecum and the vertebrate liver, as well as examining the functional transition within vertebrates. Using in vivo and in vitro experiments, single-cell/nucleus RNA-seq data and gene knockout experiments, we confirm that the amphioxus hepatic caecum and vertebrate liver are homologous organs and show that the emergence of ohnologues from two rounds of whole-genome duplications greatly contributed to the functional complexity of the vertebrate liver. Two ohnologues, kdr and flt4, play an important role in the development of liver sinusoidal endothelial cells. In addition, we found that liver-related functions such as coagulation and bile production evolved in a step-by-step manner, with gene duplicates playing a crucial role. We reconstructed the genetic footprint of the transfer of haem detoxification from the liver to the spleen during vertebrate evolution. Together, these findings challenge the previous hypothesis that organ evolution is primarily driven by regulatory elements, underscoring the importance of gene duplicates in the emergence and diversification of a complex organ.
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Affiliation(s)
- Baosheng Wu
- Shaanxi Key Laboratory of Qinling Ecological Intelligent Monitoring and Protection, School of Ecology and Environment, Northwestern Polytechnical University, Xi'an, China
- Guangdong Key Laboratory of Animal Conservation and Resource Utilization, Institute of Zoology, Guangdong Academy of Sciences, Guangzhou, China
| | - Wenjie Xu
- Shaanxi Key Laboratory of Qinling Ecological Intelligent Monitoring and Protection, School of Ecology and Environment, Northwestern Polytechnical University, Xi'an, China
| | - Kunjin Wu
- Key Laboratory of Surgical Critical Care and Life Support (Xi'an Jiaotong University), Ministry of Education, Xi'an, China
- Department of Hepatobiliary Surgery and Liver Transplantation, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Ye Li
- Shaanxi Key Laboratory of Qinling Ecological Intelligent Monitoring and Protection, School of Ecology and Environment, Northwestern Polytechnical University, Xi'an, China
| | - Mingliang Hu
- Shaanxi Key Laboratory of Qinling Ecological Intelligent Monitoring and Protection, School of Ecology and Environment, Northwestern Polytechnical University, Xi'an, China
| | - Chenguang Feng
- Shaanxi Key Laboratory of Qinling Ecological Intelligent Monitoring and Protection, School of Ecology and Environment, Northwestern Polytechnical University, Xi'an, China
| | - Chenglong Zhu
- Shaanxi Key Laboratory of Qinling Ecological Intelligent Monitoring and Protection, School of Ecology and Environment, Northwestern Polytechnical University, Xi'an, China
| | - Jiangmin Zheng
- Shaanxi Key Laboratory of Qinling Ecological Intelligent Monitoring and Protection, School of Ecology and Environment, Northwestern Polytechnical University, Xi'an, China
| | - Xinxin Cui
- Shaanxi Key Laboratory of Qinling Ecological Intelligent Monitoring and Protection, School of Ecology and Environment, Northwestern Polytechnical University, Xi'an, China
| | - Jing Li
- Key Laboratory of Surgical Critical Care and Life Support (Xi'an Jiaotong University), Ministry of Education, Xi'an, China
- Department of Hepatobiliary Surgery and Liver Transplantation, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Deqian Fan
- Shaanxi Key Laboratory of Qinling Ecological Intelligent Monitoring and Protection, School of Ecology and Environment, Northwestern Polytechnical University, Xi'an, China
| | - Fenghua Zhang
- Shaanxi Key Laboratory of Qinling Ecological Intelligent Monitoring and Protection, School of Ecology and Environment, Northwestern Polytechnical University, Xi'an, China
| | - Yuxuan Liu
- Shaanxi Key Laboratory of Qinling Ecological Intelligent Monitoring and Protection, School of Ecology and Environment, Northwestern Polytechnical University, Xi'an, China
| | - Jinping Chen
- Guangdong Key Laboratory of Animal Conservation and Resource Utilization, Institute of Zoology, Guangdong Academy of Sciences, Guangzhou, China
| | - Chang Liu
- Key Laboratory of Surgical Critical Care and Life Support (Xi'an Jiaotong University), Ministry of Education, Xi'an, China
- Department of Hepatobiliary Surgery and Liver Transplantation, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Guang Li
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Xiamen University, Xiamen, China.
| | - Qiang Qiu
- Shaanxi Key Laboratory of Qinling Ecological Intelligent Monitoring and Protection, School of Ecology and Environment, Northwestern Polytechnical University, Xi'an, China.
| | - Kai Qu
- Key Laboratory of Surgical Critical Care and Life Support (Xi'an Jiaotong University), Ministry of Education, Xi'an, China.
- Department of Hepatobiliary Surgery and Liver Transplantation, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China.
| | - Wen Wang
- Shaanxi Key Laboratory of Qinling Ecological Intelligent Monitoring and Protection, School of Ecology and Environment, Northwestern Polytechnical University, Xi'an, China.
- New Cornerstone Science Laboratory, Xi'an, China.
| | - Kun Wang
- Shaanxi Key Laboratory of Qinling Ecological Intelligent Monitoring and Protection, School of Ecology and Environment, Northwestern Polytechnical University, Xi'an, China.
- Laboratory for Marine Biology and Biotechnology, Qingdao Marine Science and Technology Center, Qingdao, China.
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15
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Feng Y, Flanagan ME, Bonakdarpour B, Jamshidi P, Castellani RJ, Mao Q, Chu X, Gao H, Liu Y, Xu J, Hou Y, Martin W, Nelson PT, Leverenz JB, Pieper AA, Cummings J, Cheng F. Single-nucleus multiome analysis of human cerebellum in Alzheimer's disease-related dementia. RESEARCH SQUARE 2024:rs.3.rs-4871032. [PMID: 39184089 PMCID: PMC11343296 DOI: 10.21203/rs.3.rs-4871032/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/27/2024]
Abstract
Although human cerebellum is known to be neuropathologically impaired in Alzheimer's disease (AD) and AD-related dementias (ADRD), the cell type-specific transcriptional and epigenomic changes that contribute to this pathology are not well understood. Here, we report single-nucleus multiome (snRNA-seq and snATAC-seq) analysis of 103,861 nuclei isolated from cerebellum from 9 human cases of AD/ADRD and 8 controls, and with frontal cortex of 6 AD donors for additional comparison. Using peak-to-gene linkage analysis, we identified 431,834 significant linkages between gene expression and cell subtype-specific chromatin accessibility regions enriched for candidate cis-regulatory elements (cCREs). These cCREs were associated with AD/ADRD-specific transcriptomic changes and disease-related gene regulatory networks, especially for RAR Related Orphan Receptor A (RORA) and E74 Like ETS Transcription Factor 1 (ELF1) in cerebellar Purkinje cells and granule cells, respectively. Trajectory analysis of granule cell populations further identified disease-relevant transcription factors, such as RORA, and their regulatory targets. Finally, we prioritized two likely causal genes, including Seizure Related 6 Homolog Like 2 (SEZ6L2) in Purkinje cells and KAT8 Regulatory NSL Complex Subunit 1 (KANSL1) in granule cells, through integrative analysis of cCREs derived from snATAC-seq, genome-wide AD/ADRD loci, and Hi-C looping data. This first cell subtype-specific regulatory landscape in the human cerebellum identified here offer novel genomic and epigenomic insights into the neuropathology and pathobiology of AD/ADRD and other neurological disorders if broadly applied.
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Affiliation(s)
- Yayan Feng
- Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Margaret E Flanagan
- Biggs Institute, University of Texas Health Science Center San Antonio, San Antonio, Texas, USA
- Department of Pathology, University of Texas Health Science Center San Antonio, San Antonio, Texas, USA
| | - Borna Bonakdarpour
- Mesulam Center for Cognitive Neurology and Alzheimer’s Disease, Ken and Ruth Davee Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Pouya Jamshidi
- Department of Pathology and Northwestern Alzheimer Disease Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Rudolph J. Castellani
- Department of Pathology and Northwestern Alzheimer Disease Center, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Qinwen Mao
- Department of Pathology, University of Utah, Salt Lake City, Utah, USA
| | - Xiaona Chu
- Department of Medical and Molecular Genetics, Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Hongyu Gao
- Department of Medical and Molecular Genetics, Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Yunlong Liu
- Department of Medical and Molecular Genetics, Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Jielin Xu
- Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Yuan Hou
- Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - William Martin
- Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Peter T Nelson
- Department of Pathology and Laboratory Medicine, University of Kentucky, Lexington, Kentucky, USA
- Department of Sanders-Brown Center on Aging, University of Kentucky, Lexington, Kentucky, USA
| | - James B. Leverenz
- Lou Ruvo Center for Brain Health, Neurological Institute, Cleveland Clinic, Cleveland, Ohio 44195, USA
| | - Andrew A. Pieper
- Helen and Robert Appel Alzheimer’s Disease Research Institute, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10021, USA
- Department of Psychiatry, Case Western Reserve University, Cleveland, OH 44106, USA
- Brain Health Medicines Center, Harrington Discovery Institute, University Hospitals Cleveland Medical Center, Cleveland, OH 44106, USA
- Geriatric Psychiatry, GRECC, Louis Stokes Cleveland VA Medical Center; Cleveland, OH 44106, USA
- Institute for Transformative Molecular Medicine, School of Medicine, Case Western Reserve University, Cleveland 44106, OH, USA
- Department of Pathology, Case Western Reserve University, School of Medicine, Cleveland, OH 44106, USA
- Department of Neurosciences, Case Western Reserve University, School of Medicine, Cleveland, OH 44106, USA
| | - Jeffrey Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, UNLV, Las Vegas, Nevada 89154, USA
| | - Feixiong Cheng
- Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
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16
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Qi G, Chhetri SB, Ray D, Dutta D, Battle A, Bhattacharjee S, Chatterjee N. Genome-wide large-scale multi-trait analysis characterizes global patterns of pleiotropy and unique trait-specific variants. Nat Commun 2024; 15:6985. [PMID: 39143063 PMCID: PMC11324957 DOI: 10.1038/s41467-024-51075-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2023] [Accepted: 07/29/2024] [Indexed: 08/16/2024] Open
Abstract
Genome-wide association studies (GWAS) have found widespread evidence of pleiotropy, but characterization of global patterns of pleiotropy remain highly incomplete due to insufficient power of current approaches. We develop fastASSET, a method that allows efficient detection of variant-level pleiotropic association across many traits. We analyze GWAS summary statistics of 116 complex traits of diverse types collected from the GRASP repository and large GWAS Consortia. We identify 2293 independent loci and find that the lead variants in nearly all these loci (~99%) to be associated with ≥ 2 traits (median = 6). We observe that degree of pleiotropy estimated from our study predicts that observed in the UK Biobank for a much larger number of traits (K = 4114) (correlation = 0.43, p-value < 2.2 × 10 - 16 ). Follow-up analyzes of 21 trait-specific variants indicate their link to the expression in trait-related tissues for a small number of genes involved in relevant biological processes. Our findings provide deeper insight into the nature of pleiotropy and leads to identification of highly trait-specific susceptibility variants.
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Affiliation(s)
- Guanghao Qi
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, WA, USA
| | - Surya B Chhetri
- Department of Biomedical Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Debashree Ray
- Department of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - Diptavo Dutta
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD, USA
| | - Alexis Battle
- Department of Biomedical Engineering, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Computer Science, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
- Department of Genetic Medicine, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Samsiddhi Bhattacharjee
- Biotechnology Research and Innovation Council-National Institute of Biomedical Genomics (BRIC-NIBMG), Kalyani, India.
| | - Nilanjan Chatterjee
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
- Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
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17
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DeGroat W, Inoue F, Ashuach T, Yosef N, Ahituv N, Kreimer A. Comprehensive network modeling approaches unravel dynamic enhancer-promoter interactions across neural differentiation. Genome Biol 2024; 25:221. [PMID: 39143563 PMCID: PMC11323586 DOI: 10.1186/s13059-024-03365-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 08/01/2024] [Indexed: 08/16/2024] Open
Abstract
BACKGROUND Increasing evidence suggests that a substantial proportion of disease-associated mutations occur in enhancers, regions of non-coding DNA essential to gene regulation. Understanding the structures and mechanisms of the regulatory programs this variation affects can shed light on the apparatuses of human diseases. RESULTS We collect epigenetic and gene expression datasets from seven early time points during neural differentiation. Focusing on this model system, we construct networks of enhancer-promoter interactions, each at an individual stage of neural induction. These networks serve as the base for a rich series of analyses, through which we demonstrate their temporal dynamics and enrichment for various disease-associated variants. We apply the Girvan-Newman clustering algorithm to these networks to reveal biologically relevant substructures of regulation. Additionally, we demonstrate methods to validate predicted enhancer-promoter interactions using transcription factor overexpression and massively parallel reporter assays. CONCLUSIONS Our findings suggest a generalizable framework for exploring gene regulatory programs and their dynamics across developmental processes; this includes a comprehensive approach to studying the effects of disease-associated variation on transcriptional networks. The techniques applied to our networks have been published alongside our findings as a computational tool, E-P-INAnalyzer. Our procedure can be utilized across different cellular contexts and disorders.
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Affiliation(s)
- William DeGroat
- Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, 679 Hoes Lane West, Piscataway, NJ, 08854, USA
| | - Fumitaka Inoue
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, Japan
| | - Tal Ashuach
- Department of Electrical Engineering and Computer Sciences and Center for Computational Biology, University of California, Berkeley, 387 Soda Hall, Berkeley, CA, 94720, USA
| | - Nir Yosef
- Department of Systems Immunology, Weizmann Institute of Science, 234 Herzl Street, Rehovot, 7610001, Israel
- Chan-Zuckerberg Biohub, 499 Illinois St, San Francisco, CA, 94158, USA
- Department of Systems Immunology, Ragon Institute of MGH, MIT, and Harvard Institute of Science, 400 Technology Square, Cambridge, MA, 02139, USA
| | - Nadav Ahituv
- Department of Bioengineering and Therapeutic Sciences, University of California, 513 Parnassus Ave, San Francisco, CA, 94143, USA
- Institute for Human Genetics, University of California, 513 Parnassus Ave, San Francisco, CA, 94143, USA
| | - Anat Kreimer
- Center for Advanced Biotechnology and Medicine, Rutgers, The State University of New Jersey, 679 Hoes Lane West, Piscataway, NJ, 08854, USA.
- Department of Biochemistry and Molecular Biology, Rutgers, The State University of New Jersey, 604 Allison Road, Piscataway, NJ, 08854, USA.
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18
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Poisner H, Faucon A, Cox N, Bick AG. Genetic determinants and phenotypic consequences of blood T-cell proportions in 207,000 diverse individuals. Nat Commun 2024; 15:6732. [PMID: 39112476 PMCID: PMC11306580 DOI: 10.1038/s41467-024-51095-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 07/29/2024] [Indexed: 08/10/2024] Open
Abstract
T-cells play a critical role in multiple aspects of human health and disease. However, to date the genetic determinants of human T-cell abundance have not been studied at scale because assays quantifying T-cell abundance are not widely used in clinical or research settings. The complete blood count clinical assay quantifies lymphocyte abundance which includes T-cells, B-cells, and NK-cells. To address this gap, we directly estimate T-cell fractions from whole genome sequencing data in over 200,000 individuals from the multi-ethnic TOPMed and All of Us studies. We identified 27 loci associated with T-cell fraction. Interrogating electronic health records identified clinical phenotypes associated with T-cell fraction, including notable changes in T-cell proportions that were highly dynamic over the course of pregnancy. In summary, by estimating T-cell fraction, we obtained new insights into the genetic regulation of T-cells and identified disease consequences of T-cell fractions across the human phenome.
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Affiliation(s)
- Hannah Poisner
- Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Annika Faucon
- Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Nancy Cox
- Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN, USA
- Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Alexander G Bick
- Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN, USA.
- Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA.
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19
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Mummey HM, Elison W, Korgaonkar K, Elgamal RM, Kudtarkar P, Griffin E, Benaglio P, Miller M, Jha A, Fox JEM, McCarthy MI, Preissl S, Gloyn AL, MacDonald PE, Gaulton KJ. Single cell multiome profiling of pancreatic islets reveals physiological changes in cell type-specific regulation associated with diabetes risk. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.03.606460. [PMID: 39149326 PMCID: PMC11326183 DOI: 10.1101/2024.08.03.606460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/17/2024]
Abstract
Physiological variability in pancreatic cell type gene regulation and the impact on diabetes risk is poorly understood. In this study we mapped gene regulation in pancreatic cell types using single cell multiomic (joint RNA-seq and ATAC-seq) profiling in 28 non-diabetic donors in combination with single cell data from 35 non-diabetic donors in the Human Pancreas Analysis Program. We identified widespread associations with age, sex, BMI, and HbA1c, where gene regulatory responses were highly cell type- and phenotype-specific. In beta cells, donor age associated with hypoxia, apoptosis, unfolded protein response, and external signal-dependent transcriptional regulators, while HbA1c associated with inflammatory responses and gender with chromatin organization. We identified 10.8K loci where genetic variants were QTLs for cis regulatory element (cRE) accessibility, including 20% with lineage- or cell type-specific effects which disrupted distinct transcription factor motifs. Type 2 diabetes and glycemic trait associated variants were enriched in both phenotype- and QTL-associated beta cell cREs, whereas type 1 diabetes showed limited enrichment. Variants at 226 diabetes and glycemic trait loci were QTLs in beta and other cell types, including 40 that were statistically colocalized, and annotating target genes of colocalized QTLs revealed genes with putatively novel roles in disease. Our findings reveal diverse responses of pancreatic cell types to phenotype and genotype in physiology, and identify pathways, networks, and genes through which physiology impacts diabetes risk.
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Affiliation(s)
- Hannah M Mummey
- Bioinformatics and Systems Biology Program, University of California San Diego, La Jolla CA
| | - Weston Elison
- Biomedical Sciences Program, University of California San Diego, La Jolla CA, USA
| | - Katha Korgaonkar
- Department of Pediatrics, University of California San Diego, La Jolla CA, USA
| | - Ruth M Elgamal
- Biomedical Sciences Program, University of California San Diego, La Jolla CA, USA
| | - Parul Kudtarkar
- Department of Pediatrics, University of California San Diego, La Jolla CA, USA
| | - Emily Griffin
- Department of Pediatrics, University of California San Diego, La Jolla CA, USA
| | - Paola Benaglio
- Department of Pediatrics, University of California San Diego, La Jolla CA, USA
| | - Michael Miller
- Center for Epigenomics, University of California San Diego, La Jolla CA, USA
| | - Alokkumar Jha
- Department of Pediatrics, Stanford School of Medicine, Stanford University, Stanford CA, USA
| | - Jocelyn E Manning Fox
- Department of Pharmacology, University of Alberta, Edmonton, Alberta, Canada
- Alberta Diabetes Institute, University of Alberta, Edmonton, Alberta, Canada
| | - Mark I McCarthy
- Wellcome Trust Center for Human Genetics, University of Oxford, Oxford, UK*
| | - Sebastian Preissl
- Center for Epigenomics, University of California San Diego, La Jolla CA, USA
- Department of Genetics, Stanford School of Medicine, Stanford University, Stanford CA, USA
| | - Anna L Gloyn
- Department of Pediatrics, Stanford School of Medicine, Stanford University, Stanford CA, USA
- Department of Genetics, Stanford School of Medicine, Stanford University, Stanford CA, USA
- Stanford Diabetes Research Center, Stanford School of Medicine, Stanford, CA, USA
| | - Patrick E MacDonald
- Alberta Diabetes Institute, University of Alberta, Edmonton, Alberta, Canada
| | - Kyle J Gaulton
- Department of Pediatrics, University of California San Diego, La Jolla CA, USA
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20
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Xu L, Liu Y. Identification, Design, and Application of Noncoding Cis-Regulatory Elements. Biomolecules 2024; 14:945. [PMID: 39199333 PMCID: PMC11352686 DOI: 10.3390/biom14080945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2024] [Revised: 07/25/2024] [Accepted: 07/30/2024] [Indexed: 09/01/2024] Open
Abstract
Cis-regulatory elements (CREs) play a pivotal role in orchestrating interactions with trans-regulatory factors such as transcription factors, RNA-binding proteins, and noncoding RNAs. These interactions are fundamental to the molecular architecture underpinning complex and diverse biological functions in living organisms, facilitating a myriad of sophisticated and dynamic processes. The rapid advancement in the identification and characterization of these regulatory elements has been marked by initiatives such as the Encyclopedia of DNA Elements (ENCODE) project, which represents a significant milestone in the field. Concurrently, the development of CRE detection technologies, exemplified by massively parallel reporter assays, has progressed at an impressive pace, providing powerful tools for CRE discovery. The exponential growth of multimodal functional genomic data has necessitated the application of advanced analytical methods. Deep learning algorithms, particularly large language models, have emerged as invaluable tools for deconstructing the intricate nucleotide sequences governing CRE function. These advancements facilitate precise predictions of CRE activity and enable the de novo design of CREs. A deeper understanding of CRE operational dynamics is crucial for harnessing their versatile regulatory properties. Such insights are instrumental in refining gene therapy techniques, enhancing the efficacy of selective breeding programs, pushing the boundaries of genetic innovation, and opening new possibilities in microbial synthetic biology.
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Affiliation(s)
- Lingna Xu
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China;
- Innovation Group of Pig Genome Design and Breeding, Research Centre for Animal Genome, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China
| | - Yuwen Liu
- Shenzhen Branch, Guangdong Laboratory for Lingnan Modern Agriculture, Key Laboratory of Livestock and Poultry Multi-Omics of MARA, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China;
- Innovation Group of Pig Genome Design and Breeding, Research Centre for Animal Genome, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518124, China
- Kunpeng Institute of Modern Agriculture at Foshan, Chinese Academy of Agricultural Sciences, Foshan 528226, China
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21
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Lin J, Luo R, Pinello L. EPInformer: a scalable deep learning framework for gene expression prediction by integrating promoter-enhancer sequences with multimodal epigenomic data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.08.01.606099. [PMID: 39131276 PMCID: PMC11312614 DOI: 10.1101/2024.08.01.606099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Transcriptional regulation, critical for cellular differentiation and adaptation to environmental changes, involves coordinated interactions among DNA sequences, regulatory proteins, and chromatin architecture. Despite extensive data from consortia like ENCODE, understanding the dynamics of cis-regulatory elements (CREs) in gene expression remains challenging. Deep learning is a powerful tool for learning gene expression and epigenomic signals from DNA sequences, exhibiting superior performance compared to conventional machine learning approaches. However, even the most advanced deep learning-based methods may fall short in capturing the regulatory effects of distal elements such as enhancers, limiting their predictive accuracy. In addition, these methods may require significant resources to train or to adapt to newly generated data. To address these challenges, we present EPInformer, a scalable deep-learning framework for predicting gene expression by integrating promoter-enhancer interactions with their sequences, epigenomic signals, and chromatin contacts. Our model outperforms existing gene expression prediction models in rigorous cross-chromosome validation, accurately recapitulates enhancer-gene interactions validated by CRISPR perturbation experiments, and identifies crucial transcription factor motifs within regulatory sequences. EPInformer is available as open-source software at https://github.com/pinellolab/EPInformer.
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Affiliation(s)
- Jiecong Lin
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Department of Pathology, Harvard Medical School, Boston, Massachusetts 02129, USA
- Department of Computer Science, The University of Hong Kong, Hong Kong, China
| | - Ruibang Luo
- Department of Computer Science, The University of Hong Kong, Hong Kong, China
| | - Luca Pinello
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Department of Pathology, Harvard Medical School, Boston, Massachusetts 02129, USA
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22
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McCutcheon SR, Rohm D, Iglesias N, Gersbach CA. Epigenome editing technologies for discovery and medicine. Nat Biotechnol 2024; 42:1199-1217. [PMID: 39075148 DOI: 10.1038/s41587-024-02320-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 06/19/2024] [Indexed: 07/31/2024]
Abstract
Epigenome editing has rapidly evolved in recent years, with diverse applications that include elucidating gene regulation mechanisms, annotating coding and noncoding genome functions and programming cell state and lineage specification. Importantly, given the ubiquitous role of epigenetics in complex phenotypes, epigenome editing has unique potential to impact a broad spectrum of diseases. By leveraging powerful DNA-targeting technologies, such as CRISPR, epigenome editing exploits the heritable and reversible mechanisms of epigenetics to alter gene expression without introducing DNA breaks, inducing DNA damage or relying on DNA repair pathways.
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Affiliation(s)
- Sean R McCutcheon
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Center for Advanced Genomic Technologies, Duke University, Durham, NC, USA
| | - Dahlia Rohm
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Center for Advanced Genomic Technologies, Duke University, Durham, NC, USA
| | - Nahid Iglesias
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
- Center for Advanced Genomic Technologies, Duke University, Durham, NC, USA
| | - Charles A Gersbach
- Department of Biomedical Engineering, Duke University, Durham, NC, USA.
- Center for Advanced Genomic Technologies, Duke University, Durham, NC, USA.
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23
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Yao D, Binan L, Bezney J, Simonton B, Freedman J, Frangieh CJ, Dey K, Geiger-Schuller K, Eraslan B, Gusev A, Regev A, Cleary B. Scalable genetic screening for regulatory circuits using compressed Perturb-seq. Nat Biotechnol 2024; 42:1282-1295. [PMID: 37872410 PMCID: PMC11035494 DOI: 10.1038/s41587-023-01964-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 08/22/2023] [Indexed: 10/25/2023]
Abstract
Pooled CRISPR screens with single-cell RNA sequencing readout (Perturb-seq) have emerged as a key technique in functional genomics, but they are limited in scale by cost and combinatorial complexity. In this study, we modified the design of Perturb-seq by incorporating algorithms applied to random, low-dimensional observations. Compressed Perturb-seq measures multiple random perturbations per cell or multiple cells per droplet and computationally decompresses these measurements by leveraging the sparse structure of regulatory circuits. Applied to 598 genes in the immune response to bacterial lipopolysaccharide, compressed Perturb-seq achieves the same accuracy as conventional Perturb-seq with an order of magnitude cost reduction and greater power to learn genetic interactions. We identified known and novel regulators of immune responses and uncovered evolutionarily constrained genes with downstream targets enriched for immune disease heritability, including many missed by existing genome-wide association studies. Our framework enables new scales of interrogation for a foundational method in functional genomics.
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Affiliation(s)
- Douglas Yao
- Program in Systems, Synthetic, and Quantitative Biology, Harvard University, Cambridge, MA, USA
| | - Loic Binan
- Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Jon Bezney
- Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA
| | - Brooke Simonton
- Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Jahanara Freedman
- Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Chris J Frangieh
- Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kushal Dey
- Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | | | - Alexander Gusev
- Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Division of Genetics, Brigham and Women's Hospital, Boston, MA, USA
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Computational and Systems Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Genentech, South San Francisco, CA, USA
| | - Brian Cleary
- Faculty of Computing and Data Sciences, Boston University, Boston, MA, USA.
- Department of Biology, Boston University, Boston, MA, USA.
- Department of Biomedical Engineering, Boston University, Boston, MA, USA.
- Program in Bioinformatics, Boston University, Boston, MA, USA.
- Biological Design Center, Boston University, Boston, MA, USA.
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24
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Qi T, Song L, Guo Y, Chen C, Yang J. From genetic associations to genes: methods, applications, and challenges. Trends Genet 2024; 40:642-667. [PMID: 38734482 DOI: 10.1016/j.tig.2024.04.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 04/15/2024] [Accepted: 04/16/2024] [Indexed: 05/13/2024]
Abstract
Genome-wide association studies (GWASs) have identified numerous genetic loci associated with human traits and diseases. However, pinpointing the causal genes remains a challenge, which impedes the translation of GWAS findings into biological insights and medical applications. In this review, we provide an in-depth overview of the methods and technologies used for prioritizing genes from GWAS loci, including gene-based association tests, integrative analysis of GWAS and molecular quantitative trait loci (xQTL) data, linking GWAS variants to target genes through enhancer-gene connection maps, and network-based prioritization. We also outline strategies for generating context-dependent xQTL data and their applications in gene prioritization. We further highlight the potential of gene prioritization in drug repurposing. Lastly, we discuss future challenges and opportunities in this field.
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Affiliation(s)
- Ting Qi
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China.
| | - Liyang Song
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China
| | - Yazhou Guo
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China
| | - Chang Chen
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China
| | - Jian Yang
- Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou 310024, China; School of Life Sciences, Westlake University, Hangzhou 310024, China.
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25
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Schipper M, Ulirsch J, Posthuma D, Ripke S, Heilbron K. Simplifying causal gene identification in GWAS loci. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.07.26.24311057. [PMID: 39132490 PMCID: PMC11312651 DOI: 10.1101/2024.07.26.24311057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Genome-wide association studies (GWAS) help to identify disease-linked genetic variants, but pinpointing the most likely causal genes in GWAS loci remains challenging. Existing GWAS gene prioritization tools are powerful, but often use complex black box models trained on datasets containing unaddressed biases. Here we present CALDERA, a gene prioritization tool that achieves similar or better performance than state-of-the-art methods, but uses just 12 features and a simple logistic regression model with L1 regularization. We use a data-driven approach to construct a truth set of causal genes in 406 GWAS loci and correct for potential confounders. We demonstrate that CALDERA is well-calibrated in external datasets and prioritizes genes with expected properties, such as being mutation-intolerant (OR = 1.751 for pLI > 90%, P = 8.45×10-3). CALDERA facilitates the prioritization of potentially causal genes in GWAS loci and may help identify novel genetics-driven drug targets.
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Affiliation(s)
- Marijn Schipper
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Jacob Ulirsch
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Illumina Artificial Intelligence Laboratory, Illumina, San Diego, CA, USA
| | - Danielle Posthuma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Department of Child and Adolescent Psychiatry and Pediatric Psychology, Section Complex Trait Genetics, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Amsterdam, The Netherlands
| | - Stephan Ripke
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Department of Psychiatry and Psychotherapy, Charité – Universitätsmedizin Berlin, Berlin, Germany
- German Center for Mental Health (DZPG), partner site Berlin/Potsdam, Berlin, Germany
| | - Karl Heilbron
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
- Department of Psychiatry and Psychotherapy, Charité – Universitätsmedizin Berlin, Berlin, Germany
- German Center for Mental Health (DZPG), partner site Berlin/Potsdam, Berlin, Germany
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26
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Lu W, Tang Y, Liu Y, Lin S, Shuai Q, Liang B, Zhang R, Cheng Y, Fang D. CatLearning: highly accurate gene expression prediction from histone mark. Brief Bioinform 2024; 25:bbae373. [PMID: 39073831 DOI: 10.1093/bib/bbae373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Revised: 06/14/2024] [Accepted: 07/16/2024] [Indexed: 07/30/2024] Open
Abstract
Histone modifications, known as histone marks, are pivotal in regulating gene expression within cells. The vast array of potential combinations of histone marks presents a considerable challenge in decoding the regulatory mechanisms solely through biological experimental approaches. To overcome this challenge, we have developed a method called CatLearning. It utilizes a modified convolutional neural network architecture with a specialized adaptation Residual Network to quantitatively interpret histone marks and predict gene expression. This architecture integrates long-range histone information up to 500Kb and learns chromatin interaction features without 3D information. By using only one histone mark, CatLearning achieves a high level of accuracy. Furthermore, CatLearning predicts gene expression by simulating changes in histone modifications at enhancers and throughout the genome. These findings help comprehend the architecture of histone marks and develop diagnostic and therapeutic targets for diseases with epigenetic changes.
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Affiliation(s)
- Weining Lu
- Beijing National Research Center for Information Science and Technology, Tsinghua University, FIT Building, Haidian District, Beijing 100084, China
| | - Yin Tang
- Liangzhu Laboratory, Zhejiang University, 1369 Wenyixi Road, Yuhang District, Hangzhou, Zhejiang, 311121, China
| | - Yu Liu
- Life Sciences Institute, Zhejiang University, 866 Yuhangtang Road, Xihu District, Hangzhou, Zhejiang, 310058, China
| | - Shiyi Lin
- Life Sciences Institute, Zhejiang University, 866 Yuhangtang Road, Xihu District, Hangzhou, Zhejiang, 310058, China
| | - Qifan Shuai
- School of Electron and Computer, Southeast University Chengxian College, 371 Heyan Road, Qixia District, Nanjing, Jiangsu 210088, China
| | - Bin Liang
- Department of Automation, Tsinghua University, 1 Tsinghua Garden, Haidian District, Beijing, 100084, China
| | - Rongqing Zhang
- Zhejiang Provincial Key Laboratory of Applied Enzymology, Yangtze Delta Region Institute of Tsinghua University, 705 Yatai Road, Jiaxing 314006, China
| | - Yu Cheng
- The Chinese University of Hong Kong, Shatin, NT, Hong Kong, 999077, China
| | - Dong Fang
- Life Sciences Institute, Zhejiang University, 866 Yuhangtang Road, Xihu District, Hangzhou, Zhejiang, 310058, China
- Department of Medical Oncology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, 88 Jiefang Road, Shangcheng District, Hangzhou, Zhejiang, 310009, China
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27
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Liu W, Zhong W, Giusti-Rodríguez P, Jiang Z, Wang GW, Sun H, Hu M, Li Y. SnapHiC-G: identifying long-range enhancer-promoter interactions from single-cell Hi-C data via a global background model. Brief Bioinform 2024; 25:bbae426. [PMID: 39222061 PMCID: PMC11367764 DOI: 10.1093/bib/bbae426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Revised: 07/05/2024] [Accepted: 08/13/2024] [Indexed: 09/04/2024] Open
Abstract
Harnessing the power of single-cell genomics technologies, single-cell Hi-C (scHi-C) and its derived technologies provide powerful tools to measure spatial proximity between regulatory elements and their target genes in individual cells. Using a global background model, we propose SnapHiC-G, a computational method, to identify long-range enhancer-promoter interactions from scHi-C data. We applied SnapHiC-G to scHi-C datasets generated from mouse embryonic stem cells and human brain cortical cells. SnapHiC-G achieved high sensitivity in identifying long-range enhancer-promoter interactions. Moreover, SnapHiC-G can identify putative target genes for noncoding genome-wide association study (GWAS) variants, and the genetic heritability of neuropsychiatric diseases is enriched for single-nucleotide polymorphisms (SNPs) within SnapHiC-G-identified interactions in a cell-type-specific manner. In sum, SnapHiC-G is a powerful tool for characterizing cell-type-specific enhancer-promoter interactions from complex tissues and can facilitate the discovery of chromatin interactions important for gene regulation in biologically relevant cell types.
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Affiliation(s)
- Weifang Liu
- Department of Biostatistics, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill, NC 27599, United States
| | - Wujuan Zhong
- Biostatistics and Research Decision Sciences, Merck & Co., Inc., 126 East Lincoln Ave, Rahway, New Jersey 07065, United States
| | - Paola Giusti-Rodríguez
- Department of Psychiatry, University of Florida, 1149 Newel Dr., Gainesville, FL 32611, United States
| | - Zhiyun Jiang
- Department of Genetics, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Chapel Hill, NC 27599, United States
| | - Geoffery W Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill, NC 27599, United States
| | - Huaigu Sun
- Department of Genetics, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Chapel Hill, NC 27599, United States
| | - Ming Hu
- Department of Quantitative Health Sciences, Lerner Research Institute, Cleveland Clinic Foundation, 9500 Euclid Avenue, Cleveland, OH 44196, United States
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, 135 Dauer Drive, Chapel Hill, NC 27599, United States
- Department of Genetics, University of North Carolina at Chapel Hill, 120 Mason Farm Road, Chapel Hill, NC 27599, United States
- Department of Computer Science, University of North Carolina at Chapel Hill, 201 S. Columbia St, Chapel Hill, NC 27599, United States
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28
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Pacheco MP, Gerard D, Mangan RJ, Chapman AR, Hecker D, Kellis M, Schulz MH, Sinkkonen L, Sauter T. Epigenetic control of metabolic identity across cell types. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.24.604914. [PMID: 39091778 PMCID: PMC11291179 DOI: 10.1101/2024.07.24.604914] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
Constraint-based network modelling is a powerful tool for analysing cellular metabolism at genomic scale. Here, we conducted an integrative analysis of metabolic networks reconstructed from RNA-seq data with paired epigenomic data from the EpiATLAS resource of the International Human Epigenome Consortium (IHEC). Applying a state-of-the-art contextualisation algorithm, we reconstructed metabolic networks across 1,555 samples corresponding to 58 tissues and cell types. Analysis of these networks revealed the distribution of metabolic functionalities across human cell types and provides a compendium of human metabolic activity. This integrative approach allowed us to define, across tissues and cell types, i) reactions that fulfil the basic metabolic processes (core metabolism), and ii) cell type-specific functions (unique metabolism), that shape the metabolic identity of a cell or a tissue. Integration with EpiATLAS-derived cell type-specific gene-level chromatin states and enhancer-gene interactions identified enhancers, transcription factors, and key nodes controlling core and unique metabolism. Transport and first reactions of pathways were enriched for high expression, active chromatin state, and Polycomb-mediated repression in cell types where pathways are inactive, suggesting that key nodes are targets of repression. This integrative analysis forms the basis for identifying regulation points that control metabolic identity in human cells.
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Affiliation(s)
- Maria Pires Pacheco
- Department of Life Sciences and Medicine, University of Luxembourg, 6, Avenue du Swing, L-4367 Belvaux, Luxembourg
| | - Déborah Gerard
- Department of Life Sciences and Medicine, University of Luxembourg, 6, Avenue du Swing, L-4367 Belvaux, Luxembourg
| | - Riley J. Mangan
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, 02139, USA
- Genetics Training Program, Harvard Medical School, Boston MA, 02115, USA
| | - Alec R. Chapman
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, 02139, USA
| | - Dennis Hecker
- Institute for Computational Genomic Medicine and Institute of Cardiovascular Regeneration, Medical Faculty, Goethe University, 60590 Frankfurt am Main, German Center for Cardiovascular Research, Partner site Rhein-Main, 60590 Frankfurt am Main, Germany
| | - Manolis Kellis
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, 02139, USA
| | - Marcel H. Schulz
- Institute for Computational Genomic Medicine and Institute of Cardiovascular Regeneration, Medical Faculty, Goethe University, 60590 Frankfurt am Main, German Center for Cardiovascular Research, Partner site Rhein-Main, 60590 Frankfurt am Main, Germany
| | - Lasse Sinkkonen
- Department of Life Sciences and Medicine, University of Luxembourg, 6, Avenue du Swing, L-4367 Belvaux, Luxembourg
| | - Thomas Sauter
- Department of Life Sciences and Medicine, University of Luxembourg, 6, Avenue du Swing, L-4367 Belvaux, Luxembourg
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29
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Guckelberger P, Doughty BR, Munson G, Rao SSP, Tan Y, Cai XS, Fulco CP, Nasser J, Mualim KS, Bergman DT, Ray J, Jagoda E, Munger CJ, Gschwind AR, Sheth MU, Tan AS, Pulido SG, Mitra N, Weisz D, Shamim MS, Durand NC, Mahajan R, Khan R, Steinmetz LM, Kanemaki MT, Lander ES, Meissner A, Aiden EL, Engreitz JM. Cohesin-mediated 3D contacts tune enhancer-promoter regulation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.12.603288. [PMID: 39026740 PMCID: PMC11257546 DOI: 10.1101/2024.07.12.603288] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Enhancers are key drivers of gene regulation thought to act via 3D physical interactions with the promoters of their target genes. However, genome-wide depletions of architectural proteins such as cohesin result in only limited changes in gene expression, despite a loss of contact domains and loops. Consequently, the role of cohesin and 3D contacts in enhancer function remains debated. Here, we developed CRISPRi of regulatory elements upon degron operation (CRUDO), a novel approach to measure how changes in contact frequency impact enhancer effects on target genes by perturbing enhancers with CRISPRi and measuring gene expression in the presence or absence of cohesin. We systematically perturbed all 1,039 candidate enhancers near five cohesin-dependent genes and identified 34 enhancer-gene regulatory interactions. Of 26 regulatory interactions with sufficient statistical power to evaluate cohesin dependence, 18 show cohesin-dependent effects. A decrease in enhancer-promoter contact frequency upon removal of cohesin is frequently accompanied by a decrease in the regulatory effect of the enhancer on gene expression, consistent with a contact-based model for enhancer function. However, changes in contact frequency and regulatory effects on gene expression vary as a function of distance, with distal enhancers (e.g., >50Kb) experiencing much larger changes than proximal ones (e.g., <50Kb). Because most enhancers are located close to their target genes, these observations can explain how only a small subset of genes - those with strong distal enhancers - are sensitive to cohesin. Together, our results illuminate how 3D contacts, influenced by both cohesin and genomic distance, tune enhancer effects on gene expression.
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30
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Verma A, Huffman JE, Rodriguez A, Conery M, Liu M, Ho YL, Kim Y, Heise DA, Guare L, Panickan VA, Garcon H, Linares F, Costa L, Goethert I, Tipton R, Honerlaw J, Davies L, Whitbourne S, Cohen J, Posner DC, Sangar R, Murray M, Wang X, Dochtermann DR, Devineni P, Shi Y, Nandi TN, Assimes TL, Brunette CA, Carroll RJ, Clifford R, Duvall S, Gelernter J, Hung A, Iyengar SK, Joseph J, Kember R, Kranzler H, Kripke CM, Levey D, Luoh SW, Merritt VC, Overstreet C, Deak JD, Grant SFA, Polimanti R, Roussos P, Shakt G, Sun YV, Tsao N, Venkatesh S, Voloudakis G, Justice A, Begoli E, Ramoni R, Tourassi G, Pyarajan S, Tsao P, O'Donnell CJ, Muralidhar S, Moser J, Casas JP, Bick AG, Zhou W, Cai T, Voight BF, Cho K, Gaziano JM, Madduri RK, Damrauer S, Liao KP. Diversity and scale: Genetic architecture of 2068 traits in the VA Million Veteran Program. Science 2024; 385:eadj1182. [PMID: 39024449 DOI: 10.1126/science.adj1182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Accepted: 05/10/2024] [Indexed: 07/20/2024]
Abstract
One of the justifiable criticisms of human genetic studies is the underrepresentation of participants from diverse populations. Lack of inclusion must be addressed at-scale to identify causal disease factors and understand the genetic causes of health disparities. We present genome-wide associations for 2068 traits from 635,969 participants in the Department of Veterans Affairs Million Veteran Program, a longitudinal study of diverse United States Veterans. Systematic analysis revealed 13,672 genomic risk loci; 1608 were only significant after including non-European populations. Fine-mapping identified causal variants at 6318 signals across 613 traits. One-third (n = 2069) were identified in participants from non-European populations. This reveals a broadly similar genetic architecture across populations, highlights genetic insights gained from underrepresented groups, and presents an extensive atlas of genetic associations.
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Affiliation(s)
- Anurag Verma
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA 19104, USA
- Department of Medicine, Division of Translational Medicine and Human Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
- Institute for Biomedical Informatics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Jennifer E Huffman
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA
- Palo Alto Veterans Institute for Research (PAVIR), Palo Alto Health Care System, Palo Alto, CA 94304, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
| | - Alex Rodriguez
- Data Science and Learning, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Mitchell Conery
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Molei Liu
- Department of Biostatistics, Columbia University's Mailman School of Public Health, New York, NY 10032, USA
| | - Yuk-Lam Ho
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA
| | - Youngdae Kim
- Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL 60439, USA
| | - David A Heise
- National Security Sciences Directorate, Cyber Resilience and Intelligence Division, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN 37831, USA
| | - Lindsay Guare
- Department of Medicine, Division of Translational Medicine and Human Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | | | - Helene Garcon
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA
| | - Franciel Linares
- R&D Systems Engineering, Information Technology Services Directorate, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN 37831, USA
| | - Lauren Costa
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA 02111, USA
| | - Ian Goethert
- Data Management and Engineering, Information Technology Services Division, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN 37831, USA
| | - Ryan Tipton
- Knowledge Discovery Infrastructure, Information Technology Services Division, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN 37831, USA
| | - Jacqueline Honerlaw
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA
| | - Laura Davies
- Computing and Computational Sciences Dir PMO, PMO, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN 37831, USA
| | - Stacey Whitbourne
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA 02111, USA
- Department of Medicine, Division of Aging, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Jeremy Cohen
- National Security Sciences Directorate, Cyber Resilience and Intelligence Division, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN 37831, USA
| | - Daniel C Posner
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA
| | - Rahul Sangar
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA 02111, USA
| | - Michael Murray
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA 02111, USA
| | - Xuan Wang
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
- Department of Population Health Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - Daniel R Dochtermann
- VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA 02130, USA
| | - Poornima Devineni
- VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA 02130, USA
| | - Yunling Shi
- VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA 02130, USA
| | - Tarak Nath Nandi
- Data Science and Learning, Argonne National Laboratory, Lemont, IL 60439, USA
| | | | - Charles A Brunette
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- Research Service, VA Boston Healthcare System, Boston, MA 02130, USA
| | - Robert J Carroll
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37211, USA
| | - Royce Clifford
- Research Department, VA San Diego Healthcare System, San Diego, CA 92161, USA
- Department of Otolaryngology, UCSD San Diego, La Jolla, CA 92093, USA
| | - Scott Duvall
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT 84148, USA
- Internal Medicine, Epidemiology, University of Utah School of Medicine, Salt Lake City, UT 84132, USA
| | - Joel Gelernter
- Psychiatry, Human Genetics, Yale University, New Haven, CT, 06520, USA
- VA Connecticut Healthcare System West Haven, West Haven, CT, 06516, USA
| | - Adriana Hung
- Medicine, Nephrology & Hypertension, VA Tennessee Valley Healthcare System & Vanderbilt University, Nashville, TN 37232, USA
| | - Sudha K Iyengar
- Departments of Population and Quantitative Health Sciences, Genetics and Genome Sciences, and Ophthalmology and Visual Sciences and the Cleveland Institute for Computational Biology, Case Western Reserve University, Cleveland, OH 44106, USA
| | - Jacob Joseph
- Medicine, Cardiology Section, VA Providence Healthcare System, Providence, RI 02908, USA
- Department of Medicine, Brown University, Providence, RI, 02908, USA
| | - Rachel Kember
- Mental Illness Research, Education and Clinical Center, Corporal Michael Crescenz VA Medical Center, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Henry Kranzler
- Mental Illness Research, Education and Clinical Center, Corporal Michael Crescenz VA Medical Center, Philadelphia, PA 19104, USA
- Department of Psychiatry, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Colleen M Kripke
- Department of Medicine, Division of Translational Medicine and Human Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Daniel Levey
- Psychiatry, Human Genetics, Yale University, New Haven, CT, 06520, USA
- Medicine, VA Connecticut Healthcare System West Haven, West Haven, CT 06516, USA
| | - Shiuh-Wen Luoh
- VA Portland Health Care System, Portland, OR 97239, USA
- Division of Hematology and Medical Oncology, Knight Cancer Institute, Oregon Health and Science University, Portland, OR 97239, USA
| | - Victoria C Merritt
- Research Department, VA San Diego Healthcare System, San Diego, CA 92161, USA
| | - Cassie Overstreet
- Psychiatry, Human Genetics, Yale University, New Haven, CT, 06520, USA
| | - Joseph D Deak
- Psychiatry, Yale University, New Haven, CT 06520, USA
- Psychiatry, VA Connecticut Healthcare System West Haven, West Haven, CT 06516, USA
| | - Struan F A Grant
- Center for Spatial and Functional Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Pediatrics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
- Divisions of Human Genetics and Endocrinology and Diabetes, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | | | - Panos Roussos
- Psychiatry, Mental Illness Research, Education and Clinical Center, James J. Peters VA Medical Center; Icahn School of Medicine at Mount Sinai, Bronx, NY 10468, USA
| | - Gabrielle Shakt
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA 19104, USA
- Department of Surgery, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Yan V Sun
- Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA 30322, USA
| | - Noah Tsao
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA 19104, USA
- Department of Surgery, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Sanan Venkatesh
- Psychiatry, Mental Illness Research, Education and Clinical Center, James J. Peters VA Medical Center; Icahn School of Medicine at Mount Sinai, Bronx, NY 10468, USA
| | - Georgios Voloudakis
- Psychiatry, Mental Illness Research, Education and Clinical Center, James J. Peters VA Medical Center; Icahn School of Medicine at Mount Sinai, Bronx, NY 10468, USA
| | - Amy Justice
- Medicine, VA Connecticut Healthcare System West Haven, West Haven, CT 06516, USA
- Internal Medicine, General Medicine, Yale University, New Haven, CT 06520, USA
- Health Policy, Yale School of Public Health, New Haven, CT 06520, USA
| | - Edmon Begoli
- Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN, 37831, USA
| | - Rachel Ramoni
- Office of Research and Development, Department of Veterans Affairs, Washington, DC, 20420, USA
| | - Georgia Tourassi
- National Center for Computational Sciences, Oak Ridge National Laboratory, Dept of Energy, Oak Ridge, TN, 37831, USA
| | - Saiju Pyarajan
- VA Cooperative Studies Program, VA Boston Healthcare System, Boston, MA 02130, USA
| | - Philip Tsao
- Medicine, Cardiology, VA Palo Alto Healthcare System, Palo Alto, CA 94304, USA
- Department of Medicine, Stanford University, Palo Alto, CA, 94304, USA
| | | | - Sumitra Muralidhar
- Office of Research and Development, Department of Veterans Affairs, Washington, DC, 20420, USA
| | - Jennifer Moser
- Office of Research and Development, Department of Veterans Affairs, Washington, DC, 20420, USA
| | - Juan P Casas
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA
| | - Alexander G Bick
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University, Nashville, TN, 37325, USA
| | - Wei Zhou
- Department of Medicine, Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research, Cambridge, MA 02142, USA
- Program in Medical and Population Genetics, Cambridge, MA 02142, USA
| | - Tianxi Cai
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | - Benjamin F Voight
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA 19104, USA
- Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
- Department of Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
- Institute of Translational Medicine and Therapeutics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Kelly Cho
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA 02111, USA
- Department of Medicine, Division of Aging, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - J Michael Gaziano
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- MVP Boston Coordinating Center, VA Boston Healthcare System, Boston, MA 02111, USA
- Department of Medicine, Division of Aging, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Ravi K Madduri
- Data Science and Learning, Argonne National Laboratory, Lemont, IL 60439, USA
| | - Scott Damrauer
- Corporal Michael Crescenz VA Medical Center, Philadelphia, PA 19104, USA
- Department of Genetics, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
- Department of Surgery, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
- Cardiovascular Institute, University of Pennsylvania - Perelman School of Medicine, Philadelphia, PA 19104, USA
| | - Katherine P Liao
- Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA 02130, USA
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
- Medicine, Rheumatology, VA Boston Healthcare System, Boston, MA 02130, USA
- Department of Medicine, Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, Boston, MA 02115, USA
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31
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Jensen CL, Chen LF, Swigut T, Crocker OJ, Yao D, Bassik MC, Ferrell JE, Boettiger AN, Wysocka J. Long range regulation of transcription scales with genomic distance in a gene specific manner. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.19.604327. [PMID: 39071420 PMCID: PMC11275926 DOI: 10.1101/2024.07.19.604327] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
While critical for tuning the timing and level of transcription, enhancer communication with distal promoters is not well understood. Here we bypass the need for sequence-specific transcription factors and recruit activators directly using CARGO-VPR, an approach for targeting dCas9-VPR using a multiplexed array of RNA guides. We show that this approach achieves effective activator recruitment to arbitrary genomic sites, even those inaccessible by single dCas9. We utilize CARGO-VPR across the Prdm8-Fgf5 locus in mESCs, where neither gene is expressed. We demonstrate that while activator recruitment to any tested region results in transcriptional induction of at least one gene, the expression level strongly depends on the genomic distance between the promoter and activator recruitment site. However, the expression-distance relationship for each gene scales distinctly in a manner not attributable to differences in 3D contact frequency, promoter DNA sequence or presence of the repressive chromatin marks at the locus.
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32
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Ivancevic A, Simpson DM, Joyner OM, Bagby SM, Nguyen LL, Bitler BG, Pitts TM, Chuong EB. Endogenous retroviruses mediate transcriptional rewiring in response to oncogenic signaling in colorectal cancer. SCIENCE ADVANCES 2024; 10:eado1218. [PMID: 39018396 PMCID: PMC466953 DOI: 10.1126/sciadv.ado1218] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 06/13/2024] [Indexed: 07/19/2024]
Abstract
Cancer cells exhibit rewired transcriptional regulatory networks that promote tumor growth and survival. However, the mechanisms underlying the formation of these pathological networks remain poorly understood. Through a pan-cancer epigenomic analysis, we found that primate-specific endogenous retroviruses (ERVs) are a rich source of enhancers displaying cancer-specific activity. In colorectal cancer and other epithelial tumors, oncogenic MAPK/AP1 signaling drives the activation of enhancers derived from the primate-specific ERV family LTR10. Functional studies in colorectal cancer cells revealed that LTR10 elements regulate tumor-specific expression of multiple genes associated with tumorigenesis, such as ATG12 and XRCC4. Within the human population, individual LTR10 elements exhibit germline and somatic structural variation resulting from a highly mutable internal tandem repeat region, which affects AP1 binding activity. Our findings reveal that ERV-derived enhancers contribute to transcriptional dysregulation in response to oncogenic signaling and shape the evolution of cancer-specific regulatory networks.
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Affiliation(s)
- Atma Ivancevic
- BioFrontiers Institute and Department of Molecular, Cellular and Developmental Biology, University of Colorado Boulder, Boulder, CO, USA
| | - David M. Simpson
- BioFrontiers Institute and Department of Molecular, Cellular and Developmental Biology, University of Colorado Boulder, Boulder, CO, USA
| | - Olivia M. Joyner
- BioFrontiers Institute and Department of Molecular, Cellular and Developmental Biology, University of Colorado Boulder, Boulder, CO, USA
| | - Stacey M. Bagby
- Division of Medical Oncology, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Lily L. Nguyen
- BioFrontiers Institute and Department of Molecular, Cellular and Developmental Biology, University of Colorado Boulder, Boulder, CO, USA
- Division of Reproductive Sciences, Department of Obstetrics and Gynecology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Ben G. Bitler
- Division of Reproductive Sciences, Department of Obstetrics and Gynecology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Todd M. Pitts
- Division of Medical Oncology, Department of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Edward B. Chuong
- BioFrontiers Institute and Department of Molecular, Cellular and Developmental Biology, University of Colorado Boulder, Boulder, CO, USA
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33
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Çelik MH, Gagneur J, Lim RG, Wu J, Thompson LM, Xie X. Identifying dysregulated regions in amyotrophic lateral sclerosis through chromatin accessibility outliers. HGG ADVANCES 2024; 5:100318. [PMID: 38872308 PMCID: PMC11260578 DOI: 10.1016/j.xhgg.2024.100318] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 06/10/2024] [Accepted: 06/11/2024] [Indexed: 06/15/2024] Open
Abstract
The high heritability of amyotrophic lateral sclerosis (ALS) contrasts with its low molecular diagnosis rate post-genetic testing, pointing to potential undiscovered genetic factors. To aid the exploration of these factors, we introduced EpiOut, an algorithm to identify chromatin accessibility outliers that are regions exhibiting divergent accessibility from the population baseline in a single or few samples. Annotation of accessible regions with histone chromatin immunoprecipitation sequencing and Hi-C indicates that outliers are concentrated in functional loci, especially among promoters interacting with active enhancers. Across different omics levels, outliers are robustly replicated, and chromatin accessibility outliers are reliable predictors of gene expression outliers and aberrant protein levels. When promoter accessibility does not align with gene expression, our results indicate that molecular aberrations are more likely to be linked to post-transcriptional regulation rather than transcriptional regulation. Our findings demonstrate that the outlier detection paradigm can uncover dysregulated regions in rare diseases. EpiOut is available at github.com/uci-cbcl/EpiOut.
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Affiliation(s)
- Muhammed Hasan Çelik
- Department of Computer Science, University of California Irvine, Irvine, CA, USA; Center for Complex Biological Systems, University of California Irvine, Irvine, CA, USA
| | - Julien Gagneur
- Department of Informatics, Technical University of Munich, Garching, Germany; Helmholtz Association - Munich School for Data Science (MUDS), Munich, Germany; Institute of Human Genetics, School of Medicine, Technical University of Munich, Munich, Germany; Institute of Computational Biology, Helmholtz Center Munich, Neuherberg, Germany
| | - Ryan G Lim
- Institute for Memory Impairments and Neurological Disorders, University of California Irvine, Irvine, CA 92697, USA
| | - Jie Wu
- Department of Biological Chemistry, University of California Irvine, Irvine, CA, USA
| | - Leslie M Thompson
- Institute for Memory Impairments and Neurological Disorders, University of California Irvine, Irvine, CA 92697, USA; Department of Biological Chemistry, University of California Irvine, Irvine, CA, USA; UCI MIND, University of California Irvine, Irvine, CA, USA; Department of Psychiatry and Human Behavior and Sue and Bill Gross Stem Cell Center, University of California Irvine, Irvine, CA, USA; Department of Neurobiology and Behavior, University of California Irvine, Irvine, CA, USA
| | - Xiaohui Xie
- Department of Computer Science, University of California Irvine, Irvine, CA, USA.
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34
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Kucinski J, Tallan A, Taslim C, Wang M, Cannon MV, Silvius KM, Stanton BZ, Kendall GC. Rhabdomyosarcoma fusion oncoprotein initially pioneers a neural signature in vivo. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.12.603270. [PMID: 39071299 PMCID: PMC11275748 DOI: 10.1101/2024.07.12.603270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Fusion-positive rhabdomyosarcoma is an aggressive pediatric cancer molecularly characterized by arrested myogenesis. The defining genetic driver, PAX3::FOXO1, functions as a chimeric gain-of-function transcription factor. An incomplete understanding of PAX3::FOXO1's in vivo epigenetic mechanisms has hindered therapeutic development. Here, we establish a PAX3::FOXO1 zebrafish injection model and semi-automated ChIP-seq normalization strategy to evaluate how PAX3::FOXO1 initially interfaces with chromatin in a developmental context. We investigated PAX3::FOXO1's recognition of chromatin and subsequent transcriptional consequences. We find that PAX3::FOXO1 interacts with inaccessible chromatin through partial/homeobox motif recognition consistent with pioneering activity. However, PAX3::FOXO1-genome binding through a composite paired-box/homeobox motif alters chromatin accessibility and redistributes H3K27ac to activate neural transcriptional programs. We uncover neural signatures that are highly representative of clinical rhabdomyosarcoma gene expression programs that are enriched following chemotherapy. Overall, we identify partial/homeobox motif recognition as a new mode for PAX3::FOXO1 pioneer function and identify neural signatures as a potentially critical PAX3::FOXO1 tumor initiation event.
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35
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Chen Y, Jiang Q, Xue Y, Chen W, Hua M. CRISPR-Cas9-mediated deletion enhancer of MECOM play a tumor suppressor role in ovarian cancer. Funct Integr Genomics 2024; 24:125. [PMID: 38995475 DOI: 10.1007/s10142-024-01399-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2024] [Revised: 06/24/2024] [Accepted: 06/25/2024] [Indexed: 07/13/2024]
Abstract
MDS1 and EVI1 complex locus (MECOM), a transcription factor encoding several variants, has been implicated in progression of ovarian cancer. The function of regulatory regions in regulating MECOM expression in ovarian cancer is not fully understood. In this study, MECOM expression was evaluated in ovarian cancer cell lines treated with bromodomain and extraterminal (BET) inhibitor JQ-1. Oncogenic phenotypes were assayed using assays of CCK-8, colony formation, wound-healing and transwell. Oncogenic phenotypes were estimated in stable sgRNA-transfected OVCAR3 cell lines. Xenograft mouse model was assayed via subcutaneous injection of enhancer-deleted OVCAR3 cell lines. The results displayed that expression of MECOM is downregulated in cell lines treated with JQ-1. Data from published ChIP-sequencing (H3K27Ac) in 3 ovarian cancer cell lines displayed a potential enhancer around the first exon. mRNA and protein expression were downregulated in OVCAR3 cells after deletion of the MECOM enhancer. Similarly, oncogenic phenotypes both in cells and in the xenograft mouse model were significantly attenuated. This study demonstrates that JQ-1 can inhibit the expression of MECOM and tumorigenesis. Deletion of the enhancer activity of MECOM has an indispensable role in inhibiting ovarian cancer progress, which sheds light on a promising opportunity for ovarian cancer treatment through the application of this non-coding DNA deletion.
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Affiliation(s)
- Yujie Chen
- Department of Gynecology and Obstetrics, Affiliated Hospital of Nantong University, 20 Xisi Road, Nantong, 226001, China
| | - Qiuwen Jiang
- Department of Gynecology and Obstetrics, Affiliated Hospital of Nantong University, 20 Xisi Road, Nantong, 226001, China
| | - Yingzhuo Xue
- Department of Gynecology and Obstetrics, Affiliated Hospital of Nantong University, 20 Xisi Road, Nantong, 226001, China
| | - Weiguan Chen
- Department of Rehabilitation Medicine, the first People's Hospital of Nantong, No. 666 Shengli Road, Nantong, 226001, China
| | - Minhui Hua
- Department of Gynecology and Obstetrics, Affiliated Hospital of Nantong University, 20 Xisi Road, Nantong, 226001, China.
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36
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Long E, Patel H, Golden A, Antony M, Yin J, Funderburk K, Feng J, Song L, Hoskins JW, Amundadottir LT, Hung RJ, Amos CI, Shi J, Rothman N, Lan Q, Choi J. High-throughput characterization of functional variants highlights heterogeneity and polygenicity underlying lung cancer susceptibility. Am J Hum Genet 2024; 111:1405-1419. [PMID: 38906146 PMCID: PMC11267514 DOI: 10.1016/j.ajhg.2024.05.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 05/23/2024] [Accepted: 05/23/2024] [Indexed: 06/23/2024] Open
Abstract
Genome-wide association studies (GWASs) have identified numerous lung cancer risk-associated loci. However, decoding molecular mechanisms of these associations is challenging since most of these genetic variants are non-protein-coding with unknown function. Here, we implemented massively parallel reporter assays (MPRAs) to simultaneously measure the allelic transcriptional activity of risk-associated variants. We tested 2,245 variants at 42 loci from 3 recent GWASs in East Asian and European populations in the context of two major lung cancer histological types and exposure to benzo(a)pyrene. This MPRA approach identified one or more variants (median 11 variants) with significant effects on transcriptional activity at 88% of GWAS loci. Multimodal integration of lung-specific epigenomic data demonstrated that 63% of the loci harbored multiple potentially functional variants in linkage disequilibrium. While 22% of the significant variants showed allelic effects in both A549 (adenocarcinoma) and H520 (squamous cell carcinoma) cell lines, a subset of the functional variants displayed a significant cell-type interaction. Transcription factor analyses nominated potential regulators of the functional variants, including those with cell-type-specific expression and those predicted to bind multiple potentially functional variants across the GWAS loci. Linking functional variants to target genes based on four complementary approaches identified candidate susceptibility genes, including those affecting lung cancer cell growth. CRISPR interference of the top functional variant at 20q13.33 validated variant-to-gene connections, including RTEL1, SOX18, and ARFRP1. Our data provide a comprehensive functional analysis of lung cancer GWAS loci and help elucidate the molecular basis of heterogeneity and polygenicity underlying lung cancer susceptibility.
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Affiliation(s)
- Erping Long
- State Key Laboratory of Respiratory Health and Multimorbidity, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| | - Harsh Patel
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Alyxandra Golden
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Michelle Antony
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Jinhu Yin
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Karen Funderburk
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - James Feng
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Lei Song
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Jason W Hoskins
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Laufey T Amundadottir
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Rayjean J Hung
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, Canada
| | - Christopher I Amos
- Institute for Clinical and Translational Research, Baylor College of Medicine, Houston, TX, USA
| | - Jianxin Shi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Nathaniel Rothman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Qing Lan
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Jiyeon Choi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
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37
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Wang Q, Antone J, Alsop E, Reiman R, Funk C, Bendl J, Dudley JT, Liang WS, Karr TL, Roussos P, Bennett DA, De Jager PL, Serrano GE, Beach TG, Van Keuren-Jensen K, Mastroeni D, Reiman EM, Readhead BP. Single cell transcriptomes and multiscale networks from persons with and without Alzheimer's disease. Nat Commun 2024; 15:5815. [PMID: 38987616 PMCID: PMC11237088 DOI: 10.1038/s41467-024-49790-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 06/13/2024] [Indexed: 07/12/2024] Open
Abstract
The emergence of single nucleus RNA sequencing (snRNA-seq) offers to revolutionize the study of Alzheimer's disease (AD). Integration with complementary multiomics data such as genetics, proteomics and clinical data provides powerful opportunities to link cell subpopulations and molecular networks with a broader disease-relevant context. We report snRNA-seq profiles from superior frontal gyrus samples from 101 well characterized subjects from the Banner Brain and Body Donation Program in combination with whole genome sequences. We report findings that link common AD risk variants with CR1 expression in oligodendrocytes as well as alterations in hematological parameters. We observed an AD-associated CD83(+) microglial subtype with unique molecular networks and which is associated with immunoglobulin IgG4 production in the transverse colon. Our major observations were replicated in two additional, independent snRNA-seq data sets. These findings illustrate the power of multi-tissue molecular profiling to contextualize snRNA-seq brain transcriptomics and reveal disease biology.
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Affiliation(s)
- Qi Wang
- ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ, 85281, USA
| | - Jerry Antone
- Division of Neurogenomics, The Translational Genomics Research Institute, Phoenix, AZ, 85004, USA
| | - Eric Alsop
- Division of Neurogenomics, The Translational Genomics Research Institute, Phoenix, AZ, 85004, USA
| | - Rebecca Reiman
- Division of Neurogenomics, The Translational Genomics Research Institute, Phoenix, AZ, 85004, USA
| | - Cory Funk
- Institute for Systems Biology, Seattle, WA, 98109, USA
| | - Jaroslav Bendl
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Joel T Dudley
- ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ, 85281, USA
| | - Winnie S Liang
- Division of Neurogenomics, The Translational Genomics Research Institute, Phoenix, AZ, 85004, USA
| | - Timothy L Karr
- ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ, 85281, USA
| | - Panos Roussos
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, 60612, USA
| | - Philip L De Jager
- Department of Neurology, Center for Translational and Computational Neuroimmunology, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Geidy E Serrano
- Civin Laboratory for Neuropathology, Banner Sun Health Research Institute, Sun City, AZ, 85351, USA
| | - Thomas G Beach
- Civin Laboratory for Neuropathology, Banner Sun Health Research Institute, Sun City, AZ, 85351, USA
| | | | - Diego Mastroeni
- ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ, 85281, USA
| | - Eric M Reiman
- Banner Alzheimer's Institute, Phoenix, AZ, 85006, USA
| | - Benjamin P Readhead
- ASU-Banner Neurodegenerative Disease Research Center, Arizona State University, Tempe, AZ, 85281, USA.
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38
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Retallick-Townsley KG, Lee S, Cartwright S, Cohen S, Sen A, Jia M, Young H, Dobbyn L, Deans M, Fernandez-Garcia M, Huckins LM, Brennand KJ. Dynamic stress- and inflammatory-based regulation of psychiatric risk loci in human neurons. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.09.602755. [PMID: 39026810 PMCID: PMC11257632 DOI: 10.1101/2024.07.09.602755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
The prenatal environment can alter neurodevelopmental and clinical trajectories, markedly increasing risk for psychiatric disorders in childhood and adolescence. To understand if and how fetal exposures to stress and inflammation exacerbate manifestation of genetic risk for complex brain disorders, we report a large-scale context-dependent massively parallel reporter assay (MPRA) in human neurons designed to catalogue genotype x environment (GxE) interactions. Across 240 genome-wide association study (GWAS) loci linked to ten brain traits/disorders, the impact of hydrocortisone, interleukin 6, and interferon alpha on transcriptional activity is empirically evaluated in human induced pluripotent stem cell (hiPSC)-derived glutamatergic neurons. Of ~3,500 candidate regulatory risk elements (CREs), 11% of variants are active at baseline, whereas cue-specific CRE regulatory activity range from a high of 23% (hydrocortisone) to a low of 6% (IL-6). Cue-specific regulatory activity is driven, at least in part, by differences in transcription factor binding activity, the gene targets of which show unique enrichments for brain disorders as well as co-morbid metabolic and immune syndromes. The dynamic nature of genetic regulation informs the influence of environmental factors, reveals a mechanism underlying pleiotropy and variable penetrance, and identifies specific risk variants that confer greater disorder susceptibility after exposure to stress or inflammation. Understanding neurodevelopmental GxE interactions will inform mental health trajectories and uncover novel targets for therapeutic intervention.
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Affiliation(s)
- Kayla G. Retallick-Townsley
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Seoyeon Lee
- Department of Psychiatry, Division of Molecular Psychiatry, Yale University School of Medicine, New Haven, CT 06511
- Department of Genetics, Wu Tsai Institute, Yale University School of Medicine, New Haven, CT 06511
| | - Sam Cartwright
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Sophie Cohen
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029
| | - Annabel Sen
- Department of Psychiatry, Division of Molecular Psychiatry, Yale University School of Medicine, New Haven, CT 06511
- Department of Genetics, Wu Tsai Institute, Yale University School of Medicine, New Haven, CT 06511
| | - Meng Jia
- Department of Psychiatry, Division of Molecular Psychiatry, Yale University School of Medicine, New Haven, CT 06511
- Department of Genetics, Wu Tsai Institute, Yale University School of Medicine, New Haven, CT 06511
| | - Hannah Young
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lee Dobbyn
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michael Deans
- Department of Psychiatry, Division of Molecular Psychiatry, Yale University School of Medicine, New Haven, CT 06511
- Department of Genetics, Wu Tsai Institute, Yale University School of Medicine, New Haven, CT 06511
| | - Meilin Fernandez-Garcia
- Department of Psychiatry, Division of Molecular Psychiatry, Yale University School of Medicine, New Haven, CT 06511
- Department of Genetics, Wu Tsai Institute, Yale University School of Medicine, New Haven, CT 06511
| | - Laura M. Huckins
- Department of Psychiatry, Division of Molecular Psychiatry, Yale University School of Medicine, New Haven, CT 06511
| | - Kristen J. Brennand
- Department of Genetics and Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Nash Family Department of Neuroscience, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029
- Department of Psychiatry, Division of Molecular Psychiatry, Yale University School of Medicine, New Haven, CT 06511
- Department of Genetics, Wu Tsai Institute, Yale University School of Medicine, New Haven, CT 06511
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39
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Melore SM, Hamilton MC, Reddy TE. HyperCas12a enables highly-multiplexed epigenome editing screens. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.08.602263. [PMID: 39026853 PMCID: PMC11257430 DOI: 10.1101/2024.07.08.602263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/20/2024]
Abstract
Interactions between multiple genes or cis-regulatory elements (CREs) underlie a wide range of biological processes in both health and disease. High-throughput screens using dCas9 fused to epigenome editing domains have allowed researchers to assess the impact of activation or repression of both coding and non-coding genomic regions on a phenotype of interest, but assessment of genetic interactions between those elements has been limited to pairs. Here, we combine a hyper-efficient version of Lachnospiraceae bacterium dCas12a (dHyperLbCas12a) with RNA Polymerase II expression of long CRISPR RNA (crRNA) arrays to enable efficient highly-multiplexed epigenome editing. We demonstrate that this system is compatible with several activation and repression domains, including the P300 histone acetyltransferase domain and SIN3A interacting domain (SID). We also show that the dCas12a platform can perform simultaneous activation and repression using a single crRNA array via co-expression of multiple dCas12a orthologues. Lastly, demonstrate that the dCas12a system is highly effective for high-throughput screens. We use dHyperLbCas12a-KRAB and a ~19,000-member barcoded library of crRNA arrays containing six crRNAs each to dissect the independent and combinatorial contributions of CREs to the dose-dependent control of gene expression at a glucocorticoid-responsive locus. The tools and methods introduced here create new possibilities for highly multiplexed control of gene expression in a wide variety of biological systems.
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Affiliation(s)
- Schuyler M. Melore
- University Program in Genetics & Genomics, Duke University, Durham, NC, USA
- Department of Biostatistics & Bioinformatics, Duke University School of Medicine, Durham, NC, USA
- Center for Advanced Genomic Technologies, Duke University, Durham, NC, USA
- Center for Combinatorial Gene Regulation, Duke University, Durham, NC, USA
| | - Marisa C. Hamilton
- University Program in Genetics & Genomics, Duke University, Durham, NC, USA
- Center for Advanced Genomic Technologies, Duke University, Durham, NC, USA
- Center for Combinatorial Gene Regulation, Duke University, Durham, NC, USA
| | - Timothy E. Reddy
- University Program in Genetics & Genomics, Duke University, Durham, NC, USA
- Department of Biostatistics & Bioinformatics, Duke University School of Medicine, Durham, NC, USA
- Center for Advanced Genomic Technologies, Duke University, Durham, NC, USA
- Center for Combinatorial Gene Regulation, Duke University, Durham, NC, USA
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40
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Huang D, Ovcharenko I. The contribution of silencer variants to human diseases. Genome Biol 2024; 25:184. [PMID: 38978133 PMCID: PMC11232194 DOI: 10.1186/s13059-024-03328-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Accepted: 06/28/2024] [Indexed: 07/10/2024] Open
Abstract
BACKGROUND Although disease-causal genetic variants have been found within silencer sequences, we still lack a comprehensive analysis of the association of silencers with diseases. Here, we profiled GWAS variants in 2.8 million candidate silencers across 97 human samples derived from a diverse panel of tissues and developmental time points, using deep learning models. RESULTS We show that candidate silencers exhibit strong enrichment in disease-associated variants, and several diseases display a much stronger association with silencer variants than enhancer variants. Close to 52% of candidate silencers cluster, forming silencer-rich loci, and, in the loci of Parkinson's-disease-hallmark genes TRIM31 and MAL, the associated SNPs densely populate clustered candidate silencers rather than enhancers displaying an overall twofold enrichment in silencers versus enhancers. The disruption of apoptosis in neuronal cells is associated with both schizophrenia and bipolar disorder and can largely be attributed to variants within candidate silencers. Our model permits a mechanistic explanation of causative SNP effects by identifying altered binding of tissue-specific repressors and activators, validated with a 70% of directional concordance using SNP-SELEX. Narrowing the focus of the analysis to individual silencer variants, experimental data confirms the role of the rs62055708 SNP in Parkinson's disease, rs2535629 in schizophrenia, and rs6207121 in type 1 diabetes. CONCLUSIONS In summary, our results indicate that advances in deep learning models for the discovery of disease-causal variants within candidate silencers effectively "double" the number of functionally characterized GWAS variants. This provides a basis for explaining mechanisms of action and designing novel diagnostics and therapeutics.
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Affiliation(s)
- Di Huang
- Intramural Research Program, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Ivan Ovcharenko
- Intramural Research Program, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20892, USA.
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41
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Oguchi A, Suzuki A, Komatsu S, Yoshitomi H, Bhagat S, Son R, Bonnal RJP, Kojima S, Koido M, Takeuchi K, Myouzen K, Inoue G, Hirai T, Sano H, Takegami Y, Kanemaru A, Yamaguchi I, Ishikawa Y, Tanaka N, Hirabayashi S, Konishi R, Sekito S, Inoue T, Kere J, Takeda S, Takaori-Kondo A, Endo I, Kawaoka S, Kawaji H, Ishigaki K, Ueno H, Hayashizaki Y, Pagani M, Carninci P, Yanagita M, Parrish N, Terao C, Yamamoto K, Murakawa Y. An atlas of transcribed enhancers across helper T cell diversity for decoding human diseases. Science 2024; 385:eadd8394. [PMID: 38963856 DOI: 10.1126/science.add8394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Accepted: 05/01/2024] [Indexed: 07/06/2024]
Abstract
Transcribed enhancer maps can reveal nuclear interactions underpinning each cell type and connect specific cell types to diseases. Using a 5' single-cell RNA sequencing approach, we defined transcription start sites of enhancer RNAs and other classes of coding and noncoding RNAs in human CD4+ T cells, revealing cellular heterogeneity and differentiation trajectories. Integration of these datasets with single-cell chromatin profiles showed that active enhancers with bidirectional RNA transcription are highly cell type-specific and that disease heritability is strongly enriched in these enhancers. The resulting cell type-resolved multimodal atlas of bidirectionally transcribed enhancers, which we linked with promoters using fine-scale chromatin contact maps, enabled us to systematically interpret genetic variants associated with a range of immune-mediated diseases.
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Affiliation(s)
- Akiko Oguchi
- RIKEN-IFOM Joint Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Institute for the Advanced Study of Human Biology, Kyoto University, Kyoto, Japan
- Department of Nephrology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Akari Suzuki
- Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Shuichiro Komatsu
- RIKEN-IFOM Joint Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- IFOM ETS - the AIRC Institute of Molecular Oncology, Milan, Italy
| | - Hiroyuki Yoshitomi
- Institute for the Advanced Study of Human Biology, Kyoto University, Kyoto, Japan
- Department of Immunology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Shruti Bhagat
- Institute for the Advanced Study of Human Biology, Kyoto University, Kyoto, Japan
| | - Raku Son
- RIKEN-IFOM Joint Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Institute for the Advanced Study of Human Biology, Kyoto University, Kyoto, Japan
- Department of Nephrology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | | | - Shohei Kojima
- Genome Immunobiology RIKEN Hakubi Research Team, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Masaru Koido
- Division of Molecular Pathology, Department of Cancer Biology, Institute of Medical Science, The University of Tokyo, Tokyo, Japan
- Laboratory of Complex Trait Genomics, Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Kazuhiro Takeuchi
- RIKEN-IFOM Joint Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Institute for the Advanced Study of Human Biology, Kyoto University, Kyoto, Japan
- Department of Medical Systems Genomics, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Keiko Myouzen
- Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Gyo Inoue
- Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Tomoya Hirai
- RIKEN-IFOM Joint Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Gastroenterological Surgery, Yokohama City University Graduate School of Medicine, Yokohama City University, Yokohama, Japan
| | - Hiromi Sano
- RIKEN-IFOM Joint Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | | | | | | | - Yuki Ishikawa
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Nao Tanaka
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Shigeki Hirabayashi
- RIKEN-IFOM Joint Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Department of Hematology and Oncology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Division of Precision Medicine, Kyushu University Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Riyo Konishi
- Inter-Organ Communication Research Team, Institute for Life and Medical Sciences, Kyoto University, Kyoto, Japan
| | - Sho Sekito
- Institute for the Advanced Study of Human Biology, Kyoto University, Kyoto, Japan
- Department of Nephro-Urologic Surgery and Andrology, Mie University Graduate School of Medicine, Mie University, Tsu, Japan
| | - Takahiro Inoue
- Department of Nephro-Urologic Surgery and Andrology, Mie University Graduate School of Medicine, Mie University, Tsu, Japan
| | - Juha Kere
- Department of Biosciences and Nutrition, Karolinska Institutet, Stockholm, Sweden
- Stem Cells and Metabolism Research Program, University of Helsinki, Helsinki, Finland
- Folkhalsan Research Center, Helsinki, Finland
| | - Shunichi Takeda
- Department of Radiation Genetics, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Shenzhen University School of Medicine, Shenzhen, Guangdong, China
| | - Akifumi Takaori-Kondo
- Department of Hematology and Oncology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Itaru Endo
- Department of Gastroenterological Surgery, Yokohama City University Graduate School of Medicine, Yokohama City University, Yokohama, Japan
| | - Shinpei Kawaoka
- Inter-Organ Communication Research Team, Institute for Life and Medical Sciences, Kyoto University, Kyoto, Japan
- Department of Integrative Bioanalytics, Institute of Development, Aging and Cancer, Tohoku University, Sendai, Japan
| | - Hideya Kawaji
- Research Center for Genome & Medical Sciences, Tokyo Metropolitan Institute of Medical Science, Tokyo, Japan
- Preventive Medicine and Applied Genomics Unit, RIKEN Center for Integrative Medical Science, Yokohama, Japan
- RIKEN Preventive Medicine and Diagnosis Innovation Program, Wako, Japan
| | - Kazuyoshi Ishigaki
- Laboratory for Human Immunogenetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Hideki Ueno
- Institute for the Advanced Study of Human Biology, Kyoto University, Kyoto, Japan
- Department of Immunology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Yoshihide Hayashizaki
- K.K. DNAFORM, Yokohama, Japan
- RIKEN Preventive Medicine and Diagnosis Innovation Program, Wako, Japan
| | - Massimiliano Pagani
- IFOM ETS - the AIRC Institute of Molecular Oncology, Milan, Italy
- Department of Medical Biotechnology and Translational Medicine, Università degli Studi, Milan, Italy
| | - Piero Carninci
- Laboratory for Transcriptome Technology, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Human Technopole, Milan, Italy
| | - Motoko Yanagita
- Institute for the Advanced Study of Human Biology, Kyoto University, Kyoto, Japan
- Department of Nephrology, Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Nicholas Parrish
- Genome Immunobiology RIKEN Hakubi Research Team, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Chikashi Terao
- Laboratory for Statistical and Translational Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Clinical Research Center, Shizuoka General Hospital, Shizuoka, Japan
- Department of Applied Genetics, School of Pharmaceutical Sciences, University of Shizuoka, Shizuoka, Japan
| | - Kazuhiko Yamamoto
- Laboratory for Autoimmune Diseases, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yasuhiro Murakawa
- RIKEN-IFOM Joint Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Institute for the Advanced Study of Human Biology, Kyoto University, Kyoto, Japan
- IFOM ETS - the AIRC Institute of Molecular Oncology, Milan, Italy
- Department of Medical Systems Genomics, Graduate School of Medicine, Kyoto University, Kyoto, Japan
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42
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Fu X, Mo S, Buendia A, Laurent A, Shao A, del Mar Alvarez-Torres M, Yu T, Tan J, Su J, Sagatelian R, Ferrando AA, Ciccia A, Lan Y, Owens DM, Palomero T, Xing EP, Rabadan R. GET: a foundation model of transcription across human cell types. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.24.559168. [PMID: 39005360 PMCID: PMC11244937 DOI: 10.1101/2023.09.24.559168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Transcriptional regulation, involving the complex interplay between regulatory sequences and proteins, directs all biological processes. Computational models of transcription lack generalizability to accurately extrapolate in unseen cell types and conditions. Here, we introduce GET, an interpretable foundation model designed to uncover regulatory grammars across 213 human fetal and adult cell types. Relying exclusively on chromatin accessibility data and sequence information, GET achieves experimental-level accuracy in predicting gene expression even in previously unseen cell types. GET showcases remarkable adaptability across new sequencing platforms and assays, enabling regulatory inference across a broad range of cell types and conditions, and uncovering universal and cell type specific transcription factor interaction networks. We evaluated its performance on prediction of regulatory activity, inference of regulatory elements and regulators, and identification of physical interactions between transcription factors. Specifically, we show GET outperforms current models in predicting lentivirus-based massive parallel reporter assay readout with reduced input data. In fetal erythroblasts, we identify distal (>1Mbp) regulatory regions that were missed by previous models. In B cells, we identified a lymphocyte-specific transcription factor-transcription factor interaction that explains the functional significance of a leukemia-risk predisposing germline mutation. In sum, we provide a generalizable and accurate model for transcription together with catalogs of gene regulation and transcription factor interactions, all with cell type specificity.
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Affiliation(s)
- Xi Fu
- Department of Systems Biology, Columbia University, New York, NY, USA
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | - Shentong Mo
- Department of Machine Learning, Carnegie Mellon University, Pittsburgh, PA, USA
- Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE
| | - Alejandro Buendia
- Department of Systems Biology, Columbia University, New York, NY, USA
| | - Anouchka Laurent
- Institute for Cancer Genetics, Columbia University, New York, NY, USA
| | - Anqi Shao
- Department of Dermatology, Columbia University, New York, NY, USA
| | | | - Tianji Yu
- Department of Systems Biology, Columbia University, New York, NY, USA
| | - Jimin Tan
- Regeneron Genetics Center, Regeneron, Tarrytown, NY, USA
| | - Jiayu Su
- Department of Systems Biology, Columbia University, New York, NY, USA
| | | | - Adolfo A. Ferrando
- Department of Dermatology, Columbia University, New York, NY, USA
- Regeneron Genetics Center, Regeneron, Tarrytown, NY, USA
| | - Alberto Ciccia
- Department of Genetics and Development, Columbia University, New York, NY, USA
| | - Yanyan Lan
- Institute for AI Industry Research, Tsinghua University, Beijing, China
| | - David M. Owens
- Institute for Cancer Genetics, Columbia University, New York, NY, USA
- Department of Pathology & Cell Biology, Columbia University, New York, NY, USA
| | - Teresa Palomero
- Institute for Cancer Genetics, Columbia University, New York, NY, USA
- Department of Pathology & Cell Biology, Columbia University, New York, NY, USA
| | - Eric P. Xing
- Department of Machine Learning, Carnegie Mellon University, Pittsburgh, PA, USA
- Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE
| | - Raul Rabadan
- Department of Systems Biology, Columbia University, New York, NY, USA
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
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43
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Perlman BS, Burget N, Zhou Y, Schwartz GW, Petrovic J, Modrusan Z, Faryabi RB. Enhancer-promoter hubs organize transcriptional networks promoting oncogenesis and drug resistance. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.02.601745. [PMID: 39005446 PMCID: PMC11244972 DOI: 10.1101/2024.07.02.601745] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Recent advances in high-resolution mapping of spatial interactions among regulatory elements support the existence of complex topological assemblies of enhancers and promoters known as enhancer-promoter hubs or cliques. Yet, organization principles of these multi-interacting enhancer-promoter hubs and their potential role in regulating gene expression in cancer remains unclear. Here, we systematically identified enhancer-promoter hubs in breast cancer, lymphoma, and leukemia. We found that highly interacting enhancer-promoter hubs form at key oncogenes and lineage-associated transcription factors potentially promoting oncogenesis of these diverse cancer types. Genomic and optical mapping of interactions among enhancer and promoter elements further showed that topological alterations in hubs coincide with transcriptional changes underlying acquired resistance to targeted therapy in T cell leukemia and B cell lymphoma. Together, our findings suggest that enhancer-promoter hubs are dynamic and heterogeneous topological assemblies with the potential to control gene expression circuits promoting oncogenesis and drug resistance.
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44
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Li Y, Tan M, Akkari-Henić A, Zhang L, Kip M, Sun S, Sepers JJ, Xu N, Ariyurek Y, Kloet SL, Davis RP, Mikkers H, Gruber JJ, Snyder MP, Li X, Pang B. Genome-wide Cas9-mediated screening of essential non-coding regulatory elements via libraries of paired single-guide RNAs. Nat Biomed Eng 2024; 8:890-908. [PMID: 38778183 PMCID: PMC11310080 DOI: 10.1038/s41551-024-01204-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 03/27/2024] [Indexed: 05/25/2024]
Abstract
The functions of non-coding regulatory elements (NCREs), which constitute a major fraction of the human genome, have not been systematically studied. Here we report a method involving libraries of paired single-guide RNAs targeting both ends of an NCRE as a screening system for the Cas9-mediated deletion of thousands of NCREs genome-wide to study their functions in distinct biological contexts. By using K562 and 293T cell lines and human embryonic stem cells, we show that NCREs can have redundant functions, and that many ultra-conserved elements have silencer activity and play essential roles in cell growth and in cellular responses to drugs (notably, the ultra-conserved element PAX6_Tarzan may be critical for heart development, as removing it from human embryonic stem cells led to defects in cardiomyocyte differentiation). The high-throughput screen, which is compatible with single-cell sequencing, may allow for the identification of druggable NCREs.
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Affiliation(s)
- Yufeng Li
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
| | - Minkang Tan
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
| | - Almira Akkari-Henić
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
| | - Limin Zhang
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
| | - Maarten Kip
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
| | - Shengnan Sun
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
| | - Jorian J Sepers
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
| | - Ningning Xu
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
| | - Yavuz Ariyurek
- Leiden Genome Technology Center, Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
| | - Susan L Kloet
- Leiden Genome Technology Center, Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands
| | - Richard P Davis
- Department of Anatomy and Embryology, The Novo Nordisk Foundation Center for Stem Cell Medicine (reNEW), Leiden University Medical Center, Leiden, the Netherlands
| | - Harald Mikkers
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
| | - Joshua J Gruber
- Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | | | - Xiao Li
- Department of Biochemistry, The Center for RNA Science and Therapeutics, Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, OH, USA.
| | - Baoxu Pang
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands.
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45
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Yang JH, Hansen AS. Enhancer selectivity in space and time: from enhancer-promoter interactions to promoter activation. Nat Rev Mol Cell Biol 2024; 25:574-591. [PMID: 38413840 DOI: 10.1038/s41580-024-00710-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/30/2024] [Indexed: 02/29/2024]
Abstract
The primary regulators of metazoan gene expression are enhancers, originally functionally defined as DNA sequences that can activate transcription at promoters in an orientation-independent and distance-independent manner. Despite being crucial for gene regulation in animals, what mechanisms underlie enhancer selectivity for promoters, and more fundamentally, how enhancers interact with promoters and activate transcription, remain poorly understood. In this Review, we first discuss current models of enhancer-promoter interactions in space and time and how enhancers affect transcription activation. Next, we discuss different mechanisms that mediate enhancer selectivity, including repression, biochemical compatibility and regulation of 3D genome structure. Through 3D polymer simulations, we illustrate how the ability of 3D genome folding mechanisms to mediate enhancer selectivity strongly varies for different enhancer-promoter interaction mechanisms. Finally, we discuss how recent technical advances may provide new insights into mechanisms of enhancer-promoter interactions and how technical biases in methods such as Hi-C and Micro-C and imaging techniques may affect their interpretation.
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Affiliation(s)
- Jin H Yang
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Koch Institute for Integrative Cancer Research, Cambridge, MA, USA
| | - Anders S Hansen
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Gene Regulation Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Koch Institute for Integrative Cancer Research, Cambridge, MA, USA.
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46
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Frenkel M, Raman S. Discovering mechanisms of human genetic variation and controlling cell states at scale. Trends Genet 2024; 40:587-600. [PMID: 38658256 DOI: 10.1016/j.tig.2024.03.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2024] [Revised: 03/29/2024] [Accepted: 03/29/2024] [Indexed: 04/26/2024]
Abstract
Population-scale sequencing efforts have catalogued substantial genetic variation in humans such that variant discovery dramatically outpaces interpretation. We discuss how single-cell sequencing is poised to reveal genetic mechanisms at a rate that may soon approach that of variant discovery. The functional genomics toolkit is sufficiently modular to systematically profile almost any type of variation within increasingly diverse contexts and with molecularly comprehensive and unbiased readouts. As a result, we can construct deep phenotypic atlases of variant effects that span the entire regulatory cascade. The same conceptual approach to interpreting genetic variation should be applied to engineering therapeutic cell states. In this way, variant mechanism discovery and cell state engineering will become reciprocating and iterative processes towards genomic medicine.
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Affiliation(s)
- Max Frenkel
- Cellular and Molecular Biology Graduate Program, University of Wisconsin, Madison, WI, USA; Medical Scientist Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Department of Biochemistry, University of Wisconsin, Madison, WI, USA.
| | - Srivatsan Raman
- Department of Biochemistry, University of Wisconsin, Madison, WI, USA; Department of Bacteriology, University of Wisconsin, Madison, WI, USA; Department of Chemical and Biological Engineering, University of Wisconsin, Madison, WI, USA.
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47
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Moeckel C, Mouratidis I, Chantzi N, Uzun Y, Georgakopoulos-Soares I. Advances in computational and experimental approaches for deciphering transcriptional regulatory networks: Understanding the roles of cis-regulatory elements is essential, and recent research utilizing MPRAs, STARR-seq, CRISPR-Cas9, and machine learning has yielded valuable insights. Bioessays 2024; 46:e2300210. [PMID: 38715516 DOI: 10.1002/bies.202300210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 04/22/2024] [Accepted: 04/23/2024] [Indexed: 05/16/2024]
Abstract
Understanding the influence of cis-regulatory elements on gene regulation poses numerous challenges given complexities stemming from variations in transcription factor (TF) binding, chromatin accessibility, structural constraints, and cell-type differences. This review discusses the role of gene regulatory networks in enhancing understanding of transcriptional regulation and covers construction methods ranging from expression-based approaches to supervised machine learning. Additionally, key experimental methods, including MPRAs and CRISPR-Cas9-based screening, which have significantly contributed to understanding TF binding preferences and cis-regulatory element functions, are explored. Lastly, the potential of machine learning and artificial intelligence to unravel cis-regulatory logic is analyzed. These computational advances have far-reaching implications for precision medicine, therapeutic target discovery, and the study of genetic variations in health and disease.
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Affiliation(s)
- Camille Moeckel
- Department of Biochemistry and Molecular Biology, Institute for Personalized Medicine, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Ioannis Mouratidis
- Department of Biochemistry and Molecular Biology, Institute for Personalized Medicine, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
- Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania, USA
| | - Nikol Chantzi
- Department of Biochemistry and Molecular Biology, Institute for Personalized Medicine, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Yasin Uzun
- Department of Biochemistry and Molecular Biology, Institute for Personalized Medicine, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
- Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania, USA
- Department of Pediatrics, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
| | - Ilias Georgakopoulos-Soares
- Department of Biochemistry and Molecular Biology, Institute for Personalized Medicine, The Pennsylvania State University College of Medicine, Hershey, Pennsylvania, USA
- Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, Pennsylvania, USA
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48
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Fazel Darbandi S, An JY, Lim K, Page NF, Liang L, Young DM, Ypsilanti AR, State MW, Nord AS, Sanders SJ, Rubenstein JLR. Five autism-associated transcriptional regulators target shared loci proximal to brain-expressed genes. Cell Rep 2024; 43:114329. [PMID: 38850535 PMCID: PMC11235582 DOI: 10.1016/j.celrep.2024.114329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 09/15/2023] [Accepted: 05/22/2024] [Indexed: 06/10/2024] Open
Abstract
Many autism spectrum disorder (ASD)-associated genes act as transcriptional regulators (TRs). Chromatin immunoprecipitation sequencing (ChIP-seq) was used to identify the regulatory targets of ARID1B, BCL11A, FOXP1, TBR1, and TCF7L2, ASD-associated TRs in the developing human and mouse cortex. These TRs shared substantial overlap in the binding sites, especially within open chromatin. The overlap within a promoter region, 1-2,000 bp upstream of the transcription start site, was highly predictive of brain-expressed genes. This signature was observed in 96 out of 102 ASD-associated genes. In vitro CRISPRi against ARID1B and TBR1 delineated downstream convergent biology in mouse cortical cultures. After 8 days, NeuN+ and CALB+ cells were decreased, GFAP+ cells were increased, and transcriptomic signatures correlated with the postmortem brain samples from individuals with ASD. We suggest that functional convergence across five ASD-associated TRs leads to shared neurodevelopmental outcomes of haploinsufficient disruption.
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Affiliation(s)
- Siavash Fazel Darbandi
- Nina Ireland Laboratory of Developmental Neurobiology, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Psychiatry and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Joon-Yong An
- School of Biosystem and Biomedical Science, College of Health Science, Korea University, Seoul, South Korea; BK21FOUR R&E Center for Learning Health Systems, Korea University, Seoul, South Korea
| | - Kenneth Lim
- Nina Ireland Laboratory of Developmental Neurobiology, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Psychiatry and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Nicholas F Page
- Department of Psychiatry and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Lindsay Liang
- Department of Psychiatry and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - David M Young
- Department of Psychiatry and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Athena R Ypsilanti
- Nina Ireland Laboratory of Developmental Neurobiology, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Psychiatry and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Matthew W State
- Department of Psychiatry and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA; Institute for Human Genetics, University of California San Francisco, San Francisco, CA 94143, USA
| | - Alex S Nord
- Department of Neurobiology, Physiology, and Behavior and Department of Psychiatry and Behavioral Sciences, Center for Neuroscience, University of California, Davis, Davis, CA 95618, USA
| | - Stephan J Sanders
- Department of Psychiatry and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA; Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA 94143, USA; Institute for Human Genetics, University of California San Francisco, San Francisco, CA 94143, USA; Institute for Developmental and Regenerative Medicine, Old Road Campus, Roosevelt Dr., Headington, Oxford OX3 7TY, UK.
| | - John L R Rubenstein
- Nina Ireland Laboratory of Developmental Neurobiology, University of California, San Francisco, San Francisco, CA 94143, USA; Department of Psychiatry and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA 94143, USA.
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Strober BJ, Zhang MJ, Amariuta T, Rossen J, Price AL. Fine-mapping causal tissues and genes at disease-associated loci. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.11.01.23297909. [PMID: 37961337 PMCID: PMC10635248 DOI: 10.1101/2023.11.01.23297909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
Heritable diseases often manifest in a highly tissue-specific manner, with different disease loci mediated by genes in distinct tissues or cell types. We propose Tissue-Gene Fine-Mapping (TGFM), a fine-mapping method that infers the posterior probability (PIP) for each gene-tissue pair to mediate a disease locus by analyzing GWAS summary statistics (and in-sample LD) and leveraging eQTL data from diverse tissues to build cis-predicted expression models; TGFM also assigns PIPs to causal variants that are not mediated by gene expression in assayed genes and tissues. TGFM accounts for both co-regulation across genes and tissues and LD between SNPs (generalizing existing fine-mapping methods), and incorporates genome-wide estimates of each tissue's contribution to disease as tissue-level priors. TGFM was well-calibrated and moderately well-powered in simulations; unlike previous methods, TGFM was able to attain correct calibration by modeling uncertainty in cis-predicted expression models. We applied TGFM to 45 UK Biobank diseases/traits (average N = 316K) using eQTL data from 38 GTEx tissues. TGFM identified an average of 147 PIP > 0.5 causal genetic elements per disease/trait, of which 11% were gene-tissue pairs. Implicated gene-tissue pairs were concentrated in known disease-critical tissues, and causal genes were strongly enriched in disease-relevant gene sets. Causal gene-tissue pairs identified by TGFM recapitulated known biology (e.g., TPO-thyroid for Hypothyroidism), but also included biologically plausible novel findings (e.g., SLC20A2-artery aorta for Diastolic blood pressure). Further application of TGFM to single-cell eQTL data from 9 cell types in peripheral blood mononuclear cells (PBMC), analyzed jointly with GTEx tissues, identified 30 additional causal gene-PBMC cell type pairs at PIP > 0.5-primarily for autoimmune disease and blood cell traits, including the biologically plausible example of CD52 in classical monocyte cells for Monocyte count. In conclusion, TGFM is a robust and powerful method for fine-mapping causal tissues and genes at disease-associated loci.
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Affiliation(s)
- Benjamin J. Strober
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Martin Jinye Zhang
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Tiffany Amariuta
- Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, CA, USA
- Department of Medicine, University of California San Diego, La Jolla, CA, USA
| | - Jordan Rossen
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Alkes L. Price
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
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50
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Cai YM, Lu ZQ, Li B, Huang JY, Zhang M, Chen C, Fan LY, Ma QY, He CY, Chen SN, Jiang Y, Li YM, Ning CB, Zhang FW, Wang WZ, Liu YZ, Zhang H, Jin M, Wang XY, Han JX, Xiong Z, Cai M, Huang CQ, Yang XJ, Zhu X, Zhu Y, Miao XP, Zhang SK, Wei YC, Tian JB. Genome-wide enhancer RNA profiling adds molecular links between genetic variation and human cancers. Mil Med Res 2024; 11:36. [PMID: 38863031 PMCID: PMC11165858 DOI: 10.1186/s40779-024-00539-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Accepted: 05/17/2024] [Indexed: 06/13/2024] Open
Abstract
BACKGROUND Dysregulation of enhancer transcription occurs in multiple cancers. Enhancer RNAs (eRNAs) are transcribed products from enhancers that play critical roles in transcriptional control. Characterizing the genetic basis of eRNA expression may elucidate the molecular mechanisms underlying cancers. METHODS Initially, a comprehensive analysis of eRNA quantitative trait loci (eRNAQTLs) was performed in The Cancer Genome Atlas (TCGA), and functional features were characterized using multi-omics data. To establish the first eRNAQTL profiles for colorectal cancer (CRC) in China, epigenomic data were used to define active enhancers, which were subsequently integrated with transcription and genotyping data from 154 paired CRC samples. Finally, large-scale case-control studies (34,585 cases and 69,544 controls) were conducted along with multipronged experiments to investigate the potential mechanisms by which candidate eRNAQTLs affect CRC risk. RESULTS A total of 300,112 eRNAQTLs were identified across 30 different cancer types, which exert their influence on eRNA transcription by modulating chromatin status, binding affinity to transcription factors and RNA-binding proteins. These eRNAQTLs were found to be significantly enriched in cancer risk loci, explaining a substantial proportion of cancer heritability. Additionally, tumor-specific eRNAQTLs exhibited high responsiveness to the development of cancer. Moreover, the target genes of these eRNAs were associated with dysregulated signaling pathways and immune cell infiltration in cancer, highlighting their potential as therapeutic targets. Furthermore, multiple ethnic population studies have confirmed that an eRNAQTL rs3094296-T variant decreases the risk of CRC in populations from China (OR = 0.91, 95%CI 0.88-0.95, P = 2.92 × 10-7) and Europe (OR = 0.92, 95%CI 0.88-0.95, P = 4.61 × 10-6). Mechanistically, rs3094296 had an allele-specific effect on the transcription of the eRNA ENSR00000155786, which functioned as a transcriptional activator promoting the expression of its target gene SENP7. These two genes synergistically suppressed tumor cell proliferation. Our curated list of variants, genes, and drugs has been made available in CancereRNAQTL ( http://canernaqtl.whu.edu.cn/#/ ) to serve as an informative resource for advancing this field. CONCLUSION Our findings underscore the significance of eRNAQTLs in transcriptional regulation and disease heritability, pinpointing the potential of eRNA-based therapeutic strategies in cancers.
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Affiliation(s)
- Yi-Min Cai
- Department of Epidemiology and Biostatistics, School of Public Health, Research Center of Public Health, Renmin Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China
- Department of Gastrointestinal Oncology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, 430071, China
- Department of Cancer Epidemiology, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, the Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450008, China
| | - Ze-Qun Lu
- Department of Epidemiology and Biostatistics, School of Public Health, Research Center of Public Health, Renmin Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China
- Department of Gastrointestinal Oncology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, 430071, China
- Department of Cancer Epidemiology, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, the Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450008, China
| | - Bin Li
- Department of Epidemiology and Biostatistics, School of Public Health, Research Center of Public Health, Renmin Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China
- Department of Gastrointestinal Oncology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, 430071, China
- Department of Cancer Epidemiology, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, the Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450008, China
| | - Jin-Yu Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Research Center of Public Health, Renmin Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China
| | - Ming Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Research Center of Public Health, Renmin Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China
| | - Can Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Research Center of Public Health, Renmin Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China
| | - Lin-Yun Fan
- Department of Epidemiology and Biostatistics, School of Public Health, Research Center of Public Health, Renmin Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China
| | - Qian-Ying Ma
- Department of Epidemiology and Biostatistics, School of Public Health, Research Center of Public Health, Renmin Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China
| | - Chun-Yi He
- Department of Epidemiology and Biostatistics, School of Public Health, Research Center of Public Health, Renmin Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China
| | - Shuo-Ni Chen
- Department of Epidemiology and Biostatistics, School of Public Health, Research Center of Public Health, Renmin Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China
| | - Yuan Jiang
- Department of Epidemiology and Biostatistics, School of Public Health, Research Center of Public Health, Renmin Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China
| | - Yan-Min Li
- Department of Epidemiology and Biostatistics, School of Public Health, Research Center of Public Health, Renmin Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China
| | - Cai-Bo Ning
- Department of Epidemiology and Biostatistics, School of Public Health, Research Center of Public Health, Renmin Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China
| | - Fu-Wei Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Research Center of Public Health, Renmin Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China
| | - Wen-Zhuo Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Research Center of Public Health, Renmin Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China
| | - Yi-Zhuo Liu
- Department of Epidemiology and Biostatistics, School of Public Health, Research Center of Public Health, Renmin Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China
| | - Heng Zhang
- Department of Epidemiology and Biostatistics, School of Public Health, Research Center of Public Health, Renmin Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China
| | - Meng Jin
- Department of Oncology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Xiao-Yang Wang
- Department of Cancer Epidemiology, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, the Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450008, China
| | - Jin-Xin Han
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Zhen Xiong
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Ming Cai
- Department of Gastrointestinal Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Chao-Qun Huang
- Department of Gastrointestinal Surgery, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, 430071, China
| | - Xiao-Jun Yang
- Department of Gastrointestinal Surgery, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, 430071, China
| | - Xu Zhu
- Department of Gastrointestinal Surgery, Renmin Hospital of Wuhan University, Wuhan, 430060, China
| | - Ying Zhu
- Department of Epidemiology and Biostatistics, School of Public Health, Research Center of Public Health, Renmin Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China
| | - Xiao-Ping Miao
- Department of Epidemiology and Biostatistics, School of Public Health, Research Center of Public Health, Renmin Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China.
- Department of Gastrointestinal Oncology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, 430071, China.
- Department of Epidemiology and Biostatistics, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, 211166, China.
| | - Shao-Kai Zhang
- Department of Cancer Epidemiology, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, the Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450008, China.
| | - Yong-Chang Wei
- Department of Gastrointestinal Oncology, Hubei Cancer Clinical Study Center, Zhongnan Hospital of Wuhan University, Wuhan, 430071, China.
| | - Jian-Bo Tian
- Department of Epidemiology and Biostatistics, School of Public Health, Research Center of Public Health, Renmin Hospital of Wuhan University, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430071, China.
- Department of Gastrointestinal Oncology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, 430071, China.
- Department of Cancer Epidemiology, Henan Engineering Research Center of Cancer Prevention and Control, Henan International Joint Laboratory of Cancer Prevention, the Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, 450008, China.
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