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Baumgarten N, Rumpf L, Kessler T, Schulz MH. A statistical approach for identifying single nucleotide variants that affect transcription factor binding. iScience 2024; 27:109765. [PMID: 38736546 PMCID: PMC11088338 DOI: 10.1016/j.isci.2024.109765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 01/30/2024] [Accepted: 04/15/2024] [Indexed: 05/14/2024] Open
Abstract
Non-coding variants located within regulatory elements may alter gene expression by modifying transcription factor (TF) binding sites, thereby leading to functional consequences. Different TF models are being used to assess the effect of DNA sequence variants, such as single nucleotide variants (SNVs). Often existing methods are slow and do not assess statistical significance of results. We investigated the distribution of absolute maximal differential TF binding scores for general computational models that affect TF binding. We find that a modified Laplace distribution can adequately approximate the empirical distributions. A benchmark on in vitro and in vivo datasets showed that our approach improves upon an existing method in terms of performance and speed. Applications on eQTLs and on a genome-wide association study illustrate the usefulness of our statistics by highlighting cell type-specific regulators and target genes. An implementation of our approach is freely available on GitHub and as bioconda package.
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Affiliation(s)
- Nina Baumgarten
- Institute of Cardiovascular Regeneration, Goethe University, 60590 Frankfurt am Main, Germany
- Institute for Computational Genomic Medicine, Goethe University, 60590 Frankfurt am Main, Germany
- Institute for Computer Science, Goethe University, 60590 Frankfurt am Main, Germany
- German Center for Cardiovascular Research, Partner Site Rhein-Main, 60590 Frankfurt am Main, Germany
| | - Laura Rumpf
- Institute of Cardiovascular Regeneration, Goethe University, 60590 Frankfurt am Main, Germany
- Institute for Computational Genomic Medicine, Goethe University, 60590 Frankfurt am Main, Germany
- Institute for Computer Science, Goethe University, 60590 Frankfurt am Main, Germany
- German Center for Cardiovascular Research, Partner Site Rhein-Main, 60590 Frankfurt am Main, Germany
| | - Thorsten Kessler
- German Heart Centre Munich, Department of Cardiology, School of Medicine and Health, Technical University of Munich, 80636 Munich, Germany
- German Centre for Cardiovascular Research, Partner Site Munich Heart Alliance, 80636 Munich, Germany
| | - Marcel H. Schulz
- Institute of Cardiovascular Regeneration, Goethe University, 60590 Frankfurt am Main, Germany
- Institute for Computational Genomic Medicine, Goethe University, 60590 Frankfurt am Main, Germany
- Institute for Computer Science, Goethe University, 60590 Frankfurt am Main, Germany
- German Center for Cardiovascular Research, Partner Site Rhein-Main, 60590 Frankfurt am Main, Germany
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2
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Rosean S, Sosa EA, O'Shea D, Raj SM, Seoighe C, Greally JM. Regulatory landscape enrichment analysis (RLEA): a computational toolkit for non-coding variant enrichment and cell type prioritization. BMC Bioinformatics 2024; 25:179. [PMID: 38714913 PMCID: PMC11075237 DOI: 10.1186/s12859-024-05794-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 04/22/2024] [Indexed: 05/12/2024] Open
Abstract
BACKGROUND As genomic studies continue to implicate non-coding sequences in disease, testing the roles of these variants requires insights into the cell type(s) in which they are likely to be mediating their effects. Prior methods for associating non-coding variants with cell types have involved approaches using linkage disequilibrium or ontological associations, incurring significant processing requirements. GaiaAssociation is a freely available, open-source software that enables thousands of genomic loci implicated in a phenotype to be tested for enrichment at regulatory loci of multiple cell types in minutes, permitting insights into the cell type(s) mediating the studied phenotype. RESULTS In this work, we present Regulatory Landscape Enrichment Analysis (RLEA) by GaiaAssociation and demonstrate its capability to test the enrichment of 12,133 variants across the cis-regulatory regions of 44 cell types. This analysis was completed in 134.0 ± 2.3 s, highlighting the efficient processing provided by GaiaAssociation. The intuitive interface requires only four inputs, offers a collection of customizable functions, and visualizes variant enrichment in cell-type regulatory regions through a heatmap matrix. GaiaAssociation is available on PyPi for download as a command line tool or Python package and the source code can also be installed from GitHub at https://github.com/GreallyLab/gaiaAssociation . CONCLUSIONS GaiaAssociation is a novel package that provides an intuitive and efficient resource to understand the enrichment of non-coding variants across the cis-regulatory regions of different cells, empowering studies seeking to identify disease-mediating cell types.
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Affiliation(s)
- Samuel Rosean
- Department of Systems and Computational Biology, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Eric A Sosa
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Dónal O'Shea
- School of Mathematics, Statistics & Applied Mathematics, National University of Ireland Galway, Galway, H91 TK33, Ireland
| | - Srilakshmi M Raj
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Cathal Seoighe
- School of Mathematics, Statistics & Applied Mathematics, National University of Ireland Galway, Galway, H91 TK33, Ireland
| | - John M Greally
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY, 10461, USA.
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3
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Marin-Bejar O, Romero-Moya D, Rodriguez-Ubreva J, Distefano M, Lessi F, Aretini P, Liquori A, Castaño J, Kozyra E, Kotmayer L, Bueno C, Cervera J, Rodriguez-Gallego JC, Nomdedeu JF, Murillo-Sanjuán L, De Heredia CD, Pérez-Martinez A, López-Cadenas F, Martínez-Laperche C, Dorado-Herrero N, Marco FM, Prósper F, Menendez P, Valcárcel D, Ballestar E, Bödör C, Bigas A, Catalá A, Wlodarski MW, Giorgetti A. Epigenome profiling reveals aberrant DNA methylation signature in GATA2 deficiency. Haematologica 2023; 108:2551-2557. [PMID: 36815365 PMCID: PMC10483368 DOI: 10.3324/haematol.2022.282305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 02/10/2023] [Indexed: 02/24/2023] Open
Affiliation(s)
- Oskar Marin-Bejar
- Regenerative Medicine Program, Bellvitge Institute for Biomedical Research (IDIBELL) and Program for Clinical Translation of Regenerative Medicine in Catalonia (P-CMRC), Barcelona.
| | - Damia Romero-Moya
- Regenerative Medicine Program, Bellvitge Institute for Biomedical Research (IDIBELL) and Program for Clinical Translation of Regenerative Medicine in Catalonia (P-CMRC), Barcelona
| | | | - Maximiliano Distefano
- Department of Hematology and Oncology, Institut de Recerca Sant Joan de Déu, Hospital Sant Joan de Déu, Barcelona
| | - Francesca Lessi
- Fondazione Pisana Per la Scienza ONLUS (FPS), San Giuliano Terme
| | - Paolo Aretini
- Fondazione Pisana Per la Scienza ONLUS (FPS), San Giuliano Terme
| | - Alessandro Liquori
- Hematology Research Group, Instituto de Investigación Sanitaria La Fe, Valencia, Spain; Centro de Investigación Biomédica en Red de Oncología (CIBERONC), Instituto de Salud Carlos III, Madrid
| | - Julio Castaño
- Advanced and Cell Therapy Services. Banc de Sang i Teixits, Barcelona
| | - Emilia Kozyra
- Division of Pediatric Hematology and Oncology, Department of Pediatrics and Adolescent Medicine, Medical Center, Faculty of Medicine; Faculty of Biology, University of Freiburg, Freiburg
| | - Lili Kotmayer
- HCEMM-SE Molecular Oncohematology Research Group, Department of Pathology and Experimental Cancer Research, Semmelweis University. Budapest, Hungary
| | - Clara Bueno
- Josep Carreras Leukaemia Research Institute. Department of Biomedicine. School of Medicine, University of Barcelona, Barcelona
| | - José Cervera
- Hematology Research Group, Instituto de Investigación Sanitaria La Fe, Valencia, Spain; Centro de Investigación Biomédica en Red de Oncología (CIBERONC), Instituto de Salud Carlos III, Madrid, Spain; Genetics Unit, Hospital Universitario y Politécnico La Fe, Valencia
| | - José Carlos Rodriguez-Gallego
- Department of Immunology, University Hospital of Gran Canaria Dr. Negrin, Canarian Health System, Las Palmas de Gran Canaria, Spain; Department of Clinical Sciences, University Fernando Pessoa Canarias, Las Palmas de Gran Canaria, Spain; Department of Medical and Surgical Sciences, School of Medicine, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria
| | - Josep F Nomdedeu
- Servei d'Hematologia Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, IIB Sant Pau/Josep Carreras Leukaemia Research Institute (IJC), Barcelona
| | - Laura Murillo-Sanjuán
- Pediatric Hematology and Oncology Division, Hospital Universitari Vall d'Hebron, Vall d'Hebron Institut de Recerca, Barcelona
| | - Cristina Díaz De Heredia
- Pediatric Hematology and Oncology Division, Hospital Universitari Vall d'Hebron, Vall d'Hebron Institut de Recerca, Barcelona
| | - Antonio Pérez-Martinez
- Pediatric Department, Universidad Autonoma de Madrid, Madrid, Spain; Hospital La Paz Institute for Health Research, Madrid, Spain; Pediatric Hemato-Oncology Department, University Hospital La Paz, Madrid
| | - Félix López-Cadenas
- Servicio de Hematología Hospital Clínico Universitario de Salamanca, salamanca, Spain; Instituto Biosanitario de Salamanca (IBSAL). Salamanca
| | - Carolina Martínez-Laperche
- Servicio de Hematología, Hospital General Universitario Gregorio Marañón. Instituto de Investigación Sanitaria Gregorio Marañón, Madrid
| | - Nieves Dorado-Herrero
- Servicio de Hematología, Hospital General Universitario Gregorio Marañón. Instituto de Investigación Sanitaria Gregorio Marañón, Madrid
| | - Francisco M Marco
- Immunology Department, Dr. Balmis General University Hospital; Institute for Health and Biomedical Research (ISABIAL), Alicante
| | - Felipe Prósper
- Centro de Investigación Biomédica en Red de Oncología (CIBERONC), Instituto de Salud Carlos III, Madrid, Spain; Area de Hemato-Oncología, CIMA Universidad de Navarra, Instituto de Investigación Sanitaria de Navarra (IDISNA), Pamplona, Spain; Servicio de Hematologia, CCUN, Clínica Universidad de Navarra, Universidad de Navarra, Pamplona
| | - Pablo Menendez
- Centro de Investigación Biomédica en Red de Oncología (CIBERONC), Instituto de Salud Carlos III, Madrid, Spain; Josep Carreras Leukaemia Research Institute. Department of Biomedicine. School of Medicine, University of Barcelona, Barcelona, Spain; Red Española de Terapias Avanzadas (TERAV) - Instituto de Salud Carlos III, Madrid, Spain; Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona
| | - David Valcárcel
- Servei d'Hematologia, Vall d'Hebron Hospital Universitari; Experimental Hematology, Vall d'Hebron Institute of Oncology (VHIO); Departament de Medicina, Universitat Autònoma de Barcelona, Barcelona
| | - Esteban Ballestar
- Epigenetics and Immune Disease Group, Josep Carreras Research Institute (IJC), Barcelona, Spain; Epigenetics in Inflammatory and Metabolic Diseases Laboratory, Health Science Center (HSC), East China Normal University (ECNU), Shanghai
| | - Csaba Bödör
- HCEMM-SE Molecular Oncohematology Research Group, Department of Pathology and Experimental Cancer Research, Semmelweis University. Budapest, Hungary
| | - Anna Bigas
- Centro de Investigación Biomédica en Red de Oncología (CIBERONC), Instituto de Salud Carlos III, Madrid, Spain; Programa de Investigación en Cáncer, IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain; Josep Carreras Leukaemia Research Institute (IJC), Barcelona
| | - Albert Catalá
- Department of Hematology and Oncology, Institut de Recerca Sant Joan de Déu, Hospital Sant Joan de Déu, Barcelona, Spain; Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Instituto de Salud Carlos III, Madrid
| | - Marcin W Wlodarski
- Department of Hematology, St. Jude Children's Research Hospital, Memphis, TN
| | - Alessandra Giorgetti
- Regenerative Medicine Program, Bellvitge Institute for Biomedical Research (IDIBELL) and Program for Clinical Translation of Regenerative Medicine in Catalonia (P-CMRC), Barcelona, Spain; Fondazione Pisana Per la Scienza ONLUS (FPS), San Giuliano Terme, Italy; Department of Pathology and Experimental Therapeutics, Faculty of Medicine and Health Sciences, Barcelona University, Barcelona.
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4
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Dwivedi SL, Heslop-Harrison P, Spillane C, McKeown PC, Edwards D, Goldman I, Ortiz R. Evolutionary dynamics and adaptive benefits of deleterious mutations in crop gene pools. TRENDS IN PLANT SCIENCE 2023; 28:685-697. [PMID: 36764870 DOI: 10.1016/j.tplants.2023.01.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 12/03/2022] [Accepted: 01/18/2023] [Indexed: 05/13/2023]
Abstract
Mutations with deleterious consequences in nature may be conditionally deleterious in crop plants. That is, while some genetic variants may reduce fitness under wild conditions and be subject to purifying selection, they can be under positive selection in domesticates. Such deleterious alleles can be plant breeding targets, particularly for complex traits. The difficulty of distinguishing favorable from unfavorable variants reduces the power of selection, while favorable trait variation and heterosis may be attributable to deleterious alleles. Here, we review the roles of deleterious mutations in crop breeding and discuss how they can be used as a new avenue for crop improvement with emerging genomic tools, including HapMaps and pangenome analysis, aiding the identification, removal, or exploitation of deleterious mutations.
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Affiliation(s)
| | - Pat Heslop-Harrison
- Key Laboratory of Plant Resources Conservation and Sustainable Utilization, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou, 510650, China; Department of Genetics and Genome Biology, University of Leicester, Leicester, LE1 7RH, UK
| | - Charles Spillane
- Agriculture and Bioeconomy Research Centre, Ryan Institute, University of Galway, University Road, Galway, H91 REW4, Ireland
| | - Peter C McKeown
- Agriculture and Bioeconomy Research Centre, Ryan Institute, University of Galway, University Road, Galway, H91 REW4, Ireland
| | - David Edwards
- School of Biological Sciences and Institute of Agriculture, University of Western Australia, Perth, WA 6009, Australia
| | - Irwin Goldman
- Department of Horticulture, College of Agricultural and Life Sciences, University of Wisconsin Madison, WI 53706, USA
| | - Rodomiro Ortiz
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Alnarp, SE 23053, Sweden.
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5
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De Vas MG, Boulet F, Joshi SS, Garstang MG, Khan TN, Atla G, Parry D, Moore D, Cebola I, Zhang S, Cui W, Lampe AK, Lam WW, Ferrer J, Pradeepa MM, Atanur SS. Regulatory de novo mutations underlying intellectual disability. Life Sci Alliance 2023; 6:e202201843. [PMID: 36854624 PMCID: PMC9978454 DOI: 10.26508/lsa.202201843] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 02/11/2023] [Accepted: 02/13/2023] [Indexed: 03/02/2023] Open
Abstract
The genetic aetiology of a major fraction of patients with intellectual disability (ID) remains unknown. De novo mutations (DNMs) in protein-coding genes explain up to 40% of cases, but the potential role of regulatory DNMs is still poorly understood. We sequenced 63 whole genomes from 21 ID probands and their unaffected parents. In addition, we analysed 30 previously sequenced genomes from exome-negative ID probands. We found that regulatory DNMs were selectively enriched in fetal brain-specific enhancers as compared with adult brain enhancers. DNM-containing enhancers were associated with genes that show preferential expression in the prefrontal cortex. Furthermore, we identified recurrently mutated enhancer clusters that regulate genes involved in nervous system development (CSMD1, OLFM1, and POU3F3). Most of the DNMs from ID probands showed allele-specific enhancer activity when tested using luciferase assay. Using CRISPR-mediated mutation and editing of epigenomic marks, we show that DNMs at regulatory elements affect the expression of putative target genes. Our results, therefore, provide new evidence to indicate that DNMs in fetal brain-specific enhancers play an essential role in the aetiology of ID.
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Affiliation(s)
- Matias G De Vas
- Section of Genetics and Genomics, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Fanny Boulet
- Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Shweta S Joshi
- Section of Genetics and Genomics, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Myles G Garstang
- Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- School of Biological Sciences, University of Essex, Colchester, UK
| | - Tahir N Khan
- Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- Department of Biological Sciences, National University of Medical Sciences, Rawalpindi, Pakistan
| | - Goutham Atla
- Section of Genetics and Genomics, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Regulatory Genomics and Diabetes, Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Barcelona, Spain
| | - David Parry
- MRC Human Genetics Unit, University of Edinburgh, Edinburgh, UK
| | - David Moore
- South-East Scotland Regional Genetics Service, Western General Hospital, Edinburgh, UK
| | - Inês Cebola
- Section of Genetics and Genomics, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Shuchen Zhang
- Institute of Reproductive and Developmental Biology, Faculty of Medicine, Imperial College London, London, UK
| | - Wei Cui
- Institute of Reproductive and Developmental Biology, Faculty of Medicine, Imperial College London, London, UK
| | - Anne K Lampe
- South-East Scotland Regional Genetics Service, Western General Hospital, Edinburgh, UK
| | - Wayne W Lam
- South-East Scotland Regional Genetics Service, Western General Hospital, Edinburgh, UK
| | - Jorge Ferrer
- Section of Genetics and Genomics, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Regulatory Genomics and Diabetes, Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Barcelona, Spain
| | - Madapura M Pradeepa
- Blizard Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK
- School of Biological Sciences, University of Essex, Colchester, UK
| | - Santosh S Atanur
- Section of Genetics and Genomics, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- NIHR Imperial Biomedical Research Centre, ITMAT Data Science Group, Imperial College London, London, UK
- Previous Institute: Centre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh, UK
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6
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PSnpBind-ML: predicting the effect of binding site mutations on protein-ligand binding affinity. J Cheminform 2023; 15:31. [PMID: 36864534 PMCID: PMC9983232 DOI: 10.1186/s13321-023-00701-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 02/17/2023] [Indexed: 03/04/2023] Open
Abstract
Protein mutations, especially those which occur in the binding site, play an important role in inter-individual drug response and may alter binding affinity and thus impact the drug's efficacy and side effects. Unfortunately, large-scale experimental screening of ligand-binding against protein variants is still time-consuming and expensive. Alternatively, in silico approaches can play a role in guiding those experiments. Methods ranging from computationally cheaper machine learning (ML) to the more expensive molecular dynamics have been applied to accurately predict the mutation effects. However, these effects have been mostly studied on limited and small datasets, while ideally a large dataset of binding affinity changes due to binding site mutations is needed. In this work, we used the PSnpBind database with six hundred thousand docking experiments to train a machine learning model predicting protein-ligand binding affinity for both wild-type proteins and their variants with a single-point mutation in the binding site. A numerical representation of the protein, binding site, mutation, and ligand information was encoded using 256 features, half of them were manually selected based on domain knowledge. A machine learning approach composed of two regression models is proposed, the first predicting wild-type protein-ligand binding affinity while the second predicting the mutated protein-ligand binding affinity. The best performing models reported an RMSE value within 0.5 [Formula: see text] 0.6 kcal/mol-1 on an independent test set with an R2 value of 0.87 [Formula: see text] 0.90. We report an improvement in the prediction performance compared to several reported models developed for protein-ligand binding affinity prediction. The obtained models can be used as a complementary method in early-stage drug discovery. They can be applied to rapidly obtain a better overview of the ligand binding affinity changes across protein variants carried by people in the population and narrow down the search space where more time-demanding methods can be used to identify potential leads that achieve a better affinity for all protein variants.
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7
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Transcription Factor-Centric Approach to Identify Non-Recurring Putative Regulatory Drivers in Cancer. RESEARCH IN COMPUTATIONAL MOLECULAR BIOLOGY : ... ANNUAL INTERNATIONAL CONFERENCE, RECOMB ... : PROCEEDINGS. RECOMB (CONFERENCE : 2005- ) 2022; 13278:36-51. [PMID: 36507923 PMCID: PMC9740185 DOI: 10.1007/978-3-031-04749-7_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Recent efforts to sequence the genomes of thousands of matched normal-tumor samples have led to the identification of millions of somatic mutations, the majority of which are non-coding. Most of these mutations are believed to be passengers, but a small number of non-coding mutations could contribute to tumor initiation or progression, e.g. by leading to dysregulation of gene expression. Efforts to identify putative regulatory drivers rely primarily on information about the recurrence of mutations across tumor samples. However, in regulatory regions of the genome, individual mutations are rarely seen in more than one donor. Instead of using recurrence information, here we present a method to identify putative regulatory driver mutations based on the magnitude of their effects on transcription factor-DNA binding. For each gene, we integrate the effects of mutations across all its regulatory regions, and we ask whether these effects are larger than expected by chance, given the mutation spectra observed in regulatory DNA in the cohort of interest. We applied our approach to analyze mutations in a liver cancer data set with ample somatic mutation and gene expression data available. By combining the effects of mutations across all regulatory regions of each gene, we identified dozens of genes whose regulation in tumor cells is likely to be significantly perturbed by non-coding mutations. Overall, our results show that focusing on the functional effects of non-coding mutations, rather than their recurrence, has the potential to identify putative regulatory drivers and the genes they dysregulate in tumor cells.
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Dietlein F, Wang AB, Fagre C, Tang A, Besselink NJM, Cuppen E, Li C, Sunyaev SR, Neal JT, Van Allen EM. Genome-wide analysis of somatic noncoding mutation patterns in cancer. Science 2022; 376:eabg5601. [PMID: 35389777 PMCID: PMC9092060 DOI: 10.1126/science.abg5601] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
We established a genome-wide compendium of somatic mutation events in 3949 whole cancer genomes representing 19 tumor types. Protein-coding events captured well-established drivers. Noncoding events near tissue-specific genes, such as ALB in the liver or KLK3 in the prostate, characterized localized passenger mutation patterns and may reflect tumor-cell-of-origin imprinting. Noncoding events in regulatory promoter and enhancer regions frequently involved cancer-relevant genes such as BCL6, FGFR2, RAD51B, SMC6, TERT, and XBP1 and represent possible drivers. Unlike most noncoding regulatory events, XBP1 mutations primarily accumulated outside the gene's promoter, and we validated their effect on gene expression using CRISPR-interference screening and luciferase reporter assays. Broadly, our study provides a blueprint for capturing mutation events across the entire genome to guide advances in biological discovery, therapies, and diagnostics.
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Affiliation(s)
- Felix Dietlein
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02215, USA.,Cancer Program, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA.,Corresponding author. (E.M.V.A.); (F.D.)
| | - Alex B. Wang
- Cancer Program, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Christian Fagre
- Cancer Program, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Anran Tang
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02215, USA.,Cancer Program, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Nicolle J. M. Besselink
- Center for Molecular Medicine and Oncode Institute, University Medical Center Utrecht, 3584 CX Utrecht, Netherlands
| | - Edwin Cuppen
- Center for Molecular Medicine and Oncode Institute, University Medical Center Utrecht, 3584 CX Utrecht, Netherlands.,Hartwig Medical Foundation, 1098 XH Amsterdam, Netherlands
| | - Chunliang Li
- Department of Tumor Cell Biology, St. Jude Children’s Research Hospital, Memphis, TN 38105, USA
| | - Shamil R. Sunyaev
- Division of Genetics, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA.,Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA
| | - James T. Neal
- Cancer Program, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Eliezer M. Van Allen
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02215, USA.,Cancer Program, Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA.,Corresponding author. (E.M.V.A.); (F.D.)
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9
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Padhi EM, Hayeck TJ, Cheng Z, Chatterjee S, Mannion BJ, Byrska-Bishop M, Willems M, Pinson L, Redon S, Benech C, Uguen K, Audebert-Bellanger S, Le Marechal C, Férec C, Efthymiou S, Rahman F, Maqbool S, Maroofian R, Houlden H, Musunuri R, Narzisi G, Abhyankar A, Hunter RD, Akiyama J, Fries LE, Ng JK, Mehinovic E, Stong N, Allen AS, Dickel DE, Bernier RA, Gorkin DU, Pennacchio LA, Zody MC, Turner TN. Coding and noncoding variants in EBF3 are involved in HADDS and simplex autism. Hum Genomics 2021; 15:44. [PMID: 34256850 PMCID: PMC8278787 DOI: 10.1186/s40246-021-00342-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 06/17/2021] [Indexed: 01/01/2023] Open
Abstract
BACKGROUND Previous research in autism and other neurodevelopmental disorders (NDDs) has indicated an important contribution of protein-coding (coding) de novo variants (DNVs) within specific genes. The role of de novo noncoding variation has been observable as a general increase in genetic burden but has yet to be resolved to individual functional elements. In this study, we assessed whole-genome sequencing data in 2671 families with autism (discovery cohort of 516 families, replication cohort of 2155 families). We focused on DNVs in enhancers with characterized in vivo activity in the brain and identified an excess of DNVs in an enhancer named hs737. RESULTS We adapted the fitDNM statistical model to work in noncoding regions and tested enhancers for excess of DNVs in families with autism. We found only one enhancer (hs737) with nominal significance in the discovery (p = 0.0172), replication (p = 2.5 × 10-3), and combined dataset (p = 1.1 × 10-4). Each individual with a DNV in hs737 had shared phenotypes including being male, intact cognitive function, and hypotonia or motor delay. Our in vitro assessment of the DNVs showed they all reduce enhancer activity in a neuronal cell line. By epigenomic analyses, we found that hs737 is brain-specific and targets the transcription factor gene EBF3 in human fetal brain. EBF3 is genome-wide significant for coding DNVs in NDDs (missense p = 8.12 × 10-35, loss-of-function p = 2.26 × 10-13) and is widely expressed in the body. Through characterization of promoters bound by EBF3 in neuronal cells, we saw enrichment for binding to NDD genes (p = 7.43 × 10-6, OR = 1.87) involved in gene regulation. Individuals with coding DNVs have greater phenotypic severity (hypotonia, ataxia, and delayed development syndrome [HADDS]) in comparison to individuals with noncoding DNVs that have autism and hypotonia. CONCLUSIONS In this study, we identify DNVs in the hs737 enhancer in individuals with autism. Through multiple approaches, we find hs737 targets the gene EBF3 that is genome-wide significant in NDDs. By assessment of noncoding variation and the genes they affect, we are beginning to understand their impact on gene regulatory networks in NDDs.
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Affiliation(s)
- Evin M Padhi
- Department of Genetics, Washington University School of Medicine, 4523 Clayton Avenue, Campus Box 8232, St. Louis, MO, 63110, USA
| | - Tristan J Hayeck
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA
- Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, USA
| | - Zhang Cheng
- Center for Epigenomics, University of California San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA, 92093, USA
| | - Sumantra Chatterjee
- Center for Human Genetics and Genomics, NYU School of Medicine, New York, NY, 10016, USA
| | - Brandon J Mannion
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | | | - Marjolaine Willems
- University of Montpellier, département de Génétique, maladies rares médecine personnalisée, U 1298, CHU Montpellier, University of Montpellier, Montpellier, France
| | - Lucile Pinson
- University of Montpellier, département de Génétique, maladies rares médecine personnalisée, U 1298, CHU Montpellier, University of Montpellier, Montpellier, France
| | - Sylvia Redon
- CHU Brest, Inserm, Univ Brest, EFS,UMR 1078, GGB, F-29200, Brest, France
| | - Caroline Benech
- CHU Brest, Inserm, Univ Brest, EFS,UMR 1078, GGB, F-29200, Brest, France
| | - Kevin Uguen
- CHU Brest, Inserm, Univ Brest, EFS,UMR 1078, GGB, F-29200, Brest, France
| | | | - Cédric Le Marechal
- CHU Brest, Inserm, Univ Brest, EFS,UMR 1078, GGB, F-29200, Brest, France
| | - Claude Férec
- CHU Brest, Inserm, Univ Brest, EFS,UMR 1078, GGB, F-29200, Brest, France
| | - Stephanie Efthymiou
- Department of Neuromuscular Disorders, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK
| | - Fatima Rahman
- Development and Behavioral Pediatrics Department, Institute of Child Health and Children Hospital, Lahore, Pakistan
| | - Shazia Maqbool
- Department of Neuromuscular Disorders, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK
- Development and Behavioral Pediatrics Department, Institute of Child Health and Children Hospital, Lahore, Pakistan
| | - Reza Maroofian
- Department of Neuromuscular Disorders, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK
| | - Henry Houlden
- Department of Neuromuscular Disorders, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK
| | | | | | | | - Riana D Hunter
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Jennifer Akiyama
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Lauren E Fries
- Center for Human Genetics and Genomics, NYU School of Medicine, New York, NY, 10016, USA
| | - Jeffrey K Ng
- Department of Genetics, Washington University School of Medicine, 4523 Clayton Avenue, Campus Box 8232, St. Louis, MO, 63110, USA
| | - Elvisa Mehinovic
- Department of Genetics, Washington University School of Medicine, 4523 Clayton Avenue, Campus Box 8232, St. Louis, MO, 63110, USA
| | - Nick Stong
- Institute for Genomic Medicine, Columbia University, New York, NY, 10027, USA
| | - Andrew S Allen
- Center for Statistical Genetics and Genomics, Duke University, Durham, NC, 27708, USA
- Division of Integrative Genomics, Duke University, Durham, NC, 27708, USA
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, 27708, USA
| | - Diane E Dickel
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | - Raphael A Bernier
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, WA, 98195, USA
| | - David U Gorkin
- Center for Epigenomics, University of California San Diego School of Medicine, 9500 Gilman Drive, La Jolla, CA, 92093, USA
- Department of Biology, Emory University, Atlanta, GA, 30322, USA
| | - Len A Pennacchio
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
- U.S. Department of Energy Joint Genome Institute, Walnut Creek, CA, 94598, USA
| | | | - Tychele N Turner
- Department of Genetics, Washington University School of Medicine, 4523 Clayton Avenue, Campus Box 8232, St. Louis, MO, 63110, USA.
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10
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Zhang Y, Ho TD, Buchler NE, Gordân R. Competition for DNA binding between paralogous transcription factors determines their genomic occupancy and regulatory functions. Genome Res 2021; 31:1216-1229. [PMID: 33975875 PMCID: PMC8256859 DOI: 10.1101/gr.275145.120] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 05/06/2021] [Indexed: 11/24/2022]
Abstract
Most eukaryotic transcription factors (TFs) are part of large protein families, with members of the same family (i.e., paralogous TFs) recognizing similar DNA-binding motifs but performing different regulatory functions. Many TF paralogs are coexpressed in the cell and thus can compete for target sites across the genome. However, this competition is rarely taken into account when studying the in vivo binding patterns of eukaryotic TFs. Here, we show that direct competition for DNA binding between TF paralogs is a major determinant of their genomic binding patterns. Using yeast proteins Cbf1 and Pho4 as our model system, we designed a high-throughput quantitative assay to capture the genomic binding profiles of competing TFs in a cell-free system. Our data show that Cbf1 and Pho4 greatly influence each other's occupancy by competing for their common putative genomic binding sites. The competition is different at different genomic sites, as dictated by the TFs' expression levels and their divergence in DNA-binding specificity and affinity. Analyses of ChIP-seq data show that the biophysical rules that dictate the competitive TF binding patterns in vitro are also followed in vivo, in the complex cellular environment. Furthermore, the Cbf1-Pho4 competition for genomic sites, as characterized in vitro using our new assay, plays a critical role in the specific activation of their target genes in the cell. Overall, our study highlights the importance of direct TF-TF competition for genomic binding and gene regulation by TF paralogs, and proposes an approach for studying this competition in a quantitative and high-throughput manner.
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Affiliation(s)
- Yuning Zhang
- Center for Genomic and Computational Biology, Duke University, Durham, North Carolina 27708, USA
- Program in Computational Biology and Bioinformatics, Duke University, Durham, North Carolina 27708, USA
| | - Tiffany D Ho
- Center for Genomic and Computational Biology, Duke University, Durham, North Carolina 27708, USA
- Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina 27708, USA
| | - Nicolas E Buchler
- Department of Molecular Biomedical Sciences, North Carolina State University, Raleigh, North Carolina 27606, USA
| | - Raluca Gordân
- Center for Genomic and Computational Biology, Duke University, Durham, North Carolina 27708, USA
- Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina 27708, USA
- Department of Computer Science, Department of Molecular Genetics and Microbiology, Duke University, Durham, North Carolina 27708, USA
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11
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Rovirosa L, Ramos-Morales A, Javierre BM. The Genome in a Three-Dimensional Context: Deciphering the Contribution of Noncoding Mutations at Enhancers to Blood Cancer. Front Immunol 2020; 11:592087. [PMID: 33117405 PMCID: PMC7575776 DOI: 10.3389/fimmu.2020.592087] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 09/21/2020] [Indexed: 11/13/2022] Open
Abstract
Associations between blood cancer and genetic predisposition, including both inherited variants and acquired mutations and epimutations, have been well characterized. However, the majority of these variants affect noncoding regions, making their mechanisms difficult to hypothesize and hindering the translation of these insights into patient benefits. Fueled by unprecedented progress in next-generation sequencing and computational integrative analysis, studies have started applying combinations of epigenetic, genome architecture, and functional assays to bridge the gap between noncoding variants and blood cancer. These complementary tools have not only allowed us to understand the potential malignant role of these variants but also to differentiate key variants, cell-types, and conditions from misleading ones. Here, we briefly review recent studies that have provided fundamental insights into our understanding of how noncoding mutations at enhancers predispose and promote blood malignancies in the context of spatial genome architecture.
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Affiliation(s)
- Llorenç Rovirosa
- 3D Chromatin Organization Group, Josep Carreras Leukaemia Research Institute (IJC), Germans Trias i Pujol, Badalona, Spain
| | - Alberto Ramos-Morales
- 3D Chromatin Organization Group, Josep Carreras Leukaemia Research Institute (IJC), Germans Trias i Pujol, Badalona, Spain
| | - Biola M Javierre
- 3D Chromatin Organization Group, Josep Carreras Leukaemia Research Institute (IJC), Germans Trias i Pujol, Badalona, Spain.,Institute for Health Science Research Germans Trias i Pujol (IGTP), Badalona, Spain
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12
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Zhu C, Miller M, Zeng Z, Wang Y, Mahlich Y, Aptekmann A, Bromberg Y. Computational Approaches for Unraveling the Effects of Variation in the Human Genome and Microbiome. Annu Rev Biomed Data Sci 2020. [DOI: 10.1146/annurev-biodatasci-030320-041014] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
The past two decades of analytical efforts have highlighted how much more remains to be learned about the human genome and, particularly, its complex involvement in promoting disease development and progression. While numerous computational tools exist for the assessment of the functional and pathogenic effects of genome variants, their precision is far from satisfactory, particularly for clinical use. Accumulating evidence also suggests that the human microbiome's interaction with the human genome plays a critical role in determining health and disease states. While numerous microbial taxonomic groups and molecular functions of the human microbiome have been associated with disease, the reproducibility of these findings is lacking. The human microbiome–genome interaction in healthy individuals is even less well understood. This review summarizes the available computational methods built to analyze the effect of variation in the human genome and microbiome. We address the applicability and precision of these methods across their possible uses. We also briefly discuss the exciting, necessary, and now possible integration of the two types of data to improve the understanding of pathogenicity mechanisms.
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Affiliation(s)
- Chengsheng Zhu
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey 08873, USA;,
| | - Maximilian Miller
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey 08873, USA;,
| | - Zishuo Zeng
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey 08873, USA;,
| | - Yanran Wang
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey 08873, USA;,
| | - Yannick Mahlich
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey 08873, USA;,
| | - Ariel Aptekmann
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey 08873, USA;,
| | - Yana Bromberg
- Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, New Jersey 08873, USA;,
- Department of Genetics, Rutgers University, Piscataway, New Jersey 08854, USA
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13
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Martin V, Zhao J, Afek A, Mielko Z, Gordân R. QBiC-Pred: quantitative predictions of transcription factor binding changes due to sequence variants. Nucleic Acids Res 2020; 47:W127-W135. [PMID: 31114870 PMCID: PMC6602471 DOI: 10.1093/nar/gkz363] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2019] [Revised: 04/13/2019] [Accepted: 05/08/2019] [Indexed: 12/12/2022] Open
Abstract
Non-coding genetic variants/mutations can play functional roles in the cell by disrupting regulatory interactions between transcription factors (TFs) and their genomic target sites. For most human TFs, a myriad of DNA-binding models are available and could be used to predict the effects of DNA mutations on TF binding. However, information on the quality of these models is scarce, making it hard to evaluate the statistical significance of predicted binding changes. Here, we present QBiC-Pred, a web server for predicting quantitative TF binding changes due to nucleotide variants. QBiC-Pred uses regression models of TF binding specificity trained on high-throughput in vitro data. The training is done using ordinary least squares (OLS), and we leverage distributional results associated with OLS estimation to compute, for each predicted change in TF binding, a P-value reflecting our confidence in the predicted effect. We show that OLS models are accurate in predicting the effects of mutations on TF binding in vitro and in vivo, outperforming widely-used PWM models as well as recently developed deep learning models of specificity. QBiC-Pred takes as input mutation datasets in several formats, and it allows post-processing of the results through a user-friendly web interface. QBiC-Pred is freely available at http://qbic.genome.duke.edu.
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Affiliation(s)
- Vincentius Martin
- Department of Computer Science, Duke University, Durham, NC 27708, USA.,Center for Genomic and Computational Biology, Duke University, Durham, NC 27708, USA
| | - Jingkang Zhao
- Center for Genomic and Computational Biology, Duke University, Durham, NC 27708, USA.,Program in Computational Biology and Bioinformatics, Duke University, Durham, NC 27708, USA
| | - Ariel Afek
- Center for Genomic and Computational Biology, Duke University, Durham, NC 27708, USA.,Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27708, USA
| | - Zachery Mielko
- Center for Genomic and Computational Biology, Duke University, Durham, NC 27708, USA.,Program in Genetics and Genomics, Duke University, Durham, NC 27708, USA
| | - Raluca Gordân
- Department of Computer Science, Duke University, Durham, NC 27708, USA.,Center for Genomic and Computational Biology, Duke University, Durham, NC 27708, USA.,Department of Biostatistics and Bioinformatics, Duke University, Durham, NC 27708, USA.,Department of Molecular Genetics and Microbiology, Duke University, Durham, NC 27708, USA
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14
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Campbell MC, Ashong B, Teng S, Harvey J, Cross CN. Multiple selective sweeps of ancient polymorphisms in and around LTα located in the MHC class III region on chromosome 6. BMC Evol Biol 2019; 19:218. [PMID: 31791241 PMCID: PMC6889576 DOI: 10.1186/s12862-019-1516-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Accepted: 09/20/2019] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Lymphotoxin-α (LTα), located in the Major Histocompatibility Complex (MHC) class III region on chromosome 6, encodes a cytotoxic protein that mediates a variety of antiviral responses among other biological functions. Furthermore, several genotypes at this gene have been implicated in the onset of a number of complex diseases, including myocardial infarction, autoimmunity, and various types of cancer. However, little is known about levels of nucleotide variation and linkage disequilibrium (LD) in and near LTα, which could also influence phenotypic variance. To address this gap in knowledge, we examined sequence variation across ~ 10 kilobases (kbs), encompassing LTα and the upstream region, in 2039 individuals from the 1000 Genomes Project originating from 21 global populations. RESULTS Here, we observed striking patterns of diversity, including an excess of intermediate-frequency alleles, the maintenance of multiple common haplotypes and a deep coalescence time for variation (dating > 1.0 million years ago), in global populations. While these results are generally consistent with a model of balancing selection, we also uncovered a signature of positive selection in the form of long-range LD on chromosomes with derived alleles primarily in Eurasian populations. To reconcile these findings, which appear to support different models of selection, we argue that selective sweeps (particularly, soft sweeps) of multiple derived alleles in and/or near LTα occurred in non-Africans after their ancestors left Africa. Furthermore, these targets of selection were predicted to alter transcription factor binding site affinity and protein stability, suggesting they play a role in gene function. Additionally, our data also showed that a subset of these functional adaptive variants are present in archaic hominin genomes. CONCLUSIONS Overall, this study identified candidate functional alleles in a biologically-relevant genomic region, and offers new insights into the evolutionary origins of these loci in modern human populations.
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Affiliation(s)
- Michael C. Campbell
- Department of Biology, College of Arts and Sciences, Howard University, Washington, DC 20059 USA
| | - Bryan Ashong
- Department of Biology, College of Arts and Sciences, Howard University, Washington, DC 20059 USA
| | - Shaolei Teng
- Department of Biology, College of Arts and Sciences, Howard University, Washington, DC 20059 USA
| | - Jayla Harvey
- Department of Biology, College of Arts and Sciences, Howard University, Washington, DC 20059 USA
| | - Christopher N. Cross
- Department of Anatomy, College of Medicine, Howard University, Washington, DC 20059 USA
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15
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Haas J, Mester S, Lai A, Frese KS, Sedaghat-Hamedani F, Kayvanpour E, Rausch T, Nietsch R, Boeckel JN, Carstensen A, Völkers M, Dietrich C, Pils D, Amr A, Holzer DB, Martins Bordalo D, Oehler D, Weis T, Mereles D, Buss S, Riechert E, Wirsz E, Wuerstle M, Korbel JO, Keller A, Katus HA, Posch AE, Meder B. Genomic structural variations lead to dysregulation of important coding and non-coding RNA species in dilated cardiomyopathy. EMBO Mol Med 2019; 10:107-120. [PMID: 29138229 PMCID: PMC5760848 DOI: 10.15252/emmm.201707838] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022] Open
Abstract
The transcriptome needs to be tightly regulated by mechanisms that include transcription factors, enhancers, and repressors as well as non‐coding RNAs. Besides this dynamic regulation, a large part of phenotypic variability of eukaryotes is expressed through changes in gene transcription caused by genetic variation. In this study, we evaluate genome‐wide structural genomic variants (SVs) and their association with gene expression in the human heart. We detected 3,898 individual SVs affecting all classes of gene transcripts (e.g., mRNA, miRNA, lncRNA) and regulatory genomic regions (e.g., enhancer or TFBS). In a cohort of patients (n = 50) with dilated cardiomyopathy (DCM), 80,635 non‐protein‐coding elements of the genome are deleted or duplicated by SVs, containing 3,758 long non‐coding RNAs and 1,756 protein‐coding transcripts. 65.3% of the SV‐eQTLs do not harbor a significant SNV‐eQTL, and for the regions with both classes of association, we find similar effect sizes. In case of deleted protein‐coding exons, we find downregulation of the associated transcripts, duplication events, however, do not show significant changes over all events. In summary, we are first to describe the genomic variability associated with SVs in heart failure due to DCM and dissect their impact on the transcriptome. Overall, SVs explain up to 7.5% of the variation of cardiac gene expression, underlining the importance to study human myocardial gene expression in the context of the individual genome. This has immediate implications for studies on basic mechanisms of cardiac maladaptation, biomarkers, and (gene) therapeutic studies alike.
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Affiliation(s)
- Jan Haas
- Department of Internal Medicine III, University of Heidelberg, Heidelberg, Germany.,DZHK (German Centre for Cardiovascular Research), Heidelberg, Germany
| | - Stefan Mester
- Department of Internal Medicine III, University of Heidelberg, Heidelberg, Germany.,DZHK (German Centre for Cardiovascular Research), Heidelberg, Germany
| | - Alan Lai
- Department of Internal Medicine III, University of Heidelberg, Heidelberg, Germany.,DZHK (German Centre for Cardiovascular Research), Heidelberg, Germany
| | - Karen S Frese
- Department of Internal Medicine III, University of Heidelberg, Heidelberg, Germany.,DZHK (German Centre for Cardiovascular Research), Heidelberg, Germany
| | - Farbod Sedaghat-Hamedani
- Department of Internal Medicine III, University of Heidelberg, Heidelberg, Germany.,DZHK (German Centre for Cardiovascular Research), Heidelberg, Germany
| | - Elham Kayvanpour
- Department of Internal Medicine III, University of Heidelberg, Heidelberg, Germany.,DZHK (German Centre for Cardiovascular Research), Heidelberg, Germany
| | - Tobias Rausch
- EMBL (European Molecular Biology Laboratory), Heidelberg, Germany
| | - Rouven Nietsch
- Department of Internal Medicine III, University of Heidelberg, Heidelberg, Germany
| | - Jes-Niels Boeckel
- Department of Internal Medicine III, University of Heidelberg, Heidelberg, Germany.,DZHK (German Centre for Cardiovascular Research), Heidelberg, Germany
| | - Avisha Carstensen
- Department of Internal Medicine III, University of Heidelberg, Heidelberg, Germany
| | - Mirko Völkers
- Department of Internal Medicine III, University of Heidelberg, Heidelberg, Germany.,DZHK (German Centre for Cardiovascular Research), Heidelberg, Germany
| | - Carsten Dietrich
- Strategy and Innovation, Siemens Healthcare GmbH, Erlangen, Germany
| | - Dietmar Pils
- Siemens AG, Corporate Technology, Vienna, Austria.,Section for Clinical Biometrics, Center for Medical Statistics, Informatics, and Intelligent Systems (CeMSIIS), Medical University of Vienna, Vienna, Austria
| | - Ali Amr
- Department of Internal Medicine III, University of Heidelberg, Heidelberg, Germany
| | - Daniel B Holzer
- Department of Internal Medicine III, University of Heidelberg, Heidelberg, Germany
| | - Diana Martins Bordalo
- Department of Internal Medicine III, University of Heidelberg, Heidelberg, Germany.,DZHK (German Centre for Cardiovascular Research), Heidelberg, Germany
| | - Daniel Oehler
- Department of Internal Medicine III, University of Heidelberg, Heidelberg, Germany.,DZHK (German Centre for Cardiovascular Research), Heidelberg, Germany
| | - Tanja Weis
- Department of Internal Medicine III, University of Heidelberg, Heidelberg, Germany.,DZHK (German Centre for Cardiovascular Research), Heidelberg, Germany
| | - Derliz Mereles
- Department of Internal Medicine III, University of Heidelberg, Heidelberg, Germany.,DZHK (German Centre for Cardiovascular Research), Heidelberg, Germany
| | - Sebastian Buss
- Department of Internal Medicine III, University of Heidelberg, Heidelberg, Germany
| | - Eva Riechert
- Department of Internal Medicine III, University of Heidelberg, Heidelberg, Germany.,DZHK (German Centre for Cardiovascular Research), Heidelberg, Germany
| | - Emil Wirsz
- Strategy and Innovation, Siemens Healthcare GmbH, Erlangen, Germany
| | | | - Jan O Korbel
- EMBL (European Molecular Biology Laboratory), Heidelberg, Germany
| | - Andreas Keller
- Department of Bioinformatics, University of Saarland, Saarbrücken, Germany
| | - Hugo A Katus
- Department of Internal Medicine III, University of Heidelberg, Heidelberg, Germany.,DZHK (German Centre for Cardiovascular Research), Heidelberg, Germany
| | - Andreas E Posch
- Strategy and Innovation, Siemens Healthcare GmbH, Erlangen, Germany
| | - Benjamin Meder
- Department of Internal Medicine III, University of Heidelberg, Heidelberg, Germany .,DZHK (German Centre for Cardiovascular Research), Heidelberg, Germany
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