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Argov CM, Shneyour A, Jubran J, Sabag E, Mansbach A, Sepunaru Y, Filtzer E, Gruber G, Volozhinsky M, Yogev Y, Birk O, Chalifa-Caspi V, Rokach L, Yeger-Lotem E. Tissue-aware interpretation of genetic variants advances the etiology of rare diseases. Mol Syst Biol 2024:10.1038/s44320-024-00061-6. [PMID: 39285047 DOI: 10.1038/s44320-024-00061-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 08/08/2024] [Accepted: 08/09/2024] [Indexed: 09/19/2024] Open
Abstract
Pathogenic variants underlying Mendelian diseases often disrupt the normal physiology of a few tissues and organs. However, variant effect prediction tools that aim to identify pathogenic variants are typically oblivious to tissue contexts. Here we report a machine-learning framework, denoted "Tissue Risk Assessment of Causality by Expression for variants" (TRACEvar, https://netbio.bgu.ac.il/TRACEvar/ ), that offers two advancements. First, TRACEvar predicts pathogenic variants that disrupt the normal physiology of specific tissues. This was achieved by creating 14 tissue-specific models that were trained on over 14,000 variants and combined 84 attributes of genetic variants with 495 attributes derived from tissue omics. TRACEvar outperformed 10 well-established and tissue-oblivious variant effect prediction tools. Second, the resulting models are interpretable, thereby illuminating variants' mode of action. Application of TRACEvar to variants of 52 rare-disease patients highlighted pathogenicity mechanisms and relevant disease processes. Lastly, the interpretation of all tissue models revealed that top-ranking determinants of pathogenicity included attributes of disease-affected tissues, particularly cellular process activities. Collectively, these results show that tissue contexts and interpretable machine-learning models can greatly enhance the etiology of rare diseases.
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Affiliation(s)
- Chanan M Argov
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, 84105, Israel
| | - Ariel Shneyour
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, 84105, Israel
| | - Juman Jubran
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, 84105, Israel
| | - Eric Sabag
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, 84105, Israel
| | - Avigdor Mansbach
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, 84105, Israel
| | - Yair Sepunaru
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, 84105, Israel
| | - Emmi Filtzer
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, 84105, Israel
| | - Gil Gruber
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, 84105, Israel
| | - Miri Volozhinsky
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, 84105, Israel
| | - Yuval Yogev
- Morris Kahn Laboratory of Human Genetics and the Genetics Institute at Soroka Medical Center, Faculty of Health Sciences, Ben Gurion University of the Negev, Beer Sheva, 84105, Israel
| | - Ohad Birk
- Morris Kahn Laboratory of Human Genetics and the Genetics Institute at Soroka Medical Center, Faculty of Health Sciences, Ben Gurion University of the Negev, Beer Sheva, 84105, Israel
- The National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer Sheva, 84105, Israel
| | - Vered Chalifa-Caspi
- Ilse Katz Institute for Nanoscale Science & Technology, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel
| | - Lior Rokach
- Department of Software & Information Systems Engineering, Faculty of Engineering Sciences, Ben-Gurion University of the Negev, Beer Sheva, 84105, Israel
| | - Esti Yeger-Lotem
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer Sheva, 84105, Israel.
- The National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer Sheva, 84105, Israel.
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2
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Wang Y, Wang Y. Identification of drug responsive enhancers by predicting chromatin accessibility change from perturbed gene expression profiles. NPJ Syst Biol Appl 2024; 10:62. [PMID: 38816426 PMCID: PMC11139989 DOI: 10.1038/s41540-024-00388-8] [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: 01/11/2024] [Accepted: 05/20/2024] [Indexed: 06/01/2024] Open
Abstract
Individual may response to drug treatment differently due to their genetic variants located in enhancers. These variants can alter transcription factor's (TF) binding strength, affect enhancer's chromatin activity or interaction, and eventually change expression level of downstream gene. Here, we propose a computational framework, PERD, to Predict the Enhancers Responsive to Drug. A machine learning model was trained to predict the genome-wide chromatin accessibility from transcriptome data using the paired expression and chromatin accessibility data collected from ENCODE and ROADMAP. Then the model was applied to the perturbed gene expression data from Connectivity Map (CMAP) and Cancer Drug-induced gene expression Signature DataBase (CDS-DB) and identify drug responsive enhancers with significantly altered chromatin accessibility. Furthermore, the drug responsive enhancers were related to the pharmacogenomics genome-wide association studies (PGx GWAS). Stepping on the traditional drug-associated gene signatures, PERD holds the promise to enhance the causality of drug perturbation by providing candidate regulatory element of those drug associated genes.
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Affiliation(s)
- Yongcui Wang
- State Key Laboratory of Phytochemistry and Plant Resources in West China, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming, 650201, China.
| | - Yong Wang
- CEMS, NCMIS, HCMS, MDIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, 100190, Beijing, China.
- Key Laboratory of Systems Biology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Hangzhou, 330106, China.
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3
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Reed TJ, Tyl MD, Tadych A, Troyanskaya OG, Cristea IM. Tapioca: a platform for predicting de novo protein-protein interactions in dynamic contexts. Nat Methods 2024; 21:488-500. [PMID: 38361019 PMCID: PMC11249048 DOI: 10.1038/s41592-024-02179-9] [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: 04/06/2023] [Accepted: 01/12/2024] [Indexed: 02/17/2024]
Abstract
Protein-protein interactions (PPIs) drive cellular processes and responses to environmental cues, reflecting the cellular state. Here we develop Tapioca, an ensemble machine learning framework for studying global PPIs in dynamic contexts. Tapioca predicts de novo interactions by integrating mass spectrometry interactome data from thermal/ion denaturation or cofractionation workflows with protein properties and tissue-specific functional networks. Focusing on the thermal proximity coaggregation method, we improved the experimental workflow. Finely tuned thermal denaturation afforded increased throughput, while cell lysis optimization enhanced protein detection from different subcellular compartments. The Tapioca workflow was next leveraged to investigate viral infection dynamics. Temporal PPIs were characterized during the reactivation from latency of the oncogenic Kaposi's sarcoma-associated herpesvirus. Together with functional assays, NUCKS was identified as a proviral hub protein, and a broader role was uncovered by integrating PPI networks from alpha- and betaherpesvirus infections. Altogether, Tapioca provides a web-accessible platform for predicting PPIs in dynamic contexts.
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Affiliation(s)
- Tavis J Reed
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Carl Icahn Laboratory, Princeton, NJ, USA
- Department of Computer Science, Princeton University, Princeton, NJ, USA
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Matthew D Tyl
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA
| | - Alicja Tadych
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Carl Icahn Laboratory, Princeton, NJ, USA
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Olga G Troyanskaya
- Lewis-Sigler Institute for Integrative Genomics, Princeton University, Carl Icahn Laboratory, Princeton, NJ, USA.
- Department of Computer Science, Princeton University, Princeton, NJ, USA.
- Flatiron Institute, Simons Foundation, New York City, NY, USA.
| | - Ileana M Cristea
- Department of Molecular Biology, Princeton University, Princeton, NJ, USA.
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4
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Xu D, Zhang C, Bi X, Xu J, Guo S, Li P, Shen Y, Cai J, Zhang N, Tian G, Zhang H, Wang H, Li Q, Jiang H, Wang B, Li X, Li Y, Li K. Mapping enhancer and chromatin accessibility landscapes charts the regulatory network of Alzheimer's disease. Comput Biol Med 2024; 168:107802. [PMID: 38056211 DOI: 10.1016/j.compbiomed.2023.107802] [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: 08/01/2023] [Revised: 10/20/2023] [Accepted: 11/29/2023] [Indexed: 12/08/2023]
Abstract
BACKGROUND Enhancers are regulatory elements that target and modulate gene expression and play a role in human health and disease. However, the roles of enhancer regulatory circuit abnormalities driven by epigenetic alterations in Alzheimer's disease (AD) are unclear. METHODS In this study, a multiomic integrative analysis was performed to map enhancer and chromatin accessibility landscapes and identify regulatory network abnormalities in AD. We identified differentially methylated enhancers and constructed regulatory networks across brain regions using AD brain tissue samples. Through the integration of snATAC-seq and snRNA-seq datasets, we mapped enhancers with DNA methylation alterations (DMA) and chromatin accessibility landscapes. Core regulatory triplets that contributed to AD neuropathology in specific cell types were further prioritized. RESULTS We revealed widespread DNA methylation alterations (DMA) in the enhancers of AD patients across different brain regions. In addition, the genome-wide transcription factor (TF) binding profiles showed that enhancers with DMA are pervasively regulated by TFs. The TF-enhancer-gene regulatory network analysis identified core regulatory triplets that are associated with brain and immune cell proportions and play important roles in AD pathogenesis. Enhancer regulatory circuits with DMA exhibited distinct chromatin accessibility patterns, which were further characterized at single-cell resolutions. CONCLUSIONS Our study comprehensively investigated DNA methylation-mediated regulatory circuit abnormalities and provided novel insights into the potential pathogenesis of AD.
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Affiliation(s)
- Dahua Xu
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan General Hospital, Hainan Affiliated Hospital, Hainan Medical University, Haikou 571199, China
| | - Chunrui Zhang
- Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100020, China
| | - Xiaoman Bi
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan General Hospital, Hainan Affiliated Hospital, Hainan Medical University, Haikou 571199, China
| | - Jiankai Xu
- College of Bioinformatics Science and Technology, Harbin Medical University, Harbin 150081, China
| | - Shengnan Guo
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan General Hospital, Hainan Affiliated Hospital, Hainan Medical University, Haikou 571199, China
| | - Peihu Li
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan General Hospital, Hainan Affiliated Hospital, Hainan Medical University, Haikou 571199, China
| | - Yutong Shen
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan General Hospital, Hainan Affiliated Hospital, Hainan Medical University, Haikou 571199, China
| | - Jiale Cai
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan General Hospital, Hainan Affiliated Hospital, Hainan Medical University, Haikou 571199, China
| | - Nihui Zhang
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan General Hospital, Hainan Affiliated Hospital, Hainan Medical University, Haikou 571199, China
| | - Guanghui Tian
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan General Hospital, Hainan Affiliated Hospital, Hainan Medical University, Haikou 571199, China
| | - Haifei Zhang
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan General Hospital, Hainan Affiliated Hospital, Hainan Medical University, Haikou 571199, China
| | - Hong Wang
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan General Hospital, Hainan Affiliated Hospital, Hainan Medical University, Haikou 571199, China
| | - Qifu Li
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan General Hospital, Hainan Affiliated Hospital, Hainan Medical University, Haikou 571199, China
| | - Hongyan Jiang
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan General Hospital, Hainan Affiliated Hospital, Hainan Medical University, Haikou 571199, China
| | - Bo Wang
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan General Hospital, Hainan Affiliated Hospital, Hainan Medical University, Haikou 571199, China.
| | - Xia Li
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan General Hospital, Hainan Affiliated Hospital, Hainan Medical University, Haikou 571199, China.
| | - Yongsheng Li
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan General Hospital, Hainan Affiliated Hospital, Hainan Medical University, Haikou 571199, China.
| | - Kongning Li
- Key Laboratory of Tropical Translational Medicine of Ministry of Education, College of Biomedical Information and Engineering, Hainan General Hospital, Hainan Affiliated Hospital, Hainan Medical University, Haikou 571199, China.
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5
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Wang Q, Zhang J, Liu Z, Duan Y, Li C. Integrative approaches based on genomic techniques in the functional studies on enhancers. Brief Bioinform 2023; 25:bbad442. [PMID: 38048082 PMCID: PMC10694556 DOI: 10.1093/bib/bbad442] [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: 08/28/2023] [Revised: 10/22/2023] [Accepted: 11/08/2023] [Indexed: 12/05/2023] Open
Abstract
With the development of sequencing technology and the dramatic drop in sequencing cost, the functions of noncoding genes are being characterized in a wide variety of fields (e.g. biomedicine). Enhancers are noncoding DNA elements with vital transcription regulation functions. Tens of thousands of enhancers have been identified in the human genome; however, the location, function, target genes and regulatory mechanisms of most enhancers have not been elucidated thus far. As high-throughput sequencing techniques have leapt forwards, omics approaches have been extensively employed in enhancer research. Multidimensional genomic data integration enables the full exploration of the data and provides novel perspectives for screening, identification and characterization of the function and regulatory mechanisms of unknown enhancers. However, multidimensional genomic data are still difficult to integrate genome wide due to complex varieties, massive amounts, high rarity, etc. To facilitate the appropriate methods for studying enhancers with high efficacy, we delineate the principles, data processing modes and progress of various omics approaches to study enhancers and summarize the applications of traditional machine learning and deep learning in multi-omics integration in the enhancer field. In addition, the challenges encountered during the integration of multiple omics data are addressed. Overall, this review provides a comprehensive foundation for enhancer analysis.
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Affiliation(s)
- Qilin Wang
- School of Engineering Medicine, Beihang University, Beijing 100191, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Junyou Zhang
- School of Engineering Medicine, Beihang University, Beijing 100191, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Zhaoshuo Liu
- School of Engineering Medicine, Beihang University, Beijing 100191, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Yingying Duan
- School of Engineering Medicine, Beihang University, Beijing 100191, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Chunyan Li
- School of Engineering Medicine, Beihang University, Beijing 100191, China
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
- Key Laboratory of Big Data-Based Precision Medicine (Ministry of Industry and Information Technology), Beihang University, Beijing 100191, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University, Beijing 100191, China
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6
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Miller CJ, Golovina E, Wicker JS, Jacobsen JC, O'Sullivan JM. De novo network analysis reveals autism causal genes and developmental links to co-occurring traits. Life Sci Alliance 2023; 6:e202302142. [PMID: 37553252 PMCID: PMC10410065 DOI: 10.26508/lsa.202302142] [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: 05/08/2023] [Revised: 07/26/2023] [Accepted: 07/27/2023] [Indexed: 08/10/2023] Open
Abstract
Autism is a complex neurodevelopmental condition that manifests in various ways. Autism is often accompanied by other conditions, such as attention-deficit/hyperactivity disorder and schizophrenia, which can complicate diagnosis and management. Although research has investigated the role of specific genes in autism, their relationship with co-occurring traits is not fully understood. To address this, we conducted a two-sample Mendelian randomisation analysis and identified four genes located at the 17q21.31 locus that are putatively causal for autism in fetal cortical tissue (LINC02210, LRRC37A4P, RP11-259G18.1, and RP11-798G7.6). LINC02210 was also identified as putatively causal for autism in adult cortical tissue. By integrating data from expression quantitative trait loci, genes and protein interactions, we identified that the 17q21.31 locus contributes to the intersection between autism and other neurological traits in fetal cortical tissue. We also identified a distinct cluster of co-occurring traits, including cognition and worry, linked to the genetic loci at 3p21.1. Our findings provide insights into the relationship between autism and co-occurring traits, which could be used to develop predictive models for more accurate diagnosis and better clinical management.
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Affiliation(s)
- Catriona J Miller
- The Liggins Institute, The University of Auckland, Auckland, New Zealand
| | - Evgeniia Golovina
- The Liggins Institute, The University of Auckland, Auckland, New Zealand
| | - Joerg S Wicker
- School of Computer Science, University of Auckland, Auckland, New Zealand
| | - Jessie C Jacobsen
- School of Biological Sciences, The University of Auckland, Auckland, New Zealand
- Centre for Brain Research, The University of Auckland, Auckland, New Zealand
| | - Justin M O'Sullivan
- The Liggins Institute, The University of Auckland, Auckland, New Zealand
- The Maurice Wilkins Centre, The University of Auckland, Auckland, Zealand
- Garvan Institute of Medical Research, Sydney, Australia
- MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK
- Singapore Institute for Clinical Sciences, Agency for Science Technology and Research, Singapore, Singapore
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7
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Zhao X, Song L, Yang A, Zhang Z, Zhang J, Yang YT, Zhao XM. Prioritizing genes associated with brain disorders by leveraging enhancer-promoter interactions in diverse neural cells and tissues. Genome Med 2023; 15:56. [PMID: 37488639 PMCID: PMC10364416 DOI: 10.1186/s13073-023-01210-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 07/10/2023] [Indexed: 07/26/2023] Open
Abstract
BACKGROUND Prioritizing genes that underlie complex brain disorders poses a considerable challenge. Despite previous studies have found that they shared symptoms and heterogeneity, it remained difficult to systematically identify the risk genes associated with them. METHODS By using the CAGE (Cap Analysis of Gene Expression) read alignment files for 439 human cell and tissue types (including primary cells, tissues and cell lines) from FANTOM5 project, we predicted enhancer-promoter interactions (EPIs) of 439 cell and tissue types in human, and examined their reliability. Then we evaluated the genetic heritability of 17 diverse brain disorders and behavioral-cognitive phenotypes in each neural cell type, brain region, and developmental stage. Furthermore, we prioritized genes associated with brain disorders and phenotypes by leveraging the EPIs in each neural cell and tissue type, and analyzed their pleiotropy and functionality for different categories of disorders and phenotypes. Finally, we characterized the spatiotemporal expression dynamics of these associated genes in cells and tissues. RESULTS We found that identified EPIs showed activity specificity and network aggregation in cell and tissue types, and enriched TF binding in neural cells played key roles in synaptic plasticity and nerve cell development, i.e., EGR1 and SOX family. We also discovered that most neurological disorders exhibit heritability enrichment in neural stem cells and astrocytes, while psychiatric disorders and behavioral-cognitive phenotypes exhibit enrichment in neurons. Furthermore, our identified genes recapitulated well-known risk genes, which exhibited widespread pleiotropy between psychiatric disorders and behavioral-cognitive phenotypes (i.e., FOXP2), and indicated expression specificity in neural cell types, brain regions, and developmental stages associated with disorders and phenotypes. Importantly, we showed the potential associations of brain disorders with brain regions and developmental stages that have not been well studied. CONCLUSIONS Overall, our study characterized the gene-enhancer regulatory networks and genetic mechanisms in the human neural cells and tissues, and illustrated the value of reanalysis of publicly available genomic datasets.
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Affiliation(s)
- Xingzhong Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence, and Department of Neurology of Zhongshan Hospital, Fudan University, 220 Handan Road, Shanghai, 200433, China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China
| | - Liting Song
- Institute of Science and Technology for Brain-Inspired Intelligence, and Department of Neurology of Zhongshan Hospital, Fudan University, 220 Handan Road, Shanghai, 200433, China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China
| | - Anyi Yang
- Institute of Science and Technology for Brain-Inspired Intelligence, and Department of Neurology of Zhongshan Hospital, Fudan University, 220 Handan Road, Shanghai, 200433, China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China
| | - Zichao Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, and Department of Neurology of Zhongshan Hospital, Fudan University, 220 Handan Road, Shanghai, 200433, China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China
| | - Jinglong Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, and Department of Neurology of Zhongshan Hospital, Fudan University, 220 Handan Road, Shanghai, 200433, China
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China
| | - Yucheng T Yang
- Institute of Science and Technology for Brain-Inspired Intelligence, and Department of Neurology of Zhongshan Hospital, Fudan University, 220 Handan Road, Shanghai, 200433, China.
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China.
| | - Xing-Ming Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence, and Department of Neurology of Zhongshan Hospital, Fudan University, 220 Handan Road, Shanghai, 200433, China.
- MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China.
- State Key Laboratory of Medical Neurobiology, Institutes of Brain Science, Fudan University, Shanghai, 200032, China.
- Internatioal Human Phenome Institutes (Shanghai), Shanghai, 200433, China.
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8
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Baur B, Shin J, Schreiber J, Zhang S, Zhang Y, Manjunath M, Song JS, Stafford Noble W, Roy S. Leveraging epigenomes and three-dimensional genome organization for interpreting regulatory variation. PLoS Comput Biol 2023; 19:e1011286. [PMID: 37428809 PMCID: PMC10358954 DOI: 10.1371/journal.pcbi.1011286] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Accepted: 06/20/2023] [Indexed: 07/12/2023] Open
Abstract
Understanding the impact of regulatory variants on complex phenotypes is a significant challenge because the genes and pathways that are targeted by such variants and the cell type context in which regulatory variants operate are typically unknown. Cell-type-specific long-range regulatory interactions that occur between a distal regulatory sequence and a gene offer a powerful framework for examining the impact of regulatory variants on complex phenotypes. However, high-resolution maps of such long-range interactions are available only for a handful of cell types. Furthermore, identifying specific gene subnetworks or pathways that are targeted by a set of variants is a significant challenge. We have developed L-HiC-Reg, a Random Forests regression method to predict high-resolution contact counts in new cell types, and a network-based framework to identify candidate cell-type-specific gene networks targeted by a set of variants from a genome-wide association study (GWAS). We applied our approach to predict interactions in 55 Roadmap Epigenomics Mapping Consortium cell types, which we used to interpret regulatory single nucleotide polymorphisms (SNPs) in the NHGRI-EBI GWAS catalogue. Using our approach, we performed an in-depth characterization of fifteen different phenotypes including schizophrenia, coronary artery disease (CAD) and Crohn's disease. We found differentially wired subnetworks consisting of known as well as novel gene targets of regulatory SNPs. Taken together, our compendium of interactions and the associated network-based analysis pipeline leverages long-range regulatory interactions to examine the context-specific impact of regulatory variation in complex phenotypes.
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Affiliation(s)
- Brittany Baur
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Junha Shin
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Jacob Schreiber
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington, United States of America
| | - Shilu Zhang
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Yi Zhang
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Mohith Manjunath
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - Jun S Song
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
- Department of Physics, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
- Cancer Center at Illinois, University of Illinois at Urbana-Champaign, Urbana, Illinois, United States of America
| | - William Stafford Noble
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Washington, United States of America
- Department of Genome Sciences, University of Washington, Seattle, Washington, United States of America
| | - Sushmita Roy
- Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
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9
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Chen X, Wang Y, Cappuccio A, Cheng WS, Zamojski FR, Nair VD, Miller CM, Rubenstein AB, Nudelman G, Tadych A, Theesfeld CL, Vornholt A, George MC, Ruffin F, Dagher M, Chawla DG, Soares-Schanoski A, Spurbeck RR, Ndhlovu LC, Sebra R, Kleinstein SH, Letizia AG, Ramos I, Fowler VG, Woods CW, Zaslavsky E, Troyanskaya OG, Sealfon SC. Mapping disease regulatory circuits at cell-type resolution from single-cell multiomics data. NATURE COMPUTATIONAL SCIENCE 2023; 3:644-657. [PMID: 37974651 PMCID: PMC10653299 DOI: 10.1038/s43588-023-00476-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Accepted: 06/06/2023] [Indexed: 11/19/2023]
Abstract
Resolving chromatin-remodeling-linked gene expression changes at cell-type resolution is important for understanding disease states. Here we describe MAGICAL (Multiome Accessibility Gene Integration Calling and Looping), a hierarchical Bayesian approach that leverages paired single-cell RNA sequencing and single-cell transposase-accessible chromatin sequencing from different conditions to map disease-associated transcription factors, chromatin sites, and genes as regulatory circuits. By simultaneously modeling signal variation across cells and conditions in both omics data types, MAGICAL achieved high accuracy on circuit inference. We applied MAGICAL to study Staphylococcus aureus sepsis from peripheral blood mononuclear single-cell data that we generated from subjects with bloodstream infection and uninfected controls. MAGICAL identified sepsis-associated regulatory circuits predominantly in CD14 monocytes, known to be activated by bacterial sepsis. We addressed the challenging problem of distinguishing host regulatory circuit responses to methicillin-resistant and methicillin-susceptible S. aureus infections. Although differential expression analysis failed to show predictive value, MAGICAL identified epigenetic circuit biomarkers that distinguished methicillin-resistant from methicillin-susceptible S. aureus infections.
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Affiliation(s)
- Xi Chen
- Center for Computational Biology, Flatiron Institute, New York, NY, USA
- Lewis-Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Yuan Wang
- Department of Computer Science, Princeton University, Princeton, NJ, USA
| | - Antonio Cappuccio
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Wan-Sze Cheng
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Venugopalan D. Nair
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Clare M. Miller
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Aliza B. Rubenstein
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - German Nudelman
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alicja Tadych
- Lewis-Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Chandra L. Theesfeld
- Lewis-Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ, USA
| | - Alexandria Vornholt
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Felicia Ruffin
- Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
| | - Michael Dagher
- Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
| | - Daniel G. Chawla
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
| | | | | | - Lishomwa C. Ndhlovu
- Division of Infectious Diseases, Department of Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Robert Sebra
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Steven H. Kleinstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
- Department of Pathology and Department of Immunobiology, Yale School of Medicine, New Haven, CT, USA
| | | | - Irene Ramos
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Vance G. Fowler
- Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
| | - Christopher W. Woods
- Division of Infectious Diseases, Department of Medicine, Duke University School of Medicine, Durham, NC, USA
| | - Elena Zaslavsky
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- These authors jointly supervised this work: Elena Zaslavsky, Olga G. Troyanskaya, Stuart C. Sealfon
| | - Olga G. Troyanskaya
- Center for Computational Biology, Flatiron Institute, New York, NY, USA
- Lewis-Sigler Institute of Integrative Genomics, Princeton University, Princeton, NJ, USA
- Department of Computer Science, Princeton University, Princeton, NJ, USA
- These authors jointly supervised this work: Elena Zaslavsky, Olga G. Troyanskaya, Stuart C. Sealfon
| | - Stuart C. Sealfon
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- These authors jointly supervised this work: Elena Zaslavsky, Olga G. Troyanskaya, Stuart C. Sealfon
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10
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Ziyani C, Delaneau O, Ribeiro DM. Multimodal single cell analysis infers widespread enhancer co-activity in a lymphoblastoid cell line. Commun Biol 2023; 6:563. [PMID: 37237005 PMCID: PMC10219981 DOI: 10.1038/s42003-023-04954-4] [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: 11/08/2022] [Accepted: 05/18/2023] [Indexed: 05/28/2023] Open
Abstract
Non-coding regulatory elements such as enhancers are key in controlling the cell-type specificity and spatio-temporal expression of genes. To drive stable and precise gene transcription robust to genetic variation and environmental stress, genes are often targeted by multiple enhancers with redundant action. However, it is unknown whether enhancers targeting the same gene display simultaneous activity or whether some enhancer combinations are more often co-active than others. Here, we take advantage of recent developments in single cell technology that permit assessing chromatin status (scATAC-seq) and gene expression (scRNA-seq) in the same single cells to correlate gene expression to the activity of multiple enhancers. Measuring activity patterns across 24,844 human lymphoblastoid single cells, we find that the majority of enhancers associated with the same gene display significant correlation in their chromatin profiles. For 6944 expressed genes associated with enhancers, we predict 89,885 significant enhancer-enhancer associations between nearby enhancers. We find that associated enhancers share similar transcription factor binding profiles and that gene essentiality is linked with higher enhancer co-activity. We provide a set of predicted enhancer-enhancer associations based on correlation derived from a single cell line, which can be further investigated for functional relevance.
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Affiliation(s)
- Chaymae Ziyani
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Olivier Delaneau
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland
| | - Diogo M Ribeiro
- Department of Computational Biology, University of Lausanne, Lausanne, Switzerland.
- Swiss Institute of Bioinformatics (SIB), Lausanne, Switzerland.
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11
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Dominguez-Alonso S, Carracedo A, Rodriguez-Fontenla C. The non-coding genome in Autism Spectrum Disorders. Eur J Med Genet 2023; 66:104752. [PMID: 37023975 DOI: 10.1016/j.ejmg.2023.104752] [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: 10/24/2022] [Revised: 03/10/2023] [Accepted: 03/19/2023] [Indexed: 04/08/2023]
Abstract
Autism Spectrum Disorders (ASD) are a group of neurodevelopmental disorders (NDDs) characterized by difficulties in social interaction and communication, repetitive behavior, and restricted interests. While ASD have been proven to have a strong genetic component, current research largely focuses on coding regions of the genome. However, non-coding DNA, which makes up for ∼99% of the human genome, has recently been recognized as an important contributor to the high heritability of ASD, and novel sequencing technologies have been a milestone in opening up new directions for the study of the gene regulatory networks embedded within the non-coding regions. Here, we summarize current progress on the contribution of non-coding alterations to the pathogenesis of ASD and provide an overview of existing methods allowing for the study of their functional relevance, discussing potential ways of unraveling ASD's "missing heritability".
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Affiliation(s)
- S Dominguez-Alonso
- Grupo de Medicina Xenómica, Center for Research in Molecular Medicine and Chronic Diseases (CiMUS), Universidad de Santiago de Compostela, Santiago de Compostela, Spain
| | - A Carracedo
- Grupo de Medicina Xenómica, Center for Research in Molecular Medicine and Chronic Diseases (CiMUS), Universidad de Santiago de Compostela, Santiago de Compostela, Spain; Grupo de Medicina Xenómica, Fundación Instituto de Investigación Sanitaria de Santiago de Compostela (FIDIS), Center for Research in Molecular Medicine and Chronic Diseases (CiMUS), Universidad de Santiago de Compostela, Santiago de Compostela, Spain
| | - C Rodriguez-Fontenla
- Grupo de Medicina Xenómica, Center for Research in Molecular Medicine and Chronic Diseases (CiMUS), Universidad de Santiago de Compostela, Santiago de Compostela, Spain.
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12
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Golovina E, Fadason T, Jaros RK, Kumar H, John J, Burrowes K, Tawhai M, O'Sullivan JM. De novo discovery of traits co-occurring with chronic obstructive pulmonary disease. Life Sci Alliance 2023; 6:6/3/e202201609. [PMID: 36574990 PMCID: PMC9795035 DOI: 10.26508/lsa.202201609] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 12/06/2022] [Accepted: 12/08/2022] [Indexed: 12/28/2022] Open
Abstract
Chronic obstructive pulmonary disease (COPD) is a heterogeneous group of chronic lung conditions. Genome-wide association studies have identified single-nucleotide polymorphisms (SNPs) associated with COPD and the co-occurring conditions, suggesting common biological mechanisms underlying COPD and these co-occurring conditions. To identify them, we have integrated information across different biological levels (i.e., genetic variants, lung-specific 3D genome structure, gene expression and protein-protein interactions) to build lung-specific gene regulatory and protein-protein interaction networks. We have queried these networks using disease-associated SNPs for COPD, unipolar depression and coronary artery disease. COPD-associated SNPs can control genes involved in the regulation of lung or pulmonary function, asthma, brain region volumes, cortical surface area, depressed affect, neuroticism, Parkinson's disease, white matter microstructure and smoking behaviour. We describe the regulatory connections, genes and biochemical pathways that underlay these co-occurring trait-SNP-gene associations. Collectively, our findings provide new avenues for the investigation of the underlying biology and diverse clinical presentations of COPD. In so doing, we identify a collection of genetic variants and genes that may aid COPD patient stratification and treatment.
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Affiliation(s)
| | - Tayaza Fadason
- Liggins Institute, University of Auckland, Auckland, New Zealand.,Maurice Wilkins Centre, University of Auckland, Auckland, New Zealand
| | - Rachel K Jaros
- Liggins Institute, University of Auckland, Auckland, New Zealand
| | - Haribalan Kumar
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Joyce John
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Kelly Burrowes
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Merryn Tawhai
- Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
| | - Justin M O'Sullivan
- Liggins Institute, University of Auckland, Auckland, New Zealand .,Maurice Wilkins Centre, University of Auckland, Auckland, New Zealand.,MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK.,Garvan Institute of Medical Research, Sydney, Australia.,Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
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13
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Hong D, Lin H, Liu L, Shu M, Dai J, Lu F, Tong M, Huang J. Complexity of enhancer networks predicts cell identity and disease genes revealed by single-cell multi-omics analysis. Brief Bioinform 2023; 24:6868525. [PMID: 36464486 DOI: 10.1093/bib/bbac508] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 10/21/2022] [Accepted: 10/24/2022] [Indexed: 12/09/2022] Open
Abstract
Many enhancers exist as clusters in the genome and control cell identity and disease genes; however, the underlying mechanism remains largely unknown. Here, we introduce an algorithm, eNet, to build enhancer networks by integrating single-cell chromatin accessibility and gene expression profiles. The complexity of enhancer networks is assessed by two metrics: the number of enhancers and the frequency of predicted enhancer interactions (PEIs) based on chromatin co-accessibility. We apply eNet algorithm to a human blood dataset and find cell identity and disease genes tend to be regulated by complex enhancer networks. The network hub enhancers (enhancers with frequent PEIs) are the most functionally important. Compared with super-enhancers, enhancer networks show better performance in predicting cell identity and disease genes. eNet is robust and widely applicable in various human or mouse tissues datasets. Thus, we propose a model of enhancer networks containing three modes: Simple, Multiple and Complex, which are distinguished by their complexity in regulating gene expression. Taken together, our work provides an unsupervised approach to simultaneously identify key cell identity and disease genes and explore the underlying regulatory relationships among enhancers in single cells.
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Affiliation(s)
- Danni Hong
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, Fujian 361102, China
| | - Hongli Lin
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, Fujian 361102, China
| | - Lifang Liu
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, Fujian 361102, China
| | - Muya Shu
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China
| | - Jianwu Dai
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Falong Lu
- State Key Laboratory of Molecular Developmental Biology, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing 100101, China.,University of Chinese Academy of Sciences, Beijing 100049, China
| | - Mengsha Tong
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, Fujian 361102, China.,National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian 361102, China
| | - Jialiang Huang
- State Key Laboratory of Cellular Stress Biology, School of Life Sciences, Faculty of Medicine and Life Sciences, Xiamen University, Xiamen, Fujian 361102, China.,National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian 361102, China
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14
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Shimizu H, Kodama M, Matsumoto M, Orba Y, Sasaki M, Sato A, Sawa H, Nakayama KI. LIGHTHOUSE illuminates therapeutics for a variety of diseases including COVID-19. iScience 2022; 25:105314. [PMID: 36246574 PMCID: PMC9549714 DOI: 10.1016/j.isci.2022.105314] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 08/08/2022] [Accepted: 10/05/2022] [Indexed: 11/26/2022] Open
Abstract
One of the bottlenecks in the application of basic research findings to patients is the enormous cost, time, and effort required for high-throughput screening of potential drugs for given therapeutic targets. Here we have developed LIGHTHOUSE, a graph-based deep learning approach for discovery of the hidden principles underlying the association of small-molecule compounds with target proteins. Without any 3D structural information for proteins or chemicals, LIGHTHOUSE estimates protein-compound scores that incorporate known evolutionary relations and available experimental data. It identified therapeutics for cancer, lifestyle related disease, and bacterial infection. Moreover, LIGHTHOUSE predicted ethoxzolamide as a therapeutic for coronavirus disease 2019 (COVID-19), and this agent was indeed effective against alpha, beta, gamma, and delta variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that are rampant worldwide. We envision that LIGHTHOUSE will help accelerate drug discovery and fill the gap between bench side and bedside. LIGHTHOUSE discovers therapeutics solely on the basis of the primary sequence The predictions of LIGHTHOUSE against multiple diseases were experimentally correct LIGHTHOUSE facilitates optimization of lead compounds as well
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Affiliation(s)
- Hideyuki Shimizu
- Department of Molecular and Cellular Biology, Medical Institute of Bioregulation, Kyushu University, Fukuoka 812-8582, Japan,Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA,Wyss Institute for Biologically Inspired Engineering, Harvard Medical School, Boston, MA 02115, USA,Department of AI Systems Medicine, M&D Data Science Center, Tokyo Medical and Dental University, Tokyo 113-8510, Japan,Corresponding author
| | - Manabu Kodama
- Department of Molecular and Cellular Biology, Medical Institute of Bioregulation, Kyushu University, Fukuoka 812-8582, Japan
| | - Masaki Matsumoto
- Department of Omics and Systems Biology, Niigata University Graduate School of Medical and Dental Sciences, Niigata 951-8510, Japan
| | - Yasuko Orba
- Division of Molecular Pathobiology, International Institute for Zoonosis Control, Hokkaido University, Sapporo 060-8638, Japan
| | - Michihito Sasaki
- Division of Molecular Pathobiology, International Institute for Zoonosis Control, Hokkaido University, Sapporo 060-8638, Japan
| | - Akihiko Sato
- Division of Molecular Pathobiology, International Institute for Zoonosis Control, Hokkaido University, Sapporo 060-8638, Japan,Drug Discovery and Disease Research Laboratory, Shionogi & Co. Ltd., Osaka 561-0825, Japan
| | - Hirofumi Sawa
- Division of Molecular Pathobiology, International Institute for Zoonosis Control, Hokkaido University, Sapporo 060-8638, Japan,International Collaboration Unit, International Institute for Zoonosis Control, Hokkaido University, Sapporo 060-8638, Japan,One Health Research Center, Hokkaido University, Sapporo 060-8638, Japan,Global Virus Network, Baltimore, MD 21201, USA,Hokkaido University, Institute for Vaccine Research and Development (HU-IVReD)
| | - Keiichi I. Nakayama
- Department of Molecular and Cellular Biology, Medical Institute of Bioregulation, Kyushu University, Fukuoka 812-8582, Japan,Corresponding author
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15
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Casella AM, Colantuoni C, Ament SA. Identifying enhancer properties associated with genetic risk for complex traits using regulome-wide association studies. PLoS Comput Biol 2022; 18:e1010430. [PMID: 36070311 PMCID: PMC9484640 DOI: 10.1371/journal.pcbi.1010430] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 09/19/2022] [Accepted: 07/23/2022] [Indexed: 11/17/2022] Open
Abstract
Genetic risk for complex traits is strongly enriched in non-coding genomic regions involved in gene regulation, especially enhancers. However, we lack adequate tools to connect the characteristics of these disruptions to genetic risk. Here, we propose RWAS (Regulome Wide Association Study), a new application of the MAGMA software package to identify the characteristics of enhancers that contribute to genetic risk for disease. RWAS involves three steps: (i) assign genotyped SNPs to cell type- or tissue-specific regulatory features (e.g., enhancers); (ii) test associations of each regulatory feature with a trait of interest for which genome-wide association study (GWAS) summary statistics are available; (iii) perform enhancer-set enrichment analyses to identify quantitative or categorical features of regulatory elements that are associated with the trait. These steps are implemented as a novel application of MAGMA, a tool originally developed for gene-based GWAS analyses. Applying RWAS to interrogate genetic risk for schizophrenia, we discovered a class of risk-associated AT-rich enhancers that are active in the developing brain and harbor binding sites for multiple transcription factors with neurodevelopmental functions. RWAS utilizes open-source software, and we provide a comprehensive collection of annotations for tissue-specific enhancer locations and features, including their evolutionary conservation, AT content, and co-localization with binding sites for hundreds of TFs. RWAS will enable researchers to characterize properties of regulatory elements associated with any trait of interest for which GWAS summary statistics are available.
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Affiliation(s)
- Alex M. Casella
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
- Medical Scientist Training Program, UMSOM, Baltimore, Maryland, United States of America
| | - Carlo Colantuoni
- Departments of Neurology and Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Seth A. Ament
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
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16
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Huang Z, Wang C, Treuter E, Fan R. An optimized 4C-seq protocol based on cistrome and epigenome data in the mouse RAW264.7 macrophage cell line. STAR Protoc 2022; 3:101338. [PMID: 35496794 PMCID: PMC9043770 DOI: 10.1016/j.xpro.2022.101338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
Chromosome conformation capture combined with high-throughput sequencing (4C-seq) is a powerful tool to map genomic DNA regions that communicate with a specific locus of interest such as functional single-nucleotide polymorphism (SNPs)-containing regions. This protocol describes detailed steps to perform 4C-seq in mouse macrophage RAW264.7 cells, starting from the primer design based on cistrome and epigenome data, sample processing, and to the bioinformatics analysis. For complete details on the use and execution of this protocol, please refer to Huang et al. (2021). 4C-seq in mouse RAW264.7 macrophage cells Applicable to any region with a proven or suspected regulatory role in transcription Integrated cistrome and epigenome data to refine the primer design of 4C-seq protocol
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17
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Fan J, Feng Y, Cheng Y, Wang Z, Zhao H, Galan EA, Liao Q, Cui S, Zhang W, Ma S. Multiplex gene quantification as digital markers for extremely rapid evaluation of chemo-drug sensitivity. PATTERNS 2021; 2:100360. [PMID: 34693378 PMCID: PMC8515010 DOI: 10.1016/j.patter.2021.100360] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 06/29/2021] [Accepted: 09/08/2021] [Indexed: 12/12/2022]
Abstract
Current administrations for precision drug uses are limited in evaluation speed. Here, we propose the use of multiplex gene-based digital markers for the extremely rapid personalized prediction of individual sensitivity to cancer drugs. We first screen the transcriptional profiles by applying two to three gene filters and scoring genes by their impact on drug sensitivity and finalize the gene lists by K-nearest neighbors cross-validation. The digital markers are cancer type dependent, are composed of tens to hundreds of gene expressions, and are rapidly quantified by reverse transcription quantitative real-time PCR (qRT-PCR) within 1–3 h after tumor sampling. The area under the receiver operating characteristic curve reached 0.88 when testing the performance of digital markers on organoids derived from colorectal cancer patient tumors. The algorithm and corresponding graphic user interface were developed to demonstrate the promise of digital markers for extremely rapid drug recommendation. Non-targeted multiplex genes are screened as digital markers for drug sensitivity Transcription level cohort of 10s to 100s genes predicts drug sensitivity Digital markers are quantified using qRT-PCR within 1–3 h Digital markers guide extremely rapid chemo-drug uses after patient hospitalization
In clinical cancer medicine, many patients require immediate chemotherapy after hospitalization. Current administrations for precision drug uses are limited in evaluation speed, including genomic sequencing and tumor organoid evaluation. An extremely rapid evaluation protocol is in high demand to realize drug recommendation within a few hours after tumor sampling. In this work, we have proposed an approach for extremely rapid and personalized drug recommendation.
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Affiliation(s)
- Jiaqi Fan
- Tsinghua University, Shenzhen International Graduate School (SIGS), Shenzhen 518055, China.,Tsinghua-Berkeley Shenzhen Institute (TBSI), Shenzhen 518055, China.,Institute for Brain and Cognitive Sciences (THUIBCS), Tsinghua University, Beijing 100084, China
| | - Yilin Feng
- Tsinghua University, Shenzhen International Graduate School (SIGS), Shenzhen 518055, China.,Tsinghua-Berkeley Shenzhen Institute (TBSI), Shenzhen 518055, China
| | - Yifan Cheng
- Tsinghua University, Shenzhen International Graduate School (SIGS), Shenzhen 518055, China.,Tsinghua-Berkeley Shenzhen Institute (TBSI), Shenzhen 518055, China
| | - Zitian Wang
- Tsinghua University, Shenzhen International Graduate School (SIGS), Shenzhen 518055, China.,Tsinghua-Berkeley Shenzhen Institute (TBSI), Shenzhen 518055, China
| | - Haoran Zhao
- Tsinghua University, Shenzhen International Graduate School (SIGS), Shenzhen 518055, China.,Tsinghua-Berkeley Shenzhen Institute (TBSI), Shenzhen 518055, China
| | - Edgar A Galan
- Tsinghua University, Shenzhen International Graduate School (SIGS), Shenzhen 518055, China.,Tsinghua-Berkeley Shenzhen Institute (TBSI), Shenzhen 518055, China
| | - Quanxing Liao
- Department of Abdominal Surgery, Affiliated Cancer Hospital and Institute of Guangzhou Medical University, Guangzhou 510095, China
| | - Shuzhong Cui
- Department of Abdominal Surgery, Affiliated Cancer Hospital and Institute of Guangzhou Medical University, Guangzhou 510095, China
| | - Weijie Zhang
- Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, China
| | - Shaohua Ma
- Tsinghua University, Shenzhen International Graduate School (SIGS), Shenzhen 518055, China.,Tsinghua-Berkeley Shenzhen Institute (TBSI), Shenzhen 518055, China.,Institute for Brain and Cognitive Sciences (THUIBCS), Tsinghua University, Beijing 100084, China
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18
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Chen X, Shi X, Neuwald AF, Hilakivi-Clarke L, Clarke R, Xuan J. ChIP-BIT2: a software tool to detect weak binding events using a Bayesian integration approach. BMC Bioinformatics 2021; 22:193. [PMID: 33858322 PMCID: PMC8051094 DOI: 10.1186/s12859-021-04108-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2020] [Accepted: 03/29/2021] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND ChIP-seq combines chromatin immunoprecipitation assays with sequencing and identifies genome-wide binding sites for DNA binding proteins. While many binding sites have strong ChIP-seq 'peak' observations and are well captured, there are still regions bound by proteins weakly, with a relatively low ChIP-seq signal enrichment. These weak binding sites, especially those at promoters and enhancers, are functionally important because they also regulate nearby gene expression. Yet, it remains a challenge to accurately identify weak binding sites in ChIP-seq data due to the ambiguity in differentiating these weak binding sites from the amplified background DNAs. RESULTS ChIP-BIT2 ( http://sourceforge.net/projects/chipbitc/ ) is a software package for ChIP-seq peak detection. ChIP-BIT2 employs a mixture model integrating protein and control ChIP-seq data and predicts strong or weak protein binding sites at promoters, enhancers, or other genomic locations. For binding sites at gene promoters, ChIP-BIT2 simultaneously predicts their target genes. ChIP-BIT2 has been validated on benchmark regions and tested using large-scale ENCODE ChIP-seq data, demonstrating its high accuracy and wide applicability. CONCLUSION ChIP-BIT2 is an efficient ChIP-seq peak caller. It provides a better lens to examine weak binding sites and can refine or extend the existing binding site collection, providing additional regulatory regions for decoding the mechanism of gene expression regulation.
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Affiliation(s)
- Xi Chen
- grid.438526.e0000 0001 0694 4940Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, 900 North Glebe Road, Arlington, VA 22203 USA ,grid.430264.7Center for Computational Biology, Flatiron Institute, Simons Foundation, 162 Fifth Avenue, New York, NY 10010 USA
| | - Xu Shi
- grid.438526.e0000 0001 0694 4940Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, 900 North Glebe Road, Arlington, VA 22203 USA
| | - Andrew F. Neuwald
- grid.411024.20000 0001 2175 4264Institute for Genome Sciences and Department Biochemistry and Molecular Biology, University of Maryland School of Medicine, Baltimore, MD 21201 USA
| | - Leena Hilakivi-Clarke
- grid.17635.360000000419368657Hormel Institute, University of Minnesota, 801 16th Ave NE, Austin, MN 55912 USA
| | - Robert Clarke
- grid.17635.360000000419368657Hormel Institute, University of Minnesota, 801 16th Ave NE, Austin, MN 55912 USA
| | - Jianhua Xuan
- Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, 900 North Glebe Road, Arlington, VA, 22203, USA.
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