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Doolittle ML, Calabrese GM, Mesner LD, Godfrey DA, Maynard RD, Ackert-Bicknell CL, Farber CR. Genetic analysis of osteoblast activity identifies Zbtb40 as a regulator of osteoblast activity and bone mass. PLoS Genet 2020; 16:e1008805. [PMID: 32497039 PMCID: PMC7326283 DOI: 10.1371/journal.pgen.1008805] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2019] [Revised: 06/30/2020] [Accepted: 04/28/2020] [Indexed: 12/21/2022] Open
Abstract
Osteoporosis is a genetic disease characterized by progressive reductions in bone mineral density (BMD) leading to an increased risk of fracture. Over the last decade, genome-wide association studies (GWASs) have identified over 1000 associations for BMD. However, as a phenotype BMD is challenging as bone is a multicellular tissue affected by both local and systemic physiology. Here, we focused on a single component of BMD, osteoblast-mediated bone formation in mice, and identified associations influencing osteoblast activity on mouse Chromosomes (Chrs) 1, 4, and 17. The locus on Chr. 4 was in an intergenic region between Wnt4 and Zbtb40, homologous to a locus for BMD in humans. We tested both Wnt4 and Zbtb40 for a role in osteoblast activity and BMD. Knockdown of Zbtb40, but not Wnt4, in osteoblasts drastically reduced mineralization. Additionally, loss-of-function mouse models for both genes exhibited reduced BMD. Our results highlight that investigating the genetic basis of in vitro osteoblast mineralization can be used to identify genes impacting bone formation and BMD.
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Affiliation(s)
- Madison L. Doolittle
- Center for Musculoskeletal Research, University of Rochester, Rochester, New York, United States of America
| | - Gina M. Calabrese
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America
| | - Larry D. Mesner
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America
- Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia, United States of America
| | - Dana A. Godfrey
- Department of Orthopedics, University of Colorado, Aurora, Colorado, United States of America
| | - Robert D. Maynard
- Center for Musculoskeletal Research, University of Rochester, Rochester, New York, United States of America
- Department of Orthopedics, University of Colorado, Aurora, Colorado, United States of America
| | - Cheryl L. Ackert-Bicknell
- Center for Musculoskeletal Research, University of Rochester, Rochester, New York, United States of America
- Department of Orthopedics, University of Colorado, Aurora, Colorado, United States of America
| | - Charles R. Farber
- Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America
- Department of Public Health Sciences, University of Virginia, Charlottesville, Virginia, United States of America
- Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, Virginia, United States of America
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52
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Wang T, Peng Q, Liu B, Liu Y, Wang Y. Disease Module Identification Based on Representation Learning of Complex Networks Integrated From GWAS, eQTL Summaries, and Human Interactome. Front Bioeng Biotechnol 2020; 8:418. [PMID: 32435638 PMCID: PMC7218106 DOI: 10.3389/fbioe.2020.00418] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Accepted: 04/14/2020] [Indexed: 12/18/2022] Open
Abstract
The study of disease-relevant gene modules is one of the main methods to discover disease pathway and potential drug targets. Recent studies have found that most disease proteins tend to form many separate connected components and scatter across the protein-protein interaction network. However, most of the research on discovering disease modules are biased toward well-studied seed genes, which tend to extend seed genes into a single connected subnetwork. In this paper, we propose N2V-HC, an algorithm framework aiming to unbiasedly discover the scattered disease modules based on deep representation learning of integrated multi-layer biological networks. Our method first predicts disease associated genes based on summary data of Genome-wide Association Studies (GWAS) and expression Quantitative Trait Loci (eQTL) studies, and generates an integrated network on the basis of human interactome. The features of nodes in the network are then extracted by deep representation learning. Hierarchical clustering with dynamic tree cut methods are applied to discover the modules that are enriched with disease associated genes. The evaluation on real networks and simulated networks show that N2V-HC performs better than existing methods in network module discovery. Case studies on Parkinson's disease and Alzheimer's disease, show that N2V-HC can be used to discover biological meaningful modules related to the pathways underlying complex diseases.
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Affiliation(s)
- Tao Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Qidi Peng
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Bo Liu
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Yongzhuang Liu
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Yadong Wang
- School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
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53
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Integrative genomics analysis of eQTL and GWAS summary data identifies PPP1CB as a novel bone mineral density risk genes. Biosci Rep 2020; 40:222598. [PMID: 32266926 PMCID: PMC7178214 DOI: 10.1042/bsr20193185] [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: 09/16/2019] [Revised: 03/03/2020] [Accepted: 04/03/2020] [Indexed: 11/17/2022] Open
Abstract
In recent years, multiple genome-wide association studies (GWAS) have identified numerous susceptibility variants and risk genes that demonstrate significant associations with bone mineral density (BMD). However, exploring how these genetic variants contribute risk to BMD remains a major challenge. We systematically integrated two independent expression quantitative trait loci (eQTL) data (N = 1890) and GWAS summary statistical data of BMD (N = 142,487) using Sherlock integrative analysis to reveal whether expression-associated variants confer risk to BMD. By using Sherlock integrative analysis and MAGMA gene-based analysis, we found there existed 36 promising genes, for example, PPP1CB, XBP1, and FDFT1, whose expression alterations may contribute susceptibility to BMD. Through a protein-protein interaction (PPI) network analysis, we further prioritized the PPP1CB as a hub gene that has interactions with predicted genes and BMD-associated genes. Two eSNPs of rs9309664 (PeQTL = 1.42 × 10-17 and PGWAS = 1.40 × 10-11) and rs7475 (PeQTL = 2.10 × 10-6 and PGWAS = 1.70 × 10-7) in PPP1CB were identified to be significantly associated with BMD risk. Consistently, differential gene expression analysis found that the PPP1CB gene showed significantly higher expression in low BMD samples than that in high BMD samples based on two independent expression datasets (P = 0.0026 and P = 0.043, respectively). Together, we provide a convergent line of evidence to support that the PPP1CB gene involves in the etiology of osteoporosis.
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54
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Dong H, Zhou W, Wang P, Zuo E, Ying X, Chai S, Fei T, Jin L, Chen C, Ma G, Liu H. Comprehensive Analysis of the Genetic and Epigenetic Mechanisms of Osteoporosis and Bone Mineral Density. Front Cell Dev Biol 2020; 8:194. [PMID: 32269995 PMCID: PMC7109267 DOI: 10.3389/fcell.2020.00194] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Accepted: 03/09/2020] [Indexed: 01/11/2023] Open
Abstract
Osteoporosis is a skeletal disorder characterized by a systemic impairment of bone mineral density (BMD). Genome-wide association studies (GWAS) have identified hundreds of susceptibility loci for osteoporosis and BMD. However, the vast majority of susceptibility loci are located in non-coding regions of the genome and provide limited information about the genetic mechanisms of osteoporosis. Herein we performed a comprehensive functional analysis to investigate the genetic and epigenetic mechanisms of osteoporosis and BMD. BMD and osteoporosis are found to share many common susceptibility loci, and the corresponding susceptibility genes are significantly enriched in bone-related biological pathways. The regulatory element enrichment analysis indicated that BMD and osteoporosis susceptibility loci are significantly enriched in 5′UTR and DNase I hypersensitive sites (DHSs) of peripheral blood immune cells. By integrating GWAS and expression Quantitative Trait Locus (eQTL) data, we found that 15 protein-coding genes are regulated by the osteoporosis and BMD susceptibility loci. Our analysis provides new clues for a better understanding of the pathogenic mechanisms and offers potential therapeutic targets for osteoporosis.
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Affiliation(s)
- Hui Dong
- Department of Oral Prosthodontics, School of Stomatology, Dalian Medical University, Dalian, China.,Department of Stomatology, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Wenyang Zhou
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Pingping Wang
- School of Life Science and Technology, Harbin Institute of Technology, Harbin, China
| | - Enjun Zuo
- Department of Oral Prosthodontics, School of Stomatology, Dalian Medical University, Dalian, China
| | - Xiaoxia Ying
- Department of Oral Prosthodontics, School of Stomatology, Dalian Medical University, Dalian, China
| | - Songling Chai
- Department of Oral Prosthodontics, School of Stomatology, Dalian Medical University, Dalian, China
| | - Tao Fei
- Department of Oral Prosthodontics, School of Stomatology, Dalian Medical University, Dalian, China
| | - Laidi Jin
- Department of Oral Prosthodontics, School of Stomatology, Dalian Medical University, Dalian, China
| | - Chen Chen
- Department of Oral Prosthodontics, School of Stomatology, Dalian Medical University, Dalian, China
| | - Guowu Ma
- Department of Oral Prosthodontics, School of Stomatology, Dalian Medical University, Dalian, China
| | - Huiying Liu
- Department of Oral Prosthodontics, School of Stomatology, Dalian Medical University, Dalian, China
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55
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Dwivedi SK, Tjärnberg A, Tegnér J, Gustafsson M. Deriving disease modules from the compressed transcriptional space embedded in a deep autoencoder. Nat Commun 2020; 11:856. [PMID: 32051402 PMCID: PMC7016183 DOI: 10.1038/s41467-020-14666-6] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Accepted: 01/22/2020] [Indexed: 01/05/2023] Open
Abstract
Disease modules in molecular interaction maps have been useful for characterizing diseases. Yet biological networks, that commonly define such modules are incomplete and biased toward some well-studied disease genes. Here we ask whether disease-relevant modules of genes can be discovered without prior knowledge of a biological network, instead training a deep autoencoder from large transcriptional data. We hypothesize that modules could be discovered within the autoencoder representations. We find a statistically significant enrichment of genome-wide association studies (GWAS) relevant genes in the last layer, and to a successively lesser degree in the middle and first layers respectively. In contrast, we find an opposite gradient where a modular protein-protein interaction signal is strongest in the first layer, but then vanishing smoothly deeper in the network. We conclude that a data-driven discovery approach is sufficient to discover groups of disease-related genes.
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Affiliation(s)
- Sanjiv K Dwivedi
- Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden
| | - Andreas Tjärnberg
- Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden
- Department of Biology, Center For Genomics and Systems Biology, New York University, New York, NY, 10008, USA
- Center for Developmental Genetics, Department of Biology, New York University, New York, NY, USA
| | - Jesper Tegnér
- Biological and Environmental Sciences and Engineering Division, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Unit of Computational Medicine, Department of Medicine, Solna, Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
- Science for Life Laboratory, Solna, Sweden
| | - Mika Gustafsson
- Bioinformatics, Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden.
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56
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Nanda H, Ponnusamy N, Odumpatta R, Jeyakanthan J, Mohanapriya A. Exploring genetic targets of psoriasis using genome wide association studies (GWAS) for drug repurposing. 3 Biotech 2020; 10:43. [PMID: 31988837 PMCID: PMC6954159 DOI: 10.1007/s13205-019-2038-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Accepted: 12/23/2019] [Indexed: 12/26/2022] Open
Abstract
Psoriasis is a chronic inflammatory disease causing itching in the body and pain in the joints. Currently, no permanent cure is available at a commercial level for this disease. Genome wide association studies (GWAS) provide a deeper insight that helps in better understanding this disease and further possible cure of this disease. The major goal of the present study is to identify potent genetic targets of psoriasis disease using GWAS approach and identify drugs for repurposing. The methods used include GWAS catalogue, GeneAnalytics, canSAR protein annotation tool, VarElect, Drug bank, Proteomics database, ProTox software. By exploring GWAS catalogue, 126 psoriasis associated genes (PAG) were identified. 68 genes found to be druggable were obtained from canSAR protein annotation tool. Localization results depict that maximum genes are present in cytoplasmic cellular components. The superpathways obtained from GeneAnalytics resulted in involvement of these genes in the immune system, Jak/Stat pathway, Th17 and Wnt pathways. Two genes Interleukin 13 (IL13) and POLI are Food and Drug Administration (FDA) approved targets. Small compounds for these targets were analysed for drug-likeliness, toxicity and mutagenecity properties. The FDA approved drug pandel was found to possess desirable properties. The medications used for psoriasis causes mild to severe side effects and does not work well always. Hence we propose drug repurposing strategy to use existing drugs for new therapies. Therefore, the drug pandel could be explored further and repurposed to treat psoriasis.
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Affiliation(s)
- Harshit Nanda
- Department of Biotechnology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu 632014 India
| | - Nirmaladevi Ponnusamy
- Department of Biotechnology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu 632014 India
| | - Rajasree Odumpatta
- Department of Biotechnology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu 632014 India
| | - Jeyaraman Jeyakanthan
- Department of Bioinformatics, Alagappa University, Karaikudi, Tamil Nadu 630004 India
| | - Arumugam Mohanapriya
- Department of Biotechnology, School of Biosciences and Technology, Vellore Institute of Technology, Vellore, Tamil Nadu 632014 India
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57
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Yang TL, Shen H, Liu A, Dong SS, Zhang L, Deng FY, Zhao Q, Deng HW. A road map for understanding molecular and genetic determinants of osteoporosis. Nat Rev Endocrinol 2020; 16:91-103. [PMID: 31792439 PMCID: PMC6980376 DOI: 10.1038/s41574-019-0282-7] [Citation(s) in RCA: 188] [Impact Index Per Article: 47.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 10/18/2019] [Indexed: 12/16/2022]
Abstract
Osteoporosis is a highly prevalent disorder characterized by low bone mineral density and an increased risk of fracture, termed osteoporotic fracture. Notably, bone mineral density, osteoporosis and osteoporotic fracture are highly heritable; however, determining the genetic architecture, and especially the underlying genomic and molecular mechanisms, of osteoporosis in vivo in humans is still challenging. In addition to susceptibility loci identified in genome-wide association studies, advances in various omics technologies, including genomics, transcriptomics, epigenomics, proteomics and metabolomics, have all been applied to dissect the pathogenesis of osteoporosis. However, each technology individually cannot capture the entire view of the disease pathology and thus fails to comprehensively identify the underlying pathological molecular mechanisms, especially the regulatory and signalling mechanisms. A change to the status quo calls for integrative multi-omics and inter-omics analyses with approaches in 'systems genetics and genomics'. In this Review, we highlight findings from genome-wide association studies and studies using various omics technologies individually to identify mechanisms of osteoporosis. Furthermore, we summarize current studies of data integration to understand, diagnose and inform the treatment of osteoporosis. The integration of multiple technologies will provide a road map to illuminate the complex pathogenesis of osteoporosis, especially from molecular functional aspects, in vivo in humans.
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Affiliation(s)
- Tie-Lin Yang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Hui Shen
- Center of Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, Tulane University, New Orleans, LA, USA
| | - Anqi Liu
- Center of Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, Tulane University, New Orleans, LA, USA
| | - Shan-Shan Dong
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Lei Zhang
- Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Jiangsu, China
| | - Fei-Yan Deng
- Center for Genetic Epidemiology and Genomics, School of Public Health, Medical College of Soochow University, Jiangsu, China
| | - Qi Zhao
- Department of Preventive Medicine, College of Medicine, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Hong-Wen Deng
- Center of Bioinformatics and Genomics, Department of Global Biostatistics and Data Science, Tulane University, New Orleans, LA, USA.
- School of Basic Medical Science, Central South University, Changsha, China.
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58
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Richardson TG, Hemani G, Gaunt TR, Relton CL, Davey Smith G. A transcriptome-wide Mendelian randomization study to uncover tissue-dependent regulatory mechanisms across the human phenome. Nat Commun 2020; 11:185. [PMID: 31924771 PMCID: PMC6954187 DOI: 10.1038/s41467-019-13921-9] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2019] [Accepted: 11/26/2019] [Indexed: 11/09/2022] Open
Abstract
Developing insight into tissue-specific transcriptional mechanisms can help improve our understanding of how genetic variants exert their effects on complex traits and disease. In this study, we apply the principles of Mendelian randomization to systematically evaluate transcriptome-wide associations between gene expression (across 48 different tissue types) and 395 complex traits. Our findings indicate that variants which influence gene expression levels in multiple tissues are more likely to influence multiple complex traits. Moreover, detailed investigations of our results highlight tissue-specific associations, drug validation opportunities, insight into the likely causal pathways for trait-associated variants and also implicate putative associations at loci yet to be implicated in disease susceptibility. Similar evaluations can be conducted at http://mrcieu.mrsoftware.org/Tissue_MR_atlas/.
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Affiliation(s)
- Tom G Richardson
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK.
| | - Gibran Hemani
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Tom R Gaunt
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - Caroline L Relton
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
| | - George Davey Smith
- MRC Integrative Epidemiology Unit (IEU), Population Health Sciences, Bristol Medical School, University of Bristol, Oakfield House, Oakfield Grove, Bristol, BS8 2BN, UK
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59
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Abstract
The common forms of metabolic diseases are highly complex, involving hundreds of genes, environmental and lifestyle factors, age-related changes, sex differences and gut-microbiome interactions. Systems genetics is a population-based approach to address this complexity. In contrast to commonly used 'reductionist' approaches, such as gain or loss of function, that examine one element at a time, systems genetics uses high-throughput 'omics' technologies to quantitatively assess the many molecular differences among individuals in a population and then to relate these to physiologic functions or disease states. Unlike genome-wide association studies, systems genetics seeks to go beyond the identification of disease-causing genes to understand higher-order interactions at the molecular level. The purpose of this review is to introduce the systems genetics applications in the areas of metabolic and cardiovascular disease. Here, we explain how large clinical and omics-level data and databases from both human and animal populations are available to help researchers place genes in the context of pathways and networks and formulate hypotheses that can then be experimentally examined. We provide lists of such databases and examples of the integration of reductionist and systems genetics data. Among the important applications emerging is the development of improved nutritional and pharmacological strategies to address the rise of metabolic diseases.
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Affiliation(s)
- Marcus Seldin
- Department of Medicine, Division of Cardiology, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Microbiology, Immunology and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Biological Chemistry and Center for Epigenetics and Metabolism, University of California, Irvine, Irvine, CA, USA
| | - Xia Yang
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Aldons J Lusis
- Department of Medicine, Division of Cardiology, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA.
- Department of Microbiology, Immunology and Molecular Genetics, University of California, Los Angeles, Los Angeles, CA, USA.
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60
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Weighill D, Tschaplinski TJ, Tuskan GA, Jacobson D. Data Integration in Poplar: 'Omics Layers and Integration Strategies. Front Genet 2019; 10:874. [PMID: 31608114 PMCID: PMC6773870 DOI: 10.3389/fgene.2019.00874] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2019] [Accepted: 08/20/2019] [Indexed: 12/20/2022] Open
Abstract
Populus trichocarpa is an important biofuel feedstock that has been the target of extensive research and is emerging as a model organism for plants, especially woody perennials. This research has generated several large ‘omics datasets. However, only few studies in Populus have attempted to integrate various data types. This review will summarize various ‘omics data layers, focusing on their application in Populus species. Subsequently, network and signal processing techniques for the integration and analysis of these data types will be discussed, with particular reference to examples in Populus.
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Affiliation(s)
- Deborah Weighill
- The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, Knoxville, TN, United States.,Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States
| | - Timothy J Tschaplinski
- The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, Knoxville, TN, United States.,Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States
| | - Gerald A Tuskan
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States
| | - Daniel Jacobson
- The Bredesen Center for Interdisciplinary Research and Graduate Education, University of Tennessee, Knoxville, Knoxville, TN, United States.,Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, United States
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61
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Zhang Q, Riddle RC, Yang Q, Rosen CR, Guttridge DC, Dirckx N, Faugere MC, Farber CR, Clemens TL. The RNA demethylase FTO is required for maintenance of bone mass and functions to protect osteoblasts from genotoxic damage. Proc Natl Acad Sci U S A 2019; 116:17980-17989. [PMID: 31434789 PMCID: PMC6731662 DOI: 10.1073/pnas.1905489116] [Citation(s) in RCA: 69] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The fat mass and obesity-associated gene (FTO) encodes an m6A RNA demethylase that controls mRNA processing and has been linked to both obesity and bone mineral density in humans by genome-wide association studies. To examine the role of FTO in bone, we characterized the phenotype of mice lacking Fto globally (FtoKO ) or selectively in osteoblasts (FtoOcKO ). Both mouse models developed age-related reductions in bone volume in both the trabecular and cortical compartments. RNA profiling in osteoblasts following acute disruption of Fto revealed changes in transcripts of Hspa1a and other genes in the DNA repair pathway containing consensus m6A motifs required for demethylation by FtoFto KO osteoblasts were more susceptible to genotoxic agents (UV and H2O2) and exhibited increased rates of apoptosis. Importantly, forced expression of Hspa1a or inhibition of NF-κB signaling normalized the DNA damage and apoptotic rates in Fto KO osteoblasts. Furthermore, increased metabolic stress induced in mice by feeding a high-fat diet induced greater DNA damage in osteoblast of FtoOc KO mice compared to controls. These data suggest that FTO functions intrinsically in osteoblasts through Hspa1a-NF-κB signaling to enhance the stability of mRNA of proteins that function to protect cells from genotoxic damage.
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Affiliation(s)
- Qian Zhang
- Department of Orthopaedic Surgery, The Johns Hopkins University, Baltimore, MD 21287
| | - Ryan C Riddle
- Department of Orthopaedic Surgery, The Johns Hopkins University, Baltimore, MD 21287
- Baltimore Veterans Administration Medical Center, Baltimore, MD 21201
| | - Qian Yang
- Department of Orthopaedic Surgery, The Johns Hopkins University, Baltimore, MD 21287
| | - Clifford R Rosen
- Center for Molecular Medicine, Maine Medical Center Research Institute, Scarborough, ME 04074
| | - Denis C Guttridge
- Department of Pediatrics, Medical University of South Carolina, Charleston, SC 29425
| | - Naomi Dirckx
- Department of Orthopaedic Surgery, The Johns Hopkins University, Baltimore, MD 21287
| | | | - Charles R Farber
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908
| | - Thomas L Clemens
- Department of Orthopaedic Surgery, The Johns Hopkins University, Baltimore, MD 21287;
- Baltimore Veterans Administration Medical Center, Baltimore, MD 21201
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62
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Blencowe M, Arneson D, Ding J, Chen YW, Saleem Z, Yang X. Network modeling of single-cell omics data: challenges, opportunities, and progresses. Emerg Top Life Sci 2019; 3:379-398. [PMID: 32270049 PMCID: PMC7141415 DOI: 10.1042/etls20180176] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2019] [Revised: 06/07/2019] [Accepted: 06/24/2019] [Indexed: 01/07/2023]
Abstract
Single-cell multi-omics technologies are rapidly evolving, prompting both methodological advances and biological discoveries at an unprecedented speed. Gene regulatory network modeling has been used as a powerful approach to elucidate the complex molecular interactions underlying biological processes and systems, yet its application in single-cell omics data modeling has been met with unique challenges and opportunities. In this review, we discuss these challenges and opportunities, and offer an overview of the recent development of network modeling approaches designed to capture dynamic networks, within-cell networks, and cell-cell interaction or communication networks. Finally, we outline the remaining gaps in single-cell gene network modeling and the outlooks of the field moving forward.
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Affiliation(s)
- Montgomery Blencowe
- Department of Integrative Biology and Physiology, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, U.S.A
| | - Douglas Arneson
- Department of Integrative Biology and Physiology, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, U.S.A
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, U.S.A
| | - Jessica Ding
- Department of Integrative Biology and Physiology, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, U.S.A
| | - Yen-Wei Chen
- Department of Integrative Biology and Physiology, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, U.S.A
- Molecular Toxicology Program, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, U.S.A
| | - Zara Saleem
- Department of Integrative Biology and Physiology, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, U.S.A
| | - Xia Yang
- Department of Integrative Biology and Physiology, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, U.S.A
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, U.S.A
- Molecular Toxicology Program, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, U.S.A
- Institute for Quantitative and Computational Biosciences, University of California, Los Angeles, 610 Charles E. Young Drive East, Los Angeles, CA 90095, U.S.A
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Ma M, Huang DG, Liang X, Zhang L, Cheng S, Cheng B, Qi X, Li P, Du Y, Liu L, Zhao Y, Ding M, Wen Y, Guo X, Zhang F. Integrating transcriptome-wide association study and mRNA expression profiling identifies novel genes associated with bone mineral density. Osteoporos Int 2019; 30:1521-1528. [PMID: 30993394 DOI: 10.1007/s00198-019-04958-z] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Accepted: 03/25/2019] [Indexed: 01/16/2023]
Abstract
UNLABELLED To scan novel candidate genes associated with osteoporosis, a two-stage transcriptome-wide association study (TWAS) of bone mineral density (BMD) was conducted. The BMD-associated genes identified by TWAS were then compared with the gene expression profiling of BMD in bone cells, B cells, and mesenchymal stem cells. We identified multiple candidate genes and gene ontology (GO) terms associated with BMD. INTRODUCTION Osteoporosis (OP) is a metabolic bone disease characterized by decrease in BMD. Our objective is to scan novel candidate genes associated with OP. METHODS A transcriptome-wide association study (TWAS) was performed by integrating the genome-wide association study (GWAS) summary of bone mineral density (BMD) with two pre-computed mRNA expression weights of peripheral blood and muscle skeleton. Then, another independent GWAS data of BMD was used to verify the discovery results. The BMD-associated genes identified between discovery and replicate TWAS were further subjected to gene ontology (GO) analysis implemented by DAVID. Finally, the BMD-associated genes and GO terms were further compared with the mRNA expression profiling results of BMD to detect the common genes and GO terms shared by both DNA-level TWAS and mRNA expression profile analysis. RESULTS TWAS identified 95 common genes with permutation P value < 0.05 for peripheral blood and muscle skeleton, such as TMTC4 in muscle skeleton and DDX17 in peripheral blood. Further comparing the genes detected by discovery-replicate TWAS with the differentially expressed genes identified by mRNA expression profiling of OP patients found 18 overlapped genes, such as MUL1 in muscle skeleton and SPTBN1 in peripheral blood. GO analysis of the genes identified by discovery-replicate TWAS detected 12 BMD-associated GO terms, such as negative regulation of cell growth and regulation of glycogen catabolic process. Further comparing the GO results of discovery-replicate TWAS and mRNA expression profiles found 6 overlapped GO terms, such as membrane and cell adhesion. CONCLUSION Our study identified multiple candidate genes and GO terms for BMD, providing novel clues for understanding the genetic mechanism of OP.
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Affiliation(s)
- M Ma
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, No.76 Yan Ta West Road, Xi'an, 710061, People's Republic of China
| | - D-G Huang
- Department of Spine Surgery, Honghui Hospital, Xi'an Jiaotong University, Xi'an, People's Republic of China
| | - X Liang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, No.76 Yan Ta West Road, Xi'an, 710061, People's Republic of China
| | - L Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, No.76 Yan Ta West Road, Xi'an, 710061, People's Republic of China
| | - S Cheng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, No.76 Yan Ta West Road, Xi'an, 710061, People's Republic of China
| | - B Cheng
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, No.76 Yan Ta West Road, Xi'an, 710061, People's Republic of China
| | - X Qi
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, No.76 Yan Ta West Road, Xi'an, 710061, People's Republic of China
| | - P Li
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, No.76 Yan Ta West Road, Xi'an, 710061, People's Republic of China
| | - Y Du
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, No.76 Yan Ta West Road, Xi'an, 710061, People's Republic of China
| | - L Liu
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, No.76 Yan Ta West Road, Xi'an, 710061, People's Republic of China
| | - Y Zhao
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, No.76 Yan Ta West Road, Xi'an, 710061, People's Republic of China
| | - M Ding
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, No.76 Yan Ta West Road, Xi'an, 710061, People's Republic of China
| | - Y Wen
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, No.76 Yan Ta West Road, Xi'an, 710061, People's Republic of China
| | - X Guo
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, No.76 Yan Ta West Road, Xi'an, 710061, People's Republic of China
| | - F Zhang
- Key Laboratory of Trace Elements and Endemic Diseases of National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, No.76 Yan Ta West Road, Xi'an, 710061, People's Republic of China.
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64
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Hsu YH, Estrada K, Evangelou E, Ackert-Bicknell C, Akesson K, Beck T, Brown SJ, Capellini T, Carbone L, Cauley J, Cheung CL, Cummings SR, Czerwinski S, Demissie S, Econs M, Evans D, Farber C, Gautvik K, Harris T, Kammerer C, Kemp J, Koller DL, Kung A, Lawlor D, Lee M, Lorentzon M, McGuigan F, Medina-Gomez C, Mitchell B, Newman A, Nielson C, Ohlsson C, Peacock M, Reppe S, Richards JB, Robbins J, Sigurdsson G, Spector TD, Stefansson K, Streeten E, Styrkarsdottir U, Tobias J, Trajanoska K, Uitterlinden A, Vandenput L, Wilson SG, Yerges-Armstrong L, Young M, Zillikens C, Rivadeneira F, Kiel DP, Karasik D. Meta-Analysis of Genomewide Association Studies Reveals Genetic Variants for Hip Bone Geometry. J Bone Miner Res 2019; 34:1284-1296. [PMID: 30888730 PMCID: PMC6650334 DOI: 10.1002/jbmr.3698] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Revised: 01/29/2019] [Accepted: 02/07/2019] [Indexed: 12/14/2022]
Abstract
Hip geometry is an important predictor of fracture. We performed a meta-analysis of GWAS studies in adults to identify genetic variants that are associated with proximal femur geometry phenotypes. We analyzed four phenotypes: (i) femoral neck length; (ii) neck-shaft angle; (iii) femoral neck width, and (iv) femoral neck section modulus, estimated from DXA scans using algorithms of hip structure analysis. In the Discovery stage, 10 cohort studies were included in the fixed-effect meta-analysis, with up to 18,719 men and women ages 16 to 93 years. Association analyses were performed with ∼2.5 million polymorphisms under an additive model adjusted for age, body mass index, and height. Replication analyses of meta-GWAS significant loci (at adjusted genomewide significance [GWS], threshold p ≤ 2.6 × 10-8 ) were performed in seven additional cohorts in silico. We looked up SNPs associated in our analysis, for association with height, bone mineral density (BMD), and fracture. In meta-analysis (combined Discovery and Replication stages), GWS associations were found at 5p15 (IRX1 and ADAMTS16); 5q35 near FGFR4; at 12p11 (in CCDC91); 11q13 (near LRP5 and PPP6R3 (rs7102273)). Several hip geometry signals overlapped with BMD, including LRP5 (chr. 11). Chr. 11 SNP rs7102273 was associated with any-type fracture (p = 7.5 × 10-5 ). We used bone transcriptome data and discovered several significant eQTLs, including rs7102273 and PPP6R3 expression (p = 0.0007), and rs6556301 (intergenic, chr.5 near FGFR4) and PDLIM7 expression (p = 0.005). In conclusion, we found associations between several genes and hip geometry measures that explained 12% to 22% of heritability at different sites. The results provide a defined set of genes related to biological pathways relevant to BMD and etiology of bone fragility. © 2019 American Society for Bone and Mineral Research.
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Affiliation(s)
- Yi-Hsiang Hsu
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, MA
- Harvard Medical School, Boston, MA
- Broad Institute, Cambridge, MA
| | - Karol Estrada
- Broad Institute, Cambridge, MA
- Department of Internal Medicine, Erasmus MC, 3000 CA Rotterdam, the Netherlands
- Department of Epidemiology, Erasmus MC, 3000 CA Rotterdam, the Netherlands
| | - Evangelos Evangelou
- Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina 45110, Greece
| | - Cheryl Ackert-Bicknell
- Center for Musculoskeletal Research, Department of Orthopaedics, University of Rochester, Rochester, New York, USA
| | - Kristina Akesson
- Department of Clinical Sciences Malmö, Lund University, Sweden
- Department of Orthopedics, Skåne University Hospital, S-205 02 Malmö, Sweden
| | - Thomas Beck
- Beck Radiological Innovations, Baltimore, MD
| | - Suzanne J Brown
- Department of Endocrinology and Diabetes, Sir Charles Gairdner Hospital, Nedlands, Australia
| | - Terence Capellini
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA
| | - Laura Carbone
- Department of Medicine at the Medical College of Georgia at Augusta University, Augusta, GA
| | - Jane Cauley
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA
| | - Ching-Lung Cheung
- Department of Medicine, The University of Hong Kong, Hong Kong, China
| | - Steven R Cummings
- California Pacific Medical Center Research Institute, San Francisco, CA
| | | | - Serkalem Demissie
- Department of Biostatistics, Boston University School of Public Health, Boston, MA
| | - Michael Econs
- Department of Medicine and Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN
| | - Daniel Evans
- California Pacific Medical Center Research Institute, San Francisco, CA
| | - Charles Farber
- Public Health Sciences, University of Virginia, Charlottesville, VA
| | - Kaare Gautvik
- Lovisenberg Diakonale Hospital, Unger-Vetlesen Institute, and University of Oslo, Institute of Basic Medical Sciences, Oslo, Norway
| | - Tamara Harris
- Laboratory of Epidemiology and Population Sciences, Intramural Research Program, NIA, Bethesda, MD
| | - Candace Kammerer
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA
| | - John Kemp
- University of Queensland Diamantina Institute, Translational Research Institute, Brisbane, Queensland, Australia
- MRC Integrative Epidemiology Unit, University of Bristol, UK
| | - Daniel L Koller
- Department of Medicine and Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN
| | - Annie Kung
- Department of Medicine, The University of Hong Kong, Hong Kong, China
| | - Debbie Lawlor
- MRC Integrative Epidemiology Unit, University of Bristol, UK
| | - Miryoung Lee
- University of Texas, School of Public Health at Bronwsville, TX
| | - Mattias Lorentzon
- Department of Internal Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, SE-413 45 Gothenburg, Sweden
| | - Fiona McGuigan
- Center for Musculoskeletal Research, Department of Orthopaedics, University of Rochester, Rochester, New York, USA
- Department of Clinical Sciences Malmö, Lund University, Sweden
| | | | - Braxton Mitchell
- Program in Personalized and Genomic Medicine, and Department of Medicine, Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, and Geriatric Research and Education Clinical Center - Veterans Administration Medical Center, Baltimore, MD
| | - Anne Newman
- Department of Epidemiology, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA
| | | | - Claes Ohlsson
- Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, SE-413 45 Gothenburg, Sweden
| | - Munro Peacock
- Department of Medicine and Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN
| | - Sjur Reppe
- Lovisenberg Diakonale Hospital, Unger-Vetlesen Institute, and University of Oslo, Institute of Basic Medical Sciences, Oslo, Norway
- Oslo University Hospital, Department of Medical Biochemistry, Oslo, Norway
| | - J Brent Richards
- Department of Human Genetics, McGill University, and Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec, Canada
| | - John Robbins
- Department of Medicine, University California at Davis, Sacramento, CA
| | | | - Timothy D Spector
- Department of Twin Research and Genetic Epidemiology, King’s College London, St Thomas’ Campus, London, UK
| | | | - Elizabeth Streeten
- Program in Personalized and Genomic Medicine, and Department of Medicine, Division of Endocrinology, Diabetes and Nutrition, University of Maryland School of Medicine, and Geriatric Research and Education Clinical Center - Veterans Administration Medical Center, Baltimore, MD
| | | | | | | | - André Uitterlinden
- Department of Internal Medicine, Erasmus MC, 3000 CA Rotterdam, the Netherlands
- Department of Epidemiology, Erasmus MC, 3000 CA Rotterdam, the Netherlands
| | - Liesbeth Vandenput
- Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, SE-413 45 Gothenburg, Sweden
| | - Scott G Wilson
- Department of Endocrinology and Diabetes, Sir Charles Gairdner Hospital, Nedlands, Australia
- Department of Twin Research and Genetic Epidemiology, King’s College London, St Thomas’ Campus, London, UK
- School of Biomedical Sciences, University of Western Australia, Nedlands, Australia
| | | | - Mariel Young
- Department of Human Evolutionary Biology, Harvard University, Cambridge, MA
| | - Carola Zillikens
- Department of Internal Medicine, Erasmus MC, 3000 CA Rotterdam, the Netherlands
- Department of Epidemiology, Erasmus MC, 3000 CA Rotterdam, the Netherlands
| | - Fernando Rivadeneira
- Department of Internal Medicine, Erasmus MC, 3000 CA Rotterdam, the Netherlands
- Department of Epidemiology, Erasmus MC, 3000 CA Rotterdam, the Netherlands
| | - Douglas P Kiel
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, MA
- Harvard Medical School, Boston, MA
- Broad Institute, Cambridge, MA
| | - David Karasik
- Hinda and Arthur Marcus Institute for Aging Research, Hebrew SeniorLife, Boston, MA
- The Azrieli Faculty of Medicine, Bar-Ilan University, Safed, Israel
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65
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Wu M, Lin Z, Ma S, Chen T, Jiang R, Wong WH. Simultaneous inference of phenotype-associated genes and relevant tissues from GWAS data via Bayesian integration of multiple tissue-specific gene networks. J Mol Cell Biol 2019; 9:436-452. [PMID: 29300920 DOI: 10.1093/jmcb/mjx059] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2017] [Accepted: 12/20/2017] [Indexed: 02/07/2023] Open
Abstract
Although genome-wide association studies (GWAS) have successfully identified thousands of genomic loci associated with hundreds of complex traits in the past decade, the debate about such problems as missing heritability and weak interpretability has been appealing for effective computational methods to facilitate the advanced analysis of the vast volume of existing and anticipated genetic data. Towards this goal, gene-level integrative GWAS analysis with the assumption that genes associated with a phenotype tend to be enriched in biological gene sets or gene networks has recently attracted much attention, due to such advantages as straightforward interpretation, less multiple testing burdens, and robustness across studies. However, existing methods in this category usually exploit non-tissue-specific gene networks and thus lack the ability to utilize informative tissue-specific characteristics. To overcome this limitation, we proposed a Bayesian approach called SIGNET (Simultaneously Inference of GeNEs and Tissues) to integrate GWAS data and multiple tissue-specific gene networks for the simultaneous inference of phenotype-associated genes and relevant tissues. Through extensive simulation studies, we showed the effectiveness of our method in finding both associated genes and relevant tissues for a phenotype. In applications to real GWAS data of 14 complex phenotypes, we demonstrated the power of our method in both deciphering genetic basis and discovering biological insights of a phenotype. With this understanding, we expect to see SIGNET as a valuable tool for integrative GWAS analysis, thereby boosting the prevention, diagnosis, and treatment of human inherited diseases and eventually facilitating precision medicine.
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Affiliation(s)
- Mengmeng Wu
- Department of Computer Science, Tsinghua University, Beijing 100084, China.,Ministry of Education Key Laboratory of Bioinformatics and Bioinformatics Division, Tsinghua National Laboratory for Information Science and Technology, Beijing 100084, China.,Department of Statistics, Stanford University, CA 94305, USA
| | - Zhixiang Lin
- Department of Statistics, Stanford University, CA 94305, USA
| | - Shining Ma
- Department of Statistics, Stanford University, CA 94305, USA
| | - Ting Chen
- Department of Computer Science, Tsinghua University, Beijing 100084, China.,Ministry of Education Key Laboratory of Bioinformatics and Bioinformatics Division, Tsinghua National Laboratory for Information Science and Technology, Beijing 100084, China
| | - Rui Jiang
- Ministry of Education Key Laboratory of Bioinformatics and Bioinformatics Division, Tsinghua National Laboratory for Information Science and Technology, Beijing 100084, China.,Department of Automation, Tsinghua University, Beijing 100084, China
| | - Wing Hung Wong
- Department of Statistics, Stanford University, CA 94305, USA
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66
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Human Systems Biology and Metabolic Modelling: A Review-From Disease Metabolism to Precision Medicine. BIOMED RESEARCH INTERNATIONAL 2019; 2019:8304260. [PMID: 31281846 PMCID: PMC6590590 DOI: 10.1155/2019/8304260] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/01/2018] [Revised: 02/07/2019] [Accepted: 05/20/2019] [Indexed: 01/06/2023]
Abstract
In cell and molecular biology, metabolism is the only system that can be fully simulated at genome scale. Metabolic systems biology offers powerful abstraction tools to simulate all known metabolic reactions in a cell, therefore providing a snapshot that is close to its observable phenotype. In this review, we cover the 15 years of human metabolic modelling. We show that, although the past five years have not experienced large improvements in the size of the gene and metabolite sets in human metabolic models, their accuracy is rapidly increasing. We also describe how condition-, tissue-, and patient-specific metabolic models shed light on cell-specific changes occurring in the metabolic network, therefore predicting biomarkers of disease metabolism. We finally discuss current challenges and future promising directions for this research field, including machine/deep learning and precision medicine. In the omics era, profiling patients and biological processes from a multiomic point of view is becoming more common and less expensive. Starting from multiomic data collected from patients and N-of-1 trials where individual patients constitute different case studies, methods for model-building and data integration are being used to generate patient-specific models. Coupled with state-of-the-art machine learning methods, this will allow characterizing each patient's disease phenotype and delivering precision medicine solutions, therefore leading to preventative medicine, reduced treatment, and in silico clinical trials.
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67
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Mesner LD, Calabrese GM, Al-Barghouthi B, Gatti DM, Sundberg JP, Churchill GA, Godfrey DA, Ackert-Bicknell CL, Farber CR. Mouse genome-wide association and systems genetics identifies Lhfp as a regulator of bone mass. PLoS Genet 2019; 15:e1008123. [PMID: 31042701 PMCID: PMC6513102 DOI: 10.1371/journal.pgen.1008123] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Revised: 05/13/2019] [Accepted: 04/03/2019] [Indexed: 11/19/2022] Open
Abstract
Bone mineral density (BMD) is a strong predictor of osteoporotic fracture. It is also one of the most heritable disease-associated quantitative traits. As a result, there has been considerable effort focused on dissecting its genetic basis. Here, we performed a genome-wide association study (GWAS) in a panel of inbred strains to identify associations influencing BMD. This analysis identified a significant (P = 3.1 x 10−12) BMD locus on Chromosome 3@52.5 Mbp that replicated in two separate inbred strain panels and overlapped a BMD quantitative trait locus (QTL) previously identified in a F2 intercross. The association mapped to a 300 Kbp region containing four genes; Gm2447, Gm20750, Cog6, and Lhfp. Further analysis found that Lipoma HMGIC Fusion Partner (Lhfp) was highly expressed in bone and osteoblasts. Furthermore, its expression was regulated by a local expression QTL (eQTL), which overlapped the BMD association. A co-expression network analysis revealed that Lhfp was strongly connected to genes involved in osteoblast differentiation. To directly evaluate its role in bone, Lhfp deficient mice (Lhfp-/-) were created using CRISPR/Cas9. Consistent with genetic and network predictions, bone marrow stromal cells (BMSCs) from Lhfp-/- mice displayed increased osteogenic differentiation. Lhfp-/- mice also had elevated BMD due to increased cortical bone mass. Lastly, we identified SNPs in human LHFP that were associated (P = 1.2 x 10−5) with heel BMD. In conclusion, we used GWAS and systems genetics to identify Lhfp as a regulator of osteoblast activity and bone mass. Osteoporosis is a common, chronic disease characterized by low bone mineral density (BMD) that puts millions of Americans at high risk of fracture. Variation in BMD in the general population is, in large part, determined by genetic factors. To identify novel genes influencing BMD, we performed a genome-wide association study in a panel of inbred mouse strains. We identified a locus on Chromosome 3 strongly associated with BMD. Using a combination of systems genetics approaches, we connected the expression of the Lhfp gene with BMD-associated genetic variants and predicted it influenced BMD by altering the activity of bone-forming osteoblasts. Using mice deficient in Lhfp, we demonstrated that Lhfp negatively regulates bone formation and BMD. These data suggest that inhibiting Lhfp may represent a novel therapeutic strategy to increase BMD and decrease the risk of fracture.
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Affiliation(s)
- Larry D. Mesner
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, United States of America
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA, United States of America
| | - Gina M. Calabrese
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, United States of America
| | - Basel Al-Barghouthi
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, United States of America
- Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, United States of America
| | - Daniel M. Gatti
- The Jackson Laboratory, Bar Harbor, ME, United States of America
| | - John P. Sundberg
- The Jackson Laboratory, Bar Harbor, ME, United States of America
| | | | - Dana. A. Godfrey
- Center for Musculoskeletal Research, University of Rochester, Rochester, NY, United States of America
| | - Cheryl L. Ackert-Bicknell
- Center for Musculoskeletal Research, University of Rochester, Rochester, NY, United States of America
| | - Charles R. Farber
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, United States of America
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA, United States of America
- Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, United States of America
- * E-mail:
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68
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Zhao Y, Blencowe M, Shi X, Shu L, Levian C, Ahn IS, Kim SK, Huan T, Levy D, Yang X. Integrative Genomics Analysis Unravels Tissue-Specific Pathways, Networks, and Key Regulators of Blood Pressure Regulation. Front Cardiovasc Med 2019; 6:21. [PMID: 30931314 PMCID: PMC6423920 DOI: 10.3389/fcvm.2019.00021] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2018] [Accepted: 02/18/2019] [Indexed: 01/23/2023] Open
Abstract
Blood pressure (BP) is a highly heritable trait and a major cardiovascular disease risk factor. Genome wide association studies (GWAS) have implicated a number of susceptibility loci for systolic (SBP) and diastolic (DBP) blood pressure. However, a large portion of the heritability cannot be explained by the top GWAS loci and a comprehensive understanding of the underlying molecular mechanisms is still lacking. Here, we utilized an integrative genomics approach that leveraged multiple genetic and genomic datasets including (a) GWAS for SBP and DBP from the International Consortium for Blood Pressure (ICBP), (b) expression quantitative trait loci (eQTLs) from genetics of gene expression studies of human tissues related to BP, (c) knowledge-driven biological pathways, and (d) data-driven tissue-specific regulatory gene networks. Integration of these multidimensional datasets revealed tens of pathways and gene subnetworks in vascular tissues, liver, adipose, blood, and brain functionally associated with DBP and SBP. Diverse processes such as platelet production, insulin secretion/signaling, protein catabolism, cell adhesion and junction, immune and inflammation, and cardiac/smooth muscle contraction, were shared between DBP and SBP. Furthermore, "Wnt signaling" and "mammalian target of rapamycin (mTOR) signaling" pathways were found to be unique to SBP, while "cytokine network", and "tryptophan catabolism" to DBP. Incorporation of gene regulatory networks in our analysis informed on key regulator genes that orchestrate tissue-specific subnetworks of genes whose variants together explain ~20% of BP heritability. Our results shed light on the complex mechanisms underlying BP regulation and highlight potential novel targets and pathways for hypertension and cardiovascular diseases.
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Affiliation(s)
- Yuqi Zhao
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Montgomery Blencowe
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Xingyi Shi
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Le Shu
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Candace Levian
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, United States
| | - In Sook Ahn
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Stuart K. Kim
- Department of Genetics, Department of Developmental Biology, Stanford University Medical Center, Stanford, CA, United States
| | - Tianxiao Huan
- The National Heart Lung and Blood Institute's Framingham Heart Study, Framingham, MA, United States
- The Population Sciences Branch and the Division of Intramural Research, National Heart, Lung and Blood Institute, Bethesda, MD, United States
| | - Daniel Levy
- The National Heart Lung and Blood Institute's Framingham Heart Study, Framingham, MA, United States
- The Population Sciences Branch and the Division of Intramural Research, National Heart, Lung and Blood Institute, Bethesda, MD, United States
| | - Xia Yang
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, United States
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69
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Benefits and Challenges of Rare Genetic Variation in Alzheimer’s Disease. CURRENT GENETIC MEDICINE REPORTS 2019. [DOI: 10.1007/s40142-019-0161-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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70
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Cary JW, Entwistle S, Satterlee T, Mack BM, Gilbert MK, Chang PK, Scharfenstein L, Yin Y, Calvo AM. The Transcriptional Regulator Hbx1 Affects the Expression of Thousands of Genes in the Aflatoxin-Producing Fungus Aspergillus flavus. G3 (BETHESDA, MD.) 2019; 9:167-178. [PMID: 30425054 PMCID: PMC6325891 DOI: 10.1534/g3.118.200870] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/06/2018] [Accepted: 11/09/2018] [Indexed: 12/21/2022]
Abstract
In filamentous fungi, homeobox proteins are conserved transcriptional regulators described to control conidiogenesis and fruiting body formation. Eight homeobox (hbx) genes are found in the genome of the aflatoxin-producing ascomycete, Aspergillus flavus While loss-of-function of seven of the eight genes had little to no effect on fungal growth and development, disruption of hbx1, resulted in aconidial colonies and lack of sclerotial production. Furthermore, the hbx1 mutant was unable to produce aflatoxins B1 and B2, cyclopiazonic acid and aflatrem. In the present study, hbx1 transcriptome analysis revealed that hbx1 has a broad effect on A. flavus gene expression, and the effect of hbx1 increases overtime, impacting more than five thousand protein-coding genes. Among the affected genes, those in the category of secondary metabolism (SM), followed by that of cellular transport, were the most affected. Specifically, regarding the effect of hbx1 on SM, we found that genes in 44 SM gene clusters where upregulated while 49 were downregulated in the absence of hbx1, including genes in the SM clusters responsible for the synthesis of asparasone, piperazine and aflavarin, all known to be associated with sclerotia. In addition, our study revealed that hbx1 affects the expression of other transcription factor genes involved in development, including the conidiation central regulatory pathway and flb genes.
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Affiliation(s)
- Jeffrey W Cary
- Food and Feed Safety Research Unit, USDA/ARS, Southern Regional Research Center, New Orleans, Louisiana
| | - Sarah Entwistle
- Department of Biological Sciences, Northern Illinois University, DeKalb, Illinois
| | - Timothy Satterlee
- Department of Biological Sciences, Northern Illinois University, DeKalb, Illinois
| | - Brian M Mack
- Food and Feed Safety Research Unit, USDA/ARS, Southern Regional Research Center, New Orleans, Louisiana
| | - Matthew K Gilbert
- Food and Feed Safety Research Unit, USDA/ARS, Southern Regional Research Center, New Orleans, Louisiana
| | - Perng K Chang
- Food and Feed Safety Research Unit, USDA/ARS, Southern Regional Research Center, New Orleans, Louisiana
| | - Leslie Scharfenstein
- Food and Feed Safety Research Unit, USDA/ARS, Southern Regional Research Center, New Orleans, Louisiana
| | - Yanbin Yin
- Department of Biological Sciences, Northern Illinois University, DeKalb, Illinois
| | - Ana M Calvo
- Department of Biological Sciences, Northern Illinois University, DeKalb, Illinois
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71
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Maynard RD, Ackert-Bicknell CL. Mouse Models and Online Resources for Functional Analysis of Osteoporosis Genome-Wide Association Studies. Front Endocrinol (Lausanne) 2019; 10:277. [PMID: 31133984 PMCID: PMC6515928 DOI: 10.3389/fendo.2019.00277] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Accepted: 04/16/2019] [Indexed: 12/13/2022] Open
Abstract
Osteoporosis is a complex genetic disease in which the number of loci associated with the bone mineral density, a clinical risk factor for fracture, has increased at an exponential rate in the last decade. The identification of the causative variants and candidate genes underlying these loci has not been able to keep pace with the rate of locus discovery. A large number of tools and data resources have been built around the use of the mouse as model of human genetic disease. Herein, we describe resources available for functional validation of human Genome Wide Association Study (GWAS) loci using mouse models. We specifically focus on large-scale phenotyping efforts focused on bone relevant phenotypes and repositories of genotype-phenotype data that exist for transgenic and mutant mice, which can be readily mined as a first step toward more targeted efforts designed to deeply characterize the role of a gene in bone biology.
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Affiliation(s)
- Robert D. Maynard
- Center for Musculoskeletal Research, University of Rochester, Rochester, NY, United States
| | - Cheryl L. Ackert-Bicknell
- Center for Musculoskeletal Research, University of Rochester, Rochester, NY, United States
- Department of Orthopaedics and Rehabilitation, University of Rochester, Rochester, NY, United States
- *Correspondence: Cheryl L. Ackert-Bicknell
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72
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Schaefer RJ, Michno JM, Jeffers J, Hoekenga O, Dilkes B, Baxter I, Myers CL. Integrating Coexpression Networks with GWAS to Prioritize Causal Genes in Maize. THE PLANT CELL 2018; 30:2922-2942. [PMID: 30413654 PMCID: PMC6354270 DOI: 10.1105/tpc.18.00299] [Citation(s) in RCA: 94] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Revised: 10/08/2018] [Accepted: 10/31/2018] [Indexed: 05/02/2023]
Abstract
Genome-wide association studies (GWAS) have identified loci linked to hundreds of traits in many different species. Yet, because linkage equilibrium implicates a broad region surrounding each identified locus, the causal genes often remain unknown. This problem is especially pronounced in nonhuman, nonmodel species, where functional annotations are sparse and there is frequently little information available for prioritizing candidate genes. We developed a computational approach, Camoco, that integrates loci identified by GWAS with functional information derived from gene coexpression networks. Using Camoco, we prioritized candidate genes from a large-scale GWAS examining the accumulation of 17 different elements in maize (Zea mays) seeds. Strikingly, we observed a strong dependence in the performance of our approach based on the type of coexpression network used: expression variation across genetically diverse individuals in a relevant tissue context (in our case, roots that are the primary elemental uptake and delivery system) outperformed other alternative networks. Two candidate genes identified by our approach were validated using mutants. Our study demonstrates that coexpression networks provide a powerful basis for prioritizing candidate causal genes from GWAS loci but suggests that the success of such strategies can highly depend on the gene expression data context. Both the software and the lessons on integrating GWAS data with coexpression networks generalize to species beyond maize.
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Affiliation(s)
- Robert J Schaefer
- Biomedical Informatics and Computational Biology Graduate Program, University of Minnesota, Minneapolis, Minnesota 55455
| | - Jean-Michel Michno
- Biomedical Informatics and Computational Biology Graduate Program, University of Minnesota, Minneapolis, Minnesota 55455
- Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, Minnesota 55108
| | - Joseph Jeffers
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota 55455
| | - Owen Hoekenga
- Cayuga Genetics Consulting Group LLC, Ithaca, New York 14850
| | - Brian Dilkes
- Department of Biochemistry, Purdue University, West Lafayette, Indiana 47907
| | - Ivan Baxter
- Donald Danforth Plant Science Center, St. Louis, Missouri 63132
- U.S. Department of Agriculture-Agricultural Research Service Plant Genetics Research Unit, St. Louis, Missouri 63132
| | - Chad L Myers
- Department of Computer Science and Engineering, University of Minnesota, Minneapolis, Minnesota 55455
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73
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Al-Barghouthi BM, Farber CR. Dissecting the Genetics of Osteoporosis using Systems Approaches. Trends Genet 2018; 35:55-67. [PMID: 30470485 DOI: 10.1016/j.tig.2018.10.004] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2018] [Revised: 10/01/2018] [Accepted: 10/22/2018] [Indexed: 02/06/2023]
Abstract
Osteoporosis is a condition characterized by low bone mineral density (BMD) and an increased risk of fracture. Traits contributing to osteoporotic fracture are highly heritable, indicating that a comprehensive understanding of bone requires a thorough understanding of the genetic basis of bone traits. Towards this goal, genome-wide association studies (GWASs) have identified over 500 loci associated with bone traits. However, few of the responsible genes have been identified, and little is known of how these genes work together to influence systems-level bone function. In this review, we describe how systems genetics approaches can be used to fill these knowledge gaps.
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Affiliation(s)
- Basel M Al-Barghouthi
- Center for Public Health Genomics, Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA
| | - Charles R Farber
- Center for Public Health Genomics, Departments of Public Health Sciences and Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA.
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74
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Li J, Liu J, Campanile G, Plastow G, Zhang C, Wang Z, Cassandro M, Gasparrini B, Salzano A, Hua G, Liang A, Yang L. Novel insights into the genetic basis of buffalo reproductive performance. BMC Genomics 2018; 19:814. [PMID: 30419816 PMCID: PMC6233259 DOI: 10.1186/s12864-018-5208-6] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2018] [Accepted: 10/30/2018] [Indexed: 12/17/2022] Open
Abstract
Background Fertility is a complex trait that has a major impact on the development of the buffalo industry. Genome-wide association study (GWAS) has increased the ability to detect genes influencing complex traits, and many important genes related to reproductive traits have been identified in ruminants. However, reproductive traits are influenced by many factors. The development of the follicle is one of the most important internal processes affecting fertility. Genes found by GWAS to be associated with follicular development may directly affect fertility. The present study combined GWAS and RNA-seq of follicular granulosa cells to identify important genes which may affect fertility in the buffalo. Results The 90 K Affymetrix Axiom Buffalo SNP Array was used to identify the SNPs, genomic regions, and genes that were associated with reproductive traits. A total of 40 suggestive loci (related to 28 genes) were identified to be associated with six reproductive traits (first, second and third calving age, calving interval, the number of services per conception and open days). Interestingly, the mRNA expressions of 25 of these genes were also observed in buffalo follicular granulosa cells. The IGFBP7 gene showed high level of expression during whole antral follicle growth. The knockdown of IGFBP7 in buffalo granulosa cells promoted cell apoptosis and hindered cell proliferation, and increased the production of progesterone and estradiol. Furthermore, a notable signal was detected at 2.3–2.7 Mb on the equivalent of bovine chromosome 5 associated with age at second calving, calving interval, and open days. Conclusions The genes associated with buffalo reproductive traits in this study may have effect on fertility by regulating of follicular growth. These results may have important implications for improving buffalo breeding programs through application of genomic information. Electronic supplementary material The online version of this article (10.1186/s12864-018-5208-6) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Jun Li
- College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China.,Department of Immunology, Zunyi Medical College, Zunyi, Guizhou, China
| | - Jiajia Liu
- College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Giuseppe Campanile
- Department of Veterinary Medicine and Animal Productions, University of Naples "Federico II", Naples, Italy
| | - Graham Plastow
- Department of Agricultural, Food & Nutritional Science, University of Alberta, Edmonton, AB, Canada
| | - Chunyan Zhang
- Department of Agricultural, Food & Nutritional Science, University of Alberta, Edmonton, AB, Canada
| | - Zhiquan Wang
- Department of Agricultural, Food & Nutritional Science, University of Alberta, Edmonton, AB, Canada
| | - Martino Cassandro
- Department of Agronomy, Food, Natural Resources, Animals and Environment, University of Padova, Agripolis, Legnaro, Italy
| | - Bianca Gasparrini
- Department of Veterinary Medicine and Animal Productions, University of Naples "Federico II", Naples, Italy
| | - Angela Salzano
- Department of Veterinary Medicine and Animal Productions, University of Naples "Federico II", Naples, Italy
| | - Guohua Hua
- College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China
| | - Aixin Liang
- College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China.
| | - Liguo Yang
- College of Animal Science and Technology, Huazhong Agricultural University, Wuhan, Hubei, China.
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75
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Squair JW, Tigchelaar S, Moon KM, Liu J, Tetzlaff W, Kwon BK, Krassioukov AV, West CR, Foster LJ, Skinnider MA. Integrated systems analysis reveals conserved gene networks underlying response to spinal cord injury. eLife 2018; 7:39188. [PMID: 30277459 PMCID: PMC6173583 DOI: 10.7554/elife.39188] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2018] [Accepted: 09/24/2018] [Indexed: 12/20/2022] Open
Abstract
Spinal cord injury (SCI) is a devastating neurological condition for which there are currently no effective treatment options to restore function. A major obstacle to the development of new therapies is our fragmentary understanding of the coordinated pathophysiological processes triggered by damage to the human spinal cord. Here, we describe a systems biology approach to integrate decades of small-scale experiments with unbiased, genome-wide gene expression from the human spinal cord, revealing a gene regulatory network signature of the pathophysiological response to SCI. Our integrative analyses converge on an evolutionarily conserved gene subnetwork enriched for genes associated with the response to SCI by small-scale experiments, and whose expression is upregulated in a severity-dependent manner following injury and downregulated in functional recovery. We validate the severity-dependent upregulation of this subnetwork in rodents in primary transcriptomic and proteomic studies. Our analysis provides systems-level view of the coordinated molecular processes activated in response to SCI.
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Affiliation(s)
- Jordan W Squair
- International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, Canada
| | - Seth Tigchelaar
- International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, Canada
| | - Kyung-Mee Moon
- Centre for High-Throughput Biology, University of British Columbia, Vancouver, Canada
| | - Jie Liu
- International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, Canada
| | - Wolfram Tetzlaff
- International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, Canada
| | - Brian K Kwon
- International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, Canada.,Department of Orthopaedics, University of British Columbia, Vancouver, Canada
| | - Andrei V Krassioukov
- International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, Canada.,GF Strong Rehabilitation Centre, Vancouver Health Authority, Vancouver, Canada.,Department of Medicine, Division of Physical Medicine and Rehabilitation, University of British Columbia, Vancouver, Canada
| | - Christopher R West
- International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, Canada.,School of Kinesiology, University of British Columbia, Vancouver, Canada
| | - Leonard J Foster
- Centre for High-Throughput Biology, University of British Columbia, Vancouver, Canada.,Department of Biochemistry and Molecular Biology and Michael Smith Laboratories, University of British Columbia, Vancouver, Canada
| | - Michael A Skinnider
- Centre for High-Throughput Biology, University of British Columbia, Vancouver, Canada
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76
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Abstract
PURPOSE OF REVIEW In recent years, the lower costs of arrays and sequencing technologies, and the better availability of data from genome-wide association studies (GWASs) have led to more reports on genetic factors that are associated with bone health. However, there remains the need for a summary of the newly identified genetic targets that are associated with bone metabolism, and the status of their functional characterization. RECENT FINDINGS GWASs revealed dozens of novel genetic loci that are associated with bone mineral density (BMD). Some of these targets have been functionally characterized, although the vast majority have not. Glypican 6, a membrane surface proteoglycan involved in cellular growth control and differentiation, was identified as a novel determinant of BMD and represents a possible drug target for treatment of osteoporosis. Pathway analysis also showed that cell-growth pathways and the SMAD proteins associated with low BMD. SUMMARY Hits that were significantly associated with BMD in different studies represent likely candidates (e.g. SOST, WNT16, ESR1 and RANKL) for functional characterization and development of osteoporosis treatments. Indeed, currently available treatment for osteoporosis (antibody against RANKL) appeared a significant target in four recent GWAS studies indicating their applicability and importance for future treatment development.
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Affiliation(s)
- Nika Lovšin
- Faculty of Pharmacy, University of Ljubljana, Ljubljana, Slovenia
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77
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Wu Z, Derks MFL, Dibbits B, Megens HJ, Groenen MAM, Crooijmans RPMA. A Novel Loss-of-Function Variant in Transmembrane Protein 263 (TMEM263) of Autosomal Dwarfism in Chicken. Front Genet 2018; 9:193. [PMID: 29930570 PMCID: PMC6001002 DOI: 10.3389/fgene.2018.00193] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2018] [Accepted: 05/11/2018] [Indexed: 12/20/2022] Open
Abstract
Autosomal dwarfism (adw) in chickens is a growth deficiency caused by a recessive mutation. Characteristic for adw is an approximately 30% growth reduction with short shank. The adw variant was first recognized in the Cornell K-strain of White Leghorns, but the genetic causal variant remained unknown. To identify the causal variant underlying the adw phenotype, fine mapping was conducted on chromosome 1, within 52-56 Mb. This region was known to harbor the causal variant from previous linkage studies. We compared whole-genome sequence data of this region from normal-sized and adw chickens in order to find the unique causal variant. We identified a novel nonsense mutation NP_001006244.1:p.(Trp59∗), in the transmembrane protein 263 gene (TMEM263), completely associated with adw. The nonsense mutation truncates the transmembrane protein within the membrane-spanning domain, expected to cause a dysfunctional protein. TMEM263 is reported to be associated with bone mineral deposition in humans, and the protein shows interaction with growth hormone 1 (GH1). Our study presents molecular genetic evidence for a novel loss-of-function variant, which likely alters body growth and development in autosomal dwarf chicken.
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78
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Yu H, You X, Li J, Zhang X, Zhang S, Jiang S, Lin X, Lin HR, Meng Z, Shi Q. A genome-wide association study on growth traits in orange-spotted grouper (Epinephelus coioides) with RAD-seq genotyping. SCIENCE CHINA-LIFE SCIENCES 2018. [DOI: 10.1007/s11427-017-9161-4] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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79
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Mochida K, Koda S, Inoue K, Nishii R. Statistical and Machine Learning Approaches to Predict Gene Regulatory Networks From Transcriptome Datasets. FRONTIERS IN PLANT SCIENCE 2018; 9:1770. [PMID: 30555503 PMCID: PMC6281826 DOI: 10.3389/fpls.2018.01770] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2018] [Accepted: 11/14/2018] [Indexed: 05/20/2023]
Abstract
Statistical and machine learning (ML)-based methods have recently advanced in construction of gene regulatory network (GRNs) based on high-throughput biological datasets. GRNs underlie almost all cellular phenomena; hence, comprehensive GRN maps are essential tools to elucidate gene function, thereby facilitating the identification and prioritization of candidate genes for functional analysis. High-throughput gene expression datasets have yielded various statistical and ML-based algorithms to infer causal relationship between genes and decipher GRNs. This review summarizes the recent advancements in the computational inference of GRNs, based on large-scale transcriptome sequencing datasets of model plants and crops. We highlight strategies to select contextual genes for GRN inference, and statistical and ML-based methods for inferring GRNs based on transcriptome datasets from plants. Furthermore, we discuss the challenges and opportunities for the elucidation of GRNs based on large-scale datasets obtained from emerging transcriptomic applications, such as from population-scale, single-cell level, and life-course transcriptome analyses.
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Affiliation(s)
- Keiichi Mochida
- Bioproductivity Informatics Research Team, RIKEN Center for Sustainable Resource Science, Yokohama, Japan
- Microalgae Production Control Technology Laboratory, RIKEN Baton Zone Program, RIKEN Cluster for Science, Technology and Innovation Hub, Yokohama, Japan
- Institute of Plant Science and Resources, Okayama University, Kurashiki, Japan
- Kihara Institute for Biological Research, Yokohama City University, Yokohama, Japan
- *Correspondence: Keiichi Mochida, Ryuei Nishii,
| | - Satoru Koda
- Graduate School of Mathematics, Kyushu University, Fukuoka, Japan
| | - Komaki Inoue
- Bioproductivity Informatics Research Team, RIKEN Center for Sustainable Resource Science, Yokohama, Japan
| | - Ryuei Nishii
- Institute of Mathematics for Industry, Kyushu University, Fukuoka, Japan
- *Correspondence: Keiichi Mochida, Ryuei Nishii,
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