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Williams EG, Pfister N, Roy S, Statzer C, Haverty J, Ingels J, Bohl C, Hasan M, Čuklina J, Bühlmann P, Zamboni N, Lu L, Ewald CY, Williams RW, Aebersold R. Multiomic profiling of the liver across diets and age in a diverse mouse population. Cell Syst 2022; 13:43-57.e6. [PMID: 34666007 PMCID: PMC8776606 DOI: 10.1016/j.cels.2021.09.005] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 07/12/2021] [Accepted: 09/14/2021] [Indexed: 01/21/2023]
Abstract
We profiled the liver transcriptome, proteome, and metabolome in 347 individuals from 58 isogenic strains of the BXD mouse population across age (7 to 24 months) and diet (low or high fat) to link molecular variations to metabolic traits. Several hundred genes are affected by diet and/or age at the transcript and protein levels. Orthologs of two aging-associated genes, St7 and Ctsd, were knocked down in C. elegans, reducing longevity in wild-type and mutant long-lived strains. The multiomics data were analyzed as segregating gene networks according to each independent variable, providing causal insight into dietary and aging effects. Candidates were cross-examined in an independent diversity outbred mouse liver dataset segregating for similar diets, with ∼80%-90% of diet-related candidate genes found in common across datasets. Together, we have developed a large multiomics resource for multivariate analysis of complex traits and demonstrate a methodology for moving from observational associations to causal connections.
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Affiliation(s)
- Evan G Williams
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg.
| | - Niklas Pfister
- Department of Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Suheeta Roy
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Cyril Statzer
- Department of Health Sciences and Technology, ETH Zürich, Zurich, Switzerland
| | - Jack Haverty
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Jesse Ingels
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Casey Bohl
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Moaraj Hasan
- Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, Zurich, Switzerland
| | - Jelena Čuklina
- Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, Zurich, Switzerland
| | - Peter Bühlmann
- Department of Mathematics, Seminar for Statistics, ETH Zürich, Zurich, Switzerland
| | - Nicola Zamboni
- Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, Zurich, Switzerland
| | - Lu Lu
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Collin Y Ewald
- Department of Health Sciences and Technology, ETH Zürich, Zurich, Switzerland
| | - Robert W Williams
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Ruedi Aebersold
- Department of Biology, Institute of Molecular Systems Biology, ETH Zürich, Zurich, Switzerland; Faculty of Science, University of Zürich, Zurich, Switzerland
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2
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Swanzey E, O'Connor C, Reinholdt LG. Mouse Genetic Reference Populations: Cellular Platforms for Integrative Systems Genetics. Trends Genet 2021; 37:251-265. [PMID: 33010949 PMCID: PMC7889615 DOI: 10.1016/j.tig.2020.09.007] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 08/28/2020] [Accepted: 09/02/2020] [Indexed: 02/07/2023]
Abstract
Interrogation of disease-relevant cellular and molecular traits exhibited by genetically diverse cell populations enables in vitro systems genetics approaches for uncovering the basic properties of cellular function and identity. Primary cells, stem cells, and organoids derived from genetically diverse mouse strains, such as Collaborative Cross and Diversity Outbred populations, offer the opportunity for parallel in vitro/in vivo screening. These panels provide genetic resolution for variant discovery and functional characterization, as well as disease modeling and in vivo validation capabilities. Here we review mouse cellular systems genetics approaches for characterizing the influence of genetic variation on signaling networks and phenotypic diversity, and we discuss approaches for data integration and cross-species validation.
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Affiliation(s)
| | - Callan O'Connor
- The Jackson Laboratory, Bar Harbor, ME, USA; Tufts Graduate School of Biomedical Sciences, Boston, MA, USA
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3
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Noll KE, Whitmore AC, West A, McCarthy MK, Morrison CR, Plante KS, Hampton BK, Kollmus H, Pilzner C, Leist SR, Gralinski LE, Menachery VD, Schäfer A, Miller D, Shaw G, Mooney M, McWeeney S, Pardo-Manuel de Villena F, Schughart K, Morrison TE, Baric RS, Ferris MT, Heise MT. Complex Genetic Architecture Underlies Regulation of Influenza-A-Virus-Specific Antibody Responses in the Collaborative Cross. Cell Rep 2020; 31:107587. [PMID: 32348764 PMCID: PMC7195006 DOI: 10.1016/j.celrep.2020.107587] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2019] [Revised: 02/20/2020] [Accepted: 04/08/2020] [Indexed: 02/07/2023] Open
Abstract
Host genetic factors play a fundamental role in regulating humoral immunity to viral infection, including influenza A virus (IAV). Here, we utilize the Collaborative Cross (CC), a mouse genetic reference population, to study genetic regulation of variation in antibody response following IAV infection. CC mice show significant heritable variation in the magnitude, kinetics, and composition of IAV-specific antibody response. We map 23 genetic loci associated with this variation. Analysis of a subset of these loci finds that they broadly affect the antibody response to IAV as well as other viruses. Candidate genes are identified based on predicted variant consequences and haplotype-specific expression patterns, and several show overlap with genes identified in human mapping studies. These findings demonstrate that the host antibody response to IAV infection is under complex genetic control and highlight the utility of the CC in modeling and identifying genetic factors with translational relevance to human health and disease.
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Affiliation(s)
- Kelsey E Noll
- Department of Microbiology and Immunology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Alan C Whitmore
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ande West
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Mary K McCarthy
- Department of Immunology and Microbiology, University of Colorado School of Medicine, Aurora, CO, USA
| | | | - Kenneth S Plante
- Department of Microbiology and Immunology, University of Texas Medical Branch at Galveston, Galveston, TX, USA
| | - Brea K Hampton
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Heike Kollmus
- Department of Infection Genetics, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Carolin Pilzner
- Department of Infection Genetics, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Sarah R Leist
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Infection Genetics, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Lisa E Gralinski
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Vineet D Menachery
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Microbiology and Immunology, University of Texas Medical Branch at Galveston, Galveston, TX, USA
| | - Alexandra Schäfer
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Darla Miller
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ginger Shaw
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Michael Mooney
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR, USA; OHSU Knight Cancer Center Institute, Oregon Health and Science University, Portland, OR, USA
| | - Shannon McWeeney
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR, USA; OHSU Knight Cancer Center Institute, Oregon Health and Science University, Portland, OR, USA; Oregon Clinical and Translational Research Institute, Oregon Health and Science University, Portland, OR, USA
| | - Fernando Pardo-Manuel de Villena
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA
| | - Klaus Schughart
- Department of Infection Genetics, Helmholtz Centre for Infection Research, Braunschweig, Germany; University of Veterinary Medicine Hannover, Hannover, Germany; Department of Microbiology, Immunology and Biochemistry, University of Tennessee Health Science Center, Memphis, TN, USA
| | - Thomas E Morrison
- Department of Immunology and Microbiology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Ralph S Baric
- Department of Microbiology and Immunology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA
| | - Martin T Ferris
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Mark T Heise
- Department of Microbiology and Immunology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA.
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4
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Soller M, Abu-Toamih Atamni HJ, Binenbaum I, Chatziioannou A, Iraqi FA. Designing a QTL Mapping Study for Implementation in the Realized Collaborative Cross Genetic Reference Population. ACTA ACUST UNITED AC 2020; 9:e66. [PMID: 31756057 DOI: 10.1002/cpmo.66] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
The Collaborative Cross (CC) mouse resource is a next-generation mouse genetic reference population (GRP) designed for high-resolution mapping of quantitative trait loci (QTL) of large effect affecting complex traits during health and disease. The CC resource consists of a set of 72 recombinant inbred lines (RILs) generated by reciprocal crossing of five classical and three wild-derived mouse founder strains. Complex traits are controlled by variations within multiple genes and environmental factors, and their mutual interactions. These traits are observed at multiple levels of the animals' systems, including metabolism, body weight, immune profile, and susceptibility or resistance to the development and progress of infectious or chronic diseases. Herein, we present general guidelines for design of QTL mapping experiments using the CC resource-along with full step-by-step protocols and methods that were implemented in our lab for the phenotypic and genotypic characterization of the different CC lines-for mapping the genes underlying host response to infectious and chronic diseases. © 2019 by John Wiley & Sons, Inc. Basic Protocol 1: CC lines for whole body mass index (BMI) Basic Protocol 2: A detailed assessment of the power to detect effect sizes based on the number of lines used, and the number of replicates per line Basic Protocol 3: Obtaining power for QTL with given target effect by interpolating in Table 1 of Keele et al. (2019).
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Affiliation(s)
- Morris Soller
- Department of Genetics, Silverman Institute for Life Sciences, Hebrew University, Jerusalem, Israel
| | - Hanifa J Abu-Toamih Atamni
- Department of Clinical Microbiology and Immunology, Sackler Faculty of Medicine, Tel Aviv University, Ramat Aviv, Tel Aviv, Israel
| | - Ilona Binenbaum
- Department of Biology, University of Patras, Patras, Greece.,Institute of Biology, Medicinal Chemistry & Biotechnology, NHRF, Athens, Greece
| | | | - Fuad A Iraqi
- Department of Clinical Microbiology and Immunology, Sackler Faculty of Medicine, Tel Aviv University, Ramat Aviv, Tel Aviv, Israel
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Li H, Auwerx J. Mouse Systems Genetics as a Prelude to Precision Medicine. Trends Genet 2020; 36:259-72. [PMID: 32037011 DOI: 10.1016/j.tig.2020.01.004] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Revised: 01/06/2020] [Accepted: 01/08/2020] [Indexed: 12/17/2022]
Abstract
Mouse models have been instrumental in understanding human disease biology and proposing possible new treatments. The precise control of the environment and genetic composition of mice allows more rigorous observations, but limits the generalizability and translatability of the results into human applications. In the era of precision medicine, strategies using mouse models have to be revisited to effectively emulate human populations. Systems genetics is one promising paradigm that may promote the transition to novel precision medicine strategies. Here, we review the state-of-the-art resources and discuss how mouse systems genetics helps to understand human diseases and to advance the development of precision medicine, with an emphasis on the existing resources and strategies.
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Li H, Wang X, Rukina D, Huang Q, Lin T, Sorrentino V, Zhang H, Bou Sleiman M, Arends D, McDaid A, Luan P, Ziari N, Velázquez-Villegas LA, Gariani K, Kutalik Z, Schoonjans K, Radcliffe RA, Prins P, Morgenthaler S, Williams RW, Auwerx J. An Integrated Systems Genetics and Omics Toolkit to Probe Gene Function. Cell Syst 2017; 6:90-102.e4. [PMID: 29199021 DOI: 10.1016/j.cels.2017.10.016] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2017] [Revised: 08/31/2017] [Accepted: 10/25/2017] [Indexed: 01/20/2023]
Abstract
Identifying genetic and environmental factors that impact complex traits and common diseases is a high biomedical priority. Here, we developed, validated, and implemented a series of multi-layered systems approaches, including (expression-based) phenome-wide association, transcriptome-/proteome-wide association, and (reverse-) mediation analysis, in an open-access web server (systems-genetics.org) to expedite the systems dissection of gene function. We applied these approaches to multi-omics datasets from the BXD mouse genetic reference population, and identified and validated associations between genes and clinical and molecular phenotypes, including previously unreported links between Rpl26 and body weight, and Cpt1a and lipid metabolism. Furthermore, through mediation and reverse-mediation analysis we established regulatory relations between genes, such as the co-regulation of BCKDHA and BCKDHB protein levels, and identified targets of transcription factors E2F6, ZFP277, and ZKSCAN1. Our multifaceted toolkit enabled the identification of gene-gene and gene-phenotype links that are robust and that translate well across populations and species, and can be universally applied to any populations with multi-omics datasets.
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Affiliation(s)
- Hao Li
- Laboratory for Integrative and Systems Physiology, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland
| | - Xu Wang
- Laboratory for Integrative and Systems Physiology, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland
| | - Daria Rukina
- Institute of Mathematics, École Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland
| | - Qingyao Huang
- Laboratory of Metabolic Signaling, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland
| | - Tao Lin
- Laboratory for Integrative and Systems Physiology, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland
| | - Vincenzo Sorrentino
- Laboratory for Integrative and Systems Physiology, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland
| | - Hongbo Zhang
- Laboratory for Integrative and Systems Physiology, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland
| | - Maroun Bou Sleiman
- Laboratory for Integrative and Systems Physiology, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland
| | - Danny Arends
- Albrecht Daniel Thaer-Institut für Agrar- und Gartenbauwissenschaften, Humboldt-Universität zu Berlin, D-10115 Berlin, Germany
| | - Aaron McDaid
- Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland; Institute of Social and Preventive Medicine, University Hospital of Lausanne, Lausanne 1010, Switzerland
| | - Peiling Luan
- Laboratory for Integrative and Systems Physiology, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland
| | - Naveed Ziari
- Laboratory for Integrative and Systems Physiology, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland
| | - Laura A Velázquez-Villegas
- Laboratory of Metabolic Signaling, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland
| | - Karim Gariani
- Laboratory for Integrative and Systems Physiology, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland
| | - Zoltan Kutalik
- Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland; Institute of Social and Preventive Medicine, University Hospital of Lausanne, Lausanne 1010, Switzerland
| | - Kristina Schoonjans
- Laboratory of Metabolic Signaling, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland
| | - Richard A Radcliffe
- Department of Pharmaceutical Sciences, University of Colorado, Aurora, CO 80045, USA
| | - Pjotr Prins
- University Medical Center Utrecht, 3584CT Utrecht, the Netherlands; Department of Genetics, Genomics and Informatics, University of Tennessee, Memphis, TN 38163, USA
| | - Stephan Morgenthaler
- Institute of Mathematics, École Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland
| | - Robert W Williams
- Department of Genetics, Genomics and Informatics, University of Tennessee, Memphis, TN 38163, USA
| | - Johan Auwerx
- Laboratory for Integrative and Systems Physiology, Institute of Bioengineering, École Polytechnique Fédérale de Lausanne, Lausanne 1015, Switzerland.
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