1
|
Holt EA, Tyler A, Lakusta-Wong T, Lahue KG, Hankes KC, Teuscher C, Lynch RM, Ferris MT, Mahoney JM, Krementsov DN. Probing the basis of disease heterogeneity in multiple sclerosis using genetically diverse mice. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.03.597205. [PMID: 38895248 PMCID: PMC11185616 DOI: 10.1101/2024.06.03.597205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Multiple sclerosis (MS) is a complex disease with significant heterogeneity in disease course and progression. Genetic studies have identified numerous loci associated with MS risk, but the genetic basis of disease progression remains elusive. To address this, we leveraged the Collaborative Cross (CC), a genetically diverse mouse strain panel, and experimental autoimmune encephalomyelitis (EAE). The thirty-two CC strains studied captured a wide spectrum of EAE severity, trajectory, and presentation, including severe-progressive, monophasic, relapsing remitting, and axial rotary (AR)-EAE, accompanied by distinct immunopathology. Sex differences in EAE severity were observed in six strains. Quantitative trait locus analysis revealed distinct genetic linkage patterns for different EAE phenotypes, including EAE severity and incidence of AR-EAE. Machine learning-based approaches prioritized candidate genes for loci underlying EAE severity ( Abcc4 and Gpc6 ) and AR-EAE ( Yap1 and Dync2h1 ). This work expands the EAE phenotypic repertoire and identifies novel loci controlling unique EAE phenotypes, supporting the hypothesis that heterogeneity in MS disease course is driven by genetic variation. Summary The genetic basis of disease heterogeneity in multiple sclerosis (MS) remains elusive. We leveraged the Collaborative Cross to expand the phenotypic repertoire of the experimental autoimmune encephalomyelitis (EAE) model of MS and identify loci controlling EAE severity, trajectory, and presentation.
Collapse
|
2
|
Keele GR. Which mouse multiparental population is right for your study? The Collaborative Cross inbred strains, their F1 hybrids, or the Diversity Outbred population. G3 (BETHESDA, MD.) 2023; 13:jkad027. [PMID: 36735601 PMCID: PMC10085760 DOI: 10.1093/g3journal/jkad027] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 12/30/2022] [Accepted: 01/23/2023] [Indexed: 02/04/2023]
Abstract
Multiparental populations (MPPs) encompass greater genetic diversity than traditional experimental crosses of two inbred strains, enabling broader surveys of genetic variation underlying complex traits. Two such mouse MPPs are the Collaborative Cross (CC) inbred panel and the Diversity Outbred (DO) population, which are descended from the same eight inbred strains. Additionally, the F1 intercrosses of CC strains (CC-RIX) have been used and enable study designs with replicate outbred mice. Genetic analyses commonly used by researchers to investigate complex traits in these populations include characterizing how heritable a trait is, i.e. its heritability, and mapping its underlying genetic loci, i.e. its quantitative trait loci (QTLs). Here we evaluate the relative merits of these populations for these tasks through simulation, as well as provide recommendations for performing the quantitative genetic analyses. We find that sample populations that include replicate animals, as possible with the CC and CC-RIX, provide more efficient and precise estimates of heritability. We report QTL mapping power curves for the CC, CC-RIX, and DO across a range of QTL effect sizes and polygenic backgrounds for samples of 174 and 500 mice. The utility of replicate animals in the CC and CC-RIX for mapping QTLs rapidly decreased as traits became more polygenic. Only large sample populations of 500 DO mice were well-powered to detect smaller effect loci (7.5-10%) for highly complex traits (80% polygenic background). All results were generated with our R package musppr, which we developed to simulate data from these MPPs and evaluate genetic analyses from user-provided genotypes.
Collapse
Affiliation(s)
- Gregory R Keele
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| |
Collapse
|
3
|
Petkevicius K, Palmgren H, Glover MS, Ahnmark A, Andréasson AC, Madeyski-Bengtson K, Kawana H, Allman EL, Kaper D, Uhrbom M, Andersson L, Aasehaug L, Forsström J, Wallin S, Ahlstedt I, Leke R, Karlsson D, González-King H, Löfgren L, Nilsson R, Pellegrini G, Kono N, Aoki J, Hess S, Sienski G, Pilon M, Bohlooly-Y M, Maresca M, Peng XR. TLCD1 and TLCD2 regulate cellular phosphatidylethanolamine composition and promote the progression of non-alcoholic steatohepatitis. Nat Commun 2022; 13:6020. [PMID: 36241646 PMCID: PMC9568529 DOI: 10.1038/s41467-022-33735-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 09/29/2022] [Indexed: 11/29/2022] Open
Abstract
The fatty acid composition of phosphatidylethanolamine (PE) determines cellular metabolism, oxidative stress, and inflammation. However, our understanding of how cells regulate PE composition is limited. Here, we identify a genetic locus on mouse chromosome 11, containing two poorly characterized genes Tlcd1 and Tlcd2, that strongly influences PE composition. We generated Tlcd1/2 double-knockout (DKO) mice and found that they have reduced levels of hepatic monounsaturated fatty acid (MUFA)-containing PE species. Mechanistically, TLCD1/2 proteins act cell intrinsically to promote the incorporation of MUFAs into PEs. Furthermore, TLCD1/2 interact with the mitochondria in an evolutionarily conserved manner and regulate mitochondrial PE composition. Lastly, we demonstrate the biological relevance of our findings in dietary models of metabolic disease, where Tlcd1/2 DKO mice display attenuated development of non-alcoholic steatohepatitis compared to controls. Overall, we identify TLCD1/2 proteins as key regulators of cellular PE composition, with our findings having broad implications in understanding and treating disease. The regulation of cellular phosphatidylethanolamine (PE) acyl chain composition is poorly understood. Here, the authors show that TLCD1 and TLCD2 proteins mediate the formation of monounsaturated fatty acid-containing PE species and promote the progression of non-alcoholic steatohepatitis.
Collapse
Affiliation(s)
- Kasparas Petkevicius
- Bioscience Metabolism, Research and Early Development Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden. .,Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden.
| | - Henrik Palmgren
- Bioscience Metabolism, Research and Early Development Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden.,Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden
| | - Matthew S Glover
- Dynamic Omics, Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, USA
| | - Andrea Ahnmark
- Bioscience Metabolism, Research and Early Development Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Anne-Christine Andréasson
- Bioscience Cardiovascular, Research and Early Development Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | | | - Hiroki Kawana
- Department of Health Chemistry, Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, Japan.,Advanced Research & Development Programs for Medical Innovation (AMED-LEAP), Tokyo, Japan
| | - Erik L Allman
- Dynamic Omics, Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, USA
| | - Delaney Kaper
- Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden
| | - Martin Uhrbom
- Bioscience Metabolism, Research and Early Development Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Liselotte Andersson
- Clinical Pharmacology and Safety Sciences, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Leif Aasehaug
- Bioscience Cardiovascular, Research and Early Development Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Johan Forsström
- Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Simonetta Wallin
- Bioscience Metabolism, Research and Early Development Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Ingela Ahlstedt
- Bioscience Metabolism, Research and Early Development Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Renata Leke
- Bioscience Metabolism, Research and Early Development Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Daniel Karlsson
- Bioscience Metabolism, Research and Early Development Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Hernán González-King
- Bioscience Cardiovascular, Research and Early Development Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Lars Löfgren
- Translational Science and Experimental Medicine, Research and Early Development Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Ralf Nilsson
- Translational Science and Experimental Medicine, Research and Early Development Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Giovanni Pellegrini
- Clinical Pharmacology and Safety Sciences, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Nozomu Kono
- Department of Health Chemistry, Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, Japan
| | - Junken Aoki
- Department of Health Chemistry, Graduate School of Pharmaceutical Sciences, The University of Tokyo, Tokyo, Japan.,Advanced Research & Development Programs for Medical Innovation (AMED-LEAP), Tokyo, Japan
| | - Sonja Hess
- Dynamic Omics, Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, USA
| | - Grzegorz Sienski
- Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Marc Pilon
- Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg, Sweden
| | | | - Marcello Maresca
- Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Xiao-Rong Peng
- Bioscience Metabolism, Research and Early Development Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| |
Collapse
|
4
|
Panganiban RA, Lu Q. A Long Non-Coding RNA "lnc"ed to Asthma Genetics. Am J Respir Cell Mol Biol 2022; 66:243-244. [PMID: 35030310 PMCID: PMC8937238 DOI: 10.1165/rcmb.2021-0534ed] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Affiliation(s)
- Ronald A Panganiban
- Harvard University T H Chan School of Public Health, 1857, Boston, Massachusetts, United States
| | - Quan Lu
- Harvard University T H Chan School of Public Health, 1857, Boston, Massachusetts, United States;
| |
Collapse
|
5
|
Tovar A, Crouse WL, Smith GJ, Thomas JM, Keith BP, McFadden KM, Moran TP, Furey TS, Kelada SNP. Integrative analysis reveals mouse strain-dependent responses to acute ozone exposure associated with airway macrophage transcriptional activity. Am J Physiol Lung Cell Mol Physiol 2022; 322:L33-L49. [PMID: 34755540 PMCID: PMC8721896 DOI: 10.1152/ajplung.00237.2021] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 11/03/2021] [Accepted: 11/04/2021] [Indexed: 01/03/2023] Open
Abstract
Acute ozone (O3) exposure is associated with multiple adverse cardiorespiratory outcomes, the severity of which varies across individuals in human populations and inbred mouse strains. However, molecular determinants of response, including susceptibility biomarkers that distinguish who will develop severe injury and inflammation, are not well characterized. We and others have demonstrated that airway macrophages (AMs) are an important resident immune cell type that are functionally and transcriptionally responsive to O3 inhalation. Here, we sought to explore influences of strain, exposure, and strain-by-O3 exposure interactions on AM gene expression and identify transcriptional correlates of O3-induced inflammation and injury across six mouse strains, including five Collaborative Cross (CC) strains. We exposed adult mice of both sexes to filtered air (FA) or 2 ppm O3 for 3 h and measured inflammatory and injury parameters 21 h later. Mice exposed to O3 developed airway neutrophilia and lung injury with strain-dependent severity. In AMs, we identified a common core O3 transcriptional response signature across all strains, as well as a set of genes exhibiting strain-by-O3 exposure interactions. In particular, a prominent gene expression contrast emerged between a low- (CC017/Unc) and high-responding (CC003/Unc) strain, as reflected by cellular inflammation and injury. Further inspection indicated that differences in their baseline gene expression and chromatin accessibility profiles likely contribute to their divergent post-O3 exposure transcriptional responses. Together, these results suggest that aspects of O3-induced respiratory responses are mediated through altered AM transcriptional signatures and further confirm the importance of gene-environment interactions in mediating differential responsiveness to environmental agents.
Collapse
Affiliation(s)
- Adelaide Tovar
- Department of Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
- Curriculum in Genetics & Molecular Biology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Wesley L Crouse
- Department of Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
- Curriculum in Bioinformatics & Computational Biology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Gregory J Smith
- Department of Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
- Curriculum in Toxicology & Environmental Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Joseph M Thomas
- Department of Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Benjamin P Keith
- Department of Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
- Curriculum in Bioinformatics & Computational Biology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Kathryn M McFadden
- Department of Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Timothy P Moran
- Department of Pediatrics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
- Center for Environmental Medicine, Asthma, and Lung Biology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Terrence S Furey
- Department of Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
- Curriculum in Genetics & Molecular Biology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
- Curriculum in Bioinformatics & Computational Biology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
- Department of Biology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Samir N P Kelada
- Department of Genetics, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
- Curriculum in Genetics & Molecular Biology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
- Curriculum in Bioinformatics & Computational Biology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
- Curriculum in Toxicology & Environmental Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
- Center for Environmental Medicine, Asthma, and Lung Biology, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| |
Collapse
|
6
|
Gordon-Larsen P, French JE, Moustaid-Moussa N, Voruganti VS, Mayer-Davis EJ, Bizon CA, Cheng Z, Stewart DA, Easterbrook JW, Shaikh SR. Synergizing Mouse and Human Studies to Understand the Heterogeneity of Obesity. Adv Nutr 2021; 12:2023-2034. [PMID: 33885739 PMCID: PMC8483969 DOI: 10.1093/advances/nmab040] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 02/17/2021] [Accepted: 03/13/2021] [Indexed: 12/12/2022] Open
Abstract
Obesity is routinely considered as a single disease state, which drives a "one-size-fits-all" approach to treatment. We recently convened the first annual University of North Carolina Interdisciplinary Nutrition Sciences Symposium to discuss the heterogeneity of obesity and the need for translational science to advance understanding of this heterogeneity. The symposium aimed to advance scientific rigor in translational studies from animal to human models with the goal of identifying underlying mechanisms and treatments. In this review, we discuss fundamental gaps in knowledge of the heterogeneity of obesity ranging from cellular to population perspectives. We also advocate approaches to overcoming limitations in the field. Examples include the use of contemporary mouse genetic reference population models such as the Collaborative Cross and Diversity Outbred mice that effectively model human genetic diversity and the use of translational models that integrate -omics and computational approaches from pre-clinical to clinical models of obesity. Finally, we suggest best scientific practices to ensure strong rigor that will allow investigators to delineate the sources of heterogeneity in the population with obesity. Collectively, we propose that it is critical to think of obesity as a heterogeneous disease with complex mechanisms and etiologies, requiring unique prevention and treatment strategies tailored to the individual.
Collapse
Affiliation(s)
| | - John E French
- Department of Nutrition, Gillings School of Global Public Health and School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA,Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, USA
| | - Naima Moustaid-Moussa
- Obesity Research Institute and Department of Nutritional Sciences, Texas Tech University, Lubbock, TX, USA
| | - Venkata S Voruganti
- Department of Nutrition, Gillings School of Global Public Health and School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA,Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, USA
| | - Elizabeth J Mayer-Davis
- Department of Nutrition, Gillings School of Global Public Health and School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Christopher A Bizon
- Renaissance Computing Institute, University of North Carolina at Chapel Hill, NC, USA
| | - Zhiyong Cheng
- Food Science and Human Nutrition Department, University of Florida, Gainesville, FL, USA
| | - Delisha A Stewart
- Department of Nutrition, Gillings School of Global Public Health and School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA,Nutrition Research Institute, University of North Carolina at Chapel Hill, Kannapolis, NC, USA
| | - John W Easterbrook
- Department of Nutrition, Gillings School of Global Public Health and School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | | |
Collapse
|
7
|
Crouse WL, Kelada SNP, Valdar W. Inferring the Allelic Series at QTL in Multiparental Populations. Genetics 2020; 216:957-983. [PMID: 33082282 PMCID: PMC7768242 DOI: 10.1534/genetics.120.303393] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2020] [Accepted: 10/12/2020] [Indexed: 12/25/2022] Open
Abstract
Multiparental populations (MPPs) are experimental populations in which the genome of every individual is a mosaic of known founder haplotypes. These populations are useful for detecting quantitative trait loci (QTL) because tests of association can leverage inferred founder haplotype descent. It is difficult, however, to determine how haplotypes at a locus group into distinct functional alleles, termed the allelic series. The allelic series is important because it provides information about the number of causal variants at a QTL and their combined effects. In this study, we introduce a fully Bayesian model selection framework for inferring the allelic series. This framework accounts for sources of uncertainty found in typical MPPs, including the number and composition of functional alleles. Our prior distribution for the allelic series is based on the Chinese restaurant process, a relative of the Dirichlet process, and we leverage its connection to the coalescent to introduce additional prior information about haplotype relatedness via a phylogenetic tree. We evaluate our approach via simulation and apply it to QTL from two MPPs: the Collaborative Cross (CC) and the Drosophila Synthetic Population Resource (DSPR). We find that, although posterior inference of the exact allelic series is often uncertain, we are able to distinguish biallelic QTL from more complex multiallelic cases. Additionally, our allele-based approach improves haplotype effect estimation when the true number of functional alleles is small. Our method, Tree-Based Inference of Multiallelism via Bayesian Regression (TIMBR), provides new insight into the genetic architecture of QTL in MPPs.
Collapse
Affiliation(s)
- Wesley L Crouse
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, North Carolina 27599
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599
| | - Samir N P Kelada
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599
- Marsico Lung Institute, University of North Carolina, Chapel Hill, North Carolina 27599
| | - William Valdar
- Department of Genetics, University of North Carolina, Chapel Hill, North Carolina 27599
- Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, North Carolina 27599
| |
Collapse
|
8
|
Mosedale M, Watkins PB. Understanding Idiosyncratic Toxicity: Lessons Learned from Drug-Induced Liver Injury. J Med Chem 2020; 63:6436-6461. [PMID: 32037821 DOI: 10.1021/acs.jmedchem.9b01297] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Idiosyncratic adverse drug reactions (IADRs) encompass a diverse group of toxicities that can vary by drug and patient. The complex and unpredictable nature of IADRs combined with the fact that they are rare makes them particularly difficult to predict, diagnose, and treat. Common clinical characteristics, the identification of human leukocyte antigen risk alleles, and drug-induced proliferation of lymphocytes isolated from patients support a role for the adaptive immune system in the pathogenesis of IADRs. Significant evidence also suggests a requirement for direct, drug-induced stress, neoantigen formation, and stimulation of an innate response, which can be influenced by properties intrinsic to both the drug and the patient. This Perspective will provide an overview of the clinical profile, mechanisms, and risk factors underlying IADRs as well as new approaches to study these reactions, focusing on idiosyncratic drug-induced liver injury.
Collapse
Affiliation(s)
- Merrie Mosedale
- Institute for Drug Safety Sciences and Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, Chapel Hill, North Carolina 27599, United States
| | - Paul B Watkins
- Institute for Drug Safety Sciences and Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, Chapel Hill, North Carolina 27599, United States
| |
Collapse
|
9
|
Keele GR, Quach BC, Israel JW, Chappell GA, Lewis L, Safi A, Simon JM, Cotney P, Crawford GE, Valdar W, Rusyn I, Furey TS. Integrative QTL analysis of gene expression and chromatin accessibility identifies multi-tissue patterns of genetic regulation. PLoS Genet 2020; 16:e1008537. [PMID: 31961859 PMCID: PMC7010298 DOI: 10.1371/journal.pgen.1008537] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2019] [Revised: 02/10/2020] [Accepted: 11/23/2019] [Indexed: 01/08/2023] Open
Abstract
Gene transcription profiles across tissues are largely defined by the activity of regulatory elements, most of which correspond to regions of accessible chromatin. Regulatory element activity is in turn modulated by genetic variation, resulting in variable transcription rates across individuals. The interplay of these factors, however, is poorly understood. Here we characterize expression and chromatin state dynamics across three tissues-liver, lung, and kidney-in 47 strains of the Collaborative Cross (CC) mouse population, examining the regulation of these dynamics by expression quantitative trait loci (eQTL) and chromatin QTL (cQTL). QTL whose allelic effects were consistent across tissues were detected for 1,101 genes and 133 chromatin regions. Also detected were eQTL and cQTL whose allelic effects differed across tissues, including local-eQTL for Pik3c2g detected in all three tissues but with distinct allelic effects. Leveraging overlapping measurements of gene expression and chromatin accessibility on the same mice from multiple tissues, we used mediation analysis to identify chromatin and gene expression intermediates of eQTL effects. Based on QTL and mediation analyses over multiple tissues, we propose a causal model for the distal genetic regulation of Akr1e1, a gene involved in glycogen metabolism, through the zinc finger transcription factor Zfp985 and chromatin intermediates. This analysis demonstrates the complexity of transcriptional and chromatin dynamics and their regulation over multiple tissues, as well as the value of the CC and related genetic resource populations for identifying specific regulatory mechanisms within cells and tissues.
Collapse
Affiliation(s)
- Gregory R. Keele
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- The Jackson Laboratory, Bar Harbor, Maine, United States of America
| | - Bryan C. Quach
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Center for Omics Discovery and Epidemiology, Research Triangle Institute (RTI) International, Research Triangle Park, North Carolina, United States of America
| | - Jennifer W. Israel
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Grace A. Chappell
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas, United States of America
| | - Lauren Lewis
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas, United States of America
| | - Alexias Safi
- Department of Pediatrics, Duke University, Durham, North Carolina, United States of America
- Center for Genomic and Computational Biology, Duke University, Durham, North Carolina, United States of America
| | - Jeremy M. Simon
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Paul Cotney
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Gregory E. Crawford
- Department of Pediatrics, Duke University, Durham, North Carolina, United States of America
- Center for Genomic and Computational Biology, Duke University, Durham, North Carolina, United States of America
| | - William Valdar
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Ivan Rusyn
- Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, Texas, United States of America
| | - Terrence S. Furey
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| |
Collapse
|
10
|
Maazi H, Hartiala JA, Suzuki Y, Crow AL, Shafiei Jahani P, Lam J, Patel N, Rigas D, Han Y, Huang P, Eskin E, Lusis AJ, Gilliland FD, Akbari O, Allayee H. A GWAS approach identifies Dapp1 as a determinant of air pollution-induced airway hyperreactivity. PLoS Genet 2019; 15:e1008528. [PMID: 31869344 PMCID: PMC6944376 DOI: 10.1371/journal.pgen.1008528] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2018] [Revised: 01/06/2020] [Accepted: 11/15/2019] [Indexed: 02/07/2023] Open
Abstract
Asthma is a chronic inflammatory disease of the airways with contributions from genes, environmental exposures, and their interactions. While genome-wide association studies (GWAS) in humans have identified ~200 susceptibility loci, the genetic factors that modulate risk of asthma through gene-environment (GxE) interactions remain poorly understood. Using the Hybrid Mouse Diversity Panel (HMDP), we sought to identify the genetic determinants of airway hyperreactivity (AHR) in response to diesel exhaust particles (DEP), a model traffic-related air pollutant. As measured by invasive plethysmography, AHR under control and DEP-exposed conditions varied 3-4-fold in over 100 inbred strains from the HMDP. A GWAS with linear mixed models mapped two loci significantly associated with lung resistance under control exposure to chromosomes 2 (p = 3.0x10-6) and 19 (p = 5.6x10-7). The chromosome 19 locus harbors Il33 and is syntenic to asthma association signals observed at the IL33 locus in humans. A GxE GWAS for post-DEP exposure lung resistance identified a significantly associated locus on chromosome 3 (p = 2.5x10-6). Among the genes at this locus is Dapp1, an adaptor molecule expressed in immune-related and mucosal tissues, including the lung. Dapp1-deficient mice exhibited significantly lower AHR than control mice but only after DEP exposure, thus functionally validating Dapp1 as one of the genes underlying the GxE association at this locus. In summary, our results indicate that some of the genetic determinants for asthma-related phenotypes may be shared between mice and humans, as well as the existence of GxE interactions in mice that modulate lung function in response to air pollution exposures relevant to humans. The genetic factors that modulate risk of asthma through gene-environment (GxE) interactions are poorly understood, due in large part to the inherent difficulties in carrying out such studies in humans. To address these challenges, we used the Hybrid Mouse Diversity Panel to elucidate the genetic architecture of asthma-related phenotypes in mice and identify loci that are associated with airway hyperreactivity (AHR) under control exposure conditions and in response to diesel exhaust particles (DEP), as a model traffic-related air pollutant. In the absence of exposure, we identified two loci on chromosomes 2 and 19 for AHR. The locus on chromosome 19 harbors Il33 and is syntenic to association signals observed for asthma at the IL33 locus in humans. In response to DEP exposure, we mapped AHR to a region on chromosome 3 and used a genetically modified mouse model to functionally demonstrate that Dapp1 is one of the genes underlying the GxE association at this locus. Collectively, our results support the concept that some of the genetic determinants for asthma-related phenotypes may be shared between mice and humans as well as the existence of GxE interactions in mice that modulate lung function in response to air pollution exposures relevant to humans.
Collapse
Affiliation(s)
- Hadi Maazi
- Departments of Molecular Microbiology and Immunology, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Jaana A. Hartiala
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
- Department of Biochemistry & Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Yuzo Suzuki
- Departments of Molecular Microbiology and Immunology, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Amanda L. Crow
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
- Department of Biochemistry & Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Pedram Shafiei Jahani
- Departments of Molecular Microbiology and Immunology, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Jonathan Lam
- Departments of Molecular Microbiology and Immunology, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Nisheel Patel
- Departments of Molecular Microbiology and Immunology, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Diamanda Rigas
- Departments of Molecular Microbiology and Immunology, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Yi Han
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
- Department of Biochemistry & Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Pin Huang
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
- Department of Biochemistry & Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Eleazar Eskin
- Department of Computer Science and Inter-Departmental Program in Bioinformatics, University of California, Los Angeles, Los Angeles, California, United States of America
| | - Aldons. J. Lusis
- Department of Human Genetics, David Geffen School of Medicine at UCLA, Los Angeles, California, United States of America
- Department of Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California, United States of America
- Department of Microbiology, Immunology, and Molecular Genetics, David Geffen School of Medicine at UCLA, Los Angeles, California, United States of America
| | - Frank D. Gilliland
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
| | - Omid Akbari
- Departments of Molecular Microbiology and Immunology, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
- * E-mail: (OA); (HA)
| | - Hooman Allayee
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
- Department of Biochemistry & Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, United States of America
- * E-mail: (OA); (HA)
| |
Collapse
|
11
|
Rychlik KA, Secrest JR, Lau C, Pulczinski J, Zamora ML, Leal J, Langley R, Myatt LG, Raju M, Chang RCA, Li Y, Golding MC, Rodrigues-Hoffmann A, Molina MJ, Zhang R, Johnson NM. In utero ultrafine particulate matter exposure causes offspring pulmonary immunosuppression. Proc Natl Acad Sci U S A 2019; 116:3443-3448. [PMID: 30808738 PMCID: PMC6397543 DOI: 10.1073/pnas.1816103116] [Citation(s) in RCA: 63] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Early life exposure to fine particulate matter (PM) in air is associated with infant respiratory disease and childhood asthma, but limited epidemiological data exist concerning the impacts of ultrafine particles (UFPs) on the etiology of childhood respiratory disease. Specifically, the role of UFPs in amplifying Th2- and/or Th17-driven inflammation (asthma promotion) or suppressing effector T cells (increased susceptibility to respiratory infection) remains unclear. Using a mouse model of in utero UFP exposure, we determined early immunological responses to house dust mite (HDM) allergen in offspring challenged from 0 to 4 wk of age. Two mice strains were exposed throughout gestation: C57BL/6 (sensitive to oxidative stress) and BALB/C (sensitive to allergen exposure). Offspring exposed to UFPs in utero exhibited reduced inflammatory response to HDM. Compared with filtered air (FA)-exposed/HDM-challenged mice, UFP-exposed offspring had lower white blood cell counts in bronchoalveolar lavage fluid and less pronounced peribronchiolar inflammation in both strains, albeit more apparent in C57BL/6 mice. In the C57BL/6 strain, offspring exposed in utero to FA and challenged with HDM exhibited a robust response in inflammatory cytokines IL-13 and Il-17. In contrast, this response was lost in offspring exposed in utero to UFPs. Circulating IL-10 was significantly up-regulated in C57BL/6 offspring exposed to UFPs, suggesting increased regulatory T cell expression and suppressed Th2/Th17 response. Our results reveal that in utero UFP exposure at a level close to the WHO recommended PM guideline suppresses an early immune response to HDM allergen, likely predisposing neonates to respiratory infection and altering long-term pulmonary health.
Collapse
Affiliation(s)
- Kristal A Rychlik
- Department of Environmental and Occupational Health, Texas A&M University, College Station, TX 77843
| | - Jeremiah R Secrest
- Department of Chemistry, Texas A&M University, College Station, TX 77843
| | - Carmen Lau
- Department of Veterinary Pathobiology, Texas A&M University, College Station, TX 77843
| | - Jairus Pulczinski
- Department of Environmental and Occupational Health, Texas A&M University, College Station, TX 77843
| | - Misti L Zamora
- Department of Atmospheric Sciences, Texas A&M University, College Station, TX 77843
| | - Jeann Leal
- Department of Veterinary Pathobiology, Texas A&M University, College Station, TX 77843
| | - Rebecca Langley
- Department of Environmental and Occupational Health, Texas A&M University, College Station, TX 77843
| | - Louise G Myatt
- Department of Environmental and Occupational Health, Texas A&M University, College Station, TX 77843
| | - Muppala Raju
- Department of Epidemiology and Biostatistics, Texas A&M University, College Station, TX 77843
| | - Richard C-A Chang
- Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, TX 77843
| | - Yixin Li
- Department of Chemistry, Texas A&M University, College Station, TX 77843
| | - Michael C Golding
- Department of Veterinary Physiology and Pharmacology, Texas A&M University, College Station, TX 77843
| | | | - Mario J Molina
- Department of Chemistry and Biochemistry, University of California, San Diego, La Jolla, CA 92093
| | - Renyi Zhang
- Department of Chemistry, Texas A&M University, College Station, TX 77843
- Department of Atmospheric Sciences, Texas A&M University, College Station, TX 77843
| | - Natalie M Johnson
- Department of Environmental and Occupational Health, Texas A&M University, College Station, TX 77843;
| |
Collapse
|
12
|
Lilue J, Doran AG, Fiddes IT, Abrudan M, Armstrong J, Bennett R, Chow W, Collins J, Collins S, Czechanski A, Danecek P, Diekhans M, Dolle DD, Dunn M, Durbin R, Earl D, Ferguson-Smith A, Flicek P, Flint J, Frankish A, Fu B, Gerstein M, Gilbert J, Goodstadt L, Harrow J, Howe K, Ibarra-Soria X, Kolmogorov M, Lelliott C, Logan DW, Loveland J, Mathews CE, Mott R, Muir P, Nachtweide S, Navarro FC, Odom DT, Park N, Pelan S, Pham SK, Quail M, Reinholdt L, Romoth L, Shirley L, Sisu C, Sjoberg-Herrera M, Stanke M, Steward C, Thomas M, Threadgold G, Thybert D, Torrance J, Wong K, Wood J, Yalcin B, Yang F, Adams DJ, Paten B, Keane TM. Sixteen diverse laboratory mouse reference genomes define strain-specific haplotypes and novel functional loci. Nat Genet 2018; 50:1574-1583. [PMID: 30275530 PMCID: PMC6205630 DOI: 10.1038/s41588-018-0223-8] [Citation(s) in RCA: 130] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2018] [Accepted: 08/02/2018] [Indexed: 12/11/2022]
Abstract
We report full-length draft de novo genome assemblies for 16 widely used inbred mouse strains and find extensive strain-specific haplotype variation. We identify and characterize 2,567 regions on the current mouse reference genome exhibiting the greatest sequence diversity. These regions are enriched for genes involved in pathogen defence and immunity and exhibit enrichment of transposable elements and signatures of recent retrotransposition events. Combinations of alleles and genes unique to an individual strain are commonly observed at these loci, reflecting distinct strain phenotypes. We used these genomes to improve the mouse reference genome, resulting in the completion of 10 new gene structures. Also, 62 new coding loci were added to the reference genome annotation. These genomes identified a large, previously unannotated, gene (Efcab3-like) encoding 5,874 amino acids. Mutant Efcab3-like mice display anomalies in multiple brain regions, suggesting a possible role for this gene in the regulation of brain development.
Collapse
MESH Headings
- Animals
- Animals, Laboratory
- Chromosome Mapping/veterinary
- Genetic Loci
- Genome
- Haplotypes/genetics
- Mice
- Mice, Inbred BALB C/genetics
- Mice, Inbred C3H/genetics
- Mice, Inbred C57BL/genetics
- Mice, Inbred CBA/genetics
- Mice, Inbred DBA/genetics
- Mice, Inbred NOD/genetics
- Mice, Inbred Strains/classification
- Mice, Inbred Strains/genetics
- Molecular Sequence Annotation
- Phylogeny
- Polymorphism, Single Nucleotide
- Species Specificity
Collapse
Affiliation(s)
- Jingtao Lilue
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK
| | - Anthony G. Doran
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK
| | - Ian T. Fiddes
- Center for Biomolecular Science and Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Monica Abrudan
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK
| | - Joel Armstrong
- Center for Biomolecular Science and Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Ruth Bennett
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
| | - William Chow
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK
| | - Joanna Collins
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK
| | - Stephan Collins
- Institut de Génétique et de Biologie Moléculaire et Cellulaire, Centre National de la Recherche Scientifique UMR7104, Institut National de la Santé et de la Recherche Médicale U964, Université de Strasbourg, 67404 Illkirch, France
- Centre des Sciences du Goût et de l’Alimentation, University of Bourgogne Franche-Comté, 21000 Dijon, France
| | - Anne Czechanski
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | - Petr Danecek
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK
| | - Mark Diekhans
- Center for Biomolecular Science and Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Dirk-Dominik Dolle
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK
| | - Matt Dunn
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK
| | - Richard Durbin
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK
- Department of Genetics, University of Cambridge, Downing Site, Cambridge CB2 3EH, UK
| | - Dent Earl
- Center for Biomolecular Science and Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Anne Ferguson-Smith
- Department of Genetics, University of Cambridge, Downing Site, Cambridge CB2 3EH, UK
| | - Paul Flicek
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK
| | - Jonathan Flint
- Brain Research Institute, University of California, 695 Charles E Young Dr S, Los Angeles, CA 90095, USA
| | - Adam Frankish
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK
| | - Beiyuan Fu
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK
| | - Mark Gerstein
- Yale Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
| | - James Gilbert
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK
| | - Leo Goodstadt
- OxFORD Asset Management, OxAM House, 6 George Street, Oxford OX1 2BW
| | - Jennifer Harrow
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK
| | - Kerstin Howe
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK
| | | | - Mikhail Kolmogorov
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA 92093, USA
| | - Chris Lelliott
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK
| | - Darren W. Logan
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK
| | - Jane Loveland
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK
| | - Clayton E. Mathews
- Department of Pathology, Immunology, and Laboratory Medicine, University of Florida, Gainesville, FL, USA
| | - Richard Mott
- Genetics Institute, University College London, Gower Street, London WC1E 6BT, UK
| | - Paul Muir
- Yale Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
| | - Stefanie Nachtweide
- Institute of Mathematics and Computer Science, University of Greifswald, Domstraße 11, 17489 Greifswald, Germany
| | - Fabio C.P. Navarro
- Yale Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
| | - Duncan T. Odom
- Cancer Research UK Cambridge Institute, University of Cambridge, Robinson Way, Cambridge, CB2 0RE, UK
- German Cancer Research Center (DKFZ), Division Signaling and Functional Genomics, 69120 Heidelberg, Germany
| | - Naomi Park
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK
| | - Sarah Pelan
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK
| | - Son K Pham
- BioTuring Inc., San Diego, California, CA92121
| | - Mike Quail
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK
| | - Laura Reinholdt
- The Jackson Laboratory, 600 Main Street, Bar Harbor, ME 04609, USA
| | - Lars Romoth
- Institute of Mathematics and Computer Science, University of Greifswald, Domstraße 11, 17489 Greifswald, Germany
| | - Lesley Shirley
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK
| | - Cristina Sisu
- Yale Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
- Department of Bioscience, Brunel University London, Uxbridge UB8 3PH, UK
| | - Marcela Sjoberg-Herrera
- Departamento de Biología Celular y Molecular, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago 8331150, Chile
| | - Mario Stanke
- Institute of Mathematics and Computer Science, University of Greifswald, Domstraße 11, 17489 Greifswald, Germany
| | - Charles Steward
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK
| | - Mark Thomas
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK
| | - Glen Threadgold
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK
| | - David Thybert
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK
- Earlham Institute, Norwich Research Park, Norwich NR4 7UZ, UK
| | - James Torrance
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK
| | - Kim Wong
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK
| | - Jonathan Wood
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK
| | - Binnaz Yalcin
- Institut de Génétique et de Biologie Moléculaire et Cellulaire, Centre National de la Recherche Scientifique UMR7104, Institut National de la Santé et de la Recherche Médicale U964, Université de Strasbourg, 67404 Illkirch, France
| | - Fengtang Yang
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK
| | - David J. Adams
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK
| | - Benedict Paten
- Center for Biomolecular Science and Engineering, University of California Santa Cruz, Santa Cruz, CA 95064, USA
| | - Thomas M. Keane
- European Molecular Biology Laboratory, European Bioinformatics Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, United Kingdom
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, CB10 1SA, UK
- School of Life Sciences, University of Nottingham, Nottingham, UK
| |
Collapse
|
13
|
Orgel K, Smeekens JM, Ye P, Fotsch L, Guo R, Miller DR, Pardo-Manuel de Villena F, Burks AW, Ferris MT, Kulis MD. Genetic diversity between mouse strains allows identification of the CC027/GeniUnc strain as an orally reactive model of peanut allergy. J Allergy Clin Immunol 2018; 143:1027-1037.e7. [PMID: 30342892 PMCID: PMC7252586 DOI: 10.1016/j.jaci.2018.10.009] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2017] [Revised: 09/17/2018] [Accepted: 10/01/2018] [Indexed: 02/08/2023]
Abstract
BACKGROUND Improved animal models are needed to understand the genetic and environmental factors that contribute to food allergy. OBJECTIVE We sought to assess food allergy phenotypes in a genetically diverse collection of mice. METHODS We selected 16 Collaborative Cross (CC) mouse strains, as well as the classic inbred C57BL/6J, C3H/HeJ, and BALB/cJ strains, for screening. Female mice were sensitized to peanut intragastrically with or without cholera toxin and then challenged with peanut by means of oral gavage or intraperitoneal injection and assessed for anaphylaxis. Peanut-specific immunoglobulins, T-cell cytokines, regulatory T cells, mast cells, and basophils were quantified. RESULTS Eleven of the 16 CC strains had allergic reactions to intraperitoneal peanut challenge, whereas only CC027/GeniUnc mice reproducibly experienced severe symptoms after oral food challenge (OFC). CC027/GeniUnc, C3H/HeJ, and C57BL/6J mice all mounted a TH2 response against peanut, leading to production of IL-4 and IgE, but only the CC027/GeniUnc mice reacted to OFC. Orally induced anaphylaxis in CC027/GeniUnc mice was correlated with serum levels of Ara h 2 in circulation but not with allergen-specific IgE or mucosal mast cell protease 1 levels, indicating systemic allergen absorption is important for anaphylaxis through the gastrointestinal tract. Furthermore, CC027/GeniUnc, but not C3H/HeJ or BALB/cJ, mice can be sensitized in the absence of cholera toxin and react on OFC to peanut. CONCLUSIONS We have identified and characterized CC027/GeniUnc mice as a strain that is genetically susceptible to peanut allergy and prone to severe reactions after OFC. More broadly, these findings demonstrate the untapped potential of the CC population in developing novel models for allergy research.
Collapse
Affiliation(s)
- Kelly Orgel
- Department of Pediatrics, University of North Carolina School of Medicine, Chapel Hill, NC; University of North Carolina Food Allergy Initiative, Chapel Hill, NC
| | - Johanna M Smeekens
- Department of Pediatrics, University of North Carolina School of Medicine, Chapel Hill, NC; University of North Carolina Food Allergy Initiative, Chapel Hill, NC
| | - Ping Ye
- Department of Pediatrics, University of North Carolina School of Medicine, Chapel Hill, NC; University of North Carolina Food Allergy Initiative, Chapel Hill, NC
| | - Lauren Fotsch
- Department of Pediatrics, University of North Carolina School of Medicine, Chapel Hill, NC; University of North Carolina Food Allergy Initiative, Chapel Hill, NC
| | - Rishu Guo
- Department of Pediatrics, University of North Carolina School of Medicine, Chapel Hill, NC; University of North Carolina Food Allergy Initiative, Chapel Hill, NC
| | - Darla R Miller
- Department of Genetics, University of North Carolina School of Medicine, Chapel Hill, NC
| | - Fernando Pardo-Manuel de Villena
- Department of Genetics, University of North Carolina School of Medicine, Chapel Hill, NC; Lineberger Comprehensive Cancer Center, Chapel Hill, NC
| | - A Wesley Burks
- Department of Pediatrics, University of North Carolina School of Medicine, Chapel Hill, NC; University of North Carolina Food Allergy Initiative, Chapel Hill, NC
| | - Martin T Ferris
- Department of Genetics, University of North Carolina School of Medicine, Chapel Hill, NC.
| | - Michael D Kulis
- Department of Pediatrics, University of North Carolina School of Medicine, Chapel Hill, NC; University of North Carolina Food Allergy Initiative, Chapel Hill, NC.
| |
Collapse
|
14
|
Mosedale M. Mouse Population-Based Approaches to Investigate Adverse Drug Reactions. Drug Metab Dispos 2018; 46:1787-1795. [DOI: 10.1124/dmd.118.082834] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2018] [Accepted: 07/06/2018] [Indexed: 01/19/2023] Open
|
15
|
McMullan RC, Ferris MT, Bell TA, Menachery VD, Baric RS, Hua K, Pomp D, Smith‐Ryan AE, de Villena FP. CC002/Unc females are mouse models of exercise-induced paradoxical fat response. Physiol Rep 2018; 6:e13716. [PMID: 29924460 PMCID: PMC6009762 DOI: 10.14814/phy2.13716] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2018] [Accepted: 05/03/2018] [Indexed: 12/13/2022] Open
Abstract
Exercise results in beneficial health outcomes and protects against a variety of chronic diseases. However, U.S. exercise guidelines recommend identical exercise programs for everyone, despite individual variation in responses to these programs, including paradoxical fat gain. Experimental models of exercise-induced paradoxical outcomes may enable the dissection of underlying physiological mechanisms as well as the evaluation of potential interventions. Whereas several studies have identified individual mice exhibiting paradoxical fat gain following exercise, no systematic effort has been conducted to identify and characterize models of paradoxical response. Strains from the Collaborative Cross (CC) genetic reference population were used due to its high levels of genetic variation, its reproducible nature, and the observation that the CC is a rich source of novel disease models, to assess the impact genetic background has on exercise responses. We identified the strain CC002/Unc as an exercise-induced paradoxical fat response model in a controlled voluntary exercise study across multiple ages in female mice. We also found sex and genetic differences were consistent with this pattern in a study of forced exercise programs. These results provide a novel model for studies to determine the mechanisms behind paradoxical metabolic responses to exercise, and enable development of more rational personalized exercise recommendations based on factors such as age, sex, and genetic background.
Collapse
Affiliation(s)
- Rachel C. McMullan
- Department of GeneticsSchool of MedicineUniversity of North Carolina at Chapel HillChapel HillNorth Carolina
- Genetics and Molecular Biology CurriculumSchool of MedicineUniversity of North Carolina at Chapel HillChapel HillNorth Carolina
- Lineberger Comprehensive Cancer CenterUniversity of North Carolina at Chapel HillChapel HillNorth Carolina
| | - Martin T. Ferris
- Department of GeneticsSchool of MedicineUniversity of North Carolina at Chapel HillChapel HillNorth Carolina
| | - Timothy A. Bell
- Department of GeneticsSchool of MedicineUniversity of North Carolina at Chapel HillChapel HillNorth Carolina
- Lineberger Comprehensive Cancer CenterUniversity of North Carolina at Chapel HillChapel HillNorth Carolina
| | - Vineet D. Menachery
- Department of EpidemiologyGillings School of Global Public HealthUniversity of North Carolina at Chapel HillChapel HillNorth Carolina
| | - Ralph S. Baric
- Department of EpidemiologyGillings School of Global Public HealthUniversity of North Carolina at Chapel HillChapel HillNorth Carolina
| | - Kunjie Hua
- Department of GeneticsSchool of MedicineUniversity of North Carolina at Chapel HillChapel HillNorth Carolina
| | - Daniel Pomp
- Department of GeneticsSchool of MedicineUniversity of North Carolina at Chapel HillChapel HillNorth Carolina
| | - Abbie E. Smith‐Ryan
- Department of Exercise and Sport ScienceCollege of Arts and SciencesUniversity of North Carolina at Chapel HillChapel HillNorth Carolina
| | - Fernando Pardo‐Manuel de Villena
- Department of GeneticsSchool of MedicineUniversity of North Carolina at Chapel HillChapel HillNorth Carolina
- Lineberger Comprehensive Cancer CenterUniversity of North Carolina at Chapel HillChapel HillNorth Carolina
| |
Collapse
|
16
|
Abstract
Endocrine disrupting chemicals (EDCs) are compounds that alter the structure and function of the endocrine system and may be contributing to disorders of the reproductive, metabolic, neuroendocrine and other complex systems. Typically, these outcomes cannot be modeled in cell-based or other simple systems necessitating the use of animal testing. Appropriate animal model selection is required to effectively recapitulate the human experience, including relevant dosing and windows of exposure, and ensure translational utility and reproducibility. While classical toxicology heavily relies on inbred rats and mice, and focuses on apical endpoints such as tumor formation or birth defects, EDC researchers have used a greater diversity of species to effectively model more subtle but significant outcomes such as changes in pubertal timing, mammary gland development, and social behaviors. Advances in genomics, neuroimaging and other tools are making a wider range of animal models more widely available to EDC researchers.
Collapse
Affiliation(s)
- Heather B Patisaul
- Center for Human Health and the Environment, W.M. Keck Center for Behavioral Biology, Department of Biological Sciences, North Carolina State University, Raleigh, NC, 27695, USA.
| | - Suzanne E Fenton
- Division of the National Toxicology Program (DNTP), NTP Laboratory, National Institute of Environmental Health Sciences (NIEHS), National Institute of Health (NIH), Research Triangle Park, NC, 27709, USA.
| | - David Aylor
- Center for Human Health and the Environment, Bioinformatics Research Center, W.M. Keck Center for Behavioral Biology, Department of Biological Sciences, North Carolina State University, Raleigh, NC, 27695, USA.
| |
Collapse
|
17
|
Mosedale M, Kim Y, Brock WJ, Roth SE, Wiltshire T, Eaddy JS, Keele GR, Corty RW, Xie Y, Valdar W, Watkins PB. Editor's Highlight: Candidate Risk Factors and Mechanisms for Tolvaptan-Induced Liver Injury Are Identified Using a Collaborative Cross Approach. Toxicol Sci 2018; 156:438-454. [PMID: 28115652 DOI: 10.1093/toxsci/kfw269] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Abstract
Clinical trials of tolvaptan showed it to be a promising candidate for the treatment of Autosomal Dominant Polycystic Kidney Disease (ADPKD) but also revealed potential for idiosyncratic drug-induced liver injury (DILI) in this patient population. To identify risk factors and mechanisms underlying tolvaptan DILI, 8 mice in each of 45 strains of the genetically diverse Collaborative Cross (CC) mouse population were treated with a single oral dose of either tolvaptan or vehicle. Significant elevations in plasma alanine aminotransferase (ALT) were observed in tolvaptan-treated animals in 3 of the 45 strains. Genetic mapping coupled with transcriptomic analysis in the liver was used to identify several candidate susceptibility genes including epoxide hydrolase 2, interferon regulatory factor 3, and mitochondrial fission factor. Gene pathway analysis revealed that oxidative stress and immune response pathways were activated in response to tolvaptan treatment across all strains, but genes involved in regulation of bile acid homeostasis were most associated with tolvaptan-induced elevations in ALT. Secretory leukocyte peptidase inhibitor (Slpi) mRNA was also induced in the susceptible strains and was associated with increased plasma levels of Slpi protein, suggesting a potential serum marker for DILI susceptibility. In summary, tolvaptan induced signs of oxidative stress, mitochondrial dysfunction, and innate immune response in all strains, but variation in bile acid homeostasis was most associated with susceptibility to the liver response. This CC study has indicated potential mechanisms underlying tolvaptan DILI and biomarkers of susceptibility that may be useful in managing the risk of DILI in ADPKD patients.
Collapse
Affiliation(s)
- Merrie Mosedale
- Institute for Drug Safety Sciences, University of North Carolina at Chapel Hill, Research Triangle Park, North Carolina 27709.,Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, Chapel Hill, North Carolina 27599
| | - Yunjung Kim
- Institute for Drug Safety Sciences, University of North Carolina at Chapel Hill, Research Triangle Park, North Carolina 27709.,Department of Genetics, UNC School of Medicine, Chapel Hill, North Carolina 27599
| | - William J Brock
- Otsuka Pharmaceutical Development and Commercialization, Inc., Rockville, Maryland 20850.,Brock Scientific Consulting, Montgomery Village, Maryland 20886
| | - Sharin E Roth
- Otsuka Pharmaceutical Development and Commercialization, Inc., Rockville, Maryland 20850
| | - Tim Wiltshire
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, Chapel Hill, North Carolina 27599.,Department of Genetics, UNC School of Medicine, Chapel Hill, North Carolina 27599
| | - J Scott Eaddy
- Institute for Drug Safety Sciences, University of North Carolina at Chapel Hill, Research Triangle Park, North Carolina 27709.,Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, Chapel Hill, North Carolina 27599
| | - Gregory R Keele
- Department of Genetics, UNC School of Medicine, Chapel Hill, North Carolina 27599
| | - Robert W Corty
- Department of Genetics, UNC School of Medicine, Chapel Hill, North Carolina 27599
| | - Yuying Xie
- Department of Genetics, UNC School of Medicine, Chapel Hill, North Carolina 27599
| | - William Valdar
- Department of Genetics, UNC School of Medicine, Chapel Hill, North Carolina 27599.,Lineberger Comprehensive Cancer Center, Chapel Hill, North Carolina 27599
| | - Paul B Watkins
- Institute for Drug Safety Sciences, University of North Carolina at Chapel Hill, Research Triangle Park, North Carolina 27709.,Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, Chapel Hill, North Carolina 27599
| |
Collapse
|
18
|
Identification of trans Protein QTL for Secreted Airway Mucins in Mice and a Causal Role for Bpifb1. Genetics 2017; 207:801-812. [PMID: 28851744 DOI: 10.1534/genetics.117.300211] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2017] [Accepted: 08/22/2017] [Indexed: 12/14/2022] Open
Abstract
Mucus hyper-secretion is a hallmark feature of asthma and other muco-obstructive airway diseases. The mucin proteins MUC5AC and MUC5B are the major glycoprotein components of mucus and have critical roles in airway defense. Despite the biomedical importance of these two proteins, the loci that regulate them in the context of natural genetic variation have not been studied. To identify genes that underlie variation in airway mucin levels, we performed genetic analyses in founder strains and incipient lines of the Collaborative Cross (CC) in a house dust mite mouse model of asthma. CC founder strains exhibited significant differences in MUC5AC and MUC5B, providing evidence of heritability. Analysis of gene and protein expression of Muc5ac and Muc5b in incipient CC lines (n = 154) suggested that post-transcriptional events were important regulators of mucin protein content in the airways. Quantitative trait locus (QTL) mapping identified distinct, trans protein QTL for MUC5AC (chromosome 13) and MUC5B (chromosome 2). These two QTL explained 18 and 20% of phenotypic variance, respectively. Examination of the MUC5B QTL allele effects and subsequent phylogenetic analysis allowed us to narrow the MUC5B QTL and identify Bpifb1 as a candidate gene. Bpifb1 mRNA and protein expression were upregulated in parallel to MUC5B after allergen challenge, and Bpifb1 knockout mice exhibited higher MUC5B expression. Thus, BPIFB1 is a novel regulator of MUC5B.
Collapse
|
19
|
Mouse Chromosome 4 Is Associated with the Baseline and Allergic IgE Phenotypes. G3-GENES GENOMES GENETICS 2017; 7:2559-2564. [PMID: 28696925 PMCID: PMC5555462 DOI: 10.1534/g3.117.042739] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Regulation of IgE concentration in the blood is a complex trait, with high concentrations associated with parasitic infections as well as allergic diseases. A/J strain mice have significantly higher plasma concentrations of IgE, both at baseline and after ovalbumin antigen exposure, when compared to C57BL/6J strain mice. Our objective was to determine the genomic regions associated with this difference in phenotype. To achieve this, we used a panel of recombinant congenic strains (RCS) derived from A/J and C57BL/6J strains. We measured IgE in the RCS panel at baseline and following allergen exposure. Using marker by marker analysis of the RCS genotype and phenotype data, we identified multiple regions associated with the IgE phenotype. A single region was identified to be associated with baseline IgE level, while multiple regions wereassociated with the phenotype after allergen exposure. The most significant region was found on Chromosome 4, from 81.46 to 86.17 Mbp. Chromosome 4 substitution strain mice had significantly higher concentration of IgE than their background parental strain mice, C57BL/6J. Our data presents multiple candidate regions associated with plasma IgE concentration at baseline and following allergen exposure, with the most significant one located on Chromosome 4.
Collapse
|
20
|
Ma SY, Guo YY, Wang SX, Shi JX, Liu J, Liu JF, Zhu P. The T Allele of rs8075977 in the 5'-Flanking Region of the PEDF Gene Is Associated with Reduced Risk of Coronary Artery Disease in Elderly Chinese Men. TOHOKU J EXP MED 2017; 241:297-308. [PMID: 28420811 DOI: 10.1620/tjem.241.297] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Coronary artery disease (CAD) is a multifactorial disease with a genetic component. Pigment epithelium-derived factor (PEDF) exerts anti-inflammatory, anti-oxidant, anti-thrombotic, and anti-angiogenic effects and thus has received increasing attention as a sensitive biomarker of atherosclerosis and CAD. To explore the potential association between PEDF single nucleotide polymorphisms (SNPs) and CAD, we performed this case-control study of consecutive elderly Chinese Han male patients (n = 416) and age-matched male controls (n = 528) without a history of CAD or electrocardiographic signs of CAD. The enrolled CAD patients (age ≥ 60 years) are not biologically related. A tag approach was used to examine 100% of common variations in the PEDF gene (r2 ≥ 0.8, minor allele frequency > 0.1). PEDF tag SNPs (tSNPs) were selected using the HapMap Data-CHB which describes the common patterns of human DNA sequence variation and Tagger program. SNPs were genotyped using ligase detection reaction (LDR). Seven tSNPs (rs8075977, rs11658342, rs1136287, rs12603825, rs12453107, rs6828 and rs11078634) were selected. Among them, only one SNP, rs8075977 (C/T) located in the 5'-flanking region, showed the significant effect on the susceptibility to CAD. The frequency of its T allele was significantly higher in the controls (52.7%) than that in the CAD group (46.2%) (adjusted OR = 0.88, 95% CI: 0.80-0.96; P = 0.005). In conclusion, the T allele of rs8075977 in the 5'-flanking region of the PEDF gene may be protective for CAD. Conversely, the C allele at this variation site is associated with CAD in elderly Chinese Han men.
Collapse
Affiliation(s)
- Shou-Yuan Ma
- Department of Geriatric Cardiology, Chinese PLA General Hospital
| | - Yuan-Yuan Guo
- Department of Cardiovascular Medicine, Shijingshan Teaching Hospital of Capital Medical University
| | - Shu-Xia Wang
- Department of Cadre Clinic, Chinese PLA General Hospital
| | - Jin-Xin Shi
- Department of Cardiovascular Medicine, Shijingshan Teaching Hospital of Capital Medical University
| | - Jie Liu
- Department of Geriatrics, Civil Aviation General Hospital
| | - Jian-Feng Liu
- Department of Geriatric Cardiology, Chinese PLA General Hospital
| | - Ping Zhu
- Department of Geriatric Cardiology, Chinese PLA General Hospital
| |
Collapse
|
21
|
Schughart K, Williams RW. The Collaborative Cross Resource for Systems Genetics Research of Infectious Diseases. Methods Mol Biol 2017; 1488:579-596. [PMID: 27933545 PMCID: PMC7120135 DOI: 10.1007/978-1-4939-6427-7_28] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
An increasing body of evidence highlights the role of host genetic variation in driving susceptibility to severe disease following pathogen infection. In order to fully appreciate the importance of host genetics on infection susceptibility and resulting disease, genetically variable experimental model systems should be employed. These systems allow for the identification, characterization, and mechanistic dissection of genetic variants that cause differential disease responses. Herein we discuss application of the Collaborative Cross (CC) panel of recombinant inbred strains to study viral pathogenesis, focusing on practical considerations for experimental design, assessment and analysis of disease responses within the CC, as well as some of the resources developed for the CC. Although the focus of this chapter is on viral pathogenesis, many of the methods presented within are applicable to studies of other pathogens, as well as to case-control designs in genetically diverse populations.
Collapse
Affiliation(s)
- Klaus Schughart
- Department of Infection Genetics, Helmholtz Centre for Infection Research & University of Veterinary Medicine Hannover, Braunschweig, Niedersachsen Germany
| | - Robert W. Williams
- Department of Microbiology, Immunology and Biochemistry, University of Tennessee Health Science Center, Memphis, Tennessee USA
| |
Collapse
|
22
|
Plethysmography Phenotype QTL in Mice Before and After Allergen Sensitization and Challenge. G3-GENES GENOMES GENETICS 2016; 6:2857-65. [PMID: 27449512 PMCID: PMC5015943 DOI: 10.1534/g3.116.032912] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Allergic asthma is common airway disease that is characterized in part by enhanced airway constriction in response to nonspecific stimuli. Genome-wide association studies have identified multiple loci associated with asthma risk in humans, but these studies have not accounted for gene-environment interactions, which are thought to be important factors in asthma. To identify quantitative trait loci (QTL) that regulate responses to a common human allergen, we applied a house dust mite mouse (HDM) model of allergic airway disease (AAD) to 146 incipient lines of the Collaborative Cross (CC) and the CC founder strains. We employed a longitudinal study design in which mice were phenotyped for response to the bronchoconstrictor methacholine both before and after HDM sensitization and challenge using whole body plethysmography (WBP). There was significant variation in methacholine responsiveness due to both strain and HDM treatment, as reflected by changes in the WBP parameter enhanced pause. We also found that distinct QTL regulate baseline [chromosome (Chr) 18] and post-HDM (Chr 19) methacholine responsiveness and that post-HDM airway responsiveness was correlated with other features of AAD. Finally, using invasive measurements of airway mechanics, we tested whether the Chr 19 QTL affects lung resistance per se using C57BL/6J mice and a consomic strain but found that QTL haplotype did not affect lung resistance. We conclude that aspects of baseline and allergen-induced methacholine responsiveness are associated with genetic variation, and that robust detection of airway resistance QTL in genetically diverse mice will be facilitated by direct measurement of airway mechanics.
Collapse
|
23
|
In Utero Cigarette Smoke Affects Allergic Airway Disease But Does Not Alter the Lung Methylome. PLoS One 2015; 10:e0144087. [PMID: 26642056 PMCID: PMC4671614 DOI: 10.1371/journal.pone.0144087] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2015] [Accepted: 11/12/2015] [Indexed: 11/19/2022] Open
Abstract
Prenatal and postnatal cigarette smoke exposure enhances the risk of developing asthma. Despite this as well as other smoking related risks, 11% of women still smoke during pregnancy. We hypothesized that cigarette smoke exposure during prenatal development generates long lasting differential methylation altering transcriptional activity that correlates with disease. In a house dust mite (HDM) model of allergic airway disease, we measured airway hyperresponsiveness (AHR) and airway inflammation between mice exposed prenatally to cigarette smoke (CS) or filtered air (FA). DNA methylation and gene expression were then measured in lung tissue. We demonstrate that HDM-treated CS mice develop a more severe allergic airway disease compared to HDM-treated FA mice including increased AHR and airway inflammation. While DNA methylation changes between the two HDM-treated groups failed to reach genome-wide significance, 99 DMRs had an uncorrected p-value < 0.001. 6 of these 99 DMRs were selected for validation, based on the immune function of adjacent genes, and only 2 of the 6 DMRs confirmed the bisulfite sequencing data. Additionally, genes near these 6 DMRs (Lif, Il27ra, Tle4, Ptk7, Nfatc2, and Runx3) are differentially expressed between HDM-treated CS mice and HDM-treated FA mice. Our findings confirm that prenatal exposure to cigarette smoke is sufficient to modify allergic airway disease; however, it is unlikely that specific methylation changes account for the exposure-response relationship. These findings highlight the important role in utero cigarette smoke exposure plays in the development of allergic airway disease.
Collapse
|
24
|
Rutledge H, Baran-Gale J, de Villena FPM, Chesler EJ, Churchill GA, Sethupathy P, Kelada SNP. Identification of microRNAs associated with allergic airway disease using a genetically diverse mouse population. BMC Genomics 2015; 16:633. [PMID: 26303911 PMCID: PMC4548451 DOI: 10.1186/s12864-015-1732-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2015] [Accepted: 06/29/2015] [Indexed: 12/17/2022] Open
Abstract
Background Allergic airway diseases (AADs) such as asthma are characterized in part by granulocytic airway inflammation. The gene regulatory networks that govern granulocyte recruitment are poorly understood, but evidence is accruing that microRNAs (miRNAs) play an important role. To identify miRNAs that may underlie AADs, we used two complementary approaches that leveraged the genotypic and phenotypic diversity of the Collaborative Cross (CC) mouse population. In the first approach, we sought to identify miRNA expression quantitative trait loci (eQTL) that overlap QTL for AAD-related phenotypes. Specifically, CC founder strains and incipient lines of the CC were sensitized and challenged with house dust mite allergen followed by measurement of granulocyte recruitment to the lung. Total lung RNA was isolated and miRNA was measured using arrays for CC founders and qRT-PCR for incipient CC lines. Results Among CC founders, 92 miRNAs were differentially expressed. We measured the expression of 40 of the most highly expressed of these 92 miRNAs in the incipient lines of the CC and identified 18 eQTL corresponding to 14 different miRNAs. Surprisingly, half of these eQTL were distal to the corresponding miRNAs, and even on different chromosomes. One of the largest-effect local miRNA eQTL was for miR-342-3p, for which we identified putative causal variants by bioinformatic analysis of the effects of single nucleotide polymorphisms on RNA structure. None of the miRNA eQTL co-localized with QTL for eosinophil or neutrophil recruitment. In the second approach, we constructed putative miRNA/mRNA regulatory networks and identified three miRNAs (miR-497, miR-351 and miR-31) as candidate master regulators of genes associated with neutrophil recruitment. Analysis of a dataset from human keratinocytes transfected with a miR-31 inhibitor revealed two target genes in common with miR-31 targets correlated with neutrophils, namely Oxsr1 and Nsf. Conclusions miRNA expression in the allergically inflamed murine lung is regulated by genetic loci that are smaller in effect size compared to mRNA eQTL and often act in trans. Thus our results indicate that the genetic architecture of miRNA expression is different from mRNA expression. We identified three miRNAs, miR-497, miR-351 and miR-31, that are candidate master regulators of genes associated with neutrophil recruitment. Because miR-31 is expressed in airway epithelia and is predicted to target genes with known links to neutrophilic inflammation, we suggest that miR-31 is a potentially novel regulator of airway inflammation. Electronic supplementary material The online version of this article (doi:10.1186/s12864-015-1732-9) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Holly Rutledge
- Department of Genetics, University of North Carolina, 120 Mason Farm Road, Chapel Hill, NC, 27599, USA.
| | - Jeanette Baran-Gale
- Department of Genetics, University of North Carolina, 120 Mason Farm Road, Chapel Hill, NC, 27599, USA. .,Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, NC, USA.
| | - Fernando Pardo-Manuel de Villena
- Department of Genetics, University of North Carolina, 120 Mason Farm Road, Chapel Hill, NC, 27599, USA. .,Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, NC, USA. .,Curriculum in Genetics and Molecular Biology, University of North Carolina, Chapel Hill, NC, USA. .,Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA.
| | | | | | - Praveen Sethupathy
- Department of Genetics, University of North Carolina, 120 Mason Farm Road, Chapel Hill, NC, 27599, USA. .,Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, NC, USA. .,Curriculum in Genetics and Molecular Biology, University of North Carolina, Chapel Hill, NC, USA. .,Lineberger Comprehensive Cancer Center, University of North Carolina, Chapel Hill, NC, USA.
| | - Samir N P Kelada
- Department of Genetics, University of North Carolina, 120 Mason Farm Road, Chapel Hill, NC, 27599, USA. .,Curriculum in Bioinformatics and Computational Biology, University of North Carolina, Chapel Hill, NC, USA. .,Curriculum in Genetics and Molecular Biology, University of North Carolina, Chapel Hill, NC, USA. .,Marsico Lung Institute, University of North Carolina, Chapel Hill, NC, USA.
| |
Collapse
|
25
|
Collaborative Cross and Diversity Outbred data resources in the Mouse Phenome Database. Mamm Genome 2015; 26:511-20. [PMID: 26286858 PMCID: PMC4602074 DOI: 10.1007/s00335-015-9595-6] [Citation(s) in RCA: 59] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2015] [Accepted: 08/10/2015] [Indexed: 10/27/2022]
Abstract
The Mouse Phenome Database was originally conceived as a platform for the integration of phenotype data collected on a defined collection of 40 inbred mouse strains--the "phenome panel." This model provided an impetus for community data sharing, and integration was readily achieved through the reproducible genotypes of the phenome panel strains. Advances in the development of mouse populations lead to an expanded role of the Mouse Phenome Database to encompass new strain panels and inbred strain crosses. The recent introduction of the Collaborative Cross and Diversity Outbred mice, which share an extensive pool of genetic variation from eight founder inbred strains, presents new opportunities and challenges for community data resources. A wide variety of molecular and clinical phenotypes are being collected across genotypes, tissues, ages, environmental exposures, interventions, and treatments. The Mouse Phenome Database provides a framework for retrieval, integration, analysis, and display of these data, enabling them to be evaluated in the context of existing data from standard inbred strains. Primary data in the Mouse Phenome Database are supported by extensive metadata on protocols and procedures. These are centrally curated to ensure accuracy and reproducibility and to provide data in consistent formats. The Mouse Phenome Database represents an established and growing community data resource for mouse phenotype data and encourages submissions from new mouse resources, enabling investigators to integrate existing data into their studies of the phenotypic consequences of genetic variation.
Collapse
|
26
|
Morgan AP, Welsh CE. Informatics resources for the Collaborative Cross and related mouse populations. Mamm Genome 2015; 26:521-39. [PMID: 26135136 DOI: 10.1007/s00335-015-9581-z] [Citation(s) in RCA: 41] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2015] [Accepted: 06/23/2015] [Indexed: 02/05/2023]
Affiliation(s)
- Andrew P Morgan
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Catherine E Welsh
- Department of Mathematics & Computer Science, Rhodes College, Memphis, TN, USA.
| |
Collapse
|
27
|
Buchner DA, Nadeau JH. Contrasting genetic architectures in different mouse reference populations used for studying complex traits. Genome Res 2015; 25:775-91. [PMID: 25953951 PMCID: PMC4448675 DOI: 10.1101/gr.187450.114] [Citation(s) in RCA: 48] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2014] [Accepted: 03/31/2015] [Indexed: 01/14/2023]
Abstract
Quantitative trait loci (QTLs) are being used to study genetic networks, protein functions, and systems properties that underlie phenotypic variation and disease risk in humans, model organisms, agricultural species, and natural populations. The challenges are many, beginning with the seemingly simple tasks of mapping QTLs and identifying their underlying genetic determinants. Various specialized resources have been developed to study complex traits in many model organisms. In the mouse, remarkably different pictures of genetic architectures are emerging. Chromosome Substitution Strains (CSSs) reveal many QTLs, large phenotypic effects, pervasive epistasis, and readily identified genetic variants. In contrast, other resources as well as genome-wide association studies (GWAS) in humans and other species reveal genetic architectures dominated with a relatively modest number of QTLs that have small individual and combined phenotypic effects. These contrasting architectures are the result of intrinsic differences in the study designs underlying different resources. The CSSs examine context-dependent phenotypic effects independently among individual genotypes, whereas with GWAS and other mouse resources, the average effect of each QTL is assessed among many individuals with heterogeneous genetic backgrounds. We argue that variation of genetic architectures among individuals is as important as population averages. Each of these important resources has particular merits and specific applications for these individual and population perspectives. Collectively, these resources together with high-throughput genotyping, sequencing and genetic engineering technologies, and information repositories highlight the power of the mouse for genetic, functional, and systems studies of complex traits and disease models.
Collapse
Affiliation(s)
- David A Buchner
- Department of Genetics and Genome Sciences, Department of Biochemistry, Case Western Reserve University, Cleveland, Ohio 44106, USA
| | - Joseph H Nadeau
- Pacific Northwest Diabetes Research Institute, Seattle, Washington 98122, USA
| |
Collapse
|
28
|
Abstract
Allergic asthma is a complex disease characterized in part by granulocytic inflammation of the airways. In addition to eosinophils, neutrophils (PMN) are also present, particularly in cases of severe asthma. We sought to identify the genetic determinants of neutrophilic inflammation in a mouse model of house dust mite (HDM)-induced asthma. We applied an HDM model of allergic asthma to the eight founder strains of the Collaborative Cross (CC) and 151 incipient lines of the CC (preCC). Lung lavage fluid was analyzed for PMN count and the concentration of CXCL1, a hallmark PMN chemokine. PMN and CXCL1 were strongly correlated in preCC mice. We used quantitative trait locus (QTL) mapping to identify three variants affecting PMN, one of which colocalized with a QTL for CXCL1 on chromosome (Chr) 7. We used lung eQTL data to implicate a variant in the gene Zfp30 in the CXCL1/PMN response. This genetic variant regulates both CXCL1 and PMN by altering Zfp30 expression, and we model the relationships between the QTL and these three endophenotypes. We show that Zfp30 is expressed in airway epithelia in the normal mouse lung and that altering Zfp30 expression in vitro affects CXCL1 responses to an immune stimulus. Our results provide strong evidence that Zfp30 is a novel regulator of neutrophilic airway inflammation.
Collapse
|