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Glenn RA, Do SC, Guruvayurappan K, Corrigan EK, Santini L, Medina-Cano D, Singer S, Cho H, Liu J, Broman K, Czechanski A, Reinholdt L, Koche R, Furuta Y, Kunz M, Vierbuchen T. A PLURIPOTENT STEM CELL PLATFORM FOR IN VITRO SYSTEMS GENETICS STUDIES OF MOUSE DEVELOPMENT. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.06.597758. [PMID: 38895226 PMCID: PMC11185710 DOI: 10.1101/2024.06.06.597758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
The directed differentiation of pluripotent stem cells (PSCs) from panels of genetically diverse individuals is emerging as a powerful experimental system for characterizing the impact of natural genetic variation on developing cell types and tissues. Here, we establish new PSC lines and experimental approaches for modeling embryonic development in a genetically diverse, outbred mouse stock (Diversity Outbred mice). We show that a range of inbred and outbred PSC lines can be stably maintained in the primed pluripotent state (epiblast stem cells -- EpiSCs) and establish the contribution of genetic variation to phenotypic differences in gene regulation and directed differentiation. Using pooled in vitro fertilization, we generate and characterize a genetic reference panel of Diversity Outbred PSCs (n = 230). Finally, we demonstrate the feasibility of pooled culture of Diversity Outbred EpiSCs as "cell villages", which can facilitate the differentiation of large numbers of EpiSC lines for forward genetic screens. These data can complement and inform similar efforts within the stem cell biology and human genetics communities to model the impact of natural genetic variation on phenotypic variation and disease-risk.
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Affiliation(s)
- Rachel A. Glenn
- Developmental Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Center for Stem Cell Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Cell and Developmental Biology Program, Weill Cornell Graduate School of Medical Sciences, Cornell University, New York, NY, USA
| | - Stephanie C. Do
- Developmental Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Center for Stem Cell Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Emily K. Corrigan
- Developmental Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Center for Stem Cell Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Present address: Eli and Edythe Broad Center for Regeneration Medicine and Stem Cell Research, University of California, San Francisco, San Francisco, CA, USA and Department of Neurology, University of California, San Francisco, San Francisco, CA, USA
| | - Laura Santini
- Developmental Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Center for Stem Cell Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Daniel Medina-Cano
- Developmental Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Center for Stem Cell Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Sarah Singer
- Developmental Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Hyein Cho
- Developmental Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Center for Stem Cell Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jing Liu
- Mouse Genetics Core Facility, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Karl Broman
- Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI USA
| | | | | | - Richard Koche
- Center for Epigenetics Research, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Yasuhide Furuta
- Mouse Genetics Core Facility, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Meik Kunz
- The Bioinformatics CRO, Sanford Florida, 32771 USA
| | - Thomas Vierbuchen
- Developmental Biology Program, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Center for Stem Cell Biology, Sloan Kettering Institute, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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Hilser JR, Lusis AJ, Allayee H. Genetics unravels protein-metabolite relationships. Trends Endocrinol Metab 2024; 35:183-184. [PMID: 38302401 PMCID: PMC10939837 DOI: 10.1016/j.tem.2024.01.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 01/19/2024] [Indexed: 02/03/2024]
Abstract
Integrating molecular traits into genetic studies enhances our understanding of how DNA variation influences complex clinical and physiological phenotypes. In a recent article, Benson and colleagues apply this systems genetics approach with proteomics and metabolomics data in plasma from humans to identify and validate several previously unrecognized causal protein-metabolite associations.
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Affiliation(s)
- James R Hilser
- Department of Population & Public Health Science, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA; Department of Biochemistry & Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Aldons J Lusis
- Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA; Department of Microbiology, Immunology, and Molecular Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA
| | - Hooman Allayee
- Department of Population & Public Health Science, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA; Department of Biochemistry & Molecular Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
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Abbas M, Diallo A, Goodney G, Gaye A. Leveraging the transcriptome to further our understanding of GWAS findings: eQTLs associated with genes related to LDL and LDL subclasses, in a cohort of African Americans. Front Genet 2024; 15:1345541. [PMID: 38384714 PMCID: PMC10879560 DOI: 10.3389/fgene.2024.1345541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 01/16/2024] [Indexed: 02/23/2024] Open
Abstract
Background: GWAS discoveries often pose a significant challenge in terms of understanding their underlying mechanisms. Further research, such as an integration with expression quantitative trait locus (eQTL) analyses, are required to decipher the mechanisms connecting GWAS variants to phenotypes. An eQTL analysis was conducted on genes associated with low-density lipoprotein (LDL) cholesterol and its subclasses, with the aim of pinpointing genetic variants previously implicated in GWAS studies focused on lipid-related traits. Notably, the study cohort consisted of African Americans, a population characterized by a heightened prevalence of hypercholesterolemia. Methods: A comprehensive differential expression (DE) analysis was undertaken, with a dataset of 17,948 protein-coding mRNA transcripts extracted from the whole-blood transcriptomes of 416 samples to identify mRNA transcripts associated with LDL, with further granularity delineated between small LDL and large LDL subclasses. Subsequently, eQTL analysis was conducted with a subset of 242 samples for which whole-genome sequencing data were available to identify single-nucleotide polymorphisms (SNPs) associated with the LDL-related mRNA transcripts. Lastly, plausible functional connections were established between the identified eQTLs and genetic variants reported in the GWAS catalogue. Results: DE analysis revealed 1,048, 284, and 94 mRNA transcripts that exhibited differential expression in response to LDL, small LDL, and large LDL, respectively. The eQTL analysis identified a total of 9,950 significant SNP-mRNA associations involving 6,955 SNPs including a subset 101 SNPs previously documented in GWAS of LDL and LDL-related traits. Conclusion: Through comprehensive differential expression analysis, we identified numerous mRNA transcripts responsive to LDL, small LDL, and large LDL. Subsequent eQTL analysis revealed a rich landscape of eQTL-mRNA associations, including a subset of eQTL reported in GWAS studies of LDL and related traits. The study serves as a testament to the important role of integrative genomics in unraveling the enigmatic GWAS relationships between genetic variants and the complex fabric of human traits and diseases.
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Affiliation(s)
- Malak Abbas
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States
| | - Ana Diallo
- School of Nursing, Virginia Commonwealth University, Richmond, VA, United States
| | - Gabriel Goodney
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States
| | - Amadou Gaye
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, United States
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