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Bravo JI, Mizrahi CR, Kim S, Zhang L, Suh Y, Benayoun BA. An eQTL-based approach reveals candidate regulators of LINE-1 RNA levels in lymphoblastoid cells. PLoS Genet 2024; 20:e1011311. [PMID: 38848448 PMCID: PMC11189215 DOI: 10.1371/journal.pgen.1011311] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2023] [Revised: 06/20/2024] [Accepted: 05/21/2024] [Indexed: 06/09/2024] Open
Abstract
Long interspersed element 1 (LINE-1; L1) are a family of transposons that occupy ~17% of the human genome. Though a small number of L1 copies remain capable of autonomous transposition, the overwhelming majority of copies are degenerate and immobile. Nevertheless, both mobile and immobile L1s can exert pleiotropic effects (promoting genome instability, inflammation, or cellular senescence) on their hosts, and L1's contributions to aging and aging diseases is an area of active research. However, because of the cell type-specific nature of transposon control, the catalogue of L1 regulators remains incomplete. Here, we employ an eQTL approach leveraging transcriptomic and genomic data from the GEUVADIS and 1000Genomes projects to computationally identify new candidate regulators of L1 RNA levels in lymphoblastoid cell lines. To cement the role of candidate genes in L1 regulation, we experimentally modulate the levels of top candidates in vitro, including IL16, STARD5, HSD17B12, and RNF5, and assess changes in TE family expression by Gene Set Enrichment Analysis (GSEA). Remarkably, we observe subtle but widespread upregulation of TE family expression following IL16 and STARD5 overexpression. Moreover, a short-term 24-hour exposure to recombinant human IL16 was sufficient to transiently induce subtle, but widespread, upregulation of L1 subfamilies. Finally, we find that many L1 expression-associated genetic variants are co-associated with aging traits across genome-wide association study databases. Our results expand the catalogue of genes implicated in L1 RNA control and further suggest that L1-derived RNA contributes to aging processes. Given the ever-increasing availability of paired genomic and transcriptomic data, we anticipate this new approach to be a starting point for more comprehensive computational scans for regulators of transposon RNA levels.
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Affiliation(s)
- Juan I. Bravo
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, California, United States of America
- Graduate program in the Biology of Aging, University of Southern California, Los Angeles, California, United States of America
| | - Chanelle R. Mizrahi
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, California, United States of America
- USC Gerontology Enriching MSTEM to Enhance Diversity in Aging Program, University of Southern California, Los Angeles, California, United States of America
| | - Seungsoo Kim
- Department of Obstetrics and Gynecology, Columbia University Irving Medical Center, New York, New York, United States of America
| | - Lucia Zhang
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, California, United States of America
- Quantitative and Computational Biology Department, USC Dornsife College of Letters, Arts and Sciences, Los Angeles, California, United States of America
| | - Yousin Suh
- Department of Obstetrics and Gynecology, Columbia University Irving Medical Center, New York, New York, United States of America
- Department of Genetics and Development, Columbia University Irving Medical Center, New York, New York, United States of America
| | - Bérénice A. Benayoun
- Leonard Davis School of Gerontology, University of Southern California, Los Angeles, California, United States of America
- Molecular and Computational Biology Department, USC Dornsife College of Letters, Arts and Sciences, Los Angeles, California, United States of America
- Biochemistry and Molecular Medicine Department, USC Keck School of Medicine, Los Angeles, California, United States of America
- USC Norris Comprehensive Cancer Center, Epigenetics and Gene Regulation, Los Angeles, California, United States of America
- USC Stem Cell Initiative, Los Angeles, California, United States of America
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Kang JB, Raveane A, Nathan A, Soranzo N, Raychaudhuri S. Methods and Insights from Single-Cell Expression Quantitative Trait Loci. Annu Rev Genomics Hum Genet 2023; 24:277-303. [PMID: 37196361 PMCID: PMC10784788 DOI: 10.1146/annurev-genom-101422-100437] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/19/2023]
Abstract
Recent advancements in single-cell technologies have enabled expression quantitative trait locus (eQTL) analysis across many individuals at single-cell resolution. Compared with bulk RNA sequencing, which averages gene expression across cell types and cell states, single-cell assays capture the transcriptional states of individual cells, including fine-grained, transient, and difficult-to-isolate populations at unprecedented scale and resolution. Single-cell eQTL (sc-eQTL) mapping can identify context-dependent eQTLs that vary with cell states, including some that colocalize with disease variants identified in genome-wide association studies. By uncovering the precise contexts in which these eQTLs act, single-cell approaches can unveil previously hidden regulatory effects and pinpoint important cell states underlying molecular mechanisms of disease. Here, we present an overview of recently deployed experimental designs in sc-eQTL studies. In the process, we consider the influence of study design choices such as cohort, cell states, and ex vivo perturbations. We then discuss current methodologies, modeling approaches, and technical challenges as well as future opportunities and applications.
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Affiliation(s)
- Joyce B Kang
- Center for Data Sciences and Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA; ,
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA;
| | | | - Aparna Nathan
- Center for Data Sciences and Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA; ,
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA;
| | - Nicole Soranzo
- Human Technopole, Milan, Italy; ,
- Department of Human Genetics, Wellcome Sanger Institute, Hinxton, United Kingdom
- British Heart Foundation Centre of Research Excellence and Department of Haematology, University of Cambridge, Cambridge, United Kingdom
| | - Soumya Raychaudhuri
- Center for Data Sciences and Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA; ,
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA;
- Centre for Genetics and Genomics Versus Arthritis, University of Manchester, Manchester, United Kingdom
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Wheeler HE, Ploch S, Barbeira AN, Bonazzola R, Andaleon A, Fotuhi Siahpirani A, Saha A, Battle A, Roy S, Im HK. Imputed gene associations identify replicable trans-acting genes enriched in transcription pathways and complex traits. Genet Epidemiol 2019; 43:596-608. [PMID: 30950127 PMCID: PMC6687523 DOI: 10.1002/gepi.22205] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2018] [Revised: 02/15/2019] [Accepted: 03/18/2019] [Indexed: 11/17/2022]
Abstract
Regulation of gene expression is an important mechanism through which genetic variation can affect complex traits. A substantial portion of gene expression variation can be explained by both local (cis) and distal (trans) genetic variation. Much progress has been made in uncovering cis-acting expression quantitative trait loci (cis-eQTL), but trans-eQTL have been more difficult to identify and replicate. Here we take advantage of our ability to predict the cis component of gene expression coupled with gene mapping methods such as PrediXcan to identify high confidence candidate trans-acting genes and their targets. That is, we correlate the cis component of gene expression with observed expression of genes in different chromosomes. Leveraging the shared cis-acting regulation across tissues, we combine the evidence of association across all available Genotype-Tissue Expression Project tissues and find 2,356 trans-acting/target gene pairs with high mappability scores. Reassuringly, trans-acting genes are enriched in transcription and nucleic acid binding pathways and target genes are enriched in known transcription factor binding sites. Interestingly, trans-acting genes are more significantly associated with selected complex traits and diseases than target or background genes, consistent with percolating trans effects. Our scripts and summary statistics are publicly available for future studies of trans-acting gene regulation.
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Affiliation(s)
- Heather E. Wheeler
- Department of BiologyLoyola University ChicagoChicagoIllinois
- Department of Computer ScienceLoyola University ChicagoChicagoIllinois
- Department of Public Health SciencesStritch School of Medicine, Loyola University ChicagoMaywoodIllinois
| | - Sally Ploch
- Department of BiologyLoyola University ChicagoChicagoIllinois
| | - Alvaro N. Barbeira
- Section of Genetic Medicine, Department of MedicineUniversity of ChicagoChicagoIllinois
| | - Rodrigo Bonazzola
- Section of Genetic Medicine, Department of MedicineUniversity of ChicagoChicagoIllinois
| | - Angela Andaleon
- Department of BiologyLoyola University ChicagoChicagoIllinois
| | | | - Ashis Saha
- Department of Computer ScienceJohns Hopkins UniversityBaltimoreMaryland
| | - Alexis Battle
- Department of Computer ScienceJohns Hopkins UniversityBaltimoreMaryland
- Department of Biomedical EngineeringJohns Hopkins UniversityBaltimoreMaryland
| | - Sushmita Roy
- Department of Biostatistics and Medical InformaticsUniversity of Wisconsin‐MadisonMadisonWisconsin
| | - Hae Kyung Im
- Section of Genetic Medicine, Department of MedicineUniversity of ChicagoChicagoIllinois
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