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Cited by 113Genetic Associations and EpidemiologyT-cell and B-cell ImmunologyGenomics and Chromatin Dynamics

Pathogenic implications for autoimmune mechanisms derived by comparative eQTL analysis of CD4+ versus CD8+ T cells

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Kasela, Silva, Kisand, Kai, Tserel, Liina, Kaleviste, Epp, Remm, Anu, Fischer, Krista, Esko, Tõnu, Westra, Harm-Jan +7 more·2017·PLOS Genetics
DOI 10.1371/journal.pgen.1006643PMC PMC5352142
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Abstract

Inappropriate activation or inadequate regulation of CD4+ and CD8+ T cells may contribute to the initiation and progression of multiple autoimmune and inflammatory diseases. Studies on disease-associated genetic polymorphisms have highlighted the importance of biological context for many regulatory variants, which is particularly relevant in understanding the genetic regulation of the immune system and its cellular phenotypes. Here we show cell type-specific regulation of transcript levels of genes associated with several autoimmune diseases in CD4+ and CD8+ T cells including a trans-acting regulatory locus at chr12q13.2 containing the rs1131017 SNP in the RPS26 gene. Most remarkably, we identify a common missense variant in IL27, associated with type 1 diabetes that results in decreased functional activity of the protein and reduced expression levels of downstream IRF1 and STAT1 in CD4+ T cells only. Altogether, our results indicate that eQTL mapping in purified T cells provides novel functional insights into polymorphisms and pathways associated with autoimmune diseases.

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  • In the pQTL-eQTL colocalisation analysis, significant pQTL loci were compared to eQTL Catalogue datasets, excluding those of Kasela et al. and Lepik et al. due to sample overlap.

    “In an pQTL–eQTL colocalisation analysis, we compared our significant pQTL loci to all eQTL Catalogue datasets18, excluding those of Kasela et al.57 and Lepik et al.29 due to sample overlap, containing gene expression, exon expression, transcript usage and txrevise event usage data, and GTEx (version 8)58 datasets containing gene expression data”

    From Genetic determinants of plasma protein levels in the Estonian population (2024) · cites this paper as Kasela et al. [57]

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Authors
Kasela, S., Kisand, K., Tserel, L., Kaleviste, E., Remm, A., Fischer, K., Esko, T., Westra, H.-J., Fairfax, B. P., Makino, S., Knight, J. C., Franke, L., Metspalu, A., Peterson, P., & Milani, L.
Affiliations
  • University of Tartu (EE)
  • Broad Institute (US)
  • Centre for Human Genetics (GB)
  • University Medical Center Groningen (NL)
Corresponding author
  • Peterson, Pärt
  • Milani, Lili
ORCID
Venue
PLOS Genetics
Publisher
Public Library of Science (PLoS)
Published
2017-03-01
Volume
13
Issue
3
Pages
e1006643
ISSN
  • 1553-7390 (ISSN-L)
  • 1553-7404
DOI
10.1371/journal.pgen.1006643
PMC
PMC5352142
OpenAlex
W2593684819
Cited by
113
References
7
Citations by year
2026: 9 · 2025: 11 · 2024: 6 · 2023: 11 · 2022: 11 · 2021: 19 · 2020: 13 · 2019: 15 · 2018: 12 · 2017: 6
FWCI
8.16
Type
journal article
Language
en
Update notices
new_version (2017-03-15)
Open access
Licence
Locations
  • PLoS Genetics (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion)
  • University of Groningen research database (University of Groningen / Centre for Information Technology) (acceptedVersion, cc-by)
  • Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Figshare (submittedVersion, cc-by)
  • Oxford University Research Archive (ORA) (University of Oxford) (submittedVersion, cc-by)
  • University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, cc-by)
Funders
  • Center of Translational Genomics, University of Tartu (SP1GVARENG)
  • Eesti Teadusagentuur (IUT20-60, IUT2-2)
  • The European Union through the European Regional Development Fund (2014-2020.4.01.15-0012)
  • ERA-Net.Rus (EGIDA)
  • Wellcome Trust (074318, 090532/Z/09/Z)
  • European Research Council (FP7/2007-2013; ERC Grant agreement number 281824)
  • Medical Research Council (98082)
  • National Institute for Health Research (Oxford Biomedical Research Centre)
  • Horizon 2020 (ePerMed (grant no. 692145))
  • Seventh Framework Programme (BBMRI-LPC (grant no 313010))
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Immunology and Microbiology › Immunology › T-cell and B-cell Immunology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
Keywords
expression quantitative trait loci, CD4+ T cells, CD8+ T cells, autoimmune disease, type 1 diabetes, IL-27, RPS26, IRF1, STAT1, cell type-specific gene regulation, genetic polymorphism, inflammatory diseases
MeSH terms
Autoimmune Diseases, Chromosome Mapping, Diabetes Mellitus, Type 1, Gene Expression Regulation, Genotype, Humans, Mutation, Ribosomal Proteins, CD4-Positive T-Lymphocytes, CD8-Positive T-Lymphocytes, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide, Quantitative Trait Loci, STAT1 Transcription Factor, Interferon Regulatory Factor-1, Genome-Wide Association Study, HEK293 Cells, Interleukin-27

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