bioRxivImmunology
Transposable elements regulate thymus development and function
J.-D. Larouche, C. M. Laumont, A. Trofimov, K. Vincent, L. Hesnard, S. Brochu, C. Cote, J. Humeau, E. Bonneil, J. Lanoix, C. Durette, P. Gendron, J.-P. Laverdure, E. R. Richie, S. Lemieux, P. Thibault, C. Perreault
Abstract
Transposable elements (TE) are repetitive sequences representing [~]45% of the human and mouse genomes and are highly expressed by medullary thymic epithelial cells (mTEC). In this study, we investigated the role of TEs on T-cell development in the thymus. We performed multi-omic analyses of TEs in human and mouse thymic cells to elucidate their role in T cell development. We report that TE expression in the human thymus is high and shows extensive age- and cell lineage-related variations. TE expression correlates with multiple transcription factors in all cell types of the human thymus. Two cell types express particularly broad TE repertoires: mTECs and plasmacytoid dendritic cells (pDC). In mTECs, transcriptomic data suggest that TEs interact with transcription factors essential for mTEC development and function (e.g., PAX1 and REL), and immunopeptidomic data showed that TEs generate MHC-I-associated peptides implicated in thymocyte education. Notably, AIRE, FEZF2, and CHD4 regulate small yet non-redundant sets of TEs in murine mTECs. Human thymic pDCs homogenously express large numbers of TEs that likely form dsRNA, which can activate innate immune receptors, potentially explaining why thymic pDCs constitutively secrete IFN LJ/{beta}. This study highlights the diversity of interactions between TEs and the adaptive immune system. TEs are genetic parasites, and the two thymic cell types most affected by TEs (mTEcs and pDCs) are essential to establishing central T-cell tolerance. Therefore, we propose that orchestrating TE expression in thymic cells is critical to prevent autoimmunity in vertebrates.
- organism: human
- organism: mouse
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: gene regulatory network inference
- research focus: cell-cell communication
- approach: statistical model
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