Assya Trofimov

Transposable elements regulate thymus development and function

Jean-David Larouche

Céline M. Laumont

Assya Trofimov

Krystel Vincent

Leslie Hesnard

Sylvie Brochu

Caroline Côté

Juliette Humeau

Eric Bonneil

Joël Lanoix

Chantal Durette

Patrick Gendron

Jean-Philippe Laverdure

Ellen R. Richie

Sébastien Lemieux

Pierre Thibault

Claude Perreault

2024-02-01

bioRxiv (prépublication)

doi.org

Transposable elements regulate thymus development and function 1

Jean-David Larouche

Céline M. Laumont

Assya Trofimov

Krystel Vincent

Leslie Hesnard

Sylvie Brochu

Caroline Côté

Juliette Humeau

Eric Bonneil

Joël Lanoix

Chantal Durette

Patrick Gendron

Jean-Philippe Laverdure

Ellen Rothman Richie

Sébastien Lemieux

Pierre Thibault

Claude Perreault

21 Transposable elements (TE) are repetitive sequences representing ~45% of the human and mouse genomes 22 and are highly expressed by medul… (voir plus)lary thymic epithelial cells (mTEC). In this study, we investigated the 23 role of transposable elements (TE), which are highly expressed by medullary thymic epithelial cells 24 (mTEC), on T-cell development in the thymus. We performed multi-omic analyses of TEs in human and 25 mouse thymic cells to elucidate their role in T cell development. We report that TE expression in the 26 human thymus is high and shows extensive ageand cell lineage-related variations. TEs interact with 27 multiple transcription factors in all cell types of the human thymus. Two cell types express particularly 28 broad TE repertoires: mTECs and plasmacytoid dendritic cells (pDC). In mTECs, TEs interact with 29 transcription factors essential for mTEC development and function (e.g., PAX1 and RELB) and generate 30 MHC-I-associated peptides implicated in thymocyte education. Notably, AIRE, FEZF2, and CHD4 31 regulate non-redundant sets of TEs in murine mTECs. Human thymic pDCs homogenously express large 32 numbers of TEs that lead to the formation of dsRNA, triggering RIG-I and MDA5 signaling and 33 explaining why thymic pDCs constitutively secrete IFN ɑ/β. This study illustrates the diversity of 34 interactions between TEs and the adaptive immune system. TEs are genetic parasites, and the two thymic 35 cell types most affected by TEs (mTEcs and pDCs) are essential to establishing central T-cell tolerance. 36 Therefore, we propose that the orchestration of TE expression in thymic cells is critical to prevent 37 autoimmunity in vertebrates. 38

2023-01-01

(published)

www.semanticscholar.org

Transposable elements regulate thymus development and function 1

Jean-David Larouche

Céline M. Laumont

Assya Trofimov

Krystel Vincent

Leslie Hesnard

Sylvie Brochu

Caroline Côté

Juliette Humeau

Eric Bonneil

Joël Lanoix

Chantal Durette

Patrick Gendron

Jean-Philippe Laverdure

Ellen Rothman Richie

Sébastien Lemieux

Pierre Thibault

Claude Perreault

21 Transposable elements (TE) are repetitive sequences representing ~45% of the human and mouse genomes 22 and are highly expressed by medul… (voir plus)lary thymic epithelial cells (mTEC). In this study, we investigated the 23 role of transposable elements (TE), which are highly expressed by medullary thymic epithelial cells 24 (mTEC), on T-cell development in the thymus. We performed multi-omic analyses of TEs in human and 25 mouse thymic cells to elucidate their role in T cell development. We report that TE expression in the 26 human thymus is high and shows extensive ageand cell lineage-related variations. TEs interact with 27 multiple transcription factors in all cell types of the human thymus. Two cell types express particularly 28 broad TE repertoires: mTECs and plasmacytoid dendritic cells (pDC). In mTECs, TEs interact with 29 transcription factors essential for mTEC development and function (e.g., PAX1 and RELB) and generate 30 MHC-I-associated peptides implicated in thymocyte education. Notably, AIRE, FEZF2, and CHD4 31 regulate non-redundant sets of TEs in murine mTECs. Human thymic pDCs homogenously express large 32 numbers of TEs that lead to the formation of dsRNA, triggering RIG-I and MDA5 signaling and 33 explaining why thymic pDCs constitutively secrete IFN ɑ/β. This study illustrates the diversity of 34 interactions between TEs and the adaptive immune system. TEs are genetic parasites, and the two thymic 35 cell types most affected by TEs (mTEcs and pDCs) are essential to establishing central T-cell tolerance. 36 Therefore, we propose that the orchestration of TE expression in thymic cells is critical to prevent 37 autoimmunity in vertebrates. 38

Two types of human TCR differentially regulate reactivity to self and non-self antigens

Assya Trofimov

Philippe Brouillard

Jean-David Larouche

Jonathan Séguin

Jean-Philippe Laverdure

Ann Brasey

Grégory Ehx

Denis-Claude Roy

Lambert Busque

Silvy Lachance

Sébastien Lemieux

Claude Perreault

2022-08-17

iScience (publié)

doi.org

Two types of human TCR differentially regulate reactivity to self and non-self antigens

Assya Trofimov

Philippe Brouillard

Jean-David Larouche

Jonathan Y. Séguin

Jean-Philippe Laverdure

A. Brasey

Grégory Ehx

D. Roy

Lambert Busque

Silvy Lachance

Sébastien Lemieux

Claude Perreault

2022-04-28

bioRxiv (preprint)

doi.org

Accounting for Variance in Machine Learning Benchmarks

Naz Sepah

Edward Raff

Kanika Madan

Vikram Voleti

Samira Ebrahimi Kahou

Strong empirical evidence that one machine-learning algorithm A outperforms another one B ideally calls for multiple trials optimizing the l… (voir plus)earning pipeline over sources of variation such as data sampling, data augmentation, parameter initialization, and hyperparameters choices. This is prohibitively expensive, and corners are cut to reach conclusions. We model the whole benchmarking process, revealing that variance due to data sampling, parameter initialization and hyperparameter choice impact markedly the results. We analyze the predominant comparison methods used today in the light of this variance. We show a counter-intuitive result that adding more sources of variation to an imperfect estimator approaches better the ideal estimator at a 51 times reduction in compute cost. Building on these results, we study the error rate of detecting improvements, on five different deep-learning tasks/architectures. This study leads us to propose recommendations for performance comparisons.

2021-01-01

MLSys (publié)

arxiv.org