Biology subjects

Auge, F.

Publications and source records attributed to Auge, F..

5 recordsLinked to original sources

bioRxiv2024

Lymphopenia drives T cell exhaustion in immunodeficient STING gain-of-function mice

STING gain-of-function (GOF) mutations are associated with the severe autoinflammatory disease designated STING Associated Vasculopathy with onset in Infancy (SAVI). Mice with the STING GOF V154M mutation develop profound T cell lymphopenia, partly due to a blockage of T cell development in the thymus. To better characterize the mechanisms of peripheral T cell dysfunctions, we conducted a transcriptomic and phenotypic analysis on sorted splenic CD4+ and CD8+ mature T cells from STING GOF V154M mice. We identify a T cell exhaustion phenotype that manifests at a terminal stage, acquired early in life but only after reaching the peripheral environment. This phenotype is independent of type I interferons and does not rely on intrinsic STING activation in either T cells or stromal cells. Mechanistically, the limited number of mature T cells that reach the periphery appear to be quickly impacted by the lymphopenic environment, experiencing heightened stimulation of the IL-7 receptor and TCR pathways, including the NFAT pathway, a key factor in T cell exhaustion. By performing transplantation experiments with STING GOF long term-hematopoietic stem cells (LT-HSCs) along with supportive wild-type bone marrow (BM) cells, we were able to prevent the T cell exhaustion of STING GOF T cells in the resulting non-lymphopenic context, demonstrating that lymphopenia is a major driver of T cell exhaustion in STING GOF mice. T cell exhaustion, although less severe, was also observed in lymphopenic mice carrying Rag1 hypomorphic mutations. In conclusion, our results, which highlight T cell exhaustion induced by lymphopenia, could have important implications for the management of patients with severe immune deficiencies. HighlightsO_LIWe describe a phenotype of T cell exhaustion in STING GOF V154M mice, which is acquired early in life and in the periphery. C_LIO_LISTING GOF-associated T cell exhaustion is independent of type I IFNs, and STING GOF/activation in T cells or in stromal cells is not sufficient for T cell exhaustion. C_LIO_LILymphopenia is a major driver of T cell exhaustion in STING GOF mice, and increased antigenic/IL-7 stimulation of T cells in the lymphopenic context of STING GOF mice could be implicated in the induction of T cell exhaustion. C_LI

immunology↗

bioRxiv2023

Large-scale computational modelling of the M1 and M2 synovial macrophages in Rheumatoid Arthritis

Macrophages play an essential role in rheumatoid arthritis (RA). Depending on their phenotype (M1 or M2), they can play a role in the initiation or resolution of inflammation. The M1/M2 ratio in RA is higher than in healthy controls. Despite this, no treatment targeting specifically macrophages is currently used in clinics. Thus, devising strategies to selectively deplete proinflammatory macrophages and promote anti-inflammatory macrophages could be a promising therapeutic approach in RA. State-of-the-art molecular interaction maps of M1 and M2 macrophages in rheumatoid arthritis are available and represent a dense source of knowledge; however, these maps remain limited by their static nature. Discrete dynamic modelling can be employed to study the emergent behaviours of these systems. Nevertheless, handling such large-scale models is challenging. Due to their massive size, it is computationally demanding to identify biologically relevant states in a cell- and disease-specific context. In this work, we developed an efficient computational framework that converts molecular interaction maps into Boolean models using the CaSQ tool. Next, we use a newly developed BMA tool version deployed to a high-performance computing cluster to identify the models steady states. The identified attractors are then validated using gene expression datasets and prior knowledge. We successfully applied our framework to generate and calibrate the first RA M1 and M2 macrophage Boolean models. Using single and double knockout simulations, we identified NFkB, JAK1/JAK2, and ERK1/Notch1 as potential targets that could selectively suppress proinflammatory macrophages, and GSK3B as a promising target that could promote anti-inflammatory macrophages in RA.

systems biology↗

bioRxiv2022

Benchmarking computational methods for B-cell receptor reconstruction from single-cell RNA-seq data

Multiple methods have recently been developed to reconstruct full-length B-cell receptors (BCRs) from single-cell RNA-seq (scRNA-seq) data. This need emerged from the expansion of scRNA-seq techniques, the increasing interest in antibody-based drug development and the importance of BCR repertoire changes in cancer and autoimmune disease progression. However, a comprehensive assessment of performance-influencing factors like the sequencing depth, read length or the number of somatic hypermutations (SHMs) as well as guidance regarding the choice of methodology are still lacking. In this work, we evaluated the ability of six available methods to reconstruct full-length BCRs using one simulated and three experimental SMART-seq datasets. In addition, we validated that the BCRs assembled in silico recognize their intended targets when expressed as monoclonal antibodies. We observed that methods like BALDR, BASIC and BRACER showed the best overall performance across the tested datasets and conditions whereas only BASIC demonstrated acceptable results on very short read libraries. Furthermore, the de novo assembly-based methods BRACER and BALDR were the most accurate in reconstructing BCRs harboring different degrees of SHMs in the variable domain, while TRUST4, MiXCR and BASIC were the fastest. Finally, we propose guidelines to select the best method based on the given data characteristics.

bioinformatics↗

bioRxiv2022

DiSiR: a software framework to identify ligand-receptor interactions at subunit level from single-cell RNA-sequencing data

Most of cell-cell interactions and crosstalks are mediated by ligand-receptor interactions. The advent of single-cell RNA-sequencing (scRNA-seq) techniques has enabled characterizing tissue heterogeneity at single-cell level. Over the past recent years, several methods have been developed to study ligand-receptor interactions at cell type level using scRNA-seq data. However, there is still no easy way to query the activity of a specific user-defined signaling pathway in a targeted way or map the interactions of the same subunit with different ligands as part of different receptor complexes. Here, we present DiSiR, a fast and easy-to-use permutation-based software framework to investigate how individual cells are interacting with each other by analyzing signaling pathways of multi-subunit ligand-activated receptors from scRNA-seq data, not only for available curated databases of ligand-receptor interactions, but also for interactions that are not listed in these databases. We show that, when utilized to infer melanoma disease map on a gold-standard dataset, DiSiR outperforms other well-known permutation-based methods, e.g., CellPhoneDB and ICELLNET. To demonstrate DiSiRs utility in exploring data and generating biologically relevant hypotheses, we apply it to COVID lung and rheumatoid arthritis (RA) synovium scRNA-seq data and highlight potential differences between inflammatory pathways at cell type level for control vs. disease samples.

cell biology↗

bioRxiv2021

ASIGNTF: AGNOSTIC SIGNATURE USING NTF: A UNIVERSAL AGNOSTIC STRATEGY TO ESTIMATE CELL-TYPES ABUNDANCE FROM TRANSCRIPTOMIC DATASETS

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSMolecular signatures for deconvolution of immune cell types have been proposed, based on a methodology that relies on the biological classification of the cell types being studied. When working with less known biological material, a data-driven approach is needed to uncover the underlying classes and construct ad hoc signatures. ResultsWe introduce a new approach, ASigNTF: Agnostic Signature using Non-negative Tensor Factorization, to perform the deconvolution of cell types from transcriptomics data (RNAseq and microarray). ASigNTF, which is based on two complementary statistical/mathematical tools: non-negative tensor factorization (for dimensionality reduction) and the Herfindahl-Hirschman index (for signature selection), can be applied to any type of tissue as long as transcriptomic data on isolated cells is available. As a direct result of the new method, we propose two new signatures for the deconvolution of immune cell types, one consisting of a relatively small set of 415 genes, which is more compatible with microarray platforms, and a larger set of 915 genes. Using external datasets, our two signatures outperform the CIBERSORT LM22 signature in deconvolution of RNA-seq data. Our signature with 415 genes allows to recognize a larger number of cell types compared to the ABIS microarray signature. ConclusionsThe paper proposes a new method, ASigNTF; applies the method, and also provides a software implementation that allows to identify molecular signatures for deconvolution of complex tissues and specifically up to 16 immune cell types from micro-array or RNA-seq data. HO_SCPLOWIGHLIGHTSC_SCPLOWO_LISeveral signatures of immune cell types have been proposed, which follow a methodology deeply rooted in the known biological classification of the investigated cell types. C_LIO_LIWhen working with less known biological material, a more agnostic, data-driven approach is required to uncover the underlying classes and construct ad hoc signatures. C_LIO_LIWe present ASigNTF, a new agnostic approach to cell type classification and signature selection supported by an application software. C_LIO_LIWe discuss the results of benchmarking our proposed signatures, ABIS-seq and CIBERSORT on external datasets. C_LI

bioinformatics↗