Biology subjects

Galeota, E.

Publications and source records attributed to Galeota, E..

3 recordsLinked to original sources

bioRxiv2024

Regulatory T-cells in multiple sclerosis are activated by Epstein-Barr Virus and produce IL-10 in the central nervous system

Regulatory T-cells (Tregs) maintain immune homeostasis, but antigens activating adaptive Tregs in human pathologies are ill-defined. EOMES+type-1 regulatory (Tr1)-like T-cells had a dysregulated homeostasis in multiple sclerosis (MS), which was related to their activation in the central nervous system (CNS). EOMES+Tr1-like cells were enriched and clonally expanded in patients cerebrospinal fluid (CSF) and were the major IL-10-producing T-cell subset in the CNS. Regulatory T-cells from PwMS produced IL-10 and IFN-{gamma} with antigens derived from Epstein-Barr Virus (EBV), but not from myelin. EOMES+Tr1-like cells responded selectively to the latency-associated antigen EBNA1, whereas FOXP3+Tregs and Th1-cells responded also to lytic EBV antigens. EBNA1-specific EOMES+Tr1-like cells were present in patients carrying the HLA-DRB1*15:01 risk allele, were associated with anti-EBNA1 IgG and disappeared upon therapeutic B-cell depletion. IL-10+EOMES+Tr1-like were present in MS brain lesions, and some were in the vicinity of EBV-infected B-cells and CD8+T-cells. Notably, EOMES+Tr1-like cells suppressed CD8+T-cell activation by EBV-infected B-cells. We propose that the insufficient protective functions of Tregs in MS are due to their aberrant anti-viral specificities that promote immune escape of a disease-associated virus.

immunology↗

bioRxiv2023

CIA: a Cluster Independent Annotation method to investigate cell identities in scRNA-seq data

Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of the transcriptional landscape of complex tissues, enabling the discovery of novel cell types and biological functions. However, the identification and classification of cells from scRNA-seq datasets remain significant challenges. To address this, we developed a new computational tool called CIA (Cluster Independent Annotation), which accurately identifies cell types across different datasets without requiring a fully annotated reference dataset or complex machine learning processes. Based on predefined cell type signatures, CIA provides a highly user-friendly and practical solution to functional annotation of single cells. Our results demonstrate that CIA outperforms other state-of-the-art approaches, while also having significantly lower computational running time. Overall, CIA simplifies the process of obtaining reproducible signature-based cell assignments that can be easily interpreted through graphical summaries providing researchers with a powerful tool to explore the complex transcriptional landscape of single cells. The CIA framework is implemented in both the Python and R programming languages, making it applicable to all main single-cell analysis frameworks, and it is available under the MIT license with its documentation at the following links: Python package: https://pypi.org/project/cia-python/ Python tutorial: https://cia-python.readthedocs.io/en/latest/tutorial/Cluster_Independent_Annotation.html R package and tutorial: https://github.com/ingmbioinfo/CIA_R

bioinformatics↗

bioRxiv2019

Dynamics of transcriptional regulation from total RNA-seq experiments

The kinetic rates of RNA synthesis, processing and degradation determine the dynamics of transcriptional regulation by governing both the abundance and the responsiveness to modulations of premature and mature RNA species. The study of RNA dynamics is largely based on the integrative analysis of total and nascent transcription, with the latter being quantified through RNA metabolic labeling. We describe here INSPEcT-, a computational method based on mathematical modeling of intronic and exonic expression, able to derive the dynamics of transcription from steady state or time course profiling of just total RNA, without requiring any information on nascent transcripts. Our approach closely recapitulates the kinetic rates obtained through RNA metabolic labeling, improves the ability to detect changes in transcripts half-lives, reduces the cost and complexity of the experiments, and can be adopted to study experimental conditions where nascent transcription cannot be readily profiled. Finally, we applied INSPEcT- to the characterization of post-transcriptional regulation landscapes in dozens of physiological and disease conditions. This approach was included in the INSPEcT Bioconductor package, which can now unveil RNA dynamics from steady state or time course data, with or without the profiling of nascent RNA.

systems biology↗