Systemic Lupus Erythematosus (SLE) is a complex immune-mediated disease, with high morbidity and mortality.Systemic Lupus Erythematosus (SLE) is a complex immune-mediated disease, with high morbidity and mortality. It is characterized by the hyperactivation of CD4+ T cells, inefficient responses of CD8+ T cells, B cell dysregulation, and abnormal lymphocyte function, resulting in chronic inflammation and the production of autoantibodies in multiple organs. These factors contribute to a lower life expectancy and an increased risk of serious infections and cardiovascular complications. It is important to highlight that SLE presents a strong gender bias, affecting women 10 times more than men, with an even higher prevalence in individuals with supernumerary X chromosomes (XXY, XXX). Given that the dosage of X-linked genes is regulated by X-chromosome inactivation (XCI), alterations in this epigenetic process have been pointed to as possible contributors to the pathogenesis of SLE. In this project, we propose to analyze multi-omics data from pairs of monozygotic (MZ) twins discordant for SLE – a unique model that allows the epigenetic contributions to be dissected independently of genetic variability – with the aim of identifying epigenetic signatures, discovering new biomarkers, and evaluating the impact of XCI dysregulation on the development of the disease. To this end, we will use genetic, epigenetic, and transcriptomic profiles from blood samples of MZ twins available in the TwinsUK database. The data will be analyzed and integrated to identify biomarkers associated with the pathogenesis of SLE, with a special focus on XCI dysregulation. The transcriptome analysis will focus on X-linked genes, including those that escape inactivation, as well as components of the XIST interactome, while the methylation profiles will allow the identification of differentially methylated regions (DMRs) between twins. The functional importance of the DMRs will be explored to identify epigenetic modifications associated with the disease. In addition, female and male samples will be compared to identify modifications specific to the inactive X chromosome (Xi). We will apply machine learning approaches to identify epigenetic signatures of the disease, including gender-biased DMRs. New strategies based on RNA-seq and whole genome sequencing (WGS) data will allow us to infer genes that escape XCI in different cell types and to analyze potential biases in the random inactivation of one of the X chromosomes. Finally, we will integrate the different levels of multi-omics data through techniques such as Canonical Correlation Analysis (CCA) and Multi-Omics Factor Analysis (MOFA), with the aim of identifying combinatorial patterns associated with XCI dysregulation in SLE and relevant biomarkers of the disease. To validate and expand our results, we will analyze the same processes in a pair of MZ twins discordant for the diagnosis of SLE, followed by our team over the last six years at Hospital de Santa Maria. We will perform WGS, RNA-seq, and Reduced Representation Bisulfite Sequencing (RRBS) on various immune cells of these twins and of their mother, to validate the conclusions obtained in the United Kingdom cohort. We will further use Single Cell RNA Sequencing (scRNA-seq) and Single Cell Bisulfite Sequencing (scBS-seq) to explore epigenetic alterations and expression patterns specific to each cell type in SLE. The integration of these data will help identify epigenomic patterns associated with the disease, with longitudinal samples providing information about the progression of the pathology and hereditary factors. In the last step, we will extend our analysis to a cohort of more than 60 patients with SLE, already established by our team at Hospital de Santa Maria. We will validate previously identified biomarkers through low-cost assays, such as RT-qPCR, IMPLICON, and RNA-FISH. The comparison between young people and adults with SLE will allow us to identify markers associated with more severe forms of the disease. This validation in a larger cohort will consolidate the relevance of these biomarkers as potential therapeutic targets. This project contributes to the United Nations 2030 Agenda, particularly to Goal 3 (Health and Well-being), by facilitating the discovery of epigenetic biomarkers with potential clinical impact. The success of the project is supported by the vast experience of the team, complemented by leading specialists who will provide support in critical areas. Furthermore, the combination of knowledge in molecular and computational biology will foster the development of young researchers, equipping them with interdisciplinary skills to address complex biological questions, aligning with Goal 4 (Quality Education). The project also adopts Open Science principles, promoting greater accessibility and collaboration in research.