Epigenetic Changes in Long COVID Patients

Long COVID-19 Syndrome, Long COVID Symptoms

Long COVID-19, COVID-19, gut, Chronic inflammation, epigenetics, methylation, omics, artificial intelligence, ai, machine learning, Multi-omics approaches, biomarker, Immune aging, genomics, transcriptomics, spatial omics, epigenomics, cytokines

The goal of this observational study is to improve the understanding of the biological mechanisms underlying long COVID and to identify molecular biomarkers that may support its diagnosis, prognosis, and future precision medicine approaches in adults with long COVID, adults who have fully recovered from COVID-19, and healthy control participants. The main questions it aims to answer are: * What molecular, immunological, epigenetic, and microbiome profiles distinguish individuals with long COVID from recovered COVID-19 participants and healthy controls? * How are viral persistence, immune dysregulation, and alterations in the gut-immune axis associated with the development and clinical manifestations of long COVID? * Which molecular biomarkers may improve disease diagnosis, patient stratification, and the identification of potential therapeutic targets? Participants will: * Undergo clinical evaluation and provide information about their medical history and symptoms. * Provide biological samples, including blood and, when clinically indicated, intestinal biopsy tissue collected during routine colonoscopy procedures. * Undergo comprehensive molecular analyses, including immunological, epigenetic, transcriptomic, proteomic, and microbiome profiling. * Have their clinical and molecular data integrated using advanced computational approaches to identify biological signatures associated with long COVID. The results of this study may improve the understanding of the biological mechanisms underlying long COVID and support the development of novel biomarkers and future precision medicine approaches for diagnosis, prognosis, patient stratification, and therapeutic target identification.

The study aims to identify the immunological and molecular mechanisms underlying Long COVID, with particular focus on persistent cardiopulmonary manifestations and the role of gut-resident immunity. The project integrates clinical, immunological, transcriptomic, epigenomic, microbiome and computational analyses to identify biomarkers associated with disease phenotypes and potential therapeutic targets. The study is both retrospective and perspective, providing a significant numerosity. The study is non-profit and its execution does not involve interventions outside of the normal clinical pathway established for the patient. Three complementary objectives will be addressed. Aim 1 will identify immunological and molecular signatures associated with Long COVID. Approximately 800 participants enrolled in the San Raffaele Hospital Long COVID outpatient cohort (OSR COVID-BIOB Clinical study, NCT04318366), together with recovered COVID-19 subjects and pre-pandemic healthy controls, will be analyzed. Clinical data include acute infection characteristics, disease course, cardiopulmonary manifestations and longitudinal follow-up. Peripheral blood samples already collected will undergo transcriptomic, epigenomic and immunological characterization. Bulk RNA sequencing and DNA methylation profiling will identify differentially expressed genes and epigenetic alterations, which will be validated in independent cohorts. Immunophenotyping will assess T-, B- and NK-cell subsets, regulatory T cells, SARS-CoV-2-specific antibodies, interferon responses, complement activation and memory B cells. Standardized sample processing, technical and biological quality controls, correction for batch effects and adjustment for relevant confounders will ensure data reliability. Aim 2 will investigate gut-resident immune cells and intestinal alterations in Long COVID using left-over intestinal biopsies collected from patients undergoing clinically indicated colonoscopy within the MedMol Biobank. Multi-omic characterization will evaluate transcriptomic and epigenomic profiles of intestinal immune and epithelial cells through bulk and single-cell RNA sequencing, spatial transcriptomics, whole-genome DNA methylation analysis and chromatin accessibility profiling. Mast-cell activation will be assessed using circulating and tissue biomarkers, while intestinal inflammation, permeability and gut microbiome composition will be evaluated through blood, stool and biopsy analyses. Computational cell-cell interaction analyses and complementary in vitro experiments will investigate communication pathways between gut-resident immune cells and systemic immune responses. Aim 3 will integrate immunological, intestinal and clinical findings using machine-learning approaches to identify molecular signatures associated with Long COVID phenotypes. Multi-omics datasets and clinical variables will be harmonized and analyzed using supervised and unsupervised learning methods to identify patient subgroups, prioritize biomarkers, predict disease evolution and generate models supporting personalized therapeutic strategies. Model performance will be evaluated using independent validation datasets and cross-validation procedures. Clinical information will be collected from approximately 800 participants, including demographic characteristics, acute COVID-19 presentation, disease progression, cardiopulmonary function, gastrointestinal manifestations and longitudinal follow-up evaluations. Peripheral blood samples will be processed according to standardized operating procedures for isolation of peripheral blood mononuclear cells, plasma and nucleic acids. Intestinal biopsies will be collected exclusively from residual tissue obtained during clinically indicated colonoscopies. Transcriptomic analyses will include bulk RNA sequencing and targeted validation of differentially expressed genes. Epigenomic analyses will include whole-genome bisulfite sequencing and complementary large-scale DNA methylation profiling. Chromatin accessibility and histone modifications will be evaluated in selected samples. Immunological characterization will include multiparametric flow cytometry, cytokine profiling, complement activity, antibody quantification and functional immune assays. Gut analyses will include transcriptomics, epigenomics, spatial transcriptomics, microbiome characterization, intestinal permeability markers and inflammatory biomarkers. Data quality will be ensured through standardized operating procedures covering patient recruitment, sample collection, processing, storage, laboratory analyses, data entry and statistical analyses. Biological samples will be pseudonymized, tracked using barcode-based systems and stored in certified biobanks under controlled conditions. Clinical and laboratory data will undergo predefined quality control procedures, including range and consistency checks, automated validation rules and regular monitoring to identify missing, inconsistent or out-of-range values. Source data verification will compare registry data with clinical records and biobank documentation where appropriate. Standardized data dictionaries, harmonized coding systems and version-controlled analytical pipelines will ensure reproducibility across participating centers. Data management will comply with the General Data Protection Regulation (GDPR). Secure databases with controlled access, automated backups and version control will be used throughout the study. Laboratory procedures will include technical and biological replicates, instrument calibration, standardized protocols and correction for batch effects to minimize technical variability. The statistical analysis plan includes descriptive statistics, differential expression and differential methylation analyses, mixed-effects models for longitudinal and cell-specific analyses, and appropriate parametric or non-parametric tests according to data distribution. Multiple-testing correction will be performed using the Benjamini-Hochberg procedure. Potential confounders, including age, sex and time from infection, will be included in multivariable analyses where appropriate. Multi-omics integration will combine transcriptomic, epigenomic, immunological, microbiome and clinical data to identify molecular pathways associated with Long COVID. Supervised machine-learning models will be developed to predict clinical outcomes, while unsupervised approaches will identify novel patient subgroups. Feature selection and biomarker prioritization will be performed using established computational methods, and biological pathway enrichment analyses will support interpretation of identified molecular signatures. Model performance will be assessed using independent validation datasets, cross-validation procedures and standard performance metrics. Power calculations indicate that the planned sample size provides adequate statistical power to detect clinically meaningful transcriptomic, epigenomic and immunological differences between Long COVID subgroups and controls. The study includes discovery and validation cohorts to increase robustness and reproducibility of identified biomarkers. Missing data will be managed using multiple imputation approaches when appropriate, together with sensitivity analyses to evaluate the impact of incomplete observations. Batch effects and technical variability will be addressed using standardized preprocessing pipelines and computational correction methods. Quality control will be performed throughout all analytical steps. Potential risks include variability in sample quality, patient heterogeneity, missing data and integration of high-dimensional datasets. These risks will be mitigated through standardized collection and processing procedures, predefined inclusion criteria, comprehensive documentation of clinical covariates, rigorous quality control, randomized laboratory processing, blinded analyses, appropriate technical controls and independent validation of computational models. Alternative analytical strategies are planned if specific methodologies prove unsuitable for individual datasets. The study is expected to generate a comprehensive molecular characterization of Long COVID by integrating systemic immunity, intestinal immune responses and clinical manifestations. The identification of robust biomarkers and molecular pathways associated with different disease phenotypes may improve patient stratification, support prediction of long-term outcomes and facilitate the development of personalized therapeutic approaches. The analytical framework developed in this project may also be applicable to other post-viral and chronic inflammatory disorders.

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