Chronic viral infections are ubiquitous in humans, with individuals carrying multiple viruses that can reactivate during physiological stress, including severe illness1. Notably, SARS-CoV-2 infection has been shown to reactivate chronic viruses such as Epstein–Barr virus and cytomegalovirus, yet the full extent, temporal dynamics and immunological impact of viral reactivation in COVID-19 remain incompletely understood2, 3, 4, 5, 6–7. Here, leveraging multi-omic longitudinal data from 1,154 hospitalized patients with COVID-19 from the Immunophenotyping Assessment in a COVID-19 Cohort (IMPACC) study, we reveal significant reactivation of Herpesviridae and Anelloviridae during acute COVID-19, with distinct temporal dynamics for different viruses, and demonstrate that reactivation correlates with disease severity, host immune effects and clinical outcomes. Although our results do not establish causation between virus reactivation and clinical outcomes, we highlight the prevalence of chronic viral reactivation during acute COVID-19 and long COVID. Our findings challenge the prevailing view that chronic viral reactivation is primarily a consequence of immunosuppression, demonstrating that reactivations occur frequently in immunocompetent individuals during severe illness and in association with increased systemic inflammation. Additionally, we demonstrate persistence of viral reactivation in convalescence, and report an association of Anelloviridae with long COVID. This study provides immune, transcriptomic and metabolomic signatures of viral reactivation that could inform future strategies to prognosticate and treat acute COVID-19 and long COVID. Chronic reactivation of distinct herpesviruses and anelloviruses occur during acute and long COVID-19, and track with disease severity, inflammation and outcomes, revealing immune signatures with prognostic potential.
Cole P. Maguire, Jing Chen, Nadine Rouphael et al.· Nature· 3 citations
Understanding vaccine durability is key to designing immunizations with long-term efficacy. Leveraging the Immune Signatures Data Resource, a compendium of transcriptomic and immunological responses from 1405 healthy adults (18+ years) across 24 vaccines, we investigated shared immune mechanisms underlying durable antibody responses. The dataset spans live (yellow fever, smallpox), recombinant viral-vector (Ebola), inactivated (influenza), and glycoconjugate (pneumococcal) vaccines.
Data preprocessing included imputation, normalization, and alignment across post-vaccination time points. We applied advanced machine learning (ML) frameworks to predict antibody immunogenicity and durability. Feature selection for high-dimensional, low-sample-size multi-omics datasets was performed using HSIC Lasso to identify predictors of antibody responses. Selected features served as input to ensemble, regularized regression, and gradient-boosting models (e.g. DT, RF, LASSO, XGB, CatBoost). We compared single-target and multi-output approaches, evaluating stacked, chained, and wrapper-based strategies, and implemented multi-layer neural networks to capture complex relationships among immune features.
Post-vaccination time points explained ∼15% of the total variance, indicating shared immune kinetics across vaccine types, while age and sex contributed minimally. Gradient-boosting and multi-output modeling approaches achieved the highest predictive accuracy across vaccines, highlighting the value of integrating correlated outcomes. Neural network models similarly captured complex, nonlinear immune signatures, albeit with reduced explainability.
Conserved transcriptional modules, particularly interferon-signaling and plasmablast-related pathways, emerged as strong predictors of antibody durability. This integrative ML framework enables identification of key immune signatures critical for developing vaccines with durable responses, advancing data-driven strategies for systems vaccinology.
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Computational and Systems Immunology (COMP)
Marco Sanna, Jing Chen, Paolo Palma et al.· Journal of Immunology· 0 citations
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