ImmuneSpace (immunespace.org) is a freely accessible database that hosts curated human immune-profiling data from a wide range of studies. It was created as the central data repository of the Human Immunology Project Consortium (HIPC), a multi-center NIH-funded program to characterize the diverse states of the human immune system and its regulation, using consistently formatted data [PMID: 23648045]. The overarching goal is to investigate human immune perturbations using state-of-the-art systems-level profiling technologies and innovative methodologies, and to make these data available to the scientific community, accessible for both humans and machines.
ImmuneSpace hosts data related to immunological exposure, demographics, cytokine profiling, cytometry, and neutralizing antibody assays. It builds on the ImmPort data model, a long-term archive of research and clinical data for the NIH [PMID: 29485622], but implements additional standardization and normalization rules, following the HIPC Data Standards initiative [PMID: 22343568, 26861911, 31272390, 32283555].
ImmuneSpace is continuously updated with data and features. Each study undergoes enhanced curation to ensure consistent use of ontology-based terminology, enabling more efficient queries both within and across studies. Study components stored in different repositories, unparsed raw data, and computationally inaccessible elements are integrated through manual curation, such as study timelines and author-determined ‘immune signatures’. Recently, we have added the ‘Finder Feature’ which simplifies complex searches through a hierarchical, tree-based interface that allows users to visually browse, search with autocomplete, or select entire branches of categorized filters while providing definitions, synonyms, and ontology links.
The HIPC Project and the ImmuneSpace platform demonstrate the feasibility and benefits of a structured approach to representing human immunological studies to elucidate system-level phenomena.
U01 AI167892
Computational and Systems Immunology (COMP)
Kerstin Westendorf, M. Kojima, James A. Overton et al.· Journal of Immunology· 0 citations
Building a knowledgebase that integrates multiple data repositories requires the concepts, relationships, and data schemas/formats to be harmonized across those repositories. One technique is designing a common data model (CDM) that encompasses all concepts and relationships, transforming the data into the CDM, and utilizing ontologies to provide shared semantics.
The Adaptive Immune Receptor Repertoire Knowledge Commons (AKC) is a publicly accessible repository that integrates data and knowledge about 1) adaptive immune receptors (AIRs) and AIR repertoires from the AIRR Data Commons, 2) AIR germline allele, genotype, haplotype, and population genetic data from the OGRDB and VDJbase, and 3) AIR specificity data from the IEDB and IRAD. We designed a CDM for the AKC based upon the Ontology for Biomedical Investigations, a community standard for scientific data integration, and we used the LinkML data modeling language for implementation.
The AKC provides a consistent CDM for study, subject, and sample information; immune exposures and other study events; sample collection; assays and processing; and data processing and analysis workflows. The CDM also provides adaptive immunity domain knowledge for chains, receptors, antigens, epitopes, and MHC/HLA. LinkML’s flexible data modeling language allows for existing data standards, such as the AIRR Standards, and ontologies from the OBO Foundry to be directly incorporated.
The foundation of the AKC is data integrated from these community-supported repositories and harmonized around a CDM based on widely adopted ontologies and data standards. The AKC assembles the critical mass of data required to develop highly accurate predictive algorithms for long-standing questions of critical importance (e.g., predicting AIR specificity, determining the contribution of AIR germline polymorphisms to disease propensity) and to ask questions across a large and diverse set of subjects with a variety of health and disease phenotypes.
The research described is supported by the National Institute of Allergy and Infectious Diseases of the National Institutes of Health under award number U24AI177622.
Computational and Systems Immunology (COMP)
Scott Christley, Felix Breden, Kevin A. Burns et al.· Journal of Immunology· 0 citations
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
CEDAR’s current capabilities, report on progress in curation, database development, and tool availability, and outline the opportunities and challenges ahead for expanding its scope and utility to the cancer research community are described.
Zeynep Koşaloğlu-Yalçın, Ibel Carri, Daniel Marrama et al.· Frontiers in Oncology· 0 citations
Revising all immune receptor records to produce resolved, standardized, and analysis-ready receptor data will enable researchers to seamlessly query large-scale repertoires for receptors with experimentally verified specificity in the IEDB, link orphan sequences to known targets, and support cross-repository studies of receptor-epitope pairs and their relationship to health and disease.
Lonneke Scheffer, Eve Richardson, R. Vita et al.· Journal of Immunology· 0 citations
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