MAKAAO, an open access resource linking human autoantibodies and autoimmune diseasesDownload PDF Download PDF CommentOpen accessPublished: 14 September 2026Fabien Maury ORCID: orcid.org/0000-0003-2854-01671,2,the MAKAAO curators and contributors,Adrien Coulet ORCID: orcid.org/0000-0002-1466-062X2 na2 &…Maud de Dieuleveult ORCID: orcid.org/0000-0002-3387-18361 na2 Communications Medicine volume 6, Article number: 482 (2026) Cite this articleSave articleView saved researchMAKAAO is an open, FAIR-compliant resource of human autoantibodies, their targets, associated diseases, and laboratory tests. It is manually curated from the literature and welcomes contributions from the autoimmune disease community.SubjectsAutoimmune diseasesDatabasesDiagnostic markersAutoimmune diseases arise from dysfunction of the adaptive immune system, whereby self-antigens are targeted by self-antibodies, named autoantibodies. These are generally detected in blood or cerebrospinal fluid, and are widely used as biomarkers for autoimmune diseases, guiding differential diagnosis. Autoimmune diseases collectively affect an estimated 3–5% of the global population1. Yet, taken individually, many of these are rare2. The scarcity and fragmentation of clinical data for individual rare autoimmune diseases, the limited economic incentive for drug development, and the concentration of expertise in a few specialized centers can contribute to delayed or missed diagnoses.Several biomedical databases covering aspects of autoimmune diseases and related concepts already exist. For example, the AAgAtlas, initiated in 2017, compiles information on autoantigens3, including a mix of manually curated and text-mined data. Another example is SAbDab, a database focused on structural information for antibodies4. Other efforts have focused only on small portions of the autoimmune landscape, such as connective tissue diseases5, or are not fully open projects. However, these resources are either not well-maintained, largely incomplete, or focus on synthetic commercial autoantibodies. None is dedicated to the exhaustive indexing of human autoantibodies in combination with their molecular targets, associated diseases, and clinical signs. Moreover, no resource connects autoimmune diseases and autoantibodies with the diagnostic assays used clinically to detect their presence. MAKAAO (MApping Knowledge about Autoimmune diseases, Autoantibodies, and Other clinical signs) aims to fill this gap, offering a central and translational resource for human autoimmunity research and care.MAKAAO knowledge baseMAKAAO catalogues 374 human autoantibodies, distributed in 70 main categories, together with their molecular targets, associated diseases, clinical signs, and the laboratory tests used to assess their presence (Fig. 1). All autoantibodies, together with their relationships to targets, diseases and clinical signs, were manually curated by experts, then systematically verified and enriched. In addition, relationships are fully traceable, with their provenance recorded via curator-documented PubMed identifiers.Fig. 1: Description of the content and provenance of the MAKAAO knowledge base.Full size imageMAKAAO catalogues human autoantibodies, their targets, associated diseases, clinical signs, and the laboratory tests used to assess their presence. The core of MAKAAO is manually curated, including its mappings to HPO and LOINC analytes (compounds measured by laboratory test as defined in the LOINC terminology). Relationships between Orphanet diseases and their clinical signs (HPO phenotypes) were obtained from Orphanet.Each antibody is associated with a list of synonyms and cross-references. We manually established mappings between MAKAAO autoantibodies and biological components of LOINC (logical observation identifiers names and codes)—a terminology providing an international standard vocabulary for medical laboratory tests and observations6—to link these autoantibodies with the laboratory tests used to assess their presence. We also mapped MAKAAO to autoantibody-positive phenotypes in the Human Phenotype Ontology (HPO)7, which is widely used to document patient phenotypes and symptoms, including to annotate electronic health records. To maintain the mappings between MAKAAO and HPO, and to contribute to the enrichment of HPO, we regularly submit newly identified MAKAAO autoantibodies that do not yet have corresponding HPO terms. Since 2021, the number of autoantibody-positive phenotypes in HPO has increased from 45 to 295. As a part of the MAKAAO project, a total of 244 modifications were made to this ontology, including 198 new terms as well as revisions, new synonyms, and cross-references. In addition, relationships between diseases and their HPO clinical signs were obtained from the Orphanet database8 and integrated into our resource.Associated services and collaborative developmentThe MAKAAO website offers an index of autoantibodies that is searchable by name or identifier, and a browsable hierarchy of autoantibody categories. This new resource follows the FAIR (findability, accessibility, interoperability, and reusability) principles of open science9. Particular attention has been devoted to ensuring that the resource is publicly accessible and interoperable with major biomedical databases and ontologies. The website and dataset have been indexed by major search engines and evaluated using the FAIR-checker service10. MAKAAO is also available as a machine-readable and well-structured knowledge graph, which can be programmatically queried through a SPARQL endpoint.MAKAAO is intended to be as comprehensive as possible; however, autoimmunity is a rapidly evolving research field, and continuous updates are necessary to maintain the completeness of the database. Consequently, we invite the immunology community and experts in autoimmune diseases to contribute to MAKAAO by submitting potentially missing information and new findings via the website contact page. Community feedback will be reviewed and incorporated, along with findings from the literature. Regarding long-term maintenance, the knowledge base will be regularly updated on our institutional server as well as in scientific data open repositories.Utility for clinical and translational researchMAKAAO enables the characterization of patient phenotypes with greater granularity. Historically, patient phenotypes have often been described in electronic health records using HPO terms as implemented, for example, in the French National Rare Disease Registry (BNDMR)11. In the context of autoimmune diseases, patient phenotypes are often defined by the presence of specific autoantibodies, and this may also influence the evolution of their pathology or enable the diagnosis of a more specific clinical subtype. However, HPO coverage of autoantibody positivity is incomplete, and its terms are not linked to a comprehensive resource about autoantibodies and their properties. The MAKAAO project addresses this limitation by systematically cataloging known autoantibodies along with additional information, and by extending HPO with new terms. In a clinical context, MAKAAO can be used by physicians to retrieve laboratory tests that could be used to detect autoantibodies associated with a specific disease. For example, “Stiff person spectrum disorder” is associated with 5 autoantibodies and 26 distinct LOINC lab tests.For research purposes, MAKAAO can also facilitate the selection of patient cohorts for epidemiologists or medical data scientists. The terms and relationships defined in MAKAAO enable the identification of patients diagnosed with a specific autoimmune disease defined in Orphanet or the Unified Medical Language System (UMLS)12 terms, or exhibiting at least one associated clinical sign (HPO terms), or having tested positive for an associated autoantibody (LOINC). To illustrate this use, we queried the clinical data warehouse of the Greater Paris University Hospitals to identify patients tested for anti-centromere antibodies, which is a complex query to build without MAKAAO. Our knowledge base offers links from anti-centromere autoantibodies to the LOINC laboratory tests which can be used to query for patients that underwent some tests. As a result, we retrieved a total of 82,738 patients tested for this specific autoantibody by one of these LOINC tests specified by MAKAAO, thus highlighting the potential of our resource for designing large-scale retrospective studies.Lastly, MAKAAO enables computational biology and translational studies by linking autoantibodies, autoimmune diseases, and molecular targets, thereby providing access to richer molecular information. The inclusion of targets referenced in UniProt establishes connections with Gene Ontology annotations, biological pathway databases, and other omics resources. For example, MAKAAO can be used to investigate molecular mechanisms that are shared across groups of diseases by identifying similarities based on dysregulated molecular functions or biological processes. In addition, it may facilitate drug repurposing by highlighting molecular commonalities between diseases.To illustrate the added value of MAKAAO relative to large language model (LLM)-powered search engines, we conducted a comparative assessment in which an LLM (ChatGPT 5.2) was queried using the following prompt: “Can you list all the human autoantibodies and categories of autoantibodies you are aware of?”. Of the 374 autoantibodies or categories of autoantibodies curated in MAKAAO, only 86 (23%) were identified in the LLM-generated response, highlighting substantial gaps in coverage.SummaryIn summary, we present a translational and easy-to-use resource dedicated to autoantibodies, their molecular targets, autoimmune diseases, and the laboratory tests used for their detection. This resource is intended for clinicians and researchers working with autoimmune diseases, especially rare ones. The knowledge base is human-curated and designed to support both clinical and translational research and to evolve through community-driven contributions. In this spirit of collaborative effort, we once again invite experts to contribute additional information and forthcoming findings, as well as feedback on the usefulness of this resource in clinical practice.Related linksMAKAAO: https://makaao.inria.fr. AAgAtlas: http://biokb.ncpsb.org/aagatlas/. SAbDab: https://opig.stats.ox.ac.uk/webapps/sabdab-sabpred/sabdab. LOINC: https://loinc.org/. HPO: https://hpo.jax.org/. Orphanet: https://www.orpha.net/. BNDMR: https://www.bndmr.fr/. UniProt: https://www.uniprot.org/. GitHub repository: https://github.com/f-maury/MAKAAO_core. MAKAAO core dataset: https://doi.org/10.5281/zenodo.17953761 MAKAAO knowledge graph: https://doi.org/10.5281/zenodo.18155507.Data availabilityGenerated data (MAKAAO core dataset, and MAKAAO knowledge graph) have been deposited on the Zenodo platform, on the MAKAAO website and the code used to process these data is also freely available on GitHub.ReferencesAhsan, H. Origins and history of autoimmunity—a brief review. Rheumatol. Autoimmun. 3, 9–14 (2023).Article CAS Google Scholar Baldovino, S., Moliner, A. M., Taruscio, D., Daina, E. & Roccatello, D. Rare diseases in Europe: from a Wide to a local perspective. Isr. Med. Assoc. J. 18, 359–363 (2016).PubMed Google Scholar Wang, D. et al. AAgAtlas 1.0: a human autoantigen database. Nucleic Acids Res. 45, D769–D776 (2017).Article CAS PubMed Google Scholar Schneider, C., Raybould, M. I. J. & Deane, C. M. SAbDab in the age of biotherapeutics: updates including SAbDab-nano, the nanobody structure tracker. 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Garcelon, A. Garofano, Y. Girardeau, B. Guillet, H. Maillard, R. Malone, K. Deiva, L. Soussand, F. Schaefer, C. Lucano, and C. Fabrizzi for their help and our research teams, HeKA and Pathophysiological basis of skeletal dysplasia, for their constant support.FundingThe authors acknowledge the Filières de Santé Maladies Rares BRAIN-TEAM and Filnemus, funded by the French Ministry of Health, and the Data Intelligence Institute of Paris (diiP), IdEx Université de Paris (ANR-18-IDEX-0001) for funding. P.N.R. was supported by NIH National Human Genome Research Institute (NHGRI) 5U24HG011449-05. F.M. received funding from Institut Imagine for further work on this project.Author informationAuthor notesThese authors contributed equally: Adrien Coulet, Maud de Dieuleveult.Authors and AffiliationsUniversité Paris Cité, INSERM, U1163 Institut Imagine, Paris, FranceFabien Maury, Killian Halberda & Maud de DieuleveultInria, Inserm, Université Paris Cité, U1346 HeKA, Paris, FranceFabien Maury, Anne-Sophie Jannot & Adrien CouletIndependent researcher, The Hague, The NetherlandsSolène GrosdidierBerlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, GermanyPeter N. RobinsonThe Jackson Laboratory for Genomic Medicine, Farmington, USAPeter N. RobinsonNeurology Department, Reference Center for Neuromuscular Diseases and ALS, Aix-Marseille University, AP-HM, La Timone Hospital, Marseille, FranceEmilien DelmontService de médecine interne, Hôpital Tenon, Assistance Publique-Hôpitaux de Paris (AP-HP), Paris, FranceChristel GérardinImmunology Department, Centre Hospitalier Lyon Sud, Hospices Civils de Lyon, Oullins-Pierre-Bénite, FranceDavid GoncalvesDepartment of Internal Medicine and Clinical Immunology, Referral Centre for rare systemic autoimmune diseases North of France, North-West, Mediterranean and Guadeloupe (CeRAINOM), CHU Lille, Univ. Lille, Inserm, U1286 - INFINITE - Institute for Translational Research in Inflammation, Lille, FranceEric HachullaNeurology Department, Centre Hospitalier Lyon Sud, Hospices Civils de Lyon, Oullins-Pierre-Bénite, FranceBastien JoubertFrench National Rare Disease Registry (BNDMR), Assistance Publique-Hôpitaux de Paris (AP-HP), Paris, FranceAnne-Sophie JannotPaediatric Neurology Department, Necker-Enfants Malades Hospital, Assistance Publique-Hôpitaux de Paris (AP-HP), Université Paris Cité, Paris, FranceIsabelle DesguerreAuthorsFabien MauryView author publicationsSearch author on:PubMed Google ScholarAdrien CouletView author publicationsSearch author on:PubMed Google ScholarMaud de DieuleveultView author publicationsSearch author on:PubMed Google ScholarConsortiathe MAKAAO curators and contributorsSolène Grosdidier, Killian Halberda, Peter N. Robinson, Emilien Delmont, Christel Gérardin, David Goncalves, Eric Hachulla, Bastien Joubert, Anne-Sophie Jannot & Isabelle DesguerreContributionsF.M. designed and implemented the knowledge base and associated services. F.M., A.C., M.D. and the MAKAAO curators and contributors conducted the literature review to collect the data; reviewed and completed the knowledge base; managed the linking with HPO; or helped in setting up and designing the project. A.C. and M.D. jointly designed and supervised the work.Corresponding authorsCorrespondence to Fabien Maury, Adrien Coulet or Maud de Dieuleveult.Ethics declarationsCompeting interestsA.C. receives funding from Sanofi and Doctolib companies, for research projects independent from MAKAAO.Peer reviewPeer review informationCommunications Medicine thanks Nils Landegren, Anish Behere and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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