{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T06:40:36Z","timestamp":1772692836007,"version":"3.50.1"},"reference-count":44,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T00:00:00Z","timestamp":1772668800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Immunol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Immune dysregulation, classified as distinct phenotypes within inborn errors of immunity (IEIs) and collectively known as primary immune regulatory disorders (PIRD) is increasingly recognized as a major contributor to morbidity; however, the quantitative cellular signatures identifying this state remain incompletely characterized.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>We aimed to describe cellular changes underlying immune dysregulation and to develop an integrative biomarker framework that links inflammatory\/regulatory imbalance with genetic background and clinical phenotypes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We performed multiparametric flow cytometry and computational modeling in 39 genetically defined IEI patients (PIRD and non-PIRDs) and 17 age-matched healthy controls. Correlation networks, t-SNE, and FlowSOM clustering were applied to examine regulatory and inflammatory compartments. An immune dysregulation score (IDS) was derived as the log-ratio of inflammatory to regulatory subsets, and clinical dysregulation was quantified by a composite presence\/absence score. Integrated models were evaluated using ROC, calibration, and decision curve analyses in accordance with TRIPOD recommendations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Patients showed significant alterations in immune networks, with consistent reductions in FOXP3\n                      <jats:sup>+<\/jats:sup>\n                      Tregs and transitional Breg cells alongside expansions of BCL6\n                      <jats:sup>+<\/jats:sup>\n                      , CD4\n                      <jats:sup>+<\/jats:sup>\n                      ICOS\n                      <jats:sup>+<\/jats:sup>\n                      BCL6\n                      <jats:sup>+<\/jats:sup>\n                      Tfh-like subsets, and activated CD8\n                      <jats:sup>+<\/jats:sup>\n                      T cells. IDS clearly distinguished patients from controls (AUC = 0.75, 95% CI 0.60\u20130.90, p &amp;lt; 0.01) and correlated inversely with clinical z scores (r = \u20130.65, p = 2.0 \u00d7 10\n                      <jats:sup>-5<\/jats:sup>\n                      ). Subgroup analyses displayed elevated IDS in patients with genetically confirmed PIRDs whereas non-PIRDs showed IDS values comparable to controls. Integrating IDS with clinical features tiered patients into three clusters that consistent with the underlying genetic causes. Composite machine-learning models modestly improved stratification of mild and moderate cases, though severe phenotypes remained heterogeneous.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our findings identify IDS as a novel quantitative biomarker that captures the regulatory\u2013inflammatory imbalance underlying immune dysregulation, distinguishes PIRDs from non-PIRD IEIs, and provides a translational framework for patient stratification. IDS may inform longitudinal monitoring and guide targeted therapeutic interventions in immune dysregulation syndromes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fimmu.2026.1735655","type":"journal-article","created":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T05:25:35Z","timestamp":1772688335000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Cellular signatures of immune dysregulation in inborn errors of immunity: development of a quantitative immune balance score"],"prefix":"10.3389","volume":"17","author":[{"given":"Dilan","family":"Inan","sequence":"first","affiliation":[{"name":"Division of Pediatric Immunology, Department of Basic Sciences of Pediatrics, Institute of Child\u2019s Health","place":["Ankara, T\u00fcrkiye"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hacer Neslihan","family":"Bildik","sequence":"additional","affiliation":[{"name":"Division of Pediatric 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Immunology, Department of Basic Sciences of Pediatrics, Institute of Child\u2019s Health","place":["Ankara, T\u00fcrkiye"]},{"name":"Division of Pediatric Allergy, Department of Pediatrics, Hacettepe University Faculty of Medicine","place":["Ankara, T\u00fcrkiye"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aslihan Berra","family":"Bolat","sequence":"additional","affiliation":[{"name":"Division of Pediatric Immunology, Department of Basic Sciences of Pediatrics, Institute of Child\u2019s Health","place":["Ankara, T\u00fcrkiye"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Deniz","family":"Cagdas","sequence":"additional","affiliation":[{"name":"Division of Pediatric Immunology, Department of Basic Sciences of Pediatrics, Institute of Child\u2019s Health","place":["Ankara, T\u00fcrkiye"]},{"name":"Division of Pediatric Immunology, Department of Pediatrics, Hacettepe University Faculty of Medicine","place":["Ankara, 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