{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T04:48:37Z","timestamp":1792039717228,"version":"4.4.0"},"reference-count":48,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T00:00:00Z","timestamp":1791936000000},"content-version":"vor","delay-in-days":13,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005089","name":"Natural Science Foundation of Beijing Municipality","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005089","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Clinical &amp; Translational Med"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>\n                      Early identification of vulnerable carotid plaques is crucial for stroke prevention, but the upstream metabolic processes linked to endothelial transition and immune recruitment remain poorly defined. We characterised lactate metabolism in ACKR1\n                      <jats:sup>+<\/jats:sup>\n                      endothelial cells (ECs) in human carotid plaques and derived a machine\u2010learning signature for discriminating plaque vulnerability.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Internal and external (GSE253903) single\u2010cell datasets were integrated to identify endothelial subpopulations, and bulk transcriptomic (GSE163154) and in\u2010house proteomic data were used to assess metabolism\u2013immune correlations. Least absolute shrinkage and selection operator (LASSO) regression selected core features for a logistic model, which was locked and applied to three independent cohorts: GSE43292 (Stary stage \u2265 IV vs. I\u2013II), GSE28829 (early vs. advanced lesions) and GSE100927 (normal vs. atherosclerotic arteries). Key molecules were validated in situ by immunohistochemistry (IHC) and spatial transcriptomics in independent plaques (five stable, five unstable).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      An ACKR1\n                      <jats:sup>+<\/jats:sup>\n                      EC subpopulation linked to neovascularisation showed hyperactive glycolysis and enhanced lactate metabolism. Computational inference linked lactate accumulation to HIF\u20101\u03b1 stability, CXCL12 release and CD8\n                      <jats:sup>+<\/jats:sup>\n                      T\u2010cell infiltration via the CXCL12\u2013CXCR4 axis, and the monocarboxylate transporters\n                      <jats:italic>SLC16A3<\/jats:italic>\n                      and\n                      <jats:italic>SLC16A4<\/jats:italic>\n                      emerged as quantitative readouts of this \u2018lactate\u2013immune\u2019 state. In unstable plaques, IHC confirmed higher atypical chemokine receptor 1 (ACKR1; geometric mean ratio [GMR]\u00a0=\u00a06.82), CD8\n                      <jats:sup>+<\/jats:sup>\n                      T\u2010cell density (GMR\u00a0=\u00a04.63), SLC16A3 (GMR\u00a0=\u00a04.74) and HIF\u20101\u03b1 (GMR\u00a0=\u00a04.02; all\n                      <jats:italic>q<\/jats:italic>\n                      \u00a0&lt;\u00a0.05), with lactylation elevated in the lipid core. A four\u2010feature model (\n                      <jats:italic>ACKR1<\/jats:italic>\n                      ,\n                      <jats:italic>SLC16A3<\/jats:italic>\n                      ,\n                      <jats:italic>SLC16A4<\/jats:italic>\n                      , CD8\n                      <jats:sup>+<\/jats:sup>\n                      T\u2010cell cytotoxicity score) performed well in training and retained discrimination in the progression\u2010stratified cohorts, while in GSE100927 it showed high disease\u2010identification performance across three arterial beds.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      Lactate metabolic reprogramming under hypoxia is associated with ACKR1\n                      <jats:sup>+<\/jats:sup>\n                      EC\u2010related CD8\n                      <jats:sup>+<\/jats:sup>\n                      T\u2010cell recruitment and plaque instability. This chain is an association\u2010based hypothesis requiring functional confirmation. The signature discriminates robustly at both the vulnerability\u2010stratification and disease\u2010identification levels, providing a quantifiable framework for molecular classification of atherosclerotic plaques.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/ctm2.70856","type":"journal-article","created":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T04:31:07Z","timestamp":1792038667000},"source":"Crossref","is-referenced-by-count":0,"title":["Multi\u2010omics integration and machine learning identify an ACKR1\n                    <sup>+<\/sup>\n                    endothelial cell\u2010based lactate\u2010immune signature associated with carotid plaque vulnerability and progression"],"prefix":"10.1002","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5147-7990","authenticated-orcid":false,"given":"Ji","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Vascular Surgery Peking Union Medical College 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