{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,6]],"date-time":"2026-11-06T04:13:24Z","timestamp":1793938404316,"version":"4.8.0"},"reference-count":50,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"the Special Funds for the Construction of High-Level Hospitals in Guangdong Province","award":["2024"],"award-info":[{"award-number":["2024"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Innate Immun"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Lupus nephritis (LN) is characterized by significant heterogeneity and a complex pathophysiology, which traditional methods struggle to fully resolve. Advanced multi-omics approaches are essential to disentangle its cellular and molecular drivers.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We employed an integrative strategy combining single-cell RNA sequencing (scRNA-seq) profiling of LN biopsies with large-scale bulk RNA-seq cohorts. We applied non-negative matrix factorization (NMF) to scRNA-seq data to define robust immune meta-programs and utilized CellChat to decode cell-cell communication networks. Leveraging these insights to overcome sample size limitations, we prioritized key pathways and developed 399 machine learning predictive models using bulk transcriptomics, validated on independent cohorts.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      ScRNA-seq analysis revealed a distinct cellular landscape, including a rare population of plasmacytoid dendritic cells (pDCs) and an expanded population of CD56dimCD16\n                      <jats:sup>+<\/jats:sup>\n                      \u2009natural killer (NK) cells expressing high levels of IFN-\u03b3 and perforin, suggesting a role in inflammatory pathology. Macrophage subpopulation CM2 emerged as a central pro-inflammatory hub, potentially driving fibrosis via autocrine signaling and epithelial activation. We observed reduced Treg-B cell interactions, suggesting a regulatory collapse. Our machine learning models, based on innate immunity, circadian rhythms, apoptosis, and NF-\u03baB signaling, achieved high diagnostic accuracy (AUC = 0.929 for innate immunity). Hub genes, including\n                      <jats:italic toggle=\"yes\">CYBB<\/jats:italic>\n                      ,\n                      <jats:italic toggle=\"yes\">CSF2RB<\/jats:italic>\n                      , and\n                      <jats:italic toggle=\"yes\">IRF8<\/jats:italic>\n                      , were confirmed to be upregulated in LN and correlated with clinical severity in external validation datasets. Molecular docking simulations suggested a potential structural basis for CYBB-dexamethasone interaction, providing a hypothesis for future verification.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>This study identifies CM2 macrophages and dysregulated pDC-NK axes as key drivers of LN. By bridging cellular interactomes with clinical predictive modeling, we provide a robust roadmap for precision detection and identifying potential therapeutic targets in LN.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/17534259261426818","type":"journal-article","created":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T12:59:31Z","timestamp":1777294771000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Advancing lupus nephritis research through multi-omics and predictive modeling"],"prefix":"10.1177","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6232-8341","authenticated-orcid":false,"given":"Lisha","family":"Mou","sequence":"first","affiliation":[{"name":"Shenzhen Second People's Hospital"},{"name":"MetaLife Lab, Shenzhen Institute of Translational Medicine, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Lu","sequence":"additional","affiliation":[{"name":"Shenzhen Second People's Hospital"},{"name":"MetaLife Lab, Shenzhen Institute of Translational Medicine, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zijing","family":"Wu","sequence":"additional","affiliation":[{"name":"Shenzhen Second People's Hospital"},{"name":"MetaLife Lab, Shenzhen Institute of Translational Medicine, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7108-5035","authenticated-orcid":false,"given":"Zuhui","family":"Pu","sequence":"additional","affiliation":[{"name":"MetaLife Lab, Shenzhen Institute of Translational Medicine, Shenzhen, China"},{"name":"Imaging Department, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People\u2019s Hospital, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,4,27]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41584-023-00925-5"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41581-021-00481-9"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41580-023-00615-w"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41576-023-00599-5"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1038\/44565"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41571-023-00830-6"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-pathmechdis-051222-113147"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-020-0752-4"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-023-06130-4"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2019.00772"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41581-023-00722-z"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1172\/JCI148277"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1186\/s43166-023-00186-w"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12969-023-00909-5"},{"key":"e_1_3_2_16_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41590-019-0398-x"},{"key":"e_1_3_2_17_2","doi-asserted-by":"publisher","DOI":"10.4049\/jimmunol.1103031"},{"key":"e_1_3_2_18_2","doi-asserted-by":"publisher","DOI":"10.1093\/ndt\/gfy125"},{"key":"e_1_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.kint.2022.05.033"},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13075-016-1050-x"},{"key":"e_1_3_2_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cell.2021.04.048"},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41596-024-01045-4"},{"key":"e_1_3_2_23_2","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.43803"},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v033.i01"},{"key":"e_1_3_2_25_2","unstructured":"R Core Team. 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