{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T23:42:02Z","timestamp":1793403722331,"version":"4.5.0"},"reference-count":58,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:00:00Z","timestamp":1788393600000},"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. Cell. Infect. Microbiol."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Ulcerative colitis (UC) is a chronic inflammatory bowel disease characterized by intestinal immune dysregulation and mucosal barrier dysfunction. Macrophages play central roles in gut immunity, yet their metabolic reprogramming and heterogeneity in UC remain insufficiently characterized.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We integrated single-cell RNA sequencing (scRNA-seq), bulk transcriptomics, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms (LASSO, SVM-RFE, and Random Forest) to identify candidate macrophage-associated metabolic regulators in UC. Pseudotime trajectory, CellChat, and functional enrichment analyses were used to assess differentiation, intercellular interactions, and pathway involvement. Diagnostic performance was evaluated using ROC curves and validated in independent datasets. Experimental validation was performed using Hematoxylin and Eosin (H&amp;amp;E) staining for histopathological assessment and Western blotting for protein expression analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>scRNA-seq identified 505 macrophage-specific genes, with pseudotime analysis suggesting differentiation branches marked by RGCC and FOSL2. Transcriptomic profiling revealed 1,058 differentially expressed genes, enriched in TNF, epithelial\u2013mesenchymal transition, and NF-\u03baB pathways. Six macrophage-associated immunometabolic genes (CYBB, CR1, INPP5D, CTSH, IFI16, and NCF4) were identified through WGCNA and machine learning analyses. These genes were strongly correlated with macrophage infiltration and cytokine signaling, showing high diagnostic performance (AUC &amp;gt; 0.98), although potential overfitting cannot be fully excluded. Consensus clustering stratified UC patients into two molecular subtypes, with Cluster 1 exhibiting a proinflammatory phenotype. H&amp;amp;E staining confirmed characteristic mucosal inflammation and epithelial damage in UC tissues, while Western blotting validated the upregulation of the six key regulators in UC tissues.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This multi-omics analysis identifies six macrophage metabolic regulators (CYBB, CR1, INPP5D, CTSH, IFI16, and NCF4) with potential diagnostic relevance in UC. Experimental validation provides supportive evidence for their association with macrophage-related immunometabolic alterations in UC.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fcimb.2026.1858403","type":"journal-article","created":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T05:22:55Z","timestamp":1788412975000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Multi-omics analysis identifies macrophage immunometabolic signatures in ulcerative colitis"],"prefix":"10.3389","volume":"16","author":[{"given":"Rui","family":"Zhang","sequence":"first","affiliation":[{"name":"Department of Colorectal Surgery, Fifth Hospital of Shanxi Medical University, Shanxi Provincial People\u2019s Hospital","place":["Taiyuan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huifang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Occupational Health, School of Public Health, MOE Key Laboratory of Coal Environmental Pathogenicity and Prevention, Shanxi Medical University","place":["Taiyuan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhifeng","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Fifth Hospital of Shanxi Medical University, Shanxi Provincial People\u2019s Hospital","place":["Taiyuan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yonggang","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Colorectal Surgery, Fifth Hospital of Shanxi Medical University, Shanxi Provincial People\u2019s Hospital","place":["Taiyuan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongwei","family":"Jia","sequence":"additional","affiliation":[{"name":"Fifth Hospital of Shanxi Medical University, Shanxi Provincial People\u2019s Hospital","place":["Taiyuan, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,3]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"5008","DOI":"10.1016\/j.csbj.2021.09.001","article-title":"Machine learning and deep learning methods that use omics data for metastasis prediction","volume":"19","author":"Albaradei","year":"2021","journal-title":"Comput. 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