{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T10:26:32Z","timestamp":1777890392552,"version":"3.51.4"},"reference-count":42,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T00:00:00Z","timestamp":1777593600000},"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>Transcriptome\u2212wide association study (TWAS) contributes to discovering novel susceptibility genes related to diseases. Single-cell RNA sequencing (scRNA-seq) has been extensively applied to characterize cellular heterogeneity in various biological and pathological conditions. However, few studies have integrated TWAS and scRNA-seq to decode the pathogenesis of myasthenia gravis (MG) at cellular resolution.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We integrated multi-omics data from GEO, genome-wide association study (GWAS) catalog, and Genotype-Tissue Expression (GTEx) databases, including scRNA-seq data, GWAS summary data, and expression quantitative trait loci. A set of risk genes genetically associated with MG were identifies using TWAS analysis. TWAS activity status across cell types in MG at single cell resolution was assessed based on these genes. Seven machine learning algorithms were performed to identify high TWAS activity related signature genes. Machine learning benchmark was incorporated to select the best model using R package \u201cmlr3\u201d. Meanwhile, we performed scRNA-seq on peripheral blood collected from MG patients and healthy controls to generate a validation dataset.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      scRNA-seq analysis revealed an elevated proportion of CD14\n                      <jats:sup>+<\/jats:sup>\n                      monocytes in MG patients, accompanied by notable transcriptomic reprogramming. Compared with other cell types, CD14\n                      <jats:sup>+<\/jats:sup>\n                      monocytes exhibited higher TWAS activity. High TWAS activity subsets mainly enriched in the MG group and functionally associated with mTORC1 signaling, complement, and inflammatory response pathways. Machine learning-based feature selection revealed six robust signature genes (FKBP15, EHMT1, CHPT1, KLC1, SCPEP1, and CFD). Among these, CFD showed elevated expression early in high TWAS activity monocyte development. Compared to control group, CFD maintained higher levels throughout disease progression in MG group. CFD\n                      <jats:sup>+<\/jats:sup>\n                      CD14\n                      <jats:sup>+<\/jats:sup>\n                      monocytes exhibited enhancer plasticity than CFD\n                      <jats:sup>-<\/jats:sup>\n                      CD14\n                      <jats:sup>+<\/jats:sup>\n                      monocytes and were related to complement and coagulation cascades, antigen processing and presentation, and proteasome pathways. CFD\n                      <jats:sup>+<\/jats:sup>\n                      CD14\n                      <jats:sup>+<\/jats:sup>\n                      monocytes in MG exhibit enhanced intercellular communication activity compared to CFD\n                      <jats:sup>-<\/jats:sup>\n                      cells, demonstrating greater signal reception and transmission capacity.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      In summary, this study systematically delineates the important role of CFD\n                      <jats:sup>+<\/jats:sup>\n                      CD14\n                      <jats:sup>+<\/jats:sup>\n                      monocytes in MG pathogenesis through multi-omics analysis, providing novel insights for a deeper understanding of MG immunopathology and potential therapeutic targets.\n                    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