{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T18:00:37Z","timestamp":1767204037286,"version":"build-2238731810"},"reference-count":51,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T00:00:00Z","timestamp":1749772800000},"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. Genet."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>End-stage renal disease (ESRD) is increasing worldwide, and although kidney transplantation improves survival, long-term graft loss\u2013driven mainly by immune-mediated rejection\u2013remains common. We aimed to delineate immune mechanisms that distinguish recipients with stable versus impaired graft function.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      Peripheral blood mononuclear cells from kidney-transplant recipients with normal (n = 10) or impaired (n = 10) renal function were profiled by single-cell RNA sequencing. Fourteen immune populations were identified; CD4\n                      <jats:sup>+<\/jats:sup>\n                      T-cell \u201cstemness\u201d was quantified using mRNAsi and EREG_mRNAsi indices, lineage trajectories were reconstructed with Monocle, and ligand\u2013receptor communication was inferred with iTalk. Findings were validated in an independent bulk RNA-seq cohort (n = 192) using differential expression and weighted gene co-expression network analysis (WGCNA).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Recipients with graft dysfunction exhibited (i) expansion of Th17 cells and contraction of Treg cells, (ii) significant loss of CD4\n                      <jats:sup>+<\/jats:sup>\n                      T-cell stem-like features (lower mRNAsi\/EREG_mRNAsi, p &amp;lt; 0.001), and (iii) pseudotime trajectories skewed toward Th17 differentiation. iTalk revealed enhanced S100A8\/A9-TLR4 signalling from myeloid cells to neutrophils, consistent with reduced circulating neutrophils and presumptive intragraft accumulation. Bulk validation confirmed the stemness deficit and identified eight hub genes (API5, CAPRIN1, CCT2, DLG1, NMD3, RDX, SENP7, S100A4) that correlated with both low stemness and poor clinical outcome. Pathway enrichment implicated cell-morphogenesis, tight-junction, and metabolic-homeostasis pathways in graft injury.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>\n                      Integrative single-cell and bulk analyses link diminished CD4\n                      <jats:sup>+<\/jats:sup>\n                      T-cell stemness, Th17-dominant polarization, and S100A4-mediated neutrophil recruitment to graft dysfunction. These signatures nominate stemness indices, Th17\/Treg balance, and the S100-TLR4 axis as candidate biomarkers and therapeutic targets to preserve allograft integrity and prolong transplant survival.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fgene.2025.1588941","type":"journal-article","created":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T00:10:22Z","timestamp":1749773422000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Reduced T-Cell stemness underlies Th17 expansion and graft dysfunction in kidney transplant recipients"],"prefix":"10.3389","volume":"16","author":[{"given":"Chang","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andu","family":"Zhu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chen","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenfan","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guocai","family":"Mao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Minjun","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianchun","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zheng","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaqian","family":"Qi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhijun","family":"Cao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,6,13]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"767","DOI":"10.1038\/s41467-022-28372-y","article-title":"Single-cell analysis of human glioma and immune cells identifies S100A4 as an immunotherapy target","volume":"13","author":"Abdelfattah","year":"2022","journal-title":"Nat. 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