T Cells With Activated STAT4 Drive the High-Risk Rejection State to Renal Allograft Failure After Kidney Transplantation.
T Cells With Activated STAT4 Drive the High-Risk Rejection State to Renal Allograft Failure After Kidney Transplantation.
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肾移植后具有激活的 STAT4 的 T 细胞导致肾同种异体移植失败的高风险排斥状态
DOI：
10.3389/fimmu.2022.895762
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发表时间：
2022
影响因子：
7.3
通讯作者：
中科院分区：
文献类型：
--
作者：
在肾移植中，排斥反应的恶化进展被认为是术后死亡的主要原因之一。然而，传统的组织学诊断在从分子层面解读排斥状态方面存在局限性，从而导致发病机制与临床表型不匹配。在此，我们通过将均匀流形近似与投影（UMAP）算法和莱顿（Leiden）算法应用于2611个公开的肾移植微阵列数据集，发现了六种排斥状态及其相应的特征基因，并揭示了一种对促进移植物丢失至关重要的高风险（HR）状态。通过从单细胞RNA测序数据中识别与这六种排斥状态相关的细胞群体，我们确定了一个T细胞群体是与HR排斥状态相关的触发发病机制的细胞。此外，通过构建基因调控网络，我们发现激活的信号转导及转录激活因子4（STAT4）作为T细胞中受蛋白酪氨酸磷酸酶非受体型6（PTPN6）调控的核心转录因子，与移植物功能不良和预后不佳密切相关。综上所述，我们的研究提供了一种新策略，有助于精确诊断肾移植排斥反应的进展，这对于探究潜在的分子发病机制具有重要作用，进而有助于进一步的临床干预。
In kidney transplantation, deteriorated progression of rejection is considered to be a leading course of postoperative mortality. However, the conventional histologic diagnosis is limited in reading the rejection status at the molecular level, thereby triggering mismatched pathogenesis with clinical phenotypes. Here, by applying uniform manifold approximation and projection and Leiden algorithms to 2,611 publicly available microarray datasets of renal transplantation, we uncovered six rejection states with corresponding signature genes and revealed a high-risk (HR) state that was essential in promoting allograft loss. By identifying cell populations from single-cell RNA sequencing data that were associated with the six rejection states, we identified a T-cell population to be the pathogenesis-triggering cells associated with the HR rejection state. Additionally, by constructing gene regulatory networks, we identified that activated STAT4, as a core transcription factor that was regulated by PTPN6 in T cells, was closely linked to poor allograft function and prognosis. Taken together, our study provides a novel strategy to help with the precise diagnosis of kidney allograft rejection progression, which is powerful in investigating the underlying molecular pathogenesis, and therefore, for further clinical intervention.
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影响因子：
46.9
作者：
Becht, Etienne;McInnes, Leland;Newell, Evan W.
通讯作者：
Newell, Evan W.
影响因子：
6.2
作者：
Hsu WT;Lin CH;Jui HY;Tseng YH;Shun CT;Hsu MC;Wu KK;Lee CM
通讯作者：
Lee CM
WGCNA：用于加权相关网络分析的 R 包。
影响因子：
3
作者：
Langfelder P;Horvath S
通讯作者：
Horvath S
诱导治疗对与排斥和耐受性相关的分子标记的表达的影响。
影响因子：
2.3
作者：
Krepsova E;Tycova I;Sekerkova A;Wohlfahrt P;Hruba P;Striz I;Sawitzki B;Viklicky O
通讯作者：
Viklicky O
影响因子：
13.6
作者：
Lefaucheur, Carmen;Viglietti, Denis;Zeevi, Adriana
通讯作者：
Zeevi, Adriana