Luo Yali, Gao Jian, Su Xinliang, Li Helian, Li Yingcen, Qi Wenhao, Han Xuling, Han Jingxuan, Zhao Yiran, Zhang Alin, Zheng Yan, Qian Feng, He Hongyu
State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences, Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, 200433, China.
State Key Laboratory of Genetic Engineering, Human Phenome Institute, School of Life Sciences, Zhangjiang Fudan International Innovation Center, Fudan University, Shanghai, 200433, China; Ministry of Education Key Laboratory of Contemporary Anthropology, School of Life Sciences, Fudan University, Shanghai, 200438, China.
EBioMedicine. 2025 Mar;113:105586. doi: 10.1016/j.ebiom.2025.105586. Epub 2025 Feb 1.
Comprehensive and in-depth research on the immunophenotype of septic patients remains limited, and effective biomarkers for the diagnosis and treatment of sepsis are urgently needed in clinical practice.
Blood samples from 31 septic patients in the Intensive Care Unit (ICU), 25 non-septic ICU patients, and 18 healthy controls were analyzed using flow cytometry for deep immunophenotyping. Metagenomic sequencing was performed in 41 fecal samples, including 13 septic patients, 10 non-septic ICU patients, and 18 healthy controls. Immunophenotype shifts were evaluated using differential expression sliding window analysis, and random forest models were developed for sepsis diagnosis or prognosis prediction.
Septic patients exhibited decreased proportions of natural killer (NK) cells and plasmacytoid dendritic cells (pDCs) in CD45 leukocytes compared with non-septic ICU patients and healthy controls. These changes statistically mediated the association of Bacteroides salyersiae with sepsis, suggesting a potential underlying mechanism. A combined diagnostic model incorporating B.salyersia, NK cells in CD45 leukocytes, and C-reactive protein (CRP) demonstrated high accuracy in distinguishing sepsis from non-sepsis (area under the receiver operating characteristic curve, AUC = 0.950, 95% CI: 0.811-1.000). Immunophenotyping and disease severity analysis identified an Acute Physiology and Chronic Health Evaluation (APACHE) II score threshold of 21, effectively distinguishing mild (n = 19) from severe (n = 12) sepsis. A prognostic model based on the proportion of total lymphocytes, Helper T (Th) 17 cells, CD4 effector memory T (T) cells, and Th1 cells in CD45 leukocytes achieved robust outcome prediction (AUC = 0.906, 95% CI: 0.732-1.000), with further accuracy improvement when combined with clinical scores (AUC = 0.938, 95% CI: 0.796-1.000).
NK cell subsets within innate immunity exhibit significant diagnostic value for sepsis, particularly when combined with B. salyersiae and CRP. In addition, T cell phenotypes within adaptive immunity are correlated with sepsis severity and may serve as reliable prognostic markers.
This project was supported by the National Key R&D Program of China (2023YFC2307600, 2021YFA1301000), Shanghai Municipal Science and Technology Major Project (2023SHZDZX02, 2017SHZDZX01), Shanghai Municipal Technology Standards Project (23DZ2202600).
对脓毒症患者免疫表型的全面深入研究仍然有限,临床实践中迫切需要用于脓毒症诊断和治疗的有效生物标志物。
使用流式细胞术对重症监护病房(ICU)的31例脓毒症患者、25例非脓毒症ICU患者和18例健康对照的血样进行分析,以进行深度免疫表型分析。对41份粪便样本进行宏基因组测序,其中包括13例脓毒症患者、10例非脓毒症ICU患者和18例健康对照。使用差异表达滑动窗口分析评估免疫表型变化,并建立随机森林模型用于脓毒症诊断或预后预测。
与非脓毒症ICU患者和健康对照相比,脓毒症患者CD45白细胞中自然杀伤(NK)细胞和浆细胞样树突状细胞(pDC)的比例降低。这些变化在统计学上介导了唾液拟杆菌与脓毒症的关联,提示了潜在的机制。一个结合了唾液拟杆菌、CD45白细胞中的NK细胞和C反应蛋白(CRP)的联合诊断模型在区分脓毒症和非脓毒症方面具有很高的准确性(受试者操作特征曲线下面积,AUC = 0.950,95%CI:0.811 - 1.000)。免疫表型分析和疾病严重程度分析确定急性生理学与慢性健康状况评估(APACHE)II评分阈值为21,可有效区分轻度(n = 19)和重度(n = 12)脓毒症。基于CD45白细胞中总淋巴细胞、辅助性T(Th)17细胞、CD4效应记忆T(T)细胞和Th1细胞比例的预后模型实现了可靠的预后预测(AUC = 0.906,95%CI:0.732 - 1.000),与临床评分结合时准确性进一步提高(AUC = 0.938,95%CI:0.796 - 1.000)。
固有免疫中的NK细胞亚群对脓毒症具有显著的诊断价值,特别是与唾液拟杆菌和CRP联合时。此外,适应性免疫中的T细胞表型与脓毒症严重程度相关,可作为可靠的预后标志物。
本项目由国家重点研发计划(2023YFC2307600,2021YFA1301000)、上海市科技重大专项(2023SHZDZX02,2017SHZDZX01)、上海市技术标准项目(23DZ2202600)资助。