Canderan Glenda, Muehling Lyndsey M, Kadl Alexandra, Ladd Shay, Bonham Catherine, Cross Claire E, Lima Sierra M, Yin Xihui, Sturek Jeffrey M, Wilson Jeffrey M, Keshavarz Behnam, Enfield Kyle B, Ramani Chintan, Bryant Naomi, Murphy Deborah D, Cheon In Su, Solga Michael, Pramoonjago Patcharin, McNamara Coleen A, Sun Jie, Utz Paul J, Dolatshahi Sepideh, Irish Jonathan M, Woodfolk Judith A
Department of Medicine, University of Virginia School of Medicine, Charlottesville, VA, USA.
Department of Pharmacology, University of Virginia School of Medicine, Charlottesville, VA, USA.
Nat Immunol. 2025 Apr;26(4):595-606. doi: 10.1038/s41590-025-02110-0. Epub 2025 Mar 26.
The variable origins of persistent breathlessness after coronavirus disease 2019 (COVID-19) have hindered efforts to decipher the immunopathology of lung sequelae. Here we analyzed hundreds of cellular and molecular features in the context of discrete pulmonary phenotypes to define the systemic immune landscape of post-COVID lung disease. Cluster analysis of lung physiology measures highlighted two phenotypes of restrictive lung disease that differed according to their impaired diffusion and severity of fibrosis. Machine learning revealed marked CCR5CD95CD8 T cell perturbations in milder lung disease but attenuated T cell responses hallmarked by elevated CXCL13 in more severe disease. Distinct sets of cells, mediators and autoantibodies distinguished each restrictive phenotype and differed from those of patients without substantial lung involvement. These differences were reflected in divergent T cell-based type 1 networks according to the severity of lung disease. Our findings, which provide an immunological basis for active lung injury versus advanced disease after COVID-19, might offer new targets for treatment.
2019冠状病毒病（COVID-19）后持续呼吸困难的多种起源阻碍了人们对肺部后遗症免疫病理学的解读。在此，我们在离散的肺部表型背景下分析了数百种细胞和分子特征，以确定COVID-19后肺部疾病的全身免疫格局。对肺生理指标的聚类分析突出了两种限制性肺病表型，它们根据其弥散受损和纤维化严重程度而有所不同。机器学习显示，在较轻的肺部疾病中存在明显的CCR5⁺CD95⁺CD8⁺ T细胞扰动，但在更严重的疾病中，以CXCL13升高为特征的T细胞反应减弱。不同的细胞、介质和自身抗体组区分了每种限制性表型，并且与没有严重肺部受累的患者不同。根据肺部疾病的严重程度，这些差异反映在基于T细胞的不同1型网络中。我们的研究结果为COVID-19后活动性肺损伤与晚期疾病提供了免疫学基础，可能会为治疗提供新的靶点。
