Distinct type 1 immune networks underlie the severity of restrictive lung disease after COVID-19

Abstract

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 CCR5⁺CD95⁺CD8⁺ 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.