American journal of respiratory and critical care medicine最新文献

Rationale: Hyperinflammatory and hypoinflammatory phenotypes previously identified in sepsis and acute respiratory distress syndrome may enable precision therapies, but their clinical relevance and translational modeling in severe pneumonia remain incompletely characterized.

Objectives: To examine biomarker-defined hyperinflammatory and hypoinflammatory phenotypes in critically ill patients with pneumonia (pulmonary sepsis), test whether the biomarkers that define these phenotypes identify subgroups and outcomes in a mouse model of bacterial pneumonia, and determine whether the mouse phenotypes respond differently to therapeutic interventions.

Methods: We performed latent class analysis (LCA) in a cohort of 548 ICU patients with pulmonary sepsis to identify inflammatory phenotypes and test the association of these phenotypes with clinical outcomes using validated classifier models. We developed a mouse model of pneumococcal pneumonia that recapitulated key aspects of these phenotypes and tested responses to dexamethasone and IL-6 receptor blockade.

Measurements and main results: Two phenotypes were identified in patients with pulmonary sepsis: a hyperinflammatory phenotype with more lung injury and increased mortality, and a hypoinflammatory phenotype with more favorable outcomes. Despite uniform pathogen exposure and baseline conditions, LCA identified 2 phenotypes in the mice with divergent trajectories (lung injury and mortality). Therapeutic benefit from anti-inflammatory interventions was observed exclusively in the more inflamed mice.

Conclusions: By integrating clinical data from human studies and experimental findings in a clinically relevant mouse model, this study provides a translational framework for developing phenotype-targeted therapies for critical illness, with the goal of improving patient outcomes.

Rationale: Airspace macrophages (AM) are implicated in both persistent inflammation and tissue repair following acute lung injury. Distinct subsets of AM are associated with lung pathology in humans, but whether unique AM signatures are specific to disease states or represent a conserved response to lung inflammation is unknown.

Objectives: We sought to test the hypothesis that conserved subsets of inflammatory and reparative AM could be identified by transcriptional programming in a human model of self-resolving acute lung injury.

Methods: A total of 15 participants underwent BAL before and at a pre-assigned time point after endobronchial exposure to bacterial endotoxin. Bronchoalveolar lavage cells were subjected to single cell RNA sequencing, and longitudinal assessment of AM programming during resolution of inflammation and lung repair was performed.

Measurements and main results: We identify transcriptionally distinct subsets of tissue resident and recruited AM present at all time points, in all participants. Two recruited AM populations increase following inflammation: one that aligns with classical monocytes and one with interstitial macrophages (IAM). Airspace macrophage subsets display unique patterns of gene expression throughout the time course. Comparison of subset-specific markers to those identified in disease states reveals that IAM express many so-called pathogenic markers during normal lung repair.

Conclusions: By applying a uniform inflammatory stimulus to healthy adults and examining BAL cells obtained at precise time points thereafter, we construct a time-resolved kinetic of AM transcriptional programming during typical lung repair. Our data demonstrate that IAM share transcriptional similarity to AM identified in disease states and suggest they may reflect a conserved cellular response to tissue injury.