Pathogen-specific host responses define distinct pneumonia endotypes in the human lung

Abstract

Pneumonia is the leading cause of death from infectious disease worldwide. The diagnosis and treatment of patients with pneumonia lag behind other major conditions, relying on syndromic definitions that lack molecular resolution and ignore underlying endotypes. We sought to test the hypothesis that dynamic pathogen-specific host responses in the alveolar space represent distinct pneumonia endotypes linked to different clinical features and outcomes. We prospectively enrolled a cohort of 690 patients (including immunocompromised patients) with known or suspected pneumonia receiving mechanical ventilation in whom the etiology of pneumonia was determined by gold-standard analysis of distal lung fluid obtained by bronchoalveolar lavage (BAL) combined with clinical adjudication. From these patients, we analyzed 792 BAL fluid samples, including 310 serial samples, using flow cytometry (482 patients) and single-cell RNA-sequencing (170 patients) and extracted daily clinical data from the electronic health record (> 15,000 patient-days). We used machine learning models to identify pathogen-specific host responses in the transcriptome of alveolar immune cells that were associated with changes in alveolar cell abundance and clinical features. Our results suggest that therapeutic strategies for pneumonia should be individualized to specific host-pathogen interactions.

Preprint at bioRxiv: https://www.biorxiv.org/content/10.64898/2026.05.12.724509

Resources

  1. Electronic health record dataset for the published SCRIPT cohort (690 patients, 15,287 patient ICU-days) is available at Physionet.

  2. Single-cell RNA-sequencing data explorer from 301 BAL samples from 205 patients or volunteers, annotated with 28 cell types (10% of cells randomly downsampled for speed).

  3. Multi-omic factor analysis (MOFA) explorer corresponding to Figure 3 and Extended Data Figure S5. Video tutorial for using the MOFA explorer.

  4. Differential gene expression analysis explorer corresponding to Figure 4 and Extended Data Figure S6. Video tutorial for using the DEG explorer.

  5. Gene set variation analysis explorer for all pseudobulk samples in the cohort. Video tutorial for using the GSVA explorer.