Search PubMedSearch

PubMed · 42417165

Pulmonary fibrosis after COVID-19 is characterized by airway abnormalities and elevated club cell secretory protein-16.

Abstract

BACKGROUNDThere are no known serum biomarkers that provide mechanistic insight or prognostic enrichment for post-COVID-19 pulmonary fibrosis.METHODSWe tested associations of serum biomarkers with radiographic fibrosis-like abnormalities (reticulation, traction bronchiectasis, or honeycombing) on thoracic computed tomography (CT) scans 4 months, 15 months, and 3 years after hospitalization in an American discovery cohort of severe-to-critical COVID-19 survivors, and externally validated findings in 2 Canadian cohorts of moderate-to-critical COVID-19 survivors. In the discovery cohort, we investigated the dose-response relationship of the biomarker with CT-derived airway-to-lung ratio. We performed single-cell RNA sequencing (scRNA-seq) of transbronchial lung biopsies from COVID-19 survivors obtained 3 years after COVID-19 hospitalization and conducted immunofluorescence analysis of COVID-19 lung explants.RESULTSAmong 150 discovery cohort participants, only higher levels of circulating club cell secretory protein-16 (CC16, encoded by the SCGB1A1 gene) at hospital discharge, 4 months, 15 months, and 3 years were associated with thoracic CT fibrosis-like abnormalities in cross-sectional and longitudinal analyses. Higher CC16 levels were associated with thoracic CT fibrosis-like abnormalities in 2 validation cohorts (n = 56 and n = 37). CC16 levels were linearly associated with increased airway-to-lung ratio. scRNA-seq revealed increased proportions of epithelial cells expressing SCGB1A1 and SCGB1A1/MUC5B in COVID-19 survivors with fibrosis. Immunofluorescence analysis of COVID-19 lung explants demonstrated increased numbers of SCGB1A1-expressing epithelial cells only in small (<100 &#x3bc;m) airways, with 3-fold more CC16/MUC5B-coexpressing cells in respiratory bronchioles..CONCLUSION. Higher CC16 levels are associated with CT fibrosis-like abnormalities for up to 3 years following moderate-to-critical COVID-19. Increased CC16 reflects dysregulated small airway epithelial progenitor cell remodeling and increased expansion of CC16+MUC5B+ epithelial cells in respiratory bronchioles after COVID-19.TRIAL REGISTRATIONNot applicable.FUNDINGDepartment of Defense, NIH, and Japan Society for the Promotion of Science for Young Scientists.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Matthew R Baldwin, Ansley E Jones, David Zhang, Chandan Gurung, Zain Khan, Anjali Saqi, Xuehan Yang, Ying Wei, Renu Nandakumar, Scarlett O Murphy, Claire F McGroder, Faisal Shaikh, Selim Arcasoy, Luke Benvenuto, Harpreet Grewal, Benjamin M Smith, Eric A Hoffman, Agnes Cy Yuen, Parteek Johal, Christopher Carlsten, Christopher J Ryerson, J Brent Richards, Alyson W Wong, Tomoko Nakanishi, Aditi S Shah, Christine Kim Garcia. 2026-07-08. Pulmonary fibrosis after COVID-19 is characterized by airway abnormalities and elevated club cell secretory protein-16.. https://doi.org/10.1172/jci.insight.199983

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Global Genomic Surveillance.

Global genomic surveillance has emerged as a foundational pillar of public health in the twenty-first century, enabling real-time tracking of pathogen evolution and informing outbreak response. This chapter examines the strategic architecture of global genomic surveillance, focusing on its application to arboviruses such as chikungunya virus (CHIKV). It explores the integration of genomic data with epidemiological, clinical, and environmental information within a One Health framework, while addressing critical challenges in governance, equity, and interoperability. The discussion covers the entire genomic surveillance workflow, from sample collection and sequencing to bioinformatic analysis and phylogenetic inference, and highlights the transformative role of artificial intelligence (AI) in predictive surveillance. By analyzing global initiatives, operational barriers, and emerging technologies, this chapter underscores the necessity of sustainable, equitable, and interoperable genomic systems to proactively address current and future infectious disease threats.

Humans

Systematic Dissection of Key Driver Perturbation Signatures in Single Cells via ECCITE-seq.

CRISPR screens, such as expanded CRISPR-compatible cellular indexing of transcriptomes and epitopes by sequencing (ECCITE-seq), enable the simultaneous measurement of transcriptomes, gRNA identity, and cell-surface protein expression at single-cell resolution to systematically interrogate gene function. This platform provides a powerful and scalable experimental approach for validating disease-associated regulators identified by large-scale association studies and other computational methods, including network-based analyses of multi-omics data. Here, as an example application, we describe an ECCITE-seq framework to characterize the transcriptomic consequences of perturbing multiple neuronal key driver genes associated with Alzheimer's disease (AD) in human-induced pluripotent stem cell (hiPSC)-derived neurons. More broadly, by integrating customized pooled gRNA libraries with different CRISPR effectors across multiple cell types, this approach allows for the assessment of the regulatory impact of candidate genes implicated in development and disease processes.

Humans

Identification of Genome-Wide Chromatin Structural Aberration in Cancer by Hi-C Analysis.

Aberrant three-dimensional genome organization is a hallmark of cancer, often driving oncogene activation through mechanisms such as enhancer hijacking. High-throughput chromosome conformation capture (Hi-C) maps these interactions on a genome-wide scale. Unlike earlier dilution-based methods, in situ Hi-C performs proximity ligation within intact nuclei, minimizing random ligation noise and enabling fine-scale structure detection. This chapter describes an optimized in situ Hi-C protocol tailored for cancer cell lines using MboI digestion and biotin-mediated pull-down to generate high-complexity libraries. We further outline a computational workflow that extends beyond standard topological mapping of compartments and topologically associating domains to identify cancer-specific aberrations. Specifically, we focus on detecting chromosomal rearrangements (structural variants) and characterizing the distinct circular topology of extrachromosomal DNA. This integrated experimental and analytical framework provides the necessary tools to dissect the spatial dysregulation underlying tumor evolution.

Humans