Daily Respiratory Research Analysis

BACKGROUND: Interstitial lung disease (ILD), represented by idiopathic pulmonary fibrosis (IPF) and progressive pulmonary fibrosis (PPF), shows poor prognosis due to progressive fibrosis. Early therapeutic intervention is required to enhance the efficacy of antifibrotic drugs, highlighting the importance of early detection of ILD progression. Although candidate biomarkers for predicting ILD progression have been recently reported through omics analyses, clinically measurable biomarkers remain unestablished. This study aimed to identify clinically measurable biomarkers that could predict the degree of ILD progression. METHODS: The serum levels of 13 candidate biomarkers were prospectively measured by chemiluminescent enzyme immunoassay and the utilities for predicting ILD progression were compared in the discovery cohort (total 252 patients). Moreover, we evaluated the utility of the identified biomarker in another independent cohort (154 patients with non-IPF-ILD) and examined the dynamics of the biomarker by immunoblotting and single-cell RNA sequencing (scRNA-seq) using samples of patients and a mouse model. RESULTS: In the discovery cohort, C-C motif chemokine ligand (CCL)17 could reliably predict ILD progression, particularly in patients with ILD other than IPF, and showed significant associations with mortality (hazard ratio [HR] 3.70; 95% confidence interval [CI] 1.19-11.49; P = 0.015; cut-off value = 418 pg/mL). Consistently, in the validation cohort, the CCL17 high group showed significantly higher mortality (HR: 2.15; 95% CI 0.99-4.69; P = 0.049), and CCL17 was identified as an independent prognostic factor from corticosteroid or immunosuppressive agents use and ILD-gender-age-physiology index. Similar to the results of serum studies, CCL17 levels in the lungs of patients with PPF and model mice were higher than those in controls. They were positively correlated with CCL17 levels in the serum, suggesting that the increased serum CCL17 levels could reflect an increase in CCL17 levels in lung tissues. The scRNA-seq analysis of lung tissues from model mice suggested that the levels of CCL17 derived primarily from conventional dendritic cells and macrophages increased, especially during the profibrotic phase. CONCLUSIONS: We identified serum CCL17 as a clinically measurable biomarker for predicting non-IPF-ILD progression. Serum CCL17 could enable the stratification of patients at risk of non-IPF-ILD progression, leading to appropriate early therapeutic intervention.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a heterogeneous syndrome, resulting in inconsistent findings across studies. Identifying a core set of genes consistently involved in COPD pathogenesis, independent of patient variability, is essential. METHODS: We integrated lung tissue sequencing data from patients with COPD across two centers. We used weighted gene co-expression network analysis and machine learning to identify 13 potential pathogenic genes common to both centers. Additionally, a gene-based model was constructed to distinguish COPD at the molecular level and validated in independent cohorts. Gene expression in specific cell types was analyzed, and Mendelian randomization was used to confirm associations between candidate genes and lung function/COPD. Preliminary in vitro functional validation was performed on prioritized core candidate genes. RESULTS: Tissue inhibitor of metalloproteinase 4 (TIMP4) was identified as a key pathogenic gene and validated in COPD cohorts. Further analysis using single-cell sequencing from mice and patients with COPD revealed that TIMP4 is involved in ciliated cells. In primary human airway epithelial cells cultured at the air-liquid interface, TIMP4 overexpression reduced ciliated cell numbers. CONCLUSIONS: We developed a 13-gene model for distinguishing COPD at the molecular level and identified TIMP4 as a potential hub pathogenic gene. This finding provides insights into shared disease mechanisms and positions TIMP4 as a promising therapeutic target for further investigation.

BACKGROUND: COPD is characterized by persistent inflammation that is responsible for remodeling the bronchovascular bundles (BVBs), which may lead to poor quality of life. Quantitative CT (QCT) scan textures of the lung can capture local disease patterns of inflammation and related respiratory morbidity. RESEARCH QUESTION: Are BVB textures, obtained from the adaptive multiple feature method, associated with systemic inflammation, morbidity, and mortality in COPD? STUDY DESIGN AND METHODS: We analyzed data from the Subpopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS; n = 2,981) and the Genetic Epidemiology of COPD (COPDGene) study (n = 10,305). The predictors included 2 QCT scan biomarkers, the BVB and CT density gradient (CTDG) textures, age, sex, BMI, race, smoking status, pack-years of smoking, CT scan-detected emphysema, and square root of the wall area of a hypothetical airway with a 10-mm lumen perimeter (Pi10). Outcomes included plasma biomarker concentrations from Meso Scale Discovery proteomics assays and CBC counts, both as markers of inflammation, along with FEV RESULTS: Increased BVB texture was associated significantly with elevated neutrophil and monocyte counts and the neutrophil to lymphocyte ratio, independent of clinical covariates, CT scan-detected emphysema, and Pi10. Elevated CTDG was associated with increased neutrophil count, NLR, and tumor necrosis factor α. Increased CTDG and BVB textures also were associated with a lower FEV INTERPRETATION: QCT scan textures may provide imaging evidence of the spatial heterogeneity of lung inflammation and overall disease burden in COPD. CLINICAL TRIAL REGISTRATION: ClinicalTrials.gov; Nos.: NCT01969344 (SPIROMICS) and NCT00608764 (COPDGene); URL: www. CLINICALTRIALS: gov.