Catherine A. Bonham

Rationale: Enlargement of lung-associated lymph nodes (LNs) predicts worse survival in all patients with interstitial lung disease (ILD). This phenomenon occurs in both connective tissue disease–associated ILD and, surprisingly, idiopathic pulmonary fibrosis (IPF), where immune-driven pathogenesis is controversial. Objectives: To determine whether immune responses in the lung LNs of patients with ILD are antigen-specific and significant to pathology and etiology. Methods: ILD lung LNs excised at transplant (30 IPF, 7 interstitial pneumonia with autoimmune features, 4 hypersensitivity pneumonitis, 5 connective tissue disease–associated ILD, 5 other ILD) and 36 donor control lung LNs were analyzed by spectral flow cytometry. Formalin-fixed lung LNs and OCT-fixed lung samples of patients with IPF were used to determine germinal center (GC) and antigen-specific responses. Serum autoantibody responses were measured by radioligand binding assay. Measurements and Main Results: All patients with ILD revealed a common adaptive immune landscape of antigen responses in lung LNs characterized by the presence of GC B cells, T follicular helper cells, and activated T cells. Immunological synapses identified in the lung LNs demonstrated that antigen stimulation is ongoing in patients with ILD. Lung LN frequencies of T follicular helper and T regulatory cells correlated with circulating antibody concentrations to ABLIM1, a recently identified autoantigen expressed widely, including in aberrant basaloid cells that are uniquely found in fibrotic lungs. Conclusions: Antigen-induced activation and development of GC in enlarged lung LNs represents a shared immunopathologic mechanism associated with progressive pulmonary fibrosis among patients with ILD, regardless of etiology. Autoantigens overexpressed in progressive pulmonary fibrosis may be key drivers of these GC responses.

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.

Investigating the molecular, cellular, and tissue-level changes caused by disease, and the effects of pharmacological treatments across these biological scales, necessitates the use of multiscale computational modeling in combination with experimentation. Many diseases dynamically alter the tissue microenvironment in ways that trigger microvascular network remodeling, which leads to the expansion or regression of microvessel networks. When microvessels undergo remodeling in idiopathic pulmonary fibrosis (IPF), functional gas exchange is impaired and lung function declines. We integrated a multiscale computational model with independent experiments to investigate how combinations of biomechanical and biochemical cues in IPF alter cell fate decisions leading to microvascular remodeling. Our computational model predicted that extracellular matrix (ECM) stiffening reduced microvessel area, which was accompanied by physical uncoupling of endothelial cell (EC) and pericytes, the cells that comprise microvessels. Nintedanib, an Food and Drug Administration-approved drug for treating IPF, was predicted to further potentiate microvessel regression by decreasing the percentage of quiescent pericytes while increasing the percentage of pericytes undergoing pericyte-myofibroblast transition in high ECM stiffnesses. Importantly, the model suggested that YAP/TAZ inhibition may overcome the deleterious effects of nintedanib by promoting EC-pericyte coupling and maintaining microvessel homeostasis. Overall, our combination of computational and experimental modeling can predict and explain how cell decisions affect tissue changes during disease and in response to treatments.

Rationale: Distinguishing connective tissue disease–associated interstitial lung disease (CTD-ILD) from idiopathic pulmonary fibrosis (IPF) can be clinically challenging. Objectives: To identify proteins that separate and classify patients with CTD-ILD and those with IPF. Methods: Four registries with 1,247 patients with IPF and 352 patients with CTD-ILD were included in analyses. Plasma samples were subjected to high-throughput proteomics assays. Protein features were prioritized using recursive feature elimination to construct a proteomic classifier. Multiple machine learning models, including support vector machine, LASSO (least absolute shrinkage and selection operator) regression, random forest, and imbalanced Random Forest, were trained and tested in independent cohorts. The validated models were used to classify each case iteratively in external datasets. Measurements and Main Results: A classifier with 37 proteins (proteomic classifier 37 [PC37]) was enriched in the biological process of bronchiole development and smooth muscle proliferation and immune responses. Four machine learning models used PC37 with sex and age score to generate continuous classification values. Receiver operating characteristic curve analyses of these scores demonstrated consistent areas under the curve of 0.85–0.90 in the test cohort and 0.94–0.96 in the single-sample dataset. Binary classification demonstrated 78.6–80.4% sensitivity and 76–84.4% specificity in the test cohort and 93.5–96.1% sensitivity and 69.5–77.6% specificity in the single-sample classification dataset. Composite analysis of all machine learning models confirmed 78.2% (194 of 248) accuracy in the test cohort and 82.9% (208 of 251) in the single-sample classification dataset. Conclusions: Multiple machine learning models trained with large cohort proteomic datasets consistently distinguished CTD-ILD from IPF. Many of the identified proteins are involved in immune pathways. We further developed a novel approach for single-sample classification, which could facilitate honing the differential diagnosis of ILD in challenging cases and improve clinical decision making.

Rationale: Contribution of central lung tissues to pathogenesis of idiopathic pulmonary fibrosis (IPF) remains unknown.Objective: To ascertain the relationship between cell types of IPF-central and IPF-peripheral lung explants using RNA sequencing (RNA-seq) transcriptome.Methods: Biopsies of paired IPF-central and IPF-peripheral along with non-IPF lungs were selected by reviewing H&E data. Criteria for differentially expressed genes (DEG) were set at false discovery rate 2. Computational cell composition deconvolution was performed. Signature scores were computed for each cell type.Findings: Comparison of central IPF versus non-IPF identified 1723 DEG (1522 upregulated and 201 downregulated). Sixty-two per cent (938/1522) of the mutually upregulated genes in central IPF genes were also upregulated in peripheral IPF versus non-IPF. Moreover, 85 IPF central-associated genes (CAG) were upregulated in central IPF versus both peripheral IPF and central non-IPF. IPF single-cell RNA-seq analysis revealed the highest CAG signature score in myofibroblasts and significantly correlated with a previously published activated fibroblasts signature (r=0.88, p=1.6×10⁻⁴). CAG signature scores were significantly higher in IPF than in non-IPF myofibroblasts (p=0.013). Network analysis of central-IPF genes identified a module significantly correlated with the deconvoluted proportion of myofibroblasts in central IPF and anti-correlated with inflammation foci trait in peripheral IPF. The module genes were over-represented in idiopathic pulmonary fibrosis signalling pathways.Interpretation: Gene expression in central IPF lung regions demonstrates active myofibroblast features that contributes to disease progression. Further elucidation of pathological transcriptomic state of cells in the central regions of the IPF lung that are relatively spared from morphological rearrangements may provide insights into molecular changes in the IPF progression.