Distinct Type 1 Immune Networks Underlie the Severity of Restrictive Lung Disease after COVID-19.

The variable etiology of persistent breathlessness after COVID-19 have confounded 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 by their impaired diffusion and severity of fibrosis. Machine learning revealed marked CCR5+CD95+ CD8+ T-cell perturbations in mild-to-moderate 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 significant lung involvement. These differences were reflected in divergent T-cell-based type 1 networks according to 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.

Multiscale computational model predicts how environmental changes and drug treatments affect microvascular remodeling in fibrotic disease.

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 due to loss of alveolar structures and lung function declines. Here, 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 (ECs) and pericytes, the cells that comprise microvessels. Nintedanib, an FDA-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 (PMT) 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 explain how cell decisions affect tissue changes during disease and in response to treatments.

Machine Learning of Plasma Proteomics Classifies Diagnosis of Interstitial Lung Disease.

RATIONALE: Distinguishing connective tissue disease associated interstitial lung disease (CTD-ILD) from idiopathic pulmonary fibrosis (IPF) can be clinically challenging. OBJECTIVES: Identify proteins that separate and classify CTD-ILD from IPF patients. METHODS: Four registries with 1247 IPF and 352 CTD-ILD patients were included in analyses. Plasma samples were subjected to high-throughput proteomics assays. Protein features were prioritized using Recursive Feature Elimination (RFE) to construct a proteomic classifier. Multiple machine learning models, including Support Vector Machine, LASSO regression, Random Forest (RF), and imbalanced-RF, were trained and tested in independent cohorts. The validated models were used to classify each case iteratively in external datasets. MEASUREMENT AND MAIN RESULTS: A classifier with 37 proteins (PC37) was enriched in 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 Area-Under-Curve 0.85-0.90 in 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 test cohort, 93.5%-96.1% sensitivity and 69.5%-77.6% specificity in single-sample classification dataset. Composite analysis of all machine learning models confirmed 78.2% (194/248) accuracy in test cohort and 82.9% (208/251) in single-sample classification dataset. CONCLUSIONS: Multiple machine learning models trained with large cohort proteomic datasets consistently distinguished CTD-ILD from IPF. Identified proteins 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.

Associations of Plasma Omega-3 Fatty Acids With Progression and Survival in Pulmonary Fibrosis.

BACKGROUND: Preclinical experiments suggest protective effects of omega-3 fatty acids and their metabolites in lung injury and fibrosis. Whether higher intake of omega-3 fatty acids is associated with disease progression and survival in humans with pulmonary fibrosis is unknown. RESEARCH QUESTION: What are the associations of plasma omega-3 fatty acid levels (a validated marker of omega-3 nutritional intake) with disease progression and transplant-free survival in pulmonary fibrosis? STUDY DESIGN AND METHODS: Omega-3 fatty acid levels were measured from plasma samples of patients with clinically diagnosed pulmonary fibrosis from the Pulmonary Fibrosis Foundation Patient Registry (n = 150), University of Virginia (n = 58), and University of Chicago (n = 101) cohorts. The N-3 index (docosahexaenoic acid + eicosapentaenoic acid) was the primary exposure variable of interest. Linear-mixed effects models with random intercept and slope were used to examine associations of plasma omega-3 fatty acid levels with changes in FVC and diffusing capacity for carbon monoxide over a period of 12 months. Cox proportional hazards models were used to examine transplant-free survival. Stratified analyses by telomere length were performed in the University of Chicago cohort. RESULTS: Most of the cohort were patients with idiopathic pulmonary fibrosis (88%) and male patients (74%). One-unit increment in log-transformed N-3 index plasma level was associated with a change in diffusing capacity for carbon monoxide of 1.43 mL/min/mm Hg per 12 months (95% CI, 0.46-2.41) and a hazard ratio for transplant-free survival of 0.44 (95% CI, 0.24-0.83). Cardiovascular disease history, smoking, and antifibrotic usage did not significantly modify associations. Omega-3 fatty acid levels were not significantly associated with changes in FVC. Higher eicosapentaenoic acid plasma levels were associated with longer transplant-free survival among University of Chicago participants with shorter telomere length (P value for interaction = .02). INTERPRETATION: Further research is needed to investigate underlying biological mechanisms and whether omega-3 fatty acids are a potential disease-modifying therapy.

Interstitial Lung Disease and Sarcoidosis.

Interstitial lung disease (ILD), a clinically recognized group of diseases resulting in pulmonary fibrosis, affects up to 200 individuals per 100,000 in the United States. Sarcoidosis has a wide range of clinical manifestations including pulmonary fibrosis. Health disparities are prevalent in both ILD and sarcoidosis around socioeconomic status, race, gender, and geographic location. This review outlines the known health disparities, discusses possible determinants of disparities, and outlines a path to achieve equity in ILD and sarcoidosis.

Central lung gene expression associates with myofibroblast features in idiopathic pulmonary fibrosis.

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 <5% and fold change >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.

High-dimensional comparison of monocytes and T cells in post-COVID and idiopathic pulmonary fibrosis.

Introduction: Up to 30% of hospitalized COVID-19 patients experience persistent sequelae, including pulmonary fibrosis (PF). Methods: We examined COVID-19 survivors with impaired lung function and imaging worrisome for developing PF and found within six months, symptoms, restriction and PF improved in some (Early-Resolving COVID-PF), but persisted in others (Late-Resolving COVID-PF). To evaluate immune mechanisms associated with recovery versus persistent PF, we performed single-cell RNA-sequencing and multiplex immunostaining on peripheral blood mononuclear cells from patients with Early- and Late-Resolving COVID-PF and compared them to age-matched controls without respiratory disease. Results and discussion: Our analysis showed circulating monocytes were significantly reduced in Late-Resolving COVID-PF patients compared to Early-Resolving COVID-PF and non-diseased controls. Monocyte abundance correlated with pulmonary function forced vital capacity and diffusion capacity. Differential expression analysis revealed MHC-II class molecules were upregulated on the CD8 T cells of Late-Resolving COVID-PF patients but downregulated in monocytes. To determine whether these immune signatures resembled other interstitial lung diseases, we analyzed samples from Idiopathic Pulmonary Fibrosis (IPF) patients. IPF patients had a similar marked decrease in monocyte HLA-DR protein expression compared to Late-Resolving COVID-PF patients. Our findings indicate decreased circulating monocytes are associated with decreased lung function and uniquely distinguish Late-Resolving COVID-PF from Early-Resolving COVID-PF, IPF, and non-diseased controls.

COVID-19 patients exhibit unique transcriptional signatures indicative of disease severity.

COVID-19 manifests a spectrum of respiratory symptoms, with the more severe often requiring hospitalization. To identify markers for disease progression, we analyzed longitudinal gene expression data from patients with confirmed SARS-CoV-2 infection admitted to the intensive care unit (ICU) for acute hypoxic respiratory failure (AHRF) as well as other ICU patients with or without AHRF and correlated results of gene set enrichment analysis with clinical features. The results were then compared with a second dataset of COVID-19 patients separated by disease stage and severity. Transcriptomic analysis revealed that enrichment of plasma cells (PCs) was characteristic of all COVID-19 patients whereas enrichment of interferon (IFN) and neutrophil gene signatures was specific to patients requiring hospitalization. Furthermore, gene expression results were used to divide AHRF COVID-19 patients into 2 groups with differences in immune profiles and clinical features indicative of severe disease. Thus, transcriptomic analysis reveals gene signatures unique to COVID-19 patients and provides opportunities for identification of the most at-risk individuals.

Advancing Lung Immunology Research: An Official American Thoracic Society Workshop Report.

The mammalian airways and lungs are exposed to a myriad of inhaled particulate matter, allergens, and pathogens. The immune system plays an essential role in protecting the host from respiratory pathogens, but a dysregulated immune response during respiratory infection can impair pathogen clearance and lead to immunopathology. Furthermore, inappropriate immunity to inhaled antigens can lead to pulmonary diseases. A complex network of epithelial, neural, stromal, and immune cells has evolved to sense and respond to inhaled antigens, including the decision to promote tolerance versus a rapid, robust, and targeted immune response. Although there has been great progress in understanding the mechanisms governing immunity to respiratory pathogens and aeroantigens, we are only beginning to develop an integrated understanding of the cellular networks governing tissue immunity within the lungs and how it changes after inflammation and over the human life course. An integrated model of airway and lung immunity will be necessary to improve mucosal vaccine design as well as prevent and treat acute and chronic inflammatory pulmonary diseases. Given the importance of immunology in pulmonary research, the American Thoracic Society convened a working group to highlight central areas of investigation to advance the science of lung immunology and improve human health.

Blood Transcriptomics Predicts Progression of Pulmonary Fibrosis and Associated Natural Killer Cells.

Rationale: Disease activity in idiopathic pulmonary fibrosis (IPF) remains highly variable, poorly understood, and difficult to predict. Objectives: To identify a predictor using short-term longitudinal changes in gene expression that forecasts future FVC decline and to characterize involved pathways and cell types. Methods: Seventy-four patients from COMET (Correlating Outcomes with Biochemical Markers to Estimate Time-Progression in IPF) cohort were dichotomized as progressors (≥10% FVC decline) or stable. Blood gene-expression changes within individuals were calculated between baseline and 4 months and regressed with future FVC status, allowing determination of expression variations, sample size, and statistical power. Pathway analyses were conducted to predict downstream effects and identify new targets. An FVC predictor for progression was constructed in COMET and validated using independent cohorts. Peripheral blood mononuclear single-cell RNA-sequencing data from healthy control subjects were used as references to characterize cell type compositions from bulk peripheral blood mononuclear RNA-sequencing data that were associated with FVC decline. Measurements and Main Results: The longitudinal model reduced gene-expression variations within stable and progressor groups, resulting in increased statistical power when compared with a cross-sectional model. The FVC predictor for progression anticipated patients with future FVC decline with 78% sensitivity and 86% specificity across independent IPF cohorts. Pattern recognition receptor pathways and mTOR pathways were downregulated and upregulated, respectively. Cellular deconvolution using single-cell RNA-sequencing data identified natural killer cells as significantly correlated with progression. Conclusions: Serial transcriptomic change predicts future FVC decline. An analysis of cell types involved in the progressor signature supports the novel involvement of natural killer cells in IPF progression.

Quantitative Measurement of IgG to Severe Acute Respiratory Syndrome Coronavirus-2 Proteins Using ImmunoCAP.

BACKGROUND: Detailed understanding of the immune response to severe acute respiratory syndrome coronavirus (SARS-CoV)-2, the cause of coronavirus disease 2019 (CO-VID-19) has been hampered by a lack of quantitative antibody assays. OBJECTIVE: The objective was to develop a quantitative assay for IgG to SARS-CoV-2 proteins that could be implemented in clinical and research laboratories. METHODS: The biotin-streptavidin technique was used to conjugate SARS-CoV-2 spike receptor-binding domain (RBD) or nucleocapsid protein to the solid phase of the ImmunoCAP. Plasma and serum samples from patients hospitalized with COVID-19 (n = 60) and samples from donors banked before the emergence of COVID-19 (n = 109) were used in the assay. SARS-CoV-2 IgG levels were followed longitudinally in a subset of samples and were related to total IgG and IgG to reference antigens using an ImmunoCAP 250 platform. RESULTS: At a cutoff of 2.5 µg/mL, the assay demonstrated sensitivity and specificity exceeding 95% for IgG to both SARS-CoV-2 proteins. Among 36 patients evaluated in a post-hospital follow-up clinic, median levels of IgG to spike-RBD and nucleocapsid were 34.7 µg/mL (IQR 18-52) and 24.5 µg/mL (IQR 9-59), respectively. Among 17 patients with longitudinal samples, there was a wide variation in the magnitude of IgG responses, but generally the response to spike-RBD and to nucleocapsid occurred in parallel, with peak levels approaching 100 µg/mL, or 1% of total IgG. CONCLUSIONS: We have described a quantitative assay to measure IgG to SARS-CoV-2 that could be used in clinical and research laboratories and implemented at scale. The assay can easily be adapted to measure IgG to mutated COVID-19 proteins, has good performance characteristics, and has a readout in standardized units.

Quantitative measurement of IgG to SARS-CoV-2 proteins using ImmunoCAP.

BACKGROUND: Detailed understanding of the immune response to SARS-CoV-2, the cause of coronavirus disease 2019 (COVID-19), has been hampered by a lack of quantitative antibody assays. OBJECTIVE: To develop a quantitative assay for IgG to SARS-CoV-2 proteins that could readily be implemented in clinical and research laboratories. METHODS: The biotin-streptavidin technique was used to conjugate SARS-CoV-2 spike receptor-binding-domain (RBD) or nucleocapsid protein to the solid-phase of the ImmunoCAP resin. Plasma and serum samples from patients with COVID-19 (n=51) and samples from donors banked prior to the emergence of COVID-19 (n=109) were used in the assay. SARS-CoV-2 IgG levels were followed longitudinally in a subset of samples and were related to total IgG and IgG to reference antigens using an ImmunoCAP 250 platform. RESULTS: Performance characteristics demonstrated 100% sensitivity and 99% specificity at a cut-off level of 2.5 µg/mL for both SARS-CoV-2 proteins. Among 36 patients evaluated in a post-hospital follow-up clinic, median levels of IgG to spike-RBD and nucleocapsid were 34.7 µg/mL (IQR 18-52) and 24.5 µg/mL (IQR 9-59), respectively. Among 17 patients with longitudinal samples there was a wide variation in the magnitude of IgG responses, but generally the response to spike-RBD and to nucleocapsid occurred in parallel, with peak levels approaching 100 µg/mL, or 1% of total IgG. CONCLUSIONS: We have described a quantitative assay to measure IgG to SARS-CoV-2 that could be used in clinical and research laboratories and implemented at scale. The assay can easily be adapted to measure IgG to novel antigens, has good performance characteristics and a read-out in standardized units.

Multi-scale models of lung fibrosis.

The architectural complexity of the lung is crucial to its ability to function as an organ of gas exchange; the branching tree structure of the airways transforms the tracheal cross-section of only a few square centimeters to a blood-gas barrier with a surface area of tens of square meters and a thickness on the order of a micron or less. Connective tissue comprised largely of collagen and elastic fibers provides structural integrity for this intricate and delicate system. Homeostatic maintenance of this connective tissue, via a balance between catabolic and anabolic enzyme-driven processes, is crucial to life. Accordingly, when homeostasis is disrupted by the excessive production of connective tissue, lung function deteriorates rapidly with grave consequences leading to chronic lung conditions such as pulmonary fibrosis. Understanding how pulmonary fibrosis develops and alters the link between lung structure and function is crucial for diagnosis, prognosis, and therapy. Further information gained could help elaborate how the healing process breaks down leading to chronic disease. Our understanding of fibrotic disease is greatly aided by the intersection of wet lab studies and mathematical and computational modeling. In the present review we will discuss how multi-scale modeling has facilitated our understanding of pulmonary fibrotic disease as well as identified opportunities that remain open and have produced techniques that can be incorporated into this field by borrowing approaches from multi-scale models of fibrosis beyond the lung.

Diagnosis and Detection of Sarcoidosis. An Official American Thoracic Society Clinical Practice Guideline.

Background: The diagnosis of sarcoidosis is not standardized but is based on three major criteria: a compatible clinical presentation, finding nonnecrotizing granulomatous inflammation in one or more tissue samples, and the exclusion of alternative causes of granulomatous disease. There are no universally accepted measures to determine if each diagnostic criterion has been satisfied; therefore, the diagnosis of sarcoidosis is never fully secure.Methods: Systematic reviews and, when appropriate, meta-analyses were performed to summarize the best available evidence. The evidence was appraised using the Grading of Recommendations, Assessment, Development, and Evaluation approach and then discussed by a multidisciplinary panel. Recommendations for or against various diagnostic tests were formulated and graded after the expert panel weighed desirable and undesirable consequences, certainty of estimates, feasibility, and acceptability.Results: The clinical presentation, histopathology, and exclusion of alternative diagnoses were summarized. On the basis of the available evidence, the expert committee made 1 strong recommendation for baseline serum calcium testing, 13 conditional recommendations, and 1 best practice statement. All evidence was very low quality.Conclusions: The panel used systematic reviews of the evidence to inform clinical recommendations in favor of or against various diagnostic tests in patients with suspected or known sarcoidosis. The evidence and recommendations should be revisited as new evidence becomes available.