Search

Despite progress in elucidation of disease mechanisms, identification of risk factors, biomarker discovery, and the approval of two medications to slow lung function decline in idiopathic pulmonary fibrosis and one medication to slow lung function decline in progressive pulmonary fibrosis, pulmonary fibrosis remains a disease with a high morbidity and mortality. In recognition of the need to catalyze ongoing advances and collaboration in the field of pulmonary fibrosis, the NHLBI, the Three Lakes Foundation, and the Pulmonary Fibrosis Foundation hosted the Pulmonary Fibrosis Stakeholder Summit on November 8-9, 2022. This workshop was held virtually and was organized into three topic areas: 1) novel models and research tools to better study pulmonary fibrosis and uncover new therapies, 2) early disease risk factors and methods to improve diagnosis, and 3) innovative approaches toward clinical trial design for pulmonary fibrosis. In this workshop report, we summarize the content of the presentations and discussions, enumerating research opportunities for advancing our understanding of the pathogenesis, treatment, and outcomes of pulmonary fibrosis.

Translational medicine has been a buzzword for over two decades. The concept was intended to be lofty, to reflect a new bench-to-bedside approach to basic and clinical research that would bridge fields, close gaps, accelerate innovation, and shorten the time and effort it takes to bring novel technologies from basic discovery to clinical application. Has this approach been successful and lived up to its promise? Despite incredible scientific advances and innovations developed within academia, successful clinical translation into real-world solutions has been difficult. This has been particularly challenging within the pulmonary field, because there have been fewer U.S. Food and Drug Administration-approved drugs and higher failure rates for pulmonary therapies than with other common disease areas. The American Thoracic Society convened a working group with the goal of identifying major challenges related to the commercialization of technologies within the pulmonary space and opportunities to enhance this process. A survey was developed and administered to 164 participants within the pulmonary arena. This report provides a summary of these survey results. Importantly, this report identifies a number of poorly recognized challenges that exist in pulmonary academic settings, which likely contribute to diminished efficiency of commercialization efforts, ultimately hindering the rate of successful clinical translation. Because many innovations are initially developed in academic settings, this is a global public health issue that impacts the entire American Thoracic Society community. This report also summarizes key resources and opportunities and provides recommendations to enhance successful commercialization of pulmonary technologies.

ObjectiveGenome-wide association studies (GWASs) have identified single nucleotide polymorphisms (SNPs) in chr11p15.5 region associated with asthma and idiopathic interstitial pneumonias (IIPs). We sought to identify functional genes for asthma by combining SNPs and mRNA expression in bronchial epithelial cells (BEC) in the Severe Asthma Research Program (SARP).MethodsCorrelation analyses of mRNA expression of six candidate genes (AP2A2, MUC6, MUC2, MUC5AC, MUC5B, and TOLLIP) and asthma phenotypes were performed in the longitudinal cohort (n = 156) with RNAseq in BEC, and replicated in the cross-sectional cohort (n = 155). eQTL (n = 114) and genetic association analysis of asthma severity (426 severe vs. 531 non-severe asthma) were performed, and compared with previously published GWASs of IIPs and asthma.ResultsHigher expression of AP2A2 and MUC5AC and lower expression of MUC5B in BEC were correlated with asthma, asthma exacerbations, and T2 biomarkers (P  0.8).ConclusionsSNPs associated with asthma in chr11p15.5 region are not associated with asthma severity neither with IIPs. Higher expression of MUC5AC and lower expression of MUC5B are risk for asthma but protective for IIPs.

Rationale: Idiopathic pulmonary fibrosis (IPF) is a devastating disease characterized by limited treatment options and high mortality. A better understanding of the molecular drivers of IPF progression is needed. Objectives: To identify and validate molecular determinants of IPF survival. Methods: A staged genome-wide association study was performed using paired genomic and survival data. Stage I cases were drawn from centers across the United States and Europe and stage II cases from Vanderbilt University. Cox proportional hazards regression was used to identify gene variants associated with differential transplantation-free survival (TFS). Stage I variants with nominal significance (P

Single-cell technologies have transformed our understanding of human tissues. Yet, studies typically capture only a limited number of donors and disagree on cell type definitions. Integrating many single-cell datasets can address these limitations of individual studies and capture the variability present in the population. Here we present the integrated Human Lung Cell Atlas (HLCA), combining 49 datasets of the human respiratory system into a single atlas spanning over 2.4 million cells from 486 individuals. The HLCA presents a consensus cell type re-annotation with matching marker genes, including annotations of rare and previously undescribed cell types. Leveraging the number and diversity of individuals in the HLCA, we identify gene modules that are associated with demographic covariates such as age, sex and body mass index, as well as gene modules changing expression along the proximal-to-distal axis of the bronchial tree. Mapping new data to the HLCA enables rapid data annotation and interpretation. Using the HLCA as a reference for the study of disease, we identify shared cell states across multiple lung diseases, including SPP1+ profibrotic monocyte-derived macrophages in COVID-19, pulmonary fibrosis and lung carcinoma. Overall, the HLCA serves as an example for the development and use of large-scale, cross-dataset organ atlases within the Human Cell Atlas.

Rationale: CC16 is a protein mainly produced by nonciliated bronchial epithelial cells (BECs) that participates in host defense. Reduced CC16 protein concentrations in BAL and serum are associated with asthma susceptibility. Objectives: Few studies have investigated the relationship between CC16 and asthma progression, and none has focused on BECs. In this study, we sought to determine if CC16 mRNA expression levels in BECs are associated with asthma severity. Methods: Association analyses between CC16 mRNA expression levels in BECs (242 asthmatics and 69 control subjects) and asthma-related phenotypes in Severe Asthma Research Program were performed using a generalized linear model. Measurements and Main Results: Low CC16 mRNA expression levels in BECs were significantly associated with asthma susceptibility and asthma severity, high systemic corticosteroids use, high retrospective and prospective asthma exacerbations, and low pulmonary function. Low CC16 mRNA expression levels were significantly associated with high T2 inflammation biomarkers (fractional exhaled nitric oxide and sputum eosinophils). CC16 mRNA expression levels were negatively correlated with expression levels of Th2 genes (IL1RL1, POSTN, SERPINB2, CLCA1, NOS2, and MUC5AC) and positively correlated with expression levels of Th1 and inflammation genes (IL12A and MUC5B). A combination of two nontraditional T2 biomarkers (CC16 and IL-6) revealed four asthma endotypes with different characteristics of T2 inflammation, obesity, and asthma severity. Conclusions: Our findings indicate that low CC16 mRNA expression levels in BECs are associated with asthma susceptibility, severity, and exacerbations, partially through immunomodulation of T2 inflammation. CC16 is a potential nontraditional T2 biomarker for asthma development and progression.

Antibodies are central effectors of the adaptive immune response, widespread used therapeutics, but also potentially disease-causing biomolecules. Antibody folding catalysts in the plasma cell are incompletely defined. Idiopathic pulmonary fibrosis (IPF) is a fatal chronic lung disease with increasingly recognized autoimmune features. We found elevated expression of FK506-binding protein 11 (FKBP11) in IPF lungs where FKBP11 specifically localized to antibody-producing plasma cells. Suggesting a general role in plasma cells, plasma cell-specific FKBP11 expression was equally observed in lymphatic tissues, and in vitro B cell to plasma cell differentiation was accompanied by induction of FKBP11 expression. Recombinant human FKBP11 was able to refold IgG antibody in vitro and inhibited by FK506, strongly supporting a function as antibody peptidyl-prolyl cis-trans isomerase. Induction of ER stress in cell lines demonstrated induction of FKBP11 in the context of the unfolded protein response in an X-box-binding protein 1 (XBP1)-dependent manner. While deficiency of FKBP11 increased susceptibility to ER stress-mediated cell death in an alveolar epithelial cell line, FKBP11 knockdown in an antibody-producing hybridoma cell line neither induced cell death nor decreased expression or secretion of IgG antibody. Similarly, antibody secretion by the same hybridoma cell line was not affected by knockdown of the established antibody peptidyl-prolyl isomerase cyclophilin B. The results are consistent with FKBP11 as a novel XBP1-regulated antibody peptidyl-prolyl cis-trans isomerase and indicate significant redundancy in the ER-resident folding machinery of antibody-producing hybridoma cells.

BackgroundThe Chr17q12-21.2 region is the strongest and most consistently associated region with asthma susceptibility. The functional genes or single nucleotide polymorphisms (SNPs) are not obvious due to linkage disequilibrium.ObjectivesWe sought to comprehensively investigate whole-genome sequence and RNA sequence from human bronchial epithelial cells to dissect functional genes/SNPs for asthma severity in the Severe Asthma Research Program.MethodsExpression quantitative trait loci analysis (n = 114), correlation analysis (n = 156) of gene expression and asthma phenotypes, and pathway analysis were performed in bronchial epithelial cells and replicated. Genetic association for asthma severity (426 severe vs 531 nonsevere asthma) and longitudinal asthma exacerbations (n = 273) was performed.ResultsMultiple SNPs in gasdermin B (GSDMB) associated with asthma severity (odds ratio, >1.25) and longitudinal asthma exacerbations (P < .05). Expression quantitative trait loci analyses identified multiple SNPs associated with expression levels of post-GPI attachment to proteins 3, GSDMB, or gasdermin A (3.1 × 10-9 -4). Higher expression levels of GSDMB correlated with asthma and greater number of exacerbations (P < .05). Expression levels of GSDMB correlated with genes involved in IFN signaling, MHC class I antigen presentation, and immune system pathways (false-discovery rate-adjusted P < .05). rs1031458 and rs3902920 in GSDMB colocalized with IFN regulatory factor binding sites and associated with GSDMB expression, asthma severity, and asthma exacerbations (P < .05).ConclusionsBy using a unique set of gene expression data from lung cells obtained using bronchoscopy from comprehensively characterized subjects with asthma, we show that SNPs in GSDMB associated with asthma severity, exacerbations, and GSDMB expression levels. Furthermore, its expression levels correlated with asthma exacerbations and antiviral pathways. Thus, GSDMB is a functional gene for both asthma susceptibility and severity.

Collagen-producing cells maintain the complex architecture of the lung and drive pathologic scarring in pulmonary fibrosis. Here we perform single-cell RNA-sequencing to identify all collagen-producing cells in normal and fibrotic lungs. We characterize multiple collagen-producing subpopulations with distinct anatomical localizations in different compartments of murine lungs. One subpopulation, characterized by expression of Cthrc1 (collagen triple helix repeat containing 1), emerges in fibrotic lungs and expresses the highest levels of collagens. Single-cell RNA-sequencing of human lungs, including those from idiopathic pulmonary fibrosis and scleroderma patients, demonstrate similar heterogeneity and CTHRC1-expressing fibroblasts present uniquely in fibrotic lungs. Immunostaining and in situ hybridization show that these cells are concentrated within fibroblastic foci. We purify collagen-producing subpopulations and find disease-relevant phenotypes of Cthrc1-expressing fibroblasts in in vitro and adoptive transfer experiments. Our atlas of collagen-producing cells provides a roadmap for studying the roles of these unique populations in homeostasis and pathologic fibrosis.

The urgent need for effective treatments for acute and chronic lung diseases underscores the significance of developing innovative preclinical human research tools. The 2023 American Thoracic Society Workshop on Precision-Cut Lung Slices (PCLSs) brought together 35 experts to discuss and address the role of human tissue–derived PCLSs as a unique tool for target and drug discovery and validation in pulmonary medicine. With increasing interest and usage, together with advancements in methods and technology, there is a growing need for consensus on PCLS methodology and readouts. The current document recommends standard reporting criteria and emphasizes the requirement for careful collection and integration of clinical metadata. We further discuss current clinically relevant readouts that can be applied to PCLSs and highlight recent developments and future steps for implementing novel technologies for PCLS modeling and analysis. The collection and correlation of clinical metadata and multiomic analysis will further advance the integration of this preclinical platform into patient endotyping and the development of tailored therapies for patients with lung disease. [ABSTRACT FROM AUTHOR]

Idiopathic pulmonary fibrosis (IPF) is a progressive, irreversible, and typically fatal lung disease characterised by subpleural fibrosis, subepithelial fibroblast foci, and microscopic honeycombing. Although understanding of the pathogenic mechanisms continues to evolve, evidence indicates that distal airway and alveolar epithelial cells are central drivers of the disease. In this Viewpoint, we review the history of naming and classifications used to define the disease now referred to as IPF, in the context of understanding the clinical presentation, causes, and pathogenesis of the disease. We aim to generate discussion on whether, given the substantial progress made in understanding the clinical, genetic, cellular, and molecular mechanisms involved in the development of IPF, a change of name should be considered. To initiate this discussion, we offer new suggestions to update the name of this disease and new approaches to classify all forms of pulmonary fibrosis.

RationaleDifferences in the lung microbial community influence idiopathic pulmonary fibrosis (IPF) progression. Whether the lung microbiome influences IPF host defense remains unknown.ObjectivesTo explore the host immune response and microbial interaction in IPF as they relate to progression-free survival (PFS), fibroblast function, and leukocyte phenotypes.MethodsPaired microarray gene expression data derived from peripheral blood mononuclear cells as well as 16S ribosomal RNA sequencing data from bronchoalveolar lavage obtained as part of the COMET-IPF (Correlating Outcomes with Biochemical Markers to Estimate Time-Progression in Idiopathic Pulmonary Fibrosis) study were used to conduct association pathway analyses. The responsiveness of paired lung fibroblasts to Toll-like receptor 9 (TLR9) stimulation by CpG-oligodeoxynucleotide (CpG-ODN) was integrated into microbiome-gene expression association analyses for a subset of individuals. The relationship between associated pathways and circulating leukocyte phenotypes was explored by flow cytometry.Measurements and main resultsDown-regulation of immune response pathways, including nucleotide-binding oligomerization domain (NOD)-, Toll-, and RIG1-like receptor pathways, was associated with worse PFS. Ten of the 11 PFS-associated pathways correlated with microbial diversity and individual genus, with species accumulation curve richness as a hub. Higher species accumulation curve richness was significantly associated with inhibition of NODs and TLRs, whereas increased abundance of Streptococcus correlated with increased NOD-like receptor signaling. In a network analysis, expression of up-regulated signaling pathways was strongly associated with decreased abundance of operational taxonomic unit 1341 (OTU1341; Prevotella) among individuals with fibroblasts responsive to CpG-ODN stimulation. The expression of TLR signaling pathways was also linked to CpG-ODN responsive fibroblasts, OTU1341 (Prevotella), and Shannon index of microbial diversity in a network analysis. Lymphocytes expressing C-X-C chemokine receptor 3 CD8 significantly correlated with OTU1348 (Staphylococcus).ConclusionsThese findings suggest that host-microbiome interactions influence PFS and fibroblast responsiveness.

Age prediction based on single cell RNA-Sequencing data (scRNA-Seq) can provide information for patients' susceptibility to various diseases and conditions. In addition, such analysis can be used to identify aging related genes and pathways. To enable age prediction based on scRNA-Seq data, we developed PolyEN, a new regression model which learns continuous representation for expression over time. These representations are then used by PolyEN to integrate genes to predict an age. Existing and new lung aging data we profiled demonstrated PolyEN's improved performance over existing methods for age prediction. Our results identified lung epithelial cells as the most significant predictors for non-smokers while lung endothelial cells led to the best chronological age prediction results for smokers. Author summary: Aging is characterized by several changes at the cellular and molecular levels. The type and rate of these changes varies between individuals and does not always correspond to their chronological age. Determining the 'molecular age' of an individual can therefore provide critical information about their susceptibility to various diseases and enable better treatment via personalized medicine. With accumulated data profiling the gene expressions for human lungs at various ages, we have developed machine learning methods to learn the individual molecular age. As we show, the transcriptome in an individual lung allows our method to accurately predict chronological age of the donor. We identified different cell types that correlate well with aging and showed that these cells consistently display aging artifacts across individuals and datasets. Specifically, we found that lung epithelial cells provide the best aging predictions for non-smokers and endothelial/smooth muscle cells as the best aging predictor for smokers. Our approach further revealed important apoptotic genes involved in the aging process of lung tissue. [ABSTRACT FROM AUTHOR]