Lung Tissue Research Consortium Research Articles

Sex-biased Regulation of Extracellular Matrix Genes in COPD.

Compared to men, women often develop COPD at an earlier age with worse respiratory symptoms despite lower smoking exposure. However, most preventive, and therapeutic strategies ignore biological sex differences in COPD. Our goal was to better understand sex-specific gene regulatory processes in lung tissue and the molecular basis for sex differences in COPD onset and severity. We analyzed lung tissue gene expression and DNA methylation data from 747 individuals in the Lung Tissue Research Consortium (LTRC), and 85 individuals in an independent dataset. We identified sex differences in COPD-associated gene regulation using gene regulatory networks. We used linear regression to test for sex-biased associations of methylation with lung function, emphysema, smoking, and age. Analyzing gene regulatory networks in the control group, we identified that genes involved in the extracellular matrix (ECM) have higher transcriptional factor targeting in females than in males. However, this pattern is reversed in COPD, with males showing stronger regulatory targeting of ECM-related genes than females. Smoking exposure, age, lung function, and emphysema were all associated with sex-specific differential methylation of ECM-related genes. We identified sex-based gene regulatory patterns of ECM-related genes associated with lung function and emphysema. Multiple factors including epigenetics, smoking, aging, and cell heterogeneity influence sex-specific gene regulation in COPD. Our findings underscore the importance of considering sex as a key factor in disease susceptibility and severity.

Overlap between COPD genetic association results and transcriptional quantitative trait loci.

Genome-wide association studies (GWAS) have identified multiple genetic loci associated with chronic obstructive pulmonary disease (COPD). When integrated with GWAS results, expression quantitative trait locus (eQTL) studies can provide insight into biological mechanisms involved in disease by identifying single nucleotide polymorphisms (SNPs) that contribute to whole gene expression. However, there are multiple genetically driven regulatory and isoform-specific effects which cannot be detected in traditional eQTL analyses. Here, we identify SNPs that are associated with alternative splicing (sQTL) in addition to eQTLs to identify novel functions for COPD associated genetic variants. We performed RNA sequencing on whole blood from 3743 subjects in the COPDGene Study. RNA sequencing data from lung tissue of 1241 subjects from the Lung Tissue Research Consortium (LTRC), and whole genome sequencing data on all subjects. Associations between all SNPs within 1000 kb of a gene (cis-) and splice and gene expression quantifications were tested using tensorQTL. In COPDGene a total of 11,869,333 SNPs were tested for association with 58,318 splice clusters, and 8,792,206 SNPs were tested for association with 70,094 splice clusters in LTRC. We assessed colocalization with COPD-associated SNPs from a published GWAS[1]. After adjustment for multiple statistical testing, we identified 28,110 splice-sites corresponding to 3,889 unique genes that were significantly associated with genotype in COPDGene whole blood, and 58,258 splice-sites corresponding to 10,307 unique genes associated with genotype in LTRC lung tissue. We found 7,576 sQTL splice-sites corresponding to 2,110 sQTL genes were shared between whole blood and lung, while 20,534 sQTL splice-sites in 3,518 genes were unique to blood and 50,682 splice-sites in 9,677 genes were unique to lung. To determine what proportion of COPD-associated SNPs were associated with transcriptional splicing, we performed colocalization analysis between COPD GWAS and sQTL data, and found that 38 genomic windows, corresponding to 38 COPD GWAS loci had evidence of colocalization between QTLs and COPD. The top five colocalizations between COPD and lung sQTLs include NPNT , FBXO38 , HHIP , NTN4 and BTC . A total of 38 COPD GWAS loci contain evidence of sQTLs, suggesting that analysis of sQTLs in whole blood and lung tissue can provide novel insights into disease mechanisms.

Lung Transcriptomics Links Emphysema to Barrier Dysfunction and Macrophage Subpopulations.

While many studies have examined gene expression in lung tissue, the gene regulatory processes underlying emphysema are still not well understood. Finding efficient non-imaging screening methods and disease-modifying therapies has been challenging, but knowledge of the transcriptomic features of emphysema may help in this effort. Our goals were to identify emphysema-associated biological pathways through transcriptomic analysis of bulk lung tissue, to determine the lung cell types in which these emphysema-associated pathways are altered, and to detect unique and overlapping transcriptomic signatures in blood and lung samples. Using RNA-sequencing data from 446 samples in the Lung Tissue Research Consortium (LTRC) and 3,606 blood samples from the COPDGene study, we examined the transcriptomic features of chest computed tomography-quantified emphysema. We also leveraged publicly available lung single-cell RNA-sequencing data to identify cell types showing COPD-associated differential expression of the emphysema pathways found in the bulk analyses. In the bulk lung RNA-seq analysis, 1,087 differentially expressed genes and 34 dysregulated pathways were significantly associated with emphysema. We observed alternative splicing of several genes and increased activity in pluripotency and cell barrier function pathways. Lung tissue and blood samples shared differentially expressed genes and biological pathways. Multiple lung cell types displayed dysregulation of epithelial barrier function pathways, and distinct pathway activities were observed among various macrophage subpopulations. This study identified emphysema-related changes in gene expression and alternative splicing, cell-type specific dysregulated pathways, and instances of shared pathway dysregulation between blood and lung.

Correlation-based network integration of lung RNA sequencing and DNA methylation data in chronic obstructive pulmonary disease

Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous, chronic inflammatory process of the lungs and, like other complex diseases, is caused by both genetic and environmental factors. Detailed understanding of the molecular mechanisms of complex diseases requires the study of the interplay among different biomolecular layers, and thus the integration of different omics data types. In this study, we investigated COPD-associated molecular mechanisms through a correlation-based network integration of lung tissue RNA-seq and DNA methylation data of COPD cases (n = 446) and controls (n = 346) derived from the Lung Tissue Research Consortium. First, we performed a SWIM-network based analysis to build separate correlation networks for RNA-seq and DNA methylation data for our case-control study population. Then, we developed a method to integrate the results into a coupled network of differentially expressed and differentially methylated genes to investigate their relationships across both molecular layers. The functional enrichment analysis of the nodes of the coupled network revealed a strikingly significant enrichment in Immune System components, both innate and adaptive, as well as immune-system component communication (interleukin and cytokine-cytokine signaling). Our analysis allowed us to reveal novel putative COPD-associated genes and to analyze their relationships, both at the transcriptomics and epigenomics levels, thus contributing to an improved understanding of COPD pathogenesis.

Colocalization analysis of 3' UTR alternative polyadenylation quantitative trait loci reveals novel mechanisms underlying associations with lung function.

While many disease-associated single nucleotide polymorphisms (SNPs) are expression quantitative trait loci (eQTLs), a large proportion of genome-wide association study (GWAS) variants are of unknown function. Alternative polyadenylation (APA) plays an important role in posttranscriptional regulation by allowing genes to shorten or extend 3' untranslated regions (UTRs). We hypothesized that genetic variants that affect APA in lung tissue may lend insight into the function of respiratory associated GWAS loci. We generated alternative polyadenylation (apa) QTLs using RNA sequencing and whole genome sequencing on 1241 subjects from the Lung Tissue Research Consortium (LTRC) as part of the NHLBI TOPMed project. We identified 56 179 APA sites corresponding to 13 582 unique genes after filtering out APA sites with low usage. We found that a total of 8831 APA sites were associated with at least one SNP with q-value < 0.05. The genomic distribution of lead APA SNPs indicated that the majority are intronic variants (33%), followed by downstream gene variants (26%), 3' UTR variants (17%), and upstream gene variants (within 1kb region upstream of transcriptional start site, 10%). APA sites in 193 genes colocalized with GWAS data for at least one phenotype. Genes containing the top APA sites associated with GWAS variants include membrane associated ring-CH-type finger 2 (MARCHF2), nectin cell adhesion molecule 2 (NECTIN2), and butyrophilin subfamily 3 member A2 (BTN3A2). Overall, these findings suggest that APA may be an important mechanism for genetic variants in lung function and chronic obstructive pulmonary disease (COPD).

Clonal Somatic Mutations in Chronic Lung Diseases Are Associated with Reduced Lung Function.

Rationale: Constantly exposed to the external environment and mutagens such as tobacco smoke, human lungs have one of the highest somatic mutation rates among all human organs. However, the relationship of these mutations to lung disease and function is not known. Objectives: To identify the prevalence and significance of clonal somatic mutations in chronic lung diseases. Methods: We analyzed the clonal somatic mutations from 1,251 samples of normal and diseased noncancerous lung tissue RNA sequencing with paired whole-genome sequencing from the Lung Tissue Research Consortium. We examined the associations of somatic mutations with lung function, disease status, and computationally deconvoluted cell types in two of the most common diseases represented in our dataset, chronic obstructive pulmonary disease (COPD; 29%) and idiopathic pulmonary fibrosis (IPF; 13%). Measurements and Main Results: Clonal somatic mutational burden was associated with reduced lung function in both COPD and IPF. We identified an increased prevalence of clonal somatic mutations in individuals with IPF compared with normal control subjects and individuals with COPD independent of age and smoking status. IPF clonal somatic mutations were enriched in disease-related and airway epithelial-expressed genes such as MUC5B in IPF. Patients who were MUC5B risk variant carriers had increased odds of developing somatic mutations of MUC5B that were explained by increased expression of MUC5B. Conclusions: Our identification of an increased prevalence of clonal somatic mutation in diseased lung that correlates with airway epithelial gene expression and disease severity highlights for the first time the role of somatic mutational processes in lung disease genetics.

Lung immune signatures define two groups of end-stage IPF patients

BackgroundThe role of the immune system in the pathobiology of Idiopathic Pulmonary Fibrosis (IPF) is controversial.MethodsTo investigate it, we calculated immune signatures with Gene Set Variation Analysis (GSVA) and applied them to the lung transcriptome followed by unbiased cluster analysis of GSVA immune-enrichment scores, in 109 IPF patients from the Lung Tissue Research Consortium (LTRC). Results were validated experimentally using cell-based methods (flow cytometry) in lung tissue of IPF patients from the University of Pittsburgh (n = 26). Finally, differential gene expression and hypergeometric test were used to explore non-immune differences between clusters.ResultsWe identified two clusters (C#1 and C#2) of IPF patients of similar size in the LTRC dataset. C#1 included 58 patients (53%) with enrichment in GSVA immune signatures, particularly cytotoxic and memory T cells signatures, whereas C#2 included 51 patients (47%) with an overall lower expression of GSVA immune signatures (results were validated by flow cytometry with similar unbiased clustering generation). Differential gene expression between clusters identified differences in cilium, epithelial and secretory cell genes, all of them showing an inverse correlation with the immune response signatures. Notably, both clusters showed distinct features despite clinical similarities.ConclusionsIn end-stage IPF lung tissue, we identified two clusters of patients with very different levels of immune signatures and gene expression but with similar clinical characteristics. Weather these immune clusters differentiate diverse disease trajectories remains unexplored.

Biomarkers of cellular senescence in idiopathic pulmonary fibrosis

BackgroundCellular senescence is a cell fate in response to diverse forms of age-related damage and stress that has been implicated in the pathogenesis of idiopathic pulmonary fibrosis (IPF). The associations between circulating levels of candidate senescence biomarkers and disease outcomes have not been specifically studied in IPF. In this study we assessed the circulating levels of candidate senescence biomarkers in individuals affected by IPF and controls and evaluated their ability to predict disease outcomes.MethodsWe measured the plasma concentrations of 32 proteins associated with senescence in Lung Tissue Research Consortium participants and studied their relationship with the diagnosis of IPF, parameters of pulmonary and physical function, health-related quality of life, mortality, and lung tissue expression of P16, a prototypical marker of cellular senescence. A machine learning approach was used to evaluate the ability of combinatorial biomarker signatures to predict disease outcomes.ResultsThe circulating levels of several senescence biomarkers were significantly elevated in persons affected by IPF compared to controls. A subset of biomarkers accurately classified participants as having or not having the disease and was significantly correlated with measures of pulmonary function, health-related quality of life and, to an extent, physical function. An exploratory analysis revealed senescence biomarkers were also associated with mortality in IPF participants. Finally, the plasma concentrations of several biomarkers were associated with their expression levels in lung tissue as well as the expression of P16.ConclusionsOur results suggest that circulating levels of candidate senescence biomarkers are informative of disease status, pulmonary and physical function, and health-related quality of life. Additional studies are needed to validate the combinatorial biomarkers signatures that emerged using a machine learning approach.

Integrating Genetics, Transcriptomics, and Proteomics in Lung Tissue to Investigate Chronic Obstructive Pulmonary Disease.

The integration of transcriptomic and proteomic data from lung tissue with chronic obstructive pulmonary disease (COPD)-associated genetic variants could provide insight into the biological mechanisms of COPD. Here, we assessed associations between lung transcriptomics and proteomics with COPD in 98 subjects from the Lung Tissue Research Consortium. Low correlations between transcriptomics and proteomics were generally observed, but higher correlations were found for COPD-associated proteins. We integrated COPD risk SNPs or SNPs near COPD-associated proteins with lung transcripts and proteins to identify regulatory cis-quantitative trait loci (QTLs). Significant expression QTLs (eQTLs) and protein QTLs (pQTLs) were found regulating multiple COPD-associated biomarkers. We investigated mediated associations from significant pQTLs through transcripts to protein levels of COPD-associated proteins. We also attempted to identify colocalized effects between COPD genome-wide association studies and eQTL and pQTL signals. Evidence was found for colocalization between COPD genome-wide association study signals and a pQTL for RHOB and an eQTL for DSP. We applied weighted gene co-expression network analysis to find consensus COPD-associated network modules. Two network modules generated by consensus weighted gene co-expression network analysis were associated with COPD with a false discovery rate lower than 0.05. One network module is related to the catenin complex, and the other module is related to plasma membrane components. In summary, multiple cis-acting determinants of transcripts and proteins associated with COPD were identified. Colocalization analysis, mediation analysis, and correlation-based network analysis of multiple omics data may identify key genes and proteins that work together to influence COPD pathogenesis.

The Association between Exposures and Disease Characteristics in Familial Pulmonary Fibrosis.

Rationale: Heterogeneous characteristics are observed in familial pulmonary fibrosis (FPF), suggesting that nongenetic factors contribute to disease manifestations. Objectives: To determine the relationship between environmental exposures and disease characteristics of FPF, including the morphological characteristics on chest computed tomography (CT) scan, and timing of FPF symptom onset, lung transplantation, or death. Methods: Subjects with FPF with an exposure questionnaire and chest CT were selected from a prospective cohort at Vanderbilt. Disease characteristics were defined by lung parenchymal findings on chest CT associated with fibrotic hypersensitivity pneumonitis (fHP) or usual interstitial pneumonia (UIP) and by time from birth to symptom onset or a composite of lung transplantation or death. After assessing the potential for confounding by sex or smoking, adjusted logistic or Cox proportional hazards regression models identified exposures associated with fHP or UIP CT findings. Findings were validated in a cohort of patients with sporadic pulmonary fibrosis enrolled in the LTRC (Lung Tissue Research Consortium) study. Results: Among 159 subjects with FPF, 98 (61.6%) were males and 96 (60.4%) were ever-smokers. Males were less likely to have CT features of fHP, including mosaic attenuation (FPF: adjusted [for sex and smoking] odds ratio [aOR], 0.27; 95% confidence interval [CI], 0.09-0.76; P = 0.01; LTRC: aOR, 0.35; 95% CI, 0.21-0.61; P = 0.0002). Organic exposures, however, were not consistently associated with fHP features in either cohort. Smoking was a risk factor for honeycombing in both cohorts (FPF: aOR, 2.19; 95% CI, 1.12-4.28; P = 0.02; LTRC: aOR, 1.69; 95% CI, 1.22-2.33; P = 0.002). Rock dust exposure may also be associated with honeycombing, although the association was not statistically-significant when accounting for sex and smoking (FPF: aOR, 2.27; 95% CI, 0.997-5.15; P = 0.051; LTRC: aOR, 1.51; 95% CI, 0.97-2.33; P = 0.07). In the FPF cohort, ever-smokers experienced a shorter transplant-free survival (adjusted hazard ratio, 1.64; 95% CI, 1.07-2.52; P = 0.02), whereas sex was not associated with differential survival (male adjusted hazard ratio, 0.75; 95% CI, 0.50-1.14; P = 0.18). Conclusions: In FPF, smoking contributes to shortened transplant-free survival and development of honeycombing, a finding that is also likely applicable to sporadic pulmonary fibrosis. Females are more likely to manifest CT features of fHP (mosaic attenuation), a finding that was incompletely explained by sex differences in exposures. These findings may have implications for pulmonary fibrosis classification and management.

Localizing the expression of Piezo2 in healthy and diseased lung tissue

Intractable air hunger remains one of the most impactful symptoms of chronic cardiopulmonary disease and current pharmacological management remains limited. However, an endogenous mechanism for air hunger relief exists. Pulmonary stretch receptors (PSRs) responding to lung inflation reduce the brainstem drive to breathe and mitigate air hunger. Harnessing this mechanism could form the basis of a novel therapy. Recent animal studies suggest Piezo2, a mechanically‐gated cation channel, causes PSR depolarization when airway smooth muscle is stretched during inflation. We have assessed whether Piezo2 is associated with PSRs in healthy and diseased human airways to determine whether Piezo2 is a potential pharmaceutical target.MethodsHuman lung biopsies from NIH Lung Tissue Research Consortium were used for identification of Piezo2 receptors in emphysema, fibrosis, bronchiolitis, and normal lung tissue. Localization of Piezo2 was determined with immunofluorescent staining and imaged with confocal microscopy (Zeiss LSM880). As PSR fiber endings are associated with airway smooth muscle, co‐staining for Smooth Muscle Antibody (SMA) and Calcitonin Gene‐Related Peptide (CGRP) was performed to determine whether Piezo2 is associated with PSR.Summary of resultsPiezo2 is associated with airway smooth muscle (as evidenced by SMA, figure 1) and neuroepithelial bodies in normal human lung tissue (evidenced by cGRP, figure 2) and expression persisted in all disease states tested.ConclusionsPiezo2 location is consistent with that of PSR in human lungs, making it a candidate for the mechanoreceptive transducer of human PSRs as has been demonstrated in animal studies. Expression persists in disease, making Piezo2 a potential therapeutic target, but it is still yet to be determined if Piezo2 is more significantly associated with airway or vascular smooth muscle in different disease states.

Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem

BackgroundAutomated segmentation of anatomical structures is a crucial step in image analysis. For lung segmentation in computed tomography, a variety of approaches exists, involving sophisticated pipelines trained and validated on different datasets. However, the clinical applicability of these approaches across diseases remains limited.MethodsWe compared four generic deep learning approaches trained on various datasets and two readily available lung segmentation algorithms. We performed evaluation on routine imaging data with more than six different disease patterns and three published data sets.ResultsUsing different deep learning approaches, mean Dice similarity coefficients (DSCs) on test datasets varied not over 0.02. When trained on a diverse routine dataset (n = 36), a standard approach (U-net) yields a higher DSC (0.97 ± 0.05) compared to training on public datasets such as the Lung Tissue Research Consortium (0.94 ± 0.13, p = 0.024) or Anatomy 3 (0.92 ± 0.15, p = 0.001). Trained on routine data (n = 231) covering multiple diseases, U-net compared to reference methods yields a DSC of 0.98 ± 0.03 versus 0.94 ± 0.12 (p = 0.024).ConclusionsThe accuracy and reliability of lung segmentation algorithms on demanding cases primarily relies on the diversity of the training data, highlighting the importance of data diversity compared to model choice. Efforts in developing new datasets and providing trained models to the public are critical. By releasing the trained model under General Public License 3.0, we aim to foster research on lung diseases by providing a readily available tool for segmentation of pathological lungs.

A robust data-driven genomic signature for idiopathic pulmonary fibrosis with applications for translational model selection.

Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive lung disease affecting ~5 million people globally. We have constructed an accurate model of IPF disease status using elastic net regularized regression on clinical gene expression data. Leveraging whole transcriptome microarray data from 230 IPF and 89 control samples from Yang et al. (2013), sourced from the Lung Tissue Research Consortium (LTRC) and National Jewish Health (NJH) cohorts, we identify an IPF gene expression signature. We performed optimal feature selection to reduce the number of transcripts required by our model to a parsimonious set of 15. This signature enables our model to accurately separate IPF patients from controls. Our model outperforms existing published models when tested with multiple independent clinical cohorts. Our study underscores the utility of elastic nets for gene signature/panel selection which can be used for the construction of a multianalyte biomarker of disease. We also filter the gene sets used for model input to construct a model reliant on secreted proteins. Using this approach, we identify the preclinical bleomycin rat model that is most congruent with human disease at day 21 post-bleomycin administration, contrasting with earlier timepoints suggested by other studies.

Development and validation of a radiological diagnosis model for hypersensitivity pneumonitis.

High-resolution computed tomography (HRCT) may be useful for diagnosing hypersensitivity pneumonitis. Here, we develop and validate a radiological diagnosis model and model-based points score.Patients with interstitial lung disease seen at the University of Michigan Health System (derivation cohort) or enrolling in the Lung Tissue Research Consortium (validation cohort) were included. A thin-section, inspiratory HRCT scan was required. Thoracic radiologists documented radiological features.The derivation cohort comprised 356 subjects (33.9% hypersensitivity pneumonitis) and the validation cohort comprised 424 subjects (15.5% hypersensitivity pneumonitis). An age-, sex- and smoking status-adjusted logistic regression model identified extent of mosaic attenuation or air trapping greater than that of reticulation ("MA-AT>Reticulation"; OR 6.20, 95% CI 3.53-10.90; p<0.0001) and diffuse axial disease distribution (OR 2.33, 95% CI 1.31-4.16; p=0.004) as hypersensitivity pneumonitis predictors (area under the receiver operating characteristic curve 0.814). A model-based score >2 (1 point for axial distribution, 2 points for "MA-AT>Reticulation") has specificity 90% and positive predictive value (PPV) 74% in the derivation cohort and specificity 96% and PPV 44% in the validation cohort. Similar model performance is seen with population restriction to those reporting no exposure (score >2: specificity 91%).When radiological mosaic attenuation or air trapping are more extensive than reticulation and disease has diffuse axial distribution, hypersensitivity pneumonitis specificity is high and false diagnosis risk low (<10%), but PPV is diminished in a low-prevalence setting.

Expression of RXFP1 Is Decreased in Idiopathic Pulmonary Fibrosis. Implications for Relaxin-based Therapies

Relaxin is a hormone that has been considered as a potential therapy for patients with fibrotic diseases. To gauge the potential efficacy of relaxin-based therapies in idiopathic pulmonary fibrosis (IPF), we studied gene expression for relaxin/insulin-like family peptide receptor 1 (RXFP1) in IPF lungs and controls. We analyzed gene expression data obtained from the Lung Tissue Research Consortium and correlated RXFP1 gene expression data with cross-sectional clinical and demographic data. We also employed ex vivo donor and IPF lung fibroblasts to test RXFP1 expression in vitro. We tested CGEN25009, a relaxin-like peptide, in lung fibroblasts and in bleomycin injury. We found that RXFP1 is significantly decreased in IPF. In patients with IPF, the magnitude of RXFP1 gene expression correlated directly with diffusing capacity of the lung for carbon monoxide (P < 0.0001). Significantly less RXFP1 was detected in vitro in IPF fibroblasts than in donor controls. Transforming growth factor-β decreased RXFP1 in both donor and IPF lung fibroblasts. CGEN25009 was effective at decreasing bleomycin-induced, acid-soluble collagen deposition in vivo. The relaxin-like actions of CGEN25009 were abrogated by RXFP1 silencing in vitro, and, in comparison with donor lung fibroblasts, IPF lung fibroblasts exhibited decreased sensitivity to the relaxin-like effects of CGEN25009. IPF is characterized by the loss of RXFP1 expression. RXFP1 expression is directly associated with pulmonary function in patients with IPF. The relaxin-like effects of CGEN25009 in vitro are dependent on expression of RXFP1. Our data suggest that patients with IPF with the highest RXFP1 expression would be predicted to be most sensitive to relaxin-based therapies.

Predictors of idiopathic pulmonary fibrosis in absence of radiologic honeycombing: A cross sectional analysis in ILD patients undergoing lung tissue sampling

BackgroundIdiopathic pulmonary fibrosis (IPF) can be diagnosed confidently and non-invasively when clinical and computed tomography (CT) criteria are met. Many do not meet these criteria due to absence of CT honeycombing. We investigated predictors of IPF and combinations allowing accurate diagnosis in individuals without honeycombing. MethodsWe utilized prospectively collected clinical and CT data from patients enrolled in the Lung Tissue Research Consortium. Included patients had no honeycombing, no connective tissue disease, underwent diagnostic lung biopsy, and had CT pattern consistent with fibrosing ILD (n = 200). Logistic regression identified clinical and CT variables predictive of IPF. The probability of IPF was assessed at various cut-points of important clinical and CT variables. ResultsA multivariable model adjusted for age and gender found increasingly extensive reticular densities (OR 2.93, CI 95% 1.55–5.56, p = 0.001) predicted IPF, while increasing ground glass densities predicted a diagnosis other than IPF (OR 0.55, CI 95% 0.34–0.89, p = 0.02). The model-based probability of IPF was 80% or greater in patients with age at least 60 years and extent of reticular density one-third or more of total lung volume; for patients meeting or exceeding these clinical thresholds the specificity for IPF is 96% (CI 95% 91–100%) with 21 of 134 (16%) biopsies avoided. ConclusionsIn patients with suspected fibrotic ILD and absence of CT honeycombing, extent of reticular and ground glass densities predict a diagnosis of IPF. The probability of IPF exceeds 80% in subjects over age 60 years with one-third of total lung having reticular densities.

A novel genomic signature with translational significance for human idiopathic pulmonary fibrosis.

The bleomycin-induced rodent lung fibrosis model is commonly used to study mechanisms of lung fibrosis and to test potential therapeutic interventions, despite the well recognized dissimilarities to human idiopathic pulmonary fibrosis (IPF). Therefore, in this study, we sought to identify genomic commonalities between the gene expression profiles from 100 IPF lungs and 108 control lungs that were obtained from the Lung Tissue Research Consortium, and rat lungs harvested at Days 3, 7, 14, 21, 28, 42, and 56 after bleomycin instillation. Surprisingly, the highest gene expression similarity between bleomycin-treated rat and IPF lungs was observed at Day 7. At this point of maximal rat-human commonality, we identified a novel set of 12 disease-relevant translational gene markers (C6, CTHRC1, CTSE, FHL2, GAL, GREM1, LCN2, MMP7, NELL1, PCSK1, PLA2G2A, and SLC2A5) that was able to separate almost all patients with IPF from control subjects in our cohort and in two additional IPF/control cohorts (GSE10667 and GSE24206). Furthermore, in combination with diffusing capacity of carbon monoxide measurements, four members of the translational gene marker set contributed to stratify patients with IPF according to disease severity. Significantly, pirfenidone attenuated the expression change of one (CTHRC1) translational gene marker in the bleomycin-induced lung fibrosis model, in transforming growth factor-β1-treated primary human lung fibroblasts and transforming growth factor-β1-treated human epithelial A549 cells. Our results suggest that a strategy focused on rodent model-human disease commonalities may identify genes that could be used to predict the pharmacological impact of therapeutic interventions, and thus facilitate the development of novel treatments for this devastating lung disease.

Correlation of Regional Emphysema and Lung Cancer: A Lung Tissue Research Consortium-Based Study

Chronic obstructive pulmonary disease and lung cancer are linked because both airflow obstruction and emphysema, on computer tomography, are independent risk factors for lung cancer. However, the local risk of malignancy relative to development of regional emphysema has not yet been defined. Specifically, it is not known if primary lung cancers are associated with regions of worse emphysema within individual patients. We performed a database analysis evaluating the association between the degree of regional emphysema as scored on computer tomography and development of primary lung cancer. We also studied the association between regional emphysema and benign lung nodules. We assembled two distinct cohorts using the National Heart, Lung, and Blood Institute's Lung Tissue Research Consortium database, hypothesizing that lung malignancy will preferentially locate in the regions of the most severe emphysema. In the Lung Tissue Research Consortium database, 624 cases met criteria for the malignant nodule cohort and 64 were included in the benign nodule cohort. When comparing location of a malignant nodule to other lung regions within the same person, the odds of having a more severe emphysema score in the location of lung cancer was 1.342 (95% confidence interval 1.112-1.620; p = 0.0022). When comparing location of a benign nodule to other lung regions within the same person, the odds of having a more severe emphysema score in the location of the benign nodule was 1.118 (95% confidence interval 0.725-1.725; p = 0.6137). Primary lung cancers are associated with areas of worse regional emphysema.

Pneumocystis jirovecii colonization is associated with enhanced Th1 inflammatory gene expression in lungs of humans with chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) is a complex disease, the pathogenesis of which remains incompletely understood. Colonization with Pneumocystis jirovecii may play a role in COPD pathogenesis; however, the mechanisms by which such colonization contributes to COPD are unknown. The objective of this study was to determine lung gene expression profiles associated with Pneumocystis colonization in patients with COPD to identify potential key pathways involved in disease pathogenesis. Using COPD lung tissue samples made available through the Lung Tissue Research Consortium (LTRC), Pneumocystis colonization status was determined by nested PCR. Microarray gene expression profiles were performed for each sample and the profiles of colonized and non-colonized samples compared. Overall, 18 participants (8.5%) were Pneumocystis-colonized. Pneumocystis colonization was associated with fold increase in expression of four closely related genes: INF-γ and the three chemokine ligands CXCL9, CXCL10, and CXCL11. These ligands are chemoattractants for the common cognate receptor CXCR3, which is predominantly expressed on activated Th1 T-lymphocytes. Although these ligand-receptor pairs have previously been implicated in COPD pathogenesis, few initiators of ligand expression and subsequent lymphocyte trafficking have been identified: our findings implicate Pneumocystis as a potential trigger. The finding of upregulation of these inflammatory genes in the setting of Pneumocystis colonization sheds light on infectious-immune relationships in COPD.

Quantitative computed tomography imaging of interstitial lung diseases.

High-resolution chest computed tomography (HRCT) is essential in the characterization of interstitial lung disease. The HRCT features of some diseases can be diagnostic. Longitudinal monitoring with HRCT can assess progression of interstitial lung disease; however, subtle changes in the volume and character of abnormalities can be difficult to assess. Accuracy of diagnosis can be dependent on expertise and experience of the radiologist, pathologist, or clinician. Quantitative analysis of thoracic HRCT has the potential to determine the extent of disease reproducibly, classify the types of abnormalities, and automate the diagnostic process. Novel software that utilizes histogram signatures to characterize pulmonary parenchyma was used to analyze chest HRCT data, including retrospective processing of clinical CT scans and research data from the Lung Tissue Research Consortium. Additional information including physiological, pathologic, and semiquantitative radiologist assessment was available to allow comparison of quantitative results, with visual estimates of the disease, physiological parameters, and measures of disease outcome. Quantitative analysis results were provided in regional volumetric quantities for statistical analysis and a graphical representation. These results suggest that quantitative HRCT analysis can serve as a biomarker with physiological, pathologic, and prognostic significance. It is likely that quantitative analysis of HRCT can be used in clinical practice as a means to aid in identifying a probable diagnosis, stratifying prognosis in early disease, and consistently determining progression of the disease or response to therapy. Further optimization of quantitative techniques and longitudinal analysis of well-characterized subjects would be helpful in validating these methods.