Biological Age, Chronological Age and Survival in Pulmonary Fibrosis: A Causal Mediation Analysis.

RATIONALE: Accelerated biological aging has been implicated in the development of interstitial lung disease (ILD) and other diseases of aging but remains poorly understood. OBJECTIVES: To identify plasma proteins that mediate the relationship between chronological age and survival association in patients with ILD. METHODS: Causal mediation analysis was performed to identify plasma proteins that mediated the chronological age-survival relationship in an idiopathic pulmonary fibrosis (IPF) discovery cohort. Proteins mediating this relationship after adjustment for false discovery were advanced for testing in an independent ILD validation cohort and explored in a chronic obstructive pulmonary disease (COPD) cohort. A proteomic-based measure of biological age was constructed and survival analysis performed assessing the impact of biological age and peripheral blood telomere length on the chronological age-survival relationship. RESULTS: Twenty-two proteins mediated the chronological age-survival relationship after adjustment for false discovery in the IPF discovery cohort (n=874), with nineteen remaining significant mediators of this relationship in the ILD validation cohort (n=983) and one mediating this relationship in the COPD cohort. Latent transforming growth factor beta binding protein 2 and ectodysplasin A2 receptor showed the strongest mediation across cohorts. A proteomic measure of biological age completely attenuated the chronological age-survival association and better discriminated survival than chronological age. Results were robust to adjustment for peripheral blood telomere length, which did not mediate the chronological age-survival relationship. CONCLUSIONS: Molecular measures of aging completely mediate the relationship between chronological age and survival, suggesting that chronological age has no direct effect on ILD survival.

Molecular Endotypes of Idiopathic Pulmonary Fibrosis: A Latent Class Analysis of Two Multicenter Observational Cohorts.

Rationale: Idiopathic pulmonary fibrosis (IPF) causes irreversible fibrosis of the lung parenchyma. While antifibrotic therapy can slow IPF progression, treatment response is variable. There exists a critical need to develop a precision medicine approach to IPF. Objective: To identify and validate biologically driven molecular endotypes of IPF. Methods: Latent class analysis (LCA) was independently performed in prospectively recruited discovery (n=875) and validation (n=347) cohorts. Twenty-five plasma biomarkers associated with fibrogenesis served as class-defining variables. The association between molecular endotype and 4-year transplant-free survival was tested using multivariable Cox regression adjusted for baseline confounders. Endotype-dependent differential treatment response to future antifibrotic exposure was then assessed in a pooled cohort of patients naïve to antifibrotic therapy at time of biomarker measurement (n=555). Results: LCA independently identified two latent classes in both cohorts (p<0.0001). WAP four-disulfide core domain protein 2 (WFDC2) was the most important determinant of class membership across cohorts. Membership in Class 2 was characterized by higher biomarker concentrations and higher risk of death or transplantation (discovery: HR 2.02 [95% CI 1.64-2.48]; p<0.001; validation: HR 1.95 [1.34-2.82]; p<0.001). In pooled analysis, significant heterogeneity in treatment effect was observed between endotypes (pinteraction=0.030), with a favorable antifibrotic response in Class 2 (HR 0.64 [0.45-0.93]; p=0.018) but not in Class 1 (HR 1.19 [0.77-1.84]; p=0.422). Conclusions: In this multicohort study, we identified two novel molecular endotypes of IPF with divergent clinical outcomes and response to antifibrotics. Pending further validation, these endotypes could enable a precision medicine approach for future IPF clinical trials.

Dendritic Cell - Fibroblast Crosstalk via TLR9 and AHR Signaling Drives Lung Fibrogenesis.

Idiopathic pulmonary fibrosis (IPF) is characterized by progressive scarring and loss of lung function. With limited treatment options, patients succumb to the disease within 2-5 years. The molecular pathogenesis of IPF regarding the immunologic changes that occur is poorly understood. We characterize a role for non-canonical aryl-hydrocarbon receptor signaling (ncAHR) in dendritic cells (DCs) that leads to production of IL-6 and IL-17, promoting fibrosis. TLR9 signaling in myofibroblasts is shown to regulate production of TDO2 which converts tryptophan into the endogenous AHR ligand kynurenine. Mice with augmented ncAHR signaling were created by crossing floxed AHR exon-2 deletion mice (AHR Δex2 ) with mice harboring a CD11c-Cre. Bleomycin was used to study fibrotic pathogenesis. Isolated CD11c+ cells and primary fibroblasts were treated ex-vivo with relevant TLR agonists and AHR modulating compounds to study how AHR signaling influenced inflammatory cytokine production. Human datasets were also interrogated. Inhibition of all AHR signaling rescued fibrosis, however, AHR Δex2 mice treated with bleomycin developed more fibrosis and DCs from these mice were hyperinflammatory and profibrotic upon adoptive transfer. Treatment of fibrotic fibroblasts with TLR9 agonist increased expression of TDO2. Study of human samples corroborate the relevance of these findings in IPF patients. We also, for the first time, identify that AHR exon-2 floxed mice retain capacity for ncAHR signaling.

A multidimensional classifier to support lung transplant referral in patients with pulmonary fibrosis.

BACKGROUND: Lung transplantation remains the sole curative option for patients with idiopathic pulmonary fibrosis (IPF), but donor organs remain scarce, and many eligible patients die before transplant. Tools to optimize the timing of transplant referrals are urgently needed. METHODS: Least absolute shrinkage and selection operator was applied to clinical and proteomic data generated as part of a prospective cohort study of interstitial lung disease (ILD) to derive clinical, proteomic, and multidimensional logit models of near-term death or lung transplant within 18 months of blood draw. Model-fitted values were dichotomized at the point of maximal sensitivity and specificity, and decision curve analysis was used to select the best-performing classifier. We then applied this classifier to independent IPF and non-IPF ILD cohorts to determine test performance characteristics. Cohorts were restricted to patients aged ≤72 years with body mass index 18 to 32 to increase the likelihood of transplant eligibility. RESULTS: IPF derivation, IPF validation, and non-IPF ILD validation cohorts consisted of 314, 105, and 295 patients, respectively. A multidimensional model comprising 2 clinical variables and 20 proteins outperformed stand-alone clinical and proteomic models. Following dichotomization, the multidimensional classifier predicted near-term outcome with 70% sensitivity and 92% specificity in the IPF validation cohort and 70% sensitivity and 80% specificity in the non-IPF ILD validation cohort. CONCLUSIONS: A multidimensional classifier of near-term outcomes accurately discriminated this end-point with good test performance across independent IPF and non-IPF ILD cohorts. These findings support refinement and prospective validation of this classifier in transplant-eligible individuals.

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.

COPD with elevated sputum group 2 innate lymphoid cells is characterized by severe disease.

Rationale: Pulmonary innate immune cells play a central role in the initiation and perpetuation of chronic obstructive pulmonary disease (COPD), however the precise mechanisms that orchestrate the development and severity of COPD are poorly understood. Objectives: We hypothesized that the recently described family of innate lymphoid cells (ILCs) play an important role in COPD. Methods: Subjects with COPD and healthy controls were clinically evaluated, and their sputum samples were assessed by flow cytometry. A mouse model of spontaneous COPD [genetically deficient in surfactant protein-D (SP-D -/- )] and ozone (O 3 ) exposure were used to examine the mechanism by which lack of functional SP-D may skew ILC2s to produce IL-17A in combination with IL-5 and IL-13, leading to a mixed inflammatory profile and more severe disease. Measurements and Main Results: COPD was characterized by poor spirometry, sputum inflammation, and the emergence of sputum GATA3 + ILCs (ILC2s), but not T-bet + ILCs (ILC1s) nor RORγt + ILCs (ILC3s). COPD subjects with elevated sputum ILC2s (the ILC2 high group) had worse spirometry and sputum neutrophilia and eosinophilia than healthy and ILC2 low subjects. This was associated with the presence of dual-positive IL-5 + IL-17A + and IL-13 + IL-17A + ILCs and nonfunctional SP-D in the sputum in ILC2 high subjects. SP-D -/- mice showed spontaneous airway neutrophilia. Lack of SP-D in the mouse lung licensed ILC2s to produce IL-17A, which was dose-dependently inhibited by recombinant SP-D. SP-D -/- mice showed enhanced susceptibility to O 3 -induced airway neutrophilia, which was associated with the emergence of inflammatory IL-13 + IL-17A + ILCs. Conclusions: We report that the presence of sputum ILC2s predicts the severity of COPD, and unravel a novel pathway of IL-17A plasticity in lung ILC2s, prevented by the immunomodulatory protein SP-D.

Proteomic Biomarkers of Survival in Idiopathic Pulmonary Fibrosis.

RATIONALE: Idiopathic pulmonary fibrosis (IPF) causes progressive lung scarring and high mortality. Reliable and accurate prognostic biomarkers are urgently needed. OBJECTIVE: To identify and validate circulating protein biomarkers of IPF survival. METHODS: High-throughput proteomic data were generated using prospectively collected plasma samples from patients with IPF from the Pulmonary Fibrosis Foundation Patient Registry (discovery cohort) and the Universities of California-Davis, Chicago, and Virginia (validation cohort). Proteins associated with three-year transplant-free survival (TFS) were identified using multivariable Cox proportional hazards regression. Those associated with TFS after adjustment for false discovery in the discovery cohort were advanced for testing in the validation cohort, with proteins maintaining TFS association with consistent effect direction considered validated. After combining cohorts, functional analyses were performed, and machine learning used to derive a proteomic signature of TFS. MAIN RESULTS: Of 2921 proteins tested in the discovery cohort (n=871), 231 were associated with differential TFS. Of these, 140 maintained TFS association with consistent effect direction in the validation cohort (n=355). After combining cohorts, validated proteins with strongest TFS association were latent-transforming growth factor beta-binding protein 2 (HR 2.43, 95% CI 2.09-2.82), collagen alpha-1(XXIV) chain (HR 2.21; 95% CI 1.86-2.39) and keratin 19 (HR 1.60; 95% CI 1.47-1.74). In decision curve analysis, a proteomic signature of TFS outperformed a similarly derived clinical prediction model. CONCLUSIONS: In largest proteomic investigation of IPF outcomes performed to date, we identified and validated 140 protein biomarkers of TFS. These results shed important light on potential drivers of IPF progression.

Telomere length and immunosuppression in non-idiopathic pulmonary fibrosis interstitial lung disease.

BACKGROUND: Studies suggest a harmful pharmacogenomic interaction exists between short leukocyte telomere length (LTL) and immunosuppressants in idiopathic pulmonary fibrosis (IPF). It remains unknown if a similar interaction exists in non-IPF interstitial lung disease (ILD). METHODS: A retrospective, multicentre cohort analysis was performed in fibrotic hypersensitivity pneumonitis (fHP), unclassifiable ILD (uILD) and connective tissue disease (CTD)-ILD patients from five centres. LTL was measured by quantitative PCR for discovery and replication cohorts and expressed as age-adjusted percentiles of normal. Inverse probability of treatment weights based on propensity scores were used to assess the association between mycophenolate or azathioprine exposure and age-adjusted LTL on 2-year transplant-free survival using weighted Cox proportional hazards regression incorporating time-dependent immunosuppressant exposure. RESULTS: The discovery and replication cohorts included 613 and 325 patients, respectively. In total, 40% of patients were exposed to immunosuppression and 22% had LTL <10th percentile of normal. fHP and uILD patients with LTL <10th percentile experienced reduced survival when exposed to either mycophenolate or azathioprine in the discovery cohort (mortality hazard ratio (HR) 4.97, 95% CI 2.26-10.92; p<0.001) and replication cohort (mortality HR 4.90, 95% CI 1.74-13.77; p=0.003). Immunosuppressant exposure was not associated with differential survival in patients with LTL ≥10th percentile. There was a significant interaction between LTL <10th percentile and immunosuppressant exposure (discovery pinteraction=0.013; replication pinteraction=0.011). Low event rate and prevalence of LTL <10th percentile precluded subgroup analyses for CTD-ILD. CONCLUSION: Similar to IPF, fHP and uILD patients with age-adjusted LTL <10th percentile may experience reduced survival when exposed to immunosuppression.

Telomere length associates with chronological age and mortality across racially diverse pulmonary fibrosis cohorts.

Pulmonary fibrosis (PF) is characterized by profound scarring and poor survival. We investigated the association of leukocyte telomere length (LTL) with chronological age and mortality across racially diverse PF cohorts. LTL measurements among participants with PF stratified by race/ethnicity were assessed in relation to age and all-cause mortality, and compared to controls. Generalized linear models were used to evaluate the age-LTL relationship, Cox proportional hazards models were used for hazard ratio estimation, and the Cochran-Armitage test was used to assess quartiles of LTL. Standardized LTL shortened with increasing chronological age; this association in controls was strengthened in PF (R = -0.28; P < 0.0001). In PF, age- and sex-adjusted LTL below the median consistently predicted worse mortality across all racial groups (White, HR = 2.21, 95% CI = 1.79-2.72; Black, HR = 2.22, 95% CI = 1.05-4.66; Hispanic, HR = 3.40, 95% CI = 1.88-6.14; and Asian, HR = 2.11, 95% CI = 0.55-8.23). LTL associates uniformly with chronological age and is a biomarker predictive of mortality in PF across racial groups.

PCSK6 and Survival in Idiopathic Pulmonary Fibrosis.

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 < 5 × 10-5) were advanced for stage II testing and meta-analyzed to identify those reaching genome-wide significance (P < 5 × 10-8). Downstream analyses were performed for genes and proteins associated with variants reaching genome-wide significance. Measurements and Main Results: After quality controls, 1,481 stage I cases and 397 stage II cases were included in the analysis. After filtering, 9,075,629 variants were tested in stage I, with 158 meeting advancement criteria. Four variants associated with TFS with consistent effect direction were identified in stage II, including one in an intron of PCSK6 (proprotein convertase subtilisin/kexin type 6) reaching genome-wide significance (hazard ratio, 4.11 [95% confidence interval, 2.54-6.67]; P = 9.45 × 10-9). PCSK6 protein was highly expressed in IPF lung parenchyma. PCSK6 lung staining intensity, peripheral blood gene expression, and plasma concentration were associated with reduced TFS. Conclusions: We identified four novel variants associated with IPF survival, including one in PCSK6 that reached genome-wide significance. Downstream analyses suggested that PCSK6 protein plays a potentially important role in IPF progression.

Longitudinal lung function and gas transfer in individuals with idiopathic pulmonary fibrosis: a genome-wide association study.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is an incurable lung disease characterised by progressive scarring leading to alveolar stiffness, reduced lung capacity, and impeded gas transfer. We aimed to identify genetic variants associated with declining lung capacity or declining gas transfer after diagnosis of IPF. METHODS: We did a genome-wide meta-analysis of longitudinal measures of forced vital capacity (FVC) and diffusing capacity of the lung for carbon monoxide (DLCO) in individuals diagnosed with IPF. Individuals were recruited to three studies between June, 1996, and August, 2017, from across centres in the US, UK, and Spain. Suggestively significant variants were investigated further in an additional independent study (CleanUP-IPF). All four studies diagnosed cases following American Thoracic Society/European Respiratory Society guidelines. Variants were defined as significantly associated if they had a meta-analysis p<5 × 10-8 when meta-analysing across all discovery and follow-up studies, had consistent direction of effects across all four studies, and were nominally significant (p<0·05) in each study. FINDINGS: 1329 individuals with a total of 5216 measures were included in the FVC analysis. 975 individuals with a total of 3361 measures were included in the DLCO analysis. For the discovery genome-wide analyses, 7 611 174 genetic variants were included in the FVC analysis and 7 536 843 in the DLCO analysis. One variant (rs115982800) located in an antisense RNA gene for protein kinase N2 (PKN2) showed a genome-wide significant association with FVC decline (-140 mL/year per risk allele [95% CI -180 to -100]; p=9·14 × 10-12). INTERPRETATION: Our analysis identifies a genetic variant associated with disease progression, which might highlight a new biological mechanism for IPF. We found that PKN2, a Rho and Rac effector protein, is the most likely gene of interest from this analysis. PKN2 inhibitors are currently in development and signify a potential novel therapeutic approach for IPF. FUNDING: Action for Pulmonary Fibrosis, Medical Research Council, Wellcome Trust, and National Institutes of Health National Heart, Lung, and Blood Institute.

Proteomic biomarkers of progressive fibrosing interstitial lung disease: a multicentre cohort analysis.

BACKGROUND: Progressive fibrosing interstitial lung disease (ILD) is characterised by parenchymal scar formation, leading to high morbidity and mortality. The ability to predict this phenotype remains elusive. We conducted a proteomic analysis to identify novel plasma biomarkers of progressive fibrosing ILD and developed a proteomic signature to predict this phenotype. METHODS: Relative plasma concentrations for 368 biomarkers were determined with use of a semi-quantitative, targeted proteomic platform in patients with connective tissue disease-associated ILD, chronic hypersensitivity pneumonitis, or unclassifiable ILD who provided research blood draws at the University of California (discovery cohort) and the University of Texas (validation cohort). Univariable logistic regression was used to identify individual biomarkers associated with 1-year ILD progression, defined as death, lung transplant, or 10% or greater relative forced vital capacity (FVC) decline. A proteomic signature of progressive fibrosing ILD was then derived with use of machine learning in the University of California cohort and validated in the University of Texas cohort. FINDINGS: The discovery cohort comprised 385 patients (mean age 63·6 years, 59% female) and the validation cohort comprised 204 patients (mean age 60·7 years, 61% female). 31 biomarkers were associated with progressive fibrosing ILD in the discovery cohort, with 17 maintaining an association in the validation cohort. Validated biomarkers showed a consistent association with progressive fibrosing ILD irrespective of ILD clinical diagnosis. A proteomic signature comprising 12 biomarkers was derived by machine learning and validated in the University of Texas cohort, in which it had a sensitivity of 0·90 and corresponding negative predictive value of 0·91, suggesting that approximately 10% of patients with a low-risk proteomic signature would experience ILD progression in the year after blood draw. Those with a low-risk proteomic signature experienced an FVC change of +85·7 mL (95% CI 6·9 to 164·4) and those with a high-risk signature experienced an FVC change of -227·1 mL (-286·7 to -167·5). A theoretical clinical trial restricted to patients with a high-risk proteomic signature would require 80% fewer patients than one designed without regard to proteomic signature. INTERPRETATION: 17 plasma biomarkers of progressive fibrosing ILD were identified and showed consistent associations across ILD subtypes. A proteomic signature of progressive fibrosing ILD could enrich clinical trial cohorts and avoid the need for antecedent progression when defining progressive fibrosing ILD for clinical trial enrolment. FUNDING: National Heart Lung and Blood Institute.

Predicting Influenza and Rhinovirus Infections in Airway Cells Utilizing Volatile Emissions.

BACKGROUND: Respiratory viral infections are common and potentially devastating to patients with underlying lung disease. Diagnosing viral infections often requires invasive sampling, and interpretation often requires specialized laboratory equipment. Here, we test the hypothesis that a breath test could diagnose influenza and rhinovirus infections using an in vitro model of the human airway. METHODS: Cultured primary human tracheobronchial epithelial cells were infected with either influenza A H1N1 or rhinovirus 1B and compared with healthy control cells. Headspace volatile metabolite measurements of cell cultures were made at 12-hour time points postinfection using a thermal desorption-gas chromatography-mass spectrometry method. RESULTS: Based on 54 compounds, statistical models distinguished volatile organic compound profiles of influenza- and rhinovirus-infected cells from healthy counterparts. Area under the curve values were 0.94 for influenza, 0.90 for rhinovirus, and 0.75 for controls. Regression analysis predicted how many hours prior cells became infected with a root mean square error of 6.35 hours for influenza- and 3.32 hours for rhinovirus-infected cells. CONCLUSIONS: Volatile biomarkers released by bronchial epithelial cells could not only be used to diagnose whether cells were infected, but also the timing of infection. Our model supports the hypothesis that a breath test could serve to diagnose viral infections.

l-Arginine supplementation in severe asthma.

BACKGROUNDDysregulation of l-arginine metabolism has been proposed to occur in patients with severe asthma. The effects of l-arginine supplementation on l-arginine metabolite profiles in these patients are unknown. We hypothesized that individuals with severe asthma with low fractional exhaled nitric oxide (FeNO) would have fewer exacerbations with the addition of l-arginine to their standard asthma medications compared with placebo and would demonstrate the greatest changes in metabolite profiles.METHODSParticipants were enrolled in a single-center, crossover, double-blind l-arginine intervention trial at UCD. Subjects received placebo or l-arginine, dosed orally at 0.05 mg/kg (ideal body weight) twice daily. The primary end point was moderate asthma exacerbations. Longitudinal plasma metabolite levels were measured using mass spectrometry. A linear mixed-effect model with subject-specific intercepts was used for testing treatment effects.RESULTSA cohort of 50 subjects was included in the final analysis. l-Arginine did not significantly decrease asthma exacerbations in the overall cohort. Higher citrulline levels and a lower arginine availability index (AAI) were associated with higher FeNO (P = 0.005 and P = 2.51 × 10-9, respectively). Higher AAI was associated with lower exacerbation events. The eicosanoid prostaglandin H2 (PGH2) and Nα-acetyl-l-arginine were found to be good predictors for differentiating clinical responders and nonresponders.CONCLUSIONSThere was no statistically significant decrease in asthma exacerbations in the overall cohort with l-arginine intervention. PGH2, Nα-acetyl-l-arginine, and the AAI could serve as predictive biomarkers in future clinical trials that intervene in the arginine metabolome.TRIAL REGISTRATIONClinicalTrials.gov NCT01841281.FUNDINGThis study was supported by NIH grants R01HL105573, DK097154, UL1 TR001861, and K08HL114882. Metabolomics analysis was supported in part by a grant from the University of California Tobacco-Related Disease Research Program program (TRDRP).

Volatile organic compound (VOC) emissions of CHO and T cells correlate to their expansion in bioreactors.

Volatile organic compound (VOC) emissions were measured from Chinese Hamster Ovary (CHO) cell and T cell bioreactor gas exhaust lines with the goal of non-invasively metabolically profiling the expansion process. Measurements of cellular 'breath' were made directly from the gas exhaust lines using polydimethylsiloxane (PDMS)-coated magnetic stir bars, which underwent subsequent thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS) analysis. Baseline VOC profiles were observed from bioreactors filled with only liquid media. After inoculation, unique VOC profiles correlated to cell expansion over the course of 8 d. Partial least squares (PLS) regression models were built to predict cell culture density based on VOC profiles of CHO and T cells (R 2 = 0.671 and R 2 = 0.769, respectively, based on a validation data set). T cell runs resulted in 47 compounds relevant to expansion while CHO cell runs resulted in 45 compounds; the 20 most relevant compounds of each cell type were putatively identified. On the final experimental days, sorbent-covered stir bars were placed directly into cell-inoculated media and into media controls. Liquid-based measurements from spent media containing cells could be distinguished from media-only controls, indicating soluble VOCs excreted by the cells during expansion. A PLS-discriminate analysis (PLS-DA) was performed, and 96 compounds differed between T cell-inoculated media and media controls with 72 compounds for CHO cells; the 20 most relevant compounds of each cell line were putatively identified. This work demonstrates that the volatilome of cell cultures can be exploited by chemical detectors in bioreactor gas and liquid waste lines to non-invasively monitor cellular health and could possibly be used to optimize cell expansion conditions 'on-the-fly' with appropriate control loop systems. Although the basis for statistical models included compounds without certain identification, this work provides a foundation for future research of bioreactor emissions. Future studies must move towards identifying relevant compounds for understanding of underlying biochemistry.

Modeling cellular metabolomic effects of oxidative stress impacts from hydrogen peroxide and cigarette smoke on human lung epithelial cells.

The respiratory system is continuously exposed to variety of biological and chemical irritants that contain reactive oxygen species, and these are well known to cause oxidative stress responses in lung epithelial cells. There is a clinical need to identify biomarkers of oxidative stress which could potentially support early indicators of disease and health management. To identify volatile biomarkers of oxidative stress, we analyzed the headspace above human bronchial epithelial cell cultures (HBE1) before and after hydrogen peroxide (H2O2) and cigarette smoke extract (CSE) exposure. Using stir bar and headspace sorptive extraction-gas chromatography-mass spectrometry, we searched for volatile organic compounds (VOC) of these oxidative measures. In the H2O2 cell peroxidation experiments, four different H2O2 concentrations (0.1, 0.5, 10, 50 mM) were applied to the HBE1 cells, and VOCs were collected every 12 h over the time course of 48 h. In the CSE cell peroxidation experiments, four different smoke extract concentrations (0%, 10%, 30%, 60%) were applied to the cells, and VOCs were collected every 12 h over the time course of 48 h. We used partial-least squares (PLS) analysis to identify putative compounds from the mass spectrometry results that highly correlated with the known applied oxidative stress. We observed chemical emissions from the cells that related to both the intensity of the oxidative stress and followed distinct time courses. Additionally, some of these chemicals are aldehydes, which are thought to be non-invasive indicators of oxidative stress in exhaled human breath. Together, these results illustrate a powerful in situ cell culture model of oxidative stress that can be used to explore the putative biological genesis of exhaled breath biomarkers that are often observed in human clinical studies.

Airway transcriptomic profiling after bronchial thermoplasty.

BACKGROUND: Bronchial thermoplasty is a nonpharmacological, device-based treatment option for a specific population of severe asthmatic subjects, but the underlying mechanisms are largely unknown. The purpose of this study is to identify potential altered pathways by bronchial thermoplasty using a transcriptomic approach. METHODS: Patients undergoing bronchial thermoplasty were recruited to the study, and a bronchial brushing sample was obtained before each bronchial thermoplasty session and sent for RNA sequencing. A variance component score test was performed to identify those genes whose expression varied after bronchial thermoplasty sessions. Differential gene expression meta-analysis of severe asthmatic subjects versus controls was performed using public repositories. Overlapping genes were included for downstream pathway and network analyses. RESULTS: 12 patients were enrolled in our study. A total of 133 severe asthma cases and 107 healthy controls from the public repositories were included in the meta-analysis. Comparison of differentially expressed genes from our study patients with the public repositories identified eight overlapping genes: AMIGO2, CBX7, NR3C2, SETBP1, SHANK2, SNTB1, STXBP1 and ZNF853. Network analysis of these overlapping genes identified pathways associated with neurophysiological processes. CONCLUSION: We have shown that bronchial thermoplasty treatment alters several gene networks that are important in asthma pathogenesis. These results potentially elucidate the disease-modifying mechanisms of bronchial thermoplasty and provide several targets for further investigation.

Headspace sorptive extraction-gas chromatography-mass spectrometry method to measure volatile emissions from human airway cell cultures.

The human respiratory tract releases volatile metabolites into exhaled breath that can be utilized for noninvasive health diagnostics. To understand the origin of this metabolic process, our group has previously analyzed the headspace above human epithelial cell cultures using solid phase microextraction-gas chromatography-mass spectrometry (SPME-GC-MS). In the present work, we improve our model by employing sorbent-covered magnetic stir bars for headspace sorptive extraction (HSSE). Sorbent-coated stir bar analyte recovery increased by 52 times and captured 97 more compounds than SPME. Our data show that HSSE is preferred over liquid extraction via stir bar sorptive extraction (SBSE), which failed to distinguish volatiles unique to the cell samples compared against media controls. Two different cellular media were also compared, and we found that Opti-MEM® is preferred for volatile analysis. We optimized HSSE analytical parameters such as extraction time (24 h), desorption temperature (300 °C) and desorption time (7 min). Finally, we developed an internal standard for cell culture VOC studies by introducing 842 ng of deuterated decane per 5 mL of cell medium to account for error from extraction, desorption, chromatography and detection. This improved model will serve as a platform for future metabolic cell culture studies to examine changes in epithelial VOCs caused by perturbations such as viral or bacterial infections, opening opportunities for improved, noninvasive pulmonary diagnostics.