Data-Driven Approach to Modeling Microfabricated Chemical Sensor Manufacturing.

We have developed a statistical model-based approach to the quality analysis (QA) and quality control (QC) of a gas micro pre-concentrator chip (µPC) performance when manufactured at scale for chemical and biochemical analysis of volatile organic compounds (VOCs). To test the proposed model, a medium-sized university-led production batch of 30 wafers of chips were subjected to rigorous chemical performance testing. We quantitatively report the outcomes of each manufacturing process step leading to the final functional chemical sensor chip. We implemented a principal component analysis (PCA) model to score individual chip chemical performance, and we observed that the first two principal components represent 74.28% of chemical testing variance with 111 of 118 viable chips falling into the 95% confidence interval. Chemical performance scores and chip manufacturing data were analyzed using a multivariate regression model to determine the most influential manufacturing parameters and steps. In our analysis, we find the amount of sorbent mass present in the chip (variable importance score = 2.6) and heater and the RTD resistance values (variable importance score = 1.1) to be the manufacturing parameters with the greatest impact on chemical performance. Other non-obvious latent manufacturing parameters also had quantified influence. Statistical distributions for each manufacturing step will allow future large-scale production runs to be statistically sampled during production to perform QA/QC in a real-time environment. We report this study as the first data-driven, model-based production of a microfabricated chemical sensor.

Inositol possesses antifibrotic activity and mitigates pulmonary fibrosis.

BACKGROUND: Myo-inositol (or inositol) and its derivatives not only function as important metabolites for multiple cellular processes but also act as co-factors and second messengers in signaling pathways. Although inositol supplementation has been widely studied in various clinical trials, little is known about its effect on idiopathic pulmonary fibrosis (IPF). Recent studies have demonstrated that IPF lung fibroblasts display arginine dependency due to loss of argininosuccinate synthase 1 (ASS1). However, the metabolic mechanisms underlying ASS1 deficiency and its functional consequence in fibrogenic processes are yet to be elucidated. METHODS: Metabolites extracted from primary lung fibroblasts with different ASS1 status were subjected to untargeted metabolomics analysis. An association of ASS1 deficiency with inositol and its signaling in lung fibroblasts was assessed using molecular biology assays. The therapeutic potential of inositol supplementation in fibroblast phenotypes and lung fibrosis was evaluated in cell-based studies and a bleomycin animal model, respectively. RESULTS: Our metabolomics studies showed that ASS1-deficient lung fibroblasts derived from IPF patients had significantly altered inositol phosphate metabolism. We observed that decreased inositol-4-monophosphate abundance and increased inositol abundance were associated with ASS1 expression in fibroblasts. Furthermore, genetic knockdown of ASS1 expression in primary normal lung fibroblasts led to the activation of inositol-mediated signalosomes, including EGFR and PKC signaling. Treatment with inositol significantly downregulated ASS1 deficiency-mediated signaling pathways and reduced cell invasiveness in IPF lung fibroblasts. Notably, inositol supplementation also mitigated bleomycin-induced fibrotic lesions and collagen deposition in mice. CONCLUSION: These findings taken together demonstrate a novel function of inositol in fibrometabolism and pulmonary fibrosis. Our study provides new evidence for the antifibrotic activity of this metabolite and suggests that inositol supplementation may be a promising therapeutic strategy for IPF.

The Precision Interventions for Severe and/or Exacerbation-Prone (PrecISE) Asthma Network: An overview of Network organization, procedures, and interventions.

Asthma is a heterogeneous disease, with multiple underlying inflammatory pathways and structural airway abnormalities that impact disease persistence and severity. Recent progress has been made in developing targeted asthma therapeutics, especially for subjects with eosinophilic asthma. However, there is an unmet need for new approaches to treat patients with severe and exacerbation-prone asthma, who contribute disproportionately to disease burden. Extensive deep phenotyping has revealed the heterogeneous nature of severe asthma and identified distinct disease subtypes. A current challenge in the field is to translate new and emerging knowledge about different pathobiologic mechanisms in asthma into patient-specific therapies, with the ultimate goal of modifying the natural history of disease. Here, we describe the Precision Interventions for Severe and/or Exacerbation-Prone Asthma (PrecISE) Network, a groundbreaking collaborative effort of asthma researchers and biostatisticians from around the United States. The PrecISE Network was designed to conduct phase II/proof-of-concept clinical trials of precision interventions in the population with severe asthma, and is supported by the National Heart, Lung, and Blood Institute of the National Institutes of Health. Using an innovative adaptive platform trial design, the PrecISE Network will evaluate up to 6 interventions simultaneously in biomarker-defined subgroups of subjects. We review the development and organizational structure of the PrecISE Network, and choice of interventions being studied. We hope that the PrecISE Network will enhance our understanding of asthma subtypes and accelerate the development of therapeutics for severe asthma.

Telemedicine use for pediatric asthma care: a mixed methods study.

OBJECTIVES: To identify factors associated with telemedicine use for asthma care among children and young adults, and to describe the parent and patient experience of asthma care over telemedicine. METHODS: Our mixed methods study consisted of an electronic health record analysis and a qualitative focus group analysis. We analyzed records for all patients aged 2-24 seen at UC Davis Health between March 19, 2020 and September 30, 2020 for a primary diagnosis of asthma. We performed multivariable logistic regression to quantify the relationships between patient characteristics and telemedicine use. We also conducted focus groups with parents and patients who received asthma care during the study period and used qualitative content analysis to identify themes from the transcripts. RESULTS: 502 patients met the inclusion criteria. Telemedicine use was significantly lower among patients with a primary language other than English (OR = 0.12, 95% CI: 0.025-0.54, p = 0.006), school-aged children (OR = 0.43, 95% CI: 0.24-0.77, p = 0.005), and patients who received asthma care from a primary care provider instead of a specialist (OR = 0.55, 95% CI: 0.34-0.91, p = 0.020). Six thematic categories emerged from focus groups: engaging with the patient, improving access to care, experience of visit, measurements, scheduling, and the future of telemedicine in asthma care. CONCLUSIONS: Alternating telemedicine with in-person visits for asthma care may result in improved access to care and reduced burdens on patients and families. Providers and researchers should work to understand the specific reasons for low telemedicine use among non-English speaking patients so that these patients receive equitable access to care.

Therapeutic targeting of argininosuccinate synthase 1 (ASS1)-deficient pulmonary fibrosis.

Argininosuccinate synthase 1 (ASS1) serves as a critical enzyme in arginine biosynthesis; however, its role in interstitial lung diseases, particularly idiopathic pulmonary fibrosis (IPF), remains largely unknown. This study aims at characterization and targeting of ASS1 deficiency in pulmonary fibrosis. We find that ASS1 was significantly decreased and inversely correlated with fibrotic status. Transcriptional downregulation of ASS1 was noted in fibroblastic foci of primary lung fibroblasts isolated from IPF patients. Genetic manipulations of ASS1 studies confirm that ASS1 expression inhibited fibroblast cell proliferation, migration, and invasion. We further show that the hepatocyte growth factor receptor (Met) receptor was activated and acted upstream of the Src-STAT3 axis signaling in ASS1-knockdown fibroblasts. Interestingly, both arginine-free conditions and arginine deiminase treatment were demonstrated to kill fibrotic fibroblasts, attenuated bleomycin-induced pulmonary fibrosis in mice, as well as synergistically increased nintedanib efficacy. Our data suggest ASS1 deficiency as a druggable target and also provide a unique therapeutic strategy against pulmonary fibrosis.

Reductions in particulate matter concentrations resulting from air filtration: A randomized sham-controlled crossover study.

One-hundred seventy-two households were recruited from regions with high outdoor air pollution (Fresno and Riverside, CA) to participate in a randomized, sham-controlled, cross-over study to determine the effectiveness of high-efficiency air filtration to reduce indoor particle exposures. In 129 households, stand-alone HEPA air cleaners were placed in a bedroom and in the main living area. In 43 households, high-efficiency MERV 16 filters were installed in central forced-air heating and cooling systems and the participating households were asked to run the system on a clean-air cycle for 15 min per hour. Participating households that completed the study received true air filtration for a year and sham air filtration for a year. Air pollution samples were collected at approximately 6-month intervals, with two measurements in each of the sham and true filtration periods. One week indoor and outdoor time-integrated samples were collected for measurement of PM2.5 , PM10 , and ultrafine particulate matter (UFP) measured as PM0.2 . Reflectance measurements were also made on the PM2.5 filters to estimate black carbon. True filtration significantly improved indoor air quality, with a 48% reduction in the geometric mean indoor PM0.2 and PM2.5 concentrations, and a 31% reduction in PM10 . Geometric mean concentrations of indoor/outdoor reflectance values, indicating fraction of particles of outdoor origin remaining indoors, decreased by 77%. Improvements in particle concentrations were greater with continuously operating stand-alone air cleaners than with intermittent central system filtration. Keeping windows closed and increased utilization of the filtration systems further improved indoor air quality.

Use of Fractional Exhaled Nitric Oxide to Guide the Treatment of Asthma: An Official American Thoracic Society Clinical Practice Guideline.

Background: The fractional exhaled nitric oxide (FENO) test is a point-of-care test that is used in the assessment of asthma. Objective: To provide evidence-based clinical guidance on whether FENO testing is indicated to optimize asthma treatment in patients with asthma in whom treatment is being considered. Methods: An international, multidisciplinary panel of experts was convened to form a consensus document regarding a single question relevant to the use of FENO. The question was selected from three potential questions based on the greatest perceived impact on clinical practice and the unmet need for evidence-based answers related to this question. The panel performed systematic reviews of published randomized controlled trials between 2004 and 2019 and followed the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) evidence-to-decision framework to develop recommendations. All panel members evaluated and approved the recommendations. Main Results: After considering the overall low quality of the evidence, the panel made a conditional recommendation for FENO-based care. In patients with asthma in whom treatment is being considered, we suggest that FENO is beneficial and should be used in addition to usual care. This judgment is based on a balance of effects that probably favors the intervention; the moderate costs and availability of resources, which probably favors the intervention; and the perceived acceptability and feasibility of the intervention in daily practice. Conclusions: Clinicians should consider this recommendation to measure FENO in patients with asthma in whom treatment is being considered based on current best available evidence.

PrecISE: Precision Medicine in Severe Asthma: An adaptive platform trial with biomarker ascertainment.

Severe asthma accounts for almost half the cost associated with asthma. Severe asthma is driven by heterogeneous molecular mechanisms. Conventional clinical trial design often lacks the power and efficiency to target subgroups with specific pathobiological mechanisms. Furthermore, the validation and approval of new asthma therapies is a lengthy process. A large proportion of that time is taken by clinical trials to validate asthma interventions. The National Institutes of Health Precision Medicine in Severe and/or Exacerbation Prone Asthma (PrecISE) program was established with the goal of designing and executing a trial that uses adaptive design techniques to rapidly evaluate novel interventions in biomarker-defined subgroups of severe asthma, while seeking to refine these biomarker subgroups, and to identify early markers of response to therapy. The novel trial design is an adaptive platform trial conducted under a single master protocol that incorporates precision medicine components. Furthermore, it includes innovative applications of futility analysis, cross-over design with use of shared placebo groups, and early futility analysis to permit more rapid identification of effective interventions. The development and rationale behind the study design are described. The interventions chosen for the initial investigation and the criteria used to identify these interventions are enumerated. The biomarker-based adaptive design and analytic scheme are detailed as well as special considerations involved in the final trial design.

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.

An environmental air sampler to evaluate personal exposure to volatile organic compounds.

A micro fabricated chip-based wearable air sampler was used to monitor the personnel exposure of volatile chemical concentrations in microenvironments. Six teenagers participated in this study and 14 volatile organic compounds (VOCs) including naphthalene, 3-decen-1-ol, hexanal, nonanal, methyl salicylate and limonene gave the highest abundance during routine daily activity. VOC exposure associated with daily activities and the location showed strong agreements with two of the participant's results. One of these subjects had the highest exposure to methyl salicylate that was supported by the use of a topical analgesic balm containing this compound. Environmental based air quality monitoring followed by the personnel exposure studies provided additional evidence associated to the main locations where the participants traveled. Toluene concentrations observed at a gas station were exceptionally high, with the highest amount observed at 1213.1 ng m-3. One subject had the highest exposure to toluene and the GPS data showed clear evidence of activities neighboring a gas station. This study shows that this wearable air sampler has potential applications including hazardous VOC exposure monitoring in occupational hazard assessment for certain professions, for example in industries that involve direct handling of petroleum products.

Long-Term Sequelae of Smoking and Cessation in Spontaneously Hypertensive Rats.

Smoking is a major risk factor for heart attack, stroke, and lung cancer. Tobacco smoke (TS) causes bronchitis, emphysema, persistent cough, and dyspnea. Smoking cessation minimizes risks of TS-related disease. To determine whether smoking cessation could reverse TS-induced pulmonary changes, 10-week-old male spontaneously hypertensive rats were exposed to TS or filtered air (FA) for 39 weeks and allowed to live out their normal lifespan. Significantly (P ≤ .05) decreased survival was noted by 21 months in TS versus FA rats. In TS rats, persistent peribronchiolar, perivascular, alveolar, and subpleural inflammation were observed with pervasive infiltration of pigmented foamy macrophages and plausible intra-alveolar fibrosis and osseous metaplasia. Alveolar airspace was significantly (P ≤ .05) increased in TS versus FA rats as was the volume of stored epithelial mucosubstances in the left central axial airway. Increased mucin contributes to airflow obstruction and increased lung infection risks. Findings suggest TS-induced changes do not attenuate with smoking cessation but result in irreversible damage similar to chronic obstructive pulmonary disease. The observed persistent pulmonary changes mirror common TS effects such as chest congestion, sputum production, and shortness of breath long after smoking cessation and represent important targets for treatment of former smokers.

Farnesyltransferase Inhibition Exacerbates Eosinophilic Inflammation and Airway Hyperreactivity in Mice with Experimental Asthma: The Complex Roles of Ras GTPase and Farnesylpyrophosphate in Type 2 Allergic Inflammation.

Ras, a small GTPase protein, is thought to mediate Th2-dependent eosinophilic inflammation in asthma. Ras requires cell membrane association for its biological activity, and this requires the posttranslational modification of Ras with an isoprenyl group by farnesyltransferase (FTase) or geranylgeranyltransferase (GGTase). We hypothesized that inhibition of FTase using FTase inhibitor (FTI)-277 would attenuate allergic asthma by depleting membrane-associated Ras. We used the OVA mouse model of allergic inflammation and human airway epithelial (HBE1) cells to determine the role of FTase in inflammatory cell recruitment. BALB/c mice were first sensitized then exposed to 1% OVA aerosol or filtered air, and half were injected daily with FTI-277 (20 mg/kg per day). Treatment of mice with FTI-277 had no significant effect on lung membrane-anchored Ras, Ras protein levels, or Ras GTPase activity. In OVA-exposed mice, FTI-277 treatment increased eosinophilic inflammation, goblet cell hyperplasia, and airway hyperreactivity. Human bronchial epithelial (HBE1) cells were pretreated with 5, 10, or 20 µM FTI-277 prior to and during 12 h IL-13 (20 ng/ml) stimulation. In HBE1 cells, FTase inhibition with FTI-277 had no significant effect on IL-13-induced STAT6 phosphorylation, eotaxin-3 peptide secretion, or Ras translocation. However, addition of exogenous FPP unexpectedly augmented IL-13-induced STAT6 phosphorylation and eotaxin-3 secretion from HBE1 cells without affecting Ras translocation. Pharmacological inhibition of FTase exacerbates allergic asthma, suggesting a protective role for FTase or possibly Ras farnesylation. FPP synergistically augments epithelial eotaxin-3 secretion, indicating a novel Ras-independent farnesylation mechanism or direct FPP effect that promotes epithelial eotaxin-3 production in allergic asthma.

The precision interventions for severe and/or exacerbation-prone asthma (PrecISE) adaptive platform trial: statistical considerations.

The Precision Interventions for Severe and/or Exacerbation-prone Asthma (PrecISE) study is an adaptive platform trial designed to investigate novel interventions to severe asthma. The study is conducted under a master protocol and utilizes a crossover design with each participant receiving up to five interventions and at least one placebo. Treatment assignments are based on the patients' biomarker profiles and precision health methods are incorporated into the interim and final analyses. We describe key elements of the PrecISE study including the multistage adaptive enrichment strategy, early stopping of an intervention for futility, power calculations, and the primary analysis strategy.

Peak detection and random forests classification software for gas chromatography/differential mobility spectrometry (GC/DMS) data.

Gas Chromatography/Differential Mobility Spectrometry (GC/DMS) is an effective tool to discern volatile chemicals. The process of correlating GC/DMS data outputs to chemical identities requires time and effort from trained chemists due to lack of commercially available software and the lack of appropriate libraries. This paper describes the coupling of computer vision techniques to develop models for peak detection and can align chemical signatures across datasets. The result is an automatically generated peak table that provides integrated peak areas for the inputted samples. The software was tested against a simulated dataset, whereby the number of detected features highly correlated to the number of actual features (r2 = 0.95). This software has also been developed to include random forests, a discriminant analysis technique that generates prediction models for application to unknown samples with different chemical signatures. In an example dataset described herein, the model achieves 3% classification error with 12 trees and 0% classification error with 48 trees. The number of trees can be optimized based on the computational resources available. We expect the public release of this software can provide other GC/DMS researchers with a tool for automated featured extraction and discriminant analysis capabilities.

Wearable Sensor System to Monitor Physical Activity and the Physiological Effects of Heat Exposure.

Mobile health monitoring via non-invasive wearable sensors is poised to advance telehealth for older adults and other vulnerable populations. Extreme heat and other environmental conditions raise serious health challenges that warrant monitoring of real-time physiological data as people go about their normal activities. Mobile systems could be beneficial for many communities, including elite athletes, military special forces, and at-home geriatric monitoring. While some commercial monitors exist, they are bulky, require reconfiguration, and do not fit seamlessly as a simple wearable device. We designed, prototyped and tested an integrated sensor platform that records heart rate, oxygen saturation, physical activity levels, skin temperature, and galvanic skin response. The device uses a small microcontroller to integrate the measurements and store data directly on the device for up to 48+ h. continuously. The device was compared to clinical standards for calibration and performance benchmarking. We found that our system compared favorably with clinical measures, such as fingertip pulse oximetry and infrared thermometry, with high accuracy and correlation. Our novel platform would facilitate an individualized approach to care, particularly those whose access to healthcare facilities is limited. The platform also can be used as a research tool to study physiological responses to a variety of environmental conditions, such as extreme heat, and can be customized to incorporate new sensors to explore other lines of inquiry.

Machine Vision Methods, Natural Language Processing, and Machine Learning Algorithms for Automated Dispersion Plot Analysis and Chemical Identification from Complex Mixtures.

Gas-phase trace chemical detection techniques such as ion mobility spectrometry (IMS) and differential mobility spectrometry (DMS) can be used in many settings, such as evaluating the health condition of patients or detecting explosives at airports. These devices separate chemical compounds in a mixture and provide information to identify specific chemical species of interest. Further, these types of devices operate well in both controlled lab environments and in-field applications. Frequently, the commercial versions of these devices are highly tailored for niche applications (e.g., explosives detection) because of the difficulty involved in reconfiguring instrumentation hardware and data analysis software algorithms. In order for researchers to quickly adapt these tools for new purposes and broader panels of chemical targets, it is critical to develop new algorithms and methods for generating libraries of these sensor responses. Microelectromechanical system (MEMS) technology has been used to fabricate DMS devices that miniaturize the platforms for easier deployment; however, concurrent advances in advanced data analytics are lagging. DMS generates complex three-dimensional dispersion plots for both positive and negative ions in a mixture. Although simple spectra of single chemicals are straightforward to interpret (both visually and via algorithms), it is exceedingly challenging to interpret dispersion plots from complex mixtures with many chemical constituents. This study uses image processing and computer vision steps to automatically identify features from DMS dispersion plots. We used the bag-of-words approach adapted from natural language processing and information retrieval to cluster and organize these features. Finally, a support vector machine (SVM) learning algorithm was trained using these features in order to detect and classify specific compounds in these represented conceptualized data outputs. Using this approach, we successfully maintain a high level of correct chemical identification, even when a gas mixture increases in complexity with interfering chemicals present.

High Asymmetric Longitudinal Field Ion Mobility Spectrometry Device for Low Power Mobile Chemical Separation and Detection.

We have developed a novel chemical sensing technique termed high asymmetric longitudinal field ion mobility spectrometry (HALF-IMS), which allows separation of ions based on mobility differences in high and low electric fields. Our device is microfabricated, has a miniature format, and uses exceptionally low power due to the lack of RF separation fields normally associated with ion mobility spectrometry (IMS) or differential mobility spectrometry (DMS). It operates at room temperature and atmospheric pressure. This HALF-IMS chip contains a microscale drift cell where spatially varying electric field regions of high and low strengths are generated by direct current (DC) applied to the electrodes that are physically placed to cause ionic separation as the ionized chemical flows along the drift cell. Power and complexity are reduced at the chip and system levels by reducing the voltage magnitude and using DC-powered electronics. A testing platform utilizing an ultraviolet (UV) photoionization source was used with custom electronic circuit boards to interface with the chip and provide data inputs and outputs. Precise control of the electrode voltages allowed filtering of the passage of the ion of interest through the drift cell, and ionic current was measured at the detector. The device was tested by scanning of electrode voltages and obtaining ion peaks for methyl salicylate, naphthalene, benzene, and 2-butanone. The current experimental setup was capable of detecting as low as ∼80 ppb of methyl salicylate and naphthalene. The use of benzene as a dopant with 2-butanone allowed one to see two ion peaks, corresponding to benzene and 2-butanone.

A cost-effectiveness analysis of reslizumab in the treatment of poorly controlled eosinophilic asthma.

Introduction: Poorly controlled severe eosinophilic asthma is difficult and costly to manage. Reslizumab, an add-on treatment for adults with severe eosinophilic asthma, reduces the number of exacerbations and improves the quality of life (QoL). The objective of this study was to evaluate the cost-effectiveness of reslizumab. Methods: A Markov model was used to compare the cost-effectiveness of add-on reslizumab with the standard-of-care (SOC) from the US societal perspective over a five-year time horizon. Efficacy and safety inputs for the model were based on data from two clinical trials (NCT01287039 and NCT01285323). Other model inputs, including mortality rates, costs, and utility, were estimated from literature, the Centers for Disease Control and Prevention (CDC), the US Department of Veterans Affairs (VA) and the Centers for Medicare and Medicaid Services (CMS). One-way, threshold, and probabilistic sensitivity analyses (PSA) were performed. Adherence, treatment response, and the placebo effect were evaluated in separate scenario analyses. Results: The base case incremental cost-effectiveness ratio (ICER) was $697 403 (2017 USD) per quality-adjusted life-years (QALYs). In the PSA, reslizumab becomes cost-effective in 50% of the iterations at a willingness-to-pay (WTP) threshold of $689 000. The model is most sensitive to the QoL improvement with reslizumab treatment in the one-way and threshold analyses. The response and adherence models had lower ICERs than the base model but still above $500 000. The ICER of the placebo effect model was $29 820. Conclusions: The improvement in QoL and exacerbation rates with reslizumab are associated with high costs, making reslizumab unlikely to be cost-effective at the $200 000 WTP threshold.

Autophagy and the unfolded protein response promote profibrotic effects of TGF-ß1 in human lung fibroblasts.

Idiopathic pulmonary fibrosis (IPF) is a lethal fibrotic lung disease in adults with limited treatment options. Autophagy and the unfolded protein response (UPR), fundamental processes induced by cell stress, are dysregulated in lung fibroblasts and epithelial cells from humans with IPF. Human primary cultured lung parenchymal and airway fibroblasts from non-IPF and IPF donors were stimulated with transforming growth factor-ß1 (TGF-ß1) with or without inhibitors of autophagy or UPR (IRE1 inhibitor). Using immunoblotting, we monitored temporal changes in abundance of protein markers of autophagy (LC3ßII and Atg5-12), UPR (BIP, IRE1α, and cleaved XBP1), and fibrosis (collagen 1α2 and fibronectin). Using fluorescent immunohistochemistry, we profiled autophagy (LC3ßII) and UPR (BIP and XBP1) markers in human non-IPF and IPF lung tissue. TGF-ß1-induced collagen 1α2 and fibronectin protein production was significantly higher in IPF lung fibroblasts compared with lung and airway fibroblasts from non-IPF donors. TGF-ß1 induced the accumulation of LC3ßII in parallel with collagen 1α2 and fibronectin, but autophagy marker content was significantly lower in lung fibroblasts from IPF subjects. TGF-ß1-induced collagen and fibronectin biosynthesis was significantly reduced by inhibiting autophagy flux in fibroblasts from the lungs of non-IPF and IPF donors. Conversely, only in lung fibroblasts from IPF donors did TGF-ß1 induce UPR markers. Treatment with an IRE1 inhibitor decreased TGF-ß1-induced collagen 1α2 and fibronectin biosynthesis in IPF lung fibroblasts but not those from non-IPF donors. The IRE1 arm of the UPR response is uniquely induced by TGF-ß1 in lung fibroblasts from human IPF donors and is required for excessive biosynthesis of collagen and fibronectin in these cells.

Early Life Wildfire Smoke Exposure Is Associated with Immune Dysregulation and Lung Function Decrements in Adolescence.

The long-term health effects of wildfire smoke exposure in pediatric populations are not known. The objectives of this study were to determine if early life exposure to wildfire smoke can affect parameters of immunity and airway physiology that are detectable with maturity. We studied a mixed-sex cohort of rhesus macaque monkeys that were exposed as infants to ambient wood smoke from a series of Northern California wildfires in the summer of 2008. Peripheral blood mononuclear cells (PBMCs) and pulmonary function measures were obtained when animals were approximately 3 years of age. PBMCs were cultured with either LPS or flagellin, followed by measurement of secreted IL-8 and IL-6 protein. PBMCs from a subset of female animals were also evaluated by Toll-like receptor (TLR) pathway mRNA analysis. Induction of IL-8 protein synthesis with either LPS or flagellin was significantly reduced in PBMC cultures from wildfire smoke-exposed female monkeys. In contrast, LPS- or flagellin-induced IL-6 protein synthesis was significantly reduced in PBMC cultures from wildfire smoke-exposed male monkeys. Baseline and TLR ligand-induced expression of the transcription factor, RelB, was globally modulated in PBMCs from wildfire smoke-exposed monkeys, with additional TLR pathway genes affected in a ligand-dependent manner. Wildfire smoke-exposed monkeys displayed significantly reduced inspiratory capacity, residual volume, vital capacity, functional residual capacity, and total lung capacity per unit of body weight relative to control animals. Our findings suggest that ambient wildfire smoke exposure during infancy results in sex-dependent attenuation of systemic TLR responses and reduced lung volume in adolescence.