Persistence of emphysema following cessation of cigarette smoke exposure requires a susceptibility factor.

Chronic obstructive pulmonary disease (COPD) is caused by cigarette smoke (CS) exposure but can often be progressive even in former smokers. Exposure of mice to CS for 22 wk causes emphysema, but whether emphysema persists after cessation of CS exposure is not clear. The purpose of this study was to determine whether emphysema persists in mice following a recovery period of 22 wk and whether a susceptibility factor, such as deficiency in the Bcl-2-interacting killer (Bik), is required for this persistence. Therefore, bik+/+ and bik-/- mice at 6-10 wk of age were exposed to 250 mg/m3 total particulate matter of CS or filtered air (FA) for 3 or 22 wk and were kept in FA for an additional 22 wk. Lungs were lavaged to quantify inflammatory cells, and sections were stained with hematoxylin and eosin to assess severity of emphysema. Exposure to CS for 3 wk increased the number of inflammatory cells in bik-/- mice compared with bik+/+ mice but not at 22 wk of exposure. At 22 wk of CS exposure, extent of emphysema was similar in bik+/+ and bik-/- mice. However, when mice were exposed to CS over the first 22 wk and were kept in FA for an additional 22 wk, emphysema remained similar in bik+/+ mice but was enhanced in bik-/- mice. These findings link increased inflammation with persistent emphysematous changes even after smoking cessation and demonstrate that a preexisting susceptibility condition is required to sustain enhanced emphysema that was initiated by long-term CS exposure.NEW & NOTEWORTHY Exposure of mice to cigarette smoke (CS) for 22 wk causes emphysema, but whether emphysema persists after an additional period of 6 mo after cessation of CS exposure has not been reported. In addition, the role of preexisting susceptibility in enhancing the persistence of CS-induced emphysema after exposure to CS has stopped has not been shown. The present study shows that a preexisting susceptibility must be present to enhance CS-induced emphysema after cessation of CS exposure.

Circulating biomarkers of airflow limitation across the life span.

BACKGROUND: Airflow limitation is a hallmark of chronic obstructive pulmonary disease, which can develop through different lung function trajectories across the life span. There is a need for longitudinal studies aimed at identifying circulating biomarkers of airflow limitation across different stages of life. OBJECTIVES: This study sought to identify a signature of serum proteins associated with airflow limitation and evaluate their relation to lung function longitudinally in adults and children. METHODS: This study used data from 3 adult cohorts (TESAOD [Tucson Epidemiological Study of Airway Obstructive Disease], SAPALDIA [Swiss Cohort Study on Air Pollution and Lung and Heart Diseases in Adults], LSC [Lovelace Smoker Cohort]) and 1 birth cohort (TCRS [Tucson Children's Respiratory Study]) (N = 1940). In TESAOD, among 46 circulating proteins, we identified those associated with FEV1/forced vital capacity (FVC) percent (%) predicted levels and generated a score based on the sum of their z-scores. Cross-sectional analyses were used to test the score for association with concomitant lung function. Longitudinal analyses were used to test the score for association with subsequent lung function growth in childhood and decline in adult life. RESULTS: After false discovery rate adjustment, serum levels of 5 proteins (HP, carcinoembryonic antigen, ICAM1, CRP, TIMP1) were associated with percent predicted levels of FEV1/FVC and FEV1 in TESAOD. In cross-sectional multivariate analyses the 5-biomarker score was associated with FEV1 % predicted in all adult cohorts (meta-analyzed FEV1 decrease for 1-SD score increase: -2.9%; 95% CI: -3.9%, -1.9%; P = 2.4 × 10-16). In multivariate longitudinal analyses, the biomarker score at 6 years of age was inversely associated with FEV1 and FEV1/FVC levels attained by young adult life (P = .02 and .005, respectively). In adults, persistently high levels of the biomarker score were associated with subsequent accelerated decline of FEV1 and FEV1/FVC (P = .01 and .001). CONCLUSIONS: A signature of 5 circulating biomarkers of airflow limitation was associated with both impaired lung function growth in childhood and accelerated lung function decline in adult life, indicating that these proteins may be involved in multiple lung function trajectories leading to chronic obstructive pulmonary disease.

Bik promotes proteasomal degradation to control low-grade inflammation.

Although chronic low-grade inflammation does not cause immediate clinical symptoms, over the longer term, it can enhance other insults or age-dependent damage to organ systems and thereby contribute to age-related disorders, such as respiratory disorders, heart disease, metabolic disorders, autoimmunity, and cancer. However, the molecular mechanisms governing low-level inflammation are largely unknown. We discovered that Bcl-2-interacting killer (Bik) deficiency causes low-level inflammation even at baseline and the development of spontaneous emphysema in female but not male mice. Similarly, a single nucleotide polymorphism that reduced Bik levels was associated with increased inflammation and enhanced decline in lung function in humans. Transgenic expression of Bik in the airways of Bik-deficient mice inhibited allergen- or LPS-induced lung inflammation and reversed emphysema in female mice. Bik deficiency increased nuclear but not cytosolic p65 levels because Bik, by modifying the BH4 domain of Bcl-2, interacted with regulatory particle non-ATPase 1 (RPN1) and RPN2 and enhanced proteasomal degradation of nuclear proteins. Bik deficiency increased inflammation primarily in females because Bcl-2 and Bik levels were reduced in lung tissues and airway cells of female compared with male mice. Therefore, controlling low-grade inflammation by modifying the unappreciated role of Bik and Bcl-2 in facilitating proteasomal degradation of nuclear proteins may be crucial in treating chronic age-related diseases.

From pre-COPD to COPD: a Simple, Low cost and easy to IMplement (SLIM) risk calculator.

BACKGROUND: The lifetime risk of developing clinical COPD among smokers ranges from 13% to 22%. Identifying at-risk individuals who will develop overt disease in a reasonable timeframe may allow for early intervention. We hypothesised that readily available clinical and physiological variables could help identify ever-smokers at higher risk of developing chronic airflow limitation (CAL). METHODS: Among 2273 Lovelace Smokers' Cohort (LSC) participants, we included 677 (mean age 54 years) with normal spirometry at baseline and a minimum of three spirometries, each 1 year apart. Repeated spirometric measurements were used to determine incident CAL. Using logistic regression, demographics, anthropometrics, smoking history, modified Medical Research Council dyspnoea scale, St George's Respiratory Questionnaire, comorbidities and spirometry, we related variables obtained at baseline to incident CAL as defined by the Global Initiative for Chronic Obstructive Lung Disease and lower limit of normal criteria. The predictive model derived from the LSC was validated in subjects from the COPDGene study. RESULTS: Over 6.3 years, the incidence of CAL was 26 cases per 1000 person-years. The strongest independent predictors were forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC) <0.75, having smoked ≥30 pack-years, body mass index (BMI) ≤25 kg·m2 and symptoms of chronic bronchitis. Having all four predictors increased the risk of developing CAL over 6 years to 85% (area under the receiver operating characteristic curve (AUC ROC) 0.84, 95% CI 0.81-0.89). The prediction model showed similar results when applied to subjects in the COPDGene study with a follow-up period of 10 years (AUC ROC 0.77, 95% CI 0.72-0.81). CONCLUSION: In middle-aged ever-smokers, a simple predictive model with FEV1/FVC, smoking history, BMI and chronic bronchitis helps identify subjects at high risk of developing CAL.

Cleaning the Flue in Wood-Burning Stoves Is a Key Factor in Reducing Household Air Pollution.

In experimental settings, replacing old wood stoves with new wood stoves results in reduced personal exposure to household air pollution. We tested this assumption by measuring PM2.5 and levoglucosan concentrations inside homes and correlated them with wood stove age. Methods: Thirty homes in the Albuquerque, NM area were monitored over a seven-day period using in-home particulate monitors placed in a common living area during the winter months. Real-time aerosol monitoring was performed, and filter samples were analyzed gravimetrically to calculate PM2.5 concentrations and chemically to determine concentrations of levoglucosan. A linear regression model with backward stepwise elimination was performed to determine the factors that would predict household air pollution measures. Results: In this sample, 73.3% of the households used wood as their primary source of heating, and 60% burned daily or almost daily. The mean burn time over the test week was 50 ± 38 h, and only one household burned wood 24/day (168 h). The average PM2.5 concentration (standard deviation) for the 30 homes during the seven-day period was 34.6 µg/m3 (41.3 µg/m3), and median (min, max) values were 15.5 µg/m3 (7.3 µg/m3, 193 µg/m3). Average PM2.5 concentrations in 30 homes ranged from 0−15 µg/m3 to >100 µg/m3. Maximum PM2.5 concentrations ranged from 100−200 µg/m3 to >3000 µg/m3. The levoglucosan levels showed a linear correlation with the total PM2.5 collected by the filters (R2 = 0.92). However, neither mean nor peak PM2.5 nor levoglucosan levels were correlated with the age (10.85 ± 8.54 years) of the wood stove (R2 ≤ 0.07, p > 0.23). The final adjusted linear regression model showed that average PM2.5 was associated with reports of cleaning the flue with a beta estimate of 35.56 (3.47−67.65) and R2 = 0.16 (p = 0.04). Discussion: Cleaning the flue and not the wood stove age was associated with household air pollution indices. Education on wood stove maintenance and safe burning practices may be more important in reducing household air pollution than the purchase of new stoves.

Clustering-based COPD subtypes have distinct longitudinal outcomes and multi-omics biomarkers.

INTRODUCTION: Chronic obstructive pulmonary disease (COPD) can progress across several domains, complicating the identification of the determinants of disease progression. In our previous work, we applied k-means clustering to spirometric and chest radiological measures to identify four COPD-related subtypes: 'relatively resistant smokers (RRS)', 'mild upper lobe-predominant emphysema (ULE)', 'airway-predominant disease (AD)' and 'severe emphysema (SE)'. In the current study, we examined the associations of these subtypes to longitudinal COPD-related health measures as well as blood transcriptomic and plasma proteomic biomarkers. METHODS: We included 8266 non-Hispanic white and African-American smokers from the COPDGene study. We used linear regression to investigate cluster associations to 5-year prospective changes in spirometric and radiological measures and to gene expression and protein levels. We used Cox-proportional hazard test to test for cluster associations to prospective exacerbations, comorbidities and mortality. RESULTS: The RRS, ULE, AD and SE clusters represented 39%, 15%, 26% and 20% of the studied cohort at baseline, respectively. The SE cluster had the greatest 5-year FEV1 (forced expiratory volume in 1 s) and emphysema progression, and the highest risks of exacerbations, cardiovascular disease and mortality. The AD cluster had the highest diabetes risk. After adjustments, only the SE cluster had an elevated respiratory mortality risk, while the ULE, AD and SE clusters had elevated all-cause mortality risks. These clusters also demonstrated differential protein and gene expression biomarker associations, mostly related to inflammatory and immune processes. CONCLUSION: COPD k-means subtypes demonstrate varying rates of disease progression, prospective comorbidities, mortality and associations to transcriptomic and proteomic biomarkers. These findings emphasise the clinical and biological relevance of these subtypes, which call for more study for translation into clinical practice. TRAIL REGISTRATION NUMBER: NCT00608764.

Racial and Ethnic Minorities Have a Lower Prevalence of Airflow Obstruction than Non-Hispanic Whites.

Racial and ethnic disparities in chronic obstructive pulmonary disease (COPD) are not well-studied. Our objective was to examine differences in limited COPD-related outcomes between three minority groups-African Americans (AAs), Hispanics, and American Indians (AIs) versus non-Hispanic Whites (NHWs), as the referent group, in separate cohorts. Separate cross-sectional evaluations were performed of three US-based cohorts of subjects at risk for COPD: COPDGene Study with 6,884 NHW and 3,416 AA smokers; Lovelace Smokers' Cohort with 1,598 NHW and 378 Hispanic smokers; and Mining Dust Exposure in the United States Cohort with 2,115 NHW, 2,682 Hispanic, and 2,467 AI miners. Prebronchodilator spirometry tests were performed at baseline visits using standard criteria. The primary outcome was the prevalence of airflow obstruction. Secondary outcomes were self-reported physician diagnosis of COPD, chronic bronchitis, and modified Medical Research Council dyspnea score. All minority groups had a lower prevalence of airflow obstruction than NHWs (adjusted ORs varied from 0.29 in AIs to 0.85 in AAs; p < 0.01 for all analyses). AAs had a lower prevalence of chronic bronchitis than NHWs. In our study, all minority groups had a lower prevalence of airflow obstruction but a greater level of self-reported dyspnea than NHWs, and covariates did not explain this association. A better understanding of racial and ethnic differences in smoking-related and occupational airflow obstruction may improve prevention and therapeutic strategies.

Predicting pathway cross-talks in ankylosing spondylitis through investigating the interactions among pathways

Given that the pathogenesis of ankylosing spondylitis (AS) remains unclear, the aim of this study was to detect the potentially functional pathway cross-talk in AS to further reveal the pathogenesis of this disease. Using microarray profile of AS and biological pathways as study objects, Monte Carlo cross-validation method was used to identify the significant pathway cross-talks. In the process of Monte Carlo cross-validation, all steps were iterated 50 times. For each run, detection of differentially expressed genes (DEGs) between two groups was conducted. The extraction of the potential disrupted pathways enriched by DEGs was then implemented. Subsequently, we established a discriminating score (DS) for each pathway pair according to the distribution of gene expression levels. After that, we utilized random forest (RF) classification model to screen out the top 10 paired pathways with the highest area under the curve (AUCs), which was computed using 10-fold cross-validation approach. After 50 bootstrap, the best pairs of pathways were identified. According to their AUC values, the pair of pathways, antigen presentation pathway and fMLP signaling in neutrophils, achieved the best AUC value of 1.000, which indicated that this pathway cross-talk could distinguish AS patients from normal subjects. Moreover, the paired pathways of SAPK/JNK signaling and mitochondrial dysfunction were involved in 5 bootstraps. Two paired pathways (antigen presentation pathway and fMLP signaling in neutrophil, as well as SAPK/JNK signaling and mitochondrial dysfunction) can accurately distinguish AS and control samples. These paired pathways may be helpful to identify patients with AS for early intervention.

Predicting pathway cross-talks in ankylosing spondylitis through investigating the interactions among pathways.

Given that the pathogenesis of ankylosing spondylitis (AS) remains unclear, the aim of this study was to detect the potentially functional pathway cross-talk in AS to further reveal the pathogenesis of this disease. Using microarray profile of AS and biological pathways as study objects, Monte Carlo cross-validation method was used to identify the significant pathway cross-talks. In the process of Monte Carlo cross-validation, all steps were iterated 50 times. For each run, detection of differentially expressed genes (DEGs) between two groups was conducted. The extraction of the potential disrupted pathways enriched by DEGs was then implemented. Subsequently, we established a discriminating score (DS) for each pathway pair according to the distribution of gene expression levels. After that, we utilized random forest (RF) classification model to screen out the top 10 paired pathways with the highest area under the curve (AUCs), which was computed using 10-fold cross-validation approach. After 50 bootstrap, the best pairs of pathways were identified. According to their AUC values, the pair of pathways, antigen presentation pathway and fMLP signaling in neutrophils, achieved the best AUC value of 1.000, which indicated that this pathway cross-talk could distinguish AS patients from normal subjects. Moreover, the paired pathways of SAPK/JNK signaling and mitochondrial dysfunction were involved in 5 bootstraps. Two paired pathways (antigen presentation pathway and fMLP signaling in neutrophil, as well as SAPK/JNK signaling and mitochondrial dysfunction) can accurately distinguish AS and control samples. These paired pathways may be helpful to identify patients with AS for early intervention.

Identification of novel targets for multiple myeloma through integrative approach with Monte Carlo cross-validation analysis.

More than one pathway is involved in disease development and progression, and two or more pathways may be interconnected to further affect the disease onset, as functional proteins participate in multiple pathways. Thus, identifying cross-talk among pathways is necessary to understand the molecular mechanisms of multiple myeloma (MM). Based on this, this paper looked at extracting potential pathway cross-talk in MM through an integrative approach using Monte Carlo cross-validation analysis. The gene expression library of MM (accession number: GSE6477) was downloaded from the Gene Expression Omnibus (GEO) database. The integrative approach was then used to identify potential pathway cross-talk, and included four steps: Firstly, differential expression analysis was conducted to identify differentially expressed genes (DEGs). Secondly, the DEGs obtained were mapped to the pathways downloaded from an ingenuity pathways analysis (IPA), to reveal the underlying relationship between the DEGs and pathways enriched by these DEGs. A subset of pathways enriched by the DEGs was then obtained. Thirdly, a discriminating score (DS) value for each paired pathway was computed. Lastly, random forest (RF) classification was used to identify the paired pathways based on area under the curve (AUC) and Monte Carlo cross-validation, which was repeated 50 times to explore the best paired pathways. These paired pathways were tested with another independently published MM microarray data (GSE85837), using in silico validation. Overall, 60 DEGs and 19 differential pathways enriched by DEGs were extracted. Each pathway was sorted based on their AUC values. The paired pathways, inhibition of matrix metalloproteases and EIF2 signaling pathway, indicated the best AUC value of 1.000. Paired pathways consisting of IL-8 and EIF2 signaling pathways with higher AUC of 0.975, were involved in 7 runs. Furthermore, it was validated consistently in separate microarray data sets (GSE85837). Paired pathways (inhibition of matrix metalloproteases and EIF2 signaling, IL-8 signaling and EIF2 signaling) exhibited the best AUC values and higher frequency of validation. Two paired pathways (inhibition of matrix metalloproteases and EIF2 signaling, IL-8 signaling and EIF2 signaling) were used to accurately classify MM and control samples. These paired pathways may be potential bio-signatures for diagnosis and management of MM.