Recent work has suggested a microbial dysbiosis association between the lung and gut in respiratory diseases. Here, we demonstrated that gut microbiome modulation attenuated emphysema development. To modulate the gut microbiome, fecal microbiota transplantation (FMT) and diet modification were adopted in mice exposed to smoking and poly I:C for the emphysema model. We analyzed the severity of emphysema by the mean linear intercept (MLI) and apoptosis by the fluorescent TUNEL assay. Microbiome analysis was also performed in feces and fecal extracellular vesicles (EVs). The MLI was significantly increased with smoking exposure. FMT or a high-fiber diet (HFD) attenuated the increase. Weight loss, combined with smoking exposure, was not noted in mice with FMT. HFD significantly decreased macrophages and lymphocytes in bronchoalveolar lavage fluid. Furthermore, IL-6 and IFN-γ were decreased in the bronchoalveolar lavage fluid and serum. The TUNEL score was significantly lower in mice with FMT or HFD, suggesting decreased cell apoptosis. In the microbiome analysis,BacteroidaceaeandLachnospiraceae, which are alleged to metabolize fiber into short-chain fatty acids (SCFAs), increased at the family level with FMT and HFD. FMT and HFD attenuated emphysema development via local and systemic inhibition of inflammation and changes in gut microbiota composition, which could provide a new paradigm in COPD treatment.

Dietary fiber functions as a prebiotic to determine the gut microbe composition. The gut microbiota influences the metabolic functions and immune responses in human health. The gut microbiota and metabolites produced by various dietary components not only modulate immunity but also impact various organs. Although recent findings have suggested that microbial dysbiosis is associated with several respiratory diseases, including asthma, cystic fibrosis, and allergy, the role of microbiota and metabolites produced by dietary nutrients with respect to pulmonary disease remains unclear. Therefore, we explored whether the gut microbiota and metabolites produced by dietary fiber components could influence a cigarette smoking (CS)-exposed emphysema model. In this study, it was demonstrated that a high-fiber diet including non-fermentable cellulose and fermentable pectin attenuated the pathological changes associated with emphysema progression and the inflammatory response in CS-exposed emphysema mice. Moreover, we observed that different types of dietary fiber could modulate the diversity of gut microbiota and differentially impacted anabolism including the generation of short-chain fatty acids, bile acids, and sphingolipids. Overall, the results of this study indicate that high-fiber diets play a beneficial role in the gut microbiota-metabolite modulation and substantially affect CS-exposed emphysema mice. Furthermore, this study suggests the therapeutic potential of gut microbiota and metabolites from a high-fiber diet in emphysema via local and systemic inflammation inhibition, which may be useful in the development of a new COPD treatment plan.

Background: Gastroesophageal reflux disease (GERD) is one of the most common causes of chronic cough and a potential risk factor for exacerbation of chronic obstructive pulmonary disease (COPD). The aim of this study was to investigate the prevalence and risk factors of GERD in patients with COPD and association between GERD and COPD exacerbation. Methods: Data were collected from the National Health Insurance Database of Korea. The subjects were 40 years old and older, who had COPD as primary or secondary diagnosis codes and utilized health care resource to receive prescriptions of COPD medication at least twice in 2009. Univariate logistic regression was performed to understand the relationship between COPD and GERD, and multiple logistic regression analysis was performed with adjustment for several confounding factors. Results: The prevalence of GERD in COPD patients was 28% (39,987/141,057). Old age, female gender, medical aid insurance type, hospitalization, and emergency room (ER) visit were associated with GERD. Most of COPD medications except inhaled muscarinic antagonists were associated with GERD. The logistic regression analysis showed that the presence of GERD was associated with increased risk of hospitalization (OR 1.54, CI 1.50 to 1.58, p<0.001) and frequent ER visits (OR 1.55, CI 1.48 to 1.62, p<0.001). Conclusions: The prevalence of GERD in patients with COPD was high. Old age, female gender, medical aid insurance type, and many COPD medications except inhaled muscarinic antagonists were associated with GERD. The presence of GERD was associated with COPD exacerbation.

Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory disease, and bacterial infection plays a role in its pathogenesis. Bacteria secrete nanometer-sized extracellular vesicles (EVs), which may induce more immune dysfunction and inflammation than the bacteria themselves. We hypothesized that the microbiome of lung EVs might have distinct characteristics depending on the presence of COPD and smoking status. We analyzed and compared the microbiomes of 13 nonsmokers with normal spirometry, 13 smokers with normal spirometry (healthy smokers) and 13 patients with COPD by using 16S ribosomal RNA gene sequencing of surgical lung tissue and lung EVs. Subjects were matched for age and sex in all groups and for smoking levels in the COPD and healthy smoker groups. Each group included 12 men and 1 woman with the same mean age of 65.5 years. In all groups, EVs consistently showed more operational taxonomic units (OTUs) than lung tissue. In the healthy smoker and COPD groups, EVs had a higher Shannon index and a lower Simpson index than lung tissue and this trend was more prominent in the COPD group. Principal component analysis (PCA) showed clusters based on sample type rather than participants’ clinical characteristics. Stenotrophomonas, Propionibacterium and Alicyclobacillus were the most commonly found genera. Firmicutes were highly present in the EVs of the COPD group compared with other samples or groups. Our analysis of the lung microbiome revealed that the bacterial communities present in the EVs and in the COPD group possessed distinct characteristics with differences in the OTUs, diversity indexes and PCA clustering.

Stem cell (SC) therapy has become a potential treatment modality for pulmonary artery hypertension (PAH), but the efficacy of human SC and priming effects have not yet been established. The mobilization and homing of hematopoietic stem cells (HSCs) are modulated by priming factors that include a bioactive lipid, sphingosine-1-phosphate (S1P), which stimulates CXCR4 receptor kinase signaling. Here, we show that priming human mesenchymal stem cells (MSCs) with S1P enhances their therapeutic efficacy in PAH. Human MSCs, similar to HSCs, showed stronger chemoattraction to S1P in transwell assays. Concomitantly, MSCs treated with 0.2 μM S1P showed increased phosphorylation of both MAPKp42/44 and AKT protein compared with nonprimed MSCs. Furthermore, S1P-primed MSCs potentiated colony forming unit-fibroblast, anti-inflammatory, and angiogenic activities of MSCs in culture. In a PAH animal model induced by subcutaneously injected monocrotaline, administration of human cord blood-derived MSCs (hCB-MSCs) or S1P-primed cells significantly attenuated the elevated right ventricular systolic pressure. Notably, S1P-primed CB-MSCs, but not unprimed hCB-MSCs, also elicited a significant reduction in the right ventricular weight ratio and pulmonary vascular wall thickness. S1P-primed MSCs enhanced the expression of several genes responsible for stem cell trafficking and angiogenesis, increasing the density of blood vessels in the damaged lungs. Thus, this study demonstrates that human MSCs have potential utility for the treatment of PAH, and that S1P priming increases the effects of SC therapy by enhancing cardiac and vascular remodeling. By optimizing this protocol in future studies, SC therapy might form a basis for clinical trials to treat human PAH.

Background Low-dose chest CT screening is recommended for smokers with the potential for lung function abnormality, but its role in predicting lung function remains unclear. Purpose To develop a deep learning algorithm to predict pulmonary function with low-dose CT images in participants using health screening services. Materials and Methods In this retrospective study, participants underwent health screening with same-day low-dose CT and pulmonary function testing with spirometry at a university affiliated tertiary referral general hospital between January 2015 and December 2018. The data set was split into a development set (model training, validation, and internal test sets) and temporally independent test set according to first visit year. A convolutional neural network was trained to predict the forced expiratory volume in the first second of expiration (FEV₁) and forced vital capacity (FVC) from low-dose CT. The mean absolute error and concordance correlation coefficient (CCC) were used to evaluate agreement between spirometry as the reference standard and deep-learning prediction as the index test. FVC and FEV₁ percent predicted (hereafter, FVC% and FEV₁%) values less than 80% and percent of FVC exhaled in first second (hereafter, FEV₁/FVC) less than 70% were used to classify participants at high risk. Results A total of 16 148 participants were included (mean age, 55 years ± 10 [SD]; 10 981 men) and divided into a development set (n = 13 428) and temporally independent test set (n = 2720). In the temporally independent test set, the mean absolute error and CCC were 0.22 L and 0.94, respectively, for FVC and 0.22 L and 0.91 for FEV₁. For the prediction of the respiratory high-risk group, FVC%, FEV₁%, and FEV₁/FVC had respective accuracies of 89.6% (2436 of 2720 participants; 95% CI: 88.4, 90.7), 85.9% (2337 of 2720 participants; 95% CI: 84.6, 87.2), and 90.2% (2453 of 2720 participants; 95% CI: 89.1, 91.3) in the same testing data set. The sensitivities were 61.6% (242 of 393 participants; 95% CI: 59.7, 63.4), 46.9% (226 of 482 participants; 95% CI: 45.0, 48.8), and 36.1% (91 of 252 participants; 95% CI: 34.3, 37.9), respectively. Conclusion A deep learning model applied to volumetric chest CT predicted pulmonary function with relatively good performance. © RSNA, 2023 Supplemental material is available for this article.