Characterization and Mortality Risk of Impaired Left Ventricular Filling in COPD.

BACKGROUND: In COPD, impaired left ventricular (LV) filling might be associated with coexisting HFpEF or due to reduced pulmonary venous return indicated by small LV size. We investigate the all-cause mortality associated with small LV or HFpEF and clinical features discriminating between both patterns of impaired LV filling. METHODS: We performed transthoracic echocardiography (TTE) in patients with stable COPD from the COSYCONET cohort to define small LV as LVEDD below the normal range and HFpEF features according to recommendations of the European Society of Cardiology. We assessed the E/A and E/e' ratios, NT-pro-BNP, hs-Troponin I, FEV1, RV, DLCo, and discriminated patients with small LV from those with HFpEF features or no relevant cardiac dysfunction as per TTE (normalTTE). The primary outcome was all-cause mortality after four and a half year. RESULTS: In 1752 patients with COPD, the frequency of small LV, HFpEF-features, and normalTTE was 8%, 16%, and 45%, respectively. Patients with small LV or HFpEF features had higher all-cause mortality rates than patients with normalTTE, HR: 2.75 (95% CI: [1.54 - 4.89]) and 2.16 (95% CI: [1.30 - 3.61]), respectively. Small LV remained an independent predictor of all-cause mortality after adjusting for confounders including exacerbation frequency and measures of RV, DLCo, or FEV1. Compared to normalTTE, patients with small LV had reduced LV filling, as indicated by lowered E/A. Yet in contrast to patients with HFpEF-features, patients with small LV had normal LV filling pressure (E/e') and lower levels of NT-pro-BNP and hs-Troponin I. CONCLUSION: In COPD, both small LV and HFpEF-features are associated with increased all-cause mortality and represent two distinct patterns of impaired LV filling.

Exploration of the sputum methylome and omics deconvolution by quadratic programming in molecular profiling of asthma and COPD: the road to sputum omics 2.0.

BACKGROUND: To date, most studies involving high-throughput analyses of sputum in asthma and COPD have focused on identifying transcriptomic signatures of disease. No whole-genome methylation analysis of sputum cells has been performed yet. In this context, the highly variable cellular composition of sputum has potential to confound the molecular analyses. METHODS: Whole-genome transcription (Agilent Human 4 × 44 k array) and methylation (Illumina 450 k BeadChip) analyses were performed on sputum samples of 9 asthmatics, 10 healthy and 10 COPD subjects. RNA integrity was checked by capillary electrophoresis and used to correct in silico for bias conferred by RNA degradation during biobank sample storage. Estimates of cell type-specific molecular profiles were derived via regression by quadratic programming based on sputum differential cell counts. All analyses were conducted using the open-source R/Bioconductor software framework. RESULTS: A linear regression step was found to perform well in removing RNA degradation-related bias among the main principal components of the gene expression data, increasing the number of genes detectable as differentially expressed in asthma and COPD sputa (compared to controls). We observed a strong influence of the cellular composition on the results of mixed-cell sputum analyses. Exemplarily, upregulated genes derived from mixed-cell data in asthma were dominated by genes predominantly expressed in eosinophils after deconvolution. The deconvolution, however, allowed to perform differential expression and methylation analyses on the level of individual cell types and, though we only analyzed a limited number of biological replicates, was found to provide good estimates compared to previously published data about gene expression in lung eosinophils in asthma. Analysis of the sputum methylome indicated presence of differential methylation in genomic regions of interest, e.g. mapping to a number of human leukocyte antigen (HLA) genes related to both major histocompatibility complex (MHC) class I and II molecules in asthma and COPD macrophages. Furthermore, we found the SMAD3 (SMAD family member 3) gene, among others, to lie within differentially methylated regions which has been previously reported in the context of asthma. CONCLUSIONS: In this methodology-oriented study, we show that methylation profiling can be easily integrated into sputum analysis workflows and exhibits a strong potential to contribute to the profiling and understanding of pulmonary inflammation. Wherever RNA degradation is of concern, in silico correction can be effective in improving both sensitivity and specificity of downstream analyses. We suggest that deconvolution methods should be integrated in sputum omics analysis workflows whenever possible in order to facilitate the unbiased discovery and interpretation of molecular patterns of inflammation.

Evolutionary Approaches to Combat Antibiotic Resistance: Opportunities and Challenges for Precision Medicine.

The rise of antimicrobial resistance (AMR) in bacterial pathogens is acknowledged by the WHO as a major global health crisis. It is estimated that in 2050 annually up to 10 million people will die from infections with drug resistant pathogens if no efficient countermeasures are implemented. Evolution of pathogens lies at the core of this crisis, which enables rapid adaptation to the selective pressures imposed by antimicrobial usage in both medical treatment and agriculture, consequently promoting the spread of resistance genes or alleles in bacterial populations. Approaches developed in the field of Evolutionary Medicine attempt to exploit evolutionary insight into these adaptive processes, with the aim to improve diagnostics and the sustainability of antimicrobial therapy. Here, we review the concept of evolutionary trade-offs in the development of AMR as well as new therapeutic approaches and their impact on host-microbiome-pathogen interactions. We further discuss the possible translation of evolution-informed treatments into clinical practice, considering both the rapid cure of the individual patients and the prevention of AMR.

Pre- and postnatal exposure of mice to concentrated urban PM2.5 decreases the number of alveoli and leads to altered lung function at an early stage of life.

Gestational exposure to air pollution is associated with negative outcomes in newborns and children. In a previous study, we demonstrated a synergistic negative effect of pre- and postnatal exposure to PM2.5 on lung development in mice. However, the means by which air pollution affects development of the lung have not yet been identified. In this study, we exposed pregnant BALB/c mice and their offspring to concentrated urban PM2.5 (from São Paulo, Brazil; target dose 600 µg/m3 for 1 h daily). Exposure was started on embryonic day 5.5 (E5.5, time of placental implantation). Lung tissue of fetuses and offspring was submitted to stereological and transcriptomic analyses at E14.5 (pseudoglandular stage of lung development), E18.5 (saccular stage) and P40 (postnatal day 40, alveolarized lung). Additionally, lung function and cellularity of bronchoalveolar lavage (BAL) fluid were studied in offspring animals at P40. Compared to control animals that were exposed to filtered air throughout gestation and postnatal life, PM-exposed mice exhibited higher lung elastance and a lower alveolar number at P40 whilst the total lung volume and cellularity of BAL fluid were not affected. Glandular and saccular structures of fetal lungs were not altered upon gestational exposure; transcriptomic signatures, however, showed changes related to DNA damage and its regulation, inflammation and regulation of cell proliferation. A differential expression was validated at E14.5 for the candidates Sox8, Angptl4 and Gas1. Our data substantiate the in utero biomolecular effect of gestational exposure to air pollution and provide first-time stereological evidence that pre- and early life-postnatal exposure compromise lung development, leading to a reduced number of alveoli and an impairment of lung function in the adult mouse.