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The fungal part of the pulmonary microbiome (mycobiome) is understudied. We report the composition of the oral and pulmonary mycobiome in participants with COPD compared to controls in a large-scale single-centre bronchoscopy study (MicroCOPD). Oral wash and bronchoalveolar lavage (BAL) was collected from 93 participants with COPD and 100 controls. Fungal DNA was extracted before sequencing of the internal transcribed spacer 1 (ITS1) region of the fungal ribosomal RNA gene cluster. Taxonomic barplots were generated, and we compared taxonomic composition, Shannon index, and beta diversity between study groups, and by use of inhaled steroids. The oral and pulmonary mycobiomes from controls and participants with COPD were dominated by Candida, and there were more Candida in oral samples compared to BAL for both study groups. Malassezia and Sarocladium were also frequently found in pulmonary samples. No consistent differences were found between study groups in terms of differential abundance/distribution. Alpha and beta diversity did not differ between study groups in pulmonary samples, but beta diversity varied with sample type. The mycobiomes did not seem to be affected by use of inhaled steroids. Oral and pulmonary samples differed in taxonomic composition and diversity, possibly indicating the existence of a pulmonary mycobiome.

Background Studies on the airway microbiome have been performed using a wide range of laboratory protocols for high-throughput sequencing of the bacterial 16S ribosomal RNA (16S rRNA) gene. We sought to determine the impact of number of polymerase chain reaction (PCR) steps (1- or 2- steps) and choice of target marker gene region (V3 V4 and V4) on the presentation of the upper and lower airway microbiome. Our analyses included lllumina MiSeq sequencing following three setups: Setup 1 (2-step PCR; V3 V4 region), Setup 2 (2-step PCR; V4 region), Setup 3 (1-step PCR; V4 region). Samples included oral wash, protected specimen brushes and protected bronchoalveolar lavage (healthy and obstructive lung disease), and negative controls. Results The number of sequences and amplicon sequence variants (ASV) decreased in order setup1 > setup2 > setup3. This trend appeared to be associated with an increased taxonomic resolution when sequencing the V3 V4 region (setup 1) and an increased number of small ASVs in setups 1 and 2. The latter was considered a result of contamination in the two-step PCR protocols as well as sequencing across multiple runs (setup 1). Although genera Streptococcus , Prevotella , Veillonella and Rothia dominated, differences in relative abundance were observed across all setups. Analyses of beta-diversity revealed that while oral wash samples (high biomass) clustered together regardless of number of PCR steps, samples from the lungs (low biomass) separated. The removal of contaminants identified using the Decontam package in R, did not resolve differences in results between sequencing setups. Conclusions Differences in number of PCR steps will have an impact of final bacterial community descriptions, and more so for samples of low bacterial load. Our findings could not be explained by differences in contamination levels alone, and more research is needed to understand how variations in PCR-setups and reagents may be contributing to the observed protocol bias.

Few studies have examined the stability of the pulmonary mycobiome. We report longitudinal changes in the oral and pulmonary mycobiome of participants with and without COPD in a large-scale bronchoscopy study (MicroCOPD). Repeated sampling was performed in 30 participants with and 21 without COPD. We collected an oral wash (OW) and a bronchoalveolar lavage (BAL) sample from each participant at two time points. The internal transcribed spacer 1 region of the ribosomal RNA gene cluster was PCR amplified and sequenced on an Illumina HiSeq sequencer. Differences in taxonomy, alpha diversity, and beta diversity between the two time points were compared, and we examined the effect of intercurrent antibiotic use. Sample pairs were dominated by Candida. We observed less stability in the pulmonary taxonomy compared to the oral taxonomy, additionally emphasised by a higher Yue-Clayton measure in BAL compared to OW (0.69 vs 0.22). No apparent effect was visually seen on taxonomy from intercurrent antibiotic use or participant category. We found no systematic variation in alpha diversity by time either in BAL (p-value 0.16) or in OW (p-value 0.97), and no obvious clusters on bronchoscopy number in PCoA plots. Pairwise distance analyses showed that OW samples from repeated sampling appeared more stable compared to BAL samples using the Bray-Curtis distance metric (p-value 0.0012), but not for Jaccard. Results from the current study propose that the pulmonary mycobiome is less stable than the oral mycobiome, and neither COPD diagnosis nor intercurrent antibiotic use seemed to influence the stability.

Nutritional risk screening, to identify patients at risk of malnutrition, is the first step in the prevention and treatment of malnutrition in hospitalized patients, and should be followed by a thorough nutritional assessment resulting in a diagnosis of malnutrition and subsequent treatment. In 2019, a consensus on criteria has been suggested for the diagnosis of malnutrition by the Global Leadership Initiative for Malnutrition (GLIM). This study investigates the diagnosis of malnutrition in hospitalized patients using nutritional risk screening and the diagnostic assessment suggested by GLIM. Hospitalized patients (excluding cancer, intensive care, and transmissible infections) who underwent nutritional risk screening (by NRS2002) were included. Nutritional risk screening was followed by anthropometric measurements including measurement of muscle mass, assessment of dietary intake and measurement of serum C-reactive protein (CRP) for inflammation in all patients. Malnutrition was diagnosed according to the GLIM-criteria. In total, 328 patients (median age 71 years, 47% women, median length of stay 7 days) were included. Nutritional risk screening identified 143 patients as at risk of malnutrition, while GLIM criteria led to a diagnosis of malnutrition in 114 patients. Of these 114 patients, 77 were also identified as at risk of malnutrition by NRS2002, while 37 patients were not identified by NRS2002. Malnutrition was evident in fewer patients than at risk of malnutrition, as expected. However, a number of patients were malnourished who were not identified by the screening procedure. More studies should investigate the importance of inflammation and reduced muscle mass, which is the main difference between nutritional risk screening and GLIM diagnostic assessment.

Background The low bacterial load in samples acquired from the lungs, have made studies on the airway microbiome vulnerable to contamination from bacterial DNA introduced during sampling and laboratory processing. We have examined the impact of laboratory contamination on samples collected from the lower airways by protected (through a sterile catheter) bronchoscopy and explored various in silico approaches to dealing with the contamination post-sequencing. Our analyses included quantitative PCR and targeted amplicon sequencing of the bacterial 16S rRNA gene. Results The mean bacterial load varied by sample type for the 23 study subjects (oral wash>1st fraction of protected bronchoalveolar lavage>protected specimen brush>2nd fraction of protected bronchoalveolar lavage; p  < 0.001). By comparison to a dilution series of know bacterial composition and load, an estimated 10–50% of the bacterial community profiles for lower airway samples could be traced back to contaminating bacterial DNA introduced from the laboratory. We determined the main source of laboratory contaminants to be the DNA extraction kit (FastDNA Spin Kit). The removal of contaminants identified using tools within the Decontam R package appeared to provide a balance between keeping and removing taxa found in both negative controls and study samples. Conclusions The influence of laboratory contamination will vary across airway microbiome studies. By reporting estimates of contaminant levels and taking use of contaminant identification tools (e.g. the Decontam R package) based on statistical models that limit the subjectivity of the researcher, the accuracy of inter-study comparisons can be improved.

The understanding of inflammation and remodeling in the bronchial wall of COPD patients with varying disease severity remains incomplete. 35 healthy controls and 47 volunteer COPD patients underwent bronchoscopy with bronchoalveolar lavage (BAL) and sampling by endobronchial biopsies in 2014-2015 as part of the MicroCOPD Study. Biopsies were embedded in glycol methyl acrylate (GMA) resin and examined by immunohistochemistry and staining for enumeration of CD3 + , CD4 + , CD8 + , CD20 + , CD68 + , EG2 + , and NE+ inflammatory cells, as well as endothelial cells (EN4) and smooth muscle actin (SMA). Mucus cells were stained by periodic acid-schiff (PAS), and toluidine blue to visualize the reticular basement membrane (RBM). The numbers of macrophages and eosinophils were higher, and vascularity increased in the submucosa in COPD patients compared with healthy controls. In healthy smokers there were lower numbers of lymphocytes (CD3 +, CD4 +, CD8 +, CD20+) than never smokers. However, COPD patients with GOLD I/II had higher numbers of eosinophils and larger smooth muscle area compared with GOLD III/IV. COPD exacerbations the last year, blood eosinophils, and use of inhaled corticosteroids did not affect levels of inflammation or remodeling. Smoking alters inflammation in healthy controls, while specific patterns of macrophages, eosinophils, and vascularity distinguish COPD from non-COPD in bronchial biopsies.

There is limited knowledge about the relationship between respiratory symptoms and quantitative high-resolution computed tomography measures of emphysema and airway wall thickness. To describe the ability of these measures of emphysema and airway wall thickness to predict respiratory symptoms in subjects with and without chronic obstructive pulmonary disease (COPD). We included 463 subjects with chronic obstructive pulmonary disease (COPD) (65% men) and 488 subjects without COPD (53% men). All subjects were current or ex-smokers older than 40 years. They underwent spirometry and high-resolution computed tomography examination, and completed an American Thoracic Society questionnaire on respiratory symptoms. Median (25th percentile, 75th percentile) percent low-attenuation areas less than -950 Hounsfield units (%LAA) was 7.0 (2.2, 17.8) in subjects with COPD and 0.5 (0.2, 1.3) in subjects without COPD. Mean (SD) standardized airway wall thickness (AWT) at an internal perimeter of 10 mm (AWT-Pi10) was 4.94 (0.33) mm in subjects with COPD and 4.77 (0.29) in subjects without COPD. Both %LAA and AWT-Pi10 were independently and significantly related to the level of dyspnea among subjects with COPD, even after adjustments for percent predicted FEV(1). AWT-Pi10 was significantly related to cough and wheezing in subjects with COPD, and to wheezing in subjects without COPD. Odds ratios (95% confidence intervals) for increased dyspnea in subjects with COPD and in subjects without COPD were 1.9 (1.5-2.3) and 1.9 (0.6-6.6) per 10% increase in %LAA, and 1.07 (1.01-1.14) and 1.11 (0.99-1.24) per 0.1-mm increase in AWT-Pi10, respectively. Quantitative computed tomography assessment of the lung parenchyma and airways may be used to explain the presence of respiratory symptoms beyond the information offered by spirometry.

Exacerbations are major contributors to morbidity and mortality in patients with chronic obstructive pulmonary disease (COPD), and respiratory bacterial and viral infections are an important trigger. However, using conventional diagnostic techniques, a causative agent is not always found. Metagenomic next-generation sequencing (mNGS) allows analysis of the complete virome, but has not yet been applied in COPD exacerbations. To study the respiratory virome in nasopharyngeal samples during COPD exacerbations using mNGS. 88 nasopharyngeal swabs from 63 patients from the Bergen COPD Exacerbation Study (2006-2010) were analysed by mNGS and in-house qPCR for respiratory viruses. Both DNA and RNA were sequenced simultaneously using an Illumina library preparation protocol with in-house adaptations. By mNGS, 24/88 samples tested positive. Sensitivity and specificity, as compared with PCR, were 96% and 98% for diagnostic targets (23/24 and 1093/1120, respectively). Additional viral pathogens detected by mNGS were herpes simplex virus type 1 and coronavirus OC43. A positive correlation was found between Cq value and mNGS viral normalized species reads (log value) (p = 0.002). Patients with viral pathogens had lower percentages of bacteriophages (p<0.001). No correlation was found between viral reads and clinical markers. The mNGS protocol used was highly sensitive and specific for semi-quantitative detection of respiratory viruses. Excellent negative predictive value implicates the power of mNGS to exclude any pathogenic respiratory viral infectious cause in one test, with consequences for clinical decision making. Reduced abundance of bacteriophages in COPD patients with viral pathogens implicates skewing of the virome during infection, with potential consequences for the bacterial populations, during infection.

Exacerbations of chronic obstructive pulmonary disease (COPD) are debilitating events and spur disease progression. Infectious causes are frequent; however, it is unknown to what extent exacerbations are caused by larger shifts in the airways' microbiota. The aim of the current study was to analyse the changes in microbial composition between stable state and during exacerbations, and the corresponding immune response. The study sample included 36 COPD patients examined at stable state and exacerbation from the Bergen COPD Cohort and Exacerbations studies, and one patient who delivered sputum on 13 different occasions during the three-year study period. A physician examined the patients at all time points, and sputum induction was performed by stringent protocol. Only induced sputum samples were used in the current study, not spontaneously expectorated sputum. Sputum inflammatory markers (IL-6, IL-8, IL-18, IP-10, MIG, TNF-α) and antimicrobial peptides (AMPs, i.e. LL-37/hCAP-18, SLPI) were measured in supernatants, whereas target gene sequencing (16S rRNA) was performed on corresponding cell pellets. The microbiome bioinformatics platform QIIME2TM and the statistics environment R were applied for bioinformatics analyses. Levels of IP-10, MIG, TNF-α and AMPs were significantly different between the two disease states. Of 36 sample pairs, 24 had significant differences in the 12 most abundant genera between disease states. The diversity was significantly different in several individuals, but not when data was analysed on a group level. The one patient case study showed longitudinal dynamics in microbiota unrelated to disease state. Changes in the sputum microbiota with changing COPD disease states are common, and are accompanied by changes in inflammatory markers. However, the changes are highly individual and heterogeneous events.

COPD exacerbations accelerate disease progression. To examine if COPD characteristics and systemic inflammatory markers predict the risk for acute COPD exacerbation (AECOPD) frequency and duration. 403 COPD patients, GOLD stage II-IV, aged 44-76 years were included in the Bergen COPD Cohort Study in 2006/07, and followed for 3 years. Examined baseline predictors were sex, age, body composition, smoking, AECOPD the last year, GOLD stage, Charlson comorbidity score (CCS), hypoxemia (PaO2<8 kPa), cough, use of inhaled steroids, and the inflammatory markers leucocytes, C-reactive protein (CRP), neutrophil gelatinase associated lipocalin (NGAL), soluble tumor necrosis factor receptor 1 (sTNF-R1), and osteoprotegrin (OPG). Negative binomial models with random effects were fitted to estimate the annual incidence rate ratios (IRR). For analysis of AECOPD duration, a generalized estimation equation logistic regression model was fitted, also adjusting for season, time since inclusion and AECOPD severity. After multivariate adjustment, significant predictors of AECOPD were: female sex [IRR 1.45 (1.14-1.84)], age per 10 year increase [1.23 (1.03-1.47)], >1 AECOPD last year before baseline [1.65 (1.24-2.21)], GOLD III [1.36 (1.07-1.74)], GOLD IV [2.90 (1.98-4.25)], chronic cough [1.64 (1.30-2.06)] and use of inhaled steroids [1.57 (1.21-2.05)]. For AECOPD duration more than three weeks, significant predictors after adjustment were: hypoxemia [0.60 (0.39-0.92)], years since inclusion [1.19 (1.03-1.37)], AECOPD severity; moderate [OR 1.58 (1.14-2.18)] and severe [2.34 (1.58-3.49)], season; winter [1.51 (1.08-2.12)], spring [1.45 (1.02-2.05)] and sTNF-R1 per SD increase [1.16 (1.00-1.35)]. Several COPD characteristics were independent predictors of both AECOPD frequency and duration.