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Background: Relationships between airway inflammation and respiratory potentially pathogenic microorganisms (PPMs) quantified using quantitative polymerase chain reaction (qPCR) in subjects with COPD are unclear. Our aim was to evaluate mediators of airway inflammation and their association with PPMs in subjects with COPD at stable state and during exacerbations., Methods: Sputum from 120 stable subjects with COPD was analyzed for bacteriology (colony-forming units; total 16S; and qPCR targeting Haemophilus influenzae, Moraxella catarrhalis, and Streptococcus pneumoniae), differential cell counts, and inflammatory mediators using the Meso-Scale Discovery Platform. Subjects were classified as colonized if any PPM was identified above the threshold of detection by qPCR. Symptoms were quantified using the visual analog scale., Results: At stable state, 60% of subjects were qPCR positive for H influenzae, 48% for M catarrhalis, and 28% for S pneumoniae. Elevated sputum concentrations of IL-1β, IL-10, and tumor necrosis factor (TNF)-α were detected in samples qPCR positive for either H influenzae or M catarrhalis. Bacterial loads of H influenzae positively correlated with IL-1β, IL-8, IL-10, TNF-α, and symptoms; and M catarrhalis correlated with IL-10 and TNF-α. H influenzae qPCR bacterial load was an independent predictor of sputum TNF-α and IL-1β. In 55 subjects with paired exacerbation data, qPCR bacterial load fold change at exacerbation in M catarrhalis but not H influenzae correlated to changes in sputum TNF-α and IL-1β concentrations., Conclusions: At stable state, H influenzae is associated with increased airway inflammation in COPD. The relationship between bacterial load changes of specific pathogens and airway inflammation at exacerbation and recovery warrants further investigation.

Background: While a subgroup of patients with exacerbations of chronic obstructive pulmonary disease (COPD) clearly benefit from antibiotics, their identification remains challenging. We hypothesised that selective assessment of the balance between the two dominant bacterial groups (Gammaproteobacteria (G) and Firmicutes (F)) in COPD sputum samples might reveal a subgroup with a bacterial community structure change at exacerbation that was restored to baseline on recovery and potentially reflects effective antibiotic treatment. Methods: Phylogenetically specific 16S rRNA genes were determined by quantitative real time PCR to derive a G:F ratio in serial sputum samples from 66 extensively-phenotyped COPD exacerbation episodes. Results: Cluster analysis based on Euclidean distance measures, generated across the 4 visit times (stable and exacerbation day: 0,14 and 42) for the 66 exacerbation episodes, revealed three subgroups designated HG, HF, and GF reflecting predominance or equivalence of the two target bacterial groups. While the other subgroups showed no change at exacerbation, the HG cluster (n = 20) was characterized by G:F ratios that increased significantly at exacerbation and returned to baseline on recovery (p<0.00001); ratios in the HG group also correlated positively with inflammatory markers and negatively with FEV1. At exacerbation G:F showed a significant receiver-operator-characteristic curve to identify the HG subgroup (AUC 0.90, p<0.0001). Conclusions: The G:F ratio at exacerbation can be determined on a timescale compatible with decisions regarding clinical management. We propose that the G:F ratio has potential for use as a biomarker enabling selective use of antibiotics in COPD exacerbations and hence warrants further clinical evaluation. [ABSTRACT FROM AUTHOR]

COPD (chronic obstructive pulmonary disease) is a heterogeneous disease associated with significant morbidity and mortality. Current diagnostic criteria based on the presence of fixed airflow obstruction and symptoms do not integrate the complex pathological changes occurring within lung, do not define different airway inflammatory patterns, nor do they define different physiological changes or differences in structure as can be defined by imaging. Over recent years, there has been interest in describing this heterogeneity and using this information to subgroup patients into COPD phenotypes. Most approaches to phenotyping have considered disease at a single scale and have not integrated information from different scales (e.g. organ-whole person, tissue-organ, cell-tissue and gene-cell) of disease to provide multi-dimensional phenotypes. Integration of disease biology with clinical expression is critical to improve understanding of this disease. When combined with biostatistical modelling, this information may lead to identification of new drug targets, new end points for clinical trials and targeted treatment for subgroups of COPD patients. It is hoped this will ultimately improve COPD outcomes and represent a move towards personalised medicine. In the present review, we will consider these aspects of multi-dimensional phenotyping in more detail. [ABSTRACT FROM AUTHOR]