Epithelial miR-141 regulates IL-13-induced airway mucus production.

IL-13-induced goblet cell metaplasia contributes to airway remodeling and pathological mucus hypersecretion in asthma. miRNAs are potent modulators of cellular responses, but their role in mucus regulation is largely unexplored. We hypothesized that airway epithelial miRNAs play roles in IL-13-induced mucus regulation. miR-141 is highly expressed in human and mouse airway epithelium, is altered in bronchial brushings from asthmatic subjects at baseline, and is induced shortly after airway allergen exposure. We established a CRISPR/Cas9-based protocol to target miR-141 in primary human bronchial epithelial cells that were differentiated at air-liquid-interface, and goblet cell hyperplasia was induced by IL-13 stimulation. miR-141 disruption resulted in decreased goblet cell frequency, intracellular MUC5AC, and total secreted mucus. These effects correlated with a reduction in a goblet cell gene expression signature and enrichment of a basal cell gene expression signature defined by single cell RNA sequencing. Furthermore, intranasal administration of a sequence-specific mmu-miR-141-3p inhibitor in mice decreased Aspergillus-induced secreted mucus and mucus-producing cells in the lung and reduced airway hyperresponsiveness without affecting cellular inflammation. In conclusion, we have identified a miRNA that regulates pathological airway mucus production and is amenable to therapeutic manipulation through an inhaled route.

Metagenomic comparison of tracheal aspirate and mini-bronchial alveolar lavage for assessment of respiratory microbiota.

Accurate and informative microbiological testing is essential for guiding diagnosis and management of pneumonia in patients who are critically ill. Sampling of tracheal aspirate (TA) is less invasive compared with mini-bronchoalveolar lavage (mBAL) and is now recommended as a frontline diagnostic approach in patients who are mechanically ventilated, despite the historical belief that TA was suboptimal due to contamination from oral microbes. Advancements in metagenomic next-generation sequencing (mNGS) now permit assessment of airway microbiota without a need for culture and, as such, provide an opportunity to examine differences between mBAL and TA at a resolution previously unachievable. Here, we engaged shotgun mNGS to assess quantitatively the airway microbiome in matched mBAL and TA specimens from a prospective cohort of critically ill adults. We observed moderate differences between sample types across all subjects; however, we found significant compositional similarity in subjects with bacterial pneumonia, whose microbial communities were characterized by dominant pathogens. In contrast, in patients with noninfectious acute respiratory illnesses, significant differences were observed between sample types. Our findings suggest that TA sampling provides a similar assessment of airway microbiota as more invasive testing by mBAL in patients with pneumonia.

Integrating host response and unbiased microbe detection for lower respiratory tract infection diagnosis in critically ill adults.

Lower respiratory tract infections (LRTIs) lead to more deaths each year than any other infectious disease category. Despite this, etiologic LRTI pathogens are infrequently identified due to limitations of existing microbiologic tests. In critically ill patients, noninfectious inflammatory syndromes resembling LRTIs further complicate diagnosis. To address the need for improved LRTI diagnostics, we performed metagenomic next-generation sequencing (mNGS) on tracheal aspirates from 92 adults with acute respiratory failure and simultaneously assessed pathogens, the airway microbiome, and the host transcriptome. To differentiate pathogens from respiratory commensals, we developed a rules-based model (RBM) and logistic regression model (LRM) in a derivation cohort of 20 patients with LRTIs or noninfectious acute respiratory illnesses. When tested in an independent validation cohort of 24 patients, both models achieved accuracies of 95.5%. We next developed pathogen, microbiome diversity, and host gene expression metrics to identify LRTI-positive patients and differentiate them from critically ill controls with noninfectious acute respiratory illnesses. When tested in the validation cohort, the pathogen metric performed with an area under the receiver-operating curve (AUC) of 0.96 (95% CI, 0.86-1.00), the diversity metric with an AUC of 0.80 (95% CI, 0.63-0.98), and the host transcriptional classifier with an AUC of 0.88 (95% CI, 0.75-1.00). Combining these achieved a negative predictive value of 100%. This study suggests that a single streamlined protocol offering an integrated genomic portrait of pathogen, microbiome, and host transcriptome may hold promise as a tool for LRTI diagnosis.