Loss of Airway Phylogenetic Diversity Is Associated with Clinical and Pathobiological Markers of Disease Development in Chronic Obstructive Pulmonary Disease.

Rationale: The airway microbiome has the potential to shape chronic obstructive pulmonary disease (COPD) pathogenesis, but its relationship to outcomes in milder disease is unestablished. Objectives: To identify sputum microbiome characteristics associated with markers of COPD in participants of the Subpopulations and Intermediate Outcome Measures of COPD Study (SPIROMICS). Methods: Sputum DNA from 877 participants was analyzed using 16S ribosomal RNA gene sequencing. Relationships between baseline airway microbiota composition and clinical, radiographic, and mucoinflammatory markers, including longitudinal lung function trajectory, were examined. Measurements and Main Results: Participant data represented predominantly milder disease (Global Initiative for Chronic Obstructive Lung Disease stage 0-2 obstruction in 732 of 877 participants). Phylogenetic diversity (i.e., range of different species within a sample) correlated positively with baseline lung function, decreased with higher Global Initiative for Chronic Obstructive Lung Disease stage, and correlated negatively with symptom burden, radiographic markers of airway disease, and total mucin concentrations (P < 0.001). In covariate-adjusted regression models, organisms robustly associated with better lung function included Alloprevotella, Oribacterium, and Veillonella species. Conversely, lower lung function, greater symptoms, and radiographic measures of small airway disease were associated with enrichment in members of Streptococcus, Actinobacillus, Actinomyces, and other genera. Baseline sputum microbiota features were also associated with lung function trajectory during SPIROMICS follow-up (stable/improved, decline, or rapid decline groups). The stable/improved group (slope of FEV1 regression ⩾66th percentile) had greater bacterial diversity at baseline associated with enrichment in Prevotella, Leptotrichia, and Neisseria species. In contrast, the rapid decline group (FEV1 slope ⩽33rd percentile) had significantly lower baseline diversity associated with enrichment in Streptococcus species. Conclusions: In SPIROMICS, baseline airway microbiota features demonstrate divergent associations with better or worse COPD-related outcomes.

Airway microbiota and immune mediator relationships differ in obesity and asthma.

BACKGROUND: Asthma and obesity are both complex conditions characterized by chronic inflammation, and obesity-related severe asthma has been associated with differences in the microbiome. However, whether the airway microbiome and microbiota-immune response relationships differ between obese persons with or without nonsevere asthma is unestablished. OBJECTIVE: We compared the airway microbiome and microbiota-immune mediator relationships between obese and nonobese subjects, with and without mild-moderate asthma. METHODS: We performed cross-sectional analyses of the airway (induced sputum) microbiome and cytokine profiles from blood and sputum using 16S ribosomal RNA gene and internal transcribed spacer region sequencing to profile bacteria and fungi, and multiplex immunoassays. Analysis tools included QIIME 2, linear discriminant analysis effect size (aka LEfSe), Piphillin, and Sparse inverse covariance estimation for ecological association inference (aka SPIEC-EASI). RESULTS: Obesity, irrespective of asthma status, was associated with significant differences in sputum bacterial community structure and composition (unweighted UniFrac permutational analysis of variance, P = .02), including a higher relative abundance of Prevotella, Gemella, and Streptococcus species. Among subjects with asthma, additional differences in sputum bacterial composition and fungal richness were identified between obese and nonobese individuals. Correlation network analyses demonstrated differences between obese and nonobese asthma in relationships between cytokine mediators, and these together with specific airway bacteria involving blood PAI-1, sputum IL-1ß, GM-CSF, IL-8, TNF-α, and several Prevotella species. CONCLUSION: Obesity itself is associated with an altered sputum microbiome, which further differs in those with mild-moderate asthma. The distinct differences in airway microbiota and immune marker relationships in obese asthma suggest potential involvement of airway microbes that may affect mechanisms or outcomes of obese asthma.

Lung Microbiota and Metabolites Collectively Associate with Clinical Outcomes in Milder Stage Chronic Obstructive Pulmonary Disease.

Rationale: Chronic obstructive pulmonary disease (COPD) is variable in its development. Lung microbiota and metabolites collectively may impact COPD pathophysiology, but relationships to clinical outcomes in milder disease are unclear. Objectives: Identify components of the lung microbiome and metabolome collectively associated with clinical markers in milder stage COPD. Methods: We analyzed paired microbiome and metabolomic data previously characterized from bronchoalveolar lavage fluid in 137 participants in the SPIROMICS (Subpopulations and Intermediate Outcome Measures in COPD Study), or (GOLD [Global Initiative for Chronic Obstructive Lung Disease Stage 0-2). Datasets used included 1) bacterial 16S rRNA gene sequencing; 2) untargeted metabolomics of the hydrophobic fraction, largely comprising lipids; and 3) targeted metabolomics for a panel of hydrophilic compounds previously implicated in mucoinflammation. We applied an integrative approach to select features and model 14 individual clinical variables representative of known associations with COPD trajectory (lung function, symptoms, and exacerbations). Measurements and Main Results: The majority of clinical measures associated with the lung microbiome and metabolome collectively in overall models (classification accuracies, >50%, P < 0.05 vs. chance). Lower lung function, COPD diagnosis, and greater symptoms associated positively with Streptococcus, Neisseria, and Veillonella, together with compounds from several classes (glycosphingolipids, glycerophospholipids, polyamines and xanthine, an adenosine metabolite). In contrast, several Prevotella members, together with adenosine, 5'-methylthioadenosine, sialic acid, tyrosine, and glutathione, associated with better lung function, absence of COPD, or less symptoms. Significant correlations were observed between specific metabolites and bacteria (Padj < 0.05). Conclusions: Components of the lung microbiome and metabolome in combination relate to outcome measures in milder COPD, highlighting their potential collaborative roles in disease pathogenesis.

Ribosome Structure, Function, and Early Evolution.

Ribosomes are among the largest and most dynamic molecular motors. The structure and dynamics of translation initiation and elongation are reviewed. Three ribosome motions have been identified for initiation and translocation. A swivel motion between the head/beak and the body of the 30S subunit was observed. A tilting dynamic of the head/beak versus the body of the 30S subunit was detected using simulations. A reversible ratcheting motion was seen between the 30S and the 50S subunits that slide relative to one another. The 30S⁻50S intersubunit contacts regulate translocation. IF2, EF-Tu, and EF-G are homologous G-protein GTPases that cycle on and off the same site on the ribosome. The ribosome, aminoacyl-tRNA synthetase (aaRS) enzymes, transfer ribonucleic acid (tRNA), and messenger ribonucleic acid (mRNA) form the core of information processing in cells and are coevolved. Surprisingly, class I and class II aaRS enzymes, with distinct and incompatible folds, are homologs. Divergence of class I and class II aaRS enzymes and coevolution of the genetic code are described by analysis of ancient archaeal species.

Type-II tRNAs and Evolution of Translation Systems and the Genetic Code.

Because tRNA is the core biological intellectual property that was necessary to evolve translation systems, tRNAomes, ribosomes, aminoacyl-tRNA synthetases, and the genetic code, the evolution of tRNA is the core story in evolution of life on earth. We have previously described the evolution of type-I tRNAs. Here, we use the same model to describe the evolution of type-II tRNAs, with expanded V loops. The models are strongly supported by inspection of typical tRNA diagrams, measuring lengths of V loop expansions, and analyzing the homology of V loop sequences to tRNA acceptor stems. Models for tRNA evolution provide a pathway for the inanimate-to-animate transition and for the evolution of translation systems, the genetic code, and cellular life.

Five checkpoints maintaining the fidelity of transcription by RNA polymerases in structural and energetic details.

Transcriptional fidelity, which prevents the misincorporation of incorrect nucleoside monophosphates in RNA, is essential for life. Results from molecular dynamics (MD) simulations of eukaryotic RNA polymerase (RNAP) II and bacterial RNAP with experimental data suggest that fidelity may involve as many as five checkpoints. Using MD simulations, the effects of different active site NTPs in both open and closed trigger loop (TL) structures of RNAPs are compared. Unfavorable initial binding of mismatched substrates in the active site with an open TL is proposed to be the first fidelity checkpoint. The leaving of an incorrect substrate is much easier than a correct one energetically from the umbrella sampling simulations. Then, the closing motion of the TL, required for catalysis, is hindered by the presence of mismatched NTPs. Mismatched NTPs also lead to conformational changes in the active site, which perturb the coordination of magnesium ions and likely affect the ability to proceed with catalysis. This step appears to be the most important checkpoint for deoxy-NTP discrimination. Finally, structural perturbations in the template DNA and the nascent RNA in the presence of mismatches likely hinder nucleotide addition and provide the structural foundation for backtracking followed by removing erroneously incorporated nucleotides during proofreading.

Flexibility-rigidity index for protein-nucleic acid flexibility and fluctuation analysis.

Protein-nucleic acid complexes are important for many cellular processes including the most essential functions such as transcription and translation. For many protein-nucleic acid complexes, flexibility of both macromolecules has been shown to be critical for specificity and/or function. The flexibility-rigidity index (FRI) has been proposed as an accurate and efficient approach for protein flexibility analysis. In this article, we introduce FRI for the flexibility analysis of protein-nucleic acid complexes. We demonstrate that a multiscale strategy, which incorporates multiple kernels to capture various length scales in biomolecular collective motions, is able to significantly improve the state of art in the flexibility analysis of protein-nucleic acid complexes. We take the advantage of the high accuracy and O(N) computational complexity of our multiscale FRI method to investigate the flexibility of ribosomal subunits, which are difficult to analyze by alternative approaches. An anisotropic FRI approach, which involves localized Hessian matrices, is utilized to study the translocation dynamics in an RNA polymerase.

Multiscale Gaussian network model (mGNM) and multiscale anisotropic network model (mANM).

Gaussian network model (GNM) and anisotropic network model (ANM) are some of the most popular methods for the study of protein flexibility and related functions. In this work, we propose generalized GNM (gGNM) and ANM methods and show that the GNM Kirchhoff matrix can be built from the ideal low-pass filter, which is a special case of a wide class of correlation functions underpinning the linear scaling flexibility-rigidity index (FRI) method. Based on the mathematical structure of correlation functions, we propose a unified framework to construct generalized Kirchhoff matrices whose matrix inverse leads to gGNMs, whereas, the direct inverse of its diagonal elements gives rise to FRI method. With this connection, we further introduce two multiscale elastic network models, namely, multiscale GNM (mGNM) and multiscale ANM (mANM), which are able to incorporate different scales into the generalized Kirchhoff matrices or generalized Hessian matrices. We validate our new multiscale methods with extensive numerical experiments. We illustrate that gGNMs outperform the original GNM method in the B-factor prediction of a set of 364 proteins. We demonstrate that for a given correlation function, FRI and gGNM methods provide essentially identical B-factor predictions when the scale value in the correlation function is sufficiently large. More importantly, we reveal intrinsic multiscale behavior in protein structures. The proposed mGNM and mANM are able to capture this multiscale behavior and thus give rise to a significant improvement of more than 11% in B-factor predictions over the original GNM and ANM methods. We further demonstrate the benefits of our mGNM through the B-factor predictions of many proteins that fail the original GNM method. We show that the proposed mGNM can also be used to analyze protein domain separations. Finally, we showcase the ability of our mANM for the analysis of protein collective motions.

Communication: Capturing protein multiscale thermal fluctuations.

Existing elastic network models are typically parametrized at a given cutoff distance and often fail to properly predict the thermal fluctuation of many macromolecules that involve multiple characteristic length scales. We introduce a multiscale flexibility-rigidity index (mFRI) method to resolve this problem. The proposed mFRI utilizes two or three correlation kernels parametrized at different length scales to capture protein interactions at corresponding scales. It is about 20% more accurate than the Gaussian network model (GNM) in the B-factor prediction of a set of 364 proteins. Additionally, the present method is able to deliver accurate predictions for some large macromolecules on which GNM fails to produce accurate predictions. Finally, for a protein of N residues, mFRI is of linear scaling (O(N)) in computational complexity, in contrast to the order of O(N(3)) for GNM.

The RNA polymerase bridge helix YFI motif in catalysis, fidelity and translocation.

The bridge α-helix in the ß' subunit of RNA polymerase (RNAP) borders the active site and may have roles in catalysis and translocation. In Escherichia coli RNAP, a bulky hydrophobic segment near the N-terminal end of the bridge helix is identified (ß' 772-YFI-774; the YFI motif). YFI is located at a distance from the active center and adjacent to a glycine hinge (ß' 778-GARKG-782) involved in dynamic bending of the bridge helix. Remarkably, amino acid substitutions in YFI significantly alter intrinsic termination, pausing, fidelity and translocation of RNAP. F773V RNAP largely ignores the λ tR2 terminator at 200µM NTPs and is strongly reduced in λ tR2 recognition at 1µM NTPs. F773V alters RNAP pausing and backtracking and favors misincorporation. By contrast, the adjacent Y772A substitution increases fidelity and exhibits other transcriptional defects generally opposite to those of F773V. All atom molecular dynamics simulation revealed two separate functional connections emanating from YFI explaining the distinct effects of substitutions: Y772 communicates with the active site through the link domain in the ß subunit, whereas F773 communicates through the fork domain in the ß subunit. I774 interacts with the F-loop, which also contacts the glycine hinge of the bridge helix. These results identified negative and positive circuits coupled at YFI and employed for regulation of catalysis, elongation, termination and translocation.

Fast and anisotropic flexibility-rigidity index for protein flexibility and fluctuation analysis.

Protein structural fluctuation, typically measured by Debye-Waller factors, or B-factors, is a manifestation of protein flexibility, which strongly correlates to protein function. The flexibility-rigidity index (FRI) is a newly proposed method for the construction of atomic rigidity functions required in the theory of continuum elasticity with atomic rigidity, which is a new multiscale formalism for describing excessively large biomolecular systems. The FRI method analyzes protein rigidity and flexibility and is capable of predicting protein B-factors without resorting to matrix diagonalization. A fundamental assumption used in the FRI is that protein structures are uniquely determined by various internal and external interactions, while the protein functions, such as stability and flexibility, are solely determined by the structure. As such, one can predict protein flexibility without resorting to the protein interaction Hamiltonian. Consequently, bypassing the matrix diagonalization, the original FRI has a computational complexity of O(N(2)). This work introduces a fast FRI (fFRI) algorithm for the flexibility analysis of large macromolecules. The proposed fFRI further reduces the computational complexity to O(N). Additionally, we propose anisotropic FRI (aFRI) algorithms for the analysis of protein collective dynamics. The aFRI algorithms permit adaptive Hessian matrices, from a completely global 3N × 3N matrix to completely local 3 × 3 matrices. These 3 × 3 matrices, despite being calculated locally, also contain non-local correlation information. Eigenvectors obtained from the proposed aFRI algorithms are able to demonstrate collective motions. Moreover, we investigate the performance of FRI by employing four families of radial basis correlation functions. Both parameter optimized and parameter-free FRI methods are explored. Furthermore, we compare the accuracy and efficiency of FRI with some established approaches to flexibility analysis, namely, normal mode analysis and Gaussian network model (GNM). The accuracy of the FRI method is tested using four sets of proteins, three sets of relatively small-, medium-, and large-sized structures and an extended set of 365 proteins. A fifth set of proteins is used to compare the efficiency of the FRI, fFRI, aFRI, and GNM methods. Intensive validation and comparison indicate that the FRI, particularly the fFRI, is orders of magnitude more efficient and about 10% more accurate overall than some of the most popular methods in the field. The proposed fFRI is able to predict B-factors for α-carbons of the HIV virus capsid (313 236 residues) in less than 30 seconds on a single processor using only one core. Finally, we demonstrate the application of FRI and aFRI to protein domain analysis.

Microbial community organization designates distinct pulmonary exacerbation types and predicts treatment outcome in cystic fibrosis.

Polymicrobial infection of the airways is a hallmark of obstructive lung diseases such as cystic fibrosis (CF), non-CF bronchiectasis, and chronic obstructive pulmonary disease. Pulmonary exacerbations (PEx) in these conditions are associated with accelerated lung function decline and higher mortality rates. An understanding of the microbial underpinnings of PEx is challenged by high inter-patient variability in airway microbial community profiles. We analyzed bacterial communities in 880 CF sputum samples and developed microbiome descriptors to model community reorganization prior to and during 18 PEx. We identified two microbial dysbiosis regimes with opposing ecology and dynamics. Pathogen-governed PEx showed hierarchical community reorganization and reduced diversity, whereas anaerobic bloom PEx displayed stochasticity and increased diversity. A simulation of antimicrobial treatment predicted better efficacy for hierarchically organized communities. This link between PEx type, microbiome organization, and treatment success advances the development of personalized clinical management in CF and, potentially, other obstructive lung diseases.

Microbial community organization designates distinct pulmonary exacerbation types and predicts treatment outcome in cystic fibrosis.

Polymicrobial infection of the airways is a hallmark of obstructive lung diseases such as cystic fibrosis (CF), non-CF bronchiectasis, and chronic obstructive pulmonary disease. Pulmonary exacerbations (PEx) in these conditions are associated with accelerated lung function decline and higher mortality rates. Understanding PEx ecology is challenged by high inter-patient variability in airway microbial community profiles. We analyze bacterial communities in 880 CF sputum samples collected during an observational prospective cohort study and develop microbiome descriptors to model community reorganization prior to and during 18 PEx. We identify two microbial dysbiosis regimes with opposing ecology and dynamics. Pathogen-governed PEx show hierarchical community reorganization and reduced diversity, whereas anaerobic bloom PEx display stochasticity and increased diversity. A simulation of antimicrobial treatment predicts better efficacy for hierarchically organized communities. This link between PEx, microbiome organization, and treatment success advances the development of personalized clinical management in CF and, potentially, other obstructive lung diseases.

Multiscale multiphysics and multidomain models--flexibility and rigidity.

The emerging complexity of large macromolecules has led to challenges in their full scale theoretical description and computer simulation. Multiscale multiphysics and multidomain models have been introduced to reduce the number of degrees of freedom while maintaining modeling accuracy and achieving computational efficiency. A total energy functional is constructed to put energies for polar and nonpolar solvation, chemical potential, fluid flow, molecular mechanics, and elastic dynamics on an equal footing. The variational principle is utilized to derive coupled governing equations for the above mentioned multiphysical descriptions. Among these governing equations is the Poisson-Boltzmann equation which describes continuum electrostatics with atomic charges. The present work introduces the theory of continuum elasticity with atomic rigidity (CEWAR). The essence of CEWAR is to formulate the shear modulus as a continuous function of atomic rigidity. As a result, the dynamics complexity of a macromolecular system is separated from its static complexity so that the more time-consuming dynamics is handled with continuum elasticity theory, while the less time-consuming static analysis is pursued with atomic approaches. We propose a simple method, flexibility-rigidity index (FRI), to analyze macromolecular flexibility and rigidity in atomic detail. The construction of FRI relies on the fundamental assumption that protein functions, such as flexibility, rigidity, and energy, are entirely determined by the structure of the protein and its environment, although the structure is in turn determined by all the interactions. As such, the FRI measures the topological connectivity of protein atoms or residues and characterizes the geometric compactness of the protein structure. As a consequence, the FRI does not resort to the interaction Hamiltonian and bypasses matrix diagonalization, which underpins most other flexibility analysis methods. FRI's computational complexity is of O(N(2)) at most, where N is the number of atoms or residues, in contrast to O(N(3)) for Hamiltonian based methods. We demonstrate that the proposed FRI gives rise to accurate prediction of protein B-Factor for a set of 263 proteins. We show that a parameter free FRI is able to achieve about 95% accuracy of the parameter optimized FRI. An interpolation algorithm is developed to construct continuous atomic flexibility functions for visualization and use with CEWAR.

Microbial community organization designates distinct pulmonary exacerbation types and predicts treatment outcome in cystic fibrosis.

Polymicrobial infection of the airways is a hallmark of obstructive lung diseases such as cystic fibrosis (CF), non-CF bronchiectasis, and chronic obstructive pulmonary disease (COPD). Intermittent pulmonary exacerbations (PEx) in these conditions are associated with lung function decline and higher mortality rates. An understanding of the microbial underpinnings of PEx is challenged by high inter-patient variability in airway microbial community profiles. We analyzed 880 near-daily CF sputum samples and developed non-standard microbiome descriptors to model community reorganization prior and during 18 PEx. We identified two communal microbial regimes with opposing ecology and dynamics. Whereas pathogen-governed dysbiosis showed hierarchical community organization and reduced diversity, anaerobic bloom dysbiosis displayed stochasticity and increased diversity. Microbiome organization modulated the relevance of pathogens and a simulation of antimicrobial treatment predicted better efficacy for hierarchically organized microbiota. This causal link between PEx, microbiome organization, and treatment success advances the development of personalized dysbiosis management in CF and, potentially, other obstructive lung diseases.

Airway bacterial community composition in persons with advanced cystic fibrosis lung disease.

BACKGROUND: The progression of lung disease in people with cystic fibrosis (pwCF) has been associated with a decrease in the diversity of airway bacterial communities. How often low diversity communities occur in advanced CF lung disease and how they may be associated with clinical outcomes is not clear, however. METHODS: We sequenced a region of the bacterial 16S ribosomal RNA gene to characterize bacterial communities in sputum from 190 pwCF with advanced lung disease (FEV1≤40% predicted), with particular attention to the prevalence and relative abundance of dominant genera. We evaluated relationships between community diversity and clinical outcomes. RESULTS: Although most of the 190 pwCF with advanced lung disease had airway bacterial communities characterized by low diversity with a dominant genus, a considerable minority (40%) did not. The absence of a dominant genus, presence of methicillin-susceptible Staphylococcus aureus, and greater bacterial richness positively correlated with lung function. Higher relative abundance of the dominant genus and greater antimicrobial use negatively correlated with lung function. PwCF with a low diversity community and dominant genus had reduced lung transplant-free survival compared to those without (median survival of 1.6 vs 2.9 years). CONCLUSIONS: A considerable proportion of pwCF with advanced lung disease do not have airway bacterial communities characterized by low diversity and a dominant genus and these individuals had better survival. An understanding of the antecedents of low diversity airway communities- and the impact these may have on lung disease trajectory - may provide avenues for improved management strategies.

Sputum Metabolites Associated with Nontuberculous Mycobacterial Infection in Cystic Fibrosis.

Nontuberculous mycobacterial (NTM) pulmonary infections in people with cystic fibrosis (CF) are associated with significant morbidity and mortality and are increasing in prevalence. Host risk factors for NTM infection in CF are largely unknown. We hypothesize that the airway microbiota represents a host risk factor for NTM infection. In this study, 69 sputum samples were collected from 59 people with CF; 42 samples from 32 subjects with NTM infection (14 samples collected before incident NTM infection and 28 samples collected following incident NTM infection) were compared to 27 samples from 27 subjects without NTM infection. Sputum samples were analyzed with 16S rRNA gene sequencing and metabolomics. A supervised classification and correlation analysis framework (sparse partial least-squares discriminant analysis [sPLS-DA]) was used to identify correlations between the microbial and metabolomic profiles of the NTM cases compared to the NTM-negative controls. Several metabolites significantly differed in the NTM cases compared to controls, including decreased levels of tryptophan-associated and branched-chain amino acid metabolites, while compounds involved in phospholipid metabolism displayed increased levels. When the metabolome and microbiome data were integrated by sPLS-DA, the models and component ordinations showed separation between the NTM and control samples. While this study could not determine if the observed differences in sputum metabolites between the cohorts reflect metabolic changes that occurred as a result of the NTM infection or metabolic features that contributed to NTM acquisition, it is hypothesis generating for future work to investigate host and bacterial community factors that may contribute to NTM infection risk in CF. IMPORTANCE Host risk factors for nontuberculous mycobacterial (NTM) infection in people with cystic fibrosis (CF) are largely unclear. The goal of this study was to help identify potential host and bacterial community risk factors for NTM infection in people with CF, using microbiome and metabolome data from CF sputum samples. The data obtained in this study identified several metabolic profile differences in sputum associated with NTM infection in CF, including 2-methylcitrate/homocitrate and selected ceramides. These findings represent potential risk factors and therapeutic targets for preventing and/or treating NTM infections in people with CF.

Effects of Fluticasone Propionate on Klebsiella pneumoniae and Gram-Negative Bacteria Associated with Chronic Airway Disease.

Inhaled corticosteroids (ICS) are commonly prescribed first-line treatments for asthma and chronic obstructive pulmonary disease (COPD). Recent evidence has shown that ICS use is associated with changes in the airway microbiome, which may impact clinical outcomes such as potential increased risk for pneumonia in COPD. Although the immunomodulatory effects of corticosteroids are well appreciated, whether ICS could directly influence the behavior of respiratory tract bacteria has been unknown. In this pilot study we explored the effects of fluticasone proprionate, a commonly prescribed inhaled corticosteroid, on respiratory bacteria with an expanded focus on Klebsiella pneumoniae, a species previously implicated in fluticasone-associated pneumonia in COPD. We observed significant effects of fluticasone proprionate on growth responses of K. pneumoniae, as well as other bacterial species isolated from asthmatic patients. Fluticasone-exposed K. pneumoniae displayed altered expression of several bacterial genes and reduced the metabolic activity of bronchial epithelial cells and their expression of human ß-defensin 2. Targeted assays identified a fluticasone metabolite from fluticasone-exposed K. pneumoniae cells, suggesting this species may be capable of metabolizing fluticasone proprionate. Collectively, these observations support the hypothesis that specific members of the airway microbiota possess the functional repertoire to respond to or potentially utilize corticosteroids in their microenvironment. These findings lay a foundation for novel research directions into the potential direct effects of ICS, often prescribed long term to patients, on the broader airway microbial community and on the behavior of specific microbial species implicated in asthma and COPD outcomes. IMPORTANCE Inhaled corticosteroids are widely prescribed for many respiratory diseases, including asthma and COPD. While they benefit many patients, corticosteroids can also have negative effects. Some patients do not improve with treatment and even experience adverse side effects. Recent studies have shown that inhaled corticosteroids can change the make-up of bacteria in the human respiratory tract. However, whether these medications can directly impact the behavior of such bacteria has been unknown. Here, we explored the effects of fluticasone propionate, a commonly prescribed inhaled corticosteroid, on Klebsiella pneumoniae and other airway bacteria of interest, including primary species isolated from adult asthma patients. We provide evidence of growth responses to direct fluticasone exposure in culture and further examined fluticasone's effects on K. pneumoniae, including gene expression changes and effects of fluticasone-exposed bacteria on airway cells. These findings indicate that members of the human airway bacterial community possess the functional ability to respond to corticosteroids, which may have implications for the heterogeneity of treatment response observed clinically.

Changes in airway bacterial communities occur soon after initiation of antibiotic treatment of pulmonary exacerbations in cystic fibrosis.

Chronic polymicrobial airway infections are a hallmark of cystic fibrosis (CF) lung disease. Antibiotic therapy is a primary treatment of CF pulmonary exacerbations (PEx); however, the impact of episodic antibiotic treatment on airway bacterial communities has not been well described. We analyzed sputum samples from adults with CF obtained immediately before and during antibiotic treatment of PEx. Sequencing of the V4 region of the bacterial 16S ribosomal RNA gene was used to assess changes in bacterial community structure during antibiotic treatment. The peak impact of antibiotic treatment was observed by day four or five of treatment. These findings advance our understanding of bacterial community dynamics during antibiotic treatment of PEx and complement recent and ongoing studies evaluating the optimal duration of antibiotic therapy for PEx.