Background Most microorganisms that comprise the lung microbiome use several iron acquisition strategies for survival. Iron and iron associated proteins are higher in the bronchoalveolar lavage fluid (BALF) of smokers with and without chronic obstructive pulmonary disease (COPD) associating with disease activity and severity. In this study we assessed whether smokers with or without COPD who have increased airway iron display a specifically altered airway microbiome. Methods 16S rRNA gene sequencing of BALF from individuals (n=181) enrolled in the bronchoscopy sub-study of the SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS) was paired with measured levels of BALF iron, the iron storage protein ferritin (ex-ferritin) and the host-iron siderophore lipocalin-2 (LCN-2) in matched participants. Results Overall, iron, ex-ferritin and LCN-2 levels were not associated with ecological diversity metrics in the BALF of participants enrolled in this cohort. However, in a differential analysis, specific taxa were found to be enriched in never-smokers with low BALF-ex-ferritin and also in smokers with high BALF-iron. In participants with COPD, an unclassified taxon, Bacteroidetes- OTU0063, was enriched in those with high BALF-iron and those with low BALF-LCN-2. Furthermore, COPD participants with high historical exacerbations and high BALF-LCN-2 showed enrichment for Streptococcus and Leptotrichia . Leptotrichia was also enriched in participants with COPD and high historical exacerbations with elevated BALF-iron and BALF-ex-ferritin. Additionally, symptomatic individuals with COPD, who had high BALF-ex-ferritin had enrichment with Moraxella . Conclusion The above data suggests that abundance of iron, and iron binding proteins may be linked to the presence of both oral commensals as well as potentially pathogenic microorganisms in the lower airways of individuals with COPD.

Background:Most microorganisms that make up the lung microbiome use several iron acquisition strategies for survival. Iron levels and iron-associated proteins are higher in the bronchoalveolar lavage fluid (BALF) of smokers with and smokers without COPD associating with disease severity. In this study we assessed whether or not smokers with and smokers without COPD who have increased airway iron display a specifically altered airway microbiome. Methods:16S ribosomal RNA gene sequencing of BALF from individuals (n=181) enrolled in the bronchoscopy substudy of the SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS) was paired with measured levels of BALF iron, the iron storage protein ferritin (ex-ferritin) and the host-iron siderophore lipocalin-2 (LCN-2) in matched participants. Results:Overall, iron, ex-ferritin and LCN-2 levels were not associated with ecological diversity metrics in the BALF of participants enrolled in this cohort. However, in a differential analysis, specific taxa were found to be enriched in never-smokers with low BALF ex-ferritin levels and also in smokers with high BALF iron levels. Of participants with COPD, an unclassified taxon, Bacteroidetes-OTU0063, was enriched in those with a high BALF iron level and those with a low BALF LCN-2 level. Furthermore, COPD participants with a high number of historical exacerbations and a high BALF LCN-2 level showed enrichment for Streptococcus and Leptotrichia. Leptotrichia was also enriched in participants with COPD and a high number of historical exacerbations with elevated BALF iron and BALF ex-ferritin levels. In addition, symptomatic individuals with COPD who had a high BALF ex-ferritin level had enrichment with Moraxella. Conclusion:The above data suggest that abundance of iron and iron-binding proteins may be linked to the presence of both oral commensals and potentially pathogenic microorganisms in the lower airways of individuals with COPD.

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.

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.

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 com-munity 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.(c) 2023 European Cystic Fibrosis Society. Published by Elsevier B.V. All rights reserved.

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 5 .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-1b, GM-CSF, IL-8, TNF-a, 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. (J Allergy Clin Immunol 2023;151:931-42.)

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.

Inhaled corticosteroids are widely prescribed for many respiratory diseases, including asthma and COPD. While they benefit many patients, corticosteroids can also have negative effects.

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.

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.

Rationale: Pulmonary infections with nontuberculous mycobacteria (NTM) are increasingly prevalent in people with cystic fibrosis (CF). Clinical outcomes following NTM acquisition are highly variable, ranging from transient self-resolving infection to NTM pulmonary disease associated with significant morbidity. Relationships between airway microbiota and variability of NTM outcomes in CF are unclear. Objective: To identify features of CF airway microbiota associated with outcomes of NTM infection. Methods: 188 sputum samples, obtained from 24 subjects with CF, each with three or more samples collected from 3.5 years prior to, and up to 6 months following incident NTM infection, were selected from a sample repository. Sputum DNA underwent bacterial 16S rRNA gene sequencing. Airway microbiota were compared based on the primary outcome, a diagnosis of NTM pulmonary disease, using Wilcoxon rank-sum testing, autoregressive integrated moving average modelling and network analyses. Measurements and main results: Subjects with and without NTM pulmonary disease were similar in clinical characteristics, including age and lung function at the time of incident NTM infection. Time-series analyses of sputum samples prior to incident NTM infection identified positive correlations between Pseudomonas, Streptococcus, Veillonella, Prevotella and Rothia with diagnosis of NTM pulmonary disease and with persistent NTM infection. Network analyses identified differences in clustering of taxa between subjects with and without NTM pulmonary disease, and between subjects with persistent versus transient NTM infection. Conclusions: CF airway microbiota prior to incident NTM infection are associated with subsequent outcomes, including diagnosis of NTM pulmonary disease, and persistence of NTM infection. Associations between airway microbiota and NTM outcomes represent targets for validation as predictive markers and for future therapies.

Chronic obstructive pulmonary disease (COPD) is heterogeneous in development, progression, and phenotypes. Little is known about the lung microbiome, sampled by bronchoscopy, in milder COPD and its relationships to clinical features that reflect disease heterogeneity (lung function, symptom burden, and functional impairment). Using bronchoalveolar lavage fluid collected from 181 never-smokers and ever-smokers with or without COPD (GOLD 0-2) enrolled in the SubPopulations and InteRmediate Outcome Measures In COPD Study (SPIROMICS), we find that lung bacterial composition associates with several clinical features, in particular bronchodilator responsiveness, peak expiratory flow rate, and forced expiratory flow rate between 25 and 75% of FVC (FEF25–75). Measures of symptom burden (COPD Assessment Test) and functional impairment (six-minute walk distance) also associate with disparate lung microbiota composition. Drivers of these relationships include members of the Streptococcus, Prevotella, Veillonella, Staphylococcus, and Pseudomonas genera. Thus, lung microbiota differences may contribute to airway dysfunction and airway disease in milder COPD.

Culture-independent studies of the cystic fibrosis (CF) airway microbiome typically rely on expectorated sputum to assess the microbial makeup of lower airways. These studies have revealed rich bacterial communities. There is often considerable overlap between taxa observed in sputum and those observed in saliva, raising questions about the reliability of expectorated sputum as a sample representing lower airway microbiota. These concerns prompted us to compare pairs of sputum and saliva samples from 10 persons with CF. Using 16S rRNA gene sequencing and droplet digital PCR (ddPCR), we analyzed 37 pairs of sputum and saliva samples, each collected from the same person on the same day. We developed an in silico postsequencing decontamination procedure to remove from sputum the fraction of DNA reads estimated to have been contributed by saliva during expectoration. We demonstrate that while there was often sizeable overlap in community membership between sample types, expectorated sputum typically contains a higher bacterial load and a less diverse community compared to saliva. The differences in diversity between sputum and saliva were more pronounced in advanced disease stage, owing to increased relative abundance of the dominant taxa in sputum. Our effort to model saliva contamination of sputum in silico revealed generally minor effects on community structure after removal of contaminating reads. Despite considerable overlap in taxa observed between expectorated sputum and saliva samples, the impact of saliva contamination on measures of lower airway bacterial community composition in CF using expectorated sputum appears to be minimal. IMPORTANCE Cystic fibrosis is an inherited disease characterized by chronic respiratory tract infection and progressive lung disease. Studies of cystic fibrosis lung microbiology often rely on expectorated sputum to reflect the microbiota present in the lower airways. Passage of sputum through the oropharynx during collection, however, contributes microbes present in saliva to the sample, which could confound interpretation of results. Using culture-independent DNA sequencing-based analyses, we characterized the bacterial communities in pairs of expectorated sputum and saliva samples to generate a model for "decontaminating" sputum in silico. Our results demonstrate that salivary contamination of expectorated sputum does not have a large effect on most sputum samples and that observations of high bacterial diversity likely accurately reflect taxa present in cystic fibrosis lower airways.

Aim: To determine treatment determinants of the respiratory microbiome in COPD patients. Methods: Stable COPD outpatients were enrolled, respiratory samples from oropharynx (oropharyngeal swab), bronchial tree (sputum), and lung (bronchoalveolar lavage) were collected and 16SrRNA gene was amplified and analyzed (Ilumina). Results: 59 COPD patients were included in the study, 24 of them reporting frequent exacerbations (≥2) the previous year. Current smoking and lung function (FEV1%) were related to a significantly different microbiome composition in the oropharynx (p<0.01 and p=0.013, distance-based PERMANOVA), and the bronchial tree (p<0.01). Frequent exacerbators reported higher antibiotic use the previous year (100% vs. 40%, p<0.001, chi-square test), and showed also a different microbial composition in both the oropharynx and the bronchial tree (p=0.0443 and p=0.0135). Patients who had used inhaled corticosteroids in the previous year showed a distinct bacterial community structure in the oropharynx (p=0.0297) and the bronchial tree (p=0.0023), and these relationships remained significant after adjustment for smoking, lung function and exacerbation frequency. Other inhaled therapies (beta-adrenergic agonists and anticolinergics) were not associated with differences in bacterial composition. The assessed clinical characteristics were not associated with a different microbial composition in the lung. Conclusion: In addition to current smoking, chronic use of inhaled corticosteroids is a determinant of the oropharyngeal and bronchial tree microbiota, and these relationships are independent of exacerbation frequency. Funded by FIS PI15/00167, BA18/0041, PI15/02042, OI18/00934 and Pla Armengol Foundation.

Sepsis commonly leads to both acute and chronic brain dysfunction (1). Numerous mechanisms likely underlie brain dysfunction during and after sepsis, including oxidative stress, microvascular and blood–brain barrier dysfunction, neurotransmitter imbalance, and neuroinflammation (2). Our understanding of these mechanisms is based on rodent studies, but there are important intrinsic differences between humans and animal models in both the response to sepsis and brain gene expression (3). Furthermore, it is likely that the human brain, supported by modern critical care, encounters significantly greater physiologic and metabolic insults during the course of critical illness than is accounted for in animal models. Postmortem studies of the human brain have provided valuable insights into the neuropathology of sepsis, including ischemia, hemorrhage, neuroaxonal injury, and innate immune activation (4). Microglia, the innate immune cells of the brain, are activated in sepsis, and markers of inflammation are increased in microglia, astrocytes, and endothelial cells (4). Although these studies have highlighted the activation of classical inflammatory mechanisms using immunohistochemical methods, only a single study has undertaken a molecular analysis of signaling in the human brain during systemic infection (5). Moreover, focused analyses cannot reveal whether innate immune activation is the predominant neuropathologic process during sepsis, or whether brain-specific pathways are also highly differentially regulated. An unbiased postmortem analysis of sepsis-related brain expression in patients requires high-quality frozen brain tissue from patients with a well-characterized cause of death and a spectrum of chronic neuropathology. We identified 89 subjects in the autopsy cohort of the ACT (Adult Changes in Thought) study who died while hospitalized (6). We determined cause of death by reviewing hospital records of the subjects’ terminal hospitalization using a structured instrument, with attention to evidence of infection (7). Patients with an acute structural brain injury at the time of death or with an indeterminate cause of death due to multifactorial critical illness were excluded. We identified 12 subjects who died of sepsis and 12 who died of a noninfectious critical illness. RNA was isolated from the parietal cortex gray matter. Age, underlying neuropathology as measured by Braak and Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) scores, dementia diagnosis, and RNA integrity were balanced between subjects with sepsis and control subjects without sepsis (Table 1). The University of Michigan Advanced Genomics Core prepared complementary DNA libraries (picoinput rRNA depletion complementary DNA kit; Takara Bio) and sequenced libraries using the Illumina Hi-Seq platform. Fragments per kilobase of transcript per million mapped reads (FPKM) values were normalized using the default setting, “classic fpkm,” within CuffDiff. Details regarding alignment, quality control, and the full differential expression analysis are available online (8), and individual-level gene count data are available at the National Center for Biotechnology Information Gene Expression Omnibus (accession number GSE135838). Some of the results of this study have been previously reported in abstract form (9). A total of 176 genes were considered significant or differentially expressed when the fold change was .1.5 and the BenjaminiHochberg false discovery rate was ,0.05. Notably, the most differentially expressed genes were immune related, including damage-associated molecular patterns (DAMPs) (S100A8, S100A9, and members of the HSP family), markers of astrocyte activation (HSPB2, GBP2, and SERPINA3), and macrophage and microglial markers (SOC3, CHI3L2, and CHI3L1). Prior studies of brain gene expression that did not take cause of death into account found that RNA integrity is significantly related to gene expression patterns (6). Likewise, we hypothesize that underlying neuropathology, such as amyloid plaques and neurofibrillary tangles, may also drive gene expression (5). Given the difficulty of obtaining suitable, well-annotated brain specimens from patients with sepsis, our modest sample was not suitable for a multivariate analysis of differential gene expression. We therefore performed dimensional reduction with weighted gene coexpression network analysis (WGCNA), which is robust for small sample sizes, reduces the burden of multiple hypothesis testing, and identifies clusters or networks of genes that are potentially dysregulated (10). WGCNA generated 35 modules of covarying genes. The association of module expression with sepsis, age, sex, RNA integrity, Braak score for neurofibrillary tangles, CERAD score for amyloid plaques, and dementia diagnosis was tested in multiple univariate analyses. The modules, their associated gene lists, and association with covariates are available online (8). Six modules were significantly correlated (P< 0.05) with sepsis (Table 2).

Rationale: Differences in cystic fibrosis (CF) airway microbiota between periods of clinical stability and exacerbation of respiratory symptoms have been investigated in efforts to better understand microbial triggers of CF exacerbations. Prior studies have often relied on a single sample or a limited number of samples to represent airway microbiota. However, the variability in airway microbiota during periods of clinical stability is not well known. Objectives: To determine the temporal variability of measures of airway microbiota during periods of clinical stability, and to identify factors associated with this variability. Methods: Sputum samples (N= 527), obtained daily from six adults with CF during 10 periods of clinical stability, underwent sequencing of the V4 region of the bacterial 16S ribosomal RNA gene. The variability in airway microbiota among samples within each period of clinical stability was calculated as the average of the Bray-Curtis similarity measures of each sample to every other sample within the same period. Outlier samples were defined as samples outside 1.5 times the interquartile range within a baseline period with respect to the average Bray-Curtis similarity. Total bacterial load was measured with droplet digital polymerase chain reaction. Results: The variation in Bray-Curtis similarity and total bacterial load among samples within the same baseline period was greater than the variation observed in technical replicate control samples. Overall, 6% of samples were identified as outliers. Within baseline periods, changes in bacterial community structure occurred coincident with changes in maintenance antibiotics (P < 0.05, analysis of molecular variance). Within subjects, bacterial community structure changed between baseline periods (P < 0.01, analysis of molecular variance). Sample-to-sample similarity within baseline periods was greater with fewer interval days between sampling. Conclusions: During periods of clinical stability, airway bacterial community structure and bacterial load vary among daily sputum samples from adults with CF. This day-to-day variation has bearing on study design and interpretation of results, particularly in analyses that rely on single samples to represent periods of interest (e.g., clinical stability vs. pulmonary exacerbation). These data also emphasize the importance of accounting for maintenance antibiotic use and granularity of sample collection in studies designed to assess the dynamics of CF airway microbiota relative to changes in clinical state.