Sputum Microbiome Research Articles

Dynamics of the airway microbiome in response to exposure to particulate matter 2.5 in patients with chronic obstructive pulmonary disease.

Particulate matter (PM) and air pollution have been suggested to be associated with chronic obstructive pulmonary disease (COPD), contributing significantly to global respiratory disease-related mortality. This study aimed to investigate whether seasonal exposure to PM influences dysbiosis in the respiratory microbiota of patients with COPD. Sputum samples were collected four times over 1 year from 102 patients with COPD, and 16S rRNA sequencing was performed. The dynamics of the airway microbiota were analyzed depending on PM exposure levels and season. The PM-low exposure group had higher α-diversity compared to the PM-high exposure group, particularly noted in spring. Some bacterial groups, including seven species such as Treponema socranskii, were more abundant in the low exposure group. Additionally, the bacterial community structure in summer significantly differed from that in other seasons, with significantly increased α-diversity in this season. The difference in the airway microbiome due to PM exposure was prominent in patients with moderate COPD. PM exposure may influence changes in the sputum microbiome depending on exposure levels and seasonal variations. Our results suggest that airway microbiomes could vary with PM exposure according to seasonal trends.

Aggregatibacter is inversely associated with inflammatory mediators in sputa of patients with chronic airway diseases and reduces inflammation in vitro

BackgroundChronic airway disease (CAD) is characterized by chronic airway inflammation and colonization of the lungs by pro-inflammatory pathogens. However, while various other bacterial species are present in the lower airways, it is not fully understood how they influence inflammation. We aimed to identify novel anti-inflammatory species present in lower airway samples of patients with CAD.MethodsPaired sputum microbiome and inflammatory marker data of adults with CAD across three separate cohorts (Australian asthma and bronchiectasis, Scottish bronchiectasis) was analyzed using Linear discriminant analysis Effect Size (LEfSE) and Spearman correlation analysis to identify species associated with a low inflammatory profile in patients.ResultsWe identified the genus Aggregatibacter as more abundant in patients with lower levels of airway inflammatory markers in two CAD cohorts (Australian asthma and bronchiectasis). In addition, the relative abundance of Aggregatibacter was inversely correlated with sputum IL-8 (Australian bronchiectasis) and IL-1β levels (Australian asthma and bronchiectasis). Subsequent in vitro testing, using a physiologically relevant three-dimensional lung epithelial cell model, revealed that Aggregatibacter spp. (i.e. A. actinomycetemcomitans, A. aphrophilus) and their cell-free supernatant exerted anti-inflammatory activity without influencing host cell viability.ConclusionsThese findings suggest that Aggregatibacter spp. might act to reduce airway inflammation in CAD patients.

Linking Microbiota Profiles to Disease Characterization in Common Variable Immunodeficiency: The Case of Granulomatous-Lymphocytic Interstitial Lung Disease.

Background and objectives: Common variable immunodeficiency (CVID) is a primary immunodeficiency characterized by decreased immunoglobulins and recurrent infections, with non-infectious complications such as granulomatous-lymphocytic interstitial lung disease (GLILD) affecting up to 30% of patients. Methods: Using high-throughput 16S rRNA gene sequencing, salivary, sputum, and fecal microbiome from CVID patients with GLILD, comparing them to CVID patients without GLILD-with immune dysregulation (dCVID) and only infections (iCVID)-and healthy controls was analyzed. Results: A total of 41 CVID patients, 7 with GLILD, and 15 healthy donors were included. Global fecal biodiversity was significantly lower in GLILD patients compared to CVID subgroups and controls. GLILD patients harbored different specific bacterial communities in all niches, with some keystone species common to dCVID. Conchiformibius, Micrococcales, and Capnocytophaga are more frequent in the sputum of GLILD patients. Saliva in GLILD shows higher frequencies of Conchiformibius and Haemophilusparainfluenzae. Fecal samples from GLILD patients have higher levels of Gemella morbilorum, Lacticaseibacillus, and Cellulosimicrobium. A non-assigned Conchiformibius spp. is consistently associated with GLILD across different niches and could be a potential pathobiont or relevant microbiological marker for GLILD. Cluster network and correlation analyses show profound dysbiosis in the sputum, saliva, and feces of GLILD patients. Conclusions: These findings highlight significant microbiome alterations in CVID patients with GLILD, particularly in the respiratory tract, suggesting a possible link to both local and systemic immune dysregulation.

The impact of anti-tuberculosis treatment on respiratory tract microbiome in pulmonary tuberculosis

The growing evidence has underscored the significance of interactions between the host and microbiota in respiratory health, presenting a novel perspective on disease management. Yet, comprehension of the respiratory microbiome shifts before and after anti-tuberculosis treatment is limited. This study compares respiratory microbiome profiles in untreated tuberculosis (UTB) and completed TB treatment (CTB) cases with healthy controls, using 16S rRNA sequencing on sputum samples. Significant reduction in sputum microbial alpha diversity was observed in both TB groups when compared to healthy controls (P < 0.05). Beta diversity analysis showed distinct clustering (P < 0.05). Linear discriminant analysis revealed an abundance of potentially pathogenic bacterial genera like Haemophilus, Pseudomonas, and Mycobacterium in the UTB group, while Streptococcus, Rothia, and Neisseria dominated in CTB samples. Healthy sputum microbiomes were enriched with Prevotella, Fusobacterium, Porphyromonadaceae_unclassified,andPeptostreptococcus. Moreover, predicted bacterial functional pathways showed significant differences among the three groups, mainly related to nutrient metabolism.These findings indicated significant microbial dysbiosis in sputum samples recovered from patients with pulmonary TB with an elevated presence of potentially pathogenic bacteria, depletion of beneficial genera, and downregulation of several essential metabolic pathways. Further exploration of respiratory microbiome-based diagnostic biomarkers and their role in targeted treatment strategies in tuberculosis is warranted.

Subtracting the background by reducing cell-free DNA’s confounding effects on Mycobacterium tuberculosis quantitation and the sputum microbiome

DNA characterisation in people with tuberculosis (TB) is critical for diagnostic and microbiome evaluations. However, extracellular DNA, more frequent in people on chemotherapy, confounds results. We evaluated whether nucleic acid dyes [propidium monoazide (PMA), PEMAX] and DNaseI could reduce this. PCR [16S Mycobacterium tuberculosis complex (Mtb) qPCR, Xpert MTB/RIF] was done on dilution series of untreated and treated (PMA, PEMAX, DNaseI) Mtb. Separately, 16S rRNA gene qPCR and sequencing were done on untreated and treated sputa before (Cohort A: 11 TB-negatives, 9 TB-positives; Cohort B: 19 TB-positives, PEMAX only) and 24-weeks after chemotherapy (Cohort B). PMA and PEMAX reduced PCR-detected Mtb DNA for dilution series and Cohort A sputum versus untreated controls, suggesting non-intact Mtb is present before treatment-start. PEMAX enabled sequencing-based Mycobacterium-detection in 7/12 (58%) TB-positive sputa where no such reads otherwise occurred. In Cohort A, PMA- and PEMAX-treated versus untreated sputa had decreased α- and increased β-diversities. In Cohort B, β-diversity differences between timepoints were only detected with PEMAX. DNaseI had negligible effects. PMA and PEMAX (but not DNaseI) reduced extracellular DNA in PCR and improved pathogen detection by sequencing. PEMAX additionally detected chemotherapy-associated taxonomic changes that would otherwise be missed. Dyes enhance microbiome evaluations especially during chemotherapy.

Metrological evaluation of DNA extraction method effects on the bacterial microbiome and resistome in sputum.

High-throughput sequencing (HTS) is one of the crucial new technologies that gives us insights into previously hidden parts of microbial communities. The DNA extraction method is an important step that can have a major impact on the results, and understanding this impact is of paramount importance for their reliable interpretation. Our results are of great value for the interpretation of sputum microbiome and resistome results obtained by targeted HTS. Our findings allow for a more rational design of future microbiome studies, which would lead to higher repeatability of results and easier comparison between different laboratories. This could also facilitate the introduction of targeted HTS in clinical microbiology for reliable identification of pathogenic bacteria and testing for antimicrobial resistance (AMR). As AMR is a major threat to public health, the improved methods for determining AMR would bring great benefits to both the healthcare system and society as a whole.

Species-level, metagenomic and proteomic analysis of microbe-immune interactions in severe asthma.

The airway microbiome in severe asthma has not been characterised at species-level by metagenomic sequencing, nor have the relationships between specific species and mucosal immune responses in 'type-2 low', neutrophilic asthma been defined. We performed an integrated species-level metagenomic data with inflammatory mediators to characterise prevalence of dominant potentially pathogenic organisms and host immune responses. Sputum and nasal lavage samples were analysed using long-read metagenomic sequencing with Nanopore and qPCR in two cross-sectional adult severe asthma cohorts, Wessex (n = 66) and Oxford (n = 30). We integrated species-level data with clinical parameters and 39 selected airway proteins measured by immunoassay and O-link. The sputum microbiome in health and mild asthma displayed comparable microbial diversity. By contrast, 23% (19/81) of severe asthma microbiomes were dominated by a single respiratory pathogen, namely H. influenzae (n = 10), M. catarrhalis (n = 4), S. pneumoniae (n = 4) and P. aeruginosa (n = 1). Neutrophilic asthma was associated with H. influenzae, M. catarrhalis, S. pneumoniae and T. whipplei with elevated type-1 cytokines and proteases; eosinophilic asthma with higher M. catarrhalis, but lower H. influenzae, and S. pneumoniae abundance. H. influenzae load correlated with Eosinophil Cationic Protein, elastase and IL-10. R. mucilaginosa associated positively with IL-6 and negatively with FGF. Bayesian network analysis also revealed close and distinct relationships of H. influenzae and M. catarrhalis with type-1 airway inflammation. The microbiomes and cytokine milieu were distinct between upper and lower airways. This species-level integrated analysis reveals central, but distinct associations between potentially pathogenic bacteria and airways inflammation in severe asthma.

Relationship between Respiratory Microbiome and Systemic Inflammatory Markers in COPD: A Pilot Study.

The respiratory microbiome may influence the development and progression of COPD by modulating local immune and inflammatory events. We aimed to investigate whether relative changes in respiratory bacterial abundance are also associated with systemic inflammation, and explore their relationship with the main clinical COPD phenotypes. Multiplex analysis of inflammatory markers and transcript eosinophil-related markers were analyzed on peripheral blood in a cohort of stable COPD patients (n = 72). Respiratory microbiome composition was analyzed by 16S rRNA microbial sequencing on spontaneous sputum. Spearman correlations were applied to test the relationship between the microbiome composition and systemic inflammation. The concentration of the plasma IL-8 showed an inverted correlation with the relative abundance of 17 bacterial genera in the whole COPD cohort. COPD patients categorized as eosinophilic showed positive relationships with blood eosinophil markers and inversely correlated with the degree of airway obstruction and the number of exacerbations during the previous year. COPD patients categorized as frequent exacerbators were enriched with the bacterial genera Pseudomonas which, in turn, was positively associated with the severity of airflow limitation and the prior year's exacerbation history. The associative relationships of the sputum microbiome with the severity of the disease emphasize the relevance of the interaction between the respiratory microbiota and systemic inflammation.

Country-specific differences in the lung tumour microbiome.

e20044 Background: Sputum examination for the early detection of lung cancer continues to be explored ( Thunnissen 2003 ), however has so far resulted in a low diagnostic yield. An increasing number of studies have evidenced a mechanistic role of the lung microbiome in the induction of lung cancer ( Liu et al., 2020 ), and the presence of bacterial biomarkers in bronchoalveolar lavage and sputum have been proposed ( Cheng et al., 2020 ). It is currently unknown whether the sputum microbiome can appropriately capture the lung tumour microbiome. Given that the lung microbiome may vary by geography, this analysis was considered over two populations: China and Italy. Methods: Microbiome analysis was performed using BioCorteX’s industry-leading knowledge graph and integrated proprietary engines v20240126_153156. Data from 9 studies was used. In total, 461 samples from lung cancer patients in China (250 sputum, 211 tumour) and 60 from Italy (34 sputum, 26 tumour) were considered. Shannon’s diversity was used to consider within-sample diversity, and dimensional scaling based (MDS) on Bray-Curtis diversity was used to consider between-sample diversity. Results: All sample were checked to have a library size greater than 100, and more than 10 unique species. Only those species present in over 1% of samples were considered to reduce noise. In both China and Italy, tumour samples had a lower diversity than sputum samples (p = 0.00016 and p &lt; 2.2e-16, respectively). Samples from Italy had a lower diversity than samples from China (p &lt; 2.22e-16). In the MDS analysis, the tumour samples from China and Italy formed two distinct, non-overlapping clusters. A third cluster was formed from the union of sputum samples from both countries. These results were independent of any study-specific effects. Conclusions: These results indicate that the sputum microbiome is not representative of the tumour microbiome, and that the divergence of the tumour microbiome away from the sputum microbiome is country specific. The results of this analysis highlight that further work into differences in the lung tumour microbiome by country is warranted, especially in light of the growing body of evidence suggesting a causative role of the microbiome in lung cancer. It is also possible that lung cancer treatments may be impacted by the country-specific nature of the lung tumour microbiome.

Comparison of the sputum microbiome between patients with stable nontuberculous mycobacterial pulmonary disease and patients requiring treatment.

We evaluated whether the sputum bacterial microbiome differs between nontuberculous mycobacteria pulmonary disease (NTM-PD) patients with stable disease not requiring antibiotic treatment and those requiring antibiotics. We collected sputum samples from 21 clinically stable NTM-PD patients (stable group) and 14 NTM-PD patients needing antibiotic treatment (treatment group). We also obtained 13 follow-up samples from the stable group. We analyzed the 48 samples using 16S rRNA gene sequencing (V3-V4 region) and compared the groups. In the linear discriminant analysis effect size (LEfSe) analysis, the species Porphyromonas pasteri, Haemophilus parahaemolyticus, Prevotella nanceiensis, and Gemella haemolysans were significantly more prevalent in the sputum of the stable group compared to the treatment group. No taxa showed significant differences in alpha-/beta-diversity or LEfSe between the 21 baseline and 13 follow-up sputum samples in the stable group. In the stable group, the genus Bergeyella and species Prevotella oris were less common in patients who achieved spontaneous culture conversion (n = 9) compared to those with persistent NTM positivity (n = 12) (effect size 3.04, p = 0.039 for Bergeyella; effect size 3.64, p = 0.033 for P. oris). In the treatment group, H. parainfluenzae was more common in patients with treatment success (n = 7) than in treatment-refractory patients (n = 7) (effect size 4.74, p = 0.013). Our study identified distinct bacterial taxa in the sputum of NTM-PD patients based on disease status. These results suggest the presence of a microbial environment that helps maintain disease stability.

Sputum bacterial microbiota signature as a surrogate for predicting disease progression of nontuberculous mycobacterial lung disease

ObjectivesPredicting progression of nontuberculous mycobacterial lung disease (NTM-LD) remains challenging. This study evaluated whether sputum bacterial microbiome diversity can be the biomarker and provide novel insights into related phenotypes and treatment timing. MethodsWe analyzed 126 sputum microbiomes of 126 patients with newly diagnosed NTM-LD due to Mycobacterium avium complex, M. abscessus complex, and M. kansasii between May 2020 and December 2021. Patients were followed for 2 years to determine their disease progression status. We identified consistently representative genera that differentiated the progressor and nonprogressor by using six methodologies. These genera were used to construct a prediction model using random forest with 5-fold cross validation. ResultsDisease progression occurred in 49 (38.6%) patients. Compared with nonprogressors, α-diversity was lower in the progressors. Significant compositional differences existed in the β-diversity between groups (p=0.001). The prediction model for NTM-LD progression constructed using seven genera (Burkholderia, Pseudomonas, Sphingomonas, Candidatus Saccharibacteria, Phocaeicola, Pelomonas, and Phascolarctobacterium) with significantly differential abundance achieved an area under curve of 0.871. ConclusionsIdentification of the composition of sputum bacterial microbiome facilitates prediction of the course of NTM-LD, and maybe used to develop precision treatment involving modulating the respiratory microbiome composition to ameliorate NTM-LD.

Bacterial interactome disturbance in chronic obstructive pulmonary disease clinical stability and exacerbations

RationaleOur understanding of airway dysbiosis in chronic obstructive pulmonary disease (COPD) remains incomplete, which may be improved by unraveling the complexity in microbial interactome.ObjectivesTo characterize reproducible features of airway bacterial interactome in COPD at clinical stability and during exacerbation, and evaluate their associations with disease phenotypes.MethodsWe performed weighted ensemble-based co-occurrence network analysis of 1742 sputum microbiomes from published and new microbiome datasets, comprising two case-control studies of stable COPD versus healthy control, two studies of COPD stability versus exacerbation, and one study with exacerbation-recovery time series data.ResultsPatients with COPD had reproducibly lower degree of negative bacterial interactions, i.e. total number of negative interactions as a proportion of total interactions, in their airway microbiome compared with healthy controls. Evaluation of the Haemophilus interactome showed that the antagonistic interaction networks of this established pathogen rather than its abundance consistently changed in COPD. Interactome dynamic analysis revealed reproducibly reduced antagonistic interactions but not diversity loss during COPD exacerbation, which recovered after treatment. In phenotypic analysis, unsupervised network clustering showed that loss of antagonistic interactions was associated with worse clinical symptoms (dyspnea), poorer lung function, exaggerated neutrophilic inflammation, and higher exacerbation risk. Furthermore, the frequent exacerbators (≥ 2 exacerbations per year) had significantly reduced antagonistic bacterial interactions while exhibiting subtle compositional changes in their airway microbiota.ConclusionsBacterial interactome disturbance characterized by reduced antagonistic interactions, rather than change in pathogen abundance or diversity, is a reproducible feature of airway dysbiosis in COPD clinical stability and exacerbations, which suggests that we may target interactome rather than pathogen alone for disease treatment.

The airway microbiome of persons with cystic fibrosis correlates with acquisition and microbiological outcomes of incident Stenotrophomonas maltophilia infection.

Chronic infection with Stenotrophomonas maltophilia in persons with cystic fibrosis (pwCF) has been linked to an increased risk of pulmonary exacerbations and lung function decline. We sought to establish whether baseline sputum microbiome associates with risk of S. maltophilia incident infection and persistence in pwCF. pwCF experiencing incident S. maltophilia infections attending the Calgary Adult CF Clinic from 2010-2018 were compared with S. maltophilia-negative sex, age (+/-2 years), and birth-cohort-matched controls. Infection outcomes were classified as persistent (when the pathogen was recovered in ≥50% of cultures in the subsequent year) or transient. We assessed microbial communities from prospectively biobanked sputum using V3-V4 16S ribosomal RNA (rRNA) gene sequencing, in the year preceding (Pre) (n = 57), at (At) (n = 22), and after (Post) (n = 31) incident infection. We verified relative abundance data using S. maltophilia-specific qPCR and 16S rRNA-targeted qPCR to assess bioburden. Strains were typed using pulse-field gel electrophoresis. Twenty-five pwCF with incident S. maltophilia (56% female, median 29 years, median FEV1 61%) with 33 total episodes were compared with 56 uninfected pwCF controls. Demographics and clinical characteristics were similar between cohorts. Among those with incident S. maltophilia infection, sputum communities did not cluster based on infection timeline (Pre, At, Post). Communities differed between the infection cohort and controls (n = 56) based on Shannon Diversity Index (SDI, p = 0.04) and clustered based on Aitchison distance (PERMANOVA, p = 0.01) prior to infection. At the time of incident S. maltophilia isolation, communities did not differ in SDI but clustered based on Aitchison distance (PERMANOVA, p = 0.03) in those that ultimately developed persistent infection versus those that were transient. S. maltophilia abundance within sputum was increased in samples from patients (Pre) relative to controls, measuring both relative (p = 0.004) and absolute (p = 0.001). Furthermore, S. maltophilia abundance was increased in sputum at incident infection in those who ultimately developed persistent infection relative to those with transient infection, measured relatively (p = 0.04) or absolute (p = 0.04), respectively. Microbial community composition of CF sputum associates with S. maltophilia infection acquisition as well as infection outcome. Our study suggests sputum microbiome may serve as a surrogate for identifying infection risk and persistence risk.

Composition of the sputum bacterial microbiome of patients with different pathomorphological forms of non-small-cell lung cancer.

Recent studies have shown that the bacterial microbiome of the respiratory tract influences the development of lung cancer. Changes in the composition of the microbiome are observed in patients with chronic inflammatory processes. Such microbiome changes may include the occurrence of bacteria that cause oxidative stress and that are capable of causing genome damage in the cells of the host organism directly and indirectly. To date, the composition of the respiratory microbiome in patients with various histological variants of lung cancer has not been studied. In the present study, we determined the taxonomic composition of the sputum microbiome of 52 patients with squamous cell carcinoma of the lung, 52 patients with lung adenocarcinoma and 52 healthy control donors, using next-generation sequencing (NGS) on the V3-V4 region of the bacterial gene encoding 16S rRNA. The sputum microbiomes of patients with different histological types of lung cancer and controls did not show significant differences in terms of the species richness index (Shannon); however, the patients differed from the controls in terms of evenness index (Pielou). The structures of bacterial communities (beta diversity) in the adenocarcinoma and squamous cell carcinoma groups were also similar; however, when analyzed according to the matrix constructed by the Bray-Curtis method, there were differences between patients with squamous cell carcinoma and healthy subjects, but not between those with adenocarcinoma and controls. Using the LEFse method it was possible to identify an increase in the content of Bacillota (Streptococcus and Bacillus) and Actinomycetota (Rothia) in the sputum of patients with squamous cell carcinoma when compared with samples from patients with adenocarcinoma. There were no differences in the content of bacteria between the samples of patients with adenocarcinoma and the control ones. The content of representatives of the genera Streptococcus, Bacillus, Peptostreptococcus (phylum Bacillota), Prevotella, Macellibacteroides (phylum Bacteroidota), Rothia (phylum Actinomycetota) and Actinobacillus (phylum Pseudomonadota) was increased in the microbiome of sputum samples from patients with squamous cell carcinoma, compared with the control. Thus, the sputum bacterial microbiome of patients with different histological types of non-small-cell lung cancer has significant differences. Further research should be devoted to the search for microbiome biomarkers of lung cancer at the level of bacterial species using whole-genome sequencing.

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.

Comparing sputum microbiota characteristics between severe and critically ill influenza patients.

Currently, limited attention has been directed toward utilizing clinical cohorts as a starting point to elucidate alterations in the lower respiratory tract (LRT) microbiota following influenza A virus (IAV) infection. Our objective was to undertake a comparative analysis of the diversity and composition of sputum microbiota in individuals afflicted by severe and critically ill influenza patients. Sputum specimens were procured from patients diagnosed with IAV infection for the purpose of profiling the microbiota using 16S-rDNA sequencing. To ascertain taxonomic differences between the severe and critically ill influenza cohorts, we leveraged Linear Discriminant Analysis Effect Size (LEfSe). Additionally, Spearman correlation analysis was employed to illuminate associations between sputum microbiota and influenza Ct values alongside laboratory indicators. Our study encompassed a total cohort of 64 patients, comprising 48 within the severe group and 16 within the critically ill group. Intriguingly, Bacteroidetes exhibited significant depletion in the critically ill cohort (p=0.031). The sputum microbiomes of the severe influenza group were hallmarked by an overrepresentation of Neisseria, Porphyromonas, Actinobacillus, Alloprevotella, TM7x, and Clostridia_UCG-014, yielding ROC-plot AUC values of 0.71, 0.68, 0.60, 0.70, 0.70, and 0.68, respectively. Notably, Alloprevotella exhibited an inverse correlation with influenza Ct values. Moreover, C-reactive protein (CRP) manifested a positive correlation with Haemophilus and Porphyromonas. The outcomes of this investigation lay the groundwork for future studies delving into the connection between the LRT microbiome and respiratory disorders. Further exploration is warranted to elucidate the intricate mechanisms underlying the interaction between IAV and Alloprevotella, particularly in disease progression.

The presence of cystic fibrosis-related diabetes modifies the sputum microbiome in cystic fibrosis disease.

Cystic fibrosis-related diabetes (CFRD) affects 40%-50% of adults with CF and is associated with a decline in respiratory health. The microbial flora of the lung is known to change with the development of CF disease, but how CFRD affects the microbiome has not been described. We analyzed the microbiome in sputa from 14 people with CF, 14 with CFRD, and two who were classed as pre-CFRD by extracting DNA and amplifying the variable V3-V4 region of the microbial 16S ribosomal RNA gene by PCR. Sequences were analyzed and sources were identified to genus level. We found that the α-diversity of the microbiome using Shannon's diversity index was increased in CFRD compared with CF. Bray Curtis dissimilarity analysis showed that there was separation of the microbiomes in CF and CFRD sputa. The most abundant phyla identified in the sputum samples were Firmicutes and Proteobacteria, Actinobacteriota and Bacteroidota, and the ratio of Firmicutes/Bacteroidota was reduced in CFRD compared with CF. Pseudomonas, Azhorizophilus, Porphyromonas, and Actinobacillus were more abundant in CFRD compared with CF, whereas Staphylococcus was less abundant. The relative abundance of these genera did not correlate with age; some correlated with a decline in FEV1/FVC but all correlated with hemoglobin A1C (HbA1c) indicating that development of CFRD mediates further changes to the respiratory microbiome in CF.NEW & NOTEWORTHY Cystic fibrosis-related diabetes (CFRD) is associated with a decline in respiratory health. We show for the first time that there was a change in the sputum microbiome of people with CFRD compared with CF that correlated with markers of raised blood glucose.

Sputum microbiome α-diversity is a key feature of the COPD frequent exacerbator phenotype.

The lung microbiome is an inflammatory stimulus whose role in COPD pathogenesis is incompletely understood. We hypothesised that the frequent exacerbator phenotype is associated with decreased α-diversity and increased lung inflammation. Our objective was to assess correlations between the frequent exacerbator phenotype, the microbiome and inflammation longitudinally during exacerbation-free periods. We conducted a case-control longitudinal observational study of the frequent exacerbator phenotype and characteristics of the airway microbiome. 81 subjects (41 frequent and 40 infrequent exacerbators) provided nasal, oral and sputum microbiome samples at two visits over 2-4 months. Exacerbation phenotype, relevant clinical factors and sputum cytokine values were associated with microbiome findings. The frequent exacerbator phenotype was associated with lower sputum microbiome α-diversity (p=0.0031). This decrease in α-diversity among frequent exacerbators was enhanced when the sputum bacterial culture was positive (p<0.001). Older age was associated with decreased sputum microbiome α-diversity (p=0.0030). Between-visit β-diversity was increased among frequent exacerbators and those who experienced a COPD exacerbation between visits (p=0.025 and p=0.014, respectively). Sputum cytokine values did not differ based on exacerbation phenotype or other clinical characteristics. Interleukin (IL)-17A was negatively associated with α-diversity, while IL-6 and IL-8 were positively associated with α-diversity (p=0.012, p=0.012 and p=0.0496, respectively). IL-22, IL-17A and IL-5 levels were positively associated with Moraxella abundance (p=0.027, p=0.0014 and p=0.0020, respectively). Even during exacerbation-free intervals, the COPD frequent exacerbator phenotype is associated with decreased sputum microbiome α-diversity and increased β-diversity. Decreased sputum microbiome α-diversity and Moraxella abundance are associated with lung inflammation.

Differential airway resistome and its correlations with clinical characteristics in Haemophilus- or Pseudomonas-predominant microbial subtypes of bronchiectasis

The prevalence and clinical correlates of antibiotic resistance genes (ARGs) in bronchiectasis are not entirely clear. We aimed to profile the ARGs in sputum from adults with bronchiectasis, and explore the association with airway microbiome and disease severity and subtypes. In this longitudinal study, we prospectively collected 118 sputum samples from stable and exacerbation visits of 82 bronchiectasis patients and 19 healthy subjects. We profiled ARGs with shotgun metagenomic sequencing, and linked these to sputum microbiome and clinical characteristics, followed by validation in an international cohort. We compared ARG profiles in bronchiectasis according to disease severity, blood and sputum inflammatory subtypes. Unsupervised clustering revealed a Pseudomonas predominant subgroup (n = 16), Haemophilus predominant subgroup (n = 48), and balanced microbiome subgroup (N = 54). ARGs of multi-drug resistance were over-dominant in the Pseudomonas-predominant subgroup, while ARGs of beta-lactam resistance were most abundant in the Haemophilus-predominant subgroup. Pseudomonas-predominant subgroup yielded the highest ARG diversity and total abundance, while Haemophilus–predominant subgroup and balanced microbiota subgroup were lowest in ARG diversity and total abundance. PBP-1A, ksgA and emrB (multidrug) were most significantly enriched in Haemophilus-predominant subtype. ARGs generally correlated positively with Bronchiectasis Severity Index, fluoroquinolone use, and modified Reiff score. 68.6% of the ARG-clinical correlations could be validated in an independent international cohort. In conclusion, ARGs are differentially associated with the dominant microbiome and clinical characteristics in bronchiectasis.

The altered sputum microbiome profile in patients with moderate and severe COPD exacerbations, compared to the healthy group in the Indian population

Background: Microbial culture-independent sequencing techniques have advanced our understanding of host-microbiome interactions in health and disease. The purpose of this study was to explore the dysbiosis of airway microbiota in patients with moderate or severe chronic obstructive pulmonary disease (COPD) and compare them with healthy controls. Methods: The COPD patients were investigated for disease severity based on airflow limitations and divided into moderate (50%≤FEV1&lt;80% predicted) and severe groups (FEV1&lt;50% predicted). Spontaneous sputum samples were collected and, the V3-V4 regions of the 16S rRNA coding gene were sequenced to examine the microbiome profile of COPD and healthy participants. Results: A total of 45 sputum samples were collected from 17 severe COPD, 12 moderate COPD cases, and 16 healthy volunteers. The bacterial alpha diversity (Shannon and Simpson’s index) significantly decreased in the moderate and severe COPD groups, compared to healthy samples. A significantly higher proportion of Firmicutes and Actinobacteria were present in moderate COPD, and Proteobacteria numbers were comparatively increased in severe COPD. In healthy samples, Bacteroidetes and Fusobacteria were more abundant in comparison to both the COPD groups. Among the most commonly detected 20 bacterial genera, Streptococcus was predominant among the COPD sputum samples, whereas Prevotella was the top genus in healthy controls. Linear discriminant analysis (LDA&gt;2) revealed that marker genera like Streptococcus and Rothia were abundant in moderate COPD. For severe COPD, the genera Pseudomonasand Leptotrichia were most prevalent, whereas Fusobacterium and Prevotella were dominant in the healthy group. Conclusions: Our findings suggest a significant dysbiosis of the respiratory microbiome in COPD patients. The decreased microbial diversity may influence the host immune response and provide microbiological biomarkers for the diagnosis and monitoring of COPD.