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.

The Lung Microbiome Predicts Mortality and Response to Azithromycin in Lung Transplant Recipients with Chronic Rejection.

Rationale: Chronic lung allograft dysfunction (CLAD) is the leading cause of death after lung transplant, and azithromycin has variable efficacy in CLAD. The lung microbiome is a risk factor for developing CLAD, but the relationship between lung dysbiosis, pulmonary inflammation, and allograft dysfunction remains poorly understood. Whether lung microbiota predict outcomes or modify treatment response after CLAD is unknown. Objectives: To determine whether lung microbiota predict post-CLAD outcomes and clinical response to azithromycin. Methods: Retrospective cohort study using acellular BAL fluid prospectively collected from recipients of lung transplant within 90 days of CLAD onset. Lung microbiota were characterized using 16S rRNA gene sequencing and droplet digital PCR. In two additional cohorts, causal relationships of dysbiosis and inflammation were evaluated by comparing lung microbiota with CLAD-associated cytokines and measuring ex vivo P. aeruginosa growth in sterilized BAL fluid. Measurements and Main Results: Patients with higher bacterial burden had shorter post-CLAD survival, independent of CLAD phenotype, azithromycin treatment, and relevant covariates. Azithromycin treatment improved survival in patients with high bacterial burden but had negligible impact on patients with low or moderate burden. Lung bacterial burden was positively associated with CLAD-associated cytokines, and ex vivo growth of P. aeruginosa was augmented in BAL fluid from transplant recipients with CLAD. Conclusions: In recipients of lung transplants with chronic rejection, increased lung bacterial burden is an independent risk factor for mortality and predicts clinical response to azithromycin. Lung bacterial dysbiosis is associated with alveolar inflammation and may be promoted by underlying lung allograft dysfunction.

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.

SNIKT: sequence-independent adapter identification and removal in long-read shotgun sequencing data.

SUMMARY: Here, we introduce SNIKT, a command-line tool for sequence-independent visual confirmation and input-assisted removal of adapter contamination in whole-genome shotgun or metagenomic shotgun long-read sequencing DNA or RNA data. AVAILABILITY AND IMPLEMENTATION: SNIKT is implemented in R and is compatible with Unix-like platforms. The source code, along with documentation, is freely available under an MIT license at https://github.com/piyuranjan/SNIKT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

AMAISE: a machine learning approach to index-free sequence enrichment.

Metagenomics holds potential to improve clinical diagnostics of infectious diseases, but DNA from clinical specimens is often dominated by host-derived sequences. To address this, researchers employ host-depletion methods. Laboratory-based host-depletion methods, however, are costly in terms of time and effort, while computational host-depletion methods rely on memory-intensive reference index databases and struggle to accurately classify noisy sequence data. To solve these challenges, we propose an index-free tool, AMAISE (A Machine Learning Approach to Index-Free Sequence Enrichment). Applied to the task of separating host from microbial reads, AMAISE achieves over 98% accuracy. Applied prior to metagenomic classification, AMAISE results in a 14-18% decrease in memory usage compared to using metagenomic classification alone. Our results show that a reference-independent machine learning approach to host depletion allows for accurate and efficient sequence detection.

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.

Rapid identification of pathogens associated with ventilator-associated pneumonia by Nanopore sequencing.

BACKGROUND: Aetiology detection is crucial in the diagnosis and treatment of ventilator-associated pneumonia (VAP). However, the detection method needs improvement. In this study, we used Nanopore sequencing to build a quick detection protocol and compared the efficiency of different methods for detecting 7 VAP pathogens. METHODS: The endotracheal aspirate (ETA) of 83 patients with suspected VAP from Peking University Third Hospital (PUTH) was collected, saponins were used to deplete host genomes, and PCR- or non-PCR-amplified library construction methods were used and compared. Sequence was performed with MinION equipment and local data analysis methods were used for sequencing and data analysis. RESULTS: Saponin depletion effectively removed 11 of 12 human genomes, while most pathogenic bacterial genome results showed no significant difference except for S. pneumoniae. Moreover, the average sequence time decreased from 19.6 h to 3.62 h. The non-PCR amplification method and PCR amplification method for library build has a similar average sensitivity (85.8% vs. 86.35%), but the non-PCR amplification method has a better average specificity (100% VS 91.15%), and required less time. The whole method takes 5-6 h from ETA extraction to pathogen classification. After analysing the 7 pathogens enrolled in our study, the average sensitivity of metagenomic sequencing was approximately 2.4 times higher than that of clinical culture (89.15% vs. 37.77%), and the average specificity was 98.8%. CONCLUSIONS: Using saponins to remove the human genome and a non-PCR amplification method to build libraries can be used for the identification of pathogens in the ETA of VAP patients within 6 h by MinION, which provides a new approach for the rapid identification of pathogens in clinical departments.

SquiggleNet: real-time, direct classification of nanopore signals.

We present SquiggleNet, the first deep-learning model that can classify nanopore reads directly from their electrical signals. SquiggleNet operates faster than DNA passes through the pore, allowing real-time classification and read ejection. Using 1 s of sequencing data, the classifier achieves significantly higher accuracy than base calling followed by sequence alignment. Our approach is also faster and requires an order of magnitude less memory than alignment-based approaches. SquiggleNet distinguished human from bacterial DNA with over 90% accuracy, generalized to unseen bacterial species in a human respiratory meta genome sample, and accurately classified sequences containing human long interspersed repeat elements.

Complete Genome Sequences of Pseudomonas lundensis Strains M101 and M105, Isolated from 1% Pasteurized Milk.

Here, we report the complete genome sequences of two strains of Pseudomonas lundensis, M101 and M105, which were isolated from 1% pasteurized milk. Long-read sequencing was performed using a MinION sequencer, and reads were assembled into circular chromosomes of 4,842,187 bp and 4,814,486 bp for M101 and M105, respectively. Both strains had additional plasmid sequences.

Metagenomics to Identify Pathogens in Diabetic Foot Ulcers and the Potential Impact for Clinical Care.

PURPOSE OF REVIEW: Diabetes mellitus may affect every third adult American by 2050, and about one-third will develop a diabetic foot ulcer (DFU) during their lifetime. The current standard of care results in healing of less than 50% of all DFUs. Many individuals with DFU develop limb-threatening infection which place them at risk for additional morbidity and mortality. We review research associated with culture-independent next-generation sequencing techniques pertaining to diabetic foot ulcers and their potential for clinical application. RECENT FINDINGS: Diabetic foot ulcers are a growing problem and clinicians are limited by their reliance on conventional culture. Metagenomic sequencing technology provides an unparalleled viewpoint of the polymicrobial constituency of DFU. The microbiome techniques used to study the microbial constituency of DFU may offer insight to improve care for these patients, but without standardized approaches in research based on real-world clinical practices, a significant knowledge gap will remain.

Toll-like receptors, environmental caging, and lung dysbiosis.

Recent studies have implicated lung microbiota in shaping local alveolar immune responses. Toll-like receptors are major sensors of microbiota and determinants of local epithelial homeostasis. The impact of toll-like receptor deficiency on lung microbiota is unknown. To determine whether the absence of toll-like receptors results in altered lung microbiota or dysbiosis, we compared lung microbiota in wild-type and toll-like receptor-deficient experimental mice using 16S ribosomal RNA gene quantification and sequencing. We used a randomized environmental caging strategy to determine the impact of toll-like receptors on lung microbiota. Lung microbiota are detectable in toll-like receptor-deficient experimental mice and exhibit considerable variability. The lung microbiota of toll-like receptor-deficient mice are altered in community composition (PERMANOVA P < 0.001), display reduced diversity (t test P = 0.0075), and bacterial burden (t test P = 0.016) compared with wild-type mice with intact toll-like receptors and associated signaling pathways. The lung microbiota of wild-type mice when randomized to cages with toll-like receptor-deficient mice converged with no significant difference in community composition (PERMANOVA P > 0.05) after 3 wk of cohousing. The lung microbiome of toll-like receptor-deficient mice is distinct from wild-type mice and may be less susceptible to the effects of caging as an environmental variable. Our observations support a role for toll-like receptor signaling in the shaping of lung microbiota.

High-Quality Genome Reconstruction of Candida albicans CHN1 Using Nanopore and Illumina Sequencing and Hybrid Assembly.

We report an improved, nearly closed, high-quality draft genome reconstruction of the Candida albicans CHN1 strain (ATCC MYA-4779), a human isolate, using Illumina and Nanopore sequencing. Covering six complete and two partial nuclear chromosomes along with a partial mitochondrial genome, this assembly is 14,787,852 bases in size, with 5,935 genes.

Lung microbiota associations with clinical features of COPD in the SPIROMICS cohort.

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.

Lung microbiota predict chronic rejection in healthy lung transplant recipients: a prospective cohort study.

BACKGROUND: Alterations in the respiratory microbiome are common in chronic lung diseases, correlate with decreased lung function, and have been associated with disease progression. The clinical significance of changes in the respiratory microbiome after lung transplant, specifically those related to development of chronic lung allograft dysfunction (CLAD), are unknown. The aim of this study was to evaluate the effect of lung microbiome characteristics in healthy lung transplant recipients on subsequent CLAD-free survival. METHODS: We prospectively studied a cohort of lung transplant recipients at the University of Michigan (Ann Arbor, MI, USA). We analysed characteristics of the respiratory microbiome in acellular bronchoalveolar lavage fluid (BALF) collected from asymptomatic patients during per-protocol surveillance bronchoscopy 1 year after lung transplantation. For our primary endpoint, we evaluated a composite of development of CLAD or death at 500 days after the 1-year surveillance bronchoscopy. Our primary microbiome predictor variables were bacterial DNA burden (total 16S rRNA gene copies per mL of BALF, quantified via droplet digital PCR) and bacterial community composition (determined by bacterial 16S rRNA gene sequencing). Patients' lung function was followed serially at least every 3 months by spirometry, and CLAD was diagnosed according to International Society of Heart and Lung Transplant 2019 guidelines. FINDINGS: We analysed BALF from 134 patients, collected during 1-year post-transplant surveillance bronchoscopy between Oct 21, 2005, and Aug 25, 2017. Within 500 days of follow-up from the time of BALF sampling, 24 (18%) patients developed CLAD, five (4%) died before confirmed development of CLAD, and 105 (78%) patients remained CLAD-free with complete follow-up. Lung bacterial burden was predictive of CLAD development or death within 500 days of the surveillance bronchoscopy, after controlling for demographic and clinical factors, including immunosuppression and bacterial culture results, in a multivariable survival model. This relationship was evident when burden was analysed as a continuous variable (per log10 increase in burden, HR 2·49 [95% CI 1·38-4·48], p=0·0024) or by tertiles (middle vs lowest bacterial burden tertile, HR 4·94 [1·25-19·42], p=0·022; and highest vs lowest, HR 10·56 [2·53-44·08], p=0·0012). In patients who developed CLAD or died, composition of the lung bacterial community significantly differed to that in patients who survived and remained CLAD-free (on permutational multivariate analysis of variance, p=0·047 at the taxonomic level of family), although differences in community composition were associated with bacterial burden. No individual bacterial taxa were definitively associated with CLAD development or death. INTERPRETATION: Among asymptomatic lung transplant recipients at 1-year post-transplant, increased lung bacterial burden is predictive of chronic rejection and death. The lung microbiome represents an understudied and potentially modifiable risk factor for lung allograft dysfunction. FUNDING: US National Institutes of Health, Cystic Fibrosis Foundation, Brian and Mary Campbell and Elizabeth Campbell Carr research gift fund.

Lung and gut microbiota are altered by hyperoxia and contribute to oxygen-induced lung injury in mice.

Inhaled oxygen, although commonly administered to patients with respiratory disease, causes severe lung injury in animals and is associated with poor clinical outcomes in humans. The relationship between hyperoxia, lung and gut microbiota, and lung injury is unknown. Here, we show that hyperoxia conferred a selective relative growth advantage on oxygen-tolerant respiratory microbial species (e.g., Staphylococcus aureus) as demonstrated by an observational study of critically ill patients receiving mechanical ventilation and experiments using neonatal and adult mouse models. During exposure of mice to hyperoxia, both lung and gut bacterial communities were altered, and these communities contributed to oxygen-induced lung injury. Disruption of lung and gut microbiota preceded lung injury, and variation in microbial communities correlated with variation in lung inflammation. Germ-free mice were protected from oxygen-induced lung injury, and systemic antibiotic treatment selectively modulated the severity of oxygen-induced lung injury in conventionally housed animals. These results suggest that inhaled oxygen may alter lung and gut microbial communities and that these communities could contribute to lung injury.

Critical Relevance of Stochastic Effects on Low-Bacterial-Biomass 16S rRNA Gene Analysis.

The bacterial microbiome of human body sites, previously considered sterile, remains highly controversial because it can be challenging to isolate signal from noise when low-biomass samples are being analyzed. We tested the hypothesis that stochastic sequencing noise, separable from reagent contamination, is generated during sequencing on the Illumina MiSeq platform when DNA input is below a critical threshold. We first purified DNA from serial dilutions of Pseudomonas aeruginosa and from negative controls using three DNA purification kits, quantified input using droplet digital PCR, and then sequenced the 16S rRNA gene in four technical replicates. This process identified reproducible contaminant signal that was separable from an irreproducible stochastic noise, which occurred as bacterial biomass of samples decreased. This approach was then applied to authentic respiratory samples from healthy individuals (n = 22) that ranged from high to ultralow bacterial biomass. Using oral rinse, bronchoalveolar lavage (BAL) fluid, and exhaled breath condensate (EBC) samples and matched controls, we were able to demonstrate (i) that stochastic noise dominates sequencing in real-world low-bacterial-biomass samples that contain fewer than 104 copies of the 16S rRNA gene per sample, (ii) that critical examination of the community composition of technical replicates can be used to separate signal from noise, and (iii) that EBC is an irreproducible sampling modality for sampling the microbiome of the lower airways. We anticipate that these results combined with suggested methods for identifying and dealing with noisy communities will facilitate increased reproducibility while simultaneously permitting characterization of potentially important low-biomass communities.IMPORTANCE DNA contamination from external sources (reagents, environment, operator, etc.) has long been assumed to be the main cause of spurious signals that appear under low-bacterial-biomass conditions. Here, we demonstrate that contamination can be separated from another, random signal generated during low-biomass-sample sequencing. This stochastic noise is not reproduced between technical replicates; however, results for any one replicate taken alone could look like a microbial community different from the controls. Using this information, we investigated respiratory samples from healthy humans and determined the narrow range of bacterial biomass where samples transition from producing reproducible microbial sequences to ones dominated by noise. We present a rigorous approach to studies involving low-bacterial-biomass samples to detect this source of noise and provide a framework for deciding if a sample is likely to be dominated by noise. We anticipate that this work will facilitate increased reproducibility in the characterization of potentially important low-biomass communities.