Multi-omic meta-analysis identifies functional signatures of airway microbiome in chronic obstructive pulmonary disease

Environmental exposures, the oral-lung axis and respiratory health: The airway microbiome goes on stage for the personalized management of human lung function

The human respiratory system is constantly exposed to environmental stimuli, sometimes including toxicants, which can trigger dysregulated lung immune responses that lead to respiratory symptoms, impaired lung function and airway diseases. Evidence supports that the microbiome in the lungs has an indispensable role in respiratory health and disease, acting as a local gatekeeper that mediates the interaction between the environmental cues and respiratory health. Moreover, the microbiome in the lungs is intimately intertwined with the oral microbiome through the oral-lung axis. Here, we discuss the intricate three-way relationship between (i) cigarette smoking, which has strong effects on the microbial community structure of the lung; (ii) microbiome dysbiosis and disease in the oral cavity; and (iii) microbiome dysbiosis in the lung and its causal role in patients suffering chronic obstructive pulmonary disease (COPD), a leading cause of morbidity and mortality worldwide. We highlight exciting outcomes arising from recently established interactions in the airway between environmental exposures, microbiome, metabolites-functional attributes and the host, as well as how these associations have the potential to predict the respiratory health status of the host through an airway microbiome health index. For completion, we argue that incorporating (synthetic) microbial community ecology in our contemporary understanding of lung disease presents challenges and also rises novel opportunities to exploit the oral-lung axis and its microbiome towards innovative airway disease diagnostics, prognostics, patient stratification and microbiota-targeted clinical interventions in the context of current therapies.

Informed development of a multi-species biofilm in chronic obstructive pulmonary disease

Recent evidence indicates that microbial biofilm aggregates inhabit the lungs of COPD patients and actively contribute towards chronic colonization and repeat infections. However, there are no contextually relevant complex biofilm models for COPD research. In this study, a meta-analysis of the lung microbiome in COPD was used to inform development of an optimized biofilm model composed of genera highly associated with COPD. Bioinformatic analysis showed that although diversity matrices of COPD microbiomes were similar to healthy controls, and internal compositions made it possible to accurately differentiate between these cohorts (AUC = 0.939). Genera that best defined these patients included Haemophilus, Moraxella and Streptococcus. Many studies fail to account for fungi; therefore, Candida albicans was included in the creation of an interkingdom biofilm model. These organisms formed a biofilm capable of tolerating high concentrations of antimicrobial therapies with no significant reductions in viability. However, combined therapies of antibiotics and an antifungal resulted in significant reductions in viable cells throughout the biofilm (p < 0.05). This biofilm model is representative of the COPD lung microbiome and results from in vitro antimicrobial challenge experiments indicate that targeting both bacteria and fungi in these interkingdom communities will be required for more positive clinical outcomes.

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

RATIONALE

Our understanding of airway dysbiosis in chronic obstructive pulmonary disease (COPD) remains incomplete, which may be improved by unraveling the complexity in microbial interactome.

OBJECTIVES

To characterize reproducible features of airway bacterial interactome in COPD at clinical stability and during exacerbation, and evaluate their associations with disease phenotypes.

METHODS

We 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.

RESULTS

Patients 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.

CONCLUSIONS

Bacterial 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.

Molecular mechanism of Jianpiyifei II granules in the treatment of chronic obstructive pulmonary disease: Network pharmacology analysis, molecular docking, and experimental assessment

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is defined by persistent airway and lung inflammation, excessive mucus production, remodeling of the airways, and damage to the alveolar tissue. Based on clinical experience, it has been observed that Jianpiyifei II (JPYF II) granules exhibit a significant therapeutic impact on individuals suffering from stable COPD. Nevertheless, the complete understanding of JPYF II's potential mode of action against COPD remains to be further clarified.

PURPOSE

To further investigate the underlying mechanism of JPYF II for treating COPD and clarify the role of the IL-17 pathway in the treatment.

METHODS

A variety of databases were utilized to acquire JPYF II's bioactive components, as well as related targets of JPYF II and COPD. Cytoscape was utilized to establish multiple interaction networks for the purpose of topological analyses and core-target screening. The Metascape was utilized to identify the function of target genes and crucial signaling pathways. To evaluate the interactions between bioactive ingredients and central target proteins, molecular docking simulations were conducted. Following that, a sequence of experiments was conducted both in the laboratory and in living organisms, which included analyzing the cell counts in bronchoalveolar lavage fluid (BALF), examining lung tissue for histopathological changes, conducting immunohistochemistry, RT‒qPCR, ELISA, and Western blotting.

RESULTS

In JPYF II, 88 bioactive ingredients were predicted to have a total of 342 targets. After conducting Venn analysis, it was discovered that 284 potential targets of JPYF II were linked to the provision of defensive benefits against COPD. The PPI network yielded a total of twenty-four core targets. The findings from the analysis of enrichment and gene‒pathway network suggested that JPYF II targeted Hsp90, MAPKs, ERK, AP-1, TNF-α, IL-6, COX-2, CXCL8, and MMP-9 as crucial elements for COPD treatment through the IL-17 pathway. Additionally, JPYF II might modulate MAPK signaling pathways and the downstream transcription factor AP-1 via IL-17 regulation. According to the findings from molecular docking, it was observed that the 24 core target proteins exhibited robust binding affinities towards the top 10 bioactive compounds. Furthermore, the treatment of COPD through the regulation of MAPKs in the IL-17 pathway was significantly influenced by flavonoids and sterols found in JPYF II. In vitro, these observations were further confirmed. In vivo results demonstrated that JPYF II reduced inflammatory cell infiltration in pulmonary tissues and the quantity of inflammatory cells in BALF obtained from LPS- and CS-stimulated mice. Moreover, the administration of JPYF II resulted in the inhibition of IL-17 mRNA and protein levels, phosphorylation levels of MAPK proteins, and expression of phosphorylated AP-1 proteins. It also suppressed the expression of downstream effector genes and proteins associated with the IL-17/MAPK/AP-1 signaling axis in lung tissues and BALF.

CONCLUSION

This research reveals that JPYF II improves COPD by controlling the IL-17/MAPK/AP-1 signaling axis within the IL-17 pathway for the first time. These findings offer potential approaches for the creation of novel medications that specifically target IL-17 and proteins involved in the IL-17 pathway to address COPD.

Lung microbiome: new insights into the pathogenesis of respiratory diseases

The lungs were long thought to be sterile until technical advances uncovered the presence of the lung microbial community. The microbiome of healthy lungs is mainly derived from the upper respiratory tract (URT) microbiome but also has its own characteristic flora. The selection mechanisms in the lung, including clearance by coughing, pulmonary macrophages, the oscillation of respiratory cilia, and bacterial inhibition by alveolar surfactant, keep the microbiome transient and mobile, which is different from the microbiome in other organs. The pulmonary bacteriome has been intensively studied recently, but relatively little research has focused on the mycobiome and virome. This up-to-date review retrospectively summarizes the lung microbiome's history, composition, and function. We focus on the interaction of the lung microbiome with the oropharynx and gut microbiome and emphasize the role it plays in the innate and adaptive immune responses. More importantly, we focus on multiple respiratory diseases, including asthma, chronic obstructive pulmonary disease (COPD), fibrosis, bronchiectasis, and pneumonia. The impact of the lung microbiome on coronavirus disease 2019 (COVID-19) and lung cancer has also been comprehensively studied. Furthermore, by summarizing the therapeutic potential of the lung microbiome in lung diseases and examining the shortcomings of the field, we propose an outlook of the direction of lung microbiome research.

Multiomics was used to clarify the mechanism by which air pollutants affect chronic obstructive pulmonary disease: A human cohort study

Exposure to air pollutants has been associated with various adverse health outcomes, including chronic obstructive pulmonary disease (COPD). However, the precise underlying mechanism by which air pollution impacts COPD through remains insufficiently understood. To elucidated the molecular mechanism by which air pollutant exposure contributes to alterations in the gut microbiome and metabolism in AECOPD patients, we employed metagenomics and untargeted metabolomics to analyse the gut microbial, faecal, and serum metabolites. The correlations among air pollutants, gut microbes, serum metabolites, and blood biochemical markers were assessed using generalised additive mixed models and Spearman correlation analysis. The findings revealed that for every 10 μg/m3 increase in PM2.5 concentration, the α-diversity of the gut flora decreased by 2.16% (95% CI: 1.80%-2.53%). We found seven microorganisms that were significantly associated with air pollutants, of which Enterococcus faecium, Bacteroides fragilis, Ruthenibacterium lactatiformans, and Subdoligranulum sp.4_3_54A2FAA were primarily associated with glycolysis. We identified 13 serum metabolites and 17 faecal metabolites significantly linked to air pollutants. Seven of these metabolites, which were strongly associated with air pollutants and blood biochemical indices, were found in both serum and faecal samples. Some of these metabolites, such as 2,5-furandicarboxylic acid, C-8C1P and melatonin, were closely associated with disturbances in lipid and fatty acid metabolism in AECOPD patients. These findings underscore the impact of air pollutants on overall metabolism based on influencing gut microbes and metabolites in AECOPD patients. Moreover, these altered biomarkers establish the biologic connection between air pollutant exposure and AECOPD outcomes.The identification of pertinent biomarkers provides valuable insights for the development of precision COPD prevention strategies.

Lower Airway Dysbiosis Augments Lung Inflammatory Injury in Mild-to-Moderate Chronic Obstructive Pulmonary Disease

Rationale: Chronic obstructive pulmonary disease (COPD) is associated with high morbidity, mortality, and healthcare costs. Cigarette smoke is a causative factor; however, not all heavy smokers develop COPD. Microbial colonization and infections are contributing factors to disease progression in advanced stages. Objectives: We investigated whether lower airway dysbiosis occurs in mild-to-moderate COPD and analyzed possible mechanistic contributions to COPD pathogenesis. Methods: We recruited 57 patients with a >10 pack-year smoking history: 26 had physiological evidence of COPD, and 31 had normal lung function (smoker control subjects). Bronchoscopy sampled the upper airways, lower airways, and environmental background. Samples were analyzed by 16S rRNA gene sequencing, whole genome, RNA metatranscriptome, and host RNA transcriptome. A preclinical mouse model was used to evaluate the contributions of cigarette smoke and dysbiosis on lower airway inflammatory injury. Measurements and Main Results: Compared with smoker control subjects, microbiome analyses showed that the lower airways of subjects with COPD were enriched with common oral commensals. The lower airway host transcriptomics demonstrated differences in markers of inflammation and tumorigenesis, such as upregulation of IL-17, IL-6, ERK/MAPK, PI3K, MUC1, and MUC4 in mild-to-moderate COPD. Finally, in a preclinical murine model exposed to cigarette smoke, lower airway dysbiosis with common oral commensals augments the inflammatory injury, revealing transcriptomic signatures similar to those observed in human subjects with COPD. Conclusions: Lower airway dysbiosis in the setting of smoke exposure contributes to inflammatory injury early in COPD. Targeting the lower airway microbiome in combination with smoking cessation may be of potential therapeutic relevance.

The application of multi-omics in the respiratory microbiome: Progresses, challenges and promises

The study of the respiratory microbiome has entered a multi-omic era. Through integrating different omic data types such as metagenome, metatranscriptome, metaproteome, metabolome, culturome and radiome surveyed from respiratory specimens, holistic insights can be gained on the lung microbiome and its interaction with host immunity and inflammation in respiratory diseases. The power of multi-omics have moved the field forward from associative assessment of microbiome alterations to causative understanding of the lung microbiome in the pathogenesis of chronic, acute and other types of respiratory diseases. However, the application of multi-omics in respiratory microbiome remains with unique challenges from sample processing, data integration, and downstream validation. In this review, we first introduce the respiratory sample types and omic data types applicable to studying the respiratory microbiome. We next describe approaches for multi-omic integration, focusing on dimensionality reduction, multi-omic association and prediction. We then summarize progresses in the application of multi-omics to studying the microbiome in respiratory diseases. We finally discuss current challenges and share our thoughts on future promises in the field.

Multiomic Investigations into Lung Health and Disease

Diseases of the lung account for more than 5 million deaths worldwide and are a healthcare burden. Improving clinical outcomes, including mortality and quality of life, involves a holistic understanding of the disease, which can be provided by the integration of lung multi-omics data. An enhanced understanding of comprehensive multiomic datasets provides opportunities to leverage those datasets to inform the treatment and prevention of lung diseases by classifying severity, prognostication, and discovery of biomarkers. The main objective of this review is to summarize the use of multiomics investigations in lung disease, including multiomics integration and the use of machine learning computational methods. This review also discusses lung disease models, including animal models, organoids, and single-cell lines, to study multiomics in lung health and disease. We provide examples of lung diseases where multi-omics investigations have provided deeper insight into etiopathogenesis and have resulted in improved preventative and therapeutic interventions.

The Lung Microbiome in COPD and Lung Cancer: Exploring the Potential of Metal-Based Drugs

Chronic obstructive pulmonary disease (COPD) and lung cancer 17 are two of the most prevalent and debilitating respiratory diseases worldwide, both associated with high morbidity and mortality rates. As major global health concerns, they impose a substantial burden on patients, healthcare systems, and society at large. Despite their distinct aetiologies, lung cancer and COPD share common risk factors, clinical features, and pathological pathways, which have spurred increasing research interest in their co-occurrence. One area of particular interest is the role of the lung microbiome in the development and progression of these diseases, including the transition from COPD to lung cancer. Exploring novel therapeutic strategies, such as metal-based drugs, offers a potential avenue for targeting the microbiome in these diseases to improve patient outcomes. This review aims to provide an overview of the current understanding of the lung microbiome, with a particular emphasis on COPD and lung cancer, and to discuss the potential of metal-based drugs as a therapeutic strategy for these conditions, specifically concerning targeting the microbiome.

Characterization of the upper respiratory tract microbiota in Chilean asthmatic children reveals compositional, functional, and structural differences

Around 155 million people worldwide suffer from asthma. In Chile, the prevalence of this disease in children is around 15% and has a high impact in the health system. Studies suggest that asthma is caused by multiple factors, including host genetics, antibiotic use, and the development of the airway microbiota. Here, we used 16S rRNA high-throughput sequencing to characterize the nasal and oral mucosae of 63 asthmatic and 89 healthy children (152 samples) from Santiago, Chile. We found that the nasal mucosa was dominated by a high abundance of Moraxella, Dolosigranulum, Haemophilus, Corynebacterium, Streptococcus, and Staphylococcus. In turn, the oral mucosa was characterized by a high abundance of Streptococcus, Haemophilus, Gemella, Veillonella, Neisseria, and Porphyromonas. Our results showed significantly (P < 0.001) lower alpha diversity and an over-abundance of Streptococcus (P < 0.01) in nasal samples from asthmatics compared to samples from healthy subjects. Community structure, as revealed by co-occurrence networks, showed different microbial interactions in asthmatic and healthy subjects, particularly in the nasal microbiota. The networks revealed keystone genera in each body site, including Prevotella, Leptotrichia, and Porphyromonas in the nasal microbiota, and Streptococcus, Granulicatella, and Veillonella in the oral microbiota. We also detected 51 functional pathways differentially abundant on the nasal mucosa of asthmatic subjects, although only 13 pathways were overrepresented in the asthmatic subjects (P < 0.05). We did not find any significant differences in microbial taxonomic (composition and structure) and functional diversity between the oral mucosa of asthmatic and healthy subjects. This study explores for the first time the relationships between the upper respiratory airways bacteriome and asthma in Chile. It demonstrates that the nasal cavity of children from Santiago harbors unique bacterial communities and identifies potential taxonomic and functional biomarkers of pediatric asthma.

The airway microbiome mediates the interaction between environmental exposure and respiratory health in humans

Exposure to environmental pollution influences respiratory health. The role of the airway microbial ecosystem underlying the interaction of exposure and respiratory health remains unclear. Here, through a province-wide chronic obstructive pulmonary disease surveillance program, we conducted a population-based survey of bacterial (n = 1,651) and fungal (n = 719) taxa and metagenomes (n = 1,128) from induced sputum of 1,651 household members in Guangdong, China. We found that cigarette smoking and higher PM_2.5 concentration were associated with lung function impairment through the mediation of bacterial and fungal communities, respectively, and that exposure was associated with an enhanced inter-kingdom microbial interaction resembling the pattern seen in chronic obstructive pulmonary disease. Enrichment of Neisseria was associated with a 2.25-fold increased risk of high respiratory symptom burden, coupled with an elevation in Aspergillus, in association with occupational pollution. We developed an individualized microbiome-based health index, which covaried with exposure, respiratory symptoms and diseases, with potential generalizability to global datasets. Our results may inform environmental risk prevention and guide interventions that harness airway microbiome.

Airway dysbiosis accelerates lung function decline in chronic obstructive pulmonary disease

Progressive lung function decline is a hallmark of chronic obstructive pulmonary disease (COPD). Airway dysbiosis occurs in COPD, but whether it contributes to disease progression remains unknown. Here, we show, through a longitudinal analysis of two cohorts involving four UK centers, that baseline airway dysbiosis in COPD patients, characterized by the enrichment of opportunistic pathogenic taxa, associates with a rapid forced expiratory volume in 1 s (FEV1) decline over 2 years. Dysbiosis associates with exacerbation-related FEV1 fall and sudden FEV1 fall at stability, contributing to long-term FEV1 decline. A third cohort in China further validates the microbiota-FEV1-decline association. Human multi-omics and murine studies show that airway Staphylococcus aureus colonization promotes lung function decline through homocysteine, which elicits a neutrophil apoptosis-to-NETosis shift via the AKT1-S100A8/A9 axis. S. aureus depletion via bacteriophages restores lung function in emphysema mice, providing a fresh approach to slow COPD progression by targeting the airway microbiome.

Butyrate inhibits iILC2-mediated lung inflammation via lung-gut axis in chronic obstructive pulmonary disease (COPD)

BACKGROUND

The study investigated the effects and underlying mechanisms of intestinal flora metabolite butyrate on inflammatory ILC2 cells (iILC2s)-mediated lung inflammation in chronic obstructive pulmonary disease (COPD).

METHODS

Mouse models of COPD and acute exacerbation of COPD (AECOPD) were established. Flow cytometry was used to detect natural ILC2 cells (nILC2s) and iILC2s in lung and colon tissues. The 16s rRNA and GC-MS were used to detect microbial flora and short chain fatty acids (SCFAs) in feces. ELISA was used to detect IL-13 and IL-4. Western blot and qRT-PCR were used to detect the relative protein and mRNA levels, respectively. In vitro experiments were performed with sorted ILC2s from colon tissues of control mice. Mice with AECOPD were treated with butyrate.

RESULTS

The nILC2s and iILC2s in lung and colon tissues of AECOPD mice were significantly higher than control groups. The abundance of the flora Clostridiaceae was significantly reduced, and the content of SCFAs, including acetate and butyrate, was significantly reduced. The in vitro experiments showed that butyrate inhibited iILC2 cell phenotype and cytokine secretion. Butyrate treatment reduced the proportion of iILC2 cells in the colon and lung tissues of mice with AECOPD.

CONCLUSIONS

The nILC2s and iILC2s in the colon tissues are involved in the course of COPD. Decreased Clostridiaceae and butyrate in AECOPD mice caused the accumulation of iILC2 cells in the intestines and lungs. Supplementation of butyrate can reduce iILC2 in the intestine and lung tissues. Our data may provide new ideas for prevention and treatment of COPD.

Global Research Trends on the Link Between the Microbiome and COPD: A Bibliometric Analysis

Background

The pathogenesis of chronic obstructive pulmonary disease (COPD) has been studied in relation to the microbiome, providing space for more targeted interventions and new treatments. Numerous papers on the COPD microbiome have been reported in the last 10 years, yet few publications have used bibliometric methods to evaluate this area.

Methods

We searched the Web of Science Core Collection for all original research articles in the field of COPD microbiome from January 2011 to August 2022 and used CiteSpace for visual analysis.

Results

A total of 505 relevant publications were obtained, and the number of global publications in this field is steadily increasing every year, with China and the USA occupying the first two spots in international publications. Imperial College London and the University of Leicester produced the most publications. Brightling C from the UK was the most prolific writer, while Huang Y and Sze M from the USA were first and second among the authors cited. The American Journal of Respiratory and Critical Care Medicine had the highest frequency of citations. The top 10 institutions, cited authors and journals are mostly from the UK and the US. In the ranking of citations, the first article was a paper published by Sze M on changes in the lung tissue's microbiota in COPD patients. The keywords "exacerbation", "gut microbiota", "lung microbiome", "airway microbiome", "bacterial colonization", and "inflammation" were identified as cutting-edge research projects for 2011-2022.

Conclusion

Based on the visualization results, in the future, we can use the gut-lung axis as the starting point to explore the immunoinflammatory mechanism of COPD, and study how to predict the effects of different treatments of COPD by identifying the microbiota, and how to achieve the optimal enrichment of beneficial bacteria and the optimal consumption of harmful bacteria to improve COPD.

The Fungal and Bacterial Interface in the Respiratory Mycobiome with a Focus on Aspergillus spp

The heterogeneity of the lung microbiome and its alteration are prevalently seen among chronic lung diseases patients. However, studies to date have primarily focused on the bacterial microbiome in the lung rather than fungal composition, which might play an essential role in the mechanisms of several chronic lung diseases. It is now well established that Aspergillus spp. colonies may induce various unfavorable inflammatory responses. Furthermore, bacterial microbiomes such as Pseudomonas aeruginosa provide several mechanisms that inhibit or stimulate Aspergillus spp. life cycles. In this review, we highlighted fungal and bacterial microbiome interactions in the respiratory tract, with a focus on Aspergillus spp.

Lung microbiome and cytokine profiles in different disease states of COPD: a cohort study

Increasing evidence indicates that respiratory tract microecological disorders may play a role in the pathogenesis of chronic obstructive pulmonary disease (COPD). Understanding the composition of the respiratory microbiome in COPD and its relevance to respiratory immunity will help develop microbiome-based diagnostic and therapeutic approaches. One hundred longitudinal sputum samples from 35 subjects with acute exacerbation of COPD (AECOPD) were analysed for respiratory bacterial microbiome using 16S ribosomal RNA amplicon sequencing technology, and the sputum supernatant was analysed for 12 cytokines using a Luminex liquid suspension chip. Unsupervised hierarchical clustering was employed to evaluate the existence of distinct microbial clusters. In AECOPD, the respiratory microbial diversity decreased, and the community composition changed significantly. The abundances of Haemophilus, Moraxella, Klebsiella, and Pseudomonas increased significantly. Significant positive correlations between the abundance of Pseudomonas and TNF-α, abundance of Klebsiella and the percentage of eosinophils were observed. Furthermore, COPD can be divided into four clusters based on the respiratory microbiome. AECOPD-related cluster was characterized by the enrichment of Pseudomonas and Haemophilus and a high level of TNF-α. Lactobacillus and Veillonella are enriched in therapy-related phenotypes and may play potential probiotic roles. There are two inflammatory endotypes in the stable state: Gemella is associated with the Th2 inflammatory endotypes, whereas Prevotella is associated with the Th17 inflammatory endotypes. Nevertheless, no differences in clinical manifestations were found between these two endotypes. The sputum microbiome is associated with the disease status of COPD, allowing us to distinguish different inflammatory endotypes. Targeted anti-inflammatory and anti-infective therapies may improve the long-term prognosis of COPD.

PLSDA-batch: a multivariate framework to correct for batch effects in microbiome data

Microbial communities are highly dynamic and sensitive to changes in the environment. Thus, microbiome data are highly susceptible to batch effects, defined as sources of unwanted variation that are not related to and obscure any factors of interest. Existing batch effect correction methods have been primarily developed for gene expression data. As such, they do not consider the inherent characteristics of microbiome data, including zero inflation, overdispersion and correlation between variables. We introduce new multivariate and non-parametric batch effect correction methods based on Partial Least Squares Discriminant Analysis (PLSDA). PLSDA-batch first estimates treatment and batch variation with latent components, then subtracts batch-associated components from the data. The resulting batch-effect-corrected data can then be input in any downstream statistical analysis. Two variants are proposed to handle unbalanced batch x treatment designs and to avoid overfitting when estimating the components via variable selection. We compare our approaches with popular methods managing batch effects, namely, removeBatchEffect, ComBat and Surrogate Variable Analysis, in simulated and three case studies using various visual and numerical assessments. We show that our three methods lead to competitive performance in removing batch variation while preserving treatment variation, especially for unbalanced batch $\times $ treatment designs. Our downstream analyses show selections of biologically relevant taxa. This work demonstrates that batch effect correction methods can improve microbiome research outputs. Reproducible code and vignettes are available on GitHub.

Akkermansia muciniphila Ameliorates Lung Injury in Smoke-Induced COPD Mice by IL-17 and Autophagy

Objective. Smoking is a primary hazard factor for chronic obstructive pulmonary disease (COPD), which induced a decrease in intestinal Akkermansia muciniphila abundance and Th17 imbalance in COPD. This study analyzed the changes of gut microbiota metabolism and Akkermansia abundance in patients with smoking-related COPD and explored the potential function of Akkermansia muciniphila in smoke-induced COPD mice. Methods. Gut microbiota diversity and metabolic profile were analyzed by 16S rRNA sequence and metabolomics in COPD patients. The IL-1β, IL-17, TNF-α, and IL-6 levels were tested by ELISA. Lung tissue damage was observed by HE staining. The expression of cleave-caspase 3, trophoblast antigen 2 (TROP2), and LC3 in lung tissues were analyzed by IHC or IF. The p-mTOR, mTOR, p62, and LC3 expression in lung tissues were tested by western blot. Results. The levels of IL-17, IL-1β, TNF-α, and IL-6 in the peripheral blood of COPD patients increased significantly. The number and alpha diversity of gut microbiota were decreased in COPD patients. The abundance of Akkermansia muciniphila in gut of COPD patients was decreased, and the metabolic phenotype and retinol metabolism were changed. In the retinol metabolism, the retinol and retinal were significantly changed. Akkermansia muciniphila could improve the alveolar structure and inflammatory cell infiltration in lung tissue, reduce the IL-17, TNF-α, and IL-6 levels in peripheral blood, promote the p-mTOR expression, and inhibit the expression of autophagy-related proteins in smoke-induced COPD mice. Conclusion. The number and alpha diversity of gut microbiota were decreased in patients with smoking-related COPD, accompanied by decreased abundance of Akkermansia muciniphila, and altered retinol metabolism function. Gut Akkermansia muciniphila ameliorated lung injury in smoke-induced COPD mice by inflammation and autophagy.

Sputum Microbiome and Chronic Obstructive Pulmonary Disease in a Rural Ugandan Cohort of Well-Controlled HIV Infection

Sub-Saharan Africa has increased morbidity and mortality related to chronic obstructive pulmonary disease (COPD). COPD among people living with HIV (PLWH) has not been well studied in this region, where HIV/AIDS is endemic. Increasing evidence suggests that respiratory microbial composition plays a role in COPD severity. Therefore, we aimed to investigate microbiome patterns and associations among PLWH with COPD in Sub-Saharan Africa. We conducted a cross-sectional study of 200 adults stratified by HIV and COPD in rural Uganda. Induced sputum samples were collected as an easy-to-obtain proxy for the lower respiratory tract microbiota. We performed 16S rRNA gene sequencing and used PICRUSt2 (version 2.2.3) to infer the functional profiles of the microbial community. We used a statistical tool to detect changes in specific taxa that searches and adjusts for confounding factors such as antiretroviral therapy (ART), age, sex, and other participant characteristics. We could cluster the microbial community into three community types whose distribution was shown to be significantly impacted by HIV. Some genera, e.g., Veillonella, Actinomyces, Atopobium, and Filifactor, were significantly enriched in HIV-infected individuals, while the COPD status was significantly associated with Gammaproteobacteria and Selenomonas abundance. Furthermore, reduced bacterial richness and significant enrichment in Campylobacter were associated with HIV-COPD comorbidity. Functional prediction using PICRUSt2 revealed a significant depletion in glutamate degradation capacity pathways in HIV-positive patients. A comparison of our findings with an HIV cohort from the United Kingdom revealed significant differences in the sputum microbiome composition, irrespective of viral suppression. IMPORTANCE Even with ART available, HIV-infected individuals are at high risk of suffering comorbidities, as shown by the high prevalence of noninfectious lung diseases in the HIV population. Recent studies have suggested a role for the respiratory microbiota in driving chronic lung inflammation. The respiratory microbiota was significantly altered among PLWH, with disease persisting up to 3 years post-ART initiation and HIV suppression. The community structure and diversity of the sputum microbiota in COPD are associated with disease severity and clinical outcomes, both in stable COPD and during exacerbations. Therefore, a better understanding of the sputum microbiome among PLWH could improve COPD prognostic and risk stratification strategies. In this study, we observed that in a virologically suppressed HIV cohort in rural Uganda, we could show differences in sputum microbiota stratified by HIV and COPD, reduced bacterial richness, and significant enrichment in Campylobacter associated with HIV-COPD comorbidity.

Leveraging 16S rRNA data to uncover vaginal microbial signatures in women with cervical cancer

Microbiota-relevant signatures have been investigated for human papillomavirus-related cervical cancer (CC), but lack consistency because of study- and methodology-derived heterogeneities. Here, four publicly available 16S rRNA datasets including 171 vaginal samples (51 CC versus 120 healthy controls) were analyzed to characterize reproducible CC-associated microbial signatures. We employed a recently published clustering approach called VAginaL community state typE Nearest CentroId clAssifier to assign the metadata to 13 community state types (CSTs) in our study. Nine subCSTs were identified. A random forest model (RFM) classifier was constructed to identify 33 optimal genus-based and 94 species-based signatures. Confounder analysis revealed confounding effects on both study- and hypervariable region-associated aspects. After adjusting for confounders, multivariate analysis identified 14 significantly changed taxa in CC versus the controls (P < 0.05). Furthermore, predicted functional analysis revealed significantly upregulated pathways relevant to the altered vaginal microbiota in CC. Cofactor, carrier, and vitamin biosynthesis were significantly enriched in CC, followed by fatty acid and lipid biosynthesis, and fermentation of short-chain fatty acids. Genus-based contributors to the differential functional abundances were also displayed. Overall, this integrative study identified reproducible and generalizable signatures in CC, suggesting the causal role of specific taxa in CC pathogenesis.

Application of metagenomic next-generation sequencing in the diagnosis of urinary tract infection in patients undergoing cutaneous ureterostomy

Objective

Urinary tract infection (UTI) is an inflammatory response of the urothelium to bacterial invasion and is a common complication in patients with cutaneous ureterostomy (CU). For such patients, accurate and efficient identification of pathogens remains a challenge. The aim of this study included exploring utility of metagenomic next-generation sequencing (mNGS) in assisting microbiological diagnosis of UTI among patients undergoing CU, identifying promising cytokine or microorganism biomarkers, revealing microbiome diversity change and compare virulence factors (VFs) and antibiotic resistance genes (ARGs) after infection.

Methods

We performed a case-control study of 50 consecutive CU patients from December 2020 to January 2021. According to the clinical diagnostic criteria, samples were divided into infected group and uninfected group and difference of urine culture, cytokines, microorganism, ARGs and VFs were compared between the two groups.

Results

Inflammatory responses were more serious in infected group, as evidenced by a significant increase in IFN-α (p=0.031), IL-1β (0.023) and IL-6 (p=0.018). Clinical culture shows that there is higher positive rate in infected group for most clinical pathogens like Escherichia coli, Klebsiella pneumoniae, Staphylococcus aureus, Candida auris etc. and the top three pathogens with positive frequencies were E. coli, K. pneumoniae, and Enterococcus faecalis. Benchmarking clinical culture, the total sensitivity is 91.4% and specificity is 76.3% for mNGS. As for mNGS, there was no significant difference in microbiome α- diversity between infected and uninfected group. Three species biomarkers including Citrobacter freundii, Klebsiella oxytoca, and Enterobacter cloacae are enriched in infected group based on Lefse. E. cloacae were significantly correlated with IL-6 and IL-10. K. oxytoca were significantly correlated with IL-1β. Besides, the unweighted gene number and weighted gene abundance of VFs or ARGs are significantly higher in infected group. Notablely, ARGs belonging to fluoroquinolones, betalatmas, fosfomycin, phenicol, phenolic compound abundance is significantly higher in infected group which may have bad effect on clinical treatment for patients.

Conclusion

mNGS, along with urine culture, will provide comprehensive and efficient reference for the diagnosis of UTI in patients with CU and allow us to monitor microbial changes in urine of these patients. Moreover, cytokines (IL-6, IL-1β, and IFN-a) or microorganisms like C. freundii, K. oxytoca or E. cloacae are promising biomarkers for building effective UTI diagnostic model of patients with CU and seriously the VFs and ARGs abundance increase in infected group may play bad effect on clinical treatment.

Airway microbiome-immune crosstalk in chronic obstructive pulmonary disease

Chronic Obstructive Pulmonary Disease (COPD) has significantly contributed to global mortality, with three million deaths reported annually. This impact is expected to increase over the next 40 years, with approximately 5 million people predicted to succumb to COPD-related deaths annually. Immune mechanisms driving disease progression have not been fully elucidated. Airway microbiota have been implicated. However, it is still unclear how changes in the airway microbiome drive persistent immune activation and consequent lung damage. Mechanisms mediating microbiome-immune crosstalk in the airways remain unclear. In this review, we examine how dysbiosis mediates airway inflammation in COPD. We give a detailed account of how airway commensal bacteria interact with the mucosal innate and adaptive immune system to regulate immune responses in healthy or diseased airways. Immune-phenotyping airway microbiota could advance COPD immunotherapeutics and identify key open questions that future research must address to further such translation.

The human lung microbiome-A hidden link between microbes and human health and diseases

Once thought to be sterile, the human lung is now well recognized to harbor a consortium of microorganisms collectively known as the lung microbiome. The lung microbiome is altered in an array of lung diseases, including chronic lung diseases such as chronic obstructive pulmonary disease, asthma, and bronchiectasis, acute lung diseases caused by pneumonia, sepsis, and COVID-19, and other lung complications such as those related to lung transplantation, lung cancer, and human immunodeficiency virus. The effects of lung microbiome in modulating host immunity and inflammation in the lung and distal organs are being elucidated. However, the precise mechanism by which members of microbiota produce structural ligands that interact with host genes and pathways remains largely uncharacterized. Multiple unique challenges, both technically and biologically, exist in the field of lung microbiome, necessitating the development of tailored experimental and analytical approaches to overcome the bottlenecks. In this review, we first provide an overview of the principles and methodologies in studying the lung microbiome. We next review current knowledge of the roles of lung microbiome in human diseases, highlighting mechanistic insights. We finally discuss critical challenges in the field and share our thoughts on broad topics for future investigation.

Multi-omics analyses of airway host-microbe interactions in chronic obstructive pulmonary disease identify potential therapeutic interventions

The mechanistic role of the airway microbiome in chronic obstructive pulmonary disease (COPD) remains largely unexplored. We present a landscape of airway microbe-host interactions in COPD through an in-depth profiling of the sputum metagenome, metabolome, host transcriptome and proteome from 99 patients with COPD and 36 healthy individuals in China. Multi-omics data were integrated using sequential mediation analysis, to assess in silico associations of the microbiome with two primary COPD inflammatory endotypes, neutrophilic or eosinophilic inflammation, mediated through microbial metabolic interaction with host gene expression. Hypotheses of microbiome-metabolite-host interaction were identified by leveraging microbial genetic information and established metabolite-human gene pairs. A prominent hypothesis for neutrophil-predominant COPD was altered tryptophan metabolism in airway lactobacilli associated with reduced indole-3-acetic acid (IAA), which was in turn linked to perturbed host interleukin-22 signalling and epithelial cell apoptosis pathways. In vivo and in vitro studies showed that airway microbiome-derived IAA mitigates neutrophilic inflammation, apoptosis, emphysema and lung function decline, via macrophage-epithelial cell cross-talk mediated by interleukin-22. Intranasal inoculation of two airway lactobacilli restored IAA and recapitulated its protective effects in mice. These findings provide the rationale for therapeutically targeting microbe-host interaction in COPD.

Gut Microbiome Signatures in the Progression of Hepatitis B Virus-Induced Liver Disease

The gut microbiome is associated with hepatitis B virus (HBV)-induced liver disease, which progresses from chronic hepatitis B, to liver cirrhosis, and eventually to hepatocellular carcinoma. Studies have analyzed the gut microbiome at each stage of HBV-induced liver diseases, but a consensus has not been reached on the microbial signatures across these stages. Here, we conducted by a systematic meta-analysis of 486 fecal samples from publicly available 16S rRNA gene datasets across all disease stages, and validated the results by a gut microbiome characterization on an independent cohort of 15 controls, 23 chronic hepatitis B, 20 liver cirrhosis, and 22 hepatocellular carcinoma patients. The integrative analyses revealed 13 genera consistently altered at each of the disease stages both in public and validation datasets, suggesting highly robust microbiome signatures. Specifically, Colidextribacter and Monoglobus were enriched in healthy controls. An unclassified Lachnospiraceae genus was specifically elevated in chronic hepatitis B, whereas Bilophia was depleted. Prevotella and Oscillibacter were depleted in liver cirrhosis. And Coprococcus and Faecalibacterium were depleted in hepatocellular carcinoma. Classifiers established using these 13 genera showed diagnostic power across all disease stages in a cross-validation between public and validation datasets (AUC = 0.65–0.832). The identified microbial taxonomy serves as non-invasive biomarkers for monitoring the progression of HBV-induced liver disease, and may contribute to microbiome-based therapies.

Integrated Microbiome and Host Transcriptome Profiles Link Parkinson’s Disease to Blautia Genus: Evidence From Feces, Blood, and Brain

A link between the gut microbiome and Parkinson’s disease (PD) has been intensively studied, and more than 100 differential genera were identified across the studies. However, the predominant genera contributing to PD remain poorly understood. Inspired by recent advances showing microbiota distribution in the blood and brain, we, here, comprehensively investigated currently available fecal microbiome data (1,914 samples) to identify significantly altered genera, which were further validated by comparison to the results from microbiome analysis of blood (85 samples) and brain (268 samples). Our data showed that the composition of fecal microbiota was different from that of blood and brain. We found that Blautia was the unique genus consistently depleted across feces, blood, and brain samples of PD patients (P < 0.05), despite using rigorous criteria to remove contaminants. Moreover, enrichment analyses revealed that host genes correlated with Blautia genus abundance were mainly involved in mitochondrial function and energy metabolism, and mapped to neurodegenerative diseases (NDDs) and metabolic diseases. A random forest classifier constructed with fecal microbiota data demonstrated that Blautia genus was an important feature contributing to discriminating PD patients from controls [receiver operating characteristic (ROC)-area under curve (AUC) = 0.704, precision-recall curve (PRC)-AUC = 0.787]. Through the integration of microbiome and transcriptome, our study depicted microbial profiles in the feces, blood, and brain of PD patients, and identified Blautia genus as a potential genus linked to PD. Further studies are greatly encouraged to determine the role of Blautia genus in the pathogenesis of PD.

Exploring the Change of Host and Microorganism in Chronic Obstructive Pulmonary Disease Patients Based on Metagenomic and Metatranscriptomic Sequencing

Background

Chronic obstructive pulmonary disease (COPD) is a universal respiratory disease resulting from the complex interactions between genes and environmental conditions. The process of COPD is deteriorated by repeated episodes of exacerbations, which are the primary reason for COPD-related morbidity and mortality. Bacterial pathogens are commonly identified in patients’ respiratory tracts both in the stable state and during acute exacerbations, with significant changes in the prevalence of airway bacteria occurring during acute exacerbation of chronic obstructive pulmonary disease (AECOPD). Therefore, the changes in microbial composition and host inflammatory responses will be necessary to investigate the mechanistic link between the airway microbiome and chronic pulmonary inflammation in COPD patients.

Methods

We performed metatranscriptomic and metagenomic sequencing on sputum samples for twelve AECOPD patients before treatment and for four of them stable COPD (stabilization of AECOPD patients after treatment). Sequencing reads were classified by Kraken2, and the host gene expression was analyzed by Hisat2 and HTseq. The correlation between genes was obtained by the Spearman correlation coefficient. Mann–Whitney U-test was applied to identify microbes that exhibit significantly different distribution in two groups.

Results

At the phyla level, the top 5 dominant phyla were Firmicutes, Actinobacteria, Proteobacteria, Bacteroidetes, and Fusobacteria. The proportion of dominant gates in metagenomic data was similar in metatranscriptomic data. There were significant differences in the abundance of specific microorganisms at the class level between the two methods. No significant difference between AECOPD and stable COPD was found. However, the different expression levels of 5 host genes were significantly increased in stable COPD and were involved in immune response and inflammatory pathways, which were associated with macrophages.

Conclusion

Our study may provide a clue to investigate the mechanism of COPD and potential biomarkers in clinical diagnosis and treatment.

Inflammatory Endotype-Associated Airway Resistome in Chronic Obstructive Pulmonary Disease

Antimicrobial resistance is a global concern in chronic respiratory diseases, including chronic obstructive pulmonary disease (COPD). The collection of antibiotic resistance genes or resistome in human airways may underlie the resistance. COPD is heterogeneous, and understanding the airway resistome in relation to patient phenotype and endotype may inform precision antibiotic therapy. Here, we characterized the airway resistome for 94 COPD participants at stable disease. Among all demographic and clinical factors, patient inflammatory endotype was associated with the airway resistome. There were distinct resistome profiles between patients with neutrophilic or eosinophilic inflammation, two primary inflammatory endotypes in COPD. For neutrophil-predominant COPD, the resistome was dominated by multidrug resistance genes. For eosinophil-predominant COPD, the resistome was diverse, with an increased portion of patients showing a macrolide-high resistome. The differential antimicrobial resistance pattern was validated by sputum culture and in vitro antimicrobial susceptibility testing. Ralstonia and Pseudomonas were the top contributors to the neutrophil-associated resistome, whereas Campylobacter and Aggregatibacter contributed most to the eosinophil-associated resistome. Multiomic analyses revealed specific host pathways and inflammatory mediators associated with the resistome. The arachidonic acid metabolic pathway and matrix metallopeptidase 8 (MMP-8) exhibited the strongest associations with the neutrophil-associated resistome, whereas the eosinophil chemotaxis pathway and interleukin-13 (IL-13) showed the greatest associations with the eosinophil-associated resistome. These results highlight a previously unrecognized link between inflammation and the airway resistome and suggest the need for considering patient inflammatory subtype in decision-making about antibiotic use in COPD and broader chronic respiratory diseases.

IMPORTANCE Antibiotics are commonly prescribed for both acute and long-term prophylactic treatment in chronic airway disorders, such as chronic obstructive pulmonary disease (COPD), and the rapid growth of antibiotic resistance is alarming globally. The airway harbors a diverse collection of microorganisms known as microbiota, which serve as a reservoir for antibiotic resistance genes or the resistome. A comprehensive understanding of the airway resistome in relation to patient clinical and biological factors may help inform decisions to select appropriate antibiotics for clinical therapies. By deep multiomic profiling and in vitro phenotypic testing, we showed that inflammatory endotype, the underlying pattern of airway inflammation, was most strongly associated with the airway resistome in COPD patients. There were distinct resistome profiles between neutrophil-predominant and eosinophil-predominant COPD that were associated with different bacterial species, host pathways, and inflammatory markers, highlighting the need of considering patient inflammatory status in COPD antibiotic management.

Pulmonary microbiome and gene expression signatures differentiate lung function in pediatric hematopoietic cell transplant candidates

Impaired baseline lung function is associated with mortality after pediatric allogeneic hematopoietic cell transplantation (HCT), yet limited knowledge of the molecular pathways that characterize pretransplant lung function has hindered the development of lung-targeted interventions. In this study, we quantified the association between bronchoalveolar lavage (BAL) metatranscriptomes and paired pulmonary function tests performed a median of 1 to 2 weeks before allogeneic HCT in 104 children in The Netherlands. Abnormal pulmonary function was recorded in more than half the cohort, consisted most commonly of restriction and impaired diffusion, and was associated with both all-cause and lung injury-related mortality after HCT. Depletion of commensal supraglottic taxa, such as Haemophilus, and enrichment of nasal and skin taxa, such as Staphylococcus, in the BAL microbiome were associated with worse measures of lung capacity and gas diffusion. In addition, BAL gene expression signatures of alveolar epithelial activation, epithelial-mesenchymal transition, and down-regulated immunity were associated with impaired lung capacity and diffusion, suggesting a postinjury profibrotic response. Detection of microbial depletion and abnormal epithelial gene expression in BAL enhanced the prognostic utility of pre-HCT pulmonary function tests for the outcome of post-HCT mortality. These findings suggest a potentially actionable connection between microbiome depletion, alveolar injury, and pulmonary fibrosis in the pathogenesis of pre-HCT lung dysfunction.

The current understanding and future directions for sputum microbiome profiling in chronic obstructive pulmonary disease

PURPOSE OF REVIEW

Next-generation sequencing (NGS) has deepened our understanding of the respiratory microbiome in health and disease. The number of microbiome studies employing sputum as an airway surrogate has continued to increase over the past decade to include multiple large multicentre and longitudinal studies of the microbiome in chronic obstructive pulmonary disease (COPD). In this review, we summarize the recent advances to our understanding of the bacteriome, virome and mycobiome in COPD.

RECENT FINDINGS

Diverse microbiome profiles are reported in COPD. The neutrophilic Haemophilus-predominant bacteriome remains a prominent COPD phenotype, relatively stable over time and during exacerbations. Studies of the virome remain limited but reveal a potential involvement of viruses and bacteriophages particularly during COPD exacerbations and advancing disease severity. Mycobiome signatures, even in stable COPD are associated with poorer clinical outcomes including mortality.

SUMMARY

The sputum microbiome in COPD is being increasingly recognized for its clinical relevance, even in the stable state. Future studies integrating microbial kingdoms holistically (i.e. bacterial, viral and fungal) will provide deeper insight into its functionality including the relevance of microbial interactions and effect of treatment on microbiome-associated clinical outcomes.

Airway Microbiome and Serum Metabolomics Analysis Identify Differential Candidate Biomarkers in Allergic Rhinitis

Allergic rhinitis (AR) is a common heterogeneous chronic disease with a high prevalence and a complex pathogenesis influenced by numerous factors, involving a combination of genetic and environmental factors. To gain insight into the pathogenesis of AR and to identity diagnostic biomarkers, we combined systems biology approach to analyze microbiome and serum composition. We collected inferior turbinate swabs and serum samples to study the microbiome and serum metabolome of 28 patients with allergic rhinitis and 15 healthy individuals. We sequenced the V3 and V4 regions of the 16S rDNA gene from the upper respiratory samples. Metabolomics was used to examine serum samples. Finally, we combined differential microbiota and differential metabolites to find potential biomarkers. We found no significant differences in diversity between the disease and control groups, but changes in the structure of the microbiota. Compared to the HC group, the AR group showed a significantly higher abundance of 1 phylum (Actinobacteria) and 7 genera (Klebsiella, Prevotella and Staphylococcus, etc.) and a significantly lower abundance of 1 genus (Pelomonas). Serum metabolomics revealed 26 different metabolites (Prostaglandin D2, 20-Hydroxy-leukotriene B4 and Linoleic acid, etc.) and 16 disrupted metabolic pathways (Linoleic acid metabolism, Arachidonic acid metabolism and Tryptophan metabolism, etc.). The combined respiratory microbiome and serum metabolomics datasets showed a degree of correlation reflecting the influence of the microbiome on metabolic activity. Our results show that microbiome and metabolomics analyses provide important candidate biomarkers, and in particular, differential genera in the microbiome have also been validated by random forest prediction models. Differential microbes and differential metabolites have the potential to be used as biomarkers for the diagnosis of allergic rhinitis.

Microbiota in the pathogenesis of COPD and its impact on the course of the disease

Chronic obstructive pulmonary disease (COPD) is a serious problem for global health. Infectious agents play a main role in the development of COPD exacerbations. Bacterial colonization of the lower respiratory tract is common in patients with stable COPD. The role of microbiota and host immune response to potential pathogens is not well studied. Microbiota composition disorders in respiratory tract are found in patients with COPD and associated with maladaptive changes in the immune system of the lungs and increased level of inflammation. This review investigates role of microbiota in the pathogenesis of COPD and its impact on the course of the disease. Some important issues such as pneumococcal vaccination and antimicrobial resistance of respiratory pathogens are also discussed.

The composition of lung microbiome in lung cancer: a systematic review and meta-analysis

Background

Although recent studies have indicated that imbalance in the respiratory microbiome composition is linked to several chronic respiratory diseases, the association between the lung microbiome and lung cancer has not been extensively studied. Conflicting reports of individual studies on respiratory microbiome alterations in lung cancer complicate the matter for specifying how the lung microbiome is linked to lung cancer. Consequently, as the first meta-analysis on this topic, we integrate publicly available 16S rRNA gene sequence data on lung tissue samples of lung cancer patients to identify bacterial taxa which differ consistently between case and control groups.

Results

The findings of the current study suggest that the relative abundance of several bacterial taxa including Actinobacteria phylum, Corynebacteriaceae and Halomonadaceae families, and Corynebacterium, Lachnoanaerobaculum, and Halomonas genera is significantly decreased (p < 0.05) in lung tumor tissues of lung cancer patients in comparison with tumor-adjacent normal tissues.

Conclusions

Despite the underlying need for scrutinizing the findings further, the present study lays the groundwork for future research and adds to our limited understanding of the key role of the lung microbiome and its complex interaction with lung cancer. More data on demographic factors and tumor tissue types would help establish a greater degree of accuracy in characterizing the lung microbial community which accords with subtypes and stages of the disease and fully capturing the changes of the lung microbiome in lung cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-021-02375-z.

A meta-analysis study of the robustness and universality of gut microbiome-metabolome associations

BACKGROUND

Microbiome-metabolome studies of the human gut have been gaining popularity in recent years, mostly due to accumulating evidence of the interplay between gut microbes, metabolites, and host health. Statistical and machine learning-based methods have been widely applied to analyze such paired microbiome-metabolome data, in the hope of identifying metabolites that are governed by the composition of the microbiome. Such metabolites can be likely modulated by microbiome-based interventions, offering a route for promoting gut metabolic health. Yet, to date, it remains unclear whether findings of microbially associated metabolites in any single study carry over to other studies or cohorts, and how robust and universal are microbiome-metabolites links.

RESULTS

In this study, we addressed this challenge by performing a comprehensive meta-analysis to identify human gut metabolites that can be predicted based on the composition of the gut microbiome across multiple studies. We term such metabolites "robustly well-predicted". To this end, we processed data from 1733 samples from 10 independent human gut microbiome-metabolome studies, focusing initially on healthy subjects, and implemented a machine learning pipeline to predict metabolite levels in each dataset based on the composition of the microbiome. Comparing the predictability of each metabolite across datasets, we found 97 robustly well-predicted metabolites. These include metabolites involved in important microbial pathways such as bile acid transformations and polyamines metabolism. Importantly, however, other metabolites exhibited large variation in predictability across datasets, suggesting a cohort- or study-specific relationship between the microbiome and the metabolite. Comparing taxonomic contributors to different models, we found that some robustly well-predicted metabolites were predicted by markedly different sets of taxa across datasets, suggesting that some microbially associated metabolites may be governed by different members of the microbiome in different cohorts. We finally examined whether models trained on a control group of a given study successfully predicted the metabolite's level in the disease group of the same study, identifying several metabolites where the model was not transferable, indicating a shift in microbial metabolism in disease-associated dysbiosis.

CONCLUSIONS

Combined, our findings provide a better understanding of the link between the microbiome and metabolites and allow researchers to put identified microbially associated metabolites within the context of other studies. Video abstract.

Microbiota Emergencies in the Diagnosis of Lung Diseases: A Meta-Analysis

Although many studies have reported that microbiota emergencies are deeply involved in the occurrence and subsequent progression of lung diseases, the present diagnosis of lung disease depends on microbiota markers, which is still poorly understood. Therefore, a meta-analysis was performed to confirm lung microbiota markers for the diagnosis of lung diseases. Literature databases were searched following the inclusion and exclusion criteria. There are 6 studies including 1347 patients and 26 comparisons to be enrolled, and then the diagnostic effect was evaluated using Stata 14.0 and Meta-disc 1.4 software. The pooled sensitivity (SEN), specificity (SPE), diagnostic likelihood ratio positive (DLR+), diagnostic likelihood ratio negative (DLR-), and diagnostic OR (DOR), as well as area under the curve (AUC) of microbiota markers in the diagnosis of lung diseases were 0.90 (95% CI: 0.83-0.94), 0.89 (95% CI: 0.76-0.95), 7.86 (95% CI: 3.39-18.21), 0.12 (95% CI: 0.06-0.21), 22.254 (95% CI: 12.83-39.59.14), and 0.95 (95% CI: 0.93-0.97), respectively. Subgroup analysis revealed that research based on Caucasian, adult, BAL fluid, PCR, pneumonia obtained higher AUC values. The microbiota markers have shown potential diagnosis value for lung diseases. But further large-scale clinical studies are still needed to verify and replicate the diagnostic value of lung microbiota markers.

Inhaled Corticosteroids and the Lung Microbiome in COPD

The Global Initiative for Chronic Obstructive Lung Disease 2021 Report recommends inhaled corticosteroid (ICS)-containing regimens as part of pharmacological treatment in patients with chronic obstructive lung disease (COPD) and frequent exacerbations, particularly with eosinophilic inflammation. However, real-world studies reveal overprescription of ICS in COPD, irrespective of disease presentation and inflammatory endotype, leading to increased risk of side effects, mainly respiratory infections. The optimal use of ICS in COPD therefore remains an area of intensive research, and additional biomarkers of benefit and risk are needed. Although the interplay between inflammation and infection in COPD is widely acknowledged, the role of the microbiome in shaping lower airway inflammation has only recently been explored. Next-generation sequencing has revealed that COPD disease progression and exacerbation frequency are associated with changes in the composition of the lung microbiome, and that the immunosuppressive effects of ICS can contribute to potentially deleterious airway microbiota changes by increasing bacterial load and the abundance of potentially pathogenic taxa such as Streptococcus and Haemophilus. Here, we explore the relationship between microbiome, inflammation, ICS use and disease phenotype. This relationship may inform the benefit:risk assessment of ICS use in patients with COPD and lead to more personalised pharmacological management.

The lung microbiome: progress and promise

The healthy lung was long thought of as sterile, but recent advances using molecular sequencing approaches have detected bacteria at low levels. Healthy lung bacteria largely reflect communities present in the upper respiratory tract that enter the lung via microaspiration, which is balanced by mechanical and immune clearance and likely involves limited local replication. The nature and dynamics of the lung microbiome, therefore, differ from those of ecological niches with robust self-sustaining microbial communities. Aberrant populations (dysbiosis) have been demonstrated in many pulmonary diseases not traditionally considered microbial in origin, and potential pathways of microbe-host crosstalk are emerging. The question now is whether and how dysbiotic microbiota contribute to initiation or perpetuation of injury. The fungal microbiome and virome are less well studied. This Review highlights features of the lung microbiome, unique considerations in studying it, examples of dysbiosis in selected disease, emerging concepts in lung microbiome-host interactions, and critical areas for investigation.

Gut Microbiota Comparison Between Intestinal Contents and Mucosa in Mice With Repeated Stress-Related Diarrhea Provides Novel Insight

Repeated stress-related diarrhea is a kind of functional bowel disorders (FBDs) that are mainly stemming from dysregulation of the microbiota–gut–brain axis mediated by a complex interplay of 5-hydroxytryptophan (5-HT). Intestinal content and intestinal mucosa microbiota belong to two different community systems, and the role of the two microbiota community systems in repeated stress-related diarrhea remains largely unknown. In order to ascertain the difference in composition and the potential function between intestinal content and intestinal mucosa microbiota response on repeated stress-related diarrhea, we collected intestinal contents and mucosa of mice with repeated stress-related diarrhea for 16S rRNA PacBio SMRT gene full-length sequencing, and with the digital modeling method of bacterial species abundance, the correlations among the two microbiota community systems and serum 5-HT concentration were analyzed. We found that the microbiotal composition differences both in intestinal contents and mucosa were consistent throughout all the phylogenetic ranks, with an increasing level of resolution. Compared with intestinal content microbiota, the diversity and composition of microbiota colonized in intestinal mucosa are more sensitive to repeated stress-related diarrhea. The PICRUSt2 of metagenomic function analysis found that repeated stress-related diarrhea is more likely to perturb the intestinal mucosa microbiota metagenomic functions involved in the neural response. We further found that the mucosal microbiota-based relative abundance model was more predictive on serum 5-HT concentration with the methods of machine-learning model established and multivariate dimensionality reduction (R² = 0.876). These findings suggest that the intestinal mucosa microbiota might serve as a novel potential prediction model for the serum 5-HT concentration involvement in the repeated stress-related diarrhea, in addition to focusing on its mechanism in the gastrointestinal dysfunction.