Lower respiratory tract microbiome composition and community interactions in smokers
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ORGANISM(S): Homo sapiens (human)
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PROVIDER: S-BSST891 | biostudies-other |
REPOSITORIES: biostudies-other
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Project description:BackgroundAlthough culture-independent methods have paved the way for characterization of the lung microbiome, the dynamic changes in the lung microbiome from neonatal stage to adult age have not been investigated.ResultsIn this study, we tracked changes in composition and diversity of the lung microbiome in C57BL/6N mice, starting from 1-week-old neonates to 8-week-old mice. Towards this, the lungs were sterilely excised from mice of different ages from 1 to 8 weeks. High-throughput DNA sequencing of the 16S rRNA gene followed by composition and diversity analysis was utilized to decipher the microbiome in these samples. Microbiome analysis suggests that the changes in the lung microbiome correlated with age. The lung microbiome was primarily dominated by phyla Proteobacteria, Firmicutes, Bacteroidetes, and Actinobacteria in all the stages from week 1 to week 8 after birth. Although Defluvibacter was the predominant genus in 1-week-old neonatal mice, Streptococcus became the dominant genus at the age of 2 weeks. Lactobacillus, Defluvibacter, Streptococcus, and Achromobacter were the dominant genera in 3-week-old mice, while Lactobacillus and Achromobacter were the most abundant genera in 4-week-old mice. Interestingly, relatively greater diversity (at the genus level) during the age of 5 to 6 weeks was observed as compared to the earlier weeks. The diversity of the lung microbiome remained stable between 6 and 8 weeks of age.ConclusionsIn summary, we have tracked the development of the lung microbiome in mice from an early age of 1 week to adulthood. The lung microbiome is dominated by the phyla Proteobacteria, Firmicutes, Bacteroidetes, and Actinobacteria. However, dynamic changes were observed at the genus level. Relatively higher richness in the microbial diversity was achieved by age of 6 weeks and then maintained at later ages. We believe that this study improves our understanding of the development of the mice lung microbiome and will facilitate further analyses of the role of the lung microbiome in chronic lung diseases.
| S-EPMC5479047 | biostudies-literature
Project description:16S rRNA gene amplicon sequencing of longitudinal Bronchoalveolar Lavage Fluid. Targeted loci
| PRJNA632552 | ENA
Project description:Analyses of human lung metagenome by nanopore sequencing
| PRJNA720304 | ENA
Project description:BackgroundLower respiratory tract infection (LRTI) is a major contributor to respiratory failure requiring intubation and mechanical ventilation. LRTI also occurs during mechanical ventilation, increasing the morbidity and mortality of intubated patients. We sought to understand the dynamics of respiratory tract microbiota following intubation and the relationship between microbial community structure and infection.ResultsWe enrolled a cohort of 15 subjects with respiratory failure requiring intubation and mechanical ventilation from the medical intensive care unit at an academic medical center. Oropharyngeal (OP) and deep endotracheal (ET) secretions were sampled within 24 h of intubation and every 48-72 h thereafter. Bacterial community profiling was carried out by purifying DNA, PCR amplification of 16S ribosomal RNA (rRNA) gene sequences, deep sequencing, and bioinformatic community analysis. We compared enrolled subjects to a cohort of healthy subjects who had lower respiratory tract sampling by bronchoscopy. In contrast to the diverse upper respiratory tract and lower respiratory tract microbiota found in healthy controls, critically ill subjects had lower initial diversity at both sites. Diversity further diminished over time on the ventilator. In several subjects, the bacterial community was dominated by a single taxon over multiple time points. The clinical diagnosis of LRTI ascertained by chart review correlated with low community diversity and dominance of a single taxon. Dominant taxa matched clinical bacterial cultures where cultures were obtained and positive. In several cases, dominant taxa included bacteria not detected by culture, including Ureaplasma parvum and Enterococcus faecalis.ConclusionsLongitudinal analysis of respiratory tract microbiota in critically ill patients provides insight into the pathogenesis and diagnosis of LRTI. 16S rRNA gene sequencing of endotracheal aspirate samples holds promise for expanded pathogen identification.
| S-EPMC4750361 | biostudies-literature
Project description:COVID-19 infection may predispose to secondary bacterial infection which is associated with poor clinical outcome especially among critically ill patients. We aimed to characterize the lower respiratory tract bacterial microbiome of COVID-19 critically ill patients in comparison to COVID-19-negative patients. We performed a 16S rRNA profiling on bronchoalveolar lavage (BAL) samples collected between April and May 2020 from 24 COVID-19 critically ill subjects and 24 patients with non-COVID-19 pneumonia. Lung microbiome of critically ill patients with COVID-19 was characterized by a different bacterial diversity (PERMANOVA on weighted and unweighted UniFrac Pr(> F) = 0.001) compared to COVID-19-negative patients with pneumonia. Pseudomonas alcaligenes, Clostridium hiranonis, Acinetobacter schindleri, Sphingobacterium spp., Acinetobacter spp. and Enterobacteriaceae, characterized lung microbiome of COVID-19 critically ill patients (LDA score > 2), while COVID-19-negative patients showed a higher abundance of lung commensal bacteria (Haemophilus influenzae, Veillonella dispar, Granulicatella spp., Porphyromonas spp., and Streptococcus spp.). The incidence rate (IR) of infections during COVID-19 pandemic showed a significant increase of carbapenem-resistant Acinetobacter baumannii (CR-Ab) infection. In conclusion, SARS-CoV-2 infection and antibiotic pressure may predispose critically ill patients to bacterial superinfection due to opportunistic multidrug resistant pathogens.
| S-EPMC8115177 | biostudies-literature
Project description:BACKGROUND:Lower respiratory tract (LRT) microbiome has been reported to associate with pulmonary diseases. Unregulated inflammation is an underlying cause of variable lung diseases. The lung microbiome may play an important role in the smoking-induced inflammatory lung diseases. What's more, the function of microbiome may be more important for understanding how microbes interact with host. Our study aims to explore the effects of smoking on the lower respiratory tract microbiome, the association between variation of lower respiratory tract microbiome and inflammation and whether smoking exposure changes the function of lower respiratory tract microbime. METHODS:Forty male mice were randomly divided into smoking group and non-smoking group, and the smoking group was exposed to cigarette smoke for 2?h per day for 90?days. After experiment, the blood samples were collected to measure the concentration of interleukin-6 (IL-6) and C reactive protein (CRP) by ELISA. Lung tissue samples were used to detect the community and diversity of lower respiratory tract microbiome through 16S rRNA gene quantification and sequencing technology. ANOSIM and STAMP were performed to analyze the differences of the microbial community structure between smoking group and non-smoking group. SPSS 24.0 software was used to analyze the correlations between microbiome and inflammation mediators through scatter plots and Spearman correlation coefficient. Microbial metabolic function was predicted by PICRUSt based on the 16?s rRNA gene quantification and sequencing results. PATRIC database was searched for the potential pathogenic bacteria in lower respiratory tract. RESULTS:Our results suggested that smoking had markedly effects on the microbiota structure of lower respiratory tract based on Bray-Curtis distance (R2?=?0.084, p?=?0.005) and on unweighted uniFrac distance (R2?=?0.131, p?=?0.002). Smoking mainly affected the abundance of microbiome which belong to Proteobacteria phyla and Firmicutes phyla. Moreover, our results also found that smoking increased the abundance of Acinetobacter, Bacillus and Staphylococcus, which were defined as pathogenic bacteria. Inflammatory mediators were observed to associate with certain microbiome at every level. Most of microbiome which were associated with inflammation belonged to Proteobacteria phyla or Firmicutes phyla. Moreover, we found that the decreased microbiome in smoking group, including Oceanospirillales, Desulfuromonadales, Nesterenkonia, and Lactobacillaceae, all were negatively correlated with IL-6 or CRP. Based on the level of inflammation, the abundance of microbiome differs. At genus level, Lactobacillus, Pelagibacterium, Geobacter and Zoogloea were significantly higher in smoking group with lower IL-6 concentration. The abundance of microbiome was not observed any statistical difference in subgroups with different weight. Three dominant genus, defined as pathogen, were found higher in the smoking group. The microbial functional prediction analysis revealed that ABC-type transport systems, transcription factors, amino acide transport and metabolism, arginine and proline metabolism et al. were distinctively decreased in smoking group, while the proportions of replication, recombination and repair, ribosome, DNA repair and recombination proteins were increased in smoking group (q?<?0.05). CONCLUSIONS:Members of Proteobacteria phyla and Firmicutes phyla played an important role in the microbial community composition and keeping a relatively balanced homeostasis. Microbiome dysbiosis might break the balance of immune system to drive lung inflammation. There might exist potential probiotics in lower respiratory tract, such as Lactobacillaceae. The altered function of Lower respiratory tract microbiome under smoking exposure may affect the physiological homeostasis of host.
| S-EPMC6892239 | biostudies-literature
Project description:BACKGROUND:Probiotics could prevent Pseudomonas aeruginosa colonization in lower respiratory tract (LRT) and reduced P. aeruginosa ventilator-associated pneumonia (VAP) rate. Recent studies also suggested that probiotics could improve lung inflammation in mice infected with P. aeruginosa. It seems that microbiota regulation may be a potential therapy for P. aeruginosa VAP patients. However, we know less about the LRT microbial composition and its correlation with prognosis in P. aeruginosa VAP patients. This study aimed to characterize LRT microbiota in P. aeruginosa VAP patients and explore the relationship between microbiota and patient prognosis. METHODS:Deep endotracheal secretions were sampled from subjects via intubation. Communities were identified by 16S ribosomal RNA gene sequencing. The relationship between microbiota and the prognosis of P. aeruginosa VAP patients were evaluated. Clinical pulmonary infection score and the survival of intensive care unit were both the indicators of patient prognosis. RESULTS:In this study, the LRT microbial composition of P. aeruginosa VAP patients was significantly different from non-infected intubation patients, and showed significant individual differences, forming two clusters. According to the predominant phylum of each cluster, these two clusters were named Pro cluster and Fir-Bac cluster respectively. Patients from Pro cluster were dominated by Proteobacteria (adj.P?<?0.001), while those from Fir-Bac cluster were dominated by Firmicutes, and Bacteroidetes (both adj.P?<?0.001). These two varied clusters (Pro and Fir-Bac cluster) were associated with the patients' primary disease (?2-test, P <?0.0001). The primary disease of the Pro cluster mainly included gastrointestinal disease (63%), and the Fir-Bac cluster was predominantly respiratory disease (89%). During the two-week dynamic observation period, despite the use of antibiotics, the dominant genera and Shannon diversity of the LRT microbiota did not change significantly in patients with P. aeruginosa VAP. In prognostic analysis, we found a significant negative correlation between Lactobacillus and clinical pulmonary infection score on the day of diagnosis (P?=?0.014); but we found no significant difference of microbial composition between survivors and non-survivors. CONCLUSIONS:LRT microbial composition was diversified among P. aeruginosa VAP patients, forming two clusters which were associated with the primary diseases of the patients.
| S-EPMC6062970 | biostudies-literature
Project description:BACKGROUND. Lower respiratory tract infection (LRTI) is a leading cause of death in children worldwide. LRTI diagnosis is challenging since non-infectious respiratory illnesses appear clinically similar and existing microbiologic tests are often falsely negative or detect incidentally-carried microbes common in children. These challenges result in antimicrobial overuse and adverse patient outcomes. Lower airway metagenomics has the potential to detect host and microbial signatures of LRTI. Whether it can be applied at scale and in a pediatric population to enable improved diagnosis and precision treatment remains unclear. METHODS. We used tracheal aspirate RNA-sequencing to profile host gene expression and respiratory microbiota in 261 children with acute respiratory failure. We developed a random forest gene expression classifier for LRTI by training on patients with an established diagnosis of LRTI (n=117) or of non-infectious respiratory failure (n=50). We then developed a classifier that integrates the: i) host LRTI probability, ii) abundance of respiratory viruses, and iii) dominance in the lung microbiome of bacteria/fungi considered pathogenic by a rules-based algorithm. RESULTS. The host classifier achieved a median AUC of 0.967 by 5-fold cross-validation, driven by activation markers of T cells, alveolar macrophages and the interferon response. The integrated classifier achieved a median AUC of 0.986 and significantly increased the confidence of patient classifications. When applied to patients with an uncertain diagnosis (n=94), the integrated classifier indicated LRTI in 52% of cases and nominated likely causal pathogens in 98% of those. CONCLUSIONS. Lower airway metagenomics enables accurate LRTI diagnosis and pathogen identification in a heterogeneous cohort of critically ill children through integration of host, pathogen, and microbiome features.
2022-12-06 | GSE212532 | GEO
Project description:Whether the presence of Candida spp. in lower respiratory tract (LRT) secretions is a marker of underlying disease, intensive care unit (ICU) treatment and antibiotic therapy or contributes to poor clinical outcome is unclear. We investigated healthy controls, patients with proposed risk factors for Candida growth in LRT (antibiotic therapy, ICU treatment with and without antibiotic therapy), ICU patients with pneumonia and antibiotic therapy and candidemic patients (for comparison of truly invasive and colonizing Candida spp.). Fungal patterns were determined by conventional culture based microbiology combined with molecular approaches (next generation sequencing, multilocus sequence typing) for description of fungal and concommitant bacterial microbiota in LRT, and host and fungal biomarkes were investigated. Admission to and treatment on ICUs shifted LRT fungal microbiota to Candida spp. dominated fungal profiles but antibiotic therapy did not. Compared to controls, Candida was part of fungal microbiota in LRT of ICU patients without pneumonia with and without antibiotic therapy (63% and 50% of total fungal genera) and of ICU patients with pneumonia with antibiotic therapy (73%) (p<0.05). No case of invasive candidiasis originating from Candida in the LRT was detected. There was no common bacterial microbiota profile associated or dissociated with Candida spp. in LRT. Colonizing and invasive Candida strains (from candidemic patients) did not match to certain clades withdrawing the presence of a particular pathogenic and invasive clade. The presence of Candida spp. in the LRT rather reflected rapidly occurring LRT dysbiosis driven by ICU related factors than was associated with invasive candidiasis.
| S-EPMC4874575 | biostudies-literature
Project description:It is widely known that pneumonia (either community acquired or hospital acquired, as like ventilator associated pneumonia (VAP)), is the most frequent type of severe infection and continues to pose a significant burden on healthcare services worldwide. Despite new diagnostic developments, most pneumonia cases continue to be difficult to diagnose clinically, partly due to acquired antibiotic resistance and the lack of a 'gold standard' method of diagnosis. In other words, the lack of a rapid, accurate diagnostic test, as well as the uncertainty of the initial etiologic diagnosis and the risk stratification, results in empirical antibiotic treatments. There are significant changes in the aetiology of patients with ventilator associated lower respiratory tract infections (VA-LRTI), which are characterised by a higher incidence of multi drug resistant organisms. Evidence suggests that when patients with VA-LRTI develop organ failure, the associated mortality can be exceptionally high with frequent complications, including acute respiratory distress syndrome, acute kidney injury, and septic shock. Appropriate antibiotic treatments must consider that the present cardiovascular failure seen in patients has a different association with the patient's mortality. Unlike patients with less severe clinical presentations, who have a higher chance of survival when the appropriate antibiotics are administered promptly, for patients with a severe subtype of the disease, the appropriateness of antibiotic treatment will impact the patient's outcome to a lesser extent. The present review highlights certain factors detectable at the time of admission that could indicate patients who are at a high risk of bacteraemia and who, therefore, merit more intense therapy and stratified care.
| S-EPMC7399936 | biostudies-literature