Ontology highlight
ABSTRACT:
PROVIDER: PRJNA940686 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR23694103_1.fastq.gz | Fastqsanger.gz | |||
| SRR23694103_2.fastq.gz | Fastqsanger.gz | |||
| SRR23694104_1.fastq.gz | Fastqsanger.gz | |||
| SRR23694104_2.fastq.gz | Fastqsanger.gz | |||
| SRR23694105_1.fastq.gz | Fastqsanger.gz |
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Project description:In this paper, we first report that EC smoking significantly increases the odds of gingival inflammation. Then, we seek to identify and explain the mechanism that underlies the relationship between EC smoking and gingival inflammation via the oral microbiome. We performed mediation analyses to assess if EC smoking affects the oral microbiome, which in turn affects gingival inflammation. For this, we collected saliva and subgingival samples from EC users and non-users and profiled their microbial compositions via 16S rRNA amplicon sequencing. We then performed α-diversity, β-diversity, and taxonomic differential analyses to survey the disparity in microbial composition between EC users and non-users. We found significant increases in α-diversity in EC users and disparities in β-diversity between EC users and non-users.
2022-12-09 | GSE201949 | GEO
Project description:Background: The microbiome is increasingly being linked to cancer risk. Little is known about the lung and oral cavity microbiomes in healthy smokers (SM), and even less for electronic cigarette (EC) users, compared healthy never-smokers (NS). Methods: In a cross-sectional pilot study of SM (N=8), EC users (N=10) and NS (N=10) saliva and bronchoscopy-collected bronchoalveolar lavage samples were collected. Bacteria species were identified through metatranscriptome profiling by RNA-sequencing to study associations with the lung and oral microbiome. Pairwise comparisons and linear modeling was assessed with false discovery rates <0.1. Results: Total bacterial load was similar for the SM, EC users and NS, and there was no differences in the bacterial diversity across groups. In the lung, there were 44 bacterial species that were statistically significantly different for SM/NS, 80% of which were decreased in the SM. There were 12 bacterial species that were different for SM/EC users, all of which were decreased, 10 of which were also identified in the SM/NS comparison. The 2 bacterial species unique to SM/EC comparison were Neisseria sp. KEM232 and Curvibacter sp. AEP1-3. From the top 5 decreased species in SM/EC, 3 were also identified in the SM/NS comparison (Neisseria elongata, Neisseria sicca, and Haemophilus parainfluenzae) and 2 of these were unique to the SM/EC comparison (Neisseria zoodegmatis and Ottowia sp. oral taxon 894). There were 8 species increased in SM compared to NS, none of which are known to be clinically significant. In the oral microbiome, 152 bacteria species were differentially abundant for the SM/NS analysis, and only 17 for the EC/NS comparison, all which were also present in SM/NS comparisons. There were 21 bacteria that were differentially abundant in both the lung and oral cavity for SM and NS, 95% also were decreased in the SM. Conclusion: Smoking and EC use do not appear to materially affect the lung microbiome, although differences are noted of unclear clinical significance. Most differentially abundant bacteria decreased, which may be due to a toxic effect of cigarette smoke, including a change in humidity or heating. Given the low number of overlapping oral and lung microbes, the oral microbiome does not appear to be a good surrogate for smoking-related effects in the lung.
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Project description:HuMiChip was used to analyze human oral and gut microbiomes, showing significantly different functional gene profiles between oral and gut microbiome. The results were used to demonstarte the usefulness of applying HuMiChip to human microbiome studies.
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Project description:HuMiChip was used to analyze human oral and gut microbiomes, showing significantly different functional gene profiles between oral and gut microbiome.
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Project description:Background: Smoking increases pulmonary inflammation, but an effect by electronic cigarette (EC) vaping is less understood. We previously reported smokers (SM) had increased lung immune cell counts and inflammatory gene expression in bronchial epithelial cells compared to EC users and never-smokers (NS). Here we report association of smoking and vaping with immune cell subtypes and gene expression in bronchoalveolar lavage. Methods: SM, EC users, and NS underwent bronchoscopy (n=28). RNASeq and the CIBERSORT computational algorithm were used to determine immune cell subtypes, along with inflammatory gene expression and microbiome metatranscriptomics. Correlations and associations were assessed across and within the tobacco-use groups, corrected for false discovery rates. Results: Classification of macrophage subtypes revealed a 2-fold increase in M0 macrophages for smokers and EC users relative to never-smokers, with a concordant decrease in M2 macrophages. There were 68, 19, and 1 significantly differentially expressed inflammatory genes (DEG) (FDR<0.05; log2-fold change>1) between SM/NS, SM/EC users, and EC users/NS respectively. CSF-1 and GATA3 expression correlated positively and inversely with M0 and M2 macrophages respectively. Correlation profiling for DEG showed distinct lung profiles for each participant group. There were 3 bacteria genera-DEG correlations and 3 bacteria genera-macrophage subtype correlations (Diffcorr>0.5 and FDR<0.1). Conclusions: In this pilot study, smoking and EC use were associated with an increase in undifferentiated M0 macrophages, but smokers differed from EC users and NS for inflammatory gene expression. The preliminary data support the hypothesis that smoking and EC use have toxic lung effects influencing inflammatory responses, but not via changes in the microbiome.
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