Oral microbiome - Saliva samples
Ontology highlight
ABSTRACT:
PROVIDER: PRJEB60183 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| ERR10931100.fastq.gz | Fastqsanger.gz | |||
| ERR10931200.fastq.gz | Fastqsanger.gz | |||
| ERR10931300.fastq.gz | Fastqsanger.gz | |||
| ERR10931400.fastq.gz | Fastqsanger.gz | |||
| ERR10931500.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:The oral cavity is home to one of the most diverse microbial community of the human body and a major entry portal for pathogens. Its homeostasis is maintained by saliva, which fulfills key functions including lubrication of food, predigesting and bacterial defense. Consequently, disruptions in saliva secretion contributes to conditions such as tooth decay and respiratory tract infections. Here we used recent improvements in mass spectrometry (MS)-based proteomics to develop a rapid workflow for mapping to map the saliva proteome quantitatively and at great depth. Microgram protein amounts retrieved from cotton swabs were processed in a single-run format, resulting in more than 3,700 quantified human proteins in 100 min measurements gradients. After separation into eight fractions, this increased to 5,500 human proteins. Remarkably, our measurements also quantified more than 2,000 microbial proteins and we find peptide evidence for more than 70 bacterial genera without any microbial culture. Co-analysis of the proteomics results with next generation sequencing data as well as MALDI Biotyper revealed strong agreement. The oral microbiome differs between individuals and changes drastically upon eating and tooth brushing. Rapid and robust shotgun technology can now simultaneously characterize the human and microbiome contributions to the proteome of a body fluid.
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