Effect of electronic cigarette and tobacco smoking on the human saliva microbial community

Study information

Needs review

study design

case-control

Citation

PMID PubMed identifier for scientific articles.

35229279

DOI Digital object identifier for electronic documents.

https://doi.org/10.1007%2Fs42770-022-00721-5

URI Uniform resource identifier for web resources.

Authors

Wang X, Mi Q, Yang J, Guan Y, Zeng W, Xiang H, Liu X, Yang W, Yang G, Li X, Cui Y, Gao Q

Journal

Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology]

Year

2022

Increasing evidence demonstrated the oral microbial community profile characteristics affected by conventional cigarettes smoking, but few studies focus on oral microbiome in response to electronic cigarettes (E-cigarettes). This study aimed to investigate the effect of E-cigarettes on the oral microbiome and to describe the difference of oral community profiles between E-cigarette smokers and tobacco smokers. 16S rRNA V4 gene sequencing was performed to investigate the oral microbial profiles of 5 E-cigarette smokers, 14 tobacco smokers, 8 quitting tobacco smokers, and 6 nonsmokers. The Chao1, ACE, and Shannon diversity indexes increased significantly in saliva samples collected from E-cigarette smokers and tobacco smokers compared to the non-smokers, and no significant difference was found in alpha diversity between E-cigarette smokers and tobacco smokers. The main phyla Proteobacteria, Firmicutes, Bacteroidetes, and Fusobacteria and major genera Neisseria, Streptococcus, Prevotellaceae, Fusobacterium, and Porphyromonas dominated in the smoking groups, while Actinobacteria, Proteobacteria, Firmicutes, Bacteroidetes, and Fusobacteria became the dominant phyla along with the genera Corynebacterium, Neisseria, Streptococcus, Actinomyces, and Porphyromonas in the nonsmokers. The differences in the phylum Actinobacteria and genus Corynebacterium contributed to various functional differences between smokers and nonsmokers. The difference on oral microbial and composition between E-cigarettes and common tobacco were associated with increased Prevotellaceae and decreased Neisseria. Additionally, smoking cessation could lead to re-establishment of the oral microbiome to that of nonsmokers. Our data demonstrate that E-cigarette smoking had different effects on the structure and composition of the oral microbial community compared to tobacco smoking. However, the short- and long-term impact of E-cigarette smoking on microbiome composition and function needs further exploration.

Experiment 1

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Needs review

Curated date: 2023/03/14

Curator: Annabelcute

Revision editor(s): Annabelcute

Subjects

Location of subjects: China

Host species Species from which microbiome was sampled (if applicable): Homo sapiens

Body site Anatomical site where microbial samples were extracted from according to the Uber Anatomy Ontology: Saliva Sailva normalis,Saliva atomaris,Saliva molecularis,Salivary gland secretion,Saliva

Condition The experimental condition / phenotype studied according to the Experimental Factor Ontology: human oral metagenome human oral metagenome

Group 0 name Corresponds to the control (unexposed) group for case-control studies: Healthy control
Group 1 name Corresponds to the case (exposed) group for case-control studies: Human oral meta genome
Group 1 definition Diagnostic criteria applied to define the specific condition / phenotype represented in the case (exposed) group: Effect of E-cigarettes on the oral microbiome
Group 0 sample size Number of subjects in the control (unexposed) group: 33
Group 1 sample size Number of subjects in the case (exposed) group: 33
Antibiotics exclusion Number of days without antibiotics usage (if applicable) and other antibiotics-related criteria used to exclude participants (if any): 6 months

Lab analysis

Sequencing type: 16S

Sequencing platform Manufacturer and experimental platform used for quantifying microbial abundance: Ion Torrent

Statistical Analysis

Data transformation Data transformation applied to microbial abundance measurements prior to differential abundance testing (if any).

relative abundances

Statistical test

LEfSe

Significance threshold p-value or FDR threshold used for differential abundance testing (if any): 0.05
MHT correction Have statistical tests be corrected for multiple hypothesis testing (MHT)?: No
LDA Score above Threshold for the linear discriminant analysis (LDA) score for studies using the popular LEfSe tool: 3

Matched on Factors on which subjects have been matched on in a case-control study: age, sex

Alpha Diversity

Shannon Estimator of species richness and species evenness: more weight on species richness: increased
Chao1 Abundance-based estimator of species richness: increased