Salivary Microbiome and Cigarette Smoking: A First of Its Kind Investigation in Jordan

Study information

Reviewed Marked as Reviewed by Claregrieve1 on 2023-5-31

study design

cross-sectional observational, not case-control

Citation

PMID PubMed identifier for scientific articles.

31905907

DOI Digital object identifier for electronic documents.

10.3390/ijerph17010256

URI Uniform resource identifier for web resources.

https://pubmed.ncbi.nlm.nih.gov/31905907/

Authors

Al-Zyoud W, Hajjo R, Abu-Siniyeh A, Hajjaj S

Journal

International journal of environmental research and public health

Year

2019

Keywords:

16S rRNA, Jordan, bioinformatics, microbiome, microbiota, next-generation sequencing, operational taxonomic unit (OTU), saliva, smoking

There is accumulating evidence in the biomedical literature suggesting the role of smoking in increasing the risk of oral diseases including some oral cancers. Smoking alters microbial attributes of the oral cavity by decreasing the commensal microbial population and increasing the pathogenic microbes. This study aims to investigate the shift in the salivary microbiota between smokers and non-smokers in Jordan. Our methods relied on high-throughput next-generation sequencing (NGS) experiments for V3-V4 hypervariable regions of the 16S rRNA gene, followed by comprehensive bioinformatics analysis including advanced multidimensional data visualization methods and statistical analysis approaches. Six genera-Streptococcus, Prevotella, Vellionella, Rothia, Neisseria, and Haemophilus-predominated the salivary microbiota of all samples with different percentages suggesting the possibility for the salivary microbiome to restored after quitting smoking. Three genera-Streptococcus, Prevotella, and Veillonella-showed significantly elevated levels among smokers at the expense of Neisseria in non-smokers. In conclusion, smoking has a definite impact on shifting the salivary microbiota in smokers. We can suggest that there is microbial signature at the genera level that can be used to classify smokers and non-smokers by Linear Discriminant Analysis Effect Size (LEfSe) based on the salivary abundance of genera. Proteomics and metabolomics studies are highly recommended to fully understand the effect of bacterial endotoxin release and xenobiotic metabolism on the bacterial interrelationships in the salivary microbiome and how they affect the growth of each other in the saliva of smokers.

Experiment 1

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Reviewed Marked as Reviewed by Claregrieve1 on 2023-5-31

Curated date: 2023/04/03

Curator: Khadeeejah

Revision editor(s): WikiWorks, Claregrieve1, Khadeeejah, Atrayees, Aiyshaaaa

Subjects

Location of subjects: Jordan

Host species Species from which microbiome was sampled. Contact us to have more species added.: 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,saliva

Condition The experimental condition / phenotype studied according to the Experimental Factor Ontology: Smoking behavior smoking,Smoking behavior,smoking behavior

Group 0 name Corresponds to the control (unexposed) group for case-control studies: Non-smokers

Group 1 name Corresponds to the case (exposed) group for case-control studies: Smokers

Group 1 definition Diagnostic criteria applied to define the specific condition / phenotype represented in the case (exposed) group: Smoking subjects who smoked at least one cigarette per day.

Group 0 sample size Number of subjects in the control (unexposed) group: 51

Group 1 sample size Number of subjects in the case (exposed) group: 49

Antibiotics exclusion Number of days without antibiotics usage (if applicable) and other antibiotics-related criteria used to exclude participants (if any): 3 months

Lab analysis

Sequencing type: 16S

16S variable region One or more hypervariable region(s) of the bacterial 16S gene: V3-V4

Sequencing platform Manufacturer and experimental platform used for quantifying microbial abundance: Illumina

Statistical Analysis

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

Statistical test: Mann-Whitney (Wilcoxon)

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)?: Yes

LDA Score above Threshold for the linear discriminant analysis (LDA) score for studies using the popular LEfSe tool: 3

Alpha Diversity

Shannon Estimator of species richness and species evenness: more weight on species richness: decreased

Chao1 Abundance-based estimator of species richness: decreased

Richness Number of species: decreased

Signature 1

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Reviewed Marked as Reviewed by Claregrieve1 on 2023-5-31

Curated date: 2023/04/03

Curator: Khadeeejah

Revision editor(s): WikiWorks, Claregrieve1, Khadeeejah, Atrayees

Source: Table 3, 4

Description: Differential microbial abundance between non-smokers versus smokers (regardless of gender).

Abundance in Group 1: increased abundance in Smokers

NCBI	Quality Control	Links
Bacillota
Fusobacteriia
Prevotella
Pseudomonadota
Streptococcus
Veillonella

Revision editor(s): WikiWorks, Claregrieve1, Khadeeejah, Atrayees

Signature 2

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Reviewed Marked as Reviewed by Claregrieve1 on 2023-5-31

Curated date: 2023/04/03

Curator: Khadeeejah

Revision editor(s): WikiWorks, Claregrieve1, Khadeeejah, Atrayees

Source: Table 3, 4

Description: Differential microbial abundance between non-smokers versus smokers (regardless of gender).

Abundance in Group 1: decreased abundance in Smokers

NCBI	Quality Control	Links
Neisseria

Revision editor(s): WikiWorks, Claregrieve1, Khadeeejah, Atrayees

Experiment 2

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Reviewed Marked as Reviewed by Claregrieve1 on 2023-5-31

Curated date: 2023/05/30

Curator: Atrayees

Revision editor(s): Atrayees, Claregrieve1, WikiWorks

Differences from previous experiment shown

Subjects

Group 1 definition Diagnostic criteria applied to define the specific condition / phenotype represented in the case (exposed) group: Smoker subjects smoked at least one cigarette per day.