Inflammatory bacteriome featuring Fusobacterium nucleatum and Pseudomonas aeruginosa identified in association with oral squamous cell carcinoma - PubMed (original) (raw)
Inflammatory bacteriome featuring Fusobacterium nucleatum and Pseudomonas aeruginosa identified in association with oral squamous cell carcinoma
Nezar Noor Al-Hebshi et al. Sci Rep. 2017.
Abstract
Studies on the possible association between bacteria and oral squamous cell carcinoma (OSCC) remain inconclusive, largely due to methodological variations/limitations. The objective of this study was to characterize the species composition as well as functional potential of the bacteriome associated with OSCC. DNA obtained from 20 fresh OSCC biopsies (cases) and 20 deep-epithelium swabs (matched control subjects) was sequenced for the V1-V3 region using Illumina's 2 × 300 bp chemistry. High quality, non-chimeric merged reads were classified to species level using a prioritized BLASTN-algorithm. Downstream analyses were performed using QIIME, PICRUSt, and LEfSe. Fusobacterium nucleatum subsp. polymorphum was the most significantly overrepresented species in the tumors followed by Pseudomonas aeruginosa and Campylobacter sp. Oral taxon 44, while Streptococcus mitis, Rothia mucilaginosa and Haemophilus parainfluenzae were the most significantly abundant in the controls. Functional prediction showed that genes involved in bacterial mobility, flagellar assembly, bacterial chemotaxis and LPS synthesis were enriched in the tumors while those responsible for DNA repair and combination, purine metabolism, phenylalanine, tyrosine and tryptophan biosynthesis, ribosome biogenesis and glycolysis/gluconeogenesis were significantly associated with the controls. This is the first epidemiological evidence for association of F. nucleatum and P. aeruginosa with OSCC. Functionally, an "inflammatory bacteriome" is enriched in OSSC.
Conflict of interest statement
The authors declare that they have no competing interests.
Figures
Figure 1
Bacteriome profile. Stacked bars showing the distribution of phyla, top 15 genera and top 25 species detected in the study population and groups.
Figure 2
Rarefaction curves showing the number of observed species as a function of sequencing depth.
Figure 3
Principal Component Analysis. Clustering of the samples based on (A) binary Jaccard index (community membership) and (B) abundance weighted Jaccard index (community structure).
Figure 4
Differentially abundant taxa. Linear Discriminant Analysis Effect Size (LEfSe) analysis showing genera (A) and species (B) that were significantly differentially abundant between the cases and controls (LDA score ≥ 3). *The difference is also significant by G-test (False discovery rate = 0).
Figure 5
Differentially enriched functions. Linear Discriminant Analysis Effect Size (LEfSe) analysis showing genes (A) and pathways (B) that were significantly differentially enriched between the cases and controls (LDA score ≥ 2.25).
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