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J Bacteriol. 2012 Apr;194(8):2082-95. doi: 10.1128/JB.06328-11. Epub 2012 Feb 10.

Effect of periodontal pathogens on the metatranscriptome of a healthy multispecies biofilm model.

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Forsyth Institute, Cambridge, Massachusetts, USA.


Oral bacterial biofilms are highly complex microbial communities with up to 700 different bacterial taxa. We report here the use of metatranscriptomic analysis to study patterns of community gene expression in a multispecies biofilm model composed of species found in healthy oral biofilms (Actinomyces naeslundii, Lactobacillus casei, Streptococcus mitis, Veillonella parvula, and Fusobacterium nucleatum) and the same biofilm plus the periodontopathogens Porphyromonas gingivalis and Aggregatibacter actinomycetemcomitans. The presence of the periodontopathogens altered patterns in gene expression, and data indicate that transcription of protein-encoding genes and small noncoding RNAs is stimulated. In the healthy biofilm hypothetical proteins, transporters and transcriptional regulators were upregulated while chaperones and cell division proteins were downregulated. However, when the pathogens were present, chaperones were highly upregulated, probably due to increased levels of stress. We also observed a significant upregulation of ABC transport systems and putative transposases. Changes in Clusters of Orthologous Groups functional categories as well as gene set enrichment analysis based on Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways showed that in the absence of pathogens, only sets of proteins related to transport and secondary metabolism were upregulated, while in the presence of pathogens, proteins related to growth and division as well as a large portion of transcription factors were upregulated. Finally, we identified several small noncoding RNAs whose predicted targets were genes differentially expressed in the open reading frame libraries. These results show the importance of pathogens controlling gene expression of a healthy oral community and the usefulness of metatranscriptomic techniques to study gene expression profiles in complex microbial community models.

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