Abstract
Background/Objectives:Periodontitis is the most prevalent inflammatory disease worldwide and is caused by a dysbiotic subgingival biofilm. Here we used metatranscriptomics to determine the functional shift from health to periodontitis, the response of individual species to dysbiosis and to discover biomarkers.Methods:Sixteen individuals were studied, from which six were diagnosed with chronic periodontitis. Illumina sequencing of the total messenger RNA (mRNA) yielded ~42 million reads per sample. A total of 324 human oral taxon phylotypes and 366,055 open reading frames from the HOMD database reference genomes were detected.Results:The transcriptionally active community shifted from Bacilli and Actinobacteria in health to Bacteroidia, Deltaproteobacteria, Spirochaetes and Synergistetes in periodontitis. Clusters of orthologous groups (COGs) related to carbohydrate transport and catabolism dominated in health, whereas protein degradation and amino acid catabolism dominated in disease. The LEfSe, random forest and support vector machine methods were applied to the 2,000 most highly expressed genes and discovered the three best functional biomarkers, namely haem binding protein HmuY from Porphyromonas gingivalis, flagellar filament core protein FlaB3 from Treponema denticola, and repeat protein of unknown function from Filifactor alocis. They predicted the diagnosis correctly for 14 from 16 individuals, and when applied to an independent study misclassified one out of six subjects only. Prevotella nigrescens shifted from commensalism to virulence by upregulating the expression of metalloproteases and the haem transporter. Expression of genes for the synthesis of the cytotoxic short-chain fatty acid butyrate was observed by Fusobacterium nucleatum under all conditions. Four additional species contributed to butyrate synthesis in periodontitis and they used an additional pathway.Conclusion:Gene biomarkers of periodontitis are highly predictive. The pro-inflammatory role of F. nucelatum is not related to butyrate synthesis.
Highlights
The microbiome of the oral cavity is the second most diverse one of the human body after the gut.[1]
The identification of numerous additional species associated with disease resulted in the ‘polymicrobial synergy and dysbiosis’ model where synergistic activities of the whole community provoked by key-stone pathogens interfere with host immune defence and cause tissue destruction.[13]
The inhouse pipeline started with the removal of ribosomal RNA reads using SortmeRNA version 1.8.23 Non-ribosomal RNA fragments were mapped against the HOMD reference sequences using the Burrows–Wheeler Aligner version 0.7.5 (− k 31, option for minimum seed length, SA (SA:Z), tag for chimera removal), and SAMtools for sorting and filtering nucleotide sequence alignments.[24]
Summary
The microbiome of the oral cavity is the second most diverse one of the human body after the gut.[1]. Understanding the mechanisms leading to such ecological catastrophes is challenging in view of the enormous complexity of the microbiomes.[3] the oral cavity is much more accessible than the gut, and just like many gut-related disorders, periodontitis results from a complex interaction between the microbiota and the host immune system. Studies of the role of the microbiota in periodontitis can help understand a potentially dangerous polymicrobial biofilm disease, but might reveal mechanisms that have a much broader relevance. Generation sequencing techniques have described the bacterial communities in great depth and shifts in the abundances of many species were observed in periodontal disease.[10,11,12] The identification of numerous additional species associated with disease resulted in the ‘polymicrobial synergy and dysbiosis’ model where synergistic activities of the whole community provoked by key-stone pathogens interfere with host immune defence and cause tissue destruction.[13]
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