Metatranscriptomics of the Human Oral Microbiome during Health and Disease

Jorth, Peter; Turner, Keith H.; Gümüş, Pınar; Nizam, Nejat; Buduneli, Nurcan; Whiteley, Marvin

doi:10.1128/mbio.01012-14

Cited by 322 publications

(368 citation statements)

References 33 publications

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“…This may in part explain why we see an apparent fall in the number of bacteria detected in Mild to moderate OSAS while the total DNA content remained similar. However, it is well documented that the microbial diversity is usually lower in the periodontal disease state than in health [36]. The numbers of periodontally relevant pathogens were greater in the patients with severe OSAS, that they made up a significantly greater proportion of the oral microbiome than in the people with no OSAS or mild/moderate OSAS.…”

Section: Discussionmentioning

confidence: 98%

Is there an association between obstructive sleep apnea syndrome and periodontal inflammation?

et al. 2015

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Objectives: To assess salivary, serum biomarkers, and subgingival bacteria as putative candidates in the potential association between obstructive sleep apnea syndrome (OSAS) and periodontal disease. Materials and methods:Fifty-two patients were grouped according to the severity of OSAS: 13 participants served as controls, 17 patients had mild-to-moderate OSAS, and 22 severe OSAS. Serum, saliva, and subgingival plaque samples were collected, clinical periodontal parameters recorded. Salivary, serum concentrations of interleukin-6 (IL-6), tumour necrosis factor (TNF-α), osteoprotegerin, sRANKL, and apelin were analysed by enzyme-linked immunosorbent assay. Bacterial counts were determined by real-time QPCR on plaque microbial DNA preparations.Results: There was a significant change in the composition of microbes in plaque particularly in severe OSAS samples (p<0.01). Statistical analyses indicated significantly higher salivary IL-6 levels in both OSAS groups compared to controls (p<0.05). Salivary apelin levels were significantly higher in Serum levels of these biomarkers and salivary osteoprotegerin, sRANKL levels were similar in the study groups. The incidence and duration of apnea positively correlated with clinical periodontal parameters (p<0.05). Conclusion:OSAS appeared to alter the tested bacteria in plaque, correlate with increasing periodontal disease severity, have additive effect on salivary IL-6.Clinical relevance: OSAS is likely to interact with periodontal disease.

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Section: Discussionmentioning

confidence: 98%

Is there an association between obstructive sleep apnea syndrome and periodontal inflammation?

et al. 2015

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“…In these oral diseases a shift of species and functions associated with the diseases, i.e. dysbiosis, is observed (Belda-Ferre 2012;Griffen 2012;Jorth 2014;Wang 2013). …”

Section: Introductionmentioning

confidence: 99%

Resilience of the Oral Microbiota in Health: Mechanisms That Prevent Dysbiosis

2017

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word count: 278Reference number: 61 Figures: 6 2 AbstractDental diseases are now viewed as a consequence of a deleterious shift in the balance of the normally stable resident oral microbiome. It is known that frequent carbohydrate consumption or reduced saliva flow can lead to caries, and excessive plaque accumulation increases the risk of periodontal diseases. However, when these disease drivers are present, while some individuals appear to be susceptible, others are more tolerant or resilient to suffering from undesirable changes in their oral microbiome. Health-maintaining mechanisms that limit the effect of disease drivers include the complex set of metabolic and functional inter-relationships that develop within dental biofilms and between biofilms and the host. In contrast, 'positive feedback loops' can develop within these microbial communities that disrupt resilience and provoke a large and abrupt change in function and structure of the ecosystem (a microbial 'regime shift'), that promotes dysbiosis and oral disease. For instance, acidification due to carbohydrate fermentation or inflammation in response to accumulated plaque select for a cariogenic or periopathogenic microbiota, respectively, in a chain of selfreinforcing events. Conversely, in tolerant individuals, health-maintaining mechanisms, including negative feedback to the drivers, can maintain resilience and promote resistance to and recovery from disease drivers. Recently studied health-maintaining mechanisms include ammonium production, limiting a drop in pH that can lead to caries, and denitrification, which could inhibit several stages of disease-associated positive feedback loops. OMICS studies comparing the microbiome of, and its interaction with, susceptible and tolerant hosts can detect markers of resilience. The neutralization or inhibition of these 'disease drivers', together with the identification and promotion of health-promoting species and functions, for example by pre-and probiotics, could enhance microbiome resilience and lead to new strategies to prevent disease.3

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“…Later, RNA is fragmented into smaller pieces (the fragment sizes in bp depend of the selected sequencing platform) followed by cDNA synthesis using reverse transcriptase and random hexamers or oligo (dT) primers which are subjected to high throughput sequencing [67]. Other methods which do not require synthesizing cDNA and involve direct sequencing of RNA were also developed to avoid the sequencing biases introduced in quantification of transcripts that arise during conversion of RNA to cDNA [68][69][70][71] (Figure 2). The processing of the meta-transcriptomic data is similar to the metagenomic data and two strategies are employed for this (1) mapping sequence reads to reference genomes and genes and (2) de novo assembly of new transcriptomes.…”

Section: Meta-transcriptomicsmentioning

confidence: 99%