The use of cultivation-independent metagenomic 16S rRNA gene-based metagenomic analysis provides a powerful tool to analyze the complex microbiomes of environmental and clinical samples. Ai and colleaques (Ai et al., 2017) suggested that alterations in the subgingival microbiome can be identified using metagenomic sequencing and used as a predictive marker of early periodontitis. Alpha-diversity (Shannon index) was the single strongest predictor of subjects’ periodontal status.
Currently, the cost of 16S rRNA gene sequencing is around 125€/sample. In order to obtain results, the data obtained from the sequencing machine must be computed with appropriate sofwares by a person trained in bioinformatics. Since the analysis process also includes manual steps, it might be difficult to perform analyses by
”feeding the data into a computer and waiting for the results”. Normally, the data must be analyzed in computer clusters due to the size of the data, which usually exceeds several gigabytes. The sequencing costs, including the need for both supercomputers and highly trained personnel, might inhibit wide usage of the method at this time.
7 CONCLUSIONS
The systemic vascular complications of periodontitis are caused either by direct bacterial invasion of the vascular wall (e.g. infective endocarditis) or indirectly by inducing systemic low-grade inflammation (e.g. atherosclerotic diseases). Many studies have shown that both periodontal pathogens and the bacteria from normal oral microbiota can escape from the oral niche and invade the extraoral vascular tissue. Local periodontitis can cause systemic low-grade inflammation. There are no other studies in which bacteria have been detected in the saccular intracranial aneurysm tissue, but the findings of Studies I to IV are in line with other studies regarding the associations between oral bacteria and cardiovascular diseases: Oral and pharyngeal bacterial DNA was detected in ruptured and unruptured intracranial aneurysm tissue samples along with possibly bacterial driven inflammation. The patients with saccular intracranial aneurysms had significantly more dental infectious foci (≥6 mm gingival pockets) than are found in the normal population in general. Fusobacterium nucleatum, which appears to be associated with the progression of periodontitis, is frequently found in the gingival pocket samples from patients with intracranial aneurysms. Active tooth brushing appears to reduce the alpha diversity and the amount of Fusobacterium nucleatum in the gingival pocket microbiome. It appears that oral bacteria might also play a role in the pathogenesis of intracranial aneurysm disease.
6.5 THE RISK OF SAMPLE CONTAMINATION
The probability of extrinsic contamination of the paper pin sample is low. The sampling process was standardized: All the samples were taken by an experienced oral and maxillofacial surgeon and a study nurse. The paper pin pack was opened by a study nurse and the pin was handled using sterile instruments. After sampling the pin was stored in a sterile eppendorf tube and frozen to -80°C within 15 minutes.
The sampling was carried out using sterile techniques.
The probability of contamination of the aneurysm tissue sample is low. Sampling was carried out using sterile techniques in a neurosurgical operation theatre. After sampling, the tissue sample was stored in a sterile eppendorf tube and refrigerated to -80°C within 15 minutes. Altogether, the probability of extrinsic contamination is very low due to standardization of the sampling procedure. However, since intrinsic contamination of the paper pin sample via saliva is possible, in Study IV the possible contaminant bacterial genera were assessed as a part of the protocol: Unusual oral bacteria, bacteria found in animal cavities, common contaminants found primarily in skin and respiratory tract and no/poorly classified bacteria were removed before the analyses.
6.6 THE USE OF MOLECULAR DETECTION OF BACTERIA IN CLINICAL PRACTICE AND FUTURE CONSIDERATIONS
The use of cultivation-independent metagenomic 16S rRNA gene-based metagenomic analysis provides a powerful tool to analyze the complex microbiomes of environmental and clinical samples. Ai and colleaques (Ai et al., 2017) suggested that alterations in the subgingival microbiome can be identified using metagenomic sequencing and used as a predictive marker of early periodontitis. Alpha-diversity (Shannon index) was the single strongest predictor of subjects’ periodontal status.
Currently, the cost of 16S rRNA gene sequencing is around 125€/sample. In order to obtain results, the data obtained from the sequencing machine must be computed with appropriate sofwares by a person trained in bioinformatics. Since the analysis process also includes manual steps, it might be difficult to perform analyses by
”feeding the data into a computer and waiting for the results”. Normally, the data must be analyzed in computer clusters due to the size of the data, which usually exceeds several gigabytes. The sequencing costs, including the need for both supercomputers and highly trained personnel, might inhibit wide usage of the method at this time.
7 CONCLUSIONS
The systemic vascular complications of periodontitis are caused either by direct bacterial invasion of the vascular wall (e.g. infective endocarditis) or indirectly by inducing systemic low-grade inflammation (e.g. atherosclerotic diseases). Many studies have shown that both periodontal pathogens and the bacteria from normal oral microbiota can escape from the oral niche and invade the extraoral vascular tissue. Local periodontitis can cause systemic low-grade inflammation. There are no other studies in which bacteria have been detected in the saccular intracranial aneurysm tissue, but the findings of Studies I to IV are in line with other studies regarding the associations between oral bacteria and cardiovascular diseases: Oral and pharyngeal bacterial DNA was detected in ruptured and unruptured intracranial aneurysm tissue samples along with possibly bacterial driven inflammation. The patients with saccular intracranial aneurysms had significantly more dental infectious foci (≥6 mm gingival pockets) than are found in the normal population in general. Fusobacterium nucleatum, which appears to be associated with the progression of periodontitis, is frequently found in the gingival pocket samples from patients with intracranial aneurysms. Active tooth brushing appears to reduce the alpha diversity and the amount of Fusobacterium nucleatum in the gingival pocket microbiome. It appears that oral bacteria might also play a role in the pathogenesis of intracranial aneurysm disease.
Figure 10. Hypothetical model explaining how TLR2, CD14 mediated inflammation could be caused by oral bacteria (especially F.nucleatum and S.mitis).
Bacteria from dental biofilm could enter the bloodstream and intracranial aneurysm.
In the aneurysm, PAMPs of the bacteria could be recognized by PRRs causing an inflammatory reaction.
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10.1126/scitranslmed.3008599.
Aarabi, G. et al. (2017) ‘Genetic Susceptibility Contributing to Periodontal and Cardiovascular Disease’, Journal of Dental Research. SAGE Publications Inc, 96(6), pp. 610–617. doi: 10.1177/0022034517699786.
Aas, J. A. et al. (2005) ‘Defining the normal bacterial flora of the oral cavity’, Journal of clinical microbiology. American Society for Microbiology, 43(11), pp. 5721–5732.
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Abusleme, L. et al. (2013) ‘The subgingival microbiome in health and periodontitis and its relationship with community biomass and inflammation’, The ISME Journal 7(5), pp. 1016-1025.
Abusleme, L. et al. (2014) ‘Influence of DNA extraction on oral microbial profiles obtained via 16S rRNA gene sequencing’, Journal of oral microbiology. Co-Action Publishing, 6, p. 10.3402/jom.v6.23990. doi: 10.3402/jom.v6.23990.
Ai, D. et al. (2017) ‘Integrated metagenomic data analysis demonstrates that a loss of diversity in oral microbiota is associated with periodontitis’, BMC genomics.
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Aoki, T.M.D. et al. (2010) ’Toll-like receptor 4 expression during cerebral aneurysm formation’, Journal of Neurosurgery JNS, 113(4), pp. 851-858.
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Aoyama, N. et al. (2018) ‘Associations among tooth loss, systemic inflammation and antibody titers to periodontal pathogens in Japanese patients with
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Aoyama, N., Suzuki, J., Kobayashi, N., et al. (2018) ‘Increased Oral Porphyromonas gingivalis Prevalence in Cardiovascular Patients with Uncontrolled Diabetes Mellitus’, International Heart Journal, 59(4), pp. 802–807. doi: 10.1536/ihj.17-480.
Figure 10. Hypothetical model explaining how TLR2, CD14 mediated inflammation could be caused by oral bacteria (especially F.nucleatum and S.mitis).
Bacteria from dental biofilm could enter the bloodstream and intracranial aneurysm.
In the aneurysm, PAMPs of the bacteria could be recognized by PRRs causing an inflammatory reaction.
REFERENCES
Aagaard, K. et al. (2014) ‘The placenta harbors a unique microbiome’, Science translational medicine, 6(237), p. 237ra65-237ra65. doi:
10.1126/scitranslmed.3008599.
Aarabi, G. et al. (2017) ‘Genetic Susceptibility Contributing to Periodontal and Cardiovascular Disease’, Journal of Dental Research. SAGE Publications Inc, 96(6), pp. 610–617. doi: 10.1177/0022034517699786.
Aas, J. A. et al. (2005) ‘Defining the normal bacterial flora of the oral cavity’, Journal of clinical microbiology. American Society for Microbiology, 43(11), pp. 5721–5732.
doi: 10.1128/JCM.43.11.5721-5732.2005.
Aboukais, R et al. (2019) ‘Absence of bacteria in intracranial aneurysms’, Journal of neurosurgery. Mar 1:1-5. doi: 10.3171/2018.12.JNS183044. [Epub ahead of print]
Abusleme, L. et al. (2013) ‘The subgingival microbiome in health and periodontitis and its relationship with community biomass and inflammation’, The ISME Journal 7(5), pp. 1016-1025.
Abusleme, L. et al. (2014) ‘Influence of DNA extraction on oral microbial profiles obtained via 16S rRNA gene sequencing’, Journal of oral microbiology. Co-Action Publishing, 6, p. 10.3402/jom.v6.23990. doi: 10.3402/jom.v6.23990.
Ai, D. et al. (2017) ‘Integrated metagenomic data analysis demonstrates that a loss of diversity in oral microbiota is associated with periodontitis’, BMC genomics.
BioMed Central, 18(Suppl 1), p. 1041. doi: 10.1186/s12864-016-3254-5.
Al-Khindi et al. (2010) ‘Cognitive and Functional Outcome After Aneurysmal Subarachnoid Hemorrhage’ Stroke 41(8), pp. e519-e536
doi:10.1161/STROKEAHA.110.581975
Allali, I. et al. (2017) ‘A comparison of sequencing platforms and bioinformatics pipelines for compositional analysis of the gut microbiome’, BMC microbiology.
BioMed Central, 17(1), p. 194. doi: 10.1186/s12866-017-1101-8.
Aoki, T.M.D. et al. (2010) ’Toll-like receptor 4 expression during cerebral aneurysm formation’, Journal of Neurosurgery JNS, 113(4), pp. 851-858.
Aoyama, N. et al. (2017) ‘Periodontitis deteriorates peripheral arterial disease in Japanese population via enhanced systemic inflammation’, Heart and Vessels, 32(11), pp. 1314–1319. doi: 10.1007/s00380-017-1003-6.
Aoyama, N. et al. (2018) ‘Associations among tooth loss, systemic inflammation and antibody titers to periodontal pathogens in Japanese patients with
cardiovascular disease.’, Journal of Periodontal Research. Wiley-Blackwell, 53(1), pp. 117–122. Available at: http://10.0.4.87/jre.12494.
Aoyama, N., Suzuki, J., Kobayashi, N., et al. (2018) ‘Increased Oral Porphyromonas gingivalis Prevalence in Cardiovascular Patients with Uncontrolled Diabetes Mellitus’, International Heart Journal, 59(4), pp. 802–807. doi: 10.1536/ihj.17-480.
Aoyama, N., Suzuki, J., Kumagai, H., et al. (2018) ‘Specific periodontopathic bacterial infection affects hypertension in male cardiovascular disease patients’, Heart and Vessels, 33(2), pp. 198–204. doi: 10.1007/s00380-017-1042-z.
Armingohar, Z. et al. (2014) ‘Bacteria and bacterial DNA in atherosclerotic plaque and aneurysmal wall biopsies from patients with and without periodontitis’, Journal of oral microbiology. Co-Action Publishing, 6, p. 10.3402/jom.v6.23408. doi:
10.3402/jom.v6.23408.
Atarbashi-Moghadam, F. et al. (2018) ‘Periopathogens in atherosclerotic plaques of patients with both cardiovascular disease and chronic periodontitis’, ARYA atherosclerosis. Isfahan Cardiovascular Research Center, Isfahan University of Medical Sciences, 14(2), pp. 53–57. doi: 10.22122/arya.v14i2.1504.
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Callahan, B. J. et al. (2016) ‘DADA2: High-resolution sample inference from
Illumina amplicon data’, Nature Methods. Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved., 13, p. 581. Available at:
http://dx.doi.org/10.1038/nmeth.3869.
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Armingohar, Z. et al. (2014) ‘Bacteria and bacterial DNA in atherosclerotic plaque and aneurysmal wall biopsies from patients with and without periodontitis’, Journal of oral microbiology. Co-Action Publishing, 6, p. 10.3402/jom.v6.23408. doi:
10.3402/jom.v6.23408.
Atarbashi-Moghadam, F. et al. (2018) ‘Periopathogens in atherosclerotic plaques of patients with both cardiovascular disease and chronic periodontitis’, ARYA atherosclerosis. Isfahan Cardiovascular Research Center, Isfahan University of Medical Sciences, 14(2), pp. 53–57. doi: 10.22122/arya.v14i2.1504.
Baba, A. et al. (2002) ‘Roles for Arg- and Lys-gingipains in the disruption of cytokine responses and loss of viability of human endothelial cells by Porphyromonas gingivalis infection.’, Biological chemistry, 383(7–8), pp. 1223–
1230.
Bag, S. et al. (2016) ‘An Improved Method for High Quality Metagenomics DNA Extraction from Human and Environmental Samples’, Scientific reports. Nature Publishing Group, 6, p. 26775. doi: 10.1038/srep26775.
Bagavad, G. J. et al. (2019) ‘Dysregulation of miR-146a by periodontal pathogens: A risk for acute coronary syndrome’, Journal of Periodontology. John Wiley & Sons, Ltd, 0(ja). doi: 10.1002/JPER.18-0466.
Bizzarro, S. et al. (2013) ‘Subgingival microbiome in smokers and non-smokers in periodontitis: an exploratory study using traditional targeted techniques and a next-generation sequencing’, Journal of Clinical Periodontology. John Wiley &
Sons, Ltd (10.1111), 40(5), pp. 483–492. doi: 10.1111/jcpe.12087.
Bo, L. et al. (2018) ‘Bioinformatic analysis of gene expression profiling of intracranial aneurysm’, Molecular medicine reports. 2017/12/29. D.A. Spandidos, 17(3), pp.
3473–3480. doi: 10.3892/mmr.2017.8367.
Bodet, C., Chandad, F. and Grenier, D. (2006) ‘Anti-inflammatory Activity of a High-molecular-weight Cranberry Fraction on Macrophages Stimulated by Lipopolysaccharides from Periodontopathogens.’, Journal of Dental Research.
Sage Publications Inc., 85(3), pp. 235–239. Available at:
http://10.0.4.153/154405910608500306.
Bolger, A. M., Lohse, M. and Usadel, B. (2014) ‘Trimmomatic: a flexible trimmer for Illumina sequence data’, Bioinformatics (Oxford, England). 2014/04/01. Oxford University Press, 30(15), pp. 2114–2120. doi: 10.1093/bioinformatics/btu170.
Bosshardt, D. D. and Lang, N. P. (2005) ‘The Junctional Epithelium: from Health to Disease’, Journal of Dental Research. SAGE Publications Inc, 84(1), pp. 9–20. doi:
10.1177/154405910508400102.
Bostanci, N. and Belibasakis, G. N. (2012) ‘Porphyromonas gingivalis: an invasive and evasive opportunistic oral pathogen’, FEMS Microbiology Letters, 333(1), pp.
1–9. Available at: http://dx.doi.org/10.1111/j.1574-6968.2012.02579.x.
Bouchard, P. et al. (2017) ’Risk factors in periodontology: a conceptual framework’, Journal of Clinical Periodontology 44: 125– 131.
doi: 10.1111/jcpe.12650.
Bozdogan, E. et al. (2016) ‘Presence of Aggregatibacter actinomycetemcomitans in saliva and cardiac tissue samples of children with congenital heart disease YR - 2016/12/1’, Indian Journal of Dental Research, (6
UL-
http://www.ijdr.in/article.asp?issn=0970-9290;year=2016;volume=27;issue=6;spage=637;epage=642;aulast=Bozdogan;t=5), p. 637 OP-642 VO-27. doi: 10.4103/0970-9290.199590.
Cagli, S. et al. (2003) ‘Failure to detect Chlamydia pneumoniae DNA in cerebral aneurysmal sac tissue with two different polymerase chain reaction methods’, Journal of neurology, neurosurgery, and psychiatry. BMJ Group, 74(6), pp. 756–759.
doi: 10.1136/jnnp.74.6.756.
Cahill, T. J. and Prendergast, B. D. (2016) ‘Infective endocarditis’, The Lancet.
Elsevier, 387(10021), pp. 882–893. doi: 10.1016/S0140-6736(15)00067-7.
Callahan, B. J. et al. (2016) ‘DADA2: High-resolution sample inference from
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http://dx.doi.org/10.1038/nmeth.3869.
Camelo-Castillo, A. J. et al. (2015) ‘Subgingival microbiota in health compared to periodontitis and the influence of smoking ’, Frontiers in Microbiology , p. 119.
Available at: https://www.frontiersin.org/article/10.3389/fmicb.2015.00119.
Caporaso, J. G. et al. (2010) ‘QIIME allows analysis of high-throughput community sequencing data’, Nature Methods. Nature Publishing Group, 7, p. 335. Available at: http://dx.doi.org/10.1038/nmeth.f.303.
Caporaso, J. G. et al. (2011) ‘Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample’, Proceedings of the National Academy of Sciences of the United States of America. 2010/06/03. National Academy of Sciences, 108 Suppl(Suppl 1), pp. 4516–4522. doi: 10.1073/pnas.1000080107.
Caporaso, J. G. et al. (2012) ‘Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms’, The ISME journal. 2012/03/08.
Nature Publishing Group, 6(8), pp. 1621–1624. doi: 10.1038/ismej.2012.8.
Carter, C. J. et al. (2017) ‘The Porphyromonas gingivalis/Host Interactome Shows Enrichment in GWASdb Genes Related to Alzheimer’s Disease, Diabetes and Cardiovascular Diseases’, Frontiers in aging neuroscience. Frontiers Media S.A., 9, p. 408. doi: 10.3389/fnagi.2017.00408.
Chakravorty, S. et al. (2007) ‘A detailed analysis of 16S ribosomal RNA gene segments for the diagnosis of pathogenic bacteria’, Journal of microbiological methods. 2007/02/22, 69(2), pp. 330–339. doi: 10.1016/j.mimet.2007.02.005.
Chalouhi, N. et al. (2012) ‘Biology of intracranial aneurysms: role of inflammation’, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism. 2012/07/11. Nature Publishing Group, 32(9), pp. 1659–1676. doi: 10.1038/jcbfm.2012.84.
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