Top

Published in:

26-12-2023 | Respiratory Microbiota | RESEARCH

High-Throughput Combined Analysis of Saliva Microbiota and Metabolomic Profile in Chinese Periodontitis Patients: A Pilot Study

Authors: Jing Ding, Jinyu Li, Chi Zhang, Lingping Tan, Chuanjiang Zhao, Li Gao

Published in: Inflammation | Issue 3/2024

Abstract

The onset and progression of periodontitis involves complicated interactions between the dysbiotic oral microbiota and disrupted host immune-inflammatory response, which can be mirrored by the changes in salivary metabolites profile. This pilot study sought to examine the saliva microbiome and metabolome in the Chinese population by the combined approach of 16s rRNA sequencing and high-throughput targeted metabolomics to discover potential cues for host-microbe metabolic interactions. Unstimulated whole saliva samples were collected from eighteen Stage III and IV periodontitis patients and thirteen healthy subjects. Full-mouth periodontal parameters were recorded. The taxonomic composition of microbiota was obtained by 16s rRNA sequencing, and the metabolites were identified and measured by ultra-high performance liquid chromatography and mass spectrometry-based metabolomic analysis. The oral microbiota composition displayed marked changes where the abundance of 93 microbial taxa differed significantly between the periodontitis and healthy group. Targeted metabolomics identified 103 differential metabolites between the patients and healthy individuals. Functional enrichment analysis demonstrated the upregulation of protein digestion and absorption, histidine metabolism, and nicotinate and nicotinamide metabolism pathways in the dysbiotic microbiota, while the ferroptosis, tryptophan metabolism, glutathione metabolism, and carbon metabolism pathways were upregulated in the patients. Correlation analysis confirmed positive relationships between the clinical parameters, pathogen abundances, and disease-related metabolite levels. The integral analysis of the saliva microbiome and metabolome yielded an accurate presentation of the dysbiotic oral microbiome and functional alterations in host-microbe metabolism. The microbial and metabolic profiling of the saliva could be a potential tool in the diagnosis, prognosis evaluation, and pathogenesis study of periodontitis.

Available only for authorised users

Hajishengallis, G. 2014. Immunomicrobial pathogenesis of periodontitis: Keystones, pathobionts, and host response. Trends in Immunology 35: 3–11. https://doi.org/10.1016/j.it.2013.09.001.CrossRefPubMed

Schenkein, H.A., P.N. Papapanou, R. Genco, and M. Sanz. 2000. Mechanisms underlying the association between periodontitis and atherosclerotic disease. Periodontology 2020 83: 90–106. https://doi.org/10.1111/prd.12304.CrossRef

Kamer, A.R., R.G. Craig, R. Niederman, J. Fortea, and M.J. de Leon. 2000. Periodontal disease as a possible cause for Alzheimer’s disease. Periodontology 2020 83: 242–271. https://doi.org/10.1111/prd.12327.

Genco, R.J., and M. Sanz. 2000. Clinical and public health implications of periodontal and systemic diseases: An overview. Periodontology 2020 83: 7–13. https://doi.org/10.1111/prd.12344.CrossRef

Meyle, J., and I. Chapple. 2000. Molecular aspects of the pathogenesis of periodontitis. Periodontology 2015 69: 7–17. https://doi.org/10.1111/prd.12104.CrossRef

Curtis, M.A., P.I. Diaz, and T.E. Van Dyke. 2000. The role of the microbiota in periodontal disease. Periodontology 2020 83: 14–25. https://doi.org/10.1111/prd.12296.CrossRef

Zhou, T., W. Xu, Q. Wang, C. Jiang, H. Li, Y. Chao and Y. Sun. 2023. The effect of the "Oral-Gut" axis on periodontitis in inflammatory bowel disease: A review of microbe and immune mechanism associations. Frontiers in Cellular and Infection Microbiology 13: 1132420. https://doi.org/10.3389/fcimb.2023.1132420.

Ding, J., C. Zhao, and L. Gao. 2023. Metabolism of periodontal pathobionts: Their regulatory roles in the dysbiotic microbiota. Molecular Oral Microbiology 38: 181–188. https://doi.org/10.1111/omi.12409.CrossRefPubMed

Belstrom, D. 2020. The salivary microbiota in health and disease. Journal of Oral Microbiology 12: 1723975. https://doi.org/10.1080/20002297.2020.1723975.CrossRefPubMedPubMedCentral

10.

Romano, F., G. Meoni, V. Manavella, G. Baima, L. Tenori, S. Cacciatore, and M. Aimetti. 2018. Analysis of salivary phenotypes of generalized aggressive and chronic periodontitis through nuclear magnetic resonance-based metabolomics. Journal of Periodontology 89: 1452–1460. https://doi.org/10.1002/JPER.18-0097.CrossRefPubMed

11.

Hirtz, C., R. O'Flynn, P.M. Voisin, D. Deville de Periere, S. Lehmann, S. Guedes, F. Amado, R. Ferreira, F. Trindade and R. Vitorino. 2021. The potential impact of salivary peptides in periodontitis. Critical Reviews in Clinical Laboratory Sciences 58:479–492. https://doi.org/10.1080/10408363.2021.1907298.

12.

Katsiki, P., K. Nazmi, B.G. Loos, M.L. Laine, K. Schaap, E. Hepdenizli, F.J. Bikker, H.S. Brand, E.C.I. Veerman, and E.A. Nicu. 2021. Comparing periodontitis biomarkers in saliva, oral rinse and gingival crevicular fluid: A pilot study. Journal of Clinical Periodontology 48: 1250–1259. https://doi.org/10.1111/jcpe.13479.CrossRefPubMedPubMedCentral

13.

Lundmark, A., Y.O.O. Hu, M. Huss, G. Johannsen, A.F. Andersson, and T. Yucel-Lindberg. 2019. Identification of salivary microbiota and its association with host inflammatory mediators in periodontitis. Frontiers in Cellular and Infection Microbiology 9: 216. https://doi.org/10.3389/fcimb.2019.00216.CrossRefPubMedPubMedCentral

14.

Kim, S., H.J. Kim, Y. Song, H.A. Lee, S. Kim, and J. Chung. 2021. Metabolic phenotyping of saliva to identify possible biomarkers of periodontitis using proton nuclear magnetic resonance. Journal of Clinical Periodontology 48: 1240–1249. https://doi.org/10.1111/jcpe.13516.CrossRefPubMed

15.

Li, Y., F. Qian, X. Cheng, D. Wang, Y. Wang, Y. Pan, L. Chen, W. Wang, and Y. Tian. 2023. Dysbiosis of oral microbiota and metabolite profiles associated with type 2 diabetes mellitus. Microbiology Spectrum 11.

16.

Salminen, A., U.K. Gursoy, S. Paju, K. Hyvarinen, P. Mantyla, K. Buhlin, E. Kononen, M.S. Nieminen, T. Sorsa, J. Sinisalo, and P.J. Pussinen. 2014. Salivary biomarkers of bacterial burden, inflammatory response, and tissue destruction in periodontitis. Journal of Clinical Periodontology 41: 442–450. https://doi.org/10.1111/jcpe.12234.CrossRefPubMed

17.

Balci, N., S. Kurgan, A. Cekici, T. Cakir, and M.A. Serdar. 2021. Free amino acid composition of saliva in patients with healthy periodontium and periodontitis. Clinical Oral Investigations. 25: 4175–4183. https://doi.org/10.1007/s00784-021-03977-7.CrossRefPubMed

18.

Guney, Z., S. Kurgan, C. Onder, C. Mammadov, M.A. Serdar, and M. Gunhan. 2023. Asymmetric and symmetric dimethylarginine gingival crevicular fluid levels in periodontitis. Journal of Periodontal Research 58: 256–261. https://doi.org/10.1111/jre.13087.CrossRefPubMed

19.

R Core Team. 2020. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.

20.

Bolyen, E., J.R. Rideout, M.R. Dillon, N.A. Bokulich, C.C. Abnet, G.A. Al-Ghalith, H. Alexander, E.J. Alm, M. Arumugam, F. Asnicar, Y. Bai, J.E. Bisanz, K. Bittinger, A. Brejnrod, C.J. Brislawn, C.T. Brown, B.J. Callahan, A.M. Caraballo-Rodriguez, J. Chase, E.K. Cope, R. Da Silva, C. Diener, P.C. Dorrestein, G.M. Douglas, D.M. Durall, C. Duvallet, C.F. Edwardson, M. Ernst, M. Estaki, J. Fouquier, J.M. Gauglitz, S.M. Gibbons, D.L. Gibson, A. Gonzalez, K. Gorlick, J. Guo, B. Hillmann, S. Holmes, H. Holste, C. Huttenhower, G.A. Huttley, S. Janssen, A.K. Jarmusch, L. Jiang, B.D. Kaehler, K.B. Kang, C.R. Keefe, P. Keim, S.T. Kelley, D. Knights, I. Koester, T. Kosciolek, J. Kreps, M.G.I. Langille, J. Lee, R. Ley, Y.X. Liu, E. Loftfield, C. Lozupone, M. Maher, C. Marotz, B.D. Martin, D. McDonald, L.J. McIver, A.V. Melnik, J.L. Metcalf, S.C. Morgan, J.T. Morton, A.T. Naimey, J.A. Navas-Molina, L.F. Nothias, S.B. Orchanian, T. Pearson, S.L. Peoples, D. Petras, M.L. Preuss, E. Pruesse, L.B. Rasmussen, A. Rivers, M.S. Robeson 2nd., P. Rosenthal, N. Segata, M. Shaffer, A. Shiffer, R. Sinha, S.J. Song, J.R. Spear, A.D. Swafford, L.R. Thompson, P.J. Torres, P. Trinh, A. Tripathi, P.J. Turnbaugh, S. Ul-Hasan, J.J.J. van der Hooft, F. Vargas, Y. Vazquez-Baeza, E. Vogtmann, M. von Hippel, W. Walters, Y. Wan, M. Wang, J. Warren, K.C. Weber, C.H.D. Williamson, A.D. Willis, Z.Z. Xu, J.R. Zaneveld, Y. Zhang, Q. Zhu, R. Knight, and J.G. Caporaso. 2019. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nature Biotechnology 37: 852–857. https://doi.org/10.1038/s41587-019-0209-9.CrossRefPubMedPubMedCentral

21.

Callahan, B.J., P.J. McMurdie, M.J. Rosen, A.W. Han, A.J. Johnson, and S.P. Holmes. 2016. DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods 13: 581–583. https://doi.org/10.1038/nmeth.3869.CrossRefPubMedPubMedCentral

22.

Cole, J.R., Q. Wang, J.A. Fish, B. Chai, D.M. McGarrell, Y. Sun, C.T. Brown, A. Porras-Alfaro, C.R. Kuske, and J.M. Tiedje. 2014. Ribosomal database project: Data and tools for high throughput rRNA analysis. Nucleic Acids Research 42: D633–D642. https://doi.org/10.1093/nar/gkt1244.CrossRefPubMed

23.

Schloss, P.D., S.L. Westcott, T. Ryabin, J.R. Hall, M. Hartmann, E.B. Hollister, R.A. Lesniewski, B.B. Oakley, D.H. Parks, C.J. Robinson, J.W. Sahl, B. Stres, G.G. Thallinger, D.J. Van Horn, and C.F. Weber. 2009. Introducing mothur: Open-source, platform-independent, community-supported software for describing and comparing microbial communities. Applied and Environment Microbiology 75: 7537–7541. https://doi.org/10.1128/AEM.01541-09.CrossRef

24.

Caporaso, J.G., J. Kuczynski, J. Stombaugh, K. Bittinger, F.D. Bushman, E.K. Costello, N. Fierer, A.G. Pena, J.K. Goodrich, J.I. Gordon, G.A. Huttley, S.T. Kelley, D. Knights, J.E. Koenig, R.E. Ley, C.A. Lozupone, D. McDonald, B.D. Muegge, M. Pirrung, J. Reeder, J.R. Sevinsky, P.J. Turnbaugh, W.A. Walters, J. Widmann, T. Yatsunenko, J. Zaneveld, and R. Knight. 2010. QIIME allows analysis of high-throughput community sequencing data. Nature Methods 7: 335–336. https://doi.org/10.1038/nmeth.f.303.CrossRefPubMedPubMedCentral

25.

Segata, N., J. Izard, L. Waldron, D. Gevers, L. Miropolsky, W.S. Garrett, and C. Huttenhower. 2011. Metagenomic biomarker discovery and explanation. Genome Biology 12: R60. https://doi.org/10.1186/gb-2011-12-6-r60.CrossRefPubMedPubMedCentral

26.

Langille, M.G., J. Zaneveld, J.G. Caporaso, D. McDonald, D. Knights, J.A. Reyes, J.C. Clemente, D.E. Burkepile, R.L. Vega Thurber, R. Knight, R.G. Beiko, and C. Huttenhower. 2013. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nature Biotechnology 31: 814–821. https://doi.org/10.1038/nbt.2676.CrossRefPubMedPubMedCentral

27.

Wen, B., Z. Mei, C. Zeng, and S. Liu. 2017. metaX: A flexible and comprehensive software for processing metabolomics data. BMC Bioinformatics 18: 183. https://doi.org/10.1186/s12859-017-1579-y.CrossRefPubMedPubMedCentral

28.

Takahashi, N. 2015. Oral microbiome metabolism: From “who are they?” to “what are they doing?” Journal of Dental Research 94: 1628–1637. https://doi.org/10.1177/0022034515606045.

29.

Melguizo-Rodriguez, L., V.J. Costela-Ruiz, F.J. Manzano-Moreno, C. Ruiz and R. Illescas-Montes. 2020. Salivary biomarkers and their application in the diagnosis and monitoring of the most common oral pathologies. International Journal of Molecular Science. 21. https://doi.org/10.3390/ijms21145173.

30.

Nguyen, T., L. Sedghi, S. Ganther, E. Malone, P. Kamarajan, and Y.L. Kapila. 2000. Host-microbe interactions: Profiles in the transcriptome, the proteome, and the metabolome. Periodontology 2020 82: 115–128. https://doi.org/10.1111/prd.12316.CrossRef

31.

Bostanci, N., M. Grant, K. Bao, A. Silbereisen, F. Hetrodt, D. Manoil, and G.N. Belibasakis. 2000. Metaproteome and metabolome of oral microbial communities. Periodontology 2021 85: 46–81. https://doi.org/10.1111/prd.12351.CrossRef

32.

Shi, B., M. Chang, J. Martin, M. Mitreva, R. Lux, P. Klokkevold, E. Sodergren, G.M. Weinstock, S.K. Haake and H. Li. 2015. Dynamic changes in the subgingival microbiome and their potential for diagnosis and prognosis of periodontitis. mBio. 6:e01926–14. https://doi.org/10.1128/mBio.01926-14.

33.

Hajishengallis, G., and R.J. Lamont. 2012. Beyond the red complex and into more complexity: The polymicrobial synergy and dysbiosis (PSD) model of periodontal disease etiology. Molecular Oral Microbiology 27: 409–419. https://doi.org/10.1111/j.2041-1014.2012.00663.x.CrossRefPubMedPubMedCentral

34.

Abusleme, L., A.K. Dupuy, N. Dutzan, N. Silva, J.A. Burleson, L.D. Strausbaugh, J. Gamonal, and P.I. Diaz. 2013. The subgingival microbiome in health and periodontitis and its relationship with community biomass and inflammation. ISME Journal 7: 1016–1025. https://doi.org/10.1038/ismej.2012.174.CrossRefPubMedPubMedCentral

35.

Overmyer, K.A., T.W. Rhoads, A.E. Merrill, Z. Ye, M.S. Westphall, A. Acharya, S.K. Shukla, and J.J. Coon. 2021. Proteomics, lipidomics, metabolomics, and 16S DNA sequencing of dental plaque from patients with diabetes and periodontal disease. Molecular and Cellular Proteomics 20: 100126. https://doi.org/10.1016/j.mcpro.2021.100126.CrossRefPubMedPubMedCentral

36.

Ozeki, M., T. Nozaki, J. Aoki, T. Bamba, K.R. Jensen, S. Murakami, and M. Toyoda. 2016. Metabolomic analysis of gingival crevicular fluid using gas chromatography/mass spectrometry. Mass Spectrom (Tokyo) 5: A0047. https://doi.org/10.5702/massspectrometry.A0047.

37.

Chen, H.W., W. Zhou, Y. Liao, S.C. Hu, T.L. Chen, and Z.C. Song. 2018. Analysis of metabolic profiles of generalized aggressive periodontitis. Journal of Periodontal Research 53: 894–901. https://doi.org/10.1111/jre.12579.CrossRefPubMed

38.

Garcia-Villaescusa, A., J.M. Morales-Tatay, D. Monleon-Salvado, J.M. Gonzalez-Darder, C. Bellot-Arcis, J.M. Montiel-Company, and J.M. Almerich-Silla. 2018. Using NMR in saliva to identify possible biomarkers of glioblastoma and chronic periodontitis. PLoS ONE 13: e0188710. https://doi.org/10.1371/journal.pone.0188710.CrossRefPubMedPubMedCentral

39.

Sedghi, L., V. DiMassa, A. Harrington, S.V. Lynch, and Y.L. Kapila. 2000. The oral microbiome: Role of key organisms and complex networks in oral health and disease. Periodontology 2021 87: 107–131. https://doi.org/10.1111/prd.12393.CrossRef

40.

Vital, M., A.C. Howe and J.M. Tiedje. 2014. Revealing the bacterial butyrate synthesis pathways by analyzing (meta)genomic data. mBio. 5:e00889. https://doi.org/10.1128/mBio.00889-14.

41.

Tsuda, H., K. Ochiai, N. Suzuki, and K. Otsuka. 2010. Butyrate, a bacterial metabolite, induces apoptosis and autophagic cell death in gingival epithelial cells. Journal of Periodontal Research 45: 626–634. https://doi.org/10.1111/j.1600-0765.2010.01277.x.CrossRefPubMed

42.

Shirasugi, M., K. Nishioka, T. Yamamoto, T. Nakaya, and N. Kanamura. 2017. Normal human gingival fibroblasts undergo cytostasis and apoptosis after long-term exposure to butyric acid. Biochemical and Biophysical Research Communications 482: 1122–1128. https://doi.org/10.1016/j.bbrc.2016.11.168.CrossRefPubMed

43.

Mayrand, D., and S.C. Holt. 1988. Biology of asaccharolytic black-pigmented Bacteroides species. Microbiological Reviews 52: 134–152. https://doi.org/10.1128/mr.52.1.134-152.1988.CrossRefPubMedPubMedCentral

44.

Slots, J., and R.J. Genco. 1984. Black-pigmented Bacteroides species, Capnocytophaga species, and Actinobacillus actinomycetemcomitans in human periodontal disease: Virulence factors in colonization, survival, and tissue destruction. Journal of Dental Research 63: 412–421. https://doi.org/10.1177/00220345840630031101.CrossRefPubMed

45.

Zhao, Y., J. Li, W. Guo, H. Li and L. Lei. 2020. Periodontitis-level butyrate-induced ferroptosis in periodontal ligament fibroblasts by activation of ferritinophagy. Cell Death Discovery. 6. https://doi.org/10.1038/s41420-020-00356-1.

46.

Cai, J., L. Sun, and F.J. Gonzalez. 2022. Gut microbiota-derived bile acids in intestinal immunity, inflammation, and tumorigenesis. Cell Host & Microbe 30: 289–300. https://doi.org/10.1016/j.chom.2022.02.004.CrossRef

47.

Wahlstrom, A., S.I. Sayin, H.U. Marschall, and F. Backhed. 2016. Intestinal crosstalk between bile acids and microbiota and its impact on host metabolism. Cell Metabolism 24: 41–50. https://doi.org/10.1016/j.cmet.2016.05.005.CrossRefPubMed

48.

Yang, R., W. Yu, L. Lin, M. Jin, S. Hu, B. Jiang, C. Mao, G. Li, J. Tang, Y. Gu, H. Chen, and E. Lu. 2023. Profiling of bile acids and activated receptor S1PR2 in gingival tissues of periodontitis patients. Journal of Periodontology 94: 564–574. https://doi.org/10.1002/JPER.22-0398.CrossRefPubMed

49.

Talebian, R., L. Panahipour, and R. Gruber. 2020. Ursodeoxycholic acid attenuates the expression of proinflammatory cytokines in periodontal cells. Journal of Periodontology 91: 1098–1104. https://doi.org/10.1002/JPER.19-0013.CrossRefPubMedPubMedCentral

50.

Cao, S., X. Meng, Y. Li, L. Sun, L. Jiang, H. Xuan, and X. Chen. 2021. Bile acids elevated in chronic periaortitis could activate farnesoid-X-receptor to suppress IL-6 production by macrophages. Frontiers in Immunology 12: 632864. https://doi.org/10.3389/fimmu.2021.632864.CrossRefPubMedPubMedCentral

51.

Bhargava, P., M.D. Smith, L. Mische, E. Harrington, K.C. Fitzgerald, K. Martin, S. Kim, A.A. Reyes, J. Gonzalez-Cardona, C. Volsko, A. Tripathi, S. Singh, K. Varanasi, H.N. Lord, K. Meyers, M. Taylor, M. Gharagozloo, E.S. Sotirchos, B. Nourbakhsh, R. Dutta, E.M. Mowry, E. Waubant, and P.A. Calabresi. 2020. Bile acid metabolism is altered in multiple sclerosis and supplementation ameliorates neuroinflammation. The Journal of Clinical Investigation 130: 3467–3482. https://doi.org/10.1172/JCI129401.CrossRefPubMedPubMedCentral

52.

Winston, J.A., and C.M. Theriot. 2020. Diversification of host bile acids by members of the gut microbiota. Gut Microbes. 11: 158–171. https://doi.org/10.1080/19490976.2019.1674124.CrossRefPubMed

53.

Collins, S.L., J.G. Stine, J.E. Bisanz, C.D. Okafor, and A.D. Patterson. 2023. Bile acids and the gut microbiota: Metabolic interactions and impacts on disease. Nature Reviews Microbiology 21: 236–247. https://doi.org/10.1038/s41579-022-00805-x.CrossRefPubMed

54.

Jones, B.V., M. Begley, C. Hill, C.G. Gahan, and J.R. Marchesi. 2008. Functional and comparative metagenomic analysis of bile salt hydrolase activity in the human gut microbiome. Proceedings of the National Academy of Sciences of the USA. 105: 13580–13585. https://doi.org/10.1073/pnas.0804437105.CrossRefPubMedPubMedCentral

55.

Sayin, S.I., A. Wahlstrom, J. Felin, S. Jantti, H.U. Marschall, K. Bamberg, B. Angelin, T. Hyotylainen, M. Oresic, and F. Backhed. 2013. Gut microbiota regulates bile acid metabolism by reducing the levels of tauro-beta-muricholic acid, a naturally occurring FXR antagonist. Cell Metabolism 17: 225–235. https://doi.org/10.1016/j.cmet.2013.01.003.CrossRefPubMed

56.

Nakagawa, M., and K.D. Setchell. 1990. Bile acid metabolism in early life: Studies of amniotic fluid. Journal of Lipid Research 31: 1089–1098.CrossRefPubMed

57.

Shoda, J., R. Mahara, T. Osuga, M. Tohma, S. Ohnishi, H. Miyazaki, N. Tanaka, and Y. Matsuzaki. 1988. Similarity of unusual bile acids in human umbilical cord blood and amniotic fluid from newborns and in sera and urine from adult patients with cholestatic liver diseases. Journal of Lipid Research 29: 847–858.CrossRefPubMed

58.

Bove, K.E., C.C. Daugherty, W. Tyson, G. Mierau, J.E. Heubi, W.F. Balistreri, and K.D. Setchell. 2000. Bile acid synthetic defects and liver disease. Pediatric and Developmental Pathology 3: 1–16. https://doi.org/10.1007/s100240050001.CrossRefPubMed

59.

Zohrer, E., K. Meinel, G. Fauler, V.A. Moser, T. Greimel, J. Zobl, A. Schlagenhauf, and J. Jahnel. 2018. Neonatal sepsis leads to early rise of rare serum bile acid tauro-omega-muricholic acid (TOMCA). Pediatric Research 84: 66–70. https://doi.org/10.1038/s41390-018-0007-y.CrossRefPubMed

60.

Liu, K., H. Meng, and J. Hou. 2012. Activity of 25-hydroxylase in human gingival fibroblasts and periodontal ligament cells. PLoS ONE 7: e52053. https://doi.org/10.1371/journal.pone.0052053.CrossRefPubMedPubMedCentral

61.

Geier, B., E.M. Sogin, D. Michellod, M. Janda, M. Kompauer, B. Spengler, N. Dubilier, and M. Liebeke. 2020. Spatial metabolomics of in situ host-microbe interactions at the micrometre scale. Nature Microbiology 5: 498–510. https://doi.org/10.1038/s41564-019-0664-6.

Title: High-Throughput Combined Analysis of Saliva Microbiota and Metabolomic Profile in Chinese Periodontitis Patients: A Pilot Study
Authors: Jing Ding
Jinyu Li
Chi Zhang
Lingping Tan
Chuanjiang Zhao
Li Gao
Publication date: 26-12-2023
Publisher: Springer US
Keywords: Respiratory Microbiota
Bleeding on Probing
Published in: Inflammation / Issue 3/2024
Print ISSN: 0360-3997
Electronic ISSN: 1573-2576
DOI: https://doi.org/10.1007/s10753-023-01948-6

2024 ESC Congress

Noise pollution dangerous for heart health

02-09-2024
Myocardial Infarction
News

Two studies underscore the adverse impact of noise pollution on the risk for and prognosis after myocardial infarction.

Antihypertensives: more effective at morning or night?

02-09-2024
Hypertension
News

Low Lp(a) levels unlikely to increase the risk of diabetes

02-09-2024
Type 2 Diabetes
News

» View more highlights on the 2024 ESC Congress hub page

A quick guide to ECGs

Electrocardiography
Training Course

Improve your ECG interpretation skills with this comprehensive, rapid, interactive course. Expert advice provides detailed feedback as you work through 50 ECGs covering the most common cardiac presentations to ensure your practice stays up to date.

PD Dr. Carsten W. Israel

Developed by: Springer Medizin

Start the cases

At a glance: The STEP trials

Obesity
Clinical Trial Summary

A round-up of the STEP phase 3 clinical trials evaluating semaglutide for weight loss in people with overweight or obesity.

Developed by: Springer Medicine

ERS Congress 2024

Springer Medicine

High-Throughput Combined Analysis of Saliva Microbiota and Metabolomic Profile in Chinese Periodontitis Patients: A Pilot Study

Abstract

2024 ESC Congress

Noise pollution dangerous for heart health

Post-TAVI follow up: deploying virtual assistants

Antihypertensives: more effective at morning or night?

Low Lp(a) levels unlikely to increase the risk of diabetes

» View more highlights on the 2024 ESC Congress hub page

A quick guide to ECGs

At a glance: The STEP trials

ERS Congress 2024

Springer Medicine

Abstract

Please log in to get access to this content

Other articles of this Issue 3/2024

Sodium Butyrate Ameliorates Atopic Dermatitis-Induced Inflammation by Inhibiting HDAC3-Mediated STAT1 and NF-κB Pathway

Colchicine Alleviates Rosacea by Inhibiting Neutrophil Inflammation Activated by the TLR2 Pathway

Low-Shear Stress Promotes Atherosclerosis via Inducing Endothelial Cell Pyroptosis Mediated by IKKε/STAT1/NLRP3 Pathway

MALT1 Protease Regulates T-Cell Immunity via the mTOR Pathway in Oral Lichen Planus

ACE2 Expressed on Myeloid Cells Alleviates Sepsis-Induced Acute Liver Injury via the Ang-(1–7)–Mas Receptor Axis

Neutrophil Extracellular Traps Induce Pyroptosis of Rheumatoid Arthritis Fibroblast-Like Synoviocytes via the NF-κB/Caspase 3/GSDME Pathway

2024 ESC Congress

Noise pollution dangerous for heart health

Post-TAVI follow up: deploying virtual assistants

Antihypertensives: more effective at morning or night?

Low Lp(a) levels unlikely to increase the risk of diabetes

» View more highlights on the 2024 ESC Congress hub page

A quick guide to ECGs

At a glance: The STEP trials