Gingival Tissue Microbiome Profiles in PD
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PROVIDER: PRJEB27126 | ENA |
REPOSITORIES: ENA
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Project description:Gingival Tissue Microbiome Profiles in Periodontitis
| PRJEB27125 | ENA
Project description:We investigated the association between subgingival bacterial profiles and gene expression patterns in gingival tissues of patients with periodontitis. A total of 120 patients undergoing periodontal surgery contributed with a minimum of two interproximal gingival papillae (range 2-4) from a maxillary posterior. Prior to tissue harvesting, subgingival plaque samples were collected from the mesial and distal aspects of each papilla. Gingival tissue RNA was extracted, reverse-transcribed, labeled, and hybridized with whole-genome microarrays (310 in total)
2009-12-21 | E-GEOD-16134 | biostudies-arrayexpress
Project description:This study evaluated the transcriptome of healthy gingival tissue in patients with a history of generalized aggressive periodontitis (GAgP) and chronic periodontitis (CP) and in subjects with no history of periodontitis (H), using microarray analysis.
2017-03-29 | GSE79705 | GEO
Project description:We examined the molecular and cellular mechanism for chronic periodontitis in patients' gingival tissue by whole transcriptome sequencing.
2016-08-18 | GSE80715 | GEO
Project description:Gingival tissue proteome is at the forefront of the periodontal research, yet have been hampered by lack of sufficient tissue-extraction methods. Using an emerging technology for tissue homogenization, pressure cycling technology (PCT), we characterize the gingival tissue proteomic profiles of both healthy (n=5) and disease (n=5) sites from five systemically healthy individuals (51.6± 4.3 years) with generalized chronic periodontitis. In brief, the tissue was lysed and digested in PCT, the peptide was further subjected to an Orbitrap Fusion mass spectrometer. Label-free quantification (LFQ) was performed by Progenesis QI using Mascot and Scaffold for identifying and sorting of protein conflict, respectively.
2020-09-25 | PXD018029 | Pride
Project description:We examined gene expression signatures in healthy and diseased gingival tissues in 90 patients. Analysis of the gingival tissue transcriptome in states of periodontal health and disease may reveal novel insights of the pathobiology of periodontitis. Keywords: gingival tissue disease state analysis
2008-12-22 | GSE10334 | GEO
Project description:Although a variety of omics studies from different tissues have been used to analyze periodontitis pathology and progression, lacks accurate signature molecules for periodontitis. This study aims to identify the potential signature molecules suitable for distinguish of patients with periodontitis using TMT-proteomics and artificial neural networks analysis. Gingival tissues were collected from one site of 15 systematically healthy individuals, in which 1 individuals have stage IV periodontitis and 4 have stage III periodontitis (Severe Periodontitis, SP), 5 stage II periodontitis (Mild Periodontitis, MP) and 5 periodontally-healthy (H). we first time carried out the protein profiles of gingival tissue in periodontitis and healthy individuals utilizing the quantitative TMT-proteomics and discovered 9 signature proteins involved in periodontitis by integrated analysis of proteomics and transcriptomics using LASSO and ANN analysis. This study provides a novel insight for potential signature molecules in gingival tissue to predict periodontitis.
2022-01-28 | PXD031302 | iProX
Project description:We investigated the association between subgingival bacterial profiles and gene expression patterns in gingival tissues of patients with periodontitis.
2009-12-22 | GSE16134 | GEO
Project description:<p><strong>AIM:</strong> The role of lipids in periodontitis has not been well studied. Thus, this study aimed to explore periodontitis-associated lipid profile changes and identify differentially expressed lipid metabolites in gingival tissues.</p><p><strong>MATERIALS AND METHODS:</strong> Gingival tissues from 38 patients with periodontitis (periodontitis group) and 38 periodontally healthy individuals (control group) were collected. A UHPLC-QTOF-MS-based non-targeted metabolomics platform was used to identify and compare the lipid profiles of the two groups. The distribution and expression of related proteins were subsequently analyzed via immunohistochemistry to further validate the identified lipids.</p><p><strong>RESULTS:</strong> Lipid profiles significantly differed between the two groups, and 20 differentially expressed lipid species were identified. Lysophosphatidylcholines (lysoPCs), diacylglycerols (DGs) and phosphatidylethanolamines (PEs) were significantly upregulated, while triacylglycerols (TGs) were downregulated in the periodontitis group. Moreover, the staining intensity of ABHD5/CGI-58, secretory phospholipase A2 (sPLA2), and sPLA2-IIA was significantly stronger in the gingival tissues of patients with periodontitis than in those of healthy controls.</p><p><strong>CONCLUSIONS:</strong> LysoPCs, DGs, and PEs were significantly upregulated, whereas TGs were downregulated in gingival tissues of patients with periodontitis. Correspondingly, the immunohistochemical staining of ABHD5/CGI-58, sPLA2 and sPLA2-IIA in gingival tissues was consistent with the downstream production of lipid classes (lysoPCs, TGs and DGs).</p>
2022-08-19 | MTBLS4684 | MetaboLights
Project description:Gene expressions relate to the pathogenesis of periodontitis and have a crucial role in local tissue destruction and susceptibility to the disease. The aims of the present study were to explore comprehensive gene expressions/transcriptomes in periodontitis-affected gingival tissues, and to identify specific biological processes. The purpose of the present study was 1) to compare comprehensive gene expression/transcriptomes of periodontitis-affected gingival tissues with those of healthy tissues by using microarray and data mining technologies, and 2) to analyze significantly differentially expressed genes which belong to pathological pathways in periodontitis by qRT-PCR. Two distinct gingival samples including healthy and periodontal-affected gingiva were taken from 3 patients with severe chronic periodontitis. Total RNAs from 6 gingival tissue samples were used for microarray and data-mining analyses. Comparisons, gene ontology, and pathway frequency analyses were performed and identified significant biological pathways in periodontitis. Quantitative reverse transcription real-time polymerase chain reaction (qRT-PCR) analyse using 14 chronic periodontitis patients including 3 patients listed above and 14 healthy individuals showed 9 differentially expressed genes in leukocyte migration and cell communication pathways.
2010-08-13 | E-GEOD-23586 | biostudies-arrayexpress