Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1238390 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR32782263_1.fastq.gz | Fastqsanger.gz | |||
| SRR32782263_2.fastq.gz | Fastqsanger.gz | |||
| SRR32782264_1.fastq.gz | Fastqsanger.gz | |||
| SRR32782264_2.fastq.gz | Fastqsanger.gz | |||
| SRR32782265_1.fastq.gz | Fastqsanger.gz |
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Project description:This study compared the subgingival microbiota of subjects with periodontitis to those with periodontal health using the Human Oral Microbe Identification Microarray (HOMIM).
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Project description:Dysbiosis of subgingival microbiome promotes the growth of periodontopathogens and the development of periodontitis, an irreversible chronic inflammatory disease. Untreated periodontitis leads to the destruction of connective tissues, alveolar bone resorption and ultimately to tooth loss. Periodontitis has been associated with inflammatory metabolic diseases such as type 2 diabetes. While periodontitis-induced inflammation is a key player in both, the development of subgingival microbiome dysbiosis and in the host-microbiome interaction, the effects of hyperglycemia on the regulation of the host genes controlling the inflammatory response and the host-microbiome interaction are still scarce. We investigated the impacts of a hyperglycemic microenvironment on the inflammatory response and gene expression of a gingival fibroblasts-macrophages coculture model stimulated with dysbiotic subgingival microbiomes. A coculture model composed of immortalized human gingival fibroblasts overlaid with U937 macrophages-likes cells were stimulated with subgingival microbiome collected from four healthy donors and four patients with periodontitis. Pro-inflammatory cytokines and matrix metalloproteinase were measured by a Luminex assay while the coculture RNA was submitted to a microarray analysis. Subgingival microbiomes were submitted to 16s rRNA gene sequencing. Data were analyzed by using an advanced multi-omics bioinformatic data integration model. Our results showed that krt76, krt27, pnma5, mansc4, rab41, thoc6, tm6sf2, and znf506 as well as the pro-inflammatory cytokines IL-1, GM-CSF, FGF2, IL-10, the metalloproteinases MMP3 and MMP8, and bacteria from the ASV 105, ASV 211, ASV 299, Prevotella, Campylobacter and Fretibacterium genera are key correlated variables contributing to periodontitis-induced inflammatory response in a hyperglycemic microenvironment. To conclude, our multi-omics integration analysis unveiled unique differentially interrelated bacterial genera, genes and pro-inflammatory cytokines involved in the regulation of the inflammatory response in a hyperglycemic microenvironment. These data also highlight the importance of considering hyperglycemic conditions in the development of new drugs or treatments for periodontal disease in link with type 2 diabetes.
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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)
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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
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Project description:We investigated differential mRNA expression in gingival tissues in states of periodontal health, gingivitis and periodontitis.
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Project description:Analysis of gingival crevicular fluid (GCF) samples may give information of the identity of unattached (planktonic) subgingival bacteria, the 35 forefront candidates for systemic dispersal via ulcerated periodontal pocket epithelium. Our study represents the first one targeting the identity of bacteria in gingival crevicular fluid. Methodology/Principal findings: We determined bacterial species diversity in GCF samples of a group of periodontitis patients and delineated contributing bacterial and host-associated factors. Subgingival paper point (PP) samples from the same sites were taken for comparison. After DNA extraction, 16S rRNA genes were PCR amplified and DNA-DNA hybridization was performed using a microarray for over 300 bacterial species or groups. Altogether 133 species from 41 genera and 8 phyla 45 were detected with 9 to 62 and 18 to 64 species in GCF and PP samples, respectively, 46 per patient. Projection to latent structures by means of partial least squares (PLS) was applied to the multivariate data analysis. PLS regression analysis showed that species of genera including Campylobacter, Selenomonas, Porphyromonas, Catonella, Tannerella, Dialister, Peptostreptococcus, Streptococcus and Eubacterium had significant positive correlations and the number of teeth with low-grade attachment loss a significant negative correlation to species diversity in GCF samples. OPLS/O2PLS discriminant analysis revealed significant positive correlations to GCF sample group membership for species of genera Campylobacter, Leptotrichia, Prevotella, Dialister, Tannerella, Haemophilus, Fusobacterium, Eubacterium, and Actinomyces. Conclusions/Significance: Among a variety of detected species those traditionally classified as Gram-negative anaerobes growing in mature subgingival biofilms were the main predictors for species diversity in GCF samples as well as responsible for distinguishing GCF samples from PP samples. GCF bacteria may provide new prospects for studying dynamic properties of subgingival biofilms. The microbial profiles of GCF and subgingival plaque were analyzed from 17 subjects with periodontal disease.
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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 Ninety non-smokers, 63 with chronic and 27 with aggressive periodontitis, each contributing with >=2 “diseased” interproximal papillae (with bleeding on probing, probing pocket depth >=4mm, and clinical attachment loss >=3mm) and a “healthy” papilla, if available (no BoP, PPD<=4mm and CAL<=2mm). RNA was extracted, amplified, reverse transcribed, labeled, and hybridized with AffymetrixU133Plus2.0 arrays. Transcriptome analysis was conducted on a total of 247 samples (from 183 diseased and 64 healthy sites).
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Project description:Periodontal infections have been associated with systemic inflammation and risk for atherosclerosis and vascular disease. We investigated the effects of comprehensive periodontal therapy on gene expression of peripheral blood monocytes. Approximately 1/3 of the patients showed substantial changes in expression in genes relevant to innate immunity, apoptosis, and cell signaling. We concluded that periodontal therapy may alter monocytic gene expression in a manner consistent with a systemic anti-inflammatory effect. Experiment Overall Design: Fifteen patients with periodontitis contributed with blood samples at four time points: 1 week prior to periodontal treatment (#1), at treatment initiation (baseline, #2), 6 weeks (#3) and 10 weeks post-baseline (#4). At baseline and 10 weeks, periodontal status was recorded and subgingival plaque samples were collected and processed by checkerboard DNA-DNA hybridization. Periodontal therapy, including periodontal surgery and extractions but no adjunctive antibiotics, was completed within 6 weeks. At each of the four time points, serum concentrations of 19 biomarkers were determined using multiplex assays for Luminex technology. Peripheral blood monocytes were purified, RNA was extracted, reverse-transcribed, labeled, and hybridized with Affymetrix U133 Plus 2.0 chips. Expression profiles were analyzed using linear random effects models. Further analysis of Gene Ontology (GO) terms summarized the expression patterns into biologically relevant categories. Treatment resulted in substantial improvement in clinical periodontal status and reduction in levels of several periodontal pathogens. Expression profiling over time revealed more than 11,000 probes sets differentially expressed at a false discovery rate of <0.05.
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