Ontology highlight
ABSTRACT:
PROVIDER: PRJNA931979 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR23350439.fastq.gz | Fastqsanger.gz | |||
| SRR23350440.fastq.gz | Fastqsanger.gz | |||
| SRR23350441.fastq.gz | Fastqsanger.gz | |||
| SRR23350442.fastq.gz | Fastqsanger.gz | |||
| SRR23350443.fastq.gz | Fastqsanger.gz |
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Project description:<p><strong>Purpose:</strong> Dental caries is characterized by a dysbiotic shift at the biofilm-tooth surface interface, yet comprehensive biochemical characterizations of the biofilm are scant. We used metabolomics to identify biochemical features of the supragingival biofilm associated with early childhood caries (ECC) prevalence and severity. </p><p><strong>Methods:</strong> The study’s analytical sample comprised 289 children ages 3-5 (51% with ECC) who attended public preschools in North Carolina and were enrolled in a community-based cross-sectional study of early childhood oral health. Clinical examinations were conducted by calibrated examiners in community locations using ICDAS criteria. Supragingival plaque collected from the facial/buccal surfaces of all primary teeth in the upper-left quadrant were analyzed using Ultra Performance Liquid Chromatography-tandem Mass Spectrometry. Associations between individual metabolites and 18 clinical traits (based on different ECC definitions and sets of tooth surfaces) were quantified using Brownian distance correlations (dCor) and linear regression modeling of log2-transformed values, applying a False Discovery Rate multiple testing correction. A tree-based pipeline optimization tool (TPOT)-machine learning process was used to identify the best-fitting ECC classification metabolite model. </p><p><strong>Results:</strong> There were 503 named metabolites identified, including microbial, host and exogenous biochemicals. Most significant ECC-metabolite associations were positive (i.e., upregulations/enrichments). The localized ECC case definition (ICDAS≥1 caries experience within the surfaces from which plaque was collected) had the strongest correlation with the metabolome (dCor p=8x10-3). Sixteen metabolites were significantly associated with ECC after multiple testing correction, including: fucose (p=3.0x10-6) and N-acetylneuraminate (p=6.8x10-6) with higher ECC prevalence; catechin (p=4.7x10-6) and epicatechin (p=2.9x10-6) with lower. Catechin, epicatechin, imidazole propionate, fucose, 9,10-DiHOME, and N-acetylneuraminate were among the top 15 metabolites in terms of ECC classification importance in the automated TPOT model. </p><p><strong>Conclusion:</strong> These supragingival biofilm metabolite findings provide novel insights in ECC biology and can serve as the basis for the development of measures of disease activity or risk assessment.</p>
2021-01-29 | MTBLS2215 | MetaboLights
Project description:Research of human vocal fold (VF) biology is hampered by several factors. The sensitive microstructure of the VF mucosa is one of them and limits the in vivo research, as biopsies carry the unbearable risk of scarring. A laryngeal organotypic model consisting of VF epithelial cells and VF fibroblasts (VFF) might overcome some of the limitations. Whereas human VFF are available in several forms, availability of VF epithelial cells is scarce. Buccal mucosa might be a good source, as it is easily accessible, and biopsies heal without scarring. For this project we generated organotypic constructs consisting of immortalized human VF fibroblasts and primary human buccal epithelial cells. The constructs (n = 3) were compared to native laryngeal mucosa on a histological and proteomic level. The engineered constructs reassembled into a mucosa-like structure, after a cultivation period of 35 days. Immunohistochemical staining confirmed a multi-layered stratified epithelium, a collagen type IV positive barrier-like structure resembling the basal membrane, and an underlying layer containing VFF. Proteomic analysis revealed a total number of 1961 proteins. Of these, 83.8% were detected in both native VF and constructs, with only 53 proteins having significantly different abundance. 15.3% of detected proteins were identified in native VF mucosa only, most likely due to endothelial, immune and muscle cells within the VF samples, while 0.9% were found only in the constructs. Based on easily available cell sources, we could demonstrate that our organotypic laryngeal mucosa model shares many characteristics with native VF mucosa. It represents a stable and reproducible in vitro model and offers a wide range of possibilities ranging from the exploration of VF biology to the testing of interventions (e.g. drug testing).
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