Oral microbial
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA663719 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:oral microbial amplicon Raw sequence reads
| PRJNA987330 | ENA
Project description:Background: The differential abundance of cell-free RNAs in bodily fluids is emerging as a promising tool for the non-invasive molecular diagnosis of cancer. Human saliva is considered a promising source of non-invasive biomarkers of diagnostic value for oral cancer detection. This study aims to identify diagnostic potent salivary RNAs in oral squamous cell carcinoma (OSCC)-patients by RNA-Sequencing. Method: Unstimulated saliva was collected from 5 normal control (NC) individuals and 9 OSCC patients (PS) with prior consent and ethical committee approvals. Total RNA isolated from cell-free saliva (CFS) supernatant was used to prepare small RNA libraries and sequenced on the Ion Torrent S5 platform. The sequencing reads were aligned to the human genome (hg19) using Bowtie 2, and the differential expression analysis was performed using RUVSeq and DESeq2. Mapped reads were screened across miRBase (v22) annotations for miRNAs and Gencode (v19) annotation for other RNAs. Reads were quantified by the Featurecount (v1.4.6) module of the R-package. The microbial-RNA enrichment analysis was determined using the One Codex platform. Result: RNA-sequencing detected protein-coding transcripts (PCTs), long-intergenic RNAs (lincRNAs), microRNAs (miRNAs), small nuclear RNAs (snRNAs), transfer RNAs (tRNAs) and pseudogenes from the saliva of PS and HC samples. Transcriptome analyses revealed 89 PCTs, 18 lincRNAs and 6 miRNAs differentially expressed between PS and HC with a log2fold change ≥ 1 or ≤ -1 and p-value < 0.05. Gene ontology and pathway enrichment analyses indicated a significant correlation of the identified PCTs and miRNAs to various cancer-related pathways that may have implications in the pathogenesis of OSCC. Interestingly, unmapped non-human reads aligned to the microbial reference genomes. Further analyses of these microbial sequence reads revealed a significant microbial dysbiosis differentiating PS from HC. Metabolic pathways and functional analysis of the identified microbial phylotypes showed gene ontologies associated with inflammation, cell proliferation, ROS generation, and a range of metabolic processes. Conclusion: We report novel panels of differentially expressed PCTs, miRNAs and lincRNAs distinguishing PS from HC. Importantly, our results also provide evidence for oral microbial dysbiosis that appears to have pathological implications in OSCC. Summarily, this study provides a comprehensive landscape of salivary RNAs that can be exploited as non-invasive biomarkers for OSCC detection.
2023-01-30 | GSE176077 | GEO
Project description:Oral microbial homeostasis is a key factor affecting oral health, and saliva plays a significant role in maintaining oral microbial homeostasis. The submandibular gland (SMG) and sublingual gland (SLG) together produce the most saliva at rest. Organic ingredients, including antimicrobial proteins, are rich and distinctive and depend on the type of acinar cells in the SMG and SLG. However, the functions of the SMG and SLG in maintaining oral microbial homeostasis have been difficult to identify and distinguish, given their unique anatomical structures. Therefore, we analyzed each gland using single-cell RNA sequencing.
2023-01-03 | GSE216476 | GEO
Project description:Oral microbial homeostasis is a key factor affecting oral health, and saliva plays a significant role in maintaining oral microbial homeostasis. The submandibular gland (SMG) and sublingual gland (SLG) together produce the most saliva at rest. Organic ingredients, including antimicrobial proteins, are rich and distinctive and depend on the type of acinar cells in the SMG and SLG. However, the functions of the SMG and SLG in maintaining oral microbial homeostasis have been difficult to identify and distinguish, given their unique anatomical structures. Therefore, we analyzed each gland using proteomics and tried to find differentially secreted proteins in the SMG and SLG.
2023-01-03 | PXD038423 | Pride