Human saliva metagenome
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA361501 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR5205370_1.fastq.gz | Fastqsanger.gz | |||
| SRR5205370_2.fastq.gz | Fastqsanger.gz | |||
| SRR5205380_1.fastq.gz | Fastqsanger.gz | |||
| SRR5205380_2.fastq.gz | Fastqsanger.gz | |||
| SRR5205390_1.fastq.gz | Fastqsanger.gz |
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Project description:Human saliva microbiota is phylogenetically divergent among host individuals yet their roles in health and disease are poorly appreciated. We employed a microbial functional gene microarray, HuMiChip 1.0, to reconstruct the global functional profiles of human saliva microbiota from ten healthy and ten caries-active adults. Saliva microbiota in the pilot population featured a vast diversity of functional genes. No significant distinction in gene number or diversity indices was observed between healthy and caries-active microbiota. However, co-presence network analysis of functional genes revealed that caries-active microbiota was more divergent in non-core genes than healthy microbiota, despite both groups exhibited a similar degree of conservation at their respective core genes. Furthermore, functional gene structure of saliva microbiota could potentially distinguish caries-active patients from healthy hosts. Microbial functions such as Diaminopimelate epimerase, Prephenate dehydrogenase, Pyruvate-formate lyase and N-acetylmuramoyl-L-alanine amidase were significantly linked to caries. Therefore, saliva microbiota carried disease-associated functional signatures, which could be potentially exploited for caries diagnosis. The DMFT INDEX (Decayed, Missing, Filled [DMF] teeth index used in dental epidemiology) values are provided for each sample We employed a microbial functional gene microarray, HuMiChip 1.0, to reconstruct the global functional profiles of human saliva microbiota from ten healthy and ten caries-active adults.
2013-08-15 | E-GEOD-49875 | biostudies-arrayexpress
Project description:Human saliva microbiota is phylogenetically divergent among host individuals yet their roles in health and disease are poorly appreciated. We employed a microbial functional gene microarray, HuMiChip 1.0, to reconstruct the global functional profiles of human saliva microbiota from ten healthy and ten caries-active adults. Saliva microbiota in the pilot population featured a vast diversity of functional genes. No significant distinction in gene number or diversity indices was observed between healthy and caries-active microbiota. However, co-presence network analysis of functional genes revealed that caries-active microbiota was more divergent in non-core genes than healthy microbiota, despite both groups exhibited a similar degree of conservation at their respective core genes. Furthermore, functional gene structure of saliva microbiota could potentially distinguish caries-active patients from healthy hosts. Microbial functions such as Diaminopimelate epimerase, Prephenate dehydrogenase, Pyruvate-formate lyase and N-acetylmuramoyl-L-alanine amidase were significantly linked to caries. Therefore, saliva microbiota carried disease-associated functional signatures, which could be potentially exploited for caries diagnosis. The DMFT INDEX (Decayed, Missing, Filled [DMF] teeth index used in dental epidemiology) values are provided for each sample
2013-08-15 | GSE49875 | GEO
Project description:Virtually every mammalian tissue exhibits rhythmic expression in thousands of genes, which activate tissue-specific processes at appropriate times of the day. Much of this rhythmic expression is thought to be driven cell-autonomously by molecular circadian clocks present throughout the body. However, increasing evidence suggests that systemic signals, and more specifically rhythmic food intake (RFI), can regulate rhythmic gene expression independently of the circadian clock. To determine the relative contribution of cell autonomous clocks versus RFI in the regulation of rhythmic gene expression, we developed a system that allows long-term manipulation of the daily rhythm of food intake in the mouse, and analyzed liver gene expression by RNA-Seq in mice fed ad libitum, only at night, or arrhythmically (mouse eating 1/8th of their daily food intake every 3 hours). We show that 70% of the cycling mouse liver transcriptome loses rhythmicity under arrhythmic feeding. Remarkably, this loss of rhythmic gene expression under arrhythmic feeding is independent of the liver circadian clock, which continues to exhibit normal oscillations in core clock gene expression. Many genes that lose rhythmicity participate in the regulation of metabolic processes such as lipogenesis and glycogenesis, likely contributing to an increased sensitivity to insulin that was observed in arrhythmically-fed mice. We also show that night-restricted feeding significantly increases the number of rhythmically expressed genes as well as the amplitude of the rhythms. Together, these results indicate that metabolic transcription factors control a large fraction of the rhythmic mouse liver transcriptome, and demonstrate that systemic signals driven by rhythmic food intake play a more important role than the cell-autonomous circadian clock in driving rhythms in liver gene expression and metabolic functions.
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Project description:Saliva is a convenient non-invasive source of liquid biopsy to monitor human health and diagnose diseases. In particular, extracellular vesicles (EVs) in saliva can potentially reveal clinically relevant information for systemic health. Recent studies have shown that RNA in saliva EVs could be exploited as biomarkers for disease diagnosis. However, there is no standardized protocol for profiling RNA in saliva EV nor clear guideline on selecting saliva fractions for biomarker analysis. To address these issues, we established a robust protocol for small RNA profiling from fractionated saliva. With this method, we performed comprehensive small RNA sequencing of four saliva fractions, including cell-free saliva (CFS), EV-depleted saliva (EV-D), exosome (EXO), and microvesicle (MV) from ten healthy volunteers. Methods: To address these issues, we established a robust protocol for small RNA profiling from fractionated saliva. With this method, we performed comprehensive small RNA sequencing of four saliva fractions, including cell-free saliva (CFS), EV-depleted saliva (EV-D), exosome (EXO), and microvesicle (MV) from ten healthy volunteers.
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