Human gut metagenome
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA295000 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:An improved methodology to overcome key issues associated with the methods of human fecal metagenomic DNA extraction
| PRJNA294995 | ENA
Project description:In this study we investigated whether gut microbiota profile of Italian healthy volunteers could differ based on their geaographical origin. To this purpose, fecal samples were collected from 31 healthy individuals living in 3 different italian regions (Lombardy, North; Lazio, Center; Apulia, South) and their respective microbiota profiles were analyzed employing 16S metagenomic sequencing method. This study identifies differences in the gut microbiota content and richness among individuals with the same ethnicity coming from three different Italian regions.
2026-01-08 | E-MTAB-8136 | biostudies-arrayexpress
Project description:We recruited 24 Mongolian volunteers,6 of which were T2D cases(sample T1-T6), 6 were prediabetes cases(sample P1-P6), and 12 were health cases(sample C1-C12). The metagenomic analysis of gut microbiota from the volunteers’ fecal samples was performed. We compared the microbial differences in the three groups, and analyzed the differences of the stool microbial function.
2022-07-30 | E-MTAB-11957 | biostudies-arrayexpress
Project description:Dog Aging Project fecal metagenomic data from the Precision Cohort
2026-04-15 | GSE312816 | GEO
Project description:This study aims to understand the systemic component of psoriasis pathogenesis since psoriasis patients have higher risk of developing diesases beyond skin inflammation. In this study, we collected sigmoidal gut biopsies to profile host transcriptomic changes associated with psoriasis patients and healthy subjects. This exepriment provided transcriptomic dataset of host response and is integrated with fecal metagenomic data and flow cytometry dataset as part of the multi-omic study.
2020-06-30 | GSE150851 | GEO
Project description:Distal gut bacteria play a pivotal role in the digestion of dietary polysaccharides by producing a large number of carbohydrate-active enzymes (CAZymes) that the host otherwise does not produce. We report here the design of a high density custom microarray that we used to spot non-redundant DNA probes for more than 6,500 genes encoding glycoside hydrolases and lyases selected from 174 reference genomes from distal gut bacteria. The custom microarray was tested and validated by the hybridization of bacterial DNA extracted from the stool samples of lean, obese and anorexic individuals. Our results suggest that a microarray-based study can detect genes from low-abundance bacteria better than metagenomic-based studies. A striking example was the finding that a gene encoding a GH6-family cellulase was present in all subjects examined, whereas metagenomic studies have consistently failed to detect this gene in both human and animal gut microbiomes. In addition, an examination of eight stool samples allowed the identification of a corresponding CAZome core containing 46 families of glycoside hydrolases and polysaccharide lyases, which suggests the functional stability of the gut microbiota despite large taxonomical variations between individuals. Fecal samples were collected from eight female subjects. Three were obese subjects of BMI kg m-2: 35, 46.8 and 51.3, respectively; age: 42, 21 and 65 years old, respectively. Three were anorexic women of BMI kg m-2: 9.8, 10 and 13.7, respectively; age: 19, 23 and 49 years old, respectively. Finally, two fecal samples from lean women of BMI kg m-2: 18.6 and 23.42 were analyzed.
2013-10-28 | E-GEOD-51760 | biostudies-arrayexpress
Project description:We explore whether a low-energy diet intervention for Metabolic dysfunction-associated steatohepatitis (MASH) improves liver disease by means of modulating the gut microbiome. 16 individuals were given a low-energy diet (880 kcal, consisting of bars, soups, and shakes) for 12 weeks, followed by a stepped re-introduction to whole for an additional 12 weeks. Stool samples were obtained at 0, 12, and 24 weeks for microbiome analysis. Fecal microbiome were measured using 16S rRNA gene sequencing. Positive control (Zymo DNA standard D6305) and negative control (PBS extraction) were included in the sequencing. We found that low-energy diet improved MASH disease without lasting alterations to the gut microbiome.
2024-12-31 | E-MTAB-14061 | biostudies-arrayexpress
Project description:<p><strong>INTRODUCTION:</strong> Mass spectrometry based metabolomics has become a promising complement and alternative to transcriptomics and proteomics in many fields including in vitro systems pharmacology. Despite several merits, metabolomics based on liquid chromatography mass spectrometry (LC-MS) is a developing area that is yet attached to several pitfalls and challenges. To reach a level of high reliability and robustness, these issues need to be tackled by implementation of refined experimental and computational protocols.</p><p><strong>OBJECTIVES:</strong> This study illustrates some key pitfalls in LC-MS based metabolomics and introduces an automated computational procedure to compensate for them.</p><p><strong>METHODS:</strong> Non-cancerous mammary gland derived cells were exposed to 21 chemicals from four pharmacological classes plus a set of 6 pesticides. Changes in the metabolome of cell lysates were assessed after 24h using LC-MS. A data processing pipeline was established and evaluated to handle issues including contaminants, carry over effects, intensity decay and inherent methodology variability and biases. A key component in this pipeline is a latent variable method called OOS-DA (optimal orthonormal system for discriminant analysis), being theoretically more easily motivated than PLS-DA in this context, as it is rooted in pattern classification rather than regression modeling.</p><p><strong>RESULTS:</strong> The pipeline is shown to reduce experimental variability/biases and is used to confirm that LC-MS spectra hold drug class specific information.</p><p><strong>CONCLUSIONS:</strong> LC-MS based metabolomics is a promising methodology, but comes with pitfalls and challenges. Key difficulties can be largely overcome by means of a computational procedure of the kind introduced and demonstrated here. The pipeline is freely available on www.github.com/stephanieherman/MS-data-processing.</p>
2017-08-23 | MTBLS401 | MetaboLights
Project description:Comparison of microbial DNA extraction methods for clinical applications
| PRJDB7617 | ENA