Ontology highlight
ABSTRACT:
PROVIDER: PRJNA731163 | ENA |
REPOSITORIES: ENA
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Project description:Kombucha Tea (KT), a fermented tea with roots in traditional Chinese medicine, has surged in worldwide popularity due to its purported health benefits. KT contains a symbiotic culture of yeast and bacterial species, many of which are considered human probiotics. The molecular basis of the health benefits of KT has yet to be thoroughly explored in any animal model. We establishC. elegansas a model to query the molecular interactions between Kombucha-associated microbes (KTM) and the host. We find that worms have an established gut microbiome after consuming a KTM-exclusive diet that mirrors the microbial community found in the fermenting culture. Remarkably, animals consuming KTMs display strikingly reduced lipid levels, yet develop and reproduce similarly toE. coli-fed animals. Critically, consumption of a non-fermenting mix of KT microbial isolates (Kombucha microbe mix) resulted in elevated fat accumulation, suggesting that KTMs do not impair nutrient absorption. To identify the host metabolic pathways altered by KTMs, we performed mRNA-seq on KTM-fed animals, finding widespread changes in lipid metabolism genes. Specifically, we found that three lysosomal lipase genes are significantly upregulated in these animals. These lipases, LIPL-1-3, have been previously shown to promote lipophagy via catabolism of lipid droplets. Consistently, KTM-fed animals display reduced levels of triglycerides and smaller lipid droplet sizes. We propose that KTM-fed animals exhibit a fasting-like metabolic state, even in the presence of sufficient nutrient availability, possibly through induction of lipophagy. Elucidating the host metabolic response to KT consumption will provide unprecedented insight into how this popular fermented beverage may impact human health and inform its use in complementary healthcare plans.
2024-01-17 | GSE236037 | GEO
Project description:Opioids such as morphine have many beneficial properties as analgesics, however, opioids may induce multiple adverse gastrointestinal symptoms. We have recently demonstrated that morphine treatment results in significant disruption in gut barrier function leading to increased translocation of gut commensal bacteria. However, it is unclear how opioids modulate the gut homeostasis. By using a mouse model of morphine treatment, we studied effects of morphine treatment on gut microbiome. We characterized phylogenetic profiles of gut microbes, and found a significant shift in the gut microbiome and increase of pathogenic bacteria following morphine treatment when compared to placebo. In the present study, wild type mice (C57BL/6J) were implanted with placebo, morphine pellets subcutaneously. Fecal matter were taken for bacterial 16s rDNA sequencing analysis at day 3 post treatment. A scatter plot based on an unweighted UniFrac distance matrics obtained from the sequences at OTU level with 97% similarity showed a distinct clustering of the community composition between the morphine and placebo treated groups. By using the chao1 index to evaluate alpha diversity (that is diversity within a group) and using unweighted UniFrac distance to evaluate beta diversity (that is diversity between groups, comparing microbial community based on compositional structures), we found that morphine treatment results in a significant decrease in alpha diversity and shift in fecal microbiome at day 3 post treatment compared to placebo treatment. Taxonomical analysis showed that morphine treatment results in a significant increase of potential pathogenic bacteria. Our study shed light on effects of morphine on the gut microbiome, and its role in the gut homeostasis.
2018-04-16 | E-MTAB-5546 | biostudies-arrayexpress
Project description:The clinical importance of microbiomes to the chronicity of wounds is widely appreciated, yet little is understood about patient-specific processes shaping wound microbiome composition. Here, a two-cohort microbiome-genome wide association study is presented through which patient genomic loci associated with chronic wound microbiome diversity were identified. Further investigation revealed that alternative TLN2 and ZNF521 genotypes explained significant inter-patient variation in relative abundance of two key pathogens, Pseudomonas aeruginosa and Staphylococcus epidermidis. Wound diversity was lowest in Pseudomonas aeruginosa infected wounds, and decreasing wound diversity had a significant negative linear relationship with healing rate. In addition to microbiome characteristics, age, diabetic status, and genetic ancestry all significantly influenced healing. Using structural equation modeling to identify common variance among SNPs, six loci were sufficient to explain 53% of variation in wound microbiome diversity, which was a 10% increase over traditional multiple regression. Focusing on TLN2, genotype at rs8031916 explained expression differences of alternative transcripts that differ in inclusion of important focal adhesion binding domains. Such differences are hypothesized to relate to wound microbiomes and healing through effects on bacterial exploitation of focal adhesions and/or cellular migration. Related, other associated loci were functionally enriched, often with roles in cytoskeletal dynamics. This study, being the first to identify patient genetic determinants for wound microbiomes and healing, implicates genetic variation determining cellular adhesion phenotypes as important drivers of infection type. The identification of predictive biomarkers for chronic wound microbiomes may serve as risk factors and guide treatment by informing patient-specific tendencies of infection.
2020-04-25 | GSE149314 | GEO
Project description:The diversity and complexity of the microbiome's genomic landscape are not always mirrored in its proteomic profile. Despite the anticipated proteomic diversity, observed complexities of microbiome sample are often lower than expected. Two main factors contribute to this discrepancy: limitations in mass spectrometry's detection sensitivity and bioinformatics challenges in metaproteomics identification. This study introduces a novel approach to evaluating sample complexity directly at the full mass spectrum (MS1) level rather than relying on peptide identifications. When analyzing under identical mass spectrometry conditions, microbiome samples displayed significantly higher complexity, as evidenced by the spectral entropy and peptide candidate entropy, compared to single-species samples. The research provides solid evidence for the complexity of microbiome in proteomics indicating the optimization potential of the bioinformatics workflow.
2024-05-27 | PXD050781 | Pride