Project description:Hundreds of microbial species were found to be transcriptionally active in the human gut microbiome based on the expression profiling of ca. 680.000 microbial genes As a part of the MetaHIT cohort 233 human stool samples were transcriptionally profiled using a custom made microarray that included probes for most prevalent microbial genes in the cohort as established by whole-genome sequencing of the same samples
Project description:The human stool samples were collected and processed for in vitro culturing under anaerobic condition using rapidAIM assay with or without SAHA, an lysine deacetylase inhibitor, for evaluating the effects of SAHA on human gut microbiome. Metaproteomics were used to analyze the microbiome composition and functions.
Project description:To test the effects of metformin on the human gut micorbiome, we fist collected human stool samples. We processed the samples in vitro culturing under anaerobic condition for 24 hours using the rapidAIM assay and either and cultured them with metformin, or DMSO as a control. We know that metformin can alter the human gut microbiome and were interested in better analyzing which functional proceses were altered.
Project description:The mice stool samples were collected for culturing with different medium for understanding the role of different medium component on the microbiome.
Project description:Dysbiotic configurations of the human gut microbiota have been linked with colorectal cancer (CRC). Human small non-coding RNAs are also implicated in CRC and recent findings suggest that their release in the gut lumen contributes to shape the gut microbiota. Bacterial small RNAs (bsRNAs) may also play a role in carcinogenesis but their role is less explored. Here, we performed small RNA and shotgun sequencing on 80 stool specimens of patients with CRC, or adenomas, and healthy subjects collected in a cross-sectional study to evaluate their combined use as a predictive tool for disease detection. We reported a considerable overlap and correlation between metagenomic and bsRNA quantitative taxonomic profiles obtained from the two approaches. Furthermore, we identified a combined predictive signature composed by 32 features from human and microbial small RNAs and DNA-based microbiome able to accurately classify CRC from healthy and adenoma samples (AUC= 0.87). In summary we reported evidence that host-microbiome dysbiosis in CRC can be observed also by altered small RNA stool profiles. Integrated analyses of the microbiome and small RNAs in the human stool may provide insights for designing more accurate tools for diagnostic purposes.
