Project description:A metaproteomics analysis was conducted on the infant fecal microbiome to characterize global protein expression in 8 samples obtained from infants with a range of early-life experiences. Samples included breast-, formula- or mixed-fed, mode of delivery, and antibiotic treatment and one set of monozygotic twins. Although label-free mass spectrometry-based proteomics is routinely used for the identification and quantification of thousands of proteins in complex samples, the metaproteomic analysis of the gut microbiome presents particular technical challenges. Among them: the extreme complexity and dynamic range of member taxa/species, the need for matched, well-annotated metagenomics databases, and the high inter-protein sequence redundancy/similarity between related members. In this study, a metaproteomic approach was developed for assessment of the biological phenotype and functioning, as a complement to 16S rRNA sequencing analysis to identify constituent taxa. A sample preparation method was developed for recovery and lysis of bacterial cells, followed by trypsin digestion, and pre-fractionation using Strong Cation Exchange chromatography. Samples were then subjected to high performance LC-MS/MS. Data was searched against the Human Microbiome Project database, and a homology-based meta-clustering strategy was used to combine peptides from multiple species into representative proteins. Bacterial taxonomies were also identified, based on species-specific protein sequences, and protein metaclusters were assigned to pathways and functional groups. The results obtained demonstrate the applicability of this approach for performing qualitative comparisons of human fecal microbiome composition, physiology and metabolism, and also provided a more detailed assessment of microbial composition in comparison to 16S rRNA.
Project description:Background: Inflammatory bowel diseases are classic polygenic disorders, with genetic loads that reflect immunopathological processes in response to intestinal microbiota. To assess gut bacterial community, microRNA (miRNA), and short chain fatty acid (SCFA) signatures associated with the activity of Crohn’s disease (CD). Methods: DNA, miRNA, and metabolites were extracted from stool samples of 15 CD patients, eight with active disease and seven in remission, and nine healthy individuals. Microbial, miRNA and SCFA profiles were assessed using datasets from 16s rRNA sequencing, Nanostring miRNA and GC-MS targeted analysis, respectively. Results: Pairwise comparisons showed that 11 and 27 taxa differed between controls and CD patients with active and inactive disease, respectively. Seven taxa were common to both comparisons, whereas eight taxa differed in CD patients. α-Diversity was lower in both CD groups than in controls. The levels of 13 miRNAs differed (p-value <0.05; FC >1.5) in CD patients and controls. Comparisons of controls with CD patients having active and inactive disease identified 12 and seven significantly different miRNAs, respectively. Of six SCFAs, the levels of two differed significantly (p-value <0.05, FC >1.5) in CD patients and controls, and the levels of four differed in patients with active and inactive CD. PLS-DA revealed models with high discriminatory powers (AUC >0.9) for bacteria and miRNA. The levels of 14 miRNAs and 39 bacterial taxa correlated with the level of SCFAs. Conclusion: CD-related gut dysbiosis correlates significantly with miRNA and SCFA profiling, indicating complex relationships among all these factors in response to intestinal inflammation.
Project description:Ample evidence indicates that insulin resistance (IR) is closely related to white adipose tissue (WAT), but the underlying mechanisms of IR pathogenesis are still unclear. Using 352 microarray datasets from seven independent studies, we identified a meta-signature which comprised of 1,413 genes. Our meta-signature was also enriched in overall WAT in in vitro and in vivo IR models. Only 12 core enrichment genes were consistently enriched across all IR models. Among the meta-signature, we identified a drug signature made up of 211 genes with expression levels that were co-regulated by thiazolidinediones and metformin using cross-species analysis. To confirm the clinical relevance of our drug signature, we found that the expression levels of 195 genes in the drug signature were significantly correlated with both homeostasis model assessment 2-IR score and body mass index. Finally, 18 genes from the drug signature were identified by protein-protein interaction network cluster. Four core enrichment genes were included in 18 genes and the expression levels of selected 8 genes were validated by quantitative PCR. These findings suggest that our signatures provide a robust set of genetic markers which can be used to provide a starting point for developing potential therapeutic targets in improving IR in WAT.
2017-12-31 | GSE102540 | GEO
Project description:DNA-barcoding in the rainforest using nanopore sequencing