Project description:Gut microbiome research is rapidly moving towards the functional characterization of the microbiota by means of shotgun meta-omics. Here, we selected a cohort of healthy subjects from an indigenous and monitored Sardinian population to analyze their gut microbiota using both shotgun metagenomics and shotgun metaproteomics. We found a considerable divergence between genetic potential and functional activity of the human healthy gut microbiota, in spite of a quite comparable taxonomic structure revealed by the two approaches. Investigation of inter-individual variability of taxonomic features revealed Bacteroides and Akkermansia as remarkably conserved and variable in abundance within the population, respectively. Firmicutes-driven butyrogenesis (mainly due to Faecalibacterium spp.) was shown to be the functional activity with the higher expression rate and the lower inter-individual variability in the study cohort, highlighting the key importance of the biosynthesis of this microbial by-product for the gut homeostasis. The taxon-specific contribution to functional activities and metabolic tasks was also examined, giving insights into the peculiar role of several gut microbiota members in carbohydrate metabolism (including polysaccharide degradation, glycan transport, glycolysis and short-chain fatty acid production). In conclusion, our results provide useful indications regarding the main functions actively exerted by the gut microbiota members of a healthy human cohort, and support metaproteomics as a valuable approach to investigate the functional role of the gut microbiota in health and disease.
Project description:Human DNA present in fecal samples can result in a small number of human reads in gut shotgun metagenomic sequencing data. However, it is currently unclear how much personal information can be reconstructed from such reads and this has not been quantitatively evaluated. Such a quantitative evaluation is necessary to clarify the ethical concerns related to data sharing and to enable the efficient use of human genetic information in stool samples, such as for research and forensics. Here, we used genomic approaches to reconstruct personal information from fecal metagenomes of 343 Japanese individuals with associated human genotype data. Our approach can be used to quantify the personal information contained within gut metagenome data.
Project description:Three human gut microbiome samples from different individuals were cultured in an optimized culture medium with or without the presence of different sugars (10 mM glucose, 20 mM fructose, 10 mM glucose + 20 mM fructose, or 10 mM kestose). Samples were cultured in technical triplicates, and were taken at 0 hr, 1hr, 5 hr, 12 hr and 24 hr of culturing for optical density and metaproteomic analyses. Cultured microbiota cells were subjected to metaproteomics analysis using LC-MS/MS and a TMT approach.
Project description:The main goal of the project is the study the associations between the gut metagenome and human health. The dataset contains data for n=7211 FINRISK 2002 participants who underwent fecal sampling. Demultiplexed shallow shotgun metagenomic sequences were quality filtered and adapter trimmed using Atropos (Didion et al., 2017), and human filtered using Bowtie2 (Langmead and Salzberg, 2012).
Project description:The main goal of the project is the study the associations between the gut metagenome and human health. The dataset contains data for n=7211 FINRISK 2002 participants who underwent fecal sampling. Demultiplexed shallow shotgun metagenomic sequences were quality filtered and adapter trimmed using Atropos (Didion et al., 2017), and human filtered using Bowtie2 (Langmead and Salzberg, 2012).
Project description:Single nucleotide polymorphisms (SNPs) are the most common type of genetic variation in gut microbial metagenome and host genome but they could not adequately represent the protein-level variants. Single amino-acid polymorphisms (SAP) derived from non-synonymous SNPs can cause functional changes of proteins and are important forces of adaption. However, SAP remain quite unexplored for human gut microbiome. Here, we present a comprehensive large-scale analysis of SAP in the gut ecosystem, introducing a rigorous computational pipeline for detecting such protein variation from 992 published human metaproteomes.