Project description:Here, we applied a microarray-based metagenomics technology termed GeoChip 5.0 to examined functional gene structure of microbes in four lakes at low and high elevations of approximately 530 and 4,600 m a.s.l., respectively.
Project description:In this study we used metaproteomics to discern the metabolism and physiology of the microorganisms occurring in the phototrophic mats of four soda lakes in the interior of British Columbia, Canada. Binned and assembled metagenomes were used as the database for protein identification.
Project description:In this study, we analyzed the microbial communities from a methane-based bio-reactor with selenate as an electron accepter. Four biological replicates were analyzed by metagenomics, of which data can be found in the SRA database (Accession number: SRP136677, SRP136696, SRP136790 and SRP136859). Based on the metagenomic data, we detected the expressed proteins using metaproteomics. This data is also included in this submission.
Project description:Gut microbes elicit specific changes in gene expression in the colon of mice. We colonized germ-free mice with microbial communities from the guts of humans, zebrafish and termites, human skin and tongue, soil and estuarine microbial mats. We used microarrays to detail the differences in global gene expression in colon tissue that are caused by the different microbial communities 28 days after gavage into the germfree animal. Three biological replicates per group, male C57BL/6 mice (12-16 weeks old)
Project description:In this study we developed metaproteomics based methods for quantifying taxonomic composition of microbiomes (microbial communities). We also compared metaproteomics based quantification to other quantification methods, namely metagenomics and 16S rRNA gene amplicon sequencing. The metagenomic and 16S rRNA data can be found in the European Nucleotide Archive (Study number: PRJEB19901). For the method development and comparison of the methods we analyzed three types of mock communities with all three methods. The communities contain between 28 to 32 species and strains of bacteria, archaea, eukaryotes and bacteriophage. For each community type 4 biological replicate communities were generated. All four replicates were analyzed by 16S rRNA sequencing and metaproteomics. Three replicates of each community type were analyzed with metagenomics. The "C" type communities have same cell/phage particle number for all community members (C1 to C4). The "P" type communities have the same protein content for all community members (P1 to P4). The "U" (UNEVEN) type communities cover a large range of protein amounts and cell numbers (U1 to U4). We also generated proteomic data for four pure cultures to test the specificity of the protein inference method. This data is also included in this submission.
Project description:Gut microbes elicit specific changes in gene expression in the colon of mice. We colonized germ-free mice with microbial communities from the guts of humans, zebrafish and termites, human skin and tongue, soil and estuarine microbial mats. We used microarrays to detail the differences in global gene expression in colon tissue that are caused by the different microbial communities 28 days after gavage into the germfree animal.
Project description:Here, we applied a microarray-based metagenomics technology termed GeoChip 5.0 to investigate spring microbial functional genes in mesocosm-simulated shallow lake ecosystems having been undergoing nutrient enrichment and warming for nine years.
Project description:Monitoring microbial communities can aid in understanding the state of these habitats. Environmental DNA (eDNA) techniques provide efficient and comprehensive monitoring by capturing broader diversity. Besides structural profiling, eDNA methods allow the study of functional profiles, encompassing the genes within the microbial community. In this study, three methodologies were compared for functional profiling of microbial communities in estuarine and coastal sites in the Bay of Biscay. The methodologies included inference from 16S metabarcoding data using Tax4Fun, GeoChip microarrays, and shotgun metagenomics.
