Project description:Sensitive models of climate change impacts would require a better integration of multi-omics approaches that connect the abundance and activity of microbial populations. Here, we show that climate is a fundamental driver of the protein abundance of microbial populations (metaproteomics), yet not their genomic abundance (16S rRNA gene amplicon sequencing), supporting the hypothesis that metabolic activity may be more closely linked to climate than community composition.
Project description:The impact of mono-chronic S. stercoralis infection on the gut microbiome and microbial activities in infected participants was explored. The 16S rRNA gene sequencing of a longitudinal study with 2 sets of human fecal was investigated. Set A, 42 samples were matched, and divided equally into positive (Pos) and negative (Neg) for S. stercoralis diagnoses. Set B, 20 samples of the same participant in before (Ss+PreT) and after (Ss+PostT) treatment was subjected for 16S rRNA sequences and LC-MS/MS to explore the effect of anti-helminthic treatment on microbiome proteomes.
Project description:Iron-rich pelagic aggregates (iron snow) were collected directly onto silicate glass filters using an electronic water pump installed below the redoxcline. RNA was extracted and library preparation was done using the NEBNext Ultra II directional RNA library prep kit for Illumina. Data was demultiplied by GATC sequencing company and adaptor was trimmed by Trimgalore. After trimming, data was processed quality control by sickle and mRNA/rRNA sequences were sorted by SortmeRNA. mRNA sequences were blast against NCBI-non redundant protein database and the outputs were meganized in MEGAN to do functional analysis. rRNA sequences were further sorted against bacterial/archeal 16S rRNA, eukaryotic 18S rRNA and 10,000 rRNA sequences of bacterial 16S rRNA, eukaryotic 18S rRNA were subset to do taxonomy analysis.
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:The objective of the study was to uncover the developmental dynamics in Bacillus subtilis biofilms on a transcriptome level. The experiment covered a developmental timeline over two months of biofilm growth.