Project description:To determine whether and how warming affects the functional capacities of the active microbial communities, GeoChip 5.0 microarray was used. Briefly, four fractions of each 13C-straw sample were selected and regarded as representative for the active bacterial community if 16S rRNA genes of the corresponding 12C-straw samples at the same density fraction were close to zero.
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: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:Purpose: This study aims to compare and analyze the differences in bacterial community composition in fecal samples from mice treated with Control(DW), Vancomycin (VAN), Ampicillin (AMP), Neomycin (NEO), Metronidazole (MET), and a combination of all antibiotics (ALL, VANM) using 16S rRNA sequencing. Methods: Each antibiotics treated mice's fecal samples were collected and stored -80'c until analyzation. DNA was extracted using the NucleoSpin DNA Stool Kit (MACHEREY-NAGEL) following the manufacturer’s protocol. Metagenomic sequencing was performed on an Illumina MiSeq platform (Illumina), targeting the V3 and V4 regions of the 16S rRNA gene according to the manufacturer's instructions. PCR products were purified using AMPure XP beads, and sequencing adapters were added using the Nextera XT Index Kit (Illumina). The library was further purified with AMPure XP beads and quantified using automated electrophoresis with the TapeStation System (Agilent). Sequencing was performed using the MiSeq v3 reagent kit (Illumina), following the manufacturer’s protocol. Results: QIIME2 (v2023.02) was used to process and analyze 16S rRNA gene amplicon sequencing data, from sequence preprocessing to taxonomic classification. Paired-end sequences were merged and quality-filtered using Deblur. The resulting amplicon sequence variants (ASVs) were used for downstream analyses. Conclusions: Our study presents a comparative analysis of bacterial community composition in fecal samples from antibiotic-treated mice. We observed that microbiota composition varied distinctly depending on the type of antibiotic administered.
Project description:Industrial anaerobic digestion (AD) represents a relevant energy source beyond today’s fossil fuels, wherein organic matter is recycled to methane gas via an intricate and complex microbial food web. Despite its potential, anaerobic reactors often undergo process instability over time, mainly caused by substrate composition perturbations, making the system unreliable for stable energy production. To ensure the reliability of AD technologies, it is crucial to identify microbial- and system responses to better understand the effect of such perturbations and ultimately detect signatures indicative of process failure . Here, we investigate the effect of microalgal organic loading rate (OLR) on the fermentation products profile, microbiome dynamics, and disruption/recovery of major microbial metabolisms. Reactors subjected to low- and high-OLR disturbances were operated and monitored for fermentation products and biogas production over time, while microbial responses were investigated via 16S rRNA gene amplicon data, shotgun metagenomics and metagenome-centric metaproteomics.
Project description:We report the use of high-throughput sequencing technology to detect the microbial composition and abundance of mice grastic contents before and after Helicobacter pylori infection or Lactobacillus paracasei ZFM54 pretreatment/treatment. The genomic DNA was obtained by the QIAamp PowerFecal DNA Kit. Then, the DNA samples were sent to BGI Genomics Co., Ltd. (Shenzhen, China) for V3-V4 region of the 16S rRNA gene high-throughput sequencing with an Illumina MiSeq platform. DNA samples were sequenced using primers 338F (forward primer sequence ACTCCTACGGGAGGCAGCAG)-806R (reverse primer sequence GGACTACHVGGGTWTCTAAT). The sequencing analyses were carried out using silva138/16s database as a reference for the assignation of Amplicon Sequence Variant (ASV) at 100% similarity.
Project description:Gut microbiota were assessed in 540 colonoscopy-screened adults by 16S rRNA gene sequencing of stool samples. Investigators compared gut microbiota diversity, overall composition, and normalized taxon abundance among these groups.
| 2255499 | ecrin-mdr-crc
Project description:16S rRNA gene amplicon sequencing of antibiotic fermentation residue
Project description:Mastitis, one of the most significant diseases affecting dairy cattle, is caused by diverse bacterial pathogens that elicit distinct host immune responses. To gain deeper insight into the molecular mechanisms underlying these pathogen-specific interactions, we conducted a comprehensive proteomic analysis of bovine somatic cells isolated from milk in relation to the bacterial species present. The study included 56 quarter-level samples collected from 24 dairy cows. Samples were grouped according to pathogen categories based on the taxonomic similarity derived from 16S rRNA amplicon sequencing. Proteomic profiling revealed patterns of protein expression associated with mastitis in general, as well as distinct, pathogen-dependent signatures. This dataset provides valuable insights into host-pathogen interactions in the bovine mammary gland