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: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.
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:IL22 induces antimicrobial peptides which influnce microbiota. We used 16s rRNA gene sequencing (16s DNA-seq) to analyze the microbiota with Fc or IL-22Fc treatment.
Project description:Controlling the progression of chronic kidney disease (CKD) at an early stage is critical for reducing disease severity. A cross-sectional study of chronic kidney disease (CKD) patients at all stages with S. stercoralis infection found that helminth infection caused gut dysbiosis, which may be involved in CKD progression. Because of the variation of gut microbiome results with helminth infection, the cross-sectional study of 16S rRNA sequencing, therefore, is insufficient to draw valid conclusions and correct the effects of S. stercoralis on the early stages of CKD. Combination with other omics approach is warrant to be better understand the disease.
Project description:Primary outcome(s): 1. Evaluation of genome abnormality and gene expression by omics analysis of tumor etc. 2. TCR repertoire analysis and RNA expression analysis etc. of T cells in tumor tissue and peripheral blood. 3. Prediction and identification of tumor neo-antigen and evaluation of immunogenicity etc. 4. Analyze ctDNA(16S rRNA PCR) and feces of patients with advanced solid malignancies over time to profile and monitor cancer-related genomic alterations 5. Assessment of the relationship between the analysis above and clinical pathological features or therapeutic efficacy etc.
Project description:Primary outcome(s): Analysis of the diversity and composition of the gut microbiome by 16S rRNA sequencing
Study Design: Observational Study Model : Others, Time Perspective : Prospective, Enrollment : 60, Biospecimen Retention : Collect & Archive- Sample with DNA, Biospecimen Description : Blood, Stool
Project description:Here we map the molecular response of a synthetic community of 32 human gut bacteria to three non-antibiotic drugs by using five omics layers, namely 16S rRNA gene profiling, metagenomics, metatranscriptomics, metaproteomics, and metabolomics. Using this controlled setting, we find that all omics methods with species resolution in their readouts are highly consistent in estimating relative species abundances across conditions. Furthermore, different omics methods can be complementary in their ability to capture functional changes in response to the drug perturbations. For example, while nearly all omics data types captured that the antipsychotic drug chlorpromazine selectively inhibits Bacteroidota representatives in the community, the metatranscriptome and metaproteome suggested that the drug induces stress responses related to protein quality control and metabolomics revealed a decrease in polysaccharide uptake, likely caused by Bacteroidota depletion. Taken together, our study provides insights into how multi-omics datasets can be utilised to reveal complex molecular responses to external perturbations in microbial communities.
