Project description:Purpose The role of intestinal flora in carcinogenesis and chemotherapy efficacy has been increasingly studied; however, comparisons between oral and intestinal flora remain limited. This study aimed to identify the microbial changes in urothelial carcinoma (UC) by analyzing oral saliva and stool samples from healthy individuals and patients. We also examined the association between microbial composition and immune checkpoint inhibitor (ICI) response. Methods A total of 20 healthy individuals and 38 patients with UC were analyzed. Among them, 27 patients with UC underwent ICI treatment. Oral saliva and stool samples were analyzed for 16S rRNA sequences to assess bacterial composition. Operational taxonomic units were generated, and phylogenetic analysis was performed using the 16S Metagenomics app whithin the Illumina BaseSpace Sequence Hub. Results Patients with UC showed higher Veillonellaceae and Prevotellaceae levels in saliva and stool, with lower levels of these bacteria associated with more prolonged overall survival and progression-free survival, particularly Veillonellaceae in stool. A higher neutrophil-to-lymphocyte ratio correlated with increased levels of these bacteria. Conclusion Veillonellaceae and Prevotellaceae are potential microbial biomarkers of survival outcomes and ICI efficacy in patients with UC. Non-invasive oral microbial sampling may facilitate personalized cancer treatment strategies.
Project description:Total DNA was extracted from stool specimens, amplified to collect amplicons of variable V3–V4 regions of the bacterial 16s rRNA gene and sequenced with MiSeq (2x300bp) Illumina platform.
Project description:Total DNA was extracted from saliva and stool of the patients, amplified to collect amplicons of variable V3–V4 regions of the bacterial 16s rRNA gene and sequenced with MiSeq (2x300bp) Illumina platform.
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:Total DNA was extracted from the stool of the patients, amplified to collect amplicons of variable V3–V4 regions (primers 341F and 805R) of the bacterial 16s rRNA gene and sequenced with MiSeq (2x300bp) Illumina platform.
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:We explore whether a low-energy diet intervention for Metabolic dysfunction-associated steatohepatitis (MASH) improves liver disease by means of modulating the gut microbiome. 16 individuals were given a low-energy diet (880 kcal, consisting of bars, soups, and shakes) for 12 weeks, followed by a stepped re-introduction to whole for an additional 12 weeks. Stool samples were obtained at 0, 12, and 24 weeks for microbiome analysis. Fecal microbiome were measured using 16S rRNA gene sequencing. Positive control (Zymo DNA standard D6305) and negative control (PBS extraction) were included in the sequencing. We found that low-energy diet improved MASH disease without lasting alterations to the gut microbiome.
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:Total bacterial DNA was isolated from water and sediment samples from a local watershed and 16S rRNA sequences were analyzed using the Illumina MiSeq v3 platform in order to generate snapshots of bacterial community profiles.
Project description:This study investigates the gut microbiome composition and diversity in three groups of rats: control, radiation enteritis model, and treatment (TG) groups. Total DNA was extracted from stool samples, PCR-amplified targeting 16S rRNA gene variable regions, and sequenced using Illumina MiSeq or NovaSeq platforms. Downstream bioinformatics analyses included sequence quality control, denoising (DADA2/OTU clustering), taxonomic classification, alpha and beta diversity evaluation, differential species abundance analysis, and functional prediction. The processed data include ASV/OTU tables, taxonomy assignments, and sample metadata.
