Project description:We applied metagenomic shotgun sequencing to investigate the effects of ZEA exposure on the change of mouse gut microbiota composition and function.
Project description:Distal gut bacteria play a pivotal role in the digestion of dietary polysaccharides by producing a large number of carbohydrate-active enzymes (CAZymes) that the host otherwise does not produce. We report here the design of a high density custom microarray that we used to spot non-redundant DNA probes for more than 6,500 genes encoding glycoside hydrolases and lyases selected from 174 reference genomes from distal gut bacteria. The custom microarray was tested and validated by the hybridization of bacterial DNA extracted from the stool samples of lean, obese and anorexic individuals. Our results suggest that a microarray-based study can detect genes from low-abundance bacteria better than metagenomic-based studies. A striking example was the finding that a gene encoding a GH6-family cellulase was present in all subjects examined, whereas metagenomic studies have consistently failed to detect this gene in both human and animal gut microbiomes. In addition, an examination of eight stool samples allowed the identification of a corresponding CAZome core containing 46 families of glycoside hydrolases and polysaccharide lyases, which suggests the functional stability of the gut microbiota despite large taxonomical variations between individuals. Fecal samples were collected from eight female subjects. Three were obese subjects of BMI kg m-2: 35, 46.8 and 51.3, respectively; age: 42, 21 and 65 years old, respectively. Three were anorexic women of BMI kg m-2: 9.8, 10 and 13.7, respectively; age: 19, 23 and 49 years old, respectively. Finally, two fecal samples from lean women of BMI kg m-2: 18.6 and 23.42 were analyzed.
Project description:Background - Prepregnancy overweight and obesity promote deleterious health impacts on both mothers during pregnancy and the offspring. Significant changes in the maternal peripheral blood mononuclear cells (PBMCs) gene expression due to obesity are well-known. However, during pregnancy the impact of overweight on immune cell gene expression and its association with maternal and infant outcomes is not well explored. Methods – Blood samples were collected from healthy normal weight (NW, BMI 18.5-24.9) or overweight (OW, BMI 25-29.9) 2nd parity pregnant women at 12, 24 and 36 weeks of pregnancy. PBMCs were isolated from the blood and subjected to mRNA sequencing. Maternal and infant microbiota were analyzed by 16S rRNA gene sequencing. Integrative multi-omics data analysis was performed to evaluate the association of gene expression with maternal diet, gut microbiota, milk composition, and infant gut microbiota. Results - Gene expression analysis revealed that 453 genes were differentially expressed in the OW women compared to NW women at 12 weeks of pregnancy, out of which 354 were upregulated and 99 were downregulated. Several up-regulated genes in the OW group were enriched in inflammatory, chemokine-mediated signaling and regulation of interleukin-8 production-related pathways. At 36 weeks of pregnancy healthy eating index score was positively associated with several genes that include, DTD1, ELOC, GALNT8, ITGA6-AS1, KRT17P2, NPW, POT1-AS1 and RPL26. In addition, at 36 weeks of pregnancy, genes involved in adipocyte functions, such as NG2 and SMTNL1, were negatively correlated to human milk 2’FL and total fucosylated oligosaccharides content collected at 1 month postnatally. Furthermore, infant Akkermansia was positively associated with maternal PBMC anti-inflammatory genes that include CPS1 and RAB7B, at 12 and 36 weeks of pregnancy. Conclusions – These findings suggest that prepregnancy overweight impacts the immune cell gene expression profile, particularly at 12 weeks of pregnancy. Further, deciphering the complex association of PBMC’s gene expression levels with maternal gut microbiome and milk composition and infant gut microbiome may aid in developing strategies to mitigate obesity-mediated effects.
Project description:Chronic acid suppression by proton pump inhibitor (PPI) has been hypothesized to alter the gut microbiota via a change in intestinal pH. To evaluate the changes in gut microbiota composition by long-term PPI treatment. Twenty-four week old F344 rats were fed with (n = 5) or without (n = 6) lansoprazole (PPI) for 50 weeks. Then, profiles of luminal microbiota in the terminal ileum were analyzed. Pyrosequencing for 16S rRNA gene was performed by genome sequencer FLX (454 Life Sciences/Roche) and analyzed by metagenomic bioinformatics.
Project description:Distal gut bacteria play a pivotal role in the digestion of dietary polysaccharides by producing a large number of carbohydrate-active enzymes (CAZymes) that the host otherwise does not produce. We report here the design of a high density custom microarray that we used to spot non-redundant DNA probes for more than 6,500 genes encoding glycoside hydrolases and lyases selected from 174 reference genomes from distal gut bacteria. The custom microarray was tested and validated by the hybridization of bacterial DNA extracted from the stool samples of lean, obese and anorexic individuals. Our results suggest that a microarray-based study can detect genes from low-abundance bacteria better than metagenomic-based studies. A striking example was the finding that a gene encoding a GH6-family cellulase was present in all subjects examined, whereas metagenomic studies have consistently failed to detect this gene in both human and animal gut microbiomes. In addition, an examination of eight stool samples allowed the identification of a corresponding CAZome core containing 46 families of glycoside hydrolases and polysaccharide lyases, which suggests the functional stability of the gut microbiota despite large taxonomical variations between individuals.
Project description:We recruited 24 Mongolian volunteers,6 of which were T2D cases(sample T1-T6), 6 were prediabetes cases(sample P1-P6), and 12 were health cases(sample C1-C12). The metagenomic analysis of gut microbiota from the volunteers’ fecal samples was performed. We compared the microbial differences in the three groups, and analyzed the differences of the stool microbial function.
Project description:We analyzed the effects of antibiotics using a popular model of gut microbiota depletion in mice by a cocktail of antibiotics. We combined intestinal transcriptome together with metagenomic analysis of the gut microbiota to develop a new bioinformatics approach that probes the links between microbial components and host functions. We found that most antibiotic-induced alterations can be explained by three factors: depletion of the microbiota; direct effects of antibiotics on host tissues; and the effects of remaining antibiotic-resistant microbes. While microbe depletion led to down-regulation of immunity, the two other factors primarily inhibited mitochondrial gene expression and amounts of active mitochondria, and induced cell death. By reconstructing and analyzing a transkingdom network, we discovered that these toxic effects were mediated by virulence/quorum sensing in antibiotic-resistant bacteria. This SuperSeries is composed of the SubSeries listed below. Refer to individual Series
Project description:Dysbiotic configurations of the human gut microbiota have been linked with colorectal cancer (CRC). Human small non-coding RNAs are also implicated in CRC and recent findings suggest that their release in the gut lumen contributes to shape the gut microbiota. Bacterial small RNAs (bsRNAs) may also play a role in carcinogenesis but their role is less explored. Here, we performed small RNA and shotgun sequencing on 80 stool specimens of patients with CRC, or adenomas, and healthy subjects collected in a cross-sectional study to evaluate their combined use as a predictive tool for disease detection. We reported a considerable overlap and correlation between metagenomic and bsRNA quantitative taxonomic profiles obtained from the two approaches. Furthermore, we identified a combined predictive signature composed by 32 features from human and microbial small RNAs and DNA-based microbiome able to accurately classify CRC from healthy and adenoma samples (AUC= 0.87). In summary we reported evidence that host-microbiome dysbiosis in CRC can be observed also by altered small RNA stool profiles. Integrated analyses of the microbiome and small RNAs in the human stool may provide insights for designing more accurate tools for diagnostic purposes.