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 used a DNA microarray chip covering 369 resistance types to investigate the relation of antibiotic resistance gene diversity with humans’ age. Metagenomic DNA from fecal samples of 123 healthy volunteers of four different age groups, i.e. pre-school Children (CH), School Children (SC), High School Students (HSS) and Adults (AD) were used for hybridization. The results showed that 80 different gene types were recovered from the 123 individuals gut microbiota, among which 25 were present in CH, 37 in SC, 58 in HSS and 72 in AD. Further analysis indicated that antibiotic resistance genes in groups of CH, SC and AD can be independently clustered, and those ones in group HSS are more divergent. The detailed analysis of antibiotic resistance genes in human gut is further described in the paper DNA microarray analysis reveals the antibiotic resistance gene diversity in human gut microbiota is age-related submitted to Sentific Reports
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:Evaluation of short-read-only, long-read-only, and hybrid assembly approaches on metagenomic samples demonstrating how they affect gene and protein prediction which is relevant for downstream functional analyses. For a human gut microbiome sample, we use complementary metatranscriptomic, and metaproteomic data to evaluate the metagenomic-based protein predictions.
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:Elucidating the role of gut microbiota in physiological and pathological processes has recently emerged as a key research aim in life sciences. In this respect, metaproteomics (the study of the whole protein complement of a microbial community) can provide a unique contribution by revealing which functions are actually being expressed by specific microbial taxa. However, its wide application to gut microbiota research has been hindered by challenges in data analysis, especially related to the choice of the proper sequence databases for protein identification. Here we present a systematic investigation of variables concerning database construction and annotation, and evaluate their impact on human and mouse gut metaproteomic results. We found that both publicly available and experimental metagenomic databases lead to the identification of unique peptide assortments, suggesting parallel database searches as a mean to gain more complete information. Taxonomic and functional results were revealed to be strongly database-dependent, especially when dealing with mouse samples. As a striking example, in mouse the Firmicutes/Bacteroidetes ratio varied up to 10-fold depending on the database used. Finally, we provide recommendations regarding metagenomic sequence processing aimed at maximizing gut metaproteome characterization, and contribute to identify an optimized pipeline for metaproteomic data analysis.
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:Next-Generation-Sequencing (NGS) technologies have led to important improvement in the detection of new or unrecognized infective agents, related to infectious diseases. In this context, NGS high-throughput technology can be used to achieve a comprehensive and unbiased sequencing of the nucleic acids present in a clinical sample (i.e. tissues). Metagenomic shotgun sequencing has emerged as powerful high-throughput approaches to analyze and survey microbial composition in the field of infectious diseases. By directly sequencing millions of nucleic acid molecules in a sample and matching the sequences to those available in databases, pathogens of an infectious disease can be inferred. Despite the large amount of metagenomic shotgun data produced, there is a lack of a comprehensive and easy-use pipeline for data analysis that avoid annoying and complicated bioinformatics steps. Here we present HOME-BIO, a modular and exhaustive pipeline for analysis of biological entity estimation, specific designed for shotgun sequenced clinical samples. HOME-BIO analysis provides comprehensive taxonomy classification by querying different source database and carry out main steps in metagenomic investigation. HOME-BIO is a powerful tool in the hand of biologist without computational experience, which are focused on metagenomic analysis. Its easy-to-use intrinsic characteristic allows users to simply import raw sequenced reads file and obtain taxonomy profile of their samples.
Project description:Lean nonalcoholic fatty liver disease (NAFLD) is increasingly recognized as a distinct clinical phenotype with limited evidence for effective non-pharmacological interventions and unclear mechanistic pathways. Aerobic exercise is recommended for NAFLD management; however, its effects and the gut microbiota–associated mechanisms in lean NAFLD remain incompletely understood. This dataset was generated from a randomized controlled trial (ClinicalTrials.gov identifier: NCT04882644). Participants assigned to the aerobic exercise intervention group provided fecal samples at baseline and after the 3-month intervention. A total of 33 paired fecal samples were included in this dataset. Gut microbiota profiles were generated using shotgun metagenomic sequencing. The dataset includes processed and de-identified species-level relative abundance tables derived from fecal samples collected before and after the intervention. These data were used to characterize exercise-induced alterations in gut microbial composition and interindividual variability in microbiota responses to aerobic exercise in lean NAFLD. The data support integrative analyses with clinical phenotypes and circulating metabolomic profiles to explore gut microbiota–associated mechanisms underlying the metabolic benefits of aerobic exercise.