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Gut microbiota regulates host metabolism and immunity. The phylogenetic composition of gut microbiota is influenced by diverse factors that include host gender. In this study, the effects of gender on gut microbial composition and its subsequent influence on serum metabolites in pigs were evaluated. The bacterial composition of feces samples was determined by 16S rRNA gene sequencing in 293 pure-bred Duroc pigs (108 gilts and 185 entire boars) and 64 validated pigs from an eight-breed mosaic F6 population. Twenty-eight F6 boars were castrated at 80 days of age to evaluate the effects of androgen on gut microbial composition. Untargeted serum metabolite features were determined in 45 boars and 26 gilts by an ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS). The study observed an obvious influence of host gender on the gut microbial composition and identified numerous sex-biased bacterial taxa. These included Veillonellaceae, Roseburia, Bulleidia, and Escherichia which showed the higher abundance in boars, and Treponema and Bacteroides which were over-represented in gilts. Castration significantly shifted the fecal microbiota composition of the boars toward that of gilts. The predicted functional pathways of the gut microbiome related to obesity and energy harvest were enriched in gilts, and positively associated with gilt-enriched bacteria. Functional pathways related to peptidases and carbohydrate metabolism were enriched in boars and positively associated with boar-enriched bacteria. Serum metabolites related to androgen and cresol metabolism were identified as sex-biased metabolites. Correlation analysis between serum metabolites and sex-biased bacteria identified that the serum concentration of androgen-related metabolites was positively correlated with Bulleidia and Escherichia, but negatively associated with Treponema, suggesting a significant interaction between gut microbiota and host sex hormone metabolism. These results offer basic knowledge of how host gender and gut microbiota influence host metabolism.
Pubmed ID: 31275280
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Open source software tool to merge paired-end reads from next-generation sequencing experiments. Designed to merge pairs of reads when original DNA fragments are shorter than twice length of reads. Can improve genome assemblies and transcriptome assembly by merging RNA-seq data.
View all literature mentionsCurated collection of human metabolite and human metabolism data which contains records for endogenous metabolites, with each metabolite entry containing detailed chemical, physical, biochemical, concentration, and disease information. This is further supplemented with thousands of NMR and MS spectra collected on purified reference metabolites.
View all literature mentionsIntegrated database resource consisting of 16 main databases, broadly categorized into systems information, genomic information, and chemical information. In particular, gene catalogs in completely sequenced genomes are linked to higher-level systemic functions of cell, organism, and ecosystem. Analysis tools are also available. KEGG may be used as reference knowledge base for biological interpretation of large-scale datasets generated by sequencing and other high-throughput experimental technologies.
View all literature mentionsSoftware which can acquire, analyze, manage, and share mass spectrometry data. MassLynx controls any Waters mass spectrometry system, from sample and solvent management components to mass spectrometer and auxiliary detectors. The software can acquire nominal mass, exact mass, MS/MS and exact mass MS/MS data. The software system also maintains and consolidates all user sample data. Optional Application Manager programs provide additional information for specific MS analyses and data.
View all literature mentionsOpen source software package for statistical programming language R to create plots based on grammar of graphics. Used for data visualization to break up graphs into semantic components such as scales and layers.
View all literature mentionsTHIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 28,2023. Algorithm for high-dimensional biomarker discovery and explanation that identifies genes, pathways, or taxa characterizing the differences between two or more biological conditions. The algorithm identifies features that are statistically different among biological classes, then performs additional tests to assess whether these differences are consistent with respect to expected biological behavior. Statistical significance and biological relevance are emphasized.
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