Integrated 16S rRNA Sequencing, Metagenomics, and Metabolomics to Characterize Gut Microbial Composition, Function, and Fecal Metabolic Phenotype in Non-obese Type 2 Diabetic Goto-Kakizaki Rats

Abstract

Type 2 diabetes mellitus (T2DM) is one of the most prevalent endocrine diseases in the world. Recent studies have shown that dysbiosis of the gut microbiota may be an important contributor to T2DM pathogenesis. However, the mechanisms underlying the roles of the gut microbiome and fecal metabolome in T2DM have not been characterized. Recently, the Goto-Kakizaki (GK) rat model of T2DM was developed to study the clinical symptoms and characteristics of human T2DM. To further characterize T2DM pathogenesis, we combined multi-omics techniques, including 16S rRNA gene sequencing, metagenomic sequencing, and metabolomics, to analyze gut microbial compositions and functions, and further characterize fecal metabolomic profiles in GK rats. Our results showed that gut microbial compositions were significantly altered in GK rats, as evidenced by reduced microbial diversity, altered microbial taxa distribution, and alterations in the interaction network of the gut microbiome. Functional analysis based on the cluster of orthologous groups (COG) and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotations suggested that 5 functional COG categories belonged to the metabolism cluster and 33 KEGG pathways related to metabolic pathways were significantly enriched in GK rats. Metabolomics profiling identified 53 significantly differentially abundant metabolites in GK rats, including lipids and lipid-like molecules. These lipids were enriched in the glycerophospholipid metabolic pathway. Moreover, functional correlation analysis showed that some altered gut microbiota families, such as Verrucomicrobiaceae and Bacteroidaceae, significantly correlated with alterations in fecal metabolites. Collectively, the results suggested that an altered gut microbiota is associated with T2DM pathogenesis.

Microbial interactions in the mosquito gut determine Serratia colonization and blood-feeding propensity

“…In the Oriental fruit fly, Bactrocera dorsalis, suppression of the dual oxidase gene (BdDuox) led to microbiome dysbiosis and an overabundance of Verrucomicrobiaceae bacteria [72]. Increases in Verrucomicrobiaceae have also been observed in mammalian systems when the microbiome transfers to a dysbiotic state [73][74][75][76]. In our analysis, Verrucomicrobiaceae was a dominant member of the microbiome of the Thailand line, yet was at relatively low abundance in the Iquitos and Juchitan lines and barely detectable in the Galveston line.…”

Study on the Salivary Microbial Alteration of Men With Head and Neck Cancer and Its Relationship With Symptoms in Southwest China

“…To estimate similarity between samples, beta diversity was evaluated by Bray-Curtis dissimilarity and weighted and unweighted UniFrac metrics and illustrated for clustering with principal coordinate analysis (PCoA) plots. Analysis of similarity (ANOSIM) testing was performed to determine group similarities among saliva bacterial community structures (Fierer et al, 2010;Peng et al, 2019). Linear discriminant analysis (LDA) with effect size measurement (LefSe) was used to explore discriminatorily abundant taxonomic characteristics between men with HNC (HNC_M) and health control (HC_M) (Segata et al, 2011).…”

<p>Effects of Metformin on the Gut Microbiota in Obesity and Type 2 Diabetes Mellitus</p>

“… 61 , 62 High fat diet and STZ induction change the gut microbiota of rats or mice. 63 , 64 Besides, metformin also had different effects on the gut microbiota of obese/diabetic and healthy humans/animals. Metformin decreased the bacterial diversity in mice fed a high-fat diet, but had no effect on bacterial diversity in mice fed a normal diet.…”