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Jan 24, 2024
Gut microbial functional and metabolic alterations in children with new-onset of type 1 diabetes (T1D) is profiled in an open access paper in Nature Communications. This work is an excellent example of multiomics advantages: here, a combination of metabolomics with gut microbiota study were utilized. As a result, the combination of 9 bacterial species and 9 fecal metabolites showed excellent discriminatory power of new-onset T1D. Below we provide a brief summary from this publication: “Functional and metabolic alterations of gut microbiota in children with new-onset of type 1 diabetes”.
In-depth multi-omics analyses revealed a deteriorated gut microbial pattern of T1D involving butyrate metabolism, LPS biosynthesis, and bile acid metabolism. The combination of 18 bacteria species and fecal metabolites as gut biomarkers excellently discriminated T1D from controls.
Metabolomic analysis was performed using fecal samples. Compared with those in the non-disease group, the levels of five metabolites (L-pyroglutamic acid, pterine, 5-hydroxytryptophol, N1-acetylspermine, and 3-(3-hydroxyphenyl)-3-hydroxypropanoic acid) increased significantly, while those of 21 metabolites, including glycoursodeoxycholic acid, glycochenodeoxycholic acid, and DL-benzylsuccinic acid, decreased significantly in the T1D group. Those metabolites belong to 11 metabolic pathways (bile acid, carbohydrate, nucleotide, and amino acid). According to KEGG pathway enrichment analysis, three pathways were significantly enriched: fructose and mannose, galactose, and caffeine metabolic pathways. We also found that the total concentrations of fecal SCFAs, butyrate, and acetic acid were significantly lower in the T1D group.
Spearman’s correlation analysis between fecal metabolites and clinical parameters revealed that the concentrations of T1D enriched metabolites, such as pterine, N1-acetylspermine, and L-pyroglutamic acid, were significantly positively correlated with serum levels of HbA1c, FBG, and TG (see figure below). Additionally, T1D reduced metabolites, such as butyric acid, acetic acid, and DL-benzylsuccinic acid, were positively correlated with butyrate-producing species, including Faecalibacterium prausnitzii, Eubacterium rectale, and Roseburia faecis, but negatively correlated with opportunistic pathogen; however, the opposite pattern was observed in case of T1D-enriched metabolites, such as pterine and L-pyroglutamic acid.
Heatmap of the Spearman’s correlation between 28 discriminatory metabolites and 35 key bacteria species as well as clinical parameters (*FDR < 0.05). The red squares indicate positive correlations, whereas the blue squares indicate negative correlations
The animal experiments further unraveled that gut microflora of T1D was a causative factor in the regulation of glucose metabolism. Butyrate and lipopolysaccharide exerted protective and destructive effects, respectively, on islet structure and function in the T1D mouse model.
As a result, this multi-omics analyses and animal experiments deciphered the functional and metabolic profile of gut dysbiosis and explored the causal relationship and underlying mechanism between the gut microbiota and glucose dysmetabolism in T1D. The main findings are:
· Decreased butyrate production and bile acid metabolism and increased lipopolysaccharide biosynthesis at the species, gene, and metabolite levels characterize T1D microbiota
· 5 metabolites increased significantly; 21 metabolites decreased significantly in T1D group
· Those 26 differential metabolites belong to 11 metabolic pathways (bile acid, carbohydrate, nucleotide, and amino acid)
· 3 pathways were significantly enriched: fructose and mannose, galactose, and caffeine (as per KEGG pathway enrichment analysis)
· The combination of 9 bacterial species and 9 fecal metabolites provides excellent discriminatory power of new-onset T1D
· Human T1D-associated gut microbiota could induce elevated fasting glucose levels and declined insulin sensitivity in antibiotic-treated mice
Similar combination of microbiota studies with metabolomics can be applied to a variety of diseases. We recommend using our proprietary TM Widely-Targeted Metabolomics, though classical untargeted approach can be used as well for the initial studies.
