Screening potential biomarkers associated with insulin resistance in high-fat diet-fed mice by integrating metagenomics and untargeted metabolomics
ABSTRACTInsulin resistance is the primary pathophysiological basis for metabolic syndrome and type 2 diabetes. Gut microbiota and microbiota-derived metabolites are pivotal in insulin resistance. However, identifying the specific microbes and key metabolites with causal roles is a challenging task,...
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| Format: | Article |
| Language: | English |
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American Society for Microbiology
2024-04-01
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| Series: | Microbiology Spectrum |
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| Online Access: | https://journals.asm.org/doi/10.1128/spectrum.04094-23 |
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| author | Yunyan Zhou Jiahui Tang Wei Du Yong Zhang Bang-Ce Ye |
| author_facet | Yunyan Zhou Jiahui Tang Wei Du Yong Zhang Bang-Ce Ye |
| author_sort | Yunyan Zhou |
| collection | DOAJ |
| description | ABSTRACTInsulin resistance is the primary pathophysiological basis for metabolic syndrome and type 2 diabetes. Gut microbiota and microbiota-derived metabolites are pivotal in insulin resistance. However, identifying the specific microbes and key metabolites with causal roles is a challenging task, and the underlying mechanisms require further exploration. Here, we successfully constructed a model of insulin resistance in mice induced by a high-fat diet (HFD) and screened potential biomarkers associated with insulin resistance by integrating metagenomics and untargeted metabolomics. Our findings showed a significant increase in the abundance of 30 species of Alistipes in HFD mice compared to normal diet (ND) mice, while the abundance of Desulfovibrio and Candidatus Amulumruptor was significantly lower in HFD mice than in ND mice. Non-targeted metabolomics analysis identified 21 insulin resistance-associated metabolites, originating from the microbiota or co-metabolized by both the microbiota and the host. These metabolites were primarily enriched in aromatic amino acid metabolism (tryptophan metabolism, tyrosine metabolism, and phenylalanine metabolism) and arginine biosynthesis. Further analysis revealed a significant association between the three distinct genera and 21 differentiated metabolites in the HFD and ND mice. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of representative genomes from 12 species of the three distinct genera further revealed the functional potential in aromatic amino acid metabolism and arginine biosynthesis. This study lays the groundwork for future investigations into the mechanisms through which the gut microbiota and its metabolites impact insulin resistance.IMPORTANCEIn this study, we aim to identify the microbes and metabolites linked to insulin resistance, some of which have not been previously reported in insulin resistance-related studies. This adds a complementary dimension to existing research. Furthermore, we establish a correlation between alterations in the gut microbiota and metabolite levels. These findings serve as a foundation for identifying the causal bacterial species and metabolites. They also offer insights that guide further exploration into the mechanisms through which these factors influence host insulin resistance. |
| first_indexed | 2024-04-24T15:02:42Z |
| format | Article |
| id | doaj.art-05bbb2795f814e9296c615cfa971d631 |
| institution | Directory Open Access Journal |
| issn | 2165-0497 |
| language | English |
| last_indexed | 2024-04-24T15:02:42Z |
| publishDate | 2024-04-01 |
| publisher | American Society for Microbiology |
| record_format | Article |
| series | Microbiology Spectrum |
| spelling | doaj.art-05bbb2795f814e9296c615cfa971d6312024-04-02T14:16:19ZengAmerican Society for MicrobiologyMicrobiology Spectrum2165-04972024-04-0112410.1128/spectrum.04094-23Screening potential biomarkers associated with insulin resistance in high-fat diet-fed mice by integrating metagenomics and untargeted metabolomicsYunyan Zhou0Jiahui Tang1Wei Du2Yong Zhang3Bang-Ce Ye4Institute of Engineering Biology and Health, Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, ChinaInstitute of Engineering Biology and Health, Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, ChinaLaboratory of Biosystem and Microanalysis, State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, ChinaInstitute of Engineering Biology and Health, Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, ChinaInstitute of Engineering Biology and Health, Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals, College of Pharmaceutical Sciences, Zhejiang University of Technology, Hangzhou, ChinaABSTRACTInsulin resistance is the primary pathophysiological basis for metabolic syndrome and type 2 diabetes. Gut microbiota and microbiota-derived metabolites are pivotal in insulin resistance. However, identifying the specific microbes and key metabolites with causal roles is a challenging task, and the underlying mechanisms require further exploration. Here, we successfully constructed a model of insulin resistance in mice induced by a high-fat diet (HFD) and screened potential biomarkers associated with insulin resistance by integrating metagenomics and untargeted metabolomics. Our findings showed a significant increase in the abundance of 30 species of Alistipes in HFD mice compared to normal diet (ND) mice, while the abundance of Desulfovibrio and Candidatus Amulumruptor was significantly lower in HFD mice than in ND mice. Non-targeted metabolomics analysis identified 21 insulin resistance-associated metabolites, originating from the microbiota or co-metabolized by both the microbiota and the host. These metabolites were primarily enriched in aromatic amino acid metabolism (tryptophan metabolism, tyrosine metabolism, and phenylalanine metabolism) and arginine biosynthesis. Further analysis revealed a significant association between the three distinct genera and 21 differentiated metabolites in the HFD and ND mice. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of representative genomes from 12 species of the three distinct genera further revealed the functional potential in aromatic amino acid metabolism and arginine biosynthesis. This study lays the groundwork for future investigations into the mechanisms through which the gut microbiota and its metabolites impact insulin resistance.IMPORTANCEIn this study, we aim to identify the microbes and metabolites linked to insulin resistance, some of which have not been previously reported in insulin resistance-related studies. This adds a complementary dimension to existing research. Furthermore, we establish a correlation between alterations in the gut microbiota and metabolite levels. These findings serve as a foundation for identifying the causal bacterial species and metabolites. They also offer insights that guide further exploration into the mechanisms through which these factors influence host insulin resistance.https://journals.asm.org/doi/10.1128/spectrum.04094-23insulin resistancegut microbiotametagenomicsnon-targeted metabolomicsbiomarkers |
| spellingShingle | Yunyan Zhou Jiahui Tang Wei Du Yong Zhang Bang-Ce Ye Screening potential biomarkers associated with insulin resistance in high-fat diet-fed mice by integrating metagenomics and untargeted metabolomics Microbiology Spectrum insulin resistance gut microbiota metagenomics non-targeted metabolomics biomarkers |
| title | Screening potential biomarkers associated with insulin resistance in high-fat diet-fed mice by integrating metagenomics and untargeted metabolomics |
| title_full | Screening potential biomarkers associated with insulin resistance in high-fat diet-fed mice by integrating metagenomics and untargeted metabolomics |
| title_fullStr | Screening potential biomarkers associated with insulin resistance in high-fat diet-fed mice by integrating metagenomics and untargeted metabolomics |
| title_full_unstemmed | Screening potential biomarkers associated with insulin resistance in high-fat diet-fed mice by integrating metagenomics and untargeted metabolomics |
| title_short | Screening potential biomarkers associated with insulin resistance in high-fat diet-fed mice by integrating metagenomics and untargeted metabolomics |
| title_sort | screening potential biomarkers associated with insulin resistance in high fat diet fed mice by integrating metagenomics and untargeted metabolomics |
| topic | insulin resistance gut microbiota metagenomics non-targeted metabolomics biomarkers |
| url | https://journals.asm.org/doi/10.1128/spectrum.04094-23 |
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