Role of an unclassified Lachnospiraceae in the pathogenesis of type 2 diabetes: a longitudinal study of the urine microbiome and metabolites
0
조회
0
다운로드
0
인용
0
참고문헌
초록
Recent investigations have revealed that the human microbiome plays an essential role in the occurrence of type 2 diabetes (T2D). However, despite the importance of understanding the involvement of the microbiota throughout the body in T2D, most studies have focused specifically on the intestinal microbiota. Extracellular vesicles (EVs) have been recently found to provide important evidence regarding the mechanisms of T2D pathogenesis, as they act as key messengers between intestinal microorganisms and the host. Herein, we explored microorganisms potentially associated with T2D by tracking changes in microbiota-derived EVs from patient urine samples collected three times over four years. Mendelian randomization analysis was conducted to evaluate the causal relationships among microbial organisms, metabolites, and clinical measurements to provide a comprehensive view of how microbiota can influence T2D. We also analyzed EV-derived metagenomic (N = 393), clinical (N = 5032), genomic (N = 8842), and metabolite (N = 574) data from a prospective longitudinal Korean community-based cohort. Our data revealed that GU174097_g, an unclassified Lachnospiraceae, was associated with T2D (β = −189.13; p = 0.00006), and it was associated with the ketone bodies acetoacetate and 3-hydroxybutyrate (r = −0.0938 and −0.0829, respectively; p = 0.0022 and 0.0069, respectively). Furthermore, a causal relationship was identified between acetoacetate and HbA1c levels (β = 0.0002; p = 0.0154). GU174097_g reduced ketone body levels, thus decreasing HbA1c levels and the risk of T2D. Taken together, our findings indicate that GU174097_g may lower the risk of T2D by reducing ketone body levels.
Abstract
Recent investigations have revealed that the human microbiome plays an essential role in the occurrence of type 2 diabetes (T2D). However, despite the importance of understanding the involvement of the microbiota throughout the body in T2D, most studies have focused specifically on the intestinal microbiota. Extracellular vesicles (EVs) have been recently found to provide important evidence regarding the mechanisms of T2D pathogenesis, as they act as key messengers between intestinal microorganisms and the host. Herein, we explored microorganisms potentially associated with T2D by tracking changes in microbiota-derived EVs from patient urine samples collected three times over four years. Mendelian randomization analysis was conducted to evaluate the causal relationships among microbial organisms, metabolites, and clinical measurements to provide a comprehensive view of how microbiota can influence T2D. We also analyzed EV-derived metagenomic (N = 393), clinical (N = 5032), genomic (N = 8842), and metabolite (N = 574) data from a prospective longitudinal Korean community-based cohort. Our data revealed that GU174097_g, an unclassified Lachnospiraceae, was associated with T2D (β = −189.13; p = 0.00006), and it was associated with the ketone bodies acetoacetate and 3-hydroxybutyrate (r = −0.0938 and −0.0829, respectively; p = 0.0022 and 0.0069, respectively). Furthermore, a causal relationship was identified between acetoacetate and HbA1c levels (β = 0.0002; p = 0.0154). GU174097_g reduced ketone body levels, thus decreasing HbA1c levels and the risk of T2D. Taken together, our findings indicate that GU174097_g may lower the risk of T2D by reducing ketone body levels.
참고문헌 (53)
- Fan, Y., "Gut microbiota in human metabolic health and disease", Nat. Rev. Microbiol, 19, pp.55~71 (2021)
- Johnson, E. L., "Microbiome and metabolic disease : revisiting the bacterial phylum Bacteroidetes", J. Mol. Med, 95, pp.1~8 (2017)
- Sanz, Y., "Understanding the role of gut microbiome in metabolic disease risk", Pediatr. Res, 77, pp.236~244 (2015)
- Zmora, N., "Personalized gut mucosal colonization resistance to empiric probiotics is associated with unique host and microbiome features", Cell, 174, pp.1388~1405.e21 (2018)
- Proal, A. D., "Inflammatory disease and the human microbiome", Discov. Med, 17, pp.257~265 (2014)
- Ahmadi Badi, S., "Microbiota-derived extracellular vesicles as new systemic regulators", Front. Microbiol, 8, pp.1610~ (2017)
- 양진호, "Diagnostic Models for Atopic Dermatitis Based on Serum Microbial Extracellular Vesicle Metagenomic Analysis: A Pilot Study", Allergy, Asthma & Immunology Research, 12(5), pp.792~805 (2020), 대한천식알레르기학회[DOI]
- An, J., "Extracellular vesicle-derived microbiome obtained from exhaled breath condensate in patients with asthma", Ann. Allergy Asthma Immunol, 126, pp.729~731 (2021)
- Lee, J. H., "Metagenome analysis using serum extracellular vesicles identified distinct microbiota in asthmatics", Sci. Rep, 10, pp.15125~ (2020)
- Kim, S. S., "Microbiome as a potential diagnostic and predictive biomarker in severe alcoholic hepatitis", Aliment. Pharmacol. Ther, 53, pp.540~551 (2021)
- Choi, Y., "Gut microbe-derived extracellular vesicles induce insulin resistance, thereby impairing glucose metabolism in skeletal muscle", Sci. Rep, 5, pp.1~11 (2015)
- VanderWeele, T. J., "Causal inference and longitudinal data: a case study of religion and mental health", Soc. Psych. Psych. Epid, 51, pp.1457~1466 (2016)
- Cho, Y. S., "A large-scale genome-wide association study of Asian populations uncovers genetic factors influencing eight quantitative traits", Nat. Genet, 41, pp.527~534 (2009)
- Kim, Y., "Cohort profile : the Korean genome and epidemiology study(KoGES)consortium", Int. J. Epidemiol, 46, pp.e20~e20 (2017)
- Pisprasert, V., "Limitations in the use of indices using glucose and insulin levels to predict insulin sensitivity : impact of race and gender and superiority of the indices derived from oral glucose tolerance test in African Americans", Diabetes care, 36, pp.845~853 (2013)
- "Rex: Excel‐based statistical software" (2018)[URL]
- Lee, E. Y., "Global proteomic profiling of native outer membrane vesicles derived from Escherichia coli", Proteomics, 7, pp.3143~3153 (2007)
- Martin, M., "Cutadapt Removes Adapter Sequences from High-Throughput Sequencing Reads", EMBnet J, 17, pp.10~12 (2011)
- Kwon, S., "CASPER : context-aware scheme for paired-end reads from high-throughput amplicon sequencing", BMC Bioinforma, 15(Suppl 9), pp.S10~ (2014)[DOI]
- Bokulich, N. A., "Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing", Nat. Methods, 10, pp.57~59 (2013)
- Rognes, T., "VSEARCH : a versatile open source tool for metagenomics", PeerJ, 4, pp.e2584~ (2016)
- Yoon, S. -H., "Introducing EzBioCloud : a taxonomically united database of 16S rRNA gene sequences and whole-genome assemblies", Int. J. Syst. Evol. Micr, 67, pp.1613~ (2017)
- Edgar, R. C., "Search and clustering orders of magnitude faster than BLAST", Bioinformatics, 26, pp.2460~2461 (2010)
- Li, K., "Analyses of the stability and core taxonomic memberships of the human microbiome", PLoS one, 8, pp.e63139~ (2013)
- Aßhauer, K. P., "Tax4Fun : predicting functional profiles from metagenomic 16S rRNA data", Bioinformatics, 31, pp.2882~2884 (2015)
- Wang, S. -Y., "Batch Normalizer : a fast total abundance regression calibration method to simultaneously adjust batch and injection order effects in liquid chromatography", time-of-flight mass spectrometry-based metabolomics data and comparison with current calibration methods, 2013, pp.85, 1037~1046
- Ondov, B. D., "Interactive metagenomic visualization in a Web browser", BMC Bioinforma, 12, pp.1~10 (2011)
- Anderson, M. J., "A new method for non-parametric multivariate analysis of variance", Austral Ecol, 26, pp.32~46 (2001)
- Benjamini, Y., "Controlling the false discovery rate : a practical and powerful approach to multiple testing", J. Roy. Stat. Soc. B Met, 57, pp.289~300 (1995)
- Gim, J., "A between ethnicities comparison of chronic obstructive pulmonary disease genetic risk", Front. Genet, 11, pp.329~ (2020)
- Greco M, F. D., "Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome", Stat. Med, 34, pp.2926~2940 (2015)
- Richard, M. A., "Altered mechanisms of genital development identified through integration of DNA methylation and genomic measures in hypospadias", Sci. Rep, 10, pp.1~12 (2020)
- Bell, J. A., "Influence of puberty timing on adiposity and cardiometabolic traits : a Mendelian randomisation study", PLoS Med, 15, pp.e1002641~ (2018)
- Staiger, D., "Instrumental variables regression with weak instruments", Econometrica, pp.557~586 (1997)
- Hausman, J. A, "Specification tests in econometrics", Econometrica, pp.1251~1271 (1978)
- Jin, H., "Causal evaluation of laboratory markers in Type 2Diabetes on cancer and vascular diseases using various mendelian randomization tools", Front. Genet, 11, pp.597420~ (2020)
- Mahendran, Y., "Association of ketone body levels with hyperglycemia and type 2 diabetes in 9, 398 Finnish men", Diabetes, 62, pp.3618~3626 (2013)
- Bowden, J., "Misconceptions on the use of MR-Egger regression and the evaluation of the InSIDE assumption", Int. J. Epidemiol, 46, pp.2097~2099 (2017)
- Wen, L., "Factors influencing the gut microbiota, inflammation, and type 2 diabetes", J. Nutr, 147, pp.1468S~1475S (2017)
- Cani, P. D., "Involvement of gut microbiota in the development of low-grade inflammation and type 2 diabetes associated with obesity", Gut microbes, 3, pp.279~288 (2012)
- Upadhyaya, S., "Type 2 diabetes and gut microbiome : at the intersection of known and unknown", Gut microbes, 6, pp.85~92 (2015)
- Sharma, S., "Gut microbiome and type 2 diabetes: where we are and where to go?", J. Nutr. Biochem, 63, pp.101~108 (2019)
- Vallianou, N. G., "Microbiome and diabetes: where are we now?", Diabetes Res. Clin. Pract, 146, pp.111~118 (2018)
- Puddu, A., "Evidence for the gut microbiota short-chain fatty acids as key pathophysiological molecules improving diabetes", Mediat. Inflamm, 2014, pp.162021~ (2014)
- O’Toole, P. W., "Gut microbiota and aging", Science, 350, pp.1214~1215 (2015)
- Vacca, M., "The controversial role of human gut lachnospiraceae", Microorganisms, 8, pp.573~ (2020)
- Ibrahim, K. S., "Characterisation of gut microbiota of obesity and type 2diabetes in a rodent model", Biosci. Microbiota Food Health, 40, pp.2019~2031 (2020)
- Sharon, G., "Specialized metabolites from the microbiome in health and disease", Cell Metab, 20, pp.719~730 (2014)
- Kohlmeier, M., "Food Science and Technology International Series", pp.147~148 (2003), Academic Press
- Kim, M. R., "Staphylococcus aureus‐derived extracellular vesicles induce neutrophilic pulmonary inflammation via both T h1 and T h17 cell responses", Allergy, 67, pp.1271~1281 (2012)
- 이영석, "Urine Microbe-Derived Extracellular Vesicles in Children With Asthma", Allergy, Asthma & Immunology Research, 13(1), pp.75~87 (2021), 대한천식알레르기학회[DOI]
- Nah, G., "Type-2 diabetics reduces spatial variation of microbiome based on extracellular vesicles from gut microbes across human body", Sci. Rep, 9, pp.1~10 (2019)
- Sasaki, K., "In vitro human colonic microbiota utilises D-β-hydroxybutyrate to increase butyrogenesis", Sci. Rep, 10, pp.1~8 (2020)