Impaired renal function and dysbiosis of gut microbiota contribute to increased trimethylamine-N-oxide in chronic kidney disease patients

Kai-Yu Xu, Geng-Hong Xia, Jun-Qi Lu, Mu-Xuan Chen, Xin Zhen, Shan Wang, Chao You, Jing Nie, Hong-Wei Zhou, Jia Yin, Kai-Yu Xu, Geng-Hong Xia, Jun-Qi Lu, Mu-Xuan Chen, Xin Zhen, Shan Wang, Chao You, Jing Nie, Hong-Wei Zhou, Jia Yin

Abstract

Chronic kidney disease (CKD) patients have an increased risk of cardiovascular diseases (CVDs). The present study aimed to investigate the gut microbiota and blood trimethylamine-N-oxide concentration (TMAO) in Chinese CKD patients and explore the underlying explanations through the animal experiment. The median plasma TMAO level was 30.33 μmol/L in the CKD patients, which was significantly higher than the 2.08 μmol/L concentration measured in the healthy controls. Next-generation sequence revealed obvious dysbiosis of the gut microbiome in CKD patients, with reduced bacterial diversity and biased community constitutions. CKD patients had higher percentages of opportunistic pathogens from gamma-Proteobacteria and reduced percentages of beneficial microbes, such as Roseburia, Coprococcus, and Ruminococcaceae. The PICRUSt analysis demonstrated that eight genes involved in choline, betaine, L-carnitine and trimethylamine (TMA) metabolism were changed in the CKD patients. Moreover, we transferred faecal samples from CKD patients and healthy controls into antibiotic-treated C57BL/6 mice and found that the mice that received gut microbes from the CKD patients had significantly higher plasma TMAO levels and different composition of gut microbiota than did the comparative mouse group. Our present study demonstrated that CKD patients had increased plasma TMAO levels due to contributions from both impaired renal functions and dysbiosis of the gut microbiota.

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
CKD patients showed significantly elevated plasma TMAO concentrations. (a) Comparison of plasma TMAO levels between the CKD patient group and the control group. (b) Comparison of plasma TMAO levels between the high GFR group (GFR ≥ 7 ml/min/1.73 m2) and the low GFR group (GFR < 7 ml/min/1.73 m2). (c) Comparison of plasma TMAO levels between the high GFR group (GFR ≥ 7 ml/min/1.73 m2) and the control group. TMAO, trimethylamine N-oxide; CKD, chronic kidney disease; GFR, glomerular filtration rate. The results are based on a Mann-Whitney U test of the TMAO concentrations.
Figure 2
Figure 2
The healthy controls exhibited significantly greater bacterial diversity than the patients. The data represent the comparison of gut bacterial profiles between the healthy controls and CKD patients, including 31 healthy controls (blue) and 30 CKD patients (red). (a,b) Average relative abundances of the predominant bacterial taxa at the phylum level and the genus level in the CKD patient and control samples. (c,d) Comparison of α-diversity between the gut microbiota of the CKD patients and controls. We used two indices to represent the α-diversity (the Shannon index and the PD whole tree). PD indicates phylogenetic diversity. The Wilcoxon rank sum test was used to determine significance in α-diversity.
Figure 3
Figure 3
CKD patients showed obvious dysbiosis of the gut microbiome. The data represent the comparison of the gut bacterial profiles between the CKD patients and the healthy controls, including 31 healthy controls (blue) and 30 CKD patients (red). (a) Principal coordinate analysis illustrating the grouping patterns of the CKD patient group and the control group based on the unweighted UniFrac distances. Each closed circle represents a sample. Distances between any pair of samples represent their dissimilarities. (b) Significantly discriminative taxa between the patients and controls were determined using Linear Discriminant Analysis Effect Size (LEfSe). Only taxa meeting the LDA significance thresholds (>3) are shown. Different coloured regions represent different groups. From the interior to the exterior, each layer represents the phylum, class, order, family, and genus level. (c) Prediction of gene functions between the CKD patients and the controls. Different coloured bar charts represent different groups. (d) Comparison of gut bacterial profiles between two CKD patient subgroups: the high GFR group (GFR ≥ 7 ml/min/1.73 m2) and the low GFR group (GFR < 7 ml/min/1.73 m2). Principal coordinate analysis illustrating the grouping patterns of the CKD patients based on the unweighted UniFrac distances. The data represent 15 GFR high patients (green) and 15 GFR low patients (yellow). Each closed circle represents a sample. (e) Comparison of raw gut mcirobiome data between two CKD patient subgroups. Different coloured bar charts represent different groups. The Mann-Whitney U test was used to determine significance between groups. *P < 0.05; **P < 0.01; ***P < 0.001.
Figure 4
Figure 4
Transplantation of the CKD patient microbiota induces an increased TMAO level and dysbiosis of the gut microbiota in antibiotic-treated mice. The data represent 12 mice transplanted with healthy control faecal samples (blue) and 13 mice transplanted with CKD patient faecal samples (red). (a) Experimental design of the faecal microbiota transplantation (FMT) in antibiotic-treated mice. (b) Comparison of the plasma TMAO levels between the groups of mice after transplant with pooled faecal samples from the CKD patients and healthy controls. The Mann-Whitney U test was used to determine significance between groups. (c) Principal coordinate analysis illustrating the grouping patterns of the two groups of mice based on the unweighted UniFrac distances. Each open circle represents a sample. Distances between any pair of samples represent their dissimilarities. (d) Significantly discriminative taxa between the two group of mice were determined using Linear Discriminant Analysis Effect Size (LEfSe). Only taxa meeting the LDA significance thresholds (>3) are shown. Different coloured bars represent different groups.

References

    1. Eckardt KU, et al. Evolving importance of kidney disease: from subspecialty to global health burden. Lancet. 2013;382:158–69. doi: 10.1016/S0140-6736(13)60439-0.
    1. Liu M, et al. Cardiovascular disease and its relationship with chronic kidney disease. Eur Rev Med Pharmacol Sci. 2014;18:2918–26.
    1. Segall L, Nistor I, Covic A. Heart failure in patients with chronic kidney disease: a systematic integrative review. Biomed Res Int. 2014;2014:937398–21. doi: 10.1155/2014/937398.
    1. Hou FF. Cardiovascular risk in Chinese patients with chronic kidney diseases: where do we stand? Chin Med J (Engl) 2005;118:883–6.
    1. Alani H, Tamimi A, Tamimi N. Cardiovascular co-morbidity in chronic kidney disease: Current knowledge and future research needs. World J Nephrol. 2014;3:156–68. doi: 10.5527/wjn.v3.i4.156.
    1. Gansevoort RT, et al. Chronic kidney disease and cardiovascular risk: epidemiology, mechanisms, and prevention. Lancet. 2013;382:339–52. doi: 10.1016/S0140-6736(13)60595-4.
    1. Ridaura VK, et al. Gut microbiota from twins discordant for obesity modulate metabolism in mice. Science. 2013;341:1241214–1241214. doi: 10.1126/science.1241214.
    1. Qin J, et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature. 2012;490:55–60. doi: 10.1038/nature11450.
    1. Abu-Shanab A, Quigley EM. The role of the gut microbiota in nonalcoholic fatty liver disease. Nat Rev Gastroenterol Hepatol. 2010;7:691–701. doi: 10.1038/nrgastro.2010.172.
    1. Vaziri ND, et al. Chronic kidney disease alters intestinal microbial flora. Kidney Int. 2013;83:308–315. doi: 10.1038/ki.2012.345.
    1. Andersen, K. et al. Intestinal Dysbiosis, Barrier Dysfunction, and Bacterial Translocation Account for CKD-Related Systemic Inflammation. J Am Soc Nephrol (2016).
    1. Tang WH, et al. Intestinal microbial metabolism of phosphatidylcholine and cardiovascular risk. N Engl J Med. 2013;368:1575–84. doi: 10.1056/NEJMoa1109400.
    1. Tang WH, et al. Intestinal microbiota-dependent phosphatidylcholine metabolites, diastolic dysfunction, and adverse clinical outcomes in chronic systolic heart failure. J Card Fail. 2015;21:91–96. doi: 10.1016/j.cardfail.2014.11.006.
    1. Koeth RA, et al. Intestinal microbiota metabolism of L-carnitine, a nutrient in red meat, promotes atherosclerosis. Nat Med. 2013;19:576–85. doi: 10.1038/nm.3145.
    1. Tang WH, et al. Gut microbiota-dependent trimethylamine N-oxide (TMAO) pathway contributes to both development of renal insufficiency and mortality risk in chronic kidney disease. Circ Res. 2015;116:448–55. doi: 10.1161/CIRCRESAHA.116.305360.
    1. Kim RB, et al. Advanced chronic kidney disease populations have elevated trimethylamine N-oxide levels associated with increased cardiovascular events. Kidney Int. 2016;89:1144–52. doi: 10.1016/j.kint.2016.01.014.
    1. Yin, J. et al. Dysbiosis of Gut Microbiota With Reduced Trimethylamine-N-Oxide Level in Patients With Large-Artery Atherosclerotic Stroke or Transient Ischemic Attack. J Am Heart Assoc4, doi:10.1161/JAHA.115.002699 (2015).
    1. Senthong V, et al. Intestinal Microbiota-Generated Metabolite Trimethylamine-N-Oxide and 5-Year Mortality Risk in Stable Coronary Artery Disease: The Contributory Role of Intestinal Microbiota in a COURAGE-Like Patient Cohort. J Am Heart Assoc. 2016;5:e002816. doi: 10.1161/JAHA.115.002816.
    1. Lozupone C, Lladser ME, Knights D, Stombaugh J, Knight R. UniFrac: an effective distance metric for microbial community comparison. Isme J. 2011;5:169–72. doi: 10.1038/ismej.2010.133.
    1. Langille MG, et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol. 2013;31:814–821. doi: 10.1038/nbt.2676.
    1. Goodrich JK, et al. Conducting a microbiome study. Cell. 2014;158:250–62. doi: 10.1016/j.cell.2014.06.037.
    1. Missailidis C, et al. Serum Trimethylamine-N-Oxide Is Strongly Related to Renal Function and Predicts Outcome in Chronic Kidney Disease. Plos One. 2016;11:e141738. doi: 10.1371/journal.pone.0141738.
    1. Bennett BJ, et al. Trimethylamine-N-oxide, a metabolite associated with atherosclerosis, exhibits complex genetic and dietary regulation. Cell Metab. 2013;17:49–60. doi: 10.1016/j.cmet.2012.12.011.
    1. Stubbs JR, et al. Serum Trimethylamine-N-Oxide is Elevated in CKD and Correlates with Coronary Atherosclerosis Burden. J Am Soc Nephrol. 2016;27:305–313. doi: 10.1681/ASN.2014111063.
    1. Rhee EP, et al. A combined epidemiologic and metabolomic approach improves CKD prediction. J Am Soc Nephrol. 2013;24:1330–8. doi: 10.1681/ASN.2012101006.
    1. Kato LM, Kawamoto S, Maruya M, Fagarasan S. The role of the adaptive immune system in regulation of gut microbiota. Immunol Rev. 2014;260:67–75. doi: 10.1111/imr.12185.
    1. Zhu Y, et al. Carnitine metabolism to trimethylamine by an unusual Rieske-type oxygenase from human microbiota. Proc Natl Acad Sci USA. 2014;111:4268–73. doi: 10.1073/pnas.1316569111.
    1. den Besten G, et al. The role of short-chain fatty acids in the interplay between diet, gut microbiota, and host energy metabolism. J Lipid Res. 2013;54:2325–40. doi: 10.1194/jlr.R036012.
    1. Peng L, Li ZR, Green RS, Holzman IR, Lin J. Butyrate enhances the intestinal barrier by facilitating tight junction assembly via activation of AMP-activated protein kinase in Caco-2 cell monolayers. J Nutr. 2009;139:1619–25. doi: 10.3945/jn.109.104638.
    1. Keku TO, Dulal S, Deveaux A, Jovov B, Han X. The gastrointestinal microbiota and colorectal cancer. Am J Physiol Gastrointest Liver Physiol. 2015;308:G351–G363. doi: 10.1152/ajpgi.00360.2012.
    1. Rios-Covian D, et al. Intestinal Short Chain Fatty Acids and their Link with Diet and Human Health. Front Microbiol. 2016;7:185. doi: 10.3389/fmicb.2016.00185.
    1. Wong J, et al. Expansion of urease- and uricase-containing, indole- and p-cresol-forming and contraction of short-chain fatty acid-producing intestinal microbiota in ESRD. Am J Nephrol. 2014;39:230–7. doi: 10.1159/000360010.
    1. Shin N, Whon TW, Bae J. Proteobacteria: microbial signature of dysbiosis in gut microbiota. Trends Biotechnol. 2015;33:496–503. doi: 10.1016/j.tibtech.2015.06.011.
    1. Lupp C, et al. Host-mediated inflammation disrupts the intestinal microbiota and promotes the overgrowth of Enterobacteriaceae. Cell Host Microbe. 2007;2:119–29. doi: 10.1016/j.chom.2007.06.010.
    1. Poza M, et al. Exploring bacterial diversity in hospital environments by GS-FLX Titanium pyrosequencing. Plos One. 2012;7:e44105. doi: 10.1371/journal.pone.0044105.
    1. Masood MI, Qadir MI, Shirazi JH, Khan IU. Beneficial effects of lactic acid bacteria on human beings. Crit Rev Microbiol. 2011;37:91–98. doi: 10.3109/1040841X.2010.536522.
    1. Falony G, et al. Population-level analysis of gut microbiome variation. Science. 2016;352:560–564. doi: 10.1126/science.aad3503.
    1. Sorensen LB. Role of the intestinal tract in the elimination of uric acid. Arthritis Rheum. 1965;8:694–706. doi: 10.1002/art.1780080429.
    1. Poesen R, et al. The Influence of CKD on Colonic Microbial Metabolism. J Am Soc Nephrol. 2016;27:1389–99. doi: 10.1681/ASN.2015030279.
    1. Takayama F, Taki K, Niwa T. Bifidobacterium in gastro-resistant seamless capsule reduces serum levels of indoxyl sulfate in patients on hemodialysis. Am J Kidney Dis. 2003;41:S142–S145. doi: 10.1053/ajkd.2003.50104.
    1. Ranganathan N, et al. Pilot study of probiotic dietary supplementation for promoting healthy kidney function in patients with chronic kidney disease. Adv Ther. 2010;27:634–647. doi: 10.1007/s12325-010-0059-9.
    1. Chapter 2: Definition, identification, and prediction of CKD progression. Kidney Int Suppl (2011) 3 63 (2013).
    1. Cooper BA, et al. A randomized, controlled trial of early versus late initiation of dialysis. N Engl J Med. 2010;363:609–619. doi: 10.1056/NEJMoa1000552.
    1. Peng X, et al. Comparison of direct boiling method with commercial kits for extracting fecal microbiome DNA by Illumina sequencing of 16S rRNA tags. J Microbiol Methods. 2013;95:455–62. doi: 10.1016/j.mimet.2013.07.015.
    1. Zhou HW, et al. BIPES, a cost-effective high-throughput method for assessing microbial diversity. Isme J. 2011;5:741–9. doi: 10.1038/ismej.2010.160.
    1. Caporaso JG, et al. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–6. doi: 10.1038/nmeth.f.303.
    1. Edgar RC. Search and clustering orders of magnitude faster than BLAST. Bioinformatics. 2010;26:2460–2461. doi: 10.1093/bioinformatics/btq461.
    1. Caporaso JG, et al. PyNAST: a flexible tool for aligning sequences to a template alignment. Bioinformatics. 2010;26:266–7. doi: 10.1093/bioinformatics/btp636.
    1. Price MN, Dehal PS, Arkin AP. FastTree: computing large minimum evolution trees with profiles instead of a distance matrix. Mol Biol Evol. 2009;26:1641–50. doi: 10.1093/molbev/msp077.
    1. McDonald D, et al. The Biological Observation Matrix (BIOM) format or: how I learned to stop worrying and love the ome-ome. Gigascience. 2012;1:7. doi: 10.1186/2047-217X-1-7.
    1. Segata N, et al. Metagenomic biomarker discovery and explanation. Genome Biol. 2011;12:R60. doi: 10.1186/gb-2011-12-6-r60.
    1. Wang Z, et al. Measurement of trimethylamine-N-oxide by stable isotope dilution liquid chromatography tandem mass spectrometry. Anal biochem. 2014;455:35–40. doi: 10.1016/j.ab.2014.03.016.
    1. Gregory JC, et al. Transmission of atherosclerosis susceptibility with gut microbial transplantation. J Biol Chem. 2015;290:5647–60. doi: 10.1074/jbc.M114.618249.
    1. Langille MG, et al. Predictive functional profiling of microbial communities using 16S rRNA marker gene sequences. Nat Biotechnol. 2013;31:814–821. doi: 10.1038/nbt.2676.

Source: PubMed

3
Předplatit