Gut metagenomic and short chain fatty acids signature in hypertension: a cross-sectional study.
Adult
Blood Pressure
/ physiology
Cross-Sectional Studies
DNA, Bacterial
/ isolation & purification
Fatty Acids, Volatile
/ analysis
Feces
/ chemistry
Female
Gastrointestinal Microbiome
/ physiology
Humans
Hypertension
/ blood
Male
Metabolomics
Metagenome
Methylamines
/ blood
Middle Aged
RNA, Ribosomal, 16S
/ genetics
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
15 04 2020
15 04 2020
Historique:
received:
07
07
2019
accepted:
24
03
2020
entrez:
17
4
2020
pubmed:
17
4
2020
medline:
15
12
2020
Statut:
epublish
Résumé
Hypertension is an independent and preventable risk factor for the development of cardiovascular diseases, however, little is known about the impact of gut microbiota composition in its development. We carried out comprehensive gut microbiota analysis and targeted metabolomics in a cross-sectional study of 29 non-treated hypertensive (HT) and 32 normotensive (NT) subjects. We determined fecal microbiota composition by 16S rRNA gene sequencing and bacterial functions by metagenomic analysis. The microbial metabolites analysed were short chain fatty acids (SCFA) both in plasma and feces, and trimethylamine N-oxide (TMAO) in plasma. The overall bacterial composition and diversity of bacterial community in the two groups were not significantly different. However, Ruminococcaceae NK4A214, Ruminococcaceae_UCG-010, Christensenellaceae_R-7, Faecalibacterium prausnitzii and Roseburia hominis were found to be significantly enriched in NT group, whereas, Bacteroides coprocola, Bacteroides plebeius and genera of Lachnospiraceae were increased in HT patients. We found a positive correlation between the HT-associated species and systolic and diastolic blood pressure after adjusted for measured confounders. SCFA showed antagonistic results in plasma and feces, detecting in HT subjects significant higher levels in feces and lower levels in plasma, which could indicate a less efficient SCFA absorption. Overall, our results present a disease classifier based on microbiota and bacterial metabolites to discriminate HT individuals from NT controls in a first disease grade prior to drug treatment.
Identifiants
pubmed: 32296109
doi: 10.1038/s41598-020-63475-w
pii: 10.1038/s41598-020-63475-w
pmc: PMC7160119
doi:
Substances chimiques
DNA, Bacterial
0
Fatty Acids, Volatile
0
Methylamines
0
RNA, Ribosomal, 16S
0
trimethyloxamine
FLD0K1SJ1A
Types de publication
Journal Article
Observational Study
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
6436Références
Esh, H. et al. 2018 ESC/ESH HYPERTENSION Guidelines, https://doi.org/10.1097/HJH (2018).
Zhou, B. et al. Worldwide trends in blood pressure from 1975 to 2015: a pooled analysis of 1479 population-based measurement studies with 19·1 million participants. The Lancet 389, 37–55 (2017).
doi: 10.1016/S0140-6736(16)31919-5
Mills, K. T. et al. HHS Public Access. Pmc 134, 441–450 (2017).
Chow, C. K. et al. Prevalence, awareness, treatment, and control of hypertension in rural and urban communities in high-, middle-, and low-income countries. JAMA - Journal of the American Medical Association 310, 959–968 (2013).
pubmed: 24002282
doi: 10.1001/jama.2013.184182
pmcid: 24002282
Rossier, B. C., Bochud, M. & Devuyst, O. The Hypertension Pandemic: An Evolutionary Perspective. Physiology 32, 112–125 (2017).
pubmed: 28202622
doi: 10.1152/physiol.00026.2016
pmcid: 28202622
Sandosh, P. & Mark, C. & F., D. A. Genetic and Molecular Aspects of Hypertension. Circulation Research 116, 937–959 (2015).
doi: 10.1161/CIRCRESAHA.116.303647
Marques, F. Z., Mackay, C. R. & Kaye, D. M. Beyond gut feelings: How the gut microbiota regulates blood pressure. Nature Reviews Cardiology 15, 20–32 (2018).
pubmed: 28836619
doi: 10.1038/nrcardio.2017.120
pmcid: 28836619
Yang, T. et al. Gut dysbiosis is linked to hypertension. Hypertension (Dallas, Tex.: 1979) 65, 1331–1340 (2015).
doi: 10.1161/HYPERTENSIONAHA.115.05315
Mell, B. et al. Evidence for a link between gut microbiota and hypertension in the Dahl rat. Physiological Genomics 47, 187–197 (2015).
pubmed: 25829393
pmcid: 4451389
doi: 10.1152/physiolgenomics.00136.2014
Li, J. et al. Gut microbiota dysbiosis contributes to the development of hypertension. Microbiome 5, 1–19 (2017).
doi: 10.1186/s40168-016-0209-7
Santisteban, M. M. et al. Hypertension-Linked Pathophysiological Alterations in the Gut. Circulation research 120, 312–323 (2017).
pubmed: 27799253
doi: 10.1161/CIRCRESAHA.116.309006
pmcid: 27799253
Koh, A., De Vadder, F., Kovatcheva-Datchary, P. & Bäckhed, F. From Dietary Fiber to Host Physiology: Short-Chain Fatty Acids as Key Bacterial Metabolites. Cell 165, 1332–1345 (2016).
pubmed: 27259147
doi: 10.1016/j.cell.2016.05.041
pmcid: 27259147
Natarajan, N. et al. Microbial short chain fatty acid metabolites lower blood pressure via endothelial G protein-coupled receptor 41. Physiological Genomics 48, 826–834 (2016).
pubmed: 27664183
pmcid: 6223570
doi: 10.1152/physiolgenomics.00089.2016
Pluznick, J. L. et al. Olfactory receptor responding to gut microbiota-derived signals plays a role in renin secretion and blood pressure regulation. Proceedings of the National Academy of Sciences 110, 4410–4415 (2013).
doi: 10.1073/pnas.1215927110
de la Cuesta-Zuluaga, J. et al. Higher Fecal Short-Chain Fatty Acid Levels Are Associated with Gut Microbiome Dysbiosis, Obesity, Hypertension and Cardiometabolic Disease Risk Factors. Nutrients 11, 51 (2018).
pmcid: 6356834
doi: 10.3390/nu11010051
Tang, W. H. W. & Hazen, S. L. The contributory role of gut microbiota in cardiovascular disease. The Journal of clinical investigation 124, 4204–4211 (2014).
pubmed: 25271725
pmcid: 4215189
doi: 10.1172/JCI72331
Wang, Z. et al. Prognostic value of choline and betaine depends on intestinal microbiota-generated metabolite trimethylamine-N-oxide. European Heart Journal 35, 904–910 (2014).
pubmed: 24497336
pmcid: 3977137
doi: 10.1093/eurheartj/ehu002
Koeth, R. A. et al. NIH Public Access. HHS Public Access 19, 576–585 (2013).
Kalnins, G. et al. Structure and Function of CutC Choline Lyase from Human Microbiota Bacterium Klebsiella pneumoniae. The Journal of biological chemistry 290, 21732–21740 (2015).
pubmed: 26187464
pmcid: 4571895
doi: 10.1074/jbc.M115.670471
Senthong, V. et al. Plasma Trimethylamine N-Oxide, a Gut Microbe–Generated Phosphatidylcholine Metabolite, Is Associated With Atherosclerotic Burden. Journal of the American College of Cardiology 67, 2620–2628 (2016).
pubmed: 27256833
pmcid: 4893167
doi: 10.1016/j.jacc.2016.03.546
de la Cuesta-Zuluaga, J. et al. Gut microbiota is associated with obesity and cardiometabolic disease in a population in the midst of Westernization. Scientific Reports 8, 1–14 (2018).
doi: 10.1038/s41598-017-17765-5
Rogers, M. A. M. & Aronoff, D. M. The influence of non-steroidal anti-inflammatory drugs on the gut microbiome. Clinical microbiology and infection: the official publication of the European Society of Clinical Microbiology and Infectious Diseases 22, 178.e1–178.e9 (2016).
doi: 10.1016/j.cmi.2015.10.003
Yan, Q. et al. Alterations of the Gut Microbiome in Hypertension. Frontiers in Cellular and Infection Microbiology 7, 1–9 (2017).
doi: 10.3389/fcimb.2017.00381
Song, S. et al. Beneficial effects of a probiotic blend on gastrointestinal side effects induced by leflunomide and amlodipine in a rat model. 1–8, https://doi.org/10.3920/BM2016.0231 (2017).
Shoaie, S. et al. Quantifying Diet-Induced Metabolic Changes of the Human Gut Microbiome. Cell Metabolism 22, 320–331 (2015).
pubmed: 26244934
doi: 10.1016/j.cmet.2015.07.001
pmcid: 26244934
Flint, H. J., Bayer, E. A., Rincon, M. T., Lamed, R. & White, B. A. Polysaccharide utilization by gut bacteria: potential for new insights from genomic analysis. 6, 121–131 (2008).
Chassard, C. & Bernalier-Donadille, A. H2 and acetate transfers during xylan fermentation between a butyrate-producing xylanolytic species and hydrogenotrophic microorganisms from the human gut. FEMS Microbiology Letters 254, 116–122 (2006).
pubmed: 16451188
doi: 10.1111/j.1574-6968.2005.00016.x
pmcid: 16451188
Bilen, M. et al. ‘Pygmaiobacter massiliensis’ sp. nov., a new bacterium isolated from the human gut of a Pygmy woman. New microbes and new infections 16, 37–38 (2016).
pubmed: 28179983
pmcid: 5284491
doi: 10.1016/j.nmni.2016.12.015
Sakamoto, M., Iino, T. & Ohkuma, M. Faecalimonas umbilicata gen. nov., sp. nov., isolated from human faeces, and reclassification of Eubacterium contortum, Eubacterium fissicatena and Clostridium oroticum as faecalicatena contorta gen. nov., comb. nov., Faecalicatena fissicatena comb. nov. International Journal of Systematic and Evolutionary Microbiology 67, 1219–1227 (2017).
pubmed: 28556772
doi: 10.1099/ijsem.0.001790
pmcid: 28556772
Zhang, M. et al. Faecalibacterium prausnitzii produces butyrate to decrease c-Myc-related metabolism and Th17 differentiation by inhibiting histone deacetylase 3. International Immunology 31, 499–514 (2019).
pubmed: 30809639
doi: 10.1093/intimm/dxz022
pmcid: 30809639
Goodrich, J. K. et al. Human genetics shape the gut microbiome. Cell 159, 789–799 (2014).
pubmed: 25417156
pmcid: 4255478
doi: 10.1016/j.cell.2014.09.053
Pluznick, J. L. Microbial Short-Chain Fatty Acids and Blood Pressure Regulation. Current Hypertension Reports 19, 1–9 (2017).
doi: 10.1007/s11906-017-0722-5
Marques, F. Z. et al. High-fiber diet and acetate supplementation change the gut microbiota and prevent the development of hypertension and heart failure in hypertensive mice. Circulation 135, 964–977 (2017).
pubmed: 27927713
doi: 10.1161/CIRCULATIONAHA.116.024545
pmcid: 27927713
Bier, A. et al. A high salt diet modulates the gut microbiota and short chain fatty acids production in a salt-sensitive hypertension rat model. Nutrients 10, 1–10 (2018).
doi: 10.3390/nu10091154
Rahat-Rozenbloom, S., Fernandes, J., Gloor, G. B. & Wolever, T. M. S. Evidence for greater production of colonic short-chain fatty acids in overweight than lean humans. International journal of obesity (2005) 38, 1525–1531 (2014).
doi: 10.1038/ijo.2014.46
Schwiertz, A. et al. Microbiota and SCFA in Lean and Overweight Healthy Subjects. 18 (2010).
Fernandes, J., Su, W., Rahat-Rozenbloom, S., Wolever, T. M. S. & Comelli, E. M. Adiposity, gut microbiota and faecal short chain fatty acids are linked in adult humans. Nutrition & diabetes 4, e121–e121 (2014).
doi: 10.1038/nutd.2014.23
Teixeira, T. F. S. et al. Higher level of faecal SCFA in women correlates with metabolic syndrome risk factors. British Journal of Nutrition 109, 914–919 (2013).
pubmed: 23200109
doi: 10.1017/S0007114512002723
pmcid: 23200109
Vogt, J. A. & Wolever, T. M. S. Fecal Acetate Is Inversely Related to Acetate Absorption from the Human Rectum and Distal Colon. The Journal of Nutrition 133, 3145–3148 (2003).
pubmed: 14519799
doi: 10.1093/jn/133.10.3145
pmcid: 14519799
Yang, T. et al. Impaired butyrate absorption in the proximal colon, low serum butyrate and diminished central effects of butyrate on blood pressure in spontaneously hypertensive rats. Acta Physiologica 226, e13256 (2019).
pubmed: 30656835
doi: 10.1111/apha.13256
pmcid: 30656835
Kim, S. et al. Imbalance of gut microbiome and intestinal epithelial barrier dysfunction in patients with high blood pressure. Clinical science (London, England: 1979) 132, 701–718 (2018).
doi: 10.1042/CS20180087
Durgan, D. J. et al. Role of the Gut Microbiome in Obstructive Sleep Apnea-Induced Hypertension. Hypertension (Dallas, Tex.: 1979) 67, 469–474 (2016).
doi: 10.1161/HYPERTENSIONAHA.115.06672
Mortensen, F. V., Nielsen, H., Mulvany, M. J. & Hessov, I. Short chain fatty acids dilate isolated human colonic resistance arteries. Gut 31, 1391–1394 (1990).
pubmed: 2265780
pmcid: 1378763
doi: 10.1136/gut.31.12.1391
Knock, G., Psaroudakis, D., Abbot, S. & Aaronson, P. I. Propionate-induced relaxation in rat mesenteric arteries: a role for endothelium-derived hyperpolarising factor. The Journal of physiology 538, 879–890 (2002).
pubmed: 11826171
pmcid: 2290101
doi: 10.1113/jphysiol.2001.013105
Miura, K., Stamler, J., Liu, K., Daviglus, M. L. & Nakagawa, H. Relation of Vegetable, Fruit, and Meat Intake to 7-Year Blood Pressure Change in Middle-aged Men The Chicago Western Electric Study. 159, 572–580 (2004).
Bird, A. R., Conlon, M. A., Christophersen, C. T. & Topping, D. L. Resistant starch, large bowel fermentation and a broader perspective of prebiotics and probiotics. Beneficial Microbes 1, 423–431 (2010).
pubmed: 21831780
doi: 10.3920/BM2010.0041
pmcid: 21831780
Jaworska, K. et al. Hypertension in rats is associated with an increased permeability of the colon to TMA, a gut bacteria metabolite. Plos One 12, e0189310–e0189310 (2017).
pubmed: 29236735
pmcid: 5728578
doi: 10.1371/journal.pone.0189310
Warrier, M. et al. The TMAO-Generating Enzyme Flavin Monooxygenase 3 Is a Central Regulator of Cholesterol Balance. Cell reports 10, 326–338 (2015).
pubmed: 25600868
pmcid: 4501903
doi: 10.1016/j.celrep.2014.12.036
Schiattarella, G. G. et al. Gut microbe-generated metabolite trimethylamine-N-oxide as cardiovascular risk biomarker: a systematic review and dose-response meta-analysis. 2948–2956, https://doi.org/10.1093/eurheartj/ehx342 (2018).
Randrianarisoa, E. et al. Relationship of Serum Trimethylamine N-Oxide (TMAO) Levels with early Atherosclerosis in Humans. Nature Publishing Group, 1–9, https://doi.org/10.1038/srep26745 (2016).
Nowiński, A. & Ufnal, M. AC SC. Nutrition, https://doi.org/10.1016/j.nut.2017.08.001 (2017).
Cho, C. E. et al. Trimethylamine- N -oxide (TMAO) response to animal source foods varies among healthy young men and is influenced by their gut microbiota composition: A randomized controlled trial. 1600324, 1–12 (2017).
Canyelles, M. et al. Trimethylamine N-Oxide: A Link among Diet, Gut Microbiota, Gene Regulation of Liver and Intestine Cholesterol Homeostasis and HDL Function. International journal of molecular sciences 19, 3228 (2018).
pmcid: 6214130
doi: 10.3390/ijms19103228
V., C. A. et al. Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. Hypertension 42, 1206–1252 (2003).
doi: 10.1161/01.HYP.0000107251.49515.c2
McAuley, P. A. et al. Physical Activity, Measures of Obesity, and Cardiometabolic Risk: The Multi-Ethnic Study of Atherosclerosis (MESA). Journal of physical activity & health 11, 831–837 (2013).
doi: 10.1123/jpah.2012-0068a
Centro de Enseñanza superior de Nutrición Humana y Dietética. Tablas de Composición de Alimentos por Medidas Caseras de Consumo Habitual en España (Food composition tables in household measures commonly consumed in Spain). (2008).
Fernández-Ballart, J. D. et al. Relative validity of a semi-quantitative food-frequency questionnaire in an elderly Mediterranean population of Spain. British Journal of Nutrition 103, 1808–1816 (2010).
pubmed: 20102675
doi: 10.1017/S0007114509993837
pmcid: 20102675
Vallbona Calbó, C., Roure Cuspinera, E., Violan Fors, M. & Alegre Martín, J. Guia de prescripció d’exercici físic per a la salut (PEFS). (2007).
Buysse, D. J., Reynolds, C. F., Monk, T. H., Berman, S. R. & Kupfer, D. J. The Pittsburgh sleep quality index: A new instrument for psychiatric practice and research. Psychiatry Research 28, 193–213 (1989).
doi: 10.1016/0165-1781(89)90047-4
Zhang, S., Wang, H. & Zhu, M.-J. A sensitive GC/MS detection method for analyzing microbial metabolites short chain fatty acids in fecal and serum samples. Talanta 196, 249–254 (2019).
pubmed: 30683360
doi: 10.1016/j.talanta.2018.12.049
pmcid: 30683360
Zhao, X., Zeisel, S. H. & Zhang, S. Rapid LC-MRM-MS assay for simultaneous quantification of choline, betaine, trimethylamine, trimethylamine N-oxide, and creatinine in human plasma and urine. ELECTROPHORESIS 36, 2207–2214 (2015).
pubmed: 26081221
doi: 10.1002/elps.201500055
pmcid: 26081221
R, R. D. C. T. & R Core Team. R: A language and environment for statistical computing. R Found Stat Comput 3, (2013).
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic acids research 41, D590–D596 (2013).
pubmed: 23193283
doi: 10.1093/nar/gks1219
pmcid: 23193283
Magoč, T. & Salzberg, S. L. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics (Oxford, England) 27, 2957–2963 (2011).
doi: 10.1093/bioinformatics/btr507
Li, D., Liu, C.-M., Luo, R., Sadakane, K. & Lam, T.-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics 31, 1674–1676 (2015).
pubmed: 25609793
doi: 10.1093/bioinformatics/btv033
pmcid: 25609793
Hyatt, D. et al. Prodigal: prokaryotic gene recognition and translation initiation site identification. BMC bioinformatics 11, 119 (2010).
pubmed: 20211023
pmcid: 2848648
doi: 10.1186/1471-2105-11-119
Durbin, R., R. Eddy, S., Krogh, A. & J. Mitchison, G. Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids 3 (1998).
Selengut, J. D. et al. TIGRFAMs and Genome Properties: tools for the assignment of molecular function and biological process in prokaryotic genomes. Nucleic acids research 35, D260–D264 (2007).
Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome biology 12, R60–R60 (2011).
pubmed: 21702898
pmcid: 3218848
doi: 10.1186/gb-2011-12-6-r60
Benjamini, Y. & Hochberg, Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society. Series B (Methodological) 57, 289–300 (1995).
doi: 10.1111/j.2517-6161.1995.tb02031.x
Liaw, A. & Wiener, M. Classification and Regression by RandomForest. Forest 23 (2001).