Two apples a day modulate human:microbiome co-metabolic processing of polyphenols, tyrosine and tryptophan

European Journal of Nutrition volume 59, pages3691–3714(2020)Cite this article

Abstract

Purpose

Validated biomarkers of food intake (BFIs) have recently been suggested as a useful tool to assess adherence to dietary guidelines or compliance in human dietary interventions. Although many new candidate biomarkers have emerged in the last decades for different foods from metabolic profiling studies, the number of comprehensively validated biomarkers of food intake is limited. Apples are among the most frequently consumed fruits and a rich source of polyphenols and fibers, an important mediator for their health-protective properties.

Methods

Using an untargeted metabolomics approach, we aimed to identify biomarkers of long-term apple intake and explore how apples impact on the human plasma and urine metabolite profiles. Forty mildly hypercholesterolemic volunteers consumed two whole apples or a sugar and energy-matched control beverage, daily for 8 weeks in a randomized, controlled, crossover intervention study. The metabolome in plasma and urine samples was analyzed via untargeted metabolomics.

Results

We found 61 urine and 9 plasma metabolites being statistically significant after the whole apple intake compared to the control beverage, including several polyphenol metabolites that could be used as BFIs. Furthermore, we identified several endogenous indole and phenylacetyl-glutamine microbial metabolites significantly increasing in urine after apple consumption. The multiomic dataset allowed exploration of the correlations between metabolites modulated significantly by the dietary intervention and fecal microbiota species at genus level, showing interesting interactions between Granulicatella genus and phenyl-acetic acid metabolites. Phloretin glucuronide and phloretin glucuronide sulfate appeared promising biomarkers of apple intake; however, robustness, reliability and stability data are needed for full BFI validation.

Conclusion

The identified apple BFIs can be used in future studies to assess compliance and to explore their health effects after apple intake. Moreover, the identification of polyphenol microbial metabolites suggests that apple consumption mediates significant gut microbial metabolic activity which should be further explored.

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References

  1. 1.

    Gao Q, Praticò G, Scalbert A et al (2017) A scheme for a flexible classification of dietary and health biomarkers. Genes Nutr 12:34. https://doi.org/10.1186/s12263-017-0587-x

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  2. 2.

    Garcia-Aloy M, Andres-Lacueva C (2018) Food intake biomarkers for increasing the efficiency of dietary pattern assessment through the use of metabolomics: unforeseen research requirements for addressing current gaps. J Agric Food Chem 66:5–7. https://doi.org/10.1021/acs.jafc.7b05586

    CAS  Article  PubMed  Google Scholar 

  3. 3.

    Hodgson JM, Prince RL, Woodman RJ et al (2016) Apple intake is inversely associated with all-cause and disease-specific mortality in elderly women. Br J Nutr 115:860–867. https://doi.org/10.1017/S0007114515005231

    CAS  Article  PubMed  Google Scholar 

  4. 4.

    Koutsos A, Tuohy K, Lovegrove J (2015) Apples and cardiovascular health—is the gut microbiota a core consideration? Nutrients 7:3959–3998. https://doi.org/10.3390/nu7063959

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Vrhovsek U, Rigo A, Tonon D, Mattivi F (2004) Quantitation of polyphenols in different apple varieties. J Agric Food Chem 52:6532–6538. https://doi.org/10.1021/jf049317z

    CAS  Article  PubMed  Google Scholar 

  6. 6.

    D’Archivio M, Filesi C, Varì R et al (2010) Bioavailability of the polyphenols: status and controversies. Int J Mol Sci 11:1321–1342. https://doi.org/10.3390/ijms11041321

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Anesi A, Mena P, Bub A et al (2019) Quantification of urinary phenyl-γ-valerolactones and related valeric acids in human urine on consumption of apples. Metabolites 9:254. https://doi.org/10.3390/metabo9110254

    CAS  Article  PubMed Central  Google Scholar 

  8. 8.

    Ulaszewska M, Vázquez-Manjarrez N, Garcia-Aloy M et al (2018) Food intake biomarkers for apple, pear, and stone fruit. Genes Nutr 13:29. https://doi.org/10.1186/s12263-018-0620-8

    Article  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Kahle K, Huemmer W, Kempf M et al (2007) Polyphenols are intensively metabolized in the human gastrointestinal tract after apple juice consumption. J Agric Food Chem 55:10605–10614. https://doi.org/10.1021/jf071942r

    CAS  Article  PubMed  Google Scholar 

  10. 10.

    Makarova E, Gornas P, Konrade I et al (2014) Acute anti-hyperglycaemic effects of an unripe apple preparation containing phlorizin in healthy volunteers: a preliminary study. J Sci Food Agric 95:560–568. https://doi.org/10.1002/jsfa.6779

    CAS  Article  PubMed  Google Scholar 

  11. 11.

    Hagl S, Deusser H, Soyalan B et al (2011) Colonic availability of polyphenols and d-(−)-quinic acid after apple smoothie consumption. Mol Nutr Food Res 55:368–377. https://doi.org/10.1002/mnfr.201000252

    CAS  Article  PubMed  Google Scholar 

  12. 12.

    Marks SC, Mullen W, Borges G, Crozier A (2009) Absorption, metabolism, and excretion of cider dihydrochalcones in healthy humans and subjects with an ileostomy. J Agric Food Chem 57:2009–2015. https://doi.org/10.1021/jf802757x

    CAS  Article  PubMed  Google Scholar 

  13. 13.

    Hollands WJ, Hart DJ, Dainty JR et al (2013) Bioavailability of epicatechin and effects on nitric oxide metabolites of an apple flavanol-rich extract supplemented beverage compared to a whole apple puree: a randomized, placebo-controlled, crossover trial. Mol Nutr Food Res 57:1209–1217. https://doi.org/10.1002/mnfr.201200663

    CAS  Article  PubMed  Google Scholar 

  14. 14.

    Gasper A, Hollands W, Casgrain A et al (2014) Consumption of both low and high (−)-epicatechin apple puree attenuates platelet reactivity and increases plasma concentrations of nitric oxide metabolites: a randomized controlled trial. Arch Biochem Biophys 559:29–37. https://doi.org/10.1016/j.abb.2014.05.026

    CAS  Article  PubMed  Google Scholar 

  15. 15.

    Auclair S, Chironi G, Milenkovic D et al (2010) The regular consumption of a polyphenol-rich apple does not influence endothelial function: a randomised double-blind trial in hypercholesterolemic adults. Eur J Clin Nutr 64:1158–1165. https://doi.org/10.1038/ejcn.2010.135

    CAS  Article  PubMed  Google Scholar 

  16. 16.

    Mennen LI, Sapinho D, Ito H et al (2006) Urinary flavonoids and phenolic acids as biomarkers of intake for polyphenol-rich foods. Br J Nutr 96:191. https://doi.org/10.1079/BJN20061808

    CAS  Article  PubMed  Google Scholar 

  17. 17.

    Rago D, Gurdeniz G, Gitte R-H, Dragsted L (2014) An explorative study of the effect of apple and apple products on the human plasma metabolome investigated by LC–MS profiling. Metabolomics 11:27–39. https://doi.org/10.1007/s11306-014-0668-4

    CAS  Article  Google Scholar 

  18. 18.

    Ravn-Haren G, Dragsted LO, Buch-Andersen T et al (2013) Intake of whole apples or clear apple juice has contrasting effects on plasma lipids in healthy volunteers. Eur J Nutr 52:1875–1889. https://doi.org/10.1007/s00394-012-0489-z

    CAS  Article  PubMed  Google Scholar 

  19. 19.

    Kristensen M, Engelsen SB, Dragsted LO (2012) LC-MS metabolomics top-down approach reveals new exposure and effect biomarkers of apple and apple-pectin intake. Metabolomics 8:64–73. https://doi.org/10.1007/s11306-011-0282-7

    CAS  Article  Google Scholar 

  20. 20.

    Rago D, Mette K, Gürdeniz G et al (2013) A LC–MS metabolomics approach to investigate the effect of raw apple intake in the rat plasma metabolome. Metabolomics 9:1202–1215. https://doi.org/10.1007/s11306-013-0534-9

    CAS  Article  Google Scholar 

  21. 21.

    Saenger T, Hubner F, Humpf HU (2017) Short-term biomarkers of apple consumption. Mol Nutr Food Res 61:1–10. https://doi.org/10.1002/mnfr.201600629

    CAS  Article  Google Scholar 

  22. 22.

    García-Conesa M-T, Chambers K, Combet E et al (2018) Meta-analysis of the effects of foods and derived products containing ellagitannins and anthocyanins on cardiometabolic biomarkers: analysis of factors influencing variability of the individual responses. Int J Mol Sci 19:694. https://doi.org/10.3390/ijms19030694

    CAS  Article  PubMed Central  Google Scholar 

  23. 23.

    Menezes R, Rodriguez-Mateos A, Kaltsatou A et al (2017) Impact of flavonols on cardiometabolic biomarkers: a meta-analysis of randomized controlled human trials to explore the role of inter-individual variability. Nutrients 9:117. https://doi.org/10.3390/nu9020117

    CAS  Article  PubMed Central  Google Scholar 

  24. 24.

    Dragsted LO, Gao Q, Scalbert A et al (2018) Validation of biomarkers of food intake—critical assessment of candidate biomarkers. Genes Nutr 13:14. https://doi.org/10.1186/s12263-018-0603-9

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Koutsos A, Riccadonna S, Ulaszewska MM et al (2019) Two apples a day lower serum cholesterol and improve cardiometabolic biomarkers in mildly hypercholesterolemic adults: a randomized, controlled, crossover trial. Am J Clin Nutr. https://doi.org/10.1093/ajcn/nqz282

    Article  PubMed Central  Google Scholar 

  26. 26.

    Koutsos A, Lima M, Conterno L et al (2017) Effects of commercial apple varieties on human gut microbiota composition and metabolic output using an in vitro colonic model. Nutrients 9:533. https://doi.org/10.3390/nu9060533

    CAS  Article  PubMed Central  Google Scholar 

  27. 27.

    Trošt K, Ulaszewska MM, Stanstrup J et al (2018) Host: microbiome co-metabolic processing of dietary polyphenols—an acute, single blinded, cross-over study with different doses of apple polyphenols in healthy subjects. Food Res Int. https://doi.org/10.1016/j.foodres.2018.06.016

    Article  PubMed  Google Scholar 

  28. 28.

    Ulaszewska MM, Trost K, Stanstrup J et al (2016) Urinary metabolomic profiling to identify biomarkers of a flavonoid-rich and flavonoid-poor fruits and vegetables diet in adults: the FLAVURS trial. Metabolomics 12:1–22. https://doi.org/10.1007/s11306-015-0935-z

    CAS  Article  Google Scholar 

  29. 29.

    Ancillotti C, Ulaszewska M, Mattivi F, Del Bubba M (2019) Untargeted metabolomics analytical strategy based on liquid chromatography/electrospray ionization linear ion trap quadrupole/orbitrap mass spectrometry for discovering new polyphenol metabolites in human biofluids after acute ingestion of Vaccinium myrti. J Am Soc Mass Spectrom 30:381–402. https://doi.org/10.1007/s13361-018-2111-y

    CAS  Article  PubMed  Google Scholar 

  30. 30.

    Chambers MC, Maclean B, Burke R et al (2012) A cross-platform toolkit for mass spectrometry and proteomics. Nat Biotechnol 30:918–920. https://doi.org/10.1038/nbt.2377

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Smith CA, Want EJ, O’Maille G et al (2006) XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal Chem 78:779–787. https://doi.org/10.1021/ac051437y

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Kuhl C, Tautenhahn R, Böttcher C et al (2012) CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets. Anal Chem 84:283–289. https://doi.org/10.1021/ac202450g

    CAS  Article  PubMed  Google Scholar 

  33. 33.

    Strimmer K (2008) A unified approach to false discovery rate estimation. BMC Bioinform 9:303. https://doi.org/10.1186/1471-2105-9-303

    Article  Google Scholar 

  34. 34.

    Strimmer K (2008) fdrtool: a versatile R package for estimating local and tail area-based false discovery rates. Bioinformatics 24:1461–1462. https://doi.org/10.1093/bioinformatics/btn209

    CAS  Article  PubMed  Google Scholar 

  35. 35.

    Wishart DS, Jewison T, Guo AC et al (2013) HMDB 3.0—the human metabolome database in 2013. Nucleic Acids Res 41:801–807. https://doi.org/10.1093/nar/gks1065

    CAS  Article  Google Scholar 

  36. 36.

    Smith A, O’maille G, Want EJ et al (2005) METLIN a metabolite mass spectral database. Ther Drug Monit 27(6):747–751. https://doi.org/10.1097/01.ftd.0000179845.53213.39

    CAS  Article  PubMed  Google Scholar 

  37. 37.

    Horai H, Arita M, Kanaya S et al (2010) MassBank: a public repository for sharing mass spectral data for life sciences. J Mass Spectrom. https://doi.org/10.1002/jms.1777

    Article  PubMed  Google Scholar 

  38. 38.

    Sud M, Fahy E, Cotter D et al (2007) LMSD: LIPID MAPS structure database. Nucleic Acids Res 35:527–532. https://doi.org/10.1093/nar/gkl838

    Article  Google Scholar 

  39. 39.

    Gerlich M, Neumann S (2013) MetFusion : integration of compound identification strategies. J Mass Spectrom. https://doi.org/10.1002/jms.3123

    Article  PubMed  Google Scholar 

  40. 40.

    Haug K, Salek RM, Conesa P et al (2013) MetaboLights—an open-access general-purpose repository for metabolomics studies and associated meta-data. Nucleic Acids Res 41:D781–D786. https://doi.org/10.1093/nar/gks1004

    CAS  Article  PubMed  Google Scholar 

  41. 41.

    Caporaso JG, Kuczynski J, Stombaugh J et al (2010) QIIME allows analysis of high-throughput community sequencing data. Nat Methods 7:335–336. https://doi.org/10.1038/nmeth.f.303

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Aronesty E (2013) Comparison of sequencing utility programs. Open Bioinforma J 7:1–8. https://doi.org/10.2174/1875036201307010001

    Article  Google Scholar 

  43. 43.

    Edgar RC (2010) Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26:2460–2461. https://doi.org/10.1093/bioinformatics/btq461

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Urpi-Sarda M, Monagas M, Khan N et al (2009) Epicatechin, procyanidins, and phenolic microbial metabolites after cocoa intake in humans and rats. Anal Bioanal Chem 394:1545–1556. https://doi.org/10.1007/s00216-009-2676-1

    CAS  Article  PubMed  Google Scholar 

  45. 45.

    Stalmach A, Edwards CA, Wightman JD, Crozier A (2011) Identification of (Poly)phenolic compounds in concord grape juice and their metabolites in human plasma and urine after juice consumption. J Agric Food Chem 59:9512–9522. https://doi.org/10.1021/jf2015039

    CAS  Article  PubMed  Google Scholar 

  46. 46.

    Van Duynhoven J, Vaughan EE, Van Dorsten F et al (2013) Interactions of black tea polyphenols with human gut microbiota: implications for gut and cardiovascular health. Am J Clin Nutr 98:1631–1641. https://doi.org/10.3945/ajcn.113.058263.INTRODUCTION

    Article  Google Scholar 

  47. 47.

    Mateos-Martín ML, Pérez-Jiménez J, Fuguet E, Torres JL (2012) Non-extractable proanthocyanidins from grapes are a source of bioavailable (epi)catechin and derived metabolites in rats. Br J Nutr 108:290–297. https://doi.org/10.1017/S0007114511005678

    CAS  Article  PubMed  Google Scholar 

  48. 48.

    Touriño S, Pérez-Jiménez J, Mateos-Martín ML et al (2011) Metabolites in contact with the rat digestive tract after ingestion of a phenolic-rich dietary fiber matrix. J Agric Food Chem 59:5955–5963. https://doi.org/10.1021/jf200159f

    CAS  Article  PubMed  Google Scholar 

  49. 49.

    Stalmach A, Steiling H, Williamson G, Crozier A (2010) Bioavailability of chlorogenic acids following acute ingestion of coffee by humans with an ileostomy. Arch Biochem Biophys 501:98–105. https://doi.org/10.1016/j.abb.2010.03.005

    CAS  Article  PubMed  Google Scholar 

  50. 50.

    Stalmach A, Mullen W, Barron D et al (2009) Metabolite profiling of hydroxycinnamate derivatives in plasma and urine after the ingestion of coffee by humans: identification of biomarkers of coffee consumption. Drug Metab Dispos 37:1749–1758. https://doi.org/10.1124/dmd.109.028019

    CAS  Article  PubMed  Google Scholar 

  51. 51.

    Stalmach A, Edwards CA, Wightman JD, Crozier A (2013) Colonic catabolism of dietary phenolic and polyphenolic compounds from Concord grape juice. Food Funct 4:52–62. https://doi.org/10.1039/C2FO30151B

    CAS  Article  PubMed  Google Scholar 

  52. 52.

    Urpi-Sarda M, Garrido I, Monagas M et al (2009) Profile of plasma and urine metabolites after the intake of almond [Prunus dulcis (Mill.) D.A. Webb] polyphenols in humans. J Agric Food Chem 57:10134–10142. https://doi.org/10.1021/jf901450z

    Article  PubMed  Google Scholar 

  53. 53.

    Redeuil K, Smarrito-Menozzi C, Guy P et al (2011) Identification of novel circulating coffee metabolites in human plasma by liquid chromatography-mass spectrometry. J Chromatogr A 1218:4678–4688. https://doi.org/10.1016/j.chroma.2011.05.050

    CAS  Article  PubMed  Google Scholar 

  54. 54.

    Nørskov NP, Hedemann MS, Lærke HN, Knudsen KEB (2013) Multicompartmental nontargeted LC–MS metabolomics: explorative study on the metabolic responses of rye fiber versus refined wheat fiber intake in plasma and urine of hypercholesterolemic pigs. J Proteome Res 12:2818–2832. https://doi.org/10.1021/pr400164b

    CAS  Article  PubMed  Google Scholar 

  55. 55.

    Bergmann H, Triebel S, Kahle K, Richling E (2010) The metabolic fate of apple polyphenols in humans. Curr Nutr Food Sci. https://doi.org/10.2174/157340110790909554

    Article  Google Scholar 

  56. 56.

    Stanstrup J, Rasmussen JE (2014) Intakes of whey protein hydrolysate and whole whey proteins are discriminated by LC–MS metabolomics. Metabolomics. https://doi.org/10.1007/s11306-013-0607-9

    Article  Google Scholar 

  57. 57.

    Stanstrup J, Schou SS, Holmer-Jensen J et al (2014) Whey protein delays gastric emptying and suppresses plasma fatty acids and their metabolites compared to casein, gluten, and fish protein. J Proteome Res 13:2396–2408. https://doi.org/10.1021/pr401214w

    CAS  Article  PubMed  Google Scholar 

  58. 58.

    Agus A, Planchais J, Sokol H (2018) Gut microbiota regulation of tryptophan metabolism in health and disease. Cell Host Microbe 23:716–724. https://doi.org/10.1016/j.chom.2018.05.003

    CAS  Article  PubMed  Google Scholar 

  59. 59.

    Gao J, Xu K, Liu H et al (2018) Impact of the gut microbiota on intestinal immunity mediated by tryptophan metabolism. Front Cell Infect Microbiol. https://doi.org/10.3389/fcimb.2018.00013

    Article  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Keszthelyi D, Troost FJ, Masclee AAM (2009) Understanding the role of tryptophan and serotonin metabolism in gastrointestinal function. Neurogastroenterol Motil 21:1239–1249. https://doi.org/10.1111/j.1365-2982.2009.01370.x

    CAS  Article  PubMed  Google Scholar 

  61. 61.

    Wlodarska M, Luo C, Kolde R et al (2017) Indoleacrylic acid produced by commensal peptostreptococcus species suppresses inflammation. Cell Host Microbe 22:25–37.e6. https://doi.org/10.1016/j.chom.2017.06.007

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Bartel B, LeClere S, Magidin M, Zolman BK (2001) Inputs to the active indole-3-acetic acid pool: de novo synthesis, conjugate hydrolysis, and indole-3-butyric acid b-oxidation. J Plant Growth Regul 20:198–216. https://doi.org/10.1007/s003440010025

    CAS  Article  Google Scholar 

  63. 63.

    Chou JC, Kuleck GA, Cohen JD, Mulbry WW (1996) Partial purification and characterization of an inducible indole-3-acetyl-l-aspartic acid hydrolase from Enterobacter agglomerans. Plant Physiol 112:1281–1287. https://doi.org/10.1104/pp.112.3.1281

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  64. 64.

    Semba RD, Trehan I, Li X et al (2017) Environmental enteric dysfunction is associated with carnitine deficiency and altered fatty acid oxidation. EBioMedicine 17:57–66. https://doi.org/10.1016/j.ebiom.2017.01.026

    Article  PubMed  PubMed Central  Google Scholar 

  65. 65.

    Smith EA, Macfarlane GT (1997) Formation of phenolic and indolic compounds by anaerobic bacteria in the human large intestine. Microb Ecol 33:180–188. https://doi.org/10.1007/s002489900020

    CAS  Article  PubMed  Google Scholar 

  66. 66.

    Russell WR, Duncan SH, Scobbie L et al (2013) Major phenylpropanoid-derived metabolites in the human gut can arise from microbial fermentation of protein. Mol Nutr Food Res 57:523–535. https://doi.org/10.1002/mnfr.201200594

    CAS  Article  PubMed  Google Scholar 

  67. 67.

    van Dorsten FA, Grün CH, van Velzen EJJ et al (2009) The metabolic fate of red wine and grape juice polyphenols in humans assessed by metabolomics. Mol Nutr Food Res 54:897–908. https://doi.org/10.1002/mnfr.200900212

    CAS  Article  Google Scholar 

  68. 68.

    Monagas M, Urpi-Sarda M, Sánchez-Patán F et al (2010) Insights into the metabolism and microbial biotransformation of dietary flavan-3-ols and the bioactivity of their metabolites. Food Funct 1:233. https://doi.org/10.1039/c0fo00132e

    CAS  Article  PubMed  Google Scholar 

  69. 69.

    Scalbert A, Williamson G (2085S) Dietary intake and bioavailability of polyphenols. J Nutr 130:2073S–2085S. https://doi.org/10.1093/jn/130.8.2073S

    CAS  Article  PubMed  Google Scholar 

  70. 70.

    Del Rio D, Rodriguez-Mateos A, Spencer JPE et al (2013) Dietary (poly)phenolics in human health: structures, bioavailability, and evidence of protective effects against chronic diseases. Antioxid Redox Signal 18:1818–1892. https://doi.org/10.1089/ars.2012.4581

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  71. 71.

    Garcia-Aloy M, Llorach R, Urpi-Sarda M et al (2014) Novel multimetabolite prediction of walnut consumption by a urinary biomarker model in a free-living population: the PREDIMED study. J Proteome Res 13:3476–3483. https://doi.org/10.1021/pr500425r

    CAS  Article  PubMed  Google Scholar 

  72. 72.

    Garcia-Aloy M, Llorach R, Urpi-Sarda M et al (2015) A metabolomics-driven approach to predict cocoa product consumption by designing a multimetabolite biomarker model in free-living subjects from the PREDIMED study. Mol Nutr Food Res 59:212–220. https://doi.org/10.1002/mnfr.201400434

    CAS  Article  PubMed  Google Scholar 

  73. 73.

    Van Gorsel H, Li C, Kerbel EL et al (1992) Compositional characterization of prune juice. J Agric Food Chem 40:784–789. https://doi.org/10.1021/jf00017a016

    Article  Google Scholar 

  74. 74.

    Poyrazoğlu E, Gökmen V, Artιk N (2002) Organic acids and phenolic compounds in pomegranates (Punica granatum L.) grown in Turkey. J Food Compos Anal 15:567–575. https://doi.org/10.1006/jfca.2002.1071

    CAS  Article  Google Scholar 

  75. 75.

    Farneti B, Masuero D, Costa F et al (2015) Is there room for improving the nutraceutical composition of apple? J Agric Food Chem 63:2750–2759. https://doi.org/10.1021/acs.jafc.5b00291

    CAS  Article  PubMed  Google Scholar 

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Acknowledgements

We thank Massimo Pindo and the FEM Sequencing Platform for performing the DNA sequencing

Funding

This project was funded in part by the AGER project “Apple fruit quality in the post-genomic era, from breeding new genotypes to post-harvest: nutrition and health”, funded by the AGER (Agribusiness and research) with Grant no. 2010–2119, internal funding of Fondazione Edmund Mach, and the European Union’s Horizon2020 research and innovation grant agreement No 696295—ERA-Net Cofund ERA-HDHL “Biomarkers for Nutrition and Health implementing the JPI HDHL objectives” (https://www.healthydietforhealthylife.eu/), projects FOODBALL (https://foodmetabolome.org/) and CABALA_DIET&HEALTH (https://www.cabalaproject.eu/). The Renetta apples were kindly provided by Consorzio Melinda S.C.A., Cles, Trentino, Italy.

Author information

Author notes

  1. Kajetan Trošt

    Present address: The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3b, 2200, Copenhagen, Denmark

  2. Jan Stanstrup

    Present address: Department of Nutrition Exercise and Sports, University of Copenhagen, Rolighedsvej 30, 1958, Frederiksberg C, Denmark

  3. Maria M. Ulaszewska and Athanasios Koutsos contributed equally.

Affiliations

  1. Department of Food Quality and Nutrition, Research and Innovation Centre, Fondazione Edmund Mach, Via E. Mach 1, San Michele all’Adige, Trentino, Italy

    Maria M. Ulaszewska, Kajetan Trošt, Jan Stanstrup, Mar Garcia-Aloy, Matthias Scholz, Francesca Fava, Urska Vrhovsek, Kieran Tuohy & Fulvio Mattivi

  2. Department of Food and Nutritional Sciences, Hugh Sinclair Unit of Human Nutrition and Institute for Cardiovascular and Metabolic Research (ICMR), University of Reading, Reading, RG6 6AP, UK

    Athanasios Koutsos & Julie Lovegrove

  3. Consiglio per la Ricerca in Agricoltura e l’Analisi dell’Economia Agraria (CREA), Food and Nutrition Research Centre, Via Ardeatina 546, 00178, Rome, Italy

    Fausta Natella & Cristina Scaccini

  4. Department of Physics, University of Trento, Via E. Mach 1, 38010, San Michele all’Adige, Italy

    Fulvio Mattivi

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  1. Maria M. Ulaszewska
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  12. Julie Lovegrove
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  13. Fulvio Mattivi
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Correspondence to Maria M. Ulaszewska or Julie Lovegrove or Fulvio Mattivi.

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Ulaszewska, M.M., Koutsos, A., Trošt, K. et al. Two apples a day modulate human:microbiome co-metabolic processing of polyphenols, tyrosine and tryptophan. Eur J Nutr 59, 3691–3714 (2020). https://doi.org/10.1007/s00394-020-02201-8

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Keywords

  • Untargeted metabolomics
  • Apples
  • Polyphenols
  • Tryptophan
  • Tyrosine
  • Orbitrap
  • Biomarker of food intake
  • Validation