Limited effects of long-term daily cranberry consumption on the gut microbiome in a placebo-controlled study of women with recurrent urinary tract infections

Timothy J Straub, Wen-Chi Chou, Abigail L Manson, Henry L Schreiber 4th, Bruce J Walker, Christopher A Desjardins, Sinéad B Chapman, Kerrie L Kaspar, Orsalem J Kahsai, Elizabeth Traylor, Karen W Dodson, Meredith A J Hullar, Scott J Hultgren, Christina Khoo, Ashlee M Earl, Timothy J Straub, Wen-Chi Chou, Abigail L Manson, Henry L Schreiber 4th, Bruce J Walker, Christopher A Desjardins, Sinéad B Chapman, Kerrie L Kaspar, Orsalem J Kahsai, Elizabeth Traylor, Karen W Dodson, Meredith A J Hullar, Scott J Hultgren, Christina Khoo, Ashlee M Earl

Abstract

Background: Urinary tract infections (UTIs) affect 15 million women each year in the United States, with > 20% experiencing frequent recurrent UTIs. A recent placebo-controlled clinical trial found a 39% reduction in UTI symptoms among recurrent UTI sufferers who consumed a daily cranberry beverage for 24 weeks. Using metagenomic sequencing of stool from a subset of these trial participants, we assessed the impact of cranberry consumption on the gut microbiota, a reservoir for UTI-causing pathogens such as Escherichia coli, which causes > 80% of UTIs.

Results: The overall taxonomic composition, community diversity, carriage of functional pathways and gene families, and relative abundances of the vast majority of observed bacterial taxa, including E. coli, were not changed significantly by cranberry consumption. However, one unnamed Flavonifractor species (OTU41), which represented ≤1% of the overall metagenome, was significantly less abundant in cranberry consumers compared to placebo at trial completion. Given Flavonifractor's association with negative human health effects, we sought to determine OTU41 characteristic genes that may explain its differential abundance and/or relationship to key host functions. Using comparative genomic and metagenomic techniques, we identified genes in OTU41 related to transport and metabolism of various compounds, including tryptophan and cobalamin, which have been shown to play roles in host-microbe interactions.

Conclusion: While our results indicated that cranberry juice consumption had little impact on global measures of the microbiome, we found one unnamed Flavonifractor species differed significantly between study arms. This suggests further studies are needed to assess the role of cranberry consumption and Flavonifractor in health and wellbeing in the context of recurrent UTI.

Trial registration: Clinical trial registration number: ClinicalTrials.gov NCT01776021 .

Keywords: Cranberry; Flavonifractor; Metagenome; Microbiome; Urinary tract infection (UTI).

Conflict of interest statement

KLK and CK are employees of Ocean Spray (Lakeville-Middleboro, MA). All other authors have no competing interests to declare.

Figures

Fig. 1
Fig. 1
Cranberry beverage consumption does not change the overall composition of the gut microbiome. a The cohort consisted of 70 women with a history of recurrent UTIs, who either consumed cranberry beverage or placebo daily for 6 months. The stool samples, collected before and after the 6-month study period, were subjected to 16S rRNA and whole metagenomic shotgun sequencing to infer gut microbial profiles and functions. b 16S rRNA-based taxonomic profiles displaying the phylum-level composition of the microbial population indicate that the composition did not change over time or due to cranberry consumption (Wilcoxon rank sum test, p > 0.3). The sample order was sorted by the relative abundance of Firmicutes. c The species richness, based on 16S OTUs, did not change significantly with cranberry beverage consumption (Wilcoxon rank sum test, p > 0.6). d A comparison of all samples at the 16S OTU level, using principal coordinate analysis (PCoA) based on Bray-Curtis dissimilarities, indicated that the samples from the cranberry cohort did not cluster into a specific group, and the trajectories from week 0 to week 24 (shown by arrows) were scattered, indicating no common shift in microbial composition through time. The magnitude of the change between timepoints, when comparing the two cohorts, was also not significant (p = 0.51). The first two principal components (PCo1 and PCo2) accounted for 13.4 and 8.9% of the variability, respectively
Fig. 2
Fig. 2
OTU41 is the only significantly different OTU between the cranberry and control cohorts. a Distribution of OTU relative abundance differences between cranberry and control cohorts. The X axis represents the difference of the log2 median fold-change of cranberry to placebo cohorts for each OTU. The Y axis is the -log10 unadjusted p-value of the Wilcoxon rank sum test for each OTU. The size of the point represents the magnitude of the median change in the OTU relative abundance in the cranberry cohort, while the color indicates the direction of said change (blue = decrease, gray = no change, red = increase). Only OTU41 (shown in the top left) was significant after correction (adjusted p = 0.02). b The relative abundance of OTU41 decreased significantly in the cranberry beverage cohort. c A phylogenetic tree, based on 16S V4 regions, of OTU41 together with the other OTUs from this study assigned to the Flavonifractor genus, as well as additional sequenced strains closely related to Flavonifractor, including Flavonifractor sp. 54, Flavonifractor plautii, Lachnospiraceae bacterium, Flintibacter butyricus, Pseudoflavonifractor sp., and Pseudoflavonifractor capillosus indicated a very close relationship between OTU41 and Flavonifractor sp. 54. Relevant bootstrap values are shown. d Alignment of the 16S V4 sequence for OTU41 and Flavonifractor sp. 54, showing only one base pair difference
Fig. 3
Fig. 3
Whole-genome comparative analysis of Flavonifractor genomes related to Flavonifractor sp. 54. Genome sequences were selected based on phylogenetic proximity to Flavonifractor sp. 54 and used to construct a whole-genome phylogeny using TBA. All bootstrap support values are 100%. Average Nucleotide Identity calculations indicated that Flavonifractor sp. 54 is in its own separate species from other references (< 95% ANI is considered a separate species)

References

    1. Cusumano CK, Pinkner JS, Han Z, Greene SE, Ford BA, Crowley JR, Henderson JP, Janetka JW, Hultgren SJ. Treatment and prevention of urinary tract infection with orally active FimH inhibitors. Sci Transl Med. 2011;3:109ra115. doi: 10.1126/scitranslmed.3003021.
    1. Garimella PS, Bartz TM, Ix JH, Chonchol M, Shlipak MG, Devarajan P, Bennett MR, Sarnak MJ. Urinary Uromodulin and risk of urinary tract infections: the cardiovascular health study. Am J Kidney Dis. 2017;69:744–751. doi: 10.1053/j.ajkd.2016.08.022.
    1. Flores-Mireles AL, Walker JN, Caparon M, Hultgren SJ. Urinary tract infections: epidemiology, mechanisms of infection and treatment options. Nat Rev Microbiol. 2015;13:269–284. doi: 10.1038/nrmicro3432.
    1. Foxman B, Gillespie B, Koopman J, Zhang L, Palin K, Tallman P, Marsh JV, Spear S, Sobel JD, Marty MJ, Marrs CF. Risk factors for second urinary tract infection among college women. Am J Epidemiol. 2000;151:1194–1205. doi: 10.1093/oxfordjournals.aje.a010170.
    1. Gupta K, Hooton TM, Naber KG, Wullt B, Colgan R, Miller LG, Moran GJ, Nicolle LE, Raz R, Schaeffer AJ, Soper DE, Infectious Diseases Society of America, European Society for Microbiology and Infectious Diseases International clinical practice guidelines for the treatment of acute uncomplicated cystitis and pyelonephritis in women: a 2010 update by the Infectious Diseases Society of America and the European Society for Microbiology and Infectious Diseases. Clin Infect Dis. 2011;52:e103–e120. doi: 10.1093/cid/ciq257.
    1. Mazzulli T. Resistance trends in urinary tract pathogens and impact on management. J Urol. 2002;168:1720–1722. doi: 10.1016/S0022-5347(05)64397-2.
    1. Foxman B. Urinary tract infection syndromes: occurrence, recurrence, bacteriology, risk factors, and disease burden. Infect Dis Clin N Am. 2014;28:1–13. doi: 10.1016/j.idc.2013.09.003.
    1. O’Brien VP, Hannan TJ, Nielsen HV, Hultgren SJ. Drug and vaccine development for the treatment and prevention of urinary tract infections. Urinary Tract Infections. 2017:589–646.
    1. Gupta K, Bhadelia N. Management of urinary tract infections from multidrug-resistant organisms. Infect Dis Clin N Am. 2014;28:49–59. doi: 10.1016/j.idc.2013.10.002.
    1. Sihra N, Goodman A, Zakri R, Sahai A, Malde S. Nonantibiotic prevention and management of recurrent urinary tract infection. Nat Rev Urol. 2018.
    1. Waller TA, Pantin SAL, Yenior AL, Pujalte GGA. Urinary tract infection antibiotic resistance in the United States: Primary Care; 2018.
    1. Bruyère F, Azzouzi AR, Lavigne J-P, Droupy S, Coloby P, Game X, Karsenty G, Issartel B, Ruffion A, Misrai V, Sotto A. Allaert F-A. A multicenter, randomized, placebo-controlled study evaluating the efficacy of a combination of Propolis and cranberry (Vaccinium macrocarpon) (DUAB®) in preventing low urinary tract infection recurrence in women complaining of recurrent cystitis. Urol Int. 2019;103:41–48. doi: 10.1159/000496695.
    1. Fu Z, Liska D, Talan D, Chung M. Cranberry reduces the risk of urinary tract infection recurrence in otherwise healthy women: a systematic review and meta-analysis. J Nutr. 2017;147:2282–2288. doi: 10.3945/jn.117.254961.
    1. Guay DRP. Cranberry and urinary tract infections. Drugs. 2009;69:775–807. doi: 10.2165/00003495-200969070-00002.
    1. Jepson RG, Williams G, Craig JC. Cranberries for preventing urinary tract infections. Cochrane Database Syst Rev. 2012;10:CD001321.
    1. Liu H. Khoo C. A randomized, double-blind, placebo-controlled pilot study to assess the urinary anti-adhesion activity following consumption of cranberry health™ cranberry supplement (P06–114-19): Current Developments in. Nutrition. 2019.
    1. Luís Â, Domingues F, Pereira L. Can cranberries contribute to reduce the incidence of urinary tract infections? A systematic review with meta-analysis and trial sequential analysis of clinical trials. J Urol. 2017;198:614–621. doi: 10.1016/j.juro.2017.03.078.
    1. Maki KC, Kaspar KL, Khoo C, Derrig LH, Schild AL, Gupta K. Consumption of a cranberry juice beverage lowered the number of clinical urinary tract infection episodes in women with a recent history of urinary tract infection. Am J Clin Nutr. 2016;103:1434–1442. doi: 10.3945/ajcn.116.130542.
    1. Mantzorou M, Giaginis C. Cranberry Consumption Against Urinary Tract Infections: Clinical Stateof- the-Art and Future Perspectives. Curr Pharm Biotechnol. 2019;19(13):1049–1063. doi: 10.2174/1389201020666181206104129.
    1. Occhipinti A, Germano A, Maffei ME. Prevention of urinary tract infection with Oximacro, a cranberry extract with a high content of a-type Proanthocyanidins: A pre-clinical double-blind controlled study. Urol J. 2016;13:2640–2649.
    1. Singh I, Gautam LK, Kaur IR. Effect of oral cranberry extract (standardized proanthocyanidin-a) in patients with recurrent UTI by pathogenic E. coli: a randomized placebo-controlled clinical research study. Int Urol Nephrol. 2016;48:1379–1386. doi: 10.1007/s11255-016-1342-8.
    1. Stapleton AE, Dziura J, Hooton TM, Cox ME, Yarova-Yarovaya Y, Chen S, Gupta K. Recurrent urinary tract infection and urinary Escherichia coli in women ingesting cranberry juice daily: a randomized controlled trial. Mayo Clin Proc. 2012;87(2):143–150. doi: 10.1016/j.mayocp.2011.10.006.
    1. Vasileiou I, Katsargyris A, Theocharis S, Giaginis C. Current clinical status on the preventive effects of cranberry consumption against urinary tract infections. Nutr Res. 2013;33:595–607. doi: 10.1016/j.nutres.2013.05.018.
    1. Vostalova J, Vidlar A, Simanek V, Galandakova A, Kosina P, Vacek J, Vrbkova J, Zimmermann BF, Ulrichova J, Student V. Are high Proanthocyanidins key to cranberry efficacy in the prevention of recurrent urinary tract infection? Phytother Res. 2015;29:1559–1567. doi: 10.1002/ptr.5427.
    1. Feliciano RP, Meudt JJ, Shanmuganayagam D, Krueger CG, Reed JD. Ratio of “A-type” to “B-type” proanthocyanidin interflavan bonds affects extra-intestinal pathogenic Escherichia coli invasion of gut epithelial cells. J Agric Food Chem. 2014;62:3919–3925. doi: 10.1021/jf403839a.
    1. Hidalgo G, Chan M, Tufenkji N. Inhibition of Escherichia coli CFT073 fliC expression and motility by cranberry materials. Appl Environ Microbiol. 2011;77:6852–6857. doi: 10.1128/AEM.05561-11.
    1. Hidalgo G, Ponton A, Fatisson J, O’May C, Asadishad B, Schinner T, Tufenkji N. Induction of a state of iron limitation in uropathogenic Escherichia coli CFT073 by cranberry-derived proanthocyanidins as revealed by microarray analysis. Appl Environ Microbiol. 2011;77:1532–1535. doi: 10.1128/AEM.02201-10.
    1. Howell AB, Vorsa N, Der Marderosian A, Foo LY. Inhibition of the adherence of P-fimbriated Escherichia coli to uroepithelial-cell surfaces by proanthocyanidin extracts from cranberries. N Engl J Med. 1998;339:1085–1086. doi: 10.1056/NEJM199810083391516.
    1. Howell AB, Reed JD, Krueger CG, Winterbottom R, Cunningham DG. Leahy M. A-type cranberry proanthocyanidins and uropathogenic bacterial anti-adhesion activity. Phytochemistry. 2005;66:2281–2291. doi: 10.1016/j.phytochem.2005.05.022.
    1. Ranfaing J, Dunyach-Remy C, Louis L, Lavigne J-P, Sotto A. Propolis potentiates the effect of cranberry (Vaccinium macrocarpon) against the virulence of uropathogenic Escherichia coli. Sci Rep. 2018;8:10706. doi: 10.1038/s41598-018-29082-6.
    1. Dorota W, Marta K, Dorota T-G. Effect of asiatic and ursolic acids on morphology, hydrophobicity, and adhesion of UPECs to uroepithelial cells. Folia Microbiol. 2013;58(3):245–252. doi: 10.1007/s12223-012-0205-7.
    1. McKay DL-Y, Oliver Chen C, Zampariello CA, Blumberg JB. Flavonoids and phenolic acids from cranberry juice are bioavailable and bioactive in healthy older adults. Food Chem. 2015;168:233–240. doi: 10.1016/j.foodchem.2014.07.062.
    1. Anhê FF, Roy D, Pilon G, Dudonné S, Matamoros S, Varin TV, Garofalo C, Moine Q, Desjardins Y, Levy E. Marette A. A polyphenol-rich cranberry extract protects from diet-induced obesity, insulin resistance and intestinal inflammation in association with increased Akkermansia spp. population in the gut microbiota of mice. Gut. 2015;64:872–883. doi: 10.1136/gutjnl-2014-307142.
    1. Anhê FF, Nachbar RT, Varin TV, Vilela V, Dudonné S, Pilon G, Fournier M, Lecours M-A, Desjardins Y, Roy D, Levy E. Marette A. A polyphenol-rich cranberry extract reverses insulin resistance and hepatic steatosis independently of body weight loss. Mol Metab. 2017;6:1563–1573. doi: 10.1016/j.molmet.2017.10.003.
    1. Denis M-C, Desjardins Y, Furtos A, Marcil V, Dudonné S, Montoudis A, Garofalo C, Delvin E, Marette A, Levy E. Prevention of oxidative stress, inflammation and mitochondrial dysfunction in the intestine by different cranberry phenolic fractions. Clin Sci. 2015;128:197–212. doi: 10.1042/CS20140210.
    1. Huang Y, Nikolic D, Pendland S, Doyle BJ, Locklear TD, Mahady GB. Effects of cranberry extracts and ursolic acid derivatives on P-fimbriated Escherichia coli, COX-2 activity, pro-inflammatory cytokine release and the NF-kappabeta transcriptional response in vitro. Pharm Biol. 2009;47:18–25. doi: 10.1080/13880200802397996.
    1. Özcan E, Sun J, Rowley DC. Sela DA. A human gut commensal ferments cranberry carbohydrates to produce Formate. Appl Environ Microbiol. 2017;83:e01097–e01017. doi: 10.1128/AEM.01097-17.
    1. Edgar RC. UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods. 2013;10:996–998. doi: 10.1038/nmeth.2604.
    1. Goodrich JK, Waters JL, Poole AC, Sutter JL, Koren O, Blekhman R, Beaumont M, Van Treuren W, Knight R, Bell JT, Spector TD, Clark AG, Ley RE. Human genetics shape the gut microbiome. Cell. 2014;159:789–799. doi: 10.1016/j.cell.2014.09.053.
    1. Zhao N, Zhan X, Guthrie KA, Mitchell CM, Larson J. Generalized Hotelling's test for paired compositional data with application to human microbiome studies. Genet Epidemiol. 2018;42:459–469. doi: 10.1002/gepi.22127.
    1. Chao A. Nonparametric estimation of the number of classes in a population. Scand Stat Theory Appl. 1984;11:265–270.
    1. Shannon CE. A mathematical theory of communication. Bell Syst Tech J. 1948;27:379–423. doi: 10.1002/j.1538-7305.1948.tb01338.x.
    1. David LA, Maurice CF, Carmody RN, Gootenberg DB, Button JE, Wolfe BE, Ling AV, Devlin AS, Varma Y, Fischbach MA, Biddinger SB, Dutton RJ, Turnbaugh PJ. Diet rapidly and reproducibly alters the human gut microbiome. Nature. 2014;505:559–563. doi: 10.1038/nature12820.
    1. Healey GR, Murphy R, Brough L, Butts CA, Coad J. Interindividual variability in gut microbiota and host response to dietary interventions. Nutr Rev. 2017;75:1059–1080. doi: 10.1093/nutrit/nux062.
    1. Matijašić BB, Obermajer T, Lipoglavšek L, Grabnar I, Avguštin G, Rogelj I. Association of dietary type with fecal microbiota in vegetarians and omnivores in Slovenia. Eur J Nutr. 2014;53:1051–1064. doi: 10.1007/s00394-013-0607-6.
    1. Portune KJ, Benítez-Páez A, Del Pulgar EMG, Cerrudo V, Sanz Y. Gut microbiota, diet, and obesity-related disorders-the good, the bad, and the future challenges. Mol Nutr Food Res. 2017;61:1600252. doi: 10.1002/mnfr.201600252.
    1. Tap J, Furet J-P, Bensaada M, Philippe C, Roth H, Rabot S, Lakhdari O, Lombard V, Henrissat B, Corthier G, Fontaine E, Doré J, Leclerc M. Gut microbiota richness promotes its stability upon increased dietary fibre intake in healthy adults. Environ Microbiol. 2015;17:4954–4964. doi: 10.1111/1462-2920.13006.
    1. Vanegas SM, Meydani M, Barnett JB, Goldin B, Kane A, Rasmussen H, Brown C, Vangay P, Knights D, Jonnalagadda S, Koecher K, Karl JP, Thomas M, Dolnikowski G, Li L, Saltzman E, Wu D, Meydani SN. Substituting whole grains for refined grains in a 6-wk randomized trial has a modest effect on gut microbiota and immune and inflammatory markers of healthy adults. Am J Clin Nutr. 2017;105:635–650. doi: 10.3945/ajcn.116.146928.
    1. Truong DT, Franzosa EA, Tickle TL, Scholz M, Weingart G, Pasolli E, Tett A, Huttenhower C, Segata N. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat Methods. 2015;12:902–903. doi: 10.1038/nmeth.3589.
    1. Franzosa EA, McIver LJ, Rahnavard G, Thompson LR, Schirmer M, Weingart G, Lipson KS, Knight R, Caporaso JG, Segata N, Huttenhower C. Species-level functional profiling of metagenomes and metatranscriptomes. Nat Methods. 2018;15:962–968. doi: 10.1038/s41592-018-0176-y.
    1. Seganfredo FB, Blume CA, Moehlecke M, Giongo A, Casagrande DS, Spolidoro JVN, Padoin AV, Schaan BD, Mottin CC. Weight-loss interventions and gut microbiota changes in overweight and obese patients: a systematic review. Obes Rev. 2017;18:832–851. doi: 10.1111/obr.12541.
    1. Bolyen E, Rideout JR, Dillon MR, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37:852–857. doi: 10.1038/s41587-019-0209-9.
    1. Bokulich NA, Dillon MR, Zhang Y, Rideout JR, Bolyen E, Li H, Albert PS. Caporaso JG. q2-longitudinal: longitudinal and paired-sample analyses of microbiome data. mSystems. 2018;3:e00219–e00218. doi: 10.1128/mSystems.00219-18.
    1. Browne HP, Forster SC, Anonye BO, Kumar N, Neville BA, Stares MD, Goulding D, Lawley TD. Culturing of “unculturable” human microbiota reveals novel taxa and extensive sporulation. Nature. 2016;533:543–546. doi: 10.1038/nature17645.
    1. Kim M, Oh H-S, Park S-C, Chun J. Towards a taxonomic coherence between average nucleotide identity and 16S rRNA gene sequence similarity for species demarcation of prokaryotes. Int J Syst Evol Microbiol. 2014;64(Pt_2):346–351. doi: 10.1099/ijs.0.059774-0.
    1. Rosa LT, Bianconi ME, Thomas GH, Kelly DJ. Tripartite ATP-independent Periplasmic (TRAP) transporters and tripartite Tricarboxylate transporters (TTT): from uptake to pathogenicity. Front Cell Infect Microbiol. 2018;8:33. doi: 10.3389/fcimb.2018.00033.
    1. Kuroda T, Tsuchiya T. Multidrug efflux transporters in the MATE family. Biochim Biophys Acta. 2009;1794:763–768. doi: 10.1016/j.bbapap.2008.11.012.
    1. Shafer WM, Qu X, Waring AJ, Lehrer RI. Modulation of Neisseria gonorrhoeae susceptibility to vertebrate antibacterial peptides due to a member of the resistance/nodulation/division efflux pump family. Proc Natl Acad Sci U S A. 1998;95:1829–1833. doi: 10.1073/pnas.95.4.1829.
    1. Reizer J, Reizer A, Saier MH., Jr A functional superfamily of sodium/solute symporters. Biochim Biophys Acta. 1994;1197:133–166. doi: 10.1016/0304-4157(94)90003-5.
    1. Coates-Brown R, Moran JC, Pongchaikul P, Darby AC, Horsburgh MJ. Comparative genomics of Staphylococcus reveals determinants of speciation and diversification of antimicrobial defense. Front Microbiol. 2018;9:2753. doi: 10.3389/fmicb.2018.02753.
    1. Lebreton F, Manson AL, Saavedra JT, Straub TJ, Earl AM, Gilmore MS. Tracing the enterococci from Paleozoic origins to the hospital. Cell. 2017;169:849–861.e13. doi: 10.1016/j.cell.2017.04.027.
    1. Shapiro BJ. What microbial population genomics has taught us about speciation. In: Polz MF, Rajora OP, editors. Population genomics: microorganisms. Cham: Springer International Publishing; 2019. pp. 31–47.
    1. Gonyar LA, Kendall MM. Ethanolamine and choline promote expression of putative and characterized fimbriae in Enterohemorrhagic Escherichia coli O157:H7. Infect Immun. 2014;82(1):193–201. doi: 10.1128/IAI.00980-13.
    1. Kendall MM, Gruber CC, Parker CT, Sperandio V. Ethanolamine controls expression of genes encoding components involved in Interkingdom signaling and virulence in Enterohemorrhagic Escherichia coli O157:H7. mBio. 2012;2(3):3.
    1. Luzader DH, Clark DE, Gonyar LA, Kendall MM. EutR is a direct regulator of genes that contribute to metabolism and virulence in Enterohemorrhagic Escherichia coli O157:H7. J Bacteriol. 2013;195(21):4947–4953. doi: 10.1128/JB.00937-13.
    1. Sintsova A, Smith S, Subashchandrabose S, Mobley HL. Role of ethanolamine utilization genes in host colonization during urinary tract infection. Infect Immun. 2017;86(3).
    1. Borths EL, Poolman B, Hvorup RN, Locher KP, Rees DC. In vitro functional characterization of BtuCD-F, theEscherichia coliABC transporter for vitamin B12Uptake. Biochemistry. 2005;44(49):16301–16309. doi: 10.1021/bi0513103.
    1. Fang H, Kang J, Zhang D. Microbial production of vitamin B12: a review and future perspectives. Microb Cell Factories. 2017;16(1):1–4. doi: 10.1186/s12934-017-0631-y.
    1. Howell AB, Botto H, Combescure C, Blanc-Potard A-B, Gausa L, Matsumoto T, Tenke P, Sotto A, Lavigne J-P. Dosage effect on uropathogenic Escherichia coli anti-adhesion activity in urine following consumption of cranberry powder standardized for proanthocyanidin content: a multicentric randomized double blind study. BMC Infect Dis. 2010;10:94. doi: 10.1186/1471-2334-10-94.
    1. Rosner B. Fundamentals of biostatistics: Nelson Education; 2015.
    1. Braune A, Blaut M. Bacterial species involved in the conversion of dietary flavonoids in the human gut. Gut Microbes. 2016;7:216–234. doi: 10.1080/19490976.2016.1158395.
    1. Kutschera M, Engst W, Blaut M, Braune A. Isolation of catechin-converting human intestinal bacteria. J Appl Microbiol. 2011;111(1):165–175. doi: 10.1111/j.1365-2672.2011.05025.x.
    1. Dinan TG, Cryan JF. Gut-brain axis in 2016: brain-gut-microbiota axis - mood, metabolism and behaviour. Nat Rev Gastroenterol Hepatol. 2017;14:69–70. doi: 10.1038/nrgastro.2016.200.
    1. Dinan TG, Cryan JF. The microbiome-gut-brain Axis in health and disease. Gastroenterol Clin N Am. 2017;46:77–89. doi: 10.1016/j.gtc.2016.09.007.
    1. Kennedy PJ, Cryan JF, Dinan TG, Clarke G. Kynurenine pathway metabolism and the microbiota-gut-brain axis. Neuropharmacology. 2017;112:399–412. doi: 10.1016/j.neuropharm.2016.07.002.
    1. Westfall S, Lomis N, Kahouli I, Dia SY, Singh SP, Prakash S. Microbiome, probiotics and neurodegenerative diseases: deciphering the gut brain axis. Cell Mol Life Sci. 2017;74:3769–3787. doi: 10.1007/s00018-017-2550-9.
    1. Coello K, Hansen TH, Sørensen N, Munkholm K, Kessing LV, Pedersen O, Vinberg M. Gut microbiota composition in patients with newly diagnosed bipolar disorder and their unaffected first-degree relatives. Brain Behav Immun. 2019;75:112–118. doi: 10.1016/j.bbi.2018.09.026.
    1. Jiang H, Ling Z, Zhang Y, Mao H, Ma Z, Yin Y, Wang W, Tang W, Tan Z, Shi J, Li L, Ruan B. Altered fecal microbiota composition in patients with major depressive disorder. Brain Behav Immun. 2015;48:186–194. doi: 10.1016/j.bbi.2015.03.016.
    1. Valles-Colomer M, Falony G, Darzi Y, Tigchelaar EF, Wang J, Tito RY, Schiweck C, Kurilshikov A, Joossens M, Wijmenga C, Claes S, Van Oudenhove L, Zhernakova A, Vieira-Silva S, Raes J. The neuroactive potential of the human gut microbiota in quality of life and depression. Nat Microbiol. 2019;4:623–632. doi: 10.1038/s41564-018-0337-x.
    1. Huang S, Mao J, Zhou L, Xiong X, Deng Y. The imbalance of gut microbiota and its correlation with plasma inflammatory cytokines in pemphigus vulgaris patients. Scand J Immunol. 2019;90:e12799. doi: 10.1111/sji.12799.
    1. Borgo F, Garbossa S, Riva A, Severgnini M, Luigiano C, Benetti A, Pontiroli AE, Morace G, Borghi E. Body mass index and sex affect diverse microbial niches within the gut. Front Microbiol. 2018;9:213. doi: 10.3389/fmicb.2018.00213.
    1. Del Chierico F, Vernocchi P, Dallapiccola B, Putignani L. Mediterranean diet and health: food effects on gut microbiota and disease control. Int J Mol Sci. 2014;15:11678–11699. doi: 10.3390/ijms150711678.
    1. Kasai C, Sugimoto K, Moritani I, Tanaka J, Oya Y, Inoue H, Tameda M, Shiraki K, Ito M, Takei Y, Takase K. Comparison of the gut microbiota composition between obese and non-obese individuals in a Japanese population, as analyzed by terminal restriction fragment length polymorphism and next-generation sequencing. BMC Gastroenterol. 2015;15:100. doi: 10.1186/s12876-015-0330-2.
    1. Siddharth J, Holway N, Parkinson SJA. Western diet ecological module identified from the “humanized” mouse microbiota predicts diet in adults and formula feeding in children. PLoS One. 2013;8:e83689. doi: 10.1371/journal.pone.0083689.
    1. Banskota S, Ghia J-E, Khan WI. Serotonin in the gut: blessing or a curse. Biochimie. 2019.
    1. Cervenka I, Agudelo LZ, Kynurenines RJL. Tryptophan’s metabolites in exercise, inflammation, and mental health. Science. 2017;357:6349. doi: 10.1126/science.aaf9794.
    1. Sofia MA, Ciorba MA, Meckel K, Lim CK, Guillemin GJ, Weber CR, Bissonnette M, Pekow JR. Tryptophan metabolism through the Kynurenine pathway is associated with endoscopic inflammation in ulcerative colitis. Inflamm Bowel Dis. 2018;24:1471–1480. doi: 10.1093/ibd/izy103.
    1. Mezrich JD, Fechner JH, Zhang X, Johnson BP, Burlingham WJ, Bradfield CA. An interaction between kynurenine and the aryl hydrocarbon receptor can generate regulatory T cells. J Immunol. 2010;185:3190–3198. doi: 10.4049/jimmunol.0903670.
    1. Peck A, Mellins ED. Precarious balance: Th17 cells in host defense. Infect Immun. 2010;78(1):32–38. doi: 10.1128/IAI.00929-09.
    1. Schwarcz R, Stone TW. The kynurenine pathway and the brain: challenges, controversies and promises. Neuropharmacology. 2017;112:237–247. doi: 10.1016/j.neuropharm.2016.08.003.
    1. León-Ponte M, Ahern GP, O’Connell PJ. Serotonin provides an accessory signal to enhance T-cell activation by signaling through the 5-HT7 receptor. Blood. 2007;109(8):3139–3146. doi: 10.1182/blood-2006-10-052787.
    1. Dagenais-Lussier X, Aounallah M, Mehraj V, El-Far M, Tremblay C, Sekaly R-P, Routy J-P, van Grevenynghe J. Kynurenine reduces memory CD4 T-cell survival by interfering with Interleukin-2 signaling early during HIV-1 infection. J Virol. 2016;90(17):7967–7979. doi: 10.1128/JVI.00994-16.
    1. Kwon YH, Wang H, Denou E, Ghia J-E, Rossi L, Fontes ME, Bernier SP, Shajib MS, Banskota S, Collins SM, Surette MG, Khan WI. Modulation of gut microbiota composition by serotonin signaling influences intestinal immune response and susceptibility to colitis. Cell Mol Gastroenterol Hepatol. 2019;7(4):709–728. doi: 10.1016/j.jcmgh.2019.01.004.
    1. Sinclair LV, Neyens D, Ramsay G, Taylor PM, Cantrell DA. Single cell analysis of kynurenine and system L amino acid transport in T cells. Nat Commun. 2018;9:1981. doi: 10.1038/s41467-018-04366-7.
    1. O’Mahony SM, Clarke G, Borre YE, Dinan TG, Cryan JF. Serotonin, tryptophan metabolism and the brain-gut-microbiome axis. Behav Brain Res. 2015;277:32–48. doi: 10.1016/j.bbr.2014.07.027.
    1. Olson P, Hunstad D. Subversion of host innate immunity by Uropathogenic Escherichia coli. Pathogens. 2016;5(1):2. doi: 10.3390/pathogens5010002.
    1. McLellan LK, Hunstad DA. Urinary Tract Infection: Pathogenesis and Outlook. Trends Mol Med. 2016.
    1. Loughman JA, Yarbrough ML, Tiemann KM, Hunstad DA. Local generation of Kynurenines mediates inhibition of neutrophil Chemotaxis by Uropathogenic Escherichia coli. Infect Immun. 2016;84:1176–1183. doi: 10.1128/IAI.01202-15.
    1. Sivick KE, Mobley HLT. Waging war against uropathogenic Escherichia coli: winning back the urinary tract. Infect Immun. 2010;78:568–585. doi: 10.1128/IAI.01000-09.
    1. Yarbrough ML, Briden KE, Mitsios JV, Weindel AL, Terrill CM, Hunstad DA, Dietzen DJ. Mass spectrometric measurement of urinary kynurenine-to-tryptophan ratio in children with and without urinary tract infection. Clin Biochem. 2018;56:83–88. doi: 10.1016/j.clinbiochem.2018.04.014.
    1. Degnan PH, Taga ME, Goodman AL. Vitamin B 12 as a modulator of gut microbial ecology. Cell Metab. 2014;20(5):769–778. doi: 10.1016/j.cmet.2014.10.002.
    1. Rowley CA, Kendall MM. To B12 or not to B12: five questions on the role of cobalamin in host-microbial interactions. PLoS Pathog. 2019;15:e1007479. doi: 10.1371/journal.ppat.1007479.
    1. Schirmer M, Franzosa EA, Lloyd-Price J, McIver LJ, Schwager R, Poon TW, Ananthakrishnan AN, Andrews E, Barron G, Lake K, Prasad M, Sauk J, Stevens B, Wilson RG, Braun J, Denson LA, Kugathasan S, McGovern DPB, Vlamakis H, Xavier RJ, Huttenhower C. Dynamics of metatranscription in the inflammatory bowel disease gut microbiome. Nat Microbiol. 2018;3:337–346. doi: 10.1038/s41564-017-0089-z.
    1. Schreiber HL, 4th, Conover MS, Chou W-C, Hibbing ME, Manson AL, Dodson KW, Hannan TJ, Roberts PL, Stapleton AE, Hooton TM, Livny J, Earl AM, Hultgren SJ. Bacterial virulence phenotypes of Escherichia coli and host susceptibility determine risk for urinary tract infections. Sci Transl Med. 2017;9:382. doi: 10.1126/scitranslmed.aaf1283.
    1. Schwartz DJ, Kalas V, Pinkner JS, Chen SL, Spaulding CN, Dodson KW, Hultgren SJ. Positively selected FimH residues enhance virulence during urinary tract infection by altering FimH conformation. Proc Natl Acad Sci U S A. 2013;110:15530–15537. doi: 10.1073/pnas.1315203110.
    1. Spaulding CN, Klein RD, Ruer S, Kau AL, Schreiber HL, Cusumano ZT, Dodson KW, Pinkner JS, Fremont DH, Janetka JW, Remaut H, Gordon JI, Hultgren SJ. Selective depletion of uropathogenic E. coli from the gut by a FimH antagonist. Nature. 2017;546:528–532. doi: 10.1038/nature22972.
    1. Raman AS, Gehrig JL, Venkatesh S, Chang H-W, Hibberd MC, Subramanian S, Kang G, Bessong PO, Lima AAM, Kosek MN, Petri WA, Jr, Rodionov DA, Arzamasov AA, Leyn SA, Osterman AL, Huq S, Mostafa I, Islam M, Mahfuz M, Haque R, Ahmed T, Barratt MJ, Gordon JI. A sparse covarying unit that describes healthy and impaired human gut microbiota development. Science. 2019;365:6449. doi: 10.1126/science.aau4735.
    1. Smith AL, Brown J, Wyman JF, Berry A, Newman DK, Stapleton AE. Treatment and prevention of recurrent lower urinary tract infections in women: a rapid review with practice recommendations. J Urol. 2018;200:1174–1191. doi: 10.1016/j.juro.2018.04.088.
    1. Li F, Hullar MAJ, Lampe JW. Optimization of terminal restriction fragment polymorphism (TRFLP) analysis of human gut microbiota. J Microbiol Methods. 2007;68:303–311. doi: 10.1016/j.mimet.2006.09.006.
    1. Caporaso JG, Lauber CL, Walters WA, Berg-Lyons D, Huntley J, Fierer N, Owens SM, Betley J, Fraser L, Bauer M, Gormley N, Gilbert JA, Smith G, Knight R. Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms. ISME J. 2012;6:1621–1624. doi: 10.1038/ismej.2012.8.
    1. Cole JR, Wang Q, Fish JA, Chai B, McGarrell DM, Sun Y, Brown CT, Porras-Alfaro A, Kuske CR, Tiedje JM. Ribosomal database project: data and tools for high throughput rRNA analysis. Nucleic Acids Res. 2014;42:D633–D642. doi: 10.1093/nar/gkt1244.
    1. Thorsen J, Brejnrod A, Mortensen M, Rasmussen MA, Stokholm J, Al-Soud WA, Sørensen S, Bisgaard H, Waage J. Large-scale benchmarking reveals false discoveries and count transformation sensitivity in 16S rRNA gene amplicon data analysis methods used in microbiome studies. Microbiome. 2016;4:62. doi: 10.1186/s40168-016-0208-8.
    1. Bray JR, Curtis JT. An ordination of the upland Forest communities of southern Wisconsin. Ecol Monogr. 1957;27:25. doi: 10.2307/1942268.
    1. Caporaso JG, Kuczynski J, Stombaugh J, Bittinger K, Bushman FD, Costello EK, Fierer N, Peña AG, Goodrich JK, Gordon JI, Huttley GA, Kelley ST, Knights D, Koenig JE, Ley RE, Lozupone CA, McDonald D, Muegge BD, Pirrung M, Reeder J, Sevinsky JR, Turnbaugh PJ, Walters WA, Widmann J, Yatsunenko T, Zaneveld J. Knight R. QIIME allows analysis of high-throughput community sequencing data. Nat Methods. 2010;7:335–336. doi: 10.1038/nmeth.f.303.
    1. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol. 1995;57(1):289–300.
    1. Eren AM, Maignien L, Sul WJ, Murphy LG, Grim SL, Morrison HG, Sogin ML. Oligotyping: differentiating between closely related microbial taxa using 16S rRNA gene data. Methods Ecol Evol. 2013;4(12):1111–1119. doi: 10.1111/2041-210X.12114.
    1. Eren AM, Morrison HG, Lescault PJ, Reveillaud J, Vineis JH, Sogin ML. Minimum entropy decomposition: unsupervised oligotyping for sensitive partitioning of high-throughput marker gene sequences. ISME J. 2015;9:968–979. doi: 10.1038/ismej.2014.195.
    1. Pruesse E, Peplies J, Glöckner FO. SINA: accurate high-throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics. 2012;28:1823–1829. doi: 10.1093/bioinformatics/bts252.
    1. Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, Lesniewski RA, Oakley BB, Parks DH, Robinson CJ, Sahl JW, Stres B, Thallinger GG, Van Horn DJ, Weber CF. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol. 2009;75:7537–7541. doi: 10.1128/AEM.01541-09.
    1. Stamatakis A. RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics. 2006;22:2688–2690. doi: 10.1093/bioinformatics/btl446.
    1. Langmead B, Salzberg SL. Fast gapped-read alignment with bowtie 2. Nat Methods. 2012;9:357–359. doi: 10.1038/nmeth.1923.
    1. Buchfink B, Xie C, Huson DH. Fast and sensitive protein alignment using DIAMOND. Nat Methods. 2015;12:59–60. doi: 10.1038/nmeth.3176.
    1. Suzek BE, Wang Y, Huang H, PB MG, Wu CH, Consortium UP. UniRef clusters: a comprehensive and scalable alternative for improving sequence similarity searches. Bioinformatics. 2015;31:926–932. doi: 10.1093/bioinformatics/btu739.
    1. Caspi R, Billington R, Fulcher CA, Keseler IM, Kothari A, Krummenacker M, Latendresse M, Midford PE, Ong Q, Ong WK, Paley S, Subhraveti P, Karp PD. The MetaCyc database of metabolic pathways and enzymes. Nucleic Acids Res. 2018;46:D633–D639. doi: 10.1093/nar/gkx935.
    1. Ye Y. Doak TG. A parsimony approach to biological pathway reconstruction/inference for genomes and metagenomes. PLoS Comput Biol. 2009;5:e1000465. doi: 10.1371/journal.pcbi.1000465.
    1. Li H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv [q-bioGN] 2013;1303:3997.
    1. Danecek P, Schiffels S, Durbin R. Multiallelic calling model in bcftools (−m) 2016.
    1. Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 2010;26:841–842. doi: 10.1093/bioinformatics/btq033.
    1. McGuire AM, Cochrane K, Griggs AD, Haas BJ, Abeel T, Zeng Q, Nice JB, MacDonald H, Birren BW, Berger BW, Allen-Vercoe E, Earl AM. Evolution of invasion in a diverse set of Fusobacterium species. mBio. 2014;5(6):e01864–e01814.
    1. Blanchette M, Kent WJ, Riemer C, Elnitski L, Smit AFA, Roskin KM, Baertsch R, Rosenbloom K, Clawson H, Green ED, Haussler D, Miller W. Aligning multiple genomic sequences with the threaded blockset aligner. Genome Res. 2004;14:708–715. doi: 10.1101/gr.1933104.
    1. Price MN, Dehal PS, Arkin AP. FastTree 2--approximately maximum-likelihood trees for large alignments. PLoS One. 2010;5:e9490. doi: 10.1371/journal.pone.0009490.
    1. Wu M, Scott AJ. Phylogenomic analysis of bacterial and archaeal sequences with AMPHORA2. Bioinformatics. 2012;28:1033–1034. doi: 10.1093/bioinformatics/bts079.
    1. Georgescu CH, Manson AL, Griggs AD, Desjardins CA, Pironti A, Wapinski I, Abeel T, Haas BJ, Earl AM. SynerClust: a highly scalable, synteny-aware orthologue clustering tool. Microbial Genomics. 2018;4(11):e000231. doi: 10.1099/mgen.0.000231.
    1. Edgar RCMUSCLE. a multiple sequence alignment method with reduced time and space complexity. BMC Bioinformatics. 2004;5:113. doi: 10.1186/1471-2105-5-113.
    1. Konstantinidis KT, Tiedje JM. Genomic insights that advance the species definition for prokaryotes. Proc Natl Acad Sci U S A. 2005;102:2567–2572. doi: 10.1073/pnas.0409727102.
    1. Walker BJ, Abeel T, Shea T, Priest M, Abouelliel A, Sakthikumar S, Cuomo CA, Zeng Q, Wortman J, Young SK, Earl AM. Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement. PLoS One. 2014;9:e112963. doi: 10.1371/journal.pone.0112963.
    1. Aziz RK, Bartels D, Best AA, DeJongh M, Disz T, Edwards RA, Formsma K, Gerdes S, Glass EM, Kubal M, Meyer F, Olsen GJ, Olson R, Osterman AL, Overbeek RA, McNeil LK, Paarmann D, Paczian T, Parrello B, Pusch GD, Reich C, Stevens R, Vassieva O, Vonstein V, Wilke A, Zagnitko O. The RAST server: rapid annotations using subsystems technology. BMC Genomics. 2008;9:75. doi: 10.1186/1471-2164-9-75.
    1. Brettin T, Davis JJ, Disz T, Edwards RA, Gerdes S, Olsen GJ, Olson R, Overbeek R, Parrello B, Pusch GD, Shukla M, Thomason JA, 3rd, Stevens R, Vonstein V, Wattam AR, Xia F. RASTtk: a modular and extensible implementation of the RAST algorithm for building custom annotation pipelines and annotating batches of genomes. Sci Rep. 2015;5:8365. doi: 10.1038/srep08365.
    1. Overbeek R, Olson R, Pusch GD, Olsen GJ, Davis JJ, Disz T, Edwards RA, Gerdes S, Parrello B, Shukla M, Vonstein V, Wattam AR, Xia F, Stevens R. The SEED and the rapid annotation of microbial genomes using subsystems technology (RAST) Nucleic Acids Res. 2014;42:D206–D214. doi: 10.1093/nar/gkt1226.
    1. Tanizawa Y, Fujisawa T, Nakamura YDFAST. a flexible prokaryotic genome annotation pipeline for faster genome publication. Bioinformatics. 2018;34:1037–1039. doi: 10.1093/bioinformatics/btx713.
    1. Kanehisa M, Sato Y, Furumichi M, Morishima K, Tanabe M. New approach for understanding genome variations in KEGG. Nucleic Acids Res. 2019;47:D590–D595. doi: 10.1093/nar/gky962.
    1. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G. Gene ontology: tool for the unification of biology. The gene ontology consortium. Nat Genet. 2000;25:25–29. doi: 10.1038/75556.
    1. The Gene Ontology Consortium The gene ontology resource: 20 years and still GOing strong. Nucleic Acids Res. 2019;47:D330–D338. doi: 10.1093/nar/gky1055.
    1. Haft DH, Selengut JD, Richter RA, Harkins D, Basu MK, Beck E. TIGRFAMs and genome properties in 2013. Nucleic Acids Res. 2013;41:D387–D395. doi: 10.1093/nar/gks1234.
    1. El-Gebali S, Mistry J, Bateman A, Eddy SR, Luciani A, Potter SC, Qureshi M, Richardson LJ, Salazar GA, Smart A, Sonnhammer ELL, Hirsh L, Paladin L, Piovesan D, Tosatto SCE, Finn RD. The Pfam protein families database in 2019. Nucleic Acids Res. 2019;47:D427–D432. doi: 10.1093/nar/gky995.
    1. Finn RD, Bateman A, Clements J, Coggill P, Eberhardt RY, Eddy SR, Heger A, Hetherington K, Holm L, Mistry J, Sonnhammer ELL, Tate J, Punta M. Pfam: the protein families database. Nucleic Acids Res. 2014;42:D222–D230. doi: 10.1093/nar/gkt1223.
    1. Taboada B, Estrada K, Ciria R, Merino E. Operon-mapper: a web server for precise operon identification in bacterial and archaeal genomes. Bioinformatics. 2018;34:4118–4120. doi: 10.1093/bioinformatics/bty496.
    1. Galperin MY, Makarova KS, Wolf YI, Koonin EV. Expanded microbial genome coverage and improved protein family annotation in the COG database. Nucleic Acids Res. 2015;43:D261–D269. doi: 10.1093/nar/gku1223.
    1. Tatusov RL, Koonin EV. Lipman DJ. A genomic perspective on protein families. Science. 1997;278:631–637. doi: 10.1126/science.278.5338.631.
    1. Consortium UP. UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res. 2019;47:D506–D515. doi: 10.1093/nar/gky1049.
    1. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215:403–410. doi: 10.1016/S0022-2836(05)80360-2.
    1. Powell S, Szklarczyk D, Trachana K, Roth A, Kuhn M, Muller J, Arnold R, Rattei T, Letunic I, Doerks T, Jensen LJ, von Mering C, Bork P. eggNOG v3.0: orthologous groups covering 1133 organisms at 41 different taxonomic ranges. Nucleic Acids Res. 2012;40:D284–D289. doi: 10.1093/nar/gkr1060.

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