1932

Abstract

The microbiome is known to regulate many aspects of host health and disease and is increasingly being recognized as a key mediator of drug action. However, investigating the complex multidirectional relationships between drugs, the microbiota, and the host is a challenging endeavor, and the biological mechanisms that underpin these interactions are often not well understood. In this review, we outline the current evidence that supports a role for the microbiota as a contributor to both the therapeutic benefits and side effects of drugs, with a particular focus on those used to treat mental disorders, type 2 diabetes, and cancer. We also provide a snapshot of the experimental and computational tools that are currently available for the dissection of drug–microbiota–host interactions. The advancement of knowledge in this area may ultimately pave the way for the development of novel microbiota-based strategies that can be used to improve treatment outcomes.

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2020-01-06
2024-03-29
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Literature Cited

  1. 1. 
    Strebhardt K, Ullrich A. 2008. Paul Ehrlich's magic bullet concept: 100 years of progress. Nat. Rev. Cancer 8:473–80
    [Google Scholar]
  2. 2. 
    Moreno L, Pearson AD. 2013. How can attrition rates be reduced in cancer drug discovery?. Expert Opin. Drug Discov. 8:4363–68
    [Google Scholar]
  3. 3. 
    Kundu P, Blacher E, Elinav E, Pettersson S 2017. Our gut microbiome: the evolving inner self. Cell 171:1481–93
    [Google Scholar]
  4. 4. 
    Veber DF, Johnson SR, Cheng HY, Smith BR, Ward KW, Kopple KD 2002. Molecular properties that influence the oral bioavailability of drug candidates. J. Med. Chem. 45:122615–23
    [Google Scholar]
  5. 5. 
    Spanogiannopoulos P, Bess EN, Carmody RN, Turnbaugh PJ 2016. The microbial pharmacists within us: a metagenomic view of xenobiotic metabolism. Nat. Rev. Microbiol. 14:5273–87
    [Google Scholar]
  6. 6. 
    Haiser HJ, Gootenberg DB, Chatman K, Sirasani G, Balskus EP, Turnbaugh PJ 2013. Predicting and manipulating cardiac drug inactivation by the human gut bacterium Eggerthella lenta. . Science 341:6143295–98
    [Google Scholar]
  7. 7. 
    Falony G, Joossens M, Vieira-Silva S, Wang J, Darzi Y et al. 2016. Population-level analysis of gut microbiome variation. Science 352:6285560–64
    [Google Scholar]
  8. 8. 
    Rothschild D, Weissbrod O, Barkan E, Kurilshikov A, Korem T et al. 2018. Environment dominates over host genetics in shaping human gut microbiota. Nature 555:7695210–15
    [Google Scholar]
  9. 9. 
    David LA, Maurice CF, Carmody RN, Gootenberg DB, Button JE et al. 2014. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505:7484559–63
    [Google Scholar]
  10. 10. 
    Forslund K, Hildebrand F, Nielsen T, Falony G, Le Chatelier E et al. 2015. Disentangling type 2 diabetes and metformin treatment signatures in the human gut microbiota. Nature 528:7581262–66
    [Google Scholar]
  11. 11. 
    Maier L, Pruteanu M, Kuhn M, Zeller G, Telzerow A et al. 2018. Extensive impact of non-antibiotic drugs on human gut bacteria. Nature 555:7698623–28
    [Google Scholar]
  12. 12. 
    Fuller AT. 1937. Is p-aminobenzenesulphonamide the active agent in prontosil therapy. ? Lancet 229:5917194–98
    [Google Scholar]
  13. 13. 
    Toda T, Ohi K, Kudo T, Yoshida T, Ikarashi N et al. 2009. Ciprofloxacin suppresses Cyp3a in mouse liver by reducing lithocholic acid-producing intestinal flora. Drug Metab. Pharmacokinet. 24:3201–8
    [Google Scholar]
  14. 14. 
    Wu H, Esteve E, Tremaroli V, Khan MT, Caesar R et al. 2017. Metformin alters the gut microbiome of individuals with treatment-naive type 2 diabetes, contributing to the therapeutic effects of the drug. Nat. Med. 23:7850–58
    [Google Scholar]
  15. 15. 
    Gershon MD. 1999. The enteric nervous system: a second brain. Hosp. Pract. 34:731–52
    [Google Scholar]
  16. 16. 
    Codagnone MG, Spichak S, O'Mahony SM, O'Leary OF, Clarke G et al. 2019. Programming bugs: microbiota and the developmental origins of brain health and disease. Biol. Psychiatry 85:2150–63
    [Google Scholar]
  17. 17. 
    Flowers SA, Baxter NT, Ward KM, Kraal AZ, McInnis MG et al. 2019. Effects of atypical antipsychotic treatment and resistant starch supplementation on gut microbiome composition in a cohort of patients with bipolar disorder or schizophrenia. Pharmacotherapy 39:2161–70
    [Google Scholar]
  18. 18. 
    Flowers SA, Evans SJ, Ward KM, McInnis MG, Ellingrod VL 2017. Interaction between atypical antipsychotics and the gut microbiome in a bipolar disease cohort. Pharmacotherapy 37:3261–67
    [Google Scholar]
  19. 19. 
    Leclercq S, Mian FM, Stanisz AM, Bindels LB, Cambier E et al. 2017. Low-dose penicillin in early life induces long-term changes in murine gut microbiota, brain cytokines and behavior. Nat. Commun. 8:15062
    [Google Scholar]
  20. 20. 
    Macedo D, Filho AJMC, Soares de Sousa CN, Quevedo J, Barichello T et al. 2017. Antidepressants, antimicrobials or both? Gut microbiota dysbiosis in depression and possible implications of the antimicrobial effects of antidepressant drugs for antidepressant effectiveness. J. Affect. Disord. 208:22–32
    [Google Scholar]
  21. 21. 
    Bahr SM, Tyler BC, Wooldridge N, Butcher BD, Burns TL et al. 2015. Use of the second-generation antipsychotic, risperidone, and secondary weight gain are associated with an altered gut microbiota in children. Transl. Psychiatry 5:10e652
    [Google Scholar]
  22. 22. 
    Bahr SM, Weidemann BJ, Castro AN, Walsh JW, DeLeon O et al. 2015. Risperidone-induced weight gain is mediated through shifts in the gut microbiome and suppression of energy expenditure. EBioMedicine 2:111725–34
    [Google Scholar]
  23. 23. 
    Plovier H, Everard A, Druart C, Depommier C, Van Hul M et al. 2017. A purified membrane protein from Akkermansia muciniphila or the pasteurized bacterium improves metabolism in obese and diabetic mice. Nat. Med. 23:1107–13
    [Google Scholar]
  24. 24. 
    Morgan AP, Crowley JJ, Nonneman RJ, Quackenbush CR, Miller CN et al. 2014. The antipsychotic olanzapine interacts with the gut microbiome to cause weight gain in mouse. PLOS ONE 9:12e115225
    [Google Scholar]
  25. 25. 
    Davey KJ, Cotter PD, O'Sullivan O, Crispie F, Dinan TG et al. 2013. Antipsychotics and the gut microbiome: Olanzapine-induced metabolic dysfunction is attenuated by antibiotic administration in the rat. Transl. Psychiatry 3:10e309
    [Google Scholar]
  26. 26. 
    Kao AC-C, Spitzer S, Anthony DC, Lennox B, Burnet PWJ 2018. Prebiotic attenuation of olanzapine-induced weight gain in rats: analysis of central and peripheral biomarkers and gut microbiota. Transl. Psychiatry 8:166
    [Google Scholar]
  27. 27. 
    Cussotto S, Strain CR, Fouhy F, Strain RG, Peterson VL et al. 2019. Differential effects of psychotropic drugs on microbiome composition and gastrointestinal function. Psychopharmacology 236:5167185
    [Google Scholar]
  28. 28. 
    Bohnert JA, Szymaniak-Vits M, Schuster S, Kern WV 2011. Efflux inhibition by selective serotonin reuptake inhibitors in Escherichia coli. J. Antimicrob. . Chemother 66:92057–60
    [Google Scholar]
  29. 29. 
    Velázquez YF, Nacheva PM. 2017. Biodegradability of fluoxetine, mefenamic acid, and metoprolol using different microbial consortiums. Environ. Sci. Pollut. Res. 24:76779–93
    [Google Scholar]
  30. 30. 
    Rodier PM, Ingram JL, Tisdale B, Nelson S, Romano J 1996. Embryological origin for autism: developmental anomalies of the cranial nerve motor nuclei. J. Comp. Neurol. 370:2247–61
    [Google Scholar]
  31. 31. 
    Vorhees CV. 1987. Behavioral teratogenicity of valproic acid: selective effects on behavior after prenatal exposure to rats. Psychopharmacology 92:2173–79
    [Google Scholar]
  32. 32. 
    Liu F, Horton-Sparks K, Hull V, Li RW, Martínez-Cerdeño V 2018. The valproic acid rat model of autism presents with gut bacterial dysbiosis similar to that in human autism. Mol. Autism 9:161
    [Google Scholar]
  33. 33. 
    Sarkar A, Lehto SM, Harty S, Dinan TG, Cryan JF, Burnet PWJ 2016. Psychobiotics and the manipulation of bacteria-gut-brain signals. Trends Neurosci 39:11763–81
    [Google Scholar]
  34. 34. 
    Burokas A, Arboleya S, Moloney RD, Peterson VL, Murphy K et al. 2017. Targeting the microbiota-gut-brain axis: Prebiotics have anxiolytic and antidepressant-like effects and reverse the impact of chronic stress in mice. Biol. Psychiatry 82:7472–87
    [Google Scholar]
  35. 35. 
    Kiely B, Desbonnet L, Garrett L, Clarke G, Dinan TG, Cryan JF 2010. Effects of the probiotic Bifidobacterium infantis in the maternal separation model of depression. Neuroscience 170:41179–88
    [Google Scholar]
  36. 36. 
    Rena G, Hardie DG, Pearson ER 2017. The mechanisms of action of metformin. Diabetologia 60:91577–85
    [Google Scholar]
  37. 37. 
    Pryor R, Cabreiro F. 2015. Repurposing metformin: an old drug with new tricks in its binding pockets. Biochem. J. 471:3307–22
    [Google Scholar]
  38. 38. 
    Stepensky D, Friedman M, Raz I, Hoffman A 2002. Pharmacokinetic-pharmacodynamic analysis of the glucose-lowering effect of metformin in diabetic rats reveals first-pass pharmacodynamic effect. Drug Metab. Dispos. 30:8861–68
    [Google Scholar]
  39. 39. 
    Bonora E, Cigolini M, Bosello O, Zancanaro C, Capretti L et al. 1984. Lack of effect of intravenous metformin on plasma concentrations of glucose, insulin, C-peptide, glucagon and growth hormone in non-diabetic subjects. Curr. Med. Res. Opin. 9:147–51
    [Google Scholar]
  40. 40. 
    Buse JB, DeFronzo RA, Rosenstock J, Kim T, Burns C et al. 2016. The primary glucose-lowering effect of metformin resides in the gut, not the circulation: results from short-term pharmacokinetic and 12-week dose-ranging studies. Diabetes Care 39:2198–205
    [Google Scholar]
  41. 41. 
    Karlsson FH, Tremaroli V, Nookaew I, Bergström G, Behre CJ et al. 2013. Gut metagenome in European women with normal, impaired and diabetic glucose control. Nature 498:745299–103
    [Google Scholar]
  42. 42. 
    Wang J, Qin J, Li Y, Cai Z, Li S et al. 2012. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature 490:741855–60
    [Google Scholar]
  43. 43. 
    de la Cuesta-Zuluaga J, Mueller NT, Corrales-Agudelo V, Velásquez-Mejía EP, Carmona JA et al. 2017. Metformin is associated with higher relative abundance of mucin-degrading Akkermansia muciniphila and several short-chain fatty acid-producing microbiota in the gut. Diabetes Care 40:154–62
    [Google Scholar]
  44. 44. 
    De Vadder F, Kovatcheva-Datchary P, Goncalves D, Vinera J, Zitoun C et al. 2014. Microbiota-generated metabolites promote metabolic benefits via gut-brain neural circuits. Cell 156:1–284–96
    [Google Scholar]
  45. 45. 
    Elbere I, Kalnina I, Silamikelis I, Konrade I, Zaharenko L et al. 2018. Association of metformin administration with gut microbiome dysbiosis in healthy volunteers. PLOS ONE 13:9e0204317
    [Google Scholar]
  46. 46. 
    Cabreiro F, Au C, Leung K-Y, Vergara-Irigaray N, Cochemé HM et al. 2013. Metformin retards aging in C. elegans by altering microbial folate and methionine metabolism. Cell 153:1228–39
    [Google Scholar]
  47. 47. 
    Sahin M, Tutuncu NB, Ertugrul D, Tanaci N, Guvener ND 2007. Effects of metformin or rosiglitazone on serum concentrations of homocysteine, folate, and vitamin B12 in patients with type 2 diabetes mellitus. J. Diabetes Complications 21:2118–23
    [Google Scholar]
  48. 48. 
    Lee H, Ko G. 2014. Effect of metformin on metabolic improvement and the gut microbiota. Appl. Environ. Microbiol. 80:5935–43
    [Google Scholar]
  49. 49. 
    Shin N-R, Lee J-C, Lee H-Y, Kim M-S, Whon TW et al. 2014. An increase in the Akkermansia spp. population induced by metformin treatment improves glucose homeostasis in diet-induced obese mice. Gut 63:5727–35
    [Google Scholar]
  50. 50. 
    Bahne E, Hansen M, Brønden A, Sonne DP, Vilsbøll T, Knop FK 2016. Involvement of glucagon-like peptide-1 in the glucose-lowering effect of metformin. Diabetes Obes. Metab. 18:10955–61
    [Google Scholar]
  51. 51. 
    Napolitano A, Miller S, Nicholls AW, Baker D, Van Horn S 2014. Novel gut-based pharmacology of metformin in patients with type 2 diabetes mellitus. PLOS ONE 9:7100778
    [Google Scholar]
  52. 52. 
    Tolhurst G, Heffron H, Lam YS, Parker HE, Habib AM et al. 2012. Short-chain fatty acids stimulate glucagon-like peptide-1 secretion via the G-protein-coupled receptor FFAR2. Diabetes 61:2364–71
    [Google Scholar]
  53. 53. 
    Larraufie P, Martin-Gallausiaux C, Lapaque N, Dore J, Gribble FM et al. 2018. SCFAs strongly stimulate PYY production in human enteroendocrine cells. Sci. Rep. 8:74
    [Google Scholar]
  54. 54. 
    Christiansen CB, Gabe MBN, Svendsen B, Dragsted LO, Rosenkilde MM, Holst JJ 2018. The impact of short-chain fatty acids on GLP-1 and PYY secretion from the isolated perfused rat colon. Am. J. Physiol. Liver Physiol. 315:1G53–65
    [Google Scholar]
  55. 55. 
    Bauer PV, Duca FA, Zaved Waise TM, Rasmussen BA, Abraham MA et al. 2018. Metformin alters upper small intestinal microbiota that impact a glucose-SGLT1-sensing glucoregulatory pathway. Cell Metab 27:1101–17.e5
    [Google Scholar]
  56. 56. 
    Sun L, Xie C, Wang G, Wu Y, Wu Q et al. 2018. Gut microbiota and intestinal FXR mediate the clinical benefits of metformin. Nat. Med. 24:121919–29
    [Google Scholar]
  57. 57. 
    Wahlström A, Sayin SI, Marschall HU, Bäckhed F 2016. Intestinal crosstalk between bile acids and microbiota and its impact on host metabolism. Cell Metab 24:141–50
    [Google Scholar]
  58. 58. 
    Islam KBMS, Fukiya S, Hagio M, Fujii N, Ishizuka S et al. 2011. Bile acid is a host factor that regulates the composition of the cecal microbiota in rats. Gastroenterology 141:51773–81
    [Google Scholar]
  59. 59. 
    Feng R, Shou JW, Zhao ZX, He CY, Ma C et al. 2015. Transforming berberine into its intestine-absorbable form by the gut microbiota. Sci. Rep. 5:12155
    [Google Scholar]
  60. 60. 
    Alolga RN, Fan Y, Chen Z, Liu LW, Zhao YJ et al. 2016. Significant pharmacokinetic differences of berberine are attributable to variations in gut microbiota between Africans and Chinese. Sci. Rep. 6:27671
    [Google Scholar]
  61. 61. 
    Wang Y, Shou JW, Li XY, Zhao ZX, Fu J et al. 2017. Berberine-induced bioactive metabolites of the gut microbiota improve energy metabolism. Metabolism 70:72–84
    [Google Scholar]
  62. 62. 
    Tian Y, Cai J, Gui W, Nichols RG, Koo I et al. 2018. Berberine directly impacts the gut microbiota to promote intestinal farnesoid x receptor activation. Drug Metab. Dispos. 47:286–93
    [Google Scholar]
  63. 63. 
    Yue S-J, Liu J, Wang A-T, Meng X-T, Yang Z-R et al. 2018. Berberine alleviates insulin resistance by reducing peripheral branched-chain amino acids. Am. J. Physiol. Metab. 316:1E73–85
    [Google Scholar]
  64. 64. 
    Baxter NT, Lesniak NA, Sinani H, Schloss PD, Koropatkin NM 2019. The glucoamylase inhibitor acarbose has a diet-dependent and reversible effect on the murine gut microbiome. mSphere 4:1e00528–18
    [Google Scholar]
  65. 65. 
    Gu Y, Wang X, Li J, Zhang Y, Zhong H et al. 2017. Analyses of gut microbiota and plasma bile acids enable stratification of patients for antidiabetic treatment. Nat. Commun. 8:11785
    [Google Scholar]
  66. 66. 
    Zhang X, Fang Z, Zhang C, Xia H, Jie Z et al. 2017. Effects of acarbose on the gut microbiota of prediabetic patients: a randomized, double-blind, controlled crossover trial. Diabetes Ther 8:2293–307
    [Google Scholar]
  67. 67. 
    Bai J, Zhu Y, Dong Y 2016. Response of gut microbiota and inflammatory status to bitter melon (Momordica charantia L.) in high fat diet induced obese rats. J. Ethnopharmacol. 194:717–26
    [Google Scholar]
  68. 68. 
    Tomas J, Mulet C, Saffarian A, Cavin J-B, Ducroc R et al. 2016. High-fat diet modifies the PPAR-γ pathway leading to disruption of microbial and physiological ecosystem in murine small intestine. PNAS 113:40E5934–43
    [Google Scholar]
  69. 69. 
    Al-Salami H, Butt G, Fawcett JP, Tucker IG, Golocorbin-Kon S, Mikov M 2008. Probiotic treatment reduces blood glucose levels and increases systemic absorption of gliclazide in diabetic rats. Eur. J. Drug Metab. Pharmacokinet. 33:2101–6
    [Google Scholar]
  70. 70. 
    Moreira GV, Azevedo FF, Ribeiro LM, Santos A, Guadagnini D et al. 2018. Liraglutide modulates gut microbiota and reduces NAFLD in obese mice. J. Nutr. Biochem. 62:143–54
    [Google Scholar]
  71. 71. 
    Zhang Q, Xiao X, Zheng J, Li M, Yu M et al. 2018. Structure moderation of gut microbiota in liraglutide-treated diabetic male rats. Exp. Biol. Med. 243:134–44
    [Google Scholar]
  72. 72. 
    Wang L, Li P, Tang Z, Yan X, Feng B 2016. Structural modulation of the gut microbiota and the relationship with body weight: compared evaluation of liraglutide and saxagliptin treatment. Sci. Rep. 6:33251
    [Google Scholar]
  73. 73. 
    Wang Z, Saha S, Van Horn S, Thomas E, Traini C et al. 2017. Gut microbiome differences between metformin- and liraglutide-treated T2DM subjects. Endocrinol. Diabetes Metab. 1:1e00009
    [Google Scholar]
  74. 74. 
    Olivares M, Neyrinck AM, Pötgens SA, Beaumont M, Salazar N et al. 2018. The DPP-4 inhibitor vildagliptin impacts the gut microbiota and prevents disruption of intestinal homeostasis induced by a Western diet in mice. Diabetologia 61:81838–48
    [Google Scholar]
  75. 75. 
    Yan X, Feng B, Li P, Tang Z, Wang L 2016. Microflora disturbance during progression of glucose intolerance and effect of sitagliptin: an animal study. J. Diabetes Res. 2016:2093171
    [Google Scholar]
  76. 76. 
    Parkin DM. 2006. The global health burden of infection-associated cancers in the year 2002. Int. J. Cancer 118:123030–44
    [Google Scholar]
  77. 77. 
    Nakatsu G, Li X, Zhou H, Sheng J, Wong SH et al. 2015. Gut mucosal microbiome across stages of colorectal carcinogenesis. Nat. Commun. 6:8727
    [Google Scholar]
  78. 78. 
    Jacquelot N, Enot DP, Flament C, Vimond N, Blattner C et al. 2016. Chemokine receptor patterns in lymphocytes mirror metastatic spreading in melanoma. J. Clin. Investig. 126:3921–37
    [Google Scholar]
  79. 79. 
    Hanahan D, Weinberg RA. 2011. Hallmarks of cancer: the next generation. Cell 144:5646–74
    [Google Scholar]
  80. 80. 
    Lin XB, Dieleman LA, Ketabi A, Bibova I, Sawyer MB et al. 2012. Irinotecan (CPT-11) chemotherapy alters intestinal microbiota in tumour bearing rats. PLOS ONE 7:7e39764
    [Google Scholar]
  81. 81. 
    Montassier E, Gastinne T, Vangay P, Al-Ghalith GA, Bruley des Varannes S et al. 2015. Chemotherapy-driven dysbiosis in the intestinal microbiome. Aliment. Pharmacol. Ther. 42:5515–28
    [Google Scholar]
  82. 82. 
    Panebianco C, Adamberg K, Jaagura M, Copetti M, Fontana A et al. 2018. Influence of gemcitabine chemotherapy on the microbiota of pancreatic cancer xenografted mice. Cancer Chemother. Pharmacol. 81:4773–82
    [Google Scholar]
  83. 83. 
    Geller LT, Barzily-Rokni M, Danino T, Jonas OH, Shental N et al. 2017. Potential role of intratumor bacteria in mediating tumor resistance to the chemotherapeutic drug gemcitabine. Science 357:63561156–60
    [Google Scholar]
  84. 84. 
    Iida N, Dzutsev A, Stewart CA, Smith L, Bouladoux N et al. 2013. Commensal bacteria control cancer response to therapy by modulating the tumor microenvironment. Science 342:6161967–70
    [Google Scholar]
  85. 85. 
    Viaud S, Saccheri F, Mignot G, Yamazaki T, Daillère R et al. 2013. The intestinal microbiota modulates the anticancer immune effects of cyclophosphamide. Science 342:6161971–76
    [Google Scholar]
  86. 86. 
    Lalani A-KA, Xie W, Lin X, Steinharter JA, Martini DJ et al. 2018. Antibiotic use and outcomes with systemic therapy in metastatic renal cell carcinoma (mRCC). J. Clin. Oncol. 36:Suppl. 6607
    [Google Scholar]
  87. 87. 
    Scott TA, Quintaneiro LM, Norvaisas P, Lui PP, Wilson MP et al. 2017. Host-microbe co-metabolism dictates cancer drug efficacy in C. elegans. . Cell 169:3442–56
    [Google Scholar]
  88. 88. 
    García-González AP, Ritter AD, Shrestha S, Andersen EC, Yilmaz LS, Walhout AJM 2017. Bacterial metabolism affects the C. elegans response to cancer chemotherapeutics. Cell 169:3431–41
    [Google Scholar]
  89. 89. 
    Yu T, Guo F, Yu Y, Sun T, Ma D et al. 2017. Fusobacterium nucleatum promotes chemoresistance to colorectal cancer by modulating autophagy. Cell 170:3548–63
    [Google Scholar]
  90. 90. 
    Aranda F, Bloy N, Pesquet J, Petit B, Chaba K et al. 2015. Immune-dependent antineoplastic effects of cisplatin plus pyridoxine in non-small-cell lung cancer. Oncogene 34:233053–62
    [Google Scholar]
  91. 91. 
    Dubin K, Callahan MK, Ren B, Khanin R, Viale A et al. 2016. Intestinal microbiome analyses identify melanoma patients at risk for checkpoint-blockade-induced colitis. Nat. Commun. 7:110391
    [Google Scholar]
  92. 92. 
    Sivan A, Corrales L, Hubert N, Williams JB, Aquino-Michaels K et al. 2015. Commensal Bifidobacterium promotes antitumor immunity and facilitates anti-PD-L1 efficacy. Science 350:62641084–89
    [Google Scholar]
  93. 93. 
    Hu Y, Le Leu RK, Christophersen CT, Somashekar R, Conlon MA et al. 2016. Manipulation of the gut microbiota using resistant starch is associated with protection against colitis-associated colorectal cancer in rats. Carcinogenesis 37:4366–75
    [Google Scholar]
  94. 94. 
    Kroemer G, Galluzzi L, Kepp O, Zitvogel L 2013. Immunogenic cell death in cancer therapy. Annu. Rev. Immunol. 31:51–72
    [Google Scholar]
  95. 95. 
    Ellerby HM, Arap W, Ellerby LM, Kain R, Andrusiak R et al. 1999. Anti-cancer activity of targeted pro-apoptotic peptides. Nat. Med. 5:91032–38
    [Google Scholar]
  96. 96. 
    Routy B, Le Chatelier E, Derosa L, Duong CPM, Alou MT et al. 2018. Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science 359:637191–97
    [Google Scholar]
  97. 97. 
    Matson V, Fessler J, Bao R, Chongsuwat T, Zha Y et al. 2018. The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients. Science 359:6371104–8
    [Google Scholar]
  98. 98. 
    Gopalakrishnan V, Spencer CN, Nezi L, Reuben A, Andrews MC et al. 2018. Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science 359:637197–103
    [Google Scholar]
  99. 99. 
    Wang F, Yin Q, Chen L, Davis MM 2017. Bifidobacterium can mitigate intestinal immunopathology in the context of CTLA-4 blockade. PNAS 115:1157–61
    [Google Scholar]
  100. 100. 
    Vétizou M, Pitt JM, Daillère R, Lepage P, Waldschmitt N et al. 2015. Anticancer immunotherapy by CTLA-4 blockade relies on the gut microbiota. Science 350:62641079–84
    [Google Scholar]
  101. 101. 
    Kato S, Hamouda N, Kano Y, Oikawa Y, Tanaka Y et al. 2017. Probiotic Bifidobacterium bifidum G9–1 attenuates 5-fluorouracil-induced intestinal mucositis in mice via suppression of dysbiosis-related secondary inflammatory responses. Clin. Exp. Pharmacol. Physiol. 44:101017–25
    [Google Scholar]
  102. 102. 
    Baldwin C, Millette M, Oth D, Ruiz MT, Luquet FM, Lacroix M 2010. Probiotic Lactobacillus acidophilus and L. casei mix sensitize colorectal tumoral cells to 5-fluorouracil-induced apoptosis. Nutr. Cancer 62:3371–78
    [Google Scholar]
  103. 103. 
    Schoener CA, Carillo-Conde B, Hutson HN, Peppas NA 2013. An inulin and doxorubicin conjugate for improving cancer therapy. J. Drug Deliv. Sci. Technol. 23:2111–18
    [Google Scholar]
  104. 104. 
    Cougnoux A, Delmas J, Gibold L, Faïs T, Romagnoli C et al. 2016. Small-molecule inhibitors prevent the genotoxic and protumoural effects induced by colibactin-producing bacteria. Gut 65:2278–85
    [Google Scholar]
  105. 105. 
    Wallace BD, Roberts AB, Pollet RM, Ingle JD, Biernat KA et al. 2015. Structure and inhibition of microbiome β-glucuronidases essential to the alleviation of cancer drug toxicity. Chem. Biol. 22:91238–49
    [Google Scholar]
  106. 106. 
    Din MO, Danino T, Prindle A, Skalak M, Selimkhanov J et al. 2016. Synchronized cycles of bacterial lysis for in vivo delivery. Nature 536:761481–85
    [Google Scholar]
  107. 107. 
    Trinder M, Daisley BA, Dube JS, Reid G 2017. Drosophila melanogaster as a high-throughput model for host-microbiota interactions. Front. Microbiol. 8:751
    [Google Scholar]
  108. 108. 
    Norvaisas P, Cabreiro F. 2018. Pharmacology in the age of the holobiont. Curr. Opin. Syst. Biol. 10:34–42
    [Google Scholar]
  109. 109. 
    Yan A, Culp E, Perry J, Lau JT, MacNeil LT et al. 2018. Transformation of the anticancer drug doxorubicin in the human gut microbiome. ACS Infect. Dis. 4:168–76
    [Google Scholar]
  110. 110. 
    Dahan D, Jude BA, Lamendella R, Keesing F, Perron GG 2018. Exposure to arsenic alters the microbiome of larval zebrafish. Front. Microbiol. 9:1323
    [Google Scholar]
  111. 111. 
    Goodrich JK, Di Rienzi SC, Poole AC, Koren O, Walters WA et al. 2014. Conducting a microbiome study. Cell 158:2250–62
    [Google Scholar]
  112. 112. 
    Poussin C, Sierro N, Boué S, Battey J, Scotti E et al. 2018. Interrogating the microbiome: experimental and computational considerations in support of study reproducibility. Drug Discov. Today 23:91644–57
    [Google Scholar]
  113. 113. 
    Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet C et al. 2018. QIIME 2: Reproducible, interactive, scalable, and extensible microbiome data science. PeerJ. Prepr. 6:e27295v2
    [Google Scholar]
  114. 114. 
    Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M et al. 2009. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75:237537–41
    [Google Scholar]
  115. 115. 
    Wood DE, Salzberg SL. 2014. Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol 15:3R46
    [Google Scholar]
  116. 116. 
    Kim D, Song L, Breitwieser FP, Salzberg SL 2016. Centrifuge: rapid and sensitive classification of metagenomic sequences. Genome Res 26:121721–29
    [Google Scholar]
  117. 117. 
    Franzosa EA, Abu-Ali G, McIver LJ, Schwager R, Huttenhower C et al. 2017. bioBakery: a meta'omic analysis environment. Bioinformatics 34:71235–37
    [Google Scholar]
  118. 118. 
    Ounit R, Lonardi S. 2016. Higher classification sensitivity of short metagenomic reads with CLARK-S. Bioinformatics 32:243823–25
    [Google Scholar]
  119. 119. 
    Afiahayati, Sato K, Sakakibara Y 2014. MetaVelvet-SL: an extension of the Velvet assembler to a de novo metagenomic assembler utilizing supervised learning. DNA Res 22:169–77
    [Google Scholar]
  120. 120. 
    Nurk S, Meleshko D, Korobeynikov A, Pevzner PA 2017. metaSPAdes: a new versatile metagenomic assembler. Genome Res 27:5824–34
    [Google Scholar]
  121. 121. 
    Seemann T. 2014. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30:142068–69
    [Google Scholar]
  122. 122. 
    Westreich ST, Treiber ML, Mills DA, Korf I, Lemay DG 2018. SAMSA2: a standalone metatranscriptome analysis pipeline. BMC Bioinform 19:1175
    [Google Scholar]
  123. 123. 
    Martinez X, Pozuelo M, Pascal V, Campos D, Gut I et al. 2016. MetaTrans: an open-source pipeline for metatranscriptomics. Sci. Rep. 6:26447
    [Google Scholar]
  124. 124. 
    Eren AM, Esen ÖC, Quince C, Vineis JH, Morrison HG et al. 2015. Anvi'o: an advanced analysis and visualization platform for ‘omics data. PeerJ 3:e1319
    [Google Scholar]
  125. 125. 
    Cheng K, Ning Z, Zhang X, Li L, Liao B et al. 2017. MetaLab: an automated pipeline for metaproteomic data analysis. Microbiome 5:1157
    [Google Scholar]
  126. 126. 
    Tyanova S, Temu T, Carlson A, Sinitcyn P, Mann M, Cox J 2015. Visualization of LC-MS/MS proteomics data in MaxQuant. Proteomics 15:81453–56
    [Google Scholar]
  127. 127. 
    Ruttkies C, Schober D, Peters K, Neumann S, Gonzalez-Beltran A et al. 2018. PhenoMeNal: processing and analysis of metabolomics data in the cloud. Gigascience 8:2giy149
    [Google Scholar]
  128. 128. 
    Caron C, Duperier C, Jacob D, Thévenot EA, Giacomoni F et al. 2014. Workflow4Metabolomics: a collaborative research infrastructure for computational metabolomics. Bioinformatics 31:91493–95
    [Google Scholar]
  129. 129. 
    Wang Q, Wang K, Wu W, Giannoulatou E, Ho JWK, Li L 2019. Host and microbiome multi-omics integration: applications and methodologies. Biophys. Rev. 11:155–65
    [Google Scholar]
  130. 130. 
    Guthrie L, Gupta S, Daily J, Kelly L 2017. Human microbiome signatures of differential colorectal cancer drug metabolism. NPJ Biofilms Microbiomes 3:127
    [Google Scholar]
  131. 131. 
    Aziz RK, Saad R, Rizkallah MR 2011. PharmacoMicrobiomics or how bugs modulate drugs: an educational initiative to explore the effects of human microbiome on drugs. BMC Bioinform 12:Suppl. 7A10
    [Google Scholar]
  132. 132. 
    Sun Y-Z, Zhang D-H, Cai S-B, Ming Z, Li J-Q, Chen X 2018. MDAD: a special resource for microbe-drug associations. Front. Cell Infect. Microbiol. 8:424
    [Google Scholar]
  133. 133. 
    Sharma AK, Jaiswal SK, Chaudhary N, Sharma VK 2017. A novel approach for the prediction of species-specific biotransformation of xenobiotic/drug molecules by the human gut microbiota. Sci. Rep. 7:9751
    [Google Scholar]
  134. 134. 
    Haiser HJ, Seim KL, Balskus EP, Turnbaugh PJ 2014. Mechanistic insight into digoxin inactivation by Eggerthella lenta augments our understanding of its pharmacokinetics. Gut Microbes 5:2233–38
    [Google Scholar]
  135. 135. 
    Heirendt L, Arreckx S, Pfau T, Mendoza SN, Richelle A et al. 2019. Creation and analysis of biochemical constraint-based models using the COBRA Toolbox v.3.0. Nat. Protoc. 14:3639–702
    [Google Scholar]
  136. 136. 
    Magnúsdóttir S, Heinken A, Kutt L, Ravcheev DA, Bauer E et al. 2017. Generation of genome-scale metabolic reconstructions for 773 members of the human gut microbiota. Nat. Biotechnol. 35:181–89
    [Google Scholar]
  137. 137. 
    Noronha A, Modamio J, Jarosz Y, Guerard E, Sompairac N et al. 2018. The Virtual Metabolic Human database: integrating human and gut microbiome metabolism with nutrition and disease. Nucleic Acids Res 47:D1D614–24
    [Google Scholar]
  138. 138. 
    Baldini F, Heinken A, Heirendt L, Magnusdottir S, Fleming RMT, Thiele I 2019. The Microbiome Modeling Toolbox: from microbial interactions to personalized microbial communities. Bioinformatics 35:13233234
    [Google Scholar]
  139. 139. 
    Harcombe WR, Riehl WJ, Dukovski I, Granger BR, Betts A et al. 2014. Metabolic resource allocation in individual microbes determines ecosystem interactions and spatial dynamics. Cell Rep 7:41104–15
    [Google Scholar]
  140. 140. 
    Machado D, Andrejev S, Tramontano M, Patil KR 2018. Fast automated reconstruction of genome-scale metabolic models for microbial species and communities. Nucleic Acids Res 46:157542–53
    [Google Scholar]
  141. 141. 
    Upton RN, Foster DJR, Abuhelwa AY 2016. An introduction to physiologically-based pharmacokinetic models. Pediatr. Anesth. 26:111036–46
    [Google Scholar]
  142. 142. 
    Guebila MB, Thiele I. 2016. Model-based dietary optimization for late-stage, levodopa-treated, Parkinson's disease patients. NPJ Syst. Biol. Appl. 2:16013
    [Google Scholar]
  143. 143. 
    Cordes H, Thiel C, Baier V, Blank LM, Kuepfer L 2018. Integration of genome-scale metabolic networks into whole-body PBPK models shows phenotype-specific cases of drug-induced metabolic perturbation. NPJ Syst. Biol. Appl. 4:110
    [Google Scholar]
  144. 144. 
    Thiele I, Clancy CM, Heinken A, Fleming RMT 2017. Quantitative systems pharmacology and the personalized drug-microbiota-diet axis. Curr. Opin. Syst. Biol. 4:43–52
    [Google Scholar]
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