1932

Abstract

In the past decade of microbiome research, we have learned about numerous adverse interactions between the microbiome and medical interventions such as drugs, radiation, and surgery. What if we could alter our microbiomes to prevent these events? In this review, we discuss potential routes to mitigate microbiome adverse events, including applications from the emerging field of microbiome engineering. We highlight cases where the microbiome acts directly on a treatment, such as via differential drug metabolism, and cases where a treatment directly harms the microbiome, such as in radiation therapy. Understanding and preventing microbiome adverse events is a difficult challenge that will require a data-driven approach involving causal statistics, multiomics techniques, and a personalized means of mitigating adverse events. We propose research considerations to encourage productive work in preventing microbiome adverse events, and we highlight the many challenges and opportunities that await.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-pharmtox-031620-031509
2021-01-06
2024-03-28
Loading full text...

Full text loading...

/deliver/fulltext/pharmtox/61/1/annurev-pharmtox-031620-031509.html?itemId=/content/journals/10.1146/annurev-pharmtox-031620-031509&mimeType=html&fmt=ahah

Literature Cited

  1. 1. 
    Natl. Inst. Health Hum. Microbiome Portf. Anal. Team. 2019. A review of 10 years of human microbiome research activities at the US National Institutes of Health, fiscal years 2007–2016. Microbiome 7:31
    [Google Scholar]
  2. 2. 
    Hitchings R, Kelly L. 2019. Predicting and understanding the human microbiome's impact on pharmacology. Trends Pharmacol. Sci. 40:495–505
    [Google Scholar]
  3. 3. 
    Spanogiannopoulos P, Bess EN, Carmody RN, Turnbaugh PJ 2016. The microbial pharmacists within us: a metagenomic view of xenobiotic metabolism. Nat. Rev. Microbiol. 14:273–87
    [Google Scholar]
  4. 4. 
    Guthrie L, Wolfson S, Kelly L 2019. The human gut chemical landscape predicts microbe-mediated biotransformation of foods and drugs. eLife 8:e42866
    [Google Scholar]
  5. 5. 
    Lebeer S, Spacova I. 2019. Exploring human host–microbiome interactions in health and disease—how to not get lost in translation. Genome Biol 20:56
    [Google Scholar]
  6. 6. 
    Sousa T, Paterson R, Moore V, Carlsson A, Abrahamsson B, Basit AW 2008. The gastrointestinal microbiota as a site for the biotransformation of drugs. Int. J. Pharm. 363:1–25
    [Google Scholar]
  7. 7. 
    Nichols RG, Peters JM, Patterson AD 2019. Interplay between the host, the human microbiome, and drug metabolism. Hum. Genom. 13:27
    [Google Scholar]
  8. 8. 
    Guthrie L, Gupta S, Daily J, Kelly L 2017. Human microbiome signatures of differential colorectal cancer drug metabolism. npj Biofilms Microbiomes 3:27
    [Google Scholar]
  9. 9. 
    Wallace BD, Wang H, Lane KT, Scott JE, Orans J et al. 2010. Alleviating cancer drug toxicity by inhibiting a bacterial enzyme. Science 330:831–35
    [Google Scholar]
  10. 10. 
    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. 22:1238–49
    [Google Scholar]
  11. 11. 
    Iizumi T, Battaglia T, Ruiz V, Perez Perez GI 2017. Gut microbiome and antibiotics. Arch. Med. Res. 48:727–34
    [Google Scholar]
  12. 12. 
    David LA, Maurice CF, Carmody RN, Gootenberg DB, Button JE et al. 2014. Diet rapidly and reproducibly alters the human gut microbiome. Nature 505:559–63
    [Google Scholar]
  13. 13. 
    Lloyd-Price J, Abu-Ali G, Huttenhower C 2016. The healthy human microbiome. Genome Med 8:51
    [Google Scholar]
  14. 14. 
    Rothschild D, Weissbrod O, Barkan E, Kurilshikov A, Korem T et al. 2018. Environment dominates over host genetics in shaping human gut microbiota. Nature 555:210–15
    [Google Scholar]
  15. 15. 
    Bachmann R, Leonard D, Delzenne N, Kartheuser A, Cani PD 2017. Novel insight into the role of microbiota in colorectal surgery. Gut 66:738–49
    [Google Scholar]
  16. 16. 
    Sokol H, Adolph TE. 2018. The microbiota: an underestimated actor in radiation-induced lesions. ? Gut 67:1–2
    [Google Scholar]
  17. 17. 
    Sharma A, Buschmann MM, Gilbert JA 2019. Pharmacomicrobiomics: the holy grail to variability in drug response. ? Clin. Pharmacol. Ther. 106:317–28
    [Google Scholar]
  18. 18. 
    Peiffer-Smadja N, Dellière S, Rodriguez C, Birgand G, Lescure FX et al. 2020. Machine learning in the clinical microbiology laboratory: Has the time come for routine practice. ? Clin. Microbiol. Infect. 26:10P1300–9
    [Google Scholar]
  19. 19. 
    Lee HL, Shen H, Hwang IY, Ling H, Yew WS et al. 2018. Targeted approaches for in situ gut microbiome manipulation. Genes 9:351
    [Google Scholar]
  20. 20. 
    Inda ME, Broset E, Lu TK, de la Fuente-Nunez C 2019. Emerging frontiers in microbiome engineering. Trends Immunol 40:952–73
    [Google Scholar]
  21. 21. 
    Angulo MT, Moog CH, Liu YY 2019. A theoretical framework for controlling complex microbial communities. Nat. Commun. 10:1045
    [Google Scholar]
  22. 22. 
    Lawson CE, Harcombe WR, Hatzenpichler R, Lindemann SR, Löffler FE et al. 2019. Common principles and best practices for engineering microbiomes. Nat. Rev. Microbiol. 17:725–41
    [Google Scholar]
  23. 23. 
    Ronda C, Chen SP, Cabral V, Yaung SJ, Wang HH 2019. Metagenomic engineering of the mammalian gut microbiome in situ. Nat. Methods 16:167–70
    [Google Scholar]
  24. 24. 
    Lam KN, Alexander M, Turnbaugh PJ 2019. Precision medicine goes microscopic: engineering the microbiome to improve drug outcomes. Cell Host Microbe 26:22–34
    [Google Scholar]
  25. 25. 
    Maini RV, Bess E, Bisanz J, Turnbaugh P, Balskus E 2019. Discovery and inhibition of an interspecies gut bacterial pathway for Levodopa metabolism. Science 364:eaau6323
    [Google Scholar]
  26. 26. 
    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 341:295–98
    [Google Scholar]
  27. 27. 
    Koppel N, Bisanz JE, Pandelia ME, Turnbaugh PJ, Balskus EP 2018. Discovery and characterization of a prevalent human gut bacterial enzyme sufficient for the inactivation of a family of plant toxins. eLife 7:e33953
    [Google Scholar]
  28. 28. 
    Ferreira MR, Andreyev HJN, Mohammed K, Truelove L, Gowan SM et al. 2019. Microbiota- and Radiotherapy-induced Gastrointestinal Side-effects (MARS) study: a large pilot study of the microbiome in acute and late-radiation enteropathy. Clin. Cancer Res. 25:6487–500
    [Google Scholar]
  29. 29. 
    Millan B, Laffin M, Madsen K 2017. Fecal microbiota transplantation: beyond Clostridium difficile. Curr. Infect. Dis. Rep 19:19–22
    [Google Scholar]
  30. 30. 
    Youngster I, Russell GH, Pindar C, Ziv-Baran T, Sauk J, Hohmann EL 2014. Oral, capsulized, frozen fecal microbiota transplantation for relapsing Clostridium difficile infection. JAMA 312:1772–78
    [Google Scholar]
  31. 31. 
    Hirsch BE, Saraiya N, Poeth K, Schwartz RM, Epstein ME, Honig G 2015. Effectiveness of fecal-derived microbiota transfer using orally administered capsules for recurrent Clostridium difficile infection. BMC Infect. Dis. 15:191
    [Google Scholar]
  32. 32. 
    Kao D, Roach B, Silva M, Beck P, Rioux K et al. 2017. Effect of oral capsule– vs colonoscopy-delivered fecal microbiota transplantation on recurrent Clostridium difficile infection: a randomized clinical trial. JAMA 318:1985–93
    [Google Scholar]
  33. 33. 
    Allegretti JR, Korzenik JR, Hamilton MJ 2014. Fecal microbiota transplantation via colonoscopy for recurrent C. difficile infection. J. Vis. Exp. 17:434–37
    [Google Scholar]
  34. 34. 
    Silverman MS, Davis I, Pillai DR 2010. Success of self-administered home fecal transplantation for chronic Clostridium difficile infection. Clin. Gastroenterol. Hepatol. 8:471–73
    [Google Scholar]
  35. 35. 
    Tariq R, Pardi DS, Bartlett MG, Khanna S 2019. Low cure rates in controlled trials of fecal microbiota transplantation for recurrent Clostridium difficile infection: a systematic review and meta-analysis. Clin. Infect. Dis. 68:1351–58
    [Google Scholar]
  36. 36. 
    Wilson BC, Vatanen T, Cutfield WS, O'Sullivan JM 2019. The super-donor phenomenon in fecal microbiota transplantation. Front. Cell. Infect. Microbiol. 9:2
    [Google Scholar]
  37. 37. 
    de Groot P, Scheithauer T, Bakker GJ, Prodan A, Levin E et al. 2019. Donor metabolic characteristics drive effects of faecal microbiota transplantation on recipient insulin sensitivity, energy expenditure and intestinal transit time. Gut 69:502–12
    [Google Scholar]
  38. 38. 
    FDA (US Food Drug Adm.). 2020. Safety alert regarding use of fecal microbiota for transplantation and risk of serious adverse events likely due to transmission of pathogenic organisms Rep., Mar. 12, 2020. https://www.fda.gov/vaccines-blood-biologics/safety-availability-biologics/safety-alert-regarding-use-fecal-microbiota-transplantation-and-risk-serious-adverse-events-likely
  39. 39. 
    Duvallet C, Zellmer C, Panchal P, Budree S, Osman M, Alm EJ 2019. Framework for rational donor selection in fecal microbiota transplant clinical trials. PLOS ONE 14:e0222881
    [Google Scholar]
  40. 40. 
    Hill C, Guarner F, Reid G, Gibson GR, Merenstein DJ et al. 2014. The International Scientific Association for Probiotics and Prebiotics consensus statement on the scope and appropriate use of the term probiotic. Nat. Rev. Gastroenterol. Hepatol 11:506–14
    [Google Scholar]
  41. 41. 
    Simpson HL, Campbell BJ. 2015. Review article: dietary fibre–microbiota interactions. Aliment. Pharmacol. Ther. 42:158–79
    [Google Scholar]
  42. 42. 
    Gibson GR, Hutkins R, Sanders ME, Prescott SL, Reimer RA et al. 2017. Expert consensus document: the International Scientific Association for Probiotics and Prebiotics (ISAPP) consensus statement on the definition and scope of prebiotics. Nat. Rev. Gastroenterol. Hepatol. 14:491–502
    [Google Scholar]
  43. 43. 
    Schrezenmeir J, de Vrese M 2001. Probiotics, prebiotics, and synbiotics—approaching a definition. Am. J. Clin. Nutr. 73:S361–64
    [Google Scholar]
  44. 44. 
    Suez J, Zmora N, Segal E, Elinav E 2019. The pros, cons, and many unknowns of probiotics. Nat. Med. 25:716–29
    [Google Scholar]
  45. 45. 
    Didari T, Solki S, Mozaffari S, Nikfar S, Abdollahi M 2014. A systematic review of the safety of probiotics. Expert Opin. Drug Saf. 13:227–39
    [Google Scholar]
  46. 46. 
    Quin C, Estaki M, Vollman DM, Barnett JA, Gill SK, Gibson DL 2018. Probiotic supplementation and associated infant gut microbiome and health: a cautionary retrospective clinical comparison. Sci 8:8283
    [Google Scholar]
  47. 47. 
    Steidler L, Hans W, Schotte L, Neirynck S, Obermeier F et al. 2000. Treatment of murine colitis by Lactococcus lactis secreting interleukin-10. Science 289:1352–55
    [Google Scholar]
  48. 48. 
    Hwang IY, Koh E, Wong A, March JC, Bentley WE et al. 2017. Engineered probiotic Escherichia coli can eliminate and prevent Pseudomonas aeruginosa gut infection in animal models. Nat. Commun. 8:15028
    [Google Scholar]
  49. 49. 
    Mimee M, Nadeau P, Hayward A, Carim S, Flanagan S et al. 2018. An ingestible bacterial-electronic system to monitor gastrointestinal health. Science 360:915–18
    [Google Scholar]
  50. 50. 
    Abedon ST, Kuhl SJ, Blasdel BG, Kutter EM 2011. Phage treatment of human infections. Bacteriophage 1:66–85
    [Google Scholar]
  51. 51. 
    Paule A, Frezza D, Edeas M 2018. Microbiota and phage therapy: future challenges in medicine. Med. Sci. 6:86
    [Google Scholar]
  52. 52. 
    Henein A. 2013. What are the limitations on the wider therapeutic use of phage. ? Bacteriophage 3:e24872
    [Google Scholar]
  53. 53. 
    Pizarro-Bauerle J, Ando H. 2020. Engineered bacteriophages for practical applications. Biol. Pharm. Bull. 43:240–49
    [Google Scholar]
  54. 54. 
    Dunne M, Rupf B, Tala M, Qabrati X, Ernst P et al. 2019. Reprogramming bacteriophage host range through structure-guided design of chimeric receptor binding proteins. Cell 29:1336–50.e4
    [Google Scholar]
  55. 55. 
    Schooley RT, Biswas B, Gill JJ, Hernandez-Morales A, Lancaster J et al. 2017. Development and use of personalized bacteriophage-based therapeutic cocktails to treat a patient with a disseminated resistant Acinetobacter baumannii infection. Antimicrob. Agents Chemother. 61:e00954
    [Google Scholar]
  56. 56. 
    Dedrick RM, Guerrero-Bustamante CA, Garlena RA, Russell DA, Ford K et al. 2019. Engineered bacteriophages for treatment of a patient with a disseminated drug-resistant Mycobacterium abscessus. Nat. . Med 25:730–33
    [Google Scholar]
  57. 57. 
    Schmitt FCF, Brenner T, Uhle F, Loesch S, Hackert T et al. 2019. Gut microbiome patterns correlate with higher postoperative complication rates after pancreatic surgery. BMC Microbiol 19:42
    [Google Scholar]
  58. 58. 
    Katzung BG. 2018. Basic and Clinical Pharmacology New York: McGraw-Hill
  59. 59. 
    Pollet RM, D'Agostino EH, Walton WG, Xu Y, Little MS et al. 2017. An atlas of β-glucuronidases in the human intestinal microbiome. Structure 25:967–77.e5
    [Google Scholar]
  60. 60. 
    Boucher JG, Boudreau A, Ahmed S, Atlas E 2015. In vitro effects of bisphenol A β-d-glucuronide (BPA-G) on adipogenesis in human and murine preadipocytes. Environ. Health Perspect. 123:1287–93
    [Google Scholar]
  61. 61. 
    Dashnyam P, Mudududdla R, Hsieh TJ, Lin TC, Lin HY et al. 2018. β-Glucuronidases of opportunistic bacteria are the major contributors to xenobiotic-induced toxicity in the gut. Sci 8:16372
    [Google Scholar]
  62. 62. 
    Sakamoto H, Yokota H, Kibe R, Sayama Y, Yuasa A 2002. Excretion of bisphenol A–glucuronide into the small intestine and deconjugation in the cecum of the rat. Biochim. Biophys. Acta Gen. Subj. 1573:171–76
    [Google Scholar]
  63. 63. 
    Kwa M, Plottel CS, Blaser MJ, Adams S 2016. The intestinal microbiome and estrogen receptor–positive female breast cancer. J. Natl. Cancer Inst. 108:djw029
    [Google Scholar]
  64. 64. 
    Awolade P, Cele N, Kerru N, Gummidi L, Oluwakemi E, Singh P 2020. Therapeutic significance of β-glucuronidase activity and its inhibitors: a review. Eur. J. Med. Chem. 187:111921
    [Google Scholar]
  65. 65. 
    Cheng KW, Tseng CH, Tzeng CC, Leu YL, Cheng TC et al. 2019. Pharmacological inhibition of bacterial β-glucuronidase prevents irinotecan-induced diarrhea without impairing its antitumor efficacy in vivo. Pharmacol. Res. 139:41–49
    [Google Scholar]
  66. 66. 
    LoGuidice A, Wallace BD, Bendel L, Redinbo MR, Boelsterli UA 2012. Pharmacologic targeting of bacterial β-glucuronidase alleviates nonsteroidal anti-inflammatory drug-induced enteropathy in mice. J. Pharmacol. Exp. Ther. 341:447–54
    [Google Scholar]
  67. 67. 
    Emami AH, Sadighi S, Shirkoohi R, Mohagheghi MA 2017. Prediction of response to irinotecan and drug toxicity based on pharmacogenomics test: a prospective case study in advanced colorectal cancer. Asian Pac. J. Cancer Prev. 18:2803–7
    [Google Scholar]
  68. 68. 
    Whitfield A, Moore B, Daniels R 2014. Classics in chemical neuroscience: levodopa. ACS Chem. Neurosci. 5:1192–97
    [Google Scholar]
  69. 69. 
    van Kessel SP, El Aidy S 2019. Contributions of gut bacteria and diet to drug pharmacokinetics in the treatment of Parkinson's disease. Front. Neurol. 10:1087
    [Google Scholar]
  70. 70. 
    Clayton TA, Baker D, Lindon JC, Everett JR, Nicholson JK 2009. Pharmacometabonomic identification of a significant host–microbiome metabolic interaction affecting human drug metabolism. PNAS 106:14728–33
    [Google Scholar]
  71. 71. 
    Lin CJ, Wu V, Wu PC, Wu CJ 2015. Meta-analysis of the associations of p-cresyl sulfate (PCS) and indoxyl sulfate (IS) with cardiovascular events and all-cause mortality in patients with chronic renal failure. PLOS ONE 10:e0132589
    [Google Scholar]
  72. 72. 
    Tsuyoshi S, Hiroki S, Koji M, Naomi T, Taichi F 2019. Amide derivative US Patent Appl. 2019/0365688
  73. 73. 
    Tsuyoshi S, Yuji N, Koji M, Naomi T, Taichi F 2020. Urea derivative US Patent Appl. 2020/0017439
  74. 74. 
    Chen YY, Chen DQ, Chen L, Liu JR, Vaziri ND et al. 2019. Microbiome–metabolome reveals the contribution of gut–kidney axis on kidney disease. J. Transl. Med. 17:5
    [Google Scholar]
  75. 75. 
    Meijers BKI, de Preter V, Verbeke K, Vanrenterghem Y, Evenepoel P 2010. p-Cresyl sulfate serum concentrations in haemodialysis patients are reduced by the prebiotic oligofructose-enriched inulin. Nephrol. Dial. Transplant. 25:219–24
    [Google Scholar]
  76. 76. 
    Rossi M, Johnson DW, Morrison M, Pascoe EM, Coombes JS et al. 2016. Synbiotics easing renal failure by improving gut microbiology (SYNERGY): a randomized trial. Clin. J. Am. Soc. Nephrol. 11:223–31
    [Google Scholar]
  77. 77. 
    Maier L, Pruteanu M, Kuhn M, Zeller G, Telzerow A et al. 2018. Extensive impact of non-antibiotic drugs on human gut bacteria. Nature 555:623–28
    [Google Scholar]
  78. 78. 
    Aslam B, Wang W, Arshad MI, Khurshid M, Muzammil S et al. 2018. Antibiotic resistance: a rundown of a global crisis. Infect. Drug Resist. 11:1645–58
    [Google Scholar]
  79. 79. 
    Zaman SB, Hussain MA, Nye R, Mehta V, Mamun KT, Hossain N 2017. A review on antibiotic resistance: Alarm bells are ringing. Cureus 9:e1403
    [Google Scholar]
  80. 80. 
    Xavier JB, Young VB, Skufca J, Ginty F, Testerman T et al. 2020. The cancer microbiome: distinguishing direct and indirect effects requires a systemic view. Trends Cancer 6:3P192–204
    [Google Scholar]
  81. 81. 
    Zhang S, Wang Q, Zhou C, Chen K, Chang H et al. 2019. Colorectal cancer, radiotherapy and gut microbiota. Chin. J. Cancer Res. 31:212–22
    [Google Scholar]
  82. 82. 
    Blanarova C, Galovicova A, Petrasova D 2009. Use of probiotics for prevention of radiation-induced diarrhea. Bratisl. Med. J. 110:98–104
    [Google Scholar]
  83. 83. 
    Gerassy-Vainberg S, Blatt A, Danin-Poleg Y, Gershovich K, Sabo E et al. 2018. Radiation induces proinflammatory dysbiosis: transmission of inflammatory susceptibility by host cytokine induction. Gut 67:97–107
    [Google Scholar]
  84. 84. 
    Liu MM, Li ST, Shu Y, Zhan HQ 2017. Probiotics for prevention of radiation-induced diarrhea: a meta-analysis of randomized controlled trials. PLOS ONE 12:e0178870
    [Google Scholar]
  85. 85. 
    Giralt J, Regadera JP, Verges R, Romero J, de la Fuente I et al. 2008. Effects of probiotic Lactobacillus casei DN-114 001 in prevention of radiation-induced diarrhea: results from multicenter, randomized, placebo-controlled nutritional trial. Int. J. Radiat. Oncol. Biol. Phys. 71:1213–19
    [Google Scholar]
  86. 86. 
    Demers M, Dagnault A, Desjardins J 2014. A randomized double-blind controlled trial: impact of probiotics on diarrhea in patients treated with pelvic radiation. Clin. Nutr. 33:761–67
    [Google Scholar]
  87. 87. 
    Cui M, Xiao H, Li Y, Zhou L, Zhao S et al. 2017. Faecal microbiota transplantation protects against radiation-induced toxicity. EMBO Mol. Med. 9:448–61
    [Google Scholar]
  88. 88. 
    Ding X, Li Q, Li P, Chen X, Xiang L et al. 2020. Fecal microbiota transplantation: a promising treatment for radiation enteritis. ? Radiother. Oncol. 143:12–18
    [Google Scholar]
  89. 89. 
    Sivan A, Corrales L, Hubert N, Williams J, Aquino-Michaels K et al. 2015. Commensal Bifidobacterium promotes antitumor immunity and facilitates anti-PD-L1 efficacy. Science 350:1084–89
    [Google Scholar]
  90. 90. 
    Gopalakrishnan V, Spencer C, Nezi L, Reuben A, Andrews M et al. 2018. Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science 359:97–103
    [Google Scholar]
  91. 91. 
    Routy B, Le Chatelier E, Derosa L, Duong CP, Alou MT et al. 2018. Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science 359:91–97
    [Google Scholar]
  92. 92. 
    Ahmed J, Kumar A, Parikh K, Anwar A, Knoll BM et al. 2018. Use of broad-spectrum antibiotics impacts outcome in patients treated with immune checkpoint inhibitors. Oncoimmunology 7:e1507670
    [Google Scholar]
  93. 93. 
    Pinato DJ, Howlett S, Ottaviani D, Urus H, Patel A et al. 2019. Association of prior antibiotic treatment with survival and response to immune checkpoint inhibitor therapy in patients with cancer. JAMA Oncol 5:1774–78
    [Google Scholar]
  94. 94. 
    Derosa L, Hellmann MD, Spaziano M, Halpenny D, Fidelle M et al. 2018. Negative association of antibiotics on clinical activity of immune checkpoint inhibitors in patients with advanced renal cell and non-small-cell lung cancer. Ann. Oncol. 29:1437–44
    [Google Scholar]
  95. 95. 
    Tinsley N, Zhou C, Tan G, Rack S, Lorigan P et al. 2020. Cumulative antibiotic use significantly decreases efficacy of checkpoint inhibitors in patients with advanced cancer. Oncologist 25:55–63
    [Google Scholar]
  96. 96. 
    Hakozaki T, Okuma Y, Omori M, Hosomi Y 2019. Impact of prior antibiotic use on the efficacy of nivolumab for non-small-cell lung cancer. Oncol. Lett. 17:2946–52
    [Google Scholar]
  97. 97. 
    Lacouture M, Keefe D, Sonis S, Jatoi A, Gernhardt D et al. 2016. A phase II study (ARCHER 1042) to evaluate prophylactic treatment of dacomitinib-induced dermatologic and gastrointestinal adverse events in advanced non-small-cell lung cancer. Ann. Oncol. 27:1712–18
    [Google Scholar]
  98. 98. 
    Yan C, Tu XX, Wu W, Tong Z, Liu LL et al. 2019. Antibiotics and immunotherapy in gastrointestinal tumors: friend or foe. ? World J. Clin. Cases 7:1253–61
    [Google Scholar]
  99. 99. 
    Pinato DJ, Gramenitskaya D, Altmann DM, Boyton RJ, Mullish BH et al. 2019. Antibiotic therapy and outcome from immune-checkpoint inhibitors. J. Immunother. Cancer 7:287
    [Google Scholar]
  100. 100. 
    Parker Inst. Cancer Immunother. 2019. Probiotics linked to poorer response to cancer immunotherapy in skin cancer patients Rep., Parker Inst. Cancer Immunother San Francisco, CA: https://www.parkerici.org/the-latest/probiotics-linked-to-poorer-response-to-cancer-immunotherapy-in-skin-cancer-patients/
  101. 101. 
    Alverdy JC, Shogan BD. 2019. Preparing the bowel for surgery: rethinking the strategy. Nat. Rev. Gastroenterol. Hepatol. 16:708–9
    [Google Scholar]
  102. 102. 
    Koskenvuo L, Lehtonen T, Koskensalo S, Rasilainen S, Klintrup K et al. 2019. Mechanical and oral antibiotic bowel preparation versus no bowel preparation for elective colectomy (MOBILE): a multicentre, randomised, parallel, single-blinded trial. Lancet 394:840–48
    [Google Scholar]
  103. 103. 
    Scales BS, Huffnagle GB. 2013. The microbiome in wound repair and tissue fibrosis. J. Pathol. 229:323–31
    [Google Scholar]
  104. 104. 
    Chowdhury AH, Adiamah A, Kushairi A, Varadhan KK, Krznaric Z et al. 2019. Perioperative probiotics or synbiotics in adults undergoing elective abdominal surgery. Ann. Surg. 31:112
    [Google Scholar]
  105. 105. 
    Haak BW, Wiersinga WJ. 2017. The role of the gut microbiota in sepsis. Lancet Gastroenterol. Hepatol. 2:135–43
    [Google Scholar]
  106. 106. 
    Pearce N, Lawlor DA. 2016. Causal inference—so much more than statistics. Int. J. Epidemiol. 45:1895–903
    [Google Scholar]
  107. 107. 
    Glass TA, Goodman SN, Hernán MA, Samet JM 2013. Causal inference in public health. Annu. Rev. Public Health 34:61–75
    [Google Scholar]
  108. 108. 
    Pingault JB, O'Reilly PF, Schoeler T, Ploubidis GB, Rijsdijk F, Dudbridge F 2018. Using genetic data to strengthen causal inference in observational research. Nat. Rev. Genet. 19:566–80
    [Google Scholar]
  109. 109. 
    Maathuis MH, Nandy P. 2016. A review of some recent advances in causal inference. Handbook of Big Data P Bühlmann, P Drineas, M Kane, M van der Laan 387–408 New York: Taylor & Francis
    [Google Scholar]
  110. 110. 
    Glymour C, Zhang K, Spirtes P 2019. Review of causal discovery methods based on graphical models. Front. Genet. 10:00524
    [Google Scholar]
  111. 111. 
    Sazal MR, Stebliankin V, Mathee K, Narasimhan G 2020. Causal inference in microbiomes using intervention calculus. bioRxiv 970624. https://doi.org/10.1101/2020.02.28.970624
    [Crossref]
  112. 112. 
    Mainali K, Bewick S, Vecchio-Pagan B, Karig D, Fagan WF 2019. Detecting interaction networks in the human microbiome with conditional Granger causality. PLOS Comput. Biol. 15:e1007037
    [Google Scholar]
  113. 113. 
    Dohlman AB, Shen X. 2019. Mapping the microbial interactome: statistical and experimental approaches for microbiome network inference. Exp. Biol. Med. 244:445–58
    [Google Scholar]
  114. 114. 
    Surana NK, Kasper DL. 2017. Moving beyond microbiome-wide associations to causal microbe identification. Nature 552:244–47
    [Google Scholar]
  115. 115. 
    Li SJ, Jiang H, Yang H, Chen W, Peng J et al. 2015. The dilemma of heterogeneity tests in meta-analysis: a challenge from a simulation study. PLOS ONE 10:e0127538
    [Google Scholar]
  116. 116. 
    Imrey PB. 2020. Limitations of meta-analyses of studies with high heterogeneity. JAMA Netw. Open 3:e1919325
    [Google Scholar]
  117. 117. 
    Costea PI, Hildebrand F, Manimozhiyan A, Bäckhed F, Blaser MJ et al. 2017. Enterotypes in the landscape of gut microbial community composition. Nat. Microbiol. 3:8–16
    [Google Scholar]
  118. 118. 
    Tataru CA, David MM. 2019. Decoding the language of microbiomes: leveraging patterns in 16S public data using word-embedding techniques and applications in inflammatory bowel disease. bioRxiv 748152. https://doi.org/10.1101/748152
    [Crossref]
  119. 119. 
    Janda JM, Abbott SL. 2007. 16S rRNA gene sequencing for bacterial identification in the diagnostic laboratory: pluses, perils, and pitfalls. J. Clin. Microbiol. 45:2761–64
    [Google Scholar]
  120. 120. 
    Quince C, Walker AW, Simpson JT, Loman NJ, Segata N 2017. Shotgun metagenomics, from sampling to analysis. Nat. Biotechnol. 35:833–44
    [Google Scholar]
  121. 121. 
    Laudadio I, Fulci V, Palone F, Stronati L, Cucchiara S, Carissimi C 2018. Quantitative assessment of shotgun metagenomics and 16S rDNA amplicon sequencing in the study of human gut microbiome. Omics J. Integr. Biol. 22:248–54
    [Google Scholar]
  122. 122. 
    Jovel J, Patterson J, Wang W, Hotte N, O'Keefe S et al. 2016. Characterization of the gut microbiome using 16S or shotgun metagenomics. Front. Microbiol. 7:459
    [Google Scholar]
  123. 123. 
    Kolmeder C, Lähteenmäki K, Wacklin P, Kotovuori A, Ritamo I et al. 2017. Tandem mass spectrometry in resolving complex gut microbiota functions. MALDI-TOF and Tandem MS for Clinical Microbiology HN Shah, SE Gharbia 502–28 New York: Wiley
    [Google Scholar]
  124. 124. 
    Bashiardes S, Zilberman-Schapira G, Elinav E 2016. Use of metatranscriptomics in microbiome research. Bioinform. Biol. Insights 10:19–25
    [Google Scholar]
  125. 125. 
    Kleiner M. 2019. Metaproteomics: much more than measuring gene expression in microbial communities. mSystems 4:00115
    [Google Scholar]
  126. 126. 
    Wang Q, Wang K, Wu W, Giannoulatou E, Ho JW, Li L 2019. Host and microbiome multi-omics integration: applications and methodologies. Biophys. Rev. 11:55–65
    [Google Scholar]
  127. 127. 
    Walter J, Armet AM, Finlay BB, Shanahan F 2020. Establishing or exaggerating causality for the gut microbiome: lessons from human microbiota–associated rodents. Cell 180:221–32
    [Google Scholar]
  128. 128. 
    Guthrie L, Kelly L. 2019. Bringing microbiome–drug interaction research into the clinic. EBioMedicine 44:708–15
    [Google Scholar]
  129. 129. 
    Douglas AE. 2019. Simple animal models for microbiome research. Nat. Rev. Microbiol. 17:764–75
    [Google Scholar]
  130. 130. 
    Nguyen TLA, Vieira-Silva S, Liston A, Raes J 2015. How informative is the mouse for human gut microbiota research. ? Dis. Models Mech. 8:1–16
    [Google Scholar]
  131. 131. 
    Hugenholtz F, de Vos WM 2017. Mouse models for human intestinal microbiota research: a critical evaluation. Cell. Mol. Life Sci. 75:149–60
    [Google Scholar]
  132. 132. 
    Wolfe D, Yazdi F, Kanji S, Burry L, Beck A et al. 2018. Incidence, causes, and consequences of preventable adverse drug reactions occurring in inpatients: a systematic review of systematic reviews. PLOS ONE 13:e0205426
    [Google Scholar]
  133. 133. 
    Roden DM, McLeod HL, Relling MV, Williams MS, Mensah GA et al. 2019. Pharmacogenomics. Lancet 394:521–32
    [Google Scholar]
  134. 134. 
    Galkin F, Aliper A, Putin E, Kuznetsov I, Gladyshev VN, Zhavoronkov A 2018. Human microbiome aging clocks based on deep learning and tandem of permutation feature importance and accumulated local effects. bioRxiv 507780. https://doi.org/10.1101/507780
    [Crossref]
  135. 135. 
    Ahadi S, Zhou W, Schüssler-Fiorenza Rose SM, Sailani MR, Contrepois K et al. 2020. Personal aging markers and ageotypes revealed by deep longitudinal profiling. Nat. Med. 26:83–90
    [Google Scholar]
  136. 136. 
    de la Cuesta-Zuluaga J, Kelley ST, Chen Y, Escobar JS, Mueller NT et al. 2019. Age- and sex-dependent patterns of gut microbial diversity in human adults. mSystems 4:00261–19
    [Google Scholar]
  137. 137. 
    Reiman D, Metwally AA, Dai Y 2018. PopPhy-CNN: a phylogenetic tree embedded architecture for convolution neural networks for metagenomic data. https://ieeexplore.ieee.org/document/9091025/
  138. 138. 
    Khan S, Kelly L. 2020. Multiclass disease classification from microbial whole-community metagenomes. Pac. Symp. Biocomput. 25:55–66
    [Google Scholar]
  139. 139. 
    Zhou Y, Gallins P. 2019. A review and tutorial of machine learning methods for microbiome host trait prediction. Front. Genet. 10:579
    [Google Scholar]
  140. 140. 
    Escobar-Zepeda A, Godoy-Lozano EE, Raggi L, Segovia L, Merino E et al. 2018. Analysis of sequencing strategies and tools for taxonomic annotation: defining standards for progressive metagenomics. Sci. Rep. 8:12034
    [Google Scholar]
  141. 141. 
    Pasolli E, Schiffer L, Manghi P, Renson A, Obenchain V et al. 2017. Accessible, curated metagenomic data through ExperimentHub. Nat. Methods 14:1023–24
    [Google Scholar]
  142. 142. 
    Pasolli E, Truong DT, Malik F, Waldron L, Segata N 2016. Machine learning meta-analysis of large metagenomic datasets: tools and biological insights. PLOS Comput. Biol. 12:e1004977
    [Google Scholar]
  143. 143. 
    Gilpin LH, Bau D, Yuan BZ, Bajwa A, Specter M, Kagal L 2019. Explaining explanations: an overview of interpretability of machine learning. Proceedings of the 5th IEEE International Conference on Data Science and Advanced Analytics80–89 Piscataway, NJ: IEEE
    [Google Scholar]
  144. 144. 
    Shrikumar A, Greenside P, Kundaje A 2017. Learning important features through propagating activation differences. arXiv:1704.02685v1 [cs]
  145. 145. 
    Ribeiro MT, Singh S, Guestrin C 2016. “Why should I trust you?” Explaining the predictions of any classifier. arXiv:1602.04938 [cs]
  146. 146. 
    Lundberg SM, Erion GG, Lee SI 2018. Consistent individualized feature attribution for tree ensembles. arXiv:1802.03888 [cs]
  147. 147. 
    Ancona M, Ceolini E, Öztireli C, Gross M 2017. Towards better understanding of gradient-based attribution methods for deep neural networks. arXiv:1711.06104 [cs]
  148. 148. 
    Nauta M, Bucur D, Seifert C 2019. Causal discovery with attention-based convolutional neural networks. Mach. Learn. Knowl. Extr. 1:312–40
    [Google Scholar]
  149. 149. 
    Kocaoglu M, Snyder C, Dimakis AG, Vishwanath S 2017. CausalGAN: learning causal implicit generative models with adversarial training Tech. Rep., Univ. Tex Austin:
  150. 150. 
    Ramachandra V. 2018. Deep learning for causal inference. arXiv:1803.00149 [econ.EM]
  151. 151. 
    Glastonbury CA, Ferlaino M, Nellåker C, Lindgren CM 2018. Adjusting for confounding in unsupervised latent representations of images. arXiv:1811.06498v2 [cs.CV]
  152. 152. 
    Shaham U, Stanton KP, Zhao J, Li H, Raddassi K et al. 2017. Removal of batch effects using distribution-matching residual networks. Bioinformatics 33:2539–46
    [Google Scholar]
/content/journals/10.1146/annurev-pharmtox-031620-031509
Loading
/content/journals/10.1146/annurev-pharmtox-031620-031509
Loading

Data & Media loading...

  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error