1932

Abstract

The human microbiome plays an important role in human health and disease. Meta-omics analyses provide indispensable data for linking changes in microbiome composition and function to disease etiology. Yet, the lack of a mechanistic understanding of, e.g., microbiome-metabolome links hampers the translation of these findings into effective, novel therapeutics. Here, we propose metabolic modeling of microbial communities through constraint-based reconstruction and analysis (COBRA) as a complementary approach to meta-omics analyses. First, we highlight the importance of microbial metabolism in cardiometabolic diseases, inflammatory bowel disease, colorectal cancer, Alzheimer disease, and Parkinson disease. Next, we demonstrate that microbial community modeling can stratify patients and controls, mechanistically link microbes with fecal metabolites altered in disease, and identify host pathways affected by the microbiome. Finally, we outline our vision for COBRA modeling combined with meta-omics analyses and multivariate statistical analyses to inform and guide clinical trials, yield testable hypotheses, and ultimately propose novel dietary and therapeutic interventions.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-micro-060221-012134
2021-10-08
2024-06-23
Loading full text...

Full text loading...

/deliver/fulltext/micro/75/1/annurev-micro-060221-012134.html?itemId=/content/journals/10.1146/annurev-micro-060221-012134&mimeType=html&fmt=ahah

Literature Cited

  1. 1. 
    Adams B, Nunes JM, Page MJ, Roberts T, Carr J et al. 2019. Parkinson's disease: a systemic inflammatory disease accompanied by bacterial inflammagens. Front. Aging Neurosci. 11:210
    [Google Scholar]
  2. 2. 
    Aden K, Rehman A, Waschina S, Pan WH, Walker A et al. 2019. Metabolic functions of gut microbes associate with efficacy of tumor necrosis factor antagonists in patients with inflammatory bowel diseases. Gastroenterology 157:1279–92.e11
    [Google Scholar]
  3. 3. 
    Alexander M, Turnbaugh PJ. 2020. Deconstructing mechanisms of diet-microbiome-immune interactions. Immunity 53:264–76
    [Google Scholar]
  4. 4. 
    Alzheimer's Assoc 2020. 2020 Alzheimer's disease facts and figures. Alzheimer's Dement 16:391–460
    [Google Scholar]
  5. 5. 
    Armstrong MJ, Okun MS. 2020. Diagnosis and treatment of Parkinson disease: a review. JAMA 323:548–60
    [Google Scholar]
  6. 6. 
    Ascherio A, Schwarzschild MA. 2016. The epidemiology of Parkinson's disease: risk factors and prevention. Lancet Neurol 15:1257–72
    [Google Scholar]
  7. 7. 
    Aurich MK, Fleming RMT, Thiele I. 2017. A systems approach reveals distinct metabolic strategies among the NCI-60 cancer cell lines. PLOS Comput. Biol. 13:e1005698
    [Google Scholar]
  8. 8. 
    Auslander N, Cunningham CE, Toosi BM, McEwen EJ, Yizhak K et al. 2017. An integrated computational and experimental study uncovers FUT9 as a metabolic driver of colorectal cancer. Mol. Syst. Biol. 13:956
    [Google Scholar]
  9. 9. 
    Baldini F, Heinken A, Heirendt L, Magnusdottir S, Fleming RMT, Thiele I. 2018. The Microbiome Modeling Toolbox: from microbial interactions to personalized microbial communities. Bioinformatics 35:2332–34
    [Google Scholar]
  10. 10. 
    Baldini F, Hertel J, Sandt E, Thinnes CC, Neuberger-Castillo L et al. 2020. Parkinson's disease-associated alterations of the gut microbiome predict disease-relevant changes in metabolic functions. BMC Biol 18:62
    [Google Scholar]
  11. 11. 
    Baloni P, Funk CC, Yan J, Yurkovich JT, Kueider-Paisley A et al. 2020. Metabolic network analysis reveals altered bile acid synthesis and metabolism in Alzheimer's disease. Cell Rep. Med 1:8100138
    [Google Scholar]
  12. 12. 
    Bashiardes S, Zilberman-Schapira G, Elinav E. 2016. Use of metatranscriptomics in microbiome research. Bioinform. Biol. Insights 10:19–25
    [Google Scholar]
  13. 13. 
    Bauer E, Thiele I. 2018. From metagenomic data to personalized in silico microbiotas: predicting dietary supplements for Crohn's disease. npj Syst. Biol. Appl. 4:27
    [Google Scholar]
  14. 14. 
    Bedarf JR, Hildebrand F, Coelho LP, Sunagawa S, Bahram M et al. 2017. Functional implications of microbial and viral gut metagenome changes in early stage L-DOPA-naïve Parkinson's disease patients. Genome Med 9:39. Erratum. 2017. Genome Med. 9:61
    [Google Scholar]
  15. 15. 
    Beger RD, Dunn W, Schmidt MA, Gross SS, Kirwan JA et al. 2016. Metabolomics enables precision medicine: “A White Paper, Community Perspective”. Metabolomics 12:149
    [Google Scholar]
  16. 16. 
    Bharti R, Grimm DG. 2019. Current challenges and best-practice protocols for microbiome analysis. Brief Bioinform 22:178–93
    [Google Scholar]
  17. 17. 
    Bohan R, Tianyu X, Tiantian Z, Ruonan F, Hongtao H et al. 2019. Gut microbiota: a potential manipulator for host adipose tissue and energy metabolism. J. Nutr. Biochem. 64:206–17
    [Google Scholar]
  18. 18. 
    Bonfili L, Cecarini V, Gogoi O, Gong C, Cuccioloni M et al. 2021. Microbiota modulation as preventative and therapeutic approach in Alzheimer's disease. FEBS J 288:2836–55
    [Google Scholar]
  19. 19. 
    Bordel S. 2018. Constraint based modeling of metabolism allows finding metabolic cancer hallmarks and identifying personalized therapeutic windows. Oncotarget 9:19716–29
    [Google Scholar]
  20. 20. 
    Boulesteix AL, Strimmer K. 2007. Partial least squares: a versatile tool for the analysis of high-dimensional genomic data. Brief Bioinform 8:32–44
    [Google Scholar]
  21. 21. 
    Brandl K, Schnabl B. 2017. Intestinal microbiota and nonalcoholic steatohepatitis. Curr. Opin. Gastroenterol. 33:128–33
    [Google Scholar]
  22. 22. 
    Brown JM, Hazen SL. 2018. Microbial modulation of cardiovascular disease. Nat. Rev. Microbiol. 16:171–81
    [Google Scholar]
  23. 23. 
    Brunk E, Sahoo S, Zielinski DC, Altunkaya A, Drager A et al. 2018. Recon3D enables a three-dimensional view of gene variation in human metabolism. Nat. Biotechnol. 36:272–81
    [Google Scholar]
  24. 24. 
    Buchel F, Saliger S, Drager A, Hoffmann S, Wrzodek C et al. 2013. Parkinson's disease: dopaminergic nerve cell model is consistent with experimental finding of increased extracellular transport of alpha-synuclein. BMC Neurosci 14:136
    [Google Scholar]
  25. 25. 
    Chen MX, Wang SY, Kuo CH, Tsai IL. 2019. Metabolome analysis for investigating host-gut microbiota interactions. J. Formos. Med. Assoc. 118:Suppl. 1S10–22
    [Google Scholar]
  26. 26. 
    Clarke G, Stilling RM, Kennedy PJ, Stanton C, Cryan JF, Dinan TG. 2014. Minireview: Gut microbiota; the neglected endocrine organ. Mol. Endocrinol. 28:1221–38
    [Google Scholar]
  27. 27. 
    Cryan JF, O'Riordan KJ, Cowan CSM, Sandhu KV, Bastiaanssen TFS et al. 2019. The microbiota-gut-brain axis. Physiol. Rev. 99:1877–2013
    [Google Scholar]
  28. 28. 
    de Maistre S, Gaillard S, Martin JC, Richard S, Boussuges A et al. 2020. Cecal metabolome fingerprint in a rat model of decompression sickness with neurological disorders. Sci. Rep. 10:15996
    [Google Scholar]
  29. 29. 
    de Souza HSP, Fiocchi C. 2018. Network medicine: a mandatory next step for inflammatory bowel disease. Inflamm. Bowel Dis. 24:671–79
    [Google Scholar]
  30. 30. 
    Delzenne NM, Rodriguez J, Olivares M, Neyrinck AM. 2020. Microbiome response to diet: focus on obesity and related diseases. Rev. Endocr. Metab. Disord. 21:369–80
    [Google Scholar]
  31. 31. 
    Di Filippo M, Colombo R, Damiani C, Pescini D, Gaglio D et al. 2016. Zooming-in on cancer metabolic rewiring with tissue specific constraint-based models. Comput. Biol. Chem. 62:60–69
    [Google Scholar]
  32. 32. 
    Diener C, Gibbons SM, Resendis-Antonio O. 2020. MICOM: metagenome-scale modeling to infer metabolic interactions in the gut microbiota. mSystems 5:e00606-19
    [Google Scholar]
  33. 33. 
    Dinan TG, Cryan JF. 2017. The microbiome-gut-brain axis in health and disease. Gastroenterol. Clin. N. Am. 46:77–89
    [Google Scholar]
  34. 34. 
    Effenberger M, Reider S, Waschina S, Bronowski C, Enrich B et al. 2021. Microbial butyrate synthesis indicates therapeutic efficacy of azathioprine in IBD patients. J. Crohn's Colitis 15:88–98
    [Google Scholar]
  35. 35. 
    Erkkinen MG, Kim M, Geschwind MD. 2018. Clinical neurology and epidemiology of the major neurodegenerative diseases. Cold Spring Harb. Perspect. Biol. 10:4a033118
    [Google Scholar]
  36. 36. 
    Fang X, Monk JM, Mih N, Du B, Sastry AV et al. 2018. Escherichia coli B2 strains prevalent in inflammatory bowel disease patients have distinct metabolic capabilities that enable colonization of intestinal mucosa. BMC Syst. Biol. 12:66
    [Google Scholar]
  37. 37. 
    Fang X, Monk JM, Nurk S, Akseshina M, Zhu Q et al. 2018. Metagenomics-based, strain-level analysis of Escherichia coli from a time-series of microbiome samples from a Crohn's disease patient. Front. Microbiol. 9:2559
    [Google Scholar]
  38. 38. 
    Forsberg EM, Huan T, Rinehart D, Benton HP, Warth B et al. 2018. Data processing, multi-omic pathway mapping, and metabolite activity analysis using XCMS Online. Nat. Protoc. 13:633–51
    [Google Scholar]
  39. 39. 
    Franzosa EA, Hsu T, Sirota-Madi A, Shafquat A, Abu-Ali G et al. 2015. Sequencing and beyond: integrating molecular ‘omics’ for microbial community profiling. Nat. Rev. Microbiol. 13:360–72
    [Google Scholar]
  40. 40. 
    Franzosa EA, Sirota-Madi A, Avila-Pacheco J, Fornelos N, Haiser HJ et al. 2019. Gut microbiome structure and metabolic activity in inflammatory bowel disease. Nat. Microbiol. 4:293–305
    [Google Scholar]
  41. 41. 
    Friedland RP, Chapman MR. 2017. The role of microbial amyloid in neurodegeneration. PLOS Pathog 13:e1006654
    [Google Scholar]
  42. 42. 
    Garza DR, Taddese R, Wirbel J, Zeller G, Boleij A et al. 2020. Metabolic models predict bacterial passengers in colorectal cancer. Cancer Metab 8:3
    [Google Scholar]
  43. 43. 
    Gershanik OS. 2018. Does Parkinson's disease start in the gut?. Arq. Neuropsiquiatr. 76:67–70
    [Google Scholar]
  44. 44. 
    Gika HG, Theodoridis GA, Plumb RS, Wilson ID. 2014. Current practice of liquid chromatography-mass spectrometry in metabolomics and metabonomics. J. Pharm. Biomed. Anal. 87:12–25
    [Google Scholar]
  45. 45. 
    Gower JC. 1975. Generalized Procrustes analysis. Psychometrika 40:33–51
    [Google Scholar]
  46. 46. 
    Goyal D, Ali SA, Singh RK. 2021. Emerging role of gut microbiota in modulation of neuroinflammation and neurodegeneration with emphasis on Alzheimer's disease. Prog. Neuropsychopharmacol. Biol. Psychiatry 106:110112
    [Google Scholar]
  47. 47. 
    Greenhalgh K, Ramiro-Garcia J, Heinken A, Ullmann P, Bintener T et al. 2019. Integrated in vitro and in silico modeling delineates the molecular effects of a synbiotic regimen on colorectal-cancer-derived cells. Cell Rep 27:1621–32.e9
    [Google Scholar]
  48. 48. 
    Gu C, Kim GB, Kim WJ, Kim HU, Lee SY. 2019. Current status and applications of genome-scale metabolic models. Genome Biol 20:121
    [Google Scholar]
  49. 49. 
    Hale VL, Jeraldo P, Chen J, Mundy M, Yao J et al. 2018. Distinct microbes, metabolites, and ecologies define the microbiome in deficient and proficient mismatch repair colorectal cancers. Genome Med 10:78
    [Google Scholar]
  50. 50. 
    Hale VL, Jeraldo P, Mundy M, Yao J, Keeney G et al. 2018. Synthesis of multi-omic data and community metabolic models reveals insights into the role of hydrogen sulfide in colon cancer. Methods 149:59–68
    [Google Scholar]
  51. 51. 
    Hammer GP, du Prel JB, Blettner M. 2009. Avoiding bias in observational studies: part 8 in a series of articles on evaluation of scientific publications. Dtsch. Arztebl. Int. 106:664–68
    [Google Scholar]
  52. 52. 
    Heinken A, Acharya G, Ravcheev DA, Hertel J, Nyga M et al. 2020. AGORA2: Large scale reconstruction of the microbiome highlights wide-spread drug-metabolising capacities. bioRxiv 2020.11.09.375451. https://doi.org/10.1101/2020.11.09.375451
    [Crossref]
  53. 53. 
    Heinken A, Basile A, Thiele I. 2021. Advances in constraint-based modelling of microbial communities. Curr. Opin. Syst. Biol. In press. https://doi.org/10.1016/j.coisb.2021.05.007
    [Crossref] [Google Scholar]
  54. 54. 
    Heinken A, Hertel J, Thiele I. 2021. Metabolic modelling reveals broad changes in gut microbial metabolism in inflammatory bowel disease patients with dysbiosis. npj Syst. Biol. Appl. 7:119
    [Google Scholar]
  55. 55. 
    Heinken A, Ravcheev DA, Baldini F, Heirendt L, Fleming RMT, Thiele I 2019. Systematic assessment of secondary bile acid metabolism in gut microbes reveals distinct metabolic capabilities in inflammatory bowel disease. Microbiome 7:75
    [Google Scholar]
  56. 56. 
    Henson MA, Phalak P. 2017. Microbiota dysbiosis in inflammatory bowel diseases: in silico investigation of the oxygen hypothesis. BMC Syst. Biol. 11:145
    [Google Scholar]
  57. 57. 
    Herrema H, Niess JH. 2020. Intestinal microbial metabolites in human metabolism and type 2 diabetes. Diabetologia 63:2533–47
    [Google Scholar]
  58. 58. 
    Hertel J, Frenzel S, Konig J, Wittfeld K, Fuellen G et al. 2019. The informative error: a framework for the construction of individualized phenotypes. Stat. Methods Med. Res. 28:1427–38
    [Google Scholar]
  59. 59. 
    Hertel J, Harms AC, Heinken A, Baldini F, Thinnes CC et al. 2019. Integrated analyses of microbiome and longitudinal metabolome data reveal microbial-host interactions on sulfur metabolism in Parkinson's disease. Cell Rep 29:1767–77.e8
    [Google Scholar]
  60. 60. 
    Hertel J, Heinken A, Martinelli F, Thiele I. 2021. Integration of constraint-based modelling with faecal metabolomics reveals large deleterious effects of Fusobacterium spp. on community butyrate production. Gut Microbes 13:11–23
    [Google Scholar]
  61. 61. 
    Hill-Burns EM, Debelius JW, Morton JT, Wissemann WT, Lewis MR et al. 2017. Parkinson's disease and Parkinson's disease medications have distinct signatures of the gut microbiome. Mov. Disord. 32:739–49
    [Google Scholar]
  62. 62. 
    Hollywood K, Brison DR, Goodacre R. 2006. Metabolomics: current technologies and future trends. Proteomics 6:4716–23
    [Google Scholar]
  63. 63. 
    Houttu V, Boulund U, Grefhorst A, Soeters MR, Pinto-Sietsma SJ et al. 2020. The role of the gut microbiome and exercise in non-alcoholic fatty liver disease. Therap. Adv. Gastroenterol. 13:1756284820941745
    [Google Scholar]
  64. 64. 
    Imhann F, Vich Vila A, Bonder MJ, Fu J, Gevers D et al. 2018. Interplay of host genetics and gut microbiota underlying the onset and clinical presentation of inflammatory bowel disease. Gut 67:108–19
    [Google Scholar]
  65. 65. 
    Inan OT, Tenaerts P, Prindiville SA, Reynolds HR, Dizon DS et al. 2020. Digitizing clinical trials. npj Digit. Med 3:101
    [Google Scholar]
  66. 66. 
    Janney A, Powrie F, Mann EH. 2020. Host-microbiota maladaptation in colorectal cancer. Nature 585:509–17
    [Google Scholar]
  67. 67. 
    Javdan B, Lopez JG, Chankhamjon P, Lee YJ, Hull R et al. 2020. Personalized mapping of drug metabolism by the human gut microbiome. Cell 181:1661–79.e22
    [Google Scholar]
  68. 68. 
    Kalia LV, Lang AE 2015. Parkinson's disease. Lancet 386:896–912
    [Google Scholar]
  69. 69. 
    Karu N, Deng L, Slae M, Guo AC, Sajed T et al. 2018. A review on human fecal metabolomics: methods, applications and the human fecal metabolome database. Anal. Chim. Acta 1030:1–24
    [Google Scholar]
  70. 70. 
    Kashyap PC, Chia N, Nelson H, Segal E, Elinav E. 2017. Microbiome at the frontier of personalized medicine. Mayo Clin. Proc. 92:1855–64
    [Google Scholar]
  71. 71. 
    Kennedy PJ, Cryan JF, Dinan TG, Clarke G. 2017. Kynurenine pathway metabolism and the microbiota-gut-brain axis. Neuropharmacology 112:399–412
    [Google Scholar]
  72. 72. 
    Kenny DJ, Plichta DR, Shungin D, Koppel N, Hall AB et al. 2020. Cholesterol metabolism by uncultured human gut bacteria influences host cholesterol level. Cell Host Microbe 28:245–57.e6
    [Google Scholar]
  73. 73. 
    Klaassen CD, Cui JY. 2015. Review: mechanisms of how the intestinal microbiota alters the effects of drugs and bile acids. Drug Metab. Dispos. 43:1505–21
    [Google Scholar]
  74. 74. 
    Knight R, Vrbanac A, Taylor BC, Aksenov A, Callewaert C et al. 2018. Best practices for analysing microbiomes. Nat. Rev. Microbiol. 16:410–22
    [Google Scholar]
  75. 75. 
    Knott ME, Manzi M, Zabalegui N, Salazar MO, Puricelli LI, Monge ME. 2018. Metabolic footprinting of a clear cell renal cell carcinoma in vitro model for human kidney cancer detection. J. Proteome Res. 17:3877–88
    [Google Scholar]
  76. 76. 
    Kolodziejczyk AA, Zheng D, Elinav E. 2019. Diet-microbiota interactions and personalized nutrition. Nat. Rev. Microbiol. 17:742–53
    [Google Scholar]
  77. 77. 
    Kowalski K, Mulak A. 2019. Brain-gut-microbiota axis in Alzheimer's disease. J. Neurogastroenterol. Motil. 25:48–60
    [Google Scholar]
  78. 78. 
    Lamichhane S, Sen P, Dickens AM, Oresic M, Bertram HC. 2018. Gut metabolome meets microbiome: a methodological perspective to understand the relationship between host and microbe. Methods 149:3–12
    [Google Scholar]
  79. 79. 
    Lang S, Schnabl B. 2020. Microbiota and fatty liver disease—the known, the unknown, and the future. Cell Host Microbe 28:233–44
    [Google Scholar]
  80. 80. 
    Lavelle A, Sokol H. 2020. Gut microbiota-derived metabolites as key actors in inflammatory bowel disease. Nat. Rev. Gastroenterol. Hepatol. 17:223–37
    [Google Scholar]
  81. 81. 
    Liu Y-X, Qin Y, Chen T, Lu M, Qian X et al.2021 A practical guide to amplicon and metagenomic analysis of microbiome data. Protein Cell 12:315–30
    [Google Scholar]
  82. 82. 
    Lloyd-Price J, Abu-Ali G, Huttenhower C 2016. The healthy human microbiome. Genome Med 8:51
    [Google Scholar]
  83. 83. 
    Louis P, Hold GL, Flint HJ. 2014. The gut microbiota, bacterial metabolites and colorectal cancer. Nat. Rev. Microbiol. 12:661–72
    [Google Scholar]
  84. 84. 
    Lynch SV, Pedersen O. 2016. The human intestinal microbiome in health and disease. N. Engl. J. Med. 375:2369–79
    [Google Scholar]
  85. 85. 
    Ma Q, Xing C, Long W, Wang HY, Liu Q, Wang RF. 2019. Impact of microbiota on central nervous system and neurological diseases: the gut-brain axis. J. Neuroinflamm. 16:53
    [Google Scholar]
  86. 86. 
    Magnusdottir 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. 35:81–89
    [Google Scholar]
  87. 87. 
    MahmoudianDehkordi S, Arnold M, Nho K, Ahmad S, Jia W et al. 2019. Altered bile acid profile associates with cognitive impairment in Alzheimer's disease—an emerging role for gut microbiome. Alzheimer's Dement 15:76–92 Erratum. 2019. Alzheimer's Dement. 15:604
    [Google Scholar]
  88. 88. 
    Maini Rekdal V, Nol Bernadino P, Luescher MU, Kiamehr S, Le C et al. 2020. A widely distributed metalloenzyme class enables gut microbial metabolism of host- and diet-derived catechols. eLife 9:e50845
    [Google Scholar]
  89. 89. 
    Mallick H, Ma S, Franzosa EA, Vatanen T, Morgan XC, Huttenhower C. 2017. Experimental design and quantitative analysis of microbial community multiomics. Genome Biol 18:228
    [Google Scholar]
  90. 90. 
    Mardinoglu A, Bjornson E, Zhang C, Klevstig M, Soderlund S et al. 2017. Personal model-assisted identification of NAD+ and glutathione metabolism as intervention target in NAFLD. Mol. Syst. Biol. 13:916
    [Google Scholar]
  91. 91. 
    Martens EC, Neumann M, Desai MS. 2018. Interactions of commensal and pathogenic microorganisms with the intestinal mucosal barrier. Nat. Rev. Microbiol. 16:457–70
    [Google Scholar]
  92. 92. 
    Mattson MP, Arumugam TV. 2018. Hallmarks of brain aging: adaptive and pathological modification by metabolic states. Cell Metab 27:1176–99
    [Google Scholar]
  93. 93. 
    McBurney MI, Davis C, Fraser CM, Schneeman BO, Huttenhower C et al. 2019. Establishing what constitutes a healthy human gut microbiome: state of the science, regulatory considerations, and future directions. J. Nutr. 149:1882–95
    [Google Scholar]
  94. 94. 
    Miraglia F, Colla E. 2019. Microbiome, Parkinson's disease and molecular mimicry. Cells 8:222
    [Google Scholar]
  95. 95. 
    Mirza B, Wang W, Wang J, Choi H, Chung NC, Ping P 2019. Machine learning and integrative analysis of biomedical big data. Genes 10:87
    [Google Scholar]
  96. 96. 
    Murgia F, Atzori L, Carboni E, Santoru ML, Hendren A et al. 2020. Metabolomics fingerprint induced by the intranigral inoculation of exogenous human alpha-synuclein oligomers in a rat model of Parkinson's disease. Int. J. Mol. Sci. 21:6745
    [Google Scholar]
  97. 97. 
    Myint KT, Aoshima K, Tanaka S, Nakamura T, Oda Y 2009. Quantitative profiling of polar cationic metabolites in human cerebrospinal fluid by reversed-phase nanoliquid chromatography/mass spectrometry. Anal. Chem. 81:1121–29
    [Google Scholar]
  98. 98. 
    Natividad JM, Lamas B, Pham HP, Michel ML, Rainteau D et al. 2018. Bilophila wadsworthia aggravates high fat diet induced metabolic dysfunctions in mice. Nat. Commun. 9:2802
    [Google Scholar]
  99. 99. 
    Needham BD, Kaddurah-Daouk R, Mazmanian SK. 2020. Gut microbial molecules in behavioural and neurodegenerative conditions. Nat. Rev. Neurosci. 21:717–31
    [Google Scholar]
  100. 100. 
    Ng SC, Shi HY, Hamidi N, Underwood FE, Tang W et al. 2017. Worldwide incidence and prevalence of inflammatory bowel disease in the 21st century: a systematic review of population-based studies. Lancet 390:101142769–78
    [Google Scholar]
  101. 101. 
    Nguyen TT, Ta QTH, Nguyen TTD, Le TT, Vo VG. 2020. Role of insulin resistance in the Alzheimer's disease progression. Neurochem. Res. 45:1481–91
    [Google Scholar]
  102. 102. 
    Ni J, Shen TD, Chen EZ, Bittinger K, Bailey A et al. 2017. A role for bacterial urease in gut dysbiosis and Crohn's disease. Sci. Transl. Med. 9:eaah6888
    [Google Scholar]
  103. 103. 
    Nicholson JK, Holmes E, Kinross J, Burcelin R, Gibson G et al. 2012. Host-gut microbiota metabolic interactions. Science 336:1262–67
    [Google Scholar]
  104. 104. 
    Nielsen J. 2017. Systems biology of metabolism: a driver for developing personalized and precision medicine. Cell Metab 25:572–79
    [Google Scholar]
  105. 105. 
    Nikolaus S, Schulte B, Al-Massad N, Thieme F, Schulte DM et al. 2017. Increased tryptophan metabolism is associated with activity of inflammatory bowel diseases. Gastroenterology 153:1504–16.e2
    [Google Scholar]
  106. 106. 
    Niu X, Zheng S, Liu H, Li S 2018. Protective effects of taurine against inflammation, apoptosis, and oxidative stress in brain injury. Mol. Med. Rep. 18:4516–22
    [Google Scholar]
  107. 107. 
    Noecker C, Chiu HC, McNally CP, Borenstein E. 2019. Defining and evaluating microbial contributions to metabolite variation in microbiome-metabolome association studies. mSystems 4:e00579-19
    [Google Scholar]
  108. 108. 
    Nuzum ND, Loughman A, Szymlek-Gay EA, Hendy A, Teo WP, Macpherson H. 2020. Gut microbiota differences between healthy older adults and individuals with Parkinson's disease: a systematic review. Neurosci. Biobehav. Rev. 112:227–41
    [Google Scholar]
  109. 109. 
    O'Brien EJ, Monk JM, Palsson BO. 2015. Using genome-scale models to predict biological capabilities. Cell 161:971–87
    [Google Scholar]
  110. 110. 
    Org E, Blum Y, Kasela S, Mehrabian M, Kuusisto J et al. 2017. Relationships between gut microbiota, plasma metabolites, and metabolic syndrome traits in the METSIM cohort. Genome Biol 18:70
    [Google Scholar]
  111. 111. 
    Orth JD, Thiele I, Palsson BO. 2010. What is flux balance analysis?. Nat. Biotechnol. 28:245–48
    [Google Scholar]
  112. 112. 
    Ozdal T, Sela DA, Xiao J, Boyacioglu D, Chen F, Capanoglu E 2016. The reciprocal interactions between polyphenols and gut microbiota and effects on bioaccessibility. Nutrients 8:78
    [Google Scholar]
  113. 113. 
    Pappalardo F, Russo G, Tshinanu FM, Viceconti M. 2019. In silico clinical trials: concepts and early adoptions. Brief Bioinform 20:1699–708
    [Google Scholar]
  114. 114. 
    Pearl J. 2009. Causal inference in statistics: an overview. Statist. Surv. 3:96–146
    [Google Scholar]
  115. 115. 
    Pearl JR, Colantuoni C, Bergey DE, Funk CC, Shannon P et al. 2019. Genome-scale transcriptional regulatory network models of psychiatric and neurodegenerative disorders. Cell Syst 8:122–35.e7
    [Google Scholar]
  116. 116. 
    Peck SC, Denger K, Burrichter A, Irwin SM, Balskus EP, Schleheck D 2019. A glycyl radical enzyme enables hydrogen sulfide production by the human intestinal bacterium Bilophila wadsworthia. PNAS 116:3171–76
    [Google Scholar]
  117. 117. 
    Peters DL, Wang W, Zhang X, Ning Z, Mayne J, Figeys D. 2019. Metaproteomic and metabolomic approaches for characterizing the gut microbiome. Proteomics 19:e1800363
    [Google Scholar]
  118. 118. 
    Prentice H, Pan C, Gharibani PM, Ma Z, Price AL et al. 2017. Analysis of neuroprotection by taurine and taurine combinations in primary neuronal cultures and in neuronal cell lines exposed to glutamate excitotoxicity and to hypoxia/re-oxygenation. Adv. Exp. Med. Biol. 975:Part 1207–16
    [Google Scholar]
  119. 119. 
    Qian G, Ho JWK. 2020. Challenges and emerging systems biology approaches to discover how the human gut microbiome impact host physiology. Biophys. Rev. 12:851–63
    [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. 
    Rastelli M, Cani PD, Knauf C. 2019. The gut microbiome influences host endocrine functions. Endocr. Rev. 40:1271–84
    [Google Scholar]
  122. 122. 
    Reed JL. 2017. Genome-scale metabolic modeling and its application to microbial communities. The Chemistry of Microbiomes: Proceedings of a Seminar Series85–92 Washington, DC: Natl. Acad. Press
    [Google Scholar]
  123. 123. 
    Regan JA, Shah SH. 2020. Obesity genomics and metabolomics: a nexus of cardiometabolic risk. Curr. Cardiol. Rep. 22:174
    [Google Scholar]
  124. 124. 
    Riccio P, Rossano R. 2019. Undigested food and gut microbiota may cooperate in the pathogenesis of neuroinflammatory diseases: a matter of barriers and a proposal on the origin of organ specificity. Nutrients 11:2714
    [Google Scholar]
  125. 125. 
    Ridlon JM, Harris SC, Bhowmik S, Kang DJ, Hylemon PB. 2016. Consequences of bile salt biotransformations by intestinal bacteria. Gut Microbes 7:22–39
    [Google Scholar]
  126. 126. 
    Robinson JL, Kocabas P, Wang H, Cholley PE, Cook D et al. 2020. An atlas of human metabolism. Sci. Signal. 13:eaaz1482
    [Google Scholar]
  127. 127. 
    Rosario D, Benfeitas R, Bidkhori G, Zhang C, Uhlen M et al. 2018. Understanding the representative gut microbiota dysbiosis in metformin-treated type 2 diabetes patients using genome-scale metabolic modeling. Front. Physiol. 9:775
    [Google Scholar]
  128. 128. 
    Ruan W, Engevik MA, Spinler JK, Versalovic J. 2020. Healthy human gastrointestinal microbiome: composition and function after a decade of exploration. Dig. Dis. Sci. 65:695–705
    [Google Scholar]
  129. 129. 
    Schapira AHV, Chaudhuri KR, Jenner P. 2017. Non-motor features of Parkinson disease. Nat. Rev. Neurosci. 18:435–50
    [Google Scholar]
  130. 130. 
    Schirmer M, Franzosa EA, Lloyd-Price J, McIver LJ, Schwager R et al. 2018. Dynamics of metatranscription in the inflammatory bowel disease gut microbiome. Nat. Microbiol. 3:337–46
    [Google Scholar]
  131. 131. 
    Schirmer M, Garner A, Vlamakis H, Xavier RJ. 2019. Microbial genes and pathways in inflammatory bowel disease. Nat. Rev. Microbiol. 17:497–511
    [Google Scholar]
  132. 132. 
    Segers K, Declerck S, Mangelings D, Heyden YV, Eeckhaut AV. 2019. Analytical techniques for metabolomic studies: a review. Bioanalysis 11:2297–318
    [Google Scholar]
  133. 133. 
    Sen P, Oresic M. 2019. Metabolic modeling of human gut microbiota on a genome scale: an overview. Metabolites 9:22
    [Google Scholar]
  134. 134. 
    Sertbas M, Ulgen K, Cakir T. 2014. Systematic analysis of transcription-level effects of neurodegenerative diseases on human brain metabolism by a newly reconstructed brain-specific metabolic network. FEBS Open Bio 4:542–53
    [Google Scholar]
  135. 135. 
    Shah P, Fritz JV, Glaab E, Desai MS, Greenhalgh K et al. 2016. A microfluidics-based in vitro model of the gastrointestinal human-microbe interface. Nat. Commun. 7:11535
    [Google Scholar]
  136. 136. 
    Shoaie S, Ghaffari P, Kovatcheva-Datchary P, Mardinoglu A, Sen P et al. 2015. Quantifying diet-induced metabolic changes of the human gut microbiome. Cell Metab 22:320–31
    [Google Scholar]
  137. 137. 
    Sonnenburg JL, Backhed F. 2016. Diet-microbiota interactions as moderators of human metabolism. Nature 535:56–64
    [Google Scholar]
  138. 138. 
    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]
  139. 139. 
    Stocchi F, Torti M. 2017. Constipation in Parkinson's disease. Int. Rev. Neurobiol. 134:811–26
    [Google Scholar]
  140. 140. 
    Sveinbjornsdottir S. 2016. The clinical symptoms of Parkinson's disease. J. Neurochem. 139:Suppl. 1318–24
    [Google Scholar]
  141. 141. 
    Tarawneh R, Holtzman DM. 2012. The clinical problem of symptomatic Alzheimer disease and mild cognitive impairment. Cold Spring Harb. Perspect. Med. 2:a006148
    [Google Scholar]
  142. 142. 
    Ternes D, Karta J, Tsenkova M, Wilmes P, Haan S, Letellier E. 2020. Microbiome in colorectal cancer: how to get from meta-omics to mechanism?. Trends Microbiol 28:401–23
    [Google Scholar]
  143. 143. 
    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]
  144. 144. 
    Thiele I, Palsson BO. 2010. A protocol for generating a high-quality genome-scale metabolic reconstruction. Nat. Protoc. 5:93–121
    [Google Scholar]
  145. 145. 
    Thiele I, Sahoo S, Heinken A, Hertel J, Heirendt L et al. 2020. Personalized whole-body models integrate metabolism, physiology, and the gut microbiome. Mol. Syst. Biol. 16:e8982
    [Google Scholar]
  146. 146. 
    Toledo JB, Arnold M, Kastenmuller G, Chang R, Baillie RA et al. 2017. Metabolic network failures in Alzheimer's disease: a biochemical road map. Alzheimer's Dement 13:965–84
    [Google Scholar]
  147. 147. 
    Tralau T, Sowada J, Luch A. 2015. Insights on the human microbiome and its xenobiotic metabolism: What is known about its effects on human physiology?. Expert Opin. Drug Metab. Toxicol. 11:411–25
    [Google Scholar]
  148. 148. 
    Unger MM, Spiegel J, Dillmann KU, Grundmann D, Philippeit H et al. 2016. Short chain fatty acids and gut microbiota differ between patients with Parkinson's disease and age-matched controls. Parkinsonism Relat. Disord. 32:66–72
    [Google Scholar]
  149. 149. 
    van der Ark KCH, van Heck RGA, Martins Dos Santos VAP, Belzer C, de Vos WM 2017. More than just a gut feeling: constraint-based genome-scale metabolic models for predicting functions of human intestinal microbes. Microbiome 5:78
    [Google Scholar]
  150. 150. 
    Van Treuren W, Dodd D. 2020. Microbial contribution to the human metabolome: implications for health and disease. Annu. Rev. Pathol. 15:345–69
    [Google Scholar]
  151. 151. 
    VanderWeele TJ. 2019. Principles of confounder selection. Eur. J. Epidemiol. 34:211–19
    [Google Scholar]
  152. 152. 
    Vogt NM, Romano KA, Darst BF, Engelman CD, Johnson SC et al. 2018. The gut microbiota-derived metabolite trimethylamine N-oxide is elevated in Alzheimer's disease. Alzheimer's Res. Ther. 10:124
    [Google Scholar]
  153. 153. 
    Wang FS, Wu WH, Hsiu WS, Liu YJ, Chuang KW. 2019. Genome-scale metabolic modeling with protein expressions of normal and cancerous colorectal tissues for oncogene inference. Metabolites 10:16
    [Google Scholar]
  154. 154. 
    Wang Y, Zhou Y, Xiao X, Zheng J, Zhou H. 2020. Metaproteomics: a strategy to study the taxonomy and functionality of the gut microbiota. J. Proteom. 219:103737
    [Google Scholar]
  155. 155. 
    Weersma RK, Zhernakova A, Fu J. 2020. Interaction between drugs and the gut microbiome. Gut 69:1510–19
    [Google Scholar]
  156. 156. 
    Wikoff WR, Anfora AT, Liu J, Schultz PG, Lesley SA et al. 2009. Metabolomics analysis reveals large effects of gut microflora on mammalian blood metabolites. PNAS 106:3698–703
    [Google Scholar]
  157. 157. 
    Wirbel J, Pyl PT, Kartal E, Zych K, Kashani A et al. 2019. Meta-analysis of fecal metagenomes reveals global microbial signatures that are specific for colorectal cancer. Nat. Med. 25:679–89
    [Google Scholar]
  158. 158. 
    Wishart DS. 2016. Emerging applications of metabolomics in drug discovery and precision medicine. Nat. Rev. Drug Discov. 15:473–84
    [Google Scholar]
  159. 159. 
    Witten DM, Tibshirani RJ. 2009. Extensions of sparse canonical correlation analysis with applications to genomic data. Stat. Appl. Genet. Mol. Biol. 8:28
    [Google Scholar]
  160. 160. 
    Wlodarska M, Luo C, Kolde R, d'Hennezel E, Annand JW et al. 2017. Indoleacrylic acid produced by commensal Peptostreptococcus species suppresses inflammation. Cell Host Microbe 22:25–37.e6
    [Google Scholar]
  161. 161. 
    Yachida S, Mizutani S, Shiroma H, Shiba S, Nakajima T et al. 2019. Metagenomic and metabolomic analyses reveal distinct stage-specific phenotypes of the gut microbiota in colorectal cancer. Nat. Med. 25:968–76
    [Google Scholar]
  162. 162. 
    Yan Y, Nguyen LH, Franzosa EA, Huttenhower C. 2020. Strain-level epidemiology of microbial communities and the human microbiome. Genome Med 12:71
    [Google Scholar]
  163. 163. 
    Yilmaz B, Juillerat P, Oyas O, Ramon C, Bravo FD et al. 2019. Microbial network disturbances in relapsing refractory Crohn's disease. Nat. Med. 25:323–36
    [Google Scholar]
  164. 164. 
    Yizhak K, Gaude E, Le Devedec S, Waldman YY, Stein GY et al. 2014. Phenotype-based cell-specific metabolic modeling reveals metabolic liabilities of cancer. eLife 3:e03641
    [Google Scholar]
  165. 165. 
    Yu H, Blair RH. 2019. Integration of probabilistic regulatory networks into constraint-based models of metabolism with applications to Alzheimer's disease. BMC Bioinform 20:386
    [Google Scholar]
  166. 166. 
    Zampieri G, Vijayakumar S, Yaneske E, Angione C 2019. Machine and deep learning meet genome-scale metabolic modeling. PLOS Comput. Biol. 15:e1007084
    [Google Scholar]
  167. 167. 
    Zhang C, Aldrees M, Arif M, Li X, Mardinoglu A, Aziz MA. 2019. Elucidating the reprograming of colorectal cancer metabolism using genome-scale metabolic modeling. Front. Oncol. 9:681
    [Google Scholar]
  168. 168. 
    Zhang X, Li L, Butcher J, Stintzi A, Figeys D 2019. Advancing functional and translational microbiome research using meta-omics approaches. Microbiome 7:154
    [Google Scholar]
  169. 169. 
    Zhu S, Jiang Y, Xu K, Cui M, Ye W et al. 2020. The progress of gut microbiome research related to brain disorders. J. Neuroinflamm. 17:25
    [Google Scholar]
  170. 170. 
    Zhu T, Goodarzi MO. 2020. Metabolites linking the gut microbiome with risk for type 2 diabetes. Curr. Nutr. Rep. 9:83–93
    [Google Scholar]
  171. 171. 
    Zierer J, Jackson MA, Kastenmuller G, Mangino M, Long T et al. 2018. The fecal metabolome as a functional readout of the gut microbiome. Nat. Genet. 50:790–95
    [Google Scholar]
  172. 172. 
    Zimmermann M, Zimmermann-Kogadeeva M, Wegmann R, Goodman AL. 2019. Mapping human microbiome drug metabolism by gut bacteria and their genes. Nature 570:462–67
    [Google Scholar]
  173. 173. 
    Zuniga C, Zaramela L, Zengler K. 2017. Elucidation of complexity and prediction of interactions in microbial communities. Microb. Biotechnol. 10:1500–22
    [Google Scholar]
/content/journals/10.1146/annurev-micro-060221-012134
Loading
/content/journals/10.1146/annurev-micro-060221-012134
Loading

Data & Media loading...

Supplementary Data

  • 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