1932

Abstract

The human microbiome is complex, variable from person to person, essential for health, and related to both the risk for disease and the efficacy of our treatments. There are robust techniques to describe microbiota with high-throughput sequencing, and there are hundreds of thousands of already-sequenced specimens in public archives. The promise remains to use the microbiome both as a prognostic factor and as a target for precision medicine. However, when used as an input in biomedical data science modeling, the microbiome presents unique challenges. Here, we review the most common techniques used to describe microbial communities, explore these unique challenges, and discuss the more successful approaches for biomedical data scientists seeking to use the microbiome as an input in their studies.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-biodatasci-020722-043017
2023-08-10
2024-05-05
Loading full text...

Full text loading...

/deliver/fulltext/biodatasci/6/1/annurev-biodatasci-020722-043017.html?itemId=/content/journals/10.1146/annurev-biodatasci-020722-043017&mimeType=html&fmt=ahah

Literature Cited

  1. 1.
    NIH (Natl. Inst. Health) HMP (Human Microbiome Proj.) Work. Group, Peterson J, Garges S, Giovanni M, McInnes P et al. 2009. The NIH Human Microbiome Project. Genome Res. 19:122317–23
    [Google Scholar]
  2. 2.
    McDonald D, Hyde E, Debelius JW, Morton JT, Gonzalez A et al. 2018. American Gut: an open platform for citizen science microbiome research. mSystems 3:3e00031–18
    [Google Scholar]
  3. 3.
    Lozupone CA, Stombaugh J, Gordon JI, Jansson JK, Knight R. 2012. Diversity, stability and resilience of the human gut microbiota. Nature 489:7415220–30
    [Google Scholar]
  4. 4.
    Huttenhower C, Gevers D, Knight R, Abubucker S, Badger JH et al. 2012. Structure, function and diversity of the healthy human microbiome. Nature 486:7402207–14
    [Google Scholar]
  5. 5.
    Grine G, Boualam MA, Drancourt M. 2017. Methanobrevibacter smithii, a methanogen consistently colonising the newborn stomach. Eur. J. Clin. Microbiol. Infect. Dis. 36:122449–55
    [Google Scholar]
  6. 6.
    Pérez JC. 2021. Fungi of the human gut microbiota: roles and significance. Int. J. Med. Microbiol. 311:3151490
    [Google Scholar]
  7. 7.
    Minot SS, Sinha R, Chen J, Chen J, Li H et al. 2011. The human gut virome: inter-individual variation and dynamic response to diet. Genome Res. 21:101616–25
    [Google Scholar]
  8. 8.
    Bobay L-M, Ochman H. 2017. The evolution of bacterial genome architecture. Front. Genet. 8:72
    [Google Scholar]
  9. 9.
    Qin J, Li R, Raes J, Arumugam M, Burgdorf KS et al. 2010. A human gut microbial gene catalogue established by metagenomic sequencing. Nature 464:728559–65
    [Google Scholar]
  10. 10.
    Chen F, Stappenbeck TS. 2019. Microbiome control of innate reactivity. Curr. Opin. Immunol. 56:107–13
    [Google Scholar]
  11. 11.
    Louis P, Flint HJ. 2017. Formation of propionate and butyrate by the human colonic microbiota: propionate and butyrate producing gut microbes. Environ. Microbiol. 19:129–41
    [Google Scholar]
  12. 12.
    Groschwitz KR, Hogan SP. 2009. Intestinal barrier function: molecular regulation and disease pathogenesis. J. Allergy Clin. Immunol. 124:13–20
    [Google Scholar]
  13. 13.
    Schaub B, Lauener R, von Mutius E. 2006. The many faces of the hygiene hypothesis. J. Allergy Clin. Immunol. 117:5969–77
    [Google Scholar]
  14. 14.
    Leinonen R, Sugawara H, Shumway M, Int. Nucleotide Seq. Database Collab 2011. The sequence read archive. Nucleic Acids Res. 39:D19–21
    [Google Scholar]
  15. 15.
    Baxter NT, Schmidt AW, Venkataraman A, Kim KS, Waldron C, Schmidt TM. 2019. Dynamics of human gut microbiota and short-chain fatty acids in response to dietary interventions with three fermentable fibers. mBio 10:1e02566–18
    [Google Scholar]
  16. 16.
    Bauermeister A, Mannochio-Russo H, Costa-Lotufo LV, Jarmusch AK, Dorrestein PC. 2022. Mass spectrometry-based metabolomics in microbiome investigations. Nat. Rev. Microbiol. 20:3143–60
    [Google Scholar]
  17. 17.
    Bhattarai Y, Kashyap PC. 2016. Germ-free mice model for studying host–microbial interactions. Mouse Models for Drug Discovery G Proetzel, MV Wiles 123–35. New York: Humana
    [Google Scholar]
  18. 18.
    Kennedy EA, King KY, Baldridge MT. 2018. Mouse microbiota models: comparing germ-free mice and antibiotics treatment as tools for modifying gut bacteria. Front. Physiol. 9:1534
    [Google Scholar]
  19. 19.
    Lauder E, Kim K, Schmidt TM, Golob JL. 2020. Organoid-derived adult human colonic epithelium responds to co-culture with a probiotic strain of Bifidobacterium longum. bioRxiv 2020.07.16.207852. https://doi.org/10.1101/2020.07.16.207852
  20. 20.
    Kosti I, Lyalina S, Pollard KS, Butte AJ, Sirota M. 2020. Meta-analysis of vaginal microbiome data provides new insights into preterm birth. Front. Microbiol. 11:476
    [Google Scholar]
  21. 21.
    Liu X, Cruz Rivera S, Moher D, Calvert MJ, Denniston AK, SPIRIT-AI and CONSORT-AI Work. Group 2020. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nat. Med. 26:91364–74
    [Google Scholar]
  22. 22.
    Schmidt TSB, Matias Rodrigues JF, von Mering C 2015. Limits to robustness and reproducibility in the demarcation of operational taxonomic units. Environ. Microbiol. 17:51689–706
    [Google Scholar]
  23. 23.
    Nat. Microbiol. 2016. Raising standards in microbiome research. Nat. Microbiol. 1:16112
    [Google Scholar]
  24. 24.
    Pollock J, Glendinning L, Wisedchanwet T, Watson M. 2018. The madness of microbiome: attempting to find consensus “best practice” for 16S microbiome studies. Appl. Environ. Microbiol. 84:7e02627–17
    [Google Scholar]
  25. 25.
    Schloss PD. 2018. Identifying and overcoming threats to reproducibility, replicability, robustness, and generalizability in microbiome research. mBio 9:3e00525–18
    [Google Scholar]
  26. 26.
    Tripathi A, Marotz C, Gonzalez A, Vázquez-Baeza Y, Song SJ et al. 2018. Are microbiome studies ready for hypothesis-driven research?. Curr. Opin. Microbiol. 44:61–69
    [Google Scholar]
  27. 27.
    Schellenberg J, Links MG, Hill JE, Hemmingsen SM, Peters GA, Dumonceaux TJ. 2011. Pyrosequencing of chaperonin-60 (cpn60) amplicons as a means of determining microbial community composition. Methods Mol. Biol. 733:143–58
    [Google Scholar]
  28. 28.
    Yarza P, Yilmaz P, Pruesse E, Glöckner FO, Ludwig W et al. 2014. Uniting the classification of cultured and uncultured bacteria and archaea using 16S rRNA gene sequences. Nat. Rev. Microbiol. 12:9635–45
    [Google Scholar]
  29. 29.
    Chakravorty S, Helb D, Burday M, Connell N, Alland D. 2007. A detailed analysis of 16S ribosomal RNA gene segments for the diagnosis of pathogenic bacteria. J. Microbiol. Methods 69:2330–39
    [Google Scholar]
  30. 30.
    Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C et al. 2013. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 41:1e1
    [Google Scholar]
  31. 31.
    Wu GD, Lewis JD, Hoffmann C, Chen Y-Y, Knight R et al. 2010. Sampling and pyrosequencing methods for characterizing bacterial communities in the human gut using 16S sequence tags. BMC Microbiol. 10:206
    [Google Scholar]
  32. 32.
    Janda JM, Abbott SL. 2007. 16S rRNA gene sequencing for bacterial identification in the diagnostic laboratory: pluses, perils, and pitfalls. J. Clin. Microbiol. 45:92761–64
    [Google Scholar]
  33. 33.
    DeSantis TZ, Hugenholtz P, Larsen N, Rojas M, Brodie EL et al. 2006. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 72:75069–72
    [Google Scholar]
  34. 34.
    Maidak BL, Olsen GJ, Larsen N, Overbeek R, McCaughey MJ, Woese CR. 1997. The RDP (Ribosomal Database Project). Nucleic Acids Res. 25:1109–11
    [Google Scholar]
  35. 35.
    Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T et al. 2013. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41:D590–96
    [Google Scholar]
  36. 36.
    Kembel SW, Wu M, Eisen JA, Green JL. 2012. Incorporating 16S gene copy number information improves estimates of microbial diversity and abundance. PLOS Comput. Biol. 8:10e1002743
    [Google Scholar]
  37. 37.
    Kennedy NA, Walker AW, Berry SH, Duncan SH, Farquarson FM et al. 2014. The impact of different DNA extraction kits and laboratories upon the assessment of human gut microbiota composition by 16S rRNA gene sequencing. PLOS ONE 9:2e88982
    [Google Scholar]
  38. 38.
    Ahn J-H, Kim B-Y, Song J, Weon H-Y. 2012. Effects of PCR cycle number and DNA polymerase type on the 16S rRNA gene pyrosequencing analysis of bacterial communities. J. Microbiol. 50:61071–74
    [Google Scholar]
  39. 39.
    Martin M. 2011. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17:110–12
    [Google Scholar]
  40. 40.
    Westcott SL, Schloss PD. 2017. OptiClust, an improved method for assigning amplicon-based sequence data to operational taxonomic units. mSphere 2:2e00073–17
    [Google Scholar]
  41. 41.
    Mahé F, Rognes T, Quince C, de Vargas C, Dunthorn M. 2014. Swarm: robust and fast clustering method for amplicon-based studies. PeerJ 2:e593
    [Google Scholar]
  42. 42.
    Nearing JT, Douglas GM, Comeau AM, Langille MGI. 2018. Denoising the Denoisers: an independent evaluation of microbiome sequence error-correction approaches. PeerJ 6:e5364
    [Google Scholar]
  43. 43.
    Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. 2016. DADA2: high resolution sample inference from Illumina amplicon data. Nat. Methods 13:7581–83
    [Google Scholar]
  44. 44.
    Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC et al. 2019. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37:8852–57
    [Google Scholar]
  45. 45.
    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]
  46. 46.
    Golob JL, Margolis E, Hoffman NG, Fredricks DN 2017. Evaluating the accuracy of amplicon-based microbiome computational pipelines on simulated human gut microbial communities. BMC Bioinform. 18:283
    [Google Scholar]
  47. 47.
    Li H, Durbin R. 2010. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics 26:5589–95
    [Google Scholar]
  48. 48.
    Lu J, Salzberg SL. 2020. Ultrafast and accurate 16S rRNA microbial community analysis using Kraken 2. Microbiome 8:124
    [Google Scholar]
  49. 49.
    Wood DE, Salzberg SL. 2014. Kraken: ultrafast metagenomic sequence classification using exact alignments. Genome Biol. 15:3R46
    [Google Scholar]
  50. 50.
    Beghini F, McIver LJ, Blanco-Míguez A, Dubois L, Asnicar F et al. 2021. Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3. eLife 10:e65088
    [Google Scholar]
  51. 51.
    Hillmann B, Al-Ghalith GA, Shields-Cutler RR, Zhu Q, Gohl DM et al. 2018. Evaluating the information content of shallow shotgun metagenomics. mSystems 3:6e00069–18
    [Google Scholar]
  52. 52.
    Franzosa EA, McIver LJ, Rahnavard G, Thompson LR, Schirmer M et al. 2018. Species-level functional profiling of metagenomes and metatranscriptomes. Nat. Methods 15:11962–68
    [Google Scholar]
  53. 53.
    Nurk S, Meleshko D, Korobeynikov A, Pevzner PA. 2017. metaSPAdes: a new versatile metagenomic assembler. Genome Res. 27:5824–34
    [Google Scholar]
  54. 54.
    Seemann T. 2014. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30:142068–69
    [Google Scholar]
  55. 55.
    Cantalapiedra CP, Hernández-Plaza A, Letunic I, Bork P, Huerta-Cepas J. 2021. eggNOG-mapper v2: functional annotation, orthology assignments, and domain prediction at the metagenomic scale. Mol. Biol. Evol. 38:125825–29
    [Google Scholar]
  56. 56.
    Huerta-Cepas J, Szklarczyk D, Heller D, Hernández-Plaza A, Forslund SK et al. 2019. eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses. Nucleic Acids Res. 47:D1D309–14
    [Google Scholar]
  57. 57.
    Golob JL, Minot SS. 2020. In silico benchmarking of metagenomic tools for coding sequence detection reveals the limits of sensitivity and precision. BMC Bioinform. 21:459
    [Google Scholar]
  58. 58.
    Minot SS, Barry KC, Kasman C, Golob JL, Willis AD. 2021. geneshot: gene-level metagenomics identifies genome islands associated with immunotherapy response. Genome Biol. 22:135
    [Google Scholar]
  59. 59.
    McDonald D, Clemente JC, Kuczynski J, Rideout JR, Stombaugh J et al. 2012. The Biological Observation Matrix (BIOM) format or: how I learned to stop worrying and love the ome-ome. Gigascience 1:17
    [Google Scholar]
  60. 60.
    Virshup I, Rybakov S, Theis FJ, Angerer P, Wolf FA. 2021. anndata: annotated data. bioRxiv 2021.12.16.473007. https://doi.org/10.1101/2021.12.16.473007
  61. 61.
    Kozlov AM, Zhang J, Yilmaz P, Glöckner FO, Stamatakis A. 2016. Phylogeny-aware identification and correction of taxonomically mislabeled sequences. Nucleic Acids Res. 44:115022–33
    [Google Scholar]
  62. 62.
    García-López M, Meier-Kolthoff JP, Tindall BJ, Gronow S, Woyke T et al. 2019. Analysis of 1,000 type-strain genomes improves taxonomic classification of Bacteroidetes. Front. Microbiol. 10:2083
    [Google Scholar]
  63. 63.
    Li H. 2015. Microbiome, metagenomics, and high-dimensional compositional data analysis. Annu. Rev. Stat. Appl. 2:73–94
    [Google Scholar]
  64. 64.
    Martin BD, Witten D, Willis AD. 2020. Modeling microbial abundances and dysbiosis with beta-binomial regression. Ann. Appl. Stat. 14:194–115
    [Google Scholar]
  65. 65.
    Lin H, Peddada SD. 2020. Analysis of compositions of microbiomes with bias correction. Nat. Commun. 11:3514
    [Google Scholar]
  66. 66.
    Janssen S, McDonald D, Gonzalez A, Navas-Molina JA, Jiang L et al. 2018. Phylogenetic placement of exact amplicon sequences improves associations with clinical information. mSystems 3:3e00021–18
    [Google Scholar]
  67. 67.
    Zheng Q, Bartow-McKenney C, Meisel JS, Grice EA. 2018. HmmUFOtu: an HMM and phylogenetic placement based ultra-fast taxonomic assignment and OTU picking tool for microbiome amplicon sequencing studies. Genome Biol. 19:82
    [Google Scholar]
  68. 68.
    Minot SS, Garb B, Roldan A, Tang A, Oskotsky T et al. 2022. Robust harmonization of microbiome studies by phylogenetic scaffolding with MaLiAmPi. bioRxiv 2022.07.26.501561. https://doi.org/10.1101/2022.07.26.501561
    [Crossref]
  69. 69.
    Gibbons SM, Duvallet C, Alm EJ. 2018. Correcting for batch effects in case-control microbiome studies. PLOS Comput. Biol. 14:4e1006102
    [Google Scholar]
  70. 70.
    Chao A, Chiu C-H, Jost L. 2014. Unifying species diversity, phylogenetic diversity, functional diversity, and related similarity and differentiation measures through Hill numbers. Annu. Rev. Ecol. Evol. Syst. 45:297–324
    [Google Scholar]
  71. 71.
    Peled JU, Gomes ALC, Devlin SM, Littmann ER, Taur Y et al. 2020. Microbiota as predictor of mortality in allogeneic hematopoietic-cell transplantation. New Engl. J. Med. 382:9822–34
    [Google Scholar]
  72. 72.
    Willis AD. 2019. Rarefaction, alpha diversity, and statistics. Front. Microbiol. 10:2407
    [Google Scholar]
  73. 73.
    Bray JR, Curtis JT. 1957. An ordination of the upland forest communities of southern Wisconsin. Ecol. Monogr. 27:4325–49
    [Google Scholar]
  74. 74.
    Jaccard P. 1912. The distribution of the flora in the alpine zone. New Phytol. 11:237–50
    [Google Scholar]
  75. 75.
    Lozupone C, Knight R. 2005. UniFrac: a new phylogenetic method for comparing microbial communities. Appl. Environ. Microbiol. 71:128228–35
    [Google Scholar]
  76. 76.
    Kruskal JB. 1964. Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika 29:11–27
    [Google Scholar]
  77. 77.
    McInnes L, Healy J, Melville J. 2020. UMAP: uniform manifold approximation and projection for dimension reduction. arXiv:1802.03426 [stat.ML]. https://doi.org/10.48550/arXiv.1802.03426
  78. 78.
    van der Maaten LJP, Hinton GE. 2008. Visualizing high-dimensional data using t-SNE. J. Mach. Learn. Res. 9:2579–605
    [Google Scholar]
  79. 79.
    Clarke KR. 1993. Non-parametric multivariate analyses of changes in community structure. Aust. J. Ecol. 18:1117–43
    [Google Scholar]
  80. 80.
    Golob JL, Pergam SA, Srinivasan S, Fiedler TL, Liu C et al. 2017. Stool microbiota at neutrophil recovery is predictive for severe acute graft versus host disease after hematopoietic cell transplantation. Clin. Infect. Dis. 65:121984–91
    [Google Scholar]
  81. 81.
    Imai J, Ichikawa H, Kitamoto S, Golob JL, Kaneko M et al. 2021. A potential pathogenic association between periodontal disease and Crohn's disease. JCI Insight 6:23e148543
    [Google Scholar]
  82. 82.
    Perez-Pascual D, Monnet V, Gardan R. 2016. Bacterial cell–cell communication in the host via RRNPP peptide-binding regulators. Front. Microbiol. 7:706
    [Google Scholar]
  83. 83.
    Duvallet C, Gibbons SM, Gurry T, Irizarry RA, Alm EJ. 2017. Meta analysis of microbiome studies identifies shared and disease-specific patterns. bioRxiv 134031. https://doi.org/10.1101/134031
  84. 84.
    Zhernakova A, Kurilshikov A, Bonder MJ, Tigchelaar EF, Schirmer M et al. 2016. Population-based metagenomics analysis reveals markers for gut microbiome composition and diversity. Science 352:6285565–69
    [Google Scholar]
  85. 85.
    France MT, Ma B, Gajer P, Brown S, Humphrys MS et al. 2020. VALENCIA: a nearest centroid classification method for vaginal microbial communities based on composition. Microbiome 8:166
    [Google Scholar]
  86. 86.
    Vital M, Howe AC, Tiedje JM. 2014. Revealing the bacterial butyrate synthesis pathways by analyzing (meta)genomic data. mBio 5:2e00889
    [Google Scholar]
  87. 87.
    Louis P, Flint HJ. 2017. Formation of propionate and butyrate by the human colonic microbiota. Environ. Microbiol. 19:129–41
    [Google Scholar]
  88. 88.
    Varga T, Czimmerer Z, Nagy L. 2011. PPARs are a unique set of fatty acid regulated transcription factors controlling both lipid metabolism and inflammation. Biochim. Biophys. Acta Mol. Basis Dis. 1812:81007–22
    [Google Scholar]
  89. 89.
    Hu E, Kim JB, Sarraf P, Spiegelman BM. 1996. Inhibition of adipogenesis through MAP kinase-mediated phosphorylation of PPARγ. Science 274:52952100–3
    [Google Scholar]
  90. 90.
    Sun M, Wu W, Liu Z, Cong Y. 2017. Microbiota metabolite short chain fatty acids, GPCR, and inflammatory bowel diseases. J. Gastroenterol. 52:1–8
    [Google Scholar]
  91. 91.
    Silva LG, Ferguson BS, Avila AS, Faciola AP. 2018. Sodium propionate and sodium butyrate effects on histone deacetylase (HDAC) activity, histone acetylation, and inflammatory gene expression in bovine mammary epithelial cells. J. Anim. Sci. 96:125244–52
    [Google Scholar]
  92. 92.
    Gao Z, He Q, Peng B, Chiao PJ, Ye J. 2006. Regulation of nuclear translocation of HDAC3 by IκBα is required for tumor necrosis factor inhibition of peroxisome proliferator-activated receptor γ function. J. Biol. Chem. 281:74540–47
    [Google Scholar]
  93. 93.
    Donohoe DR, Garge N, Zhang X, Sun W, O'Connell TM et al. 2011. The microbiome and butyrate regulate energy metabolism and autophagy in the mammalian colon. Cell Metab. 13:5517–26
    [Google Scholar]
  94. 94.
    Mathewson ND, Jenq R, Mathew AV, Koenigsknecht M, Hanash A et al. 2016. Gut microbiome–derived metabolites modulate intestinal epithelial cell damage and mitigate graft-versus-host disease. Nat. Immunol. 17:5505–13
    [Google Scholar]
  95. 95.
    Romick-Rosendale LE, Haslam DB, Lane A, Denson L, Lake K et al. 2018. Antibiotic exposure and reduced short chain fatty acid production after hematopoietic stem cell transplant. Biol. Blood Marrow Transplant. 24:122418–24
    [Google Scholar]
  96. 96.
    Haak BW, Littmann ER, Chaubard J-L, Pickard AJ, Fontana E et al. 2018. Impact of gut colonization with butyrate producing microbiota on respiratory viral infection following allo-HCT. Blood 131:262978–86
    [Google Scholar]
  97. 97.
    Kaiko GE, Ryu SH, Koues OI, Collins PL, Solnica-Krezel L et al. 2016. The colonic crypt protects stem cells from microbiota-derived metabolites. Cell 165:71708–20
    [Google Scholar]
  98. 98.
    Golob JL, DeMeules MM, Loeffelholz T, Quinn ZZ, Dame MK et al. 2019. Butyrogenic bacteria after acute graft-versus-host disease (GVHD) are associated with the development of steroid-refractory GVHD. Blood Adv. 3:192866–69
    [Google Scholar]
  99. 99.
    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 22:125–37.e6
    [Google Scholar]
  100. 100.
    Swimm A, Giver CR, DeFilipp Z, Rangaraju S, Sharma A et al. 2018. Indoles derived from intestinal microbiota act via type I interferon signaling to limit graft-versus-host disease. Blood 132:232506–19
    [Google Scholar]
  101. 101.
    Chang AE, Golob JL, Schmidt TM, Peltier DC, Lao CD, Tewari M. 2021. Targeting the gut microbiome to mitigate immunotherapy-induced colitis in cancer. Trends Cancer 7:7583–93
    [Google Scholar]
  102. 102.
    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]
  103. 103.
    DiGiulio DB, Callahan BJ, McMurdie PJ, Costello EK, Lyell DJ et al. 2015. Temporal and spatial variation of the human microbiota during pregnancy. PNAS 112:3511060–65
    [Google Scholar]
/content/journals/10.1146/annurev-biodatasci-020722-043017
Loading
/content/journals/10.1146/annurev-biodatasci-020722-043017
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