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Interrogation of the mammalian gut–brain axis using LC–MS/MS-based targeted metabolomics with in vitro bacterial and organoid cultures and in vivo gnotobiotic mouse models

Abstract

Interest in the communication between the gastrointestinal tract and central nervous system, known as the gut–brain axis, has prompted the development of quantitative analytical platforms to analyze microbe- and host-derived signals. This protocol enables investigations into connections between microbial colonization and intestinal and brain neurotransmitters and contains strategies for the comprehensive evaluation of metabolites in in vitro (organoids) and in vivo mouse model systems. Here we present an optimized workflow that includes procedures for preparing these gut–brain axis model systems: (stage 1) growth of microbes in defined media; (stage 2) microinjection of intestinal organoids; and (stage 3) generation of animal models including germ-free (no microbes), specific-pathogen-free (complete gut microbiota) and specific-pathogen-free re-conventionalized (germ-free mice associated with a complete gut microbiota from a specific-pathogen-free mouse), and Bifidobacterium dentium and Bacteroides ovatus mono-associated mice (germ-free mice colonized with a single gut microbe). We describe targeted liquid chromatography–tandem mass spectrometry-based metabolomics methods for analyzing microbially derived short-chain fatty acids and neurotransmitters from these samples. Unlike other protocols that commonly examine only stool samples, this protocol includes bacterial cultures, organoid cultures and in vivo samples, in addition to monitoring the metabolite content of stool samples. The incorporation of three experimental models (microbes, organoids and animals) enhances the impact of this protocol. The protocol requires 3 weeks of murine colonization with microbes and ~1–2 weeks for liquid chromatography–tandem mass spectrometry-based instrumental and quantitative analysis, and sample post-processing and normalization.

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Fig. 1: General workflow used to prepare the different types of microbiome models studied in this protocol.
Fig. 2: Sample collection methods and processing procedures for each of the sample types examined.
Fig. 3: Portions of metabolic pathways and LC–MS/MS-based chromatograms for the methods featured in this protocol.
Fig. 4: Heat maps representing the absolute concentrations of SCFAs measured in in vitro medium and in vivo stool samples.
Fig. 5: Heat maps representing the absolute concentrations of metabolites in the tryptophan, tyrosine and glutamate cycle pathways measured in in vitro medium and in vivo tissue samples.

Data availability

All data are publicly available in our primary research articles22,25,86. The data in Figs. 2 and 3 are published data and are available in the tables in this paper and its Supplementary Information. Data are also available upon request.

References

  1. The Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).

    Article  PubMed Central  Google Scholar 

  2. Nagpal, R. et al. Gut microbiome and aging: physiological and mechanistic insights. Nutr. Healthy Aging 4, 267–285 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Luck, B. et al. Bifidobacteria shape host neural circuits during postnatal development by promoting synapse formation and microglial function. Sci. Rep. 10, 7737 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Desbonnet, L., Clarke, G., Shanahan, F., Dinan, T. G. & Cryan, J. F. Microbiota is essential for social development in the mouse. Mol. Psychiatry 19, 146–148 (2014).

    Article  CAS  PubMed  Google Scholar 

  5. Desbonnet, L. et al. Effects of the probiotic Bifidobacterium infantis in the maternal separation model of depression. Neuroscience 170, 1179–1188 (2010).

    Article  CAS  PubMed  Google Scholar 

  6. Mayer, E. A., Knight, R., Mazmanian, S. K., Cryan, J. F. & Tillisch, K. Gut microbes and the brain: paradigm shift in neuroscience. J. Neurosci. 34, 15490–15496 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Lozupone, C. A., Stombaugh, J. I., Gordon, J. I., Jansson, J. K. & Knight, R. Diversity, stability and resilience of the human gut microbiota. Nature 489, 220–230 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Yatsunenko, T. et al. Human gut microbiome viewed across age and geography. Nature 486, 222–227 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Johnson, C. L. & Versalovic, J. The human microbiome and its potential importance to pediatrics. Pediatrics 129, 950–960 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Hollister, E. B. et al. Structure and function of the healthy pre-adolescent pediatric gut microbiome. Microbiome 3, 36 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Martin, R. et al. Role of commensal and probiotic bacteria in human health: a focus on inflammatory bowel disease. Microb. Cell Fact. 12, 71 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Engevik, M. A. et al. Human-derived Bifidobacterium dentium modulates the mammalian serotonergic system and gut–brain axis. Cell Mol. Gastroenterol. Hepatol. https://doi.org/10.1016/j.jcmgh.2020.08.002 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Luk, B. et al. Postnatal colonization with human “infant-type” Bifidobacterium species alters behavior of adult gnotobiotic mice. PLoS ONE 13, e0196510 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Yano, J. M. et al. Indigenous bacteria from the gut microbiota regulate host serotonin biosynthesis. Cell 161, 264–276 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Chang-Graham, A. L. et al. Human intestinal enteroids with inducible neurogenin-3 expression as a novel model of gut hormone secretion. Cell Mol. Gastroenterol. Hepatol. 8, 209–229 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Luczynski, P. et al. Growing up in a bubble: using germ-free animals to assess the influence of the gut microbiota on brain and behavior. Int. J. Neuropsychopharmacol. https://doi.org/10.1093/ijnp/pyw020 (2016).

  17. Desbonnet, L. et al. Gut microbiota depletion from early adolescence in mice: implications for brain and behaviour. Brain Behav. Immun. 48, 165–173 (2015).

    Article  CAS  PubMed  Google Scholar 

  18. Desbonnet, L., Garrett, L., Clarke, G., Bienenstock, J. & Dinan, T. G. The probiotic Bifidobacteria infantis: an assessment of potential antidepressant properties in the rat. J. Psychiatr. Res. 43, 164–174 (2008).

    Article  PubMed  Google Scholar 

  19. Engevik, M. A. & Versalovic, J. Biochemical features of beneficial microbes: foundations for therapeutic microbiology. Microbiol. Spectr. https://doi.org/10.1128/microbiolspec.BAD-0012-2016 (2017).

  20. Ma, Q. et al. Impact of microbiota on central nervous system and neurological diseases: the gut–brain axis. J. Neuroinflammation 16, 53 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Horvath, T. D. et al. Bacteroides ovatus colonization influences the abundance of intestinal short chain fatty acids and neurotransmitters. iScience 25, 104158 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Luck, B. et al. Neurotransmitter profiles are altered in the gut and brain of mice mono-associated with Bifidobacterium dentium. Biomolecules https://doi.org/10.3390/biom11081091 (2021).

  23. Engevik, M. A. et al. Bifidobacterium dentium fortifies the intestinal mucus layer via autophagy and calcium signaling pathways. mBio https://doi.org/10.1128/mBio.01087-19 (2019).

  24. Pokusaeva, K. et al. GABA-producing Bifidobacterium dentium modulates visceral sensitivity in the intestine. Neurogastroenterol. Motil. https://doi.org/10.1111/nmo.12904 (2017).

  25. Ihekweazu, F. D. et al. Bacteroides ovatus promotes IL-22 production and reduces trinitrobenzene sulfonic acid-driven colonic inflammation. Am. J. Pathol. 191, 704–719 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Ihekweazu, F. D. et al. Bacteroides ovatus ATCC 8483 monotherapy is superior to traditional fecal transplant and multi-strain bacteriotherapy in a murine colitis model. Gut Microbes 10, 504–520 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Tareke, E., Bowyer, J. F. & Doerge, D. R. Quantification of rat brain neurotransmitters and metabolites using liquid chromatography/electrospray tandem mass spectrometry and comparison with liquid chromatography/electrochemical detection. Rapid Commun. Mass Spectrom. 21, 3898–3904 (2007).

    Article  CAS  PubMed  Google Scholar 

  28. Zhang, M. Y. & Beyer, C. E. Measurement of neurotransmitters from extracellular fluid in brain by in vivo microdialysis and chromatography-mass spectrometry. J. Pharm. Biomed. Anal. 40, 492–499 (2006).

    Article  CAS  PubMed  Google Scholar 

  29. Kovac, A., Somikova, Z., Zilka, N. & Novak, M. Liquid chromatography–tandem mass spectrometry method for determination of panel of neurotransmitters in cerebrospinal fluid from the rat model for tauopathy. Talanta 119, 284–290 (2014).

    Article  CAS  PubMed  Google Scholar 

  30. Sato, T. et al. Single Lgr5 stem cells build crypt–villus structures in vitro without a mesenchymal niche. Nature 459, 262–265 (2009).

    Article  CAS  PubMed  Google Scholar 

  31. Ruan, W. et al. Enhancing responsiveness of human jejunal enteroids to host and microbial stimuli. J. Physiol. 598, 3085–3105 (2020).

    Article  CAS  PubMed  Google Scholar 

  32. Begou, O., Gika, H. G., Wilson, I. D. & Theodoridis, G. Hyphenated MS-based targeted approaches in metabolomics. Analyst 142, 3079–3100 (2017).

    Article  CAS  PubMed  Google Scholar 

  33. Jang, C., Chen, L. & Rabinowitz, J. D. Metabolomics and isotope tracing. Cell 173, 822–837 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Rinschen, M. M., Ivanisevic, J., Giera, M. & Siuzdak, G. Identification of bioactive metabolites using activity metabolomics. Nat. Rev. Mol. Cell Biol. 20, 353–367 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Cajka, T. & Fiehn, O. Toward merging untargeted and targeted methods in mass spectrometry-based metabolomics and lipidomics. Anal. Chem. 88, 524–545 (2016).

    Article  CAS  PubMed  Google Scholar 

  36. Mahmud, I. & Garrett, T. J. Mass spectrometry techniques in emerging pathogens studies: COVID-19 perspectives. J. Am. Soc. Mass Spectrom. 31, 2013–2024 (2020).

    Article  CAS  PubMed  Google Scholar 

  37. Anderson, B. G., Raskind, A., Habra, H., Kennedy, R. T. & Evans, C. R. Modifying chromatography conditions for improved unknown feature identification in untargeted metabolomics. Anal. Chem. 93, 15840–15849 (2021).

    Article  CAS  PubMed  Google Scholar 

  38. Horvath, T. D., Dagan, S. & Scaraffia, P. Y. Unraveling mosquito metabolism with mass spectrometry-based metabolomics. Trends Parasitol. 37, 747–761 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Lee, S. M. et al. Adaptation in a mouse colony monoassociated with Escherichia coli K-12 for more than 1,000 days. Appl. Environ. Microbiol. 76, 4655–4663 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Hoffmann, T. W. et al. Microorganisms linked to inflammatory bowel disease-associated dysbiosis differentially impact host physiology in gnotobiotic mice. ISME J. 10, 460–477 (2016).

    Article  CAS  PubMed  Google Scholar 

  41. Stehlikova, Z. et al. Crucial role of microbiota in experimental psoriasis revealed by a gnotobiotic mouse model. Front. Microbiol. https://doi.org/10.3389/fmicb.2019.00236 (2019).

  42. Klaasen, H. L. B. M., Koopman, J. P., Van den Brink, M. E., Van Wezel, H. P. N. & Beynen, A. C. Mono-association of mice with non-cultivable, intestinal, segmented, filamentous bacteria. Arch. Microbiol. 156, 148–151 (1991).

    Article  CAS  PubMed  Google Scholar 

  43. Kim, J. et al. Lactobacillus rhamnosus GG modifies the metabolome of pathobionts in gnotobiotic mice. BMC Microbiol. 21, 165 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Mao, J.-H. et al. Genetic and metabolic links between the murine microbiome and memory. Microbiome 8, 53 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Engevik, M. A. et al. Bifidobacterium dentium-derived y-glutamylcysteine suppresses ER-mediated goblet cell stress and reduces TNBS-driven colonic inflammation. Gut Microbes 13, 1–21 (2021).

    Article  PubMed  Google Scholar 

  46. Chen, Y. P. & Chen, M. J. Effects of Lactobacillus kefiranofaciens M1 isolated from kefir grains on germ-free mice. PLoS ONE 8, e78789 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Chiu, C. C. et al. Monocolonization of germ-free mice with Bacteroides fragilis protects against dextran sulfate sodium-induced acute colitis. Biomed. Res. Int. 2014, 675786 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Lee, Y. P. et al. The germ-free mice monocolonization with Bacteroides fragilis improves azoxymethane/dextran sulfate sodium induced colitis-associated colorectal cancer. Immunopharmacol. Immunotoxicol. 41, 207–213 (2019).

    Article  CAS  PubMed  Google Scholar 

  49. Donaldson, G. P. et al. Spatially distinct physiology of Bacteroides fragilis within the proximal colon of gnotobiotic mice. Nat. Microbiol. 5, 746–756 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Peterson, D. A. et al. Characterizing the interactions between a naturally primed immunoglobulin A and its conserved Bacteroides thetaiotaomicron species-specific epitope in gnotobiotic mice. J. Biol. Chem. 290, 12630–12649 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Aktar, R. et al. Human resident gut microbe Bacteroides thetaiotaomicron regulates colonic neuronal innervation and neurogenic function. Gut Microbes 11, 1745–1757 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  52. Soavelomandroso, A. P. et al. Biofilm structures in a mono-associated mouse model of Clostridium difficile infection. Front. Microbiol. 8, 2086–2086 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Tamura, M., Hirayama, K. & Itoh, K. Comparison of colonic bacterial enzymes in gnotobiotic mice monoassociated with different intestinal bacteria. Microb. Ecol. Health Dis. 9, 287–294 (1996).

    Article  Google Scholar 

  54. Liu, W.-H. et al. Alteration of behavior and monoamine levels attributable to Lactobacillus plantarum PS128 in germ-free mice. Behav. Brain Res. 298, 202–209 (2016).

    Article  CAS  PubMed  Google Scholar 

  55. Kozakova, H. et al. Colonization of germ-free mice with a mixture of three lactobacillus strains enhances the integrity of gut mucosa and ameliorates allergic sensitization. Cell Mol. Immunol. 13, 251–262 (2016).

    Article  CAS  PubMed  Google Scholar 

  56. Eberl, C. et al. Reproducible colonization of germ-free mice with the oligo-mouse-microbiota in different animal facilities. Front. Microbiol. https://doi.org/10.3389/fmicb.2019.02999 (2020).

  57. Darnaud, M. et al. A standardized gnotobiotic mouse model harboring a minimal 15-member mouse gut microbiota recapitulates SOPF/SPF phenotypes. Nat. Commun. 12, 6686 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Mark Welch, J. L., Hasegawa, Y., McNulty, N. P., Gordon, J. I. & Borisy, G. G. Spatial organization of a model 15-member human gut microbiota established in gnotobiotic mice. Proc. Natl Acad. Sci. USA 114, E9105–e9114 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Zhang, G., Mills, D. A. & Block, D. E. Development of chemically defined media supporting high-cell-density growth of lactococci, enterococci, and streptococci. Appl. Environ. Microbiol. 75, 1080–1087 (2009).

    Article  CAS  PubMed  Google Scholar 

  60. Sidhaye, J. & Knoblich, J. A. Brain organoids: an ensemble of bioassays to investigate human neurodevelopment and disease. Cell Death Differ. https://doi.org/10.1038/s41418-020-0566-4 (2020).

  61. Manousiouthakis, E. et al. Bioengineered in vitro enteric nervous system. J. Tissue Eng. Regen. Med. 13, 1712–1723 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Noel, G. et al. A primary human macrophage-enteroid co-culture model to investigate mucosal gut physiology and host-pathogen interactions. Sci. Rep. 7, 45270 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Kennedy, E. A., King, K. Y. & Baldridge, M. T. Mouse microbiota models: comparing germ-free mice and antibiotics treatment as tools for modifying gut bacteria. Front. Physiol. 9, 1534 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Luk, B. et al. in Figshare (2018).

  65. Zhang, S., Wang, H. & Zhu, M. J. A sensitive GC/MS detection method for analyzing microbial metabolites short chain fatty acids in fecal and serum samples. Talanta 196, 249–254 (2019).

    Article  CAS  PubMed  Google Scholar 

  66. Mulat, D. G. & Feilberg, A. GC/MS method for determining carbon isotope enrichment and concentration of underivatized short-chain fatty acids by direct aqueous solution injection of biogas digester samples. Talanta 143, 56–63 (2015).

    Article  CAS  PubMed  Google Scholar 

  67. Hsu, Y.-L. et al. Evaluation and optimization of sample handling methods for quantification of short-chain fatty acids in human fecal samples by GC–MS. J. Proteome Res. 18, 1948–1957 (2019).

    Article  CAS  PubMed  Google Scholar 

  68. Furuhashi, T., Sugitate, K., Nakai, T., Jikumaru, Y. & Ishihara, G. Rapid profiling method for mammalian feces short chain fatty acids by GC–MS. Anal. Biochem. 543, 51–54 (2018).

    Article  CAS  PubMed  Google Scholar 

  69. Marcos, J. et al. Targeting tryptophan and tyrosine metabolism by liquid chromatography tandem mass spectrometry. J. Chromatogr. A 1434, 91–101 (2016).

    Article  CAS  PubMed  Google Scholar 

  70. Anesi, A. et al. Metabolic profiling of human plasma and urine, targeting tryptophan, tyrosine and branched chain amino acid pathways. Metabolites https://doi.org/10.3390/metabo9110261 (2019).

  71. Whiley, L. et al. Ultrahigh-performance liquid chromatography tandem mass spectrometry with electrospray ionization quantification of tryptophan metabolites and markers of gut health in serum and plasma—application to clinical and epidemiology cohorts. Anal. Chem. 91, 5207–5216 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Hook, V. et al. Metabolomics analyses of 14 classical neurotransmitters by GC–TOF with LC–MS illustrates secretion of 9 cell-cell signaling molecules from sympathoadrenal chromaffin cells in the presence of lithium. ACS Chem. Neurosci. 10, 1369–1379 (2019).

    Article  CAS  PubMed  Google Scholar 

  73. Pan, J. X. et al. Diagnosis of major depressive disorder based on changes in multiple plasma neurotransmitters: a targeted metabolomics study. Transl. Psychiatry 8, 130 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  74. Yuan, M., Breitkopf, S. B., Yang, X. & Asara, J. M. A positive/negative ion-switching, targeted mass spectrometry-based metabolomics platform for bodily fluids, cells, and fresh and fixed tissue. Nat. Protoc. 7, 872–881 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Zheng, F. et al. Development of a plasma pseudotargeted metabolomics method based on ultra-high-performance liquid chromatography–mass spectrometry. Nat. Protoc. 15, 2519–2537 (2020).

    Article  CAS  PubMed  Google Scholar 

  76. Han, S. et al. A metabolomics pipeline for the mechanistic interrogation of the gut microbiome. Nature 595, 415–420 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Engevik, M. A. et al. Microbial metabolic capacity for intestinal folate production and modulation of host folate receptors. Front. Microbiol. 10, 2305 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  78. Engevik, M. A. et al. Loss of NHE3 alters gut microbiota composition and influences Bacteroides thetaiotaomicron growth. Am. J. Physiol. Gastrointest. Liver Physiol. 305, G697–G711 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Williamson, I. A. et al. A high-throughput organoid microinjection platform to study gastrointestinal microbiota and luminal physiology. Cell Mol. Gastroenterol. Hepatol. 6, 301–319 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  80. Dutta, D., Heo, I. & Clevers, H. Disease modeling in stem cell-derived 3D organoid systems. Trends Mol. Med. 23, 393–410 (2017).

    Article  CAS  PubMed  Google Scholar 

  81. Zhang, Y. G. & Sun, J. Study bacteria–host interactions using intestinal organoids. Methods Mol. Biol. 1576, 249–254 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Lukovac, S. et al. Differential modulation by Akkermansia muciniphila and Faecalibacterium prausnitzii of host peripheral lipid metabolism and histone acetylation in mouse gut organoids. mBio https://doi.org/10.1128/mBio.01438-14 (2014).

  83. Co, J. Y. et al. Controlling epithelial polarity: a human enteroid model for host-pathogen interactions. Cell Rep. 26, 2509–2520 e2504 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Engevik, M. A. et al. Human intestinal enteroids as a model of Clostridioides difficile-induced enteritis. Am. J. Physiol. Gastrointest. Liver Physiol. 318, G870–G888 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Wilke, G. et al. A stem-cell-derived platform enables complete Cryptosporidium development in vitro and genetic tractability. Cell Host Microbe 26, 123–134.e128 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Engevik, M. A. et al. Human-derived Bifidobacterium dentium modulates the mammalian serotonergic system and gut–brain axis. Cell Mol. Gastroenterol. Hepatol. 11, 221–248 (2021).

    Article  CAS  PubMed  Google Scholar 

  87. Fultz, R. et al. Unraveling the metabolic requirements of the gut commensal Bacteroides ovatus. Front. Microbiol. 12, 745469 (2021).

    PubMed  PubMed Central  Google Scholar 

  88. Engevik, M. A. et al. The metabolic profile of Bifidobacterium dentium reflects its status as a human gut commensal. BMC Microbiol. 21, 154 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Engevik, M. A. et al. Fusobacterium nucleatum adheres to Clostridioides difficile via the RadD adhesin to enhance biofilm formation in intestinal mucus. Gastroenterology 160, 1301–1314 e1308 (2021).

    Article  CAS  PubMed  Google Scholar 

  90. Engevik, M. A. et al. Mucin-degrading microbes release monosaccharides that chemoattract Clostridioides difficile and facilitate colonization of the human intestinal mucus layer. ACS Infect. Dis. 7, 1126–1142 (2021).

    Article  CAS  PubMed  Google Scholar 

  91. Fultz, R., Ticer, T., Glover, J., Stripe, L. & Engevik, M. A. Select streptococci can degrade Candida mannan to facilitate growth. Appl. Environ. Microbiol., aem0223721 (2021).

  92. Siddiquee, S. in Practical Handbook of the Biology and Molecular Diversity of Trichoderma Species from Tropical Regions (eds Gupta, V. K. & Tuohy, M. G.) 1–15 (Springer, 2017).

  93. Strobel, H. J. Basic laboratory culture methods for anaerobic bacteria. Methods Mol. Biol. 581, 247–261 (2009).

    Article  CAS  PubMed  Google Scholar 

  94. Kilkenny, C., Browne, W. J., Cuthi, I., Emerson, M. & Altman, D. G. Improving bioscience research reporting: the ARRIVE guidelines for reporting animal research. Vet. Clin. Pathol. 41, 27–31 (2012).

    Article  PubMed  Google Scholar 

  95. Chan, J. C., Kioh, D. Y., Yap, G. C., Lee, B. W. & Chan, E. C. A novel LCMSMS method for quantitative measurement of short-chain fatty acids in human stool derivatized with (12)C- and (13)C-labelled aniline. J. Pharm. Biomed. Anal. 138, 43–53 (2017).

    Article  CAS  PubMed  Google Scholar 

  96. Horvath, T. D., Dagan, S., Lorenzi, P. L., Hawke, D. H. & Scaraffia, P. Y. Positional stable isotope tracer analysis reveals carbon routes during ammonia metabolism of Aedes aegypti mosquitoes. FASEB J. 32, 466–477 (2018).

    Article  CAS  PubMed  Google Scholar 

  97. Engevik, K. A., Matthis, A. L., Montrose, M. H. & Aihara, E. Organoids as a model to study infectious disease. Methods Mol. Biol. 1734, 71–81 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Hill, D. R., Huang, S., Tsai, Y. H., Spence, J. R. & Young, V. B. Real-time measurement of epithelial barrier permeability in human intestinal organoids. J. Vis. Exp. https://doi.org/10.3791/56960 (2017).

  99. Mazzoli, R. & Pessione, E. The neuro-endocrinological role of microbial glutamate and GABA signaling. Front. Microbiol. 7, 1934 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  100. Julio-Pieper, M., O’Connor, R. M., Dinan, T. G. & Cryan, J. F. Regulation of the brain–gut axis by group III metabotropic glutamate receptors. Eur. J. Pharmacol. 698, 19–30 (2013).

    Article  CAS  PubMed  Google Scholar 

  101. Soeiro-de-Souza, M. G. et al. Anterior cingulate glutamate–glutamine cycle metabolites are altered in euthymic bipolar I disorder. Eur. Neuropsychopharmacol. 25, 2221–2229 (2015).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

This study was supported by an NIH K01 K12319501 grant and Global Probiotic Council 2019-19319 (M.A.E.). This work was also supported by grants from the National Institute of Diabetes and Digestive and Kidney Diseases (Grant P30-DK-56338 to Texas Medical Center Digestive Disease Center, Gastrointestinal Experimental Model Systems), NIH U01CA170930 grant (J.V.) and unrestricted research support from BioGaia AB (Stockholm, Sweden) (J.K.S. and J.V.). The Texas Children’s Hospital Department of Pathology and Immunology provides salary support to Texas Children’s Microbiome Center-Metabolomics and Proteomics Mass Spectrometry Laboratory staff, and purchased all of the reagents, the consumables and durable supplies, and the LC–MS/MS equipment described. The authors thank K. Prince (Pathology at Texas Children’s Hospital) for her assistance with the graphics contained in this manuscript.

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Authors and Affiliations

Authors

Contributions

Concept and design (T.D.H., S.J.H., M.A.E. and A.M.H.); intellectual contribution (T.D.H., S.J.H., M.A.E., N.O., J.K.S. and A.M.H.); data acquisition (T.D.H., S.J.H., M.A.E., F.I., W.R., B.L., N.O. and J.K.S.); data analysis and interpretation (T.D.H., S.J.H., M.A.E., M.B., F.I., W.R., B.L., K.M.H., N.O., J.K.S. and A.M.H.); drafting manuscript (T.D.H., S.J.H. and M.A.E.), editing manuscript (T.D.H., S.J.H., M.A.E., M.B., F.I., W.R., B.L., K.M.H., N.O., J.K.S., J.V. and A.M.H.); funding (M.A.E., J.V. and A.M.H.).

Corresponding author

Correspondence to Anthony M. Haag.

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Competing interests

J.V. and J.K.S. receive unrestricted research support from BioGaia AB, a Swedish Probiotic Company. J.K.S. is a consultant for Pastuer21, Inc. J.V. also serves on the scientific advisory boards of Biomica, an Israeli informatics enterprise, Seed, a United States-based probiotics/prebiotics company, and Plexus Worldwide, a United States-based nutrition company. T.D.H. sits on the editorial advisory board and serves as an associate editor with the STAR Protocols journal (Cell Press). All remaining authors have no financial relationships to disclose.

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Nature Protocols thanks Yu-Feng Li, Josep Rubert and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Engevik, M. A. et al. Cell Mol. Gastroenterol. Hepatol. 11, 221–248 (2020): https://doi.org/10.1016/j.jcmgh.2020.08.002

Horvath, T. D. et al. iScience 25, 104158 (2022): https://doi.org/10.1016/j.isci.2022.104158

Luck, B. et al. Biomolecules 11, 1091 (2021): https://doi.org/10.3390/biom11081091

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Supplementary Information

Supplementary Methods and Tables 1–10.

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Horvath, T.D., Haidacher, S.J., Engevik, M.A. et al. Interrogation of the mammalian gut–brain axis using LC–MS/MS-based targeted metabolomics with in vitro bacterial and organoid cultures and in vivo gnotobiotic mouse models. Nat Protoc (2022). https://doi.org/10.1038/s41596-022-00767-7

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