Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Growth effects of N-acylethanolamines on gut bacteria reflect altered bacterial abundances in inflammatory bowel disease

Nature Microbiology volume 5, pages 486–497 (2020)Cite this article

Subjects

Abstract

Inflammatory bowel diseases (IBD) are associated with alterations in gut microbial abundances and lumenal metabolite concentrations, but the effects of specific metabolites on the gut microbiota in health and disease remain largely unknown. Here, we analysed the influences of metabolites that are differentially abundant in IBD on the growth and physiology of gut bacteria that are also differentially abundant in IBD. We found that N-acylethanolamines (NAEs), a class of endogenously produced signalling lipids elevated in the stool of IBD patients and a T-cell transfer model of colitis, stimulated growth of species over-represented in IBD and inhibited that of species depleted in IBD in vitro. Using metagenomic sequencing, we recapitulated the effects of NAEs in complex microbial communities ex vivo, with Proteobacteria blooming and Bacteroidetes declining in the presence of NAEs. Metatranscriptomic analysis of the same communities identified components of the respiratory chain as important for the metabolism of NAEs, and this was verified using a mutant deficient for respiratory complex I. In this study, we identified NAEs as a class of metabolites that are elevated in IBD and have the potential to shift gut microbiota towards an IBD-like composition.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Effects of stool metabolites on bacterial growth in exponential (Vmax) and stationary (max OD600 nm) phases.
Fig. 2: NAEs are elevated in IBD patients and a mouse model of colitis.
Fig. 3: Effects of NAEs on bacterial growth.
Fig. 4: Effects of NAEs on the composition of a complex microbial community.
Fig. 5: Changes in transcriptional activity in complex microbial communities in response to NAE treatment.

Similar content being viewed by others

Data availability

Metagenomic, metatranscriptomic and transcriptomic data are available in the NCBI Sequence Read Archive as BioProject PRJNA532456. Tables of processed mouse microbial species, monoculture transcriptomic data and chemostat microbial species are available as Supplementary Datasets 4, 7 and 8, respectively. Source data for Figs. 1 and 2 and Extended Data Figs. 3 and 4 are provided with the paper.

Code availability

Custom scripts used to analyse monoculture transcriptomic data are available at https://github.com/broadinstitute/split_merge_pl. The bioBakery tools (KneadData, MetaPhlAn2 and HUMAnN2) used to process meta’omic sequencing data from the two chemostats are available via http://huttenhower.sph.harvard.edu/biobakery as source code and installable packages. Downstream analyses were conducted using custom Python and R scripts. This code (and associated usage notes) is available from the corresponding authors upon request.

References

  1. Xavier, R. J. & Podolsky, D. K. Unravelling the pathogenesis of inflammatory bowel disease. Nature 448, 427–434 (2007).

    Article  CAS  PubMed  Google Scholar 

  2. Schirmer, M., Garner, A., Vlamakis, H. & Xavier, R. J. Microbial genes and pathways in inflammatory bowel disease. Nat. Rev. Microbiol. 17, 497–511 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Plichta, D. R., Graham, D. B., Subramanian, S. & Xavier, R. J. Therapeutic opportunities in inflammatory bowel disease: mechanistic dissection of host–microbiome relationships. Cell 178, 1041–1056 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Lewis, J. D. et al. Inflammation, antibiotics, and diet as environmental stressors of the gut microbiome in pediatric Crohn’s disease. Cell Host Microbe 18, 489–500 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Hall, A. B. et al. A novel Ruminococcus gnavus clade enriched in inflammatory bowel disease patients. Genome Med. 9, 103 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  6. Dorrestein, P. C., Mazmanian, S. K. & Knight, R. Finding the missing links among metabolites, microbes, and the host. Immunity 40, 824–832 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Santoru, M. L. et al. Cross sectional evaluation of the gut-microbiome metabolome axis in an Italian cohort of IBD patients. Sci. Rep. 7, 9523 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  8. Franzosa, E. A. et al. Gut microbiome structure and metabolic activity in inflammatory bowel disease. Nat. Microbiol. 4, 293–305 (2019).

    Article  CAS  PubMed  Google Scholar 

  9. Smith, P. M. et al. The microbial metabolites, short-chain fatty acids, regulate colonic Treg cell homeostasis. Science 341, 569–573 (2013).

    Article  CAS  PubMed  Google Scholar 

  10. Blander, J. M., Longman, R. S., Iliev, I. D., Sonnenberg, G. F. & Artis, D. Regulation of inflammation by microbiota interactions with the host. Nat. Immunol. 18, 851–860 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Imhann, F. et al. Interplay of host genetics and gut microbiota underlying the onset and clinical presentation of inflammatory bowel disease. Gut 67, 108–119 (2018).

    Article  CAS  PubMed  Google Scholar 

  12. Lloyd-Price, J. et al. Multi-omics of the gut microbial ecosystem in inflammatory bowel diseases. Nature 569, 655–662 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Zhang, L. S. & Davies, S. S. Microbial metabolism of dietary components to bioactive metabolites: opportunities for new therapeutic interventions. Genome Med. 8, 46 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Ni, J. et al. A role for bacterial urease in gut dysbiosis and Crohn’s disease. Sci. Transl. Med. 9, eaah6888 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Wahlstrom, A., Sayin, S. I., Marschall, H. U. & Backhed, F. Intestinal crosstalk between bile acids and microbiota and its impact on host metabolism. Cell Metab. 24, 41–50 (2016).

    Article  PubMed  CAS  Google Scholar 

  16. Le Gall, G. et al. Metabolomics of fecal extracts detects altered metabolic activity of gut microbiota in ulcerative colitis and irritable bowel syndrome. J. Proteome Res. 10, 4208–4218 (2011).

    Article  PubMed  CAS  Google Scholar 

  17. Hansen, H. S. Role of anorectic N-acylethanolamines in intestinal physiology and satiety control with respect to dietary fat. Pharmacol. Res. 86, 18–25 (2014).

    Article  CAS  PubMed  Google Scholar 

  18. Surana, N. K. & Kasper, D. L. The yin yang of bacterial polysaccharides: lessons learned from B. fragilis PSA. Immunol. Rev. 245, 13–26 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Gevers, D. et al. The treatment-naive microbiome in new-onset Crohn’s disease. Cell Host Microbe 15, 382–392 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Read, S. & Powrie, F. Induction of inflammatory bowel disease in immunodeficient mice by depletion of regulatory T cells. Curr. Protoc. Immunol. 30, 15.13.1–15.13.10 (2001).

    Google Scholar 

  21. Fujita, Y., Matsuoka, H. & Hirooka, K. Regulation of fatty acid metabolism in bacteria. Mol. Microbiol. 66, 829–839 (2007).

    Article  CAS  PubMed  Google Scholar 

  22. Agrawal, S. et al. A genome-wide screen in Escherichia coli reveals that ubiquinone is a key antioxidant for metabolism of long-chain fatty acids. J. Biol. Chem. 292, 20086–20099 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Garrett, W. S. et al. Enterobacteriaceae act in concert with the gut microbiota to induce spontaneous and maternally transmitted colitis. Cell Host Microbe 8, 292–300 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y. & Morishima, K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 45, D353–D361 (2017).

    Article  CAS  PubMed  Google Scholar 

  25. Abubucker, S. et al. Metabolic reconstruction for metagenomic data and its application to the human microbiome. PLoS Comput. Biol. 8, e1002358 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Franzosa, E. A. et al. Relating the metatranscriptome and metagenome of the human gut. Proc. Natl Acad. Sci. USA 111, E2329–E2338 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Schneider, D. et al. Assembly of the Escherichia coli NADH:ubiquinone oxidoreductase (complex I). Biochim. Biophys. Acta 1777, 735–739 (2008).

    Article  CAS  PubMed  Google Scholar 

  28. Campbell, J. W., Morgan-Kiss, R. M. & Cronan, J. E. Jr. A new Escherichia coli metabolic competency: growth on fatty acids by a novel anaerobic beta-oxidation pathway. Mol. Microbiol. 47, 793–805 (2003).

    Article  CAS  PubMed  Google Scholar 

  29. Kaval, K. G. & Garsin, D. A. Ethanolamine utilization in bacteria. mBio 9, e00066–18 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Pasternak, B. A. et al. Lipopolysaccharide exposure is linked to activation of the acute phase response and growth failure in pediatric Crohn’s disease and murine colitis. Inflamm. Bowel Dis. 16, 856–869 (2010).

    Article  PubMed  Google Scholar 

  31. Lee, W. J. & Hase, K. Gut microbiota-generated metabolites in animal health and disease. Nat. Chem. Biol. 10, 416–424 (2014).

    Article  CAS  PubMed  Google Scholar 

  32. Donia, M. S. & Fischbach, M. A. Small molecules from the human microbiota. Science 349, 1254766 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Cani, P. D. et al. Endocannabinoids—at the crossroads between the gut microbiota and host metabolism. Nat. Rev. Endocrinol. 12, 133–143 (2016).

    Article  CAS  PubMed  Google Scholar 

  34. Alhouayek, M. & Muccioli, G. G. The endocannabinoid system in inflammatory bowel diseases: from pathophysiology to therapeutic opportunity. Trends Mol. Med. 18, 615–625 (2012).

    Article  CAS  PubMed  Google Scholar 

  35. Diep, T. A. et al. Dietary fat decreases intestinal levels of the anorectic lipids through a fat sensor. FASEB J. 25, 765–774 (2011).

    Article  CAS  PubMed  Google Scholar 

  36. Fu, J. et al. Food intake regulates oleoylethanolamide formation and degradation in the proximal small intestine. J. Biol. Chem. 282, 1518–1528 (2007).

    Article  CAS  PubMed  Google Scholar 

  37. Muccioli, G. G. et al. The endocannabinoid system links gut microbiota to adipogenesis. Mol. Syst. Biol. 6, 392 (2010).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Jourdan, T., Godlewski, G. & Kunos, G. Endocannabinoid regulation of beta-cell functions: implications for glycaemic control and diabetes. Diabetes Obes. Metab. 18, 549–557 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Di Sabatino, A. et al. The endogenous cannabinoid system in the gut of patients with inflammatory bowel disease. Mucosal Immunol. 4, 574–583 (2011).

    Article  PubMed  CAS  Google Scholar 

  40. D’Argenio, G. et al. Up-regulation of anandamide levels as an endogenous mechanism and a pharmacological strategy to limit colon inflammation. FASEB J. 20, 568–570 (2006).

    Article  PubMed  CAS  Google Scholar 

  41. Geurts, L. et al. Adipose tissue NAPE-PLD controls fat mass development by altering the browning process and gut microbiota. Nat. Commun. 6, 6495 (2015).

    Article  CAS  PubMed  Google Scholar 

  42. Geurts, L. et al. Altered gut microbiota and endocannabinoid system tone in obese and diabetic leptin-resistant mice: impact on apelin regulation in adipose tissue. Front. Microbiol. 2, 149 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Di Paola, M. et al. Oleoylethanolamide treatment affects gut microbiota composition and the expression of intestinal cytokines in Peyer’s patches of mice. Sci. Rep. 8, 14881 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Rivera-Chavez, F., Lopez, C. A. & Baumler, A. J. Oxygen as a driver of gut dysbiosis. Free Radic. Biol. Med. 105, 93–101 (2017).

    Article  CAS  PubMed  Google Scholar 

  45. Rigottier-Gois, L. Dysbiosis in inflammatory bowel diseases: the oxygen hypothesis. ISME J. 7, 1256–1261 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Albenberg, L. et al. Correlation between intraluminal oxygen gradient and radial partitioning of intestinal microbiota. Gastroenterology 147, 1055–1063 (2014).

    Article  Google Scholar 

  47. Liu, J. et al. Lipopolysaccharide induces anandamide synthesis in macrophages via CD14/MAPK/phosphoinositide 3-kinase/NF-kappaB independently of platelet-activating factor. J. Biol. Chem. 278, 45034–45039 (2003).

    Article  CAS  PubMed  Google Scholar 

  48. Maccarrone, M. et al. Lipopolysaccharide downregulates fatty acid amide hydrolase expression and increases anandamide levels in human peripheral lymphocytes. Arch. Biochem. Biophys. 393, 321–328 (2001).

    Article  CAS  PubMed  Google Scholar 

  49. Wentzel, A., Ellingsen, T. E., Kotlar, H. K., Zotchev, S. B. & Throne-Holst, M. Bacterial metabolism of long-chain n-alkanes. Appl. Microbiol. Biotechnol. 76, 1209–1221 (2007).

    Article  CAS  PubMed  Google Scholar 

  50. Arthur, J. C. et al. Intestinal inflammation targets cancer-inducing activity of the microbiota. Science 338, 120–123 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Kim, S. C. et al. Variable phenotypes of enterocolitis in interleukin 10-deficient mice monoassociated with two different commensal bacteria. Gastroenterology 128, 891–906 (2005).

    Article  CAS  PubMed  Google Scholar 

  52. Lane, D. J. 16S/23S rRNA Sequencing (John Wiley and Sons, 1991).

  53. Mishima, Y. et al. Microbiota maintain colonic homeostasis by activating TLR2/MyD88/PI3K signaling in IL-10-producing regulatory B cells. J. Clin. Invest. 130, 3702–3716 (2019).

    Article  Google Scholar 

  54. Liu, B., Tonkonogy, S. L. & Sartor, R. B. Antigen-presenting cell production of IL-10 inhibits T-helper 1 and 17 cell responses and suppresses colitis in mice. Gastroenterology 141, 653–662.e4 (2011).

    Article  CAS  PubMed  Google Scholar 

  55. Olson, T. S. et al. Expanded B cell population blocks regulatory T cells and exacerbates ileitis in a murine model of Crohn disease. J. Clin. Invest. 114, 389–398 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. McDonald, J. A. et al. Evaluation of microbial community reproducibility, stability and composition in a human distal gut chemostat model. J. Microbiol. Methods 95, 167–174 (2013).

    Article  CAS  PubMed  Google Scholar 

  57. Shishkin, A. A. et al. Simultaneous generation of many RNA-seq libraries in a single reaction. Nat. Methods 12, 323–325 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Zhu, Y. Y., Machleder, E. M., Chenchik, A., Li, R. & Siebert, P. D. Reverse transcriptase template switching: a SMART approach for full-length cDNA library construction. Biotechniques 30, 892–897 (2001).

    Article  CAS  PubMed  Google Scholar 

  59. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    Article  CAS  PubMed  Google Scholar 

  61. Abeel, T., Van Parys, T., Saeys, Y., Galagan, J. & Van de Peer, Y. GenomeView: a next-generation genome browser. Nucleic Acids Res. 40, e12 (2012).

    Article  CAS  PubMed  Google Scholar 

  62. McIver, L. J. et al. bioBakery: a meta’omic analysis environment. Bioinformatics 34, 1235–1237 (2018).

    Article  CAS  PubMed  Google Scholar 

  63. Truong, D. T. et al. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat. Methods 12, 902–903 (2015).

    Article  CAS  PubMed  Google Scholar 

  64. Franzosa, E. A. et al. Species-level functional profiling of metagenomes and metatranscriptomes. Nat. Methods 15, 962–968 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Kanehisa, M. & Goto, S. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27–30 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We are grateful to A. Vrcic and S. Figueroa-Lazu for preparing the metabolites and to T. Poon for coordinating DNA and RNA sequencing. Broad Technology Labs generated metagenomic libraries, and RNA-Seq libraries were constructed and sequenced at the Broad Institute of MIT and Harvard by the Microbial ‘Omics Core and Genomics Platform, respectively. The Microbial ‘Omics Core also provided guidance on experimental design and conducted preliminary analysis for all RNA-seq data. We are deeply indebted to T. Reimels for helpful discussions, editing the manuscript and figure generation. We thank B. Hall, D. Kenny, D. Plichta, Z. Costliow, G. Jasso, J. Rush and X. Ke for insightful discussions. This work was funded by grants from the National Institutes of Health (NIH) R24DK110499 and U54DK102557 (C.H. and R.J.X.), R01AT009708 (R.J.X.), P01DK094779 and P40OD010995 (RBS), the Crohn’s and Colitis Foundation (R.J.X.) and the Center for Microbiome Informatics and Therapeutics (R.J.X.).

Author information

Author notes

  1. These authors contributed equally: Eric A. Franzosa, Jason Bishai.

Authors and Affiliations

  1. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Nadine Fornelos, Eric A. Franzosa, Jason Bishai, Jason Lloyd-Price, Timothy D. Arthur, Ashley Garner, Julian Avila-Pacheco, Andrew C. Tolonen, Clary B. Clish, Curtis Huttenhower, Hera Vlamakis & Ramnik J. Xavier

  2. Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA

    Eric A. Franzosa, Jason Lloyd-Price & Curtis Huttenhower

  3. Chemical Biology and Therapeutics, Novartis Institutes for Biomedical Research Inc., Cambridge, MA, USA

    John W. Annand, Henry J. Haiser & Jeffrey A. Porter

  4. Departments of Medicine, Microbiology and Immunology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Akihiko Oka & R. Balfour Sartor

  5. Center for Microbiome Informatics and Therapeutics, Massachusetts Institute of Technology, Cambridge, MA, USA

    Ramnik J. Xavier

  6. Department of Molecular Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Ramnik J. Xavier

  7. Center for Computational and Integrative Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Ramnik J. Xavier

Authors
  1. Nadine Fornelos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Eric A. Franzosa
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jason Bishai
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. John W. Annand
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Akihiko Oka
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jason Lloyd-Price
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Timothy D. Arthur
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Ashley Garner
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Julian Avila-Pacheco
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Henry J. Haiser
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Andrew C. Tolonen
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Jeffrey A. Porter
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Clary B. Clish
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. R. Balfour Sartor
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Curtis Huttenhower
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Hera Vlamakis
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Ramnik J. Xavier
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

N.F., A.C.T., H.V. and R.J.X. designed the research. N.F. performed bacterial growth and chemostat experiments. E.A.F. and J.B. analysed metagenomic and metatranscriptomic data. J.W.A. performed mass spectrometry analysis. A.O. and R.B.S. provided mouse stool and helped with mass spectrometry data interpretation. J.L.-P. analysed PRISM and iHMP metabolomics data. T.D.A. and A.G. contributed chemostat communities. T.D.A. also provided bacterial isolates. J.A.-P. confirmed the presence of AEA in PRISM and iHMP stool. N.F., H.J.H., J.A.P., C.B.C., C.H., H.V. and R.J.X. supervised the project. R.B.S., C.H. and R.J.X. acquired funding.

Corresponding authors

Correspondence to Hera Vlamakis or Ramnik J. Xavier.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Validation of metabolite screen results in dose assays.

Growth curves are shown for three strains using seven metabolites in multiple concentrations. Growth was monitored over time in a volume of 40 µL per well in 384-well plates. The final concentration of DMSO per treated and control well was 0.25%. Growth curves representative of three independent tests are shown and error bars in controls represent the standard deviation of the mean of six technical replicates.

Extended Data Fig. 2 Correlation between absolute and relative NAE abundances in stool from PRISM subjects.

NAEs detected in stool from PRISM Crohn’s disease (CD) patients (n=21) in absolute abundances (ng mg-1) are plotted against their respective relative abundances8. Pearson correlation coefficients (r) are shown. Progenesis QI (nonlinear DYNAMICS) was used for the extraction of non-targeted LC-MS features and TraceFinder (Thermo Fisher Scientific) was used for the manual peak extraction of known metabolites on basis of their mass to charge ratio (m/z) and retention times determined using authentic standards.

Extended Data Fig. 3 Growth effects of NAEs on intestinal bacteria elevated in IBD.

Palmitoylethanolamide (PEA), linoleoyl ethanolamide (LEA), oleoyl ethanolamide (OEA) and arachidonoyl ethanolamide (AEA) were added to growing cells (in the range of 106 to 108 CFU mL-1) in three concentrations (0 µM, 50 µM and 100 µM), and growth was monitored in an absorbance reader in the anaerobic chamber over time. Controls contained 0.4% DMSO. Growth curves representative of two independent experiments are shown and error bars represent the standard deviation of the mean of three technical replicates.

Source data

Extended Data Fig. 4 Growth effects of NAEs on intestinal bacteria depleted or invariable in IBD.

Palmitoylethanolamide (PEA), linoleoyl ethanolamide (LEA), oleoyl ethanolamide (OEA) and arachidonoyl ethanolamide (AEA) were added to growing cells (in the range of 106 to 108 CFU mL-1) in three concentrations (0 µM, 50 µM and 100 µM), and growth was monitored in an absorbance reader in the anaerobic chamber over time. Controls contained 0.4% DMSO. Growth curves representative of two independent experiments are shown and error bars represent the standard deviation of the mean of three technical replicates.

Source data

Extended Data Fig. 5 Transcriptional responses of Bacteroides fragilis to linoleoyl ethanolamide (LEA) and arachidonoyl ethanolamine (AEA).

a, Differential gene expression between three independent exponential cultures treated for 10 minutes with a sub-inhibitory concentration (25 µM) of LEA or AEA and controls (0.04% DMSO). Differential expression was determined with edgeR, and gene functions were defined using InterPro (EMBL), the NCBI Conserved Domain Database (CDD) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Selected significantly differentially expressed genes (|log2 fold-change (treated/control)|>0.5, FDR<0.05; Benjamini-Hochberg FDR values were derived from p-values calculated using the likelihood-ratio test) are shown in color. b, Genomic environment of the differentially expressed genes using colors that correspond with (a). Genes in white were not significantly differentially expressed. Coordinate maps refer to the genome of strain B. fragilis ATCC 25285. Gene products that have been experimentally shown to be associated with the outer membrane by LC-MS/MS analysis are in bold (Wilson, M. M., Anderson, D. E. & Bernstein, H. D. Analysis of the outer membrane proteome and secretome of Bacteroides fragilis reveals a multiplicity of secretion mechanisms. PLoS ONE 10, e0117732 (2015)).

Extended Data Fig. 6 Oleic acid, oleoyl ethanolamide and linoleoyl ethanolamide do not enhance growth of a complex I mutant.

Growth of wild-type (WT) E. coli BW25113 and a derivative deficient for complex I (NADH:quinone oxidoreductase, ΔnuoB) in minimal medium (M9 with glucose 4 g L-1 and 1% trace minerals) supplemented with EtOH 0.05%, 3.5 µM, 7 µM and 14 µM of oleic acid (OA), oleoyl ethanolamide (OEA) or linoleoyl ethanolamide (LEA). Growth curves representative of two independent experiments are shown and error bars represent the standard deviation of the mean of three technical replicates.

Extended Data Fig. 7 Effects of NAEs on the composition of a complex microbial community.

a, Taxonomic abundances in chemostat B at the family level. Vertical colored bars represent the relative abundance of bacterial families in samples 1 and 12 hr after addition of DMSO (0.5%), individual NAEs, or a combination of all four NAEs (denoted as NAE-mix). Individual NAEs were added to a final concentration of 500 µM. In combination, the PEA:OEA:LEA:AEA ratio was 125:125:125:125 µM. Heatmaps show log2 fold-changes in b, family- and c, species-level taxonomic abundances between treated samples and DMSO controls (total n=41 with per-treatment n ranging from 6 to 7). Species that shifted with statistical significance in response to treatment are shown (*q<0.20, **q<0.05; FDR q-values derived from nominal two-tailed p-values of the “treatment” coefficient across per-taxon linear regression analyses). Species enriched (red) and depleted (blue) in PRISM CD stool relative to controls (q≤0.1) are indicated. d, Principal coordinate (PCo) analysis on Bray-Curtis dissimilarities between chemostat B (n=21) and PRISM (n=155) metagenomes. Times of exposure to AEA, LEA and DMSO control are indicated.

Extended Data Fig. 8 Changes in transcriptional activity in complex microbial communities in response to NAE treatment.

We used a linear model combining profiles of functional activity from both chemostats over time to identify gene families (KEGG orthologies) that were consistently differentially expressed under NAE treatment relative to DMSO controls (at the community level; total n=96 ranging from n=15 to 17 per treatment). The top 100 such orthologies ranked by mean absolute log-scaled fold change in relative expression are shown. Each of these orthologies was significantly differentially expressed under at least one treatment after correcting for multiple hypothesis testing. Pathway–treatment pairs with open circles had FDR q<0.2; those with closed circles had FDR q<0.05, while an “x” indicates that measurements were insufficient to perform the test (FDR q-values derived from nominal two-tailed p-values of the ‘treatment’ coefficient across per-orthology linear regression analyses). While trends were most significant under AEA treatment, effect sizes trended similarly under LEA and OEA treatment as well.

Extended Data Fig. 9 Changes in transcriptional activity (KEGG modules) in complex microbial communities in response to NAE treatment.

Log2 relative expression is shown for bacterial families contributing to a selection of KEGG modules in samples from chemostats A and B treated with DMSO control, individual NAEs, a combination of all four NAEs (denoted as NAE-mix), or oleic acid (OA). Family-level relative expression values were computed at the species level, averaged over replicates, and then averaged within-family while weighting by species abundance. Unknown (“x”) values represent cases where a function’s DNA and/or RNA abundance were zero for a given stratification (resulting in non-finite log2 relative expression). Col_sort refers to the measure (treatment, time or chemostat) used to order the metadata columns.

Extended Data Fig. 10 Changes in transcriptional activity (KEGG orthologies) in complex microbial communities in response to NAE treatment.

Log2 relative expression is shown for bacterial families contributing to a selection of KEGG orthologies in samples from chemostats A and B treated with DMSO control, individual NAEs, a combination of all four NAEs (denoted as NAE-mix), or oleic acid (OA). Family-level relative expression values were computed at the species level, averaged over replicates, and then averaged within-family while weighting by species abundance. Unknown (“x”) values represent cases where a function’s DNA and/or RNA abundance were zero for a given stratification (resulting in non-finite log2 relative expression). Col_sort refers to the measure (treatment, time or chemostat) used to order the metadata columns.

Supplementary information

Supplementary Information

Supplementary Tables 1–3.

Reporting Summary

Supplementary Data 1

Metabolites used in this study. Properties of metabolites used in this study, including metabolite class, name, molecular weight, HMDB ID and formula as found in the Human Metabolome Database (http://www.hmdb.ca).

Supplementary Data 2

IBD–metabolite associations. Model results (t-statistics, P values and q values) for metabolite features associated with the two IBD phenotypes (CD and UC) in the PRISM (n = 155) and iHMP (n = 132) cohorts. This list is reproduced from Franzosa et al.8 and Lloyd-Price et al.12 to include the newly validated arachidonoyl ethanolamine.

Supplementary Data 3

Metabolite effects on bacterial growth. Growth rate (Vmax) and maximum cell growth (max OD600 nm) per concentration of metabolite.

Supplementary Data 4

Rag2−/− mouse microbial species relative abundance profiles. Taxonomic composition of the Rag2−/− mouse faecal microbiota determined using MetaPhlAn263. Units are relative abundance (out of 1.0).

Supplementary Data 5

NAE effects on bacterial growth. Growth rate (Vmax) and maximum cell growth (max OD600 nm) per concentration of NAE.

Supplementary Data 6

Microbial species–NAE associations. Spearman correlations between species relative abundances and NAEs that were differentially abundant in IBD samples from the PRISM cohort (n = 155)8.

Supplementary Data 7

Transcriptomic responses of B. fragilis to LEA and AEA. Transcriptomic data of B. fragilis treated with LEA or AEA for 10 min. Differential expression analysis between DMSO- and NAE-treated cultures was conducted with edgeR60.

Supplementary Data 8

Chemostat microbial species relative abundance profiles. Taxonomic composition of the chemostat community before and after treatment with NAEs, determined using MetaPhlAn263.

Supplementary Data 9

Model results in chemostat communities. Outputs of statistical models comparing microbial species relative abundance, microbial family relative abundance, KO relative expression and KEGG module relative expression to NAE treatment (one tab per feature type) in chemostats A (total n = 66 with per-treatment n ranging from 8 to 10) and B (total n = 41 with per-treatment n ranging from 6 to 7). Columns indicate the dependent variable (for example, an individual species), covariate (for example, treatment with LEA), model coefficient, P value and FDR q value.

Supplementary Data 10

KEGG modules and KEGG orthologies in chemostat communities.

Source data

Source Data Fig. 1

Mean growth curves.

Source Data Fig. 2

Metabolite concentrations.

Source Data Extended Data Fig. 3

Growth curves.

Source Data Extended Data Fig. 4

Growth curves.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fornelos, N., Franzosa, E.A., Bishai, J. et al. Growth effects of N-acylethanolamines on gut bacteria reflect altered bacterial abundances in inflammatory bowel disease. Nat Microbiol 5, 486–497 (2020). https://doi.org/10.1038/s41564-019-0655-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41564-019-0655-7

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing