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Microbial stimulation of different Toll-like receptor signalling pathways induces diverse metabolic programmes in human monocytes

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Abstract

Microbial stimuli such as lipopolysaccharide (LPS) induce robust metabolic rewiring in immune cells known as the Warburg effect. It is unknown whether this increase in glycolysis and decrease in oxidative phosphorylation (OXPHOS) is a general characteristic of monocytes that have encountered a pathogen. Using CD14+ monocytes from healthy donors, we demonstrated that most microbial stimuli increased glycolysis, but that only stimulation of Toll-like receptor (TLR) 4 with LPS led to a decrease in OXPHOS. Instead, activation of other TLRs, such as TLR2 activation by Pam3CysSK4 (P3C), increased oxygen consumption and mitochondrial enzyme activity. Transcriptome and metabolome analysis of monocytes stimulated with P3C versus LPS confirmed the divergent metabolic responses between both stimuli, and revealed significant differences in the tricarboxylic acid cycle, OXPHOS and lipid metabolism pathways following stimulation of monocytes with P3C versus LPS. At a functional level, pharmacological inhibition of complex I of the mitochondrial electron transport chain diminished cytokine production and phagocytosis in P3C- but not LPS-stimulated monocytes. Thus, unlike LPS, complex microbial stimuli and the TLR2 ligand P3C induce a specific pattern of metabolic rewiring that involves upregulation of both glycolysis and OXPHOS, which enables activation of host defence mechanisms such as cytokine production and phagocytosis.

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Figure 1: Glycolysis is upregulated in human monocytes after stimulation with various pathogenic stimuli for 24 h.
Figure 2: LPS is unique in downregulating OXPHOS in human monocytes.
Figure 3: Human monocytes stimulated with LPS versus P3C show differential expression of metabolic genes.
Figure 4: Dose- and time-dependent metabolic rewiring in human monocytes.
Figure 5: Differential metabolic rewiring of human monocytes stimulated with LPS or P3C leads to functional differences in cytokine production and phagocytosis.

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  • 14 July 2017

    In the PDF version of this article previously published, the year of publication provided in the footer of each page and in the 'How to cite' section was erroneously given as 2017, it should have been 2016. This error has now been corrected. The HTML version of the article was not affected.

References

  1. Warburg, O., Wind, F. & Negelein, E. The metabolism of tumors in the body. J. Gen. Physiol. 8, 519–530 (1927).

    Article  CAS  Google Scholar 

  2. Michalek, R. D. et al. Cutting edge: distinct glycolytic and lipid oxidative metabolic programs are essential for effector and regulatory CD4+ T cell subsets. J. Immunol. 186, 3299–3303 (2011).

    Article  CAS  Google Scholar 

  3. Pearce, E. L. et al. Enhancing CD8 T-cell memory by modulating fatty acid metabolism. Nature 460, 103–107 (2009).

    Article  CAS  Google Scholar 

  4. Rodriguez-Prados, J. C. et al. Substrate fate in activated macrophages: a comparison between innate, classic, and alternative activation. J. Immunol. 185, 605–614 (2010).

    Article  CAS  Google Scholar 

  5. Galván-Peña, S. & O'Neill, L. A. Metabolic reprograming in macrophage polarization. Front. Immunol. 5, 420 (2014).

    PubMed  PubMed Central  Google Scholar 

  6. Tannahill, G. M. et al. Succinate is an inflammatory signal that induces IL-1β through HIF-1α. Nature 496, 238–242 (2013).

    Article  CAS  Google Scholar 

  7. Jha, A. K. et al. Network integration of parallel metabolic and transcriptional data reveals metabolic modules that regulate macrophage polarization. Immunity 42, 419–430 (2015).

    Article  CAS  Google Scholar 

  8. Mills, E. & O'Neill, L. A. Succinate: a metabolic signal in inflammation. Trends Cell Biol. 24, 313–320 (2014).

    Article  CAS  Google Scholar 

  9. Everts, B. et al. TLR-driven early glycolytic reprogramming via the kinases TBK1-IKKε supports the anabolic demands of dendritic cell activation. Nat. Immunol. 15, 323–332 (2014).

    Article  CAS  Google Scholar 

  10. Tan, Z. et al. The monocarboxylate transporter 4 is required for glycolytic reprogramming and inflammatory response in macrophages. J. Biol. Chem. 290, 46–55 (2015).

    Article  CAS  Google Scholar 

  11. Izquierdo, E. et al. Reshaping of human macrophage polarization through modulation of glucose catabolic pathways. J. Immunol. 195, 2442–2451 (2015).

    Article  CAS  Google Scholar 

  12. Huang, S. C. et al. Cell-intrinsic lysosomal lipolysis is essential for alternative activation of macrophages. Nat. Immunol. 15, 846–855 (2014).

    Article  CAS  Google Scholar 

  13. Palsson-McDermott, E. M. et al. Pyruvate kinase M2 regulates Hif-1α activity and IL-1β induction and is a critical determinant of the Warburg effect in LPS-activated macrophages. Cell Metab. 21, 65–80 (2015).

    Article  CAS  Google Scholar 

  14. Picard, M., Shirihai, O. S., Gentil, B. J. & Burelle, Y. Mitochondrial morphology transitions and functions: implications for retrograde signaling? Am. J. Physiol. Regul. Integr. Comp. Physiol. 304, R393–406 (2013).

    Article  CAS  Google Scholar 

  15. Chacko, B. K. et al. Methods for defining distinct bioenergetic profiles in platelets, lymphocytes, monocytes, and neutrophils, and the oxidative burst from human blood. Lab. Invest. 93, 690–700 (2013).

    Article  CAS  Google Scholar 

  16. Krawczyk, C. M. et al. Toll-like receptor-induced changes in glycolytic metabolism regulate dendritic cell activation. Blood 115, 4742–4749 (2010).

    Article  CAS  Google Scholar 

  17. Kelly, B., Tannahill, G. M., Murphy, M. P. & O'Neill, L. A. Metformin inhibits the production of reactive oxygen species from nADH:ubiquinone oxidoreductase to limit induction of interleukin-1β (IL-1β) and boosts interleukin-10 (IL-10) in lipopolysaccharide (LPS)-activated macrophages. J. Biol. Chem. 290, 20348–20359 (2015).

    Article  CAS  Google Scholar 

  18. O'Neill, L. A., Kishton, R. J. & Rathmell, J. A guide to immunometabolism for immunologists. Nat. Rev. Immunol. 16, 553–565 (2016).

    Article  CAS  Google Scholar 

  19. Strelko, C. L. et al. Itaconic acid is a mammalian metabolite induced during macrophage activation. J. Am. Chem. Soc. 133, 16386–16389 (2011).

    Article  CAS  Google Scholar 

  20. Takeuchi, O. et al. Differential roles of TLR2 and TLR4 in recognition of Gram-negative and Gram-positive bacterial cell wall components. Immunity 11, 443–451 (1999).

    Article  CAS  Google Scholar 

  21. Chandak, P. G. et al. Efficient phagocytosis requires triacylglycerol hydrolysis by adipose triglyceride lipase. J. Biol. Chem. 285, 20192–20201 (2010).

    Article  CAS  Google Scholar 

  22. Cifarelli, A., Pepe, G., Paradisi, F. & Piccolo, D. The influence of some metabolic inhibitors on phagocytic activity of mouse macrophages in vitro. Res. Exp. Med. 174, 197–204 (1979).

    Article  CAS  Google Scholar 

  23. Paradisi, F., D'Onofrio, C., Pepe, G., Cifarelli, A. & Piccolo, D. Phagocytosis and cellular metabolism. Ric. Clin. Lab. 9, 47–60 (1979).

    CAS  PubMed  Google Scholar 

  24. Jiang, Z., Mak, T. W., Sen, G. & Li, X. Toll-like receptor 3-mediated activation of NF-κB and IRF3 diverges at Toll-IL-1 receptor domain-containing adapter inducing IFN-β. Proc. Natl Acad. Sci. USA 101, 3533–3538 (2004).

    Article  CAS  Google Scholar 

  25. Odegaard, J. I. et al. Macrophage-specific PPARγ controls alternative activation and improves insulin resistance. Nature 447, 1116–1120 (2007).

    Article  CAS  Google Scholar 

  26. Majai, G., Sarang, Z., Csomos, K., Zahuczky, G. & Fesus, L. PPARγ-dependent regulation of human macrophages in phagocytosis of apoptotic cells. Eur. J. Immunol. 37, 1343–1354 (2007).

    Article  CAS  Google Scholar 

  27. Buck, M. D. et al. Mitochondrial dynamics controls T cell fate through metabolic programming. Cell 166, 63–76 (2016).

    Article  CAS  Google Scholar 

  28. Kelly, B. & O'Neill, L. A. Metabolic reprogramming in macrophages and dendritic cells in innate immunity. Cell Res. 25, 771–784 (2015).

    Article  Google Scholar 

  29. Lampropoulou, V. et al. Itaconate links inhibition of succinate dehydrogenase with macrophage metabolic remodeling and regulation of inflammation. Cell Metabol. 24, 158–166 (2016).

    Article  CAS  Google Scholar 

  30. Li, Y. et al. Immune responsive gene 1 (IRG1) promotes endotoxin tolerance by increasing A20 expression in macrophages through reactive oxygen species. J. Biol. Chem. 288, 16225–16234 (2013).

    Article  CAS  Google Scholar 

  31. Cheng, S. C. et al. Broad defects in the energy metabolism of leukocytes underlie immunoparalysis in sepsis. Nat. Immunol. 17, 406–413 (2016).

    Article  CAS  Google Scholar 

  32. Janssen, A. J. et al. Spectrophotometric assay for complex I of the respiratory chain in tissue samples and cultured fibroblasts. Clin. Chem. 53, 729–734 (2007).

    Article  CAS  Google Scholar 

  33. Mourmans, J. et al. Clinical heterogeneity in respiratory chain complex III deficiency in childhood. J. Neurol. Sci. 149, 111–117 (1997).

    Article  CAS  Google Scholar 

  34. Cooperstein, S. J. & Lazarow, A. A microspectrophotometric method for the determination of cytochrome oxidase. J. Biol. Chem. 189, 665–670 (1951).

    CAS  PubMed  Google Scholar 

  35. Rodenburg, R. J. Biochemical diagnosis of mitochondrial disorders. J. Inherit. Metab. Dis. 34, 283–292 (2011).

    Article  CAS  Google Scholar 

  36. Xia, J., Sinelnikov, I. V., Han, B. & Wishart, D. S. Metaboanalyst 3.0—making metabolomics more meaningful. Nucleic Acids Res. 43, W251–257 (2015).

    Article  CAS  Google Scholar 

  37. Bolstad, B. M., Irizarry, R. A., Astrand, M. & Speed, T. P. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19, 185–193 (2003).

    Article  CAS  Google Scholar 

  38. Irizarry, R. A. et al. Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res. 31, e15 (2003).

    Article  Google Scholar 

  39. Dai, M. et al. Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data. Nucleic Acids Res. 33, e175 (2005).

    Article  Google Scholar 

  40. Sartor, M. A. et al. Intensity-based hierarchical Bayes method improves testing for differentially expressed genes in microarray experiments. BMC Bioinformatics 7, 538 (2006).

    Article  Google Scholar 

  41. Blankley, S. et al. Identification of the key differential transcriptional responses of human whole blood following TLR2 or TLR4 ligation in-vitro. PLoS ONE 9, e97702 (2014).

    Article  Google Scholar 

  42. Ramilo, O. et al. Gene expression patterns in blood leukocytes discriminate patients with acute infections. Blood 109, 2066–2077 (2007).

    Article  CAS  Google Scholar 

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Acknowledgements

We would like to thank the laboratory technicians of the muscle laboratory, and in particular B. Stoltenborg, at the Translational Metabolic Laboratory (Department of Laboratory Medicine, Radboud University Nijmegen Medical Centre) and Mietske Wijers-Rouw (Department of Cell Biology, Radboud University Nijmegen Medical Centre) for excellent technical assistance. R.S. was supported by a VIDI grant from the The Netherlands Organisation for Scientific Research (NWO) and an EFSD Rising Star Grant. R.v.C. was supported by The European Union's Seventh Framework Programme (EU FP7) project TANDEM (HEALTH-F3-2012-305279). M.G.N. was supported by an ERC Consolidator Grant (no. 310372) and a Spinoza Award (NWO).

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E.L., L.B., J.M.R., A.H. and R.S. conducted most of the experiments and data analysis. G.J.H. performed all data analysis related to the transcriptome and metabolome results. R.J.R. and R.H.H. assisted in the experiments related to assessing mitochondrial function. J.A.M.F. performed the electron microscopy. L.A.B.J., R.H.H., R.v.C. and M.G.N. critically contributed to the design of the study. E.L., L.B., J.M.R., M.G.N. and R.S. wrote the manuscript together.

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Correspondence to Mihai G. Netea or Rinke Stienstra.

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The authors declare no competing financial interests.

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Supplementary Figures 1–8; Supplementary Table 1 (PDF 859 kb)

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Lachmandas, E., Boutens, L., Ratter, J. et al. Microbial stimulation of different Toll-like receptor signalling pathways induces diverse metabolic programmes in human monocytes. Nat Microbiol 2, 16246 (2017). https://doi.org/10.1038/nmicrobiol.2016.246

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