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Spatiotemporal Imaging of Cellular Energy Metabolism with Genetically-Encoded Fluorescent Sensors in Brain

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Abstract

The brain has very high energy requirements and consumes 20% of the oxygen and 25% of the glucose in the human body. Therefore, the molecular mechanism underlying how the brain metabolizes substances to support neural activity is a fundamental issue for neuroscience studies. A well-known model in the brain, the astrocyte-neuron lactate shuttle, postulates that glucose uptake and glycolytic activity are enhanced in astrocytes upon neuronal activation and that astrocytes transport lactate into neurons to fulfill their energy requirements. Current evidence for this hypothesis has yet to reach a clear consensus, and new concepts beyond the shuttle hypothesis are emerging. The discrepancy is largely attributed to the lack of a critical method for real-time monitoring of metabolic dynamics at cellular resolution. Recent advances in fluorescent protein-based sensors allow the generation of a sensitive, specific, real-time readout of subcellular metabolites and fill the current technological gap. Here, we summarize the development of genetically encoded metabolite sensors and their applications in assessing cell metabolism in living cells and in vivo, and we believe that these tools will help to address the issue of elucidating neural energy metabolism.

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References

  1. Belanger M, Allaman I, Magistretti PJ. Brain energy metabolism: focus on astrocyte-neuron metabolic cooperation. Cell Metab 2011, 14: 724–738.

    Article  PubMed  CAS  Google Scholar 

  2. Harris JJ, Jolivet R, Attwell D. Synaptic energy use and supply. Neuron 2012, 75: 762–777.

    Article  PubMed  CAS  Google Scholar 

  3. Hyder F, Rothman DL, Bennett MR. Cortical energy demands of signaling and nonsignaling components in brain are conserved across mammalian species and activity levels. Proc Natl Acad Sci U S A 2013, 110: 3549–3554.

    Article  PubMed  PubMed Central  Google Scholar 

  4. Rangaraju V, Calloway N, Ryan TA. Activity-driven local ATP synthesis is required for synaptic function. Cell 2014, 156: 825–835.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Alle H, Roth A, Geiger JR. Energy-efficient action potentials in hippocampal mossy fibers. Science 2009, 325: 1405–1408.

    Article  PubMed  CAS  Google Scholar 

  6. Mergenthaler P, Lindauer U, Dienel GA, Meisel A. Sugar for the brain: the role of glucose in physiological and pathological brain function. Trends Neurosci 2013, 36: 587–597.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Zielke HR, Zielke CL, Baab PJ. Direct measurement of oxidative metabolism in the living brain by microdialysis: a review. J Neurochem 2009, 109 Suppl 1: 24–29.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  8. Pellerin L, Magistretti PJ. Glutamate uptake into astrocytes stimulates aerobic glycolysis: a mechanism coupling neuronal activity to glucose utilization. Proc Natl Acad Sci U S A 1994, 91: 10625–10629.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Pellerin L, Magistretti PJ. Neuroscience. Let there be (NADH) light. Science 2004, 305: 50–52.

    Article  PubMed  CAS  Google Scholar 

  10. Kasischke KA, Vishwasrao HD, Fisher PJ, Zipfel WR, Webb WW. Neural activity triggers neuronal oxidative metabolism followed by astrocytic glycolysis. Science 2004, 305: 99–103.

    Article  PubMed  CAS  Google Scholar 

  11. Magistretti PJ, Allaman I. A cellular perspective on brain energy metabolism and functional imaging. Neuron 2015, 86: 883–901.

    Article  PubMed  CAS  Google Scholar 

  12. Dienel GA. Lack of appropriate stoichiometry: Strong evidence against an energetically important astrocyte-neuron lactate shuttle in brain. J Neurosci Res 2017, 95: 2103–2125.

    Article  PubMed  CAS  Google Scholar 

  13. Bergersen LH, Gjedde A. Is lactate a volume transmitter of metabolic states of the brain? Front Neuroenergetics 2012, 4: 5.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Magistretti PJ, Pellerin L. Cellular mechanisms of brain energy metabolism and their relevance to functional brain imaging. Philos Trans R Soc Lond B Biol Sci 1999, 354: 1155–1163.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Schonfeld P, Reiser G. Why does brain metabolism not favor burning of fatty acids to provide energy? Reflections on disadvantages of the use of free fatty acids as fuel for brain. J Cereb Blood Flow Metab 2013, 33: 1493–1499.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  16. Lutas A, Yellen G. The ketogenic diet: metabolic influences on brain excitability and epilepsy. Trends Neurosci 2013, 36: 32–40.

    Article  PubMed  CAS  Google Scholar 

  17. Reddan MC, Wager TD. Modeling pain using fMRI: from regions to biomarkers. Neurosci Bull 2018, 34: 208–215.

    Article  PubMed  Google Scholar 

  18. Kim E, Park H. Pairwise classifier ensemble with adaptive sub-classifiers for fMRI pattern analysis. Neurosci Bull 2017, 33: 41–52.

    Article  PubMed  Google Scholar 

  19. Guan X, Xu X, Zhang M. Region-specific iron measured by MRI as a biomarker for Parkinson’s disease. Neurosci Bull 2017, 33: 561–567.

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  20. Hiller K, Metallo CM. Profiling metabolic networks to study cancer metabolism. Curr Opin Biotechnol 2013, 24: 60–68.

    Article  PubMed  CAS  Google Scholar 

  21. Patterson GH, Knobel SM, Arkhammar P, Thastrup O, Piston DW. Separation of the glucose-stimulated cytoplasmic and mitochondrial NAD(P)H responses in pancreatic islet beta cells. Proc Natl Acad Sci U S A 2000, 97: 5203–5207.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Eto K, Tsubamoto Y, Terauchi Y, Sugiyama T, Kishimoto T, Takahashi N, et al. Role of NADH shuttle system in glucose-induced activation of mitochondrial metabolism and insulin secretion. Science 1999, 283: 981–985.

    Article  PubMed  CAS  Google Scholar 

  23. Lin SJ, Guarente L. Nicotinamide adenine dinucleotide, a metabolic regulator of transcription, longevity and disease. Curr Opin Cell Biol 2003, 15: 241–246.

    Article  PubMed  CAS  Google Scholar 

  24. Houtkooper RH, Canto C, Wanders RJ, Auwerx J. The secret life of NAD+: an old metabolite controlling new metabolic signaling pathways. Endocr Rev 2010, 31: 194–223.

    Article  PubMed  CAS  Google Scholar 

  25. Canto C, Menzies KJ, Auwerx J. NAD metabolism and the control of energy homeostasis: a balancing act between mitochondria and the nucleus. Cell Metab 2015, 22:31–53.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Tsien RY. Constructing and exploiting the fluorescent protein paintbox (Nobel Lecture). Angew Chem Int Ed Engl 2009, 48: 5612–5626.

    Article  PubMed  CAS  Google Scholar 

  27. Mehta S, Zhang J. Reporting from the field: genetically encoded fluorescent reporters uncover signaling dynamics in living biological systems. Annu Rev Biochem 2011, 80: 375–401.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Zhao Y, Yang Y. Profiling metabolic states with genetically encoded fluorescent biosensors for NADH. Curr Opin Biotechnol 2015, 31C: 86–92.

    Article  CAS  Google Scholar 

  29. Zhao Y, Yang Y. Real-time and high-throughput analysis of mitochondrial metabolic states in living cells using genetically encoded NAD+/NADH sensors. Free Radic Biol Med 2016, 100: 43–52.

    Article  PubMed  CAS  Google Scholar 

  30. Zhao Y, Jin J, Hu Q, Zhou HM, Yi J, Yu Z, et al. Genetically encoded fluorescent sensors for intracellular NADH detection. Cell Metab 2011, 14: 555–566.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  31. Cambronne XA, Stewart ML, Kim D, Jones-Brunette AM, Morgan RK, Farrens DL, et al. Biosensor reveals multiple sources for mitochondrial NAD(+). Science 2016, 352: 1474–1477.

    Article  PubMed  CAS  Google Scholar 

  32. Hung YP, Albeck JG, Tantama M, Yellen G. Imaging cytosolic NADH-NAD(+) redox state with a genetically encoded fluorescent biosensor. Cell Metab 2011, 14: 545–554.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Bilan DS, Matlashov ME, Gorokhovatsky AY, Schultz C, Enikolopov G, Belousov VV. Genetically encoded fluorescent indicator for imaging NAD(+)/NADH ratio changes in different cellular compartments. Biochim Biophys Acta 2014, 1840: 951–957.

    Article  PubMed  CAS  Google Scholar 

  34. Zhao Y, Hu Q, Cheng F, Su N, Wang A, Zou Y, et al. SoNar, a highly responsive NAD+/NADH sensor, allows high-throughput metabolic screening of anti-tumor agents. Cell Metab 2015, 21: 777–789.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Zhao Y, Wang A, Zou Y, Su N, Loscalzo J, Yang Y. In vivo monitoring of cellular energy metabolism using SoNar, a highly responsive sensor for NAD(+)/NADH redox state. Nat Protoc 2016, 11: 1345–1359.

    Article  PubMed  CAS  Google Scholar 

  36. Tao R, Zhao Y, Chu H, Wang A, Zhu J, Chen X, et al. Genetically encoded fluorescent sensors reveal dynamic regulation of NADPH metabolism. Nat Methods 2017, 14: 720–728.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Cameron WD, Bui CV, Hutchinson A, Loppnau P, Graslund S, Rocheleau JV. Apollo-NADP(+): a spectrally tunable family of genetically encoded sensors for NADP(+). Nat Methods 2016, 13: 352–358.

    Article  PubMed  CAS  Google Scholar 

  38. Imamura H, Nhat KP, Togawa H, Saito K, Iino R, Kato-Yamada Y, et al. Visualization of ATP levels inside single living cells with fluorescence resonance energy transfer-based genetically encoded indicators. Proc Natl Acad Sci U S A 2009, 106: 15651–15656.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Tsuyama T, Kishikawa J, Han YW, Harada Y, Tsubouchi A, Noji H, et al. In vivo fluorescent adenosine 5′-triphosphate (ATP) imaging of Drosophila melanogaster and Caenorhabditis elegans by using a genetically encoded fluorescent ATP biosensor optimized for low temperatures. Anal Chem 2013, 85: 7889–7896.

    Article  PubMed  CAS  Google Scholar 

  40. Berg J, Hung YP, Yellen G. A genetically encoded fluorescent reporter of ATP:ADP ratio. Nat Methods 2009, 6: 161–166.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  41. Tantama M, Martinez-Francois JR, Mongeon R, Yellen G. Imaging energy status in live cells with a fluorescent biosensor of the intracellular ATP-to-ADP ratio. Nat Commun 2013, 4: 2550.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  42. Takanaga H, Chaudhuri B, Frommer WB. GLUT1 and GLUT9 as major contributors to glucose influx in HepG2 cells identified by a high sensitivity intramolecular FRET glucose sensor. Biochim Biophys Acta 2008, 1778: 1091–1099.

    Article  PubMed  CAS  Google Scholar 

  43. Hou BH, Takanaga H, Grossmann G, Chen LQ, Qu XQ, Jones AM, et al. Optical sensors for monitoring dynamic changes of intracellular metabolite levels in mammalian cells. Nat Protoc 2011, 6: 1818–1833.

    Article  PubMed  CAS  Google Scholar 

  44. San Martin A, Ceballo S, Ruminot I, Lerchundi R, Frommer WB, Barros LF. A genetically encoded FRET lactate sensor and its use to detect the Warburg effect in single cancer cells. PLoS One 2013, 8: e57712.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. San Martin A, Ceballo S, Baeza-Lehnert F, Lerchundi R, Valdebenito R, Contreras-Baeza Y, et al. Imaging mitochondrial flux in single cells with a FRET sensor for pyruvate. PLoS One 2014, 9: e85780.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. Gruenwald K, Holland JT, Stromberg V, Ahmad A, Watcharakichkorn D, Okumoto S. Visualization of glutamine transporter activities in living cells using genetically encoded glutamine sensors. PLoS One 2012, 7: e38591.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  47. Yang H, Zhou L, Shi Q, Zhao Y, Lin H, Zhang M, et al. SIRT3-dependent GOT2 acetylation status affects the malate-aspartate NADH shuttle activity and pancreatic tumor growth. EMBO J 2015, 34: 1110–1125.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  48. Marcu R, Wiczer BM, Neeley CK, Hawkins BJ. Mitochondrial matrix Ca(2)(+) accumulation regulates cytosolic NAD(+)/NADH metabolism, protein acetylation, and sirtuin expression. Mol Cell Biol 2014, 34: 2890–2902.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  49. Chen H, Xu H, Potash S, Starkov A, Belousov VV, Bilan DS, et al. Mild metabolic perturbations alter succinylation of mitochondrial proteins. J Neurosci Res 2017, 95: 2244–2252.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  50. Oldham WM, Clish CB, Yang Y, Loscalzo J. Hypoxia-mediated increases in L-2-hydroxyglutarate coordinate the metabolic response to reductive stress. Cell Metab 2015, 22: 291–303.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Titov DV, Cracan V, Goodman RP, Peng J, Grabarek Z, Mootha VK. Complementation of mitochondrial electron transport chain by manipulation of the NAD+/NADH ratio. Science 2016, 352: 231–235.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. Roesch A, Vultur A, Bogeski I, Wang H, Zimmermann KM, Speicher D, et al. Overcoming intrinsic multidrug resistance in melanoma by blocking the mitochondrial respiratory chain of slow-cycling JARID1B(high) cells. Cancer Cell 2013, 23: 811–825.

    Article  PubMed  CAS  Google Scholar 

  53. Menger L, Vacchelli E, Adjemian S, Martins I, Ma Y, Shen S, et al. Cardiac glycosides exert anticancer effects by inducing immunogenic cell death. Sci Transl Med 2012, 4: 143ra199.

    Article  CAS  Google Scholar 

  54. Zhou B, Yu P, Lin MY, Sun T, Chen Y, Sheng ZH. Facilitation of axon regeneration by enhancing mitochondrial transport and rescuing energy deficits. J Cell Biol 2016, 214: 103–119.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  55. Deuschle K, Okumoto S, Fehr M, Looger LL, Kozhukh L, Frommer WB. Construction and optimization of a family of genetically encoded metabolite sensors by semirational protein engineering. Protein Sci 2005, 14: 2304–2314.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  56. Bermejo C, Haerizadeh F, Takanaga H, Chermak D, Frommer WB. Optical sensors for measuring dynamic changes of cytosolic metabolite levels in yeast. Nat Protoc 2011, 6: 1806–1817.

    Article  PubMed  CAS  Google Scholar 

  57. Hu HY, Wei YF, Wang DC, Su N, Chen XJ, Zhao YZ, et al. Glucose monitoring in living cells with single fluorescent protein-based sensors. Rsc Advances 2018, 8: 2485–2489.

    Article  CAS  Google Scholar 

  58. Jamali S, Klier M, Ames S, Barros LF, McKenna R, Deitmer JW, et al. Hypoxia-induced carbonic anhydrase IX facilitates lactate flux in human breast cancer cells by non-catalytic function. Sci Rep 2015, 5: 13605.

    Article  PubMed  PubMed Central  Google Scholar 

  59. Lerchundi R, Fernandez-Moncada I, Contreras-Baeza Y, Sotelo-Hitschfeld T, Machler P, Wyss MT, et al. NH4(+) triggers the release of astrocytic lactate via mitochondrial pyruvate shunting. Proc Natl Acad Sci U S A 2015, 112: 11090–11095.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  60. Erecinska M, Silver IA. Ions and energy in mammalian brain. Prog Neurobiol 1994, 43: 37–71.

    Article  PubMed  CAS  Google Scholar 

  61. Dienel GA. Brain lactate metabolism: the discoveries and the controversies. J Cereb Blood Flow Metab 2012, 32: 1107–1138.

    Article  PubMed  CAS  Google Scholar 

  62. Schurr A. Cerebral glycolysis: a century of persistent misunderstanding and misconception. Front Neurosci 2014, 8: 360.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Morrison BM, Lee Y, Rothstein JD. Oligodendroglia: metabolic supporters of axons. Trends Cell Biol 2013, 23: 644–651.

    Article  PubMed  CAS  Google Scholar 

  64. Chuquet J, Quilichini P, Nimchinsky EA, Buzsaki G. Predominant enhancement of glucose uptake in astrocytes versus neurons during activation of the somatosensory cortex. J Neurosci 2010, 30: 15298–15303.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  65. Machler P, Wyss MT, Elsayed M, Stobart J, Gutierrez R, von Faber-Castell A, et al. In vivo evidence for a lactate gradient from astrocytes to neurons. Cell Metab 2016, 23: 94–102.

    Article  PubMed  CAS  Google Scholar 

  66. Sotelo-Hitschfeld T, Niemeyer MI, Machler P, Ruminot I, Lerchundi R, Wyss MT, et al. Channel-mediated lactate release by K(+)-stimulated astrocytes. J Neurosci 2015, 35: 4168–4178.

    Article  PubMed  CAS  Google Scholar 

  67. Diaz-Garcia CM, Mongeon R, Lahmann C, Koveal D, Zucker H, Yellen G. Neuronal stimulation triggers neuronal glycolysis and not lactate uptake. Cell Metab 2017, 26: 361–374 e364.

    Google Scholar 

  68. Lundgaard I, Li B, Xie L, Kang H, Sanggaard S, Haswell JD, et al. Direct neuronal glucose uptake heralds activity-dependent increases in cerebral metabolism. Nat Commun 2015, 6: 6807.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  69. Lee Y, Morrison BM, Li Y, Lengacher S, Farah MH, Hoffman PN, et al. Oligodendroglia metabolically support axons and contribute to neurodegeneration. Nature 2012, 487: 443–448.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  70. Funfschilling U, Supplie LM, Mahad D, Boretius S, Saab AS, Edgar J, et al. Glycolytic oligodendrocytes maintain myelin and long-term axonal integrity. Nature 2012, 485: 517–521.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  71. Faubert B, Li KY, Cai L, Hensley CT, Kim J, Zacharias LG, et al. Lactate metabolism in human lung tumors. Cell 2017, 171: 358–371 e359.

    Google Scholar 

  72. Hui S, Ghergurovich JM, Morscher RJ, Jang C, Teng X, Lu W, et al. Glucose feeds the TCA cycle via circulating lactate. Nature 2017, 551: 115–118.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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Acknowledgements

This review was supported by the National Key Research and Development Program of China (2017YFA050400 and 2017YFC0906900), the National Natural Science Foundation of China (31722033, 91649123, 31671484, 31225008, and 31470833), the Shanghai Science and Technology Commission (14XD1401400, 16430723100, and 15YF1402600), Young Elite Scientists Sponsorship Program by China Association for Science and Technology (to YZ), Shanghai Young Top-notch Talent (to YZ), the State Key Laboratory of Bioreactor Engineering (to YY), and Fundamental Research Funds for the Central Universities (to YY and YZ).

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Authors and Affiliations

  1. Synthetic Biology and Biotechnology Laboratory, State Key Laboratory of Bioreactor Engineering, Shanghai Collaborative Innovation Center for Biomanufacturing Technology, East China University of Science and Technology, Shanghai, 200237, China

    Zhuo Zhang, Weicai Chen, Yuzheng Zhao & Yi Yang

  2. Optogenetics and Synthetic Biology Interdisciplinary Research Center, CAS Center for Excellence in Brain Science, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, 200031, China

    Yi Yang

  3. Shanghai Key Laboratory of New Drug Design, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China

    Zhuo Zhang, Weicai Chen & Yuzheng Zhao

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  1. Zhuo Zhang
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Correspondence to Yuzheng Zhao or Yi Yang.

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Zhang, Z., Chen, W., Zhao, Y. et al. Spatiotemporal Imaging of Cellular Energy Metabolism with Genetically-Encoded Fluorescent Sensors in Brain. Neurosci. Bull. 34, 875–886 (2018). https://doi.org/10.1007/s12264-018-0229-3

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