A Genetically Encoded Biosensor for Characterizing Transport and Metabolism of Glutarate
Source abstract
Abstract
Abstract Glutarate is a platform chemical with diversified applications. It is also an endogenous metabolite involved in various physiological processes. Deficiency in glutaryl‐CoA dehydrogenase (GcdH) for glutarate catabolism induces the inherited metabolic disorder glutaric aciduria. In this study, a genetically encoded glutarate fluorescent biosensor Glusor is constructed and optimized based on transcriptional regulator CsiR and circularly permuted yellow fluorescent protein. Glusor can quantify glutarate in human body fluids and bacteria fermentation broth with good accuracy and precision, supporting the convenient diagnosis of glutaric aciduria and glutarate production monitoring. Then, the glutarate transport is characterized independent of radioactive substrate by using Glusor expressed in Escherichia coli . The functions of transporters KgtP and YnfM in the uptake and efflux of glutarate in E. coli are identified. Glusor is also used to reveal the catabolism of glutarate in bacteria and HEK293FT cells. The role of glutarate hydroxylase in glutarate catabolism of E. coli is identified through Glusor‐supported in situ glutarate detection. Spatially resolved in vivo analysis of glutarate in HEK293FT cells is realized and GcdH inhibition and hypoxia‐induced glutarate accumulation are elucidated by using Glusor. Overall, Glusor is a versatile tool for the detection of glutarate both in vitro and in vivo.
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Research context
OpenAlex topics: Amino Acid Enzymes and Metabolism · Metabolism and Genetic Disorders · Cancer, Hypoxia, and Metabolism
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
State Key Laboratory of Microbial Technology Shandong University Qingdao 266237 P. R. China
State Key Laboratory of Microbial Metabolism Joint International Research Laboratory of Metabolic & Developmental Sciences, and School of Life Sciences & Biotechnology Shanghai Jiao Tong University Shanghai 200240 P. R. China
Funding
National Natural Science Foundation of China · Qingdao Postdoctoral Science Foundation · State Key Laboratory of Microbial Resources, Chinese Academy of Sciences · China Postdoctoral Science Foundation
References (61)
Show source-provided references
1. 10.1016/j.copbio.2015.07.00310.1016/j.copbio.2015.07.003 ↗
2. 10.1016/j.ymben.2019.03.00510.1016/j.ymben.2019.03.005 ↗
3. 10.1002/bit.2482810.1002/bit.24828 ↗
4. 10.1073/pnas.201748311710.1073/pnas.2017483117 ↗
5. 10.1016/j.ymben.2018.08.00710.1016/j.ymben.2018.08.007 ↗
6. 10.1038/s41467-019-11289-410.1038/s41467-019-11289-4 ↗
7. 10.1016/j.neuroscience.2019.01.01410.1016/j.neuroscience.2019.01.014 ↗
8. 10.1203/01.pdr.0000219387.79887.8610.1203/01.pdr.0000219387.79887.86 ↗
9. 10.1002/jimd.1256610.1002/jimd.12566 ↗
10. 10.1111/j.1742-4658.2007.06107.x10.1111/j.1742-4658.2007.06107.x ↗
11. 10.1038/s42255-023-00855-210.1038/s42255-023-00855-2 ↗
12. 10.1038/s42255-023-00878-910.1038/s42255-023-00878-9 ↗
13. 10.1016/j.cccn.2005.03.04810.1016/j.cccn.2005.03.048 ↗
14. 10.1016/j.ab.2022.11460410.1016/j.ab.2022.114604 ↗
15. 10.1016/j.chroma.2014.10.06910.1016/j.chroma.2014.10.069 ↗
16. 10.1021/acssynbio.9b0025510.1021/acssynbio.9b00255 ↗
17. 10.1021/acs.chemrev.8b0033310.1021/acs.chemrev.8b00333 ↗
18. 10.1021/acs.chemrev.4c0029310.1021/acs.chemrev.4c00293 ↗
19. 10.1146/annurev-anchem-091619-09130610.1146/annurev-anchem-091619-091306 ↗
20. 10.1038/s41467-022-34306-510.1038/s41467-022-34306-5 ↗
21. 10.1038/nmeth86610.1038/nmeth866 ↗
22. 10.1038/s41467-024-48295-010.1038/s41467-024-48295-0 ↗
23. 10.1016/j.tibtech.2023.03.00110.1016/j.tibtech.2023.03.001 ↗
24. 10.1038/s41467-018-04513-010.1038/s41467-018-04513-0 ↗
25. 10.1038/s41467-018-07563-610.1038/s41467-018-07563-6 ↗
26. 10.1128/mBio.02577-1810.1128/mBio.02577-18 ↗
27. Zhang M. · mBio · 2019 · 10
28. 10.1038/s41586-021-03819-210.1038/s41586-021-03819-2 ↗
29. 10.1021/jm030643010.1021/jm0306430 ↗
30. Desmond Molecular Dynamics System Maestro‐Desmond Interoperability Tools D. E. Shaw Research Schrödinger New York2022.
31. 10.1002/advs.20240411910.1002/advs.202404119 ↗
32. 10.1038/s41586-023-05828-910.1038/s41586-023-05828-9 ↗
33. 10.1038/s41467-020-20754-410.1038/s41467-020-20754-4 ↗
34. 10.1016/j.cmet.2022.10.00210.1016/j.cmet.2022.10.002 ↗
35. 10.1038/s41467-021-27332-210.1038/s41467-021-27332-2 ↗
36. 10.1016/j.enzmictec.2019.05.00310.1016/j.enzmictec.2019.05.003 ↗
37. 10.1046/j.1365-2958.2003.03867.x10.1046/j.1365-2958.2003.03867.x ↗
38. 10.1038/s41589-018-0136-y10.1038/s41589-018-0136-y ↗
39. 10.1038/s41586-023-06061-010.1038/s41586-023-06061-0 ↗
40. 10.1371/journal.pone.008771510.1371/journal.pone.0087715 ↗
41. 10.1016/j.bbadis.2013.04.01910.1016/j.bbadis.2013.04.019 ↗
42. 10.1038/s41594-023-01039-y10.1038/s41594-023-01039-y ↗
43. 10.1007/s00424-008-0489-210.1007/s00424-008-0489-2 ↗
44. 10.1093/brain/awq26910.1093/brain/awq269 ↗
45. 10.1016/j.ccell.2019.10.00110.1016/j.ccell.2019.10.001 ↗
46. 10.1038/ncb242410.1038/ncb2424 ↗
47. 10.1002/ana.2523310.1002/ana.25233 ↗
48. 10.1023/A:100554390448410.1023/A:1005543904484 ↗
49. 10.1016/j.ymgmr.2024.10112310.1016/j.ymgmr.2024.101123 ↗
50. 10.1002/advs.20230656010.1002/advs.202306560 ↗
51. 10.1002/advs.20241143310.1002/advs.202411433 ↗
52. 10.1039/D5CC02687C10.1039/D5CC02687C ↗
53. 10.1002/advs.20220394310.1002/advs.202203943 ↗
54. 10.1002/advs.20210082710.1002/advs.202100827 ↗
55. 10.1038/s41467-025-61549-910.1038/s41467-025-61549-9 ↗
56. 10.1038/s41551-022-00887-110.1038/s41551-022-00887-1 ↗
57. 10.1038/nprot.2006.16610.1038/nprot.2006.166 ↗
58. 10.1038/s41434-023-00428-810.1038/s41434-023-00428-8 ↗
59. 10.1093/nar/gky116910.1093/nar/gky1169 ↗
60. 10.1038/nprot.2008.13110.1038/nprot.2008.131 ↗
61. 10.1128/AEM.04023-1410.1128/AEM.04023-14 ↗