A Novel RAGE Modulator Induces Soluble RAGE to Reduce BACE1 Expression in Alzheimer's Disease
Source abstract
Abstract
Abstract β‐secretase (BACE1) is instrumental in amyloid‐β (Aβ) production, with overexpression noted in Alzheimer's disease (AD) neuropathology. The interaction of Aβ with the receptor for advanced glycation endproducts (RAGE) facilitates cerebral uptake of Aβ and exacerbates its neurotoxicity and neuroinflammation, further augmenting BACE1 expression. Given the limitations of previous BACE1 inhibition efforts, the study explores reducing BACE1 expression to mitigate AD pathology. The research reveals that the anticancer agent 6‐thioguanosine (6‐TG) markedly diminishes BACE1 expression without eliciting cytotoxicity while enhancing microglial phagocytic activity, and ameliorate cognitive impairments with reducing Aβ accumulation in AD mice. Leveraging advanced deep learning‐based tool for target identification, and corroborating with surface plasmon resonance assays, it is elucidated that 6‐TG directly interacts with RAGE, modulating BACE1 expression through the JAK2‐STAT1 pathway and elevating soluble RAGE (sRAGE) levels in the brain. The findings illuminate the therapeutic potential of 6‐TG in ameliorating AD manifestations and advocate for small molecule strategies to increase brain sRAGE levels, offering a strategic alternative to the challenges posed by the complexity of AD.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Alzheimer's disease research and treatments · Advanced Glycation End Products research · Neuroinflammation and Neurodegeneration Mechanisms
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Pharmacy Sungkyunkwan University Suwon 16419 Republic of Korea
Department of Molecular Science and Technology Ajou University Suwon 16499 Republic of Korea
Deargen Inc. Daejeon 34051 Republic of Korea
School of Biological Sciences Seoul National University Seoul 08826 Republic of Korea
Department of Microbiology College of Science and Technology Dankook University Cheonan 31116 Republic of Korea
Department of Medical Science College of Medicine CHA University Seongnam 13496 Republic of Korea
Department of Neurology Samsung Medical Center Sungkyunkwan University School of Medicine Seoul 06355 Republic of Korea
Alzheimer's Disease Convergence Research Center Samsung Medical Center Seoul 06351 Republic of Korea
Neuroscience Center Samsung Medical Center Seoul 06351 Republic of Korea
Biomedical Institute for Convergence at SKKU (BICS) Suwon 16419 Republic of Korea
Department of Health Sciences and Technology SAIHST Sungkyunkwan University Seoul 06355 Republic of Korea
Institute of Quantum Biophysics Sungkyunkwan University Suwon 16419 Republic of Korea
Funding
National Research Foundation of Korea
References (77)
Show source-provided references
1. 10.1038/nrneurol.2017.11110.1038/nrneurol.2017.111 ↗
2. 10.1007/s12272-019-01168-310.1007/s12272-019-01168-3 ↗
3. 10.1001/jamaneurol.2013.584710.1001/jamaneurol.2013.5847 ↗
4. 10.1523/JNEUROSCI.2385-16.201710.1523/JNEUROSCI.2385-16.2017 ↗
5. 10.1016/S1474-4422(20)30412-910.1016/S1474-4422(20)30412-9 ↗
6. 10.1056/NEJMoa121185110.1056/NEJMoa1211851 ↗
7. 10.1038/s41583-019-0132-610.1038/s41583-019-0132-6 ↗
8. 10.1126/sciadv.abd320710.1126/sciadv.abd3207 ↗
9. 10.1186/s13024-019-0333-510.1186/s13024-019-0333-5 ↗
10. 10.1056/NEJMra090914210.1056/NEJMra0909142 ↗
11. 10.1186/1750-1326-9-5110.1186/1750-1326-9-51 ↗
12. 10.1016/j.pharmthera.2022.10812210.1016/j.pharmthera.2022.108122 ↗
13. 10.1016/j.biopsych.2020.02.00110.1016/j.biopsych.2020.02.001 ↗
14. 10.1111/j.1474-9726.2012.00817.x10.1111/j.1474-9726.2012.00817.x ↗
15. 10.1096/fj.05-4986com10.1096/fj.05-4986com ↗
16. 10.1073/pnas.181954111610.1073/pnas.1819541116 ↗
17. 10.1002/med.2162210.1002/med.21622 ↗
18. 10.2174/1570159X1866620050302332310.2174/1570159X18666200503023323 ↗
19. 10.1523/JNEUROSCI.0028-19.201910.1523/JNEUROSCI.0028-19.2019 ↗
20. 10.1038/s41380-021-01166-210.1038/s41380-021-01166-2 ↗
21. 10.1126/scitranslmed.aao562010.1126/scitranslmed.aao5620 ↗
22. 10.1523/JNEUROSCI.5207-14.201510.1523/JNEUROSCI.5207-14.2015 ↗
23. 10.1074/jbc.M50524920010.1074/jbc.M505249200 ↗
24. 10.1523/JNEUROSCI.2766-05.200510.1523/JNEUROSCI.2766-05.2005 ↗
25. 10.1038/nrn311410.1038/nrn3114 ↗
26. 10.1038/382685a010.1038/382685a0 ↗
27. 10.1161/CIRCRESAHA.109.21221710.1161/CIRCRESAHA.109.212217 ↗
28. 10.1523/JNEUROSCI.4878-07.200810.1523/JNEUROSCI.4878-07.2008 ↗
29. 10.1038/nm89010.1038/nm890 ↗
30. 10.1073/pnas.090568610610.1073/pnas.0905686106 ↗
31. 10.1038/nm0795-69310.1038/nm0795-693 ↗
32. 10.1096/fj.08-12638310.1096/fj.08-126383 ↗
33. 10.1016/j.neurobiolaging.2010.05.02610.1016/j.neurobiolaging.2010.05.026 ↗
34. 10.1155/2018/206137610.1155/2018/2061376 ↗
35. 10.1007/s10571-015-0233-310.1007/s10571-015-0233-3 ↗
36. 10.1007/s00702-008-0069-910.1007/s00702-008-0069-9 ↗
37. 10.1124/dmd.31.6.71810.1124/dmd.31.6.718 ↗
38. 10.1080/0036551070172487110.1080/00365510701724871 ↗
39. Weiss J. W. · Cancer Res. · 1974 · 34 · 581
40. 10.1038/nm0297-13610.1038/nm0297-136 ↗
41. 10.1186/1750-1326-2-2210.1186/1750-1326-2-22 ↗
42. 10.1084/jem.2017183110.1084/jem.20171831 ↗
43. B.Shin S.Park K.Kang J. C.Ho Proceedings of the 4th Machine Learning for Healthcare Conference Proceedings of Machine Learning Research 2019 https://doi.org/10.48550/arXiv.1908.06760.
44. 10.1016/j.bbrc.2009.06.00610.1016/j.bbrc.2009.06.006 ↗
45. 10.1177/0972753121107053210.1177/09727531211070532 ↗
46. 10.3390/ijms2401086410.3390/ijms24010864 ↗
47. 10.1186/s13195-014-0089-710.1186/s13195-014-0089-7 ↗
48. 10.1126/science.286.5440.73510.1126/science.286.5440.735 ↗
49. 10.1126/scitranslmed.aad970410.1126/scitranslmed.aad9704 ↗
50. 10.1002/jcph.95010.1002/jcph.950 ↗
51. 10.1136/bmj.j84510.1136/bmj.j845 ↗
52. 10.3233/JAD-17005610.3233/JAD-170056 ↗
53. 10.1371/journal.pone.000847710.1371/journal.pone.0008477 ↗
54. 10.1007/s12035-016-0341-110.1007/s12035-016-0341-1 ↗
55. 10.1016/j.jalz.2013.11.00410.1016/j.jalz.2013.11.004 ↗
56. 10.3233/JAD-2010-10125410.3233/JAD-2010-101254 ↗
57. 10.1523/JNEUROSCI.2440-08.200810.1523/JNEUROSCI.2440-08.2008 ↗
58. 10.1126/science.113234110.1126/science.1132341 ↗
59. 10.1016/j.stemcr.2017.05.03010.1016/j.stemcr.2017.05.030 ↗
60. 10.1021/acs.jmedchem.2c0101010.1021/acs.jmedchem.2c01010 ↗
61. 10.1038/nrc229210.1038/nrc2292 ↗
62. 10.1016/j.cbi.2020.10914410.1016/j.cbi.2020.109144 ↗
63. 10.1089/ars.2012.515810.1089/ars.2012.5158 ↗
64. 10.1111/j.1600-0625.2011.01408.x10.1111/j.1600-0625.2011.01408.x ↗
65. 10.1016/j.imlet.2020.10.00410.1016/j.imlet.2020.10.004 ↗
66. 10.1186/1750-1326-7-5210.1186/1750-1326-7-52 ↗
67. 10.1371/journal.pone.004182310.1371/journal.pone.0041823 ↗
68. 10.14283/jpad.2018.1810.14283/jpad.2018.18 ↗
69. 10.1172/JCI5864210.1172/JCI58642 ↗
70. 10.1002/ddr.2197110.1002/ddr.21971 ↗
71. 10.1007/s00109-005-0688-710.1007/s00109-005-0688-7 ↗
72. 10.2337/db22-017710.2337/db22-0177 ↗
73. 10.21769/BioProtoc.287110.21769/BioProtoc.2871 ↗
74. 10.1007/s10822-013-9644-810.1007/s10822-013-9644-8 ↗
75. Y.Kim B.Shin inProceedings of the 6th Machine Learning for Healthcare Conference Proceedings of Machine Learning Research Cambridge MA2021 pp. 1–16.
76. 10.1021/jm030643010.1021/jm0306430 ↗
77. 10.1021/jm030644s10.1021/jm030644s ↗