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Proteomic analysis for the effects of non-saponin fraction with rich polysaccharide from Korean Red Ginseng on Alzheimer's disease in a mouse model

  • Sujin, Kim (Department of Biochemistry, College of Medicine, Konyang University) ;
  • Yunkwon, Nam (Department of Biochemistry, College of Medicine, Konyang University) ;
  • Min-jeong, Kim (Department of Biochemistry, College of Medicine, Konyang University) ;
  • Seung-hyun, Kwon (Veterans Medical Research Institute, Veterans Health Service Medical Center) ;
  • Junhyeok, Jeon (Department of Chemical and Biomolecular Engineering (BK21 four), Korea Advanced Institute of Science and Technology (KAIST)) ;
  • Soo Jung, Shin (Department of Biochemistry, College of Medicine, Konyang University) ;
  • Soyoon, Park (Department of Microbiology and Molecular Genetics, College of Biological Sciences, University of California) ;
  • Sungjae, Chang (Department of Biochemistry, College of Medicine, Konyang University) ;
  • Hyun Uk, Kim (Department of Chemical and Biomolecular Engineering (BK21 four), Korea Advanced Institute of Science and Technology (KAIST)) ;
  • Yong Yook, Lee (The Korean Ginseng Research Institute, Korea Ginseng Corporation) ;
  • Hak Su, Kim (Veterans Medical Research Institute, Veterans Health Service Medical Center) ;
  • Minho, Moon (Department of Biochemistry, College of Medicine, Konyang University)
  • Received : 2022.06.07
  • Accepted : 2022.09.27
  • Published : 2023.03.02

Abstract

Background: The most common type of dementia, Alzheimer's disease (AD), is marked by the formation of extracellular amyloid beta (Aβ) plaques. The impairments of axons and synapses appear in the process of Aβ plaques formation, and this damage could cause neurodegeneration. We previously reported that non-saponin fraction with rich polysaccharide (NFP) from Korean Red Ginseng (KRG) showed neuroprotective effects in AD. However, precise molecular mechanism of the therapeutic effects of NFP from KRG in AD still remains elusive. Methods: To investigate the therapeutic mechanisms of NFP from KRG on AD, we conducted proteomic analysis for frontal cortex from vehicle-treated wild-type, vehicle-treated 5XFAD mice, and NFP-treated 5XFAD mice by using nano-LC-ESI-MS/MS. Metabolic network analysis was additionally performed as the effects of NFP appeared to be associated with metabolism according to the proteome analysis. Results: Starting from 5,470 proteins, 2,636 proteins were selected for hierarchical clustering analysis, and finally 111 proteins were further selected for protein-protein interaction network analysis. A series of these analyses revealed that proteins associated with synapse and mitochondria might be linked to the therapeutic mechanism of NFP. Subsequent metabolic network analysis via genome-scale metabolic models that represent the three mouse groups showed that there were significant changes in metabolic fluxes of mitochondrial carnitine shuttle pathway and mitochondrial beta-oxidation of polyunsaturated fatty acids. Conclusion: Our results suggested that the therapeutic effects of NFP on AD were associated with synaptic- and mitochondrial-related pathways, and they provided targets for further rigorous studies on precise understanding of the molecular mechanism of NFP.

Keywords

Acknowledgement

This study was supported by the grant from the Korean Society of Ginseng (2020 to M.M), the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number: HF21C0021 to M.M.), and the Cooperative Research Program for Agriculture Science and Technology Development (PJ01428603 to M.M.), Rural Development Administration, Republic of Korea. Moreover, this work was supported by a VHS Medical Center Research Grant, Republic of Korea. (VHSMC 21022 to S.H.K.) and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2020R1F1A1049629 to H.S.K.). Furthermore, this study was supported by the Bio-Synergy Research Project (NRF-2018M3A9C4076475 to H.U.K.) from the Ministry of Science and ICT (MSIT) through the National Research Foundation of Korea (NRF).

References

  1. Long JM, Holtzman DM. Alzheimer disease: an update on pathobiology and treatment strategies. Cell 2019;179(2):312-39.  https://doi.org/10.1016/j.cell.2019.09.001
  2. Jackson J, et al. Targeting the synapse in alzheimer's disease. Front Neurosci 2019;13:735. 
  3. Sheng M, Sabatini BL, Sudhof TC. Synapses and Alzheimer's disease. Cold Spring Harb Perspect Biol 2012;4(5). 
  4. Clark KL, Noudoost B. The role of prefrontal catecholamines in attention and working memory. Front Neural Circuits 2014;8:33. 
  5. Francis PT. The interplay of neurotransmitters in Alzheimer's disease. CNS Spectr 2005;10(11 Suppl 18):6-9.  https://doi.org/10.1017/S1092852900014164
  6. German DC, et al. Disease-specific patterns of locus coeruleus cell loss. Ann Neurol 1992;32(5):667-76.  https://doi.org/10.1002/ana.410320510
  7. Reddy PH. A critical assessment of research on neurotransmitters in alzheimer's disease. J Alzheimers Dis 2017;57(4):969-74.  https://doi.org/10.3233/JAD-170256
  8. Wang W, et al. Mitochondria dysfunction in the pathogenesis of Alzheimer's disease: recent advances. Mol Neurodegener 2020;15(1):30. 
  9. Shim JS, et al. Global analysis of ginsenoside Rg1 protective effects in beta-amyloid-treated neuronal cells. J Ginseng Res 2017;41(4):566-71.  https://doi.org/10.1016/j.jgr.2016.12.003
  10. Li G, et al. High-throughput metabolomics and ingenuity pathway approach reveals the pharmacological effect and targets of Ginsenoside Rg1 in Alzheimer's disease mice. Sci Rep 2019;9(1):7040. 
  11. Wang N, et al. Ginseng polysaccharides: a potential neuroprotective agent. J Ginseng Res 2021;45(2):211-7.  https://doi.org/10.1016/j.jgr.2020.09.002
  12. Shin SJ, et al. Therapeutic effects of non-saponin fraction with rich polysaccharide from Korean red ginseng on aging and Alzheimer's disease. Free Radic Biol Med 2021;164:233-48.  https://doi.org/10.1016/j.freeradbiomed.2020.12.454
  13. Chandramouli K, Qian PY. Proteomics: challenges, techniques and possibilities to overcome biological sample complexity. Hum Genomics Proteomics 2009;2009. 
  14. Gu C, et al. Current status and applications of genome-scale metabolic models. Genome Biol 2019;20(1):121. 
  15. Lee YY, et al. Biological effects of Korean red ginseng polysaccharides in aged rat using global proteomic approach. Molecules 2020;25(13). 
  16. Joo KM, et al. Simultaneous determination of two Amadori compounds in Korean red ginseng (Panax ginseng) extracts and rat plasma by high-performance anion-exchange chromatography with pulsed amperometric detection. J Chromatogr B Analyt Technol Biomed Life Sci 2008;865(1-2):159-66.  https://doi.org/10.1016/j.jchromb.2008.02.012
  17. Geiger T, et al. Initial quantitative proteomic map of 28 mouse tissues using the SILAC mouse. Mol Cell Proteomics 2013;12(6):1709-22.  https://doi.org/10.1074/mcp.M112.024919
  18. Tyanova S, Cox J. Perseus: a bioinformatics platform for integrative analysis of proteomics data in cancer research. Methods Mol Biol 2018;1711:133-48.  https://doi.org/10.1007/978-1-4939-7493-1_7
  19. Tyanova S, et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat Methods 2016;13(9):731-40.  https://doi.org/10.1038/nmeth.3901
  20. Doncheva NT, et al. Cytoscape StringApp: network analysis and visualization of proteomics data. J Proteome Res 2019;18(2):623-32.  https://doi.org/10.1021/acs.jproteome.8b00702
  21. Agren R, et al. Identification of anticancer drugs for hepatocellular carcinoma through personalized genome-scale metabolic modeling. Mol Syst Biol 2014;10:721. 
  22. Wang H, et al. Genome-scale metabolic network reconstruction of model animals as a platform for translational research. Proc Natl Acad Sci U S A 2021;118(30). 
  23. Lieven C, et al. MEMOTE for standardized genome-scale metabolic model testing. Nat Biotechnol 2020;38(3):272-6.  https://doi.org/10.1038/s41587-020-0446-y
  24. Ebrahim A, et al. COBRApy: COnstraints-based reconstruction and analysis for Python. BMC Syst Biol 2013;7:74. 
  25. Ryu JY, Kim HU, Lee SY. Framework and resource for more than 11,000 genetranscript-protein-reaction associations in human metabolism. Proc Natl Acad Sci U S A 2017;114(45):E9740-9.  https://doi.org/10.1073/pnas.1713050114
  26. Kim HU, Kim TY, Lee SY. Framework for network modularization and Bayesian network analysis to investigate the perturbed metabolic network. BMC Systems Biology 2011;5(2):S14. 
  27. Kim PJ, et al. Metabolite essentiality elucidates robustness of Escherichia coli metabolism. Proc Natl Acad Sci U S A 2007;104(34):13638-42.  https://doi.org/10.1073/pnas.0703262104
  28. McInnes L, Healy J, Melville J. Umap: Uniform Manifold Approximation and Projection for Dimension Reduction 2018. arXiv preprint arXiv:1802.03426. 
  29. Waskom ML. Seaborn: statistical data visualization. Journal of Open Source Software 2021;6(60):3021. 
  30. Virtanen P, et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods 2020;17(3):261-72.  https://doi.org/10.1038/s41592-019-0686-2
  31. Berg D, Holzmann C, Riess O. 14-3-3 proteins in the nervous system. Nat Rev Neurosci 2003;4(9):752-62.  https://doi.org/10.1038/nrn1197
  32. Cornell B, Toyo-Oka K. 14-3-3 proteins in brain development: neurogenesis, neuronal migration and neuromorphogenesis. Front Mol Neurosci 2017;10:318. 
  33. Umahara T, et al. 14-3-3 proteins and zeta isoform containing neurofibrillary tangles in patients with Alzheimer's disease. Acta Neuropathol 2004;108(4):279-86.  https://doi.org/10.1007/s00401-004-0885-4
  34. Yuan Z, Agarwal-Mawal A, Paudel HK. 14-3-3 binds to and mediates phosphorylation of microtubule-associated tau protein by Ser9-phosphorylated glycogen synthase kinase 3beta in the brain. J Biol Chem 2004;279(25):26105-14.  https://doi.org/10.1074/jbc.M308298200
  35. Fountoulakis M, Cairns N, Lubec G. Increased levels of 14-3-3 gamma and epsilon proteins in brain of patients with Alzheimer's disease and Down syndrome. J Neural Transm Suppl 1999;57:323-35.  https://doi.org/10.1007/978-3-7091-6380-1_23
  36. Hartmann B, et al. Homozygous YME1L1 mutation causes mitochondriopathy with optic atrophy and mitochondrial network fragmentation. Elife 2016;5. 
  37. Beck JS, Mufson EJ, Counts SE. Evidence for mitochondrial UPR gene activation in familial and sporadic alzheimer's disease. Curr Alzheimer Res 2016;13(6):610-4.  https://doi.org/10.2174/1567205013666151221145445
  38. Ando K, et al. Tumor suppressor KIF1Bbeta regulates mitochondrial apoptosis in collaboration with YME1L1. Mol Carcinog 2019;58(7):1134-44.  https://doi.org/10.1002/mc.22997
  39. Egan MF, et al. Effect of COMT Val108/158 Met genotype on frontal lobe function and risk for schizophrenia. Proc Natl Acad Sci U S A 2001;98(12):6917-22.  https://doi.org/10.1073/pnas.111134598
  40. Montine TJ, Morrow JD. Fatty acid oxidation in the pathogenesis of Alzheimer's disease. Am J Pathol 2005;166(5):1283-9.  https://doi.org/10.1016/S0002-9440(10)62347-4
  41. Peng Y, et al. Central and peripheral metabolic defects contribute to the pathogenesis of alzheimer's disease: targeting mitochondria for diagnosis and prevention. Antioxid Redox Signal 2020;32(16):1188-236. https://doi.org/10.1089/ars.2019.7763