journal article·2026-02Open access

Formation of Condition‐Dependent Alpha‐Synuclein Fibril Strain in Artificial Cerebrospinal Fluid

Rūta Sniečkutė, Darius Šulskis, Arūnė Jocytė, Urtė Venclovaitė, Rimgailė Tamulytė, Mantas Žiaunys, Vytautas Smirnovas, Andrius Sakalauskas

Advanced Science· Volume 13· Issue 7

Abstract

Abstract α‐Synuclein (aSyn) is an intrinsically disordered protein involved in neurotransmission and synaptic plasticity. The pathological aggregation of this protein is a hallmark of synucleinopathies such as Parkinson's disease (PD) or Multiple System Atrophy (MSA). Misfolded aSyn, which primarily originates in the cell cytosol, transmits between neurons, promoting a prion‐like propagation. However, extracellular environments such as interstitial and cerebrospinal fluids (ISF & CSF) play a major role in its clearance and pathological transformation. The molecular components of CSF, including proteins, glycosaminoglycans, and metal ions, may influence the aggregate morphology, structure, and cytotoxicity to cells. To better understand how extracellular composition affects aggregates and their formation, artificial cerebrospinal fluid (aCSF) is employed to mimic potential aggregation processes occurring in CSF. Distinct aSyn fibrils are observed that exhibited low stability outside aCSF, and the removal of key CSF components led to its structural alterations. Cryo‐electron microscopy revealed that these fibrils possess an electron density pocket coordinated with polar basic AAs (K43, K45, H50) that is also observed in aggregates obtained from PD and MSA patients. The findings illustrate the importance of physiologically relevant conditions in studying aSyn aggregation and may explain why disease‐related fibril structure replication in vitro has not yet been successful.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Parkinson's Disease Mechanisms and Treatments · Genetic Neurodegenerative Diseases · Alzheimer's disease research and treatments

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Institute of Biotechnology Life Sciences Center Vilnius University Sauletekio al. 7 Vilnius LT‐10257 Lithuania

Institute of Biochemistry Life Sciences Center Vilnius University Sauletekio al. 7 Vilnius LT‐10257 Lithuania

Funding

Lietuvos Mokslo Taryba

References (95)

Show source-provided references
1. 10.1016/j.jchemneu.2010.12.00110.1016/j.jchemneu.2010.12.001 ↗
2. 10.1016/j.neuron.2013.09.00410.1016/j.neuron.2013.09.004 ↗
3. 10.1186/s13195-014-0073-210.1186/s13195-014-0073-2 ↗
4. 10.1101/cshperspect.a00939910.1101/cshperspect.a009399 ↗
5. 10.1016/j.neulet.2019.13431610.1016/j.neulet.2019.134316 ↗
6. 10.1186/s13024-020-00368-610.1186/s13024-020-00368-6 ↗
7. 10.3389/fnana.2014.0015910.3389/fnana.2014.00159 ↗
8. 10.3390/ijms2215833810.3390/ijms22158338 ↗
9. 10.1016/j.bbr.2022.11405910.1016/j.bbr.2022.114059 ↗
10. 10.1016/j.jbc.2022.10284010.1016/j.jbc.2022.102840 ↗
11. 10.1523/JNEUROSCI.5699-09.201010.1523/JNEUROSCI.5699-09.2010 ↗
12. 10.1111/jnc.1481010.1111/jnc.14810 ↗
13. 10.1186/s13024-018-0241-010.1186/s13024-018-0241-0 ↗
14. 10.1007/978-1-61779-551-0_2310.1007/978-1-61779-551-0_23 ↗
15. 10.1038/cdd.2011.5310.1038/cdd.2011.53 ↗
16. 10.1016/j.jmb.2022.16793010.1016/j.jmb.2022.167930 ↗
17. 10.1016/j.conb.2020.01.01010.1016/j.conb.2020.01.010 ↗
18. 10.1016/j.nbd.2022.10569610.1016/j.nbd.2022.105696 ↗
19. 10.1038/nm.426910.1038/nm.4269 ↗
20. 10.31083/j.fbl280203310.31083/j.fbl2802033 ↗
21. 10.1007/s00018-022-04240-210.1007/s00018-022-04240-2 ↗
22. 10.1038/s41374-019-0195-z10.1038/s41374-019-0195-z ↗
23. 10.14336/AD.2020.010310.14336/AD.2020.0103 ↗
24. 10.3389/fnhum.2021.73721710.3389/fnhum.2021.737217 ↗
25. 10.1016/B978-0-12-803058-5.00003-510.1016/B978-0-12-803058-5.00003-5 ↗
26. 10.1016/j.str.2022.11.01310.1016/j.str.2022.11.013 ↗
27. 10.3390/brainsci804005210.3390/brainsci8040052 ↗
28. 10.3390/jcm1111313910.3390/jcm11113139 ↗
29. 10.1126/sciadv.abc436410.1126/sciadv.abc4364 ↗
30. 10.1038/s41582-020-0333-710.1038/s41582-020-0333-7 ↗
31. 10.1016/j.nbd.2022.10596610.1016/j.nbd.2022.105966 ↗
32. 10.1038/s41598-020-73207-910.1038/s41598-020-73207-9 ↗
33. 10.1016/j.bbapap.2018.10.00610.1016/j.bbapap.2018.10.006 ↗
34. 10.1038/s41467-022-31790-710.1038/s41467-022-31790-7 ↗
35. 10.1186/s40478-024-01923-810.1186/s40478-024-01923-8 ↗
36. 10.1021/acs.jpcb.9b0238110.1021/acs.jpcb.9b02381 ↗
37. 10.1038/s41467-018-05971-210.1038/s41467-018-05971-2 ↗
38. 10.1021/acs.biochem.2c0028310.1021/acs.biochem.2c00283 ↗
39. 10.1126/sciadv.adq353910.1126/sciadv.adq3539 ↗
40. 10.1038/s41467-024-46832-510.1038/s41467-024-46832-5 ↗
41. 10.1038/s41586-022-05319-310.1038/s41586-022-05319-3 ↗
42. 10.1007/s00401-023-02550-810.1007/s00401-023-02550-8 ↗
43. 10.3390/ijms22221238210.3390/ijms222212382 ↗
44. 10.3390/biom403079510.3390/biom4030795 ↗
45. 10.1021/jp805735510.1021/jp8057355 ↗
46. 10.1093/nar/gkab37510.1093/nar/gkab375 ↗
47. 10.1038/s41586-020-2317-610.1038/s41586-020-2317-6 ↗
48. Burger D. · Neuroscience · 2021
49. 10.1073/pnas.192274111710.1073/pnas.1922741117 ↗
50. 10.1073/pnas.232163312110.1073/pnas.2321633121 ↗
51. 10.1038/s42003-025-08355-710.1038/s42003-025-08355-7 ↗
52. 10.7554/eLife.93562.410.7554/eLife.93562.4 ↗
53. 10.1038/s41419-023-05672-910.1038/s41419-023-05672-9 ↗
54. 10.1002/2211-5463.1311010.1002/2211-5463.13110 ↗
55. Frieg B. · Neuroscience · 2021
56. Sokratian A. · Neuroscience · 2022
57. 10.1021/jasms.2c0037910.1021/jasms.2c00379 ↗
58. Deyhim A. · Scientific Reports · 12
59. 10.1039/D3SC06285F10.1039/D3SC06285F ↗
60. 10.1021/ic951005d10.1021/ic951005d ↗
61. 10.1016/j.neuron.2023.04.00710.1016/j.neuron.2023.04.007 ↗
62. 10.1038/emm.2017.110.1038/emm.2017.1 ↗
63. 10.1186/s13024-021-00501-z10.1186/s13024-021-00501-z ↗
64. Burger D. · Neuroscience · 2024
65. 10.1038/s41467-018-03111-410.1038/s41467-018-03111-4 ↗
66. 10.3390/ijms2014343910.3390/ijms20143439 ↗
67. 10.1007/978-1-0716-3662-6_3610.1007/978-1-0716-3662-6_36 ↗
68. 10.1016/j.bpc.2013.06.00310.1016/j.bpc.2013.06.003 ↗
69. 10.1038/cdd.2015.7910.1038/cdd.2015.79 ↗
70. 10.1038/s41467-021-21937-310.1038/s41467-021-21937-3 ↗
71. 10.1021/acschembio.9b0031210.1021/acschembio.9b00312 ↗
72. 10.1073/pnas.173400910010.1073/pnas.1734009100 ↗
73. 10.1038/srep3799010.1038/srep37990 ↗
74. 10.1074/jbc.M109.04980910.1074/jbc.M109.049809 ↗
75. 10.1016/j.molonc.2014.10.00810.1016/j.molonc.2014.10.008 ↗
76. 10.3390/ijms2410859310.3390/ijms24108593 ↗
77. 10.1016/j.gene.2015.08.00910.1016/j.gene.2015.08.009 ↗
78. 10.3389/fnmol.2022.94719110.3389/fnmol.2022.947191 ↗
79. Bernal‐Conde L. D. · Frontiers in Neuroscience · 1399
80. 10.3389/fnagi.2021.70263910.3389/fnagi.2021.702639 ↗
81. 10.14336/AD.2020.010310.14336/AD.2020.0103 ↗
82. 10.1111/febs.1724410.1111/febs.17244 ↗
83. 10.3390/ijms2123891610.3390/ijms21238916 ↗
84. 10.1080/08940886.2017.133842410.1080/08940886.2017.1338424 ↗
85. Nečas D. · Central European Journal of Physics · 2012 · 10 · 181
86. 10.1097/00000542-199002000-0002110.1097/00000542-199002000-00021 ↗
87. 10.1038/nmeth.419310.1038/nmeth.4193 ↗
88. 10.1016/j.jsb.2015.08.00810.1016/j.jsb.2015.08.008 ↗
89. 10.1016/j.jsb.2012.09.00610.1016/j.jsb.2012.09.006 ↗
90. 10.1016/j.sbi.2020.05.01110.1016/j.sbi.2020.05.011 ↗
91. 10.1038/s41586-023-06788-w10.1038/s41586-023-06788-w ↗
92. 10.1107/S090744491000749310.1107/S0907444910007493 ↗
93. 10.1107/S205979831901147110.1107/S2059798319011471 ↗
94. 10.1002/pro.479210.1002/pro.4792 ↗
95. 10.1007/s00401-023-02563-310.1007/s00401-023-02563-3 ↗