LAMP2-Centered Autophagy Network and HDAC Inhibitors as Therapeutic Leads in Alzheimer’s and Parkinson’s Diseases: A Bioinformatic Analysis

Öz

Alzheimer hastalığı (AD) ve Parkinson hastalığı (PD) en yaygın iki nörodejeneratif hastalıktır ve her ikisi de belirgin proteinopatiler ve ilerleyici nöronal kayıpla kendisini göstermektedirler. Yapılan çalışmalar her iki hastalıkta da birleşen bir mekanizma olarak otofaji disfonksiyonuna işaret edilmektedir. Bu nedenle çalışmamızdaki temel amaç biyoinformatik teknikler kullanarak AD ve PD'de otofaji ve apoptozla ilişkili ortak upregüle olan genleri belirlemek ve bu genleri hedef alan yeni tedavi bileşenlerini ortaya çıkarmaktır. Çalışmamızda kamuya açık transkriptomik verileri (AD için GSE5281, GSE48350; PD için GSE49036) kullanarak, farklı şekilde ifade edilen genler (DEG'ler) belirlenmiştir ve bunları İnsan Otofaji Veritabanı'ndan derlenen otofaji ve apoptozla ilişkili genlerle sınırlandırılmıştır. Sonuçlarımıza göre CARD8, FXN, LAMP2, EVI2B, MYOT, P2RX7 ve MEGF10 genleri her iki durumda da sürekli olarak upregüle edilirken, ELAPOR1 downregüle edilmiştir. PPI ağ analizi sonuçlarımız LAMP2'nin lizozomal otofajik füzyonu düzenleyen merkezi bir molekül olduğunu vurgulamaktadır. Gen zenginleştirme analizleri, otofaji, lizozomal aktivite ve inflamasyonda rol oynayan yolakları işaret etmektedir. Enrichr platformunda yer alan DSigDB veritabanı kullanılarak yapılan ilaç yeniden kullanım analizi, histon deasetilaz (HDAC) inhibitörleri olan Vorinostat (suberoylanilid hidroksamik asit, SAHA), Trikostatin A (TSA) ve Valproik Asit’in (VPA) bu ortak gen ağını düzenleyebilecek umut verici ajanlar olarak öne çıktığını göstermiştir. Bu bulgular, AD ve PD'nin ortak patolojik özelliklerini ele alma potansiyeline sahip bir dizi otofaji ve apoptozla ilişkili hedef ve tedavi sunmaktadır.

Anahtar Kelimeler

• Alzheimer Hastalığı
• Parkinson Hastalığı
• Otofaji
• Apoptozis
• İlaç Yeniden Kullanımı
• HDAC İnhibitörleri
• Transkriptomik

LAMP2-Centered Autophagy Network and HDAC Inhibitors as Therapeutic Leads in Alzheimer’s and Parkinson’s Diseases: A Bioinformatic Analysis

Öz

Among neurodegenerative disorders, Alzheimer's disease (AD) and Parkinson's disease (PD) are the most commonly diagnosed, and both present with distinct proteinopathies and progressive neuronal loss. Studies have implicated autophagy dysfunction as a common mechanism in both diseases. Therefore, the primary objective of our study is to use bioinformatics techniques to identify commonly upregulated genes associated with autophagy and apoptosis in AD and PD and to develop novel therapeutic agents targeting these genes. Using publicly available transcriptomic data (GSE5281 and GSE48350 for AD; GSE49036 for PD), differentially expressed genes (DEGs) are identified and restricted to autophagy and apoptosis related genes compiled from the Human Autophagy Database. According to our results, the genes CARD8, FXN, LAMP2, EVI2B, MYOT, P2RX7, and MEGF10 are consistently upregulated in both conditions, while ELAPOR1 is downregulated. Our PPI network analysis results highlight LAMP2 as a central molecule regulating lysosomal-autophagic fusion. Gene enrichment analyses implicate pathways involved in autophagy, lysosomal activity, and inflammation. Drug repurposing analysis using the DSigDB database on the Enrichr platform has shown that the histone deacetylase (HDAC) inhibitors Vorinostat (suberoylanilide hydroxamic acid, SAHA), Trichostatin A (TSA), and Valproic Acid (VPA) stand out as promising agents that may regulate this common gene network. These findings provide a number of autophagy- and apoptosis-related targets and therapies with the potential to address shared pathological features of AD and PD.

Anahtar Kelimeler

• Alzheimer’s Disease
• Parkinson’s Disease
• Autophagy
• Apoptosis
• Drug Repurposing
• HDAC Inhibitors
• Transcriptomics

Kaynakça

1. Weller, J., & Budson, A. (2018). Current understanding of Alzheimer's disease diagnosis and treatment. F1000Res, 7, F1000 Faculty Rev-1161. doi: 10.12688/f1000research.14506.1.
2. Korczyn, A. D., & Grinberg, L. T. (2024). Is Alzheimer disease a disease? Nat Rev Neurol, 20(4), 245-251. doi: 10.1038/s41582-024-00940-4.
3. Morris, H. R., Spillantini, M. G., Sue, C. M., & Williams-Gray, C. H. (2024). The pathogenesis of Parkinson’s disease. The Lancet, 403(10423), 293–304. https://doi.org/10.1016/s0140-6736(23)01478-2.
4. Dawson, T. M., & Dawson, V. L. (2010). The role of parkin in familial and sporadic Parkinson's disease. Mov Disord, 25(Suppl 1), S32-9. doi: 10.1002/mds.22798. PMID: 20187240; PMCID: PMC4115293.
5. Lim, K. L., Ng, X. H., Grace, L. G., & Yao, T. P. (2012). Mitochondrial dynamics and Parkinson's disease: focus on parkin. Antioxid Redox Signal, 16(9), 935-49. doi: 10.1089/ars.2011.4105.
6. Uddin, M. S., Stachowiak, A., Mamun, A. A., Tzvetkov, N. T., Takeda, S., Atanasov, A. G., Bergantin, L. B., Abdel-Daim, M. M., & Stankiewicz, A. M. (2018). Autophagy and Alzheimer’s Disease: From Molecular Mechanisms to Therapeutic Implications. Frontiers in Aging Neuroscience, 10. https://doi.org/10.3389/fnagi.2018.00004.
7. Cerri, S., & Blandini, F. (2019). Role of Autophagy in Parkinson's Disease. Curr Med Chem, 26(20), 3702-3718. doi: 10.2174/0929867325666180226094351. PMID: 29484979.
8. Filippone, A., Esposito, E., Mannino, D., Lyssenko, N., & Praticò, D. (2022). The contribution of altered neuronal autophagy to neurodegeneration. Pharmacol Ther, 238, 108178. doi: 10.1016/j.pharmthera.2022.108178.

9. Nixon, R. A., & Yang, D. S. (2011). Autophagy failure in Alzheimer's disease--locating the primary defect. Neurobiol Dis, 43(1), 38-45. doi: 10.1016/j.nbd.2011.01.021.
10. Simonovitch, S., Schmukler, E., Bespalko, A., Iram, T., Frenkel, D., Holtzman, D. M., Masliah, E., Michaelson, D. M., & Pinkas-Kramarski, R. (2016). Impaired Autophagy in APOE4 Astrocytes. J Alzheimers Dis, 51(3), 915-27. doi: 10.3233/JAD-151101.
11. Barmaki, H., Nourazarian, A., & Khaki-Khatibi, F. (2023). Proteostasis and neurodegeneration: a closer look at autophagy in Alzheimer's disease. Front Aging Neurosci, 15, 1281338. doi: 10.3389/fnagi.2023.1281338.
12. Zhang, Z., Yang, X., Song, Y. Q., & Tu, J. (2021). Autophagy in Alzheimer's disease pathogenesis: Therapeutic potential and future perspectives. Ageing Res Rev, 72, 101464. doi: 10.1016/j.arr.2021.101464.
13. Hou, X., Watzlawik, J. O., Fiesel, F. C., & Springer, W. (2020). Autophagy in Parkinson's Disease. J Mol Biol, 432(8), 2651-2672. doi: 10.1016/j.jmb.2020.01.037.
14. Cerri, S., & Blandini, F. (2019). Role of Autophagy in Parkinson's Disease. Curr Med Chem, 26(20), 3702-3718. doi: 10.2174/0929867325666180226094351.
15. Qian, F., Kong, W., & Wang, S. (2022). Exploring autophagy-related prognostic genes of Alzheimer's disease based on pathway crosstalk analysis. Bosn J Basic Med Sci, 22(5), 751-771. doi: 10.17305/bjbms.2021.7019.
16. Xu, W., Su, X., Qin, J., Jin, Y., Zhang, N., & Huang, S. (2024). Identification of Autophagy-Related Biomarkers and Diagnostic Model in Alzheimer's Disease. Genes (Basel), 15(8), 1027. doi: 10.3390/genes15081027.
17. Vastrad, B., Vastrad, C., & Tengli, A. (2020). Bioinformatics analyses of significant genes, related pathways, and candidate diagnostic biomarkers and molecular targets in SARS-CoV-2/COVID-19. Gene Rep, 21, 100956. doi: 10.1016/j.genrep.2020.100956.
18. Diao, H., Li, X., Hu, S., & Liu, Y. (2012). Gene expression profiling combined with bioinformatics analysis identify biomarkers for Parkinson disease. PLoS One, 7(12), e52319. doi: 10.1371/journal.pone.0052319.
19. Li, J., Liu, W., Sun, W., Rao, X., Chen, X., & Yu, L. (2023). A Study on Autophagy Related Biomarkers in Alzheimer's Disease Based on Bioinformatics. Cell Mol Neurobiol, 43(7), 3693-3703. doi: 10.1007/s10571-023-01379-9.
20. Xicoy, H., Peñuelas, N., Vila, M., & Laguna, A. (2019). Autophagic- and Lysosomal-Related Biomarkers for Parkinson's Disease: Lights and Shadows. Cells, 8(11), 1317. doi: 10.3390/cells8111317.
21. Elango, R., Banaganapalli, B., Mujalli, A., AlRayes, N., Almaghrabi, S., Almansouri, M., Sahly, A., Jadkarim, G. A., Malik, M. Z., Kutbi, H. I., Shaik, N. A., & Alefishat, E. (2023). Potential Biomarkers for Parkinson Disease from Functional Enrichment and Bioinformatic Analysis of Global Gene Expression Patterns of Blood and Substantia Nigra Tissues. Bioinform Biol Insights, 17, 11779322231166214. doi: 10.1177/11779322231166214.
22. Liang, W. S., Dunckley, T., Beach, T. G., Grover, A., Mastroeni, D., Walker, D. G., Caselli, R. J., Kukull, W. A., McKeel, D., Morris, J. C., Hulette, C., Schmechel, D., Alexander, G. E., Reiman, E. M., Rogers, J., & Stephan, D. A. (2007). Gene expression profiles in anatomically and functionally distinct regions of the normal aged human brain. Physiol Genomics, 28(3), 311-22. doi: 10.1152/physiolgenomics.00208.2006.
23. Berchtold, N. C., Coleman, P. D., Cribbs, D. H., Rogers, J., Gillen, D. L., & Cotman, C. W. (2013). Synaptic genes are extensively downregulated across multiple brain regions in normal human aging and Alzheimer's disease. Neurobiol Aging, 34(6), 1653-61. doi: 10.1016/j.neurobiolaging.2012.11.024.
24. Dijkstra, A. A., Ingrassia, A., de Menezes, R. X., van Kesteren, R. E., Rozemuller, A. J., Heutink, P., & van de Berg, W. D. (2015). Evidence for Immune Response, Axonal Dysfunction and Reduced Endocytosis in the Substantia Nigra in Early Stage Parkinson's Disease. PLoS One, 10(6), e0128651. doi: 10.1016/j.plosone.0128651.
25. Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., & Lander, E. S. (2005). Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A, 102(43), 15545-50. doi: 10.1073/pnas.0506580102.
26. Liberzon, A., Birger, C., Thorvaldsdóttir, H., Ghandi, M., Mesirov, J. P., & Tamayo, P. (2015). The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst, 1(6), 417-425. doi: 10.1016/j.cels.2015.12.004.
27. Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res, 43(7), e47. doi: 10.1093/nar/gkv007.
28. Blighe, K., Rana, S., & Lewis, M. (2018). EnhancedVolcano: Publication-ready volcano plots with enhanced colouring and labeling. https://github.com/kevinblighe/EnhancedVolcano.
29. Kolde, R. (2012). Package ‘pheatmap’. Bioconductor. Available from: https://cran.r-project.org/package=pheatmap.
30. Jia, A., Xu, L., & Wang, Y. (2021). Venn diagrams in bioinformatics. Brief Bioinform, 22(5), bbab108. doi: 10.1093/bib/bbab108.
31. Sonsungsan, P., Aimauthon, S., Sriwichai, N., & Namchaiw, P. (2024). Unveiling mitochondria as central components driving cognitive decline in alzheimer's disease through cross-transcriptomic analysis of hippocampus and entorhinal cortex microarray datasets. Heliyon, 10(20), e39378. doi: 10.1016/j.heliyon.2024.e39378.
32. Elango, R., Banaganapalli, B., Mujalli, A., AlRayes, N., Almaghrabi, S., Almansouri, M., Sahly, A., Jadkarim, G. A., Malik, M. Z., Kutbi, H. I., Shaik, N. A., & Alefishat, E. (2023). Potential Biomarkers for Parkinson Disease from Functional Enrichment and Bioinformatic Analysis of Global Gene Expression Patterns of Blood and Substantia Nigra Tissues. Bioinform Biol Insights, 17, 11779322231166214. doi: 10.1177/11779322231166214.
33. Chin, C. H., Chen, S. H., Wu, H. H., Ho, C. W., Ko, M. T., & Lin, C. Y. (2014). cytoHubba: identifying hub objects and sub-networks from complex interactome. BMC Syst Biol, 8(Suppl 4), S11. doi: 10.1186/1752-0509-8-S4-S11.
34. Stark, C., Breitkreutz, B. J., Reguly, T., Boucher, L., Breitkreutz, A., & Tyers, M. (2006). BioGRID: a general repository for interaction datasets. Nucleic Acids Res, 34(Database issue), D535-9. doi: 10.1093/nar/gkj109.
35. Shannon, P., Markiel, A., Ozier, O., Baliga, N. S., Wang, J. T., Ramage, D., Amin, N., Schwikowski, B., & Ideker, T. (2003). Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res, 13(11), 2498-504. doi: 10.1101/gr.1239303.
36. Yin, C., Xiao, X., Balaban, V., Kandel, M. E., Lee, Y. J., Popescu, G., & Bogdan, P. (2020). Network science characteristics of brain-derived neuronal cultures deciphered from quantitative phase imaging data. Scientific Reports, 10(1), 15078. https://doi.org/10.1038/s41598-020-72013-7.
37. Evans, T. S., & Chen, B. (2022). Linking the network centrality measures closeness and degree. Communications Physics, 5(1). https://doi.org/10.1038/s42005-022-00949-5.
38. Landherr, A., Friedl, B., & Heidemann, J. (2010). A critical review of centrality measures in social networks. Business & Information Systems Engineering, 2(6), 371–385. https://doi.org/10.1007/s12599-010-0127-3.
39. Cavallaro, L., De Meo, P., Fiumara, G., & Liotta, A. (2024). On the sensitivity of centrality metrics. PLoS ONE, 19(5), e0299255. https://doi.org/10.1371/journal.pone.0299255.
40. Meghanathan, N. (2016). A comprehensive analysis of the correlation between maximal clique size and centrality metrics for complex network graphs. Egyptian Informatics Journal, 22(3), 339–355. https://doi.org/10.1016/j.eij.2016.06.004.
41. Martin, T., Zhang, X., & Newman, M. E. J. (2014). Localization and centrality in networks. arXiv (Cornell University). https://doi.org/10.48550/arxiv.1401.5093.
42. Kanehisa, M., Furumichi, M., Sato, Y., Ishiguro-Watanabe, M., & Tanabe, M. (2021). KEGG: integrating viruses and cellular organisms. Nucleic Acids Res, 49(D1), D545-D551. doi: 10.1093/nar/gkaa970.
43. Xie, Z., Bailey, A., Kuleshov, M. V., Clarke, D. J. B., Evangelista, J. E., Jenkins, S. L., Lachmann, A., Wojciechowicz, M. L., Kropiwnicki, E., Jagodnik, K. M., Jeon, M., & Ma'ayan, A. (2021). Gene Set Knowledge Discovery with Enrichr. Curr Protoc, 1(3), e90. doi: 10.1002/cpz1.90.
44. Pires, D. E. V., Blundell, T. L., & Ascher, D. B. (2015c). PKCSM: Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using Graph-Based Signatures. Journal of Medicinal Chemistry, 58(9), 4066–4072. https://doi.org/10.1021/acs.jmedchem.5b00104.
45. Banerjee, P., Eckert, A. O., Schrey, A. K., & Preissner, R. (2018c). ProTox-II: a webserver for the prediction of toxicity of chemicals. Nucleic Acids Research, 46(W1), W257–W263. https://doi.org/10.1093/nar/gky318.
46. Daina, A., Michielin, O., & Zoete, V. (2017c). SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Scientific Reports, 7(1). https://doi.org/10.1038/srep42717.
47. Faruqui, N. A., Prium, D. H., Mowna, S. A., Rahaman, T. I., Dutta, A. R., & Akter, M. F. (2020). Identification of common molecular signatures shared between Alzheimer’s and Parkinson’s diseases and therapeutic agents exploration: An integrated genomics approach. bioRxiv. https://doi.org/10.1101/2020.12.31.424962.
48. Rahman, M. H., Sarkar, B., Islam, M. S., & Abdullah, M. I. (2020). Discovering biomarkers and pathways shared by Alzheimer’s disease and Parkinson’s disease to identify novel therapeutic targets. International Journal of Engineering Research & Technology (IJERT), 9(6).
49. Kim, Y. H., Beak, S. H., Charidimou, A., & Song, M. (2016). Discovering New Genes in the Pathways of Common Sporadic Neurodegenerative Diseases: A Bioinformatics Approach. J Alzheimers Dis, 51(1), 293-312. doi: 10.3233/JAD-150769.
50. Xu, W., Su, X., Qin, J., Jin, Y., Zhang, N., & Huang, S. (2024). Identification of Autophagy-Related Biomarkers and Diagnostic Model in Alzheimer's Disease. Genes (Basel), 15(8), 1027. doi: 10.3390/genes15081027.
51. Li, H., Wang, F., Guo, X., & Jiang, Y. (2021). Decreased MEF2A Expression Regulated by Its Enhancer Methylation Inhibits Autophagy and May Play an Important Role in the Progression of Alzheimer's Disease. Front Neurosci, 15, 682247. doi: 10.3389/fnins.
52. Vastrad, B., & Vastrad, C. (2021). Bioinformatics analyses of significant genes, related pathways and candidate prognostic biomarkers in Alzheimer’s disease. bioRxiv. https://doi.org/10.1101/2021.05.06.442918.
53. Fu, L. M., & Fu, K. A. (2015). Analysis of Parkinson's disease pathophysiology using an integrated genomics-bioinformatics approach. Pathophysiology, 22(1), 15-29. doi: 10.1016/j.pathophys.2014.10.002.
54. Kristen, H., Sastre, I., Aljama, S., Fuentes, M., Recuero, M., Frank-García, A., Martin, A., Sanchez-Juan, P., Lage, C., Bullido, M. J., & Aldudo, J. (2021). LAMP2 deficiency attenuates the neurodegeneration markers induced by HSV-1 infection. Neurochem Int, 146, 105032. doi: 10.1016/j.neuint.2021.105032.
55. Orr, M. E., & Oddo, S. (2013). Autophagic/lysosomal dysfunction in Alzheimer's disease. Alzheimers Res Ther, 5(5), 53. doi: 10.1186/alzrt217.
56. Qiao, L., Hu, J., Qiu, X., Wang, C., Peng, J., Zhang, C., Zhang, M., Lu, H., & Chen, W. (2023). LAMP2A, LAMP2B and LAMP2C: similar structures, divergent roles. Autophagy, 19(11), 2837-2852. doi: 10.1080/15548627.2023.2235196.
57. Pang, S., Chen, D., Zhang, A., Qin, X., & Yan, B. (2012). Genetic analysis of the LAMP-2 gene promoter in patients with sporadic Parkinson's disease. Neurosci Lett, 526(1), 63-7. doi: 10.1016/j.neulet.2012.07.044.
58. Wu, G., Wang, X., Feng, X., Zhang, A., Li, J., Gu, K., Huang, J., Pang, S., Dong, H., Gao, H., & Yan, B. (2011). Altered expression of autophagic genes in the peripheral leukocytes of patients with sporadic Parkinson's disease. Brain Res, 1394, 105-11. doi: 10.1016/j.brainres.2011.04.013.
59. Klaver, A. C., Coffey, M. P., Aasly, J. O., & Loeffler, D. A. (2018). CSF lamp2 concentrations are decreased in female Parkinson's disease patients with LRRK2 mutations. Brain Res, 1683, 12-16. doi: 10.1016/j.brainres.2018.01.016.
60. Grochowska, K. M., Sperveslage, M., Raman, R., Failla, A. V., Głów, D., Schulze, C., Laprell, L., Fehse, B., & Kreutz, M. R. (2023b). Chaperone-mediated autophagy in neuronal dendrites utilizes activity-dependent lysosomal exocytosis for protein disposal. Cell Reports, 42(8), 112998. https://doi.org/10.1016/j.celrep.2023.112998.
61. Zheng, Y., Peng, L., Jiang, G., Zhou, J., Yang, S., Bai, L., Li, X., & He, M. (2024b). Activation of chaperone-mediated autophagy exerting neuroprotection effect on intracerebral hemorrhage-induced neuronal injury by targeting Lamp2a. Experimental Neurology, 382, 114986. https://doi.org/10.1016/j.expneurol.2024.114986.
62. Issa, A., Sun, J., Petitgas, C., Mesquita, A., Dulac, A., Robin, M., Mollereau, B., Jenny, A., Chérif-Zahar, B., & Birman, S. (2018). The lysosomal membrane protein LAMP2A promotes autophagic flux and prevents SNCA-induced Parkinson disease-like symptoms in the Drosophila brain. Autophagy, 14(11), 1898–1910. https://doi.org/10.1080/15548627.2018.1491489.
63. Kanno, H., Handa, K., Murakami, T., Aizawa, T., & Ozawa, H. (2022). Chaperone-Mediated autophagy in neurodegenerative diseases and acute neurological insults in the central nervous system. Cells, 11(7), 1205. https://doi.org/10.3390/cells11071205.
64. Chung, W., Clarke, L. E., Wang, G. X., Stafford, B. K., Sher, A., Chakraborty, C., Joung, J., Foo, L. C., Thompson, A., Chen, C., Smith, S. J., & Barres, B. A. (2013). Astrocytes mediate synapse elimination through MEGF10 and MERTK pathways. Nature, 504(7480), 394–400. https://doi.org/10.1038/nature12776.
65. Deng, L., Feng, J., & Broaddus, R. R. (2010). The novel estrogen-induced gene EIG121 regulates autophagy and promotes cell survival under stress. Cell Death and Disease, 1(4), e32. https://doi.org/10.1038/cddis.2010.9.
66. Monfort, B., Want, K., Gervason, S., & D’Autréaux, B. (2022). Recent advances in the elucidation of Frataxin biochemical function open novel perspectives for the treatment of Friedreich’s ataxia. Frontiers in Neuroscience, 16. https://doi.org/10.3389/fnins.2022.838335.
67. Territo, P. R., & Zarrinmayeh, H. (2021). P2X7 receptors in Neurodegeneration: Potential therapeutic applications From basic to clinical approaches. Frontiers in Cellular Neuroscience, 15. https://doi.org/10.3389/fncel.2021.617036.
68. Dai, Z., Liu, Z., Yang, R., Cao, W., & Ji, T. (2023). EVI2B Is a Prognostic Biomarker and Is Correlated with Monocyte and Macrophage Infiltration in Osteosarcoma Based on an Integrative Analysis. Biomolecules, 13(2), 327. https://doi.org/10.3390/biom13020327.
69. Wareham, L. K., Liddelow, S. A., Temple, S., Benowitz, L. I., Di Polo, A., Wellington, C., Goldberg, J. L., He, Z., Duan, X., Bu, G., Davis, A. A., Shekhar, K., La Torre, A., Chan, D. C., Canto-Soler, M. V., Flanagan, J. G., Subramanian, P., Rossi, S., Brunner, T., . . . Calkins, D. J. (2022b). Solving neurodegeneration: common mechanisms and strategies for new treatments. Molecular Neurodegeneration, 17(1). https://doi.org/10.1186/s13024-022-00524-0.
70. Shukla, S., & Tekwani, B. L. (2020b). Histone deacetylases inhibitors in neurodegenerative diseases, neuroprotection and neuronal differentiation. Frontiers in Pharmacology, 11. https://doi.org/10.3389/fphar.2020.00537.
71. Yoon, S., & Eom, G. H. (2016). HDAC and HDAC Inhibitor: From Cancer to Cardiovascular Diseases. Chonnam Med J, 52(1), 1-11. doi: 10.4068/cmj.2016.52.1.1.
72. Tang, J., Yan, H., & Zhuang, S. (2013). Histone deacetylases as targets for treatment of multiple diseases. Clin Sci (Lond), 124(11), 651-62. doi: 10.1042/CS20120504.
73. Shanmugam, G., Rakshit, S., & Sarkar, K. (2022). HDAC inhibitors: Targets for tumor therapy, immune modulation and lung diseases. Transl Oncol, 16, 101312. doi: 10.1016/j.tranon.2021.101312.
74. Rotili, D., Simonetti, G., Savarino, A., Palamara, A. T., Migliaccio, A. R., & Mai, A. (2009). Non-cancer uses of histone deacetylase inhibitors: effects on infectious diseases and beta-hemoglobinopathies. Curr Top Med Chem, 9(3), 272-91. doi: 10.2174/156802609788085296.
75. Yang, S. S., Zhang, R., Wang, G., & Zhang, Y. F. (2017). The development prospection of HDAC inhibitors as a potential therapeutic direction in Alzheimer's disease. Transl Neurodegener, 6, 19. doi: 10.1186/s40035-017-0089-1.
76. Chacko, S., & Ladiges, W. (2021). Therapeutic Targeting of Histone Deacetylation to Prevent Alzheimer's Disease. Emed Res, 3, 100020.
77. Oh, M., Choi, I. K., & Kwon, H. J. (2008). Inhibition of histone deacetylase1 induces autophagy. Biochem Biophys Res Commun, 369(4), 1179-83. doi: 10.1016/j.bbrc.2008.03.019.
78. Xu, K., Dai, X. L., Huang, H. C., & Jiang, Z. F. (2011). Targeting HDACs: a promising therapy for Alzheimer's disease. Oxid Med Cell Longev, 2011, 143269. doi: 10.1155/2011/143269.
79. Park, G., Tan, J., Garcia, G., Kang, Y., Salvesen, G., & Zhang, Z. (2016). Regulation of Histone Acetylation by Autophagy in Parkinson Disease. J Biol Chem, 291(7), 3531-40. doi: 10.1074/jbc.M115.675488.
80. Sharma, S., & Taliyan, R. (2015). Targeting histone deacetylases: a novel approach in Parkinson's disease. Parkinsons Dis, 2015, 303294. doi: 10.1155/2015/303294.
81. Li, H., Shi, G., Zha, H., Zheng, L., Luo, Z., & Wang, Y. (2021). Inhibition of histone deacetylase promotes a neuroprotective mechanism in an experimental model of Parkinson's disease. Arch Med Sci, 20(2), 664-674. doi: 10.5114/aoms/130287.
82. Shu, F., Xiao, H., Li, Q., Ren, X., Liu, Z., Hu, B., Wang, H., Wang, H., & Jiang, G. (2023). Epigenetic and post-translational modifications in autophagy: biological functions and therapeutic targets. Signal Transduction and Targeted Therapy, 8(1). https://doi.org/10.1038/s41392-022-01300-8.
83. Cui, L., Zhao, L., Ye, J., Yang, L., Huang, Y., Jiang, X., Zhang, Q., Jia, J., Zhang, D., & Huang, Y. (2020). The lysosomal membrane protein LAMP2 alleviates lysosomal cell death by promoting autophagic flux in ischemic cardiomyocytes. Frontiers in Cell and Developmental Biology, 8. https://doi.org/10.3389/fcell.2020.00031.
84. Xu, J., Gu, J., Pei, W., Zhang, Y., Wang, L., & Gao, J. (2023). The role of lysosomal membrane proteins in autophagy and related diseases. FEBS Journal, 291(17), 3762–3785. https://doi.org/10.1111/febs.16820.
85. Majora, M., Sondenheimer, K., Knechten, M., Uthe, I., Esser, C., Schiavi, A., Ventura, N., & Krutmann, J. (2018). HDAC inhibition improves autophagic and lysosomal function to prevent loss of subcutaneous fat in a mouse model of Cockayne syndrome. Science Translational Medicine, 10(456). https://doi.org/10.1126/scitranslmed.aam7510.
86. Rikiishi, H. (2011). Autophagic and apoptotic effects of HDAC inhibitors on cancer cells. BioMed Research International, 2011(1). https://doi.org/10.1155/2011/830260.
87. Li, T., Yin, L., Kang, X., Xue, W., Wang, N., Zhang, J., Yuan, P., Lin, L., & Li, Y. (2022). TFEB acetylation promotes lysosome biogenesis and ameliorates Alzheimer’s disease–relevant phenotypes in mice. Journal of Biological Chemistry, 298(12), 102649. https://doi.org/10.1016/j.jbc.2022.102649.
88. Dietz, K. C., & Casaccia, P. (2010). HDAC inhibitors and neurodegeneration: At the edge between protection and damage. Pharmacological Research, 62(1), 11–17. https://doi.org/10.1016/j.phrs.2010.01.011.
89. Cai, Y., Arikkath, J., Yang, L., Guo, M., Periyasamy, P., & Buch, S. (2016). Interplay of endoplasmic reticulum stress and autophagy in neurodegenerative disorders. Autophagy, 12(2), 225–244. https://doi.org/10.1080/15548627.2015.1121360.
90. Scheper, W., Nijholt, D. A., & Hoozemans, J. J. (2011). The unfolded protein response and proteostasis in Alzheimer disease. Autophagy, 7(8), 910–911. https://doi.org/10.4161/auto.7.8.15761.
91. Fouillet, A., Levet, C., Virgone, A., Robin, M., Dourlen, P., Rieusset, J., Belaidi, E., Ovize, M., Touret, M., Nataf, S., & Mollereau, B. (2012). ER stress inhibits neuronal death by promoting autophagy. Autophagy, 8(6), 915–926. https://doi.org/10.4161/auto.19716.
92. Siegel, D., Hussein, M., Belani, C., Robert, F., Galanis, E., Richon, V. M., Garcia-Vargas, J., Sanz-Rodriguez, C., & Rizvi, S. (2009b). Vorinostat in solid and hematologic malignancies. Journal of Hematology & Oncology, 2(1). https://doi.org/10.1186/1756-8722-2-31.
93. Kerr, J. S., Galloway, S., Lagrutta, A., Armstrong, M., Miller, T., Richon, V. M., & Andrews, P. A. (2009b). Nonclinical safety assessment of the Histone deacetylase inhibitor Vorinostat. International Journal of Toxicology, 29(1), 3–19. https://doi.org/10.1177/1091581809352111.
94. Yoshida, M., Kijima, M., Akita, M., & Beppu, T. (1990b). Potent and specific inhibition of mammalian histone deacetylase both in vivo and in vitro by trichostatin A. Journal of Biological Chemistry, 265(28), 17174–17179. https://doi.org/10.1016/s0021-9258(17)44885-x.
95. Vanhaecke, T., Papeleu, P., Elaut, G., & Rogiers, V. (2004b). Trichostatin A - like Hydroxamate Histone Deacetylase Inhibitors as Therapeutic Agents: Toxicological Point of View. Current Medicinal Chemistry, 11(12), 1629–1643. https://doi.org/10.2174/0929867043365099.
96. Chateauvieux, S., Morceau, F., Dicato, M., & Diederich, M. (2010b). Molecular and therapeutic potential and toxicity of valproic acid. Journal of Biomedicine and Biotechnology, 2010, 1–18. https://doi.org/10.1155/2010/479364.
97. Shnayder, N. A., Grechkina, V. V., Khasanova, A. K., Bochanova, E. N., Dontceva, E. A., Petrova, M. M., Asadullin, A. R., Shipulin, G. A., Altynbekov, K. S., Al-Zamil, M., & Nasyrova, R. F. (2023b). Therapeutic and toxic effects of valproic acid metabolites. Metabolites, 13(1), 134. https://doi.org/10.3390/metabo13010134.
98. Toxicological studies on a new macrolide antibiotic, midecamycin acetate (miocamycin). (1984b). Part IV-4. Toxicity of metabolites of miocamycin: acute toxicity of Mb2 in mice. PubMed. https://pubmed.ncbi.nlm.nih.gov/6334174/.
99. Bagley, M. C., Dale, J. W., Merritt, E. A., & Xiong, X. (2005b). Thiopeptide antibiotics. Chemical Reviews, 105(2), 685–714. https://doi.org/10.1021/cr0300441.
100. Horikoshi, T., Naganuma, H., Ohashi, Y., Ueno, T., & Nukui, H. (2000b). Enhancing effect of electric stimulation on cytotoxicity of anticancer agents against rat and human glioma cells. Brain Research Bulletin, 51(5), 371–378. https://doi.org/10.1016/s0361-9230(99)00247-6.
101. Kono, K., Takahashi, J. A., Ueba, T., Mori, H., Hashimoto, N., & Fukumoto, M. (2002b). Effects of combination chemotherapy with biscoclaurine-derived alkaloid (Cepharanthine) and nimustine hydrochloride on malignant glioma cell lines. Journal of Neuro-Oncology, 56(2), 101–108. https://doi.org/10.1023/a:1014548618440.
102. Loiodice, S., Da Costa, A. N., & Atienzar, F. (2017b). Current trends in in silico, in vitro toxicology, and safety biomarkers in early drug development. Drug and Chemical Toxicology, 42(2), 113–121. https://doi.org/10.1080/01480545.2017.1400044.