基于生物信息学筛选达格列净抗阿尔兹海默病靶基因及体外实验验证

doi:10.5246/jcps.2026.05.032

中国药学(英文版) ›› 2026, Vol. 35 ›› Issue (5): 454-466.DOI: 10.5246/jcps.2026.05.032

基于生物信息学筛选达格列净抗阿尔兹海默病靶基因及体外实验验证

刘惠婷¹^,^#, 赖志望¹^,^#, 邓水印², 郑毅³, 高红瑾⁴^,^*()

^1. 福建医科大学药学院，福建福州 350004
^2. 福建中医药大学药学院，福建福州 350122
^3. 安徽医科大学临床医学院临床医学系，安徽合肥 230031
^4. 福建医科大学省立临床医学院福建省立医院福州大学附属省立医院药学部，福建福州 350001

收稿日期:2026-01-06 修回日期:2026-02-11 接受日期:2026-03-15 出版日期:2026-05-31 发布日期:2026-05-31
通讯作者: 高红瑾

Identification of target genes for dapagliflozin against Alzheimer’s disease based on bioinformatics and verification by in vitro experiments

Huiting Liu¹^,^#, Zhiwang Lai¹^,^#, Shuiyin Deng², Yi Zheng³, Hongjin Gao⁴^,^*()

^1. School of Pharmacy, Fujian Medical University, Fuzhou 350004, Fujian, China
^2. School of Pharmacy, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, Fujian, China
^3. Department of Clinical Medicine, Clinical College of Anhui Medical University, Hefei 230031, Anhui, China
^4. Department of Pharmacy, Shengli Clinical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou 350001, Fujian, China

Received:2026-01-06 Revised:2026-02-11 Accepted:2026-03-15 Online:2026-05-31 Published:2026-05-31
Contact: Hongjin Gao
About author:
^# Huiting Liu and Zhiwang Lai contributed equally to this work.

摘要/Abstract

摘要：

现有的临床研究证实达格列净可以降低阿尔兹海默病(Alzheimer disease, AD)的发生风险，但其具体的作用机制和关键靶标尚不明确。本研究旨在通过生物信息学分析筛选达格列净AD的目标靶点并进行体外实验验证，为达格列净防治AD提供理论依据。本研究从基因表达综合(GEO)数据库获得三个基因表达谱芯片数据集(GSE118553、GSE122063、GSE132903)来鉴定差异表达基因(differentially expressed genes, DEGs)，包括242份正常人脑组织样本及320份AD的人脑组织样本。进行功能富集分析以阐明DEGs的生物学相关性。进一步使用Cytoscape软件构建蛋白互作(protein-protein interaction, PPI)网络筛选排名前五的核心靶点。通过AutodockTools和Vina软件对达格列净和五个核心靶点进行分子对接。最后，使用β-淀粉样肽(Aβ1–42)构建HT-22细胞AD模型，并通过体外实验来验证达格列净对目标靶点的作用。我们的研究共筛选出77个差异基因，通过构建PPI网络筛选出排名前五的核心靶点：胶质纤维酸性蛋白(Glial fibrillary acidic protein, GFAP)、突触小泡蛋白(Synaptophysin, SYP)、突触结合蛋白-1(Synaptotagmin-1, SYT1)、神经肽生长抑素(Somatostatin, SST)、GABA合成酶即谷氨酸脱羧酶2(Glutamate decarboxylase 2, GAD2)。分子对接结果显示这五个AD相关的核心靶点与达格列净具有较好的结合能力。其中，达格列净与SYT1具有最低的结合能。体外实验结果发现达格列净能够显著上调Aβ1–42诱导损伤的HT-22细胞中SYT1基因和蛋白的表达。

关键词: 阿尔兹海默病, 达格列净, SYT1, 分子对接, HT-22细胞

Abstract:

Recent clinical evidence suggests that dapagliflozin may confer protective effects against Alzheimer’s disease (AD); however, its precise molecular mechanisms and specific targets remain largely undefined. In the present study, we employed bioinformatics approaches to systematically screen potential molecular targets of dapagliflozin in the context of AD, followed by in vitro validation, aiming to establish a theoretical foundation for its preventive and therapeutic applications. To identify differentially expressed genes (DEGs), three gene expression microarray datasets (GSE118553, GSE122063, and GSE132903) were retrieved from the GEO database, encompassing a total of 242 normal human brain tissue samples and 320 AD brain tissue samples. Functional enrichment analyses were subsequently conducted to elucidate the biological significance of the identified DEGs. A protein-protein interaction (PPI) network was constructed using Cytoscape software to pinpoint the top five core targets. Molecular docking simulations between dapagliflozin and these core targets were performed using AutoDockTools and Vina. Finally, an AD cellular model was established by treating HT-22 cells with amyloid β-protein 1-42 (Aβ_1-42), and the modulatory effects of dapagliflozin on the core targets were experimentally validated in vitro. A total of 77 DEGs were identified. Subsequent PPI network analysis highlighted five central targets: Glial fibrillary acidic protein (GFAP), Synaptophysin, Synaptotagmin-1 (SYT1), Somatostatin (SST), and Glutamate decarboxylase 2 (GAD2). Molecular docking results demonstrated that all five targets exhibited favorable binding interactions with dapagliflozin, with SYT1 displaying the highest binding affinity (i.e., the lowest binding energy). In vitro experiments further confirmed that dapagliflozin significantly upregulated SYT1 expression at both mRNA and protein levels in the AD model.

Key words: Alzheimer disease, Dapagliflozin, Synaptotagmin-1, Molecular docking, HT-22 cells

Supporting: /attached/file/20260603/20260603010731_235.pdf

刘惠婷, 赖志望, 邓水印, 郑毅, 高红瑾. 基于生物信息学筛选达格列净抗阿尔兹海默病靶基因及体外实验验证[J]. 中国药学(英文版), 2026, 35(5): 454-466.

Huiting Liu, Zhiwang Lai, Shuiyin Deng, Yi Zheng, Hongjin Gao. Identification of target genes for dapagliflozin against Alzheimer’s disease based on bioinformatics and verification by in vitro experiments[J]. Journal of Chinese Pharmaceutical Sciences, 2026, 35(5): 454-466.

图/表 8

Table 1. Primers used for RT-qPCR analysis.

Figure 1. Results of PCA and differential expression analysis of DEGs. (A) PCA before batch correction; (B) PCA after batch correction; (C) Volcano plot of differential expression analysis; (D) Heat map of differential expression analysis.

Figure 2. Functional enrichment analyses of DEGs. (A) Circle map for GO enrichment analysis; (B) Bubble diagram of GO enrichment analysis; (C) Bubble diagram of KEGG enrichment analysis.

Figure 3. PPI network diagram of (A) 77 DEGs and (B) core genes.

Table 2. Results of molecular docking analysis of dapagliflozin with core protein targets.

Figure 4. Molecular docking interaction between dapagliflozin and SYT1.

Figure 5. Effect of dapagliflozin on normal HT-22 cell viability. In comparison to the vehicle control, *P < 0.05.

Figure 6. Effect of Dapagliflozin on the expressions of SYT1 mRNA and protein in HT-22 cells induced by Aβ1-42. (A) Detection of the relative mRNA level of SYT1 by RT-qPCR; (B) Western blotting result, *P < 0.05, ****P < 0.0001.

参考文献 36

[1]	Knopman, D.S.; Amieva, H.; Petersen, R.C.; Chételat, G.; Holtzman, D.M.; Hyman, B.T.; Nixon, R.A.; Jones, D.T. Alzheimer disease. Nat. Rev. Dis. Primers. 2021, 7, 33.
[2]	Scheltens, P.; De Strooper, B.; Kivipelto, M.; Holstege, H.; Chételat, G.; Teunissen, C.E.; Cummings, J.; van der Flier, W.M. Alzheimer’s disease. Lancet. 2021, 397, 1577–1590.
[3]	Mary, A.; Mancuso, R.; Heneka, M.T. Immune activation in Alzheimer disease. Annu. Rev. Immunol. 2024, 42, 585–613.
[4]	Alami, M.; Zerif, E.; Khalil, A.; Hajji, N.; Ramassamy, C.; Lacombe, G.; Laurent, B.; Cohen, A.A.; Wikowski, J.M.; Gris, D.; Bunt, T.; van Tellingen, O.; Hirokawa, K.; Fulop, T.; Berrougui, H. Neuroprotective effects of SGLT2 inhibitors empagliflozin and dapagliflozin on Aβ_1–42-induced neurotoxicity and neuroinflammation in cellular models of Alzheimer’s disease. J. Alzheimer’s Dis. 2025, 105, 464–480.
[5]	2024 Alzheimer’s disease facts and figures. Alzheimers Dement. 2024, 20, 3708–3821.
[6]	Zheng, Q.Y.; Wang, X. Alzheimer’s disease: insights into pathology, molecular mechanisms, and therapy. Protein Cell. 2025, 16, 83–120.
[7]	Wang, Y.X.; Hu, H.; Liu, X.Y.; Guo, X.Y. Hypoglycemic medicines in the treatment of Alzheimer’s disease: Pathophysiological links between AD and glucose metabolism. Front. Pharmacol. 2023, 14, 1138499.
[8]	Michailidis, M.; Moraitou, D.; Tata, D.A.; Kalinderi, K.; Papamitsou, T.; Papaliagkas, V. Alzheimer’s disease as type 3 diabetes: common pathophysiological mechanisms between Alzheimer’s disease and type 2 diabetes. Int. J. Mol. Sci. 2022, 23, 2687.
[9]	Dubey, S.K.; Lakshmi, K.K.; Krishna, K.V.; Agrawal, M.; Singhvi, G.; Saha, R.N.; Saraf, S.; Saraf, S.; Shukla, R.; Alexander, A. Insulin mediated novel therapies for the treatment of Alzheimer’s disease. Life Sci. 2020, 249, 117540.
[10]	Yin, H.Y.; You, S.H.; Zhang, J.Y.; Sun, L.Y.; Cao, J.M.; Liu, X.X.; Li, S.; Guo, C.Y. Proteomic alterations in the cortex and hippocampus of APP/PS1 mice: insights into potential mechanisms of Alzheimer’s disease. J. Chin. Pharm. Sci. 2025, 34, 443–457.
[11]	Nguyen, T.T.; Ta, Q.T.H.; Nguyen, T.K.O.; Nguyen, T.T.D.; Van Giau, V. Type 3 diabetes and its role implications in Alzheimer’s disease. Int. J. Mol. Sci. 2020, 21, 3165.
[12]	Wu, C.Y.; Iskander, C.; Wang, C.; Xiong, L.Y.; Shah, B.R.; Edwards, J.D.; Kapral, M.K.; Herrmann, N.; Lanctôt, K.L.; Masellis, M.; Swartz, R.H.; Cogo-Moreira, H.; MacIntosh, B.J.; Rabin, J.S.; Black, S.E.; Saskin, R.; Swardfager, W. Association of sodium-glucose cotransporter 2 inhibitors with time to dementia: A population-based cohort study. Diabetes Care. 2023, 46, 297–304.
[13]	Ibrahim, W.W.; Kamel, A.S.; Wahid, A.; Abdelkader, N.F. Dapagliflozin as an autophagic enhancer via LKB1/AMPK/SIRT1 pathway in ovariectomized/d-galactose Alzheimer’s rat model. Inflammopharmacology. 2022, 30, 2505–2520.
[14]	Samman, W.A.; Selim, S.M.; El Fayoumi, H.M.; El-Sayed, N.M.; Mehanna, E.T.; Hazem, R.M. Dapagliflozin ameliorates cognitive impairment in aluminum-chloride-induced Alzheimer’s disease via modulation of AMPK/mTOR, oxidative stress and glucose metabolism. Pharmaceuticals. 2023, 16, 753.
[15]	Henstridge, C.M.; Hyman, B.T.; Spires-Jones, T.L. Beyond the neuron–cellular interactions early in Alzheimer disease pathogenesis. Nat. Rev. Neurosci. 2019, 20, 94–108.
[16]	John, A.; Reddy, P.H. Synaptic basis of Alzheimer's disease: Focus on synaptic amyloid beta, P-tau and mitochondria. Ageing Res. Rev. 2021, 65, 101208.
[17]	Kim, S.H.; Ju, I.G.; Kim, J.H.; Eo, H.; Son, S.R.; Jang, D.S.; Oh, M.S. Linderae radix ameliorates cognitive dysfunction by inhibiting neuroinflammation and synaptic damage in Alzheimer’s disease models. Mol. Neurobiol. 2023, 60, 7196–7207.
[18]	Zhang, Y.; Guo, Y.; He, Y.; You, J.; Zhang, Y.R.; Wang, L.B.; Chen, S.D.; He, X.Y.; Yang, L.; Huang, Y.Y.; Kang, J.J.; Ge, Y.J.; Dong, Q.; Feng, J.F.; Cheng, W.; Yu, J.T. Large-scale proteomic analyses of incident Alzheimer’s disease reveal new pathophysiological insights and potential therapeutic targets. Mol. Psychiatry. 2025, 30, 2347–2361.
[19]	Jack, C.R. Jr, Andrews, J.S.; Beach, T.G.; Buracchio, T.; Dunn, B.; Graf, A.; Hansson, O.; Ho, C.; Jagust, W.; McDade, E.; Molinuevo, J.L.; Okonkwo, O.C.; Pani, L.C.; Rafii, M.S.; Scheltens, P.; Siemers, E.; Snyder, H.M.; Sperling, R.; Teunissen, C.E.; Carrillo, M.C. Revised criteria for diagnosis and staging of Alzheimer’s disease: Alzheimer’s Association Workgroup. Alzheimers. Dement. 2024, 20, 5143–5169.
[20]	Amiri, A.; Barreto, G.; Sathyapalan, T.; Sahebkar, A. siRNA therapeutics: future promise for neurodegenerative diseases. Curr. Neuropharmacol. 2021, 19, 1896–1911.
[21]	Busche, M.A.; Hyman, B.T. Synergy between amyloid-β and tau in Alzheimer’s disease. Nat. Neurosci. 2020, 23, 1183–1193.
[22]	Jiang, X.W.; Liu, W.W.; Wu, Y.T.; Wu, Q.; Lu, H.Y.; Xu, Z.H.; Gao, H.Y.; Zhao, Q.C. Notopterygium incisum extract (NRE) rescues cognitive deficits in APP/PS1 Alzhneimer’s disease mice by attenuating amyloid-beta, tau, and neuroinflammation pathology. J. Ethnopharmacol. 2020, 249, 112433.
[23]	Apaer, A.; Shi, Y.Y.; Aobulitalifu, A.; Wen, F.J.; Muhetaer, A.; Ajimu, N.; Sulitan, M.; Cheng, L. Identification of potential therapeutic targets for systemic lupus erythematosus based on GEO database analysis and Mendelian randomization analysis. Front. Genet. 2024, 15, 1454486.
[24]	Mazzucchi, S.; Palermo, G.; Campese, N.; Galgani, A.; Della Vecchia, A.; Vergallo, A.; Siciliano, G.; Ceravolo, R.; Hampel, H.; Baldacci, F. The role of synaptic biomarkers in the spectrum of neurodegenerative diseases. Expert Rev. Proteom. 2020, 17, 543–559.
[25]	Ullah, N.; El Maaiden, E.; Uddin, M.S.; Ashraf, G.M. Synaptotagmin-1: a multi-functional protein that mediates vesicle docking, priming, and fusion. Curr. Protein Pept. Sci. 2021, 22, 470–478.
[26]	Liu, J.Y.; Zhang, C.H.; Wang, J.L.; Huang, Y.F.; Shen, D.; Hu, Y.Q.; Chu, H.Y.; Yu, X.B.; Zhang, L.Y.; Ma, H.Y. A class I HDAC inhibitor BG45 alleviates cognitive impairment through the CaMKII/ITPKA/Ca²⁺ signaling pathway. Pharmaceuticals. 2022, 15, 1481.
[27]	Wu, C.; Ruan, T.T.; Yuan, Y.L.; Xu, C.S.; Du, L.J.; Wang, F.; Xu, S.J. Alterations in synaptic connectivity and synaptic transmission in Alzheimer’s disease with high physical activity. J. Alzheimer’s Dis. 2024, 99, 1005–1022.
[28]	Zhang, Y.H.; Hu, Y.Z.; Han, Z.T.; Geng, Y.; Xia, Z.; Zhou, Y.S.; Wang, Z.F.; Wang, Y.Y.; Kong, E.Y.; Wang, X.N.; Jia, J.J.; Zhang, H.H. Cattle encephalon glycoside and ignotin ameliorate palmitoylation of PSD-95 and enhance expression of synaptic proteins in the frontal cortex of a APPswe/PS1dE9 mouse model of Alzheimer’s disease. J. Alzheimers. Dis. 2022, 88, 141–154.
[29]	Premkumar, T.; Katta, B.; Lulu S, S.; Sundararajan, V. Gene expression analysis reveals GRIN1, SYT1, and SYN2 as significant therapeutic targets and drug repurposing reveals lorazepam and lorediplon as potent inhibitors to manage Alzheimer’s disease. J. Biomol. Struct. Dyn. 2024, 42, 10352–10373.
[30]	Phillips, J.M.; Winfree, R.L.; Seto, M.; Schneider, J.A.; Bennett, D.A.; Dumitrescu, L.C.; Hohman, T.J. Pathologic and clinical correlates of region-specific brain GFAP in Alzheimer’s disease. Acta Neuropathol. 2024, 148, 69.
[31]	Gammie, S.C.; Messing, A.; Hill, M.A.; Kelm-Nelson, C.A.; Hagemann, T.L. Large-scale gene expression changes in APP/PSEN1 and GFAP mutation models exhibit high congruence with Alzheimer’s disease. PLoS One. 2024, 19, e0291995.
[32]	Saiz-Sanchez, D.; Ubeda-Bañon, I.; Flores-Cuadrado, A.; Gonzalez-Rodriguez, M.; Villar-Conde, S.; Astillero-Lopez, V.; Martinez-Marcos, A. Somatostatin, olfaction, and neurodegeneration. Front. Neurosci. 2020, 14, 96.
[33]	Almeida, V.N. Somatostatin and the pathophysiology of Alzheimer’s disease. Ageing Res. Rev. 2024, 96, 102270.
[34]	Fan, X.Y.; Shi, G.; Feng, J.; Jian, L.Y. DNA hypomethylation promotes learning and memory recovery in a rat model of cerebral ischemia/reperfusion injury. Neural Regen. Res. 2023, 18, 863.
[35]	Zhu, X.K.; Wang, P.; Liu, H.J.; Zhan, J.; Wang, J.; Li, M.; Zeng, L.; Xu, P. Changes and significance of SYP and GAP-43 expression in the hippocampus of CIH rats. Int. J. Med. Sci. 2019, 16, 394–402.
[36]	Meng, L.X.; Zou, L.; Xiong, M.; Chen, J.H.; Zhang, X.Y.; Yu, T.; Li, Y.M.; Liu, C.C.; Chen, G.Q.; Wang, Z.H.; Ye, K.Q.; Zhang, Z.T. A synapsin I cleavage fragment contributes to synaptic dysfunction in Alzheimer’s disease. Aging Cell. 2022, 21, e13619.

基于生物信息学筛选达格列净抗阿尔兹海默病靶基因及体外实验验证

Identification of target genes for dapagliflozin against Alzheimer’s disease based on bioinformatics and verification by in vitro experiments

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