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Cited by 12Neuroinflammation and Neurodegeneration MechanismsAlzheimer's disease research and treatmentsDementia and Cognitive Impairment Research

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Uncovering neuroinflammation-related modules and potential repurposing drugs for Alzheimer's disease through multi-omics data integrative analysis

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Li, Shensuo, Lu, Changhao, Zhao, Zhenzhen, Lu, Dong, Zheng, Guangyong·2023·Frontiers in Aging Neuroscience
DOI 10.3389/fnagi.2023.1161405PMC PMC10272561
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Abstract

Background: Neuroinflammation is one of the key factors leading to neuron death and synapse dysfunction in Alzheimer's disease (AD). Amyloid-β (Aβ) is thought to have an association with microglia activation and trigger neuroinflammation in AD. However, inflammation response in brain disorders is heterogenous, and thus, it is necessary to unveil the specific gene module of neuroinflammation caused by Aβ in AD, which might provide novel biomarkers for AD diagnosis and help understand the mechanism of the disease. Methods: Transcriptomic datasets of brain region tissues from AD patients and the corresponding normal tissues were first used to identify gene modules through the weighted gene co-expression network analysis (WGCNA) method. Then, key modules highly associated with Aβ accumulation and neuroinflammatory response were pinpointed by combining module expression score and functional information. Meanwhile, the relationship of the Aβ-associated module to the neuron and microglia was explored based on snRNA-seq data. Afterward, transcription factor (TF) enrichment and the SCENIC analysis were performed on the Aβ-associated module to discover the related upstream regulators, and then a PPI network proximity method was employed to repurpose the potential approved drugs for AD. Results: A total of 16 co-expression modules were primarily obtained by the WGCNA method. Among them, the green module was significantly correlated with Aβ accumulation, and its function was mainly involved in neuroinflammation response and neuron death. Thus, the module was termed the amyloid-β induced neuroinflammation module (AIM). Moreover, the module was negatively correlated with neuron percentage and showed a close association with inflammatory microglia. Finally, based on the module, several important TFs were recognized as potential diagnostic biomarkers for AD, and then 20 possible drugs including ibrutinib and ponatinib were picked out for the disease. Conclusion: In this study, a specific gene module, termed AIM, was identified as a key sub-network of Aβ accumulation and neuroinflammation in AD. Moreover, the module was verified as having an association with neuron degeneration and inflammatory microglia transformation. Moreover, some promising TFs and potential repurposing drugs were presented for AD based on the module. The findings of the study shed new light on the mechanistic investigation of AD and might make benefits the treatment of the disease.

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Figure 1 Flowchart of this study, which includes four main analysis steps. *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001.
Figure 1 Flowchart of this study, which includes four main analysis steps. *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001.CC BY · PubMed Central
Figure 2 Module identification by WGCNA and primary exploration. (A) PCA dimension reduction plot for the difference between AD and NC samples in GSE44770. (B) Diagram displaying different soft-thresholding power and corresponding scale-free fit index. (C) Cluster dendrogram of genes with dissimilarity based on the topological overlap before and after merging. (D) Correlation heatmap between module eigengene and AD grouping. Each cell denoted the corresponding correlation and p-value for each module. (E) AUPRC distribution of cross-validation (CV) for 16 valid modules. (F) Precision–recall curve for the green module. The gray area represented the confidence interval of the CV. (G) Expression difference for 10 hub genes of the green module between AD and NC.
Figure 2 Module identification by WGCNA and primary exploration. (A) PCA dimension reduction plot for the difference between AD and NC samples in GSE44770. (B) Diagram displaying different soft-thresholding power and corresponding scale-free fit index. (C) Cluster dendrogram of genes with dissimilarity based on the topological overlap before and after merging. (D) Correlation heatmap between module eigengene and AD grouping. Each cell denoted the corresponding correlation and p-value for each module. (E) AUPRC distribution of cross-validation (CV) for 16 valid modules. (F) Precision–recall curve for the green module. The gray area represented the confidence interval of the CV. (G) Expression difference for 10 hub genes of the green module between AD and NC.CC BY · PubMed Central
Figure 3 Association between module activity and Aβ. (A) Boxplot of the green module ssGSEA score difference between AD and NC among three brain regions. CR, cerebellum (GSE44768); PFC, dorsolateral prefrontal cortex (GSE44770); VC, visual cortex (GSE44771). (B) Boxplot of the green module ssGSEA score difference between male (M) and female (F) in different groups. (GEO)AD, all AD patients of above microarray data from GEO; (GEO)NC, all healthy controls of above microarray data from GEO; (GTEx)NC, normal brain samples from the GTEx project. (C) Line plot showed an average Aβ plaque index of different age stages in AD model mice among 12 brain regions. For detailed region information, refer the link (https://alzmap.org). (D) Line plots of the green module score difference at different age stages between the AD model and control mice among 12 brain regions. (E) Correlation heatmap of the Aβ plaque loading index and green module score between different age stages and brain regions in AD model mice. *p < 0.05, **p < 0.01, ***p < 0.001.
Figure 3 Association between module activity and Aβ. (A) Boxplot of the green module ssGSEA score difference between AD and NC among three brain regions. CR, cerebellum (GSE44768); PFC, dorsolateral prefrontal cortex (GSE44770); VC, visual cortex (GSE44771). (B) Boxplot of the green module ssGSEA score difference between male (M) and female (F) in different groups. (GEO)AD, all AD patients of above microarray data from GEO; (GEO)NC, all healthy controls of above microarray data from GEO; (GTEx)NC, normal brain samples from the GTEx project. (C) Line plot showed an average Aβ plaque index of different age stages in AD model mice among 12 brain regions. For detailed region information, refer the link (https://alzmap.org). (D) Line plots of the green module score difference at different age stages between the AD model and control mice among 12 brain regions. (E) Correlation heatmap of the Aβ plaque loading index and green module score between different age stages and brain regions in AD model mice. *p < 0.05, **p < 0.01, ***p < 0.001.CC BY · PubMed Central
Figure 4 Functional annotation for the green module. (A) Diagram for the PPI network of the green module. Labels with red color represented immune-related genes (IRGs). A combined score above 600 was considered as a reliable PPI link. (B) Donut chart of the immune categories that IRGs in the green module involved. Enrichment analysis for green module genes using (C) GOBP, (D) KEGG, and (E) Reactome pathway set, respectively. The threshold of BH adjusted p-value was set to 0.05.
Figure 4 Functional annotation for the green module. (A) Diagram for the PPI network of the green module. Labels with red color represented immune-related genes (IRGs). A combined score above 600 was considered as a reliable PPI link. (B) Donut chart of the immune categories that IRGs in the green module involved. Enrichment analysis for green module genes using (C) GOBP, (D) KEGG, and (E) Reactome pathway set, respectively. The threshold of BH adjusted p-value was set to 0.05.CC BY · PubMed Central
Figure 5 Correlation analysis between AIM and neuron percentage. (A) Scatter plot of the relationship between the module ssGSEA score and neuron percentage predicted by the xCell algorithm in GSE44770 microarray samples. R represented the Spearman correlation coefficient. (B) Bar plot showing the Spearman correlation coefficients between 10 module hub genes and neuron percentage predicted by the xCell algorithm in GSE44770 microarray data. (C) Scatter plot of the relationship between the module ssGSEA score and neuron percentage in syn18485175 snRNA-seq samples. (D, E) were similar and based on excitatory and inhibitory neurons, respectively.
Figure 5 Correlation analysis between AIM and neuron percentage. (A) Scatter plot of the relationship between the module ssGSEA score and neuron percentage predicted by the xCell algorithm in GSE44770 microarray samples. R represented the Spearman correlation coefficient. (B) Bar plot showing the Spearman correlation coefficients between 10 module hub genes and neuron percentage predicted by the xCell algorithm in GSE44770 microarray data. (C) Scatter plot of the relationship between the module ssGSEA score and neuron percentage in syn18485175 snRNA-seq samples. (D, E) were similar and based on excitatory and inhibitory neurons, respectively.CC BY · PubMed Central
Figure 6 AIM-related cell type analysis. (A) UMAP plot of eight primary cell types in syn18485175 snRNA-seq. Ex, excitatory neurons; In, inhibitory neurons; Mic, microglia; Oli, oligodendrocytes; Opc, oligodendrocyte progenitors; Ast, astrocytes; End, endothelial; Per, pericytes. (B) UMAP plot of snRNA-seq. Each cell was colored according to AIM expression. (C) Bar plot showing AIM expression in different cell types. Blue denotes the control group and red denotes the AD group. The Wilcoxon test method was used to compare the group difference. ns: p > 0.05, *p ≤ 0.05, ***p ≤ 0.001, ****p ≤ 0.0001.
Figure 6 AIM-related cell type analysis. (A) UMAP plot of eight primary cell types in syn18485175 snRNA-seq. Ex, excitatory neurons; In, inhibitory neurons; Mic, microglia; Oli, oligodendrocytes; Opc, oligodendrocyte progenitors; Ast, astrocytes; End, endothelial; Per, pericytes. (B) UMAP plot of snRNA-seq. Each cell was colored according to AIM expression. (C) Bar plot showing AIM expression in different cell types. Blue denotes the control group and red denotes the AD group. The Wilcoxon test method was used to compare the group difference. ns: p > 0.05, *p ≤ 0.05, ***p ≤ 0.001, ****p ≤ 0.0001.CC BY · PubMed Central
Figure 7 AIM-related microglia subcluster analysis. (A) Bar plot showing AIM expression in different microglia subclusters. Blue denotes the control group and red denotes the AD group. The Wilcoxon test method was used to compare the group difference. ns: p > 0.05, **p ≤ 0.01. (B) Scatter plot of the relationship between the module score and Mic1 percentage in the snRNA-seq microglia population. R represents the Spearman correlation coefficient. (C) Heatmap of Fisher's exact test for the overlapped genes from the AIM to different microglia subtype marker genes. The numbers in each grid denote marker gene size, overlapped gene size, and adjusted p-value. (D) Heatmap of scaled gene expression in different microglia subtypes. C1, C2, C3, and C4 represent marker genes of Mic1, Mic2, Mic3, and Mic0, respectively. The top five marker genes for each subtype were labeled. The right panel represented the enriched GO biological pathways of the corresponding marker gene set. The larger text size represented more significant enrichment results. (E) Trajectory plot of cells in the reduced dimensional space. Each cell was colored by inferred states and (F) was colored by microglia subclusters. (G) Trajectory tree of cells and each cell was colored by microglia subclusters.
Figure 7 AIM-related microglia subcluster analysis. (A) Bar plot showing AIM expression in different microglia subclusters. Blue denotes the control group and red denotes the AD group. The Wilcoxon test method was used to compare the group difference. ns: p > 0.05, **p ≤ 0.01. (B) Scatter plot of the relationship between the module score and Mic1 percentage in the snRNA-seq microglia population. R represents the Spearman correlation coefficient. (C) Heatmap of Fisher's exact test for the overlapped genes from the AIM to different microglia subtype marker genes. The numbers in each grid denote marker gene size, overlapped gene size, and adjusted p-value. (D) Heatmap of scaled gene expression in different microglia subtypes. C1, C2, C3, and C4 represent marker genes of Mic1, Mic2, Mic3, and Mic0, respectively. The top five marker genes for each subtype were labeled. The right panel represented the enriched GO biological pathways of the corresponding marker gene set. The larger text size represented more significant enrichment results. (E) Trajectory plot of cells in the reduced dimensional space. Each cell was colored by inferred states and (F) was colored by microglia subclusters. (G) Trajectory tree of cells and each cell was colored by microglia subclusters.CC BY · PubMed Central
Figure 8 Transcription factor analysis for AIM. (A) Venn diagram displays the overlap between AIM-enriched TFs and Mic1-related TFs. (B) Bar plot of AIM-enriched TFs. The seven shared TFs were marked by a red box. (C) Boxplot of seven overlapped TFs involved regulon activity between the Mic1 subtype and other subtypes. (D) Bar plot of AUPRC distribution of above TFs or random seven human TFs based on GSE44770 microarray data. The model was based on a logistic algorithm. Selected TF expression was used as a feature, and sample grouping (AD or non-AD) was used as diagnostic targets. (E) Violin plot of CEBPA expression difference between NC and AD is based on three microarray datasets (GSE44768, GSE44770, and GSE44771). (F) TF network diagram shows CEBPA and its target genes in the AIM.
Figure 8 Transcription factor analysis for AIM. (A) Venn diagram displays the overlap between AIM-enriched TFs and Mic1-related TFs. (B) Bar plot of AIM-enriched TFs. The seven shared TFs were marked by a red box. (C) Boxplot of seven overlapped TFs involved regulon activity between the Mic1 subtype and other subtypes. (D) Bar plot of AUPRC distribution of above TFs or random seven human TFs based on GSE44770 microarray data. The model was based on a logistic algorithm. Selected TF expression was used as a feature, and sample grouping (AD or non-AD) was used as diagnostic targets. (E) Violin plot of CEBPA expression difference between NC and AD is based on three microarray datasets (GSE44768, GSE44770, and GSE44771). (F) TF network diagram shows CEBPA and its target genes in the AIM.CC BY · PubMed Central
Figure 9 Drug repurposing method based on the AIM. (A) Diagram demonstrated the average PPI network proximity calculation method from all drug targets to module hub genes (e.g., X = 3, Y = 10). (B) Quantile-Quantile plot (QQ plot) indicated background distribution based on random pseudo-targets conformed to Gaussian distribution well. (C) Gray density plot represents background distribution based on random three pseudo-targets and the red dashed curve represents fitted Gaussian distribution. The brown line indicated the actual proximity of the drug. (D) The scatter plot showed the rank of 1,084 approved drugs based on the adjusted p-value.
Figure 9 Drug repurposing method based on the AIM. (A) Diagram demonstrated the average PPI network proximity calculation method from all drug targets to module hub genes (e.g., X = 3, Y = 10). (B) Quantile-Quantile plot (QQ plot) indicated background distribution based on random pseudo-targets conformed to Gaussian distribution well. (C) Gray density plot represents background distribution based on random three pseudo-targets and the red dashed curve represents fitted Gaussian distribution. The brown line indicated the actual proximity of the drug. (D) The scatter plot showed the rank of 1,084 approved drugs based on the adjusted p-value.CC BY · PubMed Central

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Cites

Research works printed but not linked: 49

  1. Akiyama, 2000, Inflammation and Alzheimer's disease, Neurobiol. Aging, 21, 383, 10.1016/S0197-4580(00)00124-X · DOI · Paper page
  2. Andrews, 2021, Tutorial: guidelines for the computational analysis of single-cell RNA sequencing data, Nat. Protoc., 16, 1, 10.1038/s41596-020-00409-w · DOI
  3. Chen, 2019, Ponatinib protects mice from lethal influenza infection by suppressing cytokine storm, Front. Immunol., 10, 1393, 10.3389/fimmu.2019.01393 · DOI
  4. Cheng, 2019, Network-based prediction of drug combinations, Nat. Commun., 10, 1197, 10.1038/s41467-019-09186-x · DOI
  5. Cheng, 2021, Microglial Calhm2 regulates neuroinflammation and contributes to Alzheimer's disease pathology, Sci. Adv, 7, abe3600, 10.1126/sciadv.abe3600 · DOI
  6. Cheng, 2014, Btk inhibition targets in vivo CLL proliferation through its effects on B-cell receptor signaling activity, Leukemia, 28, 649, 10.1038/leu.2013.358 · DOI
  7. Doig, 2017, Why is research on amyloid-β failing to give new drugs for Alzheimer's disease?, ACS Chem. Neurosci., 8, 1435, 10.1021/acschemneuro.7b00188 · DOI
  8. Etienne-Manneville, 2002, Rho Gtpases in cell biology, Nature, 420, 629, 10.1038/nature01148 · DOI
  9. Feng, 2022, Integrative analysis from multicenter studies identifies a WGCNA-derived cancer-associated fibroblast signature for ovarian cancer, Front. Immunol., 13, 951582, 10.3389/fimmu.2022.951582 · DOI
  10. Fu, 2019, Targeting neuroinflammation as a therapeutic strategy for Alzheimer's disease: mechanisms, drug candidates, and new opportunities, ACS Chem. Neurosci., 10, 872, 10.1021/acschemneuro.8b00402 · DOI
  11. Gao, 2019, Transcriptional regulation of homeostatic and disease-associated-microglial genes by IRF1, LXRβ, and CEBPα, Glia, 67, 1958, 10.1002/glia.23678 · DOI
  12. 2022, Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the global burden of disease study 2019, Lancet Public Health, 7, e105, 10.1016/S2468-2667(21)00249-8 · DOI · Paper page
  13. Gomez Perdiguero, 2015, Tissue-resident macrophages originate from yolk-sac-derived erythro-myeloid progenitors, Nature, 518, 547, 10.1038/nature13989 · DOI · Paper page
  14. Griciuc, 2021, The role of innate immune genes in Alzheimer's disease, Curr. Opin. Neurol., 34, 228, 10.1097/WCO.0000000000000911 · DOI
  15. Han, 2020, Regorafenib regulates Ad pathology, neuroinflammation, and dendritic spinogenesis in cells and a mouse model of Ad, Cells, 9, 1655, 10.3390/cells9071655 · DOI
  16. Hardy, 1992, Alzheimer's disease: the amyloid cascade hypothesis, Science, 256, 184, 10.1126/science.1566067 · DOI · Paper page
  17. Heneka, 2015, Neuroinflammation in Alzheimer's disease, Lancet Neurol., 14, 388, 10.1016/S1474-4422(15)70016-5 · DOI · Paper page
  18. Jain, 2021, Role of Jak/Stat in the neuroinflammation and its association with neurological disorders, Ann Neurosci, 28, 191, 10.1177/09727531211070532 · DOI
  19. Jansen, 2019, Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer's disease risk, Nat. Genet., 51, 404, 10.1038/s41588-018-0311-9 · DOI · Paper page
  20. Jin, 2021, Inference and analysis of cell-cell communication using cellchat, Nat. Commun., 12, 1088, 10.1038/s41467-021-21246-9 · DOI · Paper page
  21. Keren-Shaul, 2017, A unique microglia type associated with restricting development of Alzheimer's disease, Cell, 169, 1276, 10.1016/j.cell.2017.05.018 · DOI · Paper page
  22. Kolodziejczyk, 2015, The technology and biology of single-cell RNA sequencing, Mol. Cell, 58, 610, 10.1016/j.molcel.2015.04.005 · DOI · Paper page
  23. Kulminski, 2022, Definitive roles of Tomm40-Apoe-Apoc1 variants in the Alzheimer's risk, Neurobiol. Aging, 110, 122, 10.1016/j.neurobiolaging.2021.09.009 · DOI
  24. Kumar, 2021, Inference of gene regulatory network from single-cell transcriptomic data using pyscenic, Methods Mol. Biol., 2328, 171, 10.1007/978-1-0716-1534-8_10 · DOI
  25. Langfelder, 2008, Wgcna: an R package for weighted correlation network analysis, BMC Bioinformatics, 9, 559, 10.1186/1471-2105-9-559 · DOI · Paper page
  26. Lannfelt, 2014, Amyloid-ß-directed immunotherapy for Alzheimer's disease, J. Intern. Med., 275, 284, 10.1111/joim.12168 · DOI
  27. Lee, 2021, Ibrutinib modulates Aβ/Tau pathology, neuroinflammation, and cognitive function in mouse models of Alzheimer's disease, Aging Cell, 20, e13332, 10.1111/acel.13332 · DOI
  28. Leng, 2021, Neuroinflammation and microglial activation in Alzheimer disease: where do we go from here?, Nat. Rev. Neurol., 17, 157, 10.1038/s41582-020-00435-y · DOI · Paper page
  29. Leroy, 2005, CEBPA point mutations in hematological malignancies, Leukemia, 19, 329, 10.1038/sj.leu.2403614 · DOI
  30. Li, 2021, Ibrutinib alleviates LPS-induced neuroinflammation and synaptic defects in a mouse model of depression, Brain Behav. Immun., 92, 10, 10.1016/j.bbi.2020.11.008 · DOI
  31. Lin, 2021, Role of calcium signaling pathway-related gene regulatory networks in ischemic stroke based on multiple WGCNA and single-cell analysis, Oxid. Med. Cell. Longev., 2021, 8060477, 10.1155/2021/8060477 · DOI
  32. Lin, 2022, Ponatinib modulates the metabolic profile of obese mice by inhibiting adipose tissue macrophage inflammation, Front. Pharmacol., 13, 1040999, 10.3389/fphar.2022.1040999 · DOI
  33. Mathys, 2019, Single-cell transcriptomic analysis of Alzheimer's disease, Nature, 570, 332, 10.1038/s41586-019-1195-2 · DOI · Paper page
  34. Micheau, 2003, Induction of Tnf receptor I-mediated apoptosis via two sequential signaling complexes, Cell, 114, 181, 10.1016/S0092-8674(03)00521-X · DOI · Paper page
  35. Miguel-Álvarez, 2015, Non-steroidal anti-inflammatory drugs as a treatment for Alzheimer's disease: a systematic review and meta-analysis of treatment effect, Drugs Aging, 32, 139, 10.1007/s40266-015-0239-z · DOI
  36. Mishra, 2012, Synapse loss induced by interleukin-1β requires pre- and post-synaptic mechanisms, J. Neuroimmune Pharmacol., 7, 571, 10.1007/s11481-012-9342-7 · DOI · Paper page
  37. Nam, 2018, Ibrutinib suppresses LPS-induced neuroinflammatory responses in Bv2 microglial cells and wild-type mice, J. Neuroinflammation, 15, 271, 10.1186/s12974-018-1308-0 · DOI
  38. Nguyen, 2017, Differential Kv1.3, Kca3.1, and Kir2.1 expression in “classically” and “alternatively” activated microglia, Glia, 65, 106, 10.1002/glia.23078 · DOI · Paper page
  39. Obradovic, 2021, Single-cell protein activity analysis identifies recurrence-associated renal tumor macrophages, Cell, 184, 2988, 10.1016/j.cell.2021.04.038 · DOI
  40. Ousman, 2012, Immune surveillance in the central nervous system, Nat. Neurosci., 15, 1096, 10.1038/nn.3161 · DOI
  41. Plescher, 2018, Plaque-dependent morphological and electrophysiological heterogeneity of microglia in an Alzheimer's disease mouse model, Glia, 66, 1464, 10.1002/glia.23318 · DOI
  42. Sampath, 2017, Cognitive dysfunction in major depression and Alzheimer's disease is associated with hippocampal-prefrontal cortex dysconnectivity, Neuropsychiatr. Dis. Treat., 13, 1509, 10.2147/NDT.S136122 · DOI
  43. Sun, 2022, Acetylcholine deficiency disrupts extratelencephalic projection neurons in the prefrontal cortex in a mouse model of Alzheimer's disease, Nat. Commun., 13, 998, 10.1038/s41467-022-28493-4 · DOI
  44. Tan, 2020, Microglial regional heterogeneity and its role in the brain, Mol. Psychiatry, 25, 351, 10.1038/s41380-019-0609-8 · DOI · Paper page
  45. Wilhelmson, 2020, Ccaat enhancer binding protein alpha (CEBPA) biallelic acute myeloid leukaemia: cooperating lesions, molecular mechanisms and clinical relevance, Br. J. Haematol., 190, 495, 10.1111/bjh.16534 · DOI
  46. Xue, 2021, Large-scale sequencing studies expand the known genetic architecture of Alzheimer's disease, Alzheimers. Dement., 13, e12255, 10.1002/dad2.12255 · DOI
  47. Yu, 2017, Mir-124 contributes to M2 polarization of microglia and confers brain inflammatory protection via the C/Ebp-α pathway in intracerebral hemorrhage, Immunol. Lett., 182, 1, 10.1016/j.imlet.2016.12.003 · DOI · Paper page
  48. Yu, 2015, Microglial Aβ receptors in Alzheimer's disease, Cell. Mol. Neurobiol., 35, 71, 10.1007/s10571-014-0101-6 · DOI · Paper page
  49. Yuksel, 2019, Trafficking and proteolytic processing of amyloid precursor protein and secretases in Alzheimer's disease development: an up-to-date review, Eur. J. Pharmacol., 856, 172415, 10.1016/j.ejphar.2019.172415 · DOI

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Metadata

Authors
Li, S., Lu, C., Zhao, Z., Lu, D., & Zheng, G.
Affiliations
  • Shanghai University of Traditional Chinese Medicine (CN)
  • University of Sassari (IT)
Corresponding author
  • Lu, Dong
  • Zheng, Guangyong
ORCID
Venue
Frontiers in Aging Neuroscience
Publisher
Frontiers Media SA
Published
2023-06-02
Volume
15
Pages
1161405-1161405
ISSN
1663-4365 (ISSN-L)
DOI
10.3389/fnagi.2023.1161405
PMC
PMC10272561
OpenAlex
W4379185140
Cited by
12
References
12
Citations by year
2026: 2 · 2025: 3 · 2024: 7
FWCI
1.24
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Aging Neuroscience (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Natural Science Foundation of China (82104521)
  • Universidade de Macau
Topics
  • Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
  • Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
Keywords
Alzheimer's disease, neuroinflammation, amyloid-beta, microglial activation, gene co-expression modules, drug repurposing, neuronal cell death, diagnostic biomarkers, ibrutinib, ponatinib

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