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Abstract

Traditional Chinese medicine (TCM) compounds have recently garnered attention for the regulation of immune cell infiltration and the prevention and treatment of Alzheimer’s disease (AD). The Liuwei Dihuang Pill (LDP) has potential in this regard; however, its specific molecular mechanism currently remains unclear. Therefore, we adopted a bioinformatics approach to investigate the infiltration patterns of different types of immune cells in AD and explored the molecular mechanism of LDP intervention, with the aim of providing a new basis for improving the clinical immunotherapy of AD patients. We found that M1 macrophages showed significantly different degrees of infiltration between the hippocampal tissue samples of AD patients and healthy individuals. Four immune intersection targets of LDP in the treatment of AD were identified; they were enriched in 206 biological functions and 30 signaling pathways. Quercetin had the best docking effect with the core immune target PRKCB. Our findings suggest that infiltrated immune cells may influence the course of AD and that LDP can regulate immune cell infiltration through multi-component, multi-target, and multi-pathway approaches, providing a new research direction regarding AD immunotherapy.

Keywords

bioinformatics Alzheimer’s disease immune cell infiltration patterns Liuwei Dihuang Pill

Introduction

Alzheimer’s disease (AD) is a chronic progressive neurodegenerative disease caused by multiple factors.¹ It is more common in pre-senile and elderly people. The clinical manifestations of this disease include memory impairment, low self-care ability, abstract thinking disorder, and personality and behavioral changes.² The main pathological features of AD are senile plaques formed by the aggregation of extracellular β-amyloid (Aβ) and neurofibrillary tangles (NFTs) formed by the hyperphosphorylation of intracellular Tau protein.^3-5 Several pathogenesis mechanisms exist for AD^6-8; however, among all, the neuroinflammatory mechanism plays a key role in AD pathogenesis.⁹ As an important part of neuroinflammation, immune cells are involved in the whole process of AD development. Furthermore, changes in their infiltration can even directly affect the therapeutic effects and clinical outcomes of AD.^10,11 Therefore, the regulation of immune cell infiltration and the inhibition of neuroinflammation have become important targets for reversing the deterioration of the pathological state of AD and for improving survival prognosis. However, it is difficult for traditional, single-target western medicines to reduce neuroinflammation and delay cognitive impairment by regulating immune cell infiltration. In recent years, traditional Chinese medicine (TCM) compounds have garnered attention in the regulation of immune cell infiltration and the prevention and treatment of AD. This popularity stems from their multi-component, multi-target, and multi-pathway characteristics, with minimal side effects.

Liuwei Dihuang Pill (LDP) was first recorded in Qian Yi’s “Key to the Therapeutics of Children’s Diseases” in the Northern Song Dynasty. It consists of 6 herbs, namely, Rehmannia glutinosa (Shudihuang in Chinese, SDH), Cornus sericea (Shanyurou in Chinese, SYR), Rhizoma dioscoreae (Shanyao in Chinese, SY), Rhizoma alismatis (Zexie in Chinese, ZX), Cortex moutan (Mudanpi in Chinese, MDP), and Poria cocos (Fuling in Chinese, FL).¹² Studies have shown that LDP can promote the repair process of the reticuloendothelial system, enhance the phagocytic ability of macrophages, improve the killing activity of natural killer cells, and promote the proliferation and differentiation of T cells and lymphocytes.^13-16 It can play a synergistic role in regulating immune cell infiltration, reducing neuroinflammation, degrading Aβ aggregation, and delaying cognitive impairment.¹⁷ However, its specific molecular mechanism currently remains unclear.

The recent development of bioinformatics has provided a more comprehensive perspective for studying the molecular mechanism of LDP; it can also aid the selection of therapeutic targets for AD. Therefore, the bioinformatics approach was adopted in this study to explore the infiltration patterns of different types of immune cells in AD and investigate the molecular mechanism of LDP intervention. In this way, this study aimed to provide a new basis for improving the clinical immunotherapy of AD patients.

Materials and Methods

Downloading the Gene Expression Omnibus Data

The GEO database was searched to find relevant data,¹⁸ with “Alzheimer’s disease” used as a keyword. The restricted search condition was “expression profiling by array,” the restricted species was “Homo sapiens,” and the obtained gene expression profile data number was GSE1297.

Screening the Differentially Expressed Genes of Alzheimer’s Disease

Based on the R (v.4.0.3) software,^19,20 the differentially expressed genes (DEGs) in the hippocampus tissue samples of AD were screened according to a |log₂ fold change (FC)| >.5 and p < .05, and visualized by a heat map and volcano plot.

Assessing Immune Cell Infiltration and Acquisition of Immune-Related Genes

The proportions of 22 kinds of immune cells in AD and normal hippocampal tissue samples were calculated using the “deconvolution method” (in Cibersort software and Perl language) to evaluate their infiltration.²¹ R (v.4.0.3) and its related packages were used to plot a histogram, distribution heat map, violin diagram, and correlation matrix to show the difference of the results (P < .05 indicated statistically significant difference). The ImmPort database was used to obtain immune-related genes.²²

Prediction of Liuwei Dihuang Pill Target Genes

All active ingredients in LDP and their corresponding potential targets were manually acquired from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database.²³ The screening conditions were as follows: drug-likeness (DL) ≥ .18 and oral bioavailability (OB) ≥ 30%.²⁴ The UniProt database was used to standardize the names of drug targets to obtain the target genes of LDP.²⁵

Construction of Protein–Protein Interaction Network

The DEGs of AD, immune-related genes, and target genes of LDP were uploaded to the Venn online mapping software to get the intersection targets.²⁶ The intersection targets were imported into the Cytoscape (v.3.8.0) software,²⁷ and the Bisogenet and CytoNCA plug-ins were used to construct a protein–protein interaction network; the core immune targets were screened according to the degree value of >61.

Gene Ontology Enrichment and Kyoto Encyclopedia of Genes and Genome Enrichment Analyses

To clarify the possible biological functions and key pathways of LDP in the treatment of AD, R (v.4.0.3) was used to perform ID conversion on the names of the intersection targets,^28,29 following which Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) pathway analyses were carried out.³⁰ Moreover, the top ten items with the smallest P values for the BP, CC, MF, and KEGG signaling pathways were selected (all items with less than ten items were selected) to plot bubble charts. As p represents the significance of enrichment, the screening condition was limited to P < .05.

Construction of “Ingredient-Target-Pathway-Disease” Network

An Ingredient-Target-Pathway-Disease (I-T-P-D) network was constructed using the Cytoscape (v.3.8.0) software to intuitively reflect the key active ingredients in LDP’s prescription for the treatment of AD targets involved in KEGG signaling pathways.

Verification of Molecular Docking

The three-dimensional (3D) crystal structures of core immune targets were obtained from the Protein Data Bank (PDB) database,³¹ while the molecules of key active ingredients were obtained from the PubChem database structure. After using Pymol (v.2.5) and AutoDockTools (v.1.5.6) software to remove the water molecules, separate the ligands, and conduct the hydro treatment,^32,33 molecular docking was carried out using the Autodock vina (v.1.2.0) software.³⁴ Herein, the docking effect between key active ingredients and core immune targets was evaluated, with a binding energy of <-5 kcal·mol^-1 as the reference standard.³⁵ A molecular docking diagram was then plotted.

Results

Screening Results for the Differentially Expressed Genes of Alzheimer’s Disease

According to the datasets GSE1297, a total of 31 hippocampal tissue samples were collected, including 22 AD patients and 9 normal individuals. After data preprocessing and gene differential expression analysis, 552 DEGs were screened in the hippocampal tissue samples of AD patients, including 249 up-regulated genes and 303 down-regulated genes (Figure 1A). The heat map showed the top 20 DEGs with most significant upregulation and downregulation (Figure 1B).

Figure 1.

DEGs of AD. (A) Heat map. (B) Volcano plot. Coral dots represent significantly up-regulated genes and cornflower blue dots represent significantly down-regulated genes.

Analysis of Immune Cell Infiltration and Acquisition of Immune-Related Genes

A total of 15 eligible hippocampal tissue samples were obtained, including 11 AD patients and 4 normal individuals. As shown in Figure 2A-C, the proportions and degrees of infiltration of 22 kinds of immune cells were analyzed across all samples, and the proportions of each immune cell in the hippocampal tissue samples of AD patients and healthy individuals were compared. These results suggest that M2 macrophages and resting CD4⁺ memory T cells in the hippocampal tissue samples of AD patients had larger distribution ratios and higher degrees of infiltration than those of healthy individuals. Moreover, the degree of infiltration of M1 macrophages differed significantly between the hippocampal tissue samples of AD patients and healthy individuals (P <.05). As shown in Figure 2D, the correlation coefficients of the proportions of 22 kinds of immune cells were calculated for the hippocampal tissue samples of AD patients. The results show that the immune cells with higher positive correlation coefficients were activated dendritic cells and activated CD4⁺ memory T cells (r = .81), while the immune cells with larger negative correlation coefficients were naive B cells and memory B cells (r = -9.0). A total of 1793 immune-related genes was collected from the ImmPort database.

Figure 2.

Immune cell infiltration landscape. (A) Histogram of the proportions of immune cells in AD patients and healthy individuals; different immune cells are distinguished with different colors. (B) Distribution heat map of immune cells in AD patients and healthy individuals; the degrees of infiltration are indicated using coral and cornflower blue, respectively. (C) Violin diagram showing proportions of immune cells in AD patients and healthy individuals; red represents AD patients and blue represents healthy individuals. (D) Correlation diagram of proportions of immune cells in AD patients; red represents positive correlation, and blue represents negative correlation.

Prediction Results of Target Genes of Liuwei Dihuang Pill

A total of 42 active ingredients of LDP were screened from the TCMSP database corresponding to the 221 potential targets. After converting the drug targets to standard gene names using UniProt database, a total of 193 target genes of LDP were obtained.

Construction Results of Protein–Protein Interaction Network

A total of 4 immune gene intersection targets were obtained for the treatment of AD with LDP, namely, PRKCB, PPP3CA, NFKBIA, and NR1I2 (Figure 3A). PPI network among these intersection targets was constructed according to the degree value of >61, and found that NFKBIA and PRKCB (which had degree values of 178 and 114, respectively) may be the core immune targets of LDP in the treatment of AD (Figure 3B).

Figure 3.

Intersection targets. (A) Venn diagram of intersection targets. (B) PPI network diagram of intersection targets. Size and color of each node reflect the degree value of the target; edge thickness reflects correlations between nodes.

Results of Gene Ontology and Kyoto Encyclopedia of Genes and Genome Enrichment Analyses

A total of 206 GO biological function items were obtained (including 175 biological processes [BP], 3 cellular components [CC], and 28 molecular functions [MF]); thirty KEGG signaling pathways were identified. As shown in Figure 4Aand B, the intersection target BP mainly involved transcription factor activity, response to carbohydrate, and protein import; CC mainly involved presynaptic cytosol, the calyx of Held, and the cytosol region; MF mainly involved transcription factor binding, nuclear receptor binding, and hormone receptor binding, among the other functions. The main highly enriched KEGG signaling pathways were the B cell receptor signaling pathway, human immunodeficiency virus 1 (HIV-1) infection, and human cytomegalovirus (HCMV) infection.

Figure 4.

Enrichment analysis of immune intersection targets of LDP against AD. (A) GO biological function enrichment. (B) KEGG signaling pathway enrichment. Pathways with significant changes (P < .05) were identified. Dot size represents number of genes and color represents P value.

“Ingredient-Target-Pathway-Disease” Network Analysis

The constructed “I-T-P-D” network contained 27 nodes and 46 edges (Figure 5), suggesting that the targets of LDP in the treatment of AD were not only regulated by a variety of key active ingredients in LDP (quercetin and kaempferol) but also related to multiple KEGG signaling pathways.

Figure 5.

“I-T-P-D” network diagram of LDP against AD. Pink squares show ingredients, yellow diamonds show targets, coral “Vs” show pathways, and lavender triangles show diseases.

Molecular Docking Verification Results

As shown in Figure 6A-D, the key active ingredients (quercetin and kaempferol) were molecularly docked with the core immune targets (NFKBIA and PRKCB). The results showed that there were strong interactions between the key active ingredients and the core immune targets (Table 1). The binding effect between quercetin and PRKCB was the best; the binding energy was -9.0 kcal·mol^-1.

Figure 6.

Molecular docking diagrams. (A) Quercetin and PRKCB. (B) Kaempferol and PRKCB. (C) Quercetin and NFKBIA. (D) Kaempferol and NFKBIA.

Table 1.

Molecular Docking Results of Key Active Ingredients and Core Immune Targets.

Core immune target	PDB ID	Active ingredient	Binding energy/(kcal/mol)
NFKBIA	6Y1J	Quercetin	-7.5
NFKBIA	6Y1J	Kaempferol	-7.4
PRKCB	2I0E	Quercetin	-9.0
PRKCB	2I0E	Kaempferol	-8.7

Discussion

Although there is no relevant record or mention of AD in TCM, according to its clinical manifestations and functional characteristics, AD can generally be classified into the categories of diseases such as “forgetfulness” and “dementia.” In TCM, the emptiness of the sea of marrow, which is thought to be caused by the depletion of kidney essence, is considered to be an important cause of AD.^36,37 LDP, as a typical formula for invigorating the kidney and filling essence,³⁸ has been confirmed by clinical and pharmacological studies to significantly improve the cognitive function of AD patients.^39-41 Moreover, its mechanism of action may be related to the regulation of immune cell infiltration and the inhibition of neuroinflammation.

In this study, the M2 macrophages and resting CD4⁺ memory T cells showed relatively high degrees of infiltration in the hippocampal tissue samples of AD patients than in those of healthy individuals. Furthermore, M1 macrophages showed significantly different degrees of infiltration in the hippocampus tissue samples of AD patients and healthy individuals (P < .05). In AD, the activation of Aβ can increase the permeability of the blood–brain barrier, which in turn would promote the entry of peripheral macrophages and CD4⁺ memory T cells into the brain.^42,43 Thus, immune infiltration can occur under the stimulation of environmental factors. This infiltration not only affords a certain degree of immune protection but also plays a certain role in promoting the formation of neuroinflammation and the pathological evolution of AD.^44,45 Moreover, the disruption of this balance is an important factor regarding the occurrence and development of AD.

In addition, a large positive correlation coefficient was identified between activated dendritic cells and activated CD4⁺ memory T cells. This suggests that there may be mutual assistance between these 2 immune cells. Previous studies have shown that activated dendritic cells can not only effectively initiate the immune response of CD4⁺ T cells in the primary immune response but can also regulate them by interacting with memory cells in almost every stage of memory cell production.^46-49 In this way, they play a key role in the reactivation and response of CD4⁺ memory T cells. A large negative correlation coefficient was identified between naive B and memory B cells, suggesting that there may have been mutual inhibition between these 2 types of immune cells. This correlation may be cause by a phenomenon whereby, after recognizing the antigen exposed by antigen-presenting cells, naive B cells can immediately activate, expand, and differentiate into memory B and plasma cells.^50-52 Therefore, focusing on the interactions between immune cells and determining their potential correlations is expected to become a new strategy for AD immunotherapy.

The PPI network suggested that LDP may directly or indirectly exert its therapeutic effect on AD by regulating 2 core immune targets: NFKBIA and PRKCB. Studies have shown that NFKBIA can not only regulate immune inflammation and tissue damage but also be closely related to the development, differentiation, and migration of immune cells.^53-55 In addition, as the most important inhibitor of the nuclear factor kappa B (NF-κB) pathway, NFKBIA usually exists in the mitochondria of the cytoplasm in a relatively static state. Once stimulated by Aβ, NFKBIA will be phosphorylated and degraded, causing NF-κB activation, and leading to AD neuronal degeneration and cognitive impairment.^56,57 As a member of the PKC family, PRKCB also plays an important role in the regulation of immune cells.⁵⁸ Previous studies have shown that PRKCB is associated with immune cell infiltration and that its increased expression not only promotes increases in the numbers of naive B cells, M1 macrophages, and other immune cells but also predicts a better prognosis.⁵⁹ Meanwhile, negative PRKCB expression leads to severe immunodeficiency and memory impairment; in particular it affects B cell polarity and metabolic reprogramming.⁶⁰

Gene Ontology analysis revealed that the biological function of LDP in the treatment of AD was mainly related to transcription factors. Studies have shown that transcription factors can regulate gene expression and participate in a variety of life activities.⁶¹ Moreover, they can play a key role in the activation or inhibition of AD-related signaling pathways.^62-64 The KEGG results showed that the pathways of LDP in the treatment of AD were mainly enriched in the B cell receptor-signaling pathway and in HIV-1 and HCMV infections. Studies have shown that activated B cells can not only induce the differentiation of effector T cells and lead to neuroinflammation^65-67 but also participate in the pathological process of AD by secreting cytokines and acting on other immune cells (such as macrophages).^45,68 Further studies have found that B cell receptors play an essential role in maintaining the survival, proliferation, and differentiation of B cells.^69,70 Therefore, inhibiting the activation of the B cell receptor-signaling pathway is of great significance in improving AD neuroinflammation and immune damage.^71,72 In the brain, HIV-1 can not only cause neuroinflammation and neuronal death by infecting immune cells (such as macrophages) but also lead to the abnormal elevation of Aβ, thereby affecting cognitive function.^73-75 The HCMV-congenital latent infection of old mouse models has been found to display typical AD-like pathological changes in the brain, such as Aβ, NFTs, and phosphorylated Tau (P-Tau).^76,77 The low immune function caused by long-term HCMV latent infection may be an important mechanism in promoting the occurrence and development of AD.^78,79

According to the “I-T-P-D” network developed in this study, quercetin and kaempferol were found to be the key active ingredients of LDP in the treatment of AD. As common flavonoids,⁸⁰ these ingredients can exhibit anti-AD activities by regulating the migration ability of macrophages, blocking the antigen presentation of macrophages, inhibiting the expression of downstream signal molecules, such as NF-kB, and preventing the excessive production of inflammatory factors.^81,82 Further research has revealed that macrophages, as important immune cells, have key roles in inhibiting immune cell infiltration and improving neuroinflammation.⁸³

In this study, the bioinformatics method was used to evaluate the immune cell infiltration patterns of AD, explore the key active ingredients and core immune targets of LDP in the treatment of AD, construct PPI and “I-T-P-D” networks, preliminarily reveal LDP’s immune mechanism of intervention on AD, and conduct molecular docking verification. Thus, the study provides a new direction for experimental research in the use of LDP for the treatment of AD through immune pathways. Moreover, this study can provide more possibilities for the clinical application of ancient prescriptions used in TCM.

Footnotes

Acknowledgments

The authors would like to thank the GEO database, R (v.4.0.3) software, Cibersort software, Perl language, ImmPort database, TCMSP database, UniProt database, Venn online mapping software, Cytoscape (v.3.8.0) software, Bisogenet and CytoNCA plug-ins, Cytoscape (v.3.8.0) software, PDB database, PubChem database, Pymol (v.2.5) software, AutoDockTools (v.1.5.6) software, and AutoDock vina (v.1.2.0) software utilized in this research for data collection and/or analysis.

Author Contributions

Chenling Zhao: conceptualization, methodology, and writing. Zhangsheng Jiang: software and writing. Liwei Tian: software, validation, and data curation. Lulu Tang: visualization and investigation. An Zhou: review and revision. Ting Dong: supervision and project administration.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project was supported by grants from the National Key R&D Program of China (2018YFC1704400 and 2018YFC1704404), Natural Science Research Project of Anhui Universities (kj2021a0547) and Graduate Scientific Research Project of Anhui Universities (yjs20210476).

ORCID iDs

Chenling Zhao

Ting Dong

References

Jaber

Zhao

Sharfman

Lukiw

. Addressing Alzheimer’s disease (AD) neuropathology using anti-microRNA (AM) strategies. Mol Neurobiol. 2019;56:8101-8108.

Reitz

Brayne

Mayeux

. Epidemiology of Alzheimer disease. Nat Rev Neurol. 2011;7:137-152.

Congdon

Sigurdsson

. Tau-targeting therapies for Alzheimer disease. Nat Rev Neurol. 2018;14:399-415.

Gallardo

Holtzman

. Amyloid-β and Tau at the crossroads of Alzheimer’s disease. Adv Exp Med Biol. 2019;1184:187-203.

Villemagne

Doré

Burnham

Masters

Rowe

. Imaging tau and amyloid-β proteinopathies in Alzheimer disease and other conditions. Nat Rev Neurol. 2018;14:225-236.

Bekdash

. The cholinergic system, the adrenergic system and the neuropathology of Alzheimer’s disease. Int J Mol Sci. 2021;22:1273.

Butterfield

Halliwell

. Oxidative stress, dysfunctional glucose metabolism and Alzheimer disease. Nat Rev Neurosci. 2019;20:148-160.

Panza

Lozupone

Logroscino

Imbimbo

. A critical appraisal of amyloid-β-targeting therapies for Alzheimer disease. Nat Rev Neurol. 2019;15:73-88.

Wang

Shen

Chuang

, et al. Neuroinflammation in Alzheimer's disease: Microglia, molecular participants and therapeutic choices. Curr Alzheimer Res. 2019;16:659-674.

10.

Janssen

Vugts

Funke

, et al. Imaging of neuroinflammation in Alzheimer’s disease, multiple sclerosis and stroke: Recent developments in positron emission tomography. Biochim Biophys Acta. 2016;1862:425-441.

11.

Regen

Hellmann-Regen

Costantini

Reale

. Neuroinflammation and Alzheimer’s disease: Implications for microglial activation. Curr Alzheimer Res. 2017;14:1140-1148.

12.

Cheng

Wang

Zhou

Zhang

. Characteristics of the traditional Liu-Wei-Di-Huang prescription reassessed in modern pharmacology. Chin J Nat Med. 2019;17:103-121.

13.

Liu

. Progress of immunomodulatory effect of Liuwei Dihuang Pill. Her. Med. 2010;29:1051-1053.

14.

Liu

Zhao

Zhang

, et al. The regulatory effect of liuwei dihuang pills on cytokines in mice with experimental autoimmune encephalomyelitis. Am J Chin Med. 2012;40:295-308.

15.

Song

Duan

Zhao

Liu

. Influence of Liuweidihuang Pills and its extracts by different techniques on immunity in mice. J Beijing Univ Tradit Chin Med. 2008;6:385-388.

16.

Wei

Zhou

. Effect of Liuwei Dihuang Pill on cellular immune aging in the elderly. Pract. Clin. Med. 2017;18:20-21.

17.

Guo

Zhao

Zhan

Weng

. Network pharmacology identifies the mechanisms of Liu Wei Di Huang Pill in the treatment of hypertension, type 2 diabetes and Alzheimer’s disease. Pharmacol Clin Chin Mater Clin Med. 2021;37:41-49.

18.

Barrett

Edgar

. Gene expression omnibus: Microarray data storage, submission, retrieval, and analysis. Methods Enzymol. 2006;411:352-369.

19.

Pan

, et al. Identification of hub genes in thyroid carcinoma to predict prognosis by integrated bioinformatics analysis. Bioengineered. 2021;12:2928-2940.

20.

Ritchie

Phipson

, et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43:e47.

21.

Liang

Duan

. Analysis of the infiltration pattern of immune cells in the endometrium of recurrent implantation failure patients based on GEO database. Chin J Tradit Chin Med Pharm. 2021;36:1984-1988.

22.

Bhattacharya

Andorf

Gomes

, et al. ImmPort: disseminating data to the public for the future of immunology. Immunol Res. 2014;58:234-239.

23.

Wang

, et al. TCMSP: A database of systems pharmacology for drug discovery from herbal medicines. J Cheminf. 2014;6:13.

24.

Wang

. Tianfoshen oral liquid: a CFDA approved clinical traditional Chinese medicine, normalizes major cellular pathways disordered during colorectal carcinogenesis. Oncotarget. 2017;8:14549-14569.

25.

UniProt Consortium . UniProt: The universal protein knowledgebase in 2021. Nucleic Acids Res. 2021;49(D1):D480-D489.

26.

Liu

. Integrated bioinformatics and network pharmacology to identify the therapeutic target and molecular mechanisms of Huangqin decoction on ulcerative Colitis. Sci Rep. 2022;12:159.

27.

Shannon

Markiel

Ozier

, et al. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res. 2003;13:2498-2504.

28.

Wang

Han

. ClusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012;16:284-287.

29.

Zhang

Zheng

, et al. Genome-wide mutation profiling and related risk signature for prognosis of papillary renal cell carcinoma. Ann Transl Med. 2019;7:427.

30.

Zhang

Zeng

Chen

Huang

Cai

. Determining protein-protein functional associations by functional rules based on gene ontology and KEGG pathway. Biochim Biophys Acta Proteins Proteom. 2021;1869:140621.

31.

Goodsell

Zardecki

Di Costanzo

, et al. RCSB protein data bank: Enabling biomedical research and drug discovery. Protein Sci. 2020;29:52-65.

32.

Baugh

Lyskov

Weitzner

Gray

. Real-time PyMOL visualization for Rosetta and PyRosetta. PLoS One. 2011;6:e21931.

33.

El-Hachem

Haibe-Kains

Khalil

Kobeissy

Nemer

. AutoDock and AutoDockTools for protein-ligand docking: Beta-site amyloid precursor protein cleaving enzyme 1 (BACE1) as a case study. Methods Mol Biol. 2017;1598:391-403.

34.

Eberhardt

Santos-Martins

Tillack

Forli

. AutoDock Vina 1.2.0: New docking methods, expanded force field, and python bindings. J Chem Inf Model. 2021;61:3891-3898.

35.

Wang

Yang

Sun

, et al. Probing the binding mechanism of substituted pyridine derivatives as effective and selective lysine-specific demethylase 1 inhibitors using 3D-QSAR, molecular docking and molecular dynamics simulations. J Biomol Struct Dyn. 2019b;37:3482-3495.

36.

Han

Zhang

. Discussion on learning and memory dysfunction in senility based on the theory of “kidney brain correlation”. Chin J Tradit Chin Med Pharm. 2020;35:5112-5116.

37.

Ren

. Liu Zuyi’s experience in treating Alzheimer’s disease by warming the kidney and activating blood circulation. Guangxi J Tradit Chin Med. 2016;22:14-15+25.

38.

Zhu

Zhang

Liu

Wang

Liu

Zhao

. Intervention effect of kidney-tonifying and essence-filling therapy on hippocampal autophagy in kidney-deficiency Alzheimer mice. Chin J Exp Tradit Med Form. 2019;25:43-48.

39.

Huang

Chen

Tan

Lan

Zhang

Deng

. Effect of Liuwei Dihuang Pill on mild cognitive impairment of kidney essence deficiency type. J Guangxi Univ Tradit Chin Med. 2015;18:8-10.

40.

Sun

. Application effect of Liuwei Dihuang Pill in adjuvant treatment of Alzheimer’s disease. Guid Chin Med. 2020;18:169-170.

41.

Wang

Guo

. Research progress of Liuwei Dihuang Pill (decoction). Zhejiang. J Integr Tradit Chin West Med. 2017;27:349-352.

42.

Mammana

Fagone

Cavalli

, et al. The role of macrophages in neuroinflammatory and neurodegenerative pathways of Alzheimer’s disease, amyotrophic lateral sclerosis, and multiple sclerosis: pathogenetic cellular effectors and potential therapeutic targets. Int J Mol Sci. 2018;19:831.

43.

Zhou

Xia

Liu

. Research progress of immune cells involved in regulation of Alzheimer’s disease. Chin. J. Immunol. 2021;37:2653-2656+2663.

44.

Fakhoury

. Microglia and astrocytes in Alzheimer’s disease: Implications for therapy. Curr Neuropharmacol. 2018;16:508-518.

45.

Wyatt-Johnson

Brutkiewicz

. The complexity of microglial interactions with innate and adaptive immune cells in Alzheimer’s disease. Front Aging Neurosci. 2020;12:592359.

46.

Lin

Huang

Zhang

Yang

. Bacillus subtilis spore-trained dendritic cells enhance the generation of memory T cells via ICAM1. Cells. 2021;10:2267.

47.

Luo

, et al. Regulatory effect of Wnt5a on the differentiation of initial T cells into memory T cells induced by mature dendritic cells. Med J Wuhan Univ. 2019;40:173-180.

48.

Luo

, et al. Role of Wnt3a expressed by dendritic cells in the activation of canonical Wnt signaling and generation of memory T cells during primary immune responses. Cell Immunol. 2016;310:99-107.

49.

Ugur

Mueller

. T cell and dendritic cell interactions in lymphoid organs: More than just being in the right place at the right time. Immunol Rev. 2019;289:115-128.

50.

Bernasconi

Onai

Lanzavecchia

. A role for Toll-like receptors in acquired immunity: Up-regulation of TLR9 by BCR triggering in naive B cells and constitutive expression in memory B cells. Blood. 2003;101:4500-4504.

51.

Crickx

Chappert

Sokal

, et al. Rituximab-resistant splenic memory B cells and newly engaged naive B cells fuel relapses in patients with immune thrombocytopenia. Sci Transl Med. 2021;13:eabc3961.

52.

Pape

Maul

Dileepan

, et al. Naive B cells with high-avidity germline-encoded antigen receptors produce persistent IgM⁺ and transient IgG⁺ memory B cells. Immunity. 2018;48:1135-1143.

53.

Rebholz

Haase

Eckelt

, et al. Crosstalk between keratinocytes and adaptive immune cells in an IkappaBalpha protein-mediated inflammatory disease of the skin. Immunity. 2007;27:296-307.

54.

Tan

Hopkins

Lim

, et al. Dominant-negative NFKBIA mutation promotes IL-1β production causing hepatic disease with severe immunodeficiency. J Clin Invest. 2020;130:5817-5832.

55.

Zhang

. Tripartite motif containing 52 positively regulates NF-κB signaling by promoting IκBα ubiquitination in lipopolysaccharide-treated microglial cell activation. Med Sci Mon Int Med J Exp Clin Res. 2020;26:e925356.

56.

Echeverria

Burgess

Gamble-George

, et al. Sorafenib inhibits nuclear factor kappa B, decreases inducible nitric oxide synthase and cyclooxygenase-2 expression, and restores working memory in APPswe mice. Neuroscience. 2009;162:1220-1231.

57.

Tang

Xiang

Zhang

. Tripterygium glycoside ameliorates neuroinflammation in a mouse model of Aβ 25-35-induced Alzheimer’s disease by inhibiting the phosphorylation of IκBα and p38. Bioengineered. 2021;12(1):8540-8554.

58.

Liu

Zhang

. Weighted analysis of gene co-expression network identifies novel immune-associated hub genes for lung adenocarcinoma in elderly patients. Chin. J. Geriatr. 2021;40:1015-1019.

59.

Pang

Chen

Wang

, et al. Comprehensive analyses of the heterogeneity and prognostic significance of tumor-infiltrating immune cells in non-small-cell lung cancer: development and validation of an individualized prognostic model. Int Immunopharm. 2020;86:106744.

60.

Tsui

Martinez-Martin

Gaya

, et al. Protein kinase C-β dictates B cell fate by regulating mitochondrial remodeling, metabolic reprogramming, and heme biosynthesis. Immunity. 2018;48:1144-1159.

61.

Patange

Ball

Wan

, et al. MYC amplifies gene expression through global changes in transcription factor dynamics. Cell Rep. 2022;38:110292.

62.

Neshan

Malakouti

Kamalzadeh

, et al. Alterations in T-cell transcription factors and cytokine gene expression in late-onset Alzheimer’s disease. J Alzheimers Dis. 2022;85:645-665.

63.

Oli

Gupta

Kumar

. FOXO and related transcription factors binding elements in the regulation of neurodegenerative disorders. J Chem Neuroanat. 2021;116:102012.

64.

Sun

Zhang

Zhuang

. Transcription factor NRF2 as a promising therapeutic target for Alzheimer’s disease. Free Radic Biol Med. 2020;159:87-102.

65.

Ding

Meng

Zhang

, et al. Prophylactic active immunization with a novel epitope vaccine improves cognitive ability by decreasing amyloid plaques and neuroinflammation in APP/PS1 transgenic mice. Neurosci Res. 2017;119:7-14.

66.

Singh

Sullivan

Hamerman

Campbell

. B cell adaptor for PI3-kinase (BCAP) modulates CD8+ effector and memory T cell differentiation. J Exp Med. 2018;215:2429-2443.

67.

Welsh

Song

Sadegh-Nasseri

. How does B cell antigen presentation affect memory CD4 T cell differentiation and longevity? Front Immunol. 2021;12:677036.

68.

Kim

Wang

Ragonnaud

, et al. Therapeutic B-cell depletion reverses progression of Alzheimer’s disease. Nat Commun. 2021;12:2185.

69.

Herzog

Reth

Jumaa

. Regulation of B-cell proliferation and differentiation by pre-B-cell receptor signalling. Nat Rev Immunol. 2009;9:195-205.

70.

Michaud

Mirlekar

Steward

Bishop

Pylayeva-Gupta

. B cell receptor signaling and protein kinase D2 support regulatory B cell function in pancreatic cancer. Front Immunol. 2022;12:745873.

71.

Cloutier

Sears-Kraxberger

Keachie

Keirstead

. Immunological demyelination triggers macrophage/microglial cells activation without inducing astrogliosis. Clin Dev Immunol. 2013:812456.

72.

Sabatino

Jr. Pröbstel

Zamvil

. B cells in autoimmune and neurodegenerative central nervous system diseases. Nat Rev Neurosci. 2019;20:728-745.

73.

Gisslén

Heslegrave

Veleva

, et al. CSF concentrations of soluble TREM2 as a marker of microglial activation in HIV-1 infection. Neurol. Neuroimmunol. Neuroinflamm. 2018;6:e512.

74.

Giunta

Hou

Zhu

, et al. HIV-1 Tat contributes to Alzheimer’s disease-like pathology in PSAPP mice. Int J Clin Exp Pathol. 2009;2:433-443.

75.

Luscombe

Avihingsanon

Supparatpinyo

, et al. Human immunodeficiency virus type 1 vpu inhibitor, BIT225, in combination with 3-drug antiretroviral therapy: Inflammation and immune cell modulation. J Infect Dis. 2021;223:1914-1922.

76.

Barbian

Lurain

Bennett

Al-Harthi

. HCMV infection induces AD pathology in astrocytes in vitro. Alzheimer's Dementia. 2020;16.

77.

Zhao

Wang

Chen

. The comparison of the three methods detecting apoptosis in cerebral cortex with HCMV congenital infection in aged mouse. Acta Univ Med Anhui. 2007:157-160.

78.

Lin

Wozniak

Cooper

Wilcock

Itzhaki

. Herpesviruses in brain and Alzheimer’s disease. J Pathol. 2002;197:395-402.

79.

Schneider

Meyer-Koenig

Hufert

. Human cytomegalovirus impairs the function of plasmacytoid dendritic cells in lymphoid organs. PLoS One. 2008;3:e3482.

80.

Sajjadi

Oskoueian

Karimi

Ebrahimi

. Amygdalus spinosissima root extract enhanced scopolamine-induced learning and memory impairment in mice. Metab Brain Dis. 2021;36:1859-1869.

81.

Cui

Wang

Cloutier , et al. Quercetin inhibits LPS-induced macrophage migration by suppressing the iNOS/FAK/paxillin pathway and modulating the cytoskeleton. Cell Adhes Migrat. 2019;13:1-12.

82.

Hämäläinen

Nieminen

Vuorela

Heinonen

Moilanen

. Anti-inflammatory effects of flavonoids: Genistein, kaempferol, quercetin, and daidzein inhibit STAT-1 and NF-kappaB activations, whereas flavone, isorhamnetin, naringenin, and pelargonidin inhibit only NF-kappaB activation along with their inhibitory effect on iNOS expression and NO production in activated macrophages. Mediat Inflamm. 2007:45673.

83.

Wolfe

Minogue

Rooney

Lynch

. Infiltrating macrophages contribute to age-related neuroinflammation in C57/BL6 mice. Mech Ageing Dev. 2018;173:84-91.

Bioinformatics-Based Approach for Exploring the Immune Cell Infiltration Patterns in Alzheimer’s Disease and Determining the Intervention Mechanism of Liuwei Dihuang Pill

Abstract

Keywords

Introduction

Materials and Methods

Downloading the Gene Expression Omnibus Data

Screening the Differentially Expressed Genes of Alzheimer’s Disease

Assessing Immune Cell Infiltration and Acquisition of Immune-Related Genes

Prediction of Liuwei Dihuang Pill Target Genes

Construction of Protein–Protein Interaction Network

Gene Ontology Enrichment and Kyoto Encyclopedia of Genes and Genome Enrichment Analyses

Construction of “Ingredient-Target-Pathway-Disease” Network

Verification of Molecular Docking

Results

Screening Results for the Differentially Expressed Genes of Alzheimer’s Disease

Analysis of Immune Cell Infiltration and Acquisition of Immune-Related Genes

Prediction Results of Target Genes of Liuwei Dihuang Pill

Construction Results of Protein–Protein Interaction Network

Results of Gene Ontology and Kyoto Encyclopedia of Genes and Genome Enrichment Analyses

“Ingredient-Target-Pathway-Disease” Network Analysis

Molecular Docking Verification Results

Discussion

Footnotes

Acknowledgments

Author Contributions

Declaration of Conflicting Interests

Funding

ORCID iDs

References