Mechanism of Action of San Ren Decoction in PLWH Treatment Determined Using Network Pharmacology Combined with Experimental Validation

Abstract

To explore the mechanism of action of San Ren Decoction (SRD) in people living with HIV (PLWH) treatment using network pharmacology, molecular docking technology, and cellular experiments. The active ingredients and potential targets of the Chinese traditional herb in SRD were determined from traditional Chinese Medicine Systems Pharmacology and Bioinformatics Analysis Tool for Molecular Mechanism of traditional Chinese medicine. The therapeutic targets of SRD in AIDS were identified using GeneCards, OMIM, DisGeNET, and DrugBank. The overlap between disease and component targets was determined to identify the potential targets of SRD in AIDS. The network of “Chinese herbs-active ingredients-targets” for SRD was accessed using Cytoscape. A protein–protein interaction network was prepared using STRING. The Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were used to perform enrichment analysis of signaling pathways. Molecular docking experiments and visualization of results were performed using Auto Dock Vina and PyMOL. Based on the results of network pharmacology, a drug-containing serum was prepared through cellular experiments. Peripheral blood mononuclear cells (PBMCs) were isolated from blood samples of PLWH and divided into three groups: the PLWH group, the PLWH + PI3K/Akt inhibitor group, and the PLWH + SRD drug-containing serum group (represented by PLWH, ZSTK474, and SRD, respectively). Healthy human PBMCs were used in the control group. After grouped culturing, quantitative polymerase chain reaction and enzyme-linked immunosorbent assay were performed to detect and confirm gene and protein expression in each group. Quercetin, luteolin, myristic acid, honokiol, arachidonic acid, and other core components were the active ingredients in SRD. The core targets of SRD in AIDS included CAV1, SRC, HSP90AA1, AKT1, PI3K, STAT1, and RAF1. Gene ontology functional enrichment analysis revealed the positive regulation of gene expression, the response to foreign stimuli, and other observations. KEGG pathway enrichment analysis showed the involvement of the PI3K/Akt, TLR, and other pathways. Molecular docking results indicated that the primary active ingredients of SRD exhibited stable binding with the core proteins. In vitro and in vivo experiments showed that the mRNA and protein levels of AKT1, Caspase8, mTOR, PI3K, STAT1, and Bcl-2 were higher in PLWH. SRD may help regulate PLWH by inhibiting the PI3K/Akt signaling pathway. The results of this study indicated that SRD may play a role in PLWH treatment through multiple components and multiple targets to regulate the PI3K/Akt signaling pathway.

Keywords

network pharmacology PLWH SRD

Introduction

Acquired immunodeficiency syndrome (AIDS) is caused by the human immunodeficiency virus (HIV), which primarily attacks immune cells and induces a progressive decrease in the population of CD4⁺ and CD8+ T lymphocytes. It compromises the immune system, making the body susceptible to various opportunistic infections and even death. Currently, antiviral therapy is widely used for treating HIV. It can significantly reduce the HIV load and prevent disease progression.¹ However, antiviral drugs face difficulties and challenges such as drug resistance, adverse drug reactions, poor patient compliance, and poor immune reconstitution.^2–4As a traditional therapeutic method, traditional Chinese medicine (TCM) is characterized by multicomponent, multitarget, and multipathway synergistic effects. TCM has been widely applied as a complementary therapy in the treatment of AIDS in China.

San Ren Decoction (SRD) is composed of eight herbs, namely Xingren (XR), Doukou (DK), Yiyiren (YYR), Huashi (HS), Tongcao (TC), Zhuye (ZY), Banxia (BX), and Houpo (HP). Previous studies conducted by our team have shown that Sanren Tang can effectively improve AIDS signs and symptoms.⁵ However, owing to the complex composition of TCM compounds, the mechanism of action of SRD in AIDS treatment remains unclear. Network pharmacology, first proposed by Hopkins A.L., has the characteristics of system and integrity, which are similar to the holistic concept of TCM. This methodology is compatible with TCM methods as it helps identify the targets of compounds and guides the development of new TCM compounds.⁶ Here, network pharmacology and molecular docking techniques were used to predict the potential targets of SRD in AIDS, the signaling pathways involved, and the active ingredients of SRD that help treat AIDS. To explore the efficacy and mechanism of action of SRD in PLWH therapy, the findings were validated experimentally.

Materials and Methods

Network pharmacology analysis of SRD in AIDS treatment

Identification of the active ingredients and targets of SRD

Using the TCM Systems Pharmacology Database and Analysis Platform⁷ (http://tcmspw.com/, TCMSP), the chemical components of the eight herbs in SRD were searched using keywords such as XR, DK, YYR, HS, TC, ZY, BX, and HP. The active ingredients of SRD were screened based on an oral bioavailability ≥30% and drug-likeness (DL) ≥0.18, and the corresponding targets were identified. In addition, the Bioinformatics Analysis Tool for Molecular Mechanism of TCM (BATMAN-TCM) was used to search the chemical components of all medicinal compounds present in SRD using keywords such as “YI YI REN,” “HUA SHI,” and “BAN XIA.” The components identified were merged using both platforms by setting the parameter score to 20 and the p value to .05. The UniProt database (https://www.uniprot.org/)⁸ and STRING (https://cn.string-db.org/) were used to convert protein and gene names and establish a drug-active ingredient-target database.

Prediction of the AIDS-related targets of SRD

Treatment targets in AIDS can be predicted using four disease gene databases: the Human Gene Annotation Database (https://www.genecards.org/, GeneCards),⁹ Online Mendelian Inheritance in Man (https://omim.org/, OMIM), DisGeNET, and the Compound and Drug Information Database (https://www.drugbank.ca, DrugBank). “Acquired Immunodeficiency Syndrome” was used as a keyword, and the relevant disease genes were searched. The results were combined with findings from previous studies and literature reviews, AIDS-related gene and protein targets were identified, duplicates were eliminated, and AIDS-related targets were identified.

Construction of an SRD-active ingredient-target network

The drug target genes and the obtained disease target genes were entered into the Venny online mapping software to identify the targets of SRD in AIDS. Protein–protein interaction (PPI) networks were prepared using STRING, and Chinese medicine, active ingredients, and target data were imported into the Cytoscape 3.8.2 software to construct a diagram of the “Chinese medicines-active ingredient-target” interaction network.

Gene ontology and Kyoto encyclopedia of genes and genomes signaling pathway enrichment analysis

The target genes of SRD in AIDS were uploaded to the DAVID database (https://david.ncifcrf.gov/home.jsp) using the following steps. The “Start Analysis” option was selected first, followed by the “official-gene-symbol” under “Select Identifier,” and the species was set to “Homo Sapiens” for gene ontology (GO) function and the Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway enrichment analysis. A p-value <.01 was considered statistically significant. The top 20 GO functions and KEGG signaling pathways were selected and uploaded to OmicShare (https://www.omicshare.com/tools/) for data visualization to create a bubble chart.

PPI network construction and core target screening

The target genes of SRD were uploaded to the target interaction database (https://string-db.org/, STRING) to construct a “disease-drug target-protein” interaction network (or PPI), illustrating the role of target proteins at the system level. The Cytoscape software was used to prepare the “key target-signaling pathway-pharmacological effect-syndrome-disease” network.

Molecular docking

The key targets were obtained using PPI and enrichment analysis. Predocking processing steps were performed on the protein macromolecules; this included the removal of water and solvent molecules, the addition of hydrogen atoms, and the setting of the molecules as receptors (saved in the PDBQT format). The active ingredients of SRD were screened in the drug-target network, and predocking processing was performed on the ingredients. The 3D structure files of active ingredients were obtained from the TCMSP database and saved in MOL2 format. All ligands were converted from the MOL2 format to the PDBQT format using the Open Babel and AutoDock tools. AutoDock Vina was used to perform the molecular docking of ligands and receptors. The docking boxes were set, and the docking parameters and computation methods were configured to complete the molecular docking process. PyMOL was used to perform visual representation.

Clinical experimental validation

Study participants

Patients with PLWH who were treated at the First Affiliated Hospital of Henan University of Chinese Medicine from January 2023 to July 2023 were selected as study participants. Healthy individuals from the same region were selected for the control group. The study was approved by the Medical Ethics Committee of the First Affiliated Hospital of Henan University of Chinese Medicine (Approval No.: 2021HL-137). All study participants provided informed consent through signed forms.

Inclusion and exclusion criteria

The inclusion criteria for PLWH: (1) HIV infection diagnosed according to Chinese Guidelines for AIDS Diagnosis and Treatment (2021 Edition); (2) aged between 18 and 60 years; (3) provision of signed informed consent. The exclusion criteria for PLWH:(1) Presence of obvious or severe opportunistic infections; (2) individuals with unconsciousness, various psychiatric disorders, dementia, or those whose family members were unwilling to cooperate.

The inclusion criteria for healthy controls:(1) HIV antibody negative; (2) aged between 18 and 60 years; (3) no diagnosis of unconsciousness, dementia, or various psychiatric disorders; (4) provision of signed informed consent.

Sample preparation

Fasting blood samples (20 mL) were collected from each participant in the morning. Peripheral blood mononuclear cells (PBMCs) were isolated by Ficoll gradient centrifugation and stored at −80°C for future use.

The mRNA expression of AKT, PI3K, STAT1, etc., was analyzed using real-time fluorescence quantitative polymerase chain reaction

The frozen PBMCs were thawed in a 37°C water bath. Total RNA was isolated from the cells using the RNeasy Mini Kit extraction kit according to the manufacturer’s instructions, following which the RNA concentration was measured. The changes in the transcription levels of serine/threonine protein kinase (AKT1), phosphatidylinositol 3-kinase (PI3K), STAT1, mTOR, and other genes were measured using quantitative polymerase chain reaction (qPCR) analysis.

Cellular experimental validation

Preparation of drug-containing rat serum

Experimental animals were purchased from Beijing Weitong Lihua Experimental Animal Technology Co. [experimental unit license number: SYXK (Yu) 2021-0015]; each animal weighed 200 ± 20 g. The rats were randomly divided into the blank control and SRD groups. The administered dose was calculated according to the clinically equivalent dose for normal adults. The blank group was administered an equivalent volume of distilled water.

The administration was performed twice per day for 7 consecutive days. One hour after the final gavage, the rats were anesthetized with chloral hydrate and blood samples were collected from their abdominal aorta using a disposable nonanticoagulant vacuum blood collection tube to prepare the drug-containing rat serum.

Cell culture and grouping

PBMCs were seeded in 6-well plates (5 × 10⁶ cells/mL), with three replicates prepared for each group. The cells were cultured in Roswell Park Memorial Institute 1,640 media supplemented with 15% fetal bovine serum and 5 ng/mL IL-2. The 6-well plates were placed in a 5% CO₂ incubator at 37°C for an overnight recovery culture. The status of the cultured cells was observed under a microscope.

The PLWH PBMCs were divided into the PLWH, PLWH + PI3K/Akt inhibitor, and PLWH +SRD-containing serum groups (represented by PLWH, ZSTK474, and SRD, respectively). PBMCs collected from healthy individuals served as the control group (represented by HC). Inhibitors and drug-containing rat serum were added, and the cells were incubated for 4 hours. After 4 hours, the 6-well plates were removed from the incubator. The cells were transferred to 15 mL centrifuge tubes and centrifuged at 251 g for 5 min. The supernatant was collected and the expression of proteins related to the PI3K/Akt signaling pathway was measured using enzyme-linked immunosorbent assay.

Statistical analysis

The general data and immunological indices were analyzed using SPSS version 21.0. Comparisons of count data were performed using the chi-square test. Measurement data conforming to a normal distribution were presented as (mean ± standard deviation) and analyzed using the t-test or one-way variance. Data not conforming to a normal distribution were analyzed using the rank-sum test. A p value of <.05 or p < .01 was considered statistically significant. The gene chip detection results were statistically analyzed online using the RT² Profiler PCR Data Analysis software.

Results

Active components of SRD useful in AIDS treatment and potential therapeutic targets of SRD in AIDS

The TCMSP and BATMAN-TCM databases were searched, and 98 active components of SRD were identified. After removing duplicates and standardizing the annotations in UniProt databases, 1,196 potential targets were obtained.

Seven thousand six hundred and eighty-seven AIDS disease-related targets were obtained from the GeneCards database. Of these, 1,745 potential AIDS targets were identified by setting a threshold where targets with a score greater than twice the median were considered potential AIDS targets. In addition, 31 targets from the DrugBank database, 243 targets from the DisGeNET database, and 178 differentially expressed AIDS targets were discovered during a previous study by our team. Two thousand and thirty-nine AIDS-related targets were identified after eliminating duplicates. Based on the intersection of the predicted targets of SRD with the AIDS targets, 339 target genes related to both AIDS and the active components of SRD were screened (Fig. 1).

FIG. 1.

Venn diagram showing 339 overlapping targets of AIDS and San Ren Decoction (SRD).

SRD herbal formula-active ingredient-target network

The Cytoscape 3.8.2 software was used to construct a network visualization diagram of the active ingredient target of SRD, which included 339 overlapping targets (Fig. 2). This network comprised 431 nodes and 1,099 edges. The top 10 active ingredients filtered based on the degree were quercetin, valine, myristic acid, arachidic acid, terpinyl acetate, aminobutyric acid, luteolin, tetrahydromagnolol, d-limonene, and 1-octene (Table 1).

FIG. 2.

Network depicting the relationship among eight types of Chinese herbs, active ingredients, and targets in SRD. The eight types of herbs were XingRen (XR), DouKou (DK), YiYiRen (YYR), DanZhuYe (DZY), HouPo (HP), TongCao (TC), and HuaShi (HS). The purple circles represent the common ingredients in SRD, the red triangles represent the individual herbs in the prescription of SRD, the pink circles represent the active ingredients of each herb, and the blue squares represent the target genes.

Table 1.

Top 10 Active Ingredients in San Ren Decoction

MOLID	Chemical constituent	Degree	Herbal source	ID
MOL000098	Quercetin	88	Amomum villosum (DK)	DK18
MOL000067	Valine	78	Pinellia ternata	BX13
MOL001393	Myristic acid	62	Amomum villosum	DK15
MOL000012	Arachidic acid	62	Prunus armeniaca	XR5
MOL004788	Terpinyl acetate	55	Prunus armeniaca	XR17
MOL000388	Gamma-aminobutyric acid	54	Pinellia ternata	BX10
MOL000006	Luteolin	40	Amomum villosum	DK13
MOL003945	Tetrahydromagnolol	40	Magnolia officinalis	HP7
MOL000023	D-limonene	35	Magnolia officinalis	HP1
MOL006935	1-octene	32	Pinellia ternata	BX2

PPI network construction

The STRING platform was accessed, the SRD targets for AIDS therapy were imported, and a PPI network was constructed (Fig. 3). The “TSV” file was retrieved from the STRING website and imported into Cytoscape. In addition, the Centiscape 2.2 plug-in was installed to analyze the PPI network and determine the core targets. The analysis revealed an average closeness centrality (Closeness) of 0.0014975, a betweenness centrality (Betweenness) of 494.56716, and an average degree centrality (Degree) of 5.68159. The thresholds of Closeness ≥ 0.0015, Betweenness ≥494.5672, and Degree ≥5.6816 were applied, and 37 core targets were successfully identified, namely AKT1, CASP8, CAV1, SRC, HSP90AA1, EGFR, HDAC1, estrogen receptor 1 (ESR1), PI3K, JUN, AR, STAT1, TP53, CTNNB1, RB1, RIPK1, RUNX2, CALM2, APP, MDM2, ITGB3, CDC42, VEGFA, ERBB2, STAT5B, XIAP, VTN, AGT, RAF1, EDN1, SNCA, FAS, F2, VDR, EGF, IGF1, and GJA1. Based on the results of the PPI network analysis, the top 10 core targets were CAV1, SRC, HSP90AA1, EGFR, HDAC1, ESR1, TP53, CTNNB1, JUN, and AKT1 (Fig. 3, Table 2).

FIG. 3.

Protein–protein interaction (PPI) network diagram depicting the potential targets of SRD in AIDS. (A) shows the PPI network diagram of potential targets of SRD in AIDS, and (B) shows the PPI network diagram of core targets.

Table 2.

Top 10 Proteins Ranked by Degree Value in the Protein–Protein Interaction Network

Protein	Node closeness value	Betweenness centrality value	Degree value
HSP90AA1	0.002247	9058.829	44
SRC	0.002278	8596.079	38
CTNNB1	0.002119	4991.679	31
TP53	0.002075	3841.182	30
CAV1	0.002105	5962.853	27
ESR1	0.002155	4262.352	26
HDAC1	0.001873	1691.412	25
EGFR	0.002053	1522.255	20
JUN	0.001786	910.583	19
AKT1	0.001901	1218.666	16

AKT1, serine/threonine protein kinase 1; CAV1, caveolin 1; CTNNB1, catenin beta 1; EGFR, epidermal growth factor receptor; ESR1, estrogen receptor 1; HDAC1, histone deacetylase 1; HSP90AA1, heat shock protein 90 alpha family class A member 1 (cancer tissue gene); JUN, Transcription Factor AP-1; SRC, tyrosine kinase; TP53, cellular tumor antigen P53.

Results of GO functional enrichment analysis and KEGG pathway enrichment analysis

GO enrichment analysis was performed using the SRD targets in AIDS. One thousand and thirty-five entries related to biological process (BP) were identified, which primarily included the positive regulation of gene expression, response to external stimuli, and response to lipopolysaccharide. There were 166 entries related to cellular components (CC), which primarily included extracellular space, cell surface, membrane raft, and mitochondria, among others. Molecular function (MF) analysis revealed 207 associated pathways, primarily related to protein binding, enzyme binding, macromolecular complex binding, cytokine activity, and receptor binding, among others. The 20 most significant processes were selected from the BP, CC, and MF analyses to create a GO functional analysis chart. One hundred and ninety-six signaling pathways were identified through KEGG pathway enrichment analysis, which primarily included the TNF, PI3K/Akt, toll-like receptor (TLR), and IL-17 signaling pathways. The 20 most significant processes were selected to construct the diagram for the KEGG signaling pathway enrichment analysis (Fig. 4).

FIG. 4.

Go and KEGG analysis for the major targets of SRD, (A) represents biological process (BP) analysis, (B) represents cellular component (CC) analysis, (C) represents molecular function (MF) analysis, and (D) represents enrichment analysis of potential target KEGG signaling pathways for AIDS treatment using SRD. GO, gene ontology; KEGG, the Kyoto Encyclopedia of Genes and Genomes.

Molecular docking

AutoDock Vina was used to perform the molecular docking analysis of the top 10 active ingredients (quercetin, valine, myristic acid, arachidic acid, terpinyl acetate, aminobutyric acid, luteolin, tetrahydromagnolol, d-limonene, and 1-octene) with the highest degree values in the “active ingredient-target” network diagram and the 10 core proteins (CAV1, SRC, HSP90AA1, EGFR, HDAC1, ESR1, TP53, CTNNB1, JUN, and AKT1) in the PPI network diagram. A binding affinity ≤−5.0 kcal/mol is considered to indicate that a small-molecule ligand has a good binding activity with the receptor protein, and a binding affinity ≤−7.0 kcal/mol indicates stronger binding activity (Fig. 5, Table 3).

FIG. 5.

Molecular docking diagrams. (A) represents the molecular docking diagram of ESR1 and quercetin; (B) represents the molecular docking diagram of AKT1 and luteolin; (C) represents the molecular docking diagram of HSP90AA1 and luteolin; (D) represents the molecular docking diagram of AKT1 and quercetin. AKT1, serine/threonine protein kinase; ESR1, estrogen receptor 1; HSP90AA1, heat shock protein 90 alpha family class A member 1 (cancer tissue gene).

Table 3.

Docking Results of the Top 10 Targets and Components

Receptor/Ligand	ESR1	SRC	CAV1	TP53	JUN	HSP90AA1	AKT1	HDAC1	EGFR	CTNNB1
1_Octene	−4.5	−4.3	−4.3	−3.5	−4.1	−3.9	−4.4	−4.2	−4.3	−4.3
Arachidic acid	−4.2	−5.2	−4.6	−3.6	−4.4	−3.6	−6.1	−4.8	−4.7	−3.4
D_Limonene	−5.7	−5.1	−5.3	−5.1	−5.8	−5.3	−6.2	−6	−5.5	−5.3
Gamma_Aminobutyric acid	−3.9	−3.9	−3.1	−4	−3.8	−3.7	−3.6	0	−3.5	−3.4
luteolin	−7.8	−8.1	−6.8	−6.9	−7.5	−9.3	−10.6	−7.6	−7.7	−7.4
Myristic Acid	−4.7	−4.9	−4.4	−4	−3.4	−4.5	−5.7	−4.9	−5	−4.5
Quercetin	−9.4	−8.4	−6.9	−7	−7.6	−7.6	−10.1	−7.8	−7.8	−6.9
Terpinyl acetate	−6.5	−6.3	−5.3	−5.2	−5.5	−5.7	−7.4	−6.1	−6.1	−5.3
Tetrahydromagnolol	−6.6	−7.3	−6.3	−5.4	−6.2	−5.7	−7.9	−6.6	−6.8	−6.7
Valine	−4.1	−4.6	−4.1	−3.8	−3.9	−4.4	−4.3	−4.2	−3.8	−4

Clinical experimental validation

Clinical study cases

No statistically significant in gender and age between the groups (Table 4), indicating comparable baseline characteristics, were revealed by the statistical analysis. CD4⁺ T cell counts were significantly lower in the PLWH group compared with the HC group, the difference was statistically significant.

Table 4.

General Information in the PLWH and HC Groups

Group	Gender			Age	CD3	CD4	CD8
Group	Total	Male	Female	Age	CD3	CD4	CD8
PLWH	20	12	8	47.21 ± 6.37	1414.42 ± 574.42	535.00 ± 225.16	840.89 ± 392.20
HC	20	13	7	42.28 ± 7.14	1495.20 ± 414.75	786.25 ± 219.74	719.91 ± 334.08
T		2.506^a		1.656	0.497	3.481	−1.024
P		0.113		0.195	0.622	0.001	0.313

χ² value.

HC, healthy control group; PLWH, people living with HIV.

Analysis of antiviral medication usage and viral load

In accordance with current guidelines in China, initiation of antiretroviral therapy is recommended immediately following HIV diagnosis, regardless of CD4⁺ T lymphocyte count. All 20 PLWH included in this study were receiving first-line treatment regimens, which consisted of two nucleoside reverse transcriptase inhibitors combined with a third drug from another class.

Among the 20 samples, viral load analysis revealed that 14 samples were below the lower limit of detection or undetectable, 2 samples had viral loads of <20 copies/mL, and the remaining 4 samples had viral loads of 26 copies/mL, 306 copies/mL, 620 copies/mL, and 20.8 copies/mL, respectively. All 20 patients were in the asymptomatic stage of infection.

Detection of the mRNA expression of AKT1, PI3K, STAT1, and other molecules in PLWH using fluorescent qPCR

Based on the 37 core targets screened by network pharmacology, the primary signaling pathways involved, and findings from relevant literature review, the genes closely related to PLWH and located in the PI3K/Akt signaling pathway were selected for PCR validation. These included AKT, PI3K, STAT1, IKK, NFKB, CASP8, RAF1, BCL2, IL8, and MTOR, among others. The criteria for significant differential expression were 2^-ΔΔCt ≥1.5 or ≤0.67 and p < .05. When the PLWH samples were compared with HC samples, statistically significant differences were observed in the expression of AKT1, PI3K, and STAT1 (Table 5).

Table 5.

Differentially Expressed mRNAs in the HIV/AIDS and Healthy Control Groups

Symbol	PLWH		HC		2^-△△Ct	p value
Symbol	AVG Delta (Ct)	Standard deviation	AVG Delta (Ct)	Standard deviation	2^-△△Ct	p value
AKT1	5.64	0.75	7.54	3.12	3.443↑	<.001
AP-1	2.11	0.90	−1.16	2.78	0.081↓	<.001
BCL-2	6.30	0.67	1.04	3.41	0.024↓	<.001
CASP8	4.75	0.71	8.28	3.74	8.613↑	<.001
mTOR	6.91	0.49	10.9	2.68	17.772↑	<.001
PDK1	7.29	0.51	8.04	1.33	8.967↑	<.001
PI3K	7.13	0.46	10.06	2.99	1.686↑	<.001
RAF1	4.58	0.42	4.56	3.00	6.095↑	<.001
STAT1	4.01	0.43	5.95	2.72	1.034	<.001

2^-ΔΔCT represents the fold change.

2^-ΔΔCT ≥ 1.5 indicates a significant increase in differential expression, whereas 2^-ΔΔCT ≤ 0.67 represents a significant decrease in differential expression.

Detection of PI3K/Akt signaling pathway-related protein expression in in vitro cell experiments

The expression of PI3K/Akt signaling pathway-related proteins in PLWH and HC groups was detected through in vitro cell experiments. Compared with the HC group, the PLWH group showed the upregulation of PDK1, mTOR, and AKT1 expression, with statistically significant differences between the two groups (p < .05, Table 6).

Table 6.

Differentially Expressed Proteins in the PLWH and HC Groups

Symbol	HC	PLWH	t	p value
PDK1	48.58 ± 16.45	82.17 ± 7.29	−3.233	.032
PI3K	313.83 ± 49.99	293.67 ± 4.65	0.696	.525
mTOR	73.00 ± 9.26	113.67 ± 10.60	−5.005	.007
RAF1	31.42 ± 4.30	30.95 ± 1.89	0.172	.872
BAD	33.00 ± 3.00	33.27 ± 2.63	−0.116	.913
AKT1	17.97 ± 2.03	26.32 ± 0.29	−7.041	.002

Compared with that in the SRD group, the expression of PDK1, PI3K, mTOR, RAF1, and AKT1 was significantly higher in the PLWH group (p < .05). Compared with that in the inhibitor group, the expression of PDK1, PI3K, mTOR, RAF1, and AKT1 was significantly lower in PLWH group (p < .05). The differences were statistically significant. Compared with that in the SRD group, the expression of PDK1 and AKT1 was significantly lower in the inhibitor group (p < .05), with a statistically significant difference between the values (Table 7 and Fig. 6).

FIG. 6.

Expression levels of PDK1, PI3K, AKT1, mTOR, Raf1, and BAD among groups PLWH, ZSTK474, and SRD. All data were presented as mean ± standard deviation.

Table 7.

Detection of Differentially Expressed Proteins in Each Group

Symbol	PLWH	ZSTK474	SRD	F	p value
PDK1	82.17 ± 7.29*	37.08 ± 6.26^#	60.17 ± 6.05^#*	10.403	.004
PI3K	293.67 ± 4.65*	249.17 ± 27.59^#	245.17 ± 22.05^#	3.079	.090
mTOR	113.67 ± 10.60*	64.17 ± 13.88^#	75.00 ± 15.26^#	9.012	.006
RAF1	30.95 ± 1.89*	23.48 ± 3.74^#	22.03 ± 1.59^#	3.600	.065
BAD	33.27 ± 2.63*	24.02 ± 4.41^#	28.22 ± 1.91	3.207	.083
AKT1	26.32 ± 0.29*	15.02 ± 0.60^#	22.98 ± 1.33^#*	82.171	.000

Compared with the PLWH group, ^#p < .05; compared with the ZSTK474 group, *p < .05.

Discussion

In this study, network pharmacology and molecular docking techniques were used to explore the mechanism of action of SRD in AIDS. Using a “medicinal herb-active ingredient-target” network diagram, quercetin, luteolin, myristic acid, tetrahydromagnolol, and d-limonene were identified as the primary active ingredients in SRD. Quercetin, a polyhydroxylated flavonoid compound extracted from rutin, exerts anti-inflammatory, antiviral, antioxidant, and immunomodulatory effects.^10,11 Quercetin exerts its anti-inflammatory effect by reducing the levels of pro-inflammatory factors IL-1β and IL-6 while increasing the levels of anti-inflammatory cytokines Interleukin 4 and IL-10.¹² Rojas et al.¹³ found that quercetin significantly suppresses the replication of hepatitis C virus. The infectivity of viral particles treated with quercetin was found to reduce by 65%. Quercetin can significantly reduce the titer of the influenza virus, inhibit virus replication, and improve the cell survival rate.¹⁴ Luteolin is a flavonoid compound with anti-inflammatory, antitumor, antiviral, and antioxidant pharmacological effects.¹⁵ Yan et al.¹⁶ infected several cell lines with two subtypes of influenza A virus, H1N1 and H3N2. Through immunofluorescence, qRT-PCR, and Western blot analysis, they demonstrated that luteolin exerts an inhibitory effect on the replication of influenza viruses by suppressing the expression of the coat protein I complex, which is involved in the entry of these viruses into cells. Luteolin¹⁷was shown to alleviate brain fog in patients with coronavirus disease 2019. The pathophysiology of brain fog may be related to hypothalamic inflammation caused by the virus, which stimulates mast cells to release microglia.

The key targets of SRD in AIDS were identified using a PPI network diagram. These included AKT1, PI3K, CAV1, SRC, HSP90AA1, EGFR, and STAT1. The multiple targets identified reflect the multitarget approach of TCM in disease treatment. Experimental results showed that, compared with healthy controls, PLWH had a significantly higher expression of AKT1, PI3K, and STAT1 but lower expression of RAF1. PI3K belongs to a lipid kinase family, which is divided into Classes I–III and is involved in immune function, inflammation regulation, cell growth, and cell proliferation.¹⁸ PI3K inhibitors can block the HIV infection of CD4⁺ T cells, and the inhibition of PI3K subunits can prevent TLR2 from aggravating HIV infection.¹⁹ AKT1 is involved in various biological processes, including metabolism, proliferation, and cell survival, and serves as a core factor in the PI3K/Akt signaling pathway. HIV-1 infection can trigger partial apoptosis in macrophages, and AKT1, a key protein for macrophage survival, increases the phosphorylation of the transcription factor Forkhead box protein O3a, thereby reducing cell viability.²⁰ Overall, the targets involved play significant roles in cell proliferation and differentiation, apoptosis, cellular factors, and lipid metabolism. Caveolin-1 (CAV1) is the primary structural protein of caveolae in smooth muscle cell membranes. Homocysteine may activate the phosphatidylinositol 3-kinase/protein kinase B (PI3K/Akt) signaling pathway by promoting CAV1 protein expression, inducing the migration and proliferation of rat vascular smooth muscle cells, which can lead to atherosclerosis.²¹ CAV1 performs multiple crucial functions in signaling and material transport during cellular activities, participating in cell proliferation, apoptosis, and differentiation. People with non-small-cell lung cancer with a high expression of CAV1 have a shorter survival period than patients with low CAV1 expression. CAV1 expression is higher in tumor tissues than in adjacent nontumorous tissues.²² The HSP90AA1 gene encodes HSP90α, which is expressed under stress. Heat shock protein 90 (HSP90) can bind to various protein kinases. The inhibition of phosphatase resulting from Akt-HSP90 binding leads to Akt dephosphorylation and inactivation, which increases cellular sensitivity to apoptosis-inducing stimuli.²³ Many viruses can use HSP90 to promote the assembly and maturation of their viral proteins. The hemagglutinin molecule of type A influenza virus subtype H1N1 can specifically bind to HSP90AA1 on the surface of host cells, facilitating the entry of H₁N₁ into the cells.²⁴ According to the results of KEGG analysis, the effect of SRD on AIDS involves multiple signaling pathways, such as the PI3K/Akt, TLR, and TNF signaling pathways. The PI3K/Akt signaling pathway is one of the most important signaling pathways that regulates cell survival, differentiation, and apoptosis. It plays a profound role not only in the occurrence and development of tumor-related diseases²⁵ but also in AIDS research. PI3K/Akt signaling contributes to macrophage apoptosis caused by HIV-1 infection, mediates the production of macrophage and CD4⁺ T-cell virus reservoirs, and induces important physiological and pathological changes, such as inflammatory responses in glial cells.^26,27 TLRs are a class of important protein molecules involved in nonspecific immunity (innate immunity) and serve as a bridge between nonspecific and specific immunity. Research on TLRs has shown that they are closely associated with immunity and inflammation as well as with various diseases, such as cancer,²⁸ viral infectious diseases,²⁹ and autoimmune diseases. TLRs are closely related to HIV infection, and the activation of TLR signaling pathways may be a potential mechanism or driving factor for HIV-related immune activation.^30,31 In addition, the activation of TLR signaling pathways can lead to the release of damage-associated molecular patterns, such as High mobility group protein B1 and Reactive oxygen species, produced owing to inflammation and tissue damage. These can act as ligands for TLRs or NOD-like receptors, thus triggering innate immune responses and inflammasome activation. The activation of inflammasomes leads to Caspase activation, which is involved in apoptosis.³²

Based on the results of molecular docking experiments, the binding energy between most core targets and active ingredients was ≤−5 kcal/mol, which indicates a stable binding between the core compounds in SRD and core receptor proteins. Among the compounds, luteolin required the lowest energy to bind with AKT1 protein and demonstrated the strongest binding affinity.

In vivo experimental results demonstrated that, compared with healthy controls, PLWH exhibited upregulated expression of AKT1, Caspase8, mTOR, PI3K, STAT1, and RAF1 and downregulated expression of AP-1 and Bcl-2. These differences were statistically significant (p < .05). The results of in vitro cell experiments demonstrated that, compared with healthy controls, PLWH exhibited a significantly higher expression of PDK1, mTOR, and AKT1 (p < .05). Compared with the PLWH group, the group treated with the SRD-containing serum had a significantly lower expression of PDK1, PI3K, mTOR, RAF1, and AKT1 (p < .05), and the same was observed in the group treated with PI3K/Akt inhibitors (p < .05). Findings from both in vivo and in vitro experiments suggested that the gene and protein expression levels of AKT1, Caspase8, mTOR, PI3K, STAT1, and Bcl-2 were elevated in PLWH. SRD may exert its regulatory effect on PLWH by inhibiting the PI3K/Akt signaling pathway.

Summary and Prospects

In summary, this study used network pharmacology, molecular docking, and experimental validation to investigate the mechanism of action of SRD in PLWH treatment. The results of the multipathway and multitarget treatment of diseases using TCM compounds were confirmed. The active compounds present in SRD, such as quercetin and luteolin, may regulate signaling pathways, such as the PI3K/Akt and TLR signaling pathways, by targeting core molecules such as AKT1, PI3K, CAV1, and STAT1, among others. Findings from both in vitro and in vivo experiments confirmed that SRD could potentially exert a regulatory effect on PLWH by inhibiting the PI3K/Akt signaling pathway.

Authors’ Contributions

J.W. and M.Z. wrote the first draft of the article. J.Q. and Z.Y. were responsible for creating charts and visualizing the article’s results. M.S. organized relevant literature and materials. Z.H. was in charge of revising the article and controlling its quality. J.W. and Q.X. took over all responsibility for the article.

Footnotes

Author Disclosure Statement

The author reports no conflicts of interest in this article.

Funding Information

This work was supported by the National Natural Science Foundation of China (82004344;82260901); the Natural Science Foundation of Henan Province (222300420059); Henan Provincial Science and Technology Research and Development Program Joint Fund (dominant discipline cultivation category) (232301420089); and the Henan Science and Technology Research Project (International Cooperation) (232102521026).

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