Skip to Content
MDPIMDPI

Advanced

IJMSInternational Journal of Molecular Sciences
PDF
  • Article
  • Open Access

23 February 2026

14 Pages

Identification of Neferine as a DOR Agonist Activating Gi and Gz Signaling: In Silico and In Vitro Studies

,
,
,
,
,
,
,
,
…
1
School of Pharmacy, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
2
Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
3
School of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
4
M. Kandiah Faculty of Medicine and Health Sciences, Universiti Tunku Abdul Rahman, Kajang 43000, Selangor, Malaysia
Int. J. Mol. Sci.2026, 27(4), 2058;https://doi.org/10.3390/ijms27042058
This article belongs to the Section Molecular Informatics
Version Notes
Order Reprints

Abstract

Benzylisoquinoline alkaloids (BIAs) exhibit diverse biological activities, such as neuroprotective effects. The delta-opioid receptor (DOR) has emerged as a promising therapeutic target due to its potential role in enhancing neuroprotection and regeneration. However, reports on the binding of BIAs to the DOR remain scarce. Here, neferine, a BIA from Nelumbo nucifera, as a potential DOR agonist. Molecular docking ranked neferine among the top of 15 BIAs. Initial binding was detected by cellular membrane chromatography and quantitatively confirmed by bio-layer interferometry, with a KD value of 37.4 μM. ONE vector G protein Optical biosensor revealed that Gi2, Gi3 and GZ signaling could be activated by neferine through DOR modulation. Consistent with the Gi/z activation, neferine dose-dependently inhibited cAMP accumulation with an EC50 of 0.25 µM. Transcriptomic analysis in DOR-overexpressing HEK293T cells indicated that neferine stimulation predominantly regulates gene networks governing cell cycle and stress adaptation. However, direct transcriptional signature for neuroprotection was not predominant in our system, suggesting that DOR signaling may exhibit context-dependent effects. In conclusion, we identified the neferine as a natural DOR agonist through in silico and in vitro approach, providing a reference for further investigation into its pharmacological potential.

1. Introduction

Benzylisoquinoline alkaloids (BIAs) have attracted widespread attention from researchers due to their diverse chemical structures and rich biological activities. Ongoing work aims to discover the pharmacological activities of BIAs and their potential targets. Several BIAs have been reported to possess neuroprotective effects [1,2,3]. Nuciferine, for example, was shown to enhance the integrity of the blood–brain barrier following ischemic stroke through the inhibition of the JAK2/STAT3 pathway [4]. Nevertheless, the specific targets for many BIAs remain to be characterized. Leveraging the structural diversity within the defined metabolic pathway to efficiently explore bioactive chemical space has proven highly effective. Previously, we assessed the antiviral potential of compounds involved in the biosynthesis pathway of BIAs from three Stephania. The results demonstrate that cepharanthine and seven other structurally analogous compounds display broad-spectrum anti-coronavirus activities [5]. Guided by the same principle, we hypothesized that the BIA pathway might also harbor compounds with same target.
G protein-coupled receptors (GPCR) represent the largest family of membrane proteins in humans and are the targets of over 30% of marketed drugs [6]. Currently, at least 80 compounds derived from traditional medicines have been reported to act directly or indirectly on GPCRs [7,8], suggesting that natural products such as BIAs may represent a source of novel GPCR ligands. A number of GPCRs have been reported to participate in the regulation of nervous system homeostasis and exhibit neuroprotective effects [9,10,11]. Recent research findings have uncovered the pivotal role of delta-opioid receptor (DOR) in neuroprotection and neural regeneration. The expression and/or activation of this receptor protects neuronal cells and tissues against damage induced by diverse factors, and also facilitate nerve regeneration [12,13,14]. This suggests that DOR may be a potential target for BIAs with neuroprotective effects.
Nelumbo nucifera, as a traditional medicinal plant, has been reported to contain multiple BIAs with potential for preventing or treating neurodegenerative diseases [15,16]. A recent study has reported that neferine is capable of suppressing the expression of inducible nitric oxide synthase, as well as the release of interleukin-6 and tumor necrosis factor-alpha in lipopolysaccharide-stimulated BV-2 microglial cells [17], suggesting its potential to inhibit neuroinflammation. Pronuciferine has demonstrated antioxidant defense effects against reactive oxygen species (ROS) in neuronal cells. A study on SH-SY5Y cells revealed that pronuciferine could inhibit H2O2-induced neuronal death and enhanced cell proliferation by 45% [18]. However, it is not yet known whether these BIAs from N. nucifera can act as ligands for DOR, which could underlie some of their observed neuroprotective activities.
In this study, we aimed to identify potential DOR ligands among BIAs from N. nucifera. Using virtual screening, we prioritized neferine from a set of 15 compounds. Its direct interaction with DOR was assessed by cellular membrane chromatography (CMC) and quantified by biolayer interferometry (BLI). Functional assays, including ONE vector G protein Optical biosensor (ONE-GO) assay and cyclic adenosine monophosphate (cAMP) assays, confirmed its agonistic activity at DOR. Finally, we employed transcriptomic analysis in DOR-overexpressing 293T cells to explore the downstream effects of neferine stimulation. This work evaluated neferine as a DOR agonist and provided a basis for further investigation into its pharmacological potential.

2. Results

2.1. Virtual Screening Identifies Neferine as a Ligand of DOR

Multiple BIA compounds have been identified in N. nucifera [19]. These compounds exhibit diverse biosynthetic pathways (Figure 1A), providing a rich pool for the discovery of potential DOR-binding candidates. Virtual screening serves as an efficient first step to prioritize candidate compounds for further experimental validation [20,21]. We evaluated the binding energies of 15 BIA compounds to DOR via molecular docking. All candidate compounds showed affinities of less than −7 kcal/mol, suggesting that several BIAs might possess the potential to bind to DOR. Among them, neferine, with an affinity value of −9.402 kcal/mol (Figure 1B, the best among the 15 compounds), was selected for further investigation, and its interaction with DOR was analyzed. The phenoxy group in neferine formed a stable hydrogen-bond interaction with the lysine residue K108 on transmembrane domain 2 of DOR, with a bond length of 3.5 Å (Figure 1C). This indicated a specific polar interaction between the ligand and receptor. The formation of this hydrogen bond enhanced the neferine’s affinity for the DOR binding pocket and contributed to maintaining neferine’s stable binding conformation at the active site.
Figure 1. Molecular docking-based screening of compounds with the ability to bind to DOR in the biosynthesis pathway of BIAs from N. nucifera. (A) The biosynthetic pathway of BIA compounds in N. nucifera. (B) Molecular docking scores for 15 BIAs and DOR. (C) The interaction between neferine and DOR.

2.2. Verification of Neferine as a DOR Ligand

The interaction between neferine and DOR was assessed using two complementary biophysical approaches. First, CMC was performed using a Trichlorocyanuric acid (TCT)-modified SiO2 column immobilized with DOR-expressing cell membranes. In this assay, compounds with affinity for DOR were selectively retained. A distinct retention peak for neferine was observed at 16.169 min compared to non-DOR expressing cell membrane, indicating specific binding to the receptor (Figure 2A,B). Second, BLI was employed for quantitative analysis. Concentration-dependent binding responses were recorded, and sensorgrams displayed characteristic association and dissociation phases for neferine (Figure 2B). Kinetic analysis yielded an equilibrium dissociation constant (KD) of 37.4 µM, confirming a direct interaction between neferine and DOR.
Figure 2. Real-time kinetic binding parameter of neferine interacting with DOR. (A) Retention behavior of neferine on DOR-Flag/CMC column. (B) Retention behavior of neferine on HEK293T/CMC column. (C) Sensorgrams showing the binding interaction between neferine and DOR using BLI. The blue and pink lines represent the actual signal values and fit signal values, respectively.

2.3. Detection of G-Protein Selectivity Mediated by DOR Under Neferine Stimulation

Although the evidence presented above suggested neferine as a ligand for DOR, further exploration of its pharmacological effects on DOR was warranted. Initially, we utilized the ONE-GO biosensor to examine whether DOR could activate G protein in response to neferine stimulation [22]. A transfection system of DOR and 10 G proteins subtypes in HEK293T cells was utilized to evaluate G protein selectivity [23]. Kinetic curves of 10 distinct G protein subtypes demonstrated that Gi2, Gi3, and Gz were significantly activated upon neferine stimulation (Figure 3A). Subsequently, the concentration-dependent curves of these three G proteins were used to determine relevant pharmacodynamic parameters. The activation levels of Gi2 (EC50 = 81.9 μM), Gi3 (EC50 = 16.1 μM), and Gz (EC50 = 15 μM) increased in a dose-dependent manner (Figure 3B–D). This indicated that neferine act as a functional DOR agonist, capable of eliciting defined G protein-mediated signaling.
Figure 3. Measurement of neferine-induced DOR activation via the ONE-GO biosensor. (A) Kinetic measurement of the effects of neferine (10 μM) on 10 G-protein subtypes in HEK293T cells upon DOR modulation. (B) Concentration-dependent curve of Gi2 protein in HEK293T cells triggered by neferine upon DOR modulation. (C) Concentration-dependent curve of Gi3 protein in HEK293T cells triggered by neferine upon DOR modulation. (D) Concentration-dependent curve of Gz protein in HEK293T cells triggered by neferine upon DOR modulation.

2.4. Detection of cAMP Response Mediated by DOR Under Neferine Stimulation

The activation of the Gi/z signaling pathway could inhibit adenylate cyclase activity through the Gi/z subunit, consequently reducing cAMP production [24,25]. We next measured cAMP levels in DOR-overexpressing HEK293T cells following neferine stimulation. As concentration increases, the presence of neferine inhibited forskolin-induced cAMP release (Figure 4A). Neferine dose-dependently inhibited forskolin-induced cAMP accumulation, with a half-maximal effective concentration (EC50) of 0.25 μM (Figure 4B). This phenomenon was not detected in cells transfected with pcDNA3.1 alone (Figure 4C); in these cells, cAMP remained at the high levels induced by Forskolin stimulation (Figure 4D). Together, these data confirmed that neferine suppressed cAMP levels specifically through DOR activation.
Figure 4. Measurement of neferine-induced DOR activation via the cAMP Glosensor. (A) Kinetic measurement of cAMP responses of DOR to neferine in HEK293T cells. (B) cAMP responses of DOR to neferine at different concentrations. (C) Kinetic measurement of cAMP responses of pcDNA3.1 to neferine in HEK293T cells. (D) cAMP responses of pcDNA3.1 to neferine at different concentrations.

2.5. Dose-Dependent Transcriptomic Profiling Induced by Neferine-Mediated DOR Activation

To delineate the downstream transcriptional consequences of DOR activation by neferine, we performed RNA sequencing on DOR-overexpressing HEK293T cells treated with a concentration gradient of neferine in 6-well plates (Figure 5A). Compared with the control group, higher concentrations of neferine (10 μM, 2 μM, 0.4 μM) influenced a larger number of genes, jointly affecting 3784 differentially expressed genes (DEGs, Figure 5B). This was consistent with its more potent activation effect on DOR. The number of DEGs affected by all four concentrations (10 μM, 2 μM, 0.4 μM, 0.016 μM) dropped to 2888 (Figure 5C). Notably, the five neferine concentrations together affected 453 DEGs (Figure 5D), while the lowest concentration (0.0032 μM) induced changes in merely 716 DEGs. This revealed a distinct dose-dependent variation in the number of DEGs in cells stimulated with different neferine concentrations.
Figure 5. Functional enrichment analysis of co-regulated DEGs based on dose–response design of neferine stimulation in 6-well plates. (A) Transcriptional profiling of DOR-overexpressing HEK293T cells treated with neferine using two different experimental designs. (B) Venn diagram of DEGs co-regulated by A (10 μM), B (2 μM) and C (0.4 μM) neferine. (C) Venn diagram of DEGs co-regulated by A (10 μM), B (2 μM), C (0.4 μM) and D (0.016 μM) neferine. (D) Venn diagram of DEGs co-regulated by A (10 μM), B (2 μM), C (0.4 μM), D (0.016 μM), E (0.0032 μM) neferine. (E) GO analysis of DEGs co-regulated by 10 μM, 2 μM, and 0.4 μM neferine. (F) KEGG analysis of DEGs co-regulated by 10 μM, 2 μM, and 0.4 μM neferine.
Functional enrichment analysis of the 3784 DEGs common to the top three concentrations (10, 2, and 0.4 µM) was performed to define the predominant biological programs. Gene Ontology (GO) analysis [26] indicated the most significantly enriched terms are presented in Figure 5E. Within the biological processes (BP) category, genes were enriched in processes pivotal to intracellular biosynthesis and quality control, specifically ribonucleoprotein complex biogenesis and the establishment of protein localization to organelles. Simultaneously, the process of macroautophagy was significantly enriched, signifying a programmed clearance mechanism. Regarding the molecular functions (MF) of these core genes, they indicated specific regulatory interactions, with significant enrichment in GTPase binding and cadherin binding. These results implied modulation of small GTPase-mediated signaling and cell–cell adhesion systems. Finally, cellular component (CC) analysis accurately localized a subset of these genes to the lysosomal membrane and lytic vacuole membrane.
Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis [27] further substantiated these themes (Figure 5F). The most significantly enriched pathways were in line with and refined the themes derived from the GO analysis. Key enriched pathways included: MAPK signaling pathway, cell cycle and cellular senescence that related to core signaling and cell fate regulation; autophagy–animal, protein processing in endoplasmic reticulum, endocytosis, which were related to cellular homeostasis and stress management; and Biosynthesis of amino acids that related to metabolic reprogramming. Notably, classical pathways directly annotated for neuroprotection were not prominently enriched in this dataset. This observation suggested that the transcriptional response to DOR activation in this simplified overexpression system may prioritize broad cellular adaptation and homeostatic control.

2.6. Cross-Validation of Transcriptomic Profiles Identifies a Core Gene Expression Program Induced by Neferine-Activated DOR

Given that the transcriptional profile from the initial dose–response design was dominated by generalized stress and adaptation pathways, we considered the possibility that technical variability common to parallel 6-well cultures might obscure more specific, receptor-mediated effects. To obtain a higher-fidelity transcriptomic signature, we therefore employed a more stringent experimental design in 10 cm dishes (Figure 5A). DOR-overexpressing cells from a single transfection batch were split into matched control and treatment (1 µM neferine) cultures. This approach yielded 221 robust DEGs, with a notable predominance of upregulated genes (153 upregulated and 68 downregulated, Figure 6A). Their functional profile; however, remained focused on fundamental cellular processes. GO analysis showed enrichment for cytoplasmic translation, ribosome biogenesis, p53-mediated signal transduction, and double-strand break repair at the biological process level, with corresponding enrichment for ribosomal components and DNA-related molecular functions (Figure S1A). KEGG analysis of these DEGs highlighted pathways including cell cycle, cellular senescence, and apoptosis (Figure S1B). Among these genes, pathways associated with neuroprotective functions were not significantly enriched.
Figure 6. Cross-validation identifies core transcriptional programs induced by Neferine upon DOR activation. (A) Volcano plot depicting DEGs from the split-sample design. (B) Venn diagram identifying the core gene set consistently regulated by three independent conditions: A (1 µM (split-sample design)), B (2 µM (dose–response design)), and C (0.4 µM (dose–response design)) neferine treatments. (C,D) Functional enrichment analysis of the 917 core genes. (C) GO analysis (D) KEGG analysis.
To distill the reliable, DOR-specific transcriptional signal, we performed an intersection analysis of DEGs derived from three independent experiments: the 1 µM neferine treatment group (from split-sample design), the 2 µM and 0.4 µM treatment groups (from dose–response design). This identified a core set of 917 genes consistently regulated across all conditions (Figure 6B). Functional analysis of this gene set revealed an exceptionally focused theme. The top enriched GO terms were almost exclusively related to DNA metabolism and integrity, including DNA replication, DNA recombination, negative regulation of cell cycle, and DNA damage response (Figure 6C). Cellular components were localized to the chromosomal region and MCM complex, and molecular functions involved ATP-dependent DNA activity and helicase activity. Concordantly, KEGG pathway analysis of the core gene set confirmed the central involvement of cell cycle, cellular senescence, apoptosis, and the p53 signaling pathway (Figure 6D).
Collectively, these analyses demonstrated that sustained DOR activation by neferine in different experimental design co-regulated a transcriptional network related to genome surveillance, DNA replication/repair, and the control of cell cycle progression and fate, rather than directly inducing a classical neuroprotective gene signature.

3. Discussion

Among various classes of druggable targets, GPCRs represent particularly promising candidates for natural products interactions due to their structural diversity, physiological ubiquity, and regulatory roles in numerous disease pathways [28]. Given the vast chemical diversity of natural products, numerous interactions with GPCRs likely remain undiscovered, and their mechanisms of action await elucidation. Studying the pharmacological activities or biological targets of metabolites sourced from natural products’ biosynthetic pathways could provide novel insights for accelerating the drug development process. We previously identified the key enzymes in the biosynthetic pathway of BIA in N. nucifera [29]. In this study, we focus on whether these BIA enzymes can bind to DOR. Although previous study using competitive radioligand binding had demonstrated that neferine could bind to DOR, in-depth signaling pathway research remains lacking [30]. This study demonstrates that neferine is a natural agonist of the DOR with a distinct Gi and Gz signaling profile [31]. By integrating in silico screening with in vitro validation across biophysical, functional, and transcriptomic levels, we provide a comprehensive characterization of its action.
The initial virtual docking screen of a BIA library identified neferine as a top candidate. Subsequently, its status as a direct DOR binder was confirmed through CMC and BLI assays. Although affinity data suggested binding interactions between the ligand and receptor, the affinity remains relatively weak compared to known small molecule ligands, such as DPDPE (a classical DOR agonist) with the pKA value of 6.9 M [32]. Notably, a number of natural products exhibit micromolar-level affinities for GPCRs [28]. These early ligand discoveries provide crucial references for subsequent drug design and optimization [33]. The Gi/o family typically includes members like Gi1, Gi2, Gi3, GoA, GoB, and Gz. All these members exhibit a high binding affinity for opioid receptors [34]. Significantly, neferine was shown to be a functional agonist via real-time G protein biosensors, selectively activating the Gi2, Gi3, and Gz subtypes. This signaling pathway was functionally verified by neferine’s inhibition of forskolin-stimulated cAMP accumulation, a classic downstream readout of Gi activation [35]. Such a specific signaling signature may hold significant pharmacological implications. Since differential G-protein coupling might determine unique physiological outcomes and side effect [36,37], it potentially differentiates neferine’s effects from those of other DOR agonists. While the current study focused on the top-ranked candidates, a systematic investigation into the structure-activity relationships of the broader BIA library would be informative. Such work could aid in the identification of new DOR ligands and the development of potential lead compounds.
To delineate the downstream transcriptional effects of DOR activation by neferine, we employed two complementary experimental designs: an initial dose–response screen in 6-well plates and a subsequent paired-sample assay in 10 cm dish. Notably, functional enrichment analyses from both approaches converged on pathways governing cellular homeostasis, stress adaptation, and cell cycle regulation, rather than revealing a transcriptional signature related to neuroprotection. In the analysis of signaling pathways across different systems, several pathways associated with viral infections or cancer were also enriched. Such pathways are large repositories of genes involved in generic host defense responses, apoptosis, and inflammatory signaling. Their enrichment here most likely reflects the activation of shared endogenous stress and immune-response modules by neferine, rather than implying a virological or oncogenic mechanism [38].
This consistent pattern suggests that the immediate, system-wide response to sustained DOR activation in this heterologous model prioritizes fundamental cellular remodeling and stabilization programs. We hypothesize that the observed focus on core housekeeping and stress-response pathways, rather than on context-specific functions like neuroprotection, may reflect the cellular context-dependence of DOR signaling. The HEK293T overexpression system, while invaluable for isolating receptor-specific effects, likely lacks the specialized transcriptional networks and cellular environment present in native neuronal cells [39]. Therefore, the biological outcomes of neferine-mediated DOR activation, particularly its potential neuroprotective efficacy, may only be fully manifested in a more physiologically relevant system. Consequently, future work should investigate the functional and transcriptional impact of neferine in primary neuronal cultures or other naturally DOR-expressing cell lines. Such studies will be crucial for determining whether the cellular stabilization programs identified here form the basis for, or are complemented by, more specialized neuroprotective mechanisms in the appropriate cellular context.

4. Materials and Methods

4.1. Cell Culture and Materials

The HEK293T Cells were chosen for their high transfection efficiency and common use in GPCR signaling and biosensor assays, facilitating the isolation of DOR-specific responses in a controlled system. This cell line was purchased from Procell (Wuhan, China) and grown in DMEM (Gibco, Waltham, MA, USA) containing 10% fetal bovine serum (Gibco, Waltham, MA, USA) and 100 μg/mL streptomycin and 100 U/mL penicillin (Beyotime, Shanghai, China). The cells were incubated at 37 ◦C with 5% CO2 in an incubator. Neferine (purity > 99.92%) was purchased from MedChemExpress (Shanghai, China).

4.2. Molecular Docking

The three-dimensional structure of the DOR (8F7S [40]) was retrieved from the Protein Data Bank (PDB), while the chemical structures of 15 BIA were obtained from PubChem. Molecular docking was performed using AutoDock Vina (version 1.1.2) [41]. A search space of 20 Å × 20 Å × 20 Å was defined to fully encompass the orthosteric binding pocket. During the docking process, full conformational flexibility was granted to all ligands. Docking calculations were carried out to obtain binding affinity scores; it is important to note that these computed scores differ from true binding free energies as they neglect protein dynamics and solvation effects [42,43]. The resulting docked poses were visualized to analyze ligand–receptor interaction details.

4.3. ONE-GO Biosensor Assay

The measurement was performed based on the ONE-GO biosensors protocol with minor modifications [44]. In brief, HEK293T cells co-expressing DOR and different G protein ONE-GO sensors (including Gi1, Gi2, Gi3, GoA, GoB, Gz, Gs, Gq, G12 and G13) were seeded into 96-well cell culture plates. The following day, after removal of the culture medium, cells were washed with phosphate-buffered saline (PBS) and incubated with BRET assay buff-er at 37 °C for 30 min. Subsequently, Nano-Glo substrate was added and incubated for 3 min, followed by the addition of drug solutions at different concentrations. Luminescence signals at 450 nm (Nluc) and 535 nm (YFP) were simultaneously measured using the BioTek Synergy Neo2 (Agilent, Chengdu, China).

4.4. cAMP Glosensor Assay

HEK293T cells (35,000 cells/well) were seeded into 96-well cell culture plates. When the cell confluency reached 80%, plasmids encoding the DOR (cloned into pcDNA3.1)/vector and GloSensor™-22F cAMP (Promega, Madison, WI, USA) were co-transfected into the cells [45]. After 24 h, the culture medium was removed and replaced with 2% v/v GloSensor™ cAMP reagent, followed by incubation at 37 °C for 90 min. Subsequently, the cells were treated with a series of drug solutions at varying concentrations for 10 min. Finally, Forskolin (final concentration 10 μM) was added, and luminescence intensity was measured using the BioTek Synergy Neo2 (Agilent, Chengdu, China).

4.5. Cell Membrane Chromatography

Stable cell lines overexpressing DOR were obtained from the Institute of Herbgenomics, Chengdu University of Traditional Chinese Medicine. In brief, the cultured cell lines or HEK293T cells (as control) were immersed in Tris-HCl buffer and subjected to sonication for 30 min. Subsequently, an ultrasonic cell disruptor was employed for further cell disruption. Cell membrane fragments were separated by centrifugation and mixed with SiO2-TCT pre-incubated with Flag antibody, then incubated with shaking in a metal bath for 1 h.
The prepared packing material was rinsed twice with physiological saline. Using a packing machine, it was continuously packed into the column core (2.0 × 10 mm) under constant pressure to yield the DOR-Flag/CMC column. Subsequently, the compound solution was analyzed with the CMC/RL 2020 (WOOKING, Shanghai, China) under the following conditions: mobile phase: 2.5 mM KH2PO4 solution, diode-array detector, flow rate: 0.2 mL/min.

4.6. Bio-Layer Interferometry

Cells were seeded in 6-well plates. Forty-eight hours after transfection with DOR, the cells were collected. Subsequently, the cells were lysed in HEPES buffer containing 0.5% Triton, 1 mM EDTA, 150 mM KCl, 0.5 mM TCEP, and cOmplete™ protease inhibitor cocktail. The DOR protein in the lysate was then enriched by using Anti-FLAG® M2 Affinity Gel (Sigma) and harvested through competitive elution. The obtained protein was immobilized on the surface of the Streptactin XT probe for one hour. Subsequently, the probe was loaded onto Gator Plus (Gator Bio, Suzhou, China) and sequentially bound to neferine at different concentrations in a gradient manner. Each concentration point was incubated for 120 s. After the compound binding, the protein was allowed to dissociate for 120 s in PBS buffer containing 1% DMSO. Moreover, non-specific signals were subtracted. The experimental temperature was kept at 30 °C, and the rotation speed was maintained at 1000 rpm.

4.7. RNA Extraction and Analysis

This section encompasses two experimental approaches: dose–response design in 6-well plates and split-sample design in 10 cm dishes. In the dose–response design groups, HEK293T cells cultured in 6-well plates were transfected with DOR for 24 h. Subsequently, they were treated with drug solutions diluted in complete medium at concentrations of 10 μM, 2 μM, 0.4 μM, 0.016 μM, 0.0032 μM, and 0 μM. Each concentration group contained three biological replicates. Twenty-four hours after treatment, cellular RNA was extracted using the TRIzol method [46] and sequenced. As for paired experimental design group, cells were seeded in 10 cm dishes and transfected with DOR for 24 h. Half of the cells were harvested as controls, while the other half were treated with 1 μM drug solution and cultured for an additional 24 h. All samples, consisting of three biological replicates, were processed using the TRIzol method for RNA extraction and subsequent sequencing.
For the RNA-seq data, normalization and filtering were carried out first. Then, differential analysis was performed using DESeq2 to identify DEGs. DEGs with an adjusted p-value (padj) < 0.05 were selected. These genes were visualized using the R language to generate Venn diagrams, volcano plots, and to conduct GO and KEGG enrichment analyses.

4.8. Statistical Analysis

For ONE-GO assay and cAMP assay, data were analyzed using ‘log(agonist) versus response’ in GraphPad Prism v.8.0. The experimental results were independently replicated three times.

5. Conclusions

In conclusion, this study, which encompassed in silico docking, direct binding validation, functional G protein pathway assays, and transcriptomics analysis, demonstrated that neferine was not merely a ligand but a functional activator that specifically interacted with Gi and Gz proteins. This systematically established neferine, derived from N. nucifera, as a novel, natural agonist of the DOR. Subsequent transcriptomic analysis revealed DOR-mediated cellular regulatory processes following neferine stimulation. These findings preliminarily demonstrated the potential biological activity of neferine, positioning it as a highly promising lead compound. The underlying mechanism of the effect of neferine on DOR warrants further investigation, with potential applications in protective therapies for neurological disorders.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijms27042058/s1.

Author Contributions

Conceptualization, L.L. and S.C.; methodology, Z.B., Z.C. and Y.L.; software, Y.S.; formal analysis, G.X.; investigation, Y.L.; resources, Y.Q. and P.J.S.; data curation, Z.X. and J.M.; writing—original draft preparation, Z.B. and Y.L.; writing—review and editing, Z.B. and L.L.; visualization, X.T. and Z.B.; funding acquisition, S.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by National Key R&D Program of China (2025YFC3508601). Natural Science Foundation of Sichuan (No. 2024ZDZX0019). the Sichuan Provincial Administration of Traditional Chinese Medicine Special Fund, for the project “Innovative Platform for Pan-Receptor Screening and Evaluation in Traditional Chinese Medicine”.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

The data presented in this study are openly available from the National Genomics Data Center (https://ngdc.cncb.ac.cn/), reference number PRJCA056150, accessed on 17 January 2026.

Acknowledgments

This work was supported by High-Performance Bioinformatics Platform of Chengdu University of Traditional Chinese Medicine.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

BIAsBenzylisoquinoline alkaloids
DORDelta-opioid receptor
GPCRG protein-coupled receptor
ONE-GOONE vector G protein Optical biosensor
CMCCellular membrane chromatography
BLIBio-layer interferometry
TCTTrichlorocyanuric acid
cAMPCyclic adenosine monophosphate
DEGsDifferentially expressed genes
GOGene Ontology
BPBiological processes
MFMolecular functions
CCCellular component
KEGGKyoto Encyclopedia of Genes and Genomes

References

  1. Zhang, C.; Li, C.; Chen, S.; Li, Z.; Jia, X.; Wang, K.; Bao, J.; Liang, Y.; Wang, X.; Chen, M.; et al. Berberine Protects against 6-OHDA-Induced Neurotoxicity in PC12 Cells and Zebrafish through Hormetic Mechanisms Involving PI3K/AKT/Bcl-2 and Nrf2/HO-1 Pathways. Redox Biol. 2017, 11, 1–11. [Google Scholar] [CrossRef] [Scilit]
  2. Lee, Y.-Y.; Park, J.-S.; Leem, Y.-H.; Park, J.-E.; Kim, D.-Y.; Choi, Y.-H.; Park, E.-M.; Kang, J.L.; Kim, H.-S. The Phosphodiesterase 10 Inhibitor Papaverine Exerts Anti-Inflammatory and Neuroprotective Effects via the PKA Signaling Pathway in Neuroinflammation and Parkinson’s Disease Mouse Models. J. Neuroinflamm. 2019, 16, 246. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  3. Jiang, W.; Li, S.; Li, X. Therapeutic Potential of Berberine against Neurodegenerative Diseases. Sci. China Life Sci. 2015, 58, 564–569. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  4. Li, J.; Liu, M.; Fan, M.; Tian, Q.; Wang, J.; Du, Y.; Yu, J.; Li, X.; Yang, L.; Zhao, M.; et al. Nuciferine Ameliorates Blood-Brain Barrier Disruption Post-Ischemic Stroke via Inhibiting the JAK2/STAT3 Pathway. Phytomedicine 2025, 143, 156829. [Google Scholar] [CrossRef] [Scilit]
  5. Leng, L.; Xu, Z.; Hong, B.; Zhao, B.; Tian, Y.; Wang, C.; Yang, L.; Zou, Z.; Li, L.; Liu, K.; et al. Cepharanthine Analogs Mining and Genomes of Stephania Accelerate Anti-Coronavirus Drug Discovery. Nat. Commun. 2024, 15, 1537. [Google Scholar] [CrossRef] [Scilit]
  6. Zhang, M.; Chen, T.; Lu, X.; Lan, X.; Chen, Z.; Lu, S. G Protein-Coupled Receptors (GPCRs): Advances in Structures, Mechanisms, and Drug Discovery. Signal Transduct. Target. Ther. 2024, 9, 88. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  7. Bi, Z.; Li, H.; Liang, Y.; Sun, D.; Liu, S.; Chen, W.; Leng, L.; Song, C.; Zhang, S.; Cong, Z.; et al. Emerging Paradigms for Target Discovery of Traditional Medicines: A Genome-Wide Pan-GPCR Perspective. Innovation 2025, 6, 100774. [Google Scholar] [CrossRef] [Scilit]
  8. Muratspahić, E.; Freissmuth, M.; Gruber, C.W. Nature-Derived Peptides: A Growing Niche for GPCR Ligand Discovery. Trends Pharmacol. Sci. 2019, 40, 309–326. [Google Scholar] [CrossRef] [Scilit]
  9. Chen, Y.-M.; He, X.-Z.; Wang, S.-M.; Xia, Y. δ-Opioid Receptors, microRNAs, and Neuroinflammation in Cerebral Ischemia/Hypoxia. Front. Immunol. 2020, 11, 421. [Google Scholar] [CrossRef] [Scilit]
  10. Couvineau, A.; Voisin, T.; Nicole, P.; Gratio, V.; Abad, C.; Tan, Y.-V. Orexins as Novel Therapeutic Targets in Inflammatory and Neurodegenerative Diseases. Front. Endocrinol. 2019, 10, 709. [Google Scholar] [CrossRef] [Scilit]
  11. Li, S.H.; Abd-Elrahman, K.S.; Ferguson, S.S.G. Targeting mGluR2/3 for Treatment of Neurodegenerative and Neuropsychiatric Diseases. Pharmacol. Ther. 2022, 239, 108275. [Google Scholar] [CrossRef] [Scilit]
  12. Xu, Y.; Shao, N.; Zhi, F.; Chen, R.; Yang, Y.; Li, J.; Xia, Y.; Peng, Y. Delta-Opioid Receptor Signaling Alleviates Neuropathology and Cognitive Impairment in the Mouse Model of Alzheimer’s Disease by Regulating Microglia Homeostasis and Inhibiting HMGB1 Pathway. Alzheimer’s Res. Ther. 2025, 17, 35. [Google Scholar] [CrossRef] [Scilit]
  13. Li, J.; Xu, Y.; Chao, Z.; Khiati, L.; Xia, Y. Delta-Opioid Receptor–Mediated Neural Protection and Regeneration. Neural Regen. Res. 2025, 21, 2931–2943. [Google Scholar] [CrossRef] [Scilit]
  14. Xu, Y.; Chen, R.; Zhi, F.; Sheng, S.; Khiati, L.; Yang, Y.; Peng, Y.; Xia, Y. δ-Opioid Receptor, Microglia and Neuroinflammation. Aging Dis. 2023, 14, 778. [Google Scholar] [CrossRef] [Scilit]
  15. Wang, Z.; Li, Y.; Ma, D.; Zeng, M.; Wang, Z.; Qin, F.; Chen, J.; Christian, M.; He, Z. Alkaloids from Lotus (Nelumbo nucifera): Recent Advances in Biosynthesis, Pharmacokinetics, Bioactivity, Safety, and Industrial Applications. Crit. Rev. Food Sci. Nutr. 2023, 63, 4867–4900. [Google Scholar] [CrossRef] [Scilit]
  16. Zhao, D.; Ma, L.; Brownlie, J.; Tonissen, K.; Pan, Y.; Feng, Y. Neuroprotective Potential of Major Alkaloids from Nelumbo nucifera (Lotus): Mechanisms and Therapeutic Implications. Int. J. Mol. Sci. 2025, 26, 8280. [Google Scholar] [CrossRef] [Scilit]
  17. Li, T.; Zhai, Y.; Zheng, T.; Xu, B. Neferine Exerts Anti-inflammatory Activity in BV-2 Microglial Cells and Protects Mice with MPTP-induced Parkinson’s Disease by Inhibiting NF-κB Activation. Mol. Med. Rep. 2023, 28, 235. [Google Scholar] [CrossRef] [Scilit]
  18. Bayazeid, O.; Nemutlu, E.; Eylem, C.C.; Yalçın, F.N. Neuroactivity of Naturally Occurring Proaporphine Alkaloid, Pronuciferine. J. Biochem. Mol. Toxicol. 2020, 34, e22601. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  19. Chen, Y.; Xu, X.; Tang, C. A Metabolomics Exploration of Young Lotus Seeds Using Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging. Molecules 2025, 30, 3242. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  20. Ballante, F.; Kooistra, A.J.; Kampen, S.; de Graaf, C.; Carlsson, J. Structure-Based Virtual Screening for Ligands of G Protein–Coupled Receptors: What Can Molecular Docking Do for You? Pharmacol. Rev. 2021, 73, 1698–1736. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  21. Raschka, S.; Kaufman, B. Machine Learning and AI-Based Approaches for Bioactive Ligand Discovery and GPCR-Ligand Recognition. Methods 2020, 180, 89–110. [Google Scholar] [CrossRef] [Scilit]
  22. Janicot, R.; Maziarz, M.; Park, J.-C.; Zhao, J.; Luebbers, A.; Green, E.; Philibert, C.E.; Zhang, H.; Layne, M.D.; Wu, J.C.; et al. Direct Interrogation of Context-Dependent GPCR Activity with a Universal Biosensor Platform. Cell 2024, 187, 1527–1546.e25. [Google Scholar] [CrossRef] [Scilit]
  23. Raskovalov, A.; Kim, D.; Cherezov, V. ONE-GO: Direct Detection of Context-Dependent GPCR Activity. Cell Res. 2024, 34, 543–544. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  24. Inoue, A.; Raimondi, F.; Kadji, F.M.N.; Singh, G.; Kishi, T.; Uwamizu, A.; Ono, Y.; Shinjo, Y.; Ishida, S.; Arang, N.; et al. Illuminating G-Protein-Coupling Selectivity of GPCRs. Cell 2019, 177, 1933–1947.e25. [Google Scholar] [CrossRef] [Scilit]
  25. Wootten, D.; Christopoulos, A.; Marti-Solano, M.; Babu, M.M.; Sexton, P.M. Mechanisms of Signalling and Biased Agonism in G Protein-Coupled Receptors. Nat. Rev. Mol. Cell Biol. 2018, 19, 638–653. [Google Scholar] [CrossRef] [Scilit]
  26. The Gene Ontology Consortium; Aleksander, S.A.; Balhoff, J.; Carbon, S.; Cherry, J.M.; Drabkin, H.J.; Ebert, D.; Feuermann, M.; Gaudet, P.; Harris, N.L.; et al. The Gene Ontology Knowledgebase in 2023. Genetics 2023, 224, iyad031. [Google Scholar] [CrossRef] [Scilit]
  27. Kanehisa, M.; Furumichi, M.; Tanabe, M.; Sato, Y.; Morishima, K. KEGG: New Perspectives on Genomes, Pathways, Diseases and Drugs. Nucleic Acids Res. 2017, 45, D353–D361. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  28. Labani, N.; Gbahou, F.; Lian, S.; Liu, J.; Jockers, R. 2023 Julius Axelrod Symposium: Plant-Derived Molecules Acting on GPCRs. Mol. Pharmacol. 2024, 105, 328–347. [Google Scholar] [CrossRef] [Scilit]
  29. Yu, Y.; Liu, Y.; Dong, G.; Jiang, J.; Leng, L.; Liu, X.; Zhang, J.; Liu, A.; Chen, S. Functional Characterization and Key Residues Engineering of a Regiopromiscuity O-Methyltransferase Involved in Benzylisoquinoline Alkaloid Biosynthesis in Nelumbo nucifera. Hortic. Res. 2023, 10, uhac276. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  30. Kumarihamy, M.; León, F.; Pettaway, S.; Wilson, L.; Lambert, J.A.; Wang, M.; Hill, C.; McCurdy, C.R.; ElSohly, M.A.; Cutler, S.J.; et al. In Vitro Opioid Receptor Affinity and in Vivo Behavioral Studies of Nelumbo nucifera Flower. J. Ethnopharmacol. 2015, 174, 57–65. [Google Scholar] [CrossRef] [Scilit]
  31. Kurose, H.; Kim, S.G. Pharmacology of Antagonism of GPCR. Biol. Pharm. Bull. 2022, 45, 669–674. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  32. Kramer, T.H.; Davis, P.; Hruby, V.J.; Burks, T.F.; Porreca, F. In Vitro Potency, Affinity and Agonist Efficacy of Highly Selective Delta Opioid Receptor Ligands. J. Pharmacol. Exp. Ther. 1993, 266, 577–584. [Google Scholar] [CrossRef] [Scilit]
  33. Wang, X.X.; Ji, X.; Lin, J.; Wong, I.N.; Lo, H.H.; Wang, J.; Qu, L.; Wong, V.K.W.; Chung, S.K.; Law, B.Y.K. GPCR-Mediated Natural Products and Compounds: Potential Therapeutic Targets for the Treatment of Neurological Diseases. Pharmacol. Res. 2024, 208, 107395. [Google Scholar] [CrossRef] [Scilit]
  34. Che, T.; Roth, B.L. Molecular Basis of Opioid Receptor Signaling. Cell 2023, 186, 5203–5219. [Google Scholar] [CrossRef] [Scilit]
  35. Jacobs, B.A.; Pando, M.M.; Jennings, E.M.; Jamshidi, R.J.; Zamora, J.C.; Chavera, T.S.; Clarke, W.P.; Berg, K.A. Signaling Characteristics and Functional Regulation of Delta Opioid-Kappa Opioid Receptor (DOP-KOP) Heteromers in Peripheral Sensory Neurons. Neuropharmacology 2019, 151, 208–218. [Google Scholar] [CrossRef] [Scilit]
  36. Lorente, J.S.; Sokolov, A.V.; Ferguson, G.; Schiöth, H.B.; Hauser, A.S.; Gloriam, D.E. GPCR Drug Discovery: New Agents, Targets and Indications. Nat. Rev. Drug Discov. 2025, 24, 458–479. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  37. Slosky, L.M.; Caron, M.G.; Barak, L.S. Biased Allosteric Modulators: New Frontiers in GPCR Drug Discovery. Trends Pharmacol. Sci. 2021, 42, 283–299. [Google Scholar] [CrossRef] [Scilit]
  38. Lee, Y.; Flores, C.C.; Lefton, M.; Bhoumik, S.; Owada, Y.; Gerstner, J.R. Integrated Transcriptome Profiling and Pan-Cancer Analyses Reveal Oncogenic Networks and Tumor-Immune Modulatory Roles for FABP7 in Brain Cancers. Int. J. Mol. Sci. 2024, 25, 12231. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  39. Saura-Esteller, J.; Sánchez-Vera, I.; Núñez-Vázquez, S.; Cosialls, A.M.; Gama-Pérez, P.; Bhosale, G.; Mendive-Tapia, L.; Lavilla, R.; Pons, G.; Garcia-Roves, P.M.; et al. Activation of the Integrated Stress Response and ER Stress Protect from Fluorizoline-Induced Apoptosis in HEK293T and U2OS Cell Lines. Int. J. Mol. Sci. 2021, 22, 6117. [Google Scholar] [CrossRef] [Scilit]
  40. Wang, Y.; Zhuang, Y.; DiBerto, J.F.; Zhou, X.E.; Schmitz, G.P.; Yuan, Q.; Jain, M.K.; Liu, W.; Melcher, K.; Jiang, Y.; et al. Structures of the Entire Human Opioid Receptor Family. Cell 2023, 186, 413–427.e17. [Google Scholar] [CrossRef] [Scilit]
  41. Trott, O.; Olson, A.J. AutoDock Vina: Improving the Speed and Accuracy of Docking with a New Scoring Function, Efficient Optimization, and Multithreading. J. Comput. Chem. 2010, 31, 455–461. [Google Scholar] [CrossRef] [Scilit]
  42. Rizzuti, B. Molecular Simulations of Proteins: From Simplified Physical Interactions to Complex Biological Phenomena. Biochim. Biophys. Acta (BBA)-Proteins Proteom. 2022, 1870, 140757. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  43. Hollingsworth, S.A.; Dror, R.O. Molecular Dynamics Simulation for All. Neuron 2018, 99, 1129–1143. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  44. Janicot, R.; Garcia-Marcos, M. Protocol to Investigate G Protein-Coupled Receptor Signaling Kinetics and Concentration-Dependent Responses Using ONE-GO Biosensors. STAR Protoc. 2024, 5, 103383. [Google Scholar] [CrossRef] [Scilit] [PubMed]
  45. Kumar, B.A.; Kumari, P.; Sona, C.; Yadav, P.N. GloSensor Assay for Discovery of GPCR-Selective Ligands. In Methods in Cell Biology; Elsevier: Amsterdam, The Netherlands, 2017; Volume 142, pp. 27–50. ISBN 978-0-12-813319-4. [Google Scholar]
  46. Rio, D.C.; Ares, M.; Hannon, G.J.; Nilsen, T.W. Purification of RNA Using TRIzol (TRI Reagent). Cold Spring Harb. Protoc. 2010, 2010, pdb.prot5439. [Google Scholar] [CrossRef] [Scilit]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Article Metrics

Citations

Article Access Statistics

Journal Statistics
Multiple requests from the same IP address are counted as one view.
Special Issue “Inflammatory Airway Diseases: Diagnosis, Pathology, Molecular Mechanisms and Treatment Options”
Previous
Immunological Analysis of Oral Cytobrush Specimens for Early Detection of Oral Cancer Biomarkers: A Comprehensive Review
Next