First, we weighed 6.9 mg of ZSS extract and mixed this with 5 ml of methanol. This was then ultrasonicated at room temperature for 15 min (300 W, 40 KHZ) and centrifuged for 10 min at 12,000 rpm. Finally, the supernatants were filtered through a 0.22 µm membrane to obtain prepared samples for UHPLC-Q-Exactive-MS analysis.

Chromatographic separation was accomplished in a Vanquis UHPLC system (Thermo Fischer Scientific, Waltham, MA, United States) equipped with a Thermo Fischer Scientific Hypersll GOLD (100 × 2.1 mm, 1.8 µm) column maintained at 35°C with a flow rate of 0.3 ml/min. The temperature of the autosampler was 5°C. Gradient elution of analytes was carried out with 0.1% acetic acid in 10 mM ammonium acetate buffer solution (A) and acetonitrile (B). We injected 1 µl of each sample after equilibration. An increasing linear gradient of solvent B (v/v) was then applied, as follows: 0–10 min, 5–20% B; 10–14 min, 20–25% B; 14–25 min, 25–35% B; 25–30 min, 35–100% B; 30–31 min, 100–100% B; 31–32 min, 100–5% B; 32–35 min, 5–5% B.

The ESI-MSⁿ experiments were executed on a Thermo Q Exactive mass spectrometer (Thermo Fischer Scientific, Massachusetts, United States) with spray voltages of 3.5 and −3.0 kV in positive and negative modes, respectively. Sheath gas and auxiliary gas were set at 40 and 15 arbitrary units, respectively. The capillary temperature was 320°C. The analyzer scanned over a mass range of m/z 150–2,000 Da for a full scan at a mass resolution of 70,000. Data dependent acquisition (DDA) MS/MS experiments were performed with HCD scans. Dynamic exclusion was implemented to remove some unnecessary information in MS/MS spectra.

The analysis of UHPLC-MS data was performed using Thermo Xcalibur Version 4.1 software (Thermo Fischer Scientific, Massachusetts, United States). We tentatively identified the compounds of ZSS by considering a range of factors, including molecular weight, retention time, fragment information obtained from the MS/MS model, further matching annotation in our in-house database of compounds, along with previous literature and standard references.

Male Sprague-Dawley rats (200 ± 20 g) were obtained from SPF (Beijing) Biotechnology Co., Ltd (Permission No. SCXK (jing) 2019–0010). All animals were housed in a breeding environment (12 h light-dark cycle, 25 ± 2°C, and 55 ± 5% relative humidity). All rats had free access to water and food. This animal research was approved by the Ethics Committee of Wuxi Hospital of Traditional Chinese Medicine (Approval ID: SKJJ2020011707) and conformed to animal welfare regulations and the ethical principles for animal protection and the relevant provisions put forward by the National Experimental Animal Welfare Ethics guidelines.

After 7 days of acclimation, the rats were randomly divided into four groups (6 rats per group): a normal group, a model group, an estazolam group, and a ZSS group. The PCPA-induced model of insomnia was established using a method that was described previously (Si et al., 2020). Rats in the model group, the estazolam group, and the ZSS group, were intraperitoneally injected with PCPA (350 mg/kg) once a day for 3 days. The normal group received the same amount of physiological saline. On the fourth day, the ZSS group was orally administered with ZSS alcohol extract (dissolved in normal saline) at a dose of 403.38 mg/kg (equivalent to a crude drug dose of 2.7 g/kg) once a day for 7 days (Du et al., 2019; Hua et al., 2021). The human equivalent dose (HED) of the dose is 0.43 g/kg, which is 2 times of the clinical dosage (Food and Drug Administration, 2005). The estazolam group was administered with 0.5 mg/kg of estazolam (dissolved in normal saline) once a day for 7 days. Rats in the other two groups were treated with an equal volume of physiological saline. At the end of the animal experiment and after fasting for 12 h, all rats were anaesthetized by an intraperitoneal injection of pentobarbital sodium (45 mg/kg). Blood samples were then collected from the abdominal aorta and hypothalamic tissues were quickly removed.

This experiment was carried out 30 min after the last drug administration. Rats were placed on their backs following an intraperitoneal injection of pentobarbital sodium (35 mg/kg), and then monitored the rats for signs of sleeping. Our main criterion for sleep was that the rats lost their righting reflex for more than 1 min. Sleep latency was recorded as the time between pentobarbital sodium injection and the loss of the righting reflex. Sleep duration was recorded from the loss of the righting reflex until recovery (Xu et al., 2019).

The hypothalamic samples were homogenized with ice-cold PBS (w/v, 1:9). The homogenate was centrifuged at 3,000 rpm for 10 min at 4°C. The supernatant was collected. The levels of 5-HT and GABA in hypothalamic tissues were measured using ELISA kits according to the manufacturer’s instructions (Nanjing JinTing Biotechnology Co., Ltd. Nanjing, China).

Hypothalamic tissues were fixed in 4% paraformaldehyde, dehydrated, embedded in paraffin, and then sectioned. Sections (4 μm thick) were then dewaxed to water, and stained with hematoxylin and eosin (HE). Finally, hypothalamic lesions were observed by microscopy, as described previously (Shen et al., 2020).

The phytochemicals identified by UHPLC-Q-Exactive-MS/MS were used for network pharmacology investigations. We used Lipinski’s rule of five related parameters to screen the active compounds: a molecular weight (MW) ≤ 500, an octanol-water partition coefficient log P (ALogP) ≤ 5, a hydrogen bond donor count (Hdon) ≤ 5, and a hydrogen bond acceptor count (Hacc) ≤ 10. The principle of screening candidate ingredients was to meet at least two of the parameters (Wang et al., 2020). Some compounds (e.g., jujuboside A) exhibited low values with explicit pharmacological effects; these were also selected for further study.

We used BATMAN-TCM (http://bionet.ncpsb.org.cn/batman-tcm/) (Ji et al., 2019) and the SwissTargetPrediction database (http://www.swisstargetprediction.ch/) (Gfeller et al., 2014) to identify the relevant biological targets of candidate ingredients from ZSS. We also retrieved disease-associated targets from the GeneCards database (https://www.genecards.org/) by using “insomnia” and “sedation and hypnosis” as keywords (Hu et al., 2020). Then, the overlapping targets between ingredient- and insomnia-associated targets were then visualized by creating Venn diagrams (https://bioinfogp.cnb.csic.es/tools/venny/index.html) (Huang et al., 2020). In addition, these overlapping targets were introduced into the STRING database (https://www.string-db.org/) to investigate protein-protein interaction (PPI) relationships; this allowed us to identify targets that were closely related to insomnia (Liang et al., 2021); the protein interaction selection score was set to >0.6. Cytoscape version 3.6.1 software (Free Software Foundation, Inc., Boston, MA, United States) was used to visualize the PPI network. Then, we used the MCODE plugin to conduct cluster analysis of the targets showing high levels of interaction (Zhang et al., 2019).

Next, the core targets obtained from MCODE cluster analysis were imported into Omicshare tools (https://www.omicshare.com/). We then performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses for the core targets and identified the key biological functions (biological processes, molecular functions, and cellular components) and related signaling pathways (Wu et al., 2020). Finally, an ingredient-target-pathway (I-T-P) network was generated by Cytoscape version 3.6.1 software, which featured a number of relationships, including the active chemical ingredients of ZSS, the core targets, and the enriched signaling pathways (An et al., 2020).

Total RNA was extracted from the rat hypothalamus with Trizol reagent (Invitrogen, Carlsbad, CA, United States). cDNA was then synthetized with a reverse transcription kit (TaKaRa, Dalian, China) by GeneExplorer PCR (Bioer Technology, Hangzhou, China). Next, the cDNA was used as a target for amplification using the TB Green Premix PCR Kit (TaKaRa, Dalian, China) and a LightCycler 480 Ⅱ (Roche, Rotkreuz, Switzerland). The real-time PCR thermal cycling protocol was as follows: 95°C for 5 min, followed by 50 cycles of 95°C for 10 s, 60°C for 10 s and 72°C for 10 s. The primer sequences were synthesized by Sangon Biotech Co., Ltd. (Shanghai, China), as follows: β-actin (Forward) 5′-CCT​CAC​TGT​CCA​CCT​TCC​A-3′ and (Reverse) 5′-GGG​TGT​AAA​ACG​CAG​CTC​A-3´; HTR1A (Forward) 5′-GGG​CAA​CTC​CAA​AGA​GCA-3′ and (Reverse) 5′-TCA​CCG​TCT​TCC​TTT​CAC​G-3´; HTR2A (Forward) 5′-TTC​CTT​GTC​ATG​CCT​GTG​T-3′ and (Reverse) 5′-ATA​GCG​GTC​CAG​GGA​GAT-3´; GABRA1 (Forward) 5′-GAC​TAT​CTT​TGG​GCC​TGG​A-3′ and (Reverse) 5′-CAT​CTT​GGG​AGG​GCT​GT-3´; GABRG2 (Forward) 5′-ACA​ATG​CCA​CCC​ACC​TT-3′ and (Reverse) 5′-TAT​CCT​CCC​GTG​TCT​CCA-3´. The relative expression of the target genes were normalized to the threshold cycle (CT) value of β-actin, and the data analysis was performed using the 2^-△△Ct method (Poh et al., 2017). Real-time PCR was performed for each sample in three replicates.

Paraffin-embedded rat hypothalamic tissues were sectioned and dewaxed to water. Then, high pressure boiling was used for antigen retrieval and endogenous catalase was blocked by 3% H₂O₂. Next, sections (4 μm thick) were incubated overnight at 4°C with HTR1A (PA5-75267, 1:200) (Invitrogen, Carlsbad, CA, United States), HTR2A (PA5-95288, 1:200) (Invitrogen, Carlsbad, CA, United States), GABRA1 (ab94585, 1:200) (Abcam, United Kingdom) and GABRG2 (ab87328, 1:200) (Abcam, United Kingdom) antibodies. The following morning, sections were washed and then incubated for 20 min at 37°C with HRP-conjugated secondary antibodies (goat anti-rabbit IgG H&L, ab205718, Abcam, 1:2,000), followed by DAB and hematoxylin staining. Finally, the stained sections were imaged and observed by NIKON ECLIPSE CI microscopy (Nikon, Japan). The integrated optical density (IOD) of the expression levels of target proteins/positive cells were then quantified with Image Pro Plus version 7.0 software (Media Cybernetics, Inc., MD, United States) (Jin et al., 2020).

Experimental data are shown as mean ± standard deviation (SD). One-way analysis of variance (ANOVA) was used for all statistical analysis followed by a Dunnettʹs test and performed in GraphPad Prism version 8 (GraphPad Software Inc., San Diego, CA, United States). p-values < 0.05 was considered to be statistically significant.

According to Lipinski’s rule, our network pharmacology study identified 21 candidate compounds (Table 2). Using associated databases, we failed to identify related targets for 6ʹʹʹ-Sinapoylspinosin and 6ʹʹʹ-p-Coumaloylspinosin. Ultimately, we identified 19 active compounds and 433 compound-related targets (Supplementary Table S1). In total, 504 insomnia-associated targets were acquired from the GeneCards database (Supplementary Table S2). After overlapping the ZSS-associated targets and the insomnia-associated targets, we identified 118 targets potential targets for ZSS in the treatment of insomnia (Supplementary Table S3). Next, we created a PPI network and performed MCODE clustering analysis in Cytoscape to identify the way the potential targets interacted and to identify kernel targets. We identified 111 nodes and 787 edges in the PPI network (Figure 3). The clustering coefficient and average neighborhood number were 0.611 and 14.180, respectively. MCODE clustering analysis identified 4 clusters. As shown in Table 3, 65 key targets were identified from 4 clusters, thus representing potential core targets for ZSS in the treatment of insomnia. Most of these targets were neuroactive ligand receptors, including serotonin receptors (e.g., HTR1A, HTR1B, HTR1E, HTR1D, HTR1F, HTR2A, HTR2B, HTR2C, HTR3A and HTR5A), GABA_A receptors (e.g., GABRA1, GABRA2, GABRA5, GABRB2 and GABRG2), dopamine receptors (e.g., DRD1, DRD2, DRD3, DRD4 and DRD5), adrenergic receptor (e.g., ADRA1A, ADRA1B, ADRA1D, ADRA2A, ADRA2B and ADRA2C), cannabinoid signaling (e.g. CNR1 and CNR2), and muscarinic acetylcholine receptors (e.g. CHRM1 and CHRM3).

GO enrichment analysis demonstrated that the core targets were associated with various neural-associated biological processes, including G protein-coupled receptor signaling pathways, anterograde trans-synaptic signaling, chemical synaptic transmission, trans-synaptic signaling, and synaptic signaling (Figure 4). Next, we applied KEGG enrichment analysis to identify the overall regulation of ZSS in the treatment of insomnia with regards to specific signaling pathways. The targets identified overlapped between neuroactive ligand-receptor interaction, serotonergic synapse, and GABAergic synapse (Figure 5).

Next, we used ingredient-target-pathway (I-T-P) sub-networks to construct serotonergic synapse and GABAergic synapse pathways and reveal the synergistic effects of ZSS to treat insomnia (Figures 6A,B). As shown in the two networks, palmitic acid, coclaurine, jujuboside A, N-nornuciferine, caaverine, magnoflorine, jujuboside B, and betulinic acid, all played roles in pathways associated with the serotonergic synapse and the GABAergic synapse.

Recent studies have shown that multiple subtypes of serotonin (5-HT) receptors In the central nervous system are mainly involved in the modulation of sleep (Cui et al., 2011). In addition, 5-HT_1A (HTR1A) and 5-HT_2A (HTR2A) have been identified as common targets for the treatment of insomnia, in both clinical and basic studies (Jiang et al., 2016; Ohno et al., 2018; Shao et al., 2020; Lv et al., 2021), as well as prevenient studies of ZSS for the treatment of insomnia (Shen et al., 2020). GABA_ARα1 (GABRA1) and GABA_ARγ2 (GABRG2) are two significant subunits of GABA receptors, and are known to play an important role in the first line treatment of insomnia (Zhong et al., 2020; Lv et al., 2021). Based on our preliminary results, we preferentially selected HTR1A, HTR2A, GABRA1, and GABRG2, as potential therapeutic targets of ZSS for further experimental validation.

Network pharmacology demonstrated that neuroactive ligand-receptor interaction (important-related targets: HTR1A, HTR2A, GABRA1 and GABRG2), serotonergic synapse (important-related targets: HTR1A and HTR2A), and GABAergic synapse (important-related targets: GABRA1 and GABRG2) signaling pathways were involved in the effects of ZSS on insomnia. We determined the expression levels of hypothalamic-related genes (Figures 7A–D). The administration of PCPA led to a significant reduction in the gene expression of HTR1A, GABRA1, and GABRG2 (both p < 0.05), and an increase in the expression levels of HTR2A mRNA (p < 0.01). We found that ZSS treatment significantly elevated the gene expression levels of HTR1A (p < 0.01), GABRA1 (p < 0.05) and GABRG2 (p < 0.01) and decreased the expression levels of HTR2A mRNA (p < 0.01).

We also used immunohistochemistry analysis to investigate the expression levels of related targets in the hypothalamus (Figures 8A,B). The injection of PCPA induced a significant reduction in the number of HTR1A-, GABRA1-, and GABRG2-positive cells (p < 0.01), and an increase in the number of HTR2A-positive cells in the hypothalamus (p < 0.01). Following ZSS treatment, the number of HTR1A-, GABRA1-, and GABRG2-positive cells increased significantly (p < 0.01); however, the number of HTR2A-positive cells decreased significantly (p < 0.01).