The present study investigated the effect of ZSSG on sleep in mice. Our findings revealed that ZSSG had no direct effect on sleep in normal mice. However, in sodium pentobarbital-induced sleeping mouse model, ZSSG significantly improved sleep by decreasing sleep latency, prolonging sleep duration, and increasing brain GABA content. Similarly, in sodium barbital-induced sleep in mice, ZSSG decreased sleep latency. According to the criteria for improving sleep in the Technical Standards for the Testing and Assessment of Health Food in China, ZSSG exhibits an effect on improving sleep. Mechanistically, ZSSG may suppress central nervous excitability by increasing the brain content of GABA to improve sleep quality in mice. Furthermore, ingredient combinations, including GABA and coumestrol, may have a synergistic effect on sleep through the regulation of the corresponding subnetwork. Additionally, new metabolites of ZSSG may have positive effects on sleep in mice. Lastly, the use of proximity network medicine shows promise in exploring the mechanism of action of herbal medicine, and the online platform we have developed holds significant ramifications for researchers in the field of TCM. The platform provides a user-friendly and cost-free tool for conducting network proximity between a set of herbal active ingredients’ targets and a set of disease (symptom) related genes. These calculations typically require specialized expertise and resources, but our platform offers a convenient solution, thereby enabling a wider range of researchers to leverage the benefits of network medicine in their research, regardless of their technical proficiency.
Some scholars in an early study found that spinosyn (10 or 15 mg/kg), a flavonoid isolated from ZSS, enhanced pentobarbital-induced sleep in mice and rats, characterized by reduced sleep latency and increased sleep duration, probably via the serotonergic system and postsynaptic 5-HT1A receptor [38, 39]. Until now, the pharmacological investigation of ZSS for the treatment of insomnia has mainly focused on rat models. For instance, extensive studies from para-chlorophenylalanine-induced insomnia model rats demonstrated that ZSS aqueous extract (30 g/kg) or ZSS alcohol extract (403.38 mg/kg) significantly enhanced the serum concentration of 5-TH and enhanced sleeping behaviors, indicating that the extract was effective in treating insomnia in rats [40, 41]. Another main ingredient GABA, one of the major inhibitory neurotransmitters in the nervous system, generates a neuroinhibitory effect by binding with its receptor. It was reported that activation of the GABA receptor is beneficial for sleep [42]. In the current study, after treatment for 30 days, ZSSG at a dose of 0.57 g/kg (including 0.25 g/kg GABA) improved sleep quality in sodium pentobarbital- and sodium barbital-treated male Balb/c mice. None of the studies showed any toxicities or side effects.
In this study, we used mass spectrometry to determine the changes in metabolites among different administration groups. Previous studies have indicated that the metabolic process is vital in the regulation of sleep-wake cycles [43, 44]. Our findings suggest that 10 metabolites demonstrated significant differences in the ZSSG group in comparison to single-agent (ZSS or GABA) group. For instance, previous studies have shown that mice administered ginseng glycoprotein exhibited increased levels in brain tissue and promoting sleep quality [45]. Moreover, uridine, a sleep-promoting substance derived from the brainstem of sleep-deprived rats, resulted in increased sleep frequency upon intravenous injection [46, 47]. Based on our results and the current literature, it is reasonable to speculate that ZSSG may have an advantage in regulating metabolism and improving sleep quality compared to single-agent formulas (ZSS or GABA).
ZSSG, a mixture of ZSS and GABA, has many components and complex pharmacological mechanisms of action and therefore may have the therapeutic characteristics of multi-target and multi-pathway regulation. To investigate the systemic regulatory mechanism of how ZSSG confers its sleep-improving effect, we used a network-based in silico methodology to characterize the relationship between ZSSG ingredients and insomnia. Network-based framework methodology, measuring the relative proximity between the disease-associated genes and drug targets in the human PPI network, has been successfully used to analyze the relationship between diseases and drugs [20, 30]. Interestingly, we found that four potential active ingredients in ZSSG and the corresponding targets may be involved in controlling sleep.
As expected, our study found that ZSSG significantly increased the brain content of GABA in mice. It was reported that activation of GABA neurons in the ventral tegmental area elicited sleep, while lesioning these neurons resulted in increased wakefulness for at least 4 months [48]. In line with this study, our results demonstrated that GABA is top 1 ranked by the proximity network (Z-score: -2.09), which indicated that GABA is the most potent active component in ZSSG in controlling sleep. This result provided further support for the reliability of the proximity network. Solute carrier family 6 member 1 (SLC6A1) encodes GABA transporter protein type 1 (GAT1), which is the most abundant GABA transporter in the brain. A previous study showed that GAT1 knock-out mice spent a longer time in rapid eye movement (REM) sleep than wild-type mice, which indicates that GAT1 plays a critical role in controlling sleep [49]. It was noted that the target of GABA, SLC6A1, is also a protein-associated with insomnia. Based on our results combined with the indicated literature, we conclude that ZSSG may suppress central nervous excitability, at least in part, by regulating the levels of GABA to improve sleep quality in mice.
With the network-based ingredient screening model, we highlighted four major ingredients (GABA, stearic acid, tetradecanoic acid, and coumestrol) of ZSSG that might be able to regulate sleep quality. Taking stearic acid as an example, experimental research has demonstrated that this ingredient binds to FBAP7 [50]. FBAP7 knock-out mice displayed an increase in wake during the light phase, indicating that FBAP7 is required for normal sleep inhibition [51]. More recently, it was reported that a reduction in stearic acid was observed in stool samples from patients with obstructive sleep apnea disease [52], which indicates that stearic acid may play an important role in controlling sleep. In particular, one additional ZSSG ingredient examined in the current study is pregnane xenobiotic receptor (PXR, NR1I2). NR1I2 knock-out mice displayed abnormal theta rhythm during sleep [53]. Coumestrol, a member of the isoflavonoid family, is a natural product with an estrogen-like structure. Coumestrol was reported to act as a human NR1I2 competitive antagonist [54]. It was noted that the target of coumestrol, NR1I2, is also insomnia-associated with the corresponding gene. Hence, to some extent, this experimental evidence supports the network-based predictions of the current study.
In addition to the roles of a single potential active ingredient, three pairwise combination ingredients (GABA plus coumestrol, GABA plus stearic acid, and coumestrol plus stearic acid), which may have synergistic effects, may be another mechanism for the improvement of sleep by ZSSG. It was reported that the network-based strategy can be successfully used to screen drug combinations for potentially new indications [21, 28]. Accordingly, we ranked all possible ingredient pairs by separation score \({S}_{AB}\), and then restricted these ingredient pairs with “Complementary Exposure” pattern to insomnia disease module. We found three network-based predicted candidate ingredient combinations for insomnia. Among the three pairs of ingredient combinations, GABA plus coumestrol or stearic acid may have synergistic effects on insomnia. This may be the reason why ZSS plus GABA as a food formulation mediates sleep quality.
The current study had several limitations. First, due to the lack of available corresponding data, the current existing relevant background information cannot guarantee that all the targets of ZSSG ingredients and the insomnia-related genes are collected, and the results cannot confirm the whole network framework of insomnia and each ingredient. The incomplete human interactome may also affect the accuracy of prediction. Second, many ingredients of ZSSG were combined to exert a sleep-improving effect. However, the network medicine framework methodology was constrained by the current algorithm and other background knowledge, and there was no good method to calculate the combined effect of more than two ingredients. This is another limitation and deserves further investigation. Third, although experimental data (the association between GABA and sleep) have validated some of the predicted positive ingredients, further experiments are necessary to validate the predicted sleep-improving effects and the corresponding molecular subnetwork.