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Background: Network pharmacology has emerged as a powerful tool to understand the therapeutic effects and mechanisms of natural products. However, there is a lack of comprehensive evaluations of network-based approaches for natural products on identifying therapeutic effects and key mechanisms. Purpose: We systematically explore the capabilities of network-based approaches on natural products, using Panax ginseng as a case study. P. ginseng is a widely used herb with a variety of therapeutic benefits, but its active ingredients and mechanisms of action on chronic diseases are not yet fully understood. Methods: Our study compiled and constructed a network focusing on P. ginseng by collecting and integrating data on ingredients, protein targets, and known indications. We then evaluated the performance of different network-based methods for summarizing known and unknown disease associations. The predicted results were validated in the hepatic stellate cell model. Results: We find that our multiscale interaction-based approach achieved an AUROC of 0.697 and an AUPR of 0.026, which outperforms other network-based approaches. As a case study, we further tested the ability of multiscale interactome-based approaches to identify active ingredients and their plausible mechanisms for breast cancer and liver cirrhosis. We also validated the beneficial effects of unreported and top-predicted ingredients, in cases of liver cirrhosis and gastrointestinal neoplasms. Conclusion: our study provides a promising framework to systematically explore the therapeutic effects and key mechanisms of natural products, and highlights the potential of network-based approaches in natural product research.

In this paper, we present a comprehensive guide for implementing artificial intelligence (AI) techniques in traditional East Asian medicine (TEAM) research. We cover essential aspects of the AI model development pipeline, including research objective establishment, data collection and preprocessing, model selection, evaluation, and interpretation. The unique considerations in applying AI to TEAM datasets, such as data scarcity, imbalance, and model interpretability, are discussed. We provide practical tips and recommendations based on best practices and our own experience. The potential of large language models in TEAM research is also highlighted. Finally, we discuss the challenges and future directions of AI application in TEAM, emphasizing the need for standardized data collection and sharing platforms.

Abstract Anorexia means loss of appetite and is a state whereby a desire to eat is either reduced or eliminated resulting in reducing or stopping food intake. Sipjeondaebo-tang (SDT) and Hyangsayukgunja-tang (HYT) are prescriptions known to have appetite-improving effects, but studies on their mechanisms and active components are insufficient. The hypothalamus is the center of appetite control, and various appetite control mechanisms are known. We used mouse hypothalamic neuronal GT1-7 cells as appetite control center cells and analyzed the difference in efficacy between SDT and HYT using microarray and network pharmacology. Microarray analysis showed that SDT and HYT affect the regulation of genes related to appetite control in the digestive tract and central nervous system. Using network pharmacology, we analyzed the differential expression of neuropeptide Y receptors, glucagon, corticotropin-releasing hormone receptors 1, and 5-hydroxytryptamine receptor 4 among the 17 anorexia-related genes selected from the comparative toxicogenomics database and also analyzed the active components that affect gene expression. In conclusion, the appetite-related genes contributed to anorexia control, and the difference in the action mechanism of the two complex prescriptions could be explained.

Allergic rhinitis (AR) is a systemic allergic disease that has a considerable impact on patients’ quality of life. Current treatments include antihistamines and nasal steroids; however, their long-term use often causes undesirable side effects. In this context, traditional Asian medicine (TAM), with its multi-compound, multi-target herbal medicines (medicinal plants), offers a promising alternative. However, the complexity of these multi-compound traits poses challenges in understanding the overall mechanisms and efficacy of herbal medicines. Here, we demonstrate the efficacy and underlying mechanisms of these multi-compound herbal medicines specifically used for AR at a systemic level. We utilized a modified term frequency–inverse document frequency method to select AR-specific herbs and constructed an herb–compound–target network using reliable databases and computational methods, such as the Quantitative Estimate of Drug-likeness for compound filtering, STITCH database for compound-target interaction prediction (with a high confidence score threshold of 0.7), and DisGeNET and CTD databases for disease-gene association analysis. Through this network, we conducted AR-related targets and pathway analyses, as well as clustering analysis based on target-level information of the herbs. Gene ontology enrichment analysis was conducted using a protein–protein interaction network. Our research identified 14 AR-specific herbs and analyzed whether AR-specific herbs are highly related to previously known AR-related genes and pathways. AR-specific herbs were found to target several genes related to inflammation and AR pathogenesis, such as PTGS2, HRH1, and TBXA2R. Pathway analysis revealed that AR-specific herbs were associated with multiple AR-related pathways, including cytokine signaling, immune response, and allergic inflammation. Additionally, clustering analysis based on target similarity identified three distinct subgroups of AR-specific herbs, corroborated by a protein–protein interaction network. Group 1 herbs were associated with the regulation of inflammatory responses to antigenic stimuli, while Group 2 herbs were related to the detection of chemical stimuli involved in the sensory perception of bitter taste. Group 3 herbs were distinctly associated with antigen processing and presentation and NIK/NF-kappa B signaling. This study decodes the principles of TAM herbal configurations for AR using a network pharmacological approach, providing a holistic understanding of drug effects beyond specific pathways.

Abstract Background Cordyceps species have been used as tonics to enhance energy, stamina, and libido in traditional Asian medicine for more than 1600 years, indicating their potential for improving reproductive hormone disorders and energy metabolic diseases. Among Cordyceps, Cordyceps militaris has been reported to prevent metabolic syndromes including obesity and benefit the reproductive hormone system, suggesting that Cordyceps militaris can also regulate obesity induced by the menopause. We investigated the effectiveness of Cordyceps militaris extraction (CME) on menopausal obesity and its mechanisms. Methods We applied an approach combining in vivo, in vitro, and in silico methods. Ovariectomized rats were administrated CME, and their body weight, area of adipocytes, liver and uterus weight, and lipid levels were measured. Next, after the exposure of MCF-7 human breast cancer cells to CME, cell proliferation and the phosphorylation of estrogen receptor and mitogen-activated protein kinases (MAPK) were measured. Finally, network pharmacological methods were applied to predict the anti-obesity mechanisms of CME. Results CME prevented overweight, fat accumulation, liver hypertrophy, and lowered triglyceride levels, some of which were improved in a dose-dependent manner. In MCF-7 cell lines, CME showed not only estrogen receptor agonistic activity through an increase in cell proliferation and the phosphorylation of estrogen receptors, but also phosphorylation of extracellular-signal-regulated kinase and p38. In the network pharmacological analysis, bioactive compounds of CME such as cordycepin, adenine, and guanosine were predicted to interact with non-overlapping genes. The targeted genes were related to the insulin signaling pathway, insulin resistance, the MARK signaling pathway, the PI3K–Akt signaling pathway, and the estrogen signaling pathway. Conclusions These results suggest that CME has anti-obesity effects in menopause and estrogenic agonistic activity. Compounds in CME have the potential to regulate obesity-related and menopause-related pathways. This study will contribute to developing the understanding of anti-obesity effects and mechanisms of Cordyceps militaris.

Introduction: Sasang Constitutional Medicine (SCM) is a type of traditional Korean medicine where patients are classified as one of four Sasang constitution types (Sasang type) and medications consisting of medicinal herbs are prescribed according to the Sasang type. Despite the importance of personalized medicine, the operation mechanism is largely unknown. To gain a better understanding, we investigated the compound information that composes Sasang type-specific personalized herbal medicines on both multivariate and univariate levels. Methods: Five machine learning classifiers including extremely randomized trees (ERT) were trained to investigate whether the Sasang type can be explained by compound information at the multivariate level. Hierarchical clustering was conducted to determine whether compounds are processed distributedly or specifically. Taxonomic and biosynthetic analyses were conducted on these compounds. A univariate level statistical test was conducted to provide more robust Sasang type-specific compound information. Results: Using the trained ERT classifier, sixty important compounds were extracted. The sixty compounds were clustered into three groups, corresponding to each Sasang type-prominent compounds, suggesting that most compounds have specific preference for the Sasang type. Structural and biosynthetic characteristics of these Sasang type-prominent compounds were determined based on taxonomy and pathway analyses. Fourteen compounds showed statistically significant relevance with the Sasang type. Additionally, we predicted the Sasang type of unknown herbs, which were confirmed by their biological effects in functional assays. Conclusion: This study investigated the personalized herbal medicines of the SCM using compound information. This study provided information on the chemical characteristics of the compounds that are essential for classifying the Sasang type of medicinal herbs, as well as predictions regarding the Sasang type of the commonly used but unidentified medicinal herbs.

A computational approach to identifying drug-target interactions (DTIs) is a credible strategy for accelerating drug development and understanding the mechanisms of action of small molecules. However, current methods to predict DTIs have mainly focused on identifying simple interactions, requiring further experiments to understand mechanism of drug. Here, we propose AI-DTI, a novel method that predicts activatory and inhibitory DTIs by combining the mol2vec and genetically perturbed transcriptomes. We trained the model on large-scale DTIs with MoA and found that our model outperformed a previous model that predicted activatory and inhibitory DTIs. Data augmentation of target feature vectors enabled the model to predict DTIs for a wide druggable targets. Our method achieved substantial performance in an independent dataset where the target was unseen in the training set and a high-throughput screening dataset where positive and negative samples were explicitly defined. Also, our method successfully rediscovered approximately half of the DTIs for drugs used in the treatment of COVID-19. These results indicate that AI-DTI is a practically useful tool for guiding drug discovery processes and generating plausible hypotheses that can reveal unknown mechanisms of drug action.

Skin photoaging induced by ultraviolet (UV) irradiation contributes to the formation of thick and coarse wrinkles. Humans are exposed to UV light throughout their lives. Therefore, it is crucial to determine the time-sequential effects of UV on the skin. In this study, we irradiated the mouse back skin with UV light for eight weeks and observed the changes in gene expressions via microarray analysis every week. There were more downregulated genes (514) than upregulated genes (123). The downregulated genes had more functional diversity than the upregulated genes. Additionally, the number of downregulated genes did not increase in a time-dependent manner. Instead, time-dependent kinetic patterns were observed. Interestingly, each kinetic cluster harbored functionally enriched gene sets. Since collagen changes in the dermis are considered to be a major cause of photoaging, we hypothesized that other gene sets contributing to photoaging would exhibit kinetics similar to those of the collagen-regulatory genes identified in this study. Accordingly, co-expression network analysis was conducted using 11 well-known collagen-regulatory seed genes to predict genes with similar kinetics. We ranked all downregulated genes from 1 to 504 based on their expression levels, and the top 50 genes were suggested to be involved in the photoaging process. Additionally, to validate and support our identified top 50 gene lists, we demonstrated that the genes (FN1, CCDC80, PRELP, and TGFBR3) we discovered are downregulated by UV irradiation in cultured human fibroblasts, leading to decreased collagen levels, which is indicative of photoaging processes. Overall, this study demonstrated the time-sequential genetic changes in chronically UV-irradiated skin and proposed 50 genes that are involved in the mechanisms of photoaging.

Sensorimotor information provided by mossy fibers (MF) is mapped to high-dimensional space by a huge number of granule cells (GrC) in the cerebellar cortex’s input layer. Significant studies have demonstrated the computational advantages and primary contributor of this expansion recoding. Here, we propose a novel perspective on the expansion recoding where each GrC serve as a kernel basis function, thereby the cerebellum can operate like a kernel machine that implicitly use high dimensional (even infinite) feature spaces. We highlight that the generation of kernel basis function is indeed biologically plausible scenario, considering that the key idea of kernel machine is to memorize important input patterns. We present potential regimes for developing kernels under constrained resources and discuss the advantages and disadvantages of each regime using various simulation settings.

Chung-Sang-Bo-Ha-Hwan (CSBHH) is an herbal prescription widely used to treat various chronic respiratory diseases. To investigate the system-level treatment mechanisms of CSBHH in respiratory tract diseases, we identified 56 active ingredients of CSBHH and evaluated the degree of overlap between their targets and respiratory tract disease-associated proteins. We then investigated the respiratory tract disease-related signaling pathways associated with CSBHH targets. Enrichment analysis showed that the CSBHH targets were significantly associated with various signaling pathways related to inflammation, alveolar structure, and tissue fibrosis. Experimental validation was conducted using phorbol-12-myristate-13-acetate (PMA)-stimulated NCI-H292 cells by analyzing the mRNA expression levels of biomarkers (IL-1β and TNF-α for inflammation; GSTP1, GSTM1, and PTEN for apoptosis) derived from network pharmacological analysis, in addition to the mucin genes MUC5AC and MUC2, to investigate the phlegm-expelling effect of CSBHH. The mRNA expression levels of these genes were consistent with network pharmacological predictions in a concentration-dependent manner. These results suggest that the therapeutic mechanisms of CSBHH in respiratory tract diseases could be attributed to the simultaneous action of multiple active ingredients in the herbal prescription.

Nonalcoholic fatty liver disease (NAFLD) is the most common type of chronic liver disease and lacks guaranteed pharmacological therapeutic options. In this study, we applied a network-based framework for comprehensively identifying candidate flavonoids for the prevention and/or treatment of NAFLD. Flavonoid-target interaction information was obtained from combining experimentally validated data and results obtained using a recently developed machine-learning model, AI-DTI. Flavonoids were then prioritized by calculating the network proximity between flavonoid targets and NAFLD-associated proteins. The preventive effects of the candidate flavonoids were evaluated using FFA-induced hepatic steatosis in HepG2 and AML12 cells. We reconstructed the flavonoid-target network and found that the number of re-covered compound-target interactions was significantly higher than the chance level. Proximity scores have successfully rediscovered flavonoids and their potential mechanisms that are reported to have therapeutic effects on NAFLD. Finally, we revealed that discovered candidates, particularly glycitin, significantly attenuated lipid accumulation and moderately inhibited intracellular reactive oxygen species production. We further confirmed the affinity of glycitin with the predicted target using molecular docking and found that glycitin targets are closely related to several proteins involved in lipid metabolism, inflammatory responses, and oxidative stress. The predicted network-level effects were validated at the levels of mRNA. In summary, our study offers and validates network-based methods for the identification of candidate flavonoids for NAFLD.

Pattern identification (PI), a unique diagnostic system of traditional Asian medicine, is the process of inferring the pathological nature or location of lesions based on observed symptoms. Despite its critical role in theory and practice, the information processing principles underlying PI systems are generally unclear. We present a novel framework for comprehending the PI system from a machine learning perspective. After a brief introduction to the dimensionality of the data, we propose that the PI system can be modeled as a dimensionality reduction process and discuss analytical issues that can be addressed using our framework. Our framework promotes a new approach in understanding the underlying mechanisms of the PI process with strong mathematical tools, thereby enriching the explanatory theories of traditional Asian medicine.

Background: Despite the importance of accurate Sasang type diagnosis, a unique form of Korean medicine, there have been concerns about consistency among diagnoses. We investigate a data-driven integrative diagnostic model by applying machine learning to a multicenter clinical dataset with comprehensive features. Methods: Extremely randomized trees (ERT), support vector machines, multinomial logistic regression, and K-nearest neighbor were applied, and performances were evaluated by cross-validation. The feature importance of the classifier was analyzed to understand which information is crucial in diagnosis. Results: The ERT classifier showed the highest performance, with an overall f1 score of 0.60 ± 0.060. The feature classes of body measurement, personality, general information, and cold–heat were more decisive than others in classifying Sasang types. Costal angle was the most informative feature. In pairwise classification, we found Sasang type-dependent distinctions that body measurement features played a key role in TE-SE and TE-SY datasets, while personality and cold–heat features showed importance in SE-SY dataset. Conclusion: Current study investigated a comprehensive diagnostic model for Sasang type using machine learning and achieved better performance than previous studies. This study helps data-driven decision making in clinics by revealing key features contributing to the Sasang type diagnosis.

Background: This study aimed at determining the effect of the herbal mixture estrogen inhibition formula (EIF) and its possible mechanisms by precocious puberty animal models and network pharmacology-based analysis. Methods: Precocious puberty animal models were established by a single injection of 300 μg danazol, then female rats were administered EIF, vaginal openings were monitored, uterus and pituitary indices were determined. The levels of ALP, E2, LH, and FSH were measured using ELISA kits. Real-time PCR was performed to evaluate the mRNA expression of GnRH, UNC5C, and netrin-1 in hypothalamic tissues. We applied network pharmacological analysis to predict potential targets and pathways of EIF. Results: EIF delayed danazol-induced early vaginal opening. In the onset model, EIF reduced the increased levels of serum ALP, E2, LH, and FSH; as well as mRNA expressions of GnRH, Netrin-1, and UNC5C. Moreover, long-term administration of EIF not only diminished all impaired factors but also had no effect on the normal development of the animals. The gene set enrichment analysis showed that the targets of EIF are mainly associated with the GnRH signaling and ovarian steroidogenesis pathways. Conclusion: EIF could be used in preclinical research for the treatment of precocious puberty by the inhibition of HPGA pre-maturation.

One of the central goals in systems neuroscience is to understand how information is encoded in the brain, and the standard approach is to identify the relation between a stimulus and a neural response. However, the feature of a stimulus is typically defined by the researcher's hypothesis, which may cause biases in the research conclusion. To demonstrate potential biases, we simulate four likely scenarios using deep neural networks trained on the image classification dataset CIFAR-10 and demonstrate the possibility of selecting suboptimal/irrelevant features or overestimating the network feature representation/noise correlation. Additionally, we present studies investigating neural coding principles in biological neural networks to which our points can be applied. This study aims to not only highlight the importance of careful assumptions and interpretations regarding the neural response to stimulus features but also suggest that the comparative study between deep and biological neural networks from the perspective of machine learning can be an effective strategy for understanding the coding principles of the brain.

Climbing fibers (CFs) generate complex spikes (CS) and Ca2+ transients in cerebellar Purkinje cells (PCs), serving as instructive signals. The so-called 'all-or-none' character of CSs has been questioned since the CF burst was described. Although recent studies have indicated a sensory-driven enhancement of PC Ca2+ signals, how CF responds to sensory events and contributes to PC dendritic Ca2+ and CS remains unexplored. Here, single or simultaneous Ca2+ imaging of CFs and PCs in awake mice revealed the presynaptic CF Ca2+ amplitude encoded the sensory input’s strength and directly influenced post-synaptic PC dendritic Ca2+ amplitude. The sensory-driven variability in CF Ca2+ amplitude depended on the number of spikes in the CF burst. Finally, the spike number of the CF burst determined the PC Ca2+ influx and CS properties. These results reveal the direct translation of sensory information-coding CF inputs into PC Ca2+, suggesting the sophisticated role of CFs as error signals.

Background: Fermented black ginseng (FBG) is produced through several cycles of steam treatment of raw ginseng, at which point its color turns black. During this process, the original ginsenoside components of raw ginseng (e.g., Re, Rg1, Rb1, Rc, and Rb2) are altered, and less-polar ginsenosides are generated (e.g., Rg3, Rg5, Rk1, and Rh4). The aim of this study was to determine the effect of FBG on wound healing. Methods: The effects of FBG on tube formation and on scratch wound healing were measured using human umbilical vein endothelial cells (HUVECs) and HaCaT cells, respectively. Protein phosphorylation of mitogen-activated protein kinase was evaluated via Western blotting. Finally, the wound-healing effects of FBG were assessed using an experimental cutaneous wounds model in mice. Results and Conclusion: The results showed that FBG enhanced the tube formation in HUVECs and migration in HaCaT cells. Western blot analysis revealed that FBG stimulated the phosphorylation of p38 and extracellular signal-regulated kinase in HaCaT cells. Moreover, mice treated with 25 μg/mL of FBG exhibited faster wound closure than the control mice did in the experimental cutaneous wounds model in mice. Keywords: ginseng, human umbilical vein endothelial cells, vascular endothelial growth factor, wound healing

In recent years, several therapeutic drugs have been rationally designed and synthesized based on the novel knowledge gained from investigating the actions of biologically active chemicals derived from foods, plants, and medicinal herbs. One of the major advantages of these naturalistic chemicals is their ability to interact with multiple targets in the body resulting in a combined beneficial effect. Ginseng is a perennial herb (Araliaceae family), a species within the genus Panax, and a highly valued and popular medicinal plant. Evidence for the medicinal and health benefits of Panax ginseng and its components in preventing neurodegeneration has increased significantly in the past decade. The beneficial effects of P. ginseng on neurodegenerative diseases have been attributed primarily to the antioxidative and immunomodulatory activities of its ginsenoside components. Mechanistic studies on the neuroprotective effects of ginsenosides revealed that they act not only as antioxidants but also as modulators of intracellular neuronal signaling and metabolism, cell survival/death genes, and mitochondrial function. The goal of the present paper is to provide a brief review of recent knowledge and developments concerning the beneficial effects as well as the mechanism of action of P. ginseng and its components in the treatment and prevention of neurodegenerative diseases. Keywords: antioxidant, ginsenosides, neurodegenerative diseases, Panax ginseng

Obesity is a low-grade systemic inflammatory disease involving adipocytokines. As though Anmyungambi decoction (AMGB) showed significant improvement on obesity in a clinical trial, the molecular mechanism of AMGB in obesity remains unknown. Therefore, we explored the potential mechanisms of action of AMGB on obesity through network pharmacological approaches. We revealed that targets of AMGB are significantly associated with obesity-related and adipocyte-elevated genes. Evodiamine, berberine, genipin, palmitic acid, genistein, and quercetin were shown to regulate adipocytokine signaling pathway proteins which mainly involved tumor necrosis factor receptor 1, leptin receptor. In terms of the regulatory pathway of lipolysis in adipocytes, norephedrine, pseudoephedrine, quercetin, and limonin were shown to affect adrenergic receptor-beta, protein kinase A, etc. We also found that AMGB has the potentials to enhance the insulin signaling pathway thereby preventing type II diabetes mellitus. Additionally, AMGB was discovered to be able to control not only insulin-related proteins but also inflammatory mediators and apoptotic regulators and caspases, hence reducing hepatocyte injury in nonalcoholic fatty liver disease. Our findings help develop a better understanding of how AMGB controls obesity.

Anemarrhenae rhizome and Phellodendri cortex have historically been used for the treatment of precocious puberty (PP) in oriental medicine. Our study aimed to evaluate the effect of APE, a mixture of the extracts from these herbs, against danazol-induced PP in female rats. The offspring were injected danazol to establish the PP model, and then treated with APE daily, and observed for vaginal opening. At the end of the study, the levels of gonadotropic hormones, such as estradiol, follicle-stimulating hormone, and luteinizing hormone, were determined by ELISA. Moreover, the mRNA expression of GnRH, netrin-1, and UNC5C in hypothalamic tissues was determined by real-time PCR. Network pharmacological analysis was performed to predict the active compounds of APE and their potential actions. APE treatment delayed vaginal opening in rats with PP. In addition, APE treatment reduced LH levels and suppressed UNC5C expression. Gene set enrichment analysis revealed that the targets of APE were significantly associated with GnRH signaling and ovarian steroidogenesis pathways. In conclusion, APE may be used as a therapeutic remedy to inhibit the activation of the hypothalamic–pituitary–gonadal axis.

Panax ginseng has been used since ancient times based on the traditional Asian medicine theory and clinical experiences, and currently, is one of the most popular herbs in the world. To date, most of the studies concerning P. ginseng have focused on specific mechanisms of action of individual constituents. However, in spite of many studies on the molecular mechanisms of P. ginseng, it still remains unclear how multiple active ingredients of P. ginseng interact with multiple targets simultaneously, giving the multidimensional effects on various conditions and diseases. In order to decipher the systems-level mechanism of multiple ingredients of P. ginseng, a novel approach is needed beyond conventional reductive analysis. We aim to review the systems-level mechanism of P. ginseng by adopting novel analytical framework–network pharmacology. Here, we constructed a compound-target network of P. ginseng using experimentally validated and machine learning-based prediction results. The targets of the network were analyzed in terms of related biological process, pathways, and diseases. The majority of targets were found to be related with primary metabolic process, signal transduction, nitrogen compound metabolic process, blood circulation, immune system process, cell-cell signaling, biosynthetic process, and neurological system process. In pathway enrichment analysis of targets, mainly the terms related with neural activity showed significant enrichment and formed a cluster. Finally, relative degrees analysis for the target-disease association of P. ginseng revealed several categories of related diseases, including respiratory, psychiatric, and cardiovascular diseases.

The primary somatosensory (S1) cortex plays an important role in the perception and discrimination of touch and pain mechanosensations. Conventionally, neurons in the somatosensory system including S1 cortex have been classified into low/high threshold (HT; non-nociceptive/nociceptive) or wide dynamic range (WDR; convergent) neurons by their electrophysiological responses to innocuous brush-stroke and noxious forceps-pinch stimuli. Besides this “noxiousness” (innocuous/noxious) feature, each stimulus also includes other stimulus features: “texture” (brush hairs/forceps-steel arm), “dynamics” (dynamic stroke/static press) and “intensity” (weak/strong). However, it remains unknown how S1 neurons inclusively process such diverse features of brushing and pinch at the single-cell and population levels. Using in vivo two-photon Ca2+ imaging in the layer 2/3 neurons of the mouse S1 cortex, we identified clearly separated response patterns of the S1 neural population with distinct tuning properties of individual cells to texture, dynamics and noxiousness features of cutaneous mechanical stimuli. Among cells other than broadly tuned neurons, the majority of the cells showed a highly selective response to the difference in texture, but low selectivity to the difference in dynamics or noxiousness. Between the two low selectivity features, the difference in dynamics was slightly more specific, yet both could be decoded using the response patterns of neural populations. In addition, more neurons are recruited and stronger Ca2+ responses are evoked as the intensity of forceps-pinch is gradually increased. Our results suggest that S1 neurons encode various features of mechanosensations with feature-dependent differential selectivity of single cells and distributed response patterns of populations. Moreover, we raise a caution about describing neurons by a single stimulus feature ignoring other aspects of the sensory stimuli.

Glutamate toxicity has been implicated in neuronal cell death in both acute CNS injury and in chronic diseases. In our search for neuroprotective agents obtained from natural sources that inhibit glutamate toxicity, an endophytic fungus, Fusarium solani JS-0169 isolated from the leaves of Morus alba, was found to show potent inhibitory activity. Chemical investigation of the cultures of the fungus JS-0169 afforded isolation of six compounds, including one new γ-pyrone (1), a known γ-pyrone, fusarester D (2), and four known naphthoquinones: karuquinone B (3), javanicin (4), solaniol (5), and fusarubin (6). To identify the protective effects of the isolated compounds (1−6), we assessed their inhibitory effect against glutamate-induced cytotoxicity in HT22 cells. Among the isolates, compound 6 showed significant neuroprotective activity on glutamate-mediated HT22 cell death. In addition, the informatics approach using in silico systems pharmacology identified that compound 6 may exert its neuroprotective effect by controlling the amount of ubiquinone. The results suggest that the metabolites produced by the endophyte Fusarium solani JS-0169 might be related to the neuroprotective activity of its host plant, M. alba.

This study was conducted to evaluate the biological activities of Pueraria lobata (PL) on menopause-related metabolic diseases and to explore the underlying mechanism of PL by network pharmacological analyses. We used ovariectomized (OVX) rats as a postmenopausal model and administered PL at different doses (50, 100, and 200 mg/kg). In OVX rats, decreased uterine weights and PPAR-γ (peroxisome proliferator-activated receptor-gamma) mRNA expression in the thigh muscle were significantly recovered after PL administration. PL also significantly alleviated OVX-induced increases in total cholesterol, triglyceride, alanine aminotransferase (ALT/GPT), and aspartate aminotransferase (AST/GOT) levels. To identify the systems-level mechanism of PL, we performed network pharmacological analyses by predicting the targets of the potential bioactive compounds and their associated pathways. We identified 61 targets from four potential active compounds of PL: formononetin, beta-sitosterol, 3’-methoxydaidzein, and daidzein-4,7-diglucoside. Pathway enrichment analysis revealed that among female sex hormone-related pathways, the estrogen signaling pathways, progesterone-mediated oocyte maturation, oxytocin signaling pathways, and prolactin signaling pathways were associated with multiple targets of PL. In conclusion, we found that PL improved various indicators associated with lipid metabolism in the postmenopausal animal model, and we also identified that its therapeutic effects are exerted via multiple female sex hormone-related pathways.

Cordyceps militaris is a well-known medicinal mushroom. It is non-toxic and has clinical health benefits including cancer inhibition. However, the anticancer effects of C. militaris cultured in brown rice on breast cancer have not yet been reported. In this study, we simultaneously investigated the anticancer effects of cordycepin and an extract of C. militaris cultured in brown rice on MCF-7 human breast cancer cells using a cell viability assay, cell staining with Hoechst 33342, and an image-based cytometric assay. The C. militaris concentrate exhibited significant MCF-7 cell inhibitory effects, and its IC50 value was 73.48 µg/mL. Cordycepin also exhibited significant MCF-7 cell inhibitory effects, and its IC50 value was 9.58 µM. We applied network pharmacological analysis to predict potential targets and pathways of cordycepin. The gene set enrichment analysis showed that the targets of cordycepin are mainly associated with the hedgehog signaling, apoptosis, p53 signaling, and estrogen signaling pathways. We further verified the predicted targets related to the apoptosis pathway using western blot analysis. The C. militaris concentrate and cordycepin exhibited the ability to induce apoptotic cell death by increasing the cleavage of caspase-7 -8, and -9, increasing the Bcl-2-associated X protein/ B-cell lymphoma 2 (Bax/Bcl-2) protein expression ratio, and decreasing the protein expression of X-linked inhibitor of apoptosis protein (XIAP) in MCF-7 cells. Consequently, the C. militaris concentrate and cordycepin exhibited significant anticancer effects through their ability to induce apoptosis in breast cancer cells.

Natural products, including traditional herbal medicine (THM), are known to exert their therapeutic effects by acting on multiple targets, so researchers have employed network pharmacology methods to decipher the potential mechanisms of THM. To conduct THM-network pharmacology (THM-NP) studies, researchers have employed different tools and databases for constructing and analyzing herb−compound−target networks. In this study, we attempted to capture the methodological trends in THM-NP research. We identified the tools and databases employed to conduct THM-NP studies and visualized their combinatorial patterns. We also constructed co-author and affiliation networks to further understand how the methodologies are employed among researchers. The results showed that the number of THM-NP studies and employed databases/tools have been dramatically increased in the last decade, and there are characteristic patterns in combining methods of each analysis step in THM-NP studies. Overall, the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) was the most frequently employed network pharmacology database in THM-NP studies. Among the processes involved in THM-NP research, the methodology for constructing a compound−target network has shown the greatest change over time. In summary, our analysis describes comprehensive methodological trends and current ideas in research design for network pharmacology researchers.

Chronic exposure to cisplatin, a potent anticancer drug, causes irreversible kidney damage. In this study, we investigated the protective effect and mechanism of nine lupane- and ceanothane-type triterpenoids isolated from jujube (Ziziphus jujuba Mill., Rhamnaceae) on cisplatin-induced damage to kidney epithelial LLC-PK1 cells via mitogen-activated protein kinase (MAPK) and apoptosis pathways. Cisplatin-induced LLC-PK1 cell death was most significantly reduced following treatment with 3-dehydroxyceanothetric acid 2-methyl ester (3DC2ME). Additionally, apoptotic cell death was significantly reduced. Expression of c-Jun N-terminal kinase (JNK), extracellular signal-regulated kinase (ERK), and p38 was markedly suppressed by 3DC2ME, indicating inhibition of the MAPK pathway. Treatment with 3DC2ME also significantly reduced expression of active caspase-8 and -3, Bcl-2-associated X protein (Bax), and B cell lymphoma 2 (Bcl-2), indicating the inhibition of apoptosis pathways in the kidneys. We also applied the network pharmacological analysis and identified multiple targets of 3DC2ME related to MAPK signaling pathway and apoptosis.

Platycodon grandiflorum (PG) is widely used in Asia for its various beneficial effects. Although many studies were conducted to understand the molecular mechanisms of PG, it is still unclear how the combinations of multiple ingredients work together to exert its therapeutic effects. The aim of the present study was to provide a comprehensive review of the systems-level mechanisms of PG by adopting network pharmacological analysis. We constructed a compound⁻target⁻disease network for PG using experimentally validated and machine-leaning-based prediction results. Each target of the network was analyzed based on previously known pharmacological activities of PG. Gene ontology analysis revealed that the majority of targets were related to cellular and metabolic processes, responses to stimuli, and biological regulation. In pathway enrichment analyses of targets, the terms related to cancer showed the most significant enrichment and formed distinct clusters. Degree matrix analysis for target⁻disease associations of PG suggested the therapeutic potential of PG in various cancers including hepatocellular carcinoma, gastric cancer, prostate cancer, small-cell lung cancer, and renal cell carcinoma. We expect that network pharmacological approaches will provide an understanding of the systems-level mechanisms of medicinal herbs and further develop their therapeutic potentials.