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Abstract Samul-tang (SM) is a traditional multi-ingredient herbal formula that is widely used in clinical practice for treating female infertility. Despite its known therapeutic benefits, the complexity of its action mechanisms owing to the combination of multiple compounds has limited its research and integration into modern pharmacological science. To address this challenge, we identified 38 herbal compounds as the major ingredients in SM and generated their transcriptome data from aged-mice ovaries by administering these individual compounds. This dataset provides a resource for investigating the action mechanisms of SM and assist in identifying effective combinations of ingredients that can reproduce the therapeutic effects of SM.

Abstract Traditional herbal medicine, rooted in a long history of use in East Asia, combines several herbs to create treatments showing high efficacy with minimal side effects, for specific diseases. Such combination therapies represent a potential reservoir of new drugs for treating multifactorial and incurable chronic diseases. However, the complexity of their mechanisms of action due to the combination of multiple compounds, has limited their research integration into modern pharmacological science. To address this challenge, we constructed drug-induced transcriptome data for herbal medicines through systematic experiments, analyzed with the aid of various omics databases. We introduce KORE-Map 1.0 (Korean medicine Omics Resource Extension Map), the first comprehensive resource of drug-derived transcriptome data for representative tonifying herbal medicines, effective in enhancing the immune system. This dataset aims to provide novel insights into the combinatorial mechanisms of these herbal medicines and to aid in the discovery of new therapeutic targets and indications for various incurable diseases.

Korean diesel particulate matter 20 (KDP20) is a pollutant comprising a complex mixture of carbon and chemical irritants. Although particulate matter and nasal inflammation are strongly associated, the underlying molecular mechanism based on systematic transcriptome analysis remains unknown. In this study, genome-wide gene expression profiles of mouse nasal tissues were determined following exposure to KDP20 for 5 and 10 days and compared with those of the control (n = 4/group). We identified 758 significant differentially expressed genes (DEGs) and classified them as 5-day-specific, 10-day-specific, and common among groups based on their expression patterns. The terms “regulation of alpha-beta T cell differentiation,” “macrophage differentiation,” and “cell adhesion mediated by integrin” were significantly enriched in each group. Receiver operating characteristic analysis revealed six genes as potential predictive biomarkers. The differential expression of these six genes was validated using quantitative RT-PCR (n = 3/group). Furthermore, a possible mechanism for nasal inflammation was suggested through the binding analysis between metal ions and genes. The genes identified in this study may play important roles in regulating the mechanism of nasal inflammation induced by diesel particles, especially immune cell regulation, and may function as markers for diesel particle-induced nasal inflammation.

Pharmacogenomic analysis based on drug transcriptomic signatures is widely used to identify mechanisms of action and pharmacological indications. Despite accumulating reports on the efficacy of medicinal herbs, related transcriptome-level analyses are lacking. The aim of the present study was to elucidate the underlying molecular mechanisms of action of Bupleuri Radix (BR), a widely used herbal medicine, through a systematic transcriptomic analysis. We analyzed the drug-responsive transcriptome profiling of A549 lung cancer cell line after treating them with multiple doses of BR water (W-BR) and ethanol (E-BR) extracts and their phytochemicals. In vitro validation experiments were performed using both A549 and the immortalized human keratinocyte line HaCaT. Pathway enrichment analysis revealed the anti-cancer effects of BR treatment via inhibition of cell proliferation and induction of apoptosis. Enhanced cell adhesion and migration were observed with the W-BR but not with the E-BR. Comparison with a disease signature database validated an indication of the W-BR for skin disorders. Moreover, W-BR treatment showed the wound-healing effect in skin and lung cells. The main active ingredients of BR showed only the anti-cancer effect of the E-BR and not the wound healing effect of the W-BR, suggesting the need for research on minor ingredients of BR.

Atopic dermatitis (AD) is chronic allergic contact dermatitis with immune dysregulation. Veronica persica has pharmacological activity that prevents asthmatic inflammation by ameliorating inflammatory cell activation. However, the potential effects of the ethanol extract of V. persica (EEVP) on AD remain elusive. This study evaluated the activity and underlying molecular pathway of EEVP in two AD models: dinitrochlorobenzene (DNCB)-induced mice and interferon (IFN)-γ/tumor necrosis factor (TNF)-α-stimulated human HaCaT keratinocytes. EEVP attenuated the DNCB-induced increase in serum immunoglobulin E and histamine levels, mast cell counts in toluidine-blue-stained dorsal skin, inflammatory cytokine (IFN-γ, interleukin [IL]-4, IL-5, and IL-13) levels in cultured splenocytes, and the mRNA expression of IL6, IL13, IL31 receptor, CCR-3, and TNFα in dorsal tissue. Additionally, EEVP inhibited the IFN-γ/TNF-α-induced mRNA expression of IL6, IL13, and CXCL10 in HaCaT cells. Furthermore, EEVP restored the IFN-γ/TNF-α-induced downregulation of heme oxygenase (HO)-1 in HaCaT cells by inducing nuclear factor erythroid 2-related factor 2 (Nrf2) expression. A molecular docking analysis demonstrated that EEVP components have a strong affinity to the Kelch-like ECH-associated protein 1 Kelch domain. In summary, EEVP inhibits inflammatory AD by attenuating immune cell activation and inducing the Nrf2/HO-1 signaling pathway in skin keratinocytes.

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.

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.

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.