Eun-Hye Seo

Jakyakgamcho-tang (JGT), the simplest form of herbal medicine, comprises Paeoniae Radix (PR) and Glycyrrhizae Radix et Rhizoma (GR). It has been used to treat muscle-related diseases and inflammation. However, its pharmacological effects may vary with the proportions of ingredients and preparatory factors such as the extraction method. Nevertheless, gene expression datasets systematically reflecting these variables are lacking. A total of 513 transcriptome profiles were created with three concentrations and three replicates of RNA-seq data. This dataset structure will enable multidimensional analysis of the effects of various JGT preparation factors on gene expression; these factors include the PR to GR proportional ratio (2:1, 1:1, and 1:2), solvent (water or 70% ethanol), and extraction method (combined or individual extraction method). The HepG2, C2C12, and PC12 cell lines were targeted. All raw and preprocessed data are available through GEO. Standardized metadata and ingredient data are also provided. This dataset provides a foundation for exploring traditional herbal formulations effects on cellular transcriptomic responses and can facilitate the scientific optimization of herbal medicines.

Herbal medicines have been used for centuries to manage various disease; however, their therapeutic mechanisms remain complex and insufficiently understood

Herbs, such as Aconitum carmichaeli Debeaux (ACD), have long been used as therapies, but it is difficult to identify which organs of the human body are affected by the various compounds. In this study, we predicted the organ where the drug predominantly acts using bioinformatics and verified it using transcriptomics. We constructed a computer-aided brain system network (BSN) and intestinal system network (ISN). We predicted the action points of ACD using network pharmacology (NP) analysis and predicted the dockable proteins acting in the BSN and ISN using statistical-based docking analysis. The predicted results were verified using ACD-induced transcriptome analysis. The predicted results showed that both the NP and docking analyses predominantly acted on the BSN and showed better hit rates in the hub nodes. In addition, we confirmed through verification experiments that the SW1783 cell line had more than 10 times more differentially expressed genes than the HT29 cell line and that the dominant acting organ is the brain, using network dimension spanning analysis. In conclusion, we found that ACD preferentially acts in the brain rather than in the intestine, and this multi-bioinformatics-based approach is expected to be used in future studies of drug efficacy and side effects.

Phosphoserine phosphatase (PSPH) is one of the key enzymes of the L-serine synthesis pathway. PSPH is reported to affect the progression and survival of several cancers in an L-serine synthesis-independent manner, but the mechanism remains elusive. We demonstrate that PSPH promotes lung cancer progression through a noncanonical L-serine-independent pathway. PSPH was significantly associated with the prognosis of lung cancer patients and regulated the invasion and colony formation of lung cancer cells. Interestingly, L-serine had no effect on the altered invasion and colony formation by PSPH. Upon measuring the phosphatase activity of PSPH on a serine-phosphorylated peptide, we found that PSPH dephosphorylated phospho-serine in peptide sequences. To identify the target proteins of PSPH, we analyzed the protein phosphorylation profile and the PSPH-interacting protein profile using proteomic analyses and found one putative target protein, IRS-1. Immunoprecipitation and immunoblot assays validated a specific interaction between PSPH and IRS-1 and the dephosphorylation of phospho-IRS-1 by PSPH in lung cancer cells. We suggest that the specific interaction and dephosphorylation activity of PSPH have novel therapeutic potential for lung cancer treatment, while the metabolic activity of PSPH, as a therapeutic target, is controversial.