Identification and validation of key target genes of penning formula for treating rat chronic endometritis model

• Published in: Scientific reports Vol. 15; no. 1; pp. 22358 - 14
• Main Authors: Zhihui, Liu, Jiani, Shi, Chen, Chen, Yuqiong, Yuan, Guorun, Chen, Qianru, Zhou
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.07.2025; Nature Portfolio
• Subjects: 631/154/555; 692/308/1426; Active ingredient; Animals; Chronic Disease; Chronic endometritis; Disease Models, Animal; Drugs, Chinese Herbal - pharmacology; Drugs, Chinese Herbal - therapeutic use; Endometritis - drug therapy; Endometritis - genetics; Endometritis - metabolism; Endometritis - pathology; Female; Gene Expression Profiling; Gene Expression Regulation - drug effects; Gene Regulatory Networks - drug effects; Humanities and Social Sciences; Key target gene; MicroRNAs - genetics; Molecular Docking Simulation; multidisciplinary; Penning formula; Rats; Rats, Sprague-Dawley; Regulatory network; Science; Science (multidisciplinary); Transcriptome; Chronic endometritis; Active ingredient; Penning formula; Key target gene; Regulatory network
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-025-02585-9

This study analyzed transcriptome sequencing data from rat samples, categorized into control (normal), model (chronic endometritis, CE), and test (treated with Penning Formula, PNF) groups, to identify key target genes of PNF in CE treatment and elucidate the specific mechanism of PNF treatment for CE. Differentially expressed genes (DEGs) between the model-control (DEGs1) and model-test (DEGs2) groups were compared, revealing 47 PNF-related genes (PRGs), primarily involved in stress response and prolactin signaling. These PRGs were cross-referenced with 909 target genes (TGs) of 25 active ingredients of PNF obtained from public databases, resulting in the identification of three key target genes: Bhmt, Scn10a, and Esr2. Further analysis, including functional pathway enrichment, chromosomal mapping, expression profiling, gene interactions, regulatory networks, and molecular docking, highlighted their involvement in stress response and prolactin signaling, with distinct chromosomal distributions and differential expression across groups. Additionally, miRNA predictions, such as rno-miR-29a-3p, were made, and significant binding interactions between key genes and active ingredients were observed. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) validation confirmed a marked down-regulation of Bhmt, Scn10a, and Esr2 in the treatment group, corroborating the therapeutic effect of PNF on CE. This study unveils novel targets and offers valuable insights into the mechanisms underlying PNF’s therapeutic action for CE.