Downregulation in Infectious Pneumonia: A Multi-Modal Study Combining Bioinformatics, Animal Models, and Reverse Pharmacology.

Abstract

: Infectious pneumonia remains a major global health challenge with high morbidity and mortality, especially among vulnerable groups. Current diagnostic approaches lack sufficient specificity and accuracy. This study aimed to identify core diagnostic genes, explore their biological functions, and predict potential natural compounds targeting these genes to improve diagnostic and therapeutic strategies.: Gene expression profiles from the GEO database (GSE103119) were analyzed to identify differentially expressed genes (DEGs). Hub genes were selected by integrating protein-protein interaction (PPI) networks and multiple machine learning algorithms. Expression patterns of the identified hub gene were validated in a murine pneumonia model. Reverse network pharmacology was applied to screen natural compounds, followed by molecular docking and molecular dynamics simulations to evaluate binding affinity and complex stability.: A total of 2550 DEGs were identified.was consistently determined as a hub gene through PPI and machine learning analyses, showing significant downregulation in infectious pneumonia patients. Animal experiments confirmed pronounced reduction oftranscription in both lung tissue and whole blood of pneumonia model mice. Two natural compounds, pyrogallol and tectorigenin, were identified as potential ligands for. Molecular simulations confirmed stable binding with the target protein, with tectorigenin exhibiting superior binding affinity.: This study proposesas a promising diagnostic biomarker for infectious pneumonia and suggests tectorigenin as a candidate compound for further therapeutic development.