RESUMEN

BACKGROUND: Nonalcoholic Steatohepatitis (NASH) results from complex liver conditions involving metabolic, inflammatory, and fibrogenic processes. Despite its burden, there has been a lack of any approved food-and-drug administration therapy up till now. PURPOSE: Utilizing machine learning (ML) algorithms, the study aims to identify reliable potential genes to accurately predict the treatment response in the NASH animal model using biochemical and molecular markers retrieved using bioinformatics techniques. METHODS: The NASH-induced rat models were administered various microbiome-targeted therapies and herbal drugs for 12 weeks, these drugs resulted in reducing hepatic lipid accumulation, liver inflammation, and histopathological changes. The ML model was trained and tested based on the Histopathological NASH score (HPS); while (0-4) HPS considered Improved NASH and (5-8) considered non-improved, confirmed through rats' liver histopathological examination, incorporates 34 features comprising 20 molecular markers (mRNAs-microRNAs-Long non-coding-RNAs) and 14 biochemical markers that are highly enriched in NASH pathogenesis. Six different ML models were used in the proposed model for the prediction of NASH improvement, with Gradient Boosting demonstrating the highest accuracy of 98% in predicting NASH drug response. FINDINGS: Following a gradual reduction in features, the outcomes demonstrated superior performance when employing the Random Forest classifier, yielding an accuracy of 98.4%. The principal selected molecular features included YAP1, LATS1, NF2, SRD5A3-AS1, FOXA2, TEAD2, miR-650, MMP14, ITGB1, and miR-6881-5P, while the biochemical markers comprised triglycerides (TG), ALT, ALP, total bilirubin (T. Bilirubin), alpha-fetoprotein (AFP), and low-density lipoprotein cholesterol (LDL-C). CONCLUSION: This study introduced an ML model incorporating 16 noninvasive features, including molecular and biochemical signatures, which achieved high performance and accuracy in detecting NASH improvement. This model could potentially be used as diagnostic tools and to identify target therapies.

Asunto(s)

Modelos Animales de Enfermedad , Aprendizaje Automático , Enfermedad del Hígado Graso no Alcohólico , Animales , Enfermedad del Hígado Graso no Alcohólico/tratamiento farmacológico , Enfermedad del Hígado Graso no Alcohólico/genética , Enfermedad del Hígado Graso no Alcohólico/patología , Ratas , Hígado/patología , Hígado/metabolismo , Hígado/efectos de los fármacos , Masculino , Proteínas Señalizadoras YAP/genética , Biomarcadores/sangre , MicroARNs/genética

RESUMEN

BACKGROUND: Nonalcoholic fatty pancreatitis (NAFP) presents a pressing challenge within the domain of metabolic disorders, necessitating further exploration to unveil its molecular intricacies and discover effective treatments. Our focus was to delve into the potential therapeutic impact of ZBiotic, a specially engineered strain of probiotic B. subtilis, in managing NAFP by targeting specific genes linked with necroptosis and the TNF signaling pathway, including TNF, ZBP1, HSPA1B, and MAPK3, along with their upstream epigenetic regulator, miR-5192, identified through bioinformatics. METHODS: Rats were subjected to either a standard or high-fat, high-sucrose diet (HFHS) for eight weeks. Subsequently, they were divided into groups: NAFP model, and two additional groups receiving daily doses of ZBiotic (0.5 ml and 1 ml/kg), and the original B. subtilis strain group (1 ml/kg) for four weeks, alongside the HFHS diet. RESULTS: ZBiotic exhibited remarkable efficacy in modulating gene expression, leading to the downregulation of miR-5192 and its target mRNAs (p < 0.001). Treatment resulted in the reversal of fibrosis, inflammation, and insulin resistance, evidenced by reductions in body weight, serum amylase, and lipase levels (p < 0.001), and decreased percentages of Caspase and Nuclear Factor Kappa-positive cells in pancreatic sections (p < 0.01). Notably, high-dose ZBiotic displayed superior efficacy compared to the original B. subtilis strain, highlighting its potential in mitigating NAFP progression by regulating pivotal pancreatic genes. CONCLUSION: ZBiotic holds promise in curbing NAFP advancement, curbing fibrosis and inflammation while alleviating metabolic and pathological irregularities observed in the NAFP animal model. This impact was intricately linked to the modulation of necroptosis/TNF-mediated pathway-related signatures.