Dataset Information

Integrating pharmacokinetics, tissue distribution, RNA sequencing, and network pharmacology identifies STAT1 and SERPINE1 as potential targets of moscatilin in treating liver diseases

ABSTRACT: This study established a quantitative analysis method for moscatilin using ultrahigh-performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry (UHPLC-Q-TOF/MS) to investigate its pharmacokinetics and tissue distribution in Kunming mice. Additionally, RNA sequencing (RNA-seq) and network pharmacology were employed to elucidate the molecular mechanisms underlying moscatilin's therapeutic potential in liver diseases, including non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH), viral hepatitis, liver injury, liver fibrosis, liver cirrhosis, and liver cancer. The analytical method was validated for selectivity, carryover, linearity, precision, accuracy, dilution integrity, matrix effect, recovery, and stability following US Food and Drug Administration (FDA) and the European Medicines Agency (EMA) bioanalytical guidelines. Pharmacokinetic analysis revealed rapid absorption of moscatilin in vivo, with a bioavailability of 85.82%. Tissue distribution studies indicated widespread presence across various organs, with elevated concentrations in the lungs and liver. RNA-seq and network pharmacology analysis identified signal transducer and activator of transcription 1 (STAT1) and serine protease inhibitor clade E member 1 (SERPINE1) as potential targets for moscatilin in liver disease treatment.

ORGANISM(S): Mus musculus

PROVIDER: GSE312822 | GEO | 2025/12/12

REPOSITORIES: GEO

ACCESS DATA

Dataset's files

Source:

			Action	DRS
		Other
	GSE312822_processed_data_file.txt.gz	Txt

Items per page:

1 - 2 of 2

Similar Datasets

Project description:Mardinoglu2014 - Genome-scale metabolic model (HMR version 2.0) - human hepatocytes (iHepatocytes2322) This model is described in the article: Genome-scale metabolic modelling of hepatocytes reveals serine deficiency in patients with non-alcoholic fatty liver disease. Mardinoglu A, Agren R, Kampf C, Asplund A, Uhlen M, Nielsen J. Nat Commun 2014; 5: 3083 Abstract: Several liver disorders result from perturbations in the metabolism of hepatocytes, and their underlying mechanisms can be outlined through the use of genome-scale metabolic models (GEMs). Here we reconstruct a consensus GEM for hepatocytes, which we call iHepatocytes2322, that extends previous models by including an extensive description of lipid metabolism. We build iHepatocytes2322 using Human Metabolic Reaction 2.0 database and proteomics data in Human Protein Atlas, which experimentally validates the incorporated reactions. The reconstruction process enables improved annotation of the proteomics data using the network centric view of iHepatocytes2322. We then use iHepatocytes2322 to analyse transcriptomics data obtained from patients with non-alcoholic fatty liver disease. We show that blood concentrations of chondroitin and heparan sulphates are suitable for diagnosing non-alcoholic steatohepatitis and for the staging of non-alcoholic fatty liver disease. Furthermore, we observe serine deficiency in patients with NASH and identify PSPH, SHMT1 and BCAT1 as potential therapeutic targets for the treatment of non-alcoholic steatohepatitis. This model is hosted on BioModels Database and identified by: MODEL1402200003. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Dataset Information

Integrating pharmacokinetics, tissue distribution, RNA sequencing, and network pharmacology identifies STAT1 and SERPINE1 as potential targets of moscatilin in treating liver diseases

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets