We computationally analyze network toxicology and transcriptomic data to identify biomarkers of acetaminophen-induced liver injury, revealing estrogen receptor 1 and purine nucleoside phosphorylase as diagnostic targets.
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Research Article
We computationally analyze network toxicology and transcriptomic data to identify biomarkers of acetaminophen-induced liver injury, revealing estrogen receptor 1 and purine nucleoside phosphorylase as diagnostic targets.
Acetaminophen-induced liver injury is a significant public health concern, yet reliable early biomarkers are lacking. This study aimed to identify candidate biomarkers for acetaminophen-induced hepatotoxicity using a computational approach integrating network toxicology, transcriptomics, and machine learning. Potential acetaminophen targets were predicted using online platforms, yielding 140 candidates. Hepatotoxicity-related genes (n = 657) were retrieved from GeneCards, and 38 overlapping genes were identified. Differentially expressed genes from the GSE74000 dataset (n = 1,978) were analyzed. Functional enrichment was performed to identify relevant pathways. A random forest model prioritized 20 feature genes, and molecular docking evaluated binding affinities with acetaminophen. DEGs were primarily associated with mitochondrial dysfunction and ribosome biogenesis. Functional enrichment highlighted xenobiotic metabolism and oxidative stress pathways. Estrogen receptor 1 and purine nucleoside phosphorylase were top-ranked feature genes, showing significant expression differences and strong docking interactions with acetaminophen. This computational protocol systematically predicts candidate biomarkers for acetaminophen-induced liver injury, providing molecular insights and candidates for experimental validation.
Acetaminophen (APAP) is a widely administered antipyretic and pain reliever, but excessive intake can lead to severe hepatotoxicity and potentially liver failure1,2. The damage to the liver is anticipated to occur once the drug is metabolized, and the subsequent toxic metabolite is N-acetyl-p-benzoquinone imine (NAPQI), which results in mitochondrial oxidative stress, altered mitochondrial respiration, and the mitochondrial permeability transition, eventually causing necrotic, rather than apoptotic, hepatocyte death3,4. APAP overdose remains the leadin....
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This protocol outlines a computational method to define the possible biomarkers of acetaminophen-induced liver damage, which takes advantage of network toxicology, transcriptomics, machine learning, and molecular docking (Figure 1). The protocol is aimed at researchers who have access to bioinformatics tools, transcriptomic datasets, and molecular docking software.
Procedure
Step 1: Identification of acetaminophen targets
Retrieve the SMILES representation of acetaminophen (APAP) from PubChem. Use online platforms (ChEMBL, SwissTargetPrediction, ....
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Identification of overlapping genes for APAP and hepatotoxicity and their functional analysis
To determine potential molecular targets of APAP and investigate their biological roles, predictions were generated using four computational tools: ChEMBL, SwissTargetPrediction, STITCH, and SEA. Following the removal of duplicate entries and integration of overlapping results, a non-redundant list of 140 candidate targets was compiled. Additionally, 657 hepatotoxicity-related genes were extracted from the G.......
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APAP is a frequently prescribed analgesic and antipyretic agent; however, excessive intake can result in significant hepatotoxicity, sometimes culminating in acute liver failure38. Despite advances in elucidating the pathophysiological mechanisms of APAP-induced liver injury, there remains a scarcity of reliable biomarkers for accurate diagnosis, prognosis, and mechanistic investigation. Thus, there is an urgent need to identify potential novel biomarkers and approaches to further investigate APAP.......
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The authors have nothing to disclose.
The authors thank Tianjin University of Traditional Chinese Medicine for providing institutional support. We extend our gratitude to the developers of public databases (GeneCards, PubChem, STRING) and open-source tools that enabled this research. Special thanks to colleagues from the School of Chinese Materia Medica and College of Integrative Chinese and Western Medicine for their valuable discussions and technical assistance. We also acknowledge the contributors of the GSE74000 dataset for making their data publicly available.
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| Name | Company | Catalog Number | Comments |
|---|---|---|---|
| Databases & Web Servers | |||
| STRING | https://cn.string-db.org/ | PPI network construction; Functional enrichment | |
| Sendo Academic Tools | https://www.xiantaozi.com/ | GO, KEGG, and GSEA enrichment analysis | |
| PubChem | https://pubchem.ncbi.nlm.nih.gov/ | Retrieval of chemical structures (APAP) | |
| GeneCards | https://www.genecards.org/ | Retrieval of hepatotoxicity-related genes | |
| GEO Database | https://www.ncbi.nlm.nih.gov/geo/ | Transcriptomic dataset retrieval (GSE74000) | |
| ChEMBL | https://www.ebi.ac.uk/chembl/ | Target prediction | |
| SwissTargetPrediction | http://www.swisstargetprediction.ch/ | Target prediction | |
| STITCH | http://stitch.embl.de/ | Interaction prediction | |
| SEA | http://sea.bkslab.org/ | Similarity ensemble approach for target prediction | |
| Protein Data Bank (PDB) | https://www.rcsb.org/ | Protein structure retrieval (ESR1, PNP) | |
| Software | |||
| R (Version 4.x.x) | https://www.r-project.org/ | Statistical analysis and data processing | |
| AutoDock Tools / Vina | http://autodock.scripps.edu/ | Molecular docking preparation and simulation | |
| PyMOL | https://pymol.org/ | Visualization of molecular structures | |
| Cytoscape | https://cytoscape.org/ | Network visualization |
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