Dual-omics analysis of the effect and mechanism of Jiedu Huayu granule in acute liver failure

Dual-omics analysis of the effect and mechanism of Jiedu Huayu granule in acute liver failure | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Dual-omics analysis of the effect and mechanism of Jiedu Huayu granule in acute liver failure wang Na, Hao Pei, fenglan Wu, Han pei, Ri-yun Zhang, Rui qin, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8434231/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background: Acute liver failure is a severe condition with high mortality, requiring effective treatments. Jiedu Huayu Granule, a traditional Chinese medicine, shows protective effects, but its molecular mechanisms remain unclear. This study aims to investigate the therapeutic mechanisms of Jiedu Huayu Granule in acute liver failure using integrated transcriptomic and proteomic analyses. Results: Transcriptomic and proteomic profiling of rat models identified three key genes (Fth1, Necap2, and Prdx2) whose expression was upregulated in acute liver failure and subsequently downregulated by Jiedu Huayu Granule treatment. Animal and cellular experiments confirmed these expression changes. Bioinformatics analysis linked these genes to crucial pathways including ribosome function, chemokine signaling, and fatty acid metabolism. Molecular docking predicted stable binding interactions between active components of the herbal formula and the proteins encoded by these key genes. Conclusion: Jiedu Huayu Granule may exert its therapeutic effects against acute liver failure by modulating the expression of Fth1, Necap2, and Prdx2 and their associated pathways. These findings provide a molecular basis for the clinical application of Jiedu Huayu Granule and suggest novel potential targets for acute liver failure treatment. Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Biological sciences/Drug discovery Biological sciences/Molecular biology Acute liver failure Jiedu huayu granule Transcriptome sequencing Proteome sequencing Molecular docking Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 1. Introduction Acute liver failure (ALF) is a critical condition resulting from severe liver damage, progressing rapidly with a broad spectrum of symptoms, including jaundice, coagulation dysfunction, hepatorenal syndrome, hepatic encephalopathy, and ascites [ 1 ]. Epidemiological studies indicate that the etiology of ALF is multifactorial. In Europe and the United States, particularly among young and middle-aged populations, acetaminophen (APAP) overdose is the primary cause, though other factors such as viral hepatitis, drug-induced liver injury, gestational liver failure, and autoimmune hepatitis also contribute [ 2 , 3 ]. While liver transplantation remains the standard treatment for ALF, it carries a significant risk of postoperative complications, with a mortality rate of 33% [ 4 ]. Research into ALF caused by APAP overdose has revealed potential treatments, including 4-methylpyrazole, carmengadipil, dimethylbiguanide, and methylene blue; however, N-acetylcysteine remains the only approved clinical intervention [ 5 ]. Thus, early diagnosis and prompt treatment are critical to improving patient outcomes. Jiedu Huayu Granule (JDHYG) is a traditional Chinese medicine formulation composed of six herbs: Artemisia capillaris , Herba Hedyotidis Diffusae , Radix Paeoniae Rubra , Rheum officinale , Radix Curcumae , and Rhizoma Acori Tatarinowii . JDHYG has been used clinically for years, and both in vitro and in vivo studies in rats have demonstrated its ability to inhibit NF-κB-mediated inflammatory pathways, providing protective effects against lipopolysaccharide (LPS)-induced liver injury [ 6 ]. In an ALF rat model, JDHYG significantly improved liver function and survival rates, with these effects closely linked to reduced inflammation and enhanced liver cell regeneration [ 7 ]. Toxicity assessments in rats administered varying doses of JDHYG revealed no damage to vital organs such as the liver, lungs, or kidneys, with histopathological analysis confirming the preservation of their structural integrity [ 8 ]. Despite these promising findings, the precise molecular mechanisms underlying JDHYG’s therapeutic effects in ALF remain poorly understood. Therefore, further investigation into its molecular actions and potential therapeutic benefits is essential to establish a scientific basis for its application in liver disease treatment. Proteomics is a powerful approach for investigating protein expression, structure, function, and interactions within specific cells, tissues, body fluids, and organisms. Alterations in protein levels can serve as indicators of underlying pathological or biological processes. Thus, analyzing protein composition and abundance is essential for identifying disease biomarkers and elucidating therapeutic mechanisms [ 9 ]. Transcriptomics, on the other hand, focuses on the study of gene transcription and its regulation within cells [ 10 ]. Liu et al. identified potential therapeutic targets for various clinical types of chronic kidney disease (CKD) by integrating plasma proteome and transcriptome data, providing a solid foundation for developing targeted CKD therapies [ 11 ]. Building on this approach, the current study applies transcriptomic and proteomic analyses to explore the molecular mechanisms of JDHYG in the treatment of ALF. The integration of multi-level biological data enables comprehensive examination of gene functions, regulatory networks, and molecular interactions, offering new insights into the complex pathways that underlie biological processes and disease mechanisms. This study employed transcriptomic and proteomic sequencing data from rat samples to identify biomarkers influencing the progression of ALF under JDHYG treatment. Bioinformatics tools were used to perform detailed analyses of the biological functions, pathways, and molecular regulatory networks associated with these biomarkers, which were then validated through animal experiments and molecular docking. The findings provide robust scientific evidence for a deeper understanding of ALF pathogenesis and support the clinical application of JDHYG as a therapeutic intervention. 2. Materials and methods 2.1 Reagents JDHY, a traditional Chinese medicinal formulation, consists of six herbs: Artemisia capillaries (30 g), Herba Hedyotidis Diffusae (30 g), Radix paeoniae rubra (50 g), Rheum officinale (15 g), Radix curcumae (15 g), and Rhizoma Acori Tatarinowii (15 g)(plant name has been checked with http://mpns.kew.org ). Each 1 g of JDHY granule is equivalent to 4.7 g of raw herbs and was supplied by Jiangyin Tian Jiang Pharmaceutical Co., Ltd. (Jiangsu, China). Previous studies on this compound, analyzed by HPLC, identified paeoniflorin as a key active component [ 7 ]. The compounds D-galactosamine hydrochloride (D-GalN, G0500) and lipopolysaccharide (LPS, L2880) were obtained from Sigma-Aldrich. 2.2 Animal and cells groups This study adhered to the ethical guidelines outlined in the Institutional Animal Care and Use Committee's (IACUC) protocol for the humane treatment and use of experimental animals (DW20230830-152). After one week of standard treatment, the rats were divided into three groups: control group (c), Alf group (m) and treatment group (z). Male SD rats (270 g, 8 weeks old) were purchased from Hunan slake Jingda experimental animal Co., Ltd. (license number scxk (Xiang) 2019-0004). Specifically, rats in ALF group were intraperitoneally injected with D-GalN (550 mg/kg body weight) and LPS (20 µ g/kg body weight) to form a model at one time [ 12 ]. The dose conversion of the animal model was based on the method described in the FDA publication "estimation of the maximum safe starting dose in the initial clinical trial of therapeutic drugs in adult healthy volunteers", assuming an adult weight of 70 kg, one dose per day. The conversion formula is used to explain the difference in metabolic rate between human body size and rats, ensuring that the dose given to animals is reasonably close to the relevant dose of human body. Rats in the jdhy treatment group were orally administered with jdhy at a dose of 3 g/kg/ day for 3 consecutive days. After the last oral administration of jdhy, d-galn/lps stimulation was given according to the ALF modeling method. The control group was given equal volume of distilled water (1ml / 100g / D) by gavage.. Six rats in each experimental group were randomly selected for transcriptome sequencing (RNA SEQ), and the remaining three rats were subjected to proteome sequencing, both of which were performed using rat liver. In parallel, hepatic stellate cells (HSCs) were cultured in DMEM supplemented with 10% fetal bovine serum at 37°C and 5% CO2 to 70% confluence. Cells were then seeded into 96-well plates at a density of 2 × 10 4 cells per well, and divided into control, model, and JDHY treatment groups. ALF in the cell model was induced by incubation with D-galactose (8 mM) for 1 hour, followed by LPS (1 µg/mL) treatment. JDHY was administered at a concentration of 40 µg/mL based on LDH detection to determine the optimal intervention dose. 2.3 Identification of Chemical Components in JDHYG by UHPLC-Q-Orbitrap-MS The chemical composition of JDHYG was determined using an UltiMate 3000 RS HPLC system with an AQ-C18150 × 2.1 mm, 1.8 µm column (Welch). The chromatographic column was maintained at 35°C. Gradient elution was performed with aqueous phase A (0.1% formic acid) and organic phase B (methanol) at a flow rate of 0.30 mL/min. The gradient program used for elution is summarized in the table below. Time(min) A(%) B(%) 1 98 2 5 80 20 10 50 50 15 20 80 20 5 95 27 5 95 28 98 2 2.4 Detection of serum ALT , AST , TBIL , and plasma PT Serum levels of ALT , AST , and TBIL in rats were measured using a fully automated biochemical analyzer (Shenzhen Mindray Biomedical Electronics Co., Ltd., ES-480 model), while PT in rat plasma was assessed using a coagulation analyzer (DIAGNOSTICA STAGO, France, STA R MAX). 2.5 Hematoxylin and Eosin ( H&E ) Staining Liver tissues were preserved in 4% formalin (cat. no. G1101-25ml, Servicebio, Wuhan, China), cut into 3mm × 3mm × 3mm pieces, soaked in water for 6 hours, dried in a desiccator for 24 hours, and subsequently embedded, sectioned, stained, and imaged. Liver histopathology was examined using a light microscope (Nikon Eclipse E100, Nikon, Japan). 2.6 TUNEL Staining For TUNEL staining, liver tissue paraffin sections from SD rats were deparaffinized in xylene and rehydrated. Sections were incubated with proteinase K (G1205, Servicebio, Wuhan, China) in a 37°C incubator for 22 minutes, followed by incubation with membrane permeabilizing solution (G1204, Servicebio, Wuhan, China) for 20 minutes at room temperature. After a 10-minute incubation with buffer at room temperature, sections were treated with a TUNEL Kit (G1502, Servicebio, Wuhan, China) containing TDT enzyme, dUTP, and buffer in a 1:5:50 ratio in a 37°C incubator for 1 hour. Following DAPI (G1012, Servicebio, Wuhan, China) staining, images were captured under a fluorescence microscope (Nikon, Sendai, Ishinomaki, Japan) and analyzed using ImageJ software. 2.7 Differential expression analysis Prior to RNA-seq and proteomic data analysis, quality control was performed. For RNA-seq, raw count values were derived using FeatureCounts software (v2.0.6) [ 13 ] and transformed into fragments per kilobase million (FPKM) using the FPKM computational formula. Principal component analysis (PCA) was then performed to assess sample correlation and variance using the “prcomp” function in the “stats” package (v4.3.1) [ 14 ]. For proteome sequencing, data preprocessing was conducted using MaxQuant software (v2.1.4.0) [ 15 ], applying the TMT-9plex method for quantitative analysis. Differentially expressed genes (DEGs) or proteins (DEPs) were identified between groups (Group M vs Group Z, Group M vs Group C) using the “DESeq2” package (v1.38.0) [ 16 ] for RNA-seq and ANOVA for proteomics, with significance set at p 0.5. Volcano plots, marking the top five up- and down-regulated genes/proteins by log 2 FC, and heatmaps were generated using the “ggplot2” package (v3.3.6) [ 17 ] and the “pheatmap” package (v1.0.12) [ 18 ] for visualization. 2.8 Functional network analysis and identification of key genes The DEGs or DEPs across the three experimental groups were determined by comparing the opposite trends in DEGs1/DEPs1 and DEGs2/DEPs2 using the ggvenn package (v0.1.10) [ 19 ]. Specifically, the analysis focused on the overlap between down-regulated genes/proteins in DEGs1/DEPs1 and up-regulated genes/proteins in DEGs2/DEPs2, as well as up-regulated genes/proteins in DEGs1/DEPs1 and down-regulated genes/proteins in DEGs2/DEPs2, leading to the identification of DEGs or DEPs from both sets. To further explore the biological functions of these DEGs or DEPs, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were performed using the clusterProfiler package (v4.7.1.3) [ 20 ], with a significance threshold of p.adjust < 0.05. The results were visualized using the ggplot2 package. Protein-protein interaction (PPI) networks for DEGs or DEPs were analyzed using data from the STRING database ( https://string-db.org/ ) with a confidence score > 0.9 for DEGs and a score = 0.15 for DEPs (species set to Rattus norvegicus). The networks were visualized using Cytoscape software (v3.9.0) [ 21 ]. Additionally, the PANTHER database ( http://www.pantherdb.org ) was used to categorize DEPs based on their functional annotations. The key genes were identified as the intersection between DEGs and DEPs using the VennDiagram package (v1.7.3) [ 22 ], and their expression patterns were further analyzed using both RNA-seq and proteomic data through the DESeq2 package. 2.9 Enrichment analysis of key genes The Spearman correlation between key genes and all other genes in the RNA-seq data was calculated using the psych package (v2.2.9) [ 23 ]. Gene set enrichment analysis (GSEA) was then performed using the clusterProfiler package. The background gene set was selected from the Molecular Signatures Database (MSigDB) ( https://www.gsea-msigdb.org/gsea/msigdb/index.jsp ) as "c2.cp.kegg.v2023.1.Hs.symbols.gmt". The top 10 pathways with the highest enrichment scores (p.adjust < 0.05) were visualized using the enrichplot package (v1.18.0) [ 24 ]. 2.10 Positioning and distributions of key genes The chromosomal localization of the key genes was obtained from the Ensembl database ( https://asia.ensembl.org/index.html ), and their positions were visualized using the Circos package (v1.2.0) [ 25 ]. Tissue distribution data for the key genes were examined using the Multi-Gene Query function of the Genotype-Tissue Expression (GTEx) database (v8, https://www.gtexportal.org/home/ ). For further investigation of subcellular localization, the fast all sequence annotation (FASTA) sequence format for the key genes was searched in the NCBI database, and predicted scores for subcellular localization were obtained from the mRNALocater database ( http://bio-bigdata.cn/mRNALocater/ ). Additionally, a Friends analysis was performed to explore the relationships between key genes using the GOSemSim package (v2.28.1) [ 26 ]. 2.11 Networks of key genes prediction The ElMMO ( http://www.mirz.unibas.ch/ElMMo3/ ) and microcosm ( http://www.ebi.ac.uk/enright-srv/microcosm/htdocs/targets/v5/ ) databases were queried to identify microRNAs (miRNAs) targeting key genes, thereby exploring the regulatory relationships between these genes and miRNAs. Targeted miRNAs were determined through the overlap of two miRNA sets using the "ggvenn" package. Transcription factors (TFs) interacting with key genes were identified via the ChEA3 database ( https://amp.pharm.mssm.edu/ChEA3 ). A miRNA-mRNA-TF regulatory network was constructed using Cytoscape, incorporating the intersection of miRNAs and the top 20 TFs with the highest scores. Disease associations related to key genes and ALF were identified by searching the Comparative Toxicogenomics Database (CTD) ( https://ctdbase.org/ ), focusing on the top 10 diseases ranked by inference score. The network was visualized using Cytoscape software. 2.12 Prediction of drugs and molecular docking Drug-gene interactions related to key genes were obtained from the Drug-Gene Interaction Database (DGIdb) ( https://dgidb.org ) with a combined score > 2000. Molecular docking was then employed to construct a computational binding model to predict drug-gene interactions. The three-dimensional structures of drugs were retrieved from PubChem ( https://pubchem.ncbi.nlm.nih.gov/ ), while receptor protein structures of key genes were sourced from the Protein Data Bank (PDB) ( http://www.rcsb.org ). Molecular docking was performed using the CB-Dock platform ( https://cadd.labshare.cn/cb-dock/php/manual.php ), guided by drug-gene interactions to predict binding affinity. Binding energy was calculated and visualized using PyMOL software (v 3.0.3) [ 27 ]. To identify active ingredients in the six traditional Chinese medicines in JDHY, searches were performed in the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) ( https://www.91tcmsp.com/#/home ) for Artemisia capillaris, Hedyotis diffusa, Radix Paeoniae Rubra, Rheum officinale, Curcuma aromatica, and Acorus tatarinowii. Potential active ingredients were identified, and an ADME evaluation system (absorption, distribution, metabolism, and excretion) was used to select those with oral bioavailability (OB) ≥ 30% and drug likeness (DL) ≥ 0.1 for subsequent analysis. Active ingredients with higher OB were prioritized for molecular docking simulation. The 3D molecular structures of these ingredients were obtained from PubChem ( https://pubchem.ncbi.nlm.nih.gov/ ), and protein structures of key genes predicted by AlphaFold were obtained from UniProt ( https://www.uniprot.org/ ). Molecular docking simulation analysis was conducted using the CB-Dock platform ( https://cadd.labshare.cn/cb - dock/index.php). 2.13 Immunohistochemical staining For immunohistochemical staining, paraffin-embedded rat liver tissue sections were dewaxed and rehydrated. Primary antibodies (fth1 [1:50], necap2 [1:300], and prdx2 [1:600]) were applied to the sections and incubated overnight at 4°C. The sections were then incubated with HRP-labeled goat anti-rabbit IgG (1:200, gb23303, ServiceBio) for 50 minutes at room temperature. Following incubation with DAB substrate (g1212, ServiceBio), the sections were counterstained with hematoxylin. Images were captured using a light microscope, and analysis was performed using AipathWell® Software. 2.14 Real-time quantitative PCR analysis Primers were synthesized by Wuhan Saville Biotechnology Co., Ltd. (Supplementary Table 1). The reaction system was 20 µL, and reverse transcription was carried out using the Swescript All-in-One RT Supermix for qPCR (One Step gDNA Remover) kit (Wuhan Saville Biotechnology Co., Ltd.; product number: g3337). The thermal cycling conditions were as follows: pretreatment at 95°C for 30 seconds, denaturation at 95°C for 15 seconds, annealing and extension at 60°C for 30 seconds, with 40 cycles in total. The real-time PCR system (Agilent, model: AriaMX, California, USA) was used to determine the CT values for each gene, and relative gene expression was calculated using the 2 − △△ CT method. 2.15 Western blot analysis Total protein was extracted from liver tissue, and protein concentration was determined using a BCA assay. Proteins were separated by 10% and 15% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and transferred to PVDF membranes. After blocking with 5 mL of 5% skim milk solution for 30 minutes, the membranes were incubated overnight at 4°C with primary antibodies: ACTIN (1:2000), FTH1 (1:1000), NECAP2 (1:1000), and Prdx2 (1:1000). After 24 hours, the membranes were incubated with a secondary antibody (1:5000) for 30 minutes at room temperature. Protein bands were visualized using an ECL assay, and the gray value of the bands was quantified using ImageJ software. 2.16 LDH and CCK-8 analysis Cytotoxicity of drugs on cells was measured using LDH release at 490 nm, and HSC cell viability was assessed using the CCK-8 reagent at OD450 nm. 2.17 Statistical analysis Bioinformatics analyses were performed using the R programming language (v 4.2.2). The Wilcoxon rank-sum test was applied to assess differences between two groups, with statistical significance set at p < 0.05. 3. Results 3.1 JDHYG chemical composition analysis HHPLC-Q-Orbitrap-MS was employed to determine the chemical composition of JDHYG in both positive and negative ion modes. The total ion current (TIC) chromatogram is presented in Fig. 1 . By comparing the obtained mass spectrometry data with standard materials and the mzCloud mass spectrometry library, 468 compounds were identified in the samples. Of these, 326 compounds had a comprehensive score exceeding 60 points. The active ingredients of JDHYG include flavonoids, anthraquinones, terpenes, and others (Supplementary Table 2) . 3.2 JDHYG reduces serum ALT , AST , TBIL , and plasma PT levels Compared to the model group, the JDHYG group exhibited significant reductions in ALT , AST , TBIL , and PT levels (Fig. 2), suggesting that JDHYG improves liver function by modulating these biomarkers. 3.3 H&E s taining results Pathological changes were further examined in the control, JDHYG, and ALF groups using H&E staining at varying magnifications (Fig. 3). In the normal group, the liver tissue membrane structure was clear, and the boundaries between liver lobules were indistinct. The central vein was centrally located in the lobules, surrounded by hepatocytes and hepatic sinusoids arranged radially. In the ALF group, extensive hepatocyte necrosis (black arrow), nuclear fragmentation, and dissolution were observed. Hepatic steatosis was more pronounced (green arrow), with circular vacuoles of varying sizes appearing in the cytoplasm. Clusters of chromatin at the edges of liver cell nuclei (light green arrows) and visible bruising (orange arrows) were also evident. In the JDHYG group, localized hepatocyte necrosis (black arrow), nuclear fragmentation, increased cytoplasmic eosinophilia, and mild bleeding (yellow arrow) were observed. Multiple hepatocellular steatosis (green arrow) with small vacuoles appeared in the cytoplasm, chromatin edge clustering (light green arrow), and rare bruising (orange arrow) were noted. 3.4 Further exploration of TUNEL changes in liver tissue of rats in each group To assess the apoptosis rate, TUNEL staining was performed. Compared to the control group, the TUNEL positivity rate was elevated in both the ALF and JDHYG groups. Specifically, the apoptosis rate was approximately 13% in the ALF group, compared to 3.5% in the JDHYG group (Fig. 4). 3.5 Diverse array of functions observed in DEGs Gene expression variability within individual samples and across groups was clearly demonstrated (Fig. 5A). A total of 6,265 DEGs1 were identified between the M and Z groups, with 3,648 genes exhibiting higher expression in group Z and 2,617 genes showing reduced expression (Fig. 5B). In group C, 5,567 genes with elevated expression and 3,089 genes with reduced expression were classified as DEG2 (Fig. 5C). An additional 2,863 DEGs were identified (Fig. 5D). GO analysis revealed that these DEGs were associated with multiple biological processes (BP), including leukocyte migration, chemotaxis, taxis, positive regulation of cell adhesion, and positive regulation of the MAPK cascade. Cellular component (CC) analysis highlighted the external side of the plasma membrane, actin cytoskeleton, cell leading edge, membrane raft, and membrane microdomain. In terms of molecular function (MF), enriched categories included actin binding, phospholipid binding, signaling receptor activator activity, receptor-ligand activity, and GTPase regulator activity (Fig. 5E). KEGG pathway analysis identified significant pathways such as leukocyte transendothelial migration, hematopoietic cell lineage, tuberculosis, leishmaniasis, and chemokine signaling (Fig. 5F). Additionally, a complex PPI network was constructed, demonstrating close protein-level interactions among the DEGs (Fig. 5G). 3.6 Variable biological function of the DEPs involved A total of 114 DEPs1 were identified (84 upregulated and 30 downregulated) (Fig. 6A), along with 270 DEPs2 (70 upregulated and 200 downregulated) (Fig. 6B), yielding 67 overlapping DEPs (Fig. 6C). Enrichment analysis revealed significant associations with various GO BPs, including olefinic compound metabolism, diterpenoid metabolism, terpenoid metabolism, isoprenoid metabolism, and hormone metabolism; GO CCs such as the cell cortex, ribosome, cell body fiber, blood microparticles, and septin rings; and GO MFs including oxidoreductase activity (involving the reduction of molecular oxygen to water), retinol binding, ubiquitin-protein transferase inhibitor activity, and carboxylesterase activity (Fig. 6D). The "Biosynthesis of unsaturated fatty acids" pathway was enriched in DEPs, involving genes like Fads2, Fads1, and Acnat2. The PPI network revealed an interaction between Fth1 and Prdx2 at the protein level (Fig. 6E). Additionally, a detailed summary of the functional and structural characteristics of the DEPs is provided in Supplementary Table 3 , highlighting Fth1’s association with ferritin heavy chain (gene name), ferritin heavy chain (PANTHER family/subfamily), and storage protein (PANTHER protein class). 3.7 Promising effectiveness of three identified key genes After removing duplicate genes, the intersection of DEGs and DEPs identified four common genes (Fig. 7A). Among these, three key genes—Fth1, Necap2, and Prdx2 (FTH1, NECAP2, and PRDX2 in humans)—were selected, while Igh-6 was excluded due to the absence of a homolog in the human genome. RNA-seq data revealed reduced expression of these three key genes in the C and Z groups compared to group M (Fig. 7B). Similarly, proteomic data showed diminished levels of these genes in both the C and Z groups relative to group M. No significant differences were observed between the C and Z groups, indicating that JDHY followed a similar trend to the control group (Fig. 7C). A strong positive correlation was found among the three key genes, with all correlation coefficients exceeding 0.7 (Fig. 7D). Notably, these genes were significantly correlated with the ribosome, fatty acid metabolism, and chemokine signaling pathways, suggesting that they may play a critical role in regulating ALF through these pathways (Fig. 7E-G). 3.8 Varied distributions exhibited in key genes All three key genes were identified as residing on distinct euchromatic chromosomes. Specifically, the NECAP2 gene is located on chromosome 1, PRDX2 is on chromosome 19, and FTH1 is situated on chromosome 11 (Fig. 8A). Subsequent analysis revealed the widespread expression of these genes across various tissues. Notably, NECAP2 exhibited the highest expression in the spleen, FTH1 was most abundant in blood and fibroblasts, and PRDX2 showed the highest expression in the adrenal gland (Fig. 8B). The subcellular localization of the key genes was also examined, revealing their presence in multiple cellular compartments. Notably, the three genes showed the highest expression levels in the cytoplasm and the lowest in the mitochondria (Fig. 8C). Additionally, FTH1 demonstrated the strongest correlation with the other two key genes, suggesting its significant role in regulating their expression (Fig. 8D). 3.9 Prediction of networks and interaction for key genes A total of 13 miRNAs were identified as associated with the three key genes across two databases, with 11 miRNAs linked to NECAP2. Among the top 20 TFs, 15 were found to be related to FTH1 (Fig. 9A). Notably, the top 10 diseases associated with each gene highlighted the involvement of all three key genes in chemical and drug-induced liver injury, hyperplasia, necrosis, weight loss, and prenatal exposure delayed effects (Fig. 9B). Additionally, drug-gene interaction analysis identified 36 drugs associated with the three key genes: FTH1 was linked to 13 drugs, PRDX2 to 24 drugs, and NECAP2 to one drug. Intriguingly, all three genes shared a common drug, cyclosporin A (Fig. 9C). The highest-scoring drugs associated with the key genes are presented in Table 1 . Molecular docking simulations were performed using 7-ACA for FTH1 and Congo red for PRDX2, due to the unavailability of three-dimensional structures for potassium nitrate and cyclosporin A. The docking results indicated stable binding interactions, with binding energies of -5.1 kcal/mol for FTH1 and 7-ACA, and − 11.5 kcal/mol for PRDX2 and Congo red. FTH1 formed two hydrogen bonds with 7-ACA, involving two distinct SER residues (Fig. 9D). PRDX2, on the other hand, interacted with Congo red through four hydrogen bonds, including a GLY residue, a THR residue, and two GLU residues (Fig. 9E). For active ingredients of each key gene, those with higher OB were prioritized for molecular docking analysis. The selected compounds are listed in Table 2 . The 3D molecular structures of six active ingredients were sourced from the PubChem database, while protein structures for FTH1, NECAP2, and PRDX2 were predicted by AlphaFold and retrieved from the UniProt database. Molecular docking simulations were then performed using the CB-Dock database, with Table 3 presenting the minimum binding energies from these simulations. The results are visually depicted in Supplementary Figs. 1–3 . Table 1 The scores for drugs between key genes. Drug Combined.Score Genes POTASSIUM NITRATE 6403.334808 FTH1 7-ACA 4739.656184 FTH1 Congo red 3054.58855 PRDX2 cyclosporin A 194220.8108 PRDX2;NECAP2;FTH1 Table 2 Selection of Active Ingredients of Key Components in Six Traditional Chinese Medicines Mol ID Molecule Name OB (%) DL component MOL008045 4'-Methylcapillarisin 72.18 0.35 Artemisia capillaris MOL000471 aloe-emodin 83.37 0.24 Rheum officinale MOL007016 Paeoniflorigenone 65.33 0.36 Radix Paeoniae Rubra MOL000098 quercetin 46.43 0.27 Herba Hedyotidis Diffusae MOL003571 spathulenol 81.61 0.12 Rhizoma Acori Tatarinowii MOL004313 Zedoarolide B 135.55 0.21 Radix Curcumae Table 3 Molecular Docking Results Table for FTH1, NECAP2, and PRDX2 Gene Molecule PubChem CID Vina Score(kcal/mol) FTH1(P02794) 4'-Methylcapillarisin 5320438 -6.6 aloe-emodin 10207 -6.7 Paeoniflorigenone 70698143 -6.7 quercetin 5280343 -6.3 spathulenol 92231 -5.6 Zedoarolide B 73353446 -6.2 NECAP2(Q9NVZ3) 4'-Methylcapillarisin 5320438 -6.9 aloe-emodin 10207 -7.1 Paeoniflorigenone 70698143 -7.1 quercetin 5280343 -6.9 Zedoarolide B 73353446 -6.3 PRDX2(P32119) 4'-Methylcapillarisin 5320438 -6.4 aloe-emodin 10207 -6.3 Paeoniflorigenone 70698143 -6.7 quercetin 5280343 -7 spathulenol 92231 -5.8 Zedoarolide B 73353446 -6.1 3.10 Animal experiment mRNA and protein expression Immunohistochemistry (Fig. 10A, Supplementary Table 4 ) and Western blot (Fig. 10B, 10C) were employed for protein expression analysis, while qPCR (Fig. 10D) validated mRNA expression. The results revealed elevated protein and mRNA levels of FTH1, PRDX2, and NECAP2 in the ALF model, with a reduction observed following JDHYG treatment. 3.11 JDHYG inhibits apoptosis of hepatic stellate cells The modeling effect was first confirmed (Fig. 11A). CCK8 assays indicated reduced cell proliferation in the model group, and LDH assays showed increased LDH levels, suggesting apoptosis of HSCs. LDH assays were used to determine the non-toxic concentration of traditional Chinese medicine (Fig. 11B), with 40 µg/mL identified as the lowest non-toxic dose. This concentration was applied in subsequent interventions. To evaluate the therapeutic effects of the drugs (Fig. 11C), cells were pretreated with JDHYG for 1 hour before D-galn/LPS induction. After 24 hours, LDH levels were measured, showing a significant reduction following drug treatment. CCK8 assays further revealed a marked increase in HSC proliferation after drug intervention. 3.12 Cell experiment mRNA and protein expression Western blot (Fig. 11D, 11E) and qPCR (Fig. 11F) confirmed a decrease in the protein and mRNA expression of FTH1, PRDX2, and NECAP2 following JDHYG treatment. Discussion ALF is a critical clinical condition characterized by rapid hepatocyte damage, often leading to elevated mortality [ 1 ]. Research highlights that ALF results from complex pathological and physiological processes, including mitochondrial oxidative stress, inflammation, and apoptosis [ 28 ]. JDHYG, a granule formulation derived from six traditional Chinese medicinal herbs, has shown protective effects against D-Galn + LPS-induced liver injury in experimental settings [ 7 ]. This study employed RNA sequencing and proteomic analysis to identify numerous DEGs and DEPs associated with ALF. Bioinformatics analysis revealed that these DEGs and DEPs are involved in several biological processes and pathways, such as cell migration, fatty acid metabolism, and chemical signaling. Notably, Fth1, Necap2, and Prdx2 were identified as potential key players in ALF pathogenesis, a hypothesis validated by experimental data. Additionally, the study predicted the miRNA regulatory networks, transcription factor interactions, and potential drug-binding profiles of these key genes, thereby presenting novel therapeutic targets for ALF treatment. The NECAP2 (Adaptin ear-binding coat-associated protein 2) gene plays a pivotal role in regulating endocytosis by maintaining receptor levels on the cell surface. It orchestrates the formation of the clathrin coat on early endosomes, a key step in the rapid endocytic cycle. Elevated NECAP2 expression has been observed in low-grade gliomas and hepatocellular carcinoma (HCC), where it is strongly associated with aggressive tumor behavior and poor patient prognosis. Specifically, in low-grade gliomas, NECAP2 is implicated in immune cell infiltration and oxidative stress, while in HCC, its expression is regulated by NF-YA and contributes to the modulation of the MEK/ERK signaling pathway [ 29 , 30 ]. PRDX2 (Peroxiredoxin 2), an antioxidant enzyme, protects cells from oxidative stress and may enhance the antiviral activity of CD8(+) T cells [ 31 ]. It also plays a role in hepatocyte senescence induced by Cr (VI), which is potentially linked to liver disease development [ 32 ]. Furthermore, low PRDX2/3 expression is associated with poor prognosis in patients with HCC, suggesting that PRDX2 may contribute to HCC progression and correlate with cell proliferation [ 33 ]. As an iron storage protein, FTH1 (Ferritin Heavy Chain 1) regulates iron metabolism and oxidative stress, providing protective functions in liver diseases. Alterations in FTH1 expression influence hepatocyte susceptibility to ferroptosis, a process closely associated with liver disease progression [ 34 ]. FTH1 may also serve as a promising therapeutic target for liver diseases, with potential for improving disease prognosis through modulation of its expression or function [ 35 ]. Experimental findings from PCR, Western blot, and immunohistochemistry in this study demonstrate that JDHYG significantly reduces the mRNA and protein levels of NECAP2, PRDX2, and FTH1. In conclusion, NECAP2, PRDX2, and FTH1 are critical factors in the onset and progression of liver failure and related liver diseases. Modulating these molecules affects essential cellular processes and is closely linked to tumor malignancy and patient prognosis. Therefore, targeting these molecules could offer novel strategies for the early diagnosis and treatment of liver diseases. To explore the specific mechanisms of FTH1, NECAP2, and PRDX2 in ALF, GSEA enrichment analysis revealed that the enriched pathways for these proteins include ribosome biogenesis, chemokine signaling, and fatty acid metabolism. Ribosomes are essential for protein synthesis, and when hepatocytes are damaged, ribosomal function is compromised, impairing the production of vital proteins required for liver repair and normal function [ 36 ]. Chemokines play a pivotal role in immune responses by attracting immune cells to sites of inflammation [ 37 ]. In ALF, increased chemokine production can lead to immune cell accumulation in the liver, exacerbating the inflammatory response and hepatocyte injury. Furthermore, fatty acid metabolism is essential for energy production and cellular signaling, and disruptions in this pathway during ALF can impair energy availability and cellular function, hindering the liver’s ability to repair and regenerate [ 38 ]. Collectively, the elevated expression of NECAP2, PRDX2, and FTH1 contributes to ALF by modulating ribosome function, chemokine signaling, and fatty acid metabolism. In drug prediction, 7-ACA exhibits strong binding interactions with FTH1, and Congo red binds to PRDX2. 7-ACA (7-aminocephalosporanic acid), a key intermediate in β-lactam antibiotic synthesis, is essential for producing various semi-synthetic cephalosporins [ 39 ]. While 7-ACA itself is not used as a standalone drug, it is integral to the development of cephalosporin antibiotics, which possess broad-spectrum antibacterial properties and are primarily used to treat infections caused by sensitive bacteria [ 40 ]. Congo red, a traditional histological dye, is used to detect amyloid deposits. It binds to amyloid fibrils, prevents misfolding, stabilizes protein monomers, reduces toxic oligomers, and has shown potential in improving models of neurodegenerative diseases such as Alzheimer’s disease [ 41 ]. In summary, both 7-ACA and Congo red show considerable promise in drug development, potentially offering new therapeutic insights for diseases associated with FTH1 and PRDX2. Previous studies have demonstrated that JDHY can inhibit the inflammatory pathway mediated by NF-κB. In the pathogenesis of ALF, NF-κB, as a critical transcription factor, is rapidly activated, triggering the transcription of a series of pro-inflammatory cytokines and chemokines. This initiates a robust inflammatory cascade, exacerbating liver damage [ 42 ]. JDHY effectively blocks this process, reducing the release of inflammatory mediators and mitigating liver inflammation [ 42 ]. Notably, JDHY also upregulates CD163/sCD163 levels through the NF-κB signaling pathway [ 43 ]. CD163 is predominantly expressed on the surface of mononuclear macrophages, and its soluble form, sCD163, can be shed into the bloodstream. In the ALF state, immune dysregulation and excessive inflammation occur. JDHY enhances the ability of mononuclear macrophages to clear inflammatory mediators and pathogen-associated molecular patterns by regulating CD163/sCD163 levels while inhibiting the excessive activation of inflammatory cells. This precise modulation of immune responses further alleviates the inflammatory state of the liver [ 43 ]. Additionally, JDHY has been shown to alleviate oxidative stress and mitochondrial damage in liver cells, likely by promoting the expression of the PI3K/AKT/mTOR signaling pathway [ 44 ]. Therapeutic mechanisms of JDHY particles on liver regeneration have been analyzed, with findings showing that JDHY promotes liver regeneration by enhancing DNA replication and improving cholesterol metabolism, thereby preventing D-GalN/LPS-induced ALF in rats [ 45 ]. JDHY also alleviates liver injury by regulating the expression levels of IL-2, TLR4, and PCNA in rats [ 7 ]. In conclusion, the multiple mechanisms of action of JDHY, as revealed in previous studies, provide compelling evidence for the results of this study from various perspectives, highlighting the multi-target and multi-pathway characteristics of JDHY in ALF treatment. While prior studies have extensively explored these mechanisms, this study successfully identifies three key genes—NECAP2, PRDX2, and FTH1—associated with the therapeutic effect of JDHY on ALF. Through combined proteomics and transcriptomics analysis, this study emphasizes the role of these specific genes in ALF and related biological processes, while molecular docking further reveals substances with affinity for these key genes, enhancing the previously established multi-level therapeutic mechanisms. In conclusion, this study successfully identified NECAP2, PRDX2, and FTH1 as key genes involved in the treatment of ALF through a combined analysis of proteomics and transcriptomics, demonstrating that JDHYG may exert its therapeutic effects by modulating the expression of these genes. However, certain limitations remain, particularly the lack of experimental validation through techniques such as gene knockout. Future research should aim to validate the functions of these key genes experimentally and further elucidate the underlying mechanisms of JDHYG’s action. Additionally, at the molecular level, single-cell sequencing facilitates the analysis of the expression dynamics of FTH1, PRDX2, and NECAP2 across different liver cell subpopulations before and after JDHY treatment, providing single-cell resolution insights into the cell types involved. Simultaneously, protein-protein interaction omics will be employed to construct a comprehensive interaction network between these three key proteins and JDHY’s active ingredients, enabling a deeper understanding of their molecular regulatory mechanisms. Further investigation into the clinical applicability of JDHY in treating ALF will also be explored. This series of in-depth and comprehensive research efforts is expected to provide a solid theoretical foundation and practical guidance for ALF treatment strategies, advancing medical technologies in this field and delivering significant clinical benefits to patients. Abbreviations Abbreviations Full name NCBI National Center for Biotechnology Information GTEx Genotype-Tissue Expression miRNA MicroRNAs TFs Transcription Factors CTD Comparative Toxicogenomics Database PPI Protein-Protein Interaction GEO Gene Expression Omnibus MSigDB Molecular Signatures Database DGIdb Drug-Gene Interaction Database PDB Protein Data Bank CB-Dock Cavity-Detection Guided Blind Docking GO Gene Ontology KEGG Kyoto Encyclopedia of Genes and Genomes GSEA Gene Set Enrichment Analysis LPS Lipopolysaccharide D-GalN D- galactosamine Hydrochloride CKD Chronic Kidney Disease APAP Acetaminophen IACUC Institutional Animal Care and Use Committee HSCs Hepatic Stellate Cells PCA Principal Component Analysis PANTHER Protein ANalysis THrough Evolutionary Relationships Declarations Ethics approval All experimental protocols were approved by the ethics licensing Committee of Guangxi University of traditional Chinese medicine (No : DW20230830-152). All experimental methods were in accordance with ARRIVE guidelines (https://arriveguidelines.org). Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Publication Consent Not Applicable Authors' contributions Hao Pei, Feng-lan Wu, and Na wang consistent contributions should be regarded as joint first authors. Hao Pei and Yue-qiao Chen : Conceptualization. Hao Pei: methodology. Na wang: software. Han Pei and Rui Qin: validation. Feng-lan Wu and Ri-yun Zhang: formal analysis. Yan-yan Zhang: investigation. Yue-qiao Chen: resources. Hao Pei : data curation. Hao Pei: writing - original draft preparation. Yue-qiao Chen: writing—review and editing. Hao Pei: visualization. Na wang: supervision. Yue-qiao Chen: project administration. Yue-qiao Chen: funding acquisition. All authors have read and agreed to the published version of the manuscript. Funding sources This work was supported by National Natural Science Foundation Project (grant numbers: 82060848, 82160888) , Guangxi University of Traditional Chinese Medicine Introduction Doctoral Research Initiation Fund Project: (grant numbers: 2023BS030) and Guangxi Natural Science Foundation Project (grant numbers: 2023GXNSFAA026361, 2024GXNSFBA010218). Acknowledgements We thank Bullet Edits Limited for the linguistic editing and proofreading of the manuscript. Special thanks to the Teacher from the Medical Molecular Laboratory of Guangxi University of Traditional Chinese Medicine, In conclusion, we extend our thanks to everyone who has supported and assisted us along the way. Without your support, this research would not have been possible. Availability of data and materials The transcriptome data generated and analyzed in this study can be viewed in NCBI database, No.: PRJNA1401552, website: https://www.ncbi.nlm.nih.gov/sra/PRJNA1401552. 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A Straightforward Approach to Synthesize 7-Aminocephalosporanic Acid In Vivo in the Cephalosporin C Producer Acremonium chrysogenum. J. Fungi . 8 (5), 450 (2022). Frid, P., Anisimov, S. V. & Popovic, N. Congo red and protein aggregation in neurodegenerative diseases. Brain Res. Rev. 53 (1), 135–160 (2007). Qiu, H. et al. The underlying mechanisms of Jie-Du-Hua-Yu granule for protecting rat liver failure. Drug. Des. Devel. Ther. 13 , 589–600 (2019). Bai, W. et al. [Retracted] Chinese Herb Jiedu Huayu Granules Inhibiting Immune and Inflammatory Response of Rats with Acute Liver Failure by Regulating the NF-κB Signaling Pathway. Biomed. Res. Int. ; 2022 (1). (2022). Lin, Y. et al. Jiedu Huayu Extract Alleviate Acute Liver Failure via Promotion of GPX4 Expression and Inhibition of D-GalN/LPS-Induced Ferroptosis. Nat. Prod. Commun. ; 19 (12). (2024). Wang, T. et al. Jie-Du‐Hua‐Yu Granules Promote Liver Regeneration in Rat Models of Acute Liver Failure: miRNA‐mRNA Expression Analysis. 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Supplementary Files SupplementaryTable2.xlsx SupplementaryTable3.xlsx SupplementaryTable1.xlsx SupplementaryTable4.xlsx SupplementaryFigure2.tif SupplementaryFigure3.tif GraphicalAbstracts.tif SupplementaryFigure1.tif WBFigures.pdf file.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 10 Apr, 2026 Reviews received at journal 17 Mar, 2026 Reviewers agreed at journal 09 Mar, 2026 Reviews received at journal 08 Mar, 2026 Reviewers agreed at journal 26 Feb, 2026 Reviewers invited by journal 24 Feb, 2026 Editor assigned by journal 16 Feb, 2026 Editor invited by journal 14 Jan, 2026 Submission checks completed at journal 14 Jan, 2026 First submitted to journal 13 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8434231","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":596145377,"identity":"66fb15b1-449c-45ed-b5c7-033546b73179","order_by":0,"name":"wang Na","email":"","orcid":"","institution":"First Clinical Medical College, Guangxi University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"wang","middleName":"","lastName":"Na","suffix":""},{"id":596145378,"identity":"afb61bc5-7e61-4eca-bcf5-993d44a28041","order_by":1,"name":"Hao Pei","email":"","orcid":"","institution":"First Clinical Medical College, Guangxi University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Hao","middleName":"","lastName":"Pei","suffix":""},{"id":596145379,"identity":"1ce43ed0-1bb8-4308-b73a-b80a5a3bd14b","order_by":2,"name":"fenglan Wu","email":"","orcid":"","institution":"First Clinical Medical College, Guangxi University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"fenglan","middleName":"","lastName":"Wu","suffix":""},{"id":596145381,"identity":"76dd5df7-93b2-468d-9005-6387a3fafeb2","order_by":3,"name":"Han pei","email":"","orcid":"","institution":"Chongqing Medical And Pharmaceutical College","correspondingAuthor":false,"prefix":"","firstName":"Han","middleName":"","lastName":"pei","suffix":""},{"id":596145383,"identity":"eaf484b1-99d0-4a95-9416-8b1bae35ee79","order_by":4,"name":"Ri-yun Zhang","email":"","orcid":"","institution":"Laibin City Traditional Chinese Medicine Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ri-yun","middleName":"","lastName":"Zhang","suffix":""},{"id":596145385,"identity":"6887cf99-f28a-434b-a910-acf4c55a8af1","order_by":5,"name":"Rui qin","email":"","orcid":"","institution":"Nanning Fifth People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"qin","suffix":""},{"id":596145387,"identity":"82104b5f-874c-4c25-b47e-7f08618f192e","order_by":6,"name":"yueqiao Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYBACNvbmgw8SKmzs+InWwsdzLNngwZm0ZMkGYrXISfioST5sO8S44QDRDpPgYZBIbDvAbHw8eQPDj4ptRGiR7j1gkHDuDp/ZmWcFjD1nbhOhReZcQkJC2TNmsxs5BsyMbcRokcgxOJDAdphx8wwStBg2JLQdZtwgQbQWYCAzJAADWQLol4NE+UW+vfn4zx+gqGxP3vjgRwURWpBAgsEBktSDtZCqYxSMglEwCkYIAABhNUDSP7Ag3QAAAABJRU5ErkJggg==","orcid":"","institution":"First Clinical Medical College, Guangxi University of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"yueqiao","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2025-12-23 13:24:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8434231/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8434231/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103442913,"identity":"1d4cbff9-e1b5-42cf-867d-7b8f7227ab05","added_by":"auto","created_at":"2026-02-25 17:45:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1518067,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of natural products in JDHYG using total ion chromatogram. Note: The black color in column 1 represents the total ion current in negative ion mode, while the red color in column 2 represents the total ion current in positive ion mode.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8434231/v1/9e65214f78b04c539a393139.png"},{"id":103442914,"identity":"458a4378-2d7d-4136-a85f-f7809704fe8e","added_by":"auto","created_at":"2026-02-25 17:45:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1589741,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of \u003cem\u003eALT\u003c/em\u003e, \u003cem\u003eAST\u003c/em\u003e, \u003cem\u003eTBIL\u003c/em\u003e, and \u003cem\u003ePT\u003c/em\u003eexpression in each group. The abscissa represents each group, and the ordinate represents the levels of \u003cem\u003eALT\u003c/em\u003e, \u003cem\u003eAST\u003c/em\u003e, \u003cem\u003eTBIL\u003c/em\u003e, and \u003cem\u003ePT\u003c/em\u003e. Columns of different colors represent different groups. * indicates p \u0026lt; 0.05, ** indicates p \u0026lt; 0.01, *** indicates p \u0026lt; 0.001, and **** indicates p \u0026lt; 0.0001, reflecting the significance of the differences between groups.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8434231/v1/70eeb48ba593ad5b4a255ec8.png"},{"id":104397690,"identity":"ec819b3c-aca8-4212-8508-ac9c285a8d7e","added_by":"auto","created_at":"2026-03-11 11:54:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1300296,"visible":true,"origin":"","legend":"\u003cp\u003eResults of H\u0026amp;E staining in control, JDHYG and ALF groups. From top to bottom were the control group, the JDHYG group, and the ALF group. The tissue samples of different groups presented specific morphological characteristics through H\u0026amp;E staining, which could be used for comparative analysis of the structural and cellular morphological changes of tissues under different treatment conditions. From left to right corresponded to different observation scales, and the lengths shown by the scale bars were 400μm, 200μm, and 100μm in sequence. These scale bars provided a reference for the sizes of the tissue and cellular structures in the images, helping to determine the differences in the morphology, size, and distribution of tissue cells among different groups.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8434231/v1/e5de47e953bc39820ee83a27.png"},{"id":103506971,"identity":"48426144-c856-406d-84b8-81d36bcf3a20","added_by":"auto","created_at":"2026-02-26 13:40:05","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":387985,"visible":true,"origin":"","legend":"\u003cp\u003eResults of TUNEL staining of three biomarkers in control, JDHYG and ALF groups at high magnification (40X). The images were arranged horizontally. From left to right were the control group, the JDHYG group, and the ALF group, which were used to compare the sample conditions under different treatment conditions. It represented the image that combined the results of TUNEL staining and DAPI staining. In this image, the overall state of the cells could be observed comprehensively. TUNEL staining was used to detect cell apoptosis. The red fluorescence signals in the figure represented TUNEL - positive apoptotic cells, based on which the occurrence of cell apoptosis could be determined.DAPI was a fluorescent dye that could bind tightly to DNA, presenting blue fluorescence. It was used to label the cell nuclei and show the quantity and distribution of cells. \"100 μm\" scale bars were marked at the bottom of each image, providing a size reference for the cells and structures in the images, which facilitated the comparison of differences in cell morphology, quantity, and distribution among different groups.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8434231/v1/64f9399c90931e8e25eb9987.png"},{"id":103442918,"identity":"3194bb45-1284-4df5-b304-d57a2e36afc7","added_by":"auto","created_at":"2026-02-25 17:45:52","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":568253,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of DEGs. (A-1) Overall gene expression levels in different samples. The abscissa was the logarithm of gene expression levels (log10 (TPM)), and the ordinate was the number of genes. Curves in different colors in the figure represented the gene-expression level distribution of different sample groups. (A-2) Results of principal component analysis of the sample. The abscissa was the first principal component (PC1), and the ordinate was the second principal component (PC2). Points in different colors and shapes represented different samples, and the ellipses indicated the clustering of sample groups. (A-3) Results of correlation analyses for different samples. The color of each square in the figure represented the correlation coefficient between samples. The closer the color was to red, the stronger the correlation (close to 1), and the closer the color was to blue, the weaker the correlation (close to 0). (B-1) Volcano map of the differential gene DEGs1 in the Z and M groups. The abscissa was the logarithm of the fold - change (log2FC), and the ordinate was the negative logarithm of the significance of the difference (-log10 (p - value)). Red points represented up - regulated differential genes, green points represented down-regulated differential genes, and gray points represented genes with no significant difference.(B-2) Heatmap of the differential gene DEGs1 in the Z and M groups. The abscissa was the samples, and the ordinate was the differential genes. The color ranging from blue to red indicated that the gene expression level increased from low to high. (C-1) Volcano map of the differential gene DEGs2 in the C and M groups. (C-2) Heatmap of the differential gene DEGs2 in the C and M groups. (D-1) Venn diagram of DEGs1 down-regulated genes and DEGs2 up-regulated genes. (D-2) Venn diagram of DEGs2 down-regulated genes and DEGs1 up-regulated genes. (E) Results of GO enrichment analysis of DEGs. The abscissa was the GO functional categories, and the ordinate was the number of enriched differential genes. The colors of the bar charts represented different GO functional categories, such as biological processes, cellular components, and molecular functions. (F) Results of KEGG enrichment analysis of DEGs. Note: This KEGG image is quoted from KEGG database, and the relevant literature is: 1.Kanehisa M, Furumichi M, Sato Y, Matsuura Y, Ishiguro-Watanabe M. KEGG: biological systems database as a model of the real world. Nucleic Acids Research. 2024 Oct 17;53(D1):D672–7. 2.Kanehisa M. Toward understanding the origin and evolution of cellular organisms. Protein Science. 2019 Sep 9;28(11):1947–51. 3.Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Research. 2000;28(1):27-30. (G) Results of PPI network construction. The nodes in the figure represented proteins (corresponding to the proteins encoded by differential genes), and the connections between the nodes indicated the existence of protein - protein interaction relationships.\u003c/p\u003e\n\u003cp\u003eDEGs: Differentially expressed genes; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; PPI: Protein-protein interaction.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8434231/v1/f4b25f8e7fa7c57baac88612.png"},{"id":103507466,"identity":"acc0989e-d7d1-4263-82d2-a25537a2bcb2","added_by":"auto","created_at":"2026-02-26 13:41:24","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":400380,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of DEPs.\u003cstrong\u003e \u003c/strong\u003e(A-1) Volcano map of the differential gene DEPs1 in the Z and M groups. The abscissa was the logarithm of the fold - change (log2FoldChange), and the ordinate was the negative logarithm of the significance of the difference (-log10 (p - value)). Red points represented up - regulated differential proteins, green points represented down - regulated differential proteins, and gray points represented proteins with no significant difference. (A-2) Heatmap of clustering of the differential gene DEPs1 in the Z and M groups. The circular area in the figure showed the expression of differential proteins in different samples. The color ranging from blue to red indicated that the protein expression level increased from low to high. \"Exp\" in the center represented the expression level (Expression), and the corresponding value range of the color bar was from - 2 to 2. The information of different samples and proteins was labeled on the outer side of the circular area. (B-1) Volcano map of the differential gene DEPs2 in the C and M groups. The abscissa was the logarithm of the fold - change (log2FoldChange), and the ordinate was the negative logarithm of the significance of the difference (-log10 (p - value)). Red points represented up - regulated differential proteins, green points represented down - regulated differential proteins, and gray points represented proteins with no significant difference. (B-2) Heatmap of clustering of the differential gene DEPs2 in the C and M groups. (C-1) Venn diagram of DEPs1 down-regulated genes and DEPs2 up-regulated proteins. (C-2) Venn diagram of DEPs2 down-regulated genes and DEPs1 up-regulated proteins. (D) Results of GO enrichment analysis of DEPs. The abscissa was the number of genes (Gene Count numbers), and the ordinate was the GO functional categories (including biological process BP, cellular component CC, and molecular function MF). The colors of the bar charts represented different GO functional categories. Red represented the biological process, blue represented the cellular component, and green represented the molecular function. (E) Results of KEGG enrichment analysis of DEPs. The figure showed the interaction relationships among different proteins in the KEGG pathway. Nodes represented proteins, and the connections between the nodes indicated the existence of interactions among proteins. Different proteins were labeled with different names beside the nodes.\u003c/p\u003e\n\u003cp\u003eDEPs: Differentially expressed proteins; GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; PPI: Protein-protein interaction.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-8434231/v1/f0c8a9e29ea999e0c533ea5a.png"},{"id":103507673,"identity":"2ac8d32d-ae55-4a31-a5ba-4062bb88ff59","added_by":"auto","created_at":"2026-02-26 13:43:09","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":379378,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of biomarkers. (A) Venn diagram of DEGs and DEPs. (B-1) Expression of FTH1 in groups M, C and Z in RNA-seq data. the significance differences among different groups were marked with (*p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, and ns for no significant difference).(B-2) Expression of NECAP2 in groups M, C and Z in RNA-seq data. the significance differences among different groups were marked with (*p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, and ns for no significant difference). (B-3) Expression of PRDX2 in groups M, C and Z in RNA-seq data. the significance differences among different groups were marked with (*p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, and ns for no significant difference). (C-1) Expression of FTH1 in groups M, C and Z in proteome sequencing data. the significance differences among different groups were marked with (*p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, and ns for no significant difference). (C-2) Expression of NECAP2 in groups M, C and Z in proteome sequencing data. the significance differences among different groups were marked with (*p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, and ns for no significant difference). (C-3) Expression of PRDX2 in groups M, C and Z in proteome sequencing data. the significance differences among different groups were marked with (*p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, and ns for no significant difference). (D) The correlation between all three key genes. Each red circle in the figure represented a gene, and the number inside the circle was the correlation coefficient, with a value range from - 1 to 1. The closer it was to 1, the stronger the positive correlation was; the closer it was to - 1, the stronger the negative correlation was; and the closer it was to 0, the weaker the correlation was. (E) Results of the analysis of GSEA of FTH1. The abscissa was the ranked gene-sets (Rank in Ordered Dataset), and the ordinate was the running enrichment score (Running Enrichment Score). Curves in different colors in the figure represented different gene - sets (Term Name), and the colored bar areas below showed the position distribution of each gene - set in the gene ranking. (F) Results of the analysis of GSEA of NECAP2. (G) Results of the analysis of GSEA of PRDX2.\u003c/p\u003e\n\u003cp\u003eDEGs: Differentially expressed genes; DEPs : Differentially expressed proteins; GSEA: Gene set enrichment analysis.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-8434231/v1/bd77d859d18df79ff9807ccb.png"},{"id":103442920,"identity":"d441a243-7cf1-4f34-a9e7-3bc4c014c31f","added_by":"auto","created_at":"2026-02-25 17:45:52","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":189515,"visible":true,"origin":"","legend":"\u003cp\u003ePositioning and distributions of biomarkers. (A) Results of biomarkers in chromosomal localisation. The labels on the outer - ring indicate the corresponding biomarker names, and the blue short lines on the ring represent the positions of the biomarkers on the chromosome. (B) Biomarker-Organ localisation network. The abscissa represents the names of different organ tissues, and the ordinate represents the sample clustering situation. The color bar represents the gene expression level (TPM). From white to dark blue, it indicates that the expression level increases from low to high. The values above the figure represent the specific expression values in different tissues. (C) Subcellular localisation of biomarkers. The abscissa represents the biomarker names, and the ordinate represents the score (Score). The bars in different colors represent different sub - cellular structures, and the legend on the right indicates the names of the sub - cellular structures corresponding to the colors. (D) Friends analysis of biomarkers. The box - plot shows the correlation among FTH1, PRDX2, and NECAP2.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-8434231/v1/3e1331340a113315cb2d9c68.png"},{"id":103507875,"identity":"2a2e2317-f6f6-4f2e-8a45-22e6bb8c2d26","added_by":"auto","created_at":"2026-02-26 13:46:08","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":537982,"visible":true,"origin":"","legend":"\u003cp\u003eLocalization and regulatory network of prognostic genes. (A) The miRNA-TF-mRNA pathway network. The orange nodes in the figure represent mRNAs (NECAP2, PRDX2, FTH1), the green nodes represent transcription factors (TFs), and the blue nodes represent micro - RNAs (miRNAs). The connections between nodes indicate that there are regulatory relationships among them. (B) Biomarker-Disease network. The orange nodes represent biomarkers (FTH1, PRDX2, NECAP2), and the blue nodes represent diseases (such as Kidney Diseases, Liver Diseases, etc.). The connections between nodes indicate that there are associations between biomarkers and diseases. (C) Network diagram of biomarker-drug targeting. The orange nodes represent biomarkers (NECAP2, PRDX2, FTH1), and the blue nodes represent drug - related components. The connections between nodes indicate that there are targeting relationships between biomarkers and drug components. (D) Molecular docking results for FTH1_7-ACA. The figure shows the docking structure of FTH1 protein (the green part) and 7 - ACA molecule (the orange and blue parts), demonstrating the spatial position relationship of their binding. (E) Molecular docking results for PRDX2_Congo_red. The figure shows the docking structure of PRDX2 protein (the green part) and Congo red molecule (the orange and blue parts), demonstrating the spatial position relationship of their binding.\u003c/p\u003e\n\u003cp\u003emiRNA: MicroRNAs; TFs: Transcription factors.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-8434231/v1/85b11a0484538c0bee98ef1e.png"},{"id":103442929,"identity":"065b6cd4-ce4d-47e9-b3b1-419ab729aff1","added_by":"auto","created_at":"2026-02-25 17:45:53","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":341688,"visible":true,"origin":"","legend":"\u003cp\u003eAnimal experiment mRNA and protein expression.\u003cstrong\u003e \u003c/strong\u003e(A)\u003cstrong\u003e \u003c/strong\u003eResults of immunohistochemistry. The image shows the immunohistochemical staining results of three biomarkers, Fth1, Necap2, and Prdx2, in the control group, the JDHYG group, and the ALF group. The tissue sections of different groups present different staining intensities and patterns, reflecting the localization and relative expression levels of the biomarkers in the tissues. (B) Results of Western Blot. The figure shows the protein bands of Fth1 (21kDa), Necap2 (37kDa), Prdx2 (22kDa), and β - actin (42kDa, as an internal reference) in the control group, the JDHYG group, and the ALF group. The presence and intensity of the bands can be used to preliminarily determine the expression of the corresponding proteins. (C) Protein expression of biomarkers. The abscissa represents the groups, and the ordinate represents the relative protein expression levels. The columns in different colors represent different groups. * indicates p \u0026lt; 0.05, ** indicates p \u0026lt; 0.01, *** indicates p \u0026lt; 0.001, and **** indicates p \u0026lt; 0.0001, reflecting the significance of the differences between groups. (D) Results of qPCR identification of biomarkers. The abscissa represents the groups, and the ordinate represents the relative mRNA expression levels. The columns in different colors represent different groups. *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, and ****p \u0026lt; 0.0001, showing the significance of the differences in mRNA expression between groups.\u003c/p\u003e","description":"","filename":"Figure10.png","url":"https://assets-eu.researchsquare.com/files/rs-8434231/v1/c13b388e047ed628ac02ed1b.png"},{"id":103442924,"identity":"5c3d7245-b35c-4770-b40b-3432f8d3ad7b","added_by":"auto","created_at":"2026-02-25 17:45:52","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":281626,"visible":true,"origin":"","legend":"\u003cp\u003eCell experiment mRNA and protein expression. (A) Modelling effects of cellular models. The three left - hand images showed the cell morphologies of the control group, the JDHYG group, and the ALF group, with a scale of 200μm. The line chart and bar chart on the right showed the optical density (OD) values (reflecting cell viability, etc.) and relative lactate dehydrogenase (LDH) levels (measuring cell damage) at different time points (0h, 24h, 48h, 72h). The control group was represented by red, and the ALF group was represented by green. **** in the figure indicated p \u0026lt; 0.0001, showing significant differences between groups. (B) Results of LDH detection. The bar chart showed the relative LDH levels at different concentrations (0, 5, 10, 20, 40, 60, 80, 100, 200 μg/mL). ns indicated no significant difference, and ****p \u0026lt; 0.0001, which was used to reflect the differences between groups at different concentrations. (C) The therapeutic effect of drugs on cells. The line chart and bar chart showed the OD values (reflecting cell viability, etc.) and relative LDH levels (measuring cell damage) of the control group (red), the ALF group (green), and the JDHYG group (blue) at different time points (0h, 24h, 48h, 72h). **** in the figure indicated p \u0026lt; 0.0001, showing significant differences between groups. (D-E) Protein expression of biomarkers. (F) Results of qPCR identification of biomarkers. The bar chart showed the relative mRNA expression levels of three biomarkers, Fth1, Necap2, and Prdx2, in the control group (red), the JDHYG group (yellow), and the ALF group (green). *** p \u0026lt; 0.001, and ****p \u0026lt; 0.0001, showing significant differences in mRNA expression between groups.\u003c/p\u003e","description":"","filename":"Figure11.png","url":"https://assets-eu.researchsquare.com/files/rs-8434231/v1/2f3875adb6207b562b81af6f.png"},{"id":104407377,"identity":"9c45af6b-b1c2-4912-af32-82d34a2a419e","added_by":"auto","created_at":"2026-03-11 12:37:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8597552,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8434231/v1/3d9eb6da-c6e1-4838-86e4-a3e425ecaa46.pdf"},{"id":104397510,"identity":"bad6d619-d8d8-455e-ba61-32547b1a91d4","added_by":"auto","created_at":"2026-03-11 11:50:06","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":71118,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8434231/v1/1b4b04b9f2b1246bc5cfc20d.xlsx"},{"id":103442915,"identity":"9f2286f8-4aa3-4060-8416-ba461ace15d3","added_by":"auto","created_at":"2026-02-25 17:45:52","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":12002,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8434231/v1/512edb76aa012b887a30a936.xlsx"},{"id":103507865,"identity":"84bc30ba-bf84-4237-9c44-29ebf32283c7","added_by":"auto","created_at":"2026-02-26 13:46:05","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":10980,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8434231/v1/41c100a950206f2a385b993c.xlsx"},{"id":103507843,"identity":"49b7be72-082b-4bfe-af29-50337b6dab3b","added_by":"auto","created_at":"2026-02-26 13:45:59","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":10760,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8434231/v1/42309f259a222e195b31745e.xlsx"},{"id":103442923,"identity":"42204f29-667d-467c-ae15-505376d13503","added_by":"auto","created_at":"2026-02-25 17:45:52","extension":"tif","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":4004248,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure2.tif","url":"https://assets-eu.researchsquare.com/files/rs-8434231/v1/b935c3c66543224f77ef451e.tif"},{"id":103442928,"identity":"ee83db0d-5dff-46d6-bb0e-c530ce8fed4e","added_by":"auto","created_at":"2026-02-25 17:45:53","extension":"tif","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":7230472,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure3.tif","url":"https://assets-eu.researchsquare.com/files/rs-8434231/v1/fd81ea8f83fc6ce3532682bb.tif"},{"id":103442925,"identity":"813d0af9-fb19-4d85-88e0-512294933dbd","added_by":"auto","created_at":"2026-02-25 17:45:52","extension":"tif","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":2533036,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstracts.tif","url":"https://assets-eu.researchsquare.com/files/rs-8434231/v1/3bc56b8fd21fa33d49500a91.tif"},{"id":103442932,"identity":"3f7ddc21-ee43-454a-9380-89c6e0a61767","added_by":"auto","created_at":"2026-02-25 17:45:53","extension":"tif","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":7392524,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-8434231/v1/be64596877bf229765a7844d.tif"},{"id":103507339,"identity":"66161513-0d4f-4983-9cd1-1eceff6a7f0a","added_by":"auto","created_at":"2026-02-26 13:41:03","extension":"pdf","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":865556,"visible":true,"origin":"","legend":"","description":"","filename":"WBFigures.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8434231/v1/4520c7d18c014c10d232db04.pdf"},{"id":103442931,"identity":"5123617f-2202-4bd4-8eea-7326e6ba99a5","added_by":"auto","created_at":"2026-02-25 17:45:53","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":12338,"visible":true,"origin":"","legend":"","description":"","filename":"file.docx","url":"https://assets-eu.researchsquare.com/files/rs-8434231/v1/5a223ab90730e42bceaec333.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dual-omics analysis of the effect and mechanism of Jiedu Huayu granule in acute liver failure","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAcute liver failure (ALF) is a critical condition resulting from severe liver damage, progressing rapidly with a broad spectrum of symptoms, including jaundice, coagulation dysfunction, hepatorenal syndrome, hepatic encephalopathy, and ascites [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Epidemiological studies indicate that the etiology of ALF is multifactorial. In Europe and the United States, particularly among young and middle-aged populations, acetaminophen (APAP) overdose is the primary cause, though other factors such as viral hepatitis, drug-induced liver injury, gestational liver failure, and autoimmune hepatitis also contribute [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. While liver transplantation remains the standard treatment for ALF, it carries a significant risk of postoperative complications, with a mortality rate of 33% [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Research into ALF caused by APAP overdose has revealed potential treatments, including 4-methylpyrazole, carmengadipil, dimethylbiguanide, and methylene blue; however, N-acetylcysteine remains the only approved clinical intervention [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Thus, early diagnosis and prompt treatment are critical to improving patient outcomes.\u003c/p\u003e \u003cp\u003eJiedu Huayu Granule (JDHYG) is a traditional Chinese medicine formulation composed of six herbs: \u003cem\u003eArtemisia capillaris\u003c/em\u003e, \u003cem\u003eHerba Hedyotidis Diffusae\u003c/em\u003e, \u003cem\u003eRadix Paeoniae Rubra\u003c/em\u003e, \u003cem\u003eRheum officinale\u003c/em\u003e, \u003cem\u003eRadix Curcumae\u003c/em\u003e, and \u003cem\u003eRhizoma Acori Tatarinowii\u003c/em\u003e. JDHYG has been used clinically for years, and both \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003ein vivo\u003c/em\u003e studies in rats have demonstrated its ability to inhibit NF-κB-mediated inflammatory pathways, providing protective effects against lipopolysaccharide (LPS)-induced liver injury [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In an ALF rat model, JDHYG significantly improved liver function and survival rates, with these effects closely linked to reduced inflammation and enhanced liver cell regeneration [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Toxicity assessments in rats administered varying doses of JDHYG revealed no damage to vital organs such as the liver, lungs, or kidneys, with histopathological analysis confirming the preservation of their structural integrity [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Despite these promising findings, the precise molecular mechanisms underlying JDHYG\u0026rsquo;s therapeutic effects in ALF remain poorly understood. Therefore, further investigation into its molecular actions and potential therapeutic benefits is essential to establish a scientific basis for its application in liver disease treatment.\u003c/p\u003e \u003cp\u003eProteomics is a powerful approach for investigating protein expression, structure, function, and interactions within specific cells, tissues, body fluids, and organisms. Alterations in protein levels can serve as indicators of underlying pathological or biological processes. Thus, analyzing protein composition and abundance is essential for identifying disease biomarkers and elucidating therapeutic mechanisms [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Transcriptomics, on the other hand, focuses on the study of gene transcription and its regulation within cells [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Liu et al. identified potential therapeutic targets for various clinical types of chronic kidney disease (CKD) by integrating plasma proteome and transcriptome data, providing a solid foundation for developing targeted CKD therapies [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Building on this approach, the current study applies transcriptomic and proteomic analyses to explore the molecular mechanisms of JDHYG in the treatment of ALF. The integration of multi-level biological data enables comprehensive examination of gene functions, regulatory networks, and molecular interactions, offering new insights into the complex pathways that underlie biological processes and disease mechanisms.\u003c/p\u003e \u003cp\u003eThis study employed transcriptomic and proteomic sequencing data from rat samples to identify biomarkers influencing the progression of ALF under JDHYG treatment. Bioinformatics tools were used to perform detailed analyses of the biological functions, pathways, and molecular regulatory networks associated with these biomarkers, which were then validated through animal experiments and molecular docking. The findings provide robust scientific evidence for a deeper understanding of ALF pathogenesis and support the clinical application of JDHYG as a therapeutic intervention.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Reagents\u003c/h2\u003e \u003cp\u003eJDHY, a traditional Chinese medicinal formulation, consists of six herbs: \u003cem\u003eArtemisia capillaries\u003c/em\u003e (30 g), \u003cem\u003eHerba Hedyotidis Diffusae\u003c/em\u003e (30 g), \u003cem\u003eRadix paeoniae rubra\u003c/em\u003e (50 g), \u003cem\u003eRheum officinale\u003c/em\u003e (15 g), \u003cem\u003eRadix curcumae\u003c/em\u003e (15 g), and \u003cem\u003eRhizoma Acori Tatarinowii\u003c/em\u003e (15 g)(plant name has been checked with \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://mpns.kew.org\u003c/span\u003e\u003cspan address=\"http://mpns.kew.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Each 1 g of JDHY granule is equivalent to 4.7 g of raw herbs and was supplied by Jiangyin Tian Jiang Pharmaceutical Co., Ltd. (Jiangsu, China). Previous studies on this compound, analyzed by HPLC, identified paeoniflorin as a key active component [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The compounds D-galactosamine hydrochloride (D-GalN, G0500) and lipopolysaccharide (LPS, L2880) were obtained from Sigma-Aldrich.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Animal and cells groups\u003c/h2\u003e \u003cp\u003e This study adhered to the ethical guidelines outlined in the Institutional Animal Care and Use Committee's (IACUC) protocol for the humane treatment and use of experimental animals (DW20230830-152). After one week of standard treatment, the rats were divided into three groups: control group (c), Alf group (m) and treatment group (z). Male SD rats (270 g, 8 weeks old) were purchased from Hunan slake Jingda experimental animal Co., Ltd. (license number scxk (Xiang) 2019-0004). Specifically, rats in ALF group were intraperitoneally injected with D-GalN (550 mg/kg body weight) and LPS (20 \u0026micro; g/kg body weight) to form a model at one time [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. The dose conversion of the animal model was based on the method described in the FDA publication \"estimation of the maximum safe starting dose in the initial clinical trial of therapeutic drugs in adult healthy volunteers\", assuming an adult weight of 70 kg, one dose per day. The conversion formula is used to explain the difference in metabolic rate between human body size and rats, ensuring that the dose given to animals is reasonably close to the relevant dose of human body. Rats in the jdhy treatment group were orally administered with jdhy at a dose of 3 g/kg/ day for 3 consecutive days. After the last oral administration of jdhy, d-galn/lps stimulation was given according to the ALF modeling method. The control group was given equal volume of distilled water (1ml / 100g / D) by gavage.. Six rats in each experimental group were randomly selected for transcriptome sequencing (RNA SEQ), and the remaining three rats were subjected to proteome sequencing, both of which were performed using rat liver.\u003c/p\u003e \u003cp\u003eIn parallel, hepatic stellate cells (HSCs) were cultured in DMEM supplemented with 10% fetal bovine serum at 37\u0026deg;C and 5% CO2 to 70% confluence. Cells were then seeded into 96-well plates at a density of 2 \u0026times; 10\u003csup\u003e4\u003c/sup\u003e cells per well, and divided into control, model, and JDHY treatment groups. ALF in the cell model was induced by incubation with D-galactose (8 mM) for 1 hour, followed by LPS (1 \u0026micro;g/mL) treatment. JDHY was administered at a concentration of 40 \u0026micro;g/mL based on LDH detection to determine the optimal intervention dose.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Identification of Chemical Components in JDHYG by UHPLC-Q-Orbitrap-MS\u003c/h2\u003e \u003cp\u003eThe chemical composition of JDHYG was determined using an UltiMate 3000 RS HPLC system with an AQ-C18150 \u0026times; 2.1 mm, 1.8 \u0026micro;m column (Welch). The chromatographic column was maintained at 35\u0026deg;C. Gradient elution was performed with aqueous phase A (0.1% formic acid) and organic phase B (methanol) at a flow rate of 0.30 mL/min. The gradient program used for elution is summarized in the table below.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime(min)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Detection of serum \u003cem\u003eALT\u003c/em\u003e, \u003cem\u003eAST\u003c/em\u003e, \u003cem\u003eTBIL\u003c/em\u003e, and plasma \u003cem\u003ePT\u003c/em\u003e\u003c/h2\u003e \u003cp\u003eSerum levels of \u003cem\u003eALT\u003c/em\u003e, \u003cem\u003eAST\u003c/em\u003e, and \u003cem\u003eTBIL\u003c/em\u003e in rats were measured using a fully automated biochemical analyzer (Shenzhen Mindray Biomedical Electronics Co., Ltd., ES-480 model), while \u003cem\u003ePT\u003c/em\u003e in rat plasma was assessed using a coagulation analyzer (DIAGNOSTICA STAGO, France, STA R MAX).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.5 Hematoxylin and Eosin\u003c/b\u003e (\u003cb\u003eH\u0026amp;E\u003c/b\u003e) \u003cb\u003eStaining\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eLiver tissues were preserved in 4% formalin (cat. no. G1101-25ml, Servicebio, Wuhan, China), cut into 3mm \u0026times; 3mm \u0026times; 3mm pieces, soaked in water for 6 hours, dried in a desiccator for 24 hours, and subsequently embedded, sectioned, stained, and imaged. Liver histopathology was examined using a light microscope (Nikon Eclipse E100, Nikon, Japan).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 TUNEL Staining\u003c/h2\u003e \u003cp\u003eFor TUNEL staining, liver tissue paraffin sections from SD rats were deparaffinized in xylene and rehydrated. Sections were incubated with proteinase K (G1205, Servicebio, Wuhan, China) in a 37\u0026deg;C incubator for 22 minutes, followed by incubation with membrane permeabilizing solution (G1204, Servicebio, Wuhan, China) for 20 minutes at room temperature. After a 10-minute incubation with buffer at room temperature, sections were treated with a TUNEL Kit (G1502, Servicebio, Wuhan, China) containing TDT enzyme, dUTP, and buffer in a 1:5:50 ratio in a 37\u0026deg;C incubator for 1 hour. Following DAPI (G1012, Servicebio, Wuhan, China) staining, images were captured under a fluorescence microscope (Nikon, Sendai, Ishinomaki, Japan) and analyzed using ImageJ software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Differential expression analysis\u003c/h2\u003e \u003cp\u003ePrior to RNA-seq and proteomic data analysis, quality control was performed. For RNA-seq, raw count values were derived using FeatureCounts software (v2.0.6) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] and transformed into fragments per kilobase million (FPKM) using the FPKM computational formula. Principal component analysis (PCA) was then performed to assess sample correlation and variance using the \u0026ldquo;prcomp\u0026rdquo; function in the \u0026ldquo;stats\u0026rdquo; package (v4.3.1) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. For proteome sequencing, data preprocessing was conducted using MaxQuant software (v2.1.4.0) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], applying the TMT-9plex method for quantitative analysis. Differentially expressed genes (DEGs) or proteins (DEPs) were identified between groups (Group M \u003cem\u003evs\u003c/em\u003e Group Z, Group M \u003cem\u003evs\u003c/em\u003e Group C) using the \u0026ldquo;DESeq2\u0026rdquo; package (v1.38.0) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] for RNA-seq and ANOVA for proteomics, with significance set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |log\u003csub\u003e2\u003c/sub\u003e Fold Change (FC)| \u0026gt; 0.5. Volcano plots, marking the top five up- and down-regulated genes/proteins by log\u003csub\u003e2\u003c/sub\u003eFC, and heatmaps were generated using the \u0026ldquo;ggplot2\u0026rdquo; package (v3.3.6) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and the \u0026ldquo;pheatmap\u0026rdquo; package (v1.0.12) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] for visualization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Functional network analysis and identification of key genes\u003c/h2\u003e \u003cp\u003eThe DEGs or DEPs across the three experimental groups were determined by comparing the opposite trends in DEGs1/DEPs1 and DEGs2/DEPs2 using the ggvenn package (v0.1.10) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Specifically, the analysis focused on the overlap between down-regulated genes/proteins in DEGs1/DEPs1 and up-regulated genes/proteins in DEGs2/DEPs2, as well as up-regulated genes/proteins in DEGs1/DEPs1 and down-regulated genes/proteins in DEGs2/DEPs2, leading to the identification of DEGs or DEPs from both sets. To further explore the biological functions of these DEGs or DEPs, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were performed using the clusterProfiler package (v4.7.1.3) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], with a significance threshold of p.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The results were visualized using the ggplot2 package. Protein-protein interaction (PPI) networks for DEGs or DEPs were analyzed using data from the STRING database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org/\u003c/span\u003e\u003cspan address=\"https://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with a confidence score\u0026thinsp;\u0026gt;\u0026thinsp;0.9 for DEGs and a score\u0026thinsp;=\u0026thinsp;0.15 for DEPs (species set to Rattus norvegicus). The networks were visualized using Cytoscape software (v3.9.0) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Additionally, the PANTHER database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.pantherdb.org\u003c/span\u003e\u003cspan address=\"http://www.pantherdb.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to categorize DEPs based on their functional annotations. The key genes were identified as the intersection between DEGs and DEPs using the VennDiagram package (v1.7.3) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], and their expression patterns were further analyzed using both RNA-seq and proteomic data through the DESeq2 package.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Enrichment analysis of key genes\u003c/h2\u003e \u003cp\u003eThe Spearman correlation between key genes and all other genes in the RNA-seq data was calculated using the psych package (v2.2.9) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Gene set enrichment analysis (GSEA) was then performed using the clusterProfiler package. The background gene set was selected from the Molecular Signatures Database (MSigDB) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/msigdb/index.jsp\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/msigdb/index.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) as \"c2.cp.kegg.v2023.1.Hs.symbols.gmt\". The top 10 pathways with the highest enrichment scores (p.adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were visualized using the enrichplot package (v1.18.0) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Positioning and distributions of key genes\u003c/h2\u003e \u003cp\u003eThe chromosomal localization of the key genes was obtained from the Ensembl database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://asia.ensembl.org/index.html\u003c/span\u003e\u003cspan address=\"https://asia.ensembl.org/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and their positions were visualized using the Circos package (v1.2.0) [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Tissue distribution data for the key genes were examined using the Multi-Gene Query function of the Genotype-Tissue Expression (GTEx) database (v8, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gtexportal.org/home/\u003c/span\u003e\u003cspan address=\"https://www.gtexportal.org/home/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). For further investigation of subcellular localization, the fast all sequence annotation (FASTA) sequence format for the key genes was searched in the NCBI database, and predicted scores for subcellular localization were obtained from the mRNALocater database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bio-bigdata.cn/mRNALocater/\u003c/span\u003e\u003cspan address=\"http://bio-bigdata.cn/mRNALocater/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Additionally, a Friends analysis was performed to explore the relationships between key genes using the GOSemSim package (v2.28.1) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.11 Networks of key genes prediction\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe ElMMO (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.mirz.unibas.ch/ElMMo3/\u003c/span\u003e\u003cspan address=\"http://www.mirz.unibas.ch/ElMMo3/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and microcosm (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ebi.ac.uk/enright-srv/microcosm/htdocs/targets/v5/\u003c/span\u003e\u003cspan address=\"http://www.ebi.ac.uk/enright-srv/microcosm/htdocs/targets/v5/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) databases were queried to identify microRNAs (miRNAs) targeting key genes, thereby exploring the regulatory relationships between these genes and miRNAs. Targeted miRNAs were determined through the overlap of two miRNA sets using the \"ggvenn\" package. Transcription factors (TFs) interacting with key genes were identified \u003cem\u003evia\u003c/em\u003e the ChEA3 database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://amp.pharm.mssm.edu/ChEA3\u003c/span\u003e\u003cspan address=\"https://amp.pharm.mssm.edu/ChEA3\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). A miRNA-mRNA-TF regulatory network was constructed using Cytoscape, incorporating the intersection of miRNAs and the top 20 TFs with the highest scores. Disease associations related to key genes and ALF were identified by searching the Comparative Toxicogenomics Database (CTD) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ctdbase.org/\u003c/span\u003e\u003cspan address=\"https://ctdbase.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), focusing on the top 10 diseases ranked by inference score. The network was visualized using Cytoscape software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.12 Prediction of drugs and molecular docking\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eDrug-gene interactions related to key genes were obtained from the Drug-Gene Interaction Database (DGIdb) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dgidb.org\u003c/span\u003e\u003cspan address=\"https://dgidb.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with a combined score\u0026thinsp;\u0026gt;\u0026thinsp;2000. Molecular docking was then employed to construct a computational binding model to predict drug-gene interactions. The three-dimensional structures of drugs were retrieved from PubChem (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), while receptor protein structures of key genes were sourced from the Protein Data Bank (PDB) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.rcsb.org\u003c/span\u003e\u003cspan address=\"http://www.rcsb.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Molecular docking was performed using the CB-Dock platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cadd.labshare.cn/cb-dock/php/manual.php\u003c/span\u003e\u003cspan address=\"https://cadd.labshare.cn/cb-dock/php/manual.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), guided by drug-gene interactions to predict binding affinity. Binding energy was calculated and visualized using PyMOL software (v 3.0.3) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo identify active ingredients in the six traditional Chinese medicines in JDHY, searches were performed in the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.91tcmsp.com/#/home\u003c/span\u003e\u003cspan address=\"https://www.91tcmsp.com/#/home\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) for Artemisia capillaris, Hedyotis diffusa, Radix Paeoniae Rubra, Rheum officinale, Curcuma aromatica, and Acorus tatarinowii. Potential active ingredients were identified, and an ADME evaluation system (absorption, distribution, metabolism, and excretion) was used to select those with oral bioavailability (OB)\u0026thinsp;\u0026ge;\u0026thinsp;30% and drug likeness (DL)\u0026thinsp;\u0026ge;\u0026thinsp;0.1 for subsequent analysis.\u003c/p\u003e \u003cp\u003eActive ingredients with higher OB were prioritized for molecular docking simulation. The 3D molecular structures of these ingredients were obtained from PubChem (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and protein structures of key genes predicted by AlphaFold were obtained from UniProt (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.uniprot.org/\u003c/span\u003e\u003cspan address=\"https://www.uniprot.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Molecular docking simulation analysis was conducted using the CB-Dock platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cadd.labshare.cn/cb\u003c/span\u003e\u003cspan address=\"https://cadd.labshare.cn/cb\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e - dock/index.php).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.13 Immunohistochemical staining\u003c/h2\u003e \u003cp\u003eFor immunohistochemical staining, paraffin-embedded rat liver tissue sections were dewaxed and rehydrated. Primary antibodies (fth1 [1:50], necap2 [1:300], and prdx2 [1:600]) were applied to the sections and incubated overnight at 4\u0026deg;C. The sections were then incubated with HRP-labeled goat anti-rabbit IgG (1:200, gb23303, ServiceBio) for 50 minutes at room temperature. Following incubation with DAB substrate (g1212, ServiceBio), the sections were counterstained with hematoxylin. Images were captured using a light microscope, and analysis was performed using AipathWell\u0026reg; Software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.14 Real-time quantitative PCR analysis\u003c/h2\u003e \u003cp\u003ePrimers were synthesized by Wuhan Saville Biotechnology Co., Ltd. (Supplementary Table\u0026nbsp;1). The reaction system was 20 \u0026micro;L, and reverse transcription was carried out using the Swescript All-in-One RT Supermix for qPCR (One Step gDNA Remover) kit (Wuhan Saville Biotechnology Co., Ltd.; product number: g3337). The thermal cycling conditions were as follows: pretreatment at 95\u0026deg;C for 30 seconds, denaturation at 95\u0026deg;C for 15 seconds, annealing and extension at 60\u0026deg;C for 30 seconds, with 40 cycles in total. The real-time PCR system (Agilent, model: AriaMX, California, USA) was used to determine the CT values for each gene, and relative gene expression was calculated using the 2\u003csup\u003e\u0026minus; △△ CT\u003c/sup\u003e method.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e2.15 Western blot analysis\u003c/h2\u003e \u003cp\u003eTotal protein was extracted from liver tissue, and protein concentration was determined using a BCA assay. Proteins were separated by 10% and 15% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and transferred to PVDF membranes. After blocking with 5 mL of 5% skim milk solution for 30 minutes, the membranes were incubated overnight at 4\u0026deg;C with primary antibodies: ACTIN (1:2000), FTH1 (1:1000), NECAP2 (1:1000), and Prdx2 (1:1000). After 24 hours, the membranes were incubated with a secondary antibody (1:5000) for 30 minutes at room temperature. Protein bands were visualized using an ECL assay, and the gray value of the bands was quantified using ImageJ software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e2.16 LDH and CCK-8 analysis\u003c/h2\u003e \u003cp\u003eCytotoxicity of drugs on cells was measured using LDH release at 490 nm, and HSC cell viability was assessed using the CCK-8 reagent at OD450 nm.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e2.17 Statistical analysis\u003c/h2\u003e \u003cp\u003eBioinformatics analyses were performed using the R programming language (v 4.2.2). The Wilcoxon rank-sum test was applied to assess differences between two groups, with statistical significance set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.1 JDHYG chemical composition analysis\u003c/h2\u003e \u003cp\u003eHHPLC-Q-Orbitrap-MS was employed to determine the chemical composition of JDHYG in both positive and negative ion modes. The total ion current (TIC) chromatogram is presented in \u003cb\u003eFig.\u0026nbsp;1\u003c/b\u003e. By comparing the obtained mass spectrometry data with standard materials and the mzCloud mass spectrometry library, 468 compounds were identified in the samples. Of these, 326 compounds had a comprehensive score exceeding 60 points. The active ingredients of JDHYG include flavonoids, anthraquinones, terpenes, and others \u003cb\u003e(Supplementary Table\u0026nbsp;2)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.2 JDHYG reduces serum \u003cem\u003eALT\u003c/em\u003e, \u003cem\u003eAST\u003c/em\u003e, \u003cem\u003eTBIL\u003c/em\u003e, and plasma \u003cem\u003ePT\u003c/em\u003e levels\u003c/h2\u003e \u003cp\u003eCompared to the model group, the JDHYG group exhibited significant reductions in \u003cem\u003eALT\u003c/em\u003e, \u003cem\u003eAST\u003c/em\u003e, \u003cem\u003eTBIL\u003c/em\u003e, and \u003cem\u003ePT\u003c/em\u003e levels (Fig.\u0026nbsp;2), suggesting that JDHYG improves liver function by modulating these biomarkers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e3.3 H\u0026amp;E\u003c/b\u003e s\u003cb\u003etaining results\u003c/b\u003e\u003c/h2\u003e \u003cp\u003ePathological changes were further examined in the control, JDHYG, and ALF groups using H\u0026amp;E staining at varying magnifications (Fig.\u0026nbsp;3). In the normal group, the liver tissue membrane structure was clear, and the boundaries between liver lobules were indistinct. The central vein was centrally located in the lobules, surrounded by hepatocytes and hepatic sinusoids arranged radially. In the ALF group, extensive hepatocyte necrosis (black arrow), nuclear fragmentation, and dissolution were observed. Hepatic steatosis was more pronounced (green arrow), with circular vacuoles of varying sizes appearing in the cytoplasm. Clusters of chromatin at the edges of liver cell nuclei (light green arrows) and visible bruising (orange arrows) were also evident. In the JDHYG group, localized hepatocyte necrosis (black arrow), nuclear fragmentation, increased cytoplasmic eosinophilia, and mild bleeding (yellow arrow) were observed. Multiple hepatocellular steatosis (green arrow) with small vacuoles appeared in the cytoplasm, chromatin edge clustering (light green arrow), and rare bruising (orange arrow) were noted.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Further exploration of TUNEL changes in liver tissue of rats in each group\u003c/h2\u003e \u003cp\u003eTo assess the apoptosis rate, TUNEL staining was performed. Compared to the control group, the TUNEL positivity rate was elevated in both the ALF and JDHYG groups. Specifically, the apoptosis rate was approximately 13% in the ALF group, compared to 3.5% in the JDHYG group (Fig.\u0026nbsp;4).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Diverse array of functions observed in DEGs\u003c/h2\u003e \u003cp\u003eGene expression variability within individual samples and across groups was clearly demonstrated (Fig.\u0026nbsp;5A). A total of 6,265 DEGs1 were identified between the M and Z groups, with 3,648 genes exhibiting higher expression in group Z and 2,617 genes showing reduced expression (Fig.\u0026nbsp;5B). In group C, 5,567 genes with elevated expression and 3,089 genes with reduced expression were classified as DEG2 (Fig.\u0026nbsp;5C). An additional 2,863 DEGs were identified (Fig.\u0026nbsp;5D). GO analysis revealed that these DEGs were associated with multiple biological processes (BP), including leukocyte migration, chemotaxis, taxis, positive regulation of cell adhesion, and positive regulation of the MAPK cascade. Cellular component (CC) analysis highlighted the external side of the plasma membrane, actin cytoskeleton, cell leading edge, membrane raft, and membrane microdomain. In terms of molecular function (MF), enriched categories included actin binding, phospholipid binding, signaling receptor activator activity, receptor-ligand activity, and GTPase regulator activity (Fig.\u0026nbsp;5E). KEGG pathway analysis identified significant pathways such as leukocyte transendothelial migration, hematopoietic cell lineage, tuberculosis, leishmaniasis, and chemokine signaling (Fig.\u0026nbsp;5F). Additionally, a complex PPI network was constructed, demonstrating close protein-level interactions among the DEGs (Fig.\u0026nbsp;5G).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Variable biological function of the DEPs involved\u003c/h2\u003e \u003cp\u003eA total of 114 DEPs1 were identified (84 upregulated and 30 downregulated) (Fig.\u0026nbsp;6A), along with 270 DEPs2 (70 upregulated and 200 downregulated) (Fig.\u0026nbsp;6B), yielding 67 overlapping DEPs (Fig.\u0026nbsp;6C). Enrichment analysis revealed significant associations with various GO BPs, including olefinic compound metabolism, diterpenoid metabolism, terpenoid metabolism, isoprenoid metabolism, and hormone metabolism; GO CCs such as the cell cortex, ribosome, cell body fiber, blood microparticles, and septin rings; and GO MFs including oxidoreductase activity (involving the reduction of molecular oxygen to water), retinol binding, ubiquitin-protein transferase inhibitor activity, and carboxylesterase activity (Fig.\u0026nbsp;6D). The \"Biosynthesis of unsaturated fatty acids\" pathway was enriched in DEPs, involving genes like Fads2, Fads1, and Acnat2. The PPI network revealed an interaction between Fth1 and Prdx2 at the protein level (Fig.\u0026nbsp;6E). Additionally, a detailed summary of the functional and structural characteristics of the DEPs is provided in \u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e, highlighting Fth1\u0026rsquo;s association with ferritin heavy chain (gene name), ferritin heavy chain (PANTHER family/subfamily), and storage protein (PANTHER protein class).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Promising effectiveness of three identified key genes\u003c/h2\u003e \u003cp\u003eAfter removing duplicate genes, the intersection of DEGs and DEPs identified four common genes (Fig.\u0026nbsp;7A). Among these, three key genes\u0026mdash;Fth1, Necap2, and Prdx2 (FTH1, NECAP2, and PRDX2 in humans)\u0026mdash;were selected, while Igh-6 was excluded due to the absence of a homolog in the human genome. RNA-seq data revealed reduced expression of these three key genes in the C and Z groups compared to group M (Fig.\u0026nbsp;7B). Similarly, proteomic data showed diminished levels of these genes in both the C and Z groups relative to group M. No significant differences were observed between the C and Z groups, indicating that JDHY followed a similar trend to the control group (Fig.\u0026nbsp;7C). A strong positive correlation was found among the three key genes, with all correlation coefficients exceeding 0.7 (Fig.\u0026nbsp;7D). Notably, these genes were significantly correlated with the ribosome, fatty acid metabolism, and chemokine signaling pathways, suggesting that they may play a critical role in regulating ALF through these pathways (Fig.\u0026nbsp;7E-G).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Varied distributions exhibited in key genes\u003c/h2\u003e \u003cp\u003eAll three key genes were identified as residing on distinct euchromatic chromosomes. Specifically, the NECAP2 gene is located on chromosome 1, PRDX2 is on chromosome 19, and FTH1 is situated on chromosome 11 (Fig.\u0026nbsp;8A). Subsequent analysis revealed the widespread expression of these genes across various tissues. Notably, NECAP2 exhibited the highest expression in the spleen, FTH1 was most abundant in blood and fibroblasts, and PRDX2 showed the highest expression in the adrenal gland (Fig.\u0026nbsp;8B). The subcellular localization of the key genes was also examined, revealing their presence in multiple cellular compartments. Notably, the three genes showed the highest expression levels in the cytoplasm and the lowest in the mitochondria (Fig.\u0026nbsp;8C). Additionally, FTH1 demonstrated the strongest correlation with the other two key genes, suggesting its significant role in regulating their expression (Fig.\u0026nbsp;8D).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e3.9 Prediction of networks and interaction for key genes\u003c/h2\u003e \u003cp\u003eA total of 13 miRNAs were identified as associated with the three key genes across two databases, with 11 miRNAs linked to NECAP2. Among the top 20 TFs, 15 were found to be related to FTH1 (Fig.\u0026nbsp;9A). Notably, the top 10 diseases associated with each gene highlighted the involvement of all three key genes in chemical and drug-induced liver injury, hyperplasia, necrosis, weight loss, and prenatal exposure delayed effects (Fig.\u0026nbsp;9B). Additionally, drug-gene interaction analysis identified 36 drugs associated with the three key genes: FTH1 was linked to 13 drugs, PRDX2 to 24 drugs, and NECAP2 to one drug. Intriguingly, all three genes shared a common drug, cyclosporin A (Fig.\u0026nbsp;9C). The highest-scoring drugs associated with the key genes are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Molecular docking simulations were performed using 7-ACA for FTH1 and Congo red for PRDX2, due to the unavailability of three-dimensional structures for potassium nitrate and cyclosporin A. The docking results indicated stable binding interactions, with binding energies of -5.1 kcal/mol for FTH1 and 7-ACA, and \u0026minus;\u0026thinsp;11.5 kcal/mol for PRDX2 and Congo red. FTH1 formed two hydrogen bonds with 7-ACA, involving two distinct SER residues (Fig.\u0026nbsp;9D). PRDX2, on the other hand, interacted with Congo red through four hydrogen bonds, including a GLY residue, a THR residue, and two GLU residues (Fig.\u0026nbsp;9E).\u003c/p\u003e \u003cp\u003eFor active ingredients of each key gene, those with higher OB were prioritized for molecular docking analysis. The selected compounds are listed in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The 3D molecular structures of six active ingredients were sourced from the PubChem database, while protein structures for FTH1, NECAP2, and PRDX2 were predicted by AlphaFold and retrieved from the UniProt database. Molecular docking simulations were then performed using the CB-Dock database, with Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presenting the minimum binding energies from these simulations. The results are visually depicted in \u003cb\u003eSupplementary Figs.\u0026nbsp;1\u0026ndash;3\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe scores for drugs between key genes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrug\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCombined.Score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGenes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePOTASSIUM NITRATE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6403.334808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFTH1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7-ACA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4739.656184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFTH1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCongo red\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3054.58855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePRDX2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ecyclosporin A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e194220.8108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePRDX2;NECAP2;FTH1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSelection of Active Ingredients of Key Components in Six Traditional Chinese Medicines\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMol ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMolecule Name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOB (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ecomponent\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMOL008045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4'-Methylcapillarisin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eArtemisia capillaris\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMOL000471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ealoe-emodin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e83.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRheum officinale\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMOL007016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePaeoniflorigenone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRadix Paeoniae Rubra\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMOL000098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003equercetin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHerba Hedyotidis Diffusae\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMOL003571\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003espathulenol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRhizoma Acori Tatarinowii\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMOL004313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZedoarolide B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e135.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRadix Curcumae\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMolecular Docking Results Table for FTH1, NECAP2, and PRDX2\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMolecule\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePubChem CID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVina Score(kcal/mol)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFTH1(P02794)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4'-Methylcapillarisin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5320438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ealoe-emodin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePaeoniflorigenone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70698143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003equercetin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5280343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003espathulenol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZedoarolide B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73353446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNECAP2(Q9NVZ3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4'-Methylcapillarisin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5320438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ealoe-emodin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-7.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePaeoniflorigenone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70698143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-7.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003equercetin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5280343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZedoarolide B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73353446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePRDX2(P32119)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4'-Methylcapillarisin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5320438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ealoe-emodin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10207\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePaeoniflorigenone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70698143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003equercetin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5280343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003espathulenol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-5.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZedoarolide B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73353446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-6.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e3.10 Animal experiment mRNA and protein expression\u003c/h2\u003e \u003cp\u003eImmunohistochemistry (Fig.\u0026nbsp;10A, \u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e) and Western blot (Fig.\u0026nbsp;10B, 10C) were employed for protein expression analysis, while qPCR (Fig.\u0026nbsp;10D) validated mRNA expression. The results revealed elevated protein and mRNA levels of FTH1, PRDX2, and NECAP2 in the ALF model, with a reduction observed following JDHYG treatment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003e3.11 JDHYG inhibits apoptosis of hepatic stellate cells\u003c/h2\u003e \u003cp\u003eThe modeling effect was first confirmed (Fig.\u0026nbsp;11A). CCK8 assays indicated reduced cell proliferation in the model group, and LDH assays showed increased LDH levels, suggesting apoptosis of HSCs. LDH assays were used to determine the non-toxic concentration of traditional Chinese medicine (Fig.\u0026nbsp;11B), with 40 \u0026micro;g/mL identified as the lowest non-toxic dose. This concentration was applied in subsequent interventions. To evaluate the therapeutic effects of the drugs (Fig.\u0026nbsp;11C), cells were pretreated with JDHYG for 1 hour before D-galn/LPS induction. After 24 hours, LDH levels were measured, showing a significant reduction following drug treatment. CCK8 assays further revealed a marked increase in HSC proliferation after drug intervention.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003e3.12 Cell experiment mRNA and protein expression\u003c/h2\u003e \u003cp\u003eWestern blot (Fig.\u0026nbsp;11D, 11E) and qPCR (Fig.\u0026nbsp;11F) confirmed a decrease in the protein and mRNA expression of FTH1, PRDX2, and NECAP2 following JDHYG treatment.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eALF is a critical clinical condition characterized by rapid hepatocyte damage, often leading to elevated mortality [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Research highlights that ALF results from complex pathological and physiological processes, including mitochondrial oxidative stress, inflammation, and apoptosis [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. JDHYG, a granule formulation derived from six traditional Chinese medicinal herbs, has shown protective effects against D-Galn\u0026thinsp;+\u0026thinsp;LPS-induced liver injury in experimental settings [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. This study employed RNA sequencing and proteomic analysis to identify numerous DEGs and DEPs associated with ALF. Bioinformatics analysis revealed that these DEGs and DEPs are involved in several biological processes and pathways, such as cell migration, fatty acid metabolism, and chemical signaling. Notably, Fth1, Necap2, and Prdx2 were identified as potential key players in ALF pathogenesis, a hypothesis validated by experimental data. Additionally, the study predicted the miRNA regulatory networks, transcription factor interactions, and potential drug-binding profiles of these key genes, thereby presenting novel therapeutic targets for ALF treatment.\u003c/p\u003e \u003cp\u003eThe NECAP2 (Adaptin ear-binding coat-associated protein 2) gene plays a pivotal role in regulating endocytosis by maintaining receptor levels on the cell surface. It orchestrates the formation of the clathrin coat on early endosomes, a key step in the rapid endocytic cycle. Elevated NECAP2 expression has been observed in low-grade gliomas and hepatocellular carcinoma (HCC), where it is strongly associated with aggressive tumor behavior and poor patient prognosis. Specifically, in low-grade gliomas, NECAP2 is implicated in immune cell infiltration and oxidative stress, while in HCC, its expression is regulated by NF-YA and contributes to the modulation of the MEK/ERK signaling pathway [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. PRDX2 (Peroxiredoxin 2), an antioxidant enzyme, protects cells from oxidative stress and may enhance the antiviral activity of CD8(+) T cells [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. It also plays a role in hepatocyte senescence induced by Cr (VI), which is potentially linked to liver disease development [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Furthermore, low PRDX2/3 expression is associated with poor prognosis in patients with HCC, suggesting that PRDX2 may contribute to HCC progression and correlate with cell proliferation [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. As an iron storage protein, FTH1 (Ferritin Heavy Chain 1) regulates iron metabolism and oxidative stress, providing protective functions in liver diseases. Alterations in FTH1 expression influence hepatocyte susceptibility to ferroptosis, a process closely associated with liver disease progression [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. FTH1 may also serve as a promising therapeutic target for liver diseases, with potential for improving disease prognosis through modulation of its expression or function [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Experimental findings from PCR, Western blot, and immunohistochemistry in this study demonstrate that JDHYG significantly reduces the mRNA and protein levels of NECAP2, PRDX2, and FTH1. In conclusion, NECAP2, PRDX2, and FTH1 are critical factors in the onset and progression of liver failure and related liver diseases. Modulating these molecules affects essential cellular processes and is closely linked to tumor malignancy and patient prognosis. Therefore, targeting these molecules could offer novel strategies for the early diagnosis and treatment of liver diseases.\u003c/p\u003e \u003cp\u003eTo explore the specific mechanisms of FTH1, NECAP2, and PRDX2 in ALF, GSEA enrichment analysis revealed that the enriched pathways for these proteins include ribosome biogenesis, chemokine signaling, and fatty acid metabolism. Ribosomes are essential for protein synthesis, and when hepatocytes are damaged, ribosomal function is compromised, impairing the production of vital proteins required for liver repair and normal function [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Chemokines play a pivotal role in immune responses by attracting immune cells to sites of inflammation [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In ALF, increased chemokine production can lead to immune cell accumulation in the liver, exacerbating the inflammatory response and hepatocyte injury. Furthermore, fatty acid metabolism is essential for energy production and cellular signaling, and disruptions in this pathway during ALF can impair energy availability and cellular function, hindering the liver\u0026rsquo;s ability to repair and regenerate [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Collectively, the elevated expression of NECAP2, PRDX2, and FTH1 contributes to ALF by modulating ribosome function, chemokine signaling, and fatty acid metabolism.\u003c/p\u003e \u003cp\u003eIn drug prediction, 7-ACA exhibits strong binding interactions with FTH1, and Congo red binds to PRDX2. 7-ACA (7-aminocephalosporanic acid), a key intermediate in β-lactam antibiotic synthesis, is essential for producing various semi-synthetic cephalosporins [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. While 7-ACA itself is not used as a standalone drug, it is integral to the development of cephalosporin antibiotics, which possess broad-spectrum antibacterial properties and are primarily used to treat infections caused by sensitive bacteria [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Congo red, a traditional histological dye, is used to detect amyloid deposits. It binds to amyloid fibrils, prevents misfolding, stabilizes protein monomers, reduces toxic oligomers, and has shown potential in improving models of neurodegenerative diseases such as Alzheimer\u0026rsquo;s disease [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. In summary, both 7-ACA and Congo red show considerable promise in drug development, potentially offering new therapeutic insights for diseases associated with FTH1 and PRDX2.\u003c/p\u003e \u003cp\u003ePrevious studies have demonstrated that JDHY can inhibit the inflammatory pathway mediated by NF-κB. In the pathogenesis of ALF, NF-κB, as a critical transcription factor, is rapidly activated, triggering the transcription of a series of pro-inflammatory cytokines and chemokines. This initiates a robust inflammatory cascade, exacerbating liver damage [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. JDHY effectively blocks this process, reducing the release of inflammatory mediators and mitigating liver inflammation [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Notably, JDHY also upregulates CD163/sCD163 levels through the NF-κB signaling pathway [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. CD163 is predominantly expressed on the surface of mononuclear macrophages, and its soluble form, sCD163, can be shed into the bloodstream. In the ALF state, immune dysregulation and excessive inflammation occur. JDHY enhances the ability of mononuclear macrophages to clear inflammatory mediators and pathogen-associated molecular patterns by regulating CD163/sCD163 levels while inhibiting the excessive activation of inflammatory cells. This precise modulation of immune responses further alleviates the inflammatory state of the liver [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Additionally, JDHY has been shown to alleviate oxidative stress and mitochondrial damage in liver cells, likely by promoting the expression of the PI3K/AKT/mTOR signaling pathway [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Therapeutic mechanisms of JDHY particles on liver regeneration have been analyzed, with findings showing that JDHY promotes liver regeneration by enhancing DNA replication and improving cholesterol metabolism, thereby preventing D-GalN/LPS-induced ALF in rats [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. JDHY also alleviates liver injury by regulating the expression levels of IL-2, TLR4, and PCNA in rats [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In conclusion, the multiple mechanisms of action of JDHY, as revealed in previous studies, provide compelling evidence for the results of this study from various perspectives, highlighting the multi-target and multi-pathway characteristics of JDHY in ALF treatment. While prior studies have extensively explored these mechanisms, this study successfully identifies three key genes\u0026mdash;NECAP2, PRDX2, and FTH1\u0026mdash;associated with the therapeutic effect of JDHY on ALF. Through combined proteomics and transcriptomics analysis, this study emphasizes the role of these specific genes in ALF and related biological processes, while molecular docking further reveals substances with affinity for these key genes, enhancing the previously established multi-level therapeutic mechanisms.\u003c/p\u003e \u003cp\u003eIn conclusion, this study successfully identified NECAP2, PRDX2, and FTH1 as key genes involved in the treatment of ALF through a combined analysis of proteomics and transcriptomics, demonstrating that JDHYG may exert its therapeutic effects by modulating the expression of these genes. However, certain limitations remain, particularly the lack of experimental validation through techniques such as gene knockout. Future research should aim to validate the functions of these key genes experimentally and further elucidate the underlying mechanisms of JDHYG\u0026rsquo;s action. Additionally, at the molecular level, single-cell sequencing facilitates the analysis of the expression dynamics of FTH1, PRDX2, and NECAP2 across different liver cell subpopulations before and after JDHY treatment, providing single-cell resolution insights into the cell types involved. Simultaneously, protein-protein interaction omics will be employed to construct a comprehensive interaction network between these three key proteins and JDHY\u0026rsquo;s active ingredients, enabling a deeper understanding of their molecular regulatory mechanisms. Further investigation into the clinical applicability of JDHY in treating ALF will also be explored. This series of in-depth and comprehensive research efforts is expected to provide a solid theoretical foundation and practical guidance for ALF treatment strategies, advancing medical technologies in this field and delivering significant clinical benefits to patients.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 284px;\"\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFull name\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eNCBI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eNational Center for Biotechnology Information\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eGTEx\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eGenotype-Tissue Expression\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003emiRNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eMicroRNAs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eTFs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eTranscription Factors\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eCTD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eComparative Toxicogenomics Database\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003ePPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eProtein-Protein Interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eGEO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eGene Expression Omnibus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eMSigDB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eMolecular Signatures Database\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eDGIdb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eDrug-Gene Interaction Database\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003ePDB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eProtein Data Bank\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eCB-Dock\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eCavity-Detection Guided Blind Docking\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eGO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eGene Ontology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eKEGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eKyoto Encyclopedia of Genes and Genomes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eGSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eGene Set Enrichment Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eLPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eLipopolysaccharide\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eD-GalN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eD- galactosamine Hydrochloride\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eCKD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eChronic Kidney Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eAPAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eAcetaminophen\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eIACUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eInstitutional Animal Care and Use Committee\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eHSCs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eHepatic Stellate Cells\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003ePCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003ePrincipal Component Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003ePANTHER\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 284px;\"\u003e\n \u003cp\u003eProtein ANalysis THrough Evolutionary Relationships\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll experimental protocols were approved by the ethics licensing Committee of Guangxi University of traditional Chinese medicine (No : DW20230830-152). All experimental methods were in accordance with ARRIVE guidelines (https://arriveguidelines.org).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePublication Consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHao Pei, Feng-lan Wu, and Na wang consistent contributions should be regarded as joint first authors. Hao Pei and Yue-qiao Chen : Conceptualization. Hao Pei: methodology. Na wang: software. Han Pei and Rui Qin: validation. Feng-lan Wu and Ri-yun Zhang: formal analysis. Yan-yan Zhang: investigation. Yue-qiao Chen: resources. Hao Pei : data curation. Hao Pei: writing - original draft preparation. Yue-qiao Chen: writing\u0026mdash;review and editing. Hao Pei: visualization. Na wang: supervision. Yue-qiao Chen: project administration. Yue-qiao Chen: funding acquisition. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by National Natural Science Foundation Project (grant numbers: 82060848, 82160888) , Guangxi University of Traditional Chinese Medicine Introduction Doctoral Research Initiation Fund Project: (grant numbers: 2023BS030) and Guangxi Natural Science Foundation Project (grant numbers: 2023GXNSFAA026361, 2024GXNSFBA010218).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Bullet Edits Limited for the linguistic editing and proofreading of the manuscript.\u0026nbsp;Special thanks to the Teacher from the Medical Molecular Laboratory of Guangxi University of Traditional Chinese Medicine, In conclusion, we extend our thanks to everyone who has supported and assisted us along the way. Without your support, this research would not have been possible.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe transcriptome data generated and analyzed in this study can be viewed in NCBI database, No.: PRJNA1401552, website: https://www.ncbi.nlm.nih.gov/sra/PRJNA1401552. The proteome data generated and analyzed in this study can be viewed in Pride database, No.: PXD073039, website: http://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD073039\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eShingina, A. et al. Acute Liver Failure Guidelines. \u003cem\u003eAm. J. Gastroenterol.\u003c/em\u003e \u003cb\u003e118\u003c/b\u003e (7), 1128\u0026ndash;1153 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArshad, M. A., Murphy, N. \u0026amp; Bangash, M. N. Acute liver failure. \u003cem\u003eClin. 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(2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, T. et al. Jie-Du‐Hua‐Yu Granules Promote Liver Regeneration in Rat Models of Acute Liver Failure: miRNA‐mRNA Expression Analysis. \u003cem\u003eEvidence-Based Complement. Altern. Med.\u003c/em\u003e ;\u003cb\u003e2020\u003c/b\u003e(1). (2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Acute liver failure, Jiedu huayu granule, Transcriptome sequencing, Proteome sequencing, Molecular docking","lastPublishedDoi":"10.21203/rs.3.rs-8434231/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8434231/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eAcute liver failure is a severe condition with high mortality, requiring effective treatments. Jiedu Huayu Granule, a traditional Chinese medicine, shows protective effects, but its molecular mechanisms remain unclear. This study aims to investigate the therapeutic mechanisms of Jiedu Huayu Granule in acute liver failure using integrated transcriptomic and proteomic analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eTranscriptomic and proteomic profiling of rat models identified three key genes (Fth1, Necap2, and Prdx2) whose expression was upregulated in acute liver failure and subsequently downregulated by Jiedu Huayu Granule treatment. Animal and cellular experiments confirmed these expression changes. Bioinformatics analysis linked these genes to crucial pathways including ribosome function, chemokine signaling, and fatty acid metabolism. Molecular docking predicted stable binding interactions between active components of the herbal formula and the proteins encoded by these key genes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eJiedu Huayu Granule may exert its therapeutic effects against acute liver failure by modulating the expression of Fth1, Necap2, and Prdx2 and their associated pathways. These findings provide a molecular basis for the clinical application of Jiedu Huayu Granule and suggest novel potential targets for acute liver failure treatment.\u003c/p\u003e","manuscriptTitle":"Dual-omics analysis of the effect and mechanism of Jiedu Huayu granule in acute liver failure","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-25 17:45:47","doi":"10.21203/rs.3.rs-8434231/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-10T17:51:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-17T07:32:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"209822593678840390352534634170276453882","date":"2026-03-09T07:53:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-09T01:04:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"267963366819200375949147158549322450237","date":"2026-02-26T14:54:32+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-24T07:08:30+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-16T15:06:57+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-01-14T15:32:24+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-14T05:04:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-01-14T04:58:34+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"db159801-69d9-42c5-bd26-701814352b03","owner":[],"postedDate":"February 25th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":63429847,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":63429848,"name":"Health sciences/Diseases"},{"id":63429849,"name":"Biological sciences/Drug discovery"},{"id":63429850,"name":"Biological sciences/Molecular biology"}],"tags":[],"updatedAt":"2026-04-29T04:24:24+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-25 17:45:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8434231","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8434231","identity":"rs-8434231","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}