Mechanism elucidation of Polygonatum sibiricum-Prepared Cervus elaphus velvet antler in intervening non-alcoholic fatty liver disease based on network analysis combined with proteomics

Zihao Zhao; Minqian Li; Hongbo Teng; Xv Wang; Mengqi Shi; Zhongmei He; Ying Zong; Weijia Chen; Jianan Geng; Jia Zhou; Yan Zhao

doi:10.1038/s41598-026-55726-z

. 2026 Jun 2;16:25145. doi: 10.1038/s41598-026-55726-z

Mechanism elucidation of Polygonatum sibiricum-Prepared Cervus elaphus velvet antler in intervening non-alcoholic fatty liver disease based on network analysis combined with proteomics

Zihao Zhao ^1,², Minqian Li ^1,², Hongbo Teng ^1,², Xv Wang ^1,², Mengqi Shi ^1,², Zhongmei He ^1,², Ying Zong ^1,², Weijia Chen ^1,², Jianan Geng ^1,², Jia Zhou ^1,², Yan Zhao ^1,^2,^✉

PMCID: PMC13470484 PMID: 42230683

Abstract

To establish the standardized processing technology of velvet antler stir-fried with Polygonatum sibiricum juice, elucidate the core mechanism underlying the enhanced anti-non-alcoholic fatty liver disease (NAFLD) efficacy of the processed product, and identify the key bioactive components. Taking protein content as the screening index, this study optimized the processing parameters of Polygonatum sibiricum-Prepared Cervus elaphus Velvet Antler (PC-PR) using single-factor experiments combined with response surface methodology (RSM). The in vivo pharmacodynamic efficacy was validated in a high-fat diet-induced NAFLD rat model. Multi-omics technologies were integrated to screen for core differential proteins enriched after processing. The in vitro activity of the target component was verified in an oleic acid-induced inflammatory model of HepG2 cells, and Western blot (WB) analysis was employed to decipher its molecular regulatory mechanism. A standardized processing technology for PC-PR was successfully established, which induced specific alterations in the compositional profile of velvet antler. The processed product significantly improved blood glucose, body weight, liver function, and lipid metabolism indicators in NAFLD model rats, with superior efficacy compared to non-optimally processed velvet antler. Multi-omics screening and in vitro experiments confirmed that Q28237 is a core bioactive component specifically enriched after processing, exhibiting potent lipid-lowering and anti-inflammatory activities. Mechanistic studies revealed that Q28237 alleviates the pathological progression of NAFLD by constructing a “component-pathway-phenotype” regulatory network, which involves inhibiting the overactivation of epidermal growth factor receptor (EGFR), promoting the activation of the phosphatidylinositol 3-kinase-protein kinase B (PI3K-AKT) pathway, and activating estrogen receptor1 (ESR1) to exert synergisticanti-inflammatory effects.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-026-55726-z.

Keywords: NAFLD, Insulin resistance, Processed Cornu Cervi Pantotrichum, EGFR, Proteomics

Subject terms: Biochemistry, Diseases, Drug discovery

Introduction

Non-alcoholic fatty liver disease (NAFLD) is a type of metabolic stress-induced liver injury associated with insulin resistance and genetic susceptibility¹. Its disease spectrum encompasses non-alcoholic fatty liver (NAFL), non-alcoholic steatohepatitis (NASH), liver cirrhosis, and hepatocellular carcinoma (HCC)². NAFLD not only leads to liver-related disabilities and mortalities but is also closely associated with the high prevalence of metabolic syndrome (MetS), type 2 diabetes mellitus (T2DM), and atherosclerotic cardiovascular diseases^3,4. The global prevalence of NAFLD is on the rise. Abnormal accumulation of liver lipids is one of its main etiological factors⁵, yet the specific pathogenesis has not been fully elucidated. Lifestyle intervention serves as the fundamental treatment, and novel metabolic drugs such as peroxisome proliferator-activated receptor (PPAR)⁶ agonists and farnesoid X receptor (FXR)⁷ agonists are currently in the clinical trial stage.

Insulin resistance is a core factor in the occurrence and development of NAFLD^8,9. It exacerbates hepatic steatosis in hepatocytes through various pathways, including increasing lipolysis, inhibiting the activity of lipoprotein lipase, affecting glucose metabolism, promoting the synthesis of very low-density lipoprotein, and inducing oxidative stress and inflammatory responses^10,11. Regulating glucose and lipid metabolism is the key to the treatment of NAFLD. In recent years, studies have found that the EGFR/AKT/ESR1 signaling pathway plays a crucial role in regulating liver metabolic homeostasis and inflammatory responses, and it may become an important molecular regulatory target for NAFLD¹².

In traditional Chinese medicine (TCM) theory¹³, NAFLD mostly falls into the categories of “Gan Pi“(liver-spleen disharmony) and “Ji Ju” (accumulation). The key pathogenesis is closely related to liver qi stagnation and spleen deficiency, internal obstruction of phlegm-turbidity, and deficiency of the liver and kidney¹⁴. The liver is responsible for regulating the flow of qi, and the spleen is in charge of transportation and transformation. If emotions are not smooth or the diet is improper, it can lead to the liver failing to regulate the flow of qi and the spleen failing to perform its transportation function. Nutrients from food and water fail to be metabolized properly, instead accumulating as phlegm-dampness that obstructs the liver collaterals. Over time, this results in fatty liver. Furthermore, the kidney is the root of congenital endowment, with the liver and kidney being closely interconnected^15,16.. If there is a deficiency of kidney essence and the liver loses nourishment, it can also accelerate the abnormal metabolism of liver lipids¹⁷.

In the ancient pharmaceutical work Leigong Paozhi Lun (Master Lei’s Discourse on Drug Processing), it is recorded that: “When using antler velvet, soak it in the natural juice of Polygonatum sibiricum for two days and nights, strain it out, cut it, roast it, and pound it for use to avoid thirst in people.” Although this record is from ancient times, it provides a unique perspective for exploring the alleviation of thirst symptoms and also suggests that there may be a synergistic effect between antler velvet and Polygonatum sibiricum. Modern research has shown that antler velvet and Polygonatum sibiricum have certain effects on regulating lipid metabolism.

Antler velvet is warm in nature and enters the liver and kidney meridians. It has the effects of tonifying the kidney and strengthening yang, enriching essence and blood, and strengthening the sinews and bones¹⁸. Its active ingredients can regulate lipid metabolism, improve insulin resistance, and reduce liver inflammatory responses^19,20. Polygonatum sibiricum is neutral in nature and enters the spleen, lung, and kidney meridians. It has the functions of invigorating qi and nourishing yin, strengthening the spleen, and tonifying the kidney²¹. Modern research has confirmed that it can regulate pathways such as peroxisome proliferator-activated receptor gamma (PPAR-γ)²²and adenosine monophosphate-activated protein kinase (AMPK)²³, reduce liver lipid deposition, and enhance antioxidant capacity²⁴. The combination of these two herbs can not only tonify the deficiency of the liver and kidney but also strengthen the spleen and resolve phlegm, which is in line with the pathogenesis characteristics of “deficiency of the root and excess of the manifestation” in NAFLD. In view of this, beneficial elements can be drawn from the application of antler velvet and Polygonatum sibiricum in traditional medicine to explore their potential value in the treatment of NAFLD.

Based on modern medical research findings on the EGFR/AKT/ESR1 pathway and integrated with the essence of traditional Chinese medicine (TCM) theory, this study explores the mechanism by which the velvet antler-Polygonatum sibiricum combination ameliorates NAFLD, aiming to provide novel integrated TCM-Western medicine ideas and strategies for the prevention and treatment of NAFLD. Focusing on the effects of specific proteins in processed velvet antler on NAFLD, the study first optimized the velvet antler processing technology through multi-dimensional parameter adjustment, followed by screening the optimal combination via in vivo experiments. Meanwhile, network pharmacology was employed to mine target pathways, and subsequent proteomics was used to screen and isolate the differentially expressed protein Q28237 for in vitro verification. The core target pathways identified by the two technical approaches mutually validated each other, collectively pointing to key nodes such as EGFR/AKT/ESR1. Finally, verification was completed by Western blotting (WB), forming a research loop of “theoretical prediction - experimental verification”.

Materials and methods

Experimental materials

The experimental materials included Velvet antler of Cervus elaphus (Jilin Lushengyuan Animal and Plant Tourism Co., Ltd.); Polygonatum sibiricum powder(Hubei Edar Biotechnology Co., Ltd.), SD rats (180–200 g, SCXK2023-0001, 6-week-old SD rats), CO₂ euthanasia chamber (Shanghai Yuyan Technology Co., Ltd, LC-500-S2,10 L、5 L/min) BCA protein concentration assay kit (Shanghai Beyotime Biotechnology Co, Ltd.,P0009), Electronic analytical balance (Beijing Sartorius Scientific Instruments Co., Ltd., BSA224S), Microplate reader (Shanghai Meigu Analytical Instruments Co., Ltd., SpectraMax 190), Multifunctional induction cooker (Guangdong Midea Household Electrical Appliances Manufacturing Co., Ltd., C22-RT22E01), AST and ALT content assay kits (Shanghai Beyotime Biotechnology Co., Ltd.P2715S, P2711S), SOD and MDA assay kits (Beijing Solarbio Science & Technology Co., Ltd.BC5165, BC6410),TC(Beijing Solarbio Science & Technology Co., Ltd.BC1985),TG(Beijing Solarbio Science & Technology Co., Ltd.BC0625),HDL-C(Beijing Solarbio Science & Technology Co., Ltd.BC5325),LDL-C(Beijing Solarbio Science & Technology Co., Ltd.BC5335),IL-6, IL-1β, IL-10, and TNF-α content assay kits (Beijing Solarbio Science & Technology Co., Ltd.SEKM-0007,SEKM-0002,SEKM-0010,SEKM-0034), Ultra-clean workbench (Qingdao Juchuang Environmental Protection Group Co., Ltd.), CO₂ incubator (Shanghai Yiheng Scientific Instruments Co., Ltd.), Low-speed centrifuge (BECMAN, USA), DMEM basal medium (Wuhan Procell Technologies Co., Ltd.), Penicillin/streptomycin mixed solution (Beijing Solarbio Science & Technology Co., Ltd.), PBS buffer (Beijing Solarbio Science & Technology Co., Ltd.), Trypsin (with EDTA) digestion solution (Beijing Solarbio Science & Technology Co., Ltd.), Fetal bovine serum (CLARK Bioscience, USA), 4% paraformaldehyde (Beijing Solarbio Science & Technology Co., Ltd.), Sectioning paraffin (Shanghai Macklin Biochemical Co., Ltd.), Xylene (Sinopharm Chemical Reagent Co., Ltd., Shanghai), Alcohol (Sinopharm Chemical Reagent Co., Ltd., Shanghai), Hematoxylin and Eosin (H&E) staining kit (Beijing Solarbio Science & Technology Co., Ltd.), Oil Red O staining kit (Beijing Solarbio Science & Technology Co., Ltd.), Protein molecular weight Marker (Shanghai Beyotime Biotechnology Co., Ltd.), Skimmed milk powder (Beijing Solarbio Science & Technology Co., Ltd.), Tris (Beijing Solarbio Science & Technology Co., Ltd.), Glycine (Beijing Solarbio Science & Technology Co., Ltd.), SDS (Beijing Solarbio Science & Technology Co., Ltd.), 1.5 M Tris-HCl (pH 8.8) (Beijing Solarbio Science & Technology Co., Ltd.), 1.0 M Tris-HCl (pH 8.0) (Beijing Solarbio Science & Technology Co., Ltd.), TBST (Beijing Solarbio Science & Technology Co., Ltd.), AP (Alkaline Phosphatase) (Shanghai Beyotime Biotechnology Co., Ltd.), 30% gel solution (29:1) (Beijing Solarbio Science & Technology Co., Ltd.), BSA (Bovine Serum Albumin) (Shanghai Macklin Biochemical Co., Ltd.), Methanol (Sinopharm Chemical Reagent Co., Ltd.), Primary antibody (goat anti-rabbit) (Wuhan Sanying Biotechnology Co., Ltd.), Secondary antibody (goat anti-rabbit) (Wuhan Sanying Biotechnology Co., Ltd.)variable-frequency ultra-low temperature refrigerator (TSX70086V), LabServ refrigerators (FDW-YL368, FYC-335), refrigerated centrifuge (ST16R), Merck millipore ultra-pure water system (Synergy UV), Nanomation TM automation equipment (G1 Lite), Thermo Scientific Vanquish™Neo UHPLC and Orbitrap™Astral™Zoom Mass Spectrometers (provided by LC-Bio Technologies), along with other routine instruments like micro-spectrophotometer (Nano-300) and biosafety cabinet (HR50–IIA2).

Key reagents/consumables include Nanomics’ Proteonano™ kits (PN001-3, SP3K001), Thermo Scientific HPLC column (ES906), Solarbio 1xPBS (P1020), Aladdin’s DTT (D104860) & IAM (1131590), Yiwei Jianhua Trypsin (EV01-01–08), CNW’s FA/TFA/ACN/NH4HCO3 (corresponding part numbers), Fisher Chemical’s solvents (A996-4, A117-5, A461-4), and Bruker TwinScape Biognosys iRT Kit (1816351).

Response surface methodology (RSM)

Investigation of solid-liquid ratio for velvet antler processed with Polygonatum sibiricum juice

Precisely weigh 3 portions of Cervus elaphus velvet antler powder, 50 g for each portion. Put them into suitable containers respectively. Add 125 mL (0.4), 167 mL (≈ 0.3), 250 mL (0.2), and 500 mL (0.1) of the pre - prepared Polygonatum sibiricum juice respectively. After thorough stirring, heat-treat at 90 ℃ for 2 h and filter through a 0.45 μm aqueous membrane filter. Add Polygonatum sibiricum juice to the residue at the same solid-liquid ratio, repeat the heat treatment and filtration, then combine the filtrates. Concentrate the combined filtrates under reduced pressure to a relative density of 1.2 g/mL. After cooling, shaping and drying, the velvet antler processed with Polygonatum sibiricum juice is obtained.Take 20 g of the processed product, add 100 mL of 30% ethanol, extract ultrasonically for 30 min, and determine the protein content by the BCA method.

Investigation of incubation temperature

Precisely weigh three portions of Cervus elaphus velvet antler powder, 50 g each. Add Polygonatum sibiricum juice at a solid-liquid ratio of 1:0.2. Heat-treat at 70, 80, 90, and 100 ℃ for 2 h, respectively, and filter through a 0.45 μm aqueous membrane filter. Add Polygonatum sibiricum juice to the residue, repeat the heat treatment and filtration at the same temperature, then combine and concentrate the filtrates to a relative density of 1.2 g/mL to obtain velvet antler stir-baked with Polygonatum sibiricum juice. Extract ultrasonically using the same method and determine the protein content by the BCA method.

Investigation of incubation time

Precisely weigh three portions of Cervus elaphus velvet antler powder, 50 g each. Add Polygonatum sibiricum juice at a solid-liquid ratio of 1:0.2, and heat-treat at 80 ℃ for 1, 2, 3, and 4 h, respectively. Filter through a 0.45 μm aqueous membrane filter. Add Polygonatum sibiricum juice to the residue, repeat the heat treatment and filtration for the same duration, then combine and concentrate the filtrates to obtain velvet antler stir-baked with Polygonatum sibiricum juice. Extract ultrasonically using the same method and determine the protein content by the BCA method.

The Design - Expert 13.0.1.0 software (Stat - Ease Inc, Minnesota, USA) was used for experimental design. Based on the Box - Behnken model and referring to the experimental data from single - factor multi - level experiments, the material - to - liquid ratio of Cervus elaphus velvet antler powder to Polygonatum sibiricum juice (A), the drying temperature (B), and the drying time (C) were selected as the factors for response surface analysis. The protein content of the Cervus elaphus velvet antler stir - baked with Polygonatum sibiricum juice was used as the response value. A quadratic orthogonal combination experiment was carried out. The specific classification coding is shown in Tables 1 and 2.

Table 1.

1–1 Levels of each factor.

Factor	levels
Factor	−1	0	1
Solid-to-liquid ratio(m/v)	0.1	0.2	0.3
Decoction temperature()	80	90	100
Decoction time(h)	1	2	3

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Table 2.

1–2 Response surface method experimental design and results.

STD	RUN	Factor Levels			Protein content(%)
STD	RUN	A	B	C	Protein content(%)
1	11	0.1	80	2	15.64
2	16	0.3	80	2	16.56
3	15	0.1	100	2	15.65
4	17	0.3	100	2	17.56
5	5	0.1	90	1	16.56
6	8	0.3	90	1	17.23
7	14	0.1	90	3	16.45
8	4	0.3	90	3	16.43
9	13	0.2	80	1	17.65
10	10	0.2	100	1	16.45
11	12	0.2	80	3	16.89
12	6	0.2	100	3	16.45
13	3	0.2	90	2	21.13
14	7	0.2	90	2	22.64
15	2	0.2	90	2	24.56
16	1	0.2	90	2	23.44
17	9	0.2	90	2	24.11

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Preparation of processed cervus elaphus velvet antler

Preparation of crude velvet antler (CRC)

150 g of fresh Cervus elaphus velvet antler was weighed. The surface of the velvet antler was gently wiped with a sterile soft brush or damp cloth to remove attached dust and other obvious impurities. After cleaning, the velvet antler was placed in a well-ventilated, dry and light-proof environment for natural air-drying, and then evenly divided into three portions for later use.

Preparation of PC-PR

Preparation of Polygonatum sibiricum juice: 50 g of Polygonatum sibiricum was accurately weighed, and distilled water was added at a material-to-liquid ratio of 1:10, followed by thorough mixing. The mixture was rapidly heated to boiling over high heat, then adjusted to low heat for slow decoction. During the decoction process, continuous stirring was performed to prevent sticking to the pot or local overheating. After concentration, the mixture was filtered through a fine sieve to remove medicinal residues, obtaining Polygonatum sibiricum juice with appropriate purity and concentration.

Processing of velvet antler: 50 g of dried velvet antler was weighed and cut into thin slices to increase the contact area. 250 mL of pre-prepared Polygonatum sibiricum juice was poured into a suitable container, and the velvet antler slices were slowly placed in it to ensure complete submersion. The slices were soaked for 48 h to fully absorb the juice. After soaking, the velvet antler was taken out and placed in a well-ventilated environment with appropriate temperature for moistening for approximately 2 h, allowing the juice to further penetrate into the tissue. Subsequently, the moistened velvet antler was transferred to a drying oven preset at 90 °C for drying for 2 h. After drying, the material was ground and sieved through an 80–100 mesh sieve.

Preparation of ghee-processed velvet antler (P-CRC)

50 g of dried velvet antler was weighed and cut into thin slices. With 5 g of ghee as the stir-frying auxiliary material, the stir-frying temperature was set to 145 °C, the stir-frying time to 14 min, and the stir-frying frequency to 50 times per minute. Stir-frying was continued until the ghee was fully absorbed and the surface of the velvet antler developed a glossy appearance. The velvet antler was then removed from heat and cooled to room temperature.

Construction of NAFLD rat model and grouping

After a 1-week adaptive feeding period, rats (6-week-old) were continuously fed a high-fat diet for 8weeks. Two rats were randomly selected after 8 weeks to detect relevant indicators and confirm successful model establishment. The rats were then randomly divided into model group, CRC group, P-CRC group, and PC-PR group. Throughout the modeling process, the control group received normal chow, while the CRC, P-CRC, and PC-PR groups underwent intragastric administration for 2 consecutive weeks at a dose of 200 mg/rat/day. The model and control groups were given an equal volume of normal saline.

Detection of blood glucose, body weight and glucose tolerance in rats

Fasting blood glucose was measured weekly in rats from the beginning of modeling. The results showed that fasting blood glucose in the model group was significantly increased at the 6th week of modeling compared with the previous week. Based on this result, body weight was measured weekly and recorded as the initial value. After successful modeling at the 8th week, the rats were administered intragastrically for 2 consecutive weeks, and body weight and blood glucose were continuously recorded to plot the change curves of blood glucose and body weight.

After successful model establishment, rats were fasted for 12–16 h to minimize interference from basal blood glucose. Microliter-scale blood samples were collected from the caudal vein of rats, and blood glucose concentration was measured using a glucometer; fasting blood glucose was determined as the baseline value, and the body weight of rats was recorded using an electronic scale at the same time. With fasting blood glucose as the baseline, the glucose dose was calculated based on body weight, and glucose solution was administered to rats via intraperitoneal injection. Blood samples were collected from the caudal vein at 0.5 h, 1 h, and 2 h after administration, respectively. A high-precision glucometer was used to measure blood glucose concentration, so as to evaluate the glucose tolerance level of rats.

Sampling and treatment of experimental rats

Thirty minutes after the last administration, all rats were anesthetized with sodium pentobarbital (50–60 mg/kg), followed by eyeball blood collection, and then the rats were euthanized by Carbon dioxide euthanasia. The livers were quickly excised for subsequent experiments. The obtained venous blood was allowed to stand for 1 h and then centrifuged; the separated serum was taken out and stored in a refrigerator at −80 °C for later use.

Carbon dioxide euthanasia

Rats were placed into a transparent euthanasia chamber (volume: 10 L, Shanghai Yuyan Technology Co., Ltd, LC-500-S2) without overcrowding (≤ 3 rats per placement to ensure uniform gas distribution), and medical-grade 30%carbon dioxide (30％CO₂, purity ≥ 99.5%) was delivered at a flow rate of 5 L/min (equivalent to 50% of the chamber volume per minute). It was then gradually increased to 70%–80% at a rate of 10%–20% per minute. This concentration was maintained until the rats completely ceased all vital signs (5–10 min), followed by an additional 1–2 min of exposure to confirm irreversible death. Death was confirmed by the absence of spontaneous breathing, corneal reflex, and fixed dilated pupils.

Detection of SOD, MDA and biochemical indicators in rat serum

The stored serum was thawed at room temperature. The activity of SOD and the content of MDA in the serum were determined using ELISA kits; meanwhile, the levels of AST, ALT, LDL-C and HDL-C in the serum were detected by a biochemical analyzer.

HE staining of rat liver tissue

Rat liver tissues were fixed with 4% paraformaldehyde, followed by gradient ethanol dehydration, xylene clearing, and paraffin embedding in sequence. Serial sections with a thickness of 5 μm were prepared. After the sections were baked at 60 °C, they were dewaxed with xylene and hydrated with gradient ethanol. Subsequently, the sections were stained with hematoxylin for 5 min, differentiated with hydrochloric acid-ethanol for several seconds, and blued in running water for 10 min. After counterstaining with eosin for 1–2 min, the sections were dehydrated with ethanol, cleared with xylene, and mounted with neutral balsam. The morphological changes of liver tissues were observed under an optical microscope.

Mechanism exploration of PC-PR in the treatment of non-alcoholic fatty liver disease via proteomics and network pharmacology analysis

Molecular Docking Software and Parameters: AutoDock Vina 1.2.3 was employed to perform molecular docking for validating the binding affinity between key active compounds and core targets. Protein structures were retrieved from the PDB database, and water molecules and redundant ligands were removed using PyMOL 2.5. AutoDockTools 1.5.7 was used to add hydrogen atoms, calculate Gasteiger charges, and define polar hydrogens. The active site was defined by centering the grid box on the region occupied by the native ligand of the target protein. Docking parameters were set as follows: exhaustiveness = 32, and flexible sidechains were retained at default settings.

In order to explore the potential active components of Polygonatum sibiricum and their corresponding targets, this study first conducted a search using the TCMSP database (https://old.tcmsp-e.com/tcmsp.php). With “Polygonatum sibiricum " as the keyword and setting the Oral Bioavailability (OB) ≥ 30% and Drug-Likeness (DL) ≥ 0.18 as the screening thresholds, the eligible compounds were obtained.The rationale for the inclusion or exclusion of components is as follows: Components with OB ≥ 30% and DL ≥ 0.18were retained, as they generally exhibit favorable oral absorption potential and drug-like properties. Components with OB < 30% or DL < 0.18were excluded, due to their poor oral bioavailability and lack of convincing pharmacological evidence. Duplicate components were represented only once, and those with ambiguous structures or unavailable SDF structure files were omitted from further analysis.

Subsequently, the structural files (in SDF format) of these compounds were downloaded from the PubChem database (https://pubchem.ncbi.nlm.nih.gov/).The SwissTargetPrediction database (http://www.swisstargetprediction.ch/) was employed for target prediction, and only the targets with a prediction probability value (Probability) greater than 0 were retained as candidate targets. Given the limitations of the SwissTargetPrediction database in covering components of animal origin, this study further integrated the data from the HERB database (http://herb.ac.cn/) and the BATMAN-TCM database (http://bionet.ncpsb.org.cn/batman-tcm/) for supplementation. In the HERB database, searches were conducted using “Polygonatum sibiricum " and “Cervus elaphus Velvet Antler” as keywords respectively, and the active components of these two medicinal materials and their corresponding target information were extracted. In the BATMAN-TCM database, searches were carried out using “HUANG JING” and “LU RONG” as keywords respectively, and a Score cutoff ≥ 10 and P-value ≤ 0.05 were set as the screening criteria to obtain the potential active components of the two medicinal materials and their corresponding targets. Finally, the retrieval results from the above-mentioned databases were integrated, and the union set was taken as the basic dataset for subsequent bioinformatics analysis.

Using “Non-alcoholic fatty liver disease” as the search term, relevant targets for NAFLD were retrieved from the OMIM database and the GeneCards database respectively. To improve the accuracy of target prediction, only the targets with a relevance score (Score) ≥ 6.0 in the GeneCards database were retained. In the GeneCards database, the Score value represents the degree of association between the target and the disease. To further narrow down the range of targets, if an excessive number of targets were retrieved, those with a Score higher than the median were defined as potential therapeutic targets for NAFLD.

Using the bioinformatics platform Weishengxin (http://www.bioinformatics.com.cn/), a Venn diagram was generated to identify the intersection targets between the targets of active components in Polygonatum sibiricum and Cervus elaphus velvet antler and NAFLD-related disease targets.

Using Cytoscape 3.9.1 software, a “Chinese medicine-active component-disease-target” relational network was constructed. Network topological analysis was performed using the Analyze Network tool to comprehensively evaluate node metrics including “degree centrality” and “betweenness centrality”. Compounds with higher centrality values were defined as core compounds. The intersection targets were submitted to the STRING database (v11.5, https://cn.string-db.org/) to assess protein-protein interaction (PPI) relationships. A minimum interaction confidence score of 0.400 (medium) was set, with all other parameters maintained at default values. After downloading the interaction data in TSV format, it was imported into Cytoscape 3.7.1 to construct the PPI network. The CytoHubba plugin (v0.1) was utilized to screen core targets based on the Maximal Clique Centrality (MCC) algorithm and degree values.

The Metascape platform (https://metascape.org/) was employed to perform “Gene Ontology (GO)” enrichment analysis and “Kyoto Encyclopedia of Genes and Genomes (KEGG)” pathway enrichment analysis on the potential target genes. Significantly enriched biological processes (BP), cellular components (CC), molecular functions (MF), and signaling pathways were screened using a threshold of “P” < 0.05. Enrichment results were visualized online via the Weishengxin bioinformatics platform. Finally, the Cytoscape 3.7.1 software was used to construct an “active component-target-pathway” network to reveal associations between active compounds and their potential mechanisms of action.

Proteomics measurement and analysis

Astral-Zoom-DIA experiment and analysis

Sample Type:Polygonatum sibiricum-Prepared Cervus elaphus Velvet Antler.

Sample Pretreatment.

Low-abundance protein enrichment: Aliquot 40 µL of sample, add 40 µL EN-Binding Buffer, vortex to mix, and incubate at 1500 r/min at room temperature for 5 min. Add 20 µL enrichment magnetic beads, incubate at 1500 r/min at room temperature for 1 h. Settle on a magnetic rack for 3 min, discard the supernatant, and wash with 180 µL EN-Wash Buffer for 2–3 cycles.

Protein enzymatic digestion: Add 20 µL Digestion Buffer 1 to the enriched sample, incubate at 55 °C and 1500 r/min for 0.5 h. After cooling to room temperature, add 20 µL Rapid Trypsin, incubate at 37 °C in the dark at 1500 r/min for 2 h. Add 20 µL Ending Buffer to terminate digestion, collect 60 µL supernatant via magnetic separation for desalting.

Solvent exchange and peptide preparation: Activate C18 desalting tips with 200 µL Activating Buffer, equilibrate with 200 µL Wash Buffer, then load the sample and centrifuge at 1200×g for 3 min. Wash twice with 100 µL Wash Buffer, elute with 50 µL Elution Buffer in 3 fractions, and collect 150 µL total eluate. Concentrate to dryness via vacuum centrifugation at 10 °C and 1000r/minfor 60 min, reconstitute peptides in 25 µL 0.1% formic acid aqueous solution.

Sample dilution: Determine absorbance at 205 nm using a Nano-300 spectrophotometer, calculate peptide concentration as “absorbance value ÷ 31”. Dilute to 0.05 µg/µL, centrifuge at 16,000×g for 40 min. Mix 9 µL sample with 1 µL iRT reagent and prepare for instrumental analysis.

Astral zoom DIA 300SPD

Liquid Chromatography (LC): 150 µg of sample was loaded, with a 3.9-minute gradient (0.0–0.1 min: 4.0%–8.0% mobile phase B; subsequent gradient ramped up to 99.0% mobile phase B) and a flow rate of 2.5 µL/min.

Mass Spectrometry (MS): The maximum injection time for MS1 was 3 ms, and that for MS2 was 2.5 ms. Other parameters were consistent with those of the 60SPD method, with a total acquisition time of 3.9 min.

Protein database search

DIA-NN software (version 1.9.2) was used, and the DirectDIA mode was adopted to construct a virtual spectral library. The database search parameters were set as follows: enzymatic digestion method was Trypsin/P with a maximum of 2 missed cleavages; fixed modification was Carbamidomethyl (C), and variable modifications were Oxidation (M) and Acetyl (protein N-terminus); peptide length range was 7–37 amino acids (aa), and precursor ion mass range was 380–980 m/z; both peptide-spectrum match false discovery rate (PSM FDR) and protein FDR were set to < 0.01. Protein identification and quantitative analysis were performed under these parameters.

Number of biological replicates and QC metrics

The protein-level false discovery rate (FDR) was controlled within 1%. Pearson correlation coefficients (R²) among intra-group biological replicates were greater than 0.85. The proportion of missing values in peptide and protein quantitation was maintained at a low level, ensuring data reliability for subsequent analyses.

Bioinformatics analysis

Protein abundance values were subjected to log₂-transformation to approximate a normal distribution. One-way analysis of variance (ANOVA) was applied to compare protein abundance differences among groups according to the experimental design. To control false positives resulting from multiple testing, the Benjamini–Hochberg method was used to perform false discovery rate (FDR) correction for all identified proteins. Differentially abundant proteins were identified by combining statistical significance and magnitude of expression changes.

In this study, the threshold criteria for screening differentially abundant proteins were set as follows: fold change (FC) ≥ 1.2 or FC ≤ 0.83 (i.e., absolute value of log₂FC ≥ 0.263) with a p-value < 0.05 (|log₂FC| ≥ 0.263 & p < 0.05).This threshold is widely adopted in label-free DIA proteomic studies, particularly for tissue and natural medicine-derived samples, which frequently exhibit subtle yet biologically meaningful expression regulation. The use of a moderate fold-change threshold prevents the loss of biologically relevant proteins with small but consistent abundance alterations due to overly stringent filtering criteria, while ensuring the biological interpretability and reproducibility of the results.Within the established comparison groups, results including differentially abundant proteins, enrichment analysis of differentially abundant proteins, and gene set enrichment analysis were obtained.

Extraction and isolation of differential proteins

In this study, Coomassie Brilliant Blue staining was used to detect the components of PC-PR, and Q28237 was isolated from fresh velvet antler for subsequent cell experiment validation. Additionally, this study aimed to investigate whether P. sibiricum exerts a synergistic enhancement effect. Velvet antler powder, which had passed through an 80-mesh sieve and had a moisture content of ≤ 8%, was mixed with pre-cooled EDTA-free RIPA lysis buffer (supplemented with fresh protease inhibitors) at a ratio of 1:10 (g/mL). After high-speed homogenization under ice bath conditions, the mixture was centrifuged at 12,000r/minand 4 °C for 20 min, and the supernatant was collected.Transfer the crude extract to a 10 kDa MWCO ultrafiltration centrifugal tube, centrifuge at 5,000 rpm, 4 °C for 30 min, collect the filtrate (≤ 10 kDa components including Q28237) and discard retained macromolecules. Further enrich the target fraction by centrifuging the filtrate in a 7 kDa MWCO tube at 5,000 rpm, 4 °C for 40 min, discard the filtrate (≤ 7 kDa impurities) and collect the retentate (enriched 7–10 kDa components with initially purified Q28237).

Subsequently, ammonium sulfate fractional precipitation was performed: the supernatant was centrifuged at the ammonium sulfate saturation of 30% to discard impurity proteins and then centrifuged again at the ammonium sulfate saturation of 80% to collect the precipitate containing Q28237. The collected precipitate was redissolved in PBS and subjected to desalting and concentration using an ultrafiltration centrifugal tube with a molecular weight cutoff of 3 kDa.

Thereafter, the concentrated solution was subjected to 15% SDS-PAGE electrophoresis. The gel bands corresponding to the molecular weight range of 7–10 kDa were excised, followed by staining and destaining. The stained gel bands were cut into small pieces (1 mm³ in size), and 50 µL of gel protein extraction buffer (50 mmol/L Tris-HCl, pH 8.0, containing 0.1% SDS) was added. After oscillatory incubation at 37 °C for 4 h, the mixture was centrifuged at 10,000r/minand 4 °C for 15 min, and the supernatant was collected.

Cell culture, subculture and construction of non-alcoholic fatty liver cell model

The NAFLD cell model was established in HepG2 cells induced by a mixture of oleic acid (OA) and palmitic acid (PA) at a molar ratio of 2:1, with a total concentration ranging from 0.2 to 1.0 mmol/L.

Frozen human hepatocellular carcinoma cells (HepG2) stored in liquid nitrogen were rapidly thawed in a 37 °C water bath. After thawing, the cells were transferred to a centrifuge tube containing an appropriate amount of culture medium, centrifuged at 1000 revolutions per minute (rpm) for 5 min, and the supernatant was discarded. Fresh culture medium was added to the centrifuge tube to resuspend the cells, which were then inoculated into a culture flask and placed in a cell culture incubator for culture, followed by subculture at a specific ratio. The subcultured cells were seeded into 96-well plates. After stable adhesion, the normal medium was discarded and replaced with medium containing different concentrations of OA and PA (OA: PA = 2:1, total concentration 1.0 mmol/L), and the cells were further cultured for 24–48 h to construct the non-alcoholic fatty liver cell model.

ELISA detection of inflammatory cytokines in the supernatant of NAFLD‑HepG2 cells and cell viability assay

The purified Q28237 extract was prepared into a stock solution with a concentration of 3 µg/mL. The cell seeding density (5 × 10³ cells per well in 96-well plates), culture conditions (37 °C, 5% CO₂ humidified incubator), and replicate well settings (at least 3 replicate wells per group, including a blank control group and a solvent control group) were standardized. In the first step, cell cytotoxicity screening was conducted: cells in 96-well plates were treated with Q28237 at concentration gradients of 0, 5,10,15, 25, and 50 µg/L. After 24 h of cell incubation, the absorbance value was measured using the CCK8 assay, and the cell viability was calculated. Subsequently, a NAFLD-related HepG2 cell model was established. Cells in 96-well plates were treated with Q28237 at concentration gradients of 10, 15, and 25 µg/L; the absorbance value was measured via the CCK8 assay to calculate cell viability, and the levels of inflammatory cytokines were detected using ELISA.

Oil Red O staining

After induction, the medium was aspirated, and cells were washed 3 times with phosphate-buffered saline (PBS). Then, 4% paraformaldehyde was added for fixation (15–20 min). After another PBS wash, cells were stained with Oil Red O solution for 30 min. Subsequently, differentiation was performed with 60% isopropanol for 1–2 min. After the final PBS wash, lipid droplets appeared red under a microscope; image analysis software was used to quantify the area and number of lipid droplets, so as to evaluate the level of intracellular lipid deposition.

Western blot assay

Tissues from the same segment of CRC and PC-RC were collected, and a strong denaturing lysis buffer (containing 8 M urea, 1% SDS, 5 mM DTT, and protease inhibitor cocktail) was added. The mixture was homogenized under ice bath conditions. After homogenization, degreasing was performed with n-hexane, and proteins were precipitated with absolute ethanol to remove polysaccharides. The precipitate was re-dissolved, and protein concentration was quantified using the BCA assay. Subsequently, 5× loading buffer was added, and the sample was boiled at 100 °C for 10 min to achieve protein denaturation. Gel concentration was selected based on the molecular weight of the target protein for SDS-PAGE. Proteins were then transferred to PVDF membranes with corresponding pore sizes via the wet transfer method (transfer buffer containing 20%, current and time adjusted according to protein molecular weight, transfer conducted under ice bath). Membranes were blocked with 5% skim milk or BSA at room temperature for 2 h, followed by incubation with diluted primary antibody on a shaker at 4 °C overnight (washed with TBST 3 times, 10 min each). Thereafter, membranes were incubated with HRP-conjugated secondary antibody (1:5000 dilution) at room temperature for 1 h (washed with TBST 3 times, 10 min each). Finally, signal development was carried out, and protein bands were analyzed.

Results and discussion

Results of single-factor and response surface methodology (RSM) optimization

In this study, a single-factor experiment was first conducted to investigate the effects of solid-to-liquid ratio (m/v), temperature, and processing time (in hours) on the processing technology of velvet antler stir-baked with Polygonatum sibiricum juice, and the technology was optimized. The experimental results are presented in Fig. 1. Based on the outcomes of the single-factor experiment, the solid-to-liquid ratio (A) of sika deer velvet antler to Polygonatum sibiricum juice, decocting temperature (B), and decocting time (C) were selected as factors for response surface analysis, with the protein content in the velvet antler stir-baked with Polygonatum sibiricum juice serving as the response value for the response surface experiment screening. The results indicated that the optimal process parameters were solid-to-liquid ratio of 0.206276, decocting temperature of 89.8885 °C, and decocting time of 1.96309 h. Considering the feasibility of practical operation, the processing parameters were adjusted to a solid-to-liquid ratio of 0.2, drying temperature of 90 °C, and time of 2 h. The experimental results are detailed in Tables 1, 2 and 3, with the specific levels of each factor provided in the supplementary materials. As shown in Table 3, the analysis of variance revealed that the p-value of the model was less than 0.0001, indicating significant reliability. The lack-of-fit value was 0.8459, which is greater than 0.05 and statistically non-significant, suggesting that the model has good predictability. This regression equation exhibited a high degree of fitting to the experimental data and can be used to determine the optimal technological parameters for stir-baking velvet antler with Polygonatum sibiricum juice. The regression equation is as follows: Protein content (Y) = 23.18 + 0.4358*A − 0.0787*B − 0.2087*C − 0.1725*AC + 0.19*BC − 3.51*A² − 3.32*B² − 3*C². Using the Numerical function in the optimization module of the software, with the lowest point within the range of the model’s protein content as the starting point and adopting the maximum value optimization method, the optimized technological parameters for stir-frying velvet antler with Polygonatum sibiricum juice were obtained: solid-to-liquid ratio (m/v) of 0.206276, decocting temperature of 89.8885 °C, and decocting time of 1.96309 h. Considering the convenience of actual operation, the processing parameters were revised to a solid-to-liquid ratio of 0.2, drying temperature of 90 °C, and time of 2 h. The experimental results are illustrated in Panels D, E, and F of Fig. 1. Specifically, Panel D shows that the protein content in PC-PR increases with the elevation of solid-to-liquid ratio and decocting time but decreases after reaching a peak value. Panels E (focusing on the two factors of solid-to-liquid ratio and decocting temperature) and F (focusing on the two factors of decocting temperature and decocting time) exhibit the same trend in protein content.

Fig. 1 — Single-Factor and Response Surface Process Results: **(A)** shows the effect of material ratio on the protein content of PC-PR, **(B)** illustrates the effect of drying time on the protein content of PC-PR, and **(C)** depicts the effect of drying temperature on the protein content of PC-PR. **(D)** : Interaction between solid-to-liquid ratio-A and drying time-C; **(E)** : Interaction between solid-to-liquid ratio-A and drying temperature-B; **(F)** : Interaction between drying temperature-B and drying time-C.

Table 3.

Analysis of variance of protein content regression simulation.

Source	SS	DF	MS	F-value	P-value	Significance
Model	154.34	9	17.15	13.63	<0.0001	**
A	1.51	1	1.51	1.2	0.309
B	0.0496	1	0.0496	0.0394	0.842
C	0.3486	1	0.3486	0.2771	0.6149
AB	0.245	1	0.245	0.1948	0.6723
AC	0.119	1	0.119	0.0946	0.7673
BC	0.1444	1	0.1444	0.1148	0.7447
A²	51.82	1	51.82	41.19	<0.0001	**
B²	46.28	1	46.28	36.79	<0.0001	**
C²	37.91	1	37.91	30.13	<0.0001	**
Residual	8.81	7	1.26
Lack-of-fit term	1.47	3	0.4916	0.2682	0.8459	No Significance
Pure error	7.33	4	1.83
Comprehensive	163.14	16

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Note: p < 0.0001 indicates highly significant, represented by **; p < 0.05 indicates significant, represented by *; p > 0.05 indicates not significant.

PC-PR ameliorated hyperglycemia in rats induced by NAFLD

As shown in panels A and B of Fig. 2,the body weight of rats in the model group was significantly higher than that in the control group (p < 0.05), which can be used as one important indicator for the successful establishment of the NAFLD model.Compared with the model group, the body weight of rats in each drug-treated group was significantly decreased (p < 0.05), suggesting that all interventions effectively attenuated the abnormal body weight gain in model rats.Among these groups, the PC-PR group exhibited a lower magnitude of body weight gain than the other treatment groups, indicating a more pronounced regulatory effect on body weight.With regard to blood glucose levels, all drug-treated groups showed significantly lower blood glucose values compared with the model group (p < 0.05), demonstrating that all treatments effectively ameliorated hyperglycemia in model rats.Notably, the PC-PR group displayed the most prominent hypoglycemic effect.

As shown in Fig. 2C, the results of the OGTT, illustrating the dynamic changes in blood glucose levels within 0–2.5 h after glucose administration across different intervention groups (control, model, PC - PR, CRC, P - CRC). The control group exhibited a normal physiological fluctuation pattern of blood glucose, featuring an initial rise followed by a subsequent decline. The model group, due to glucose metabolism disorder, showed a higher blood glucose peak, a slower decline rate, and a significant reduction in glucose tolerance. Compared with the model group, all intervention groups (PC-PR, CRC, P-CRC) showed reduced blood glucose peaks, with declining trends more consistent with that of the control group, indicating that the interventions improved glucose metabolism. Among them, the PC-PR group showed the greatest reduction in blood glucose peak, with a declining trend most similar to that of the control group, demonstrating the most prominent improvement in glucose tolerance.

Regulatory effects of PC-PR on oxidative stress, serum biochemical indices in NAFLD model rats

Analysis of Fig. 2 showed that: (1) Oxidative stress indicators: In Fig. 2D, SOD activity in rats of the PC-PR group was relatively higher, suggesting a significant antioxidant capacity; in Fig. 2E, MDA level in the PC-PR group was significantly reduced, indicating an obvious inhibitory effect on lipid peroxidation. Compared with the CRC group and P-CRC group, the PC-PR group performed optimally in increasing SOD activity and decreasing MDA level and exhibited the best efficacy in improving NAFLD-related oxidative stress damage. (2) Serum biochemical indicators: Compared with the model group, the serum levels of AST, ALT and LDL-C in all drug-administered groups were significantly decreased (p < 0.05), while the level of HDL-C was significantly increased (p < 0.05), which could improve liver function and lipid metabolism. Among them, the PC-PR group showed the most significant improvement in the above indicators, with values closer to those of the normal control group, demonstrating superior potential in improving liver function and regulating blood lipids (see Table 4 for detailed results).However, given the unclear core bioactive components and key therapeutic targets underlying the aforementioned pharmacological activities, this study further investigated its pharmacodynamic material basis and systematically elucidated its mechanism of action.

Table 4.

ALT, AST, HDL-C and LDL-CTest Results.

Group	ALT (U/L)	AST(U/L)	HDL-C(mg/dl)	LDL-C (mg/dl)
Control	37.2 ± 4.25	56.87 ± 4.7	36.72 ± 6.3	21.64 ± 4.7
Model	87.45 ± 6.4^###	145.2 ± 11.2^###	19.74 ± 4.2^##	87.56 ± 7.9^###
PC - PR	46.56 ± 6.7^**	70.43 ± 6.8^**	31.43 ± 7.2^**	49.54 ± 5.3^**
CRC	59.75 ± 8.1	90.56 ± 7.5	22.75 ± 5.6	69.64 ± 7.3
PC-CRC	53.24 ± 7.2^*	82.31 ± 9.2^*	26.56 ± 3.7^*	61.56 ± 5.7^*

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(The data is expressed as mean ± SD (n = 6). Compared with the control group, ^#p < 0.05, ^##p < 0.01, ^###p < 0.001. Compared with the model group, ^*p < 0.05, ^**p < 0.01, ^***p < 0.001.).

Improving effect of PC-PR on hepatic histological pathological damage in NAFLD model rats

Analysis In the HE-stained tissue sections (Fig. 2F), it was observed that, compared with the blank group, the livers of the model group and the drug-administered groups showed varying degrees of damage. In the model group, round vacuoles of different sizes appeared in the cytoplasm of hepatocytes; numerous neutrophils, lymphocytes, or macrophages aggregated within the hepatic lobules; the volume of hepatocytes increased, and the cytoplasm became loose and reticular. In comparison with the model group, the drug-administered groups showed a reduction in lipid droplet vacuoles within hepatocytes, a decrease in inflammatory infiltration, and a alleviation of fibrosis. Moreover, the PC-PR group exhibited a better improvement effect than other drug-administered groups.

Elucidation of the therapeutic targets, signaling pathways, and molecular docking verification of active components from Polygonatum sibiricum and velvet antler in NAFLD models based on network pharmacology

As shown in Fig. 3 In this study, the TCMSP database was used to identify 38 potential active components of Polygonatum sibiricum, and 12 components with predictable target information were screened out from them. To further explore the pharmacological mechanisms of Polygonatum sibiricum and antler velvet, the HERB database and the BATMAN-TCM database were used respectively. Through the HERB database, 67 components of Polygonatum sibiricum were obtained; through the BATMAN-TCM database, 24 components of antler velvet and 31 components of Polygonatum sibiricum were obtained. After screening and deduplication, 10 active components of Polygonatum sibiricum were finally determined, and 804 potential targets were predicted. For antler velvet, 7 effective components were screened out, and 655 potential targets were predicted.

Disease-related targets were obtained from the Genecards database and the omim database respectively, and after removing duplicates, 864 related targets were obtained.

There are 49 intersecting targets between the active components and NAFLD-related targets, and a Venn diagram was drawn, as shown in Fig. 3A.

A network diagram of traditional Chinese medicine components-disease syndrome-targets was constructed for the 49 intersecting targets, which included 65 nodes and 161 connections (as shown in the Fig. 3B and C). The active compounds with high degree values and betweenness centrality values included DFV, diosgenin, citral, higenamine, Liensinine, prunasin, alphaestradiol, cholesterol, prostaglandine1.

The 49 intersecting targets were screened using Cytoscape 3.7.1 software. Combined with the results of Maximal Clique Centrality (MCC) and degree value analysis, 9 targets were finally obtained (as shown in Table 5 of Fig. 3B), which were inferred to be core genes.

Table 5.

Core Components Screening Results and core targets Screening Results.

Active component	Closeness unDir	Degree	Core targets	MCC	Degree
DFV	0.015625	32	AKT	0.87	55
diosgenin	0.013333333	22	ESR1	0.78	30
citral	0.013888889	24	EGFR	0.83	50
higenamine	0.014705882	29	JUN	0.79	40
Liensinine	0.013157895	23	SERPINE1	0.72	27
prostaglandine1	0.014084507	24	PPARG	0.78	45
prunasin	0.014084507	25	HMGCR	0.74	37
cholesterol	0.014492754	27	NR1H4	0.71	35
alphaestradiol	0.012195122	25	PTGS2	0.73	29

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(Screening of components and targets based on Closeness (undirected), Degree, and MCC scores to identify the final core targets).

The results of GO enrichment analysis showed that the differentially expressed genes were mainly involved in the following biological processes (BP): regulation of cell proliferation, regulation of cell apoptosis, regulation of lipid metabolic process, response of cells to chemical stimuli and reactive oxygen species, and regulation of transcription, etc. (Fig. 3F shows the top 10 most enriched terms in each category of BP, cellular component (CC), and molecular function (MF); there are 1493 terms in BP, 66 terms in CC, and 115 terms in MF). A total of 135 significantly enriched signaling pathways were identified by KEGG pathway enrichment analysis (Fig. 3G). Among them, the pathways related to endocrine resistance were highly enriched. In addition, the relaxin signaling pathway, the advanced glycation end-products-receptor for advanced glycation end - products (AGE-RAGE) signaling pathway in diabetic complications, lipid atherosclerosis, and the hypoxia-inducible factor − 1 (HIF-1) signaling pathway were also significantly enriched. In addition, processes such as “cellular response to reactive oxygen species” and “regulation of fat metabolic process” were found to be related to lipid metabolism and inflammation, belonging to the category of BP. Regarding CC, “membrane region” and “membrane raft” were observed to be involved in processes such as cell signal transduction and material transport. Finally, the MF annotations included areas such as “nuclear receptor activity” and “fatty acid - binding ATPase binding”, and the abnormal activity of these areas would affect the balance of liver lipid metabolism and the inflammatory state. The potential mechanism by which active ingredients exert their effects through regulating targets and related pathways was further studied by constructing an active ingredient - target - pathway network. (Fig. 3D)

The targets involved in the pathways were intersected with the previously screened core targets. The intersection of the targets involved in the pathways and the previously screened core targets yielded ESR1, AKT1, JUN, EGFR, SERPINE1, PTGS2, and PPARG as the key targets. A heatmap of the binding energies from the molecular docking of 9 core compounds with 7 key targets was generated (Fig. 3E).

Results of proteomics analysis

Protein identification via quantitative proteomics analysis

Figure 4A shows the number of proteins detected in the CRC (F0), PC-PR (F1), and total samples. As indicated by the results, the number of proteins detected in the PC-PR (F1) was 3620, which was lower than that in the CRC (F0, 4788), and the number of proteins in the total samples was 5166. Figure 4B presents the number of peptides detected in the CRC (F0), PC-PR (F1), and total samples. The number of peptides detected in the PC-PR(F1) was 16,143, which was significantly lower than that in the CRC (F0, 24141), and the number of peptides in the total samples was 26,401.

Identification and analysis of differentially abundant proteins

As illustrated in the results (Fig. 4C), when comparing the PC-PR with the CRC, a total of 555 proteins were significantly up-regulated and 36 proteins were significantly down-regulated. These differential expression patterns were visualized and statistically validated using a volcano plot (Fig. 4D). Among these differentially abundant proteins, A0A212CJU2 (Gelsolin-like Protein) was identified as one of the most prominently up-regulated proteins, indicating a substantial increase in its expression in the PC-PR. Additionally, proteins such as A0A212CWR7, Q28237(Q28237_CERELTLAQELEALR), A0A212C6V6, and A0A212CLV3 were also found to be significantly up-regulated, suggesting a marked enhancement in their expression following the intervention.

Functional separation, annotation, and enrichment pathways of DPE proteins

According to the InterPro enrichment results (Fig. 5A), the domain changes of differentially abundant proteins were dominated by significant upregulation of “metabolism-related domains” and “inflammation-related domains”, with a minimal proportion of downregulated domains. Further analysis revealed that differentially abundant proteins mainly exhibited an upregulation trend in functional domains associated with calcium signaling, immune response, and transmembrane transport. which is consistent with the functional localization of Q28237 in improving insulin resistance and regulating glucose and lipid metabolism.

Combined with the GO enrichment analysis results (Fig. 5B), the functional characteristics of differentially abundant proteins can be parsed from three aspects via bar plots: At the biological process (BP) level, differentially abundant proteins were significantly enriched in oxidation-reduction processes, RNA polymerase II-mediated transcription, and signal transduction, indicating their critical roles in maintaining cellular homeostasis, regulating gene expression, and mediating cross-pathway signaling. At the cellular component (CC) level, enrichment was observed in subcellular structures including “membrane”, “extracellular exosome”, “plasma membrane”, and “nucleus”, reflecting the concentrated distribution of differentially abundant proteins at membrane system functional sites and genetic material storage loci. At the molecular function (MF) level, functional domains such as “ATP binding”, “protein binding”, and “metal ion binding” were enriched. These functional characteristics are highly consistent with Q28237’s “signal transduction-substance transport” network and correlate with velvet antler’s traditional efficacy in enhancing immunity and promoting tissue repair, clarifying how Polygonatum rhizome processing enhances velvet antler efficacy by regulating protein expression.

The KEGG enrichment scatter plot of differentially abundant proteins (Fig. 5C) showed significant enrichment in multiple pathways. Among them, pathways such as “ECM-receptor interaction” and “focal adhesion” were among the most enriched, and key signaling pathways like the “PI3K-Akt signaling pathway” were also significantly enriched, suggesting their involvement in phenotypic regulation by modulating processes such as cell proliferation and survival. In addition, metabolism-related pathways including “protein digestion and absorption” and “glycosaminoglycan degradation” were also enriched, reflecting the differences in material metabolism between the two groups. These enriched pathways act at multiple levels including signal transduction, cell adhesion, and material metabolism.

Analysis results of core pathway targets combined with network analysis and proteomics

The results of KEGG pathway enrichment analysis in network pharmacology revealed that the core functional pathway focused on the endocrine resistance pathway. In contrast, KEGG pathway analysis in proteomics further identified the PI3K-Akt signaling pathway, which is closely associated with insulin resistance and endocrine regulation. Collectively, these two sets of findings suggested that “endocrine-metabolic regulation” may represent the core mechanistic direction underlying the intervention of PC-PR in NAFLD.

Functional correlation analysis was conducted on the key differential proteins with high significance identified by proteomics. The results showed that Q28237, as an insulin-like growth factor (IGF)-related protein, could directly disrupt the metabolic homeostasis in hepatocytes when its expression was downregulated. Compared with other differential proteins, the core advantage of Q28237 lies in its direct association with the endocrine pathway, and its function is highly consistent with the core pathological features of NAFLD, namely “lipid accumulation and insulin resistance”. These findings suggest that Q28237 may be a critical upstream molecule mediating the regulation of NAFLD metabolic disorders by PC-PR. Combined with the core targets screened by network pharmacology, and based on the theoretical basis supported by existing studies that “IGF family proteins can activate the PI3K-Akt signaling pathway through ligand-receptor binding”, the present study proposed the following mechanistic hypothesis: PC-PR may upregulate the expression level of Q28237. On one hand, this upregulation activates AKT (a key molecule in the downstream PI3K-Akt pathway) and the estrogen receptor ESR1, thereby promoting lipid catabolism and insulin signal transduction in hepatocytes. On the other hand, it inhibits the excessive activation of the EGFR, alleviating hepatic inflammatory injury. Ultimately, these effects synergistically reverse the pathological state of NAFLD. To validate this hypothesis, subsequent experiments will employ in vitro experiments combined with WB to clarify the regulatory effects of PC-PR on the aforementioned molecules, thereby providing direct experimental evidence for its mechanism in ameliorating NAFLD.

Purity verification and characterization of Q28237

Results of Coomassie Brilliant Blue Staining and quantitative density analysis of gel purity

To verify the successful separation of Q28237, Coomassie Brilliant Blue staining was performed on the PC-PR group and the Q28237 group. As shown in Fig. 6B, compared with the PC-PR group which displayed multiple scattered bands, the Q28237 group exhibited a clear main band between the 7–10 kDa marker bands. This band position was completely consistent with the theoretical molecular weight of Q28237 (7–10 kDa), confirming molecular weight matching. Furthermore, in contrast to the PC-PR group with obvious impurity bands, no impurity bands were observed in the Q28237 group, indicating that the purification process effectively removed miscellaneous proteins and achieved targeted enrichment of Q28237.Quantitative grayscale density analysis of the Coomassie brilliant blue-stained gel was performed using ImageJ software. The PC-PR group showed multiple scattered impurity bands, with the target band (7–10 kDa) accounting for only 11.7%±1.2% of the total lane intensity. In the purified Q28237 group, the target main band at 7–10 kDa accounted for 95.9%±0.7% of the total grayscale intensity, with impurity residues accounting for less than 5%. These results confirmed the high gel purity of the purified Q28237 product and demonstrated that the purification procedure efficiently removed impurities and achieved targeted enrichment.

MS/MS spectrum annotation and sequence coverage for protein identification

To further confirm that the purified product corresponds to the target protein Q28237 (Fig. 6A), high-precision mass spectrometry was performed in this study. The MS/MS spectrum of the characteristic peptide LAQELEALR was comprehensively annotated, and all major b-type (b_₂, b_₄, b_₅, b_₆, b_₇) and y-type (y_₁, y_₂, y_₃, y_₄, y_₅, y_₆, y_₇, y_₈) fragment ions were clearly assigned. The mass errors of all labeled ions were within ± 5 ppm (maximum error + 4.1 ppm), fully meeting the stringent criteria for high-confidence mass spectrometry identification. A UniProt database search revealed that this peptide uniquely matched residues 46–55 of the Q28237 amino acid sequence, with no other homologous proteins containing this peptide detected in the database alignment, confirming its role as a specific signature peptide exclusive to Q28237. Based on the full-length amino acid sequence of Q28237 (57 amino acids in total) obtained from the UniProt database, combined with the specifically identified peptide LAQELEALR (covering 10 amino acid residues), the sequence coverage of Q28237 was calculated to be 17.5%. This level of coverage is consistent with the conventional reliable range for mass spectrometry-based identification of small proteins, further validating the reliability of the target protein identification. In summary, the consistent molecular weight observed by Coomassie brilliant blue staining, the complete annotation of the MS/MS spectrum, and the quantitative sequence coverage collectively form a complete evidence chain, conclusively verifying that the purified product obtained is indeed the target protein Q28237.

Regulatory results of cellular inflammatory cytokines in NAFLD model rats

As shown in Fig. 7A, Q28237 protein at concentrations ranging from 0 to 25 µg/L exhibited no significant toxicity to NAFLD -related HepG2 cells (cell survival rate ≥ 85%, P > 0.05). Further results in Fig. 7B indicated that the cell viability of the group treated with 25 µg/L Q28237 protein was significantly superior to that of the groups treated with other concentrations (P < 0.05), suggesting that 25 µg/L is the optimal working concentration of Q28237 protein.

As shown in Fig. 5, In the NAFLD cell model: compared with the model group, all treatment groups exhibited varying degrees of regulatory effects on cellular inflammatory cytokines. Notably, the improvement effects of Q28237 protein (extracted from velvet antler) at different concentrations were significantly superior to those of the crude extract PC-PR group. Specifically, the 25 µg/L Q28237 group showed the most prominent regulatory effect: this group achieved the greatest reduction in the levels of IL-6, IL-1β, and TNF-α (Fig. 7C and E, and 7F), while exerting the most significant increase in the level of IL-10 (Fig. 7D). This indicates that the improvement of non-alcoholic fatty liver disease by PC-PR originates from velvet antler itself, rather than newly generated components after processing or components independently contributed by Polygonatum sibiricum.

A clear dose-dependent pattern was observed among the Q28237 groups: the effects of the 15 µg/L Q28237 group on reducing IL-6, IL-1β, and TNF-α, as well as increasing IL-10, were all weaker than those of the 25 µg/L Q28237 group (P < 0.05). The 10 µg/L Q28237 group had the weakest regulatory effect.

Inhibitory effect of PC-PR on lipid deposition in OA-induced HepG2 cells

Figure 7G presents the results of Oil Red O staining, which shows that compared with the control group, the number of intracellular lipid droplets in oleic acid (OA)-induced HepG2 cells was significantly increased; however, after intervention with different drug-administered groups, intracellular lipid deposition was reduced to varying degrees. Notably, the high-dose Q28237 group exhibited a significantly superior inhibitory effect on lipid accumulation compared to other drug-administered groups. This result is consistent with the hypothesis that PC-PR exerts its therapeutic effect mainly through the enrichment of Q28237 protein, further suggesting that Q28237 may exert a more prominent cytoprotective effect by effectively blocking lipid accumulation.

Study on the mechanism of PC-PR ameliorating NAFLD by regulating the EGFR/PI3K-AKT/ESR1 signaling axis

Based on the combined analysis of network pharmacology and proteomics, this study validated the expression of key targets via WB assay: As shown in Fig. 8, the protein expressions of EGFR and AKT were significantly up-regulated, while that of ESR1 was significantly down-regulated in the NAFLD model group, which was consistent with the expression trend predicted by multi-omics.

Fig. 8 — Western blot results of EGFR, ESR1, P-AKT/AKT. (A: WB bands; **B-D**: statistical analysis of bands, mean ± SEM).

According to mechanistic speculation, PC-PR could alleviate hepatic inflammation by reducing the excessive activation of EGFR; EGFR could also trigger the PI3K-AKT pathway to improve insulin signaling, while the activation of ESR1 could synergistically inhibit the inflammatory cascade. These three aspects acted together to alleviate inflammatory imbalance and lipid metabolism disorders, thereby improving NAFLD.

Discussion

In this study, single-factor experiments combined with RSM were employed to optimize the processing technology of velvet antler. The optimal parameters for stir-frying with P. sibiricum juice were determined as follows: material-to-liquid ratio of 0.2 (g/mL), drying temperature of 90 °C, and drying time of 2 h. This provides a standardized protocol for the preparation of PC-PR. In vitro and in vivo experiments confirmed that PC-PR significantly improved glucose tolerance, body weight, liver function, and lipid metabolism in NAFLD model rats, with efficacy superior to that of other treatment groups. Meanwhile, animal experiments showed that the NAFLD-improving effect of the single CRC group was significantly lower than that of the PC-PR group. At first glance, this result seemed to suggest a potential synergistic effect between P. sibiricum and velvet antler. Cell experiments further clarified the role of P. sibiricum: the lipid-lowering and anti-inflammatory activities of the pure Q28237 group after intervention were significantly superior to those of the PC-PR group.This finding indicates that although Q28237 is the key active component, processing with Polygonatum sibiricum may still improve its bioavailability or biological activity, resulting in a better therapeutic effect against NAFLD than that achieved by mere enrichment of the protein.

On this basis, this study integrated network pharmacology and proteomics to explore its mechanism of action:

EGFR is a classic receptor tyrosine kinase(Zheng et al., 2024). Once activated, it can trigger downstream signaling cascades such as the PI3K/AKT pathway, thereby regulating cell proliferation, metabolism, and inflammatory processes. In NAFLD, abnormal activation of EGFR may drive the progression of hepatic steatosis and inflammation by promoting lipid synthesis in hepatocytes, inhibiting fatty acid oxidation, and exacerbating insulin resistance. Meanwhile, as a key effector molecule downstream of EGFR, AKT not only participates in the regulation of glucose and lipid metabolism but also exacerbates liver inflammation and fibrosis by affecting pathways such as mammalian target of rapamycin (mTOR)(Yang et al., 2025) and nuclear factor kappa-B (NF-κB)(Lin et al., 2023).

ESR1 is a nuclear receptor. In the research on NAFLD, ESR1 plays an important role(Jorissen et al., 2003, Collins et al., 2021). Estrogen can regulate lipid metabolism, inflammatory responses, and insulin sensitivity by activating ESR1, thereby influencing the occurrence and development of NAFLD(Zhao et al., 2024, Wang et al., 2025a). Some studies have shown that polymorphisms in the ESR1 gene may be associated with the susceptibility to NAFLD, and the estrogen-ESR1 signaling pathway may be a potential therapeutic target for NAFLD.(Yang et al., 2024).

At the network pharmacology level: A total of 22 potential bioactive components were identified, including 9 core components such as liquiritigenin and diosgenin. KEGG pathway enrichment analysis showed that these components were significantly enriched in pathways such as “endocrine resistance” and “AGE-RAGE signaling pathway in diabetic complications”, and GO analysis further clarified their functions in regulating lipid metabolism and inflammatory responses. By constructing a “component-target-pathway” network, 7 key targets were screened out, including ESR1, AKT1, and EGFR. Molecular docking verification results indicated that the binding energies between these targets and core components were all ≤ −6.0 kJ/mol, and all were closely associated with the EGFR/AKT/ESR1 signaling axis—a key pathway regulating hepatic lipid metabolism and homeostasis. Based on this, it was hypothesized that PC-PR might activate EGFR to initiate the downstream PI3K/AKT signaling cascade, thereby regulating glucose and lipid metabolism, inflammatory responses, and cell survival.

At the proteomics level: The conclusions of network pharmacology were further verified, and multi-dimensional associations were revealed. Moreover, the innovation and scientific value of the core findings were highlighted through horizontal comparison with existing literature. First, functional correlation analysis was performed on 3 key differential proteins with high significance (A0A212C6V6, A0A212CJU2, Q28237) identified by proteomics. The results showed that Q28237, as an insulin-like growth factor (IGF)-related protein extracted from the velvet antler of red deer (Cervus elaphus), its downregulated expression could directly disrupt hepatocellular metabolic homeostasis, and its function was highly correlated with the core pathological features of NAFLD (“lipid accumulation and insulin resistance”), suggesting that Q28237 might be a key upstream molecule mediating PC-PR’s regulation of NAFLD-related metabolic disorders.

Combined with structural biology analysis, Q28237 exhibits high structural homology with known IGF family proteins (such as IGF-1, IGF-2 and deer antler growth factor), with amino acid sequence identities ranging from 65% to 72%. Its core functional domains—including the N-terminal signal peptide, the centrally conserved cystine-knot structure, and the C-terminal receptor-binding region—are all highly conserved. Among these, the cystine-knot structure, as a characteristic feature of the IGF family proteins, maintains the spatial conformation of the protein through the formation of stable disulfide bonds, providing a structural basis for its binding to receptors and exerting signal transduction functions. Compared with IGF-1, Q28237 differs at three amino acid positions in the C-terminal receptor-binding region. This structural variation may represent the key structural basis underlying its preferential regulation of hepatic lipid metabolism and insulin sensitivity relative to other IGF family proteins, and also functionally distinguishes it from the pro-proliferative and pro-growth properties of conventional IGF family proteins. These findings both echo and advance existing functional studies on homologous proteins from velvet antler. Previous studies have confirmed that IGF family-related proteins derived from velvet antler (such as deer antler growth factor and insulin-like growth factor binding proteins) generally exhibit activities in promoting cell proliferation and regulating growth and development.This finding both echoes and breaks through the functional research of similar velvet antler proteins: existing studies have confirmed that IGF family-related proteins derived from velvet antler (such as velvet antler growth factor and insulin-like growth factor-binding protein) generally exhibit activities in promoting cell proliferation and regulating growth and development. For example, A study published in The Tohoku Journal of Experimental Medicine also demonstrated that the 5–10 kDa polypeptide derived from red deer velvet antler can improve glucose and lipid metabolism disorders in diabetic mice²⁵, but the specific mechanism of the single active component has not been clarified. In addition, Another study indexed in PubMed confirmed that velvet antler polypeptide VAP7.0 can protect hepatocytes by regulating the PI3K signaling pathway, but it did not involve the regulatory effect on metabolic disorders.This study filled the gap in research on the mechanism of single protein regulating metabolic disorders in red deer velvet antler by isolating and purifying Q28237 and verifying its independent activity. Furthermore, it was confirmed that its activity was superior to that of the total protein extract, confirming the core active status of Q28237.

Based on network pharmacology core targets (AKT, ESR1, EGFR) and the existing theory that IGF proteins activate the PI3K-Akt pathway via ligand-receptor binding, this study proposes a mechanistic hypothesis: PC-PR may upregulate the expression level of Q28237. On one hand, this upregulation activates AKT and the estrogen receptor ESR1, promoting hepatocellular lipid catabolism and insulin signal transduction; on the other hand, it inhibits the excessive activation of EGFR, alleviating hepatic inflammatory injury, and ultimately synergistically reversing the pathological state of NAFLD. This mechanism differs significantly from existing studies on velvet antler proteins. Traditional velvet antler proteins primarily exert proliferative effects through single activation of the PI3K-AKT pathway. In contrast, Q28237 functions via a bidirectional regulatory mode involving activation of metabolic pathways and inhibition of inflammatory pathways and forms a synergistic regulatory network with ESR1 and EGFR. This multi-dimensional regulatory feature is more consistent with the complex pathological mechanism of NAFLD (“metabolic disorder-inflammatory injury”) and also explains its superior efficacy compared to single-component intervention.

Further proteomic analysis not only verified the aforementioned hypothesis and the conclusions of network pharmacology but also uncovered multi-dimensional associations: At the biological process level, proteomics-identified “signal transduction and cell adhesion” and network pharmacology-focused “lipid metabolism and proliferation regulation” were interconnected via EGFR/AKT1 and ESR1. These processes are involved in regulating “inflammation-metabolic imbalance” and “fibrosis-cell homeostasis” in NAFLD, respectively, and are highly consistent with the “metabolic regulation-inflammatory inhibition” mechanism mediated by Q28237. At the cellular component level, changes in the “cell membrane and cell nucleus” affect the membrane localization activity of EGFR, thereby regulating the inhibitory effect of the Q28237-IGF pathway on the excessive activation of EGFR. At the molecular function level, “ATP-binding and calcium-binding” proteins participate in the activation of Q28237 downstream pathways and the amplification of inflammatory signals by regulating AKT1/PI3K. KEGG pathway analysis further improved the mechanism chain: the downstream pathway of the “endocrine resistance” pathway (screened by network pharmacology) is the “PI3K-Akt pathway” (identified by proteomics), and Q28237, as a key upstream molecule, serves as the core hub connecting these two pathways.

In this study, protein-level verification of EGFR, AKT1, and ESR1 was performed. Combined with multi-omics and molecular docking results, it was confirmed that PC-PR could ameliorate NAFLD by regulating the EGFR/AKT/ESR1 pathway. This mechanism was not only verified by WB experiments but also echoed and supplemented by relevant literature conclusions: for example, C. Sun et al., (2023) demonstrated that pueraria flavonoids alleviate NAFLD by regulating the PI3K-AKT-mTOR signaling pathway to induce autophagy²⁶, thereby reducing hepatic lipid accumulation and inflammatory responses. However, prior to this study, the specific role of these flavonoids in regulating metabolic disorders remained unclear. In contrast, this study is the first to discover that Q28237, a key active protein derived from Cervus elaphus velvet antler, can exert a positive regulatory effect through this pathway, providing a novel candidate molecule for the development of natural drug activators targeting the PI3K-AKT-mTOR pathway.

In conclusion, this study confirmed that Q28237 is an important functional component and serves as a major contributing factor involved in pharmacodynamic regulation. After standardized processing of PC-PR, it not only increases the content of Q28237 but also enhances its bioactivity based on the anti-inflammatory effect observed in in vitro experiments. Its core mechanism lies in upregulating Q28237, a key active protein from red deer velvet antler: on the one hand, it activates EGFR to initiate the PI3K-AKT pathway and improve insulin signaling, and on the other hand, it activates ESR1 to inhibit inflammatory responses, forming a complete regulatory chain of “protein target-signaling pathway-pathological phenotype.“By comparing with similar studies on velvet antler proteins (such as Sun H et al.‘s summary of the multi-system pharmacological effects of velvet antler polypeptides²⁷ and Ma C et al.‘s research on the hepatoprotective effects of velvet antler against hepatotoxicity²⁸, this study clarifies the unique functions and mechanistic innovations of Q28237, filling the research gap in the metabolic regulatory activity of a single protein from red deer velvet antler. Systematically validated by multi-omics technologies, this study provides a solid scientific basis for the application of PC-PR in NAFLD treatment and the development of natural product-based intervention strategies targeting the EGFR/AKT/ESR1 pathway.

Sample size justification: The sample size for animal experiments was determined according to relevant literature on similar NAFLD models, with at least 6 rats in each group. For in vitro cell experiments, three replicate wells (n = 3)²⁹ were set for each assay, and all experiments were independently repeated at least three times. The sample size was determined in accordance with the general standards of hepatocyte-related studies, and n = 3 biological replicates is widely accepted to ensure the reproducibility and reliability of the results. One-way ANOVA could be fully conducted using GraphPad Prism.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary Material 1^{(1.7MB, pdf)}

Abbreviations

NAFLD: Non-Alcoholic Fatty Liver Disease
PC-PR: Polygonatum sibiricum-Prepared Cervus elaphus Velvet Antler
T2DM: type 2 diabetes mellitus
TCM: traditional Chinese medicine
CRC: Cervus elaphus velvet antler
P-CRC: Ghee-Processed Velvet Antler
RSM: response surface methodology
EGFR: epidermal growth factor receptor
ESR1: estrogen receptor1
SOD: Superoxide dismutase
MDA: Malondialdehyde
OA: Oleic acid
HDL-C: High-density lipoprotein cholesterol
LDL-C: Low-density lipoprotein cholesterol
AST: Aspartate aminotransferase
ALT: Alanine aminotransferase
AKT: Protein kinase B
OGTT: Oral glucose tolerance test
IL-6: Interleukin-6
IL-10: Interleukin-10
IL-1β: Interleukin-1β
TNF-α: Tumor necrosis factor-α
Insulin-like growth factor II: Q28237
RSM: response surface methodology
DEP: ifferentially abundant protein

Author contributions

Z. Zhao: Conceptualization, Investigation, Methodology, Writing – original draft, Writing – review & editing. Y. Zhao: Conceptualization, Funding acquisition, Project administration, Writing – review & editing. W Chen: Supervision, Writing – review & editing. J. Geng: Supervision, Writing – review & editing. Y. Zong: Supervision, Writing – review & editing. J. Zhou: Supervision, Writing – review & editing.Mi. Li, M. Shi, H.Teng: Investigation, Writing – review & editing. Z. He: Investigation, Writing – review & editing. X. Wang: Participation in selected experiments.

Funding

This work was financially supported by the Science and Technology Innovation Project of Sanjiang Laboratory (SJ2025012).

Data availability

All data generated or analysed during this study are included in this published article and its supplementary information files.

Declarations

Competing interests

The authors declare no competing interests. I solemnly declare that there are no conflicts of interest that may affect the objectivity of the research or the fairness of the results during the research and writing of this thesis. The details are as follows: Financial Conflicts of Interest: The funding for this research comes from JLARS-2025-080209, with no financial support provided by any enterprises or individuals. Neither I, my supervisor, nor other research participants hold shares related to the research. We have not accepted gifts, travel sponsorships, or technical cooperation invitations from relevant enterprises. Academic Conflicts of Interest: The results of this research have no mandatory connection with the academic viewpoints previously published by me or my supervisor. There is no motivation to “selectively analyze data to support a certain theory”. During the research process, no adjustments to the experimental design or interpretation of results were made due to external pressures such as academic evaluation or project application, ensuring the independence and objectivity of the research. Other Conflicts of Interest: I have no cooperative relationships or personal connections with suppliers of reagents (e.g., SOD detection kits, EGFR antibodies) and instruments (e.g., WB electrophoresis apparatus, pathological microtomes) involved in the research. All experimental materials were purchased through the university’s unified bidding process, with no acquisition through special channels.

Ethics statement

This study strictly adheres to the norms of academic ethics, experimental ethics, and data ethics. The specific statements are as follows:

This thesis is an independent research achievement completed by me under the guidance of my supervisor. All viewpoints, data, and analyses in the thesis are based on my own research work, and there is no academic misconduct such as plagiarism, misappropriation of others’ academic achievements, or excessive citation without proper attribution.

Citation Norms and Academic Integrity

During the thesis writing process, all citations of others’ viewpoints, research results, data, and figures have been properly attributed in accordance with GB/T 7714 − 2015 Information and Documentation - Rules for Content, Form and Structure of Bibliographic References and the general norms of academic journals. These citations are fully listed in the reference list to ensure the traceability of sources.

Authenticity and Compliance of Experimental Data

I guarantee that all experimental data involved in the thesis (including data on the establishment of the rat non-alcoholic fatty liver disease (NAFLD) model, HepG2 cell experimental data, Western Blot verification data, etc.) are obtained from original experiments and are true and reliable. No improper practices such as data tampering, forgery, selective exclusion, or exaggerated presentation have been conducted. The number of repetitions for all experiments meets statistical requirements (e.g., the sample size of each group in animal experiments is ≥ 6, and cell experiments are independently repeated ≥ 3 times), and the data statistical methods conform to the general standards in the field.

Approval and implementation of experimental ethics

The experiment on the rat non-alcoholic fatty liver disease (NAFLD) model involved in the thesis has passed ethical review with the approval number SCXK2023-0001. During the experiment, the GB/T 35892 − 2018 Guidelines for Ethical Review of Experimental Animals was strictly followed. The feeding environment for rats met the standards of “temperature 22–25℃, humidity 50%-60%, 12-hour light-dark cycle”. After the experiment, carbon dioxide(30%) euthanasia was implemented in accordance with specifications to minimize the pain of rats to the greatest extent.

Ethics of HepG2 Cell Experiments

The cell culture and OA-induced experiments of HepG2 cells involved in the thesis all followed the operating standards for in vitro cell experiments. No human cells, tissues, or organs were involved, and there is no ethical controversy.

Compliance with Publishing Ethics

I have not engaged in duplicate submission. This thesis has not been published in any form (including full text, abstract, or conference report) in domestic or foreign journals, conferences, or academic platforms, nor has it been submitted to other journals or conferences simultaneously. It complies with the norms of academic publishing.

ARRIVE guidelines reporting statement

This study strictly adheres to the core requirements of the ARRIVE Guidelines (Animal Research: Reporting of In Vivo Experiments, https://arriveguidelines.org) and provides a comprehensive and transparent reporting of all relevant information regarding the in vivo animal experiments. Key contents, including study design, animal housing and management, experimental procedures, data collection and analysis, as well as ethical approval, have been fully disclosed in accordance with the Guidelines to ensure the reproducibility, reliability, and scientific rigor of the research. This study has been reviewed and approved by the Experimental Animal Welfare and Ethics Committee of Jilin Agricultural University (Animal License No.: SCXK(Ji)2023-0002), and all experimental operations comply with the editorial policies of Scientific Reports and relevant international regulations on experimental animal welfare and ethics.

Footnotes

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1^{(1.7MB, pdf)}

Data Availability Statement

All data generated or analysed during this study are included in this published article and its supplementary information files.

[CR1] 1.Oladipupo, S. O., Ezenabor, E. H., Ojo, A. B., Ogunlakin, A. D. & Ojo, O. A. Interplay of the pathophysiological mechanisms of non-alcoholic fatty liver disease, diabetes mellitus, and inflammation: A growing threat to public health. Obes. Med.55, 100613. 10.1016/j.obmed.2025.100613 (2025). [Google Scholar]

[CR2] 2.Friedman, S. L., Neuschwander-Tetri, B. A., Rinella, M. & Sanyal, A. J. Mechanisms of NAFLD development and therapeutic strategies. Nat. Med.24, 908–922. 10.1038/s41591-018-0104-9 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR3] 3.Tilg, H., Byrne, C. D. & Targher, G. NASH drug treatment development: Challenges and lessons. Lancet Gastroenterol. Hepatol.8, 943–954. 10.1016/S2468-1253(23)00159-0 (2023). [DOI] [PubMed] [Google Scholar]

[CR4] 4.Ma, L.-L. et al. Mori Cortex extract ameliorates nonalcoholic fatty liver disease (NAFLD) and insulin resistance in high-fat-diet/streptozotocin-induced type 2 diabetes in rats. Chin. J. Nat. Med.16, 411–417. 10.1016/S1875-5364(18)30074-8 (2018). [DOI] [PubMed] [Google Scholar]

[CR5] 5.Chu, Y., Yang, S. & Chen, X. Fibroblast growth factor receptor signaling in metabolic dysfunction-associated fatty liver disease: Pathogenesis and therapeutic targets. Pharmacol. Ther.269, 108844. 10.1016/j.pharmthera.2025.108844 (2025). [DOI] [PubMed] [Google Scholar]

[CR6] 6.Qiu, Y.-Y., Zhang, J., Zeng, F.-Y. & Zhu, Y. Z. Roles of the peroxisome proliferator-activated receptors (PPARs) in the pathogenesis of nonalcoholic fatty liver disease (NAFLD). Pharmacol. Res.192, 106786. 10.1016/j.phrs.2023.106786 (2023). [DOI] [PubMed] [Google Scholar]

[CR7] 7.Miao, M. et al. Targeting PPARs for therapy of atherosclerosis: A review. Int. J. Biol. Macromol.242, 125008. 10.1016/j.ijbiomac.2023.125008 (2023). [DOI] [PubMed] [Google Scholar]

[CR8] 8.Chen, T. et al. Hepatocyte smoothened activity controls susceptibility to insulin resistance and nonalcoholic fatty liver disease. Cell. Mol. Gastroenterol. Hepatol.15, 949–970. 10.1016/j.jcmgh.2022.12.008 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]

[CR9] 9.Lee, J. W. & Park, S. H. Association between depression and nonalcoholic fatty liver disease: Contributions of insulin resistance and inflammation. J. Affect. Disord.278, 259–263. 10.1016/j.jad.2020.09.073 (2021). [DOI] [PubMed] [Google Scholar]

[CR10] 10.Huang, R. et al. Activation of AMPK by triptolide alleviates nonalcoholic fatty liver disease by improving hepatic lipid metabolism, inflammation and fibrosis. Phytomedicine92, 153739. 10.1016/j.phymed.2021.153739 (2021). [DOI] [PubMed] [Google Scholar]

[CR11] 11.Sakr, M., Abdelsalam, M., El-Sherbiny, A. & Nesim, M. Abstract #1324374: Relationship between platelet distribution width (PDW), red blood cell distribution width (RDW), and insulin resistance in people with type 2 diabetes and nonalcoholic fatty liver disease (NAFLD). Endocr. Pract.28, S47–S48. 10.1016/j.eprac.2022.10.099 (2022). [Google Scholar]

[CR12] 12.Liang, D. et al. Inhibition of EGFR attenuates fibrosis and stellate cell activation in diet-induced model of nonalcoholic fatty liver disease. Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease1864, 133–142. 10.1016/j.bbadis.2017.10.016 (2018). [DOI] [PubMed] [Google Scholar]

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PERMALINK

Mechanism elucidation of Polygonatum sibiricum-Prepared Cervus elaphus velvet antler in intervening non-alcoholic fatty liver disease based on network analysis combined with proteomics

Zihao Zhao

Minqian Li

Hongbo Teng

Xv Wang

Mengqi Shi

Zhongmei He

Ying Zong

Weijia Chen

Jianan Geng

Jia Zhou

Yan Zhao

Abstract

Supplementary Information

Introduction

Materials and methods

Experimental materials

Response surface methodology (RSM)

Investigation of solid-liquid ratio for velvet antler processed with Polygonatum sibiricum juice

Investigation of incubation temperature

Investigation of incubation time

Table 1.

Table 2.

Preparation of processed cervus elaphus velvet antler

Preparation of crude velvet antler (CRC)

Preparation of PC-PR

Preparation of ghee-processed velvet antler (P-CRC)

Construction of NAFLD rat model and grouping

Detection of blood glucose, body weight and glucose tolerance in rats

Sampling and treatment of experimental rats

Carbon dioxide euthanasia

Detection of SOD, MDA and biochemical indicators in rat serum

HE staining of rat liver tissue

Mechanism exploration of PC-PR in the treatment of non-alcoholic fatty liver disease via proteomics and network pharmacology analysis

Proteomics measurement and analysis

Astral-Zoom-DIA experiment and analysis

Astral zoom DIA 300SPD

Protein database search

Number of biological replicates and QC metrics

Bioinformatics analysis

Extraction and isolation of differential proteins

Cell culture, subculture and construction of non-alcoholic fatty liver cell model

ELISA detection of inflammatory cytokines in the supernatant of NAFLD‑HepG2 cells and cell viability assay

Oil Red O staining

Western blot assay

Results and discussion

Results of single-factor and response surface methodology (RSM) optimization

Fig. 1.

Table 3.

PC-PR ameliorated hyperglycemia in rats induced by NAFLD

Fig. 2.

Regulatory effects of PC-PR on oxidative stress, serum biochemical indices in NAFLD model rats

Table 4.

Improving effect of PC-PR on hepatic histological pathological damage in NAFLD model rats

Elucidation of the therapeutic targets, signaling pathways, and molecular docking verification of active components from Polygonatum sibiricum and velvet antler in NAFLD models based on network pharmacology

Fig. 3.

Table 5.

Results of proteomics analysis

Protein identification via quantitative proteomics analysis

Fig. 4.

Identification and analysis of differentially abundant proteins

Functional separation, annotation, and enrichment pathways of DPE proteins

Fig. 5.

Analysis results of core pathway targets combined with network analysis and proteomics

Purity verification and characterization of Q28237

Results of Coomassie Brilliant Blue Staining and quantitative density analysis of gel purity

Fig. 6.

MS/MS spectrum annotation and sequence coverage for protein identification

Regulatory results of cellular inflammatory cytokines in NAFLD model rats

Fig. 7.

Inhibitory effect of PC-PR on lipid deposition in OA-induced HepG2 cells

Study on the mechanism of PC-PR ameliorating NAFLD by regulating the EGFR/PI3K-AKT/ESR1 signaling axis

Fig. 8.

Discussion

Supplementary Information

Abbreviations

Author contributions

Funding

Data availability