We mixed freeze-dried powders of APP (0.1240 g) with 5 mL of 6 mol/L hydrochloric acid, and the mixture was solidified under liquid nitrogen and vacuum. Then, the mixture was hydrolyzed in a constant temperature drying oven at 110°C for 24 h. The impurities were removed by filtration. Subsequently, 0.5 mL of the filtrate was vacuum dried, dissolved in 1 mL deionized water, and dried again. This process was repeated twice. Finally, the powder was dissolved in sample buffer, filtered, and analyzed by an automatic amino acid analyzer (Sykam, Germany) using a LCA K06/Na chromatographic column (4.6 mm × 150 mm) with 0.12 N sodium citrate solution (pH 3.45) and 0.2 N sodium citrate solution (pH 10.85) as mobile phases A and B, respectively. Flow rates of the eluting and derivative pumps were 0.45 mL/min and 0.25 mL/min, respectively. The detection wavelengths were 570 nm and 440 nm. The injection volume was 0.05 mL. The gradient temperature control set to 58°C–74°C. The pressure of the column was maintained at 30–40 bar (Liu et al., 2016).

The amino acid analyses of APA (0.1190 g) and APH (0.1520 g) were also processed by the same method.

The freeze-dried powders of APP (10 mg) were dissolved in deionized water to obtain a 0.1 mg/mL solution. The solution was filtered using a 0.45 μm microporous membrane. The secondary microstructure of APP, including α-helix and β-sheet, were analyzed by CD, which was performed at an ambient temperature of 20°C. The thickness of the quartz cell was 0.1 cm. The scanning range was 190–260 nm. The scanning speed was 50 nm/min. The data interval was 0.2 nm. The bandwidth was 2 nm. The sensitivity was 20 mdeg. The response time was 0.5 s. The proportion of secondary structures (α-helix, β-sheet, β-turn and random coil) in APP were analyzed using the CDNN version 2.1 software (Applied Photophysics Ltd., Leatherhead, UK) (Han et al., 2024).

The CD analyses of APA (10 mg) and APH (10 mg) were also processed by the same method.

The freeze-dried powders of APP (5 mg) were mixed with 200 mg of dried KBr. The mixture was pressed into discs at 15.0 MPa for 3 min. Then the mixture was loaded in the sample holder of the 470 FT-IR spectrometer (Nicolet, USA) and the spectra in the range of 4,000 to 400 cm^-1 were recorded (Fu et al., 2023).

The FT-IR spectroscopy analyses of APA (5 mg) and APH (5 mg) were also processed by the same method.

The freeze-dried powders of APP (5 mg) were dissolved in 200 μL of 25 mmol/L ammonium bicarbonate. The extract was centrifuged and the supernatant was harvested. Then, 2 μL of 1 mol/L dithiothreitol (DTT) was added to the supernatant and the solution was incubated in a water bath at 60°C for 40 min. Subsequently, we added 8 μL of freshly prepared 1 mol/L iodoacetamide to the solution, incubated in the dark for 30 min at room temperature, and desalinated. Then the freeze-dried sample was dissolved in 10 μL of loading buffer and analyzed by LC-MS/MS using the Easy nLC1200-QEaxtive plus system (Thermo Scientific, USA) in a Thermo Scientific C18 column (50 mm × 150 mm, 3 μm) with a flow rate of 300 nL/min. The mobile phase was composed of solution A (aqueous solution of 0.1% formic acid) and solution B (0.1% formic acid in 80% acetonitrile). The following gradient elution program was used: 0–3 min, 2%–8% B; 3–42 min, 8%–20% B; 42–48 min, 20%–35% B; 48–49 min, 35%–100% B; 49–60 min, 100% B. The chromatographic peaks were monitored in the ESI⁺ mode. The Orbitrap analyser acquired a high-resolution mass spectrum at full scan (m/z 200-1,600) with resolution of 70,000. The product ions were measured at a resolution of 17,500. The capillary temperature was 275°C and the spray voltage was 1,800 V. The maximum filling time for the entire scan and the MS/MS scan was set at 50 ms and 45 ms, respectively. The dynamic exclusion time was set at 30 s. We identified and selected peptide sequences with a false discovery rate (FDR) ≤ 1%.

The peptide spectroscopy analyses of APA (5 mg) and APH (5 mg) were also processed by the same method.

We purchased 54 male Kunming mice (SPF level, weighing 18–20 g) from the Jinan Pengyue Experimental Animal Breeding Co., Ltd (Shandong, China, SYXK (LU)2019-0003). All the animals were housed under standard animal room conditions (temperature 24°C ± 2°C, humidity 55%–60%, 12/12 h light/dark cycles) with standard food and water ad libitum. All the protocols for the animal experiments were performed according to the National Institutes of Health (NIH) guidelines for the care and use of experimental animals and were approved by the Institutional Animal Care and Use Committee, School of Pharmacy, Bin Zhou Medical University (Ethics Approval Number: 2021-087).

After 7 days of adaptive feeding, the animals were randomly divided into the following 9 groups (n = 6 each): Con; Mod; aspirin group (Asp, 40 mg/kg); low-dose APP group (LAPP, 100 mg/kg); HAPP (200 mg/kg); low-dose APH group (LAPH, 100 mg/kg); HAPH (200 mg/kg); low-dose APA group (LAPA, 100 mg/kg); and HAPA (200 mg/kg). The mice were treated via intragastric administration for 10 days and the mice in the Con and Mod were administered the same volumes of normal saline. After 10 days, mice from all the other groups except the Con were intraperitoneally injected with 0.5% carrageenan (type I) solution after the last gavage to establish the thrombus model (Yang et al., 2022). After 48 h of modeling, the mice were anesthetized with 10% chloral hydrate and blood samples were obtained from the abdominal aorta. The whole blood was divided into two parts: (1) half of the whole blood was added into an anticoagulant tube containing heparin; (2) the remaining whole blood was added into an anticoagulant tube containing 3.2% sodium citrate.

The total tail lengths (L₁) and the black tail lengths (L₂) were recorded for all the mice in the 9 groups after blood collection. The BTP was estimated with the following formula: BTP  % = L 2 / L 1 × 100 %

We collected 400 µL of whole blood from anticoagulant tubes with heparin sodium. Then, we measured the high, medium and low shear values of the whole blood using a rheometer (XL1000, Zhongchi Weiye Technology Development Co., Ltd, China). The plasma viscosity was also measured. The blood samples in the sodium citrate-containing vacuum tubes were centrifuged at 3,500 r/min for 15 min at 4°C. The supernatant was harvested as a plasma sample to be used. Then, we measured the PT, APTT, TT, and FIB for all the groups using the plasma samples with an automatic coagulation analyzer (MEN-C100A, Shandong Meiyilin Electronic Instrument Co., Ltd, China) and commercial kits according to manufacturers’ instructions. These indicators were used to evaluate the anti-coagulant efficacies of APP, APH, and APA.

We incubated 100 µL of blood plasma from the vacuum tubes with heparin sodium with 300 µL of cold ethanol for 10 min. Then, the extract was centrifuged at 4,000 rpm for 15 min at 4°C. The supernatant was harvested for use. 1 mL of the supernatant was dried by nitrogen blowing at 4°C. Then, it was re-dissolved using 100 µL of 80% methanol, centrifuged at 15,000 rpm for 15 min at 4°C, and the supernatant was stored for further analysis.

Chromatographic separation was performed on a Dionex Ultimate 3000 UHPLC system equipped with a WPS-3000 TRS autosampler, a TCC-3000RS column oven, and an HPG-3400RS binary pump (Dionex Softron GmbH-Part of Thermo Fisher Scientific, Germany). An ACQUITY UPLC BEH C18 column (150 mm × 2.1 mm, 1.7 µm) was operated at 40°C. The mobile phase was composed of (A) 0.1% FA in water and (Setiabakti et al.) ACN with a flow rate of 0.3 mL/min. The elution parameters were as follows: 0.00–1.00 min, 5% B; 1.00–4.00 min, 5%–30% B; 4.00–10.00 min, 30% B; 10.00–11.00 min, 30%–75% B; 11.00–13.00 min, 75%–90% B; 13.00–15.10 min, 90%–5% B; and 15.10–17.00 min, 5% B.

The LC-MS/MS analysis was performed on a Q-Exactive Focus Orbitrap MS (Thermo Electron, Germany) equipped with a heated electrospray ionization (HESI) source. In a single analytical run, the peaks were monitored in both the positive and negative ion modes. The ion source parameters were as follows: (1) ion spray voltages were set at 3.5 kV in the positive ion mode and 3.0 kV in the negative ion mode; (2) Nitrogen (purity ≥99.99%) was used as the sheath gas and the auxiliary gas at a flow rate of 30 and 10 arbitrary units, respectively; (3) capillary temperature was set at 320°C and vaporizer temperature was set at 400°C. The orbitrap mass analyzer was used to acquire a high-resolution mass spectrum at full scan in a mass range of m/z 70-1,050 at a resolution of 70,000 (Shi et al., 2023).

The LC-MS/MS data was processed using the Compound Discoverer 3.0 software (Thermo Fisher Scientific, MA, USA) for noise cancellation, baseline correction and normalization so that reliable data was obtained for parameters, including m/z, peak intensity and retention time. At the same time, some data were verified by manual screening in the process of data processing to ensure the accuracy and reliability of the results. Then, principal component analysis (PCA) and orthogonal to partial least squares-discriminate analysis (OPLS-DA) was performed for the processed datasets using the SIMCA-P 14.0 software (Umetrics, Sweden). S-plots were used to screen differential metabolites in the tail vein thrombosis model after treatment with HAPP, HAPH, and HAPA in combination with other judgment methods, such as variable importance in projection (VIP) (generated in the OPLS-DA mode) and p-value (formed from relative intensity). Finally, we extracted further information for the differential metabolites, including molecular formulas, molecular weights and codes, using the HMDB dataset (http://www.hmdb.ca/), KEGG dataset (https://www.kegg.jp/), and VMH dataset (http://www.vmh.life).

The amino acid compositions of APP, APH, and APA are shown in Figures 1A–D. We detected 16 amino acids in APP, APH, and APA, including 2 acidic amino acids (Figure 1A), 3 basic amino acids (Figure 1B), 7 hydrophobic amino acids (Figure 1D) and 4 hydrophilic amino acids (Figure 1C). The total amino acid content of APP was 653.359 mg/g with higher levels of Glu (13.03%), Asp (9.15%), and Leu (8.78%), and lower levels of Pro (2.90%) and Met (1.82%). The total amino acid content of APA was 829.027 mg/g with higher levels of Glu (15.70%), Ala (12.03%), and Gly (10.17%), and lower levels of Pro (2.70%) and Met (1.51%). The total amino acid content of APH was 663.553 mg/g with higher levels of Leu (12.04%), Ala (11.81%), and Glu (11.56%), and lower levels of Met (1.10%) and Ile (0.71%). The structures and functions of the proteins or peptides are determined by the composition and sequence of the amino acids (Perron et al., 2019). Therefore, the comparison of the activity of APP, APH and APA with similar amino acid composition will be further revealed.

CD is often used to rapidly determine the secondary structure, folding, and binding properties of the macromolecules, including proteins or peptides (Yoo et al., 2023). The CD spectra of most disordered or unfolded proteins show a negative peak in the region of 190–200 nm (Miles et al., 2021). As shown in Figure 1E, the CD spectrum of APP exhibited a strong negative band at 196 nm and a positive band at 224 nm. The CD spectrum of APH exhibited a strong negative band at 197 nm and a positive band at 250 nm. The CD spectrum of APA exhibited a strong negative band at 195 nm and a positive band at 216 nm. Further analysis using the CDNN software showed that the α-helix, β-sheet, β-turn and random coil of APP accounted for 16.7%, 44.2%, 21.0% and 42.6%, respectively; the α-helix, β-sheet, β-turn and random coil of APA accounted for 16.6%, 44.1%, 20.9% and 43.0%, respectively; and the α-helix, β-sheet, β-turn and random coil of APH accounted for 16.7%, 45.1%, 21.1% and 42.2%, respectively. Therefore, the proportion of the secondary structures in APP, APH and APA did not show significant differences.

FT-IR spectroscopy is a valuable tool for studying the secondary structural composition and changes in the proteins. A peptide generates the following infrared-active amide vibrational modes under normal conditions: amide A (3,300 cm^-1), amide B (3,100-3,050 cm^-1), amide I (1,700-1,600 cm^-1), and amide II (1,570-1,540 cm^-1). Amide I band mostly consists of C=O stretching vibrations and C-N groups. Amide II band consists primarily of N-H bending, but also C-N and C-C stretching vibrations (Bicak, 2023). As shown in Figure 1F, the bands around 3,300 cm^-1 and 3,065 cm^-1 were generated by Fermi resonances between amide II and N-H stretching vibrations, whereas the strong band at 1,695 cm^-1 represented the signal of amide I absorption peak. The FT-IR spectra of APP, APH, and APA exhibited absorption peak signals at 2,870 cm^-1 and 2,965 cm^-1, which represented the CH₃ stretching vibrations. They also exhibited a spectral band at 1,405 cm^-1 for the C-N stretching vibrations. The absorption peaks at 1,250 cm^-1 and 1,120 cm^-1 represented the C-O stretching vibrations in the alcohol and carboxylic acid groups, respectively. The presence of broad absorption peaks at 910 cm^-1 and 1,450 cm^-1 suggested presence of a benzene ring in APP, APH, and APA. In summary, the infrared absorption spectra were similar for APP, APH and APA.

The mass spectra of APP, APH, and APA were analyzed with the PEAKS StudioX software (BSI, Canada). Firstly, peptides with low confidence based on −10log p < 20 and peak area = 0 were removed from the analysis. Subsequently, we identified 1,688 peptides (length: 3–27 amino acids; molecular weight: 300–2,900 Da) in APP, 959 peptides (length: 7–22 amino acids; molecular weight: 600–2,300 Da) in APA, and 313 peptides (length: 7–24 amino acids; molecular weight: 750–2,700 Da) in APH. Furthermore, we identified 171 identical peptides between APP and APA, 78 identical peptides between APP and APH, and 107 identical peptides between APH and APA. The identical polypeptide in APP, APA, and APH are shown in Supplementary Table S1.

Metabonomics was used to investigate the mechanisms underlying the anti-thrombotic effects of HAPP, HAPH and HAPA in the mice tail vein thrombosis model. Plasma samples from the Con, Mod, HAPP, HAPH, and HAPA were analyzed in the ESI⁺ and ESI⁻ modes using the UHPLC-Q-Exactive Orbitrap MS/MS, and their base peak ions (BPI) diagrams of the Con, Mod, HAPP, HAPH and HAPA were depicted in Supplementary Figure S1, which exhibited clear separation of all peaks within a span of 15 min in both ESI⁺ and ESI⁻ modes. Furthermore, the metabolic profiles of various groups showed significant differences. This suggested that the treatments altered the plasma metabolite profiles in each group. The differences in the plasma metabolite profiles of the Con, Mod, HAPP, HAPH, and HAPA were analyzed using the PCA score plots. As shown in Figures 3A, B, the five groups of samples showed a clear separation trend in ESI⁺ and ESI⁻ modes. In the ESI⁺ and ESI⁻ modes, samples from the Con and Mod showed clear separation. Furthermore, samples from the treatment groups were clearly separated from the Mod samples. These results demonstrated significant differences in the endogenous metabolites between the Mod and the Con, HAPP, HAPH, and HAPA.

The supervised OPLS-DA model was used to identify potential biomarker metabolites between the Con, Mod, HAPP, HAPH, and HAPA. The plasma metabolite profile of the Mod showed significantly differences from the plasma metabolite profiles of the Con, HAPP, HAPH, and HAPA in both ESI⁺ and ESI⁻ modes (Figures 3C–J). The reliability of the OPLS-DA model was assessed using the R²Y and Q² parameters (Supplementary Table S2). In ESI⁺ mode, the R²Y and Q² values were 0.981 and 0.852, respectively, while in ESI⁻ mode, the corresponding values were 0.988 and 0.876. In general, when the values of R²Y and Q² are close to 1, it shows that the model is reliable and stable (Wei et al., 2023). Furthermore, seven-round cross validation and 200 time-permutation testing also showed the robustness of OPLS-DA models (Supplementary Figure S2). The S-plot scores (Supplementary Figure S3) were used to screen and identify the potential metabolic biomarkers in the OPLS-DA model. The differential metabolites between groups were evaluated using VIP >1 and t-test p < 0.05 as threshold parameters. The molecular information was then imported into the HMDB database and compared with the relevant values retrieved by HMDB. The theoretical fragments were used to infer the structure and fragment attribution of the compounds and potential biomarkers related to tail vein thrombosis were identified. Based on this comprehensive analysis, we identified 24 compounds with significant differences in their plasma concentrations between the Con and the Mod. These included bile acids, fatty acids, glycerophospholipids, and others (Supplementary Table S3; Figure 4 ). Among these, 19, 23, and 20 metabolites were significantly normalized after administration of HAPP, HAPH, and HAPA in the mice, respectively. Venn diagram showed that 16 of the 24 metabolites overlapped between HAPP, HAPH, and HAPA, accounting for 66.7% of the total differential metabolites (Figure 3K). It is suggested that HAPP, HAPH and HAPA might have a common mechanism in the treatment of tail vein thrombosis.

We identified significant differences in the plasma levels of 24 metabolites between the Mod and the Con (p < 0.05; Figure 4). The Mod showed significantly lower levels of glycochenodeoxycholic acid, cholic acid, chenodeoxycholic acid, LysoPE (0:0/16:0), eicosapentaenoic acid, linoleic acid, 1-oleoyl-2-hydroxy-sn-glycero-3-phosphoethanolamine, deoxycholic acid, 1-hydroxy-2-eicosadienoyl-sn-glycero-3-phosphoethanolamine, and 1-Stearoyl-2-hydroxy-sn-glycero-3-phosphoethanolamine (p < 0.05) and significantly higher levels of L-alpha-lysophosphatidylcholine, 1-[(9Z)-hexadecenoyl]-sn-glycero-3-phosphocholine, PC (O-18:1 (9Z)/2:0), 1-homo-gamma-linolenoyl-glycero-3-phosphocholine, palmitoylcarnitine, platelet-activating factor, 1-octadecyl-Sn-glycero-3-phosphocholine, phosphatidylcholine, 1-icosanoyl-sn-glycero-3-phosphocholine, 1-alpha-linolenoyl-2-linoleoyl-phosphatidylcholine, cis-5-tetradecenoylcarnitine, 1-heptadecanoyl-sn-glycero-3-phosphocholine, 1-[(11Z,14Z)]-icosadienoyl-sn-glycero-3-phosphocholine and 1-arachidonoyl-sn-glycero-3-phosphocholine compared with the Con (p < 0.05). Subsequently, we imported the information about these metabolites into the MetaboAnalyst 5.0 database (http://www.metaboanalyst.ca/) to predict the thrombus-related metabolic pathways in mice. The 24 metabolites with significant differences between the Mod and Con correlated with linoleic acid metabolism, primary bile acid biosynthesis, ether lipid metabolism, and glycerophospholipid metabolism (Figure 5A).

The differences in the plasma metabolites between the HAPP, HAPH, and HAPA and the Mod were analyzed to determine their potential therapeutic effects and mechanism of action. The results revealed that the intervention of HAPP resulted in the reversal of 19 metabolites among the 24 potential biomarkers listed in Supplementary Table S3, including glycochenodeoxycholic acid, cholic acid, chenodeoxycholic acid, L-alpha-lysophosphatidylcholine, 1-[(9Z)-hexadecenoyl]-sn-glycero-3-phosphocholine, PC(O-18:1 (9Z)/2:0), 1-homo-gamma-linolenoyl-glycero-3-phosphocholine, LysoPE (0:0/16:0), palmitoylcarnitine, platelet-activating factor, 1-Octadecyl-sn-glycero-3-phosphocholine, eicosapentaenoic acid, linoleic acid, phosphatidylcholine, 1-icosanoyl-sn-glycero-3-phosphocholine, 1-alpha-linolenoyl-2-linoleoyl-phosphatidylcholine, 1-Oleoyl-2-hydroxy-sn-glycero-3-phosphoethanolamine, deoxycholic acid, and 1-hydroxy-2-eicosadienoyl-sn-glycero-3-phosphoethanolamine. Likewise, 23 metabolites among the 24 potential biomarkers were significantly reversed after the intervention of HAPH, including glycochenodeoxycholic acid, cholic acid, chenodeoxycholic acid, L-alpha-lysophosphatidylcholine, 1-[(9Z)-hexadecenoyl]-sn-glycero-3-phosphocholine, PC(O-18:1 (9Z)/2:0), 1-homo-gamma-linolenoyl-glycero-3-phosphocholine, LysoPE (0:0/16:0), palmitoylcarnitine, platelet-activating factor, 1-Octadecyl-sn-glycero-3-phosphocholine, eicosapentaenoic acid, linoleic acid, phosphatidylcholine, 1-icosanoyl-sn-glycero-3-phosphocholine, 1-alpha-linolenoyl-2-linoleoyl-phosphatidylcholine, cis-5-Tetradecenoylcarnitine, deoxycholic acid, 1-hydroxy-2-eicosadienoyl-sn-glycero-3-phosphoethanolamine, 1-Stearoyl-2-hydroxy-sn-glycero-3-phosphoethanolamine, 1-heptadecanoyl-sn-glycero-3-phosphocholine, 1-[(11Z,14Z)]-icosadienoyl-sn-glycero-3-phosphocholine, and 1-arachidonoyl-sn-glycero-3-phosphocholine. There were 20 metabolites among the 24 differential metabolites are significantly reversed after the intervention of HAPA, including glycochenodeoxycholic acid, cholic acid, chenodeoxycholic acid, L-alpha-lysophosphatidylcholine, 1-[(9Z)-hexadecenoyl]-sn-glycero-3-phosphocholine, PC(O-18:1 (9Z)/2:0), 1-homo-gamma-linolenoyl-glycero-3-phosphocholine, LysoPE (0:0/16:0), palmitoylcarnitine, platelet-activating factor, 1-Octadecyl-sn-glycero-3-phosphocholine, eicosapentaenoic acid, linoleic acid, phosphatidylcholine, 1-icosanoyl-sn-glycero-3-phosphocholine, 1-alpha-linolenoyl-2-linoleoyl-phosphatidylcholine, cis-5-Tetradecenoylcarnitine, 1-heptadecanoyl-sn-glycero-3-phosphocholine, 1-[(11Z,14Z)]-icosadienoyl-sn-glycero-3-phosphocholine, and 1-arachidonoyl-sn-glycero-3-phosphocholine. As shown in Figures 5B–D, HAPP, HAPH and HAPA could reverse the disorder of linoleic acid metabolism, primary bile acid biosynthesis and ether lipid metabolism induced by tail vein thrombosis in mice. The above results suggested significant anti-thrombotic effects of HAPP, HAPH, and HAPA. Furthermore, the anti-thrombotic mechanisms of HAPP, HAPH, and HAPA were similar.