Materials
Standard products of HG (CAS No.: 5843-65-2; Cat No. CHB180121) and [6]-GR (CAS No.: 23513-14-6; Cat No. CHB180306) were obtained from Chroma Biotechnology Co. Ltd (Chengdu, China). DOX hydrochloride injection was purchased from Shenzhen Main Luck pharmaceutical Inc. (batch number: 1710E1, Shenzhen, China). Dobutamine hydrochloride (DH) injection (Batch number: 1803203, Shanghai, China) was purchased from SPH NO.1 Biochemical & Pharmaceutical CO., LTD.
Animal Handling
Male Sprague - Dawley (SD) rats (200 ± 10 g) were purchased from the Beijing Keyu Animal Breeding Center (Beijing, China) with a permission number of SCXK-(jing) 2018-0010. Rats were fed in the Fifth Medical Center of PLA General Hospital. Rats in the control group were intragastrically given normal saline. Simultaneously, rats in the other groups were given DOX hydrochloride injection in the doses of 2.5 mg/kg body weight twice a week for six times. Thus, the accumulative doses of DOX was 15 mg/kg body weight.[13-15] As for the judgment of CHF model, hemodynamic indices were comprehensively assessed by a RM6240 multi-channel physiological signal acquisition system (Chengdu Instrument Factory, Sichuan, China) as our previous studies.[16-19] When the values of +dp/dtmax were reduced to 50% of the control group, CHF model was successfully prepared.
Forty rats with successfully prepared CHF model were randomly assigned into five groups of eight rats in each group: DOX group, DH positive group (50 μg/kg/d), HG group (5 mg/kg/d), [6]-GR group (5 mg/kg/d), and HG/[6]-GR compatibility group (10 mg/kg/d). All rats were intraperitoneally injected with corresponding drugs once a day for seven consecutive days. It should be noted that CHF rats intraperitoneally injected with 5 mg/kg/d HG and [6]-GR showed a good therapeutic effect in our previous study.[20] Hemodynamic indices were assessed after the final injection. All animals were sacrificed to collect serum samples and cardiac tissues for HE staining, TUNEL staining, and metabolomic analysis.
Detection of pharmacodynamic indices
Serum biochemical indices, including BNP, NT-proBNP, LDH, CK-MB, and AST were determined on a Synergy hybrid reader (Biotek, Winooski, USA). Hematoxylin-eosin (H&E) staining was carried out for showing myocardial morphological changes. Terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay was performed to indicate the cytotoxicity, cell damage and its recovery. Left ventricular myocardial tissue of rats was cut longitudinally. Next, cardiac tissue were fixed with 4% paraformaldehyde solution and embedded in paraffin for pathological observation. All the sections were observed under a Nikon microscope and analyzed by a Pro-Plus 7200 software.
Preparation of serum metabolomics samples
Firstly, the serum samples were thawed at 4°C. 200 μL of the serum was mixed with 600 μL of methanol to precipitate the protein. After centrifugation (13,800 g, 4℃, 10 min), the supernatant was transferred into a polypropylene tube and filtered via a syringe filter (0.22 μm) for obtaining the injection sample. Simultaneously, to assess the stability and reproducibility of serum metabolomics samples, the quality control (QC) sample was prepared by mixing all individual samples with10 uL aliquots in each.
Chromatography analysis
The serum samples were measured on an Agilent 1290 series UHPLC system (Agilent Technologies, Santa Clara, USA) coupled with a ZORBAX RRHD 300 SB-C18 column (100 × 2.1 mm, 1.8-μm, Agilent Technologies, Santa Clara, USA) for chromatography and separation. During the analysis, the setting conditions were as follows: sample maintaining temperature, 4 °C; injection volume: 4 μL; column temperature: 30 °C; flow rate, 0.30 mL/min. The mobile phases were composed as solvent A (0.1% formic acid in acetonitrile), and solvent B (0.1% formic acid in water). The gradient elution was set as Table 1. To ensure the stability and repeatability of the UHPLC-Q-TOF/MS systems, QC sample was injected followed by a blank sample after each injecting.
Mass spectrometry analysis
Mass spectrometry analysis was performed using an Agilent 6550A Q-TOF/MS instrument (Agilent Technologies, Santa Clara, USA) coupled with an electrospray ionization (ESI) source in both positive and negative ionization mode in the full scan mode (80 - 1200 m/z). The setting conditions in mass spectrometry analysis were as follows: gas temperature: 225 °C in positive ionization mode and 200 °C in negative ionization mode; nozzle voltage: 500 V in both positive and negative mode; electrospray capillary voltage, 4.0 kV in positive ionization mode and 3.0 kV in negative ionization mode; nebulizer: 45 pisg (positive) and 35 pisg (negative); gas flow rare: 11 L/min; mass range: from 80 to 1000 m/z; sheath gas flow: 12 L/min; sheath gas temperature: 350 °C.
Data processing and multivariate data analysis
After statistical analysis by MetaboAnalyst 4.0 (http://www.MetaboAnalyst.ca/),[21] the raw data were converted into “data_normalized.csv” format. Then, the normalized file in positive mode and negative mode were imported into the SIMCA-P program (version 14.1, MKS Umetrics) for multivariate analysis. Principal component analysis (PCA) was performed after concentration and normalization to check the overall metabolism of each sample group, and observe sample aggregation, dispersion and abnormal values. Next, partial least-squares discriminant analysis (PLS-DA) was used to identify the main difference variables that caused the aggregation and discretization. Subsequently, 100 iteration permutation tests were performed to avoid the over-fitting of PLS-DA. Potential biomarkers were selected according to the parameters of variable VIP > 1 and |Pcorr| > 0.58 from PLS-DA. SPSS 23.0 software with the t-test was used to test the peak areas of differential metabolites and determine the differences of biomarkers between groups (p-value threshold was set at 0.05).
Potential metabolites identification and pathway analysis
Furthermore, a MassHunter Profinder software (version B.06.00, Agilent, California, USA) was used to detect the sample data for peak detection and alignment. Full scan mode was employed and the mass range was 80 to 1000 m/z. The online biochemical database HMDB database (http://www.hmdb.ca/) and METLIN (http://metlin.scripps.edu/) were used to identify the potential metabolites. MetaboAnalyst 4.0 was used for the pathway analysis. Finally, to identify and visualize the affected metabolic pathways, the biomarkers were put into MetaboAnalyst 4.0 based on the pathway library of Rattus norvegicus (rat). In the present study, the bioactive components, possible biomarkers and potential mechanisms of HG and [6]-GR in the treatment of CHF induced by DOX were comprehensively elucidated using the serum metabolomics strategy.
Statistical analysis
All data were analyzed using SPSS 23.0 software program (Chicago, United States) and GraphPad Prism 8.2.0 software (GraphPad Software). The differences of data between groups were assessed by one-way analysis of variance (ANOVA). Values in the text were presented as mean ± SD. P < 0.05 was considered statistically significant. P < 0.01 was considered highly significant.