Regulation of energy metabolism by combination therapy attenuates cardiac metabolic remodeling in heart failure

Cardiac metabolic remodeling is recognized as an important hallmark of heart failure (HF), while strategies that target energy metabolism have therapeutic potential in treating HF. Shen-Fu formula (S-F) is a standardized herbal preparation frequently used in clinical practice and is a promising combinatorial therapy for HF-related metabolic remodeling. Herein, we performed an untargeted multi-omics analysis using transcriptomics, proteomics, and metabolomics on HF mice induced by transverse aortic constriction (TAC). Integrated and pathway-driven analyses were used to reveal the therapeutic targets associated with S-F treatment. The cardioprotective effect and potential mechanism of S-F were verified by the results from echocardiography, hemodynamics, histopathology, and biochemical assays. As a result, S-F significantly alleviated myocardial fibrosis and hypertrophy, thus reducing the loss of heart function during adverse cardiac remodeling in TAC mice. Integrated omics analysis showed that S-F synergistically mediated the metabolic flexibility of fatty acids and glucose in cardiac energy metabolism. These effects of S-F were confirmed by the activation of AMP-activated protein kinase (AMPK) and its downstream targets in the failing heart. Collectively, our results demonstrated that S-F suppressed cardiac metabolic remodeling through activating AMPK-related pathways via energy-dependent mechanisms.


RNA Extraction, library construction, and sequencing
Total RNA from myocardial tissue was extracted with TRIzol reagent (

Data processing and bioinformatics analysis
Raw data were processed using the fastp tool (version 0.18.0 Functional analysis of the differentially expressed genes was performed using the KEGG database.

High pH Reverse Phase Separation
The peptide mixture was re-dissolved in 20 mM ammonium formate in water (pH 10.0, adjusted with ammonium hydroxide). The samples were then fractionated by high pH separation using an The linear gradient was starting from 5% B to 45% B in 40 min with a flow rate of 1 ml/min. A total of 12 fractions were collected, each fraction was dried in a vacuum concentrator for the next step.

Low pH nano-HPLC Orbitrap-MS/MS analysis
Peptide fractions were reconstituted with 30 μl of 0.1% formic acid in water. The samples were further analyzed on an Easy-nLC 1000 system connected to a Q Exactive Hybrid Quadrupole-Orbitrap system (Thermo Fisher Scientific, Waltham, MA, USA). After loading onto an Acclaim PepMap C18 100 μm × 2 cm trap column at a flow rate of 10 μl/min for 3 min, samples were applied onto an Acclaim PepMap C18 75 μm × 15 cm analytical column. The column flow rate was 300 nl/min, and the temperature was maintained at 40°C. The mobile phase consisted of 0.1% formic acid in water (mobile phase A) and 0.1% formic acid in acetonitrile (mobile phase B) with a linear gradient, from 2% B to 40% B in 70 min. The Q Exactive mass spectrometer was operated in the data-dependent acquisition mode to switch automatically between MS and MS/MS acquisition. The following program settings were applied: m/z scan range, 350-1550 Da; electrospray voltage, 2 kV; full MS resolution, 70000; MS/MS resolution, 17500; collision energy as 27 eV in NCE model.

Data processing and quantitative analysis
For protein identification and quantification, the raw data of mass spectra were extracted, charge state deconvoluted and deisotoped by Mascot Distiller version 2.6 (Matrix Science, London, UK).
The processed data were then transformed into MGF format by Proteome Discovery Protein identifications were accepted if they could achieve an FDR less than 1.0% by the Scaffold Local FDR algorithm. Confident protein identification involved at least two unique peptides.
Protein relative quantification was based on the ratios of reporter ions, which reflect the relative abundance of peptides. The Mascot search results were averaged using medians and quantified.
Proteins with fold change in a comparison > 1.2 or < 0.83 and unadjusted significance level p < 0.05 were considered differentially expressed.
The mobile phase A was 0.1% formic acid in water for positive ion mode, and 5 mmol/l ammonium acetate in water for negative, and the mobile phase B was acetonitrile. The elution gradient was set as follows: 0-1 min, 1% B; 1-8 min, 99%-1% B; 8-10 min, 1% B. The flow rate was 0.5 ml/min. In order to test the repeatability of the analytical system, the QC samples were injected after every ten samples throughout the analytical workflow. Base peak chromatograms and PCA scores plots demonstrated good technical repeatability from QC samples, indicating that the analysis system was stable and all the data were under control.

Data processing and multivariate analysis
MS raw data (.raw) files were converted to the mzML format using ProteoWizard and processed by R package XCMS, including retention time alignment, peak detection and peak matching. Then data filtering was carried out, including estimating missing values, data filtering, data normalization and Pareto scaling. The processed data were further imported into the SIMCA 14.1 software package (Umetrics, Umeå, Sweden) for multivariate data analysis. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were performed to maximize class differences while minimizing the variability unrelated to class. The goodness-of-fit was quantified by R 2 , while the predictive ability was indicated by Q 2 . A cross-15 validation procedure and testing with 200 random permutations were performed to avoid overfitting of supervised OPLS-DA models. Furthermore, potential biomarkers were selected based on the variable importance of project (VIP) statistics and the two-tailed p-values calculated by Student's t-test. The metabolites were identified by matching the accurate mass of observed peaks with those in the HMDB and KEGG databases within a mass accuracy window of 10 ppm.