The framework flow chart is shown below:

Chip GSE125042 expression matrix was obtained from the Gene Expression Omnibus (GEO) (http://www.ncbi.nlm.nih.gov/geo/) database; these included 10 samples after cecum ligation perforation (CLP) induction (CLP group), and 10 samples as controls (Sham group).

Background correction and matrix data normalization were performed using the robust multiarray average algorithm. Differential analysis of expression profiles between the Sham and CLP groups was performed using the Limma R package Wilcoxon test analysis, with ∣log2FC∣ >0.2630 and adj. p<0.05 as the screening condition.

Co-expression network analysis was performed using the weighted gene co-expression network analysis (WGCNA) R package to reveal the correlation of genes and determine positively related gene modules. The soft threshold power was set to 20; feature genes were then calculated, modules were clustered hierarchically and similar modules were merged.

Once the number in sets exceeds four, it is more difficult to produce a graphical output in the form of a Venn diagram. The Upset Venn diagram was plotted using UpSet R package, and the intersection between the up-down differentially expressed genes (DEGs) and WGCNA module genes in sepsis-induced cardiomyocyte was visualized using the Upset Venn diagram.

The ClusterProfiler R package was used to enrich the up-regulated differentially expressed genes and modular genes, which was followed by gene ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and the plot of GO clustering figure. The ggplot2 R package was employed to plot the GO secondary classification column and KEGG annotation statistical chart.

The up-regulated DEGs and modular genes were analyzed using the enrichplot R package, the enrichment scores (ES) were calculated to estimate the significative degree of ES and determine the biological significance of the gene set. Multiple hypothesis tests were therefore conducted to calculate the positive false discovery rate (FDR). FDR<0.25 and p<0.05 were used to analyze the false-positive rate of the value of expression.

Sixty healthy male Wistar rats were purchased from the Laboratory Animal Science Institute of Chinese Academy of Medical Sciences and placed in SPF environment (humidity 50±5%, temperature 20–22°C), of which 40 rats were subjected to LPS (Solarbio, 10 mg/kg). Intraperitoneal injection to replicate the rat model with septic cardiomyopathy, and the remaining 20 in sham operation group (Sham) were administered an intraperitoneal injection of the same amount of normal saline (Beijing Tiantan Biological Products Co., Ltd.). 20 rats with septic cardiomyopathy were injected intraperitoneally with curcumin (LPS+Cur) (Solarbio) at 100 mg/kg 1 hour before modeling. The remaining two groups (Sham, LPS) were injected with the same amount of normal saline. The rats were anesthetized for 12 hours, and then sacrificed with pentobarbital sodium (Shanghai Xinya Pharmaceutical Co., Ltd.) (200 mg/kg). The chest cavity was opened, 1 ml blood was taken from the heart, and myocardial tissue was frozen in liquid nitrogen for further determination. Thereafter, the survival of rats that could drink and eat freely were observed, and the survival rate of the three groups within seven days was calculated. All procedures were undertaken in accordance with the National Laboratory Animal Management Regulations and the National Laboratory Animal Management Implementation Rules.

After the blood was taken from the heart, it was left to stand at room temperature for 1 h, centrifuged at 3000 r/min for 5 min. The supernatant was taken, the levels of tumor necrosis factor-α (TNF-α), interleukin 6 (IL-6) and interleukin 10 (IL-10) in rat serum were detected using ELISA kit (abcam), the protein was expressed in pg/mg and the levels of brain natriuretic peptide (BNP), troponin I (cTnI), creatine kinase (CK) and creatine kinase MB (CK-MB) were detected using an automated biochemical analyzer.

Myocardial cells (H9C2) of the rat were obtained from Shanghai Zhong Qiao Xin Zhou Biotechnology Co. Ltd., all the samples were treated with 90% high glucose DMEM (Gbico), 10% FBS (Gbico) and 1% double antibody (Gbico), then incubated at 37°C, 95% air and 5% carbon dioxide. When the fusion rate reached 75%, the samples were treated with LPS (1 μg/ml) for 6 h to construct a model of LPS-induced myocardial injury.

Twelve hours before adding LPS, each well was given different curcumin concentrations (0, 10, 30, 50, 100 μM), and six replicate wells were set in each group. CCK-8 kit (Solarbio) was used to detect the effect of curcumin on the proliferation activity of H9C2 cells after LPS treatment, then the cells were inoculated into 96-well plates (5*103 cells/well), 10 μl CCK-8 solution was added after 24 hours of culture. After incubation at room temperature for 4 h, the OD value at 450 nm was detected using microplate reader.

Using the same cell treatment as above, Annexin V-FITC an apoptosis detection kit (Procell) was used. Cells were seeded into six well plates, treated with LPS for 6 h, and washed twice with PBS. The cells then were resuspended in binding buffer and incubated with Annexin V-FITC/PI staining solution in the dark for 20 minutes at room temperature. The apoptosis rate of cardiomyocytes was analyzed using a C6 flow cytometer (BD).

The total RNA in the cells was lysed using Trizol (Solarbio), the cDNA library was synthesized using the reverse transcription kit (Thermo), and then the target gene was amplified using the quantitative polymerase chain reaction (PCR) instrument Quantstudio7flex (Thermo). 2 μL cDNA, 10 μL 2×FastFire qPCR PreMix (Tiangen), a total of 0.6 μL upstream and downstream primers, and 6.8 μL RNase-Free Water were added to the system. An amplification reaction was carried out using the following condition: pre-denaturation at 95°C for 60 s, denaturation at 95°C for 5 s, annealing at 60°C for 15 s, 40 cycles. In this study, 2−ΔCT was used to calculate the relative level of expression of the target gene. The experiment was carried out three times and the average value was taken. All primer sequences were designed and synthesized by Shanghai GenePharma Co., Ltd. The primer sequences are shown in Table 1.

The protein in the cells were extracted using protein kit (Solarbio), separated with 10% SDS-PAGE, transferred to PVDF film, then blocked with 5% skimmed milk at room temperature for 2 h, and washed three times (10 min every time) using TriTween buffer. Then the samples were added to the primary antibody (abcam) diluted by dilution buffer, incubated at 4°C overnight, washed three times with tris-buffered solution, and added to the diluted secondary antibody (ab205718) for incubation. After washing three times (10 min every time), an ECL Chemiluminescence kit (Thermo-Fisher) was used for detection. The expression levels of TLR1 and protein were normalized with GAPDH and 284 Lamin A as internal parameters.

With crystal structure of human TLR1 (PDB ID: 6nih) as a protein receptor, the crystal water and other small molecules in the crystal structure in the pymol1.7 program were deleted, hydrogen atoms were added and saved in the program. The two-dimensional structure of curcumin was plotted with chemdraw 15.0 and saved in cdx format. Chem3D 15.0 was loaded and the MM2 force field was adopted to minimize energy. The autodocktool1.5.6 program was loaded, charged, and assigned to the atom type. As the protein remains rigid, all the rotatable bonds of curcumin were set to be flexible and saved in pdbqt format for further analysis.

Autodock vina1.1.2 was applied for molecular docking. The docking parameters were set as follows: the center coordinates of the conformation search box: center_x=103.3, center_y=−35.634, center_z=16.573. Box size: size_x=70, size_y=52, size_z=54. Other parameters: exhaustiveness=100; num_modes=9; energy_range=4. The best conformation of affinity was selected as the result of molecular docking, and subsequent analysis was performed.

All experimental data in this research were presented in terms of (x±sd), calculated and plotted using GraphPad 8. An independent-sample T test was applied in group comparison and one-way analysis of variance in comparison among groups, LSD-t was used for post-hoc pairwise comparison. Results with p<0.05 were considered statistically significant.

We performed differential expression analysis between the CLP and Sham groups based on the GSE125042 microarray expression matrix. A total of 2876 DEGs were screened with ∣log2FC∣ >0.2630 and adj. p<0.05, of which 1424 genes were upregulated and 1452 genes were down regulated. Volcano plots of DEG expression are shown in Figure 1A, and a heat map of DEG cluster analysis is shown in Figure 1B.

Figure 1.

Screening and identification of differential genes for myocardial injury in sepsis. (A) Differentially expressed genes are shown as a volcano map, with red indicating up-regulated genes and blue indicating down regulated genes. (B) The expression of differential genes in each sample is represented by a heat map.

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Weighted gene co-expression network analysis of whole genes based on the GSE125042 microarray expression matrix was performed to reveal genes and co-expression networks highly associated with myocardial injury in sepsis. WGCNA analysis classified genes into a total of 20 modules (Figure 2A and B), where a heat map of module-phenotype correlations showed that genes in the Brown module were most highly associated with myocardial injury in sepsis (Figure 2C and D, p<0.05, R=0.94). Interestingly, TLR1 was also present in the Brown module with the highest correlation (Table 2).

Figure 2.

Weighted gene co-expression network analysis (WGCNA) of gene in septic cardiomyocytes. (A) Co-expression module-level cluster tree identified by WGCNA; (B) cluster tree diagram: the relationship between septic cardiomyocyte samples; (C) relationship between sepsis model and module relevance; (D) scatter diagram between members of the Brown module and genetic sense.

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The Upset Venn diagram can better display the quantitative results of multiple interaction sets than a traditional Venn diagram. The common gene distribution of the up/down regulated DEGs and WGCNA modules of the highest correlation is shown in Figure 3. Since TLR1 is an up-regulated gene and located in the Brown module, the up-regulated DEGs were intersected with the Brown module of highest relevance to obtain 601 genes. According to the enrichment analysis, these 601 genes were significantly enriched in the generation of ribosomal protein complexes, ribose RNA-related metabolism, myeloblast migration and other related biological processes; in phagocytic vesicles, membrane rafts and other related cellular components; as well as in the binding of a Toll-like receptor, amide binding and other related molecular functions. According to the analysis on KEGG pathway, these 601 genes are mainly enriched in NF-κB signaling pathway, autophagy, mitochondrial autophagy and other pathways. In addition, these genes are also involved in proteasome, RNA transport, TGF-beta signaling pathway and JAK-STAT signaling pathways. The correlation can be clarified by visualizing the interactive network of sepsis-related pathways and corresponding genes. See Figure 4 and Table 3 for details.

Figure 3.

Upset Venn diagram of up/down regulated differential expression genes and gene in weighted gene co-expression network analysis modules. The histogram above represents the number of genes contained in each group, the bar in the lower left corner shows the number of genes contained in each type, and the dots and rows in the lower right corner indicate the types of events included.

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Figure 4.

Functional enrichment analysis on the up/down regulated differential expression genes and genes in weighted gene co-expression network analysis module. (A) Annotation of the entries of 601 genes in biological process (BP), molecular function, and cell composition, of which BP takes the top 15. (B) The top 15 of Kyoto Encyclopedia of Genes and Genomes enrichment analysis.

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We performed GSEA analysis of 601 genes obtained by taking the intersection of the upregulated DEGs with the Brown module with the highest correlation, and obtained 62 pathways. Three gene sets were statistically significant, and nine gene sets had an incidence rate of <0.25, mainly including human papillomavirus infection, chemokine signaling pathway, NF-κB signaling pathway and other related processes. The results are shown in Table 4. NF-κB signaling pathways were significantly enriched (NES=1.146, p=0.024) in sepsis-induced cardiomyocytes, with an enrichment score of 0.545. It is suggested that TLR1 may affect the survival of cardiomyocytes by interacting with the NF-κB signaling pathway, serving as a molecular intervention target for septic cardiomyopathy, see Figure 5 for details.

Figure 5.

Gene set enrichment analysis on the up/down-regulated differential expression genes and genes in weighted gene co-expression network analysis module. These 601 genes were significantly enriched in the NF-κB signaling pathway, and the green curve represents the change in gene density identified in the RNA sequence.

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Lipopolysaccharide was used to construct a rat model of sepsis, and the effect of intraperitoneal injection of curcumin on rats with sepsis was observed. Curcumin increased the overall survival of rats seven days after LPS induction. Blood was collected from the rats’ hearts, and the expression levels of inflammatory factors and myocardial enzymes in serum were detected. The results showed that curcumin effectively reduced the expression of pro-inflammatory factors TNF-α and IL-6, up-regulated the expression of anti-inflammatory factors IL-10, and down regulated the expression levels of BNP, cTn?, CK and CK-MB (p<0.05) (Figure 6). Then we further examined the effect of curcumin on the model of myocardial injury induced by LPS through in vitro cell experiments, we first added 0, 10, 30, 50 and 100 μM curcumin to H9C2 cells in order, then we treated the samples with LPS to induce cardiomyocyte injury after 12 hours. The QPCR showed that curcumin can inhibit the expression levels of TLR1 mRNA and NF-κB mRNA in H9C2 cells. Western blot detected that the protein expression of TLR1 and p-NF-κB was down regulated. After double staining detection with CCK-8 and Annexin/PI, it was found that curcumin can improve the survival rate of cells and inhibit apoptosis after LPS treatment, which is dose-dependent (p<0.05). See Figure 7 for details.

Figure 6.

Curcumin improves myocardial damage induced by LPS in rats with sepsis. (A) 7-day survival curves of rats in each group; (B) expression levels of inflammatory factors TNF-α, IL-6 and IL-10 in serum of rats in each group; (C) expression levels of cardiac enzymes BNP, cTn, CK and CK-MB in serum of rats in each group. Note: *p<0.05, ****p<0.0001.

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Figure 7.

Curcumin increases the cell survival rate of LPS-treated cardiomyocytes. (A) Effect of different concentrations of curcumin on the relative expression levels of TLR1 mRNA in cells after LPS treatment of H9C2. (B) Effect of different concentrations of curcumin on the relative expression levels of NF-κB mRNA in cells after LPS treatment of H9C2. (C) WB plots showing the effect of different concentrations of curcumin on the relative expression levels of TLR1 and NF-κB protein in cells after LPS treatment of H9C2. (D) Effect of different concentrations of curcumin on the cell survival rate after LPS treatment of H9C2. (E) Effect of different concentrations of curcumin on the apoptosis rate of cells after LPS treatment of H9C2. Note: *p<0.05, **p<0.01, ****p<0.0001.

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The molecular structure of curcumin and TLR1 is shown in Figure 8. Molecular docking shows that TLR1 has no obvious ligand-binding pockets or cavities. Curcumin can only bind to the area composed of multiple loops on the surface of TLR1, and the binding energy of the two can be −7.1 kcal/mol. This area has strong hydrophilicity when exposed to a solvent environment. Curcumin has multiple hydroxyl groups and oxygen, which can form hydrogen bonds with the receptor to stabilize binding (Figure 9A). It can be seen from Figure 9 that the hydroxy oxygen of curcumin (O27) can form a hydrogen bond with the side chain amino group of Gln54, and the heavy atom distance is 2.88 Å. The curcumin carbonyl oxygen (O12) can form a hydrogen bond with the side chain amino group of Lys104, with a heavy atom distance of 3.15 Å. These two hydrogen bonds are essential for the binding of curcumin. In addition, there is also a hydrophobic accumulation between Phe123 and the curcumin benzene ring, which can facilitate binding to a certain extent (Figure 9B and C).

Figure 8.

Molecular structure of ligand receptor. (A) The molecular two-dimensional structure of curcumin. (B) The protein structure of TLR1.

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Figure 9.

Molecular docking detecting the binding of curcumin to TLR1. (A) The overall binding of curcumin and TLR1. (B) The binding mode of curcumin and TLR1. (C) Three-dimensional interaction view of curcumin and TLR1.

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