Temporal analysis of doxorubicin-induced cardiac toxicity and hypertrophy

Figures

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  Fig. 1. AC16 cells exposed to DOX exhibit morphological and cellular stress level alterations. a Drug screening results showing changes in the mitochondrial potential caused by 23 distinct compounds (n = 6 for each concentration) and controls (no drug treatment) (n = 40 plates of cells). The y-axis represents the normalized mitochondrial membrane potential estimated from confocal images. The values were normalized to the mean values of the control samples from the same experimental batch. The subtitles indicate the drugs, and the x-axis denotes concentrations. The minimum, low, moderate, and high concentrations of drugs were derived from MitoPlate I-1 assays. Notably, the MitoPlate I-1 assay does not provide absolute concentrations but instead establishes a predefined order of concentrations (minimum, low, moderate, and high). This hierarchical arrangement facilitates comparative analysis across treatments under standardized experimental conditions. To compare the treated groups with the controls, we performed one-way ANOVA on each drug-treated group, followed by Student’s t test. The data points shown are measured values from distinct wells. b Confocal images showing the size increases…

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  Fig. 2. Scheme of the study design and workflow for transcriptomic and metabolomic analyses. a RNA sequencing. The raw sequencing data from AC16 cells and publicly available datasets were aligned to the human reference genome. The public datasets included samples from normal tissues or cells treated with DOX. We then used the gene counts to perform RNA-seq analysis and metabolic model simulation. b RNA-seq analysis. We used the gene expression data to identify DEGs and subsequently performed functional and pathway analyses to identify the DOX-affected pathways. c GEM reconstruction and simulation. By integrating the RNA-seq data and a human metabolic model, we obtained a sample-specific GEM for each sample. These models were used to simulate metabolic fluxes and gene knockout effects, suggesting the occurrence of DOX-induced metabolic alterations between the groups. The figure was created using BioRender.com.

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  Fig. 3. DOX treatment of AC16 cells for different durations resulted in transcriptomic differences. a Bubble plots showing the most significant DEGs with the greatest differences between DOX-D2 (right), DOX-D4 (middle), and DOX-D6 (left) and the control (n = 3 per condition). The colors are log2-fold changes, and the sizes of the dots represent negative log10-transformed adjusted P values. b A PPI subnetwork generated from DOX-D6 DEGs. c Bar plots showing enrichment analysis results for upregulated (left) and downregulated DEGs (right) in the DOX-treated samples compared with the control samples. d GSEA results split into pathways with NES > 0 (left) and NES < 0 (right). e Bar plots showing the IPA results of the upregulated (left) and downregulated DEGs (right). f Enrichment analysis results of the PPI nodes constructed from the identified DEGs. g Enrichment analysis (left) and GSEA results (right) for public studies. This panel illustrates the results of overlapping enrichment analysis (left) and GSEA (right) based on DEGs identified from RNA-seq data obtained from various public studies. The studies are categorized into three groups: cultured cardiac cells, heart tissue, and other…

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  Fig. 4. Meta-analysis and sample-specific metabolic models generated from public transcriptomic datasets. a Boxplots showing the percentiles of the fitted thresholds for fitting two Gaussian distributions for the RAN-seq data and constructing genome-scale metabolic network models. The expression thresholds are in the 90th and 50th percentiles, and the nonexpression thresholds are between the 50th and 5th percentiles. b The probability distribution of the logTPM values and the fitted thresholds of a sample in our RNA-seq data (one sample obtained from the DOX-D6 group). This is an example showing how the thresholds were fitted. c PCA results indicating model component similarity between context-specific models. The colors represent the groups (left) and cell lines (right) of the samples. VSMC: vascular smooth muscle cell; SI: small intestine. d Volcano plot showing the fold changes (x-axis) and -log-transformed p values (y-axis) of the task scores calculated using the control (n = 143) and DOX models (n = 221). e Venn diagrams showing the number of key metabolic genes found in the knockout analysis. The diagrams on the left show the numbers of common key metabolic genes found in the…

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  Fig. 5. Metabolic flux measurement and simulation of the sample-specific GEMs reconstructed from AC16 cell RNA-seq data. a Rates of electron flow in the electron transport chain from different metabolic substrates that produce NADH and FADH2. Slopes of MitoPlate S-1 substrate utilization indicating the relative amount of reduced cytochrome c subjected to different treatments (colors) and added substrates (x-axis). The colors of the dots represent the pathways in which the substrates are involved. The data are presented as the means ± standard deviations. P values were obtained via t tests to compare the DOX and control samples following a significant two-tailed one-way ANOVA result (P < 0.05). Significance levels: *P < 0.05. b Cellular respiration and oxygen consumption rates. Mitochondrial metabolic parameters were inferred from the oxygen consumption rates measured by O2K high-resolution respirometry. The parameters were normalized on the basis of estimated cell volume (top), cell dry weight (middle) and cell number (bottom). While AC16 cells treated with DOX presented a greater OCR than the controls did, a different trend was observed for the parameters normalized by the dry weight of…

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  Fig. 6. Metabolomics data and predicted metabolic fluxes. a Heatmap and hierarchical clustering of the measured metabolite concentrations in the AC16 samples (n = 3 per group) subjected to different treatments. The heatmap colors represent the z scores calculated for the substrates. The bottom colors are the groups of the samples (blue: control; orange: DOX-D2; green: DOX-D4; red: DOX-D6). b Metabolic parameters derived from raw metabolomics data. The data are presented as the means ± SDs. P values were obtained from multiple comparison t tests following a significant two-tailed one-way ANOVA result (P < 0.05). Significance levels: ****P < 0.0001, ***P < 0.001, **P < 0.01, and *P < 0.05. c Absolute concentration (nmol/gram protein dry weight) of energy transfer cofactors and redox cofactors measured by capillary electrophoresis mass spectrometry. The data are presented as the means ± SDs. The data points were measurements of the samples (n = 3 per group). P values were obtained from t tests as multiple comparison tests following a significant two-tailed one-way ANOVA result (P < 0.05). d Pentose phosphate pathway (PPP) and e glycolysis pathway. The subplots in (d) and (e) illustrate…

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  Fig. 7. DOX-induced metabolic changes predicted via GEMs and captured in the metabolomics data. a Randomly sampled OXPHOS fluxes in GEMs normalized to the number of cells in the control (blue), DOX-D2 (orange), DOX-D4 (green), and DOX-D6 (red) groups. b Venn diagram showing the intersections of important metabolic genes predicted via sample-specific GEMs. The yellow boxes indicate some of the genes that are exclusively important for DOX-D2 (left) and DOX-D4&D6 (right). The gray boxes list the metabolic pathways in which these genes are involved. The percentages are the proportions of how many genes are associated with the pathways. In this figure, DOX-D4 & DOX-D6 represent the merged gene data from these two groups, reflecting the effects of mid- to long-term DOX toxicity on cardiac cells (Supplementary Fig. 9). c Volcano plots showing metabolite fold changes and adjusted p values of the DOX-D2 (left) or DOX-D4 and DOX-D6 (right) samples compared with the control samples. The green circles indicate significantly decreased metabolites, and the yellow circles indicate significantly increased metabolites. Similar to (b), the DOX-D4 and DOX-D6 data were merged to represent their combined…

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  Fig. 8. DOX affects respiration and fatty acid metabolism in AC16 cells and causes cell cycle arrest and senescence. a Seahorse analyzer-measured OCR values (left) and the mitochondrial parameters (right) inferred from the data. N = 4 or 5. The error bars represent the standard deviations. b A bar chart showing the proportions of cells in the G1, G2, and S phases in the control and DOX-treated groups. A lower proportion of cells in the G2 and S phases was observed in the DOX-D2 (G2: 2.71 ± 0.25%, S-phase: 4.53 ± 0.79%), DOX-D4 (G2: 3.47 ± 0.34%, S-phase: 7.25 ± 2.34%), and DOX-D6 conditions (G2: 13.18 ± 0.10%, S-phase: 20.59 ± 1.84%) than in the control conditions (G2: 19.29 ± 0.85%, S-phase: 28.00 ± 1.40%). Each data point represents the cells in one culture plate (n = 3 per group). The P value in the figure was obtained from Tukey’s test of the proportions of cells in the S phase following a significant two-tailed mixed-design one-way ANOVA (P < 0.05). c Nontargeted metabolomic profiling results showing the relative carnitine concentrations in the DOX-treated and control samples. d Percentages of positive senescent cells detected in (e). Data points represent the mean percentages of…

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  Fig. 9. Potential progression of cardiotoxicity induced by high- and low-dose DOX treatment. a This figure illustrates the possible progression of drug-induced cardiotoxicity (DIC) based on our in vitro and in silico experiments. Our hypothesis suggests that cardiac cells with healthy mitochondria are more resistant to DOX, while those with a higher proportion of damaged mitochondria are more susceptible. High-dose DOX treatment results in increased apoptosis and oxidative stress, leading to cell death in both resistant and susceptible cells. In contrast, cells treated with a lower dose of DOX survive longer. In susceptible cells, DOX treatment reduces energy production, increases apoptosis, and induces inflammation. Resistant cells, on the other hand, maintain higher energy production through enhanced electron transport chain (ETC) activity and glycolysis, leading to a reduced apoptosis rate and improved survival. However, surviving cells, regardless of resistance, develop inflammation, hypertrophy, fibrosis, and senescence. b The biological pathways affected in cardiac cells by DOX treatment. The increase or decrease of these pathways is inferred from biological evidence and…

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  Fig. 10. Pathways associated with DOX-induced cardiotoxicity. This graph illustrates the signaling (blue rectangles) and metabolic pathways (purple rectangles) potentially associated with DOX-induced cardiotoxicity. These pathways can increase (black arrows) or inhibit (red dashed lines) various biological processes (red rounded rectangles), such as oxidative stress, calcium release, fibrosis, inflammation, and apoptosis. Ligands (green rounded rectangles) may bind to receptors (light blue) on the cell membrane, activating signaling processes.