Modeling cardiomyocyte signaling and metabolism predicts genotype-to-phenotype mechanisms in hypertrophic cardiomyopathy

Abstract

Familial hypertrophic cardiomyopathy (HCM) is a significant precursor of heart failure and sudden cardiac death, primarily caused by mutations in sarcomeric and structural proteins. Despite the extensive research on the HCM genotype, the complex and context-specific nature of many signaling and metabolic pathways linking the HCM genotype to phenotype has hindered therapeutic advancements for patients. Here, we have developed a computational model of HCM encompassing cardiomyocyte signaling and metabolic networks and their associated interactions. Utilizing a stochastic logic-based ODE approach, we linked cardiomyocyte signaling to the metabolic network through a gene regulatory network and post-translational modifications. We validated the model against published data on activities of signaling species in the HCM context and transcriptomes of two HCM mouse models (i.e., R403Q-αMyHC and R92W-TnT). Our model predicts that HCM mutation induces changes in metabolic functions such as ATP synthase deficiency and a transition from fatty acids to carbohydrate metabolism. The model indicated major shifts in glutamine-related metabolism and increased apoptosis after HCM-induced ATP synthase deficiency. We predicted that the transcription factors STAT, SRF, GATA4, TP53, and FoxO are the key regulators of cardiomyocyte hypertrophy and apoptosis in HCM in alignment with experiments. Moreover, we identified shared (e.g., activation of PGC1α by AMPK, and FHL1 by titin) and context-specific mechanisms (e.g., regulation of Ca2+ sensitivity by titin in HCM patients) that may control genotype-to-phenotype transition in HCM across different species or mutations. We also predicted potential combination drug targets for HCM (e.g., mavacamten plus ROS inhibitors) preventing or reversing HCM phenotype (i.e., hypertrophic growth, apoptosis, and metabolic remodeling) in cardiomyocytes. This study provides new insights into mechanisms linking genotype to phenotype in familial hypertrophic cardiomyopathy and offers a framework for assessing new treatments and exploring variations in HCM experimental models.

Keywords: Cardiac metabolism; Cardiac signaling; Genotype-to-phenotype; Hypertrophic cardiomyopathy; Network modeling; Systems biology.

Figure 1

Systems modeling of genotype to phenotype in HCM. (A) Mapping HCM mutation in sarcomeric proteins to the hypertrophic remodeling of cardiomyocytes through signaling and metabolic networks. (B) The pipeline used to develop the systems biology model of HCM. This model connects the expanded version of the cardiomyocyte signaling network model to a genome-scale metabolic network model of the heart (iCardio [10] through a gene regulatory network model.

Figure 2

Signaling network of the HCM model. Schematic of expanded cardiomyocyte signaling network model in HCM. The model incorporates 86 signaling species connected through 187 reactions.

Figure 3

Gene regulatory network of the HCM model. This network links 23 transcription factors (TFs) (blue ovals) to their directly regulated 1078 genes (orange rectangles). In a circular layout, genes regulated by one TF are positioned close to it, while those co-regulated by multiple TFs are associated with an AND gate (green triangle).

Figure 4:

The HCM model accurately predicts classified qualitative changes in the activity of signaling nodes and cardiomyocyte transcriptome measured by independent experimental studies. (A) Comparison of model predictions with experimental data of 32 signaling nodes from literature. (B) The in silico transcriptome indicates upregulated and downregulated differentially expressed genes in HCM. (C) Comparison of model-predicted DEGs with 41 DEGs in αMyHC-mutant mouse hearts (R403Q-αMyHC) [39]. (D) Comparison of model-predicted DEGs12 with 49 DEGs in TnT-mutant mouse hearts (R92W-TnT) [39].

Figure 5:

The model predicts significant remodeling in metabolic functions and sarcomere structure/organization in HCM. (A) Top significant (p-value<0.025) downregulated and (B) upregulated metabolic functions in HCM predicted by the model using the TIDEs approach before and after applying feedback from the metabolic network model to the signaling (i.e., lower ATP synthase and level). The TIDEs approach identifies metabolic functions linked to DEGs by applying gene expression fold changes using gene-protein-reaction (GPR) rules to weight network reactions. TIDEs’ task scores are determined by averaging the reaction weights within a task [10]. (C) Gene Ontology analysis results for downregulated and (D) upregulated DEGs predicted by the model. (E) Results of ClinVar enrichment analysis for upregulated DEGs predicted by the model. (F) HCM-induced changes in hypertrophic growth and (G) apoptosis indexes predicted by the model. The Whiskers show 5 and 95 percentiles. Fifty in silico samples for each condition were used for statistical analysis (ordinary one-way ANOVA followed by Dunnett’s multiple comparison test with single pooled variance: * p-value<0.05;**** p-value<0.0001). All analyses in (C-E) were conducted by Enrichr, a web-based enrichment analysis platform, and combined scores computed by multiplying the log of the p-value from the Fisher exact test by the z-score of the deviation from the expected rank were used for ranking biological functions and diseases [53].

Figure 6

Morris global sensitivity analysis identifies major reactions regulating cardiomyocyte phenotype in different contexts. Important and less-important reactions in the HCM network regulating (A) the hypertrophic growth and (B) apoptosis of cardiomyocytes were identified based on the Morris index (μ*) larger or smaller than 0.01 (arbitrary criteria set by the designer), respectively. (C) Context-dependent regulatory reactions governing the cardiomyocyte response in three HCM contexts of mouse R403Q-αMyHC, mouse R92W-TnT, and human HCM patients. The top 20 reactions in each context are illustrated with three bar indicators comparing the normalized Morris indexes of each reaction.

Figure 7

The model predicts potential drug targets in the HCM context. (A) Efficacy of combination pharmacotherapy on hypertrophic cardiomyopathy. Data represent all pairwise combinations of inhibiting (Ymax = 1 to 0.1) node activity and their impact on hypertrophic growth and apoptosis indexes in HCM. Red points indicate potential drug targets preventing/reversing cardiomyocyte remodeling. (B) Model-predicted hypertrophic growth and (C) apoptosis indexes (Mean±SD) in selected drug targets in the HCM context. Fifty in silico samples for each condition were used for statistical analysis (ordinary one-way ANOVA followed by Tukey’s multiple comparison test with single pooled variance:*** p-value<0.001;**** p-value<0.0001). (D) Model-predicted metabolic functions that were upregulated and (E) downregulated in analyzed drug interventions.