Identification of Aging and Mitochondria-Related Hub Genes in Dilated Cardiomyopathy

Summary

This protocol integrates multidimensional transcriptomic data with machine learning to identify aging- and mitochondria-related genes in dilated cardiomyopathy for biomarker discovery and molecular subtyping.

Abstract

Dilated cardiomyopathy (DCM) is characterized by left ventricular dilation and systolic dysfunction and is associated with mitochondrial dysfunction and immune-inflammatory activation. However, aging-related molecular signatures and mitochondrial regulatory pathways in DCM remain incompletely understood. This study analyzed six bulk transcriptomic datasets and one single-cell RNA sequencing dataset from the Gene Expression Omnibus database. After data normalization, batch correction, and cell-type annotation, aging- and mitochondria-related candidate genes were identified using differential expression analysis, weighted gene co-expression network analysis, and protein-protein interaction network construction. Core genes were further screened using least absolute shrinkage and selection operator regression, random forest, and support vector machine-recursive feature elimination. Immune-cell infiltration, cell-cell communication, and molecular subtyping analyses were performed to characterize the cardiac immune microenvironment in DCM. A total of 66 aging-related genes and 16 mitochondria-related genes were associated with DCM and were mainly enriched in hypoxia-inducible factor-1 signaling, oxidative phosphorylation, and nitric oxide synthase-related pathways. Machine learning and single-cell RNA sequencing analyses identified SERPINE1, TGFB2, CYBB, and TLR2 as core genes. CYBB and TLR2 were highly expressed in monocytes and macrophages, whereas SERPINE1 and TGFB2 were predominantly expressed in stromal cells. Immune landscape analysis showed increased pro-inflammatory macrophage activation and altered cell-cell communication in DCM samples. Based on core gene expression, DCM samples were divided into two molecular subtypes associated with vascular endothelial growth factor signaling and primary bile acid biosynthesis, respectively. This protocol provides an integrated framework for identifying candidate biomarkers and molecular subtypes in DCM.

Introduction

Dilated cardiomyopathy (DCM) is a myocardial disorder characterized by left ventricular dilation and impaired systolic function. It is the third most common cause of heart failure and the leading indication for cardiac transplantation worldwide¹. Population-based studies estimate a prevalence of approximately 1 in 250 adults, with a higher prevalence in males and a substantial proportion of cases attributable to monogenic variants². These findings indicate that both genetic susceptibility and environmental factors contribute to the onset and progression of DCM.

The pathogenesis of DCM involves interconnected processes, including inflammatory activation, oxidative stress, cardiomyocyte apoptosis, and dysregulated profibrotic signaling. Inflammatory genetic polymorphisms, including tumor necrosis factor-α promoter variants, have been associated with susceptibility to viral DCM³. Increased oxidative stress has also been associated with cardiomyocyte death and left ventricular dysfunction in human DCM subtypes⁴. In addition, aberrant activation of Wnt/β-catenin and calcineurin/nuclear factor of activated T cells signaling promotes myocardial hypertrophy and interstitial fibrosis, thereby contributing to disease progression^5,6. Mitochondrial dysfunction is another important component of DCM because cardiomyocytes have high energy demands. Disruption of mitochondrial biogenesis, calcium homeostasis, mitophagy, and mitochondrial DNA integrity can impair oxidative phosphorylation and contribute to progressive cardiac dysfunction^7,8,9,10.

Despite these mechanistic findings, important knowledge gaps remain. In particular, the temporal and causal relationships between mitochondrial structural remodeling and bioenergetic dysfunction during DCM initiation and progression have not been fully defined¹¹. Several therapeutic strategies have been investigated. Stem cell therapy has shown regenerative potential through paracrine, cytoprotective, and immunomodulatory effects, but optimization of cell sources, delivery routes, and post-transplantation survival remains necessary¹². Gene therapy approaches, including adeno-associated virus-based delivery and clustered regularly interspaced short palindromic repeats-based genome editing, also offer potential precision treatment strategies. However, limitations related to cardiac tropism, vector immunogenicity, and long-term safety remain unresolved¹³.

Public transcriptomic datasets from repositories such as the Gene Expression Omnibus (GEO) are widely used for biomarker discovery in DCM. These resources provide access to multicenter clinical cohorts, support cost-effective and reproducible investigations, and can improve statistical power through cross-dataset integration¹⁴. Transcriptomic profiling also enables genome-wide candidate gene screening, molecular subtyping, and pathway-level analysis¹⁵. However, public datasets have inherent limitations, including technical batch effects, clinical and etiological heterogeneity, limited capacity for causal inference, and incomplete longitudinal or prognostic information¹⁶. Therefore, findings derived from public transcriptomic datasets are most suitable for hypothesis generation and candidate biomarker prioritization and require validation in independent cohorts and experimental models.

Many bioinformatic studies of DCM rely primarily on differential expression analysis, which may generate false-positive findings and does not fully characterize gene co-expression networks or cellular heterogeneity within cardiac tissue. To address these limitations, the present study employed an integrated analytical strategy combining complementary methods. Bulk transcriptomic analysis provides tissue-level expression profiles suitable for case-control comparisons. Weighted gene co-expression network analysis (WGCNA) identifies gene modules associated with phenotypic traits and enables prioritization of functionally related gene sets rather than individual differentially expressed genes. Protein-protein interaction (PPI) network analysis identifies highly connected genes on the basis of network topology. Three machine learning algorithms—least absolute shrinkage and selection operator regression, random forest, and support vector machine-recursive feature elimination—were used to identify candidate biomarkers across the integrated datasets¹⁷. Single-cell RNA sequencing (scRNA-seq) was then used to examine cell-type-specific expression patterns and intercellular communication networks¹⁸.

Although mitochondrial dysfunction and aging-related molecular changes have each been investigated in DCM, their combined associations with disease-related transcriptional changes remain underexplored. The present study integrated multiple bulk transcriptomic and scRNA-seq datasets to identify aging- and mitochondria-related hub genes in DCM, characterize the cardiac immune microenvironment, and examine molecular subtypes based on the identified genes. This integrated approach was used to prioritize candidate biomarkers and provide a basis for subsequent mechanistic and validation studies.

Protocol

All animal procedures were reviewed and approved by the Laboratory Animal Ethics Committee of the Second Affiliated Hospital of Henan University of Chinese Medicine (Approval No. HNSZYYYJS2023011150). All procedures were conducted in accordance with the Guidelines for Ethical Review of Laboratory Animal Welfare (GB/T 35892-2018) and the 3R principles of Replacement, Reduction, and Refinement. The reagents, databases, software, and equipment used in this study are listed in the Table of Materials. 

1. Data resources and experimental materials
Male SPF-grade CTNTR141W transgenic mice with a spontaneous dilated cardiomyopathy (DCM) phenotype and a body weight of 25 ± 2 g were used as the model group. Age-matched male SPF-grade C57BL/6J mice with a body weight of 25 ± 2 g were used as the control group. Each group included 12 mice. All animals were obtained from institutions holding valid laboratory animal production licenses and were housed in an SPF-grade barrier environment at 22 ± 2 °C and 40%–60% relative humidity under a 12 h light/dark cycle, with free access to sterilized food and water. After 1 week of acclimatization, all mice were maintained under the same conditions for an additional 4 weeks before cardiac function assessment and sample collection. All mice were 6–8 weeks old at the start of the experiment. Mice were deeply anesthetized and euthanized by cervical dislocation.

Seven public transcriptomic datasets of left ventricular myocardial tissue from patients with DCM were retrieved from the Gene Expression Omnibus (GEO)¹⁹ database. These datasets included six bulk transcriptomic datasets and one single-cell RNA sequencing (scRNA-seq) dataset, GSE145154. Both CD45-positive and CD45-negative fractions were included in the analysis. Both CD45-positive and CD45-negative cell fractions were combined before clustering. Sample identity was used as the main batch variable for Harmony integration. Normal left ventricular and DCM left ventricular samples from GSE145154 were included, specifically GSM4307515, GSM4307516, GSM4307520, and GSM4307521. The datasets used in this study were GSE145154, GSE5406, GSE42955, GSE57338, GSE79962, GSE116250, and GSE141910. All samples other than DCM were excluded, and only control samples (Control group) and DCM samples (DCM group) were retained. No samples were removed after quality control. Sample information of included GEO datasets is summarized as follows: GSE5406 contained 102 samples (16 control and 86 DCM samples); GSE42955 contained 17 samples (5 control and 12 DCM samples); GSE57338 contained 231 samples (136 control and 95 DCM samples); GSE79962 contained 20 samples (11 control and 9 DCM samples); GSE116250 contained 51 samples (14 control and 37 DCM samples); and GSE141910 contained 322 samples (161 control and 161 DCM samples).

2. Bulk transcriptome data preprocessing
Raw expression matrices and clinical annotation files for the six bulk datasets were downloaded using the GEOquery package²⁰. Raw CEL files were retrieved for the Affymetrix microarray datasets, and raw count matrices were retrieved for the RNA-seq datasets. Background correction, quantile normalization, and expression calculation for the microarray data were performed using the robust multi-array average algorithm implemented in the affy package²¹.

RNA-seq count data were normalized using the trimmed mean of M-values method in the edgeR package²² and were converted to log₂-transformed counts per million values. Probe identifiers were converted to official gene symbols using platform-specific annotation files. When multiple probes mapped to the same gene, the mean expression value was calculated.

Technical batch effects across datasets were removed using the ComBat algorithm in the sva package²³. Dataset source and detection platform were specified as batch factors. Principal component analysis was performed before and after batch correction to evaluate the effectiveness of batch-effect removal.

3. Single-cell transcriptome data preprocessing and cell annotation
The gene expression matrix from GSE145154 was imported into Seurat to construct a Seurat object using Seurat version 5²⁴. Low-quality cells were excluded using the following thresholds: 200–6,000 detected genes per cell, total unique molecular identifier count greater than 500, and mitochondrial gene percentage below 25%. Cells outside these quality-control thresholds were excluded as low-quality or ruptured cells. We excluded low-quality cells solely using the quality control thresholds described above.

Log normalization was performed using the NormalizeData function with a scale factor of 10,000. The top 3,000 highly variable genes were selected using the FindVariableFeatures function with the vst method. The data were scaled using ScaleData, followed by principal component analysis for linear dimensionality reduction.

Batch effects were corrected using the Harmony algorithm²⁵ through the RunHarmony function, with sample identity specified as the grouping variable. The first 15 principal components were used to cluster cells using the FindNeighbors and FindClusters functions. Clustering was performed using the Leiden algorithm at a resolution of 0.15. Nonlinear dimensionality reduction and visualization were performed using uniform manifold approximation and projection.

Cell types were annotated using canonical marker genes together with automated annotation using the SingleR package²⁶. The marker genes were as follows: B cells, IGKC, MS4A1, and CD79A; cardiomyocytes, TNNI3, MYL2, and ACTC1; endothelial cells, VWF, PECAM1, and EGFL7; macrophages, C1QC, C1QB, and C1QA; monocytes, S100A8, S100A9, and G0S2; natural killer cells, NKG7, GNLY, and CCL5; smooth muscle cells, MYL9, TAGLN, and ACTA2; stromal cells, FBLN1, LUM, and DCN; and T cells, CD3E, CD3G, and CD3D.

4. Differential expression analysis and gene set enrichment scoring
A linear model was constructed using the limma package²⁷ to compare gene expression between the DCM and healthy control groups. Genes with P value < 0.05 and an absolute fold change greater than 1.5, corresponding to an absolute log₂ fold change greater than 0.58, were defined as significantly differentially expressed.

Single-sample gene set enrichment analysis was performed to calculate enrichment scores for the aging-related and mitochondria-related gene sets in each sample²⁸. Differences in enrichment scores between the DCM and healthy control groups were evaluated using the Wilcoxon rank-sum test, with P value < 0.05 considered statistically significant.

At the single-cell level, aging-related and mitochondrial module scores were calculated using the AddModuleScore function in Seurat. Differences in module scores between groups were assessed using the Wilcoxon rank-sum test.

Aging-related gene signatures were retrieved from the CellAge database (https://genomics.senescence.info/cells/), and mitochondria-related gene sets were obtained from GeneCards (https://www.genecards.org/). The complete gene lists used for scoring are provided in Supplementary File 1.

5. Weighted gene co-expression network construction
The top 5000 protein-coding genes with the highest expression variance in bulk transcriptomic data were retained for network construction. The pickSoftThreshold function was applied to calculate the scale-free topology fit index under multiple soft-thresholding powers. The optimal threshold was determined as the minimum power yielding a scale-free network with an R² value above 0.9. Accordingly, a soft-thresholding power of β = 5 was adopted for subsequent network analysis.

A signed weighted co-expression network was constructed using the blockwiseModules function with a minimum module size of 30. Pearson correlation coefficients were calculated between each module eigengene and the aging-related or mitochondrial enrichment score. Modules with an absolute correlation coefficient greater than 0.4 and P < 0.001 were considered significantly associated modules.

Genes within significantly associated modules were intersected with differentially expressed genes to identify DCM-associated aging candidate genes and DCM-associated mitochondria candidate genes.

6. Functional enrichment analysis
Functional enrichment analyses, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, were conducted on candidate genes using the clusterProfiler package²⁹. GO enrichment covered three standard categories: biological process, cellular component, and molecular function.

All analyses were performed with human species annotation, false discovery rate (FDR) for P‑value correction, and a q‑value threshold of 0.05. Gene sets were restricted to a size range of 10–500 genes, and terms with an FDR < 0.05 were defined as statistically significant. Finally, GO enrichment results were visualized via grouped bar charts, while KEGG enrichment outcomes were displayed using bubble plots.

7. PPI network construction and hub gene screening
Candidate genes were submitted to the STRING version 11.5 database³⁰, with the organism set to Homo sapiens and the interaction confidence threshold set to a combined score greater than 0.7. Disconnected nodes were hidden, and the interaction data were exported in tab-separated values format.

The interaction data were imported into Cytoscape version 3.9.1 for visualization³¹. Node topological scores were calculated using the CytoHubba plugin³² with three algorithms: Degree, maximum neighborhood component, and maximal clique centrality.

Core functional modules within the network were identified using the MCODE plugin³³ with the following default parameters: degree cutoff, 2; k-core, 2; node score cutoff, 0.2; and maximum depth, 100. Genes ranked among the top 10 by all three topological algorithms were intersected with genes in the MCODE core subnetwork to identify the final protein-protein interaction hub genes.

8. Machine learning-based core gene selection and diagnostic model building
To ensure reproducibility and balanced representation, the integrated bulk transcriptomic dataset was randomly split into training and validation sets at a 7:3 ratio using a fixed random seed (seed = 123456). This split was stratified by disease group (DCM vs. control) to maintain consistent class proportions across both sets. Prior to splitting, batch effects from different dataset sources were corrected using the sva package, and the integrated samples were treated as a unified cohort during the random allocation.

Three machine learning algorithms were applied to screen candidate genes. First, LASSO logistic regression was performed via the cv.glmnet function in the glmnet package³⁴. A 5-fold cross-validation binary classification model was constructed, with the AUC adopted as the evaluation metric. Genes with non-zero coefficients at lambda.min were reserved as candidate genes.

Second, a random forest classification model with 500 decision trees was built using the randomForest package³⁵. The number of variables sampled for each split was set to the square root of the total number of features. Gene importance was quantified based on the Gini coefficient, and the top 10 genes with the highest importance scores were retained.

Third, SVM-RFE analysis was implemented using the rfe function in the caret package³⁶. Feature numbers were set ranging from 1–10, and 5-fold cross-validation was adopted for model training. The gene subset with the optimal cross-validation accuracy was ultimately selected.

Genes identified by all three algorithms were defined as the final core aging- and mitochondria-related genes in DCM. Diagnostic models were then constructed using 10 classification algorithms: decision tree, gradient boosting machine, boosted generalized linear model, k-nearest neighbors, logistic regression, neural network, partial least squares, random forest, support vector machine, and extreme gradient boosting.

Receiver operating characteristic curves were generated using the pROC package³⁷. Area under the curve, accuracy, sensitivity, and specificity were calculated to evaluate diagnostic performance in the training and validation sets.

SHapley Additive exPlanations analysis was performed to calculate the contribution of each core gene to model predictions³⁸. Summary plots and per-sample waterfall plots were generated. A final diagnostic model with an area under the curve greater than 0.8 in the validation set was considered to have good diagnostic performance.

9. Cell-cell communication inference
Cell-cell communication networks in the cardiac microenvironment were inferred using the CellChat package³⁹. A CellChat object was constructed using the CellChatDB.human database. Differentially expressed ligands and receptors were identified using identifyOverExpressedGenes, and significant interaction pairs were filtered using identifyOverExpressedInteractions.

Communication probabilities between cell types were calculated using computeCommunProb. The global cell-type-level communication network was aggregated using aggregateNet. The number of interactions and the communication strength between each pair of cell types were quantified and visualized using heatmaps and bar charts.

10. Immune cell infiltration quantification
Enrichment scores for 28 immune cell types were calculated for each bulk sample using single-sample gene set enrichment analysis²⁸ and an immune cell signature gene set⁴⁰. The Wilcoxon rank-sum test was used to compare immune cell enrichment scores between the DCM and healthy control groups. P < 0.05 was considered statistically significant.

Pearson correlation analysis was conducted to evaluate the association between core gene expression levels and immune cell enrichment scores. All correlations with P < 0.05 were regarded as statistically significant.

11. Consensus clustering for molecular subtyping
Unsupervised consensus clustering of DCM samples was performed using core gene expression profiles via the ConsensusClusterPlus package⁴¹. The clustering parameters were set as a maximum cluster number of 6, 1000 resampling iterations, and a resampling proportion of 0.8. Partitioning around medoids with Euclidean distance was adopted for clustering, and a fixed random seed was used to ensure reproducibility.

The optimal subtype number was determined according to delta area plot and consensus cluster stability scores, with K = 2 finally identified. Principal component analysis was further performed to verify the distinct separation of the two molecular subtypes.

Gene set variation analysis⁴² was applied to calculate sample-specific KEGG pathway enrichment scores. The limma package²⁷ was utilized to detect differential pathway activation between subtypes, and a P value of less than 0.05 was defined as statistically significant.

12. Echocardiographic assessment of cardiac function
Mice were anesthetized via intraperitoneal injection of 1% sodium pentobarbital (30 mg/kg) and fixed in a supine position on a thermostatic operating table. After chest hair removal, ultrasound coupling gel was evenly applied to the precordial area.

Two-dimensional-guided M-mode echocardiography was performed at the level of the left ventricular papillary muscles using a small-animal ultrasound system. Three consecutive stable cardiac cycles were captured to measure left ventricular end-diastolic diameter, end-systolic diameter, ejection fraction, and fractional shortening. All echocardiographic assessments were conducted blindly by a professional ultrasonographer.

Three mice were randomly selected from each group for echocardiographic examination, and these 6 animals in total were subsequently sacrificed for myocardial tissue collection and ELISA measurement. The remaining experimental animals underwent additional parallel laboratory assays, and their data were not included in the present study.

13. Myocardial tissue collection, protein extraction, and enzyme-linked immunosorbent assay
After echocardiographic assessment, mice were euthanized under deep anesthesia. Heart tissues were rapidly harvested via median thoracotomy, and left ventricular myocardium was dissected on ice. Isolated tissues were thoroughly rinsed with ice‑cold phosphate‑buffered saline to eliminate residual intracardiac blood. After blotting excess liquid with sterile filter paper, samples were immediately snap‑frozen in liquid nitrogen and stored at −80 °C for subsequent protein extraction, with repeated freeze–thaw cycles strictly avoided.

Frozen myocardial tissues were weighed and cut into approximately 1 mm³ fragments on ice. Tissues were lysed in ice‑cold RIPA lysis buffer containing protease and phosphatase inhibitors at a standardized ratio of 100 µL buffer per 10 mg tissue. Samples were fully homogenized mechanically on ice and incubated for 30 min to achieve complete cell lysis.

Lysates were centrifuged at 12,000 × g for 15 min at 4 °C. The resulting supernatants were collected in enzyme‑free tubes, and total protein concentration was quantified using a bicinchoninic acid protein assay kit following the manufacturer’s protocols. All samples were normalized to an identical protein concentration with lysis buffer.

Protein expression levels of the four hub genes in myocardial lysates were measured using the corresponding enzyme‑linked immunosorbent assay (ELISA) kits. Serially diluted standards and normalized tissue lysates were added in duplicate (100 µL per well) to pre‑coated microplates. Plates were incubated for 2 h at room temperature and thoroughly washed with the kit‑supplied wash buffer.

Each well was supplemented with enzyme‑conjugated antibody and incubated for 1 h at room temperature, followed by a thorough wash. Substrate chromogen solution was then added, and the plates were incubated for 20 min at room temperature in the dark. The color reaction was terminated with the stop solution, and absorbance values were measured at 450 nm (reference wavelength: 570 nm) using a full‑wavelength microplate reader.

14. Statistical analysis
All statistical analyses and data visualizations were performed using R version 4.2.3. For ELISA concentration measurements of each target gene (TGFB2, SERPINE1, CYBB, TLR2), the Shapiro-Wilk test was first applied to assess the normality of the data in the Control and DCM groups separately. Subsequently, an F-test was used to evaluate the homogeneity of variances between the two groups. The method for intergroup comparison was determined according to the results of the variance homogeneity test: if variances were homogeneous (P ≥ 0.05), an unpaired Student’s t-test was adopted to compare mean values between groups; if variances were heterogeneous (P < 0.05), the corrected Welch’s t-test was utilized for analysis. All tests were two-tailed, and the threshold for statistical significance was set at P < 0.05. Data were visualized as boxplots overlaid with jittered individual data points. The P values of all tests and the type of t-test implemented were annotated in detail on each plot.

Results

Data preprocessing and differential expression analysis
All six bulk transcriptomic datasets underwent standardized preprocessing and batch-effect correction before downstream analysis. Microarray data were normalized using the robust multi-array average algorithm, whereas RNA-seq count data were normalized using the trimmed mean of M-values method. The ComBat algorithm was applied to remove technical batch effects associated with the dataset source and detection platform. Principal component analysi...

Discussion

The integrated multilayered workflow combined bulk transcriptomic meta-analysis, weighted gene co-expression network construction, ensemble machine learning, single-cell transcriptomic validation, and in vivo animal-model verification. Four aging- and mitochondria-related hub genes CYBB, SERPINE1, TGFB2, and TLR2 were identified as candidate diagnostic biomarkers for dilated cardiomyopathy (DCM). Integration of six independent left ventricular transcriptomic datasets from the Gene Expression Omnibus repository, ...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
Aquasonic Clear Ultrasound Gel	Parker Laboratories, Inc.	Mar-34	Used for small-animal echocardiographic imaging.
BCA Protein Assay Kit	Thermo Fisher Scientific	23227	Detection at 562 nm; range, 20–2,000 µg/mL; used for total protein quantification of mouse heart lysates.
CellAge database	Human Ageing Genomic Resources	https://genomics.senescence.info/cells/	Source of aging-related gene signatures.
CytoHubba, Cytoscape plugin	Cytoscape App Store	Version 0.1	Used for node-topology scoring in protein-protein interaction networks.
Cytoscape	Cytoscape Consortium	Version 3.9.1	Used for visualization of protein-protein interaction networks.
Full-wavelength microplate reader	Thermo Fisher Scientific	Multiskan FC	Used to measure absorbance in ELISA assays.
Gene Expression Omnibus	National Center for Biotechnology Information	https://www.ncbi.nlm.nih.gov/geo/	Public repository used to obtain transcriptomic datasets.
GeneCards	Weizmann Institute of Science	https://www.genecards.org/	Source of mitochondria-related gene sets.
Halt Protease and Phosphatase Inhibitor Cocktail, 100×, EDTA-free	Thermo Fisher Scientific	78441	Stored at 4 °C; added to RIPA buffer at 10 µL/mL immediately before use.
Liquid nitrogen	Local laboratory gas supplier	Not applicable	Used for snap-freezing myocardial tissue.
Male SPF-grade C57BL/6J mice, 6–8 weeks old, 25 ± 2 g	Beijing Vital River Laboratory Animal Technology Co., Ltd.	Not applicable	Animal Production License No. SCXK (Jing) 2021-0006; used as normal controls.
Male SPF-grade CTNTR141W transgenic DCM mice, 6–8 weeks old, 25 ± 2 g	Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences	Not applicable	Animal Production License No. SCXK (Jing) 2021-0065; used as the spontaneous DCM model.
MCODE, Cytoscape plugin	Cytoscape App Store	Version 2.0.2	Used to identify core functional subnetworks in protein-protein interaction networks.
Mouse CYBB ELISA Kit	Biogradetech	A-QEK09250-96wells	Used in this study to measure CYBB in mouse myocardial tissue lysates.
Mouse PAI-1 ELISA Kit	EK-BIO	ML30970	Used in this study to measure PAI-1, the SERPINE1-encoded protein, in mouse myocardial tissue lysates.
Mouse TGF-β2 ELISA Kit	ElaBoX	SEKM-0036	Used in this study to measure TGF-β2 in mouse myocardial tissue lysates.
Mouse TLR-2 ELISA Kit	Solarbio	SEKM-0163	Used in this study to measure TLR-2 in mouse myocardial tissue lysates.
Phosphate-buffered saline, pH 7.4, calcium- and magnesium-free	Biological Industries	02-024-1ACS	Sterile 1× solution; stored at 4 °C; used for tissue washing and dilution.
R package: caret	CRAN	Version 6.0-94	Used for support vector machine-recursive feature elimination.
R package: CellChat	CellChat developers	Version 1.6.1	Used for cell-cell communication inference from single-cell RNA sequencing data.
R package: clusterProfiler	Bioconductor	Version 4.8.3	Used for functional enrichment analysis.
R package: ConsensusClusterPlus	Bioconductor	Version 1.64.0	Used for unsupervised consensus clustering.
R package: edgeR	Bioconductor	Version 3.42.4	Used for RNA sequencing data normalization with the trimmed mean of M-values method.
R package: GEOquery	Bioconductor	Version 2.68.0	Used to download data from the Gene Expression Omnibus.
R package: glmnet	CRAN	Version 4.1-8	Used for least absolute shrinkage and selection operator logistic regression.
R package: limma	Bioconductor	Version 3.56.2	Used for differential expression analysis and statistical modeling.
R package: pROC	CRAN	Version 1.18.5	Used for receiver operating characteristic curve analysis.
R package: randomForest	CRAN	Version 4.7-1.2	Used for random forest machine learning.
R package: Seurat	CRAN	Version 5.0.1	Used for single-cell RNA sequencing data analysis.
R package: SingleR	Bioconductor	Version 2.2.0	Used for automated cell-type annotation.
R package: sva	Bioconductor	Version 3.48.0	Used for ComBat batch-effect correction.
Refrigerated centrifuge	Sigma-Aldrich	SIGMA 3-K	Used for centrifugation of myocardial tissue lysates.
RIPA Lysis and Extraction Buffer	Thermo Fisher Scientific	89900	Ready-to-use 1× solution; stored at 4 °C; supplemented with protease and phosphatase inhibitors before use.
Small-animal ultrasound imaging system	VINNO Technology Co., Ltd.	VINN06LAB	Used for echocardiographic assessment of cardiac function.
Sodium pentobarbital	Sinopharm Chemical Reagent Co.	20040428	Prepared as a 1% solution, 10 mg/mL, in sterile saline; used for intraperitoneal anesthesia at 30 mg/kg.
STRING database	STRING Consortium	Version 11.5	Used for protein-protein interaction network construction.
TGrinder H24 Tissue Homogenizer	TIANGEN	OSE-TH-01	Used to homogenize mouse myocardial tissue in RIPA buffer at 6.0 m/s for 30–60 s over 2–3 cycles.
Thermostatic animal platform/heated operating table for small animals	Shanghai Yuyan Scientific Instrument Co., Ltd.	T-30350	Used to maintain mice at 37 °C during echocardiography; operating range, room temperature to 50 °C.