Abstract
Autophagy of myocardial cells involves the interaction of multiple molecular signaling pathways, and regulatory factors, while existing methods are difficult to handle. This study utilized the variational autoencoder (VAE) model to reveal the characteristic distribution of myocardial cell energy autophagy under different exercise conditions. First, this paper is based on mass spectrometry analysis, enzyme-linked immunosorbent assay ELISA (Enzyme-Linked Immunosorbent Assay) to determine the cardiomyocyte metabolite concentration data, and RNA-Seq (Ribonucleic Acid-Sequencing) to collect genes related to cardiomyocyte energy metabolism and autophagy expression data; in the VAE model, this paper utilizes the full connectivity layer to encode the data into potential representations, and reconstructs the numerical data through the numerical data decoder. The loss function is defined as the data reconstruction error and KL (Kullback-Leibler) scatter, and Adam is used to optimize the training process; the features are analyzed and the classification performance is verified under different motion conditions based on RF (Random Forest); the relationship between the features and metabolite concentration and gene expression is analyzed by LASSO (Least Absolute Shrinkage and Selection Operator) regression model to analyze the relationship between features and metabolite concentration and gene expression; the features in the latent space are downscaled using t-SNE (t-distributed Stochastic Neighbor Embedding) to visualize the feature distribution; finally, CRISPR-Cas9 (Clustered Regularly Interspaced Short Palindromic Repeats-Cas9) knockdown experiments to reveal the importance of AMPK, PGC1A, CPT1B, and SIRT1 in cardiomyocyte autophagy and energy metabolism, which provide potential targets for future gene-based therapies.