砂河 孝行

Background: Although advances in next-generation sequencing have accelerated the identification of genetic variants in cardiomyopathy, interpreting variants of uncertain significance (VUS) remains a clinical challenge. Evo 2 is a high-resolution genomic artificial intelligence model capable of predicting pathogenicity across large sequence contexts and enabling mechanistic interpretation; however, its application in cardiovascular genetics is limited. Here, we evaluated the utility of Evo 2 for assessing the pathogenicity and underlying mechanisms of cardiomyopathy-associated variants.Methods: We used Evo 2 to predict the pathogenicity of single-nucleotide variants in cardiomyopathy-related genes listed on ClinVar. We assessed the ability of the model to identify characteristic structural features in both coding and noncoding regions using internal representation such as embeddings, and to infer the molecular mechanisms of variants within these regions.Results: Evo 2 demonstrated high predictive accuracy for pathogenicity, achieving an AUROC of 0.983 and an AUPRC of 0.915. Notably, sparse autoencoders (SAEs) from embeddings identified features corresponding to higher-order structural features, including coiled-coil and actin-binding domains characteristic of cardiomyopathy-related proteins, and accurately detected mutations known to disrupt these domains. The model recognized the binding motif of the cardiac-enriched transcription factor TBX5 with SAEs and accurately predicted a single-nucleotide polymorphism affecting TBX5 binding affinity after supervised fine-tuning.Conclusions: Evo 2 demonstrated strong performance for both predicting pathogenicity and extracting biological features of cardiomyopathy-associated variants. It may represent a powerful emerging tool for evaluating VUS in cardiovascular medicine.

Cardiac fibroblasts (CFs) are the predominant non-myocyte cell type in the heart and play central roles in extracellular matrix remodeling and intercellular signaling during cardiac physiology and pathology. However, the bioenergetic basis underlying CF functions remains poorly understood, mainly due to the lack of tools for visualizing intracellular adenosine triphosphate (ATP) dynamics with high spatiotemporal resolution. Here, we established immortalized murine cardiac fibroblasts stably expressing the genetically encoded ATP indicator GO-ATeam2 based on Förster Resonance Energy Transfer (FRET). The resulting CF7/GO-ATeam2 cell line allows real-time and quantitative monitoring of cytosolic ATP levels in living cells. CF7/GO-ATeam2 cells exhibited robust proliferation and quick responses to change of cytosolic ATP level. We demonstrated dynamic cytosolic ATP imaging upon pharmacological perturbations of oxidative phosphorylation and glycolysis, as well as under growth factor stimulation. Our work provides the CF7/GO-ATeam2 platform, a versatile cellular resource for dissecting the metabolic regulation of cardiac fibroblasts, offering new opportunities to explore energy dynamics in cardiac physiology and disease.

BACKGROUND AND AIMS: Pulmonary arterial hypertension (PAH) is a severe disease with limited effective therapies, making the discovery of new therapeutic targets crucial. While single-cell RNA sequencing (sc-RNA seq) offers a powerful tool for this purpose, its application is hampered by the scarcity of patient samples. This study addresses the problem of how to efficiently identify novel, functionally relevant disease-associated genes from limited publicly available data. METHODS: We employed transfer learning by fine-tuning Geneformer, a deep learning model, with public sc-RNA seq data from patients with PAH to create a specialized model called PAH-former. This model was used to perform in silico perturbation analysis to identify and rank candidate genes predicted to influence the disease state. For validation, we performed RNA interference-mediated knockdown of top novel candidate genes in human pulmonary artery endothelial cells and measured the expression of SRY-Box Transcription Factor 18 (SOX18), a signature gene of pulmonary arterial hypertension. RESULTS: In silico perturbation analysis identified 134 candidate genes whose deletion was predicted to shift cells towards a disease phenotype. These included known disease-related genes as well as many novel ones. Subsequent in vitro validation demonstrated that knockdown of the candidate genes resulted in a significant increase in the expression of SOX18. CONCLUSIONS: Our novel platform, PAH-former, provides a powerful and broadly applicable strategy for disease-related gene discovery. This approach enables the identification and validation of new candidate genes from limited data, promising to advance cell-specific mechanistic insights and accelerate therapeutic development for rare diseases like PAH. (248/300 words).