Graph neural network-based prediction of direct reprogramming factors using gene regulatory networks with microRNA-mediated regulation

Direct reprogramming (DR) converts somatic cells directly into target cell types while bypassing an intermediate pluripotent state, such as induced pluripotent stem cells. In practice, DR is achieved by transfecting multiple transcription factors (TFs); prior research has shown that combining microRNAs (miRNAs) with TFs further improves reprogramming efficiency. However, experimentally identifying effective TFs and miRNA combinations is difficult and costly, underscoring the need for robust in silico prediction approaches. We developed a graph neural network-based method to predict TFs that induce DR across diverse human cell types while explicitly modeling miRNA-mediated transcriptional regulation. By constructing a gene regulatory network integrating TF-target gene, TF-miRNA, miRNA-target gene, and gene-gene interactions, we implemented a Graph Attention Network v2 that predicts DR-inducing TFs while learning interaction importance and capturing transcriptional activation and repression. This approach outperformed existing methods in predicting experimentally validated DR-inducing TFs. Moreover, high-ranking predictions for previously unexplored tissues included TFs known to be associated with the development of the corresponding tissues, supporting the biological relevance of the results. Overall, the proposed method provides a practical in regenerative medicine.