SRA

The fate of differentiated cells in our body can be reverted and changed by nuclear reprogramming. In this way, cells valuable for therapeutic purposes and disease modeling are produced. However, the efficiency of this process is low, partly due to the properties of the somatic donor which stabilize its differentiated fate and compromise reprogramming-associated cell fate changes. The identity of these reprogramming barriers is not fully understood.Here, we developed an artificial intelligence-based approach that models reprogramming and identifies the chromatin modification H3K27ac as a novel epigenetic barrier to reprogramming-induced cell fate changes. Using reprogramming by nuclear transfer to eggs of Xenopus laevis as a model system, we profiled chromatin modifications in cell types alongside gene expression patterns before and after reprogramming. We built computational models that integrate the generated data to accurately predict reprogramming outcomes on a transcriptome level. By leveraging our predictive models, we find that genes resisting inactivation during reprogramming display specific chromatin modification barcodes, including the known reprogramming barrier H3K4me3 alongside a novel candidate barrier, H3K27ac. Reducing H3K27ac levels using p300/CBP inhibitors before reprogramming correlated with an improved downregulation of genes linked to H3K27ac-modified enhancers during reprogramming. Importantly, these effects were accompanied by an improvement in the embryonic development of the resulting NT-embryos. In summary, our study developed 'Digital Reprogramming”, an artificial intelligence approach capable of predicting resistance to cell-fate reprogramming and implicates H3K27ac as a critical barrier to reprogramming-associated cell fate changes. Overall design: In total 114 samples from mRNA-seq, single or paired-ended from neurula stage 18 endoderm and gastrula stage 11 ectoderm samples; or from neurula stage 18 meso-ectoderm and gastrula stage 11 endoderm samples.