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Llorente-Armijo, S.

Publications and source records attributed to Llorente-Armijo, S..

2 recordsLinked to original sources

Digital Reprogramming Decodes Epigenetic Barriers of Cell Fate Changes

The fates of differentiated cells in our body can be induced to change by nuclear reprogramming. In this way, cells valuable for therapeutic purposes and disease modeling can be produced. However, the efficiency of this process is low, partly due to properties of somatic donor nuclei which stabilize their differentiated fate but also act as barriers reprogramming-associated cell fate changes. The identity of these reprogramming barriers is not fully understood. Here, we developed an artificial intelligence-based approach to model nuclear reprogramming and used it to identify the chromatin modification H3K27ac as an epigenetic barrier to reprogramming-induced cell fate changes. Using reprogramming by nuclear transfer (NT) to eggs of Xenopus laevis as a model system, we profiled chromatin modifications in differentiated cell types alongside gene expression patterns before and after reprogramming. Our model integrated the data and by leveraging model predictions, we find that genes resisting inactivation during reprogramming display chromatin modification barcodes. This revealed H3K27ac as a novel candidate barrier to NT reprogramming. Reducing H3K27ac levels using p300/CBP inhibitors before reprogramming led to an improved downregulation of genes linked to H3K27ac-modified enhancers after reprogramming. Importantly, these effects were accompanied by improved embryonic development of the resulting nuclear transfer embryos. In summary, our study identified H3K27ac as a safeguarding mechanism of cellular identities and as a reprogramming barrier during NT. Hence, the here-developed Digital Reprogramming" approach is capable of modelling and improving current cell-fate reprogramming strategies.

genomics↗

Molecular characterization of breast cancer cell response to metabolic drugs

Metabolic reprogramming is a hallmark of cancer. We and other authors have previously shown that breast cancer subtypes present metabolism differences. In this study, breast cancer cell lines were treated with metformin and rapamycin. The response was heterogeneous across various breast cancer cells, leading to cell cycle disruption in specific conditions. The molecular effects of these treatments were characterized using sublethal doses, SNP genotyping and mass spectrometry-based proteomics. Protein expression was analyzed using probabilistic graphical models, showing that treatments elicit various responses in some biological processes, providing insights into cell responses to metabolism drugs. Moreover, a flux balance analysis approach using protein expression values was applied, showing that predicted growth rates were comparable with cell viability measurements and suggesting an increase in reactive oxygen species response enzymes due to metformin treatment. In addition, a method to assess flux differences in whole pathways was proposed. Our results show that these various approaches provide complementary information, which can be used to suggest hypotheses about the drugs mechanisms of action and the response to drugs that target metabolism.

systems biology↗