Abstract
The direct reprogramming of adult skin fibroblasts to neurons is thought to be controlled by a small set of interacting gene regulators. Here, we investigate how the interaction dynamics between these regulating factors coordinate cellular decision making in direct neuronal reprogramming. We put forward a quantitative model of the governing gene regulatory system, supported by measurements of mRNA expression. We found that nPTB needs to feed back into the direct neural conversion network most likely via PTB in order to accurately capture quantitative gene interaction dynamics and correctly predict the outcome of various overexpression and knockdown experiments. This was experimentally validated by nPTB knockdown leading to successful neural conversion. We also proposed a novel analytical technique to dissect system behaviour and reveal the influence of individual factors on resulting gene expression. Overall, we demonstrate that computational analysis is a powerful tool for understanding the mechanisms of direct (neuronal) reprogramming, paving the way for future models that can help improve cell conversion strategies.
Highlights
Because they are abundant and easy to harvest
We have previously shown that cells that are converted by knocking down REST adopt a transcriptome that more closely resembles that of a neuron, compared to the reprogramming approach involving the overexpression of miR-9/9* and miR-1246
We put forward a quantitative model of the core gene regulatory network (GRN) governing direct reprogramming of human adult fibroblasts to neurons
Summary
Because they are abundant and easy to harvest. Many different cocktails of transcription factors have been successfully applied. We measured the expression levels of key transcription factors at different time points during a neuronal reprogramming experiment, and used the resulting experimental data to evaluate the literature-based model. We found that this literature-based model was not able to explain the experimentally observed dynamic behaviour, and that the data suggested a missing feedback loop in the gene regulatory network. Models based on network hypotheses without this crucial interaction could not reproduce the correct system behaviour Together, these results suggest that nPTB feeds back into the neural conversion gene network playing an activating role in the expression of PTB, for example by blocking the negative self-regulation of PTB. By incorporating the mechanisms of different conversion methods into one explanatory framework, this work aims towards an integrated and predictive understanding of cellular decision making in direct neuronal reprogramming
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