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A quantitative model of cellular decision making in direct neuronal reprogramming.

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Merlevede, Adriaan 
Legault, Emilie M 
Drugge, Viktor 
Barker, Roger A 
Drouin-Ouellet, Janelle 


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.



Aged, Cellular Reprogramming, Cellular Reprogramming Techniques, Computational Biology, Female, Fibroblasts, Gene Expression, Gene Expression Regulation, Gene Regulatory Networks, Humans, Middle Aged, Models, Theoretical, Nerve Tissue Proteins, Neurons, Polypyrimidine Tract-Binding Protein, Primary Cell Culture, Stochastic Processes, Transcription Factors

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Springer Science and Business Media LLC


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Medical Research Council (MC_PC_17230)
JD-O is receiving salary support from the Fonds du Québec en Recherche, Santé (FRQS) and Parkinson Quebec. VO gratefully acknowledges the support of the US National Institutes of Health (USPHS grant R01HL119102). We thank Joachim Eriksson for preliminary work on building the literature-based network