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Photo of Victor Olariu

Victor Olariu

Senior lecturer

Photo of Victor Olariu

A quantitative model of cellular decision making in direct neuronal reprogramming

Author

  • Adriaan Merlevede
  • Emilie M. Legault
  • Viktor Drugge
  • Roger A Barker
  • Janelle Drouin-Ouellet
  • Victor Olariu

Summary, in English

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.

Department/s

  • Computational Biology and Biological Physics - Has been reorganised

Publishing year

2021-01-15

Language

English

Publication/Series

Scientific Reports

Volume

11

Issue

1

Document type

Journal article

Publisher

Nature Publishing Group

Topic

  • Bioinformatics and Systems Biology
  • Neurology
  • Other Physics Topics

Keywords

  • direct reprogramming
  • neuron
  • fibroblast
  • PTB
  • nPTB
  • miR-124
  • REST

Status

Published

Project

  • Computational Science for Health and Environment
  • Modelling cellular decision making in direct reprogramming to neurons

ISBN/ISSN/Other

  • ISSN: 2045-2322