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Foto på Victor Olariu

Victor Olariu

Universitetslektor

Foto på Victor Olariu

A quantitative model of cellular decision making in direct neuronal reprogramming

Författare

  • 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.

Avdelning/ar

  • Beräkningsbiologi och biologisk fysik - Har omorganiserats

Publiceringsår

2021-01-15

Språk

Engelska

Publikation/Tidskrift/Serie

Scientific Reports

Volym

11

Issue

1

Dokumenttyp

Artikel i tidskrift

Förlag

Nature Publishing Group

Ämne

  • Bioinformatics and Systems Biology
  • Neurology
  • Other Physics Topics

Nyckelord

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

Status

Published

Projekt

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

ISBN/ISSN/Övrigt

  • ISSN: 2045-2322