The epigenetic Oct4 gene regulatory network: stochastic analysis of different cellular reprogramming approaches.
Simone BrunoDomitilla Del VecchioPublished in: bioRxiv : the preprint server for biology (2023)
In the last decade, several experimental studies have shown how chromatin modifications (histone modifications and DNA methylation) and their effect on DNA compaction have a critical effect on cellular reprogramming, i.e., the conversion of differentiated cells to a pluripotent state. In this paper, we compare three reprogramming approaches that have been considered in the literature: (a) prefixed overexpression of transcription factors (TFs) alone (Oct4), (b) prefixed overexpression of Oct4 and DNA methylation "eraser" TET, and (c) prefixed overexpression of Oct4 and H3K9me3 eraser JMJD2. To this end, we develop a model of the pluritpotency gene regulatory network, that includes, for each gene, a circuit recently published encapsulating the main interactions among chromatin modifications and their effect on gene expression. We then conduct a computational study to evaluate, for each reprogramming approach, latency and variability. Our results show a faster and less stochastic reprogramming process when also eraser enzymes are overexpressed, consistent with previous experimental data. However, TET overexpression leads to a faster and more efficient reprogramming compared to JMJD2 overexpression when the recruitment of DNA methylation by H3K9me3 is weak and the MBD protein level is sufficiently low such that it does not hamper TET binding to methylated DNA. The model developed here provides a mechanistic understanding of the outcomes of former experimental studies and is also a tool for the development of optimized reprogramming approaches that combine TF overexpression with modifiers of chromatin state.
Keyphrases
- dna methylation
- gene expression
- transcription factor
- genome wide
- cell proliferation
- optical coherence tomography
- dna damage
- copy number
- systematic review
- induced apoptosis
- genome wide identification
- single molecule
- machine learning
- type diabetes
- randomized controlled trial
- metabolic syndrome
- weight loss
- oxidative stress
- electronic health record
- cell free
- adipose tissue
- signaling pathway
- insulin resistance
- cell cycle arrest
- network analysis