miRNA Combinatorics and its Role in Cell State Control—A Probabilistic Approach

Shelly Mahlab-Aviv, Nathan Linial, Michal Linial*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

A hallmark of cancer evolution is that the tumor may change its cell identity and improve its survival and fitness. Drastic change in microRNA (miRNA) composition and quantities accompany such dynamic processes. Cancer samples are composed of cells’ mixtures of varying stages of cancerous progress. Therefore, cell-specific molecular profiling represents cellular averaging. In this study, we consider the degree to which altering miRNAs composition shifts cell behavior. We used COMICS, an iterative framework that simulates the stochastic events of miRNA-mRNA pairing, using a probabilistic approach. COMICS simulates the likelihood that cells change their transcriptome following many iterations (100 k). Results of COMICS from the human cell line (HeLa) confirmed that most genes are resistant to miRNA regulation. However, COMICS results suggest that the composition of the abundant miRNAs dictates the nature of the cells (across three cell lines) regardless of its actual mRNA steady-state. In silico perturbations of cell lines (i.e., by overexpressing miRNAs) allowed to classify genes according to their sensitivity and resilience to any combination of miRNA perturbations. Our results expose an overlooked quantitative dimension for a set of genes and miRNA regulation in living cells. The immediate implication is that even relatively modest overexpression of specific miRNAs may shift cell identity and impact cancer evolution.

Original languageAmerican English
Article number772852
JournalFrontiers in Molecular Biosciences
Volume8
DOIs
StatePublished - 21 Dec 2021

Bibliographical note

Publisher Copyright:
Copyright © 2021 Mahlab-Aviv, Linial and Linial.

Keywords

  • CLIP-Seq
  • Markov chain
  • TargetScan
  • ceRNA
  • cell line
  • cell simulation
  • miRNA binding site
  • miRNA-target prediction

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