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Single-Cell Transcriptomes Reveal Characteristics of MicroRNAs in Gene Expression Noise Reduction

Hu, Tao, Wei, Lei, Li, Shuailin, Cheng, Tianrun, Zhang, Xuegong, Wang, Xiaowo·2021·Genomics, Proteomics & Bioinformatics
DOI 10.1016/j.gpb.2021.05.002PMC PMC8864250
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Abstract

Isogenic cells growing in identical environments show cell-to-cell variations because of the stochasticity in gene expression. High levels of variation or noise can disrupt robust gene expression and result in tremendous consequences for cell behaviors. In this work, we showed evidence from single-cell RNA sequencing data analysis that microRNAs (miRNAs) can reduce gene expression noise at the mRNA level in mouse cells. We identified that the miRNA expression level, number of targets, target pool abundance, and miRNA–target interaction strength are the key features contributing to noise repression. miRNAs tend to work together in cooperative subnetworks to repress target noise synergistically in a cell type-specific manner. By building a physical model of post-transcriptional regulation and observing in synthetic gene circuits, we demonstrated that accelerated degradation with elevated transcriptional activation of the miRNA target provides resistance to extrinsic fluctuations. Together, through the integrated analysis of single-cell RNA and miRNA expression profiles, we demonstrated that miRNAs are important post-transcriptional regulators for reducing gene expression noise and conferring robustness to biological processes.

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Metadata

Authors
Hu, T., Wei, L., Li, S., Cheng, T., Zhang, X., & Wang, X.
Affiliations
Ministry of Education Key Laboratory of Bioinformatics, Center for Synthetic and Systems Biology, Bioinformatics Division, Beijing National Research Center for Information Science and Technology, Department of Automation, Tsinghua University , Beijing 100084 , China
ORCID
Venue
Genomics, Proteomics & Bioinformatics
Publisher
Oxford University Press (OUP)
Published
2021-06-01
Volume
19
Issue
3
Pages
394-407
ISSN
  • 1672-0229
  • 2210-3244
DOI
10.1016/j.gpb.2021.05.002
PMC
PMC8864250
Cited by
12
References
11
Language
en
Licence
Funders
  • National Science Foundation of China (61773230, 61721003)
  • NSFC

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