Quantification of transcription noise’s impact on cell fate commitment with digital resolutions

Author:

Jiao Feng12,Tang Moxun3ORCID

Affiliation:

1. Guangzhou Center for Applied Mathematics, Guangzhou University , Guangzhou 510006, P.R. China

2. College of Mathematics and Information Sciences, Guangzhou University , Guangzhou 510006, P.R. China

3. Department of Mathematics, Michigan State University , East Lansing, MI 48824, USA

Abstract

Abstract Motivation Gene transcription is a random and noisy process. Tremendous efforts in single-cell studies have been mapping transcription noises to phenotypic variabilities between isogenic cells. However, the exact role of the noise in cell fate commitment remains largely descriptive or even controversial. Results For a specified cell fate, we define the jumping digit I of a critical gene as a statistical threshold that a single cell has approximately an equal chance to commit the fate as to have at least I transcripts of the gene. When the transcription is perturbed by a noise enhancer without changing the basal transcription level E0, we find a crossing digit k such that the noise catalyzes cell fate change when I > k while stabilizes the current state when I < k; k remains stable against enormous variations of kinetic rates. We further test the reactivation of latent HIV in 22 integration sites by noise enhancers paired with transcriptional activators. Strong synergistic actions are observed when the activators increase transcription burst frequency, whereas no synergism, but antagonism, is often observed if activators increase burst size. The synergistic efficiency can be predicted accurately by the ratio I/E0. When the noise enhancers double the noise, the activators double the burst frequency, and I/E0≥7, their combination is 10 times more effective than their additive effects across all 22 sites. Availability and implementation The jumping digit I may provide a novel probe to explore the phenotypic consequences of transcription noise in cell functions. Code is freely available at http://cam.gzhu.edu.cn/info/1014/1223.htm. The data underlying this article are available in the article and in its online supplementary material. Supplementary information Supplementary data are available at Bioinformatics online.

Funder

Natural Science Foundation of China grants

Changjiang Scholars and Innovative Research Team in the University

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

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