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Stochastic Modeling of Gene Regulation by Noncoding Small RNAs in the Strong Interaction Limit.

ABSTRACT: Noncoding small RNAs (sRNAs) are known to play a key role in regulating diverse cellular processes, and their dysregulation is linked to various diseases such as cancer. Such diseases are also marked by phenotypic heterogeneity, which is often driven by the intrinsic stochasticity of gene expression. Correspondingly, there is significant interest in developing quantitative models focusing on the interplay between stochastic gene expression and regulation by sRNAs. We consider the canonical model of regulation of stochastic gene expression by sRNAs, wherein interaction between constitutively expressed sRNAs and mRNAs leads to stoichiometric mutual degradation. The exact solution of this model is analytically intractable given the nonlinear interaction term between sRNAs and mRNAs, and theoretical approaches typically invoke the mean-field approximation. However, mean-field results are inaccurate in the limit of strong interactions and low abundances; thus, alternative theoretical approaches are needed. In this work, we obtain analytical results for the canonical model of regulation of stochastic gene expression by sRNAs in the strong interaction limit. We derive analytical results for the steady-state generating function of the joint distribution of mRNAs and sRNAs in the limit of strong interactions and use the results derived to obtain analytical expressions characterizing the corresponding protein steady-state distribution. The results obtained can serve as building blocks for the analysis of genetic circuits involving sRNAs and provide new insights into the role of sRNAs in regulating stochastic gene expression in the limit of strong interactions.

SUBMITTER: Kumar N

PROVIDER: S-EPMC6129477 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Stochastic Modeling of Gene Regulation by Noncoding Small RNAs in the Strong Interaction Limit.

Kumar Niraj N Zarringhalam Kourosh K Kulkarni Rahul V RV

Biophysical journal 20180601 11

Noncoding small RNAs (sRNAs) are known to play a key role in regulating diverse cellular processes, and their dysregulation is linked to various diseases such as cancer. Such diseases are also marked by phenotypic heterogeneity, which is often driven by the intrinsic stochasticity of gene expression. Correspondingly, there is significant interest in developing quantitative models focusing on the interplay between stochastic gene expression and regulation by sRNAs. We consider the canonical model ...[more]

PMID: 29874604

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Project description:BackgroundTranscriptional regulation is a fundamental process in biological systems, where transcription factors (TFs) have been revealed to play crucial roles. In recent years, in addition to TFs, an increasing number of non-coding RNAs (ncRNAs) have been shown to mediate post-transcriptional processes and regulate many critical pathways in both prokaryotes and eukaryotes. On the other hand, with more and more high-throughput biological data becoming available, it is possible and imperative to quantitatively study gene regulation in a systematic and detailed manner.ResultsMost existing studies for inferring transcriptional regulatory interactions and the activity of TFs ignore the possible post-transcriptional effects of ncRNAs. In this work, we propose a novel framework to infer the activity of regulators including both TFs and ncRNAs by exploring the expression profiles of target genes and (post)transcriptional regulatory relationships. We model the integrated regulatory system by a set of biochemical reactions which lead to a log-bilinear problem. The inference process is achieved by an iterative algorithm, in which two linear programming models are efficiently solved. In contrast to available related studies, the effects of ncRNAs on transcription process are considered in this work, and thus more reasonable and accurate reconstruction can be expected. In addition, the approach is suitable for large-scale problems from the viewpoint of computation. Experiments on two synthesized data sets and a model system of Escherichia coli (E. coli) carbon source transition from glucose to acetate illustrate the effectiveness of our model and algorithm.ConclusionOur results show that incorporating the post-transcriptional regulation of ncRNAs into system model can mine the hidden effects from the regulation activity of TFs in transcription processes and thus can uncover the biological mechanisms in gene regulation in a more accurate manner. The software for the algorithm in this paper is available upon request.

Project description:Many herpesviruses express small noncoding RNAs (sncRNAs), including microRNAs (miRNAs), that may play roles in regulating lytic and latent infections. None have yet been reported in varicella-zoster virus (VZV; also known as human herpesvirus 3 [HHV-3]). Here we analyzed next-generation sequencing (NGS) data for small RNAs in VZV-infected fibroblasts and human embryonic stem cell-derived (hESC) neurons. Two independent bioinformatics analyses identified more than 20 VZV-encoded 20- to 24-nucleotide RNAs, some of which are predicted to have stem-loop precursors potentially representing miRNAs. These sequences are perfectly conserved between viruses from three clades of VZV. One NGS-identified sequence common to both bioinformatics analyses mapped to the repeat regions of the VZV genome, upstream of the predicted promoter of the immediate early gene open reading frame 63 (ORF63). This miRNA candidate was detected in each of 3 independent biological repetitions of NGS of RNA from fibroblasts and neurons productively infected with VZV using TaqMan quantitative PCR (qPCR). Importantly, transfected synthetic RNA oligonucleotides antagonistic to the miRNA candidate significantly enhanced VZV plaque growth rates. The presence of 6 additional small noncoding RNAs was also verified by TaqMan qPCR in productively infected fibroblasts and ARPE19 cells. Our results show VZV, like other human herpesviruses, encodes several sncRNAs and miRNAs, and some may regulate infection of host cells.IMPORTANCE Varicella-zoster virus is an important human pathogen, with herpes zoster being a major health issue in the aging and immunocompromised populations. Small noncoding RNAs (sncRNAs) are recognized as important actors in modulating gene expression, and this study demonstrates the first reported VZV-encoded sncRNAs. Many are clustered to a small genomic region, as seen in other human herpesviruses. At least one VZV sncRNA was expressed in productive infection of neurons and fibroblasts that is likely to reduce viral replication. Since sncRNAs have been suggested to be potential targets for antiviral therapies, identification of these molecules in VZV may provide a new direction for development of treatments for painful herpes zoster.

Dataset Information

Stochastic Modeling of Gene Regulation by Noncoding Small RNAs in the Strong Interaction Limit.

Publications

Stochastic Modeling of Gene Regulation by Noncoding Small RNAs in the Strong Interaction Limit.

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