Generating dynamic gene expression patterns without the need for regulatory circuits

Abstract

Advancements in synthetic biology have significantly enhanced our capacity to design and implement intricate, time-varying genetic circuits for controlling the expression of recombinant proteins. However, these circuits often necessitate the inclusion of regulatory genes solely responsible for coordinating the expression of other genes. In certain cases, particularly when working with compact genetic structures like viral genomes, it may be desirable to avoid introducing such auxiliary gene products while still encoding complex expression dynamics. In this study, we present compelling evidence that manipulating the placement and strengths of promoters, terminators, and RNase cleavage sites within a computational model of a bacteriophage genome is adequate for achieving a range of fundamental gene expression patterns.

Introduction

Every genome contains a diverse array of RNA and protein sequences, which play vital roles in the growth and survival of organisms. The production of these biomolecules is crucial, as they often need to be present in varying quantities relative to one another, and this demand can change over time. Therefore, organisms possess the remarkable ability to modulate gene expression levels, enabling them to meet specific lifestyle requirements and adapt to shifting environmental conditions. Extensive research has characterized numerous gene-regulatory elements, and scientists have been engineering cells to produce specific protein products for many years. By manipulating factors such as promoter strength, terminator efficiency, and ribosome binding sites, among others, cells can generate different amounts of recombinant gene products, spanning a wide range of expression levels. This flexibility in gene expression regulation allows organisms to finely tune their molecular machinery and respond dynamically to the demands of their internal and external environments.

Materials and methods

Gene expression simulations

We employed the gene expression simulation platform Pinetree (version 0.3.0) to conduct our experiments on mRNA transcript expression within a bacteriophage genome. The simulated genomes consisted of a small number of genes, ranging from three to ten in our specific simulations. Our simulations encompassed the processes of mRNA translation and protein production, assuming that all genes had equal strengths in terms of ribosome binding sites. However, for the purposes of this study, we focused solely on analyzing mRNA transcript levels.

Results

Evolutionary simulation of phage gene expression

Our objective is to explore the range of gene expression patterns that can be achieved in a bacteriophage by manipulating a small set of regulatory components. We recognized that designing genomes in a rational manner would be challenging, except for simple cases, and exhaustively enumerating all possible genomes would be impractical. Therefore, we adopted an evolutionary approach to computationally engineer genomes that can replicate a diverse set of predefined gene expression time-course profiles.

To implement this strategy, we utilized a stochastic gene expression platform called Pinetree, which simulates the molecular-level dynamics of phage infection and generates time-course data representing RNA abundance [29]. It is important to note that Pinetree does not directly operate on DNA sequences; instead, it relies on parameter files that define the genomic location and strengths of individual genes, promoters, terminators, and other regulatory elements. We extended the functionality of Pinetree to facilitate evolutionary simulations, where discrete generations consist of individual phage infection simulations, and the resulting time-course of RNA species serves as the phenotype of interest. In our evolutionary approach, the genome provided as input to Pinetree varies from generation to generation through mutations, selection, and genetic drift, specifically affecting the placement and strengths of promoters, terminators, and RNase cleavage sites.

Discussion

In all organisms, the regulation of gene expression is vital for achieving specific levels of gene activity that vary dynamically over time. In this study, we employed computational modeling to illustrate that bacteriophages can exhibit diverse and dynamic gene expression patterns without the need for elaborate regulatory molecules or complex genetic circuitry. By manipulating the strengths of promoters, terminators, and RNase binding sites, our evolutionary simulation platform successfully generated genomes that matched a wide range of distinct gene expression time-course patterns. These results challenge the notion that networks of interacting promoters and transcription factors are essential for achieving specific gene expression designs. Instead, our findings demonstrate the potential for de novo generation of gene expression patterns through simple genomic modifications.

Acknowledgments

This work made use of high-performance computing resources provided by the Texas Advanced Computing Center (TACC) at The University of Texas at Austin.

Citation: Shah SB, Hill AM, Wilke CO, Hockenberry AJ (2022) Generating dynamic gene expression patterns without the need for regulatory circuits. PLoS ONE 17(5): e0268883. https://doi.org/10.1371/journal.pone.0268883

Editor: William Ott, University of Houston, UNITED STATES

Received: July 28, 2021; Accepted: May 10, 2022; Published: May 26, 2022

Copyright: © 2022 Shah et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All code and data required to reproduce this work are available at: https://github.com/SahilBShah/pinetree-evolution and https://doi.org/10.5281/zenodo.4592577.

Funding: S.B.S received support from the Texas Institute for Discovery Education in Science (TIDES) in the College of Natural Sciences at the University of Texas at Austin. C.O.W. was supported by a National Institutes of Health grant R01 GM088344, as well as support from the Jane and Roland Blumberg Centennial Professorship in Molecular Evolution and the Dwight W. and Blanche Faye Reeder Centennial Fellowship in Systematic and Evolutionary Biology at The University of Texas at Austin. A.J.H. was supported by National Institutes of Health award F32 GM130113. The Texas Advanced Computing Center (TACC) at The University of Texas at Austin provided high-performance computing resources. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0268883#abstract0