Dmitry Nevozhay

Negative autoregulation linearizes the dose–response and suppresses the heterogeneity of gene expression

Although several recent studies have focused on gene autoregulation, the effects of negative feedback (NF) on gene expression are not fully understood. Our purpose here was to determine how the strength of NF regulation affects the characteristics of gene expression in yeast cells harboring chromosomally integrated transcriptional cascades that consist of the yEGFP reporter controlled by (i) the constitutively expressed tetracycline repressor TetR or (ii) TetR repressing its own expression. Reporter gene expression in the cascade without feedback showed a steep (sigmoidal) dose–response and a wide, nearly bimodal yEGFP distribution, giving rise to a noise peak at intermediate levels of induction. We developed computational models that reproduced the steep dose–response and the noise peak and predicted that negative autoregulation changes reporter expression from bimodal to unimodal and transforms the dose–response from sigmoidal to linear. Prompted by these predictions, we constructed a “linearizer” circuit by adding TetR autoregulation to our original cascade and observed a massive (7-fold) reduction of noise at intermediate induction and linearization of dose–response before saturation. A simple mathematical argument explained these findings and indicated that linearization is highly robust to parameter variations. These findings have important implications for gene expression control in eukaryotic cells, including the design of synthetic expression systems.

Mapping the Environmental Fitness Landscape of a Synthetic Gene Circuit

Gene expression actualizes the organismal phenotypes encoded within the genome in an environment-dependent manner. Among all encoded phenotypes, cell population growth rate (fitness) is perhaps the most important, since it determines how well-adapted a genotype is in various environments. Traditional biological measurement techniques have revealed the connection between the environment and fitness based on the gene expression mean. Yet, recently it became clear that cells with identical genomes exposed to the same environment can differ dramatically from the population average in their gene expression and division rate (individual fitness). For cell populations with bimodal gene expression, this difference is particularly pronounced, and may involve stochastic transitions between two cellular states that form distinct sub-populations. Currently it remains unclear how a cell populations growth rate and its subpopulation fractions emerge from the molecular-level kinetics of gene networks and the division rates of single cells. To address this question we developed and quantitatively characterized an inducible, bistable synthetic gene circuit controlling the expression of a bifunctional antibiotic resistance gene in Saccharomyces cerevisiae. Following fitness and fluorescence measurements in two distinct environments (inducer alone and antibiotic alone), we applied a computational approach to predict cell population fitness and subpopulation fractions in the combination of these environments based on stochastic cellular movement in gene expression space and fitness space. We found that knowing the fitness and nongenetic (cellular) memory associated with specific gene expression states were necessary for predicting the overall fitness of cell populations in combined environments. We validated these predictions experimentally and identified environmental conditions that defined a “sweet spot” of drug resistance. These findings may provide a roadmap for connecting the molecular-level kinetics of gene networks to cell population fitness in well-defined environments, and may have important implications for phenotypic variability of drug resistance in natural settings.

Transferring a synthetic gene circuit from yeast to mammalian cells

The emerging field of synthetic biology builds gene circuits for scientific, industrial, and therapeutic needs. Adaptability of synthetic gene circuits across different organisms could enable a synthetic biology pipeline, where circuits are designed in silico, characterized in microbes and reimplemented in mammalian settings for practical usage. However, the processes affecting gene circuit adaptability have not been systematically investigated. Here we construct a mammalian version of a negative feedback-based “linearizer” gene circuit previously developed in yeast. The first naïve mammalian prototype was non-functional, but a computational model suggested that we could recover function by improving gene expression and protein localization. After rationally developing and combining new parts as the model suggested, we regained function and could tune target gene expression in human cells linearly and precisely as in yeast. The steps we have taken should be generally relevant for transferring any gene circuit from yeast into mammalian cells.

Stress-response balance drives the evolution of a network module and its host genome

Stress response genes and their regulators form networks that underlie drug resistance. These networks often have an inherent tradeoff: their expression is costly in the absence of stress, but beneficial in stress. They can quickly emerge in the genomes of infectious microbes and cancer cells, protecting them from treatment. Yet, the evolution of stress resistance networks is not well understood. Here, we use a two‐component synthetic gene circuit integrated into the budding yeast genome to model experimentally the adaptation of a stress response module and its host genome in three different scenarios. In agreement with computational predictions, we find that: (i) intra‐module mutations target and eliminate the module if it confers only cost without any benefit to the cell; (ii) intra‐ and extra‐module mutations jointly activate the module if it is potentially beneficial and confers no cost; and (iii) a few specific mutations repeatedly fine‐tune the modules noisy response if it has excessive costs and/or insufficient benefits. Overall, these findings reveal how the timing and mechanisms of stress response network evolution depend on the environment.

Cheburator Software for Automatically Calculating Drug Inhibitory Concentrations from In Vitro Screening Assays

In the process of new cancer drug development, as the first step of their assessment, their activities are usually studied in vitro against a panel of cancer cell lines. The results of these in vitro drug screening assays are commonly expressed as inhibitory concentration 50% (IC50): the concentration of the tested agent that inhibits the proliferation of the cancer cell population to 50% of the theoretically possible effect (absolute IC50) or maximum effect practically achieved by the drug (relative IC50). The currently available software for calculating IC50 values requires manual data entry, is time consuming, and is prone to calculation errors. Thus, we have developed open source, free, easy-to-use software for performing standardized data evaluations and automatically calculating the IC50. This software eliminates the laborious and error-prone manual entry of data, substantially reduces the amount of time spent for data analysis. It has been extensively used in our department as the main tool for in vitro data processing during the past several years and can be useful for other research groups working in the area of anticancer drug discovery, either alone or combined with other software packages. The current version of our program, Cheburator, together with sample data, source code, and documentation, is freely available at the following URL: http://www.cheburator.nevozhay.com (it is free for academic use, but a license is required for commercial use).

Phenotypic heterogeneity promotes adaptive evolution

Genetically identical cells frequently display substantial heterogeneity in gene expression, cellular morphology and physiology. It has been suggested that by rapidly generating a subpopulation with novel phenotypic traits, phenotypic heterogeneity (or plasticity) accelerates the rate of adaptive evolution in populations facing extreme environmental challenges. This issue is important as cell-to-cell phenotypic heterogeneity may initiate key steps in microbial evolution of drug resistance and cancer progression. Here, we study how stochastic transitions between cellular states influence evolutionary adaptation to a stressful environment in yeast Saccharomyces cerevisiae. We developed inducible synthetic gene circuits that generate varying degrees of expression stochasticity of an antifungal resistance gene. We initiated laboratory evolutionary experiments with genotypes carrying different versions of the genetic circuit by exposing the corresponding populations to gradually increasing antifungal stress. Phenotypic heterogeneity altered the evolutionary dynamics by transforming the adaptive landscape that relates genotype to fitness. Specifically, it enhanced the adaptive value of beneficial mutations through synergism between cell-to-cell variability and genetic variation. Our work demonstrates that phenotypic heterogeneity is an evolving trait when populations face a chronic selection pressure. It shapes evolutionary trajectories at the genomic level and facilitates evolutionary rescue from a deteriorating environmental stress.

Two-Dimensionality of Yeast Colony Expansion Accompanied by Pattern Formation

Yeasts can form multicellular patterns as they expand on agar plates, a phenotype that requires a functional copy of the FLO11 gene. Although the biochemical and molecular requirements for such patterns have been examined, the mechanisms underlying their formation are not entirely clear. Here we develop quantitative methods to accurately characterize the size, shape, and surface patterns of yeast colonies for various combinations of agar and sugar concentrations. We combine these measurements with mathematical and physical models and find that FLO11 gene constrains cells to grow near the agar surface, causing the formation of larger and more irregular colonies that undergo hierarchical wrinkling. Head-to-head competition assays on agar plates indicate that two-dimensional constraint on the expansion of FLO11 wild type (FLO11) cells confers a fitness advantage over FLO11 knockout (flo11Δ) cells on the agar surface.

Linearizer Gene Circuits with Negative Feedback Regulation

Gene functional studies consist of phenotyping cells with altered gene expression. Improving the precision of current gene expression control techniques would enable more detailed studies of gene function. Here, we provide protocols for building synthetic gene constructs for tuning the expression of a gene in all the cells of a population precisely and uniformly, achieving expression levels proportional to the extracellular inducer concentration.

Efflux Pump Control Alters Synthetic Gene Circuit Function

Synthetic biology aims to design new biological systems for predefined purposes, such as the controlled secretion of biofuels, pharmaceuticals, or other chemicals. Synthetic gene circuits regulating an efflux pump from the ATP-binding cassette (ABC) protein family could achieve this. However, ABC efflux pumps can also drive out intracellular inducer molecules that control the gene circuits. This will introduce an implicit feedback that could alter gene circuit function in ways that are poorly understood. Here, we used two synthetic gene circuits inducible by tetracycline family molecules to regulate the expression of a yeast ABC pump (Pdr5p) that pumps out the inducer. Pdr5p altered the dose-responses of the original gene circuits substantially in Saccharomyces cerevisiae. While one aspect of the change could be attributed to the efflux pumping function of Pdr5p, another aspect remained unexplained. Quantitative modeling indicated that reduced regulator gene expression in addition to efflux pump function could fully explain the altered dose-responses. These predictions were validated experimentally. Overall, we highlight how efflux pumps can alter gene circuit dynamics and demonstrate the utility of mathematical modeling in understanding synthetic gene circuit function in new circumstances.

Negative Regulation Gene Circuits for Efflux Pump Control

Synthetic biologists aim to design biological systems for a variety of industrial and medical applications, ranging from biofuel to drug production. Synthetic gene circuits regulating efflux pump protein expression can achieve this by driving desired substrates such as biofuels, pharmaceuticals, or other chemicals out of the cell in a precisely controlled manner. However, efflux pumps may introduce implicit negative feedback by pumping out intracellular inducer molecules that control gene circuits, which then can alter gene circuit function. Therefore, synthetic gene circuits must be carefully designed and constructed for precise efflux control. Here, we provide protocols for quantitatively modeling and building synthetic gene constructs for efflux pump regulation.