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 module's 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

UCHL1/PGP 9.5 Dynamic in neuro-immune-cutaneous milieu : focusing on axonal nerve terminals and epidermal keratinocytes in psoriatic itch

Psoriasis is an immunogenetic skin disease manifesting as plaque lesions on the skin. Patients with psoriasis frequently suffer from itch, an unpleasant sensation causing a desire to scratch. Psoriatic itch is mainly transmitted by unmyelinated C-fibers; however, the exact molecular mechanism of psoriatic itch is still unexplained. Protein gene product 9.5 (PGP 9.5) is a panneurological marker commonly used for analysis of peripheral peptidergic and nonpeptidergic nerves and identification of cutaneous neuro-immune-endocrine cells. However, some studies suggested that nonneuronal cells, like keratinocytes, may also express PGP 9.5. This phenomenon might be linked with impaired axonal transport, keratinocyte injury, or dysfunctions of neuro-immune-cutaneous connections. The aim of this study was to analyze the expression of PGP 9.5 in psoriatic skin. We observed significantly altered density of PGP 9.5-positive axonal nerve terminals in pruritic lesional (p=0.04) and nonlesional psoriatic skin (p>0.001) compared with controls. In contrast, no significant differences were observed between psoriatic skin without itch and controls. Furthermore, PGP 9.5 expression by suprabasal keratinocytes (SBKs) was significantly increased in itchy skin lesions (p=0.007) compared to skin without itch, and a positive correlation was observed between PGP 9.5 expression and itch intensity (r=0.64; p=0.02). Our findings indicate changes in peripheral innervations and psoriatic keratinocytes, which may influence neuro-immune-cutaneous homeostasis and modulate itch transmission

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.publishe

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 population's 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

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)

The built-in converter for.csv files.

<p>The line containing the data from the first row of the plate is selected in the picture. Other rows have to follow immediately after the first row. All rows before and after the plate data will be discarded. Each row is then divided into separate values using the delimiter specified in the combo box below (semicolon is the default, but other delimiters are also supported). Since each row could contain more values than the number of wells, the user also has to specify in the edit box below which value in the row will be treated as the optical density value for the first well. Data for the subsequent wells have to follow immediately after, separated by delimiters. Note that the user can use a period or comma as the decimal separator for data files. Cheburator will automatically convert all decimal separators into periods, which are the default.</p

Example test plate designed to be processed in Cheburator.

<p>The sample plate with 1 control row (H), 1 row for the reference drug (A), and 4 rows for 4 novel studied compounds (B, C, D, and E, respectively) tested in 4 concentrations. Please note that not all rows have to be used on the plate, and different ranges of concentrations for different compounds can be applied in the test. Concentrations are highlighted in red. Control of untreated cells (CC) and control of media (CM) are in blue and green, respectively.</p

The Graphs tab after the analysis.

<p>It is showing the dose-response curves, individual values of percentages of inhibition for each data point, method of calculation used, IC₅₀ values, its type, 95% confidence intervals, and goodness of fit (for regression analysis).</p