Timing and Variability of Galactose Metabolic Gene Activation Depend on the Rate of Environmental Change

A modular cloning (MoClo) toolkit for reliable intracellular protein targeting in the yeast Saccharomyces cerevisiae

Modular Cloning (MoClo) allows the combinatorial assembly of plasmids from standardized genetic parts without the need of error-prone PCR reactions. It is a very powerful strategy which enables highly flexible expression patterns without the need of repetitive cloning procedures. In this study, we describe an advanced MoClo toolkit that is designed for the baker's yeast Saccharomyces cerevisiae and optimized for the targeting of proteins of interest to specific cellular compartments. Comparing different targeting sequences, we developed signals to direct proteins with high specificity to the different mitochondrial subcompartments, such as the matrix and the intermembrane space (IMS). Furthermore, we optimized the subcellular targeting by controlling expression levels using a collection of different promoter cassettes; the MoClo strategy allows it to generate arrays of expression plasmids in parallel to optimize gene expression levels and reliable targeting for each given protein and cellular compartment. Thus, the MoClo strategy enables the generation of protein-expressing yeast plasmids that accurately target proteins of interest to various cellular compartments.

Rate thresholds in cell signaling have functional and phenotypic consequences in non-linear time-dependent environments

All cells employ signal transduction pathways to respond to physiologically relevant extracellular cytokines, stressors, nutrient levels, hormones, morphogens, and other stimuli that vary in concentration and rate in healthy and diseased states. A central unsolved fundamental question in cell signaling is whether and how cells sense and integrate information conveyed by changes in the rate of extracellular stimuli concentrations, in addition to the absolute difference in concentration. We propose that different environmental changes over time influence cell behavior in addition to different signaling molecules or different genetic backgrounds. However, most current biomedical research focuses on acute environmental changes and does not consider how cells respond to environments that change slowly over time. As an example of such environmental change, we review cell sensitivity to environmental rate changes, including the novel mechanism of rate threshold. A rate threshold is defined as a threshold in the rate of change in the environment in which a rate value below the threshold does not activate signaling and a rate value above the threshold leads to signal activation. We reviewed p38/Hog1 osmotic stress signaling in yeast, chemotaxis and stress response in bacteria, cyclic adenosine monophosphate signaling in Amoebae, growth factors signaling in mammalian cells, morphogen dynamics during development, temporal dynamics of glucose and insulin signaling, and spatio-temproral stressors in the kidney. These reviewed examples from the literature indicate that rate thresholds are widespread and an underappreciated fundamental property of cell signaling. Finally, by studying cells in non-linear environments, we outline future directions to understand cell physiology better in normal and pathophysiological conditions.

Public good-driven release of heterogeneous resources leads to genotypic diversification of an isogenic yeast population

Understanding the basis of biological diversity remains a central problem in evolutionary biology. Using microbial systems, adaptive diversification has been studied in (a) spatially heterogeneous environments, (b) temporally segregated resources, and (c) resource specialization in a homogeneous environment. However, it is not well understood how adaptive diversification can take place in a homogeneous environment containing a single resource. Starting from an isogenic population of yeast Saccharomyces cerevisiae, we report rapid adaptive diversification, when propagated in an environment containing melibiose as the carbon source. The diversification is driven due to a public good enzyme α-galactosidase, which hydrolyzes melibiose into glucose and galactose. The diversification is driven by mutations at a single locus, in the GAL3 gene in the S. cerevisiae GAL/MEL regulon. We show that metabolic co-operation involving public resources could be an important mode of generating biological diversity. Our study demonstrates sympatric diversification of yeast starting from an isogenic population and provides detailed mechanistic insights into the factors and conditions responsible for generating and maintaining the population diversity.

Automatic synchronisation of the cell cycle in budding yeast through closed-loop feedback control

The cell cycle is the process by which eukaryotic cells replicate. Yeast cells cycle asynchronously with each cell in the population budding at a different time. Although there are several experimental approaches to synchronise cells, these usually work only in the short-term. Here, we build a cyber-genetic system to achieve long-term synchronisation of the cell population, by interfacing genetically modified yeast cells with a computer by means of microfluidics to dynamically change medium, and a microscope to estimate cell cycle phases of individual cells. The computer implements a controller algorithm to decide when, and for how long, to change the growth medium to synchronise the cell-cycle across the population. Our work builds upon solid theoretical foundations provided by Control Engineering. In addition to providing an avenue for yeast cell cycle synchronisation, our work shows that control engineering can be used to automatically steer complex biological processes towards desired behaviours similarly to what is currently done with robots and autonomous vehicles.

The binary response of the GAL/MEL genetic switch of Saccharomyces cerevisiae is critically dependent on Gal80p-Gal4p interaction

Studies on the Saccharomyces cerevisiae GAL/MEL genetic switch have revealed that its bistability is dependent on ultrasensitivity that can be altered or abolished by disabling different combinations of nested feedback loops. In contrast, we have previously demonstrated that weakening of the interaction between Gal80p and Gal4p alone is sufficient to abolish the ultrasensitivity (Das Adhikari et al. 2014). Here, we demonstrate that altering the epistatic interaction between Gal80p and Gal4p also abolishes the bistability, and the switch response to galactose becomes graded instead of binary. However, the GAL/MEL switch of wild-type and epistatically altered strains responded in a graded fashion to melibiose. The properties of the epistatically altered strain resemble Kluyveromyces lactis, which separated from the Saccharomyces lineage 100 mya before whole-genome duplication (WGD). Based on the results reported here, we propose that epistatic interactions played a crucial role in the evolution of the fine regulation of S. cerevisiae GAL/MEL switch following WGD.

The lag-phase during diauxic growth is a trade-off between fast adaptation and high growth rate

Bi-phasic or diauxic growth is often observed when microbes are grown in a chemically defined medium containing two sugars (for example glucose and lactose). Typically, the two growth stages are separated by an often lengthy phase of arrested growth, the so-called lag-phase. Diauxic growth is usually interpreted as an adaptation to maximise population growth in multi-nutrient environments. However, the lag-phase implies a substantial loss of growth during the switch-over. It therefore remains unexplained why the lag-phase is adaptive. Here we show by means of a stochastic simulation model based on the bacterial PTS system that it is not possible to shorten the lag-phase without incurring a permanent growth-penalty. Mechanistically, this is due to the inherent and well established limitations of biological sensors to operate efficiently at a given resource cost. Hence, there is a trade-off between lost growth during the diauxic switch and the long-term growth potential of the cell. Using simulated evolution we predict that the lag-phase will evolve depending on the distribution of conditions experienced during adaptation. In environments where switching is less frequently required, the lag-phase will evolve to be longer whereas, in frequently changing environments, the lag-phase will evolve to be shorter.