Integrative model of the response of yeast to osmotic shock

Strong delayed negative feedback

In this paper, we analyze the strong feedback limit of two negative feedback schemes which have proven to be efficient for many biological processes (protein synthesis, immune responses, breathing disorders). In this limit, the nonlinear delayed feedback function can be reduced to a function with a threshold nonlinearity. This will considerably help analytical and numerical studies of networks exhibiting different topologies. Mathematically, we compare the bifurcation diagrams for both the delayed and non-delayed feedback functions and show that Hopf classical theory needs to be revisited in the strong feedback limit.

Yeast cell responses and survival during periodic osmotic stress are controlled by glucose availability

Natural environments of living organisms are often dynamic and multifactorial, with multiple parameters fluctuating over time. To better understand how cells respond to dynamically interacting factors, we quantified the effects of dual fluctuations of osmotic stress and glucose deprivation on yeast cells using microfluidics and time-lapse microscopy. Strikingly, we observed that cell proliferation, survival, and signaling depend on the phasing of the two periodic stresses. Cells divided faster, survived longer, and showed decreased transcriptional response when fluctuations of hyperosmotic stress and glucose deprivation occurred in phase than when the two stresses occurred alternatively. Therefore, glucose availability regulates yeast responses to dynamic osmotic stress, showcasing the key role of metabolic fluctuations in cellular responses to dynamic stress. We also found that mutants with impaired osmotic stress response were better adapted to alternating stresses than wild-type cells, showing that genetic mechanisms of adaptation to a persistent stress factor can be detrimental under dynamically interacting conditions.

A sodium-dependent trehalose transporter contributes to anhydrobiosis in insect cell line, Pv11

Pv11 is the only animal cell line that, when preconditioned with a high concentration of trehalose, can be preserved in the dry state at room temperature for more than one year while retaining the ability to resume proliferation. This extreme desiccation tolerance is referred to as anhydrobiosis. Here, we identified a transporter that contributes to the recovery of Pv11 cells from anhydrobiosis. In general, the solute carrier 5 (SLC5)-type secondary active transporters cotransport Na+ and carbohydrates including glucose. The heterologous expression systems showed that the transporter belonging to the SLC5 family, whose expression increases upon rehydration, exhibits Na+-dependent trehalose transport activity. Therefore, we named it STRT1 (sodium-ion trehalose transporter 1). We report an SLC5 family member that transports a naturally occurring disaccharide, such as trehalose. Knockout of the Strt1 gene significantly reduced the viability of Pv11 cells upon rehydration after desiccation. During rehydration, when intracellular trehalose is no longer needed, Strt1-knockout cells released the disaccharide more slowly than the parental cell line. During rehydration, Pv11 cells became roughly spherical due to osmotic pressure changes, but then returned to their original spindle shape after about 30 min. Strt1-knockout cells, however, required about 50 min to adopt their normal morphology. STRT1 probably regulates intracellular osmolality by releasing unwanted intracellular trehalose with Na+, thereby facilitating the recovery of normal cell morphology during rehydration. STRT1 likely improves the viability of dried Pv11 cells by rapidly alleviating the significant physical stresses that arise during rehydration.

Data integration strategies for whole-cell modeling

Data makes the world go round-and high quality data is a prerequisite for precise models, especially for whole-cell models (WCM). Data for WCM must be reusable, contain information about the exact experimental background, and should-in its entirety-cover all relevant processes in the cell. Here, we review basic requirements to data for WCM and strategies how to combine them. As a species-specific resource, we introduce the Yeast Cell Model Data Base (YCMDB) to illustrate requirements and solutions. We discuss recent standards for data as well as for computational models including the modeling process as data to be reported. We outline strategies for constructions of WCM despite their inherent complexity.

Cellular Stress Impact on Yeast Activity in Biotechnological Processes-A Short Overview

The importance of Saccharomyces cerevisiae yeast cells is known worldwide, as they are the most used microorganisms in biotechnology for bioethanol and biofuel production. Also, they are analyzed and studied for their similar internal biochemical processes to human cells, for a better understanding of cell aging and response to cell stressors. The special ability of S. cerevisiae cells to develop in both aerobic and anaerobic conditions makes this microorganism a viable model to study the transformations and the way in which cellular metabolism is directed to face the stress conditions due to environmental changes. Thus, this review will emphasize the effects of oxidative, ethanol, and osmotic stress and also the physiological and genetic response of stress mitigation in yeast cells.

Advances in S. cerevisiae Engineering for Xylose Fermentation and Biofuel Production: Balancing Growth, Metabolism, and Defense

Genetically engineering microorganisms to produce chemicals has changed the industrialized world. The budding yeast Saccharomyces cerevisiae is frequently used in industry due to its genetic tractability and unique metabolic capabilities. S. cerevisiae has been engineered to produce novel compounds from diverse sugars found in lignocellulosic biomass, including pentose sugars, like xylose, not recognized by the organism. Engineering high flux toward novel compounds has proved to be more challenging than anticipated since simply introducing pathway components is often not enough. Several studies show that the rewiring of upstream signaling is required to direct products toward pathways of interest, but doing so can diminish stress tolerance, which is important in industrial conditions. As an example of these challenges, we reviewed S. cerevisiae engineering efforts, enabling anaerobic xylose fermentation as a model system and showcasing the regulatory interplay's controlling growth, metabolism, and stress defense. Enabling xylose fermentation in S. cerevisiae requires the introduction of several key metabolic enzymes but also regulatory rewiring of three signaling pathways at the intersection of the growth and stress defense responses: the RAS/PKA, Snf1, and high osmolarity glycerol (HOG) pathways. The current studies reviewed here suggest the modulation of global signaling pathways should be adopted into biorefinery microbial engineering pipelines to increase efficient product yields.

Positive feedback induces switch between distributive and processive phosphorylation of Hog1

Cellular decision making often builds on ultrasensitive MAPK pathways. The phosphorylation mechanism of MAP kinase has so far been described as either distributive or processive, with distributive mechanisms generating ultrasensitivity in theoretical analyses. However, the in vivo mechanism of MAP kinase phosphorylation and its activation dynamics remain unclear. Here, we characterize the regulation of the MAP kinase Hog1 in Saccharomyces cerevisiae via topologically different ODE models, parameterized on multimodal activation data. Interestingly, our best fitting model switches between distributive and processive phosphorylation behavior regulated via a positive feedback loop composed of an affinity and a catalytic component targeting the MAP kinase-kinase Pbs2. Indeed, we show that Hog1 directly phosphorylates Pbs2 on serine 248 (S248), that cells expressing a non-phosphorylatable (S248A) or phosphomimetic (S248E) mutant show behavior that is consistent with simulations of disrupted or constitutively active affinity feedback and that Pbs2-S248E shows significantly increased affinity to Hog1 in vitro. Simulations further suggest that this mixed Hog1 activation mechanism is required for full sensitivity to stimuli and to ensure robustness to different perturbations.

Two activating phosphorylation sites of Pbs2 MAP2K in the yeast HOG pathway are differentially dephosphorylated by four PP2C phosphatases Ptc1-Ptc4

To cope with an increased external osmolarity, the budding yeast Saccharomyces cerevisiae activates the Hog1 mitogen-activated kinase (MAPK) through the High-Osmolarity Glycerol (HOG) pathway, which governs adaptive responses to osmostress. In the HOG pathway, two apparently redundant upstream branches, termed SLN1 and SHO1, activate cognate MAP3Ks Ssk2/22 and Ste11, respectively. These MAP3Ks, when activated, phosphorylate and thus activate the Pbs2 MAP2K, which in turn phosphorylates and activates Hog1. Previous studies have shown that protein tyrosine phosphatases (PTP) and the serine/threonine protein phosphatases type 2C (PP2C) negatively regulate the HOG pathway to prevent its excessive and inappropriate activation, which is detrimental to cell growth. The tyrosine phosphatases Ptp2 and Ptp3 dephosphorylate Hog1 at Tyr-176, whereas the PP2Cs Ptc1 and Ptc2 dephosphorylate Hog1 at Thr-174. In contrast, the identities of phosphatases that dephosphorylate Pbs2 remained less clear. Here, we examined the phosphorylation status of Pbs2 at the activating phosphorylation sites Ser-514 and Thr-518 (S514 and T518) in various mutants, both in the unstimulated and osmostressed conditions. Thus, we found that Ptc1-Ptc4 collectively regulate Pbs2 negatively, but each Ptc acts differently to the two phosphorylation sites in Pbs2. T518 is predominantly dephosphorylated by Ptc1, whereas the effect of Ptc2-Ptc4 could be seen only when Ptc1 is absent. Conversely, S514 can be dephosphorylated by any of Ptc1-4 to an appreciable extent. We also show that Pbs2 dephosphorylation by Ptc1 requires the adaptor protein Nbp2 that recruits Ptc1 to Pbs2, thus highlighting the complex processes involved in regulating adaptive responses to osmostress.

Neuron populations use variable combinations of short-term feedback mechanisms to stabilize firing rate

Neurons tightly regulate firing rate and a failure to do so leads to multiple neurological disorders. Therefore, a fundamental question in neuroscience is how neurons produce reliable activity patterns for decades to generate behavior. Neurons have built-in feedback mechanisms that allow them to monitor their output and rapidly stabilize firing rate. Most work emphasizes the role of a dominant feedback system within a neuronal population for the control of moment-to-moment firing. In contrast, we find that respiratory motoneurons use 2 activity-dependent controllers in unique combinations across cells, dynamic activation of an Na+ pump subtype, and rapid potentiation of Kv7 channels. Both systems constrain firing rate by reducing excitability for up to a minute after a burst of action potentials but are recruited by different cellular signals associated with activity, increased intracellular Na+ (the Na+ pump), and membrane depolarization (Kv7 channels). Individual neurons do not simply contain equal amounts of each system. Rather, neurons under strong control of the Na+ pump are weakly regulated by Kv7 enhancement and vice versa along a continuum. Thus, each motoneuron maintains its characteristic firing rate through a unique combination of the Na+ pump and Kv7 channels, which are dynamically regulated by distinct feedback signals. These results reveal a new organizing strategy for stable circuit output involving multiple fast activity sensors scaled inversely across a neuronal population.

The old and the new about the contractile vacuole of Trypanosoma cruzi

Osmoregulation is a conserved cellular process required for the survival of all organisms. In protists, the need for robust compensatory mechanisms that can maintain cell volume and tonicity within physiological range is even more relevant, as their life cycles are often completed in different environments. Trypanosoma cruzi, the protozoan pathogen responsible for Chagas disease, is transmitted by an insect vector to multiple types of mammalian hosts. The contractile vacuole complex (CVC) is an organelle that senses and compensates osmotic changes in the parasites, ensuring their survival upon ionic and osmotic challenges. Recent work shows that the contractile vacuole is also a key component of the secretory and endocytic pathways, regulating the selective targeting of surface proteins during differentiation. Here we summarize our current knowledge of the mechanisms involved in the osmoregulatory processes that take place in the vacuole, and we explore the new and exciting functions of this organelle in cell trafficking and signaling.

Yeast osmoregulation - glycerol still in pole position

In response to osmotic dehydration cells sense, signal, alter gene expression, and metabolically counterbalance osmotic differences. The main compatible solute/osmolyte that accumulates in yeast cells is glycerol, which is produced from the glycolytic intermediate dihydroxyacetone phosphate. This review covers recent advancements in understanding mechanisms involved in sensing, signaling, cell-cycle delays, transcriptional responses as well as post-translational modifications on key proteins in osmoregulation. The protein kinase Hog1 is a key-player in many of these events, however, there is also a growing body of evidence for important Hog1-independent mechanisms playing vital roles. Several missing links in our understanding of osmoregulation will be discussed and future avenues for research proposed. The review highlights that this rather simple experimental system—salt/sorbitol and yeast—has developed into an enormously potent model system unravelling important fundamental aspects in biology.

Biologia futura: combinatorial stress responses in fungi

In the ever-changing fungal environment, fungi have to cope with a wide array of very different stresses. These stresses frequently act in combination rather than independently, i.e., they quickly follow one another or occur concomitantly. Combinatorial stress response studies revealed that the response of fungi to a stressor is highly dependent on the simultaneous action of other stressors or even on earlier stresses to which the fungi adapted. Several important phenomena were discovered, such as stress pathway interference, acquired stress tolerance, stress response memory or stress cross-protection/sensitization, which cannot be interpreted when we study the consequences of a single stressor alone. Due to the interactions between stressors and stress responses, a stress response that develops under a combined stress is not the simple summation of stress responses observed during single stress treatments. Based on the knowledge collected from single stress treatment experiments, we cannot predict how fungi will respond to a certain combination of stresses or even whether this combination will be more harmful than single stress treatments. This uncertainty warns us that if we want to understand how fungi adapt to a certain habitat (e.g., to the human body) to find a point of weakness in this adaptation, we must understand how the fungi cope with combinations of stresses, rather than with single stressors.

A yeast cell cycle model integrating stress, signaling, and physiology

The cell division cycle in eukaryotic cells is a series of highly coordinated molecular interactions that ensure that cell growth, duplication of genetic material, and actual cell division are precisely orchestrated to give rise to two viable progeny cells. Moreover, the cell cycle machinery is responsible for incorporating information about external cues or internal processes that the cell must keep track of to ensure a coordinated, timely progression of all related processes. This is most pronounced in multicellular organisms, but also a cardinal feature in model organisms such as baker's yeast. The complex and integrative behavior is difficult to grasp and requires mathematical modeling to fully understand the quantitative interplay of the single components within the entire system. Here, we present a self-oscillating mathematical model of the yeast cell cycle that comprises all major cyclins and their main regulators. Furthermore, it accounts for the regulation of the cell cycle machinery by a series of external stimuli such as mating pheromones and changes in osmotic pressure or nutrient quality. We demonstrate how the external perturbations modify the dynamics of cell cycle components and how the cell cycle resumes after adaptation to or relief from stress.

Modelling of glucose repression signalling in yeast Saccharomyces cerevisiae

Saccharomyces cerevisiae has a sophisticated signalling system that plays a crucial role in cellular adaptation to changing environments. The SNF1 pathway regulates energy homeostasis upon glucose derepression; hence, it plays an important role in various processes, such as metabolism, cell cycle and autophagy. To unravel its behaviour, SNF1 signalling has been extensively studied. However, the pathway components are strongly interconnected and inconstant; therefore, elucidating its dynamic behaviour based on experimental data only is challenging. To tackle this complexity, systems biology approaches have been successfully employed. This review summarizes the progress, advantages and disadvantages of the available mathematical modelling frameworks covering Boolean, dynamic kinetic, single-cell models, which have been used to study processes and phenomena ranging from crosstalks to sources of cell-to-cell variability in the context of SNF1 signalling. Based on the lessons from existing models, we further discuss how to develop a consensus dynamic mechanistic model of the entire SNF1 pathway that can provide novel insights into the dynamics of nutrient signalling.

The Effect of Lithium on the Budding Yeast Saccharomyces cerevisiae upon Stress Adaptation

Lithium salts are used in the treatment of mood disorders, cancer, and Alzheimer's disease. It has been shown to prolong life span in several phyla; however, not yet in budding yeast. In our study, we investigate the influence of lithium on yeast cells' viability by characterizing protein aggregate formation, cell volume, and molecular crowding in the context of stress adaptation. While our data suggest a concentration-dependent growth inhibition caused by LiCl, we show an extended long-term survival rate as an effect of lithium addition upon glucose deprivation. We show that caloric restriction mitigates the negative impact of LiCl on cellular survival. Therefore, we suggest that lithium could affect glucose metabolism upon caloric restriction, which could explain the extended long-term survival observed in our study. We find furthermore that lithium chloride did not affect an immediate salt-induced Hsp104-dependent aggregate formation but cellular adaptation to H₂O₂ and acute glucose starvation. We presume that different salt types and concentrations interfere with effective Hsp104 recruitment or its ATP-dependent disaggregase activity as a response to salt stress. This work provides novel details of Li⁺ effect on live eukaryotic cells which may also be applicable in further research on the treatment of cancer, Alzheimer's, or other age-related diseases in humans.

D-Xylose Sensing in Saccharomyces cerevisiae: Insights from D-Glucose Signaling and Native D-Xylose Utilizers

Extension of the substrate range is among one of the metabolic engineering goals for microorganisms used in biotechnological processes because it enables the use of a wide range of raw materials as substrates. One of the most prominent examples is the engineering of baker's yeast Saccharomyces cerevisiae for the utilization of d-xylose, a five-carbon sugar found in high abundance in lignocellulosic biomass and a key substrate to achieve good process economy in chemical production from renewable and non-edible plant feedstocks. Despite many excellent engineering strategies that have allowed recombinant S. cerevisiae to ferment d-xylose to ethanol at high yields, the consumption rate of d-xylose is still significantly lower than that of its preferred sugar d-glucose. In mixed d-glucose/d-xylose cultivations, d-xylose is only utilized after d-glucose depletion, which leads to prolonged process times and added costs. Due to this limitation, the response on d-xylose in the native sugar signaling pathways has emerged as a promising next-level engineering target. Here we review the current status of the knowledge of the response of S. cerevisiae signaling pathways to d-xylose. To do this, we first summarize the response of the native sensing and signaling pathways in S. cerevisiae to d-glucose (the preferred sugar of the yeast). Using the d-glucose case as a point of reference, we then proceed to discuss the known signaling response to d-xylose in S. cerevisiae and current attempts of improving the response by signaling engineering using native targets and synthetic (non-native) regulatory circuits.

SBMLWebApp: Web-Based Simulation, Steady-State Analysis, and Parameter Estimation of Systems Biology Models

In systems biology, biological phenomena are often modeled by Ordinary Differential Equations (ODEs) and distributed in the de facto standard file format SBML. The primary analyses performed with such models are dynamic simulation, steady-state analysis, and parameter estimation. These methodologies are mathematically formalized, and libraries for such analyses have been published. Several tools exist to create, simulate, or visualize models encoded in SBML. However, setting up and establishing analysis environments is a crucial hurdle for non-modelers. Therefore, easy access to perform fundamental analyses of ODE models is a significant challenge. We developed SBMLWebApp, a web-based service to execute SBML-based simulation, steady-state analysis, and parameter estimation directly in the browser without the need for any setup or prior knowledge to address this issue. SBMLWebApp visualizes the result and numerical table of each analysis and provides a download of the results. SBMLWebApp allows users to select and analyze SBML models directly from the BioModels Database. Taken together, SBMLWebApp provides barrier-free access to an SBML analysis environment for simulation, steady-state analysis, and parameter estimation for SBML models. SBMLWebApp is implemented in Java™ based on an Apache Tomcat® web server using COPASI, the Systems Biology Simulation Core Library (SBSCL), and LibSBMLSim as simulation engines. SBMLWebApp is licensed under MIT with source code freely available. At the end of this article, the Data Availability Statement gives the internet links to the two websites to find the source code and run the program online.

Intrinsically disordered protein biosensor tracks the physical-chemical effects of osmotic stress on cells

Cell homeostasis is perturbed when dramatic shifts in the external environment cause the physical-chemical properties inside the cell to change. Experimental approaches for dynamically monitoring these intracellular effects are currently lacking. Here, we leverage the environmental sensitivity and structural plasticity of intrinsically disordered protein regions (IDRs) to develop a FRET biosensor capable of monitoring rapid intracellular changes caused by osmotic stress. The biosensor, named SED1, utilizes the Arabidopsis intrinsically disordered AtLEA4-5 protein expressed in plants under water deficit. Computational modeling and in vitro studies reveal that SED1 is highly sensitive to macromolecular crowding. SED1 exhibits large and near-linear osmolarity-dependent changes in FRET inside living bacteria, yeast, plant, and human cells, demonstrating the broad utility of this tool for studying water-associated stress. This study demonstrates the remarkable ability of IDRs to sense the cellular environment across the tree of life and provides a blueprint for their use as environmentally-responsive molecular tools.

Intrinsically disordered protein biosensor tracks the physical-chemical effects of osmotic stress on cells

Cell homeostasis is perturbed when dramatic shifts in the external environment cause the physical-chemical properties inside the cell to change. Experimental approaches for dynamically monitoring these intracellular effects are currently lacking. Here, we leverage the environmental sensitivity and structural plasticity of intrinsically disordered protein regions (IDRs) to develop a FRET biosensor capable of monitoring rapid intracellular changes caused by osmotic stress. The biosensor, named SED1, utilizes the Arabidopsis intrinsically disordered AtLEA4-5 protein expressed in plants under water deficit. Computational modeling and in vitro studies reveal that SED1 is highly sensitive to macromolecular crowding. SED1 exhibits large and near-linear osmolarity-dependent changes in FRET inside living bacteria, yeast, plant, and human cells, demonstrating the broad utility of this tool for studying water-associated stress. This study demonstrates the remarkable ability of IDRs to sense the cellular environment across the tree of life and provides a blueprint for their use as environmentally-responsive molecular tools.

RTG Signaling Sustains Mitochondrial Respiratory Capacity in HOG1-Dependent Osmoadaptation

Mitochondrial RTG-dependent retrograde signaling, whose regulators have been characterized in Saccharomyces cerevisiae, plays a recognized role under various environmental stresses. Of special significance, the activity of the transcriptional complex Rtg1/3 has been shown to be modulated by Hog1, the master regulator of the high osmolarity glycerol pathway, in response to osmotic stress. The present work focuses on the role of RTG signaling in salt-induced osmotic stress and its interaction with HOG1. Wild-type and mutant cells, lacking HOG1 and/or RTG genes, are compared with respect to cell growth features, retrograde signaling activation and mitochondrial function in the presence and in the absence of high osmostress. We show that RTG2, the main upstream regulator of the RTG pathway, contributes to osmoadaptation in an HOG1-dependent manner and that, with RTG3, it is notably involved in a late phase of growth. Our data demonstrate that impairment of RTG signaling causes a decrease in mitochondrial respiratory capacity exclusively under osmostress. Overall, these results suggest that HOG1 and the RTG pathway may interact sequentially in the stress signaling cascade and that the RTG pathway may play a role in inter-organellar metabolic communication for osmoadaptation.

Multiscale models quantifying yeast physiology: towards a whole-cell model

The yeast Saccharomyces cerevisiae is widely used as a cell factory and as an important eukaryal model organism for studying cellular physiology related to human health and disease. Yeast was also the first eukaryal organism for which a genome-scale metabolic model (GEM) was developed. In recent years there has been interest in expanding the modeling framework for yeast by incorporating enzymatic parameters and other heterogeneous cellular networks to obtain a more comprehensive description of cellular physiology. We review the latest developments in multiscale models of yeast, and illustrate how a new generation of multiscale models could significantly enhance the predictive performance and expand the applications of classical GEMs in cell factory design and basic studies of yeast physiology.

Building predictive signaling models by perturbing yeast cells with time-varying stimulations resulting in distinct signaling responses

This protocol provides a step-by-step approach to perturb single cells with time-varying stimulation profiles, collect distinct signaling responses, and use these to infer a system of ordinary differential equations to capture and predict dynamics of protein-protein regulation in signal transduction pathways. The models are validated by predicting the signaling activation upon new cell stimulation conditions. In comparison to using standard step-like stimulations, application of diverse time-varying cell stimulations results in better inference of model parameters and substantially improves model predictions.

For complete details on the use and results of this protocol, please refer to Jashnsaz et al. (2020).

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Diverse time-varying cell stimulations result in distinct signaling activation dynamics

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Signaling models fit step stimuli responses well but result in poor predictions

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Distinct responses upon diverse time-varying stimulations improve model predictions

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Temporal stimulation of pathways result in novel signaling dynamics and mechanisms

Increased xerotolerance of Saccharomyces cerevisiae during an osmotic pressure ramp over several generations

Although mechanisms involved in response of Saccharomyces cerevisiae to osmotic challenge are well described for low and sudden stresses, little is known about how cells respond to a gradual increase of the osmotic pressure (reduced water activity; aw ) over several generations as it could encounter during drying in nature or in food processes. Using glycerol as a stressor, we propagated S. cerevisiae through a ramp of the osmotic pressure (up to high molar concentrations to achieve testing-to-destruction) at the rate of 1.5 MPa day-1 from 1.38 to 58.5 MPa (0.990-0.635 aw ). Cultivability (measured at 1.38 MPa and at the harvest osmotic pressure) and glucose consumption compared with the corresponding sudden stress showed that yeasts were able to grow until about 10.5 MPa (0.926 aw ) and to survive until about 58.5 MPa, whereas glucose consumption occurred until 13.5 MPa (about 0.915 aw ). Nevertheless, the ramp conferred an advantage since yeasts harvested at 10.5 and 34.5 MPa (0.778 aw ) showed a greater cultivability than glycerol-shocked cells after a subsequent shock at 200 MPa (0.234 aw ) for 2 days. FTIR analysis revealed structural changes in wall and proteins in the range 1.38-10.5 MPa, which would be likely to be involved in the resistance at extreme osmotic pressure.

Potassium and Sodium Salt Stress Characterization in the Yeasts Saccharomyces cerevisiae, Kluyveromyces marxianus, and Rhodotorula toruloides

Biotechnology requires efficient microbial cell factories. The budding yeast Saccharomyces cerevisiae is a vital cell factory, but more diverse cell factories are essential for the sustainable use of natural resources. Here, we benchmarked nonconventional yeasts Kluyveromyces marxianus and Rhodotorula toruloides against S. cerevisiae strains CEN.PK and W303 for their responses to potassium and sodium salt stress. We found an inverse relationship between the maximum growth rate and the median cell volume that was responsive to salt stress. The supplementation of K⁺ to CEN.PK cultures reduced Na⁺ toxicity and increased the specific growth rate 4-fold. The higher K⁺ and Na⁺ concentrations impaired ethanol and acetate metabolism in CEN.PK and acetate metabolism in W303. In R. toruloides cultures, these salt supplementations induced a trade-off between glucose utilization and cellular aggregate formation. Their combined use increased the beta-carotene yield by 60% compared with that of the reference. Neural network-based image analysis of exponential-phase cultures showed that the vacuole-to-cell volume ratio increased with increased cell volume for W303 and K. marxianus but not for CEN.PK and R. toruloides in response to salt stress. Our results provide insights into common salt stress responses in yeasts and will help design efficient bioprocesses. IMPORTANCE Characterization of microbial cell factories under industrially relevant conditions is crucial for designing efficient bioprocesses. Salt stress, typical in industrial bioprocesses, impinges upon cell volume and affects productivity. This study presents an open-source neural network-based analysis method to evaluate volumetric changes using yeast optical microscopy images. It allows quantification of cell and vacuole volumes relevant to cellular physiology. On applying salt stress in yeasts, we found that the combined use of K⁺ and Na⁺ improves the cellular fitness of Saccharomyces cerevisiae strain CEN.PK and increases the beta-carotene productivity in Rhodotorula toruloides, a commercially important antioxidant and a valuable additive in foods.

Osmolyte Signatures for the Protection of Aspergillus sydowii Cells under Halophilic Conditions and Osmotic Shock

Aspergillus sydowii is a moderate halophile fungus extensively studied for its biotechnological potential and halophile responses, which has also been reported as a coral reef pathogen. In a recent publication, the transcriptomic analysis of this fungus, when growing on wheat straw, showed that genes related to cell wall modification and cation transporters were upregulated under hypersaline conditions but not under 0.5 M NaCl, the optimal salinity for growth in this strain. This led us to study osmolyte accumulation as a mechanism to withstand moderate salinity. In this work, we show that A. sydowii accumulates trehalose, arabitol, mannitol, and glycerol with different temporal dynamics, which depend on whether the fungus is exposed to hypo- or hyperosmotic stress. The transcripts coding for enzymes responsible for polyalcohol synthesis were regulated in a stress-dependent manner. Interestingly, A. sydowii contains three homologs (Hog1, Hog2 and MpkC) of the Hog1 MAPK, the master regulator of hyperosmotic stress response in S. cerevisiae and other fungi. We show a differential regulation of these MAPKs under different salinity conditions, including sustained basal Hog1/Hog2 phosphorylation levels in the absence of NaCl or in the presence of 2.0 M NaCl, in contrast to what is observed in S. cerevisiae. These findings indicate that halophilic fungi such as A. sydowii utilize different osmoadaptation mechanisms to hypersaline conditions.

Microfluidic measurement of individual cell membrane water permeability

This paper reports a microfluidic lab-on-chip for dynamic particle sizing and real time individual cell membrane permeability measurements. To achieve this, the device measures the impedance change of individual cells or particles at up to ten time points after mixing with different media, e.g. dimethyl sulfoxide or DI water, from separate inlets. These measurements are enabled by ten gold electrode pairs spread across a 20 mm long microchannel. The device measures impedance values within 0.26 s after mixing with other media, has a detection throughput of 150 samples/second, measures impedance values at all ten electrodes at this rate, and allows tracking of individual cell volume changes caused by cell osmosis in anisosmotic fluids over a 1.3 s postmixing timespan, facilitating accurate individual cell estimates of water permeability. The design and testing were performed using yeast cells (Saccharomyces cerevisiae). The relationship between volume and impedance in both polystyrene calibration beads as well as the volume-osmolality relationship in yeast were demonstrated. Moreover, we present the first noninvasive and non-optically-based water permeability measurements in individual cells.

Building patient-specific models for receptor tyrosine kinase signaling networks

Cancer progresses due to changes in the dynamic interactions of multidimensional factors associated with gene mutations. Cancer research has actively adopted computational methods, including data-driven and mathematical model-driven approaches, to identify causative factors and regulatory rules that can explain the complexity and diversity of cancers. A data-driven, statistics-based approach revealed correlations between gene alterations and clinical outcomes in many types of cancers. A model-driven mathematical approach has elucidated the dynamic features of cancer networks and identified the mechanisms of drug efficacy and resistance. More recently, machine learning methods have emerged that can be used for mining omics data and classifying patient. However, as the strengths and weaknesses of each method becoming apparent, new analytical tools are emerging to combine and improve the methodologies and maximize their predictive power for classifying cancer subtypes and prognosis. Here, we introduce recent advances in cancer systems biology aimed at personalized medicine, with focus on the receptor tyrosine kinase signaling network.

Stress-induced growth rate reduction restricts metabolic resource utilization to modulate osmo-adaptation time

A near-constant feature of stress responses is a downregulation or arrest of the cell cycle, resulting in transient growth slowdown. To investigate the role of growth slowdown in the hyperosmotic shock response of S. cerevisiae, we perturbed the G1/S checkpoint protein Sic1 to enable osmo-stress response activation with diminished growth slowdown. We document that in this mutant, adaptation to stress is accelerated rather than delayed. This accelerated recovery of the mutant proceeds by liquidation of internal glycogen stores, which are then shunted into the osmo-shock response. Therefore, osmo-adaptation in wild-type cells is delayed because growth slowdown prevents full accessibility to cellular glycogen stores. However, faster adaptation comes at the cost of acute sensitivity to subsequent osmo-stresses. We suggest that stress-induced growth slowdown acts as an arbiter to regulate the resources devoted to osmo-shock, balancing short-term adaptation with long-term robustness.

Rapid and reversible cell volume changes in response to osmotic stress in yeast

Saccharomyces cerevisiae has evolved diverse mechanisms to osmotic changes: the cell wall, ion and water transport systems, and signaling cascades. At the present time, little is known about the mechanisms involved in short-term responses of osmotic stress in yeast or their physiological state during this process. We conducted studies of flow cytometry, wet weight measurements, and electron microscopy to evaluate the modifications in cell volume and the cell wall induced by osmotic stress. In response to osmotic challenges, we show very fast and drastic changes in cell volume (up to 60%), which were completed in less than eight seconds. This dramatic change was completely reversible approximately 16 s after returning to an isosmotic solution. Cell volume changes were also accompanied by adaptations in yeast metabolism observed as a reduction by 50% in the respiratory rate, measured as oxygen consumption. This effect was also fully reversible upon returning to an isosmotic solution. It is noteworthy that we observed a significant recovery in oxygen consumption during the first 10 min of the osmotic shock. The rapid adjustment of the cellular volume may represent an evolutionary advantage, allowing greater flexibility for survival.

Trehalose as an osmolyte in Candidatus Accumulibacter phosphatis

Abstract

Candidatus Accumulibacter phosphatis is an important microorganism for enhanced biological phosphorus removal (EBPR). In a previous study, we found a remarkable flexibility regarding salinity, since this same microorganism could thrive in both freshwater- and seawater-based environments, but the mechanism for the tolerance to saline conditions remained unknown. Here, we identified and described the role of trehalose as an osmolyte in Ca. Accumulibacter phosphatis. A freshwater-adapted culture was exposed to a single batch cycle of hyperosmotic and hypo-osmotic shock, which led to the release of trehalose up to 5.34 mg trehalose/g volatile suspended solids (VSS). Long-term adaptation to 30% seawater-based medium in a sequencing batch reactor (SBR) gave a stable operation with complete anaerobic uptake of acetate and propionate along with phosphate release of 0.73 Pmol/Cmol, and complete aerobic uptake of phosphate. Microbial analysis showed Ca. Accumulibacter phosphatis clade I as the dominant organism in both the freshwater- and seawater-adapted cultures (> 90% presence). Exposure of the seawater-adapted culture to a single batch cycle of hyperosmotic incubation and hypo-osmotic shock led to an increase in trehalose release upon hypo-osmotic shock when higher salinity is used for the hyperosmotic incubation. Maximum trehalose release upon hypo-osmotic shock was achieved after hyperosmotic incubation with 3× salinity increase relative to the salinity in the SBR adaptation reactor, resulting in the release of 11.9 mg trehalose/g VSS. Genome analysis shows the possibility of Ca. Accumulibacter phosphatis to convert glycogen into trehalose by the presence of treX, treY, and treZ genes. Addition of trehalose to the reactor led to its consumption, both during anaerobic and aerobic phases. These results indicate the flexibility of the metabolism of Ca. Accumulibacter phosphatis towards variations in salinity.

Key points

• Trehalose is identified as an osmolyte in Candidatus Accumulibacter phosphatis.

• Ca. Accumulibacter phosphatis can convert glycogen into trehalose.

• Ca. Accumulibacter phosphatis clade I is present and active in both seawater and freshwater.

Electronic supplementary material

The online version of this article (10.1007/s00253-020-10947-8) contains supplementary material, which is available to authorized users.

Diverse Cell Stimulation Kinetics Identify Predictive Signal Transduction Models

Computationally understanding the molecular mechanisms that give rise to cell signaling responses upon different environmental, chemical, and genetic perturbations is a long-standing challenge that requires models that fit and predict quantitative responses for new biological conditions. Overcoming this challenge depends not only on good models and detailed experimental data but also on the rigorous integration of both. We propose a quantitative framework to perturb and model generic signaling networks using multiple and diverse changing environments (hereafter "kinetic stimulations") resulting in distinct pathway activation dynamics. We demonstrate that utilizing multiple diverse kinetic stimulations better constrains model parameters and enables predictions of signaling dynamics that would be impossible using traditional dose-response or individual kinetic stimulations. To demonstrate our approach, we use experimentally identified models to predict signaling dynamics in normal, mutated, and drug-treated conditions upon multitudes of kinetic stimulations and quantify which proteins and reaction rates are most sensitive to which extracellular stimulations.

Parameterization of mechanistic models from qualitative data using an efficient optimal scaling approach

Quantitative dynamical models facilitate the understanding of biological processes and the prediction of their dynamics. These models usually comprise unknown parameters, which have to be inferred from experimental data. For quantitative experimental data, there are several methods and software tools available. However, for qualitative data the available approaches are limited and computationally demanding. Here, we consider the optimal scaling method which has been developed in statistics for categorical data and has been applied to dynamical systems. This approach turns qualitative variables into quantitative ones, accounting for constraints on their relation. We derive a reduced formulation for the optimization problem defining the optimal scaling. The reduced formulation possesses the same optimal points as the established formulation but requires less degrees of freedom. Parameter estimation for dynamical models of cellular pathways revealed that the reduced formulation improves the robustness and convergence of optimizers. This resulted in substantially reduced computation times. We implemented the proposed approach in the open-source Python Parameter EStimation TOolbox (pyPESTO) to facilitate reuse and extension. The proposed approach enables efficient parameterization of quantitative dynamical models using qualitative data.

Optimal Design of Single-Cell Experiments within Temporally Fluctuating Environments

Modern biological experiments are becoming increasingly complex, and designing these experiments to yield the greatest possible quantitative insight is an open challenge. Increasingly, computational models of complex stochastic biological systems are being used to understand and predict biological behaviors or to infer biological parameters. Such quantitative analyses can also help to improve experiment designs for particular goals, such as to learn more about specific model mechanisms or to reduce prediction errors in certain situations. A classic approach to experiment design is to use the Fisher information matrix (FIM), which quantifies the expected information a particular experiment will reveal about model parameters. The Finite State Projection based FIM (FSP-FIM) was recently developed to compute the FIM for discrete stochastic gene regulatory systems, whose complex response distributions do not satisfy standard assumptions of Gaussian variations. In this work, we develop the FSP-FIM analysis for a stochastic model of stress response genes in S. cerevisae under time-varying MAPK induction. We verify this FSP-FIM analysis and use it to optimize the number of cells that should be quantified at particular times to learn as much as possible about the model parameters. We then extend the FSP-FIM approach to explore how different measurement times or genetic modifications help to minimize uncertainty in the sensing of extracellular environments, and we experimentally validate the FSP-FIM to rank single-cell experiments for their abilities to minimize estimation uncertainty of NaCl concentrations during yeast osmotic shock. This work demonstrates the potential of quantitative models to not only make sense of modern biological data sets, but to close the loop between quantitative modeling and experimental data collection.

Genomic Insights Into Sugar Adaptation in an Extremophile Yeast Zygosaccharomyces rouxii

The osmotolerant Zygosaccharomyces rouxii is known for its trait to survive in extreme high sugar environments. This ability determines its role in the fermentation process and leads to yeast spoilage in the food industry. However, our knowledge of the gene expression in response to high sugar stress remains limited. Here, we conducted RNA-sequencing (RNA-seq) under different sugar concentrations of the spoilage yeast, Z. rouxii, which exhibit extremely high tolerance to sugar stress. The obtained differentially expressed genes (DEGs) are significantly different to that of the Saccharomyces cerevisiae, which is sensitive to extreme high sugar stress. Most of the DEGs participated in the “glucan synthesis,” “transmembrane transport,” “ribosome,” etc. In this work, we also demonstrated that the gene ZYRO0B03476g (ZrKAR2) encoding Kar2p can significantly affect the growth of Z. rouxii under high sugar stress. In addition, we combined with a previous study on the genome sequence of Z. rouxii, indicating that several gene families contain significantly more gene copies in the Z. rouxii lineage, which involved in tolerance to sugar stress. Our results provide a gene insight for understanding the high sugar tolerance trait, which may impact food and biotechnological industries and improve the osmotolerance in other organisms.

Thoughts on the evolution of Core Environmental Responses in yeasts

The model yeasts, Saccharomyces cerevisiae and Schizosaccharomyces pombe, display Core Environmental Responses (CERs) that include the induction of a core set of stress genes in response to diverse environmental stresses. CERs underlie the phenomenon of stress cross-protection, whereby exposure to one type of stress can provide protection against subsequent exposure to a second type of stress. CERs have probably arisen through the accumulation, over evolutionary time, of protective anticipatory responses (“adaptive prediction”). CERs have been observed in other evolutionarily divergent fungi but, interestingly, not in the pathogenic yeast, Candida albicans. We argue that this is because we have not looked in the right place. In response to specific host inputs, C. albicans does activate anticipatory responses that protect it against impending attack from the immune system. Therefore, we suggest that C. albicans has evolved a CER that reflects the environmental challenges it faces in host niches.

•

We review Core Environmental Responses (CERs) in domesticated and pathogenic yeasts.

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CERs probably evolved through the accumulation of protective anticipatory responses.

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Evolutionarily diverse yeasts display CERs, but the pathogen, Candida albicans, does not.

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C. albicans has evolved an alternative CER that protects against immune clearance.

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This has implications for the investigation of CERs in other fungi.

A scalable method for parameter-free simulation and validation of mechanistic cellular signal transduction network models

The metabolic modelling community has established the gold standard for bottom-up systems biology with reconstruction, validation and simulation of mechanistic genome-scale models. Similar methods have not been established for signal transduction networks, where the representation of complexes and internal states leads to scalability issues in both model formulation and execution. While rule- and agent-based methods allow efficient model definition and execution, respectively, model parametrisation introduces an additional layer of uncertainty due to the sparsity of reliably measured parameters. Here, we present a scalable method for parameter-free simulation of mechanistic signal transduction networks. It is based on rxncon and uses a bipartite Boolean logic with separate update rules for reactions and states. Using two generic update rules, we enable translation of any rxncon model into a unique Boolean model, which can be used for network validation and simulation-allowing the prediction of system-level function directly from molecular mechanistic data. Through scalable model definition and simulation, and the independence of quantitative parameters, it opens up for simulation and validation of mechanistic genome-scale models of signal transduction networks.

A genetic toolkit for the analysis of metabolic changes in Drosophila provides new insights into metabolic responses to stress and malignant transformation

Regulation of the energetic metabolism occurs fundamentally at the cellular level, so analytical strategies must aim to attain single cell resolution to fully embrace its inherent complexity. We have developed methods to utilize a toolset of metabolic FRET sensors for assessing lactate, pyruvate and 2-oxoglutarate levels of Drosophila tissues in vivo by imaging techniques. We show here how the energetic metabolism is altered by hypoxia: While some larval tissues respond to low oxygen levels by executing a metabolic switch towards lactic fermentation, the fat body and salivary glands do not alter their energetic metabolism. Analysis of tumor metabolism revealed that depending on the genetic background, some tumors undergo a lactogenic switch typical of the Warburg effect, while other tumors do not. This toolset allows for developmental and physiologic studies in genetically manipulated Drosophila individuals in vivo.

Hierarchical optimization for the efficient parametrization of ODE models

Motivation

Mathematical models are nowadays important tools for analyzing dynamics of cellular processes. The unknown model parameters are usually estimated from experimental data. These data often only provide information about the relative changes between conditions, hence, the observables contain scaling parameters. The unknown scaling parameters and corresponding noise parameters have to be inferred along with the dynamic parameters. The nuisance parameters often increase the dimensionality of the estimation problem substantially and cause convergence problems.

Results

In this manuscript, we propose a hierarchical optimization approach for estimating the parameters for ordinary differential equation (ODE) models from relative data. Our approach restructures the optimization problem into an inner and outer subproblem. These subproblems possess lower dimensions than the original optimization problem, and the inner problem can be solved analytically. We evaluated accuracy, robustness and computational efficiency of the hierarchical approach by studying three signaling pathways. The proposed approach achieved better convergence than the standard approach and required a lower computation time. As the hierarchical optimization approach is widely applicable, it provides a powerful alternative to established approaches.

Availability and implementation

The code is included in the MATLAB toolbox PESTO which is available at http://github.com/ICB-DCM/PESTO.

Supplementary information

Supplementary data are available at Bioinformatics online.

Osmolyte homeostasis controls single-cell growth rate and maximum cell size of Saccharomyces cerevisiae

Cell growth is well described at the population level, but precisely how nutrient and water uptake and cell wall expansion drive the growth of single cells is poorly understood. Supported by measurements of single-cell growth trajectories and cell wall elasticity, we present a single-cell growth model for yeast. The model links the thermodynamic quantities, such as turgor pressure, osmolarity, cell wall elasto-plasticity, and cell size, applying concepts from rheology and thin shell theory. It reproduces cell size dynamics during single-cell growth, budding, and hyper-osmotic or hypo-osmotic stress. We find that single-cell growth rate and final size are primarily governed by osmolyte uptake and consumption, while bud expansion requires additionally different cell wall extensibilities between mother and bud. Based on first principles the model provides a more accurate description of size dynamics than previous attempts and its analytical simplification allows for easy combination with models for other cell processes.

Efficient parameterization of large-scale dynamic models based on relative measurements

MOTIVATION

Mechanistic models of biochemical reaction networks facilitate the quantitative understanding of biological processes and the integration of heterogeneous datasets. However, some biological processes require the consideration of comprehensive reaction networks and therefore large-scale models. Parameter estimation for such models poses great challenges, in particular when the data are on a relative scale.

RESULTS

Here, we propose a novel hierarchical approach combining (i) the efficient analytic evaluation of optimal scaling, offset and error model parameters with (ii) the scalable evaluation of objective function gradients using adjoint sensitivity analysis. We evaluate the properties of the methods by parameterizing a pan-cancer ordinary differential equation model (>1000 state variables, >4000 parameters) using relative protein, phosphoprotein and viability measurements. The hierarchical formulation improves optimizer performance considerably. Furthermore, we show that this approach allows estimating error model parameters with negligible computational overhead when no experimental estimates are available, providing an unbiased way to weight heterogeneous data. Overall, our hierarchical formulation is applicable to a wide range of models, and allows for the efficient parameterization of large-scale models based on heterogeneous relative measurements.

AVAILABILITY AND IMPLEMENTATION

Supplementary code and data are available online at http://doi.org/10.5281/zenodo.3254429 and http://doi.org/10.5281/zenodo.3254441.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Yeast Systems Biology: Model Organism and Cell Factory

For thousands of years, the yeast Saccharomyces cerevisiae (S. cerevisiae) has served as a cell factory for the production of bread, beer, and wine. In more recent years, this yeast has also served as a cell factory for producing many different fuels, chemicals, food ingredients, and pharmaceuticals. S. cerevisiae, however, has also served as a very important model organism for studying eukaryal biology, and even today many new discoveries, important for the treatment of human diseases, are made using this yeast as a model organism. Here a brief review of the use of S. cerevisiae as a model organism for studying eukaryal biology, its use as a cell factory, and how advances in systems biology underpin developments in both these areas, is provided.

Transcriptome Kinetics of Saccharomyces cerevisiae in Response to Viral Killer Toxin K1

The K1 A/B toxin secreted by virus-infected Saccharomyces cerevisiae strains kills sensitive cells via disturbance of cytoplasmic membrane functions. Despite decades of research, the mechanisms underlying K1 toxicity and immunity have not been elucidated yet. In a novel approach, this study aimed to characterize transcriptome changes in K1-treated sensitive yeast cells in a time-dependent manner. Global transcriptional profiling revealed substantial cellular adaptations in target cells resulting in 1,189 differentially expressed genes in total. Killer toxin K1 induced oxidative, cell wall and hyperosmotic stress responses as well as rapid down-regulation of transcription and translation. Essential pathways regulating energy metabolism were also significantly affected by the toxin. Remarkably, a futile cycle of the osmolytes trehalose and glycogen was identified probably representing a critical feature of K1 intoxication. In silico analysis suggested several transcription factors involved in toxin-triggered signal transduction. The identified transcriptome changes provide valuable hints to illuminate the still unknown molecular events leading to K1 toxicity and immunity implicating an evolutionarily conserved response at least initially counteracting ionophoric toxin action.

Microfluidic and computational study of structural properties and resistance to flow of blood clots under arterial shear

The ability of a blood clot to modulate blood flow is determined by the clot’s resistance, which depends on its structural features. For a flow with arterial shear, we investigated the characteristic patterns relating to clot shape, size, and composition on the one hand, and its viscous resistance, intraclot axial flow velocity, and shear distributions on the other. We used microfluidic technology to measure the kinetics of platelet, thrombin, and fibrin accumulation at a thrombogenic surface coated with collagen and tissue factor (TF), the key clot-formation trigger. We subsequently utilized the obtained data to perform additional calibration and validation of a detailed computational fluid dynamics model of spatial clot growth under flow. We then ran model simulations to gain insights into the resistance of clots formed under our experimental conditions. We found that increased thrombogenic surface length and TF surface density enhanced the bulk thrombin and fibrin generation in a nonadditive, synergistic way. The height of the platelet deposition domain—and, therefore, clot occlusivity—was rather robust to thrombogenic surface length and TF density variations, but consistently increased with time. Clot viscous resistance was non-uniform and tended to be higher in the fibrin-rich, inner “core” region of the clot. Interestingly, despite intraclot structure and viscous resistance variations, intraclot flow velocity variations were minor compared to the abrupt decrease in flow velocity around the platelet deposition region. Our results shed new light on the connection between the structure of clots under arterial shear and spatiotemporal variations in their resistance to flow.

Signaling pathways in context

The last decade has seen a rise in the development of methods and models to analyze cellular networks on all levels. The applications of this knowledge are, however, often confined to specifics of the network in concrete conditions and leveraging it is hampered by the lack of information about this context and its implications on the system. While not all cellular networks have been deciphered yet, even for well-studied networks their versatility in different contexts is barely considered. Here, we focus on challenges and potentials when integrating signaling networks into their encompassing structures. We highlight three different consequences of this process: a) its fundamental importance for whole-cell and large-scale models, b) significant changes in contextual behavior imposed on entire systems by genetic variations, and c) species-specific conservation or divergence of signaling motifs can give important clues on how to handle cellular context. While important studies have been conducted on these topics to some extent, an increased focus on developing and exploiting solutions for integrative contextualization should turn out as a fruitful path for both theoretical and experimental research.

Adaptive information processing of network modules to dynamic and spatial stimuli

BACKGROUND

Adaptation and homeostasis are basic features of information processing in cells and seen in a broad range of contexts. Much of the current understanding of adaptation in network modules/motifs is based on their response to simple stimuli. Recently, there have also been studies of adaptation in dynamic stimuli. However a broader synthesis of how different circuits of adaptation function, and which circuits enable a broader adaptive behaviour in classes of more complex and spatial stimuli is largely missing.

RESULTS

We study the response of a variety of adaptive circuits to time-varying stimuli such as ramps, periodic stimuli and static and dynamic spatial stimuli. We find that a variety of responses can be seen in ramp stimuli, making this a basis for discriminating between even similar circuits. We also find that a number of circuits adapt exactly to ramp stimuli, and dissect these circuits to pinpoint what characteristics (architecture, feedback, biochemical aspects, information processing ingredients) allow for this. These circuits include incoherent feedforward motifs, inflow-outflow motifs and transcritical circuits. We find that changes in location in such circuits where a signal acts can result in non-adaptive behaviour in ramps, even though the location was associated with exact adaptation in step stimuli. We also demonstrate that certain augmentations of basic inflow-outflow motifs can alter the behaviour of the circuit from exact adaptation to non-adaptive behaviour. When subject to periodic stimuli, some circuits (inflow-outflow motifs and transcritical circuits) are able to maintain an average output independent of the characteristics of the input. We build on this to examine the response of adaptive circuits to static and dynamic spatial stimuli. We demonstrate how certain circuits can exhibit a graded response in spatial static stimuli with an exact maintenance of the spatial mean-value. Distinct features which emerge from the consideration of dynamic spatial stimuli are also discussed. Finally, we also build on these results to show how different circuits which show any combination of presence or absence of exact adaptation in ramps, exact mainenance of time average output in periodic stimuli and exact maintenance of spatial average of output in static spatial stimuli may be realized.

CONCLUSIONS

By studying a range of network circuits/motifs on one hand and a range of stimuli on the other, we isolate characteristics of these circuits (structural) which enable different degrees of exact adaptive and homeostatic behaviour in such stimuli, how they may be combined, and also identify cases associated with non-homeostatic behaviour. We also reveal constraints associated with locations where signals may act to enable homeostatic behaviour and constraints associated with augmentations of circuits. This consideration of multiple experimentally/naturally relevant stimuli along with circuits of adaptation of relevance in natural and engineered biology, provides a platform for deepening our understanding of adaptive and homeostatic behaviour in natural systems, bridging the gap between models of adaptation and experiments and in engineering homeostatic synthetic circuits.

Label-free spatio-temporal monitoring of cytosolic mass, osmolarity, and volume in living cells

Microorganisms adapt their biophysical properties in response to changes in their local environment. However, quantifying these changes at the single-cell level has only recently become possible, largely relying on fluorescent labeling strategies. In this work, we utilize yeast (Saccharomyces cerevisiae) to demonstrate label-free quantification of changes in both intracellular osmolarity and macromolecular concentration in response to changes in the local environment. By combining a digital holographic microscope with a millifluidic chip, the temporal response of cellular water flux was successfully isolated from the rate of production of higher molecular weight compounds, in addition to identifying the produced compounds in terms of the product of their refractive index increment [Formula: see text] and molar mass. The ability to identify, quantify and temporally resolve multiple biophysical processes in living cells at the single cell level offers a crucial complement to label-based strategies, suggesting broad applicability in studies of a wide-range of cellular processes.

Osmolytes resist against harsh osmolarity: Something old something new

From the halophilic bacteria to human, cells have to survive under the stresses of harsh environments. Hyperosmotic stress is a process that triggers cell shrinkage, oxidative stress, DNA damage, and apoptosis and it potentially contributes to a number of human diseases. Remarkably, by high salts and organic solutes concentrations, a variety of organisms struggle with these conditions. Different strategies have been developed for cellular osmotic adaptations among which organic osmolyte synthesis/accumulation is a conserved once. Osmolytes are naturally occurring solutes used by cells of several halophilic (micro) organisms to preserve cell volume and function. In this review, the osmolytes diversity and their protective roles in harsh hyperosmolar environments from bacteria to human cells are highlighted. Moreover, it provides a close look at mammalian kidney osmoregulation at a molecular level. This review provides a concise view on the recent developments and advancements on the applications of osmolytes. Identification of disease-related osmolytes and their targeted-delivery may be used as a therapeutic measurement for treatment of the pathological conditions and the inherited diseases related to protein misfolding and aggregation. The molecular and cellular aspects of cell adaptation against harsh environmental osmolarity will benefit the development of effective drugs for many diseases.