Signal-dependent dynamics of transcription factor translocation controls gene expression

Single-cell stimulus-response gene expression trajectories reveal the stimulus specificities of dynamic responses by single macrophages

Macrophages induce the expression of hundreds of genes in response to immune threats. However, current technology limits our ability to capture single-cell inducible gene expression dynamics. Here, we generated high-resolution time series single-cell RNA sequencing (scRNA-seq) data from mouse macrophages responding to six stimuli, and imputed ensembles of real-time single-cell gene expression trajectories (scGETs). We found that dynamic information contained in scGETs substantially contributes to macrophage stimulus-response specificity (SRS). Dynamic information also identified correlations between immune response genes, indicating biological coordination. Furthermore, we showed that the microenvironmental context of polarizing cytokines profoundly affects scGETs, such that measuring response dynamics offered clearer discrimination of the polarization state of individual macrophage cells than single time-point measurements. Our findings highlight the important contribution of dynamic information contained in the single-cell expression responses of immune genes in characterizing the SRS and functional states of macrophages.

Dynamic phosphorylation of Hcm1 promotes fitness in chronic stress

Cell survival depends upon the ability to adapt to changing environments. Environmental stressors trigger an acute stress response program that rewires cell physiology, downregulates proliferation genes and pauses the cell cycle until the cell adapts. Here, we show that dynamic phosphorylation of the yeast cell cycle-regulatory transcription factor Hcm1 is required to maintain fitness in chronic stress. Hcm1 is activated by cyclin dependent kinase (CDK) and inactivated by the phosphatase calcineurin (CN) in response to stressors that signal through increases in cytosolic Ca2+. Expression of a constitutively active, phosphomimetic Hcm1 mutant reduces fitness in stress, suggesting Hcm1 inactivation is required. However, a comprehensive analysis of Hcm1 phosphomutants revealed that Hcm1 activity is also important to survive stress, demonstrating that Hcm1 activity must be toggled on and off to promote gene expression and fitness. These results suggest that dynamic control of cell cycle regulators is critical for survival in stressful environments.

Enhanced cellular longevity arising from environmental fluctuations

Cellular longevity is regulated by both genetic and environmental factors. However, the interactions of these factors in the context of aging remain largely unclear. Here, we formulate a mathematical model for dynamic glucose modulation of a core gene circuit in yeast aging, which not only guided the design of pro-longevity interventions but also revealed the theoretical principles underlying these interventions. We introduce the dynamical systems theory to capture two general means for promoting longevity-the creation of a stable fixed point in the "healthy" state of the cell and the "dynamic stabilization" of the system around this healthy state through environmental oscillations. Guided by the model, we investigate how both of these can be experimentally realized by dynamically modulating environmental glucose levels. The results establish a paradigm for theoretically analyzing the trajectories and perturbations of aging that can be generalized to aging processes in diverse cell types and organisms.

Quantifying redox transcription factor dynamics as a tool to investigate redox signalling

A critical feature of the cellular antioxidant response is the induction of gene expression by redox-sensitive transcription factors. In many cells, activating these transcription factors is a dynamic process involving multiple redox steps, but it is unclear how these dynamics should be measured. Here, we show how the dynamic profile of the Schizosaccharomyces pombe Pap1 transcription factor is quantifiable by three parameters: signal amplitude, signal time and signal duration. In response to increasing hydrogen peroxide concentrations, the Pap1 amplitude decreased while the signal time and duration showed saturable increases. In co-response plots, these parameters showed a complex, non-linear relationship to the mRNA levels of four Pap1-regulated genes. We also demonstrate that hydrogen peroxide and tert-butyl hydroperoxide trigger quantifiably distinct Pap1 activation profiles and transcriptional responses. Based on these findings, we propose that different oxidants and oxidant concentrations modulate the Pap1 dynamic profile, leading to specific transcriptional responses. We further show how the effect of combination and pre-exposure stresses on Pap1 activation dynamics can be quantified using this approach. This method is therefore a valuable addition to the redox signalling toolbox that may illuminate the role of dynamics in determining appropriate responses to oxidative stress.

Solving stochastic gene-expression models using queueing theory: A tutorial review

Stochastic models of gene expression are typically formulated using the chemical master equation, which can be solved exactly or approximately using a repertoire of analytical methods. Here, we provide a tutorial review of an alternative approach based on queueing theory that has rarely been used in the literature of gene expression. We discuss the interpretation of six types of infinite-server queues from the angle of stochastic single-cell biology and provide analytical expressions for the stationary and nonstationary distributions and/or moments of mRNA/protein numbers and bounds on the Fano factor. This approach may enable the solution of complex models that have hitherto evaded analytical solution.

Quantifying and correcting bias in transcriptional parameter inference from single-cell data

The snapshot distribution of mRNA counts per cell can be measured using single-molecule fluorescence in situ hybridization or single-cell RNA sequencing. These distributions are often fit to the steady-state distribution of the two-state telegraph model to estimate the three transcriptional parameters for a gene of interest: mRNA synthesis rate, the switching on rate (the on state being the active transcriptional state), and the switching off rate. This model assumes no extrinsic noise, i.e., parameters do not vary between cells, and thus estimated parameters are to be understood as approximating the average values in a population. The accuracy of this approximation is currently unclear. Here, we develop a theory that explains the size and sign of estimation bias when inferring parameters from single-cell data using the standard telegraph model. We find specific bias signatures depending on the source of extrinsic noise (which parameter is most variable across cells) and the mode of transcriptional activity. If gene expression is not bursty then the population averages of all three parameters are overestimated if extrinsic noise is in the synthesis rate; underestimation occurs if extrinsic noise is in the switching on rate; both underestimation and overestimation can occur if extrinsic noise is in the switching off rate. We find that some estimated parameters tend to infinity as the size of extrinsic noise approaches a critical threshold. In contrast when gene expression is bursty, we find that in all cases the mean burst size (ratio of the synthesis rate to the switching off rate) is overestimated while the mean burst frequency (the switching on rate) is underestimated. We estimate the size of extrinsic noise from the covariance matrix of sequencing data and use this together with our theory to correct published estimates of transcriptional parameters for mammalian genes.

FOXO transcription factors as mediators of stress adaptation

The forkhead box protein O (FOXO, consisting of FOXO1, FOXO3, FOXO4 and FOXO6) transcription factors are the mammalian orthologues of Caenorhabditis elegans DAF-16, which gained notoriety for its capability to double lifespan in the absence of daf-2 (the gene encoding the worm insulin receptor homologue). Since then, research has provided many mechanistic details on FOXO regulation and FOXO activity. Furthermore, conditional knockout experiments have provided a wealth of data as to how FOXOs control development and homeostasis at the organ and organism levels. The lifespan-extending capabilities of DAF-16/FOXO are highly correlated with their ability to induce stress response pathways. Exogenous and endogenous stress, such as cellular redox stress, are considered the main drivers of the functional decline that characterizes ageing. Functional decline often manifests as disease, and decrease in FOXO activity indeed negatively impacts on major age-related diseases such as cancer and diabetes. In this context, the main function of FOXOs is considered to preserve cellular and organismal homeostasis, through regulation of stress response pathways. Paradoxically, the same FOXO-mediated responses can also aid the survival of dysfunctional cells once these eventually emerge. This general property to control stress responses may underlie the complex and less-evident roles of FOXOs in human lifespan as opposed to model organisms such as C. elegans.

Biphasic amplitude oscillator characterized by distinct dynamics of trough and crest

Biphasic amplitude dynamics (BAD) of oscillation have been observed in many biological systems. However, the specific topology structure and regulatory mechanisms underlying these biphasic amplitude dynamics remain elusive. Here, we searched all possible two-node circuit topologies and identified the core oscillator that enables robust oscillation. This core oscillator consists of a negative feedback loop between two nodes and a self-positive feedback loop of the input node, which result in the fast and slow dynamics of the two nodes, thereby achieving relaxation oscillation. Landscape theory was employed to study the stochastic dynamics and global stability of the system, allowing us to quantitatively describe the diverse positions and sizes of the Mexican hat. With increasing input strength, the size of the Mexican hat exhibits a gradual increase followed by a subsequent decrease. The self-activation of input node and the negative feedback on input node, which dominate the fast dynamics of the input node, were observed to regulate BAD in a bell-shaped manner. Both deterministic and statistical analysis results reveal that BAD is characterized by the linear and nonlinear dependence of the oscillation trough and crest on the input strength. In addition, combining with computational and theoretical analysis, we addressed that the linear response of trough to input is predominantly governed by the negative feedback, while the nonlinear response of crest is jointly regulated by the negative feedback loop and the self-positive feedback loop within the oscillator. Overall, this study provides a natural and physical basis for comprehending the occurrence of BAD in oscillatory systems, yielding guidance for the design of BAD in synthetic biology applications.

Optogenetic control of YAP reveals a dynamic communication code for stem cell fate and proliferation

YAP is a transcriptional regulator that controls pluripotency, cell fate, and proliferation. How cells ensure the selective activation of YAP effector genes is unknown. This knowledge is essential to rationally control cellular decision-making. Here we leverage optogenetics, live-imaging of transcription, and cell fate analysis to understand and control gene activation and cell behavior. We reveal that cells decode the steady-state concentrations and timing of YAP activation to control proliferation, cell fate, and expression of the pluripotency regulators Oct4 and Nanog. While oscillatory YAP inputs induce Oct4 expression and proliferation optimally at frequencies that mimic native dynamics, cellular differentiation requires persistently low YAP levels. We identify the molecular logic of the Oct4 dynamic decoder, which acts through an adaptive change sensor. Our work reveals how YAP levels and dynamics enable multiplexing of information transmission for the regulation of developmental decision-making and establishes a platform for the rational control of these behaviors.

Quantifying dynamic pro-inflammatory gene expression and heterogeneity in single macrophage cells

Macrophages must respond appropriately to pathogens and other pro-inflammatory stimuli in order to perform their roles in fighting infection. One way in which inflammatory stimuli can vary is in their dynamics-that is, the amplitude and duration of stimulus experienced by the cell. In this study, we performed long-term live cell imaging in a microfluidic device to investigate how the pro-inflammatory genes IRF1, CXCL10, and CXCL9 respond to dynamic interferon-gamma (IFNγ) stimulation. We found that IRF1 responds to low concentration or short duration IFNγ stimulation, whereas CXCL10 and CXCL9 require longer or higherconcentration stimulation to be expressed. We also investigated the heterogeneity in the expression of each gene and found that CXCL10 and CXCL9 have substantial cell-to-cell variability. In particular, the expression of CXCL10 appears to be largely stochastic with a subpopulation of nonresponding cells across all the stimulation conditions tested. We developed both deterministic and stochastic models for the expression of each gene. Our modeling analysis revealed that the heterogeneity in CXCL10 can be attributed to a slow chromatin-opening step that is on a similar timescale to that of adaptation of the upstream signal. In this way, CXCL10 expression in individual cells can remain stochastic in response to each pulse of repeated stimulation, which we also validated by experiments. Together, we conclude that pro-inflammatory genes in the same signaling pathway can respond to dynamic IFNγ stimulus with very different response features and that upstream signal adaptation can contribute to shaping heterogeneous gene expression.

BioNetGMMFit: estimating parameters of a BioNetGen model from time-stamped snapshots of single cells

Mechanistic models are commonly employed to describe signaling and gene regulatory kinetics in single cells and cell populations. Recent advances in single-cell technologies have produced multidimensional datasets where snapshots of copy numbers (or abundances) of a large number of proteins and mRNA are measured across time in single cells. The availability of such datasets presents an attractive scenario where mechanistic models are validated against experiments, and estimated model parameters enable quantitative predictions of signaling or gene regulatory kinetics. To empower the systems biology community to easily estimate parameters accurately from multidimensional single-cell data, we have merged a widely used rule-based modeling software package BioNetGen, which provides a user-friendly way to code for mechanistic models describing biochemical reactions, and the recently introduced CyGMM, that uses cell-to-cell differences to improve parameter estimation for such networks, into a single software package: BioNetGMMFit. BioNetGMMFit provides parameter estimates of the model, supplied by the user in the BioNetGen markup language (BNGL), which yield the best fit for the observed single-cell, time-stamped data of cellular components. Furthermore, for more precise estimates, our software generates confidence intervals around each model parameter. BioNetGMMFit is capable of fitting datasets of increasing cell population sizes for any mechanistic model specified in the BioNetGen markup language. By streamlining the process of developing mechanistic models for large single-cell datasets, BioNetGMMFit provides an easily-accessible modeling framework designed for scale and the broader biochemical signaling community.

Dynamical Information Synergy in Biochemical Signaling Networks

Biological cells encode information about their environment through biochemical signaling networks that control their internal state and response. This information is often encoded in the dynamical patterns of the signaling molecules, rather than just their instantaneous concentrations. Here, we analytically calculate the information contained in these dynamics for a number of paradigmatic cases in the linear regime, for both static and time-dependent input signals. When considering oscillatory output dynamics, we report on the emergence of synergy between successive measurements, meaning that the joint information in two measurements exceeds the sum of the individual information. We extend our analysis numerically beyond the scope of linear input encoding to reveal synergetic effects in the cases of frequency or damping modulation, both of which are relevant to classical biochemical signaling systems.

High-throughput functional characterization of combinations of transcriptional activators and repressors

Despite growing knowledge of the functions of individual human transcriptional effector domains, much less is understood about how multiple effector domains within the same protein combine to regulate gene expression. Here, we measure transcriptional activity for 8,400 effector domain combinations by recruiting them to reporter genes in human cells. In our assay, weak and moderate activation domains synergize to drive strong gene expression, whereas combining strong activators often results in weaker activation. In contrast, repressors combine linearly and produce full gene silencing, and repressor domains often overpower activation domains. We use this information to build a synthetic transcription factor whose function can be tuned between repression and activation independent of recruitment to target genes by using a small-molecule drug. Altogether, we outline the basic principles of how effector domains combine to regulate gene expression and demonstrate their value in building precise and flexible synthetic biology tools. A record of this paper's transparent peer review process is included in the supplemental information.

NFAT and NF-κB dynamically co-regulate TCR and CAR signaling responses in human T cells

While it has been established that the responses of T cells to antigens are combinatorially regulated by multiple signaling pathways, it remains elusive what mechanisms cells utilize to quantitatively modulate T cell responses during pathway integration. Here, we show that two key pathways in T cell signaling, calcium/nuclear factor of activated T cells (NFAT) and protein kinase C (PKC)/nuclear factor κB (NF-κB), integrate through a dynamic and combinatorial strategy to fine-tune T cell response genes. At the cis-regulatory level, the two pathways integrate through co-binding of NFAT and NF-κB to immune response genes. Pathway integration is further regulated temporally, where T cell receptor (TCR) and chimeric antigen receptor (CAR) activation signals modulate the temporal relationships between the nuclear localization dynamics of NFAT and NF-κB. Such physical and temporal integrations together contribute to distinct modes of expression modulation for genes. Thus, the temporal relationships between regulators can be modulated to affect their co-targets during immune responses, underscoring the importance of dynamic combinatorial regulation in cellular signaling.

Coupled oscillator cooperativity as a control mechanism in chronobiology

Control of dynamical processes is vital for maintaining correct cell regulation and cell-fate decisions. Numerous regulatory networks show oscillatory behavior; however, our knowledge of how one oscillator behaves when stimulated by two or more external oscillatory signals is still missing. We explore this problem by constructing a synthetic oscillatory system in yeast and stimulate it with two external oscillatory signals. Letting model verification and prediction operate in a tight interplay with experimental observations, we find that stimulation with two external signals expands the plateau of entrainment and reduces the fluctuations of oscillations. Furthermore, by adjusting the phase differences of external signals, one can control the amplitude of oscillations, which is understood through the signal delay of the unperturbed oscillatory network. With this we reveal a direct amplitude dependency of downstream gene transcription. Taken together, these results suggest a new path to control oscillatory systems by coupled oscillator cooperativity.

Transcription factor localization dynamics and DNA binding drive distinct promoter interpretations

Environmental information may be encoded in the temporal dynamics of transcription factor (TF) activation and subsequently decoded by gene promoters to enact stimulus-specific gene expression programs. Previous studies of this behavior focused on the encoding and decoding of information in TF nuclear localization dynamics, yet cells control the activity of TFs in myriad ways, including by regulating their ability to bind DNA. Here, we use light-controlled mutants of the yeast TF Msn2 as a model system to investigate how promoter decoding of TF localization dynamics is affected by changes in the ability of the TF to bind DNA. We find that yeast promoters directly decode the light-controlled localization dynamics of Msn2 and that the effects of changing Msn2 affinity on that decoding behavior are highly promoter dependent, illustrating how cells could regulate TF localization dynamics and DNA binding in concert for improved control of gene expression.

Remembering through the genome: the role of chromatin states in brain functions and diseases

Chromatin is the physical substrate of the genome that carries the DNA sequence and ensures its proper functions and regulation in the cell nucleus. While a lot is known about the dynamics of chromatin during programmed cellular processes such as development, the role of chromatin in experience-dependent functions remains not well defined. Accumulating evidence suggests that in brain cells, environmental stimuli can trigger long-lasting changes in chromatin structure and tri-dimensional (3D) organization that can influence future transcriptional programs. This review describes recent findings suggesting that chromatin plays an important role in cellular memory, particularly in the maintenance of traces of prior activity in the brain. Inspired by findings in immune and epithelial cells, we discuss the underlying mechanisms and the implications for experience-dependent transcriptional regulation in health and disease. We conclude by presenting a holistic view of chromatin as potential molecular substrate for the integration and assimilation of environmental information that may constitute a conceptual basis for future research.

FOXO nuclear shuttling dynamics are stimulus-dependent and correspond with cell fate

FOXO transcription factors are regulators of cellular homeostasis linked to increased lifespan and tumor suppression. FOXOs are activated by diverse cell stresses including serum starvation and oxidative stress. FOXO activity is regulated through posttranslational modifications that control shuttling of FOXO proteins to the nucleus. In the nucleus, FOXOs up-regulate genes in multiple, often conflicting pathways, including cell-cycle arrest and apoptosis. How cells control FOXO activity to ensure the proper response for a given stress is an open question. Using quantitative immunofluorescence and live-cell imaging, we found that the dynamics of FOXO nuclear shuttling is stimulus-dependent and corresponds with cell fate. H2O2 treatment leads to an all-or-none response where some cells show no nuclear FOXO accumulation, while other cells show a strong nuclear FOXO signal. The time that FOXO remains in the nucleus increases with the dose and is linked with cell death. In contrast, serum starvation causes low-amplitude pulses of nuclear FOXO and predominantly results in cell-cycle arrest. The accumulation of FOXO in the nucleus is linked with low AKT activity for both H2O2 and serum starvation. Our findings suggest the dynamics of FOXO nuclear shuttling is one way in which the FOXO pathway dictates different cellular outcomes.

Modulation of transcription factor dynamics allows versatile information transmission

Cells detect changes in their environment and generate responses, often involving changes in gene expression. In this paper we use information theory and a simple transcription model to analyze whether the resulting gene expression serves to identify extracellular stimuli and assess their intensity when they are encoded in the amplitude, duration or frequency of pulses of a transcription factor's nuclear concentration (or activation state). We find, for all cases, that about three ranges of input strengths can be distinguished and that maximum information transmission occurs for fast and high activation threshold promoters. The three input modulation modes differ in the sensitivity to changes in the promoters parameters. Frequency modulation is the most sensitive and duration modulation, the least. This is key for signal identification: there are promoter parameters that yield a relatively high information transmission for duration or amplitude modulation and a much smaller value for frequency modulation. The reverse situation cannot be found with a single promoter transcription model. Thus, pulses of transcription factors can selectively activate the "frequency-tuned" promoter while prolonged nuclear accumulation would activate promoters of all three modes simultaneously. Frequency modulation is therefore highly selective and better suited than the other encoding modes for signal identification without requiring other mediators of the transduction process.

Resonant learning in scale-free networks

Large networks of interconnected components, such as genes or machines, can coordinate complex behavioral dynamics. One outstanding question has been to identify the design principles that allow such networks to learn new behaviors. Here, we use Boolean networks as prototypes to demonstrate how periodic activation of network hubs provides a network-level advantage in evolutionary learning. Surprisingly, we find that a network can simultaneously learn distinct target functions upon distinct hub oscillations. We term this emergent property resonant learning, as the new selected dynamical behaviors depend on the choice of the period of the hub oscillations. Furthermore, this procedure accelerates the learning of new behaviors by an order of magnitude faster than without oscillations. While it is well-established that modular network architecture can be selected through evolutionary learning to produce different network behaviors, forced hub oscillations emerge as an alternative evolutionary learning strategy for which network modularity is not necessarily required.

A transcriptional activator from Rhizophagus irregularis regulates phosphate uptake and homeostasis in AM symbiosis during phosphorous starvation

Introduction

Phosphorus (P) is one of the most important nutrient elements for plant growth and development. Under P starvation, arbuscular mycorrhizal (AM) fungi can promote phosphate (Pi) uptake and homeostasis within host plants. However, the underlying mechanisms by which AM fungal symbiont regulates the AM symbiotic Pi acquisition from soil under P starvation are largely unknown. Here, we identify a HLH domain containing transcription factor RiPho4 from Rhizophagus irregularis.

Methods

To investigate the biological functions of the RiPho4, we combined the subcellular localization and Yeast One-Hybrid (Y1H) experiments in yeasts with gene expression and virus-induced gene silencing approach during AM symbiosis.

Results

The approach during AM symbiosis. The results indicated that RiPho4 encodes a conserved transcription factor among different fungi and is induced during the in planta phase. The transcription of RiPho4 is significantly up-regulated by P starvation. The subcellular localization analysis revealed that RiPho4 is located in the nuclei of yeast cells during P starvation. Moreover, knock-down of RiPho4 inhibits the arbuscule development and mycorrhizal Pi uptake under low Pi conditions. Importantly, RiPho4 can positively regulate the downstream components of the phosphate (PHO) pathway in R. irregularis.

Discussion

In summary, these new findings reveal that RiPho4 acts as a transcriptional activator in AM fungus to maintain arbuscule development and regulate Pi uptake and homeostasis in the AM symbiosis during Pi starvation.

Using the AKAR3-EV biosensor to assess Sch9p- and PKA-signalling in budding yeast

Budding yeast uses the TORC1-Sch9p and cAMP-PKA signalling pathways to regulate adaptations to changing nutrient environments. Dynamic and single-cell measurements of the activity of these cascades will improve our understanding of the cellular adaptation of yeast. Here, we employed the AKAR3-EV biosensor developed for mammalian cells to measure the cellular phosphorylation status determined by Sch9p and PKA activity in budding yeast. Using various mutant strains and inhibitors, we show that AKAR3-EV measures the Sch9p- and PKA-dependent phosphorylation status in intact yeast cells. At the single-cell level, we found that the phosphorylation responses are homogenous for glucose, sucrose, and fructose, but heterogeneous for mannose. Cells that start to grow after a transition to mannose correspond to higher normalized Förster resonance energy transfer (FRET) levels, in line with the involvement of Sch9p and PKA pathways to stimulate growth-related processes. The Sch9p and PKA pathways have a relatively high affinity for glucose (K0.5 of 0.24 mM) under glucose-derepressed conditions. Lastly, steady-state FRET levels of AKAR3-EV seem to be independent of growth rates, suggesting that Sch9p- and PKA-dependent phosphorylation activities are transient responses to nutrient transitions. We believe that the AKAR3-EV sensor is an excellent addition to the biosensor arsenal for illuminating cellular adaptation in single yeast cells.

Quantitative Imaging Analysis of NF-κB for Mathematical Modeling Applications

Mathematical models can integrate different types of experimental datasets, reconstitute biological systems in silico, and identify previously unknown molecular mechanisms. Over the past decade, mathematical models have been developed based on quantitative observations, such as live-cell imaging and biochemical assays. However, it is difficult to directly integrate next-generation sequencing (NGS) data. Although highly dimensional, NGS data mostly only provides a "snapshot" of cellular states. Nevertheless, the development of various methods for NGS analysis has led to much more accurate predictions of transcription factor activity and has revealed various concepts regarding transcriptional regulation. Therefore, fluorescence live-cell imaging of transcription factors can help alleviate the limitations in NGS data by supplementing temporal information, linking NGS to mathematical modeling. This chapter introduces an analytical method for quantifying dynamics of nuclear factor kappaB (NF-κB) which forms aggregates in the nucleus. The method may also be applicable to other transcription factors regulated in a similar fashion.

Impact of Saccharomyces cerevisiae on the Field of Single-Molecule Biophysics

Cellular functions depend on the dynamic assembly of protein regulator complexes at specific cellular locations. Single Molecule Tracking (SMT) is a method of choice for the biochemical characterization of protein dynamics in vitro and in vivo. SMT follows individual molecules in live cells and provides direct information about their behavior. SMT was successfully applied to mammalian models. However, mammalian cells provide a complex environment where protein mobility depends on numerous factors that are difficult to control experimentally. Therefore, yeast cells, which are unicellular and well-studied with a small and completely sequenced genome, provide an attractive alternative for SMT. The simplicity of organization, ease of genetic manipulation, and tolerance to gene fusions all make yeast a great model for quantifying the kinetics of major enzymes, membrane proteins, and nuclear and cellular bodies. However, very few researchers apply SMT techniques to yeast. Our goal is to promote SMT in yeast to a wider research community. Our review serves a dual purpose. We explain how SMT is conducted in yeast cells, and we discuss the latest insights from yeast SMT while putting them in perspective with SMT of higher eukaryotes.

The regulatory mechanism of the yeast osmoresponse under different glucose concentrations

Cells constantly respond to environmental changes by modulating gene expression programs. These responses may demand substantial costs and, thus, affect cell growth. Understanding the regulation of these processes represents a key question in biology and biotechnology. Here, we studied the responses to osmotic stress in glucose-limited environments. By analyzing seventeen osmotic stress-induced genes and stress-activated protein kinase Hog1, we found that cells exhibited stronger osmotic gene expression response and larger integral of Hog1 nuclear localization during adaptation to osmotic stress under glucose-limited conditions than under glucose-rich conditions. We proposed and verified that in glucose-limited environment, glycolysis intermediates (representing "reserve flux") were limited, which required cells to express more glycerol-production enzymes for stress adaptation. Consequently, the regulatory mechanism of osmoresponse was derived in the presence and absence of such reserve flux. Further experiments suggested that this reserve flux-dependent stress-defense strategy may be a general principle under nutrient-limited environments.

Optimal transcriptional regulation of dynamic bacterial responses to sudden drug exposures

Cellular responses to the presence of toxic compounds in their environment require prompt expression of the correct levels of the appropriate enzymes, which are typically regulated by transcription factors that control gene expression for the duration of the response. The characteristics of each response dictate the choice of regulatory parameters such as the affinity of the transcription factor to its binding sites and the strength of the promoters it regulates. Although much is known about the dynamics of cellular responses, we still lack a framework to understand how different regulatory strategies evolved in natural systems relate to the selective pressures acting in each particular case. Here, we analyze a dynamical model of a typical antibiotic response in bacteria, where a transcriptionally repressed enzyme is induced by a sudden exposure to the drug that it processes. We identify strategies of gene regulation that optimize this response for different types of selective pressures, which we define as a set of costs associated with the drug, enzyme, and repressor concentrations during the response. We find that regulation happens in a limited region of the regulatory parameter space. While responses to more costly (toxic) drugs favor the usage of strongly self-regulated repressors, responses where expression of enzyme is more costly favor the usage of constitutively expressed repressors. Only a very narrow range of selective pressures favor weakly self-regulated repressors. We use this framework to determine which costs and benefits are most critical for the evolution of a variety of natural cellular responses that satisfy the approximations in our model and to analyze how regulation is optimized in new environments with different demands.

Transcriptional noise adjusted for expression levels reveals genes with high transcriptional noise that are highly expressed, functionally related, and co-regulated in yeast

Understanding the relationship between variability in single-cell and non-single-cell gene expression studies will aid in understanding the role of and mechanisms that lead to variability in biological systems. Studies on the variation of gene expression levels in yeast normally focus on single cells and use the coefficient of variance (CV) as a measure of noise. The CV is typically negatively correlated with gene expression levels, so most of the studies using yeast find that genes with high transcriptional noise are lowly expressed. We find adjusting noise for expression levels using linear/natural log polynomial, and local fits and analyzing many non-single-cell RNA-seq sets identifies genes with high median transcriptional noise that are different than those that have high median CVs. Interestingly, these genes are heavily regulated by transcription factors that are related to variability and stochastic processes based on single-cell studies, including Msn2p, Msn4p, Hsf1p, and Crz1p but are not associated with genes with high median CVs based on non-single-cell gene expression data. In addition, adjusting noise for expression levels in a single-cell RNA-seq data set adds value by finding genes that have noisy gene expression levels and their associated transcription factors that are not found to be associated with genes with high CVs in the single-cell expression data or a comparable non-single-cell gene expression data. Lastly, S. cerevisiae genes with noisy expression tend to have orthologs with noisy gene expression in C. albicans, indicating transcriptional noise is evolutionarily conserved.

Network design principle for robust oscillatory behaviors with respect to biological noise

Oscillatory behaviors, which are ubiquitous in transcriptional regulatory networks, are often subject to inevitable biological noise. Thus, a natural question is how transcriptional regulatory networks can robustly achieve accurate oscillation in the presence of biological noise. Here, we search all two- and three-node transcriptional regulatory network topologies for those robustly capable of accurate oscillation against the parameter variability (extrinsic noise) or stochasticity of chemical reactions (intrinsic noise). We find that, no matter what source of the noise is applied, the topologies containing the repressilator with positive autoregulation show higher robustness of accurate oscillation than those containing the activator-inhibitor oscillator, and additional positive autoregulation enhances the robustness against noise. Nevertheless, the attenuation of different sources of noise is governed by distinct mechanisms: the parameter variability is buffered by the long period, while the stochasticity of chemical reactions is filtered by the high amplitude. Furthermore, we analyze the noise of a synthetic human nuclear factor κB (NF-κB) signaling network by varying three different topologies and verify that the addition of a repressilator to the activator-inhibitor oscillator, which leads to the emergence of high-robustness motif—the repressilator with positive autoregulation—improves the oscillation accuracy in comparison to the topology with only an activator-inhibitor oscillator. These design principles may be applicable to other oscillatory circuits.

Fungi-on-a-Chip: microfluidic platforms for single-cell studies on fungi

This review highlights new advances in the emerging field of 'Fungi-on-a-Chip' microfluidics for single-cell studies on fungi and discusses several future frontiers, where we envisage microfluidic technology development to be instrumental in aiding our understanding of fungal biology. Fungi, with their enormous diversity, bear essential roles both in nature and our everyday lives. They inhabit a range of ecosystems, such as soil, where they are involved in organic matter degradation and bioremediation processes. More recently, fungi have been recognized as key components of the microbiome in other eukaryotes, such as humans, where they play a fundamental role not only in human pathogenesis, but also likely as commensals. In the food sector, fungi are used either directly or as fermenting agents and are often key players in the biotechnological industry, where they are responsible for the production of both bulk chemicals and antibiotics. Although the macroscopic fruiting bodies are immediately recognizable by most observers, the structure, function, and interactions of fungi with other microbes at the microscopic scale still remain largely hidden. Herein, we shed light on new advances in the emerging field of Fungi-on-a-Chip microfluidic technologies for single-cell studies on fungi. We discuss the development and application of microfluidic tools in the fields of medicine and biotechnology, as well as in-depth biological studies having significance for ecology and general natural processes. Finally, a future perspective is provided, highlighting new frontiers in which microfluidic technology can benefit this field.

Quantifying information of intracellular signaling: progress with machine learning

Cells convey information about their extracellular environment to their core functional machineries. Studying the capacity of intracellular signaling pathways to transmit information addresses fundamental questions about living systems. Here, we review how information-theoretic approaches have been used to quantify information transmission by signaling pathways that are functionally pleiotropic and subject to molecular stochasticity. We describe how recent advances in machine learning have been leveraged to address the challenges of complex temporal trajectory datasets and how these have contributed to our understanding of how cells employ temporal coding to appropriately adapt to environmental perturbations.

Frequency modulation of a bacterial quorum sensing response

In quorum sensing, bacteria secrete or release small molecules into the environment that, once they reach a certain threshold, trigger a behavioural change in the population. As the concentration of these so-called autoinducers is supposed to reflect population density, they were originally assumed to be continuously produced by all cells in a population. However, here we show that in the α-proteobacterium Sinorhizobium meliloti expression of the autoinducer synthase gene is realized in asynchronous stochastic pulses that result from scarcity and, presumably, low binding affinity of the key activator. Physiological cues modulate pulse frequency, and pulse frequency in turn modulates the velocity with which autoinducer levels in the environment reach the threshold to trigger the quorum sensing response. We therefore propose that frequency-modulated pulsing in S. meliloti represents the molecular mechanism for a collective decision-making process in which each cell's physiological state and need for behavioural adaptation is encoded in the pulse frequency with which it expresses the autoinducer synthase gene; the pulse frequencies of all members of the population are then integrated in the common pool of autoinducers, and only once this vote crosses the threshold, the response behaviour is initiated.

Principles, mechanisms and functions of entrainment in biological oscillators

Entrainment is a phenomenon in which two oscillators interact with each other, typically through physical or chemical means, to synchronize their oscillations. This phenomenon occurs in biology to coordinate processes from the molecular to organismal scale. Biological oscillators can be entrained within a single cell, between cells or to an external input. Using six illustrative examples of entrainable biological oscillators, we discuss the distinctions between entrainment and synchrony and explore features that contribute to a system's propensity to entrain. Entrainment can either enhance or reduce the heterogeneity of oscillations within a cell population, and we provide examples and mechanisms of each case. Finally, we discuss the known functions of entrainment and discuss potential functions from an evolutionary perspective.

Synthetic gene networks recapitulate dynamic signal decoding and differential gene expression

Cells live in constantly changing environments and employ dynamic signaling pathways to transduce information about the signals they encounter. However, the mechanisms by which dynamic signals are decoded into appropriate gene expression patterns remain poorly understood. Here, we devise networked optogenetic pathways that achieve dynamic signal processing functions that recapitulate cellular information processing. Exploiting light-responsive transcriptional regulators with differing response kinetics, we build a falling edge pulse detector and show that this circuit can be employed to demultiplex dynamically encoded signals. We combine this demultiplexer with dCas9-based gene networks to construct pulsatile signal filters and decoders. Applying information theory, we show that dynamic multiplexing significantly increases the information transmission capacity from signal to gene expression state. Finally, we use dynamic multiplexing for precise multidimensional regulation of a heterologous metabolic pathway. Our results elucidate design principles of dynamic information processing and provide original synthetic systems capable of decoding complex signals for biotechnological applications.

Temporal signaling, population control, and information processing through chromatin-mediated gene regulation

Chromatin regulation is a key pathway cells use to regulate gene expression in response to temporal stimuli, and is becoming widely used as a platform for synthetic biology applications. Here, we build a mathematical framework for analyzing the response of genetic circuits containing chromatin regulators to temporal signals in mammalian cell populations. Chromatin regulators can silence genes in an all-or-none fashion at the single-cell level, with individual cells stochastically transitioning between active, reversibly silent, and irreversibly silent gene states at constant rates over time. We integrate this mode of regulation with classical gene regulatory motifs, such as autoregulatory and incoherent feedforward loops, to determine the types of responses achievable with duration-dependent signaling. We demonstrate that repressive regulators without long-term epigenetic memory can filter out high frequency noise, and as part of an autoregulatory loop can precisely tune the fraction of cells in a population that expresses a gene of interest. Additionally, we find that repressive regulators with epigenetic memory can sum up and encode the total duration of their recruitment in the fraction of cells irreversibly silenced and, when included in a feed forward loop, enable perfect adaptation. Last, we use an information theoretic approach to show that all-or-none stochastic silencing can be used by populations to transmit information reliably and with high fidelity even in very simple genetic circuits. Altogether, we show that chromatin-mediated gene control enables a repertoire of complex cell population responses to temporal signals and can transmit higher information levels than previously measured in gene regulation.

Using average transcription level to understand the regulation of stochastic gene activation

Gene activation is a random process, modelled as a framework of multiple rate-limiting steps listed sequentially, in parallel or in combination. Together with suitably assumed processes of gene inactivation, transcript birth and death, the step numbers and parameters in activation frameworks can be estimated by fitting single-cell transcription data. However, current algorithms require computing master equations that are tightly correlated with prior hypothetical frameworks of gene activation. We found that prior estimation of the framework can be facilitated by the traditional dynamical data of mRNA average level M(t), presenting discriminated dynamical features. Rigorous theory regarding M(t) profiles allows to confidently rule out the frameworks that fail to capture M(t) features and to test potential competent frameworks by fitting M(t) data. We implemented this procedure for a large number of mouse fibroblast genes under tumour necrosis factor induction and determined exactly the 'cross-talking n-state' framework; the cross-talk between the signalling and basal pathways is crucial to trigger the first peak of M(t), while the following damped gentle M(t) oscillation is regulated by the multi-step basal pathway. This framework can be used to fit sophisticated single-cell data and may facilitate a more accurate understanding of stochastic activation of mouse fibroblast genes.

Profiling transcription factor activity dynamics using intronic reads in time-series transcriptome data

Activities of transcription factors (TFs) are temporally modulated to regulate dynamic cellular processes, including development, homeostasis, and disease. Recent developments of bioinformatic tools have enabled the analysis of TF activities using transcriptome data. However, because these methods typically use exon-based target expression levels, the estimated TF activities have limited temporal accuracy. To address this, we proposed a TF activity measure based on intron-level information in time-series RNA-seq data, and implemented it to decode the temporal control of TF activities during dynamic processes. We showed that TF activities inferred from intronic reads can better recapitulate instantaneous TF activities compared to the exon-based measure. By analyzing public and our own time-series transcriptome data, we found that intron-based TF activities improve the characterization of temporal phasing of cycling TFs during circadian rhythm, and facilitate the discovery of two temporally opposing TF modules during T cell activation. Collectively, we anticipate that the proposed approach would be broadly applicable for decoding global transcriptional architecture during dynamic processes.

Mean-field theory accurately captures the variation of copy number distributions across the mRNA life cycle

We consider a stochastic model where a gene switches between two states, an mRNA transcript is released in the active state, and subsequently it undergoes an arbitrary number of sequential unimolecular steps before being degraded. The reactions effectively describe various stages of the mRNA life cycle such as initiation, elongation, termination, splicing, export, and degradation. We construct a mean-field approach that leads to closed-form steady-state distributions for the number of transcript molecules at each stage of the mRNA life cycle. By comparison with stochastic simulations, we show that the approximation is highly accurate over all the parameter space, independent of the type of expression (constitutive or bursty) and of the shape of the distribution (unimodal, bimodal, and nearly bimodal). The theory predicts that in a population of identical cells, any bimodality is gradually washed away as the mRNA progresses through its life cycle.

The high osmolarity glycerol (HOG) pathway in fungi†

While fungi are widely occupying nature, many species are responsible for devastating mycosis in humans. Such niche diversity explains how quick fungal adaptation is necessary to endow the capacity of withstanding fluctuating environments and to cope with host-imposed conditions. Among all the molecular mechanisms evolved by fungi, the most studied one is the activation of the phosphorelay signalling pathways, of which the high osmolarity glycerol (HOG) pathway constitutes one of the key molecular apparatus underpinning fungal adaptation and virulence. In this review, we summarize the seminal knowledge of the HOG pathway with its more recent developments. We specifically described the HOG-mediated stress adaptation, with a particular focus on osmotic and oxidative stress, and point out some lags in our understanding of its involvement in the virulence of pathogenic species including, the medically important fungi Candida albicans, Cryptococcus neoformans, and Aspergillus fumigatus, compared to the model yeast Saccharomyces cerevisiae. Finally, we also highlighted some possible applications of the HOG pathway modifications to improve the fungal-based production of natural products in the industry.

Electrically synchronizing and modulating the dynamics of ERK activation to regulate cell fate

Intracellular signaling dynamics play fundamental roles in cell biology. Precise modulation of the amplitude, duration, and frequency of signaling activation will be a powerful approach to investigate molecular mechanisms as well as to engineer signaling to control cell behaviors. Here, we showed a practical approach to achieve precise amplitude modulation (AM), frequency modulation (FM), and duration modulation (DM) of MAP kinase activation. Alternating current (AC) electrical stimulation induced synchronized ERK activation. Amplitude and duration of ERK activation were controlled by varying stimulation strength and duration. ERK activation frequencies were arbitrarily modulated with trains of short AC applications with accurately defined intervals. Significantly, ERK dynamics coded by well-designed AC can rewire PC12 cell fate independent of growth factors. This technique can be used to synchronize and modulate ERK activation dynamics, thus would offer a practical way to control cell behaviors in vivo without the use of biochemical agents or genetic manipulation.

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Alternating-current (AC) electric field activates ERK independently of growth factors

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AC stimulation length modulates the amplitude and duration of ERK activation

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On-off time interval of AC modulates the frequency of ERK activation peaks

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Electrical modulation of ERK dynamics promotes neuronal differentiation of PC12 cells

Mapping the dynamic transfer functions of eukaryotic gene regulation

Biological information can be encoded within the dynamics of signaling components, which has been implicated in a broad range of physiological processes including stress response, oncogenesis, and stem cell differentiation. To study the complexity of information transfer across the eukaryotic promoter, we screened 119 dynamic conditions-modulating the pulse frequency, amplitude, and pulse width of light-regulating the binding of an epigenome editor to a fluorescent reporter. This system revealed tunable gene expression and filtering behaviors and provided a quantification of the limit to the amount of information that can be reliably transferred across a single promoter as ∼1.7 bits. Using a library of over 100 orthogonal chromatin regulators, we further determined that chromatin state could be used to tune mutual information and expression levels, as well as completely alter the input-output transfer function of the promoter. This system unlocks the information-rich content of eukaryotic gene regulation.

Progress and challenges in understanding the regulation and function of p53 dynamics

The dynamics of p53 expression provide a mechanism to increase differentiation between cellular stresses and specificity in appropriate responses. Here, we review recent advances in our understanding of the molecular mechanisms regulating p53 dynamics and the functions of the dynamics in the regulation of p53-dependent cell stress responses. We also compare dynamic encoding in the p53 system with that found in other important cell signaling systems, many of which can interact with the p53 network. Finally, we highlight some of the current challenges in understanding dynamic cell signaling within a larger cellular network context.

HES1 protein oscillations are necessary for neural stem cells to exit from quiescence

Quiescence is a dynamic process of reversible cell cycle arrest. High-level persistent expression of the HES1 transcriptional repressor, which oscillates with an ultradian periodicity in proliferative neural stem cells (NSCs), is thought to mediate quiescence. However, it is not known whether this is due to a change in levels or dynamics. Here, we induce quiescence in embryonic NSCs with BMP4, which does not increase HES1 level, and we find that HES1 continues to oscillate. To assess the role of HES1 dynamics, we express persistent HES1 under a moderate strength promoter, which overrides the endogenous oscillations while maintaining the total HES1 level within physiological range. We find that persistent HES1 does not affect proliferation or entry into quiescence; however, exit from quiescence is impeded. Thus, oscillatory expression of HES1 is specifically required for NSCs to exit quiescence, a finding of potential importance for controlling reactivation of stem cells in tissue regeneration and cancer.

A novel two-layer-integrated microfluidic device for high-throughput yeast proteomic dynamics analysis at the single-cell level

Current microfluidic methods for studying multicell strains (e.g., m-types) with multienvironments (e.g., n-types) require large numbers of inlets/outlets (m*n), a complicated procedure or expensive machinery. Here, we developed a novel two-layer-integrated method to combine different PDMS microchannel layers with different functions into one chip by a PDMS through-hole array, which improved the design of a PDMS-based microfluidic system. Using this method, we succeeded in converting 2 × m × n inlets/outlets into m + n inlets/outlets and reduced the time cost of loading processing (from m × n to m) of the device for studying multicell strains (e.g., m-types) in varied multitemporal environments (i.e., n-types). Using this device, the dynamic behavior of the cell-stress-response proteins was studied when the glucose concentration decreased from 2% to a series of lower concentrations. Our device could also be widely used in high-throughput studies of various stress responses, and the new concept of a multilayer-integrated fabrication method could greatly improve the design of PDMS-based microfluidic systems.

T cells selectively filter oscillatory signals on the minutes timescale

T cells experience complex temporal patterns of stimulus via receptor-ligand-binding interactions with surrounding cells. From these temporal patterns, T cells are able to pick out antigenic signals while establishing self-tolerance. Although features such as duration of antigen binding have been examined, our understanding of how T cells interpret signals with different frequencies or temporal stimulation patterns is relatively unexplored. We engineered T cells to respond to light as a stimulus by building an optogenetically controlled chimeric antigen receptor (optoCAR). We discovered that T cells respond to minute-scale oscillations of activation signal by stimulating optoCAR T cells with tunable pulse trains of light. Systematically scanning signal oscillation period from 1 to 150 min revealed that expression of CD69, a T cell activation marker, reached a local minimum at a period of ∼25 min (corresponding to 5 to 15 min pulse widths). A combination of inhibitors and genetic knockouts suggest that this frequency filtering mechanism lies downstream of the Erk signaling branch of the T cell response network and may involve a negative feedback loop that diminishes Erk activity. The timescale of CD69 filtering corresponds with the duration of T cell encounters with self-peptide-presenting APCs observed via intravital imaging in mice, indicating a potential functional role for temporal filtering in vivo. This study illustrates that the T cell signaling machinery is tuned to temporally filter and interpret time-variant input signals in discriminatory ways.

NFAT indicates nucleocytoplasmic damped oscillation via its feedback modulator

Cell signaling and the following gene regulation are tightly regulated to keep homeostasis. NF-κB is a famous key transcription factor for inflammatory cell regulations that obtain a closed feedback loop with IκB. Similarly, we show here, NFAT is also tightly regulated via its downstream target, down syndrome critical region (DSCR)-1. In primary cultured endothelium, either shear stress or VEGF treatment revealed quick NFAT1 nuclear localization following the DSCR-1 transactivation, which in turn induced NFAT1 cytoplasm sequestration. Interestingly, both NFAT and DSCR-1 can be competitive substrates for calcineurin phosphatase and DSCR-1 is known to unstable protein, which caused NFAT1-nucleocytoplasmic damped oscillation via sustained shear stress or VEGF stimulation in endothelial cell (EC)s. To understand the molecular mechanism underlying the NFAT1 oscillation, we built a mathematical model of spatiotemporal regulation of NFAT1 combined with calcineurin and DSCR-1. Theoretically, manipulation of DSCR-1 expression in simulation predicted that DSCR-1 reduction would cause nuclear retention of dephosphorylated NFAT1 and disappearance of NFAT1 oscillation. To confirm this in ECs, DSCR-1 knockdown analysis was performed. DSCR-1 reduction indeed increased dephosphorylated NFAT1 in both the nucleus and cytoplasm, which eventually led to nuclear retention of NFAT1. Taken together, these studies suggest that DSCR-1 is a responsible critical factor for NFAT1 nucleocytoplasmic oscillation in shear stress or VEGF treated ECs. Our mathematical model successfully reproduced the experimental observations of NFAT1 dynamics. Combined mathematical and experimental approaches would provide a quantitative understanding way for the spatiotemporal NFAT1 feedback system.

A model explaining mRNA level fluctuations based on activity demands and RNA age

Cellular RNA levels typically fluctuate and are influenced by different transcription rates and RNA degradation rates. However, the understanding of the fundamental relationships between RNA abundance, environmental stimuli, RNA activities, and RNA age distributions is incomplete. Furthermore, the rates of RNA degradation and transcription are difficult to measure in transcriptomic experiments in living organisms, especially in studies involving humans. A model based on activity demands and RNA age was developed to explore the mechanisms of RNA level fluctuations. Using single-cell time-series gene expression experimental data, we assessed the transcription rates, RNA degradation rates, RNA life spans, RNA demand, accumulated transcription levels, and accumulated RNA degradation levels. This model could also predict RNA levels under simulation backgrounds, such as stimuli that induce regular oscillations in RNA abundance, stable RNA levels over time that result from long-term shortage of total RNA activity or from uncontrollable transcription, and relationships between RNA/protein levels and metabolic rates. This information contributes to existing knowledge.

Detected cellular RNA levels usually fluctuate. The understanding of the fundamental relationships between RNA level fluctuations, the rates of RNA degradation and transcription, environmental stimuli, RNA activities, and RNA age distributions is incomplete. In the present research, we developed a model based on the demands of RNA (related to intrinsic and/or extrinsic information), RNA age (determines the survival time and biological activity of an RNA), transcription, and RNA degradation to explain the mechanism underlying intracellular RNA level fluctuations. We also explored applicability of the model for analysing dynamic processes between interacting biomolecules, such as the relationship between RNA and protein level fluctuations. Using single-cell time-series gene expression experimental data, we assessed some biological parameters, such as transcription rates, RNA degradation rates, and RNA life spans. This model could also predict RNA levels under simulation backgrounds, such as stimuli that induce regular oscillations in RNA abundance, stable RNA levels over time that result from long-term shortage of total RNA activity or from uncontrollable transcription, and relationships between RNA/protein levels and metabolic rates. This information contributes to existing knowledge and provides a new perspective for future studies.

Light-Stress Response Mediated by the Transcription Factor KlMga2 in the Yeast Kluyveromyces lactis

In unicellular organisms like yeasts, which do not have specialized tissues for protection against environmental challenges, the presence of cellular mechanisms to respond and adapt to stress conditions is fundamental. In this work, we aimed to investigate the response to environmental light in Kluyveromyces lactis. Yeast lacks specialized light-sensing proteins; however, Saccharomyces cerevisiae has been reported to respond to light by increasing hydrogen peroxide level and triggering nuclear translocation of Msn2. This is a stress-sensitive transcription factor also present in K. lactis. To investigate light response in this yeast, we analyzed the different phenotypes generated by the deletion of the hypoxia responsive and lipid biosynthesis transcription factor KlMga2. Alterations in growth rate, mitochondrial functioning, ROS metabolism, and fatty acid biosynthesis provide evidence that light was a source of stress in K. lactis and that KlMga2 had a role in the light-stress response. The involvement of KlMsn2 and KlCrz1 in light stress was also explored, but the latter showed no function in this response.

An optogenetic method for interrogating YAP1 and TAZ nuclear-cytoplasmic shuttling

The shuttling of transcription factors and transcriptional regulators into and out of the nucleus is central to the regulation of many biological processes. Here we describe a new method for studying the rates of nuclear entry and exit of transcriptional regulators. A photo-responsive LOV (light-oxygen-voltage) domain from Avena sativa is used to sequester fluorescently labelled transcriptional regulators YAP1 and TAZ (also known as WWTR1) on the surface of mitochondria and to reversibly release them upon blue light illumination. After dissociation, fluorescent signals from the mitochondria, cytoplasm and nucleus are extracted by a bespoke app and used to generate rates of nuclear entry and exit. Using this method, we demonstrate that phosphorylation of YAP1 on canonical sites enhances its rate of nuclear export. Moreover, we provide evidence that, despite high intercellular variability, YAP1 import and export rates correlate within the same cell. By simultaneously releasing YAP1 and TAZ from sequestration, we show that their rates of entry and exit are correlated. Furthermore, combining the optogenetic release of YAP1 with lattice light-sheet microscopy reveals high heterogeneity of YAP1 dynamics within different cytoplasmic regions, demonstrating the utility and versatility of our tool to study protein dynamics. This article has an associated First Person interview with Anna M. Dowbaj, joint first author of the paper.