Enhancement of cellular memory by reducing stochastic transitions

Transcriptional heterogeneity shapes stress-adaptive responses in yeast

In response to stress, cells activate signaling pathways that coordinate broad changes in gene expression to enhance cell survival. Remarkably, complex variations in gene expression occur even in isogenic populations and in response to similar signaling inputs. However, the molecular mechanisms underlying this variability and their influence on adaptive cell fate decisions are not fully understood. Here, we use scRNA-seq to longitudinally assess transcriptional dynamics during osmoadaptation in yeast. Our findings reveal highly heterogeneous expression of the osmoresponsive program, which organizes into combinatorial patterns that generate distinct cellular programs. The induction of these programs is favored by global transcriptome repression upon stress. Cells displaying basal expression of the osmoresponsive program are hyper-responsive and resistant to stress. Through a transcription-focused analysis of more than 300 RNA-barcoded deletion mutants, we identify genetic factors that shape the heterogeneity of the osmostress-induced transcriptome, define regulators of stress-related subpopulations and find a link between transcriptional heterogeneity and increased cell fitness. Our findings provide a regulatory map of the complex transcriptional phenotypes underlying osmoadaptation in yeast and highlight the importance of transcriptional heterogeneity in generating distinct adaptive strategies.

Versatile xylose and arabinose genetic switches development for yeasts

Inducible transcription systems are essential tools in genetic engineering, where tight control, strong inducibility and fast response with cost-effective inducers are highly desired. However, existing systems in yeasts are rarely used in large-scale fermentations due to either cost-prohibitive inducers or incompatible performance. Here, we developed powerful xylose and arabinose induction systems in Saccharomyces cerevisiae, utilizing eukaryotic activators XlnR and AraRA from Aspergillus species and bacterial repressors XylR and AraRR. By integrating these signals into a highly-structured synthetic promoter, we created dual-mode systems with strong outputs and minimal leakiness. These systems demonstrated over 4000- and 300-fold regulation with strong activation and rapid response. The dual-mode xylose system was fully activated by xylose-rich agricultural residues like corncob hydrolysate, outperforming existing systems in terms of leakiness, inducibility, dynamic range, induction rate, and growth impact on host. We validated their utility in metabolic engineering with high-titer linalool production and demonstrated the transferability of the XlnR-based xylose induction system to Pichia pastoris, Candida glabrata and Candida albicans. This work provides robust genetic switches for yeasts and a general strategy for integrating activation-repression signals into synthetic promoters to achieve optimal performance.

Living in a noisy world-origins of gene expression noise and its impact on cellular decision-making

The expression level of a gene can vary between genetically identical cells under the same environmental condition-a phenomenon referred to as gene expression noise. Several studies have now elucidated a central role of transcription factors in the generation of expression noise. Transcription factors, as the key components of gene regulatory networks, drive many important cellular decisions in response to cellular and environmental signals. Therefore, a very relevant question is how expression noise impacts gene regulation and influences cellular decision-making. In this Review, we summarize the current understanding of the molecular origins of expression noise, highlighting the role of transcription factors in this process, and discuss the ways in which noise can influence cellular decision-making. As advances in single-cell technologies open new avenues for studying expression noise as well as gene regulatory circuits, a better understanding of the influence of noise on cellular decisions will have important implications for many biological processes.

The bistable mitotic switch in fission yeast

In favorable conditions, eukaryotic cells proceed irreversibly through the cell division cycle (G1-S-G2-M) in order to produce two daughter cells with the same number and identity of chromosomes of their progenitor. The integrity of this process is maintained by "checkpoints" that hold a cell at particular transition points of the cycle until all requisite events are completed. The crucial functions of these checkpoints seem to depend on irreversible bistability of the underlying checkpoint control systems. Bistability of cell cycle transitions has been confirmed experimentally in frog egg extracts, budding yeast cells and mammalian cells. For fission yeast cells, a recent paper by Patterson et al. (2021) provides experimental evidence for an abrupt transition from G2 phase into mitosis, and we show that these data are consistent with a stochastic model of a bistable switch governing the G2/M checkpoint. Interestingly, our model suggests that their experimental data could also be explained by a reversible/sigmoidal switch, and stochastic simulations confirm this supposition. We propose a simple modification of their experimental protocol that could provide convincing evidence for (or against) bistability of the G2/M transition in fission yeast.

Phase separation provides a mechanism to drive phenotype switching

Phenotypic switching plays a crucial role in cell fate determination across various organisms. Recent experimental findings highlight the significance of protein compartmentalization via liquid-liquid phase separation in influencing such decisions. However, the precise mechanism through which phase separation regulates phenotypic switching remains elusive. To investigate this, we established a mathematical model that couples a phase separation process and a gene expression process with feedback. We used the chemical master equation theory and mean-field approximation to study the effects of phase separation on the gene expression products. We found that phase separation can cause bistability and bimodality. Furthermore, phase separation can control the bistable properties of the system, such as bifurcation points and bistable ranges. On the other hand, in stochastic dynamics, the droplet phase exhibits double peaks within a more extensive phase separation threshold range than the dilute phase, indicating the pivotal role of the droplet phase in cell fate decisions. These findings propose an alternative mechanism that influences cell fate decisions through the phase separation process. As phase separation is increasingly discovered in gene regulatory networks, related modeling research can help build biomolecular systems with desired properties and offer insights into explaining cell fate decisions.

Cellular forgetting, desensitisation, stress and ageing in signalling networks. When do cells refuse to learn more?

Recent findings show that single, non-neuronal cells are also able to learn signalling responses developing cellular memory. In cellular learning nodes of signalling networks strengthen their interactions e.g. by the conformational memory of intrinsically disordered proteins, protein translocation, miRNAs, lncRNAs, chromatin memory and signalling cascades. This can be described by a generalized, unicellular Hebbian learning process, where those signalling connections, which participate in learning, become stronger. Here we review those scenarios, where cellular signalling is not only repeated in a few times (when learning occurs), but becomes too frequent, too large, or too complex and overloads the cell. This leads to desensitisation of signalling networks by decoupling signalling components, receptor internalization, and consequent downregulation. These molecular processes are examples of anti-Hebbian learning and 'forgetting' of signalling networks. Stress can be perceived as signalling overload inducing the desensitisation of signalling pathways. Ageing occurs by the summative effects of cumulative stress downregulating signalling. We propose that cellular learning desensitisation, stress and ageing may be placed along the same axis of more and more intensive (prolonged or repeated) signalling. We discuss how cells might discriminate between repeated and unexpected signals, and highlight the Hebbian and anti-Hebbian mechanisms behind the fold-change detection in the NF-κB signalling pathway. We list drug design methods using Hebbian learning (such as chemically-induced proximity) and clinical treatment modalities inducing (cancer, drug allergies) desensitisation or avoiding drug-induced desensitisation. A better discrimination between cellular learning, desensitisation and stress may open novel directions in drug design, e.g. helping to overcome drug resistance.

A computational model of stem cells' internal mechanism to recapitulate spatial patterning and maintain the self-organized pattern in the homeostasis state

The complex functioning of multi-cellular tissue development relies on proper cell production rates to replace dead or differentiated specialized cells. Stem cells are critical for tissue development and maintenance, as they produce specialized cells to meet the tissues' demands. In this study, we propose a computational model to investigate the stem cell's mechanism, which generates the appropriate proportion of specialized cells, and distributes them to their correct position to form and maintain the organized structure in the population through intercellular reactions. Our computational model focuses on early development, where the populations overall behavior is determined by stem cells and signaling molecules. The model does not include complicated factors such as movement of specialized cells or outside signaling sources. The results indicate that in our model, the stem cells can organize the population into a desired spatial pattern, which demonstrates their ability to self-organize as long as the corresponding leading signal is present. We also investigate the impact of stochasticity, which provides desired non-genetic diversity; however, it can also break the proper boundaries of the desired spatial pattern. We further examine the role of the death rate in maintaining the system's steady state. Overall, our study sheds light on the strategies employed by stem cells to organize specialized cells and maintain proper functionality. Our findings provide insight into the complex mechanisms involved in tissue development and maintenance, which could lead to new approaches in regenerative medicine and tissue engineering.

Optimal phenotypic adaptation in fluctuating environments

Phenotypic adaptation is a universal feature of biological systems navigating highly variable environments. Recent empirical data support the role of memory-driven decision making in cellular systems navigating uncertain future nutrient landscapes, wherein a distinct growth phenotype emerges in fluctuating conditions. We develop a simple stochastic mathematical model to describe memory-driven cellular adaptation required for systems to optimally navigate such uncertainty. In this framework, adaptive populations traverse dynamic environments by inferring future variation from a memory of prior states, and memory capacity imposes a fundamental trade-off between the speed and accuracy of adaptation to new fluctuating environments. Our results suggest that the observed growth reductions that occur in fluctuating environments are a direct consequence of optimal decision making and result from bet hedging and occasional phenotypic-environmental mismatch. We anticipate that this modeling framework will be useful for studying the role of memory in phenotypic adaptation, including in the design of temporally varying therapies against adaptive systems.

Increased gene expression variability hinders the formation of regional mechanical conflicts leading to reduced organ shape robustness

To relate gene networks and organ shape, one needs to address two wicked problems: i) Gene expression is often variable locally, and shape is reproducible globally; ii) gene expression can have cascading effects on tissue mechanics, with possibly counterintuitive consequences for the final organ shape. Here, we address such wicked problems, taking advantage of simpler plant organ development where shape only emerges from cell division and elongation. We confirm that mutation in VERNALIZATION INDEPENDENCE 3 (VIP3), a subunit of the conserved polymerase-associated factor 1 complex (Paf1C), increases gene expression variability in Arabidopsis. Then, we focused on the Arabidopsis sepal, which exhibits a reproducible shape and stereotypical regional growth patterns. In vip3 sepals, we measured higher growth heterogeneity between adjacent cells. This even culminated in the presence of negatively growing cells in specific growth conditions. Interestingly, such increased local noise interfered with the stereotypical regional pattern of growth. We previously showed that regional differential growth at the wild-type sepal tip triggers a mechanical conflict, to which cells resist by reinforcing their walls, leading to growth arrest. In vip3, the disturbed regional growth pattern delayed organ growth arrest and increased final organ shape variability. Altogether, we propose that gene expression variability is managed by Paf1C to ensure organ robustness by building up mechanical conflicts at the regional scale, instead of the local scale.

Sequence and chromatin features guide DNA double-strand break resection initiation

DNA double-strand breaks (DSBs) are cytotoxic genome lesions that must be accurately and efficiently repaired to ensure genome integrity. In yeast, the Mre11-Rad50-Xrs2 (MRX) complex nicks 5'-terminated DSB ends to initiate nucleolytic processing of DSBs for repair by homologous recombination. How MRX-DNA interactions support 5' strand-specific nicking and how nicking is influenced by the chromatin context have remained elusive. Using a deep sequencing-based assay, we mapped MRX nicks at single-nucleotide resolution next to multiple DSBs in the yeast genome. We observed that the DNA end-binding Ku70-Ku80 complex directed DSB-proximal nicks and that repetitive MRX cleavage extended the length of resection tracts. We identified a sequence motif and a DNA meltability profile that is preferentially nicked by MRX. Furthermore, we found that nucleosomes as well as transcription impeded MRX incisions. Our findings suggest that local DNA sequence and chromatin features shape the activity of this central DSB repair complex.

Bistability regulates TNFR2-mediated survival and death of T-regulatory cells

A subgroup of T cells called T-regulatory cells (Tregs) regulates the body's immune responses to maintain homeostasis and self-tolerance. Tregs are crucial for preventing illnesses like cancer and autoimmunity. However, contrasting patterns of Treg frequency are observed in different autoimmune diseases. The commonality of tumour necrosis factor receptor 2 (TNFR2) defects and decrease in Treg frequency on the onset of autoimmunity demands an in-depth study of the TNFR2 pathway. To unravel this mystery, we need to study the mechanism of cell survival and death in Tregs. Here, we construct an ordinary differential equation (ODE)-based model to capture the mechanism of cell survival and apoptosis in Treg cells via TNFR2 signalling. The sensitivity analysis reveals that the input stimulus, the concentration of tumour necrosis factor (TNF), is the most sensitive parameter for the model system. The model shows that the cell goes into survival or apoptosis via bistable switching. Through hysteretic switching, the system tries to cope with the changing stimuli. In order to understand how stimulus strength and feedback strength influence cell survival and death, we compute bifurcation diagrams and obtain cell fate maps. Our results indicate that the elevated TNF concentration and increased c-Jun N-terminal kinase (JNK) phosphorylation are the major contributors to the death of T-regulatory cells. Biological evidence cements our hypothesis and can be controlled by reducing the TNF concentration. Finally, the system was studied under stochastic perturbation to see the effect of noise on the system's dynamics. We observed that introducing random perturbations disrupts the bistability, reducing the system's bistable region, which can affect the system's normal functioning.

Differential regulation of mRNA stability modulates transcriptional memory and facilitates environmental adaptation

Transcriptional memory, by which cells respond faster to repeated stimuli, is key for cellular adaptation and organism survival. Chromatin organization has been shown to play a role in the faster response of primed cells. However, the contribution of post-transcriptional regulation is not yet explored. Here we perform a genome-wide screen to identify novel factors modulating transcriptional memory in S. cerevisiae in response to galactose. We find that depletion of the nuclear RNA exosome increases GAL1 expression in primed cells. Our work shows that gene-specific differences in intrinsic nuclear surveillance factor association can enhance both gene induction and repression in primed cells. Finally, we show that primed cells present altered levels of RNA degradation machinery and that both nuclear and cytoplasmic mRNA decay modulate transcriptional memory. Our results demonstrate that mRNA post-transcriptional regulation, and not only transcription regulation, should be considered when investigating gene expression memory.

Maintenance of abiotic stress memory in plants: Lessons learned from heat acclimation

Plants acquire enhanced tolerance to intermittent abiotic stress by employing information obtained during prior exposure to an environmental disturbance, a process known as acclimation or defense priming. The capacity for stress memory is a critical feature in this process. The number of reports related to plant stress memory (PSM) has recently increased, but few studies have focused on the mechanisms that maintain PSM. Identifying the components involved in maintaining PSM is difficult due in part to the lack of clear criteria to recognize these components. In this review, based on what has been learned from genetic studies on heat acclimation memory, we propose criteria for identifying components of the regulatory networks that maintain PSM. We provide examples of the regulatory circuits formed by effectors and regulators of PSM. We also highlight strategies for assessing PSMs, update the progress in understanding the mechanisms of PSM maintenance, and provide perspectives for the further development of this exciting research field.

Long-Term Adaptation to Galactose as a Sole Carbon Source Selects for Mutations Outside the Canonical GAL Pathway

Galactose is a secondary fermentable sugar that requires specific regulatory and structural genes for its assimilation, which are under catabolite repression by glucose. When glucose is absent, the catabolic repression is attenuated, and the structural GAL genes are fully activated. In Saccharomyces cerevisiae, the GAL pathway is under selection in environments where galactose is present. However, it is unclear the adaptive strategies in response to long-term propagation in galactose as a sole carbon source in laboratory evolution experiments. Here, we performed a 4,000-generation evolution experiment using 48 diploid Saccharomyces cerevisiae populations to study adaptation in galactose. We show that fitness gains were greater in the galactose-evolved population than in identically evolved populations with glucose as a sole carbon source. Whole-genome sequencing of 96 evolved clones revealed recurrent de novo single nucleotide mutations in candidate targets of selection, copy number variations, and ploidy changes. We find that most mutations that improve fitness in galactose lie outside of the canonical GAL pathway. Reconstruction of specific evolved alleles in candidate target of selection, SEC23 and IRA1, showed a significant increase in fitness in galactose compared to glucose. In addition, most of our evolved populations (28/46; 61%) fixed aneuploidies on Chromosome VIII, suggesting a parallel adaptive amplification. Finally, we show greater loss of extrachromosomal elements in our glucose-evolved lineages compared with previous glucose evolution. Broadly, these data further our understanding of the evolutionary pressures that drive adaptation to less-preferred carbon sources.

Prophylactic and therapeutic insights into trained immunity: A renewed concept of innate immune memory

Trained immunity is a renewed concept of innate immune memory that facilitates the innate immune system to have the capacity to remember and train cells via metabolic and transcriptional events to enable them to provide nonspecific defense against the subsequent encounters with a range of pathogens and acquire a quicker and more robust immune response, but different from the adaptive immune memory. Reversing the epigenetic changes or targeting the immunological pathways may be considered potential therapeutic approaches to counteract the hyper-responsive or hypo-responsive state of trained immunity. The efficient regulation of immune homeostasis and promotion or inhibition of immune responses is required for a balanced response. Trained immunity-based vaccines can serve as potent immune stimuli and help in the clearance of pathogens in the body through multiple or heterologous effects and confer protection against nonspecific and specific pathogens. This review highlights various features of trained immunity and its applications in developing novel therapeutics and vaccines, along with certain detrimental effects, challenges as well as future perspectives.

Phenotypic plasticity as a facilitator of microbial evolution

Tossed about by the tides of history, the inheritance of acquired characteristics has found a safe harbor at last in the rapidly expanding field of epigenetics. The slow pace of genetic variation and high opportunity cost associated with maintaining a diverse genetic pool are well-matched by the flexibility of epigenetic traits, which can enable low-cost exploration of phenotypic space and reactive tuning to environmental pressures. Aiding in the generation of a phenotypically plastic population, epigenetic mechanisms often provide a hotbed of innovation for countering environmental pressures, while the potential for genetic fixation can lead to strong epigenetic-genetic evolutionary synergy. At the level of cells and cellular populations, we begin this review by exploring the breadth of mechanisms for the storage and intergenerational transmission of epigenetic information, followed by a brief review of common and exotic epigenetically regulated phenotypes. We conclude by offering an in-depth coverage of recent papers centered around two critical issues: the evolvability of epigenetic traits through Baldwinian adaptive phenotypic plasticity and the potential for synergy between epigenetic and genetic evolution.

Shifts in evolutionary balance of phenotypes under environmental changes

Environments shape communities by driving individual interactions and the evolutionary outcome of competition. In static, homogeneous environments a robust, evolutionary stable, outcome is sometimes reachable. However, inherently stochastic, this evolutionary process need not stabilize, resulting in a dynamic ecological state, often observed in microbial communities. We use evolutionary games to study the evolution of phenotypic competition in dynamic environments. Under the assumption that phenotypic expression depends on the environmental shifts, existing periodic relationships may break or result in formation of new periodicity in phenotypic interactions. The exact outcome depends on the environmental shift itself, indicating the importance of understanding how environments influence affected systems. Under periodic environmental fluctuations, a stable state preserving dominant phenotypes may exist. However, rapid environmental shifts can lead to critical shifts in the phenotypic evolutionary balance. This might lead to environmentally favoured phenotypes dominating making the system vulnerable. We suggest that understanding of the robustness of the system's current state is necessary to anticipate when it will shift to a new equilibrium via understanding what level of perturbations the system can take before its equilibrium changes. Our results provide insights in how microbial communities can be steered to states where they are dominated by desired phenotypes.

Altered expression response upon repeated gene repression in single yeast cells

Cells must continuously adjust to changing environments and, thus, have evolved mechanisms allowing them to respond to repeated stimuli. While faster gene induction upon a repeated stimulus is known as reinduction memory, responses to repeated repression have been less studied so far. Here, we studied gene repression across repeated carbon source shifts in over 1,500 single Saccharomyces cerevisiae cells. By monitoring the expression of a carbon source-responsive gene, galactokinase 1 (Gal1), and fitting a mathematical model to the single-cell data, we observed a faster response upon repeated repressions at the population level. Exploiting our single-cell data and quantitative modeling approach, we discovered that the faster response is mediated by a shortened repression response delay, the estimated time between carbon source shift and Gal1 protein production termination. Interestingly, we can exclude two alternative hypotheses, i) stronger dilution because of e.g., increased proliferation, and ii) a larger fraction of repressing cells upon repeated repressions. Collectively, our study provides a quantitative description of repression kinetics in single cells and allows us to pinpoint potential mechanisms underlying a faster response upon repeated repression. The computational results of our study can serve as the starting point for experimental follow-up studies.

Cells have to continuously adjust to their environment and cope with changing temperature, stress conditions, or metabolic resources. Yeast cells exposed to repeated carbon source shifts have shown to be “primed” by their first exposure, exhibiting enhanced gene expression of specific genes later on. However, how cells respond to a repeated repressive stimulus, e.g., withdrawal of metabolic resources, has been so far much less studied.

For this, we investigated the expression kinetics of a carbon source-responsive gene across repeated repressions. We measured single-cell expression and used mathematical modeling to evaluate potential causes underlying an observed faster repression response upon a repeated stimulus. Specifically, we investigated whether i) an increased dilution due to e.g., proliferation, ii) an increased fraction of repressing cells, or iii) different kinetic parameters in the repeated repression cause the observed faster response in the second repression. Leveraging quantitative mathematical model comparison, we discovered that the faster response is mediated by a shortened estimated time between carbon source shift and protein production termination at the single-cell level.

Our study provides a quantitative description of repression kinetics in single cells and allows us to pinpoint potential mechanisms underlying a faster response upon repeated repression.

Phenotypic selection during laboratory evolution of yeast populations leads to a genome-wide sustainable chromatin compaction shift

In a previous study, we have shown how microbial evolution has resulted in a persistent reduction in expression after repeatedly selecting for the lowest PGAL1-YFP-expressing cells. Applying the ATAC-seq assay on samples collected from this 28-day evolution experiment, here we show how genome-wide chromatin compaction changes during evolution under selection pressure. We found that the chromatin compaction was altered not only on GAL network genes directly impacted by the selection pressure, showing an example of selection-induced non-genetic memory, but also at the whole-genome level. The GAL network genes experienced chromatin compaction accompanying the reduction in PGAL1-YFP reporter expression. Strikingly, the fraction of global genes with differentially compacted chromatin states accounted for about a quarter of the total genome. Moreover, some of the ATAC-seq peaks followed well-defined temporal dynamics. Comparing peak intensity changes on consecutive days, we found most of the differential compaction to occur between days 0 and 3 when the selection pressure was first applied, and between days 7 and 10 when the pressure was lifted. Among the gene sets enriched for the differential compaction events, some had increased chromatin availability once selection pressure was applied and decreased availability after the pressure was lifted (or vice versa). These results intriguingly show that, despite the lack of targeted selection, transcriptional availability of a large fraction of the genome changes in a very diverse manner during evolution, and these changes can occur in a relatively short number of generations.

Buffering variability in cell regulation motifs close to criticality

Bistable biological regulatory systems need to cope with stochastic noise to fine tune their function close to bifurcation points. Here, we study stability properties of this regime in generic systems to demonstrate that cooperative interactions buffer system variability, hampering noise-induced regime shifts. Our analysis also shows that, in the considered cooperativity range, impending regime shifts can be generically detected by statistical early warning signals from distributional data. Our generic framework, based on minimal models, can be used to extract robustness and variability properties of more complex models and empirical data close to criticality.

A computational model of stem cells' decision-making mechanism to maintain tissue homeostasis and organization in the presence of stochasticity

The maintenance of multi-cellular developed tissue depends on the proper cell production rate to replace the cells destroyed by the programmed process of cell death. The stem cell is the main source of producing cells in a developed normal tissue. It makes the stem cell the lead role in the scene of a fully formed developed tissue to fulfill its proper functionality. By focusing on the impact of stochasticity, here, we propose a computational model to reveal the internal mechanism of a stem cell, which generates the right proportion of different types of specialized cells, distribute them into their right position, and in the presence of intercellular reactions, maintain the organized structure in a homeostatic state. The result demonstrates that the spatial pattern could be harassed by the population geometries. Besides, it clearly shows that our model with progenitor cells able to recover the stem cell presence could retrieve the initial pattern appropriately in the case of injury. One of the fascinating outcomes of this project is demonstrating the contradictory roles of stochasticity. It breaks the proper boundaries of the initial spatial pattern in the population. While, on the flip side of the coin, it is the exact factor that provides the demanded non-genetic diversity in the tissue. The remarkable characteristic of the introduced model as the stem cells' internal mechanism is that it could control the overall behavior of the population without need for any external factors.

Periodic synchronization of isolated network elements facilitates simulating and inferring gene regulatory networks including stochastic molecular kinetics

Background

The temporal progression of many fundamental processes in cells and organisms, including homeostasis, differentiation and development, are governed by gene regulatory networks (GRNs). GRNs balance fluctuations in the output of their genes, which trace back to the stochasticity of molecular interactions. Although highly desirable to understand life processes, predicting the temporal progression of gene products within a GRN is challenging when considering stochastic events such as transcription factor–DNA interactions or protein production and degradation.

Results

We report a method to simulate and infer GRNs including genes and biochemical reactions at molecular detail. In our approach, we consider each network element to be isolated from other elements during small time intervals, after which we synchronize molecule numbers across all network elements. Thereby, the temporal behaviour of network elements is decoupled and can be treated by local stochastic or deterministic solutions. We demonstrate the working principle of this modular approach with a repressive gene cascade comprising four genes. By considering a deterministic time evolution within each time interval for all elements, our method approaches the solution of the system of deterministic differential equations associated with the GRN. By allowing genes to stochastically switch between on and off states or by considering stochastic production of gene outputs, we are able to include increasing levels of stochastic detail and approximate the solution of a Gillespie simulation. Thereby, CaiNet is able to reproduce noise-induced bi-stability and oscillations in dynamically complex GRNs. Notably, our modular approach further allows for a simple consideration of deterministic delays. We further infer relevant regulatory connections and steady-state parameters of a GRN of up to ten genes from steady-state measurements by identifying each gene of the network with a single perceptron in an artificial neuronal network and using a gradient decent method originally designed to train recurrent neural networks. To facilitate setting up GRNs and using our simulation and inference method, we provide a fast computer-aided interactive network simulation environment, CaiNet.

Conclusion

We developed a method to simulate GRNs at molecular detail and to infer the topology and steady-state parameters of GRNs. Our method and associated user-friendly framework CaiNet should prove helpful to analyze or predict the temporal progression of reaction networks or GRNs in cellular and organismic biology. CaiNet is freely available at https://gitlab.com/GebhardtLab/CaiNet.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04541-6.

Silent transcription intervals and translational bursting lead to diverse phenotypic switching

For complex process of gene expression, we use theoretical analysis and stochastic simulations to study the phenotypic diversity induced by silent transcription intervals and translational bursting.

Multiple transcription auto regulatory loops can act as robust oscillators and decision-making motifs

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We have shown that:

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Negative transcription auto regulation can speed up the response time at the cost of reduced steady state protein levels. Under strong binding conditions, one can increase the steady state protein level by increasing the gene copy number without a compromise on the response time.

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Multiple negative transcription autoregulatory motifs can be tuned for both the response time as well as steady state protein levels by varying the gene copy number.

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Multiple negative autoregulatory loops can act as robust genetic oscillators.

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Dual feedback motifs constructed with multiple negative and positive autoregulatory loop components can act as robust oscillators and bistable decision making units within the transcription factor networks.

Response time decides how fast a gene can react against an external signal at the transcription level in a signalling cascade. The steady state protein levels of the responding genes decide the coupling between two consecutive members of a signalling cascade. A negative autoregulatory loop (NARL) present in a transcription factor network can speed up the response time of the regulated gene at the cost of reduced steady state protein level. We present here a multi NARL motif which can be tuned for both the steady state protein level as well as response time in the required direction. Remarkably, there exists an optimum Hill coefficient n_opt≅4 at which the response time of the NARL motif is at minimum. When the Hill coefficient is n < n_opt, then under strong binding conditions, one can raise the steady state protein level by increasing the gene copy number with almost no change in the response time of the multi NARL motif. Using detailed computational analysis, we show that the coupled multi NARL and positive auto regulatory loop (PARL) motifs can act as an oscillator as well as decision making component which are robust against extrinsic fluctuations in the control parameters. We further demonstrate that the period of oscillation of the coupled multi NARL-PARL dual feedback oscillator can also be fine-tuned by the gene copy number apart from the inducer concentration. We finally demonstrate robustness of bistable dual feedback decision making motifs with multi autoregulatory loop component.

Mechanism of life-long maintenance of neuron identity despite molecular fluctuations

Cell fate is maintained over long timescales, yet molecular fluctuations can lead to spontaneous loss of this differentiated state. Our simulations identified a possible mechanism that explains life-long maintenance of ASE neuron fate in Caenorhabditis elegans by the terminal selector transcription factor CHE-1. Here, fluctuations in CHE-1 level are buffered by the reservoir of CHE-1 bound at its target promoters, which ensures continued che-1 expression by preferentially binding the che-1 promoter. We provide experimental evidence for this mechanism by showing that che-1 expression was resilient to induced transient CHE-1 depletion, while both expression of CHE-1 targets and ASE function were lost. We identified a 130 bp che-1 promoter fragment responsible for this resilience, with deletion of a homeodomain binding site in this fragment causing stochastic loss of ASE identity long after its determination. Because network architectures that support this mechanism are highly conserved in cell differentiation, it may explain stable cell fate maintenance in many systems.

Modeling aging and its impact on cellular function and organismal behavior

Aging is a complex phenomenon of functional decay in a biological organism. Although the effects of aging are readily recognizable in a wide range of organisms, the cause(s) of aging are ill defined and poorly understood. Experimental methods on model organisms have driven significant insight into aging as a process, but have not provided a complete model of aging. Computational biology offers a unique opportunity to resolve this gap in our knowledge by generating extensive and testable models that can help us understand the fundamental nature of aging, identify the presence and characteristics of unaccounted aging factor(s), demonstrate the mechanics of particular factor(s) in driving aging, and understand the secondary effects of aging on biological function. In this review, we will address each of the above roles for computational biology in aging research. Concurrently, we will explore the different applications of computational biology to aging in single-celled versus multicellular organisms. Given the long history of computational biogerontological research on lower eukaryotes, we emphasize the key future goals of gradually integrating prior models into a holistic map of aging and translating successful models to higher-complexity organisms.

Computational analysis of GAL pathway pinpoints mechanisms underlying natural variation

Quantitative traits are measurable phenotypes that show continuous variation over a wide phenotypic range. Enormous effort has recently been put into determining the genetic influences on a variety of quantitative traits with mixed success. We identified a quantitative trait in a tractable model system, the GAL pathway in yeast, which controls the uptake and metabolism of the sugar galactose. GAL pathway activation depends both on galactose concentration and on the concentrations of competing, preferred sugars such as glucose. Natural yeast isolates show substantial variation in the behavior of the pathway. All studied yeast strains exhibit bimodal responses relative to external galactose concentration, i.e. a set of galactose concentrations existed at which both GAL-induced and GAL-repressed subpopulations were observed. However, these concentrations differed in different strains. We built a mechanistic model of the GAL pathway and identified parameters that are plausible candidates for capturing the phenotypic features of a set of strains including standard lab strains, natural variants, and mutants. In silico perturbation of these parameters identified variation in the intracellular galactose sensor, Gal3p, the negative feedback node within the GAL regulatory network, Gal80p, and the hexose transporters, HXT, as the main sources of the bimodal range variation. We were able to switch the phenotype of individual yeast strains in silico by tuning parameters related to these three elements. Determining the basis for these behavioral differences may give insight into how the GAL pathway processes information, and into the evolution of nutrient metabolism preferences in different strains. More generally, our method of identifying the key parameters that explain phenotypic variation in this system should be generally applicable to other quantitative traits.

Microbes adopt elaborate strategies for the preferred uptake and use of nutrients to cope with complex and fluctuating environments. As a result, yeast strains originating from different ecological niches show significant variation in the way they induce genes in the galactose metabolism (GAL) pathway in response to nutrient signals. To identify the mechanistic sources of this variation, we built a mathematical model to simulate the dynamics of the galactose metabolic regulation network, and studied how parameters with different biological implications contributed to the natural variation. We found that variations in the behavior of the galactose sensor Gal3p, the negative feedback node Gal80p, and the hexose transporters HXT were critical elements in the GAL pathway response. Tuning single parameters in silico was sufficient to achieve phenotype switching between different yeast strains. Our computational approach should be generally useful to help pinpoint the genetic and molecular bases of natural variation in other systems.

Variation in the modality of a yeast signaling pathway is mediated by a single regulator

Bimodal gene expression by genetically identical cells is a pervasive feature of signaling networks and has been suggested to allow organisms to hedge their ‘bets’ in uncertain conditions. In the galactose-utilization (GAL) pathway of Saccharomyces cerevisiae, gene induction is unimodal or bimodal depending on natural genetic variation and pre-induction conditions. Here, we find that this variation in modality arises from regulation of two features of the pathway response: the fraction of cells that show induction and their level of expression. GAL3, the galactose sensor, controls the fraction of induced cells, and titrating its expression is sufficient to control modality; moreover, all the observed differences in modality between different pre-induction conditions and among natural isolates can be explained by changes in GAL3’s regulation and activity. The ability to switch modality by tuning the activity of a single protein may allow rapid adaptation of bet hedging to maximize fitness in complex environments.

Exact Probability Landscapes of Stochastic Phenotype Switching in Feed-Forward Loops: Phase Diagrams of Multimodality

Feed-forward loops (FFLs) are among the most ubiquitously found motifs of reaction networks in nature. However, little is known about their stochastic behavior and the variety of network phenotypes they can exhibit. In this study, we provide full characterizations of the properties of stochastic multimodality of FFLs, and how switching between different network phenotypes are controlled. We have computed the exact steady-state probability landscapes of all eight types of coherent and incoherent FFLs using the finite-butter Accurate Chemical Master Equation (ACME) algorithm, and quantified the exact topological features of their high-dimensional probability landscapes using persistent homology. Through analysis of the degree of multimodality for each of a set of 10,812 probability landscapes, where each landscape resides over 10⁵–10⁶ microstates, we have constructed comprehensive phase diagrams of all relevant behavior of FFL multimodality over broad ranges of input and regulation intensities, as well as different regimes of promoter binding dynamics. In addition, we have quantified the topological sensitivity of the multimodality of the landscapes to regulation intensities. Our results show that with slow binding and unbinding dynamics of transcription factor to promoter, FFLs exhibit strong stochastic behavior that is very different from what would be inferred from deterministic models. In addition, input intensity play major roles in the phenotypes of FFLs: At weak input intensity, FFL exhibit monomodality, but strong input intensity may result in up to 6 stable phenotypes. Furthermore, we found that gene duplication can enlarge stable regions of specific multimodalities and enrich the phenotypic diversity of FFL networks, providing means for cells toward better adaptation to changing environment. Our results are directly applicable to analysis of behavior of FFLs in biological processes such as stem cell differentiation and for design of synthetic networks when certain phenotypic behavior is desired.

Tunable phenotypic variability through an autoregulatory alternative sigma factor circuit

Genetically identical individuals in bacterial populations can display significant phenotypic variability. This variability can be functional, for example by allowing a fraction of stress prepared cells to survive an otherwise lethal stress. The optimal fraction of stress prepared cells depends on environmental conditions. However, how bacterial populations modulate their level of phenotypic variability remains unclear. Here we show that the alternative sigma factor σV circuit in Bacillus subtilis generates functional phenotypic variability that can be tuned by stress level, environmental history and genetic perturbations. Using single-cell time-lapse microscopy and microfluidics, we find the fraction of cells that immediately activate σV under lysozyme stress depends on stress level and on a transcriptional memory of previous stress. Iteration between model and experiment reveals that this tunability can be explained by the autoregulatory feedback structure of the sigV operon. As predicted by the model, genetic perturbations to the operon also modulate the response variability. The conserved sigma-anti-sigma autoregulation motif is thus a simple mechanism for bacterial populations to modulate their heterogeneity based on their environment.

Synchronization of gene expression across eukaryotic communities through chemical rhythms

The synchronization is a recurring phenomenon in neuroscience, ecology, human sciences, and biology. However, controlling synchronization in complex eukaryotic consortia on extended spatial-temporal scales remains a major challenge. Here, to address this issue we construct a minimal synthetic system that directly converts chemical signals into a coherent gene expression synchronized among eukaryotic communities through rate-dependent hysteresis. Guided by chemical rhythms, isolated colonies of yeast Saccharomyces cerevisiae oscillate in near-perfect synchrony despite the absence of intercellular coupling or intrinsic oscillations. Increased speed of chemical rhythms and incorporation of feedback in the system architecture can tune synchronization and precision of the cell responses in a growing cell collectives. This synchronization mechanism remain robust under stress in the two-strain consortia composed of toxin-sensitive and toxin-producing strains. The sensitive cells can maintain the spatial-temporal synchronization for extended periods under the rhythmic toxin dosages produced by killer cells. Our study provides a simple molecular framework for generating global coordination of eukaryotic gene expression through dynamic environment.

Bistability in cell signalling and its significance in identifying potential drug targets

MOTIVATION

Bistability is one of the salient dynamical features in various all-or-none kinds of decision-making processes. The presence of bistability in a cell signalling network plays a key role in input-output (I/O) relation. Our study is aiming to capture and emphasise the role of motif structure influencing the I/O relation between two nodes in the context of bistability. Here, a model-based analysis is made to investigate the critical conditions responsible for the emergence of different bistable protein-protein interaction (PPI) motifs and their possible applications to find the potential drug targets.

RESULTS

The global sensitivity analysis is used to identify sensitive parameters and their role in maintaining the bistability. Additionally, the bistable switching through hysteresis is explored to develop an understanding of the underlying mechanisms involved in the cell signalling processes, when significant motifs exhibiting bistability have emerged. Further, we elaborate the application of the results by the implication of the emerged PPI motifs to identify potential drug-targets in three cancer networks, which is validated with existing databases. The influence of stochastic perturbations that could hinder desired functionality of any signalling networks is also described here.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

An Overview on Identification and Regulatory Mechanisms of Long Non-coding RNAs in Fungi

For decades, more and more long non-coding RNAs (lncRNAs) have been confirmed to play important functions in key biological processes of different organisms. At present, most identified lncRNAs and those with known functional roles are from mammalian systems. However, lncRNAs have also been found in primitive eukaryotic fungi, and they have different functions in fungal development, metabolism, and pathogenicity. In this review, we highlight some recent researches on lncRNAs in the primitive eukaryotic fungi, particularly focusing on the identification of lncRNAs and their regulatory roles in diverse biological processes.

Memorizing environmental signals through feedback and feedforward loops

Cells in diverse organisms can store the information of previous environmental conditions for long periods of time. This form of cellular memory adjusts the cell's responses to future challenges, providing fitness advantages in fluctuating environments. Many biological functions, including cellular memory, are mediated by specific recurring patterns of interactions among proteins and genes, known as 'network motifs.' In this review, we focus on three well-characterized network motifs - negative feedback loops, positive feedback loops, and feedforward loops, which underlie different types of cellular memories. We describe the latest studies identifying these motifs in various molecular processes and discuss how the topologies and dynamics of these motifs can enable memory encoding and storage.

Variation, Variegation and Heritable Gene Repression in S. cerevisiae

Phenotypic heterogeneity provides growth advantages for a population upon changes of the environment. In S. cerevisiae, such heterogeneity has been observed as "on/off" states in the expression of individual genes in individual cells. These variations can persist for a limited or extended number of mitotic divisions. Such traits are known to be mediated by heritable chromatin structures, by the mitotic transmission of transcription factors involved in gene regulatory circuits or by the cytoplasmic partition of prions or other unstructured proteins. The significance of such epigenetic diversity is obvious, however, we have limited insight into the mechanisms that generate it. In this review, we summarize the current knowledge of epigenetically maintained heterogeneity of gene expression and point out similarities and converging points between different mechanisms. We discuss how the sharing of limiting repression or activation factors can contribute to cell-to-cell variations in gene expression and to the coordination between short- and long- term epigenetic strategies. Finally, we discuss the implications of such variations and strategies in adaptation and aging.

Cell memory of epithelial-mesenchymal plasticity in cancer

Fundamental biological processes of cell identity and cell fate determination are controlled by complex regulatory networks. These processes require molecular mechanisms that confer cellular phenotypic memory and state persistence. In this minireview, we will summarize mechanisms of cell memory based on regulatory hysteretic feedback loops and explore epigenetic mechanisms widely represented in nature, with special focus on epithelial-to-mesenchymal plasticity. We will also discuss the functional consequences of cell memory and epithelial-to-mesenchymal plasticity dynamics during development and cancer metastasis.

Stable maintenance of hidden switches as a strategy to increase the gene expression stability

In response to severe genetic and environmental perturbations, wild-type organisms can express hidden alternative phenotypes adaptive to such adverse conditions. While our theoretical understanding of the population-level fitness advantage and evolution of phenotypic switching under variable environments has grown, the mechanism by which these organisms maintain phenotypic switching capabilities under static environments remains to be elucidated. Here, using computational simulations, we analyzed the evolution of gene circuits under natural selection and found that different strategies evolved to increase the gene expression stability near the optimum level. In a population comprising bistable individuals, a strategy of maintaining bistability and raising the potential barrier separating the bistable regimes was consistently taken. Our results serve as evidence that hidden bistable switches can be stably maintained during environmental stasis-an essential property enabling the timely release of adaptive alternatives with small genetic changes in the event of substantial perturbations.

Cell-to-cell diversification in ERBB-RAS-MAPK signal transduction that produces cell-type specific growth factor responses

Growth factors regulate cell fates, including their proliferation, differentiation, survival, and death, according to the cell type. Even when the response to a specific growth factor is deterministic for collective cell behavior, significant levels of fluctuation are often observed between single cells. Statistical analyses of single-cell responses provide insights into the mechanism of cell fate decisions but very little is known about the distributions of the internal states of cells responding to growth factors. Using multi-color immunofluorescent staining, we have here detected the phosphorylation of seven elements in the early response of the ERBB-RAS-MAPK system to two growth factors. Among these seven elements, five were analyzed simultaneously in distinct combinations in the same single cells. Although principle component analysis suggested cell-type and input specific phosphorylation patterns, cell-to-cell fluctuation was large. Mutual information analysis suggested that each cell type uses multitrack (bush-like) signal transduction pathways under conditions in which clear fate changes have been reported. The clustering of single-cell response patterns indicated that the fate change in a cell population correlates with the large entropy of the response, suggesting a bet-hedging strategy is used in decision making. A comparison of true and randomized datasets further indicated that this large variation is not produced by simple reaction noise, but is defined by the properties of the signal-processing network.

Chromatin regulatory genes differentially interact in networks to facilitate distinct GAL1 activity and noise profiles

Controlling chromatin state constitutes a major regulatory step in gene expression regulation across eukaryotes. While global cellular features or processes are naturally impacted by chromatin state alterations, little is known about how chromatin regulatory genes interact in networks to dictate downstream phenotypes. Using the activity of the canonical galactose network in yeast as a model, here, we measured the impact of the disruption of key chromatin regulatory genes on downstream gene expression, genetic noise and fitness. Using Trichostatin A and nicotinamide, we characterized how drug-based modulation of global histone deacetylase activity affected these phenotypes. Performing epistasis analysis, we discovered phenotype-specific genetic interaction networks of chromatin regulators. Our work provides comprehensive insights into how the galactose network activity is affected by protein interaction networks formed by chromatin regulators.

Activation of Clustered IFNγ Target Genes Drives Cohesin-Controlled Transcriptional Memory

Cytokine activation of cells induces gene networks involved in inflammation and immunity. Transient gene activation can have a lasting effect even in the absence of ongoing transcription, known as long-term transcriptional memory. Here we explore the nature of the establishment and maintenance of interferon γ (IFNγ)-induced priming of human cells. We find that, although ongoing transcription and local chromatin signatures are short-lived, the IFNγ-primed state stably propagates through at least 14 cell division cycles. Single-cell analysis reveals that memory is manifested by an increased probability of primed cells to engage in target gene expression, correlating with the strength of initial gene activation. Further, we find that strongly memorized genes tend to reside in genomic clusters and that long-term memory of these genes is locally restricted by cohesin. We define the duration, stochastic nature, and molecular mechanisms of IFNγ-induced transcriptional memory, relevant to understanding enhanced innate immune signaling.

Capturing and Understanding the Dynamics and Heterogeneity of Gene Expression in the Living Cell

The regulation of gene expression is a fundamental process enabling cells to respond to internal and external stimuli or to execute developmental programs. Changes in gene expression are highly dynamic and depend on many intrinsic and extrinsic factors. In this review, we highlight the dynamic nature of transient gene expression changes to better understand cell physiology and development in general. We will start by comparing recent in vivo procedures to capture gene expression in real time. Intrinsic factors modulating gene expression dynamics will then be discussed, focusing on chromatin modifications. Furthermore, we will dissect how cell physiology or age impacts on dynamic gene regulation and especially discuss molecular insights into acquired transcriptional memory. Finally, this review will give an update on the mechanisms of heterogeneous gene expression among genetically identical individual cells. We will mainly focus on state-of-the-art developments in the yeast model but also cover higher eukaryotic systems.

Epigenetic Mechanisms Contribute to Evolutionary Adaptation of Gene Network Activity under Environmental Selection

How evolution can be facilitated by epigenetic mechanisms has received refreshed attention recently. To explore the role epigenetic inheritance plays in evolution, we subject isogenic wild-type yeast cells expressing P_GAL1-YFP (yellow fluorescent protein) to selection by daily sorting based on reporter expression. We observe expression-level reductions in multiple replicates sorted for the lowest expression that persist for several days, even after lifting the selection pressure. Reduced expression is due to factors in the galactose (GAL) network rather than global factors. Results using a constitutively active GAL network are in overall agreement with findings with the wild-type network. We find that the local chromatin environment of the reporter has a significant effect on the observed phenotype. Genome sequencing, chromatin immunoprecipitation (ChIP)-qPCR, and sporulation analysis provide further insights into the epigenetic and genetic contributors to the expression changes observed. Our work provides a comprehensive example of the role played by epigenetic mechanisms on gene network evolution.

Luo et al. demonstrate how epigenetic mechanisms contribute to the evolution of gene network activity. Subjecting yeast cells to repeated environmental selection based on the activity of the galactose network, they observe sustained changes in reporter expression level. They characterize the epigenetic and genetic factors contributing to the observed phenotypes.

A Non-genetic Mechanism Involving the Integrin β4/Paxillin Axis Contributes to Chemoresistance in Lung Cancer

Tumor heterogeneity and cisplatin resistance are major causes of tumor relapse and poor survival. Here, we show that in lung cancer, interaction between paxillin (PXN) and integrin β4 (ITGB4), components of the focal adhesion (FA) complex, contributes to cisplatin resistance. Knocking down PXN and ITGB4 attenuated cell growth and improved cisplatin sensitivity, both in 2D and 3D cultures. PXN and ITGB4 independently regulated expression of several genes. In addition, they also regulated expression of common genes including USP1 and VDAC1, which are required for maintaining genomic stability and mitochondrial function, respectively. Mathematical modeling suggested that bistability could lead to stochastic phenotypic switching between cisplatin-sensitive and resistant states in these cells. Consistently, purified subpopulations of sensitive and resistant cells re-created the mixed parental population when cultured separately. Altogether, these data point to an unexpected role of the FA complex in cisplatin resistance and highlight a novel non-genetic mechanism.

The Stress-Responsive Alternative Sigma Factor SigB of Bacillus subtilis and Its Relatives: An Old Friend With New Functions

Alternative sigma factors have led the core RNA polymerase (RNAP) to recognize different sets of promoters to those recognized by the housekeeping sigma A-directed RNAP. This change in RNAP promoter selectivity allows a rapid and flexible reformulation of the genetic program to face environmental and metabolic stimuli that could compromise bacterial fitness. The model bacterium Bacillus subtilis constitutes a matchless living system in the study of the role of alternative sigma factors in gene regulation and physiology. SigB from B. subtilis was the first alternative sigma factor described in bacteria. Studies of SigB during the last 40 years have shown that it controls a genetic universe of more than 150 genes playing crucial roles in stress response, adaption, and survival. Activation of SigB relies on three separate pathways that specifically respond to energy, environmental, and low temperature stresses. SigB homologs, present in other Gram-positive bacteria, also play important roles in virulence against mammals. Interestingly, during recent years, other unexpected B. subtilis responses were found to be controlled by SigB. In particular, SigB controls the efficiencies of spore and biofilm formation, two important features that play critical roles in adaptation and survival in planktonic and sessile B. subtilis communities. In B. subtilis, SigB induces the expression of the Spo0E aspartyl-phosphatase, which is responsible for the blockage of sporulation initiation. The upregulated activity of Spo0E connects the two predominant adaptive pathways (i.e., sporulation and stress response) present in B. subtilis. In addition, the RsbP serine-phosphatase, belonging to the energy stress arm of the SigB regulatory cascade, controls the expression of the key transcription factor SinR to decide whether cells residing in the biofilm remain in and maintain biofilm growth or scape to colonize new niches through biofilm dispersal. SigB also intervenes in the recognition of and response to surrounding microorganisms, a new SigB role that could have an agronomic impact. SigB is induced when B. subtilis is confronted with phytopathogenic fungi (e.g., Fusarium verticillioides) and halts fungal growth to the benefit of plant growth. In this article, we update and review literature on the different regulatory networks that control the activation of SigB and the new roles that have been described the recent years.

A computational model of stem cell molecular mechanism to maintain tissue homeostasis

Stem cells, with their capacity to self-renew and to differentiate to more specialized cell types, play a key role to maintain homeostasis in adult tissues. To investigate how, in the dynamic stochastic environment of a tissue, non-genetic diversity and the precise balance between proliferation and differentiation are achieved, it is necessary to understand the molecular mechanisms of the stem cells in decision making process. By focusing on the impact of stochasticity, we proposed a computational model describing the regulatory circuitry as a tri-stable dynamical system to reveal the mechanism which orchestrate this balance. Our model explains how the distribution of noise in genes, linked to the cell regulatory networks, affects cell decision-making to maintain homeostatic state. The noise effect on tissue homeostasis is achieved by regulating the probability of differentiation and self-renewal through symmetric and/or asymmetric cell divisions. Our model reveals, when mutations due to the replication of DNA in stem cell division, are inevitable, how mutations contribute to either aging gradually or the development of cancer in a short period of time. Furthermore, our model sheds some light on the impact of more complex regulatory networks on the system robustness against perturbations.

The past determines the future: sugar source history and transcriptional memory

Transcriptional reinduction memory is a phenomenon whereby cells “remember” their transcriptional response to a previous stimulus such that subsequent encounters with the same stimulus can result in altered gene expression kinetics. Chromatin structure is thought to play a role in certain transcriptional memory mechanisms, leading to questions as to whether and how memory can be actively maintained and inherited to progeny through cell division. Here we summarize efforts towards dissecting chromatin-based transcriptional memory inheritance of GAL genes in Saccharomyces cerevisiae. We focus on methods and analyses of GAL (as well as MAL and INO) memory in single cells and discuss the challenges in unraveling the underlying mechanisms in yeast and higher eukaryotes.

Drought-Induced Stress Priming in Two Distinct Filamentous Saprotrophic Fungi

Sessile organisms constantly face environmental fluctuations and especially drought is a common stressor. One adaptive mechanism is "stress priming," the ability to cope with a severe stress ("triggering") by retaining information from a previous mild stress event ("priming"). While plants have been extensively investigated for drought-induced stress priming, no information is available for saprotrophic filamentous fungi, which are highly important for nutrient cycles. Here, we investigated the potential for drought-induced stress priming of one strain each of two ubiquitous species, Neurospora crassa and Penicillium chrysogenum. A batch experiment with 4 treatments was conducted on a sandy soil: exposure to priming and/or triggering as well as non-stressed controls. A priming stress was caused by desiccation to pF 4. The samples were then rewetted and after 1-, 7-, or 14-days of recovery triggered (pF 6). After triggering, fungal biomass, respiration, and β-glucosidase activity were quantified. P. chrysogenum showed positive stress priming effects. After 1 day of recovery, biomass as well as β-glucosidase activity and respiration were 0.5 to 5 times higher during triggering. Effects on biomass and activity decreased with prolonged recovery but lasted for 7 days and minor effects were still detectable after 14 days. Without triggering, stress priming had a temporary negative impact on biomass but this reversed after 14 days. For N. crassa, no stress priming effect was observed on the tested variables. The potential for drought-induced stress priming seems to be species specific with potentially high impact on composition and activity of fungal communities considering the expected increase of drought events.

How Do Cells Adapt? Stories Told in Landscapes

Cells adapt to changing environments. Perturb a cell and it returns to a point of homeostasis. Perturb a population and it evolves toward a fitness peak. We review quantitative models of the forces of adaptation and their visualizations on landscapes. While some adaptations result from single mutations or few-gene effects, others are more cooperative, more delocalized in the genome, and more universal and physical. For example, homeostasis and evolution depend on protein folding and aggregation, energy and protein production, protein diffusion, molecular motor speeds and efficiencies, and protein expression levels. Models provide a way to learn about the fitness of cells and cell populations by making and testing hypotheses.

Primed to be strong, primed to be fast: modeling benefits of microbial stress responses

Organisms are prone to different stressors and have evolved various defense mechanisms. One such defense mechanism is priming, where a mild preceding stress prepares the organism toward an improved stress response. This improved response can strongly vary, and primed organisms have been found to respond with one of three response strategies: a shorter delay to stress, a faster buildup of their response or a more intense response. However, a universal comparative assessment, which response is superior under a given environmental setting, is missing. We investigate the benefits of the three improved responses for microorganisms with an ordinary differential equation model, simulating the impact of an external stress on a microbial population that is either naïve or primed. We systematically assess the resulting population performance for different costs associated with priming and stress conditions. Our results show that independent of stress type and priming costs, the stronger primed response is most beneficial for longer stress phases, while the faster and earlier responses increase population performance and survival probability under short stresses. Competition increases priming benefits and promotes the early stress response. This dependence on the ecological context highlights the importance of including primed response strategies into microbial stress ecology.