Dissecting Timing Variability in Yeast Meiosis

Coordination between endoderm progression and mouse gastruloid elongation controls endodermal morphotype choice

Embryonic development is highly robust. Morphogenetic variability between embryos (under ideal conditions) is largely quantitative. This robustness stands in contrast to in vitro embryo-like models, which, like most organoids, can display a high degree of tissue morphogenetic variability. The source of this difference is not fully understood. We use the mouse gastruloid model to study the morphogenetic progression of definitive endoderm (DE) and its divergence. We first catalog the different morphologies and characterize their statistics. We then learn predictive models for DE morphotype based on earlier expression and morphology measurements. Finally, we analyze these models to identify key drivers of morphotype variability and devise gastruloid-specific and global interventions that can lower this variability and steer morphotype choice. In the process, we identify two types of coordination lacking in the in vitro model but required for robust gut-tube formation. This approach can help improve the quality and usability of 3D embryo-like models.

Multi-signal regulation of the GSK-3β homolog Rim11 controls meiosis entry in budding yeast

Starvation in diploid budding yeast cells triggers a cell-fate program culminating in meiosis and spore formation. Transcriptional activation of early meiotic genes (EMGs) hinges on the master regulator Ime1, its DNA-binding partner Ume6, and GSK-3β kinase Rim11. Phosphorylation of Ume6 by Rim11 is required for EMG activation. We report here that Rim11 functions as the central signal integrator for controlling Ume6 phosphorylation and EMG transcription. In nutrient-rich conditions, PKA suppresses Rim11 levels, while TORC1 retains Rim11 in the cytoplasm. Inhibition of PKA and TORC1 induces Rim11 expression and nuclear localization. Remarkably, nuclear Rim11 is required, but not sufficient, for Rim11-dependent Ume6 phosphorylation. In addition, Ime1 is an anchor protein enabling Ume6 phosphorylation by Rim11. Subsequently, Ume6-Ime1 coactivator complexes form and induce EMG transcription. Our results demonstrate how various signaling inputs (PKA/TORC1/Ime1) converge through Rim11 to regulate EMG expression and meiosis initiation. We posit that the signaling-regulatory network elucidated here generates robustness in cell-fate control.

A stochastic vs deterministic perspective on the timing of cellular events

Cells are the fundamental units of life, and like all life forms, they change over time. Changes in cell state are driven by molecular processes; of these many are initiated when molecule numbers reach and exceed specific thresholds, a characteristic that can be described as "digital cellular logic". Here we show how molecular and cellular noise profoundly influence the time to cross a critical threshold-the first-passage time-and map out scenarios in which stochastic dynamics result in shorter or longer average first-passage times compared to noise-less dynamics. We illustrate the dependence of the mean first-passage time on noise for a set of exemplar models of gene expression, auto-regulatory feedback control, and enzyme-mediated catalysis. Our theory provides intuitive insight into the origin of these effects and underscores two important insights: (i) deterministic predictions for cellular event timing can be highly inaccurate when molecule numbers are within the range known for many cells; (ii) molecular noise can significantly shift mean first-passage times, particularly within auto-regulatory genetic feedback circuits.

Deep learning-driven imaging of cell division and cell growth across an entire eukaryotic life cycle

The life cycle of biomedical and agriculturally relevant eukaryotic microorganisms involves complex transitions between proliferative and non-proliferative states such as dormancy, mating, meiosis, and cell division. New drugs, pesticides, and vaccines can be created by targeting specific life cycle stages of parasites and pathogens. However, defining the structure of a microbial life cycle often relies on partial observations that are theoretically assembled in an ideal life cycle path. To create a more quantitative approach to studying complete eukaryotic life cycles, we generated a deep learning-driven imaging framework to track microorganisms across sexually reproducing generations. Our approach combines microfluidic culturing, life cycle stage-specific segmentation of microscopy images using convolutional neural networks, and a novel cell tracking algorithm, FIEST, based on enhancing the overlap of single cell masks in consecutive images through deep learning video frame interpolation. As proof of principle, we used this approach to quantitatively image and compare cell growth and cell cycle regulation across the sexual life cycle of Saccharomyces cerevisiae. We developed a fluorescent reporter system based on a fluorescently labeled Whi5 protein, the yeast analog of mammalian Rb, and a new High-Cdk1 activity sensor, LiCHI, designed to report during DNA replication, mitosis, meiotic homologous recombination, meiosis I, and meiosis II. We found that cell growth preceded the exit from non-proliferative states such as mitotic G1, pre-meiotic G1, and the G0 spore state during germination. A decrease in the total cell concentration of Whi5 characterized the exit from non-proliferative states, which is consistent with a Whi5 dilution model. The nuclear accumulation of Whi5 was developmentally regulated, being at its highest during meiotic exit and spore formation. The temporal coordination of cell division and growth was not significantly different across three sexually reproducing generations. Our framework could be used to quantitatively characterize other single-cell eukaryotic life cycles that remain incompletely described. An off-the-shelf user interface Yeastvision provides free access to our image processing and single-cell tracking algorithms.

Rie1 and Sgn1 form an RNA-binding complex that enforces the meiotic entry cell fate decision

Budding yeast cells have the capacity to adopt few but distinct physiological states depending on environmental conditions. Vegetative cells proliferate rapidly by budding while spores can survive prolonged periods of nutrient deprivation and/or desiccation. Whether or not a yeast cell will enter meiosis and sporulate represents a critical decision that could be lethal if made in error. Most cell fate decisions, including those of yeast, are understood as being triggered by the activation of master transcription factors. However, mechanisms that enforce cell fates posttranscriptionally have been more difficult to attain. Here, we perform a forward genetic screen to determine RNA-binding proteins that affect meiotic entry at the posttranscriptional level. Our screen revealed several candidates with meiotic entry phenotypes, the most significant being RIE1, which encodes an RRM-containing protein. We demonstrate that Rie1 binds RNA, is associated with the translational machinery, and acts posttranscriptionally to enhance protein levels of the master transcription factor Ime1 in sporulation conditions. We also identified a physical binding partner of Rie1, Sgn1, which is another RRM-containing protein that plays a role in timely Ime1 expression. We demonstrate that these proteins act independently of cell size regulation pathways to promote meiotic entry. We propose a model explaining how constitutively expressed RNA-binding proteins, such as Rie1 and Sgn1, can act in cell fate decisions both as switch-like enforcers and as repressors of spurious cell fate activation.

The transcriptional regulator Ume6 is a major driver of early gene expression during gametogenesis

The process of gametogenesis is orchestrated by a dynamic gene expression program, where a vital subset constitutes the early meiotic genes. In budding yeast, the transcription factor Ume6 represses early meiotic gene expression during mitotic growth. However, during the transition from mitotic to meiotic cell fate, early meiotic genes are activated in response to the transcriptional regulator Ime1 through its interaction with Ume6. While it is known that binding of Ime1 to Ume6 promotes early meiotic gene expression, the mechanism of early meiotic gene activation remains elusive. Two competing models have been proposed whereby Ime1 either forms an activator complex with Ume6 or promotes Ume6 degradation. Here, we resolve this controversy. First, we identify the set of genes that are directly regulated by Ume6, including UME6 itself. While Ume6 protein levels increase in response to Ime1, Ume6 degradation occurs much later in meiosis. Importantly, we found that depletion of Ume6 shortly before meiotic entry is detrimental to early meiotic gene activation and gamete formation, whereas tethering of Ume6 to a heterologous activation domain is sufficient to trigger early meiotic gene expression and produce viable gametes in the absence of Ime1. We conclude that Ime1 and Ume6 form an activator complex. While Ume6 is indispensable for early meiotic gene expression, Ime1 primarily serves as a transactivator for Ume6.

Identification of 14-3-3 proteins, Polo kinase, and RNA-binding protein Pes4 as key regulators of meiotic commitment in budding yeast

The initiation of the cell division process of meiosis requires exogenous signals that activate internal gene regulatory networks. Meiotic commitment ensures the irreversible continuation of meiosis, even upon withdrawal of the meiosis-inducing signals. A loss of meiotic commitment can cause highly abnormal polyploid cells and can ultimately lead to germ cell tumors. Despite the importance of meiotic commitment, only a few genes involved in commitment are known. In this study, we have discovered six new regulators of meiotic commitment in budding yeast: the Bcy1 protein involved in nutrient sensing, the meiosis-specific kinase Ime2, Polo kinase Cdc5, RNA-binding protein Pes4, and the 14-3-3 proteins Bmh1 and Bmh2. Decreased levels of these proteins cause a failure to establish or maintain meiotic commitment. Importantly, we found that Bmh1 and Bmh2 are involved in multiple processes throughout meiosis and in meiotic commitment. First, cells depleted of both Bmh1 and Bmh2 trigger the pachytene checkpoint, likely due to a role in DNA double-strand break repair. Second, Bmh1 interacts directly with the middle meiosis transcription factor Ndt80, and both Bmh1 and Bmh2 maintain Ndt80 levels. Third, Bmh1 and Bmh2 bind to Cdc5 and enhance its kinase activity. Finally, Bmh1 binds to Pes4, which regulates the timing of the translation of several mRNAs in meiosis II and is required to maintain meiotic commitment. Our results demonstrate that meiotic commitment is actively maintained throughout meiosis, with the 14-3-3 proteins and Polo kinase serving as key regulators of this developmental program.

Cdc14 spatiotemporally dephosphorylates Atg13 to activate autophagy during meiotic divisions

Autophagy is a conserved eukaryotic lysosomal degradation pathway that responds to environmental and cellular cues. Autophagy is essential for the meiotic exit and sporulation in budding yeast, but the underlying molecular mechanisms remain unknown. Here, we show that autophagy is maintained during meiosis and stimulated in anaphase I and II. Cells with higher levels of autophagy complete meiosis faster, and genetically enhanced autophagy increases meiotic kinetics and sporulation efficiency. Strikingly, our data reveal that the conserved phosphatase Cdc14 regulates meiosis-specific autophagy. Cdc14 is activated in anaphase I and II, accompanying its subcellular relocation from the nucleolus to the cytoplasm, where it dephosphorylates Atg13 to stimulate Atg1 kinase activity and thus autophagy. Together, our findings reveal a meiosis-tailored mechanism that spatiotemporally controls meiotic autophagy activity to ensure meiosis progression, exit, and sporulation.

Controlling gene expression timing through gene regulatory architecture

Gene networks typically involve the regulatory control of multiple genes with related function. This connectivity enables correlated control of the levels and timing of gene expression. Here we study how gene expression timing in the single-input module motif can be encoded in the regulatory DNA of a gene. Using stochastic simulations, we examine the role of binding affinity, TF regulatory function and network size in controlling the mean first-passage time to reach a fixed fraction of steady-state expression for both an auto-regulated TF gene and a target gene. We also examine how the variability in first-passage time depends on these factors. We find that both network size and binding affinity can dramatically speed up or slow down the response time of network genes, in some cases predicting more than a 100-fold change compared to that for a constitutive gene. Furthermore, these factors can also significantly impact the fidelity of this response. Importantly, these effects do not occur at “extremes” of network size or binding affinity, but rather in an intermediate window of either quantity.

Meiosis initiation: a story of two sexes in all creatures great and small

Meiosis facilitates diversity across individuals and serves as a major driver of evolution. However, understanding how meiosis begins is complicated by fundamental differences that exist between sexes and species. Fundamental meiotic research is further hampered by a current lack of human meiotic cells lines. Consequently, much of what we know relies on data from model organisms. However, contextualising findings from yeast, worms, flies and mice can be challenging, due to marked differences in both nomenclature and the relative timing of meiosis. In this review, we set out to combine current knowledge of signalling and transcriptional pathways that control meiosis initiation across the sexes in a variety of organisms. Furthermore, we highlight the emerging links between meiosis initiation and oncogenesis, which might explain the frequent re-expression of normally silent meiotic genes in a variety of human cancers.

Transcription levels of a noncoding RNA orchestrate opposing regulatory and cell fate outcomes in yeast

Transcription through noncoding regions of the genome is pervasive. How these transcription events regulate gene expression remains poorly understood. Here, we report that, in S. cerevisiae, the levels of transcription through a noncoding region, IRT2, located upstream in the promoter of the inducer of meiosis, IME1, regulate opposing chromatin and transcription states. At low levels, the act of IRT2 transcription promotes histone exchange, delivering acetylated histone H3 lysine 56 to chromatin locally. The subsequent open chromatin state directs transcription factor recruitment and induces downstream transcription to repress the IME1 promoter and meiotic entry. Conversely, increasing transcription turns IRT2 into a repressor by promoting transcription-coupled chromatin assembly. The two opposing functions of IRT2 transcription shape a regulatory circuit, which ensures a robust cell-type-specific control of IME1 expression and yeast meiosis. Our data illustrate how intergenic transcription levels are key to controlling local chromatin state, gene expression, and cell fate outcomes.

Dormancy-to-death transition in yeast spores occurs due to gradual loss of gene-expressing ability

Dormancy is colloquially considered as extending lifespan by being still. Starved yeasts form dormant spores that wake-up (germinate) when nutrients reappear but cannot germinate (die) after some time. What sets their lifespans and how they age are open questions because what processes occur-and by how much-within each dormant spore remains unclear. With single-cell-level measurements, we discovered how dormant yeast spores age and die: spores have a quantifiable gene-expressing ability during dormancy that decreases over days to months until it vanishes, causing death. Specifically, each spore has a different probability of germinating that decreases because its ability to-without nutrients-express genes decreases, as revealed by a synthetic circuit that forces GFP expression during dormancy. Decreasing amounts of molecules required for gene expression-including RNA polymerases-decreases gene-expressing ability which then decreases chances of germinating. Spores gradually lose these molecules because they are produced too slowly compared with their degradations, causing gene-expressing ability to eventually vanish and, thus, death. Our work provides a systems-level view of dormancy-to-death transition.

Synchronized emergence under diatom sperm competition

Appropriate timing of mating is crucial for the success of individuals. However, we know little about factors that explain variation in mating time in unicellular organisms. Unicellular eukaryotes often have facultative sexuality, that is, the less frequent sex is occasionally induced after long clonal reproduction. Thus, males originated from clonemates could be non-negligible mating rivals. Using a centric diatom whose clonal cells differentiate into either male or female, we analysed whether males (spermatogonium) compete or cooperate with each other. By analysing differentiation timing with hypotheses based on evolutionary game theory, we estimated that a substantial part of the variation in the mating timing of the diatom can be explained by results of optimization through interactions among selfish individuals rather than cooperation among clonemates. However, the competition is fiercer than expected owing to excessive synchronization, which was realized by adjustment of meiotic duration: cells completed mitotic division in the earlier mating phase took longer to enter into meiosis, whereas late-dividing cells entered into meiosis more quickly. Adjacent cells tended to synchronize, and model analyses suggest that cell-cell interaction can create a gap between the optimal and actual decisions. Our results provide insights into the evolution of cellular decision making and its restriction.

Temporal precision of molecular events with regulation and feedback

Cellular behaviors such as migration, division, and differentiation rely on precise timing, and yet the molecular events that govern these behaviors are highly stochastic. We investigate regulatory strategies that decrease the timing noise of molecular events. Autoregulatory feedback increases noise. Yet we find that in the presence of regulation by a second species, autoregulatory feedback decreases noise. To explain this finding, we develop a method to calculate the optimal regulation function that minimizes the timing noise. The method reveals that the combination of feedback and regulation minimizes noise by maximizing the number of molecular events that must happen in sequence before a threshold is crossed. We compute the optimal timing precision for all two-node networks with regulation and feedback, derive a generic lower bound on timing noise, and discuss our results in the context of neuroblast migration during Caenorhabditis elegans development.

Regulated repression governs the cell fate promoter controlling yeast meiosis

Intrinsic signals and external cues from the environment drive cell fate decisions. In budding yeast, the decision to enter meiosis is controlled by nutrient and mating-type signals that regulate expression of the master transcription factor for meiotic entry, IME1. How nutrient signals control IME1 expression remains poorly understood. Here, we show that IME1 transcription is regulated by multiple sequence-specific transcription factors (TFs) that mediate association of Tup1-Cyc8 co-repressor to its promoter. We find that at least eight TFs bind the IME1 promoter when nutrients are ample. Remarkably, association of these TFs is highly regulated by different nutrient cues. Mutant cells lacking three TFs (Sok2/Phd1/Yap6) displayed reduced Tup1-Cyc8 association, increased IME1 expression, and earlier onset of meiosis. Our data demonstrate that the promoter of a master regulator is primed for rapid activation while repression by multiple TFs mediating Tup1-Cyc8 recruitment dictates the fate decision to enter meiosis.

Yeast Sporulation and [SMAUG+] Prion: Faster Is Not Always Better

Chakravarty et al. (2019) and Itakura et al. (2019) report that the yeast RNA-binding protein Vts1 can convert into the [SMAUG⁺] prion state and delay meiosis commitment in response to starvation. It enables budding yeast to optimize their sporulation efficiency depending on how quickly nutrient availability fluctuates in their environment.

Stochastic fluctuations in apoptotic threshold of tumour cells can enhance apoptosis and combat fractional killing

Fractional killing, which is a significant impediment to successful chemotherapy, is observed even in a population of genetically identical cancer cells exposed to apoptosis-inducing agents. This phenomenon arises not from genetic mutation but from cell-to-cell variation in the activation timing and level of the proteins that regulates apoptosis. To understand the mechanism behind the phenomenon, we formulate complex fractional killing processes as a first-passage time (FPT) problem with a stochastically fluctuating boundary. Analytical calculations are performed for the FPT distribution in a toy model of stochastic p53 gene expression, where the cancer cell is killed only when the p53 expression level crosses an active apoptotic threshold. Counterintuitively, we find that threshold fluctuations can effectively enhance cellular killing by significantly decreasing the mean time that the p53 protein reaches the threshold level for the first time. Moreover, faster fluctuations lead to the killing of more cells. These qualitative results imply that fluctuations in threshold are a non-negligible stochastic source, and can be taken as a strategy for combating fractional killing of cancer cells.

Control strategies for the timing of intracellular events

While the timing of intracellular events is essential for many cellular processes, gene expression inside a single cell can exhibit substantial cell-to-cell variability, raising the question of how cells ensure precision in event timing despite such stochasticity. We address this question by analyzing a biologically reasonable model of gene expression in the context of first passage time (FPT), focusing on two experimentally measurable statistics: mean FPT (MFPT) and timing variability (TV). We show that (1) transcriptional burst size (BS) and burst frequency (BF) can minimize the TV; (2) translational BS monotonically reduces the MFPT to a nonzero low bound; (3) the timescale of promoter kinetics can minimize both the MFPT and the TV, depending on the ratio of the on-switching rate over the off-switching rate; and (4) positive feedback regulation of any form can all minimize the TV, whereas negative feedback regulation of transcriptional BF or BS always enhances the TV. These control strategies can have broad implications for diverse cellular processes relying on precise temporal triggering of events.

Stochastic and Heterogeneous Cancer Cell Migration: Experiment and Theory

Cell migration, an essential process for normal cell development and cancer metastasis, differs from a simple random walk: the mean-square displacement (〈(Δr)²(t)〉) of cells sometimes shows non-Fickian behavior, and the spatiotemporal correlation function (G(r, t)) of cells is often non-Gaussian. We find that this intriguing cell migration should be attributed to heterogeneity in a cell population, even one with a homogeneous genetic background. There are two limiting types of heterogeneity in a cell population: cellular heterogeneity and temporal heterogeneity. Cellular heterogeneity accounts for the cell-to-cell variation in migration capacity, while temporal heterogeneity arises from the temporal noise in the migration capacity of single cells. We illustrate that both cellular and temporal heterogeneity need to be taken into account simultaneously to elucidate cell migration. We investigate the two-dimensional migration of A549 lung cancer cells using time-lapse microscopy and find that the migration of A549 cells is Fickian but has a non-Gaussian spatiotemporal correlation. We find that when a theoretical model considers both cellular and temporal heterogeneity, the model reproduces all of the anomalous behaviors of cancer cell migration.

Learning time-varying information flow from single-cell epithelial to mesenchymal transition data

Cellular regulatory networks are not static, but continuously reconfigure in response to stimuli via alterations in protein abundance and confirmation. However, typical computational approaches treat them as static interaction networks derived from a single time point. Here, we provide methods for learning the dynamic modulation of relationships between proteins from static single-cell data. We demonstrate our approach using TGFß induced epithelial-to-mesenchymal transition (EMT) in murine breast cancer cell line, profiled with mass cytometry. We take advantage of the asynchronous rate of transition to EMT in the data and derive a pseudotime EMT trajectory. We propose methods for visualizing and quantifying time-varying edge behavior over the trajectory, and a metric of edge dynamism to predict the effect of drug perturbations on EMT.

Integration of Multiple Metabolic Signals Determines Cell Fate Prior to Commitment

Cell-fate decisions are central to the survival and development of both uni- and multicellular organisms. It remains unclear when and to what degree cells can decide on future fates prior to commitment. This uncertainty stems from experimental and theoretical limitations in measuring and integrating multiple signals at the single-cell level during a decision process. Here, we combine six-color live-cell imaging with the Bayesian method of statistical evidence to study the meiosis/quiescence decision in budding yeast. Integration of multiple upstream metabolic signals predicts individual cell fates with high probability well before commitment. Cells "decide" their fates before birth, well before the activation of pathways characteristic of downstream cell fates. This decision, which remains stable through several cell cycles, occurs when multiple metabolic parameters simultaneously cross cell-fate-specific thresholds. Taken together, our results show that cells can decide their future fates long before commitment mechanisms are activated.

Prolonged cyclin-dependent kinase inhibition results in septin perturbations during return to growth and mitosis

By investigating how yeast cells coordinate polarity and division in a special type of cell division called return to growth, Gihana et al. discover that although checkpoints are normally beneficial, prolonged activation of the morphogenesis checkpoint is instead detrimental to the cell.

We investigated how Saccharomyces cerevisiae coordinate polarization, budding, and anaphase during a unique developmental program called return to growth (RTG) in which cells in meiosis return to mitosis upon nutrient shift. Cells reentering mitosis from prophase I deviate from the normal cell cycle by budding in G2 instead of G1. We found that cells do not maintain the bipolar budding pattern, a characteristic of diploid cells. Furthermore, strict temporal regulation of M-phase cyclin-dependent kinase (CDK; M-CDK) is important for polarity establishment and morphogenesis. Cells with premature M-CDK activity caused by loss of checkpoint kinase Swe1 failed to polarize and underwent anaphase without budding. Mutants with increased Swe1-dependent M-CDK inhibition showed additional or more penetrant phenotypes in RTG than mitosis, including elongated buds, multiple buds, spindle mispositioning, and septin perturbation. Surprisingly, the enhanced and additional phenotypes were not exclusive to RTG but also occurred with prolonged Swe1-dependent CDK inhibition in mitosis. Our analysis reveals that prolonged activation of the Swe1-dependent checkpoint can be detrimental instead of beneficial.

Temporal precision of regulated gene expression

Important cellular processes such as migration, differentiation, and development often rely on precise timing. Yet, the molecular machinery that regulates timing is inherently noisy. How do cells achieve precise timing with noisy components? We investigate this question using a first-passage-time approach, for an event triggered by a molecule that crosses an abundance threshold and that is regulated by either an accumulating activator or a diminishing repressor. We find that either activation or repression outperforms an unregulated strategy. The optimal regulation corresponds to a nonlinear increase in the amount of the target molecule over time, arises from a tradeoff between minimizing the timing noise of the regulator and that of the target molecule itself, and is robust to additional effects such as bursts and cell division. Our results are in quantitative agreement with the nonlinear increase and low noise of mig-1 gene expression in migrating neuroblast cells during Caenorhabditis elegans development. These findings suggest that dynamic regulation may be a simple and powerful strategy for precise cellular timing.

Cells control important processes with precise timing, even though their underlying molecular machinery is inherently imprecise. In the case of Caenorhabditis elegans development, migrating neuroblast cells produce a molecule until a certain abundance is reached, at which time the cells stop moving. Precise timing of this event is critical to C. elegans development, and here we investigate how it can be achieved. Specifically, we investigate regulation of the molecule production by either an accumulating activator or a diminishing repressor. Our results are consistent with the nonlinear increase and low noise of gene expression observed in the C. elegans cells.

A regulatory circuit of two lncRNAs and a master regulator directs cell fate in yeast

Transcription of long noncoding RNAs (lncRNAs) regulates local gene expression in eukaryotes. Many examples of how a single lncRNA controls the expression of an adjacent or nearby protein-coding gene have been described. Here we examine the regulation of a locus consisting of two contiguous lncRNAs and the master regulator for entry into yeast meiosis, IME1. We find that the cluster of two lncRNAs together with several transcription factors form a regulatory circuit by which IME1 controls its own promoter and thereby promotes its own expression. Inhibition or stimulation of this unusual feedback circuit affects timing and rate of IME1 accumulation, and hence the ability for cells to enter meiosis. Our data demonstrate that orchestrated transcription through two contiguous lncRNAs promotes local gene expression and determines a critical cell fate decision.

Single-cell variability in multicellular organisms

Noisy gene expression is of fundamental importance to single cells, and is therefore widely studied in single-celled organisms. Extending these studies to multicellular organisms is challenging since their cells are generally not isolated, but individuals in a tissue. Cell–cell coupling via signalling, active transport or pure diffusion, ensures that tissue-bound cells are neither fully independent of each other, nor an entirely homogeneous population. In this article, we show that increasing the strength of coupling between cells can either increase or decrease the single-cell variability (and, therefore, the heterogeneity of the tissue), depending on the statistical properties of the underlying genetic network. We confirm these predictions using spatial stochastic simulations of simple genetic networks, and experimental data from animal and plant tissues. The results suggest that cell–cell coupling may be one of several noise-control strategies employed by multicellular organisms, and highlight the need for a deeper understanding of multicellular behaviour.

While gene expression noise in single-celled organisms is well understood, it is less so in the context of tissues. Here the authors show that coupling between cells in tissues can increase or decrease cell-to-cell variability depending on the level of noise intrinsic to the regulatory networks.

SAIC: an iterative clustering approach for analysis of single cell RNA-seq data

Background

Research interests toward single cell analysis have greatly increased in basic, translational and clinical research areas recently, as advances in whole-transcriptome amplification technique allow scientists to get accurate sequencing result at single cell level. An important step in the single-cell transcriptome analysis is to identify distinct cell groups that have different gene expression patterns. Currently there are limited bioinformatics approaches available for single-cell RNA-seq analysis. Many studies rely on principal component analysis (PCA) with arbitrary parameters to identify the genes that will be used to cluster the single cells.

Results

We have developed a novel algorithm, called SAIC (Single cell Analysis via Iterative Clustering), that identifies the optimal set of signature genes to separate single cells into distinct groups. Our method utilizes an iterative clustering approach to perform an exhaustive search for the best parameters within the search space, which is defined by a number of initial centers and P values. The end point is identification of a signature gene set that gives the best separation of the cell clusters. Using a simulated data set, we showed that SAIC can successfully identify the pre-defined signature gene sets that can correctly separated the cells into predefined clusters. We applied SAIC to two published single cell RNA-seq datasets. For both datasets, SAIC was able to identify a subset of signature genes that can cluster the single cells into groups that are consistent with the published results. The signature genes identified by SAIC resulted in better clusters of cells based on DB index score, and many genes also showed tissue specific expression.

Conclusions

In summary, we have developed an efficient algorithm to identify the optimal subset of genes that separate single cells into distinct clusters based on their expression patterns. We have shown that it performs better than PCA method using published single cell RNA-seq datasets.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-4019-5) contains supplementary material, which is available to authorized users.

Stochastic timing in gene expression for simple regulatory strategies

Timing is essential for many cellular processes, from cellular responses to external stimuli to the cell cycle and circadian clocks. Many of these processes are based on gene expression. For example, an activated gene may be required to reach in a precise time a threshold level of expression that triggers a specific downstream process. However, gene expression is subject to stochastic fluctuations, naturally inducing an uncertainty in this threshold-crossing time with potential consequences on biological functions and phenotypes. Here, we consider such ‘timing fluctuations’ and we ask how they can be controlled. Our analytical estimates and simulations show that, for an induced gene, timing variability is minimal if the threshold level of expression is approximately half of the steady-state level. Timing fluctuations can be reduced by increasing the transcription rate, while they are insensitive to the translation rate. In presence of self-regulatory strategies, we show that self-repression reduces timing noise for threshold levels that have to be reached quickly, while self-activation is optimal at long times. These results lay a framework for understanding stochasticity of endogenous systems such as the cell cycle, as well as for the design of synthetic trigger circuits.

The chromatin remodeling complex Swi/Snf regulates splicing of meiotic transcripts in Saccharomyces cerevisiae

Despite its relatively streamlined genome, there are important examples of regulated RNA splicing in Saccharomyces cerevisiae, such as splicing of meiotic transcripts. Like other eukaryotes, S. cerevisiae undergoes a dramatic reprogramming of gene expression during meiosis, including regulated splicing of a number of crucial meiosis-specific RNAs. Splicing of a subset of these is dependent upon the splicing activator Mer1. Here we show a crucial role for the chromatin remodeler Swi/Snf in regulation of splicing of meiotic genes and find that the complex affects meiotic splicing in two ways. First, we show that Swi/Snf regulates nutrient-dependent downregulation of ribosomal protein encoding RNAs, leading to the redistribution of spliceosomes from this abundant class of intron-containing RNAs (the ribosomal protein genes) to Mer1-regulated transcripts. We also demonstrate that Mer1 expression is dependent on Snf2, its acetylation state and histone H3 lysine 9 acetylation at the MER1 locus. Hence, Snf2 exerts systems level control of meiotic gene expression through two temporally distinct mechanisms, demonstrating that it is a key regulator of meiotic splicing in S. cerevisiae. We also reveal an evolutionarily conserved mechanism whereby the cell redirects its energy from maintaining its translational capacity to the process of meiosis.

Transcription of the mating-type-regulated lncRNA IRT1 is governed by TORC1 and PKA

Cell fate decisions are controlled by multiple cell-intrinsic and -extrinsic factors. In budding yeast, the decision to enter gametogenesis or sporulation is dictated by nutrient availability and mating type. Recently, we showed that in diploid cells harbouring opposite mating types (MATa and MATα), the protein kinase A (PKA) and target of rapamycin complex I (TORC1) signalling pathways integrate at the promoter of the master regulatory transcription factor IME1 to control sporulation via nutrient availability (Weidberg, et al. 2016). In cells with a single mating type (MATa or MATα), however, IME1 is repressed by transcription through the IME1 promoter of a long non-coding RNA called IRT1, which prevents this cell type from undergoing sporulation. Here, we investigated the role of nutrient signalling in mating-type control of IME1. We find that expression of IRT1, like IME1 itself, depends on nutrient availability and the activities of PKA and TORC1. IRT1 transcription is repressed when nutrients are ample and TORC1 and PKA are active. In contrast, inhibition of PKA and TORC1 is sufficient to recruit Rme1 to the IRT1 promoter and induce IRT1-mediated repression of IME1. Finally, we provide evidence that IRT1 and IME1 are co-repressed by the Tup1-Cyc8 complex when nutrients are ample. Thus, in cells with a single mating-type nutrient availability regulates mating-type repression of IME1 and sporulation. Our results indicate that there is a hierarchy between nutrient and mating-type signals in controlling the decision to enter sporulation.

First-passage time approach to controlling noise in the timing of intracellular events

In the noisy cellular environment, gene products are subject to inherent random fluctuations in copy numbers over time. How cells ensure precision in the timing of key intracellular events despite such stochasticity is an intriguing fundamental problem. We formulate event timing as a first-passage time problem, where an event is triggered when the level of a protein crosses a critical threshold for the first time. Analytical calculations are performed for the first-passage time distribution in stochastic models of gene expression. Derivation of these formulas motivates an interesting question: Is there an optimal feedback strategy to regulate the synthesis of a protein to ensure that an event will occur at a precise time, while minimizing deviations or noise about the mean? Counterintuitively, results show that for a stable long-lived protein, the optimal strategy is to express the protein at a constant rate without any feedback regulation, and any form of feedback (positive, negative, or any combination of them) will always amplify noise in event timing. In contrast, a positive feedback mechanism provides the highest precision in timing for an unstable protein. These theoretical results explain recent experimental observations of single-cell lysis times in bacteriophage [Formula: see text] Here, lysis of an infected bacterial cell is orchestrated by the expression and accumulation of a stable [Formula: see text] protein up to a threshold, and precision in timing is achieved via feedforward rather than feedback control. Our results have broad implications for diverse cellular processes that rely on precise temporal triggering of events.

Temporal Expression of a Master Regulator Drives Synchronous Sporulation in Budding Yeast

Yeast cells enter and undergo gametogenesis relatively asynchronously, making it technically challenging to perform stage-specific genomic and biochemical analyses. Cell-to-cell variation in the expression of the master regulator of entry into sporulation, IME1, has been implicated to be the underlying cause of asynchronous sporulation. Here, we find that timing of IME1 expression is of critical importance for inducing cells to undergo sporulation synchronously. When we force expression of IME1 from an inducible promoter in cells incubated in sporulation medium for 2 hr, the vast majority of cells exhibit synchrony during premeiotic DNA replication and meiotic divisions. Inducing IME1 expression too early or too late affects the synchrony of sporulation. Surprisingly, our approach for synchronous sporulation does not require growth in acetate-containing medium, but can be achieved in cells grown in rich medium until saturation. Our system requires solely IME1, because the expression of the N6-methyladenosine methyltransferase IME4, another key regulator of early sporulation, is controlled by IME1 itself. The approach described here can be combined easily with other stage-specific synchronization methods, and thereby applied to study specific stages of sporulation, or the complete sporulation program.

Positive Feedback Keeps Duration of Mitosis Temporally Insulated from Upstream Cell-Cycle Events

Cell division is characterized by a sequence of events by which a cell gives rise to two daughter cells. Quantitative measurements of cell-cycle dynamics in single cells showed that despite variability in G1-, S-, and G2 phases, duration of mitosis is short and remarkably constant. Surprisingly, there is no correlation between cell-cycle length and mitotic duration, suggesting that mitosis is temporally insulated from variability in earlier cell-cycle phases. By combining live cell imaging and computational modeling, we showed that positive feedback is the molecular mechanism underlying the temporal insulation of mitosis. Perturbing positive feedback gave rise to a sluggish, variable entry and progression through mitosis and uncoupled duration of mitosis from variability in cell cycle length. We show that positive feedback is important to keep mitosis short, constant, and temporally insulated and anticipate it might be a commonly used regulatory strategy to create modularity in other biological systems.

Integrated live imaging and molecular profiling of embryoid bodies reveals a synchronized progression of early differentiation

Embryonic stem cells can spontaneously differentiate into cell types of all germ layers within embryoid bodies (EBs) in a highly variable manner. Whether there exists an intrinsic differentiation program common to all EBs is unknown. Here, we present a novel combination of high-throughput live two-photon imaging and gene expression profiling to study early differentiation dynamics spontaneously occurring within developing EBs. Onset timing of Brachyury-GFP was highly variable across EBs, while the spatial patterns as well as the dynamics of mesendodermal progression following onset were remarkably similar. We therefore defined a 'developmental clock' using the Brachyury-GFP signal onset timing. Mapping snapshot gene expression measurements to this clock revealed their temporal trends, indicating that loss of pluripotency, formation of primitive streak and mesodermal lineage progression are synchronized in EBs. Exogenous activation of Wnt or BMP signaling accelerated the intrinsic clock. CHIR down-regulated Wnt3, allowing insights into dependency mechanisms between canonical Wnt signaling and multiple genes. Our findings reveal a developmental clock characteristic of an early differentiation program common to all EBs, further establishing them as an in vitro developmental model.

Nutrient Control of Yeast Gametogenesis Is Mediated by TORC1, PKA and Energy Availability

Cell fate choices are tightly controlled by the interplay between intrinsic and extrinsic signals, and gene regulatory networks. In Saccharomyces cerevisiae, the decision to enter into gametogenesis or sporulation is dictated by mating type and nutrient availability. These signals regulate the expression of the master regulator of gametogenesis, IME1. Here we describe how nutrients control IME1 expression. We find that protein kinase A (PKA) and target of rapamycin complex I (TORC1) signalling mediate nutrient regulation of IME1 expression. Inhibiting both pathways is sufficient to induce IME1 expression and complete sporulation in nutrient-rich conditions. Our ability to induce sporulation under nutrient rich conditions allowed us to show that respiration and fermentation are interchangeable energy sources for IME1 transcription. Furthermore, we find that TORC1 can both promote and inhibit gametogenesis. Down-regulation of TORC1 is required to activate IME1. However, complete inactivation of TORC1 inhibits IME1 induction, indicating that an intermediate level of TORC1 signalling is required for entry into sporulation. Finally, we show that the transcriptional repressor Tup1 binds and represses the IME1 promoter when nutrients are ample, but is released from the IME1 promoter when both PKA and TORC1 are inhibited. Collectively our data demonstrate that nutrient control of entry into sporulation is mediated by a combination of energy availability, TORC1 and PKA activities that converge on the IME1 promoter.

Noise Expands the Response Range of the Bacillus subtilis Competence Circuit

Gene regulatory circuits must contend with intrinsic noise that arises due to finite numbers of proteins. While some circuits act to reduce this noise, others appear to exploit it. A striking example is the competence circuit in Bacillus subtilis, which exhibits much larger noise in the duration of its competence events than a synthetically constructed analog that performs the same function. Here, using stochastic modeling and fluorescence microscopy, we show that this larger noise allows cells to exit terminal phenotypic states, which expands the range of stress levels to which cells are responsive and leads to phenotypic heterogeneity at the population level. This is an important example of how noise confers a functional benefit in a genetic decision-making circuit.

Fluctuations, or “noise”, in the response of a system is usually thought to be harmful. However, it is becoming increasingly clear that in single-celled organisms, noise can sometimes help cells survive. This is because noise can enhance the diversity of responses within a cell population. In this study, we identify a novel benefit of noise in the competence response of a population of Bacillus subtilis bacteria, where competence is the ability of bacteria to take in DNA from their environment when under stress. We use computational modeling and experiments to show that noise increases the range of stress levels for which these bacteria exhibit a highly dynamic response, meaning that they are neither unresponsive, nor permanently in the competent state. Since a dynamic response is thought to be optimal for survival, this study suggests that noise is exploited to increase the fitness of the bacterial population.

Water-Transfer Slows Aging in Saccharomyces cerevisiae

Transferring Saccharomyces cerevisiae cells to water is known to extend their lifespan. However, it is unclear whether this lifespan extension is due to slowing the aging process or merely keeping old yeast alive. Here we show that in water-transferred yeast, the toxicity of polyQ proteins is decreased and the aging biomarker 47Q aggregates at a reduced rate and to a lesser extent. These beneficial effects of water-transfer could not be reproduced by diluting the growth medium and depended on de novo protein synthesis and proteasomes levels. Interestingly, we found that upon water-transfer 27 proteins are downregulated, 4 proteins are upregulated and 81 proteins change their intracellular localization, hinting at an active genetic program enabling the lifespan extension. Furthermore, the aging-related deterioration of the heat shock response (HSR), the unfolded protein response (UPR) and the endoplasmic reticulum-associated protein degradation (ERAD), was largely prevented in water-transferred yeast, as the activities of these proteostatic network pathways remained nearly as robust as in young yeast. The characteristics of young yeast that are actively maintained upon water-transfer indicate that the extended lifespan is the outcome of slowing the rate of the aging process.

The asymmetry of telomere replication contributes to replicative senescence heterogeneity

In eukaryotes, the absence of telomerase results in telomere shortening, eventually leading to replicative senescence, an arrested state that prevents further cell divisions. While replicative senescence is mainly controlled by telomere length, the heterogeneity of its onset is not well understood. This study proposes a mathematical model based on the molecular mechanisms of telomere replication and shortening to decipher the causes of this heterogeneity. Using simulations fitted on experimental data obtained from individual lineages of senescent Saccharomyces cerevisiae cells, we decompose the sources of senescence heterogeneity into interclonal and intraclonal components, and show that the latter is based on the asymmetry of the telomere replication mechanism. We also evidence telomere rank-switching events with distinct frequencies in short-lived versus long-lived lineages, revealing that telomere shortening dynamics display important variations. Thus, the intrinsic heterogeneity of replicative senescence and its consequences find their roots in the asymmetric structure of telomeres.

Noise in gene expression is coupled to growth rate

Genetically identical cells exposed to the same environment display variability in gene expression (noise), with important consequences for the fidelity of cellular regulation and biological function. Although population average gene expression is tightly coupled to growth rate, the effects of changes in environmental conditions on expression variability are not known. Here, we measure the single-cell expression distributions of approximately 900 Saccharomyces cerevisiae promoters across four environmental conditions using flow cytometry, and find that gene expression noise is tightly coupled to the environment and is generally higher at lower growth rates. Nutrient-poor conditions, which support lower growth rates, display elevated levels of noise for most promoters, regardless of their specific expression values. We present a simple model of noise in expression that results from having an asynchronous population, with cells at different cell-cycle stages, and with different partitioning of the cells between the stages at different growth rates. This model predicts non-monotonic global changes in noise at different growth rates as well as overall higher variability in expression for cell-cycle–regulated genes in all conditions. The consistency between this model and our data, as well as with noise measurements of cells growing in a chemostat at well-defined growth rates, suggests that cell-cycle heterogeneity is a major contributor to gene expression noise. Finally, we identify gene and promoter features that play a role in gene expression noise across conditions. Our results show the existence of growth-related global changes in gene expression noise and suggest their potential phenotypic implications.

Is the Cell Nucleus a Necessary Component in Precise Temporal Patterning?

One of the functions of the cell nucleus is to help regulate gene expression by controlling molecular traffic across the nuclear envelope. Here we investigate, via stochastic simulation, what effects, if any, does segregation of a system into the nuclear and cytoplasmic compartments have on the stochastic properties of a motif with a negative feedback. One of the effects of the nuclear barrier is to delay the nuclear protein concentration, allowing it to behave in a switch-like manner. We found that this delay, defined as the time for the nuclear protein concentration to reach a certain threshold, has an extremely narrow distribution. To show this, we considered two models. In the first one, the proteins could diffuse freely from cytoplasm to nucleus (simple model); and in the second one, the proteins required assistance from a special class of proteins called importins. For each model, we generated fifty parameter sets, chosen such that the temporal profiles they effectuated were very similar, and whose average threshold time was approximately 150 minutes. The standard deviation of the threshold times computed over one hundred realizations were found to be between 1.8 and 7.16 minutes across both models. To see whether a genetic motif in a prokaryotic cell can achieve this degree of precision, we also simulated five variations on the coherent feed-forward motif (CFFM), three of which contained a negative feedback. We found that the performance of these motifs was nowhere near as impressive as the one found in the eukaryotic cell; the best standard deviation was 6.6 minutes. We argue that the significance of these results, the fact and necessity of spatio-temporal precision in the developmental stages of eukaryotes, and the absence of such a precision in prokaryotes, all suggest that the nucleus has evolved, in part, under the selective pressure to achieve highly predictable phenotypes.

Control of relative timing and stoichiometry by a master regulator

Developmental processes in cells require a series of complex steps. Often only a single master regulator activates genes in these different steps. This poses several challenges: some targets need to be ordered temporally, while co-functional targets may need to be synchronized in both time and expression level. Here we study in single cells the dynamic activation patterns of early meiosis genes in budding yeast, targets of the meiosis master regulator Ime1. We quantify the individual roles of the promoter and protein levels in expression pattern control, as well as the roles of individual promoter elements. We find a consistent expression pattern difference between a non-cofunctional pair of genes, and a highly synchronized activation of a co-functional pair. We show that dynamic control leading to these patterns is distributed between promoter, gene and external regions. Through specific reciprocal changes to the promoters of pairs of genes, we show that different genes can use different promoter elements to reach near identical activation patterns.

The physics of bacterial decision making

The choice that bacteria make between sporulation and competence when subjected to stress provides a prototypical example of collective cell fate determination that is stochastic on the individual cell level, yet predictable (deterministic) on the population level. This collective decision is performed by an elaborated gene network. Considerable effort has been devoted to simplify its complexity by taking physics approaches to untangle the basic functional modules that are integrated to form the complete network: (1) A stochastic switch whose transition probability is controlled by two order parameters-population density and internal/external stress. (2) An adaptable timer whose clock rate is normalized by the same two previous order parameters. (3) Sensing units which measure population density and external stress. (4) A communication module that exchanges information about the cells' internal stress levels. (5) An oscillating gate of the stochastic switch which is regulated by the timer. The unique circuit architecture of the gate allows special dynamics and noise management features. The gate opens a window of opportunity in time for competence transitions, during which the circuit generates oscillations that are translated into a chain of short intervals with high transition probability. In addition, the unique architecture of the gate allows filtering of external noise and robustness against variations in circuit parameters and internal noise. We illustrate that a physics approach can be very valuable in investigating the decision process and in identifying its general principles. We also show that both cell-cell variability and noise have important functional roles in the collectively controlled individual decisions.

Pulsatile dynamics in the yeast proteome

The activation of transcription factors in response to environmental conditions is fundamental to cellular regulation. Recent work has revealed that some transcription factors are activated in stochastic pulses of nuclear localization, rather than at a constant level, even in a constant environment [1-12]. In such cases, signals control the mean activity of the transcription factor by modulating the frequency, duration, or amplitude of these pulses. Although specific pulsatile transcription factors have been identified in diverse cell types, it has remained unclear how prevalent pulsing is within the cell, how variable pulsing behaviors are between genes, and whether pulsing is specific to transcriptional regulators or is employed more broadly. To address these issues, we performed a proteome-wide movie-based screen to systematically identify localization-based pulsing behaviors in Saccharomyces cerevisiae. The screen examined all genes in a previously developed fluorescent protein fusion library of 4,159 strains [13] in multiple media conditions. This approach revealed stochastic pulsing in ten proteins, all transcription factors. In each case, pulse dynamics were heterogeneous and unsynchronized among cells in clonal populations. Pulsing is the only dynamic localization behavior that we observed, and it tends to occur in pairs of paralogous and redundant proteins. Taken together, these results suggest that pulsatile dynamics play a pervasive role in yeast and may be similarly prevalent in other eukaryotic species.

The meiotic-mitotic initiation switch in budding yeast maintains its function robustly against sensitive parameter perturbations

Experiments show that the meiotic-mitotic initiation switch in budding yeast functions robustly during the early hours of meiosis initiation. In this study, we explain these experimental observations first by understanding how this switching occurs during the early hours of meiosis by studying the temporal variation of this switch at the gene expression level. Then, we investigate the effects on this meiotic-mitotic switching from the perturbations of the most sensitive parameters in budding yeast meiosis initiation network. We use a mathematical model of meiosis initiation in budding yeast for this task and find the most sensitive group of parameters that influence the expressions of meiosis and mitosis initiators at all stages of the meiotic-mitotic switch. The results indicate that the transition region of the switch, where a double negative feedback loop between meiosis (Ime2) and mitosis (Cdk1/Cln3) initiators plays a major role, shows lower robustness. Feedback loops are frequently observed serving as a major robust adaption mechanism in many biological networks. Consequences of this less robust region appear in the transition region of the resulting switches. Most importantly, despite the differences observed in the transition region, we find that the meiotic-mitotic switch robustly maintains its main function of transition from meiosis to mitosis when the nutrients are re-supplied, against the perturbations in the sensitive parameters.

Stochastic galactokinase expression underlies GAL gene induction in a GAL3 mutant of Saccharomyces cerevisiae

GAL1 and GAL3 are paralogous signal transducers that functionally inactivate Gal80p to activate the Gal4p-dependent transcriptional activation of GAL genes in Saccharomyces cerevisiae in response to galactose. Unlike a wild-type strain, the gal3∆ strain shows delayed growth kinetics as a result of the signaling function of GAL1. The mechanism ensuring that GAL1 is eventually expressed to turn on the GAL switch in the gal3∆ strain remains a paradox. Using galactose and histidine growth complementation assays, we demonstrate that 0.3% of the gal3∆ cell population responds to galactose. This is corroborated by flow cytometry and microscopic analysis. The galactose responders and nonresponders isolated from the galactose-adapted population attain the original bimodal state and this phenotype is found to be as hard wired as a genetic trait. Computational analysis suggests that the log-normal distribution in GAL4 synthesis can lead to bimodal expression of GAL80, resulting in the bimodal expression of GAL genes. Heterozygosity at the GAL80 but not at the GAL1, GAL2 or GAL4 locus alters the extent of bimodality of the gal3∆ cell population. We suggest that the asymmetric expression pattern between GAL1 and GAL3 results in the ability of S. cerevisiae to activate the GAL pathway by conferring nongenetic heterogeneity.

Positive feedback of NDT80 expression ensures irreversible meiotic commitment in budding yeast

In budding yeast, meiotic commitment is the irreversible continuation of the developmental path of meiosis. After reaching meiotic commitment, cells finish meiosis and gametogenesis, even in the absence of the meiosis-inducing signal. In contrast, if the meiosis-inducing signal is removed and the mitosis-inducing signal is provided prior to reaching meiotic commitment, cells exit meiosis and return to mitosis. Previous work has shown that cells commit to meiosis after prophase I but before entering the meiotic divisions. Since the Ndt80 transcription factor induces expression of middle meiosis genes necessary for the meiotic divisions, we examined the role of the NDT80 transcriptional network in meiotic commitment. Using a microfluidic approach to analyze single cells, we found that cells commit to meiosis in prometaphase I, after the induction of the Ndt80-dependent genes. Our results showed that high-level expression of NDT80 is important for the timing and irreversibility of meiotic commitment. A modest reduction in NDT80 levels delayed meiotic commitment based on meiotic stages, although the timing of each meiotic stage was similar to that of wildtype cells. A further reduction of NDT80 resulted in the surprising finding of inappropriately uncommitted cells: withdrawal of the meiosis-inducing signal and addition of the mitosis-inducing signal to cells at stages beyond metaphase I caused return to mitosis, leading to multi-nucleate cells. Since Ndt80 enhances its own transcription through positive feedback, we tested whether positive feedback ensured the irreversibility of meiotic commitment. Ablating positive feedback in NDT80 expression resulted in a complete loss of meiotic commitment. These findings suggest that irreversibility of meiotic commitment is a consequence of the NDT80 transcriptional positive feedback loop, which provides the high-level of Ndt80 required for the developmental switch of meiotic commitment. These results also illustrate the importance of irreversible meiotic commitment for maintaining genome integrity by preventing formation of multi-nucleate cells.

Noise genetics: inferring protein function by correlating phenotype with protein levels and localization in individual human cells

To understand gene function, genetic analysis uses large perturbations such as gene deletion, knockdown or over-expression. Large perturbations have drawbacks: they move the cell far from its normal working point, and can thus be masked by off-target effects or compensation by other genes. Here, we offer a complementary approach, called noise genetics. We use natural cell-cell variations in protein level and localization, and correlate them to the natural variations of the phenotype of the same cells. Observing these variations is made possible by recent advances in dynamic proteomics that allow measuring proteins over time in individual living cells. Using motility of human cancer cells as a model system, and time-lapse microscopy on 566 fluorescently tagged proteins, we found 74 candidate motility genes whose level or localization strongly correlate with motility in individual cells. We recovered 30 known motility genes, and validated several novel ones by mild knockdown experiments. Noise genetics can complement standard genetics for a variety of phenotypes.

A nutrient dependant switch explains mutually exclusive existence of meiosis and mitosis initiation in budding yeast

Nutrients from living environment are vital for the survival and growth of any organism. Budding yeast diploid cells decide to grow by mitosis type cell division or decide to create unique, stress resistant spores by meiosis type cell division depending on the available nutrient conditions. To gain a molecular systems level understanding of the nutrient dependant switching between meiosis and mitosis initiation in diploid cells of budding yeast, we develop a theoretical model based on ordinary differential equations (ODEs) including the mitosis initiator and its relations to budding yeast meiosis initiation network. Our model accurately and qualitatively predicts the experimentally revealed temporal variations of related proteins under different nutrient conditions as well as the diverse mutant studies related to meiosis and mitosis initiation. Using this model, we show how the meiosis and mitosis initiators form an all-or-none type bistable switch in response to available nutrient level (mainly nitrogen). The transitions to and from meiosis or mitosis initiation states occur via saddle node bifurcation. This bidirectional switch helps the optimal usage of available nutrients and explains the mutually exclusive existence of meiosis and mitosis pathways.