Recognition of Histone Crotonylation by Taf14 Links Metabolic State to Gene Expression.

Metabolic signaling to chromatin often underlies how adaptive transcriptional responses are controlled. While intermediary metabolites serve as co-factors for histone-modifying enzymes during metabolic flux, how these modifications contribute to transcriptional responses is poorly understood. Here, we utilize the highly synchronized yeast metabolic cycle (YMC) and find that fatty acid ß-oxidation genes are periodically expressed coincident with the ß-oxidation byproduct histone crotonylation. Specifically, we found that H3K9 crotonylation peaks when H3K9 acetylation declines and energy resources become limited. During this metabolic state, pro-growth gene expression is dampened; however, mutation of the Taf14 YEATS domain, a H3K9 crotonylation reader, results in de-repression of these genes. Conversely, exogenous addition of crotonic acid results in increased histone crotonylation, constitutive repression of pro-growth genes, and disrupted YMC oscillations. Together, our findings expose an unexpected link between metabolic flux and transcription and demonstrate that histone crotonylation and Taf14 participate in the repression of energy-demanding gene expression.

Diploid-dominant life cycles characterize the early evolution of Fungi.

Most of the described species in kingdom Fungi are contained in two phyla, the Ascomycota and the Basidiomycota (subkingdom Dikarya). As a result, our understanding of the biology of the kingdom is heavily influenced by traits observed in Dikarya, such as aerial spore dispersal and life cycles dominated by mitosis of haploid nuclei. We now appreciate that Fungi comprises numerous phylum-level lineages in addition to those of Dikarya, but the phylogeny and genetic characteristics of most of these lineages are poorly understood due to limited genome sampling. Here, we addressed major evolutionary trends in the non-Dikarya fungi by phylogenomic analysis of 69 newly generated draft genome sequences of the zoosporic (flagellated) lineages of true fungi. Our phylogeny indicated five lineages of zoosporic fungi and placed Blastocladiomycota, which has an alternation of haploid and diploid generations, as branching closer to the Dikarya than to the Chytridiomyceta. Our estimates of heterozygosity based on genome sequence data indicate that the zoosporic lineages plus the Zoopagomycota are frequently characterized by diploid-dominant life cycles. We mapped additional traits, such as ancestral cell-cycle regulators, cell-membrane- and cell-wall-associated genes, and the use of the amino acid selenocysteine on the phylogeny and found that these ancestral traits that are shared with Metazoa have been subject to extensive parallel loss across zoosporic lineages. Together, our results indicate a gradual transition in the genetics and cell biology of fungi from their ancestor and caution against assuming that traits measured in Dikarya are typical of other fungal lineages.

Competition for DNA binding between paralogous transcription factors determines their genomic occupancy and regulatory functions.

Most eukaryotic transcription factors (TFs) are part of large protein families, with members of the same family (i.e., paralogous TFs) recognizing similar DNA-binding motifs but performing different regulatory functions. Many TF paralogs are coexpressed in the cell and thus can compete for target sites across the genome. However, this competition is rarely taken into account when studying the in vivo binding patterns of eukaryotic TFs. Here, we show that direct competition for DNA binding between TF paralogs is a major determinant of their genomic binding patterns. Using yeast proteins Cbf1 and Pho4 as our model system, we designed a high-throughput quantitative assay to capture the genomic binding profiles of competing TFs in a cell-free system. Our data show that Cbf1 and Pho4 greatly influence each other's occupancy by competing for their common putative genomic binding sites. The competition is different at different genomic sites, as dictated by the TFs' expression levels and their divergence in DNA-binding specificity and affinity. Analyses of ChIP-seq data show that the biophysical rules that dictate the competitive TF binding patterns in vitro are also followed in vivo, in the complex cellular environment. Furthermore, the Cbf1-Pho4 competition for genomic sites, as characterized in vitro using our new assay, plays a critical role in the specific activation of their target genes in the cell. Overall, our study highlights the importance of direct TF-TF competition for genomic binding and gene regulation by TF paralogs, and proposes an approach for studying this competition in a quantitative and high-throughput manner.

Redox rhythm reinforces the circadian clock to gate immune response.

Recent studies have shown that in addition to the transcriptional circadian clock, many organisms, including Arabidopsis, have a circadian redox rhythm driven by the organism's metabolic activities. It has been hypothesized that the redox rhythm is linked to the circadian clock, but the mechanism and the biological significance of this link have only begun to be investigated. Here we report that the master immune regulator NPR1 (non-expressor of pathogenesis-related gene 1) of Arabidopsis is a sensor of the plant's redox state and regulates transcription of core circadian clock genes even in the absence of pathogen challenge. Surprisingly, acute perturbation in the redox status triggered by the immune signal salicylic acid does not compromise the circadian clock but rather leads to its reinforcement. Mathematical modelling and subsequent experiments show that NPR1 reinforces the circadian clock without changing the period by regulating both the morning and the evening clock genes. This balanced network architecture helps plants gate their immune responses towards the morning and minimize costs on growth at night. Our study demonstrates how a sensitive redox rhythm interacts with a robust circadian clock to ensure proper responsiveness to environmental stimuli without compromising fitness of the organism.

A noisy linear map underlies oscillations in cell size and gene expression in bacteria.

During bacterial growth, a cell approximately doubles in size before division, after which it splits into two daughter cells. This process is subjected to the inherent perturbations of cellular noise and thus requires regulation for cell-size homeostasis. The mechanisms underlying the control and dynamics of cell size remain poorly understood owing to the difficulty in sizing individual bacteria over long periods of time in a high-throughput manner. Here we measure and analyse long-term, single-cell growth and division across different Escherichia coli strains and growth conditions. We show that a subset of cells in a population exhibit transient oscillations in cell size with periods that stretch across several (more than ten) generations. Our analysis reveals that a simple law governing cell-size control-a noisy linear map-explains the origins of these cell-size oscillations across all strains. This noisy linear map implements a negative feedback on cell-size control: a cell with a larger initial size tends to divide earlier, whereas one with a smaller initial size tends to divide later. Combining simulations of cell growth and division with experimental data, we demonstrate that this noisy linear map generates transient oscillations, not just in cell size, but also in constitutive gene expression. Our work provides new insights into the dynamics of bacterial cell-size regulation with implications for the physiological processes involved.

Epigenetic switching as a strategy for quick adaptation while attenuating biochemical noise.

Epigenetic switches are bistable, molecular systems built from self-reinforcing feedback loops that can spontaneously switch between heritable phenotypes in the absence of DNA mutation. It has been hypothesized that epigenetic switches first evolved as a mechanism of bet-hedging and adaptation, but the evolutionary trajectories and conditions by which an epigenetic switch can outcompete adaptation through genetic mutation remain unknown. Here, we used computer simulations to evolve a mechanistic, biophysical model of a self-activating genetic circuit, which can both adapt genetically through mutation and exhibit epigenetic switching. We evolved these genetic circuits under a fluctuating environment that alternatively selected for low and high protein expression levels. In all tested conditions, the population first evolved by genetic mutation towards a region of genotypes where genetic adaptation can occur faster after each environmental transition. Once in this region, the self-activating genetic circuit can exhibit epigenetic switching, which starts competing with genetic adaptation. We show a trade-off between either minimizing the adaptation time or increasing the robustness of the phenotype to biochemical noise. Epigenetic switching was superior in a fast fluctuating environment because it adapted faster than genetic mutation after an environmental transition, while still attenuating the effect of biochemical noise on the phenotype. Conversely, genetic adaptation was favored in a slowly fluctuating environment because it maximized the phenotypic robustness to biochemical noise during the constant environment between transitions, even if this resulted in slower adaptation. This simple trade-off predicts the conditions and trajectories under which an epigenetic switch evolved to outcompete genetic adaptation, shedding light on possible mechanisms by which bet-hedging strategies might emerge and persist in natural populations.

Conservation and divergence of C-terminal domain structure in the retinoblastoma protein family.

The retinoblastoma protein (Rb) and the homologous pocket proteins p107 and p130 negatively regulate cell proliferation by binding and inhibiting members of the E2F transcription factor family. The structural features that distinguish Rb from other pocket proteins have been unclear but are critical for understanding their functional diversity and determining why Rb has unique tumor suppressor activities. We describe here important differences in how the Rb and p107 C-terminal domains (CTDs) associate with the coiled-coil and marked-box domains (CMs) of E2Fs. We find that although CTD-CM binding is conserved across protein families, Rb and p107 CTDs show clear preferences for different E2Fs. A crystal structure of the p107 CTD bound to E2F5 and its dimer partner DP1 reveals the molecular basis for pocket protein-E2F binding specificity and how cyclin-dependent kinases differentially regulate pocket proteins through CTD phosphorylation. Our structural and biochemical data together with phylogenetic analyses of Rb and E2F proteins support the conclusion that Rb evolved specific structural motifs that confer its unique capacity to bind with high affinity those E2Fs that are the most potent activators of the cell cycle.

Gene duplication and co-evolution of G1/S transcription factor specificity in fungi are essential for optimizing cell fitness.

Transcriptional regulatory networks play a central role in optimizing cell survival. How DNA binding domains and cis-regulatory DNA binding sequences have co-evolved to allow the expansion of transcriptional networks and how this contributes to cellular fitness remains unclear. Here we experimentally explore how the complex G1/S transcriptional network evolved in the budding yeast Saccharomyces cerevisiae by examining different chimeric transcription factor (TF) complexes. Over 200 G1/S genes are regulated by either one of the two TF complexes, SBF and MBF, which bind to specific DNA binding sequences, SCB and MCB, respectively. The difference in size and complexity of the G1/S transcriptional network across yeast species makes it well suited to investigate how TF paralogs (SBF and MBF) and DNA binding sequences (SCB and MCB) co-evolved after gene duplication to rewire and expand the network of G1/S target genes. Our data suggests that whilst SBF is the likely ancestral regulatory complex, the ancestral DNA binding element is more MCB-like. G1/S network expansion took place by both cis- and trans- co-evolutionary changes in closely related but distinct regulatory sequences. Replacement of the endogenous SBF DNA-binding domain (DBD) with that from more distantly related fungi leads to a contraction of the SBF-regulated G1/S network in budding yeast, which also correlates with increased defects in cell growth, cell size, and proliferation.

Exact and efficient hybrid Monte Carlo algorithm for accelerated Bayesian inference of gene expression models from snapshots of single-cell transcripts.

Single cells exhibit a significant amount of variability in transcript levels, which arises from slow, stochastic transitions between gene expression states. Elucidating the nature of these states and understanding how transition rates are affected by different regulatory mechanisms require state-of-the-art methods to infer underlying models of gene expression from single cell data. A Bayesian approach to statistical inference is the most suitable method for model selection and uncertainty quantification of kinetic parameters using small data sets. However, this approach is impractical because current algorithms are too slow to handle typical models of gene expression. To solve this problem, we first show that time-dependent mRNA distributions of discrete-state models of gene expression are dynamic Poisson mixtures, whose mixing kernels are characterized by a piecewise deterministic Markov process. We combined this analytical result with a kinetic Monte Carlo algorithm to create a hybrid numerical method that accelerates the calculation of time-dependent mRNA distributions by 1000-fold compared to current methods. We then integrated the hybrid algorithm into an existing Monte Carlo sampler to estimate the Bayesian posterior distribution of many different, competing models in a reasonable amount of time. We demonstrate that kinetic parameters can be reasonably constrained for modestly sampled data sets if the model is known a priori. If there are many competing models, Bayesian evidence can rigorously quantify the likelihood of a model relative to other models from the data. We demonstrate that Bayesian evidence selects the true model and outperforms approximate metrics typically used for model selection.

The evolution of a G1/S transcriptional network in yeasts.

The G1-to-S cell cycle transition is promoted by the periodic expression of a large set of genes. In Saccharomyces cerevisiae G1/S gene expression is regulated by two transcription factor (TF) complexes, the MBF and SBF, which bind to specific DNA sequences, the MCB and SCB, respectively. Despite extensive research little is known regarding the evolution of the G1/S transcription regulation including the co-evolution of the DNA binding domains with their respective DNA binding sequences. We have recently examined the co-evolution of the G1/S TF specificity through the systematic generation and examination of chimeric Mbp1/Swi4 TFs containing different orthologue DNA binding domains in S. cerevisiae (Hendler et al. in PLoS Genet 13:e1006778. doi: 10.1371/journal.pgen.1006778 , 2017). Here, we review the co-evolution of G1/S transcriptional network and discuss the evolutionary dynamics and specificity of the MBF-MCB and SBF-SCB interactions in different fungal species.

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.

Programming stress-induced altruistic death in engineered bacteria.

Programmed death is often associated with a bacterial stress response. This behavior appears paradoxical, as it offers no benefit to the individual. This paradox can be explained if the death is 'altruistic': the killing of some cells can benefit the survivors through release of 'public goods'. However, the conditions where bacterial programmed death becomes advantageous have not been unambiguously demonstrated experimentally. Here, we determined such conditions by engineering tunable, stress-induced altruistic death in the bacterium Escherichia coli. Using a mathematical model, we predicted the existence of an optimal programmed death rate that maximizes population growth under stress. We further predicted that altruistic death could generate the 'Eagle effect', a counter-intuitive phenomenon where bacteria appear to grow better when treated with higher antibiotic concentrations. In support of these modeling insights, we experimentally demonstrated both the optimality in programmed death rate and the Eagle effect using our engineered system. Our findings fill a critical conceptual gap in the analysis of the evolution of bacterial programmed death, and have implications for a design of antibiotic treatment.

Protein sequestration generates a flexible ultrasensitive response in a genetic network.

Ultrasensitive responses are crucial for cellular regulation. Protein sequestration, where an active protein is bound in an inactive complex by an inhibitor, can potentially generate ultrasensitivity. Here, in a synthetic genetic circuit in budding yeast, we show that sequestration of a basic leucine zipper transcription factor by a dominant-negative inhibitor converts a graded transcriptional response into a sharply ultrasensitive response, with apparent Hill coefficients up to 12. A simple quantitative model for this genetic network shows that both the threshold and the degree of ultrasensitivity depend upon the abundance of the inhibitor, exactly as we observed experimentally. The abundance of the inhibitor can be altered by simple mutation; thus, ultrasensitive responses mediated by protein sequestration are easily tuneable. Gene duplication of regulatory homodimers and loss-of-function mutations can create dominant negatives that sequester and inactivate the original regulator. The generation of flexible ultrasensitive responses is an unappreciated adaptive advantage that could explain the frequent evolutionary emergence of dominant negatives.

Chytrid fungi.

Medina and Buchler provide an introduction to chytrid fungi, an early diverging fungal lineage exhibiting characteristics found in both animals and fungi.

Genetic transformation of Spizellomyces punctatus, a resource for studying chytrid biology and evolutionary cell biology.

Chytrids are early-diverging fungi that share features with animals that have been lost in most other fungi. They hold promise as a system to study fungal and animal evolution, but we lack genetic tools for hypothesis testing. Here, we generated transgenic lines of the chytrid Spizellomyces punctatus, and used fluorescence microscopy to explore chytrid cell biology and development during its life cycle. We show that the chytrid undergoes multiple rounds of synchronous nuclear division, followed by cellularization, to create and release many daughter 'zoospores'. The zoospores, akin to animal cells, crawl using actin-mediated cell migration. After forming a cell wall, polymerized actin reorganizes into fungal-like cortical patches and cables that extend into hyphal-like structures. Actin perinuclear shells form each cell cycle and polygonal territories emerge during cellularization. This work makes Spizellomyces a genetically tractable model for comparative cell biology and understanding the evolution of fungi and early eukaryotes.

Transcriptional regulation by the numbers: models.

The expression of genes is regularly characterized with respect to how much, how fast, when and where. Such quantitative data demands quantitative models. Thermodynamic models are based on the assumption that the level of gene expression is proportional to the equilibrium probability that RNA polymerase (RNAP) is bound to the promoter of interest. Statistical mechanics provides a framework for computing these probabilities. Within this framework, interactions of activators, repressors, helper molecules and RNAP are described by a single function, the "regulation factor". This analysis culminates in an expression for the probability of RNA polymerase binding at the promoter of interest as a function of the number of regulatory proteins in the cell.

Transcriptional regulation by the numbers: applications.

With the increasing amount of experimental data on gene expression and regulation, there is a growing need for quantitative models to describe the data and relate them to their respective context. Thermodynamic models provide a useful framework for the quantitative analysis of bacterial transcription regulation. This framework can facilitate the quantification of vastly different forms of gene expression from several well-characterized bacterial promoters that are regulated by one or two species of transcription factors; it is useful because it requires only a few parameters. As such, it provides a compact description useful for higher-level studies (e.g. of genetic networks) without the need to invoke the biochemical details of every component. Moreover, it can be used to generate hypotheses on the likely mechanisms of transcriptional control.

Evolutionary innovation, fungal cell biology, and the lateral gene transfer of a viral KilA-N domain.

Fungi are found in diverse ecological niches as primary decomposers, mutualists, or parasites of plants and animals. Although animals and fungi share a common ancestor, fungi dramatically diversified their life cycle, cell biology, and metabolism as they evolved and colonized new niches. This review focuses on a family of fungal transcription factors (Swi4/Mbp1, APSES, Xbp1, Bqt4) derived from the lateral gene transfer of a KilA-N domain commonly found in prokaryotic and eukaryotic DNA viruses. These virus-derived fungal regulators play central roles in cell cycle, morphogenesis, sexual differentiation, and quiescence. We consider the possible origins of KilA-N and how this viral DNA binding domain came to be intimately associated with fungal processes.

Enhancer Histone Acetylation Modulates Transcriptional Bursting Dynamics of Neuronal Activity-Inducible Genes.

Neuronal activity-inducible gene transcription correlates with rapid and transient increases in histone acetylation at promoters and enhancers of activity-regulated genes. Exactly how histone acetylation modulates transcription of these genes has remained unknown. We used single-cell in situ transcriptional analysis to show that Fos and Npas4 are transcribed in stochastic bursts in mouse neurons and that membrane depolarization increases mRNA expression by increasing burst frequency. We then expressed dCas9-p300 or dCas9-HDAC8 fusion proteins to mimic or block activity-induced histone acetylation locally at enhancers. Adding histone acetylation increased Fos transcription by prolonging burst duration and resulted in higher Fos protein levels and an elevation of resting membrane potential. Inhibiting histone acetylation reduced Fos transcription by reducing burst frequency and impaired experience-dependent Fos protein induction in the hippocampus in vivo. Thus, activity-inducible histone acetylation tunes the transcriptional dynamics of experience-regulated genes to affect selective changes in neuronal gene expression and cellular function.

Efficient analysis of stochastic gene dynamics in the non-adiabatic regime using piecewise deterministic Markov processes.

Single-cell experiments show that gene expression is stochastic and bursty, a feature that can emerge from slow switching between promoter states with different activities. In addition to slow chromatin and/or DNA looping dynamics, one source of long-lived promoter states is the slow binding and unbinding kinetics of transcription factors to promoters, i.e. the non-adiabatic binding regime. Here, we introduce a simple analytical framework, known as a piecewise deterministic Markov process (PDMP), that accurately describes the stochastic dynamics of gene expression in the non-adiabatic regime. We illustrate the utility of the PDMP on a non-trivial dynamical system by analysing the properties of a titration-based oscillator in the non-adiabatic limit. We first show how to transform the underlying chemical master equation into a PDMP where the slow transitions between promoter states are stochastic, but whose rates depend upon the faster deterministic dynamics of the transcription factors regulated by these promoters. We show that the PDMP accurately describes the observed periods of stochastic cycles in activator and repressor-based titration oscillators. We then generalize our PDMP analysis to more complicated versions of titration-based oscillators to explain how multiple binding sites lengthen the period and improve coherence. Last, we show how noise-induced oscillation previously observed in a titration-based oscillator arises from non-adiabatic and discrete binding events at the promoter site.