Periodic mRNA synthesis and degradation co-operate during cell cycle gene expression

Cellular energy regulates mRNA degradation in a codon-specific manner

Codon optimality is a major determinant of mRNA translation and degradation rates. However, whether and through which mechanisms its effects are regulated remains poorly understood. Here we show that codon optimality associates with up to 2-fold change in mRNA stability variations between human tissues, and that its effect is attenuated in tissues with high energy metabolism and amplifies with age. Mathematical modeling and perturbation data through oxygen deprivation and ATP synthesis inhibition reveal that cellular energy variations non-uniformly alter the effect of codon usage. This new mode of codon effect regulation, independent of tRNA regulation, provides a fundamental mechanistic link between cellular energy metabolism and eukaryotic gene expression.

SLAM-Drop-seq reveals mRNA kinetic rates throughout the cell cycle

RNA abundance is tightly regulated in eukaryotic cells by modulating the kinetic rates of RNA production, processing, and degradation. To date, little is known about time‐dependent kinetic rates during dynamic processes. Here, we present SLAM‐Drop‐seq, a method that combines RNA metabolic labeling and alkylation of modified nucleotides in methanol‐fixed cells with droplet‐based sequencing to detect newly synthesized and preexisting mRNAs in single cells. As a first application, we sequenced 7280 HEK293 cells and calculated gene‐specific kinetic rates during the cell cycle using the novel package Eskrate. Of the 377 robust‐cycling genes that we identified, only a minor fraction is regulated solely by either dynamic transcription or degradation (6 and 4%, respectively). By contrast, the vast majority (89%) exhibit dynamically regulated transcription and degradation rates during the cell cycle. Our study thus shows that temporally regulated mRNA degradation is fundamental for the correct expression of a majority of cycling genes. SLAM‐Drop‐seq, combined with Eskrate, is a powerful approach to understanding the underlying mRNA kinetics of single‐cell gene expression dynamics in continuous biological processes.

Nearly maximal information gain due to time integration in central dogma reactions

Living cells process information about their environment through the central dogma processes of transcription and translation, which drive the cellular response to stimuli. Here, we study the transfer of information from environmental input to the transcript and protein expression levels. Evaluation of both experimental and analogous simulation data reveals that transcription and translation are not two simple information channels connected in series. Instead, we demonstrate that the central dogma reactions often create a time-integrating information channel, where the translation channel receives and integrates multiple outputs from the transcription channel. This information channel model of the central dogma provides new information-theoretic selection criteria for the central dogma rate constants. Using the data for four well-studied species we show that their central dogma rate constants achieve information gain because of time integration while also keeping the loss because of stochasticity in translation relatively low (<0.5 bits).

Evolutionary trade-off and mutational bias could favor transcriptional over translational divergence within paralog pairs

How changes in the different steps of protein synthesis-transcription, translation and degradation-contribute to differences of protein abundance among genes is not fully understood. There is however accumulating evidence that transcriptional divergence might have a prominent role. Here, we show that yeast paralogous genes are more divergent in transcription than in translation. We explore two causal mechanisms for this predominance of transcriptional divergence: an evolutionary trade-off between the precision and economy of gene expression and a larger mutational target size for transcription. Performing simulations within a minimal model of post-duplication evolution, we find that both mechanisms are consistent with the observed divergence patterns. We also investigate how additional properties of the effects of mutations on gene expression, such as their asymmetry and correlation across levels of regulation, can shape the evolution of paralogs. Our results highlight the importance of fully characterizing the distributions of mutational effects on transcription and translation. They also show how general trade-offs in cellular processes and mutation bias can have far-reaching evolutionary impacts.

On the gain of entrainment in the n-dimensional ribosome flow model

The ribosome flow model (RFM) is a phenomenological model for the flow of particles along a one-dimensional chain of n sites. It has been extensively used to study ribosome flow along the mRNA molecule during translation. When the transition rates along the chain are time-varying and jointly T-periodic the RFM entrains, i.e. every trajectory of the RFM converges to a unique T-periodic solution that depends on the transition rates, but not on the initial condition. In general, entrainment to periodic excitations like the 24 h solar day or the 50 Hz frequency of the electric grid is important in numerous natural and artificial systems. An interesting question, called the gain of entrainment (GOE) in the RFM, is whether proper coordination of the periodic translation rates along the mRNA can lead to a larger average protein production rate. Analysing the GOE in the RFM is non-trivial and partial results exist only for the RFM with dimensions n = 1, 2. We use a new approach to derive several results on the GOE in the general n-dimensional RFM. Perhaps surprisingly, we rigorously characterize several cases where there is no GOE, so to maximize the average production rate in these cases, the best choice is to use constant transition rates all along the chain.

A dynamical stochastic model of yeast translation across the cell cycle

Translation is a central step in gene expression, however its quantitative and time-resolved regulation is poorly understood. We developed a discrete, stochastic model for protein translation in S. cerevisiae in a whole-transcriptome, single-cell context. A "base case" scenario representing an average cell highlights translation initiation rates as the main co-translational regulatory parameters. Codon usage bias emerges as a secondary regulatory mechanism through ribosome stalling. Demand for anticodons with low abundancy is shown to cause above-average ribosome dwelling times. Codon usage bias correlates strongly both with protein synthesis rates and elongation rates. Applying the model to a time-resolved transcriptome estimated by combining data from FISH and RNA-Seq experiments, it could be shown that increased total transcript abundance during the cell cycle decreases translation efficiency at single transcript level. Translation efficiency grouped by gene function shows highest values for ribosomal and glycolytic genes. Ribosomal proteins peak in S phase while glycolytic proteins rank highest in later cell cycle phases.

StaRTrEK:in silico estimation of RNA half-lives from genome-wide time-course experiments without transcriptional inhibition

Background

Gene expression is the result of the balance between transcription and degradation. Recent experimental findings have shown fine and specific regulation of RNA degradation and the presence of various molecular machinery purposely devoted to this task, such as RNA binding proteins, non-coding RNAs, etc. A biological process can be studied by measuring time-courses of RNA abundance in response of internal and/or external stimuli, using recent technologies, such as the microarrays or the Next Generation Sequencing devices. Unfortunately, the picture provided by looking only at the transcriptome abundance may not gain insight into its dynamic regulation. By contrast, independent simultaneous measurement of RNA expression and half-lives could provide such valuable additional insight. A computational approach to the estimation of RNAs half-lives from RNA expression time profiles data, can be a low-cost alternative to its experimental measurement which may be also affected by various artifacts.

Results

Here we present a computational methodology, called StaRTrEK (STAbility Rates ThRough Expression Kinetics), able to estimate half-life values basing only on genome-wide gene expression time series without transcriptional inhibition. The StaRTrEK algorithm makes use of a simple first order kinetic model and of a \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$l_1$$\end{document}l1-norm regularized least square optimization approach to find its parameter values. Estimates provided by StaRTrEK are validated using simulated data and three independent experimental datasets of two short (6 samples) and one long (48 samples) time-courses.

Conclusions

We believe that our algorithm can be used as a fast valuable computational complement to time-course experimental gene expression studies by adding a relevant kinetic property, i.e. the RNA half-life, with a strong biological interpretation, thus providing a dynamic picture of what is going in a cell during the biological process under study.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04730-x.

Prediction of Time Series Gene Expression and Structural Analysis of Gene Regulatory Networks Using Recurrent Neural Networks

Methods for time series prediction and classification of gene regulatory networks (GRNs) from gene expression data have been treated separately so far. The recent emergence of attention-based recurrent neural network (RNN) models boosted the interpretability of RNN parameters, making them appealing for the understanding of gene interactions. In this work, we generated synthetic time series gene expression data from a range of archetypal GRNs and we relied on a dual attention RNN to predict the gene temporal dynamics. We show that the prediction is extremely accurate for GRNs with different architectures. Next, we focused on the attention mechanism of the RNN and, using tools from graph theory, we found that its graph properties allow one to hierarchically distinguish different architectures of the GRN. We show that the GRN responded differently to the addition of noise in the prediction by the RNN and we related the noise response to the analysis of the attention mechanism. In conclusion, this work provides a way to understand and exploit the attention mechanism of RNNs and it paves the way to RNN-based methods for time series prediction and inference of GRNs from gene expression data.

The asymmetric distribution of RNA polymerase II and nucleosomes on replicated daughter genomes is caused by differences in replication timing between the lagging and the leading strand

Chromatin features are thought to have a role in the epigenetic transmission of transcription states from one cell generation to the next. It is unclear how chromatin structure survives disruptions caused by genomic replication or whether chromatin features are instructive of the transcription state of the underlying gene. We developed a method to monitor budding yeast replication, transcription, and chromatin maturation dynamics on each daughter genome in parallel, with which we identified clusters of secondary origins surrounding known origins. We found a difference in the timing of lagging and leading strand replication on the order of minutes at most yeast genes. We propose a model in which the majority of old histones and RNA polymerase II (RNAPII) bind to the gene copy that replicated first, while newly synthesized nucleosomes are assembled on the copy that replicated second. RNAPII enrichment then shifts to the sister copy that replicated second. The order of replication is largely determined by genic orientation: If transcription and replication are codirectional, the leading strand replicates first; if they are counterdirectional, the lagging strand replicates first. A mutation in the Mcm2 subunit of the replicative helicase Mcm2-7 that impairs Mcm2 interactions with histone H3 slows down replication forks but does not qualitatively change the asymmetry in nucleosome distribution observed in the WT. We propose that active transcription states are inherited simultaneously and independently of their underlying chromatin states through the recycling of the transcription machinery and old histones, respectively. Transcription thus actively contributes to the reestablishment of the active chromatin state.

Analysis of mRNA Dynamics Using RNA Sequencing Data

The RNA abundance of each gene is determined by its rates of transcription and RNA decay. Biochemical experiments that measure these rates, including transcription inhibition and metabolic labelling, are challenging to perform and are largely limited to in vitro settings. Most transcriptomic studies have focused on analyzing changes in RNA abundances without attributing those changes to transcriptional or posttranscriptional regulation. Estimating differential transcription and decay rates of RNA molecules would enable the identification of regulatory factors, such as transcription factors, RNA binding proteins, and microRNAs, that govern large-scale shifts in RNA expression. Here, we describe a protocol for estimating differential stability of RNA molecules between conditions using standard RNA-sequencing data, without the need for transcription inhibition or metabolic labeling. We apply this protocol to in vivo RNA-seq data from individuals with Alzheimer's disease and demonstrate how estimates of differential stability can be leveraged to infer the regulatory factors underlying them.

Time-resolved single-cell sequencing identifies multiple waves of mRNA decay during the mitosis-to-G1 phase transition

Accurate control of the cell cycle is critical for development and tissue homeostasis, and requires precisely timed expression of many genes. Cell cycle gene expression is regulated through transcriptional and translational control, as well as through regulated protein degradation. Here, we show that widespread and temporally controlled mRNA decay acts as an additional mechanism for gene expression regulation during the cell cycle in human cells. We find that two waves of mRNA decay occur sequentially during the mitosis-to-G1 phase transition, and we identify the deadenylase CNOT1 as a factor that contributes to mRNA decay during this cell cycle transition. Collectively, our data show that, akin to protein degradation, scheduled mRNA decay helps to reshape cell cycle gene expression as cells move from mitosis into G1 phase.

Cell-Projection Pumping of Fibroblast Contents into Osteosarcoma SAOS-2 Cells Correlates with Increased SAOS-2 Proliferation and Migration, as well as Altered Morphology

We earlier reported that cell-projection pumping transfers fibroblast contents to cancer cells and this alters the cancer cell phenotype. Here, we report on single-cell tracking of time lapse recordings from co-cultured fluorescent fibroblasts and SAOS-2 osteosarcoma cells, tracking 5201 cells across 7 experiments. The fluorescent lipophilic marker DiD was used to label fibroblast organelles and to trace the transfer of fibroblast cytoplasm into SAOS-2 cells. We related SAOS-2 phenotypic change to levels of fluorescence transfer from fibroblasts to SAOS-2 cells, as well as what we term 'compensated fluorescence', that numerically projects mother cell fluorescence post-mitosis into daughter cells. The comparison of absolute with compensated fluorescence allowed us to deduct if the phenotypic effects in mother SAOS-2 cells were inherited by their daughters. SAOS-2 receipt of fibroblast fluorescence correlated by Kendall's tau with cell-profile area and without evidence of persistence in daughter cells (median tau = 0.51, p < 0.016); negatively and weakly with cell circularity and with evidence of persistence (median tau = -0.19, p < 0.05); and very weakly with cell migration velocity and without evidence of persistence (median tau = 0.01, p < 0.016). In addition, mitotic SAOS-2 cells had higher rates of prior fluorescence uptake (median = 64.9 units/day) than non-dividing cells (median = 35.6 units/day, p < 0.016) and there was no evidence of persistence post-mitosis. We conclude that there was an appreciable impact of cell-projection pumping on cancer cell phenotype relevant to cancer histopathological diagnosis, clinical spread and growth, with most effects being 'reset' by cancer cell mitosis.

Kinetics of Gene Expression Changes in Equine Fetal Interzone and Anlagen Cells Over 14 Days of Induced Chondrogenesis

Within developing synovial joints, interzone and anlagen cells progress through divergent chondrogenic pathways to generate stable articular cartilage and transient hypertrophic anlagen cartilage, respectively. Understanding the comparative cell biology between interzone and anlagen cells may provide novel insights into emergent cell-based therapies to support articular cartilage regeneration. The aim of this study was to assess the kinetics of gene expression profiles in these skeletal cell lines after inducing chondrogenesis in culture. Interzone and anlagen cells from seven equine fetuses were isolated and grown in a TGF-β1 chondrogenic inductive medium. Total RNA was isolated at ten time points (0, 1.5, 3, 6, 12, 24, 48, 96, 168, and 336 h), and gene expression for 93 targeted gene loci was measured in a microfluidic RT-qPCR system. Differential transcriptional responses were observed as early as 1.5 h after the initiation of chondrogenesis. Genes with functional annotations that include transcription regulation responded to the chondrogenic stimulation earlier (1.5–96 h) than genes involved in signal transduction (1.5–336 h) and the extracellular matrix biology (3–336 h). Between interzone and anlagen cell cultures, expression levels of 73 out of the 93 targeted genes were not initially different at 0 h, but 47 out of the 73 genes became differentially expressed under the chondrogenic stimulation. While interzone and anlagen cells are both chondrogenic, they display clear differences in response to the same TGF-β1 chondrogenic stimulation. This study provides new molecular insight into a timed sequence of the divergent developmental fates of interzone and anlagen cells in culture over 14 days.

RNA-biology ruling cancer progression? Focus on 3'UTRs and splicing

The protein-coding regions of mRNAs have the information to make proteins and hence have been at the center of attention for understanding altered protein functions in disease states, including cancer. Indeed, the discovery of genomic alterations and driver mutations that change protein levels and/or activity has been pivotal in our understanding of cancer biology. However, to better understand complex molecular mechanisms that are deregulated in cancers, we also need to look at non-coding parts of mRNAs, including 3'UTRs (untranslated regions), which control mRNA stability, localization, and translation efficiency. Recently, these rather overlooked regions of mRNAs are gaining attention as mounting evidence provides functional links between 3'UTRs, protein functions, and cancer-related molecular mechanisms. Here, roles of 3'UTRs in cancer biology and mechanisms that result in cancer-specific 3'-end isoform variants will be reviewed. An increased appreciation of 3'UTRs may help the discovery of new ways to explain as of yet unknown oncogene activation and tumor suppressor inactivation cases in cancers, and provide new avenues for diagnostic and therapeutic applications.

Simple Kinetic Models in Molecular Chronobiology

Circadian rhythms are constituted by a complex dynamical system with intertwined feedback loops, molecular switches, and self-sustained oscillations. Mathematical modeling supports understanding available heterogeneous kinetic data, highlights basic mechanisms, and can guide experimental research. Here, we introduce the basic steps from a biological question to simple models providing insight into gene-regulatory mechanisms. We illustrate the general approach by three examples: modeling decay processes, clock-controlled genes, and self-sustained oscillations.

The Making and Breaking of RNAs: Dynamics of Rhythmic RNA Expression in Mammals

The identification and characterization of rhythmically expressed mRNAs have been an active area of research over the past 20 years, as these mRNAs are believed to produce the daily rhythms in a wide range of biological processes. Circadian transcriptome studies have used mature mRNA as a primary readout and focused largely on rhythmic RNA synthesis as a regulatory mechanism underlying rhythmic mRNA expression. However, RNA synthesis, RNA degradation, or a combination of both must be rhythmic to drive rhythmic RNA profiles, and it is still unclear to what extent rhythmic synthesis leads to rhythmic RNA profiles. In addition, circadian RNA expression is also often tissue specific. Although a handful of genes cycle in all or most tissues, others are rhythmic only in certain tissues, even though the same core clock mechanism is believed to control the rhythmic RNA profiles in all tissues. This review focuses on the dynamics of rhythmic RNA synthesis and degradation and discusses how these steps collectively determine the rhythmicity, phase, and amplitude of RNA accumulation. In particular, we highlight a possible role of RNA degradation in driving tissue-specific RNA rhythms. By unifying findings from experimental and theoretical studies, we will provide a comprehensive overview of how rhythmic gene expression can be achieved and how each regulatory step contributes to tissue-specific circadian transcriptome output in mammals.

INSPEcT-GUI Reveals the Impact of the Kinetic Rates of RNA Synthesis, Processing, and Degradation, on Premature and Mature RNA Species

The abundance of RNA species and their response to perturbations are set by the kinetics rates of RNA synthesis, processing, and degradation. However, the visualization, interpretation, and manipulation of these data require familiarity with mathematical modeling and command line tools. INSPEcT-GUI is an R-Shiny interface that allows researchers without specific training to effortlessly explore how the fine kinetic regulation of the RNA life cycle can shape gene expression programs. In particular, it allows to: (i) interactively visualize gene-level RNA dynamics; (ii) refine the model fit of experimental data; (iii) test alternative regulatory models; (iv) explore, independently from the availability of data, how the combined action of the RNA kinetic rates impacts on premature and mature RNA. INSPEcT-GUI is freely available within the R/Bioconductor package INSPEcT at http://bioconductor.org/packages/INSPEcT/. An HTML vignette including documentation on the tool startup and usage, executable examples, and a video demonstration, are available at: http://bioconductor.org/packages/release/bioc/vignettes/INSPEcT/inst/doc/INSPEcT_GUI.html.

YTHDF2 promotes mitotic entry and is regulated by cell cycle mediators

The N6-methyladenosine (m6A) modification regulates mRNA stability and translation. Here, we show that transcriptomic m6A modification can be dynamic and the m6A reader protein YTH N6-methyladenosine RNA binding protein 2 (YTHDF2) promotes mRNA decay during cell cycle. Depletion of YTHDF2 in HeLa cells leads to the delay of mitotic entry due to overaccumulation of negative regulators of cell cycle such as Wee1-like protein kinase (WEE1). We demonstrate that WEE1 transcripts contain m6A modification, which promotes their decay through YTHDF2. Moreover, we found that YTHDF2 protein stability is dependent on cyclin-dependent kinase 1 (CDK1) activity. Thus, CDK1, YTHDF2, and WEE1 form a feedforward regulatory loop to promote mitotic entry. We further identified Cullin 1 (CUL1), Cullin 4A (CUL4A), damaged DNA-binding protein 1 (DDB1), and S-phase kinase-associated protein 2 (SKP2) as components of E3 ubiquitin ligase complexes that mediate YTHDF2 proteolysis. Our study provides insights into how cell cycle mediators modulate transcriptomic m6A modification, which in turn regulates the cell cycle.

ECHO: an application for detection and analysis of oscillators identifies metabolic regulation on genome-wide circadian output

MOTIVATION

Time courses utilizing genome scale data are a common approach to identifying the biological pathways that are controlled by the circadian clock, an important regulator of organismal fitness. However, the methods used to detect circadian oscillations in these datasets are not able to accommodate changes in the amplitude of the oscillations over time, leading to an underestimation of the impact of the clock on biological systems.

RESULTS

We have created a program to efficaciously identify oscillations in large-scale datasets, called the Extended Circadian Harmonic Oscillator application, or ECHO. ECHO utilizes an extended solution of the fixed amplitude oscillator that incorporates the amplitude change coefficient. Employing synthetic datasets, we determined that ECHO outperforms existing methods in detecting rhythms with decreasing oscillation amplitudes and in recovering phase shift. Rhythms with changing amplitudes identified from published biological datasets revealed distinct functions from those oscillations that were harmonic, suggesting purposeful biologic regulation to create this subtype of circadian rhythms.

AVAILABILITY AND IMPLEMENTATION

ECHO's full interface is available at https://github.com/delosh653/ECHO. An R package for this functionality, echo.find, can be downloaded at https://CRAN.R-project.org/package=echo.find.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Promoter-dependent nuclear RNA degradation ensures cell cycle-specific gene expression

Cell cycle progression depends on phase-specific gene expression. Here we show that the nuclear RNA degradation machinery plays a lead role in promoting cell cycle-dependent gene expression by triggering promoter-dependent co-transcriptional RNA degradation. Single molecule quantification of RNA abundance in different phases of the cell cycle indicates that relative curtailment of gene expression in certain phases is attained even when transcription is not completely inhibited. When nuclear ribonucleases are deleted, transcription of the Saccharomyces cerevisiae G1-specific axial budding gene AXL2 is detected throughout the cell cycle and its phase-specific expression is lost. Promoter replacement abolished cell cycle-dependent RNA degradation and rendered the RNA insensitive to the deletion of nuclear ribonucleases. Together the data reveal a model of gene regulation whereby RNA abundance is controlled by promoter-dependent induction of RNA degradation.

Spt6 Association with RNA Polymerase II Directs mRNA Turnover During Transcription

Spt6 is an essential histone chaperone that mediates nucleosome reassembly during gene transcription. Spt6 also associates with RNA polymerase II (RNAPII) via a tandem Src2 homology domain. However, the significance of Spt6-RNAPII interaction is not well understood. Here, we show that Spt6 recruitment to genes and the nucleosome reassembly functions of Spt6 can still occur in the absence of its association with RNAPII. Surprisingly, we found that Spt6-RNAPII association is required for efficient recruitment of the Ccr4-Not de-adenylation complex to transcribed genes for essential degradation of a range of mRNAs, including mRNAs required for cell-cycle progression. These findings reveal an unexpected control mechanism for mRNA turnover during transcription facilitated by a histone chaperone.

A transcriptome-wide analysis deciphers distinct roles of G1 cyclins in temporal organization of the yeast cell cycle

Oscillating gene expression is crucial for correct timing and progression through cell cycle. In Saccharomyces cerevisiae, G1 cyclins Cln1-3 are essential drivers of the cell cycle and have an important role for temporal fine-tuning. We measured time-resolved transcriptome-wide gene expression for wild type and cyclin single and double knockouts over cell cycle with and without osmotic stress. Clustering of expression profiles, peak time detection of oscillating genes, integration with transcription factor network dynamics, and assignment to cell cycle phases allowed us to quantify the effect of genetic or stress perturbations on the duration of cell cycle phases. Cln1 and Cln2 showed functional differences, especially affecting later phases. Deletion of Cln3 led to a delay of START followed by normal progression through later phases. Our data and network analysis suggest mutual effects of cyclins with the transcriptional regulators SBF and MBF.

Central dogma rates and the trade-off between precision and economy in gene expression

Steady-state protein abundance is set by four rates: transcription, translation, mRNA decay and protein decay. A given protein abundance can be obtained from infinitely many combinations of these rates. This raises the question of whether the natural rates for each gene result from historical accidents, or are there rules that give certain combinations a selective advantage? We address this question using high-throughput measurements in rapidly growing cells from diverse organisms to find that about half of the rate combinations do not exist: genes that combine high transcription with low translation are strongly depleted. This depletion is due to a trade-off between precision and economy: high transcription decreases stochastic fluctuations but increases transcription costs. Our theory quantitatively explains which rate combinations are missing, and predicts the curvature of the fitness function for each gene. It may guide the design of gene circuits with desired expression levels and noise.

Contributions of regulated transcription and mRNA decay to the dynamics of gene expression

Organisms have acquired sophisticated regulatory networks that control gene expression in response to cellular perturbations. Understanding of the mechanisms underlying the coordinated changes in gene expression in response to external and internal stimuli is a fundamental issue in biology. Recent advances in high-throughput technologies have enabled the measurement of diverse biological information, including gene expression levels, kinetics of gene expression, and interactions among gene expression regulatory molecules. By coupling these technologies with quantitative modeling, we can now uncover the biological roles and mechanisms of gene regulation at the system level. This review consists of two parts. First, we focus on the methods using uridine analogs that measure synthesis and decay rates of RNAs, which demonstrate how cells dynamically change the regulation of gene expression in response to both internal and external cues. Second, we discuss the underlying mechanisms of these changes in kinetics, including the functions of transcription factors and RNA-binding proteins. Overall, this review will help to clarify a system-level view of gene expression programs in cells. This article is categorized under: Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs RNA Turnover and Surveillance > Regulation of RNA Stability RNA Methods > RNA Analyses in vitro and In Silico.

A feature-based integrated scoring scheme for cell cycle-regulated genes prioritization

Prioritization of cell cycle-regulated genes from expression time-profiles is still an open problem. The point at issue is the surprisingly poor overlap among ranked lists obtained from different experimental protocols. Instead of developing a general-purpose computational methodology for detecting periodic signals, we focus on the budding yeast mitotic cell cycle. The reason being that the current availability of a total of 12 datasets, produced by 6 independent groups using 4 different synchronization methods, permits a re-analysis and re-consideration of this problem in a more reliable and extensive data domain. Notably, budding yeast is a model organism for studying cancer and testing new drugs. Here we propose a novel multi-feature score (called PERLA, PERiodicity, Regulation and Lag-Autocorrelation) that integrates different features of cell cycle-regulated gene expression time-profiles. We obtained increased performances on a wide range of benchmarks and, most importantly, a substantially increased overlap of the top ranking genes among different datasets, thus proving the effectiveness of the proposed prioritization algorithm. Examples on how to use PERLA to gain new insight into the biology of the cell cycle, are provided in a final dedicated section.

Prss56 expression in the rodent hypothalamus: Inverse correlation with pro-opiomelanocortin suggests oscillatory gene expression in adult rat tanycytes

We recently reported that the number of hypothalamic tanycytes expressing pro-opiomelanocortin (Pomc) is highly variable among brains of adult rats. While its cause and significance remain unknown, identifying other variably expressed genes in tanycytes may help understand this curious phenomenon. In this in situ hybridization study, we report that the Prss56 gene, which encodes a trypsin-like serine protease and is expressed in neural stem/progenitor cells, shows a similarly variable mRNA expression in tanycytes of adult rats and correlates inversely with tanycyte Pomc mRNA. Prss56 was expressed in α1, β1, subsets of α2, and some median eminence γ tanycytes, but virtually absent from β2 tanycytes. Prss56 was also expressed in vimentin positive tanycyte-like cells in the parenchyma of the ventromedial and arcuate nuclei, and in thyrotropin beta subunit-expressing cells of the pars tuberalis of the pituitary. In contrast to adults, Prss56 expression was uniformly high in tanycytes in adolescent rats. In mice, Prss56-expressing tanycytes and parenchymal cells were also observed but fewer in number and without significant variations. The results identify Prss56 as a second gene that is expressed variably in tanycytes of adult rats. We propose that the variable, inversely correlating expression of Prss56 and Pomc reflect periodically oscillating gene expression in tanycytes rather than stable expression levels that vary between individual rats. A possible functional link between Prss56 and POMC, and Prss56 as a potential marker for migrating tanycytes are discussed.

Stability estimation of autoregulated genes under Michaelis-Menten-type kinetics

Feedback loops are typical motifs appearing in gene regulatory networks. In some well-studied model organisms, including Escherichia coli, autoregulated genes, i.e., genes that activate or repress themselves through their protein products, are the only feedback interactions. For these types of interactions, the Michaelis-Menten (MM) formulation is a suitable and widely used approach, which always leads to stable steady-state solutions representative of homeostatic regulation. However, in many other biological phenomena, such as cell differentiation, cancer progression, and catastrophes in ecosystems, one might expect to observe bistable switchlike dynamics in the case of strong positive autoregulation. To capture this complex behavior we use the generalized family of MM kinetic models. We give a full analysis regarding the stability of autoregulated genes. We show that the autoregulation mechanism has the capability to exhibit diverse cellular dynamics including hysteresis, a typical characteristic of bistable systems, as well as irreversible transitions between bistable states. We also introduce a statistical framework to estimate the kinetics parameters and probability of different stability regimes given observational data. Empirical data for the autoregulated gene SCO3217 in the SOS system in Streptomyces coelicolor are analyzed. The coupling of a statistical framework and the mathematical model can give further insight into understanding the evolutionary mechanisms toward different cell fates in various systems.

Transcriptional timing and noise of yeast cell cycle regulators-a single cell and single molecule approach

Gene expression is a stochastic process and its appropriate regulation is critical for cell cycle progression. Cellular stress response necessitates expression reprogramming and cell cycle arrest. While previous studies are mostly based on bulk experiments influenced by synchronization effects or lack temporal distribution, time-resolved methods on single cells are needed to understand eukaryotic cell cycle in context of noisy gene expression and external perturbations. Using smFISH, microscopy and morphological markers, we monitored mRNA abundances over cell cycle phases and calculated transcriptional noise for SIC1, CLN2, and CLB5, the main G1/S transition regulators in budding yeast. We employed mathematical modeling for in silico synchronization and for derivation of time-courses from single cell data. This approach disclosed detailed quantitative insights into transcriptional regulation with and without stress, not available from bulk experiments before. First, besides the main peak in G1 we found an upshift of CLN2 and CLB5 expression in late mitosis. Second, all three genes showed basal expression throughout cell cycle enlightening that transcription is not divided in on and off but rather in high and low phases. Finally, exposing cells to osmotic stress revealed different periods of transcriptional inhibition for CLN2 and CLB5 and the impact of stress on cell cycle phase duration. Combining experimental and computational approaches allowed us to precisely assess cell cycle progression timing, as well as gene expression dynamics.

A Multi-Parameter Analysis of Cellular Coordination of Major Transcriptome Regulation Mechanisms

To understand cellular coordination of multiple transcriptome regulation mechanisms, we simultaneously measured transcription rate (TR), mRNA abundance (RA) and translation activity (TA). This revealed multiple insights. First, the three parameters displayed systematic statistical differences. Sequentially more genes exhibited extreme (low or high) expression values from TR to RA, and then to TA; that is, cellular coordination of multiple transcriptome regulatory mechanisms leads to sequentially enhanced gene expression selectivity as the genetic information flow from the genome to the proteome. Second, contribution of the stabilization-by-translation regulatory mechanism to the cellular coordination process was assessed. The data enabled an estimation of mRNA stability, revealing a moderate but significant positive correlation between mRNA stability and translation activity. Third, the proportion of mRNA occupied by un-translated regions (UTR) exhibited a negative relationship with the level of this correlation, and was thus a major determinant of the mode of regulation of the mRNA. High-UTR-proportion mRNAs tend to defy the stabilization-by-translation regulatory mechanism, staying out of the polysome but remaining stable; mRNAs with little UTRs largely followed this regulation. In summary, we quantitatively delineated the relationship among multiple transcriptome regulation parameters, i.e., cellular coordination of corresponding regulatory mechanisms.

Genome-Wide Mapping of Decay Factor-mRNA Interactions in Yeast Identifies Nutrient-Responsive Transcripts as Targets of the Deadenylase Ccr4

The Ccr4 (carbon catabolite repression 4)-Not complex is a major regulator of stress responses that controls gene expression at multiple levels, from transcription to mRNA decay. Ccr4, a “core” subunit of the complex, is the main cytoplasmic deadenylase in Saccharomyces cerevisiae; however, its mRNA targets have not been mapped on a genome-wide scale. Here, we describe a genome-wide approach, RNA immunoprecipitation (RIP) high-throughput sequencing (RIP-seq), to identify the RNAs bound to Ccr4, and two proteins that associate with it, Dhh1 and Puf5. All three proteins were preferentially bound to lowly abundant mRNAs, most often at the 3′ end of the transcript. Furthermore, Ccr4, Dhh1, and Puf5 are recruited to mRNAs that are targeted by other RNA-binding proteins that promote decay and mRNA transport, and inhibit translation. Although Ccr4-Not regulates mRNA transcription and decay, Ccr4 recruitment to mRNAs correlates better with decay rates, suggesting it imparts greater control over transcript abundance through decay. Ccr4-enriched mRNAs are refractory to control by the other deadenylase complex in yeast, Pan2/3, suggesting a division of labor between these deadenylation complexes. Finally, Ccr4 and Dhh1 associate with mRNAs whose abundance increases during nutrient starvation, and those that fluctuate during metabolic and oxygen consumption cycles, which explains the known genetic connections between these factors and nutrient utilization and stress pathways.

Asymmetric cell division requires specific mechanisms for adjusting global transcription

Most cells divide symmetrically into two approximately identical cells. There are many examples, however, of asymmetric cell division that can generate sibling cell size differences. Whereas physical asymmetric division mechanisms and cell fate consequences have been investigated, the specific problem caused by asymmetric division at the transcription level has not yet been addressed. In symmetrically dividing cells the nascent transcription rate increases in parallel to cell volume to compensate it by keeping the actual mRNA synthesis rate constant. This cannot apply to the yeast Saccharomyces cerevisiae, where this mechanism would provoke a never-ending increasing mRNA synthesis rate in smaller daughter cells. We show here that, contrarily to other eukaryotes with symmetric division, budding yeast keeps the nascent transcription rates of its RNA polymerases constant and increases mRNA stability. This control on RNA pol II-dependent transcription rate is obtained by controlling the cellular concentration of this enzyme.

Transcriptome-wide analysis of the Trypanosoma cruzi proliferative cycle identifies the periodically expressed mRNAs and their multiple levels of control

Trypanosoma cruzi is the protozoan parasite causing American trypanosomiasis or Chagas disease, a neglected parasitosis with important human health impact in Latin America. The efficacy of current therapy is limited, and its toxicity is high. Since parasite proliferation is a fundamental target for rational drug design, we sought to progress into its understanding by applying a genome-wide approach. Treating a TcI linage strain with hydroxyurea, we isolated epimastigotes in late G1, S and G2/M cell cycle stages at 70% purity. The sequencing of each phase identified 305 stage-specific transcripts (1.5-fold change, p≤0.01), coding for conserved cell cycle regulated proteins and numerous proteins whose cell cycle dependence has not been recognized before. Comparisons with the parasite T. brucei and the human host reveal important differences. The meta-analysis of T. cruzi transcriptomic and ribonomic data indicates that cell cycle regulated mRNAs are subject to sub-cellular compartmentalization. Compositional and structural biases of these genes- including CAI, GC content, UTR length, and polycistron position- may contribute to their regulation. To discover nucleotide motifs responsible for the co-regulation of cell cycle regulated genes, we looked for overrepresented motifs at their UTRs and found a variant of the cell cycle sequence motif at the 3' UTR of most of the S and G2 stage genes. We additionally identified hairpin structures at the 5' UTRs of a high proportion of the transcripts, suggesting that periodic gene expression might also rely on translation initiation in T. cruzi. In summary, we report a comprehensive list of T. cruzi cell cycle regulated genes, including many previously unstudied proteins, we show evidence favoring a multi-step control of their expression, and we identify mRNA motifs that may mediate their regulation. Our results provide novel information of the T. cruzi proliferative proteins and the integrated levels of their gene expression control.

Coordinated Pulses of mRNA and of Protein Translation or Degradation Produce EGF-Induced Protein Bursts

Protein responses to extracellular cues are governed by gene transcription, mRNA degradation and translation, and protein degradation. In order to understand how these time-dependent processes cooperate to generate dynamic responses, we analyzed the response of human mammary cells to the epidermal growth factor (EGF). Integrating time-dependent transcript and protein data into a mathematical model, we inferred for several proteins their pre-and post-stimulus translation and degradation coefficients and found that they exhibit complex, time-dependent variation. Specifically, we identified strategies of protein production and degradation acting in concert to generate rapid, transient protein bursts in response to EGF. Remarkably, for some proteins, for which the response necessitates rapidly decreased abundance, cells exhibit a transient increase in the corresponding degradation coefficient. Our model and analysis allow inference of the kinetics of mRNA translation and protein degradation, without perturbing cells, and open a way to understanding the fundamental processes governing time-dependent protein abundance profiles.

Cell cycle time series gene expression data encoded as cyclic attractors in Hopfield systems

Modern time series gene expression and other omics data sets have enabled unprecedented resolution of the dynamics of cellular processes such as cell cycle and response to pharmaceutical compounds. In anticipation of the proliferation of time series data sets in the near future, we use the Hopfield model, a recurrent neural network based on spin glasses, to model the dynamics of cell cycle in HeLa (human cervical cancer) and S. cerevisiae cells. We study some of the rich dynamical properties of these cyclic Hopfield systems, including the ability of populations of simulated cells to recreate experimental expression data and the effects of noise on the dynamics. Next, we use a genetic algorithm to identify sets of genes which, when selectively inhibited by local external fields representing gene silencing compounds such as kinase inhibitors, disrupt the encoded cell cycle. We find, for example, that inhibiting the set of four kinases AURKB, NEK1, TTK, and WEE1 causes simulated HeLa cells to accumulate in the M phase. Finally, we suggest possible improvements and extensions to our model.

Cell cycle—the process in which a parent cell replicates its DNA and divides into two daughter cells—is an upregulated process in many forms of cancer. Identifying gene inhibition targets to regulate cell cycle is important to the development of effective therapies. Although modern high throughput techniques offer unprecedented resolution of the molecular details of biological processes like cell cycle, analyzing the vast quantities of the resulting experimental data and extracting actionable information remains a formidable task. Here, we create a dynamical model of the process of cell cycle using the Hopfield model (a type of recurrent neural network) and gene expression data from human cervical cancer cells and yeast cells. We find that the model recreates the oscillations observed in experimental data. Tuning the level of noise (representing the inherent randomness in gene expression and regulation) to the “edge of chaos” is crucial for the proper behavior of the system. We then use this model to identify potential gene targets for disrupting the process of cell cycle. This method could be applied to other time series data sets and used to predict the effects of untested targeted perturbations.

An improved MS2 system for accurate reporting of the mRNA life cycle

The MS2-MCP system enables researchers to image multiple steps of the mRNA life cycle with high temporal and spatial resolution. However, for short-lived mRNAs, the tight binding of the MS2 coat protein (MCP) to the MS2 binding sites (MBS) protects the RNA from being efficiently degraded, and this confounds the study of mRNA regulation. Here, we describe a reporter system (MBSV6) with reduced affinity for the MCP, which allows mRNA degradation while preserving single-molecule detection determined by single-molecule FISH (smFISH) or live imaging. Constitutive mRNAs (MDN1 and DOA1) and highly-regulated mRNAs (GAL1 and ASH1) endogenously tagged with MBSV6 in Saccharomyces cerevisiae degrade normally. As a result, short-lived mRNAs were imaged throughout their complete life cycle. The MBSV6 reporter revealed that, in contrast to previous findings, coordinated recruitment of mRNAs at specialized structures such as P-bodies during stress did not occur, and mRNA degradation was heterogeneously distributed in the cytoplasm.

Reconciling conflicting models for global control of cell-cycle transcription

Models for the control of global cell-cycle transcription have advanced from a CDK-APC/C oscillator, a transcription factor (TF) network, to coupled CDK-APC/C and TF networks. Nonetheless, current models were challenged by a recent study that concluded that the cell-cycle transcriptional program is primarily controlled by a CDK-APC/C oscillator in budding yeast. Here we report an analysis of the transcriptome dynamics in cyclin mutant cells that were not queried in the previous study. We find that B-cyclin oscillation is not essential for control of phase-specific transcription. Using a mathematical model, we demonstrate that the function of network TFs can be retained in the face of significant reductions in transcript levels. Finally, we show that cells arrested at mitotic exit with non-oscillating levels of B-cyclins continue to cycle transcriptionally. Taken together, these findings support a critical role of a TF network and a requirement for CDK activities that need not be periodic.

Comprehensive analysis of nucleocytoplasmic dynamics of mRNA in Drosophila cells

Eukaryotic mRNAs undergo a cycle of transcription, nuclear export, and degradation. A major challenge is to obtain a global, quantitative view of these processes. Here we measured the genome-wide nucleocytoplasmic dynamics of mRNA in Drosophila cells by metabolic labeling in combination with cellular fractionation. By mathematical modeling of these data we determined rates of transcription, export and cytoplasmic decay for 5420 genes. We characterized these kinetic rates and investigated links with mRNA features, RNA-binding proteins (RBPs) and chromatin states. We found prominent correlations between mRNA decay rate and transcript size, while nuclear export rates are linked to the size of the 3'UTR. Transcription, export and decay rates are each associated with distinct spectra of RBPs. Specific classes of genes, such as those encoding cytoplasmic ribosomal proteins, exhibit characteristic combinations of rate constants, suggesting modular control. Binding of splicing factors is associated with faster rates of export, and our data suggest coordinated regulation of nuclear export of specific functional classes of genes. Finally, correlations between rate constants suggest global coordination between the three processes. Our approach provides insights into the genome-wide nucleocytoplasmic kinetics of mRNA and should be generally applicable to other cell systems.

All mRNAs start from production in the nucleus, undergo exportation through nuclear pores and finally are degraded in the cytoplasm. A comprehensive characterization of the kinetic rates of all mRNAs is an important prerequisite for a global understanding of the regulation of the transcriptome and the cell. By conducting a time-series experiment and building a mathematical model, we trace the dynamics of mRNAs from the nucleus to the cytoplasm and determine the rates at each kinetic step at transcriptome-wide level. This information allows us to associate mRNA kinetic rates with a wealth of biological features and made some intriguing discoveries. We show mRNA decay is positively linked to transcript length while mRNA export is negatively linked to the length of the 3' UTR. We show binding of splicing factors is associated with faster rates of mRNA export. We provide evidence for global coordination between nuclear export an decay of mRNA. We show genes sharing specific functions tend to have similar nucleoplasmic kinetics, in which ribosomal proteins possessing special kinetic features exclusively stand out. Altogether, our integrated approach to quantitatively determine the rates of kinetic steps on a gene-by-gene basis provides a blueprint to obtain the global understanding of RNA regulation.

Solving the influence maximization problem reveals regulatory organization of the yeast cell cycle

The Influence Maximization Problem (IMP) aims to discover the set of nodes with the greatest influence on network dynamics. The problem has previously been applied in epidemiology and social network analysis. Here, we demonstrate the application to cell cycle regulatory network analysis for Saccharomyces cerevisiae. Fundamentally, gene regulation is linked to the flow of information. Therefore, our implementation of the IMP was framed as an information theoretic problem using network diffusion. Utilizing more than 26,000 regulatory edges from YeastMine, gene expression dynamics were encoded as edge weights using time lagged transfer entropy, a method for quantifying information transfer between variables. By picking a set of source nodes, a diffusion process covers a portion of the network. The size of the network cover relates to the influence of the source nodes. The set of nodes that maximizes influence is the solution to the IMP. By solving the IMP over different numbers of source nodes, an influence ranking on genes was produced. The influence ranking was compared to other metrics of network centrality. Although the top genes from each centrality ranking contained well-known cell cycle regulators, there was little agreement and no clear winner. However, it was found that influential genes tend to directly regulate or sit upstream of genes ranked by other centrality measures. The influential nodes act as critical sources of information flow, potentially having a large impact on the state of the network. Biological events that affect influential nodes and thereby affect information flow could have a strong effect on network dynamics, potentially leading to disease. Code and data can be found at: https://github.com/gibbsdavidl/miergolf.

Relationship between regulatory pattern of gene expression level and gene function

Regulation of gene expression levels is essential for all living systems and transcription factors (TFs) are the main regulators of gene expression through their ability to repress or induce transcription. A balance between synthesis and degradation rates controls gene expression levels. To determine which rate is dominant, we analyzed the correlation between expression levels of a TF and its regulated gene based on a mathematical model. We selected about 280,000 expression patterns of 355 TFs and 647 regulated genes using DNA microarray data from the Gene Expression Omnibus (GEO) data repository. Based on our model, correlation between the expressions of TF–regulated gene pairs corresponds to tuning of the synthesis rate, whereas no correlation indicates excessive synthesis and requires tuning of the degradation rate. The gene expression relationships between TF–regulated gene pairs were classified into four types that correspond to different gene regulatory mechanisms. It was surprising that fewer than 20% of these genes were governed by the familiar regulatory mechanism, i.e., through the synthesis rate. Moreover, we performed pathway analysis and found that each classification type corresponded to distinct gene functions: cellular regulation pathways were dominant in the type with synthesis rate regulation and terms associated with diseases such as cancer, Parkinson’s disease, and Alzheimer’s disease were dominant in the type with degradation rate regulation. Interestingly, these diseases are caused by the accumulation of proteins. These results indicated that gene expression is regulated structurally, not arbitrarily, according to the gene function. This funding is indicative of a systematic control of transcription processes at the whole-cell level.

Delineating functional principles of the bow tie structure of a kinase-phosphatase network in the budding yeast

Background

Kinases and phosphatases (KP) form complex self-regulating networks essential for cellular signal processing. In spite of having a wealth of data about interactions among KPs and their substrates, we have very limited models of the structures of the directed networks they form and consequently our ability to formulate hypotheses about how their structure determines the flow of information in these networks is restricted.

Results

We assembled and studied the largest bona fide kinase-phosphatase network (KP-Net) known to date for the yeast Saccharomyces cerevisiae. Application of the vertex sort (VS) algorithm on the KP-Net allowed us to elucidate its hierarchical structure in which nodes are sorted into top, core and bottom layers, forming a bow tie structure with a strongly connected core layer. Surprisingly, phosphatases tend to sort into the top layer, implying they are less regulated by phosphorylation than kinases. Superposition of the widest range of KP biological properties over the KP-Net hierarchy shows that core layer KPs: (i), receive the largest number of inputs; (ii), form bottlenecks implicated in multiple pathways and in decision-making; (iii), and are among the most regulated KPs both temporally and spatially. Moreover, top layer KPs are more abundant and less noisy than those in the bottom layer. Finally, we showed that the VS algorithm depends on node degrees without biasing the biological results of the sorted network. The VS algorithm is available as an R package (https://cran.r-project.org/web/packages/VertexSort/index.html).

Conclusions

The KP-Net model we propose possesses a bow tie hierarchical structure in which the top layer appears to ensure highest fidelity and the core layer appears to mediate signal integration and cell state-dependent signal interpretation. Our model of the yeast KP-Net provides both functional insight into its organization as we understand today and a framework for future investigation of information processing in yeast and eukaryotes in general.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-017-0418-0) contains supplementary material, which is available to authorized users.

Connecting virulence pathways to cell-cycle progression in the fungal pathogen Cryptococcus neoformans

Proliferation and host evasion are critical processes to understand at a basic biological level for improving infectious disease treatment options. The human fungal pathogen Cryptococcus neoformans causes fungal meningitis in immunocompromised individuals by proliferating in cerebrospinal fluid. Current antifungal drugs target "virulence factors" for disease, such as components of the cell wall and polysaccharide capsule in C. neoformans. However, mechanistic links between virulence pathways and the cell cycle are not as well studied. Recently, cell-cycle synchronized C. neoformans cells were profiled over time to identify gene expression dynamics (Kelliher et al., PLoS Genet 12(12):e1006453, 2016). Almost 20% of all genes in the C. neoformans genome were periodically activated during the cell cycle in rich media, including 40 genes that have previously been implicated in virulence pathways. Here, we review important findings about cell-cycle-regulated genes in C. neoformans and provide two examples of virulence pathways-chitin synthesis and G-protein coupled receptor signaling-with their putative connections to cell division. We propose that a "comparative functional genomics" approach, leveraging gene expression timing during the cell cycle, orthology to genes in other fungal species, and previous experimental findings, can lead to mechanistic hypotheses connecting the cell cycle to fungal virulence.

Histone H3K4 and H3K36 Methylation Independently Recruit the NuA3 Histone Acetyltransferase in Saccharomyces cerevisiae

Histone post-translational modifications (PTMs) alter chromatin structure by promoting the interaction of chromatin-modifying complexes with nucleosomes. The majority of chromatin-modifying complexes contain multiple domains that preferentially interact with modified histones, leading to speculation that these domains function in concert to target nucleosomes with distinct combinations of histone PTMs. In Saccharomyces cerevisiae, the NuA3 histone acetyltransferase complex contains three domains, the PHD finger in Yng1, the PWWP domain in Pdp3, and the YEATS domain in Taf14; which in vitro bind to H3K4 methylation, H3K36 methylation, and acetylated and crotonylated H3K9, respectively. While the in vitro binding has been well characterized, the relative in vivo contributions of these histone PTMs in targeting NuA3 is unknown. Here, through genome-wide colocalization and by mutational interrogation, we demonstrate that the PHD finger of Yng1, and the PWWP domain of Pdp3 independently target NuA3 to H3K4 and H3K36 methylated chromatin, respectively. In contrast, we find no evidence to support the YEATS domain of Taf14 functioning in NuA3 recruitment. Collectively our results suggest that the presence of multiple histone PTM binding domains within NuA3, rather than restricting it to nucleosomes containing distinct combinations of histone PTMs, can serve to increase the range of nucleosomes bound by the complex. Interestingly, however, the simple presence of NuA3 is insufficient to ensure acetylation of the associated nucleosomes, suggesting a secondary level of acetylation regulation that does not involve control of HAT-nucleosome interactions.

Investigating Conservation of the Cell-Cycle-Regulated Transcriptional Program in the Fungal Pathogen, Cryptococcus neoformans

The pathogenic yeast Cryptococcus neoformans causes fungal meningitis in immune-compromised patients. Cell proliferation in the budding yeast form is required for C. neoformans to infect human hosts, and virulence factors such as capsule formation and melanin production are affected by cell-cycle perturbation. Thus, understanding cell-cycle regulation is critical for a full understanding of virulence factors for disease. Our group and others have demonstrated that a large fraction of genes in Saccharomyces cerevisiae is expressed periodically during the cell cycle, and that proper regulation of this transcriptional program is important for proper cell division. Despite the evolutionary divergence of the two budding yeasts, we found that a similar percentage of all genes (~20%) is periodically expressed during the cell cycle in both yeasts. However, the temporal ordering of periodic expression has diverged for some orthologous cell-cycle genes, especially those related to bud emergence and bud growth. Genes regulating DNA replication and mitosis exhibited a conserved ordering in both yeasts, suggesting that essential cell-cycle processes are conserved in periodicity and in timing of expression (i.e. duplication before division). In S. cerevisiae cells, we have proposed that an interconnected network of periodic transcription factors (TFs) controls the bulk of the cell-cycle transcriptional program. We found that temporal ordering of orthologous network TFs was not always maintained; however, the TF network topology at cell-cycle commitment appears to be conserved in C. neoformans. During the C. neoformans cell cycle, DNA replication genes, mitosis genes, and 40 genes involved in virulence are periodically expressed. Future work toward understanding the gene regulatory network that controls cell-cycle genes is critical for developing novel antifungals to inhibit pathogen proliferation.

Measures of RNA metabolism rates: Toward a definition at the level of single bonds

We give an overview of experimental and computational methods to estimate RNA metabolism rates genome-wide. We then advocate a local definition of RNA metabolism rate at the level of individual phosphodiester bonds. Rates of formation and disappearance of individual bonds are unambiguously defined, in contrast to rates of complete transcripts. We show that over previous approaches, the recently developed transient transcriptome sequencing (TT-seq) protocol allows for estimation of metabolism rates of individual bonds with least positional bias.

Inference of gene regulation functions from dynamic transcriptome data

To quantify gene regulation, a function is required that relates transcription factor binding to DNA (input) to the rate of mRNA synthesis from a target gene (output). Such a ‘gene regulation function’ (GRF) generally cannot be measured because the experimental titration of inputs and simultaneous readout of outputs is difficult. Here we show that GRFs may instead be inferred from natural changes in cellular gene expression, as exemplified for the cell cycle in the yeast S. cerevisiae. We develop this inference approach based on a time series of mRNA synthesis rates from a synchronized population of cells observed over three cell cycles. We first estimate the functional form of how input transcription factors determine mRNA output and then derive GRFs for target genes in the CLB2 gene cluster that are expressed during G2/M phase. Systematic analysis of additional GRFs suggests a network architecture that rationalizes transcriptional cell cycle oscillations. We find that a transcription factor network alone can produce oscillations in mRNA expression, but that additional input from cyclin oscillations is required to arrive at the native behaviour of the cell cycle oscillator.

DOI:http://dx.doi.org/10.7554/eLife.12188.001

Living cells rely on networks of genes to control their behavior, including how they grow, develop and respond to stress. Genes encode instructions needed to make proteins and other molecules, and much of the control is exerted at the first stage of protein production, known as transcription. During this process, a gene is copied to make molecules known as transcripts that may later be used as templates to make proteins.

Many genes encode proteins that act to regulate transcription. Therefore, an individual gene may receive inputs from other genes, and these inputs affect how much transcript the gene produces, which can be considered as the gene’s output. While these inputs and outputs can often be wired together to form a network, it is less clear exactly how all the different inputs at a gene interact to determine its output. These interactions are known as “gene regulation functions”, and knowing them would be an important step towards understanding gene networks, which would help us to predict how cells will behave in different situations.

Gene regulation functions are difficult to measure directly, so researchers would like to find other ways to assess them indirectly. A recently developed experimental technique called “dynamic transcriptome analysis” seemed promising as it measures both the inputs and outputs of all genes in a cell over time. Hillenbrand et al. used this technique to infer gene regulation functions with one or two inputs in yeast cells.

Comparing these estimates with experimental data from previous studies showed that these inferred gene regulation functions could successfully predict the output of a gene based on its inputs. Hillenbrand et al. then used these estimates to search and model a well-known genetic network that is thought to be part of the molecular clockwork that controls the timing of events that cause a cell to divide.

Currently, the approach used by Hillenbrand et al. treats gene regulation functions like “black boxes”. This means that, while an output can be predicted if the inputs are known, it cannot reveal all of the detailed mechanisms behind it. Gaining insights into the inner workings of these black boxes will require taking more data into account, such as how abundant the proteins that regulate transcription are, where they are located within cells or whether they are active or not. Therefore, the next challenge is to incorporate these kinds of data to gain a fuller picture of how gene networks operate within cells.

DOI:http://dx.doi.org/10.7554/eLife.12188.002