Cell-Size-Dependent Transcription of FLC and Its Antisense Long Non-coding RNA COOLAIR Explain Cell-to-Cell Expression Variation

AraLeTA: An Arabidopsis leaf expression atlas across diurnal and developmental scales

Mature plant leaves are a composite of distinct cell types, including epidermal, mesophyll, and vascular cells. Notably, the proportion of these cells and the relative transcript concentrations within different cell types may change over time. While gene expression data at a single-cell level can provide cell-type-specific expression values, it is often too expensive to obtain these data for high-resolution time series. Although bulk RNA-seq can be performed in a high-resolution time series, RNA-seq using whole leaves measures average gene expression values across all cell types in each sample. In this study, we combined single-cell RNA-seq data with time-series data from whole leaves to assemble an atlas of cell-type-specific changes in gene expression over time for Arabidopsis (Arabidopsis thaliana). We inferred how the relative transcript concentrations of different cell types vary across diurnal and developmental timescales. Importantly, this analysis revealed 3 subgroups of mesophyll cells with distinct temporal profiles of expression. Finally, we developed tissue-specific gene networks that form a community resource: an Arabidopsis Leaf Time-dependent Atlas (AraLeTa). This allows users to extract gene networks that are confirmed by transcription factor-binding data and specific to certain cell types at certain times of day and at certain developmental stages. AraLeTa is available at https://regulatorynet.shinyapps.io/araleta/.

Genome-wide quantification of RNA flow across subcellular compartments reveals determinants of the mammalian transcript life cycle

Dissecting the regulatory mechanisms controlling mammalian transcripts from production to degradation requires quantitative measurements of mRNA flow across the cell. We developed subcellular TimeLapse-seq to measure the rates at which RNAs are released from chromatin, exported from the nucleus, loaded onto polysomes, and degraded within the nucleus and cytoplasm in human and mouse cells. These rates varied substantially, yet transcripts from genes with related functions or targeted by the same transcription factors and RNA-binding proteins flowed across subcellular compartments with similar kinetics. Verifying these associations uncovered a link between DDX3X and nuclear export. For hundreds of RNA metabolism genes, most transcripts with retained introns were degraded by the nuclear exosome, while the remaining molecules were exported with stable cytoplasmic lifespans. Transcripts residing on chromatin for longer had extended poly(A) tails, whereas the reverse was observed for cytoplasmic mRNAs. Finally, machine learning identified molecular features that predicted the diverse life cycles of mRNAs.

Proximal termination generates a transcriptional state that determines the rate of establishment of Polycomb silencing

The mechanisms and timescales controlling de novo establishment of chromatin-mediated transcriptional silencing by Polycomb repressive complex 2 (PRC2) are unclear. Here, we investigate PRC2 silencing at Arabidopsis FLOWERING LOCUS C (FLC), known to involve co-transcriptional RNA processing, histone demethylation activity, and PRC2 function, but so far not mechanistically connected. We develop and test a computational model describing proximal polyadenylation/termination mediated by the RNA-binding protein FCA that induces H3K4me1 removal by the histone demethylase FLD. H3K4me1 removal feeds back to reduce RNA polymerase II (RNA Pol II) processivity and thus enhance early termination, thereby repressing productive transcription. The model predicts that this transcription-coupled repression controls the level of transcriptional antagonism to PRC2 action. Thus, the effectiveness of this repression dictates the timescale for establishment of PRC2/H3K27me3 silencing. We experimentally validate these mechanistic model predictions, revealing that co-transcriptional processing sets the level of productive transcription at the locus, which then determines the rate of the ON-to-OFF switch to PRC2 silencing.

A kinetic dichotomy between mitochondrial and nuclear gene expression processes

Oxidative phosphorylation (OXPHOS) complexes, encoded by both mitochondrial and nuclear DNA, are essential producers of cellular ATP, but how nuclear and mitochondrial gene expression steps are coordinated to achieve balanced OXPHOS subunit biogenesis remains unresolved. Here, we present a parallel quantitative analysis of the human nuclear and mitochondrial messenger RNA (mt-mRNA) life cycles, including transcript production, processing, ribosome association, and degradation. The kinetic rates of nearly every stage of gene expression differed starkly across compartments. Compared with nuclear mRNAs, mt-mRNAs were produced 1,100-fold more, degraded 7-fold faster, and accumulated to 160-fold higher levels. Quantitative modeling and depletion of mitochondrial factors LRPPRC and FASTKD5 identified critical points of mitochondrial regulatory control, revealing that the mitonuclear expression disparities intrinsically arise from the highly polycistronic nature of human mitochondrial pre-mRNA. We propose that resolving these differences requires a 100-fold slower mitochondrial translation rate, illuminating the mitoribosome as a nexus of mitonuclear co-regulation.

Functional consequences of somatic polyploidy in development

Polyploid cells contain multiple genome copies and arise in many animal tissues as a regulated part of development. However, polyploid cells can also arise due to cell division failure, DNA damage or tissue damage. Although polyploidization is crucial for the integrity and function of many tissues, the cellular and tissue-wide consequences of polyploidy can be very diverse. Nonetheless, many polyploid cell types and tissues share a remarkable similarity in function, providing important information about the possible contribution of polyploidy to cell and tissue function. Here, we review studies on polyploid cells in development, underlining parallel functions between different polyploid cell types, as well as differences between developmentally-programmed and stress-induced polyploidy.

COOLAIR and PRC2 function in parallel to silence FLC during vernalization

Noncoding transcription induces chromatin changes that can mediate environmental responsiveness, but the causes and consequences of these mechanisms are still unclear. Here, we investigate how antisense transcription (termed COOLAIR) interfaces with Polycomb Repressive Complex 2 (PRC2) silencing during winter-induced epigenetic regulation of Arabidopsis FLOWERING LOCUS C (FLC). We use genetic and chromatin analyses on lines ineffective or hyperactive for the antisense pathway in combination with computational modeling to define the mechanisms underlying FLC repression. Our results show that FLC is silenced through pathways that function with different dynamics: a COOLAIR transcription-mediated pathway capable of fast response and in parallel a slow PRC2 switching mechanism that maintains each allele in an epigenetically silenced state. Components of both the COOLAIR and PRC2 pathways are regulated by a common transcriptional regulator (NTL8), which accumulates by reduced dilution due to slow growth at low temperature. The parallel activities of the regulatory steps, and their control by temperature-dependent growth dynamics, create a flexible system for registering widely fluctuating natural temperature conditions that change year on year, and yet ensure robust epigenetic silencing of FLC.

Role of methylation in vernalization and photoperiod pathway: a potential flowering regulator?

Recognized as a pivotal developmental transition, flowering marks the continuation of a plant's life cycle. Vernalization and photoperiod are two major flowering pathways orchestrating numerous florigenic signals. Methylation, including histone, DNA and RNA methylation, is one of the recent foci in plant development. Considerable studies reveal that methylation seems to show an increasing potential regulatory role in plant flowering via altering relevant gene expression without altering the genetic basis. However, little has been reviewed about whether and how methylation acts on vernalization- and photoperiod-induced flowering before and after FLOWERING LOCUS C (FLC) reactivation, what role RNA methylation plays in vernalization- and photoperiod-induced flowering, how methylation participates simultaneously in both vernalization- and photoperiod-induced flowering, the heritability of methylation memory under the vernalization/photoperiod pathway, and whether and how methylation replaces vernalization/photoinduction to regulate flowering. Our review provides insight about the crosstalk among the genetic control of the flowering gene network, methylation (methyltransferases/demethylases) and external signals (cold, light, sRNA and phytohormones) in vernalization and photoperiod pathways. The existing evidence that RNA methylation may play a potential regulatory role in vernalization- and photoperiod-induced flowering has been gathered and represented for the first time. This review speculates about and discusses the possibility of substituting methylation for vernalization and photoinduction to promote flowering. Current evidence is utilized to discuss the possibility of future methylation reagents becoming flowering regulators at the molecular level.

A kinetic dichotomy between mitochondrial and nuclear gene expression drives OXPHOS biogenesis

Oxidative phosphorylation (OXPHOS) complexes, encoded by both mitochondrial and nuclear DNA, are essential producers of cellular ATP, but how nuclear and mitochondrial gene expression steps are coordinated to achieve balanced OXPHOS biogenesis remains unresolved. Here, we present a parallel quantitative analysis of the human nuclear and mitochondrial messenger RNA (mt-mRNA) life cycles, including transcript production, processing, ribosome association, and degradation. The kinetic rates of nearly every stage of gene expression differed starkly across compartments. Compared to nuclear mRNAs, mt-mRNAs were produced 700-fold higher, degraded 5-fold faster, and accumulated to 170-fold higher levels. Quantitative modeling and depletion of mitochondrial factors, LRPPRC and FASTKD5, identified critical points of mitochondrial regulatory control, revealing that the mitonuclear expression disparities intrinsically arise from the highly polycistronic nature of human mitochondrial pre-mRNA. We propose that resolving these differences requires a 100-fold slower mitochondrial translation rate, illuminating the mitoribosome as a nexus of mitonuclear co-regulation.

The σB alternative sigma factor circuit modulates noise to generate different types of pulsing dynamics

Single-cell approaches are revealing a high degree of heterogeneity, or noise, in gene expression in isogenic bacteria. How gene circuits modulate this noise in gene expression to generate robust output dynamics is unclear. Here we use the Bacillus subtilis alternative sigma factor σB as a model system for understanding the role of noise in generating circuit output dynamics. σB controls the general stress response in B. subtilis and is activated by a range of energy and environmental stresses. Recent single-cell studies have revealed that the circuit can generate two distinct outputs, stochastic pulsing and a single pulse response, but the conditions under which each response is generated are under debate. We implement a stochastic mathematical model of the σB circuit to investigate this and find that the system's core circuit can generate both response types. This is despite one response (stochastic pulsing) being stochastic in nature, and the other (single response pulse) being deterministic. We demonstrate that the main determinant for whichever response is generated is the degree with which the input pathway activates the core circuit, although the noise properties of the input pathway also biases the system towards one or the other type of output. Thus, our work shows how stochastic modelling can reveal the mechanisms behind non-intuitive gene circuit output dynamics.

Integrating analog and digital modes of gene expression at Arabidopsis FLC

Quantitative gene regulation at the cell population level can be achieved by two fundamentally different modes of regulation at individual gene copies. A 'digital' mode involves binary ON/OFF expression states, with population-level variation arising from the proportion of gene copies in each state, while an 'analog' mode involves graded expression levels at each gene copy. At the Arabidopsis floral repressor FLOWERING LOCUS C (FLC), 'digital' Polycomb silencing is known to facilitate quantitative epigenetic memory in response to cold. However, whether FLC regulation before cold involves analog or digital modes is unknown. Using quantitative fluorescent imaging of FLC mRNA and protein, together with mathematical modeling, we find that FLC expression before cold is regulated by both analog and digital modes. We observe a temporal separation between the two modes, with analog preceding digital. The analog mode can maintain intermediate expression levels at individual FLC gene copies, before subsequent digital silencing, consistent with the copies switching OFF stochastically and heritably without cold. This switch leads to a slow reduction in FLC expression at the cell population level. These data present a new paradigm for gradual repression, elucidating how analog transcriptional and digital epigenetic memory pathways can be integrated.

Modelling capture efficiency of single-cell RNA-sequencing data improves inference of transcriptome-wide burst kinetics

MOTIVATION

Gene expression is characterized by stochastic bursts of transcription that occur at brief and random periods of promoter activity. The kinetics of gene expression burstiness differs across the genome and is dependent on the promoter sequence, among other factors. Single-cell RNA sequencing (scRNA-seq) has made it possible to quantify the cell-to-cell variability in transcription at a global genome-wide level. However, scRNA-seq data are prone to technical variability, including low and variable capture efficiency of transcripts from individual cells.

RESULTS

Here, we propose a novel mathematical theory for the observed variability in scRNA-seq data. Our method captures burst kinetics and variability in both the cell size and capture efficiency, which allows us to propose several likelihood-based and simulation-based methods for the inference of burst kinetics from scRNA-seq data. Using both synthetic and real data, we show that the simulation-based methods provide an accurate, robust and flexible tool for inferring burst kinetics from scRNA-seq data. In particular, in a supervised manner, a simulation-based inference method based on neural networks proves to be accurate and useful when applied to both allele and nonallele-specific scRNA-seq data.

AVAILABILITY AND IMPLEMENTATION

The code for Neural Network and Approximate Bayesian Computation inference is available at https://github.com/WT215/nnRNA and https://github.com/WT215/Julia_ABC, respectively.

Whole-mount smFISH allows combining RNA and protein quantification at cellular and subcellular resolution

Multicellular organisms result from complex developmental processes largely orchestrated through the quantitative spatiotemporal regulation of gene expression. Yet, obtaining absolute counts of messenger RNAs at a three-dimensional resolution remains challenging, especially in plants, owing to high levels of tissue autofluorescence that prevent the detection of diffraction-limited fluorescent spots. In situ hybridization methods based on amplification cycles have recently emerged, but they are laborious and often lead to quantification biases. In this article, we present a simple method based on single-molecule RNA fluorescence in situ hybridization to visualize and count the number of mRNA molecules in several intact plant tissues. In addition, with the use of fluorescent protein reporters, our method also enables simultaneous detection of mRNA and protein quantity, as well as subcellular distribution, in single cells. With this method, research in plants can now fully explore the benefits of the quantitative analysis of transcription and protein levels at cellular and subcellular resolution in plant tissues.

Reference genes for quantitative Arabidopsis single molecule RNA fluorescence in situ hybridization

Subcellular mRNA quantities and spatial distributions are fundamental for driving gene regulatory programmes. Single molecule RNA fluorescence in situ hybridization (smFISH) uses fluorescent probes to label individual mRNA molecules, thereby facilitating both localization and quantitative studies. Validated reference mRNAs function as positive controls and are required for calibration. Here we present selection criteria for the first set of Arabidopsis smFISH reference genes. Following sequence and transcript data assessments, four mRNA probe sets were selected for imaging. Transcript counts per cell, correlations with cell size, and corrected fluorescence intensities were all calculated for comparison. In addition to validating reference probe sets, we present sample preparation steps that can retain green fluorescent protein fluorescence, thereby providing a method for simultaneous RNA and protein detection. In summary, our reference gene analyses, modified protocol, and simplified quantification method together provide a firm foundation for future quantitative single molecule RNA studies in Arabidopsis root apical meristem cells.

Promoter-pervasive transcription causes RNA polymerase II pausing to boost DOG1 expression in response to salt

Eukaryotic genomes are pervasively transcribed by RNA polymerase II. Yet, the molecular and biological implications of such a phenomenon are still largely puzzling. Here, we describe noncoding RNA transcription upstream of the Arabidopsis thaliana DOG1 gene, which governs salt stress responses and is a key regulator of seed dormancy. We find that expression of the DOG1 gene is induced by salt stress, thereby causing a delay in seed germination. We uncover extensive transcriptional activity on the promoter of the DOG1 gene, which produces a variety of lncRNAs. These lncRNAs, named PUPPIES, are co-directionally transcribed and extend into the DOG1 coding region. We show that PUPPIES RNAs respond to salt stress and boost DOG1 expression, resulting in delayed germination. This positive role of pervasive PUPPIES transcription on DOG1 gene expression is associated with augmented pausing of RNA polymerase II, slower transcription and higher transcriptional burst size. These findings highlight the positive role of upstream co-directional transcription in controlling transcriptional dynamics of downstream genes.

Gene expression model inference from snapshot RNA data using Bayesian non-parametrics

Gene expression models, which are key towards understanding cellular regulatory response, underlie observations of single-cell transcriptional dynamics. Although RNA expression data encode information on gene expression models, existing computational frameworks do not perform simultaneous Bayesian inference of gene expression models and parameters from such data. Rather, gene expression models-composed of gene states, their connectivities and associated parameters-are currently deduced by pre-specifying gene state numbers and connectivity before learning associated rate parameters. Here we propose a method to learn full distributions over gene states, state connectivities and associated rate parameters, simultaneously and self-consistently from single-molecule RNA counts. We propagate noise from fluctuating RNA counts over models by treating models themselves as random variables. We achieve this within a Bayesian non-parametric paradigm. We demonstrate our method on the Escherichia coli lacZ pathway and the Saccharomyces cerevisiae STL1 pathway, and verify its robustness on synthetic data.

Coupling gene expression dynamics to cell size dynamics and cell cycle events: Exact and approximate solutions of the extended telegraph model

The standard model describing the fluctuations of mRNA numbers in single cells is the telegraph model which includes synthesis and degradation of mRNA, and switching of the gene between active and inactive states. While commonly used, this model does not describe how fluctuations are influenced by the cell cycle phase, cellular growth and division, and other crucial aspects of cellular biology. Here, we derive the analytical time-dependent solution of an extended telegraph model that explicitly considers the doubling of gene copy numbers upon DNA replication, dependence of the mRNA synthesis rate on cellular volume, gene dosage compensation, partitioning of molecules during cell division, cell-cycle duration variability, and cell-size control strategies. Based on the time-dependent solution, we obtain the analytical distributions of transcript numbers for lineage and population measurements in steady-state growth and also find a linear relation between the Fano factor of mRNA fluctuations and cell volume fluctuations. We show that generally the lineage and population distributions in steady-state growth cannot be accurately approximated by the steady-state solution of extrinsic noise models, i.e. a telegraph model with parameters drawn from probability distributions. This is because the mRNA lifetime is often not small enough compared to the cell cycle duration to erase the memory of division and replication. Accurate approximations are possible when this memory is weak, e.g. for genes with bursty expression and for which there is sufficient gene dosage compensation when replication occurs.

Concentration fluctuations in growing and dividing cells: Insights into the emergence of concentration homeostasis

Intracellular reaction rates depend on concentrations and hence their levels are often regulated. However classical models of stochastic gene expression lack a cell size description and cannot be used to predict noise in concentrations. Here, we construct a model of gene product dynamics that includes a description of cell growth, cell division, size-dependent gene expression, gene dosage compensation, and size control mechanisms that can vary with the cell cycle phase. We obtain expressions for the approximate distributions and power spectra of concentration fluctuations which lead to insight into the emergence of concentration homeostasis. We find that (i) the conditions necessary to suppress cell division-induced concentration oscillations are difficult to achieve; (ii) mRNA concentration and number distributions can have different number of modes; (iii) two-layer size control strategies such as sizer-timer or adder-timer are ideal because they maintain constant mean concentrations whilst minimising concentration noise; (iv) accurate concentration homeostasis requires a fine tuning of dosage compensation, replication timing, and size-dependent gene expression; (v) deviations from perfect concentration homeostasis show up as deviations of the concentration distribution from a gamma distribution. Some of these predictions are confirmed using data for E. coli, fission yeast, and budding yeast.

Antisense Transcription in Plants: A Systematic Review and an Update on cis-NATs of Sugarcane

Initially, natural antisense transcripts (NATs, natRNAs, or asRNAs) were considered repressors; however, their functions in gene regulation are diverse. Positive, negative, or neutral correlations to the cognate gene expression have been noted. Although the first studies were published about 50 years ago, there is still much to be investigated regarding antisense transcripts in plants. A systematic review of scientific publications available in the Web of Science databases was conducted to contextualize how the studying of antisense transcripts has been addressed. Studies were classified considering three categories: “Natural antisense” (208), artificial antisense used in “Genetic Engineering” (797), or “Natural antisense and Genetic Engineering”-related publications (96). A similar string was used for a systematic search in the NCBI Gene database. Of the 1132 antisense sequences found for plants, only 0.8% were cited in PubMed and had antisense information confirmed. This value was the lowest when compared to fungi (2.9%), bacteria (2.3%), and mice (54.1%). Finally, we present an update for the cis-NATs identified in Saccharum spp. Of the 1413 antisense transcripts found in different experiments, 25 showed concordant expressions, 22 were discordant, 1264 did not correlate with the cognate genes, and 102 presented variable results depending on the experiment.

Single seeds exhibit transcriptional heterogeneity during secondary dormancy induction

Seeds are highly resilient to the external environment, which allows plants to persist in unpredictable and unfavorable conditions. Some plant species have adopted a bet-hedging strategy to germinate a variable fraction of seeds in any given condition, and this could be explained by population-based threshold models. Here, in the model plant Arabidopsis (Arabidopsis thaliana), we induced secondary dormancy (SD) to address the transcriptional heterogeneity among seeds that leads to binary germination/nongermination outcomes. We developed a single-seed RNA-seq strategy that allowed us to observe a reduction in seed transcriptional heterogeneity as seeds enter stress conditions, followed by an increase during recovery. We identified groups of genes whose expression showed a specific pattern through a time course and used these groups to position the individual seeds along the transcriptional gradient of germination competence. In agreement, transcriptomes of dormancy-deficient seeds (mutant of DELAY OF GERMINATION 1) showed a shift toward higher values of the germination competence index. Interestingly, a significant fraction of genes with variable expression encoded translation-related factors. In summary, interrogating hundreds of single-seed transcriptomes during SD-inducing treatment revealed variability among the transcriptomes that could result from the distribution of population-based sensitivity thresholds. Our results also showed that single-seed RNA-seq is the method of choice for analyzing seed bet-hedging-related phenomena.

Phase separation of HRLP regulates flowering time in Arabidopsis

RNA binding proteins mediate posttranscriptional RNA metabolism and play regulatory roles in many developmental processes in eukaryotes. Despite their known effects on the floral transition from vegetative to reproductive growth in plants, the underlying mechanisms remain largely obscure. Here, we show that a hitherto unknown RNA binding protein, hnRNP R-LIKE PROTEIN (HRLP), inhibits cotranscriptional splicing of a key floral repressor gene FLOWERING LOCUS C (FLC). This, in turn, facilitates R-loop formation near FLC intron I to repress its transcription, thereby promoting the floral transition in Arabidopsis thaliana. HRLP, together with the splicing factor ARGININE/SERINE-RICH 45, forms phase-separated nuclear condensates with liquid-like properties, which is essential for HRLP function in regulating FLC splicing, R-loop formation, and RNA Polymerase II recruitment. Our findings reveal that inhibition of cotranscriptional splicing of FLC by nuclear HRLP condensates constitutes the molecular basis for down-regulation of FLC transcript levels to ensure the reproductive success of Arabidopsis.

Mechanisms of cellular mRNA transcript homeostasis

For most genes, mRNA transcript abundance scales with cell size to ensure a constant concentration. Scaling of mRNA synthesis rates with cell size plays an important role, with regulation of the activity and abundance of RNA polymerase II (Pol II) now emerging as a key point of control. However, there is also considerable evidence for feedback mechanisms that kinetically couple the rates of mRNA synthesis, nuclear export, and degradation to allow cells to compensate for changes in one by adjusting the others. Researchers are beginning to integrate results from these different fields to reveal the mechanisms underlying transcript homeostasis. This will be crucial for moving beyond our current understanding of relative gene expression towards an appreciation of how absolute transcript levels are linked to other aspects of the cellular phenotype.

Feedback from nuclear RNA on transcription promotes robust RNA concentration homeostasis in human cells

RNA concentration homeostasis involves coordinating RNA abundance and synthesis rates with cell size. Here, we study this in human cells by combining genome-wide perturbations with quantitative single-cell measurements. Despite relative ease in perturbing RNA synthesis, we find that RNA concentrations generally remain highly constant. Perturbations that would be expected to increase nuclear mRNA levels, including those targeting nuclear mRNA degradation or export, result in downregulation of RNA synthesis. This is associated with reduced abundance of transcription-associated proteins and protein states that are normally coordinated with RNA production in single cells, including RNA polymerase II (RNA Pol II) itself. Acute perturbations, elevation of nuclear mRNA levels, and mathematical modeling indicate that mammalian cells achieve robust mRNA concentration homeostasis by the mRNA-based negative feedback on transcriptional activity in the nucleus. This ultimately acts to coordinate RNA Pol II abundance with nuclear mRNA degradation and export rates and may underpin the scaling of mRNA abundance with cell size.

Investigating Histone Modification Dynamics by Mechanistic Computational Modeling

The maintenance of transcriptional states regulated by histone modifications and controlled switching between these states are fundamental concepts in our understanding of nucleosome-mediated epigenetic memory. Any approach relying on genome-wide bioinformatic analyses alone offers limited scope for dissecting the molecular mechanisms involved in maintenance and switching. Mechanistic mathematical models-describing the dynamics of histone modifications at individual genomic loci-offer an alternative way to investigate these mechanisms. These models, in conjunction with quantitative experimental data-ChIP data, quantification of mRNA levels, and single-cell fluorescence tracking in clonal lineages-can generate predictions that drive more targeted experiments, allowing us to understand mechanisms that would be challenging to unravel by a purely experimental approach. In this chapter, we describe a generic stochastic modeling framework that can be used to capture histone modification dynamics and associated molecular processes-including transcription and read-write feedback by chromatin modifying complexes-at individual genomic loci. Using a specific example-transcriptional silencing by Polycomb-mediated H3K27 methylation-we demonstrate how to construct and simulate a stochastic histone modification model. We provide a step-by-step guide to programming simulations for such a model and discuss how to analyze the simulation output.

Heterogeneous recruitment abilities to RNA polymerases generate nonlinear scaling of gene expression with cell volume

While most genes' expression levels are proportional to cell volumes, some genes exhibit nonlinear scaling between their expression levels and cell volume. Therefore, their mRNA and protein concentrations change as the cell volume increases, which often have crucial biological functions such as cell-cycle regulation. However, the biophysical mechanism underlying the nonlinear scaling between gene expression and cell volume is still unclear. In this work, we show that the nonlinear scaling is a direct consequence of the heterogeneous recruitment abilities of promoters to RNA polymerases based on a gene expression model at the whole-cell level. Those genes with weaker (stronger) recruitment abilities than the average ability spontaneously exhibit superlinear (sublinear) scaling with cell volume. Analysis of the promoter sequences and the nonlinear scaling of Saccharomyces cerevisiae's mRNA levels shows that motifs associated with transcription regulation are indeed enriched in genes exhibiting nonlinear scaling, in concert with our model.

Cold-induced Arabidopsis FRIGIDA nuclear condensates for FLC repression

Plants use seasonal temperature cues to time the transition to reproduction. In Arabidopsis thaliana, winter cold epigenetically silences the floral repressor locus FLOWERING LOCUS C (FLC) through POLYCOMB REPRESSIVE COMPLEX 2 (PRC2)1. This vernalization process aligns flowering with spring. A prerequisite for silencing is transcriptional downregulation of FLC, but how this occurs in the fluctuating temperature regimes of autumn is unknown2-4. Transcriptional repression correlates with decreased local levels of histone H3 trimethylation at K36 (H3K36me3) and H3 trimethylation at K4 (H3K4me3)5,6, which are deposited during FRIGIDA (FRI)-dependent activation of FLC7-10. Here we show that cold rapidly promotes the formation of FRI nuclear condensates that do not colocalize with an active FLC locus. This correlates with reduced FRI occupancy at the FLC promoter and FLC repression. Warm temperature spikes reverse this process, buffering FLC shutdown to prevent premature flowering. The accumulation of condensates in the cold is affected by specific co-transcriptional regulators and cold induction of a specific isoform of the antisense RNA COOLAIR5,11. Our work describes the dynamic partitioning of a transcriptional activator conferring plasticity in response to natural temperature fluctuations, thus enabling plants to effectively monitor seasonal progression.

Changes in ploidy affect vascular allometry and hydraulic function in Mangifera indica trees

The enucleated vascular elements of the xylem and the phloem offer an excellent system to test the effect of ploidy on plant function because variation in vascular geometry has a direct influence on transport efficiency. However, evaluations of conduit sizes in polyploid plants have remained elusive, most remarkably in woody species. We used a combination of molecular, physiological and microscopy techniques to model the hydraulic resistance between source and sinks in tetraploid and diploid mango trees. Tetraploids exhibited larger chloroplasts, mesophyll cells and stomatal guard cells, resulting in higher leaf elastic modulus and lower dehydration rates, despite the high water potentials of both ploidies in the field. Both the xylem and the phloem displayed a scaling of conduits with ploidy, revealing attenuated hydraulic resistance in tetraploids. Conspicuous wall hygroscopic moieties in the cells involved in transpiration and transport indicate a role in volumetric adjustments as a result of turgor change in both ploidies. In autotetraploids, the enlargement of organelles, cells and tissues, which are critical for water and photoassimilate transport at long distances, point to major physiological novelties associated with whole-genome duplication.

The vernalization-induced long non-coding RNA VAS functions with the transcription factor TaRF2b to promote TaVRN1 expression for flowering in hexaploid wheat

Vernalization is a physiological process in which prolonged cold exposure establishes flowering competence in winter plants. In hexaploid wheat, TaVRN1 is a cold-induced key regulator that accelerates floral transition. However, the molecular mechanism underlying the gradual activation of TaVRN1 during the vernalization process remains unknown. In this study, we identified the novel transcript VAS (TaVRN1 alternative splicing) as a non-coding RNA derived from the sense strand of the TaVRN1 gene only in winter wheat, which regulates TaVRN1 transcription for flowering. VAS was induced during the early period of vernalization, and its overexpression promoted TaVRN1 expression to accelerate flowering in winter wheat. VAS physically associates with TaRF2b and facilitates docking of the TaRF2b-TaRF2a complex at the TaVRN1 promoter during the middle period of vernalization. TaRF2b recognizes the Sp1 motif within the TaVRN1 proximal promoter region, which is gradually exposed along with the disruption of a loop structure at the TaVRN1 locus during vernalization, to activate the transcription of TaVRN1. The tarf2b mutants exhibited delayed flowering, whereas transgenic wheat lines overexpressing TaRF2b showed earlier flowering. Taken together, our data reveal a distinct regulatory mechanism by which a long non-coding RNA facilitates the transcription factor targeting to regulate wheat flowering, providing novel insights into the vernalization process and a potential target for wheat genetic improvement.

Hybrid protein assembly-histone modification mechanism for PRC2-based epigenetic switching and memory

The histone modification H3K27me3 plays a central role in Polycomb-mediated epigenetic silencing. H3K27me3 recruits and allosterically activates Polycomb Repressive Complex 2 (PRC2), which adds this modification to nearby histones, providing a read/write mechanism for inheritance through DNA replication. However, for some PRC2 targets, a purely histone-based system for epigenetic inheritance may be insufficient. We address this issue at the Polycomb target FLOWERING LOCUS C (FLC) in Arabidopsis thaliana, as a narrow nucleation region of only ~three nucleosomes within FLC mediates epigenetic state switching and subsequent memory over many cell cycles. To explain the memory's unexpected persistence, we introduce a mathematical model incorporating extra protein memory storage elements with positive feedback that persist at the locus through DNA replication, in addition to histone modifications. Our hybrid model explains many features of epigenetic switching/memory at FLC and encapsulates generic mechanisms that may be widely applicable.

Quantitative imaging of RNA polymerase II activity in plants reveals the single-cell basis of tissue-wide transcriptional dynamics

The responses of plants to their environment are often dependent on the spatiotemporal dynamics of transcriptional regulation. While live-imaging tools have been used extensively to quantitatively capture rapid transcriptional dynamics in living animal cells, the lack of implementation of these technologies in plants has limited concomitant quantitative studies in this kingdom. Here, we applied the PP7 and MS2 RNA-labelling technologies for the quantitative imaging of RNA polymerase II activity dynamics in single cells of living plants as they respond to experimental treatments. Using this technology, we counted nascent RNA transcripts in real time in Nicotiana benthamiana (tobacco) and Arabidopsis thaliana. Examination of heat shock reporters revealed that plant tissues respond to external signals by modulating the proportion of cells that switch from an undetectable basal state to a high-transcription state, instead of modulating the rate of transcription across all cells in a graded fashion. This switch-like behaviour, combined with cell-to-cell variability in transcription rate, results in mRNA production variability spanning three orders of magnitude. We determined that cellular heterogeneity stems mainly from stochasticity intrinsic to individual alleles instead of variability in cellular composition. Together, our results demonstrate that it is now possible to quantitatively study the dynamics of transcriptional programs in single cells of living plants.

Cell identity specification in plants: lessons from flower development

Multicellular organisms display a fascinating complexity of cellular identities and patterns of diversification. The concept of 'cell type' aims to describe and categorize this complexity. In this review, we discuss the traditional concept of cell types and highlight the impact of single-cell technologies and spatial omics on the understanding of cellular differentiation in plants. We summarize and compare position-based and lineage-based mechanisms of cell identity specification using flower development as a model system. More than understanding ontogenetic origins of differentiated cells, an important question in plant science is to understand their position- and developmental stage-specific heterogeneity. Combinatorial action and crosstalk of external and internal signals is the key to cellular heterogeneity, often converging on transcription factors that orchestrate gene expression programs.

A new era for plant science: spatial single-cell transcriptomics

To achieve a complete understanding of how organisms function, there is a need to study their fundamental unit, the cell, in its spatial context. In recent years, we have seen fast-paced technological progress to study the transcriptional content of single cells and their spatial relationships. This review highlights modern advancements in single-cell RNA-sequencing, provides an overview of the technologies that led the plant field toward spatial transcriptomics, and describes the available spatial transcriptomics approaches providing examples of their application to plant tissues. In addition, it discusses the integration of these methods to study plant tissues. Taken together, we propose a central role of spatial transcriptomics approaches in plant science.

Long Non-Coding RNAs, the Dark Matter: An Emerging Regulatory Component in Plants

Long non-coding RNAs (lncRNAs) are pervasive transcripts of longer than 200 nucleotides and indiscernible coding potential. lncRNAs are implicated as key regulatory molecules in various fundamental biological processes at transcriptional, post-transcriptional, and epigenetic levels. Advances in computational and experimental approaches have identified numerous lncRNAs in plants. lncRNAs have been found to act as prime mediators in plant growth, development, and tolerance to stresses. This review summarizes the current research status of lncRNAs in planta, their classification based on genomic context, their mechanism of action, and specific bioinformatics tools and resources for their identification and characterization. Our overarching goal is to summarize recent progress on understanding the regulatory role of lncRNAs in plant developmental processes such as flowering time, reproductive growth, and abiotic stresses. We also review the role of lncRNA in nutrient stress and the ability to improve biotic stress tolerance in plants. Given the pivotal role of lncRNAs in various biological processes, their functional characterization in agriculturally essential crop plants is crucial for bridging the gap between phenotype and genotype.

Does Gene Expression Noise Play a Functional Role in Plants?

Gene expression in individual cells can be surprisingly noisy. In unicellular organisms this noise can be functional; for example, by allowing a subfraction of the population to prepare for environmental stress. The role of gene expression noise in multicellular organisms has, however, remained unclear. In this review, we discuss how new techniques are revealing an unexpected level of variability in gene expression between and within genetically identical plants. We describe recent progress as well as speculate on the function of transcriptional noise as a mechanism for generating functional phenotypic diversity in plants.

Differential Gene Expression with an Emphasis on Floral Organ Size Differences in Natural and Synthetic Polyploids of Nicotiana tabacum (Solanaceae)

Floral organ size, especially the size of the corolla, plays an important role in plant reproduction by facilitating pollination efficiency. Previous studies have outlined a hypothesized organ size pathway. However, the expression and function of many of the genes in the pathway have only been investigated in model diploid species; therefore, it is unknown how these genes interact in polyploid species. Although correlations between ploidy and cell size have been shown in many systems, it is unclear whether there is a difference in cell size between naturally occurring and synthetic polyploids. To address these questions comparing floral organ size and cell size across ploidy, we use natural and synthetic polyploids of Nicotiana tabacum (Solanaceae) as well as their known diploid progenitors. We employ a comparative transcriptomics approach to perform analyses of differential gene expression, focusing on candidate genes that may be involved in floral organ size, both across developmental stages and across accessions. We see differential expression of several known floral organ candidate genes including ARF2, BIG BROTHER, and GASA/GAST1. Results from linear models show that ploidy, cell width, and cell number positively influence corolla tube circumference; however, the effect of cell width varies by ploidy, and diploids have a significantly steeper slope than both natural and synthetic polyploids. These results demonstrate that polyploids have wider cells and that polyploidy significantly increases corolla tube circumference.

Cellular Heterogeneity in Pressure and Growth Emerges from Tissue Topology and Geometry

Cell-to-cell heterogeneity prevails in many systems, as exemplified by cell growth, although the origin and function of such heterogeneity are often unclear. In plants, growth is physically controlled by cell wall mechanics and cell hydrostatic pressure, alias turgor pressure. Whereas cell wall heterogeneity has received extensive attention, the spatial variation of turgor pressure is often overlooked. Here, combining atomic force microscopy and a physical model of pressurized cells, we show that turgor pressure is heterogeneous in the Arabidopsis shoot apical meristem, a population of stem cells that generates all plant aerial organs. In contrast with cell wall mechanical properties that appear to vary stochastically between neighboring cells, turgor pressure anticorrelates with cell size and cell neighbor number (local topology), in agreement with the prediction by our model of tissue expansion, which couples cell wall mechanics and tissue hydraulics. Additionally, our model predicts two types of correlations between pressure and cellular growth rate, where high pressure may lead to faster- or slower-than-average growth, depending on cell wall extensibility, yield threshold, osmotic pressure, and hydraulic conductivity. The meristem exhibits one of these two regimes, depending on conditions, suggesting that, in this tissue, water conductivity may contribute to growth control. Our results unravel cell pressure as a source of patterned heterogeneity and illustrate links between local topology, cell mechanical state, and cell growth, with potential roles in tissue homeostasis.

Noncoding SNPs influence a distinct phase of Polycomb silencing to destabilize long-term epigenetic memory at Arabidopsis FLC

In Arabidopsis thaliana, the cold-induced epigenetic regulation of FLOWERING LOCUS C (FLC) involves distinct phases of Polycomb repressive complex 2 (PRC2) silencing. During cold, a PHD-PRC2 complex metastably and digitally nucleates H3K27me3 within FLC On return to warm, PHD-PRC2 spreads across the locus delivering H3K27me3 to maintain long-term silencing. Here, we studied natural variation in this process in Arabidopsis accessions, exploring Lov-1, which shows FLC reactivation on return to warm, a feature characteristic of FLC in perennial Brassicaceae This analysis identifies an additional phase in this Polycomb silencing mechanism downstream from H3K27me3 spreading. In this long-term silencing (perpetuated) phase, the PHD proteins are lost from the nucleation region and silencing is likely maintained by the read-write feedbacks associated with H3K27me3. A combination of noncoding SNPs in the nucleation region mediates instability in this long-term silencing phase with the result that Lov-1 FLC frequently digitally reactivates in individual cells, with a probability that diminishes with increasing cold duration. We propose that this decrease in reactivation probability is due to reduced DNA replication after flowering. Overall, this work defines an additional phase in the Polycomb mechanism instrumental in natural variation of silencing, and provides avenues to dissect broader evolutionary changes at FLC.

The Features and Regulation of Co-transcriptional Splicing in Arabidopsis

Precursor mRNA (pre-mRNA) splicing is essential for gene expression in most eukaryotic organisms. Previous studies from mammals, Drosophila, and yeast show that the majority of splicing events occurs co-transcriptionally. In plants, however, the features of co-transcriptional splicing (CTS) and its regulation still remain largely unknown. Here, we used chromatin-bound RNA sequencing to study CTS in Arabidopsis thaliana. We found that CTS is widespread in Arabidopsis seedlings, with a large proportion of alternative splicing events determined co-transcriptionally. CTS efficiency correlated with gene expression level, the chromatin landscape and, most surprisingly, the number of introns and exons of individual genes, but is independent of gene length. In combination with enhanced crosslinking and immunoprecipitation sequencing analysis, we further showed that the hnRNP-like proteins RZ-1B and RZ-1C promote efficient CTS globally through direct binding, frequently to exonic sequences. Notably, this general effect of RZ-1B/1C on splicing promotion is mainly observed at the chromatin level, not at the mRNA level. RZ-1C promotes CTS of multiple-exon genes in association with its binding to regions both proximal and distal to the regulated introns. We propose that RZ-1C promotes efficient CTS of genes with multiple exons through cooperative interactions with many exons, introns, and splicing factors. Our work thus reveals important features of CTS in plants and provides methodologies for the investigation of CTS and RNA-binding proteins in plants.

Double or Nothing? Cell Division and Cell Size Control

Size is a fundamental property that must be tightly regulated to ensure that cells and tissues function efficiently. Dynamic size control allows unicellular organisms to adapt to environmental changes, but cell size is also integral to multicellular development, affecting tissue size and structure. Despite clear evidence for homeostatic cell size maintenance, we are only now beginning to understand cell size regulation in the actively dividing meristematic tissues of higher plants. We discuss here how coupled advances in live cell imaging and modelling are uncovering dynamic mechanisms for size control mediated at the cellular level. We argue that integrated models of cell growth and division will be necessary to predict cell size and fully understand multicellular growth and development.

Identification of lncRNAs Responsive to Infection by Plasmodiophora brassicae in Clubroot-Susceptible and -Resistant Brassica napus Lines Carrying Resistance Introgressed from Rutabaga

Clubroot disease, caused by Plasmodiophora brassicae Woronin, is a major threat to the production of Brassica' crops. Resistance to different P. brassicae pathotypes has been reported in the A genome, chromosome A08; however, the molecular mechanism of this resistance, especially the involvement of long noncoding RNAs (lncRNAs), is not understood. We have used a strand-specific lncRNA-Seq approach to catalog lncRNAs from the roots of clubroot-susceptible and -resistant Brassica napus lines. In total, 530 differentially expressed (DE) lncRNAs were identified, including 88% of long intergenic RNAs and 11% natural antisense transcripts. Sixteen lncRNAs were identified as target mimics of the microRNAs (miRNAs) and eight were identified as the precursors of miRNAs. KEGG pathway analysis of the DE lncRNAs showed that the cis-regulated target genes mostly belong to the phenylpropanoid biosynthetic pathway (15%) and plant-pathogen interactions (15%) while the transregulated target genes mostly belong to carbon (18%) and amino acid biosynthesis pathway (19%). In all, 24 DE lncRNAs were identified from chromosome A08, which is known to harbor a quantitative trait locus conferring resistance to different P. brassicae pathotypes; however, eight of these lncRNAs showed expression only in the resistant plants. These results could form the basis for future studies aimed at delineating the roles of lncRNAs in plant-microbe interactions.

Emergence of robust patterns from local rules during plant development

The formation of spatial and temporal patterns is an essential component of organismal development. Patterns can be observed on every level from subcellular to organismal and may emerge from local rules that correspond to the interactions between molecules, cells, or tissues. The emergence of robust patterns may seem in contradiction with the prominent heterogeneity at subcellular and cellular scales, however it has become increasingly clear that heterogeneity can be instrumental for pattern formation. Here we review recent examples in plant development, involving genetic regulation, cell arrangement, growth and signal gradient. We discuss how patterns emerge from local rules, whether heterogeneity is stochastic or can be patterned, and whether stochastic noise is amplified or requires filtering for robust patterns to be achieved. We also stress the importance of modelling in investigating such questions.

Homeostasis of protein and mRNA concentrations in growing cells

Many experiments show that the numbers of mRNA and protein are proportional to the cell volume in growing cells. However, models of stochastic gene expression often assume constant transcription rate per gene and constant translation rate per mRNA, which are incompatible with these experiments. Here, we construct a minimal gene expression model to fill this gap. Assuming ribosomes and RNA polymerases are limiting in gene expression, we show that the numbers of proteins and mRNAs both grow exponentially during the cell cycle and that the concentrations of all mRNAs and proteins achieve cellular homeostasis; the competition between genes for the RNA polymerases makes the transcription rate independent of the genome number. Furthermore, by extending the model to situations in which DNA (mRNA) can be saturated by RNA polymerases (ribosomes) and becomes limiting, we predict a transition from exponential to linear growth of cell volume as the protein-to-DNA ratio increases.

Ploidy and Size at Multiple Scales in the Arabidopsis Sepal

Ploidy and size phenomena are observed to be correlated across several biological scales, from subcellular to organismal. Two kinds of ploidy change can affect plants. Whole-genome multiplication increases ploidy in whole plants and is broadly associated with increases in cell and organism size. Endoreduplication increases ploidy in individual cells. Ploidy increase is strongly correlated with increased cell size and nuclear volume. Here, we investigate scaling relationships between ploidy and size by simultaneously quantifying nuclear size, cell size, and organ size in sepals from an isogenic series of diploid, tetraploid, and octoploid Arabidopsis thaliana plants, each of which contains an internal endopolyploidy series. We find that pavement cell size and transcriptome size increase linearly with whole-organism ploidy, but organ area increases more modestly due to a compensatory decrease in cell number. We observe that cell size and nuclear size are maintained at a constant ratio; the value of this constant is similar in diploid and tetraploid plants and slightly lower in octoploid plants. However, cell size is maintained in a mutant with reduced nuclear size, indicating that cell size is scaled to cell ploidy rather than to nuclear size. These results shed light on how size is regulated in plants and how cells and organisms of differing sizes are generated by ploidy change.

Cell size control and gene expression homeostasis in single-cells

Growth of a cell and its subsequent division into daughters is a fundamental aspect of all cellular living systems. During these processes, how do individual cells correct size aberrations so that they do not grow abnormally large or small? How do cells ensure that the concentration of essential gene products are maintained at desired levels, in spite of dynamic/stochastic changes in cell size during growth and division? Both these questions have fascinated researchers for over a century. We review how advances in singe-cell technologies and measurements are providing unique insights into these questions across organisms from prokaryotes to human cells. More specifically, diverse strategies based on timing of cell-cycle events, regulating growth, and number of daughters are employed to maintain cell size homeostasis. Interestingly, size homeostasis often results in size optimality - proliferation of individual cells in a population is maximized at an optimal cell size. We further discuss how size-dependent expression or gene-replication timing can buffer concentration of a gene product from cell-to-cell size variations within a population. Finally, we speculate on an intriguing hypothesis that specific size control strategies may have evolved as a consequence of gene-product concentration homeostasis.

Frequency Modulation of Transcriptional Bursting Enables Sensitive and Rapid Gene Regulation

Gene regulation is a complex non-equilibrium process. Here, we show that quantitating the temporal regulation of key gene states (transcriptionally inactive, active, and refractory) provides a parsimonious framework for analyzing gene regulation. Our theory makes two non-intuitive predictions. First, for transcription factors (TFs) that regulate transcription burst frequency, as opposed to amplitude or duration, weak TF binding is sufficient to elicit strong transcriptional responses. Second, refractoriness of a gene after a transcription burst enables rapid responses to stimuli. We validate both predictions experimentally by exploiting the natural, optogenetic-like responsiveness of the Neurospora GATA-type TF White Collar Complex (WCC) to blue light. Further, we demonstrate that differential regulation of WCC target genes is caused by different gene activation rates, not different TF occupancy, and that these rates are tuned by both the core promoter and the distance between TF-binding site and core promoter. In total, our work demonstrates the relevance of a kinetic, non-equilibrium framework for understanding transcriptional regulation.

Antisense transcription-dependent chromatin signature modulates sense transcript dynamics

Antisense transcription is widespread in genomes. Despite large differences in gene size and architecture, we find that yeast and human genes share a unique, antisense transcription-associated chromatin signature. We asked whether this signature is related to a biological function for antisense transcription. Using quantitative RNA-FISH, we observed changes in sense transcript distributions in nuclei and cytoplasm as antisense transcript levels were altered. To determine the mechanistic differences underlying these distributions, we developed a mathematical framework describing transcription from initiation to transcript degradation. At GAL1, high levels of antisense transcription alter sense transcription dynamics, reducing rates of transcript production and processing, while increasing transcript stability. This relationship with transcript stability is also observed as a genome-wide association. Establishing the antisense transcription-associated chromatin signature through disruption of the Set3C histone deacetylase activity is sufficient to similarly change these rates even in the absence of antisense transcription. Thus, antisense transcription alters sense transcription dynamics in a chromatin-dependent manner.

Use it or average it: stochasticity in plant development

A process that is stochastic has a probabilistic or randomly determined outcome. At the molecular level, all processes are stochastic; but development is highly reproducible, suggesting that plants and other multicellular organisms have evolved mechanisms to ensure robustness (achieving correct development despite stochastic and environmental perturbations). Mechanisms of robustness can be discovered through isolating mutants with increased variability in phenotype; such mutations do not necessarily change the average phenotype. Surprisingly, some developmental robustness mechanisms actually exploit stochasticity as a useful source of variation. For example, gene expression is stochastic and can be utilized to create subtle differences between identical cells that can initiate the patterning of specialized cell types. Stochasticity can also be used to promote robustness through spatiotemporal averaging-stochasticity can be averaged out across space and over time. Thus, organisms often harness stochasticity to ensure robust development.