Noise in gene expression is coupled to growth rate

Histone deacetylase Hos2 regulates protein expression noise by potentially modulating the protein translation machinery

Non-genetic variations derived from expression noise at transcript or protein levels can result in cell-to-cell heterogeneity within an isogenic population. Although cells have developed strategies to reduce noise in some cellular functions, this heterogeneity can also facilitate varying levels of regulation and provide evolutionary benefits in specific environments. Despite several general characteristics of cellular noise having been revealed, the detailed molecular pathways underlying noise regulation remain elusive. Here, we established a dual-fluorescent reporter system in Saccharomyces cerevisiae and performed experimental evolution to search for mutations that increase expression noise. By analyzing evolved cells using bulk segregant analysis coupled with whole-genome sequencing, we identified the histone deacetylase Hos2 as a negative noise regulator. A hos2 mutant down-regulated multiple ribosomal protein genes and exhibited partially compromised protein translation, indicating that Hos2 may regulate protein expression noise by modulating the translation machinery. Treating cells with translation inhibitors or introducing mutations into several Hos2-regulated ribosomal protein genes-RPS9A, RPS28B and RPL42A-enhanced protein expression noise. Our study provides an effective strategy for identifying noise regulators and also sheds light on how cells regulate non-genetic variation through protein translation.

A Genomewide Evolution-Based CRISPR/Cas9 with Donor-Free (GEbCD) for Developing Robust and Productive Industrial Yeast

Developing more robust and productive industrial yeast is crucial for high-efficiency biomanufacturing. However, the challenges posed by the long time required and the low abundance of mutations generated through genomewide evolutionary engineering hinder the development and optimization of desired hosts for industrial applications. To address these issues, we present a novel solution called the Genomewide Evolution-based CRISPR/Cas with Donor-free (GEbCD) system, in which nonhomologous-end-joining (NHEJ) repair can accelerate the acquisition of highly abundant yeast mutants. Together with modified rad52 of the DNA double-strand break repair in Saccharomyces cerevisiae, a hypermutation host was obtained with a 400-fold enhanced mutation ability. Under multiple environmental stresses the system could rapidly generate millions of mutants in a few rounds of iterative evolution. Using high-throughput screening, an industrial S. cerevisiae SISc-Δrad52-G4-72 (G4-72) was obtained that is strongly robust and has higher productivity. G4-72 grew stably and produced ethanol efficiently in multiple-stress environments, e.g. high temperature and high osmosis. In a pilot-scale fermentation with G4-72, the fermentation temperature was elevated by 8 °C and ethanol production was increased by 6.9% under the multiple stresses posed by the industrial fermentation substrate. Overall, the GEbCD system presents a powerful tool to rapidly generate abundant mutants and desired hosts, and offers a novel strategy for optimizing microbial chassis with regard to demands posed in industrial applications.

Modification of Seurat v4 for the Development of a Phase Assignment Tool Able to Distinguish between G2 and Mitotic Cells

Single-cell RNA sequencing (scRNAseq) is a rapidly advancing field enabling the characterisation of heterogeneous gene expression profiles within a population. The cell cycle phase is a major contributor to gene expression variance between cells and computational analysis tools have been developed to assign cell cycle phases to cells within scRNAseq datasets. Whilst these tools can be extremely useful, all have the drawback that they classify cells as only G1, S or G2/M. Existing discrete cell phase assignment tools are unable to differentiate between G2 and M and continuous-phase-assignment tools are unable to identify a region corresponding specifically to mitosis in a pseudo-timeline for continuous assignment along the cell cycle. In this study, bulk RNA sequencing was used to identify differentially expressed genes between mitotic and interphase cells isolated based on phospho-histone H3 expression using fluorescence-activated cell sorting. These gene lists were used to develop a methodology which can distinguish G2 and M phase cells in scRNAseq datasets. The phase assignment tools present in Seurat were modified to allow for cell cycle phase assignment of all stages of the cell cycle to identify a mitotic-specific cell population.

Microfluidic single-cell measurements of oxidative stress as a function of cell cycle position

Single-cell measurements routinely demonstrate high levels of variation between cells, but fewer studies provide insight into the analytical and biological sources of this variation. This is particularly true of chemical cytometry, in which individual cells are lysed and their contents separated, compared to more established single-cell measurements of the genome and transcriptome. To characterize population-level variation and its sources, we analyzed oxidative stress levels in 1278 individual Dictyostelium discoideum cells as a function of exogenous stress level and cell cycle position. Cells were exposed to varying levels of oxidative stress via singlet oxygen generation using the photosensitizer Rose Bengal. Single-cell data reproduced the dose-response observed in ensemble measurements by CE-LIF, superimposed with high levels of heterogeneity. Through experiments and data analysis, we explored possible biological sources of this heterogeneity. No trend was observed between population variation and oxidative stress level, but cell cycle position was a major contributor to heterogeneity in oxidative stress. Cells synchronized to the same stage of cell division were less heterogeneous than unsynchronized cells (RSD of 37-51% vs 93%), and mitotic cells had higher levels of reactive oxygen species than interphase cells. While past research has proposed changes in cell size during the cell cycle as a source of biological noise, the measurements presented here use an internal standard to normalize for effects of cell volume, suggesting a more complex contribution of cell cycle to heterogeneity of oxidative stress.

Evaluating single-cell variability in proteasomal decay

Gene expression is a stochastic process that leads to variability in mRNA and protein abundances even within an isogenic population of cells grown in the same environment. This variation, often called gene-expression noise, has typically been attributed to transcriptional and translational processes while ignoring the contributions of protein decay variability across cells. Here we estimate the single-cell protein decay rates of two degron GFPs in Saccharomyces cerevisiae using time-lapse microscopy. We find substantial cell-to-cell variability in the decay rates of the degron GFPs. We evaluate cellular features that explain the variability in the proteasomal decay and find that the amount of 20s catalytic beta subunit of the proteasome marginally explains the observed variability in the degron GFP half-lives. We propose alternate hypotheses that might explain the observed variability in the decay of the two degron GFPs. Overall, our study highlights the importance of studying the kinetics of the decay process at single-cell resolution and that decay rates vary at the single-cell level, and that the decay process is stochastic. A complex model of decay dynamics must be included when modeling stochastic gene expression to estimate gene expression noise.

Shift in vacuolar to cytosolic regime of infecting Salmonella from a dual proteome perspective

By applying dual proteome profiling to Salmonella enterica serovar Typhimurium (S. Typhimurium) encounters with its epithelial host (here, S. Typhimurium infected human HeLa cells), a detailed interdependent and holistic proteomic perspective on host-pathogen interactions over the time course of infection was obtained. Data-independent acquisition (DIA)-based proteomics was found to outperform data-dependent acquisition (DDA) workflows, especially in identifying the downregulated bacterial proteome response during infection progression by permitting quantification of low abundant bacterial proteins at early times of infection when bacterial infection load is low. S. Typhimurium invasion and replication specific proteomic signatures in epithelial cells revealed interdependent host/pathogen specific responses besides pointing to putative novel infection markers and signalling responses, including regulated host proteins associated with Salmonella-modified membranes.

Optimal metabolic strategies for microbial growth in stationary random environments

In order to grow in any given environment, bacteria need to collect information about the medium composition and implement suitable growth strategies by adjusting their regulatory and metabolic degrees of freedom. In the standard sense, optimal strategy selection is achieved when bacteria grow at the fastest rate possible in that medium. While this view of optimality is well suited for cells that have perfect knowledge about their surroundings (e.g. nutrient levels), things are more involved in uncertain or fluctuating conditions, especially when changes occur over timescales comparable to (or faster than) those required to organize a response. Information theory however provides recipes for how cells can choose the optimal growth strategy under uncertainty about the stress levels they will face. Here we analyse the theoretically optimal scenarios for a coarse-grained, experiment-inspired model of bacterial metabolism for growth in a medium described by the (static) probability density of a single variable (the 'stress level'). We show that heterogeneity in growth rates consistently emerges as the optimal response when the environment is sufficiently complex and/or when perfect adjustment of metabolic degrees of freedom is not possible (e.g. due to limited resources). In addition, outcomes close to those achievable with unlimited resources are often attained effectively with a modest amount of fine tuning. In other terms, heterogeneous population structures in complex media may be rather robust with respect to the resources available to probe the environment and adjust reaction rates.

Effective bet-hedging through growth rate dependent stability

Microbes in the wild face highly variable and unpredictable environments and are naturally selected for their average growth rate across environments. Apart from using sensory regulatory systems to adapt in a targeted manner to changing environments, microbes employ bet-hedging strategies where cells in an isogenic population switch stochastically between alternative phenotypes. Yet, bet-hedging suffers from a fundamental trade-off: Increasing the phenotype-switching rate increases the rate at which maladapted cells explore alternative phenotypes but also increases the rate at which cells switch out of a well-adapted state. Consequently, it is currently believed that bet-hedging strategies are effective only when the number of possible phenotypes is limited and when environments last for sufficiently many generations. However, recent experimental results show that gene expression noise generally decreases with growth rate, suggesting that phenotype-switching rates may systematically decrease with growth rate. Such growth rate dependent stability (GRDS) causes cells to be more explorative when maladapted and more phenotypically stable when well-adapted, and we show that GRDS can almost completely overcome the trade-off that limits bet-hedging, allowing for effective adaptation even when environments are diverse and change rapidly. We further show that even a small decrease in switching rates of faster-growing phenotypes can substantially increase long-term fitness of bet-hedging strategies. Together, our results suggest that stochastic strategies may play an even bigger role for microbial adaptation than hitherto appreciated.

Transcription factor binding process is the primary driver of noise in gene expression

Noise in expression of individual genes gives rise to variations in activity of cellular pathways and generates heterogeneity in cellular phenotypes. Phenotypic heterogeneity has important implications for antibiotic persistence, mutation penetrance, cancer growth and therapy resistance. Specific molecular features such as the presence of the TATA box sequence and the promoter nucleosome occupancy have been associated with noise. However, the relative importance of these features in noise regulation is unclear and how well these features can predict noise has not yet been assessed. Here through an integrated statistical model of gene expression noise in yeast we found that the number of regulating transcription factors (TFs) of a gene was a key predictor of noise, whereas presence of the TATA box and the promoter nucleosome occupancy had poor predictive power. With an increase in the number of regulatory TFs, there was a rise in the number of cooperatively binding TFs. In addition, an increased number of regulatory TFs meant more overlaps in TF binding sites, resulting in competition between TFs for binding to the same region of the promoter. Through modeling of TF binding to promoter and application of stochastic simulations, we demonstrated that competition and cooperation among TFs could increase noise. Thus, our work uncovers a process of noise regulation that arises out of the dynamics of gene regulation and is not dependent on any specific transcription factor or specific promoter sequence.

Single-cell RNA-seq reveals early heterogeneity during aging in yeast

The budding yeast Saccharomyces cerevisiae (S. cerevisiae) has relatively short lifespan and is genetically tractable, making it a widely used model organism in aging research. Here, we carried out a systematic and quantitative investigation of yeast aging with single-cell resolution through transcriptomic sequencing. We optimized a single-cell RNA sequencing (scRNA-seq) protocol to quantitatively study the whole transcriptome profiles of single yeast cells at different ages, finding increased cell-to-cell transcriptional variability during aging. The single-cell transcriptome analysis also highlighted key biological processes or cellular components, including oxidation-reduction process, oxidative stress response (OSR), translation, ribosome biogenesis and mitochondrion that underlie aging in yeast. We uncovered a molecular marker of FIT3, indicating the early heterogeneity during aging in yeast. We also analyzed the regulation of transcription factors and further characterized the distinctive temporal regulation of the OSR by YAP1 and proteasome activity by RPN4 during aging in yeast. Overall, our data profoundly reveal early heterogeneity during aging in yeast and shed light on the aging dynamics at the single cell level.

Digital models in biotechnology: Towards multi-scale integration and implementation

Industrial biotechnology encompasses a large area of multi-scale and multi-disciplinary research activities. With the recent megatrend of digitalization sweeping across all industries, there is an increased focus in the biotechnology industry on developing, integrating and applying digital models to improve all aspects of industrial biotechnology. Given the rapid development of this field, we systematically classify the state-of-art modelling concepts applied at different scales in industrial biotechnology and critically discuss their current usage, advantages and limitations. Further, we critically analyzed current strategies to couple cell models with computational fluid dynamics to study the performance of industrial microorganisms in large-scale bioprocesses, which is of crucial importance for the bio-based production industries. One of the most challenging aspects in this context is gathering intracellular data under industrially relevant conditions. Towards comprehensive models, we discuss how different scale-down concepts combined with appropriate analytical tools can capture intracellular states of single cells. We finally illustrated how the efforts could be used to develop digitals models suitable for both cell factory design and process optimization at industrial scales in the future.

Gene regulation by a protein translation factor at the single-cell level

Gene expression is inherently stochastic and pervasively regulated. While substantial work combining theory and experiments has been carried out to study how noise propagates through transcriptional regulations, the stochastic behavior of genes regulated at the level of translation is poorly understood. Here, we engineered a synthetic genetic system in which a target gene is down-regulated by a protein translation factor, which in turn is regulated transcriptionally. By monitoring both the expression of the regulator and the regulated gene at the single-cell level, we quantified the stochasticity of the system. We found that with a protein translation factor a tight repression can be achieved in single cells, noise propagation from gene to gene is buffered, and the regulated gene is sensitive in a nonlinear way to global perturbations in translation. A suitable mathematical model was instrumental to predict the transfer functions of the system. We also showed that a Gamma distribution parameterized with mesoscopic parameters, such as the mean expression and coefficient of variation, provides a deep analytical explanation about the system, displaying enough versatility to capture the cell-to-cell variability in genes regulated both transcriptionally and translationally. Overall, these results contribute to enlarge our understanding on stochastic gene expression, at the same time they provide design principles for synthetic biology.

In the cell, proteins can bind to DNA to regulate transcription as well as to RNA to regulate translation. However, cells have mainly evolved to exploit transcription factors as specific gene regulators, while translation factors have remained as global modulators of expression. Consequently, transcription regulation has attracted much attention over the last years to unveil design principles of genetic organization and to engineer synthetic circuits for cell reprogramming. In this work, the phage MS2 coat protein was exploited to regulate the expression of a green fluorescent protein at the level of translation. This synthetic system was instrumental to gain fundamental knowledge on stochasticity and regulation at an overlooked level within the genetic information flow.

Sources of transcription variation in Plasmodium falciparum

Variation in transcript abundance can contribute to both short-term environmental response and long-term evolutionary adaptation. Most studies are designed to assess differences in mean transcription levels and do not consider other potentially important and confounding sources of transcriptional variation. Detailed quantification of variation sources will improve our ability to detect and identify the mechanisms that contribute to genome-wide transcription changes that underpin adaptive responses. To quantify innate levels of expression variation, we measured mRNA levels for more than 5000 genes in the malaria parasite, Plasmodium falciparum, among clones derived from two parasite strains across biologically and experimentally replicated batches. Using a mixed effects model, we partitioned the total variation among four sources - between strain, within strain, environmental batch effects, and stochastic noise. We found 646 genes with significant variation attributable to at least one of these sources. These genes were categorized by their predominant variation source and further examined using gene ontology enrichment analysis to associate function with each source of variation. Genes with environmental batch effect and within strain transcript variation may contribute to phenotypic plasticity, while genes with between strain variation may contribute to adaptive responses and processes that lead to parasite strain-specific survival under varied conditions.

Application of an Escherichia coli triple reporter strain for at-line monitoring of single-cell physiology during L-phenylalanine production

Biotechnological production processes are sustainable approaches for the production of biobased components such as amino acids for food and feed industry. Scale-up from ideal lab-scale bioreactors to large-scale processes is often accompanied by loss in productivity. This may be related to population heterogeneities of cells originating from isogenic cultures that arise due to dynamic non-ideal conditions in the bioreactor. To better understand this phenomenon, deeper insights into single-cell physiologies in bioprocesses are mandatory before scale-up. Here, a triple reporter strain (3RP) was developed by chromosomally integrating the fluorescent proteins mEmerald, CyOFP1, and mTagBFP2 into the L-phenylalanine producing Escherichia coli strain FUS4 (pF81_kan) to allow monitoring of growth, oxygen availability, and general stress response of the single cells. Functionality of the 3RP was confirmed in well-mixed lab-scale fed-batch processes with glycerol as carbon source in comparison to the strain without fluorescent proteins, leading to no difference in process performance. Fluorescence levels could successfully reflect the course of related process state variables, revealed population heterogeneities during the transition between different process phases and potentially subpopulations that exhibit superior process performance. Furthermore, indications were found for noise in gene expression as regulation strategy against environmental perturbation.

Genome-wide gene expression noise in Escherichia coli is condition-dependent and determined by propagation of noise through the regulatory network

Although it is well appreciated that gene expression is inherently noisy and that transcriptional noise is encoded in a promoter’s sequence, little is known about the extent to which noise levels of individual promoters vary across growth conditions. Using flow cytometry, we here quantify transcriptional noise in Escherichia coli genome-wide across 8 growth conditions and find that noise levels systematically decrease with growth rate, with a condition-dependent lower bound on noise. Whereas constitutive promoters consistently exhibit low noise in all conditions, regulated promoters are both more noisy on average and more variable in noise across conditions. Moreover, individual promoters show highly distinct variation in noise across conditions. We show that a simple model of noise propagation from regulators to their targets can explain a significant fraction of the variation in relative noise levels and identifies TFs that most contribute to both condition-specific and condition-independent noise propagation. In addition, analysis of the genome-wide correlation structure of various gene properties shows that gene regulation, expression noise, and noise plasticity are all positively correlated genome-wide and vary independently of variations in absolute expression, codon bias, and evolutionary rate. Together, our results show that while absolute expression noise tends to decrease with growth rate, relative noise levels of genes are highly condition-dependent and determined by the propagation of noise through the gene regulatory network.

Genome-wide flow cytometry measurements reveal that gene expression noise in bacteria is highly condition-dependent; while absolute noise levels of all genes decrease with growth-rate, theoretical modeling shows that the relative noise levels of different genes are determined by the propagation of noise through the gene regulatory network (GRN). Thus GRN structure controls not only mean expression but also noise levels.

Pareto optimality between growth-rate and lag-time couples metabolic noise to phenotypic heterogeneity in Escherichia coli

Despite mounting evidence that in clonal bacterial populations, phenotypic variability originates from stochasticity in gene expression, little is known about noise-shaping evolutionary forces and how expression noise translates to phenotypic differences. Here we developed a high-throughput assay that uses a redox-sensitive dye to couple growth of thousands of bacterial colonies to their respiratory activity and show that in Escherichia coli, noisy regulation of lower glycolysis and citric acid cycle is responsible for large variations in respiratory metabolism. We found that these variations are Pareto optimal to maximization of growth rate and minimization of lag time, two objectives competing between fermentative and respiratory metabolism. Metabolome-based analysis revealed the role of respiratory metabolism in preventing the accumulation of toxic intermediates of branched chain amino acid biosynthesis, thereby supporting early onset of cell growth after carbon starvation. We propose that optimal metabolic tradeoffs play a key role in shaping and preserving phenotypic heterogeneity and adaptation to fluctuating environments.

Identifying chromatin features that regulate gene expression distribution

Gene expression variability, differences in the number of mRNA per cell across a population of cells, is ubiquitous across diverse organisms with broad impacts on cellular phenotypes. The role of chromatin in regulating average gene expression has been extensively studied. However, what aspects of the chromatin contribute to gene expression variability is still underexplored. Here we addressed this problem by leveraging chromatin diversity and using a systematic investigation of randomly integrated expression reporters to identify what aspects of chromatin microenvironment contribute to gene expression variability. Using DNA barcoding and split-pool decoding, we created a large library of isogenic reporter clones and identified reporter integration sites in a massive and parallel manner. By mapping our measurements of reporter expression at different genomic loci with multiple epigenetic profiles including the enrichment of transcription factors and the distance to different chromatin states, we identified new factors that impact the regulation of gene expression distributions.

Translational control of gene expression noise and its relationship to ageing in yeast

Gene expression noise influences organism evolution and fitness but is poorly understood. There is increasing evidence that the functional roles of components of the translation machinery influence noise intensity. In addition, modulation of the activities of at least some of these same components affects the replicative lifespan of a broad spectrum of organisms. In a novel comparative approach, we modulate the activities of the translation initiation factors eIFG1 and eIF4G2, both of which are involved in the process of recruiting ribosomal 43S preinitiation complexes to the 5' end of eukaryotic mRNAs. We show that tagging of the cell wall using a fluorescent dye allows us to follow gene expression noise as different yeast strains progress through successive cycles of replicative ageing. This procedure reveals a relationship between global protein synthesis rate and gene expression noise (cell-to-cell heterogeneity), which is accompanied by a parallel correlation between gene expression noise and the replicative age of mother cells. An alternative approach, based on microfluidics, confirms the interdependence between protein synthesis rate, gene expression noise and ageing. We additionally show that it is important to characterize the influence of the design of the microfluidic device on the nutritional state of the cells during such experiments. Analysis of the noise data derived from flow cytometry and fluorescence microscopy measurements indicates that both the intrinsic and the extrinsic noise components increase as a function of ageing.

Contributions to the 'noise floor' in gene expression in a population of dividing cells

Experiments with cells reveal the existence of a lower bound for protein noise, the noise floor, in highly expressed genes. Its origins are still debated. We propose a minimal model of gene expression in a proliferating bacterial cell population. The model predicts the existence of a noise floor and it semi-quantitatively reproduces the curved shape of the experimental noise vs. mean protein concentration plots. When the cell volume increases in a different manner than does the mean protein copy number, the noise floor level is determined by the cell population’s age structure and by the dependence of the mean protein concentration on cell age. Additionally, the noise floor level may depend on a biological limit for the mean number of bursts in the cell cycle. In that case, the noise floor level depends on the burst size distribution width but it is insensitive to the mean burst size. Our model quantifies the contributions of each of these mechanisms to gene expression noise.

Physiological RNA dynamics in RNA-Seq analysis

Physiological RNA dynamics cause problems in transcriptome analysis. Physiological RNA accumulation affects the analysis of RNA quantification, and physiological RNA degradation affects the analysis of the RNA sequence length, feature site and quantification. In the present article, we review the effects of physiological degradation and accumulation of RNA on analysing RNA sequencing data. Physiological RNA accumulation and degradation probably led to such phenomena as incorrect estimations of transcription quantification, differential expressions, co-expressions, RNA decay rates, alternative splicing, boundaries of transcription, novel genes, new single-nucleotide polymorphisms, small RNAs and gene fusion. Thus, the transcriptomic data obtained up to date warrant further scrutiny. New and improved techniques and bioinformatics software are needed to produce accurate data in transcriptome research.

Characterizing and inferring quantitative cell cycle phase in single-cell RNA-seq data analysis

Cellular heterogeneity in gene expression is driven by cellular processes, such as cell cycle and cell-type identity, and cellular environment such as spatial location. The cell cycle, in particular, is thought to be a key driver of cell-to-cell heterogeneity in gene expression, even in otherwise homogeneous cell populations. Recent advances in single-cell RNA-sequencing (scRNA-seq) facilitate detailed characterization of gene expression heterogeneity and can thus shed new light on the processes driving heterogeneity. Here, we combined fluorescence imaging with scRNA-seq to measure cell cycle phase and gene expression levels in human induced pluripotent stem cells (iPSCs). By using these data, we developed a novel approach to characterize cell cycle progression. Although standard methods assign cells to discrete cell cycle stages, our method goes beyond this and quantifies cell cycle progression on a continuum. We found that, on average, scRNA-seq data from only five genes predicted a cell's position on the cell cycle continuum to within 14% of the entire cycle and that using more genes did not improve this accuracy. Our data and predictor of cell cycle phase can directly help future studies to account for cell cycle-related heterogeneity in iPSCs. Our results and methods also provide a foundation for future work to characterize the effects of the cell cycle on expression heterogeneity in other cell types.

Topology-dependent interference of synthetic gene circuit function by growth feedback

Growth-mediated feedback between synthetic gene circuits and host organisms leads to diverse emerged behaviors, including growth bistability and enhanced ultrasensitivity. However, the range of possible impacts of growth feedback on gene circuits remains underexplored. Here, we mathematically and experimentally demonstrated that growth feedback affects the functions of memory circuits in a network topology-dependent way. Specifically, the memory of the self-activation switch is quickly lost due to the growth-mediated dilution of the circuit products. Decoupling of growth feedback reveals its memory, manifested by its hysteresis property across a broad range of inducer concentration. On the contrary, the toggle switch is more refractory to growth-mediated dilution and can retrieve its memory after the fast-growth phase. The underlying principle lies in the different dependence of active and repressive regulations in these circuits on the growth-mediated dilution. Our results unveil the topology-dependent mechanism on how growth-mediated feedback influences the behaviors of gene circuits.

Simulation and inference algorithms for stochastic biochemical reaction networks: from basic concepts to state-of-the-art

Stochasticity is a key characteristic of intracellular processes such as gene regulation and chemical signalling. Therefore, characterizing stochastic effects in biochemical systems is essential to understand the complex dynamics of living things. Mathematical idealizations of biochemically reacting systems must be able to capture stochastic phenomena. While robust theory exists to describe such stochastic models, the computational challenges in exploring these models can be a significant burden in practice since realistic models are analytically intractable. Determining the expected behaviour and variability of a stochastic biochemical reaction network requires many probabilistic simulations of its evolution. Using a biochemical reaction network model to assist in the interpretation of time-course data from a biological experiment is an even greater challenge due to the intractability of the likelihood function for determining observation probabilities. These computational challenges have been subjects of active research for over four decades. In this review, we present an accessible discussion of the major historical developments and state-of-the-art computational techniques relevant to simulation and inference problems for stochastic biochemical reaction network models. Detailed algorithms for particularly important methods are described and complemented with Matlab^® implementations. As a result, this review provides a practical and accessible introduction to computational methods for stochastic models within the life sciences community.

The strength and pattern of natural selection on gene expression in rice

Levels of gene expression underpin organismal phenotypes^1,2, but the nature of selection that acts on gene expression and its role in adaptive evolution remain unknown^1,2. Here we assayed gene expression in rice (Oryza sativa)³, and used phenotypic selection analysis to estimate the type and strength of selection on the levels of more than 15,000 transcripts^4,5. Variation in most transcripts appears (nearly) neutral or under very weak stabilizing selection in wet paddy conditions (with median standardized selection differentials near zero), but selection is stronger under drought conditions. Overall, more transcripts are conditionally neutral (2.83%) than are antagonistically pleiotropic⁶ (0.04%), and transcripts that display lower levels of expression and stochastic noise^7-9 and higher levels of plasticity⁹ are under stronger selection. Selection strength was further weakly negatively associated with levels of cis-regulation and network connectivity⁹. Our multivariate analysis suggests that selection acts on the expression of photosynthesis genes^4,5, but that the efficacy of selection is genetically constrained under drought conditions¹⁰. Drought selected for earlier flowering^11,12 and a higher expression of OsMADS18 (Os07g0605200), which encodes a MADS-box transcription factor and is a known regulator of early flowering¹³-marking this gene as a drought-escape gene^11,12. The ability to estimate selection strengths provides insights into how selection can shape molecular traits at the core of gene action.

Development and characterization of Escherichia coli triple reporter strains for investigation of population heterogeneity in bioprocesses

Background

Today there is an increasing demand for high yielding robust and cost efficient biotechnological production processes. Although cells in these processes originate from isogenic cultures, heterogeneity induced by intrinsic and extrinsic influences is omnipresent. To increase understanding of this mechanistically poorly understood phenomenon, advanced tools that provide insights into single cell physiology are needed.

Results

Two Escherichia coli triple reporter strains have been designed based on the industrially relevant production host E. coli BL21(DE3) and a modified version thereof, E. coli T7E2. The strains carry three different fluorescence proteins chromosomally integrated. Single cell growth is followed with EmeraldGFP (EmGFP)-expression together with the ribosomal promoter rrnB. General stress response of single cells is monitored by expression of sigma factor rpoS with mStrawberry, whereas expression of the nar-operon together with TagRFP657 gives information about oxygen limitation of single cells. First, the strains were characterized in batch operated stirred-tank bioreactors in comparison to wildtype E. coli BL21(DE3). Afterwards, applicability of the triple reporter strains for investigation of population heterogeneity in bioprocesses was demonstrated in continuous processes in stirred-tank bioreactors at different growth rates and in response to glucose and oxygen perturbation simulating gradients on industrial scale. Population and single cell level physiology was monitored evaluating general physiology and flow cytometry analysis of fluorescence distributions of the triple reporter strains. Although both triple reporter strains reflected physiological changes that were expected based on the expression characteristics of the marker proteins, the triple reporter strain based on E. coli T7E2 showed higher sensitivity in response to environmental changes. For both strains, noise in gene expression was observed during transition from phases of non-growth to growth. Apparently, under some process conditions, e.g. the stationary phase in batch cultures, the fluorescence response of EmGFP and mStrawberry is preserved, whereas TagRFP657 showed a distinct response.

Conclusions

Single cell growth, general stress response and oxygen limitation of single cells could be followed using the two triple reporter strains developed in this study. They represent valuable tools to study population heterogeneity in bioprocesses significantly increasing the level of information compared to the use of single reporter strains.

Controlling cell-to-cell variability with synthetic gene circuits

Cell-to-cell variability originating, for example, from the intrinsic stochasticity of gene expression, presents challenges for designing synthetic gene circuits that perform robustly. Conversely, synthetic biology approaches are instrumental in uncovering mechanisms underlying variability in natural systems. With a focus on reducing noise in individual genes, the field has established a broad synthetic toolset. This includes noise control by engineering of transcription and translation mechanisms either individually, or in combination to achieve independent regulation of mean expression and its variability. Synthetic feedback circuits use these components to establish more robust operation in closed-loop, either by drawing on, but also by extending traditional engineering concepts. In this perspective, we argue that major conceptual advances will require new theory of control adapted to biology, extensions from single genes to networks, more systematic considerations of origins of variability other than intrinsic noise, and an exploration of how noise shaping, instead of noise reduction, could establish new synthetic functions or help understanding natural functions.

Cell Size-Based Decision-Making of a Viral Gene Circuit

Latently infected T cells able to reinitiate viral propagation throughout the body remain a major barrier to curing HIV. Distinguishing between latently infected cells and uninfected cells will advance efforts for viral eradication. HIV decision-making between latency and active replication is stochastic, and drug cocktails that increase bursts of viral gene expression enhance reactivation from latency. Here, we show that a larger host-cell size provides a natural cellular mechanism for enhancing burst size of viral expression and is necessary to destabilize the latent state and bias viral decision-making. Latently infected Jurkat and primary CD4+ T cells reactivate exclusively in larger activated cells, while smaller cells remain silent. In addition, reactivation is cell-cycle dependent and can be modulated with cell-cycle-arresting compounds. Cell size and cell-cycle dependent decision-making of viral circuits may guide stochastic design strategies and applications in synthetic biology and may provide important determinants to advance diagnostics and therapies.

Bohn-Wippert et al. investigate reactivation of T cells latently infected with HIV. They discover that only larger cells exit latency, while smaller cells remain silent. Viral expression bursts are cell size and cell-cycle dependent, presenting dynamic cell states, capable of active control, as sources of viral fate determination.

Stimulus response-based fine-tuning of polyhydroxyalkanoate pathway in Halomonas

Optimization of intracellular biosynthesis process involving regulation of multiple gene expressions is dependent on the efficient and accurate expression of each expression unit independently. However, challenges of analyzing intermediate products seriously hinder the application of high throughput assays. This study aimed to develop an engineering approach for unsterile production of poly(3-hydroxybutyrate-co-4-hydroxybutyrate) or (P3HB4HB) by recombinant Halomonas bluephagenesis (H. bluephagenesis) constructed via coupling the design of GFP-mediated transcriptional mapping and high-resolution control of gene expressions (HRCGE), which consists of two inducible systems with high- and low-dynamic ranges employed to search the exquisite transcription level of each expression module in the presence of γ-butyrolactone, the intermediate for 4-hydroxybutyrate (4HB) synthesis. It has been successful to generate a recombinant H. bluephagenesis, namely TD68-194, able to produce over 36 g/L P3HB4HB consisting of 16 mol% 4HB during a 7-L lab-scale fed-batch growth process, of which cell dry weight and PHA content reached up to 48.22 g/L and 74.67%, respectively, in 36 h cultivation. HRCGE has been found useful for metabolic pathway construction.

Experimental evolution reveals a general role for the methyltransferase Hmt1 in noise buffering

Cell-to-cell heterogeneity within an isogenic population has been observed in prokaryotic and eukaryotic cells. Such heterogeneity often manifests at the level of individual protein abundance and may have evolutionary benefits, especially for organisms in fluctuating environments. Although general features and the origins of cellular noise have been revealed, details of the molecular pathways underlying noise regulation remain elusive. Here, we used experimental evolution of Saccharomyces cerevisiae to select for mutations that increase reporter protein noise. By combining bulk segregant analysis and CRISPR/Cas9-based reconstitution, we identified the methyltransferase Hmt1 as a general regulator of noise buffering. Hmt1 methylation activity is critical for the evolved phenotype, and we also show that two of the Hmt1 methylation targets can suppress noise. Hmt1 functions as an environmental sensor to adjust noise levels in response to environmental cues. Moreover, Hmt1-mediated noise buffering is conserved in an evolutionarily distant yeast species, suggesting broad significance of noise regulation.

Experimental evolution in yeast reveals that the methyltransferase Hmt1 functions as a mediator connecting environmental stimuli to cellular noise; Hmt1-mediated noise buffering is conserved in an evolutionarily distant yeast.

Why haploinsufficiency persists

Haploinsufficiency describes the decrease in organismal fitness observed when a single copy of a gene is deleted in diploids. We investigated the origin of haploinsufficiency by creating a comprehensive dosage sensitivity data set for genes under their native promoters. We demonstrate that the expression of haploinsufficient genes is limited by the toxicity of their overexpression. We further show that the fitness penalty associated with excess gene copy number is not the only determinant of haploinsufficiency. Haploinsufficient genes represent a unique subset of genes sensitive to copy number increases, as they are also limiting for important cellular processes when present in one copy instead of two. The selective pressure to decrease gene expression due to the toxicity of overexpression, combined with the pressure to increase expression due to their fitness-limiting nature, has made haploinsufficient genes extremely sensitive to changes in gene expression. As a consequence, haploinsufficient genes are dosage stabilized, showing much more narrow ranges in cell-to-cell variability of expression compared with other genes in the genome. We propose a dosage-stabilizing hypothesis of haploinsufficiency to explain its persistence over evolutionary time.

Exactly solvable model of gene expression in a proliferating bacterial cell population with stochastic protein bursts and protein partitioning

Many of the existing stochastic models of gene expression contain the first-order decay reaction term that may describe active protein degradation or dilution. If the model variable is interpreted as the molecule number, and not concentration, the decay term may also approximate the loss of protein molecules due to cell division as a continuous degradation process. The seminal model of that kind leads to gamma distributions of protein levels, whose parameters are defined by the mean frequency of protein bursts and mean burst size. However, such models (whether interpreted in terms of molecule numbers or concentrations) do not correctly account for the noise due to protein partitioning between daughter cells. We present an exactly solvable stochastic model of gene expression in cells dividing at random times, where we assume description in terms of molecule numbers with a constant mean protein burst size. The model is based on a population balance equation supplemented with protein production in random bursts. If protein molecules are partitioned equally between daughter cells, we obtain at steady state the analytical expressions for probability distributions similar in shape to gamma distributions, yet with quite different values of mean burst size and mean burst frequency than would result from fitting of the classical continuous-decay model to these distributions. For random partitioning of protein molecules between daughter cells, we obtain the moment equations for the protein number distribution and thus the analytical formulas for the squared coefficient of variation.

Novel approach to quantitative spatial gene expression uncovers genetic stochasticity in the developing Drosophila eye

Robustness in development allows for the accumulation of genetically based variation in expression. However, this variation is usually examined in response to large perturbations, and examination of this variation has been limited to being spatial, or quantitative, but because of technical restrictions not both. Here we bridge these gaps by investigating replicated quantitative spatial gene expression using rigorous statistical models, in different genotypes, sexes, and species (Drosophila melanogaster and D. simulans). Using this type of quantitative approach with molecular developmental data allows for comparison among conditions, such as different genetic backgrounds. We apply this approach to the morphogenetic furrow, a wave of differentiation that patterns the developing eye disc. Within the morphogenetic furrow, we focus on four genes, hairy, atonal, hedgehog, and Delta. Hybridization chain reaction quantitatively measures spatial gene expression, co-staining for all four genes simultaneously. We find considerable variation in the spatial expression pattern of these genes in the eye between species, genotypes, and sexes. We also find that there has been evolution of the regulatory relationship between these genes, and that their spatial interrelationships have evolved between species. This variation has no phenotypic effect, and could be buffered by network thresholds or compensation from other genes. Both of these mechanisms could potentially be contributing to long term developmental systems drift.

A Simple and Flexible Computational Framework for Inferring Sources of Heterogeneity from Single-Cell Dynamics

Single-cell time-lapse data provide the means for disentangling sources of cell-to-cell and intra-cellular variability, a key step for understanding heterogeneity in cell populations. However, single-cell analysis with dynamic models is a challenging open problem: current inference methods address only single-gene expression or neglect parameter correlations. We report on a simple, flexible, and scalable method for estimating cell-specific and population-average parameters of non-linear mixed-effects models of cellular networks, demonstrating its accuracy with a published model and dataset. We also propose sensitivity analysis for identifying which biological sub-processes quantitatively and dynamically contribute to cell-to-cell variability. Our application to endocytosis in yeast demonstrates that dynamic models of realistic size can be developed for the analysis of single-cell data and that shifting the focus from single reactions or parameters to nuanced and time-dependent contributions of sub-processes helps biological interpretation. Generality and simplicity of the approach will facilitate customized extensions for analyzing single-cell dynamics.

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.

Predicting bacterial growth conditions from mRNA and protein abundances

Cells respond to changing nutrient availability and external stresses by altering the expression of individual genes. Condition-specific gene expression patterns may thus provide a promising and low-cost route to quantifying the presence of various small molecules, toxins, or species-interactions in natural environments. However, whether gene expression signatures alone can predict individual environmental growth conditions remains an open question. Here, we used machine learning to predict 16 closely-related growth conditions using 155 datasets of E. coli transcript and protein abundances. We show that models are able to discriminate between different environmental features with a relatively high degree of accuracy. We observed a small but significant increase in model accuracy by combining transcriptome and proteome-level data, and we show that measurements from stationary phase cells typically provide less useful information for discriminating between conditions as compared to exponentially growing populations. Nevertheless, with sufficient training data, gene expression measurements from a single species are capable of distinguishing between environmental conditions that are separated by a single environmental variable.

Diverse Spatial Expression Patterns Emerge from Unified Kinetics of Transcriptional Bursting

How transcriptional bursting relates to gene regulation is a central question that has persisted for more than a decade. Here, we measure nascent transcriptional activity in early Drosophila embryos and characterize the variability in absolute activity levels across expression boundaries. We demonstrate that boundary formation follows a common transcription principle: a single control parameter determines the distribution of transcriptional activity, regardless of gene identity, boundary position, or enhancer-promoter architecture. We infer the underlying bursting kinetics and identify the key regulatory parameter as the fraction of time a gene is in a transcriptionally active state. Unexpectedly, both the rate of polymerase initiation and the switching rates are tightly constrained across all expression levels, predicting synchronous patterning outcomes at all positions in the embryo. These results point to a shared simplicity underlying the apparently complex transcriptional processes of early embryonic patterning and indicate a path to general rules in transcriptional regulation.

Virulence function of the Ustilago maydis sterol carrier protein 2

The peroxisomal sterol carrier protein 2 (Scp2) of the biotrophic maize pathogen Ustilago maydis was detected in apoplastic fluid, suggesting that it might function as a secreted effector protein. Here we analyze the role of the scp2 gene during plant colonization. We used reverse genetics approaches to delete the scp2 gene, determined stress sensitivity and fatty acid utilization of mutants, demonstrated secretion of Scp2, used quantitative reverse transcription polymerase chain reaction for expression analysis and expressed GFP-Scp2 fusion proteins for protein localization. scp2 mutants were strongly attenuated in virulence and this defect manifested itself during penetration. Scp2 localized to peroxisomes and peroxisomal targeting was necessary for its virulence function. Deletion of scp2 in U. maydis interfered neither with growth nor with peroxisomal β-oxidation. Conventionally secreted Scp2 protein could not rescue the virulence defect. scp2 mutants displayed an altered localization of peroxisomes. Our results show a virulence function for Scp2 during penetration that is probably carried out by Scp2 in peroxisomes. We speculate that Scp2 affects the lipid composition of membranes and in this way ensures the even cellular distribution of peroxisomes.

Correcting the Mean-Variance Dependency for Differential Variability Testing Using Single-Cell RNA Sequencing Data

Cell-to-cell transcriptional variability in otherwise homogeneous cell populations plays an important role in tissue function and development. Single-cell RNA sequencing can characterize this variability in a transcriptome-wide manner. However, technical variation and the confounding between variability and mean expression estimates hinder meaningful comparison of expression variability between cell populations. To address this problem, we introduce an analysis approach that extends the BASiCS statistical framework to derive a residual measure of variability that is not confounded by mean expression. This includes a robust procedure for quantifying technical noise in experiments where technical spike-in molecules are not available. We illustrate how our method provides biological insight into the dynamics of cell-to-cell expression variability, highlighting a synchronization of biosynthetic machinery components in immune cells upon activation. In contrast to the uniform up-regulation of the biosynthetic machinery, CD4+ T cells show heterogeneous up-regulation of immune-related and lineage-defining genes during activation and differentiation.

Statistical mechanics for metabolic networks during steady state growth

Which properties of metabolic networks can be derived solely from stoichiometry? Predictive results have been obtained by flux balance analysis (FBA), by postulating that cells set metabolic fluxes to maximize growth rate. Here we consider a generalization of FBA to single-cell level using maximum entropy modeling, which we extend and test experimentally. Specifically, we define for Escherichia coli metabolism a flux distribution that yields the experimental growth rate: the model, containing FBA as a limit, provides a better match to measured fluxes and it makes a wide range of predictions: on flux variability, regulation, and correlations; on the relative importance of stoichiometry vs. optimization; on scaling relations for growth rate distributions. We validate the latter here with single-cell data at different sub-inhibitory antibiotic concentrations. The model quantifies growth optimization as emerging from the interplay of competitive dynamics in the population and regulation of metabolism at the level of single cells.

Mean-Independent Noise Control of Cell Fates via Intermediate States

Stochasticity affects accurate signal detection and robust generation of correct cell fates. Although many known regulatory mechanisms may reduce fluctuations in signals, most simultaneously influence their mean dynamics, leading to unfaithful cell fates. Through analysis and computation, we demonstrate that a reversible signaling mechanism acting through intermediate states can reduce noise while maintaining the mean. This mean-independent noise control (MINC) mechanism is investigated in the context of an intracellular binding protein that regulates retinoic acid (RA) signaling during zebrafish hindbrain development. By comparing our models with experimental data, we find that the MINC mechanism allows for sharp boundaries of gene expression without sacrificing boundary accuracy. In addition, this MINC mechanism can modulate noise to levels that we show are beneficial to spatial patterning through noise-induced cell fate switching. These results reveal a design principle that may be important for noise regulation in many systems that control cell fate determination.

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Mean-independent noise control allows noise attenuation without affecting the mean

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Intermediate states enable such control through proportional coupling

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This controls spatial gene expression noise without shifting boundary locations

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Specific noise levels are required for successful downstream boundary sharpening

Population heterogeneity in microbial bioprocesses: origin, analysis, mechanisms, and future perspectives

Population heterogeneity is omnipresent in all bioprocesses even in homogenous environments. Its origin, however, is only so well understood that potential strategies like bet-hedging, noise in gene expression and division of labour that lead to population heterogeneity can be derived from experimental studies simulating the dynamics in industrial scale bioprocesses. This review aims at summarizing the current state of the different parts of single cell studies in bioprocesses. This includes setups to visualize different phenotypes of single cells, computational approaches connecting single cell physiology with environmental influence and special cultivation setups like scale-down reactors that have been proven to be useful to simulate large-scale conditions. A step in between investigation of populations and single cells is studying subpopulations with distinct properties that differ from the rest of the population with sub-omics methods which are also presented here. Moreover, the current knowledge about population heterogeneity in bioprocesses is summarized for relevant industrial production hosts and mixed cultures, as they provide the unique opportunity to distribute metabolic burden and optimize production processes in a way that is impossible in traditional monocultures. In the end, approaches to explain the underlying mechanism of population heterogeneity and the evidences found to support each hypothesis are presented. For instance, population heterogeneity serving as a bet-hedging strategy that is used as coordinated action against bioprocess-related stresses while at the same time spreading the risk between individual cells as it ensures the survival of least a part of the population in any environment the cells encounter.

Division rate, cell size and proteome allocation: impact on gene expression noise and implications for the dynamics of genetic circuits

The cell division rate, size and gene expression programmes change in response to external conditions. These global changes impact on average concentrations of biomolecule and their variability or noise. Gene expression is inherently stochastic, and noise levels of individual proteins depend on synthesis and degradation rates as well as on cell-cycle dynamics. We have modelled stochastic gene expression inside growing and dividing cells to study the effect of division rates on noise in mRNA and protein expression. We use assumptions and parameters relevant to Escherichia coli, for which abundant quantitative data are available. We find that coupling of transcription, but not translation rates to the rate of cell division can result in protein concentration and noise homeostasis across conditions. Interestingly, we find that the increased cell size at fast division rates, observed in E. coli and other unicellular organisms, buffers noise levels even for proteins with decreased expression at faster growth. We then investigate the functional importance of these regulations using gene regulatory networks that exhibit bi-stability and oscillations. We find that network topology affects robustness to changes in division rate in complex and unexpected ways. In particular, a simple model of persistence, based on global physiological feedback, predicts increased proportion of persister cells at slow division rates. Altogether, our study reveals how cell size regulation in response to cell division rate could help controlling gene expression noise. It also highlights that understanding circuits' robustness across growth conditions is key for the effective design of synthetic biological systems.

Division rate, cell size and proteome allocation: impact on gene expression noise and implications for the dynamics of genetic circuits

The cell division rate, size and gene expression programmes change in response to external conditions. These global changes impact on average concentrations of biomolecule and their variability or noise. Gene expression is inherently stochastic, and noise levels of individual proteins depend on synthesis and degradation rates as well as on cell-cycle dynamics. We have modelled stochastic gene expression inside growing and dividing cells to study the effect of division rates on noise in mRNA and protein expression. We use assumptions and parameters relevant to Escherichia coli, for which abundant quantitative data are available. We find that coupling of transcription, but not translation rates to the rate of cell division can result in protein concentration and noise homeostasis across conditions. Interestingly, we find that the increased cell size at fast division rates, observed in E. coli and other unicellular organisms, buffers noise levels even for proteins with decreased expression at faster growth. We then investigate the functional importance of these regulations using gene regulatory networks that exhibit bi-stability and oscillations. We find that network topology affects robustness to changes in division rate in complex and unexpected ways. In particular, a simple model of persistence, based on global physiological feedback, predicts increased proportion of persister cells at slow division rates. Altogether, our study reveals how cell size regulation in response to cell division rate could help controlling gene expression noise. It also highlights that understanding circuits' robustness across growth conditions is key for the effective design of synthetic biological systems.

Computational Investigation of Environment-Noise Interaction in Single-Cell Organisms: The Merit of Expression Stochasticity Depends on the Quality of Environmental Fluctuations

Organisms need to adapt to changing environments and they do so by using a broad spectrum of strategies. These strategies include finding the right balance between expressing genes before or when they are needed, and adjusting the degree of noise inherent in gene expression. We investigated the interplay between different nutritional environments and the inhabiting organisms’ metabolic and genetic adaptations by applying an evolutionary algorithm to an agent-based model of a concise bacterial metabolism. Our results show that constant environments and rapidly fluctuating environments produce similar adaptations in the organisms, making the predictability of the environment a major factor in determining optimal adaptation. We show that exploitation of expression noise occurs only in some types of fluctuating environment and is strongly dependent on the quality and availability of nutrients: stochasticity is generally detrimental in fluctuating environments and beneficial only at equal periods of nutrient availability and above a threshold environmental richness. Moreover, depending on the availability and nutritional value of nutrients, nutrient-dependent and stochastic expression are both strategies used to deal with environmental changes. Overall, we comprehensively characterize the interplay between the quality and periodicity of an environment and the resulting optimal deterministic and stochastic regulation strategies of nutrient-catabolizing pathways.

Independent regulation of gene expression level and noise by histone modifications

The inherent stochasticity generates substantial gene expression variation among isogenic cells under identical conditions, which is frequently referred to as gene expression noise or cell-to-cell expression variability. Similar to (average) expression level, expression noise is also subject to natural selection. Yet it has been observed that noise is negatively correlated with expression level, which manifests as a potential constraint for simultaneous optimization of both. Here, we studied expression noise in human embryonic cells with computational analysis on single-cell RNA-seq data and in yeast with flow cytometry experiments. We showed that this coupling is overcome, to a certain degree, by a histone modification strategy in multiple embryonic developmental stages in human, as well as in yeast. Importantly, this epigenetic strategy could fit into a burst-like gene expression model: promoter-localized histone modifications (such as H3K4 methylation) are associated with both burst size and burst frequency, which together influence expression level, while gene-body-localized ones (such as H3K79 methylation) are more associated with burst frequency, which influences both expression level and noise. We further knocked out the only “writer” of H3K79 methylation in yeast, and observed that expression noise is indeed increased. Consistently, dosage sensitive genes, such as genes in the Wnt signaling pathway, tend to be marked with gene-body-localized histone modifications, while stress responding genes, such as genes regulating autophagy, tend to be marked with promoter-localized ones. Our findings elucidate that the “division of labor” among histone modifications facilitates the independent regulation of expression level and noise, extend the “histone code” hypothesis to include expression noise, and shed light on the optimization of transcriptome in evolution.

Gene expression noise, or cell-to-cell expression variability, has been a topic of intense interest for more than a decade. The prevailing model of “burst-like transcription” mediated by the promoter transitions between on and off states explains the formation of noise in eukaryotes. Albeit widely accepted, the cis- elements that determine the burst frequency and burst size remain largely unknown. Here we systematically examined the relationship between transcriptional burst frequency/size and all major histone modifications in various cell types, including human embryonic cells, mouse embryonic stem cells, and yeast, and found that histone markers can be divided into two groups based on their associations with burst frequency/ size. Coincidently, promoter-localized histone markers are associated with both burst size and burst frequency whereas gene-body-localized ones are more associated with burst frequency. We further knocked out a gene that is responsible for “writing” a gene-body histone mark in yeast, and found that burst frequency is indeed reduced. Our findings reveal a new mechanism of transcriptional burst regulation and shed light on the simultaneous optimization of gene expression level and noise in evolution.

A multiplex culture system for the long-term growth of fission yeast cells

Maintenance of long‐term cultures of yeast cells is central to a broad range of investigations, from metabolic studies to laboratory evolution assays. However, repeated dilutions of batch cultures lead to variations in medium composition, with implications for cell physiology. In Saccharomyces cerevisiae, powerful miniaturized chemostat setups, or ministat arrays, have been shown to allow for constant dilution of multiple independent cultures. Here we set out to adapt these arrays for continuous culture of a morphologically and physiologically distinct yeast, the fission yeast Schizosaccharomyces pombe, with the goal of maintaining constant population density over time. First, we demonstrated that the original ministats are incompatible with growing fission yeast for more than a few generations, prompting us to modify different aspects of the system design. Next, we identified critical parameters for sustaining unbiased vegetative growth in these conditions. This requires deletion of the gsf2 flocculin‐encoding gene, along with addition of galactose to the medium and lowering of the culture temperature. Importantly, we improved the flexibility of the ministats by developing a piezo‐pump module for the independent regulation of the dilution rate of each culture. This made it possible to easily grow strains that have different generation times in the same assay. Our system therefore allows for maintaining multiple fission yeast cultures in exponential growth, adapting the dilution of each culture over time to keep constant population density for hundreds of generations. These multiplex culture systems open the door to a new range of long‐term experiments using this model organism. © 2017 The Authors. Yeast published by John Wiley & Sons, Ltd.

Positive-feedback, ratiometric biosensor expression improves high-throughput metabolite-producer screening efficiency in yeast

Biosensors are valuable and versatile tools in synthetic biology that are used to modulate gene expression in response to a wide range of stimuli. Ligand responsive transcription factors are a class of biosensor that can be used to couple intracellular metabolite concentration with gene expression to enable dynamic regulation and high-throughput metabolite producer screening. We have established the Saccharomyces cerevisiae WAR1 transcriptional regulator and PDR12 promoter as an organic acid biosensor that can be used to detect varying levels of para-hydroxybenzoic acid (PHBA) production from the shikimate pathway and output green fluorescent protein (GFP) expression in response. The dynamic range of GFP expression in response to PHBA was dramatically increased by engineering positive-feedback expression of the WAR1 transcriptional regulator from its target PDR12 promoter. In addition, the noise in GFP expression at the population-level was controlled by normalising GFP fluorescence to constitutively expressed mCherry fluorescence within each cell. These biosensor modifications increased the high-throughput screening efficiency of yeast cells engineered to produce PHBA by 5,000-fold, enabling accurate fluorescence activated cell sorting isolation of producer cells that were mixed at a ratio of 1 in 10,000 with non-producers. Positive-feedback, ratiometric transcriptional regulator expression is likely applicable to many other transcription-factor/promoter pairs used in synthetic biology and metabolic engineering for both dynamic regulation and high-throughput screening applications.