Stochastic Expression Research Articles

A transcriptional cycling model recapitulates chromatin-dependent features of noisy inducible transcription.

Activation of gene expression in response to environmental cues results in substantial phenotypic heterogeneity between cells that can impact a wide range of outcomes including differentiation, viral activation, and drug resistance. An important source of gene expression noise is transcriptional bursting, or the process by which transcripts are produced during infrequent bursts of promoter activity. Chromatin accessibility impacts transcriptional bursting by regulating the assembly of transcription factor and polymerase complexes on promoters, suggesting that the effect of an activating signal on transcriptional noise will depend on the initial chromatin state at the promoter. To explore this possibility, we simulated transcriptional activation using a transcriptional cycling model with three promoter states that represent chromatin remodeling, polymerase binding and pause release. We initiated this model over a large parameter range representing target genes with different chromatin environments, and found that, upon increasing the polymerase pause release rate to activate transcription, changes in gene expression noise varied significantly across initial promoter states. This model captured phenotypic differences in activation of latent HIV viruses integrated at different chromatin locations and mediated by the transcription factor NF-κB. Activating transcription in the model via increasing one or more of the transcript production rates, as occurs following NF-κB activation, reproduced experimentally measured transcript distributions for four different latent HIV viruses, as well as the bimodal pattern of HIV protein expression that leads to a subset of reactivated virus. Importantly, the parameter ‘activation path’ differentially affected gene expression noise, and ultimately viral activation, in line with experimental observations. This work demonstrates how upstream signaling pathways can be connected to biological processes that underlie transcriptional bursting, resulting in target gene-specific noise profiles following stimulation of a single upstream pathway.

Microfluidics for single-cell noise measurements in budding yeast

The combination of microfluidics and time-lapse microscopy is a powerful one, with the potential for the generation of time-series data for single-cell measurements across a whole population of cells. This project aims to employ these tools for the development of technologies and strategies that facilitate the study of stochastic events at the single-cell level. A key focus of the work is the development of microfluidic and image analysis techniques. Such developments within this project are being applied to study the specific example of noise within the expression of the yeast GCN4 transcriptional regulator. This is activated during amino acid starvation, through a novel post-transcriptional regulatory system. The majority of research into gene expression noise thus far has focused on transcriptional noise, so the impact of post-transcriptional regulation on noise is relatively poorly understood. Biological systems show many apparently deterministic behaviours at the macroscopic level, but their underlying mechanisms are often stochastic in nature. Noise in gene expression generates heterogeneity across clonal cell populations, and is an important factor in many cellular processes (e.g. circadian rhythms). Even within the well-known “repressilator” synthetic gene circuit, noise had a significant impact on the circuit output. An appreciation of stochasticity in gene expression is therefore crucial for a complete understanding of biology, and will also be essential for the successful design of future synthetic biological systems.

Activation domains can decouple the mean and noise of gene expression.

Regulatory mechanisms set a gene's average level of expression, but a gene's expression constantly fluctuates around that average. These stochastic fluctuations, or expression noise, play a role in cell-fate transitions, bet hedging in microbes, and the development of chemotherapeutic resistance in cancer. An outstanding question is what regulatory mechanisms contribute to noise. Here, we demonstrate that, for a fixed mean level of expression, strong activation domains (ADs) at low abundance produce high expression noise, while weak ADs at high abundance generate lower expression noise. We conclude that differences in noise can be explained by the interplay between a TF's nuclear concentration and the strength of its AD's effect on mean expression, without invoking differences between classes of ADs. These results raise the possibility of engineering gene expression noise independently of mean levels in synthetic biology contexts and provide a potential mechanism for natural selection to tune the noisiness of gene expression.

Gene regulation in Escherichia coli is commonly selected for both high plasticity and low noise.

Bacteria often respond to dynamically changing environments by regulating gene expression. Despite this regulation being critically important for growth and survival, little is known about how selection shapes gene regulation in natural populations. To better understand the role natural selection plays in shaping bacterial gene regulation, here we compare differences in the regulatory behaviour of naturally segregating promoter variants from Escherichia coli (which have been subject to natural selection) to randomly mutated promoter variants (which have never been exposed to natural selection). We quantify gene expression phenotypes (expression level, plasticity and noise) for hundreds of promoter variants across multiple environments and show that segregating promoter variants are enriched for mutations with minimal effects on expression level. In many promoters, we infer that there is strong selection to maintain high levels of plasticity, and direct selection to decrease or increase cell-to-cell variability in expression. Taken together, these results expand our knowledge of how gene regulation is affected by natural selection and highlight the power of comparing naturally segregating polymorphisms to de novo random mutations to quantify the action of selection.

Gene copy number and negative feedback differentially regulate transcriptional variability of segmentation clock genes

SummaryTimely progression of a genetic program is critical for embryonic development. However, gene expression involves inevitable fluctuations in biochemical reactions leading to substantial cell-to-cell variability (gene expression noise). One of the important questions in developmental biology is how pattern formation is reproducibly executed despite these unavoidable fluctuations in gene expression. Here, we studied the transcriptional variability of two paired zebrafish segmentation clock genes (her1 and her7) in multiple genetic backgrounds. Segmentation clock genes establish an oscillating self-regulatory system, presenting a challenging yet beautiful system in studying control of transcription variability. In this study, we found that a negative feedback loop established by the Her1 and Her7 proteins minimizes uncorrelated variability whereas gene copy number affects variability of both RNAs in a similar manner (correlated variability). We anticipate that these findings will help analyze the precision of other natural clocks and inspire the ideas for engineering precise synthetic clocks in tissue engineering.

Stochastic analysis of a complex gene-expression model

Gene expression has been extensively studied in terms of Markov processes, but its stochastic mechanisms including how noisy sources contribute to expression levels still remain not fully understood. To reveal more reasonable mechanisms, it is needed to consider more molecular events occurring in gene-expression processes. Here we introduce and analyze a general gene-expression model described by a chemical master equation. This model simultaneously considers negative feedback, bursting and promoter cyclic structure, thus extending most gene-expression models in the existing literature. First, we uncover the hierarchy of this complex transcription process by deriving the probability-generating function expressed by an Euler integral formula. Second, we further derive analytical mRNA distribution, which includes previous results as its special cases and provides new insights into the roles of chromatin remodeling and feedback in fine-tuning the mRNA noise. Third, we show that the multi-OFF mechanism always reduces the mRNA noise, but the effect of feedback on the mRNA noise is complex, depending on the detail of the multi-OFF mechanism. The overall analysis provides a paradigm for investigation into expression noise and gene-expression mechanisms in more complex cases.

PRC1 chromatin factors strengthen the consistency of neuronal cell fate specification and maintenance in C. elegans.

In the nervous system, the specific identity of a neuron is established and maintained by terminal selector transcription factors that directly activate large batteries of terminal differentiation genes and positively regulate their own expression via feedback loops. However, how this is achieved in a reliable manner despite noise in gene expression, genetic variability or environmental perturbations remains poorly understood. We addressed this question using the AIY cholinergic interneurons of C. elegans, whose specification and differentiation network is well characterized. Via a genetic screen, we found that a loss of function of PRC1 chromatin factors induces a stochastic loss of AIY differentiated state in a small proportion of the population. PRC1 factors act directly in the AIY neuron and independently of PRC2 factors. By quantifying mRNA and protein levels of terminal selector transcription factors in single neurons, using smFISH and CRISPR tagging, we observed that, in PRC1 mutants, terminal selector expression is still initiated during embryonic development but the level is reduced, and expression is subsequently lost in a stochastic manner during maintenance phase in part of the population. We also observed variability in the level of expression of terminal selectors in wild type animals and, using correlation analysis, established that this noise comes from both intrinsic and extrinsic sources. Finally, we found that PRC1 factors increase the resistance of AIY neuron fate to environmental stress, and also secure the terminal differentiation of other neuron types. We propose that PRC1 factors contribute to the consistency of neuronal cell fate specification and maintenance by protecting neurons against noise and perturbations in their differentiation program.

Impact of optogenetic pulse design on CA3 learning and replay: A neural model

SummaryOptogenetic manipulation of hippocampal circuitry is an important tool for investigating learning in vivo. Numerous approaches to pulse design have been employed to elicit desirable circuit and behavioral outcomes. Here, we systematically test the outcome of different single-pulse waveforms in a rate-based model of hippocampal memory function at the level of mnemonic replay extension and de novo synaptic weight formation in CA3 and CA1. Lower-power waveforms with long forward or forward and backward ramps yield more natural sequence replay dynamics and induce synaptic plasticity that allows for more natural memory replay timing, in contrast to square or backward ramps. These differences between waveform shape and amplitude are preserved with the addition of noise in membrane potential, light scattering, and protein expression, improving the potential validity of predictions for in vivo work. These results inform future optogenetic experimental design choices in the field of learning and memory.

Ligand-receptor promiscuity enables cellular addressing.

In multicellular organisms, secreted ligands selectively activate, or "address," specific target cell populations to control cell fate decision-making and other processes. Key cell-cell communication pathways use multiple promiscuously interacting ligands and receptors, provoking the question of how addressing specificity can emerge from molecular promiscuity. To investigate this issue, we developed a general mathematical modeling framework based on the bone morphogenetic protein (BMP) pathway architecture. We find that promiscuously interacting ligand-receptor systems allow a small number of ligands, acting in combinations, to address a larger number of individual cell types, defined by their receptor expression profiles. Promiscuous systems outperform seemingly more specific one-to-one signaling architectures in addressing capability. Combinatorial addressing extends to groups of cell types, is robust to receptor expression noise, grows more powerful with increases in the number of receptor variants, and is maximized by specific biochemical parameter relationships. Together, these results identify design principles governing cellular addressing by ligand combinations.

Standardization of regulatory nodes for engineering heterologous gene expression: a feasibility study.

SummaryThe potential of LacI/P trc , XylS/P m , AlkS/P alkB , CprK/P DB3 and ChnR/P chnB regulatory nodes, recruited from both Gram‐negative and Gram‐positive bacteria, as the source of parts for formatting expression cargoes following the Standard European Vector Architecture (SEVA) has been examined. The five expression devices, which cover most known regulatory configurations in bacteria were assembled within exactly the same plasmid backbone and bearing the different functional segments arrayed in an invariable DNA scaffold. Their performance was then analysed in an Escherichia coli strain of reference through the readout of a fluorescence reporter gene that contained strictly identical translation signal elements. This approach allowed us to describe and compare the cognate expression systems with quantitative detail. The constructs under scrutiny diverged considerably in their capacity, expression noise, inducibility and ON/OFF ratios. Inspection of such a variance exposed the different constraints that rule the optimal arrangement of functional DNA segments in each case. The data highlighted also the ease of standardizing inducer‐responsive devices subject to transcriptional activation as compared to counterparts based on repressors. The study resulted in a defined collection of formatted expression cargoes lacking any cross talk while offering a panoply of choices to potential users and help interoperability of the specific constructs.

Synthetic cell-based materials extract positional information from morphogen gradients.

Biomaterials composed of synthetic cells have the potential to adapt and differentiate guided by physicochemical environmental cues. Inspired by biological systems in development, which extract positional information (PI) from morphogen gradients in the presence of uncertainties, we here investigate how well synthetic cells can determine their position within a multicellular structure. To calculate PI, we created and analyzed a large number of synthetic cellular assemblies composed of emulsion droplets connected via lipid bilayer membranes. These droplets contained cell-free feedback gene circuits that responded to gradients of a genetic inducer acting as a morphogen. PI is found to be limited by gene expression noise and affected by the temporal evolution of the morphogen gradient and the cell-free expression system itself. The generation of PI can be rationalized by computational modeling of the system. We scale our approach using three-dimensional printing and demonstrate morphogen-based differentiation in larger tissue-like assemblies.

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 (pF81kan) 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.

Double-edged role of resource competition in gene expression noise and control.

Despite extensive investigation demonstrating that resource competition can significantly alter the deterministic behaviors of synthetic gene circuits, it remains unclear how resource competition contributes to the gene expression noise and how this noise can be controlled. Utilizing a two-gene circuit as a prototypical system, we uncover a surprising double-edged role of resource competition in gene expression noise: competition decreases noise through introducing a resource constraint but generates its own type of noise which we name as "resource competitive noise." Utilization of orthogonal resources enables retainment of the noise reduction conferred by resource constraint while removing the added resource competitive noise. The noise reduction effects are studied using three negative feedback types: negatively competitive regulation (NCR), local, and global controllers, each having four placement architectures in the protein biosynthesis pathway (mRNA or protein inhibition on transcription or translation). Our results show that both local and NCR controllers with mRNA-mediated inhibition are efficacious at reducing noise, with NCR controllers demonstrating a superior noise-reduction capability. We also find that combining feedback controllers with orthogonal resources can improve the local controllers. This work provides deep insights into the origin of stochasticity in gene circuits with resource competition and guidance for developing effective noise control strategies.

A DNA repair pathway can regulate transcriptional noise to promote cell fate transitions

Stochastic fluctuations in gene expression (‘noise’) are often considered detrimental, but fluctuations can also be exploited for benefit (e.g., dither). We show here that DNA base-excision repair amplifies transcriptional noise to facilitate cellular reprogramming. Specifically, the DNA-repair protein Apex1, which recognizes both naturally occurring and unnatural base modifications, amplifies expression noise while homeostatically maintaining mean-expression levels. This amplified expression noise originates from shorter duration, higher intensity, transcriptional bursts generated by Apex1-mediated DNA supercoiling.

Noise decomposition and control in synthetic gene circuits coupled by resource competition

Despite extensive investigation demonstrating that resource competition can significantly alter the circuits' deterministic behaviors, a fundamental issue is how resource competition contributes to the gene expression noise and how the noise can be controlled. Utilizing a two-gene circuit as a prototypical system, we uncover a surprising double-edged role of resource competition in gene expression noise: the competition decreases noise through a resource constraint but generates its own type of noise which we name as ¯¯resource competitive noise.'' Utilization of orthogonal resources enables retaining the noise reduction conferred by resource constraint while removing the added resource competitive noise.

Exact distributions for stochastic gene expression models with arbitrary promoter architecture and translational bursting.

Gene expression in individual cells is inherently variable and sporadic, leading to cell-to-cell variability in mRNA and protein levels. Recent single-cell and single-molecule experiments indicate that promoter architecture and translational bursting play significant roles in controlling gene expression noise and generating the phenotypic diversity that life exhibits. To quantitatively understand the impact of these factors, it is essential to construct an accurate mathematical description of stochastic gene expression and find the exact analytical results, which is a formidable task. Here, we develop a stochastic model of bursty gene expression, which considers the complex promoter architecture governing the variability in mRNA expression and a general distribution characterizing translational burst. We derive the analytical expression for the corresponding protein steady-state distribution and all moment statistics of protein counts. We show that the total protein noise can be decomposed into three parts: the low-copy noise of protein due to probabilistic individual birth and death events, the noise due to stochastic switching between promoter states, and the noise resulting from translational busting. The theoretical results derived provide quantitative insights into the biochemical mechanisms of stochastic gene expression.

Modeling noise propagation in time-delayed auto-inhibitory genetic circuits

The abundance of specific protein molecules in genetically identical cell populations exposed to the same external environment can show remarkable cell-to-cell variations as biochemical reactions are inherently stochastic and occur with low numbers of molecular copies. Such variations in gene products are commonly known as gene expression noise. One of the mechanisms for cells to reduce such noise is auto-regulatory negative feedback (auto-inhibition), commonly found across organisms. This auto-inhibition is subjected to unavoidable time-delays associated with transcriptional and translational processes. Sufficient time-delays and strong auto-inhibition can generate sustained oscillations in gene products, which is a common mechanism for precise timekeeping in many biomolecular clocks. While the importance of time-delays in the generation of oscillations is well appreciated, its role in stochastic dynamics is not well understood in the absence of sustained oscillations. Here, we investigate the interplay between the feedback strength and the time-delay to study the noise propagation in the non-oscillatory regime using linear stability analysis, the linear noise approximation, and stochastic simulations. From a simple auto-regulatory model with one protein species (no delay), we systematically introduce one-step and two-step time-delays by incorporating intermediate dynamics with additional second and third species, respectively. Interestingly, the negative feedback in the presence of time-delay can show counterintuitive noise behavior to our common perception about its role as a noise buffer.

Gene Expression Noise Dynamics Unveil Functional Heterogeneity of Ageing Hematopoietic Stem Cells

Gene Expression Noise Dynamics Unveil Functional Heterogeneity of Ageing Hematopoietic Stem Cells

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.

Volumetric compression develops noise-driven single-cell heterogeneity

Recent studies have revealed that extensive heterogeneity of biological systems arises through various routes ranging from intracellular chromosome segregation to spatiotemporally varying biochemical stimulations. However, the contribution of physical microenvironments to single-cell heterogeneity remains largely unexplored. Here, we show that a homogeneous population of non-small-cell lung carcinoma develops into heterogeneous subpopulations upon application of a homogeneous physical compression, as shown by single-cell transcriptome profiling. The generated subpopulations stochastically gain the signature genes associated with epithelial-mesenchymal transition (EMT; VIM, CDH1, EPCAM, ZEB1, and ZEB2) and cancer stem cells (MKI67, BIRC5, and KLF4), respectively. Trajectory analysis revealed two bifurcated paths as cells evolving upon the physical compression, along each path the corresponding signature genes (epithelial or mesenchymal) gradually increase. Furthermore, we show that compression increases gene expression noise, which interplays with regulatory network architecture and thus generates differential cell-fate outcomes. The experimental observations of both single-cell sequencing and single-molecule fluorescent in situ hybridization agrees well with our computational modeling of regulatory network in the EMT process. These results demonstrate a paradigm of how mechanical stimulations impact cell-fate determination by altering transcription dynamics; moreover, we show a distinct path that the ecology and evolution of cancer interplay with their physical microenvironments from the view of mechanobiology and systems biology, with insight into the origin of single-cell heterogeneity.