Noise in protein expression scales with natural protein abundance

High-throughput optimized prime editing mediated endogenous protein tagging for pooled imaging of protein localization

The subcellular organization of proteins carries important information on cellular state and gene function, yet currently there are no technologies that enable accurate measurement of subcellular protein localizations at scale. Here we develop an approach for pooled endogenous protein tagging using prime editing, which coupled with an optical readout and sequencing, provides a snapshot of proteome organization in a manner akin to perturbation-based CRISPR screens. We constructed a pooled library of 17,280 pegRNAs designed to exhaustively tag 60 endogenous proteins spanning diverse localization patterns and explore a large space of genomic and pegRNA design parameters. Pooled measurements of tagging efficiency uncovered both genomic and pegRNA features associated with increased efficiency, including epigenetic states and interactions with transcription. We integrate pegRNA features into a computational model with predictive value for tagging efficiency to constrain the design space of pegRNAs for large-scale peptide knock-in. Lastly, we show that combining in-situ pegRNA sequencing with high-throughput deep learning image analysis, enables exploration of subcellular protein localization patterns for many proteins in parallel following a single pooled lentiviral transduction, setting the stage for scalable studies of proteome dynamics across cell types and environmental perturbations.

Self-diffusion is temperature independent on active membranes

Molecular transport maintains cellular structures and functions. For example, lipid and protein diffusion sculpts the dynamic shapes and structures on the cell membrane that perform essential cellular functions, such as cell signaling. Temperature variations in thermal equilibrium rapidly change molecular transport properties. The coefficient of lipid self-diffusion increases exponentially with temperature in thermal equilibrium, for example. Hence, maintaining cellular homeostasis through molecular transport is hard in thermal equilibrium in the noisy cellular environment, where temperatures can fluctuate widely due to local heat generation. In this paper, using both molecular and lattice-based modeling of membrane transport, we show that the presence of active transport originating from the cell's cytoskeleton can make the self-diffusion of the molecules on the membrane robust to temperature fluctuations. The resultant temperature-independence of self-diffusion keeps the precision of cellular signaling invariant over a broad range of ambient temperatures, allowing cells to make robust decisions. We have also found that the Kawasaki algorithm, the widely used model of lipid transport on lattices, predicts incorrect temperature dependence of lipid self-diffusion in equilibrium. We propose a new algorithm that correctly captures the equilibrium properties of lipid self-diffusion and reproduces experimental observations.

Transcriptional noise, gene activation, and roles of SAGA and Mediator Tail measured using nucleotide recoding single-cell RNA-seq

We describe a time-resolved nascent single-cell RNA sequencing (RNA-seq) approach that measures gene-specific transcriptional noise and the fraction of active genes in S. cerevisiae. Most genes are expressed with near-constitutive behavior, while a subset of genes show high mRNA variance suggestive of transcription bursting. Transcriptional noise is highest in the cofactor/coactivator-redundant (CR) gene class (dependent on both SAGA and TFIID) and strongest in TATA-containing CR genes. Using this approach, we also find that histone gene transcription switches from a low-level, low-noise constitutive mode during M and M/G1 to an activated state in S phase that shows both an increase in the fraction of active promoters and a switch to a noisy and bursty transcription mode. Rapid depletion of cofactors SAGA and MED Tail indicates that both factors play an important role in stimulating the fraction of active promoters at CR genes, with a more modest role in transcriptional noise.

Noise robustness and metabolic load determine the principles of central dogma regulation

The processes of gene expression are inherently stochastic, even for essential genes required for growth. How does the cell maximize fitness in light of noise? To answer this question, we build a mathematical model to explore the trade-off between metabolic load and growth robustness. The model provides insights for principles of central dogma regulation: Optimal protein expression levels for many genes are in vast overabundance. Essential genes are transcribed above a lower limit of one message per cell cycle. Gene expression is achieved by load balancing between transcription and translation. We present evidence that each of these regulatory principles is observed. These results reveal that robustness and metabolic load determine the global regulatory principles that govern gene expression processes, and these principles have broad implications for cellular function.

Transcription Attenuation in Synthetic Promoters in Nonoverlapping Tandem Formation

Closely spaced promoters are ubiquitous in prokaryotic and eukaryotic genomes. How their structure and dynamics relate remains unclear, particularly for tandem formations. To study their transcriptional interference, we engineered two pairs and one trio of synthetic promoters in nonoverlapping, tandem formation, in single-copy plasmids transformed into Escherichia coli cells. From in vivo measurements, we found that these promoters in tandem formation can have attenuated transcription rates. The attenuation strength can be widely fine-tuned by the promoters' positioning, natural regulatory mechanisms, and other factors, including the antibiotic rifampicin, which is known to hamper RNAP promoter escape. From this, and supported by in silico models, we concluded that the attenuation in these constructs emerges from premature terminations generated by collisions between RNAPs elongating from upstream promoters and RNAPs occupying downstream promoters. Moreover, we found that these collisions can cause one or both RNAPs to falloff. Finally, the broad spectrum of possible, externally regulated, attenuation strengths observed in our synthetic tandem promoters suggests that they could become useful as externally controllable regulators of future synthetic circuits.

Noise robustness and metabolic load determine the principles of central dogma regulation

The processes of gene expression are inherently stochastic, even for essential genes required for growth. How does the cell maximize fitness in light of noise? To answer this question, we build a mathematical model to explore the trade-off between metabolic load and growth robustness. The model predicts novel principles of central dogma regulation: Optimal protein expression levels for many genes are in vast overabundance. Essential genes are transcribed above a lower limit of one message per cell cycle. Gene expression is achieved by load balancing between transcription and translation. We present evidence that each of these novel regulatory principles is observed. These results reveal that robustness and metabolic load determine the global regulatory principles that govern gene expression processes, and these principles have broad implications for cellular function.

Quantitative comparison of single-cell RNA sequencing versus single-molecule RNA imaging for quantifying transcriptional noise

Stochastic fluctuations (noise) in transcription generate substantial cell-to-cell variability. However, how best to quantify genome-wide noise, remains unclear. Here we utilize a small-molecule perturbation (IdU) to amplify noise and assess noise quantification from numerous scRNA-seq algorithms on human and mouse datasets, and then compare to noise quantification from single-molecule RNA FISH (smFISH) for a panel of representative genes. We find that various scRNA-seq analyses report amplified noise, without altered mean-expression levels, for ~90% of genes and that smFISH analysis verifies noise amplification for the vast majority of genes tested. Collectively, the analyses suggest that most scRNA-seq algorithms are appropriate for quantifying noise including a simple normalization approach, although all of these systematically underestimate noise compared to smFISH. From a practical standpoint, this analysis argues that IdU is a globally penetrant noise-enhancer molecule-amplifying noise without altering mean-expression levels-which could enable investigations of the physiological impacts of transcriptional noise.

Living in a noisy world-origins of gene expression noise and its impact on cellular decision-making

The expression level of a gene can vary between genetically identical cells under the same environmental condition-a phenomenon referred to as gene expression noise. Several studies have now elucidated a central role of transcription factors in the generation of expression noise. Transcription factors, as the key components of gene regulatory networks, drive many important cellular decisions in response to cellular and environmental signals. Therefore, a very relevant question is how expression noise impacts gene regulation and influences cellular decision-making. In this Review, we summarize the current understanding of the molecular origins of expression noise, highlighting the role of transcription factors in this process, and discuss the ways in which noise can influence cellular decision-making. As advances in single-cell technologies open new avenues for studying expression noise as well as gene regulatory circuits, a better understanding of the influence of noise on cellular decisions will have important implications for many biological processes.

A library of reporters of the global regulators of gene expression in Escherichia coli

The topology of the transcription factor network (TFN) of Escherichia coli is far from uniform, with 22 global regulator (GR) proteins controlling one-third of all genes. So far, their production rates cannot be tracked by comparable fluorescent proteins. We developed a library of fluorescent reporters for 16 GRs for this purpose. Each consists of a single-copy plasmid coding for green fluorescent protein (GFP) fused to the full-length copy of the native promoter. We tracked their activity in exponential and stationary growth, as well as under weak and strong stresses. We show that the reporters have high sensitivity and specificity to all stresses tested and detect single-cell variability in transcription rates. Given the influence of GRs on the TFN, we expect that the new library will contribute to dissecting global transcriptional stress-response programs of E. coli. Moreover, the library can be invaluable in bioindustrial applications that tune those programs to, instead of cell growth, favor productivity while reducing energy consumption.IMPORTANCECells contain thousands of genes. Many genes are involved in the control of cellular activities. Some activities require a few hundred genes to run largely synchronous transcriptional programs. To achieve this, cells have evolved global regulator (GR) proteins that can influence hundreds of genes simultaneously. We have engineered a library of Escherichia coli strains to track the levels over time of these, phenotypically critical, GRs. Each strain has a single-copy plasmid coding for a fast-maturing green fluorescent protein whose transcription is controlled by a copy of the natural GR promoter. By allowing the tracking of GR levels, with sensitivity and specificity, this library should become of wide use in scientific research on bacterial gene expression (from molecular to synthetic biology) and, later, be used in applications in therapeutics and bioindustries.

Effect of genomic and cellular environments on gene expression noise

BACKGROUND

Individual cells from isogenic populations often display large cell-to-cell differences in gene expression. This "noise" in expression derives from several sources, including the genomic and cellular environment in which a gene resides. Large-scale maps of genomic environments have revealed the effects of epigenetic modifications and transcription factor occupancy on mean expression levels, but leveraging such maps to explain expression noise will require new methods to assay how expression noise changes at locations across the genome.

RESULTS

To address this gap, we present Single-cell Analysis of Reporter Gene Expression Noise and Transcriptome (SARGENT), a method that simultaneously measures the noisiness of reporter genes integrated throughout the genome and the global mRNA profiles of individual reporter-gene-containing cells. Using SARGENT, we perform the first comprehensive genome-wide survey of how genomic locations impact gene expression noise. We find that the mean and noise of expression correlate with different histone modifications. We quantify the intrinsic and extrinsic components of reporter gene noise and, using the associated mRNA profiles, assign the extrinsic component to differences between the CD24+ "stem-like" substate and the more "differentiated" substate. SARGENT also reveals the effects of transgene integrations on endogenous gene expression, which will help guide the search for "safe-harbor" loci.

CONCLUSIONS

Taken together, we show that SARGENT is a powerful tool to measure both the mean and noise of gene expression at locations across the genome and that the data generatd by SARGENT reveals important insights into the regulation of gene expression noise genome-wide.

Duplicate Gene Expression and Possible Mechanisms of Paralog Retention During Bacterial Genome Expansion

Gene duplication contributes to the evolution of expression and the origin of new genes, but the relative importance of different patterns of duplicate gene expression and mechanisms of retention remains debated and particularly poorly understood in bacteria. Here, we investigated gene expression patterns for two lab strains of the cyanobacterium Acaryochloris marina with expanding genomes that contain about 10-fold more gene duplicates compared with most bacteria. Strikingly, we observed a generally stoichiometric pattern of greater combined duplicate transcript dosage with increased gene copy number, in contrast to the prevalence of expression reduction reported for many eukaryotes. We conclude that increased transcript dosage is likely an important mechanism of initial duplicate retention in these bacteria and may persist over long periods of evolutionary time. However, we also observed that paralog expression can diverge rapidly, including possible functional partitioning, for which different copies were respectively more highly expressed in at least one condition. Divergence may be promoted by the physical separation of most Acaryochloris duplicates on different genetic elements. In addition, expression pattern for ancestrally shared duplicates could differ between strains, emphasizing that duplicate expression fate need not be deterministic. We further observed evidence for context-dependent transcript dosage, where the aggregate expression of duplicates was either greater or lower than their single-copy homolog depending on physiological state. Finally, we illustrate how these different expression patterns of duplicated genes impact Acaryochloris biology for the innovation of a novel light-harvesting apparatus and for the regulation of recA paralogs in response to environmental change.

What can we learn when fitting a simple telegraph model to a complex gene expression model?

In experiments, the distributions of mRNA or protein numbers in single cells are often fitted to the random telegraph model which includes synthesis and decay of mRNA or protein, and switching of the gene between active and inactive states. While commonly used, this model does not describe how fluctuations are influenced by crucial biological mechanisms such as feedback regulation, non-exponential gene inactivation durations, and multiple gene activation pathways. Here we investigate the dynamical properties of four relatively complex gene expression models by fitting their steady-state mRNA or protein number distributions to the simple telegraph model. We show that despite the underlying complex biological mechanisms, the telegraph model with three effective parameters can accurately capture the steady-state gene product distributions, as well as the conditional distributions in the active gene state, of the complex models. Some effective parameters are reliable and can reflect realistic dynamic behaviors of the complex models, while others may deviate significantly from their real values in the complex models. The effective parameters can also be applied to characterize the capability for a complex model to exhibit multimodality. Using additional information such as single-cell data at multiple time points, we provide an effective method of distinguishing the complex models from the telegraph model. Furthermore, using measurements under varying experimental conditions, we show that fitting the mRNA or protein number distributions to the telegraph model may even reveal the underlying gene regulation mechanisms of the complex models. The effectiveness of these methods is confirmed by analysis of single-cell data for E. coli and mammalian cells. All these results are robust with respect to cooperative transcriptional regulation and extrinsic noise. In particular, we find that faster relaxation speed to the steady state results in more precise parameter inference under large extrinsic noise.

Regulation of Chromatin Modifications through Coordination of Nucleus Size and Epithelial Cell Morphology Heterogeneity

Cell morphology heterogeneity within epithelial collectives is a pervasive phenomenon intertwined with tissue mechanical properties. Despite its widespread occurrence, the underlying mechanisms driving cell morphology heterogeneity and its consequential biological ramifications remain elusive. Here, we investigate the dynamic evolution of epithelial cell morphology and nucleus morphology during crowding, unveiling a consistent correlation between the two. Our investigation reveals a persistent log-normal probability distribution characterizing both cell and nucleus areas across diverse crowding stages and epithelial model systems. We showed that this morphological diversity arises from asymmetric partitioning during cell division and is perpetuated through actomyosin-mediated regulation of cell-nucleus size coordination. Moreover, we provide insights into the impact of nucleus morphology on chromatin dynamics, demonstrating that constraining nucleus area leads to downregulation of the euchromatic mark H3K9ac and upregulation of the heterochromatic mark H3K27me3 through modulation of histone demethylase UTX expression. These findings under-score the significance of cell morphology heterogeneity as a driver of chromatin state diversity, shaping functional variability within epithelial tissues.

Contributions of transcriptional noise to leukaemia evolution: KAT2A as a case-study

Transcriptional noise is proposed to participate in cell fate changes, but contributions to mammalian cell differentiation systems, including cancer, remain associative. Cancer evolution is driven by genetic variability, with modulatory or contributory participation of epigenetic variants. Accumulation of epigenetic variants enhances transcriptional noise, which can facilitate cancer cell fate transitions. Acute myeloid leukaemia (AML) is an aggressive cancer with strong epigenetic dependencies, characterized by blocked differentiation. It constitutes an attractive model to probe links between transcriptional noise and malignant cell fate regulation. Gcn5/KAT2A is a classical epigenetic transcriptional noise regulator. Its loss increases transcriptional noise and modifies cell fates in stem and AML cells. By reviewing the analysis of KAT2A-depleted pre-leukaemia and leukaemia models, I discuss that the net result of transcriptional noise is diversification of cell fates secondary to alternative transcriptional programmes. Cellular diversification can enable or hinder AML progression, respectively, by differentiation of cell types responsive to mutations, or by maladaptation of leukaemia stem cells. KAT2A-dependent noise-responsive genes participate in ribosome biogenesis and KAT2A loss destabilizes translational activity. I discuss putative contributions of perturbed translation to AML biology, and propose KAT2A loss as a model for mechanistic integration of transcriptional and translational control of noise and fate decisions. This article is part of a discussion meeting issue 'Causes and consequences of stochastic processes in development and disease'.

Mitigating transcription noise via protein sharing in syncytial cells

Bursty transcription allows nuclei to concentrate the work of transcribing mRNA into short, intermittent intervals, potentially reducing transcriptional interference. However, bursts of mRNA production can increase noise in protein abundances. Here, we formulate models for gene expression in syncytia, or multinucleate cells, showing that protein abundance noise may be mitigated locally via spatial averaging of diffuse proteins. Our modeling shows a universal reduction in protein noise, which increases with the average number of nuclei per cell and persists even when the number of nuclei is itself a random variable. Experimental data comparing distributions of a cyclin mRNA that is conserved between brewer's yeast and a closely related filamentous fungus Ashbya gossypii confirm that syncytism is permissive of greater levels of transcriptional noise. Our findings suggest that division of transcriptional labor between nuclei allows syncytia to sidestep tradeoffs between efficiency and precision of gene expression.

Reduction in metabolic noise reveals rejuvenation following transient severe caloric restriction

Among land vertebrates, the laying hen stands out due to its great reproductive efficiency: producing an egg daily all year long. This production rate makes the laying hen a special model animal to study the general process of reproduction and aging. One unique aspect of hens is their ability to undergo reproductive plasticity and to rejuvenate their reproductive tract during molting, a standard industrial feed restriction protocol for transiently pausing reproduction, followed by improved laying efficiency almost to peak production. Here we use longitudinal metabolomics, immunology, and physiological assays to show that molting promotes reproduction, compresses morbidity, and restores youthfulness when applied to old hens. We identified circulating metabolic biomarkers that quantitatively predict the reproduction and age of individuals. Lastly, we introduce metabolic noise, a robust, unitless, and quantifiable measure for heterogeneity of the complete metabolome as a general marker that can indicate the rate of aging of a population. Indeed, metabolic noise increased with age in control hens, whereas molted hens exhibited reduced noise following molting, indicating systemic rejuvenation. Our results suggest that metabolic noise can be used as a quick and universal proxy for assessing successful aging treatments, accelerating the timeline for drug development.

A dual fluorescence protein expression system detects cell cycle dependent protein noise

Inherently identical cells exhibit significant phenotypic variation. It can be essential for many biological processes and is known to arise from stochastic, 'noisy', gene expression that is determined by intrinsic and extrinsic components. It is now obvious that the noise varies as a function of inducer concentration. However, its fluctuation over the cell cycle is limited. Applying dual colour fluorescence protein reporter system, Cyan Fluorescent Protein (CFP) and Yellow fluorescent protein (YFP) tagged multi-copy plasmids, we determine variation of the noise components over the phases in lac promoter induced by Isopropyl β-D-1-thiogalactopyranoside (IPTG) and in presence of additional Magnesium, Mg2+ ion. We, also, estimate the how such system deviates from observations of single-copy plasmid. Found 25 % difference between multi-copy system and single-copy system clarifies that observed noise is considerable and estimates population behaviour during the cell cycle. We show that total variation in cells induced with IPTG is determined by higher extrinsic than intrinsic noise. It increases from Lag to Exponential phase and decreases from Retardation to Stationary phase. By observing slow and fast dividing cells, we show that 5 mM Mg2+ increases population homogeneity compared to 2.5 mM Mg2+ in the environment. The experimental data obtained using dual colour fluorescence protein reporter system demonstrates that protein expression noise, depending on intra cellular ionic concentration, is tightly controlled by phase of the cell.

Phenotypic Patterning through Copy Number Adaptation to Environmental Gradients

We recently described a paradigm for engineering bacterial adaptation using plasmids coupled to the same origin of replication. In this study, we use plasmid coupling to generate spatially separated and phenotypically distinct populations in response to heterogeneous environments. Using a custom microfluidic device, we continuously tracked engineered populations along induced gradients, enabling an in-depth analysis of the spatiotemporal dynamics of plasmid coupling. Our observations reveal a pronounced phenotypic separation within 4 h exposure to an opposing gradient of AHL and arabinose. Additionally, by modulating the burden strength balance between coupled plasmids, we demonstrate the inherent limitations and tunability of this system. Intriguingly, phenotypic separation persists for an extended time, hinting at a biophysical spatial retention mechanism reminiscent of natural speciation processes. Complementing our experimental data, mathematical models provide invaluable insights into the underlying mechanisms and guide optimization of plasmid coupling for prospective applications of environmental copy number adaptation engineering across separated domains.

Design patterns of biological cells

Design patterns are generalized solutions to frequently recurring problems. They were initially developed by architects and computer scientists to create a higher level of abstraction for their designs. Here, we extend these concepts to cell biology to lend a new perspective on the evolved designs of cells' underlying reaction networks. We present a catalog of 21 design patterns divided into three categories: creational patterns describe processes that build the cell, structural patterns describe the layouts of reaction networks, and behavioral patterns describe reaction network function. Applying this pattern language to the E. coli central metabolic reaction network, the yeast pheromone response signaling network, and other examples lends new insights into these systems.

The interplay between metabolic stochasticity and cAMP-CRP regulation in single E. coli cells

The inherent stochasticity of metabolism raises a critical question for understanding homeostasis: are cellular processes regulated in response to internal fluctuations? Here, we show that, in E. coli cells under constant external conditions, catabolic enzyme expression continuously responds to metabolic fluctuations. The underlying regulatory feedback is enabled by the cyclic AMP (cAMP) and cAMP receptor protein (CRP) system, which controls catabolic enzyme expression based on metabolite concentrations. Using single-cell microscopy, genetic constructs in which this feedback is disabled, and mathematical modeling, we show how fluctuations circulate through the metabolic and genetic network at sub-cell-cycle timescales. Modeling identifies four noise propagation modes, including one specific to CRP regulation. Together, these modes correctly predict noise circulation at perturbed cAMP levels. The cAMP-CRP system may thus have evolved to control internal metabolic fluctuations in addition to external growth conditions. We conjecture that second messengers may more broadly function to achieve cellular homeostasis.

Quantifying dynamic pro-inflammatory gene expression and heterogeneity in single macrophage cells

Macrophages must respond appropriately to pathogens and other pro-inflammatory stimuli in order to perform their roles in fighting infection. One way in which inflammatory stimuli can vary is in their dynamics-that is, the amplitude and duration of stimulus experienced by the cell. In this study, we performed long-term live cell imaging in a microfluidic device to investigate how the pro-inflammatory genes IRF1, CXCL10, and CXCL9 respond to dynamic interferon-gamma (IFNγ) stimulation. We found that IRF1 responds to low concentration or short duration IFNγ stimulation, whereas CXCL10 and CXCL9 require longer or higherconcentration stimulation to be expressed. We also investigated the heterogeneity in the expression of each gene and found that CXCL10 and CXCL9 have substantial cell-to-cell variability. In particular, the expression of CXCL10 appears to be largely stochastic with a subpopulation of nonresponding cells across all the stimulation conditions tested. We developed both deterministic and stochastic models for the expression of each gene. Our modeling analysis revealed that the heterogeneity in CXCL10 can be attributed to a slow chromatin-opening step that is on a similar timescale to that of adaptation of the upstream signal. In this way, CXCL10 expression in individual cells can remain stochastic in response to each pulse of repeated stimulation, which we also validated by experiments. Together, we conclude that pro-inflammatory genes in the same signaling pathway can respond to dynamic IFNγ stimulus with very different response features and that upstream signal adaptation can contribute to shaping heterogeneous gene expression.

Transcriptional Stochasticity as a Key Aspect of HIV-1 Latency

This review summarizes current advances in the role of transcriptional stochasticity in HIV-1 latency, which were possible in a large part due to the development of single-cell approaches. HIV-1 transcription proceeds in bursts of RNA production, which stem from the stochastic switching of the viral promoter between ON and OFF states. This switching is caused by random binding dynamics of transcription factors and nucleosomes to the viral promoter and occurs at several time scales from minutes to hours. Transcriptional bursts are mainly controlled by the core transcription factors TBP, SP1 and NF-κb, the chromatin status of the viral promoter and RNA polymerase II pausing. In particular, spontaneous variability in the promoter chromatin creates heterogeneity in the response to activators such as TNF-α, which is then amplified by the Tat feedback loop to generate high and low viral transcriptional states. This phenomenon is likely at the basis of the partial and stochastic response of latent T cells from HIV-1 patients to latency-reversing agents, which is a barrier for the development of shock-and-kill strategies of viral eradication. A detailed understanding of the transcriptional stochasticity of HIV-1 and the possibility to precisely model this phenomenon will be important assets to develop more effective therapeutic strategies.

Escherichia coli cells are primed for survival before lethal antibiotic stress

Non-genetic factors can cause significant fluctuations in gene expression levels. Regardless of growing in a stable environment, this fluctuation leads to cell-to-cell variability in an isogenic population. This phenotypic heterogeneity allows a tiny subset of bacterial cells in a population called persister cells to tolerate long-term lethal antibiotic effects by entering into a non-dividing, metabolically repressed state. We occasionally noticed a high variation in persister levels, and to explore this, we tested clonal populations starting from a single cell using a modified Luria-Delbrück fluctuation test. Although we kept the conditions same, the diversity in persistence level among clones was relatively consistent: varying from ~60- to 100- and ~40- to 70-fold for ampicillin and apramycin, respectively. Then, we divided and diluted each clone to observe whether the same clone had comparable persister levels for more than one generation. Replicates had similar persister levels even when clones were divided, diluted by 1:20, and allowed to grow for approximately five generations. This result explicitly shows a cellular memory passed on for generations and eventually lost when cells are diluted to 1:100 and regrown (>seven generations). Our result demonstrates (1) the existence of a small population prepared for stress ("primed cells") resulting in higher persister numbers; (2) the primed memory state is reproducible and transient, passed down for generations but eventually lost; and (3) a heterogeneous persister population is a result of a transiently primed reversible cell state and not due to a pre-existing genetic mutation. IMPORTANCE Antibiotics have been highly effective in treating lethal infectious diseases for almost a century. However, the increasing threat of antibiotic resistance is again causing these diseases to become life-threatening. The longer a bacteria can survive antibiotics, the more likely it is to develop resistance. Complicating matters is that non-genetic factors can allow bacterial cells with identical DNA to gain transient resistance (also known as persistence). Here, we show that a small fraction of the bacterial population called primed cells can pass down non-genetic information ("memory") to their offspring, enabling them to survive lethal antibiotics for a long time. However, this memory is eventually lost. These results demonstrate how bacteria can leverage differences among genetically identical cells formed through non-genetic factors to form primed cells with a selective advantage to survive antibiotics.

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.

Out-of-equilibrium gene expression fluctuations in the presence of extrinsic noise

Cell-to-cell variability in protein concentrations is strongly affected by extrinsic noise, especially for highly expressed genes. Extrinsic noise can be due to fluctuations of several possible cellular factors connected to cell physiology and to the level of key enzymes in the expression process. However, how to identify the predominant sources of extrinsic noise in a biological system is still an open question. This work considers a general stochastic model of gene expression with extrinsic noise represented as fluctuations of the different model rates, and focuses on the out-of-equilibrium expression dynamics. Combining analytical calculations with stochastic simulations, we characterize how extrinsic noise shapes the protein variability during gene activation or inactivation, depending on the prevailing source of extrinsic variability, on its intensity and timescale. In particular, we show that qualitatively different noise profiles can be identified depending on which are the fluctuating parameters. This indicates an experimentally accessible way to pinpoint the dominant sources of extrinsic noise using time-coarse experiments.

The one-message-per-cell-cycle rule: A conserved minimum transcription level for essential genes

The inherent stochasticity of cellular processes leads to significant cell-to-cell variation in protein abundance. Although this noise has already been characterized and modeled, its broader implications and significance remain unclear. In this paper, we revisit the noise model and identify the number of messages transcribed per cell cycle as the critical determinant of noise. In yeast, we demonstrate that this quantity predicts the non-canonical scaling of noise with protein abundance, as well as quantitatively predicting its magnitude. We then hypothesize that growth robustness requires an upper ceiling on noise for the expression of essential genes, corresponding to a lower floor on the transcription level. We show that just such a floor exists: a minimum transcription level of one message per cell cycle is conserved between three model organisms: Escherichia coli, yeast, and human. Furthermore, all three organisms transcribe the same number of messages per gene, per cell cycle. This common transcriptional program reveals that robustness to noise plays a central role in determining the expression level of a large fraction of essential genes, and that this fundamental optimal strategy is conserved from E. coli to human cells.

Multidimensional characterization of inducible promoters and a highly light-sensitive LOV-transcription factor

The ability to independently control the expression of different genes is important for quantitative biology. Using budding yeast, we characterize GAL1pr, GALL, MET3pr, CUP1pr, PHO5pr, tetOpr, terminator-tetOpr, Z₃EV, blue-light inducible optogenetic systems El222-LIP, El222-GLIP, and red-light inducible PhyB-PIF3. We report kinetic parameters, noise scaling, impact on growth, and the fundamental leakiness of each system using an intuitive unit, maxGAL1. We uncover disadvantages of widely used tools, e.g., nonmonotonic activity of MET3pr and GALL, slow off kinetics of the doxycycline- and estradiol-inducible systems tetOpr and Z₃EV, and high variability of PHO5pr and red-light activated PhyB-PIF3 system. We introduce two previously uncharacterized systems: strongLOV, a more light-sensitive El222 mutant, and ARG3pr, which is induced in the absence of arginine or presence of methionine. To demonstrate fine control over gene circuits, we experimentally tune the time between cell cycle Start and mitosis, artificially simulating near-wild-type timing. All strains, constructs, code, and data ( https://promoter-benchmark.epfl.ch/ ) are made available.

Memory of stochastic single-cell apoptotic signaling promotes chemoresistance in neuroblastoma

Gene expression noise is known to promote stochastic drug resistance through the elevated expression of individual genes in rare cancer cells. However, we now demonstrate that chemoresistant neuroblastoma cells emerge at a much higher frequency when the influence of noise is integrated across multiple components of an apoptotic signaling network. Using a JNK activity biosensor with longitudinal high-content and in vivo intravital imaging, we identify a population of stochastic, JNK-impaired, chemoresistant cells that exist because of noise within this signaling network. Furthermore, we reveal that the memory of this initially random state is retained following chemotherapy treatment across a series of in vitro, in vivo, and patient models. Using matched PDX models established at diagnosis and relapse from individual patients, we show that HDAC inhibitor priming cannot erase the memory of this resistant state within relapsed neuroblastomas but improves response in the first-line setting by restoring drug-induced JNK activity within the chemoresistant population of treatment-naïve tumors.

Understanding the Stringent Response: Experimental Context Matters

As rapidly growing bacteria begin to exhaust essential nutrients, they enter a state of reduced growth, ultimately leading to stasis or quiescence. Investigation of the response to nutrient limitation has focused largely on the consequences of amino acid starvation, known as the "stringent response." Here, an uncharged tRNA in the A-site of the ribosome stimulates the ribosome-associated protein RelA to synthesize the hyperphosphorylated guanosine nucleotides (p)ppGpp that mediate a global slowdown of growth and biosynthesis. Investigations of the stringent response typically employ experimental methodologies that rapidly stimulate (p)ppGpp synthesis by abruptly increasing the fraction of uncharged tRNAs, either by explicit amino starvation or by inhibition of tRNA charging. Consequently, these methodologies inhibit protein translation, thereby interfering with the cellular pathways that respond to nutrient limitation. Thus, complete and/or rapid starvation is a problematic experimental paradigm for investigating bacterial responses to physiologically relevant nutrient-limited states.

Enhanced Transcriptional Strength of HIV-1 Subtype C Minimizes Gene Expression Noise and Confers Stability to the Viral Latent State

Stochastic fluctuations in gene expression emanating from the HIV-1 long terminal repeat (LTR), amplified by the Tat positive feedback circuit, determine the choice between viral infection fates: active transcription (ON) or transcriptional silence (OFF). The emergence of several transcription factor binding site (TFBS) variant strains in HIV-1 subtype C (HIV-1C), especially those containing the duplication of the NF-κB motif, mandates the evaluation of the effect of enhanced transcriptional strength on gene expression noise and its influence on viral fate selection switch. Using a panel of subgenomic LTR-variant strains containing different copy numbers of the NF-κB motif (ranging from 0 to 4), we used flow cytometry, mRNA quantification, and pharmacological perturbations to demonstrate an inverse correlation between promoter strength and gene expression noise in Jurkat T cells and primary CD4⁺ T cells. The inverse correlation is consistent in clonal cell populations at constant intracellular concentrations of Tat and when NF-κB levels were regulated pharmacologically. Further, we show that strong LTRs containing at least two copies of the NF-κB motif in the enhancer establish a more stable latent state and demonstrate more rapid latency reversal than weak LTRs containing fewer motifs. We also demonstrate a cooperative binding of NF-κB to the motif cluster in HIV-1C LTRs containing two, three, or four NF-κB motifs (Hill coefficient [H] = 2.61, 3.56, and 3.75, respectively). The present work alludes to a possible evolution of the HIV-1C LTR toward gaining transcriptional strength associated with attenuated gene expression noise with implications for viral latency. IMPORTANCE Over the past two consecutive decades, HIV-1 subtype C (HIV-1C) has been undergoing directional evolution toward augmenting the transcriptional strength of the long terminal repeat (LTR) by adding more copies of the existing transcription factor binding site (TFBS) by sequence duplication. Additionally, the duplicated elements are genetically diverse, suggesting broader-range signal receptivity by variant LTRs. The HIV-1 promoter is inherently noisy, and the stochastic fluctuations in gene expression of variant LTRs may influence the active transcription (ON)/transcriptional silence (OFF) latency decisions. The evolving NF-κB motif variations of HIV-1C offer a powerful opportunity to examine how the transcriptional strength of the LTR might influence gene expression noise. Our work here shows that the augmented transcriptional strength of the HIV-1C LTR leads to concomitantly reduced gene expression noise, consequently leading to stabler latency maintenance and rapid latency reversal. The present work offers a novel lead toward appreciating the molecular mechanisms governing HIV-1 latency.

Compartmentalized Cell-Free Expression Systems for Building Synthetic Cells

One of the grand challenges in bottom-up synthetic biology is the design and construction of synthetic cellular systems. One strategy toward this goal is the systematic reconstitution of biological processes using purified or non-living molecular components to recreate specific cellular functions such as metabolism, intercellular communication, signal transduction, and growth and division. Cell-free expression systems (CFES) are in vitro reconstitutions of the transcription and translation machinery found in cells and are a key technology for bottom-up synthetic biology. The open and simplified reaction environment of CFES has helped researchers discover fundamental concepts in the molecular biology of the cell. In recent decades, there has been a drive to encapsulate CFES reactions into cell-like compartments with the aim of building synthetic cells and multicellular systems. In this chapter, we discuss recent progress in compartmentalizing CFES to build simple and minimal models of biological processes that can help provide a better understanding of the process of self-assembly in molecularly complex systems.

Genome-Wide Analysis of Gene Expression Noise Brought About by Transcriptional Regulation in Pseudomonas aeruginosa

The part of expression noise that is brought about by transcriptional regulation (represented here as NTR) is an important criterion for estimating the regulatory mode of a gene. However, characterization of NTR is an under-explored area, and there is little knowledge regarding the genome-wide NTR in the model pathogen Pseudomonas aeruginosa. Here, with a library of dual-color transcriptional reporters, we estimated the NTR for over 90% of the promoters in P. aeruginosa. Most promoters exhibit low NTR, while 42 and 115 promoters with high NTR were screened out in the exponential and the stationary growth phases, respectively. Specifically, a rearrangement of NTR was found in promoters involved in amino acid metabolism when bacteria enter the exponential phase. In addition, during the stationary phase, high NTR was found in a wide range of iron-related promoters involving siderophore synthesis and heme uptake, ExsA-regulated promoters involving bacterial virulence, and FleQ-regulated promoters involving biofilm development. We also found a large-scale negative dependence of transcriptional regulation between high-NTR promoters belonging to different functional categories. Our findings offer a global view of transcriptional heterogeneity in P. aeruginosa. IMPORTANCE The phenotypic diversity of Pseudomonas aeruginosa is frequently observed in research, suggesting that bacteria adopt strategies such as bet-hedging to survive ever-changing environments. Gene expression noise (GEN) is the major source of phenotypic diversity. Large GEN from transcriptional regulation (represented as NTR) represent an evolutionary necessity to maintain the copy number diversity of certain proteins in the population. Here, we provide a system-wide view of NTR in P. aeruginosa under nutrient-rich and stressed conditions. High NTR was found in genes involved in flagella biosynthesis and amino acid metabolism under both conditions. Specially, iron acquisition genes exhibited high NTR in the stressed condition, suggesting a great diversity of iron physiology in P. aeruginosa. We further revealed a global negative dependence of transcriptional regulation between those high-NTR genes under the stressed condition, suggesting a mutually exclusive relationship between different bacterial survival strategies.

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.

Dynamic and single cell characterization of a CRISPR-interference toolset in Pseudomonas putida KT2440 for β-ketoadipate production from p-coumarate

Pseudomonas putida KT2440 is a well-studied bacterium for the conversion of lignin-derived aromatic compounds to bioproducts. The development of advanced genetic tools in P. putida has reduced the turnaround time for hypothesis testing and enabled the construction of strains capable of producing various products of interest. Here, we evaluate an inducible CRISPR-interference (CRISPRi) toolset on fluorescent, essential, and metabolic targets. Nuclease-deficient Cas9 (dCas9) expressed with the arabinose (8K)-inducible promoter was shown to be tightly regulated across various media conditions and when targeting essential genes. In addition to bulk growth data, single cell time lapse microscopy was conducted, which revealed intrinsic heterogeneity in knockdown rate within an isoclonal population. The dynamics of knockdown were studied across genomic targets in exponentially-growing cells, revealing a universal 1.75 ± 0.38 h quiescent phase after induction where 1.5 ± 0.35 doublings occur before a phenotypic response is observed. To demonstrate application of this CRISPRi toolset, β-ketoadipate, a monomer for performance-advantaged nylon, was produced at a 4.39 ± 0.5 g/L and yield of 0.76 ± 0.10 mol/mol from p-coumarate, a hydroxycinnamic acid that can be derived from grasses. These cultivation metrics were achieved by using the higher strength IPTG (1K)-inducible promoter to knockdown the pcaIJ operon in the βKA pathway during early exponential phase. This allowed the majority of the carbon to be shunted into the desired product while eliminating the need for a supplemental carbon and energy source to support growth and maintenance.

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Developed an inducible dCas9-based CRISPR interference toolset in Pseudomonas putida KT2440.

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Characterized single-cell dynamics of fluorescent and essential gene knockdown.

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Applied the toolset for glucose-free production of β-ketoadipate from p-coumarate.

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Produced β-ketoadipate at titer of 4.39 ± 0.5 g/L and 0.76 ± 0.10 mol/mol yield.

Stochastic dynamics of Type-I interferon responses

Interferon (IFN) activates the transcription of several hundred of IFN stimulated genes (ISGs) that constitute a highly effective antiviral defense program. Cell-to-cell variability in the induction of ISGs is well documented, but its source and effects are not completely understood. The molecular mechanisms behind this heterogeneity have been related to randomness in molecular events taking place during the JAK-STAT signaling pathway. Here, we study the sources of variability in the induction of the IFN-alpha response by using MxA and IFIT1 activation as read-out. To this end, we integrate time-resolved flow cytometry data and stochastic modeling of the JAK-STAT signaling pathway. The complexity of the IFN response was matched by fitting probability distributions to time-course flow cytometry snapshots. Both, experimental data and simulations confirmed that the MxA and IFIT1 induction circuits generate graded responses rather than all-or-none responses. Subsequently, we quantify the size of the intrinsic variability at different steps in the pathway. We found that stochastic effects are transiently strong during the ligand-receptor activation steps and the formation of the ISGF3 complex, but negligible for the final induction of the studied ISGs. We conclude that the JAK-STAT signaling pathway is a robust biological circuit that efficiently transmits information under stochastic environments.

We investigate the impact of intrinsic and extrinsic noise on the reliability of interferon signaling. Information must be transduced robustly despite existing biochemical variability and at the same time the system has to allow for cellular variability to tune it against changing environments. Getting insights into stochasticity in signaling networks is crucial to understand cellular dynamics and decision-making processes. To this end, we developed a detailed stochastic computational model based on single cell data. We are able to show that reliability is achieved despite high noise at the receptor level.

Non-genetic resistance facilitates survival while hindering the evolution of drug resistance due to intraspecific competition

Rising rates of resistance to antimicrobial drugs threaten the effective treatment of infections across the globe. Drug resistance has been established to emerge from non-genetic mechanisms as well as from genetic mechanisms. However, it is still unclear how non-genetic resistance affects the evolution of genetic drug resistance. We develop deterministic and stochastic population models that incorporate resource competition to quantitatively investigate the transition from non-genetic to genetic resistance during the exposure to static and cidal drugs. We find that non-genetic resistance facilitates the survival of cell populations during drug treatment while hindering the development of genetic resistance due to competition between the non-genetically and genetically resistant subpopulations. Non-genetic resistance in the presence of subpopulation competition increases the fixation times of drug resistance mutations, while increasing the probability of mutation before population extinction during cidal drug treatment. Intense intraspecific competition during drug treatment leads to extinction of susceptible and non-genetically resistant subpopulations. Alternating between drug and no drug conditions results in oscillatory population dynamics, increased resistance mutation fixation timescales, and reduced population survival. These findings advance our fundamental understanding of the evolution of resistance and may guide novel treatment strategies for patients with drug-resistant infections.

Alteration of DNA supercoiling serves as a trigger of short-term cold shock repressed genes of E. coli

Cold shock adaptability is a key survival skill of gut bacteria of warm-blooded animals. Escherichia coli cold shock responses are controlled by a complex multi-gene, timely-ordered transcriptional program. We investigated its underlying mechanisms. Having identified short-term, cold shock repressed genes, we show that their responsiveness is unrelated to their transcription factors or global regulators, while their single-cell protein numbers' variability increases after cold shock. We hypothesized that some cold shock repressed genes could be triggered by high propensity for transcription locking due to changes in DNA supercoiling (likely due to DNA relaxation caused by an overall reduction in negative supercoiling). Concomitantly, we found that nearly half of cold shock repressed genes are also highly responsive to gyrase inhibition (albeit most genes responsive to gyrase inhibition are not cold shock responsive). Further, their response strengths to cold shock and gyrase inhibition correlate. Meanwhile, under cold shock, nucleoid density increases, and gyrases and nucleoid become more colocalized. Moreover, the cellular energy decreases, which may hinder positive supercoils resolution. Overall, we conclude that sensitivity to diminished negative supercoiling is a core feature of E. coli's short-term, cold shock transcriptional program, and could be used to regulate the temperature sensitivity of synthetic circuits.

Mathematical modelling of activation-induced heterogeneity in TNF, IL6, NOS2, and IL1β expression reveals cell state transitions underpinning macrophage responses to LPS

Background: Despite extensive work on macrophage heterogeneity, the mechanisms driving activation induced heterogeneity (AIH) in macrophages remain poorly understood. Here, we aimed to develop mathematical models to explore theoretical cellular states underpinning the empirically observed responses of macrophages following lipopolysaccharide (LPS) challenge.

Methods: We obtained empirical data following primary and secondary responses to LPS in two in vitro cellular models (bone marrow-derived macrophages or BMDMs, and RAW 264.7 cells) and single-cell protein measurements for four key inflammatory mediators: TNF, IL-6, pro-IL-1β, and NOS2, and used mathematical modelling to understand heterogeneity.

Results: For these four factors, we showed that macrophage community AIH is dependent on LPS dose and that altered AIH kinetics in macrophages responding to a second LPS challenge underpin hypo-responsiveness to LPS. These empirical data can be explained by a mathematical three-state model including negative, positive, and non-responsive states (NRS), but they are also compatible with a four-state model that includes distinct reversibly NRS and non-responsive permanently states (NRPS). Our mathematical model, termed NoRM (Non-Responsive Macrophage) model identifies similarities and differences between BMDM and RAW 264.7 cell responses. In both cell types, transition rates between states in the NoRM model are distinct for each of the tested proteins and, crucially, macrophage hypo-responsiveness is underpinned by changes in transition rates to and from NRS.

Conclusions: Overall, we provide a mathematical model for studying macrophage ecology and community dynamics that can be used to elucidate the role of phenotypically negative macrophage populations in AIH and, primary and secondary responses to LPS.

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.

Noise reduction by upstream open reading frames

Gene expression is prone to burst production, making it a highly noisy process that requires additional controls. Upstream open reading frames (uORFs) are widely present in the 5' leader sequences of 30-50% of eukaryotic messenger RNAs^1-3. The translation of uORFs can repress the translation efficiency of the downstream main coding sequences. Whether the low translation efficiency leads to a different variation, or noise, in gene expression has not been investigated, nor has the direct biological impact of uORF-repressed translation. Here we show that uORFs achieve low but precise protein production in plant cells, possibly by reducing the protein production rate. We also demonstrate that, by buffering a stable TIMING OF CAB EXPRESSION 1 (TOC1) protein production level, uORFs contribute to the robust operation of the plant circadian clock. Our results provide both an action model and the biological impact of uORFs in translational control to mitigate transcriptional noise for precise protein production.

Stochastic Emergence of Two Distinct Self-Replicators from a Dynamic Combinatorial Library

Unraveling how chemistry can give rise to biology is one of the greatest challenges of contemporary science. Achieving life-like properties in chemical systems is therefore a popular topic of research. Synthetic chemical systems are usually deterministic: the outcome is determined by the experimental conditions. In contrast, many phenomena that occur in nature are not deterministic but caused by random fluctuations (stochastic). Here, we report on how, from a mixture of two synthetic molecules, two different self-replicators emerge in a stochastic fashion. Under the same experimental conditions, the two self-replicators are formed in various ratios over several repeats of the experiment. We show that this variation is caused by a stochastic nucleation process and that this stochasticity is more pronounced close to a phase boundary. While stochastic nucleation processes are common in crystal growth and chiral symmetry breaking, it is unprecedented for systems of synthetic self-replicators.

Dynamic gene expression and growth underlie cell-to-cell heterogeneity in Escherichia coli stress response

Individual bacteria that share identical genomes and growth environments can display substantial cell-to-cell differences in expression of stress-response genes and single-cell growth rates. This phenotypic heterogeneity can impact the survival of single cells facing sudden stress. However, the windows of time that cells spend in vulnerable or tolerant states are often unknown. We quantify the temporal expression of a suite of stress-response reporters, while simultaneously monitoring growth. We observe pulsatile expression across genes with a range of stress-response functions, finding that single-cell growth rates are often anticorrelated with reporter levels. These dynamic phenotypic differences have a concrete link to function, in which individual cells undergoing a pulse of elevated expression and slow growth are predisposed to survive antibiotic exposure.

Cell-to-cell heterogeneity in gene expression and growth can have critical functional consequences, such as determining whether individual bacteria survive or die following stress. Although phenotypic variability is well documented, the dynamics that underlie it are often unknown. This information is important because dramatically different outcomes can arise from gradual versus rapid changes in expression and growth. Using single-cell time-lapse microscopy, we measured the temporal expression of a suite of stress-response reporters in Escherichia coli, while simultaneously monitoring growth rate. In conditions without stress, we found several examples of pulsatile expression. Single-cell growth rates were often anticorrelated with reporter levels, with changes in growth preceding changes in expression. These dynamics have functional consequences, which we demonstrate by measuring survival after challenging cells with the antibiotic ciprofloxacin. Our results suggest that fluctuations in both gene expression and growth dynamics in stress-response networks have direct consequences on survival.

Sequence-dependent model of genes with dual σ factor preference

Escherichia coli uses σ factors to quickly control large gene cohorts during stress conditions. While most of its genes respond to a single σ factor, approximately 5% of them have dual σ factor preference. The most common are those responsive to both σ⁷⁰, which controls housekeeping genes, and σ³⁸, which activates genes during stationary growth and stresses. Using RNA-seq and flow-cytometry measurements, we show that 'σ⁷⁰⁺³⁸ genes' are nearly as upregulated in stationary growth as 'σ³⁸ genes'. Moreover, we find a clear quantitative relationship between their promoter sequence and their response strength to changes in σ³⁸ levels. We then propose and validate a sequence dependent model of σ⁷⁰⁺³⁸ genes, with dual sensitivity to σ³⁸ and σ⁷⁰, that is applicable in the exponential and stationary growth phases, as well in the transient period in between. We further propose a general model, applicable to other stresses and σ factor combinations. Given this, promoters controlling σ⁷⁰⁺³⁸ genes (and variants) could become important building blocks of synthetic circuits with predictable, sequence-dependent sensitivity to transitions between the exponential and stationary growth phases.

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.

Mathematical modelling of activation-induced heterogeneity in TNF, IL6, NOS2, and IL1β expression reveals cell state transitions underpinning macrophage responses to LPS

Background: Despite extensive work on macrophage heterogeneity, the mechanisms driving activation induced heterogeneity (AIH) in macrophages remain poorly understood. Here, we aimed to develop mathematical models to explore theoretical cellular states underpinning the empirically observed responses of macrophages following lipopolysaccharide (LPS) challenge. Methods: We obtained empirical data following primary and secondary responses to LPS in two in vitro cellular models (bone marrow-derived macrophages or BMDMs, and RAW 264.7 cells) and single-cell protein measurements for four key inflammatory mediators: TNF, IL-6, pro-IL-1β, and NOS2, and used mathematical modelling to understand heterogeneity. Results: For these four factors, we showed that macrophage community AIH is dependent on LPS dose and that altered AIH kinetics in macrophages responding to a second LPS challenge underpin hypo-responsiveness to LPS. These empirical data can be explained by a mathematical three-state model including negative, positive, and non-responsive states (NRS), but they are also compatible with a four-state model that includes distinct reversibly NRS and non-responsive permanently states (NRPS). Our mathematical model, termed NoRM (Non-Responsive Macrophage) model identifies similarities and differences between BMDM and RAW 264.7 cell responses. In both cell types, transition rates between states in the NoRM model are distinct for each of the tested proteins and, crucially, macrophage hypo-responsiveness is underpinned by changes in transition rates to and from NRS. Conclusions: Overall, we provide a mathematical model for studying macrophage ecology and community dynamics that can be used to elucidate the role of phenotypically negative macrophage populations in AIH and, primary and secondary responses to LPS.

Analytical kinetic model of native tandem promoters in E. coli

Closely spaced promoters in tandem formation are abundant in bacteria. We investigated the evolutionary conservation, biological functions, and the RNA and single-cell protein expression of genes regulated by tandem promoters in E. coli. We also studied the sequence (distance between transcription start sites ‘d_TSS’, pause sequences, and distances from oriC) and potential influence of the input transcription factors of these promoters. From this, we propose an analytical model of gene expression based on measured expression dynamics, where RNAP-promoter occupancy times and d_TSS are the key regulators of transcription interference due to TSS occlusion by RNAP at one of the promoters (when d_TSS ≤ 35 bp) and RNAP occupancy of the downstream promoter (when d_TSS > 35 bp). Occlusion and downstream promoter occupancy are modeled as linear functions of occupancy time, while the influence of d_TSS is implemented by a continuous step function, fit to in vivo data on mean single-cell protein numbers of 30 natural genes controlled by tandem promoters. The best-fitting step is at 35 bp, matching the length of DNA occupied by RNAP in the open complex formation. This model accurately predicts the squared coefficient of variation and skewness of the natural single-cell protein numbers as a function of d_TSS. Additional predictions suggest that promoters in tandem formation can cover a wide range of transcription dynamics within realistic intervals of parameter values. By accurately capturing the dynamics of these promoters, this model can be helpful to predict the dynamics of new promoters and contribute to the expansion of the repertoire of expression dynamics available to synthetic genetic constructs.

Tandem promoters are common in nature, but investigations on their dynamics have so far largely relied on synthetic constructs. Thus, their regulation and potentially unique dynamics remain unexplored. We first performed a comprehensive exploration of the conservation of genes regulated by these promoters in E. coli and the properties of their input transcription factors. We then measured protein and RNA levels expressed by 30 Escherichia coli tandem promoters, to establish an analytical model of the expression dynamics of genes controlled by such promoters. We show that start site occlusion and downstream RNAP occupancy can be realistically captured by a model with RNAP binding affinity, the time length of open complex formation, and the nucleotide distance between transcription start sites. This study contributes to a better understanding of the unique dynamics tandem promoters can bring to the dynamics of gene networks and will assist in their use in synthetic genetic circuits.

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.

IκBα is required for full transcriptional induction of some NFκB-regulated genes in response to TNF in MCF-7 cells

Inflammatory stimuli triggers the degradation of three inhibitory κB (IκB) proteins, allowing for nuclear translocation of nuclear factor-κB (NFκB) for transcriptional induction of its target genes. Of these three, IκBα is a well-known negative feedback regulator that limits the duration of NFκB activity. We sought to determine whether IκBα's role in enabling or limiting NFκB activation is important for tumor necrosis factor (TNF)-induced gene expression in human breast cancer cells (MCF-7). Contrary to our expectations, many more TNF-response genes showed reduced induction than enhanced induction in IκBα knockdown cells. Mathematical modeling was used to investigate the underlying mechanism. We found that the reduced activation of some NFκB target genes in IκBα-deficient cells could be explained by the incoherent feedforward loop (IFFL) model. In addition, for a subset of genes, prolonged NFκB activity due to loss of negative feedback control did not prolong their transient activation; this implied a multi-state transcription cycle control of gene induction. Genes encoding key inflammation-related transcription factors, such as JUNB and KLF10, were found to be best represented by a model that contained both the IFFL and the transcription cycle motif. Our analysis sheds light on the regulatory strategies that safeguard inflammatory gene expression from overproduction and repositions the function of IκBα not only as a negative feedback regulator of NFκB but also as an enabler of NFκB-regulated stimulus-responsive inflammatory gene expression. This study indicates the complex involvement of IκBα in the inflammatory response to TNF that is induced by radiation therapy in breast cancer.

Independent control of mean and noise by convolution of gene expression distributions

Gene expression noise can reduce cellular fitness or facilitate processes such as alternative metabolism, antibiotic resistance, and differentiation. Unfortunately, efforts to study the impacts of noise have been hampered by a scaling relationship between noise and expression level from individual promoters. Here, we use theory to demonstrate that mean and noise can be controlled independently by expressing two copies of a gene from separate inducible promoters in the same cell. We engineer low and high noise inducible promoters to validate this result in Escherichia coli, and develop a model that predicts the experimental distributions. Finally, we use our method to reveal that the response of a promoter to a repressor is less sensitive with higher repressor noise and explain this result using a law from probability theory. Our approach can be applied to investigate the effects of noise on diverse biological pathways or program cellular heterogeneity for synthetic biology applications.