Functional roles of pulsing in genetic circuits

A Statistical Approach Reveals Designs for the Most Robust Stochastic Gene Oscillators

The engineering of transcriptional networks presents many challenges due to the inherent uncertainty in the system structure, changing cellular context, and stochasticity in the governing dynamics. One approach to address these problems is to design and build systems that can function across a range of conditions; that is they are robust to uncertainty in their constituent components. Here we examine the parametric robustness landscape of transcriptional oscillators, which underlie many important processes such as circadian rhythms and the cell cycle, plus also serve as a model for the engineering of complex and emergent phenomena. The central questions that we address are: Can we build genetic oscillators that are more robust than those already constructed? Can we make genetic oscillators arbitrarily robust? These questions are technically challenging due to the large model and parameter spaces that must be efficiently explored. Here we use a measure of robustness that coincides with the Bayesian model evidence, combined with an efficient Monte Carlo method to traverse model space and concentrate on regions of high robustness, which enables the accurate evaluation of the relative robustness of gene network models governed by stochastic dynamics. We report the most robust two and three gene oscillator systems, plus examine how the number of interactions, the presence of autoregulation, and degradation of mRNA and protein affects the frequency, amplitude, and robustness of transcriptional oscillators. We also find that there is a limit to parametric robustness, beyond which there is nothing to be gained by adding additional feedback. Importantly, we provide predictions on new oscillator systems that can be constructed to verify the theory and advance design and modeling approaches to systems and synthetic biology.

Stochastic Simulation of Biomolecular Networks in Dynamic Environments

Simulation of biomolecular networks is now indispensable for studying biological systems, from small reaction networks to large ensembles of cells. Here we present a novel approach for stochastic simulation of networks embedded in the dynamic environment of the cell and its surroundings. We thus sample trajectories of the stochastic process described by the chemical master equation with time-varying propensities. A comparative analysis shows that existing approaches can either fail dramatically, or else can impose impractical computational burdens due to numerical integration of reaction propensities, especially when cell ensembles are studied. Here we introduce the Extrande method which, given a simulated time course of dynamic network inputs, provides a conditionally exact and several orders-of-magnitude faster simulation solution. The new approach makes it feasible to demonstrate—using decision-making by a large population of quorum sensing bacteria—that robustness to fluctuations from upstream signaling places strong constraints on the design of networks determining cell fate. Our approach has the potential to significantly advance both understanding of molecular systems biology and design of synthetic circuits.

Simulation algorithms have become indispensable tools in modern quantitative biology, providing deep insight into many biochemical systems, including gene regulatory networks. However, current stochastic simulation approaches handle the effects of fluctuating extracellular signals and upstream processes poorly, either failing to give qualitatively reliable predictions or being very inefficient computationally. Here we introduce the Extrande method, a novel approach for simulation of biomolecular networks embedded in the dynamic environment of the cell and its surroundings. The method is accurate and computationally efficient, and hence fills an important gap in the field of stochastic simulation. In particular, we employ it to study a bacterial decision-making network and demonstrate that robustness to fluctuations from upstream signaling places strong constraints on the design of networks determining cell fate.

Stepwise Progression of Embryonic Patterning

It is long established that the graded distribution of Dorsal transcription factor influences spatial domains of gene expression along the dorsoventral (DV) axis of Drosophila melanogaster embryos. However, the more recent realization that Dorsal levels also change with time raises the question of whether these dynamics are instructive. An overview of DV axis patterning is provided, focusing on new insights identified through quantitative analysis of temporal changes in Dorsal target gene expression from one nuclear cycle to the next ('steps'). Possible roles for the stepwise progression of this patterning program are discussed including (i) tight temporal regulation of signaling pathway activation, (ii) control of gene expression cohorts, and (iii) ensuring the irreversibility of the patterning and cell fate specification process.

Oscillatory control of Delta-like1 in cell interactions regulates dynamic gene expression and tissue morphogenesis

Shimojo et al. developed a live-imaging system and found that Notch ligand Delta-like1 (Dll1) protein expression oscillates in neural progenitors and presomitic mesoderm cells, and this regulates dynamic gene expression and tissue morphogenesis.

Notch signaling regulates tissue morphogenesis through cell–cell interactions. The Notch effectors Hes1 and Hes7 are expressed in an oscillatory manner and regulate developmental processes such as neurogenesis and somitogenesis, respectively. Expression of the mRNA for the mouse Notch ligand Delta-like1 (Dll1) is also oscillatory. However, the dynamics of Dll1 protein expression are controversial, and their functional significance is unknown. Here, we developed a live-imaging system and found that Dll1 protein expression oscillated in neural progenitors and presomitic mesoderm cells. Notably, when Dll1 expression was accelerated or delayed by shortening or elongating the Dll1 gene, Dll1 oscillations became severely dampened or quenched at intermediate levels, as modeled mathematically. Under this condition, Hes1 and Hes7 oscillations were also dampened. In the presomitic mesoderm, steady Dll1 expression led to severe fusion of somites and their derivatives, such as vertebrae and ribs. In the developing brain, steady Dll1 expression inhibited proliferation of neural progenitors and accelerated neurogenesis, whereas optogenetic induction of Dll1 oscillation efficiently maintained neural progenitors. These results indicate that the appropriate timing of Dll1 expression is critical for the oscillatory networks and suggest the functional significance of oscillatory cell–cell interactions in tissue morphogenesis.

Mechanisms of information decoding in a cascade system of gene expression

Biotechnology advances have allowed investigation of heterogeneity of cellular responses to stimuli on the single-cell level. Functionally, this heterogeneity can compromise cellular responses to environmental signals, and it can also enlarge the repertoire of possible cellular responses and hence increase the adaptive nature of cellular behaviors. However, the mechanism of how this response heterogeneity is generated remains elusive. Here, by systematically analyzing a representative cellular signaling system, we show that (1) the upstream activator always amplifies the downstream burst frequency (BF) but the noiseless activator performs better than the noisy one, remarkably for small or moderate input signal strengths, and the repressor always reduces the downstream BF but the difference in the reducing effect between noiseless and noise repressors is very small; (2) both the downstream burst size and mRNA mean are a monotonically increasing function of the activator strength but a monotonically decreasing function of the repressor strength; (3) for repressor-type input, there is a noisy signal strength such that the downstream mRNA noise arrives at an optimal level, but for activator-type input, the output noise intensity is fundamentally a monotonically decreasing function of the input strength. Our results reveal the essential mechanisms of both signal information decoding and cellular response heterogeneity, whereas our analysis provides a paradigm for analyzing dynamics of noisy biochemical signaling systems.

Transcription Dynamics in Living Cells

The transcription cycle can be roughly divided into three stages: initiation, elongation, and termination. Understanding the molecular events that regulate all these stages requires a dynamic view of the underlying processes. The development of techniques to visualize and quantify transcription in single living cells has been essential in revealing the transcription kinetics. They have revealed that (a) transcription is heterogeneous between cells and (b) transcription can be discontinuous within a cell. In this review, we discuss the progress in our quantitative understanding of transcription dynamics in living cells, focusing on all parts of the transcription cycle. We present the techniques allowing for single-cell transcription measurements, review evidence from different organisms, and discuss how these experiments have broadened our mechanistic understanding of transcription regulation.

Optogenetic Control of Calcium Oscillation Waveform Defines NFAT as an Integrator of Calcium Load

It is known that the calcium-dependent transcription factor NFAT initiates transcription in response to pulsatile loads of calcium signal. However, the relative contributions of calcium oscillation frequency, amplitude, and duty cycle to transcriptional activity remain unclear. Here, we engineer HeLa cells to permit optogenetic control of intracellular calcium concentration using programmable LED arrays. This approach allows us to generate calcium oscillations of constant peak amplitude, in which frequency is varied while holding duty cycle constant, or vice versa. Using this setup and mathematical modeling, we show that NFAT transcriptional activity depends more on duty cycle, defined as the proportion of the integrated calcium concentration over the oscillation period, than on frequency alone. This demonstrates that NFAT acts primarily as a signal integrator of cumulative load rather than a frequency-selective decoder. This approach resolves a fundamental question in calcium encoding and demonstrates the value of optogenetics for isolating individual dynamical components of larger signaling behaviors.

Fluctuations and synchrony of RNA synthesis in nucleoli

Ribosomal RNA (rRNA) sequences are synthesized at exceptionally high rates and, together with ribosomal proteins (r-proteins), are utilized as building blocks for the assembly of pre-ribosomal particles. Although it is widely acknowledged that tight regulation and coordination of rRNA and r-protein production are fundamentally important for the maintenance of cellular homeostasis, still little is known about the real-time kinetics of the ribosome component synthesis in individual cells. In this communication we introduce a label-free MicroRaman spectrometric approach for monitoring rRNA synthesis in live cultured cells. Remarkably high and rapid fluctuations of rRNA production rates were revealed by this technique. Strikingly, the changes in the rRNA output were synchronous for ribosomal genes located in separate nucleoli of the same cell. Our findings call for the development of new concepts to elucidate the coordination of ribosomal components production. In this regard, numerical modeling further demonstrated that the production of rRNA and r-proteins can be coordinated, regardless of the fluctuations in rRNA synthesis. Overall, our quantitative data reveal a spectacular interplay of inherently stochastic rates of RNA synthesis and the coordination of gene expression.

p53 Pulses Diversify Target Gene Expression Dynamics in an mRNA Half-Life-Dependent Manner and Delineate Co-regulated Target Gene Subnetworks

The transcription factor p53 responds to DNA double-strand breaks by increasing in concentration in a series of pulses of fixed amplitude, duration, and period. How p53 pulses influence the dynamics of p53 target gene expression is not understood. Here, we show that, in bulk cell populations, patterns of p53 target gene expression cluster into groups with stereotyped temporal behaviors, including pulsing and rising dynamics. These behaviors correlate statistically with the mRNA decay rates of target genes: short mRNA half-lives produce pulses of gene expression. This relationship can be recapitulated by mathematical models of p53-dependent gene expression in single cells and cell populations. Single-cell transcriptional profiling demonstrates that expression of a subset of p53 target genes is coordinated across time within single cells; p53 pulsing attenuates this coordination. These results help delineate how p53 orchestrates the complex DNA damage response and give insight into the function of pulsatile signaling pathways.

The mechanisms shaping the single-cell transcriptional landscape

Recent technological and computational advances in understanding the transcriptional and chromatin features of single cells have begun answering longstanding questions in the extent and impact of biological heterogeneity. Here, we outline the intrinsic and extrinsic mechanisms that underlie the transcriptional and functional diversity within superficially homogeneous populations, and we discuss how fascinating new studies have afforded novel insight into each mechanism. The studies are chosen in part to include initial reports of novel functional genomics tools where the eventual applications will clearly have profound impact on our understanding the dynamics of cell-to-cell transcriptional variation-from individual cells to whole organisms.

Coordination of cell decisions and promotion of phenotypic diversity in B. subtilis via pulsed behavior of the phosphorelay

The phosphorelay of Bacillus subtilis, a kinase cascade that activates master regulator Spo0A ~ P in response to starvation signals, is the core of a large network controlling the cell's decision to differentiate into sporulation and other phenotypes. This article reviews recent advances in understanding the origins and purposes of the complex dynamical behavior of the phosphorelay, which pulses with peaks of activity coordinated with the cell cycle. The transient imbalance in the expression of two critical genes caused by their strategic placement at opposing ends of the chromosome proved to be the key for this pulsed behavior. Feedback control loops in the phosphorelay use these pulses to implement a timer mechanism, which creates several windows of opportunity for phenotypic transitions over multiple generations. This strategy allows the cell to coordinate multiple differentiation programs in a decision process that fosters phenotypic diversity and adapts to current conditions.

Crossroad between linear and nonlinear transcription concepts in the discovery of next-generation sequencing systems-based anticancer therapies

The unprecedented potential of standard and new next-generation sequencing applications and methods to explore cancer genome evolution and tumor heterogeneity as well as transcription networks in time and space shapes the development of next-generation therapeutics. However, biomedical and pharmaceutical research for overcoming heterogeneity-based therapeutic resistance is at an important crossroads. Focus on linear transcription-based drug development targeting dynamics of simple intrapatient structured genome diversity represents a realistic medium-term goal. By contrast, the discovery of nonlinear transcription drugs for targeting structural and functional genome and transcriptome heterogeneity represents a long-term rational strategy. This review compares effectiveness, challenges and expectations between linear and nonlinear drugs targeting simple intrapatient variation and aberrant transcriptional biocircuits, respectively.

NF-κB oscillations translate into functionally related patterns of gene expression

Several transcription factors (TFs) oscillate, periodically relocating between the cytoplasm and the nucleus. NF-κB, which plays key roles in inflammation and cancer, displays oscillations whose biological advantage remains unclear. Recent work indicated that NF-κB displays sustained oscillations that can be entrained, that is, reach a persistent synchronized state through small periodic perturbations. We show here that for our GFP-p65 knock-in cells NF-κB behaves as a damped oscillator able to synchronize to a variety of periodic external perturbations with no memory. We imposed synchronous dynamics to prove that transcription of NF-κB-controlled genes also oscillates, but mature transcript levels follow three distinct patterns. Two sets of transcripts accumulate fast or slowly, respectively. Another set, comprising chemokine and chemokine receptor mRNAs, oscillates and resets at each new stimulus, with no memory of the past. We propose that TF oscillatory dynamics is a means of segmenting time to provide renewing opportunity windows for decision.

From noise to synthetic nucleoli: can synthetic biology achieve new insights?

Synthetic biology aims to re-organise and control biological components to make functional devices. Along the way, the iterative process of designing and testing gene circuits has the potential to yield many insights into the functioning of the underlying chassis of cells. Thus, synthetic biology is converging with disciplines such as systems biology and even classical cell biology, to give a new level of predictability to gene expression, cell metabolism and cellular signalling networks. This review gives an overview of the contributions that synthetic biology has made in understanding gene expression, in terms of cell heterogeneity (noise), the coupling of growth and energy usage to expression, and spatiotemporal considerations. We mainly compare progress in bacterial and mammalian systems, which have some of the most-developed engineering frameworks. Overall, one view of synthetic biology can be neatly summarised as "creating in order to understand."

Gene expression variability in clonal populations: Causes and consequences

During the last decade it has been shown that among cell variation in gene expression plays an important role within clonal populations. Here, we provide an overview of the different mechanisms contributing to gene expression variability in clonal populations. These are ranging from inherent variations in the biochemical process of gene expression itself, such as intrinsic noise, extrinsic noise and bistability to individual responses to variations in the local micro-environment, a phenomenon called phenotypic plasticity. Also genotypic variations caused by clonal evolution and phase variation can contribute to gene expression variability. Consequently, gene expression studies need to take these fluctuations in expression into account. However, frequently used techniques for expression quantification, such as microarrays, RNA sequencing, quantitative PCR and gene reporter fusions classically determine the population average of gene expression. Here, we discuss how these techniques can be adapted towards single cell analysis by integration with single cell isolation, RNA amplification and microscopy. Alternatively more qualitative selection-based techniques, such as mutant screenings, in vivo expression technology (IVET) and recombination-based IVET (RIVET) can be applied for detection of genes expressed only within a subpopulation. Finally, differential fluorescence induction (DFI), a protocol specially designed for single cell expression is discussed.

How to make an oscillator

A cell-free approach reveals how genetic circuits can produce robust oscillations of proteins and other components.

Evolving modular genetic regulatory networks with a recursive, top-down approach

Being able to design genetic regulatory networks (GRNs) to achieve a desired cellular function is one of the main goals of synthetic biology. However, determining minimal GRNs that produce desired time-series behaviors is non-trivial. In this paper, we propose a 'top-down' approach to evolving small GRNs and then use these to recursively boot-strap the identification of larger, more complex, modular GRNs. We start with relatively dense GRNs and then use differential evolution (DE) to evolve interaction coefficients. When the target dynamical behavior is found embedded in a dense GRN, we narrow the focus of the search and begin aggressively pruning out excess interactions at the end of each generation. We first show that the method can quickly rediscover known small GRNs for a toggle switch and an oscillatory circuit. Next we include these GRNs as non-evolvable subnetworks in the subsequent evolution of more complex, modular GRNs. Successful solutions found in canonical DE where we truncated small interactions to zero, with or without an interaction penalty term, invariably contained many excess interactions. In contrast, by incorporating aggressive pruning and the penalty term, the DE was able to find minimal or nearly minimal GRNs in all test problems.

Frequency modulation of ERK activation dynamics rewires cell fate

Transient versus sustained ERK MAP kinase (MAPK) activation dynamics induce proliferation versus differentiation in response to epidermal (EGF) or nerve (NGF) growth factors in PC‐12 cells. Duration of ERK activation has therefore been proposed to specify cell fate decisions. Using a biosensor to measure ERK activation dynamics in single living cells reveals that sustained EGF/NGF application leads to a heterogeneous mix of transient and sustained ERK activation dynamics in distinct cells of the population, different than the population average. EGF biases toward transient, while NGF biases toward sustained ERK activation responses. In contrast, pulsed growth factor application can repeatedly and homogeneously trigger ERK activity transients across the cell population. These datasets enable mathematical modeling to reveal salient features inherent to the MAPK network. Ultimately, this predicts pulsed growth factor stimulation regimes that can bypass the typical feedback activation to rewire the system toward cell differentiation irrespective of growth factor identity.

Know the Single-Receptor Sensing Limit? Think Again

How cells reliably infer information about their environment is a fundamentally important question. While sensing and signaling generally start with cell-surface receptors, the degree of accuracy with which a cell can measure external ligand concentration with even the simplest device-a single receptor-is surprisingly hard to pin down. Recent studies provide conflicting results for the fundamental physical limits. Comparison is made difficult as different studies either suggest different readout mechanisms of the ligand-receptor occupancy, or differ on how ligand diffusion is implemented. Here we critically analyse these studies and present a unifying perspective on the limits of sensing, with wide-ranging biological implications.

Gene Expression Variability Underlies Adaptive Resistance in Phenotypically Heterogeneous Bacterial Populations

The root cause of the antibiotic resistance crisis is the ability of bacteria to evolve resistance to a multitude of antibiotics and other environmental toxins. The regulation of adaptation is difficult to pinpoint due to extensive phenotypic heterogeneity arising during evolution. Here, we investigate the mechanisms underlying general bacterial adaptation by evolving wild-type Escherichia coli populations to dissimilar chemical toxins. We demonstrate the presence of extensive inter- and intrapopulation phenotypic heterogeneity across adapted populations in multiple traits, including minimum inhibitory concentration, growth rate, and lag time. To search for a common response across the heterogeneous adapted populations, we measured gene expression in three stress-response networks: the mar regulon, the general stress response, and the SOS response. While few genes were differentially expressed, clustering revealed that interpopulation gene expression variability in adapted populations was distinct from that of unadapted populations. Notably, we observed both increases and decreases in gene expression variability upon adaptation. Sequencing select genes revealed that the observed gene expression trends are not necessarily attributable to genetic changes. To further explore the connection between gene expression variability and adaptation, we propagated single-gene knockout and CRISPR (clustered regularly interspaced short palindromic repeats) interference strains and quantified impact on adaptation to antibiotics. We identified significant correlations that suggest genes with low expression variability have greater impact on adaptation. This study provides evidence that gene expression variability can be used as an indicator of bacterial adaptive resistance, even in the face of the pervasive phenotypic heterogeneity underlying adaptation.

Intracellular Information Processing through Encoding and Decoding of Dynamic Signaling Features

Cell signaling dynamics and transcriptional regulatory activities are variable within specific cell types responding to an identical stimulus. In addition to studying the network interactions, there is much interest in utilizing single cell scale data to elucidate the non-random aspects of the variability involved in cellular decision making. Previous studies have considered the information transfer between the signaling and transcriptional domains based on an instantaneous relationship between the molecular activities. These studies predict a limited binary on/off encoding mechanism which underestimates the complexity of biological information processing, and hence the utility of single cell resolution data. Here we pursue a novel strategy that reformulates the information transfer problem as involving dynamic features of signaling rather than molecular abundances. We pursue a computational approach to test if and how the transcriptional regulatory activity patterns can be informative of the temporal history of signaling. Our analysis reveals (1) the dynamic features of signaling that significantly alter transcriptional regulatory patterns (encoding), and (2) the temporal history of signaling that can be inferred from single cell scale snapshots of transcriptional activity (decoding). Immediate early gene expression patterns were informative of signaling peak retention kinetics, whereas transcription factor activity patterns were informative of activation and deactivation kinetics of signaling. Moreover, the information processing aspects varied across the network, with each component encoding a selective subset of the dynamic signaling features. We developed novel sensitivity and information transfer maps to unravel the dynamic multiplexing of signaling features at each of these network components. Unsupervised clustering of the maps revealed two groups that aligned with network motifs distinguished by transcriptional feedforward vs feedback interactions. Our new computational methodology impacts the single cell scale experiments by identifying downstream snapshot measures required for inferring specific dynamical features of upstream signals involved in the regulation of cellular responses.

Single cell studies have shown that differential patterns in the dynamics of signaling proteins, transcription factor activity, gene expression, etc. produce distinct downstream outcomes. The opposite also holds true where particular cellular outcomes have been found to be associated with the dynamical pattern of one or more signaling molecules. Signaling pathways, therefore, serve as signal processing units to inform specific downstream regulation. However, the functional capabilities of the dynamic aspects of signaling are not well understood. To address this issue, we developed a new approach that evaluates information processing between dynamic features in signaling patterns and transcriptional regulatory activity. Our work demonstrates that the information transfer occur through decoding of temporal history of signals rather than only through instantaneous correlations. Moreover, our results identify regulatory network motifs as the critical components in the information processing and filtering of variability in signaling dynamics to produce distinct patterns of downstream transcriptional responses. Our methodology can be broadly applied to single cell scale data on experimentally accessible downstream measures to infer dynamic aspects of upstream signaling.

Combinatorial gene regulation by modulation of relative pulse timing

Studies of individual living cells have revealed that many transcription factors activate in dynamic, and often stochastic, pulses within the same cell. However, it has remained unclear whether cells might modulate the relative timing of these pulses to control gene expression. Here, using quantitative single-cell time-lapse imaging of Saccharomyces cerevisiae, we show that the pulsatile transcription factors Msn2 and Mig1 combinatorially regulate their target genes through modulation of their relative pulse timing. The activator Msn2 and repressor Mig1 pulsed in either a temporally overlapping or non-overlapping manner during their transient response to different inputs, with only the non-overlapping dynamics efficiently activating target gene expression. Similarly, under constant environmental conditions, where Msn2 and Mig1 exhibit sporadic pulsing, glucose concentration modulated the temporal overlap between pulses of the two factors. Together, these results reveal a time-based mode of combinatorial gene regulation. Regulation through relative signal timing is common in engineering and neurobiology, and these results suggest that it could also function broadly within the signaling and regulatory systems of the cell.

Types or States? Cellular Dynamics and Regenerative Potential

Many of our organs can maintain and repair themselves during homeostasis and injury, as a result of the action of tissue-specific, multipotent stem cells. However, recent evidence from mammalian systems suggests that injury stimulates dramatic plasticity, or transient changes in cell potential, in both stem cells and more differentiated cells. Planarian flatworms possess abundant stem cells, making them an exceptional model for understanding the cellular behavior underlying homeostasis and regeneration. Recent discoveries of cell lineages and regeneration-specific events provide an initial framework for unraveling the complex cellular contributions to regeneration. In this review, we discuss the concept of cellular plasticity in the context of planarian regeneration, and consider the possibility that pluripotency may be a transient, probabilistic state exhibited by stem cells.

Intra-tumor heterogeneity of cancer cells and its implications for cancer treatment

Recent studies have revealed extensive genetic and non-genetic variation across different geographical regions of a tumor or throughout different stages of tumor progression, which is referred to as intra-tumor heterogeneity. Several causes contribute to this phenomenon, including genomic instability, epigenetic alteration, plastic gene expression, signal transduction, and microenvironmental differences. These variables may affect key signaling pathways that regulate cancer cell growth, drive phenotypic diversity, and pose challenges to cancer treatment. Understanding the mechanisms underlying this heterogeneity will support the development of effective therapeutic strategies.

Nanofabricated structures and microfluidic devices for bacteria: from techniques to biology

Nanofabricated structures and microfluidic technologies are increasingly being used to study bacteria because of their precise spatial and temporal control. They have facilitated studying many long-standing questions regarding growth, chemotaxis and cell-fate switching, and opened up new areas such as probing the effect of boundary geometries on the subcellular structure and social behavior of bacteria. We review the use of nano/microfabricated structures that spatially separate bacteria for quantitative analyses and that provide topological constraints on their growth and chemical communications. These approaches are becoming modular and broadly applicable, and show a strong potential for dissecting the complex life of bacteria at various scales and engineering synthetic microbial societies.

Noise and low-level dynamics can coordinate multicomponent bet hedging mechanisms

To counter future uncertainty, cells can stochastically express stress response mechanisms to diversify their population and hedge against stress. This approach allows a small subset of the population to survive without the prohibitive cost of constantly expressing resistance machinery at the population level. However, expression of multiple genes in concert is often needed to ensure survival, requiring coordination of infrequent events across many downstream targets. This raises the question of how cells orchestrate the timing of multiple rare events without adding cost. To investigate this, we used a stochastic model to study regulation of downstream target genes by a transcription factor. We compared several upstream regulator profiles, including constant expression, pulsatile dynamics, and noisy expression. We found that pulsatile dynamics and noise are sufficient to coordinate expression of multiple downstream genes. Notably, this is true even when fluctuations in the upstream regulator are far below the dissociation constants of the regulated genes, as with infrequently activated genes. As an example, we simulated the dynamics of the multiple antibiotic resistance activator (MarA) and 40 diverse downstream genes it regulates, determining that low-level dynamics in MarA are sufficient to coordinate expression of resistance mechanisms. We also demonstrated that noise can play a similar coordinating role. Importantly, we found that these benefits are present without a corresponding increase in the population-level cost. Therefore, our model suggests that low-level dynamics or noise in a transcription factor can coordinate expression of multiple stress response mechanisms by engaging them simultaneously without adding to the overall cost.

A Gene Gravity Model for the Evolution of Cancer Genomes: A Study of 3,000 Cancer Genomes across 9 Cancer Types

Cancer development and progression result from somatic evolution by an accumulation of genomic alterations. The effects of those alterations on the fitness of somatic cells lead to evolutionary adaptations such as increased cell proliferation, angiogenesis, and altered anticancer drug responses. However, there are few general mathematical models to quantitatively examine how perturbations of a single gene shape subsequent evolution of the cancer genome. In this study, we proposed the gene gravity model to study the evolution of cancer genomes by incorporating the genome-wide transcription and somatic mutation profiles of ~3,000 tumors across 9 cancer types from The Cancer Genome Atlas into a broad gene network. We found that somatic mutations of a cancer driver gene may drive cancer genome evolution by inducing mutations in other genes. This functional consequence is often generated by the combined effect of genetic and epigenetic (e.g., chromatin regulation) alterations. By quantifying cancer genome evolution using the gene gravity model, we identified six putative cancer genes (AHNAK, COL11A1, DDX3X, FAT4, STAG2, and SYNE1). The tumor genomes harboring the nonsynonymous somatic mutations in these genes had a higher mutation density at the genome level compared to the wild-type groups. Furthermore, we provided statistical evidence that hypermutation of cancer driver genes on inactive X chromosomes is a general feature in female cancer genomes. In summary, this study sheds light on the functional consequences and evolutionary characteristics of somatic mutations during tumorigenesis by propelling adaptive cancer genome evolution, which would provide new perspectives for cancer research and therapeutics.

Structured nucleosome fingerprints enable high-resolution mapping of chromatin architecture within regulatory regions

Transcription factors canonically bind nucleosome-free DNA, making the positioning of nucleosomes within regulatory regions crucial to the regulation of gene expression. Using the assay of transposase accessible chromatin (ATAC-seq), we observe a highly structured pattern of DNA fragment lengths and positions around nucleosomes in Saccharomyces cerevisiae, and use this distinctive two-dimensional nucleosomal “fingerprint” as the basis for a new nucleosome-positioning algorithm called NucleoATAC. We show that NucleoATAC can identify the rotational and translational positions of nucleosomes with up to base-pair resolution and provide quantitative measures of nucleosome occupancy in S. cerevisiae, Schizosaccharomyces pombe, and human cells. We demonstrate the application of NucleoATAC to a number of outstanding problems in chromatin biology, including analysis of sequence features underlying nucleosome positioning, promoter chromatin architecture across species, identification of transient changes in nucleosome occupancy and positioning during a dynamic cellular response, and integrated analysis of nucleosome occupancy and transcription factor binding.

Advances in computational approaches for prioritizing driver mutations and significantly mutated genes in cancer genomes

Cancer is often driven by the accumulation of genetic alterations, including single nucleotide variants, small insertions or deletions, gene fusions, copy-number variations, and large chromosomal rearrangements. Recent advances in next-generation sequencing technologies have helped investigators generate massive amounts of cancer genomic data and catalog somatic mutations in both common and rare cancer types. So far, the somatic mutation landscapes and signatures of >10 major cancer types have been reported; however, pinpointing driver mutations and cancer genes from millions of available cancer somatic mutations remains a monumental challenge. To tackle this important task, many methods and computational tools have been developed during the past several years and, thus, a review of its advances is urgently needed. Here, we first summarize the main features of these methods and tools for whole-exome, whole-genome and whole-transcriptome sequencing data. Then, we discuss major challenges like tumor intra-heterogeneity, tumor sample saturation and functionality of synonymous mutations in cancer, all of which may result in false-positive discoveries. Finally, we highlight new directions in studying regulatory roles of noncoding somatic mutations and quantitatively measuring circulating tumor DNA in cancer. This review may help investigators find an appropriate tool for detecting potential driver or actionable mutations in rapidly emerging precision cancer medicine.

cis Determinants of Promoter Threshold and Activation Timescale

Although the relationship between DNA cis-regulatory sequences and gene expression has been extensively studied at steady state, how cis-regulatory sequences affect the dynamics of gene induction is not known. The dynamics of gene induction can be described by the promoter activation timescale (AcTime) and amplitude threshold (AmpThr). Combining high-throughput microfluidics with quantitative time-lapse microscopy, we control the activation dynamics of the budding yeast transcription factor, Msn2, and reveal how cis-regulatory motifs in 20 promoter variants of the Msn2-target-gene SIP18 affect AcTime and AmpThr. By modulating Msn2 binding sites, we can decouple AmpThr from AcTime and switch the SIP18 promoter class from high AmpThr and slow AcTime to low AmpThr and either fast or slow AcTime. We present a model that quantitatively explains gene-induction dynamics on the basis of the Msn2-binding-site number, TATA box location, and promoter nucleosome organization. Overall, we elucidate the cis-regulatory logic underlying promoter decoding of TF dynamics.

Vizardous: interactive analysis of microbial populations with single cell resolution

MOTIVATION

Single cell time-lapse microscopy is a powerful method for investigating heterogeneous cell behavior. Advances in microfluidic lab-on-a-chip technologies and live-cell imaging render the parallel observation of the development of individual cells in hundreds of populations possible. While image analysis tools are available for cell detection and tracking, biologists are still confronted with the challenge of exploring and evaluating this data.

RESULTS

We present the software tool Vizardous that assists scientists with explorative analysis and interpretation tasks of single cell data in an interactive, configurable and visual way. With Vizardous, lineage tree drawings can be augmented with various, time-resolved cellular characteristics. Associated statistical moments bridge the gap between single cell and the population-average level.

AVAILABILITY AND IMPLEMENTATION

The software, including documentation and examples, is available as executable Java archive as well as in source form at https://github.com/modsim/vizardous.

CONTACT

k.noeh@fz-juelich.de.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Real-time imaging of bHLH transcription factors reveals their dynamic control in the multipotency and fate choice of neural stem cells

The basic-helix-loop-helix (bHLH) transcription factors Ascl1/Mash1, Hes1, and Olig2 regulate the fate choice of neurons, astrocytes, and oligodendrocytes, respectively; however, these factors are coexpressed in self-renewing multipotent neural stem cells (NSCs) even before cell fate determination. This fact raises the possibility that these fate determination factors are differentially expressed between self-renewing and differentiating NSCs with unique expression dynamics. Real-time imaging analysis utilizing fluorescent proteins is a powerful strategy for monitoring expression dynamics. Fusion with fluorescent reporters makes it possible to analyze the dynamic behavior of specific proteins in living cells. However, it is technically challenging to conduct long-term imaging of proteins, particularly those with low expression levels, because a high-sensitivity and low-noise imaging system is required, and very often bleaching of fluorescent proteins and cell toxicity by prolonged laser exposure are problematic. Furthermore, to analyze the functional roles of the dynamic expression of cellular proteins, it is essential to image reporter fusion proteins that are expressed at comparable levels to their endogenous expression. In this review, we introduce our recent reports about the dynamic control of bHLH transcription factors in multipotency and fate choice of NSCs, focusing on real-time imaging of fluorescent reporters fused with bHLH transcription factors. Our imaging results indicate that bHLH transcription factors are expressed in an oscillatory manner by NSCs, and that one of them becomes dominant during fate choice. We propose that the multipotent state of NSCs correlates with the oscillatory expression of several bHLH transcription factors, whereas the differentiated state correlates with the sustained expression of a single bHLH transcription factor.

Interplay between stochasticity and negative feedback leads to pulsed dynamics and distinct gene activity patterns

Gene expression is an inherently stochastic process that depends on the structure of the biochemical regulatory network in which the gene is embedded. Here we study the dynamical consequences of the interplay between stochastic gene switching and the widespread negative feedback regulatory loop in a simple model of a biochemical regulatory network. Using a simplified hybrid simulation approach, in which only the gene activation is modeled stochastically, we find that stochasticity in gene switching by itself can induce pulses in the system, providing also analytical insights into their origin. Furthermore, we find that this simple network is able to reproduce both exponential and peaked distributions of gene active and inactive times similar to those that have been observed experimentally. This simplified hybrid simulation approach also allows us to link these patterns to the dynamics of the system for each gene state.

High-throughput microfluidics to control and measure signaling dynamics in single yeast cells

Microfluidics coupled to quantitative time-lapse fluorescence microscopy is transforming our ability to control, measure and understand signaling dynamics in single living cells. Here we describe a pipeline that incorporates multiplexed microfluidic cell culture, automated programmable fluid handling for cell perturbation, quantitative time-lapse microscopy and computational analysis of time-lapse movies. We illustrate how this setup can be used to control the nuclear localization of the budding yeast transcription factor Msn2. By using this protocol, we generate oscillations of Msn2 localization and measure the dynamic gene expression response of individual genes in single cells. The protocol allows a single researcher to perform up to 20 different experiments in a single day, while collecting data for thousands of single cells. Compared with other protocols, the present protocol is relatively easy to adopt and of higher throughput. The protocol can be widely used to control and monitor single-cell signaling dynamics in other signal transduction systems in microorganisms.

Single-cell chromatin accessibility reveals principles of regulatory variation

Cell-to-cell variation is a universal feature of life that impacts a wide range of biological phenomena, from developmental plasticity^1,2 to tumor heterogeneity³. While recent advances have improved our ability to document cellular phenotypic variation^4–8 the fundamental mechanisms that generate variability from identical DNA sequences remain elusive. Here we reveal the landscape and principles of cellular DNA regulatory variation by developing a robust method for mapping the accessible genome of individual cells via assay for transposase-accessible chromatin using sequencing (ATAC-seq). Single-cell ATAC-seq (scATAC-seq) maps from hundreds of single-cells in aggregate closely resemble accessibility profiles from tens of millions of cells and provides insights into cell-to-cell variation. Accessibility variance is systematically associated with specific trans-factors and cis-elements, and we discover combinations of trans-factors associated with either induction or suppression of cell-to-cell variability. We further identify sets of trans-factors associated with cell-type specific accessibility variance across 8 cell types. Targeted perturbations of cell cycle or transcription factor signaling evoke stimulus-specific changes in this observed variability. The pattern of accessibility variation in cis across the genome recapitulates chromosome topological domains⁹de novo, linking single-cell accessibility variation to three-dimensional genome organization. All together, single-cell analysis of DNA accessibility provides new insight into cellular variation of the “regulome.”

A noisy linear map underlies oscillations in cell size and gene expression in bacteria

During bacterial growth, a cell approximately doubles in size before division, after which it splits into two daughter cells. This process is subjected to the inherent perturbations of cellular noise and thus requires regulation for cell-size homeostasis. The mechanisms underlying the control and dynamics of cell size remain poorly understood owing to the difficulty in sizing individual bacteria over long periods of time in a high-throughput manner. Here we measure and analyse long-term, single-cell growth and division across different Escherichia coli strains and growth conditions. We show that a subset of cells in a population exhibit transient oscillations in cell size with periods that stretch across several (more than ten) generations. Our analysis reveals that a simple law governing cell-size control-a noisy linear map-explains the origins of these cell-size oscillations across all strains. This noisy linear map implements a negative feedback on cell-size control: a cell with a larger initial size tends to divide earlier, whereas one with a smaller initial size tends to divide later. Combining simulations of cell growth and division with experimental data, we demonstrate that this noisy linear map generates transient oscillations, not just in cell size, but also in constitutive gene expression. Our work provides new insights into the dynamics of bacterial cell-size regulation with implications for the physiological processes involved.

Accurate encoding and decoding by single cells: amplitude versus frequency modulation

Cells sense external concentrations and, via biochemical signaling, respond by regulating the expression of target proteins. Both in signaling networks and gene regulation there are two main mechanisms by which the concentration can be encoded internally: amplitude modulation (AM), where the absolute concentration of an internal signaling molecule encodes the stimulus, and frequency modulation (FM), where the period between successive bursts represents the stimulus. Although both mechanisms have been observed in biological systems, the question of when it is beneficial for cells to use either AM or FM is largely unanswered. Here, we first consider a simple model for a single receptor (or ion channel), which can either signal continuously whenever a ligand is bound, or produce a burst in signaling molecule upon receptor binding. We find that bursty signaling is more accurate than continuous signaling only for sufficiently fast dynamics. This suggests that modulation based on bursts may be more common in signaling networks than in gene regulation. We then extend our model to multiple receptors, where continuous and bursty signaling are equivalent to AM and FM respectively, finding that AM is always more accurate. This implies that the reason some cells use FM is related to factors other than accuracy, such as the ability to coordinate expression of multiple genes or to implement threshold crossing mechanisms.

Signals, and hence information, can generally be transmitted either by amplitude (AM) or frequency (FM) modulation, as used, for example, in the transmission of radio waves since the 1930s. Both types of modulation are known to play a role in biology with AM conventionally associated with signaling and gene expression, and FM used to reliably transmit electrical signals over large distances between neurons. Surprisingly, FM was recently also observed in gene regulation, making their roles less distinct than previously thought. Although the engineering advantages and disadvantages of AM and FM are well understood, the equivalent question in biological systems is still largely unsolved. Here, we propose a simple model of signaling by receptors (or ion channels) with subsequent gene regulation, thus implementing both AM and FM in different types of biological pathways. We then compare the accuracy in the production of target proteins. We find that FM can be more accurate than AM only for a single receptor with fast signaling, whereas AM is more accurate in slow gene regulation and with signaling by multiple receptors. Finally, we propose possible reasons that cells use FM despite the potential decrease in accuracy.

Impact of prenatal environmental stress on cortical development

Prenatal exposure of the developing brain to various types of environmental stress increases susceptibility to neuropsychiatric disorders such as autism, attention deficit hyperactivity disorder and schizophrenia. Given that even subtle perturbations by prenatal environmental stress in the cerebral cortex impair the cognitive and memory functions, this review focuses on underlying molecular mechanisms of pathological cortical development. We especially highlight recent works that utilized animal exposure models, human specimens or/and induced Pluripotent Stem (iPS) cells to demonstrate: (1) molecular mechanisms shared by various types of environmental stressors, (2) the mechanisms by which the affected extracortical tissues indirectly impact the cortical development and function, and (3) interaction between prenatal environmental stress and the genetic predisposition of neuropsychiatric disorders. Finally, we discuss current challenges for achieving a comprehensive understanding of the role of environmentally disturbed molecular expressions in cortical maldevelopment, knowledge of which may eventually facilitate discovery of interventions for prenatal environment-linked neuropsychiatric disorders.

Limits on information transduction through amplitude and frequency regulation of transcription factor activity

Signaling pathways often transmit multiple signals through a single shared transcription factor (TF) and encode signal information by differentially regulating TF dynamics. However, signal information will be lost unless it can be reliably decoded by downstream genes. To understand the limits on dynamic information transduction, we apply information theory to quantify how much gene expression information the yeast TF Msn2 can transduce to target genes in the amplitude or frequency of its activation dynamics. We find that although the amount of information transmitted by Msn2 to single target genes is limited, information transduction can be increased by modulating promoter cis-elements or by integrating information from multiple genes. By correcting for extrinsic noise, we estimate an upper bound on information transduction. Overall, we find that information transduction through amplitude and frequency regulation of Msn2 is limited to error-free transduction of signal identity, but not signal intensity information.

Noise facilitates transcriptional control under dynamic inputs

Cells must respond sensitively to time-varying inputs in complex signaling environments. To understand how signaling networks process dynamic inputs into gene expression outputs and the role of noise in cellular information processing, we studied the immune pathway NF-κB under periodic cytokine inputs using microfluidic single-cell measurements and stochastic modeling. We find that NF-κB dynamics in fibroblasts synchronize with oscillating TNF signal and become entrained, leading to significantly increased NF-κB oscillation amplitude and mRNA output compared to non-entrained response. Simulations show that intrinsic biochemical noise in individual cells improves NF-κB oscillation and entrainment, whereas cell-to-cell variability in NF-κB natural frequency creates population robustness, together enabling entrainment over a wider range of dynamic inputs. This wide range is confirmed by experiments where entrained cells were measured under all input periods. These results indicate that synergy between oscillation and noise allows cells to achieve efficient gene expression in dynamically changing signaling environments.

Dynamic stem cell heterogeneity

Recent lineage-tracing studies based on inducible genetic labelling have emphasized a crucial role for stochasticity in the maintenance and regeneration of cycling adult tissues. These studies have revealed that stem cells are frequently lost through differentiation and that this is compensated for by the duplication of neighbours, leading to the consolidation of clonal diversity. Through the combination of long-term lineage-tracing assays with short-term in vivo live imaging, the cellular basis of this stochastic stem cell loss and replacement has begun to be resolved. With a focus on mammalian spermatogenesis, intestinal maintenance and the hair cycle, we review the role of dynamic heterogeneity in the regulation of adult stem cell populations.

Heredity and self-organization: partners in the generation and evolution of phenotypes

In this review we examine the role of self-organization in the context of the evolution of morphogenesis. We provide examples to show that self-organized behavior is ubiquitous, and suggest it is a mechanism that can permit high levels of biodiversity without the invention of ever-increasing numbers of genes. We also examine the implications of self-organization for understanding the "internal descriptions" of organisms and the concept of a genotype-phenotype map.

How to train your microbe: methods for dynamically characterizing gene networks

Gene networks regulate biological processes dynamically. However, researchers have largely relied upon static perturbations, such as growth media variations and gene knockouts, to elucidate gene network structure and function. Thus, much of the regulation on the path from DNA to phenotype remains poorly understood. Recent studies have utilized improved genetic tools, hardware, and computational control strategies to generate precise temporal perturbations outside and inside of live cells. These experiments have, in turn, provided new insights into the organizing principles of biology. Here, we introduce the major classes of dynamical perturbations that can be used to study gene networks, and discuss technologies available for creating them in a wide range of microbial pathways.

Microbial individuality: how single-cell heterogeneity enables population level strategies

Much of our knowledge of microbial life is only a description of average population behaviours, but modern technologies provide a more inclusive view and reveal that microbes also have individuality. It is now acknowledged that isogenic cell-to-cell heterogeneity is common across organisms and across different biological processes. This heterogeneity can be regulated and functional, rather than just reflecting tolerance to noisy biochemistry. Here, we review recent advances in our understanding of microbial heterogeneity, with an emphasis on the pervasiveness of heterogeneity, the mechanisms that sustain it, and how heterogeneity enables collective function.

Biased allelic expression in human primary fibroblast single cells

The study of gene expression in mammalian single cells via genomic technologies now provides the possibility to investigate the patterns of allelic gene expression. We used single-cell RNA sequencing to detect the allele-specific mRNA level in 203 single human primary fibroblasts over 133,633 unique heterozygous single-nucleotide variants (hetSNVs). We observed that at the snapshot of analyses, each cell contained mostly transcripts from one allele from the majority of genes; indeed, 76.4% of the hetSNVs displayed stochastic monoallelic expression in single cells. Remarkably, adjacent hetSNVs exhibited a haplotype-consistent allelic ratio; in contrast, distant sites located in two different genes were independent of the haplotype structure. Moreover, the allele-specific expression in single cells correlated with the abundance of the cellular transcript. We observed that genes expressing both alleles in the majority of the single cells at a given time point were rare and enriched with highly expressed genes. The relative abundance of each allele in a cell was controlled by some regulatory mechanisms given that we observed related single-cell allelic profiles according to genes. Overall, these results have direct implications in cellular phenotypic variability.