Interplay between gene expression noise and regulatory network architecture

Priestia megaterium cells are primed for surviving lethal doses of antibiotics and chemical stress

Antibiotic resistant infections kill millions worldwide yearly. However, a key factor in recurrent infections is antibiotic persisters. Persisters are not inherently antibiotic-resistant but can withstand antibiotic exposure by entering a non-dividing state. This tolerance often results in prolonged antibiotic usage, increasing the likelihood of developing resistant strains. Here, we show the existence of "primed cells" in the Gram-positive bacterium Priestia megaterium, formerly known as Bacillus megaterium. These cells are pre-adapted to become persisters prior to lethal antibiotic stress. Remarkably, this prepared state is passed down through multiple generations via epigenetic memory, enhancing survival against antibiotics and other chemical stress. Previously, two distinct types of persisters were proposed: Type I and Type II, formed during stationary and log phases, respectively. However, our findings reveal that primed cells contribute to an increase in persisters during transition and stationary phases, with no evidence supporting distinct phenotypes between Type I and Type II persisters.

Chromatin-based memory as a self-stabilizing influence on cell identity

Cell types are traditionally thought to be specified and stabilized by gene regulatory networks. Here, we explore how chromatin memory contributes to the specification and stabilization of cell states. Through pervasive, local, feedback loops, chromatin memory enables cell states that were initially unstable to become stable. Deeper appreciation of this self-stabilizing role for chromatin broadens our perspective of Waddington's epigenetic landscape from a static surface with islands of stability shaped by evolution, to a plasticine surface molded by experience. With implications for the evolution of cell types, stabilization of resistant states in cancer, and the widespread plasticity of complex life.

Emergence of temporal noise hierarchy in co-regulated genes of multi-output feed-forward loop

Natural variations in gene expression, called noise, are fundamental to biological systems. The expression noise can be beneficial or detrimental to cellular functions. While the impact of noise on individual genes is well-established, our understanding of how noise behaves when multiple genes are co-expressed by shared regulatory elements within transcription networks remains elusive. This lack of understanding extends to how the architecture and regulatory features of these networks influence noise. To address this gap, we study the multi-output feed-forward loop motif. The motif is prevalent in bacteria and yeast and influences co-expression of multiple genes by shared transcription factors (TFs). Focusing on a two-output variant of the motif, the present study explores the interplay between its architecture, co-expression (symmetric and asymmetric) patterns of the two genes, and the associated noise dynamics. We employ a stochastic modeling approach to investigate how the binding affinities of the TFs influence symmetric and asymmetric expression patterns and the resulting noise dynamics in the co-expressed genes. This knowledge could guide the development of strategies for manipulating gene expression patterns through targeted modulation of TF binding affinities.

Stable developmental patterns of gene expression without morphogen gradients

Gene expression patterns in developing organisms are established by groups of cross-regulating target genes that are driven by morphogen gradients. As development progresses, morphogen activity is reduced, leaving the emergent pattern without stabilizing positional cues and at risk of rapid deterioration due to the inherently noisy biochemical processes at the cellular level. But remarkably, gene expression patterns remain spatially stable and reproducible over long developmental time spans in many biological systems. Here we combine spatial-stochastic simulations with an enhanced sampling method (Non-Stationary Forward Flux Sampling) and a recently developed stability theory to address how spatiotemporal integrity of a gene expression pattern is maintained in developing tissue lacking morphogen gradients. Using a minimal embryo model consisting of spatially coupled biochemical reactor volumes, we study a prototypical stripe pattern in which weak cross-repression between nearest neighbor expression domains alternates with strong repression between next-nearest neighbor domains, inspired by the gap gene system in the Drosophila embryo. We find that tuning of the weak repressive interactions to an optimal level can prolong stability of the expression patterns by orders of magnitude, enabling stable patterns over developmentally relevant times in the absence of morphogen gradients. The optimal parameter regime found in simulations of the embryo model closely agrees with the predictions of our coarse-grained stability theory. To elucidate the origin of stability, we analyze a reduced phase space defined by two measures of pattern asymmetry. We find that in the optimal regime, intact patterns are protected via restoring forces that counteract random perturbations and give rise to a metastable basin. Together, our results demonstrate that metastable attractors can emerge as a property of stochastic gene expression patterns even without system-wide positional cues, provided that the gene regulatory interactions shaping the pattern are optimally tuned.

Drug tolerance and persistence in bacteria, fungi and cancer cells: Role of non-genetic heterogeneity

A common feature of bacterial, fungal and cancer cell populations upon treatment is the presence of tolerant and persistent cells able to survive, and sometimes grow, even in the presence of usually inhibitory or lethal drug concentrations, driven by non-genetic differences among individual cells in a population. Here we review and compare data obtained on drug survival in bacteria, fungi and cancer cells to unravel common characteristics and cellular pathways, and to point their singularities. This comparative work also allows to cross-fertilize ideas across fields. We particularly focus on the role of gene expression variability in the emergence of cell-cell non-genetic heterogeneity because it represents a possible common basic molecular process at the origin of most persistence phenomena and could be monitored and tuned to help improve therapeutic interventions.

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

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

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.

Gram-positive bacteria are primed for surviving lethal doses of antibiotics and chemical stress

Antibiotic resistance kills millions worldwide yearly. However, a major contributor to recurrent infections lies in a small fraction of bacterial cells, known as persisters. These cells are not inherently antibiotic-resistant, yet they lead to increased antibiotic usage, raising the risk of developing resistant progenies. In a bacterial population, individual cells exhibit considerable fluctuations in their gene expression levels despite being cultivated under identical, stable conditions. This variability in cell-to-cell characteristics (phenotypic diversity) within an isogenic population enables persister cells to withstand antibiotic exposure by entering a non-dividing state. We recently showed the existence of "primed cells" in E. coli. Primed cells are dividing cells prepared for antibiotic stress before encountering it and are more prone to form persisters. They also pass their "prepared state" down for several generations through epigenetic memory. Here, we show that primed cells are common among distant bacterial lineages, allowing for survival against antibiotics and other chemical stress, and form in different growth phases. They are also responsible for increased persister levels in transition and stationary phases compared to the log phase. We tested and showed that the Gram-positive bacterium Bacillus megaterium, evolutionarily very distant from E. coli, forms primed cells and has a transient epigenetic memory that is maintained for 7 generations or more. We showed this using ciprofloxacin and the non-antibiotic chemical stress fluoride. It is well established that persister levels are higher in the stationary phase than in the log phase, and B. megaterium persisters levels are nearly identical from the early to late-log phase but are ~2-fold and ~4-fold higher in the transition and stationary phase, respectively. It was previously proposed that there are two distinct types of persisters: Type II forms in the log phase, while Type I forms in the stationary phase. However, we show that primed cells lead to increased persisters in the transition and stationary phase and found no evidence of Type I or II persisters with distant phenotypes. Overall, we have provided substantial evidence of the importance of primed cells and their transitory epigenetic memories to surviving stress.

Systems Genome: Coordinated Gene Activity Networks, Recurring Coordination Modules, and Genome Homeostasis in Developing Neurons

As human progenitor cells differentiate into neurons, the activities of many genes change; these changes are maintained within a narrow range, referred to as genome homeostasis. This process, which alters the synchronization of the entire expressed genome, is distorted in neurodevelopmental diseases such as schizophrenia. The coordinated gene activity networks formed by altering sets of genes comprise recurring coordination modules, governed by the entropy-controlling action of nuclear FGFR1, known to be associated with DNA topology. These modules can be modeled as energy-transferring circuits, revealing that genome homeostasis is maintained by reducing oscillations (noise) in gene activity while allowing gene activity changes to be transmitted across networks; this occurs more readily in neuronal committed cells than in neural progenitors. These findings advance a model of an "entangled" global genome acting as a flexible, coordinated homeostatic system that responds to developmental signals, is governed by nuclear FGFR1, and is reprogrammed in disease.

Yuragi biomarker concept for evaluating human induced pluripotent stem cells using heterogeneity-based Raman finger-printing

Considering the fundamental mechanism causing singularity phenomena, we performed the following abduction: Assuming that a multicellular system is driven by spontaneous fluctuation of each cell and dynamic interaction of the cells, state transition of the system would be experimentally predictable from cellular heterogeneity. This study evaluates the abductive hypothesis by analyzing cellular heterogeneity to distinguish pre-state of state transition of differentiating cells with Raman spectroscopy and human induced pluripotent stem cells (hiPSCs) technique. Herein, we investigated the time development of cellular heterogeneity in Raman spectra during cardiomyogenesis of six hiPSC lines and tested two types of analyses for heterogeneity. As expected, some spectral peaks, possibly attributed to glycogen, correctively exhibited higher heterogeneity, prior to intensity changes of the spectrum in the both analyses in the all cell-lines tested. The combination of spectral data and heterogeneity-based analysis will be an approach to the arrival of biology that uses not only signal intensity but also heterogeneity as a biological index.

Stochastic chemical kinetics of cell fate decision systems: From single cells to populations and back

Stochastic chemical kinetics is a widely used formalism for studying stochasticity of chemical reactions inside single cells. Experimental studies of reaction networks are generally performed with cells that are part of a growing population, yet the population context is rarely taken into account when models are developed. Models that neglect the population context lose their validity whenever the studied system influences traits of cells that can be selected in the population, a property that naturally arises in the complex interplay between single-cell and population dynamics of cell fate decision systems. Here, we represent such systems as absorbing continuous-time Markov chains. We show that conditioning on non-absorption allows one to derive a modified master equation that tracks the time evolution of the expected population composition within a growing population. This allows us to derive consistent population dynamics models from a specification of the single-cell process. We use this approach to classify cell fate decision systems into two types that lead to different characteristic phases in emerging population dynamics. Subsequently, we deploy the gained insights to experimentally study a recurrent problem in biology: how to link plasmid copy number fluctuations and plasmid loss events inside single cells to growth of cell populations in dynamically changing environments.

Deep-learning-assisted Sort-Seq enables high-throughput profiling of gene expression characteristics with high precision

Owing to the nondeterministic and nonlinear nature of gene expression, the steady-state intracellular protein abundance of a clonal population forms a distribution. The characteristics of this distribution, including expression strength and noise, are closely related to cellular behavior. However, quantitative description of these characteristics has so far relied on arrayed methods, which are time-consuming and labor-intensive. To address this issue, we propose a deep-learning-assisted Sort-Seq approach (dSort-Seq) in this work, enabling high-throughput profiling of expression properties with high precision. We demonstrated the validity of dSort-Seq for large-scale assaying of the dose-response relationships of biosensors. In addition, we comprehensively investigated the contribution of transcription and translation to noise production in Escherichia coli, from which we found that the expression noise is strongly coupled with the mean expression level. We also found that the transcriptional interference caused by overlapping RpoD-binding sites contributes to noise production, which suggested the existence of a simple and feasible noise control strategy in E. coli.

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.

Diverse regulatory pathways modulate bet hedging of competence induction in epigenetically-differentiated phase variants of Streptococcus pneumoniae

Despite enabling Streptococcus pneumoniae to acquire antibiotic resistance and evade vaccine-induced immunity, transformation occurs at variable rates across pneumococci. Phase variants of isolate RMV7, distinguished by altered methylation patterns driven by the translocating variable restriction-modification (tvr) locus, differed significantly in their transformation efficiencies and biofilm thicknesses. These differences were replicated when the corresponding tvr alleles were introduced into an RMV7 derivative lacking the locus. RNA-seq identified differential expression of the type 1 pilus, causing the variation in biofilm formation, and inhibition of competence induction in the less transformable variant, RMV7domi. This was partly attributable to RMV7domi's lower expression of ManLMN, which promoted competence induction through importing N-acetylglucosamine. This effect was potentiated by analogues of some proteobacterial competence regulatory machinery. Additionally, one of RMV7domi's phage-related chromosomal island was relatively active, which inhibited transformation by increasing expression of the stress response proteins ClpP and HrcA. However, HrcA increased competence induction in the other variant, with its effects depending on Ca2+ supplementation and heat shock. Hence the heterogeneity in transformation efficiency likely reflects the diverse signalling pathways by which it is affected. This regulatory complexity will modulate population-wide responses to synchronising quorum sensing signals to produce co-ordinated yet stochastic bet hedging behaviour.

Characterizing the landscape of gene expression variance in humans

Gene expression variance has been linked to organismal function and fitness but remains a commonly neglected aspect of molecular research. As a result, we lack a comprehensive understanding of the patterns of transcriptional variance across genes, and how this variance is linked to context-specific gene regulation and gene function. Here, we use 57 large publicly available RNA-seq data sets to investigate the landscape of gene expression variance. These studies cover a wide range of tissues and allowed us to assess if there are consistently more or less variable genes across tissues and data sets and what mechanisms drive these patterns. We show that gene expression variance is broadly similar across tissues and studies, indicating that the pattern of transcriptional variance is consistent. We use this similarity to create both global and within-tissue rankings of variation, which we use to show that function, sequence variation, and gene regulatory signatures contribute to gene expression variance. Low-variance genes are associated with fundamental cell processes and have lower levels of genetic polymorphisms, have higher gene-gene connectivity, and tend to be associated with chromatin states associated with transcription. In contrast, high-variance genes are enriched for genes involved in immune response, environmentally responsive genes, immediate early genes, and are associated with higher levels of polymorphisms. These results show that the pattern of transcriptional variance is not noise. Instead, it is a consistent gene trait that seems to be functionally constrained in human populations. Furthermore, this commonly neglected aspect of molecular phenotypic variation harbors important information to understand complex traits and disease.

Predicting attractors from spectral properties of stylized gene regulatory networks

How the architecture of gene regulatory networks shapes gene expression patterns is an open question, which has been approached from a multitude of angles. The dominant strategy has been to identify nonrandom features in these networks and then argue for the function of these features using mechanistic modeling. Here we establish the foundation of an alternative approach by studying the correlation of network eigenvectors with synthetic gene expression data simulated with a basic and popular model of gene expression dynamics: Boolean threshold dynamics in signed directed graphs. We show that eigenvectors of the network adjacency matrix can predict collective states (attractors). However, the overall predictive power depends on details of the network architecture, namely the fraction of positive 3-cycles, in a predictable fashion. Our results are a set of statistical observations, providing a systematic step towards a further theoretical understanding of the role of network eigenvectors in dynamics on graphs.

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.

Detection of genes with differential expression dispersion unravels the role of autophagy in cancer progression

The majority of gene expression studies focus on the search for genes whose mean expression is different between two or more populations of samples in the so-called "differential expression analysis" approach. However, a difference in variance in gene expression may also be biologically and physiologically relevant. In the classical statistical model used to analyze RNA-sequencing (RNA-seq) data, the dispersion, which defines the variance, is only considered as a parameter to be estimated prior to identifying a difference in mean expression between conditions of interest. Here, we propose to evaluate four recently published methods, which detect differences in both the mean and dispersion in RNA-seq data. We thoroughly investigated the performance of these methods on simulated datasets and characterized parameter settings to reliably detect genes with a differential expression dispersion. We applied these methods to The Cancer Genome Atlas datasets. Interestingly, among the genes with an increased expression dispersion in tumors and without a change in mean expression, we identified some key cellular functions, most of which were related to catabolism and were overrepresented in most of the analyzed cancers. In particular, our results highlight autophagy, whose role in cancerogenesis is context-dependent, illustrating the potential of the differential dispersion approach to gain new insights into biological processes and to discover new biomarkers.

Probing transient memory of cellular states using single-cell lineages

The inherent stochasticity in the gene product levels can drive single cells within an isoclonal population to different phenotypic states. The dynamic nature of this intercellular variation, where individual cells can transition between different states over time, makes it a particularly hard phenomenon to characterize. We reviewed recent progress in leveraging the classical Luria-Delbrück experiment to infer the transient heritability of the cellular states. Similar to the original experiment, individual cells were first grown into cell colonies, and then, the fraction of cells residing in different states was assayed for each colony. We discuss modeling approaches for capturing dynamic state transitions in a growing cell population and highlight formulas that identify the kinetics of state switching from the extent of colony-to-colony fluctuations. The utility of this method in identifying multi-generational memory of the both expression and phenotypic states is illustrated across diverse biological systems from cancer drug resistance, reactivation of human viruses, and cellular immune responses. In summary, this fluctuation-based methodology provides a powerful approach for elucidating cell-state transitions from a single time point measurement, which is particularly relevant in situations where measurements lead to cell death (as in single-cell RNA-seq or drug treatment) or cause an irreversible change in cell physiology.

Precision of morphogen-driven tissue patterning during development is enhanced through contact-mediated cellular interactions

Cells in developing embryos reliably differentiate to attain location-specific fates, despite fluctuations in morphogen concentrations that provide positional information and in molecular processes that interpret it. We show that local contact-mediated cell-cell interactions utilize inherent asymmetry in the response of patterning genes to the global morphogen signal yielding a bimodal response. This results in robust developmental outcomes with a consistent identity for the dominant gene at each cell, substantially reducing the uncertainty in the location of boundaries between distinct fates.

Multi-modular structure of the gene regulatory network for specification and commitment of murine T cells

T cells develop from multipotent progenitors by a gradual process dependent on intrathymic Notch signaling and coupled with extensive proliferation. The stages leading them to T-cell lineage commitment are well characterized by single-cell and bulk RNA analyses of sorted populations and by direct measurements of precursor-product relationships. This process depends not only on Notch signaling but also on multiple transcription factors, some associated with stemness and multipotency, some with alternative lineages, and others associated with T-cell fate. These factors interact in opposing or semi-independent T cell gene regulatory network (GRN) subcircuits that are increasingly well defined. A newly comprehensive picture of this network has emerged. Importantly, because key factors in the GRN can bind to markedly different genomic sites at one stage than they do at other stages, the genes they significantly regulate are also stage-specific. Global transcriptome analyses of perturbations have revealed an underlying modular structure to the T-cell commitment GRN, separating decisions to lose "stem-ness" from decisions to block alternative fates. Finally, the updated network sheds light on the intimate relationship between the T-cell program, which depends on the thymus, and the innate lymphoid cell (ILC) program, which does not.

Genome-wide inference reveals that feedback regulations constrain promoter-dependent transcriptional burst kinetics

Gene expression in mammalian cells is highly variable and episodic, resulting in a series of discontinuous bursts of mRNAs. A challenge is to understand how static promoter architecture and dynamic feedback regulations dictate bursting on a genome-wide scale. Although single-cell RNA sequencing (scRNA-seq) provides an opportunity to address this challenge, effective analytical methods are scarce. We developed an interpretable and scalable inference framework, which combined experimental data with a mechanistic model to infer transcriptional burst kinetics (sizes and frequencies) and feedback regulations. Applying this framework to scRNA-seq data generated from embryonic mouse fibroblast cells, we found Simpson's paradoxes, i.e. genome-wide burst kinetics exhibit different characteristics in two cases without and with distinguishing feedback regulations. We also showed that feedbacks differently modulate burst frequencies and sizes and conceal the effects of transcription start site distributions on burst kinetics. Notably, only in the presence of positive feedback, TATA genes are expressed with high burst frequencies and enhancer-promoter interactions mainly modulate burst frequencies. The developed inference method provided a flexible and efficient way to investigate transcriptional burst kinetics and the obtained results would be helpful for understanding cell development and fate decision.

Polarization and cell-fate decision facilitated by the adaptor Ste50p in Saccharomyces cerevisiae

In response to pheromone, many proteins localize on the plasma membrane of yeast cell to reform it into a polarized shmoo structure. The adaptor protein Ste50p, known as a pheromone signal enhancer critical for shmoo polarization, has never been explored systematically for its localization and function in the polarization process. Time-lapse single-cell imaging and quantitation shown here characterizes Ste50p involvement in the establishment of cell polarity. We found that Ste50p patches on the cell cortex mark the point of shmoo initiation, these patches could move, and remain associated with the growing shmoo tip in a pheromone concentration time-dependent manner until shmoo maturation. A Ste50p mutant impaired in patch localization suffers a delay in polarization. By quantitative analysis we show that polarization correlates with the rising levels of Ste50p, enabling rapid cell responses to pheromone that correspond to a critical level of Ste50p at the initial G1 phase. We exploited the quantitative differences in the pattern of Ste50p expression to correlate with the cell-cell phenotypic heterogeneity, showing Ste50p involvement in the cellular differentiation choice. Taken together, these findings present Ste50p to be part of the early shmoo development phase, suggesting that Ste50p may be involved with the polarisome in the initiation of polarization, and plays a role in regulating the polarized growth of shmoo during pheromone response.

Navigating Environmental Transitions: the Role of Phenotypic Variation in Bacterial Responses

The ability of bacteria to respond to changes in their environment is critical to their survival, allowing them to withstand stress, form complex communities, and induce virulence responses during host infection. A remarkable feature of many of these bacterial responses is that they are often variable across individual cells, despite occurring in an isogenic population exposed to a homogeneous environmental change, a phenomenon known as phenotypic heterogeneity. Phenotypic heterogeneity can enable bet-hedging or division of labor strategies that allow bacteria to survive fluctuating conditions. Investigating the significance of phenotypic heterogeneity in environmental transitions requires dynamic, single-cell data. Technical advances in quantitative single-cell measurements, imaging, and microfluidics have led to a surge of publications on this topic. Here, we review recent discoveries on single-cell bacterial responses to environmental transitions of various origins and complexities, from simple diauxic shifts to community behaviors in biofilm formation to virulence regulation during infection. We describe how these studies firmly establish that this form of heterogeneity is prevalent and a conserved mechanism by which bacteria cope with fluctuating conditions. We end with an outline of current challenges and future directions for the field. While it remains challenging to predict how an individual bacterium will respond to a given environmental input, we anticipate that capturing the dynamics of the process will begin to resolve this and facilitate rational perturbation of environmental responses for therapeutic and bioengineering purposes.

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.

Synthesizing genome regulation data with vote-counting

The increasing availability of high-throughput datasets allows amalgamating research information across a large body of genome regulation studies. Given the recent success of meta-analyses on transcriptional regulators, epigenetic marks, and enhancer:gene associations, we expect that such surveys will continue to provide novel and reproducible insights. However, meta-analyses are severely hampered by the diversity of available data, concurring protocols, an eclectic amount of bioinformatics tools, and myriads of conceivable parameter combinations. Such factors can easily bar life scientists from synthesizing omics data and substantially curb their interpretability. Despite statistical challenges of the method, we would like to emphasize the advantages of joining data from different sources through vote-counting and showcase examples that achieve a simple but highly intuitive data integration.

Diversification of heat shock transcription factors expanded thermal stress responses during early plant evolution

The copy numbers of many plant transcription factor (TF) genes substantially increased during terrestrialization. This allowed TFs to acquire new specificities and thus create gene regulatory networks (GRNs) with new biological functions to help plants adapt to terrestrial environments. Through characterizing heat shock factor (HSF) genes MpHSFA1 and MpHSFB1 in the liverwort Marchantia polymorpha, we explored how heat-responsive GRNs widened their functions in M. polymorpha and Arabidopsis thaliana. An interspecies comparison of heat-induced transcriptomes and the evolutionary rates of HSFs demonstrated the emergence and subsequent rapid evolution of HSFB prior to terrestrialization. Transcriptome and metabolome analyses of M. polymorpha HSF-null mutants revealed that MpHSFA1 controls canonical heat responses such as thermotolerance and metabolic changes. MpHSFB1 also plays essential roles in heat responses, as well as regulating developmental processes including meristem branching and antheridiophore formation. Analysis of cis-regulatory elements revealed development- and stress-related TFs that function directly or indirectly downstream of HSFB. Male gametophytes of M. polymorpha showed higher levels of thermotolerance than female gametophytes, which could be explained by different expression levels of MpHSFA1U and MpHSFA1V on sex chromosome. We propose that the diversification of HSFs is linked to the expansion of HS responses, which enabled coordinated multicellular reactions in land plants.

Binary oppositions, algebraic holography and stochastic rules in genetic informatics

The article is devoted to the author's results of the algebraic analysis of molecular genetic systems, including a set of structured DNA alphabets and long nucleotide sequences in single-stranded DNA of eukaryotic and prokaryotic genomes. A connection of the system of DNA n-plets alphabets with principles of algebraic holography is shown, which concerns a popular theme of holography principles in genetically inherited physiology. In addition, a relation between DNA n-plets alphabets and the Poincaré disk model of Lobachevski hyperbolic geometry is revealed. This relation can explain known facts of the relationship of physiological phenomena with hyperbolic geometry. Considering long DNA sequences as a bunch of many parallel texts written in different n-plets alphabets led to the discovery of some universal rules of the stochastic organization of genomic DNAs. These rules are discussed concerning the general problem of the biological dualism "probability-vs-determinism". In general, the presented results give pieces of evidence in favor of the efficiency of a model approach to living organisms as quantum-informational algebraic-harmonic essences.

Data mining and mathematical models in cancer prognosis and prediction

Cancer is a fetal and complex disease. Individual differences of the same cancer type or the same patient at different stages of cancer development may require distinct treatments. Pathological differences are reflected in tissues, cells and gene levels etc. The interactions between the cancer cells and nearby microenvironments can also influence the cancer progression and metastasis. It is a huge challenge to understand all of these mechanistically and quantitatively. Researchers applied pattern recognition algorithms such as machine learning or data mining to predict cancer types or classifications. With the rapidly growing and available computing powers, researchers begin to integrate huge data sets, multi-dimensional data types and information. The cells are controlled by the gene expressions determined by the promoter sequences and transcription regulators. For example, the changes in the gene expression through these underlying mechanisms can modify cell progressing in the cell-cycle. Such molecular activities can be governed by the gene regulations through the underlying gene regulatory networks, which are essential for cancer study when the information and gene regulations are clear and available. In this review, we briefly introduce several machine learning methods of cancer prediction and classification which include Artificial Neural Networks (ANNs), Decision Trees (DTs), Support Vector Machine (SVM) and naive Bayes. Then we describe a few typical models for building up gene regulatory networks such as Correlation, Regression and Bayes methods based on available data. These methods can help on cancer diagnosis such as susceptibility, recurrence, survival etc. At last, we summarize and compare the modeling methods to analyze the development and progression of cancer through gene regulatory networks. These models can provide possible physical strategies to analyze cancer progression in a systematic and quantitative way.

Global coordination level in single-cell transcriptomic data

Genes are linked by underlying regulatory mechanisms and by jointly implementing biological functions, working in coordination to apply different tasks in the cells. Assessing the coordination level between genes from single-cell transcriptomic data, without a priori knowledge of the map of gene regulatory interactions, is a challenge. A ‘top-down’ approach has recently been developed to analyze single-cell transcriptomic data by evaluating the global coordination level between genes (called GCL). Here, we systematically analyze the performance of the GCL in typical scenarios of single-cell RNA sequencing (scRNA-seq) data. We show that an individual anomalous cell can have a disproportionate effect on the GCL calculated over a cohort of cells. In addition, we demonstrate how the GCL is affected by the presence of clusters, which are very common in scRNA-seq data. Finally, we analyze the effect of the sampling size of the Jackknife procedure on the GCL statistics. The manuscript is accompanied by a description of a custom-built Python package for calculating the GCL. These results provide practical guidelines for properly pre-processing and applying the GCL measure in transcriptional data.

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

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

Transcriptional and post-transcriptional control of epithelial-mesenchymal plasticity: why so many regulators?

The dynamic transition between epithelial-like and mesenchymal-like cell states has been a focus for extensive investigation for decades, reflective of the importance of Epithelial-Mesenchymal Transition (EMT) through development, in the adult, and the contributing role EMT has to pathologies including metastasis and fibrosis. Not surprisingly, regulation of the complex genetic networks that underlie EMT have been attributed to multiple transcription factors and microRNAs. What is surprising, however, are the sheer number of different regulators (hundreds of transcription factors and microRNAs) for which critical roles have been described. This review seeks not to collate these studies, but to provide a perspective on the fundamental question of whether it is really feasible that so many regulators play important roles and if so, what does this tell us about EMT and more generally, the genetic machinery that controls complex biological processes.

Principles for the design of multicellular engineered living systems

Remarkable progress in bioengineering over the past two decades has enabled the formulation of fundamental design principles for a variety of medical and non-medical applications. These advancements have laid the foundation for building multicellular engineered living systems (M-CELS) from biological parts, forming functional modules integrated into living machines. These cognizant design principles for living systems encompass novel genetic circuit manipulation, self-assembly, cell-cell/matrix communication, and artificial tissues/organs enabled through systems biology, bioinformatics, computational biology, genetic engineering, and microfluidics. Here, we introduce design principles and a blueprint for forward production of robust and standardized M-CELS, which may undergo variable reiterations through the classic design-build-test-debug cycle. This Review provides practical and theoretical frameworks to forward-design, control, and optimize novel M-CELS. Potential applications include biopharmaceuticals, bioreactor factories, biofuels, environmental bioremediation, cellular computing, biohybrid digital technology, and experimental investigations into mechanisms of multicellular organisms normally hidden inside the "black box" of living cells.

Multiple neural bHLHs ensure the precision of a neuronal specification event in Caenorhabditis elegans

Neural bHLH transcription factors play a key role in the early steps of neuronal specification in many animals. We have previously observed that the Achaete-Scute HLH-3, the Olig HLH-16 and their binding partner the E-protein HLH-2 activate the terminal differentiation program of a specific class of cholinergic neurons, AIY, in Caenorhabditis elegans. Here we identify a role for a fourth bHLH, the Neurogenin NGN-1, in this process, raising the question of why so many neural bHLHs are required for a single neuronal specification event. Using quantitative imaging we show that the combined action of different bHLHs is needed to activate the correct level of expression of the terminal selector transcription factors TTX-3 and CEH-10 that subsequently initiate and maintain the expression of a large battery of terminal differentiation genes. Surprisingly, the different bHLHs have an antagonistic effect on another target, the proapoptotic BH3-only factor EGL-1, normally not expressed in AIY and otherwise detrimental for its specification. We propose that the use of multiple neural bHLHs allows robust neuronal specification while, at the same time, preventing spurious activation of deleterious genes.

Summary: During neuronal specification, the combined action of several neural bHLHs ensures the robust activation of terminal selector transcription factor expression and prevents the activation of deleterious genes.

The Impact of Self-Loops on Boolean Networks Attractor Landscape and Implications for Cell Differentiation Modelling

Boolean networks are a notable model of gene regulatory networks and, particularly, prominent theories discuss how they can capture cellular differentiation processes. One frequent motif in gene regulatory networks, especially in those circuits involved in cell differentiation, is autoregulation. In spite of this, the impact of autoregulation on Boolean network attractor landscape has not yet been extensively discussed in literature. In this paper we propose to model autoregulation as self-loops, and analyse how the number of attractors and their robustness may change once they are introduced in a well-known and widely used Boolean networks model, namely random Boolean networks. Results show that self-loops provide an evolutionary advantage in dynamic mechanisms of cells, by increasing both number and maximal robustness of attractors. These results provide evidence to the hypothesis that autoregulation is a straightforward functional component to consolidate cell dynamics, mainly in differentiation processes.

SplicingFactory-splicing diversity analysis for transcriptome data

MOTIVATION

Alternative splicing contributes to the diversity of RNA found in biological samples. Current tools investigating patterns of alternative splicing check for coordinated changes in the expression or relative ratio of RNA isoforms where specific isoforms are up- or down-regulated in a condition. However, the molecular process of splicing is stochastic and changes in RNA isoform diversity for a gene might arise between samples or conditions. A specific condition can be dominated by a single isoform, while multiple isoforms with similar expression levels can be present in a different condition. These changes might be the result of mutations, drug treatments or differences in the cellular or tissue environment. Here, we present a tool for the characterization and analysis of RNA isoform diversity using isoform level expression measurements.

RESULTS

We developed an R package called SplicingFactory, to calculate various RNA isoform diversity metrics, and compare them across conditions. Using the package, we tested the effect of RNA-seq quantification tools, quantification uncertainty, gene expression levels and isoform numbers on the isoform diversity calculation. We analyzed a set of CD34+ hematopoietic stem cells and myelodysplastic syndrome samples and found a set of genes whose isoform diversity change is associated with SF3B1 mutations.

AVAILABILITY AND IMPLEMENTATION

The SplicingFactory package is freely available under the GPL-3.0 license from Bioconductor for the Windows, MacOS and Linux operating systems (https://www.bioconductor.org/packages/release/bioc/html/SplicingFactory.html).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A dynamic actin-dependent nucleoskeleton and cell identity

Actin is an essential regulator of cellular functions. In the eukaryotic cell nucleus, actin regulates chromatin as a bona fide component of chromatin remodelling complexes, it associates with nuclear RNA polymerases to regulate transcription and is involved in co-transcriptional assembly of nascent RNAs into ribonucleoprotein complexes. Actin dynamics are, therefore, emerging as a major regulatory factor affecting diverse cellular processes. Importantly, the involvement of actin dynamics in nuclear functions is redefining the concept of nucleoskeleton from a rigid scaffold to a dynamic entity that is likely linked to the three-dimensional organization of the nuclear genome. In this review, we discuss how nuclear actin, by regulating chromatin structure through phase separation may contribute to the architecture of the nuclear genome during cell differentiation and facilitate the expression of specific gene programs. We focus specifically on mitochondrial genes and how their dysregulation in the absence of actin raises important questions about the role of cytoskeletal proteins in regulating chromatin structure. The discovery of a novel pool of mitochondrial actin that serves as 'mitoskeleton' to facilitate organization of mtDNA supports a general role for actin in genome architecture and a possible function of distinct actin pools in the communication between nucleus and mitochondria.

Single-nucleus RNA-seq2 reveals functional crosstalk between liver zonation and ploidy

Single-cell RNA-seq reveals the role of pathogenic cell populations in development and progression of chronic diseases. In order to expand our knowledge on cellular heterogeneity, we have developed a single-nucleus RNA-seq2 method tailored for the comprehensive analysis of the nuclear transcriptome from frozen tissues, allowing the dissection of all cell types present in the liver, regardless of cell size or cellular fragility. We use this approach to characterize the transcriptional profile of individual hepatocytes with different levels of ploidy, and have discovered that ploidy states are associated with different metabolic potential, and gene expression in tetraploid mononucleated hepatocytes is conditioned by their position within the hepatic lobule. Our work reveals a remarkable crosstalk between gene dosage and spatial distribution of hepatocytes.

Does transcriptional heterogeneity facilitate the development of genetic drug resistance?

Non-genetic forms of antimicrobial (drug) resistance can result from cell-to-cell variability that is not encoded in the genetic material. Data from recent studies also suggest that non-genetic mechanisms can facilitate the development of genetic drug resistance. We speculate on how the interplay between non-genetic and genetic mechanisms may affect microbial adaptation and evolution during drug treatment. We argue that cellular heterogeneity arising from fluctuations in gene expression, epigenetic modifications, as well as genetic changes contribute to drug resistance at different timescales, and that the interplay between these mechanisms enhance pathogen resistance. Accordingly, developing a better understanding of the role of non-genetic mechanisms in drug resistance and how they interact with genetic mechanisms will enhance our ability to combat antimicrobial resistance. Also see the video abstract here: https://youtu.be/aefGpdh-bgU.

Imaging of native transcription and transcriptional dynamics in vivo using a tagged Argonaute protein

A flexible method to image unmodified transcripts and transcription in vivo would be a valuable tool to understand the regulation and dynamics of transcription. Here, we present a novel approach to follow native transcription, with fluorescence microscopy, in live C. elegans. By using the fluorescently tagged Argonaute protein NRDE-3, programmed by exposure to defined dsRNA to bind to nascent transcripts of the gene of interest, we demonstrate transcript labelling of multiple genes, at the transcription site and in the cytoplasm. This flexible approach does not require genetic manipulation, and can be easily scaled up by relying on whole-genome dsRNA libraries. We apply this method to image the transcriptional dynamics of the heat-shock inducible gene hsp-4 (a member of the hsp70 family), as well as two transcription factors: ttx-3 (a LHX2/9 orthologue) in embryos, and hlh-1 (a MyoD orthologue) in larvae, respectively involved in neuronal and muscle development.

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

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

The Axes of Life: A roadmap for understanding dynamic multiscale systems

The biological challenges facing humanity are complex, multi-factorial, and are intimately tied to the future of our health, welfare, and stewardship of the Earth. Tackling problems in diverse areas, such as agriculture, ecology, and health care require linking vast data sets that encompass numerous components and spatio-temporal scales. Here, we provide a new framework and a road map for using experiments and computation to understand dynamic biological systems that span multiple scales. We discuss theories that can help understand complex biological systems and highlight the limitations of existing methodologies and recommend data generation practices. The advent of new technologies such as big data analytics and artificial intelligence can help bridge different scales and data types. We recommend ways to make such models transparent, compatible with existing theories of biological function, and to make biological data sets readable by advanced machine learning algorithms. Overall, the barriers for tackling pressing biological challenges are not only technological, but also sociological. Hence, we also provide recommendations for promoting interdisciplinary interactions between scientists.

Small RNA-driven feed-forward loop: fine-tuning of protein synthesis through sRNA-mediated crosstalk

Often in bacterial regulatory networks, small non-coding RNAs (sRNA) interact with several mRNA species. The competition among mRNAs for binding to the common pool of sRNA might lead to crosstalk between the mRNAs. This is similar to the competing endogenous RNA effect that leads to complex gene regulation with stabilized gene expression in Eukaryotes. Here, we study an sRNA-driven feed-forward loop (sFFL) where the top-tier regulator, an sRNA, translationally activates the target protein (TP) as well as a transcriptional activator of the TP through binding to the respective mRNAs. We show that the sRNA-mediated crosstalk between the two mRNA species enables the sFFL to function in three different regimes depending on the synthesis rate of the transcriptional activator mRNA. Of these three regimes, there exists a sensitive regime where the TP level shows interesting features depending on the precise mechanism of target translation. In the case of translation entirely from sRNA-mRNA bound complexes, the TP level becomes maximum around the sensitive regime. Through stochastic analysis and simulations, we show that relative fluctuations in the TP level is minimized here. For translation both from mRNA and sRNA-mRNA bound complexes, the target expression shows a threshold response across the sensitive regime.

Lung mediated auditory contrast enhancement improves the Signal-to-noise ratio for communication in frogs

Environmental noise is a major source of selection on animal sensory and communication systems. The acoustic signals of other animals represent particularly potent sources of noise for chorusing insects, frogs, and birds, which contend with a multi-species analog of the human "cocktail party problem" (i.e., our difficulty following speech in crowds). However, current knowledge of the diverse adaptations that function to solve noise problems in nonhuman animals remains limited. Here, we show that a lung-to-ear sound transmission pathway in frogs serves a heretofore unknown noise-control function in vertebrate hearing and sound communication. Inflated lungs improve the signal-to-noise ratio for communication by enhancing the spectral contrast in received vocalizations in ways analogous to signal processing algorithms used in hearing aids and cochlear implants. Laser vibrometry revealed that the resonance of inflated lungs selectively reduces the tympanum's sensitivity to frequencies between the two spectral peaks present in conspecific mating calls. Social network analysis of continent-scale citizen science data on frog calling behavior revealed that the calls of other frog species in multi-species choruses can be a prominent source of environmental noise attenuated by the lungs. Physiological modeling of peripheral frequency tuning indicated that inflated lungs could reduce both auditory masking and suppression of neural responses to mating calls by environmental noise. Together, these data suggest an ancient adaptation for detecting sound via the lungs has been evolutionarily co-opted to create auditory contrast enhancement that contributes to solving a multi-species cocktail party problem.

Stochastic Differential Equations for Practical Simulation of Gene Circuits

The Chemical Langevin Equation approach allows simple stochastic simulation of gene circuits under many practical situations where the number of molecules of the species involved is not extremely low. Here, we describe methods and a computational framework to simulate a population of cells containing gene circuits of interest. These methods account for both intrinsic and extrinsic noise sources, and allow us to have both individual cell-related species and population-related ones. The protocol covers aspects related to proper description of the system and setting the software tools. It also helps to deal with the optimization of data storage and the simulation precision versus computational time issue. Finally, it also gives practical tests to assess the validity of the underlying technical assumptions.

Quantitative measurements of early alphaviral replication dynamics in single cells reveals the basis for superinfection exclusion

While decades of research have elucidated many steps of the alphavirus lifecycle, the earliest replication dynamics have remained unclear. This missing time window has obscured early replicase strand-synthesis behavior and prevented elucidation of how the first events of infection might influence subsequent viral competition. Using quantitative live-cell and single-molecule imaging, we observed the initial replicase activity and its strand preferences in situ and measured the trajectory of replication over time. Under this quantitative framework, we investigated viral competition, where one alphavirus is able to exclude superinfection by a second homologous virus. We show that this appears as an indirect phenotypic consequence of a bidirectional competition between the two species, coupled with the rapid onset of viral replication and a limited total cellular carrying capacity. Together, these results emphasize the utility of analyzing viral kinetics within single cells.

Precision of tissue patterning is controlled by dynamical properties of gene regulatory networks

During development, gene regulatory networks allocate cell fates by partitioning tissues into spatially organised domains of gene expression. How the sharp boundaries that delineate these gene expression patterns arise, despite the stochasticity associated with gene regulation, is poorly understood. We show, in the vertebrate neural tube, using perturbations of coding and regulatory regions, that the structure of the regulatory network contributes to boundary precision. This is achieved, not by reducing noise in individual genes, but by the configuration of the network modulating the ability of stochastic fluctuations to initiate gene expression changes. We use a computational screen to identify network properties that influence boundary precision, revealing two dynamical mechanisms by which small gene circuits attenuate the effect of noise in order to increase patterning precision. These results highlight design principles of gene regulatory networks that produce precise patterns of gene expression.

Identification of FoxP circuits involved in locomotion and object fixation in Drosophila

The FoxP family of transcription factors is necessary for operant self-learning, an evolutionary conserved form of motor learning. The expression pattern, molecular function and mechanisms of action of the Drosophila FoxP orthologue remain to be elucidated. By editing the genomic locus of FoxP with CRISPR/Cas9, we find that the three different FoxP isoforms are expressed in neurons, but not in glia and that not all neurons express all isoforms. Furthermore, we detect FoxP expression in, e.g. the protocerebral bridge, the fan-shaped body and in motor neurons, but not in the mushroom bodies. Finally, we discover that FoxP expression during development, but not adulthood, is required for normal locomotion and landmark fixation in walking flies. While FoxP expression in the protocerebral bridge and motor neurons is involved in locomotion and landmark fixation, the FoxP gene can be excised from dorsal cluster neurons and mushroom-body Kenyon cells without affecting these behaviours.

Plasticity via feedback reduces the cost of developmental instability

Costs of plasticity are thought to have important physiological and evolutionary consequences. A commonly predicted cost to plasticity is that plastic genotypes are likely to suffer from developmental instability. Adaptive plasticity requires that the developing organism can in some way sense what environment it is in or how well it is performing in that environment. These two information pathways—an “environmental signal” or a “performance signal” that indicates how well a developing phenotype matches the optimum in the current environment—can differ in their consequences for the organism and its evolution. Here, we consider how developmental instability might emerge as a side‐effect of these two distinct mechanisms. Because a performance cue allows a regulatory feedback loop connecting a trait to a feedback signal, we hypothesized that plastic genotypes using a performance signal would be more developmentally robust compared to those using a purely environmental signal. Using a numerical model of a network of gene interactions, we show that plasticity comes at a cost of developmental instability when the plastic response is mediated via an environmental signal, but not when it is mediated via a performance signal. We also show that a performance signal mechanism can evolve even in a constant environment, leading to genotypes preadapted for plasticity to novel environments even in populations without a history of environmental heterogeneity.