Genetic flexibility of regulatory networks

RuX: A Novel, Flexible, and Sensitive Mifepristone-Induced Transcriptional Regulation System

Inducible gene regulation methods are indispensable in diverse biological applications, yet many of them have severe limitations in their applicability. These include inducer toxicity, a limited variety of organisms the given system can be used in, and side effects of the induction method. In this study, a novel inducible system, the RuX system, was created using a mutant ligand-binding domain of the glucocorticoid receptor (CS1/CD), used together with various genetic elements such as the Gal4 DNA-binding domain or Cre recombinase. The RuX system is shown to be capable of over 1000-fold inducibility, has flexible applications, and is offered for use in cell cultures.

Single-cell network biology characterizes cell type gene regulation for drug repurposing and phenotype prediction in Alzheimer's disease

Dysregulation of gene expression in Alzheimer's disease (AD) remains elusive, especially at the cell type level. Gene regulatory network, a key molecular mechanism linking transcription factors (TFs) and regulatory elements to govern gene expression, can change across cell types in the human brain and thus serve as a model for studying gene dysregulation in AD. However, AD-induced regulatory changes across brain cell types remains uncharted. To address this, we integrated single-cell multi-omics datasets to predict the gene regulatory networks of four major cell types, excitatory and inhibitory neurons, microglia and oligodendrocytes, in control and AD brains. Importantly, we analyzed and compared the structural and topological features of networks across cell types and examined changes in AD. Our analysis shows that hub TFs are largely common across cell types and AD-related changes are relatively more prominent in some cell types (e.g., microglia). The regulatory logics of enriched network motifs (e.g., feed-forward loops) further uncover cell type-specific TF-TF cooperativities in gene regulation. The cell type networks are also highly modular and several network modules with cell-type-specific expression changes in AD pathology are enriched with AD-risk genes. The further disease-module-drug association analysis suggests cell-type candidate drugs and their potential target genes. Finally, our network-based machine learning analysis systematically prioritized cell type risk genes likely involved in AD. Our strategy is validated using an independent dataset which showed that top ranked genes can predict clinical phenotypes (e.g., cognitive impairment) of AD with reasonable accuracy. Overall, this single-cell network biology analysis provides a comprehensive map linking genes, regulatory networks, cell types and drug targets and reveals cell-type gene dysregulation in AD.

Deriving disease modules from the compressed transcriptional space embedded in a deep autoencoder

Disease modules in molecular interaction maps have been useful for characterizing diseases. Yet biological networks, that commonly define such modules are incomplete and biased toward some well-studied disease genes. Here we ask whether disease-relevant modules of genes can be discovered without prior knowledge of a biological network, instead training a deep autoencoder from large transcriptional data. We hypothesize that modules could be discovered within the autoencoder representations. We find a statistically significant enrichment of genome-wide association studies (GWAS) relevant genes in the last layer, and to a successively lesser degree in the middle and first layers respectively. In contrast, we find an opposite gradient where a modular protein-protein interaction signal is strongest in the first layer, but then vanishing smoothly deeper in the network. We conclude that a data-driven discovery approach is sufficient to discover groups of disease-related genes.

Limits to a classic paradigm: most transcription factors in E. coli regulate genes involved in multiple biological processes

Transcription factors (TFs) are important drivers of cellular decision-making. When bacteria encounter a change in the environment, TFs alter the expression of a defined set of genes in order to adequately respond. It is commonly assumed that genes regulated by the same TF are involved in the same biological process. Examples of this are methods that rely on coregulation to infer function of not-yet-annotated genes. We have previously shown that only 21% of TFs involved in metabolism regulate functionally homogeneous genes, based on the proximity of the gene products’ catalyzed reactions in the metabolic network. Here, we provide more evidence to support the claim that a 1-TF/1-process relationship is not a general property. We show that the observed functional heterogeneity of regulons is not a result of the quality of the annotation of regulatory interactions, nor the absence of protein–metabolite interactions, and that it is also present when function is defined by Gene Ontology terms. Furthermore, the observed functional heterogeneity is different from the one expected by chance, supporting the notion that it is a biological property. To further explore the relationship between transcriptional regulation and metabolism, we analyzed five other types of regulatory groups and identified complex regulons (i.e. genes regulated by the same combination of TFs) as the most functionally homogeneous, and this is supported by coexpression data. Whether higher levels of related functions exist beyond metabolism and current functional annotations remains an open question.

CRP Interacts Specifically With Sxy to Activate Transcription in Escherichia coli

Horizontal gene transfer through natural competence is an important driving force of bacterial evolution and antibiotic resistance development. In several Gram-negative pathogens natural competence is regulated by the concerted action of cAMP receptor protein (CRP) and the transcriptional co-regulator Sxy through a subset of CRP-binding sites (CRP-S sites) at genes encoding competence factors. Despite the wealth of knowledge on CRP’s structure and function it is not known how CRP and Sxy act together to activate transcription. In order to get an insight into the regulatory mechanism by which these two proteins activate gene expression, we performed a series of mutational analyses on CRP and Sxy. We found that CRP contains a previously uncharacterized region necessary for Sxy dependent induction of CRP-S sites, here named “Sxy Interacting Region” (SIR) encompassing residues Q194 and L196. Lost promoter induction in SIR mutants could be restored in the presence of specific complementary Sxy mutants, presenting evidence for a direct interaction of CRP and Sxy proteins in transcriptional activation. Moreover, we identified constitutive mutants of Sxy causing higher levels of CRP-S site promoter activation than wild-type Sxy. Both suppressor and constitutive mutations are located within the same area of Sxy.

Birth and Resuscitation of (p)ppGpp Induced Antibiotic Tolerant Persister Cells

Transient antibiotic treatment typically eradicates most sensitive bacteria except a few survivors called persisters. The second messenger (p)ppGpp plays a key role in persister formation in Escherichia coli populations but the underlying mechanisms have remained elusive. In this study we induced (p)ppGpp synthesis by modulating tRNA charging and then directly observed the stochastic appearance, antibiotic tolerance, and resuscitation of persister cells using live microscopy. Different physiological parameters of persister cells as well as their regularly growing ancestors and sisters were continuously monitored using fluorescent reporters. Our results confirmed previous findings that high (p)ppGpp levels are critical for persister formation, but the phenomenon remained strikingly stochastic without any correlation between (p)ppGpp levels and antibiotic tolerance on the single-cell level. We could not confirm previous notions that persisters exhibit markedly low concentrations of intracellular ATP or were linked to post-transcriptional effects of (p)ppGpp through the activation of small genetic elements known as toxin-antitoxin (TA) modules. Instead, we suggest that persister cell formation under regular conditions is driven by the transcriptional response to increased (p)ppGpp levels.

Construction of Boolean logic gates based on dual-vector circuits of multiple gene regulatory elements

Gene circuits are constructed to run complex logical operations for the precise regulation of biological metabolic processes. At present, the implementation of most genetic circuits is based on the regulatory mechanism of various circuit components, but we hope to realize complex logic gates through biological metabolic pathways of organisms. In this study, we matched the regulatory elements of different functional mechanisms to build a Boolean logic gate model by means of a dual-vector circuit. In Escherichia coli, we made 12 circuit logic gate modules and validated the functions of four of the logic gates, including "AND", "NAND", "OR" and "NOR" by the expression and analysis of a reporter gene. The inputs were converted into outputs by an intermediate product of the host metabolism. The results indicated that these logic gate circuits had the expected efficacy and regulatory characteristics. Our study provides new ideas for designing genetic circuits and precisely controlling metabolic pathways.

Deconstructing a multiple antibiotic resistance regulation through the quantification of its input function

Many essential bacterial responses present complex transcriptional regulation of gene expression. To what extent can the study of these responses substantiate the logic of their regulation? Here, we show how the input function of the genes constituting the response, i.e., the information of how their transcription rates change as function of the signals acting on the regulators, can serve as a quantitative tool to deconstruct the corresponding regulatory logic. To demonstrate this approach, we consider the multiple antibiotic resistance (mar) response in Escherichia coli. By characterizing the input function of its representative genes in wild-type and mutant bacteria, we recognize a dual autoregulation motif as main determinant of the response, which is further adjusted by the interplay with other regulators. We show that basic attributes, like its reaction to a wide range of stress or its moderate expression change, are associated with a strong negative autoregulation, while others, like the buffering of metabolic signals or the lack of memory to previous stress, are related to a weak positive autoregulation. With a mathematical model of the input functions, we identify some constraints fixing the molecular attributes of the regulators, and also notice the relevance of the bicystronic architecture harboring the dual autoregulation that is unique in E. coli. The input function emerges then as a tool to disentangle the rationale behind most of the attributes defining the mar phenotype. Overall, the present study supports the value of characterizing input functions to deconstruct the complexity of regulatory architectures in prokaryotic and eukaryotic systems.

Many cellular responses result from the integration of numerous regulatory signals. To deconstruct the regulation of one of these responses, which enables resistance to multiple antibiotics in Escherichia coli, a team led by Juan F. Poyatos at the National Center for Biotechnology in Madrid studied the response input function by combining theoretical models and experiments. This function quantifies the rate of transcription of the genes constituting the response with respect to the signals acting on its cognate regulators. By examining how the shape of the function changes in different situations, e.g., when a given regulator is mutated, the team identified the implications for the specificity and dynamics of the response of a dual autoregulation at the core of the control architecture. The use of input functions as quantitative tools allows us to reverse engineer the complex regulations that dictate essential physiological functions in both prokaryotic and eukaryotic cells.

Repressor logic modules assembled by rolling circle amplification platform to construct a set of logic gates

Small molecule metabolites and their allosterically regulated repressors play an important role in many gene expression and metabolic disorder processes. These natural sensors, though valuable as good logic switches, have rarely been employed without transcription machinery in cells. Here, two pairs of repressors, which function in opposite ways, were cloned, purified and used to control DNA replication in rolling circle amplification (RCA) in vitro. By using metabolites and repressors as inputs, RCA signals as outputs, four basic logic modules were constructed successfully. To achieve various logic computations based on these basic modules, we designed series and parallel strategies of circular templates, which can further assemble these repressor modules in an RCA platform to realize twelve two-input Boolean logic gates and a three-input logic gate. The RCA-output and RCA-assembled platform was proved to be easy and flexible for complex logic processes and might have application potential in molecular computing and synthetic biology.

Noise propagation with interlinked feed-forward pathways

Functionally similar pathways are often seen in biological systems, forming feed-forward controls. The robustness in network motifs such as feed-forward loops (FFLs) has been reported previously. In this work, we studied noise propagation in a development network that has multiple interlinked FFLs. A FFL has the potential of asymmetric noise-filtering (i.e., it works at either the “ON” or the “OFF” state in the target gene). With multiple, interlinked FFLs, we show that the propagated noises are largely filtered regardless of the states in the input genes. The noise-filtering property of an interlinked FFL can be largely derived from that of the individual FFLs, and with interlinked FFLs, it is possible to filter noises in both “ON” and “OFF” states in the output. We demonstrated the noise filtering effect in the developmental regulatory network of Caenorhabditis elegans that controls the timing of distal tip cell (DTC) migration. The roles of positive feedback loops involving blmp-1 and the degradation regulation of DRE-1 also studied. Our analyses allow for better inference from network structures to noise-filtering properties, and provide insights into the mechanisms behind the precise DTC migration controls in space and time.

Engineering Synthetic cis-Regulatory Elements for Simultaneous Recognition of Three Transcriptional Factors in Bacteria

Recognition of cis-regulatory elements by transcription factors (TF) at target promoters is crucial to gene regulation in bacteria. In this process, binding of TFs to their cognate sequences depends on a set of physical interactions between these proteins and specific nucleotides in the operator region. Previously, we showed that in silico optimization algorithms are able to generate short sequences that are recognized by two different TFs of Escherichia coli, namely, CRP and IHF, thus generating an AND logic gate. Here, we expanded this approach in order to engineer DNA sequences that can be simultaneously recognized by three unrelated TFs (CRP, IHF, and Fis). Using in silico optimization and experimental validation strategies, we were able to obtain a candidate promoter (Plac-CFI1) regulated by only two TFs with an AND logic, thus demonstrating a limitation in the design. Subsequently, we modified the algorithm to allow the optimization of extended sequences, and were able to design two synthetic promoters (PCFI20-1 and PCFI22-5) that were functional in vivo. Expression assays in E. coli mutant strains for each TF revealed that while CRP positively regulates the promoter activities, IHF and Fis are strong repressors of both the promoter variants. Taken together, our results demonstrate the potential of in silico strategies in bacterial synthetic promoter engineering. Furthermore, the study also shows how small modifications in cis-regulatory elements can drastically affect the final logic of the resulting promoter.

Evolutionary Design of Choline-Inducible and -Repressible T7-Based Induction Systems

By assembly and evolutionary engineering of T7-phage-based transcriptional switches made from endogenous components of the bet operon on the Escherichia coli chromosome, genetic switches inducible by choline, a safe and inexpensive compound, were constructed. The functional plasticity of the BetI repressor was revealed by rapid and high-frequency identification of functional variants with various properties, including those with high stringency, high maximum expression level, and reversed phenotypes, from a pool of BetI mutants. The plasmid expression of BetI mutants resulted in the choline-inducible (Bet-ON) or choline-repressible (Bet-OFF) switching of genes under the pT7/betO sequence at unprecedentedly high levels, while keeping the minimal leaky expression in uninduced conditions.

Evolution of Intra-specific Regulatory Networks in a Multipartite Bacterial Genome

Reconstruction of the regulatory network is an important step in understanding how organisms control the expression of gene products and therefore phenotypes. Recent studies have pointed out the importance of regulatory network plasticity in bacterial adaptation and evolution. The evolution of such networks within and outside the species boundary is however still obscure. Sinorhizobium meliloti is an ideal species for such study, having three large replicons, many genomes available and a significant knowledge of its transcription factors (TF). Each replicon has a specific functional and evolutionary mark; which might also emerge from the analysis of their regulatory signatures. Here we have studied the plasticity of the regulatory network within and outside the S. meliloti species, looking for the presence of 41 TFs binding motifs in 51 strains and 5 related rhizobial species. We have detected a preference of several TFs for one of the three replicons, and the function of regulated genes was found to be in accordance with the overall replicon functional signature: house-keeping functions for the chromosome, metabolism for the chromid, symbiosis for the megaplasmid. This therefore suggests a replicon-specific wiring of the regulatory network in the S. meliloti species. At the same time a significant part of the predicted regulatory network is shared between the chromosome and the chromid, thus adding an additional layer by which the chromid integrates itself in the core genome. Furthermore, the regulatory network distance was found to be correlated with both promoter regions and accessory genome evolution inside the species, indicating that both pangenome compartments are involved in the regulatory network evolution. We also observed that genes which are not included in the species regulatory network are more likely to belong to the accessory genome, indicating that regulatory interactions should also be considered to predict gene conservation in bacterial pangenomes.

The influence of transcriptional regulatory networks on the evolution of bacterial pangenomes has not yet been elucidated, even though the role of transcriptional regulation is widely recognized. Using the model symbiont Sinorhizobium meliloti we have predicted the regulatory targets of 41 transcription factors in 51 strains and 5 other rhizobial species, showing a correlation between regulon diversity and pangenome evolution, through upstream sequence diversity and accessory genome composition. We have also shown that genes not wired to the regulatory network are more likely to belong to the accessory genome, thus suggesting that inclusion in the regulatory circuits may be an indicator of gene conservation. We have also highlighted a series of transcription factors that preferentially regulate genes belonging to one of the three replicons of this species, indicating the presence of replicon-specific regulatory modules, with peculiar functional signatures. At the same time the chromid shares a significant part of the regulatory network with the chromosome, indicating an additional way by which this replicon integrates itself in the pangenome.

Function does not follow form in gene regulatory circuits

Gene regulatory circuits are to the cell what arithmetic logic units are to the chip: fundamental components of information processing that map an input onto an output. Gene regulatory circuits come in many different forms, distinct structural configurations that determine who regulates whom. Studies that have focused on the gene expression patterns (functions) of circuits with a given structure (form) have examined just a few structures or gene expression patterns. Here, we use a computational model to exhaustively characterize the gene expression patterns of nearly 17 million three-gene circuits in order to systematically explore the relationship between circuit form and function. Three main conclusions emerge. First, function does not follow form. A circuit of any one structure can have between twelve and nearly thirty thousand distinct gene expression patterns. Second, and conversely, form does not follow function. Most gene expression patterns can be realized by more than one circuit structure. And third, multifunctionality severely constrains circuit form. The number of circuit structures able to drive multiple gene expression patterns decreases rapidly with the number of these patterns. These results indicate that it is generally not possible to infer circuit function from circuit form, or vice versa.

Multilevel Regulation and Translational Switches in Synthetic Biology

In contrast to the versatility of regulatory mechanisms in natural systems, synthetic genetic circuits have been so far predominantly composed of transcriptionally regulated modules. This is about to change as the repertoire of foundational tools for post-transcriptional regulation is quickly expanding. We provide an overview of the different types of translational regulators: protein, small molecule and ribonucleic acid (RNA) responsive and we describe the new emerging circuit designs utilizing these tools. There are several advantages of achieving multilevel regulation via translational switches and it is likely that such designs will have the greatest and earliest impact in mammalian synthetic biology for regenerative medicine and gene therapy applications.

Effects of Four Different Regulatory Mechanisms on the Dynamics of Gene Regulatory Cascades

Gene regulatory cascades (GRCs) are common motifs in cellular molecular networks. A given logical function in these cascades, such as the repression of the activity of a transcription factor, can be implemented by a number of different regulatory mechanisms. The potential consequences for the dynamic performance of the GRC of choosing one mechanism over another have not been analysed systematically. Here, we report the construction of a synthetic GRC in Escherichia coli, which allows us for the first time to directly compare and contrast the dynamics of four different regulatory mechanisms, affecting the transcription, translation, stability, or activity of a transcriptional repressor. We developed a biologically motivated mathematical model which is sufficient to reproduce the response dynamics determined by experimental measurements. Using the model, we explored the potential response dynamics that the constructed GRC can perform. We conclude that dynamic differences between regulatory mechanisms at an individual step in a GRC are often concealed in the overall performance of the GRC, and suggest that the presence of a given regulatory mechanism in a certain network environment does not necessarily mean that it represents a single optimal evolutionary solution.

Coupled positive and negative feedbacks produce diverse gene expression patterns in colonies

Formation of patterns is a common feature in the development of multicellular organism as well as of microbial communities. To investigate the formation of gene expression patterns in colonies, we build a mathematical model of two-dimensional colony growth, where cells carry a coupled positive-and-negative-feedback circuit. We demonstrate that the model can produce sectored, target (concentric), uniform, and scattered expression patterns of regulators, depending on gene expression dynamics and nutrient diffusion. We reconstructed the same regulatory structure in Escherichia coli cells and found gene expression patterns on the surface of colonies similar to the ones produced by the computer simulations. By comparing computer simulations and experimental results, we observed that very simple rules of gene expression can yield a spectrum of well-defined patterns in a growing colony. Our results suggest that variations of the protein content among cells lead to a high level of heterogeneity in colonies.

Importance Formation of patterns is a common feature in the development of microbial communities. In this work, we show that a simple genetic circuit composed of a positive-feedback loop and a negative-feedback loop can produce diverse expression patterns in colonies. We obtained similar sets of gene expression patterns in the simulations and in the experiments. Because the combination of positive feedback and negative feedback is common in intracellular molecular networks, our results suggest that the protein content of cells is highly diversified in colonies.

Formation of patterns is a common feature in the development of microbial communities. In this work, we show that a simple genetic circuit composed of a positive-feedback loop and a negative-feedback loop can produce diverse expression patterns in colonies. We obtained similar sets of gene expression patterns in the simulations and in the experiments. Because the combination of positive feedback and negative feedback is common in intracellular molecular networks, our results suggest that the protein content of cells is highly diversified in colonies.

Expanding the logic of bacterial promoters using engineered overlapping operators for global regulators

The understanding of how the architecture of cis-regulatory elements at bacterial promoters determines their final output is of central interest in modern biology. In this work, we attempt to gain insight into this process by analyzing complex promoter architectures in the model organism Escherichia coli. By focusing on the relationship between different TFs at the genomic scale in terms of their binding site arrangement and their effect on the target promoters, we found no strong constraint limiting the combinatorial assembly of TF pairs in E. coli. More strikingly, overlapping binding sites were found equally associated with both equivalent (both TFs have the same effect on the promoter) and opposite (one TF activates while the other repress the promoter) effects on gene expression. With this information on hand, we set an in silico approach to design overlapping sites for three global regulators (GRs) of E. coli, specifically CRP, Fis, and IHF. Using random sequence assembly and an evolutionary algorithm, we were able to identify potential overlapping operators for all TF pairs. In order to validate our prediction, we constructed two lac promoter variants containing overlapping sites for CRP and IHF designed in silico. By assaying the synthetic promoters using a GFP reporter system, we demonstrated that these variants were functional and activated by CRP and IHF in vivo. Taken together, presented results add new information on the mechanisms of signal integration in bacterial promoters and provide new strategies for the engineering of synthetic regulatory circuits in bacteria.

Mutations in transcriptional regulators allow selective engineering of signal integration logic

Bacterial cells monitor their environment by sensing a set of signals. Typically, these environmental signals affect promoter activities by altering the activity of transcription regulatory proteins. Promoters are often regulated by more than one regulatory protein, and in these cases the relevant signals are integrated by certain logic. In this work, we study how single amino acid substitutions in a regulatory protein (GalR) affect transcriptional regulation and signal integration logic at a set of engineered promoters. Our results suggest that point mutations in regulatory genes allow independent evolution of regulatory logic at different promoters.

IMPORTANCE

Gene regulatory networks are built from simple building blocks, such as promoters, transcription regulatory proteins, and their binding sites on DNA. Many promoters are regulated by more than one regulatory input. In these cases, the inputs are integrated and allow transcription only in certain combinations of input signals. Gene regulatory networks can be easily rewired, because the function of cis-regulatory elements and promoters can be altered by point mutations. In this work, we tested how point mutations in transcription regulatory proteins can affect signal integration logic. We found that such mutations allow context-dependent engineering of signal integration logic at promoters, further contributing to the plasticity of gene regulatory networks.

Latent phenotypes pervade gene regulatory circuits

BACKGROUND

Latent phenotypes are non-adaptive byproducts of adaptive phenotypes. They exist in biological systems as different as promiscuous enzymes and genome-scale metabolic reaction networks, and can give rise to evolutionary adaptations and innovations. We know little about their prevalence in the gene expression phenotypes of regulatory circuits, important sources of evolutionary innovations.

RESULTS

Here, we study a space of more than sixteen million three-gene model regulatory circuits, where each circuit is represented by a genotype, and has one or more functions embodied in one or more gene expression phenotypes. We find that the majority of circuits with single functions have latent expression phenotypes. Moreover, the set of circuits with a given spectrum of functions has a repertoire of latent phenotypes that is much larger than that of any one circuit. Most of this latent repertoire can be easily accessed through a series of small genetic changes that preserve a circuit's main functions. Both circuits and gene expression phenotypes that are robust to genetic change are associated with a greater number of latent phenotypes.

CONCLUSIONS

Our observations suggest that latent phenotypes are pervasive in regulatory circuits, and may thus be an important source of evolutionary adaptations and innovations involving gene regulation.

Engineering multicellular logic in bacteria with metabolic wires

Aromatic biodegradation pathways of environmental bacteria are vast sources of matching trios of enzymes, substrates and regulators that can be refactored to run logic operations through cell-to-cell communication. As a proof of concept, the connection between two Pseudomonas putida strains using benzoic acid as the wiring molecule is presented. In this system, a sender strain harboring the TOL pathway for biodegradation of aromatics processed toluene as input and generated benzoate as the output signal. Diffusion of such metabolic intermediate to the medium was then sensed by a second strain (the receiver) that used benzoate as input for a new logic gate producing a visual output (i.e., light emission). The setup was functional irrespective of whether sender and receiver cells were in direct contact or in liquid culture. These results highlight the potential of environmental metabolic pathways as sources of building blocks for the engineering of multicellular logic in prokaryotic systems.

A mixed incoherent feed-forward loop allows conditional regulation of response dynamics

Expression of the SodA superoxide dismutase (MnSOD) in Escherichia coli is regulated by superoxide concentration through the SoxRS system and also by Fur (Ferric uptake regulator) through a mixed incoherent feed forward loop (FFL) containing the RyhB small regulatory RNA. In this work I theoretically analyze the function of this feed forward loop as part of the network controlling expression of the two cytoplasmic superoxide dismutases, SodA and SodB. I find that feed forward regulation allows faster response to superoxide stress at low intracellular iron levels compared to iron rich conditions. That is, it can conditionally modulate the response time of a superimposed transcriptional control mechanism.

Robustness, evolvability, and the logic of genetic regulation

In gene regulatory circuits, the expression of individual genes is commonly modulated by a set of regulating gene products, which bind to a gene's cis-regulatory region. This region encodes an input-output function, referred to as signal-integration logic, that maps a specific combination of regulatory signals (inputs) to a particular expression state (output) of a gene. The space of all possible signal-integration functions is vast and the mapping from input to output is many-to-one: For the same set of inputs, many functions (genotypes) yield the same expression output (phenotype). Here, we exhaustively enumerate the set of signal-integration functions that yield identical gene expression patterns within a computational model of gene regulatory circuits. Our goal is to characterize the relationship between robustness and evolvability in the signal-integration space of regulatory circuits, and to understand how these properties vary between the genotypic and phenotypic scales. Among other results, we find that the distributions of genotypic robustness are skewed, so that the majority of signal-integration functions are robust to perturbation. We show that the connected set of genotypes that make up a given phenotype are constrained to specific regions of the space of all possible signal-integration functions, but that as the distance between genotypes increases, so does their capacity for unique innovations. In addition, we find that robust phenotypes are (i) evolvable, (ii) easily identified by random mutation, and (iii) mutationally biased toward other robust phenotypes. We explore the implications of these latter observations for mutation-based evolution by conducting random walks between randomly chosen source and target phenotypes. We demonstrate that the time required to identify the target phenotype is independent of the properties of the source phenotype.

Multi-input regulation and logic with T7 promoters in cells and cell-free systems

Engineered gene circuits offer an opportunity to harness biological systems for biotechnological and biomedical applications. However, reliance on native host promoters for the construction of circuit elements, such as logic gates, can make the implementation of predictable, independently functioning circuits difficult. In contrast, T7 promoters offer a simple orthogonal expression system for use in a variety of cellular backgrounds and even in cell-free systems. Here we develop a T7 promoter system that can be regulated by two different transcriptional repressors for the construction of a logic gate that functions in cells and in cell-free systems. We first present LacI repressible T7lacO promoters that are regulated from a distal lac operator site for repression. We next explore the positioning of a tet operator site within the T7lacO framework to create T7 promoters that respond to tet and lac repressors and realize an IMPLIES gate. Finally, we demonstrate that these dual input sensitive promoters function in an E. coli cell-free protein expression system. Our results expand the utility of T7 promoters in cell based as well as cell-free synthetic biology applications.

Expanding the boolean logic of the prokaryotic transcription factor XylR by functionalization of permissive sites with a protease-target sequence

The σ54-dependent prokaryotic regulator XylR implements a one-input/one-output actuator that transduces the presence of the aromatic effector m-xylene into transcriptional activation of the cognate promoter Pu. Such a signal conversion involves the effector-mediated release of the intramolecular repression of the N-terminal A domain on the central C module of XylR. On this background, we set out to endow this regulator with additional signal-sensing capabilities by inserting a target site of the viral protease NIa in permissive protein locations that once cleaved in vivo could either terminate XylR activity or generate an effector-independent, constitutive transcription factor. To find optimal protein positions to this end, we saturated the xylR gene DNA with a synthetic transposable element designed for randomly delivering in-frame polypeptides throughout the sequence of any given protein. This Tn5-based system supplies the target gene with insertions of a selectable marker that can later be excised, leaving behind the desired (poly) peptides grafted into the protein structure. Implementation of such knock-in-leave-behind (KILB) method to XylR was instrumental to produce a number of variants of this transcription factor (TF) that could compute in vivo two inputs (m-xylene and protease) into a single output following a logic that was dependent on the site of the insertion of the NIa target sequence in the TF. Such NIa-sensitive XylR specimens afforded the design of novel regulatory nodes that entered protease expression as one of the signals recognized in vivo for controlling Pu. This approach is bound to facilitate the functionalization of TFs and other proteins with new traits, especially when their forward engineering is made difficult by, for example, the absence of structural data.

The influence of assortativity on the robustness and evolvability of gene regulatory networks upon gene birth

Gene regulatory networks (GRNs) represent the interactions between genes and gene products, which drive the gene expression patterns that produce cellular phenotypes. GRNs display a number of characteristics that are beneficial for the development and evolution of organisms. For example, they are often robust to genetic perturbation, such as mutations in regulatory regions or loss of gene function. Simultaneously, GRNs are often evolvable as these genetic perturbations are occasionally exploited to innovate novel regulatory programs. Several topological properties, such as degree distribution, are known to influence the robustness and evolvability of GRNs. Assortativity, which measures the propensity of nodes of similar connectivity to connect to one another, is a separate topological property that has recently been shown to influence the robustness of GRNs to point mutations in cis-regulatory regions. However, it remains to be seen how assortativity may influence the robustness and evolvability of GRNs to other forms of genetic perturbation, such as gene birth via duplication or de novo origination. Here, we employ a computational model of genetic regulation to investigate whether the assortativity of a GRN influences its robustness and evolvability upon gene birth. We find that the robustness of a GRN generally increases with increasing assortativity, while its evolvability generally decreases. However, the rate of change in robustness outpaces that of evolvability, resulting in an increased proportion of assortative GRNs that are simultaneously robust and evolvable. By providing a mechanistic explanation for these observations, this work extends our understanding of how the assortativity of a GRN influences its robustness and evolvability upon gene birth.

Constraint and contingency in multifunctional gene regulatory circuits

Gene regulatory circuits drive the development, physiology, and behavior of organisms from bacteria to humans. The phenotypes or functions of such circuits are embodied in the gene expression patterns they form. Regulatory circuits are typically multifunctional, forming distinct gene expression patterns in different embryonic stages, tissues, or physiological states. Any one circuit with a single function can be realized by many different regulatory genotypes. Multifunctionality presumably constrains this number, but we do not know to what extent. We here exhaustively characterize a genotype space harboring millions of model regulatory circuits and all their possible functions. As a circuit's number of functions increases, the number of genotypes with a given number of functions decreases exponentially but can remain very large for a modest number of functions. However, the sets of circuits that can form any one set of functions becomes increasingly fragmented. As a result, historical contingency becomes widespread in circuits with many functions. Whether a circuit can acquire an additional function in the course of its evolution becomes increasingly dependent on the function it already has. Circuits with many functions also become increasingly brittle and sensitive to mutation. These observations are generic properties of a broad class of circuits and independent of any one circuit genotype or phenotype.

Circuit-level input integration in bacterial gene regulation

Gene regulatory circuits can receive multiple simultaneous inputs, which can enter the system through different locations. It is thus necessary to establish how these genetic circuits integrate multiple inputs as a function of their relative entry points. Here, we use the dynamic circuit regulating competence for DNA uptake in Bacillus subtilis as a model system to investigate this issue. Specifically, we map the response of single cells in vivo to a combination of (i) a chemical signal controlling the constitutive expression of key competence genes, and (ii) a genetic perturbation in the form of copy number variation of one of these genes, which mimics the level of stress signals sensed by the bacteria. Quantitative time-lapse fluorescence microscopy shows that a variety of dynamical behaviors can be reached by the combination of the two inputs. Additionally, the integration depends strongly on the relative locations where the two perturbations enter the circuit. Specifically, when the two inputs act upon different circuit elements, their integration generates novel dynamical behavior, whereas inputs affecting the same element do not. An in silico bidimensional bifurcation analysis of a mathematical model of the circuit offers good quantitative agreement with the experimental observations, and sheds light on the dynamical mechanisms leading to the different integrated responses exhibited by the gene regulatory circuit.

Effects of growth and mutation on pattern formation in tissues

In many developing tissues, neighboring cells enter different developmental pathways, resulting in a fine-grained pattern of different cell states. The most common mechanism that generates such patterns is lateral inhibition, for example through Delta-Notch coupling. In this work, we simulate growth of tissues consisting of a hexagonal arrangement of cells laterally inhibiting their neighbors. We find that tissue growth by cell division and cell migration tends to produce ordered patterns, whereas lateral growth leads to disordered, patchy patterns. Ordered patterns are very robust to mutations (gene silencing or activation) in single cells. In contrast, mutation in a cell of a disordered tissue can produce a larger and more widespread perturbation of the pattern. In tissues where ordered and disordered patches coexist, the perturbations spread mostly at boundaries between patches. If cell division occurs on time scales faster than the degradation time, disordered patches will appear. Our work suggests that careful experimental characterization of the disorder in tissues could pinpoint where and how the tissue is susceptible to large-scale damage even from single cell mutations.

Digital clocks: simple Boolean models can quantitatively describe circadian systems

The gene networks that comprise the circadian clock modulate biological function across a range of scales, from gene expression to performance and adaptive behaviour. The clock functions by generating endogenous rhythms that can be entrained to the external 24-h day–night cycle, enabling organisms to optimally time biochemical processes relative to dawn and dusk. In recent years, computational models based on differential equations have become useful tools for dissecting and quantifying the complex regulatory relationships underlying the clock's oscillatory dynamics. However, optimizing the large parameter sets characteristic of these models places intense demands on both computational and experimental resources, limiting the scope of in silico studies. Here, we develop an approach based on Boolean logic that dramatically reduces the parametrization, making the state and parameter spaces finite and tractable. We introduce efficient methods for fitting Boolean models to molecular data, successfully demonstrating their application to synthetic time courses generated by a number of established clock models, as well as experimental expression levels measured using luciferase imaging. Our results indicate that despite their relative simplicity, logic models can (i) simulate circadian oscillations with the correct, experimentally observed phase relationships among genes and (ii) flexibly entrain to light stimuli, reproducing the complex responses to variations in daylength generated by more detailed differential equation formulations. Our work also demonstrates that logic models have sufficient predictive power to identify optimal regulatory structures from experimental data. By presenting the first Boolean models of circadian circuits together with general techniques for their optimization, we hope to establish a new framework for the systematic modelling of more complex clocks, as well as other circuits with different qualitative dynamics. In particular, we anticipate that the ability of logic models to provide a computationally efficient representation of system behaviour could greatly facilitate the reverse-engineering of large-scale biochemical networks.

The influence of assortativity on the robustness of signal-integration logic in gene regulatory networks

Gene regulatory networks (GRNs) drive the cellular processes that sustain life. To do so reliably, GRNs must be robust to perturbations, such as gene deletion and the addition or removal of regulatory interactions. GRNs must also be robust to genetic changes in regulatory regions that define the logic of signal-integration, as these changes can affect how specific combinations of regulatory signals are mapped to particular gene expression states. Previous theoretical analyses have demonstrated that the robustness of a GRN is influenced by its underlying topological properties, such as degree distribution and modularity. Another important topological property is assortativity, which measures the propensity with which nodes of similar connectivity are connected to one another. How assortativity influences the robustness of the signal-integration logic of GRNs remains an open question. Here, we use computational models of GRNs to investigate this relationship. We separately consider each of the three dynamical regimes of this model for a variety of degree distributions. We find that in the chaotic regime, robustness exhibits a pronounced increase as assortativity becomes more positive, while in the critical and ordered regimes, robustness is generally less sensitive to changes in assortativity. We attribute the increased robustness to a decrease in the duration of the gene expression pattern, which is caused by a reduction in the average size of a GRN's in-components. This study provides the first direct evidence that assortativity influences the robustness of the signal-integration logic of computational models of GRNs, illuminates a mechanistic explanation for this influence, and furthers our understanding of the relationship between topology and robustness in complex biological systems.

Continuous computation in engineered gene circuits

In this paper we consider the problem of representation and measurement in genetic circuits, and investigate how they can affect the reliability of engineered systems. We propose a design scheme, based on the notion of continuous computation, which addresses these issues. We illustrate the methodology by showing how a concept from computer architecture (namely, branch prediction) may be implemented in vivo, using a distributed approach. Simulation results confirm the in-principle feasibility of our method, and offer valuable insights into its future laboratory validation.

A survey of sRNA families in α-proteobacteria

We have performed a computational comparative analysis of six small non-coding RNA (sRNA) families in α-proteobacteria. Members of these families were first identified in the intergenic regions of the nitrogen-fixing endosymbiont S. meliloti by a combined bioinformatics screen followed by experimental verification. Consensus secondary structures inferred from covariance models for each sRNA family evidenced in some cases conserved motifs putatively relevant to the function of trans-encoded base-pairing sRNAs i.e., Hfq-binding signatures and exposed anti Shine-Dalgarno sequences. Two particular family models, namely αr15 and αr35, shared own sub-structural modules with the Rfam model suhB (RF00519) and the uncharacterized sRNA family αr35b, respectively. A third sRNA family, termed αr45, has homology to the cis-acting regulatory element speF (RF00518). However, new experimental data further confirmed that the S. meliloti αr45 representative is an Hfq-binding sRNA processed from or expressed independently of speF, thus refining the Rfam speF model annotation. All the six families have members in phylogenetically related plant-interacting bacteria and animal pathogens of the order of the Rhizobiales, some occurring with high levels of paralogy in individual genomes. In silico and experimental evidences predict differential regulation of paralogous sRNAs in S. meliloti 1021. The distribution patterns of these sRNA families suggest major contributions of vertical inheritance and extensive ancestral duplication events to the evolution of sRNAs in plant-interacting bacteria.

Structure and function of the D-galactose network in enterobacteria

Galactose is important for the survival and virulence of bacteria. In Escherichia coli, galactose is utilized by the Leloir pathway, which is controlled by a complex network. To shed light on the potential functions the galactose network could perform, we performed bioinformatical analysis of reference genome sequences belonging to the Enterobacteriaceae family. We found that several genomes have reduced numbers of components compared to the E. coli galactose system, suggesting that the network can be optimized for different environments. Typically, genes are removed by deletions; however, in Yersinia pestis, the galactose mutarotase (galM) gene is inactivated by a single-base-pair deletion. Lack of GalM activity indicates that the two anomers of d-galactose are used for different purposes, α-d-galactose as a carbon source and β-d-galactose for induction of UDP-galactose synthesis for biosynthetic glycosylation. We demonstrate that activity of the galM gene can be restored by different single-base-pair insertions. During the evolution of Y. pestis to become a vector-transmitted systemic pathogen, many genes were converted to pseudogenes. It is not clear whether pseudogenes are present to maintain meiotrophism or are in the process of elimination. Our results suggest that the galM pseudogene has not been deleted because its reactivation may be beneficial in certain environments.

Evolution of bacteria to populate a new environment necessarily involves reengineering of their molecular network. Members of the Enterobacteriaceae family of bacteria have diverse lifestyles and can function in a wide range of environments. In this study we performed bioinformatical analysis of 34 reference genome sequences belonging to the Enterobacteriaceae family to gain insight into the natural diversity of the d-galactose utilization network. Our bioinformatical analysis shows that in several species, some genes of the network are completely missing or are inactivated by large deletions. The only exception is the galactose mutarotase (galM) gene of Yersinia pestis, which is converted to a pseudogene by a single-base-pair deletion. In this paper, we discuss the possible consequences of galM inactivation on network function. We suggest that galM was converted to a pseudogene rather than being deleted in evolution because its reactivation can be beneficial in certain environments.

Direct and indirect effects in the regulation of overlapping promoters

Optimal response to environmental stimuli often requires activation of certain genes and repression of others. Dual function regulatory proteins play a key role in the differential regulation of gene expression. While repression can be achieved by any DNA binding protein through steric occlusion of RNA polymerase in the promoter region, activation often requires a surface on the regulatory protein to contact RNAP and thus facilitate transcription initiation. RNAP itself is also a DNA binding protein, therefore it can function as a transcriptional repressor. Searching the Escherichia coli promoter database we found that ∼14% of the identified ‘forward’ promoters overlap with a promoter oriented in the opposite direction. In this article we combine a mathematical model with experimental analysis of synthetic regulatory regions to investigate interference of overlapping promoters. We find that promoter interference depends on the characteristics of overlapping promoters. The model predicts that promoter strength and interference can be regulated separately, which provides unique opportunities for regulation. Our experimental data suggest that in principle any DNA binding protein can be used for both activation and repression of promoter transcription, depending on the context. These findings can be exploited in the construction of synthetic networks.

Implementing an OR-NOT (ORN) logic gate with components of the SOS regulatory network of Escherichia coli

Whether biological or electronic, man-engineered computation is based on logic circuits assembled with binary gates that are interconnected to perform Boolean operations. We report here the rewiring of the SOS system of Escherichia in a fashion that makes the output of both the recA and lexA promoters to faithfully follow the pattern of a binary composite OR-NOT gate (ORN) in which the inputs are DNA damage (e.g. nalidixic acid addition) and IPTG as an exogenous signal. Unlike other non-natural gates whose implementation requires changes in genes and promoters of the genome of the host cells, this ORN was brought about by the sole addition of wild-type bacteria with a plasmid encoding a module for LacI(q)-dependent expression of lexA. Specifically, we demonstrate that the interplay between native, chromosomally-encoded components of the SOS system and the extra parts engineered in such a plasmid made the desired performance to happen without any modification of the core DNA-damage response network. It is thus possible to artificially interface autonomous cell networks with a predetermined logic by means of Boolean gates built with regulatory elements already functioning in the recipient organism.

The logicome of environmental bacteria: merging catabolic and regulatory events with Boolean formalisms

The regulatory and metabolic networks that rule biodegradation of pollutants by environmental bacteria are wired to the rest of the cellular physiology through both transcriptional factors and intermediary signal molecules. In this review, we examine some formalisms for describing catalytic/regulatory circuits of this sort and advocate the adoption of Boolean logic for combining transcriptional and enzymatic occurrences in the same biological system. As an example, we show how known regulatory and metabolic actions that bring about biodegradation of m-xylene by Pseudomonas putida mt-2 can be represented as clusters of binary operations and then reconstructed as a digital network. Despite the many simplifications, Boolean tools still capture the gross behaviour of the system even in the absence of kinetic constants determined experimentally. On this basis, we argue that still with a limited volume of data binary formalisms allow us to penetrate the raison d'être of extant regulatory and metabolic architectures.

Timing of gene transcription in the galactose utilization system of Escherichia coli

In the natural environment, bacterial cells have to adjust their metabolism to alterations in the availability of food sources. The order and timing of gene expression are crucial in these situations to produce an appropriate response. We used the galactose regulation in Escherichia coli as a model system for understanding how cells integrate information about food availability and cAMP levels to adjust the timing and intensity of gene expression. We simulated the feast-famine cycle of bacterial growth by diluting stationary phase cells in fresh medium containing galactose as the sole carbon source. We followed the activities of six promoters of the galactose system as cells grew on and ran out of galactose. We found that the cell responds to a decreasing external galactose level by increasing the internal galactose level, which is achieved by limiting galactose metabolism and increasing the expression of transporters. We show that the cell alters gene expression based primarily on the current state of the cell and not on monitoring the level of extracellular galactose in real time. Some decisions have longer term effects; therefore, the current state does subtly encode the history of food availability. In summary, our measurements of timing of gene expression in the galactose system suggest that the system has evolved to respond to environments where future galactose levels are unpredictable rather than regular feast and famine cycles.