Mode of regulation and the insulation of bacterial gene expression

Plug-in repressor library for precise regulation of metabolic flux in Escherichia coli

In metabolic engineering, enhanced production of value-added chemicals requires precise flux control between growth-essential competing and production pathways. Although advances in synthetic biology have facilitated the exploitation of a number of genetic elements for precise flux control, their use requires expensive inducers, or more importantly, needs complex and time-consuming processes to design and optimize appropriate regulator components, case-by-case. To overcome this issue, we devised the plug-in repressor libraries for target-specific flux control, in which expression levels of the repressors were diversified using degenerate 5' untranslated region (5' UTR) sequences employing the UTR Library Designer. After we validated a wide expression range of the repressor libraries, they were applied to improve the production of lycopene from glucose and 3-hydroxypropionic acid (3-HP) from acetate in Escherichia coli via precise flux rebalancing to enlarge precursor pools. Consequently, we successfully achieved optimal carbon fluxes around the precursor nodes for efficient production. The most optimized strains were observed to produce 2.59 g/L of 3-HP and 11.66 mg/L of lycopene, which were improved 16.5-fold and 2.82-fold, respectively, compared to those produced by the parental strains. These results indicate that carbon flux rebalancing using the plug-in library is a powerful strategy for efficient production of value-added chemicals in E. coli.

The relation between crosstalk and gene regulation form revisited

Genes differ in the frequency at which they are expressed and in the form of regulation used to control their activity. In particular, positive or negative regulation can lead to activation of a gene in response to an external signal. Previous works proposed that the form of regulation of a gene correlates with its frequency of usage: positive regulation when the gene is frequently expressed and negative regulation when infrequently expressed. Such network design means that, in the absence of their regulators, the genes are found in their least required activity state, hence regulatory intervention is often necessary. Due to the multitude of genes and regulators, spurious binding and unbinding events, called “crosstalk”, could occur. To determine how the form of regulation affects the global crosstalk in the network, we used a mathematical model that includes multiple regulators and multiple target genes. We found that crosstalk depends non-monotonically on the availability of regulators. Our analysis showed that excess use of regulation entailed by the formerly suggested network design caused high crosstalk levels in a large part of the parameter space. We therefore considered the opposite ‘idle’ design, where the default unregulated state of genes is their frequently required activity state. We found, that ‘idle’ design minimized the use of regulation and thus minimized crosstalk. In addition, we estimated global crosstalk of S. cerevisiae using transcription factors binding data. We demonstrated that even partial network data could suffice to estimate its global crosstalk, suggesting its applicability to additional organisms. We found that S. cerevisiae estimated crosstalk is lower than that of a random network, suggesting that natural selection reduces crosstalk. In summary, our study highlights a new type of protein production cost which is typically overlooked: that of regulatory interference caused by the presence of excess regulators in the cell. It demonstrates the importance of whole-network descriptions, which could show effects missed by single-gene models.

Genes differ in the frequency at which they are expressed and in the form of regulation used to control their activity. The basic level of regulation is mediated by different types of DNA-binding proteins, where each type regulates particular gene(s). We distinguish between two basic forms of regulation: positive—if a gene is activated by the binding of its regulatory protein, and negative—if it is active unless bound by its regulatory protein. Due to the multitude of genes and regulators, spurious binding and unbinding events, called “crosstalk”, could occur. How does the form of regulation, positive or negative, affect the extent of regulatory crosstalk? To address this question, we used a mathematical model integrating many genes and many regulators. As intuition suggests, we found that in most of the parameter space, crosstalk increased with the availability of regulators. We propose, that crosstalk is usually reduced when networks are designed such that minimal regulation is needed, which we call the ‘idle’ design. In other words: a frequently needed gene will use negative regulation and conversely, a scarcely needed gene will employ positive regulation. In both cases, the requirement for the regulators is minimized. In addition, we demonstrate how crosstalk can be calculated from available datasets and discuss the technical challenges in such calculation, specifically data incompleteness.

Biological Pathway Specificity in the Cell-Does Molecular Diversity Matter?

Biology arises from the crowded molecular environment of the cell, rendering it a challenge to understand biological pathways based on the reductionist, low-concentration in vitro conditions generally employed for mechanistic studies. Recent evidence suggests that low-affinity interactions between cellular biopolymers abound, with still poorly defined effects on the complex interaction networks that lead to the emergent properties and plasticity of life. Mass-action considerations are used here to underscore that the sheer number of weak interactions expected from the complex mixture of cellular components significantly shapes biological pathway specificity. In particular, on-pathway-i.e., "functional"-become those interactions thermodynamically and kinetically stable enough to survive the incessant onslaught of the many off-pathway ("nonfunctional") interactions. Consequently, to better understand the molecular biology of the cell a further paradigm shift is needed toward mechanistic experimental and computational approaches that probe intracellular diversity and complexity more directly. Also see the video abstract here https://youtu.be/T19X_zYaBzg.

Fluorescent Lactic Acid Bacteria and Bifidobacteria as Vehicles of DNA Microbial Biosensors

Control and quantification of effector molecules such as heavy metals, toxins or other target molecules is of great biotechnological, social and economic interest. Microorganisms have regulatory proteins that recognize and modify the gene expression in the presence or absence of these compounds (effector molecules) by means of binding to gene sequences. The association of these recognizing gene sequences to reporter genes will allow the detection of effector molecules of interest with high sensitivity. Once investigators have these two elements-recognizing gene sequences and reporter genes that emit signals-we need a suitable vehicle to introduce both elements. Here, we suggest lactic acid bacteria (LAB) and bifidobacteria as promising carrier microorganisms for these molecular biosensors. The use of fluorescent proteins as well as food-grade vectors and clustered regularly interspaced short palindromic repeats (CRISPR) are indispensable tools for introducing biosensors into these microorganisms. The use of these LAB and bifidobacteria would be of special interest for studying the intestinal environment or other complex ecosystems. The great variety of species adapted to many environments, as well as the possibility of applying several protocols for their transformation with recognizing gene sequences and reporter genes are considerable advantages. Finally, an effort must be made to find recognizable gene sequences.

A simple comparison of the extrinsic noise in gene expression between native and foreign regulations in Escherichia coli

Living cells reorganize their gene expression through regulatory machineries in response to external perturbations. The contribution of the regulation to the noise in gene expression is of great interest. In this study, we evaluate the contribution of both native and foreign regulations to the extrinsic noise in gene expression. We analyzed the gene expression data of a mini-library containing 70 genetic constructs of 136 clones into which the gfp gene had been chromosomally incorporated under the control of either native or foreign regulation. We found that the substitution of native by foreign regulation, i.e., the insertion of the P_tet promoter, triggered a decrease in the extrinsic noise, which was independent of the protein abundance. The reanalyses of varied genomic data sets verified that the noisy gene expression mediated by native regulations is a common feature, regardless of the diversity in the genetic approaches used. Disturbing native regulations by a synthetic promoter reduced the extrinsic noise in gene expression in Escherichia coli. It indicated that the extrinsic noise in gene expression caused by the native regulation could be further repressed. These results suggest a tendency of released regulation leading to reduced noise and a linkage between noise and plasticity in the regulation of gene expression.

Intrinsic limits to gene regulation by global crosstalk

Gene regulation relies on the specificity of transcription factor (TF)–DNA interactions. Limited specificity may lead to crosstalk: a regulatory state in which a gene is either incorrectly activated due to noncognate TF–DNA interactions or remains erroneously inactive. As each TF can have numerous interactions with noncognate cis-regulatory elements, crosstalk is inherently a global problem, yet has previously not been studied as such. We construct a theoretical framework to analyse the effects of global crosstalk on gene regulation. We find that crosstalk presents a significant challenge for organisms with low-specificity TFs, such as metazoans. Crosstalk is not easily mitigated by known regulatory schemes acting at equilibrium, including variants of cooperativity and combinatorial regulation. Our results suggest that crosstalk imposes a previously unexplored global constraint on the functioning and evolution of regulatory networks, which is qualitatively distinct from the known constraints that act at the level of individual gene regulatory elements.

Limited specificity of transcription factor-DNA interactions leads to crosstalk in gene regulation. Here the authors consider global crosstalk in regulatory networks of growing size and complexity, and show that it imposes constraints on gene regulation and on the evolution of regulatory networks.

Mathematical modeling of the apo and holo transcriptional regulation in Escherichia coli

Transcription factors can bind to DNA either with their effector bound (holo conformation), or as free proteins (apo conformation).

Dynamic interactive events in gene regulation using E. coli dehydrogenase as a model

Different approaches in gene expression analysis always provide a snapshot view of cellular events. During the bacterial growth, the decisions are dynamically made with participation of various genes and their interactions with modulating factors. We have selected Escherichia coli dehydrogenases as a model to capture these interactions. We have treated the cells with hydrogen peroxide with very low level and asked the questions how cellular physiology has modulated itself to survive post-shock conditions. We hypothesized that while global regulators and associated gene network dictate the overall cellular intelligence, specific redox-sensitive classes of enzymes like dehydrogenase-mediated modulation could provide the option to cell for survival under peroxide after-effect. To understand the dynamic gene interaction, we used multidimensional scaling of genes and overlaid with minimum spanning tree to understand the clustering patterns under different conditions. Study shows that under peroxide after-effect, it is the interplay of ArcA (global regulator), with ldhA (involved in intermediary metabolism) and ndh (managing co-factor NADH), that emerges as modulating association. Knockout mutants of global regulators confirmed the promoter activity trend through gene expression change for dehydrogenases.

Strategy revealing phenotypic differences among synthetic oscillator designs

Considerable progress has been made in identifying and characterizing the component parts of genetic oscillators, which play central roles in all organisms. Nonlinear interaction among components is sufficiently complex that mathematical models are required to elucidate their elusive integrated behavior. Although natural and synthetic oscillators exhibit common architectures, there are numerous differences that are poorly understood. Utilizing synthetic biology to uncover basic principles of simpler circuits is a way to advance understanding of natural circadian clocks and rhythms. Following this strategy, we address the following questions: What are the implications of different architectures and molecular modes of transcriptional control for the phenotypic repertoire of genetic oscillators? Are there designs that are more realizable or robust? We compare synthetic oscillators involving one of three architectures and various combinations of the two modes of transcriptional control using a methodology that provides three innovations: a rigorous definition of phenotype, a procedure for deconstructing complex systems into qualitatively distinct phenotypes, and a graphical representation for illuminating the relationship between genotype, environment, and the qualitatively distinct phenotypes of a system. These methods provide a global perspective on the behavioral repertoire, facilitate comparisons of alternatives, and assist the rational design of synthetic gene circuitry. In particular, the results of their application here reveal distinctive phenotypes for several designs that have been studied experimentally as well as a best design among the alternatives that has yet to be constructed and tested.

Optogenetic characterization methods overcome key challenges in synthetic and systems biology

Systems biologists aim to understand how organism-level processes, such as differentiation and multicellular development, are encoded in DNA. Conversely, synthetic biologists aim to program systems-level biological processes, such as engineered tissue growth, by writing artificial DNA sequences. To achieve their goals, these groups have adapted a hierarchical electrical engineering framework that can be applied in the forward direction to design complex biological systems or in the reverse direction to analyze evolved networks. Despite much progress, this framework has been limited by an inability to directly and dynamically characterize biological components in the varied contexts of living cells. Recently, two optogenetic methods for programming custom gene expression and protein localization signals have been developed and used to reveal fundamentally new information about biological components that respond to those signals. This basic dynamic characterization approach will be a major enabling technology in synthetic and systems biology.

Correcting direct effects of ethanol on translation and transcription machinery confers ethanol tolerance in bacteria

The molecular mechanisms of ethanol toxicity and tolerance in bacteria, although important for biotechnology and bioenergy applications, remain incompletely understood. Genetic studies have identified potential cellular targets for ethanol and have revealed multiple mechanisms of tolerance, but it remains difficult to separate the direct and indirect effects of ethanol. We used adaptive evolution to generate spontaneous ethanol-tolerant strains of Escherichia coli, and then characterized mechanisms of toxicity and resistance using genome-scale DNAseq, RNAseq, and ribosome profiling coupled with specific assays of ribosome and RNA polymerase function. Evolved alleles of metJ, rho, and rpsQ recapitulated most of the observed ethanol tolerance, implicating translation and transcription as key processes affected by ethanol. Ethanol induced miscoding errors during protein synthesis, from which the evolved rpsQ allele protected cells by increasing ribosome accuracy. Ribosome profiling and RNAseq analyses established that ethanol negatively affects transcriptional and translational processivity. Ethanol-stressed cells exhibited ribosomal stalling at internal AUG codons, which may be ameliorated by the adaptive inactivation of the MetJ repressor of methionine biosynthesis genes. Ethanol also caused aberrant intragenic transcription termination for mRNAs with low ribosome density, which was reduced in a strain with the adaptive rho mutation. Furthermore, ethanol inhibited transcript elongation by RNA polymerase in vitro. We propose that ethanol-induced inhibition and uncoupling of mRNA and protein synthesis through direct effects on ribosomes and RNA polymerase conformations are major contributors to ethanol toxicity in E. coli, and that adaptive mutations in metJ, rho, and rpsQ help protect these central dogma processes in the presence of ethanol.

Linear superposition and prediction of bacterial promoter activity dynamics in complex conditions

Bacteria often face complex environments. We asked how gene expression in complex conditions relates to expression in simpler conditions. To address this, we obtained accurate promoter activity dynamical measurements on 94 genes in E. coli in environments made up of all possible combinations of four nutrients and stresses. We find that the dynamics across conditions is well described by two principal component curves specific to each promoter. As a result, the promoter activity dynamics in a combination of conditions is a weighted average of the dynamics in each condition alone. The weights tend to sum up to approximately one. This weighted-average property, called linear superposition, allows predicting the promoter activity dynamics in a combination of conditions based on measurements of pairs of conditions. If these findings apply more generally, they can vastly reduce the number of experiments needed to understand how E. coli responds to the combinatorially huge space of possible environments.

Bacteria face complex conditions in important settings such as our body and in biotechnological applications such as biofuel production. Understanding how bacteria respond to complex conditions is a hard problem: the number of conditions that need to be tested grows exponentially with the number of nutrients, stresses and other factors that make up the environment. To overcome this exponential explosion, we present an approach that allows computing the dynamics of gene expression in a complex condition based on measurements in simple conditions. This is based on the main discovery in this paper: using accurate promoter activity measurements, we find that promoter activity dynamics in a cocktail of media is a weighted average of the dynamics in each medium alone. The weights in the average are constant across time, and can be used to predict the dynamics in arbitrary cocktails based only on measurements on pairs of conditions. Thus, dynamics in complex conditions is, for the vast majority of genes, much simpler than it might have been; this simplicity allows new mathematical formula for accurate prediction in new conditions.

A network characteristic that correlates environmental and genetic robustness

As scientific advances in perturbing biological systems and technological advances in data acquisition allow the large-scale quantitative analysis of biological function, the robustness of organisms to both transient environmental stresses and inter-generational genetic changes is a fundamental impediment to the identifiability of mathematical models of these functions. An approach to overcoming this impediment is to reduce the space of possible models to take into account both types of robustness. However, the relationship between the two is still controversial. This work uncovers a network characteristic, transient responsiveness, for a specific function that correlates environmental imperturbability and genetic robustness. We test this characteristic extensively for dynamic networks of ordinary differential equations ranging up to 30 interacting nodes and find that there is a power-law relating environmental imperturbability and genetic robustness that tends to linearity as the number of nodes increases. Using our methods, we refine the classification of known 3-node motifs in terms of their environmental and genetic robustness. We demonstrate our approach by applying it to the chemotaxis signaling network. In particular, we investigate plausible models for the role of CheV protein in biochemical adaptation via a phosphorylation pathway, testing modifications that could improve the robustness of the system to environmental and/or genetic perturbation.

Advances in the ways that living systems can be perturbed in order to study how they function and sharp reductions in the cost of computer resources have allowed the collection of large amounts of data. The aim of biological system modeling is to analyze this data in order to pin down the precise interactions of molecules that underlie the observed functions. This is made difficult due to two features of biological systems: (1) Living things do not show an appreciable loss of function across large ranges of environmental factors. (2) Their function is inherited from parent to child more or less unchanged in spite of random mutations in genetic sequences. We find that these two features are more correlated in a specific subset of networks and show how to use this observation to find networks in which these two features appear together. Working within this smaller space of networks may make it easier to find suitable underlying models from data.

The effects of transcription factor competition on gene regulation

Transcription factor (TF) molecules translocate by facilitated diffusion (a combination of 3D diffusion around and 1D random walk on the DNA). Despite the attention this mechanism received in the last 40 years, only a few studies investigated the influence of the cellular environment on the facilitated diffusion mechanism and, in particular, the influence of "other" DNA binding proteins competing with the TF molecules for DNA space. Molecular crowding on the DNA is likely to influence the association rate of TFs to their target site and the steady state occupancy of those sites, but it is still not clear how it influences the search in a genome-wide context, when the model includes biologically relevant parameters (such as: TF abundance, TF affinity for DNA and TF dynamics on the DNA). We performed stochastic simulations of TFs performing the facilitated diffusion mechanism, and considered various abundances of cognate and non-cognate TFs. We show that, for both obstacles that move on the DNA and obstacles that are fixed on the DNA, changes in search time are not statistically significant in case of biologically relevant crowding levels on the DNA. In the case of non-cognate proteins that slide on the DNA, molecular crowding on the DNA always leads to statistically significant lower levels of occupancy, which may confer a general mechanism to control gene activity levels globally. When the "other" molecules are immobile on the DNA, we found a completely different behavior, namely: the occupancy of the target site is always increased by higher molecular crowding on the DNA. Finally, we show that crowding on the DNA may increase transcriptional noise through increased variability of the occupancy time of the target sites.

Mechanisms of protein sequence divergence and incompatibility

Alignments of orthologous protein sequences convey a complex picture. Some positions are utterly conserved whilst others have diverged to variable degrees. Amongst the latter, many are non-exchangeable between extant sequences. How do functionally critical and highly conserved residues diverge? Why and how did these exchanges become incompatible within contemporary sequences? Our model is phosphoglycerate kinase (PGK), where lysine 219 is an essential active-site residue completely conserved throughout Eukaryota and Bacteria, and serine is found only in archaeal PGKs. Contemporary sequences tested exhibited complete loss of function upon exchanges at 219. However, a directed evolution experiment revealed that two mutations were sufficient for human PGK to become functional with serine at position 219. These two mutations made position 219 permissive not only for serine and lysine, but also to a range of other amino acids seen in archaeal PGKs. The identified trajectories that enabled exchanges at 219 show marked sign epistasis - a relatively small loss of function with respect to one amino acid (lysine) versus a large gain with another (serine, and other amino acids). Our findings support the view that, as theoretically described, the trajectories underlining the divergence of critical positions are dominated by sign epistatic interactions. Such trajectories are an outcome of rare mutational combinations. Nonetheless, as suggested by the laboratory enabled K219S exchange, given enough time and variability in selection levels, even utterly conserved and functionally essential residues may change.

Orthologs are proteins in different species sharing the same function and structure. However, the mechanisms that underline the divergence of different sequences from a single ancestor remain unclear, particularly because many amino acid exchanges between orthologs result in loss of function (incompatibility). We aimed at disentangling an ancient divergence event within the active-site of a universally spread enzyme that mediates ATP synthesis. Using laboratory evolution experiments, we found that an exchange in a functionally critical active-site residue that is incompatible within contemporary orthologs is enabled by few mutations. These mutations lead to transition sequences in which, unlike the extant sequences, a wide range of amino acids is tolerated. Our experiment reveals the properties of these transition sequences that may resemble the historical ancestral states that underlined this divergence event, and the mechanisms that led to incompatibility within the contemporary orthologs. Our results support theoretical predictions and reshape our understanding of protein structure-function. That a given position is entirely conserved and essential for function does not indicate that it will never exchange, but rather, that the exchange may depend on changes in many other positions.

Dissecting specific and global transcriptional regulation of bacterial gene expression

Gene expression is regulated by specific transcriptional circuits but also by the global expression machinery as a function of growth. Simultaneous specific and global regulation thus constitutes an additional--but often neglected--layer of complexity in gene expression. Here, we develop an experimental-computational approach to dissect specific and global regulation in the bacterium Escherichia coli. By using fluorescent promoter reporters, we show that global regulation is growth rate dependent not only during steady state but also during dynamic changes in growth rate and can be quantified through two promoter-specific parameters. By applying our approach to arginine biosynthesis, we obtain a quantitative understanding of both specific and global regulation that allows accurate prediction of the temporal response to simultaneous perturbations in arginine availability and growth rate. We thereby uncover two principles of joint regulation: (i) specific regulation by repression dominates the transcriptional response during metabolic steady states, largely repressing the biosynthesis genes even when biosynthesis is required and (ii) global regulation sets the maximum promoter activity that is exploited during the transition between steady states.

Indirect and suboptimal control of gene expression is widespread in bacteria

Gene regulation in bacteria is usually described as an adaptive response to an environmental change so that genes are expressed when they are required. We instead propose that most genes are under indirect control: their expression responds to signal(s) that are not directly related to the genes' function. Indirect control should perform poorly in artificial conditions, and we show that gene regulation is often maladaptive in the laboratory. In Shewanella oneidensis MR-1, 24% of genes are detrimental to fitness in some conditions, and detrimental genes tend to be highly expressed instead of being repressed when not needed. In diverse bacteria, there is little correlation between when genes are important for optimal growth or fitness and when those genes are upregulated. Two common types of indirect control are constitutive expression and regulation by growth rate; these occur for genes with diverse functions and often seem to be suboptimal. Because genes that have closely related functions can have dissimilar expression patterns, regulation may be suboptimal in the wild as well as in the laboratory.

Genetic circuit performance under conditions relevant for industrial bioreactors

Synthetic genetic programs promise to enable novel applications in industrial processes. For such applications, the genetic circuits that compose programs will require fidelity in varying and complex environments. In this work, we report the performance of two synthetic circuits in Escherichia coli under industrially relevant conditions, including the selection of media, strain, and growth rate. We test and compare two transcriptional circuits: an AND and a NOR gate. In E. coli DH10B, the AND gate is inactive in minimal media; activity can be rescued by supplementing the media and transferring the gate into the industrial strain E. coli DS68637 where normal function is observed in minimal media. In contrast, the NOR gate is robust to media composition and functions similarly in both strains. The AND gate is evaluated at three stages of early scale-up: 100 mL shake flask experiments, a 1 mL MTP microreactor, and a 10 L bioreactor. A reference plasmid that constitutively produces a GFP reporter is used to make comparisons of circuit performance across conditions. The AND gate function is quantitatively different at each scale. The output deteriorates late in fermentation after the shift from exponential to constant feed rates, which induces rapid resource depletion and changes in growth rate. In addition, one of the output states of the AND gate failed in the bioreactor, effectively making it only responsive to a single input. Finally, cells carrying the AND gate show considerably less accumulation of biomass. Overall, these results highlight challenges and suggest modified strategies for developing and characterizing genetic circuits that function reliably during fermentation.

Computational models for large-scale simulations of facilitated diffusion

The binding of site-specific transcription factors to their genomic target sites is a key step in gene regulation. While the genome is huge, transcription factors belong to the least abundant protein classes in the cell. It is therefore fascinating how short the time frame is that they require to home in on their target sites. The underlying search mechanism is called facilitated diffusion and assumes a combination of three-dimensional diffusion in the space around the DNA combined with one-dimensional random walk on it. In this review, we present the current understanding of the facilitated diffusion mechanism and identify questions that lack a clear or detailed answer. One way to investigate these questions is through stochastic simulation and, in this manuscript, we support the idea that such simulations are able to address them. Finally, we review which biological parameters need to be included in such computational models in order to obtain a detailed representation of the actual process.