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Bacteria have evolved complex regulatory networks to control the activity of transcription and translation, and thus the growth rate, over a range of environmental conditions. Reengineering RNA polymerase and ribosomes allows modifying naturally evolved regulatory networks and thereby profoundly reorganizing the manner in which bacteria allocate resources to different cellular functions. This opens new opportunities for our fundamental understanding of microbial physiology and for a variety of applications. Recent breakthroughs in genome engineering and the miniaturization and automation of culturing methods have offered new perspectives for the reengineering of the transcription and translation machinery in bacteria as well as the development of novel in vitro and in vivo gene expression systems. We review different examples from the unifying perspective of resource reallocation, and discuss the impact of these approaches for microbial systems biology and biotechnological applications., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Microbial physiology exhibits growth laws that relate the macromolecular composition of the cell to the growth rate. Recent work has shown that these empirical regularities can be derived from coarse-grained models of resource allocation. While these studies focus on steady-state growth, such conditions are rarely found in natural habitats, where microorganisms are continually challenged by environmental fluctuations. The aim of this paper is to extend the study of microbial growth strategies to dynamical environments, using a self-replicator model. We formulate dynamical growth maximization as an optimal control problem that can be solved using Pontryagin's Maximum Principle. We compare this theoretical gold standard with different possible implementations of growth control in bacterial cells. We find that simple control strategies enabling growth-rate maximization at steady state are suboptimal for transitions from one growth regime to another, for example when shifting bacterial cells to a medium supporting a higher growth rate. A near-optimal control strategy in dynamical conditions is shown to require information on several, rather than a single physiological variable. Interestingly, this strategy has structural analogies with the regulation of ribosomal protein synthesis by ppGpp in the enterobacterium Escherichia coli. It involves sensing a mismatch between precursor and ribosome concentrations, as well as the adjustment of ribosome synthesis in a switch-like manner. Our results show how the capability of regulatory systems to integrate information about several physiological variables is critical for optimizing growth in a changing environment.

The ability to control growth is essential for fundamental studies of bacterial physiology and biotechnological applications. We have engineered an Escherichia coli strain in which the transcription of a key component of the gene expression machinery, RNA polymerase, is under the control of an inducible promoter. By changing the inducer concentration in the medium, we can adjust the RNA polymerase concentration and thereby switch bacterial growth between zero and the maximal growth rate supported by the medium. We show that our synthetic growth switch functions in a medium-independent and reversible way, and we provide evidence that the switching phenotype arises from the ultrasensitive response of the growth rate to the concentration of RNA polymerase. We present an application of the growth switch in which both the wild-type E. coli strain and our modified strain are endowed with the capacity to produce glycerol when growing on glucose. Cells in which growth has been switched off continue to be metabolically active and harness the energy gain to produce glycerol at a twofold higher yield than in cells with natural control of RNA polymerase expression. Remarkably, without any further optimization, the improved yield is close to the theoretical maximum computed from a flux balance model of E. coli metabolism. The proposed synthetic growth switch is a promising tool for gaining a better understanding of bacterial physiology and for applications in synthetic biology and biotechnology., (© 2015 The Authors. Published under the terms of the CC BY 4.0 license.)

Motivation: Time-series observations from reporter gene experiments are commonly used for inferring and analyzing dynamical models of regulatory networks. The robust estimation of promoter activities and protein concentrations from primary data is a difficult problem due to measurement noise and the indirect relation between the measurements and quantities of biological interest., Results: We propose a general approach based on regularized linear inversion to solve a range of estimation problems in the analysis of reporter gene data, notably the inference of growth rate, promoter activity, and protein concentration profiles. We evaluate the validity of the approach using in silico simulation studies, and observe that the methods are more robust and less biased than indirect approaches usually encountered in the experimental literature based on smoothing and subsequent processing of the primary data. We apply the methods to the analysis of fluorescent reporter gene data acquired in kinetic experiments with Escherichia coli. The methods are capable of reliably reconstructing time-course profiles of growth rate, promoter activity and protein concentration from weak and noisy signals at low population volumes. Moreover, they capture critical features of those profiles, notably rapid changes in gene expression during growth transitions., Availability and Implementation: The methods described in this article are made available as a Python package (LGPL license) and also accessible through a web interface. For more information, see https://team.inria.fr/ibis/wellinverter., (© The Author 2015. Published by Oxford University Press.)

The inference of regulatory interactions and quantitative models of gene regulation from time-series transcriptomics data has been extensively studied and applied to a range of problems in drug discovery, cancer research, and biotechnology. The application of existing methods is commonly based on implicit assumptions on the biological processes under study. First, the measurements of mRNA abundance obtained in transcriptomics experiments are taken to be representative of protein concentrations. Second, the observed changes in gene expression are assumed to be solely due to transcription factors and other specific regulators, while changes in the activity of the gene expression machinery and other global physiological effects are neglected. While convenient in practice, these assumptions are often not valid and bias the reverse engineering process. Here we systematically investigate, using a combination of models and experiments, the importance of this bias and possible corrections. We measure in real time and in vivo the activity of genes involved in the FliA-FlgM module of the E. coli motility network. From these data, we estimate protein concentrations and global physiological effects by means of kinetic models of gene expression. Our results indicate that correcting for the bias of commonly-made assumptions improves the quality of the models inferred from the data. Moreover, we show by simulation that these improvements are expected to be even stronger for systems in which protein concentrations have longer half-lives and the activity of the gene expression machinery varies more strongly across conditions than in the FliA-FlgM module. The approach proposed in this study is broadly applicable when using time-series transcriptome data to learn about the structure and dynamics of regulatory networks. In the case of the FliA-FlgM module, our results demonstrate the importance of global physiological effects and the active regulation of FliA and FlgM half-lives for the dynamics of FliA-dependent promoters.

We have developed a new screening methodology for identifying all genes that control the expression of a target gene through genetic or metabolic interactions. The screen combines mutant libraries with luciferase reporter constructs, whose expression can be monitored in vivo and over time in different environmental conditions. We apply the method to identify the genes that control the expression of the gene acs, encoding the acetyl coenzyme A synthetase, in Escherichia coli. We confirm most of the known genetic regulators, including CRP-cAMP, IHF and components of the phosphotransferase system. In addition, we identify new regulatory interactions, many of which involve metabolic intermediates or metabolic sensing, such as the genes pgi, pfkA, sucB and lpdA, encoding enzymes in glycolysis and the TCA cycle. Some of these novel interactions were validated by quantitative reverse transcriptase-polymerase chain reaction. More generally, we observe that a large number of mutants directly or indirectly influence acs expression, an effect confirmed for a second promoter, sdhC. The method is applicable to any promoter fused to a luminescent reporter gene in combination with a deletion mutant library.

Gene expression is controlled by the joint effect of (i) the global physiological state of the cell, in particular the activity of the gene expression machinery, and (ii) DNA-binding transcription factors and other specific regulators. We present a model-based approach to distinguish between these two effects using time-resolved measurements of promoter activities. We demonstrate the strength of the approach by analyzing a circuit involved in the regulation of carbon metabolism in E. coli. Our results show that the transcriptional response of the network is controlled by the physiological state of the cell and the signaling metabolite cyclic AMP (cAMP). The absence of a strong regulatory effect of transcription factors suggests that they are not the main coordinators of gene expression changes during growth transitions, but rather that they complement the effect of global physiological control mechanisms. This change of perspective has important consequences for the interpretation of transcriptome data and the design of biological networks in biotechnology and synthetic biology.

Gene regulatory networks consist of direct interactions, but also include indirect interactions mediated by metabolism. We investigate to which extent these indirect interactions arising from metabolic coupling influence the dynamics of the system. To this end, we build a qualitative model of the gene regulatory network controlling carbon assimilation in Escherichia coli, and use this model to study the changes in gene expression following a diauxic shift from glucose to acetate. In particular, we compare the relative variation in the steady-state concentrations of enzymes and transcription regulators during growth on glucose and acetate, as well as the dynamic response of gene expression to the exhaustion of glucose and the subsequent assimilation of acetate. We find significant differences between the dynamics of the system in the absence and presence of metabolic coupling. This shows that interactions arising from metabolic coupling cannot be ignored when studying the dynamics of gene regulatory networks., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

Background: Fluorescent and luminescent reporter genes have become popular tools for the real-time monitoring of gene expression in living cells. However, mathematical models are necessary for extracting biologically meaningful quantities from the primary data., Results: We present a rigorous method for deriving relative protein synthesis rates (mRNA concentrations) and protein concentrations by means of kinetic models of gene expression. We experimentally and computationally validate this approach in the case of the protein Fis, a global regulator of transcription in Escherichia coli. We show that the mRNA and protein concentration profiles predicted from the models agree quite well with direct measurements obtained by Northern and Western blots, respectively. Moreover, we present computational procedures for taking into account systematic biases like the folding time of the fluorescent reporter protein and differences in the half-lives of reporter and host gene products. The results show that large differences in protein half-lives, more than mRNA half-lives, may be critical for the interpretation of reporter gene data in the analysis of the dynamics of regulatory systems., Conclusions: The paper contributes to the development of sound methods for the interpretation of reporter gene data, notably in the context of the reconstruction and validation of models of regulatory networks. The results have wide applicability for the analysis of gene expression in bacteria and may be extended to higher organisms.

Twelve populations of Escherichia coli evolved in and adapted to a glucose-limited environment from a common ancestor. We used two-dimensional protein electrophoresis to compare two evolved clones, isolated from independently derived populations after 20,000 generations. Exceptional parallelism was detected. We compared the observed changes in protein expression profiles with previously characterized global transcription profiles of the same clones; this is the first time such a comparison has been made in an evolutionary context where these changes are often quite subtle. The two methodologies exhibited some remarkable similarities that highlighted two different levels of parallel regulatory changes that were beneficial during the evolution experiment. First, at the higher level, both methods revealed extensive parallel changes in the same global regulatory network, reflecting the involvement of beneficial mutations in genes that control the ppGpp regulon. Second, both methods detected expression changes of identical gene sets that reflected parallel changes at a lower level of gene regulation. The protein profiles led to the discovery of beneficial mutations affecting the malT gene, with strong genetic parallelism across independently evolved populations. Functional and evolutionary analyses of these mutations revealed parallel phenotypic decreases in the maltose regulon expression and a high level of polymorphism at this locus in the evolved populations.

A major task of contemporary biology is to understand and predict the functioning of regulatory networks. We use expression data to deduce the regulation network connecting the sigma factors of Synechocystis PCC6803, the most global regulators in bacteria. Synechocystis contains one group 1 (SigA) and four group 2 (SigB, SigC, SigD and SigE) sigma factors. From the relative abundance of the sig mRNA measured in the wild-type and the four group 2 sigma mutants, we derive a network of the influences of each sigma factor on the transcription of all other sigma factors. Internal or external stimuli acting on only one of the sigma factors will thus indirectly modify the expression of most of the others. From this model, we predict the control points through which the circadian time modulates the expression of the sigma factors. Our results show that the cross regulation between the group 1 and group 2 sigma factors is very important for the adaptation of the bacterium to different environmental and physiological conditions.

Background: The cyanobacterium Synechocystis PCC6803 contains one group 1 (sigA) and four group 2 (sigB, sigC, sigD and sigE) sigma factors. The activity of these multiple sigma factors determines the transcriptional program of this bacterium. We wanted to study the role of the group 2 sigma factors in Synechocystis. We have therefore constructed mutants of each of the group 2 sigma factors and investigated their crosstalk., Results: We used quantitative RT-PCR analysis to measure the relative abundance of the sig mRNAs in the four sigma mutants. Our data indicate that a network of mutual transcriptional regulation links the expression of the sigma genes. Accordingly, an environmental stress acting on only one of the sigma factors will indirectly modify the expression of most of the other sigma factors. This was confirmed by the transcriptional analysis of the sig mRNAs as a function of nitrogen starvation., Conclusion: Taken together, our observations suggest that the crosstalk regulation between all group 1 and group 2 genes could be important for the adaptation of the bacterium to different environmental and physiological conditions.