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

A logical way to reprogram plants

Living cells constantly monitor their external and internal environments for changing conditions, stresses or developmental cues. Networks of genetically encoded components sense and process these signals following pre-defined rules in such a way that specific combinations of the presence or absence of certain signals activate suitable responses. Many biological signal integration mechanisms approximate Boolean logic operations, whereby presence or absence of signals are computed as variables with values described as either true or false, respectively. Boolean logic gates are commonly used in algebra and in computer sciences, and have long been recognized as useful information processing devices in electronic circuits. In these circuits, logic gates integrate multiple input values and produce an output signal according to pre-defined Boolean logic operations. Recent implementation of these logic operations using genetic components to process information in living cells has allowed genetic circuits to enable novel traits with decision-making capabilities. Although several literature reports describe the design and use of these logic gates to introduce new functions in bacterial, yeast and mammalian cells, similar approaches in plants remain scarce, likely due to challenges posed by the complexity of plants and the lack of some technological advances, e.g., species-independent genetic transformation. In this mini review, we have surveyed recent reports describing synthetic genetic Boolean logic operators in plants and the different gate architectures used. We also briefly discuss the potential of deploying these genetic devices in plants to bring to fruition a new generation of resilient crops and improved biomanufacturing platforms.

Combinational Regularity Analysis (CORA) - a new method for uncovering complex causation in medical and health research

BACKGROUND

Modern configurational comparative methods (CCMs) of causal inference, such as Qualitative Comparative Analysis (QCA) and Coincidence Analysis (CNA), have started to make inroads into medical and health research over the last decade. At the same time, these methods remain unable to process data on multi-morbidity, a situation in which at least two chronic conditions are simultaneously present. Such data require the capability to analyze complex effects. Against a background of fast-growing numbers of patients with multi-morbid diagnoses, we present a new member of the family of CCMs with which multiple conditions and their complex conjunctions can be analyzed: Combinational Regularity Analysis (CORA).

METHODS

The technical heart of CORA consists of algorithms that have originally been developed in electrical engineering for the analysis of multi-output switching circuits. We have adapted these algorithms for purposes of configurational data analysis. To demonstrate CORA, we provide several example applications, both with simulated and empirical data, by means of the eponymous software package CORA. Also included in CORA is the possibility to mine configurational data and to visualize results via logic diagrams.

RESULTS

For simple single-condition analyses, CORA's solution is identical with that of QCA or CNA. However, analyses of multiple conditions with CORA differ in important respects from analyses with QCA or CNA. Most importantly, CORA is presently the only configurational method able to simultaneously explain individual conditions as well as complex conjunctions of conditions.

CONCLUSIONS

Through CORA, problems of multi-morbidity in particular, and configurational analyses of complex effects in general, come into the analytical reach of CCMs. Future research aims to further broaden and enhance CORA's capabilities for refining such analyses.

Picking the right metaphors for addressing microbial systems: economic theory helps understanding biological complexity

Any descriptive language is necessarily metaphoric and interpretative. Two somewhat overlapping-but not identical-languages have been thoroughly employed in the last decade to address the issue of regulatory complexity in biological systems: the terminology of network theory and the jargon of electric circuitry. These approaches have found many formal equivalences between the layout of extant genetic circuits and the architecture of man-made counterparts. However, these languages still fail to describe accurately key features of biological objects, in particular the diversity of signal-transfer molecules and the diffusion that is inherent to any biochemical system. Furthermore, current formalisms associated with networks and circuits can hardly face the problem of multi-scale regulatory complexity-from single molecules to entire ecosystems. We argue that the language of economic theory might be instrumental not only to portray accurately many features of regulatory networks, but also to unveil aspects of the biological complexity problem that remain opaque to other types of analyses. The main perspective opened by the economic metaphor when applied to control of microbiological activities is a focus on metabolism, not gene selfishness, as the necessary background to make sense of regulatory phenomena. As an example, we analyse and reinterpret the widespread phenomenon of catabolite repression with the formal frame of the consumer's choice theory.

A navigation guide of synthetic biology tools for Pseudomonas putida

Pseudomonas putida is a microbial chassis of huge potential for industrial and environmental biotechnology, owing to its remarkable metabolic versatility and ability to sustain difficult redox reactions and operational stresses, among other attractive characteristics. A wealth of genetic and in silico tools have been developed to enable the unravelling of its physiology and improvement of its performance. However, the rise of this microbe as a promising platform for biotechnological applications has resulted in diversification of tools and methods rather than standardization and convergence. As a consequence, multiple tools for the same purpose have been generated, whilst most of them have not been embraced by the scientific community, which has led to compartmentalization and inefficient use of resources. Inspired by this and by the substantial increase in popularity of P. putida, we aim herein to bring together and assess all currently available (wet and dry) synthetic biology tools specific for this microbe, focusing on the last 5 years. We provide information on the principles, functionality, advantages and limitations, with special focus on their use in metabolic engineering. Additionally, we compare the tool portfolio for P. putida with those for other bacterial chassis and discuss potential future directions for tool development. Therefore, this review is intended as a reference guide for experts and new 'users' of this promising chassis.

Subcellular Architecture of the xyl Gene Expression Flow of the TOL Catabolic Plasmid of Pseudomonas putida mt-2

Despite intensive research on the biochemical and regulatory features of the archetypal catabolic TOL system borne by pWW0 of Pseudomonas putida strain mt-2, the physical arrangement and tridimensional logic of the xyl gene expression flow remains unknown. In this work, the spatial distribution of specific xyl mRNAs with respect to the host nucleoid, the TOL plasmid, and the ribosomal pool has been investigated. In situ hybridization of target transcripts with fluorescent oligonucleotide probes revealed that xyl mRNAs cluster in discrete foci, adjacent but clearly separated from the TOL plasmid and the cell nucleoid. Also, they colocalize with ribosome-rich domains of the intracellular milieu. This arrangement was maintained even when the xyl genes were artificially relocated to different chromosomal locations. The same held true when genes were expressed through a heterologous T7 polymerase-based system, which likewise led to mRNA foci outside the DNA. In contrast, rifampin treatment, known to ease crowding, blurred the confinement of xyl transcripts. This suggested that xyl mRNAs exit from their initiation sites to move to ribosome-rich points for translation—rather than being translated coupled to transcription. Moreover, the results suggest the distinct subcellular motion of xyl mRNAs results from both innate properties of the sequences and the physical forces that keep the ribosomal pool away from the nucleoid in P. putida. This scenario is discussed within the background of current knowledge on the three-dimensional organization of the gene expression flow in other bacteria and the environmental lifestyle of this soil microorganism.

Liquid brains, solid brains

Cognitive networks have evolved a broad range of solutions to the problem of gathering, storing and responding to information. Some of these networks are describable as static sets of neurons linked in an adaptive web of connections. These are 'solid' networks, with a well-defined and physically persistent architecture. Other systems are formed by sets of agents that exchange, store and process information but without persistent connections or move relative to each other in physical space. We refer to these networks that lack stable connections and static elements as 'liquid' brains, a category that includes ant and termite colonies, immune systems and some microbiomes and slime moulds. What are the key differences between solid and liquid brains, particularly in their cognitive potential, ability to solve particular problems and environments, and information-processing strategies? To answer this question requires a new, integrative framework. This article is part of the theme issue 'Liquid brains, solid brains: How distributed cognitive architectures process information'.

Modeling somatic computation with non-neural bioelectric networks

The field of basal cognition seeks to understand how adaptive, context-specific behavior occurs in non-neural biological systems. Embryogenesis and regeneration require plasticity in many tissue types to achieve structural and functional goals in diverse circumstances. Thus, advances in both evolutionary cell biology and regenerative medicine require an understanding of how non-neural tissues could process information. Neurons evolved from ancient cell types that used bioelectric signaling to perform computation. However, it has not been shown whether or how non-neural bioelectric cell networks can support computation. We generalize connectionist methods to non-neural tissue architectures, showing that a minimal non-neural Bio-Electric Network (BEN) model that utilizes the general principles of bioelectricity (electrodiffusion and gating) can compute. We characterize BEN behaviors ranging from elementary logic gates to pattern detectors, using both fixed and transient inputs to recapitulate various biological scenarios. We characterize the mechanisms of such networks using dynamical-systems and information-theory tools, demonstrating that logic can manifest in bidirectional, continuous, and relatively slow bioelectrical systems, complementing conventional neural-centric architectures. Our results reveal a variety of non-neural decision-making processes as manifestations of general cellular biophysical mechanisms and suggest novel bioengineering approaches to construct functional tissues for regenerative medicine and synthetic biology as well as new machine learning architectures.

Metabolic perceptrons for neural computing in biological systems

Synthetic biological circuits are promising tools for developing sophisticated systems for medical, industrial, and environmental applications. So far, circuit implementations commonly rely on gene expression regulation for information processing using digital logic. Here, we present a different approach for biological computation through metabolic circuits designed by computer-aided tools, implemented in both whole-cell and cell-free systems. We first combine metabolic transducers to build an analog adder, a device that sums up the concentrations of multiple input metabolites. Next, we build a weighted adder where the contributions of the different metabolites to the sum can be adjusted. Using a computational model fitted on experimental data, we finally implement two four-input perceptrons for desired binary classification of metabolite combinations by applying model-predicted weights to the metabolic perceptron. The perceptron-mediated neural computing introduced here lays the groundwork for more advanced metabolic circuits for rapid and scalable multiplex sensing.

Advances and challenges in logical modeling of cell cycle regulation: perspective for multi-scale, integrative yeast cell models

The eukaryotic cell cycle is robustly designed, with interacting molecules organized within a definite topology that ensures temporal precision of its phase transitions. Its underlying dynamics are regulated by molecular switches, for which remarkable insights have been provided by genetic and molecular biology efforts. In a number of cases, this information has been made predictive, through computational models. These models have allowed for the identification of novel molecular mechanisms, later validated experimentally. Logical modeling represents one of the youngest approaches to address cell cycle regulation. We summarize the advances that this type of modeling has achieved to reproduce and predict cell cycle dynamics. Furthermore, we present the challenge that this type of modeling is now ready to tackle: its integration with intracellular networks, and its formalisms, to understand crosstalks underlying systems level properties, ultimate aim of multi-scale models. Specifically, we discuss and illustrate how such an integration may be realized, by integrating a minimal logical model of the cell cycle with a metabolic network.

High-resolution analysis of the m-xylene/toluene biodegradation subtranscriptome of Pseudomonas putida mt-2

Pseudomonas putida mt-2 metabolizes m-xylene and other aromatic compounds through the enzymes encoded by the xyl operons of the TOL plasmid pWW0 along with other chromosomally encoded activities. Tiling arrays of densely overlapping oligonucleotides were designed to cover every gene involved in this process, allowing dissection of operon structures and exposing the interplay of plasmid and chromosomal functions. All xyl sequences were transcribed in response to aromatic substrates and the 3'-termini of both upper and lower mRNA operons extended beyond their coding regions, i.e. the 3'-end of the lower operon mRNA penetrated into the convergent xylS regulatory gene. Furthermore, xylR mRNA for the master m-xylene responsive regulator of the system was decreased by aromatic substrates, while the cognate upper operon mRNA was evenly stable throughout its full length. RNA sequencing confirmed these data at a single nucleotide level and refined the formerly misannotated xylL sequence. The chromosomal ortho route for degradation of benzoate (the ben, cat clusters and some pca genes) was activated by this aromatic, but not by the TOL substrates, toluene or m-xylene. We advocate this scenario as a testbed of natural retroactivity between a pre-existing metabolic network and a new biochemical pathway implanted through gene transfer.

Retropath: automated pipeline for embedded metabolic circuits

Metabolic circuits are a promising alternative to other conventional genetic circuits as modular parts implementing functionalities required for synthetic biology applications. To date, metabolic design has been mainly focused on production circuits. Emergent applications such as smart therapeutics, however, require circuits that enable sensing and regulation. Here, we present RetroPath, an automated pipeline for embedded metabolic circuits that explores the circuit design space from a given set of specifications and selects the best circuits to implement based on desired constraints. Synthetic biology circuits embedded in a chassis organism that are capable of controlling the production, processing, sensing, and the release of specific molecules were enumerated in the metabolic space through a standard procedure. In that way, design and implementation of applications such as therapeutic circuits that autonomously diagnose and treat disease, are enabled, and their optimization is streamlined.

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.

Increasing signal specificity of the TOL network of Pseudomonas putida mt-2 by rewiring the connectivity of the master regulator XylR

Prokaryotic transcription factors (TFs) that bind small xenobiotic molecules (e.g., TFs that drive genes that respond to environmental pollutants) often display a promiscuous effector profile for analogs of the bona fide chemical signals. XylR, the master TF for expression of the m-xylene biodegradation operons encoded in the TOL plasmid pWW0 of Pseudomonas putida, responds not only to the aromatic compound but also, albeit to a lesser extent, to many other aromatic compounds, such as 3-methylbenzylalcohol (3MBA). We have examined whether such a relaxed regulatory scenario can be reshaped into a high-capacity/high-specificity regime by changing the connectivity of this effector-sensing TF within the rest of the circuit rather than modifying XylR structure itself. To this end, the natural negative feedback loop that operates on xylR transcription was modified with a translational attenuator that brings down the response to 3MBA while maintaining the transcriptional output induced by m-xylene (as measured with a luxCDABE reporter system). XylR expression was then subject to a positive feedback loop in which the TF was transcribed from its own target promoters, each known to hold different input/output transfer functions. In the first case (xylR under the strong promoter of the upper TOL operon, Pu), the reporter system displayed an increased transcriptional capacity in the resulting network for both the optimal and the suboptimal XylR effectors. In contrast, when xylR was expressed under the weaker Ps promoter, the resulting circuit unmistakably discriminated m-xylene from 3MBA. The non-natural connectivity engineered in the network resulted both in a higher promoter activity and also in a much-increased signal-to-background ratio. These results indicate that the working regimes of given genetic circuits can be dramatically altered through simple changes in the way upstream transcription factors are self-regulated by positive or negative feedback loops.

It is generally taken for granted that promoters regulated by transcriptional factors (TFs) that respond to small molecules control their specificity to given effectors by tightening or relaxing the intrinsic dual interaction between the TF and the particular inducer. One such promoter is Pu, which drives expression of an operon for the biodegradation of m-xylene by the soil bacterium P. putida mt-2. While XylR, the chief TF of this system, binds this substrate and activates Pu, the same regulator responds, to a lesser extent, to 3-methylbenzylalcohol and thus also activates the promoter. This work provides evidence that such natural effector promiscuity of the system can be altogether suppressed by replacing the naturally occurring negative autoregulation loop that governs XylR expression with an equivalent positive feedback loop. Based on this result, we argue that signal specificity of a given regulatory device depends not only on the TF involved but also on TF connectivity to upstream signals and downstream targets.

Production and characterization of a recombinant single-chain antibody (scFv) for tracing the σ54 factor of Pseudomonas putida

The number of alternative sigma factor molecules per bacterial cell determines either stochasticity or evenness of transcription of cognate promoters. An approach for examining the abundance of sigmas in any sample of bacterial origin is explained here which relies on the production of a recombinant highly specific, high-affinity single-chain variable Fv domain (scFv) targeted towards unique protein sites of the factor. Purposely, a super-binder scFv recognizing a distinct epitope of the less abundant sigma σ(54) of Pseudomonas putida (also known as σ(N)) was obtained and its properties examined in detail. To this end, an scFv library was generated from mRNA extracted from lymphocytes of mice immunized with the purified σ(54) protein of this bacterium. The library was displayed on a phage system and subjected to various rounds of panning with purified σ(54) for capturing extreme binders. The resulting high-affinity anti-σ(54) phage antibody (Phab) clone named C2 strongly attached a small region located between positions 172 and 183 of the primary amino acid sequence of σ(54) that overlaps its core RNA polymerase-binding region. The purified scFv-C2 detected minute amounts of σ(54) in whole cell protein extracts not only of P. putida but also Escherichia coli cells and putatively in other bacteria as well. The affinity constant of the purified antibody was measured by surface plasmon resonance (SPR) and found to have a K(D) (k(off)/k(on)) in the range of 2×10(-9)M. The considerable affinity and specificity of this recombinant antibody makes it a tool of choice for quantitative studies on gene expression of σ(54)-dependent promoters in P. putida and other Gram-negative bacteria.

Importance of metabolic coupling for the dynamics of gene expression following a diauxic shift in Escherichia coli

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.

The logic layout of the TOL network of Pseudomonas putida pWW0 plasmid stems from a metabolic amplifier motif (MAM) that optimizes biodegradation of m-xylene

Background

The genetic network of the TOL plasmid pWW0 of the soil bacterium Pseudomonas putida mt-2 for catabolism of m-xylene is an archetypal model for environmental biodegradation of aromatic pollutants. Although nearly every metabolic and transcriptional component of this regulatory system is known to an extraordinary molecular detail, the complexity of its architecture is still perplexing. To gain an insight into the inner layout of this network a logic model of the TOL system was implemented, simulated and experimentally validated. This analysis made sense of the specific regulatory topology out on the basis of an unprecedented network motif around which the entire genetic circuit for m-xylene catabolism gravitates.

Results

The most salient feature of the whole TOL regulatory network is the control exerted by two distinct but still intertwined regulators (XylR and XylS) on expression of two separated catabolic operons (upper and lower) for catabolism of m-xylene. Following model reduction, a minimal modular circuit composed by five basic variables appeared to suffice for fully describing the operation of the entire system. In silico simulation of the effect of various perturbations were compared with experimental data in which specific portions of the network were activated with selected inducers: m-xylene, o-xylene, 3-methylbenzylalcohol and 3-methylbenzoate. The results accredited the ability of the model to faithfully describe network dynamics. This analysis revealed that the entire regulatory structure of the TOL system enables the action an unprecedented metabolic amplifier motif (MAM). This motif synchronizes expression of the upper and lower portions of a very long metabolic system when cells face the head pathway substrate, m-xylene.

Conclusion

Logic modeling of the TOL circuit accounted for the intricate regulatory topology of this otherwise simple metabolic device. The found MAM appears to ensure a simultaneous expression of the upper and lower segments of the m-xylene catabolic route that would be difficult to bring about with a standard substrate-responsive single promoter. Furthermore, it is plausible that the MAM helps to avoid biochemical conflicts between competing plasmid-encoded and chromosomally-encoded pathways in this bacterium.

A composite feed-forward loop I4-FFL involving IHF and Crc stabilizes expression of the XylR regulator of Pseudomonas putida mt-2 from growth phase perturbations

Genetic networks are typically composed of a series of connected motifs that confer specific logic and dynamic properties to the resulting circuits. While some feed forward loop (FFL) variants abound in such networks, others (e.g. the type-4 incoherent FFL or I4-FFL) are virtually absent from the known regulatory devices. We report here that the key node that rules the expression of the m-xylene biodegradation pathway of the soil bacterium Pseudomonas putida mt-2 merges opposite physiological effects of the growth phase by means of a regulatory device based on the rarely found I4-FFL motif. Specifically, the FFL includes the integration host factor (IHF), which both co-activates the master P(u) promoter and represses transcription of its cognate regulatory gene xylR at the onset of the stationary phase. On the other hand, the catabolite repression control (Crc) protein inhibits translation of XylR during exponential growth. By computing these two conflicting regulatory actions within a composite I4-FFL gate, cells shield the expression of XylR from perturbations caused by the growth phase, thereby ensuring a steady supply of the regulator regardless of physiological conditions. This device thus endows xylR expression with a degree of robustness in respect to the growth phase that could hardly be achieved with e.g. a simple constitutive promoter.

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.