Bacterial Killers Engineered to Exterminate Pathogenic Microbes.

A new study reports a synthetic bacterium that uses conjugation to transfer toxic genes that selectively kill pathogenic cells. The work represents a novel strategy for targeting pathogens, which could be the basis for a new generation of precision antimicrobials.

Variety Is the Spice of Life: Diverse Social Networks Are Associated With Social Cohesion and Well-Being.

Both homophily and heterophily are observed in humans. Homophily reinforces homogeneous social networks, and heterophily creates new experiences and collaborations. However, at the extremes, high levels of homophily can cultivate prejudice toward out-groups, whereas high levels of heterophily can weaken in-group support. Using data from 24,726 adults (M = 46 years; selected from 10,398 English neighborhoods) and the composition of their social networks based on age, ethnicity, income, and education, we tested the hypothesis that a middle ground between homophily and heterophily could be the most beneficial for individuals. We found that network homophily, mediated by perceived social cohesion, is associated with higher levels of subjective well-being but that there are diminishing returns, because at a certain point increasing network homophily is associated with lower social cohesion and, in turn, lower subjective well-being. Our results suggest that building diverse social networks provides benefits that cannot be attained by homogeneous networks.

Signaling in microbial communities with open boundaries.

Microbial communities such as swarms or biofilms often form at the interfaces of solid substrates and open fluid flows. At the same time, in laboratory environments these communities are commonly studied using microfluidic devices with media flows and open boundaries. Extracellular signaling within these communities is therefore subject to different constraints than signaling within classic, closed-boundary systems such as developing embryos or tissues, yet is understudied by comparison. Here, we use mathematical modeling to show how advective-diffusive boundary flows and population geometry impact cell-cell signaling in monolayer microbial communities. We reveal conditions where the intercellular signaling lengthscale depends solely on the population geometry and not on diffusion or degradation, as commonly expected. We further demonstrate that diffusive coupling with the boundary flow can produce signal gradients within an isogenic population, even when there is no flow within the population. We use our theory to provide new insights into the signaling mechanisms of published experimental results, and we make several experimentally verifiable predictions. Our research highlights the importance of carefully evaluating boundary dynamics and environmental geometry when modeling microbial cell-cell signaling and informs the study of cell behaviors in both natural and synthetic systems.

A suppressor tRNA-mediated feedforward loop eliminates leaky gene expression in bacteria.

Ligand-inducible genetic systems are the mainstay of synthetic biology, allowing gene expression to be controlled by the presence of a small molecule. However, 'leaky' gene expression in the absence of inducer remains a persistent problem. We developed a leak dampener tool that drastically reduces the leak of inducible genetic systems while retaining signal in Escherichia coli. Our system relies on a coherent feedforward loop featuring a suppressor tRNA that enables conditional readthrough of silent non-sense mutations in a regulated gene, and this approach can be applied to any ligand-inducible transcription factor. We demonstrate proof-of-principle of our system with the lactate biosensor LldR and the arabinose biosensor AraC, which displayed a 70-fold and 630-fold change in output after induction of a fluorescence reporter, respectively, without any background subtraction. Application of the tool to an arabinose-inducible mutagenesis plasmid led to a 540-fold change in its output after induction, with leak decreasing to the level of background mutagenesis. This study provides a modular tool for reducing leak and improving the fold-induction within genetic circuits, demonstrated here using two types of biosensors relevant to cancer detection and genetic engineering.

Allosteric regulation within the highly interconnected structural scaffold of AraC/XylS homologs tolerates a wide range of amino acid changes.

To create bacterial transcription "circuits" for biotechnology, one approach is to recombine natural transcription factors, promoters, and operators. Additional novel functions can be engineered from existing transcription factors such as the E. coli AraC transcriptional activator, for which binding to DNA is modulated by binding L-arabinose. Here, we engineered chimeric AraC/XylS transcription activators that recognized ara DNA binding sites and responded to varied effector ligands. The first step, identifying domain boundaries in the natural homologs, was challenging because (i) no full-length, dimeric structures were available and (ii) extremely low sequence identities (≤10%) among homologs precluded traditional assemblies of sequence alignments. Thus, to identify domains, we built and aligned structural models of the natural proteins. The designed chimeric activators were assessed for function, which was then further improved by random mutagenesis. Several mutational variants were identified for an XylS•AraC chimera that responded to benzoate; two enhanced activation to near that of wild-type AraC. For an RhaR•AraC chimera, a variant with five additional substitutions enabled transcriptional activation in response to rhamnose. These five changes were dispersed across the protein structure, and combinatorial experiments testing subsets of substitutions showed significant non-additivity. Combined, the structure modeling and epistasis suggest that the common AraC/XylS structural scaffold is highly interconnected, with complex intra-protein and inter-domain communication pathways enabling allosteric regulation. At the same time, the observed epistasis and the low sequence identities of the natural homologs suggest that the structural scaffold and function of transcriptional regulation are nevertheless highly accommodating of amino acid changes.

Emergent spatiotemporal population dynamics with cell-length control of synthetic microbial consortia.

The increased complexity of synthetic microbial biocircuits highlights the need for distributed cell functionality due to concomitant increases in metabolic and regulatory burdens imposed on single-strain topologies. Distributed systems, however, introduce additional challenges since consortium composition and spatiotemporal dynamics of constituent strains must be robustly controlled to achieve desired circuit behaviors. Here, we address these challenges with a modeling-based investigation of emergent spatiotemporal population dynamics using cell-length control in monolayer, two-strain bacterial consortia. We demonstrate that with dynamic control of a strain's division length, nematic cell alignment in close-packed monolayers can be destabilized. We find that this destabilization confers an emergent, competitive advantage to smaller-length strains-but by mechanisms that differ depending on the spatial patterns of the population. We used complementary modeling approaches to elucidate underlying mechanisms: an agent-based model to simulate detailed mechanical and signaling interactions between the competing strains, and a reductive, stochastic lattice model to represent cell-cell interactions with a single rotational parameter. Our modeling suggests that spatial strain-fraction oscillations can be generated when cell-length control is coupled to quorum-sensing signaling in negative feedback topologies. Our research employs novel methods of population control and points the way to programming strain fraction dynamics in consortial synthetic biology.

Humans adapt to social diversity over time.

Humans have evolved cognitive processes favoring homogeneity, stability, and structure. These processes are, however, incompatible with a socially diverse world, raising wide academic and political concern about the future of modern societies. With data comprising 22 y of religious diversity worldwide, we show across multiple surveys that humans are inclined to react negatively to threats to homogeneity (i.e., changes in diversity are associated with lower self-reported quality of life, explained by a decrease in trust in others) in the short term. However, these negative outcomes are compensated in the long term by the beneficial influence of intergroup contact, which alleviates initial negative influences. This research advances knowledge that can foster peaceful coexistence in a new era defined by globalization and a socially diverse future.

Bayesian inference of distributed time delay in transcriptional and translational regulation.

MOTIVATION: Advances in experimental and imaging techniques have allowed for unprecedented insights into the dynamical processes within individual cells. However, many facets of intracellular dynamics remain hidden, or can be measured only indirectly. This makes it challenging to reconstruct the regulatory networks that govern the biochemical processes underlying various cell functions. Current estimation techniques for inferring reaction rates frequently rely on marginalization over unobserved processes and states. Even in simple systems this approach can be computationally challenging, and can lead to large uncertainties and lack of robustness in parameter estimates. Therefore we will require alternative approaches to efficiently uncover the interactions in complex biochemical networks. RESULTS: We propose a Bayesian inference framework based on replacing uninteresting or unobserved reactions with time delays. Although the resulting models are non-Markovian, recent results on stochastic systems with random delays allow us to rigorously obtain expressions for the likelihoods of model parameters. In turn, this allows us to extend MCMC methods to efficiently estimate reaction rates, and delay distribution parameters, from single-cell assays. We illustrate the advantages, and potential pitfalls, of the approach using a birth-death model with both synthetic and experimental data, and show that we can robustly infer model parameters using a relatively small number of measurements. We demonstrate how to do so even when only the relative molecule count within the cell is measured, as in the case of fluorescence microscopy. AVAILABILITY AND IMPLEMENTATION: Accompanying code in R is available at https://github.com/cbskust/DDE_BD. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Long-range temporal coordination of gene expression in synthetic microbial consortia.

Synthetic microbial consortia have an advantage over isogenic synthetic microbes because they can apportion biochemical and regulatory tasks among the strains. However, it is difficult to coordinate gene expression in spatially extended consortia because the range of signaling molecules is limited by diffusion. Here, we show that spatio-temporal coordination of gene expression can be achieved even when the spatial extent of the consortium is much greater than the diffusion distance of the signaling molecules. To do this, we examined the dynamics of a two-strain synthetic microbial consortium that generates coherent oscillations in small colonies. In large colonies, we find that temporally coordinated oscillations across the population depend on the presence of an intrinsic positive feedback loop that amplifies and propagates intercellular signals. These results demonstrate that synthetic multicellular systems can be engineered to exhibit coordinated gene expression using only transient, short-range coupling among constituent cells.

A synthetic system for asymmetric cell division in Escherichia coli.

We describe a synthetic genetic circuit for controlling asymmetric cell division in Escherichia coli in which a progenitor cell creates a differentiated daughter cell while retaining its original phenotype. Specifically, we engineered an inducible system that can bind and segregate plasmid DNA to a single position in the cell. Upon cell division, colocalized plasmids are kept by one and only one of the daughter cells. The other daughter cell receives no plasmid DNA and is irreversibly differentiated from its sibling. In this way, we achieved asymmetric cell division through asymmetric plasmid partitioning. We then used this system to achieve physical separation of genetically distinct cells by tying motility to differentiation. Finally, we characterized an orthogonal inducible circuit that enables the simultaneous asymmetric partitioning of two plasmid species, resulting in cells that have four distinct differentiated states. These results point the way toward the engineering of multicellular systems from prokaryotic hosts.

The morphological affinity of the Early Pleistocene footprints from Happisburgh, England, with other footprints of Pliocene, Pleistocene, and Holocene age.

Fossil hominin footprints provide a direct source of evidence of locomotor behavior and allow inference of other biological data such as anthropometrics. Many recent comparative analyses of hominin footprints have used 3D analytical methods to assess their morphological affinities, comparing tracks from different locations and/or time periods. However, environmental conditions can sometimes preclude 3D digital capture, as was the case at Happisburgh (England) in 2013. Consequently, we use here a 2D geometric morphometric approach to investigate the evolutionary context of the Happisburgh tracks. The comparative sample of hominin tracks comes from eight localities that span a broad temporal range from the Pliocene to Late Holocene. The results show disparity in the shapes of tracks ascribed to hominins from the Pliocene (presumably Australopithecus afarensis), Pleistocene (presumably Homo erectus and Homo antecessor), and Holocene (Homo sapiens). Three distinct morphological differences are apparent between time samples: changes in adduction of the hallux, changes in the shape and position of the medial longitudinal arch impression, and apparent changes in foot proportions. Linear dimensions classified the potential H. antecessor tracks from Happisburgh as being most similar to the presumed H. erectus prints from Ileret. We demonstrate using 2D geometric morphometric methods and linear dimensions that the Happisburgh tracks are morphologically similar to other presumed Homo tracks and differ from the Laetoli footprints. The probable functional implications of these results fit well with previous comparative analyses of hominin tracks at other sites.

Engineering Orthogonal Methyltransferases to Create Alternative Bioalkylation Pathways.

S-adenosyl-l-methionine (SAM)-dependent methyltransferases (MTs) catalyse the methylation of a vast array of small metabolites and biomacromolecules. Recently, rare carboxymethylation pathways have been discovered, including carboxymethyltransferase enzymes that utilise a carboxy-SAM (cxSAM) cofactor generated from SAM by a cxSAM synthase (CmoA). We show how MT enzymes can utilise cxSAM to catalyse carboxymethylation of tetrahydroisoquinoline (THIQ) and catechol substrates. Site-directed mutagenesis was used to create orthogonal MTs possessing improved catalytic activity and selectivity for cxSAM, with subsequent coupling to CmoA resulting in more efficient and selective carboxymethylation. An enzymatic approach was also developed to generate a previously undescribed co-factor, carboxy-S-adenosyl-l-ethionine (cxSAE), thereby enabling the stereoselective transfer of a chiral 1-carboxyethyl group to the substrate.

Using cellular fitness to map the structure and function of a major facilitator superfamily effluxer.

The major facilitator superfamily (MFS) effluxers are prominent mediators of antimicrobial resistance. The biochemical characterization of MFS proteins is hindered by their complex membrane environment that makes in vitro biochemical analysis challenging. Since the physicochemical properties of proteins drive the fitness of an organism, we posed the question of whether we could reverse that relationship and derive meaningful biochemical parameters for a single protein simply from fitness changes it confers under varying strengths of selection. Here, we present a physiological model that uses cellular fitness as a proxy to predict the biochemical properties of the MFS tetracycline efflux pump, TetB, and a family of single amino acid variants. We determined two lumped biochemical parameters roughly describing Km and Vmax for TetB and variants. Including in vivo protein levels into our model allowed for more specified prediction of pump parameters relating to substrate binding affinity and pumping efficiency for TetB and variants. We further demonstrated the general utility of our model by solely using fitness to assay a library of tet(B) variants and estimate their biochemical properties.

Structure and Biocatalytic Scope of Coclaurine N-Methyltransferase.

Benzylisoquinoline alkaloids (BIAs) are a structurally diverse family of plant secondary metabolites, which have been exploited to develop analgesics, antibiotics, antitumor agents, and other therapeutic agents. Biosynthesis of BIAs proceeds via a common pathway from tyrosine to (S)-reticulene at which point the pathway diverges. Coclaurine N-methyltransferase (CNMT) is a key enzyme in the pathway to (S)-reticulene, installing the N-methyl substituent that is essential for the bioactivity of many BIAs. In this paper, we describe the first crystal structure of CNMT which, along with mutagenesis studies, defines the enzymes active site architecture. The specificity of CNMT was also explored with a range of natural and synthetic substrates as well as co-factor analogues. Knowledge from this study could be used to generate improved CNMT variants required to produce BIAs or synthetic derivatives.

Modeling mechanical interactions in growing populations of rod-shaped bacteria.

Advances in synthetic biology allow us to engineer bacterial collectives with pre-specified characteristics. However, the behavior of these collectives is difficult to understand, as cellular growth and division as well as extra-cellular fluid flow lead to complex, changing arrangements of cells within the population. To rationally engineer and control the behavior of cell collectives we need theoretical and computational tools to understand their emergent spatiotemporal dynamics. Here, we present an agent-based model that allows growing cells to detect and respond to mechanical interactions. Crucially, our model couples the dynamics of cell growth to the cell's environment: Mechanical constraints can affect cellular growth rate and a cell may alter its behavior in response to these constraints. This coupling links the mechanical forces that influence cell growth and emergent behaviors in cell assemblies. We illustrate our approach by showing how mechanical interactions can impact the dynamics of bacterial collectives growing in microfluidic traps.

Engineered temperature compensation in a synthetic genetic clock.

Synthetic biology promises to revolutionize biotechnology by providing the means to reengineer and reprogram cellular regulatory mechanisms. However, synthetic gene circuits are often unreliable, as changes to environmental conditions can fundamentally alter a circuit's behavior. One way to improve robustness is to use intrinsic properties of transcription factors within the circuit to buffer against intra- and extracellular variability. Here, we describe the design and construction of a synthetic gene oscillator in Escherichia coli that maintains a constant period over a range of temperatures. We started with a previously described synthetic dual-feedback oscillator with a temperature-dependent period. Computational modeling predicted and subsequent experiments confirmed that a single amino acid mutation to the core transcriptional repressor of the circuit results in temperature compensation. Specifically, we used a temperature-sensitive lactose repressor mutant that loses the ability to repress its target promoter at high temperatures. In the oscillator, this thermoinduction of the repressor leads to an increase in period at high temperatures that compensates for the decrease in period due to Arrhenius scaling of the reaction rates. The result is a transcriptional oscillator with a nearly constant period of 48 min for temperatures ranging from 30 °C to 41 °C. In contrast, in the absence of the mutation the period of the oscillator drops from 60 to 30 min over the same temperature range. This work demonstrates that synthetic gene circuits can be engineered to be robust to extracellular conditions through protein-level modifications.

An Enzyme Cascade for Selective Modification of Tyrosine Residues in Structurally Diverse Peptides and Proteins.

Bioorthogonal chemistry enables a specific moiety in a complex biomolecule to be selectively modified in the presence of many reactive functional groups and other cellular entities. Such selectivity has become indispensable in biology, enabling biomolecules to be derivatized, conjugated, labeled, or immobilized for imaging, biochemical assays, or therapeutic applications. Methyltransferase enzymes (MTase) that accept analogues of the cofactor S-adenosyl methionine have been widely deployed for alkyl-diversification and bioorthogonal labeling. However, MTases typically possess tight substrate specificity. Here we introduce a more flexible methodology for selective derivatization of phenolic moieties in complex biomolecules. Our approach relies on the tandem enzymatic reaction of a fungal tyrosinase and the mammalian catechol-O-methyltransferase (COMT), which can effect the sequential hydroxylation of the phenolic group to give an intermediate catechol moiety that is subsequently O-alkylated. When used in this combination, the alkoxylation is highly selective for tyrosine residues in peptides and proteins, yet remarkably tolerant to changes in the peptide sequence. Tyrosinase-COMT are shown to provide highly versatile and regioselective modification of a diverse range of substrates including peptide antitumor agents, hormones, cyclic peptide antibiotics, and model proteins.

Effects of cell cycle noise on excitable gene circuits.

We assess the impact of cell cycle noise on gene circuit dynamics. For bistable genetic switches and excitable circuits, we find that transitions between metastable states most likely occur just after cell division and that this concentration effect intensifies in the presence of transcriptional delay. We explain this concentration effect with a three-states stochastic model. For genetic oscillators, we quantify the temporal correlations between daughter cells induced by cell division. Temporal correlations must be captured properly in order to accurately quantify noise sources within gene networks.

Sources of Variability in a Synthetic Gene Oscillator.

Synthetic gene oscillators are small, engineered genetic circuits that produce periodic variations in target protein expression. Like other gene circuits, synthetic gene oscillators are noisy and exhibit fluctuations in amplitude and period. Understanding the origins of such variability is key to building predictive models that can guide the rational design of synthetic circuits. Here, we developed a method for determining the impact of different sources of noise in genetic oscillators by measuring the variability in oscillation amplitude and correlations between sister cells. We first used a combination of microfluidic devices and time-lapse fluorescence microscopy to track oscillations in cell lineages across many generations. We found that oscillation amplitude exhibited high cell-to-cell variability, while sister cells remained strongly correlated for many minutes after cell division. To understand how such variability arises, we constructed a computational model that identified the impact of various noise sources across the lineage of an initial cell. When each source of noise was appropriately tuned the model reproduced the experimentally observed amplitude variability and correlations, and accurately predicted outcomes under novel experimental conditions. Our combination of computational modeling and time-lapse data analysis provides a general way to examine the sources of variability in dynamic gene circuits.

Timing and Variability of Galactose Metabolic Gene Activation Depend on the Rate of Environmental Change.

Modulation of gene network activity allows cells to respond to changes in environmental conditions. For example, the galactose utilization network in Saccharomyces cerevisiae is activated by the presence of galactose but repressed by glucose. If both sugars are present, the yeast will first metabolize glucose, depleting it from the extracellular environment. Upon depletion of glucose, the genes encoding galactose metabolic proteins will activate. Here, we show that the rate at which glucose levels are depleted determines the timing and variability of galactose gene activation. Paradoxically, we find that Gal1p, an enzyme needed for galactose metabolism, accumulates more quickly if glucose is depleted slowly rather than taken away quickly. Furthermore, the variability of induction times in individual cells depends non-monotonically on the rate of glucose depletion and exhibits a minimum at intermediate depletion rates. Our mathematical modeling suggests that the dynamics of the metabolic transition from glucose to galactose are responsible for the variability in galactose gene activation. These findings demonstrate that environmental dynamics can determine the phenotypic outcome at both the single-cell and population levels.