Tunable signal processing in synthetic MAP kinase cascades.

The flexibility of MAPK cascade responses enables regulation of a vast array of cell fate decisions, but elucidating the mechanisms underlying this plasticity is difficult in endogenous signaling networks. We constructed insulated mammalian MAPK cascades in yeast to explore how intrinsic and extrinsic perturbations affect the flexibility of these synthetic signaling modules. Contrary to biphasic dependence on scaffold concentration, we observe monotonic decreases in signal strength as scaffold concentration increases. We find that augmenting the concentration of sequential kinases can enhance ultrasensitivity and lower the activation threshold. Further, integrating negative regulation and concentration variation can decouple ultrasensitivity and threshold from the strength of the response. Computational analyses show that cascading can generate ultrasensitivity and that natural cascades with different kinase concentrations are innately biased toward their distinct activation profiles. This work demonstrates that tunable signal processing is inherent to minimal MAPK modules and elucidates principles for rational design of synthetic signaling systems.

Mechanisms of noncanonical binding dynamics in multivalent protein-protein interactions.

Protein multivalency can provide increased affinity and specificity relative to monovalent counterparts, but these emergent biochemical properties and their mechanistic underpinnings are difficult to predict as a function of the biophysical properties of the multivalent binding partners. Here, we present a mathematical model that accurately simulates binding kinetics and equilibria of multivalent protein-protein interactions as a function of the kinetics of monomer-monomer binding, the structure and topology of the multidomain interacting partners, and the valency of each partner. These properties are all experimentally or computationally estimated a priori, including approximating topology with a worm-like chain model applicable to a variety of structurally disparate systems, thus making the model predictive without parameter fitting. We conceptualize multivalent binding as a protein-protein interaction network: ligand and receptor valencies determine the number of interacting species in the network, with monomer kinetics and structural properties dictating the dynamics of each species. As predicted by the model and validated by surface plasmon resonance experiments, multivalent interactions can generate several noncanonical macroscopic binding dynamics, including a transient burst of high-energy configurations during association, biphasic equilibria resulting from interligand competition at high concentrations, and multiexponential dissociation arising from differential lifetimes of distinct network species. The transient burst was only uncovered when extending our analysis to trivalent interactions due to the significantly larger network, and we were able to predictably tune burst magnitude by altering linker rigidity. This study elucidates mechanisms of multivalent binding and establishes a framework for model-guided analysis and engineering of such interactions.

Trajectory-based energy landscapes of gene regulatory networks.

Multistability and natural biological variability can result in significant heterogeneity within a cell population, leading to challenges in understanding and modulating cell behavior. Energy landscapes can offer qualitatively intuitive visualizations of cell phenotype and facilitate a more quantitative understanding of cellular dynamics, but current methods for landscape generation are mathematically involved and often require specific system properties (e.g., ergodicity or independent gene/protein probability distributions) that do not always hold. Here, we present a simple kinetic Monte Carlo-based method for landscape generation from a system of ordinary differential equations using only simulation trajectories initialized throughout the phase space of interest. The resulting landscape produces three quantitative features relevant to understanding cell behavior: stability (reflected by the depth or potential of landscape valleys), velocity (representing average directional movement on the landscape), and variance in velocity (indicative of landscape positions with heterogeneous movements). We applied this method to a genetic toggle switch, a core decision-making network in binary cellular responses, to elucidate effects of biologically relevant intrinsic and extrinsic cues. Intrinsic noise, such as stochasticity in transcription-translation and differences in cell cycle position, manifests through changes in valley width and position, reflecting increased population heterogeneity and more probabilistic cell fate transitions. The landscapes also capture the effect of an external inducer, revealing a quantitative correlation between the rate of cell fate transition and the energy barrier above a threshold inducer concentration determined by the permissivity of the valley. Further, in tracking dynamically changing landscapes under time-varying external cues, we unexpectedly found that an oscillatory inducer input can modulate cell fate heterogeneity and lead to periodic cell fate transitions entrained to the input frequency, depending on the intrinsic degradation rate of the switch. The landscape generation approach outlined herein is generalizable to other network topologies and may provide new quantitative insights into their dynamics.

Variable cellular decision-making behavior in a constant synthetic network topology.

BACKGROUND: Modules of interacting components arranged in specific network topologies have evolved to perform a diverse array of cellular functions. For a network with a constant topological structure, its function within a cell may still be tuned by changing the number of instances of a particular component (e.g., gene copy number) or by modulating the intrinsic biochemical properties of a component (e.g., binding strength or catalytic efficiency). How such perturbations affect cellular response dynamics remains poorly understood. Here, we explored these effects in a common decision-making motif, cross-antagonism with autoregulation, by synthetically constructing this network in yeast. RESULTS: We employed the engineering design strategy of reuse to build this topology with a single protein building block, TetR, creating necessary components through TetR mutations and fusion partners. We then studied the impact of several topology-preserving perturbations - strength of cross-antagonism, number of operator sites in a promoter, and gene dosage - on decision-making behavior. We found that reducing TetR repression strength, which hinders cross-antagonism, resulted in a loss of mutually exclusive cell responses. Unexpectedly, increasing the number of operator sites also impeded decision-making exclusivity, which may be a consequence of the averaging effect that arises when multiple transcriptional activators and repressors are accommodated at a given locus. Stochastic simulations of this topology revealed that, even for networks with high TetR repression strength and a low number of operator sites, increasing gene dosage can reduce exclusivity in response dynamics. We further demonstrated this result experimentally by quantifying gene copy numbers in selected yeast clones with differing phenotypic responses. CONCLUSIONS: Our study illustrates how parameters that do not change the topological structure of a decision-making network can nonetheless exert significant influence on its response dynamics. These findings should further inform the study of native motifs, including the effects of topology-preserving mutations, and the robust engineering of synthetic networks.

Robust hematopoietic progenitor cell commitment in the presence of a conflicting cue.

Hematopoietic lineage commitment is regulated by cytokines and master transcription factors, but it remains unclear how a progenitor cell chooses a lineage in the face of conflicting cues. Through transcript counting in megakaryocyte-erythroid progenitors undergoing erythropoiesis, we show that the expression levels of the pro-erythropoiesis transcription factor EKLF (also known as KLF1) and receptor EpoR are inversely correlated with their pro-megakaryopoiesis counterparts, FLI-1 and TpoR (also known as MPL). Notably, as progenitors commit to the erythrocyte lineage, EpoR is upregulated and TpoR is strongly downregulated, thus boosting the potency of the pro-erythropoiesis cue erythropoietin and effectively eliminating the activity of the pro-megakaryopoiesis cue thrombopoietin. Based on these findings, we propose a new model for exclusive decision making that explicitly incorporates signals from extrinsic cues, and we experimentally confirm a model prediction of temporal changes in transcript noise levels in committing progenitors. Our study suggests that lineage-specific receptor levels can modulate potencies of cues to achieve robust commitment decisions.

Broad-specificity mRNA-rRNA complementarity in efficient protein translation.

Studies of synthetic, well-defined biomolecular systems can elucidate inherent capabilities that may be difficult to uncover in a native biological context. Here, we used a minimal, reconstituted translation system from Escherichia coli to identify efficient ribosome binding sites (RBSs) in an unbiased, high-throughput manner. We applied ribosome display, a powerful in vitro selection method, to enrich only those mRNA sequences which could direct rapid protein translation. In addition to canonical Shine-Dalgarno (SD) motifs, we unexpectedly recovered highly efficient cytosine-rich (C-rich) sequences that exhibit unmistakable complementarity to the 16S rRNA of the small subunit of the ribosome, indicating that broad-specificity base-pairing may be an inherent, general mechanism for efficient translation. Furthermore, given the conservation of ribosomal structure and function across species, the broader relevance of C-rich RBS sequences identified through our in vitro evolution approach is supported by multiple, diverse examples in nature, including C-rich RBSs in several bacteriophage and plants, a poly-C consensus before the start codon in a lower eukaryote, and Kozak-like sequences in vertebrates.

Rapid retinoic acid-induced trophoblast cell model from human induced pluripotent stem cells.

A limited number of accessible and representative models of human trophoblast cells currently exist for the study of placentation. Current stem cell models involve either a transition through a naïve stem cell state or precise dynamic control of multiple growth factors and small-molecule cues. Here, we demonstrated that a simple five-day treatment of human induced pluripotent stem cells with two small molecules, retinoic acid (RA) and Wnt agonist CHIR 99021 (CHIR), resulted in rapid, synergistic upregulation of CDX2. Transcriptomic analysis of RA + CHIR-treated cells showed high similarity to primary trophectoderm cells. Multipotency was verified via further differentiation towards cells with syncytiotrophoblast or extravillous trophoblast features. RA + CHIR-treated cells were also assessed for the established criteria defining a trophoblast cell model, and they possess all the features necessary to be considered valid. Collectively, our data demonstrate a facile, scalable method for generating functional trophoblast-like cells in vitro to better understand the placenta.

Engineering signal peptides for enhanced protein secretion from Lactococcus lactis.

Lactococcus lactis is an attractive vehicle for biotechnological production of proteins and clinical delivery of therapeutics. In many such applications using this host, it is desirable to maximize secretion of recombinant proteins into the extracellular space, which is typically achieved by using the native signal peptide from a major secreted lactococcal protein, Usp45. In order to further increase protein secretion from L. lactis, inherent limitations of the Usp45 signal peptide (Usp45sp) must be elucidated. Here, we performed extensive mutagenesis on Usp45sp to probe the effects of both the mRNA sequence (silent mutations) and the peptide sequence (amino acid substitutions) on secretion. We screened signal peptides based on their resulting secretion levels of Staphylococcus aureus nuclease and further evaluated them for secretion of Bacillus subtilis α-amylase. Silent mutations alone gave an increase of up to 16% in the secretion of α-amylase through a mechanism consistent with relaxed mRNA folding around the ribosome binding site and enhanced translation. Targeted amino acid mutagenesis in Usp45sp, combined with additional silent mutations from the best clone in the initial screen, yielded an increase of up to 51% in maximum secretion of α-amylase while maintaining secretion at lower induction levels. The best sequence from our screen preserves the tripartite structure of the native signal peptide but increases the positive charge of the n-region. Our study presents the first example of an engineered L. lactis signal peptide with a higher secretion yield than Usp45sp and, more generally, provides strategies for further enhancing protein secretion in bacterial hosts.

Facile Display of Homomultivalent Proteins for In Vitro Selections.

Low-affinity protein binders are emerging as valuable domains for therapeutic applications because of their higher specificity when presented in multivalent ligands that increase the overall strength and selectivity of receptor binding. De novo discovery of low-affinity binders would be enhanced by the large library sizes attainable with in vitro selection systems, but these platforms generally maximize recovery of high-affinity monovalent binders. Here, we present a facile technology that uses rolling circle amplification to create homomultivalent libraries. We show proof of principle of this approach in ribosome display with off-rate selections of a bivalent ligand against monovalent and bivalent targets, thereby demonstrating high enrichment (up to 166-fold) against a low-affinity target that is bivalent but not monovalent. This approach to homomultivalent library construction can be applied to any binder tolerant of N- and C-terminal fusions and provides a platform for performing in vitro display selections with controlled protein valency and orientation.

Designing Multivalent and Multispecific Biologics.

In the era of precision medicine, multivalent and multispecific therapeutics present a promising approach for targeted disease intervention. These therapeutics are designed to interact with multiple targets simultaneously, promising enhanced efficacy, reduced side effects, and resilience against drug resistance. We dissect the principles guiding the design of multivalent biologics, highlighting challenges and strategies that must be considered to maximize therapeutic effect. Engineerable elements in multivalent and multispecific biologic design-domain affinities, valency, and spatial presentation-must be considered in the context of the molecular targets as well as the balance of important properties such as target avidity and specificity. We illuminate recent applications of these principles in designing protein and cell therapies and identify exciting future directions in this field, underscored by advances in biomolecular and cellular engineering and computational approaches. Expected final online publication date for the Annual Review of Chemical and Biomolecular Engineering , Volume 15 is June 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Inertial effect of cell state velocity on the quiescence-proliferation fate decision in breast cancer.

Energy landscapes can provide intuitive depictions of population heterogeneity and dynamics. However, it is unclear whether individual cell behavior, hypothesized to be determined by initial position and noise, is faithfully recapitulated. Using the p21-/Cdk2-dependent quiescence-proliferation decision in breast cancer dormancy as a testbed, we examined single-cell dynamics on the landscape when perturbed by hypoxia, a dormancy-inducing stress. Combining trajectory-based energy landscape generation with single-cell time-lapse microscopy, we found that initial position on a p21/Cdk2 landscape did not fully explain the observed cell-fate heterogeneity under hypoxia. Instead, cells with higher cell state velocities prior to hypoxia, influenced by epigenetic parameters, tended to remain proliferative under hypoxia. Thus, the fate decision on this landscape is significantly influenced by "inertia", a velocity-dependent ability to resist directional changes despite reshaping of the underlying landscape, superseding positional effects. Such inertial effects may markedly influence cell-fate trajectories in tumors and other dynamically changing microenvironments.

Pulsed stimuli entrain p53 to synchronize single cells and modulate cell-fate determination.

Entrainment to an external stimulus enables a synchronized oscillatory response across a population of cells, increasing coherent responses by reducing cell-to-cell heterogeneity. It is unclear whether the property of entrainability extends to systems where responses are intrinsic to the individual cell, rather than dependent on coherence across a population of cells. Using a combination of mathematical modeling, time-lapse fluorescence microscopy, and single-cell tracking, we demonstrated that p53 oscillations triggered by DNA double-strand breaks (DSBs) can be entrained with a periodic damage stimulus, despite such synchrony not known to function in effective DNA damage responses. Surprisingly, p53 oscillations were experimentally entrained over a wider range of DSB frequencies than predicted by an established computational model for the system. We determined that recapitulating the increased range of entrainment frequencies required, non-intuitively, a less robust oscillator and wider steady-state valley on the energy landscape. Further, we show that p53 entrainment can lead to altered expression dynamics of downstream targets responsible for cell fate in a manner dependent on target mRNA stability. Overall, this study demonstrates that entrainment can occur in a biological oscillator despite the apparent lack of an evolutionary advantage conferred through synchronized responses and highlights the potential of externally entraining p53 dynamics to reduce cellular variability and synchronize cell-fate responses for therapeutic outcomes.

Synthetic conversion of a graded receptor signal into a tunable, reversible switch.

The ability to engineer an all-or-none cellular response to a given signaling ligand is important in applications ranging from biosensing to tissue engineering. However, synthetic gene network 'switches' have been limited in their applicability and tunability due to their reliance on specific components to function. Here, we present a strategy for reversible switch design that instead relies only on a robust, easily constructed network topology with two positive feedback loops and we apply the method to create highly ultrasensitive (n(H)>20), bistable cellular responses to a synthetic ligand/receptor complex. Independent modulation of the two feedback strengths enables rational tuning and some decoupling of steady-state (ultrasensitivity, signal amplitude, switching threshold, and bistability) and kinetic (rates of system activation and deactivation) response properties. Our integrated computational and synthetic biology approach elucidates design rules for building cellular switches with desired properties, which may be of utility in engineering signal-transduction pathways.

Hematopoiesis and its disorders: a systems biology approach.

Scientists have traditionally studied complex biologic systems by reducing them to simple building blocks. Genome sequencing, high-throughput screening, and proteomics have, however, generated large datasets, revealing a high level of complexity in components and interactions. Systems biology embraces this complexity with a combination of mathematical, engineering, and computational tools for constructing and validating models of biologic phenomena. The validity of mathematical modeling in hematopoiesis was established early by the pioneering work of Till and McCulloch. In reviewing more recent papers, we highlight deterministic, stochastic, statistical, and network-based models that have been used to better understand a range of topics in hematopoiesis, including blood cell production, the periodicity of cyclical neutropenia, stem cell production in response to cytokine administration, and the emergence of imatinib resistance in chronic myeloid leukemia. Future advances require technologic improvements in computing power, imaging, and proteomics as well as greater collaboration between experimentalists and modelers. Altogether, systems biology will improve our understanding of normal and abnormal hematopoiesis, better define stem cells and their daughter cells, and potentially lead to more effective therapies.

Robust network topologies for generating switch-like cellular responses.

Signaling networks that convert graded stimuli into binary, all-or-none cellular responses are critical in processes ranging from cell-cycle control to lineage commitment. To exhaustively enumerate topologies that exhibit this switch-like behavior, we simulated all possible two- and three-component networks on random parameter sets, and assessed the resulting response profiles for both steepness (ultrasensitivity) and extent of memory (bistability). Simulations were used to study purely enzymatic networks, purely transcriptional networks, and hybrid enzymatic/transcriptional networks, and the topologies in each class were rank ordered by parametric robustness (i.e., the percentage of applied parameter sets exhibiting ultrasensitivity or bistability). Results reveal that the distribution of network robustness is highly skewed, with the most robust topologies clustering into a small number of motifs. Hybrid networks are the most robust in generating ultrasensitivity (up to 28%) and bistability (up to 18%); strikingly, a purely transcriptional framework is the most fragile in generating either ultrasensitive (up to 3%) or bistable (up to 1%) responses. The disparity in robustness among the network classes is due in part to zero-order ultrasensitivity, an enzyme-specific phenomenon, which repeatedly emerges as a particularly robust mechanism for generating nonlinearity and can act as a building block for switch-like responses. We also highlight experimentally studied examples of topologies enabling switching behavior, in both native and synthetic systems, that rank highly in our simulations. This unbiased approach for identifying topologies capable of a given response may be useful in discovering new natural motifs and in designing robust synthetic gene networks.

A cell-free approach to identify binding hotspots in plant immune receptors.

Plant immune receptors are often difficult to express heterologously, hindering study of direct interactions between these receptors and their targets with traditional biochemical approaches. The cell-free method ribosome display (RD) enables expression of such recalcitrant proteins by keeping each nascent polypeptide chain tethered to its ribosome, which can enhance protein folding by virtue of its size and solubility. Moreover, in contrast to an in planta readout of receptor activity such as a hypersensitive response that conflates binding and signaling, RD enables direct probing of the interaction between plant immune receptors and their targets. Here, we demonstrate the utility of this approach using tomato recognition of Trichoderma viride ethylene-inducing xylanase (EIX) as a case study. Leveraging the modular nature of the tomato LeEIX2 and LeEIX1 leucine-rich repeat (LRR) receptors, we applied an entropy-informed algorithm to maximize the information content in our receptor segmentation RD experiments to identify segments implicated in EIX binding. Unexpectedly, two distinct EIX-binding hotspots were discovered on LeEIX2 and both hotspots are shared with decoy LeEIX1, suggesting that their contrasting receptor functions are not due to differential modes of ligand binding. Given that most plant immune receptors are thought to engage targets via their LRR sequences, this approach should be of broad utility in rapidly identifying their binding hotspots.

Engineering a Minimal Leucine-rich Repeat IgG-binding Module.

Sea lamprey immunization can yield leucine-rich repeat (LRR) protein binders analogous to globular antibodies developed from mammals. A novel minimal LRR was discovered through lamprey immunization with human immunoglobulin G Fc domain (IgG Fc). Initial attempts to solubly express this LRR protein, VLRB.IgGFc, in Escherichia coli proved challenging, so it was analyzed using the cell-free method ribosome display. In ribosome display, VLRB.IgGFc was found to bind specifically to the Fc domain of IgG, with little observed cross-reactivity to IgA or IgM. The minimal repeat protein architecture of VLRB.IgGFc may facilitate modular LRR extensions to incorporate additional or augmented functionality within a continuous, structurally defined scaffold. We exploited this modularity to design a chimera of a well-characterized, soluble LRR repebody and the initially insoluble VLRB.IgGFc to produce soluble Repe-VLRB.IgGFc. The minimal IgG Fc-binding module, Repe-VLRB.IgGFc, and future-engineered variants thereof should be useful additions to the biotechnological toolbox for detecting, purifying, or targeting IgGs. More generally, this two-step approach of minimal LRR binder discovery via sea lamprey immunization followed by modular augmentation of functionality may be of general utility in protein engineering.

MVsim is a toolset for quantifying and designing multivalent interactions.

Arising through multiple binding elements, multivalency can specify the avidity, duration, cooperativity, and selectivity of biomolecular interactions, but quantitative prediction and design of these properties has remained challenging. Here we present MVsim, an application suite built around a configurational network model of multivalency to facilitate the quantification, design, and mechanistic evaluation of multivalent binding phenomena through a simple graphical user interface. To demonstrate the utility and versatility of MVsim, we first show that both monospecific and multispecific multivalent ligand-receptor interactions, with their noncanonical binding kinetics, can be accurately simulated. Further, to illustrate the conceptual insights into multivalent systems that MVsim can provide, we apply it to quantitatively predict the ultrasensitivity and performance of multivalent-encoded protein logic gates, evaluate the inherent programmability of multispecificity for selective receptor targeting, and extract rate constants of conformational switching for the SARS-CoV-2 spike protein and model its binding to ACE2 as well as multivalent inhibitors of this interaction. MVsim and instructional tutorials are freely available at https://sarkarlab.github.io/MVsim/ .

Model-guided engineering of DNA sequences with predictable site-specific recombination rates.

Site-specific recombination (SSR) is an important tool in synthetic biology, but its applications are limited by the inability to predictably tune SSR reaction rates. Facile rate manipulation could be achieved by modifying the DNA substrate sequence; however, this approach lacks rational design principles. Here, we develop an integrated experimental and computational method to engineer the DNA attachment sequence attP for predictably modulating the inversion reaction mediated by the recombinase Bxb1. After developing a qPCR method to measure SSR reaction rate, we design, select, and sequence attP libraries to inform a machine-learning model that computes Bxb1 inversion rate as a function of attP sequence. We use this model to predict reaction rates of attP variants in vitro and demonstrate their utility in gene circuit design in Escherichia coli. Our high-throughput, model-guided approach for rationally tuning SSR reaction rates enhances our understanding of recombinase function and expands the synthetic biology toolbox.