A male-derived nonribosomal peptide pheromone controls female schistosome development.

Schistosomes cause morbidity and death throughout the developing world due to the massive numbers of eggs female worms deposit into the blood of their host. Studies dating back to the 1920s show that female schistosomes rely on constant physical contact with a male worm both to become and remain sexually mature; however, the molecular details governing this process remain elusive. Here, we uncover a nonribosomal peptide synthetase that is induced in male worms upon pairing with a female and find that it is essential for the ability of male worms to stimulate female development. We demonstrate that this enzyme generates ß-alanyl-tryptamine that is released by paired male worms. Furthermore, synthetic ß-alanyl-tryptamine can replace male worms to stimulate female sexual development and egg laying. These data reveal that peptide-based pheromone signaling controls female schistosome sexual maturation, suggesting avenues for therapeutic intervention and uncovering a role for nonribosomal peptides as metazoan signaling molecules.

Designing Biological Circuits: Synthetic Biology Within the Operon Model and Beyond.

In 1961, Jacob and Monod proposed the operon model of gene regulation. At the model's core was the modular assembly of regulators, operators, and structural genes. To illustrate the composability of these elements, Jacob and Monod linked phenotypic diversity to the architectures of regulatory circuits. In this review, we examine how the circuit blueprints imagined by Jacob and Monod laid the foundation for the first synthetic gene networks that launched the field of synthetic biology in 2000. We discuss the influences of the operon model and its broader theoretical framework on the first generation of synthetic biological circuits, which were predominantly transcriptional and posttranscriptional circuits. We also describe how recent advances in molecular biology beyond the operon model-namely, programmable DNA- and RNA-binding molecules as well as models of epigenetic and posttranslational regulation-are expanding the synthetic biology toolkit and enabling the design of more complex biological circuits.

Synthetic biology in the clinic: engineering vaccines, diagnostics, and therapeutics.

Synthetic biology is a design-driven discipline centered on engineering novel biological functions through the discovery, characterization, and repurposing of molecular parts. Several synthetic biological solutions to critical biomedical problems are on the verge of widespread adoption and demonstrate the burgeoning maturation of the field. Here, we highlight applications of synthetic biology in vaccine development, molecular diagnostics, and cell-based therapeutics, emphasizing technologies approved for clinical use or in active clinical trials. We conclude by drawing attention to recent innovations in synthetic biology that are likely to have a significant impact on future applications in biomedicine.

A Deep Learning Approach to Antibiotic Discovery.

Due to the rapid emergence of antibiotic-resistant bacteria, there is a growing need to discover new antibiotics. To address this challenge, we trained a deep neural network capable of predicting molecules with antibacterial activity. We performed predictions on multiple chemical libraries and discovered a molecule from the Drug Repurposing Hub-halicin-that is structurally divergent from conventional antibiotics and displays bactericidal activity against a wide phylogenetic spectrum of pathogens including Mycobacterium tuberculosis and carbapenem-resistant Enterobacteriaceae. Halicin also effectively treated Clostridioides difficile and pan-resistant Acinetobacter baumannii infections in murine models. Additionally, from a discrete set of 23 empirically tested predictions from >107 million molecules curated from the ZINC15 database, our model identified eight antibacterial compounds that are structurally distant from known antibiotics. This work highlights the utility of deep learning approaches to expand our antibiotic arsenal through the discovery of structurally distinct antibacterial molecules.

A White-Box Machine Learning Approach for Revealing Antibiotic Mechanisms of Action.

Current machine learning techniques enable robust association of biological signals with measured phenotypes, but these approaches are incapable of identifying causal relationships. Here, we develop an integrated "white-box" biochemical screening, network modeling, and machine learning approach for revealing causal mechanisms and apply this approach to understanding antibiotic efficacy. We counter-screen diverse metabolites against bactericidal antibiotics in Escherichia coli and simulate their corresponding metabolic states using a genome-scale metabolic network model. Regression of the measured screening data on model simulations reveals that purine biosynthesis participates in antibiotic lethality, which we validate experimentally. We show that antibiotic-induced adenine limitation increases ATP demand, which elevates central carbon metabolism activity and oxygen consumption, enhancing the killing effects of antibiotics. This work demonstrates how prospective network modeling can couple with machine learning to identify complex causal mechanisms underlying drug efficacy.

Targeting Antibiotic Tolerance, Pathogen by Pathogen.

Antibiotic tolerance, the capacity of genetically susceptible bacteria to survive the lethal effects of antibiotic treatment, plays a critical and underappreciated role in the disease burden of bacterial infections. Here, we take a pathogen-by-pathogen approach to illustrate the clinical significance of antibiotic tolerance and discuss how the physiology of specific pathogens in their infection environments impacts the mechanistic underpinnings of tolerance. We describe how these insights are leading to the development of species-specific therapeutic strategies for targeting antibiotic tolerance and highlight experimental platforms that are enabling us to better understand the complexities of drug-tolerant pathogens in in vivo settings.

Universal Chimeric Antigen Receptors for Multiplexed and Logical Control of T Cell Responses.

T cells expressing chimeric antigen receptors (CARs) are promising cancer therapeutic agents, with the prospect of becoming the ultimate smart cancer therapeutics. To expand the capability of CAR T cells, here, we present a split, universal, and programmable (SUPRA) CAR system that simultaneously encompasses multiple critical "upgrades," such as the ability to switch targets without re-engineering the T cells, finely tune T cell activation strength, and sense and logically respond to multiple antigens. These features are useful to combat relapse, mitigate over-activation, and enhance specificity. We test our SUPRA system against two different tumor models to demonstrate its broad utility and humanize its components to minimize potential immunogenicity concerns. Furthermore, we extend the orthogonal SUPRA CAR system to regulate different T cell subsets independently, demonstrating a dually inducible CAR system. Together, these SUPRA CARs illustrate that multiple advanced logic and control features can be implemented into a single, integrated system.

Next-Generation Machine Learning for Biological Networks.

Machine learning, a collection of data-analytical techniques aimed at building predictive models from multi-dimensional datasets, is becoming integral to modern biological research. By enabling one to generate models that learn from large datasets and make predictions on likely outcomes, machine learning can be used to study complex cellular systems such as biological networks. Here, we provide a primer on machine learning for life scientists, including an introduction to deep learning. We discuss opportunities and challenges at the intersection of machine learning and network biology, which could impact disease biology, drug discovery, microbiome research, and synthetic biology.

A high-throughput synthetic biology approach for studying combinatorial chromatin-based transcriptional regulation.

The construction of synthetic gene circuits requires the rational combination of multiple regulatory components, but predicting their behavior can be challenging due to poorly understood component interactions and unexpected emergent behaviors. In eukaryotes, chromatin regulators (CRs) are essential regulatory components that orchestrate gene expression. Here, we develop a screening platform to investigate the impact of CR pairs on transcriptional activity in yeast. We construct a combinatorial library consisting of over 1,900 CR pairs and use a high-throughput workflow to characterize the impact of CR co-recruitment on gene expression. We recapitulate known interactions and discover several instances of CR pairs with emergent behaviors. We also demonstrate that supervised machine learning models trained with low-dimensional amino acid embeddings accurately predict the impact of CR co-recruitment on transcriptional activity. This work introduces a scalable platform and machine learning approach that can be used to study how networks of regulatory components impact gene expression.

Engineering Models to Scale.

Developing organisms can regulate and scale patterns to achieve consistent proportions despite immense changes in size. In this issue, Cao et al. build a model system, using engineered E. coli, to understand how regulatory dynamics can produce pattern scaling without the need for a morphogen gradient.

Portable, On-Demand Biomolecular Manufacturing.

Synthetic biology uses living cells as molecular foundries for the biosynthesis of drugs, therapeutic proteins, and other commodities. However, the need for specialized equipment and refrigeration for production and distribution poses a challenge for the delivery of these technologies to the field and to low-resource areas. Here, we present a portable platform that provides the means for on-site, on-demand manufacturing of therapeutics and biomolecules. This flexible system is based on reaction pellets composed of freeze-dried, cell-free transcription and translation machinery, which can be easily hydrated and utilized for biosynthesis through the addition of DNA encoding the desired output. We demonstrate this approach with the manufacture and functional validation of antimicrobial peptides and vaccines and present combinatorial methods for the production of antibody conjugates and small molecules. This synthetic biology platform resolves important practical limitations in the production and distribution of therapeutics and molecular tools, both to the developed and developing world.

Rapid, Low-Cost Detection of Zika Virus Using Programmable Biomolecular Components.

The recent Zika virus outbreak highlights the need for low-cost diagnostics that can be rapidly developed for distribution and use in pandemic regions. Here, we report a pipeline for the rapid design, assembly, and validation of cell-free, paper-based sensors for the detection of the Zika virus RNA genome. By linking isothermal RNA amplification to toehold switch RNA sensors, we detect clinically relevant concentrations of Zika virus sequences and demonstrate specificity against closely related Dengue virus sequences. When coupled with a novel CRISPR/Cas9-based module, our sensors can discriminate between viral strains with single-base resolution. We successfully demonstrate a simple, field-ready sample-processing workflow and detect Zika virus from the plasma of a viremic macaque. Our freeze-dried biomolecular platform resolves important practical limitations to the deployment of molecular diagnostics in the field and demonstrates how synthetic biology can be used to develop diagnostic tools for confronting global health crises. PAPERCLIP.

Systematic identification of factors for provirus silencing in embryonic stem cells.

Embryonic stem cells (ESCs) repress the expression of exogenous proviruses and endogenous retroviruses (ERVs). Here, we systematically dissected the cellular factors involved in provirus repression in embryonic carcinomas (ECs) and ESCs by a genome-wide siRNA screen. Histone chaperones (Chaf1a/b), sumoylation factors (Sumo2/Ube2i/Sae1/Uba2/Senp6), and chromatin modifiers (Trim28/Eset/Atf7ip) are key determinants that establish provirus silencing. RNA-seq analysis uncovered the roles of Chaf1a/b and sumoylation modifiers in the repression of ERVs. ChIP-seq analysis demonstrates direct recruitment of Chaf1a and Sumo2 to ERVs. Chaf1a reinforces transcriptional repression via its interaction with members of the NuRD complex (Kdm1a, Hdac1/2) and Eset, while Sumo2 orchestrates the provirus repressive function of the canonical Zfp809/Trim28/Eset machinery by sumoylation of Trim28. Our study reports a genome-wide atlas of functional nodes that mediate proviral silencing in ESCs and illuminates the comprehensive, interconnected, and multi-layered genetic and epigenetic mechanisms by which ESCs repress retroviruses within the genome.

Discovery of a structural class of antibiotics with explainable deep learning.

The discovery of novel structural classes of antibiotics is urgently needed to address the ongoing antibiotic resistance crisis1-9. Deep learning approaches have aided in exploring chemical spaces1,10-15; these typically use black box models and do not provide chemical insights. Here we reasoned that the chemical substructures associated with antibiotic activity learned by neural network models can be identified and used to predict structural classes of antibiotics. We tested this hypothesis by developing an explainable, substructure-based approach for the efficient, deep learning-guided exploration of chemical spaces. We determined the antibiotic activities and human cell cytotoxicity profiles of 39,312 compounds and applied ensembles of graph neural networks to predict antibiotic activity and cytotoxicity for 12,076,365 compounds. Using explainable graph algorithms, we identified substructure-based rationales for compounds with high predicted antibiotic activity and low predicted cytotoxicity. We empirically tested 283 compounds and found that compounds exhibiting antibiotic activity against Staphylococcus aureus were enriched in putative structural classes arising from rationales. Of these structural classes of compounds, one is selective against methicillin-resistant S. aureus (MRSA) and vancomycin-resistant enterococci, evades substantial resistance, and reduces bacterial titres in mouse models of MRSA skin and systemic thigh infection. Our approach enables the deep learning-guided discovery of structural classes of antibiotics and demonstrates that machine learning models in drug discovery can be explainable, providing insights into the chemical substructures that underlie selective antibiotic activity.

Reactive metabolic byproducts contribute to antibiotic lethality under anaerobic conditions.

Understanding how bactericidal antibiotics kill bacteria remains an open question. Previous work has proposed that primary drug-target corruption leads to increased energetic demands, resulting in the generation of reactive metabolic byproducts (RMBs), particularly reactive oxygen species, that contribute to antibiotic-induced cell death. Studies have challenged this hypothesis by pointing to antibiotic lethality under anaerobic conditions. Here, we show that treatment of Escherichia coli with bactericidal antibiotics under anaerobic conditions leads to changes in the intracellular concentrations of central carbon metabolites, as well as the production of RMBs, particularly reactive electrophilic species (RES). We show that antibiotic treatment results in DNA double-strand breaks and membrane damage and demonstrate that antibiotic lethality under anaerobic conditions can be decreased by RMB scavengers, which reduce RES accumulation and mitigate associated macromolecular damage. This work indicates that RMBs, generated in response to antibiotic-induced energetic demands, contribute in part to antibiotic lethality under anaerobic conditions.

Using targeted chromatin regulators to engineer combinatorial and spatial transcriptional regulation.

The transcription of genomic information in eukaryotes is regulated in large part by chromatin. How a diverse array of chromatin regulator (CR) proteins with different functions and genomic localization patterns coordinates chromatin activity to control transcription remains unclear. Here, we take a synthetic biology approach to decipher the complexity of chromatin regulation by studying emergent transcriptional behaviors from engineered combinatorial, spatial, and temporal patterns of individual CRs. We fuse 223 yeast CRs to programmable zinc finger proteins. Site-specific and combinatorial recruitment of CRs to distinct intralocus locations reveals a range of transcriptional logic and behaviors, including synergistic activation, long-range and spatial regulation, and gene expression memory. Comparing these transcriptional behaviors with annotated CR complex and function terms provides design principles for the engineering of transcriptional regulation. This work presents a bottom-up approach to investigating chromatin-mediated transcriptional regulation and introduces chromatin-based components and systems for synthetic biology and cellular engineering.

Toehold switches: de-novo-designed regulators of gene expression.

Efforts to construct synthetic networks in living cells have been hindered by the limited number of regulatory components that provide wide dynamic range and low crosstalk. Here, we report a class of de-novo-designed prokaryotic riboregulators called toehold switches that activate gene expression in response to cognate RNAs with arbitrary sequences. Toehold switches provide a high level of orthogonality and can be forward engineered to provide average dynamic range above 400. We show that switches can be integrated into the genome to regulate endogenous genes and use them as sensors that respond to endogenous RNAs. We exploit the orthogonality of toehold switches to regulate 12 genes independently and to construct a genetic circuit that evaluates 4-input AND logic. Toehold switches, with their wide dynamic range, orthogonality, and programmability, represent a versatile and powerful platform for regulation of translation, offering diverse applications in molecular biology, synthetic biology, and biotechnology.

Integrating biological redesign: where synthetic biology came from and where it needs to go.

Synthetic biology seeks to extend approaches from engineering and computation to redesign of biology, with goals such as generating new chemicals, improving human health, and addressing environmental issues. Early on, several guiding principles of synthetic biology were articulated, including design according to specification, separation of design from fabrication, use of standardized biological parts and organisms, and abstraction. We review the utility of these principles over the past decade in light of the field's accomplishments in building complex systems based on microbial transcription and metabolism and describe the progress in mammalian cell engineering.

Paper-based synthetic gene networks.

Synthetic gene networks have wide-ranging uses in reprogramming and rewiring organisms. To date, there has not been a way to harness the vast potential of these networks beyond the constraints of a laboratory or in vivo environment. Here, we present an in vitro paper-based platform that provides an alternate, versatile venue for synthetic biologists to operate and a much-needed medium for the safe deployment of engineered gene circuits beyond the lab. Commercially available cell-free systems are freeze dried onto paper, enabling the inexpensive, sterile, and abiotic distribution of synthetic-biology-based technologies for the clinic, global health, industry, research, and education. For field use, we create circuits with colorimetric outputs for detection by eye and fabricate a low-cost, electronic optical interface. We demonstrate this technology with small-molecule and RNA actuation of genetic switches, rapid prototyping of complex gene circuits, and programmable in vitro diagnostics, including glucose sensors and strain-specific Ebola virus sensors.