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.

Genomic characterization of metastatic patterns from prospective clinical sequencing of 25,000 patients.

Metastatic progression is the main cause of death in cancer patients, whereas the underlying genomic mechanisms driving metastasis remain largely unknown. Here, we assembled MSK-MET, a pan-cancer cohort of over 25,000 patients with metastatic diseases. By analyzing genomic and clinical data from this cohort, we identified associations between genomic alterations and patterns of metastatic dissemination across 50 tumor types. We found that chromosomal instability is strongly correlated with metastatic burden in some tumor types, including prostate adenocarcinoma, lung adenocarcinoma, and HR+/HER2+ breast ductal carcinoma, but not in others, including colorectal cancer and high-grade serous ovarian cancer, where copy-number alteration patterns may be established early in tumor development. We also identified somatic alterations associated with metastatic burden and specific target organs. Our data offer a valuable resource for the investigation of the biological basis for metastatic spread and highlight the complex role of chromosomal instability in cancer progression.

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.

Cancer Burden Is Controlled by Mural Cell-ß3-Integrin Regulated Crosstalk with Tumor Cells.

Enhanced blood vessel (BV) formation is thought to drive tumor growth through elevated nutrient delivery. However, this observation has overlooked potential roles for mural cells in directly affecting tumor growth independent of BV function. Here we provide clinical data correlating high percentages of mural-ß3-integrin-negative tumor BVs with increased tumor sizes but no effect on BV numbers. Mural-ß3-integrin loss also enhances tumor growth in implanted and autochthonous mouse tumor models with no detectable effects on BV numbers or function. At a molecular level, mural-cell ß3-integrin loss enhances signaling via FAK-p-HGFR-p-Akt-p-p65, driving CXCL1, CCL2, and TIMP-1 production. In particular, mural-cell-derived CCL2 stimulates tumor cell MEK1-ERK1/2-ROCK2-dependent signaling and enhances tumor cell survival and tumor growth. Overall, our data indicate that mural cells can control tumor growth via paracrine signals regulated by ß3-integrin, providing a previously unrecognized mechanism of cancer growth control.

Charting the Complexity of the Marine Microbiome through Single-Cell Genomics.

Marine bacteria and archaea play key roles in global biogeochemistry. To improve our understanding of this complex microbiome, we employed single-cell genomics and a randomized, hypothesis-agnostic cell selection strategy to recover 12,715 partial genomes from the tropical and subtropical euphotic ocean. A substantial fraction of known prokaryoplankton coding potential was recovered from a single, 0.4 mL ocean sample, which indicates that genomic information disperses effectively across the globe. Yet, we found each genome to be unique, implying limited clonality within prokaryoplankton populations. Light harvesting and secondary metabolite biosynthetic pathways were numerous across lineages, highlighting the value of single-cell genomics to advance the identification of ecological roles and biotechnology potential of uncultured microbial groups. This genome collection enabled functional annotation and genus-level taxonomic assignments for >80% of individual metagenome reads from the tropical and subtropical surface ocean, thus offering a model to improve reference genome databases for complex microbiomes.

Higher-Order Clustering of the Transmembrane Anchor of DR5 Drives Signaling.

Receptor clustering on the cell membrane is critical in the signaling of many immunoreceptors, and this mechanism has previously been attributed to the extracellular and/or the intracellular interactions. Here, we report an unexpected finding that for death receptor 5 (DR5), a receptor in the tumor necrosis factor receptor superfamily, the transmembrane helix (TMH) alone in the receptor directly assembles a higher-order structure to drive signaling and that this structure is inhibited by the unliganded ectodomain. Nuclear magnetic resonance structure of the TMH in bicelles shows distinct trimerization and dimerization faces, allowing formation of dimer-trimer interaction networks. Single-TMH mutations that disrupt either trimerization or dimerization abolish ligand-induced receptor activation. Surprisingly, proteolytic removal of the DR5 ectodomain can fully activate downstream signaling in the absence of ligand. Our data suggest a receptor activation mechanism in which binding of ligand or antibodies to overcome the pre-ligand autoinhibition allows TMH clustering and thus signaling.

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.

Molecular and cellular basis of genetically inherited skeletal muscle disorders.

Neuromuscular disorders comprise a diverse group of human inborn diseases that arise from defects in the structure and/or function of the muscle tissue - encompassing the muscle cells (myofibres) themselves and their extracellular matrix - or muscle fibre innervation. Since the identification in 1987 of the first genetic lesion associated with a neuromuscular disorder - mutations in dystrophin as an underlying cause of Duchenne muscular dystrophy - the field has made tremendous progress in understanding the genetic basis of these diseases, with pathogenic variants in more than 500 genes now identified as underlying causes of neuromuscular disorders. The subset of neuromuscular disorders that affect skeletal muscle are referred to as myopathies or muscular dystrophies, and are due to variants in genes encoding muscle proteins. Many of these proteins provide structural stability to the myofibres or function in regulating sarcolemmal integrity, whereas others are involved in protein turnover, intracellular trafficking, calcium handling and electrical excitability - processes that ensure myofibre resistance to stress and their primary activity in muscle contraction. In this Review, we discuss how defects in muscle proteins give rise to muscle dysfunction, and ultimately to disease, with a focus on pathologies that are most common, best understood and that provide the most insight into muscle biology.

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.

Spliceosome Profiling Visualizes Operations of a Dynamic RNP at Nucleotide Resolution.

Tools to understand how the spliceosome functions in vivo have lagged behind advances in the structural biology of the spliceosome. Here, methods are described to globally profile spliceosome-bound pre-mRNA, intermediates, and spliced mRNA at nucleotide resolution. These tools are applied to three yeast species that span 600 million years of evolution. The sensitivity of the approach enables the detection of canonical and non-canonical events, including interrupted, recursive, and nested splicing. This application of statistical modeling uncovers independent roles for the size and position of the intron and the number of introns per transcript in substrate progression through the two catalytic stages. These include species-specific inputs suggestive of spliceosome-transcriptome coevolution. Further investigations reveal the ATP-dependent discard of numerous endogenous substrates after spliceosome assembly in vivo and connect this discard to intron retention, a form of splicing regulation. Spliceosome profiling is a quantitative, generalizable global technology used to investigate an RNP central to eukaryotic gene expression.

Tracking Cancer Evolution Reveals Constrained Routes to Metastases: TRACERx Renal.

Clear-cell renal cell carcinoma (ccRCC) exhibits a broad range of metastatic phenotypes that have not been systematically studied to date. Here, we analyzed 575 primary and 335 metastatic biopsies across 100 patients with metastatic ccRCC, including two cases sampledat post-mortem. Metastatic competence was afforded by chromosome complexity, and we identify 9p loss as a highly selected event driving metastasis and ccRCC-related mortality (p = 0.0014). Distinct patterns of metastatic dissemination were observed, including rapid progression to multiple tissue sites seeded by primary tumors of monoclonal structure. By contrast, we observed attenuated progression in cases characterized by high primary tumor heterogeneity, with metastatic competence acquired gradually and initial progression to solitary metastasis. Finally, we observed early divergence of primitive ancestral clones and protracted latency of up to two decades as a feature of pancreatic metastases.

Interleukin-33 activates regulatory T cells to suppress innate γδ T cell responses in the lung.

Foxp3+ regulatory T (Treg) cells expressing the interleukin (IL)-33 receptor ST2 mediate tissue repair in response to IL-33. Whether Treg cells also respond to the alarmin IL-33 to regulate specific aspects of the immune response is not known. Here we describe an unexpected function of ST2+ Treg cells in suppressing the innate immune response in the lung to environmental allergens without altering the adaptive immune response. Following allergen exposure, ST2+ Treg cells were activated by IL-33 to suppress IL-17-producing γδ T cells. ST2 signaling in Treg cells induced Ebi3, a component of the heterodimeric cytokine IL-35 that was required for Treg cell-mediated suppression of γδ T cells. This response resulted in fewer eosinophil-attracting chemokines and reduced eosinophil recruitment into the lung, which was beneficial to the host in reducing allergen-induced inflammation. Thus, we define a fundamental role for ST2+ Treg cells in the lung as a negative regulator of the early innate γδ T cell response to mucosal injury.

On the composition of the preimmune repertoire of T cells specific for Peptide-major histocompatibility complex ligands.

Millions of T cells are produced in the thymus, each expressing a unique alpha/beta T cell receptor (TCR) capable of binding to a foreign peptide in the binding groove of a host major histocompatibility complex (MHC) molecule. T cell-mediated immunity to infection is due to the proliferation and differentiation of rare clones in the preimmune repertoire that by chance express TCRs specific for peptide-MHC (pMHC) ligands derived from the microorganism. Here we review recent findings that have altered our understanding of how the preimmune repertoire is established. Recent structural studies indicate that a germline-encoded tendency of TCRs to bind MHC molecules contributes to the MHC bias of T cell repertoires. It has also become clear that the preimmune repertoire contains functionally heterogeneous subsets including recent thymic emigrants, mature naive phenotype cells, memory phenotype cells, and natural regulatory T cells. In addition, sensitive new detection methods have revealed that the repertoire of naive phenotype T cells consists of distinct pMHC-specific populations that consistently vary in size in different individuals. The implications of these new findings for the clonal selection theory, self-tolerance, and immunodominance are discussed.

Mechanism of Transcription Anti-termination in Human Mitochondria.

In human mitochondria, transcription termination events at a G-quadruplex region near the replication origin are thought to drive replication of mtDNA by generation of an RNA primer. This process is suppressed by a key regulator of mtDNA-the transcription factor TEFM. We determined the structure of an anti-termination complex in which TEFM is bound to transcribing mtRNAP. The structure reveals interactions of the dimeric pseudonuclease core of TEFM with mobile structural elements in mtRNAP and the nucleic acid components of the elongation complex (EC). Binding of TEFM to the DNA forms a downstream "sliding clamp," providing high processivity to the EC. TEFM also binds near the RNA exit channel to prevent formation of the RNA G-quadruplex structure required for termination and thus synthesis of the replication primer. Our data provide insights into target specificity of TEFM and mechanisms by which it regulates the switch between transcription and replication of mtDNA.

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.

Author Correction: CCR5AS lncRNA variation differentially regulates CCR5, influencing HIV disease outcome.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.