Schistosome Infection Impacts Hematopoiesis.

Helminth infections are common in animals. However, the impact of a helminth infection on the function of hematopoietic stem cells (HSCs) and other hematopoietic cells has not been comprehensively defined. In this article, we describe the hematopoietic response to infection of mice with Schistosoma mansoni, a parasitic flatworm that causes schistosomiasis. We analyzed the frequency or number of hematopoietic cell types in the bone marrow, spleen, liver, thymus, and blood and observed multiple hematopoietic changes caused by infection. Schistosome infection impaired bone marrow HSC function after serial transplantation. Functional HSCs were present in the infected liver. Infection blocked bone marrow erythropoiesis and augmented spleen erythropoiesis, observations consistent with the anemia and splenomegaly prevalent in schistosomiasis patients. This work defines the hematopoietic response to schistosomiasis, a debilitating disease afflicting more than 200 million people, and identifies impairments in HSC function and erythropoiesis.

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.

A niche-derived non-ribosomal peptide triggers planarian sexual development.

Germ cells are regulated by local microenvironments (niches), which secrete instructive cues. Conserved developmental signaling molecules act as niche-derived regulatory factors, yet other types of niche signals remain to be identified. Single-cell RNA-sequencing of sexual planarians revealed niche cells expressing a non-ribosomal peptide synthetase (nrps). Inhibiting nrps led to loss of female reproductive organs and testis hyperplasia. Mass spectrometry detected the dipeptide ß-alanyl-tryptamine (BATT), which is associated with reproductive system development and requires nrps and a monoamine-transmitter-synthetic enzyme (AADC) for its production. Exogenous BATT rescued the reproductive defects after nrps or aadc inhibition, restoring fertility. Thus, a non-ribosomal, monoamine-derived peptide provided by niche cells acts as a critical signal to trigger planarian reproductive development. These findings reveal an unexpected function for monoamines in niche-germ cell signaling. Furthermore, given the recently reported role for BATT as a male-derived factor required for reproductive maturation of female schistosomes, these results have important implications for the evolution of parasitic flatworms and suggest a potential role for non-ribosomal peptides as signaling molecules in other organisms.

Discovery of antibiotics that selectively kill metabolically dormant bacteria.

There is a need to discover and develop non-toxic antibiotics that are effective against metabolically dormant bacteria, which underlie chronic infections and promote antibiotic resistance. Traditional antibiotic discovery has historically favored compounds effective against actively metabolizing cells, a property that is not predictive of efficacy in metabolically inactive contexts. Here, we combine a stationary-phase screening method with deep learning-powered virtual screens and toxicity filtering to discover compounds with lethality against metabolically dormant bacteria and favorable toxicity profiles. The most potent and structurally distinct compound without any obvious mechanistic liability was semapimod, an anti-inflammatory drug effective against stationary-phase E. coli and A. baumannii. Integrating microbiological assays, biochemical measurements, and single-cell microscopy, we show that semapimod selectively disrupts and permeabilizes the bacterial outer membrane by binding lipopolysaccharide. This work illustrates the value of harnessing non-traditional screening methods and deep learning models to identify non-toxic antibacterial compounds that are effective in infection-relevant contexts.

Synthetic genetic circuits to uncover the OCT4 trajectories of successful reprogramming of human fibroblasts.

Reprogramming human fibroblasts to induced pluripotent stem cells (iPSCs) is inefficient, with heterogeneity among transcription factor (TF) trajectories driving divergent cell states. Nevertheless, the impact of TF dynamics on reprogramming efficiency remains uncharted. We develop a system that accurately reports OCT4 protein levels in live cells and use it to reveal the trajectories of OCT4 in successful reprogramming. Our system comprises a synthetic genetic circuit that leverages noise to generate a wide range of OCT4 trajectories and a microRNA targeting endogenous OCT4 to set total cellular OCT4 protein levels. By fusing OCT4 to a fluorescent protein, we are able to track OCT4 trajectories with clonal resolution via live-cell imaging. We discover that a supraphysiological, stable OCT4 level is required, but not sufficient, for efficient iPSC colony formation. Our synthetic genetic circuit design and high-throughput live-imaging pipeline are generalizable for investigating TF dynamics for other cell fate programming applications.

Engineering microbial division of labor for plastic upcycling.

Plastic pollution is rapidly increasing worldwide, causing adverse impacts on the environment, wildlife and human health. One tempting solution to this crisis is upcycling plastics into products with engineered microorganisms; however, this remains challenging due to complexity in conversion. Here we present a synthetic microbial consortium that efficiently degrades polyethylene terephthalate hydrolysate and subsequently produces desired chemicals through division of labor. The consortium involves two Pseudomonas putida strains, specializing in terephthalic acid and ethylene glycol utilization respectively, to achieve complete substrate assimilation. Compared with its monoculture counterpart, the consortium exhibits reduced catabolic crosstalk and faster deconstruction, particularly when substrate concentrations are high or crude hydrolysate is used. It also outperforms monoculture when polyhydroxyalkanoates serves as a target product and confers flexible tuning through population modulation for cis-cis muconate synthesis. This work demonstrates engineered consortia as a promising, effective platform that may facilitate polymer upcycling and environmental sustainability.

Engineering synthetic phosphorylation signaling networks in human cells.

Protein phosphorylation signaling networks play a central role in how cells sense and respond to their environment. Here, we describe the engineering of artificial phosphorylation networks in which "push-pull" motifs-reversible enzymatic phosphorylation cycles consisting of opposing kinase and phosphatase activities-are assembled from modular protein domain parts and then wired together to create synthetic phosphorylation circuits in human cells. We demonstrate that the composability of our design scheme enables model-guided tuning of circuit function and the ability to make diverse network connections; synthetic phosphorylation circuits can be coupled to upstream cell surface receptors to enable fast-timescale sensing of extracellular ligands, while downstream connections can regulate gene expression. We leverage these capabilities to engineer cell-based cytokine controllers that dynamically sense and suppress activated T cells. Our work introduces a generalizable approach for designing and building phosphorylation signaling circuits that enable user-defined sense-and-respond function for diverse biosensing and therapeutic applications.

Leveraging artificial intelligence in the fight against infectious diseases.

Despite advances in molecular biology, genetics, computation, and medicinal chemistry, infectious disease remains an ominous threat to public health. Addressing the challenges posed by pathogen outbreaks, pandemics, and antimicrobial resistance will require concerted interdisciplinary efforts. In conjunction with systems and synthetic biology, artificial intelligence (AI) is now leading to rapid progress, expanding anti-infective drug discovery, enhancing our understanding of infection biology, and accelerating the development of diagnostics. In this Review, we discuss approaches for detecting, treating, and understanding infectious diseases, underscoring the progress supported by AI in each case. We suggest future applications of AI and how it might be harnessed to help control infectious disease outbreaks and pandemics.

Rapid and Multiplexed Nucleic Acid Detection using Programmable Aptamer-Based RNA Switches.

Rapid, simple, and low-cost diagnostic technologies are crucial tools for combatting infectious disease. Here, we describe a class of aptamer-based RNA switches called aptaswitches that recognize specific target nucleic acid molecules and respond by initiating folding of a reporter aptamer. Aptaswitches can detect virtually any sequence and provide a fast and intense fluorescent readout, generating signals in as little as 5 minutes and enabling detection by eye with minimal equipment. We demonstrate that aptaswitches can be used to regulate folding of six different fluorescent aptamer/fluorogen pairs, providing a general means of controlling aptamer activity and an array of different reporter colors for multiplexing. By coupling isothermal amplification reactions with aptaswitches, we reach sensitivities down to 1 RNA copy/µL in one-pot reactions. Application of multiplexed one-pot reactions against RNA extracted from clinical saliva samples yields an overall accuracy of 96.67% for detection of SARS-CoV-2 in 30 minutes. Aptaswitches are thus versatile tools for nucleic acid detection that can be readily integrated into rapid diagnostic assays.

BioAutoMATED: An end-to-end automated machine learning tool for explanation and design of biological sequences.

The design choices underlying machine-learning (ML) models present important barriers to entry for many biologists who aim to incorporate ML in their research. Automated machine-learning (AutoML) algorithms can address many challenges that come with applying ML to the life sciences. However, these algorithms are rarely used in systems and synthetic biology studies because they typically do not explicitly handle biological sequences (e.g., nucleotide, amino acid, or glycan sequences) and cannot be easily compared with other AutoML algorithms. Here, we present BioAutoMATED, an AutoML platform for biological sequence analysis that integrates multiple AutoML methods into a unified framework. Users are automatically provided with relevant techniques for analyzing, interpreting, and designing biological sequences. BioAutoMATED predicts gene regulation, peptide-drug interactions, and glycan annotation, and designs optimized synthetic biology components, revealing salient sequence characteristics. By automating sequence modeling, BioAutoMATED allows life scientists to incorporate ML more readily into their work.

Discovering small-molecule senolytics with deep neural networks.

The accumulation of senescent cells is associated with aging, inflammation and cellular dysfunction. Senolytic drugs can alleviate age-related comorbidities by selectively killing senescent cells. Here we screened 2,352 compounds for senolytic activity in a model of etoposide-induced senescence and trained graph neural networks to predict the senolytic activities of >800,000 molecules. Our approach enriched for structurally diverse compounds with senolytic activity; of these, three drug-like compounds selectively target senescent cells across different senescence models, with more favorable medicinal chemistry properties than, and selectivity comparable to, those of a known senolytic, ABT-737. Molecular docking simulations of compound binding to several senolytic protein targets, combined with time-resolved fluorescence energy transfer experiments, indicate that these compounds act in part by inhibiting Bcl-2, a regulator of cellular apoptosis. We tested one compound, BRD-K56819078, in aged mice and found that it significantly decreased senescent cell burden and mRNA expression of senescence-associated genes in the kidneys. Our findings underscore the promise of leveraging deep learning to discover senotherapeutics.

Detection of viral RNAs at ambient temperature via reporter proteins produced through the target-splinted ligation of DNA probes.

Nucleic acid assays are not typically deployable in point-of-care settings because they require costly and sophisticated equipment for the control of the reaction temperature and for the detection of the signal. Here we report an instrument-free assay for the accurate and multiplexed detection of nucleic acids at ambient temperature. The assay, which we named INSPECTR (for internal splint-pairing expression-cassette translation reaction), leverages the target-specific splinted ligation of DNA probes to generate expression cassettes that can be flexibly designed for the cell-free synthesis of reporter proteins, with enzymatic reporters allowing for a linear detection range spanning four orders of magnitude and peptide reporters (which can be mapped to unique targets) enabling highly multiplexed visual detection. We used INSPECTR to detect a panel of five respiratory viral targets in a single reaction via a lateral-flow readout and ~4,000 copies of viral RNA via additional ambient-temperature rolling circle amplification of the expression cassette. Leveraging synthetic biology to simplify workflows for nucleic acid diagnostics may facilitate their broader applicability at the point of care.

Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii.

Acinetobacter baumannii is a nosocomial Gram-negative pathogen that often displays multidrug resistance. Discovering new antibiotics against A. baumannii has proven challenging through conventional screening approaches. Fortunately, machine learning methods allow for the rapid exploration of chemical space, increasing the probability of discovering new antibacterial molecules. Here we screened ~7,500 molecules for those that inhibited the growth of A. baumannii in vitro. We trained a neural network with this growth inhibition dataset and performed in silico predictions for structurally new molecules with activity against A. baumannii. Through this approach, we discovered abaucin, an antibacterial compound with narrow-spectrum activity against A. baumannii. Further investigations revealed that abaucin perturbs lipoprotein trafficking through a mechanism involving LolE. Moreover, abaucin could control an A. baumannii infection in a mouse wound model. This work highlights the utility of machine learning in antibiotic discovery and describes a promising lead with targeted activity against a challenging Gram-negative pathogen.

Perspective on Schistosomiasis Drug Discovery: Highlights from a Schistosomiasis Drug Discovery Workshop at Wellcome Collection, London, September 2022.

In September 2022, the Drug Discovery Unit at the University of Dundee, UK, organised an international meeting at the Wellcome Collection in London to explore the current clinical situation and challenges associated with treating schistosomiasis. The aim of this meeting was to discuss the need for new treatments in view of the clinical situation and to ascertain what the key requirements would be for any potential new anti-schistosomals. This information will be essential to inform ongoing drug discovery efforts for schistosomiasis. We also discussed the potential drug discovery pathway and associated criteria for progressing compounds to the clinic. To date, praziquantel (PZQ) is the only drug available to treat all species causing schistosomiasis, but it is often unable to completely clear parasites from an infected patient, partially due to its inactivity against juvenile worms. PZQ-mediated mass drug administration campaigns conducted in endemic areas (e.g., sub-Saharan Africa, where schistosomiasis is primarily prevalent) have contributed to reducing the burden of disease but will not eliminate the disease as a public health problem. The potential for Schistosoma to develop resistance towards PZQ, as the sole treatment available, could become a concern. Consequently, new anthelmintic medications are urgently needed, and this Perspective aims to capture some of the learnings from our discussions on the key criteria for new treatments.

Autocatalytic base editing for RNA-responsive translational control.

Genetic circuits that control transgene expression in response to pre-defined transcriptional cues would enable the development of smart therapeutics. To this end, here we engineer programmable single-transcript RNA sensors in which adenosine deaminases acting on RNA (ADARs) autocatalytically convert target hybridization into a translational output. Dubbed DART VADAR (Detection and Amplification of RNA Triggers via ADAR), our system amplifies the signal from editing by endogenous ADAR through a positive feedback loop. Amplification is mediated by the expression of a hyperactive, minimal ADAR variant and its recruitment to the edit site via an orthogonal RNA targeting mechanism. This topology confers high dynamic range, low background, minimal off-target effects, and a small genetic footprint. We leverage DART VADAR to detect single nucleotide polymorphisms and modulate translation in response to endogenous transcript levels in mammalian cells.

A self-propagating, barcoded transposon system for the dynamic rewiring of genomic networks.

In bacteria, natural transposon mobilization can drive adaptive genomic rearrangements. Here, we build on this capability and develop an inducible, self-propagating transposon platform for continuous genome-wide mutagenesis and the dynamic rewiring of gene networks in bacteria. We first use the platform to study the impact of transposon functionalization on the evolution of parallel Escherichia coli populations toward diverse carbon source utilization and antibiotic resistance phenotypes. We then develop a modular, combinatorial assembly pipeline for the functionalization of transposons with synthetic or endogenous gene regulatory elements (e.g., inducible promoters) as well as DNA barcodes. We compare parallel evolutions across alternating carbon sources and demonstrate the emergence of inducible, multigenic phenotypes and the ease with which barcoded transposons can be tracked longitudinally to identify the causative rewiring of gene networks. This work establishes a synthetic transposon platform that can be used to optimize strains for industrial and therapeutic applications, for example, by rewiring gene networks to improve growth on diverse feedstocks, as well as help address fundamental questions about the dynamic processes that have sculpted extant gene networks.

Helminth infection impacts hematopoiesis.

Helminth infections are common in animals. However, the impact of a helminth infection on the function of hematopoietic stem cells (HSCs) and other hematopoietic cells has not been comprehensively defined. Here we describe the hematopoietic response to infection of mice with Schistosoma mansoni, a parasitic flatworm which causes schistosomiasis. We analyzed the frequency or number of hematopoietic cell types in the bone marrow, spleen, liver, thymus, and blood, and observed multiple hematopoietic changes caused by infection. Schistosome infection impaired bone marrow HSC function after serial transplantation. Functional HSCs were present in the infected liver. Infection blocked bone marrow erythropoiesis and augmented spleen erythropoiesis, observations consistent with the anemia and splenomegaly prevalent in schistosomiasis patients. This work defines the hematopoietic response to schistosomiasis, a debilitating disease afflicting more than 200 million people, and identifies impairments in HSC function and erythropoiesis.

Synthetic genetic circuits to uncover and enforce the OCT4 trajectories of successful reprogramming of human fibroblasts.

Reprogramming human fibroblasts to induced pluripotent stem cells (iPSCs) is inefficient, with heterogeneity among transcription factor (TF) trajectories driving divergent cell states. Nevertheless, the impact of TF dynamics on reprogramming efficiency remains uncharted. Here, we identify the successful reprogramming trajectories of the core pluripotency TF, OCT4, and design a genetic controller that enforces such trajectories with high precision. By combining a genetic circuit that generates a wide range of OCT4 trajectories with live-cell imaging, we track OCT4 trajectories with clonal resolution and find that a distinct constant OCT4 trajectory is required for colony formation. We then develop a synthetic genetic circuit that yields a tight OCT4 distribution around the identified trajectory and outperforms in terms of reprogramming efficiency other circuits that less accurately regulate OCT4. Our synthetic biology approach is generalizable for identifying and enforcing TF dynamics for cell fate programming applications.