O-glycosylation contributes to mammalian glycoRNA biogenesis.

There is an increasing appreciation for the role of cell surface glycans in modulating interactions with extracellular ligands and participating in intercellular communication. We recently reported the existence of sialoglycoRNAs, where mammalian small RNAs are covalently linked to N-glycans through the modified base acp3U and trafficked to the cell surface. However, little is currently known about the role for O-glycosylation, another major class of carbohydrate polymer modifications. Here, we use parallel genetic, enzymatic, and mass spectrometry approaches to demonstrate that O-linked glycan biosynthesis is responsible for the majority of sialoglycoRNA levels. By examining the O-glycans associated with RNA from cell lines and colon organoids we find known and previously unreported O-linked glycan structures. Further, we find that O-linked glycans released from small RNA from organoids derived from ulcerative colitis patients exhibit higher levels of sialylation than glycans from healthy organoids. Together, our work provides flexible tools to interrogate O-linked glycoRNAs (O-glycoRNA) and suggests that they may be modulated in human disease.

Customizable gene sensing and response without altering endogenous coding sequences.

Synthetic biology aims to modify cellular behaviors by implementing genetic circuits that respond to changes in cell state. Integrating genetic biosensors into endogenous gene coding sequences using clustered regularly interspaced short palindromic repeats and Cas9 enables interrogation of gene expression dynamics in the appropriate chromosomal context. However, embedding a biosensor into a gene coding sequence may unpredictably alter endogenous gene regulation. To address this challenge, we developed an approach to integrate genetic biosensors into endogenous genes without modifying their coding sequence by inserting into their terminator region single-guide RNAs that activate downstream circuits. Sensor dosage responses can be fine-tuned and predicted through a mathematical model. We engineered a cell stress sensor and actuator in CHO-K1 cells that conditionally activates antiapoptotic protein BCL-2 through a downstream circuit, thereby increasing cell survival under stress conditions. Our gene sensor and actuator platform has potential use for a wide range of applications that include biomanufacturing, cell fate control and cell-based therapeutics.

Rapid discovery and evolution of nanosensors containing fluorogenic amino acids.

Binding-activated optical sensors are powerful tools for imaging, diagnostics, and biomolecular sensing. However, biosensor discovery is slow and requires tedious steps in rational design, screening, and characterization. Here we report on a platform that streamlines biosensor discovery and unlocks directed nanosensor evolution through genetically encodable fluorogenic amino acids (FgAAs). Building on the classical knowledge-based semisynthetic approach, we engineer ~15 kDa nanosensors that recognize specific proteins, peptides, and small molecules with up to 100-fold fluorescence increases and subsecond kinetics, allowing real-time and wash-free target sensing and live-cell bioimaging. An optimized genetic code expansion chemistry with FgAAs further enables rapid (~3 h) ribosomal nanosensor discovery via the cell-free translation of hundreds of candidates in parallel and directed nanosensor evolution with improved variant-specific sensitivities (up to ~250-fold) for SARS-CoV-2 antigens. Altogether, this platform could accelerate the discovery of fluorogenic nanosensors and pave the way to modify proteins with other non-standard functionalities for diverse applications.

A Krüppel-like factor establishes cellular heterogeneity during schistosome tegumental maintenance.

Schistosomes are blood-dwelling parasitic flatworms that rely on a syncytial surface coat, known as the tegument, for long-term survival and immune evasion in the blood of their human hosts. Previous studies have shown that cells within the tegumental syncytium are perpetually turned over and renewed by somatic stem cells called neoblasts. Yet, little is known about this renewal process on a molecular level. Here, we characterized a Krüppel-like factor 4 (klf4) using a combination of bulk and single cell RNAseq approaches and demonstrate that klf4 is essential for the maintenance of a specific tegumental lineage, resulting in the loss of a subpopulation of molecularly-unique tegument cells. Thus, klf4 is critical for maintaining the balance between different tegumental progenitor pools, thereby fine-tuning the molecular composition of the mature tegument. Understanding these distinct tegumental cell populations is expected to provide insights into parasite defense mechanisms and suggest new avenues for therapeutics.

Rapid, Multiplexed, and Enzyme-Free Nucleic Acid Detection Using Programmable Aptamer-Based RNA Switches.

Rapid, simple, and low-cost diagnostic technologies are crucial tools for combatting infectious disease. We describe a class of aptamer-based RNA switches or aptaswitches that recognize target nucleic acid molecules and initiate folding of a reporter aptamer. Aptaswitches can detect virtually any sequence and provide an intense fluorescent readout without intervening enzymes, generating signals in as little as 5 minutes and enabling detection by eye with minimal equipment. Aptaswitches can be used to regulate folding of seven fluorogenic aptamers, providing a general means of controlling aptamers and an array of multiplexable reporter colors. Coupling isothermal amplification reactions with aptaswitches, we reach sensitivities down to 1 RNA copy/µL in one-pot reactions. Application of multiplexed all-in-one reactions against RNA 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 are readily integrated into rapid diagnostic assays.

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.

Horizontal gene transfer of a functional cki homolog in the human pathogen Schistosoma mansoni.

Schistosomes are parasitic flatworms responsible for the neglected tropical disease schistosomiasis, causing devastating morbidity and mortality in the developing world. The parasites are protected by a skin-like tegument, and maintenance of this tegument is controlled by a schistosome ortholog of the tumor suppressor TP53. To understand mechanistically how p53-1 controls tegument production, we identified a cyclin dependent kinase inhibitor homolog (cki) that was co-expressed with p53-1. RNA interference of cki resulted in a hyperproliferation phenotype, that, in combination with p53-1 RNA interference yielded abundant tumor-like growths, indicating that cki and p53-1 are bona fide tumor suppressors in Schistosoma mansoni. Interestingly, cki homologs are widely present throughout parasitic flatworms but evidently absent from their free-living ancestors, suggesting this cki homolog came from an ancient horizontal gene transfer event. This in turn implies that the evolution of parasitism in flatworms may have been aided by a highly unusual means of metazoan genetic inheritance.

A niche-derived nonribosomal 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 nonribosomal 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 Aromatic L-amino acid decarboxylase (AADC) for its production. Exogenous BATT rescued the reproductive defects after nrps or aadc inhibition, restoring fertility. Thus, a nonribosomal, 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 nonribosomal peptides as signaling molecules in other organisms.

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 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.

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.

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 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.

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.

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.