helixCAM: A platform for programmable cellular assembly in bacteria and human cells.

Interactions between cells are indispensable for signaling and creating structure. The ability to direct precise cell-cell interactions would be powerful for engineering tissues, understanding signaling pathways, and directing immune cell targeting. In humans, intercellular interactions are mediated by cell adhesion molecules (CAMs). However, endogenous CAMs are natively expressed by many cells and tend to have cross-reactivity, making them unsuitable for programming specific interactions. Here, we showcase "helixCAM," a platform for engineering synthetic CAMs by presenting coiled-coil peptides on the cell surface. helixCAMs were able to create specific cell-cell interactions and direct patterned aggregate formation in bacteria and human cells. Based on coiled-coil interaction principles, we built a set of rationally designed helixCAM libraries, which led to the discovery of additional high-performance helixCAM pairs. We applied this helixCAM toolkit for various multicellular engineering applications, such as spherical layering, adherent cell targeting, and surface patterning.

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.

An Integrated Genome-wide CRISPRa Approach to Functionalize lncRNAs in Drug Resistance.

Resistance to chemotherapy plays a significant role in cancer mortality. To identify genetic units affecting sensitivity to cytarabine, the mainstay of treatment for acute myeloid leukemia (AML), we developed a comprehensive and integrated genome-wide platform based on a dual protein-coding and non-coding integrated CRISPRa screening (DICaS). Putative resistance genes were initially identified using pharmacogenetic data from 760 human pan-cancer cell lines. Subsequently, genome scale functional characterization of both coding and long non-coding RNA (lncRNA) genes by CRISPR activation was performed. For lncRNA functional assessment, we developed a CRISPR activation of lncRNA (CaLR) strategy, targeting 14,701 lncRNA genes. Computational and functional analysis identified novel cell-cycle, survival/apoptosis, and cancer signaling genes. Furthermore, transcriptional activation of the GAS6-AS2 lncRNA, identified in our analysis, leads to hyperactivation of the GAS6/TAM pathway, a resistance mechanism in multiple cancers including AML. Thus, DICaS represents a novel and powerful approach to identify integrated coding and non-coding pathways of therapeutic relevance.

A swapped genetic code prevents viral infections and gene transfer.

Engineering the genetic code of an organism has been proposed to provide a firewall from natural ecosystems by preventing viral infections and gene transfer1-6. However, numerous viruses and mobile genetic elements encode parts of the translational apparatus7-9, potentially rendering a genetic-code-based firewall ineffective. Here we show that such mobile transfer RNAs (tRNAs) enable gene transfer and allow viral replication in Escherichia coli despite the genome-wide removal of 3 of the 64 codons and the previously essential cognate tRNA and release factor genes. We then establish a genetic firewall by discovering viral tRNAs that provide exceptionally efficient codon reassignment allowing us to develop cells bearing an amino acid-swapped genetic code that reassigns two of the six serine codons to leucine during translation. This amino acid-swapped genetic code renders cells resistant to viral infections by mistranslating viral proteomes and prevents the escape of synthetic genetic information by engineered reliance on serine codons to produce leucine-requiring proteins. As these cells may have a selective advantage over wild organisms due to virus resistance, we also repurpose a third codon to biocontain this virus-resistant host through dependence on an amino acid not found in nature10. Our results may provide the basis for a general strategy to make any organism safely resistant to all natural viruses and prevent genetic information flow into and out of genetically modified organisms.

Recording gene expression order in DNA by CRISPR addition of retron barcodes.

Biological processes depend on the differential expression of genes over time, but methods to make physical recordings of these processes are limited. Here we report a molecular system for making time-ordered recordings of transcriptional events into living genomes. We do this through engineered RNA barcodes, based on prokaryotic retrons1, that are reverse transcribed into DNA and integrated into the genome using the CRISPR-Cas system2. The unidirectional integration of barcodes by CRISPR integrases enables reconstruction of transcriptional event timing based on a physical record through simple, logical rules rather than relying on pretrained classifiers or post hoc inferential methods. For disambiguation in the field, we will refer to this system as a Retro-Cascorder.

Realizing the potential of synthetic biology.

Synthetic biology, despite still being in its infancy, is increasingly providing valuable information for applications in the clinic, the biotechnology industry and in basic molecular research. Both its unique potential and the challenges it presents have brought together the expertise of an eclectic group of scientists, from cell biologists to engineers. In this Viewpoint article, five experts discuss their views on the future of synthetic biology, on its main achievements in basic and applied science, and on the bioethical issues that are associated with the design of new biological systems.

Reprogramming to recover youthful epigenetic information and restore vision.

Ageing is a degenerative process that leads to tissue dysfunction and death. A proposed cause of ageing is the accumulation of epigenetic noise that disrupts gene expression patterns, leading to decreases in tissue function and regenerative capacity1-3. Changes to DNA methylation patterns over time form the basis of ageing clocks4, but whether older individuals retain the information needed to restore these patterns-and, if so, whether this could improve tissue function-is not known. Over time, the central nervous system (CNS) loses function and regenerative capacity5-7. Using the eye as a model CNS tissue, here we show that ectopic expression of Oct4 (also known as Pou5f1), Sox2 and Klf4 genes (OSK) in mouse retinal ganglion cells restores youthful DNA methylation patterns and transcriptomes, promotes axon regeneration after injury, and reverses vision loss in a mouse model of glaucoma and in aged mice. The beneficial effects of OSK-induced reprogramming in axon regeneration and vision require the DNA demethylases TET1 and TET2. These data indicate that mammalian tissues retain a record of youthful epigenetic information-encoded in part by DNA methylation-that can be accessed to improve tissue function and promote regeneration in vivo.

Enhanced Bacterial Immunity and Mammalian Genome Editing via RNA-Polymerase-Mediated Dislodging of Cas9 from Double-Strand DNA Breaks.

The ability to target the Cas9 nuclease to DNA sequences via Watson-Crick base pairing with a single guide RNA (sgRNA) has provided a dynamic tool for genome editing and an essential component of adaptive immune systems in bacteria. After generating a double-stranded break (DSB), Cas9 remains stably bound to DNA. Here, we show persistent Cas9 binding blocks access to the DSB by repair enzymes, reducing genome editing efficiency. Cas9 can be dislodged by translocating RNA polymerases, but only if the polymerase approaches from one direction toward the Cas9-DSB complex. By exploiting these RNA-polymerase/Cas9 interactions, Cas9 can be conditionally converted into a multi-turnover nuclease, mediating increased mutagenesis frequencies in mammalian cells and enhancing bacterial immunity to bacteriophages. These consequences of a stable Cas9-DSB complex provide insights into the evolution of protospacer adjacent motif (PAM) sequences and a simple method of improving selection of highly active sgRNAs for genome editing.

Multiplexed in situ protein imaging using DNA-barcoded antibodies with extended hybridization chain reactions.

Antibodies have long served as vital tools in biological and clinical laboratories for the specific detection of proteins. Conventional methods employ fluorophore or horseradish peroxidase-conjugated antibodies to detect signals. More recently, DNA-conjugated antibodies have emerged as a promising technology, capitalizing on the programmability and amplification capabilities of DNA to enable highly multiplexed and ultrasensitive protein detection. However, the nonspecific binding of DNA-conjugated antibodies has impeded the widespread adoption of this approach. Here, we present a novel DNA-conjugated antibody staining protocol that addresses these challenges and demonstrates superior performance in suppressing nonspecific signals compared to previously published protocols. We further extend the utility of DNA-conjugated antibodies for signal-amplified in situ protein imaging through the hybridization chain reaction (HCR) and design a novel HCR DNA pair to expand the HCR hairpin pool from the previously published 5 pairs to 13, allowing for flexible hairpin selection and higher multiplexing. Finally, we demonstrate highly multiplexed in situ protein imaging using these techniques in both cultured cells and tissue sections.

Uncovering the dynamics and consequences of RNA isoform changes during neuronal differentiation.

Static gene expression programs have been extensively characterized in stem cells and mature human cells. However, the dynamics of RNA isoform changes upon cell-state-transitions during cell differentiation, the determinants and functional consequences have largely remained unclear. Here, we established an improved model for human neurogenesis in vitro that is amenable for systems-wide analyses of gene expression. Our multi-omics analysis reveals that the pronounced alterations in cell morphology correlate strongly with widespread changes in RNA isoform expression. Our approach identifies thousands of new RNA isoforms that are expressed at distinct differentiation stages. RNA isoforms mainly arise from exon skipping and the alternative usage of transcription start and polyadenylation sites during human neurogenesis. The transcript isoform changes can remodel the identity and functions of protein isoforms. Finally, our study identifies a set of RNA binding proteins as a potential determinant of differentiation stage-specific global isoform changes. This work supports the view of regulated isoform changes that underlie state-transitions during neurogenesis.

Engineered Living Materials For Sustainability.

Recent advances in synthetic biology and materials science have given rise to a new form of materials, namely engineered living materials (ELMs), which are composed of living matter or cell communities embedded in self-regenerating matrices of their own or artificial scaffolds. Like natural materials such as bone, wood, and skin, ELMs, which possess the functional capabilities of living organisms, can grow, self-organize, and self-repair when needed. They also spontaneously perform programmed biological functions upon sensing external cues. Currently, ELMs show promise for green energy production, bioremediation, disease treatment, and fabricating advanced smart materials. This review first introduces the dynamic features of natural living systems and their potential for developing novel materials. We then summarize the recent research progress on living materials and emerging design strategies from both synthetic biology and materials science perspectives. Finally, we discuss the positive impacts of living materials on promoting sustainability and key future research directions.

Publisher Correction: DNA sequencing at 40: past, present and future.

In this Review, the year of publication of reference 54 should be 2005, not 2015. In Box 2, "1982: GenBank ( https://www.ncbi.nlm.nih.gov/genbank/statistics/ )" should read "1982: Genbank/ENA/DDBJ" and "2007: NCBI Short Read Archive" should read "2007: NCBI and ENA Short Read Archives"; this is because the launches of these American, European and Japanese databases were coordinated. These errors have not been corrected.

Regulation of lifespan by neural excitation and REST.

The mechanisms that extend lifespan in humans are poorly understood. Here we show that extended longevity in humans is associated with a distinct transcriptome signature in the cerebral cortex that is characterized by downregulation of genes related to neural excitation and synaptic function. In Caenorhabditis elegans, neural excitation increases with age and inhibition of excitation globally, or in glutamatergic or cholinergic neurons, increases longevity. Furthermore, longevity is dynamically regulated by the excitatory-inhibitory balance of neural circuits. The transcription factor REST is upregulated in humans with extended longevity and represses excitation-related genes. Notably, REST-deficient mice exhibit increased cortical activity and neuronal excitability during ageing. Similarly, loss-of-function mutations in the C. elegans REST orthologue genes spr-3 and spr-4 elevate neural excitation and reduce the lifespan of long-lived daf-2 mutants. In wild-type worms, overexpression of spr-4 suppresses excitation and extends lifespan. REST, SPR-3, SPR-4 and reduced excitation activate the longevity-associated transcription factors FOXO1 and DAF-16 in mammals and worms, respectively. These findings reveal a conserved mechanism of ageing that is mediated by neural circuit activity and regulated by REST.

Molecular electronics sensors on a scalable semiconductor chip: A platform for single-molecule measurement of binding kinetics and enzyme activity.

For nearly 50 years, the vision of using single molecules in circuits has been seen as providing the ultimate miniaturization of electronic chips. An advanced example of such a molecular electronics chip is presented here, with the important distinction that the molecular circuit elements play the role of general-purpose single-molecule sensors. The device consists of a semiconductor chip with a scalable array architecture. Each array element contains a synthetic molecular wire assembled to span nanoelectrodes in a current monitoring circuit. A central conjugation site is used to attach a single probe molecule that defines the target of the sensor. The chip digitizes the resulting picoamp-scale current-versus-time readout from each sensor element of the array at a rate of 1,000 frames per second. This provides detailed electrical signatures of the single-molecule interactions between the probe and targets present in a solution-phase test sample. This platform is used to measure the interaction kinetics of single molecules, without the use of labels, in a massively parallel fashion. To demonstrate broad applicability, examples are shown for probe molecule binding, including DNA oligos, aptamers, antibodies, and antigens, and the activity of enzymes relevant to diagnostics and sequencing, including a CRISPR/Cas enzyme binding a target DNA, and a DNA polymerase enzyme incorporating nucleotides as it copies a DNA template. All of these applications are accomplished with high sensitivity and resolution, on a manufacturable, scalable, all-electronic semiconductor chip device, thereby bringing the power of modern chips to these diverse areas of biosensing.

Low-N protein engineering with data-efficient deep learning.

Protein engineering has enormous academic and industrial potential. However, it is limited by the lack of experimental assays that are consistent with the design goal and sufficiently high throughput to find rare, enhanced variants. Here we introduce a machine learning-guided paradigm that can use as few as 24 functionally assayed mutant sequences to build an accurate virtual fitness landscape and screen ten million sequences via in silico directed evolution. As demonstrated in two dissimilar proteins, GFP from Aequorea victoria (avGFP) and E. coli strain TEM-1 ß-lactamase, top candidates from a single round are diverse and as active as engineered mutants obtained from previous high-throughput efforts. By distilling information from natural protein sequence landscapes, our model learns a latent representation of 'unnaturalness', which helps to guide search away from nonfunctional sequence neighborhoods. Subsequent low-N supervision then identifies improvements to the activity of interest. In sum, our approach enables efficient use of resource-intensive high-fidelity assays without sacrificing throughput, and helps to accelerate engineered proteins into the fermenter, field and clinic.

L1CAM is not associated with extracellular vesicles in human cerebrospinal fluid or plasma.

L1CAM is a transmembrane protein expressed on neurons that was presumed to be found on neuron-derived extracellular vesicles (NDEVs) in human biofluids. We developed a panel of single-molecule array assays to evaluate the use of L1CAM for NDEV isolation. We demonstrate that L1CAM is not associated with extracellular vesicles in human plasma or cerebrospinal fluid and therefore recommend against its use as a marker in NDEV isolation protocols.

Comparative transcriptome analysis of acne vulgaris, rosacea, and hidradenitis suppurativa supports high-dose dietary zinc as a therapeutic agent.

Acne vulgaris, rosacea, and hidradenitis suppurativa are enduring inflammatory skin conditions that frequently manifest with akin clinical attributes, posing a considerable challenge for their distinctive diagnosis. While these conditions do exhibit certain resemblances, they also demonstrate distinct underlying pathophysiological mechanisms and treatment modalities. Delving into both the molecular parallels and disparities among these three disorders can yield invaluable insights for refined diagnostics, effective management, and targeted therapeutic interventions. In this report, we present a comparative analysis of transcriptomic data across these three diseases, elucidating differentially expressed genes and enriched pathways specific to each ailment, as well as those shared among them. Specifically, we identified multiple zinc-binding proteins (SERPINA1, S100A7, S100A8, S100A9 and KRT16) as consistently highly upregulated genes across all three diseases. Our hypothesis suggests that these proteins could bind and sequester zinc, potentially leading to localized zinc deficiency and heightened inflammation. We identified high-dose dietary zinc as a promising therapeutic approach and confirmed its effectiveness through validation in an acne mouse model.

Impaired neural stress resistance and loss of REST in bipolar disorder.

Neurodevelopmental changes and impaired stress resistance have been implicated in the pathogenesis of bipolar disorder (BD), but the underlying regulatory mechanisms are unresolved. Here we describe a human cerebral organoid model of BD that exhibits altered neural development, elevated neural network activity, and a major shift in the transcriptome. These phenotypic changes were reproduced in cerebral organoids generated from iPS cell lines derived in different laboratories. The BD cerebral organoid transcriptome showed highly significant enrichment for gene targets of the transcriptional repressor REST. This was associated with reduced nuclear REST and REST binding to target gene recognition sites. Reducing the oxygen concentration in organoid cultures to a physiological range ameliorated the developmental phenotype and restored REST expression. These effects were mimicked by treatment with lithium. Reduced nuclear REST and derepression of REST targets genes were also observed in the prefrontal cortex of BD patients. Thus, an impaired cellular stress response in BD cerebral organoids leads to altered neural development and transcriptional dysregulation associated with downregulation of REST. These findings provide a new model and conceptual framework for exploring the molecular basis of BD.

Deep representation learning improves prediction of LacI-mediated transcriptional repression.

Recent progress in DNA synthesis and sequencing technology has enabled systematic studies of protein function at a massive scale. We explore a deep mutational scanning study that measured the transcriptional repression function of 43,669 variants of the Escherichia coli LacI protein. We analyze structural and evolutionary aspects that relate to how the function of this protein is maintained, including an in-depth look at the C-terminal domain. We develop a deep neural network to predict transcriptional repression mediated by the lac repressor of Escherichia coli using experimental measurements of variant function. When measured across 10 separate training and validation splits using 5,009 single mutations of the lac repressor, our best-performing model achieved a median Pearson correlation of 0.79, exceeding any previous model. We demonstrate that deep representation learning approaches, first trained in an unsupervised manner across millions of diverse proteins, can be fine-tuned in a supervised fashion using lac repressor experimental datasets to more effectively predict a variant's effect on repression. These findings suggest a deep representation learning model may improve the prediction of other important properties of proteins.