Prime editing (PE) enables precise genetic modifications using canonical prime editing guide RNA (pegRNA), with the reverse transcription template and primer binding site (RTT–PBS) attached to the 3′ ends of CRISPR–Cas guide RNAs. Although PE ribonucleoprotein (RNP) delivery holds great therapeutic potential, its weak genomic editing capability limits therapeutic applications. Here we present structure-guided engineering of the PE complex using non-canonical pegRNAs (npegRNAs), with the RTT–PBS integrated within the single guide RNA loops, to improve PE efficiency. This approach demonstrates enhanced precise editing rates across various genomic sites and cell types, and improves therapeutic gene correction in a tyrosinaemia mouse model. Cas9-associated npegRNAs are more resistant to exonuclease degradation, probably enhancing the PE complex’s targeting efficiency in living cells. Using PE RNP delivery, npegRNAs achieve increased average editing yields of 26.8-fold over canonical pegRNAs and 5.9-fold over engineered pegRNAs (epegRNAs). Furthermore, npegRNA-mediated RNPs increased the efficiency of installing disease-relevant mutations up to 123-fold in human cell lines, including Jurkat T cells and induced pluripotent stem cells. Collectively, our findings demonstrate a robust PE strategy and highlight the potential of npegRNAs for therapeutic PE applications. Prime editing using non-canonical prime editing guide RNAs, where the reverse transcription template and primer binding site is integrated within the single guide RNA loops rather than attached to the 3′ ends of CRISPR–Cas guide RNAs, increases editing efficiency.

The NAD+ cap has been discovered in RNAs across prokaryotes and eukaryotes, suggesting a possible role of NAD capping in gene regulation. Current NAD-capped RNA (NAD-RNA) profiling methods lack precision in 5’-end mapping or bias against small NAD-RNAs. Here, we introduce precision NAD-RNA sequencing (pNAD-seq), which combines a two-step enrichment strategy with high-throughput sequencing to achieve single-nucleotide resolution of 5’-ends and unprecedented sensitivity for identifying small NAD-RNAs. We further develop NAD-linkSeq to determine full-length NAD-RNA sequences. Applying these methods to E. coli, we uncover a vast repertoire of NAD-RNAs, including tRNAs, rRNAs, intragenic transcripts, and antisense RNAs, many of which are significantly shorter than regular mRNAs, implying specialized biogenesis. High-resolution mapping reveals conserved promoter architectures driving NAD-RNA production and condition-dependent initiation dynamics: under nitrogen limitation, some NAD-RNA undergo TSS switching, alternative promoter usage, and coordinated expression shifts, correlating with metabolic stress responses. This report presents findings that offer a comprehensive view of NAD-RNAs in E. coli and introduces reliable methods for genome-wide profiling of NAD-RNAs across different organisms, which will facilitate the functional characterization of NAD-capping. Some RNAs carry an NAD+ cap that may regulate genes, but current methods miss small RNAs and precise start sites. Here, the authors develop two profiling methods that map NAD-capped RNAs at single-base resolution, uncover many new species in E. coli, and reveal stress-responsive promoter activity.

Mangroves are ecosystems located at land–sea transition zones, where they are continuously exposed to plant biomass and plastic pollution. Their soils harbor extensive microbial diversity with potential for discovering polymer-degrading enzymes. Here, we perform a microcosm experiment to examine how mangrove soil microbial communities respond to inputs of lignocellulose or polyethylene terephthalate (PET) in the presence and absence of seawater, and to explore the selection of putative PET-active enzymes (PETases) using gene- and genome-resolved metagenomics. Incubation conditions lead to a gradual increase in salinity, resulting in the enrichment of halophilic taxa, including spore-forming bacteria and archaeal species, particularly in seawater-depleted treatments. Lignocellulose input is the primary driver of soil microbial community restructuring, followed by seawater presence. In dry, lignocellulose-amended microcosms (L treatment), microbial diversity is significantly reduced, while lignocellulolytic taxa within the phyla Bacillota and Actinomycetota are enriched. Twelve potential PETases are identified in the L treatment, sharing >70% sequence similarity with known PETases, and three are predicted to be thermostable. Two putative PETases from Microbulbifer species display distinct sequence and structural features, thereby expanding the currently limited PETase sequence landscape. This study demonstrates that perturbing environmental microbiomes with plant-derived polymers represents a promising strategy for capturing novel PETases. Mangroves are increasingly exposed to plastic pollution. In this study, the authors performed microcosm experiments to show that mangrove soil microbial communities are reshaped by lignocellulose inputs yielding potential halophilic PET-degrading enzymes as promising candidates for plastic degradation.

Bacterial secreted proteins, particularly effectors delivered by specialized secretion systems, are key mediators of virulence and host–pathogen interactions. However, accurate computational identification remains challenging, as many existing methods rely heavily on sequence similarity or handcrafted features, and often focus on a single secretion system. Recent studies have reported that some bacterial effectors may be associated with more than one secretion system, highlighting the complexity of secretion system annotation and motivating the development of system-aware computational prediction approaches. Here, we present PLM-Effector, a hybrid deep learning framework that integrates modern protein language models (PLMs) with multiple neural architectures via a two-layer ensemble stacking strategy. By extracting complementary features from N- and C-terminal regions, PLM-Effector enables secretion-type-aware prediction across five major bacterial secretion systems (T1SS–T4SS and T6SS), with each system modeled independently. Systematic benchmarking shows that embeddings from protein-specific PLMs (ESM-1b, ESM2_t33, ProtT5) are more discriminative than those from general-purpose language models (e.g. BERT, BioBERT). Leveraging these representations, PLM-Effector achieves superior performance on an independent test set, with macro F1-scores of 0.9848, 0.8649, 0.9899, 0.9620, and 0.9728 for secreted proteins of T1SS–T4SS and T6SS, respectively, outperforming existing tools and homology-based baselines. Implemented as an accessible web server (http://www.mgc.ac.cn/PLM-Effector/) with source code and datasets available (https://github.com/zhengdd0422/PLM-Effector/), PLM-Effector provides a reproducible and user-friendly platform for both small-scale and genome-wide secreted protein discovery, facilitating advances in the study of bacterial pathogenesis.

Bacterial therapeutics hold great promise for cancer treatment by targeting oxygen-poor tumor regions and complementing existing therapies. However, current approaches often struggle with safety concerns and complex engineering. Developing a safe, effective delivery platform relying entirely on natural bacterial biosynthesis remains a challenge. Here we show that attenuated Serratia marcescens serves as a powerful biohybrid platform for cancer therapy by leveraging its natural biosynthesis of prodigiosin, a photosensitive pigment. We engineer S. marcescens to yield high prodigiosin levels, which exhibit strong intrinsic anti-cancer activity and near-infrared photosensitivity. In female mouse models of melanoma and colorectal cancer, this platform triggers robust systemic immune responses, including enhanced T cell recruitment and long-term memory against tumor recurrence. Furthermore, the bacteria induces tumor cell death via mitophagy, while photothermal properties of prodigiosin enables rapid, light-controlled bacterial clearance post-treatment. These findings establish S. marcescens as a versatile, self-regulating biosynthetic platform for precise and safe cancer immunotherapy. Bacterial therapies for cancer face safety and complexity challenges. Here, the authors develop an engineered Serratia marcescens platform that leverages natural pigment biosynthesis to trigger antitumor immunity and enable rapid bacterial clearance, controlled by near-infrared light.

The microbiome is widely involved in host metabolism, with many omics studies suggesting that it is important for metabolic health. Although studies in this area have made great strides in furthering our understanding of the role of the microbiome in health and disease, key challenges still hinder the safe clinical application of gut microbiota-targeted therapies. These limitations include a lack of confirmation of causality between the gut microbiota and host health, insights into the molecular mechanisms by which the gut microbiota functions to affect host health, and the development of therapeutic strategies that accurately regulate the function of the gut microbiota towards specific microbial enzyme targets without affecting its overall composition and viability. Microbial enzymes with various functions and activities have attracted the attention of many researchers in the past few years, especially microbiota–host isozymes, which are enzymes in the microbiome and the host that share a similar function. Such isozymes, as well as microbial-specific enzymes involved in basic biological processes of the gut microbiota, metabolism of nutrients, and synthesis of active metabolites and interactions in microbial-host communities, are the key mediators of gut microbiota–host crosstalk and have received much attention. In this Review, we provide a holistic understanding of the multifaceted role of gut microbial enzymes, including providing guidance for their discovery, while highlighting the great potential of gut microbial enzyme-oriented therapies for precision medicine. In this Review, Jiang and colleagues describe how gut microbial enzymes regulate host metabolic health and diseases. They also discuss the methodology for studying gut microbial enzymes and their potential clinical applications.

Prime editing enables precise genome modifications without DNA double-strand breaks, yet bacterial applications are limited by low efficiency and small edit sizes. Here, we develop PE-STAR, Prime Editing with SOS-Triggered and RecJ-Augmented Repair, to enhance prime editing in E. coli. Removing three inhibitory 3′→5′ exonucleases (SbcB, ExoX, and XseA) improved edited-strand retention, and extending post-transformation outgrowth increased editing efficiency. RecJ overexpression strengthened 5′-directed processing during flap resolution and gap expansion, biasing repair toward incorporation of the reverse-transcribed edited strand. To enrich edited cells, we integrated an SOS-responsive counter-selection circuit that links PE3-associated dual nicking to LexA-dependent gRNA expression targeting a plasmid encoding the toxin CcdB, thereby eliminating unedited cells. PE-STAR achieved up to 80%–90% editing efficiency for short-fragment modifications, representing up to 16-fold improvement across loci. The platform supported insertions, deletions, and replacements of up to 46 bp with high efficiency. Furthermore, installing an attB site by prime editing, followed by Bxb1 integrase recombination, enabled chromosomal integration of 3.2 and 8.0 kb cassettes with 100% recombination efficiency among screened colonies, including GFP reporter and riboflavin biosynthetic pathway. PE-STAR expands both the efficiency and functional scope of bacterial prime editing for programmable genome engineering.

Understanding the role of gene expression in cellular function and tissue organization requires spatial and quantitative detection of individual RNA molecules. Yet, the widespread adoption of automated single-molecule fluorescence in situ hybridization (smFISH) has been limited by the cost of equipment and the complexity of experimental procedures. We present autoFISH, an affordable, user-friendly platform that removes these barriers through open-source hardware components, accessible control software, and integrated analysis tools. The system demonstrates broad applicability by enabling both conventional and signal-amplified smFISH protocols and incorporates an optimized tissue-clearing method that preserves nuclear structures. Testing across multiple cell types and tissue preparations validates the system’s reliability and reproducibility, offering a practical solution for scaling spatial transcriptomics research and advancing discoveries in cellular and developmental biology, while significantly reducing costs and the technical expertise required. AutoFISH provides a cost-effective, open-source toolbox for automated smFISH, offering integrated hardware, software, and optimized protocols to streamline spatial gene-expression profiling in cell lines and cleared tissue samples.

A systematic understanding of cellular metabolism is essential for engineering yeast and uncovering the principles of metabolic robustness and evolution, yet much of its metabolic space remains unexplored. Although yeast genome-scale metabolic models have been reconstructed and curated for over two decades, more than 90% of the yeast metabolome remains uncovered. Here, to address this gap, we have developed an integrated workflow that combines retrobiosynthesis, deep learning-based enzyme annotation and enzyme–substrate prediction to systematically explore yeast underground metabolism. Using the framework, we reconstruct a yeast metabolic twin model, Yeast-MetaTwin, comprising 16,244 metabolites, 1,976 metabolic genes and 59,865 reactions. The model reveals systematic differences in Km distributions between the known and underground networks and identifies key hub metabolites linking the underground network. Moreover, Yeast-MetaTwin predicts by-product formation in yeast cell factories, and we experimentally validate two genes converting geraniol to geranial during geraniol biosynthesis. Cellular underground metabolism plays crucial roles in enzyme promiscuity, metabolism and biological evolution, but it has hardly been investigated. Here the authors combine retrobiosynthesis with deep learning enzyme annotation and enzyme–substrate prediction methods to explore it, reconstructing the yeast metabolic twin model, Yeast-MetaTwin.