The twin-arginine translocation (Tat) system is a mechanistically unique protein transport pathway moving folded proteins across membranes. It is found in all domains of life and is essential for bacterial virulence and plant photosynthesis. The membrane proteins, TatA, TatB and TatC form a core complex to which substrate proteins bind, triggering the recruitment of additional TatA protomers to form the transport site. Here we present cryo-electron microscopy structures of the prototypical TatBC complex from E. coli and the atypical complexes from Nitratifactor salsuginis and Myxococcus xanthus in a resting state, alongside TatAC substrate-bound TatBC and TatABC complexes from E. coli in the early stages of transport. These structures demonstrate that substrate proteins associate with the core complex solely through their N-terminal signal peptides. The Tat targeting sequences of the signal peptides make specific contacts with TatC, and the peptide body is clamped by TatB. The core complex contains highly tilted transmembrane helices that drive extreme local membrane thinning. On the basis of our structures and biochemical and functional analyses, we propose a model for the early steps in Tat transport. Cryo-EM structural, biochemical and functional analyses of the bacterial twin-arginine translocation system for protein transport across membranes reveal mechanisms of substrate binding.

Bacterial consortia, with broad metabolism and environmental resilience, show promise for bioaugmentation treatment of plastic wastes. Consortia design principles and plastic degradation enhancements in laboratory and simulated-system studies were reviewed. Practical barriers include narrow polymer scope, long treatment duration, limited scalability, ecological risks, and scarce techno-economic assessments. Bioaugmentation can be realized through the development of bacterial formulations, the integration of pretreatment–bioaugmentation workflows, and the implementation of long-term field trials.

Thermus thermophilus Argonaute (TtAgo) is a DNA-guided endonuclease promising for gene editing and molecular diagnostics. However, its high-temperature dependence (65–85 °C) restricts its utility in moderate-temperature scenarios. Here we show that combining deoxycytidine triphosphate (dCTP) with the single-stranded DNA-binding protein from T. thermophilus (TthSSB) enables robust TtAgo activity within 37–60 °C. Mechanistic studies reveal that dCTP increases TtAgo’s conformational flexibility, while TthSSB promotes substrate recruitment, synergistically driving efficient DNA cleavage. Leveraging this mechanism, we develop ERCB-TtAgo (exponential amplification reaction by dCTP/TthSSB-activated TtAgo), a one-step isothermal diagnostic platform for microRNA detection. It achieves femtomolar sensitivity within 40 min and accurately distinguishes hepatocellular carcinoma patients from controls in a 151-sample clinical cohort, matching RT‑qPCR performance. Our work not only surmounts a key limitation of TtAgo but also offers an approach for modulating nuclease activity, paving the way for intelligent biosensing platforms with broad applicability. Thermus thermophilus Argonaute (TtAgo) requires high temperatures for activity. Here, authors show that dCTP and a single stranded DNA binding protein enable TtAgo to work at moderate temperatures, allowing them to develop an isothermal miRNA detection platform for the diagnosis of hepatocellular carcinoma.

Precise and efficient replacement of large genomic DNA segments without inducing double-strand breaks (DSBs) remains a central challenge in genome engineering. Traditional homologous recombination relies on DSBs and long homologous arms, yet it remains inefficient, while recombinase or integrase systems suffer from residual sequences at integration sites. Prime editing (PE), limited by the processivity of reverse transcriptase, struggles to integrate large fragments (>100 bp). To address this challenge, we introduce Prime Editing–Microhomology-Enabled Replacement (PREMIER), a DSB-free platform by installing single-stranded microhomology arms at donor and genomic junctions via PE. In cell lines, PREMIER achieved a mean efficiency of 63.4% (median 65.2%) in diverse target sites, with peak efficiencies reaching 85.9%, exceeding homology-directed repair by 10–20-fold and reducing off-target integrations by over 100-fold compared to nonhomologous end joining. It bypasses the need for long homology arms, simplifies donor preparation, achieves targeted replacement of sequences up to 10.3 kb. In vivo, PREMIER integrates a 6.2-kb oncogene cassette into the mouse liver. Additionally, PREMIER replaces murine Trp53 with human TP53 CDS, generating functional humanized mice. Altogether, PREMIER provides a precise, high-efficiency, and DSB-free strategy for large-scale genome rewriting, offering a powerful tool for complex modeling and therapeutic genome editing.

Transposon-encoded IscB was defined as the evolutionary ancestor of CRISPR-Cas9. This compact RNA-guided endonuclease has since been engineered for genome-editing applications. We previously repurposed IscB and related Cas9s as efficient RNA editors by removing their double-stranded DNA (dsDNA) recognition module, the target-adjacent motif (TAM)/protospacer adjacent motif-interacting domain. Here, we report four cryo-electron microscopy structures of IscB, with or without TAM-interaction domain (TID), in complex with single-stranded nucleic acid (ssNA) targets. Structures reveal that, regardless of TID presence, IscB engages ssNA using the same mechanism. IscB initially facilitates formation of a 10-nt seed duplex with ssNA; further base-pairing is blocked by an alternatively positioned HNH nuclease that acts as a roadblock. In this intermediate state, neither HNH nor RuvC is competent for target cleavage. Only upon full duplex formation is the HNH roadblock dislodged by the duplex extension between guide RNA and ssNA. HNH and RuvC nuclease active sites become exposed as the result. A similar set of conformational rearrangements likely governs IscB activity during dsDNA target interrogation. Guided by the structural and mechanistic insights, we introduced mutations to either improve ssNA binding or ease HNH dislodging. Both approaches improved the RNA-targeting efficiency of IscB in vitro and in human cells.

Transcriptional interference is rarely documented in bacteria. In cyanobacteria, phycobilisomes are the major light-harvesting antennae, and their degradation under non-optimal conditions follows a tightly regulated genetic program leading to the production of NblA, a widely conserved proteolytic adapter. NblA production is regulated at both the transcriptional and post-transcriptional levels. Here, we uncover an additional regulatory layer mediated by an antisense RNA conserved in heterocyst-forming cyanobacteria. In Nostoc sp. PCC 7120, transcription of the abundant antisense RNA (as_nblA) limits nblA mRNA accumulation. A strain unable to transcribe as_nblA produces an excess of NblA, ultimately so harmful that suppressor mutations of nblA expression accumulate rapidly, underscoring the essential role of as_nblA. Rifampicin time-series experiments and the inability of as_nblA to regulate nblA expression in trans support transcriptional interference as the primary regulatory mechanism of as_nblA. Mathematical modeling of nblA expression, supported by biological data, shows that as_nblA plays a pivotal role in preventing leaky nblA expression under non-inducing conditions. Our work highlights the importance of antisense RNA-mediated regulation, particularly transcriptional interference, in establishing thresholds that prevent spurious expression of genes encoding critical cellular functions.

The microbiome actively influences antimicrobial resistance (AMR) dynamics by shaping both ecological and evolutionary processes. However, the extent of its role in resistance emergence, transmission and persistence remains unclear. Traditional AMR research has mainly focused on genetic mechanisms and pathogen-level dynamics. In contrast, the intersection of AMR and the microbiome, including resistance-gene reservoirs, microbial competition and community-mediated selection, remains poorly represented, especially in a modelling context. Here we present a structured framework for incorporating microbiome–AMR interactions into predictive models. We identify key microbiome-mediated processes shaping AMR across different levels of complexity, describe how these can be quantitatively integrated into models, and identify critical data gaps that limit current approaches. By bridging microbiome ecology, AMR biology and mathematical modelling, we set out research priorities and strategies to improve resistance prediction and guide microbiome-targeted interventions. The microbiome plays a significant yet underexplored role in antimicrobial resistance by influencing ecological and evolutionary processes. This Perspective proposes a framework to integrate microbiome–AMR interactions into predictive models while highlighting key mechanisms and data gaps to improve resistance understanding and interventions.

Microbial inoculants are increasingly promoted as sustainable alternatives or complements to conventional agricultural inputs, yet their field performance remains highly variable. This review examines how ecological processes governing root microbiome assembly constrain inoculant establishment, persistence, and function across agricultural systems. We synthesize current evidence on the roles of environmental filtering, host-mediated selection, microbial interactions, and context dependency in shaping inoculant outcomes. We further evaluate the promise and limitations of core-microbiome concepts and synthetic communities as emerging strategies for microbiome-informed inoculant design, emphasizing that their practical translation remains challenged by methodological variability, ecological complexity, formulation constraints, and regulatory barriers. By integrating ecological theory with applied microbiology, this review highlights why many inoculants fail to deliver consistent agronomic benefits and outlines a more potential framework for developing context-aware, field-relevant microbiome-based solutions for sustainable agroecosystem management.

Xylanases are central to lignocellulosic biomass degradation, yet current methods lack the specificity to resolve how enzymes distinguish complex xylan structures decorated with arabinofuranose (Araf) and 4-O-methyl-glucuronic acid (MeGlcA). Here, we report a suite of chemically-defined activity-based probes (ABPs) that enable the selective detection of arabinoxylan- and glucuronoxylan-specific xylanases (AXXs and GXXs). These cyclophellitol-derived ABPs covalently label retaining xylanases at their active sites, allowing precise mapping of substrate specificity across diverse glycoside hydrolase families. Crystallographic and mass spectrometric analyses reveal the molecular basis of probe selectivity, while in-gel and pull-down assays demonstrate their effectiveness in profiling xylanase activities in complex bacterial and fungal proteomes, including cellulosomes. By integrating activity-based protein profiling (ABPP) with sequence similarity networks (SSNs), we further show that xylanase specificity can be predicted from sequence alone, enabling rapid functional annotation of uncharacterized xylanases. This chemoproteomic strategy provides a powerful platform for discovering and engineering substrate-specific enzymes for biomass valorisation, microbial ecology, and biotechnological applications. Xylan is a complex plant polysaccharide with chemical decorations that shape its degradation. Here, the authors develop selective probes for xylanases targeting these structures in biological samples, offering a powerful approach for enzyme discovery and optimization for biotechnology.

Nicotinamide adenine dinucleotide (NAD+) is one of the most important metabolic coenzymes that not only drives redox reactions and energy production but also acts as a critical substrate for several enzymes involved in immune signalling, DNA repair and epigenetic regulation. Viral infections are known as potent modulators of NAD+ metabolism, with pathogens such as SARS-CoV-2, influenza A virus, Zika virus, herpes simplex virus and human immunodeficiency virus altering NAD+ biosynthesis and consumption to benefit their persistence and replication. In this review, we summarize the current understanding of NAD+ metabolism and its regulatory enzymes: sirtuins, poly (ADP-ribose) polymerases and CD38/CD157. We then discuss the interplay between NAD+ homeostasis and virus infection. Understanding how diverse viruses manipulate NAD+ metabolism could lead to broad-spectrum antiviral strategies grounded in metabolic resilience.

Accurate RNA splicing is essential for gene expression and protein function, yet the mechanisms governing splice site recognition remain incompletely understood. Aberrant splicing caused by mutations can lead to severe diseases, including cancer and genetic disorders, underscoring the need for accurate computational tools to predict splice sites and detect disruptions. Existing methods have made significant advances in splice site prediction but are often limited in handling long-range dependencies due to high computational costs, a factor critical to splicing regulation. Moreover, many models lack interpretability, hindering efforts to elucidate the underlying biological mechanisms. Here, we present SpliceSelectNet (SSNet), a hierarchical Transformer-based deep learning model that predicts splice sites from DNA sequences spanning up to 100 kb. By integrating local and global attention mechanisms, SSNet efficiently captures both proximal and distal regulatory signals while maintaining single-nucleotide resolution. Across multiple benchmark datasets, SSNet achieves state-of-the-art performance in splice site prediction and aberrant splicing detection. Systematic in silico mutagenesis demonstrates that attention scores reflect functional sequence importance, supporting their biological relevance. Long-range sequence perturbation experiments further show that SSNet captures distal regulatory effects beyond conventional receptive fields. Together, these results establish SSNet as a biologically interpretable framework for modeling long-range splicing regulation from genomic sequence.