Transposon-insertion sequencing (Tn-seq) couples transposon mutagenesis with next-generation sequencing to identify the transposon insertion site for thousands of mutants in parallel. It is a powerful technology with a myriad of uses beyond the identification of essential genes required for a cell to grow and divide. Tn-seq is particularly useful as a high-throughput method to assign function to function-unknown genes, which have increased steadily with the abundance of newly sequenced bacterial genomes. Tn-seq has now been adapted for use in over 100 bacterial species. Here, we summarize the applications of Tn-seq for querying bacterial physiology and discuss some of the possible applications for the future.

Natural transformation enables bacteria to internalize extracellular DNA, driving adaptation and the spread of antibiotic resistance. The membrane protein ComEC mediates translocation of single-stranded DNA (ssDNA) across the cytoplasmic membrane while degrading the complementary strand, yet the structural basis of its activity remains incompletely defined. Here, we report a cryo-electron microscopy structure of full-length ComEC from Neomoorella carbonis in a pre-translocation state, revealing a three-domain architecture and a conserved transmembrane channel captured in a closed conformation. Structural analysis indicates that conformational rearrangements of channel-lining helices would be required to accommodate ssDNA. Biochemical assays show that, relative to the isolated β-lactamase-like domain, full-length ComEC degrades DNA more efficiently and exhibits position-dependent cleavage of phosphodiester bonds within the DNA substrate. Importantly, coating of the DNA by the periplasmic DNA receptor ComEA suppresses endonucleolytic cleavage and enhances 5’ʹ terminal cleavage, thereby directing ComEC towards productive processing of transforming DNA during natural transformation.

Live-cell monitoring of sequence-specific nucleic acids is essential to understanding genome organization, RNA regulation, and disease progression. CRISPR-cas and Argonaute (Ago) systems provide programmable, guide-directed recognition of DNA or RNA and are increasingly used as platforms for in vivo bioimaging. This review summarizes the structural and mechanistic features of representative CRISPR and Ago effectors and discusses design strategies for sensitive, specific, and multiplexed imaging of genomic loci, extrachromosomal DNA, and endogenous RNA in living cells. We compare the analytical performance and limitations of CRISPR- and Ago-based imaging, with particular emphasis on the major technical and biological challenges affecting their accuracy, applicability, and reliability. Finally, this review offers insights into developing high-resolution and user-friendly bioimaging platforms for fundamental biology and future translational applications.

Strigolactones (SLs) are a class of plant hormones that regulate diverse developmental processes and environmental responses. SLs also play important roles as allelochemicals in interactions with arbuscular mycorrhizal fungi (AMF) and root parasitic plants in the rhizosphere. Since their discovery as plant hormones nearly 20 years ago, SL biosynthesis, transport, and signaling have been extensively studied, primarily by characterizing mutants with increased shoot branching and by utilizing reverse genetic approaches in various plant species. Emerging evidence has revealed a series of new components of SL biology, expanding our knowledge of how a single plant species produces various types of SLs with diverse chemical structures and how SLs are released from roots into the soil. However, the bioactive forms of SLs that function as plant hormones and the mechanisms underlying their root-to-shoot transport have not yet been clearly elucidated. In this review, we summarize the current understanding of SL biosynthesis and transport in Arabidopsis thaliana and Oryza sativa. In addition, we discuss the physiological functions of different SL species as plant hormones and rhizosphere signaling molecules, which largely remain unresolved.

Evolution guides biological systems to populate ecological niches, with viruses among the most successful examples of this principle. Viruses evolved over billions of years to efficiently transfer genetic information. Although viruses are highly diverse, most have converged towards remarkable similarity in the size and shape of their capsids. By contrast, generative models for protein design enable the creation of protein architectures that are absent from nature. Here we investigate whether protein assemblies designed by artificial intelligence can be functionalized to construct nucleic acid transport vehicles that are independent of evolutionary trajectories. By combining natural protein domains with synthetic protein assemblies, we create more than 100 bottom-up RNA transfer vehicles with unique sizes and shapes. These vehicles surpass the RNA transfer efficiency of widely used delivery vehicles by several orders of magnitude. In addition, we demonstrate that their tropism can be programmed by incorporation of computationally designed peptide binders and use them to deliver therapeutically relevant cargo RNAs into a wide range of cellular models. We show the in vivo biodistribution of one of these vehicles in a mouse at near-single-cell resolution, confirm its safety, and use it to perform a gene-editing treatment strategy for Duchenne muscular dystrophy in patient-derived cells and a pig. Our work demonstrates how proteins created by generative artificial intelligence can be harnessed for the rational engineering of RNA transport systems with the desired properties by overcoming the limitations of natural protein diversity. Synthetic virus-like protein architectures designed by artificial intelligence show superior ability to deliver RNA into cells, compared with their naturally occurring counterparts, by overcoming the evolutionary constraints limiting viral evolution.

Much of the human genome’s non-protein-coding fraction acts directly through RNA, yet the structural and functional roles encoded in these sequences remain poorly understood. Applying deep learning is hindered by scarce RNA structural data and it remains unclear what biological constraints such models can recover directly from the abundant RNA sequences alone. Here, to address these challenges, we developed NucleicBERT, a self-supervised masked-language model that learns contextual representations from single sequences without evolutionary information. Explainable artificial intelligence analyses show that the model organizes RNA sequences in latent space and encodes structural properties indicating that biologically meaningful constraints are learned from sequence correlations alone. When fine-tuned for downstream structural and functional tasks, NucleicBERT requires only single sequences while matching or exceeding current RNA prediction models. This alignment-free framework addresses the scarcity of annotated 3D RNA data while providing a rapid, computational complement to experimental techniques. By bridging abundant unlabelled sequence data with scarce structural annotations, NucleicBERT advances RNA structure prediction and informs how large language models encode biological information. RNA structure and function are hard to infer because annotations are scarce, despite abundant sequence data. Upadhyay et al. trained a self-supervised model on large-scale RNA data that derives biologically meaningful patterns from sequence correlations.

RNA interference (RNAi) shows great potential to protect crops against fungal diseases, yet reported protection efficiencies vary greatly, and our understanding of the factors responsible for this variance remains limited.  In this meta-analysis, we evaluated 89 studies that compare the efficiency of host-induced gene silencing (HIGS) and spray-induced gene silencing (SIGS) in controlling fungal diseases, focusing on biotrophic, hemibiotrophic, and necrotrophic fungi, the use of formulations, and the dsRNA design as explanatory factors for differences between reported efficiency values.  Our results indicate that SIGS is slightly more effective, particularly against biotrophs. Surprisingly, SIGS studies using formulations did not outperform those applying naked dsRNA. We also assessed parameters of RNA design. Differences in dsRNA length and the number of constructs and number of targets showed no consistent significant effect on resistance in either HIGS or SIGS. However, HIGS studies reported significantly higher efficiency when targeting genes closer to the 3′ end and SIGS when targeting genes closer to the 5′ end.  We discuss potential reasons for the reported patterns, such as variability in dsRNA uptake mechanisms, intercellular trafficking, and Dicer processing, and conclude that more research is needed to understand the biological mechanisms determining RNAi efficiency for fungal control.

Individual mammalian mRNAs and proteins are typically believed to originate from single genomic loci, with isoform diversity arising through cis-splicing of pre-mRNA. Whether mRNA from distant genes can undergo trans-splicing to generate functionally relevant chimeric transcripts has remained unclear. Here we develop a pipeline combining long-read direct RNA sequencing with non-targeted and targeted validation to identify chimeric transcripts in macrophages. Chromatin conformation capture studies reveal that inflammation induces interchromosomal DNA interactions, positioning parent genes proximally to facilitate the formation of chimeric mRNA. Notably, we identify a protein-coding chimeric mRNA representing a fusion between the pore-forming protein gasdermin D (GSDMD) and a C-terminal domain translated out of frame from Tmem106a (Gsdmd-Tmem106a) in mice. We show that inflammasome priming upregulates Gsdmd-Tmem106a, with the protein localizing to the plasma membrane. After activation of the inflammasome, GSDMD–TMEM106A directly interacts with canonical GSDMD N termini to accelerate and enhance pore formation and IL-1β release. Finally, we show that GSDMD–TMEM106A balances host defence and immunopathology in vivo: its loss protects against lethal sepsis but compromises antibacterial defence, whereas overexpression enhances host protection while increasing sepsis lethality. We establish that protein-coding chimeric mRNAs formed by regulated transcript fusion events are operative during inflammation and immunity. Inflammation induces interchromosomal DNA interactions that bring parent genes into close proximity, facilitating the formation of chimeric mRNAs that encode physiologically relevant, functional proteins.