Mannitol is a widely distributed sugar alcohol and a primary carbon source for diverse microbial communities, serving as a substrate for producing high-value metabolites. In Gammaproteobacteria, the conserved mtl operon is essential for mannitol metabolism, encoding the phosphotransferase system enzyme II (EIIMtl; mtlA), mannitol-1-phosphate dehydrogenase (mtlD), and the repressor MtlR (mtlR). While operon transcription is known to be subject to catabolite repression and dependent on cAMP receptor protein (CRP), the molecular mechanism of MtlR-mediated repression has remained unclear, as it lacks a canonical DNA-binding domain. Here, we demonstrate that MtlR represses transcription by utilizing CRP as a DNA-anchoring platform. In the absence of mannitol, MtlR forms a complex with CRP at the mtl operator, with recruitment specificity determined by the precise spacing between adjacent CRP-binding sites. With mannitol, the dephosphorylation of membrane-bound EIIMtl triggers the sequestration of MtlR at the membrane, thereby relieving repression and inducing operon expression. We reveal a dual regulatory mechanism: CRP spacing-dependent MtlR recruitment and EIIMtl-mediated MtlR sequestration. This dual-control strategy is selectively conserved across Gammaproteobacteria, with Enterobacterales retaining both functional modules, while other lineages exhibit distinct evolutionary divergence. This study highlights a non-canonical, broadly conserved strategy for integrating metabolic and environmental signals into bacterial gene control.

Bacterial genomes contain multiple ribosomal RNA (rRNA) operons that by homologous recombination facilitate genome rearrangements. Here, we show that a Staphylococcus aureus temperate bacteriophage stimulates homologous recombination between the rRNA operons flanking the prophage and promotes a novel route of horizontal gene transfer HGT we term rrn-linked lateral transduction. By polymerase chain reaction, sequencing of phage-packaged bacterial DNA, and phage transduction assays, we show that upon induction of the prophage, large circles of DNA formed by rRNA operon recombination are packaged and transduced by the phage via the mechanism of lateral transduction. This phenomenon is likely to be widely occurring, with prophage-linked rRNA operon recombination also shown here in Salmonella. Our results challenge the concepts of mobile and core genomes and establish rrn-linked lateral transduction as a new form of gene transfer involving rRNA operons.

The clinical translation of phage therapy for multidrug-resistant infections is constrained by the lack of rapid, standardized therapeutic phage selection. Here, we introduce digital phage susceptibility testing (dPhaST), an automated droplet digital PCR workflow that quantifies phage-induced DNA release as a molecular signature of lysis. By targeting conserved 16S rRNA regions, dPhaST measures lytic activity across diverse bacterial pathogens within 3 h. Across 122 phage–host combinations involving 19 bacterial strains from six species, dPhaST shows 95.9% concordance with spot tests while resolving weak and heterogeneous lytic activities that are not readily distinguished phenotypically. It remains robust during the early infection window despite phage-encoded nuclease activity and tolerates phage cross-contamination better than spot tests. The method captures defense-mediated interactions involving CRISPR–Cas and Sir2-HerA systems. In this work, we show that automated digital quantification enables rapid and mechanistically informative profiling of early phage lytic efficacy across Gram-positive and Gram-negative pathogens. In this study, the authors present their automated droplet digital PCR workflow, capable of quantifying phage lytic activity.

Emerging large language models (LLMs) can infer gene functions directly from gene lists, enabling hypothesis generation without predefined gene sets. However, these LLM-derived predictions are qualitative, and principled statistical validation is lacking. Here, we develop an embedding-based statistical framework that transforms gene and function descriptions into vector representations, enabling statistical testing of gene-gene and gene-function relationships and quantitative prioritization of de novo functional hypotheses inferred by LLMs. We benchmark seven state-of-the-art embedding models using curated and retrieval-augmented literature-derived gene descriptions across diverse biological contexts. OpenAI’s text-embedding-3-large and Google’s gemini-embedding-001 perform best, capturing gene-gene functional relationships in 88.7-92.5% of Gene Ontology biological processes and approximately 98.6% of canonical pathways. In gene-function association analyses, these models achieve high sensitivity (95.2-98.4%) and specificity (72.7-84.3%). Through contamination analysis and evaluation using experimentally informed protein assembly gene sets, our framework distinguishes biologically meaningful LLM-inferred hypotheses from noise, outperforming confidence-based inference and conventional enrichment analysis. We further develop the open-source R package DEGEmbedR and demonstrate its utility for interpreting a drug perturbation-derived differentially expressed gene (DEG) signature lacking significant conventional enrichment results. Together, these results establish LLM-derived embeddings as a quantitative foundation for functional genomics and the statistical validation of LLM-based gene function inference. Whilst large language models can infer gene functions from gene lists, these predictions lack validation. Here the authors develop a framework to transform gene and function descriptions into vector representations, enabling statistical testing of gene-gene and gene-function relationships.

Efficient purification of specific RNAs from lysates remains a major challenge. Existing methods, such as MS2 tagging or oligonucleotide hybridization, require protein immobilization or sequence-specific hybridization, limiting scalability and compatibility with diverse RNAs. Current approaches often have low recovery, exhibit slow kinetics, and generate background contamination, limiting their use in RNA–protein interaction studies. To overcome these limitations, we developed FS2, an RNA sequence that binds Sephadex beads with high affinity, thus acting as an affinity tag for protein-free, rapid, and simple RNA purification. FS2 consists of two copies of the dextran-binding D8 aptamer embedded within the highly stable F30 three-way junction RNA scaffold, which promotes aptamer folding and enhances avidity. Systematic optimization revealed that FS2 exhibits rapid binding kinetics, efficient purification, and efficient elution under mild conditions (50°C, 10 mM EDTA), representing substantial improvements over existing RNA purification systems. We validated the utility of FS2 by purifying an FS2-tagged MYC mRNA fragment from HEK293T cells and demonstrating that N6-methyladenosine (m6A)-containing mRNAs can be pulled down along with the m6A-binding protein YTHDF2. Overall, FS2 provides a highly simple and efficient RNA-based affinity tag for recovering RNA from complex mixtures and lysates for diverse applications. 

The human genome encodes ~1,900 secreted proteins, many of which mediate intercellular communication. Secreted proteins do not act cell-autonomously, limiting systematic approaches to characterize their functions. Here we introduce SecAct (Secreted Activity, https://secact.ccr.cancer.gov ), a computational framework that infers the signaling activities of 1,170 human secreted proteins from spatial, single-cell and bulk transcriptomic data. The inference model harnesses precomputed intercellular signaling signatures trained on 1,258 spatial transcriptomics samples spanning 37 cancer types. Transcriptomics data from antisecreted protein therapies validate SecAct’s accuracy in predicting the repression of secreted protein activity following treatment. For spatial and single-cell transcriptomics data, SecAct provides interactive modules for analyzing secreted protein-mediated cell–cell communication. Applying SecAct to 54 cancer immunotherapy cohorts comprising 5,174 patients, we identified secreted proteins associated with tumor immunity. In vivo experiments validated lymphocyte antigen 86 (LY86), whose function in cancer was previously unknown, as an antitumor regulator. SecAct is a framework that leverages transcriptomic data to infer secreted protein signaling activities.