The selection of small-molecule-binding RNA aptamers enables the creation of ligand-responsive RNA tools, yet most aptamers fail to operate reliably across diverse environments. Scaffolded selection addresses this limitation by preserving the tertiary architecture of a riboswitch scaffold while driving the evolution of a new ligand-binding pocket. Using the purine riboswitch aptamer, we previously generated dopamine-binding aptamers. Here, we report the crystal structures of two representative variants in their apo and dopamine-bound states to define how they recognize ligand while maintaining scaffold integrity. The structures demonstrate that scaffolded selection enforces global fold conservation and retains the defining tertiary interactions of the parental riboswitch. Local remodeling, triggered by deletions acquired during selection, rewires the three-way junction to build extensive interaction networks that host the dopamine-binding pocket. A deeply buried potassium ion anchors ligand recognition, while the RNA engages the catechol ring through stacking and hydrogen bonding interactions. Structure probing shows minimal conformational changes upon ligand binding, indicating that the aptamers adopt a largely preorganized fold. The balance between structural stability and local plasticity expands the capacity of a single RNA fold to recognize chemically distinct ligands and positions scaffolded selection as a powerful platform for engineering robust RNA-based sensing and regulatory devices.

The CRISPR-Cas9 system exhibits limited trans-cleavage activity, restricting its utility for nucleic acid detection. Here, we present a split RNA-guided Cas9 system (SCas9) that overcomes this limitation. By partitioning single-guide RNA into functionally independent scaffold and spacer modules, SCas9 enables amplification-free detection of single-stranded DNA and RNA with picomolar sensitivity within 30 min and single-nucleotide specificity. Incorporation of λ exonuclease permits PAM-independent detection of double-stranded DNA without cis-cleavage activity. Coupling SCas9 with recombinase polymerase amplification or reverse transcription–recombinase polymerase amplification extends detection of double-stranded DNA and long RNA to attomolar sensitivity. We further developed an autocatalytic SCas9 strategy for attomolar microRNA detection. SCas9 demonstrates broad substrate compatibility and was validated in clinical specimens, including human papillomavirus 16 in vaginal secretions and miR-21 in colorectal cancer tissues, highlighting its potential for point-of-care diagnostics. CRISPR-Cas9 lacks collateral cleavage for nucleic acid diagnostics. Here, authors show that splitting the guide RNA into scaffold and spacer converts Cas9 into SCas9, enabling rapid, single-nucleotide-specific DNA/RNA sensing, PAM-independent dsDNA detection, and attomolar clinical assays.

Plant viruses are a diverse class of infectious agents affecting agricultural crop production, plant health, and international movement of plant material. While availability and accessibility of scientific data related to plant viruses is essential for plant protection activities, scientific research, diagnostics, plant breeding, and beyond, publicly available data on plant viruses are at present not easily accessible; they are non-standardized, scattered across different resources, poorly interlinked, and not easily searchable by scientists or digital agents. We present PVDive (Plant Virus Diversity Database; https://pvdive.dsmz.de), a freely accessible resource providing high-quality, standardized, and integrated data associated with plant viruses. PVDive primarily offers information at the level of individual plant virus isolates and samples originating from the plant virus collection of the Leibniz Institute DSMZ (Germany). The data are integrated with information on plant virus species and host plants available in existing public resources and their access is centralized in a single platform. Through an intuitive, user-friendly interface with a number of search functionalities and a RESTful application programming interface, PVDive improves findability, accessibility, retrievability, and interoperability of plant virus data. To support data exploration, the platform also includes tools such as advanced search, sequence viewer, host gallery, and interactive data views.

The National Center for Biotechnology Information (NCBI) provides three principal resources containing DNA, RNA, and protein sequence data: the Sequence Read Archive (SRA), GenBank®, and the Reference Sequence (RefSeq™) collection. All resources are free to the public. SRA and GenBank are part of the International Nucleotide Sequence Database Collaboration and share data daily. After summarizing the current state of these resources, we review significant updates in the past two years, including these to the Submission Portal, the primary interface for submitting data to SRA and GenBank. We also describe access points for these data including NCBI Datasets’ website and programmatic interface, and present improvements in NCBI’s main sequence analysis and visualization tools, such as annotation pipelines, BLAST, and the Multiple Comparative Genome Viewer.

Two recent studies provide additional mechanistic insights into how this can work. Zeng et al. demonstrate that pathogen-triggered immune signaling in aerial tissues induces root exudation of heptadecanoic acid, which selectively enriches protective rhizosphere bacteria. Gilliard et al. reveal that plants perceive a Bacillus-derived lipopeptide through membrane biophysical remodeling rather than canonical receptor-mediated recognition. Although these mechanisms have been demonstrated in rice (Oryza sativa) and Arabidopsis thaliana respectively, together they provide complementary mechanistic examples that motivate a testable hypothesis about whether pathogen-triggered microbiota recruitment and microbial-signal perception can be functionally connected.

Messenger RNA modifications regulate transcript stability and translation efficiency across diverse biological systems, but the consequences of toxin-mediated mRNA modification in bacteria remain poorly understood. RhsP2 was recently identified as an RNA-targeting interbacterial ADP-ribosyltransferase toxin, but whether mRNAs are physiological substrates remained unknown. Here, we show that RhsP2 targets mRNA coding sequences and in doing so prevents their efficient translation. eCLIP-seq reveals preferential association of RhsP2 with UAAG-enriched motifs, and biochemical analyses showed that these sequences are preferred substrates for ribose 2′-OH ADP-ribosylation. Unexpectedly, modified mRNAs were broadly stabilized despite impaired protein synthesis, demonstrating that RhsP2 uncouples transcript stability from productive translation. Monosome- and disome-seq analyses show that RhsP2-modified mRNAs induce ribosome stalling and collision, and in vitro translation reactions demonstrate that ADP-ribosylation of mRNAs is sufficient to block translation elongation. Together, these findings establish mRNA ADP-ribosylation as a mechanism of toxin-mediated translational sabotage in which chemically modified transcripts accumulate while disrupting protein synthesis.

TransCirc is an evidence-integrated database for evaluating the translation potential of circular RNAs (circRNAs) and their putative products. Here, we present TransCirc 2.0, a major update that expands this resource from a primarily human-focused database to 10 vertebrate species, comprising more than 2.8 million circRNAs and increasing coverage by approximately 10-fold through the integration of standardized full-length circRNA sequences. Within the original multi-evidence framework, we incorporate additional evidence, including translation initiation sites, internal ribosome entry sites, N6-methyladenosine, ribosome/polysome association, and mass spectrometry (MS), together with circRNA-specific open reading frame and sequence-composition annotations. In light of recent advances in AI-based RNA modeling, we further incorporate multiple regulatory features predicted by TranslationAI and LAMAR. The circRNA-encoded proteins are further annotated for degron-associated stability and degradation properties, while MS-supported peptides are evaluated for potential MHC-I presentation. Collectively, these updates establish a cross-species framework linking full-length circRNA sequences with multi-level translation evidence and downstream product annotations, supporting systematic evaluation of circRNA translation potential and putative translation products. TransCirc 2.0 is freely available at https://www.biosino.org/transcirc/.

proMGE is a resource of prokaryotic mobile genetic elements (MGEs) that systematically catalogues MGEs from prokaryotes, their intercellular spread, antimicrobial resistance gene carriage, and MGE nesting interactions. We present proMGE2 featuring a 6X expansion, a total of >16.4 million MGEs from 670k high-quality isolate genomes, and 180k metagenome-assembled genomes (MAGs), spanning diverse MGE categories: transposons and insertion sequences, phages, phage-like elements, conjugative elements, mobility islands, and integrons. proMGE2 includes MGEs from MAGs derived from environmental microbiomes, encompassing ~50k samples across several major habitat types. proMGE2 provides a standardized MGE reference collection of >5 million non-redundant MGE sequences drawn from our curated database, and enables identification of MGEs in user-provided genomes. proMGE2 captures MGE diversity and their functional potential at unprecedented scale to support future research of mobile genetic elements and transfer of genetic material in microbes. The database is available at http://promge.embl.de/.

Microbial peptides represent a vast yet largely underexplored component of microbial dark matter, offering a valuable reservoir of bioactive molecules. Artificial Intelligence (AI) has revolutionized peptide mining, demonstrating high-throughput screening capabilities in tasks such as functional prediction and de novo design. To fully unlock the potential of microbial resource, we present miProbe (https://www.biosino.org/iMAC/miProbe), an integrative platform hosting 1.08 × 109 microbial peptides from disparate habitats. Uniquely, miProbe bridges the gap between raw sequences and AI applications by providing 8 TB of precomputed protein language model embeddings (e.g., ESM2-650M and ProtT5-XL-U50). Our analysis reveals substantial variation in peptide diversity and functional properties across habitats and microbial taxonomic groups, and highlights the utility of large-scale embedding resources in functional peptide mining. Equipped with robust APIs for seamless model integration, miProbe establishes a diverse and scalable data resource to accelerate AI-driven exploration of microbial dark matter and the rational design of novel therapeutics. Authors present miProbe, a platform aimed to consolidate peptide data and functional characterisation, and presenting a resource to accelerate AI-driven exploration of microbial dark matter.

Accurate phage–host prediction underpins ecological interpretation and experimental validation of uncultivated phages, yet most current approaches rely heavily on metagenome-assembled genomes (MAGs). This reliance implicitly assumes that MAG-level quality metrics, such as completeness and contamination, ensure scaffold-level correctness. Here, we demonstrate that this assumption can be violated under particular challenging conditions. Tetranucleotide frequency (TNF) analyses reveal that CRISPR-associated regions often diverge compositionally from their host genomes, increasing the risk of misbinning, while experimentally validated phage–host genome pairs show consistent TNF separation between phages and their hosts, challenging compositional binning assumptions. Through controlled simulations, we further show that the incorporation of substantial amounts of TNF-matched, non-host phage sequences into high-quality MAGs does not substantially alter standard completeness and contamination estimates, rendering such errors largely undetectable. Together, these results expose a systematic and underappreciated source of bias in MAG-based phage–host inference and highlight the need for explicit scaffold-level validation beyond conventional genome-quality metrics. We further propose a practical scaffold-level validation framework that integrates structural linkage and taxonomic consistency to improve the reliability of phage–host inference from MAGs.