RMH

Biofilms, in which microbes are encased within a self-produced matrix, represent the primary mode of microbial life. Yet, our understanding of the molecular mechanisms governing biofilm formation remains incomplete, limiting the development of targeted interventions against biofilm-associated infections. By selecting for Vibrio cholerae biofilms, we identified mutations in the polyamine-regulated, bifunctional enzyme MbaA that alter its enzymatic activity in metabolizing the conserved second messenger cyclic diguanylate (c-di-GMP). These mutations occur in both the c-di-GMP-producing GGDEF domain and the c-di-GMP-hydrolyzing EAL domain of MbaA. Mutagenesis and enzyme kinetic analyses revealed regulatory crosstalk between these domains, where mutations in either domain enhanced production of c-di-GMP by MbaA while concurrently reducing its hydrolytic activity. In the wild-type enzyme, c-di-GMP metabolism is post-translationally regulated by specific polyamines. Biofilm-selected mutations not only shift MbaA’s enzymatic activity but also disrupt this regulation, rendering the enzyme insensitive to external polyamine signaling. Our findings illustrate how evolutionary pressure can rewire existing enzymes, promoting a biofilm-associated lifestyle.

Fungal genomics has expanded rapidly over the past three decades, yet this growth has been phylogenetically uneven and many taxonomic gaps persist. Here we review the field from early efforts such as the Fungal Genome Initiative to the current era of third-generation long-read sequencing and compile publicly available assemblies to quantify trends in contiguity, completeness, long-read adoption and taxonomic representation. We argue that fungal genomics has moved from a period of data scarcity to one in which the principal constraints are taxonomic bias, inconsistent annotation, sparse metadata and the interpretation of complex genome structure. Substantial phylogenetic gaps remain, particularly outside Dikarya and among unculturable taxa, and only a minority of assemblies are annotated. We therefore identify priorities for the fungal community, including coordinated efforts to close major taxonomic gaps, improved repository metrics to facilitate the identification of high-quality assemblies and standardized genome annotation, which is lacking for most assemblies. This study examines how long-read sequencing has transformed fungal genome assemblies and uncovered genome features once hidden in fragmented drafts, while also revealing that taxonomic bias and uneven annotation continue to constrain the field.

Biochar soil application has been widely promoted as an option for carbon dioxide removal; however, its effects on crop productivity remain uncertain. In this Review, we synthesize evidence from global field experiments to identify the key drivers of biochar-promoted crop productivity change. Biochar increased crop productivity by an average of 12.8% globally, with the productivity of tap-root crops (32%) being substantially greater than staple cereals with fibrous-root systems (8%). Productivity increases are generally application-rate-dependent and are most pronounced in warm, acid, clay-rich soils. Biochar enhances crop productivity through nutrient enhancement and soil conditioning effects. Biochar improves soil structure, water retention and biological functioning via its porous architecture and reactive surfaces. Although productivity benefits decline over time, the positive effects can persist for more than 3 years after application. As biochar ages, interactions between clay minerals and organic matter further enhance soil aggregation, resource retention and acquisition and crop tolerance to drought and salinity. Future research should examine biochar-mediated resilience across diverse soil–crop systems and expand field testing to under-represented crops and regions to guide the effective deployment of biochar for sustainable food production. Improving crop production is essential to meet the rising food demand of the increasing global population. This Review synthesizes global evidence that biochar enhances crop productivity, explores the underlying mechanisms and considers how biochar can contribute to sustainable food production.

Research on plant viruses depends on taxonomy, nucleic acid sequences, host and disease records, molecular targets, and antiviral assays. These data are distributed across separate resources, making integrated retrieval and comparison difficult. PlantVirDisConDB (https://dpai.ccnu.edu.cn/PVDCDB) is freely accessible without login or registration and links these data through curated plant virus records. At the July 2026 reporting date, it comprised, in round numbers, 5000 virus and subviral agent records and 38 000 sequence accessions. It also included approximately1900 hosts, 9000 virus–host associations, 4800 disease names, 5000 structure records, and 25 000 small molecules. Direct assay records for plant viruses are distinguished from general antiviral background data; experimental and predicted structures, and measured activities and model outputs, are also labelled separately. Linked record pages support retrieval across these data types, access to individual sequence records, target searches, and molecular similarity searches. An activity prediction module ranks candidate molecule–virus pairs with applicability warnings and does not imply experimental or field efficacy. PlantVirDisConDB links viral nucleic acid entities to hosts, phenotypes, targets, and antiviral information and is updated through continuing curation and user feedback.

Small self-cleaving ribozymes are catalytic RNAs discovered in viroid-like agents, which are replicating circular RNAs (circRNAs) postulated as relics of a prebiotic RNA world. In the last decade, however, small ribozymes have also been detected across the tree of life, from bacterial to human genomes, and more recently, in unusual circRNA viruses. Here we report the conserved occurrence of diverse small ribozymes within the linear genomes of typical single- and double-stranded RNA viruses infecting fungi and plants. We identified type I hammerhead ribozymes in the 5′-UTRs of chrysovirids and fusarivirids, displaying self-cleaving activity in vitro and in vivo. Similar hammerhead, as well as hepatitis delta and twister ribozymes, occur in diverse megabirna-, hypo-, fusagra-, toti-, or tombus-like viruses. The ribozymes appear as isolated motifs within the UTRs or as tandem pairs that encompass small RNA segments (186–399 nt) resembling Zetavirus-like sequences. In vivo characterization of the 5′-UTR from a fungal chrysovirus revealed that its RNA self-cleaving activity is essential for protein translation initiation. Analogous experiments in plants with diverse ribozyme motifs confirmed that self-cleaving activity alone can induce cap-independent translation. We conclude that RNA self-cleaving activity, historically linked to the rolling circle replication of viroid-like circRNAs, appears to be co-opted by some linear RNA viruses for translational roles.

Prebiotics and fermentable fibre modulate gut microbiota composition and function in ways considered beneficial to health. However, both microbiota and clinical responses can vary widely among individuals, which reduces their efficacy as dietary interventions across populations and thus represents a key challenge for the field. In this Review, we discuss potential reasons for the individualized responses to prebiotics and putative prebiotic fibres and how this variability influences interactions among gut microbial species as well as between microorganisms and fibre structures. We further describe strategies for predicting and optimizing responses to prebiotic and putative prebiotic fibre treatments. Specifically, we highlight methods for targeted and predictable individual responses using systems biology, mechanistic modelling and machine learning approaches. We also consider how the physicochemical structures of fermentable fibre influence similarity in gut microbial responses. By integrating this knowledge, solutions are proposed to overcome inter-individual variability and improve the clinical efficacy and utility of prebiotic fibres. Prebiotics and dietary fibre show highly individualized effects on gut microbiota and clinical outcomes, challenging their consistent effectiveness across populations. This Review examines the drivers of this variability, presents predictive approaches and discusses strategies to optimize prebiotic responses across individuals.

Evolutionary relationships among homologous enzymes can be leveraged to introduce large numbers of amino acid substitutions for improved functional properties. Here, we combine evolution-informed design (EVdesign) with rational design to engineer improved poly(ethylene terephthalate) (PET) hydrolases (PETases). Designs derived from multiple PETases reveal strong parent-dependent differences in success and performance, with Thermobifida fusca TfCut2 emerging as the most design-amenable scaffold. Increasing sequence diversification reduces the number of active enzymes but broadens performance variance, enabling discovery of high-performing outliers. Four rational mutations overcome a thermostability bottleneck in TfCut2 and this variant provides a robust scaffold for iterative EVdesign, which further improves activity. Overall, we experimentally test 603 designs from eight parent PETases. More than 400 variants containing 12-68 substitutions were able to depolymerize PET, nearly all outperforming their parent enzymes, and many approach benchmark LCC-ICCG performance in partial conversion assays. These results establish EVdesign as a robust strategy for generating diverse, high-activity enzymes. Here the authors combine computational and rational protein design to generate hundreds of PET-depolymerizing enzymes, many more active than their parent enzymes, and show that iterative design can further improve performance for plastic recycling.

The protein sequence space is incredibly vast and identifying functional variants is like finding a needle in a haystack. In 1997, Boder and Wittrup developed a method to display proteins on the surface of yeast, unlocking the ability to screen large libraries of mutants to identify proteins with desired characteristics.

RNA-targeted small-molecule discovery is challenged by the scarcity of experimentally characterized RNA–ligand complexes and by the limited transferability of current computational approaches across chemically diverse systems. Here we present P-RISE, a physics-grounded framework for RNA–ligand binding prediction, and use it to identify the physical determinants underlying reliable performance. Systematic evaluation of force fields, ionic environments, dielectric treatments, and entropy corrections reveals three coupled requirements: preservation of RNA-binding pocket integrity under ion-sensitive conditions; an effective electrostatic error cancellation regime enabled by an elevated internal dielectric constant; and an empirically observed coupling between entropy correction and solvent modeling. Integrating these features, P-RISE provides stable affinity ranking, physics-grounded delineation of its physicochemical applicability domain, accurate metal ion site localization, and improved binding pose discrimination relative to established RNA docking and scoring approaches. Crucially, benchmarking against AlphaFold3 reveals that co-folding models, optimized for structure generation rather than energetic ranking, benefit from an explicit physics-based evaluation layer. Furthermore, diverse machine-learning comparisons demonstrate that purely data-driven methods lack stability and generalization in this data-poor regime. Ultimately, P-RISE provides a mechanistic understanding of RNA–ligand modeling, serving as a principled physical baseline that defines reliable prediction boundaries, and guides future data-driven integration.

The circadian clock orchestrates daily physiological processes through coordinated regulation of clock-controlled gene expression across multiple molecular layers. However, systematic characterization of this multilayered rhythmic regulation remains challenging because relevant information is distributed across independent datasets and rhythmicity is evaluated using diverse analytical approaches. Here, we developed CircaVal, a gene-centric multi-omics platform for the integrated exploration and interpretation of circadian regulatory features in humans, mice, and baboons. To support this framework, CircaVal processes public datasets using standardized, data-type-specific pipelines, and gene rhythmicity is integrated with tissue-specific expression, cross-species orthology, functional annotations, and multi-omics regulatory features. CircaVal further introduces an integrated rhythmicity score (IRS), which combines concordance across multiple rhythm-detection methods with reproducibility across independent datasets to provide a quantitative basis for prioritizing candidate rhythmic genes. The platform enables users to examine gene rhythmicity across datasets, identify tissue-enriched rhythmic genes, compare rhythmicity across species, and explore associated regulatory features. Additional functions include interactive gene-centric exploration, genome-browser visualization, rhythmicity analysis of user-uploaded time-series data, and programmatic data access. Together, these capabilities provide an integrated framework for rhythmic gene prioritization and gene-level interpretation of circadian regulatory features across tissues, species, datasets, and omics data types. CircaVal is freely available at https://CircaVal.com.

Genome expansion and contraction are driven by transposable element activity, but the underlying dynamics remain enigmatic due to a lack of historical records tracing these changes. Here, we present PrinTE, a forward-time simulator of whole-genome sequences with customizable transposon dynamics for identifying the evolutionary parameters most consistent with an observed genome. Across simulations spanning four orders of magnitude, PrinTE recovers transposon insertion and deletion rates with less than 6% error. We demonstrate PrinTE on Arabidopsis and the fungal subdivision Pucciniomycotina, including myrtle rust (Austropuccinia psidii), which expanded its genome size to 1,018 Mb through transposon amplifications. Our simulations show that recombination-based removal of LTR retrotransposons alone cannot prevent genome obesity, and that preventing their invasion in the first place is more effective. We hypothesize that A. psidii might have experienced a prolonged expansion phase followed by a potent and ongoing period of contraction. The authors present PrinTE, a freely available simulator that evolves genomes forward in time to reveal how transposable elements drive genome expansion and contraction, and use it to test hypotheses about genome size evolution in fungi and plants.