Recent data indicate plastics and associated chemicals pose risks to all forms of life - from plants, microbes, and animals to humans and their developing embryos. Plastics decompose over time into Micro and Nanoplastics (MNPs), which are present as particles or fibrils. MNPs are surrounded by other substances through hydrophobic and hydrophilic interactions, known as eco-coronas in the environment, and within living organisms as bio-coronas. We highlight key biological phenomena that may help us better understand the unique and complex impact of a Trojan horse-like exposure to the bio-corona’s cargo of MNPs. These could modify, enhance, or inhibit hormones and other active substances essential for homeostasis. The investigation of MNPs with bio-corona is of high public health relevance.

Methanogenic environments harbor complex prokaryotic consortia that drive the anaerobic conversion of organic matter into methane and carbon dioxide. Beyond being key to the global carbon cycle, such processes have been steered into powerful biotechnologies for energy production in bioreactors. Decoding the functional and taxonomic landscape of these communities requires comprehensive surveys, including bacteriophages and plasmids, which, however, remain vastly underrepresented in current databases. Here, we present the microbial Catalogue of Anaerobic Digestion (CAnDi), a database comprising 36 480 prokaryotic genomes spanning 11 149 species recovered from 1256 metagenomic samples collected across bioreactors worldwide. The reconstructed metagenomes also encode ∼1.9 million viral and over 130 000 plasmid sequences. Identity clustering revealed ∼536 000 candidate viral species, of which 85% are novel relative to existing databases, as well as over 15 000 unique plasmids. Clusters of viral and plasmid proteins were generated de novo, totalling 1 721 182 and 123 813 distinct protein families, respectively. Together, these resources substantially advance previous systematic approaches to characterize anaerobic digestion microbiomes, providing an expanded foundation for investigating the ecological and evolutionary roles of the virome and plasmidome in methane-producing ecosystems. CAnDi is accessible via https://www.candi-db.org/.

Homologous genes, especially those sharing close genomic locations and forming gene clusters, often have compensatory roles, therefore pose remarkable challenges for deciphering the functions and relationships of these genes. A comprehensive database of local homologous gene clusters with multimodal information would greatly facilitate such research. Here, we present LoGIC (https://omicslab.genetics.ac.cn/LoGIC/), a Local Gene-cluster Integrative Catalog that includes 20 306 local gene clusters identified from 30 representative eukaryotic species. In addition to the basic genomic features of each gene cluster, LoGIC also allows users to examine the sequence similarity of gene members within a cluster, the functional preference of clustered genes within a species, and the cross-species conservation of gene clusters. Information of tissue-wide expression profile and relationship with chromatin Hi-C structure is also available for human gene clusters. Together, LoGIC can facilitate studies of tandem gene duplication, paralog functional divergence, lineage-specific gene-family expansion, and evolutionary conservation or remodelling of homologous gene clusters across-species.

Antibiotic resistance is a global health threat requiring antimicrobials with alternative modes of action. Growing evidence indicates that bacteria organize key intracellular processes through biomolecular condensates and other forms of spatial organization, yet whether such spatial organization can be exploited as an antimicrobial target remains unclear. Here, we identify intrinsically disordered antimicrobial peptides, generated from the Drosophila sleep-inducing protein Nemuri, that inhibit bacterial growth by targeting ribosomal organization. Mechanistically, these peptides enter bacterial cells without overt membrane disruption and accumulate in ribosome-rich regions, where they bind broadly to RNA and ribosomal surfaces through multivalent interactions. This engagement remodels ribosome-rich regions into condensates with reduced ribosomal mobility, thereby suppressing global protein synthesis and distinguishing these peptides from canonical site-specific ribosome-targeting antibiotics. Furthermore, Nemuri-derived peptides exhibit broad-spectrum antimicrobial activity, including activity against drug-resistant pathogens, with low cytotoxicity toward Drosophila and mammalian cells. Together, our findings identify the organization and material dynamics of bacterial ribosomes as a biophysical vulnerability that can be exploited by antimicrobial peptides, revealing how a naturally evolved metazoan host-defense system turns intracellular organization itself into an antibacterial target. Nishinami et al. show that peptides from the sleep-inducing protein Nemuri, condense bacterial ribosomes and inhibit protein synthesis, revealing a biophysical antimicrobial strategy targeting intracellular organization.

Anthropogenic nitrogen (N) enrichment profoundly influences the growth of N-fixing plants, but the drivers underlying their highly variable biomass responses remain poorly quantified. Through a meta-analysis of 2,110 paired observations across 76 N-fixing plant species from 121 experiments, we demonstrate that N enrichment increases N-fixing plant biomass by 14.2% on average, with responses ranging from −99.4% to 1301.3%. Intraspecific variability (26.9%; i.e., variation among individuals within species) and plant phylogeny (21.1%) represent substantial yet frequently overlooked sources of uncertainty in biomass responses of N-fixing plants to N enrichment, even though environmental conditions (climatic and edaphic factors) and experimental design (N enrichment rate and duration) account for a significant portion of biomass response variation. Intraspecific variability, phylogeny, and experimental design collectively explain a larger proportion of variance in root than in shoot biomass responses, whereas environmental conditions show the opposite trend. These results provide quantitative evidence that phylogeny and intraspecific variability are indispensable for accurately predicting the response of N-fixing plants to global change. Nitrogen enrichment alters the growth of nitrogen-fixing plants, but responses vary widely. This meta-analysis shows that phylogeny and intraspecific variation are major contributors to that variability.

Protospacer adjacent motif (PAM) requirements constrain the targeting scope of CRISPR-Cas9 systems and limit genome editing applications that require precise nuclease positioning. Although broad-PAM and near-PAMless Cas9 variants have expanded the editable genome, robust targeting across certain PAM families remains difficult, particularly at NNAN PAMs. Here, we apply Sequence Display to generate multiplexed, large-scale sequence–activity datasets for Staphylococcus lugdunensis Cas9 (SlugCas9) across the NNAN PAM family, including NNAA, NNAT, NNAC and NNAG. These datasets quantitatively resolve how individual and combinatorial mutations reshape nuclease activity across distinct PAM contexts. By integrating Sequence Display-derived datasets with protein language models, we construct fine-grained activity landscapes and identify SlugCas9 variants with enhanced and broadened activity across NNAN PAMs. The resulting variants reprogram PAM recognition beyond the native G-compatible landscape of SlugCas9 and enable improved base editing at selected endogenous NNAN-constrained sites in cultured mammalian cells, including disease-relevant sites in APOC3 and PCSK9. Together, our results establish a strategy for reprogramming PAM preference in compact Cas9 nucleases and highlight Sequence Display as a general platform for evolving genome editors toward specific and difficult-to-access PAM spaces. Compact CRISPR-Cas9 enzymes are easier to deliver but recognize few targets. Here, authors use Sequence Display to generate large-scale SlugCas9 sequence–activity datasets and apply protein language models to engineer variants that recognize difficult NNAN PAMs and edit selected human genomic sites.

Safe and effective gene delivery remains a central challenge for therapeutic applications. While non-viral and viral vectors have enabled substantial progress, their reliance on non-human components often triggers immune responses, limiting their use in chronic treatments. Here, we develop DeepDelivery, an artificial intelligence-driven platform to identify human proteins with scaffold potential for mRNA delivery. Proteome-scale screening nominated 512 candidates, and experimental validation show that 12 of 15 selected human proteins functioned as scaffold components that facilitated particle formation and mRNA delivery within a modular engineering framework. Notably, several tripartite motif (TRIM) family proteins, typically linked to antiviral responses, exhibit strong assembly and delivery activity. Model interpretability and perturbation analyses identify TRIM domain features that influenced prediction scores and guide TRIM25-specific domain engineering to improve delivery. Together, these results establish a framework for mining the human proteome for scaffold components of programmable mRNA delivery systems and for further exploring the design space of human-derived delivery architectures. Protein-based carriers could provide biocompatible options for mRNA delivery. Here, authors develop an AI-guided platform to identify and engineer human-derived protein scaffolds that assemble into particles and support mRNA delivery in cultured cells.

Filamentous fungi of the genus Aspergillus, because of their exceptional secretory capacity and metabolic versatility have been extensively applied in traditional food fermentation and industrial biotechnology for production of diverse bioproducts. Their history of domestication and industrial application has enabled major progress in enzyme production; however, challenges associated with the filamentous growth and strain engineering for future biomanufacturing development still remain. This review summarizes the advances across the spectrum of strain-improvement methodologies, from classical mutagenesis, protoplast fusion, genome shuffling, and adaptive laboratory evolution to the modern era of CRISPR enabled genome editing, synthetic biology, and systems-level metabolic engineering. Previous reviews have primarily focused on individual Aspergillus species, specific industrial products or particular strain improvement approaches. In this review, we provide a comprehensive overview of classical and modern strain engineering technologies and industrial scale up across a wide range of Aspergillus species for industrial enzyme production. In particular, emerging non-GMO evolution tools such as atmospheric and room temperature plasma (ARTP) mutagenesis, and modern molecular biotechnology coupled with microdroplet-based ultrahigh-throughput screening, now allow the researchers to generate and screen a large number of Aspergillus species mutants with single-cell precision. The discovery of high-performance strains with enhanced secretion, stress tolerance, substrate utilization, and favorable enzymatic profiles for food and bioprocessing applications has been accelerated by the strain innovations. Collectively, these advances establish A. oryzae and the related species as increasingly reliable, scalable, and programmable hosts that can meet the biomanufacturing requirements of future circular and sustainable bioeconomies.

Biomolecular condensates have so far been studied in terms of their structural, compositional, and functional properties. However, condensate enzymatic activity - a key aspect of cellular metabolism - remains unexplored due to the complexity of the system. In this study, using a combination of experimental, computational and theoretical techniques, we have discovered that the non-equilibrium activity which originates from catalytic reactions couples with the environment through various feedback mechanisms across five orders of magnitude of length scales. We observe that condensed enzymes catalyze more rapidly in the presence of crowding proteins and show that the increased enzymatic activity within these droplets stems from the emergence of lower-energy protein conformations induced by the highly crowded environment. Despite the crowding in the environment of the droplet, which might suggest an effective increase in its overall viscosity, we find that it becomes more agile, as evidenced by the observation of enhanced diffusion and macroscopic flow, due to the enzymatic activity. These findings shed light on the dynamic interplay between enzymatic activity, composition and crowding in condensates, and their roles on the mobility and accessibility of various functional units in these environments, expanding our understanding of liquid-liquid phase separation in metabolically active conditions. Enzymes operate in highly crowded cellular environments, yet the impact of crowding on their function remains poorly understood. Here, the authors demonstrate that enzyme activity increases molecular mobility in crowded protein droplets, suggesting a feedback mechanism between catalysis and the physical properties of the surrounding environment.

Although machine learning has transformed protein structure prediction of folded protein ground states with remarkable accuracy, intrinsically disordered proteins and regions (IDPs/IDRs) are defined by diverse and dynamical structural ensembles that are predicted with low confidence by algorithms such as AlphaFold and RoseTTAFold. We present a machine learning method, IDPForge (Intrinsically Disordered Protein, FOlded and disordered Region GEnerator), that exploits a transformer protein language diffusion model to create all-atom IDP ensembles and IDR ensembles that maintains the folded domains. IDPForge does not require sequence-specific training, back transformations from coarse-grained representations, nor ensemble reweighting. The created IDP/IDR conformational ensembles show good agreement with both NMR and SAXS solution experimental data, and options for biasing with experimental restraints are available if desired. IDPForge with these diverse capabilities facilitates integrative and structural studies for proteins that contain intrinsic disorder, and is available as an open source resource for general use. Intrinsically disordered proteins and regions (IDPs/IDRs) are defined by diverse structural ensembles. We present IDPForge that exploits a transformer protein language diffusion model to create all-atom IDP and IDR ensembles that maintain folded domains with options to conform to experimental data.