Bacteria use diverse defence systems to resist phage predation, many of which cluster within mobile genetic elements (MGEs) and defence islands. In Pseudomonas aeruginosa, genomic and pathogenicity islands—such as the pathogenicity islands (PAPI), genomic islands (PAGI), and Liverpool epidemic strain islands (LESGI)—have been linked to virulence and adaptation, but their contribution to the organization and spread of defence systems remains unexplored. Here, we show that these islands serve as hubs for the assembly and spread of defence systems, revealing an underappreciated role in shaping the bacterium’s antiviral arsenal. We identify 11 conserved hotspots that encode defence and anti-defence genes, but rarely co-occur with virulence factors, resistance genes, or interbacterial competition modules. The frequent co-occurrence of defence and anti-defence genes within these loci points to an ongoing, intense molecular arms race between bacteria, MGEs, and lytic phages. Notably, these hotspots are found beyond their original island contexts, appearing across diverse Pseudomonas species and, in some cases, other genera. Together, our findings expand the known bacterial immunity landscape in P. aeruginosa, redefine the roles of these islands as defence and anti-defence reservoirs, and establish a framework for scalable discovery and annotation of novel defence and anti-defence systems in bacterial genomes.

The intensive and repeated use of agrochemicals, including synthetic pesticides, herbicides, and fertilizers, has led to persistent contamination of agricultural soils, endangering soil health, ecosystem services, biodiversity, and sustainable food production. Soil microbiomes, with their remarkable metabolic versatility, represent a promising resource for in situ remediation of these pollutants. This review provides an integrated overview of the enzymatic and regulatory mechanisms underpinning microbial remediation, placing greater emphasis on enzymatic degradation as the central process driving pollutant breakdown. The biodegradation of soil pollutants is orchestrated by a network of microbial enzymes, including organophosphorus hydrolases, dehalogenases, oxidoreductases, dioxygenases, plastic-degrading and alkane-catabolising enzymes, that catalyse oxidation, hydrolysis, and dehalogenation reactions, transforming toxic compounds into less harmful intermediates that feed into metabolic pathways. Understanding the relationship between these enzymes, their encoding genes, and microbial hosts is crucial for designing robust bioremediation strategies. Complementing these biochemical processes, quorum sensing (QS) is discussed as a regulatory system that modulates microbial cooperation, biofilm formation, and catabolic gene expression during degradation. Emerging strategies, including microbial consortia design and synthetic biology-based engineering, are evaluated with a focus on the integration of QS-mediated interactions. Critical challenges, including soil heterogeneity, abiotic inhibition of QS signals, enzyme instability, biosafety concerns related to engineered strains, and horizontal gene transfer, are discussed. Future perspectives highlight enzyme engineering, QS-based biosensors, artificial intelligence-driven modelling, and synthetic QS circuits as tools to optimise bioremediation outcomes. Collectively, these insights outline pathways for advancing ecologically sound and sustainable approaches to the remediation of agrochemical-contaminated soils.

Awareness is rising that antifungal resistance poses a threat to agriculture, food safety, biodiversity and human health. There is a limited number of antifungals available and resistance to all of them has been reported. The development of novel antifungals is complex, as eukaryotic organisms have very few selective drug targets that distinguish them from the infected plant, human or animal host.  Yeasts produce different compounds with antifungal activity, ranging from small molecules such as iron chelators, biosurfactants and volatile organic compounds, to proteins like myocins and hydrolytic enzymes. Those could be further developed into new antifungals; however, there is a scarcity of fundamental knowledge on their chemical structure, their mode of action, their biosynthesis and its regulation. Given the opportunities that yeasts display as industrial hosts and the synthetic biology tools available, a deeper understanding of these molecular aspects could enable a wider range of yet underexplored applications for the producer yeast and their molecules, from biocontrol to food preservation and human health.  To facilitate this exploration, we here consolidate current molecular knowledge on these compounds, suggest readily available methodologies to screen for different molecule classes in natural yeast isolates and discuss how they could be further studied and engineered towards their eventual application.

Globular proteins are expected to assume folds with fixed secondary structures, α-helices and β-sheets. Fold-switching proteins challenge this expectation by remodeling their secondary and/or tertiary structures in response to cellular stimuli. Though these shape-shifting proteins were once thought to be haphazard evolutionary by-products with little intrinsic biological relevance, recent work has shown that evolution has selected for their dual-folding behavior, which plays critical roles in biological processes across all kingdoms of life. The widening scope of fold switching draws attention to the ways it challenges conventional wisdom, raising fundamental unanswered questions about protein structure, biophysics, and evolution. Here we discuss the progress being made to answer these questions and suggest future directions for the field.

Filamentous fungi can convert a wide variety of naturally occurring chemical compounds, including organic biomass and waste streams, into a range of products. They have long been used for industrial organic acid production and food preparation. In this review, we will discuss production of products such as organic acids, lipids, small molecules, enzymes, materials, and foods, and highlight advances in metabolic and protein engineering, including CRISPR-Cas9-mediated strain improvements. We discuss to what extent these products are already being made on a commercial scale, as well as what is still required to make certain promising concepts industrially and commercially relevant. Despite significant progress, the systematic application of synthetic biology to filamentous fungi remains in its infancy, with many opportunities for discovery and innovation as new strains and genetic tools are developed. The integration of fungal biotechnology into circular and bio-based economies promises to address critical challenges in waste management, resource sustainability, and the development of new materials for terrestrial and extraterrestrial applications, but requires further developments in genetic engineering and process design.

The human genome contains a sophisticated array of elements that regulate gene activity and organismal functions. Developing a large window foundation model capable of efficiently processing long sequence inputs is essential yet challenging for decoding the multi-layered and complex landscape of the cis-regulatory elements. Here, we introduce OmniReg-GPT, a generative foundation model designed for the low-resource pretraining of long genomic sequences by optimized attention mechanism. During pretraining, OmniReg-GPT captures the complete distribution of regulatory elements across nucleotide to megabase scales with efficient training speed and memory usage. We demonstrate exceptional performance in downstream regulotary applications spanning the entire spectrum of genomic scales, including various cis-regulatory elements identification, context dependent gene expression prediction, single-cell chromatin accessibility analysis, and 3D chromatin contact modeling. As a generative model, OmniReg-GPT also holds the potential to generate candidate cell-type-specific enhancers through prompt engineering. Overall, OmniReg-GPT extends the boundaries of foundation models in the genomic field, and provides a valuable pretraining model resource which can be extensively applied for genomic researches. Understanding long-range genomic regulation is a key challenge for DNA foundation models. Here, authors develop OmniReg-GPT with a hybrid local-global attention architecture, enabling efficient analysis of multi-scale regulatory features across long DNA sequences.

Resistance to antibiotics is approaching crisis levels for organisms such as the ESKAPEE pathogens (includes Enterococcus faecium, Staphylococcus aureus, Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, Enterobacter spp., and Escherichia coli) that often are acquired in hospitals. These organisms sometimes have acquired plasmids that confer resistance to most if not all beta-lactam antibiotics. We have been developing alternative means for dealing with antibiotic resistant microbes that cause infections in humans by developing viruses (bacteriophages) that attack and kill them. One of these pathogens, K. pneumoniae, has one of the highest propensities for antimicrobial resistance. We identified many phages that have lytic capacity against limited numbers of clinical isolates, and through experimental evolution over the course of 30 days, were able to vastly expand the host ranges of these phages to kill a broader range of clinical K. pneumoniae isolates including MDR (multi-drug resistant) and XDR (extensively-drug resistant) isolates. Most interestingly, they were capable of inhibiting growth of clinical isolates both on solid and in liquid medium over extended periods. That we were able to extend the host ranges of multiple naïve antibiotic resistant K. pneumoniae through experimental phage evolution suggests that such a technique may be applicable to other antibiotic-resistant organisms to help stem the tide of antibiotic resistance and offer further options for medical treatments. Ghatbale et al. adapted a co-evolutionary technique to develop Klebsiella pneumoniae phages to be highly active longitudinally against K. pneumoniae clinical isolates, including drug resistant isolates.

Current methods for protein engineering are constrained by limited understanding of sequence–function relationships, the difficulty of designing complex properties by artificial intelligence methods and labor-intensive directed evolution. Here, to enable continuous and scalable protein evolution and systematic exploration of protein adaptive landscapes, we established an industrial-grade automation platform featuring high throughput, high efficiency, enhanced reliability and minimal human intervention (operational for ~1 month). We then developed new genetic circuits for the OrthoRep continuous evolution system to achieve growth-coupled evolution for proteins with diverse and complex functionalities. This included improving lactate sensitivity of LldR via dual selection and increasing operator selectivity for LmrA using the NIMPLY circuit. We integrated these components into an all-in-one laboratory, iAutoEvoLab, and evolved proteins from inactive precursors to fully functional entities, such as a T7 RNA polymerase fusion protein CapT7 with mRNA capping properties, which can be directly applied to in vitro mRNA transcription and mammalian systems. Our system represents a versatile tool for protein engineering and expands the scope for investigating the origins and evolutionary trajectories of protein functions. This study reports on an industrial-grade, large-scale, all-in-one integrated and automated laboratory (iAutoEvoLab), combined with a genetic circuit-controlled, growth-coupled continuous evolution system based on OrthoRep, which can evolve proteins with diverse and complex functionalities. These include protein–protein interactions, protein–DNA interactions, proteins requiring both protein–DNA and protein–ligand interactions, and fusion proteins with low to near-zero activities.

Bacteriophages and bacteria frequently occupy the same ecological niches, driving complex and dynamic host–virus interactions. In Pseudomonas aeruginosa, phages from the Migulavirinae subfamily, tail fibre proteins (TFPs) are crucial to host recognition. These proteins, located within the phage tail structure, are subject to frequent recombination and may play a key role in shaping host range. This study investigates the molecular basis of host specificity in Litunavirus and Luzseptimavirus phages, focusing on the structure and variation of their TFPs. Host spectrum analysis divided phages into three categories; however, contrary to expectations, no direct correlation was found between TFP recombination history and host range, most likely because subsequent single amino acid changes in the pyocin knob regions, critical for adsorption, altered the host spectrum after the recombination event. Notably, phages sharing highly similar pyocin knob 2 domain architectures displayed identical host spectra, suggesting a strong link between this region and host specificity. Despite high sequence variability, all TFPs adopted a conserved trimeric fold with five regions: N-terminal, GrpE-like, GDSL-like with a carbohydrate-binding module, pyocin knob, and C-terminal. Structural similarities to bacterial PilA and pyocins were noted. Variation in the pyocin knob region, especially substitutions involving polar residues, was partially correlated with host range, likely via hydrogen bonding with the O-antigen. The GrpE-like domain resembled type IV pili, suggesting a role in reversible attachment, while the GDSL-like domain may support enzymatic processing of the O-antigen. Our findings support a multi-step adsorption mechanism of Migulavirinae phages, initiated by random encounters with the bacterial surface, followed by specific, stable interactions between the pyocin knob region and the bacterial lipopolysaccharide (LPS) O-antigen. Final stabilization involves additional interactions with the LPS core region. While the GrpE-like domain may contribute to transient stabilization near the surface, its structural similarity to PilA suggests a possible evolutionary convergence rather than a direct pilus-binding function. Despite high sequence variability, TFPs maintain conserved structural features, allowing for modular adaptations that precisely adjust host specificity. Importantly, the lack of a direct link between TFP recombination and host range suggests that factors beyond recombination influence phage host specificity.

BreakTag is a scalable next-generation sequencing-based method for the unbiased characterization of programmable nucleases and guide RNAs at multiple levels. BreakTag allows off-target nomination, nuclease activity assessment and the characterization of scission profile, that, in Cas9-based gene editing, is mechanistically linked with the indel repair outcome. The method relies on digestion of genomic DNA by Cas9 and guide RNAs in ribonucleoprotein format, followed by enrichment of blunt and staggered DNA double-strand breaks generated by CRISPR nucleases at on- and off-target sequences. Next-generation sequencing and data analysis with BreakInspectoR allows high-throughput characterization of Cas nuclease activity, specificity, protospacer adjacent motif frequency and scission profile. Here we first describe a detailed BreakTag protocol for the nomination of CRISPR off-targets and multilevel characterization of engineered Cas variants and second, we describe a step-by-step protocol for data analysis using BreakInspectoR. Third, we provide a web interface for XGScission, a machine learning model amenable to training with scission-aware BreakTag data to predict the relative frequency of blunt and staggered double-strand breaks at new sequences unseen by the model. XGScission allows a preselection of target sequences predicted to be cut in staggered configuration that are preferably repaired as single-nucleotide templated insertions. Furthermore, XGScisson can be used to assess sequence determinants of blunt and staggered cleavage by SpCas9 and engineered nuclease variants. As a companion strategy, we describe HiPlex for the generation of hundreds to thousands of single guide RNAs in pooled format for the production of robust BreakTag datasets. The BreakTag library preparation takes ~6 h, and the entire protocol can be completed in ~3 d, including sequencing, data analysis with BreakInspectoR and XGScission model training. BreakTag is a scalable next-generation sequencing-based method for the unbiased characterization of programmable nucleases and guide RNAs that allows off-target and nuclease activity assessment, as well as the characterization of scission profiles.

In living organisms, proteins perform key functions required for life activities by interacting to form complexes. Determining the protein complex structure is crucial for understanding and mastering biological functions. Although AlphaFold2 makes a revolutionary breakthrough in predicting protein monomeric structures, accurately capturing inter-chain interaction signals and modeling the structures of protein complexes remain a formidable challenge. In this work, we report DeepSCFold, a pipeline for improving protein complex structure modeling. DeepSCFold uses sequence-based deep learning models to predict protein-protein structural similarity and interaction probability, providing a foundation for identifying interaction partners and constructing deep paired multiple-sequence alignments (MSAs) for protein complex structure prediction. Benchmark results show that DeepSCFold significantly increases the accuracy of protein complex structure prediction compared with state-of-the-art methods. For multimer targets from CASP15, DeepSCFold achieves an improvement of 11.6% and 10.3% in TM-score compared to AlphaFold-Multimer and AlphaFold3, respectively. Furthermore, when applied to antibody-antigen complexes from the SAbDab database, DeepSCFold enhances the prediction success rate for antibody-antigen binding interfaces by 24.7% and 12.4% over AlphaFold-Multimer and AlphaFold3, respectively. These results demonstrate that DeepSCFold effectively captures intrinsic and conserved protein-protein interaction patterns through sequence-derived structure-aware information, rather than relying solely on sequence-level co-evolutionary signals. In this work the authors present DeepSCFold, a pipeline which improves the protein complex structure prediction accuracy by capturing intrinsic and conserved protein-protein interaction patterns through sequence-derived structure-aware information.

Structure-based sequence redesign or inverse folding can significantly enhance structural stability but often compromises functional activity when performed using existing models. Here, we introduce ABACUS-T, a multimodal inverse folding model that improves precision and minimizes functional loss. ABACUS-T unifies several important features into one framework: detailed atomic sidechains and ligand interactions, a pre-trained protein language model, multiple backbone conformational states, and evolutionary information from multiple sequence alignment (MSA). Redesigned proteins show notable improvements: an allose binding protein achieves 17-fold higher affinity while retaining conformational change; redesigned endo-1,4-β-xylanase and TEM β-lactamase maintain or surpass wild-type activity; and OXA β-lactamase gains altered substrate selectivity. All achieve substantially increase thermostability (∆Tm ≥ 10 °C). In each test case, these enhancements are achieved by testing only a few sequences, each containing dozens of simultaneously mutated residues. ABACUS-T thus offers a promising tool for reengineering functional proteins in biotechnological applications. Improving protein stability by inverse folding often compromises function. Here, the authors develop ABACUS-T, a multimodal inverse folding model that combines structural and evolutionary information to redesign proteins with enhanced stability while preserving biological activity.