Bis(3′-5′)-cyclic dimeric guanosine monophosphate (c-di-GMP) plays a crucial role in bacterial signaling pathways, allowing bacterial cells to respond to various environmental stimuli. The prevalence of c-di-GMP and its potential applications underscore the necessity for developing tools and methods to regulate intracellular c-di-GMP levels. Optogenetic control of c-di-GMP dynamics is particularly attractive because it enables tunable and spatiotemporal regulation of c-di-GMP metabolism. The development of sensitive optogenetic control systems requires highly active, light-responsive c-di-GMP synthases. Here, we report an engineered, highly active photosensitive c-di-GMP synthase, BphS-13. This engineered c-di-GMP synthase was developed from a near-infrared (NIR) light-activable bacteriophytochrome c-di-GMP synthase, BphS, using a three-step directed evolution process that included error-prone PCR, in vitro homologous recombination, and site-directed mutagenesis. After two rounds of this directed evolution strategy, we generated a BphS variant with 13 mutations, referred to as BphS-13. The diguanylate cyclase (DGC) activity of BphS-13 was approximately 13 times higher than that of the original BphS, and it exhibited tightly regulated DGC activity in response to NIR light with minimal leakage in the dark. We then demonstrated the effectiveness of BphS-13 in controlling biofilm dynamics. Overall, this study highlights BphS-13 as a highly active and photosensitive tool for optogenetic applications in biotechnology and suggests its future potential application in mammalian systems for precise control of gene expression, particularly given the lack of native c-di-GMP signaling pathways in mammalian cells.

Advances in metagenomic sequencing over the past two decades have identified vast numbers of previously uncharacterised small open reading frames that may encode microproteins (<50aa). Although computational tools have accelerated gene sequence prediction from metagenomic data, the function of most annotated proteins remains unknown and untested, especially in the context of host-microbiome interactions. Here, we present a scalable phenotypic screening pipeline to identify gut microbiome-derived proteins that modulate host function. Using the nematode worm Caenorhabditis elegans as a whole animal model that is amenable to systematic screening approaches, our pipeline integrates high-throughput cloning, expression and delivery to worms via feeding, followed by behavioral phenomics screening. We apply this approach to a pilot library of 126 uncharacterised microproteins (< 50 aa) from healthy human gut metagenomes, identifying a set of high-interest targets with potential activity and ultimately validating a microprotein that modulates host fatty acid metabolism when expressed. With protein-based therapies increasingly recognised as a promising alternative to traditional small molecules, this work highlights the potential of a target-agnostic approach for the systematic screening and discovery of novel bioactive proteins.

Biomolecular condensates formed via liquid-liquid phase separation (LLPS) play vital roles in cellular organization and function. Computational prediction of phase-separating proteins (PSPs) is increasingly used to prioritize candidates at proteome scale, making robust, well-designed benchmarks essential for fair evaluation and iterative improvement of PSP predictors. We first show that a recently released PSP benchmark is substantially confounded by the imbalances in taxonomic origin and intrinsic-disorder compositions between positive and negative sets, allowing predictors to achieve high apparent performance by exploiting non-LLPS shortcuts and obscuring their true ability to distinguish PSPs. To minimize these effects, we construct a taxonomy-aware, disorder-matched PSP benchmark. Using this benchmark, we find that absolute sequence and biophysical feature values of PSPs differ markedly across taxa, whereas LLPS-associated feature shifts relative to taxon-specific proteome backgrounds are comparatively conserved. Benchmarking twenty PSP predictors under this framework reveals pronounced taxon-dependent variation in performance. Moreover, PSPs lacking IDRs consistently constitute a more challenging regime across methods, motivating routine disorder-stratified evaluation. Our taxonomy-aware, disorder-matched benchmarking framework reduces shortcut-driven biases, enables more interpretable evaluation of PSP predictors, and provides guidance for developing models that capture transferable LLPS-associated signals rather than dataset- or taxon-specific shortcuts.

The diversity in specificity mechanisms across DNA-targeting systems highlights the incredible complexity of the broader microbial immune repertoire and the many internal barriers phages must consider and overcome during infection. Much like CRISPR-Cas and RM, we expect the field to continue discovering new classes of DNA-targeting systems, compounding on the complexity of the DNA-targeting pool and, perhaps, defining new restriction mechanisms. In addition, bacterial genomes encode, on average, 5–10 known defense systems, which begs the question of how DNA-targeting systems may behave in the presence of other defense systems. Costa et al have shown evidence of synergistic effects between DNA-targeting system pairs, such as type II Zorya and type III Druantia, on phage restriction in E. coli, where antiphage activity of any individual system is minimal, but significantly improves when the two systems are active. Synergistic patterns likely differ between species due to different ecological environments, predatory agents, and bacterial life cycles, so similar studies across other bacteria will likely reveal new inter-defense compatibilities. Despite significant progress in the discovery of DNA-targeting systems and their mechanisms for phage detection, major gaps in knowledge on how these systems restrict phage or plasmid DNA in vivo still need to be filled. In addition, more work is needed to understand the regulation of these systems in their native strains, as well as how host nucleoid-binding proteins may influence or regulate immune systems. A deeper understanding in these areas will greatly contribute to advancing efforts in phage therapeutics, comprehending microbial ecology and microbiome evolution, and biotechnological applications of DNA-targeting systems.

Anti-CRISPR (Acr) proteins have long exemplified the viral counterattack against CRISPR-Cas immunity. By contrast, comparatively little is known about host proteins that may increase Cas effector activity. Recent work on a compact type V nuclease, Cas12p, demonstrates that this phage-associated effector depends on the bacterial thioredoxin TrxA for efficient DNA cleavage. TrxA binds a dedicated thioredoxin-binding (TB) domain on Cas12p through a redox-sensitive interaction, promoting an active conformation competent for DNA cleavage. This finding adds to a small but growing set of CRISPR activators and highlights that CRISPR-Cas systems are not static defense modules but dynamic networks shaped by auxiliary factors that can fine-tune their activity.

Bacterial pathogenicity arises from complex genetic interactions that are difficult to characterise through single-gene deletions. CRISPR base editors can generate multiplexed gene knockouts, yet this technology remains unexplored for dissecting bacterial pathogenicity. Here, we developed a base-editing pipeline for multi-gene knockouts while revealing that target-independent editing can contribute to variability in clonal fitness. In the model pathogen Salmonella Typhimurium, we employed curable plasmids containing a cytidine deaminase base editor and a multi-spacer CRISPR array to introduce premature stop codons in up to nine genes encoding SPI-2 T3SS effector proteins. Target bases were efficiently edited, producing a multi-knockout strain that showed reduced virulence in vivo relative to single knockouts. However, whole-genome sequencing revealed off-target cytidine deaminase activity, which affected virulence in vivo in a clone-dependent manner. A statistical power analysis predicted how many edited mutants are needed to confidently measure fitness functions in the face of off-target mutations. Our work shows the potential and current limitations of multiplexed base editing in bacterial pathogens and highlights the need for properly addressing off-target mutations when deploying base editors to interrogate genotype-phenotype relationships.

In multicellular systems, organized phenotypic heterogeneity emerges from the interplay of processes spanning scales from molecular to population-level. Using Bacillus subtilis, we investigated feedback between the collective process of colony expansion and the distribution of spore development among individual cells, a process triggered by starvation. Biofilms are commonly studied using a strain with inhibited sporulation. Intact regulation yielded high-frequency sporulation early in biofilm growth. Biofilm composition was organized by a wave of sporulation driving biofilms toward dormancy from within. However, expansion was also maintained by non-sporulating cells in a narrow front at the external edge. Along with mathematical modeling, we also used mutants with altered biofilm morphogenesis to probe the relationship between colony expansion and sporulation. Sporulation dynamics were patterned by radial expansion, but the faster biofilms spread, the greater the separation of growth and sporulation distributions. We demonstrate essential interplay between cell behavior and the physics of collective expansion that organizes differentiation among cells.

The rapidly growing universe of predicted protein structures offers opportunities for data driven exploration but requires computationally scalable and interpretable tools. We developed a method to detect catalytic features in protein structures, providing insights into enzyme function and mechanism. A library of 6780 3D coordinate sets describing enzyme catalytic sites, referred to as templates, has been collected from manually curated examples of 762 enzyme catalytic mechanisms described in the Mechanism and Catalytic Site Atlas. For template searching we optimized the geometric-matching algorithm Jess. We implemented RMSD and residue orientation filters to differentiate catalytically informative matches from spurious ones. We validated this approach on a non-redundant set of high quality experimental (n=3751, <40% amino acid identity) enzyme structures with well annotated catalytic sites as well as predicted structures of the human proteome. We show matching catalytic templates solely on structure is more sensitive than sequence- and 3D-structure-based approaches in identifying homology between distantly related enzymes. Since geometric matching does not depend on conserved sequence motifs or even common evolutionary history, we are able to identify examples of structural active site similarity in highly divergent and possibly convergent enzymes. Such examples make interesting case studies into the evolution of enzyme function. Though not intended for characterizing substrate-specific binding pockets, the speed and knowledge-driven interpretability of our method make it well suited for expanding enzyme active-site annotation across large predicted proteomes. We provide the method and template library as a Python module, Enzyme Motif Miner, at https://github.com/rayhackett/enzymm.