lthough protein engineering and laboratory evolution have been used to optimize prime editors, we show that previous changes that improve prime editor efficiency also compromise protein stability and expression level, limiting performance. To address these limitations, we apply structure-informed artificial intelligence-guided methods such as the inverse-folding network ProteinMPNN to redesign the reverse transcriptase (RT)domains of engineered and evolved prime editors while preserving regions essential for catalysis. Redesigned RTs are extensively mutated, with 30–163 amino acid substitutions, and exhibit enhanced folding stability and soluble expression and up to twofold higher intracellular prime editor protein levels following mRNA delivery. Redesigned PE8 prime editors demonstrate enhanced editing efficiencies across multiple ex vivo contexts, including in several human primary cell types and via several delivery modalities. In mice, editing efficiency is up to 2.9-fold higher than that of state-of-the-art PE6, PE7 and PEmax prime editors. These findings demonstrate a generalizable approach for augmenting laboratory evolution to improve genome editing agents. A computational redesign strategy improves evolved prime editors.

Quantitative analysis of bacterial dynamics in time-lapse microscopy requires robust tracking pipelines, yet selecting and optimizing algorithms for specific experiments remains challenging. Indeed, microbiologists are confronted with numerous algorithms that must be carefully chosen and parameterized to achieve optimal tracking for their experiments. We present an automated methodology to determine optimal tracking configurations for microbiological applications. It is based on TrackMate 8, a novel version of the TrackMate Fiji plugin extended with microbiology-specific tools. Our approach systematically evaluates algorithm-parameter combinations optimizing biologically relevant metrics (e.g., cell-cycle accuracy, bacterial morphology) and includes: (1) integration of deep-learning algorithms (Omnipose, YOLO, Trackastra) adequate for bacterial images in TrackMate; (2) a TrackMate-Helper extension for parameter optimization; and (3) a tracking and segmentation editor for tracking ground-truth generation. We demonstrate the effectiveness of the methodology on two use cases showing its adaptability to diverse experimental conditions. This methodology enables microbiologists with a widely applicable, automated framework to optimize tracking pipelines, facilitating quantitative analysis in bacterial imaging.

Drug-resistant bacterial infections increasingly evade available antimicrobials, and many existing antimicrobial peptides remain limited by instability, toxicity to mammalian membranes and high manufacturing cost. Here we introduce a modular peptide technology that self-assembles into nanofibres on bacterial surfaces through a membrane-anchoring biphenyl group, a diphenylalanine linker and a cationic minimalistic peptide that together enable selective disruption of drug-resistant pathogens. Using cryogenic electron microscopy, molecular dynamics simulations, lipid-nanoparticle membrane mimetics and binding thermodynamics, we show that the peptide first forms short nanofibres that dock onto phosphatidylglycerol and then elongates into nanofibres that penetrate and destabilize the bacterial membrane without inducing resistance. The nanofibres retain antibacterial activity when recycled from killed bacteria and outperform vancomycin and several classical antimicrobial peptides against dense bacterial populations in vitro. In a mouse model of methicillin-resistant Staphylococcus aureus pneumonia, inhaled peptide nanofibres eradicate pulmonary infection and restore lung architecture without detectable toxicity. This modular strategy enables the design of potent, selective and low-cost antimicrobials. A modular peptide technology that self-assembles into nanofibres on bacterial surfaces through a membrane-anchoring biphenyl group, a diphenylalanine linker and a cationic peptide enables disruption of drug-resistant pathogens.

Staphylococcus aureus is a leading cause of lethal bacteremia and pneumonia, which are driven by potent virulence factors such as T-cell superantigens and alpha hemolysin. S. aureus has among the highest rates of antibiotic resistance, yet no vaccines or alternative therapies are available. Here, we developed a repertoire of potent, high-affinity nanobodies (Nbs) targeting key toxins in S. aureus infection, including Hla and superantigens SEB, SEC, and TSST-1. Comprehensive cryo-EM and AlphaFold3 analyses of these Nbs, which were elicited with clinical cocktail vaccines, revealed diverse neutralizing epitopes and mechanisms that provide insights for immunotherapy and vaccine strategies. Guided by these findings, we engineered stable, multivalent, and multifunctional Nb constructs. These constructs included an aerosolizable trimeric Nb with enhanced neutralization activity against Hla and SEC, and a decameric Nb-IgG-Fc fusion construct with pM or better potencies against a wide range of major toxins in S. aureus sepsis (SEB, SEC, TSST-1, and Hla). These multifunctional Nbs demonstrated protective activity in murine models of pneumonia and sepsis, underscoring their potential as versatile immunotherapies that address the complex virulence of S. aureus. Our work lays a foundation for precision immunotherapies beyond current treatment options to combat complex bacterial infections with multiple virulence mechanisms. Here, the authors develop a repertoire of multivalent nanobodies that target key toxins in Staphylococcus aureus infection that show protective activity in murine models of sepsis and pneumonia.

The evolution of the eukaryotic cell paved the way for the emergence of all complex life on Earth. Despite its significance, the environmental context of early eukaryote evolution is largely unknown. Here we use the geological record to reconstruct the habitats of the oldest known fossil eukaryotes, approximately 1.75–1.4 billion years old. Our integrated palaeontological, sedimentological and geochemical analyses show that although fossil eukaryotes are found in samples deposited in a range of environments from coastal to offshore, they are almost entirely restricted to those from settings with oxygenated bottom waters. This distribution suggests these organisms were aerobes (obligate, facultative and/or microaerophilic) and, given their size and morphological complexity, probably possessed mitochondria. Furthermore, their near absence from otherwise fossiliferous anoxic samples suggests a benthic habit, as planktonic eukaryotes would be expected to be present in both oxic and anoxic samples. We propose that eukaryotes were largely restricted to oxic benthic habitats for much of the Proterozoic eon, only expanding into planktonic habitats during the Neoproterozoic era (1–0.54 billion years ago). This late ecological expansion could account for the mismatch between the appearance of eukaryotic body fossils and molecular biomarkers and explain the stepwise increase in eukaryote diversity during the Neoproterozoic era. Integrated palaeontological, sedimentological and geochemical analyses of ancient rocks from Australia show that early eukaryotes were largely restricted to oxygenated benthic habitats, probably possessed mitochondria by 1.75 billion years ago, and may have expanded into planktonic habitats much later, perhaps during the Neoproterozoic era.

Measurements of glycans modifying glycoproteins are hampered by the lack of standards that reflect the wide diversity in structure typically observed. To this end we exploit a large library of N-glycan standards comprised of a unique collection of 226 N-glycans including oligomannose, hybrid, and complex-type and apply a method employing porous graphitised carbon (PGC) and liquid chromatography mass spectrometry (PGC-LC-MS) to provide a high-resolution separation and characterization of underivatized N-glycan structures. Chromatogram libraries arising from this study include retention time data, diagnostic fragments, and validated structural assignments, providing a robust platform for both targeted and discovery-based glycomics. Here we establish this generated data as an N-glycopedia, the resource in which researchers can compare this collective data to N-glycans under study and overcome the limitations of only having compositional data and predicted structures. The technology is easily expandable to include additional N-glycans as new standards become available. Researchers created N-glycopedia, a reference library of 226 sugar molecules found on proteins, enabling a native glycomics method to precisely identify and measure these sugars, which influence immunity and disease, without chemical pre-treatment.

L-Tryptophan is an important aromatic amino acid with wide applications across the food, pharmaceutical, and feed industries. However, its efficient microbial production remains challenging due to complex metabolic networks and multi-level feedback regulation. In this study, we constructed a highly efficient E. coli cell factory for L-tryptophan biosynthesis by combining systematic metabolic engineering with high-throughput screening. Initially, a tnaC-based biosensor was developed and coupled with atmospheric and room temperature plasma (ARTP) mutagenesis to isolate high-performance chassis strains. Central carbon metabolism was subsequently reprogrammed to minimize carbon loss and channel metabolic fluxes toward essential precursors, phosphoenolpyruvate (PEP) and erythrose-4-phosphate (E4P). To further alleviate pathway bottlenecks, promoter engineering was utilized to balance the intracellular supplies of L-glutamine, L-serine, and phosphoribosyl pyrophosphate (PRPP). This targeted intervention yielded a 21.61% increase in L-tryptophan accumulation. Product transport systems were then engineered to enhance extracellular secretion and mitigate intracellular toxicity. Following the optimization of inoculum size and feeding strategies in a 5 L bioreactor, the final engineered strain (W-24) produced 50.83 g/L of L-tryptophan within 40 h, achieving a yield of 0.185 g/g glucose. This multi-modular engineering framework establishes a robust platform for L-tryptophan biosynthesis and provides a scalable strategy for the industrial production of other valuable aromatic compounds. 

The performance of prime-editing (PE) systems has been improved by systematic engineering of their protein and small RNA components but the structured RNA motifs appended to the 3′ end of PE guide RNAs (pegRNAs)—a key determinant of pegRNA stability and editing efficiency—have not been extensively studied. We introduce PE-PRISM, a high-throughput pooled screen to identify and optimize these 3′ RNA motifs in human cells. Here, using PE-PRISM, we evaluated 2,858 RNA motifs across four iterative libraries, including natural and engineered pseudoknots, G-quadruplexes and reverse transcriptase recruitment elements. We applied structure-guided mutagenesis and combinatorial variant screening to refine hits, culminating in the engineered and evolved pseudoknot variants tevo2.0, eHAV and eSBRMV1-A. In a screen correcting 847 pathogenic ClinVar variants, the top-performing motifs improved PE efficiency over the widely used tevopreQ1 motif for >90% of edits. They also increased PE efficiencies for correcting disease-associated mutations in primary human cells and in vivo in mouse brain and liver. Prime editing is improved with engineered RNA-stabilizing motifs.

Ribosomally synthesized and post-translationally modified peptides (RiPPs) are natural products with diverse structures and functions. Here, we report the discovery of a family of RiPPs whose biosynthetic gene clusters are widespread in the Bacillota genomes and often co-localize with those of lasso peptides, another distinct family of RiPPs. The synthesis of both kinds of RiPPs relies on specific interactions between small adapter protein domains known as RiPP recognition elements (RREs) with their precursor peptides. As these latter share a conserved RRE-binding motif, conflicts between the two biosynthetic pathways may emerge. Through biochemical and structural studies, we reveal how the two RiPP biosynthetic systems evolved to discriminate between their cognate precursors and leader peptidases, allowing them to coexist within a single host. Thus, our study provides insights into the evolutionary diversification of RiPP families. One bacterium can produce many ribosomally synthesized and post-translationally modified peptides (RiPPs) from different families. Here, the authors show how two closely related RiPP families in Bacillota co-evolved to exclude non-cognate precursor peptides, preventing biosynthetic pathway interference.

Genetically encoded fluorescent biosensors (GEFBs) are invaluable tools for spatiotemporal metabolite monitoring in cellular metabolism, yet their development for many key metabolites is hampered by a lack of specific biorecognition elements. Here, we report a versatile strategy to engineer metabolite-responsive GEFBs by leveraging the allosteric properties of regulatory domains from allosteric enzymes. Using regulatory domains from chorismate mutase, 2-acetolactate synthase, and d-citramalate synthase as biorecognition elements, we construct three biosensors for specific l-phenylalanine, l-valine, and l-isoleucine detection. We further demonstrate that multi-ligand-binding regulatory domains can be exploited to derive diverse specific biosensors, and apply this strategy to develop two S-adenosyl-l-methionine biosensors and an S-methyl-5’-thioadenosine biosensor. We also showcase the utility of these biosensors for real-time, in situ tracking of target metabolites in living cells, as well as bioprocess monitoring and clinical diagnostics. Overall, this study establishes a flexible strategy that provides insights to construct GEFBs targeting other metabolites. Genetically encoded fluorescent biosensors are valuable tools for metabolite monitoring. Here, authors report a versatile strategy to design such biosensors via regulatory domains of allosteric enzymes, showing that the developed tools support sensitive detection of target metabolites in vitro and in live cells.

UV exposure causes not only cytosine-to-thymine (C > T) substitutions in dipyrimidine sequences but also many non-canonical mutation classes (e.g. A > T, T > C, and AC > TT). While C > T substitutions are thought to arise from mutagenic bypass of cyclobutane pyrimidine dimers (CPDs), the photoproduct(s) that cause non-canonical mutation classes, which are responsible for key driver mutations in melanoma, are unclear. Here, we use lesion-specific photolyases and whole genome sequencing of yeast irradiated with predominately UVB light to dissect the origins of different classes of UV mutations. These data reveal that CPDs are responsible for ∼60% of all UV mutations and cause not only C > T mutations but also a subset of non-canonical mutation classes, particularly in CT sequence contexts. Our data also indicate that UV-induced pyrimidine (6–4) pyrimidone photoproducts (6–4PPs) are responsible for slightly less than half of all UV-induced mutations in yeast and are the primary cause of T > C substitutions in TT sequences and C > A substitutions. Finally, ∼5% of UV mutations are resistant to both CPD and 6–4PP photolyases, and these are comprised of A > T and AC > TT substitutions in purine-containing dinucleotides. Collectively, these findings define the photoproducts responsible for different UV mutation classes across a eukaryotic genome and indicate that many A > T and AC > TT substitutions arise from atypical UV photoproducts.