RMH

Betanin is a natural red pigment that accumulates within plant vacuoles. It is hypothesized that vacuolar sequestration could also restrict the extracellular accumulation of heterologously produced betanin in the yeast Yarrowia lipolytica. To investigate the role of vacuolar functions in influencing extracellular betanin levels, we employed the TUNEYALI platform to generate a promoter-engineering library targeting 21 vacuole-associated genes. Screening 470 transformants identified nine variants with enhanced extracellular betanin accumulation. Whole-genome sequencing revealed that all nine isolates harbored disruptions in YALI1_C31684g, the gene most closely related to Saccharomyces cerevisiaeVPS8, which encodes a CORVET-specific tethering subunit. Reverse engineering confirmed that partial knockdown of YALI1_C31684g led to a 1.18-fold increase in total betanin production and a 61.7% increase in extracellular betanin levels, whereas complete deletion of the gene reduced extracellular accumulation. Quantitative PCR analysis showed that the promoter knockout construct maintained approximately 47% of wild-type transcript levels, suggesting that a partial loss of function is more effective than a complete loss of function in enhancing extracellular betanin. Confocal microscopy demonstrated extensive vacuolar fragmentation in the knockdown strain, with the highest levels of extracellular betanin correlating with the most pronounced fragmentation phenotype. Collectively, these findings identify YALI1_C31684g as a key regulator of extracellular betanin accumulation in Y. lipolytica and demonstrate that modulating vacuolar trafficking functions can enhance the extracellular fraction of metabolites stored in vacuoles.

Enzymes are essential catalysts in many cellular processes. Understanding their interactions with small molecules, such as regulators, cofactors, and most importantly, substrates, is crucial for understanding the biochemical processes that occur in cells. Correctly interpreting the roles of small molecules that interact with enzymes is key to elucidating enzyme function. Recently, enzyme-small molecule interaction prediction has attracted growing interest from computational methods, especially deep learning. As a result, researchers have published several datasets and numerous models with remarkable performance. In this work, we critically examine one of the most popular datasets and four models trained on it, identifying similarity-induced information leakage that may overinflate reported model performance in out-of-distribution applications. We show that the inspected models are susceptible to information leakage, and their performance is not better than statistical baselines when the leakage is removed. Furthermore, controlling for leakage due to protein sequence similarity alone still yields high predictive performance, whereas performance decreases substantially when common substrates shared between training and test sets are removed from one of them, and approaches random prediction when structurally similar ligands are separated between splits. Thus, the investigated models generalize considerably better across enzyme sequence space than across ligand chemical space, indicating that their reported performance depends strongly on the small-molecule similarity between training and evaluation data.

Antimicrobial resistance is a major global health threat, with disproportionate impact in regions with limited diagnostic infrastructure. To address this challenge, we developed BADLOCK (Bacterial and AMR Detection by SHERLOCK), a rapid, low-cost molecular diagnostic platform for direct detection of bacterial pathogens and resistance genes from clinical samples. BADLOCK operates as a one-pot CRISPR-Cas13a reaction capable of detecting nine bacterial species and four major resistance genes directly from positive blood culture. It requires only a heat block and a basic fluorometer for operation. We validated BADLOCK on 194 sequentially collected clinical blood culture specimens growing Gram-negative rods, supplemented with 69 mock samples generated from banked isolates enriched for targeted resistance genes. In all, we conducted 2224 individual reactions, achieving 97.6% accuracy (2171 of 2224) at the reaction level. At the assay level, 89.5% (274 of 306) showed perfect or partial concordance with gold-standard species and resistance gene detection, including 255 assays with perfect concordance and 19 with partial concordance (correct detection of at least one pathogen). This included an evaluation of BADLOCK as a potential culture-free diagnostic for urinary tract infections (UTIs), achieving 98.0% reaction-level accuracy. At the assay level, 95.3% (41 of 43) were partially or perfectly concordant with gold-standard detection of both species and resistance genes. To our knowledge, this represents the first demonstration of the CRISPR-Cas13a diagnostic platform on clinical bloodstream infections to date and supports BADLOCK’s potential as a practical and scalable solution for rapid pathogen and resistance gene detection in resource-constrained settings.

Engineering G protein-coupled receptors (GPCRs) for biosensing applications remains challenging due to structural and functional constraints when such proteins are expressed in a heterologous host. OrthoRep offers the continuous accumulation and selection of signal-enhancing mutations in yeast, yet it has not been applied to GPCR evolution. We apply OrthoRep-driven mutagenesis to evolve opioid GPCRs and improve yeast-based biosensors: first, we express the human μ-opioid receptor (OPRM1) on the OrthoRep p1 plasmid and couple ligand activation to growth. Serial cell passaging of the biosensor generated functionally diverse mutants. However, mutations that decouple receptor activation from selection arose over longer passaging campaigns, halting evolution. We resolve this issue using a site-specific recombinase that re-introduces evolved variants onto the p1 landing pad of an unmutated biosensor, allowing continued selection. This led to an ~18-fold improvement in sensitivity of a mutant OPRM1 over wt-OPRM1. Additional polymerases were added to the platform, broadening the mutational profiles available. Assembling these components forms the platform we call HERO (Heterologous Engineering of Receptors using OrthoRep). Lastly, we deploy HERO using automation to improve the human δ-opioid receptor's weak response to an agonist by ~40-fold. Sensitivity also improved for a structurally dissimilar ligand assayed on the same evolved variants.

Tailocins are phage tail-like bacteriocins derived from prophages that have lost the ability to package viral genomes while retaining bactericidal activity against related bacterial strains. In Pseudomonas aeruginosa, these elements are referred to as pyocins and contribute to ecological fitness and antimicrobial applications. Despite lacking essential phage components, pyocin-encoding gene clusters share substantial sequence homology with phage tail genes, creating a major challenge for genome annotation. Current prophage prediction tools, which rely on phage gene homology, can frequently misclassify pyocins as prophages, potentially confounding genomic analyses and downstream experimental design. Here, we present TattleTail, a rule-based bioinformatic pipeline for identifying canonical pyocin-encoding gene clusters in P. aeruginosa. The current implementation targets the characteristic trpE–trpG-associated genomic architecture of these loci and integrates the detection of conserved pyocin-associated genes with the exclusion of canonical phage features, enabling discrimination between pyocin-encoding gene clusters and prophage regions. Evaluation across manually curated and independently selected P. aeruginosa genomes, together with P. aeruginosa genomes computationally depleted of their pyocin loci and non-P. aeruginosa controls, demonstrated accurate identification of canonical pyocin loci without false-positive predictions. Application to clinical isolates identified representative pyocin clusters, which were experimentally validated by mitomycin C induction, transmission electron microscopy, and bactericidal activity assays, supporting that predicted loci in these isolates correspond to functional pyocin particles. TattleTail complements existing prophage prediction tools and supports accurate annotation of phage-derived elements.

Syringotoxins are chlorinated cyclic lipopeptides from Pseudomonas syringae that were structurally characterized more than three decades ago. The corresponding biosynthetic gene cluster required for their production, however, has not been experimentally assigned. Based on targeted genetics, metabolite profiling, and structure analysis, we identify a divergent syringomycin-type nonribosomal peptide synthetase gene cluster in P. syringae DSM 1242 and show that psyt is the syringotoxin biosynthetic gene cluster. Deletion of psyt abolishes syringotoxin production and renders the mutant susceptible to grazing by the amoebal predator Dictyostelium discoideum. Besides nanomolar amoebicidal activity, syringotoxin B shows strong antifungal activity, particularly against Candida pathogens. Comparative genomic and structural analyses place psyt within the syringomycin-type lipopeptide family, while the activity loss after macrolactone opening links syringotoxin bioactivity to its cyclic lipopeptide architecture. Together, this work assigns the biosynthetic origin of syringotoxin and establishes this cyclic lipopeptide as a defense factor required for full resistance of P. syringae to amoebal predation.

Bacteria inhabiting the mammalian gut coexist in dense communities where contact-dependent antagonism mechanisms are widespread. The type VI secretion system (T6SS) is an interbacterial toxin delivery pathway prevalent among gut Bacteroidales, yet its function in naturally evolved microbiomes remains poorly defined. Here, we examine the role of the T6SS in Bacteroides within a physiologically relevant gut community derived from wild mice (the WildR microbiome). Using newly developed genetic tools and a strategy for functional replacement of strains within the WildR community, we demonstrate that the WildR isolate B. acidifaciens employs a T6SS to antagonize co-resident Bacteroidales. We also show that loss of T6SS function compromises the long-term maintenance of B. acidifaciens in the community but not its initial colonization, establishing the system as a determinant of strain persistence. The T6SS we identified resides on an integrative and conjugative element (ICE). ICE-seq, a targeted sequencing approach, reveals that the T6SS-ICE is distributed among select Bacteroidales and Muribaculaceae species in the WildR microbiome, between which it appears to be recently exchanged. We also show that transfer of the T6SS-ICE to WildR isolate Phocaeicola vulgatus confers transient colonization benefits in mice, but is linked to eventual population decline. Our findings demonstrate that the T6SS can stabilize the presence of specific strains within a complex, co-evolved gut microbiome, yet its value is context dependent and constrained by the ecological and physiological landscape of the host community.

Viral-induced DNA damage triggers both eukaryotic and prokaryotic innate immunity. Here, we report that self-DNA ends generated routinely during normal termination of bacterial replication suffice to trigger the P2-encoded OLD antiphage system. Normally, these physiological triggers are cleared by the dual helicase-nuclease activities of the conserved RecBCD enzyme, which acts as an immune safeguard analogous to mammalian TREX1 exonuclease and plant DBR1 debranching enzyme. However, when this clearance fails, the transient self-DNA ends persist and are recognized by ATP-bound OLD dimers via a positively charged channel across their ATPase subunits. ATP hydrolysis then activates OLD to cleave tRNAs, causing cytotoxicity. Single-cell microscopy reveals that dozens of OLD molecules associate with kilobases of adjacent DNA, amplifying the sparse physiological DNA end signal. Our findings reveal how an endogenous byproduct poses a constant autoimmune threat in bacteria, highlighting a universal molecular conflict across kingdoms and the convergent solutions evolved to resolve it.