G protein-coupled receptors (GPCRs) recognize ligands on the cell surface, initiating intracellular signaling pathways that control a variety of biological processes, from neurotransmission and hormone regulation to light detection and smell. As entryways into these pathways, GPCRs are key pharmacological targets, with 30% of FDA-approved drugs targeting them. High-throughput GPCR-based sensors in yeast are proven platforms for the identification of novel GPCR ligands. Most human GPCRs (hGPCRs), however, led to small increases in the signal after activation, hindering the development of high-throughput (HT) assays. To streamline the generation of HT assays for biomedically important hGPCRs, here we analyze five fluorescent reporters in the context of hGPCR-based sensors. Using the serotonin receptor 4 (HTR4)-based sensor as a testbed, we identify YPet, a yellow fluorescent protein previously evolved for improved intracellular fluorescence, as the optimal fluorescent reporter when using flow cytometry, fluorescence-activated cell sorting, or a fluorescent plate reader. YPet increases the dynamic range of hGPCR-based sensors in general, enabling the engineering of HTR4-, MC4R- S1PR2-, HTR1A-, and Mel1A-based sensors with vastly higher increases in signal than previously engineered sensors. YPet even allowed the construction of a functional HTR1D-based sensor, a sensor that had been difficult for the field to construct. Finally, the fast maturation of YPet reduces the time to readout from 4 h to 30 min, unlocking point-of-care diagnostic applications previously inaccessible to hGPCR-based sensors in yeast. Looking ahead, the identification of YPet as the optimal fluorescent reporter for yeast hGPCR-based sensors opens the door to the standardized generation of hGPCR high-throughput assays in this host, and sets the stage for ultrahigh-throughput single-cell experiments toward the identification of new ligands for known GPCRs, GPCR deorphanization, and GPCR engineering to bind designer ligands.

Horizontal gene transfer is a pivotal element in the evolution of microbes, enabling them to acquire novel genes and phenotypes. Integrative and conjugative elements (ICEs) are a type of mobile genetic element that can integrate into the host genome and propagate during chromosome replication and cell division. The induction of ICE gene expression results in the excision of the ICE gene, the production of conserved conjugation machinery, a Type IV Secretion System, and the potential for DNA transfer to appropriate receptors. It has been observed that ICEs frequently contain cargo genes that do not typically align with the ICE life cycle. These genes often result in the manifestation of phenotypes that are of particular interest. The bacterium Sphingopyxis granuli strain TFA is being studied for its ability to degrade the contaminant tetralin present in crude oils. Genomic analysis identified eight possible integrative mobile elements in S. granuli TFA. Most of these regions exhibited a distribution pattern that was restricted to the species, and they lacked some functional modules that are characteristic of a complete ICE. This finding suggests the presence of degenerate structures or limited mobilization capacity. However, only two of the detected elements exhibited the capacity to retain all the modules necessary for transfer, integration, and maintenance. These elements also contained a cargo module that included genes associated with lipid metabolic pathways and resistance mechanisms. Among them, ICE3 was distinguished as the sole complete functional ICE that was also present in other species. Transcriptomic analysis under multiple stress conditions revealed differential and consistent activation of ICE3 genes, demonstrating their direct contribution to bacterial resilience and suggesting a key adaptive role in response to adverse environmental changes.

Viral-mediated bacterial mortality and the prevalence of lysogeny are two key parameters for understanding the role of viral activity in aquatic ecosystems. The viral production assay is most commonly used to assess these parameters, with lytic and mitomycin C-induced viral production rates prevalently extracted using the linear regression or increment-based (VIPCAL) approach. A literature survey shows that 64% of the 89 viral production studies used the linear regression approach for lytic and 48% employed VIPCAL for lysogenic viral production rates. Our comparative evaluation highlights significant differences between these two approaches of estimating viral production rates. To refine estimations, we enhanced VIPCAL to VIPCAL-SE by incorporating standard error of the means to rigorously identify maxima–minima pairs, accounting for biological and ecological variabilities between replicates. We also included a bacterial net generation time endpoint to reduce estimation bias due to potential secondary infections, particularly relevant in more productive ecosystems. VIPCAL-SE is now available as a part of the viralprod R package and provides an opportunity for further standardisation in the field of aquatic viral ecology.

With the development of synthetic biology, an evolution system in vivo has been applied to accelerate the construction of cell factories. In this study, an efficient in vivo evolution system was developed for regulation of single and multiple genes in Bacillus amyloliquefaciens. First, the CRISPR/Cas9n-AID base editor was constructed through integration expression of the fused Cas9n protein and activation-induced cytidine deaminase (AID), and the base conversion efficiency from C to T was as high as 90% in single-gene editing. Subsequently, the evolution template (XP43) with an editable RBS sequence (GGGGGGGG) was designed for in vivo evolution through two strategies. By next-generation sequencing of RBS mutation libraries, the extended sgRNA strategy was confirmed to be the optimal evolution scheme. Using the alkaline protease gene (aprE) as the single gene target, the evolution program was initiated to successfully obtain a series of mutant strains with gradient AprE activities. Furthermore, multiple key genes (dhemA, SAM2, and hemEHY) were evolved simultaneously to balance the heme metabolic network, and the optimal mutant strain (HZHA-C2) produced 14.02 mg/L heme, 93% higher than the control strain. Finally, the overexpression of the hemH gene further increased the heme titer by 49%. By a fed-batch fermentation strategy, the heme titer of the optimal engineered strain (HZHA2/pHY-hemH) was improved by 64%, achieving 32.61 mg/L.

Chromatin is intrinsically repressive, limiting access to DNA, implying a major regulatory role. Studies with nuclei support this model. However, we have shown previously that genomic DNA is almost completely accessible in living budding yeast and human cells, except for centromeric chromatin. The fission yeast, Schizosaccharomyces pombe, possesses heterochromatin similar to mammalian heterochromatin at the pericentromeric repeats, telomeres and the silenced mating type loci. S. pombe heterochromatin is marked by histone H3K9 di- and tri-methylation (H3K9me2/3) and heterochromatin protein 1 (HP1/Swi6), potentially repressing genes by preventing access to the DNA. Here, we developed a copper-inducible DNA methyltransferase system to measure accessibility in living S. pombe cells. We find that euchromatin and heterochromatin are generally accessible, indicating that heterochromatin does not represent a significant block to DNA methyltransferases in vivo. S. pombe centromeres are much more accessible than budding yeast and human centromeres. In contrast, S. pombe chromatin is mostly inaccessible in isolated nuclei, primarily due to tight nucleosome spacing on gene bodies, with very little linker DNA. We conclude that S. pombe euchromatin and heterochromatin are both highly dynamic in vivo, suggesting that the H3K9me/HP1 system does not repress transcription by preventing access to DNA.

Creating artificial organelles that sequester and release specific proteins in response to a small molecule in mammalian cells is an attractive approach for regulating protein function. In this work, by combining phase-separated condensates formed by the tandem fusion of two oligomeric proteins with a trimethoprim (TMP)-responsive nanobody switch for GFP (GFPLAMA; ligand-modulated antibody fragment), we developed a synthetic condensate system that initially sequesters GFP-tagged proteins within condensates and rapidly releases them into the cytoplasm upon TMP treatment. The released proteins can then be resequestered by washing out the TMP. This system enabled user-defined, temporal, rapid, and reversible control of cellular processes, including membrane ruffling mediated by exogenously expressed GFP-Vav2 and modulation of the cellular localization of endogenous ERK2-GFP generated by genome knock-in. Our results highlight the utility of the GFPLAMA-based synthetic condensate platform as a novel, chemically switchable tool for regulating protein function through controlled protein sequestration and release in mammalian cells.

Biosynthetic gene clusters (BGCs) are responsible the biosynthesis of many natural products, including a multitude of effective therapeutics and their precursors. Advances in genomic data collection as well as computational techniques have made it possible to identify BGCs at scale. However, accurately determining the types of BGC-encoded products from genomic content remains elusive. Here, we introduce BGCat (BGC annotation tool), a machine learning method for fine-grained structural classification of BGC-encoded products, leveraging the NPClassifier natural product nomenclature. Our method leverages a pre-trained protein language model for creating meaningful gene representations and a deep neural network for class label prediction. We show the method outperforms state-of-the-art approaches in coarse-grained product classification and is effective for detailed classification. We implement a clustering-based augmentation strategy for BGC-product relationships, addressing a crucial gap in the available datasets. We then introduce the concept of product class profiles (PCPs) of gene cluster families (GCFs), associating each GCF with a probabilisitc distribution of product types and offering a new perspective on GCF functions. Lastly, we use BGCat to provide new product class labels for over 100k BGCs in antiSMASH DB that presently have minimal information about their products.

Synthetic biology is being widely applied in tumor therapy, ranging from attenuating microbial toxicity to constructing synthetic gene circuits and developing CAR-T cells, all of which are reshaping the landscape of cancer immunotherapy. In this review, we summarize recent advances in microbial-based therapeutics that leverage bacteria’s natural tropism for hypoxic tumor regions to deliver immunomodulatory payloads with high spatial precision. Parallel progress in CAR-T cell engineering has led to the development of armored and logic-gated constructs designed to overcome challenges such as antigen heterogeneity, the immunosuppressive tumor microenvironment, and T cell exhaustion. Synthetic biology further integrates these platforms via programmable genetic circuits capable of performing Boolean logic operations, ensuring therapeutic activation only in the presence of tumor-specific biomarkers. While this convergence offers the unprecedented precision, safety, and potency in reprogramming anti-tumor immunity, the clinical translation of these complex systems faces significant hurdles. Despite challenges in clinical translation-including safety concerns, immune clearance, and manufacturing complexity-the field is advancing toward multifunctional “smart” therapies, synergistic microbial-cell combinations, and personalized treatment strategies. Together, these innovations are defining a new generation of precision-engineered immunotherapies with the potential to transform the treatment of refractory malignancies.

Pseudomonas aeruginosa is a human opportunistic pathogen, capable of producing a wide range of metabolites, including pyomelanin. This pigment results from alterations in tyrosine catabolism. Melanin synthesis from tryptophan has never been reported in Pseudomonas. In this study, we describe a tryptophan-derived melanin in P. aeruginosa PAH, a strain that was isolated from a fibrocystic patient. PAH produced a brown pigment when grown in LB or L-tryptophan-supplemented media. Structural analysis revealed this pigment was composed by two fractions differing in NaOH solubility: a soluble one consistent of pyomelanin, and an insoluble fraction with a complex structure containing substituted indolic units. A pyomelanin inhibitor enhanced total melanin synthesis, mainly the insoluble fraction, and a tryptophan 2,3-dioxygenase inhibitor decreased pigment formation. Metabolomic profiling identified distinct indolic compounds and low levels of anthranilate in PAH cultures. Genomic and transcriptomic analyses revealed the presence of mutations and downregulation of genes related to pyoverdine biosynthesis. Furthermore, iron supplementation in the culture medium reduced melanin production. Overall, tryptophan arises as a key compound for melanin production in PAH, expanding the diversity of melanins synthesized by this genus. Furthermore, iron deprivation emerges as a critical factor triggering melanin biosynthesis, probably as a survival strategy enabling persistence in the fibrocystic lung environment.

Several luciferases have been developed for imaging and biosensing, and the collection continues to grow as new applications are pursued. The current workflow for luciferase optimization, while successful, remains laborious and inefficient. Mutant libraries are generated in vitro and screened, “winning” mutants are picked by hand, and the isolated sequences are subjected to additional rounds of mutagenesis and screening. Here, we present a streamlined platform for luciferase engineering that removes the need for manual library generation during each cycle. We purposed an orthogonal DNA replication (OrthoRep) system for continuous hypermutation of a well-known luciferase (GeNL). Short cycles of culturing and screening were sufficient to evolve the enzyme, with no repetitive manual library generation necessary. New GeNL variants were identified that exhibit improved light outputs with a noncognate and inexpensive luciferin. We further characterized the novel luciferases in cell models. Collectively this work establishes OrthoRep and continuous hypermutation as a viable method to engineer luciferases, and sets the stage for more rapid development of bioluminescent reporters.

Metabolic regulation─the dynamic biochemical network governing energy transduction, substrate conversion, and metabolic flux─represents a fundamental determinant of microbial viability and functional output. While temporal metabolite fluctuations provide critical insights into metabolic network dynamics, conventional analytical platforms face fundamental limitations in real-time monitoring within native microbial environments. This study presents a novel whole-cell electrochemical biosensing platform integrating carbon dot (CD)-engineered Escherichia coli with advanced cyclic voltammetry (CV) for dynamic metabolic interrogation. This biohybrid system synergizes microbial biochemical specificity with CD-enhanced electron transfer efficiency, achieving nearly a 20-fold amplification in the electrochemical signal amplitude through quantum-enhanced charge transport mechanisms. The platform enables precise quantification of redox-active metabolites via distinct voltammetric fingerprints, as demonstrated through the detection of lactic acid─a pivotal biomarker in industrial biotechnology and clinical diagnostics. Featuring a modular bioarchitectural design, this technology permits seamless adaptation across diverse microbial systems, offering unprecedented capabilities for real-time bioprocess optimization, dynamic metabolic pathway analysis, and pathogen metabolic profiling. By interfacing nanomaterial-enhanced electrochemistry with synthetic biology, our platform surmounts traditional analytical constraints, establishing a versatile analytical tool for spatiotemporal mapping of metabolic networks in complex biological matrices.