We introduce a new algorithm, DartUniFrac, and a near-optimal implementation with GPU acceleration, up to three orders of magnitude faster than the state of the art and scaling to millions of samples (pairwise) and billions of taxa. DartUniFrac connects UniFrac with weighted Jaccard similarity and exploits sketching algorithms for fast computation. We benchmark DartUniFrac against exact UniFrac implementations, demonstrating that DartUniFrac is statistically indistinguishable from them on real-world microbiome and metagenomic datasets.

Understanding how to engineer transcriptional regulation in plants is key to advancing both fundamental knowledge and practical applications in plant biology. Native gene promoters, while widely used, are constrained by evolutionary pressures that limit their modularity, tunability, and predictability across genetic backgrounds and species. Synthetic promoters, artificial DNA sequences composed of defined cis-regulatory elements (CREs) for recruitment of gene-specific transcription factors (TFs) and general transcriptional machinery, provide a powerful alternative for achieving fine-tuned transcriptional control. This review examines the design and application of synthetic promoters in plants, emphasizing current strategies, ongoing challenges, and avenues for innovation. We cover the structure of plant promoter architecture, including the contributions of core, proximal, and distal regions, and highlight how promoter grammar (i.e., motif identity, motif distance from transcription start site, spacing between motifs, helical phase of TF binding, motif orientation, and combinatorial interactions between motifs) impacts transcriptional activity. We outline how synthetic promoters are designed and validated via high-throughput reporter assays. Applications of synthetic promoters are discussed across functional genomics studies, biosensor creation, logic gate-based genetic circuits, and practical crop engineering, with examples covering constitutively expressing, hormone-responsive, pathogen-inducible, and abiotic stress-responsive promoter designs. We discuss traditional and emerging computational frameworks that enable CRE identification, novel synthetic promoter generation, and prediction of promoter sequence activity in silico to inform the rational design of promoters with predictable performance and spatiotemporal expression. We emphasize the importance of integrating experimental studies and computational approaches through iterative Design-Build-Test-Learn (DBTL) cycles to standardize and optimize frameworks for synthetic promoter development. By combining insights from plant promoter studies with advances in both plant-specific and non-plant synthetic promoter generation and computational modeling, researchers can expand synthetic promoter libraries to enable complex man-driven transcriptional regulation across various plant systems.

Across diverse contexts, bacteria experience loss of electron acceptors due to fluctuating environmental conditions, leading to growth-arrest and reductive stress. Yet, microbial metabolism has been primarily studied with cells growing under nutrient-replete conditions. To study how cells preserve metabolic flux under reductively stressed growth-arrest, we explored how the opportunistic pathogen Pseudomonas aeruginosa remodels its metabolism under such conditions. During anaerobic survival on glucose, P. aeruginosa utilizes the upper Embden-Meyerhoff-Parnas pathway and pentose-phosphate pathway to generate metabolite precursors for a previously undescribed phosphoketolase (herein termed xfp) used to produce acetyl-P and indirectly ATP via subsequent acetate formation. This re-routing bypasses P. aeruginosa's canonical glucose-catabolizing Entner-Doudoroff pathway (EDP), allowing for metabolic flux without exacerbating reductive stress. Moreover, anaerobic survival on diverse carbon sources triggers purine degradation and metabolite accumulation, requiring xfp to maintain metabolic balance and viability. Thus, our data suggest that phosphoketolases may play an additional role in ribonucleotide balance. This study expands our understanding of P. aeruginosa's anaerobic survival strategies and serves as a reminder that large gaps remain in our understanding of growth arrest physiology even in well-studied model organisms, highlighting the potential for basic discovery in the realm of non-growth metabolism.

While protein language models (PLMs) have shown great promise for protein design, their performance is fundamentally constrained by the diversity and completeness of available training data. In particular, PLMs often struggle to extrapolate to sequences that fall outside the distribution spanned by their training sets, limiting their ability to discover proteins in sparsely sampled regions of sequence space. Here we test the hypothesis that experimentally expanding training diversity can convert extrapolation into interpolation and thereby enable discovery of functional sequences beyond natural protein manifolds. Using large-scale gene synthesis and DNA shuffling, we generate libraries that span a broad region of fluorescent protein sequence space and create chimeric variants that bridge between distant homologs. Functional screening for blue fluorescence yields thousands of active variants distributed across diverse sequence lineages. Fine-tuning ProtGPT2 on this expanded dataset enables generation of diverse fluorescent proteins, including designs that extend beyond the regions occupied by known natural sequences while retaining function. This work illustrates how synthetic approaches can help address key limitations in machine learning-guided protein design, especially for small or sparsely populated protein families, by actively creating novel sequences across unexplored but functional regions of sequence space.

Natural evolution is high-dimensional; organisms adapt to many pressures at once, across substrates, environments, and genetic backgrounds. Yet most directed evolution methods flatten this landscape to a single selection axis, hiding tradeoffs, and limiting what can be learned. Phage-assisted continuous evolution (PACE) is uniquely suited for multivariate selection because horizontal gene transfer couples genotype to propagation and allows the same phage lineage to traverse different selection environments. In practice, implementing this at scale has been prohibitive because each selection demands its own host culture, and every culture must be held for days to weeks within a narrow, infectable density window using continuously responsive bioreactors. In this work, TurboPRANCE is presented as an open-source, queueable robotic platform that integrates ~200 independently controlled turbidostats with 96 parallel PACE lagoons under closed-loop control. Each turbidostat operates as a fully separate unit that can be equilibrated and initiated on its own schedule, enabling asynchronous starts and sustained operation without intervention. Automated media formulation, programmable dosing, on-deck sterilization, and adaptive scheduling coordinate growth control with the changing needs of the robotic workflow, dynamically adjusting dilution and transfer timing around formulation, sampling, and handling steps to keep each culture at consistent infectable densities despite unpredictable method demands. Cultures can be multiplexed and titrated into lagoons at defined ratios, swapped in and out on a schedule, or kept fully separate across experiments, creating a combinatorial space of selection pressures and programs that is effectively unbounded. Additionally, to enable high-throughput evolutionary tracking that scales with TurboPRANCE, Nanopore long-read sequencing was combined with DeepVariant, a deep learning-based variant caller, enabling population-level tracking of evolving variants. The result is a system that generates high-resolution time-resolvable evolutionary trajectories and large parallel datasets spanning diverse selection regimes, yielding dense, multivariate training data to map and engineer complex fitness landscapes at scale.

Nucleobases, the fundamental units of DNA and RNA, undergo diverse chemical modifications and lesions that regulate genomic stability, epigenetic states, and cellular functions. Nucleobase-editing enzymes, including deaminases, methyltransferases/demethylases, and DNA glycosylases, catalyze precise base conversions, modification/demodification, or excision reactions at high resolution without inducing double-stranded breaks. Originally studied in transcriptomic diversification, epigenetic regulation, and DNA repair, these enzymes are now increasingly repurposed as programmable actuators in biosensing and bioimaging applications. In this review, we first outline the catalytic principles of representative nucleobase-editing enzymes, emphasizing substrate recognition, reaction mechanisms, and physiological functions. We then highlight how these enzymes are integrated into biosensing and bioimaging modules across three major modes: nucleobase conversion, where site-directed deamination enables facile fluorescent protein reporter translation or specific DNA probe hybridization signals; nucleobase modification/demodification, where methylation and demethylation events regulate downstream selective enzymatic biocatalysis or programmble functional nucleic acid activation; and nucleobase erasure, where glycosylase-mediated base excisions facilitate specific probe accommodation or effient enzyme-catalyzed amplification. Such nucleobase-editing enzyme-driven systems offer high specificity, efficient amplification, and high compatibility with physiological conditions, enabling sensitive and spatiotemporally resolved monitoring of nucleic acids, proteins, and cellular processes. Finally, we discuss the advantages, challenges, and future directions of this emerging field. Advances in enzyme engineering, delivery strategies, and multiple circuitry integration are expected to yield next-generation bioanalytical platforms with improved precision, scalability, and clinical applicability. Collectively, these developments establish nucleobase-editing enzymes as a versatile molecular toolbox that bridges fundamental enzymology with applied biotechnology for diagnostics, therapeutic monitoring, and synthetic biology.

Polyhydroxyalkanoates (PHA) are promising biodegradable alternatives to petroleum-based plastics, but high production costs limit their deployment. In this study, we evaluated a cosubstrate strategy combining vanillic acid (VA), representative of lignin-derived aromatics, with palm oil (PO) recovered from oil palm fruit residues to enhance PHA synthesis in Pseudomonas putida. Cofeeding 4 g/L VA and 4 g/L PO increased PHA titer to 1.13 g/L, a 52.7% improvement over VA alone, and yielded a new monomer, 3-hydroxyhexanoate (3HHx). Integrated transcriptome and metabolomic analyses revealed that co-substrate metabolism attenuated TCA flux, enhanced β-oxidation and hydroxyacyl precursor pools, and altered NAD(P)H turnover and membrane lipids. These shifts were correlated with expanded monomer diversity. This study demonstrates that cofeeding lignin-derived aromatics with agricultural oils provides a synergistic route to improve PHA yield and diversify monomer composition, linking biomass valorization with circular, low-carbon material production.

The Clostridium estertheticum complex (CEC) consists of closely related bacterial species that are mostly isolated from meat processing environment. Genome mining studies have recently established CEC as a source of class I bacteriocins. However, up until now, class II bacteriocins have not been reported from CEC. In the present study, we determined the presence of class II bacteriocin biosynthetic clusters in 33 CEC genomes through genome mining followed by bacteriocin production through heterologous expression in E. coli. Six biosynthetic gene clusters belonging to class IIa (n = 1), IIb (n = 2), IId (n = 2) and an undefined class containing three precursor peptides were identified in six different CEC strains. Using molecular biology, we developed dedicated expression vectors for the class IIa bacteriocin, clesteriocin A. Its precursor peptide, CleA, and protease, CleB150, were initially expressed in insoluble form in E. coli. Through a systematic analysis of suitable solubility enhancing protein tags, both CleA and CleB150 were expressed in significant amounts of soluble fractions using tandem tags derived from Small Ubiquitin-like Modifier, superfolder Green fluorescent protein, Maltose binding protein and N-utilization substance. Mature clesteriocin A was obtained following in vitro maturation reactions between the tagged CleA and CleB150. The novel bacteriocin displayed antimicrobial activity against Listeria monocytogenes, which was mediated by the mptC gene. By combining genome mining and molecular biology, we have shown CEC is a source of novel class II bacteriocins that have potential for application as food biopreservatives.