Microbial communities are shaped by complex metabolic interactions, whereby the byproducts of one organism influence the physiology of others. This is exemplified in the microbial nitrogen cycle, where diffusion of free intermediates can drastically reshape the chemical landscape of the environment. One such intermediate, nitrous oxide (N2O), is often overlooked as biologically inert. However, emerging evidence suggests this gas may inhibit the activity of some cobalamin-dependent enzymes through a reaction with the cofactor. This raises the possibility that, through such an interaction, N2O-producing organisms may shape the microbial communities in which they reside, selecting against organisms that rely on these sensitive cobalamin enzymes. At the plant root, a hotspot of microbial activity, the impact of such interactions may be especially important. To investigate this, we focused on microbial N2O production and its effect on methionine biosynthesis, a ubiquitous bacterial process carried out by cobalamin-dependent (MetH) or independent (MetE) methyltransferases. In this study, we show that deleting metE and forcing reliance on MetH sensitizes the denitrifier Pseudomonas aeruginosa to exogenous and self-produced N2O. We extend these findings to plant-associated bacteria, where we find that a significant portion of an Arabidopsis thaliana rhizosphere culture collection relies exclusively on cobalamin-dependent methionine synthases and experimentally demonstrate their sensitivity to N2O. Finally, we show that the growth of one MetH-reliant rhizosphere isolate is suppressed in co-culture with N2O-producing P. aeruginosa. Together, these findings suggest that N2O producers can shape microbial ecology at the plant root.

Aflatoxin contamination in agricultural products poses a critical global food safety threat, demanding an urgent paradigm shift from conventional postcontamination quantification to proactive early warning strategies. This comprehensive review critically examines the revolutionary transition toward biomarker-driven biosensing platforms that enable the interception of aflatoxigenesis before toxin accumulation. By systematically analyzing stage-specific biomarkers─including fungal spores/volatile organic compounds, regulatory genes, metabolic proteins, and critical biosynthetic precursors─we elucidate how temporal biosensing creates crucial intervention windows. The review further evaluates advances in nanomaterial-enhanced transducers, multiplexed detection platforms, and AI-integrated sensor arrays while addressing persistent challenges in bioreceptor specificity and field deployment. Future progress hinges on the development of quantitative biomarker–toxin correlation models, advancing point-of-care devices, and implementing integrated monitoring networks. Distinct from existing reviews, this work presents a novel chronobiology-aligned framework for aflatoxin contamination by integrating biomarker discovery with biosensing innovation to enable proactive risk management and strengthen global food security.

Lignin is the most abundant renewable source of aromatic carbon on Earth and a central yet historically underutilized component of lignocellulosic biomass. Its complex and heterogeneous molecular architecture has long constrained efficient and selective conversion into value-added products, despite its high aromatic carbon content and chemical functionality. Recent advances in lignin extraction, fractionation, modification, and application-driven design have substantially expanded the range of achievable material and chemical performance within circular bioeconomy frameworks. This review provides a comprehensive and critically integrated assessment of lignin valorization that explicitly links plant biosynthesis and structural diversity to industrial convertibility, functional materials development, and sustainability performance. Green extraction technologies—including deep eutectic solvent and hydrotropic systems—are evaluated with respect to lignin structural quality, energy demand, solvent recovery, and downstream compatibility. Targeted chemical and enzymatic modification strategies enabling more reproducible lignin streams are discussed alongside applications in carbon fibers, nanomaterials, adhesives, bioplastics, cementitious systems, and additive manufacturing. Quantitative benchmarking against fossil-based incumbents identifies application domains where lignin-derived materials already achieve comparable performance, as well as areas where intrinsic structural limitations remain. In parallel, catalytic depolymerization pathways toward renewable aromatic chemicals are assessed from both mechanistic and systems-level perspectives. Environmental and economic implications are critically examined using recent life-cycle and techno-economic evidence, highlighting the influence of allocation choices, energy integration, and comparison with lignin incineration for energy recovery. Overall, this review clarifies how application-targeted lignin design and system-level sustainability assessment are essential for translating lignin’s biological complexity into scalable, competitive solutions for sustainable materials and chemicals.

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.

This study identified a putative cold-adapted acetylxylan esterase in Glutamicibacter soli Em07 via a metagenome-assembled genome. The gene encoding this enzyme was cloned and heterologously expressed in E. coli. SDS-PAGE gel electrophoresis analysis showed that the protein has a molecular weight of 33.24 kDa. Using 1-naphthyl acetate as a substrate, the enzyme activity was optimal at 20 °C and pH 9. Furthermore, the enzyme exhibited excellent cold adaptation, alkali resistance, and salt tolerance. It demonstrated OCP pesticide-degrading activity: 66.48% degradation of carbaryl, 92.14% of cypermethrin, and 97.78% of malathion, underscoring its strong potential in environmental remediation. Notably, this esterase emerged as the first to simultaneously possess cold adaptation, alkali resistance, and salt tolerance. These results positioned the enzyme as a promising candidate for bioremediation strategies in multiextreme environments. Further research will investigate its activity on other persistent organic pollutants.

In eukaryotic cells, essential functions are often confined within organelles enclosed by lipid membranes. Increasing evidence, however, highlights the role of membrane-less organelles (MLOs), formed through liquid-liquid phase separation (LLPS). MLO assemblies are typically initiated by driver proteins, which form a scaffold to recruit additional client molecules. By leveraging expanding MLO datasets and modern machine learning approaches, we developed LLPSight, an ML-based predictor of LLPS-driving proteins. The model was trained using rigorously curated datasets: a positive set of proteins experimentally confirmed to drive LLPS in vivo and a negative set of soluble, unstructured proteins not associated with LLPS. For the features, we employed a cutting-edge approach using embeddings from protein Language Models. LLPSight achieves the highest F1 score (0.885) among existing tools, enabling more efficient discovery of new LLPS drivers eagerly awaited by researchers for experimental validation. An additional key feature of LLPSight is its ability to perform proteome-wide analyses; application to the human proteome yielded promising targets. LLPSight can be obtained from authors upon request.

Translation is carried out by the most conserved assemblies in biology. Among these assemblies, the ribosome and RNase P are central players. These ancient ribonucleoprotein complexes achieved structural and functional maturity by the last universal common ancestor (LUCA) of life. In prior work, we reconstructed the evolutionary history of the ribosome using its three-dimensional structure, based on accretion and molecular fingerprints that date back to life’s earliest stages. Here, we extend our structural phylogenetic framework—based on the accretion model—to RNase P, a ribonucleoprotein responsible for processing pre-tRNAs. By sampling RNase P RNA (RPR) sequences and structures across phylogeny and partitioning them into RNA fragments based on insertion fingerprints, we characterize the state of RPR at LUCA and reconstruct the chronology of its emergence. The chronology reveals that RNase P, like the ribosome, accreted modular RNA elements over evolution, while preserving the structure of preexisting elements, thus maintaining a structural record. We used interactions with tRNA to link and unify the evolutionary trajectories of RPR and rRNA. These results support the view that RNase P and the ribosome coevolved as part of a functionally integrated system. The ancestral catalytic sites of rRNA and RPR formed by the same process, fusion of two stem–elbow–stem elements. Analysis of these two coevolving RNAs also suggests that some of their accreted elements share common ancestry. Application of the accretion model requires correct secondary structures and was successful for RPR only when the traditional secondary structure was corrected by reorganizing a pseudoknot.

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.