Mechanically active RNAs represent an emerging class of biomolecules whose function derives from resisting molecular forces. Among them, exoribonuclease-resistant RNAs (xrRNAs) achieve this by folding into a ring-like topology that physically blocks 5' to 3' degradation. However, despite years of structural insight, the rational design of such mechanically functional RNA devices has remained elusive. Here, we describe a mechanics-aware RNA design approach that enables de novo engineering of functional xrRNAs. We first identify structural determinants of force resistance by perturbing pseudoknot architecture in a model xrRNA and quantifying resulting efficiencies in the stalling of exoribonuclease XRN1. We then implement these rules in a design framework that integrates explicit topological constraints with molecular dynamics-guided optimization. The resulting synthetic xrRNAs reproduce the ring-like architecture and stall exoribonuclease XRN1 with wild-type-like efficiency. Our top-performing constructs exhibit minimal sequence similarity to known xrRNAs and evade detection by covariance models, yet remain fully functional in vitro. Together, our results show that mechanical function can be rationally designed independent of evolutionary ancestry, laying the groundwork for the design of RNA elements that modulate decay and fine-tune the mechanical stability of engineered transcripts.

Metabolic enzymes have traditionally been regarded as highly specific catalysts; however, many can catalyze multiple reactions. To systematically investigate the prevalence of such enzyme promiscuity, we used nontargeted metabolomics to measure dynamic metabolite ion profiles in in vitro assays with 667 successfully purified E. coli enzymes in a natural intracellular metabolome extract. Notably, nearly half of these enzymes elicited significant changes in ion traces. Using a machine learning-derived multivariate classifier at a false-discovery rate of 33%, we identified unexpected changes in 135 putatively annotated metabolite ion traces, indicating the presence of so far unknown promiscuous activities in 11% of the tested enzymes, most of which have yet to be recognized for their ability to catalyze multiple reactions. Notably, we found that nucleotide-related substrates or cofactors were enriched among the newly identified reactants. For 11 promiscuous enzymes, we successfully reconstructed 22 complete reaction stoichiometries, four of which were validated experimentally. Key findings include the nucleoside phosphorylase DeoA, for which we expanded the substrate range to include pyrimidines relevant to carbon and energy utilization, and the N-acetylmannosamine kinase (NanK), which displayed both cofactor and sugar substrate promiscuity. Additionally, CobC, a putative adenosylcobalamin/α-ribazole phosphatase, catalyzes flavin mononucleotide dephosphorylation, suggesting a generalist role in vitamin biosynthesis pathways. Beyond specific examples, the results suggest that metabolism harbors a wealth of underexplored catalytic flexibility, relevant for functional annotation, evolution, and genome-scale metabolic models. Metabolomics-based activity profiling of 667 purified E. coli enzymes reveals a surprising level of promiscuity, with many enzymes catalyzing unexpected reactions.

Bacteria and phages have coexisted for billions of years engaging in continuous evolutionary arms races that drive reciprocal adaptations and resistance mechanisms. Among the diverse antiviral strategies developed by bacteria, modification or masking phage receptors as well as their physical removal via extracellular vesicles are the first line of defense. These vesicles play a pivotal role in bacterial survival by mitigating the effects of various environmental threats, including predation by bacteriophages. The secretion of extracellular vesicles represents a highly conserved evolutionary trait observed across all domains of life. Bacterial extracellular vesicles (BEVs) are generated by a wide variety of Gram (+), Gram (−), and atypical bacteria, occurring under both natural and stress conditions, including phage infection. This review addresses the multifaceted role of BEVs in modulating bacteria–phage interactions, considering the interplay from both bacterial and phage perspectives. We focus on the dual function of BEVs as both defensive agents that inhibit phage infection and as potential facilitators that may inadvertently enhance bacterial susceptibility to phages. Furthermore, we discuss how bacteriophages can influence BEV production, affecting both the quantity and molecular composition of vesicles. Finally, we provide an overview of the ecological relevance and efficacy of BEV–phage interplay across diverse environments and microbial ecosystems. omv

Antimicrobial resistance and the emergence of multi-drug-resistant pathogens necessitate holistic care. Novel antimicrobial drug discovery involves an in-depth assessment of quorum sensing (QS) signaling and cell-cell communication. Bacteria regulate their metabolism to cope with complex host-environmental changes through QS signaling. Previous studies suggest that the social behaviors of bacteria include biofilm formation, virulence, and drug resistance mediated by QS. Over several decades, autoinducer receptors, signaling pathways, and the regulatory networks that control gene expression have corroborated QS signaling molecules. Multiple QS systems and chemical structural diversity signaling molecules have been sporadically reported, but there is no comprehensive review of these findings. This review systematically and comprehensively summarizes the paper, addressing bacterial QS-based cell-cell communication and the potential mechanisms of QS systems in bacterial drug resistance. Furthermore, a status quo update has been established for novel QS systems in the realms of pathogenicity, poly-microbial interactions, and/or antimicrobial resistance patterns. Nevertheless, the applications of QS systems would invoke newer incorporations for futuristic research values. Thus, the complexity and evolutionary insights of QS systems, quorum sensing signaling molecules (QSSMs), and regulatory mechanisms need a “cloud” based repurposing for the design of novel agents in antimicrobial resistance (AMR) and communication.

Artificial intelligence models are transforming protein research, enabling advances in areas ranging from structure prediction to the design of functional enzymes. However, these models operate as black boxes, and their underlying working principles remain unclear. Here we survey emerging applications of explainable artificial intelligence (XAI) to protein language models and describe the potential of XAI in protein research. We organize existing work around four points in a typical modelling pipeline: the data used for training; the user-provided inputs; the internal model architecture; and input–output relationships. Across these contexts, we highlight methods and applications of XAI. In addition, from published studies we distil five potential roles for XAI in protein research: Evaluator, Multitasker, Engineer, Coach and Teacher, with the evaluator role being the only one widely adopted so far. While our analysis focuses on protein language models, our categorization is broadly applicable to any other architecture. We conclude by highlighting critical areas of application for the future and outlining a path to advance the interpretability of protein artificial intelligence. Hunklinger and Ferruz provide an overview of explainable artificial intelligence methods for protein language models.

Komagataella phaffii is used to manufacture biologic medicines, food proteins, reagents, and materials. Despite its increasing prevalence, further improvements to its productivity would enhance its economic and operational benefits. Genomic engineering represents one approach to increase its cell-specific productivity. We hypothesized that combining the metrics for the relative essentiality of genes with biological inference for relevance to protein secretion could identify genes that, when disrupted, would improve specific productivity in the resulting strains. The essentiality of genes in K. phaffii (NRRL Y-11430) were predicted through a genome-wide knockout screen using CRISPR-Cas9. Based on the results from this screen, we selected and subsequently disrupted the least essential genes from two gene groups heavily associated with secretion, namely those relating to the cell wall and vacuolar transport. Strains of K. phaffii with single gene disruptions from these gene sets showed significantly improved production of a monoclonal antibody (mAb). These strains exhibited no discernible differences in growth or apparent profiles of host cell proteins when compared to the parental strain. The best-performing strains consistently showed 2-3x enhancements in specific productivity and titers across scales (3–150 mL), culture formats (plates, flasks, bioreactors), and processing operations (batch and fed-batch). This study demonstrates how combining data on gene essentiality and prior knowledge of biological pathways related to a phenotypic trait of interest (here protein secretion) can inform strain engineering to enhance the trait. This study expands the catalog of genetically engineered strains of K. phaffii with improved productivity. These strains support the long-term goal of achieving low-cost, high-volume production of recombinant proteins using this host. Further engineering of these strains and optimization of fermentation processes could enable volumetric productivities comparable to those of other established hosts used to produce mAbs and other complex recombinant proteins.

The human gut harbors a complex microbial ecosystem that supports host homeostasis. Gut microbiota (GM) ferment dietary fiber to produce short-chain fatty acids (SCFAs), which modulate immune function and influence osteoclastogenesis and osteoblast activity. Exosomes, derived from gut and bone cells, containing proteins, lipids, and RNA, mediated bioactive signaling that affects bone metabolism. This review focuses on how GM maintains health through immunomodulation and suppression of gastrointestinal inflammation, while exerting distal effects on bone physiological and pathological conditions.

The microbial valorization of glycerol, a major biodiesel byproduct, into high-value chemicals remains challenging at industrially competitive titers. Here we engineered Corynebacterium glutamicum for high-level 1,3-propanediol (1,3-PDO) production, validating each step via fed-batch fermentation. First, C. glutamicum ATCC 13032 was metabolically engineered to produce 138 g l−1 1,3-PDO from glucose–glycerol and 100.9 g l−1 from glycerol alone. Key engineering strategies included establishing glycerol uptake and 1,3-PDO biosynthetic pathways, minimizing byproducts and optimizing fed-batch fermentation. We then transferred these strategies to a newly isolated strain, C. glutamicum SC97. Further engineering, including antibiotic-free plasmid addiction system and sucCD overexpression, enabled 141.5 g l−1 1,3-PDO at 2.95 g l−1 h−1 without antibiotics. Scalability was demonstrated at 30-l and 300-l pilot-scale fermentations, reaching 120.2 g l−1 and 127.8 g l−1 of 1,3-PDO, respectively. Techno-economic and life-cycle assessments support industrial feasibility and environmental impact, providing a robust blueprint for sustainable microbial 1,3-PDO production at scale. This study reports on engineered Corynebacterium glutamicum capable of converting glycerol, a major biodiesel byproduct, into 1,3-propanediol at industrially relevant titers. Through integrated strain design, optimization of fed-batch conditions, implementation of antibiotic-free systems and validation in pilot-scale fermentation, this study establishes a scalable blueprint for sustainable 1,3-propanediol biomanufacturing.

The rising rate of drug-resistant fungal infections and the emergence of intrinsically resistant pathogens pose growing clinical challenges. Because fungi are closely related to mammals, developing antifungals without toxic off-target effects is difficult. Targeted gene repression can model drug-mediated inhibition and reveal gene dosage sensitivity, but traditional approaches in the fungal pathogen Candida albicans are labor intensive and low throughput. Here we adapt pooled CRISPR interference (CRISPRi) screening in C. albicans to enable large-scale functional genomic analysis. We assess repression sensitivity of 130 essential genes conserved in fungi without close homologues in humans and identify highly dosage-sensitive genes across multiple pathways. Screening across ten environmental conditions reveals environment-dependent effects on gene sensitivity. Extending these experiments to two drug-resistant clinical isolates shows that many fitness defects are conserved across genetic backgrounds. Thus, CRISPRi pooled screening enables rapid, large-scale functional genomics across diverse genetic backgrounds in C. albicans. This study presents a pooled CRISPRi screening approach in Candida albicans to investigate essential genes at scale, revealing environment- and strain-dependent gene sensitivities and conserved vulnerabilities relevant to antifungal drug discovery.

Dysregulated extracellular proteolytic activity is a prominent hallmark of cancer and can thus be exploited for tumor detection and therapeutic development. However, the discovery of tumor-responsive probes has been hindered by the lack of methods to directly screen proteolytic events in specific tissue samples. Here we report PSurf, a platform that enables the identification of tissue-specific protease sensors with tissue specimens. Through differential selection of tumor-specific sequences over healthy tissue, PSurf identifies context-specific tumor-activated probes that precisely distinguish metastatic lesions in lung tissue slices. Using these substrates, we engineered nanobody-targeted biosensors that release urinary reporters upon tumor-specific cleavage in vivo, enabling precise non-invasive tumor detection in a mouse lung metastasis model. PSurf provides a foundation for developing conditionally activated agents through tissue-specific activity mapping and probe discovery. Dysregulated extracellular proteolytic activity is a hallmark of cancer that can be exploited for tumor detection and therapeutic development. The authors developed a platform that screens proteolytic events in tissue samples and differentially selects tumor-specific sequences, enabling the design of biosensors detecting tumor-specific cleavage of peptide substrates in tissue samples and in vivo.