Type I toxin-antitoxin (TA) systems are widely distributed in the prokaryotic world, known for their antitoxin RNA interfering with the cognate toxin mRNA translation. Beyond their canonical role in plasmid maintenance, these systems represent evolutionarily optimized regulatory modules with rapid kinetics and modular architectures. Here, we repurposed them as post-transcriptional regulatory RNA devices via reverse engineering. After isolating the core of type I TA pairs and demonstrating the independence between their structure and repression function, we reconstructed artificial TA pairs termed SRTS-OPRTS. A platform for generating orthogonal SRTS-OPRTS pairs with cross-species application (Bacillus subtilis, Escherichia coli, and Corynebacterium glutamicum) was developed by introducing structure and energy constraints. As an individual expressing element or a co-expressing 3′ UTR tag within specific mRNA, SRTS achieved quantitative regulation of the gene with 3′ UTR cognate OPRTS. Such portability enabled convenient construction of dynamic mutually inhibitory switches, where genes tagged by SRTS and OPRTS could regulate each other. Leveraging this approach, a selective lethal system was further constructed to enrich high-fluorescent mutants, resulting in up to 11.32-fold enhancement in mean fluorescence intensity. Overall, these synthetic RNA devices provide portable tools for gene regulation and offer a robust foundation for constructing dynamic genetic circuits.

Antimicrobial resistance (AMR) is one of the most pressing global health challenges, often referred to as a ‘silent pandemic.’ Towards this, ARKbase is developed as an integrated, curated, value-added knowledgebase for AMR, with focus on WHO Bacterial Priority Pathogens. ARKbase has three core modules namely—Database, Insight, and Comparative Analysis Module—each offering curated data, including data analysed with custom-built pipelines. The Database Module has curated reference genomes and pan genomes along with antimicrobial susceptibility testing (AST) profiles. The Insight Module has 14 sub-modules and offers deep annotations encompassing antimicrobial resistance genes, virulence factors, structure annotation, host–pathogen interactions, biosynthetic gene clusters, known antibiotics, protein–protein interactions, drug targets, co-targets, drug-target interactions, machine learning models, and curated transcriptomic datasets for antibiotic exposure. The Comparative Analysis module offers a simple interface for comparing antimicrobial resistance genes, virulence factors, and drug targets among different priority pathogens. The data included in ARKbase are quality-checked and curated as per CLSI standards for AST, EUCAST guidelines for genome sequence, and the FAIR data principles. ARKbase is the first comprehensive knowledgebase focusing on WHO priority pathogens and AWaRe classification, enabling a combined evidence approach towards a holistic understanding of AMR. Available at https://datascience.imtech.res.in/anshu/arkbase.

Nanopore sequencing enables direct, single-molecule interrogation of biopolymers and shows promise for analyzing not only DNA and RNA but also chemically modified bases, proteins, and other polymers. Expanded DNA alphabets, such as those found in xenonucleic acids (XNAs), open new possibilities for diagnostics, therapeutics, data storage, and engineered biology. However, robust sequencing strategies for these modified molecules remain lacking. While nanopore-based tools exist for some noncanonical bases, they often require extensive experimental calibration by measuring each base across many sequence contexts, which limits scalability and increases cost. In this work, we investigate computational methods for predicting the ionic current signals produced during nanopore sequencing of DNA containing noncanonical XNA bases, aiming to reduce the need for experimental calibration. We compare a sequence-based predictive model with two structure-aware approaches: one using graph-based molecular representations and another adapting a generative language model to molecular SMILES. Our findings show that while sequence context captures much of the signal variability, incorporating structural and chemical information improves predictive accuracy in specific cases. These results highlight the value of structural data representations and model design in scaling XNA sequencing, and suggest this framework could extend to modeling ionic currents from other complex biomolecules, such as proteins.

Microplastics have emerged as major environmental hazards that require efficient, cost-effective, and sustainable remediation technologies. This study introduces an integrative platform for the remediation and upcycling of microplastics by algae, while synergizing with plastic upcycling, wastewater treatment, and algal production. The strategy employs a mechanism that enhances hydrophobic interactions between the cell surface and microplastics, enabling rapid aggregation and removal. The platform achieves a superior microplastic removal efficiency of 91.4% within 1 hour, with a capacity of 0.1-gram microplastic per gram of biomass. Furthermore, the study demonstrates an upcycling strategy that converts microplastics-enriched cyanobacteria into plastic composites with unique performance. This work also integrates microplastic removal with cyanobacterial bioproduction and wastewater treatment, offering an approach that synergizes remediation with these value-added processes. Ultimately, this platform provides a viable and sustainable pathway to address microplastic pollution by creating value through plastic upcycling, wastewater nutrient removal, and CO2-based bioproduction. Microplastics (MPs) represent an environmental hazard which must be resolved by efficient, cheap, and sustainable remediation technology. Here the authors use an engineered algae to capture MPs and treat wastewater, the captured algae-plastic mix is upcycled into a tougher bioplastic composite.

Microplastic biofilms, known as the “plastisphere”, harbor diverse microbial communities and serve as reservoirs for antibiotic resistance genes (ARGs). This review discussed the mechanisms driving bacterial community alteration on microplastics and delineated the pathways through which ARGs transfer within microplastic biofilms. We expected to provide a comprehensive understanding of the ecological and human health impacts associated with microplastic biofilms and ARGs, thereby informing strategies to mitigate plastic pollution and its risks.

Cell-to-cell variability often limits the efficiency of microbial bioproduction, yet how individual cells fluctuate over time and how these fluctuations shape population-level output remain unclear. To address this issue, we tracked a heterologous betaxanthin pathway in Escherichia coli using microfluidics-assisted time-lapse microscopy, allowing simultaneous measurement of fluctuations in betaxanthin, its biosynthetic enzyme DOD and growth across generations. Here we show that over 50% of high betaxanthin producers become medium or low producers after two divisions. Betaxanthin variation primarily originates from DOD noise, with a smaller contribution from growth rate fluctuations. We further develop a stochastic model to explore various control circuits and find that pathway enzyme or metabolite-based growth selection strategies are most effective in enhancing production. We experimentally validate the model by coupling enzyme expression to nutrient availability, which enriches high producers and boosts titer by 4.4-fold. Our results highlight key sources of metabolic heterogeneity and provide a framework for designing robust microbial processes. Cell-to-cell variability limits efficient microbial production. Here, the authors track single cells to reveal enzyme noise as the main source of bioproduction variation, and by coupling growth to pathway performance, they selectively enrich high producers and substantially boost overall titres.

Protein structure prediction has a long history of benchmarking efforts such as critical assessment of structure prediction, continuous automated model evaluation and critical assessment of prediction of interactions. With the rise of artificial intelligence-based methods for prediction of macromolecular complexes, benchmarking with large datasets and robust, unsupervised scores to compare predictions against a reference has become essential. Also, the increasing size and complexity of experimentally determined reference structures by crystallography or cryogenic electron microscopy poses challenges for structure comparison methods. Here we review the current state of the art in scoring methodologies, identify existing limitations and present more suitable approaches for scoring of tertiary and quaternary structures, protein–protein interfaces and protein–ligand complexes. Our methods are designed to scale efficiently, enabling the assessment of large, complex systems. All developments are available in the structure benchmarking framework of OpenStructure. OpenStructure is open source software and available for free at https://openstructure.org/ . This study advances methods for benchmarking macromolecular complex predictions by introducing a scalable open-source framework used in recent community assessments to compare structures, interfaces and ligand interactions against experimental data.

CRISPR-cas provide sequence-specific mechanisms for targeting foreign DNA or RNA and have been widely used in genome editing and DNA detection. Type V CRISPR–Cas systems are characterized by a single RNA-guided RuvC domain-containing effector, Cas12. Here, through comprehensive mining of large-scale genomic and metagenomic data from microbial sources, we identified a new Class 2 CRISPR–Cas effector superfamily, designated Casδ, comprising three members with protein sizes ranging from 867 to 936 amino acids. Biochemical analyses revealed that Casδ-1 functions as a single RNA-guided endonuclease with specific recognition of 5′-RYR-3′ protospacer-adjacent motifs, where R represents A or G, and Y represents T or C. Casδ-1 exhibits robust double-stranded DNA cleavage activity and target-dependent trans-cleavage activity. Casδ-1 mediates efficient genome editing across species, achieving up to 60% indel rates in human cells while generating homozygous knockout lines in two agriculturally important monocot species (Oryza sativa and Zea mays) through stable transformation. Structural and evolutionary analyses reveal Casδ as an evolutionary transitional nuclease bridging Cas12n and canonical type V systems, featuring a C-terminal loop that is essential for activity. Collectively, Casδ is an evolutionarily distinct, compact (<1000 aa), tracrRNA-free CRISPR system enabling versatile cross-kingdom genome editing.

Transfer RNA (tRNA) modifications tune translation rates and codon optimality, thereby optimizing co-translational protein folding. However, the mechanisms by which tRNA modifications modulate codon optimality and trigger phenotypes remain unclear. Here, we show that ribosomes stall at specific modification-dependent codon pairs in wobble uridine modification (U34) mutants. This triggers ribosome collisions and a coordinated hierarchical response of cellular quality control pathways. High-resolution ribosome profiling reveals an unexpected functional diversity of U34 modifications during decoding. For instance, 5-carbamoylmethyluridine (ncm5U) exhibits distinct effects at the A and P sites. Importantly, ribosomes only slow down at a fraction of codons decoded by hypomodified tRNA, and the decoding speed of most codons remains unaffected. However, the translation speed of a codon largely depends on the identity of A- and P-site codons. Stalling at modification-dependent codon pairs induces ribosome collisions, triggering ribosome-associated quality control (RQC) and preventing protein aggregation by degrading aberrant nascent peptides and messenger RNAs. Inactivation of RQC stimulates the expression of molecular chaperones that remove protein aggregates. Our results demonstrate that loss of tRNA modifications primarily disrupts translation rates of suboptimal codon pairs, showing the coordinated regulation and adaptability of cellular surveillance systems. These systems ensure efficient and accurate protein synthesis and maintain protein homeostasis.

Biosensors based on transcription factors (TFs) have shown extensive applications in synthetic biology. Due to the complex multi-domain structure of effector-TF-DNA, computational design of TFs remains a challenge. Here, we present the successful structure-guided computational design of the access tunnel, ligand binding, allosteric transition process for an allulose-responsive PsiR. It enables a 20-fold increase in sensitivity, reducing the EC50 of PsiR-allulose biosensors (PABs) from 16 mM to 0.8 mM, and delivers a PAB box possessing the detection range from 10 μM to 100 mM. We further validate its broader applicability in enhancing sensitivity of LacI-IPTG biosensor. Based on the developed PABs, we present the inducer-free allulose-mediated auto-inducible protein expression system, and demonstrate an allulose-triggered CRISPR interference circuit for dynamic metabolic regulation. It facilitates a 68% increase in allulose titer and achieves a high yield of 0.43 g/g glucose. This work provides the versatile TF toolbox for developing allulose-triggered regulation circuits in biotechnology application. Transcription-factor biosensors enable programmable metabolism. Here, the authors integrate structure and computation for mechanism-guided iterative redesign of the allulose biosensor PsiR, increasing sensitivity and dynamic range, enabling allulose-triggered expression system and CRISPRi circuit.

Microbes precisely control their composition and geometry across diverse growth conditions, yet the mechanisms coordinating these processes remain unclear. Here, we integrate quantitative proteomics, microscopy, and biochemical measurements to reveal a biophysical principle linking these properties in Escherichia coli: cytoplasmic and membrane protein densities maintain a tightly conserved ratio across growth conditions, while the periplasmic density varies. Building on this observation, we develop a mathematical model demonstrating that maintaining this density ratio constrains the surface-to-volume ratio as a nonlinear function of proteome composition, specifically the ribosomal proteome fraction and partitioning between cellular compartments. The model holds under guanosine tetraphosphate perturbations that alter ribosome levels, further demonstrating that cellular geometry is not strictly determined by growth rate. These findings provide a biophysical framework for geometry control, underscoring density maintenance as a key physiological constraint that shapes cellular phenotypes. Chure et al. analyse experimental data to show that E. coli bacteria maintain stable protein density ratios between cytoplasm and membranes. In addition, they develop a biophysical model that predicts surface-to-volume ratio from ribosomal content and protein partitioning across cell compartments.

Vaccines are the most effective tool in preventing and managing infectious diseases. One of the critical challenges in vaccine development is the selection of suitable target antigens from the thousands of proteins produced by pathogens. Artificial intelligence is anticipated to play a significant role in addressing this challenge. In this study, we develop a framework termed PLGDL for protective antigen prediction that employs Protein Language and Geometric Deep Learning models. This framework leverages both primary sequence features and three-dimensional structural features of protein antigens, thereby reducing the biases associated with manually curated features. Our integrated model exhibits robustness across both constructed and public datasets and is applicable to viruses, bacteria, and eukaryotic pathogens. Notably, when applied to the ongoing Mpox outbreak, our model not only quickly identifies multiple known antigens but also discovers a protective antigen: G10R. Here, our study provides a high-performance screening tool for protective vaccine antigen prediction by synergistically utilizing the capabilities of protein language and geometric deep learning models, providing substantive insights and methodological advancements for rapid vaccine development. Vaccines are the most effective tool in managing infectious disease and characterizing features of protective epitopes could help in prediction methods. Here the authors use protein language and geometric deep learning frameworks to investigate primary sequence features and structural features to identify and predict potential antigens, showing prediction of a protective mpox epitope using this method.

Heterogeneity within clonal cell populations remains a critical bottleneck within bioprocess engineering, notably by undermining bioproduction yields. Efforts to mitigate its impact have, however, been hampered by technological difficulties quantifying metabolism at the single-cell level. Here, we propose a framework based on single-cell biosensor analysis that enables robust characterization of cell’s metabolic states, leveraging it to detect and isolate isogeneic heterogeneity in response to environmental perturbations and within microbial cell factories. We identify acute and gradual glucose depletion to induce differentiation of metabolically distinct subpopulations and reveal these subpopulations to exhibit differential production capabilities, with lower intracellular pH subpopulations exhibiting enhanced product accumulation within violacein-producing strains but reduced yields within lycopene-producing strains. Lastly, we highlight galactose cultivation as a method to modulate subpopulation dynamics towards higher-producing lycopene phenotypes. Altogether, our research provides insights into subpopulation differentiation and establishes promising avenues for the engineering of more robust and higher-producing strains. Heterogeneity within clonal cell populations affects bioprocess engineering. Here, the authors report a biosensor-based toolkit to investigate phenotypic heterogeneity in engineered yeast, reveal pH-based subpopulations and metabolite production states, and modulate/shift subpopulation dynamics to increase lycopene production.