Water environments serve as critical sources and conduits for antimicrobial resistance (AMR). The complex water cycle facilitates the transmission of antibiotics, resistant bacteria and resistance genes that have been released through anthropogenic activities, thereby reshaping AMR dynamics across different environmental compartments. Among these resistance determinants, inactivating antibiotic resistance genes (inactivating ARGs) encode enzymes that degrade or modify antibiotics to reduce their bioactivity. They play a complex and dual role bridging environmental reservoirs and clinical risk. While they can threaten the therapeutic effectiveness of antibiotics when transferred horizontally to pathogens, they also act as cooperative traits that lower local concentrations of bioactive antibiotics and relax selection for resistance. In turn, reduced antibiotic exposure can enhance ecosystem functions and stability in both host-associated and environmental microbiomes through ecosystem resilience and community protection. Inactivating ARGs represent a crucial intersection between AMR challenges and microbial community stability. Here we discuss the risks and ecological benefits of inactivating ARGs, and present intervention strategies aiming to both protect ecological resilience and limit resistance development in pathogens. Water environments are pivotal in the spread of antimicrobial resistance, acting as conduits for antibiotics and resistance genes. Here, the authors explore the dual role of inactivating antibiotic resistance genes, highlighting their ecological benefits and risks, and propose strategies to enhance ecosystem resilience while curbing resistance in pathogens.

Proteins interact with several RNA types to facilitate a broad spectrum of cellular functions. However, the underlying interaction data are sparse, and existing methods for predicting RNA-binding residues (RBRs) in protein sequences are almost exclusively RNA type-agnostic, limiting their utility. To this end, we introduced RNAdetector, a sequence-based method that accurately predicts messenger RNA-, ribosomal RNA-, small nuclear RNA (snRNA)-, and transfer RNA-binding residues and type-agnostic RBRs. RNAdetector employs an innovative deep transformer network architecture and transfer learning, which together produce a large boost to predictive performance and minimize cross-predictions, defined as incorrectly predicting wrong types of RBRs. Moreover, our design has a low computational footprint and produces accurate predictions in about 9 s per protein, facilitating analysis of large collections of proteins. A comparative evaluation on a low-similarity test dataset showed that RNAdetector provided substantially more accurate RNA-type-specific predictions, a much shorter runtime, additionally covered snRNA, and significantly reduced cross-predictions compared to the only other RNA-type-specific predictor that is cross-prediction prone. Moreover, we empirically showed that RNAdetector’s predictions of type-agnostic RBRs are modestly more accurate than those generated by several representative predictors of RNA-type-agnostic RBRs and RNA-binding proteins. http://biomine.cs.vcu.edu/servers/RNAdetector/.

Since their discovery, CRISPR-Cas systems have been widely applied in areas ranging from genome editing to biosensing, owing to their specific, RNA-guided target recognition. Their performance in complex biological environments has been extensively studied, particularly to optimize guide RNA (gRNA) design and minimize off-target cleavage. Here, we focus on the kinetic inhibition of the interaction between Cas12a—a Class 2, Type V effector—and its target, caused by interference from non-cognate background nucleic acids. This effect is particularly relevant for sensing applications in complex mixtures or cellular contexts, where genome- and transcriptome-derived sequences may impede CRISPR-Cas activity. Using in vitro assays under defined conditions, we systematically examine the influence of background single-stranded RNA and double-stranded DNA (dsDNA) on reaction kinetics. We find that both the purine-to-pyrimidine ratio and the GC content of the gRNA seed region significantly affect kinetic inhibition by background polynucleotides. gRNAs with low GC content and a high purine fraction in the seed region were least affected by background sequences. A gRNA with high uracil content in the seed region exhibited particularly strong inhibition in the presence of a dsDNA background. Experiments with dCas12a-based gene activation in living cells indicate that our in vitro findings may also be relevant for in vivo applications.

Directed evolution methods face trade-offs between the control of discrete approaches and the throughput of modern continuous systems. Here, we engineered a method called lytic selection and evolution (LySE) for near-continuous evolution of bacterial gene clusters while maintaining discrete checkpoints. We developed a hypermutagenic T7 DNA polymerase variant fused to a dual adenine-cytosine deaminase to install all possible transition mutations at similar frequencies. By relieving pressure from maintaining genome fidelity, we obtained mutation rates of 3.82 × 10−5 substitutions per base. For biocontainment, the T7 DNA polymerase was encoded on an accessory plasmid, while the target gene cluster was encoded on a T7 DNA polymerase-lacking T7 phagemid. Alternating cycles of lysis and transduction enable selective replication and mutagenesis of target genes, while off-target genomic mutations are discarded. LySE evolved a 25-fold increase in tetA-encoded tigecycline resistance in 5 cycles, and a 50.9% increase in endpoint biomass of a bacterial strain that uses the polyethylene terephthalate monomer, ethylene glycol, as its sole carbon source. Our method balances speed and control for directed bacterial evolution. The lytic selection and evolution (LySE) system uses a plasmid-encoded, hypermutagenic T7 DNA polymerase and T7 phagemid-encoded target genes to enable controllable, near-continuous directed evolution of large gene clusters.

Phenotypic heterogeneity, a feature of both bacteria and eukaryotic cells, arises from inherent cell-to-cell variability. In eukaryotes, single-cell RNA sequencing has led to an explosion in understanding how heterogeneity impacts different cell types and states in organs and tissues. While single-cell RNA sequencing analyses in bacteria have lagged behind eukaryotic studies, recent technological advances now enable similar, high-resolution studies to be performed at scale in bacteria, yielding fundamental insights into how heterogeneity influences bacterial physiology, metabolism, antibiotic resistance, pathogenesis and interactions within complex microbial communities. Here we review recent advances in bacterial single-cell RNA sequencing, including the methods developed so far and what has been learned from their application. We also discuss technological and computational challenges going forwards, the need for standardization and how that could be achieved, and how this emerging field is now poised to revolutionize our understanding of bacterial physiology, infection biology and interactions within bacterial communities, such as the microbiota. This Review summarizes recent advances in bacterial single-cell transcriptomics approaches, while also discussing technological and computational challenges remaining to be met and the need for standardization going forwards.

Targeting DNA payloads into human induced pluripotent stem cells (hiPSCs) typically requires multiple inefficient steps, slowing the testing of gene circuits and cell-fate programmes. Here we show that STRAIGHT-IN Dual enables simultaneous, allele-specific, single-copy integration of two DNA constructs efficiently within 1 week. STRAIGHT-IN Dual leverages the STRAIGHT-IN platform for near-scarless payload integration, facilitating the recycling of components for further modifications. Using STRAIGHT-IN Dual, we investigate how promoter choice and gene syntax influence transgene silencing and how these design features affect reporter expression and forward programming of hiPSCs into neurons, motor neurons and endothelial cells. We also incorporate a grazoprevir-inducible synthetic gene switch that complements tetracycline-inducible control, providing tunable and temporally controlled expression of different transcription factors within the same cell. STRAIGHT-IN Dual generates homogeneous engineered hiPSC populations, accelerating synthetic biology design–build–test cycles in stem cells and enabling controlled comparisons of circuit performances. STRAIGHT-IN Dual enables allele-specific integration of two DNA constructs into hiPSCs within 1 week. The system was used to programme hiPSCs into distinct cell types and for tunable expression of different transcription factors within the same cell.