Interspecies interactions can influence the physiology of competing species, shaping their long-term evolutionary trajectories. Although interspecific competition’s role in community dynamics is well-documented, its impact on evolutionary outcomes and mechanisms is less explored. Here, we investigate how interspecies competition affects antibiotic resistance evolution in the gut pathogen Salmonella enterica within synthetic microbial communities. Specifically, we examine how the presence of an interspecific competitor, E. coli, modulates resistance evolution at low streptomycin concentrations. Our findings reveal that interspecies competition results in the selection of S. enterica mutants with higher resistance levels by increasing the likelihood of accumulating resistance mutations that follow a trajectory of negative fitness epistasis. We show that this effect is driven by the enhanced expression of the cryptic aminoglycoside transferase gene (aadA). Our study thus links antibiotic resistance evolution to competition-induced physiological changes, emphasizing the interplay between interspecies interaction and adaptation to environmental conditions.

Base editing is a precision genome-editing methodology that enables the programmable installation of point mutations with high efficiency. Two major classes of base editors have been developed: cytosine base editors introducing C·G-to-T·A edits, and adenine base editors introducing A·T-to-G·C edits. Since their introduction, base editor use has expanded substantially, and base editors have been applied in many biological and biomedical applications. In this Primer, we provide an overview of the use of base editors in mammalian cells for high-throughput base-editing screens, disease modelling and therapeutic applications. We cover important considerations in experimental design of base-editing experiments, data analysis and interpretation, and best practices for reporting results. We also discuss potential challenges related to reproducibility and limitations, and outline options for optimization and troubleshooting. Our goal is to aid both new and experienced researchers in effectively implementing base editing in their own work. Base editing enables precise, programmable single-base changes using cytosine or adenine editors. This Primer outlines their use in mammalian cells for screening, disease modelling and therapy, covering experimental design, analysis, limitations and best-practice guidance to support effective implementation.

The 3D architecture and dynamics of the genome are crucial for regulation of genome stability, transcription and cellular function. CRISPR-based live imaging technologies have enabled real-time visualization of specific genomic loci and transcripts in living cells. These tools harness customized guide RNAs and nuclease-deactivated Cas effectors to achieve precise genomic targeting, and recent methodological advances provide the 3D spatiotemporal resolution required to decipher real-time chromatin communication. These methods are elucidating the biophysical properties of chromatin, linking dynamic enhancer–promoter interactions directly to transcription, and revealing the role of 3D genome dynamics in basic cellular processes and disease. Here, we summarize the development of CRISPR-based live-cell imaging techniques, highlight the complementary 3D microscopy and analysis methods compatible with these methods, and offer perspectives on their applications to uncover fundamental principles that govern genome dynamics and function. In this Review, Zhu et al. outline the various CRISPR-based tools recently built for dynamic DNA and RNA imaging, which have provided insights into various molecular mechanisms. The authors also discuss the important parameters to consider when using these tools, and how to approach the quantitative image analysis.

Similar to proteins, many RNAs fold into three-dimensional (3D) structures to perform biological functions. Here we present the trRosettaRNA server, a web-based platform for automated RNA 3D structure prediction using deep learning. The primary input is the nucleotide sequence of a target RNA, with the option to upload custom multiple sequence alignments and secondary structures. The server uses an end-to-end neural network for automated 3D structure prediction, followed by an energy optimization step to resolve structural violations. As an automated server, trRosettaRNA is distinguished by its state-of-the-art modeling accuracy, flexible input options and comprehensive visualization of prediction results. trRosettaRNA has been successfully applied in various contexts, including predicting structures for Rfam families lacking known 3D structures, where representative cases of high-confidence structure predictions were found to align well with subsequent experimental observations. Utilizing up to 5 central processing unit (CPU) cores in parallel on our computer cluster, the server takes a median time of about 1 h to predict structures for RNA sequences with about 200 nucleotides. The standalone package for trRosettaRNA offers distinct advantages such as enhanced data privacy for sensitive sequences, the ability to bypass server queues and integration into high-throughput automated pipelines. Importantly, the open-source nature of the package empowers researchers to directly modify the codebase for specialized research needs or to develop derivative tools by fine-tuning the underlying neural network. The web server and standalone package of trRosettaRNA are available at https://yanglab.qd.sdu.edu.cn/trRosettaRNA/ and https://github.com/YangLab-SDU/trRosettaRNA2 , respectively. The trRosettaRNA server is a web-based platform for automated and accurate RNA 3D structure prediction using deep learning. This protocol also describes how to use the standalone package locally, which is beneficial for large-scale applications.

Functional metagenomics has emerged as an effective tool for discovering novel enzymes directly from environmental samples, overcoming the limitations of traditional culture-based methods. In this study, we used a functional metagenomic approach on stool samples from Axis kuhlii, an endemic deer species from Indonesia, to identify active cellulases. We created an efficient workflow for expression of metagenomic sequences directly in Komagatella phaffii by combining metagenomic sequencing to investigate enzyme diversity, multiplex PCR to build a genes library, and rolling circle amplification (RCA) to streamline the cloning process, eliminating the need for intermediate Escherichia coli transformation and propagation steps. Furthermore, a semi-high-throughput screening method was used to evaluate multiple samples at once, allowing for the rapid identification of active enzymes. Using this approach, we discovered five endoglucanases and three β-glucosidases with confirmed enzyme activity. This study shows that functional metagenomics can bridge the gap between computational predictions and experimental validation, providing a reliable platform for enzyme discovery and characterization from complex environmental microbiomes. 

Reconstructing the precise biosynthesis of structurally complex natural esters, such as monoterpene esters, in engineered microbes remains a major challenge, owing to the limited repertoire of highly selective alcohol acyltransferases and the lack of compatible pathway modularity. Here, we establish a dual-substrate microbial platform to profile the activities of alcohol acyltransferase (AAT) to synthesize three distinct classes of monoterpene esters: monoterpenyl esters, monoterpenoate esters, and monoterpenyl monoterpenoate esters, enabling access to both natural and non-natural monoterpene ester biosynthetic pathways. Through structure-guided critical residue engineering and dual-substrate molar ratio tuning, we achieve selective biosynthesis of >C2 acyl-CoA-derived monoterpene esters, despite competing intracellular acetyl-CoA. Coculture engineering further redistributed metabolic fluxes between acyl-CoA and alcohol precursors, yielding 11.50 g/L linalyl acetate and 3.16 g/L geranyl butyrate in 1-L bioreactor. This study expands the biosynthetic space of monoterpene esters and provides a versatile strategy to control AAT selectivity, offering a plug-and-play, scalable framework for ester biomanufacturing. Microbial biosynthesis of monoterpene esters remains a major challenge due to the limited repertoire of highly selective alcohol acyltransferases (AATs). Here the authors discovered and engineered AATs for various monoterpene esters using a dual-substrate microbial platform.

Assembly line biosynthesis creates numerous structurally diverse natural products using a common modular synthetic strategy. The collinearity between the architectures of modular polyketide synthases (PKS) and the structures of their polyketide products would seem to render these biosynthetic machineries excellent platforms for designer biosynthesis, yet reliable strategies to reprogram these assembly lines without diminishing their activities have not been identified. Here, as a best practice for PKS engineering, we demonstrate the reprogramming of the mediomycin PKS without significant loss of productivity. Using in vitro CRISPR/Cas9 gene editing followed by heterologous expression, we reconstruct an inaccessible drug lead of the fibrinogen receptor, tetrafibricin, at 82 ± 3 mg/L yield, retaining 26% productivity after five-step module editing using an evolution-supported cut site, downstream of the acyltransferase domain. A macrocyclic aminopolyol is also accessed through thioesterase swapping. These results pave the way toward the rational reprogramming of PKSs to access desired complex organic molecules. The collinearity between the architectures of modular polyketide synthases (PKS) and the structures of their polyketide products would suggest these biosynthetic machineries are excellent platforms for designer biosynthesis, yet reliable strategies to reprogram these assembly lines without diminishing their activities have not been identified. Here, the authors demonstrate the reprogramming of the mediomycin PKS without significant loss of productivity, and reconstruct an inaccessible drug lead of the fibrinogen receptor, tetrafibricin, at 82 mg/L yield.

Engineering proteins with desired functions remains challenging and usually requires multiple rounds of screening and selection. Here, we present Sequence Display, a platform that generates large-scale protein sequence–activity datasets in a single round. Sequence Display enables multiplexed assessment of individual variant activity within a single experiment, offering a robust approach to mapping detailed sequence–function relationships. We demonstrate the platform’s broad applicability by generating datasets for cytosine deaminase, uracil glycosylase inhibitor, aminoacyl-tRNA synthetase and a compact Cas9 nuclease. Integrating these datasets obtained from Sequence Display with pretrained protein language models, fine-grained, variant-specific activity landscapes can be constructed. We discovered several Cas9 variants with expanded protospacer-adjacent motif recognition and evolved aminoacyl-tRNA synthetase variants capable of recognizing different noncanonical amino acids. Together, this study establishes Sequence Display as a powerful tool for mapping protein activity landscapes and accelerating the discovery of optimized proteins for biological and medical applications. Sequence Display maps protein variant activities to a sequencing-based readout.