The advent of nano-technology has revolutionized approaches to identifying and combating bacterial infections. This review highlights the latest applications of nanoparticles (NPs) for bacterial detection and treatment, with a focus on their translational potential in clinical settings. We discuss advanced nanotechnology-enabled biosensing platforms that offer ultra-sensitive, rapid and precise diagnostics capabilities crucial for addressing antibiotic-resistant pathogens. In addition to detection, various nanoparticles demonstrate multiple antibacterial mechanisms and function as targeted drug-delivery vehicles. The review also examines current clinical trials involving nanoparticle-based therapeutics, underscoring their promise for overcoming antimicrobial resistance. 

1,5-Pentanediol (1,5-PDO) is a high-value chemical with broad uses in polymer, cosmetic, and pharmaceutical industries. Although diverse biosynthetic pathways have been constructed, current recombinant strains typically rely on plasmid-based overexpression, which necessitates antibiotics and hinders industrial-scale production.  We developed a robust, plasmid-free E. coli platform for de novo 1,5-PDO synthesis by integrating pathway genes (davB, davA, gabT, yahK, car, sfp and yqhD) into the chromosome of a lysine-hyperproducing strain via CRISPR/Cas9. Screening of carboxylic acid reductases identified Nocardia iowensis CAR-Ni as the most effective, yielding a base strain (D13) that produced 0.672 g/L 1,5-PDO. Integrated analysis confirmed the alcohol dehydrogenase (ADH)-mediated reduction of 5-hydroxypentanal (5-HP) as an underappreciated bottleneck. We subsequently screened ten endogenous ADHs and selected YjgB for computational optimization. Docking-guided saturation mutagenesis at position E205 yielded the variant YjgB(E205C), which exhibited a 3.34-fold increase in in vitro activity, reduced 5-HP accumulation, and elevated the titer to 0.935 g/L. Enhancing NADPH supply by integrating pntAB further raised the shake-flask titer to 1.5 g/L. In a 5-L fed-batch bioreactor, the final strain (D91) achieved 12.1 g/L 1,5-PDO (yield of 0.225 mol/mol glucose) without antibiotics or inducers. To our knowledge, this is the highest reported 1,5-PDO titer in E. coli. This study establishes a scalable, sustainable biosynthetic platform through synergistic metabolic engineering and computational enzyme optimization.

Experimental evolution is a powerful method that has been instrumental for revealing core mechanisms of adaptation and coevolution. It has mostly been used in very simple settings of one or two species. Yet, it is now increasingly being employed in more complex community settings that include indirect effects, higher-order interactions, and multidimensional selection typical of natural communities. Here we synthesize the emerging field of experimental evolution in communities and show how community context reshapes selection and evolutionary trajectories, beyond what single-species or pairwise designs predict. We conducted a systematic literature survey targeting multi-species, multi-generation evolution, identifying 100 such studies with the number increasing recently. Despite this progress, most experiments are biased toward microbial systems and competitive interactions, leaving major gaps for predicting evolution in realistic communities. We discuss community ecology concepts in the light of experimental evolution, together with designs that address these concepts. We emphasize three main research areas: indirect and higher-order interactions that make selection multidimensional, eco-evolutionary feedbacks linking trait change to community dynamics, and genetic constraints that shape responses across interaction networks. We then discuss routes to increase ecological realism with field experiments and conclude by outlining key research fronts for experimental evolution in communities.

RNA pseudouridylation is one of the most prevalent post-transcriptional modifications, occurring universally across all organisms. Although pseudouridines have been extensively studied in bacterial tRNAs and rRNAs, their presence and role in bacterial mRNA remain poorly characterized. Here, we used a bisulfite-based deep sequencing approach to provide a comprehensive and quantitative measurement of bacterial pseudouridines using E. coli, to provide proof of concept. We identified 1,954 high-confidence sites in 1,331 transcripts, which is 29 times above previous estimates and representing almost 30% of the transcriptome. Furthermore, pseudouridines were significantly associated with mRNA stability and enriched in transcripts associated with secondary metabolite production and adaptation to diverse environments. Finally, we mapped pseudouridines in oral microbiome samples of human subjects, demonstrating the broad applicability of our approach in complex microbiomes. This way, we observe that, although uridines are required for modification, mRNAs from GC-rich bacteria harbored more pseudouridine sites than AT-rich genomes in our dataset. Altogether, our work highlights the advantages of mapping bacterial pseudouridines and provides a tool to study posttranscription regulation in microbial communities. Pseudouridines are RNA modifications occurring in all organisms. Here, Sharma et al. identify and quantify pseudouridines in E. coli bacteria, where this modification is associated with mRNA stability, and map pseudouridines in human oral microbiome samples.

Hyperspectral imaging (HSI) is an advanced sensing modality that captures spatial and spectral information simultaneously, enabling non-invasive and label-free characterization of material, chemical and biological properties. This Primer overviews HSI, with an emphasis on the imaging workflow and data processing pipeline. We introduce the essential physical principles and sensor architectures, using Earth observation HSI systems as a representative example. Key steps in data acquisition, calibration and correction that determine data structure and quality are discussed. We summarize common hyperspectral data forms and highlight core analytical techniques, including dimensionality reduction, classification and spectral unmixing, together with emerging artificial intelligence-based methods that increasingly influence current HSI research. Six representative application fields are briefly surveyed to illustrate how HSI enables sub-visual feature extraction for quantitative interpretation and decision-making. We also discuss persistent challenges, including hardware trade-offs, acquisition variability and high-dimensional complexity alongside promising advances such as computational imaging, physics-guided modelling, cross-modal fusion and scalable learning frameworks. Best practices for dataset sharing, reproducibility and metadata documentation to support transparent research are included. Scalable, real-time and embedded HSI, enabled by sensor miniaturization and artificial intelligence, will push HSI into a cross-disciplinary platform for transformative scientific and societal impact. This Primer introduces hyperspectral imaging (HSI) through a concise, imaging-centric perspective, linking sensor platforms, data types and representative datasets across application domains. It highlights how platform characteristics shape data properties and downstream analysis, providing a unified reference for understanding and comparing HSI systems and data.

Accurate characterization of multi-state protein conformations is crucial for understanding their functional mechanisms and advancing targeted therapies. Extracting coevolutionary constraints from homologous sequences helps reveal protein structure and function, which can be automatically captured by MSA Transformer leveraging attention mechanisms. Making use of the multi-conformational coevolutionary signals captured by MSA Transformer, we introduce in this study EvoSplit to disentangle coevolutionary signals associated with distinct conformations to guide protein structure predictions. EvoSplit outperforms AF-Cluster on 85 fold-switching proteins and successfully models the conformations of proteins beyond AlphaFold2’s training set. We then identify 54 candidates with potential conformational diversity for cancer-related human proteins. Notably, for five GTPases, EvoSplit consistently predicts two conformations, one of which has not been previously reported. As an important example, the protein–protein interaction analysis provides new insights into novel HRAS function-associated conformations. Furthermore, the validity of these newly identified conformations is examined by evolutionary analysis and extensive molecular dynamics simulations. Understanding multi-state protein conformations is essential for elucidating their functions and developing targeted therapies. Here, the authors introduce EvoSplit, leveraging MSA Transformer to disentangle coevolutionary signals associated with distinct conformations, outperforming AF-Cluster in modeling fold-switching proteins and identifying new conformations of GTPases and HRAS.