Maize (Zea Mays) is one of the world’s most important staple crops, providing food for humans and feed for livestock. However, its production is threatened by a range of stresses, including crop diseases, which significantly reduce yields, particularly in smallholder farming systems. Traditional disease detection methods, such as visual inspection, are often labor-intensive, subjective, and prone to error, leading to delayed interventions and widespread crop losses. This study uses unmanned aerial vehicle (UAV) remote sensing and machine learning (ML) to investigate the feasibility of detecting maize leaf diseases in a smallholder farm located in the Mopani District of Limpopo Province, South Africa. UAV-derived vegetation indices including NDVI, GNDVI, and NDRE were combined with UAV multispectral bands and the three ML algorithms, namely – support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost), to first distinguish healthy from diseased plants and then to classify specific maize diseases. The SVM algorithm achieved the highest accuracy in both, distinguishing healthy and diseased crops from other land cover classes (91.73%) and in distinguishing specific diseases (89.41%). Among the diseases identified, Southern Corn Leaf Blight was classified with the highest user’s accuracy, while phosphorus deficiency had the lowest user’s classification accuracy. The results demonstrate the potential of integrating UAV-based multispectral imaging and ML for precision agriculture by providing timely, spatially detailed disease information that enables targeted management practices, reducing crop losses and enhancing food security for smallholder farmers.

Methylomonas sp. DH-1 is a Gram-negative methanotrophic bacterium that utilizes methane as a carbon source and presents a potential chassis for sustainable production of value-added metabolites from this greenhouse gas. Realizing this potential requires the development of reliable and tailored genetic engineering methodologies. Recent progress has expanded the molecular toolbox for this strain, including optimized transformation methods to overcome host restriction barriers and plasmid-free genome engineering using cell-penetrating Cre recombinase. In addition, a tunable promoter library for tunable transcriptional control and a synthetic small regulatory RNA platform for post-transcriptional modulation of gene expression have been established. Promising complementary engineering strategies, including adaptive laboratory evolution, synthetic consortia, and systems biology approaches can be used to improve strain robustness and metabolic performance. The review highlights these advances, along with metabolic engineering efforts that have enabled the synthesis of diverse value-added chemicals in Methylomonas sp. DH-1. Collectively, these developments provide the foundation for metabolic engineering and synthetic biology in Methylomonas sp. DH-1 and may inform the design of genetic tools in other methanotrophs.

Antibiotic resistance is rising globally, demanding faster, more reliable routes to design antimicrobial candidates. Although artificial-intelligence-based methods have accelerated antimicrobial discovery, most are designed to screen fixed libraries or generate candidates broadly, rather than optimize existing peptide scaffolds under practical design constraints. Here, to address this challenge, we present APEX generative optimization (ApexGO). ApexGO uses a transformer variational autoencoder that embeds peptide sequences in a continuous latent space, whereas Bayesian optimization efficiently proposes sequence edits to boost antimicrobial potency. Unlike traditional approaches, ApexGO generates peptide sequences through modifications of template peptides, opening avenues for peptide design and antibiotic discovery. Using ten peptides as templates, ApexGO generated optimized derivatives with enhanced antimicrobial properties. We chemically synthesized 100 of these compounds and conducted comprehensive in vitro characterizations, including assessments of antimicrobial activity, mechanism of action, secondary structure and cytotoxicity. In particular, ApexGO achieved an 85% ground-truth experimental hit rate and a 72% success rate in enhancing antimicrobial activity against Gram-negative pathogens, outperforming previously reported methods for antibiotic discovery and optimization. In two preclinical mouse models of Acinetobacter baumannii infection, artificial-intelligence-optimized molecules exhibited potent anti-infective activity superior to their template controls and comparable with or exceeding that of last-resort antibiotic. These findings highlight the potential of ApexGO as a generative artificial intelligence approach for peptide design and antibiotic optimization, offering a powerful tool to accelerate antibiotic discovery. Torres et al. present ApexGO, a generative approach capable of redesigning peptide antibiotics to better kill drug-resistant bacteria. They validated candidates in laboratory tests and mouse infections and matching or outperforming standard antibiotics.

Prokaryotic organisms have evolved unique strategies to acquire immunity against the constant threat of bacteriophage (phage) and mobile genetic elements. Hna is a broadly distributed anti-phage immune system that confers resistance against diverse phage by eliciting an abortive infection response. Using a combination of biochemistry, cryo-electron microscopy, and single-molecule fluorescence imaging, we reveal that Hna functions as a 3’—5’ single-stranded DNA exonuclease that forms an auto-inhibited dimer under physiological ATP concentrations. Biochemical and mutational analyses demonstrate that Hna catalytic outputs are governed by kinetic partitioning between ATPase and nuclease active sites. Disruption of this balance enhances DNA cleavage and causes cellular toxicity. Furthermore, we show that a phage-encoded single-stranded DNA-binding protein (5 A SSB) destabilizes the autoinhibited Hna dimer and shifts catalytic partitioning toward dysregulated nuclease activation. Conversely, phage escape mutants encode SSB variants that evade Hna surveillance by adopting higher order stoichiometries with enhanced DNA binding affinity. Our work establishes the molecular basis of Hna-mediated antiphage activity and provides insights into how phage-encoded proteins can directly stimulate a bacterial immune response. Bacteria use diverse immune systems to defend against viral infection. Here, Hooper et al. show that the Hna system is an ATP-regulated DNA nuclease activated by phage proteins. They further reveal how viruses can both trigger and evade this bacterial immune response.

The biological oxidation of ammonia, the first step of nitrification, is central to biological water purification processes for nitrogen removal. For drinking water treatment, particularly sourced from groundwater, low concentrations of available copper often limit the efficiency of nitrification. Copper dosing both enhances nitrification and affects the composition of the nitrifying microbial community. The mechanisms underlying the effect of copper on nitrifying community composition, ammonia oxidation, and subsequent nitrogen removal processes remain unknown. The objective of this study was to confirm the effects of copper availability on the relative abundance of complete (comammox) and canonical ammonia-oxidizing bacteria (AOB) in nitrifying communities within the drinking water treatment plant and to determine differences in their copper transport mechanisms. Comparative metagenomic analysis revealed that, unlike most AOB, many comammox Nitrospira encode PcoB/CopB-type high-affinity copper uptake systems, indicating that they are more competitive in low-copper environments. This niche adaptation was confirmed in laboratory-scale bioreactors, which showed that comammox Nitrospira became dominant under copper-limited conditions, while AOB dominated at high copper concentrations. Furthermore, specific detection of comammox amoA mRNA by catalyzed reporter deposition-fluorescent in situ hybridization (CARD-FISH) confirmed that the transcriptional activity of comammox Nitrospira was higher compared to AOB under copper limitation. Thus, these results suggest that copper availability may play an important role in shaping the dominant ammonia-oxidizing bacterial guild, with potential implications for engineered water treatment processes.