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.

Plant cell walls constitute a central battleground in the molecular arms race, shaping plant–pathogen interactions. Pathogens deploy hydrolytic enzymes to breach plant cell walls, whereas plants perceive cell wall damage through sophisticated resistance mechanisms. Co-evolutionary dynamics between both organisms continue, as pathogens evolve strategies to evade host recognition while plants counteract the activity of hydrolytic enzymes. Consequently, in this interorganismal interaction, plant cell walls emerge as critical for the outcome of the disease. In this review, we expand the classical arms race model and incorporate the contribution of plant cell walls in plant immunity and filamentous pathogen virulence.

Complex intra- and inter-kingdom interactions shape microbiota stability and influence the success of microbial invasion into the plant holobiont. Microbial chitinases, enzymes central to fungal physiology and nutrient acquisition, can also function as secreted effector-like proteins that modulate community dynamics by restricting access to occupied ecological niches, such as plant roots, or by contributing to the destabilization of resident consortia. Beyond these antimicrobial functions, microbial chitinases can facilitate plant colonization by interfering with chitin-triggered immunity, suggesting that chitinase diversification has accompanied microbial adaptation to distinct host-associated niches. In this review, we examine microbial chitinase functions in ecological contexts and discuss how structural variation can tune chitinolytic activity, substrate engagement, and biological function. We argue that the ecological roles of microbial chitinases cannot be inferred from catalytic activity alone, but emerge from the interplay between enzyme architecture, substrate accessibility, synergistic enzyme activities, regulated spatiotemporal expression, and the microbial or host context in which chitin is encountered.

Gene expression quantification through genomics methods is crucial for understanding diverse biological contexts. Among these methods, ribosome profiling (Ribo-seq) stands out as a valuable tool for uncovering post-transcriptional gene expression regulation by providing a comprehensive view of the translatome. While current protocols are time-intensive with limited variations, we introduced the use of the Benzonase enzyme to generate ribosome footprints from a polysome-enriched fraction, streamlining the workflow and reducing time, cost and potential sources of bias. Comparing translatome from Benzonase- and RNAse I-derived footprints reveals minimal differences, with both separating from RNA-seq-based transcriptome quantification. Benzonase acts more gently on ribosomes, yielding larger footprints and a weaker triplet periodicity compared to RNAse I, while enabling its application to low-input samples. We further demonstrate Ribo-seq application in primary neuronal cultures, using Benzonase to digest the post-mitochondrial supernatant, thereby bypassing the labor-intensive ribosome/polysome purification step. The introduction of such protocol variations for Ribo-seq, especially for challenging low-input samples, offers a significant advancement of this resource for the community.

Agricultural soil microbiomes experience frequent disturbance from intensive management and may therefore be better equipped to withstand climate warming than microbiomes in undisturbed natural soils. Here we test this by combining a continental-scale warming microcosm experiment across 100 paired agricultural–natural sites with a global meta-analysis and three microbiome manipulation experiments (microbial suspensions, cross-inoculation and synthetic communities). Agricultural soils showed a higher resistance of soil multifunctionality to warming than natural soils, consistent across the meta-analysis. Resistance of microbial community composition was the strongest predictor of functional resistance and was confirmed in artificial soils inoculated with agricultural versus natural microbial suspensions. Introducing soil microbiomes from agricultural ecosystems into previously undisturbed natural soils enhanced functional resistance to warming. Metagenomic analysis revealed that microbial life-history strategies play a crucial role in regulating the resistance of soil microbial community to warming, with communities dominated by stress-tolerant strategies conferring significantly stronger resistance. Our work highlights the potential of microbiome engineering to strengthen ecosystem functioning under climate change. Using warming microcosms across 100 paired sites, a global meta-analysis and microbiome manipulation experiments, this study shows that agricultural microbiomes confer higher compositional and multifunctionality resistance to warming and can transfer this resistance to natural soils.

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.

Predicting how mutations affect protein stability and protein-protein ppi binding affinity is crucial for protein engineering and drug development. Although several computational tools have been developed for these tasks, they often require specialized expertise and are difficult to integrate into unified workflows. Here, we present PythiaStudio (https://pythiastudio.wulab.xyz), a comprehensive web platform that integrates our recently developed Pythia, Pythia-PPI, and Pythia-Pocket models with complementary protein analysis tools. The platform enables users to predict mutational effects on protein stability and protein-protein binding affinity, ligand binding pocket, through an intuitive interface. Additional features include fitness and structure prediction. PythiaStudio provides interactive visualization tools, including mutation heatmaps, sortable result tables, and structure viewers. Importantly, the platform offers an integrated engineering workflow that combines stability and fitness predictions to guide rational protein design. We demonstrate the utility of this workflow through multiple cases, including different glycoside hydrolases and amidases. In these cases, the two-step computational redesign strategy successfully improved both thermostability and catalytic activity. PythiaStudio democratizes access to state-of-the-art deep learning-based protein engineering methods, enabling researchers without computational expertise to perform sophisticated protein engineering.