Resource competition theory typically assumes static traits and continuous supply of resources. Yet microbial communities often experience feast-famine cycles and rapid trait change. To investigate coexistence under these nonequilibrium conditions, we integrate modern coexistence theory (MCT) with a genome-scale metabolic model that explicitly links resource use (traits) to metabolic fluxes and growth. MCT partitions competitive interactions into niche and fitness differences, to predict when trait-driven departures from neutrality result in coexistence or exclusion. Using dynamic flux balance analysis, we define a function that maps trait-resource matching to niche and fitness differences between species in a two-species two-resource system. This mapping shows that niche and fitness differences are not independently tunable under resource competition: changes in transporter-mediated resource uptake and changes in resource concentration ratios generate constrained trajectories through coexistence space. Specifically, we show that the minimum niche difference required for coexistence increases linearly with the absolute difference in maximal growth rates on limiting resources, showing how limiting similarity between species can emerge from intracellular metabolic constraints. Furthermore, we find that in batch culture simulations, initial conditions (inoculum size, total resource concentration) determine the timescale of the transient growth phase, with niche differences saturating and fitness differences increasing as the timescale grows, thereby governing competition outcomes. Finally, we test these predictions experimentally using E. coli strains with targeted resource transporter knockouts under both equal and skewed resource concentrations. Our results confirm that transporter-mediated trait changes and resource concentration ratio modulation can be harnessed to engineer coexistence. Together, our work demonstrates that trait-resource matching imposes structured constraints on the joint evolution of niche and fitness differences, thereby shaping biodiversity maintenance in microbial communities under nonequilibrium conditions.

Biotic-abiotic interfaced configurations hold great promise for application in renewable energy and artificial photosynthesis systems. Recent advances in synthetic biology, computational, and visualization techniques, along with enhanced high-resolution characterization, have enabled a deeper fundamental understanding of the interface, which, in turn, has improved electron transfer processes and the design architecture. These developed configurations open new routes to mimic the photosynthetic apparatus or add new applications based on biotic and abiotic catalytic reactions. Aiming to surpass natural systems, researchers have examined methods to reconfigure these block sets into new designs. This review focuses on the advances in artificial photosynthesis and coupled biotic-abiotic biohybrid systems. The work presents the development of artificial photosynthesis configurations aimed at generating light-induced energy or fuels. The use of natural photosynthetic proteins, inorganic photocatalysts, and advanced biohybrid materials is presented and discussed, aiming to enable future biotic-abiotic design and the ambitious goal of developing real-world applications.

Considerable progress has been made in AI-driven RNA structure prediction, but the resulting models often lack complete atomic details or suffer from severe stereochemical distortions and incorrect local interactions. We present RNArefine, an AI-guided hierarchical framework for atomic-level RNA structure refinement. RNArefine first predicts base-pairing and base-stacking interactions using geometric attention networks and then integrates the interactions with physics-based force fields to guide a two-step refinement strategy consisting of Monte Carlo conformational sampling followed by L-BFGS energy optimization. Large-scale benchmark experiments on both sequence-based prediction models and cryo-EM-derived structures demonstrated that RNArefine consistently improves stereochemical quality, interaction fidelity and physically penalized structural accuracy while preserving global topology. When applied to blind CASP16 RNA prediction models, RNArefine improved ranking scores for 28 of the top 30 groups. These results establish RNArefine as a robust open-source framework for transforming raw RNA folds into physically realistic atomic models for downstream structural and therapeutic applications.

Microbiologically induced concrete corrosion (MICC) poses a severe challenge to the long-term durability of infrastructure, particularly in sewer networks and marine environments, which is driven by microbial metabolic activities that attack cement hydrates (Ca(OH)2, C-S-H) mainly caused by biogenic sulfuric acid (from sulfur-oxidizing bacteria) or organic acids (from fungi), converting them into expansive gypsum and ettringite, and then cause cracking and spalling. This article reviews advances in mechanisms, key microorganisms, and protection strategies of MICC to enhance our understanding of MICC and provide a guideline for effective protection. The corrosion mechanisms differ by environment: sewers exhibit three-stage pH-driven succession, marine biofilms can either accelerate or inhibit corrosion, while fungi dominate in agricultural and historical settings. Core functional microorganisms involved in MICC include sulfur-oxidizing bacteria (SOB), sulfate-reducing bacteria (SRB), and acid-producing fungi (AF), following pH-dependent succession, while indicator microorganisms for protection efficacy include typical SOB, SRB, and AF that are involved in MICC, as well as general antimicrobial indicator strains (E. coli and Staphylococcus aureus) which are used only to assess broad antimicrobial activity and do not represent MICC-specific resistance. Multi-scale deterioration proceeds from microstructural decalcification and pore coarsening to macroscopic mass loss and compressive strength reduction. Protection strategies are categorized into: (i) corrosion-resistant materials (e.g., calcium aluminate cement and alkali-activated materials), (ii) antimicrobial additives (e.g., nano-ZnO and Cu2O), (iii) surface coatings (e.g., superhydrophobic coatings and electrodeposited Cu/Cu2O layers), and (iv) ecological regulation. However, significant gaps remain between laboratory efficacy and field performance, highlighting the need for long-term validation, multi-scale characterization, intelligent responsive materials, eco-compatible protection systems, and standardized microbial exposure systems.

With the rise of antimicrobial resistance, urinary tract infections (UTIs) have become increasingly more difficult to treat, prompting renewed interest in bacteriophage (phage) therapy as an alternative or adjunct to antibiotics. UTIs are an attractive target for phage therapy because they generate a high density of actively replicating bacteria that supports phage propagation, and because the urinary tract is readily accessible for administration and monitoring. Yet studies of phage therapy for UTIs report mixed outcomes, including failures to meet clinical and microbiological endpoints. Here we follow the population dynamics of a clinical Escherichia coli UTI strain and two phages, HP3 and ES19, to which the strain appears susceptible by standard testing. Despite this apparent susceptibilty, both phages fail to suppress the strain, with resistance emerging almost immediately. Using the measured mutation rate, our mathematical model shows that traditional resistance cannot account for these dynamics. We instead demonstrate, including by a phage-specific population analysis profile assay we developed, that heteroresistance drives this rapid failure, offering a plausible explanation for treatment failures in UTI phage therapy

Plant roots are broadly colonized by endophytic fungi with saprotrophic capabilities, but our understanding of whether they function in ways that are beneficial or detrimental to the host remains limited to model organisms. We hypothesized that endophytic fungi broadly affect plant access to soil nutrients, particularly organic forms that are typically not directly available to the plant. To address this, we paired 41 fungal endophytes with switchgrass (Panicum virgatum L.) and provided either inorganic or organic forms of nitrogen (N) and phosphorus (P). We evaluated how the fungi affected plant tissue N and P as well as plant growth. We also examined if these outcomes could be predicted from fungal phylogenetic relationships, in vitro traits of the fungi, or characteristics of the habitat from which fungi were isolated. There was substantial variation in both plant N (0.05-0.63%) and P (0.02-0.10%) acquisition that depended on the interaction of fungus and nutrient treatment. More fungi were beneficial for plant N than for P and shoot nutrients generally increased more than root nutrients from fungal associations. However, fungal effects on plant nutrients were not predicted by fungal traits, habitat traits, or fungal phylogenetic relationships. This unpredictability highlights a key challenge for incorporating endophytes into nutrient management strategies. Improving our ability to predict endophyte impacts on host nutrient acquisition will require identifying the mechanisms underlying observed beneficial effects and scaling up to realistic, diverse root microbial communities.

Microbial communities spontaneously colonize pristine environments, yet how species growth kinetics and individual cell variation shape community assembly remains poorly understood. Here, we use time-lapse microscopy imaging to track the division of individual founder cells in communities composed of up to seven soil bacterial isolates grown on nutrient surfaces. With cell lineage tracking, we quantify species-specific absolute biomass formation and growth kinetics from early growth through stationary phase. The reproductive success of individual founder cells depended on the timing of their first cell division, which determined their access to the primary substrate and their maximum growth rates. In mixed-species communities, founder cell success also depended on species-specific, substrate-dependent growth rates and yields. In addition, spatial factors – such as cells’ positioning, distances to non-kin neighbors, and identities of co-occurring species – further influenced outcomes. In spatially structured communities, interspecific interactions were globally governed by competition for primary substrates. We also observed cross-feeding of leaked metabolites, reflected in fluctuating paired interaction strengths and interaction signs. Species-pair interactions differed locally, with cells within distances of less than 15 µm exhibiting opposite interaction behaviors. Global pairwise interactions predicted from monoculture growth kinetics were observed in approximately half of the measured pairs, whereas measured paired interactions generally weakened in combinations of three or more species. Using a spatially explicit agent-based Monod growth model that includes interspecific interactions, we accurately predicted the compositions of seven-member communities. Overall, our results indicate that emergent, spatially mediated interspecific interactions between cells of different bacterial species primarily drive local and temporal changes in individual cell growth rates, which in turn determine final biomass formation. Because most natural microbial habitats are spatially structured, stochastic founder-cell positioning and fitness differences are key determinants of locally formed interaction patterns and species coexistence.

Genome-scale metabolic models (GSMMs) are important aids towards system-level understanding of the metabolic physiology of the gut microbes and for rational microbiome engineering. While large-scale repositories of GSMMs for gut-associated bacteria are available, strain-level variability and the continuous discovery of novel taxa through metagenomics and culturomics underscore the need for scalable, ab initio reconstruction tools. Here, we present CarveMe-GutMicrobes, a client-side framework for rapid reconstruction of metabolic models directly from (meta)genomic input. Building upon the original CarveMe framework, CarveMe-GutMicrobes incorporates an expanded, gut-microbe-centric biochemical database that includes reactions, metabolites, and gene-protein-reaction (GPR) associations curated specifically for Bacteria and Archaea inhabiting the human gut. The tool supports taxonomic restriction of the reference database to improve context-specific accuracy. To test the CarveMe-GutMicrobes and to address the paucity of experimental data for non-model gut taxa, we generated new experimental datasets on metabolite secretion profiles and gene essentiality. CarveMe-GutMicrobes models demonstrated high predictive performance performance against these as well as previously available datasets. By integrating curated resources, extending reaction coverage, and offering new empirical datasets, CarveMe-GutMicrobes provides a scalable platform for high-resolution metabolic reconstruction towards broader adoption of GSMMs in gut microbiome research.

Bacteria carry a large repertoire of antiviral defence systems, our knowledge of which is expanding rapidly. Several bioinformatics tools now exist to identify them. Though powerful, these tools can differ in the models they use and the nomenclature they return, thus a single tool could both miss an annotation and disagree with its peers.  Here we describe Ptolemaea, a pipeline for harmonizing phage-defence annotations across multiple tools by reconciling PADLOC, DefenseFinder, and a bidirectional BLAST. Over a common predicted set of proteins, Ptolemaea provides a consensus annotation list per genome. The pipeline is not intended to outperform or replace its component tools; its purpose is to maximize the number of defence systems recovered from a genome and to make disagreements between tools explicit and resolvable. We demonstrate the pipeline on 700 complete genomes spanning the ESKAPE pathogens and E. coli, recovering 32,509 defence annotations, of which 50.6% were supported by more than one annotation source.

Designing binders against novel protein targets remains a central challenge in computational drug discovery. Here we introduce BoltzProt-1, a pipeline for generating protein binders, including nanobodies, with improved hit rates and favorable developability properties. At its core lie a refined iteration of BoltzGen's generative model and a novel protein-protein interaction prediction model, BoltzPPI. Employing BoltzPPI instead of BoltzGen's standard structure-prediction confidence metrics to rank nanobody (VHH) designs increases the confirmed-binder hit rate from 3.3% to 8.0% across 10 novel targets. Assessed on 10 additional targets used in prior literature, the BoltzProt-1 pipeline obtains nanobody screening hits for 7 of 10 targets, surpassing the 6 of 10 previously reported by Chai-2. Finally, evaluating the developability of BoltzProt-1-designed nanobodies in terms of stability, aggregation, purity, polyspecificity and hydrophobicity reveals that 58% of its confirmed binders pass every criterion, exceeding both BoltzGen (40%) and clinical-stage VHH controls (21%).

The 5′ untranslated region (5′ UTR) is a key regulatory element that governs mRNA translation and protein output. However, existing computational methods typically address isolated tasks such as functional prediction or sequence optimization, limiting their ability to support rational design across the full 5′ UTR engineering workflow. Here, we present UTRGen, a unified modeling framework for 5′ UTRs that integrates sequence generation, multi-property prediction, and constrained function-guided design. UTRGen is pre-trained autoregressively on large-scale 5′ UTR datasets from multiple species and subsequently adapted to diverse downstream regulatory tasks. Across systematic evaluations, UTRGen generates novel and diverse 5′ UTRs while preserving sequence, structural, and functional characteristics of natural UTRs. After task-specific fine-tuning, UTRGen achieves state-of-the-art performance across 14 benchmark datasets, improving translation efficiency prediction by up to 11.1%, expression level prediction by up to 13.2%, and mean ribosome load prediction by up to 3.0% relative to the strongest baselines. It also achieved the best overall performance for internal ribosome entry site identification. To enable controllable design, we formulate function-guided 5′ UTR design as a GRPO-based refinement process over a pre-trained autoregressive sequence prior, using composite rewards to encode functional objectives and biological constraints while regularizing toward the natural 5′ UTR distribution. The resulting sequences show consistently improved predicted translation efficiency and expression levels across cellular contexts, and reveal interpretable sequence features associated with high activity, including reduced C content, fewer upstream AUGs, and depletion of inhibitory motifs. Together, our results establish a unified modeling strategy for 5′ UTR design and lay a foundation for programmable control of translation.