Cyanobacteria are promising for sustainable bioproduction but are vulnerable to predation, particularly by the mixotrophic protist Poterioochromonas, which threatens large-scale cultivation. This study investigates the predation susceptibility of three fast-growing cyanobacterial strains-Synechococcus elongatus UTEX 2973 (S2973), Synechococcus elongatus PCC 11801 (S11801), and Synechococcus sp. PCC 11901 (S11901)-to Poterioochromonas sp. CCMP 2740, using a reproducible predator-prey model. Grazing experiments, microscopy, and growth measurements revealed that while all strains were susceptible, S11801 and S11901 exhibited significantly higher resistance than S2973. This resistance was linked to their natural formation of microcolonies, which offered spatial protection and size exclusion against the predator. Engineering S2973 to aggregate enhanced its predation tolerance, confirming cell aggregation as a protective mechanism. Additionally, increasing temperature to 38 degrees C effectively eliminated the predator while supporting robust cyanobacterial growth, presenting a practical control strategy. These findings offer valuable insights for strain selection and predator management in cyanobacterial biomanufacturing, highlighting cell aggregation as an innate defense and temperature regulation as a practical control method.

Kinetic modeling of biochemical reactions and bioreactor systems can enhance and quantify knowledge gained from cell culture experiments and has many applications in bioprocess design and optimization. The Microbial and Algal Growth Modeling Application (MAGMA) is a user-friendly MATLAB-based software for streamlining the development of kinetic models for various bioreactor systems. This study details the MAGMA workflow by demonstrating the creation of kinetic models with systems of ordinary differential equations (ODEs), model fitting by solving inverse problems, statistical evaluation of model fitting quality, and visual display of simulation results. Two case studies (microalgae growth and Rhodococcus jostii plastic fermentation) have been provided to validate MAGMA applicability. It also includes a proof-of-concept for utilizing OpenAI GPT-4o's graph interpretation capability to automate tabulation of time course culture data from figures/plots in relevant literature, which can be used to calibrate model parameters. MAGMA is open source and compiled with MATLAB Runtime.

Microplastics have emerged as major environmental hazards that require efficient, cost-effective, and sustainable remediation technologies. This study introduces an integrative platform for the remediation and upcycling of microplastics by algae, while synergizing with plastic upcycling, wastewater treatment, and algal production. The strategy employs a mechanism that enhances hydrophobic interactions between the cell surface and microplastics, enabling rapid aggregation and removal. The platform achieves a superior microplastic removal efficiency of 91.4% within 1 hour, with a capacity of 0.1-gram microplastic per gram of biomass. Furthermore, the study demonstrates an upcycling strategy that converts microplastics-enriched cyanobacteria into plastic composites with unique performance. This work also integrates microplastic removal with cyanobacterial bioproduction and wastewater treatment, offering an approach that synergizes remediation with these value-added processes. Ultimately, this platform provides a viable and sustainable pathway to address microplastic pollution by creating value through plastic upcycling, wastewater nutrient removal, and CO2-based bioproduction.

CyanoCyc is a web portal that integrates an exceptionally rich database collection of information about cyanobacterial genomes with an extensive suite of bioinformatics tools. It was developed to address the needs of the cyanobacterial research and biotechnology communities. The 277 annotated cyanobacterial genomes currently in CyanoCyc are supplemented with computational inferences including predicted metabolic pathways, operons, protein complexes, and orthologs; and with data imported from external databases, such as protein features and Gene Ontology (GO) terms imported from UniProt. Five of the genome databases have undergone manual curation with input from more than a dozen cyanobacteria experts to correct errors and integrate information from more than 1,765 published articles. CyanoCyc has bioinformatics tools that encompass genome, metabolic pathway and regulatory informatics; omics data analysis; and comparative analyses, including visualizations of multiple genomes aligned at orthologous genes, and comparisons of metabolic networks for multiple organisms. CyanoCyc is a high-quality, reliable knowledgebase that accelerates scientists’ work by enabling users to quickly find accurate information using its powerful set of search tools, to understand gene function through expert mini-reviews with citations, to acquire information quickly using its interactive visualization tools, and to inform better decision-making for fundamental and applied research.

Lignocellulosic biorefineries may be applied to produce value-added products, such as chemicals, biofuels and bioplastics, from biomass, thereby reducing carbon emissions compared with fossil fuel-based products. However, efficient biomass valorization remains challenging owing to limitations in yields and economic viability. In this Review, we discuss engineering strategies to improve lignocellulosic biomass-based production, including approaches to optimize biomass deconstruction, substrate utilization, productivity, strain robustness and fermentation stability. We further highlight the importance of systems and synthetic biology tools, artificial intelligence, and automation in the design and scale-up of lignocellulosic biorefineries, emphasizing the integration of techno-economic analysis and life-cycle assessment. These tools may assist in investigating microbial metabolism, accelerating microbial metabolic engineering, enhancing substrate-to-product bioconversion, and optimizing the economics and environmental impact of biorefineries. Biomass, such as lignocellulose, can be processed into value-added products and energy, offering a promising solution for carbon-neutral chemical and fuel production. This Review discusses engineering strategies, including systems and synthetic biology approaches, to optimize lignocellulose biorefineries.

Three-dimensional structure and dynamics are essential for protein function. Advancements in hydrogen-deuterium exchange (HDX) techniques enable probing protein dynamic information in physiologically relevant conditions. HDX-coupled mass spectrometry (HDX-MS) has been broadly applied in pharmaceutical industries. However, it is challenging to obtain dynamics information at the single amino acid resolution and time consuming to perform the experiments and process the data. Here, we demonstrate the first deep learning model, artificial intelligence-based HDX (AI-HDX), that predicts intrinsic protein dynamics based on the protein sequence. It uncovers the protein structural dynamics by combining deep learning, experimental HDX, sequence alignment, and protein structure prediction. AI-HDX can be broadly applied to drug discovery, protein engineering, and biomedical studies. As a demonstration, we elucidated receptor-binding domain structural dynamics as a potential mechanism of anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibody efficacy and immune escape. AI-HDX fundamentally differs from the current AI tools for protein analysis and may transform protein design for various applications.

Microplastics (MPs) are gaining increasing attention in recent years due to their potential harm to the entire ecosystem. Recent studies on MPs have mostly focused on millimeter (mm) to micrometer (mu m) grade, but their hazardous level increases with the decrease in size. This study provided new findings on the removal of Polystyrene (PS) and Poly(methyl methacrylate) (PMMA) ranging from 200 nm to 5 mu m in the aquatic environment using isolated fungal strains. Three isolates were chosen via screening of a library of 230 fungal isolates (fast growth, dye degradation, spore production, and pellet formation). Aspergillus niger could completely remove 200 nm PS and 5 mu m PMMA at 0.005 % and 0.01 % solid loading. Bjerkandera adusta and Phanerochaete chrysosporium showed assimilation potential on 500 nm PS and 5 mu m PMMA, respectively. These findings can potentially be used in wastewater treatment plants to further remove MPs and minimize plastics pollution in natural waters.

Photosynthetic terpene production represents one of the most carbon and energy-efficient ffi cient routes for converting CO2 2 into hydrocarbon. In photosynthetic organisms, metabolic engineering has led to limited success in enhancing terpene productivity, partially due to the low carbon partitioning. In this study, we employed systems biology analysis to reveal the strong competition for carbon substrates between primary metabolism (e.g., sucrose, glycogen, and protein synthesis) and terpene biosynthesis in Synechococcus elongatus PCC 7942. We then engineered key " source " and " sink " enzymes. The " source " limitation was overcome by knocking out either sucrose or glycogen biosynthesis to significantly fi cantly enhance limonene production via altered carbon partitioning. Moreover, a fusion enzyme complex with geranyl diphosphate synthase (GPPS) and limonene synthase (LS) was designed to further improve pathway kinetics and substrate channeling. The synergy between " source " and " sink " achieved a limonene titer of 21.0 mg/L. Overall, the study demonstrates that balancing carbon fl ux between primary and secondary metabolism can be an effective ff ective approach to enhance terpene bioproduction in cyanobacteria. The design of " source " and " sink " synergy has significant fi cant potential in improving natural product yield in photosynthetic species.

Chemical pollution threatens human health and ecosystem sustainability. Persistent organic pollutants (POPs) like per- and polyfluoroalkyl substances (PFAS) are expensive to clean up once emitted. Innovative and synergistic strategies are urgently needed, yet process integration and cost-effectiveness remain challenging. An in-situ PFAS remediation system is developed to employ a plant-derived biomimetic nano-framework to achieve highly efficient adsorption and subsequent fungal biotransformation synergistically. The multiple component framework is presented as Renewable Artificial Plant for In-situ Microbial Environmental Remediation (RAPIMER). RAPIMER exhibits high adsorption capacity for the PFAS compounds and diverse adsorption capability toward co-contaminants. Subsequently, RAPIMER provides the substrates and contaminants for in situ bioremediation via fungus Irpex lacteus and promotes PFAS detoxification. RAPIMER arises from cheap lignocellulosic sources, enabling a broader impact on sustainability and a means for low-cost pollutant remediation.

Photosynthetic terpene production represents one of the most carbon and energy-efficient routes for converting CO2 into hydrocarbon. In photosynthetic organisms, metabolic engineering has led to limited success in enhancing terpene productivity, partially due to the low carbon partitioning. In this study, we employed systems biology analysis to reveal the strong competition for carbon substrates between primary metabolism (e.g., sucrose, glycogen, and protein synthesis) and terpene biosynthesis in Synechococcus elongatus PCC 7942. We then engineered key "source" and "sink" enzymes. The "source" limitation was overcome by knocking out either sucrose or glycogen biosynthesis to significantly enhance limonene production via altered carbon partitioning. Moreover, a fusion enzyme complex with geranyl diphosphate synthase (GPPS) and limonene synthase (LS) was designed to further improve pathway kinetics and substrate channeling. The synergy between "source" and "sink" achieved a limonene titer of 21.0 mg/L. Overall, the study demonstrates that balancing carbon flux between primary and secondary metabolism can be an effective approach to enhance terpene bioproduction in cyanobacteria. The design of "source" and "sink" synergy has significant potential in improving natural product yield in photosynthetic species.

Algal biofuel is regarded as one of the ultimate solutions for renewable energy, but its commercialization is hindered by growth limitations caused by mutual shading and high harvest costs. We overcome these challenges by advancing machine learning to inform the design of a semi-continuous algal cultivation (SAC) to sustain optimal cell growth and minimize mutual shading. An aggregation-based sedimentation (ABS) strategy is then designed to achieve low-cost biomass harvesting and economical SAC. The ABS is achieved by engineering a fast-growing strain, Synechococcus elongatus UTEX 2973, to produce limonene, which increases cyanobacterial cell surface hydrophobicity and enables efficient cell aggregation and sedimentation. SAC unleashes cyanobacterial growth potential with 0.1 g/L/hour biomass productivity and 0.2 mg/L/hour limonene productivity over a sustained period in photobioreactors. Scaling-up the SAC with an outdoor pond system achieves a biomass yield of 43.3 g/m 2 /day, bringing the minimum biomass selling price down to approximately $281 per ton.

Terpenoids are a large group of secondary metabolites with broad industrial applications. Engineering cyanobacteria is an attractive route for the sustainable production of commodity terpenoids. Currently, a major obstacle lies in the low productivity attained in engineered cyanobacterial strains. Traditional metabolic engineering to improve pathway kinetics has led to limited success in enhancing terpenoid productivity. In this study, we reveal thermodynamics as the main determinant for high limonene productivity in cyanobacteria. Through overexpressing the primary sigma factor, a higher photosynthetic rate was achieved in an engineered strain of Synechococcus elongatus PCC 7942. Computational modeling and wet lab analyses showed an increased flux toward both native carbon sink glycogen synthesis and the non-native limonene synthesis from photosynthate output. On the other hand, comparative proteomics showed decreased expression of terpene pathway enzymes, revealing their limited role in determining terpene flux. Lastly, growth optimization by enhancing photosynthesis has led to a limonene titer of 19 mg/L in 7 days with a maximum productivity of 4.3 mg/L/day. This study highlights the importance of enhancing photosynthesis and substrate input for the high productivity of secondary metabolic pathways, providing a new strategy for future terpenoid engineering in phototrophs.

Evolutionarily conserved ecto-nucleoside triphosphate diphosphohydrolases (referred to NTPDases' below) are important ecto-nucleotidases that are able to hydrolyse NTPs and NDPs in the environment to the monophosphate form. NTPDases are found in a variety of eukaryotic organisms including medical pathogens. However, pathogenic roles of these NTPDases in medical and plant pathogens are still very obscure. Here, we demonstrate that conidial germination, appressorium formation and pathogenicity of rice blast fungus Magnaporthe oryzae that had been pretreated with NTPDase-specific inhibitors were significantly reduced, suggesting that NTPDases of M.oryzae play an important role in its infection. Our findings may provide a new avenue for powerful fungicide development and the control of rice blast.